diff --git a/.gitignore b/.gitignore index 5fdc2c870..a8c557457 100644 --- a/.gitignore +++ b/.gitignore @@ -52,9 +52,6 @@ coverage.xml # Sphinx documentation docs/_build/ -# PyBuilder -target/ - # Jupyter Notebook .ipynb_checkpoints @@ -124,3 +121,5 @@ fabric.properties # Data downloaded from Yellowbrick data/ .vscode/settings.json + +yellowbrick/datasets/fixtures diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 954fcd2e8..053be200c 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -43,6 +43,10 @@ The typical workflow for a contributor to the codebase is as follows: We believe that *contribution is collaboration* and therefore emphasize *communication* throughout the open source process. We rely heavily on GitHub's social coding tools to allow us to do this. +Ideally, any pull request should be capable of resolution within 6 weeks of being opened. This timeline helps to keep our pull request queue small and allows Yellowbrick to maintain a robust release schedule to give our users the best experience possible. However, the most important thing is to keep the dialogue going! And if you're unsure whether you can complete your idea within 6 weeks, you should still go ahead and open a PR and we will be happy to help you scope it down as needed. + +If we have comments or questions when we evaluate your pull request and receive no response, we will also close the PR after this period of time. Please know that this does not mean we don't value your contribution, just that things go stale. If in the future you want to pick it back up, feel free to address our original feedback and to reference the original PR in a new pull request. + ### Forking the Repository The first step is to fork the repository into your own account. This will create a copy of the codebase that you can edit and write to. Do so by clicking the **"fork"** button in the upper right corner of the Yellowbrick GitHub page. diff --git a/DESCRIPTION.rst b/DESCRIPTION.rst index 270c5871a..1b42fad27 100644 --- a/DESCRIPTION.rst +++ b/DESCRIPTION.rst @@ -42,6 +42,7 @@ Classification Visualization - **Class Prediction Error**: shows error and support in classification - **Classification Report**: visual representation of precision, recall, and F1 - **ROC/AUC Curves**: receiver operator characteristics and area under the curve +- **Precision-Recall Curves**: precision vs recall for different probability thresholds - **Confusion Matrices**: visual description of class decision making - **Discrimination Threshold**: find a threshold that best separates binary classes @@ -57,6 +58,7 @@ Clustering Visualization - **K-Elbow Plot**: select k using the elbow method and various metrics - **Silhouette Plot**: select k by visualizing silhouette coefficient values +- **Intercluster Distance Maps**: show relative distance and size of clusters Model Selection Visualization ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -71,6 +73,11 @@ Text Visualization - **t-SNE Corpus Visualization**: use stochastic neighbor embedding to project documents - **Dispersion Plot**: visualize how key terms are dispersed throughout a corpus +Target Visualization +~~~~~~~~~~~~~~~~~~~~ + +- **Feature Correlation**: visualize the correlation between the dependent variables and the target + ... and more! Visualizers are being added all the time; be sure to check the examples_ (or even the develop_ branch) and feel free to contribute your ideas for new Visualizers! .. _examples: http://www.scikit-yb.org/en/latest/api/index.html diff --git a/README.md b/README.md index 14db205f4..6c43e72ce 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,9 @@ [![Build Status](https://travis-ci.com/DistrictDataLabs/yellowbrick.svg?branch=develop)](https://travis-ci.com/DistrictDataLabs/yellowbrick) [![Build status](https://ci.appveyor.com/api/projects/status/11abg00ollbdf4oy?svg=true)](https://ci.appveyor.com/project/districtdatalabs/yellowbrick) [![Coverage Status](https://coveralls.io/repos/github/DistrictDataLabs/yellowbrick/badge.svg?branch=master)](https://coveralls.io/github/DistrictDataLabs/yellowbrick?branch=master) -[![Code Health](https://landscape.io/github/DistrictDataLabs/yellowbrick/master/landscape.svg?style=flat)](https://landscape.io/github/DistrictDataLabs/yellowbrick/master) +[![Total Alerts](https://img.shields.io/lgtm/alerts/g/DistrictDataLabs/yellowbrick.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/DistrictDataLabs/yellowbrick/alerts/) +[![Language Grade: Python](https://img.shields.io/lgtm/grade/python/g/DistrictDataLabs/yellowbrick.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/DistrictDataLabs/yellowbrick/context:python) + [![PyPI version](https://badge.fury.io/py/yellowbrick.svg)](https://badge.fury.io/py/yellowbrick) [![Documentation Status](https://readthedocs.org/projects/yellowbrick/badge/?version=latest)](http://yellowbrick.readthedocs.io/en/latest/?badge=latest) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1206239.svg)](https://doi.org/10.5281/zenodo.1206239) @@ -44,6 +46,7 @@ Visualizers are estimators (objects that learn from data) whose primary objectiv - **Class Prediction Error**: shows error and support in classification - **Classification Report**: visual representation of precision, recall, and F1 - **ROC/AUC Curves**: receiver operator characteristics and area under the curve +- **Precision-Recall Curves**: precision vs recall for different probability thresholds - **Confusion Matrices**: visual description of class decision making - **Discrimination Threshold**: find a threshold that best separates binary classes @@ -57,6 +60,7 @@ Visualizers are estimators (objects that learn from data) whose primary objectiv - **K-Elbow Plot**: select k using the elbow method and various metrics - **Silhouette Plot**: select k by visualizing silhouette coefficient values +- **Intercluster Distance Maps**: show relative distance and size of clusters #### Model Selection Visualization @@ -69,6 +73,10 @@ Visualizers are estimators (objects that learn from data) whose primary objectiv - **t-SNE Corpus Visualization**: use stochastic neighbor embedding to project documents. - **Dispersion Plot**: visualize how key terms are dispersed throughout a corpus +#### Target Visualization + +- **Feature Correlation**: visualize the correlation between the dependent variables and the target + And more! Visualizers are being added all the time, so be sure to check the examples (or even the develop branch) and feel free to contribute your ideas for Visualizers! ## Installing Yellowbrick @@ -168,7 +176,7 @@ $ python -m tests.images -C tests/test_visualizer.py Glob syntax can be used to move multiple files. For example to reset all the classifier tests: ``` -$ python -m tests.images tests/test_classifier/* +$ python -m tests.images tests/test_classifier/* ``` Though it is recommended that specific test cases are targeted, rather than updating entire directories. diff --git a/docs/about.rst b/docs/about.rst index bdc89cc02..d11fc67e7 100644 --- a/docs/about.rst +++ b/docs/about.rst @@ -23,6 +23,18 @@ However, model selection is a bit more nuanced than simply picking the "right" o The **model selection triple** was first described in a 2015 SIGMOD_ paper by Kumar et al. In their paper, which concerns the development of next-generation database systems built to anticipate predictive modeling, the authors cogently express that such systems are badly needed due to the highly experimental nature of machine learning in practice. "Model selection," they explain, "is iterative and exploratory because the space of [model selection triples] is usually infinite, and it is generally impossible for analysts to know a priori which [combination] will yield satisfactory accuracy and/or insights." + +Who is Yellowbrick for? +----------------------- + +Yellowbrick ``Visualizers`` have multiple use cases: + + - For data scientists, they can help evaluate the stability and predictive value of machine learning models and improve the speed of the experimental workflow. + - For data engineers, Yellowbrick provides visual tools for monitoring model performance in real world applications. + - For users of models, Yellowbrick provides visual interpretation of the behavior of the model in high dimensional feature space. + - For teachers and students, Yellowbrick is a framework for teaching and understanding a large variety of algorithms and methods. + + Name Origin ----------- The Yellowbrick package gets its name from the fictional element in the 1900 children's novel **The Wonderful Wizard of Oz** by American author L. Frank Baum. In the book, the yellow brick road is the path that the protagonist, Dorothy Gale, must travel in order to reach her destination in the Emerald City. @@ -68,7 +80,7 @@ Jupyter Notebooks: - `Data Science Delivered: ML Regression Predications `_ Slides: - - `Machine Learning Libraries You'd Wish You'd Known About (PyData Budapest 2017) `_ + - `Machine Learning Libraries You'd Wish You'd Known About (PyData Budapest 2017) `_ - `Visualizing the Model Selection Process `_ - `Visualizing Model Selection with Scikit-Yellowbrick `_ - `Visual Pipelines for Text Analysis (Data Intelligence 2017) `_ diff --git a/docs/api/classifier/class_balance.py b/docs/api/classifier/class_balance.py deleted file mode 100644 index 0aa30ff7b..000000000 --- a/docs/api/classifier/class_balance.py +++ /dev/null @@ -1,29 +0,0 @@ -import pandas as pd - -from sklearn.ensemble import RandomForestClassifier -from sklearn.model_selection import train_test_split - -from yellowbrick.classifier import ClassBalance - - -if __name__ == '__main__': - # Load the regression data set - data = pd.read_csv("../../../examples/data/occupancy/occupancy.csv") - - features = ["temperature", "relative humidity", "light", "C02", "humidity"] - classes = ['unoccupied', 'occupied'] - - # Extract the numpy arrays from the data frame - X = data[features].as_matrix() - y = data.occupancy.as_matrix() - - # Create the train and test data - X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) - - # Instantiate the classification model and visualizer - forest = RandomForestClassifier() - visualizer = ClassBalance(forest, classes=classes) - - visualizer.fit(X_train, y_train) # Fit the training data to the visualizer - visualizer.score(X_test, y_test) # Evaluate the model on the test data - g = visualizer.poof(outpath="images/class_balance.png") # Draw/show/poof the data diff --git a/docs/api/classifier/class_balance.rst b/docs/api/classifier/class_balance.rst deleted file mode 100644 index 32e6a1cd4..000000000 --- a/docs/api/classifier/class_balance.rst +++ /dev/null @@ -1,51 +0,0 @@ -.. -*- mode: rst -*- - -Class Balance -============= - -Oftentimes classifiers perform badly because of a class imbalance. A class balance chart can help prepare the user for such a case by showing the support for each class in the fitted -classification model. - -.. code:: python - - from sklearn.model_selection import train_test_split - - # Load the classification data set - data = load_data("occupancy") - - # Specify the features of interest and the classes of the target - features = ["temperature", "relative humidity", "light", "C02", "humidity"] - classes = ["unoccupied", "occupied"] - - # Extract the numpy arrays from the data frame - X = data[features].as_matrix() - y = data.occupancy.as_matrix() - - # Create the train and test data - X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) - -.. code:: python - - from sklearn.ensemble import RandomForestClassifier - - from yellowbrick.classifier import ClassBalance - - # Instantiate the classification model and visualizer - forest = RandomForestClassifier() - visualizer = ClassBalance(forest, classes=classes) - - visualizer.fit(X_train, y_train) # Fit the training data to the visualizer - visualizer.score(X_test, y_test) # Evaluate the model on the test data - g = visualizer.poof() # Draw/show/poof the data - - -.. image:: images/class_balance.png - - -API Reference -------------- - -.. automodule:: yellowbrick.classifier.class_balance - :members: ClassBalance - :undoc-members: - :show-inheritance: diff --git a/docs/api/classifier/class_prediction_error.rst b/docs/api/classifier/class_prediction_error.rst index 61d5471c6..b7e2ddf85 100644 --- a/docs/api/classifier/class_prediction_error.rst +++ b/docs/api/classifier/class_prediction_error.rst @@ -23,7 +23,7 @@ The class prediction error chart provides a way to quickly understand how good y .. code:: python from sklearn.ensemble import RandomForestClassifier - + from yellowbrick.classifier import ClassPredictionError # Instantiate the classification model and visualizer @@ -45,7 +45,7 @@ The class prediction error chart provides a way to quickly understand how good y API Reference ------------- -.. automodule:: yellowbrick.classifier.class_balance +.. automodule:: yellowbrick.classifier.class_prediction_error :members: ClassPredictionError :undoc-members: :show-inheritance: diff --git a/docs/api/classifier/classification_report.rst b/docs/api/classifier/classification_report.rst index cfde533f4..8ac9a8a65 100644 --- a/docs/api/classifier/classification_report.rst +++ b/docs/api/classifier/classification_report.rst @@ -18,9 +18,9 @@ The classification report visualizer displays the precision, recall, F1, and sup ] classes = ["unoccupied", "occupied"] - # Extract the numpy arrays from the data frame - X = data[features].as_matrix() - y = data.occupancy.as_matrix() + # Extract the instances and target + X = data[features] + y = data.occupancy # Create the train and test data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) diff --git a/docs/api/classifier/confusion_matrix.py b/docs/api/classifier/confusion_matrix.py index 6bbae0cf2..c5a0952c4 100644 --- a/docs/api/classifier/confusion_matrix.py +++ b/docs/api/classifier/confusion_matrix.py @@ -1,23 +1,36 @@ -from sklearn.datasets import load_digits +from sklearn.datasets import load_digits, load_iris from sklearn.linear_model import LogisticRegression -from sklearn.model_selection import train_test_split +from sklearn.model_selection import train_test_split as tts from yellowbrick.classifier import ConfusionMatrix if __name__ == '__main__': - # Load the regression data set digits = load_digits() - X = digits.data - y = digits.target - - X_train, X_test, y_train, y_test = train_test_split(X,y, test_size =0.2, random_state=11) - + digit_X = digits.data + digit_y = digits.target + d_X_train, d_X_test, d_y_train, d_y_test = tts( + digit_X, digit_y, test_size=0.2 + ) model = LogisticRegression() + digit_cm = ConfusionMatrix(model, classes=[0,1,2,3,4,5,6,7,8,9]) + digit_cm.fit(d_X_train, d_y_train) + digit_cm.score(d_X_test, d_y_test) + d = digit_cm.poof(outpath="images/confusion_matrix_digits.png") - #The ConfusionMatrix visualizer taxes a model - cm = ConfusionMatrix(model, classes=[0,1,2,3,4,5,6,7,8,9]) - cm.fit(X_train, y_train) # Fit the training data to the visualizer - cm.score(X_test, y_test) # Evaluate the model on the test data - g = cm.poof(outpath="images/confusion_matrix.png") # Draw/show/poof the data + iris = load_iris() + iris_X = iris.data + iris_y = iris.target + iris_classes = iris.target_names + i_X_train, i_X_test, i_y_train, i_y_test = tts( + iris_X, iris_y, test_size=0.2 + ) + model = LogisticRegression() + iris_cm = ConfusionMatrix( + model, classes=iris_classes, + label_encoder={0: 'setosa', 1: 'versicolor', 2: 'virginica'} + ) + iris_cm.fit(i_X_train, i_y_train) + iris_cm.score(i_X_test, i_y_test) + i = iris_cm.poof(outpath="images/confusion_matrix_iris.png") diff --git a/docs/api/classifier/confusion_matrix.rst b/docs/api/classifier/confusion_matrix.rst index 764fd8676..878b910a8 100644 --- a/docs/api/classifier/confusion_matrix.rst +++ b/docs/api/classifier/confusion_matrix.rst @@ -51,8 +51,37 @@ scikit-learn documentation on `confusion matrices `_ (or anything with an `inverse_transform` method that performs the mapping), or a `dict` with the encoding-to-string mapping as in the example below: + +.. code:: python + + iris = load_iris() + X = iris.data + y = iris.target + classes = iris.target_names + + X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2) + + model = LogisticRegression() + + iris_cm = ConfusionMatrix( + model, classes=classes, + label_encoder={0: 'setosa', 1: 'versicolor', 2: 'virginica'} + ) + + iris_cm.fit(X_train, y_train) + iris_cm.score(X_test, y_test) + + iris_cm.poof() + + +.. image:: images/confusion_matrix_iris.png API Reference diff --git a/docs/api/classifier/images/binary_precision_recall.png b/docs/api/classifier/images/binary_precision_recall.png new file mode 100644 index 000000000..e3602a65e Binary files /dev/null and b/docs/api/classifier/images/binary_precision_recall.png differ diff --git a/docs/api/classifier/images/class_balance.png b/docs/api/classifier/images/class_balance.png deleted file mode 100644 index 4287577cf..000000000 Binary files a/docs/api/classifier/images/class_balance.png and /dev/null differ diff --git a/docs/api/classifier/images/confusion_matrix.png b/docs/api/classifier/images/confusion_matrix.png deleted file mode 100644 index 96d1776bc..000000000 Binary files a/docs/api/classifier/images/confusion_matrix.png and /dev/null differ diff --git a/docs/api/classifier/images/confusion_matrix_digits.png b/docs/api/classifier/images/confusion_matrix_digits.png new file mode 100644 index 000000000..120c56d8d Binary files /dev/null and b/docs/api/classifier/images/confusion_matrix_digits.png differ diff --git a/docs/api/classifier/images/confusion_matrix_iris.png b/docs/api/classifier/images/confusion_matrix_iris.png new file mode 100644 index 000000000..f536db142 Binary files /dev/null and b/docs/api/classifier/images/confusion_matrix_iris.png differ diff --git a/docs/api/classifier/images/multiclass_precision_recall.png b/docs/api/classifier/images/multiclass_precision_recall.png new file mode 100644 index 000000000..923c9b915 Binary files /dev/null and b/docs/api/classifier/images/multiclass_precision_recall.png differ diff --git a/docs/api/classifier/images/multiclass_precision_recall_full.png b/docs/api/classifier/images/multiclass_precision_recall_full.png new file mode 100644 index 000000000..ec5e7ca51 Binary files /dev/null and b/docs/api/classifier/images/multiclass_precision_recall_full.png differ diff --git a/docs/api/classifier/images/rocauc_binary.png b/docs/api/classifier/images/rocauc_binary.png new file mode 100644 index 000000000..2e9e7ce6e Binary files /dev/null and b/docs/api/classifier/images/rocauc_binary.png differ diff --git a/docs/api/classifier/images/rocauc_multiclass.png b/docs/api/classifier/images/rocauc_multiclass.png new file mode 100644 index 000000000..0da15e398 Binary files /dev/null and b/docs/api/classifier/images/rocauc_multiclass.png differ diff --git a/docs/api/classifier/index.rst b/docs/api/classifier/index.rst index 2b162809a..b4a949c78 100644 --- a/docs/api/classifier/index.rst +++ b/docs/api/classifier/index.rst @@ -8,15 +8,16 @@ Classification models attempt to predict a target in a discrete space, that is a - :doc:`classification_report`: A visual classification report that displays precision, recall, and F1 per-class as a heatmap. - :doc:`confusion_matrix`: A heatmap view of the confusion matrix of pairs of classes in multi-class classification. - :doc:`rocauc`: Graphs the receiver operating characteristics and area under the curve. -- :doc:`class_balance`: Visual inspection of the target to show the support of each class to the final estimator. +- :doc:`prcurve`: Plots the precision and recall for different probability thresholds. +- :doc:`../target/class_balance`: Visual inspection of the target to show the support of each class to the final estimator. - :doc:`class_prediction_error`: An alternative to the confusion matrix that shows both support and the difference between actual and predicted classes. - :doc:`threshold`: Shows precision, recall, f1, and queue rate over all thresholds for binary classifiers that use a discrimination probability or score. Estimator score visualizers wrap scikit-learn estimators and expose the -Estimator API such that they have fit(), predict(), and score() methods -that call the appropriate estimator methods under the hood. Score +Estimator API such that they have ``fit()``, ``predict()``, and ``score()`` +methods that call the appropriate estimator methods under the hood. Score visualizers can wrap an estimator and be passed in as the final step in -a Pipeline or VisualPipeline. +a ``Pipeline`` or ``VisualPipeline``. .. code:: python @@ -27,8 +28,11 @@ a Pipeline or VisualPipeline. from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split - from yellowbrick.classifier import ClassificationReport, ROCAUC - from yellowbrick.classifier import ClassBalance, ClassPredictionError + from yellowbrick.target import ClassBalance + from yellowbrick.classifier import ROCAUC + from yellowbrick.classifier import PrecisionRecallCurve + from yellowbrick.classifier import ClassificationReport + from yellowbrick.classifier import ClassPredictionError from yellowbrick.classifier import DiscriminationThreshold .. toctree:: @@ -37,6 +41,6 @@ a Pipeline or VisualPipeline. classification_report confusion_matrix rocauc - class_balance + prcurve class_prediction_error threshold diff --git a/docs/api/classifier/prcurve.py b/docs/api/classifier/prcurve.py new file mode 100644 index 000000000..f4ac5224a --- /dev/null +++ b/docs/api/classifier/prcurve.py @@ -0,0 +1,81 @@ +#!/usr/bin/env python + +import os +import pandas as pd +import matplotlib.pyplot as plt + +from sklearn.naive_bayes import MultinomialNB +from sklearn.linear_model import RidgeClassifier +from sklearn.ensemble import RandomForestClassifier +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import LabelEncoder + +from yellowbrick.classifier import PrecisionRecallCurve + + +# Location of downloaded datasets from Yellowbrick +FIXTURES = os.path.join( + os.path.dirname(__file__), "..", "..", "..", "yellowbrick", "datasets", "fixtures" +) + + +def load_binary(split=True): + data = pd.read_csv(os.path.join(FIXTURES, "spam", "spam.csv")) + + target = "is_spam" + features = [col for col in data.columns if col != target] + + X = data[features] + y = data[target] + + if split: + return train_test_split(X, y, test_size=0.2, shuffle=True) + return X, y + + +def load_multiclass(split=True): + data = pd.read_csv(os.path.join(FIXTURES, "game", "game.csv")) + + # Encode the categorical variables + data.replace({'x':0, 'o':1, 'b':2}, inplace=True) + + # Extract the numpy arrays from the data frame + X = data.iloc[:, data.columns != 'outcome'] + y = LabelEncoder().fit_transform(data['outcome']) + + if split: + return train_test_split(X, y, test_size=0.2, shuffle=True) + return X, y + + +def draw_binary(outpath=None): + _, ax = plt.subplots(figsize=(9,6)) + + X_train, X_test, y_train, y_test = load_binary(split=True) + + oz = PrecisionRecallCurve(RidgeClassifier(), ax=ax) + oz.fit(X_train, y_train) + oz.score(X_test, y_test) + oz.poof(outpath=outpath) + + +def draw_multiclass(outpath=None, simple=True): + _, ax = plt.subplots(figsize=(9,6)) + + X_train, X_test, y_train, y_test = load_multiclass() + + if simple: + oz = PrecisionRecallCurve(RandomForestClassifier(), ax=ax) + else: + oz = PrecisionRecallCurve(MultinomialNB(), ax=ax, per_class=True, iso_f1_curves=True, fill_area=False, micro=False) + + oz.fit(X_train, y_train) + oz.score(X_test, y_test) + oz.poof(outpath=outpath) + + + +if __name__ == '__main__': + draw_binary(outpath="images/binary_precision_recall.png") + draw_multiclass(simple=True, outpath="images/multiclass_precision_recall.png") + draw_multiclass(simple=False, outpath="images/multiclass_precision_recall_full.png") diff --git a/docs/api/classifier/prcurve.rst b/docs/api/classifier/prcurve.rst new file mode 100644 index 000000000..d1762b80e --- /dev/null +++ b/docs/api/classifier/prcurve.rst @@ -0,0 +1,94 @@ +.. -*- mode: rst -*- + +Precision-Recall Curves +======================= + +Precision-Recall curves are a metric used to evaluate a classifier's quality, +particularly when classes are very imbalanced. The precision-recall curve +shows the tradeoff between precision, a measure of result relevancy, and +recall, a measure of how many relevant results are returned. A large area +under the curve represents both high recall and precision, the best case +scenario for a classifier, showing a model that returns accurate results +for the majority of classes it selects. + +Binary Classification +--------------------- + +.. code:: python + + from sklearn.linear_model import RidgeClassifier + from sklearn.model_selection import train_test_split as tts + from yellowbrick.classifier import PrecisionRecallCurve + + # Load the dataset and split into train/test splits + data = load_spam() + X = data[[col for col in data.columns if col != "is_spam"]] + y = data["is_spam"] + + X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2, shuffle=True) + + # Create the visualizer, fit, score, and poof it + viz = PrecisionRecallCurve(RidgeClassifier()) + viz.fit(X_train, y_train) + viz.score(X_test, y_test) + viz.poof() + + +.. image:: images/binary_precision_recall.png + +The base case for precision-recall curves is the binary classification case, and this case is also the most visually interpretable. In the figure above we can see the precision plotted on the y-axis against the recall on the x-axis. The larger the filled in area, the stronger the classifier is. The red line annotates the *average precision*, a summary of the entire plot computed as the weighted average of precision achieved at each threshold such that the weight is the difference in recall from the previous threshold. + +Multi-Label Classification +-------------------------- + +To support multi-label classification, the estimator is wrapped in a `OneVsRestClassifier `_ to produce binary comparisons for each class (e.g. the positive case is the class and the negative case is any other class). The Precision-Recall curve is then computed as the micro-average of the precision and recall for all classes: + +.. code:: python + + from sklearn.ensemble import RandomForestClassifier + from sklearn.preprocessing import LabelEncoder + + # Load dataset and encode categorical variables + data = load_game() + data.replace({'x':0, 'o':1, 'b':2}, inplace=True) + + # Create train/test splits + X = data.iloc[:, data.columns != 'outcome'] + y = LabelEncoder().fit_transform(data['outcome']) + + X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2, shuffle=True) + + # Create the visualizer, fit, score, and poof it + viz = PrecisionRecallCurve(RandomForestClassifier()) + viz.fit(X_train, y_train) + viz.score(X_test, y_test) + viz.poof() + +.. image:: images/multiclass_precision_recall.png + +A more complex Precision-Recall curve can be computed, however, displaying the each curve individually, along with F1-score ISO curves (e.g. that show the relationship between precision and recall for various F1 scores). + +.. code:: python + + from sklearn.naive_bayes import MultinomialNB + + oz = PrecisionRecallCurve( + MultinomialNB(), per_class=True, iso_f1_curves=True, + fill_area=False, micro=False + ) + viz.fit(X_train, y_train) + viz.score(X_test, y_test) + viz.poof() + +.. image:: images/multiclass_precision_recall_full.png + + +.. seealso:: `Scikit-Learn: Model Selection with Precision Recall Curves `_ + +API Reference +------------- + +.. automodule:: yellowbrick.classifier.prcurve + :members: PrecisionRecallCurve + :undoc-members: + :show-inheritance: diff --git a/docs/api/classifier/rocauc.py b/docs/api/classifier/rocauc.py index a0a4593fb..98708dd32 100644 --- a/docs/api/classifier/rocauc.py +++ b/docs/api/classifier/rocauc.py @@ -1,29 +1,77 @@ import pandas as pd +import matplotlib.pyplot as plt -from sklearn.linear_model import LogisticRegression +from sklearn.svm import LinearSVC +from sklearn.linear_model import LogisticRegression, RidgeClassifier from sklearn.model_selection import train_test_split from yellowbrick.classifier import ROCAUC -if __name__ == '__main__': - # Load the regression data set - data = pd.read_csv("../../../examples/data/occupancy/occupancy.csv") +def load_occupancy(): + # Load the binary classification data set + room = pd.read_csv("../../../examples/data/occupancy/occupancy.csv") features = ["temperature", "relative humidity", "light", "C02", "humidity"] classes = ['unoccupied', 'occupied'] # Extract the numpy arrays from the data frame - X = data[features].as_matrix() - y = data.occupancy.as_matrix() + X = room[features].values + y = room.occupancy.values + + return X, y, classes + + +def load_game(): + # Load multi-class classification dataset + game = pd.read_csv('../../../examples/data/game/game.csv') + + classes = ["win", "loss", "draw"] + game.replace({'loss':-1, 'draw':0, 'win':1, 'x':2, 'o':3, 'b':4}, inplace=True) + + # Extract the numpy arrays from the data frame + X = game.iloc[:, game.columns != 'outcome'] + y = game['outcome'] + + return X, y, classes + + +def rocauc(X, y, model, outpath, **kwargs): + # Create a new figure and axes + _, ax = plt.subplots() + + # Instantiate the classification model and visualizer + visualizer = ROCAUC(model, ax=ax, **kwargs) # Create the train and test data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) - # Instantiate the classification model and visualizer - logistic = LogisticRegression() - visualizer = ROCAUC(logistic) + visualizer.fit(X_train, y_train) + visualizer.score(X_test, y_test) + + # Save to disk + visualizer.poof(outpath=outpath) + +if __name__ == '__main__': + + # Occupancy data visualization + X, y, classes = load_occupancy() + + # Draw the binary rocauc + rocauc( + X, y, LogisticRegression(), "images/rocauc_binary.png", classes=classes + ) + + # Draw a single binary decision curve + rocauc( + X, y, LinearSVC(), "images/rocauc_binary.png", + micro=False, macro=False, per_class=False + ) + + # Game data visualization + X, y, classes = load_game() - visualizer.fit(X_train, y_train) # Fit the training data to the visualizer - visualizer.score(X_test, y_test) # Evaluate the model on the test data - g = visualizer.poof(outpath="images/rocauc.png") # Draw/show/poof the data + # Draw the multiclass roc_auc + rocauc( + X, y, RidgeClassifier(), "images/rocauc_multiclass.png", classes=classes + ) diff --git a/docs/api/classifier/rocauc.rst b/docs/api/classifier/rocauc.rst index 72b0ef619..5291dc0d0 100644 --- a/docs/api/classifier/rocauc.rst +++ b/docs/api/classifier/rocauc.rst @@ -3,8 +3,11 @@ ROCAUC ====== -A ROCAUC (Receiver Operating Characteristic/Area Under the Curve) plot allows the user to visualize the tradeoff between the classifier's -sensitivity and specificity. +A ``ROCAUC`` (Receiver Operating Characteristic/Area Under the Curve) plot allows the user to visualize the tradeoff between the classifier's sensitivity and specificity. + +The Receiver Operating Characteristic (ROC) is a measure of a classifier's predictive quality that compares and visualizes the tradeoff between the model's sensitivity and specificity. When plotted, a ROC curve displays the true positive rate on the Y axis and the false positive rate on the X axis on both a global average and per-class basis. The ideal point is therefore the top-left corner of the plot: false positives are zero and true positives are one. + +This leads to another metric, area under the curve (AUC), which is a computation of the relationship between false positives and true positives. The higher the AUC, the better the model generally is. However, it is also important to inspect the "steepness" of the curve, as this describes the maximization of the true positive rate while minimizing the false positive rate. .. code:: python @@ -17,30 +20,75 @@ sensitivity and specificity. features = ["temperature", "relative humidity", "light", "C02", "humidity"] classes = ["unoccupied", "occupied"] - # Extract the numpy arrays from the data frame - X = data[features].as_matrix() - y = data.occupancy.as_matrix() + # Extract the instances and target + X = data[features] + y = data.occupancy # Create the train and test data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) .. code:: python + from yellowbrick.classifier import ROCAUC from sklearn.linear_model import LogisticRegression - from yellowbrick.classifier import ROCAUC + # Instantiate the visualizer with the classification model + visualizer = ROCAUC(LogisticRegression(), classes=classes) + + visualizer.fit(X_train, y_train) # Fit the training data to the visualizer + visualizer.score(X_test, y_test) # Evaluate the model on the test data + g = visualizer.poof() # Draw/show/poof the data + + +.. image:: images/rocauc_binary.png + + +.. warning:: + Binary classification using a Scikit-learn-style estimator with only a + ``decision_function``, triggers an ``IndexError`` because the predictions + will be a 1D array, meaning there is only sufficient information to plot a + single curve. More on this bug can be found in this `notebook `_. The bug was addressed in a `July 2018 PR `_ + and will be fixed in v0.9, where the solution will be to set the ``micro``, + ``macro``, and ``per-class`` parameters of ``ROCAUC`` to ``False``. + + +Multi-class ROCAUC Curves +######################### + +Yellowbrick's ``ROCAUC`` Visualizer does allow for plotting multiclass classification curves. +ROC curves are typically used in binary classification, and in fact the Scikit-Learn ``roc_curve`` metric is only able to perform metrics for binary classifiers. Yellowbrick addresses this by binarizing the output (per-class) or to use one-vs-rest (micro score) or one-vs-all (macro score) strategies of classification. + +.. code:: + + # Load multi-class classification dataset + game = load_game() + + classes = ["win", "loss", "draw"] + + # Encode the non-numeric columns + game.replace({'loss':-1, 'draw':0, 'win':1, 'x':2, 'o':3, 'b':4}, inplace=True) + + # Extract the instances and target + X = game.iloc[:, game.columns != 'outcome'] + y = game['outcome'] + + # Create the train and test data + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + +.. code:: + + from sklearn.linear_model import RidgeClassifier - # Instantiate the classification model and visualizer - logistic = LogisticRegression() - visualizer = ROCAUC(logistic) + visualizer = ROCAUC(RidgeClassifier(), classes=classes) visualizer.fit(X_train, y_train) # Fit the training data to the visualizer visualizer.score(X_test, y_test) # Evaluate the model on the test data g = visualizer.poof() # Draw/show/poof the data +By default with multi-class ROCAUC visualizations, a curve for each class is plotted, in addition to the micro- and macro-average curves for each class. This enables the user to inspect the tradeoff between sensitivity and specificity on a per-class basis. Note that for multi-class ``ROCAUC``, at least one of the ``micro``, ``macro``, or ``per_class`` parameters must be set to ``True`` (by default, all are set to ``True``). +.. image:: images/rocauc_multiclass.png -.. image:: images/rocauc.png API Reference diff --git a/docs/api/cluster/elbow.py b/docs/api/cluster/elbow.py index 06e530ac0..fd362308d 100644 --- a/docs/api/cluster/elbow.py +++ b/docs/api/cluster/elbow.py @@ -1,27 +1,55 @@ -# Clustering Evaluation Imports -from functools import partial +#!/usr/bin/env python -from sklearn.cluster import MiniBatchKMeans -from sklearn.datasets import make_blobs as sk_make_blobs +""" +Generate images for the elbow plot documentation. +""" -from yellowbrick.cluster import KElbowVisualizer +# Import necessary modules +import matplotlib.pyplot as plt -# Helpers for easy dataset creation -N_SAMPLES = 1000 -N_FEATURES = 12 -SHUFFLE = True +from sklearn.cluster import KMeans +from sklearn.datasets import make_blobs +from yellowbrick.cluster import KElbowVisualizer -# Make blobs partial -make_blobs = partial(sk_make_blobs, n_samples=N_SAMPLES, n_features=N_FEATURES, shuffle=SHUFFLE) +def draw_elbow(path="images/elbow.png"): + # Generate synthetic dataset with 8 blobs + X, y = make_blobs( + centers=8, n_features=12, n_samples=1000, + shuffle=True, random_state=42 + ) -if __name__ == '__main__': - # Make 8 blobs dataset - X, y = make_blobs(centers=8) + # Create a new figure to draw the clustering visualizer on + _, ax = plt.subplots() # Instantiate the clustering model and visualizer + model = KMeans() + visualizer = KElbowVisualizer(model, ax=ax, k=(4,12)) + + visualizer.fit(X) # Fit the data to the visualizer + visualizer.poof(outpath=path) # Draw/show/poof the data + + +def draw_calinski_harabaz(path="images/calinski_harabaz.png"): + # Generate synthetic dataset with 8 blobs + X, y = make_blobs( + centers=8, n_features=12, n_samples=1000, + shuffle=True, random_state=42 + ) + + # Create a new figure to draw the clustering visualizer on + _, ax = plt.subplots() + # Instantiate the clustering model and visualizer - visualizer = KElbowVisualizer(MiniBatchKMeans(), k=(4,12)) + model = KMeans() + visualizer = KElbowVisualizer( + model, ax=ax, k=(4,12), + metric='calinski_harabaz', timings=False + ) + visualizer.fit(X) # Fit the data to the visualizer + visualizer.poof(outpath=path) # Draw/show/poof the data - visualizer.fit(X) # Fit the training data to the visualizer - visualizer.poof(outpath="images/elbow.png") # Draw/show/poof the data + +if __name__ == '__main__': + draw_elbow() + draw_calinski_harabaz() diff --git a/docs/api/cluster/elbow.rst b/docs/api/cluster/elbow.rst index 66721225b..50e3d8277 100644 --- a/docs/api/cluster/elbow.rst +++ b/docs/api/cluster/elbow.rst @@ -3,29 +3,56 @@ Elbow Method ============ -The elbow method for :math:`K` selection visualizes multiple clustering models with different values for :math:`K`. Model selection is based on whether or not there is an "elbow" in the curve; e.g. if the curve looks like an arm, if there is a clear change in angle from one part of the curve to another. +The ``KElbowVisualizer`` implements the "elbow" method to help data scientists select the optimal number of clusters by fitting the model with a range of values for :math:`K`. If the line chart resembles an arm, then the "elbow" (the point of inflection on the curve) is a good indication that the underlying model fits best at that point. + +To demonstrate, in the following example the ``KElbowVisualizer`` fits the ``KMeans`` model for a range of :math:`K` values from 4 to 11 on a sample two-dimensional dataset with 8 random clusters of points. When the model is fit with 8 clusters, we can see an "elbow" in the graph, which in this case we know to be the optimal number. .. code:: python from sklearn.datasets import make_blobs - # Make 8 blobs dataset - X, y = make_blobs(centers=8) + # Create synthetic dataset with 8 random clusters + X, y = make_blobs(centers=8, n_features=12, shuffle=True, random_state=42) .. code:: python - from sklearn.cluster import MiniBatchKMeans - + from sklearn.cluster import KMeans from yellowbrick.cluster import KElbowVisualizer # Instantiate the clustering model and visualizer - visualizer = KElbowVisualizer(MiniBatchKMeans(), k=(4,12)) + model = KMeans() + visualizer = KElbowVisualizer(model, k=(4,12)) - visualizer.fit(X) # Fit the training data to the visualizer - visualizer.poof() # Draw/show/poof the data + visualizer.fit(X) # Fit the data to the visualizer + visualizer.poof() # Draw/show/poof the data + +.. image:: images/elbow.png +By default, the scoring parameter ``metric`` is set to ``distortion``, which +computes the sum of squared distances from each point to its assigned center. +However, two other metrics can also be used with the ``KElbowVisualizer`` -- ``silhouette`` and ``calinski_harabaz``. The ``silhouette`` score calculates the mean Silhouette Coefficient of all samples, while the ``calinski_harabaz`` score computes the ratio of dispersion between and within clusters. -.. image:: images/elbow.png +The ``KElbowVisualizer`` also displays the amount of time to train the clustering model per :math:`K` as a dashed green line, but is can be hidden by setting ``timings=False``. In the following example, we'll use the ``calinski_harabaz`` score and hide the time to fit the model. + +.. code:: python + + from sklearn.cluster import KMeans + from yellowbrick.cluster import KElbowVisualizer + + # Instantiate the clustering model and visualizer + model = KMeans() + visualizer = KElbowVisualizer( + model, k=(4,12), metric='calinski_harabaz', timings=False + ) + + visualizer.fit(X) # Fit the data to the visualizer + visualizer.poof() # Draw/show/poof the data + + +.. image:: images/calinski_harabaz.png + +It is important to remember that the "elbow" method does not work well if the data +is not very clustered. In this case, you might see a smooth curve and the optimal value of :math:`K` will be unclear. API Reference ------------- diff --git a/docs/api/cluster/icdm.py b/docs/api/cluster/icdm.py new file mode 100644 index 000000000..0438a90c2 --- /dev/null +++ b/docs/api/cluster/icdm.py @@ -0,0 +1,27 @@ +# Clustering Evaluation Imports +from functools import partial + +from sklearn.cluster import KMeans +from sklearn.datasets import make_blobs as sk_make_blobs + +from yellowbrick.cluster import InterclusterDistance + +# Helpers for easy dataset creation +N_SAMPLES = 1000 +N_FEATURES = 12 +SHUFFLE = True + +# Make blobs partial +make_blobs = partial(sk_make_blobs, n_samples=N_SAMPLES, n_features=N_FEATURES, shuffle=SHUFFLE) + + +if __name__ == '__main__': + # Make 8 blobs dataset + X, y = make_blobs(centers=12) + + # Instantiate the clustering model and visualizer + # Instantiate the clustering model and visualizer + visualizer = InterclusterDistance(KMeans(9)) + + visualizer.fit(X) # Fit the training data to the visualizer + visualizer.poof(outpath="images/icdm.png") # Draw/show/poof the data diff --git a/docs/api/cluster/icdm.rst b/docs/api/cluster/icdm.rst new file mode 100644 index 000000000..42af8e868 --- /dev/null +++ b/docs/api/cluster/icdm.rst @@ -0,0 +1,35 @@ +.. -*- mode: rst -*- + +Intercluster Distance Maps +========================== + +Intercluster distance maps display an embedding of the cluster centers in 2 dimensions with the distance to other centers preserved. E.g. the closer to centers are in the visualization, the closer they are in the original feature space. The clusters are sized according to a scoring metric. By default, they are sized by membership, e.g. the number of instances that belong to each center. This gives a sense of the relative importance of clusters. Note however, that because two clusters overlap in the 2D space, it does not imply that they overlap in the original feature space. + +.. code:: python + + from sklearn.datasets import make_blobs + + # Make 12 blobs dataset + X, y = make_blobs(centers=12, n_samples=1000, n_features=16, shuffle=True) + +.. code:: python + + from sklearn.cluster import KMeans + from yellowbrick.cluster import InterclusterDistance + + # Instantiate the clustering model and visualizer + visualizer = InterclusterDistance(KMeans(9)) + + visualizer.fit(X) # Fit the training data to the visualizer + visualizer.poof() # Draw/show/poof the data + + +.. image:: images/icdm.png + +API Reference +------------- + +.. automodule:: yellowbrick.cluster.icdm + :members: InterclusterDistance + :undoc-members: + :show-inheritance: diff --git a/docs/api/cluster/images/calinski_harabaz.png b/docs/api/cluster/images/calinski_harabaz.png new file mode 100644 index 000000000..6cddfeeea Binary files /dev/null and b/docs/api/cluster/images/calinski_harabaz.png differ diff --git a/docs/api/cluster/images/elbow.png b/docs/api/cluster/images/elbow.png index 164df5841..decf30c5f 100644 Binary files a/docs/api/cluster/images/elbow.png and b/docs/api/cluster/images/elbow.png differ diff --git a/docs/api/cluster/images/icdm.png b/docs/api/cluster/images/icdm.png new file mode 100644 index 000000000..51c5b5a0d Binary files /dev/null and b/docs/api/cluster/images/icdm.png differ diff --git a/docs/api/cluster/index.rst b/docs/api/cluster/index.rst index 5ae6ad2c7..7e971fc23 100644 --- a/docs/api/cluster/index.rst +++ b/docs/api/cluster/index.rst @@ -3,10 +3,11 @@ Clustering Visualizers ====================== -Clustering models are unsupervised methods that attempt to detect patterns in unlabeled data. There are two primary classes of clustering algorithm: *agglomerative* clustering links similar data points together, whereas *centroidal* clustering attempts to find centers or partitions in the data. Yellowbrick provides the ``yellowbrick.cluster`` module to visualize and evaluate clustering behavior. Currently we provide two visualizers to evaluate centroidal mechanisms, particularly K-Means clustering, that help us to discover an optimal :math:`K` parameter in the clustering metric: +Clustering models are unsupervised methods that attempt to detect patterns in unlabeled data. There are two primary classes of clustering algorithm: *agglomerative* clustering links similar data points together, whereas *centroidal* clustering attempts to find centers or partitions in the data. Yellowbrick provides the ``yellowbrick.cluster`` module to visualize and evaluate clustering behavior. Currently we provide several visualizers to evaluate centroidal mechanisms, particularly K-Means clustering, that help us to discover an optimal :math:`K` parameter in the clustering metric: - :doc:`elbow`: visualize the clusters according to some scoring function, look for an "elbow" in the curve. - :doc:`silhouette`: visualize the silhouette scores of each cluster in a single model. +- :doc:`icdm`: visualize the relative distance and size of clusters. Because it is very difficult to ``score`` a clustering model, Yellowbrick visualizers wrap scikit-learn clusterer estimators via their ``fit()`` method. Once the clustering model is trained, then the visualizer can call ``poof()`` to display the clustering evaluation metric. @@ -15,3 +16,4 @@ Because it is very difficult to ``score`` a clustering model, Yellowbrick visual elbow silhouette + icdm diff --git a/docs/api/contrib/index.rst b/docs/api/contrib/index.rst index e91e566ed..d79960a3c 100644 --- a/docs/api/contrib/index.rst +++ b/docs/api/contrib/index.rst @@ -11,3 +11,4 @@ The ``yellowbrick.contrib`` package contains a variety of extra tools and experi boundaries statsmodels scatter + missing/index diff --git a/docs/api/contrib/missing/bar.py b/docs/api/contrib/missing/bar.py new file mode 100644 index 000000000..51695b844 --- /dev/null +++ b/docs/api/contrib/missing/bar.py @@ -0,0 +1,23 @@ +import numpy as np +from sklearn.datasets import make_classification + +# Create dummy data +X, y = make_classification( + n_samples=400, n_features=10, n_informative=2, n_redundant=3, + n_classes=2, n_clusters_per_class=2, random_state=854 + ) + +# assign some NaN values +X[X > 1.5] = np.nan +features = ["Feature {}".format(str(n)) for n in range(10)] + +from yellowbrick.contrib.missing import MissingValuesBar + +viz = MissingValuesBar(features=features) +viz.fit(X) +viz.poof(outpath="images/missingbar.png") + + +viz = MissingValuesBar(features=features) +viz.fit(X, y=y) +viz.poof(outpath="images/missingbar_with_targets.png") diff --git a/docs/api/contrib/missing/bar.rst b/docs/api/contrib/missing/bar.rst new file mode 100644 index 000000000..1a77ea633 --- /dev/null +++ b/docs/api/contrib/missing/bar.rst @@ -0,0 +1,60 @@ +.. -*- mode: rst -*- + +MissingValues Bar +============================= + +The MissingValues Bar visualizer creates a bar graph that counts the number of missing values per feature column. + +If the target y is supplied to fit, then produces a stacked bar chart. + +**Setup** + +.. code:: python + + import numpy as np + from sklearn.datasets import make_classification + + X, y = make_classification( + n_samples=400, n_features=10, n_informative=2, n_redundant=3, + n_classes=2, n_clusters_per_class=2, random_state=854 + ) + # assign some NaN values + X[X > 1.5] = np.nan + features = ["Feature {}".format(str(n)) for n in range(10)] + +------------------------------------------- +Without Targets Supplied +------------------------------------------- + +.. code:: python + + from yellowbrick.contrib.missing import MissingValuesBar + + viz = MissingValuesBar(features=features) + viz.fit(X) + viz.poof() + +.. image:: images/missingbar.png + +------------------------------------------- +With Targets (y) Supplied +------------------------------------------- + +.. code:: python + + from yellowbrick.contrib.missing import MissingValuesBar + + viz = MissingValuesBar(features=features) + viz.fit(X, y=y) # supply the targets via y + viz.poof() + +.. image:: images/missingbar_with_targets.png + + +API Reference +------------- + +.. automodule:: yellowbrick.contrib.missing.bar + :members: MissingValuesBar + :undoc-members: + :show-inheritance: diff --git a/docs/api/contrib/missing/dispersion.py b/docs/api/contrib/missing/dispersion.py new file mode 100644 index 000000000..e09cdb6dd --- /dev/null +++ b/docs/api/contrib/missing/dispersion.py @@ -0,0 +1,23 @@ +import numpy as np +from sklearn.datasets import make_classification + +# Create dummy data +X, y = make_classification( + n_samples=400, n_features=10, n_informative=2, n_redundant=3, + n_classes=2, n_clusters_per_class=2, random_state=854 + ) + +# assign some NaN values +X[X > 1.5] = np.nan +features = ["Feature {}".format(str(n)) for n in range(10)] + +from yellowbrick.contrib.missing import MissingValuesDispersion + +viz = MissingValuesDispersion(features=features) +viz.fit(X) +viz.poof(outpath="images/missingdispersion.png") + + +viz = MissingValuesDispersion(features=features) +viz.fit(X, y=y) +viz.poof(outpath="images/missingdispersion_with_targets.png") diff --git a/docs/api/contrib/missing/dispersion.rst b/docs/api/contrib/missing/dispersion.rst new file mode 100644 index 000000000..3c36e6139 --- /dev/null +++ b/docs/api/contrib/missing/dispersion.rst @@ -0,0 +1,60 @@ +.. -*- mode: rst -*- + +MissingValues Dispersion +============================= + +The MissingValues Dispersion visualizer creates a chart that maps the position of missing values by the order of the index. + +**Setup** + +.. code:: python + + import numpy as np + from sklearn.datasets import make_classification + + X, y = make_classification( + n_samples=400, n_features=10, n_informative=2, n_redundant=3, + n_classes=2, n_clusters_per_class=2, random_state=854 + ) + # assign some NaN values + X[X > 1.5] = np.nan + features = ["Feature {}".format(str(n)) for n in range(10)] + +------------------------------------------- +Without Targets Supplied +------------------------------------------- + +.. code:: python + + from yellowbrick.contrib.missing import MissingValuesDispersion + + viz = MissingValuesDispersion(features=features) + viz.fit(X) + viz.poof() + +.. image:: images/missingdispersion.png + +------------------------------------------- +With Targets (y) Supplied +------------------------------------------- + +.. code:: python + + from yellowbrick.contrib.missing import MissingValuesDispersion + + viz = MissingValuesDispersion(features=features) + viz.fit(X, y=y) # supply the targets via y + viz.poof() + +.. image:: images/missingdispersion_with_targets.png + + + + +API Reference +------------- + +.. automodule:: yellowbrick.contrib.missing.dispersion + :members: MissingValuesDispersion + :undoc-members: + :show-inheritance: diff --git a/docs/api/contrib/missing/images/missingbar.png b/docs/api/contrib/missing/images/missingbar.png new file mode 100644 index 000000000..b4cb4b7ff Binary files /dev/null and b/docs/api/contrib/missing/images/missingbar.png differ diff --git a/docs/api/contrib/missing/images/missingbar_with_targets.png b/docs/api/contrib/missing/images/missingbar_with_targets.png new file mode 100644 index 000000000..9b8af9eb5 Binary files /dev/null and b/docs/api/contrib/missing/images/missingbar_with_targets.png differ diff --git a/docs/api/contrib/missing/images/missingdispersion.png b/docs/api/contrib/missing/images/missingdispersion.png new file mode 100644 index 000000000..662729b5b Binary files /dev/null and b/docs/api/contrib/missing/images/missingdispersion.png differ diff --git a/docs/api/contrib/missing/images/missingdispersion_with_targets.png b/docs/api/contrib/missing/images/missingdispersion_with_targets.png new file mode 100644 index 000000000..0729760d7 Binary files /dev/null and b/docs/api/contrib/missing/images/missingdispersion_with_targets.png differ diff --git a/docs/api/contrib/missing/index.rst b/docs/api/contrib/missing/index.rst new file mode 100644 index 000000000..552364bce --- /dev/null +++ b/docs/api/contrib/missing/index.rst @@ -0,0 +1,15 @@ +.. -*- mode: rst -*- + +Missing Values +======================= + +MissingValues visualizers are a variant of feature visualizers that specifically show places in a dataset that have missing values (numpy NaN). + +- :doc:`bar`: visualize the count of missing values by feature. +- :doc:`dispersion`: visualize the position of missing values by position in the index. + +.. toctree:: + :maxdepth: 2 + + bar + dispersion diff --git a/docs/api/contrib/scatter.rst b/docs/api/contrib/scatter.rst index b766b3131..58e4f08fc 100644 --- a/docs/api/contrib/scatter.rst +++ b/docs/api/contrib/scatter.rst @@ -22,7 +22,7 @@ A scatter visualizer simply plots two features against each other and colors the .. code:: python - from yellowbrick.features import ScatterVisualizer + from yellowbrick.contrib.scatter import ScatterVisualizer visualizer = ScatterVisualizer(x="light", y="C02", classes=classes) diff --git a/docs/api/datasets.rst b/docs/api/datasets.rst index 3a57fe0cb..b8e4a0618 100644 --- a/docs/api/datasets.rst +++ b/docs/api/datasets.rst @@ -69,6 +69,7 @@ The following code snippet can be found at the top of the ``examples/examples.ip Unless otherwise specified, most of the examples currently use one or more of the listed datasets. Each dataset has a ``README.md`` with detailed information about the data source, attributes, and target. Here is a complete listing of all datasets in Yellowbrick and their associated analytical tasks: + - **bikeshare**: suitable for regression - **concrete**: suitable for regression - **credit**: suitable for classification/clustering diff --git a/docs/api/features/images/jointplot.png b/docs/api/features/images/jointplot.png index dc9bc4954..89e116936 100644 Binary files a/docs/api/features/images/jointplot.png and b/docs/api/features/images/jointplot.png differ diff --git a/docs/api/features/images/jointplot_hex.png b/docs/api/features/images/jointplot_hex.png index 94df0d526..ee5964296 100644 Binary files a/docs/api/features/images/jointplot_hex.png and b/docs/api/features/images/jointplot_hex.png differ diff --git a/docs/api/features/images/normalized_sampled_parallel_coordinates.png b/docs/api/features/images/normalized_sampled_parallel_coordinates.png index e8b07ee94..fbefb8dec 100644 Binary files a/docs/api/features/images/normalized_sampled_parallel_coordinates.png and b/docs/api/features/images/normalized_sampled_parallel_coordinates.png differ diff --git a/docs/api/features/images/parallel_coordinates.png b/docs/api/features/images/parallel_coordinates.png index 6617799cd..459030b96 100644 Binary files a/docs/api/features/images/parallel_coordinates.png and b/docs/api/features/images/parallel_coordinates.png differ diff --git a/docs/api/features/images/radviz.png b/docs/api/features/images/radviz.png index ea42f5180..679a6ffce 100644 Binary files a/docs/api/features/images/radviz.png and b/docs/api/features/images/radviz.png differ diff --git a/docs/api/features/images/rank1d_shapiro.png b/docs/api/features/images/rank1d_shapiro.png index b2e1726d4..5d7bcb29d 100644 Binary files a/docs/api/features/images/rank1d_shapiro.png and b/docs/api/features/images/rank1d_shapiro.png differ diff --git a/docs/api/features/images/rank2d_covariance.png b/docs/api/features/images/rank2d_covariance.png index 2ed1386a4..d7a3bf57c 100644 Binary files a/docs/api/features/images/rank2d_covariance.png and b/docs/api/features/images/rank2d_covariance.png differ diff --git a/docs/api/features/images/rank2d_pearson.png b/docs/api/features/images/rank2d_pearson.png index 82b2e3841..54916b3e7 100644 Binary files a/docs/api/features/images/rank2d_pearson.png and b/docs/api/features/images/rank2d_pearson.png differ diff --git a/docs/api/features/importances.rst b/docs/api/features/importances.rst index f25fce811..4fa7a8767 100644 --- a/docs/api/features/importances.rst +++ b/docs/api/features/importances.rst @@ -78,8 +78,8 @@ regression dataset: ] # Extract the instances and target - X = concrete[feats] - y = concrete.strength + X = data[features] + y = data.strength When using a model with a ``coef_`` attribute, it is better to set ``relative=False`` to draw the true magnitude of the coefficient (which may diff --git a/docs/api/features/index.rst b/docs/api/features/index.rst index 9d5b77de5..b9b769a75 100644 --- a/docs/api/features/index.rst +++ b/docs/api/features/index.rst @@ -22,7 +22,7 @@ At the moment we have the following feature analysis visualizers implemented: - :doc:`manifold`: visualize high dimensional data using manifold learning - :doc:`importances`: rank features by relative importance in a model - :doc:`rfecv`: select a subset of features by importance -- :doc:`jointplot`: plot 2D correlation between features and target +- :doc:`jointplot`: (aka Jointplots) plot 2D correlation between features and target Feature analysis visualizers implement the ``Transformer`` API from scikit-learn, meaning they can be used as intermediate transform steps diff --git a/docs/api/features/jointplot.py b/docs/api/features/jointplot.py index 13ac0af5b..d86dff126 100644 --- a/docs/api/features/jointplot.py +++ b/docs/api/features/jointplot.py @@ -5,12 +5,8 @@ def jointplot(X, y, outpath, **kwargs): - # Create a new figure and axes - fig = plt.figure() - ax = fig.add_subplot(111) - # Create the visualizer - visualizer = JointPlotVisualizer(ax=ax, **kwargs) + visualizer = JointPlotVisualizer(**kwargs) visualizer.fit(X, y) visualizer.transform(X) diff --git a/docs/api/features/manifold.rst b/docs/api/features/manifold.rst index 269ee38a3..c05147517 100644 --- a/docs/api/features/manifold.rst +++ b/docs/api/features/manifold.rst @@ -69,7 +69,7 @@ this by assigning a color to each label and showing the labels in a legend. "temperature", "relative humidity", "light", "C02", "humidity" ] - # Extract the data from the data frame. + # Extract the instances and target X = data[features] y = data.occupancy @@ -106,7 +106,18 @@ the ``f_classif`` score to find the 3 best features in our occupancy dataset. ("viz", Manifold(manifold='isomap', target='discrete')), ]) - X, y = load_occupancy_data() + # Load the classification dataset + data = load_data("occupancy") + + # Specify the features of interest + features = [ + "temperature", "relative humidity", "light", "CO2", "humidity" + ] + + # Extract the instances and target + X = data[features] + y = data.occupancy + model.fit(X, y) model.named_steps['viz'].poof() diff --git a/docs/api/features/pcoords.py b/docs/api/features/pcoords.py index 70fa7c9c2..e65454dee 100644 --- a/docs/api/features/pcoords.py +++ b/docs/api/features/pcoords.py @@ -15,9 +15,9 @@ def load_occupancy_data(): features = ["temperature", "relative humidity", "light", "C02", "humidity"] classes = ['unoccupied', 'occupied'] - # Extract the numpy arrays from the data frame - X = data[features].as_matrix() - y = data.occupancy.as_matrix() + # Extract the instances and target + X = data[features] + y = data.occupancy return X, y, features, classes diff --git a/docs/api/features/pcoords.rst b/docs/api/features/pcoords.rst index b21c98749..8cc96bc86 100644 --- a/docs/api/features/pcoords.rst +++ b/docs/api/features/pcoords.rst @@ -18,6 +18,7 @@ Data scientists use this method to detect clusters of instances that have simila ] classes = ["unoccupied", "occupied"] + # Extract the instances and target X = data[features] y = data.occupancy diff --git a/docs/api/features/radviz.py b/docs/api/features/radviz.py index b61ab5096..45de47299 100644 --- a/docs/api/features/radviz.py +++ b/docs/api/features/radviz.py @@ -9,9 +9,9 @@ features = ["temperature", "relative humidity", "light", "C02", "humidity"] classes = ['unoccupied', 'occupied'] -# Extract the numpy arrays from the data frame -X = data[features].as_matrix() -y = data.occupancy.as_matrix() +# Extract the instances and target +X = data[features] +y = data.occupancy # Instantiate the visualizer visualizer = RadViz(classes=classes, features=features) diff --git a/docs/api/features/radviz.rst b/docs/api/features/radviz.rst index be5de18a0..425b097da 100644 --- a/docs/api/features/radviz.rst +++ b/docs/api/features/radviz.rst @@ -20,7 +20,7 @@ picture of your data. RadViz will raise a DataWarning to inform you of the percent missing. If you do receive this warning, you may want to look at imputation strategies. -A good starting place is `scikit-learn Imputer. `_ +A good starting place is the `scikit-learn Imputer. `_ .. code:: python @@ -31,9 +31,9 @@ A good starting place is `scikit-learn Imputer. +# Created: Tue Sept 11 12:09:40 2018 -0400 +# +# ID: binning.py [] kautumn06@gmail.com $ + +""" +Generates images for the balanced binning reference documentation. +""" + +########################################################################## +## Imports +########################################################################## + +from yellowbrick.target import BalancedBinningReference +from sklearn.datasets import load_diabetes + + +def balanced_binning_reference(path="images/balanced_binning_reference.png"): + # Load a regression data set + data = load_diabetes() + + # Extract the target variable + y = data['target'] + + # Instantiate and fit the visualizer + visualizer = BalancedBinningReference() + visualizer.fit(y) + return visualizer.poof(outpath=path) + + + +if __name__ == '__main__': + balanced_binning_reference() diff --git a/docs/api/target/binning.rst b/docs/api/target/binning.rst new file mode 100644 index 000000000..a716cfffe --- /dev/null +++ b/docs/api/target/binning.rst @@ -0,0 +1,43 @@ +.. -*- mode: rst -*- + +Balanced Binning Reference +========================== + +Frequently, machine learning problems in the real world suffer from the curse of dimensionality; you have fewer training instances than you'd like and the predictive signal is distributed (often unpredictably!) across many different features. + +Sometimes when the your target variable is continuously-valued, there simply aren't enough instances to predict these values to the precision of regression. In this case, we can sometimes transform the regression problem into a classification problem by binning the continuous values into makeshift classes. + +To help the user select the optimal number of bins, the ``BalancedBinningReference`` visualizer takes the target variable ``y`` as input and generates a histogram with vertical lines indicating the recommended value points to ensure that the data is evenly distributed into each bin. + + +.. code:: python + + from yellowbrick.target import BalancedBinningReference + + # Load the a regression data set + data = load_data("concrete") + + # Extract the target of interest + y = data["strength"] + + # Instantiate the visualizer + visualizer = BalancedBinningReference() + + visualizer.fit(y) # Fit the data to the visualizer + visualizer.poof() # Draw/show/poof the data + + +.. image:: images/balanced_binning_reference.png + +.. seealso:: + + To learn more, please read Rebecca Bilbro's article `"Creating Categorical Variables from Continuous Data." `_ + + +API Reference +------------- + +.. automodule:: yellowbrick.target.binning + :members: BalancedBinningReference + :undoc-members: + :show-inheritance: diff --git a/docs/api/target/class_balance.py b/docs/api/target/class_balance.py new file mode 100644 index 000000000..6a7fae6fc --- /dev/null +++ b/docs/api/target/class_balance.py @@ -0,0 +1,54 @@ +# class_balance +# Generates images for the class balance documentation. +# +# Author: Benjamin Bengfort +# Created: Thu Jul 19 12:09:40 2018 -0400 +# +# ID: class_balance.py [] benjamin@bengfort.com $ + +""" +Generates images for the class balance documentation. +""" + +########################################################################## +## Imports +########################################################################## + +from yellowbrick.target import ClassBalance +from yellowbrick.datasets import load_occupancy, load_game + +from sklearn.model_selection import train_test_split + +def compare_class_balance(path="images/class_balance_compare.png"): + data = load_occupancy() + + features = ["temperature", "relative_humidity", "light", "C02", "humidity"] + classes = ['unoccupied', 'occupied'] + + # Extract the numpy arrays from the data frame + X = data[features] + y = data["occupancy"] + + # Create the train and test data + _, _, y_train, y_test = train_test_split(X, y, test_size=0.2) + + # Instantiate the classification model and visualizer + visualizer = ClassBalance(labels=classes) + + visualizer.fit(y_train, y_test) + return visualizer.poof(outpath=path) + + +def balance_class_balance(path="images/class_balance.png"): + data = load_game() + y = data["outcome"] + + oz = ClassBalance(labels=["draw", "loss", "win"]) + oz.fit(y) + return oz.poof(outpath=path) + + + +if __name__ == '__main__': + compare_class_balance() + balance_class_balance() diff --git a/docs/api/target/class_balance.rst b/docs/api/target/class_balance.rst new file mode 100644 index 000000000..7dd9bf3f0 --- /dev/null +++ b/docs/api/target/class_balance.rst @@ -0,0 +1,72 @@ +.. -*- mode: rst -*- + +Class Balance +============= + +One of the biggest challenges for classification models is an imbalance of classes in the training data. Severe class imbalances may be masked by relatively good F1 and accuracy scores -- the classifier is simply guessing the majority class and not making any evaluation on the underrepresented class. + +There are several techniques for dealing with class imbalance such as stratified sampling, down sampling the majority class, weighting, etc. But before these actions can be taken, it is important to understand what the class balance is in the training data. The ``ClassBalance`` visualizer supports this by creating a bar chart of the *support* for each class, that is the frequency of the classes' representation in the dataset. + +.. code:: python + + from yellowbrick.datasets import load_game + from yellowbrick.target import ClassBalance + + # Load the classification data set + data = load_game() + + # Specify the target + y = data["outcome"] + + visualizer = ClassBalance(labels=["draw", "loss", "win"]) + visualizer.fit(y) + visualizer.poof() + +.. image:: images/class_balance.png + +The resulting figure allows us to diagnose the severity of the balance issue. In this figure we can see that the ``"win"`` class dominates the other two classes. One potential solution might be to create a binary classifier: ``"win"`` vs ``"not win"`` and combining the ``"loss"`` and ``"draw"`` classes into one class. + +.. warning:: + The ``ClassBalance`` visualizer interface has changed in version 0.9, a classification model is no longer required to instantiate the visualizer, it can operate on data only. Additionally the signature of the fit method has changed from ``fit(X, y=None)`` to ``fit(y_train, y_test=None)``, passing in ``X`` is no longer required. + +If a class imbalance must be maintained during evaluation (e.g. the event being classified is actually as rare as the frequency implies) then *stratified sampling* should be used to create train and test splits. This ensures that the test data has roughly the same proportion of classes as the training data. While scikit-learn does this by default in ``train_test_split`` and other ``cv`` methods, it can be useful to compare the support of each class in both splits. + +The ``ClassBalance`` visualizer has a "compare" mode, where the train and test data can be passed to ``fit()``, creating a side-by-side bar chart instead of a single bar chart as follows: + +.. code:: python + + from sklearn.model_selection import train_test_split + from yellowbrick.model_selection import ClassBalance + + # Load the classification data set + data = load_data('occupancy') + + # Specify the features of interest and the target + features = ["temperature", "relative_humidity", "light", "C02", "humidity"] + classes = ["unoccupied", "occupied"] + + # Extract the instances and target + X = data[features] + y = data["occupancy"] + + # Create the train and test data + _, _, y_train, y_test = train_test_split(X, y, test_size=0.2) + + # Instantiate the classification model and visualizer + visualizer = ClassBalance(labels=classes) + + visualizer.fit(y_train, y_test) + return visualizer.poof() + +.. image:: images/class_balance_compare.png + +This visualization allows us to do a quick check to ensure that the proportion of each class is roughly similar in both splits. This visualization should be a first stop particularly when evaluation metrics are highly variable across different splits. + + +API Reference +------------- + +.. automodule:: yellowbrick.target.class_balance + :members: ClassBalance + :undoc-members: + :show-inheritance: diff --git a/docs/api/target/feature_correlation.py b/docs/api/target/feature_correlation.py new file mode 100644 index 000000000..e451b4371 --- /dev/null +++ b/docs/api/target/feature_correlation.py @@ -0,0 +1,71 @@ +# feature_correlation +# Generates images for the feature correlation documentation. +# +# Author: Zijie (ZJ) Poh +# Created: Tue Jul 31 20:21:32 2018 -0700 +# +# +""" +Generates images for the feature correlation documentation. +""" + +########################################################################## +## Imports +########################################################################## + +import numpy as np +import pandas as pd +from sklearn import datasets + +from yellowbrick.target import FeatureCorrelation + + +########################################################################## +## Plotting Functions +########################################################################## + +def feature_correlation_pearson( + path="images/feature_correlation_pearson.png"): + data = datasets.load_diabetes() + X, y = data['data'], data['target'] + feature_names = np.array(data['feature_names']) + + visualizer = FeatureCorrelation(labels=feature_names) + visualizer.fit(X, y) + visualizer.poof(outpath=path, clear_figure=True) + + +def feature_correlation_mutual_info_classification( + path="images/feature_correlation_mutual_info_classification.png"): + data = datasets.load_wine() + X, y = data['data'], data['target'] + feature_names = np.array(data['feature_names']) + X_pd = pd.DataFrame(X, columns=feature_names) + + feature_to_plot = ['alcohol', 'ash', 'hue', 'proline', 'total_phenols'] + + visualizer = FeatureCorrelation(method='mutual_info-classification', + feature_names=feature_to_plot) + visualizer.fit(X_pd, y, random_state=0) + visualizer.poof(outpath=path, clear_figure=True) + + +def feature_correlation_mutual_info_regression( + path="images/feature_correlation_mutual_info_regression.png"): + data = datasets.load_diabetes() + X, y = data['data'], data['target'] + feature_names = np.array(data['feature_names']) + + discrete_features = [False for _ in range(len(feature_names))] + discrete_features[1] = True + + visualizer = FeatureCorrelation(method='mutual_info-regression', + labels=feature_names, sort=True) + visualizer.fit(X, y, discrete_features=discrete_features, random_state=0) + visualizer.poof(outpath=path, clear_figure=True) + + +if __name__ == '__main__': + feature_correlation_pearson() + feature_correlation_mutual_info_classification() + feature_correlation_mutual_info_regression() diff --git a/docs/api/target/feature_correlation.rst b/docs/api/target/feature_correlation.rst new file mode 100644 index 000000000..c1c4166e5 --- /dev/null +++ b/docs/api/target/feature_correlation.rst @@ -0,0 +1,89 @@ +.. -*- mode: rst -*- + +Feature Correlation +=================== + +This visualizer calculates Pearson correlation coefficients and mutual information between features and the dependent variable. +This visualization can be used in feature selection to identify features with high correlation or large mutual information with the dependent variable. + +Pearson Correlation +------------------- + +The default calculation is Pearson correlation, which is perform with ``scipy.stats.pearsonr``. + +.. code:: python + + from sklearn import datasets + from yellowbrick.target import FeatureCorrelation + + # Load the regression data set + data = datasets.load_diabetes() + X, y = data['data'], data['target'] + feature_names = np.array(data['feature_names']) + + visualizer = FeatureCorrelation(labels=feature_names) + visualizer.fit(X, y) + visualizer.poof() + +.. image:: images/feature_correlation_pearson.png + +Mutual Information - Regression +------------------------------- + +Mutual information between features and the dependent variable is calculated with ``sklearn.feature_selection.mutual_info_classif`` when ``method='mutual_info-classification'`` and ``mutual_info_regression`` when ``method='mutual_info-regression'``. +It is very important to specify discrete features when calculating mutual information because the calculation for continuous and discrete variables are different. +See `scikit-learn documentation `_ for more details. + +.. code:: python + + from sklearn import datasets + from yellowbrick.target import FeatureCorrelation + + # Load the regression data set + data = datasets.load_diabetes() + X, y = data['data'], data['target'] + feature_names = np.array(data['feature_names']) + + discrete_features = [False for _ in range(len(feature_names))] + discrete_features[1] = True + + visualizer = FeatureCorrelation(method='mutual_info-regression', + labels=feature_names) + visualizer.fit(X, y, discrete_features=discrete_features, random_state=0) + visualizer.poof() + +.. image:: images/feature_correlation_mutual_info_regression.png + +Mutual Information - Classification +----------------------------------- + +By fitting with a pandas DataFrame, the feature labels are automatically obtained from the column names. +This visualizer also allows sorting of the bar plot according to the calculated mutual information (or Pearson correlation coefficients) and selecting features to plot by specifying the names of the features or the feature index. + +.. code:: python + + from sklearn import datasets + from yellowbrick.target import FeatureCorrelation + + # Load the regression data set + data = datasets.load_diabetes() + X, y = data['data'], data['target'] + feature_names = np.array(data['feature_names']) + X_pd = pd.DataFrame(X, columns=feature_names) + + feature_to_plot = ['alcohol', 'ash', 'hue', 'proline', 'total_phenols'] + + visualizer = FeatureCorrelation(method='mutual_info-classification', + feature_names=feature_to_plot, sort=True) + visualizer.fit(X_pd, y, random_state=0) + visualizer.poof() + +.. image:: images/feature_correlation_mutual_info_classification.png + +API Reference +------------- + +.. automodule:: yellowbrick.target.feature_correlation + :members: FeatureCorrelation + :undoc-members: + :show-inheritance: diff --git a/docs/api/target/images/balanced_binning_reference.png b/docs/api/target/images/balanced_binning_reference.png new file mode 100644 index 000000000..f287419a2 Binary files /dev/null and b/docs/api/target/images/balanced_binning_reference.png differ diff --git a/docs/api/target/images/class_balance.png b/docs/api/target/images/class_balance.png new file mode 100644 index 000000000..1393e7497 Binary files /dev/null and b/docs/api/target/images/class_balance.png differ diff --git a/docs/api/target/images/class_balance_compare.png b/docs/api/target/images/class_balance_compare.png new file mode 100644 index 000000000..59fdd2210 Binary files /dev/null and b/docs/api/target/images/class_balance_compare.png differ diff --git a/docs/api/target/images/feature_correlation_mutual_info_classification.png b/docs/api/target/images/feature_correlation_mutual_info_classification.png new file mode 100644 index 000000000..5bbb2cf0a Binary files /dev/null and b/docs/api/target/images/feature_correlation_mutual_info_classification.png differ diff --git a/docs/api/target/images/feature_correlation_mutual_info_regression.png b/docs/api/target/images/feature_correlation_mutual_info_regression.png new file mode 100644 index 000000000..3d00a22b9 Binary files /dev/null and b/docs/api/target/images/feature_correlation_mutual_info_regression.png differ diff --git a/docs/api/target/images/feature_correlation_pearson.png b/docs/api/target/images/feature_correlation_pearson.png new file mode 100644 index 000000000..40dc20e89 Binary files /dev/null and b/docs/api/target/images/feature_correlation_pearson.png differ diff --git a/docs/api/target/index.rst b/docs/api/target/index.rst new file mode 100644 index 000000000..52f135d75 --- /dev/null +++ b/docs/api/target/index.rst @@ -0,0 +1,26 @@ +.. -*- mode: rst -*- + +Target Visualizers +================== + +Target visualizers specialize in visually describing the dependent variable for supervised modeling, often referred to as ``y`` or the target. + +The following visualizations are currently implemented: + +- :doc:`binning`: Generate histogram with vertical lines showing the recommended value point to bin data into evenly distributed bins. +- :doc:`class_balance`: Visual inspection of the target to show the support of each class to the final estimator. +- :doc:`feature_correlation`: Plot correlation between features and dependent variables. + +.. code:: python + + # Target Visualizers Imports + from yellowbrick.target import BalancedBinningReference + from yellowbrick.target import ClassBalance + from yellowbrick.target import FeatureCorrelation + +.. toctree:: + :maxdepth: 2 + + binning + class_balance + feature_correlation diff --git a/docs/api/text/corpus.rst b/docs/api/text/corpus.rst index 02989148c..2c0c91f87 100644 --- a/docs/api/text/corpus.rst +++ b/docs/api/text/corpus.rst @@ -3,7 +3,7 @@ Loading a Text Corpus ===================== -As in the previous sections, Yellowbrick has provided a sample dataset to run the following cells. In particular, we are going to use a text corpus wrangled from the `Baleen RSS Corpus `_ to present the following examples. If you haven't already downloaded the data, you can do so by running: +As in the previous sections, Yellowbrick has provided a sample dataset to run the following cells. In particular, we are going to use a text corpus wrangled from the `Baleen RSS Corpus `_ to present the following examples. If you haven't already downloaded the data, you can do so by running: :: @@ -82,7 +82,7 @@ This is a fairly long bit of code, so let's walk through it step by step. The da ├── 56d62adec1808113ffb88054.txt └── 56d70f17c180810560aec345.txt -Each of the documents in the corpus is stored in a text file labeled with its hash signature in a directory that specifies its label or category. Therefore the first step after checking to make sure the specified path exists is to list all the directories in the `hobbies` directory—this gives us each of our categories, which we will store later in the bunch. +Each of the documents in the corpus is stored in a text file labeled with its hash signature in a directory that specifies its label or category. Therefore the first step after checking to make sure the specified path exists is to list all the directories in the `hobbies` directory---this gives us each of our categories, which we will store later in the bunch. The second step is to create placeholders for holding filenames, text data, and labels. We can then loop through the list of categories, list the files in each category directory, add those files to the files list, add the category name to the target list, then open and read the file to add it to data. diff --git a/docs/api/text/dispersion.py b/docs/api/text/dispersion.py index a0976c023..66dfab43a 100644 --- a/docs/api/text/dispersion.py +++ b/docs/api/text/dispersion.py @@ -37,8 +37,8 @@ def dispersion(target_words, text, outpath, **kwargs): corpus = load_corpus("../../../examples/data/hobbies") # Convert corpus into a list of all words from beginning to end - text = [word for doc in corpus.data for word in doc.split()] - + # text = [word for doc in corpus.data for word in doc.split()] + text = [doc.split() for doc in corpus.data] # Select target words to visualize target_words = ['Game', 'player', 'score', 'oil', 'Man'] diff --git a/docs/api/text/dispersion.rst b/docs/api/text/dispersion.rst index 6852ceda3..800440492 100644 --- a/docs/api/text/dispersion.rst +++ b/docs/api/text/dispersion.rst @@ -16,8 +16,8 @@ After importing the visualizer, we can :doc:`load the corpus ` # Load the text data corpus = load_corpus("hobbies") - # create a list of words from the corpus text - text = [word for doc in corpus.data for word in doc.split()] + # Create a list of words from the corpus text + text = [doc.split() for doc in corpus.data] # Choose words whose occurence in the text will be plotted target_words = ['Game', 'player', 'score', 'oil', 'Man'] diff --git a/docs/api/text/images/dispersion_docs.png b/docs/api/text/images/dispersion_docs.png index 7687bf50e..b2f623209 100644 Binary files a/docs/api/text/images/dispersion_docs.png and b/docs/api/text/images/dispersion_docs.png differ diff --git a/docs/changelog.rst b/docs/changelog.rst index d3205a603..ebb822017 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -3,6 +3,63 @@ Changelog ========= +Version 0.9 +----------- +* Tag: v0.9_ +* Deployed: Wednesday, November 14, 2018 +* Contributors: Rebecca Bilbro, Benjamin Bengfort, Zijie (ZJ) Poh, Kristen McIntyre, Nathan Danielsen, David Waterman, Larry Gray, Prema Roman, Juan Kehoe, Alyssa Batula, Peter Espinosa, Joanne Lin, @rlshuhart, @archaeocharlie, @dschoenleber, Tim Black, @iguk1987, Mohammed Fadhil, Jonathan Lacanlale, Andrew Godbehere, Sivasurya Santhanam, Gopal Krishna + +Major Changes: + - Target module added for visualizing dependent variable in supervised models. + - Prototype missing values visualizer in contrib module. + - ``BalancedBinningReference`` visualizer for thresholding unbalanced data (undocumented). + - ``CVScores`` visualizer to instrument cross-validation. + - ``FeatureCorrelation`` visualizer to compare relationship between a single independent variable and the target. + - ``ICDM`` visualizer, intercluster distance mapping using projections similar to those used in pyLDAVis. + - ``PrecisionRecallCurve`` visualizer showing the relationship of precision and recall in a threshold-based classifier. + - Enhanced ``FeatureImportance`` for multi-target and multi-coefficient models (e.g probabilistic models) and allows stacked bar chart. + - Adds option to plot PDF to ``ResidualsPlot`` histogram. + - Adds document boundaries option to ``DispersionPlot`` and uses colored markers to depict class. + - Added alpha parameter for opacity to the scatter plot visualizer. + - Modify ``KElbowVisualizer`` to accept a list of k values. + - ``ROCAUC`` bugfix to allow binary classifiers that only have a decision function. + - ``TSNE`` bugfix so that title and size params are respected. + - ``ConfusionMatrix`` bugfix to correct percentage displays adding to 100. + - ``ResidualsPlot`` bugfix to ensure specified colors are both in histogram and scatterplot. + - Fixed unicode decode error on Py2 compatible Windows using Hobbies corpus. + - Require matplotlib 1.5.1 or matplotlib 2.0 (matplotlib 3.0 not supported yet). + - Deprecated percent and sample_weight arguments to ``ConfusionMatrix`` fit method. + - Yellowbrick now depends on SciPy 1.0 and scikit-learn 0.20. + +Minor Changes: + - Removed hardcoding of ``SilhouetteVisualizer`` axes dimensions. + - Audit classifiers to ensure they conform to score API. + - Fix for ``Manifold`` ``fit_transform`` bug. + - Fixed ``Manifold`` import bug. + - Started reworking datasets API for easier loading of examples. + - Added ``Timer`` utility for keeping track of fit times. + - Added slides to documentation for teachers teaching ML/Yellowbrick. + - Added an FAQ to the documentation. + - Manual legend drawing utility. + - New examples notebooks for regression and clustering. + - Example of interactive classification visualization using ipywidgets. + - Example of using Yellowbrick with PyTorch. + - Repairs to ``ROCAUC`` tests and binary/multiclass ``ROCAUC`` construction. + - Rename tests/random.py to tests/rand.py to prevent NumPy errors. + - Improves ``ROCAUC``, ``KElbowVisualizer``, and ``SilhouetteVisualizer`` documentation. + - Fixed visual display bug in ``JointPlotVisualizer``. + - Fixed image in ``JointPlotVisualizer`` documentation. + - Clear figure option to poof. + - Fix color plotting error in residuals plot quick method. + - Fixed bugs in ``KElbowVisualizer``, ``FeatureImportance``, Index, and Datasets documentation. + - Use LGTM for code quality analysis (replacing Landscape). + - Updated contributing docs for better PR workflow. + - Submitted JOSS paper. + + +.. _v0.9: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.9 + + Version 0.8 ----------- * Tag: v0.8_ @@ -35,10 +92,10 @@ Minor Changes: .. _v0.8: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.8.0 -Version 0.7.0 -------------- +Version 0.7 +----------- -* Tag: v0.7.0_ +* Tag: v0.7_ * Deployed: Thursday, May 17, 2018 * Contributors: Benjamin Bengfort, Nathan Danielsen, Rebecca Bilbro, Larry Gray, Ian Ozsvald, Jeremy Tuloup, Abhishek Bharani, Raúl Peralta Lozada, Tabishsada, Kristen McIntyre, Neal Humphrey @@ -70,12 +127,12 @@ Deprecation Warnings: **NOTE**: These deprecation warnings originally mentioned deprecation in 0.7, but their life was extended by an additional version. -.. _v0.7.0: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.7.0 +.. _v0.7: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.7 -Version 0.6.0 -------------- +Version 0.6 +----------- -* Tag: v0.6.0_ +* Tag: v0.6_ * Deployed: Saturday, March 17, 2018 * Contributors: Benjamin Bengfort, Nathan Danielsen, Rebecca Bilbro, Larry Gray, Kristen McIntyre, George Richardson, Taylor Miller, Gary Mayfield, Phillip Schafer, Jason Keung @@ -104,7 +161,7 @@ Deprecation Warnings: - ``ScatterPlotVisualizer`` is being moved to contrib in 0.7 - ``DecisionBoundaryVisualizer`` is being moved to contrib in 0.7 -.. _v0.6.0: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.6.0 +.. _v0.6: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.6 Version 0.5 ----------- diff --git a/docs/contributing.rst b/docs/contributing.rst index 260b6a657..9765ec1ed 100644 --- a/docs/contributing.rst +++ b/docs/contributing.rst @@ -156,7 +156,7 @@ Visualizers interact with scikit-learn objects by intersecting with them at the Creating a visualizer means defining a class that extends ``Visualizer`` or one of its subclasses, then implementing several of the methods described above. A barebones implementation is as follows:: - import matplotlib.pyplot as plot + import matplotlib.pyplot as plt from yellowbrick.base import Visualizer @@ -173,7 +173,7 @@ Creating a visualizer means defining a class that extends ``Visualizer`` or one if self.ax is None: self.ax = self.gca() - self.ax.plot(X) + self.ax.plt(X) def finalize(self): self.set_title("My Visualizer") diff --git a/docs/faq.rst b/docs/faq.rst new file mode 100644 index 000000000..036ad10c3 --- /dev/null +++ b/docs/faq.rst @@ -0,0 +1,162 @@ +.. -*- mode: rst -*- + +Frequently Asked Questions +========================== + +Welcome to our frequently asked questions page. We're glad that you're using Yellowbrick! If your question is not captured here, please submit it to our `Google Groups Listserv `_. This is an email list/forum that you, as a Yellowbrick user, can join and interact with other users to address and troubleshoot Yellowbrick issues. The Google Groups Listserv is where you should be able to receive the quickest response. We would welcome and encourage you to join the group so that you can respond to others' questions! You can also ask questions on `Stack Overflow `_ and tag them with "yellowbrick". Finally, you can add issues on GitHub and you can tweet or direct message us on Twitter `@scikit_yb `_. + + +How can I change the size of a Yellowbrick plot? +------------------------------------------------ + +You can change the ``size`` of a plot by passing in the desired dimensions in pixels on instantiation of the visualizer: + +.. code:: python + + # Import the visualizer + from yellowbrick.features import RadViz + + # Instantiate the visualizer using the ``size`` param + visualizer = RadViz( + classes=classes, features=features, size=(1080, 720) + ) + + ... + + +Note: we are considering adding support for passing in ``size`` in inches in a future Yellowbrick release. For a convenient inch-to-pixel converter, check out `www.unitconversion.org `_. + +How can I change the title of a Yellowbrick plot? +--------------------------------------------------- + +You can change the ``title`` of a plot by passing in the desired title as a string on instantiation: + + +.. code:: python + + from yellowbrick.classifier import ROCAUC + from sklearn.linear_model import RidgeClassifier + + # Create your custom title + my_title = "ROCAUC Curves for Multiclass RidgeClassifier" + + # Instantiate the visualizer passing the custom title + visualizer = ROCAUC( + RidgeClassifier(), classes=classes, title=my_title + ) + + ... + + +How can I change the color of a Yellowbrick plot? +------------------------------------------------- + +To customize coloring in your plot, use the ``colors`` or ``cmap`` (or ``colormap``) arguments. Note that different visualizers may require slightly different arguments depending on how they construct the plots. + +For instance, the :doc:`api/features/manifold` accepts a ``colors`` argument, for which ``discrete`` targets should be the name of one of the :doc:`api/palettes` or a list of `matplotlib colors `_ represented as strings: +For instance, the :doc:`api/features/manifold` accepts a ``colors`` argument, for which ``discrete`` targets should be the name of a palette from the Yellowbrick :doc:`api/palettes` or a list of `matplotlib colors `_ represented as strings: + +.. code:: python + + from yellowbrick.features.manifold import Manifold + + visualizer = Manifold( + manifold="tsne", target="discrete", colors=["teal", "orchid"] + ) + + ... + + +... whereas for ``continuous`` targets, ``colors`` should be a colormap: + + +.. code:: python + + from yellowbrick.features.manifold import Manifold + + visualizer = Manifold( + manifold="isomap", target="continuous", colors="YlOrRd" + ) + + ... + + +Other visualizers accept a ``cmap`` argument: + +.. code:: python + + from sklearn.linear_model import LogisticRegression + from yellowbrick.classifier import ConfusionMatrix + + visualizer = ConfusionMatrix( + LogisticRegression(), cmap="YlGnBu" + ) + + ... + +Or a ``colormap`` argument: + +.. code:: python + + from yellowbrick.features import ParallelCoordinates + + # Instantiate the visualizer + visualizer = ParallelCoordinates( + classes=classes, features=features, colormap="PRGn" + ) + + ... + +The :doc:`api/regressor/residuals` accepts color argument for the training and test points, ``train_color`` and ``test_color``, respectively: + +.. code:: python + + from yellowbrick.regressor import ResidualsPlot + from sklearn.linear_model import ElasticNet + + visualizer = ResidualsPlot( + model=ElasticNet() + train_color=train_color, # color of points model was trained on + test_color=train_color, # color of points model was tested on + line_color=line_color # color of zero-error line + ) + + +How can I save a Yellowbrick plot? +----------------------------------- + +Save your Yellowbrick plot by passing an ``outpath`` into ``poof()``: + +.. code:: python + + from sklearn.cluster import MiniBatchKMeans + from yellowbrick.cluster import KElbowVisualizer + + visualizer = KElbowVisualizer(MiniBatchKMeans(), k=(4,12)) + + visualizer.fit(X) + visualizer.poof(outpath="kelbow_minibatchkmeans.png") + + ... + +Most backends support png, pdf, ps, eps and svg to save your work! + + +How can I make overlapping points show up better? +---------------------------------------------------- + +You can use the ``alpha`` param to change the opacity of plotted points (where ``alpha=1`` is complete opacity, and ``alpha=0`` is complete transparency): + +.. code:: python + + from yellowbrick.contrib.scatter import ScatterVisualizer + + visualizer = ScatterVisualizer( + x="light", y="C02", classes=classes, alpha=0.5 + ) + + +How can I access the sample datasets used in the examples? +--------------------------------------------------------------- + +Visit the :doc:`api/datasets` page. diff --git a/docs/gallery.rst b/docs/gallery.rst index bccdb9bc7..f78f56a4d 100644 --- a/docs/gallery.rst +++ b/docs/gallery.rst @@ -1,13 +1,10 @@ .. -*- mode: rst -*- -==================== -Example Gallery -======= Gallery -==================== +======= -Features Analysis ------------------ +Feature Analysis +---------------- .. image:: api/features/images/radviz.png :width: 200px @@ -104,7 +101,7 @@ Regression Visualizers :target: api/regressor/alphas.html#alpha-selection -Clasification Visualizers +Classification Visualizers -------------------------- .. image:: api/classifier/images/classification_report.png @@ -113,23 +110,41 @@ Clasification Visualizers :alt: GaussianNB Classification Report :target: api/classifier/classification_report.html#classification-report -.. image:: api/classifier/images/confusion_matrix.png +.. image:: api/classifier/images/confusion_matrix_digits.png :width: 200px :height: 100px - :alt: Logistic Regression Confusion Matrix + :alt: Logistic Regression Confusion Matrix with Numeric Labels :target: api/classifier/confusion_matrix.html#confusion-matrix -.. image:: api/classifier/images/rocauc.png +.. image:: api/classifier/images/confusion_matrix_iris.png + :width: 200px + :height: 100px + :alt: Logistic Regression Confusion Matrix with Class Name Labels + :target: api/classifier/confusion_matrix.html#plotting-with-class-names + +.. image:: api/classifier/images/rocauc_binary.png :width: 200px :height: 100px - :alt: ROC Curves for Logistic Regression + :alt: Binary ROC Curves for Logistic Regression :target: api/classifier/rocauc.html#rocauc -.. image:: api/classifier/images/class_balance.png +.. image:: api/classifier/images/rocauc_multiclass.png + :width: 200px + :height: 100px + :alt: Multiclass ROC Curves + :target: api/classifier/rocauc.html#multi-class-rocauc-curves + +.. image:: api/classifier/images/binary_precision_recall.png + :width: 200px + :height: 100px + :alt: Precision-Recall Curves + :target: api/classifier/prcurve.html + +.. image:: api/classifier/images/multiclass_precision_recall_full.png :width: 200px :height: 100px - :alt: Class Balance for Random Forest Classifier - :target: api/classifier/class_balance.html#class-balance + :alt: Multi-Label Precision-Recall Curves + :target: api/classifier/prcurve.html#multi-label-classification .. image:: api/classifier/images/class_prediction_error.png :width: 200px @@ -158,6 +173,12 @@ Clustering Visualizers :alt: Silhoutte Plot of Mini Batch Kmeans Clustering :target: api/cluster/silhouette.html#silhouette-visualizer +.. image:: api/cluster/images/icdm.png + :width: 200px + :height: 100px + :alt: Intercluster Distance Maps + :target: api/cluster/icdm.html#intercluster-distance-maps + Model Selection Visualizers --------------------------- @@ -179,6 +200,18 @@ Model Selection Visualizers :alt: Learning Curve for KMeans :target: api/model_selection/learning_curve.html#clustering +.. image:: api/model_selection/images/cv_scores_classifier.png + :width: 200px + :height: 100px + :alt: CV Scores for MultinomialNB Classification + :target: api/model_selection/cross_validation.html#classification + +.. image:: api/model_selection/images/cv_scores_regressor.png + :width: 200px + :height: 100px + :alt: CV Scores for Ridge Regression + :target: api/model_selection/cross_validation.html#regression + Text Modeling Visualizers --------------------------- @@ -194,7 +227,13 @@ Text Modeling Visualizers :alt: TSNE Projection of Documents :target: api/text/tsne.html#t-sne-corpus-visualization -Decision Boundaries Vizualizer +.. image:: api/text/images/dispersion_docs.png + :width: 200px + :height: 100px + :alt: Dispersion Plot + :target: api/text/dispersion.html#dispersion-plot + +Decision Boundaries Visualizer ------------------------------ .. image:: api/contrib/images/knn_decisionviz.png @@ -202,3 +241,36 @@ Decision Boundaries Vizualizer :height: 100px :alt: Nearest Neighbor Boundary Visualizer :target: api/contrib/boundaries.html#decisionboundaries-vizualizer + +Target Visualizers +------------------ + +.. image:: api/target/images/balanced_binning_reference.png + :width: 200px + :height: 100px + :alt: Balanced Binning Reference + :target: api/target/binning.html#balanced-binning-reference + +.. image:: api/target/images/class_balance_compare.png + :width: 200px + :height: 100px + :alt: Class Balance + :target: api/target/class_balance.html#class-balance + +.. image:: api/target/images/feature_correlation_pearson.png + :width: 200px + :height: 100px + :alt: Feature Correlation Pearson Correlation Coefficients + :target: api/target/feature_correlation.html#pearson-correlation + +.. image:: api/target/images/feature_correlation_mutual_info_regression.png + :width: 200px + :height: 100px + :alt: Feature Correlation Mutual Information - Regression + :target: api/target/feature_correlation.html#mutual-information-regression + +.. image:: api/target/images/feature_correlation_mutual_info_classification.png + :width: 200px + :height: 100px + :alt: Feature Correlation Mutual Information - Classification + :target: api/target/feature_correlation.html#mutual-information-classification diff --git a/docs/index.rst b/docs/index.rst index 2b4afed15..8fb3ebee9 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -33,10 +33,10 @@ Feature Visualization Classification Visualization ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -- :doc:`api/classifier/class_balance`: see how the distribution of classes affects the model - :doc:`api/classifier/class_prediction_error`: shows error and support in classification - :doc:`api/classifier/classification_report`: visual representation of precision, recall, and F1 - :doc:`ROC/AUC Curves `: receiver operator characteristics and area under the curve +- :doc:`api/classifier/prcurve`: precision vs recall for different probability thresholds - :doc:`Confusion Matrices `: visual description of class decision making - :doc:`Discrimination Threshold `: find a threshold that best separates binary classes @@ -52,6 +52,7 @@ Clustering Visualization - :doc:`K-Elbow Plot `: select k using the elbow method and various metrics - :doc:`Silhouette Plot `: select k by visualizing silhouette coefficient values +- :doc:`api/cluster/icdm`: show relative distance and size/importance of clusters Model Selection Visualization ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -59,6 +60,13 @@ Model Selection Visualization - :doc:`api/model_selection/validation_curve`: tune a model with respect to a single hyperparameter - :doc:`api/model_selection/learning_curve`: show if a model might benefit from more data or less complexity +Target Visualization +~~~~~~~~~~~~~~~~~~~~ + +- :doc:`api/target/binning`: generate a histogram with vertical lines showing the recommended value point to bin the data into evenly distributed bins +- :doc:`api/target/class_balance`: see how the distribution of classes affects the model +- :doc:`api/target/feature_correlation`: display the correlation between features and dependent variables + Text Visualization ~~~~~~~~~~~~~~~~~~ @@ -97,8 +105,10 @@ The following is a complete listing of the Yellowbrick documentation for this ve evaluation contributing matplotlib + teaching gallery about + faq code_of_conduct changelog diff --git a/docs/requirements.txt b/docs/requirements.txt index 5aac82590..0cc16735a 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,5 +1,5 @@ # Library Dependencies -matplotlib>=1.5.1 +matplotlib>=1.5.1,<3.0 scipy>=0.19 scikit-learn>=0.19 numpy>=1.13.0 diff --git a/docs/teaching.rst b/docs/teaching.rst new file mode 100644 index 000000000..a9cd2063d --- /dev/null +++ b/docs/teaching.rst @@ -0,0 +1,24 @@ +.. -*- mode: rst -*- + +Yellowbrick for Teachers +======================== + +For teachers and students of machine learning, Yellowbrick can be used as a framework for teaching and understanding a large variety of algorithms and methods. In fact, Yellowbrick grew out of teaching data science courses at Georgetown's School of Continuing Studies! + +The following slide deck presents an approach to teaching students about the machine learning workflow (the model selection triple), including: + +- feature analysis +- feature importances +- feature engineering +- algorithm selection +- model evaluation for classification and regression +- cross-validation +- hyperparameter tuning +- the scikit-learn API + +.. raw:: html + + + + +Teachers are welcome to `download the slides `_ via SlideShare as a PowerPoint deck, and to add them to their course materials to assist in teaching these important concepts. diff --git a/examples/agodbehere/PytorchExample.html b/examples/agodbehere/PytorchExample.html new file mode 100644 index 000000000..b5f57a448 --- /dev/null +++ b/examples/agodbehere/PytorchExample.html @@ -0,0 +1,12513 @@ + + + +PytorchExample + + + + + + + + + + + + + + + + + + + +
+
+ +
+
+
In [1]:
+
+
+
from sklearn.datasets import make_circles, load_iris
+from sklearn.model_selection import train_test_split
+
+import torch
+
+import numpy as np
+
+import yellowbrick as yb
+import matplotlib
+import matplotlib.pylab as plt
+
+# dtype = torch.long
+# device = torch.device("cpu")
+
+ +
+
+
+ +
+
+
+
+
+

Load data & prepare

+
+
+
+
+
+
In [2]:
+
+
+
X, y = make_circles(n_samples=1000, noise=0.1)
+
+# 75/25 train/test split
+orig_X_train, orig_X_test, orig_y_train, orig_y_test = train_test_split(X, y, test_size=0.25)
+
+# Transform data into tensors.
+X = torch.tensor(orig_X_train, dtype=torch.float)
+y = torch.tensor(orig_y_train, dtype=torch.long)
+
+ +
+
+
+ +
+
+
+
+
+

Visualize data

+
+
+
+
+
+
In [3]:
+
+
+
import yellowbrick.contrib.scatter
+visualizer = yellowbrick.contrib.scatter.ScatterVisualizer()
+
+visualizer.fit(orig_X_train, orig_y_train)
+visualizer.poof()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

Basic Neural Net

3 things are needed for an optimization problem:

+
    +
  1. model
    2. +
  2. Loss function
    3. +
  3. Optimizer
    4. +
+ +
+
+
+
+
+
In [4]:
+
+
+
from torch import nn
+
+# Sequential model allows easy model experimentation
+model = nn.Sequential(
+          nn.Linear(2, 16),    # input dim 2. 16 neurons in first layer.
+          nn.ReLU(),           # ReLU activation
+          #nn.Dropout(p=0.2),  # Optional dropout
+          nn.Linear(16, 4),     # Linear from 16 neurons down to 2
+          nn.ReLU(),
+          nn.Linear(4,2),
+          nn.Softmax(dim=1)    # Softmax activation to normalize output weights
+        )
+
+
+# Loss function. CrossEntropy is valid for classification problems.
+loss_fn = nn.CrossEntropyLoss()
+
+# Optimizer. Many to choose from. 
+optimizer = torch.optim.Adam(params=model.parameters())
+
+# Optimizer iterations
+for i in range(1000):
+    # Clear the gradient at the start of each step.
+    optimizer.zero_grad()
+    
+    # Compute the forward pass
+    output = model(X)
+    
+    # Compute the loss
+    loss = loss_fn(output, y)
+    
+    # Backprop to compute the gradients
+    loss.backward()
+    
+    # Update the model parameters
+    optimizer.step()
+
+print(loss.item())
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
0.46360188722610474
+
+
+
+ +
+
+ +
+
+
+
+
+

What do the activation regions look like?

(an exercise in Tensor math)

+ +
+
+
+
+
+
In [5]:
+
+
+
%matplotlib inline
+
+# Make a grid 
+ns = 25
+xx, yy = np.meshgrid(np.linspace(-1.5, 1.5, 2*ns), np.linspace(-1.5, 1.5, 2*ns))
+# Shape of each is [ns, ns]
+
+# Combine into a single tensor
+G = torch.tensor(np.array([xx, yy]), dtype=torch.float)
+# Shape is [2, ns, ns]
+
+# reshape to be convenient to work with
+G = G.reshape((2, G.shape[1]*G.shape[2])).transpose(0,1)
+# Now a tensor of shape [ns*ns, 2]. Sequence of x,y coordinate pairs
+
+result = model(G).detach()
+# For each row (sample) in G, get the prediction under the model
+# The variables inside the model are tracked for gradients. 
+# Call "detach()" to stop tracking gradient for further computations.
+# Result is shape [ns*ns, 2] since model takes 2-dim vectors and generates a 2-dim prediction
+
+c0 = result[:,0]
+# weights assigned to class 0
+
+c1 = result[:,1]
+# weights assigned to class 1
+
+plt.hexbin(G[:,0].detach().numpy(), G[:,1].detach().numpy(), c0.numpy(), gridsize=ns, cmap='viridis')
+# Gridsize is half that of the meshgrid for clean rendering.
+
+plt.title("Class 0 Activation")
+plt.axis('equal')
+plt.show()
+plt.hexbin(G[:,0].detach().numpy(), G[:,1].detach().numpy(), c1.numpy(), gridsize=ns, cmap='viridis')
+plt.title("Class 1 Activation")
+plt.axis('equal')
+plt.show()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

What is the classification performance?

Case study in working with Yellowbrick

+ +
+
+
+
+
+
In [6]:
+
+
+
from sklearn.base import BaseEstimator
+
+class NetWrapper(BaseEstimator):
+    """
+    Wrap our model as a BaseEstimator
+    """
+    _estimator_type = "classifier"
+    # Tell yellowbrick this is a classifier
+    
+    def __init__(self, model):
+        # save a reference to the model
+        self.model = model
+        self.classes_ = None
+    
+    def fit(self, X, y):
+        # save the list of classes
+        self.classes_ = list(set(i for i in y))
+    
+    def predict_proba(self, X):
+        """
+        Define predict_proba or decision_function
+        
+        Compute predictions from model. 
+        Transform input into a Tensor, compute the prediction, 
+        transform the prediction back into a numpy array
+        """
+        v = model(torch.tensor(X, dtype=torch.float)).detach().numpy()
+        print("v:", v.shape)
+        return v
+        
+
+wrapped_net = NetWrapper(model)
+# Wrap the model
+
+# Use ROCAUC as per usual
+ROCAUC = yb.classifier.ROCAUC(wrapped_net)
+
+ROCAUC.fit(orig_X_train, orig_y_train)
+print(orig_X_test.shape, orig_y_test.shape)
+print(orig_X_train.shape, orig_y_train.shape)
+ROCAUC.score(orig_X_test, orig_y_test)
+ROCAUC.poof()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
(250, 2) (250,)
+(750, 2) (750,)
+v: (250, 2)
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
+
+

Custom Modules

Implementing new functionality, e.g. radial activation regions for "circular" neurons

+ +
+
+
+
+
+
In [7]:
+
+
+
# weight: a * (x-c)^T(x-c), a is a real number
+
+class Circle(torch.nn.Module):
+    """
+    Extend torch.nn.Module for a new "layer" in a neural network
+    """
+    def __init__(self, k, data):
+        """
+        k is the number of neurons to use
+        data is passed in to use as samples to initialize centers
+        """
+        super().__init__()
+        
+        # k is not a Parameter, so there is no gradient and this is not updated in optimization
+        self.k = int(k)
+        
+        # Parameters always have gradients computed
+        self.alpha = torch.nn.Parameter(torch.normal(mean=torch.zeros(k), std=torch.ones(k)*0.5).unsqueeze(1))
+        self.C = torch.nn.Parameter(data[np.random.choice(data.shape[0], k, replace=False), :].unsqueeze(1))
+        
+        
+    def forward(self, x): 
+        diff = (x - self.C)        
+        # compact way of writing inner products, outer products, etc.
+        tmp = torch.einsum('kij,kij->ki', [diff, diff])
+
+        return (self.alpha * torch.einsum('kij,kij->ki', [diff, diff])).transpose(0,1)
+
+ +
+
+
+ +
+
+
+
In [8]:
+
+
+
from tqdm import tqdm
+loss_fn = torch.nn.CrossEntropyLoss()
+model = nn.Sequential(
+          Circle(16, X),
+          nn.ReLU(),
+          nn.Linear(16,4),
+          nn.ReLU(),
+          nn.Linear(4,2),
+          nn.Softmax(dim=1)
+        )
+optimizer = torch.optim.Adam(params=model.parameters())
+for i in tqdm(range(1000)):
+    optimizer.zero_grad()
+    output = model(X)
+    loss = loss_fn(output, y)
+    loss.backward()
+    optimizer.step()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
100%|██████████| 1000/1000 [00:26<00:00, 37.85it/s]
+
+
+
+ +
+
+ +
+
+
+
In [9]:
+
+
+
%matplotlib inline
+
+ns = 25
+xx, yy = np.meshgrid(np.linspace(-1.5, 1.5, 2*ns), np.linspace(-1.5, 1.5, 2*ns))
+G = torch.tensor(np.array([xx, yy]), dtype=torch.float)
+
+
+# reshape...
+G = G.reshape((2, G.shape[1]*G.shape[2])).transpose(0,1)
+result = model(G).detach()
+
+c0 = result[:,0]
+c1 = result[:,1]
+
+plt.hexbin(G[:,0].detach().numpy(), G[:,1].detach().numpy(), c0.numpy(), gridsize=ns, cmap='viridis')
+plt.title("Class 0 Activation")
+plt.axis('equal')
+plt.show()
+plt.hexbin(G[:,0].detach().numpy(), G[:,1].detach().numpy(), c1.numpy(), gridsize=ns, cmap='viridis')
+plt.title("Class 1 Activation")
+plt.axis('equal')
+plt.show()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + + + +
+ +
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
In [10]:
+
+
+
wrapped_net = NetWrapper(model)
+ROCAUC = yb.classifier.ROCAUC(wrapped_net)
+
+ROCAUC.fit(orig_X_train, orig_y_train)
+wrapped_net.predict_proba(orig_X_test)
+ROCAUC.score(orig_X_test, orig_y_test)
+ROCAUC.poof()
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
v: (250, 2)
+v: (250, 2)
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
In [11]:
+
+
+
%matplotlib inline
+
+# Show the centers of each "kernel" 
+
+centers = model[0].C.squeeze().detach().numpy()
+scales = model[0].alpha.squeeze().detach().numpy()
+
+plt.scatter(centers[:,0], centers[:,1])
+plt.scatter(X[:,0], X[:,1], alpha=0.1)
+plt.axis('equal')
+
+print(centers.shape)
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
(16, 2)
+
+
+
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+
In [12]:
+
+
+
%matplotlib inline
+from matplotlib import cm
+
+# Show the contours of the activation regions of each kernel
+
+ns = 25
+xx, yy = np.meshgrid(np.linspace(-2, 2, ns), np.linspace(-2, 2, ns))
+G = torch.tensor(np.array([xx, yy]), dtype=torch.float)
+G = G.reshape((2, G.shape[1]*G.shape[2])).transpose(0,1)
+G = G.expand(centers.shape[0], ns*ns, 2)
+Z = torch.tensor(scales).unsqueeze(1) * torch.einsum('kij,kij->ki', [G-torch.tensor(centers).unsqueeze(1), G-torch.tensor(centers).unsqueeze(1)])
+
+plt.scatter(centers[:,0], centers[:,1])
+plt.scatter(X[:,0], X[:,1], alpha=0.1)
+cmap = cm.get_cmap('tab20')
+for i in range(Z.shape[0]):
+    if scales[i] > 0:   
+        plt.contour(np.linspace(-2, 2, ns), np.linspace(-2, 2, ns), Z[i].reshape(ns, ns), [-0.5,0.5], antialiased=True, colors=[cmap(i)], alpha=0.8, linestyles='dotted')
+    else:
+        plt.contour(np.linspace(-2, 2, ns), np.linspace(-2, 2, ns), Z[i].reshape(ns, ns), [-0.5,0.5], antialiased=True, colors=[cmap(i)], alpha=0.3, linestyles='solid')
+
+plt.axis('equal')
+
+ +
+
+
+ +
+
+ + +
+ +
+ + +
+
/Users/andrewgodbehere/environments/torch/lib/python3.7/site-packages/matplotlib/contour.py:1230: UserWarning: No contour levels were found within the data range.
+  warnings.warn("No contour levels were found"
+
+
+
+ +
+ +
Out[12]:
+ + + + +
+
(-2.0, 2.0, -2.0, 2.0)
+
+ +
+ +
+ +
+ + + + +
+ +
+ +
+ +
+
+ +
+
+
+ + + + + + diff --git a/examples/agodbehere/PytorchExample.ipynb b/examples/agodbehere/PytorchExample.ipynb new file mode 100644 index 000000000..ddd67b323 --- /dev/null +++ b/examples/agodbehere/PytorchExample.ipynb @@ -0,0 +1,432 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.datasets import make_circles, load_iris\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "import torch\n", + "\n", + "import numpy as np\n", + "\n", + "import yellowbrick as yb\n", + "import matplotlib\n", + "import matplotlib.pylab as plt\n", + "\n", + "# dtype = torch.long\n", + "# device = torch.device(\"cpu\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load data & prepare" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X, y = make_circles(n_samples=1000, noise=0.1)\n", + "\n", + "# 75/25 train/test split\n", + "orig_X_train, orig_X_test, orig_y_train, orig_y_test = train_test_split(X, y, test_size=0.25)\n", + "\n", + "# Transform data into tensors.\n", + "X = torch.tensor(orig_X_train, dtype=torch.float)\n", + "y = torch.tensor(orig_y_train, dtype=torch.long)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualize data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import yellowbrick.contrib.scatter\n", + "visualizer = yellowbrick.contrib.scatter.ScatterVisualizer()\n", + "\n", + "visualizer.fit(orig_X_train, orig_y_train)\n", + "visualizer.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Basic Neural Net\n", + "\n", + "3 things are needed for an optimization problem:\n", + "1. model\n", + "2. Loss function\n", + "3. Optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from torch import nn\n", + "\n", + "# Sequential model allows easy model experimentation\n", + "model = nn.Sequential(\n", + " nn.Linear(2, 16), # input dim 2. 16 neurons in first layer.\n", + " nn.ReLU(), # ReLU activation\n", + " #nn.Dropout(p=0.2), # Optional dropout\n", + " nn.Linear(16, 4), # Linear from 16 neurons down to 2\n", + " nn.ReLU(),\n", + " nn.Linear(4,2),\n", + " nn.Softmax(dim=1) # Softmax activation to normalize output weights\n", + " )\n", + "\n", + "\n", + "# Loss function. CrossEntropy is valid for classification problems.\n", + "loss_fn = nn.CrossEntropyLoss()\n", + "\n", + "# Optimizer. Many to choose from. \n", + "optimizer = torch.optim.Adam(params=model.parameters())\n", + "\n", + "# Optimizer iterations\n", + "for i in range(1000):\n", + " # Clear the gradient at the start of each step.\n", + " optimizer.zero_grad()\n", + " \n", + " # Compute the forward pass\n", + " output = model(X)\n", + " \n", + " # Compute the loss\n", + " loss = loss_fn(output, y)\n", + " \n", + " # Backprop to compute the gradients\n", + " loss.backward()\n", + " \n", + " # Update the model parameters\n", + " optimizer.step()\n", + "\n", + "print(loss.item())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What do the activation regions look like?\n", + "\n", + "(an exercise in Tensor math)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "# Make a grid \n", + "ns = 25\n", + "xx, yy = np.meshgrid(np.linspace(-1.5, 1.5, 2*ns), np.linspace(-1.5, 1.5, 2*ns))\n", + "# Shape of each is [ns, ns]\n", + "\n", + "# Combine into a single tensor\n", + "G = torch.tensor(np.array([xx, yy]), dtype=torch.float)\n", + "# Shape is [2, ns, ns]\n", + "\n", + "# reshape to be convenient to work with\n", + "G = G.reshape((2, G.shape[1]*G.shape[2])).transpose(0,1)\n", + "# Now a tensor of shape [ns*ns, 2]. Sequence of x,y coordinate pairs\n", + "\n", + "result = model(G).detach()\n", + "# For each row (sample) in G, get the prediction under the model\n", + "# The variables inside the model are tracked for gradients. \n", + "# Call \"detach()\" to stop tracking gradient for further computations.\n", + "# Result is shape [ns*ns, 2] since model takes 2-dim vectors and generates a 2-dim prediction\n", + "\n", + "c0 = result[:,0]\n", + "# weights assigned to class 0\n", + "\n", + "c1 = result[:,1]\n", + "# weights assigned to class 1\n", + "\n", + "plt.hexbin(G[:,0].detach().numpy(), G[:,1].detach().numpy(), c0.numpy(), gridsize=ns, cmap='viridis')\n", + "# Gridsize is half that of the meshgrid for clean rendering.\n", + "\n", + "plt.title(\"Class 0 Activation\")\n", + "plt.axis('equal')\n", + "plt.show()\n", + "plt.hexbin(G[:,0].detach().numpy(), G[:,1].detach().numpy(), c1.numpy(), gridsize=ns, cmap='viridis')\n", + "plt.title(\"Class 1 Activation\")\n", + "plt.axis('equal')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What is the classification performance?\n", + "\n", + "Case study in working with Yellowbrick" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.base import BaseEstimator\n", + "\n", + "class NetWrapper(BaseEstimator):\n", + " \"\"\"\n", + " Wrap our model as a BaseEstimator\n", + " \"\"\"\n", + " _estimator_type = \"classifier\"\n", + " # Tell yellowbrick this is a classifier\n", + " \n", + " def __init__(self, model):\n", + " # save a reference to the model\n", + " self.model = model\n", + " self.classes_ = None\n", + " \n", + " def fit(self, X, y):\n", + " # save the list of classes\n", + " self.classes_ = list(set(i for i in y))\n", + " \n", + " def predict_proba(self, X):\n", + " \"\"\"\n", + " Define predict_proba or decision_function\n", + " \n", + " Compute predictions from model. \n", + " Transform input into a Tensor, compute the prediction, \n", + " transform the prediction back into a numpy array\n", + " \"\"\"\n", + " v = model(torch.tensor(X, dtype=torch.float)).detach().numpy()\n", + " print(\"v:\", v.shape)\n", + " return v\n", + " \n", + "\n", + "wrapped_net = NetWrapper(model)\n", + "# Wrap the model\n", + "\n", + "# Use ROCAUC as per usual\n", + "ROCAUC = yb.classifier.ROCAUC(wrapped_net)\n", + "\n", + "ROCAUC.fit(orig_X_train, orig_y_train)\n", + "print(orig_X_test.shape, orig_y_test.shape)\n", + "print(orig_X_train.shape, orig_y_train.shape)\n", + "ROCAUC.score(orig_X_test, orig_y_test)\n", + "ROCAUC.poof()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Custom Modules\n", + "\n", + "Implementing new functionality, e.g. radial activation regions for \"circular\" neurons" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# weight: a * (x-c)^T(x-c), a is a real number\n", + "\n", + "class Circle(torch.nn.Module):\n", + " \"\"\"\n", + " Extend torch.nn.Module for a new \"layer\" in a neural network\n", + " \"\"\"\n", + " def __init__(self, k, data):\n", + " \"\"\"\n", + " k is the number of neurons to use\n", + " data is passed in to use as samples to initialize centers\n", + " \"\"\"\n", + " super().__init__()\n", + " \n", + " # k is not a Parameter, so there is no gradient and this is not updated in optimization\n", + " self.k = int(k)\n", + " \n", + " # Parameters always have gradients computed\n", + " self.alpha = torch.nn.Parameter(torch.normal(mean=torch.zeros(k), std=torch.ones(k)*0.5).unsqueeze(1))\n", + " self.C = torch.nn.Parameter(data[np.random.choice(data.shape[0], k, replace=False), :].unsqueeze(1))\n", + " \n", + " \n", + " def forward(self, x): \n", + " diff = (x - self.C) \n", + " # compact way of writing inner products, outer products, etc.\n", + " tmp = torch.einsum('kij,kij->ki', [diff, diff])\n", + "\n", + " return (self.alpha * torch.einsum('kij,kij->ki', [diff, diff])).transpose(0,1)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from tqdm import tqdm\n", + "loss_fn = torch.nn.CrossEntropyLoss()\n", + "model = nn.Sequential(\n", + " Circle(16, X),\n", + " nn.ReLU(),\n", + " nn.Linear(16,4),\n", + " nn.ReLU(),\n", + " nn.Linear(4,2),\n", + " nn.Softmax(dim=1)\n", + " )\n", + "optimizer = torch.optim.Adam(params=model.parameters())\n", + "for i in tqdm(range(1000)):\n", + " optimizer.zero_grad()\n", + " output = model(X)\n", + " loss = loss_fn(output, y)\n", + " loss.backward()\n", + " optimizer.step()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "ns = 25\n", + "xx, yy = np.meshgrid(np.linspace(-1.5, 1.5, 2*ns), np.linspace(-1.5, 1.5, 2*ns))\n", + "G = torch.tensor(np.array([xx, yy]), dtype=torch.float)\n", + "\n", + "\n", + "# reshape...\n", + "G = G.reshape((2, G.shape[1]*G.shape[2])).transpose(0,1)\n", + "result = model(G).detach()\n", + "\n", + "c0 = result[:,0]\n", + "c1 = result[:,1]\n", + "\n", + "plt.hexbin(G[:,0].detach().numpy(), G[:,1].detach().numpy(), c0.numpy(), gridsize=ns, cmap='viridis')\n", + "plt.title(\"Class 0 Activation\")\n", + "plt.axis('equal')\n", + "plt.show()\n", + "plt.hexbin(G[:,0].detach().numpy(), G[:,1].detach().numpy(), c1.numpy(), gridsize=ns, cmap='viridis')\n", + "plt.title(\"Class 1 Activation\")\n", + "plt.axis('equal')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "wrapped_net = NetWrapper(model)\n", + "ROCAUC = yb.classifier.ROCAUC(wrapped_net)\n", + "\n", + "ROCAUC.fit(orig_X_train, orig_y_train)\n", + "wrapped_net.predict_proba(orig_X_test)\n", + "ROCAUC.score(orig_X_test, orig_y_test)\n", + "ROCAUC.poof()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "# Show the centers of each \"kernel\" \n", + "\n", + "centers = model[0].C.squeeze().detach().numpy()\n", + "scales = model[0].alpha.squeeze().detach().numpy()\n", + "\n", + "plt.scatter(centers[:,0], centers[:,1])\n", + "plt.scatter(X[:,0], X[:,1], alpha=0.1)\n", + "plt.axis('equal')\n", + "\n", + "print(centers.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "from matplotlib import cm\n", + "\n", + "# Show the contours of the activation regions of each kernel\n", + "\n", + "ns = 25\n", + "xx, yy = np.meshgrid(np.linspace(-2, 2, ns), np.linspace(-2, 2, ns))\n", + "G = torch.tensor(np.array([xx, yy]), dtype=torch.float)\n", + "G = G.reshape((2, G.shape[1]*G.shape[2])).transpose(0,1)\n", + "G = G.expand(centers.shape[0], ns*ns, 2)\n", + "Z = torch.tensor(scales).unsqueeze(1) * torch.einsum('kij,kij->ki', [G-torch.tensor(centers).unsqueeze(1), G-torch.tensor(centers).unsqueeze(1)])\n", + "\n", + "plt.scatter(centers[:,0], centers[:,1])\n", + "plt.scatter(X[:,0], X[:,1], alpha=0.1)\n", + "cmap = cm.get_cmap('tab20')\n", + "for i in range(Z.shape[0]):\n", + " if scales[i] > 0: \n", + " plt.contour(np.linspace(-2, 2, ns), np.linspace(-2, 2, ns), Z[i].reshape(ns, ns), [-0.5,0.5], antialiased=True, colors=[cmap(i)], alpha=0.8, linestyles='dotted')\n", + " else:\n", + " plt.contour(np.linspace(-2, 2, ns), np.linspace(-2, 2, ns), Z[i].reshape(ns, ns), [-0.5,0.5], antialiased=True, colors=[cmap(i)], alpha=0.3, linestyles='solid')\n", + "\n", + "plt.axis('equal')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/bbengfort/cluster.ipynb b/examples/bbengfort/cluster.ipynb index 5b329f52e..687004da9 100644 --- a/examples/bbengfort/cluster.ipynb +++ b/examples/bbengfort/cluster.ipynb @@ -2,10 +2,8 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, + "execution_count": 94, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" @@ -24,13 +22,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "import sys \n", "sys.path.append(\"../..\")\n", "\n", + "import numpy as np\n", "import yellowbrick as yb \n", "import matplotlib.pyplot as plt \n", "\n", @@ -41,10 +40,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, + "execution_count": 3, + "metadata": {}, "outputs": [], "source": [ "# Helpers for easy dataset creation \n", @@ -67,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -248,6 +245,182 @@ "visualizer.fit(X)\n", "visualizer.poof()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Intercluster Distance Map" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [], + "source": [ + "def prop_to_size(prop, mi=0, ma=5, power=0.5):\n", + " \"\"\"\n", + " Scale a property to be used as a size \n", + " \"\"\"\n", + " prop = np.asarray(prop)\n", + "\n", + " return mi + (ma - mi)*(((prop - prop.min()) / (prop.max() - prop.min()))**power)" + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.manifold import MDS \n", + "\n", + "## Make 12 blobs dataset \n", + "X, y = make_blobs(centers=12)\n", + "\n", + "## Fit KMeans model on dataset \n", + "model = KMeans(9).fit(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 317, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[25000. 400. 400. 3084.0800499\n", + " 3084.0800499 24853.13301574 25000. 400.\n", + " 400. ] [89.20620581 11.28379167 11.28379167 31.33198317 31.33198317 88.94379091\n", + " 89.20620581 11.28379167 11.28379167]\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 317, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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axHj5+hci2klNvJgTnb0D3PLYq8Q5HFy4pnLcBH60lMR4zl65hMr8LG5/ooba5o4FilQIMRsN7d388ska1pZJAh+NbDYbpy8thjDc+fQWBgPDVockhDhFksSLUxYMhfjNM6+Tn5nCmiX52GzTHwVhSW4G6ysK+c3Tr9PdL/OECBGJGjt6uP2J11hVmv/G1TQRfYxEvojAcFA6uwoRA+R6mjhlz++uZWBomDOWlZzw+AN3/ZoH7/7tScufd/GlXHf9DW/8XpiVRlNnLw+9spuPXrB+3uMVQkxfV/8gdzz5GiuKcliSK2Vv0W6ktOblvXXc88IOPnz+uhk1vAghIock8eKUDA0H+ceOQ5xbVXbSF8GqdRuwjxp2busrL3Fon2b9WeectJ01Zfn8fet+Gjt6KMhMnfe4hRBTCwSNFtuCzDQq8mUUmlhhs9k4c0Upz+48zNM7DnLxacusDkkIMQuSxItTsquuidTEeFKTTp58p2rtOqrWrgPg+LGj/OXeP3DRO67gzHPfctKyDrud0pwMXtl/lCvPqJr3uIUQkwuHwzz48m6GAsOcXllkdThijjnsdjaqJTy94xCFmWmsXjK/M7sKIeae1MSLU7K/oZX8abSc/+YXN+F0Onn/1ddOuExJdhr76lvnMjwhxCxt3nMEfayFs5aXSrlFjEqMd3L2yiXc88J2jrd3Wx2OEGKGJIkXp6SutWvKkWgO7N3Dzq01vO3yq0hOmTjhz0hJpKW7j0BQOlsJYaV99S087t/PxqolOBzyNRHLslOTWFWaz51Pv07PwJDV4QghZkD+O4tT0jc4NOV4w8889lcAzr34kkmXc9jtxNntDAYkiRfCKs1dvdz1/DbOWF5CSkK81eGIBVCel0l2ahK/f3Yrw8GQ1eEIIaZJknhxShx2O6FQeNJl/K+9QmFJKQXFJZMuBxAKh7DLpXshLBEIBvndM1tZUZhDXnqK1eGIBXRaeQHd/UM87t9vdShCiGmSJF6ckryMFDr7BiZ8vr21hY62ViqXr5xyW32DAZwOB0kyk6AQlnh6x0FC4TCVBTISzWIzMvTki7qOupZOq8MRQkyDJPHilFTkZdLS1Tfh8x3tbQBk5eZNua2Wrl6W5GZIJzohLHC0tYvndx+hemmx/A0uUonOONaW53PP5u0yEZQQUUCSeHFK1pYV0NDePWFJTeXylfz6z0/wwY9fN+W2jrR0Ur2seK5DFEJMIRAMcu/m7awuzSMx3ml1OMJCZbmZ2O02nth2wOpQhBBTkCRenJKCzFRKczI4cPzUhoZs6epjYCggYxULYYGndxwkRJjyvEyrQxERYMPSYl7aK2U1QkQ6SeLFKbvqrCr2HW+jp39wVusPB0NsOXiM95yzGueoGV6FEPPvaGunlNGIEyQ641hTJmU1QkQ6SeLFKcvPSOXy01eyec8R+gYDM1o3GAzxgj7C2rICVpdKK7wQC2k4GOLezTukjEacZKSs5sntB60ORQgxAUnixZw4e+US3rpuGc/sPMSxtq5prdPe089TOw5SmZ/FVWetklZAIRbYloP1BEJBKaMR49pQWcQLe2pp7+m3OhQhxDhkLD8xZ85bVU5Jdjr3vLCDg8fbqMjPojAr9YQSmWAoRFt3Pwcb2+joG+DKM6rYUFkkCbyYU4HhII2dPfQOBLDZIDMlkdy0FOx2Oc5GDA0H+fvWfZy+rET+/sS4EuOdLMnN5MntB/injWutDkcIMYYk8WJOVRZk4XnXJnbXNfPC3iNsPdRAojMOZ5yDYChE72CA/PQUzlpRyvrKIpIT5BK+mBvBUIg9x5p5YU8dh5raSU2MJ8HpgLAxB8FQMMjasgI2qTKW5GZYHa7lXtRHSEmMJzs1yepQRASrKsnl8W0HOH9VBQWZqVaHI4QYRZJ4MeecDgfrKgpZV1HIcDBEa3cfQ8NBHHYbuekpxMdJ51Uxtxo7erj3hR10DwyxrCCLy6tX4rCfWC04GBjmUGM7dzxZw+rSPK44Q5GcEG9RxNbqGxzi6R0H2VRVbnUoIsI54xwsK8jisa37+NiFG6wORwgxitTEi3kV57BTkJnKktwMirPTJYEXc04fa+Hmv71MVkoiF66poCwv86QEHiDBGUdVaR5vW7eM1u4+bnrkZVq7J56oLJY9s/MweekppCclWB2KiALLi3I42NhGbXOH1aEIIUaRJF4IEbVqmzu46zk/Z60oZVlRzrRqu+McdjYsLaYkO43bHn+N3oGhBYg0cnT2DvDi3jqZk0FMm8NuZ2VxLo9u2Us4PP7EfkKIhSdJvBAiKg0GhvnD89tYV1FITlryjNdfXpRDRnICD7+6Zx6ii1xP7zhISXYaSTKkpJiBivwsWrp62d9wahP7CSHmjiTxQoio9PzuWhKccZRkp0+4zO9u9XH1lW/j0D497vPrygvZ19DKocb2+QozovQNBthyqB5VnGt1KCLK2G02lhZms3nPEatDEUKYJIkXQkSdYXOSsFWleeM/Hwhwz69v5/G/PDjpdhwOOxX5mWzWtfMRZsTZeqiB7NRkmdhJzEp5biYHG9sWbV8SISKNJPFCiKhzpKUDp8NORnLiuM9/5XOf4O8P3U9ufuGU26osyGJ3XTOBYGxPLx8Oh3l+Ty3LCrOtDkVEKYfDTnF2Oq/uP2Z1KEIIJIkXQkShhrZu0idI4AHWrD+db/34FqrWrptyW06Hg+REJ40dvXMZYsQ50NhGYDhI7iz6DwgxYnlhNi/vq4v5k14hooEk8UKIqNPc3Udq4sRjvF/92X+ntLxi2ttLiY+P+anlX9R1lOdlyuys4pSkJiWQkuBk55Emq0MRYtGTJF4IEXWCoRD2OUxGbXZbTA+d19Hbz976FsrzM60ORcSAyoJsNu9ZHP1IhIhkksQLIaJOelICA0PDc7a9ocAwyYmx29mz5kA9hZmpOB0y2Zo4dUVZabR09VHf1mV1KEIsapLECyGiTnF2Gl39g3OyrXA4TGffIEWZaXOyvUi09VADFflZVochYoTdZqMwK42ddVJSI4SVJIkXQkSd8rws2nv7CQyfeue6xs4ectOSSJmkxj6atXb30dU/SHZqktWhiBhSkp3G9tpGq8MQYlGTJF4IEXVSE+NZVZrPgeNtky533fU38Os/P0HlCjXhMgeOt7GpqnyuQ4wY+lgLeenJ0qFVzKmctGQ6egfo6BuwOhQhFi1J4oUQUemtpy3lUFM7fYOBWW/jaGsnwVCY9ZVFcxhZZNl+pJGirIlntRViNmw2G3kZyRxeJLMdCxGJJIkXQkSlgsxULlq7lJf31REMhma8fnf/INtrG/nAprXEx8Vmh8++wQB1LR0UZqZaHYqIQUVZaew51mx1GEIsWpLECyGi1gVrKlhZlMuzuw/TP4MW+ZauPp7fXcu7zqyK6Q6f+4+3kpWahMMh/+rF3CvMTONYq4xQI4RV4qwOQAghZstms/Hec1bz9I5D/GPnIZYVZrO0IJu4CZLW/sEAe44109LVxz+fdxqrSvMXOOKFtfNIIwUZ0gov5kecw07GJDMnCyHmlyTxQoioZrPZuPi0pVSV5PLEtgM8tnUfOWnJZCQnkBRvjP3e3T9IZ98g3f2DVC8r5l8u3DDpjK+xIBwOs7e+lfNXV1gdiohhWalJQOxOlCZEJJMkXggRE4qz0/nYhRvo6O3ncFMHda2ddPUNYrfZqMjPoiQ7naUF2STGL45/e519A4TCYZITYncSK2E9I4nvszoMIRalxfFtJoRYNDJTklhfmRTTI85MR0N7N+lJCVaHIWJcRlICDEgSL4QVpLeTEELEoGOt3aQnSxIv5pddOk0LYRn56xNCiBhU29xuljoIIYSIRZLECyFEjAmHwxxt7SInNdnqUIQQQswTSeKFECLGjHRqTZJOrUIIEbMkiRdCiBjT0C718EIIEeskiRdCiBjT0Tvwxhj5QgghYpMk8UIIEWM6+wZIdMoIwkIIEcskiRdCiBhjtMRLEi+EELFMknghhIgxXX1STiOEELFOknghhIgxXf1DksQLIUSMkyReCCFiTGffgAwvKYQQMU6KJoUQIoYMBoYZDoZwOqSNZiZqD+7n1zf/hGNHaikpq+Djn7uessqlVoclhBATkv/yQggRQ3oHAyTGx2Gz2awOJWoMDQ3xv9/6Co319bzlkss4Xn+U//3WjQQCQ1aHJoQQE5IkXgghYkgwFMIhCfyM7N62lc72Nt56+VV85LrP8rZ3XkVHWyu7t221OjQhhJiQJPFCCBFDQqGwtMLPUMPRIwAUFBUbP4tLzMfrLItJCCGmIkm8EELEkFBYkviZGhwYAMAZHw9AnNPoFDww0G9ZTEIIMRVJ4oUQIoYYSbzVUUSX+IQEAAKBgPnTqIVPSEi0LCYhhJiKJPFCCBFD7DYb4bDVUUSXkfKZ5uMNADQ1GD+LSsssi0kIIaYiQ0wKIUQMMZJ4yeJnYo1rA2npGTz+5wfo7elm89OPk5mdw6p1660OTQghJiQt8UIIEUPsdkniZyohMYkbvvcjCktKefbxRyksLsHzje8Rb9bICyFEJJKWeCGEiCEOu52gJPEzVla5jK//98+sDkMIIaZNWuKFECKGpCbGMzA0LK3xQgiUUhVKqZfmcHt3KqXeMeaxQqXUzXO1DzF90hIvhBAxJD7OQZzDTiAYIj7OYXU4QogYp7U+DnzW6jgWI0nihRAixmQkJ9I/GJAkXogoopT6OHAlkAQUAT8FrgLWAl8E4gEPEASe11r/P6XUt4DlQC6QA/wceB+wErgaOA7kKaUeBgqAv2itv6uUWgL8n7mvfuCTgAP4M9AKPAL0mNsIAa9qrf/NDPVTSqkbgAzgM0AT8Aet9TlKqV3Ac8AaoA34kNa6d87fLAFIOY0QQsSc9KR4+ocCVochhJi5NK31O4EfYiTI78VIsK8Fvg28VWt9HlCilLrEXKdfa/0O4H7gnVrrK4EfAP9sPp8K/AuwCbhMKeUC/gf4mdb6QvP+D8xlC4FLtdY/Aq4B3FrrjcBupdRIw2+N1vpi4Cbg42PiTwZ+b8a4B/jUHLwnYgLSEi+EEDEmPTlRknghotPr5s8OYLfWOqyUasdIxPOAR5RSAGnAMnPZLaPW2WXebwdGZivza607AZRSr2C00p8GfEUp9WXABoz8wziktR4y718DfFEpVQm8aC4HUGP+PI6RtI8W0Fo/a95/AbhsZi9fzIS0xAshRIzJTEmkf2jY6jCEEDM3UY/0MFAHXGK2nt8EvDTFOiNWKaVSzZb0s4GdGK3kXza39SngXnPZ0Kj1rgM+rbW+ANiA0ZI/1f6cZks/wLnmvsQ8kSReCCFiTEayJPFCxJgA4AWeUUq9jNHCvXea67YBf8RoGb9Pa70Lo8b+m0qpZ4DfANvGWW878JxS6imMuveXp7m/LyulngdKgFunuY6YBdtCDkNWU1NTARxau3YtCQkJC7ZfIUTsqKmpobq62uowItruo808skVzblW51aGIWBcK4uxtBaisrq4+bHE0wmJKqcNAldZ6wOJQFgVpiRdCiBhTlJVKV9+g1WEIIYSYR9KxVQghYkxGciJ2m43+wQBJCU6rw4l64XCYweEgw8EQ4XCYcDhMKBzGhg2b3YYdsNltJMbF4XBI25hYvLTWFVbHsJhIEi+EEDHGZrNRmpNOa08fpQkZVocT8QLBIB09A3T2DdA/NEzvwBA9g0P0DgzRNxigfyiAzWbDYbdhs9mwYbzHwJtJPTAcDOJ0OEhOcJKSEE9KYjwpCU6SE+JJSXSSlZJEamL8G+sKIcSpkCReCCFiUEV+Fgcb2yjNkSR+tJGEvbWnj6bOXpo6e+jqGyQpwUliXBzOODvxcQ4SnA7yM1JIcDqIj3PgsE/dwh4OhwkEQwwFhhkcDjIYCNI4MMhwMMTQcJC+QWMUv7z0FPIzUshJSyYnLVkSeyHErEgSL4QQMag4O42thxusDsNyoXCYlq5ejjR3UtvcQVffAIkJTpLj40hJjKcsN4OUhHjs9lNPom02G/FxRtKfOsEyQ8NBuvoGaezo5XBTO71mYl+UlUZFfhal2elRVQIVCoamXkgIMS8kiRdCiBhUlJW2aDu3BoaDNLR3c7i5g9rmDhx2GxnJiZTkpFFVmovdwlbv+DgHuenJ5Ka/OUfO0HDZrcxoAAAgAElEQVSQtp5+ttce5/ndtWSnJlFZkMWSnAwyUxIjupW+s3+Q/MgNT4iYJkm8EELEoJHOrX2DAZKjqGV3toLBELUtHeyrb6WhvZuURCcZyYmsXZJHYnxkv/74OAeFmakUZqYSCofp6B3gcFMH/kPHccbZWVqQzcriHDJTkqwO9STtPf3kp1kdhRCLkyTxQggRg2w2GytLcqlv62J5UY7V4cyb7v5B9LEWdh9rNurY01OoXlZMXJSOEmO32chOTSI7NYlwOEzvYICmzl52H20iNy2FNWX5LMnNmFaN/kJo7+mHtESrwxBiUZIkXgghYtSaJfk87t8fc0l8KBymvq2LXXVN1Ld1k5OWxOrSvJi74mCz2UhNjCc1MZ6K/Exaunp5ee9RNu+pZfWSfFYW55KSEG9ZfMPBEJ19g4Ak8UJYQZJ4IYSIUcsLc7hn83aCwVBMjF8eDIXYe6yFbbXHCYUhPyOFM5YXR0yr9Hyy22zkZ6SSn5FK78AQdS2d+A8dZ0luBhuWFpOduvClNg3t3ZTmpi/4foUQBknihRAiRiUnOCnLy6Shozuqh5oMhcMcamzntf1HsdvtlOdnkpG8eFt/UxLjWVGUw3B+iIb2Hh5+dTeV+VmcvrSYtKSEBYujob2bs5cVQW/rgu0zlrk8vjggH8gDEoBDwBDQ5/e6A1bGJiKTJPFCCBHDTisroObAsahM4sPhMMfaunhl31EGA0Eq8jMisnOnVeIcdpbkplOUlUpdayf3v7STquI8XJWFJM1zZ95wOExzVy8VBVkcPShJ/HS4PL4s4Bqg2LwVmbe/Am8F1gKOcVb9s8vjCwFHgUswkvvDQANQDzzq97qPznf8IvJIEi+EEDFsZXEuj27ZRzgcjuihCsdq6uzhlX1H6egdoCwvg9y05KiKfyHFOexU5mdRkp3OkeZO7t28g9PKC1hTVkB83Hg54alr6e4jOzWJzOREJHt8k8vjSwZcQPWo22/9Xvd/Y3Qe+F9gGLAD24FtwEXmOiGMxLwFaAX+DOQCNwJB3kzwVwIBYORM7RLgqMvjeytwA1ADvGb+POL3usPz94qFlSSJF0KIGJaTlkxGcgKt3f0njE0eqQYDw7y0t47a5g5Kc9JZWpAlyfs0xcc5WF6UzcBQgMNNHew62sR5qyooz8uc830da+tiXXnhnG832rg8PgdQ4Pe66837TUCK+XQTRiJ91OXxXQV8dtSq9wLf8Hvde10e34eA0wCf3+uuH7N9O/As8ENgDfA9jAR+GfBpoAC42OXxlZn7LQC+xJv5XavL49vo97r3uTw+u9/rltm5Yogk8UIIEePWVxax51hzxCfxR1o6eH5XLWlJCZy+tGhRdFidD4nxTqpKc+nsHeDZXYcozclg48olczZefigc5nh7N1eduWpOthdtXB5fKnAp8C7gcmA/sNHvdQddHt+XMEpcaoBjfq877PL4CoFdQC/wdeB2v9d93OXxnQ7g97rvBu4eb19m0v03l8f3AkbZzY3AW/xe9/fNWLqBWwEb8G3gDIzcbh3GVYDVwAFzcz8z9/kwRiv/Lmmlj27yH1IIIWLcGctKON7RQyAYtDqUcQ0Ghnlm5yGe2XGYyoJMlhdlSwI/BzJSEtlQWURP/xD3v7iT2uaOOdluQ1s3eekpFGUtvlmeXB7fdzBKXe4HrgIeA7wjz/u97lv8XvdDwHHgarP1+zhwJlDp97q/ZybwZwKvuDy+c6azX7/X3WXu7zBGq/3I4x1AFfAQ8H3gJWC53+t+xYzl86Na33djlOD8F7AD2O/y+L4x2/dCWE9a4oUQIsZlpCSysjiXw00drIiwMeNHt75vWFooyfscc9jtLC/KntNW+YNNbVzqWj6HUUYus0zlk4DX73W3YbSo/xyjNXvzeKPGuDy+1cBvMFrCm4BH/F73gTGLfQYYMLc3LX6vu83l8a0aWxLj97oPAx9weXzvB24GXnB5fGVmgj96uZ8DP3d5fCXAFRgnBTlmzDaM2vqn/F738HRjEtaSJF4IIRaBTaqMu5/3s7wwOyJqzIeDIV7QR6ht6mB5UZaMOjPPRlrlDzV2cP9LO7lo7VKKs2c+xntP/yB9gwFWL8mfhygjg1mHfglGDfsV5sNbgD/5ve4/AH+YZN3zgb8Ag8D7/V73I+MskwZ8CLjTbGGfNr/XHTIT7rixJxB+r/tel8f3D2DTSALv8vhsY0tm/F73MYwSnFvNbQFsxLiqUOfy+G7FKPlpnElsYuFJk4cQQiwCSwuySIiLo6Wrz+pQ6B0c4q+vaVq6etmwtFAS+AUy0ipfmZ/F4/797KxrJByeWUn0voZWNq5cgtMxP6PeWM0cBlIDf8NIbH8ALPV73X+axrqXYiTCDUC13+u+b4JFSzFGqnlmlmFuB3473hN+r7vZLOfB7DD7kMvjm3DygFEJ/ivAezFe+/cwkvnfujy+vFnGKBaAJPFCCLEI2Gw2zltVzoHGNkvjaO7s5aGXd5OYEEdVSa6Uz1ggKzWJdeUFbD3UwPO7awkGpzdgSTAYor69mzOWl85zhAvL5fHZXB6fC8DvdbcD9wEfAZb4ve6v+r3u2mluaghjaMfz/V533STLjfQwn+0ZdYA3R8CZTDpwJXCXOZHUhPxe97Df637A73VfglFjfzPGSUwvvHF1QkQY+VCEEGKRcFUU0dbTz8CQNZM/7mto4ZEte1mSm0FZbkZElPUsVonxTtaVF9LY2csjW/bSPzj1MVHb0sGygmyyU2PnyonL47sAeAGocXl8ywH8XveNfq/7Lr/XPTjNbcSZ6/0DuMDvdTdPscoB4IMYCf9sZAOdUy3k97pvBb6A0cLum+7G/V639nvdXwBW+b3uPpfH5wS2uDy+77o8vpnXYIl5I0m8EEIsEskJTqqXFqPrWxZ0v6FwmJf31fHS3jrWLMmL+KEuF4s4h53VpbnEOew89MruSUutQuEwB463ce6q8gWMcP64PD6Xy+N7FPgHsARjzPXDs9hOHPCcy+O7AU4oT5mQ3+vu8Hvd94wdE36a+zsTKANens7yfq/7p8CPgE+5PL4rZ7KvUTX3acAe4GvAQZfHd/1kJTpi4UgSL4QQi8iFayo51tY9rZbXuRAIBnli635qmzpwVRSSkhi/IPsV02Oz2ajIz6QoO41HajRHWsYfhvJwUzv5GSksL8xe4Ajnnlnn/SJwNsYMpyv8XvftsxyV5UvAOczwBMDl8WW7PL6vujy+ihnu7zMYJS6/mcE6XwdeZ9TQlDPh97rb/F73P2OMQb8FY0jNLeYoN8JCMjqNEEIsIhkpiWxcuYSddU2csXx+v4MDw0H+7t/PUCDI2rJ8KZ+JYPkZKSTFx/H09kO8ZXU5lQVvJuvBUIi99S3868XVUf0Zujy+pX6v+6Df6252eXwfwBgisv0UtrcW+BZwn9/rvmeGq6cA3wFWuzy+j85g0qWfA0/7ve4py2lG+L3uIXPW1mmVB02ynRrgUpfH907gGoyx8GOOUsoJ/BqoAILAdRiN3v+HManWPuATWmvLh+K0zbRn+qmoqampAA6tXbuWhAS5EiOEmLmamhqqq6utDiOq9Q0G+NGDz7Gpqoz0pPn5XzwUGOaxrfsJhUOsKMqJ6uRvMekZGGJXXRPnVpWz3JxTYM/RZhwOG/9ywYaTlh8cHGTHjh0AldXV1YcXNNhpcnl8iRjJ9peAK/xe96NzsE0HxsRKFcAav9fdNIttfB0jkf8ZcP3Y8d/HLLsCODzeuPQz3OcFgNPvdT9xKtsZtb184B7gS36v+9W52KbVlFJXAR/RWn9AKXUJRqmVA/BqrZ9VSt0JPKS1fsDKOEFa4oUQYtFJTnBy0dpKthysZ6Mqm/PtDw0H+dvWfRBGEvgok5oYz5qyfDbvOQJAeV4mBxvb+Nxl05pYNOK4PL6zgF8Bq4E7MDqxzoXTgQ3ANbNJ4E3fw+ik+gXgDJfH92m/17199AJmZ9tPA5/CGDHmy7MN2Bxh5haMcpw5SeIx6vOXAy+6PL4fAd+eQYdgG0Zy7MTIR+MmuJ846pZg3hKBePN+PEZL+ch6dnO707kfD/yb3+seGhXaXiBOKWXHGOEnAHxAax1USsUDhUyjY/FCkCReCCEWoY2qjOd319LW3Ud22tx1NA0MB/n71n2Ew7CiKDImlhIzk5IQz5qyPDbvOcLBxjbWVRRRkJlqdVgz5vL4/g34MVAPXOb3uv82V9v2e92vujy+cowZWWe7jTBwvcvj24lRm98O4PL4rsUYvSYdo25/GHgU+JPL41uGkbslcXJSmzDq5mT85HUf8C6Xx3cbRjlMnPncdBLfkeVG7o/c9mKUmdwI/JvL49uDUYbimMY27aO2OXZfI7f51IZxhWZ0Et+DcYVlD5ALXGEm8OUYJz+dgH+e45oWSeKFEGIRio9zcIlrOU/vOMhbVlfMSbI9HAzxxLYDDAdDrCyWFvholpIQz4rCbJ7eeYi3r19hdTiz1Q48AFw7kxryqbg8Prvf6w6ZM5+eMr/XfbvL47tjVF18IUYCP4hRh/0s0A+sBNZzYsI+0loNMLo+eqSVe2xy3IpxUnABRufe8ZL4iZL1yW59QCOQB6wCukZtc/Rt9DYj1fXAY1rrG5VSS4CnlFKnaa1rgRVKqU9gdO692tIokSReCCEWreplxWzeU0ttcwcV+VmntK1wOMxzuw7TNxRgVUmuJPAxoKGjm3Orynlky16Ks9NZkpthdUhTcnl8RcAGv9f9iN/r/q3L4/vdDDqNTtcPXB7fOuByv9cdnIsNjo7R73V/H/j+XGx3PC6PbxD4OLDR73W3zvG2V2Ik870YQ1MOM3GpzMjJyEipTDwnnqDEc/IJxXgnFzMtoRlbTjO2n0H7qMfazFj/rJT6rNZ6H9ANTG+GtHkmSbwQQixSDrudD5x7Grc+9ioFmakkxTtnva1th49zvKObdeWFksDHgKaOXkJhOG9VOY2dPfz2mddxX3YO6cmJVoc2IXMM9QeBBJfHV+H3unvmOoE3x4W/FnhqrhJ4C9wCvB1jyMjH5nLDfq97L7zRmfgxjPHsPafaIXeB/Rj4pVLqOYwk/ysYQ4jeqZQawrjq8AnrwnuTJPFCCLGIlWSnc97qMrYcrGeTKptVAn6kuYOth4/jqijAbpcEPtoNDYc41NTB5dUriXPYKclOp7N3gN/8YyufuvRMnHGRVwnh8vg+jNFxtRGj/r1nnnZVhdEZ9eF52v6883vd24Cl87ybAEYZ0H8Aa1we3/vnutV/vmite4APjPPUuQsdy1RksichhFjkLlq7FLvNRm3z+BP9TKa9p59/7DxEVUkuCU5pF4oF+xtaWFOWT15GyhuPrSrNYzgU4oGXd7GQQ1NPh8vjux74PfAKcKaZpM6XkfFtX5vHfUQ9v9cd9HvdX8Qo2zkXY1bbImujij2SxAshxCLndDj4wLmnsauumf6h6V/1HhgK8Hf/fpbkZpCeLHN/xIKRMpoNlSfmWzabjTOXlbCvoZXndtVaFN2ESoD7gUv8XnfzPO+rGqPee+8872deuTy+f3J5fHUuj29ep+D1e92/Bi7FGIryV/O5r8VIknghhBCUZKdz/upythysn1ZLazAU4qkdB0lNcFIYhcMPipMNBYIcaurgwjWVxDlOTg8cDjsbVRlPbD/A3voWCyI8kcvjyzXvfgn45zFjfc+Xl4GfRHE9/IhOoBRjvPt55fe6nwHehjHevZhDcu1TCCEEYJTV7DzSyMHjbSwzZ+uciP/wcXr6h1hblr9A0Yn5FA6Dbmg9qYxmrOQEJ2ctL+Hu57dx/RWbSHBY0wfC5fF9DXC7PL4z/V53HcYoKPPO73X/fiH2swBqzJ/rmbuJnybk97pfgjdmuv028Au/1310vvcb66QlXgghBABxDjsfvWA9BxrbaO7snXC51u4+ttcel7HgY8ihxnZSE5ycXjl12XJuegrFWek88PJuS+rjXR7ffwDfBf4OzMlY7dPcr83l8WW6PL7ZD+MUOdoxhknMXOD9LgU+Dzzp8vgKF3jfMUeSeCGEEG/ITU/hQ+e7ePXAUXoHT65OCIZCPLPzEGV5mdKRNUYc7+ihZ2CQi09bimOcMprxrFmST11rB7vqZj1h6ay4PL7rgP8B7gH+1e91L+R43WkYye/nF3Cf88IcerMfY+bXhdzvPuCdGP0YnnB5fJE/+UAEkyReCCHECVYU5fB21wpe1HUMB0/MkfyHjxMMhSmYpORCRI/uvkHqWjq5dP0KEmcwT4DdbqN6aQlPbD8wj9GdyOXxbQJuBh4FPur3uhekhGaUkbPWiJjoZw78Edi10Dv1e92bgXcBCrjLLLERsyBJvBBCiJNsqiqjqiSXV/cffaNkoqXLKKNZUZQtZTQxYDAQZPexFi5cU0lW6swbZLNSkyjISJuHyCa0AyOJ/5BFkwcNmD9jYigmv9d9rd/rvsOifT8FuIELgdOsiCEWyLVQIYQQJ7HZbFx11ipue/w1dtY1s6o0l2d2SRlNrAiFwuw62sy6ikLK8mZfFr2iKAf62+YwspO5PL4UIOz3uruAf5/XnU1uJImP+stQLo/PDjisnEnV73Xf6vL4HjE7JotZkJZ4IYQQ43I6HHz0gvU0d/bwxLYDhKSMJiaEwrDnWDOFmSmsrzi1voXzPUOvmWz+FviHy+Oz9OzRrL8/CKyyMo45sgbocnl877QyiJEE3uXxXevy+M6yMpZoJEm8EEKICaUnJfDB89ay5UA9mckJUkYT5cJh2FvfQnJCPG9ZXRkNn+c3gPcAd1lQAz+e/wTutjqIOVANJGKclFjKvNLyVeBBl8dXbHU80USSeCGEEJPacaSJ81aVc7yzh+auiYeeFJEtHIZ9Da3EO+y8zbV83AmdIonL47sc+CbGTJ8/tTgcAPxe9x1+r/tPVscxB6qBHiJg5lm/190LXAWkA/dKR9fpi+y/YCGEEJZq7e7j1f3HOGtFKZdtWEltc4ck8lFoJIG32eDS9StwRn4CnwXcBmwHPmMOiWg5l8fncHl8p7k8vhKrYzlFZwCvL/AQnRPye93bgU8CmwCPxeFEjcj+KxZCCGGpJ/z7Kc/LIsEZR256yhuJfFOHJPLRImSW0DjsNt6xfiXOuKho6MwCjgAf93vdg1YHM0o6sA24zupAZsssX1nPm7O2Roq7gQeA77o8vqlnHROSxAshhBhffVsXO482oUpy3ngsNz2Fy09X1LV2cry928LoxHSEQqCPNhHvjOPtG1YS74yKBB6/130Q2Oj3urdYHctofq+7HXgKuCaKyz7swHeAu6wOZDTzastngPf4ve4Gq+OJBjJOmBBCiHH97fV9LC/Mwek4MVfJTkvmijMUf3t9H32Dw1QWZBH5/SMXn6HhELuPNpGblsKFaysjvgYe3iij+TbwLb/XPb9jV87ez4H7gcuBhy2OZcb8Xnc38F9WxzEev9fdiDGZFy6PLzuCj4GIEPl/0UIIIRZcbXMHda2dLC/MGff5zJQkrjprFRBmZ10Tw6GIKK0Vpu7+IfyHG1hRlMPFpy2NigTe9BPgs0C51YFM4mGgHiPOqOLy+Na5PL4Pujy+6U/PawGXx/d+oNbl8cXCcJ7zJmr+qoUQQiycF/URKvIyJx0HPCneyTs2rKQoKw3/oeP0D1o2b4wYpamzl91Hmzl/VQXVy0rmfSz3uWKOWf4x4D/9XvfrVsczEXOoy1uBTS6PL9vqeGboSxgdhiN91tlngSHgV+ZcAWIc8sYIIYQ4QXf/IDvrmqgsmDo/cTjsnFtVxhnLi9l2pIn2noEp1xHzIxyGQ00d1Ld3c3n1SioLsqwOadrMiZy8wB7gexaHMx0/BUqjqdzD5fGdCXwY+KXf6+6xOp7JmGU11wNnAx+0OJyIJUm8EEKIE2w5UE9BRirx0xzFxGazUVWSzzvWL+dgYxt1LZ2EI2JAwMUjEAyxq66JYCjEVWeuIjc96mbWvRpQwI1+r3vI6mCm4ve6O/1ed5c55ORqq+OZisvjSwTuxCgD+qa10Uzb7zBGAvqey+OLtzqYSCRJvBBCiDeEQmE261qWFc68SqAwK42rzlpF/9Aw22uP0z8UCRNsxr7W7j5eP9hASU46l21YSXJCRJc7T+QJjOTyIasDmaGfAs9HwZCI3wBWA9f5ve5Oq4OZDnMM+xuBMmCjxeFEJBmdRgghxBt0fQs2m42s1KRZrZ+WlMCVZ1Sx40gjWw41UJqdTnF2moxeMw8CwRAHjrcxFBjm0vXLKcpKszqkWfN73bUYwx5Gm5uAa4FfuDy+d0fKpFTjeB74gd/r/pvVgczQo8BSv9ddZ3UgkUha4oUQQrzhhT21VOSdWi213W5jXUUh7z6rit7BIWmVnwcjre8FGam895w1UZvAuzy+TJfHd1+0jkLi97o18DXgXcDXLQ7nJC6PLwnA73U/4ve6b7Q6npnye93hkQTe5fEVWx1PpJEkXgghBGAkhrXNHZTlZczJ9jJTkrjyjCpWleazrbaRY63dUit/igLBEHuOtXCstYtL1y9nU1VZtMzAOpEbgPcR+aOlTObHwK+Bb7s8vk9aHcwIl8dXALzu8vg+b3Usp8rl8d0I7HZ5fLlWxxJJJIkXQggBwJ5jzeRnpOKwz91Xw+hW+b6hIbYebqC1u3/Otr9YhEJhjjR3siUGWt9HuDy+HOALwF1+r3ur1fHMllm7/Qngl8Bmi8MBwOXxlWEM01gGROxwnTPwEJAKfNHqQCJJ1NbEDwaGOd7RQ0N7N119AwwFgwwPh7DbbMQ7HSQ64yjITKUoK42M5ERsUpAphBCT2nGkiaLs+UkMR1rla5s7eHX/MY61dlGRn0l6cjQ3wM6/UBgaO7o52tpFUVYaV51ZNev+ChHoGiCJCJ09dCbMseOvBXB5fDaMqwt/9nvdgwsdi8vjOwu4D8gALvV73c8vdAxzze9173J5fA8Cn3B5fN/ye90yli1RlMT3DQbYWdfIocYO6lo6aOvpJy0pnrSkBBLjnTjsNuw2G+EwBEMhAsNBag7W09U3iN1mozQ3g4q8TKpK8yjJTrf65QghRETpGxyirqWDtUvy520fNpuNivwsynIz2N/QxmsHjhEf56AiP4uUxKgcUWXehMPQ0t3LkeZOMlMSefv6FRRkplod1pwxJ/D5DPCs3+veYXU8c+xtwL3ALpfHd43f635loXbs8vhKMTqxNgIXRvKkWbNwM/Be4P3Aby2OJSJEfBJf39bFy3vreP1wA9mpyeSmJbN6ST4ZyYnTnoWufzBAa08f+xtaeW73YfLSUzi3qpw1Zfk4HVFdSyiEEHNif0Mb2anJOBzzX2Vpt9tZWZJLZWE2u+ua2Hq4gfSkBEpzMhZ9Mh8OQ1tPH3UtXSTEObhgTSWlOemxeDU5AWPc8lhKMgHwe92Puzy+yzBmRn3R5fH9CPj2fLYeuzy+JX6vu87vdR91eXwfBh6PlqEkZ+ApQGNcwZEkHrCFF7CXUU1NTQVwaO3atSQkTHwJNRwOs/1II8/tOkxzVy9luZksLcgiMf7U/7mHwmEa2rs51NhG72CAs1cs4bxV5aQmyjwCQkSDmpoaqqurrQ4j5tz1nJ9wmFmND3+qBgLD7K5rYmddE06HncLMNHIzUphmO01MCAyHqG/vormrl9TEBFzlhVTkZ027scoyoSDO3laAyurq6sMWRxNRXB5fBsYstP8K/NXvdV8xx9u3AZuAz2G0Tr/V73U/O5f7iDQuj28dcNjvdXfN1z6UUk6MjsoVQBC4Tmu9x3zuw8DntdYRMW59xLXEd/T2c/9Luzje3k1VSR4blhZjn8MWCLvNRkl2OiXZ6fT0D6LrW3llXx3vPns1p5UVxGJrhxBCTCoQDLLnWAsXr620ZP+Jzjg2LC1mXXkhda2d7DjSyOHmDvLSUyjKTiPRGbtXTDv7Bmlo76azb4ClBdm8Y0MJuWnJMf1dZHa6PBt40O91B6yOZ76YLeHXujy+e4BkAJfHlwr8CLhttqUu5uyrVwOfBdYBXRjj1fvnIu5I5ve6ty3Abt4JxGmtNymlLgG+D7xPKbUBo99DxPxxRkwSHw6Hee3AMf5SoynPzeSi05bOafI+ntSkBKqXFdPa3ceDL+9m2+HjvPvs1dIqL4RYVGqbO0hOcM7J1c5T4XDYqcjPoiI/i/aefvYca2bb4eOkJDrJS0shOy2JuAUo95lvA0PDNHf10dLdi8NmY21ZAcuKskl0LppSos9gDC1ZAcT8JD5+r/uxUb+ejpGAf8bl8dUBNebtl36vu37sui6PLx5YBVQDg36v+/cYIwv+EDgMfBJjdJ/eeX0R4zD7NcSZN+ck9xNH3RJG3RKBeHMZxwS3OIzXO/p+OXA+8BeMlnKH+XjcDO6Pvr3V73X3jXppe4E4pZQdSAcCSqkc4D8xRlO67ZTfvDkSEUl8V98A9764k+Pt3WxaWUZGSuKC7j8nLZm3rlvKjiONeB9+nvdtXMOaJQULGoMQQljlUGM7ORE24klWahIbVRlnLCvhUFM7BxvbqDnQTnKik6yUJHLTk0l0RsRX2JTCYejuH6Slu4/O3gGC4TDleZlcsLqSoqy0yC+ZmUMuj8+BUV7y58U4C6ff637W5fGVAB8GzsNIzt8N/BHA5fHdwJsz144knCNqXR7fcozk934ggFFOc6GZUMPJrcS2UT9HbhMlzLO92cfcH0ngR36fL9fNwTbaOPE9BujBOMHcA+QCVwJ3AB4gosbHtfw/YGt3H7c9/ho56ckL0vo+EYfdjquiiNbuPu7ZvINLXQOcu6rckliEEGIhHWnuIDst2eowxuWMc7CyOJeVxbkEhoPUt3VzuLmdHbWNOBwOMpMTyUpNJC0pAUcEJcODgSBd/QO0dw/Q0TdAcoKTyvwszlxWQm56yqJK3Mc4B8gH7rY6EKv4ve4OjJFWboY3aue7zadfAX4GhIFhYADYj9Faf4g3W6Qnavke3eI9Xsv3yLKnkrSP1zruYPYt4TM5OfhH+YgAACAASURBVBgdw3y5HnhMa32jUmoJcAQ4CNyC8R6uVkr9RGv9hXmMYVosTeIbO3q4/YnXqMzPYqkFnanGk5OWzP9v777D5DwLcw//ps/23ou6Rn0srdwrGDAu2MYBHJrpNUNIJpATkhwOJAcIECYcMglJaCEYkkBoxmCKe7fklTXqo767Wml7r9O+88esjGyrrTSz35Tnvq69Vrs7O98jeT37zDtvuW7tYh7aeYiZaIxXr1+a03MTRSS/GYbBscGxjHkMPhuH3cai2nIW1ZaTSBj0j03SNTDKscFR9nb347LZKXQ7KHI5KClwLVixn43GGZ+ZZXxqlqnZKJOzUSwWqCktYnlDJS3VZZQWLuwrzBnsdpLl9NdmB8kUp+4iEwr4HgUePcvNc3YNwUlzC3atnH2KzqdITkt6HckpNad70uLizE8kTn3F4OW7Bg3z+3/nIaAD2BAOhyc9Hs9i4L8yocCDiSV+cHyKbz74PMvqK1lcW2FWjNMqcjm5bs1iHt/TgcNm47q1i82OJCKSFiOTMyQMgwKT58PPl9Vqoa68mLryYjYvbyIeTzAyNcPQxDQDo5P0jkzMFXsbTocdp82GzWbB5bDjtNtwOey4HDacNhsWi4XTjdUYBsSNBLPROJFonNlYjEg0TiQeJxpPEIvHmZ6NvVjY68qLqZ6bu1/sdmoA6PQuAR7Nwe0PJUVCAZ9BspjHz3Qbrz/4XZKn5NaEAr4fpTjCPwDf9ng8T5Ccs/+X4XB4wdccnA9TSvzETIRvPvwCi2vKM67An+R2Orh29SIe3nWYQpeDzcubzI4kIpJyJ4bHKcuBUWKbzUpVSSFVJYWsaKgCIB5PMDo1w1QkytRslOnZKBOzUaZmIgxNTDM1E2UmGiVhzE0WtiQPpDIMA8NIzmew2ywUOh0UuhwUupyUFbkocbtwO+0UupwUu50q7PPzepKLBUUuxjMkT6UdTvUdh8PhCeAtZ/jaUZJTwjKCKSX+1y/sp6K4gGVzD7SZqsDl4JpVrdz3/D5aa8qoLcud0/JERAC6h8YoKcjNHblsNiuVJYWca6KQYRgkEgbG3J8tluQJ4CdLvaTO3CirRuHlooQCvjjJvfHzmil7dfWMTLAuS3Z/KS5wsbKhih89vYtEYuEOxhIRWQiZvKh1oVgsFmw2K3abFYfdht1mxWq1qMCnmNcf/B+vP/gZs3NI7vD6g3Vef7DR7BxmMaXEr2upy6qV+cvqK5mYifDk3g6zo4iIpNSxwTEqM2x7Sck9Xn+wnORWitm1+EIyltcfLCC5V/6fmhzFNKaU+IXeB/5iWSwWNi9r4sGdh+gbnTA7johISkRicWaisazZb12y2qUkdwd5xOwgkhtCAd80sI0MmqO+0LL/6LsFUuR2sqK+kp9v2Wd2FBGRlBifnsXttGvaiCyEtrn37aamkFzTDmyaO0Qs76jEz8OyhiqODY7SO6LReBHJfhMzEY3Cy0JpAw6HAr6U7yYiea0dKAQ8Zgcxg0r8PFgtFpqrythyMO9OihaRHDQ+PYvLkZcDWLLwdpHHp7RK2px8ZaftrLfKURqCmadl9ZU8tvsIr/OuwKURLBHJYhMzERw2lXhJv1DA91mzM0hO2kfy5NaHzQ5iBrXQeSp0OSgvchM62sNlK5rNjiMicsHGpmZxZ9lJrZJ9vP5gITA7t7e3SMqEAr4Y8B9m5zCLptNcgCW1FTy3X1NqRCS7jUzOUODUWM58jQwN8o9f+CwffPNt+N7xJn5y77+bHSnT/TEw6vUHdWKipJzXH2z1+oPvzcfFrSrxF6CmrIiekQmicQ0qiEj2Gp+e0cLWeUokEnzls3/Jzm1buf51t1Bb38DP//tenntCOyeexSpgJBTwaVcISYebgG8BeTc9Qo/eF8BmtVLodtA7MkFzVZnZcURELkg8kcBm1VjOfOzfs4vOw4e4863v5Ja77mZyYpzRkWFq6xvMjpbJGoFus0NIzjo+974ByKtTOXPq0bvn+DE+8Kbb+MKn/Gm/VmmBixPD42m/johIusQTBtoifn46Dx8C4OC+PXzkrXfif9/beeSBX1BQUGhysozWwO+LlkiqnfzZajQ1hQlypsQn4nG+8Q9fIjI7syDXKyt00zUwuiDXEhFJh2SJV4ufj+npSQCOdRzlng9/jLYrruax3z7AI7++3+RkGa0ROGF2CMlZJ3+28u7lsLNOp/F4PJ8+29fD4fDfpDbOhfvVT35Ix6EDC3a9yuJCDvYMLtj1RERSzTAMLKjEz4fL5Qbg1Te/gRtuupVLr76O559+gl3bt3HjrXeYnC7zeP1BC/D/gOfNziI5qx+IoxJ/2q/7ga8AifTHuTDHOo7w0x/8B3e9/V388LvfXJBrFrkdjE/PLsi1RETSwWq1YGCYHSOrNLUuAmBsNHnwaDwWA8DlcpmWKZOFAj4DyJgBP8k9oYAv7vUHLwc6zc6y0M5a4sPh8Kc9Hk8jMBkOh7+0QJnmJRaL8W+BL9K6dBk3v/HNC1bibRYL0XjGPq8RETknq8VCIqESPx+rN2yktqGRR379SwwDuo4eBuDSq683OVlm8vqDRUAp0Kd94iVdQgFf+7lvlXvOZ068nwyey/aLH/2AzqOHuf3ut9Pf2wNAJBKh93h6F8LbrFZiKvEiksVsGomfN7vdzic+8wVWrdvA4797gOGBft754Y/RduXVZkfLVDeRXHi4wewgkru8/uCNXn/w/WbnWGjn3GIyHA6PAd9bgCwXZN+O7RiJBF/92//94ucO79/Hn3/oXXz3Fw+m7boJw8Bm1VxSEcleDrtdgxEXoK6xiT//24x8cToTFcy9nzQ1heS6DwAbgYWZjpEhzlniPR7Pe4Bd4XB469zHXwAOhMPhb6c73Pl449vexfjY73eJCf7d39DYsoi73v6utF43nkjgsOXd4WAikkNKC11Mz0bNjiG57WTP0A+apFOUPDz76Fy703wMeAdwzymffgD4isfjcYXD4a+nM9z5WLXe+4rPlZaVcenV16X1upFYHJdDJV5Esld5oZuh8SmzY0huO9kzYqamkFwXQyX+Fd4HXDc3pQaAcDj8uMfjuRl4CDC9xL9cOqfQnGpoYpomndYqIlmstNDFbFRrDSWtTvYM/aBJOsXJwxJ/roWtiVML/EnhcHiADN5yciEMT0zTWl1qdgwRkQtW7HYRialbSVo9C3wC0BHnkk5W8vCJ4rmetcQ8Hk9tOBzuO/WTHo+nDsjruSTjMxEaK1XiRSR7lRQ4mY1ploOkTyjgCwEhs3NIzvskv19EnTfOVeKDwK88Hs+fAlsAC7CZ5OFP/5bmbBnLMAxGJ2doKC8xO4qIyAUrdjs1nUbSyusPlgCNwNFQwKcTEiUtQgHfoNkZzHDW6TThcPg/gH8lucXkFMktor4DfDscDgfTHy8zjU3NUuR2UuR2mh1FROSCFbtdzEZjGIb2ipe0uQnYB3jMDiK5y+sP3uP1B99hdo6Fdq7daRqBm4EJkuX9k+FweHghgmWyI33DbFraaHYMEZGLYrdZKSlwMjEToaTAZXYcyU09c+/rgR1mBpGc9lGS6y7uNTvIQjrXwtbvkHwG/QmSc+C/kvZEGS4eT3B8aJxLlzeZHUVE5KK1VpczqG0mJX1OnviukS9JpwaSJwPnlXPNiW8Kh8M3AXg8noeA7emPlNk6BkZYWldBVUmh2VFERC5aa00Ze7r6zY4huetkiW8wNYXkLK8/aCH583XiXLfNNecaiY+c/EM4HI6e+nG+6ugf4UpPq9kxRERSoqGilPFprTeU9AgFfFPAKCrxkj5VgAONxJ9TSlY/HTlyhGg8+7aZH5mO0D84ydiJo7T3dJgdRyRvtbe3mx0hZ0xH4xzuOk6tbRaLxWJ2HMkyDpuVlbXn3G75j4CDCxBH8tPJJ4h5V+ItZ9uVwOPxzALdp3yqae5jC2CEw+Gl87lYe3v7YuBItKgKrNm1zXw8keDhnYe587I1rF9UZ3YckbzV3t5OW1ub2TFyyud//CiXLm/W4laZv0Qcx+QgwJK2trajJqeRPOX1B4uAeCjgmzE7y0I610j8ynRcNBqL43BmV4nf1dnL0rpK1rXWmh1FRCSlTi5uVYmXdPD6g63AklDA95jZWSQ3hQK+SbMzmOGsJT4cDqdlzsieY314lzan467TYnB8ir6RSd5++yV6uVlEck5rTRm7O/tYXFthdhTJTe8GPuP1B8tCAd+42WEkt3j9wfcDTaGA77NmZ1lo51rYmhYzkRjHh8bMuPS8xRMJth0+zhuvWEOxDncSkRy0rL6KAW0zKenTTnIa7iVmB5Gc9HbgFrNDmMGUEn9rm4fQ0R5GJzN76lLCMNhy4BgrG6pY26JpNCKSmxorSrAAY9qlRtLj5Ep0LWaRlPL6g1ZgE7//GcsrppT4luoy/uCKNTwd7mQiQ39pGIZB+8FuSgvc3HXlWk2jEZGcZbFYWL+onu7BUbOjSA4KBXw9JHcOUYmXVFsGlKISv7A2LG7gts0entjbwdhUZo3IJwyDrQe7cdhtvON6Lw5bdi3CFRGZr9XNNfSN5uXaMFkY7ajES+qd/JnKyxI/333iU+rS5c3YrVZ++txeLl/ZnBGnoMbjCbYcPEZpoZt3Xn8JTrsKvIjkviW1FUzMRJiNxnA5TP3VILnpr9CBkZJ65UAPsNvsIGYwbST+pI1LG3nrtRt4/lA3Ozt7SSRScp7UBekfm+TBHYdoqizlnhtU4EUkfzjsNlY2VGfNpgOSXUIB385QwBc2O4fkllDA9y9AYyjgi5qdxQyml3hIvoz7p7ddhcNm5eFdhxmemF7Q68fjCbYfOc72Iyd481XreMvV6zWFRkTyzrpFdfSMTJgdQ3KQ1x+0eP3Bd3v9wbvMziK5wesPWgBCAZ95o78my4gSD1Ba6OZdN2zklk0r2XLwGDs7eonG42m9pmEY9I5M8OCOQ5QXFeB/w9Ws0S40IpKnPI1VDE1MMxuNmR1Fcsxc0foo8Emzs0jOeL3XH9zj9QfTcjBpNsiYEg/JHRI2LW3kT2+7iiK3k99uP0j7oeMp34oyGo9z4MQgD+88zIETg7z5qnX84TUbKNI+8CKSxwpdTryL6zncO2R2FMlNvwAu9/qD9WYHkZxwO9AKdJodxCwZuXqptNDN267dwNjUDFsPdvNMuBOH3caimnIaKkouaNFVImEwPDnN0b5hekYmWNlYzR9es4GldRXaPlJEZM4VK1v41kPtrGqq0WOjpNp9wN8AtwLfMjmLZLG5qTRvAH4TCvgya4vDBZSRJf6k0kI3N25YxvVrlxA+3s+z+4/x0M7D2KwWSgtclBS4qCwuoMjtxGa1YrNaMAyIJRJEY3GGJ6YZmZxhfGaWiekIlSUFXLK4gXdcdwllRW6z/3oiIhmnqbKUiqICTgyP01hZanYcyS07SI6a3o5KvFycjUATySeGeSujS/xJdpuVtS11rG2pwzAMhiamOTE8TvfgGB0DIxzqGSIajxOPG1itFuw2Ky6HnebKUi5d3kRDRQn1FSXabUZE5BwsFgvXrF7EIzsPq8RLSoUCPsPrD94HtHn9QUs+L0iUi3Y7kAB+ZXYQM2VFiT+VxWKhqqSQqpJC1rXWmR1HRCTnrGut476t+5iciWitkKTan4YCPq2clov1HPD5UMDXb3YQM2XUwlYRETGf027jsuVNHOwZNDuK5JiTBd7rD2pOq1ywUMD3QCjg+99m5zCbSryIiLzC5Stb6B4cI6LtJiXFvP7gO4ATXn+w2uwskn28/uDNXn+w1ewcmSDrptOIiEj6VZUUsmlpI3uO9XPJkgaz48icRCLBVCTK9GyM6Uh07s9RJmYjTMxEmJ6NEosnMAyDuGGQSBhYLRasVgtWiwWLxUKh006h20mJ20Why0GB00GBy06h00Ghy4ndlvbxve1AOfAe4MvpvpjkDq8/WAB8H/gdcLfJcUynEi8iIqf16vVL+cp9T7GysZpCl8PsOHknkUgwOjXL4PgU/eOT9A1PMjQ5hc1qxWm3YbdacdhsOOw2nHYbxW4nVcWFydJuSa4hw2IBDIyEQYLkdsvRWJxILEb/+CTR4TixWIJoPE4kntzZraTARW1ZEbVlRVQVF1JRUogjhcU+FPDt8vqDjwMf8fqDXwkFfImU3bnkuj8EKoB/NjtIJlCJFxGR0yotdHP1qlb2dPWyeXmz2XFyXiyeoGdknK7B0RcLu8Nmo9DtpMjpoKasiKX1FWkdKU8kYHI2wtjULPu7B5mK9jA9G32x2DdXltJUVYrbedFP6v4Z+C/gJuCBiw4u+eKjwB7gcbODZAKVeBEROaNrVi/mmf1djE3NUFqotYipNh2JcmxwjKN9w3QPjlHgtFNW5F6Qwn46ViuUFDgpKfj9rkSnFvvtR0/w+N6jVJcUsqSugtbKUqou7FI/BXqBP0IlXs6D1x+8FNgM+LQ9aZJKvIiInFGhy8Gr1i7l+UPdXLVKa8lSYWxqho7+EQ73DjM0MU3p3MGFm5Y24rBn3n4Tpxb7pqqSF09AP9QzTLirjz/cUD/v+wwFfBGvP/ge4HDqE0uOuhQYAb5ndpBMoRIvIiJndaWnhSf3HWVgbIrq0kKz42SlRMKge2iMPV19nBieoKLYTXVJIcvrK7FaLWbHmxer9ffntdgvInoo4NMIvJy3UMD3z15/8LuhgG/S7CyZIvOe8ouISEZx2G3c1raKF44cJ57QGsT5mI5E2XG0hx8+vZMn93XgsFu5dHkjKxurqCwpyLoC/3IXm97rDy71+oP3ef3BpSkJJDnJ6w8uAVCBfymVeBEROaf1i+pYVFPOnq4+s6NkhcGxSR7ddYT/fnInh3uHWFpbwSWL62moKMn64n6qWDx+sXcxA7wG+JuLTyO5yOsPrgEOev3B95qdJdOoxIuIyDlZLBbuvHwNJ4bHGZqYNjtOxhqdnOHhnYe5v30/s9EYm5Y24mmqpqTQZXa0tDjaP3JR3x8K+I4D/w94m9cf9KYklOSa/wtMAPeZHSTTqMSLiMh5KS1wccdlq2k/1K1pNS8zNRPhqb0d/PS5PURiMdqWNdJaU5aRC1VTpW90kkQiJZuEfJHkgsXPp+LOJHd4/cErgDcCXw4FfANm58k0ufvoIiIiKbdhUb2m1ZxiJhrj+YPd/OiZ3QxPTrNpaSOLasqx5dCUmdOJxuIc7Rvm8pUXf35AKOAbAb4A3OL1B6+76DuUnOD1By3A35HcivSrJsfJSNqdRkREztvJaTVf/cVTNFWVUVlcYHYkUxiGQfj4AFsOHKO0wMWGxfW4HTazYy2Ygz1DrG6upaqkCCZnUnGXQcAJhFJxZ5ITWoGNwF+GAr4Js8NkIpV4ERGZl9ICF3detoafbdnDq9YtxWHPn/IKMD49yxN7OxiZnGFNcy1F7os+vTSrHB+eIJ4w2LS0EUjNmTuhgG8a+BwkR2B1mI+EAr6OuV2Lxs3OkqlU4kVEZN42LK6nY2CYZ/d3cfXqRVgtuT19BF46+l5XVox3cT05PmvmFUYnZzg+NMbtm1clT5NNXPTuNC/h9Qc3At/x+oN3hQI+HQSVp7z+4A3A46GAb9DsLJlMc+JFROSC3LLJQ0VxATuO9pgdJe3Gp2d54IUDbDt8gjXNtbTWlOVdgZ+JxggfH+RV65ZQVuRO12UGgCXAt73+oDpKHvL6gzcCjwAfMTtLptP/ICIickFsVitvvWYDo1OzHO0bNjtOWhiGwf7uAX763B5sFgvexfV5N30GIJ5IsKern01LG2muKkvbdUIBXxfgB65HJS7veP3BEuBbQBj4tslxMp5KvIiIXLAit5N3v2oj+7oHGByfMjtOSsXjCZ7a18GWg8fydvQd5p7IHB+kuaqUda21C3HJbwO/Ab6kk1zzzpeBFuA9c+sk5CxU4kVE5KLUlRdz99Xr2XLgGNOzUbPjpMR0JMoDL+znxPAE3iX5Ofp+UufAKHarlWtWLcKyAGsf5ha1fgCIAR9L+wUlI3j9wdcAHwICoYDvGbPzZAOVeBERuWirm2t49bqlPBXuJBKNmR3nogyOTfKzLXuxWiysbanFbs3fX5U9w+MMT0xzo3cZNtvC/TvMTau5GvjEgl1UzDZGci78p80Oki3y95FJRERS6rq1i9m0pIEn93UQjaV215KFcrRvmPvb99NYXsKSugryYNOdM+obneD40Dg3b1pJkcu54NcPBXy7QgFf3OsPNnj9wdcteABZECcXMIcCvi3AjZpGc/5U4kVEJCUsFgs3b1rJuta6ZJGPZ0+RNwyDF44c5/E9R1ndXENteZHZkUzVPzZJ18AYN7d5KC8y/UCvrwE/8/qDm8wOIqk1dyrrD7z+4OfhxalUcp5U4kVEJGUsFgtv2LyKVY3VPLk3O0bkDcNgy4Fj7Onqx7uonpKChR91ziT9Y5N09o/y+o0rM+VEXh8wSLLI15kdRlLqU8DdwKjZQbKRSryIiKSUxWLhzsvXsKa5lif2Hs3oOfKGYfBMuItDvUOsb63D6civ02dfrndkgq7+UW7etJLq0kKz4wAQCvh6gTuAauAnXn/QZXIkSQGvP3g7yVN6fwB8yeQ4WUklXkREUs5isXD7pavYsLiex/d0MBPJvF1rDMPgqX0ddAyMsL61Doc9v38lHh+eoHtojFvaPFSVZEaBPykU8G0D3g1cBfy1uWnkYnn9wfXA94HngfdrGs2Fye9HLBERSRuLxcKtmzxctaqFR3cfYWgic9arGYbBs/u76BoYY11rLfYF3Hkl0xiGwaGeIQbHJrmtbRUVmTGF5hVCAd8PgXcCXzE7i1y0ZSSnSN2phawXLn8ftUREJO0sFguvXr+MN1+1ji0HuujsHzE7EoZhsPVgN0d6h5MFPo+3kIzF4+zq7MNmtXL7ZaspK3KbHemsQgHfvaGAb8TrDxZ4/cF3mp1H5sfrDzoBQgHfz4BVoYCv2+RIWS1/H7lERGTBrG2p40Ovu4zDvcPs7OjBMMx79XxnRy/7jw+wrrUur0fgp2ajbD/aS2t1OTddsgK3w252pPn4EPAfXn9Qe4pnCa8/uBjY7fUH/wAgFPDNmJso++Xvo5eIiCyohooSfLdcQcIweDrcacoWlF0Do2w7coK1eT4Hfmh8il2dvVy+opkrPC1YrVm3If4/At8FPuv1Bz83t1WhZCivP7gCeAKoAo6amyZ35O8jmEgeMAyDoYlpDpwYJNw9QEf/CLMZvFOI5L5it5P33biZpXWVPLrrCGNTCzcYNzI5zSO7DrOqqRp3nu5CYxgGnf2jHO0b4fUbV7KysdrsSBckFPDFgfcC3wD+Evh7FfnM5PUH1wCPA27gVaGAr93kSDkjq147E5Hz0z82ybP7u9h26DgJDErcLiwWiMbijE9HqC8v5qpVraxfVI/Tnp9lRsxjt1m587LVtFSVcX97mEU15XiaqrGm8XjUmWiM324/SEtVKWWF+blD4dRslAPHBykpdHHH5Wsodmf3fvihgC/h9Qc/BMwA95DcprDX3FRyKq8/2EhyBD4CXB8K+PaYHCmnWBZyXmJ7e/ti4Mi6detwufLzQVQknaLxOA/vOMxT+zppri5lWV0lRS/7RZ0wDHpHJjjcM0QskeAtV61nSV2FSYnnr729nba2NrNjSIoMT0zz42d30zc6yeZljZQWpn5hZSKR4DfbDxJPxFlWX5Xy+890hmHQNThGz/AEly1vYlVzDZZUPWFKxHFMDgIsaWtrO5qaO52fuRH4RaGA76jXH7QCVaGAr9+MLPJKXn/wU8APQwHfIbOz5BqVeJEcMTUb5TuPbGMmEqVtaSNup+Oc39M9NMaOoz28fuMKrvS0LkDKi6cSn3tO7hbzq2370zIq/+z+Tjr7R1nbWkf2Tf2+OKeOvl+7ZjGlBSn+3ZsBJf5UXn/wr0ie8HpnKOB7zuw8+WhuB5ovA98MBXw7zc6TyzQnXiQHxOIJ/v2RbViAqzyt51XgAZoqS7luzWJ+s/0gLxw+nt6QImdgsVi4bEUzH7/1SuKJBI/uOsJoiubKH+0b5sDxQVY11+RVgTcMg86BUXZ19uFdXM8tm1amvsBnpp8D08BjXn/wHrPD5BuvP1gLPAj8MfBak+PkPJV4kRzw2O4jTM5G2bikYd4vkxe5nVy5soWfbd3LyKTO3BDzVBQX8L4b23jNhqU8u7+L5w92MzV74Se9TkeiPLm3g5WN1TjyZCtJwzDoG51k2+ETJBIGb7xiDatbalM3fSbDhQK+XcClwNPAd73+4N97/cHzG9WQi+L1BzcCW0n++78tFPAFTI6U8/LjUU0kh41MTvPY7iNsXtp42l/U2557mk999H28/w9u4c8/+C4ef/DXr7hNWZGblqpyfrXtwEJEFjmj5Kh8C5+841pWNlbx2O4jbD9ygsg8d1UyDIOn93VSXuSmNE8Wsg5PTBM62sPQxDSvXr+U129ckS+j7y8RCvgGgZuAfwL+BFhtbqLc5/UHrwGeA2zAtaGA7z9NjpQXVOJFstzzh7qpryihwPXKwaax0RH++Yt/S2R2htfcegd2h4Nvf+0r9PW8cuqMp7GKvcf6GJueXYjYImdV6HLwuktW8Ge3X019eTEP7jjEnq4+4vHEeX3/0b4Rjg+Ps6Q2exZtX6jx6Vl2dvRybHCMKz2t3HnZahorS/Nm9P10QgFfNBTw+YD1oYBvB4DXH7zV6w9qV74U8vqDJ1eiPwf8PXBJKOB73sRIeUU/zCJZ7oXDJ1jXWnfarw329xGNRmlqXcTl172K8bExerq7sFlfua2kw26jprSQfcf6uWxFc7pji5yX0kI3d16+hmtWL+K32w/y29BBltRWsLSuAucZThidjkR5al8HKxuqsvEQo/NiGAZjU7N0D40zE43StqyJFQ1V2KwamztVKODbC+D1By8B7gfavf7gu+em3cgFmpui9L+A93v9wY2hgG+Y5H79soBU4kWy2HQkysjkDGVFp9+Wb9HS5azb2Ebo+S2Ent8CwDs+lrxe7AAAHZFJREFU5KOq9vSlv7yogGODoyrxknGqS4t423VeuofGeHJvB7/bcYjasiKW1VdRWVzw4u0Mw+Cpvbk7jSaeSNAzMkHfyCQ2m5V1LbWsbKrGYdN5D2cTCvi2e/3BNwP/DGzz+oOfBb4UCvgufNFFnvL6g+uBfwc2Af8N5OYz5SygEi+SxUYmZyh0Oc64HZ/FYqF50VL6e3u47U1/yIO/vI8ffucbeNZuoHXJ0lfcvqzQzYnh8XTHFrlgTZWl3H31em5t89B+qJun93WCBRbXlNNaXU7X4CgnRsbZtKTB7KgpNTkb4fjQBEMTUzRVlnLDuiU0VJTk9ZSZ+QoFfP/j9QcfA4LA/wVu8fqD14QCvoXbazuLzY2+/xPJk3KHgDeFAr4fm5sqv+l1N5EsljCMs04XaH/mSX79sx9xy113c91rb+Yt73o/kcgsT5xmcSuAxQKJhH6fSeYrdju5fu0S/tcbr+Ouy9cyHYnxwAv7uW/rPmpKC3NibHAmGqN7cIwdHb3sOzZAc1Upb7pyHa/1Ls/7Oe8XKhTw9YcCvruB24GvhwI+w+sP2uYWZsppzO37ztyrFrUkX81YowJvPo3Ei2SxQqeDmciZd+0Y6EueQD4zPQXA7Exy722H8/THrc9EYpQUZPdR7JJfrFYLnqZqPE3V/Gb7ASZmIsxG42w50E1ZoYvK4gKqSgqwZ8F0E8MwmJiJMDA2xejUDNF4gkU15Vy+opmW6jLNd0+hUMD3i1M+fAvwA68/+DvgU6GAr92kWBnF6w8WAB8D/F5/8MpQwHcEuCsU8J3f6nJJO5V4kSxWXuTGYknOjS84zQFPa7ybsNnt/Py/7mVkaJCtTz2BxWql7crTDzoNTUyzurkm3bFFUm4mEmPL/mO8fuMKyosKmJqN0j00xpG+YdoPnaDA5aC8yE1ZgYviAmdGFGLDMJiNxhmbnmV0aoaRyRlcDjtL6yrYtKyR2tLinF2Ym2F+CvwZ8FfA815/8EdAAHguH6faeP3BcuBdwJ8DjcADJLeORAU+s6jEi2Qxi8XCysYaugZGWdlY/Yqvty5Zyh9/6jP8+Pvf4aFf3kdVbR0f/rNPsWzlqlfc1jAM+kcnuWXTyoWILpJST+3roKzITXlRcpFrocvBioYqVjRUEY3H6RmeoGtwlJ6RCYaOTeNy2Cl0OShyOShdgGKfLOwxxqYjTMxEmJqNMjkbwWG1UlNWxNLaSlqqyygtdGmazAILBXwzQMDrD34L+ATwcWANsB7A6w/aQgFf3MSIC2Zu9P0wUAE8RfLQpsfMTSVnYjGMhXuS2d7evhg4sm7dOlyu3Ns1QMQMR3qHuffx7dy4YdkZF7iej+NDY3T2j/Dx267K6BLR3t5OW1ub2TEkg0zMRPjyz5/g2tWLKXafezpYPJFgZHKGwfEpBsam6BudYGhiGqfdhsNuw2GzYrdZcdptOO02XA47LrsNm9WKxZJ88myxAAYYJNemGIZBJBYnEoszG02+j8YTxGJxIvE4kWgchy1Z2GvLiqkqKaSqpJACpz2j/387p0Qcx+QgwJK2trajJqdJCa8/WAK0hgK+3V5/sBjYTXK0/uuhgC9sbrrUmtvn/U3AFXP76uP1Bz8AbNO0osynkXiRLLe4tpyGihLCxwdY3XRhU2Fi8QQ7Onq5++r12V0oJC89suswdWXF51XgAWxW64slemVj8nPxRILJmQjTkRhTs1GmI1EmZ6NMzswyMjnD1GyEaDyBkTCIG2AYCSwWC1aL5cX3BU47RS4HhW4ntaVFFLgcFLocFDiT792OLC/seSIU8I2TLO4ApSRHpD8KfNzrDz4K/AT4z1DAN2BOwovj9QddwPUkF/feDVQD+73+YFko4BsNBXzfMDWgnDeVeJEsZ7FYuOuKtfzjL5+hqriA2rLieX2/YRg8f6ibNc01mg8vWWdkcpqtB47x6vWv3DJ1PmxWK6WFbkoLUxRMckIo4DsOvM3rD/4p8D7gHuBrwCPAgNcfXAsUAu2ZPF/c6w9WArFQwDcGvBX4DjAN/Ar4F+DhTM4vp6cSL5IDKosLeMf1l/C9x7aztqWWluqy8/q+aDxO+8FuitxO7rx8TZpTiqTe1oPd1FeU4D7Nwm6RVAkFfL3A54HPe/3B5cChuS99Ang3cMLrD/4SeAZ4PhTw7TAl6ByvP1gDtAGbgdcA15Cc6/9PwH3AG4CHQgHftGkhM5DH47kc+GI4HL7B4/HUAt8guT7ABtwTDocPeTyem4H/Q3Ij23bgj8LhsCkLoFXiRXLEsvpK3ndjG//55A66h8ZY01JLacHp154kDINjA6PsOdaPd1Edt21ehcOe+VvwiZwqFk/wbLiLy1fqhGFZOKGA7+ApH34CeIjk1JS3AO8H9gMeAK8/+FFgBnge2D+3iDZlvP6gleTe7ZcAM6GA71GvP1gE9PD7s4B2AF8g+eoBoYBvCLg/lTkyhdcftJHsto6zvHdxmv8WHo/nz4F3ApNzn/oS8P1wOPxDj8fzKmCVx+PpA74M3BAOhwfmvqca6E/7X+40VOJFckhLdRl/cttVPL7nCM/s68LpsFE2t62ezWplJhpLLuibmKKhvIS3X+dlRUOV2bFFLsieY324nHZKC91mR5E8FQr4BoF7gXvnCvUKkqXupD+Z+xwAXn9whOR8+o/OffzXwATQC8wCMZK7wxQBNSR7mh0oA6ZCAd9/zn3fT0iOsteTLKYAvwEeDQV8k15/8IMkXy14IRTwjXr9QQtgm1vIeraC6z7lzXXK+5NvDpKj0qe+2Uk+YbCf5mu2Uz5/8nZnus3p7uNs92k9zfee6f5PvS8LySddp54VwNy/113A9+Y+vhrY4fF4HgSOknwl4xpgJ/AVj8ezFPhmOBw2pcCDSrxIznHabbxmw3KuX7uEI73DHB8a48TIBJF4nNICF6uba1hUUz7vufMimebpfZ0sqa0wO4YI8OIe6uG5t5NWkSzxbcBioIG5RbNzpf8vSBb2U/0Tye0tr3vZ5094/cENQAnJkf440Emy+E8BJV5/8CleWpptXn/w1CJsfdn7U0euTy265h+ksMDC4fCPPR7P4lM+tRgYDofDr/F4PJ8G/hfJ/7avIvnKxwTwhMfjeSYcDu9f6LygEi+Ssxw2Gysbq0+7f7xItusZHufE8DgbFtebHUXkjM5Q7F/82tx2lpUkR92dJHvZIMliX0iyoEdJFsaeUMA3e7GZ5kblrZx9RN4OFPDSkXj3yz5++ai8g9OPhJ9rpH4+o/lnezvdbV7+ZOXk3/t85rAPklw/AMlR+88BTwNbw+FwD4DH43mcZKFXiRcRETkfzx04Rkt12UWdjSBitrkTYQfn3hbymvG5t5w190rHmZ6g9JzHXTwJ3EJyes11JF9B2Qas83g81cAIcAXJxa+mUIkXEZGsMhuNse3wcW5Yt8TsKCKSoeZeBZmde7sQfwZ80+PxfAQYBd4WDoeHPR7Pp0iuPwD4YTgc3nXxaS+MSryIiGSVgz2DFBc4KdC2kiKSQuFw+CjJ0XXC4XAH8NrT3Oa/gP9a2GSnl3cLF0REJLvt6eqnTguzRSTPqcSLiEjWSCQM9nT10VRVanYUERFTqcSLiEjW6BwYwWG3UeRymh1FRMRUKvEiIpI19nX3U1P68m21RUTyj0q8iIhkBcMw2NnRq6k0IiKoxIuISJYYHJ9iYjZCRZHb7CgiIqZTiRcRkaxwpG+YquJCLDrgSUREJV5ERLJD18Ao5RqFFxEBVOJFRCRLdPSPUlVSaHYMEZGMoBIvIiIZLxqPMzA2qZF4EZE5KvEiIpLxekcmKHI7sVn1a0tEBFTiRUQkC5wYHqe0QAc8iYicpBIvIiIZr2tglNJCTaURETlJJV5ERDJe58AolcVa1CoicpJKvIiIZLzhiWlNpxEROYVKvIiIZLRoLE40Fsdht5kdRUQkY6jEi4hIRhufieBy2nVSq4jIKVTiRUQko41Pz+J22M2OISKSUVTiRUQko03MzOLUVBoRkZdQiRcRkYw2MR3BaddIvIjIqfSoKCIiGW1sehaXQyPx8/Xob37Fd4KBl3zuHR/y8drb7jQpkYikkkq8iIhktDHNib8gnYcPAnDn2+7Bak2+8L7cs9rMSCKSQnpUFBGRjBaNxbFZNftzvjoOH6SiqprXveGNWK02Cgp1WJZILlGJFxGRjJYwDKzaXnJeEokEXUcPE4/F+aO3/wEYBpddewPv//gncTp1aJZILlCJFxGRjJZIGNgdGomfj9HhISqraqisqeGK617FC1ue5bnHH6GhuYU3vvUes+OJSAqoxIuISEZLJAwsaCR+Piqqqvm7f/nOix+3XXktH332KfZs36YSL5IjNLQhIiIZzWqzYGCYHSOrdB09zL3/GuSFLc8AMD01CYDL7TYzloikkEbiRUQko1ktFhIJlfj5KCop5fEHf83Tjz7E9a+7md3btwFww+tvMzmZiKSKRuJFRCSj2axWDEMlfj4qq6rxf/pz1DY08rv7f8bkxATv+ujH2XzlNWZHE5EU0Ui8iIhkNKfdxsRM3OwYWWfVei+fCfyT2TFEJE00Ei8iIhmttMDFTCRqdgwRkYyiEi8iIhmtpMBFJKaReBGRU6nEi4hIRispcDEbVYkXETmVSryIiGS0kgIns7GY2TFERDKKSryIiGS0YrdTI/EiIi+j3WkkZWLxBEMTU0RicWLxBAnDwG61YrdZKSt0U+R2mh1RRLJQsdvFbDSGYRhYLDq5VUQEVOLlAiUSBj0j45wYHqdrYJSO/hH6RicpcNqx22xYrRYsluTt4gmDqdkohS4HLVWltNaU01hZSlNlKYUuh9l/FRHJcHabFbfDzmw0htupxwwREVCJl3kan55l26HjPBXuJJEwKC10UVboZml9JZuXNWGznX6GlmEYTMxEGByfYu+xfrYcOMbY9CxrW2q50tNKa3WZRthE5IyqSgoZm55ViRcRmaMSL+dkGAYd/SM8u7+L3V191JUVs3FJAxXFBed9HxaLhZICFyUFrhc/F4nFOdI7xHcfeYHSAidXr17EhkX1uBz6sRSRl2qtKWNwfJrasmKzo4iIZAS1JTmrkclpfvzsHroHR1lUU85rvctx2m0puW+n3YanqYaVjdX0jk7wxN4Ofv3CAd54+RrWttRqZF5EXtRSXc6R3mGzY4iIZAyVeDktwzBoP3Sc+9vDtFaX8RrvcqxpKtUWi4X68hLqy0sYGJviJ8/uZkdHD7dfuppiLYYVEaChvJjx6VmzY4iIZAxtMSmvMDI5zbcf3sZvth/gypUtrGmpTVuBf7nq0kJuXL+MsalZ/uEXT7GrsxfDMBbk2iKSuWrKipiOxojGtdWkiAioxMvLdPSP8LVfPoMFeNX6pZQVuRc8g81m5ZIlDWxc0shPnt3N/e1hFXmRPGezWmkoL2FkYsbsKCIiGUElXl50sGeQ7zy8jfWL6hd09P1MqksLuWHdUnZ19vLjZ3cTTyRMzSMi5mqtKWNgfMrsGCIiGUElXgA41DPE9x7dTtuyRhoqSsyO8yKn3ca1qxdzuHeYHz+zm0RCI/Ii+aqlupzRKY3Ei4iASrwAXQOjfO+xF9i8rIma0iKz47yC3Wblak8rh3uHuL99n6bWiOSpZXUVDI5PkdBjgIiISny+m5yJ8N1HklNoasoyr8CfZLNZuXrVIkJHe2g/dNzsOCJigtJCNzWlhQyMTZodRUTEdCrxee6+rfuoKimkqbLU7Cjn5LDbuGx5M/e3hxmZnDY7joiYYH1rPd2DY2bHEBExnUp8Htvd1cv+4wNsWFRvdpTzVlbkprW6jB8/u0fTakTykKepmv4xLW4VEVGJz1OTMxF+8uweNi1rxGbLrh+DVc019AyPa1qNSB5qqCjBZrUwpgWuIpLnsqu9Scrc3x6muqQwIxeynovVYmHzsibubw/rF7lInrFYLKxfVMcxTakRkTynEp+Hhiem2dXZy/osmkbzcmVFburKinj2QJfZUURkga1urqVPi1tFJM+pxOehrYeO0VhZgj3LptG83LKGKp4LH9Mx7CJ5ZkltBdFonLHpWbOjiIiYJrtbnMxbNB7nufAxltVXmR3lopUWuHA77ew91m92FBFZQHablcs9zRw6MWh2FBER06jE55m9Xf24nXZKC1xmR0mJxbUVPLWvw+wYIrLALl3WzInhceLxhNlRRERMYTc7gCysp8KdLK6tMDtGyjRVlbKrs5fekQnqyovNjiNy0WYiMfZ299PZP0LPyDjRWAK3005TZSmLa8tZ0VCd9VPhUqGiuIBl9VUc7R9hWX2l2XFERBacSnweicTiHBsYZcOiOrOjpIzVYqG2rIgjfcMq8ZLVZiIxHtx5kK0HuikvclNRXEBdeTF2q5VILM7xoTF2dPQSie3mVeuWcpWnFavVYnZsU13laeFHz+xSiReRvKQSn0d6hscpLnBis+bWKF55UQGd/aNcsbLF7CgiF6Sjf4QfPBGixO3ihnVLKHA6XnGbxrlTlUcnZ3hqXwehoyd427VeKooLFjpuxlhWX4XdamVgbIrq0kKz44iILKjcanNyVieGxylx58Zc+FNVFhfQOTBidgyRC3Kkd5hvP9SOp7GazcubTlvgT1VW5Oa6NYspcjn5l99uYWhieoGSZh6r1cLVqxZxuHfI7CgiIgtOJT6PdA2MUl7kTvt1tjz5GH/xkffy/rtu5i8+/B6eeuTBtF6vrMjN8MQ0s9FYWq8jkmpjUzN877EX2LS08cWR9vNhsVhY1VxDU0Up33v0BWJ5vLhz49JGhiammNB2kyKSZ1Ti80jHwAiVJel9ybn3eDdf//LnSMTjvOa2O4nFY3zjH75Ix+GDabum1WKhpMBJz8hE2q4hkg4/fW4vDRWlp13PEXp+C5/++If5wB/cyic/eA8P/eq+V9xmRWMV0XiCx/ccXYC0manQ5eCGtUvZ1dVndhQRkQWlEp9HBsenKEvz1pIV1TX87df+jb/64le58vpXU11bh8ViwWazpfW6hS4ng+NTab2GSCqdGB7ncO8Qa1tqX/G1vp7j/OMXPsPE+Bivu+MubFYb//H1rxF6fstLbmexWNi4pIHHdh8hEsvfQ8+u9LQwPj3LcB5PLRKR/KMSnycSCYN4wkj7bhZOp5PmRYuxO+x8+k8+wt4d27n7PR+kedGStF7XZrXk9ZQCyT5bDx6jpbrstP9PnjjWRUNTC3e/54O8+Z73cfd7PwjA/j07X3HbIreTskI3uzp70545U7kcdl7nXZ7X/wYikn9U4vNENB7HZrVisSzMlnSGYfD+j3+SVes28MN//wZ7d25P6/WsFguxhEq8ZI8DJwbPOA/eu/ly/vZr/8rl196QvO3e3QA0NJ1+B6basiIO9+T34s62ZU0kDINeTasTkTyhEp8nFqq8n1RcUsq1r7mJt33go8TjcR777QMLen2RTBaNxxkcn6a88NwLzR/9za/41Y//m/qmZi675vrT3qaypJDOgdFUx8wqdpuVmzetZHdXH4ZhmB1HRCTtVOLzhN1qJZ5IpP2X285tW/nUR9/HM48+BMDUZHJUrKAgvQtqE4aBPcf2v5fcNRuNY7dazzm97dc/+x++EwxQUVWN/9Ofw+k6/ZoWl8PGdCSajqhZZV1LHUUuB12D+f2ERkTygw57yhNWqwWb1UIiYWCzpW9UvnnREoYH+/n+N79O59HDbH3qcaxWK9fc+Lq0XRMgnjB0FL1kDavF8uKT6jO9Sva7X/yU//zWv9DQ3Mon/+bvqKp55QLYkxIJI+cOcbsQVquFOy5bzbcf3kZ9WTFOh37FiUju0qN+HqkqKWQ0zXspV1RV4/8/n6e2vp4H7/85LrebP/6rz7LMszqt152ajVCV5u0zRVKlwGnH5bAzHTn92QadRw7xg29+HYBV6zbw5MO/5ef/fS87t2097e1HJmdOu01lPlpcW8GlyxvZfvSE2VFERNJKwxR5ZFF1OUPjU1Sm+Zj2lWvW8em/D6b1GqdKJAzGpyM0VJQs2DVFLobFYqG1uoy+0QkW11a84uuP/uZXJOYWaj/y6/tf/PyNt97B+k2XvuL2A+NTrGyoSl/gLHPTJSv56v1P0z04RlPV+R+iJSKSTVTi80hLdRlbD3abHSPlRqdmqCwuwGlP7170Iqm0aVkjv3nhwGlL/D0f/hj3fPhj53U/8USCE0PjvOnKtamOmLWcdhtvuWod3354GzWlhZpWIyI5SdNp8khjZSnjM7l3NPng+BQt1eVmxxCZl9XNNURicfpHJy/qfg6eGGRxbTk1pUUpSpYbktNqmjStRkRylkp8HqkrL2ZiOkI8x/ZTH52aobWmzOwYIvPisNm464q1tB8+TjR+Yaetjk3Pcqh3mDsuS++ak2x10yUrmInG6R4cMzuKiEjKqcTnEafdRnN1GceHxs2OkjIJw6BvdJIlp5mSIJLpVjfXsGlJA0/v65x3kZ+cifD0vk7uuHSVFnWfwclpNTs6epiciZgdR0QkpVTi88zVq1o52jdsdoyU6R4co6GiRDtzSNa6bfMqVjZW88jOIwyOT53X93QOjPDY7qPcdMly2pY1pTlhdltcW8FNl6zg6fD8nyiJiGQylfg8s7q5hplIjLE0bzW5UI70DXPVqlazY4hcMKvVwp2XreaNl69h26HjPBPu5MTwOLH4S6e9RaIxjvYN89juI3T1j/KB127mSo9+9s/HlZ4W1jTXsvVAt05zFZGcoSX7ecZhs3G5p5lDJwbZuLTR7DgXZWxqhtlIjDXNZz4ERyQbWCwW1i+qY2VjFaGjPWw9cIznD3bjdtix2axE43Fi8QRLaiu4bbOHVU01OtxpHiwWC7dftopvPtjO7q4+1rXWmR1JROSiqcTnocuWN/PEng7WxxNZfcrpwZ4hrlzVktV/B5FTuRx2LlvRzGUrmonFEwxPThOLJ3DabVQUFWC1pu+05VznsNl4x3Vegg88S9fAKC3VWgwvItlN7ScPlRcVsGFRPbs6e8yOcsFGJ2foG53ksuXNZkcRSQu7zUpNaRENFSVUlRSqwKdASYGLe27YyK7OXoYnps2OIyJyUVTi89Qtm1bSPzZF/9jF7VFthoRh8Pyhbm5r81Ba6DY7johkkabKUt505Tqe3d/FRI6sDRKR/KQSn6eK3E7edOVath06TjyeXfvG7z3WT0NFCW3LsntOv4iYY/2iOm5t8/Dkvk4mZ7X1pIhkp4WeE28DiET0oJkJltaUsa65in3Heljbkh0LvcanZhkYGeO9r96kn6M8NjurEVS5ON7WGmLRKE/sPcrlK5pxOx1mR8pOiRe37bSZGUMkH1kWcrut9vb2a4AnFuyCIiIishCubWtre9LsECL5ZKFH4rcC1wInAJ26ISIikt1sQAPJ3+8isoAWdCReREREREQunha2ioiIiIhkGZV4EREREZEsoxIvIiIiIpJlVOJFRERERLKMSryIiIiISJZRiRcRERERyTIq8SIiIiIiWUYlXkREREQkyyz0ia0ikuU8Hs9iYD+wBzAAJ3AceE84HD7m8XjuAT4GOEgOFHwzHA5/be57rwa+Ove1QeC94XC4Y8H/EiIiIllOI/EiciGOh8PhS8Lh8MZwOLwWeB74R4/H80HgT4Dbw+HwJcB1wDs8Hs/75r7v+8D75r72feBrZoQXERHJdirxIpIKjwMrgb8G/OFw+ARAOBweAd4F7PJ4PC7gr8Ph8I6579kBtJoRVkREJNtpOo2IXBSPx+MA7gaeA94z9/5F4XB47ykf3jv3PVbgM8DPFialiIhIblGJF5EL0ejxeLbP/dkFbAE+QbLEW872jR6Pxwl8l+Tjz+fTGVJERCRXqcSLyIU4Pjev/SU8Hs9hYDPJ6TUnP3c9cHM4HP4Lj8dTDNxHclHrHeFwOLpQgUVERHKJ5sSLSCp9GfiKx+OpB/B4PNXAV4CDc1+/d+7PbwmHw7PmRBQREcl+FsMwzM4gIllkbovJR8Ph8OIzfP2PgQ8ACZIDBf8aDoeDHo9nI7CN5NaUJ0fgj4fD4VvSHlpERCTHqMSLiIiIiGQZTacREREREckyKvEiIiIiIllGJV5EREREJMuoxIuIiIiIZBmVeBERERGRLKMSLyIiIiKSZVTiRURERESyjEq8iIiIiEiW+f9MY/nsZ58ZUQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib.lines import Line2D\n", + "\n", + "def intercluster_distance(model, ax=None):\n", + " # Create the figure if an axes isn't passed in \n", + " if ax is None:\n", + " fig, ax = plt.subplots(figsize=(9,6))\n", + " else:\n", + " fig = plt.gcf()\n", + " \n", + " ## Get centers \n", + " ## TODO: is this how sklearn stores centers in all models? \n", + " C = model.cluster_centers_\n", + "\n", + " ## Compute the sizes of the clusters \n", + " scores = np.bincount(model.predict(X))\n", + " size = prop_to_size(scores, 400, 25000)\n", + "\n", + " ## Use MDS to plot centers \n", + " Cm = MDS().fit_transform(C)\n", + " ax.scatter(Cm[:,0], Cm[:,1], s=size, c='#2e719344', edgecolor='#2e719399', linewidth=1)\n", + "\n", + " ## Annotate the clustes with their labels \n", + " for i, pt in enumerate(Cm):\n", + " ax.text(s=str(i), x=pt[0], y=pt[1], va=\"center\", ha=\"center\", fontweight='bold', size=13)\n", + " \n", + " ## Set the title \n", + " ax.set_title(\"Intercluster Distance Map (via Multidimensional Scaling)\")\n", + " \n", + " # Create origin grid \n", + " ax.set_xticks([0])\n", + " ax.set_yticks([0])\n", + " ax.set_xticklabels([])\n", + " ax.set_yticklabels([])\n", + " ax.set_xlabel(\"PC2\")\n", + " ax.set_ylabel(\"PC1\")\n", + " \n", + " # Create a regular legend with target \"size\" descriptor \n", + "# handles = tuple([\n", + "# Line2D([0], [0], color=\"none\", marker=\"o\", markersize=i, markerfacecolor='none', markeredgecolor=\"#999999\", markeredgewidth=1, markevery=i)\n", + "# for i in [3,9,18]\n", + "# ])\n", + "# ax.legend([handles], ['membership',], loc='best')\n", + "\n", + " # Create the size legend on an inner axes \n", + " lax = fig.add_axes([.9, 0.25, 0.3333, 0.5], frameon=False, facecolor=\"none\")\n", + " make_size_legend(scores, size, lax)\n", + " \n", + " return ax \n", + "\n", + "intercluster_distance(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 307, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib.patches import Circle \n", + "\n", + "def make_size_legend(scores, areas, ax=None):\n", + " # Create the figure if an axes isn't passed in \n", + " if ax is None:\n", + " _, ax = plt.subplots()\n", + "\n", + " ## Compute the sizes of the clusters \n", + " radii = np.sqrt(areas / np.pi)\n", + " scaled = np.interp(radii, (radii.min(), radii.max()), (.1, 1))\n", + " print(size, radii)\n", + " \n", + " # Compute the locations of the 25th, 50th, and 75th percentiles of the score \n", + " indices = np.array([\n", + " np.where(scores==np.percentile(scores, p, interpolation='nearest'))[0][0]\n", + " for p in (25, 50, 75)\n", + " ])\n", + " \n", + " # Draw circles with their various sizes \n", + " for idx in indices:\n", + " center = (-0.30, 1-scaled[idx])\n", + " c = Circle(center, scaled[idx], facecolor=\"none\", edgecolor=\"#2e7193\", linewidth=1.5, linestyle=\"--\", label=\"bob\")\n", + " ax.add_patch(c)\n", + " \n", + " ax.annotate(\n", + " scores[idx], (-0.30, 1-(2*scaled[idx])), xytext=(1, 1-(2*scaled[idx])), \n", + " arrowprops=dict(arrowstyle=\"wedge\", color=\"#2e7193\"), va='center', ha='center',\n", + " )\n", + " \n", + " # Draw size legend title \n", + " ax.text(s=\"membership\", x=0, y=1.2, va='center', ha='center')\n", + " \n", + " ax.set_xlim(-1.4,1.4)\n", + " ax.set_ylim(-1.4,1.4)\n", + " ax.set_xticks([])\n", + " ax.set_yticks([])\n", + " for name in ax.spines:\n", + " ax.spines[name].set_visible(False)\n", + " \n", + " ax.grid(False)\n", + " \n", + " return ax " + ] } ], "metadata": { @@ -266,7 +439,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.2" } }, "nbformat": 4, diff --git a/examples/clustering.ipynb b/examples/clustering.ipynb new file mode 100644 index 000000000..899a118f4 --- /dev/null +++ b/examples/clustering.ipynb @@ -0,0 +1,249 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib as mpl \n", + "import matplotlib.pyplot as plt \n", + "\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.datasets import make_blobs\n", + "\n", + "from yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer\n", + "\n", + "mpl.rcParams[\"figure.figsize\"] = (9,6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Yellowbrick — Clustering Evaluation Examples\n", + "\n", + "The Yellowbrick library is a diagnostic visualization platform for machine learning that allows data scientists to steer the model selection process. It extends the scikit-learn API with a new core object: the `Visualizer`. Visualizers allow models to be fit and transformed as part of the scikit-learn pipeline process, providing visual diagnostics throughout the transformation of high-dimensional data.\n", + "\n", + "In machine learning, clustering models are unsupervised methods that attempt to detect patterns in unlabeled data. There are two primary classes of clustering algorithms: *agglomerative* clustering which links similar data points together, and *centroidal* clustering which attempts to find centers or partitions in the data.\n", + "\n", + "Currently, Yellowbrick provides two visualizers to evaluate *centroidal* mechanisms, particularly K-Means clustering, that help users discover an optimal $K$ parameter in the clustering metric:\n", + "- `KElbowVisualizer` visualizes the clusters according to a scoring function, looking for an \"elbow\" in the curve. \n", + "- `SilhouetteVisualizer` visualizes the silhouette scores of each cluster in a single model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load the Data\n", + "\n", + "For the following examples, we'll use scikit-learn's `make_blobs()` function to create a sample two-dimensional dataset with 8 random clusters of points." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate synthetic dataset with 8 blobs\n", + "X, y = make_blobs(n_samples=1000, n_features=12, centers=8, shuffle=True, random_state=42)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Elbow Method \n", + "\n", + "K-Means is a simple unsupervised machine learning algorithm that groups data into the number $K$ of clusters specified by the user, even if it is not the optimal number of clusters for the dataset. \n", + "\n", + "Yellowbrick's `KElbowVisualizer` implements the “elbow” method of selecting the optimal number of clusters by fitting the K-Means model with a range of values for $K$. If the line chart looks like an arm, then the “elbow” (the point of inflection on the curve) is a good indication that the underlying model fits best at that point.\n", + "\n", + "In the following example, the `KElbowVisualizer` fits the model for a range of $K$ values from 4 to 11, which is set by the parameter `k=(4,12)`. When the model is fit with 8 clusters we can see an \"elbow\" in the graph, which in this case we know to be the optimal number since we created our synthetic dataset with 8 clusters of points. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the clustering model and visualizer\n", + "model = KMeans()\n", + "visualizer = KElbowVisualizer(model, k=(4,12))\n", + "\n", + "visualizer.fit(X) # Fit the data to the visualizer\n", + "visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default, the scoring parameter `metric` is set to `distortion`, which computes the sum of squared distances from each point to its assigned center. However, two other metrics can also be used with the `KElbowVisualizer`—`silhouette` and `calinski_harabaz`. The `silhouette` score is the mean silhouette coefficient for all samples, while the `calinski_harabaz` score computes the ratio of dispersion between and within clusters.\n", + " \n", + "The `KElbowVisualizer` also displays the amount of time to fit the model per $K$, which can be hidden by setting `timings=False`. In the following example, we'll use the `calinski_harabaz` score and hide the time to fit the model." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the clustering model and visualizer \n", + "model = KMeans()\n", + "visualizer = KElbowVisualizer(model, k=(4,12), metric='calinski_harabaz', timings=False)\n", + "\n", + "visualizer.fit(X) # Fit the data to the visualizer\n", + "visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is important to remember that the Elbow method does not work well if the data is not very clustered. In this case, you might see a smooth curve and the optimal value of $K$ will be unclear.\n", + "\n", + "You can learn more about the Elbow method at Robert Grove's [Blocks](https://bl.ocks.org/rpgove/0060ff3b656618e9136b)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Silhouette Visualizer \n", + "\n", + "Silhouette analysis can be used to evaluate the density and separation between clusters. The score is calculated by averaging the silhouette coefficient for each sample, which is computed as the difference between the average intra-cluster distance and the mean nearest-cluster distance for each sample, normalized by the maximum value. This produces a score between -1 and +1, where scores near +1 indicate high separation and scores near -1 indicate that the samples may have been assigned to the wrong cluster.\n", + "\n", + "The `SilhouetteVisualizer` displays the silhouette coefficient for each sample on a per-cluster basis, allowing users to visualize the density and separation of the clusters. This is particularly useful for determining cluster imbalance or for selecting a value for $K$ by comparing multiple visualizers.\n", + "\n", + "Since we created the sample dataset for these examples, we already know that the data points are grouped into 8 clusters. So for the first `SilhouetteVisualizer` example, we'll set $K$ to 8 in order to show how the plot looks when using the optimal value of $K$. \n", + "\n", + "Notice that graph contains homogeneous and long silhouettes. In addition, the vertical red-dotted line on the plot indicates the average silhouette score for all observations." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the clustering model and visualizer \n", + "model = KMeans(8)\n", + "visualizer = SilhouetteVisualizer(model)\n", + "\n", + "visualizer.fit(X) # Fit the data to the visualizer\n", + "visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the next example, let's see what happens when using a non-optimal value for $K$, in this case, 6. \n", + "\n", + "Now we see that the width of clusters 1 and 2 have both increased and their silhouette coefficient scores have dropped. This occurs because the width of each silhouette is proportional to the number of samples assigned to the cluster. The model is trying to fit our data into a smaller than optimal number of clusters, making two of the clusters larger (wider) but much less cohesive (as we can see from their below-average scores)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the clustering model and visualizer \n", + "model = KMeans(6)\n", + "visualizer = SilhouetteVisualizer(model)\n", + "\n", + "visualizer.fit(X) # Fit the data to the visualizer\n", + "visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/examples/examples.ipynb b/examples/examples.ipynb index cbd7b2491..bd49a3cb5 100644 --- a/examples/examples.ipynb +++ b/examples/examples.ipynb @@ -3,7 +3,9 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "%matplotlib inline" @@ -15,13 +17,15 @@ "source": [ "# Yellowbrick Examples \n", "\n", - "Ths notebook is a sample of the examples that yellowbrick provides." + "This notebook is a sample of the examples that Yellowbrick provides." ] }, { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "import os\n", @@ -55,9 +59,9 @@ "outputs": [ { "data": { - "image/png": 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MLyU+c0hrFOmtPn3LHDp0iJaWFtauXUsgEOCee+7h4osvBmDv3r1ccsklOBwO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\ntV5YWIjNmzcjKSkJ77//vkNqAoCLFy+ivr4eqampSElJQX5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\n", 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" ] }, "metadata": {}, @@ -86,7 +90,9 @@ { "cell_type": "code", "execution_count": 4, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "from download import download_all \n", @@ -135,29 +141,33 @@ "\n", "Feature analysis visualizers are designed to visualize instances in data space in order to detect features or targets that might impact downstream fitting. Because ML operates on high-dimensional data sets (usually at least 35), the visualizers focus on aggregation, optimization, and other techniques to give overviews of the data. It is our intent that the steering process will allow the data scientist to zoom and filter and explore the relationships between their instances and between dimensions.\n", "\n", - "At the moment we have three feature analysis visualizers implemented:\n", + "At the moment, we have the following feature analysis visualizers available:\n", + "\n", + "- Direct Data Visualization: plot 2D correlation between features and target\n", + "- Parallel Coordinates: plot instances as lines along vertical axes to detect classes or clusters\n", + "- PCA Projection: project higher dimensions into a visual space using PCA\n", + "- RadViz Visualizer: plot data points along axes ordered around a circle to detect separability\n", + "- Rank Features: rank single and pairs of features to detect covariance\n", "\n", - "- Rank2D: rank pairs of features to detect covariance \n", - "- RadViz: plot data points along axes ordered around a circle to detect separability \n", - "- Parallel Coordinates: plot instances as lines along vertical axes to detect clusters \n", + "Feature analysis visualizers implement the `Transformer` API from scikit-learn, meaning they can be used as intermediate transform steps in a `Pipeline` (particularly a `VisualPipeline`).\n", "\n", - "Feature analysis visualizers implement the `Transformer` API from Scikit-Learn, meaning they can be used as intermediate transform steps in a `Pipeline` (particularly a `VisualPipeline`). They are instantiated in the same way, and then fit and transform are called on them, which draws the instances correctly. Finally `poof` or `show` is called which displays the image. " + "They are instantiated in the same way, and then fit and transform are called on them, which draws the instances correctly. Finally `poof` or `show` is called which displays the image. " ] }, { "cell_type": "code", "execution_count": 5, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "# Feature Analysis Imports \n", - "# NOTE that all these are available for import from the `yellowbrick.features` module \n", - "from yellowbrick.features.rankd import Rank1D, Rank2D \n", - "from yellowbrick.features.radviz import RadViz \n", - "from yellowbrick.features.pcoords import ParallelCoordinates \n", - "from yellowbrick.features.jointplot import JointPlotVisualizer\n", - "from yellowbrick.features.pca import PCADecomposition\n", - "from yellowbrick.features.scatter import ScatterVisualizer" + "from yellowbrick.features import JointPlotVisualizer\n", + "from yellowbrick.features import ParallelCoordinates\n", + "from yellowbrick.features import PCADecomposition\n", + "from yellowbrick.features import Rank1D, Rank2D \n", + "from yellowbrick.features import RadViz " ] }, { @@ -189,8 +199,8 @@ " ]\n", "\n", "# Extract the numpy arrays from the data frame \n", - "X = data[features].as_matrix()\n", - "y = data.default.as_matrix()" + "X = data[features]\n", + "y = data.default" ] }, { @@ -207,9 +217,9 @@ "outputs": [ { "data": { - "image/png": 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TkxOffPIJY8eOJT4+vtTvbdiwgdzcXJYuXcqbb75JTk4OAJMnTyYsLIzExEQG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\nSY+Bh4iIiKTHwENERETSY+AhIiIi6f0DgaIzgVDKzqsAAAAASUVORK5CYII=\n", + "image/png": 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\n", 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mICCAoKAgnJz+t1+sh4cHHh4exdalUqmYM2dOgeMNGzZk/fr1BY5v3boVgM8+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+ "image/png": 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4+PAhgwcPZurUqYwcORJFURg4cCCOjo74+fkRHByMn58f5ubmLF68WK92Ku33VOPj4zl16hQmJiaEhISU6PoaNWrQtWtX/v3vfzN06NBir8/JyeH06dM4OzvLTFUIISi//xcnmJU8IUNhluZdMlBPDK9SL++ys7MrUUAFGDBggC7l1sqVK43YKyGEEKJwlfr27/Xr1/Hx8WHr1q3069eP1157jd9//52mTZtSq1Ytjh8/joWFBWvWrGHVqlXUrl2be/fucf/+fUJCQkockIUQQpSfirj9W14q9Uz1z7Kysujbty/R0dEcP34cNzc3Nm/eTG5uLhcuXNBd9/7771OjRg0JqEIIUUmZqsr2qswq9Uz1r1q3bg08vi38JOGxnZ1dgR0zhBBCVF5Veab6TAXVki7zrqRrr4QQQlD5Z5tl8czc/i0NJycnJk2aVNHdEEII8ZyptF+pMTb5So0QQuRXXv8vhlg1K1v5RxeeflEFeaZu/wohhHj2VclbpP9HgqoQQohyJQuVqrAZTd4g82Z6qcuVJEuCEEKI58tzH1SFEEKUr6q8+leCqhBCiHIlt3+FEEIIA5GZqhBCCGEgVXmmWpVXNgshhBDl6pkIqpcuXcLX15ehQ4fi7+/PzZs3Wbx4MX5+fgwePJjdu3eTl5eHr68vhw4d4vbt2/Tp04ebN29WdNeFEEL8hWyoX8EOHz6Mi4sLkydP5vjx4+zbt49r164RExNDTk4OPj4+eHp6smjRIt577z3q1KnDlClTqFevXkV3XQghxF9U5du/z0RQHTRoEGvXriUwMBBbW1tatmzJmTNnCAgIACAvL4/r16/TqlUr3NzcOHXqFB07dqzgXgshhChMZZ9tlsUzcft3//79uLu7ExkZiZeXF/Hx8Xh4eBAVFUVkZCS9evWiYcOGnDp1ivPnz9OmTRs2bNhQ0d0WQghRCLn9W8GcnZ0JDg5m5cqVaLVali1bxo4dO/D39+fhw4d069YNRVGYMWMG4eHh1K9fH29vb15//XVeeeWViu6+EEKI58QzEVQbNWpETExMvmPOzs4Frvvmm290P2/fvt3o/RJCCFF68kxVCCGEMJDKfgu3LCSoCiGEKFcyU63Ctr7cnluOGaUq8+Dw52Q/eqRXe9WsrPQqJ4QQovJ77oOqEEKI8iW3f4UQQggDkdu/QgghhIFU5ZnqM7H5Q1EWLVpEfHx8RXdDCCFEKZiqVGV6VWbPdFAVQgghKpNKffs3NzeXOXPmkJKSglarZfz48dy7d4+VK1fi4OBAbm4uTZs25dixY3zxxRcsXboUAE9PTxITEyu490IIIQpjUslnm2VRqYNqbGws9vb2zJ07lz/++IOhQ4fy8OFD4uPjqVmzJqNHj67oLgohhCglVRV+qFqpg+q5c+c4ceIEv/76K/B45qpSqbC3twfg1VdfLbScoijl1kchhBClYyJBtWI0bdqUunXr8t5775Gdnc3KlSvZsWMHd+/excHBgaSkJOrWrYulpSXp6ekAXL9+nfv371dwz4UQQhRFZVp1l/NU6qDq6+vLzJkzGTp0KJmZmfj7+zN79mxGjhxJjRo1MDN73H1nZ2dsbW3x9vbGycmJF198sYJ7LoQQoqJotVpCQkL4/fffsbCwICwsjMaNGwPw3//+l7lz5+quPXXqFJ9//jkuLi707NmTFi1aANCtWzf+/ve/l7rtSh1ULSwsWLBgQYHjnTt3LnBs5cqV5dAjIYQQZWXsZ6r79u1DrVazZcsWTp06xbx583QxolWrVkRFRQGwe/duXnjhBTp27Mjhw4fp27cvs2bNKlPblTqoCiGEqHqM/Uz1xIkTdOjQAQBXV1dOnz5d4JqHDx+yfPly/v3vfwNw+vRpzpw5w9ChQ3FwcGDmzJm88MILpW5bgqoQQohypTIx7jPVzMxMbGxsdO9NTU3Jy8vTPTIEiIuLw8vLCwcHB+DxGh5nZ2fat2/P9u3bCQsLY9myZaVuW4KqHrTo91fWozwtWRkP9Spby7a6XuWEEOJ5Y2NjQ1ZWlu69VqvNF1ABduzYkS9otm3bFqv/yyLWvXt3vQIqyI5KQgghypmJqapMr6dxc3MjISEBeLwQ6cnioycyMjJQq9XUq1dPd2zmzJns2bMHgCNHjtC6dWu9xiYzVSGEEOXK2AuVunfvTmJiIr6+viiKwty5c9m4cSONGjWia9euXLp0iQYNGuQrM3HiRKZPn05MTAxWVlaEhYXp1bYEVSGEEOXK2N9TNTExITQ0NN8xJycn3c8uLi6sWLEi3/mGDRvqVgWXqe0y11BO1qxZo9tZ6Wkke40QQlRexr79W5GemZmq7PMrhBCisjNaUI2Pj+f7778nOzub9PR0hg0bxv79+zl//jxTpkzh1q1b7N27l0ePHmFvb094eDg7d+7kyy+/RKvV8tFHHzF9+nSaNm2Kk5MTDx48oHfv3rRr165A5hoPDw/27NlTIHuNEEKIykdlUrlnm2Vh1JlqVlYWGzZs4JtvviEiIoKtW7dy7NgxIiIicHZ2JiIiAhMTE0aOHElSUhIAdnZ2up0vbt68SXx8PPb29kydOhUoPHPNV199xbx58yR7jRBCPANMZO9f/bRq1QoAW1tbnJycUKlU1KhRg9zcXMzNzQkKCqJ69ercunWLvLw8AJo0aaIrb29vr8tI88RfM9fk5eWRnp5OjRo1npq9RgghRMWT1G96UhWRiDY3N5d9+/YRGxvLo0ePGDBggC5dm8mfdtowKWTXjcIy19SuXZsHDx4UyF4jhBCi8pGgauhGzcywsrLC19cXgDp16pCWllaisoVlrrGwsCg0e40QQghRnlTKc5rROycnh9OnT/NO8CZu3ckoVdl7h1c8/aJCPMrT6lUOZJtCIYTxPfl/0dnZGUtLS6O1c6x7lzKV9/juewP1xPBkSieEEKJcye1fIYQQwkBM5Cs1VZfPb4fJvJleqjKrjl/Xq61zHbvqVW6D65t6lQNQ/7xB77JCCCFK57kPqkIIIcqXsff+rUgSVIUQQpSryr5/b1lIUBVCCFGuZKGSEEIIYSBV+fZv1R2ZEEIIUc5kpiqEEKJcyTPVCpKZmcmMGTPIyMggLS0Nf39/nJ2d+fjjj7G2tqZWrVpYWloyb948oqKi2LlzJyqVit69ezNs2LCK7r4QQohCSOq3CpKSkkKfPn3o0aMHqampBAQEYG1tzYIFC2jevDlLly4lNTWVCxcusGvXLqKjowF49913eeONNySnqhBCVEKS+q2C1K5dm8jISPbu3YuNjQ15eXmkpaXRvHlzANzd3dm1axfnzp3jxo0bDB8+HID79++TkpIiQVUIISohWf1bQTZs2ICrqyv+/v4cPXqUgwcPUrduXS5cuECzZs345ZdfgMfp4Jo1a8a6detQqVRERETw0ksvVXDvhRBCPG8qdVDt0qULYWFh7Nq1C1tbW0xNTZk9ezbTp0+nevXqmJub4+joSMuWLWnXrh1+fn6o1WpcXFxwdHSs6O4LIYQoRFX+Sk2lDqpt27Zl586d+Y5t3ryZVatW4eDgwNKlSzE3NwcgMDCQwMDAiuimEEKIUlCZSFCtNGrVqsWIESOoXr06tra2zJs3r6K7JIQQohRkoVIl4uXlhZeXl8Hq2/pye245li5Jebpbfb3aqp6WoFe5f1nX0qvcihO3WHIoWa+yQR2c9ConhBDPs2cuqAohhHi2yTNVIYQQwkAkqAohhBAGIguVhBBCCANRmZpWdBeMplL9ueDp6VnkuWvXruHj41OOvRFCCCFKR2aqQgghypU8U/2TwjLH7N69myZNmnDp0iUURWHp0qVcvHiRRYsWYW5ujo+PD2+//XaBujQaDbNmzeLChQs0bNgQtVoNwM2bN5k1axY5OTlYWlryz3/+M1+5b7/9ls2bN5OXl4dKpSI8PJyIiAgcHR0ZMmQI9+/f59133yU+Pl7Pj0UIIYSxmMgz1f8pLHOMo6Mjbm5uhIaGsnnzZlavXk337t3JyckhNja2yLq+++47cnJy2Lp1Kzdu3GDPnj0AzJ8/n4CAADp16sSRI0dYtGgREyZM0JW7fPkya9aswcrKitmzZ/Pjjz/i7e1NUFAQQ4YMYefOnfTr10+Pj0MIIYSxGXumqtVqCQkJ4ffff8fCwoKwsDAaN26sOx8WFsbJkyextrYGYMWKFeTm5jJp0iSys7N54YUX+PTTT7Gysip126UOqoVljoHHWwoCuLm5ceDAAQCaNGlSbF2XL1/GxcUFgPr161OvXj0Azp07x+rVq1m3bh2KomBmlr+btWrVIjg4GGtray5evIirqysNGzbE2tqaCxcusGPHDlasWFHaoQkhhKgC9u3bh1qtZsuWLZw6dYp58+axcuVK3fkzZ86wbt06HBwcdMfCwsLo27cvAwYMYM2aNWzZskWX+aw0Sv3nwpPMMYsWLcLLywtFUQA4ffo0ACdPnqRZs2aPK3/KFL9Zs2acOnUKgNTUVFJTU4HHWWcmTZpEVFQUH3/8cb4dlDIyMli2bBlLly4lLCwMS0tLXR98fHxYsWIFjo6O+T4sIYQQlYfK1KRMr6c5ceIEHTp0AMDV1VUXn+DxLDYlJYXZs2fj6+tLXFxcgTIdO3bk8OHDeo2t1DPVwjLHqNVqtm3bRkREBFZWVixYsIBz5849ta6uXbuSmJiIt7c39evXx97eHoDg4GBCQkLIyckhOzubGTNm6MrY2Njg5ubG4MGDMTMzw87OjrS0NAC6detGaGgoCxcuLO2whBBClBNjf081MzMTGxsb3XtTU1Py8vIwMzPj4cOHDB06lHfffReNRsOwYcNwdnYmMzMTW1tbAKytrcnIKN32tU+UOqgWljkmICCAoKAgnJz+t1+sh4cHHh4exdalUqmYM2dOgeMNGzZk/fr1BY5v3boVgM8++6zQ+jQaDQ0aNCj2qzlCCCEqlrGfqdrY2JCVlaV7r9VqdY8RraysGDZsmO55adu2bTl79qyuTLVq1cjKysLOzk6vtstlCVZ4eDgBAQEFXlevXjVYGydPnsTHx4dRo0ZV6ZVlQgjxrDP27V83NzcSEh4nMDl16hQtWrTQnbt8+TJ+fn5oNBpyc3M5efIkrVu3xs3NjYMHDwKQkJCAu7u7XmMzyPdUo6Kiij0/ZswYxowZY4imiuTm5saOHTuM2oYQQojKr3v37iQmJuLr64uiKMydO5eNGzfSqFEjunbtSv/+/fHx8cHc3Jz+/fvTvHlz3n//fYKDg9m6dSv29vYsXrxYr7Zl8wc9PFkYVWqaXMN25CmOJd/Rq9xrY4ai71PpyZlPf5YuhHi+GTufqomJCaGhofmO/fnxZGBgIIGBgfnO165du9DHjqUlQVUIIUS5kg31hRBCCAORbQqFEEIIA6nKQbVSjSwmJobly5cXeX7q1Km6FV1CCCFEZSMzVSGEEOXquX6mGh8fz/fff092djbp6ekMGzaM/fv3c/78eaZMmcKtW7fYu3cvjx49wt7envDwcKZNm0a/fv3o3LkzycnJzJ8/nzVr1hRa//Hjx5k7dy52dnaYmpri6uoKPP6azs6dO1GpVPTu3Zthw4bpyhSWKadfv36888477NmzB1NTUxYuXEjr1q3p3bu3gT4qIYQQhmDyvCcpz8rKYu3atYwaNYqYmBjCw8MJDQ0lLi6Oe/fuERERQWxsLBqNhqSkJLy9vdm2bRsAcXFxDBo0qMi6P/74YxYvXkxERAQvvvgiABcuXGDXrl1ER0ezefNm9u3bx8WLF3VlnmTK2bBhA+vXryciIgJbW1vc3d358ccf0Wg0JCQk0K1bt7J8NkIIIYzA2Js/VKQS3f5t1aoVALa2tjg5OaFSqahRowa5ubmYm5sTFBRE9erVuXXrFnl5eXh4eBAWFsbdu3dJTEwkKCioyLpv376ty2bj5ubGlStXOHfuHDdu3NBlCLh//z4pKSm6MkVlyvH29iYqKgqtVkv79u2xsLDQ60MRQggh9FGikK9SqQo9npuby759+/jXv/7FrFmz0Gq1KIqCSqXirbfeIiwsDE9PT8zNzYus29HRkeTkZACSkpKAx1lqmjVrxqZNm4iKimLAgAG89NJLujJFZcp57bXXuHr16lNnx0IIISrOcz9TLbKwmRlWVlb4+voCUKdL1OIRAAAgAElEQVROHV3GmAEDBtC5c2e+/vrrYusIDQ1lypQp2NjYYG1tTY0aNWjZsiXt2rXDz88PtVqNi4sLjo6OujJFZcqxsLCgX79+fPvttzRv3rwsQxNCCGEkz/VCpQEDBuh+7tixIx07dgQe3xLesGFDkeU0Gg3u7u75toYqjIuLC19++WWB44VtIzVv3jzdz3/NlPPndr29vYttUwghRMWp7LPNsjDKV2r27t3L8uXLCQkJAeDGjRsEBwcXuK5NmzZ89NFHBmt36tSppKWlsWrVKoPVKYQQwrAkqJZSjx496NGjh+59/fr1n5rJxhD+PJMVQgghypts/qCHohZuPb2gfn+dqRStXuUysvP0KveKo7Ve5b66+Afvqf6mV1mAVcplvcsKIZ4dz/UzVSGEEMKQVCZVd/MHCapCCCHKlwRVIYQQwkCq8O1fvUaWkJDAli1bDN0XPD09izx37do1fHx8DN6mEEIIYSh6zVSffFdVCCGEKC1VFd5QX6+gGh8fz6FDh7h+/Tpbt24FwMfHhyVLlrBt2zauXbvGnTt3uHHjBtOmTaNDhw6F1qPRaJg1axYXLlygYcOGqNVqAG7evMmsWbPIycnB0tKSf/7zn/nKffvtt2zevJm8vDxUKhXh4eFERETg6OjIkCFDuH//Pu+++y7x8fH6DE8IIYQxVeFnqka5sW1hYcG6deuYMWMGERERRV733XffkZOTw9atW5k4cSKPHj0CYP78+QQEBBAVFcXIkSNZtGhRvnKXL19mzZo1xMTE0KxZM3788Ue8vb356quvgMe7LfXr188YQxNCCFFWJqZle1ViBluo9GRTe/hfVpu6devqZp+FuXz5Mi4uLsDjDSLq1asHwLlz51i9ejXr1q1DURTMzPJ3s1atWgQHB2Ntbc3FixdxdXWlYcOGWFtbc+HCBXbs2MGKFSsMNTQhhBCiRPQOqra2tty5cweNRkNWVhbXrl3TnSvp5gjNmjXjm2++4e9//zupqamkpqYCj7PUjBgxAjc3N5KTk/nPf/6jK5ORkcGyZcv44YcfAHj33Xd1Ad3Hx4cVK1bg6OiIg4ODvkMTQghhRLL5QyHs7Ozw9PRk0KBBNGzYkMaNG5e6jq5du5KYmIi3tzf169fH3t4egODgYEJCQsjJySE7O5sZM2boytjY2ODm5sbgwYMxMzPDzs5OlxmnW7duhIaGsnDhQn2HJYQQwtgq+S3cstArqObl5WFubk5oaGiBc2PHjtX97OTkVOyevyqVijlz5hQ43rBhQ9avX1/g+JNFUZ999lmh9Wk0Gho0aFDsV3OEEEJUMAmq/3Pw4EE2bdqky0BTEuHh4Rw7dqzA8blz59KwYcPSdqFQJ0+eZM6cOXz44YeYVOFbC0II8ayT279/0qlTJzp16lSqMmPGjGHMmDGlbapU3Nzc2LFjh1HbEEIIIYoj2xSWJ32z2+iplo2FXuWsX6iuV7n6NzL0KgdwIztP7ww3kt1GiGeM3P4VQgghDESCqhBCCGEYsk2hEEIIYShVeKGSQUZWXNaa5cuXExMTU+J6pk6dWuT50tQlhBBClDeDzFQla40QQogSk2eqxSsua83TJCcnM336dKysrLCysqJGjRoA7N69m4iICExMTHB3d2fSpEm6MhqNhtmzZ3Pr1i3S0tJ48803GTduHD179iQ2NpaaNWsSHR1NVlYWo0aNMsQQhRBCGIjKyEFVq9USEhLC77//joWFBWFhYfl2/YuIiOCbb74BHn9NdMyYMSiKQseOHfnb3/4GgKurKxMnTix12xX+THXBggV89NFHeHp6smbNGi5evMi9e/dYvnw5X375JVZWVkyePJnExERdmZs3b+Lq6oq3tzc5OTl07NiRCRMm0K9fP7755huGDBnC9u3bCQ8Pr8CRCSGEKJSRn6nu27cPtVrNli1bOHXqFPPmzWPlypUAXL16le3btxMbG4uJiQl+fn5069YNKysrWrduzapVq8rUttGC6p+z1hTnz5lq3NzcuHjxIleuXOHu3buMHj0agKysLK5cuaIrU7NmTZKSkjh69Cg2Nja6TDgDBw4kKCiINm3aULt2bWrXrm3gUQkhhKjsTpw4ocvj7erqyunTp3Xn6taty7p16zD9vxXIeXl5WFpacubMGVJTUwkICKBatWpMmzaNpk2blrptg/258OesNQ8ePMiXtaY4Tk5O/PzzzwC6gb/44ovUq1ePDRs2EBUVxdChQ3F1ddWViY+Px9bWlsWLFzNixAiys7NRFIUGDRpga2vLqlWrGDRokKGGJoQQwoBUJqZlej1NZmYmNjY2uvempqbk5eUBYG5ujoODA4qiMH/+fF5++WWaNGlCnTp1GD16NFFRUfzjH/9g8uTJeo3NYDNVfbPWTJ06leDgYNavX4+DgwOWlpY4ODgwfPhwAgICdJvk9+rVS1emXbt2TJw4kVOnTmFhYUHjxo1JS0vD0dERHx8fwsLCJFONEEJUVkZ+pmpjY0NWVpbuvVarzZeXOycnh+nTp2Ntba1L6uLs7Kybvb722mukpaWhKEqJU5k+YZCgWtKsNYVp1KhRoV+T6d+/P/379y+yru3btxdan0ajYeDAgboPRwghRCVj5Geqbm5ufP/99/Tu3ZtTp07RokUL3TlFUfjggw/w8PDQPWKEx4lfatasyahRozh79iz16tUrdUAFAwTVkmStUavVjBw5ssDxJk2aFBqI9bVkyRKOHTtW5gfNQgghjMfYOyp1796dxMREfH19URSFuXPnsnHjRho1aoRWq+Wnn35CrVZz6NAhAIKCghg9ejSTJ0/m4MGDmJqa8umnn+rVdpmDakmy1lhYWBSbV9VQgoKCjN6GEEKIys3ExKTAhM3JyUn3c1JSUqHl1qxZU+a2K/wrNc8iE32TzajKd2uuh2qNXuWq19YvS02j6uZ6lQPQ6lnulmS3EeLZI5s/CCGEEAYiQVUIIYQwDFUV3lBfgqoQQojyVYVnqpXqz4VFixYRHx9f5PmAgACSk5PLsUdCCCFEyclMVQghRPkq50Wb5anEQTUzM5MZM2aQkZFBWloa/v7+7N69m5CQEJycnIiJieH27duMHTuWzz//nH379uHg4MCjR48YN24cHh4ehda7Z88eVq5ciYODA7m5ubq9FhcvXszx48fRarUMHz48345Kt27dIiQkhJycHNLT0xk/fjxOTk5MnjyZuLg4AMaPH8+IESN0+woLIYSoJCSoQkpKCn369KFHjx66TYcdHR0LXHf27FkOHTpEXFwcubm59OvXr8g6c3NzmTdvHvHx8dSsWVO3u8XBgwe5du0aMTEx5OTk4OPjg6enp67cxYsXeffdd/Hw8ODkyZMsX76cjRs3Uq1aNS5cuEDt2rW5du2aBFQhhKiEFAmqULt2bSIjI9m7dy82Nja6zYmfeJKVJjk5mVdeeQVTU1NMTU1xdnYuss67d+9So0YN7O3tAXj11VcBOHfuHGfOnCEgIAB4vA3i9evXdeXq1KnDypUriYuLQ6VS6fri7e1NfHw89evX56233irp0IQQQgiDKPGfCxs2bMDV1ZVFixbh5eWFoihYWFiQnp4OwG+//QZAs2bNSEpKQqvVolardccLU6tWLR48eMDdu3eB/+1y0bRpUzw8PIiKiiIyMpJevXrRsGFDXbnPPvuM/v37s3DhQjw8PHQB3cvLi8TERL777jsJqkIIUVmpTMr2qsRKPFPt0qULYWFh7Nq1C1tbW0xNTfHz8+Pjjz+mfv36vPDCCwC89NJLdOrUCR8fH+zt7TE3N8+XHSBf42ZmzJ49m5EjR1KjRg3ddW+++SY//fQT/v7+PHz4kG7duuVL4+Pl5cWCBQtYs2YNdevW5Y8//gDA0tKSNm3acPfuXWrWrKn3hyKEEMKI9Nio/llR4qDatm1bdu7cWeB4t27d8r2/c+cOdnZ2xMXFoVar6dOnD/Xq1Suy3s6dO9O5c+cCx6dNm1bg2JP9g52cnOjbt2+h9Wk0Gry9vYsbihBCiIokmz+UnL29PadPn2bgwIGoVCq8vb25ffs2wcHBBa7t1asX/v7+Bmt7xIgR2Nvb065dO4PVKYQQwrBkoVIpmJiYFJoypzyy1GzYsMHobQghhBBFkc0fypFiWr4fd83qFnqVs7TTr1xtS/3H90ij6FUuM0+//DaZeVrJbiNERZGZqhBCCGEgElSFEEIIA5GgKoQQQhhGVV6oVHVHJoQQQpSzShNUExISmDp1apHnly9fTkxMTDn2SAghhFHIjkpCCCGEgciOSnDp0iWmTZuGmZkZWq2WxYsXEx0dXSA9W0BAAE2aNOHSpUsoisLSpUupU6dOoXUmJyczffp0rKyssLKyokaNGgDs3r2biIgITExMcHd3Z9KkSboyGo2G2bNnc+vWLdLS0njzzTcZN24cPXv2JDY2lpo1axIdHU1WVhajRo0q48cjhBDC4Cr5bLMsSjyyw4cP4+LiwsaNGxk7diz79u3TpWfbtGkTq1at4sGDBwC4ubkRFRVFr169WL16dZF1LliwgI8++oiIiAhdhpp79+6xfPlyIiIiiImJITU1lcTERF2Zmzdv4urqyvr164mLi+OLL77AxMSEfv368c033wCwfft23nnnHb0+ECGEEEJfJZ6pDho0iLVr1xIYGIitrS0tW7YsMj1b27ZtgcfB9cCBA0XWefnyZV3OUzc3Ny5evMiVK1e4e/euLrdqVlYWV65c0ZWpWbMmSUlJHD16FBsbG9RqNQADBw4kKCiINm3aULt2bWrXrl2az0EIIUQ5kdW/wP79+3F3dycyMhIvLy/i4+OLTM92+vRpAE6ePEmzZs2KrNPJyYmff/45X5kXX3yRevXqsWHDBqKiohg6dCiurq66MvHx8dja2rJ48WJGjBhBdnY2iqLQoEEDbG1tWbVqFYMGDSr9JyGEEKJ8mJiU7VWJlXim6uzsTHBwMCtXrkSr1bJs2TJ27NhRaHq2bdu2ERERgZWVFQsWLCiyzqlTpxIcHMz69etxcHDA0tISBwcHhg8fTkBAABqNhgYNGtCrVy9dmXbt2jFx4kROnTqFhYUFjRs3Ji0tDUdHR3x8fAgLC2PhwoVl+EiEEEIYVRWeqZY4qDZq1KjAV1qcnZ0LvTYoKAgnJye96gTo378//fv3z3ds7Nixup+3b99eaH0ajYaBAwdiamr61LaFEEJUEAmq+lOr1YwcObLA8SZNmhAaGmqwdpYsWcKxY8dYtWqVweoUQgghSsPgQfWvKd4sLCzKJe1bUFCQ0dsQQghhADJTrbp8fjtM5s30UpWp5fmhXm2NOFX0SujibHB9s1zbC9er1PNDUsYJUTZVefXvcx9UhRBClDMJqkIIIYSBVOFtCqvunwtCCCFEOXumguqxY8eYMGFCgeOffPIJN27c0GWyKeo6IYQQlYCRs9RotVpmz57N4MGDCQgIICUlJd/5rVu3MmDAAHx8fPj+++8BuHv3LiNGjMDf35/x48fz6NEjvYb2TAXVosyYMYP69etXdDeEEEKUgKIyKdPrafbt24darWbLli1MnDiRefPm6c6lp6cTFRXFF198wfr161myZAlqtZoVK1bQt29foqOjefnll9myZYteYzN6UM3MzGTcuHGMGDFC1+GAgABmz55NQEAAQ4cOJT09nWPHjuHt7Y2/vz9fffVVkfWlpKQwcuRIBgwYQGxsLAABAQEkJycbeyhCCCEMwcgz1RMnTtChQwcAXF1dddvgAvz666+8+uqrWFhYYGtrS6NGjTh79my+Mh07duTw4cN6Dc3oC5VSUlLo06cPPXr0IDU1lYCAABwdHXFzcyM0NJTNmzezevVqunfvTk5Oji5QFiU3N1e3VWL//v3p2rWrsYcghBDiGZKZmanbNhfA1NSUvLw8zMzMyMzMxNbWVnfO2tqazMzMfMetra3JyMjQq22jB9XatWsTGRnJ3r17sbGxIS8vDyg8k02TJk2eWp+rqysWFhbA4w35r127ZqSeCyGEMAbFyKt/bWxsyMrK0r3XarWYmZkVei4rKwtbW1vd8WrVqpGVlYWdnZ1ebRv99u+GDRtwdXVl0aJFeHl5oSgKUHgmG5MSZB/47bffyMvL4+HDhyQnJ9OoUSPjdV4IIYTBKUrZXk/j5uZGQkICAKdOnaJFixa6cy4uLpw4cYKcnBwyMjJITk6mRYsWuLm5cfDgQQASEhJwd3fXa2xGn6l26dKFsLAwdu3aha2tLaampqjV6gKZbM6dO1ei+iwtLRk1ahQPHjxg7Nix1KxZ08gjEEIIYUjakkTGMujevTuJiYn4+vqiKApz585l48aNNGrUiK5duxIQEIC/vz+KojBhwgQsLS15//33CQ4OZuvWrdjb27N48WK92lYpipFHV4iAgABCQkJKlMnGWHJycjh9+jQx/d4r9TaF5b1tYHm3J4xDtikUld2T/xednZ2xtLQ0WjsZD/X7usoTttWtDNQTw6uUOyqFh4dz7NixAsfnzp2rS4QuhBBCVDYVElSflrVmzJgxjBkzppx6I4QQojxpy/3+aPmplDNVIaoiyW4jxGMV8NSx3EhQFUIIUa6q8ky1SmxTKIQQQlQGMlMVQghRrqrwRPXZm6kWts/vf//7X8LDwwHw9PQs8johhBAVT6uU7VWZVYmZaqtWrWjVqlVFd0MIIUQJyEKlUoiPj+f7778nOzub9PR0hg0bxv79+zl//jxTpkzh1q1b7N27l0ePHmFvb094eDjTpk2jX79+dO7cmeTkZObPn8+aNWuKbGPZsmX88ccfWFhYsGDBAs6fP88XX3zB0qVLDT0cIYQQBqat6A4YkVFu/2ZlZbF27VpGjRpFTEwM4eHhhIaGEhcXx71794iIiCA2NhaNRkNSUhLe3t5s27YNgLi4OAYNGlRs/T169GDTpk106dKF1atXG2MIQgghRKkZJag+uRVra2uLk5MTKpWKGjVqkJubi7m5OUFBQUyfPp1bt26Rl5eHh4cHycnJ3L17l8TERLp06VJs/a+99hrweNPkS5cuGWMIQgghjMTYG+pXJKM8U1UVkdYnNzeXffv2ERsby6NHjxgwYACKoqBSqXjrrbcICwvD09MTc3PzYutPSkrC0dGR48eP07x5c2MMQQghhJFU9sVGZVGuC5XMzMywsrLC19cXgDp16pCWlgbAgAED6Ny5M19//fVT69m3bx+RkZFYW1szf/58zp49a9R+CyGEMBxZqFQKAwYM0P3csWNHOnbsCDy+Jbxhw4Yiy2k0Gtzd3Z+auaawfYM9PDzw8PAAIDExscjrhBBCVLyqvFCpUnylZu/evSxfvpyQkBAAbty4QXBwcIHr2rRpw0cffVTOvRNCCCFKplIE1R49etCjRw/d+/r168tMUwghqqgqfPe3cgRVIUTR9M1uA5LhRlRO2iocVSWoCiGEKFdVN6Q+g3v/CiGEEJWVzFSFEEKUq6r8PdVymakmJCSwZcuWMtdz7NgxJkyYUOD4J598wo0bN1i+fDkxMTFFXieEEKLiyY5KZfTku6rGMmPGDKPWL4QQwnC0VfiparnMVOPj45kwYQI+Pj66Yz4+Ply7do3ly5cTHBxMYGAgvXv35tChQ8XWlZKSwsiRIxkwYACxsbGA5E4VQohnicxUjczCwoJ169aRmJjIhg0b6NChQ5HX5ubmsnLlSrRaLf3796dr167l2FMhhBCiaBUWVP+89+OTrDZ169ZFrVYXW87V1RULCwsAnJycuHbtmvE6KYQQwuCq8kKlcguqtra23LlzB41GQ1ZWVr5gWFRWm8L89ttv5OXloVarSU5OplGjRsborhBCCCOp7Ldwy6LcgqqdnR2enp4MGjSIhg0b0rhxY73qsbS0ZNSoUTx48ICxY8dSs2ZNA/dUCCGEMVXlhUrlElTz8vIwNzcnNDS0wLmxY8fqfnZycip2z18PDw+io6MLHH9S5s91PclaI4QQonKRmWoZHDx4kE2bNuky0JREeHg4x44dK3B87ty5NGzY0IC9E0IIIQzH6EG1U6dOdOrUqVRlxowZw5gxY4zUIyGEEBVJNtQXBrHB9c2K7oJ4zuib4Uay2whj0lThLOUSVIUQQpSrqjxTlSw1QgghhIHITFUIIUS50lTATDU7O5vJkydz584drK2tmT9/Pg4ODvmumT9/PidPniQvL4/Bgwfj4+PDvXv36NmzJy1atACgW7du/P3vfy+ynXKfqRaXseZJlpmiTJ06lYSEhHzH0tPTdSuL33zzTXJycgq9TgghROWgVZQyvfQRExNDixYtiI6O5u2332bFihX5zh89epQrV66wZcsWYmJiWLt2Lffv3+e3336jb9++REVFERUVVWxAhQqYqRo6Y02dOnVK9XUdIYQQFasiFiqdOHGCwMBA4HEc+mtQffXVV3Vb5gJoNBrMzMw4ffo0Z86cYejQoTg4ODBz5kxeeOGFItsp96AaHx/PoUOHuH79Olu3bgUeZ6xZsmRJicpHR0ezfv16NBoNn3zyCaampgQFBenqEkIIUbkZe6FSbGwskZGR+Y7VqlULW1tbAKytrcnIyMh33tLSEktLS3Jzc5k6dSqDBw/G2tqapk2b4uzsTPv27dm+fTthYWEsW7asyLafuYVKbm5uREZGMmrUKBYuXFjR3RFCCFHJeHt7s3PnznwvW1tbsrKyAMjKysLOzq5Aufv37xMYGIiTkxP/+Mc/AGjbtq1uh77u3bvz22+/Fdt2pQiqSin+annttdeAx1P1S5cuGatLQgghjESjKGV66cPNzY2DBw8Cj9f2uLu75zufnZ3N8OHDGThwIB9++KHu+MyZM9mzZw8AR44coXXr1sW2UyGrf4vLWPM0v/76K25ubhw/fpzmzZsbsZdCCCGMoSJSv/n5+REcHIyfnx/m5uYsXrwYgAULFuDl5cXJkye5evUqsbGxxMbGAo+3xp04cSLTp08nJiYGKysrwsLCim2nQoJqWTLW/PLLLwwbNgyVSsXcuXNLNcsVQghR8TQVEFWtrKwKfRY6ZcoUAFxcXBg+fHihZYtL9PJX5R5US5qxpjDz5s0r9PiTRUoHDhwo9johhBAVryrvqFSuQbUkGWvUajUjR44scLxJkyaFBmIhhBCisijXoFqSjDUWFhalmmoLIYR4tmiq7kRVtikUQhQk2W2EMcntXyGEEMJAKmKhUnmpFN9TFUIIIaoCmakKIYQoV3L7VwghhDCQqrxQ6Zm6/fsktdufPUkld+3aNXx8fIq8TgghROVQEanfysszP1N9kkquNFsdCiGEqDjaKrxQyShBNTMzkxkzZpCRkUFaWhr+/v7s3r2bkJAQnJyciImJ4fbt24wdO5bPP/+cffv24eDgwKNHjxg3bpwuI0BhZs+ezfXr16lVqxbz589n165dXLx4EV9fX2MMRQghhCgxowTVlJQU+vTpQ48ePUhNTSUgIABHR8cC1509e5ZDhw4RFxdHbm4u/fr1e2rdfn5+uLq6smDBArZu3YqNjY0xhiCEEMJIqvIzVaME1dq1axMZGcnevXuxsbEhLy8v3/knm+AnJyfzyiuvYGpqiqmpKc7OzsXWa25ujqurK/A4jU9iYiKvvPKKMYYghBDCSCr7c9GyMMpCpQ0bNuDq6sqiRYvw8vJCURQsLCxIT08H0CV5bdasGUlJSWi1WtRq9VOTv+bm5vLf//4XQFK/CSHEM6oi8qmWF6PMVLt06UJYWBi7du3C1tYWU1NT/Pz8+Pjjj6lfvz4vvPACAC+99BKdOnXCx8cHe3t7zM3NMTMrukvm5uZERUWRkpJC/fr1mThxIjt27DDGEIQQQhiJLFQqpbZt27Jz584Cx7t165bv/Z07d7CzsyMuLg61Wk2fPn2oV69ekfU+yb7+ZwMGDND9/NcUcEIIIUR5qtCv1Njb23P69GkGDhyISqXC29ub27dvExwcXODaXr164e/vXwG9FEIIYUiyUMlITExM+PTTTwscl9RvQjybJLuNKImqvFDpmd/8QQghxLOlsi82KotnaptCIYQQojKTmaoQQohyVZXzqUpQFUIIUa6qclB9pm7/Tp06lYSEhHzH0tPTCQkJAf6Xnaaw64QQQlQOGq1Spldl9szPVOvUqaMLqkIIISq/yh4Yy8JoQfXSpUtMmzYNMzMztFotixcvJjo6muPHj6PVahk+fDi9evUiICCAJk2acOnSJRRFYenSpdSpU6fIeqOjo1m/fj0ajYZPPvkEU1NTgoKCdBs/CCGEEBXFaLd/Dx8+jIuLCxs3bmTs2LHs27ePa9euERMTw6ZNm1i1ahUPHjwAHm+OHxUVRa9evVi9enWx9bq5uREZGcmoUaNYuHChsbovhBDCSKry7V+jBdVBgwZhZ2dHYGAgmzdv5v79+5w5c4aAgAACAwPJy8vj+vXrwONtDeFxwLx06VKx9b722msAvPrqq0+9VgghROUjQVUP+/fvx93dncjISLy8vIiPj8fDw4OoqCgiIyPp1asXDRs2BOD06dMAnDx5kmbNmhVb76+//gpIlhohhHhWVeWgarRnqs7OzgQHB7Ny5Uq0Wi3Lli1jx44d+Pv78/DhQ7p166ZLML5t2zYiIiKwsrJiwYIFxdb7yy+/MGzYMFQqFXPnztXlZhVCCPFsqOyBsSyMFlQbNWpETExMvmNFJSEPCgrCycnpqXXOmzev0ON/zU5T1HVCCCGEMVW6r9So1WpGjhxZ4HiTJk0IDQ2tgB4JIYQwJJmpGtFfM9JYWFhIlhohhKjCJKiK58oG1zfLvc0Rp/RLLF8RfdWHvuN7XkjKuOdLXgUE1ezsbCZPnsydO3ewtrZm/vz5ODg45Lvm/fff548//sDc3BxLS0vWrVtHSkoKU6dORaVS0bx5c+bMmYOJSdFrfJ+pbQqFEEIIfcTExNCiRQuio6N5++23WbFiRYFrUlJSiImJISoqinXr1gHw6aefMn78eKKjo1EUhf379xfbjgRVIYQQ5aoivkS/gtAAACAASURBVFJz4sQJOnToAEDHjh05cuRIvvO3b9/mwYMHvPfee/j5+fH9998DcObMGV5//XVducOHDxfbjtz+FUIIUa6M/Uw1NjaWyMjIfMdq1aqFra0tANbW1mRkZOQ7n5uby4gRIxg2bBj379/Hz88PFxcXFEVBpVIVWe6vJKgKIYQoVxoj7y/g7e2Nt7d3vmNjxowhKysLgKysLOzs7PKdr127Nr6+vpiZmVGrVi1atWrFpUuX8j0/LazcX8ntXyGEEOWqIm7/urm5cfDgQQASEhJwd3fPd/7w4cOMGzcOeBw8z58/T9OmTXn55Zc5duyYrtyTrXKLYvCZamZmJjNmzCAjI4O0tDT8/f3ZvXt3gUw0Fy9eZNGiRZibm+Pj48Pbb79doK5jx46xatUqTExMSE9PZ/DgwQwZMoSffvqJ8PBwFEUhKyuLxYsX89NPP3H58mWCg4PRaDS8/fbbxMXFYWlpaeghCiGEeMb4+fkRHByMn58f5ubmLF68GIAFCxbg5eVFp06d+PHHH/Hx8cHExISgoCAcHBwIDg5m1qxZLFmyhKZNm9KzZ89i2zF4UE1JSaFPnz706NGD1NRUAgICcHR0xM3NjdDQUDZv3szq1avp3r07OTk5xMbGFltfamoqX3311f9v797DYk77P4C/B01FJbQSQhKeZdFps1jWWak21WQrrd1tLVIPcshhtyVsaMPjkPBQhKLTYlmH8Cj2YDfbRawfKuTUpAM6aJrD74+ume0wh+98Zzru53VdrouZub+HnZ15z/d73/fnhlgshouLC6ZNm4b79+8jIiICpqamiI6OxtmzZ+Hn5wd3d3csXboUGRkZcHBwoEAlhJAWqDnmqerr62P79u0NHl++fLns76tXr27wvIWFBQ4fPsx4P1oPVRMTExw8eBDnz5+HgYEBhEIhgLor0UjLCVpYWKjcnrW1NbhcLgDAysoKjx8/hqmpKTZs2ICOHTuioKAANjY2MDAwgL29Pa5evYqUlBQEBARo+9QIIYRoARV/UMOBAwcwYsQI+Pj44Ndff5Xdw87OzkaPHj3qrESjbAKt1F9//QWRSASBQIAHDx6gb9++CAgIwIULF2BgYICQkBBZUX0vLy/s27cPJSUlGDx4sLZPjRBCiBaIxOLmPoRGo/VQHT9+PNavX48zZ87A0NAQ7du3h0AgaLASzb179xhtTygUYs6cOSgtLcX8+fPRtWtXuLq6wtfXF/r6+jAxMQGfzwcADB8+HI8ePYKvr6+2T4sQQghRSeuhOnLkSPz44491HvPz82uwEo2DgwMcHBxUbs/S0hJbt26t89jKlSvlvlYsFqNjx45wdnZmceSEEEKaAt3+bWQ7d+6UDVmuTd6IYEXy8/MRGBgId3d32TqthBBCWh4KVQ2pWnUmMDAQgYGBcp/z8PBgtA9zc3OcOHFC7WMjhBDStJqjoH5TaRFXqoS0ltVmSMvCdnUbgFa4aU5t+UqVKioRQgghWkJXqoQQQppUW75SpVAlhBDSpChUCSGEEC2hUCWEEEK0hEJViZSUFFy+fBlv375FYWEhPv30U1y8eBH379/H8uXL8eLFC5w/fx6VlZXo0qULdu7ciZUrV8LFxQUfffQRcnJysGnTJuzdu1fu9v38/BqscNO1a1eEhobixYsX4PP5mDBhAhYuXIipU6ciMTERxsbGOHr0KMrLyzFnzhxNT5EQQghhRCujf8vLy7Fv3z7MmTMH8fHx2LlzJ8LCwpCUlITS0lLExsYiMTERIpEIt27dAo/HQ2pqKgAgKSkJnp6eSrdvY2ODuLg4ODo6Ys+ePXj+/DlGjBiB/fv3IykpCQkJCWjXrh1cXFxw+vRpAMDJkycxY8YMbZweIYQQLZKIJRr9acm0cvv3X//6FwDA0NAQlpaW4HA46Ny5M6qrq6Gjo4Pg4GB07NgRL168gFAohIODA9avX4/i4mJcu3YNwcHBSrdff4UbY2Nj3Lp1C7/++isMDAwgEAgA1BSKCA4Ohr29PUxMTGBiYqKN0yOEEKJF4hYejJrQSqhyOBy5j1dXVyMtLQ2JiYmorKyEu7s7JBIJOBwOXF1dsX79eowePRo6OjpKt19/hZuUlBQYGhoiLCwMjx49wvHjxyGRSNCrVy8YGhoiOjpa5dUvIYSQ5iFdWawtatSBSh06dIC+vj4++eQTAMA777wjW1HG3d0dH330EaPSgvVXuHn58iWWLFmCrKwscLlc9O3bF3w+H6ampvDy8sL69esRERHRmKdGCCGENKBxqLq7u8v+PnbsWIwdOxZAzS3hAwcOKGwnEolga2tbZ+UaReqvcNOlSxecPHlS4XY9PDzQvn17pqdACCGkCbX0flFNNMuUmvPnz2PHjh1Ys2YNAODZs2cICQlp8Dp7e3u1trtlyxb89ttviI6O1sZhEkIIaQTUp6plU6ZMwZQpU2T/7tmzp8qVbJhQNeCJEEJI85OIm/sIGg8VfyD/SF9kXWruQyDNjO0KN7S6jeba8kAlWqWGEEII0RK6UiWEENKkqE+VEEII0ZK2PPq3UW7/pqen49ixY42xaUIIIa0clSlUk3SuKiGEEFKfuA0PVGqUUE1JSUFGRgaePn2K48ePAwC8vLywZcsWpKam4smTJygqKsKzZ8+wcuVKfPjhh3K3I51z2q5dOxQWFmLmzJnw9fXF9evXsXPnTkgkEpSXlyMyMhLXr1/Hw4cPERISApFIBDc3NyQlJUFXV7cxTpEQQghpoFlG/3K5XPz3v//F6tWrERsbq/S1BQUF2L17N44fP47Y2FgUFRXh/v37iIiIQFxcHKZMmYKzZ89i+vTpuHjxIkQiETIyMuDg4ECBSgghLRDd/tWC2vOSpKva9OjRQ7bCjCLW1tbgcrkAACsrKzx+/BimpqbYsGEDOnbsiIKCAtjY2MDAwAD29va4evUqUlJSEBAQ0HgnQwghhLWWHoyaaLRQNTQ0RFFREUQiEcrLy/HkyRPZc4pWtZHnr7/+gkgkgkAgwIMHD9C3b18EBATgwoULMDAwQEhIiCywvby8sG/fPpSUlGDw4MFaPydCCCGaoyk1LBgZGWH06NHw9PSEubk5+vbty2o7QqEQc+bMQWlpKebPn4+uXbvC1dUVvr6+0NfXh4mJiWzlm+HDh+PRo0fw9fXV5qkQQgghjDRKqAqFQujo6CAsLKzBc0FBQbK/W1paqqz5a2lpia1bt9Z5bOXKlXJfKxaL0bFjRzg7O7M4akIIIU2hLZcp1HqoXrlyBYcOHZKtQMPEzp078dtvvzV43M3NjfE28vPzERgYCHd3dxgYGDBuRwghpGlRQX01jBs3DuPGjVOrTWBgIAIDA+U+5+HhwWgb5ubmjBY8J4QQ0ryao0/17du3WLZsGYqKitCpUyds2rQJXbt2lT2fnp6Offv2Aai5ks7MzMSPP/6IqqoqzJ07F/369QMAeHt7w8nJSeF+qEwh+Uc6MGICq3a0ug2h1W001xyjf+Pj4zFw4EAEBQXh9OnTiIqKwtdffy17fuzYsbLCRf/9739hY2MDS0tLJCYm4vPPP8cXX3zBaD+0Sg0hhJA2LzMzU1ZoaOzYsfjll1/kvu7Fixc4ceKE7O5pdnY2/ve//8HX1xerVq1CWVmZ0v3QlSohhJAm1dhXqomJiTh48GCdx7p16wZDQ0MAQKdOnfDmzRu5bWNiYvDZZ5/J6iMMGzYMPB4PQ4cOxe7du7Fr1y6EhIQo3DeFKiGEkCbV2LV/eTweeDxenccCAwNRXl4OACgvL4eRkVHD4xKL8b///Q+LFy+WPTZ58mTZaydPnox169Yp3Xej3/5VtmLNjh07EB8f39iHQAghpAVpjjKFNjY2uHLlCoCaXLK1tW3wmnv37sHCwgJ6enqyx/z9/XHz5k0AwC+//IIhQ4Yo3U+jX6nSijWEEEJqa46BSt7e3ggJCYG3tzd0dHQQGRkJANi8eTOmTZuGYcOGIS8vD+bm5nXarVmzBuvWrYOOjg5MTExUXqk2eqgqW7FGlRUrVkAikeD58+eoqKjApk2bYGlpicjISGRnZ6O0tBSDBw9GeHg4PvnkE6xbtw5WVla4cuUKLl++rNZcWUIIIW2Xvr4+tm/f3uDx5cuXy/7u6OgIR0fHOs8PGTIECQkJjPfT4kf/mpub49ChQwgKCkJERATKyspgZGSEmJgYJCcnIysrCwUFBeDxeEhNTQUAJCcnN7ifTgghpGUQiyUa/WnJmiVU1SlRNXLkSAA1q9Xk5eVBV1cXxcXFCA4ORmhoKCoqKlBdXQ1HR0dcunQJRUVFKCgoUHnfmxBCSPOQSCQa/WnJmmT0r7IVa1S5ffs27OzscOPGDVhZWSE9PR3Pnz/Htm3bUFxcjAsXLkAikaBjx45wcHDAhg0b4Orq2ohnQwghRBO09JuGNFmxJj09HRcvXoRYLEZ4eDj09PQQFRUFX19fcDgcmJubg8/nw9zcHF5eXvDx8aG+VEIIIc2i0UOV6Yo1isyePbvBCOLk5GS5rxWJRJg6darc+UeEEEJahpbeL6qJRg1VJivWCAQC+Pv7N3jcwsJCrX0dPnwYSUlJ2LZtm7qHSQghpAlJxKLmPoRG06ihymTFGi6Xq3JNVSZmzZqFWbNmabwdQgghjYtClRACgFa3IezR6jZ/a8uh2uLnqRJCCCGtBV2pEkIIaVISUdu9UqVQJYQQ0qTa8u1fClVCCCFNikKVEEII0ZK2HKo0UIkQQgjREq1dqZaVlWH16tV48+YN+Hw+fHx88NNPP2HNmjWwtLREfHw8Xr58iaCgIOzatQtpaWno2rUrKisrsXDhQjg4OMjdrpOTE+zs7HD//n107twZW7ZsgVgsbrAvFxcXzJgxA+fOnUP79u0RERGBIUOGwMnJSVunSAghRAva8pWq1kL10aNHmD59OqZMmYKCggL4+fnB1NS0wevu3r2LjIwMJCUlobq6Gi4uLkq3+/btW7i4uMDe3h6bN2/GsWPH8P777zfYl4+PD2xtbXH16lWMGTMG6enpWLhwobZOjxBCiJZQqDJgYmKCgwcP4vz58zAwMIBQKKzzvHS5npycHLz33nto37492rdvj6FDhyo/wA4dYG9vDwCwsbFBeno6nJyc5O6Lx+MhLi4OYrEYo0aNApfL1dbpEUII0RJxGw5VrfWpHjhwACNGjMD333+PadOmQSKRgMvlorCwEABw584dAMCAAQNw69YtiMViCAQC2eOKCIVC3L17FwCQmZmJAQMGyN0XANjZ2SE/Px9JSUnw9PTU1qkRQgghjGjtSnX8+PFYv349zpw5A0NDQ7Rv3x7e3t5Yu3Ytevbsie7duwMABg0ahHHjxsHLywtdunSBjo4OOnRQfhj79u3Ds2fP0LNnTyxevBg3btxosC+BQAAulwsXFxecPXsWVlZW2jo1QgghWkS3fxkYOXIkfvzxxwaPT5o0qc6/i4qKYGRkhKSkJAgEAkyfPh1mZmZKt/3dd99BV1dX5b6AmuXfeDweizMghBDSFChUtahLly7Izs6Gh4cHOBwOeDweXr58iZCQkAavdXR0VGvbK1asAJ/PR3R0tLYOlxBCiJa15TKFHIm0Q/IfpqqqCtnZ2Yh3mYey54VqtWW7UklzYLs6Sms6x9aCVqohTYnN6jbS78WhQ4fWuTuobV2nhWnUvvhsqJaORPuo+EMbRoHaclCgEvLPQGUKCSGENCnqUyWEEEK0hEKVEEII0RKJWNzch9BoKFQJIYQ0qbZ8pUoDlQghhBAt0dqVal5eHlauXIkOHTpALBYjMjISR48exR9//AGxWIzPPvsMjo6O8PPzg4WFBfLy8iCRSLB161a88847cre5YsUKSCQSPH/+HBUVFdi0aRMsLS0RGRmJ7OxslJaWYvDgwQgPD8cnn3yCdevWwcrKCleuXMHly5exZs0abZ0eIYQQLaErVQZ+/vlnDBs2DDExMQgKCkJaWhqePHmC+Ph4HDp0CNHR0Xj9+jWAmsL4cXFxcHR0xJ49e5Ru19zcHIcOHUJQUBAiIiJQVlYGIyMjxMTEIDk5GVlZWSgoKACPx0NqaioAIDk5maoqEUJICyUWizT605JpLVQ9PT1hZGSEL7/8EkeOHMGrV69w+/Zt+Pn54csvv4RQKMTTp08B1JQZBGrCNS8vT+l2pa+1trZGXl4edHV1UVxcjODgYISGhqKiogLV1dVwdHTEpUuXUFRUhIKCAgwZMkRbp0YIIUSLJCKRRn9aMq2F6sWLF2Fra4uDBw9i2rRpSElJgYODA+Li4nDw4EE4OjrC3NwcAJCdnQ0AuHHjBgYMGKB0u7dv35a91srKCunp6Xj+/Dm2bNmC4OBgvH37FhKJBB07doSDgwM2bNgAV1dXbZ0WIYQQwpjW+lSHDh2KkJAQ7N69G2KxGNu3b8epU6fg4+ODiooKTJo0CQYGBgCA1NRUxMbGQl9fH5s3b1a63fT0dFy8eBFisRjh4eHQ09NDVFQUfH19weFwYG5uDj6fD3Nzc3h5ecHHx4f6UgkhpAVry32qWgvVPn36ID4+vs5jihYgDw4OhqWlJaPtzp49G2PHjq3zWHJystzXikQiTJ06FUZGRoy2TQghpOk1Z6heuHABZ8+eRWRkZIPnjh8/joSEBHTo0AHz58/H+PHjUVxcjKVLl+Lt27fo3r07wsPDoa+vr3D7zT5PVSAQwN/fv8HjFhYWam3n8OHDSEpKwrZt27R1aIQQQhpBc4Xq+vXrcfXqVfzrX/9q8FxhYSHi4uKQnJyMqqoq+Pj4YPTo0YiKioKzszPc3d2xd+9eHDt2DJ999pnCfTR5qMbFxdX5N5fLbfAYG7NmzcKsWbMYv166OE+n7t3U3lePboZqt2kOBmbypyqp0lrOrzVh+14QwlZVVZXabQQCAYC/vx8bS3OFqo2NDSZNmoRjx441eO7mzZuwtrYGl8sFl8tFnz59cPfuXWRmZmLu3LkAgLFjx2LLli0tK1RbiurqagCA6/4Narf11vbBNJpPWbVqPefXmrB7LwhhSzoglI3q6mro6elp8WjqEvx5oNG2DQCJiYk4ePBgnce+++47ODk54bfffpPbpqysDIaGf19QdOrUCWVlZXUe79SpE968eaN03//YUO3UqRMGDhwIHR0dcDic5j4cQghpdhKJBNXV1ejUqVNzH4pGeDye2rUKDAwMUF5eLvt3eXk5DA0NZY/r6emhvLxc5Zidf2yotmvXrs6vEkIIIWjUK9SWbNiwYdi2bRuqqqogEAiQk5ODgQMHwsbGBleuXIG7uzvS09Nha2urdDv/2FAlhBBCYmJi0KdPH0ycOBF+fn7w8fGBRCLB4sWLoauri/nz5yMkJATHjx9Hly5d5I4aro0jaeweaUIIIeQfglapIYQQQrSEQpUQQgjREgpVQgghREtooBJpM/bv348ZM2aga9euzX0ord7Vq1cVPjdmzJgmPJKWSyAQgMvlsmobFhYGHo8nt7IPad0oVGsJCwtDaGio7N/Lly9XWfAfAAoKCmBqair79+3bt1v00nMPHz7Eo0ePMGjQIJiamjbqPN2zZ89i0qRJ6NCB+f9q7u7ucHV1hZubG4yNjRm369ixIxYsWIB33nkHHh4eGDt2LONzu3XrFt577z3G+6qtqcJc2TKJisp6BgcHK/xvoGwU4+nTpxU+xyRUpZV55FEWRJqGOdugY/Meenh4YOTIkeDxeBg4cKBa+/voo48QHR2NgoICuLq6wtXVVbbgiCpsPlMABXlTodG/AI4cOYLdu3ejtLRU9iUukUgwYMCABlU55HF2dsaKFSswZswYHDhwACdPnsQPP/ygsp30S0IikeDVq1cwNzfHTz/9pLSNsu26ubmp3Ofhw4dx4cIFvHr1Cm5ubnj8+HGdHxKKsA2677//Hunp6Rg9ejQ8PT0ZLaTw+vVrnDp1CqdOnYKZmRl4PB5GjRrFeJ/3799HdHQ0MjMz4eHhgU8//RSdO3dW2mbx4sV4+vSp7AtOnUUZ4uPjcfLkScZhzjbo/Pz85D7O4XBw6NAhuc9dv35d4fbef/99hc+xDUWpCRMmgMPhNCh3x+FwcPHiRYXtVq5cqfC58PBwlft1cXFhFXTqvocAIBaLkZGRgeTkZJSUlMDV1RVOTk5qFU4oLi7Ghg0bcOnSJUydOhUBAQHo06eP0jZsPlNAzYpfycnJrIKcMEehWkt0dDTmzZundruioiIsW7YMxcXFsLOzw/Lly9X+tfz06VPs3LlT5ReH9Es3KysL+vr6sLa2xq1btyAUCrF3716V+/H29saRI0cwe/ZsxMXFwcPDQ+GqP7VpEnRisVj2gS4sLISXlxdcXFygo6OjtF1OTg6ioqLw888/o3fv3vjqq68wefJkpcd4+vRpnDhxAoaGhvDy8oJIJEJsbCwSEhJUHuerV6/w448/Ii0tDV27doWXlxccHBwYnSPAPMzZBh0b8mqcSs2cOVPhc9JQrE0ikagMRU1pGuaaBp26P8gkEgnS09ORlJSER48eoWPHjnB2dlZZhzwnJwcpKSm4fPky3n//fXh5eUEoFGLNmjVISUlhdJ5sPlMAuyAnzNHtXwCXL1/G+PHjYWxs3OBLSNkXj9Tdu3dRWFgIGxsb/PXXX3jx4oXa/5P26tULubm5Kl+3ZMkSAIC/v3+dEP3iiy8Y7Uf6xSj9wmQa/kZGRvD19cXIkSMRFRWFJUuWMAo6iUSCq1ev4ocffpBdCZaUlGDevHnYv3+/3DZHjhzBiRMnYGBgAE9PT2zcuBFCoRBeXl5K9+Xp6QlXV1ds2bIFPXv2lD3+119/MTrHly9f4tmzZygpKYGlpSXOnTuHxMREfP/990rb1Q/z1atXQyQSYe7cuXLDXNltXGWhquz2p6LbpoWFhUqOXLFLly6xaic1c+ZMhVd6yn7gTJs2TaMwb9eunWypyKSkJNmqI6qCTt33EAA2b96Mixcv4v3338ecOXMwbNgwiMViuLu7qwzVr7/+Gl5eXggMDKyzjJiHh4fKc2TzmQIaBvmRI0cgFAqxaNEiRkFOmKFQBVBaWgqg5kuVjR07dmDPnj3o2bMnsrKysGDBApw6dUplu9q3Afl8Prp1Y75iTnFxMV6/fg0jIyOUlJTIzkGV6dOnw9fXF8+ePcOcOXMwadIkRu3YBt2UKVNgZ2cHPz+/OuW9Hjx4oLANn89HZGQkzM3NZY/p6OggLCxM6TGeO3euzhcyn89H9+7dsXjxYpXnx+PxoKenBx6Ph4ULF8p+bMhblrA+dcOcbdAp629Udmw9evRQGuTySMcXyAtHJlf9W7ZsUWt/UpqGOdugY/ODrF+/fkhJSalzFdyuXTvs3LlT5XHGx8eDz+ejpKQExcXF4PP5sLa2hq+vr8q2bD5TgGZBTpij27+1CIVCPHjwoM4tqGHDhqlsJxKJUFlZiSdPnqBPnz4Qi8WM+ipq3wbU1dXF0KFD0b59e0bHeu7cOWzatAmdO3fGmzdv8M0332DcuHGM2ubk5ODevXvo378/Bg0axKjN1q1b4enpWSfoAODPP/+EtbW1wnZlZWV1/ltUV1ervEVVUlKCa9euQSgUQiKRgM/ny5ZeUuY///kP4uPjUV1djbdv36Jfv35KB9zU9vDhQ/Tr14/Ra+uTXklJScNckRcvXigMOmXrCEdFRSEgIEBun6yivtjw8HCsXLkSfn5+sjbS41XUDwvU/MA0MTHB06dPGzzXq1cvhe2kEhMTwePxEBkZ2eBYg4ODFbbTNMyPHz+O6dOnN7jd++TJE/Tu3VthO3XfQ6Dm/5lz587JVrzi8/kqf/hJrVq1CllZWaisrERlZSX69OmD48ePM2rL5jMlxefz63yulH12CTsUqrX4+/tDIBDIBqlwOBxGvzrPnTuH3bt3QyQSyW5fBQQEKHy9poONpIRCIQoLC2FiYsL4Q1V/IIiOjg569OgBX19fpX1HbIMuISEBMTExsnYdOnTA+fPnlbaZNWsW+vfvj3v37kFXVxf6+vqIjo5Wua+PP/4YiYmJ+O677/D5559j7dq1OHCA2RJTFy9exNGjR1FdXQ2JRILS0lJGdxsA9cOcbdDdvXsXgwcPltsny6Qvtri4GE+fPkXfvn0ZD8TKz8/H5s2b8fDhQ1hZWWHZsmUwMzNT2S4jIwMffvghUlNTGzw3Y8YMhe00DXO2QcfmB5mnpycmT56M3377Dd27d0dFRQW2b9+ucl9AzcC/5ORkhIaGYvHixVi4cCHjdaXZfKYAzYKcMEfFH2qpqqpCXFwcdu3ahV27djEKVKCmIPPx48dhbGyMgIAApKWlKX19Tk6OrH/jzJkzeP78Oc6fP48zZ84wPtbff/8dbm5u+OKLLxAVFYXExERG7aqqqtC9e3c4OTmhV69eKCgogEAgQEhIiNJ2QUFBuH79OhISEvDDDz/gzz//ZLS/I0eOIC4uDmPHjkV4eDgGDBigso1EIkFYWBgsLCwQExPD+Nb2O++8Ay6Xi/LycvTt21f2xcrEtm3bEBgYCDMzM8yYMYPxFTxQc8syPT0dLi4uOHPmTJ3pVfJIf9jExcVh27ZtWLZsGXbt2qU0UAFg8ODBAAArKytcunQJBw4cQEZGBqMpEsnJyfDx8UF0dDRmzpzJ+P+1VatWwdPTE0ePHoWzszNWrVrFqN2HH34IAHByckJZWRmys7NRVVUFV1dXpe1MTEwA1AzE2bhxI+bNm4fIyEi0a8fsq2rp0qUAgBs3buDJkyeM/99R9z0EaqZwzZ07F6ampti4caNa3UddunQBh8NBRUWF2lOx2HymgJofZadPn8aYMWNw5swZ6OrqqrVfwgyFai12dnbIyMjAs2fPZH+YaN++PbhcrmwAUO3+CnmWLFmCJUuWQEdHB3v37sX8+fMRFRUFoVDI+Fi3bduGw4cPw8TEBPPmzUN8fDyjdsXFxVi8eDE+/PBDBAYGorq6GosWLVK58C7boOvevTu6d++O8vJyODg4qNwPIr5xfQAAEsNJREFUUPPfs6qqCpWVleBwOBCJRIz21aNHDyQlJUFfXx+RkZF4/fo1o3bS45TeCnN3d0dBQQHjtmzDnG3QhYSEoE+fPli0aBFMTU1V/iACavrwTpw4gV27diE5ORkxMTGM9tW+fXuMGzcOhoaGmDBhAsRiMaN2UitWrEBBQQE++OADPHr0iHEosw1ztkHH5j3kcDgoLCxEeXk5KioqUFFRwWhfADBkyBDs379f1uf/9u1bxm3ZfKYAzYKcMEcDlWopKirCd999V+f2L5N+HFtbWyxZsgQFBQUIDQ1lXESA7WAjoGZAhLGxMTgcDnR1dRlPGSgrK0NOTg4sLS2Rk5OD8vJylJSUqPxCYBt0hoaGSEtLk/23ZHKOvr6+iI2NxejRozFu3DiV6xdKhYWF4fnz55g2bRpSU1PVGiyjo6OD33//HUKhEBkZGSgpKWHclm2YS4NOV1cXFRUVmD17NpycnFS2q6qqgo+PD4Caq9dz586pbGNsbCwrFqCnp6fy9q90UJS+vj727dsHe3t73Lx5U3YlydTLly+xdetWAMCkSZNUjoqVkoY5UDO9h8l8cYB90LF5DwMDA3HhwgV8/PHHmDRpEj7++GNG+wJq+pWlC19fuXKF0dgNKTafKUCzICfMUajWkpubq7L4gjw+Pj5IS0tD//79kZKSgh07djBqN3/+fLi5uclubX3zzTeM99mnTx9ERkaitLQUe/furTNiUZnQ0FAsW7YMfD4fZmZm+Oabb3DmzBmV83PZBt369evx+PFjBAcHIyYmBl9//bXKNlOnTpX93dHRUeWgL3lzMblcLv744w/GE+PXrl2L3NxczJ8/H//5z38wf/58Ru0A9mGubtBJBzZ16dIFP/30E+zs7HDz5k2lA3Ckg5qKi4vh7u6O4cOH486dOyoXopb2JxobGyM3N1c23YvpFCzpYL/evXvj5s2bGDZsGO7evatyMJimYc426MLCwvDixQvZe6hqzUwAsLe3h6WlJfLz83HmzBm1iqLk5eXV6atWp7th/fr1yM/PV+szBWgW5IQ5GqhUS1hYGFxdXfHuu+/KHmPyJTJr1iwEBgbi6NGjmDp1KhISEhgNOrh+/TrWrl0LoVAIR0dH9OrVCzwej9GxCoVCJCYm4t69e7C0tISXlxejY42Pj0dsbKzs9hbTQQ611R99KA+bcnNs5zYq6/sODAxU+BzArvSfFNvCCtKgy8vLg0gkqhN0hw8fVtiuMSoqPX36lNEAoPq+/fZbrF27VuHz9SsqcblcCAQC6OrqKv3hqmlFJaDmDlB+fj769u3LOOiKioqwe/duWcjNmzdPZRWuI0eO4ODBg7CyssKDBw8QEBDAOMS9vLywYMEC2NjYIDMzE/v372c8UOnt27dISEhAXl4erKysMHPmTEYDFesHeUhICKv3nihHoVqLi4sLysvLZf9mOuHcz88PsbGx8Pf3R2xsLGbPns3odpWvry927dqFf//739i3bx+8vb1VTsKW1qiVF1pMaqO6uLhg//792L17N6ZNm4aDBw8iKipK4evZBh2bL0d5Iz6lmH74f/75Z+Tn52P48OGwsLBQORiDTVBJsQ3zxgq6nTt3qvwRUd+nn36q8jy12U4qISEBn3zyidrtVIU526Dz8/ODo6OjLOTS09OxZ88epW3c3Nxw7Ngx6OrqorKyErNmzWJUnQxAg+8Ipt8ZABAQEID+/ftjxIgRuHHjBvh8vsoCJYBmQU6Yo9u/tTCdQlGfUChEREQE7Ozs8OuvvzIeqCLtFwXAuF/0l19+wXvvvSd3uD+TUK0/yEHVCGe2k/hrB2deXh4eP36MQYMGKZ37Jw2SgoICREREoLi4GNOmTcOgQYMYhcyWLVvw4sUL5OTkgMvlYu/evSqPv/aXyps3b/D06VOYm5szei9qB1j9MFdG1fSXlStXsgosZWGtSHP9pj5z5gyrUFVVxCIxMRGnTp2qE3RMrx5r91OfPXtW5eu7desmm1eup6en1u1fMzMzREVFYeTIkbh9+za4XK7sh7Kqz3FpaalslPOkSZNkx62Kvr6+rJ/6o48+YjxYjaiHQhV/TziX9yFnMlApPDwc165dA4/HQ1paGjZt2sRov2z6Rb/66isAQOfOnbFixQpG+6lN3UEOmgZd7QL+M2bMwKNHj1QW8P/mm2/w+eefIyoqCnZ2dlixYgWj+XSZmZk4cuQI/Pz8MGPGDMYjogH15xrXxibMlWEbdGzaNeYKRco0VpizDbr+/fvj5MmTcHBwwO3bt2FsbCwLcEU/kiQSCdzc3GBtbY07d+5AKBTKyoiq6pPlcDjIz89Hfn4+gJqpRNIfyqpCdcCAAcjMzIStrS3+7//+Dz179pTNr1bWBaRJkBPmKFRRUxGpfnkygPkXTr9+/WQDMJiM3pRau3YtEhMTYWtrC319faxbt45x2wcPHshGDquDzcAhgH3QnT59WlbAf/bs2YxKor19+xYffPABdu/ejf79+zOeTycSiVBVVSUbncx0biPw91xjf39/BAQEwMPDg3GoahLm8rANuuYKSDYa61jZBp10MFbt+d6hoaFKuwFqD+5zcXGR/V1ZN4aUoi6Qb7/9VmXbzMxMXL16FTo6OrK7YlOnTlXZXaVJkBPmKFQBjBgxAoDqgSna1qFDB3h7e7Nqm5ubi5EjR8rmngHMasMaGBjIBmKpc6XLNujYFPDX1dVFRkYGxGIxsrKyGI84nT17Ntzd3VFcXAwej4fPPvuMUTtA/bnGtWkS5s2tKa+KmwLboFPUt6hsJL+i2/iffvqp0qpRyjCp0ayo0pOqH3OaBDlhjkIVysumtVQbNmzABx980GT7Yxt0zs7OahfwX7duHTZt2oSSkhIcOHAAa9asYbQvR0dHjBo1Co8ePULv3r3VmuBua2uL4OBgtecaA5qFuTxNGXQjR46U+7iqkc1Myz8q0ljnqO2g+/3339Vuo8kPDk3a/vTTT6x+pKu72AJRjkK1ldq5c2eThirboJs1axY++OAD3Lt3DxYWFrJSe8r06NFDVjCACW1MwwgODkZ6ejreffddWFpaYvz48Yz3zzbMT548Kbdsn6Kgk5o7dy54PB7Gjx9fZwGGzZs3K2xz7do1xMTE1Fks4tChQ1iwYIHc16taSYdpren6odShQweYmZlh2bJlStspmuLCNsxbSz+1Jm3b2l2H1opCtZXicDhYsGABLCwsZLcbla3+oSl1g07eqOKcnBykpaUpnPYh7deprq5GZWUlzMzMUFBQgK5duypdEkzajx0fHw9ra2vY2Njg1q1buHXrlsrjrL+4gYmJCV69eoUffvhB5eIGmob58ePH5YaqoqCTWr58OZKTk7Fjxw6MGTMGPB4P/fr1U1roPjw8HKtWrUKPHj1UHhfw98hmpqU6Fdm2bRtevnyJIUOG4M6dO9DR0YFAIICnp6fS4gOLFi2Co6MjPD09kZmZieXLl2PPnj2Mw7w+6qfWfjsiH4VqK9VUayCyDTppBZy0tDT07t1bFnTPnz9X2EbaJ7x06VIsWbJEti9VASUt3h4TE4M5c+YAqLmd+/nnn6s8v5ycHABAVlYW9PX1YW1tjVu3bkEoFKoMVU3CHKipOuTm5lbnhxGTSj6WlpZYvnw5iouLsWHDBjg7O8Pe3h4LFy6UjQ+oz8zMDKNGjWJ0XLUtXrwYHA4HYrEYT548Qd++fdUaiKWnp4eTJ09CV1cXAoEAQUFB2LFjB2bNmiV7rxRRd4pLS9Fct39Jy0Ch2kq5uLjIvvylS7E1BrZBJ52edP78edmtYldXV0ZB9+TJE9lVl6mpqdIgrq2iokI2j/fPP/9EVVWVyjbSkaH+/v7Yu3ev7PEvvvhCZVtNwhz4e0UVdV25cgWpqanIycnBxx9/jFWrVkEoFGLOnDk4efKk3DbdunVDaGgo3n33XdmVibKqT1K1+1Zfv36tVilNoGbJQOmgNi6Xi5KSEnC5XJWF+dlMcVGmJfRTA43bV023f1sGCtVWSrrCDJ/Ph0gkQvfu3eHs7Nxo+2MbdKWlpXj8+DH69OmD3NxcRitqWFpaYtmyZRg2bBiysrIwZMgQRvvasGEDIiIiZOXbmM4XBjRb3IBNmAM151m/35CJkydPwsfHp8GgnKCgIIVtpPWB1VmerD5DQ0PZdAymJk6cCG9vbwwbNgy3bt3ChAkTcPToUVhZWSltx2aKC9Cy+6kB7fRVt5R+aiIflSlspWbOnIljx45h9erVsjmkms6PVGb16tUQCASyoOvcubPKIg4A8Mcff2Dt2rUoLi6Gqakp1qxZo7KQt1gsxoULF/Dw4UNYWlrKRgw3Vp1aoKb4w6ZNm2BsbCy7IpNWn1ElJyenTpiHhITA3NxcZTs/Pz84OTnB2tqacWk8oOZWfHZ2dp27FKp+UMnrG2VSbKR2mcqioiKMGjVK5X/L+u7evYvc3FwMGDAAAwcORHFxcZ2pYIqoW+EKqBkYp6x+siI5OTlITk7GtWvX6vRTKyNdkq52P3X//v0Z7Y/t+wHUlDdV1E+t7JY6m1KMRH10pdpKSVcZqaysVLniiDasW7dOFnSOjo6Mg87Ozk5u+UdldWrbtWtXZ6UaKbbl+5hMGZg6dSomTpyI4uLiOlV5mNSotbS0RHR0dIPHmYS5dAqEOv2GQUFBat+lULdvNDExETwer857O2jQIBgZGWHHjh0YPXo0bGxsVB5r7QFrubm5OH/+PKP6xGwrXLWGfmpAs77qf2I/dWtCodpKTZkyBbt27cLgwYMxc+ZMtYoVsKHtoGuJdWo7dOjQoDYx2xq1gOow79+/P06cOCErG8e037CkpKTBXQpV1O0blV59SfuNaxMKhfj2228Z1cqWDliTSCS4c+cO40XO2Va4ag391IBmfdUtpZ+ayEeh2kr16NEDV69eRXV1NfT09Or0AzWl1jL/j63GDPLc3Fzk5eXJVjYRCASM+g01vUvBpG9UGqaKCiYoWxihtvo/SL788ktG7dhWuGpt/dSA+n3VTd1PTdRDodpKbd68GWFhYSrXfGxsbX1uXGMep5OTE6u1bdncpZDXN6oJpv3Nta/W+Xw+43mvtra2WLJkidoVrhYtWgQnJ6cG81tV2bhxI7Kzs/H777/X6aeePHmywjbu7u6MjkkeTd6PBQsWYOLEicjNzYWHh4esn1pVNaW4uDhW/dREPRSqrZSVlRUcHBya+zCaVFubMnD06FHExcXJ1rZlerWgzl0KbfWNsiW9EgJqSl0yvT3r4+ODtLQ09O/fHykpKUpr8NbXUvupAe28H03dT03UQ6HaSk2cOBEzZ86sM9qQaUk+bWoJ8/9a4tw/Jm3VXdtWSp27FNrqG2Wr/tX4xo0bMWHCBJXtli5disDAQBw9ehTBwcEIDw9ntKB2S+6nBrTzfjR1PzVRD4VqKxUXF4cvv/wShoaGTbK/ljz/rznn/gHs5/+pu7atlDp3KbTVN8pW/avxgwcPMmrH4XBgb2+P6OhoTJ8+ndEyg0DL7qcGtPN+NHU/NVEPhWorZWJiotbarZpq63Vq2daoBdjXqWW7tq0271Iw7Rtli+3VuFAoREREBOzs7PDrr7/KrnRVac391ACz96Op+6mJeihUWyk9PT34+/vXGc7fmAX1W8P8v+aa+wewm//Hdm3bpr5LoQm2V+Ph4eG4du0aeDwe0tLSGFfHon5qxTTppybMUai2UuosTaYNrWH+X3PM/QOafv5fU9+l0ATbq/F+/frJKhqpc67UT60Y235qoh4K1VaqqRdWb23z/5pq7h/Q9PP/mvouhSbYXo2zRf3UirHtpybqodq/hJF/Qp1atjVqAXZ1atlKTU1t8FhT/8hqqcrKyvD48WN069YNMTExGD9+PKOwTE1NRUJCQrOPpmfC398f+/fvx/Lly7F582b4+fkxuuL09vbG8OHDYWBgADs7O2zfvh1Hjx5tgiP+Z6FQJYzU/+Ay/SDPmzevwfy/2NhYpW2k4ahundraV2uGhoYwMjICl8tVe+6fFJO5fwDN/2sL3N3dG/RTy7u92xIsWrQIzs7OuHDhAqytrXHkyBFGt5sfPnxYp5/6vffeY7TwA1EP3f4ljLTk+X/NOfcPoPl/bQH1UxNtoVAljLTk+X/NOfcPoPl/bQH1UxNtoVAljLTm+X+NOfcPoPl/bUFTj6YnbReFKmGkrc//Yzv3D6D5f20BDfQi2kKhShhp6/P/2M79A2j+HyHkbxSqhJG2Pv+P7dw/gOb/EUL+RqFKGGnrdWrZXokD7OvUEkLaHgpVwkhbr1PL9kocYF+nlhDS9lDxB9KovvrqK+zdu7e5D0MltpV4CCGkNgpV0qj+/e9/o7y8vFXM/yOEEE3R7V/SqGj+HyHkn4SuVAkhhBAtadfcB0AIIYS0FRSqhBBCiJZQqBJCCCFaQqFKCCGEaAmFKiGEEKIl/w++pl+9jk7o/wAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -254,7 +264,7 @@ "\n", "visualizer.fit(X, y) # Fit the data to the visualizer\n", "visualizer.transform(X) # Transform the data\n", - "visualizer.poof() # Draw/show/poof the data" + "visualizer.poof() # Draw/show/poof the data" ] }, { @@ -264,9 +274,9 @@ "outputs": [ { "data": { - "image/png": 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VEEIIKv9oszyqzPRvWbi7u/Phhx9WdDeEEEI8ZirtIzWmJo/UCCFEYeb6uzjDrln5yt87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\nf4RhdgtcWxfRAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -279,7 +289,7 @@ "\n", "visualizer.fit(X, y) # Fit the data to the visualizer\n", "visualizer.transform(X) # Transform the data\n", - "visualizer.poof() # Draw/show/poof the data" + "visualizer.poof() # Draw/show/poof the data" ] }, { @@ -288,15 +298,17 @@ "source": [ "### RadViz \n", "\n", - "RadViz is a multivariate data visualization algorithm that plots each feature dimension uniformely around the circumference of a circle then plots points on the interior of the circle such that the point normalizes its values on the axes from the center to each arc. This meachanism allows as many dimensions as will easily fit on a circle, greatly expanding the dimensionality of the visualization. \n", + "RadViz is a multivariate data visualization algorithm that plots each feature dimension uniformly around the circumference of a circle then plots points on the interior of the circle such that the point normalizes its values on the axes from the center to each arc. This mechanism allows as many dimensions as will easily fit on a circle, greatly expanding the dimensionality of the visualization. \n", "\n", - "Data scientists use this method to dect separability between classes. E.g. is there an opportunity to learn from the feature set or is there just too much noise?" + "Data scientists use this method to detect separability between classes. E.g. is there an opportunity to learn from the feature set or is there just too much noise?" ] }, { "cell_type": "code", "execution_count": 10, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "# Load the classification data set\n", @@ -307,8 +319,8 @@ "classes = ['unoccupied', 'occupied']\n", "\n", "# Extract the numpy arrays from the data frame \n", - "X = data[features].as_matrix()\n", - "y = data.occupancy.as_matrix()" + "X = data[features]\n", + "y = data.occupancy" ] }, { @@ -318,9 +330,9 @@ "outputs": [ { "data": { - "image/png": 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YLnjKYVnh5dwBhA2JA0jMODte4Z6VPxXbtyLXYuWWN7/lpR/+KBEGzqX9LqQu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\n", 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jx0lMTOTQoUPEx8c3uWZ8fDxvvvkmCoWCt99+m4SEBOLi4jh8+DDTp08nMzOz0+/9QpIQCNHDNTQ0cPbsWWpqaqitrcVgMDS5ocD5T4iNN22NRkN0dDT+/v5dctO2Wq1Nko3i4mL0ej1Wa9OFXBcmDhqNhssuuwylUnnJ12+vxvcaHR1NZGQkf//73/H29mbLli0MGzaMXbt2Aec/6QcFBfGHP/yB/Px8Nm7ciM1mw8vLq9l7CgoKIiUlhaeeeoprr70WOP8HesGCBcyfP5+Kigo2bdrULBZHSZZarebcuXPExcWRlZVF3759WzzXkcYbQGJiIhkZGQ7ff2P848aNY926dWzevJn77ruvXa/f0/mrVSxIiWmyhqDRgpToS5oumDZtGu+99x7Lli0jOTmZgIAAh+clJCRw9dVXs2zZMqxWK2PHjrWPyDz00EN8/PHHAKxbt46AgIBmjwHcd999pKenk5yc3OS133jjDYqLi4mKimLVqlV88sknACQmJjJp0iSWLVuGyWRixIgR9O3bl9WrV/O73/2Ot956i7CwsC5pQigJgRA9SENDA2VlZRQXF6PT6YDznzAvu+wyQkNDiYmJ6bIbfXt5eXkRHBxMcHBwq+dZrVb0ej21tbVUVVWRnZ2NxWIBQKPREBUV5ZQkISwsjFtvvZUVK1ZgsVjo378/V199tf34pEmT+M1vfsORI0dQq9UMHDiQs2fPMnToUF555ZVmf6iXLFnC7bffbv+Dftddd/Hwww+zceNGdDodd999d7viuuWWW/j9739v/z501C9/+UseeOABtm/fTnR0dLPjF8Z/zTXXMH/+fD777DOGDBnS4Wv1VM/OHwucXzNQqNURExLIgpRo++OdFRwc3OrUzrJly+z/vu2227jtttuaHA8LC+PVV19t9jxHj33wwQcOr7Fu3bomN/XGBBTg9ttv5/bbb29y/oABA1odJeoMhe3ijxr/YzQayczMJCUlRdofC9ENWrr59+vXj6ioKAIDA10cYdew2WzU1NRQXFzM2bNnsVqt2Gw2pyYJvdGbb75JSEgI119/vatD6XIGk5mSmjoig/26fCGhK6SlpfHpp592+722rfu6+38nhXATFRUV5OXlNbn59+3bl6SkJI+5+TuiUCjQaDRoNBqGDRsGNE0SsrOz7UlCSEgIcXFxDrdQmc1m9uzZw8GDB9Hr9QwePJiZM2cyaNAgJ7+jnm/16tWcPXvW4SdUT+CvVhEXEeTqMLpMT6kfISMEQnQTq9XKmTNnOHXqFGazmbCwMOLj4wkK8pw/ZF3JZrNRXV1NTk4ONTU1qNVq4uLiiIyMRKFQsH79+mYLvhpX1w8cONBFUQvhPmSEQAgnMplM5ObmUlxcjEKhIDo6msmTJ+Pt7e3q0Ho8hUJBSEiIffuV0WgkNzeXH3/8kdraWoervy0WC7t37242pyuE6DhJCIS4RDU1NWRnZ1NdXY23tzfx8fEkJia2e/W4cMzHx4ekpCSSkpL46quvWjzv9OnTzgtKCA8mCYEQnVBaWkpOTg4mk4mgoCCGDh1q3z8uul5r0yxKpZKKigrCw8OdGJEQnkcSAiHaqaGhgczMTM6dO0dkZCTjx4+X9TVOkpSUREBAAHp986I0EydOJD8/n0OHDtnrz8uuBc9mtpgwmGrxVwehUrpH0aXXX3+diRMn2qsLtua5554jNja2ydZDZ5CEQIg2VFRUkJGRgc1mIzk52V6OVDiPt7c3K1as4N///rd9lwbAqFGjmDlzpj0BKCoqYs+ePfj4+DBy5Mg2aycI92K1WUg/tZ3Ciix0Ri2BPiHEhCeROnguXoqenQQ2Vs7sySQhEMIBq9VKdnY2BQUFhIWFMWnSJBkNcLFBgwaxevVqfvzxR/u2w8Zqf4369+9P//79MRgMHD16lNraWuLi4hg8eLCs6fAA6ae282PxT30pdMYq+9cTYud36jUbGhp48MEHOXPmDBaLhdtuu43+/fuzbt06rFYrffv25bnnnuPEiRPNHrvjjjscNqy699576dOnD2VlZUyfPp1Vq1axevVq5s6dy6RJk3jsscfIz8/HarVy3333MWHCBHbs2MErr7xCWFgYDQ0NxMbGdsn3rCMkIRDiAjqdjqNHj2IwGBg6dChXXnml3Eh6EJVKxfDhw9s8z9/fn4kTJ2Kz2cjNzWXXrl0EBwczcuRIfH19nRCp6Gpmi4nCiiyHxworshg7cHanpg/ef/99wsLCeO6559DpdFx77bWo1WpeeOEF4uLi2LRpE7m5uTz66KP8+c9/bvJYS4qKinjrrbcICgpi+fLlTXbIbNq0idDQUNatW0dVVRU333wzW7du5emnn2bLli2EhIS4bDRBEgLR69lsNgoLC8nOzsbPz4+RI0d6dKGg3kShUBAfH098fDxarZbvvvuOhoYGkpOT6devn6vDEx1gMNWiM2odHtMZtRhMtQT7dXxhaW5uLpMnTwYgMDCQuLg4du/eTVxcHHC+dDVAeXl5s8cudGFJn8TERPsi4xEjRjTrXnjo0CF7O2Sz2cy5c+fQaDSEhoYCuGxasme1KxPCiWw2GxkZGXz++ecYDAbS0tKYMmWKJAMeKiQkhOnTp3P55Zdz7tw5du7cSXZ2drNGUaJn8lcHEejjeCdPoE8I/urOFfyKi4vj4MGDwPkRwuzsbKKjo+3bWV9//XU+//xzLrvssmaPNTasAsjK+mn0Ijc3l7q6OiwWC0ePHm3WvfCaa65h/fr1vPHGG8yZM4eIiAhqamqorKwEcNi8yhlkhED0OjabjZMnT5Kbm0tycnK7hqCF52icdkhJSSE3N5fPPvuM5ORkBgwY4OrQRCtUSjUx4UlN1hA0iglP6vRug6VLl/LII4+wbNkyjEYjd999N3FxcTz00EN4eXnRp08fbr31Vvr27dvsMbVa7bBhlbe3N/feey/l5eXMmTOHxMRE+7Ebb7yRNWvWcPPNN6PT6Vi+fDlqtZpHH32UX/ziF2g0Gocts51BSheLXuXMmTNkZmYSFxdHfHy8rA8Q2Gw2jh07RlFREaNHj+5UJ0LhHO6wy+DMmTP83//9Hxs3bnR1KM1I6WIhgHPnzvH9998TFRXF7NmzJREQdgqFgpSUFIYNG8bhw4c5evQo48aNk0JTPZCXQsmE2PmMHTjb7eoQuAMZIRAerbq6mvT0dDQaDWPGjJGCNaJNJpOJgwcPYjKZGD9+PP7+/q4OSYguISMEolcyGAykp6ejUqmYPn06arV8ihDto1armTx5cpOfodTUVPkZEh5PEgLhURo/3TU0NJCamiqf7kSn+fv7c/nll1NdXc2+ffvQaDSMHj1aRpmEx5KEQHiMrKwsioqKGD9+PBqNxtXhCA+h0WiYOXMm586d4/PPPychIYHBgwe7OiwhupzUIRBuT6/Xs3PnTtRqNVdeeaUkA6Jb9OnTh9mzZ6PT6dizZw8mk8nVIQnRpWSEQLi1H3/8keLiYqZPny4laUW3UygUDB8+3J4UDB06VEYLhMeQEQLhlgwGAzt37sTb25uZM2dKMiCcKjAwkKuuukpGC4RHkREC4XZkVED0BDJaIDyNjBAItyGjAqInktEC4SlkhEC4BRkVED2ZjBYITyAjBKJHMxgMfP755zIqINyCjBYIdyYjBKLHOnXqFLm5uUybNk0SAeE2Lhwt2L17NyNHjiQyMtLVYQnRJhkhED2OzWbj0KFDVFZWMmvWLEkGhFsKDAxk9uzZ5OXlkZWV5epwhGiTJASiR7FYLOzevZvw8HDGjh3r6nCEuCQKhYIpU6agUCj46quvaKGXnBA9giQEosfQ6/Xs2LGDMWPGMGjQIFeHI0SXGTZsGPHx8ezYsQOj0ejqcIRwSBIC0SOUlpayb98+Zs6cSWhoqKvDEaLL9evXj2nTprFr1y6qqqpcHY4QzUhCIFwuKyuL3NxcZs+e7bBHtxCeIiAggDlz5vD9999z6tQpV4cjRBOSEAiXsdlsfP311wD2eVYhPJ1SqSQtLY3KykoOHTrk6nCEsJOEQLiEyWRi586dxMXFkZSU5OpwhHAqhULB2LFjCQsLY/fu3VgsFleHJIQkBML5tFotn3/+OVOnTqVfv36uDkcIlxk8eDCjR49mx44d6PV6V4cjejlJCIRTFRQUcOjQIebMmUNAQICrwxHC5UJDQ5k5cyb79u3j7Nmzrg5H9GKSEAinycvLo6CggLS0NJRKpavDEaLH8PHx4aqrruLo0aOUlpa6OhzRS0lCIJwiJyeHsrIypk6dKosHhXDAy8uLmTNnkpWVRVFRkavDEb2QJASi2x0/fpyqqiomTZrk6lCE6NEUCgVXXHEFJ0+epKCgwNXhiF5GEgLRrbKystDr9YwfP97VoQjhFhQKBdOnTyc/P19qFQinkoRAdJuMjAxMJpP0JBCigxQKBdOmTaOkpITc3FxXhyN6CUkIRLc4duwYVquVUaNGuToUIdzW5MmTOXv2rIwUCKeQhEB0uePHj2M0Ghk5cqSrQxHC7U2aNImioiJZUyC6nSQEokvl5ORQW1vLmDFjXB2KEB5jypQpnDp1SnYfiG4lCYHoMnl5eVRUVJCamurqUITwKI0LDbOzs6VOgeg2khCILpGfn09JSQkTJ050dShCeCSFQsGMGTM4duyYVDQU3UISAg+yZcsWHn30UdauXdviOQcOHGDVqlXNHj9x4gTp6emdum55eTl5eXlMnjy5U88XQrSPQqEgLS2Nw4cPU1tb6+pwhIeRhMDDBAcHt5oQtGTnzp2cPHmyw8+rq6sjPT2dyy+/XCoQCuEEjcWL9u3bh9lsdnU4woOoXB2A6FpFRUUsXbqUjRs3smfPHl588UUCAwPRaDQkJCQwfvx48vPzuf3226msrOSKK65g6dKlfPjhh3h7e5OcnMyIESPadS2r1cqXX37JjBkz8PKS3FIIZ1Gr1UyZMoUvv/ySmTNnSjIuuoQkBB7KYrHwxBNP8P777xMREcFvfvMb+zGj0cjLL7+MxWJhxowZ3HPPPSxevJiIiIh2JwMA+/btIzU1FT8/v+54C0KIVmg0GoYOHcrBgwdlIa/oEvKxzkNVVlYSGBhIREQEAOPGjbMfGzJkCGq1Gj8/P1SqzuWEP/zwA1FRUfbXF0I434ABA/D29pZqhqJLSELgocLDw9Hr9VRWVgLnb+CNHA0vKhQKrFZru167sLCQ+vp6hgwZ0jXBCiE6bdSoURQUFFBRUeHqUISbk4TAQ3l5efHII49wxx13cOutt1JcXNzqaEBKSgrvvPMO+/fvb/V1a2pqOH78uDQrEqIHufzyyzlw4AD19fWuDkW4MYXNZrM5OmA0GsnMzCQlJQUfHx9nxyW6wGuvvcZtt92GWq3m/vvvZ+rUqSxatKjTr9fQ0MDOnTu56qqr8Pb27sJIhRCXymAw8N///pfZs2fLIl/hUFv3dfmp8WABAQEsXbqUG2+8EZvNxty5czv0fLPZjFarxWw2Y7PZ2LNnD9OmTZNkQIgeyN/fn7Fjx/LVV1+5OhThpmSEQDRjtVrZtWsX3377LXV1dfj5+RETE8OsWbMYMGCAq8MTQrTixIkTmEwmhg8f7upQRA8jIwSiw3bs2MHu3bupq6sDzhcfys7OJjMz08WRCSHakpCQgE6nk0ZIosMkIRBNGI1Gvv32W4fHvv32W1m0JIQbmDhxIpmZmfakXoj2kIRANFFRUYHJZHJ4rKGhQbY2CeEGGrsjynoC0RGSEIgmNBoNSqXS4TEvLy80Go2TIxJCdIafnx8DBgzgxIkTrg5FuAlJCEQTAQEBjBw50uGxESNGEBgY6OSIhBCdlZCQQEFBgUwdiHaRhEA0s3DhQvr06WOvaKhQKBg+fDiLFy92cWRCiI6aOnWqTB2IdpHmRqKZzMxMli1bhp+fH+Xl5URERBAaGurqsIQQnXDh1EFCQoKrwxE9mIwQiCaqqqowGAxERUURGhrKkCFDJBkQws3J1IFoD0kIhJ3NZmP//v1MmjTJ1aEIIbqYTB2ItkhCIOy+//57Ro4c2emWyEKInkt2HYi2SEIggKZTBUIIzyRTB6I1khAImSoQoheRqQPREkkIhEwVCNGL+Pn5MXDgQI4fP+7qUEQPIwlBL1dTU4Ner5epAiF6kaFDh1JYWIgHq3V1AAAgAElEQVTRaHR1KKIHkYSgl0tPT2fChAmuDkMI4WQTJkzg4MGDrg5D9CCSEPRi5eXlBAcHO+yLLYTwbMHBwVgsFvR6vatDET2EJAS92Pfff8/o0aNdHYYQwkVSU1NJT093dRiih5CEoJcqKiqiX79+spBQiF7Mz88PHx8ftFqtq0MRPYAkBL2QzWYjIyODlJQUV4cihHCxcePGyVoCAUhC0Cvl5uYSGxuLl5f85xeit/P29iY0NJSysjJXhyJcTO4IvYzNZuPkyZMMGTLE1aEIIXqIUaNGceTIEVeHIVxMEoJe5tixYyQnJ6NQKFwdihCih1AqlURHR5Ofn+/qUIQLSULQi1gsFoqKioiJiXF1KEKIHiYpKYmsrCxsNpurQxEuIglBL3L48GHZZiiEcEihUDB06FCys7NdHYpwEUkIegmTyYRWq+Wyyy5zdShCiB4qLi6OU6dOYbVaXR2KcAFJCHqJgwcPMm7cOFeH0SsYTGZyy2sxmMyuDkWIDhs+fDgZGRmuDkO4gFSl6QVMJhP19fWEhIS4OhSPZrZYeWDbIT7OLKRAq2dASAALUmJ4dv5YVErJvYV76N+/P8eOHcNms8ni415G/kr1ApmZmYwYMcLVYXi8B7Yd4sV9xzldpcdqg9NVel7cd5z7tqbLiIFwK0OGDOHkyZOuDkM4mSQEHs5ms1FeXk5ERISrQ/FoBpOZjzILHR57fX8OiU9vZfgzH7Nqazpmi8zPip5t0KBBnDp1ytVhCCeThMDDnTlzRrYZOkFJTR2FWsdd4yxWW5MRgwe2HXJydEJ0jEKhICQkRHoc9DKSEHi4EydOkJCQ4OowPF5ksB8DQgLade7HmWdk+kD0eCNGjODo0aOuDkM4kSQEHkyv1+Pr6ys9C5zAX61iQUr7RmIKtTpKauq6OSIhLo2vry8NDQ2YzZK89hZyp/BgR48eZeTIka4Oo9d4dv5YVk5LZFBoIF6A0svxCu2YkEAig/2cG1wHydZJAZCcnExWVparwxBOItsOPZTVakWv1xMUFOTqUHoNldKL5xel8uTc0ZTU1PGX/2bx8jfNq74tSInGX90zf/Vq6kzcuzWdL0+WcqbaQLTGnxnx/XhhUSrBfmpXhyecrF+/flKToBfpmX+VxCU7efIk8fHxrg6jV/JXq4iLCOL5RamolF58nHmGQq2OmJBAFqRE8+z8sa4OsZnGGgr/+O4ktcafRgUKtAb+dTCPDzMKuG18PE/NHY7JosdfHYRKKQlCb9C3b19KS0vp16+fq0MR3UwSAg+Vn5/PrFmzXB1Gr3bxiEFksF+PHRlorKEAoFZa0fiaqa5XYbKcn1XUmxo4W7WHf367HR9lPYE+IcSEJ5E6eC5eCqVTYzWYzD3+++lJkpKS2Lt3ryQEvYD8NnmgqqoqQkJCpMpYD9E4YtBTNdZQ8FLYWJpcyqioGsL8zGjrlRwpDmZDZiRLk0u5ckil/Tk6YxU/Fn8NwITY+U6J8+LpjAsrQZos1haTBEkgLo1KpUKlUlFfX4+vr6+rwxHdSH47PNDRo0eZOHGiq8MQbqKxhsLFN/1wfwsz46sYGqHH19vi8LmFFVmMHTi7W6cPWprOaKzrsDe3DG2dqVm5aIBVW9PZmllIaU0d0aH+LEoZ0OlS0uW6ejJKtAyPDCEisHfdGBu3II4fP97VoYhuJAmBhzGbzVgsFnx8fFwdiugB2vPpODLYj0GhPoyKqnF4PCbEREvN73RGLQZTLcF+4V0VcjP3bU3nFQeLMxsdKa6y/7sxSbDabPz3ZBkZpT8V1imoMvDivuOYzFb+dv0Eh6914fcL4FiplmKtnkc/+4FjZdXYAAUwMiqUr++Zg28vGXEICQmhurpa+ht4uN7x09yLZGVlkZyc7OowhIt1pNGSv1rFrCEhhPm1vMWwpVIWgT4h+Ku7Zjrk4uTFbLGyams6r+9vORloyWvfZNNgtTk89uq32eiNDTx45XBiQgLwV6sorTZwx8Zv+K6wnHJ9Az5eYGwhCbJxPglJff4/vHz9BHLO1TJraCQDwgI7HGcjd5jWGDRoEPn5+QwaNMjVoYhu0jN/8kSnnTt3ThoZiSaLBOGnT84Azy9KbXb+k9dM5p/f7CW0laTAkZjwpEueLmgpebHabA63bbZHS8lAo/Xfn2L996eI1vhiaLBSaTA1Od5SMnChrLM1zHj5c/vX4f5qMu+fT22DpV03doPJTKFWz0v7jvPpj0U9vkNmbGwse/fulYTAg0lC4EEaGhpQqeQ/aW/XWqOljzPP8OTc0c1uVmEBgZhtA4C8Nl/fZgNvpZr4vuNIHTz3kuNtKXkJ8un+n+Uz1fVd9loVBhORf/gABRAd4s/i4Y7XK1yYAJ2uatr/oq3EzZWUSiVWq1WmDTxYz0pBxSU5deoUgwcPdnUYwsVaa7TUWtnke9Nuw9AQ3ObrKxRgtppQKBSXvOWwteTlwgWE7sQGFGrPr1f4+Yavm1R7NJjMXPf2l/Y22S3pqf0u+vbty9mzZ10dhugmkhB4kDNnzhAdHe3qMISLtdZoqbWyyWqVN3fN+B3Bvn3bdZ2C8mOYLaa2T2xFa8lLeyi9FNw6Lpb+wf54AQNDAwh0wshCe73z/WmS//gR9374HUv+8QVBD77HJ1lFbT6vp/a7iI+PJycnx9VhiG4iCYGHsNls2Gw2aWQkWm201FbZZC+FkkVjV1JRF4Ot9Wl49CYt3+ZuxWpzvCWxPVpLXtozZWCx2vjsRDHFNQb6BvlyzbD+LGpnkylnKdAa+OtXJ9iSWdzu5/TUfhe+vr4YjUZXhyG6idw9PERJSQlRUVGuDkP0EBc2WlIqYFBoICunJbarbHJ9g43X0i+joq7tG3Lu2e9JP7W903G2lrz8LDWOldMS22wrXVpbjw0oqa3n5W+yUXt5OWX9QXcaEx3q6hBaFBgYSG1travDEN1AYbM5/hxgNBrJzMwkJSVF9rS7gb179zJx4kTUaqkvL37Sme1sueW1JD69laXJJU0KFbUk0CeURWNWdXq3wU+L7Jr3fFApvTCYzPz6gwP862DbCx4bJffTcKy0ulPx9BQK4P9NGsILi8f3qB0HVVVV5OXlMXZsz+vJIVrX1n3dvdNoYdfQ0CDJgGimM2WTG4fxNx47X7t+VFQ1YX4WvBTnFxReTGesuqTiRG31fPBXq3hj6SRC/NRszTi/NbEtx0qrCfdXYzJbqDV1fkrDlWzAq9/m4KVQ8NJ1jgspuUJoaCharbbtE4Xb6Tlpp+g0rVaLRqNxdRjCQzQO41ttCjZkRvLoF0M5UBjsMBkAUHn5dElxosbkxdFIRmPSsO32K9r9R6vCYCIq2I+bxw5mQEgASgV4ueFuuVe+yebuDw5gtrSjOIKTeHt7YzJd2oJS0fNIQuABsrOzGTp0qKvDEB7k4jUISZe1vOK9hVnHbhEbHsSA0NbXFFzoRLmOo8VV/HD/PH5cvYifj3e/luA2zicFv3j/mx6zFTE2Npa8vPZP4Qj3IAmBB6itrSU4uO3940K0V+Mn8h8emMvGm7wJ8Wto8VyLzURNfVWLx7tSa4sQW3K0RMuD/zlMXEQQf7tuAkMiOl9i2JX+fegUyc98xKqt6S4fLejfvz9FRW1vnxTuRRICN2c0GmXtgOg2x4p2crbmaJvnffrDK5e0/bAjnp0/ll9OHoqyA8P//z6Uh8FkRqX04rv7rkHlpn/5Ghs0PbDtkEvjaKxUaG2p65VwS276ayEanTx5kvh49xsGFT2f2WKisCKrXec2WOv5+PCLTZICs8VETV0F9SYdNXUVl1zEqJFK6cWqy5PoyESFzmQmr+L8VrlgPzV3TU7oklhcpSdUMoyOjpZRAg8juwzcXFlZGUlJSa4OQ3ggg6kWnbH9q8m1hjK+y93G+Lj5fJe7jfyKLOoaaji/gc5GgDqEARHJpA6ee8kljxt3QrRW/rc1f1owDi+Fgg8zCijUGv4X4flFh230ReoRGisZdnQHSVeKjY3l22+/JSamZxWCEp0nCYEHkEYjojv4q4MI9AlBZ2z/+oATpemU1pxGayi94NHzd1i9ScuPxV9TXWdi6pCFAJ1u+du4luDCpkit8fZSEBv+083z4q2OGl9vqusb0Ph6U2Ew/q8DYTGFWh3RIQFo/LzJK69F978tjN4KBQuHx/CHq0cx9aXPmnVL7G59An3R+Ho79ZoX8/b2xmzuGYscRdeQhMCNmUwmWT8guo1KqSYmPIkfi79u93NsWC5KBpr74cxhbnqvngarFzqjmahgPxamxPD8otQOFeB5dv5YSmsMbPyhoM1zzVYb9SZzs8TjwjoNEYG+9v//63UTmhV1Mlww7RAb/tP2yFMPLWbAHzZRbXLefHppbT0T/rLd5a2SFQqFdD/0ILKGwI2VlJTQr18/V4chPFjq4LkMi5pCoE/XldIN9TOj9Kqn1mjGBhTV1PHyN9mMfG4bNXXnP2kbTGZyy2tbnCevN5kZ+tQH7UoG4PwYxeSXPu1QnBfXRfBXq0iJDCUlMrRJYhHop6byqZs49fBi7poYx6hIDVcNucy+ZTO4m8ooN7ZKduUCQ41GQ3W1e1eEFD+R0sVubP/+/YwYMQJ/f39XhyI8nNlioqAil/+e+GeLBYray2CCB3YkUm9uvo7AX6Wgf0gA2joT5/QmIoN8WTR8ALemDmbtZ0c5WlRB/0A135XqOnXtW8YO5o0bJjvlE3XjCEOfAB8e3P49r3+TQ3eMIfQN8OHl6ycwNbavfZTDWUpLS6msrJR1TG6irfu6JARubPfu3aSlpbk6DNFLmC0m3tz3BL6qS58v/yInhHcz+3dBVB03KiqUA/fNdfowu8Fk5lBhBR/8cJqXvs7u8tdXAMOjQvj2nqvx7eCajM6yWCzs27ePGTNmOOV64tK0dV+XKQMhRLuolGoGhnTNTXxGnBZflQW10kqfABNqZcc+O3f2eQBHiqu4Z8uBDj/vUvmrVUyL68vN4+K65fVtwNFiLVNe+qxbXt8RpVIptQg8iCwqdFNSkEi4wpDIVMpyTl3y6yi9YM3lJ1EprYT5Wams8+JIcQgbj/XDalOgVlrR+Jqprldhsvz0ucVLYWNpcimjomoI8zNTWafiSHGw/Xnt9daBk/xpYWqHdzd0haR+ISgVYOmm7Y0ZpVrKdfVOmz6QhYWeQxICN1VaWioLCoXTRYd2Xc+MyOCfFgz2CbBy5ZBKArwbqDOrGBFZ6/CGvzS5tElL5j4BZvvXGzIj231tiw2OlWpJHRDRZe+nPcwWKw9vP4yP0guDuXs+WVusNjJKtFwxxDl/HxoXFoaEhDjleqL7yJSBmyopKSEqKsrVYYhexlcdSIC663YcXGzSwFpmxlfRJ8CM0uunG/7S5FLUSiujomocPm9UVE2Hpw9yzjp/dfwD2w7x4r7j3ZYMwPm1BMMuc15vk6ioKIqLi512PdF9JCFwUwaDQXYXCJe4esSd3fbaLY06j4qqIcLfRJif422IoX5mNL4dK5LzdnpuR8O7JDV1Jv7x3cluv44N+OOeY91+nUZ9+vTh7NmzTrue6D6SEAghOsTXOwBn/+kI/V8iUFnneJazqk5FdX3HZkC/OFnGrzcfuKTOgXuyi7nhn1/yTvrJNnsL3Ls1nVqjcyr7ObPXgSws9ByyhsANyYJC4UoGUy10y476llXVqSg3qDlSHNxkDUGjI8XBTRYftter32bj5QUvXTuhQ887U1HDwHUf2b/efLQQNnzL9SkxvHHjZIL9mv5+Gkxm9pxsvYJjV3J2rwNZWOgZJCFwQ7KgULiSvzoIP3UQdaZap12z8Ya/8dj5n/tRUTWE+pmpumDRYWe9/HU2M+P7tbuwj1ZX3yQZuNDmzEI+XPM+Vw7txz3ThlFcU8esoZHUmS0Uag2djrGj/NUqIoP9nHY9WVjoGSQhcEMlJSUMHz7c1WGIXqRpXX814YEDOVOZ2a3XtFhpsssAwGpTsCEzki0/9nW4LbGzrvvnXgBCvOGVpVOYOOgyaowNaA0mao1mUgeE25OFPo9taj1u4LPsUj7L/mlEQO3kyVmr43pz3aZxYaEkBO5NEgI3VFdXR0BAgKvDEG7CbDFhMNXirw5CpezYVJPZYuWBbYf4OLOQAq2euHAf7hh3lnC/M90U7U+e/HIwJTpfhzd8k8WLc/qunzbTNsCydxw3c4rwVRKl8e/UZIkT+x4BUGeyOHXKIDQ0lLy8PKdcS3QfSQiE8FBWm4X0U9sprMhCZ9QS6BNCTHgSqYPn4qVo3kfAkcZtcl4KG0tTSpkxuBLv9j31ki0bcZan9w1yzsXaobzeQnm986ZJLsWA0ECnThn4+PhgNBqddj3RPSQhEMJDpZ/a3qR1sc5YZf96Quz8Np9vMJn5KLMQ4HxBoPjmi/m605AIPRpfG9X1slCtoxakRLukCqNwb7LtUAgPZLaYKKzIcnissCKLepOOmroKzJaWGxWV1NRRUKUnQG1mbLRrWtz+clwJw/oEuuTa7mr5mEE8O3+sq8MQbkhSSDfTQnNKIZowmGrRGbUOj+mMVXx85EUMptpWpxE0Pl7ckFLC2P7VhPpanBF2M/1DtGjr6ukf7EtRTb1LYnAngWolry2Z5PROjsIzyE+NmzEajfj6OrfnuXA//uogAn1aXvFtMNUANvs0Qvqp7c3OefyzN7lySCVh/pYWKwh2N1+lDRQ6SQba6dbx8TJVIDpNEgI3o9PpCApyzsph4b5USjUx4UntPr+wIqvJ9EFZTQ0xwee6I7QO8fKCO8cVNnv8Utofe6plowfypwXjXHZ9tVotCwvdnKSSbqa2tpbAQJlTFa0zW0wk9puA1WqhqOoEOqMWf3XQ/0YGmtMZtRhMtQT7hQPwQ1GxvVywq0UHmwhQm9GbVF3W/tgT/fvm6S69flBQEDqdDh8fH5fGITpPEgI3U1tbS//+/V0dhuihHG01jA5NIDFqCr4qPz754W/ojFXNnhfoE4K/+vzIk9liIj7Cj4xCFeH+rk8KFAoYGqLl8NmILmt/7EkUQOmj17k6DIKCgqitrSU8PNzVoYhOkikDN1NbWytTBqJFjVsNz9/0z68ROF66nxOl+/FVB7Y4jRATnoSXl5IDedv48NCf2XviFQLVrk8G4HxC8KtJZQwMqe7S9seeIFAF5j+tIELj+s6ngYGB1Na6R50G4ZgkBG6moaFBGhsJh1rbalhQcYxKfSmjB8xiWNQUAn1CAQWBPqEMi5pC6uC5fJf3H34s/hq96fzuBJ8eNH7o5QVrLj/Tpe2P3Z2vyouitTe4Ogy7ximDixmNRjZtar3cszMVFxeze/duV4fRI/WgX3khxKVobauh3qjl48N/IdAnlJjwJBaMXkl9g8FezthsMZFTetDJEXeMQnF+eNwRixXqzb1jDYGvyosbRg3k9aWTe9T2Qh8fH+rrm+8GOXfuHJs2bWLJkiUuiKq5/fv3k5eXR1pamqtD6XEkIXAzUodAtKRxq6GjNQKNWqpWWFNficXWcpGinqC1rY8+KvjznGz2nQpgR14kVXXeXdL0qKdZPmYQry2Z1CO3FrbU+vjVV1/l5MmT/PWvfyU7O5uqqvM/n2vWrCEhIYErr7yS0aNHc/r0aSZNmkRtbS1Hjx5l8ODBPPvss6xevRqbzUZJSQkGg4E//vGPxMXFsX79ej755BMUCgVz587llltuYfXq1Wi1WrRaLa+88grPPfccpaWlnD17lrS0NFauXMnrr79OfX09o0eP5u2332bt2rXExcXx3nvvUV5ezuLFi/nlL39JSEgI06dPZ/r06TzxxBMAhISEsG7dOo+dtvW83xgheqmObDW8eJuhxdrQXWE5jZcXXB6n54mZJ/nDzBxuTCnBS+FZCfTHx85gtrjXWom77rqL+Ph46urqmDhxIuvXr+fxxx9n7dq1ABQVFXHffffxzjvv8K9//Yvly5ezadMmDh06RE3N+TUjMTEx/Otf/+Kee+7h2Wef5eTJk2zfvp13332Xd955h127dtmbK02cOJENGzag1+sZNWoUb731Fps3b2bDhg0olUruvPNO5s2bx8yZM1uM+dy5c7z11lvccccdPPLIIzz22GOsX7+e6dOn8+abb3b798xVel6aKYTotNTBcwHsuwzA8Q3x4m2GKi9vZ4XY7by8PHf3gc5oJvaJLZx57Hp8e+AoQWuys7PZv38/n376KQDV1efLYYeEhBAVFQWAv78/8fHxwPk1CY11DSZOnAjA6NGjWbduHdnZ2RQXF3PrrbfaXys/Px+AwYMH2183IyOD/fv3ExgYiMnU+gjYhaOv0dHR9rVaubm5/P73vwfOr+EaNGjQJX0fejL3+okSLQ7LCQHgpVAyIXY+YwfOpra+kl3H3rYvErzQhdsMAYJ8w1Aq1D1+2qCjRkXVsOXHvh41fVBV38DIP23jxIOLXR1KM47+Pnl5eWG1WomNjWXBggXMnz+fiooK+0LD9vxNO3bsGOPGjeP7779nyJAhxMbGEh8fz5tvvolCoeDtt98mISGBHTt22F9vy5YtBAUF8Yc//IH8/Hw2btyIzWazxwPniymdO3eOuLg4srKy6Nu3rz3mRoMHD+aPf/wjUVFRHDp0iHPnXF+wq7tIQiCEB1Ip1YQG9GNARHKTjoeNYsKTUCnVTc7vE5xMafVhZ4bZ7Rp3H5zTe9bOnJPlOpS/WU/F75cQEtizS5mHh4fT0NCAXq/n008/ZePGjeh0Ou6+++52v8bevXv54osvsFqtPPXUU8TExDBp0iSWLVuGyWRixIgR9pt5o0mTJvGb3/yGI0eOoFarGThwIGfPnmXo0KG88sorJCcnc8stt/D73/+eqKgoLrvsMofXXrt2Lb/73e8wm80oFAqefPLJS/p+9GQKWwur1IxGI5mZmaSkpEjlqR7kyy+/ZMaMGa4OQ7gJR4WKWmpmpDMaeXn3k1wW5NxRAput9QWDl+KcHh79YphHjRBcLGf1fGL7tNy3wpm64+/T6tWrmTt3LtOnu7YSoydo674uIwRuxGazyZSB6JALpxAMplr7NsOLWW0WjhXtROOv6NYbtCPdea0QX5okA2qlFY2vmep6lcckCUOe3sZdk4bwwuLxLtuGmJubyxdffMHp06fZv38/48eP5/LLL0epVLb9ZNFjSELgRqxWa5O5LSHaS6VU2xcQOtJY4dDHw/5+K70gQG2mrkHZpAdCVb2KH88FsuHo+R4I7p4kvPptDvvzyzlw31ynJwW5ubm89dZb9nl5rVbLzp07OXv2LDfeeOMlv/7TTz99ya8h2kcSAjciCYHoDq1VOHR3CiA6uJ7R/Wqb9ECI8DczbaCWidFaGiwKfFQ2t2+UdKS4ivu2pvPX6yY49bq7du2yJwNN4jlyhLS0tBbn5kXPI3cXN2K1WmXKQHS51iocujurFcp06hZ7IHgrwV9tQ3nBVsWlyaVOjrLrfJx5BoPJuSWcG7f7dfSY6HkkIXAjkgyI7tBY4dATnanxwVtJiz0QHHHnRknFNQZKauqces2AgIBOHRM9jyQEbuTC/bNCdJWOVDh0FzYbFGqVrNsbS3W9CmMH1ga4c6OkyGA/IoP9nHrN1NRUh48HBweTkJDg1FjEpZGEwI1IQiC6S+rguST2m4iixfZB7idAreD6pLLz5Ys70AOkqk5Fdb17Lq+aEdfX6X0O0tLSSElJafJYUFAQt9xyi+wycDPu+VPfSykUCkkIRLfwUihJ6j+N46X7XR1Kl1AoIMz//JqAUD8TPqr2JwRHioPdcreBt5eCvzl5QSGASqXi5ptvpri4mF27djFmzBgSExNRqeT24m7kv5gbkTUEojuplZ5ZgGxsf10LHR3AaoP6BlCrzo8MNO4ycIZgtZKnrxlNVb2JHT8WAzaG9w/j5+PjUSmVVBmM7M0pY+3nR2nPx4D/N3kowX6uq8gYFRVFTExMs9EC4T4kIRBCAJBV5BmjAxdTKGhxImRPbiibs/o5rQ6Bv0rBVYn9+du1E+in8bc/vnrWSIfnT4vrx8NzRnKqvIZfbNhPRmkllXUNBKqVKAC9yUJ0SACLhsfw7Pyx3Rq78HySEAghADhRfMDVITiVwaSwNz7qzl4HkwdEsOaqFPqHBBIbHtSpOf7BEcHsvvsqDCYzJTV19oWDjf929rqBlsgopnvrGT9FQogupaurokibg8YvgoigaIflii8WFtSPkppaJ0TXM6hVNoJ8LNSbu2bhmwLwU3lhMFuJ8FeTNiSS15ZM7NJhfH+1iriIn7pUXvjvnqCF1jjCTUhCIIQHMVnq2PzdM5gsTfeix/cdx+T4xc0aGl1o7OA5fPJDTneH2G3yq9RkVwQyKrKaCH9Lmz0SLnU3wcV9EUp/vwR/tarHfWoXor3kJ1YID/JB+rPNkgGAk2UHqagtYv7ou1tMCiKC+nd3eN3CaoW9p4J5JyMaq03Blqy+LB9ZwrSBrVdf7OxuAi+FrUlfhMaSx2eqdIyKiehxn9qFaC/321sjhHBIV1eF0Wxo8XiVoYSvsjdhtrTc3vjacb/tjtC6jckMqz4dyvqjMfb+AyaLFxuO9sNgcjxEYLHAF7mhnd5NsDS5lCuHVNInwNyk5PHWo5s6/T48gcVikboDbk5GCNxMYy0CaXIkLlZSndvmOXnnjlBWfZoBEcmkDp7bbLQg2DeMmIjhFJZndFeYnXLx1LTVBl/lB/HvH2IcNiIK8rG0WHvApoDPcyM61cBIrbS22BchyLuYmjoDwX7+Do97Op1OR2BgoKvDEJdAEgI3ExAQgF6vJyhIhiVFU2U1p9p1nt6k5cfirwGYEDu/2fEp8YvY0MMSAr0J3v0hEj9vK4XVvhTW+Lc63F9dr6KyTkWfgOYliKvqVBgavOgTYOrwVkONr7nFvgga3wYKqspJ8RvQ7tfzJLW1tZIQuDlJCNxMUFAQtbW1khCIJswWE6XVeR16TmFFFmMHzm62A2HD/se7MrRLZrHCAzsSMVnaPxxtsnhxpITbLWEAABxLSURBVDi4ScvjRgaTkkdm5DaZ/29vy+PWEo2aejUDQiPaHaOn0el0aDQaV4chLoGMO7uZxoRAiAt1poWxzqjFYGr6s6Srq+rKsNqlpZ1qNhvUGhXc/UnHkoFGG4/14/OcMM7pVZitcE6vIr/Kh4Ghxmbz/+1tedyYaDhiYUCvnS4A5IOKB5ARAjcTFBREcXGxq8MQPYjZYsJibSBArUFvan9SEOgTgr+66R/wzYf+3KkYbDba3ObXUZV1KtbsGtLp6oFWm4INmZFs+bEvGl8zhgYvHpnheJ3FqKgae5GitjQuRhwVVUOon5mqOhVm6wB+c+XPOhWnp9Dr9fj7996EyBNIQuBmGtcQCGG1WUg/tZ3Ciix0Ri0qL8cFcIJ9I6ipL2/2eEx4koOCRQ2diqUxGWjx0z7ntweqHHzQr2tQ4K9u/sTvi7qmyVBjJcI+AaYW5/8bWx63p2JhY6Kx7ce+XJMUyt+WpBEWIHPnNptNFju7OUkI3Iy0QBaN0k9tty8OBDBbjQCovHwwW00E+oQQE57E2EGzOXR6hz1xaHw8dfDcLo+p3AD6GogMO/91pRb2lESwvyic+UPPOZzT/yY/BBuKJp+4u6PJUFsLDVsrUhTmq2JYZAh/uGoUKVGhVNc3SPEh4XHkp1kIN2S2mCisyHJ4zMfbj7lJvyTYN8w+AjAhdj5jB87GYKrFXx3UYinjmNDhFFa1vcOgpSmCUD/409dDHH7SdjTUfqQ4mF2nBqBUKNjyYz0R/mbC/EPILGu5nkJntbbQsLFIkRfw2yuSmDMsmrEx4YDjfgERgb5dHp8QriYJgRBuqLVFhHpjNSov72Y3fZVSTbBfeCuvaSYybC75FRm0NfJrtYHSQULQ2ifti+f0H589kReWxBER6Nukac9vtn5HZlnbNRU648KkJMzPTHW9N4eKgvji1ABunzCQv103AZWy6ZuXyoOit5CEQAg35K8OItAnBJ2x+a4AR4sFW2O2WHlg2yE2/5BPcU0dCWHh3D+twj4C4Ggk4Ez1+dX6F2tPOeDGOf3RMX3tn7QvbNqj8fNpM+ZXr5vA7MQo3tifzbovjrV5fiOrTcHGzEjCgidz+9QElF4BLNRbeEOG/y+JxWKR9QMeQH4D3JCXl5eUCe3lVEo1MeFJTdYQNHK8WLBlq7am8/I32favT1T2446P+nFj4immxRqo0kFwIPh4Yx/m35zVl+uTyi5p3r+wWs84mu/bv218HM9+6Xg6pNHsxCgGhAXy+NwxaOsbePnr7BbPHRUVyoe3zWBXTglDIoIZGxPe5Ob/v07C4hJIlULPIAmBG+rTpw9nz54lMjLS1aEIF2pcFHgpiwV/KCpvkgxcaMPxwWw4fv7fF3f2A5oM/3e04h9AtCbA4eMJfUNQAY73A0C4v5oBYT/dfJ5fmArAq19nc/Fy2+GRGr6+Zw6+ahU/nzCkQ/GJ9istLaVv376uDkNcIoWthQbWRqORzMxMUlJS8PFpewhPOI9WqyUvL48xY8a4OhTRA5gtpjYXC15MV2di4BNb0NZ3bpvhpVIC2qeWtThMr9XV0//xD6g3N73Fh/mqOLXmOgL9mr9Pg8nMsVItueW1+Hh7MW1wX1n85yR79+5l0qRJeHt7uzoU0Yq27usyQuCGNBoNWm3HqtIJz9XWYsGL1ZvMhK55v9mn6Utx38R4/nnkNFX1LX2ub+r/TRna6px9SKAv+j/exIkyLe8cyiNIreaGMYOajAxczF+tInVABKkDem/5YFexWCySDHgASQjckKKrS8KJXqNcV8/I5z7qsmRg+Yj+rP9ZGgC/nzeWQU980GZS8PNxsfZh/rYk9A3hD3NlJEwIZ5CEwE3JwkLRHo3b+UJ9vZn56uccLemakaWyR68jQtO0TG2gn5ryJ5dRUKnjw6P5oIA5if05WFjBlowCrv3/7d1rUJTnoQfw/7s3lpvisqAsgouIFwREZb2hIgZIezJNTqbNNM2Zk9NxOp580KTpyTntcXoZ23FGk55k5tR+iJ1k5oyZfkg7NunpmEFDsQgTFUFBRF2Qm8hykUVgd91lL+/5kMIJEUR02Wf33f9vhhndXZY/A+z73+d93ufJy8CLG8ycza8w3OVQOfiXGaFSU1M5sZBmNXkp4Z9b7qB7xAkJCMqogARg8PBLMDzi3HymIQFv7Fk/9f81S5PwT0XZQfjqFI76+vr4OqQQvHA0QplMJthsNtExKEz9+/824L/P30TXiPPLfQSC9LwHd619ZBmg6DMwMMArDBSCIwQRihMLaSauCR/aBsfw4cW2oD6vWiXhX7fl4J1vbQ7q81Lk8/l8nFCoECwEEYoTC2mSa8KHO/edOH7+Jk7fuIuukeDvhrl/Ww5+8+2tQX9einx8LVIOFoIIplarObEwik3OE/jkWg967gdnM6DVSVrsXrMCn1v7cee+AxlJCXg+bzlHBmhGnFCoLCwEESwlJQVDQ0NYtiy428RSZPi3P1/G8dpbT/z5iRpgpTERAw4P/nPvehwoyZu676ubDfGqAJqNzWbjhEIF4V96BDOZTOjo6GAhiEKuCR/+p35+OwJODuymJujxrfXLZ9zZb9JXNxsimk1/fz+2b98uOgYFCQtBBOPEwujVMTyOcc/jrQo46bUdq/FmSS7f9VPQcEKhsvBVIYJxMg89DvOS/58HMNuIANF8zbINDkUwFoIIp9fr4XK5EBcXN/eDSTFWJiciMUYz6yjBiiXxeG5dOg7sWouMpHiOCFDQDQ0NwWjkvhFKwrcLES4nJwdtbcG95pzCX5xOg3+xzLz63z9vzkLLfzyP33x7K9akLmYZoAXR1taGnBxuKa0kfKWIcEajEU1NTaJjkAD/9XwRVJKET1vuoPe+E8uT4vFCXgZPDVBIuN1uxMbGio5BQcRCEOEkSYJGo4HP54NGwx9nNNGoVXjvHy048g8beYkghZTD4UB8fLzoGBRkfBuhAGazGd3d3aJjkCCTlwiyDFCo8HSBMrEQKEBGRgZ6enpExyCiKGG325GcnCw6BgUZC4ECTC5hzMuAiGiheb1enp5UKBYChVi2bBkGBgZExyAihevs7MTKlStFx6AFwEKgEKtWrUJ7e7voGESkcL29vUhPTxcdgxYAC4FC6PV6eDwe0TGISMEmT0uqVDx0KBF/qgqSmJiIsbEx0TGISKH6+vq4u6GCsRAoyOrVq2G1WkXHICKFun37NrKzZ14hkyIfC4GCJCUlcfdDIlowXq8XOp1OdAxaICwECqPT6TiXgIiCbmRkBIsXLxYdgxYQC4HC5OXl4dq1a6JjEJHCtLS0YP369aJj0AJiIVAYg8GAkZERLlJEREHj9/sxMTHBzYwUjoVAgTIzM3Hnzh3RMYhIIW7cuIHc3FzRMWiBsRAoUE5ODq82IKKgsdlsvNwwCrAQKJBKpUJcXBwcDofoKEQU4QYGBpCamio6BoUAC4FCFRQUoKmpSXQMIopw169f52TCKMFCoFAJCQl48OABAoGA6ChEFKE8Hg/UajV3N4wSLAQKxpULiehpNDc3Iz8/X3QMChEWAgXLyMhAT0+P6BhEFIFkWcbIyAgMBoPoKBQiLAQKJkkSkpOTMTw8LDoKEUWY7u5umM1m0TEohFgIFC4/P58rFxLRvLW3t2PVqlWiY1AIsRAonE6ngyzL8Hq9oqMQUYQYHx9HfHw8VCoeIqIJf9pRIC8vDy0tLaJjEFGEaG5uRkFBgegYFGIsBFEgJSUFg4ODvASRiObk8XjgdrsRHx8vOgqFGAtBlNiwYQMXKiKiOV2+fBkWi0V0DBKAhSBKLFu2DPfu3eNcAiKaldPphN/vx6JFi0RHIQFYCKLI5s2b0dDQIDoGEYWp+vp6jg5EMRaCKGIwGPDgwQO43W7RUYgozIyMjCAmJgaxsbGio5AgLARRxmKx4NKlS6JjEFGYaWhoQFFRkegYJBALQZRJSEiASqXC+Pi46ChEFCYGBgawZMkSaLVa0VFIIBaCKFRUVIT6+nrRMYgoTFy9ehWFhYWiY5BgLARRSK/XIz4+nnscEBF6enqwfPlyqNVq0VFIMBaCKLV582Y0NjaKjkFEAsmyjNbWVuTm5oqOQmGAhSBKaTQapKSkoK+vT3QUIhLEarVi9erVkCRJdBQKAywEUaygoADNzc2iYxCRAIFAAJ2dnVi5cqXoKBQmWAiimEqlgtlsRkdHh+goRBRi165d4wZGNA0LQZRbs2YNrFYrlzQmiiJOpxPDw8MwmUyio1AYYSGIcpIkYceOHairqxMdhYhCQJZl1NbWori4WHQUCjMsBIRFixZhyZIl6O7uFh2FiBZYa2srVq1ahZiYGNFRKMywEBCALycY3rhxg6cOiBTM6XRiYGAA2dnZoqNQGGIhIAA8dUCkdDxVQHNhIaApPHVApFw8VUBzYSGgaXjqgEh5HA4HTxXQnDSiA1B4mTx18Je//AVOpxNDQ0MwGo0oLi7mAiZEEUiWZdTV1WHPnj2io1CYYyGgh3R1deHSpUuQZRnAl1ujtra24uWXX8aGDRsEpyOi+bh+/TpycnJ4qoDmxFMGNE0gEMDp06enysAkWZZx+vRpBAIBQcmIaL4cDgcGBwc5ukePhYWAphkcHMT9+/dnvG90dBQDAwMhTkRET2LyVAGvKqDHxUJA0+h0uqe6n4jCA08V0HyxENA0BoMBmZmZM96XkZGB5OTkECciovkaHByE3W7nqQKaFxYCesh3vvMdJCYmTrtNq9XihRdeEJSIiB6Xy+VCQ0MDdu7cKToKRRheZUAPSU1NxVtvvYWmpqapyw6zs7PR2NiI9PR0SJIkOiIRzcDv9+PcuXPYu3cvVCq+36P5YSFQmLa2Nrzzzjt48OABXC4XSkpKcPDgQfz2t7/FuXPnoNFocOjQoakFiH71q19BrVZDp9Ph2LFjMBqNAICYmBhs2bJl2nPn5ubi4sWL2LZtm4hvjYjmUFNTg61bt0Kv14uOQhGIFVJBxsbG8KMf/QiHDh3CyZMn8fHHH8NqteL999/HpUuX8Ic//AHvvvsuDh8+DAA4cuQIfvazn+HkyZMoLy/H7373u0c+//LlyxEXF4e2trZQfDtENA9XrlxBZmYm5/nQE2MhUJCqqips3boVZrMZAKBWq3Hs2DHExcVh586dkCQJJpMJfr8fdrsd7777LtatWwfgy6HGx5mNXFBQgL6+PgwNDS3kt0JE89DT0wOfz8eliempsBAoyODgIDIyMqbdFh8fD4fDgYSEhGm3jY+PIzU1FQDQ2NiIjz76CN///vcf6+vs2rULly9fxoMHD4KWnYiezOjoKKxWK4qKikRHoQjHQqAgJpMJ/f390267c+cOAoEAnE7n1G1Op3PqKoLTp0/jF7/4BU6cOAGDwfBYX0elUqG0tBTV1dVcuZBIoImJCdTW1mLPnj2c7EtPjYVAQUpLS3H+/Hn09PQAALxeL44ePQq1Wo3a2loEAgH09fUhEAjAYDDg008/xUcffYSTJ08+NLIwF71ejy1btqCmpmYhvhUimoMsy6iurkZJSQk0Gs4Pp6fH3yIFSUhIwNGjR/HTn/4UsizD6XSitLQUr732Gnw+H7773e8iEAjg5z//Ofx+P44cOYK0tDQcPHgQAGCxWPD6668/9tczGo1IT09HU1MTNz0iCrEvvvgC+fn5004HEj0NSf76LjZ/5/F40NLSgry8PC59SY908eJFmEymeY8yENGTuXnzJnw+H/Ly8kRHoQgy13GdpwzoqW3ZsgVWq5VXHhCFQE9PD+x2O8sABR0LAT01SZJQWlqKK1euYHh4WHQcIsW6e/cuurq6sH37dtFRSIFYCCgoVCoVysrKUF9fj5GREdFxiBTHZrPBarVi165dvKKAFgQLAQWNSqVCeXk5Lly4gNHRUdFxiBRjcHAQra2tvLyQFhQLAQWVWq1GeXk56urqMD4+LjoOUcS7d+8empqaUFpayjJAC4qFgIJOo9GgvLwcNTU1cDgcouMQRazh4WE0NDTgmWee4e6FtOD4G0YLQqvVoqKiAjU1NRgbGxMdhyjiDA0NoaGhAeXl5SwDFBL8LaMFM1kK6urqcP/+fdFxiCJGf38/mpqaUFZWxjJAIcPfNFpQGo0GFRUVuHDhAux2u+g4RGHv7t27aG1t5WkCCjn+ttGCU6vVqKiowOXLl3Hv3j3RcYjC1p07d9De3s4JhCQECwGFxOQ6BVevXkVfX5/oOERh5/bt2+jq6sLu3btZBkgIFgIKGZVKhWeeeQbd3d1oaWkRHYcoLMiyjPr6eoyNjXHRIRKKhYBCSpIkbN++HRqNBufPn0cgEBAdiUgYn8+HqqoqpKamYuPGjaLjUJRjISAh1q5dizVr1uDMmTPweDyi4xCFnMPhQGVlJYqKirBixQrRcYhYCEic1NRU7N69G1VVVbwCgaJKX18f6urqUF5ejqSkJNFxiACwEJBgcXFxePbZZ9HU1ISOjg7RcYgWXEtLC7q6ulBRUQGdTic6DtEUFgISTq1Wo7S0FKOjo6ivr4csy6IjEQWdLMs4f/48NBoNduzYwcmDFHZYCChsbNy4EampqaiqqoLP5xMdhyhoPB4PKisrsXr1aqxdu1Z0HKIZsRBQWFmxYgWKiopw5swZboxEimC321FVVYXdu3dj6dKlouMQzYqFgMJOUlISysrKUFdXh7t374qOQ/TEbt++jatXr+LZZ59FXFyc6DhEj8RCQGFJp9OhoqICNpsNtbW18Pv9oiMRPbaJiQn89a9/hcvlwt69e6FWq0VHIpqTRnQAotlIkoSioiLY7XZUVlZi48aNSEtLEx2L6JE6OzthtVpRXFyMhIQE0XGIHhtHCCjsGQwGfPOb30Rvby9HCyhsTUxMoLq6Gg6HAxUVFSwDFHE4QkARQZIkWCwWjhZQWOKoACkBRwgoonC0gMIJRwVISThCQBGHowUUDjgqQErDEQKKWBwtIBE4KkBKxRECimhfHy0oLCyEyWQSHYsUqqOjA21tbRwVIEXiCAEpwuRoweDgIM6ePcvdEymoBgYGUFlZCbfbzVEBUiyOEJBiSJKEwsJCeL1eNDY2wuVywWKx8MWbntjIyAgaGhpgMBhQXl4OlYrvoUi5WAhIcbRaLbZu3Qq32436+noAgMVigV6vF5yMIoXD4UB9fT1iY2NRUlICrVYrOhLRgmMhIMXS6/XYtWsXxsfH8cUXXyAuLg6bN2+GRsNfe5qZx+NBfX09AoEAtm3bhtjYWNGRiEKGr4ykeImJiSgtLYXdbkd1dTVSUlJQUFDA4V+a4vP50NjYCIfDAYvFgsTERNGRiEKOhYCixuR5YJvNhrNnzyIzMxNr166FJEmio5EggUAAzc3NGBoawqZNm5CcnCw6EpEwLAQUddLS0pCWlobOzk5UVlYiOzsbq1atYjGIIn6/Hzdv3kRvby/y8/NRWFgoOhKRcCwEFLWysrJgNpvR1dWFzz//HAkJCdiwYQP3rVewsbExNDc3w+PxYM2aNVi/fr3oSERhg4WAopokScjKykJWVhbGxsbQ2NgIj8eDtWvXwmQycdRAAWRZRldXF9rb25GYmIhNmzax9BHNgIWA6O8WLVqEnTt3wu/349atW2htbYXRaEReXh4vO4tAbrcbzc3NGB0dRVZWFsrKyljwiB6BhYDoa9RqNXJzc5Gbm4uhoSHU1tZCkiTk5+dz0plAPp8P1dXVuHz5MpxOJ8xmM8rKymA2m6c9rr+/H9evX4dWq0VBQQGSkpLEBCaKMCwERI+QkpKC0tJSTExM4Nq1a2hoaEBmZiZWr17NyxZD7Pe//z1aW1un/t/e3o7Ozk7s378f6enpaG1tRX9/P5YtW4aSkhKuN0E0T/yLIXoMOp0OmzdvhizL6O3txblz5wAAGRkZyMrK4sFngfX29k4rA5P8fj8+/vhjFBUVYd26dSgoKBCQjkgZ+CpGNA+SJCEjIwMZGRkIBALo7e1FXV0dfD4fkpOTkZOTg/j4eNExFae7u3vW+xwOB0pLS0OYhkiZWAiInpBKpUJmZiYyMzMBAMPDw2hubobL5UJsbCxycnJgNBo5ke0pyLKM/v5+9Pb2zvoYbl5FFBwsBERBkpycjO3btwMAXC4X2tra0NzcDLVaDbPZjMzMTM47eAw+nw9dXV3o6emBLMtIS0vDc889B6vVCqfT+dDjLRaLgJREysNCQLQA4uLisGHDBgBfHuC6u7vxt7/9DbIsQ6/XT62WyB0YAafTCZvNhv7+fng8HqjVamRlZWHPnj3TCtSrr76KkydPwuFwTN1WWFiIXbt2iYhNpDiSLMvyTHd4PB60tLQgLy8PMTExoc5FpFhutxs2mw02mw1utxsAoqYkfPXg7/V6Icsy4uLiYDKZsGzZMuh0ukd+vs/nw40bN+ByuWA2m7F06dIQJSeKfHMd1zlCQBRier1+anXESZMlobGxUTEl4VEH/y1btsx58J+JRqNBfn7+jPedOnUKHR0deOutt542Ot58800cO3ZsWsaamhqcPn0aR48exYEDB3D8+HHcunULY2NjPG1BisBCQBQG5ioJHo9n2uNjYmKQkJCAxMTEqQ+dTheSCYyyLMPtdmN8fBwOhwPj4+MYHx+H1+ud9pinPfiL9N577z3y/uPHjwMAzpw5A6PRyEJAisBCQBSmZioJkzwez9QB2WazwWq1PlQagC/XT9Dr9VCpVFCr1VCpVNM+JEmCLMsIBAIIBALw+/1T/w4EAnC5XPD5fA89b2xs7FQhycjIQEJCQlgc9JuamrBv3z7Y7XZ873vfw/vvv4/PPvsMMTEx+PWvf42VK1ciPT0dJ06cgFarRX9/P15++WVcuHABN2/exKuvvopXXnkFe/fuxWeffYbe3l4cOnQIsbGxiI2NxeLFiwEAxcXFOHXqFP70pz9Bq9Vi/fr1+OUvf4k//vGPAIAf/vCH2LdvH9dFoIjCQkAUgWJiYhATEwOj0fjIx01MTMDtdk87yH/14C/L8kMl4asfsbGxEbWPg0ajwQcffIC7d+9i//79sz6uv78fn3zyCa5fv4433ngDZ8+excDAAA4cOIBXXnll6nFvv/02Xn/9dRQXF+PEiRPo6OiYum/p0qV48cUXYTQaUVBQAL1ej/b2dhiNRvT29rIMUMRhISBSMJ1OFxbv3EMlNzcXkiQhJSVlai7GpK/On87JyYFWq0ViYiIyMzOh0+mwePHih0ZZurq6pg7smzZtmlYIvu6ll17CqVOnYDKZ8PzzzwfxuyIKDV4UTUSK8fU5FDqdDoODg5BlGTdv3pz1cbPJzs7GlStXAAAtLS0zfr1AIAAA+MY3voG6ujqcPXuWhYAiEkcIiEixfvCDH0xtfrRo0aJ5f/5PfvIT/PjHP8YHH3wAg8Hw0KVaeXl5ePvtt5GdnY1t27bBYrHAbrdzh0WKSFyHgIgoSA4fPoyKioqpFSuJwslcx3WeMiAiCoJ9+/ZhbGyMZYAiFk8ZEBEFwYcffig6AtFT4QgBERERsRAQERERCwERERGBhYCIiIjAQkBERERgISAiIiKwEBAREREesQ7B5AKGExMTIQtDREREC2PyeD7LAsWzFwKv1wsAsFqtCxCLiIiIRPB6vdDr9Q/dPuteBoFAAE6nE1qt9rF3BiMiIqLwJMsyvF4v4uPjoVI9PGNg1kJARERE0YOTComIiIiFgIiIiFgIiIiICCwEREREBOD/AM94h2wUsTQVAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -351,7 +363,7 @@ "\n", "Parallel coordinates displays each feature as a vertical axis spaced evenly along the horizontal, and each instance as a line drawn between each individual axis. This allows many dimensions; in fact given infinite horizontal space (e.g. a scrollbar) an infinite number of dimensions can be displayed! \n", "\n", - "Data scientists use this method to detect clusters of instances that have similar classes, and to note features that have high varaince or different distributions. " + "Data scientists use this method to detect clusters of instances that have similar classes, and to note features that have high variance or different distributions. " ] }, { @@ -370,8 +382,8 @@ "classes = ['unoccupied', 'occupied']\n", "\n", "# Extract the numpy arrays from the data frame \n", - "X = data[features].as_matrix()\n", - "y = data.occupancy.as_matrix()" + "X = data[features]\n", + "y = data.occupancy" ] }, { @@ -381,9 +393,9 @@ "outputs": [ { "data": { - "image/png": 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LNdZKCbeYhgQ0Qgghdum5rY9Q8oZYP5wGDUeMK9Xe2UivMx1hkHT+VZiUGppQ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twYBXhO06UxtvG46UfN1lRSuVxeLDiID/nW9adf/Z/zcJ64oHn55Zf5x3/8RwC++c1v8vnPf55X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\nmOtQzEUXXcSVV17JPffcA2RdlrfddtuQ75v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\n", 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607snaXoT2GaB3vxcSk7fLtu2ZdpJItm32KeETCoSqu1RLKNJye6fYWglkUh2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sxOF3UUWU+n/Je9Ngy7L0LO9Z057OdKccKrOquqpnqbtpSzSSwEAjIQRIxgpDCJsQ4bAxtkMe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\n+HEJKlSS2eMCRCQc1WorBYiPqGgYpCkXxmNar3n1uGRWd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maVcvoV6GHe7tVzA7/q69L3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\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9ULxx5rmz8JxsIutBxlU+GamcL9n/i1w4yQw8UuokdsOot9Y6NbaOtWZaTilsidFCLh4S\nPNoEBjcQ44CiQ2vphJgCSdt2ikWr6J2lC+Iakvm8wKWmVWRnIk6LzgsPYdlZ1r1l2WtiKrc33/Fi\nFBIxW9JkXQEqkaJCpcgRsFtH1oPl0drm3YLspgqdeGyvQ0V41BpisMwLyToRcJTBI3qkIQjBcoiJ\n43XJOsi5AqBDZKpcZgOVaCVwu5gXj8vdlC4kuuA561Y8/3BFXWj2KstTexM++tg+jzYiFD5rB7yP\nnLtABAqjmU0qlE6s+kDvQr6hv/3jsmxm5NT8MI4XTtb85//ic/zjL77Gf/lzH+apgzl3FhsJ/xtE\ngPu1RwvuLi70M6U1vOfabqYENzxctXz5wTl7dcn7ru2y8YHXzxoeZNeSj4kn92e0zvPGeUNhNB+4\nvs+VmQjAj9cdzz9acN4OvHHW4ELgaN3yv/3hq1vQ3Qeu7/Jvv/82P/rEFR7bnfLh71DEfOHuCb/8\nu9/g/mIDKTEpLC+drKiM5l2Hc4YQ+cP7ZxwtW5ado8+M+FlpeO+NXT742CG39qZMjOLhquVLD855\nsGp5sGwlHkBChUhAbSS7x3vpJsY8oimtytfjSNCWhGcXpVDwGTORcjE8ng7jfy8XMG8tlk1+/nld\nsF+LszGGyGIYeNh09L7PeXCjMQJispy1UM0DVyYdk6Lg0bpi0UswZVWIszElw6wUZohWSUYjOZGw\n9ZaNK2SMRSSlgZQU1gRMgnkhcTFtrs47L8XIEEyOQRAG07yykCKLIWRx6sXvprQA+ogCpHRRQRTx\n/4CM6esEQSlUdgYZLd3iEEcGjBQ0ISi6IFC9jY9Mc4jsTi0QD9nokkffovsRbZx0UmIe5fgtL2s0\nbgRIMhrqMq7C6IIqO5SmpdCopeDLxhQl46DCeBENb2UOstakpKVgAqzxJCO/S5edo1BKALGNMvIv\npTgrtIiR+wTeG1InGqLCQF0kdiwo4xk8nLcANmtKE1XhmRSJGEUUPkqfxxFofvkYPbp4E5NyoFCB\nEA1dzLogr1kMf0I1MnI5WsDyG6/Ab7wiX9UUzIqCaV1ydVJz+6DGKvj8nWNWDt68Xxm/J1v/SExK\niVkvjSUCrRt4+bQDhExZW9k5OR9pnMwQQybgytxS5QVZ5pp1TqcW2a+Mq8Zh05hNsRkUhbIcTGrq\n0tIHy8ZZQGbUr5+veLDsWfeBIY0jq7GASUwyPybBtkgZoXZlDgqrR3eUKpjXNZPK4v1YhERS8oQQ\nWPke5z0xi5RDApLJtGBDG2DIN7jx90gKhlBsW699pkC6SC6cYHSNTcvIvEi43EL24QIg5TMBU6fA\npErURrKz9ivHrPR8DZjayFk7FRJwSJw1mr1JojSe3hlOW8mFndVyoSaEuhlSYqZl3GbzTXYIlrU3\nXNzyA4ZE72Xp2HY7Qsow74tHan0pnySfVa2LtG7gwXpAs2BSaK7OKz5284CVk/HLaSMJv5vg8CSs\nkhBGBTQu4GLM4Xlv/xiLmHEs+YM+znvPp155xF/7R7/NX/rQU/yNP/s+eh84bnoGH9gMHh8Tn797\n+ib9zKSwvP/GHk/uT3n5dM1J0/OF+wNXZhUfuLHHKucyvffaDq+dNd80Skop8dpZw6sZyHe87nEx\ncvd8za984XWOmp7Saj5x64BfeP9tPnLrgGvzCR+5dYD9FkVMjJF/8qU3+MdfeJVFO2C1YvCRo6bh\n6lQW/LvLDQ+WG47XHX2QBa0yooP56ONXePJgRm01D1YbvvJgwf1Vx/3zDYvBbbuKGoHYKWQHO+QP\nXAT3UryQpMsqBgCBrY14f3hz8TIeigsUQLr07+MovS40+3XFrLKQEpsh8GDV0YeQU5ZTFnImjPIU\nJtE7AYAmYIiWB2vF9VnHtIjc2u1ZdBWailIN7FQ+uw5LWq+2tNrVYNAqUOgWlHRZrQ5M64TPhoCx\nQ2BVJGaNjNGaUkWmViEyZYNWltUQiCGSVNzSaWUMDjZCUOA9RHWJsBvEU+mCoh0SqAhKif7M5Gtb\nxTxqyhZpqSLwaRTQwvEGCi2al4n1WJOym0hwHCaP7I1R2Q2WsRIRvJLsJhi1j2CQrpoUx+e07gAA\nIABJREFUNbLRM3k8My0CdYbtpZxnNVJ5UfI6xPZsGIJsMIU6ry6YMCZix7FQdqOeJdEMVkWUjk0R\nBHBoJEZGMqXgtI25+1Vm3YwUfRI6KZt3DWgtoaCFFS7YiMaTCJwsUNCJQSn6qLIoODEtInuzHmsS\nlf4TW8h86yMCawcqDTQmcveNU1LWotzejZgUJf7d2S1yGSTwri4sVsvOu4kRH8Th4oMCLTuPvglZ\n/CQ2vp3Ss197DqYDh1P5/9rEzAKQcUSIF9V34w2r1rDsLWtnWfcVs9Jypgo2p2QapSLhWXUNy04W\ngdpa5iXs1Z7d2nN7t6cwcjtTXGQvtU4iCnzQuFATkqEuLYXSTKymNLL73/SByjhm1jM4R+OFndMN\nmt6L8HbtNb0TQZjKzIOIzpkfWfycE7TJgrCU3VCknDWV08UFay35IAmFMYq5AZ0cV2aeaZUwBFxS\n9F5leqRm0VvOWpszP+Drx/Oc5SJJ3u+60jKrAu1gefV8xk7tMAS0Eav1mC4+or0P6p6JCTTO8Npy\ngpQmEmQmv6PK3Q7zTefVm/5+aVEaz6Lwlsc3LtKctbx21mKAndrw5MGMibWcND3nm54+JnrnCUqi\nKualIUUkkZfvrdPywxQCtz4Rm55/+NkX+M2XHvCf/Fvv42ffc4tH627bmXEh8HDVfpN+ZlYVfOjm\nAYtM/z1pek6anutzAeJ942j1TaMkHyLPP1pwvO44anpWnWMIka8/OOeffukNFt3AtCz4yaeu8XPv\nfYz33zjgyqz6tkXMqhv45U99jc+8fsyycxRacZJJwjemFV2IvPBoIQJvJxqsQmtu7Ez4+O1D3nVV\nyML3lxu+cbTkzmLD/cWG1l8Uphq2qdJjEayQosZaQ64mRKgb5ToJubj4bufBeA6Oz3nRvSjESQi4\nkFi0jkdNJ8aCmL4JA6CALhiqvAGryoDzmpSTn1MyLNqa3bJjVjgqq1gNM4Y0ZTU4Su0IyUMSB97M\nDkyqgItWdB9K4YMUSH0Q/L9WwqEavJHCIAfRjoGORil2K0NpoOk6FIkmaELIDBWdKBAHjsrvlSkz\nkVaprB0B7wJDzj0S448mkFAh5dwjMRooFbcaSWGOyXs1Untd1LgeGqepjZgpaiN5TCGCIYtqVcTk\njauPchPcapeSwntAmRzdIEVJyM+/8ZYhGKwRvcu8DNSFz4R4LpxTgFI+P2fKdnkxk7igs61dxNZj\nCnfIRc0QJC6h9ZapFYNGaSTOojKJ2sqozmUasYiP80jNSPcnKYkvWAeDdsU2FNKoROsuNvjjYVSi\nsJpKK2aVYb+27E8Lrk9+cES8d1QhI/vurTaci0tZvrL0geW5XP4KRW1g8AajDYVKXJ/JIiwWYXGE\nbII8dvwejWanhqkeKK3gqLUSy3LKtr/SyIhop4zolNgMGm+k9QYiFBMLoMw1Y4JZGbm2o7i9W+NT\nwXlnOW8Vx03ipZOeznsSwpiZFeqiMjdyQQxRYfLoyqqYBWiWeaW4PrMYI24Go00e/SRC8nRDZNEn\nXBgDwbK4NwqOfPABlHBcfFAMCfqQx0f+ImtDTE9y6xO9T0QEeXKSi4NCbJJERco4bUvgoJbW7P7E\nsVd5diuXw87UNpV71VlKU7LsalZDTZ8MPg/151VB4xTOJw6nHY0zHG9KztqS3cnA1AYmNohgTWvm\npTjPQpBZtDZw3hXcW1dIsRLZKTxGSaBn4ywX+9pvf7y1lX/5GBeSy4VFAM67wPl9yQ8qgINpydM7\nBZuQWHWSjh18ICiFtRqjFSmKEyXxwy1U3s7RB1k4vnG85G/+n5/jTz/9Bv/pT3+A2/tTHiw7XAg5\nxFG0MMKf2eHGTo1Sir1JyUcfP+R00/PyieQ4gexyL4+SRj3MsnOcbnqaweFC5LOvPuJXv3GPdSdZ\nTz/97HX+/HO3eNfVHQ5nFR+++a2LmBePlvzdTz7PSycrms6hNCy7wG5lMUZzb7Xh/nLDsg/b0c/h\npObDjx/w/uu71EZzZ7HhxaMVb5w3HK07+pC258YosEXlkWDeyVZWbVubMaZtYTEKUOPbHDaO55tR\nwrGaTywG8CmxGRLLvpNrffu8uZP6lucgf006xbIzn5cBU0aGpImxQGspRO42huuTltJ65sWKk01N\nExU7deL6bCAkgeuddQU7lehHOq/zOC1R1gO9kw7stBCjw9Qmml50KQDzMlIYS6k1ax/oe88QAJ3Y\nrYPgKbJoN6HoQ3YQ6YhBnEwJYVuREsoqiqgwJhGDjH58EjOEiiK2NVG66VapXNxku7SRe16BghS3\nI6PWGboAhdZMbGRe+K1jK+UuUYha4guiyAwCY8E1Zh5JF1tF0UhOC0k6CgmGaDhvC5a9oTQpFzU+\ni6YvPjOUSAam2jMv5WtD0AxOxLYuatHtKAENljZkOi+0QcCA3aAxuqAOgVkR2akcu3WkLnophDM+\npA8XzyUjIydrIirHU1wYPOqsLzLKgC6wpmRqbRZCq9wZ7zhqAlx7W6f7H/l4RxUynkCtNQdTuL++\n/C9btBNjszUhYWMEmFhF1ArvZGtk8FQ6iIAr5wQVOrCTRWpVESg026JFOhqSetoOmrW3LFvL/bV0\nHAqVKAqxSlcaVMZSayU3+XlZ8+7rh+zXFbu1JvgV9xZnFGnBQQm3djSLTqBubVaXr/qSsy7rSFKg\nMIl5KeFr8yqxUyUOJrBba65OFdMi4mPExQFnBxHqNorTzrDpC3oPIQZWg6Z1YsMO5DC2UAnRUSGV\ntZbOSmUiUZoXW+W6ZDpBSIIyv6y8r5Tjxk5gWkuqbGUTKaVsYVfSaekMIczw0RKSJVGisBiDXABl\nYl4LkFBrzSvApCg5nAzMq5bWKR6uJ0QKrs4cCQlsazYFrVcURj4zYuSJvY5ZGXjUlDxcT7FIe3tS\nSuejd6P98I8uOrvc1h+Pty4cDni0GXiUoW/TQnM4KShtzbpzbAZHzCJOa6SrFZPsqr/V879Tjs5H\nDuqCzeD5tRce8MUHZ/zlDz/Ff/Bj7wJKjppORNNBbMJv1c+AiGUPJiVHuZD5xO0r1IV0yEY9zGbw\nrHrPpneEGPl/vnaXX3/pIa0LHE5Lfv65m/z0ex7j9v6cw2n5bYuYf/21e/zPv/cCD1Yd3eC2WqWZ\nFVbNw+M1Z11H7+XcnlWSi/Shm/tUheXOecMrp2IVP2sH/KUPZjt6Jd+VknRkjBbCdwqJoLLOJcXv\n+XNViIttYo3s1LWi93C6cdsx1Ld7zssdmPFdMYC1Iio2WgppnyyF8VQp0SXHZtDEKNlmd4aS5672\n7E8ctfXcX8047wTedjAZUCR2a08zGA5qvw00HIIW40Pp2TjLcrC5Uyti9rbPS02qSBHOsyNzyIG9\nKoin0CghoNvgGZLQaH3UqGjwOmGjuIYiSjozKJERAEGTtUcB79WW2eWjwSGtndEFa7TEEIgOUuWc\nI6Grmwg+mNwJEd5WkQXCExtkFJPXopjPg5RGkrnefkaQuWGDIWERi3TE5nLIR806aJa9xahSfkbW\nulQmYi51akZtCkgnfG4TSQVcEEfSagDXyoMMKQdSRsIlvMbJRqOYZOigOKzmRaIsAiZvZIVro/Cx\n2BYlIr/QlEZTWcvOpGRaGEoLs1Is2J3znLSBZSf6txAjOz/A6uIdVcgoLH10HG/eKnb8Zj/65RtK\n6z0OEZXOysDuJHBlMmA1nLUlZ4MREJxTaAyTzCwxmTAZsJJYmoFBVkdc0vRRk6KiQ1H4hMusBK1F\nuX17T/HMoeXqLGDTI5pNw8nSMURZ+PenMpTZT4rrMwmDa3rNWWfpQiFjkmAJ0YrC34mYrc0jodNW\nFm2rNELZhZACMQSSNnR9ZDUkWh/oA7lIki5LQk7okLZhATK3zYI0dWltD7lVqMb5qIGpdRzueOqc\n71SbuCVaaqQFOnjFxhW0rqJxBV3GdY8iZUXEaAmnm2hNco4+Ws4GCedc9vIJ71Xn3NpxaF1w73zK\nE/tTrG5QOMl2GgrOesu0CqQU8REq6zntS15fWo7WJQrNpAzsVA4NrAZLF+y37KT8cR3fbYHauMjG\n9YDsfmalYV4qSJrNMBAyuNHqLABM6U0anXfScdY53n0w4WEz8HDZ8w8+/SK//fIR/9FPPsdPPn2V\nde9ZZ92Mj5FlN/D5u6dcm9e8K+tnlFJc3xHCa12YLR/mtbOG9eAJQb7PKPiVz7/KZ14/oY+RG7s1\nf+F9t/mpZ65xY3fKwbcpYnrn+R9+9wV+/cX7PFw0gm2PYfu4R40EWfZecpuqwvDk/owfvX2Fyhru\nnm945Uxez6oP3/Q5bDEK+f9tts2TRLSbcnfme43hNcjIyGaRXYjQehmBfyvdzFuPsXBJQKlA28yi\nyf8oLBj5XIasn2t62Kk8lY6YQnPei4vPJcPzx3PeddhyWDue3lvzxmrKsrcoFbk+HUhKNlpHm4JZ\nEcgRkJy21VZo6vKoYwTbFbnVcNZ52Rga6ZjOqqy3ywUFQFKJZBRTBaVx0lkOVjrjl3R8RicKJTEm\n2+RpHbNO6QKY1/kR1a/ysNzkLCVZhAstnZKx6yAbVOmgiAvIMkRFO4jFe2IDs0oy4LRmaw8PWWMy\nssNkdJ7JuZGc8K0kqFZlDYoOok/J4NAmd9trI6G80zLbufPnqJV0zMUaLmyfWSVarZFn1nmDjzKm\nM0o2nBM8SQms0EXhvSx6CXusCukYTa0UcrMyYU3u5BQFs6KmKg0mXwE+BpZdpOk9XZDziqioC0Nl\nEjFGVs7QdX/0DeTbPd5RhcxHbsy59/LZJcV64GIJGm9asizNtGNnIjlAfRCLbx8TvhPa4HlX5ITS\nQBE9HisExWTpgtiiR+tykbUvIAu05CIZrkxrlt1A45yMfrR0NOZlYK8W+u1Jk1i0EsgY0RhVCe9A\nyYlTGhk7zcuAydlQ71YOrVqsgSGULNqK005GKYvesOoVxxu9rWx9VLlFnUhJ9h9ShJktW0Ejaa5k\nyFHMuxGVLlKz/UghzmhssWkmdgrPYzuOWR0kh6n2AmvSQrnsgmLdGc66ilVfsXGW3lsSBmskTdea\nxJ6N1IUm+Z4+Gha9YtnDxiV6l3DRXOLwQGlE+BnpePFE8+VHJQfTjsPJiv3KE5LBxZKmVzy916OD\nRxeW3WpJ7xOvnE1phhk7taIwYhd1QcR1Q7woYMYxwLiTfWsn5QdxRGA1BFYZrWCBSWEgBZJRDIOc\niwmx+o83w3dSUfPiWcufvn3AG6uW42bgi/dP+Vv/1x/wM+9+jL/+p97Frf055+0gKfFZOHC07jh5\ni34G2OphTpqeZecwCk42PaUx/P3feZ4v3TvHp8QTe1P+4gdv8+NPX+dwWrE/+dZFzL3zhr/zm1/l\ny/fPeLhsBSyXREPVdANH646VE4qsNYZr84pPPH7ItDDcWWx49azhbta/fLsjQt65kjUSF18H3vaH\npZDPX9woWcgaFV1Ib2v0NJ6/BeROgnQnxoIgJQgpMmTfdsovMkEGosm1f9YW7JSOqogcTAaWbZkX\nXMuLxxPedy2yO/HcnG+4s6g5bWo2TkZTChlrPWyqfJ8Qc8VmKBh/Cx8lkPGsvXhr1oM4R5UxVCZS\npsSkyK5Lk0f3UZGU3JeV1gQvhc+VmYzF20wPTnmzOSIxCs12PKI1TLS8Dmu0wPYuaUJcUvhg6ND5\ne8XtJEniipSsdMsR6N2UlBH+muVQsBzY6mAmRaC0cm6N7/dohR85OCFJ950Y8+hfM3hIyEim1AGr\nxUDig2LhC867MtN7JZ27MlEiCDJ+QmKT1PbUS5CDLMMWiue8xjlZ98Y8Ja0i1gpktA1SPC10xbTQ\nXJ8X3LpS8vhewe7EEEPkvOs5ahwn64G1SwwhSaRUPs86F3Eh5WIQQFOYxLXJn1AgntJ3+DNPNDx/\ntMN5VyKadjiYKI5ax8XLVaxjybqJHBjPtWnHxpUSYpjEhdMFke9qJYIyqwMFEYeWVmNKKGUw+a5k\ndWJawuEkMasgBoc1xzx9IEVKSrDxEpa4HiwP1gIzEqV2YFIM7NYiQJ1pQFlKI8rxiRVtjNKKmAwp\nFShdo/SMSpdMkuLAGDSBzgdWeMkUCVEq6BEkoWQ2PMbTQ9rexHS2ORdW2qU+SIXfeyliTAJjPYeT\ngYOJ36raS5tnxrB1ah2vLRs3ofOGPokTqrRGoEg2sW9lLOe6hg7LelCcOkXnRKfTx3IrFpZDbgg7\nhWdWBUpDtpIrPg/cPZ9xf22ZV1KEDUFxb13SeyEkX5023F3IbPZ9u2dYnXj5ZMarZzU7VeL2fkcZ\nBgYz46gpZeYfJbNm8FHAW+nCwXHZEfTDGul4YDUS8RCRnclb/hRlt583Ot8SgvbDOn73zhn/zntv\ncNIHvvpwwaJz/Ivn7/C5u2f80sef4Rd/5DY7VcFpO+Q8G/lzWT8D8Ad3TtgMnmXvqIzh/rKlIPFf\n/8aXeeF4RUiJd13Z4d/78JN85PFD9icl+5OKD9/a3xZD29f06iP+/qe+xktHK07bQQJIfWRIcN62\nnHeemKTztVsXfOTWPnt1ycN1x0vHK46a/m0Hel6MDb63400OOS3PMyRJiJbjuz9roaRwUekiVJA0\nMj8i/lIhc3n7ty3e1cVfRofiorfM8cyKwE49SFRJUAQszx/tcn3eUef7RB8inZNixygPKkoneZD8\ntpTNAymNC1jabibGO0EzWMiMrEIJSM/auM2as3nkb1SiDYqBUeMz6pPS1mjggrBexsyilN8TneUE\nqIvusNHZrZkVkylCn8YrT9aC0QlmVMiAP/Ld9sLUYE1EA30UcnozSMFjleQkFSZuRcZj0TjmAIqt\nXt4JvcVliPi291YEzEaEtaWSsVDvFa23nHWi7ymzc1UCOQUEe7m7/qbxolZURcIE4XcNOQ5BqYJS\nK+a14cas5HBWEKNh0QcWXeCzdzyffkO4M7WNzEpydAFsnOj+ei+6zhBVHu69+RxOg6JUf0JdS5+/\nP+PGbMEvfeQRr55d5VOvT1l1kdM2YbECp3uTCFhzFgqadcHVaWCvatg4y2oo6aPOnn+xAyoKyVTK\npaRUpmBMojZJyIQjcjslOZEoWHSFtOZMYLf0XJ96bs47em9YDZZNVpIv+5ohRGaljJAqCyEVgMJF\nS0qGSTFhv54wKQo6FzhpHcdtIASPC+2b8OM7VSIEGIqRaaBzJofMhWXDm3HbWRgc8k1MgsY8B7Xj\nXVcGrs4GDqaOnUqq8ZjpnI0znG8s512VYVgan2wu/qQfMGZyDMFlOq9iCOB8pI81fXizKBskQmGn\nklTX3QnsVjAvVd4RCD20thbnCz4P3Ni7xq39DSF6CYl0tYTCucRuOdAO0tbeqT2vnU24s6i430wo\nVaCyLYsuseosx5uEYhA2g5a29sREvIPppMKlJCRgH3FRiKmJixv/5ZvAD7q4CbBlbYDc4CxcjPt4\ns1Lsh3n8H19/yF/+wE3e84HH+TevHnNn0fLG+Zq/+8mv8usv3eev/9h7+MDNfWJKrHu/tRcPmdIL\nCKF3CEyt4cGqI8XI3/6tr/DGWQMoPvDYHn/lo0/z3hv7zEv7LYuYECL/y++/xD/5wmu8fLJiiMIt\nal2kcwOnjccnWZzmpXBursxKjpuBP7h7xtnGfc/v5ff6+G03UOXxBbztqtSQCxfUtmhJQQTDY3Eg\n520erOQXd/lqHB83ilHH8/1im6Fo+oLBW4Gj6YhOkmEWouLResLEOpSW7vAQoXEGydMxaBLbnO00\nAjpzfp2CKjt/fFBskNFPiJJwNyAjfu8RLpaRX0ITiVp2X0UuTsaxkLxi+QVKAyRPQBboIajckVH5\n907Z5cQ2okX+noiWjIgQW3dA5XtrIiqDy5lLI6E3JU2fFDrJ12aFh1Ls0f2YOD2IDuYiAyllAWz+\nVHIBNyItLgtoya9RcugsLbl4yl2UcXTVOOF3jZED4+bVqIsC7/I5oBWUhWJ/arkyKbk2n1AVJedd\n4qQZWPeBk6MBH0eHp8RgbFyic2mrLxs7/5Uds7YQ4nzeJIqu8lJH6u2f5n8sxzuqkAHNHz66xdeP\n4a9+EP7c0y2fek1z2lpCVn1cvEUXo6YBuLcxwJS9MrFbSYLvJojdbUu81MJTKFWisKLxcFHRelHH\nbwaFNprSJBFJRfHlp6QkzTRqDiYDe2Xg1u6GiQ1snOXOsubBuuKsFaDUEA2gKTTMS8tuLQjyR5vA\niycdQ9xACqgkFXxMo5LrYin1UaAUhZHCxGUHRIs4swYvtxCjRMi8Vw/MiiDtWiMFQ0Cx9oZ2WfNw\nXYGKqMQ2vDEgnRajIXgv5GI8Qwic9SLgdUHRxxIX9HYOLa9QbqATHdmpAgfTxLVZ4nAiIzdrEkV2\nWblkaIeCxhU4SoKrGVLJvBLHyrOHiZREGti5gtYLQbI0jt4ZXBioizUbp3jtfEbjKg5rT23F3nnU\nWLpw0cYMAbrw5sto0fTS0td51wXUCspJSYijfkD+O9opf1jdGpAz/DLgezzzx7Lxh/naAH7lK/f5\nd999wL//o8/y6Vcf8bm755y3A5997YSXjlb8wnsf4y995FmuzWv67GxKsHVluBCorOZ407FoO/6b\n33o+0381H725z1/5+DM8dThn9m2KmLN1x3/1m1/md1454o3zJgunE83gWQ2ezo8uIokVuLE75Xjd\n8dVHS5rh+4savOhDZq3E+Jfv8j2Xh+fb78/f/NbP+3Kxffnxl4/xfBmLmSzBydk8F/8OeQGNJrsX\ngzic8r+tfEGpPEonAb+phE/iCiozGwVGbpQWh05eWIeg0DpSaXmVVyei52i95NSNzsg+JgoiKiWC\nFst0ZcRoMZoTxmNbAOgw9le2HJbBKdqgGaLZ5uKldEFkH59GMvcAPMEKVyYEYb/I/VHhFDn3KW2B\nnii5J3pv0FoKt73a4wMCs/M6C2ZNNpnEPO5J2ypDpQueTQICF+TzUTwdcwaUQxP9WFBJN4gEDkXn\nxchgcsEjhHURi+/VhuvzCU8czDmcVfQ+sNg4TlvH8rSlCwnnHGuXWHZyvbhRn5cAneSziIqkFCla\n6XglMo1ZbYsnuBhZRsZ7p6LSxbc73f/Yj3dUIbNTGO4TaWPgf/piogCe2WspdMFJWzFEuY2LENhx\neV8x/v9yEIDPY3uep/bXzGzglbMJ99Y1q8ESgqJBoQNoJAogJoVLGoemIlDogC2kQ1PqkDNMxHr4\n6tmMIQozQNpqiUIrqsIicfSB2o5J2HC0CRxtPIUJGGSBV9vqXLHxVpTmY+WdLoIspfshhYvOtreZ\ndTxzteXadGBixQEz5LyNMXnbeXEhDT4zCJwhyP4OH+SmMIqdU97P+VDIKCpcoLK59M5alZjYyE6d\n2KtEwT8tZexmtUJrSUlNFAypxFBTVTN2qzmP7e5yc29OTI4v3FnwpQenvH66pHWCY75zfpZdPCVG\nT6itgJVKPcVXLTFt6IPiuNlnt57z7GFPZUUPc9bWzAe2uozWeVonDq/LZN1xIRjeuk3IiejjQqK5\n0C/UtcVdGk99J8fI9/u4GEK9+fhhdI/G45+9eMbxsuM//vmP89y1E3775Ye8eLziuBn43794h9+/\nc84vfewZfuKZa8wry2YQqzOIrmM9ON44XfH3PvV1jjcDldX8qdtX+GufeJbHdidMCsv+pORDN99c\nxHzp3gl/5ze+whfunnG8GbAqse4dvU9sMuDFKrgyK3hsd8bJpufTrx7xgwocf7s/5s19zO+uibr8\nWX+7x6m3PO6ttuyRfzP+rDedTwlIOUgQD9ulUzEkzaF17FRCyD5pLc1Q4pOij5qdMmzZIsveUNmU\nyd6w6uzWgeOiuPVmZWASFUOIDFHjg6ZL4hQyOlJ5hbMCmKtsyN2AHGyopOCJaGLKTiQl+pGdbFMO\n8SJjyEWVC8JM09XSJSJl3QqKQrutjrAPBucUQxYtp5xNpLPraacMMtJDKMA6i59vVR1WR9rs2moH\nIwnbyM+ZF2KxljVAipOxsy7i4MzDyV11q8TZJMHEZmvckGgcLxydpIjx/2XvTWMty87zvGcNezjj\nHWvoqu6unjiqKYmSRYqwIysRRNlRNMGw5MCxEQP5EwOxIiFGgMQIEttBEiRSggRIFASxEEFG/iRW\nGEuwZYWyRCmiJDISyW4OPXcNXcOtO55xD2vIj2/tc25VVze7yWZ3id0LqLr3nnvu2fvss/fa7/q+\ndzAoNNYatkrFZl+n+IUpTXvEjWOf8q4iBJFylwEoRB0d07a7Kr2QhYUo3WVEWSWcqCZKkK/3evW7\nfhYYFo6+9RQmikIWGNvydc7mt3bcV0Dm3LDl1knFSZSKRkvOsyc5W9bw3efhygncmvtUnbmbvgmy\ncjEctZr6EGaVZbvfslG2fPfgmGmjuTbtcbLMRYOfIuN71mNpU4Ky5rCynNQiXzOIGiGk2IDgk8ui\nUSuUbzTkDnqZZ6PnGeSexkdmdWDhpPRYtdL+UQoMYpSESg7CviOf6SQnFCb5Rt7yxLa0hrZ7LYPM\nU2Zr50sfxWly2lhOlob9JmdWGxZOPGK6oUDUPlGtgtVcalHdOZ3KUR1YGBaB7SIy7ndRC132hxgw\nKG3JTUluSoqsx6joMy5H7PR7oA0vHc65ejjlmf1jjp69SdVWxOiwypEnI6deOvucr5jWlv2ZZtQ7\noLSe1ilGec25cc2yMXx1f4OI5aHxHGvEXj3EAdulZafn8UrT+kjVuhQhIZ9B6wJNiLStZ+m88I5O\nyVi7ISujO03KFtWdqUcdyNGAtgYXfEr9fefAxDvdZvr9vSWT3/gs/9Vf/Vd5ZGfI567u89kX97gx\nq3huf8Iv/M7T/LkXdvkb3/c4Fzf6FEYqZy5E/vClW/zK51/kpHL0c8tfeOwMf+27H2V7UEqO1V0g\nJsbI//HFy/yvf/gcX9s7pnZiC3+8bBLfQT6fQaY5N+5ztGz44vXj+4ZfdPe4VwXlzTz/tZ7zWq/7\nxs4VqbxqLQu6EEOqdmoO64yAeJaMc0mrlpueoXVmNTcVmaJuNJkWcJHbuJKwT5Oy7OOLAAAgAElE\nQVRtvUkLuc4AzqiwSqV2wVBj0C5xaYwmM+IQbJPjuU5/p5PkOtApAIXLohIHJbcB7RV1AiWNVywx\nq8pHnsIatTKpNaOEh1IECq+pvaRsNwFaxG23dqLclGRvTwyaRdAsXYHVYnZ3YVDBEE7qjFmdZNy1\nkIRL6xnlXmTmyclXgjaljSi8MtnfwohQxGrhAy1bnaIDMoxWbJWRUeEhehatY9nCtWOpiuTGiV9Z\n8n3p2cC4CKsqyl1T//ouGmHpRBY+re0qmwlg07TkRvgyy9awdBbnVXqeYZB5tnsNG6Vn992atbR0\nns1R4HwOexPFUTrpj5zjj68HLg1q3rfjuDHpi7trVAnU3F10VyxCTjPz1EmvNy49Hzs755O9Y27P\nLF+4OeLFoyEntWHeZoSYSX8wxlVcwGnZd5YiCjIDJo9YJY6MilQC9FIJmVYmtYQ8hdES766kJbR0\nhrrVtLHzNpEutyEyzB0745atomVcenq5eNUYZCIodEASiMVIaVlblk5z3Ny5nw4xjOpORkn6vpPH\n0r07C2RWkretFi8Bo6U1VVhxJN7oWc4MCs4NCx4YD3lgvMlWf4TSfZat5mRR88WbJ7x4OOV4eZOT\n5ZKqbZDip0s5VTEF6Sk8UNURZTQ6wYZZk3O4zNjuN+TaUVea3dGcR7ZrTmrDn94YUznFwxsnLFq4\nObPsL3IU4jaqlCKzSiY8LT1caxRWG8pCsqeM8kSlk2mgODw33gvfxwcWTUPVruWu95r0V20CAPfq\n9kRnGWA1K4DzbhhfOg78zf/l0/zav/cjPLjR5/HtMZ+9fIsvvHLEtHZ85sVbPLM/5Uc/dIEf/dBF\nAD71pcv8k6eusmgdG2XGDzx+lp/+6CNslgXWvBrELKqGX/zMV/nU05e5fLQg+sCi9jSsG82Fgp1+\nwaRxPHswe839fW98vaGog8EEaW0XOlAHae0f1xnOQy8LDIs2VaottRfCZ24CwUVQkYUTmwfrE68F\nyFVAm4CKmpBaIvkKlIgDe+ssVSApNBVtNEQXwCRCfBTSbWakUqx053UVV5VyIQ6nRGmxMsIFJ2HE\njbSeQpBMN63FhDRLRFtxMderapCPnjZoapd8VoKmidA00jrXKiaz1CC+Lo3lcJGTmciocJwd1DTe\ncJKqNJPaMqlFRVSkOAGrO46LVLG6zniMGqUziJpBodnoiQ3Hso2cVJErx2IKG5F9sMZDUDiU2HoI\nw0fuXTok/mRYKc2UvmtBF+XzXz8WU9ssKaBCR+xVKTgzJv8ZK12IqIAhmQo80Lf8tce+hafpqXFf\nAZkzA8+FzUhpHaNLgWdvHfG5m2MqJ8X+y/Me2dyxkzcoa1k6S4zS3vF47gQzUvo7rEoq7zicZzy7\n36OXSb95VmVkJgcPVVgDIYEXMZHY5OYfU/83eOHNWB/JjcVpMTgySkqeXTZGGxQ+iPFdYA0iFEKE\n3erXjLKAMRGigA4SQ9waCUULteIkWhonpbylE1SvVZe9JCuFOrWeuv6sT6VMC0mGqBhkmkJHrFEo\n5VMSt4RQ9vLIVqk4M8g4MxDDo8oJ+XfRGJZOM2stzcRybQKLdkbrj2h8iw813tWgPI13NC5QeylT\nxmTfbbXkVOmYPHCCeNvgwur4vnxs2O41zJvAfmu5MK7Is8hzByVfujlGq8j5YY0LsD+3TJoMFaU8\n5hEuST9q4T7ETtWVjvkp8ptWKnGCFEbJv8woSmPY7A1ABTQWHzpCsIQ/ik9PvMMY7V6jgzavo+D9\nth23gb/4P/wmf/If/Bts9HIubfd53+6I339xj5cOF1w/WfCrn3+RP7qyD8D/+aUrLJ1nu5fzw++/\nwE9+58OMygytFFv9nCfPr0HMC/sT/sFvfpHfe/kW+5MlrRcPjdPKnFFhWDSe6/M3mB773vg6Q5RL\nwXssgUxBGz1gmLmMJqSKghE3W0lOlhtdbnwKsZQomBAljBcQekDUKyJrBNDi19UpeYyRZGwXAo3T\nhCDE4DYYFk7m2zxVZvJkHCeLyqTi0QrlhWdCEhh0QEf8YxRKBRqED9N4qKIlNGrV4jepsoMyay5R\nx0VRkRgkZ69p9aoFI+0iySkKTjZ6XGWotL+58ZRGPIyqVKmfNwqqDIP42kgLTTK0+pnBakOIgdoH\nbs8CtRORgtJrPo1P3mCLKE3B7r2Kqim5q7s1OBEbkdT6S2qxLs9vVaNXp3odqXptLegQJdLBI9mB\nifqjlRzXUgt5eukNC/8u5chcGMvJu7dYMqsjxmiePLPgyrFhf9knoGjJuNkYdrOKrbFjUuVieIT0\nW0+rmoTdr5i0GZNWDqr4d0i1Y1A0XNySNkPjU/KzN6kvuoYynUNlR2jzMdIE6dFqTLqBvVq9Y5Os\nsZ+L1HlcOrZKx6hoGWRiMLdsNMdVxsEyY9ZYbs9zWd0EKZt27q+dL4wLwpDvcoqk0QPGKAZWU+aa\nzHSGS57NnmOn79jtB7b6mu2+kCdDyKl9xv5CMakE9VcxJ4ac3fGAM9FxezZjb77glZMjjhc1y1aq\nGC6IpDZGWUUIc16C9goV0Fbhg0uZHJxSWolhX/ARpfOVFPHxbbklzWrDB8/MGOWOg2XGU7dGlBnS\nfw+a/UV2R8vsNNmxasOpo5+UBnedXx6g8eSaO5LFgZTEK6BYaUWmhQRtjGZoDcMYoJO8xwjaSHhi\n64VD82ZP9m/D4YHv+m9+naf+7o8xKjPOj/s8sjPmc1du84cv32Z/0fDU9WNAqq/nhyV/6UMX+PEn\nH6LM5Pq8G8T8869c47/89FM8feOIWS3MuNMj1wIcT+r3PoFvxYgYWlIFV8liJKJpgsWElo3CMy5a\n5rWhdhYfFf1MBBeZkQpJ1RpSVigKcFEWfGvC8SlIqqTd1JFfO3NBCWqMkOa9pV/9RapBp2WoXgsR\npE2TiKjyZuhSp7tcNam+3t0CeXtaIl0CukbjtIBzFSFUJKuIiF85C2lUombfSabgVY+99vbkWHQq\nwtUdqwMtp+bENajpji0YI3vhfExuxuvqdWT9WXXbKu+ySfhWjvsKyFw9dsxdLX4PRculzUAv93zv\ng7A/P+F3XtrmeFng0ey3PY5OAluF2G+3zt5FfJRmxt0fsSNQtXLSzxtDCGJoNMpFL9+4wLzSTJND\nok9ErDtvm2vQ4tPvFFLlGBUtO71WYu9tKt+lj7oNhuPasL+wSYIokewxyg2y8Yo6IfWuTLc+jdKq\nAiiMprRaqg7JoXJYwG7fc37oubgB50aWc6OSrX6OUUZ6wCbHqBKjBxjdw5oemsDzBxO+9MptXjw8\nZm9WMaubpN6RfTcElA4URhw6fZBgsoimDULWq+rILE0eWisiGTFqfBsx1hKCJiiD89AEMQcskyHe\nrA4sG3hke05mA9dnBV/bG7JRBjZ6AecN+4sM0JRWQjuVSjyX1AvqrBMjAqzCqYuqu8C6s6Bj5985\nGZxuKN1JgyzuxqiAxiXfCfEmiXTcpfRc72nRQkJ+t/SY0vjIf/1P+ezf+Us8sj1kkGdc2hzw+O4G\nn728x1PXj5gBD28M+NEPX+BHPnSRMssIMbLVz/nI+S20VrSt5xd/98v88uee4+rBguquY9hd2a8i\nb783vgXDQAwMMocxkabVzLxl6XJ6tmKYiwvt4TIyqy2HS8uiVZRWKtbOKyonF5AxkUJHvBP3dB/F\n2C4mI1Jx1FWstMRRZsA8tTB8EjTIx65W/3f2/yYpU6UKE1fBuJ0MeuUArMT8rrPk6MIjXfJacWG9\ngNSnpM1adaEIpG3Ldy7Jqb2T9vma8CBVIUFRnSeNEnWRIbVmPJWDOnlvdWaY3XvrFItddVn2aQ0k\n5Hmn20HrY3N6xNV/Ml5lYfT15qk3uVaI93Dk/1aN+wrILJrIn3/smEmdsWykXaJbhdKG3V6fn/nu\nAX98WfGlm5EmyHHdP1VJlroMxDtuU93XtcdrSyS0kcxp6kyxaBSFgaikJymy4xSY+KoTQm5ynaFT\nbsVASevuRFdM6gznPYO8RSlJLl20JgWsdcqiLgupC+Vat7Y0UiodWMlHiUkxo7VYmY8KGPdgp/Rs\n9jTb/UxAhi3oZ4rC5vSLHGP6aD1kXA4ZlD1i8Dy7f8iVw2OuT65wspwzqRoaF4QbFCI6OnItrSEf\nVLpRSDCn9G+FBF2YKCm+rSgHuuRV76M05lS6tBRkrSfXgczUDPPIMFf0LAzLnN8GdgcF584eYbXj\nxqzgpaMRZ4cS1DatNQfzLMHScOfHkD5epVQ6kde+MIY7r8vTplyvdd2eXj+chjX1PW+Wp17llJta\nrtMkFECpIEF1HRzVHaQVj6Bv5xrCJ/77f85v/+1PcnZQsFFmbPYLHt7q8/6dEb8E/Mz3PsoPPHaW\nwhpciGz3C548v4nWihvHC37+//5jfutrr3B074P/ruEf3S/DozlpM0wbZA5QHheldT+txKVXa5kT\nAiJyWLiIQWNT+wOgalNemoWB8kRiWrilVOckwDBECDGZiJJa1anlE+U6D4kc3IkWgo+Y5DBrVaTI\nIipIS8ToDtAIb0Z4hCKt7qgBQ+1Fnh5U4haq5BEjVWVZJEW0cuQJpKm0DyLiEA2oC3rlLdP4tbTa\nGLG/EFAlFV6tpUW2PfAMM0dEsXCaRbP2pyHK/q+4LUlg0vFqZM7rwoIFrFndSbID3iuqRE/oFKli\noCqvR7on+VSxkkq7WpGQrY4r0z+VjAKTEj29liyqM6UxRkBe7WDj3Zq19MLxmM/+XsGTuw0//b2X\nUOoMe4vIjcmcRV1TTxtcnHG233J74dMNZn176nqZ8Y5KTPe1UznJ33gMPkZoNMns+1X7o1Wkbxq2\n+w2GyMwZFo2l9V1YmMZFIVDptHqoGmiC5TBmBHprnByTV8Gpao5GyHSDzGCNTie7ThEJio1SMSrE\nXE8cJSPWyOsZrehZy6CwoCzaFGSmwJgSYyyH84bn9xdMlsfMmoqqbVg6ly7iiPcSG6C0SqsG2S2t\nDeMeEBXeS3uobgPTVjFvVJJ5S9+69cK7ybW8j1ER5Mhql0LUBNQYJeZZ/Tynl3V9U03j5Pvzozmg\nuTEfsT8f8sEzkXGR0cY+TSh4fBc5tl7aOMs2qY/SP59yZIKPuCjhdyEIPyjECPpu9tR6dOnBp/99\nM+OOCsHd9da7SjP3Kry+0cXRn4Xxr/2P/4JP/7s/TJkZHt0ZMigsD270+SXgBx8/R24NrQ93gJjP\nPH+dn/snn+MLtybv9O6/N141JCdofRpHxAgjo/UeuzLM6eZSjSctwtIZHRBOiveRMlP0ssig59Ha\n0bSwDJbGSdVXvFuCEFjRqT2d2r9KFpDiYm4EbKBTblFaTChFloCPJchcpzvX33WlPKASuVajlQCE\nYVLmtJmAgMatwYkPmmWbwkKt7KPW0vZahVhqqQZpHWhaebx2HV2hc2MXx90YFItWc6gycisp1eOi\nxWeaymuqVqfgR5vUWnEFpDSRzHb+WGKQ10m5O2+gXEdGRWCr73A+UrWWpTeJpiAgqbDCnVQqgcOw\nbi8ZBYVR5EkYUlipzuep9W61cKAKo+jnmkFuGeUZ273e23Nacp8BmVJbPIEv7md89bf2+fCZCRc2\nDUeLJftzR+slk2VQBPq55nih2K/MHYTadd7s3RUZWN/O7tVNjBg8o0ICJy9s1Dw4rtnpt8lyWgIf\nb0wLnj/ocWNaMG8lzXqZeBt3lvfUHdvSSIvCZgarkrJGSyx9aRXjnqafQT+LbPcDG6WcSLmJ0krK\nCkLUHCw1k0oTMeTGEtE0bcv+vKaqp0yalmnd4nwCLCHSeuSiNhIqlxkpbZa5dF11Yq4H76m9ZD0t\nW1i0Ba2Xi6hqJFQss1DmEWjTUY6rScoqKSdv9C3bfcM477N0huM6ChE56FUmzLDQyUgLJpVhUg/w\n9LiwCaU1VC4XTlAM6xqqEu6KLcwpO/RuL1LacPJa6ECtUpHaST5I6504+vpAG8CHkNK+Q8qxklZV\nVxo+PbqAwNdTNb3Z8W7oivzQ//Rb/Pbf/iRXjuacGRZs9cYAFJmhcWsQA5H/4re+yC98+imO7ibC\nvDfuo9E1O077TAsJ2EUnxGAEsPhV0yee8kGSxzzQtJFpq8lIiddWDPC0Fc+TxhtaJ07jSgkYAals\nhiAtqeQul2b2gEOneBRN04hzsAasjeQGciPVc6JwZbq3JGIotZ7TlMyLUrERJadSgcYpnFI4D1VQ\n0IhFv1VC8l1VLVSq+CT/Lx8VNkSc17jUInNxLT93DoiBWWWZ2GTeqqXqT6oYOaeok5Bk1sr8p9Ii\nrWuPGR3p6HwRteIChXjnPU8chk+zixIBWyuMkTiMTCusteTJJyxTlsxk7AwKzg4KLmz2OTcq2ChN\nCoUF4qkoiRBguXwLz73XHvcVkPmxD4/5zecOePYw0gTPF24t+eKtwHZWszWUvmmIOuUIQZFHzhrH\n4dzQvAqk3Kst1I2OAxHSRyhSsp2yYlQIE3/RGm7Nc2a1yPAWjeFgmUn/t8mIquv/rTkzp29uhRJX\nUW20pFAjp0tuFL1c07eRMotsFJLtVBhHL9MpBE4CLgdFwc6wx06/T2YshYbeZMENNeXq0YTnJw37\nc7dysfVB9sJoRT+TsLRMy6qHqCRIzkfaENHOcLwkEYiF7Fy1ohqxNhLbliJvGRYiERTr8bjGiKmd\no3RBiBkKAR6VV0wbMeiblZadgeVjDxVsDBr2j445WEw5qioWrWc/ER+eP4hMqoZB3rA31cyanBjl\nAjC6S9uW/wR4qcTFYcXJ0amtJ5+lTHYq9ZJzFSlLiw/ScOokhtABX1agaNX6CYHWRZat0O06Z+Uu\nOygkcNU5WYb1y9zx9b0hlZk/+vf/MrdnNXlyRmtcYGdQ8B3nNjlZ1vybv/Lb/NbzB+/wnr433vjo\nwEw3x3rA4u7ZML1Xe35NJW2AxkW0i+QK8kzUO7nxNK3c8CXoUYuqKEqMgXBoUmtJSZCh0REdYuLR\niKFfALyTbVitkheNVC+UVpgI6M4na1056tZQQYklf2kDPROTiEGvYhF8gDYqlNe0SEXHmrBqYQVx\nzCG38p58SLEzXgCdjxLu25WOQ4wEB0ErgjYYE1IatsDA2ot7cOtVatmtDfaMjigDlrjiCmVap9Z2\nMvBDUVhLP0UXjHsGHTSzxrB0EZMW2YPCsNO3bJUGbTSLRhbHIkCpuHpcc7gw9DIlgg9jkh9OZNl4\nCIEfObv1Fp5zrz3uKyDzuy/uM2lqHhx59meKZcyIaA7aHkdHgbMlRB1YJvfZ1ssJ/mrW+enRgZbT\nP6+rNZEWq0WpdHvZY1J7epnDUyB+aF16qVr5sYi0ULNdQFHmeC8JoPPWraS3bYToAgMjoZfDUmO0\n3ByF+S+IN0bFrLH4LGOQF+z0C4ZlRt06bk4rnr19yKy+Re1aWi+1gtZFvHQz6eWGflRpJZKqBlHC\n6EwIEmEQ5GKrg6V2SvwQQgpXUxGCJzeBUR4ospRk25OLQCEXtfMaVEamC4ZFST8rqKOmMJZhDsNC\nMcwjG4XnytExN6ZzJnXLK4eBV448KsnLC+PpZZGNPLJMYNAHzbmRY+kMt6aWEN+aWsVrgYmVrfbq\nv9NTrUortu73Ao5i90BMgEkp6Q2TJJeJExRVBB9kYrYWHwPeC/DpSvJdgendUJHpxsf/u3/Gi//x\nT3H5aA6wAjG/+/x1fuJ//m3m7/D+vTe+kXG6MkP6XvP6Z/ZrE0ADUEWoGrjz6l1/Ly7sXb11/Tud\n6sKn28cdXzKufpJFG0GL3lRLgnXX4pcOe+dpoxA595pQLLYNYI0sQkvrVzyYNpDStSVMsQ3yXAmB\nlBaTOPhqMQk0kcJKpcZ1oARJy05kTbyX8FvjDLXpwIKISkpDmlOsZExFLeHASlSrpYV+phkUht1B\nzpl+xm4/p19YMmtYNIFZI4KOfqYZ5JqeVfjguXJScfmw4epxy4sHAZ/uI9ZAYaRVWDvx4nJBzC0D\nKZsqeftkNnK2zN6dQKYr3VVYBqViW2uOK83cy830ZvVabaFuRDQu3eKFOGtToyAi/JSu7LWu3uTU\nqf4WosJ5y9yLQgbkssuMKIV2epYzw4LdYclGr0z22BJ4VrmW48WS/VnFrUXLrBbPtKoN3PAwbDwX\nNjIubWX4KGheq3TZxMB02XDlaCknV0gMnCRDdEGalUYrioSUN3PFMDNEJZWWGMUNd1Jrpi20VWTi\nDC4GIW/pSKZaDJ6BCZjiVCUCwfQ+yt+HIKXE3FgGWclmP6fMMvq5YpiLHHqrFyiyOZPFlGWzwPuW\n2dIxWUi7Zrev2MgVJ7XlaGk5XmbJDyjHaEMvyygk7ITvfWiT3AyIqkArTfSOJkDVepz3NOliab1b\nc2I8IgOMAY+QlaPzq6qJ93L8HWvQ0KmZQlyb23VEvNPn0Kt92+91pkkp+97TbRre3f3IanR1vNMk\n5Nfe2rfHeOw//zU++9c/AsB3nNvk3/7fPs0/fvrGO7xX741vbhjuDFfoRBWvBWa+EYq7Wl0jp0HJ\n6dGZL7zu9XjH84Uft+Lpv/al+hqjA1Dyfccn6TzBTiuKuufZFAljdFeNSswfxbrSEdZp2fKCMkdG\nb1BRWlg212z3DOfGGQ+OC8ZlRghwVAVuzxqOl56F8xAjrY/cnLbcnjUYPaMwGq3kM3AhUreBykPt\nk0JVSzRCpmWf6hZmjfCaYlSExAmVFO7Uwoo6vQdF5aT6b5yir1/vXv3WjvsKyOS2YKfXIypDllkO\nZhVz30Xn3U2NdBR4HtxsGRaOnZ5jo9eiiexNMr5yMGBSZ/hoVjecO9VMsL4g7r1KsArGuWVQ5BKY\npjVKaQyOvl3ywFizWbYiP8wVpVEcLHq8cJDx/EHg6rFjb+ZZtpFFCy/sO64cOTZKzbiILNvAcRUF\n0QZxqIwRQeRKkRlDaWCnD1t9iw9I2JdTHFWKvRkpLK0gBk+IHpRDa0mK3lCRRS0VLJ9Y9w2SzaS9\nTA8hmShZrSlsxmZhyHO9Uif1MsdAL7C2JVc1OjS0bcO+Cyn1VGSLjdPMW7G0njcZgQKlCnKd0bOa\n7UGg9g1NG2iDo3KeLgHgT15xuDAFpqtj/+pJaP2ItIbu8QxlQIyVsfbVJ7cYV91ZfYFUoQ7JdyiI\nu2/X65VJJa6k1Z23kDz39P68OcLw6ed9O4OXu8cn/vFTAGR/91ff4T15b7x1oxNSdKigg+ffyBl+\n9xzdvV6kTGZxklMn3PlOXdTJpMXQLiTqjF5LtWNc8WJCZMXfOb1NixjNWZ14f2mykPTrdWVmfZ1L\nS0hp6No7INVvIdyC92r1/E5erYO0d6y4/61bOZmltMI3iQqCj1QuUDnx74pIpf9wGTmpIy8fNfyp\nDRhdJfWSwqeA1jbFp/gUgtu9HSE5S/XYGFKcg7TYuvcYkRZRpsXQNDOBnhEw00aTjF+BVqo0mY6U\nRqXb1hrUtf61q29v9bivgMyl7TP84O4Gn7u8x7986XS/3DPMPGWsmPicJlpA0WA4Wnoe25ozKAzz\nxhKIOGXY7DkmVUcPk6yQNzpKI2zyEGHaOIzxXBwZerkmM01qPWhynXF+VPDgZo9hYehnBf28h9WW\nl/aWfOprB/zR5QnXpzXHS6icWPQdJv5TpiN9qxjkiph8YcpMs1FojI7MG82iiewv4NZcrsUy05Qa\nPIHGeVAepTyKSOhibX3Xx5SVSmak9Fg1ChelEmS05JcMCkU/V2yXnq1+Q2FqjKoBhwueeaOYe0Vd\niWtnCIY2DJLnghF1gc6xJmdcGJQOZNYRCWjVYrXEEBcaBjmU44yNsuTcaMwoL/iHwN/6+Pul4uJ8\nSqIm5RiJm24IAvZcEO6KD0JiltKqPBbCGnj4kKThSbYeYkiOwiQjP4lNiAmMrIi+dL/vwtxOvQYd\nsVjGafByx3erldjrQTEBTlJJI8keO2B0KpwyxFOT5qtf473x3rh/xukaI9x5pr6Zlfnp564NTiOK\nZYBlEzBAz0pLt8gCmReOn48yR7mok3LIYzWEqFPlWl7fIKaWUcVVYCMIuFEhEruIAxvIk9QZpU5x\naJKTTAIP0m5eE4QzHUTRk16zDRYfjThSe3EKFu4KlFZUQCEGXGhovPhqdVWexkW8F98uF4TTuCIl\nx3UAcGYQ37KkIM2NVHccCueEhxNSS05sQhATUCVgxiSir0r7HKLcAxsHWus1+VmBjmJJ4SIsHbLw\n7s4AJdQLjCaGN37P/WbHfQVknrt8m197+jIPbjT8+YcrHhg25MYzqS0HC3G8zc2C+TLw7OGQOloO\n64LfeukcHdlsaCUxd5QpHj9rOThZcNyEN5V8W3nYKuSDmbcwrSNXg+N9ZywPbFg0FmM0B0tLfiLq\npdZ5TqpDXpksuTlpOFo66jaydFL0zK2QUH0E78zq4lk6uVy3eoaNQrMMhlszSW8uLAyySGkC0yZS\nO8+0ggkRHSXBVetIm/hCPhqiF5JbG4TbYY2iZxWjnuKDZxQXNiKD3DNdzNhbwKyJNC5ytITjpTDv\nNWC0pvU2Ja9qWmdoYwZKkxux9w9RclPEa6BBK7kwc6sYZDk7/T5nxyMe3hjzgXPbfPTiLjuj4R3H\n+h8Cf/8vf89bcPa8PaNpGpoGGhxNG2gihBConKd2ntoH2jaIVNwHWpfCK0NIP4dkIhhwLtJ6LxOU\nCzgEpDVO+tIuCqBz/hSg8/K4KK5iAm3ys48B56H1kjLdxiRJ95EmBJrW0yQw6GLE+Y54GFYlbbgr\nU+q98d54w6Orbr8VDklrHoxRURSJyQfMI5WK0gS2S0eZJe2NCtStZlJlLL3BB4NWgZ71lFmLQkxO\nW69TJlAkJp2xD6xUPB5R+zSNplJJ2kxIlRezhmipeKSjSqCgAxYi2lAxJBAUicHjI7QxiIdU8m3p\n/FiUEoKwWTkbd60lhVbCUdFGAjRbl+4jXcVJgWuR+BwlCrDcSkp2XwM5iZCspR2f7Cm60bkmxxSl\nUCRgIy7AXSK3VHG0jmgDQyPPcUGxdJraddV9OS6WSKnfPvnhfQVkysEtfsIGzs4AACAASURBVO47\nj9eyOATxbrYS4/78YZ/LR30mtUVFSx+o6S4beSu1i8yrmsrn8ltj2ehFqrql8nfms3TIeOU7cGoc\n1VAA5zc1syqw9PCVW56b08jFDakOHC0b5jU4HxJDPFLaKDLqHHaGho2iYLufY1XG5ePA1RPHSSXk\n4Mav207zxnNTe/pZy1YpgGLRRKaVyP80HkKn2BKUv6amCZLOFPTzyDAXCffuQFFax7JxhCiVpZNF\nYFaLTHCzFGn33jxPKa1CBo5IC63LztAWskyIy70sUFq1Osm1KimsFYVVv8+j22O+58FtnjizycNb\nAzZ6+Yow9+0w8jwnzwHyN/23ziUAk/41LiTuT1hVnOR7ATw+RnEGTqCm9SHJ6eWx1id1VQI/bQjU\nqaoVUtWqTWRjtwIvaVte8qOqRgBX7T2tl9dunEs+PQLAZFvrfy49PmvfgzvvjXuNtwLMrOcMUeZ0\nFpfStnIYZt4wn1gyAoPcsT0QRdKZUQMxMKlypo14ptQpSXqct5TWs/QSd9J4TYgBQ0j0OJFvr1xm\notwjch0o80jPtoxyR2HF5M8qqcyE5NprlCwsxcBU2jhGSZijURCVom4U89YyawyLVq/aYzFVfo1C\ngm+iIii1cg3uWkTSrlIr/l8X29C1cqZ38H0S/Ota6l2rXK35gV0zrxM/CCgUFZSKpExA+WVHz1DE\nFDIsQcOFJs0vihapIM3atw9e3FdA5vJ0lz/8f0dkON4/mNPkJbdPckZ5xpmtDXZGBWdGituzmqNl\nTeUDWeuZNn4FRFrgqIXcN1zoW3Y3Ss4MM3pZZDKf8+Jxw95snU6cKSnDiS/AnftTA5ePA0MjH/os\nRCZNw5XjhkxFhjnkGVgtYYSDXHNxnHNhXHJm2KOX59KaaaRU+PC2pyzmXD1YclgFlq0QpnwQINOG\nyHGtOKrllOnM/QJCku2GJjDIApkJWJVyjqyiUA5rPZn2lMZTt2LQF1A0TjFrJVJ+3mpcchnW2tLP\npO1zph/wxFNlyI4kq+jZnHGRszvscWbY5yMXdvn+R87x8OaAMtPcnFRcPppxY7Lk2qTmyslNtFZs\nljkPbfa5tDXkwc0+uX37+qZvZIQ7QAWrLCmfKijdTdsFMdprToEIl27w0g5bg5Hu++7xDnzcPTq5\nd0hVHWlhdTJvUjss4oKn9bzqdbvPKHbtswhtCMQgqjQf168VEyiS1O+wqsJ0qo7VtnxIfxtw3suK\nzEVuL5tX7f83Mv5iBv8S8L/wN173M/nylZv8vf/rd/j1q9/O/sffzuNuEnA3TrcbvvkFTiTx/pqc\no+b0o93X9fbnreGwylITzEuLSesEj+yqXUNM3lqn3HJrpZk1EaMtuckprFR6BrlfZeJlOuDRKCXV\nEJDrWStWip7SOs72A5mpiBGWTnNSWU4qATbLVriQWokqapB7+tahdJJsB3F6V4hJKkRaZ5i38q9q\nxUQvpoqTVZFe7ujbwCATQ9WYQJFLjr+i/k2GrTFVVmL3+UQM4i0j61GdWuFG4iJSi6lBPNG0VfRi\nyo1Sbx+8UPF04/8dGnVd8/TTT/MTn/oqN+Z37s4AePTsEGMMVeupnE+TrXiiuCA3/NzCcuk5/ee5\nhg+cMXzycUuvzNjuKWY1fOGVms+/0nJcxaThF9M4F0+vI159EeYmErwSUmmEQQYfONtjd1BQeyWS\ntCAs9FGB2PEXMMwSP4NI4zwHs5orx45bM5i1irrttna3942EsGd4BmWgn4nLb894RoXkUBE8s1Yz\nqQ1Vo6lOOTa2KTsEdHJnhDKHXCupaSKVlVwr8kwxyg27wx4PbQ65sDFkkPcpbUYTJJBz2Xo6nxqt\nFEUCJYPc8uBmn0e2h5wdljQucPV4zpXjBbemy9VN3GrNuXHJpc2BPHfUE/Ooe5yCLh3L5q7qhQ/p\nhnyP6kUHLE5XL5wPd4AJH7uKhnBUfPjGTv81/2bN1YkJHHc96O74dkCDuFZLdMBDqlWycosxmWkl\nd2IXpf0UkfcUUuuoMzoEhYtyTnUtqxg7snK3LTH7czL7rPxvuuPVtbuaGAnp+Lx0tPiGjsnXGx14\nea3P/BsZznuuHc754iu3+fWnL/P/fO0Vrrw9HlzvjTc0uioKhF/8W+if/+W3cbuJr5G+f2OQ+G5w\ndbc45I1uu9MlddyRsJoXxJOr49KIb5lCrl0XjdAEgqKNnT9WoNSRXhYorJcKUFDJGiKm+AJRubbB\nMq81S6dZtpommATMIr3Ms1E4zvQ8/aLz0ZH5oE4Ows4raifbBgF0rROOTGEDozwyyJOnjRK5+CLF\nCSkURSa+Y/0CzvYy/vpjH+XJJ5+kKIo3eOy+sXFfVWTODAbcmM/ueGwOPL0nj+XA2Y0eW72MnYHm\n/Mhypq9Yesf+3OGCwIGv3ai4fCJ28V++5XnhwPN9Dyo++cQQpQznNy0PL5Ys9iqOlpEuu/jVU2tH\nYJPReHhglBEjnFQtSwdfurFkXCx5aFPTyzMh3QY4WUQmFegYmbZefGYaOF4GySQ6deg1XhC7lsTY\nng4EBY1bu1S2HuapzN9oOKosPma4AMsmBZYFI06WRozjrIK8ABWFn5NbRd8qdvoZZ0Yl58dDNsse\n54ZDtnoFCw+9zDAqM3b7BQ9uDdjt54QYeeFgxvO3pxws6tXNO0YxT5o3jmf2JjyzNxF/nX7Bw5sD\nvu+hHfq5YVK1XD6ac/V4wfWTBa8cL/iDl2/Tz+UY/PIfP/91qxff7BAPCFEHaKXIrILkA6P1Oswt\nIkGQq88mAZLYEYsTL6UDHYSYpIcSfKdYk429jyvAEIKADnwgJHlmIK4A27rKwqrSotJzBIAIkPEB\nHOJO7BNxWUClpm+MKNE6gBKESxC9AK+uquSCVJm+mJKov9XjqZ/9IT788IVvyWtbY3jkzJhHzoz5\nie9+/DWfFxPguzlZ8PztCU/fOOIPXt7jyzcOuXxUsXivS/YtGneTgL+Vo2uSsPrqU62lSORghVQf\nm6hSAKVO13yKQ1CRqCWGQKz6Q1IprtswgU4O3jmIi2eMVkFMOqPMwTGsJdnxVEJOjCpx2SwLktIn\n7Xr3O5CFdRfb+9rj9cBVdzxkw9PGsDfPeY6IRWIUChvX4cZaiMMxbbeTgatEfFHK8EpSh5U20Msk\nLLSw7SpuYVIL/yjTkelAw2Nv/NP7ZsZ9BWSqtmWztCilOFq+mijUADdOljz2iOHvfGKD85sWpTwa\ny6TWPH3T88xeYLMc88Gl50+uzLmxhIWD33254jMvL+mZwMAGRn3hksSgpdUSTepxQncfvRdV6ca0\nZWCgX4BvYRmEJDtrAtu9hsIKOXjewsKdvrBEnz8qHedzx3bfsVE4xqWjtLA/V+wtSpaNZuHkrC90\noPJKAthaLVEMTfd6cnpnWpGbyCgTYm9mFJlWKQrAUmQZvSxno8wZ90oyY7DGsDsouLTV58PnN3l4\nc4DRmv15xY3JkpNly1dunfDM7QnjMmOnX/Dw1oAf+eAFYoy8cCig5jCBGolZMGRGE0LkeNFwc7Ik\n05qNXs5WP+fiuM8Tu2OMiuzPa64eL3jlRFb+tfNJ/m3ItMZoRWakimS1GDwZnRx7ESAiF1Zy+FVd\nEFtiz6dDpFdrIhFN106ymnwHCEI4BTi6jKYgvJKwvvH72DlnCsm5A7wh+gS8EoG2+76reiBqqW7o\n5BDaGQII8S6uWlXxlGleSJOH1ZrMikOzSb4MMXFfXKpI1t5Tt55l7albqcyEDrh4aIPnj68evuHr\n8K0a37Mz5HP/0U+97du911BKUWSWSztjLu2M+aEPPsjPvsZzQ4hUrWNvuuDy0YIXDiY8c3PC09f3\neW5/wt6sYfpex+tNjI7jwqmvb3bcvczsflanfr5bJSPbdUSck58yEDM7I3OJd5EaMZ9rosFpsEGM\n3yCmeaK7qUtbJrEI8ajVFkNqQ2UkN10VyTJxEg6JjHKajwKs7CA6hZJWXSTC+h34gFT7UwsoBFFZ\ndcGaNpGAVRTxUKeiipBymQSEOSeL3cYLD0gCi6FpIraNycSui3phZTURgk4VIFaV3s6jLa4xkszB\nrNWySsHtQcbbNe4rIDMsFCZTXBxbHtsu+cDZjJ0y8A9+85gXksWIBz7zsuczLx9ybgD/6Q8/wCce\n3uCl5ZJb0wXXT6a8cFhxsAi0MTLKItMWukLfwkMTMvoFfP+juzyxM2Z/3vIn1455/mBO04ZkEy0n\nvYuvvoTmHuYLkQDGZLrWenhl1vVkA6X27JSB3Eqvs7RS2hsV0lsdF47cSPLrrNG00UCITGrDcW1x\nXhPoNP9ygtsO0Sux349R3Ckzq9gaFJwd9fjw2U2+/6Ez5EVB48RTYN620pZrxTCuDoGjRcPvHc34\n/NVDdgcFT+yO+OiDO3zi4V3aGDlIYONgXvPCwZRnE6jZ6hU8ujPkkx94gBgjLx7OeGF/xuGiZt44\nrNaMSsvZYSlqHi9tphcPZozLjI0yY6uX89DWgI88IK6P//qHHjxVlYiv+X3HI2m8X1c97qh+hJV3\ngkuPdZOInAGixjJKJSmhgKIVVwVWrSeXrubOQyYQqRr5XVcxAQFLXURCbg1WexqvxIU5dGQ8+Xsf\nPN7FVbpsbg2l0eQ2JzMqBbbJ+1RKrds+qa1X16KMalfk20jtQpKiC7D5vZdvv+XX5Tcy9v+Tn2Br\nY/xO78Y3NLRW9IuMR4oNHtnd4C++74HXfK7zgUXTsj+tuDpZ8NLBlMuHC64cT3nu5hHXphX7s5ql\nf3tqEt++4/VaPvf6eV0L6oi7Brn5WpNzcaPHQ1sDMm3QWm73lw8X3JpV1N6jUQxzy6WtPjuDgsNl\ny/68YlI5lq1LcSjCQWsjK/UTyIKnzAyDzNDPLVu9nFEp1iDRC7ipnbSITOLitGkFY01ERU1uJYPP\nmtQi9o5p7TmsHMcLx6yRRZmQkaNUZC1YqxIfTlrdLs1VUnGSqJxFA4taFLN1ENDiVMREGJUwsmAs\np9ri0t2Q+TTZRcTOY4uk+JL5VMzyIMtgt/f2ya/vK47MFfZ4aMsSVYv3rZjPKYMxFrD8oz864pf+\naHGPCcExtoGLo0C/JOUVGRpnaYPFKkMTFNdPaiaJEKaAfgaP7+Q8up3ROs+NSc21SWTasPIJcf7e\nIYKne78SWxmJSuRwRkWGtuXCRkVhRWbXpmyNNohUTSLipR0UkPZAZiRa3aCJqiN2iWtBmSn6ucHq\njNIarLEpCDEk0yNFv7A8vDngsZ0hj+6MeGJniIswqx3TuqFq/QoUWC2Erv15xc1pxaR2iSNT8uS5\nDT764A6PbA/xsaugzLk5WXJcNTQu0M9F5v7I9pCHN/qg4MUDATVHS6nUZFoz7mVs93Myo1k0nlnd\nsmg8mVFs9HL+ync9wq998eUkIU4k09BxQNLnoNYrBKNUStNWK65OVynpWkQgr9XByrVSJyT5sUia\n29R+WXFHEvnPqI67cvrTjsnPpvtp3QJaqYSirNQyo8mtXu3f6WFTtakrOdfOUzlRG9UJeC5ax7Lx\nVD4BFyfArDOyUgmI/c5XrnNnI/adH3/vX3k//9lPfvx1n/NWcmT+rIyYAPK8btmf1VybzLl2vODq\n4YJXJgtuTBa8dPuI28uWyaJleQ8l5Z/1EX7xb6J//lfekW3nqeLRBlZV0VHPcnHc47subHNhc4BR\nmjIzvHww5cs3j9mbVUQi/Szjg2dHfOyhHeoQefb2hGvHSw7mNZXzFFYlmbJf+1IFqZYXmeHsqGCn\n3+PCZp9Hk5Jz3jgGmUVpaHwk14pBYVm2nhihnxtKa+hllvPjHrv9gszI/WDetrx8OOML14545vYJ\nN6ZLZlWL1pqNMuOhrQEXxiWFMcwaz7x2krPnxG9mXGZsFgW3l0uuHS34yt4JV4/nTKsWrTRlpnl4\no8dHLm5yaWNIEzw+yGJq0Tomy4Zl62mjqG9DlAVX5QLzxhOJ9KzioVHOf/jR828LR+a+AjJxYx+T\naTJT0MtGvLBf88dXj3nm1pLr05pJXROiZ1F5nj0ST4E1EpeS5SiHf+u7L/BXvusB+oXl5nTKZ18+\n5Mu35tyetdyYNOxNAh3BXQG9DB7fHvCBsxscLz1Xjue8cjyndjGBmNNJr3cj/5iyOkQmHYOiCVK6\nE6MhLwxyrQmBVRvE6MQozzTjUov1f2nZ6JWcGQ54aHPMhfGQvYXn1rTlZNngQuTMoEArOJg30kII\ncHu+ZN545nUjFtEKzo96fPDsmO+6uM0jOyN6VjOpWk6WLUdVJTHtSBWhZw21c7x0OOfGdMmy9Qxy\ny4Vxj++8sMVHL25zYaOPUYrDRcOVozlXjuccL2tqF8iMZqPMubQ14NHtIUYrXjyY8eLBGtTkVrNZ\nSptpkFu5YTeOn/zOS3zqqSvpmOhVnolWUqHoYu9JJFbnPa2DNgoY6RQ47QrUxdVEEu4BPyFJC9U6\nfRylUu885aGoQPAQVMS7mGIeuCcw6UYvM/QyQ24NRilyo8XLxQtImTeOReNYtIFpLQnlVStApZND\nu8jK+yfTkuVktEy8v/r5l+/rG9tWobnx93+GzH79Iu+7Eci8meETuX3eOA5nFdcmS64dz7kxWfLK\nZMHeZMmtacXtecXt6YJ566n8vRZb99d4J4FMN6QdzyoTz2jIjebMsOSD5zb40LkNyjwj04pF4/jC\ntQOuniyoXcBoxYMbfT728C4PbvR44XDBC/sTbk0rTqo2EW8VzvnkHCwVDKNlPtgc5Jwd9DgzLLi0\nOeTSdl8ccoFhYVPbOzIuMlDiLWW0YlRmWK3ZHRRc2Oiz2RMFa4yRo0XNV/dO+JNrhzy7N2F/XlO1\njsIKgHpid4OdQYFRMKmlmiTRBJ5BnnF2VDDMM/amS75y65j/79ohV48WVM6TGdnmxx7e4Uc+cIEz\no1IqPTEybxwny5bDqhZ7B++pk3No5RyHi4bgHf/OE713H5D5p6/s8aVbU24vWmrXoO4KwDBaU9qc\nzbLkoZ0hl4Yl/+y5A377xVfLFAzw6Jbm44+UPDge4oLhynHLK5OWSeXZmzYcLZoVD0Yh5NhRFhgU\nNVUTqaNK+UByUrbhdML16RvavQAOd/y+VJGNnkrtB0U/zxgVBbujHu/b3eT7HtzlyYu7PLo9XJFg\nu3HjZMGnvnyVL9885mTZYJTi/LBEacXerKL1Aas1s9Yxqxr2ZzVHy4bGRzZKy6M7I37g0bM8eWGL\n3Bp8iNyeVRwuaqZ1S544KDuDkp7V3JxWPHf7hL1ZQ+PlhH90e8BHHtjkyQe2ODvqMcgMx1XLtaM5\nLxzO2J9XVK1HK8WwyHhos89j20PKzPLy4atBzVYvZ7df8skPXeTTz14X1U2MawM571cXtkteKaf5\nJqdHV5WRaocAijIzqFXLqFMUCS9FTOUi1Sk+yWuN3Bh6uWGYWwa5lWqL0ehUBfI+UjnPUVVzuGiY\nVS3z1jGtpPJUeVHZhdQGc14mO2s0ReK+ZEZjlUIbze985mt84XXOpPtx/O8/8zF++mMfeMPPfw/I\nvHWj9YG6lUrn4aLi+qTixmTB9cmCm5MFe9Oa/UXD4bziaFlzPK9Yuigthbd5X+8HIHN6WCUutZ7O\nkVcx7hVSjXhgi/PjHsZoMgVfuTXhmdsnnCxbIrBR5nznhU2+98IWJ43n+f0JLx/OOVjW1I04Cnd8\nk85eQfh+hjLXnB/12O4VXNjo8fjumGFuWLrAIDNopVi6QGE1oyKTKg2RQW4prMxvD4z7nB+VKzsL\n5wM3p0v+5NoBT9045qWDGdOmpfWBjSLn/Kjk8d0Rw8ISQuSoalJr2hMijArLQ5t9AG5NlvzB5X2e\nunHM7VmFD5EyM1zaGvDD7z/PDzx2LnUMpNI9WdYcLmRxphQrsUauAn9h3L77gMxPfeqrHFQVu4PI\nA0PLuFdwdlTy+PaIj17c5H1nNqiDZ17VLJ1nVjdMli1Hy4bfe3HKb3x1ds/sr2Gm+ejFLT7x6C6l\nhWdvH/Hs3rG0SpaBhV8z3RVQGs+F0Zx+rjmcGw6qInFWulX+a4GW9e80RmpE6an93HBps8+Pf/gh\nzm0O0BoWTZeCLdWBncRzeWDU45HtIWV2JzHu2b0TfuMr13jhYMZJ1VAYw4WNEq0Mt2dLFq2jNAal\noW4D146mXDmpWDQOaxQXxn1+4LGz/OAT5yVh2wXq1nHtZMHhsiGESGENoyLjwqgkEHnuYMZzexOO\nly2N94zLjCd2xzx5foP3nRlzZthjs8yYt45rxwte2J9yYyL7AlBmlosbPR7dHjIuMq4cL3jxYMrx\nsiHEyM/94Hfw3/7Ol+94nwJK1qTfnjX0cktp5XtthPAq8mK18nypW2nTzFtZebyetNpoxSC39DLL\nMLcMi4x+ZuhlWuIbrLSYGhc5rloO5xX784Zp3TBL1ZV57Vg6sQSIqfXTxSdoFSmsJbcCWKwxFFZT\naOE+tT7yjz79NDdfcw/v//F9Fzb4/Z/9Ueyb9AZ6D8i8/SMkpVrdeqZ1y9G84vq04uZ0yd50wY1J\nza3ZgsO5VH9PqpZp3TBZVNRefEG+2Yrg/QZkTo/TS0droLSW3WHBh86Oed/ZTYZ5hjWK29MlX7h+\nxK3ZEudlvnx0e8jHL+1wZlDy/P6U525PuTFdMpEYb5EpS7l1pYrsRAzbg5wzA/Ede3hzwKWtPnXi\nywxyIxy4GNkoMxSKJojz+qjMMKlK88C4x9Yp49F53fLS4YzPX93nmb0p10/mCQwpdvs5Fzf7PLw1\nILeGuvUcVQ0xRBaNwxjNdq/goa0+tfNcPpjx6edv8uztCdPKoTUMc8tHHtjix77jIh86u8HCCRdx\n0TgO5zUHy0bAj/J8fNi8G4HMc9yYC6rLjeLMwPKxh4b8+Ic3GfUyjpct08YzqcQNl2hRyuK8oUlt\nm1eO5/z+s1e59qoijXiy7BY1T5xZcG7YclJn3J5nHC4yjpaWpe/ISWr1N6CwKNw9WfKn2fjrYYGt\nfp7kydIikdIkbPYKvufBLf7cQzuM8pyoYNlKXTgzGmsUu4OSnUHBxY0+l9IJt3oXIfCnrxzyL565\nzrXjBdO6lT7qqKC0lr1ZxaRuKYxmUGQMc80Lt6c8dfOEw0rAymYv57subPFXv/NhHtsdc1I5Wu+5\nPau5NVsyqx2ZUfRzy9lByQPjkv15w7N7J7xwOGdWC9Lf7hc8cWbE+3fHPLoz5Nyox3Y/x4XI9ZMl\nL+z//+y9d5Bl133f+Tnn3PTy6xwnY4BBIAASkWIQRYkUaUuW7JWVbFFrr7dcK7PWWnntrdq1V7Ve\nWV5VyeZubVmy16ZlSZZEJUpUoChRgSSYwAAQBBEGk2Pn7hdvvufuH+e+np6IwWBCz8z7VjV6MNP9\n0g3ne36/7+/77XBio083MnUvJQ2Z2j1SoeE5rPRDvm3vFF87uULJtrCLkcVEF0FpqbH+7ycp/SjB\nTzL8OL3ieLYUgpKtCpKiqLo2lYKo1FwTyuYoCUIQphlBnNIOE1qBqai0w5h+lNKLUlOyT8zzbiVF\nuTbOmBJR7JAkShky6ipF1bWKiAfTVkoyzb/89ItXcyncFlDAF37i/Tyxb+qafn9IZLY3BhuDIDak\nZ90PWOnFLLR9lvohS52AVT9irRcVhCfBj1J6cUI/NHlBGRd7+25nInMhBhV6z5I0Si7zjTIPzjaZ\nqZWwpCTRGS+ebXFsvYcfpwgBE9USb50b4S1TTVaDmCOrXY6sdlkPYsI0LYbQBbJok5t1TmIpSbnQ\nwoyUHKbqJe4dq1NyFGGqKdtmojZINY4labg2QbF5KhdVGs9WzNRKTNdLm/5eWues+xEvL7V4/swG\nR1a7bPgRUZrh2YrRksPO0SqTFReljPTAT1KyDPpxgmcbecFM3aMfp3zt1BqfObzEmXbRepKC8arH\nu/dO8YEDc4xXPbpRYlpeQUyvH/Cg07/7iMzzfp+Pfm2ZQ6sRvVhvjkELASVbMtco8djsOJN1DwAl\nsiKhWVNzE2wZkecBUepzaKnHn7+qOeY7nCMb5yopCk3FSZAZpEIRpKqgOhfCJKHWlY3nGqV3htEv\nOMKoz7vpxb9Vl1CrOEU8+sBj0vgSuLZkrl7hHXsn2D1ap+5aRElGXEzleMUOfqLqMlJymW+W2dGs\nYKstnjZpypeOr/LM0SXOtn38JKPqWoxXPGquzWovpBXFKGEEYLONEmfaPs8eX+X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5YlrIUJh9b6fOboKpY8iCsVYxUzLfXEbI0f31e5Ka9pWxGZ794/wW99a+GSxnFgyEhJmgUlA+K0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\nOHrhSN5++21uvvlm4vE4N954I8888wwbN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\n", 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" ] }, "metadata": {}, @@ -433,9 +445,9 @@ "source": [ "### Data Visualization \n", "\n", - "The `ScatterVisualizer` and `JointPlotVisualizer` display data for one or two dimensions. \n", + "#### Joint Plot\n", "\n", - "#### Scatter Visualization" + "The `JointPlotVisualizer` plots a feature against the target and shows the distribution of each via a histogram on each axis." ] }, { @@ -446,77 +458,28 @@ }, "outputs": [], "source": [ - "# Load the classification data set\n", - "data = load_data('occupancy') \n", - "\n", - "# Specify the features of interest and the classes of the target \n", - "features = [\"temperature\", \"relative humidity\", \"light\", \"C02\", \"humidity\"]\n", - "classes = ['unoccupied', 'occupied']\n", - "\n", - "# Extract the numpy arrays from the data frame \n", - "X = data[features]\n", - "y = data.occupancy" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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L69atsWjRIv3jvLw87NixA08//TQmT56M0tJS5OTkQKPRQBAEREREoKqqCpcv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\nL4tu3sjl9VwqtFrM+j4Xh4qvK9ImR1f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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "visualizer = ScatterVisualizer(x='light', y='C02', classes=classes)\n", - "\n", - "visualizer.fit(X, y)\n", - "visualizer.transform(X)\n", - "visualizer.poof()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Joint Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Load the data\n", + "# Load the concrete data\n", "df = load_data('concrete')\n", + "\n", + "# Specify the feature and target variables\n", "feature = 'cement'\n", "target = 'strength'\n", "\n", - "# Get the X and y data from the DataFrame \n", + "# Extract the instance data and the target\n", "X = df[feature]\n", "y = df[target]" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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A4XCMdjN0iW4Fy2AwAADsdntZZhterxdOp1Pz8+iB8dRXgPo71hkr/WWMwe/3p8Y+YjC6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s3wtRPLNAJTvQ55NteM5HoVaD/6W8ZhrPjC83PMNQ0V/wvPz3Hr80X+y09c10t8pDCqHYvtsBcE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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -524,45 +487,23 @@ } ], "source": [ + "# Instantiate the visualizer\n", "visualizer = JointPlotVisualizer(feature=feature, target=target)\n", "\n", - "visualizer.fit(X, y)\n", - "visualizer.poof()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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EMRJQwiIIgiBGAkpYBEEQxEhACYsgCIIYCShhEQRBECMBJSyCIAhiJKCERRAEQYwElLAI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\nD/umYCVpNtNQ0BqD0gqpFNasnZ8yTUPPHAvt6F0UNUQikciZwJ4tWKMZ0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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "visualizer = JointPlotVisualizer(feature=feature, target=target, joint_plot='hex')\n", - "\n", - "visualizer.fit(X, y)\n", - "visualizer.poof()" + "visualizer.fit(X, y) # Fit the data to the visualizer\n", + "visualizer.poof() # Draw/show/poof the data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Projection " + "### PCA Projection " ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 17, "metadata": { "collapsed": true }, @@ -580,20 +521,20 @@ " ]\n", "\n", "# Extract the numpy arrays from the data frame \n", - "X = data[features].as_matrix()\n", - "y = data.default.as_matrix()" + "X = data[features]\n", + "y = data.default" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": 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v1bpwZbmqN4VqIm/L97kto25KhRyrJmRiSVaGaJATqu8zkZSJfvNPnToVv/vd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\nI5PJRHl5ufVxbW1tozd0Z9anTx969+6NwWCgT58++Pv78/e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\n", 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UXNBIhQKFCgKK7pTyEKjnwtMovgCiKCIWi8Hv94NhGPT29qK9vV23iyjLsmhtbUVra2vB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\n4sCBA6hUKrp168akSZOoX78+8+bN86gfQ8mY5Jo1a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+ "image/png": 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\n", 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" ] }, "metadata": {}, @@ -634,66 +575,90 @@ "source": [ "## Regressor Evaluation \n", "\n", - "Regression models attempt to predict a target in a continuous space. Regressor score visualizers display the instances in model space to better understand how the model is making predictions. We currently have implemented two regressor evaluations:\n", + "The Yellowbrick library is a diagnostic visualization platform for machine learning that allows data scientists to steer the model selection process. It extends the scikit-learn API with a new core object: the Visualizer. Visualizers allow visual models to be fit and transformed as part of the scikit-learn pipeline process, providing visual diagnostics throughout the transformation of high-dimensional data.\n", "\n", - "- Residuals Plot: plot the difference between the expected and actual values \n", - "- Prediction Error: plot expected vs. the actual values in model space \n", + "Estimator score visualizers *wrap* scikit-learn estimators and expose the Estimator API such that they have `fit()`, `predict()`, and `score()` methods that call the appropriate estimator methods under the hood. Score visualizers can wrap an estimator and be passed in as the final step in a `Pipeline` or `VisualPipeline`.\n", "\n", - "Estimator score visualizers _wrap_ Scikit-Learn estimators and expose the Estimator API such that they have `fit()`, `predict()`, and `score()` methods that call the appropriate estimator methods under the hood. Score visualizers can wrap an estimator and be passed in as the final step in a `Pipeline` or `VisualPipeline`." + "In machine learning, regression models attempt to predict a target in a continuous space. Yellowbrick has implemented the following regressor score visualizers that display the instances in model space to better understand how the model is making predictions:\n", + "- `AlphaSelection` visual tuning of regularization hyperparameters\n", + "- `PredictionError` plot the expected vs. the actual values in model space \n", + "- `Residuals Plot` plot the difference between the expected and actual values" ] }, { "cell_type": "code", - "execution_count": 23, - "metadata": {}, + "execution_count": 20, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "# Regression Evaluation Imports \n", + "from sklearn.linear_model import Lasso, LassoCV, Ridge, RidgeCV\n", + "from sklearn.model_selection import cross_val_predict, train_test_split\n", "\n", - "from sklearn.linear_model import Ridge, Lasso \n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "from yellowbrick.regressor import PredictionError, ResidualsPlot" + "from yellowbrick.datasets import load_concrete\n", + "from yellowbrick.regressor import AlphaSelection, PredictionError, ResidualsPlot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Residuals Plot \n", + "#### Load Regression Dataset\n", + "\n", + "Yellowbrick provides several datasets wrangled from the [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/). For the following examples, we'll use the `concrete` dataset, since it is well-suited for regression tasks.\n", "\n", - "A residual plot shows the residuals on the vertical axis and the independent variable on the horizontal axis. If the points are randomly dispersed around the horizontal axis, a linear regression model is appropriate for the data; otherwise, a non-linear model is more appropriate." + "The `concrete` dataset contains 1030 instances and 9 attributes. Eight of the attributes are explanatory variables, including the age of the concrete and the materials used to create it, while the target variable `strength` is a measure of the concrete's compressive strength (MPa)." ] }, { "cell_type": "code", - "execution_count": 24, - "metadata": {}, + "execution_count": 21, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ - "# Load the data\n", - "df = load_data('concrete')\n", + "# Use Yellowbrick to load the concrete dataset\n", + "data = load_concrete()\n", + "\n", + "# Save the data in a Pandas DataFrame\n", + "df = pd.DataFrame(data['data'], columns=data['feature_names'], dtype='float')\n", + "\n", + "# Save feature names as a list and target variable as a string\n", "feature_names = ['cement', 'slag', 'ash', 'water', 'splast', 'coarse', 'fine', 'age']\n", "target_name = 'strength'\n", "\n", "# Get the X and y data from the DataFrame \n", - "X = df[feature_names].as_matrix()\n", - "y = df[target_name].as_matrix() \n", + "X = df[feature_names]\n", + "y = df[target_name]\n", "\n", "# Create the train and test data \n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Residuals Plot \n", + "\n", + "A residual is the difference between the observed value of the target variable (y) and the predicted value (ŷ), i.e. the error of the prediction. The `ResidualsPlot` Visualizer shows the difference between residuals on the vertical axis and the dependent variable on the horizontal axis, allowing you to detect regions within the target that may be susceptible to more or less error. \n", + "\n", + "If the points are randomly dispersed around the horizontal axis, a linear regression model is usually well-suited for the data; otherwise, a non-linear model is more appropriate. The following example shows a fairly random, uniform distribution of the residuals against the target in two dimensions. This seems to indicate that our linear model is performing well." + ] + }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { - "image/png": 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IihpPUKXbt+LEmtMZdIVbiAZdGk6sOT2t1xkNuizNbNr/Em/tfJJN+1+iy9Kc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\nKIqyY8cO5Y477pjS+2F2m8Nj0NTUxOOPPx7/98GDB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CCG688Ubeffdd3G431dXVXH311fzgBz9gzZo17N+/n9GjR/P555/j9XopKyvjRz/6EQsXLozafklJCfn5+TQ0NPD888/z05/+NLyvV155hc8++4za2lr27NnDVVddFV7vxz/+MR6Pp8V4b7/9dmbNmhX+fPDgQQYPHhzeZlZWFjt37gx/BtizZw9ZWVnceeedlJaWMmHCBO68805cLld4Ga/Xi2VZUes9+OCD3HbbbeGCiZG/RaLrOnv37o17njdv3hz3976OPD4QwqLS2IXbrMAQXmyKiwwtnzzbJBSl97rP5bXr23TV8TWftyTtIxnaIeZ2kznI9vDYY4+xdOlSrr76avx+f/j7xsZGBg0a1Ob6kydPDnfxDbXWSLSK8ps7DrLrqAeBIDMtOHHvOurBub+GRVNGtvNIWuJ0OrHb7aSlpYU/Z2dns2rVKnbt2sXChQuZN28elZWV/PCHP+RHP/pReB2n04nX6+W5555j//793HrrrVx33XXhbR85cgSPx8O///u/U1lZSVFREXfccUf4Zrjpppu46aabgOC5DI0B4LXXXkt4/DabLWr8kccDYLfb2b17N8uXL6e4uJiHHnqIl156iSVLloSXcblcqKoaXu+TTz5h6NChzJw5k40bN0btozmBQIApU6a0ek03b97M9773vYSOpy8ijy/Ilv1/w11ZhmJXsOMATNyijGGD85kxen7XD7QDyGvXt+no8fn9/jaFTuS8JTlBIucuRGe1Qyy6XQi9+eabVFZW8pOf/ASXy4WiKEyePJmNGzcya9Ys1q9fz+mnn95l+9dNi68O16KpCmbE95qq8NXhWi6dWJAUN1lzTjrpJAAGDx7Mn//8Z95//33S09MxDKPFsuPHjwdg2LBhLfqnlZaWcuqpp/LCCy9QV1fHggUL2Lp1KzNmzGhzDNdff31MC8zdd9/NmWeeGf6cl5dHRUVF+HNlZSV5eXlR6+Tn55Ofn09xcTEA8+fP5w9/+EPc/W/ZsoWPP/6Y9evX4/f7aWhoYOnSpaxcubLNsUsGHoapU1Zb0qK4pqIolNWWMLXwAukmk0gGCF2pHbpdCM2bN497772XG264AcMw+OUvf8nYsWO5//77eeqppxgzZgwXXXRRl+3f7dfxBAxSHS0P3RMwcPt1clKTr9hDkezPP/8806ZN4/rrr+cf//gHn376aYtl41VVLikpYeLEiQBkZmayYMECPv3004SE0Msvv5zQWKdMmcL+/fs5dOgQeXl5vPPOOzz55JNRywwZMoT8/Hz27dvHmDFj2LBhQ5tNXu+44w7uuOMOADZu3Mjzzz8vRZCkVby6G0/AHfPvwRtw49XdZGg5PTAyiaRjvLHpMW4464GeHkafpCu1Q7cLodTUVH7zm9+0+P6ll17qlv1nOO0xRRBAqsNGhrNr3zDPP/98HnroIdauXUtGRgaaprWw+sSjpKQkKrB6zpw5PPzww9x+++1JG6PNZuOBBx7gpptuCqe6jxs3DoCbb76Zhx56iLy8PO6//36WLl2KrusUFhbyyCOPhLfxH//xH/zzn/+ktraW2bNn8/Of/5wf/OAHSRvjQEQ3Ldx+PWnB/b0Zw9QxLR2XPQOf0TK70OXIwGXP6IGRSSSSnqArtcOAK6ho11SKh2ezpawm6nvTEswoyEnKBHPFFVe0+vn0009vUVcn1joQjM35+OOPo75rbpmZOXMmb775ZmeGG5Nzzz2Xc889t8X3zz77bPjfEyZMYPXq1THXf+qpp+Juf9asWVEB2pLWsSzBml2H+OpwbdiaWTw8m4WTCpMS3N+bsITJtgMfUFZbgifgBiHQzQAOWwo0HaoQgoLsIukWk0gkSWHACSGAhZOCaeGb9leFJ5YZBTnh7yWS3sSaXYfYUlaDpipha2ZIyCcjuL83se3AB5RWbkJRFBRFQQAoILBQhILLkUFBdhHTRvW9OmMSiaR3MiCFkKoqLJoykvNGZWPZHAPC1SDpm0QG90fS1cH9PUGs4GhFUXDYUkixpXFu0fWkp2RLS1ASGUjuVomkNQakEAph11TSuiAwWiJJFj0V3N8TxAuO9ukNaJpNiqAkMZDcrb0N7Y4XMZ/8YU8PQxKBfAWQSHoxPR3c35247BmkOmIHQMvg6OQScrcCUe7WNbsO9eSwJJIeQQohiaQXEwruNy0R9b1pCYqHZ/crd4ZNs1OQXYQQ0ccqg6OTS1vuVt20MEwdt68Gw0ysWr9E0pcZ0K4xiaQvEArij3Rj9Nfg/lAQdFltCd6AWwZHdwHx3a0B/rnvbxxr2IMn4CY14vyritYDo5VIuh4phCSSXk4ouP/SiQV9LrC1vcG4qqIxY/R8Jgw/n2ONteSmZeNy9I8YqN5CPHfrsLRvOHTsIIoazNrz6g2UVm4C6LUtTSSSziKFkETSR7Brap8JjO5oMG5ovS1lNRz3BshyVYStXzKINzlE1lKLdI9Zlk5OSiWKKluadDXaHS/G/F4GUfcMUghJJJKk05HaR7pp8fKWffy/XWXUeAIEDAuHTWV3VR2WEFwxdVSHx9Of08Q7cmyx3K1Th6eg+/yEK1dGIFuaSPozUgj1cT788EPWrVtHQ0MDV111FWeffXZPD0kywEmk9lEkISvQ1vIaXt+2H49ukuG0kZvqRAhBpdvL69sOcNmkwnaLmP6cJt6ZY4vlblUwWbs9A68uW5pIBhb969WoH/Pqq69y1llnsXDhQubOnRtuqzF37lweeughfvWrX7F27doOb3/9+vVcdNFFXHjhhXG7yNfX1/OLX/yC+fPnc/HFF7N161YA7r33Xs444wwWLFgQcz3TNFm0aBE/+clPOjzGrkI3LWo8fnTT6umh9AtCwbixCNU+iiRkPfIEDBoDBgrg9ukc8/gBUBWFsuON1DR9bg/9OU28PcfW2j0ecrfaNVVm7UkGLNIilEQeffRRdu3aRXV1NT6fj8LCQrKzs3n66acTWt/v97NmzZqYzUlLS0u57bbbuO6669i+fTs333wzixYtCv++atUqbrjhhg6N2zRNHnzwQf74xz+Sl5fHVVddxZw5czj55JNbLPvwww9zzjnn8PTTTxMIBPD5fECwV9rixYu5++67Y+7jhRdeYOzYsTQ0tHzb7Cn6s7WgJ2lP7aOW1qPgfxVFoSFgkJMqUBUFEcNdEwvD1PHqblz2DARav63KnWjF8fbe4zJrTzIQGdBCyLSCtTJc9oykvO3cc889AKxevZp9+/axdOnSdq1fXV3N66+/HlMIlZSUMG/ePAAKCgqw24PjFUKwcuVKZs+ezaRJkzo07u3btzNq1CgKC4NxA5deeikfffRRCyHkdrv58ssvefTRRwFwOBw4HA4g2Py1rKws5vYrKipYt24dt956K3/60586NMauYCD18OpOWgvGjdXYODKV22W3MSjFTkOTxciygusoqkJhVmrcQPHmzVpTHRnkpo/DExhCatM9Gklfr8qdaMXx9t7joay9qYUXhAWltARJ+jsDUgiFHpoHj36Dz2zs8loZuq6zfPlyDhw4gGVZLFmyhKFDh3Lvvfdis9mwLIsnn3yS3//+93z77bf89re/5bbbbovaRmlpKSeddBJCCF566SVuv/12AF588UU2bNiA2+3mwIEDXHfddVHrXX/99TQ2NrYY0913382ZZ54JQGVlJfn5+eHf8vLy2L59e4t1ysrKyMnJ4d5772X37t1MmjSJZcuWkZqaGvf4V6xYwZ133hlzHD3FQOrh1RMkWvso0nqkqQqT8rPYeaSWxoABCDRVIS8jhaunjY57PZo3a/XqDRw6toVhaSOp04tbLN/Xq3InYnXrzD1u0+wyMLobkFlivYMBKYRCD00EKGrX18p4/fXXyc7OZsWKFdTW1rJ48WKuv/56pk6dyp133smmTZtwu93ceuutYRdYJEeOHKGxsZFbbrmFyspKioqK+PnPfw7AjTfeyI033tjqvl9++eWkHYdhGHz99dfcf//9FBcX89BDD/GHP/yBJUuWtLrOJ598Qk5ODpMnT2bjxo1JG0tnGUg9vHqC9tQ+GpObzu7Kehw2lfFDB6EA5fUeMp12ZhTmMn1E/OKRsZq1QvBvOyelklq/jqqeED2xLFN9jUSsbjUev7zHJZIEGHBCKPKhGRkU2JW1MkpLS9m8eXPYymIYBhdccAFvvPEGN910ExkZGWELT2vrn3rqqbzwwgvU1dWxYMECtm7dyowZM9rcdyIWoby8PCoqKsK/VVZWkpeX12Kd/Px88vPzKS4OvmHPnz8/bmA1wJYtW/j4449Zv349fr+fhoYGli5dysqVK9sce1cykHp4dReR8Tmhv6HWah9ZQvDmjoNBi5Hf4ODx4D1amJXK+LxMrioexblj88h0OdoULPGatTo0P1OHp7Czwux3VbnbsrrJe1wiSYwBJ4TiPTS7qlbGmDFjyM/P59Zbb8Xn87Fq1Sq2bNnC9773PW677Tbefvtt/uu//ouf//znWFbLzKWSkhImTpwIQGZmJgsWLODTTz9NSAglYhGaMmUK+/fv59ChQ+Tl5fHOO+/w5JNPtlhuyJAh5Ofns2/fPsaMGcOGDRsYO3Zs3G3fcccd3HHHHQBs3LiR559/vsdFELQvjkUSn1jxOW25mteXualz2oKxK04b4/MyCRgWE/IyuWZ6fDdYc0LNWmOlfac6MrhkchELJ2v9ro5QW1a39tzj/bnOkkTSFgPuju+JDtfXXnst+/btY/HixVx77bWMGDGCyZMn8/TTT3PjjTfy6quvsnjxYnJzc9F1nSeeeCJq/ZKSEiZMmBD+PGfOHD799NOkjc9ms/HAAw9w0003cckll3DxxRczbty48O8333wzlZWVANx///0sXbqUyy67jG+++YZbb70VgP/4j//g2muv5bvvvmP27Nm8/vrrSRtfV7FwUiEzCoKiN5Tu3V+sBd1JyNXs1RuC8TmBBnZXbGLz/g9iLq+bFqXHfS1iVxw2lb3H3O3efyJp35Fp4v2NeMfW1j1uWUHL3IoPd/DYRztZ8eEO3txxEKtZk1+JpD8z4CxCoYdmKCYoRDJrZVxxxRVRnx0OB48//niL5V555ZUW37311lstvmtunZk5c2a4jlCyOPfcczn33HNj/vbss8+G/z1hwgRWr17dYpmnnnqqzX3MmjWLWbNmdXyQSaYv9/DqLUTF5wjwGSa6KRAIthzcRpn7FC6ffFJUqrbbr+M3Yk+0HY1dkWnfsWnrHpeZkxLJABRCcOKhefDoN/jNRvnQHKBEugNk0GjHiHQ1B0WQBSgoKGiKjx2HD6MotqhJNcNpx2mLXRcoVuxKIm4bmfYdn+axWqECi83dZiAzJ7uT5j3HZBZZzzAghVDooXly7ukodks+NAcYspBi8gjH5wQa0E1BZJ8qU6RgihT+caCaeUXDwxYHu6ZySlYKdZaIG7vSkesk077jE3lOazx+thw6RkFWGkVDB0XFTcqssq7h7vdOAfQ2l5N0LwNSCIXQVDtpKWk9PQxJDIQQMQPak4F0BySPkKt5d8UmBAIlLIQsyupz+ezgMTy6CXzF6aOGhEXM7IIMyp05cesMyeuUfCLPaWaKA5umcrjeA8D4vMzwcjKrTDKQ6BdCSFVVAoFAuMqxpO9jmmaXXE9ZSDH5TBt1IaaALQe3oSpeLOEKiqADBaBAmsOGQ9OiRIyqxI9dkdcp+TQ/p5qqkJ/h4ki9l8oGH+OGDEJTFZk5KRlw9AshZLPZ8Hq9eDweNE1L2JKg6zqBQKCLR9dz9MXjE0JgmiamaWKzJf/2lIUUuwChUt4wkfUHnVQ31GKKFKoaDAanqVhCMDzDFZ58m3efb63OkLxOiZNo6nusc1o0dBAAh457OO4NkJvmlJmTkgFHvxBCABkZGRiGEbMOT2vs3buXKVOmdOGoepa+eHyKouBwOLpEBIEsMtcZWptwQ+6WcUOysYRGWd2JTvGT87PCky3E7j4fC3md2qa9MVSxzqmiKIzPy+SUoYP46VlF/bbEgEQSj34jhIAOTZ793Z3W34+vvchCiu0n3oRrChHlbhmfl8mY3AyEqMCmqYwbEh2Em6iIibxOAAHTxKEFizPK6xSkvTFUbd37eRmu7hm4pAUyW6xnkU8TyYBDFlJsH6EJF4iacNfsOhR2t0TSbtmdAAAgAElEQVTisKkUZqWhmxYB0wx/b1qC4uHZCYuYBRMK0E2Lz76r4pNvK/nsuyp002LBhIK2V+5lhNLVg+UFkrO9eDFUre1H3vsSSUv6lUVIIkkEWUgxcdqacCPT4iMJucMcmtbhHl9vf1OGXVM5+6ShURaht78p65GssY60oeiqUg0djaGS975E0hIphCQDltYCdfsjsRqiJkJbE67PMGO6WywB10wf3eaE25q4aC7AXOqJ/Xd31lhnxExXlQDobAzVQLr3JZK2kEJIIunHdKQhaiSJTLjxuqCrqtJ29/kY4iLZWWOdaSraUTHTlSUAZKxb3+Sxi0q5cfVJPT0MSTOkEJL0KB21VEgSI9QQVVGUYENUvSHcZ2/G6Pltrp/ohNted0tU9/kY4iJZWWOddU11Rsx0dQmAeAJU0rdo3mojhAyi7h6kEJL0CJ21VEjaJqohagSKolBWW8LUwgsSEp8Xjx9Bg99gz9F6fLrZ6oSbqLsl1H1++LD44qIzFo86b4Bvj9azu6qekqr6Drum2hIzNR4/dk2NKf66ugSAjPeRSJKDFEKSHqE1S4UpYGze+Ul/qA9Ey1NkQ9QWvwXceHV33L5cza0pTpvG5GFZXF08Gqe942I10e7zHbF4BAIm1/3l72wtr8EbMPCbFvkZLq6aOhKt6X5qj2uqNTEjhOBgbSO/+7wkLA5DlqYQ3eW+kvE+EknnkEJI0u3EtFQI8BsWWw5u4//uzMHlcCYluyYZlqfOxJf0JOGGqHpDy98cGbjsGXHXjxUbs6fazbu7yzsd6JtI9/mOWDyu+8vf2XTwKKqqYNdUvIbJ4ToPb2w/yDXTR4eXS9Q11ZqY+bqyDgVQlZaWpkiZ1lzMpTmgeLiTS8bnAwNToEskvQ0phCTdTixLhc8w0U0LTfGR7tQxhTMp2TUxLU8Vm/DqFqeNmR93Yu3rXepDDVFDxx9CCEFBdlHcibere30VpDnwGRYO24lttGYpSdTiUecNsLW8JnxtVIVgC1gFKtxefLpJSpMlqz2uqeZixmnT0BQlqmI2nDg3w3JOWLtCYu7iCcPY9N0HHHXvwetz8+6OdGyaA8PS8UrX8IDiue/vivr8P/46qYdGIgkhhZCk22lhqRCgmwJQMIUTS6QAnZ90W1ieREhwCbaXb+ejfXlMHT6kVWHTH7qfTxt1IQBltSV4A25cERNuPLoi0DdSWO6r8xHwugEozEolzWnvdKDvt00xTM4mcaUoQatQwDAxLUGNx8/wzNR2u6aaW6Z00+KpdV/HdDl6AgaNestihrvKPuJQzZagIEehzncMy9KxqQ7sNme7g9glEknykEJI0u00t1RYgECgAF5zOCLituxMdk1zy1PI6gQKdtWHqvhaFTb9pfu5qmjMGD2fqYUXtMsF0xWBvpHCMkVTGZOXScCwmJCXyTXTR3f6fJ48eBApdg0hTlhkUpssQIYlcNmDx9NRwRWyTOmmFffcpNmj45/CghwFnx68B1XFQAEMS8cunKC0P4hd0n+Q2WE9S7cKIV3X+eUvf0l5eTmBQIB/+7d/4+STT+aee+5BURTGjRvH8uXLUdXeP8FIOkekpcITcGOJFLzmcOr06CaxncmuibI8RVidAEyRgiVSWhU2/a37uU2zxw2Mbk6yA31bE5YOm8reY+52bSvWtkMxRNNH5IRjhCAoLlw2jRmFuTxw0dSkxHm1dW5sgeqo5UOC3G9YTUJcoBAUS0IIfIZBSpNISySIXSIZiHSlfuhWIbRmzRqysrJ44oknOH78OIsWLWL8+PEsWbKEWbNm8cADD/DRRx9x4YXxzfaSvk9zS8WHpcfZWl6f1OyaSMtTcOoJTj4KIsryFEvYyO7nidWpSTSQvKtdbaFtXzdtNEIIth2uDccEnToyl1duOAeHI3mxN/HOzdat0ULIZQ8Gpjf4jxMS4gKlSQwp6KYgxRb8KZEgdknf5e73TuFIo97Tw+iTdKV+6FYhNH/+fC666CIg+CakaRq7du3itNNOA2D27Nl8/vnnUgj1U2JNmiFLxeWTs1GUQ0kvDjdt1IVYAr4+sgPd8OHRHZS7c6n0jOSUoQKlKeunubCRlXvjZ221N5C8q11toW1vrzjOjTPH8seT8/n2aD0nDx5EpssRtV4ysgDbk9Fm0+wMzhhHVcNGFFRAQQgNRTEQaAjAApQEgtglkoFKV+qHbhVCaWlpADQ0NPCLX/yCJUuW8Nhjj4VjONLS0nC7EzOT79y5Mylj2rx5c1K201vpDcdnCcH6Mjelx334DYHTpnBKVgqzCzJQIwJOC4FhOYJG3SLNLrAFqlu8XUeS6LF9eshJae14qr2NlLkVLKEhOMqx2uOMzHAwPsfF9m1bW6w3Qgj2+luOe4TfYPPm1seVLHrDtWuNdYfq2V3jDV+/40BZeTl79+7lvMJBMddJ90avc/jwYSwhWj3/8TAswbu7qiFGOaJ3K48wzFeFTVX4turE94neh8mg+bXTzMFUHs8jK/UYDi1AwHRgWS5U1cKhBUB3kmEbhnU0l83Heu91D9Gb781k0N3H19/PZzJIpn5oTrcHSx85coSf/exnXH/99Vx22WU88cQT4d8aGxsZNCj2Q7Q5kydPxunsXIzG5s2b+d73vtepbfRmesvxvbnjIHVOW1Ql4TpLUO7M6XD2VaLHppsWbx/dwXBXDsOEwFlVT2WDj4BhodtU5n+viO9PGdlqOvzMU3umjlBvuXaxCJ3TghEtf2sApk6bEvM8TZ9+wor03aFyTioc0eFyBDUeP1kVO1t1tY2bNLmFq60r7sNYtHbtKnbks7WsCrvlxxIpCGxYus7UISlcPrnvWIJ6872ZDDp6fH6/v8Mv6P35fCZCoucuWfqhOd0qhI4ePcqPf/xjHnjgAc444wwAJk6cyMaNG5k1axbr16/n9NNP784hSbqYns6+ioxNURSF8XmZjBsyiIBpYpiC88fltzkJy8q90XQ03ifSnfT3jX7OmRVbMCVCe11tPX0fQqy4IpheMLTP1KWSdA0yYywxulI/dKsQ+v3vf099fT2/+93v+N3vfgfAsmXLeOihh3jqqacYM2ZM2Aco6R/0dPZVrAlTUxVcqg3sDIig52TT2Xgfu6aS6dQSEh6tVV5ubwxXT9+HIHuDSWT3+c7QlfqhW4XQfffdx3333dfi+5deeqk7hyHpRno6+6orgp77asuNZNEdgeSJtEZpTy+ynr4PI5EWRomk/XSlfpAFFSVdSm/IvupI885Y9PWWG8kkWee0NVprygsnKi+3x8LSG+5DiUTSO5FCSNLldPWk2RbtmTDjWXu6uuVG5L57O13p5onZlJfWKy8namHp6ftQIpH0TqQQknQ5oUlzXtFwjtR7GDYolVSHDd20OO4JdJuLKd6E2Za1pyuDbWPtO91bz/TpotdbmrrCzROrKW/4t05UXpYxOhKJJBZSCEk6RHviZJpP9C67Db8RbI7p1c0udzElMta2rD1dGWwba9+7a7ys2XWozzR37SyRQdEtmvJGkIzKyzJGR9JT3P3eKUB0ZWntjhfjriOzyroeKYQk7aIjcTLNJ/rdlXWU13kYkZnK+LxMoHMuptaETqJjTcTa01XBtq3tW1X6VnPXeMQToq0FRednjuPbqi1oihLqSoGQlZclEkkXIIWQpF20N06m+URvWoIKtxdNVahs8DFuyCA0VemQi6ktoRNrrFvLqjCt+qgCdolae7oi2LY3pHV3FfGuT4gWQdGBBraXbeR4YDS6WcAgRwVOLUB2WiaFTVlj/YGBnnkokfQmpBCSJExH4mSaT/QB00Q3LeyaSsCwCJhmsKYP7Z/444mySycW8NXhWgC8uoFDU8hx7iRVO0xtnYe3v8phZM54po26sFVrj2kJFAVSbO1P106UjhQG7CsT6F93HOQfB6px2W0trk8hsYOifYaJYQlc2hHq9Qvx+qaA8JI5aDgzRo/picNIKjLzUCLpfUghJEmYjlgvUmwaihIUFZqq4NBOFNJz2FQc2omO4O1xMRmWiCvKZo0czFflNdR4AvgMkzMK9jMyrQxNEYCgzlPFLu8xLCE49aSLo6w9QghKquo54vaS43LwxCe7wpNVsoNtW0vrtoSgeHh2h5uc9iSWJVi94wC/+7yUgGFi11TyM1wUDR0Uvj7DckTLoGgBuhnsyK4pPnSjnjqfRZojg+1H3CyYZPV68dcWXZ15KOndPHZRKbrwRH33P/46qYdGIwkhhZAkYdpjvYicuEuq6qnx+Bk+KJWioYPIz3A1xQi5olxm7XExNepWXFH27u5ySo/W4wmYKJiMyT6MqljhZQQCUxh8W7WJaSPnRll7vjpcGzVeiJ6skh1sG8vSND7HFWVp6ksT6Jpdh9h44CgB44RwOVLvBWB8XiaegEGjbrUIirYIXhcF8OkGTtYxNtOk3m/j4NFsqt1jGZ6VnvA4usJ61plt9oY2H5K+hwym7nqkEJIkTHuK0kW6RYqHZ4ctLH7DZOrwHEbnpOO0qR12MaXZ1VZFmdOm8V7JYYQAy7IYnOon1W4AwWblkZ3G/bqH455jqFo2l04sYF7RcB58/yscmhZ1jF05WcVK6962dRONgVpc9gwEWp+ZQEOTvctuw2FTESLYHl5RoMLtZdyQQaQ6bKTZBTbNTkF2UThGSAUUFCzLh02FjCatOchpMDmvimc+/QuPXP6TNsfQFdazZGyzP8eDSSR9GSmEJO2irTiZeG6RULPTu+ZMCtcR6ujbtU1VWhVlJ+U4+fJADfnpDqoUEIqv1e0I4PcbSnH700h12BiTm46wQLO3nNy6erKyaypZLhvbDrzPXv829n71PqmODHLTx+EJDCHV4ej2MbWXyMk+Lz2Fw/WesPDUTQuvbnDWSUOxBaoBwsHPZbUleANubKqLOn8AEXH6FYICaZCjkprGRnLS0uKOoT3Ws0TvwWRY5HpTmw+JRHICKYQk7aKtonRtuUWEEQyITXXYOu1iainKVCbk7sGhlrPglBp8hoOy+ly2V47EZ9hwNVmFTiAwTCeWSDtRv6eynoPHG8Np/ZF0x2QVyqIyhIFdsePVGzh0bAvD0kZSpxf3yJjaQ+RkH3IrVjb4CBgWDpvG6aOGsHBSIVu3BoWQqmjMGD2fqYUX4NXd/OGLb7ApawnnzEeQZg9QUlXBGSeNDX/XXMgk6n5qzcJzyfh8/GZDVJPXZLm0ZJsPiaR3IoWQpEPEEjGJukWSNXE3F2V7Kz9hb1UphqmgqSouu8643CMAfFszjIlDyoNxQopAQcWwFDxWtDvOYQtORsGJ+8TE1B2TVautJVSFnJRKav06qnri3PXGCbT5ZD8+L5NxQwbh1Q1mjRrMlcWjYq5n0+ykkMUxjx2XYmeQM1q0CqBRt1M0NB9o3VV19klDE3I/tbTwWHxX9QmvuqtxaL6oJq9uv5E0l1ZrFtWLx4+gxuPvE9mAEkl/QwohSdJI1C2S7Ae9XVMZ5FQ5cvyEiHDZNby6iSVgRMYx3ts7nTSHnXG5x7FrPoQIpvKnaodwphzDaw6jTp8CqBRmpTIhL5O9x9wdimHqqMsvXmsJh+Zn6vAUdlaYvb5PVqzJ/qyThrY5VrdfxxQah+pymJRXSbRVSFDnywu7xSKFjNOm4dUNvjx4DMOywoLFtAQB0wzHe4VEeCwLT6Z9B+m27wgYCk7NFtXkdUrhvKS5tJqL9zS7jXd3l/Poxzt7fTZgcyKrgcsil5K+jBRCkqSRqFukK2guIkK1f3TTwpZiMHtsFlOGT+WS8flsOfAO31XvRFGCIkVTfKTb9gFQpxeT5rRzzfTRAO0SNJ0NqI3XWiLVkcElk4u4eIIS1a+tN9LRnl6h+8eZfSa7Kr+gMKuGDKeB22/j0PEcnvz+j4ATlkdVgd2VdRH3mMq+Y24WTSnk/24/yNFGf/j7wWlOrp0+GrumUuPxR1l4FAxc2mFAQSCwIBi4HdHkNZ5LC2i3NSdkUX1zx8E+kw0YQgiLLfv/1qIa+LRRF6IqWtsbkLQbmRnWtfTOJ6mkT9JRt0gyaCEiFEixa6TYNOy2NH54xkxcDieGqVNRfxBUFbsm0E2LUDiuSztMrX8i0wtOWK3aE8PU2YDayCyqSIQQDM8q4u2vj/SJOkIh2hsDFnn/jBh8Lp6An+o6Nym2DC6Zmk9GagpwwvJ4sLYxbHW0a8H6TwdqG/jiu2oUaHLNCoQQ4YBraBm0rCo+NMXXtEQwey1EqMlrLCvX9BE5WEKw4sMdUddkRJNLuC36ajp9pbELd2XZiWrgEdazGaPn9/DoJJL2I4WQBIjtzumI6bujbpH2Ylgi6i28eSp2CIFgdO54XA4nliV4a2cJtQ21BMv2EbWcpviYOjylQ2NN1qQWyqIqLd8GwsDV9LZ9oH48W8v7luWgI0TePxYauemDW7TlyHDacdo0Kht8UaUQICimth+p5ZwxeRQNzYxyjW0/UsuCSQUtBLslUjCFC03xBq9RxCad9gx8hoMUu2hh5Xrn6zK2ltW2uCZ7/W5mntr2sfbFdHrD1HGbFSjNsiojrWfSTdY2sohi70IKoQFOc3dOil3j5Nx0inL2UFFf2sL0bVlWXHHUUbdIe8f77q5qsip2RllGmqdiuyLGDUGLzfbDPoanuposAAACu6bgsNlw2dNZMLkIUwiOewLtGnuyJrVQFpV5NIfxk08O1xH6fx/u6HOWg46QyP1j11TGDcngoz1HcET8JgTkpjopr/cGW7fYbeH2LRB9HSIFV2MAvPZhZDm+C7tUEcHWLEcastnwSUnUfZaT6owrfEuP+8JtZOLRF9PpvbobQ3ix07KUQ8h6lqHl9MDIJJKOI4XQACfkzlEVOFjbSIXbS2395zQ2VuKy20ixaXj1BkoqvuRI3T5MS08oLqA9bhFPwEg47iU0XgQxLSOhVGy3rx5DOMlypaIqJ9KqVdWO1xzWFBMUdJjopsCpBTubd9T9lOxJTVU0MlJOxJ/0NctBZ2nr/rm6eDRrvy6nwu0Ni45hg1ycPDiD4z49qnVLiMjr0CJo2TGVXWUfhUW033RS6y+g3phCqiMoaCLvs3jC12+IhK5JX0ynd9kzsCkuwGz5myMDlz2j+wclkXQSKYQGMJFvtbsr6zhS78WmmozKqsESNMXPBGNtTCtAtfsQLnt60uICDMPi/r9tY+PBozQGDNIcNmaNHMyv50/DZms5CSTiftIUJaaYiUyrDmaHgUs7HOxpZaUwImcSB+qGs/1IFapqb7f7qSsntb5oOUgWrblnnXaNa6aP5suDxzCFFXZ/mZZg1sjBLbbT2nWIFFyRIvqZz/Yjmj0eI++zeNfEaVMSviZd0ci3K7FpdjK0fNyiLNoFLYIvEtItJumLSCE0gAm91TptGhVub1On9QAuWwAhFASErSWmFXwDFFjhcNLOxAUYps6v3v8H/9hfD4qNVLuGEILPv6vi/r9t45EFM1odbzzLyPq9lTEDlk1LRKynUqcXU69PQlU8ZNj2cqxhH7UNmxme6opKpW+P+6mrJrW+aDnoLJYw2Xbgg7iZSa2d7wUTCnj7m7IOXQebZkco6TQGILWl9yfKAtfaNTklKyXha9LVruSuIM82iWGD81t1QUs6h8wQ636kEBrAhN5qvboRdi/4DAdew4HLrgczbxBYTf+vKGqznJqWcQFt1dAJTXAHa0oYlnKUBacEqz9/VXkSokl4bDx4NKbgacsykmJrvSfXzorjTMrPZPvh4+HfBTbStH1kOg7g1W0IWqbSQ+Lup66c1Pqa5aCzhCpsx8tMine+O3MdErXAtXZNRvibVzBvm2Q38u1KFEWNqgYu6wi1n+e+vyv8bxk43fNIIdTHSGZH7ZCl4cuDx8LbMoVGWX0uRYMrCCUcq01JxZpia9H5IBQXkGgNndAEZwkQKFHVn7dVBlsnNDbFDI0dPCjmeEPuqhAhy4jPMONajM4bm49NVcNjTHNArqsKpxaqJxMaZzCVvtIzHrvmaLf7qSsmtb5gOUhWgb1WK2y3YoFs7Xx39DokaoFr7Zps3lzd7n32NB15rgTdZDIwWtL3kUKoj9AVHbXhxFvtvmNuDh5vDKYleyYy0kwjVTmCUwuQ6swgw5WD2xstQCLjAhIpDOcN+Nl/bHdQVinB1htBFAoGHWVfbT6NegppDhvDBqXGHe+7lUdaWEZMIeK+yWe6HFETlyIaeG+nPzwQu6agmxaGJTCtRraWleM1XJw5eihajGrPPUFvtByErHyHakpoDNST5hhEYU7HC+zFq7DdXZlJ7bHA9cZrkihd9VyRSPoSUgj1EZLR/ToWobfai8eP4L+/2s+eajd+w6TemMboIedwwclZpDozUFU1HLPRPC6grSDmi8eP4N3d5ew8coi8lFoUVOya0tQk08SumjhTAsw/eSse3YGi5SNEgBqP2eINNTTeYb4qxk2aHPW7Susd6SPf5EMTl2GqUUUYU2waumlhCYFXd2CKFIYPSsWmKqzZdahd5zmZlrvezpb9H7CzfCOGFXSlegK11Ho2YpiC08Ze3O7txauw3V2ZSX3BApcMuuq5IpH0JaQQ6gMkWqyvM5Ov067xw1PHxt1Ga3EBx/3+cNB1qIBdcNwBFHy8tu1bvj3qxaamYInUYKaWaeHQVFR0FMVCCAWbapLt8qAqe3nlH49RGzgJj1VM8fDcFm+oNlWJ+Rbenjf52EUYFRyagkct5IzRw8LnPNGAacsS/HXHQb48dBTTgnRn/37DNkydr4/swLCC1ZRNS2AJgd+ADfu3Ut5YxOWTT0JVlah7Kx5R1wUl3PJC0P2ZSX3Z2tMWfbWytUSSbKQQ6gO0lS1V5w3w2XdVLczbiZb6j6StB3+suIA0u42DtY1NPZ9M6rx+zig8yLjcOtLsAWrrXGQ7RlBvTI2q4aObFnZVIFAQioIqgun6lgBF8ZPt3IfdUNlSNhVI7A21+Zt8ik3DZ5iYQqA2D3CCqCKMjX43uuXEZw7jqH8ijgivTqyA6eYxMZYlWLZ2K1/sr8K0BHZNJT/DhdFUhqA/vmG7ffUEjEZAwbCC1jQIehtteNlWdhiBhqooUfdnureeyVNNGnUjpuieWjiXkqp66rz7UBUvlnAxyDWGqYVze+Ao+yddWdl6IFlEO8tz398VFTCt3fFi0rYtM9ASQwqhPkBbWSzr9laEs6Eizdul3nrGTmxfM8iO8O7uciwhME2LQ8cbOavwIJOGHkNBQaBiU31o6l4URYmq4aMqHkQwVwtEKD0/iKKAYQaXs6mT2v2GqikK6/dWthn7EKrkPLXwAuq8dfxs9XbK6/xY4ihOm0ZeegpFQwdFBUy3ltq99/gpbDhQjaooqFpwH0fqvQDYNLVfvmEbwolupWBXfWERFMKrOwhYDl7fdoAxuRk4bCqpDhtCCN7bf5xNr3zOyOy0mNfm7a8P882xU7CpY1EVH5ZIodyj8fbXh/uloOwJuqI+lYw5kvRF+tdTuZ8SymIxreiJxrQEk/Oz2FVRF2XeFkKwp7qe/95TwyMf7mTFhzt4c8dBLKv9FqK2CJnXJ+RlYgoLb8DPhKH1RKaXCQSWCIoaBYs6vZhK34VU+uaS6crDtDROSKDQMYApgv2/VMUXfkNNlHAFaqIrUK/ZdSjm8qpiY+W6g+w84uZAbSNH6r1UN/g4XO/h68o6iodnh0VMKPPNqzecSO2u2MSB6vUYZvRxKApUuL00+Ns3/t6MYeq4fTUYpk6WKxW/NRyBIFoHCcrduaiKnbLjjZhN1j6Akqp6jnkNKtxenE0tLSKvTaTLRmDDFOkIbGGXTajQp6RzxHuuRN7v7aG9f3cSSW9ACqE+wsJJhcwoCLqkPIFgnZIZBTmcOzYv/DlESVU9h+s9mBaEnmWtPYwiJ7WOEDKvB5+lCmkOnQynjgAsgmIGgsJGVbyoTT2+DEtj8rDRDB10CsFpLfpt0RIqQijoVgqWSGnXG2pbsQ+xJtLVOw6w4UA1Q9JTyExxIISg1uPnWKMfTVG4ePyI4Libp3YLmkoBQJr9CC57S7GpmxaaSp+pAK2bFjUef4vzZAmTLfv/xtrtv+Odr37H2u2/Y8eh9xmZezb1gTH4jOB58+p29hwbRqVnIqawECjhuDHTElQ2+FCUoGs0YAYtgZHXJnRPxaK9glgSn9aeK13ReFgKWElvRbrG+gitZbHophVl3g5NNKqioKmEJ6DmAZCJVO5NhMiijPtrGqn3qdT77AxKCT5UhQjWonZoCpZw0eC343KceNjq1jC+PHSUdNteNMWPEEERpFsaiiLwmcMwLI0ZBYm/obY39kE3LTYdOoZhBhuwhlAUhcaAgVcPruO0aydSu1HwGSa6KQhGOYFCgIJM2Fcjorqia6rCzMLBvd4t1pZbo7UihycPBVU9n9e37aW6oRZLpDAkPZ2ioYPQTUFhVmp4cgyYJgEjOCHaNTWqJ1jo2gzkliLdTTKz47oy5qi/IYso9i5695NZ0ibNzduhiUYIyE2xRb2dRb5Nx3TvVG5i24EPorbflsUotH/LAo9uYKGxsyodmsQBBMVQik1lxshp3H7eFG498xQunViAqio4bXZGDzmPQ42X4tZPxhCZmMKGR3dwqL6QemNqu99QE6lAHWnxcPt1DFPgsKkcbfTT0HSObKqCJeBoo591eyuAE6ndQREUsmUFZZDfSMGjOxg+KLXJ4hGMgDpz9FC+3wfiWuK5NeIVOTx8vIQFE4fx/LWzuXHmNM4aM5yR2WkoisLMkblcPW10+P50aBoOm4oA8jNcUfdnSOR0hctGEp9QkoTsiScZiEiLUB8h3tt6ZMp4aELPS08ho5kpOvQwSqRyb2TdoLYsRgsnFYZ7lWko/G1PPioKk/LcDHIaNPrtTBw2jTL3eNaWlsYZvxNvo590p874ofn86OQRZLoc7X44t1YZ2GgqlvjEJ7uixnDx+BFkpNgZnOZk39H6qPOiKjA8M5UdR45zzhgvOalOhmUVUeX+B9HuPIGljEBV7CBVIHgAACAASURBVIwbMogRmanYNIVTC3O5YsqoXh8o2pZb49wxaW0XOUzJYfGpY1pkDFmWQFGC96ffMMlLT0ELNFI09ETl8Oa1ngZaS5H+wEDsideTyIyw5CGFUB+hrcJnkebtT/ZU8NXhWo4cacSrG2H3Q+hh5PYdb3NS21PxzzZ7PYVQVYUbTx3LK1u+o9rtw2OYfHFoFP8st8hNNUmxpzMyfwKlVS0z22KNPxlZbrEmUsMS2Joe0M3HUDw8G7cvwM7DNrymiWkCiqAwMxUV+Ki0glpPgNw0JyfljKXWV8EgZwWa4sMUKXjN4dQbUxiZbfGzs4uwa2qfSh1uy61hCGfCRQ5D1oVQrFGG0x51fdPsNn77znoaFKVVkTNQChr2N6SAlfRFpBDqAyRa+Cw0AV0+qZBNh46xtboRrb6SNIeNWSMHs2BCARB077jsGXj1+mAT1YjNuhwZ2NWUsMUoGLNjoSpq3G7zqQ4bZ44eyuffVQGCynoP1Y0GRxshN9XLqs+2MCpnMKcMyQkLsNbGnwxi1RN64pNdLZYLjeGeOZMJGCYbDxylutGHxzJJs2nU+XTqKo4zJD2FLFewHXlJVQP7jo1hQt6UcGq3aPpTSnW07mJIVi+urqAtt0aWKzVG8cnoNish4lkvQ9f3vMJBTJ02pU2R058LGvZHpICV9EWkEOoDtDcI8e1vyrBrKtOHpJI7NC9sEXr7mzIWTh7B9kMf4Q3U4zMaUZRgM1W75gxX7tUtH55APT49gBAGwZwoBUWxNWUFxe719Ov501i2dgt//Oe3HPfqoAgWTahiWn4jGSk6Ht1Bgz4Mm+NUQuFpXR1EGZpIazz+FufQtAQB08QwBP/91X72HWsgYFo0+g0yXQ5y05wcOu7BsixyXI6wEHXYgmP3GyoOW3rU9mK5AJIVmN6VJOLWiCw+2bzNSiSJtm2QIqf/Iq9tfCK7z4MMnu5ppBBKIl1VTbU9QYiR1iNVUXDZT6z31eFaRqR/zd6qTaAo2FQ7pmViiAA2zc4peTOZNupCLMvCp1sIERkgLRBCx6fbWu31ZLOpKIpFXrqOJRTmjq3i9JG1IBRMAS6bTobzIH7LQb0xLeb4u4rIcyiEoKSqvqkStkWdL8CB2gbG52XismtkpTpw+w1008K0BINS7KiqgmmJsEgozEplQl4me4+523QBtJZtBS3djD1JW26NyOKTrVm2ZNsGiUTS15BCKAl0dTXV9gQhhqxHTpuGz7CiJm9vwM+hmt1h14bdloJNBNO/XfYMJgw/n+NeA5tqYTTVvmmOYZoc9xwjKzU32h0iTL7c9z6DnZu4ZaafOq9GhtM4kTsmwFIEpqWQoh3BbUxuSovvniDKyHO4pzpYZ0lVFEKns6rRh1kpMCzB4LQUclKD/9ZUBS2i5o1LDf7JpDntXDN9NEBc8ZtIYHpvcZMl6taI1WYlhEyhlki6B+2OF2XAdJKQQigJxHMFJMtXnmgQYmTfr5o6L4f0qnCbiHSnTsBoiJqUFUVBEQq1nnqeXLcJtz8Fh9bICJeFomgoioXSVClHCFDVAO/t/AMZKZlR7p1tBz7g26pNuGxBa9CgFIN0h0nAVPEZJ45bCIHWVFhxRkFhtwZRLpxUiGFZ/H1fJX7DIsWuMSQ9BUsE6/7UeAJh0agqwaT4wWlOjjX6o2reNBeg8Sb2cN2heNlWrYiKnqIzbo0Mp500B6hKQ1TsFHSf9S+eZVb2wJJIJM3pESH01VdfsXLlSl588UUOHDjAPffcg6IojBs3juXLl6OqfecB1ZorQFXgta372Vpeg083O20lSvRtPdT3y7IENiUoPA7XezCF4LrphWhmy8wfn2HiNx3hCs6WlUqD7iBdMZqyrAQKBqpiAgqaqkW5d6YWXkBZbQmaqqIEF8dq+p9Ns8BUQQRjshVFIcuVyQ/POBWXo/stA17dJBAqKyCCtYJC59EwBUPTnRxt9CMQ2DSVSXlZlFTXoykKfsOMKUDjTa6hukOJZFv1dSxhsuPQB4zO2EnAcGOKVLzmMOr0KZiW0qr1L1niJJ5lFuiRHli9OUBe0juQ8UHtoyv0Q7cLoWeffZY1a9bgcrkAeOSRR1iyZAmzZs3igQce4KOPPuLCCy9sYyu9h9ZcASVV9RyobWBUTlrcgNH2Eu9tPSTKJualkWr38rXXCnZ411Q0ReGSCaPYVd4s80cE1/Oaw8Nv76pqp86fT6r9IKARDJRuKh6o2AitGnLvjBk6PWz1cGgqfmFhKgq6peC0CVQEQlHQ1KCVJT/rlE6LIEuYuH017Zpg1uw6xO7Kepw2DdHU+qPS7WvqaaZg1xRsmkKtN0Cd10+6w8G3R91cN/0kLpkwguO+ABC0AKmqkpBL1KbZE8626uuEYqGcmoIQGpg+0mx7ARg95LwW1r9ku5TjWWZD/24rgDtZ9IUAeYmkr9FV+qHbhdDIkSN55plnuOuuuwDYtWsXp512GvD/2XvzIMmu67zzd+9bcs/a967qbnQD3Y3egQbBFSRFmSBIEKYoiZRGjHB4xhGzhUbhcChkjsb0jCxzxpItRVi2JhwzE6EZURQXmwtEEABBiiDABSDQ+97orfa9Kiv3t97542W+yqzKqq6qru4G0PVFIAJdlZn18i33nHvOd74PnnjiCX72s5+9oxKhRkTmqs1FRNfqLAQ2Shhd6455oVwmLk+SNifp21bi0U6NsgrUmS1XUXDcZZM/hp5kPttM0T9Y91ma8SgjWUV/0xy6LCJQCGEQM6J1ryvZOVAirHqkIgYKB8/2sT2J7UHBNmiKepRckwWrm3/ywJNr/u5LUQ0w16xTXDv9gzUHmGqSWBWbrHKEhABdCNoTESazJUYXSnQko+zramJnawJdaigUz18aXRawlYKTo7cOrmudtnonYykXKqprmJoPaHQk53n64Z5lyc1ap8vWgtVI2idG5gKxzyW/06XHufFhntzTtenVyXcKQX4LW3gn4U7lD3c9EXryyScZGRkJ/62UChfPRCJBLpdb0+ecO3duU47n+PHjt/0ZyVKWS3Ol0F+q7PrMZop0JwwmJ8brXmt5ildft2iK3HpX6CvFKyM5ri4UUNgITHY3JXhiW6rOy6qKMessTcY1UIE1hKn7mFyjVCwyPL2di2d9oroEOuhRrbiyDF6EF0fnQE0sPwDRz1Ndhyi4C8zxJh42jlNvtaGLKBfP38Cx09jMIRDEJEhDUvZ8zo+38fpIH2nTISqjfHhbMz/75UkShgzFDdeDCecsc+6NwNbCgawzz/nCLxifmKDbOLji+xYsjxvDM0Q0QUopoq7NbNnFrbTsPtKh0eT7OB6YmkL6HpmZIgD/6eog25JmeLwZYGhkhNGcw/am5QH0+clxespTS77f4jnXvShqVuPk7KlVv+tm3Jt3C0UvT8aeQyLwcfHxqMou2G6Jn7/5U1wvEV5311c8f/x88JIlaHz+Vkft9V2KrO2hgCaz+swp+ltv0pyYxdBsvvXLV2jWe+jS9yPE7bflfeVxbfQUrlpuHHtl9BTeTOvbpirk+oqC46/7eXwn3ZsbwTvl+71TjnOzsFn5w1Lcc7J0bT+vUCiQTqdXefUiDhw4QCRye7u448eP8+ijj97WZwAcPVpf4o/oGnYky57OdEOS7IceP7imitC3z9xEps5zpGUCTZTwVIyi282I+WF+7dCOute6nsPomZ8SUUbFA0vg+x4+klhslhm1mxfnjJDfUrs7n4wN8cbQLJ7yMTUNrTIqfrSvmUT6EvPzl/FLZXzfQ5MaUkbQhEAphZIP8IP5KEX7AD0JjdboJKZmkUqkKDo9tLfu5gMxl6aogesryrrGi7ON2yC34lNUv6MpTRzbwTAXX+MZCxw+dGjFVpPj+byaOxv+u69vUUfI1DT+uw/u4c9evtCwunchN05HV2edFEHJcblcmKaru3tZpaFouzy4/8CqLcxbVfg2696806i2t86MQUckRkTLI4VLhREGgMLnRGGQ8cLB8Lons+M0t3WuOF222vlrhKXXtxZdFYuP6uahyThNUp+qHJ+ObvjkGKGnvXtTqjW/eOMV0F0M0eBeVC57D+wmFb23BPnbaUu+U+7NjWKj38+yrE3boK8V7/nqhRV/906aKNvoudto/rAU9zwRevjhh3n99dd5/PHHeeWVV3jve997rw9p3WhEZH7uwkhQ9q9ZU9bjueN4PoMzr5AyblANKpookzJuMDgjcLyBus+pTidFda3y/mD820cR1W0cr8BPb0xxaWoBXyk+e2g7ECyIvlJcnckyOJdHSsn2lgSfO7KD/tSFsLxvaFFcv4zjOfiuh6vS5O0eMs4udA3ipsmCc5h5y+FQb5RPHQj4L9WgX7X9EGK5vcUzB/o4NfgSw3OXKdhZEmaa/tbl7a6Sk6NgZUGAWlJKuNUEViMJAk0KTIIR/tZ4pGFQtj0PhahrcUJgHipQdSP1Vaw0HbVZnJjNnHy63c9abG/p2H43Ue0SSsFi/q/wfI2YNkHCPIgiuO7JUoF4oq3hZ25kuuxWEhNU/q4uPWLaGMEzpYLvLALj3M2SM9BF9G1PkN/MtuQWbg9LxRVhi0C9VmxW/nDPx7P+4A/+gL/4i7/g85//PI7j8OSTG+eP3GvUOjg/s78/XICLdlAiX4/nTqZUJCKrC3YtBBE5RqZUDJ3hS7ZF2TWDBVZA1NBIRirEZyGwXBPXj6CUYjJX4punBkPn9O+cG+JrJ28yXbDQNIkmA3NSx7MZmrtUcVYPJsscX8P1I7gqyXjxozx7uYurM4W6o5PS4My4y2zRDYnaqYjBuYnMiiJ7b17/AWdGXmc6n6Fge0znM5wZeZ0TN18KX+srj0vjr+F4FpZTCNp0bjkkPa8lwKx2TVZyPNeEpL853vDYtzUn0Ja0UlZzSF/N3X0t8H3Fd84O8eUfnuXLL53lXzx/kv98+ia+36C/tI7P+jc/OseXf3iW75wdWtdnLeXl5Nxd+EqrSC0oBALP11EYaKKMFGUgOHfXshYHups31WF+tetb/Z0UZaQoAcHzWt04wGIyfbuQQmNby57w3qxiswnyVS+36rO8nvetJnq53s/bwhbuFTYrf7gnFaFt27bxjW98A4CdO3fyla985V4cxh1FI6+rsuvhKYVcltwshy4sDFlmMRFSKBVQKnRZ4sr4S8zkh5gvZLC8KFm7C1020RLJEDMCKwxfgRSKkWwbngoWfCkEw5kC12ayDLQk+eapQSZzAb/JrASfi1ML/PtXpvhHR+YRQqJrgoLl4itVYX2U+NmNURw3wmS+zIMdaTQpQsXm4UwxNCg93NvC4wPtzBYsmmtsKiAIegulEhfGz+H6FRuPyvd1fcWF8bMcGQh26KcGX+La1AmkkPgq2M27fsBX0rXImgLMrSQIGmk1PTbQxrH+tpAUXXvsv3lkO1KINRlMbobi8rPnhzk+PMvVmVyoiv3a4AzHh+f41588uq6q0mZUBJZOTPoqjqvSaJRQ+MR1A9v3EAhcFWGmIElGguTYchUf3tUVfv+85aJJeKy/fcPaUre6vp85OMCTe7p4/uwbOG5h2R5jM6s1d5Igf7uVxS3Ryy28k3En8od73hq7l3B9Fbpj3ylxNU0IXrk2ue5FKxVNY+pJbDeP6ytszw93mFLAxfFTGJoWqB+LEinjOldmu7npd/BAa4amiEvB1pkodHB6cmf4uTMFi2zZ4T+8epmYqXF6bI6uVKzu9wXbpeQIXBXFEDYFy8X1/VBkMGfrDM3bRI0ShtYctocuT2UZz5YwdUlzzEQpxVdP3OBvT15nNFMOJ7Ye6khxZToI5oYssKc5i5SSwMKrek4EtlsgV86SiqbDiSRDCxboahLkK8XuzqPrCjArSRCsFEj9CsekUcIjpViTaObtBp9qInV1JhdOvBmVvuvPb07x7bND/Prh7Wv6/ptlg7F0YlKhU/J6SOrXEWiBrhQeludyfLSZH1wbxdQkfU1x9iWgKWaGIpdvDs/ieiqsHN6Ovs9qEhMxM8KOtr1By7cmE9rsas1a7Eg2ittNYtdj2bOFLdwPuC8ToeqO6vnz0zRPnNt0cbVa3kXIFVrnoqVrBg/3HODU8C/qkiBRqcmUHR/bVZi6huv7+Ar6UrM8f/VRzk/D+3akeOHMGIlkc8jXmClY5MoO6ahBayJCyXHJ2x5awaI9EUEpyFtBguH6krzdQ0v0Bn5diV/huJJ/9MhlkqaD5UXQtQHm7YNM5EooFF3JGL7vcGl6kslc0C7ra4oxmSszli0ymikgZFD9ScVTFByThOng+tRNrjh+FFdF6tWZBRh6BGyJbgQaR3t63repUzhLA+mtKg1rUWK+3eCTsxxyZaeSPPpEdZuya+IpDc9XvDE8wzMH+teUwGxWRaARL2fBOYhSirbYFAKLvG1weryZnwz2oVUEPofm8piOhqFJvnN2iDNjQRWTyim401yVuylnsJodyUawGUnseix77jW2BCm3cDdwXyZCIVdDLSfu3s7iu7RkHdE13poOpsdqsdZF62D/x/jJtUks5yZJ0yVv6wwvJHm4Yx6foBpioMJEJarbRHWbTDmC48dpjZqkK0rJtudTsF2SEZ393U1oMjBkbYoa5CyHtngET/n4lXHEdNQg5x3CKyl8b4iE6ZCzdBxP0hIvI5AoBGnTRZPXsVwf1+umLx2hM3aWuJzhA9scspbO1blmOtPvQwrBWLbIUKbAjtYUPekYu9tTjOXaeLBtnIAuEiR6oLD8XppjcQSRhuRTISQxI3nXyKe3Yz1xu8EnFTHQNcXBjqsMNM0Q122KrslItp3TkzvxfNacwGxmRaBRO3Fn50f55N5uFsoL/Nb/90uuzpbIWW7oe5eK6GQsl4WSfccMWlcjgd/Jas2dxmYlsWu17LlXuF8FKTeTJK39s79e1+vfSVNmm437LhG6k+7YS0vWJcdlKBOQifd2NdW9di2LVsH2uTz7ED+4HKM55lKwg8W6vylP0gzIoEqpcEqn7JqUXTP0xepOmvzqgX6uzuSYyJZAwUBLIkzMNCnY393EqdG5ShIEmpTEDa2SLGnk3CN883SKuG4xV1T8k2NXA46OCtpFVXmA7sQMH919gGzhDR5oHYeKN1k64vJIzzTX599kb9cH2dYcp2C7PDbQRrIScCeLDwNBRcuIufgqStHtYUfHE5VrIe+pOnOVlAqEZPiN4HaCj6FJDrRfo2SNIIWPEGDqLunICMmIjm72rjmB2cyKwGrVsuxChOEFO0isI3qFswYIwUzJ5epMdtO5Kuvhz2x2teZOoprYRXVtU5LYtVr23CtsCVJu4W7ivkuE7hRRsFGCZWoaEV1jIlcKCcVVrGXRSkUMOpJRpNRZKC/ugq7ONnO0ZzpcJIRQQECKdn2NnnQMTQoiUvLxPb38+qGg6vMff3p5WQK4t7MJgWB3e4qs5TCRK6KUYG9nkLiZuqQjGefylEPatGiKVhKwyse4nl9pzxVw3BydyemQe1FtqAkkXckZiq5NzDBIR806TZ6HOpu5PHWAc9MFjm2LEzOTHOrtqEsQlrYzdBHloa4jy9oZmzla7vuK754b5hunbjI4X8D3fba3Jvn80R185sDAmtqoS49no8HH9Rw03kKX/uJ5FaALn4H0KO0tqXV9382uCDSqlrUnori+Cu4GIeqkJGyl2N6S3HSuyrttLLxRYld2PHQp0Guu90bbWrdT5bxTWKpSXkXV0mczJA62sIVa3HeJ0HraAusJqo0SLE0KupJRBufzdXoza120XM9hT4egL2UwtGCFgnAv3+zDkIIjvUUMaVN0TK7NNnFueic96RgPdaS5MJFhPlvmX790luaYySPbWjnS18Kp0fpkzVfw+aM7wuCcMPRldhL//Qce4n957hQFR5G1dFIRF0EwgeapIAnzVBD00hE3nG4Lac9CkTJtZrMZEtEOHh9or/ueQgge7EjzG4e389EHu2/ZzsiVsxw/dYWD/Y8jK+Prm+1bBUFQ/dsT17kwuUDBcfF9mMiXGVkIqnxVLaZG8H3Ft84OhiTgVNQIj2cjwSdvzWG7pSDxhbrRbEOz+cgDiXV93p2sCFSfG8fz6UpGGc8Wl1Xy2qMaUopN5ao02oxURTNPjs7dVrX3XqFRYldV5tY13pZtrdtFHSdw6e9uoRe2hS1sBPddIlTbFqhF7eK7kaC6UoJVbUNFdG3Ni5bruXzz+LfIlm4gKfIb+03OT6Z46XoPjidoiZu0pj7IP37/fhy/gBRxvn12jJZ0lrLjcXl6gZGFAuWSy4mROUxdcmlqgc8f2cEj21pXnH6qBuelAXJoPk9XOk5ESzKRb6cpOgE1VR9fKebLXZScGGXXJG44YSKkCQ9d+CgBvYnXQAzwP/7Kb/PClYkVj2Ml+L7iexfGOT02z43heX6WPxtel2+fHeK1wWlihr4plQDH8zk5OsfFqQXyloMQguqhjS2U+NrJm3x6f2Nysu8r/vD7J/nF4DSup8JpObeiz7KhykRFMqCK2iAhlvx7PVhLUrZWwmojjtxASxxdCkayRRxXYeiCgaYEe5OKVMTY1MpU7WakKuUwkSuFSuu721P8ziMP3FG3+c3ESm18XZPoGvz+R/dTdr23XVvrdhEzUrctSLmZleE7iS3hxLcH7rtECBbbAs9PjjdcfDdSXl+Jd7G04rKWB/Obx79FoXy+ItYnSZgu7+mf5fEd7Xxg96cYaFl0tI8SBLEvHHsg5LL8T9/6JbqUlXFzEQopfuPUTf79Z9/Dx/f0rrqAup5D2cmRjqTQNUlPOk7C1PF8nx/f6GO+aLOvM0s64pIt65ycb+ZH15Po2gSfeqiVve0TCCHQhIehBcHf8yVNUQ+4zrdPf4fffs/n6s4JQKZsr3p+aq9L1VPq+PAsP785xZvDc9iuh6FJulMx9nSmb4v3lbMcZitSA0t93XwFQ/N55opWnfRAFd8+O8TPb06FI+5KKcaygW+Zrsllx+N4PplSEV1YpKLphslGMtpCxIhTdorLfhcx4iSjLev6fmvBegmrjZ4bhaC3Kc4Tu7rJ2w5J00CTgiZrHkOTOJ7PE7u6bnlPrgW1m5GqlIMQwbMphODSZJZnzw+/Y1pkt2rjl13vbdfW2gzomrFhTuCdqAxv4d2P+zIRqrYFespTPLj/QF0gjurahsnUq+1uaysuq6FkW2RL15cpFoPEdYfY3hIltkJrz/UcxhemGF/Iomlm+HOlAr2kwbk8/+oHZ+hIRsPFoRYrBb5D/b9KS8zk1esT2K5icL6Lv7vURtwMEiFN04kbPp1Rk1MTOwDY0TxLc9RGKYGvJFIYVK1CsqXrlGyLmBmhOWquaeFaaXd8dSbHcKaAryASCBExng2Ug/d2NW2Y9xUEVY3lyt7BvaDJxveA4/m8MTyD5ytkDSlGCsFkvkxfUzw8noCDNMjgzCtE5BiGLGPqSR7uOQCq3n5C1wx2dx7j4tjP8fFCs0GJxu7OY3eEM3H85ku8Nfkm2hLCqqdgV9dH65KWla7Pw11NXJ7KomsicKTXgw1DT9nhO2eHGl73jaK6GXljaJaJXCmUjfCVojcVw9TlbQ9EVHE3Kg73s97PRiUO3m0csbuJW02ZvZunyu7LRKgKXYplgViIYDd5uLdlWbvhVkF1M3gXs4X5igXA8vdJUWK2ME8r7Yxni/Sk48RNvaaVdh2hinxit8bQQitvZrsAmCmUWSgF7Z2YEezkT4zM4XoO792eoC3RQsyMrDipcXkqy0Ptu3hzeAbHc1BKUfYkJTdwZPd8FU6R6ZrO+ZkH+W/e9wl+ef2vkEJDiCpnaFGZerYwT5fWyddO3uDSZBZTl6suXDnLoWRbJCMOvooCQTsz0C6iLgALQUhQ32jAMDTJewba+f7FUQrWoou4AhKGxkBzouF9kLMcPJ+G1912fXRNhMfz7Plhbk7/pM5PznbznBt9nQ59O/BY3fsf2fHxQBl87jJFJ0vcWPRk20wECdoNpuZPIYWLIKhsRXUNy/U5MXSK/3KulZgZKIc/tbePkYUCubJDKmogcJGiHFwnoTPQkuB/+MCe0G7F0CT/7jvXWIgYmx6wntnfT95yeeXaROARp0t6KxVCuH3l5LtZcXgn6f1sNjYicXAnJ4K38O7GfZ0IwfIdhFdRm748lV028r7WoHo7kxhtiRZ8FUer+DLVwlcx/sNPh3ht6HQYdN63vYMD7W+RK5+nko+QNF0e7pjAdmxeH48wnS+QNF0sz2RwvsC+ziTKPcHg1CTzCw4+cVLR7SSMyeWTGggWStdB7KC/OQkoBufz+ApmCxbRSmIVM3XytktrXFF2PEy9CUUC1y/hK4Xvq5A3VPZM/uLVISLGOH//1mTIodnTmQ5aaksWLl95XJv8MTvTp9FECU/FkK0p8l47jucT0TU6EhEmcqVQbDBnBfIFH9jZueHF77MHt3N8aI6/uzBMznIBRTpqsq8rzeeO7Gj4uamIQTKi052Kha2ZKnRNcKy/LWwJnRmbpis6Tn3VSeD6kHXHcT2nbvG/W/o3z54f5uzYGL3xRYsXx/NDDypNlElGHFzf5KsnbvD1kzfZ1pzg5OgM7+8fZlt6Fk2U8VSMktcDHKyTHXA8nyuZMr09tw5Y6628SCn4/NEdXJnOYnsengdZy6bs+MRM7bYrKXe74vB21/u501iPxMGWdcgWNor7OhFyfbVsB6FJQW86zviSkfe7tQuLmRHSsZ0Uyueprwr5DGZaefbiOCU3mGCaK1nM5Qs0H75MqvJ8CwFSgu8LdrZmUeI6D7Qu0BRxKbkRJvLzLOQVD7VPoJQAIdEoU7QuYDs+MbOe8+ITVKJiuo2pS2zXQwqJJhbDtxQCKQS+H5ynhKmzo62Z5+xu4to1grmy6ucprkynuTQ3R3syQuChtsihqSaftQtX4DP2JhHdx3EDS5HOpjwJ7wKa7KokUUkear1CU2QiVF3uadrD0w8f2fC1kFLwrz91lGMDbbw+NE3R9mhLRDjat3Igqu7iFeSzRwAAIABJREFUq8ToKllXk4L37+jksweDSbOc5WC7hUrCW58QKBQO5RWnY1YKDpuhwlvdVQsRw1OxuoTc9nxMTeKpKL6KcnkqG/rUPdiR5v39w/Qkh1BKBveVKJPUr9GVjGJoR8PPyVkOltvY3LV63dfaMm0EQ5Mc6GniXz5/ism8FZr/diUj/G9PHbktkca7XXF4u+v9vJ3wTmwlNnKeX4otQvWdx32dCBUcv+EOYk9nGsv1sD0P5XLXd2GfOfIZvnHcp2zfRIoSvooRj+zge5djFO2aCSYFShWJ6ja1SZMg+H17wqYtPourgkQlFXFIRcYrjt8SIWpDsMRXbijOuPjToBKFiNGV9BlZKCBl8LcjhgZKYVQcvKu0mff0t/H9iyM8f6Wb3c1Z+pvnAmK1pXN5ponnr3TQk7YQYrGlVeXQVJPP6sLleg5Dc5cp2mVQHppUQQKHIK6P84Ed+9CkQZNxhog5hKcCMnNUtylZ5zk5mOaxBzYuwCal4NcPb+eZA/1rDkTV+0TXZOBQXzET/bWDi9pDqYiBqSeWJRvV62cQWbNi9maq8Nbuqqu+YVVjl0AWQVHyenF9jcl8GSlEpVpksyM9i1KyYs6rKi01jbixWN1yPQeh8sSMxg7n1et+u5WXr58YZKZgoZQKDYFnChZfPzHI547svOX7b3VuluJOVxzejno/bzfcz63ELdwe7utEKGHIhouaEILDfa1rGk/dbAG/xV3wg8SM3exqNfjs4T0MZ8rMvfhDjJoHXAE522ChrNMW91E1DvWgMAU4qqo7E1RjNAFxw6XsGhXCb82Emw+e76FrWs3fUKRjDzBa1EKexULJYb5k0d8Ux1NQclxsz6ctHuFDD3RxrL+N1wansT24OPcgf3c5Q1S3Al8yqeN4DpmijeP5PNzVxHzJQYiAQ1NyAj7O49vbMTRJrpxhNj+FEF54qEIodE2hVI5//iu7+dHVDNfGh3D14CWSILGyPcWbQ6c4un19AmyNrumtAtFqwolRXaPsenhKIStfwtAkh3o7uDHVXcMRCs64LgVpvWfNx7yZKry1u+oF5yAAMW0MTZQpuya26ifrHsT2PGzXx9AEhiaJ6XaQ0AmJIqgKapVMu2znKNoLXJ54k+G5S9hunkcHBDl/lqx7iGoSXw1YELSCILi3TE2rENTXVnlZKNmcHJsLlct9pcLJv5NjcyyUbJpi5orvX8u5WYq3a8XhfsP93kq8k1iNTP1OJ1Lf14mQfgtBt7ipr7jw3SkBvxMjc+jSI2GUKLkml6Z9XroyxbH+tiCwVFCsJB/Kh7MTST60Yx5EJcwqBSKwzPB8FR6PUuDVfHetUlZy/cCvrGhHybr9tEZniOpW3dTY9y6McXpsnoGWBLvbU+Qsh3TUxHK9wFG8Oc4XHnmAiKHx5R+eJWbomLrE830sT1ByTFzfR5culusx43vMlyQdyQgSgRAwV7B4Y3iW1phJ0hTgz7K/U6KUj2hwSpVSeOh8cGeK8Rk74EgRtPNUxTFeqALzxQwdqY5bnn/fV3z77FBl6guSkZWvaTXxaSRAWX2PJgSvXJtc8R55Zn8/31UfZnBG1EyNJXi45yDMtq1wlPVYiwovELbMFNqqo/r1u2rJgnOYrLMfVImya6JrJpoUmFpARPZ9RU86qBhWq1sCESZBAFEzxffO/JCidanCExMI6dNi3EQIwXhhf13AmitanB6dY75kh22tqiRCyfFuWXm5OhPoaVWnCGvlD8qOx9WZLI/2t6/09hWxloqD4/ksWF543Fu4u9hqJW5hI7ivEyFovIM41NvMB3d2rrqYbRZpsmi7jGeLtCeinB6bpcU8g2QUQ5QoCJPxfDvfOLmPX32wh4GWBMPzBUqui+0GrQXX93nhrS6EgINdeVLRYKT9/GSC/V15mqJe6PEUtAjAVxE0GYSkwLm+atHRzpnpPmy3gwda4emH+zjQtx9d0/nUw9t4344gmagSXxtVTuaKVngeu5JRhjJ5NCmwXYXrB/YQMhwjU8wVbTqSURzXZ09XEwd7mmiNnCGlXyGX93ktD9VLsJRV4ikYy2Qw9ARZSydpOgCLFh8K8rYRJFy36DJVRRB/fnMKz1dh8F0qgrg0AR6aL+Arxb6upmX3QfX/V7pHpBT82qEdON7AsuTk+NzxNd0/q6nwFq0sb9x4juncIEU7h+1FKdgADoa0wlH9R3bUt9AaPROH+wZ4et82vndxhNNj81iuR1cyiq8UezrTKESllXYNQ9PCJEgpRcHpIle+GYzhh9dOoQvBjuZ5fuc9e2iOxcN76OVrE8yVbASL03e1kgi3qrzsbk8TNbQ69e0qoobG7vZ0g3etrbq7UsXh6X3bQjmAG8MzvJo7u6Vfcw+x1Urcwnpw3ydCtTuIhZLNT65Ncm4iwxtDs+vWtFkPadJ1ff7FC6d4bXA6bJ8c7b5OV+dkxYVdkDAddreO4Xo+3zk3wD/90D7+7U/Oc2kqG1R7VFDJSZg6z13p4fuXXZIRl7Jj4CoNIcZ578A8KPArvCFNwp7uxxhdKJItXcfzC5Rdk+GFNk5O7GC+VOJY7012t2aYzrzO//OzF2mKP8Cl2d1kSm5o17GSVURt+2BPZxpPKUYXipRsNwzWi0FKkLNc2hKKnO3yse5mWiNnSRuXK6yUxlAqEGjM2zH6Wtq4MPoTEqZD3Aw4Tq4vsb3g/F+ZbaIr1TgLqpKLdZng66eG+dmNKTQpQv2favCtFUGsE3XUA56M7wetlyrRW5OCEyNzdRyo1e4RQ5N0JJNAsuFxrhagV1PhBRicOYeQAsv18fwMUc3FVzoKIxzVl6K+hbbarrr250urYXCQrmSUuDFOuaL90tO8hxffStAdvcRSUrjjKcpOjqhuY2jJ8Luen1igJxVjLFsMqzlCwFi2yG8c3n7LZ6spZnK0r5U3h2bqnlvfVxwbaFvWFltPdXelc/Ods0PLxD639GventiMoYItvLtw3ydCVRia5Kc3psIEZ+kO/umHe8KHJ2c1JllDPWlyqWs5EC6eX3r+FM+eH6LoePhKYWqKrl3TOJ5aEjwF25vnuDozxxc/dpSi6/GXP7uM5XoMZwqARszQcH2HkqMxX5IYWvDfC1e6kEJyoCtHOuoiRIyy38fju54kohuMLWT585ePE9GTnJmeZ7ZY4rHeGxztmUEhQAg8v8B8/iz54iwnRneGdh2+Ug29tpa2D/Z3N+O4Hr8cmiUV1clbQUuvOkkVONQbWK6P6zvEtdEVkyABFB0NIYKWDGKAqxOvcmXiDRQC25MY0sfQfGxPcnysnYszu5gplImbehhAq+Ti4fnLzBcy2J7B+ckUo9kuOuKKuJlEoYd6RNuaE+F1q02Aa3kyS411MyUbIWi4K10rsbYaoI8PzzKVL9OZjPJof1tdgF5Jhdf3g3aiqFTfHM9HE0FjVAgPpXSqo/pDc0ELTaHVBfeVdtW1P1+eFBytCzRZyyd/7jReZDkpXKEw9SQxIxW+p+yaFG035KNN5svYro+pS1pjJh/e1bXqOavib3/nQ/z237zKydE5yo5H1NA4NtDG3/7Oh5a9dqNK8tVzsKVfs3HcTSuMzRwq2MK7C1uJUAUrL2aKm9Mv89zpeUpO8PD0NO8hbjZekOOmTsLQ+faZIb5x6ibDmSKgkEBvU5ztLUmEEPzNietIsTh6njBt0hEXT4FUoo4TkzAd5gtFCo4baqTMFS2mz5XD4zU0Wam6BJWimJSUXfjh9T5eH5U81KHz6LZtHBvoIqIHu6COZJJktJWi7TCeLTJXKPJAawZfVeX9Av4DQtCbmiGqb8dTgslciW+eGqzz2nI9h1w5i6siPLW3D1hsHxzoaWWmaCMFzBUyFB0PRJDuFB2XG7M5tArhVorlFhJ1UFB0TQrFVn7/qd/kPx//j4EBpRQ4nobjVRKRss4Lb3USNcr8s+++yZG+No70Bbv8U0MvcWXiTcquhVIuhixxuCfLvo4xLE+St0yGs61cmduN7QVVtFTEWDY1ZGqBUrJSCsfz64x1m2NmQ15T9R5ZC7H2O2eG+HevXGA8WwpH1//+6gS+p/jskSAJdTyfgY4n8BSMZxZVeDtTA9ycDUZz/eqJqySYIvz/QOKyaGf57rnLnJvwNsR3W5ow1Y73pyI+MTNSN4FWhQC2tTzEmeEfhcEpZqToSbSw4Bxkb1cTD3aksT0vJEyvleRsmhr/5R9/hIWSzdWZLLvb0w3fu/S5r5q0mpq25iRmS79m/bgXVhibOVSwhXcXthKhClZazJqMsyT0axQdE1l5eK5NvclDLQ9xcfahhqTJ710Y4SvHrzNbsDA0wUzBJlu2mS3ajC0UsTyfqXwJQ5MkTJ24oVOwjQrPxQ0c3Su8nsAzLIapJ8Jd0yPbWnnl2mRdoI3pGrap4/o+rucTMzR0NCKGhuMpUtFWjg108cm93eTKc8SMFIYWOKJ/5fh1CpZLwnRIR6oqygrPX3SRj+k2Ud2m4MSQQjCSKTBXtOhImpy4+RIXxs9hu3kcP4rl97K9/Qn++a8coOC4pCIGj15o5fXBGa7P5rB9FTqo65qk6PgkhMT2IhUxyYWG18hH8lbmg/zTj76PmRvXsNwSths4wZuah9B8hAgm50zp05308DDIlBwuV6pYSrlo3mVc30YpJ/xsAUR0BXgQcdjfOYkCzk3v4pG+1lAVufb+0KSgKxllLFvE0CRmZdqudvppo6O8rq/4859fZDRTDPyyKiPgo5kif/7qRT61f9sSknYPh3oe4qmDzcQjQStwKjdEyclXZrKqqW0w2F5NSAQC149yeayMlMZt8d0aYbFCeABYnECzXYO29B40QV1wKrt5ms0MSimy7hE0KYjJQOj0cG/LuqsGTTFzVWJ09bmPGVqgi1RTgWqKGiyUbNqT0VX/xtY02fpxt4Up1zJUsNUmWxnv9KmwW2GrXltBo8VM4BLTxgLyrfJDtq4QgrgxztG+Rdl+gKN9rdiex1/89BInRmYZzhSYzpfJW4Fx50yxzOB8PmxZ+H4wMl50XLKW4sJUGoRCq6grB/wIRdHt4VBvRxgEntnfzwd2dpKOmpWKDaRjBtua4rRGDToSUXa0JelKmAy0JPn1Q9v5f3/7fQykLvDCuf+T507/Jd8/85ecuPkCT+7pxpCSVNQgW9bIWnow1SMWG1QCKLkmZbfGv4xAP+YXV1/k3Ojr2G4eEBjSIqlf5+b0T3j+0mhIrH5mfz97OtNIITEr38PUJHFDIxU1aI6b7O5opuj14avGO0Lba6Ul2UtvU3DeXRXB8aNI4aIJDykqFQ8BulS8t3+OtngkbHEBnJ8Yp2Bn8Xy34d8wZPDtfAXb0rPge1yYXOA7Z4fQRDBlGOgwBdjTmaYrFaM7FcNyg9ZTlUP1zP7+MCGq3iNrHeWdKwVE7KVVJSFgaL7AV45fCwNH9b49OZrl76/l0TUjbJkpFdwfhiZRBImaUlUPNYUuYa7chZT1QUCTgjNj08wXZnE9h9vBM/v7OdTbykRxP1czH2Wi/A+w8u/j149+htHMWwhEwIurnNaYodMWm0Lgrvu8rRfV5/7yVJaxbBGlVGiUO1e0+Mm1yVt+RjXZq70vgA0nb+8UBFXguXXfH7dqJVYVzDcT1aGChr+zc5Scxr97O2AtootbuD1sVYQqqDVs9JQftD20EobMIYXCclXFUV3H0CKU7RyfPNzM0/t3hD3u752/yamRYRzXprrjzpYdyq5H3NSxXB9TSjQpieqSsu2hkOEE2AtvdRHRJUd6ihiyRNExGcu38/5dT9QFAU8pPvpgN5bn8o2Tg2TKDq4XLOBNMYP+pgTbmhNMT3t8+rG9fPbgdk4NvdiwLFxyfAZautjdnuKHV8a5PtfC0d5pqrNXwesVo9k2PBUEUt9XSBT/92sX6YqeRikPKQTBtPKi2OGZsemwtSCl4Kl9ffxfv7hSEYQM2jKaCIQdXc/nqX3baI79Nr+8/gLXpl5DqSCxUIDltTFe+giPbFsMLM2xOLbfTVS7tOx6ukqyuy3L1XkfT2lh68p2DQwtjtXAxR1AyKqgpKA56vHRB5sQQoRJR6Opof/qkZ08tbcvrH7VBr4qh6aWK7bSKH7tewuuH+gONeivub7PqdH5ZZWKpZyUWuNKpXLYXhMFW7A4NZbggY6H+btLncTrukY+TcZZInKM5886JCPpDXMpqi2Q8xMLeD5oms6+rm4GnGnKTp65QgbXr9apFj3NIprF735wB0ok7yh/xNAk+7ub+PHVibpzrRT0puOcm8jwzIH+W/792vvC8oKE6N2qX6OUz4mbL2yYa3MvWomrDRXEzNSaxUu38O7EViJUgV/R0rk+m2MkU0Ah+JWdg8j44u5EKYWrHBSQirZVpg4kzTGd4zd/wNT8aR5IF2ndp3NhKs3LNwOujOP5AblXBcHKkJJt6Tgj2SKCoA0iETTFIhS9Iyx4KXADHSGpS37loV6kFMv66jFDZ19XM7oULJSdcKKrGpTfOg+PH96xall4OvcWCbMLheTJvb1cmoxxbf4i/alZ4oaNkHFGsm2cnuwP5QQ85dPblECXFrosY7uBWKPrB/pEQKX9Uahb1FrjEQZakjXTQMFrfaXYVjExNTTJhx76NI/vfJJnz53lrZlpsuUmomacR7bVu5MbmqSv9REWslcQQgWMFwW+kji+rGvnVVtXmtTpb9nHxfFXg6pXzYh1VT1ZIpBSUHIMNBkPvk8lyfj4nl6e2NXFx/f0Una9UCxRSlFHnq0mNpoQPHdhZEWX9ZV4Ej0Jg9aYSbZs1103pRRNUQNTbxxwagNJI2+ypTpCCo0fXT9b9xlNxtmQz6MJ/ba4FLUtkGQkWG5Oj81z08oxYmawvAiasELJg2o1oCXe1FDnaC1YLwH3I7u6+erxG3W6RT3pQLdorYG5dprs1dctPvT4wbtWCbqbhGOASfc8ucmRDXNt7kUrcaWhAqUU21r2bLXF7nNsJUIVfPvsEK8NTvNgR5oHO9I4nk1v6hSgAS7VoK2UwvVsBjNNfO/CeEi+fWvyTaRwAUnSdDnSM03Z8fj+Wz04nk/RdjA1SSoakGjbklFaElEEULAdYqZOf1Mi1GRBpIgaEDWocytf2levVrI++mB33UIYMbQwKVlNa8ZycuzvjnB6zEWTgn3drXj++5myLI60R/nYgw/w6o1ZdDNDpmSTihhcm8mxpzONrzx8FUNUCM5VPSIAx4+ia/G6Rc3QJL95ZDtfO3mTmYIVcjG6klE+e2ggXMwB8o7iHx4K/KlWW+Sf2b+Xr73Riu3mUfgEcTSoTuVtnbJrohSB6B9wuLeFxx44wEz+JjO5ERCK2o6GryRCBB4iGasb3TTC635mbJ4/+sFplAraN5broUvBXNEmbmo8NtCGITXOjAeJTdTQWCjZxA2diKGtS2eoX5d8cl8fz54fCb3lpIS4YfCpfX0ko40X7kaBZKk32dJR/dopv2o7OLhei6KIG+FSrNYCuTxXZiGSpTXSu4REHbRce5rXH5w2SsBtipkc7mutI0pXj3m9gdnQJE0R7a4kJPeCcOx6DjlvAmFsnGtzr6wwaiuk1aGCaiVrC/c37vtEyPcV3zo7yF/+7Aq264VCevu7JLoo4/o6UV3g+i5KBQV8pSQ5dxcnRuZC8m0gFrf4UEsh2N+Z5aVrXSglw2myVGVX3JuOs6czjeMFYnwxQ6uYXS5+hu367O0K+DCrBZVG5ftahdtblYU/8fBDaHKiptKkoUmDK9NweuxKIDLZ08KHd3XhKcWfvXwBX0HZhaLeQ0y7Fqg6Kyoq1T6XplNMl4s8d2GEZ/b3U3Y9xrNFPv5QL0rBa4PTlByflphJ2XX46fVJfnFzOtTu6W+OEzV0HuxI8bnDjZ3eAUzD5EDvAa5MvIkSAgmUXQ/H85gtdeGj0xo32NPZFE6NSSF46tB/y4mbLzA8d4myU8RyPVwv8MgquyYZqwvNeDT8O5enssyV7DBIXpzIBOfLWUySv37qJv3NcX71wR6G5guM50pcn8nRHDPZ393Mns500F6VgpOjcyi1ss5QT6vij596BCEEvxicJld2SEUN3re9g3/1iSN87+LIpgWS2rZO1QjW0CTRJVWnKpdiM9zAs45HtGSjyXobD09Fydrd7Ox8Ytl7blX52CgBtzYwx4zFY73Tgfl2Kzl3m3AMwabKVSUMlk/gref+uBdWGI0qpFuVoC3AViLEs+eHeX1wpqIHs6hiK4VJx0AMKUpoMkLZ1ajOUAXu23E0KTg/Mc7upqDaYmjBbrbqbdQc83i0z6Q1EefBji4UGg+0pbgxl6fseAgheGxg8eGvBsGC5VTG7gPuzJdnz7KrLUXBckg02J3Wlu9rd4m1CrfbmvdwdapxWdg0zLCsnykVefmtG5ybcBFisYpRTcKe2tvH0HwhnK6J6F28v9+iPTaDJkvkLYOxfDsz5X3s6Uzz5tAMf/3mdeZLNgXLpei4tEQNjvW3ETcEP7g8ymSujFsZQY/okh0tCYbn8wgpeenKGN+/MMrnj+5Ycae7dKfXEm+ip3kPn3n0CeKGucwvLghALocHnuLIwMdDUcX5koXlFjg+XGRhfLGC5vmKsWyR3nRwzT1fcX4yw2yhjOMrmmMmCsiWHN6aziEAXWp4FWuQnO0wshBMt1VFFzMlG6WgLdFYZ6jg+Oi65H9/+pFQfbwnHQ+vx1N7+8hbLm9V7CRuJ5DUtnUypSI/v3KSsnv7XIrVWiBpQ6M5ZgIitPGQooyvoih0mqKL/Ke1VD5uV8vnbgbmzajk3CvtopiRQhcxFs16an63jvvjXlphLK2Q3ku8U5zl380+Y3CfJ0Kurzg9Nh/6YlX5IoGKrU3RrRpiVlOgoGVS8npRlVOXtwxMPYnjFYjqGkqBVSE/C+Cph66QMGw8FSdrd/H0/s/RFI2GD7/Ao2DPEzNS4cLw9ZM3MSoaNVVcnFxgOFMMA2ktasv3dcrHNQq3qm8vD3WtXBb2lcfZ4ZcYnrvMQn6e3niMktdTMd6U4QJbteTw/YCc7St4ZXCA7uQuYoZNT1MbybjJnmTwt1+5NslgpsDO1hRFxyVvOWTLduVvKi5PZYO2kaFRsDxKtsOpYsCLSUcNdE2Qtxx2DgatnEY73Vvt9AxNrugLdqinBcf3ODFyA9dTpKIGB3uaOdrXGra4qsKIVZG/kuOSKVpYXjCI7quqzW1QxRvOFNnRmkQTElmZDlQqEAesii42x4KWXSME1UNV9+9dFVuIpUE0omsc6Gnmc4d3EDFuTxSuqnDd37o5XIrVWiB7WqPs2tYa/k6h46lkpQpTP2m1lsrH7RJwVwrMjueTKdqbGqg3o5KTsxzylosmqWvlwZ3VLgqSiG5yamRTuDZbVhhbeDvgvk6ECs6iQnRVD6Y6OeJ4PmOFfTzSHSOmj1Ow5/FUhJLXG7pyA8TMCP2te7lWqbYEKs8+QtigFCYOINFEmZbITW5MvcJjOz9Bc0zn1OAPlk1e7N/2Ma7N5uqSICD8d5VXU8VSw8eVdolnxhd4ev/HGyYLrufwxo3nGJw5h6qMzWuiXOFuwIJzGIC85fLm8Cz7upqQIlBTrpJLXaXRlmgnapj4voPrFXG9KCOVVpfj++StYMxWCsHQQhHX90GA7Qbq2kFLS1X+fkXbx9DwPJ9L0wtEDG3Vne7Snd5SE9WRTAFPKR6u+IIppfi3L59noWzTHI2EfCXX83m0v43/+VcPhvYnf/rj85Vz5XN+IsNCOVDIlkJQdjxihoYQ4KvAOsKrJIrJihBj4LcWTK6ZaMt0hqr8FE1IHhtoQ7enG37HRkH0rekcz18a3bR2yGZyKVaqtPRZLo+uoQqz1srHegi4K7WkXM+h7ORIR1J4vuKvT17jrekcVmXqc393Ex/Z1U1TzNxwUrQZlRzfV/z9W+McH5mta+dXW693WruoS99PT3v3FtdmC+8arCkRGhoa4tSpU3z605/mS1/6EhcuXOCLX/wix44du9PHd0eRMGSdLxbUSvprvHd7F08//ChZq8SP37rO+Qm7Tm+lmoQ8uuMAmggCR9HKokuBX2mLKCyU0lBoGJrGeOYyrvcxzgz/aMVx9qLd1XBRH2hOsLcrzfXZfMPAsdZdcTVZ8JXH8ZsvMjx7kVx5BiryACKUlxLEtDGyzn4UOpoE11MIQyxT/S05HlL4uPYvaY5MENVtio7J+/uT/Hy4v/L3FsfBy46HUkFFxfH8in+YCrWLAr6Rj3ABNOaKNrmys+ad7lITVV2TzBYsWuMmWsUX7OLkAqMLgWBhWzyCUkELDBb9xapWKTtbk/zd+WEuTS2QKdp4ykcphdQEthckdKaUKAmmIcNA1xo3aYkaQWWIQJah9pr5SvHNU4PhpGJ/c5xj/W1hlakWd6sdsplcipUqLcePT6+pPbLWe3otBNyVWlJPP9zLmeEfhpsS24vy1mwTP77Rh6nrdCYjoODHVyf46vEbHO5r3TApeTNGx589P8yZsUy4eYNFX7wHO9J3lNcEIITc4tps4V2FNSVCX/ziF/nCF77Aj370I27evMkXv/hF/uRP/oRvfOMbd/r47ih0KeoWz2pwLzkujw20oUnB//H358JRdduTRISitISTIYUIF4Y3bjzHjekz2F4QzAUKIYL2SlQ3Kdk58tbcGsbZlyMe0fmtozuBxpNUK+2KPV9V/v5i68RXHs+f+U9M54YJlewq8gC61HH9QHRPE2WkKGN7CR7rb+fcRCb8jKrqL0AyohMXJ0kYQ4RaQqbDY30zGFJyY2FvnU5L1NBQKJQdJEWGtngYwbmgbprL8Xx0Tax5p/uts4P8YnA6sDDRBK7vs1CyK8cteaAtFQroKSVwPYWUga7RZL5MX1OchZId+s+dHJnlynSW2aJFVNdIRgzyZQflK5QE2/PpTcVojkXQRNB2NTRBWyLC/q5mfAX7upr4/NF64rcUggfaUmxvTYQtjpOjc1y3cjy2ZJ9xt/VXNpNLsVoLZLXfxXSDssmcAAAgAElEQVRIRRa5Q7VYWvm4Fc9npZZUqfQaQgX6Vpbr43h5epILPNbrcGpyF+fGMyigMxllvmTj+WrDpOTbHR2vTYZrN2+O5zOZL/Mbh7ffNe2itxPX5p2KRmKJ7xTe0LsJa0qELMviqaee4g//8A/59Kc/zbFjx3Ddxsq87zQ0Wjw/sLMTpZaPNhua5GDP8lH1WkznBtGkhvADPlEVAh+FIm6mQIk1j7NXsXSCZSUzzNrETinFpckFxnMlWmMmf/rj8+FO9sTgC8H4+DIohPAwNAPHUzh+FF9Fw4BSdVZfemyHelLMLkyglKxYWQTJjCYkD7ZlGMp6QfJgOfhKsb05AcC12RyGJrFdr55+qaio/AbikwI41t+26k632vKI6hpvDs+GIpMQHIcmBQXbJe165G0HXwWJSNFxGV0ooAj+HTM0tP42Xr42wZmxIPHLWi6t8Qg5yyVh6uxoTTJbsJjKlytK2YK9Xc184dEH+PiDPfzhCyc5MTrH4FyBuaLN+7Z38FtHdqAvmew7PTZfaXUu/lyTgiuZcth2rOLtaOWw1Fh4syoRtQaZ2+KB3tBiW1o2nOharcK0UjVNlx7Z0nWao6JiTqsqelKCbelZzkzuoOh4KAKdsWoLM2boG6rC3e7oeG0yLER9Zdb1AqHVOzU6v4UtvFuxpkRI0zRefPFFXn75ZX7v936PH/7wh0j57pCNb7R4Anz5h2fXPKpeRa1ejyZ03BovK4WPr3y2tewhGW1Z1zj7eiZYahO7qxkLz5ThqD4QjvzjXELhU+tBFR6rUkQNQUST9Lcd4uj2w5RdD0+pFXfdj/QavJgthRo8VY8yNIUQDjGjRNwItHxaogaPb28nbuq0JiK8fG0iUO6uvEeXQUIipCCiS5SC927v4LMHl7vdwyKB+OToHJmSTUSXXJnKhkkQBElZMmKwUA7MX+OGjhRB+035FQXtymuLthdMhk0soElByXGxXR9NCnRNUHI8BIKOZJSmqMnRba3ETY3/9ckjxE2db50ZZCxbQgoR3ifXZnM8e2GYzx5a/A6rVXgsVy2r8Nwr/ZVG8H3Fd84NLWvrfe7IDv7hgdvXsak1yIwZOkLYaOJ6oMnlH131eWhUYVrpXEtRRooSfmUcPHRiExDVbXTNQuCRMh1QEQzNCD3lNlqFu50JtUbJcFiZrdEc28IWbhfvhmmwtWJNidAf/dEf8Vd/9Vd86UtforOzk+eee44//uM/vtPHdldRu3jOFa0NtSBq9XoMLfi9pwL9ISl0Hup6NJShX03ltHacfb2jpdXE7uN7erk5PEJ/X3dd0KyO/O9KFxFIVMMmHJhakm0texkt7ONPf3x+2Zjv0mMr2VbFMLVMYM+hELgI4RE3FP/1I0M0xXfx+K4niehG+N5nzw1zeXqB0UyJsuvhK7B9kChiUiIQ9DTFSEYMnj0/3JCX8d1zw3z1xHUmciU8H4RQLJQcWmImMvRsg/ZEpNIKU7x6fYrZgoXl+kQDgzH8SiuzJxUDFLmyQ9zUKxyjIFFKmjoLJRvH95ECDF3SFDV5bKCNuKnjeD7fPDXIZK4UVpcAJnMlvnlqkE/vX0yiV6vwRPTGbcB7ob/SCM+eH+ZrJ28ymSuFVa7xbImvnrhOwXaXtQDXA9dzGJq7HJhuVDLqqKER1TVaExmeOriXmLm+5GOlc+1XpDAkQYU7MKENJiOzZZ1tqUGe6F8gHXWwvAgLVjea7CRo/W6sCnc7o+Nvp2R4C1t4t2DVRGhsLFCYTaVS/O7v/m74s9///d+/80d2D7HRFsRSGXdDj2CoCL7y2N11jGM7Pxm+di2TObczWlp2PRRiWVULgpH/iJHC821c32GxKgSgyNo7uTR/hG9eKOOrQfZVpqygfsw3qmsMzecr+jYR0rGdFMrnCUwq3IrSNghh4PhFpnNnuDJu8siOT4Qk5DeGZ8hbwdSYJgXCD+pUvgJf+TzS38qR3laEaDxibDkef/aTCwzN58lbbjitJYRgoWTzvh0dzBRtbDfgGO3pbGJPRzqwBPEUz10coex4RCp+aFIKNE3yi5vT+CoIwI6nWCjb+ErRHjfJlh1GF4o4nh9oNynF0/u2AUESPZIp1LXAIKhwjWQKzBUtulKx8PquFNQeao42DGp3Qn9lvcJ+judzYmSOmYJVx/uaLVoMZQqMZ0tcmc4ucujWUR3yfcV3z11mPj9fqSou+o8hwHHzuH4BWN9zsdK5dn2NdGwnqsIRotICkwJ8pXGkeyYQC0WQNF2aoyPk3Qhz1qF1JR6NzvFGn++3SzK8hS28W7BqIvSFL3xhmR9TFUIIfvSjH92xA7uXuJ1d11pHj++0ymkqYhDRGwegqBGh5PZQducRaAjhV5zbBSW3jXnnMUxdMJnP4Ho+ju9zoLsFANvzOD40y89vTPPmyCwFO+DMPD7Qzr/8B5/h26chW7qGIUuAQAidmBGI4y2V4c9ZDmXHp2C7GJrEIAgYjuejCAjHmqivZlV5GVX8zYlrXJrKkLdcXC9wWpcOFU0nheP7PNLXiq4Jjva1cnFyYVEoUVN0p2IoBVP5Ushx8ZViOmcFprCWpCMZpT0RYTpf5sZ8gYSp05WK0VpRjBZC8L2LI2GCpmh83hv9fLXx8tWwGforGxX2y1kOmZJdESENXjdTsMhZTmBBU+HRbIRQHExElemNxyrVxUX/sej/z957R0l21fe+n71PqFzVOc305KSZ0WhmJJkkIYEIAssg2djIBq5t3vVbXgZzzbO5Dm+Zew2+wDXYdxl4z+u9B9jLmGQbg2UMAoQkkAALjTSjCZqcuqdzrFwn7f3+OFXV1XF6QqMR6u9aCl3h1D6ndp3927/f9/f9WsY1GWQudq3v2/mLHO5/hL7Jk+SZwlNR+qeb6EqOY0iJVZ0vpgylJWLGIPvW3LGswGMlbDBeSDHCVVwfrBKibywsGQg9+uijP61x3HC42l3XlQY4K9V5YRmSbU1RskrPC+a8QHFycistESe0NqCEpywGc21I++WAxPE9hnMlKr7i4lSRU2M5BIJM1OLiZJGEbdCdiRO3jGqpaYT/+u8+/+frfwGlpnn0+f8vJI3Pudc3yvAbQpCtOHWGklsNgqr+tAQKDg2EBNdd1UCsVpqEcIF86OglSu5MEASggLIfYJuSjS0pPvDaXVWis8ezlybr2S1DCrpSMfqni+QqPk0xm/FihVzFo+T5JG0LrQNaExECpUO3dwH3bu8hbluzrmstQGuJR+htijOUK88690BpOpKRednEpdrLVxpXK+yXilg0xey6CKnWUHT9KrdGELWMegfclRCKa4RmKS3KQXeDB5nACzQRY3HRvuVktZYKIPZvuJfetjv46+8/i20mSCfKZGLfrUpJCDSahG2CCP3YXretaVmBzEraYKyKEa5iFdcHy+IInTt3ji9+8YuUSmG7sVKKS5cu8YUvfGGlx/eC4Vp3XTdCa+mr16YYiLTM240eGQo9zbLezQgUMWMQKRyS9jimeZicv4cLk0VKXoAgVFLWhBpASmuKXqhoO150aEtEGK92Tx0dmubpvjE2tcZ54+Y4Jk7YmQX1ICVmp7Bkgj/+xrM81TdO/1SBouthSInnK1TjCQgYL1b4wdlRBKJeoqsFE5Mlh5F8GaFFneRag9LUy1+WIRcU3Qu94zSTpTCbcXoshxCCtkSkPmClZ1SlA6X5wblRTEPOKzk2csd+Ze8GvnzwPKOFCq4fkHM8NCFHqbFzr3Eh/WkvateiSWQZkv1rWzgxmmUkX653U0kBCdukOxWrH/dKCMWNhOaaaGnNg8xTUXpb98zLrF5NxkUQYMkighSN3XpNsTiWmQlLciLWwHkLS3RShCqfMWt5WakXygZjFau4HljKVmMuXuzE6mUFQu9///u55557eOaZZ3jggQf4wQ9+wNatW1d6bDcEbrRd15XwOaSYH8zlHY+f9I0Tt00y1hES5gXquj+Wh11Vkx4rdpOKmOQr3qwSVbbs1nf+BcfDV4qJolMvZykNg9kih4cttrbkkFXCs2WEXWhrm7fzZ985xg/Pj2JIUdfryZY9FGH5AcIFTorwHCp+QP906NX1q/s3zjpvpQVR28CthKKMdUHG6jEODk7y2OlhHrh53aySp8DnzPgoIzlNWyJKSI72Q90hIZBV9QNDCqaqZqsY4UJf6xpqRGOA9tbdvSiteapvjKND0xiGZM2czj1YOWPMxdA4d65Vk+gtu3rrYpB9U+F3k4xY7OrK1M8Trqytf3agKsl6tzDl7ETpElLE+c+b9iPF7Dl/JRmXxpb8RjX3WgPD7JK42ZCVCu8DiCuzkvhp6z6tYhWruDosKxBSSvG+970P3/fZuXMnDz74IA8++OB1G4RSiv/+3/87J0+exLZt/vzP/5z16xdulX6p4lq4Bo3BXG2xEfjEjEFmSNI1YrUgIgfxg1Za4xH8IMwAKQ1ShgubIcJSiFPVV/FVmEGLmPDy3ov0JMeJWw6BFhjV41f8CLa1iW3dr+Gpb36/vksWQrCzq4nT4zlG8g6WFAQaorbENo266nQtQHnTjjX182qJR9jQkmAkX6LiBVR8VQ+EDAGZqE1POsZzg1Mo7fGK9Unu3NDE8wOPYIkBXtbjUGizuDDZzPdza8g6HoYQlP2A9kSUouuRitr4gaoraL9sXdu86ztXvfhfjlzkQP8EFTcMPGryBTVu0tyMwLW6kF8OC82d3V1N9Y62uVhO8CKl4Bf3rOcXdvUyWXJ4+PgAp8byi9q/LIS5590YiEgBJ0dzjBQqOH7Auib49+cvXZPRamNLfqOaO4SlMZgpiR8cmOR8dhsb0prW2Ci24RCzkldkJXEj6j6tYhUvVqxknLCsQCgWi+G6Lhs2bODYsWPcdtttOI5zXQYA8Mgjj+C6Ll/5ylc4dOgQH/vYx/ibv/mb63b864mVXrQWE6i7Gq6Br8Kyz9xOlVt6mjk80FdvdQ+h655XWpfIlbMEpLi5p5mRfJmgyjWSQnBmLFs3TkVX284DuP+mcba3ThIo0Eh0tWRV9nuZ9vYxXDG5NF2m6PrEGxZhIQRb2tJUvGm60zGyFa+eGdJaE7dN7trcha8URc+vm4tahuTBfRs5MjiJKQW2EXJJhABLCuK2xY6OJMp/mr7RYcanXHylaYkEuCo0yE1YHrs6Ryj5AV8/3lEt5XkkbU1HIgkyFJaMmAb71rRw301r+cbxSwtyx2q2Hj++OBZakQiYqHZWyar4XQ0l12e8UOF7p4dnOcjXgtsayq7DRHGK1kTzFbeM17DQ3HlucGqWYGMt0AOuqBvKMiSdqRjvum3zvGCrkVPX+LsxhODx/hzfGD8yL6ivvf4rBy9wcapAxDRY15Rge0f6moxW/cBbVM29kcBfg9agtWTKvYUN7Wnu2dJEPHJlDQ2rre6rWAwLKUrDKol6KaxknCD0Qi1hc/AP//APPProo3ziE5/g7W9/O+vXr0cpxec+97nrMoiPfvSj7Nmzh5//+Z8H4M477+SJJ55Y8LWO43D06FE+8IEPMDExcU2f67outm0v+/WlqrBeTSzQNiVx6/r51pZcn6Lr41e9JUwpSNgmsap2zWJois0/h5LnU3Y8pGEsONaS6xOoImIBHSGNoOJHCLTGlKHKblAlxUJ4I1d6tgKRQJOJ+kjBnMfD4ykdQwPpiMVYsbLgeaiqJ5gbRlIgqte5mi1oPNfad6eB8UIFN1BzFK3DVvio4WPIGc1qga6WOGZ3cWkNecciYgbYRqgRFL7aQMooyQU1aPSs9vGS65NzvFmv8ZVGEmZQGi1Oap1QftX+xGgQX7RNiaECPHy09qkxoKQwSUQSC167pTC9xNzRgOMH9esWMQ2aF5hPV4J512XO70YDKggw5pQYG+fodNmdEeWcg8b5vtS5Nb5Oa0XJzS/62ridQghZH+tcXMlvfe59ZaXvGz9tXOl988WGqz2/1tZWPv7xj7N7924ikdmbltq69Z7/8r8zNr54I8RE6cV5Xdc3X/l9qRFLXbsariROuFIs69f4zne+k/vvv59kMsnnP/95jhw5wh133HFdBgBQKBRIJpP1vw3DwPd9THPx4Xmeh+sufhNcLpZ7jIqv8NTsoKEcBPieT9S89p1dxVcUvIBaDADgB+AFAa7n4+uFm7I1UHFm67k0jlUFwYJjDa29DLQIu30aT00piSlcbKNqHKvBCySVwKjaD4Svk/WAIlxE53aIzRTdNK7vYQiJ74MpwA1mn48GbENgS9AKvIbAyw8USilippz1fbmuS9mvttvPiedDUr9GmEH9+KJhULO1tMOxxywfy2h8VCPxMXQF1136BqUJFamVmn1eQuvQOkSBH4Tk81qgW7vmWoNP2IRgSkHe87ENH9NQ9WOH+jYe+XKeiLH8zJDSGj9QC84dT2lMKbAE6GrQqYKAXKlyXeY0LPy7cQNdDTRnmapQDgKMqpntYmOeO9+lnn98CDOCs3/bes5/GyHwPA+NoOwGCzw/M7blNrw3frYJGEY4J4UQCK2uy73rhcSLffyXw9Wcn+d5l3/RzyiudT4s59pdTZywXCzrCJ/+9KfnPXby5Ene+973XvMAAJLJJMVisf63UuqyJ/fII48sGjkuF8888wy33nrrZV/nBYqPPHJk0ef/5HU3X1Oa2wsU7/zCEzx2ehgdzOxGldaYpsHPbe5kZ1fTrGBnsc9vHOvg4CA9PT0ASBwMMc07bt3PT/oL/MvhPgamC+zrvsDG5ikihoOUccpBDxPFCt3JfkBUu4EEQmj6c2v53rm1nBrNYptG2E5Mdcfrubz7jrNkokFdnrHG5Sh7Fv9+ej/vuWM3v3TLenxf8acPH+KpvvFZOkQfvncvpin5x0Pn+YcD55goueiqqGFbIsKD+zbULSqeeeYZ+u12nu6b4KFj/eQdD0HIXQKIWwZdSZ937T0GiLqbe9zy6gFbwTFQOvyj4BoYUpKK+AgEtiFJRa1qgJTkzXt+Z8myyGTJ4aOPHOVA/3j9MUmAZVQYyEJTPMbL13cQtw1OjeZY1xznh+fHZ9mAQGjsOZDN8wvbniEVmVmUpRCYUhLoCP/pFe9fdpms5Pp86DvP1dvZawiU5snzo9yxsWNB0c1rndOw8O+m7Pk8eW6USqnIm2/ZMuuzS67PH96zm1TEWvbvbSH+066uDHdv7iITs2dxsM6OPMbZ0flq7ts6b2P/hnuZLDn8z+8dXbTU9of37F4WuXm595WrQa5Spn9yjN6WdtLR2Ip8xuWwkud3I+Bqz6+W9VkKv/s/34qnS4s+/2ItjS2ea11eR9lyrt3VxAnLxRUfxfM8nnjiCW655ZbrMgCA/fv389hjj/HmN7+ZQ4cOsW3btut27OuBle7+mCw5XJgs4M7ZBQshcH3FxckCP79zLecmChhSUPECJksOmajNKze2z1qw5o/VpyPyKLacQgjFd48+znAhzrGhXbQm4xwe3cxzIz5lN8/Gtg62t6dpinyLxqKEqKZSWmOjxMw1DSTmcIcbt0xipslUpYOO5HCY+VGaICRaMJBvIxOLc9fmTgBMU/LR+/YveFN3vICvHu5jquyidOg71paIsLMzw5Gh6bpFha/0LKJsbbQJ26TkhcFQ0bMo+zZJ20foMBPjKYltKJRqzIJp+nMpbunMghBoHfI7autlo/ZRDXO5YjETMtEKPWmbgWyZHa1nWdc0Scr2yLsWhrGe33zFq9FI/urx54mYRl2Hpw5VwQ+maIlJErZHY2t3GMhppCgzUZxird215JxqDBBOjeaYLLt0p2J10nbZ82mKWgsGQdero2mh341thN1ZBR3ykmJy5rkagfhKuDWNMhfZssv3z45wdHiap/smiFkGjq8wpSRbcWmKdXBT6zZi1hCVBcROb2Rys+t5fOrxL4LuJ2o6VPwIiF5+9+5fw7bCca00f3EVK49V7tDiWMk4YVmB0NzMz3ve8x7e/e53X7dBvP71r+eHP/whDz74IFprPvKRj1y3Y18PvKA3yGoQ8sbtPfzw3Cj/7dvPMZAroZQmZpucGc/x5u1rsG1jwbF2RB4jakzUCwJSKnrSBX5p51H++fgeIGyDL7qCkeIUkjKvWusCol6aqpWVYqbLmozk4rRJyfXr3lwCWNeUIBl/OUX/CAYDWKJM2bPpz7cyXNzBLT3NZKqcjbltzGdHwgVpT+/r+MNvPMuPz48hqq3zyYiFFGUMIVjXnKgv0EVPUXJ9hIBMNLz+NWf7qGmyb00zvc1J0rEyvn8SpcLdf8WToDWOHwrlZSsmJ8ZSHBhay/pMkYTtIaqO9L5SpCJWXc247DqM5Sd5qr/I8yPF6iIv2dZ8mrg1xNr4NG3rIkyXAxJWuWbfSVMkwDbP873j/87bbn1b/fvpTEYZzJUwheL1mw/SGitgSI1WkkBrfB1eXcFMCVLpGK2J5stOm0aC9J6eZk6O5hjMlaj4AXvXtPCy9W0k7IXn7WJz+koX2sUMQrtSMXL5/CwZgrlBzpUKmlqG5Mnzo/XgOG6bHB+e5vDQNIYUZKKhAOSJ0U7evvdW3nxL8zyx0xuZ3Pypx79IzDxDTeoiZrnAGT71+Bd5/z3/6bqrV69iFTcaVjJOuKq8UrFYrPuQXQ9IKfnQhz503Y53vbHSN8iWeISNLUkuTIRZoUZEDMnG1iRtyShfOnSBfMUlHbGQIswYPdM/wTu++Dife3AfMSuFZVgNY3WJGJP1IKiRF9OdLpGrFBFEquUfg4gh6ZtW3NJhkYrMWDwEVd5LybPZ3NpOf3aC/myR7lQMUwq603E2tSTpySQ4M76TZ/raSNgeFd8m0AaBctjUOtOhtFgb8/GRLM9eymAaM5mSQpV8bEjJptZU3YIjYcm6IaplSNoSEVrjEcAjafvs6kzy0POX+O5xn73dGW7qKJCO+GQrJj8caeJ7Z9tpS4KvomhMHM/n5Hia29dO1EuQbqDIOy6b2rfyT898jVz5HEoVKXo2vttFzLqVjHWEYuUsrmcQs0zAoT1eRmkDV0kMIaodcIJc+Rx+4NW/n+0daQKt2d/5JB2J/AynSqqQYK08Sr5V1W0Kg850bNNly2Jz28pFtWNta3saNwj4wGt2EbdNTNm3rDl9tdINtd/N030TBFrVy3Nb2lJE3TC7uViQcyWCprVOy0MDYZu70lF8ZfD8SJaSGwa2rfHQcHckX+afnxvgLbs3zvODgxvTxytXKYMOS9WzIUD385VDpzk+UloR9epVrOJGwUrGCcsKhF772tfW6+paa3K53HXNCL0YsJI3SMuQvH3vRvqnSpyZyOFUu1ZsI1w0Hty3kZLrc3BgEqPh5i2E5p7NA2xtfZ5vHPoRiUia7qbtvHLDnQRK8+PsCQQz3SqNMKWmJVZksmwjEBhS0p2J86oNHWSdITLRS6ja+3T4r7MTGbJ+ibs3d+IEAVIIPF/zk/5xToxksU3JRMkhahrhwgPYBqzJxIlUy2mCYOE25nqgsJu4ZVCoWjZAGAy5QcDhQYNPPPY8yYhJspxnz6bNHByYpCsVYzhXZF/XedakJ0jaLn5wgP+8P3x/1jE5OZbisXMZCq5NOhojEYGudApDCoquz1CuxMXcdlomL7A2PUHUdKn4Nmcm27DtHKXK8ygNvoKI4dKZuMhIURGLTQGy2mIPliGqukcKIaxZV75W1nrLrl60hn88dIHxfJbWDYW6SCXUFK/D4KfoGiRsn5Jr4wTr+Y1X/eJl59NipVxDCrQfGvLGbXPZc/pqbSJUtbvw3ESeS9NFNILepji/sncDb+kI2Lvv5ssGOUsJms4EaBPY+hAJcYmU7SFEnJzTRd5JIqpq6IFWmEIuaH7biBvRx6t/coyo6bBQD13EdHi2f4BkdLaK/ap69SpWsXwsKxD6/OdnpLaFEKTT6Vns7ZcCVvoG+dbd4eLz5UPnuDAZEsI2tCR5+94NvHV3LwcHJkKX9IZunrs3DLCvewyNIFA2U6Uso/n/4PEzw5TUPjrsJgxpEqiZ7E4tK+QrwXA+StQKG+gTlkF3KoZtSvLeXi5kNa3RUWKWS94xOT6W5vsXujGNCTa0JPnIm/ajBXzgoQNorWlNhG7yRcdnuuSSr3hsbE3SkYyyvSNN2Qt5J5YsUnLzswMhHZJolfIpunmKXqTOP1JaM112iVvg+JqDlxzak0kSfpmNm8LshRSCjZkTrE0PIYXEkgrbCInGbiBJ43P72klcpfnX5ztJo5FSouv+5mFGTAiDQyObOTK6oR4IlVyf3vQRIubsfiOtBRl7GEMEQMhZCj3Qqu3yQjPX+KNW1pJSIARsak2xo72EKat9YVrPWuuEgOdGtmMYGTa3tmMIu3r0GfiBN8/Tbrml3OXM6WuxiXjoWD+HBqZmZaMMIetlz2tVba8FaC2RIyTNC7iBrupAlWmKnufuDS08fmEtUgiMBkXqxUxxG3EjKcr3trRT8SPVcthsVHybQC1Mml5Vr17FKpaHJQOhr3/960u++f7777+ug3kxYKVukFIKHtizjvt2rV1QUHFLW5po1eAUwJSKLa3T1BZyX2l8FeZw0vYw5YrP6WlFV1crQozQyMkVwEghga8tlIZ0dMYaQSnFDy+MM5hrRdCMbZTxVIy1mSTrmsPARGnNN45f4g3bezg8NF0f43TZrevjFL2w+2okX6mLCaYiFkEQJWLGcPwyAoEXOPjKR2tFxBC8ct043z7TgyAkPiuleFXvELd0F0nZHmXf5lKuleMD3RwZmuZPXnczb9zeybeOPIbrR1Fa4/punQhtSYWLRCO4ubPAN0+2EzFNFFU/Mq25Y2Mnzw5MUgt1Am1Q9MLFJR11sI1K/ToLNIaAQEPE9PBVDFH1VDPDXjmUloDCVzATI6h6WasWXNimRNKCRtYzd43RltaSHZ2bEDIKzF7YlrKLsAzjikq5S83pq20UmBtAGVLUidHPDU7R3XJZ+bIl4QWKZy9N4gVuVSFdIoWqz08TyY72HE9cVMTsSJ34rjX0NsVfVMFBOqHQYosAACAASURBVBoD0QvUOEI1aLToJRNfOBB6oQneq7g++GkQpV/sXmHXiiUDoaeeegqAvr4+Ll68yF133YVhGDz55JNs2bLlJRkIrTRqSr1zkYnZ7FvTwoG+caQUJGyPlB1qn9jVLqraTdIQFaQIA5Dz+Vdza8dTjBcvobVCaYmrWtjecz//480Bhy5N0RyP1K0RvntqkKJboTnmUnQtxksRlPIJVIl1zYmwQ8oyeW5wih0dGYqOj20KBIKSF2AZEtcP0FrgBoqYZTCYK/FLe3o50v8dLk2dpOhME6iavo9qIGULbu4cQ2vBd86uIed4vHbjJfb3TGHKkNwcszy2tg7hei4FZ109y+T6hZBzFCod1a9bTd9Ia0hHfFrjio5UlI5klJs6k+xfE+Otu7fz3759tO5/VkOgNNu7Oyl7NnHbI2J4SFM3PA+aCob0sKpBUKDDzFLWSaO1TyrioYmRjm3il28Ny1qNwYUiwnQlTVN0et53nnXS9SAIZi9sl7OLuF6l3KttFLhcAFX05osWLhdKab5y8AKPnR4iYVfo3VZES4kpBb6aEfzMRDw2t5m4QaSuot2ZivIreze86MpFv3v3r9W7xiKmg1PtGvsvd/8a3zwxdEMSvFexihcLlgyEPvrRjwLwrne9i4ceeoiWlrAOnc1mec973rPyo3sJYKHSxmL40jvu5Fe/8AQHByaZLBkUXJvmWEAmalU5NeGNMNBRlI4COUouvHL7/0bcDJgoDGOYzTTH0nzrxACD2SmG82VOjOXIRC22tyfZ3XaWrW3TRE2XXMXk6EiKb5zqYKxYQSlNcyLC6bEcvZk4T54fZqxYIVDhouYFHj0pxWhRUvapL9ItMZuuxPOcGjmIEALLiAIOvgpT/RqBrgYQUsDW1mmeH9uIEwTs6SpUg6BGCNY3Z5nyA1IRC0GKuJ2i7BUIQ7KZDEujEWuuYpK00/zundvoTZ1kIn+Espvn4aMpfnHndqCNp/om69pGr9rYzgdfv4ePfOcoLfFz8woqhgSlPQIlkEIjRYAhAiqexVSlhXKwm3t3r2VTW+csgnNjcBEozTfP7uF1mw7SFisgq11jw/kYj17cw6s2zVif1Ba25dpFXI9S7tU2CiwWQAl8UpEKsUX0Py7XmeYFii8fPM+xoSymIfFVhLJvE7M8aoLQNbkHZIxb167BNOxq+3y4mXghic9XC9uy+P3X//qCkhMrwV9cbcVfxUsJy+IIjY6O0tTUVP87FosxNra4TPgqLo/LOWEvBNs2+Opv3k227HJmPIfjpOibeLa6+NcWKU056EFXv9oZbZYIa1o2AfD1I331Tp7d3c1orTkyPIXyn2FX50j9WOmoz8t6J/CV4qETXaFjfdRiIFtiYLqIZRqsTcfozxa4e+Mgm5qnyER9Co7FSKGNWHwLhjDxApfh7KHwuNVyVKCNepBS8Q2kMDBl2GGViQbctSWNKSTpiIevZgQRa4hbHhs7Y9WbdOhqf2r4AE6g8FVYJgHwlQQtECj6s828ccc6RqZ/hFM6T8wy69mUs2MH+OXdt/Gnb3gdQ7kS3el4fSHf3p5gIeHT0BoDdBAKToaXP0xBdSf7GSoItnW+fMFSVC24cIOAii/59tnbsITLplafnkwPPzwzhCsF2YpLSzwya2Ere/l5PCtdva6lOZpH16OUezUL7fwASpGxjhAzBokYDhe9CJELU/X5frnOtNrzBwcm+d6pYWxTEgQKJSSXcq1sbR0K7UoQVU88KPk9mIbNnp4mXru1e9mL+o0gWrgY0tEYu3pmE9SvJ3/xWsydV7GKFyuWFQjdfffd/OZv/iZveMMbUErx8MMP86Y3vWmlx/YzjeU4YS+GTMzm1t42lH4jUVNyaeokZS+LE9iUgx6y3s1AuDDe0tM866boeAFfOXiB4XwZLwjF5hw/IGpqOtaO1YOgmveUQHBzV4HvntE4vk//dDFcgHVok/CarV1kCz9mc8sYKtRPJBMN6EiOcnH6IP9yvJOkXWZ9chKEwBDV4pXWRE1R5dxUdXNULcsSRYo4e9a0kC+kwC+EJY+G9I5WEd66e3v9vPaufz0nR3NUnHMYIiBQRpgUECBkhIrfw5q2WxAoYsYgvtJU/IBo1cC1MZuyuS1dP64feMTMQXyf2TyrhjWhFnRpgAaSdGtslLLnYBnzF9NaEPHspclqOU/QmszQlUmjhWBDJkJnVzfvuWP7LK4YQMyayYChwzKUG4Qlxopv89FHzvChe2/DvE42GVe70DYGUHH5HEnzApYhiZomnleZNd8v15lWe94NAkCHZHop0Erz3MhGlILO5CgtsQAhYhT8NWS9mzGk4NhwlrfuXrdohql2TloFlxUtvJIM7mJYqWzL9Qh6r7ZDcBUrh88+cGxVUHGFsaxA6I//+I/59re/zU9+8hOEELz73e/mnnvuWemx/cziSp2wF4MUBvs33Mue3nsoOXm+d2aaw0P5utDfjpbYvF37Pz53gb7pIrYhyVY8Co7HVMmlK+UTNV0EocGqoKq8LATpiE9TLEAhMaQgakqyZY9jw9PsXZPmpo4chjBRGgKlqiULTdIexpJtpCJpCq5N3HKpWbVKKQiUDMtJUhD4Gk8pAgWnJtJoI+De7es4OrCb5y79OCTBqvDdhoBsqYl/e/4Uv7BzG7ZlEyjBqantCDYjRaVaGgR0mSfO53nFhu5qpqCEIULyc63lXRNqOJfcPMO5CTrTHfUFquzlcYNK9fuZ/x3oOdoEoUEtgMaUFT7+6NPc2ruBN+1YQ9Hz64tfoDWv3tzJG7b3sK09zfGq/EANCwWxNZiGFWbARg5QcPy69pQQmku5Fp4ZmuRPHz7ER+/bf9k5dCW40oW2FkDVyOyeb83pigvn+009r1myM+0N23vqz9sY9WsihUCYklesbyVlDmMKA9NQdduUGkquz2TJwTJk/fovlPlwKk+xJt1fp8XPEi183TuvOIM7Fzd6tuVaOgRXsYoXM5YMhI4dO8auXbt4+umnaWlp4d57ZzIVTz/9NLfffvuKD/DFiqV2fQuVNurPLWDncDmYhkU63sIDe1q4b9fM5x4+dHDWDdYLFKfH8kRMg7FChbzj1Vu2p8oGuYpJS1whatmXKoquxYbmNnyMehty2Q+YLLugy/XAQgqQhkEyYpJ3PJK2x9qMpOAanBxPs7drlJqJiFagtKDgpoiZAlOWKXoWZycyfPdsO4p+Dl2a5BUb2zD1GtL2MDErJHAHyiAenWQq+098+ekUu3t2s6791fXFJdAz0g5lP0bOydXtHJSOEugYhig3tLyHnBcniPDpJ/qIRUbqC1TESOIFMbQuA7WAIzx2LUFVy55BeE41QcayZyGI8aVnz/OVgxdY15yo2z5ETIOyF453T3czt/a2cGRompLrE7NMfAVHhqb4Sd/4ggvm3vWvxws0T559ppq9CLvpnhvZiCEFT/WNL0pW/mnDVzNk9rkou3kmilNLEquHciWmSi5lz6clHqErFWMoV0YIcH1Fs32EpsiFKkfIQAqHpHkOgGl3D31TRf7vH56k4gX1a6m05tDAjAq157sk7aFZ32VdXEH389TZb3Fx4tBVZXBreOhYf70kHTHD4OlGyrastJXQKm5cGL//+cu/aAVwo3SrLXmX/PKXv8yHP/xhPvnJT857TgjB3//936/YwF5MaAx6DCEuu+ubVdqYg5qdw9zjLncndrlWaMcPaEtEOD+Rr4v4CaDiw4XpZppiY9jVm7TWYBpwZqINpFWfLEpretNxvEBR9u1qYFEBQpXnWnBQ9uyqurTimyc7qXgBN3XkSEd88o7J6ckmnhlYy7pmyDkeZ8c1pmEhhSAiFdnyBP90KE/M6sBVzSQsj9t6Rrmlq2ZsauL6RU4NHyDQELe7552zbYTmsDU7B41JOegmKs/UW959pQmU4sxEhtFymZ1dkfoCBTBRbidl5TGEBw1dbkqZ+CSQ5DGq5TFDKqTwUFow5KzlzFQ5lBCQgq3taU6O5hjIlliTibOjMwPAwYFJ9q9t4U9eFwoMfufkAGf6AorVoAjmL5hSGLRlXsVXj/u0xIK6incNNZHIxjLfC4XLzffWRDNxe2TB90YMyf/x9QM8cX4UTyksKelKRtnd3cRY0cEUPil7GMswsAyq8g3VjI4xyH+MrkVrAylmSj1P901wbiJfv/4Ajl+gM+mhdUizrwW5AogYDn0Tx68og1vj8W1pS5OJ2fNK0pYh6ap6v90o2ZYb2WttFatYSSwZCH34wx8GZgsqAhQKhZecoOJCWCjVXfGCkPRryEVr7I2ljblO2GubtyOFydeP9F33FHrtRrexJcHRwWnKgY9SVM0/4dTEFmKWyY72HJasELES7OjczXfPxRDCxfUVtinpScXY1p7i1Fgey7DJuZ00R2r8j3AxlkIzWGgj0Aam8IlHHB4528n3znXRHPUJtM2dG0Z48OajZKKh9cXxdJofXVrLXesH2NI6TdL2yFZMzk028dj5tTjCYGvrVPVa1ehCGgUMTJ1kT/c2DvRnwx23obEMB0WEl61rm3UdJp3d5IsFNjRNIXCYLEmOjTbz8OlmLHMErTU7u5o4ODCJ1mDIPQBEjSFMWSZQJifHMgyWbmayVGFv50m2tIxgSl3NJoRLaHPcZrhvJnNR9nxGChUMKRjOl9nansaQol56eNOONXznxAAf+s5hJgsl7GGHpG2yq6uJXV1N8xbM7nScqBWh6M3X5EnYJt3p+FXPleuJy833mB1ZtDPtu6eGGMyWqordAq01g7kSCMEDN69jR7vE0ocQwqjzx7xAo9EYokLMdFnfMtugNtCKS9PF+vUHiJhJCq5NwnLr/m61kRQ9g6RXJLaAN9vcDK7rBvzX7/dx/lsXqXghB23fmhZ+4aY19ZJ07fsbypUBZnnovZC4kb3WVrGKlcSy8uaPPfYYBw4c4Hd+53d429vexuTkJO973/t4xzvesdLju6Exl1gYKM0PL4zSloiwu6t5lpjc3EWs5nh9aeok5TlO2A8dXRnCYqP3U0c6iusH9fGBQEjBs0MbaUq2s6cnWuff/Eqpb55fVKA0b9+3IXT9rmzj/OgPGJqeORcluhkstLK38yRr0hPY0gmtLsYzPDe8kbvW9bOrIyRnawSpiM/tayfY0lKgOebUs0qpiM/e7lE2NOWJWgHtCQetwQ9CjpHSYfaj5E5xcvICfdPQlThBa3qClO0Rt9Pc1LWbS4UdHB7KhkatwI8vbeDR810UKnlGCwItDCwpqHg+R4amMaSkIxVFKc140WE438VEMYUUFVw/wmjeoSUxxdqmCJ3JPE5gUvFVnWBuCEnMGMIPWtGYdV8r11dYhsAL1Cz39ZLj84ffeIZvnxhgIFvGCwIM3yFbdhkvOozkyrx8Q/usBTNum7xsXduC+kev2th+Q5TFapg7300RZVvn3vrjC3Wm7ehI8flnykgpiEsTqPGhwkByV1eat+3p5eGjj4fZJgFRyyBqhkXMiJGgM9UyL5NjGwYaMev6W6bNcK6VTc2DyDmihcOFNppiRWLan+dy0ZjBBfjVLzzB8xMlotEoETP0zHu6b4znh6fpzsTrgqgQluCG82W2daRvmGzLjei19lLDKjH6p49l3Sk//elP8xd/8Rd885vfZM+ePXzwgx/kXe9610s6EJpLLNRac3R4iouTRS5OFpgoOvSk42zvSIdu5nNq7I1E58YulJUmLN5301qeujjORNFhsuRgm5LedJzXbO3EV7C1PcWbblpT71TyAsUdGzvwleLYcHbezVFKQVsiTtvGe/GDmXORwkTzT+RKwwQKAi1oiga8vHeChCVZl56qZnU0or44aLrTJcpetZOr+mjE0KzNFCl5Zp3DYZkaP/BBWARKk62Y/PORMd64ZZSb2scwpEQg0brE2bEDbOuE+3a9ITTdPHSBbLnCcN6h7BkEWiNRYIIlJUXPZyBbYktbilNjOYbzZSaKDqMFD9cHxy/hKfB1keboJLbhVMtlokqeFvhaI4MSeTfPZNEmE7PpmypiGeFZWYac5b5+YTrPwHSZiZJDxQ8INEitMGXIObo4XaR7Kj5vwfzwvXv504cP8VTf+Cz9ow/fu/eq58hKYO58P3H0DPs3/NzM8wt0ph0enMTxVN1WJm6ZxKzwt+YGmu0dGWzLnp9tEiC0prd1B0cm5mdZDBl6njXabgCY1q0cGfHZ1DJN0vYpuCajhXYyyZeRc4+QilzCWCCjVSuLZcsuBwcmkXN0r4QQDOXK7OxqYrxYqXPIABw/YGtb+obJttyIXmurWMVKY9lbxs2bN/NXf/VXvOUtbyGRSOAtJKzyEsJcYuHJ0RzjBaeaLQl35bXU947OzKI1dtOwZhGjV5qw+I3jl7AMyf27e3l+JMtEycHxAw4PTbGtPcOFySJ/9fjzxCwTxw+9zcpVkunuribu2txJJmYv2s1UOxc/8EhaQxixCEprHF/hK4WvNFtapoiY3kwJouopJgCzof08LDBpLENX/alCXSDLmOHj1Iw9T46nMaVkXdNk2LWlNKYMzVCjpqB/8iT/fLSFH12c4ujQFGXPxw9CPzMZvhzHU2BBtuxxTuXYu6Yl9GbTMFZ0cP2gquCteeCmUfZ0FUhHXaKGRiFwg1D9una8KccAHSUdNWhLRBjOl9EqjOTWZOL1YNf1Fa6vOD+RJ1fx6/YgSodlHkOE3XQ1+5JZ19yUfPS+/XVScaP+0QuBy/HaanNksU6rRo7bXFsZoD5fYpZkS5X/tFR2daAwsGCp51f2bgi93BoyH6/Y2MXRobspBS5ThQIRM0lz2gagpG5ha2fXrKxn7TNqODOeo+IF84Q3hRAEaFrjNrYhGSlU6mXm9c1JfvmW9Vd8nVcaN5LX2ipWsdJY1h2zra2ND3/4wxw9epSPf/zjfOxjH6Onp2elx3ZDY6468HC+jCEFCTvsmDKqxprD+TKbWlPcvq51WTurlSQszs42CW7ubuL4SJbBXIkTI1nGiw5rMwm2d6Q5OZqdR+qtvXex8lyjxkpjZ5wUgphlUPGh4nvYpibvGiTtmYW9VgrzqlxXXbUMEXLGIkMIiSFNhPDROkAKTdkzuJBt57Hz7WSioe0IWtZ0pet+YqOFKQ4NDOAFUXylsaTE8wOUDpWb0bXW/LD7LVSs1jiBoi1pcGmqTBAYOAG85aZRXrFuqhrGSZQOsM2wH84NDKQUaKU4PZ7hlRt6mCq7DOfLBCokk9++rpW4ZdUX4Js6MxwemAz92aqWINQ60gBfazIxk00tqUUD4bhtvqDE6JVoDZ9rK9P4WbetayUTC4OUxbKrsHSpR0oxL/Nhyj6evTQ5y8095Mi0cvvGfbOynnMJ0rXAzXH8WY8LQlPjpliENU2JWQa0t69rJWItr/1+Fav4WcCN0inWiGUFQn/5l3/JI488wq//+q8Tj8fp7e3lve9970qP7YbGXHXgWidIS9ymOWphSInrKwSamzozy66xX0/Coq80kyWnfpPPOx4Fx8eQIU/i9FiOkXwFQ0hKXtgNNZQr4yvFYK4MaIYWIPXOLc8tpJLd07SFmJ2iUusUqgUzQNGxeH4swc+tnUJTVY3Wmoip0AiSdoBS4CmBGwi0Ak/Lqrq0JmZFyZVdlIjybyd3oYgCBYquRd61aIoGKKVxUFDlEE2WDNwgAjoMCGv6P0qDBWG6CTBFeJ4IweHBSba3nmFPV4HXrCuSr1gcHU2woz0PWlCTq3GC0GRVVsnSecfkuaEkD5/uZHfPNGvSce7Y2FHXSfqje3aTilj1BdgLFP/Pj09iSwNPhOcYZj7Ca5YwJds7MqRi1lUHwsvpQFxKLPBy779SIb65c3MxNNrKVLyAZETwcxvi/L+//PJ5r52bXYXLl3rmZj4ux5FZ6DNqqAVuPzozMOtxpTSv3NDBKze289zgFI4fzAoUV7GKVbywWFYglEwmkVLy1a9+ld/+7d8mkUisdo0xc9M8ODAJhPoiPdWWWKXBDQJsw+Dt+zZc0a74WgmLtd35t46N0TR8tK5V46mAZy5N4PoBppRMlBzaEpGqa30YC0yUKhwbmcbxA2TV0LU5anHLmpYFuU6wsEr2mdGDJCMtBErXeRW+CsnEp6eaePhMO1pLburIkbT9+sLv+AagMKUiYmocX3JhOsbaTIWY6SEEVDwPU8KU24MWUQSQjFjkKy590y2kO0fCspsOu9cCHXBiPMNIwQ8ViQl1kmQ1EPJU2HkmCLMv3ckY7akYezvPsql5JPQuE4JkxOPlvZNYhsYJjIYSiMAJDAwN/3ZiK2cmo0wUfTIxG6PKDwHqmbXaYly7hnnHozUeIR4xEQJyFQ+vViqU0JGKsaM9s6jA4nLmwlKZmqXsXtDysu+/El7bQnNzqcxRzVZmqlTmR2cexvUu4vgFHjl++ooEDZdb6rlWjsyX3nEnb/r0Q5wv6XrX2G3rWvnSO+7Eto1V7s0qVnEDYlmB0Cc+8QmGh4c5duwYv/Vbv8VXv/pVTpw4wR/90R+t9PhuaDTeNLe0pTgxkqurAxsCbIwrz+JUd+X37ey+6ptmbXeOpr47//LB82igMxllMFci0IpsJTQ9bY7ZZKIWU2WXkXzIX5BVfSFXBZwcyxO1zAW5TguqZGuoeIqp8jR5t4eUPUrUdHB8m/58Kz+51IkhKnzrdDffOd1BxCrz27dfQoigqmkU+odJNHnHpD+bZG2mUj+80hopBa3xCJ2pKP3TJdoSEZqjFsfHN1NwPLa3Z2mPu0gZijduas7iBWf53tnu+jFUtfwUM0IOR8w0SUasalDkszY9EZKuhcAU4GmN1hJDBNVeN1Ev/UkhKHomp8ajIA16UhaZuI3AJ2F7jBWCRUukqYjF3jWtTJRc+qeL2KakUHYwTZOoZXJzTzM7u5p40441y54Dc+fCUpmapexe+vI7L/v+K+G1LTQ3l9MReX70++TLx65J0PBKcLUcGds2+Iu71rFl582zdISu9birWMUqVg7LCoSefPJJvva1r/HAAw+QTCb527/9W97ylre85AOhGixD8o79m+btnK8oi7OkCevyg6CS6/MfF8dmdSQFSjNaqCCE4FUb2gEYypcRCAqux02dGbpSUX7SN4EXKAwpsKTACaoKyL7PYK604EI+SyVbQ8UPKDo+vtZo7fLZp02cYCO2UWGiKIlHogh8IoaB1gFOILC0JB0NZgQedShopxCkoz472nK4gYkbaMxq2UpqUOIi29q20JWKkYrYuEHA6bEcj55fixSKzNpJdBAKPKZsn9t6xvGCgO+cCQOKsh/yepQAoSVl38dRmqzjYRslIqaLFBLLCDCFIlK1b9BopNChrg1h91DMNtjRvZfXT7UyWXaZLJbZ0nyaDc1TpGyfim/Tkd7KfTvftuD82bummUApTo3lGMyVyOchlUrRFLXY3p7hyNAUZyfyV8S7WU6mRhAsavfSN3mSw2PtGNJc9P0124rl8NqutiPyelnSXAmu1Q+s5ge4ilWs4sbHsgKhWjto7Ubkuu68FtGXOq41pX4tJqwwU3L4j4tjPHZmhIRtYrkO3d26zmECgacUOzozbG1P0xKzqwFOEq01zw1NoctgGeHuP6bDrirP1zhesCDXqVE1uOIHlL2gbo6ac0xG8lDyXIQIAxIncIlbBq2JKLeua2V9U5JzE1O4wSAxy0Wpui4eACXPJG77aC1CF/JqyQsBpiwjcLCMKLu607xmSzf/6/HjHB4eZ2trAaUbrr8AtGBne4FHzoYKxcoIy3amIbGkoOiGCtNaC/KORdG1aI07SIIqaycstYXaRXFMQxMzXSp+BKfSS6Kyi7xzibFChf1d59nYPFp1hQ/ViV3vOAcvPsLtm+Z/nzMcFMm65iTjo4r2zjYihoFpyLoO0ZXoSS0nU2PJ4pJ2L65fxDIzi76/JrOwHF7b1XZEXm9LmqWCnBvdD2wVq3ghcCMSnK8nlhUI3Xvvvfze7/0e2WyWv/u7v+Nf//Vfue+++1Z6bC9KXE3q+3rseGslB9swiFdbjsfLHidHc2xtD3VKhBDYhkGgwuBoV1cTEdMgYhoUHZ+edBxLSppiNqaUdbJuoBWv3tS1INeprho8fAAvUATV7iuN4vhoEyVfEBCampq1QDpQTBQrTJdjvPPWDj5+36186vvDSHEGJcBAhORqFEeHU+zoKJCJhLwmVQ2yfKUpVEy+dGgYU1gc6J+g4ikKjkfa9khFPeap3wloivns6jQo+0nOjOeBmlGsQTJq4/oBGuhJpxktttIW769/L0KEysZKSyxD852zezENn6ZYE9s6WjgylEOjMYWiPT42y60eBK6vOdB3iH3r76lrRjUuyI2B9PEjikem53/nV6In1ZipEfh1M1qNWc/UCJa2v7DNxKzAtIa5JdLl8NqutiNyuZY0l8NygpxV9/VVvBBYFVF8YXHZQOjcuXO89a1v5aabbqKnp4fh4WF+4zd+gwMHDvw0xveSwFI73pojemuiGV8VF+3oaSw5zJhSCkYKFba2p+lIRtFah51iVR0T0xC8Yn07f3LPzRQ9n0dPXeIbx07RP62pNbZrNN3p+JLt/7vX3k2uUuLY0AnQZQqOydGRFN861VHt9KJu8W5KSTpmoTXc1NHEa7d2Y9sG73/dO/nByW9xdPAwEdOl5NlcmGriwFAnttnP/p5xDBEqWgsBSitOjGfwAoGrPUZymq8d6ePseJ6xgiZbtshEq23MYiYkyjsmU2WL6UoFN1AIDRFLEjPNmTZ6DZ5SnJroYUfbAGGRUYUKQcLADQRR08U2FclIK1vbw7b16bJLRzKGLYsk7BmdLVEbgACtikwUpvhxX2XBBbkWSDuBvmY9KcuQ7OnOcGHs+8TNYQxRJtAxSn4XG9rvqn6fclH7i3Ut28FqX1YH43Iyoo2Zo0ZcriNyMYsOpRQdqeUHJ5cLcq6nmOm1ltZWsYpV/PSwZCD0qU99is997nNAqC79B3/wB3z2s5/lz/7sz9i3b99PZYAvahlKgwAAIABJREFUBSy4463ybSq+xZcPfIuUPUrEqNCcaKJ3TrfM3JLD9o5wYT5dzFN0fdwg4MF9G3nq4hg/ujCGGyiipkF3KoYpBd883s/69AksfZK710+T7bI4N9nEwaENrGlK8st71y/IdZrLazKlwbnJDr5zpocLUy5Kq6o4YghDhNpKhgizRIakwZvM4FVb38wPLnZzfniMi1MBGpOeDJzPbiMVsdjfU8QL8pQ8myNDTTzRH3J9hBBMlB1aig4Fx0MpwdGRJK9aPxXyiaoCjQLNifEMrYkkzfGwa6zm9l4rSXakYrTGbW5f10bU1Pj6JEnTh/oxwA18fBVh/9o1SDkTlDbF7KrwYzNF1yIZ8WflpARQ9Gy+dnSMwZy3ZNYhYcmryp7MRW/qBNlc6Mwe6iRVaI5coDfVCWwAlhYk3FstLy6X+3a5jGjtfd8aGboiLl3jGEtODgi7DC9MHGM03zerg2yhIGQ5Qc71EDNVSvN4f45vjB9ZLa2tYhUvEiwZCH3961/n29/+NqOjo3zyk5/kM5/5DOPj4/z1X/81d955509rjD/zWGjHW/EDvCBA6ThNkYtQdUmfLmUpu7O5Q3NLDkIIdnRmSPpFWjs6+OAbbsEQgn88dCG8GQfUUySGFFwc/wFOqQ8hBTHbJGZp2uMT/NIt63jF5jsW3dHO5TVFTY+NzWPctdFgstxBtuyGgYEGUwosQxK3BRFZJOda9E8X+fhjx2ZlQ3Z2tfLomXHErKlpMFa5mZPTUS5OjHEppzk9kicaqdmbzPwTKE1zPMr3zneyra3IuqYKlqHxlWC0EKc/uw1phAtWOmqTili0JiI0x20miy5SCLrSMS5NlxjMlXhFbzOCASDMRimt0VpzcKiJs1NjvGZrF1LKelYD4D8ujHNmsol93WP1C10zYx3Mt5IM3Drnp36Gc7IOphTXrCflBx6D0yeJWWY9MydDMzQGp0/iB2GJbilBQgTX1XKhljnqroyyddfuZR+vcYxPn/93Lo4frf9Wanw6peFSYeeCmbblBDnXQ8z0oWP9nJgss3bN/K641db5VazixsSSgVAikaCjo4OOjg4OHz7M/fffz2c+8xkMY1UJ9Xpj1o7XzVPxbcpBFzFjmJnCjqhbRlyaOslNPa+h7IeB0EKLJmhevj403/z8gbN19+tYVcl2KFdGioA7egfR1Xb56sdgGJLp4llmGD6zsRCvqZbd2dg8zYbmdYxaBnnHq3aAKe7ZNMSOtizpiI8bRDBMl5zfPCsbcvfmLr74zHmmym5dpLI7HWozFRyfdS1dBBQ4O5oP2+iFIG4bKK2YLFXIOz5CCN66Y4LOlIfjS3wVlqda4w737xpnyr0FNwiwpOTMeJ7+6RK9mQQxs2qA6vmhV1wmzsXsNhwvoD0+RsL2yDkmz4+m+PaZDkyRxVWKN2xfE3q07VhTX+D+12NbUVqzvS1HKuKF0gG5VjD24wZqXiAE87MO16onVXLyTBanq9mgMJ9lGYKoaSxIMl5KLLAx0zM343I1ZSBTiqtuIx/LX0TMya4IIXh+6Ahns21IaS0YhFwuyLlWMdNa1knOKXFLAV85eKEuCrmaJVrFKm4sLBkINXaGNTc3r7bLryAad7zDuQk+/WQfyYhH0rxAI+lXE2YlJotZ/vLxA+SdKHHb5ObuJvataeHwUIN7d0uMt+zqxQsUp8fyRMw5vk0CsuUsliwjseeNqXGxnKs6vCCvSYTu352m4u9+7VbidhMJy+TrR/sZzT5B0pwOvbOkQdwOgPMIIch6t9SzIZmYzS1rWuqE7prbPUAqarG7qwlDSkbHJ3HMCLZhoLSuOtAHRCxJxNRsbcuC1tiGqnuVaQ2WPEfe34khw/Pd0ZlhW0ea33r5VgwheOzMEP/XD0/h+YrhfJmpkstAbBNHhlLELQ8viOCp8HcRs00GsiW8QHF0aJpzE0frC9wbt/Xwx99s45Hz4wgqCBHjtt5OPvj6PfzF48cWnANzsw7X2on4vTPTOEEEQzjUwtyaX1lzPLNsknENjhfwT89d5HTVU2shP7qVXOC9QDGSm1iYT6fB9YsoXUKrdH3ONGbalhPkXEvwWcs6zcXJ0RwXpwqsb0msErBXsSA++8Dse8KNRp42fv/z9f//WewgWzIQmrXbj0ZXfDAvNqwEIdI0LDrTHcTsEZQ2CHQMQ8yICQoEbqBwAhulo/Ub66GBKfavbeFPXnfzjHv3oYNIKZguuTh+UBdTbNyxZisGppEE4c4bS8xOETHjPHvh4Xn6Rtu7X41tJHH8Qr3cUkPcTtHb1FYvrzy4r5dvPDdNybUouj4NuSdixiA5bxdFl3o2pLZgxayZ6Rkozd41zQCcm8gz7fo4nuD/Z+/NgyQ7yzvd52x5cq99X3qRelHvi0DGtAUyGLNZbNM29r3j4Ma9gT0RRNhjFvvi6yCwR/J4JojwBDcCOxwEg30dtiRjZBmDAGuEQQhk0eq91d1Sb7XvS+55tu/+cTKzs6qyqrKqsqqrqr+HAKRaTp3vZOb5fud9f+/7aqrK7qYI01kDT0DE0zG0NBHDxtA8Avpc4acqeeoDF5i2HgbAcf1BsF995U3ODkzyemEQbdjwuzynLYd03iZrK+ScAKau+pX4QMZxyTte6byztsOrfZOAv8H9+UfeWnEY6kqjDqupRLRdjwvDSeJ6B1HjZnG8LX5U0aOjfl/VvXeK1VZPnb3NnekUpq7RHgshEAzNZufMozs7MIbrJfjQoeqPX+3fPz80TdbKsytuENTzfgSy8GJkbZeZrMYPbyRQ1SzthQ7v5d3QqxE5axGfxdTaTNnXXE8wmsph6tqc3l6rMWBLJJL1YUkh9MYbb/Cud70LgNHR0dI/i0J/mRdeeGH9z3ATsh69RuaLquJmmXU7iOo3Kd7xi6mIrNuJKHv5ym+s8zfN4g26aKIun37dXV/HgfZD3Jw4s6BqqLthH5cGfjC3v5GV4sLAK3zzYj+JXIC9TXk01U+3BXUNgf975Ztg1k6Ss5O+WXpeSbum5FCVHOFAtBQNWWzDEsIfZ+L7n1I0tbaBUOhtCPO9a0PcmEyRsB3SKcFsVqMzvvDp3BMKpjpG1soTCpg4nt+k0XE9zg5OMZu1yLseOdtFVxVyjkvGcnA931x9N0kJdmEMSd90issjM6VU3s3JJO/b34VpaBWHoZaPZpnJWtSHAitKeVXDbNbi/OAUM9lWDrem6a2bJBKwUZQwCaudXa2PVn2s5y7389PbEwzMpkub+eBshumsRXPELFQmRmk0LxHShpmezfIv5xvobNjD3va3EgnUrUkUlVd7hQImWbcDTbkB+BHInOPieC5DqXZQdGzXY3A2AzCnG/pKRM5qxGfxczsweHfWmOW65B2X3vrIAqN2tQZsiUSyviwphL773e9u1HlsKWrZa2QxUfXBh7oBOD90GNv1iAdGMDWLmBnnxnQ9Ge/wgmMtdmMtF1bFZorl068f3t2FrvmdhItVQ72N+zjU/U6ev/iXcwSSX8nmUWeO8NP+46gKdMUnQdgoSohDnYdLfqci5VVxhqaUmjsCuCKI7Zoc7747R6vShgXw5L9eLG0m/kR7/9r/241RpjL+ptwYDnBjwuPGVJju+vzC6y0UoqbDf/r5TsIBk6+83IdA4+zAJDNZG1Xxo0y24+J6vvjzCiJI4JfV64o/gy3veJi6ykzWpniJPCHom0nz9Pnb/MeHH1jytS83edeaH9wYYSproaByYewBLo/vJKhb1IXq2NvaSF2VEd687fLU2dv0z6S5PZXG0BSipkFd0CCRs2gKm/77U79QSuMKIJGbZHpwhKvDP6Eu1LyiuWDlVKr2mrX9935IDGHqftfujNPBmcEW7symEQJUFaazeXY1RnnrjuYlB63WkscP9nDjxg1S+J9HU9forY+UHkLKWUn1n0QiWT+WFEJdXSufbbTdqWWvEVheVPli4AAhHRwvja5G+G8vXq14rKVurOVRlgXTr4U/U+rCeAuWkyagR8Bo4UErs3B8huXgeAJTs0jm07w2spuLYzsJGTYnurv49UdOLBgJUl4VVzRU265A4Ee2jne3VoyGlG9YU5l8xaof1xMMzWZoiQWZTOcZT+VI5m2+cbmNE50JoqZb8gc5norj6YQFnLvzD6StJC2mQc7tYCLdUhIzhqpgASj+8QFMTcX2PGzHI2DqhEwd09CJBVSms3lSloPn+RtwLGBwfSxB1spX7P1U/po3RcwFr/lasV2PyyOzdMRCpVSoKzTSdoikleejR+qrfo8+ff42fTNpv+pP998Hqbxd8JopuMIjZEAscNfUrykOrucC4HkuGSu56rlglau9VGbto4yk9/Mf97TzN2dGuDOdAzVDzPTTr64nmM1aZG1nQye8q6rCO3viHDl2N0X9L1cG/Ne77Jaxkuo/yfZms/mB7keq6iwtuUsteo0UqVZU3T2e//+rqWxZKi3w7MW+wvF0DL0Ogb8xC+HMGZ9huZ4fvRCCmbzOjQkHVUvQGg1SFwwwnfUWXX95VRwkqQvFaInt4eFdv4SpL2wQOf8ci+k91xNkbYes45UM1QKFg231vPjmMKm8A4qC7Wm8dKeBUzuLVTz+tQoZDp4nyDlpNEXFUPPo6k1OtKd5Y6IJy/HXqKkKmqIgFIgFdfa31jGZzjObtdjbUsfP72olY9l8+/UBMjm7MHy1GOHxcOxX+c7Ff8NyUnPmxrmeUlMhXem62a5HxnIqpkIbQwHe8UBb1ccrN9lHAzrJwlrTlkM8qCMEdNcpBR+bn75VFbd0DIFXmM2mrmou2FIl7aGAya7mTgL6FKOpGVRFoTli0hQ2cYWHpqjEQwFcIVDndxkvML8IoFaUf27XWv0nkUjWFymEVkgteo0UWa2oWsuNdX5aoFyMza/UujCc5P1793Fj9GfYrihsc/5/Xh+L4wgVxfVI5vwuyvWhwKLrX7JXTeE8ZrMWP7gxwuWR2QXeK01RyFku/3yln2TewbIsmqdd9rXG6K4LFYaV+qbygKaCrvLdN9swNIWHWpPUB13yboCY6WEUolIoFFJ1Cjvqp+iItTOUsLBcUYii+P6h1mgIXVVpi4VojYV4uKeJP/ylw3ie4F/fGMH2/AiEpipEAzrv3j1AT3wYywkumBu3s/Wxmgnpxa6bqWv0TafZ1xpnT0uc7vowACHD755dPg19KZJ5e47JvqlwXinLwXI89jTXsa81TlD3sL0ghprH0JQ5qT4FteQLW2ou2GKFB8uVtIcDOntaYrzwxrD/uuOb4jX8tgs52614TZcecqxVNLmvlrVW/0kktWY7Vn6tBSmEVshae42Us1pRtdoba6XNJpm3yeQd+mbSjCSzJcNveyxEb32EXa2PYjkeFwYvoKs5EjmdCyNRnrvaAnioCngBv4T9aGfDsucxv1dNuUfq/OAUU1mLjkLFDzBnHMONyURh/b4gS+ZthhNZ2qIhxlIpYoEsGTuApuqkhQA0XurfyUt9Dqd2RfnI4U5c618q9j6KBCxaIgJXhBBA1nIZT+VoCAdoKaSvPCHojIdRFd9EDbCnOcZEOo9AoCkquurSFZ9ELQyZLZmrlbu9n4qRrfntAVYipJe6boqi4Hge/3p9iICul6JBzRGTjx/fueIqqPLIUl3QpCkSpDEU4K9/4+1Eg/7MtH+/OUb/5GsoikLOUQqpM4Gm6qXrXWkuWDWFB8sJ/189upNvXxmc8/4t9p5SFKXiNV1syLHrwT9eaeWVvgnSlkMkoPNIbzN/8t5j6PraxEutvEnrFcWSSO5XpBBaBbUKda9VVFV7Y11qs4mZBn0z6cJsMkp/cziRBaAuGOStu9/LCzfbuD42yku3kqQtD1XxU1MOgKKwpzlWdcqlnKJfBmA6a6EAQ4m7FT+aqhTSdH4Zcn0oQEM4QFL1qItHUBXBjvjrvKN3GlXJkswbvDlVz8+GdtAQDnKyuwlNVfjie48R0ATfvvDDuaNMCr2P6kJ1/OZbDvPmZIZ03kFRBD+5NY6ha9iuIKArdMYWbq5HOht5fXSGoYTfTygasIgGbHRVW9CGMmslsZ00OdvlJ3fGyTuuX51XH+Gh1jpO9iw+z20l121PSxzX85jOWLTENMDvhu0nCKuvalzOZB8NGqWfe9uDv0zI8NNftpvDFS6aqqNr/vuzWIU4f+N+7nI/r/ZN4goPsyBK5/ullhP+pqHxa8d3lo5TFJeuJzjRtVCcLzXk+Gd95/jp7cOg6KXhxT++NcYfPX+OP/3giaqv3XqwXBRLIpGsDimEVkEtQ921EFWLdfp1CkbfpQzZHzjgV6fdnaRF6d+LGJrKQ21NPH2un0Re4An/a6YOrdEgbfEwRzobq065lJ93MS2XtZ3S07xaNixWUxWmM3mujye4PZUCFFQVNNelHjjUcpMHG0cI6Bqup1EXdHm4a4K6oMGrQ7vRVIW372otrXupAaMndu6dcy3/5crAgs3Vcjz2t/lRDU1RyDsuI8ksXqFSKRaMgxLG0Czma45QIMYLb86gAmPJnB/B8DyujSUYnMnwR+8+UtVrDFS8bgpweWSGwdkMd6bTgODBoM6OhigxM0BAV7kwPM0v7+8k57hVvW+XNNmXUZ76zFizXB95haGZN/0qRCNa2rDLKVaklXuY2qJB9rXGK/qllhL+K/kcLTbkWAh/KG4kYJO257ameKVvYtGU5kaxWBQLVm5Cl2wOpFF6cyCF0BqoRah7LaKqGOl5bcDvR1MXNPyKLl0jazvMTI4zELjDxeHFzblv29lCT8FDUr4hdcZC9NSHS/6KjO3ger6nJu+IwjFUv9Tc9djTEluxGCz3SAU0bc7vW46H5bqEVJ3RVI6M7dxtBCkgmXeYTGfYUT/lbwz4YxsAXAFdsUkuGbt4ZEfznM1wqQGj8ynfXNN5m/4ZP+LiuB5PTl4kZ7toCnTGw6VrN5y0SVsd1Jv9c44lhKCjfh/fvp7kRzdHyVg2dUGjNCZkOJHh//nuWf7brzxc8TUu3+B3N0XJ5B3C5tzrNpnJM5u1CQd0hBDkHJfzg9NcHUvQEQ/THguSs13++HvnEYKq+l+t9P2pawbxUDMP7/rAsimcZ87f4c50ioCmoSgCzxOlqFZvQ2RFfqmVnGfFIcf4qc+0HSDnLBT06YJnaH5PqI1iqSjWakzoEonkLlIIbRJWI6qevdTH35+9zUQ6j+V4JHIWrudxuLOBh9rqmRHw0zvjvDmR5FjXQoNqcRxAxDTmpD7mjLUwfQ/IjYkUHXVhWmNBxlM5Mpbjd1e2XQ52hPjVozuXPd/5katyj5SmKrTHQqUUXUBXCWgaluOhKQrddRGmMxZjySwZ28N2BXp6BlPLl9ICTmEoKgJChsVbe8N89PCOOZv8YqZty7b45yuvc3kkT9qaKxI+cKCbp87extA0AgWfiOsJfnJnnLZokF1NUXY3xUqRo5TXygNtbQzPzBVbvS2PMnv+Cv2JTGlDK4o7gcJPbo8viDpUiuZdHU3QN5MupQ7bYyEGZzOkLAddUwloKjnHb+Rnux5e1iJnu9yaSBANBjjS2Vh6fast21/N+3Op2WWOJ7g+kSCZd0hbuZLZPBLQURWFPS3xVfXYqeY8Kw05Bj+AN5xqxhUL00yRgE5HPLzi86kVi0WxYGkTukQiWR4phLYotuvxzLk7jCazhSaAkLT8AaeXR2bZ2+KPPAgZOrM5u7TRlBMO6HNGWmiqQkj13xLl09T7plNk7bvVQ22xkD/pXfjjKX7lQC+msbhHYSmPUvnfLppyhxIZGkMBNFVhT0sMx/UIBTQGZjIMzKZxPBchIJXXSFsG9SGvJIKE8KuGbC+EpoR57nJ/aZO3XY+pjN9ksTFsEgs24gmX124/z6WhS1hOkrZgmKzRwax9mFf7JknlHT56pJcbk8mSCALIOw5jiRx3plMMzGYwda2U1slYHg+0PcbxXl9s6WqErOMbs13hYTsCQ1sYobMcb07UYbH2CsXzKEbv9rXGyTsut6ZShAz/ewLhG7KF3w5gJmvheGLBRnqvRj2kbY9Lw9MI4fuX/NYDopDSXV2EcSUsFhm8MNGM600s8Oy9fVfLPU2LLRbFgsomdIlkKcpnhy3H/VBhdk8+2d///vd5/vnn+dKXvgTAuXPneOKJJ9A0jVOnTvGpT33qXpzWlmIqk2dgJl2aYu56wm/op0Ai5zeSA3+jqw8aZG2HaNkTdrkhu5K/4lhXA0L43ZyTOZvXBqdojZh0xEOMpfLYrkdA1+iuC3L66I4lz3Upj1L5387aLvvb6vgPR3fwjgfaSp6jJycv+iMuVIibBp4HeeGS9zQujMT4ud4pdNWPhPiVWgJLdKNpAc4PTfO+/V18+/VBnj53m/6ZDAq+Qfn0sR30xl7njdGfYTs24PfDieo3mEzn+fGdXl54Y4jXBia5NZXiaGdDSUjcmkqTdZxCasvfxMtN3jHTQFUUXngjxfmh/tJ1NVQVTfXTMMWUngCiAZ2IacyJOizVXqGnPsxDbXXcmEySsRw0TSFq6sRNA8fzsBx/uGrxLxT/P2O7ZG2HkKGXon95p3KJ+XpiagozOZuWaBAlnSeVt0tpQl1V+cih9R1Gulhk8Ei3xx89f25O1djbd7XwJ+89tq7nsxyLRbEWM6FLJPcTa9UUGy6E/st/+S+89NJLPPTQQ6WvfeELX+DLX/4yPT09fPKTn+TKlSscOHBgo09tyyHK3Lia6puIfY/z3Kf+I52NHOqom9Ofp9xIWslfUeqGqyrEggZt0SCDsxm66sKc2tU6p3poqWhQNU0jl/N2HO1s4Me3xsjZLrNZG8txfU+QpvLdN9sRQnCoLUVrzMMTIZJWOwnnEJrqp/+eOX+Hf77cz2gyV4jEKAwlMjxz7g1+efcrKORRFX/sh0DDcjWixjBDiQYyFrzSN8FM1sLUVB5qr8f1BBPpPFHTIJm30QqdtFVFYTiZ5WNHezE0taxRpVLy7fTNpPE8wWzORlUVgrpGS8SkPmTwSG/zHNGzVHuFiGnwa8d34nmC3//WGUYTORQBo8ksQcMXN7qmYmoqhqYSNnRm8/7w3evjsyTz7hyDcsTY2FtB3hXUBQ2mMvk5TRAV/G7bludtyHnMT9/pusqffvBETfsI1YpD3e/EcrKMJu6Qs1NL+tskW4OvfuSyNEyvkVpoig3/hJ84cYJ3v/vdPPXUUwCkUiksy6K3138CPHXqFC+//LIUQsvQGDbpqQ+XPDWqopQ6/8aDAUKGToK7kZ8PH+7lQ4cqN60rUvRXVBIv5b1kuusjRM3K1UPzqbZp5GLejozlcLC9jrTl+CXnroeqqiA8DFXBE/D8m528cNPjPfsaGElCzlEwtHHaYyEebI5zbWyWsVSO8qyQqij0xq4gRLZMNwoUHFTFI2x4hA0L2zPRVT+ecml0hr2tdViuLyIawwHqQwE/FVeo3GoIBjja0UjGchZcw6tjCa6MzKBpCjFTJ235Hh4UOLWrbUHUoZr2Cv9w6TbnCn+nNRYsGaYFvqk7bOhECtfeUBVyrmAs5U9uNzR/jponBN+5Orjq8R6LNUNciojhr+3aWILRVK4UYSymF+/1DK5Kw3Lns5p1r4b5ZfMhI0Zv0yFO7nwvAb26mXESyXalFppi3YTQM888w9e//vU5X3vyySd5//vfzyuvvFL6WiqVIhqNlv49EonQ3z+34qYSly5dqsl5njlzpibHuRecjDl8dyrDdN7F9SCgCOp0QXvApW9wCFNXqMtP05V3OHNmfMljOZ4gbXtEDJW07XGrfwJzno8lDhiGx9vjFh0RgW6Nc/bs8sedmRxnptJgUQXeuHyxVO019/c8/uL8OBcnM+QdgakraEBABfCNQK7rNzXUVYW8LXjtdpJwITqVA2YTCZRsAg+YSeTQCoNT/fJ/j574aMVz1lWPiVyAqaRHQLNJp1IEgamMw62BARQUctksTUGdHXETIQSW6zGcztOfSvIn//QjFAX6ExYPNgRRCn/zTH+CtOPhCUF72KAtqGO5gjbD5cNtHufPn51zHmfOnKFLCG7kk1yfyZWuw976IF15h1deHeOfz48yk8iiFy6hCTQHBInCvqwJl2ze9YWyCpomsLMZcgI0FZqCOnWex3fOXKYjN1bxtVgMTwh+OLDw3B7tjt2t8FsEXVWI5aaJOlmiQbBcQUDzwEkRy7lcOHd2yd+/l1Sz7lreV0bsi0w5t0rtLZL2NInMFFMTM7QbC4cvbwRb+b5ZDdt9fSths1yL9dQU6yaETp8+zenTp5f9uWg0SjqdLv17Op0mHl++RPXQoUOY5to8DWfOnOHkyZNrOsa95PhxwYOX+zk76JfP14cCHO9q5H37u0jbDm9cvsgjb3l4yWNUMjIfbK9jZ7fJYnvZB37h8IqegIeDfYtGNR5ZJArxf3/rNW5kVWLRGEUbaM5xMAMOqqKSyVtomoZZSC2lbYeD3U1MZqw5KZ8Hmv3fHnXGmCjzorQYFiHj7kysUmW+8ANEA8koddEYTWGz5MkIhgV/evoXCAd0XnxjZE7E5+roLLlchj0dYR5oq8P1BHfyoyS1EPvb6kjlbayBDGYAUCg0g/R/N+cKdu4/SFssVDqf8vfmWx6uHH2YyuRpGYJGa6rQyfkuHYofUWmOmFiOR9DQaAwFUBSFQx0NWK6LqQkMLY8ngqQt2HPw0Ip8Qs9e7GPW1OnsKJsM7wkGzcZlo0tnzpzhPz/+zmW7Sm9Gllt3Le4rxdc7pMPg5ZcIqAtL+l1jlqNHjmy4P2ir3zeXY7Xry+fzNXtArwVbyeRczbVbT01xz5Pf0WgUwzDo6+ujp6eHl156SZqlq2Sp3immoVX1dF/JyHxhaKaU6lnrGBFYWbM72/UYTWb5yZ3xBb6ioK4TMXQ668Jk0mki0SiGqpJ3XaKmwaGOhgWjKzKWw+GOBv7XmyOFgaF+WsyjUojKxxPwykAroJREkBC+QbktFsLQVD5yuLfkc0rlHUZTObrqwqUUoqYqtEbwtWEdAAAgAElEQVSDDM7Osq9FRcHA7/BMyUhdRCk7l/nNMItUSh3GTKPk3xpKZFAK0+A1RaU1GuTndrQQNLSSSD7cUc+l4VkUxaPdvExIG0ZTsrgiRNboIBJYvKFjpddprcNjN8sMrpWkuKpZ92qOW2T+g0nMzNEdniFk6AsadMqy+a2P9AfVntVoinsuhAC++MUv8pnPfAbXdTl16hRHjx6916e0pVhtY8elbuqqonK4o4FLIzNrnphdzYZXvgEMzWZ4cyJJQ8iYE5EBaIkGOdrZwKs3M4XmkQqP7mgjWCgpL28BAL7X48OHevjWlX4SOZtEziZn2zy6Y2FKrxhUsb0AroiSytu4nkdbLERbLMivHtsJ+JGYmGmU1tQ3neIrPxbECiMnhBBcH5uhK3KFE60TxEwbWw/y3j1xfnS7i8bQ3dfKK4zYqA8GePZiX2kDnJkcZzjYt2R0pOghsh2XwZk0/YkMluOh6wonu5p44n3HEQpzrrmu9nFr7EWi+i0oDN3QlCz1gVtcHnih6g7Fqx0YvNg6NrJirUg1c87mU826PSHmvJYriXTNfzDxRJC8a6IoFsF5RQmybF4iqcxKNcU9EUKPPPIIjzzySOnfjx07xtNPP30vTuW+ZqmbetZ2eWxPO48f6qnZ0/pSG175BtASDRLQ1NJU++bIXUNoLBjgSx96C2fP6rQ/sK9U1fPsxT5+1jeKomRRlTCqapQiWJbnsaMhyt6WOs4PTtIausSR9ikcTyWgeSUBJAAhNLLubh5qa2Y4mSVnu5za1cqJ7iYEgif/9eKCza23IUo4oJO1HQKaxhvjCdrDV9jTNAwoheiUxZG2MVzP419vdRPWtYLACnH62A6+c3VwzgY4I6prdvj4wR5euTOBoiq0x4KoikJXXZi9LXG+9foAHz7cO+eav39/O3+fHMNylNJYFUNTCeraijoUr3ZgcDkbZTZejKXaOix2zatZ9w8Hksya+oqOC5UfTAQ6WbcTTbnpDwgupXA3V9n8vX4tJfc3a9UUmyIiJLk3VHNT34in9Yzl8NM74wQ0/4lXVZRSuidlOTSG/f4y5Y3tgrpaqupxXIdM9qe0BN9EV7Jk7QCzVjvHet/N4wd7cIUoTXzvn0nwSOcMoGC5/t/TVYGiCEAl6ewh4Rxlf5vK7qYY46kcv/Xze/lZ/yRnB6YXbG7F3jdvjCcYnE1QH7RI5XI83D4JKKiKguMJHM8XXA82zfKD2x3YjkJLxOQ3Tuziffu7+K//61LFyNzZwSnetrOlVFk3H1cIgobGOx5oX9AVvFKKKu+mCGh5TE3HbxhAKTG3klTLWgYGe57gB/0JvjWxUFRulDdotam95dYNcH0mN8c/VM1xYfEHk1n7MLbr0RiZwXY2V9n8aqJqEslmQwqh+5i1bGbzWYsf4qd3xnnxzVHChobjCXRVKZyPQjrvpxvqQ+aije2eOfOPZHKXiRgqoBHQXepDA1j5V1HVXagoHO1s4Ic3RnG9LDHTb54IYHsarlDwhIepaaScPQihcG1stjQ/7C9fvs6bE8mS/6eIpio8c+4Ou5sivLXrFqHeGxiqBfjma09ogEHO8TthCyAWsNnfEmA6F0BRFT5woJupTJ6pTJ66oN9NW8EhoOe4OjrJrakck2mLlqhZcYMp3zzLU4JQOUVV6lBspbAcF9sVpchQQA9jalGqZbUDg5+73M/VqSzdXZQ2/Vf7JknmbT5+fNeGRBSK183UtQUCcrnU3lLrnslZpVl881nuuIs/mKhkvOO87/B+HC+96Oy2aqll9GY1UTWJZLMhhdAWpVY3s9VuZkXW8kRYvIkGNI1IQGc8lSOZs4kFDZojJrubojie4OPHd/B/vHVP5RSelSeRvVlqakipyFghkb1J1soTCpj+JpXN80+XDJJ5g5jplIzTAAgF2wviiSDXxhIl83FPfRjb87gzncIVgt1N0dKm6XqCgZk0JztuUhd4AwW/Cs3vbg2a4uIJBU8oBTcOZB1/qGdA0xiayfD/nbnBrck0r/VPYmjw9t4BuuOTNHal6MgbBJU6Lg1DRzyK4/pNBss3mJWmqIodii8MvFIwZCulrtOT2Va+fXWk6g1sNWbnYiSmNGNNiFIvoX+7McqbE0mOdzVWHVFY7ecgYuj0TacZSWZLhQHtsRD7WuPLpvaWWnfMNDD1yue93HGXezAJBUz8Bgmro9bRm1oY5u931rOhYvkYja1UQXYvkEJoi1Hrm9laK3dW+0Q4/ybaHDG5OZFAURTSlkNT2EQg6K4LM5G2Fj2nyfQ0qpIFFn5fVbJMpqfpDrSjqgr/28kHeP7qEKPpcepDvn+niK4pxEK7GclppdElxY3R9QTJvMOr/RMMzKQxdY32WIgdDSZxM0MsMITC3U7IxXSTAqiKi1L4mCmKYDDRVBrqOZnJc3FohrpQgO76CO3hS3REh3FcBc+DWMDhZNcE9aEAZ0ceKJynOmeDKd88gVJ0A1g0qnew+1388OYYIW0ITcnhiiBZt5OEc7jiBrac2FhJ+rQYiSlSFJ2+MBLkHbeq989aPwffuTqIK/xmksU1DSeyeELw6yeqi0pVWrehqeytDzI7b7ZftVHWtT6YLEWtoze1NMxLJPcSKYS2GOsVil5uM6s0cmAtT4Tzb6K7GiNcGpomW+ja7AqP7rpIYYjp4jfVpkgDngijKbkF3/NEiKZIw5w1/tqxXTx11kOdhK7YJCHDwvFCtMQf5ONv+RjjKYvZnFVKUwG8OZFAUxxiRg5DNVFQfNESmmbXvhyGai/428UrIhCoiiBlBRhJNXN+dBdC+IJFVZXS/Lf9rWHqDd+75Bbc237ESqU7PsnFsZ2MJLN010cWXIsPPtTNK3cm5szHeqS3mQ8+1E0l0pbHcPogkcBBVCWHJ4KIwq2g/Fqvh/+jGMGawRcHo6lcKTpkaGop2rbc+6fS56Da9FrxfXugrQ5NUUop0ICuoioK79vftaq1FXm0O8ag2bgqMbNeLQXWI3pTC8O8RLIZkEJoC3EvQtGOs3AI5SO9zfzJe4+RtFb/RDj/JmrqOm3xEJ4QeAIe3d1WmrK+1E01FDCJh3aRzl1mblTIIx7aXUgn3OVDh3pQFDg72MBgJkVLJMP+tp18+PAeVEWhMWzOOWfXc2kPX+Lho5OYukXOCeC4KvFgFlXRCBoGnufAvL5E/kBVhYjZTDj8GC9dnmYkaTOazJJzPHQFNEXljfEE+1rj6GqeiGEhUPGES9FoDRDULYK6xUzORFNZcC2+9foAhqaWZsAVI0LFqrHFrr0AXDHXE+Qb0TWmMvk5TSNrJbqLEayBwcHSqBJDUxACOuKhqnw68z8HK02vlYvw/W117GmJzxlAm7adJefnVTqfctGiKmsXM7UuUliP6E0tPYYSyb1ECqEtxL0IRf/R8+f48a0xfzM0NIQQ/PjWGH/0/Dn++H3HVv1EOP8mqqkK7bEQg7MZuuvCJRFUzU319MmP8swZSGRvoipZPBEiHtrN6ZMfXfCzqqrw+KEuOsKX6Zt6HcdLo3uvc67vJsd2/BKGps05r7h+gbbGYRTF761UH3TRlDSe0PBQCGgqeaEjxN2okFJI86iqzu6WAxzrPUYw0M/fnb1JxnJojem0RoOMp3IMJ7IAPNQWwRUhNCVbShMVy/qLviJNVXhLT/OCtFW5KCg3TJc3+CvfkBfbwBzXw/EE//3FyyRzNq8NTpVmfxV7OdVCdD9+sIcbN24wq2mFZpIKHfHQHDP6Uu+f+Z+DlabX5ovw8t5TK4lkLBYx6yq8cPeqP1Il1it6s56pPIlko5BCaAux0aHojOXwSt9ExQjUK30T2K63pifC+TfRfa117GyMYurqim6quqbz62/9VbJWnsn0NE2RhgWRoCKeJ3jqZ98gnbtC0SpsObNk8j8D4MTO95b+3oWhcSLGCEL40Rl/+GoxbeWhCN9sHTKC2K6C4xUqxlAw9TB72k5ybMcv4XkO79gd4exAlF2NsVL65+roLEOJDCPJLHta4mTdDqL6DQxNJW87eAIEHq8N1nN9IsPHj+/kI/M29qXEcTpv89TZ29yYTC5IbVXawIoVe/41VbAcryTU9rfVlY67VtGtqgrv7Ilz5NgR9rbEeX10tiR8Yfn3T/nnYH56LagLwnoWlNCigq1WkYzF0tQ38kmWmWyz4axX9GazdAeXSNaCFEJbiI0ORQ8nMqQtpzTItJx0wTO0lifCxW6iq60ECgVMugPtS/7MP126VagyU0o+HrtQjVXeUPDDh3t5x+4I37loY7t64WdKMggFga5SckZrmkk02MjbHvgP5BxoiDRhuYIzt7/P8Mw1UvkE7SGDvNfJrH0Y14PehjCeEAzNZpjNWWjqYdqiQWYyNxGkSNk6b0zU8XJ/F1FDQ0NdkOpZShz3z2QwNI2ArlZMbZVf+6Cu8d9fvFz63UDh94QQJaFWfM/VSnQbmspvnNi1IKqy3Pun/HNQTK8FNMHRtlvsbJghYli4IkTCamM2t4fmSHjBMdYayVgqTX19JleqRNtMrGf0ZjNFv+4XZCVY7ZBCaIuxkaHojniYSEBfMNATIBLQ6YiHa/JEOP8mul43Vdv1uDwyTHswx9zBTQq2K8jMaygYC8aJmnGyVqrw+8LvPo2GqrgEdZ2cXezF43FlrJWnLt0k57jM5q7wSNdtHmgYxNA0grqGoeYx1BtMpvP8uH9HyaDbURfmD37xEM3RILZ7mA999fs4uSnUQAOWp7CzUUVR4JX+iQXRn8XEseX44q480gILU1vFaz2Vyc85tqbebWpZnN8WUvWai+7Vvn+K7/fXBqZQ8EXQvuYRdNVXp5qSo8G8za2xH9K8a+HYkLW+b5eKxOUdsSkrpmT0RiKpjBRCW4yNvJmFC8bookeoSHmH5yLr+URYq55JybxNKm/gmqEFVWYCQUCPzpndVOy5c330ZwQNjaCO341Z6MRCjUym09huGlcEGUg08aM7XUykpwFojxk0hUZxPD+9BWBoClkb6swRVLowNA3PEyjAK30TfPhwL33TKRJ5gZs3iQU0ynVMMQpX7KhdpJI43t8WL/Udmk+l1FalyFLRszOaylE81HqJ7pW+f8o/B3uaw6Qz59GV8silXxY/PHMNx118bMhq37dLReJMXdnUFVMyeiORzEUKoS3KSm9mjmuTzCVwhEl9KFy1oPiT9x5bUDW2WIfnWrNU+fZqiJkGoYBZ8OLcpDwqpAA9jfsXbJjFMQYD09fIWkkihfEGB7vfxZ+9cAFVyWG7Jj/unwIEaSuLoiiYmkdQt7BdcD0F2/UIFVKMId1CV/O4brhkEi5GaYpRuMTCbgClKNx8KoljgCcnL1a8DpVSW5UiS4qisKclzseO9vKLezo2ZQTB0FQ+dLCZf3wtXxCdc+enrdeE9qXS1Hvrg5vuOkk2F3Lq/OZCCqFtjidcXrv9fa4MX8JyUthekLzXyY7mR/nQoR3L9oPRdZU//eCJin2E1puleiatRgrd3bwOAcxpKBgP7ebkzoWzm1RF48TO93Kk511k7WRpvMFUJk/agnAgSt51sBwPRfEb9CGgfxYSOZ2o6SCEwBWQtlyEEHgiyFt66lDVOKrqC5LyKM0jvc189+LsnPOoFIWrtL5ycbxSP9lSadfNPDcqbMZojNSTtVJ4FJooFE43ZKzfhPbFrldX3lnmNyUSyWZCCqFtzrk73+fS4N1xCr5P5Sa3xwXPXX6s6n4w4YC+ICWznizXM6mjsfI8p+W4u3kdZSS9n6hpc7C9gw8d2nV33EYFdM2YE1UoT40UjcWeJ0rHSOQFb07Vc6x9HFEwZquKQFMdXC9HV+QHKEqErNvBrH14TpTmT957jJGREe5YSikKd2pXA5975w4c114QtVosdbhSP1kt064bOY28mMK8NvIqKBRiQsq6T2hf7HqdOTO+Ln9PIpGsD1IIbWM84TI4dQ3fNzvXHBzWh7kwNL5p5wEt1zMpbVf2vyxHrTb7+amRorE4Uphyn7FdXrzVie16PNSaIG76TReVwv96AgJqlqh+EyEEu1ofK52Hrqt86ngbDx0+ytBsisnETxhN/ITnL3+PcPnkcaEu2fl5tWtdi4fkXkwjzzs2GdufHZe3M4CCqYdK7QvWm8Wu10aKQcn2RFaGbQxSCG1jHJEjYyVKvolyNCWH5aQ3XXWL49p+CkqPLNkzKWKsLiJUpBaG0fKIS0+9793pjIfJ2A4Xh2fQVZ0f3unl0rhKT53gHTsuEzLs0sBWr9CHqCk0xvv3Lyz7Dwd0EumfcmvijN+pWlHI2imuj/o9j/qSB6oat1K+1tL1XeME88VYjxEwiwmKoui6Pf4DIvoNFFQMLeQPPRUKiqKiKtV3iK4VnhA8e7FvQ8WgRCJZPVIIbWN0JUgkECdjzSz4niuCBPTIpqlu8YTLuTvfZ2D6GhkrSTgQY29DB69PPoim3t3Mih4X3br36YdKERfHtZlMT/PjW0le6ZvhtcEpFMATWUK6haqo6KqKrgpCho6mKijkybspAsZcQ6/j2gxMXyukeTyU0ggRQd/UVS6Mt6Cpcz/Ci3V+rnR9i5GlWomF8nSmglOaY6ap+qq6US8XXXrucj9nB8boDA+Vro3f70klaKhz+kJtJD8cSDJr6jWfByjZPnz1I5cX/Z40Um88UghtY1RFo6dxH9OZux4hH0HG6eBIZ8umCdmfu/N9ro/+bE7kQ/Gu81ATXJ/et8Djcvbs0kJoI9MShqZSH9I5d+d7JaERCsR4S2cHI4lWbk9n0JQAtggRUS0cz0/rZWwHBYWAHsbUoguOm7FmSWQncIW7oJdTzs4Q0c6Qct/C3BlrlcvjK13fYmTpxM6FfXZWg5/OtOiIvE5IG0ZTsrgiRNbtYDj90Iqjj0tFlz5woJvzQ9MYWr7QCqH43vZ7QgV11q1ibCls1+P6TI7Ojo2bByiRSNaGFELbnGM7fglPwJXhi1hOulQ1trPl0U0zD6gU+ZhnVlZUhbAxzOce+xBZh6pEzb3wqMBCoZGzUyjiOr/79hjXp/fzxkQCyxvD9PyyfX+Uhe8Ymsy28u2rI6Voge16zOZdXh9+BddzKbZxBErzxwQKEb0fVTFJOHNbGcwvj1/0+ipKTaMmMdOgI/J6WWsCv7FhVL9JRwRi5smqj7WcWf5tO1vIWA6RQLAwo+1uvwFR6NwUCaxfxdhiJPM2eady2na95gFKJJK1IYXQNkdVNB7e9V6O9b5rVX2ENoKsnSRjJRds1OA/1TtemsZwdU/16+FRWY6lhMbQ7HV+ef8v8NFAL7O5Pfz9q/9AWB8GcrjCJOt2knAOc35omvft7+I7Vwc5PzTNnYFRHrEvY2oq4JdjlweFhFABFVUMonAIwd3ZW/PL45e6vhkryUhikrZ465rfEwoujcFRrArV443BURRc5kevFmM5szz4gk/Agr5QCgrKOleMLUbMNHyPUgXWYx6gRCJZO1II3SfomkFDpOlen0ZFQkaMcCBG1k4t/N4KnuqXiyKsV1qiotAQkLVdkrlp/se/vYah17G7KcpI+hAR81DJP1MUMBnL4enzt3ljPImmKkQDDpqSw/E0FDxUZX6VnOZHlfQcOFkydmjR8viK11dAznHJOQH+35f6CAVG1xw5y9pJAloeIdTC2JG7zQ0DWn5FaarlBgw3hs1S1d6sfdhfZ6EvVECPsLf98IZUjM3H0FT21geZ9cSGzAOUbE2kD2hzIYWQ5J5TPsqiXEystA/MclGE9UpLzBEaBYGRtV1cT5BzAtyactnTIrg6mqBvJs3+tjpcMdcTFDS0kggCsF0DV4QQIoPraQR1j+KlEULB8RQ0VUEQ5v962xGChrlo6rDS9c05LrbrknU7CQX8a7LWyFnxOihKqjSOpNjcMGREqxK05d6u5RpCllftDacPEgkc5GC7ya8c2EvACKxqDbXg0e4Yg2bjhswDlEgka0cKIUnNWY1R+diOX8IVHgNTV8k7mbn9cqpkuShCrdIS89dXLjTyjoftenhCoCiCgUQjA7N5PJFgf1sdQGnYahHXE+xtiXNxeLp0/p7QyDjtmOqbgIIrVPRCVMgTKq4QaHjkvU5aotFlr3P5qJCMlSTnBEhZbaTdnSiKg0Bfc+RsvuAq1bhVIWgrebuOdDRwrKuBi8MzFQXFUn2SJlI5LgxPcaSjkeZocMVrWQuqIoebSiRbCSmEJDVjtUZlT7icuf19+qeuk7fTBI0wnfUPrri0e6n5T7VISyy1Pl/IwWt951CVLBnLYDDZxPlRv2P1aCrHnpY4PfVhHmqr48Zkcs7m/r79XdyYTM75e6PZA2SzaXrrptAUQc5TQfjrytgGaaWHB9sfrWpdxVEhh7p+kb8/e5nXx35MV3yA5sBNbBHCo4uEc4SM5a0pcjZ/NluoSkFbydt1dnCKE92NfP7dh5cUFOV9knI5h8f+4ntcG09gux6GprKvJc6Lv/0egsGNvd3J4aYSydZACiFJzViNUdnzBE/97Bukc1fwC/wVLDfNm6NnS5v3SljpWImVsNz6Hmh7jG9caiRsWPzwZgJX3BVxluNhuS4R0+DXju8EWLC5zxdxAc3gJ6O7mcg9xEOtBq+P2YylcqX5aL/98wf40KGVpbG+fXWE8cQF9jaPUqzsMpQcqnKjkDY7uqbI2WKz2ZaiGm9XtYLisb/4HldGZlBVvzJPCMGVkRke+4vv8ZPfff+S5yCjNxLJ/YkUQpKasFqj8j9dukUiexNNudv72nY9hIC+qZWXdtdyXlY51ayvON1eYNIS9RhOZEu+noCuoil+xKp4PvM393IRl3d9s+3P72xFVxVcVPa2wgPNgqzt8MiOZj52dMeK13BhaJy24DBCqP6A2AKegKA2xI7mUzW5XvNnsy1FrbxdE6kc18YTC6KPqqpwbTzBRCq3IE12r9otSCTVoH36b+b8uxy5sT7IRx9JTShuZpUobmbzsV2PyyPDZT1gBI7nYbkeacthIjXNP126huetfJxGMS1Rq6f7pdaXzNn0TfsVWUc7G3A9wb7WOB3xEAB5x6UtGuQtvU1LRqY84fCuPVE+99h+fvNAM59/92GeeP9xTvb41X4Zy0FTFd6+q5WPHl6ZCCquwXLSaEoOXVVKA2KF8P8b1PK868H6FR93rdTK23VheKrQWXohtutxYXhqwdeLUb7i3wI/yvfc5f6q/qZEItn6yIiQpCasZjNL5m1SeQPXDBVKxcWcKIXtBbkwlENT++/5aIJK6xNCcG0swWgqx1+8/AZRU+dwRz3Huxq5MDxNb0OEva1x9jTHOX10B6ZR2e901yN1FctJEQ7E0NQ6NPUkqqLWLMIVMw0CeqTUgNBv6igo9hyvD9cTNje2ASHUztt1pMP/2flduIt/40jH3AjVvWq3cC+N3BKJZCFSCElqwmo2s2IqyW+Id2OOCFIUQc7tQFWNTTGaYP76XE9waWSasWSOnvoIUdP/KJ0bnK7K4Fukkkcq78yiiinO3fl+ySNVC+Otoakc6Wzh1lg7MeMWRY+QgkBXFXobN74BYZFaeLuao0H2tcRLHqEinic40F63QHRsdLuFzWTklkgkd5GpMUnNePxgDye6/afuYhppqc2sKC6m8oeYye8iYxkIIcjYBsOpXhLOkdKxKqXWKmG7HlOZ/KIpkrXw+MEejnU1cHV0lheuD3Gmf4rZnO0PwCiIuGI0AagqNVf0SBX6IZfWYLt+5ZXjVrfuarBdj1O7Wulu+gVSzm5szwQEAT3Coa5H7kkDwiJFb9fn332Y33/XIT732H7etSeKJyqnIxfjxd9+Dwfa61EUBccTKIrCgfZ6Xvzt9yz42Y1qt1CkaOQWQiwwckskknuHfAyR1IzVGJWLIum1gWP88M1moqZDXaiOPS0NFEcmVLMpbYTpVS34anY3xWiNBaF/koCmMpLIokCpT1C10YSiR6o9WD40FEDBER6ZKoaGVlPtVOnaHGp/B6d2NRLQLGLB+D2LBM1HUwW3x14s9Tsq7ydVTSuFYFDnJ7/7/qrST+vdbqGc1Ri5JRLJxiCFkKTmrCSNUy6enmqJ8/ro7IJmg9VsShsxY6zoKQnoKnVqAFPXEEKgKDCSzLKnJV76+9VEE+Z7pMoRAgL64t2YVyL8Kl2bojfmXnuv5jN/eG3WTnF99GcAVbdSsF0PVVX4hd3tVQvxalJyaymxLxq59QqivGjk/sU9nSs6puT+QFaKrT9SCEk2BYam8hsndi3Y3KvxiWyU6bXcU6KpCm3RIEOJDKqiYLt+n6AAWtXRhLkeqbtDQ8H3SPU07l80UlOt8NtIQ7Dj2lX3Dlrs9xcbXjswvXwrhdVEBauJYtYi2rhSI7dEItk4pBCSbBpW2wNoo0yv8z0l+1rjAIym/NSWqWsc76re4Hs3NXMIuDs01BVBXKuDkzsre3ZWIm5qeW0Wi4h4wuXcne+vOp1VpOLw2uL3ytKEi53HWqKCS0UxaxFtXKmRWyKRbBxSCEk2HSutkKpkenU9geW6mLpWM9PrfE+Joijsb6tjd1OM/W1xPn5814qjK3dTM0cZSe8natocbO+g25paVESsRNzUwhDseYJvXuzj1f4JXA+i5tyISC3SWTBveO387wVimFqUZy/2VYzMuEKsS+SrlhG1F3/7PQuqxg6011U0cku2L3Ly/OZDCiHJlqdcoKgKpd4+eceltz7Cv1wZqJlpupKnpNgocTXHXywKdubM9KK/sxJxs1ZDsOcJ/vDbZ3n59hiuJzA0lfZYCKdQlffBAx1rSmeVM39oa5Hi0NZvXx1ZNDLz6ANt6xIVrGVEbSVGbolEsnFIISTZFhQFylNnb3NnOoWpa/TWR9jXGq+paXq9RnisJAq2UnGzlh49/3jxDj+5M+4X9ysCIWA4kQVA11TesTtSVTqrWhYb2nqw+1388wtXFo3MvGdfZ0Wx4nq+mT2oV5+iK8d2LLLWDCoxgoG5r89qS+ybo0FpjJZINhFSCEm2Baqq8IED3ZwdnBBqcHkAAB0PSURBVGJHY4SAppU2TU2h5sbgez1ZfCXiZrXizXY9Xu2bYCyZJed4uJ4//ywS0FEU6K6P4AhzyXTWYlVvi7HY0NapTH7JyEzOceeIw2LX78FEhnjA4L++cKl0faqJ3OXyFn/8/P8kFhjhwTqL2bxO/0Qj7Q0/j6YZ61JiL5FUQvv038jKsXVGCiHJhrKeU76TeZuc7W5Yp+B7yWrEzUrFWzJvc3UsQcZ2UQBV8dNUybyN43loKtSHwkums1bbn2j+0NZq0oHl4vDc4BRvTCRRFQXPE7x0a4yrY7N4QvDRI8vPafvj5/8n7dG+QpNLhTrTJd42ysXRH9PT/M4Vd72WSCSblw0VQslkks9+9rOkUils2+YP/uAPOH78OOfOneOJJ55A0zROnTrFpz71qY08LckGsBENDze6U/BmYD0jU0FdI5l3iJk6yZxdEjoKkLNdTnT5EZHF0llr6VQ9vxS/2nTghw/38p59nfzvf/sjmsJm6WeFEIwmszxz7g6/crBnSdE4lU4TC4yUOn0DKAooqOxumOH/fNtu2uIbP5NNsj346kcuS8N0jaiVpthQIfS1r32Nn/u5n+MTn/gEN2/e5NOf/jTf/OY3+cIXvsCXv/xlenp6+OQnP8mVK1c4cODARp6aZJ3ZiIaHG9kp+H4g57jUBw3cgjE6ZTl4ni8KOmIhfnFPB7B4Oms1LFWKX206MJm3GU/m0Oe93qqiMDCTZiqTpy0WWvQcro2NEAlYzO327RMN2NycHJNCSCLZBNRKU2yoEPrEJz5BIBAAwHVdTNMklUphWRa9vf5meOrUKV5++WUphLYRG9nUrxbDOyU+MdPgSGcj18ZmUVWFmOmiKgod8RC7m2ILom/z01mrYblS/GrTgaKCiFnq6+Xsa23nB1cDRAML57yl7QD7WttXsCKJRLJe1EpTrJsQeuaZZ/j6178+52tPPvkkR44cYXx8nM9+9rN8/vOfJ5VKEY1GSz8TiUTo7+9f9viXLl2qyXmeOXOmJsfZrGyG9c3mXW71T2BqCzehvCv40St56syVV/UstrYeoKNRkLY9IoZAt8Y5e3Z8xce/12yG1y6WSxB1sjwYUrBcwXDa4uZImsmpaf7z5Bh764M82h1DrVA1thzz1+cJlxv5czgVBq1eHzyHO9G4ZINGx/Nfc1NTiHo5xjPOHNkjgJagzu2rlxlYJh07nqgj0jQG844wnqjj1tWr3Frh2rYbcn1r499/Y2UP+tv9elfDemqKdRNCp0+f5vTp0wu+fu3aNX7v936Pz33uc7z1rW8llUqRTqdL30+n08Tj8WWPf+jQIUxzbd6IM2fOcPLkyTUdYzOzWdZnux4/Sl5c9Pu/8MjhFUeENsva1ovNsr7jx+96u84NTpHX8+ztDLOvNY6iKMx6gkGzccXpzUrrS+amuHH+exhKhbSacNh/6EFiwYURp0r+s+MP9HJjMsF4Ol9qXtgaDfLx47t45Mjy53rw0OFS1VjEsEjbAZJWO09++BMEzcCK17adkOurTD6fX/YB/fe/u5fhtI37pe17/VZDNdduPTXFhqbG3nzzTX7nd36HP//zP2f//v0ARKNRDMOgr6+Pnp4eXnrpJWmW3mZI787WpVid9p59nfzx987PaUsAtU1vLtdZerFS/Er+M11TeKA5zkPtKjNZi/pQYEXjT4JmgCc/9Emm0mmujY2wr7Wdxkhk9YuTSCQ1p1aaYkOF0Je+9CUsy+KJJ54A/BP+yle+whe/+EU+85nP4Loup06d4ujRoxt5WpINoJJ352hnA6d2tZae2CWbl5zjIgQLfF5Qu9YEy3WWrmTAXsx/pmsqugaffewgOcdddbuGxkiEt+16YOWLkUgk606tNMWGCqGvfOUrFb9+7Ngxnn766Y08FckGU973ZjZr8YMbI1wameHf+ybWpZReUls2qjXBSkvxlxuBkXPcbdM7SiKRzKVWmkI2VNzCrGdzwvXC0FReujXGhaGZdS2ll9SWjUpvrrQU/37sHSWRSGqLFEJbkI1oTrhebGQpvaS2bGRrgmpL8TdCoG3FBw7J5kb79N+U/lmOz7j3SCG0BdmI5oTrxVKpjGTOpm86RW9DVG44m5D1Gji7VtZLoG3lBw6JRFI9UghtMbZ6RKVSKqM4IHM0leMvXn6DqCk3nM3MvR44O5/1Emhb+YFDIpFUz+bdMSUVKUZUKlGs3tnMFFMZridKX7s2lmBwNkNbNEjUvLvhPHd5+caa2xnb9ZjK+H1wJMtTFGi1Soct9cAhXxOJZPsgI0JbjO1gDi1PZSRzNqOpHF11fpO+IlslwrUeeJ7gB/0JvjVxUaZk7hHLVaPVol2ARCLZHNxfO8w2oFJEBXxz6NHOhi0hGoqpjM+/+zD/6e17OdndxP62ujm9Y2BrRLjWg+cu93N1KgswJyWzmgiZjCqtju3wwCGRSKpDRoS2INtlsKihqfQ2REvpsPncjxtOMSUzf3bXSiNk0ui7NmQ3dMl6IivFNhdSCG1BNmv1zmqQG85cqvGAVZOSkUbftbNdHjgkEsnSSCG0hdls1TurRW44dymmZGYqfK/aCNlWryzcLGynBw6JRLI4UghJ7jmVNhyAmZx1320+xQjZwODgnK+vJEImjb61Zbs8cEgkkspIISTZNBiaSn0wcN97Wx4/2MONGzdIwaoiZNLoK5FIJNUjhZBkUyG9LX6E7J09cY4cO7yqlIz0XUkkEkn1yDuiZNMgm9jNZS0NAh8/2MOJbn9WV9F8fb/6riSSzYSsGNt8yIiQZNMgvS21Qxp9JRKJpDrknVGyaZDeltpTy7ET641s/iiRSO4FMiIk2TRIb8v9iWz+KJFI7iVyZ9mm2K7HbN7dck/X0tty/1E0yMPaR4pIJBLJSpERoW1G+dP1rf4JfpS8uKWerqW35f5CNn+USCT3GimEthnl5eem5m8uW7H8XDaxuz+QBnnJ/Yb26b8p/bOsINscyEetbYQsP5dsNWphkJcma4lEshZkRGgbIZ+uJVuNtRjkpclaIpHUAhkR2kbI8nPJVmS1BnlpspZIJLVARoS2EbL8XLIVWY1BXpqsJRJJrZBCaJtRfIo+PzRN3hUAHO1s4NSuVmzXk5uDZNOyEoO8TANLtirSIL35kEJom1H+dP1vP80xE63j0sgM/943IT0Ukm2DTANLJJJaIcMD2xRDUzk/nuHC0AwgPRSS7UUxDex6Ys7XXU9wtLNBRj4lEknVyLvFNsV2Pa7P5GQpvWTbIruQSySSWiBTY9uUZN4m74iK35MeCsl2QHYhl0gktUAKoW1KzDQw9co+IOmhkGwnZBdyiUSyFuTj0zbF0FT21gelh0IikUg2CTf+8CP3+hQkFZC74Tbm0e6Y9FBIJBKJRLIEMjW2jVEV6aGQSCQSiWQppBC6D5AeColEIpFIKiPDAxKJRCKRSO5bpBCSSCQSiURy3yKFkEQikUgkkvsWKYQkEolEIpHct2yoWTqTyfDpT3+aRCKBYRj82Z/9GW1tbZw7d44nnngCTdM4deoUn/rUpzbytCQSiUQikWwxaqUpNjQi9PTTT3Pw4EH+9m//lscff5y/+qu/AuALX/gCX/rSl/i7v/s7zp8/z5UrVzbytCQSiUQikWwxaqUpNjQi9IlPfALXdQEYGhoiHo+TSqWwLIve3l4ATp06xcsvv8yBAwc28tQkEolEIpFsIWqlKdZNCD3zzDN8/etfn/O1J598kiNHjvCbv/mbXL9+na997WukUimi0WjpZyKRCP39/cse/9KlSzU5zzNnztTkOJuV7by+7bw2kOvbymzntYFc32qp1b51P7KemmLdhNDp06c5ffp0xe/99V//NTdu3OC3fuu3ePbZZ0mn06XvpdNp4vH4ssc/dOgQprm2JoFnzpzh5MmTazrGZmY7r287rw3k+rYy23ltINe3GPl8flmhU4t9aztSzbVbT02xoR6hv/zLv+TZZ58FfJWmaRrRaBTDMOjr60MIwUsvvcTDDz+8kaclkUgkEolki1ErTbGhHqGPfexj/P7v/z7f+MY3cF2XJ598EoAvfvGLfOYzn8F1XU6dOsXRo0c38rQkEolEIpFsMWqlKTZUCDU3N/PVr351wdePHTvG008/vZGnIpFIJBKJZAtTK00hGypKJJKqsF2PqUwe2/Xu9alIJBJJzZDT5yUSyZJ4nuC5y/2cH5omYzmEAzpHOxt4/GAPqqrc69OTSLYMDzzxTQb++OP3+jQk85ARIYlEsiTPXe7ntYEpAMIB/9nptYEpnru8fJsLiUQi2exIISSRSBbFdj3OD02jzYv8aKrC+aFpmSaT/P/t3XlUVPX/x/HnDKuCpqZ2JMPcC1FTcymXOpaamhBkhppLpqlJlBmBCopCFqlZmpW2mJJF5kZlHkWt45ILddyP2fEXkohSopWAOcPM/f3BYQK3bwpHmJnX47+5n7l3Pu/7YT73zf3c+XxEnJ4SIRG5qvMXrRRaiq5YVmgp4vxF602ukYhIxVIiJCJXVcPHyzEcdqnq3p7U8PG6yTUSEalYSoRE5Kq8PMy0DaiNzW6U2W6zG7QNqI2Xh7oQEXFu6sVE5JpCWt1B+4Z1ABzDZO0b1iGk1R2VWS0RkQqhn8+LyDWZzSYeax1I/6CGnL9opYaPl+4EiYjLUG8mIv+Jl4eZOtV9LkuCNNGiiDgz3RESkRuiiRZFxBXojpCI3BBNtCgirkCJkIhcN020KCKuQomQiFw3TbQocv3+b2pYZVdBrkCJkIhcN020KCKuQomQiFw3TbQoIq5CvZWI3BBNtCgirkA/nxeRG6KJFkXEFSgREpFyKZloUUTEGenfNxEREXFbSoRERETEbSkREhEREbelREhERETclhIhERERcVtKhERERMRtKRESERERt6VESERERNyW002oaBjFaxtZLJYKOd7Fixcr5DhVlSvH58qxgeJzZq4cGyi+Kym5JpVco0qr6OuWq7nWubsZTEZlffINOn/+PL/88ktlV0NEROQyLVq0oEaNGmW26br131zp3N0MTpcI2e12CgoK8PLywmQyVXZ1REREMAwDq9WKn58fZnPZp0503bq2a527m8HpEiERERGRiqKHpUVERMRtKRESERERt6VESERERNyWEiERERFxW043j1B57N+/nzlz5pCSkkJWVhaxsbGYTCaaN2/O9OnTK+Vp9YpgtVqZMmUKJ0+exGKxMH78eJo1a+Yy8dlsNuLi4sjMzMRkMjFjxgx8fHxcJr4SeXl5hIeH8/HHH+Pp6elS8YWFheHv7w9Aw4YNefLJJ3n11Vfx8PCgW7duREZGVnINb9yiRYvYsmULVquVwYMH06lTJ5dpu9WrV7NmzRqgeG6dI0eOkJKS4jJtZ7VaiY2N5eTJk5jNZhITEyv8u2e320lISODo0aN4e3uTlJREo0aNKjAK51Fl+wHDTSxevNh49NFHjSeeeMIwDMMYO3assWvXLsMwDCM+Pt7YuHFjZVavXFauXGkkJSUZhmEY586dMx544AGXii89Pd2IjY01DMMwdu3aZYwbN86l4jMMw7BYLMZzzz1n9O7d2zh27JhLxffPP/8YoaGhZbaFhIQYWVlZht1uN0aPHm0cPny4kmpXPrt27TLGjh1r2Gw2Iz8/35g/f75LtV1pCQkJRmpqqsu0nWEU9y1RUVGGYRjG9u3bjcjIyApvvw0bNhgxMTGGYRjG3r17jXHjxpWv0k6qKvcDzvlvyg0IDAxkwYIFjteHDx+mU6dOAPTo0YMffvihsqpWbo888ggvvPACUDwfg4eHh0vF9/DDD5OYmAhATk4ONWvWdKn4AJKTk4mIiKB+/fqAa/19/vzzz1y4cIFRo0YxfPhwMjIysFgsBAYGYjKZ6Natm9PGt337dlq0aMGECRMYN24cDz74oEu1XYmDBw9y7Ngx+vfv7zJtB9C4cWNsNht2u538/Hw8PT0rvP1++uknunfvDsA999zDoUOHyl1vZ1SV+wG3GRrr06cP2dnZjteGYTgmtvLz8+P8+fOVVbVy8/PzAyA/P5+oqChefPFFkpOTXSY+AE9PT2JiYkhPT2f+/Pns2LHDZeJbvXo1derUoXv37ixevBhwrb9PX19fnnnmGZ544gmOHz/OmDFjqFmzpqPcz8+PEydOVGINb9y5c+fIycnh/fffJzs7m/Hjx7tU25VYtGgREyZMID8/3zG0Ac7ddgDVq1fn5MmT9O3bl3PnzvH++++TkZFRoe136Tnz8PCgqKgIT0+3ufwCVbsfcK+WKKX0mG9BQUGZBnFGp06dYsKECQwZMoQBAwYwe/ZsR5krxAfFd01efvllBg0aVGYtIGePb9WqVZhMJnbu3MmRI0eIiYnh7NmzjnJnj69x48Y0atQIk8lE48aNqVGjBn/++aej3Jnjq1WrFk2aNMHb25smTZrg4+PD6dOnHeXOHFuJv//+m8zMTLp06UJ+fj4FBQWOMmeP75NPPqFbt25MmjSJU6dOMWLECKxWq6O8IuLz9/cvc87sdrvbJUFQtfsBtxkau1RQUBC7d+8GYOvWrdx7772VXKMbd+bMGUaNGkV0dDQDBw4EXCu+tWvXsmjRIgCqVauGyWQiODjYZeJbvnw5n376KSkpKdx9990kJyfTo0cPl4lv5cqVvP766wDk5uZy4cIFqlevzm+//YZhGGzfvt1p4+vQoQPbtm3DMAxHbPfdd5/LtB1ARkYG9913H1B8Uffy8nKJtgOoWbOmY22rW265haKiogrvO9u3b8/WrVsB2LdvHy1atChfpZ1UVe4H3GqJjezsbF566SVWrFhBZmYm8fHxWK1WmjRpQlJSEh4eHpVdxRuSlJTE+vXradKkiWPb1KlTSUpKcon4CgsLmTx5MmfOnKGoqIgxY8bQtGlTl2m/0oYNG0ZCQgJms9ll4rNYLEyePJmcnBxMJhMvv/wyZrOZWbNmYbPZ6NatGxMnTqzsat6wN954g927d2MYBhMnTqRhw4Yu03YAH374IZ6enowcORIovpi7StsVFBQwZcoU/vjjD6xWK8OHDyc4OLhC26/kV2O//PILhmEwa9YsmjZtWoFROIeq3A+4VSIkIiIiUprbDo2JiIiIKBESERERt6VESERERNyWEiERERFxW0qERERExG0pERK5CbKzswkODiY0NJTHHnuM/v378/TTT5eZfO96rV69mtjYWADGjBlDbm7uVd87f/58fvzxx+s6fsuWLcu8zs/Pp127dpd9zp49ewgLC7uuY4nIlZXuK0JDQ+nTpw9RUVGcOXOGgwcPMnXq1Kvue+LECaZMmXLFss8//5zPP/8cuP7v45YtW1iyZMllx3EV7je9pUglqV+/PmlpaY7Xc+fOJTExkYULF5b72B988ME1yzMyMujcuXO5PsPf359evXqxbt06Ro0a5di+du1aHn/88XIdW0T+VbqvMAyDN998k6ioKD777DNat2591f1ycnKuukzF4MGDb7g+hw8frpDjVFVKhEQqyb333suWLVsA6NmzJ23atOHIkSN89tlnbNu2jaVLl2K322nVqhXTp0/Hx8eHtWvX8t577+Hv78/tt99O9erVHfsvW7aMevXqMWPGDH766Se8vLx47rnnsFgsHDp0iLi4ON555x18fX1JSEjgzz//xNfXl/j4eIKCgsjOziY6OprCwkLatm17xTo//vjjJCcnOxKhixcv8v333xMTEwPAvHnz2LlzJ3/99Re1a9dmwYIF1KtXz7F/ycLHzz//fJl6N2jQgDfeeIM9e/Zgs9kIDw93TOAn4s5MJhPPP/88Xbt2ZdmyZaSnp5OSksKSJUtYs2YNZrOZNm3aMHPmTJKSksjOzmbGjBk88sgjzJ49G7vdTvPmzWnYsCHw73cvPj6eAwcOULt2bWbNmkVAQADDhg0jMjKSzp07k52dzfDhw1m8eDGpqakABAQEkJOT4zjOd999x1tvvYXdbueOO+5g5syZ1K1bl549exISEsL27du5cOECycnJBAcHV84J/A80NCZSCaxWK+vXr6d9+/aObT169GDDhg2cPXuWFStWkJqaSlpaGrfeeisfffQRubm5zJkzh+XLl/PFF1+UWb+oREpKCoWFhaxfv54lS5awcOFC+vXrR3BwMElJSbRs2ZKYmBiio6NZs2YNiYmJjtlcExMTCQ8PJy0trUy9SuvUqRN///03v/76KwCbNm2iS5cu3HLLLWRlZfHrr7+SmprKhg0bCAwM5Ouvv/5P52PFihUArFmzhpUrV7J58+brHsoTcVXe3t40atSIunXrAlBUVMSiRYtYtWoVq1evxmQykZubS1xcHMHBwUyfPh2A48ePs3TpUpKTky87ZseOHUlLS6NXr168+uqrV/3sZs2aERERQURERJk7v3l5eUybNo2FCxfy9ddf0759e2bOnOkor1WrFitXriQiIsKxRFJVpTtCIjfJ77//TmhoKFA83XybNm2YNGmSo7zkLszu3bvJyspi0KBBQHHSFBQUxN69e2nXrp2jMxwwYAC7du0q8xkZGRkMGjQIs9lMvXr1WLduXZnygoICDh06xOTJkx3bCgsLOXfuHHv27GHu3LkAhISEEBcXd1kMJpOJsLAwvvnmG6KiokhLS3PcuWnUqBExMTF8+eWXZGZmsm/fPgIDA//TuSlZcLYknsLCQo4ePerU61iJVCSTyYSvry8Anp6etGvXjoEDB/LQQw8xdOhQbrvtNo4fP15mn5LFTS/l6+tLSEgIAKGhobz11lvXXZ8DBw7Qpk0bx52mJ598ksWLFzvKu3fvDkDz5s3ZuHHjdR//ZlIiJHKTXPqM0KV8fHwAsNls9O3b15GIFBQUYLPZ2LlzJ3a73fH+K61gfem2rKwsGjRo4Hhtt9vx9vYuU4/Tp09Tq1YtoPh5BCjudE0m0xXrGRYWxqhRoxgyZAiZmZmOBTkPHTrEpEmTGDlyJH369MFsNnPpCj4mk6lMDCUrfdtsNqKjo+nduzcAZ8+edQz7ibg7i8VCZmYmeXl5jm3vvvsu+/btY+vWrYwePZo5c+Zctl9J4nQps/nfwSDDMMr0GyXf2aKiomvWqfT3uGS/0vuU9GdX60eqEg2NiVQxnTt3Jj09nby8PAzDICEhgaVLl9KhQwf2799Pbm4udrudb7/99rJ9O3bsyPr16zEMg7y8PJ566iksFgseHh7YbDZq1KjBnXfe6UiEduzYwdChQwG4//77+eqrrwDYuHEjFovlivULCAggICCA+fPnExoa6ujoMjIy6NSpE4MHD6ZZs2bs2LEDm81WZt/atWtz7NgxoPg/yj/++AOALl26sGLFCqxWKwUFBQwZMoT9+/dXwNkUcW52u50FCxbQtm1bxx3Ws2fP0rdvX1q0aMELL7xA165dOXr0KB4eHv8zgYHiO66bN28GYNWqVdx///1A2e/npk2bHO+/0nHbtm3L/v37yc7OBuCLL74o9w8yKovuCIlUMXfddReRkZGMGDECu93O3XffzbPPPouPjw9xcXGMHDmSatWq0axZs8v2HTJkCElJSY7b3vHx8fj7+9O9e3emT59OcnIys2fPJiEhgQ8//BAvLy/mzZuHyWRi2rRpREdHk5qaSuvWrfHz87tqHcPDw3nllVdIT093bOvXrx+RkZEMGDAALy8vWrZs6egkS79nw4YN9OvXj1atWhEUFARAREQEWVlZhIWFUVRURHh4uNN2qiLlVXoYvaQPmDt3LkePHgWgTp06REREMHDgQKpVq0aDBg0ICwvDarVy/vx5oqOjGThw4FWPX7NmTTZt2sTbb7/NbbfdxmuvvQbA6NGjiY2NZdWqVTz00EOO93fs2JGYmBjHsDxA3bp1mTlzJpGRkVitVgICAq75rFFVptXnRURExG1paExERETclhIhERERcVtKhERERMRtKRESERERt6VESERERNyWEiERERFxW0qERERExG0pERIRERG39f+3xXro6gDTRAAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": {}, @@ -702,8 +667,8 @@ ], "source": [ "# Instantiate the linear model and visualizer \n", - "ridge = Ridge()\n", - "visualizer = ResidualsPlot(ridge)\n", + "model = Ridge()\n", + "visualizer = ResidualsPlot(model)\n", "\n", "visualizer.fit(X_train, y_train) # Fit the training data to the visualizer\n", "visualizer.score(X_test, y_test) # Evaluate the model on the test data \n", @@ -716,40 +681,19 @@ "source": [ "### Prediction Error Plot \n", "\n", - "Plots the actual targets from the dataset against the predicted values generated by our model. This allows us to see how much variance is in the model. Data scientists diagnose this plot by comparing against the 45 degree line, where the prediction exactly matches the model. " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Load the data\n", - "df = load_data('concrete')\n", - "feature_names = ['cement', 'slag', 'ash', 'water', 'splast', 'coarse', 'fine', 'age']\n", - "target_name = 'strength'\n", - "\n", - "# Get the X and y data from the DataFrame \n", - "X = df[feature_names].as_matrix()\n", - "y = df[target_name].as_matrix() \n", - "\n", - "# Create the train and test data \n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)" + "Yellowbrick's `PredictionError` Visualizer plots the actual targets from the dataset against the predicted values generated by the model. This allows us to see how much variance is in the model. Data scientists can diagnose regression models using this plot by comparing against the 45-degree line, where the prediction exactly matches the model." ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { - "image/png": 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sZufOnfjjjz8glUoxbtw4dOvWDQsXLgSHw0GbNm2wbNkycLnUNyXm0xcg4oZ0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\n", 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" ] }, "metadata": {}, @@ -758,8 +702,8 @@ ], "source": [ "# Instantiate the linear model and visualizer \n", - "lasso = Lasso()\n", - "visualizer = PredictionError(lasso)\n", + "model = Lasso()\n", + "visualizer = PredictionError(model)\n", "\n", "visualizer.fit(X_train, y_train) # Fit the training data to the visualizer\n", "visualizer.score(X_test, y_test) # Evaluate the model on the test data \n", @@ -770,59 +714,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Regularization Alpha Selection \n", - "\n", - "Regularization is designed to penalize model complexity, therefore the higher the alpha, the less complex the model, decreasing the error due to variance (overfit). Alphas that are too high on the other hand increase the error due to bias (underfit). It is important, therefore to choose an optimal Alpha such that the error is minimized in both directions.\n", + "### Alpha Selection Visualizer\n", "\n", - "The AlphaSelection Visualizer demonstrates how different values of alpha influence model selection during the regularization of linear models. Generally speaking, alpha increases the affect of regularization, e.g. if alpha is zero there is no regularization and the higher the alpha, the more the regularization parameter influences the final model." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.linear_model import RidgeCV\n", - "from sklearn.linear_model import LassoCV\n", + "The `AlphaSelection` Visualizer demonstrates how different values of alpha influence model selection during the regularization of linear models. Since regularization is designed to penalize model complexity, the higher the alpha, the less complex the model, decreasing the error due to variance (overfit). However, alphas that are too high increase the error due to bias (underfit). Therefore, it is important to choose an optimal alpha such that the error is minimized in both directions.\n", "\n", - "from sklearn.pipeline import make_pipeline\n", - "from sklearn.preprocessing import PolynomialFeatures\n", - "from yellowbrick.regressor import AlphaSelection\n", - "from yellowbrick.regressor import ManualAlphaSelection" + "To do this, typically you would you use one of the \"RegressionCV” models in scikit-learn. E.g. instead of using the `Ridge` (L2) regularizer, use `RidgeCV` and pass a list of alphas, which will be selected based on the cross-validation score of each alpha. This visualizer wraps a “RegressionCV” model and visualizes the alpha/error curve. If the visualization shows a jagged or random plot, then potentially the model is not sensitive to that type of regularization and another is required (e.g. L1 or Lasso regularization)." ] }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Load the data\n", - "df = load_data('concrete')\n", - "feature_names = ['cement', 'slag', 'ash', 'water', 'splast', 'coarse', 'fine', 'age']\n", - "target_name = 'strength'\n", - "\n", - "# Get the X and y data from the DataFrame \n", - "X = df[feature_names].as_matrix()\n", - "y = df[target_name].as_matrix() \n", - "\n", - "# Create the train and test data \n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { - "image/png": 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Bp6cn0tPTiz0nMDAQhw8fxunTp+Hl5YVZs2YhNDQU7u7uqFKliqliExGRFdl9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\n", 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\n", 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" ] }, "metadata": {}, @@ -831,36 +739,16 @@ ], "source": [ "# Create a list of alphas to cross-validate against \n", - "alphas = np.logspace(-12, -0.5, 400)\n", + "alphas = np.logspace(-10, 1, 400)\n", "\n", "# Instantiate the linear model and visualizer \n", "model = LassoCV(alphas=alphas)\n", "visualizer = AlphaSelection(model)\n", "\n", - "visualizer.fit(X_train, y_train) # Fit the training data to the visualizer\n", + "visualizer.fit(X, y) # Fit the data to the visualizer\n", "g = visualizer.poof() # Draw/show/poof the data" ] }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "# # Create a list of alphas to cross-validate against \n", - "# alphas = np.logspace(-12, -0.5, 400)\n", - "\n", - "# # Instantiate the linear model and visualizer \n", - "# model = make_pipeline(\n", - "# PolynomialFeatures(2),\n", - "# Ridge()\n", - "# )\n", - "# visualizer = ManualAlphaSelection(RidgeCV(), alphas=alphas,)\n", - "\n", - "# visualizer.fit(X_train, y_train) # Fit the training data to the visualizer\n", - "# g = visualizer.poof() # Draw/show/poof the data" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -878,27 +766,20 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", - " \"This module will be removed in 0.20.\", DeprecationWarning)\n" - ] - } - ], + "execution_count": 25, + "metadata": { + "collapsed": true + }, + "outputs": [], "source": [ "# Classifier Evaluation Imports \n", "\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.linear_model import LogisticRegression \n", "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.cross_validation import train_test_split\n", + "from sklearn.model_selection import train_test_split\n", "\n", - "from yellowbrick.classifier import ClassificationReport, ROCAUC, ClassBalance, ConfusionMatrix, DecisionBoundariesVisualizer" + "from yellowbrick.classifier import ClassificationReport, ROCAUC, ClassBalance, ConfusionMatrix" ] }, { @@ -912,7 +793,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 26, "metadata": { "collapsed": true }, @@ -935,14 +816,14 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { - "image/png": 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IiIhpKJiIiIiIaSiYiIiIiGkomIiIiIhpKJiIiIiIaSiYiIiIiGkomIiIiIhpKJiIiIiI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\n", 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\n", 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" ] }, "metadata": {}, @@ -963,19 +844,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Confusion Matrix" + "### Confusion Matrix \n", + "\n", + "The `ConfusionMatrix` visualizer displays the accuracy score of the model, i.e., it shows how each of the test values predicted classes compare to their actual classes. It provides the numerical scores as well as a color-coded heatmap to provide data scientists a clearer view of the model performance on each of the individual classes and is particularly useful for imbalanced datasets. More information can be found by looking at the [scikit-learn documentation](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html)." ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] }, "metadata": {}, @@ -1003,7 +886,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 29, "metadata": { "collapsed": true }, @@ -1026,14 +909,14 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { - "image/png": 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Iiv15Pvl517YTVrkocb4OCvx1icCTF246/mGVi3DFx0GB05cpevL8Dfs3LLCvYlGu+HiR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+ "image/png": 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\n", 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" ] }, "metadata": {}, @@ -1061,8 +944,10 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": {}, + "execution_count": 31, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "# Load the classification data set\n", @@ -1082,14 +967,14 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { - "image/png": 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1bdq0csdvRtOmTfXqq6/q+eeflzFG3t7eWrx4MUdAcNu5zLXHNgEAAL4HnI4BAABWECEA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\nCAEAAFYQIQAAwAoiBAAAWEGEAAAAK/4f/7j2wOMqDLcAAAAASUVORK5CYII=\n", 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\n", 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" ] }, "metadata": {}, @@ -1106,107 +991,80 @@ "g = visualizer.poof() # Draw/show/poof the data" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Decision Boundaries " - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "# # Instantiate the classification model and visualizer \n", - "# forest = RandomForestClassifier()\n", - "# visualizer = DecisionBoundariesVisualizer(forest, features=[1,3], classes=classes)\n", - "\n", - "# visualizer.fit(X_train, y_train) # Fit the training data to the visualizer\n", - "# visualizer.score(X_test, y_test)\n", - "# g = visualizer.poof() # Draw/show/poof the data" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Clustering Evaluation \n", "\n", - "Clustering models are unsupervised methods that attempt to detect patterns in unlabeled data. There are two primary classes of clustering algorithm: _agglomerative_ clustering links similar data points together, whereas _centroidal_ clustering attempts to find centers or partitions in the data. Yellowbrick provides the `yellowbrick.cluster` module to visualize and evaluate clustering behavior. Currently we provide two visualizers to evaluate _centroidal_ mechanisms, particularly K-Means clustering, that help us to discover an optimal $K$ parameter in the clustering metric:\n", + "The Yellowbrick library is a diagnostic visualization platform for machine learning that allows data scientists to steer the model selection process. It extends the scikit-learn API with a new core object: the `Visualizer`. Visualizers allow models to be fit and transformed as part of the scikit-learn pipeline process, providing visual diagnostics throughout the transformation of high-dimensional data.\n", "\n", - "- Elbow Visualizer: visualize the clusters according to some scoring function, look for an \"elbow\" in the curve. \n", - "- Silhouette Visualizer: visualize the silhouette scores of each cluster in a single model. \n", + "In machine learning, clustering models are unsupervised methods that attempt to detect patterns in unlabeled data. There are two primary classes of clustering algorithms: *agglomerative* clustering which links similar data points together, and *centroidal* clustering which attempts to find centers or partitions in the data.\n", "\n", - "Because it is very difficult to `score` a clustering model, Yellowbrick visualizers wrap Scikit-Learn \"clusterer\" estimators via their `fit()` method. Once the clustering model is trained, then the visualizer can call `poof()` to display the clustering evaluation metric." + "Currently, Yellowbrick provides two visualizers to evaluate *centroidal* mechanisms, particularly K-Means clustering, that help users discover an optimal $K$ parameter in the clustering metric:\n", + "- `KElbowVisualizer` visualizes the clusters according to a scoring function, looking for an \"elbow\" in the curve. \n", + "- `SilhouetteVisualizer` visualizes the silhouette scores of each cluster in a single model." ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Clustering Evaluation Imports \n", - "from functools import partial\n", - "\n", - "from sklearn.cluster import KMeans, MiniBatchKMeans\n", - "from sklearn.datasets import make_blobs as sk_make_blobs\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.datasets import make_blobs\n", "\n", "from yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer" ] }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "# Helpers for easy dataset creation \n", - "N_SAMPLES = 1000 \n", - "N_FEATURES = 12 \n", - "SHUFFLE = True \n", - "\n", - "# Make blobs partial \n", - "make_blobs = partial(sk_make_blobs, n_samples=N_SAMPLES, n_features=N_FEATURES, shuffle=SHUFFLE)" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Silhouette Visualizer \n", + "### Load the Data\n", "\n", - "The Silhouette Coefficient is used when the ground-truth about the dataset is unknown and computes the density of clusters computed by the model. The score is computed by averaging the silhouette coefficient for each sample, computed as the difference between the average intra-cluster distance and the mean nearest-cluster distance for each sample, normalized by the maximum value. This produces a score between 1 and -1, where 1 is highly dense clusters and -1 is completely incorrect clustering. \n", - "\n", - "The Silhouette Visualizer displays the silhouette coefficient for each sample on a per-cluster basis, visualizing which clusters are dense and which are not. This is particularly useful for determining cluster imbalance, or for selecting a value for $K$ by comparing multiple visualizers. " + "For the following examples, we'll use scikit-learn's `make_blobs()` function to create a sample two-dimensional dataset with 8 random clusters of points." ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ - "# Make 8 blobs dataset \n", - "X, y = make_blobs(centers=8)" + "# Generate synthetic dataset with 8 blobs\n", + "X, y = make_blobs(n_samples=1000, n_features=12, centers=8, shuffle=True, random_state=42)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Elbow Method \n", + "\n", + "K-Means is a simple unsupervised machine learning algorithm that groups data into the number $K$ of clusters specified by the user, even if it is not the optimal number of clusters for the dataset. \n", + "\n", + "Yellowbrick's `KElbowVisualizer` implements the “elbow” method of selecting the optimal number of clusters by fitting the K-Means model with a range of values for $K$. If the line chart looks like an arm, then the “elbow” (the point of inflection on the curve) is a good indication that the underlying model fits best at that point.\n", + "\n", + "In the following example, the `KElbowVisualizer` fits the model for a range of $K$ values from 4 to 11, which is set by the parameter `k=(4,12)`. When the model is fit with 8 clusters we can see an \"elbow\" in the graph, which in this case we know to be the optimal number since we created our synthetic dataset with 8 clusters of points." ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { - "image/png": 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ldwoim1hkEBE5E7MRCGwidwqiCmGRQUTkTFQA/BvKnaL6sE+GorDIICJyJno/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\nAK5fvy7Gzqenp6O4uBivXr3C4OAgPB4PnE6nOAa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\n", 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" ] }, "metadata": {}, @@ -1214,45 +1072,35 @@ } ], "source": [ - "# Instantiate the clustering model and visualizer \n", - "model = MiniBatchKMeans(6)\n", - "visualizer = SilhouetteVisualizer(model)\n", + "# Instantiate the clustering model and visualizer\n", + "model = KMeans()\n", + "visualizer = KElbowVisualizer(model, k=(4,12))\n", "\n", - "visualizer.fit(X) # Fit the training data to the visualizer\n", - "visualizer.poof() # Draw/show/poof the data" + "visualizer.fit(X) # Fit the data to the visualizer\n", + "visualizer.poof() # Draw/show/poof the data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Elbow Method \n", + "### Silhouette Visualizer \n", "\n", - "The elbow method for $K$ selection visualizes multiple clustering models with different values for $K$. Model selection is based on whether or not there is an \"elbow\" in the curve; e.g. if the curve looks like an arm, if there is a clear change in angle from one part of the curve to another. " - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Make 8 blobs dataset \n", - "X, y = make_blobs(centers=8)" + "Silhouette analysis can be used to evaluate the density and separation between clusters. The score is calculated by averaging the silhouette coefficient for each sample, which is computed as the difference between the average intra-cluster distance and the mean nearest-cluster distance for each sample, normalized by the maximum value. This produces a score between -1 and +1, where scores near +1 indicate high separation and scores near -1 indicate that the samples may have been assigned to the wrong cluster.\n", + "\n", + "The `SilhouetteVisualizer` displays the silhouette coefficient for each sample on a per-cluster basis, allowing users to visualize the density and separation of the clusters. This is particularly useful for determining cluster imbalance or for selecting a value for $K$ by comparing multiple visualizers." ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { - "image/png": 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7o9pRSjUppFTQq743I1rW5Gh0AosOnFM7jhBCiHJKURQstFZqxyjVpJBSyWcD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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -1261,10 +1109,11 @@ ], "source": [ "# Instantiate the clustering model and visualizer \n", - "visualizer = KElbowVisualizer(MiniBatchKMeans(), k=(4,12))\n", + "model = KMeans(6)\n", + "visualizer = SilhouetteVisualizer(model)\n", "\n", - "visualizer.fit(X) # Fit the training data to the visualizer\n", - "visualizer.poof() # Draw/show/poof the data" + "visualizer.fit(X) # Fit the data to the visualizer\n", + "visualizer.poof() # Draw/show/poof the data" ] }, { @@ -1290,7 +1139,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 37, "metadata": { "collapsed": true }, @@ -1373,7 +1222,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 38, "metadata": { "collapsed": true }, @@ -1385,8 +1234,10 @@ }, { "cell_type": "code", - "execution_count": 49, - "metadata": {}, + "execution_count": 39, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "# Load the data and create document vectors \n", @@ -1399,14 +1250,14 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { - "image/png": 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ccr2Z5rpzXZNAIBgcRqSxTqwetltwnC/I66UacshgksvBRLcLv25w/qwj+N6p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\nPs3WB1E9sSu8TSEwTIEsHxrmEZlMsLBDFCwe81qqji7qNQrWLT7/+c8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GFuQdLhwO9yRz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0MIITh+3lkNWUOlMp4AkZhOZEonoOU/4KOxKfY++xxd9sNfCCb0g+hGjAOP/pjtazqZ07WCY/tW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\nAAAAAElFTkSuQmCC\n", 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\n", 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S94lPjvQSJRLJMCNV3xJJCeE3vASEH+2KcwledgbK4W5QQNRVY+oqAeFHR3rUEsl4Qhpq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MAXWLrVCME+KGuKF9H36zikr/RGoCk5hVfzLl3mrlUSsUYxj161Uoxgma0Fg4ZRnzJi0maofxGgFloBWKcYL6JSsU4wxdMwiYlaO9DIVCUUBUzlqhUCgUihJHGWuFQqFQKEocZawVCoVCoShxlLFWKBQKhaLEUcZaoVAoFIoSRxlrhUKhUChKHGWsFQqFQqEocZSxVox7HNcmbHWo0ZAKhWLMopqiKMYtrnTZcXgrjR37iMZ68Hr81FfOZP7kJWhC3aeWEpYdoTMSosIXxDR8o70chaLkUMZaMW7ZcXgrHzS8i+xw0Ko82FqMg6H3AFg4Zdkor04BYLs2L+1YR3Pn+9iOhaGbTKyYxor5X8BQrVIVigTq16AYl8RiFrvvfx7r9SO47RZalYnn5Gr8X5hGQ/s+5k1arPpmlwAv7VhHQ9sehCbQNA1X2jS07eGlHes4d+EVo708haJkULFAxbjkvfufoufFBmSPgzB1ZI9DdGMLPevex7IjRO3waC/xmMeyIzR3vo/QRMrjQhM0d/Z+TgqFohdlrBXjDicao3XTPnRdT3lcaILYG614XA9eIzBKq1PE6YyEsB0r6zbbseiMhEZ4RQpF6aKMtWLcYYW6iLX24PUEAJmyze2MMcGdrELgJUCFL4ihm1m3GbpJhS84witSKEoXZawV44JkeZYZLMcbLKPcW4PPLEcIgUQihCBQG2ThwrNGe7kKwDR8TKyYhnRTb6ikK5lYMU1VhSsUSSj3QlEUXDeKa4fQjCCa5i3ecfqRZ004cx6Hn95GuTdIubcaRzpormDqhafg8RVvPYrBsWL+FzKrwat7q8EVCsVRlLFWFBQpHVobVxPu2IjrhND0IIHKM6mpX4kQeu4dDJIdh7dyMPQeQmjomoHt9Mqzpn5mHlPFSTS9tB2rNYy3JkDdioXMWXlBwdegGDqGZnDuwiuUzlqhyIEy1oqC0tq4mq7WpxFCRwgf0g3T1fo0AMFJNxT0WI5r09C+D5HW4EQIjcauA5z1lS8w+9rzsEJd6GVenO4o0nZAV9mfUsM0fNSWTxntZSgUJYsy1oqC4bpRwh0bMzxoIXTCHRuprru2oCHxqB3GsnuyFovF5Vk+o5wPHnuFppfeIxrqxhsso27FAuasvABNGW2FQjFGUFcrRcFw7RCuk11u4zqtuHZhpTheI4DX48+6zTR8eI0Au1ev59BTb2GHLXSfBztsceipt9i9en1B16JQKBTFRBlrRcHQjCCanl1uo+k1aEZhpTi6ZlBfORMp3ZTHpXSZVDUTYpKml7Yj0jxooWs0vbQdJxor6HoUCoWiWChjPUawOsK0vrUfq6N0O29pmpdA5ZlI6aQ8LqVDoPLMolSFz5+8hOOCC9A1D47roGsejgsuYP7kJVihLqKh7OfLag1jhboKvh6FQqEoBipnXeLYls2WK39Cy9ZdON1R9DIvE5bMZemaGzHM0vv4aupXAvRVg7ei6TWU9VWDFwNNaCycsox5kxYTtcN4jUAihx3XW9vhzC5ZZk0AM1helDUNB8e1idphDM3Edq2U96NQKI5d1FWgxNly5U9o3PAuQhMIj45r2TRueJctV/6EFetuHu3lZSCETnDSDVTXXTsiOus4umYQMCtTH/N6qFuxgENPvYXUBDHHxaNrCFdSt2IhutdT9HXlS1wv3tC+l1B3A44bw9BMasrrmVQ5S431VCiOcZSxLmGsjjAtW3dlHXTQsnUXVkcYs7I0e1xrmhfNnDzay2Dmlz/Oi3saCW3eiejsQZb5CJ48naVXlVYXs7hevDvahmWHEUJg2Tbt4WZsuzcyoMZ6KhTHLupWvYTp3tuE0x3Nus0JW3TvbRrhFY09Hnx5N39YOJUtf7uClvlTcKVL28Yd/Pozq9h537O4jpt7J0UmrhcHiPQZagAERGNhEIKG9n04rj2Kq1QMh46IxVuHjtARyT64RKHIhfKsS5iyWXXoZV5cK/MirQdMymbVjcKqxg5R2+GlPU3ommDmpp1M2NMImkCaBq2t3Xzw328CMO+GT4zyOnv14tBbyZ4w1vSGx13pJHTj6aF+RWlj2TZXrt3E1gMtdFs2ZabBkukTWHPFckxDXX4V+aM86xLGrAwwYcncrIMOJiyZW7Ih8FIhFI4S6omixRwm7joMSemEmONiQ0lIuOJ6cU1oGd3YNKGhCT2hG1eMLa5cu4kNuxuw+uolLMdlw+4Grly7abSXphhjKGNd4ixdcyP15yxCMw2k7aKZBvXnLGLpmhtHe2klTzDgJRjwYnZHMdMqwj261nvxLAEJV1wvDuAzAkjZd3Mm6R3zKSWTqmaqqvAxRkfEYuuBFjQt7QZM09h6oEWFxBWDQv36SxzDNFix7masjjDde5som1WnPOo88Ro6K2bV8UxXBKvMxLB69d9SSoJlPjQhMIok4YpLsPKVXs2fvASAw217caTEcS08ukmVfyKTqmYltivGDnuPdNJt2XiytLUNWzZ7j3Ry0tTaUViZYiyijPUYwawMYJ40Y7SXMeZYuWweAB/Mn0rNm/vxeHSCZT5mBMuQjltwCVd/IztzSa/S9eJKZz32mVVbQZlpYGUpYgyYBrNqK0ZhVYqxiroKKMY1uqZxw5kLiJw+m7fvfYbwK7tx2nswAmZRRmb2N7IT8pNeJevFTdSoyLFMpc9kyfQJbNjdkBIKd12XJbPqqPSZo7g6xVhDGWvFMYHP6+H0Wz6NE41hhbowg+UFb4oy0MjOhvZ9zJu0WHnJxxhrrlieqAYPWzYB02DJrDrWXLF8tJemGGOoK4fimEL3evBPrinKvvMZ2TmWpFeDzbsrMjENg3VXnU1HxGLvkU5m1VYoj1oxJNQvUKEoEHEJlu1kSsHGkvRqqHl3Rf9U+kxVTKYYFuqXp1AUiFwjO4vtnTrRGD2HW4etG4/n3W0nlpJ333F4a4FWWppEbYf9oS72hzqJ2k7uFygUI4jyrBWKAhKXWDW078OyI5iGj0lVM4sqvXIdl92r19P00ntEQ914g2XUrVjAnJUXoGWRDQ3EsZh3d1yXn27awSOv7uFQexgQTKn0c/Xi2Vy/fD66pnwaxegzvn51CsUoM9DIzmKxe/V6Dj31FkLX0H0e7LDFoafeAgbfSnW85d3zYfXmnTywZSctXdFEq9dD7WEe2LwTTQhuOHPBKK9QoVBhcIWiKMQlWCMR+m56aTsizYMWujakVqrxvHs2xlLePV+itsOGPY20hWMpPdmFELT2WGzY1ahC4oqSQBlrxZjHcW3CVscxOZXKCnURDYWzbxtCK9XRzruPNKFwlOauCDE3s3GJ7Uqau3sIhbNPvlMoRpLx9ctTHFOoqmUwg+V4g2XY4cw+0+YQW6mORt59tAgGvNSV+9jd0omTNjDH0AQTy/wEA95RWp1CcRRlrBUFIWo7hMJRggEvXkMfkWMOt1vYeED3eqhbsSCRs44znFaqA+Xds2mv0x8bS/psr6Fz9ux63m1sS8lZSympCXg5Z279iH2fFYqBKO1fkqLkcVyX1Zt38tLepoSxXjGrjpXL5hW1ivZYrFpOJ24UZ375XKB33KfVGsasCRSklWpy69NsUYy6ihm9x+3cTzTWg2n4AAFCYsUiYybSsXLZPFwpj1aDS5haFeDqxbMTveUVitFmfF/NFEVn9eadPLX9ELom8Hl0wjGbp7YfAihqFW2hq5bHUoepuOFsaN9Dj9WN3yxj0qWzOeNLX8FuDRellWq2KMaOhpcBSYWvFl0zaA830WN14fdWUO6tGTORDl3TuGnFQr68dB6HO3oAyeTKgPKoFSWFMtaKIRO1HV7a04Su9YYOXSmJOS4eXeOlPU1cu2Ru0S54heoWZtl2ondzt2VTZhosmT6BNVcsxzRK8+fx3odb2NGwlajdg5QuHRGNtnATUkoWTS18z+lsUQyJxLJ7kEjK+4rRInYYoQmisTBl3moEYkxFOryGzowijEtVKApB6camFCVPKBwl1BNFStgf6uKtQ62J/7Y1tNLcFSnasQtVtXzl2k1s2N2A1XeTYTkuG3Y3cOXaTcVY9rBxXJtdja8TiXUDsi/HKonEutnV+HpRKuLjUYxkXOngSgcpXdy+/+KfRe/fR+VO8UiHQqEYOspYK4ZE1HaI2i5VPpMDrV00dUZwXIkmBI4raQtb/O7PB4q6hvmTl3BccAG65sFxHXTNw3HBBXlXLXdELLYeaEkZXwigaRpbD7TQEcmssB5twlYHYas9RRMMvbrgsNVO2Ooo+DGzaa81oaMJHSE0tL7/4p53799HIyrjUZ+tUIw0pR2XUow4bjSK3RrCqAmieTMlK+kFZUe6o7wf6sbnOXpxllIyodzH1v0tRJc7KaHwQlaND7db2N4jnXRbNp4sLTnDls3eI50lOHxBgBhgW/8bh0w8ihHPWfceSWAafnq9+97HfEaAHqsLnzeAIF5VPT712QrFSKN+QQoApOPQ9NBqOjdvxGkLoVcHqVh2JnXXrUToR41qekFZbZmXnc3tRGLg9egYmiBY5mNGsIzWniihcJTJlYGiVo0nVy0Phlm1FZSZBpaT2RAjYBrMqq0Y1rriFHKGdsCsoMysIhxtT7XLEsq8lQTMwqw5nWza6/mTzgCgsWM/lh2h0l9HVaAekFh2dFzrsxWKkUYZ6yzYHR1E9+/FO2MWRuX46oPcH00PrabtT08jdB3h9eH2hGn709MA1K+8AcgsKAMwDY0qvxfXlSyqr8Lr0RPba/zeREOJ0aoaH4hKn8mS6RPYsLshJRTuui5LZtUNuyq8kAM24uiawZy6U9nR8DKW3YMrXTShYXr8zKk7tWge7EBRjPmTzxizOmuFYqygctZJuJbFrr/5Au+cfgI7LrmAd04/gV1/8wVcq/Ryl4XEjUbp3LwxxYMGELpO5+aNuNHedovxgrJkNCEIBkxsV6JpImGoHVeyYnYdXkPPauQBdE3w0p6mUe29vOaK5ZwzZxKmrmE7Lqaucc6cSay5YvhV1fEBG3bYShmwsXv1+mHtd8GUpSyYvIS6yulUBeqoq5zOgslLWDBl6bDXnItsPc/THxupvugKxbGE+jUlseeaK+l86X8RmoYwPEjLovOl/2XPNVcy91frRnt5RcNuDeG0hRBeX8Y2p70VuzWEOWkywUCvp9xlxRISLU0IpteUY+oaVV6TjqhFjd/Litl1iYYScSOfnNeOkxwqHw1Mw2DdVWcXXGeda8DG7GvPG3JIfDQmeylvWaEYXdSvrg+7o4PuV19GpOVPhabR/erL2B0d4zYkbtQE0auDuD2Z8hq9qgajJtj7PE0gpeTNgyFsV+LRNYIBk+Oqyvji4jlcu2Ru1uKxuJEPxzJlRcmh8tGk0mcWtJgsPmBD92Ua5PiADf/kmmEdY6i5+sGg+q8rFKWB+rX1Ed2/FzfcnXWbGw4T3b93hFc0cmheLxXLzkQ6qeFo6ThULDszURW+evNOOqMxggEvhiaIOS4tXVEqvAYrl83Da+hZOz95DZ0Vs+oyBiUkh8qLSdR2ONwRHtFwe3zARtZtQxywMRrEO5fZTiyl//qOw1tHe2kKxTGF8qz78M6YhRYoQ2bJT2uBAN4Zs0ZhVSNH3XUrAXqrwdtb0atqEtXgcLS4zNA1ZtaWM62mLBEKFwhsVzJQzVQ8JP7SniZae6IZofJcDCUMO1p9y6E4AzaKSX8DOo71/usKRamgfml9GJWVlJ1+RiJnHUe6LuWnnzFuQ+BxhK5Tv/IGJn7x2qw66/S8s64JdK333/G8czDg7VdDrWsaN5y5oN9QeX8MJwxb7Ar0rAYuSaYVH6RR6AEbhWSg81vo/usKhWLoKGOdxOxfrGHPNVfS/erLuOEwWiBA+elnMPsXa0Z7aSOG5vViTpqc8fhAeedqv8m6N/ez9UBLTg82HirPl6GOwcxVgT6cvuVZJ1CVTUd7opOWjTszZFqzrz2vYDrrQjPQ+Z03aXFB+q8PhkJq0hWK8YQy1klopsncX607JnXWuYjnneOeapx4Hnr9zsMF92CHE4YtZgV6NgO3a/X/oL0SocJfmyLTAph3wyeGXUxWDPI5v+mdy6BwXcmSIxNCagXXpCsU44kxaawL2bIyG0ZlJcYJJxV8v2OdbHnnpTMmsnlfYT3Y+OfrN6JDDsMGA16qfCYHhwgQAAAgAElEQVTtEQuPrqWsbzgV6FknUFku9htt4EjAJV63OZBMa7Q9SMe1aQs3EbXDGFqWivW+85utc9lwu5Jli0xov+/A2dyO0PWsNzsKxbHOmDLWo1kwVCqM5kU+W945FI7y39sPZniwjitp6AxzuCPMjGB+LTDTP9/agMF5s2xm1BgZHa8HCsM6rsvPtu5iX6iT91vDeHRBMOBlRrAMVzKsCvRseVy3PYbbEQNTw5EOepIhT5dpFaOr2WBINpQRK0xHpAXT8FHuTfX84+e3GJru9MhErCdK+8Yd+Fw/5Xow8bxCaNIVivHCmDLWpdiycqQY7Yt8Msl55/RctpSS/aFuQuEorpTc9uQbnD27PusNVXqEJP3z7Y5J3j7sRdDJjJqjBj9XGDa+n8mVASzHJdRt0djZg6lrXHPGnLwr0LO/98w52lqVB63KhIiLLlJvAtJlWvGuZkLXRsWDTDaUhu7B1L30RDsBEgY72/ktlKY7W2TCbY8hO2JEvZJybzXJitJCadIVirHOmDHWxSwYGgukXuR1ot3dHHrqDWB0w4Tpuez9oe7EHOu6Ch8R28m4ocoWIVkyfQJb9jdnfL77O6bhNQ4xM6hhO7mHQ6R/T2YEy5lWI4k5LpU+k2uXzB1WFCbrBCpTwzi5Cu2VCMmGJl2mVcyuZgMRzw0bmplhKMv6DLRlR7A9Nl7DX9ThG9kiE/GbHbfHzohMjCVN+mjjRGP0HG4FBP7J1SoaMc4YM8a6lFtWFpujF3lBVzRENBZODHAI/2kjM790Dh7f6HUBi3uqG3Y10tIVwdA0gmUm02t6L7LpN1TZIiRPvnuQD9t7mDsxPWQu2NY4mZvPPYOaADnDsNm+J5oQeA2djh6rIN+TbHncuSvPR5vcScvGHf3KtEaiq1ky6blhXTfoCB+h0n+0U5sQgnJfENuJcfrMT1IdqCuqdjpbZEKYGp6Tq7E2HUmJTJSqJr3UcB2X9/79KfY9/L9EmjsAQWBqDTOvOot5139CFeiNE8aMsS6FlpW5Zj0Xi/hFvkd0ErG66J0m3Nv6M3wkxPbtL3LCyaOn3Y3nsi9ZdBxX/3oT5V4DTaR6yMla7PQIiSslGtBtxXr/nfbaGr+XCeVleUVORuJ70m8e90Zwrju/35qCeFczO5zZeKcYHmR6bli6LpbdQ1e0NSNH7fUEim6oIXtkAsD3V8dRHahDeztaspr0UsR1XDZd/u98+OyfcXosNF1D8xiED4bY/cBzaJqmCvTGCWPGWA8kHSp2y8p8Zz0XCzNYjqcmQFtzI6SVWmkVHlq0wziuPerdpCZX+plU4R/QUCZ7vlLCgdYuQmGLmOMSidnsbu5k7sRK4vZ6sJ9v/HvyX/93EEfKRCV4Mb4n2fK4utfTr3dcjK5m/XV2y5YbFkLD5wkQsbop81YD4EoHgWBq1bwR+/5ki0xMnTiP+f+0BGk5Smc9CHbe+wyNL27HtWK9Y14luFbv789q66Zhw7uqQG+cMGaMNQy/ZeVQyWfWczHRvR5qls+g4fe7U+YuS1dinlJDTIuVRDepfG6okj3fA61dNHVGEEKgCUGZaaAJONwRpjbgHdLn67gurpQ0d0c41B4GBFMq/Vy9eHbRvyf5UKiuZrk6u/XXfazMW4PjunRF2ojY3SAlZd4qpJSJ1EqxGbDC3KupYrI8caIxGl94B2ynVzGYdB/vxmzcmIPV0qEK9MYJY8pYD7Vl5XDINet54hevHZGQ+IKvfJIDLe8QfbUFt9NGqzAwT6nB/4VpGIZZlG5SQyHXDVXC8333IKFuC9HnQkspmVDuY2ZtOV5N598+cyqTK/2D/nxXb97JM+99yJSqAJMq/cQcF63vZiCfwrJia/g1vTcsOdyuZrk6u2XLDUNvjtrQTXwePwGzAk1oCKFxqHUHQogBu8IVmpGYGjYUxso4UCvURazbQngM0AQkz8npmy9vTqhUBXrjhNL9Jg7AYFtWDod8Zz0XG4/HZPbK8/jgs+8iOxy0Kg/C1ArWTapQ5HNDtXLZPNojFm8dDOHSO3ozWOZjRt+Uqo6ohdfQhtRMJTkfHi8sA3IqBkZawz9QuDwX+XZ2y5Ybdl0bIUBPa4SihnOMvXGgZrAc34QKIrUV2F09uJaTuPlFE3iC5Uw6Z5EKgY8Tjs1f5SDId9bzSJDI9fn7cn2ap6gym+Ew0A2Vrmn84zkf4c2DrQXtMDYcxcBY0vDnO2AjW254YsU0DrdnH/d6rA/nGGof+tEiXgNhdfQgpaTnYAgn2lu86JtQxdyV56sCvXGEMtY5iM96juespeMgYzHQdao+fuaIVoXHc31zak4k3HyIwMSpePzZZyaXOl5D55w59QUtGBxqJfhY0/D3F+KG1M5u2XLDAK3hwwUZzjFWwsX5MFbHgSbXQAQmV6N7DWoXz2HBLZ/GMwIKGcXIUXrfvhKk7rqVSNelZe0jxA5/CIBn8hSkdJGOMyIV4TD6VemFZrAFg7nyyUNVDBRSwz8SBiw5xI0QuNJBEzpImTUlkp4bHu5wjrEWLs6HsToOtFA1EIrSRxnrPBC6jtA0PHV1eIITEKYHNI329c8ihDYiFeGQX1V6qXk7AxnYfAsGB5NPHopiIN0jd2VvxzOPruUdkh9pAzZ30mIa2vfR3Pk+tmNh6CYTK6Yxd9LilOdl+z4MdzjHWAsX50N/0QppueidYDilbQCHUwOhGBuM/tV8DJCoCDc8KWdsJCvCc1Wl1159DbtCb5aMtzMYA5urYHAw+eShKAbiHvl/v3uIg+3dCd23oQnOnFmHoaWPEclkpA3YroZXsJ0oNYH6hOTKdqLsaniFhVOW5bx5GOpwjrEaLs5FekGedCQ9697HeqMVb9jklckPqpGdilFFfevyIF4Rno14Rfhor2HHe+s5GHoP24mlGIsdh7cOuF/HtQlbHTiunfLv4RI3sOGYnWJgV2/eOaj95MonR20n6+viNwD55ppXLptHhdegpSuaMNTBgJfOaCznmnMZsHzPZ77nP/l48ZuD+L/jx4vfPAz0fYiHxwdjXOPh4mzEw8VjlfmTl3BccAG65qF73QGsTUfwuX4qqiYkBq7sXr1+tJepOEYZe7fAo0ApVIQPtAatsoomERqUt5M+KtFyei/AphHAN0yvvJAFW4XKJ+fKd9uuRCA4+bhgIgQeX/+GXY1csui4fnXfw813DjaEnut4YauzaN5vvsVtY5F4xGF2zcls3vkTqPST7M+okZ2K0UQZ6zxIrwiPIx2HimUjUxE+0Br8SxZjaTZ6lo+zP2ORHLaNxDrpiXUh6C1WMjRjWCHcQhZsDbfXd77h+OQ161rvuuPjPlu6o1z9601MqvBnfe1wDdhgQ+i5jgeyaMVS/em3S03vPxyctghOe2zEBq4oFPmgwuB5MuFvv0j5kmUI04u0omj+ANUXXkzddSsHtR83GsVqOIwbjQ56DXXXraT6wovR/IGUNUz+8o14Pf6sr8lmLJLDqFK6ROxwbzMFAdFYGIkcdAg3mbiBzcZgNdTxfLLjypTH85V45RuOz7bm+LhPXQjKvUa/r40bMCndlMfzMWBDCaHnOl7ArBzU92GwJIeLHddB1zwcF1xQknr/oRAfuJJ1mxrZqRglxv5tcJFJl0tpFVVULD+L+htvRg/kf9ErhOxK6Dr1K29g4hevzZj+NRhvJzmM6koXKd1E5yNXurjSQRfGkL2wwUio8mnvOdSe8IMJx6ev2XEloXDvDVWwzExMAusvlD/UCuvkz0IiEzIsgRjw/A90PE1oRfV+h1OgNhYoxsAVhWK4jLlf2EiPqUyXS0krSufWzeiVVYOSbBVyGIjm9Wa0OB2MsUgOo8Z7Q8cbC2tC69XsMjwvLJeBHUy1+FB7wg82HJ+85obOMK6U1FX4EnO5B3rtUA2Y1whgeny0h5v75pT3GmuvJ0CVf2K/5z/X8YYrz8qHUu3tXQjizUYaN/wfkeZOfBMrqD/nBNURTDFqjBljPRoNQQo1xGMkhoFIy2GGWMjsGSdj67EBjUV63tFnBBI5a68Z6JuVPTwvLJeBHUp7z8H2hB9svjt5zYc7wtz25BtEslSbDxTKH6wB0zUDpKAn2onQBEIIJC490U6q/LnnS/d3vPHu/Y4UMj5oRuSW7ykUxWTM5KzjnqnbE07xTJseWl20YxZKslVM6ZfruOy871m2XPVTNl91P6988UEOPrQFIQf+aJPzjj5PJQGzmoC3Cr+noqA5yGwSqqHKsYZy7KHku72GzoxgBWfPrh9yrjxfHNdGSonfLAcEUkpA4DfLkVIOW0Y3FHlW8toGI+XL9vxCygFHCse1+b/7/sihp97Ajdh4KgO4EVtJtxSjypi41R6tMZWFkmwVU/q1e/X6RG5N93kSelCAeTd8ot/X9dc3eiS8sEJWi8fpL/edKxw/UM585bJ5xByXF3Y1ELZsast8g5qvPVA3ufg2x3WIORHKfUHKZHWiwYkQGjEnmjVnXewudYOVkmV7fl3FDACaOvdn3UepddqDo++joWU3R55+HREFrydAubcGEANKt0rx/SjGFyX/rXKjUXp2bMc+cgQtkFnhWswxlYWSbBVL+uVEYzS9tD2lCAYGpwdND6OORA5yuHKsZHLlvvsLxzuuy30b3+v3dfH9bj3QQti2CXg9LJ0xMa+RmQMZOyBlm2l4EwZZCA09yRim1wyMVEvTwUrJsj1/R8PLgKTCV5uyDyklQoiS6bSX7X24oRiywwZTJ2J1AVDu7b2hTpdupfQriIUxNA+Tq2azcOryUX8/ivFFyRrr5By1HWohsnsXekUlnsmT0UwT+i6YQ/VM8ylUc6NRqj/9GaRt0/XKVpz2VvSqmkSufDD7ij+/c/PGfvczWKxQF9FQeEzoQZPPEbrBCVOqeWlPEx7j6AVtKCHmfHPf6fnuXK9L3u739FZq/2nnhxi6yDkycyBjB6Rsc1wHx43RGQlR4Tv6Pc5WMzASLU2zSckkEheXhva9GQ1V+nu+ZfcgkZRLN7FNCI1dja/jN8vQhD4qfcX784CT34dW5UGrMpE9DiCIxsKUe6sBLUO6Ff9Muq32RIFgS+dBmjrf5+wFlymDrSgYJWusk6unNZ8fadtE9uzG+uAAekUlek0Q8/jjBu2Z5lOolu055YuXUHPpX+CZMDFxvMEUvQ0kuxoqZrAcs8qP1d6D5tFTPOxS0YOm3HS1htjveHhz0lz+97RPcCRiIyXUBkyCgcGFmGHondJyve7K02ZlbNeEi88TY+OewwN2YBtYN723z7NM3VbuDRK2OtE1A8uOZq3cLkZP7myGK707Wle0NWGEEBrvHHyRjx1/TsIIZeum5kqn9/n0ep7xaIGULmGrHZ+ZmuIYib7iuaISye9DmBqek6uJbmxBaAJXujjSQXNJkW7FP5Nuq73XAxf0SSAlje372P7hJj4ydUVR3o/i2KMkjXV6jjp64ADSttFME9eOISwLN9SCb/6CAT3TbB5vPhKqbM9pf349wjBSZFbpz3O6Ogk9+QekbTPpxn/IuqZssqshnSPHZc/PnqdrXxPd77egeQzMYDllMyaCK0tGD5p8jt4P27R0djG9eSvLHZfXzvoMMcdlxaw6/vGcjwy6aGuoue/012nCxdRiWK6H1p4oe490Jm2XzKx+nwn+NkwtRsQ2ePN9weKZZw26FWhPrBuk7OsydhQhBD5PGafN/CS6ZiRC35FYV8KQFnKE40CGK1nW1xVtTTNC0Ni+F9PwJbzgbN3UNKGjCR2JTDlHbl8Tl7g0cDjvYbDkikqkvw//F6YBEHujFbocPAE/k85alCLditrh3v9iYUgrFpe4fNi2hwWTl6octqIglOS3KF49Lbw+cF2c1hBC0xCBADgOvgUL0cvLEZqGtO0ML7Y/j3fC334xZ6EakFcxW/INhZQS6/392KEQMmYT3b0bgPqv3Fg0WVm8sMw/uQbXsomGuog0tqGZOnOuObck9KDJ58h1JaHuKEKAFBrT9rzNm8suBsNk26G2Ie1/qLnvo6+LpRhiy/XQEa1lZrAssd+Z1e8zqawFELho+D3Q3r2HHYfNQbcC9XvKAInjZla7m4YvkbfOZkjn1J+W2K+Ubkoh2mD18LkMV33lTD4IbU8xQlJK/J4yNM1I8YKztR8VCEzDD6RGEQRQ5q1CpFs2ittXPN+oRPL7ELogcPl03L+YyiQxnY8sOifj5tdrBDA0D650Ejczyft2nFjJzsFWjD1KMqESr54GkFYMGUu6azc9vYZa1/uVPfUn82r4yY9ySqishsPEGg8jndQLqnQcYk0NWA2HgVQ5lvX+fmJNzeC4vTcQ0QhtT/930WRlKYVlQlA2o47gSTMInjyD8ln1zL72vJIY45d8jmKOS8w52h7T19OFv7sTOOoFp+NEY/QcbsWJZho+GJ40a8WsOmZU9hpiQ3Nw0dCFw/yJ7XzY+gYrZtUhpcNEfxtxiyVlbzczXdOH2Ap0FpOqZg/YlrS/aVm7G1+jrmIGnZEjHOn+kFD3hxzp/pDOyBHqK2cUbMSl49rMn7yE+sqZfd3t+qRknnLKvL31D+nTtbK1H50/6QzmT1qS8tjxtYuYU3fqkNqyDod8J4Vlex/HT1rER0/6eNYola4ZTK6ajUi7jEop8RkBvJ7AmB5soigtStKzTqmeNj0IjwfpOEgp8QR7jbgbiaBXVmUUlw0k8wr/+S20iiqklWkY9MoqQr/7DZ1bNxPZtRM0DSMYxHP8dGIfHMAOhcB1+eDOr1F55llM+NsvolcHcbo6sUOhlDtrYXjQfN6iycqyFpZpGprXxO7oGdXCsuTUQ7JkzaNreHQNR/Ya1oi/nJ6yCiDTC3Ydl92r19P00ntEQ914g2X9zhIeaivS65bM4pdbXyDUrSembAXLTabXlNPQvo/rlpyGIEx32MayRcp2GDhsm0/3sGzbchnSiRXH0+u3CmTf/zPirznIN5z+0ePOItR9CMuOJnW56yXdCx6oAcv8yWekPOb2tbYtZme1dPIdtDKURjILpy6nqfN9Gtv3IektpvN7ygiYVeNmsImiNCjZb1Jy9bRWUYnd0YYRnICUkvC2t5BWDPP442l++GcpBV0pIfQ03K4OKpafRefWzRkSKinpzUvrOkbtROzmJmJNzdhHjiAdFwEYE+t6vea+HHfFsjN7c9QxGxGX80iJXhMETSuarCw+aMAOW5nbRqmwrL/UQ/mSZbT/z7Nouk6wzEtTZwRNurw/+2M4hpnVC87QjndF+ODxV3Bsh4X/8MmU46ZLs8pMg27LxnYlutZ/9a/tRji+ysO0al/CWMf7f1t2BNuN8HfLTmDDe+8SiUVTtkPqRd6JxrBCXZjBcnSvJ+dFv79t4VhXv4Y0GgvT0L6XCl8wo4d4Y8d+5k8+I+/2pvkYLl0zmFQ1OxEWjofeBTC1Zl7WY2Xrppb+WPq5MTQT27V6ve0iVU4PdlLYYLrQaULj7AWXsf3DTXzYtgfHieH1BFJuQJQGW1EISvabk1w9HWtppvUPv6fl12uwDn6A5jEx6idhTp6SURyWqwFJ/Y03o1dWpUioyhcvofPlLQkD7p0+HQA7dAS7rRWjugYjWJt4PJ6/nrn650i7N0ctoxGE4UGvCSaeV6xZ16MxaCCXPK2/wr2qCz5B9YUX07l5I9PKIrjeat6sn8fm0z9BtWFkeMEpIX4p6T7QTDTUhRtz6NjZgJAw76aLMjxsQxM8tu39JN20ybmzQsyr7cayIxnVv8lGKz1cHjdaumYwpXpW30X+qKGOX+SF1Nh537P9RgAGuujrmoFX+rEaOxNGfiBDqusebDeGoXkQCHRx9Kc7mOKswRiu+ZOXIKVkV+Pr9FgdSCRl3iqklImc+VARQuNAyzsjprcuZq90TWh8ZOoKFkxemhFF2P7h5pLUlCvGHiVrrONoXi/eqcdRd91KOrdswhOcgDA9CZ11euFXrgYkeiCQIaGyW0O0/+mpo964EHhnzMBTX0f3m2/gX7AQLW3CltPeitPRnqj6bnv6v9F83sS6CjHrOt1jSyZeQNb00nas1jBmTYC6FQsLXliWjzxtoNRD15bNzHroPxLne15NkHN1g6v76RqWHOLvPtBMpKmjtxpZE7iRGAeffAPNo2d0Z0vXTdeX7eVIZwt78DOjpjyjiCpfozXQRX73T4fWPW6gMH9/a5pSM5fmzveHPDM7mXwMV9wblNLFb5bhMwMJT/5Q6w6EEDl10QN5lCOhGU9mJHqlp9+cjfR7VIxvSt5Yw9EuZk5bW0oXM+k4yFgMN9KTEm7OpwFJsoSqP29cmF708go0X2ZIPdlrrv/KjQjD6NMSH0HzBag674IhNzzJJ2er6RrzbvgEs689r1+DPhziNwptv/sVHS88O6DUbaDUQ3IqIH6+vdBvO9FEiL8rQjTUlZKSFYaO7vNkdGdL101rwmWivw0hBKFui2nVEk2IjOrfuHH6sG0vYStMwAwwtWZWitHq7yI/nO5xA7WInX99781W1tGXh/WCjL0cyHCldOSywnREWjANX1/Lzb73mEMXnUvTXAzNeL6M1KSw0XyPivFJSX9bsnYxq6zCnDYNq6/oS8ZsNJ+P0O9+k5BKDbYBSX/eOEDZ6WfghrtTynjSvWah69RdtxJp23S8+AJud5iuV7YiDGNIU8EG0+9b93oKWkyWcqNwpBNr9/9RXmNSN8shHglOj2YUsvd5PMT/weOv4MYchBavxJZ4g+UIXcvozpaumza1GB4thouWqEKPe/DJIWMp4fm9QTbujdJtdVNmlnHmrCDzJpFRt5V+kR9q97h8jHz/xVqFDeVmM1zJ3qAQAseNEbF6q96TDfZAofdcHmUhNeOlyrHwHhUjS0knTpIlWJo/gF5VSaypkfBbbx6VSgmBXllF+/PrM6RSce85n1B03XUrqb7wYjR/AGlF0fwBqi+8mNm/WJP18XSvuemh1bQ/vx6kRAv4hzwVLNfFvD8ZU6GI3yjYYQtNk7gRi/YGg6a9qTccybK5+M1ONrnbUFIBc1ZewJRPndprCF2J0DR8E6t6G76QWUQX103HsVwPlttrRONV6HGSQ8bx0Hl3TIII0B2TPLX9EKs378y5xngEIOu2AYr84kY+67Y+Iw/Zp2XFPeKz5n+BFfM/z1nzv8DCKcsKlv9M9wYTVeCit8BNclQi11/oPR9pWDw3n41i6q1HkmPhPSpGlpL1rLPlQc1pM5Cui7V/P1pZOcIwjhZ0CTEsqdRA3nguL72QU8EK2e97oJx3f89PvlEQHg/CY4Lj0NWiM3GGg9b3FtM95kL2Ptd0jYX/8EmEhINPvoHu8yTWlK2ILq6bjuesXanR0lNNfaCFYLmZqOJODhkPtVVp4nlDLPIrRCV/sUK56d5g8qxziYsrHXRhDBh6z9ejHEx19lhksBXoCkUuSvYbky0PKoTAnDwVu7ERf18XM5ImIBVCKtVfO9CB2oTmm7PNh0JczAejU04m40ZB0zFqaog1N+PEwLbA9Gf3mIvR+3zeTRehefS8iujS9daNXbNYVF/N7NrurP22c7UqPdwRxmvoWYvg4gylyG80Kvnj5JIQZatGTzRCcaJI2VuVPlDoPV9pWDGrs0uFY+E9KkaOkjXW/RZ9eTxogbIMQw3Fk0rlohg52+FczIc64zrbjYI5bQYATkcrOp1o/oE95kL1PofBFdH1Pwozu4GKh847o7GEzlrXBFJKWrqj3PbkG7T1WBmjM4e6vmRGqpI/Tr6jNbO2DhWCMm81c2vmMnPiiTmrqPP1KEeiOnu0ORbeo2LkKNlvznCLvkaSQs+r7u9iPvOL59BzuHVAozCcKuWsNwpC4DluOtMuvIRZXzilIB7zYBlMEV36KMxsIeOo7dDcFcF2Xd46GMJ2JYYuCAa8uK6L0AQR2xlw5OZA64vaTsoNQzrFruRPZzASooG8wXxz44PxKEeqOns0ORbeo6L4lKyxhv7zoBOvuY7mXzxU0NnQxVrrUHO2yRdzoyrAvoc38PI1q3OGtYeb8x7I6yuFfuPDwXFdVm/eyUt7m/jzhyE6IhZCCDy6IOZKjnRH8OgaJ0xJjYTkm8dO3n/cWPfnlUPhK/mzr2lwEqJ+pWquTThpCthAKI9SoSg8Qkops22IRqO88847fPSjH8U7Ct5qMv11z8rVVWs0KMaadt73bNaw+NRPnpQIa8eLyfQyL698+aGsOW/dq3Pyv12Jf3J1Tk9usMVpxSY9nD2UFo73bXwv4SW/dSiEK3slYRPLfUyp8uO68M7hVk45vjaj8Cwac/iPy5f3qw9P3n/yax1X8smFU/v1yotN2OrgpR2/yXqOHNdhxfzPD+j15RtCVyiyUUp2ZKwzJm53h1L0NVoUek25wtozv3gO+x7ekFJMJqXEtR20Pi9QSknXviYMn4eXv/xQXgVnI+H15UO6sTA9PpACkFnbiPZHcvV3JOZgOxJNEwghaA1bTA+WgQ6I3glhupbqQQ80cjN9/8nk65VnozA9pb1IzL5e4qlry0dCpLpwKRSlwZgw1scyucLaO370FM2bdqYUk0nbwVPhQ4je50RaOhDQa3yFyLvgrBRINxbt4WZ6op34zXLKfcG8jUdy9bdH1zB0QXyyZsw92jhlalUAPc2o5Rq5mb7/dOIjQAfyypMphDebHJKv9dpMr24lWOZlek05gvwkRKoLl0JROqg4VokzUPMNo9JP65v7Mr1uQwfX5WN3/RWn/vvVlM+qp3xWPSSP8SxAkxUnGqNrfzNd+5uK0qwl3VhIJNFYuK8ALJyYi5zccKM/khun6FpvMVk8A+TR+sZ3upKrTp/Npz5yHAHDIBpzCBgGn1w4NefIzfTGLMnk8srT6W+m9Y7DW/PeR7zhSzhm80HXdA51BfmwPcaB1nZ0zcNxwQU5JUT5zoFWKBTFR90WlzgDSbmCJ8+gccP2FK9bSkn3/maslk5e+fJDmFUBOnY2UD6nPmVyFAy+yUoc13HZ+dNn2ffIi4QPtQLgn8EkteUAACAASURBVFLD7KvPYu71nyhYIVp6gw1XOrjSQQiRGNmo9xnyXC0c0xunzOi7ATrSHaXS76Hc42HF7Dq+uHg27ZEYV542i27LHlBnPdD+4+TjlSdTCG82MyQv2Nc2nQPiePa0uly++GwCZu6bh3w10wqFovgoYz0GGEjK1bbt/ZRisu79zUSa29EMHU+5D+m4WB3ddO9vpnxmXcp+hzr7evfq9ex+4DmiLZ2JHtrhQ0fY+cBzCE3LGVrPt3gt3VhoQkcTOhIXIbSUkLBp+DA0k7DV0W+ON71xykfqq1kyYwJ/eeJ0ggGTh1/ZwzWPbsmo5M6X9P3X+L0ZI0BzUYie0v2F5F2p0dAlaY84BMzca1FduBSK0kH92sYAA+lyk71u6bhYoS4EAm/waNMYX20FkZZOyqZNGLBtZz440RgNG97FautOnYglBLHWbho3/F+/Wu5YOMqOHz1F65v7sdp7cha6pRsLgcDrCfTlrMuOhsf7wuGbd/8+R9OP7I1TILWSO199deZ6+99/vhTCm42H5MOxzLRArpB8elGb6sKlUJQGyliPIbJVaCd73T0N7UjXxVdXSdn0ieC6uDGbwPG1SNtBMwROJDasjllWqItoc0fKRKw40naINHdmhNbj7U/3PLyB7vdb0DwGZrAczWvkLHRLNxZV/olU+euQUhJzetuIAsTsCJqm51WxnN44pdCV3On7H4h041gIb3YoIfmBitrGk2a6I2Kx90gns2orqPTlEV5QlAy5mg0Nlscee4y9e/dyyy23DOn1t99+O5/85Cc566yzhr2WfBi7vzoFkOp19xxu5Y3b1uL2xOg+0Ew01IUbc9A8Ov5JVZzxH9cjI7FhaafNYDneukq03Q1Ix03ZJgwd38SKjND67tXrOfRfb9DT0NYbAXBdIs3tAJTPrBuws1p/DTYsO0JnJETArGTrnifQ0qRWg8nxDreSeyia9IGM42C92WwXscGG5HNJtMZ6Fy7Ltrly7Sa2Hmih27IpMw2WTJ/AmiuWYxrqMljKDLbZ0HhFfUvHCbrXQ/mMOiadvYid969P5JOFJnBtBzti8/5/bhq2VEv3eph09iI63j2UkrOWUmLWlFF/zkcAEm1Rodfrl66b4o0LIbBCXchpE/IqdIsbC1e6bP9wc8LI6bpBR/gIlf7ajNfkm+Mdath4qANTILdxzMebzXURyzckP5oSrcJoyXNz5dpNbNjdgNZX+W85Lht2N3Dl2k2su+rsoh1XMXziyobhpKj646233uKqq66iq6uLm266CY/Hw49+9CO8Xi/V1dV85zvfobKyknvuuYfXX38dgE996lNcddVViX38+c9/5u677+bHP/4x77zzDg899BCGYVBXV8cPf/hDtALdUChjXQIU8oI184vnsPsXL/R6sLaDMHS8wXLKZkzM2Rs8X+asvADXddn3yP8SPtQGQGBqDbP+dgWu67Llqp8mjFf1CdOIhrrRTQPNo6d449J2cGMO3mD+hW7pRk66LpbdQ1e0lXJvqrHPN8c71Eru3avX88F/v4kNeLxG3vr1fI1jLm82n4tYPiH5QhS1DZaR7IzWEbHYeqAl46KpaRpbD7TQEbFUSLxEKUazoWT8fj8PPvggoVCIz3/+8wD8+te/pr6+nkceeYT777+fxYsXc/DgQX7zm99g2zaXX345S5b0RrnefPNNtmzZwurVq6mtreWee+7hS1/6EhdddBGPP/44XV1dVFYW5rejjPUoUowLlt0exltbiX9STSIEHi8qS/Zg+wvd5hPS1XSNBTddzNwvn0/P4TZA4p9cw56fPZ8x7avppfd6jzkliDdYTqSpI+GNC0NH6CLvQrdsRk4IDZ8nQMTqpsxbjSBzdnU+DDZsbPVE2fTYK7S2diemdgUDXmYEy3LeFBXCOBbyIjYaEq2R7Iy290gn3ZaNJ0u0I2zZ7D3SyUlTMyMzitGnkM2GsnHqqacihKC2tha/3w9AfX09AKeffjo/+MEPqK2t5bTTTuudI+DxcOKJJ7Jnzx4ANm3aRHd3N0ZfKuWOO+7ggQce4Fe/+hWzZs3i/PPPH/La0lHGehQpxgUrecylnnZxMmsCGFUBdt73bEbodtZ1H2fvQ88NKqTbG3qfCPTfFlXzGEgpkTG7t+gNenPplo1/Sg3HfeqUvAvd+jNyZd4aHCkRCBzXGVLF8mAruX/2p22EG9qQpoGmCRwpaerqbSAyLWoPGNbvzzhK6aJpOoaW28sr5EVspCVaIx12n1VbQZlpYKXVWAAETINZtRUFO5aisAxH2ZAPb7/9NgDNzc1Eo1Fc16WpqYm6ujpeeeUVZsyYwezZs3nssce4+uqricVivPnmm1x66aUA3HjjjTQ2NvKtb32LH/zgB6xbt46bbrqJ2tpa/vmf/5n169cnnjtclLEeJYp1wco1D3vfwxuyzrpufHE7dld00DOw4wzUFtVbW8HEFQto23YA/+QaKuZPIXjyDObf/Ek8g/ix9WfkhBAEyyaxfO5fYLtW1nRCvqmG/MLGDhubu/hYuff/s3fmcXKVZb7/vuecqtNV1Wulu5PuhNBZSdgGUCBAgrihgjr343XEO5CRzAUnDvjRUQdl1Ln4QRw+o+hcPyxRUGG4XgdnnBlHRcGFPQHxCgIGSNIkkK23VK+1nDrnvO/9o7oqVV1V3bV2VXfq91fSdarOe2o5z/s8z+/3ezDibsY6QhGLVb3ts5b1ZwZHpRRha5SYHcFr+Ni570dzVlgqfRObT4nWfJfdW5u8bDq5M9WzTkJKyabV3Y0SeB2jUmZD+RCLxfiLv/gLIpEIN998M0opPv7xjyOEoK2tjX/4h38gGAzy29/+liuuuALbtnn3u9/NaaedlnqNP/uzP+MXv/gFP/nJTzjzzDP5q7/6KwKBAH6/n0suuaSs9aWjEaxrhGresGYzUXnmL+/KticVMPL0XtpPX5n59wJmYCeRntHPhBkMsPHT701cWxmTvObKAL1GE16aMp5TjVZDKGIx4jgMr+2h56WDIEAq0ATYjsR/3to5ry89OIbCA9hOlCZvgGazo6AKS6VvYvM51rIWZff7r7woxQaPxB38XoNNq7u5/8qLKn6uBiqLSpgN5cIHPvABPvCBD2T9/cILs39zn/3sZ7P+duutt6b+/Z3vfCf177e97W1lrSsfGsG6RqjmDSufiUr06GjO7FfaDm44jrTdrNJ5oZakc2X0yeBV7iSvYjPAarQaklntvrdsJBSxWLpvADNqY/k8jKzv4frrLp3zNTShsX7ZefS0r+XZ/T8DSPXbobAKSzVuYvMh0aqFM5rXMHjgI29ZlDprKS2kEwItADKMZgTRtMUzjrISZkOLAY1gXSPMxw1rpolKvuxX8xjoAS9ajv5nMZak+TL6UsxX8qGYDLBarYZkVnvXzlf57RkrMU5dgT8aJ9zkoaPNz73/b/+skpL0bD9iTTIeG8bnac5is89VYVnIN7FaOaO1NnkXDZlMKZfRwR1EJp7ACr+AkhGE7sfrO5NA2xY6lm5HiIXxfSgExZgNLUY0gnUNMd83rLzZr4LOTetwpqyM44u1JJ3NFrXSKCQDrGarYdt5a/juM/vQNYGNRrjVl2KDz8XGTs/2PboXgSAWnwLICNjFSM8W2k1sPsvuixWjgzuYGv05TvwgrjuKQCBtC1vsZkomvk/BZdfVeJUNVAqNX0cNUYsbVr7s9zgbvPysOJctai1QqVaDtCyc0RBGRxDNTJQXx2M2SwImy1p9KelWsnc8Gxt7ZrYvhEaT4SdqT2HZkZT87EQZlrHQndFqBSktIhNPIoTAdUKpFooQAtcOYXhXEpl4kvbuaxZVSfxExuK+E8wzSjU3mc8b1kx7UhD4etrRvcasWXEplpq1RrmtBuW6DN29g8mdT+KOhdDbg7RcuJnua7dnsLH1GVans7Gxc2X7gelsOmqHsd04Pk/zghuWMV9OZA0kIJ0Q0g2BEihlI0j/fjsoZSPdUaQTQvP21HClDVQKjV9VBTCfbkyVgHQl/ff8JqememZWXI6lZj2gnFbD0N07GHv45whdR5hNyGiEsYd/DsDS7deVxMbOle0LIWhuCtLq6+Lc1e/F721ZMAFvoX33Fws0I4imB1FyCiE8oNIlhAZCeND0FjQjWMNVNlBJLIw7Qp1jPt2YKoF9O36ZU2sN2ZrqYo6tR5TaapCWxeTOJxH6jAEhus7kzifp2nZNSWzsLI01CqlcBILlHetpaap8+6CaVZGF9t2vFZKM7UoxtTXNxN+6manRn6MbQRx7aLp9ojC8iQDtb928qErg1azePP744xw9epQrrriioq9bSTSCdZmo5RCEUpDPaSxdUw0JPbQeMOc8diGVxItpNTijIdyxEMJsynrMHR/FGQ3hXdZTEhv7lJ5NKKXYN/T/CMcnQCkCZhtKKaSSFctIc1VFlmxez8r/eQFNZnPZ38uF9t2vBY4ztp/EdUYQWoBA21sJLru+bKZ2x9LtAEQmHkdJBynD6EYAT9OpKTb4YsB8VG/ma8xlOTixf0kVQC2GIJSD2ZzGrFCEl2/7KWMvvIEVCmP4PEzsGaB57VKEyPSgLlR/vVBhdATR24PIaCTrMb2tA6PjeHmxWDa2JjSEEJiGD0P3YmgeNKFzePRVhBBFZ6T5Mo7MqojB2OgAA//Wz6tHn6Z96/qyb3gL7btfC4wO7mAy9CCufRDXDqGUgzX1O6KTO1m+7v+WFbCF0Akuu4727msK0lnny+7rXaddjepNLBbjxhtv5MiRI9i2zbve9S4mJyf58Ic/zKc//WmWLVvGwYMHOeOMM/jSl77E5OQkn//85xkdHQXgC1/4AqeccgrvfOc7Ofvsszlw4AAXXHABk5OTvPDCC6xatYqvfvWr7Nmzh1tvvRXXdRkdHeWmm27inHPOKWnNjWBdJqplblKt0uVsTmPx0ARDT7yS0F03eVCuJD4RJnxgmOZV3ZmvU4T+ei7UI3lNM01aLtyc6lknoVyXlgs3p1jhpcB24+wd/B3R+CRSuWhCx/T4aTY7Cs5IXekQtad4feRFhiffyMo4VNzNqIpMWaMJeZgQuL8fx/6gxSGnvBteLZzIFhKSjG3XPogTH0YIMV2FkEQmniJ09HaW9H6i7PNomplGIsveHKVn99INoelB/K2bae++lrGhuwmPP0E8+gLKjSA0P2bgTPyt9aHTrlb15l/+5V9Yvnw53/jGNzhw4ACPPvook5OTABw4cIDvfOc7+Hw+3vGOdzA8PMy9997Lpk2b+PM//3MOHDjAjTfeyA9+8AMOHz7MfffdR1dXF+eddx7/+q//yhe/+EXe/va3MzExwb59+/jsZz/LKaecwk9+8hP+/d//vRGsa4VKm5tUm9CVV2ttOyiVMEhJQdNoWtJCbGSSwMrO1PHF6q/zod7Ja93XJsqIkzufxB0fRW/rSLHBy8Huw08Sjo0itESGrZApnbXP0zprRppeEgxNDRB3ojR5/ARm2JT2iY1pFRSJZUdIjjuTkw5y3EbvMsvzoa+BE9lCgnRCuM4Irh3KqkyBQ2TiETqWba96FpvUYwuhI0QTSkaYGv05kYnHkW4YJ/4Grj2KEALpWsSjLyPd+tBpV6t689prr6VK3319fbS2tjIyMgLAypUraW5OJCJdXV1YlsWePXt4+umn+fnPEwTT8fFxANrb2+nt7QXA7/ezdu1aAFpaWrAsi+7ubu68806ampoIh8Op1y0FJ/avqUKopLnJXISuSmShubTW7WeuZeCR3VnHBk7uQjkumiFwY3ZFXcnqnbwmdJ2l26+ja9s1WTrrUuFKh2NThxGaDqi0k4FlR2jzdc2akSZLggiB7cZAKKL2tKFKUzCVcazpOztVQXGVi1QypcXVWgy0tsR3p9xyda2cyBYCNCOI0AIo5eTIDD1IGa26tOq4HnsGUVIIopPP4PWdmrGZECR02x6zPnTa1arerFmzhhdffJF3vOMdHDx4kK9//ev8t//23wBybKxg9erVvP/97+d973sfx44d41//9V/zHpuOW265ha997WusWbOGb37zmxw+fLik9UIjWFcElTI3mY38Nfj4blzH5diuvWVnobmcxgDGXngjuzwuBO1nruS8b38UN2xVrFRdCNGtnkri3mWVuaFaTgTbtVJGKOk/dle6BAO9BdmnuspJsMiFQAhBzIkQUO0IoRF3Yji6naqg6LqONj3dS0mF95wOhDfxvpftQ99wIssLTTMJtL0Va+p3wPHxnAqFYQTR9LZEv1haVQuIST22EJlESSVtlAyjZCRrM6GUnXhc1V6nXa3qzYc//GH+7u/+jquuugrXddm2bVuqH50L27dv5/Of/zw//OEPmZqa4vrrry/oPO9///v5xCc+QWtrK8uWLZv1HHOh8auqIMo1N5mN/DX24qEEQ9vXVLEsdKamerZBHN5WP1TQ0nK2a13M5LVkpqBNZzoxJ4JSEiE0AmYbpy7fnPe56SVBTehoQkdNBwGlJFJJdKGlAnB6BcU7YWL54pjndOC7YmXqORXzoW84keVEcNn1RCd3Epl4CnAQwoOud6CUi2O9xsD+v0r1kKvRIz6ux84kSgrNg9ACCM2PEAbpmwkhPNOPN9eFTrsa1RvTNLnttttyPvbDH/4w57/vvPPOrGOfeuqpnP/+8Y9/DMC2bdvYtm1byetMRyNY1xHykb+UK3HCMXQzc0qQ0DUGH/0jvZefk3AhKzMTnY9BHEnMRnQrlrxWjwS1fEjPFJqbggRU+3SJGk5acioePf8kqPSSoEBgevzTpLEE4SaRPWcG4GQFJXZsggPWboZibyRueFqjXD0fEEJn+br/S+jo7UQmHkHKKK59DKUsDG8vIFI9ZKh8jzhdj52+EVBK4Ws5H+mG0T3BFAEumfUrpQjUiU67Ub1J4MS74gJQK+vEfOQvN2bj8ZugpZeBFOEDw8RHJtl19Z34lrWXTc6az0EcAO1nrmT4iVcQaaS2Yshr9U5Qy4fsTMFXUOCcWRJMDv2IxcN4DB+GbuZ8Hd30EOhdwmlsYUPDFnTeIYTOkt5P0LFsO278KIOv34BSVtYx1eoRH9djP4l0R9H0DgIz2OBKvZgI3FoAr28D/taL606nfaJXb4RSSuV6wLIsXnrpJU4//XTMMkk1CwX1YJ14PAAdz247N61neNde3OjxLHRq/xCx4XE0Qyd4Vh9oGsqVLL/srKLL4vOZmaYH2NixKeKhKYRQeIOtmMHjmfxcwda1bF6+7acpqVkSpb4HtUApm8LkdzS9JNjdspKTu87A5ynf6KSB6sKJH+VI/9VZPWQApSx613wPo0o94lrorE/EOFItNH7ZaagH68R82a3meSiVcStXJoIcAjPYnMq4iyVn1SIzTWeAGz4vxvIg0nbo3rKBjZ9+75zrTq554LHdiaxc0/AGmwn0dSXIVnVIUMuHfJnCbNaUjZLgwka+HjKApndUtUecqcfO9/cTN3Otd9RvrXCeMZf43pXOvK4nSf5KBpy129/J8svOwvB7cMIxlJQ0dbcSOLkr43lJclYhSAZOJxLPIK3t2/HLgp7vWjbRo6O4VrasIt/xuRjgmsdg7IXXi1vzeAQlFUpKYsPjhA8Mp44p5j2oJyjlEhq4gyP7ruZI/9Uc2Xc1oYE7UGlDGpJIBvoTNVBLaeHEjyKlNffBdYRkD3nmZ6qUu+i8vBuoLGr2S49PRAi/NkRgdXeCaVxj1Jt14szS9MzRlr+/4fvIWPYGolByVjnSqVIz8nIZ4OlrFsJA8+goVyKEIB6aQk0bt1TSXW0+kc+8AhLEo8YYyvxuXPXgtlUo8vWQ661H3EB9Yd5/8U7cYdfW2xl5ei9u2EIPmHRuWscF91+P4a3dDaherBPnCoS66aG5r5tlbzk1r8yqkPJvOYGzVDOTchngGWvWNMxgM7GhCRCgHBdpu2hQEXe1QhB3YkzGQrQ0BfEa2T3IYpDfvEInMvEEg+5pDE4erssxlJbjFjXIpBzMtaFZCJjp6V1vXtz1CmlZFTMnKgeHDh3iU5/6VIasa3h4mDvuuIObbrqpaued9+i4a+vtDD66G6EJhEdHxh0GH93Nrq23s+WBT873clKoF+vEQgNhuTKrUgNnMVO7ZhLW8lqdFrjJmLnmZAvACk2hUHjbmlh6yWlVkZqlw5EOT7z6AMOTb+C4cQzdS1fLSraccgVGid+TfOYVAJPhAxyJ/R6lByvOpShndKMrJTt27uGJ14ZSwXrL6sSIUF2r/CZi9g1N7d22ikW+HnIDmVCuy9DdOxK2v2Mh9PZgyvZ35gjbWqGrq6uqgRrmOVhHxyYY3vUqQsu0aBOaYOTpvcQnIjUtidfaOrGY0nS5MqtSA+fsU7vCGVO7cpXHy9lkZK1ZCAJ93fiXB+kqkKBWCTzx6gMMjPUjNIGmaUjlMDDWzxOvPsBbN15Z0mvmJx4pYq6G8rRk/LXcQQbFlJPTAzqQ+veOnft58OXD6JqgyaMTsR0efDlhp3jd5g1Fr2kuJL22QUsYd6StU7q1d9tqoDoYuntHaqCOMJuQ0QhjDyeqKUu3l1ZNmTl16+/+7u/4l3/5Fw4dOpRyNLvsssvYvXs3N998M7quY5omN998c+o1XNflc5/7HOvWreOyyy5LZdvve9/7OO+883j11cQUvTvvvJPm5ma+9KUv8dJLL9HZ2cnhw4e56667WLFiRcFrnpdgnZSbHPzt88SmptB0DU3T0bXjN1Y3Eif82hDes/rmY0k5UWumbSml6ZkuZMWglMA5W0ZuHZtM6abzVQXK3WTkXvOZc/bLKyVPizsxhiffyLnhHJ58I7XJKxb5zCsc1yairQKR4ztRBpeikHJyRkB3QrjOMZQC3ZPYWMSO9WBol6E4vl5dEzzRP8Q1m9ZVtCSulMv4yA+xo3uRMoYQBroniOHtQwhRdSZ1A7WBtCwmdz6ZlUELXWdy55N0bbumpJL4zKlbDz74IMFgkK997WtMTU3xgQ98gE2bNvGFL3yBW265hY0bN/KrX/2KW2+9lRtuuAHHcfjMZz7Dm9/8Zq688koOHTqUeu1wOMzll1/OF7/4RT796U/z+OOPY5omY2Nj/Nu//RuhUIhLL7206DXPSyRKDR/o8SJ8BiouU+zqZMDW/V4Cq7tne5l5Q63E95V09cqF2UhrhQayfBm5tB2EIMPgBPIT1krdZBS75krL0yZjIRw3jpajzOu4cSZjIZY09xb9upCbeNTa+g5eH28Hmc0IL5VLUWg5OT2gO/YAjj00PXskji299DXtIt4h2TX6pxmvMxq1CEWsomZ8z4XRwR2Ex3+JZrQhbQuQOPGEAsDwrsTfsimhFW70fxcVnNEQ7lgIYWZvgN3xUZzRUEm+/TOnbg0PD3PhhYmWUnNzM2vWrOHgwYMMDQ2xceNGAM4999yURemrr75Kc3MzkUi2BA/g1FNPBaCnpwfLsjh8+DBnnXUWAMFgkNWrVxe95qqzU9IlUVqzgef0VpSUgEBO34CUVHRuWlcXrPBaIhkIlTvt0ysl0oqjbKcs0pR0JS//7wd58kP/xFN/cQe7PnIne+54CDl9npkysbmQLiOTlo3hT6zbG2zJeXw1pFSFrrlcedpMtDQFMfJYghq6l5am0rO7JPGod+299K75Hr1r72VJ78dZ2rYGpWTGseVwKZL98ZyPTZeTMwO6xHVCCBJDQ1w7hKEpDN1gtf9FdJG5uezwmQT9lQuY6WvxmCdjeLpB6CAUjj2O0HxEJ3fNKXdrYOHB6Aiit+f+TeltHRgdpf3eklO3AA4ePMjPfvYzfve73wEwNTXFnj17WLFiBd3d3bzySoIf8uyzz9LX1wfAaaedxre//W3+67/+K/V4OmZO41q3bh3PP5+oMo6Pj3PgwIGi11z1zHqmJKr11j9h4nN/wH5xHBlzET6N7gtO4YL7C5tistixdvs7kVKy/77HiBweA8DX24GSEunKorNB6Uqe+vNvMvTkKyhHonl0rGAz9kQUKG0ISFFTu6hMVSAdhZa0qzHZy2s00dWyMtWzTkJJRVf7yrJZ4ZBNPKo0l6IQY450wltiApONmN7bK+UgcAj6TUJTk/j1SSadJQC4UrFlTXdFS+CZ5DuBx+zDY65ESZu49QbSGUfTfQuWHd5AfmimScuFm1M96ySU69Jy4eaSWeEzp27dc889fP/73+d//I//gWVZXH/99SxZsoQvf/nL3HzzzSil0HWdr3zlK6nXaGpq4n/9r//FZz/7Wb7xjW/Mer5LLrmExx9/nA9/+MN0dnbS1NSEx1PcvafqdqOudHhizwNZkig55aCOxLn4rVfha2+45qRjzx0Pceinv0e5Cs2jp1zLSrHRfOWfHuSP//hfkP4xK2jqbqXt1OVccN91FSNl7bnjoZyEtUrZfxZb0o4eHWXnR+7KyQGQls0F936spFJ8NdjghaCSOuvQwB05hju4NHe8h+Cy65DS4si+q6cDuiQWfh5S2aqGGTgLITT6Qy537/sbRiKKDp/JljWVZ4NnruU4lHKxIi/RFDiDmUVCofnpXXtvoyReY1QijmSwwcdH0ds66o4NPhf6+/t55ZVXuPzyyxkdHeW9730vjzzyCF5v/sE9M1H1zDqfJEoENE46/6xGoJ6BZDaoeQxIizGlZIOuZTP4yEvgSkgnRImE3MkKhSs6irLaU7uK1XdXiwNgaAZv3XhlRXXWhaCSXIq5jDlmEt50I5jqWRveIELoKOVy5urL+O55b62qzjrv5CgZQ9P85OrmFcsOL0fC1kB1IXSdpduvo2vbNXWhsy4FPT09fO1rX+O+++7DdV0+85nPFBWoYZ4IZrWWRC0kVGrOs2vZTLx8mPhkDGHo0zyB41C2i+HzVrQ8Xc2pXaWUtMvVdc8Fr9FUMpms1ijEmCM9oBveZQjhmWaDL0Fo/lRwF0KvKJlsJqS0aOn4U5RyiE4+ndpcNLe9jejkLpSMZj2nUHb4YnBEO1GgmWZJZLJ6gN/v5667EyBq/AAAIABJREFU7irrNeYlWNdaErWQUG42mDnVapKp/kGUSozUzCA9GBrdbzutKrrkQpjexUqpSt3EzOeM7oWI2Yw5cgV0YN4y0FyB1Ne8idYl/x3d04WmmYSEkbOcX+gs5sXgiNbAiYF5jZgn+jzSQlBuNpg51crE2+YnMjCGZhiIaVtOdI3uzRs45fp3V/tyslCqlKrUTcx8z+hejJgZ0OfLeCRXIA2P/xKhGalAWorPdvpIyMXkiNbA4kYjva1DlJoN5ioVJy054+MRWtYuxdPcxNK3ns76j7+7amMwZ0OpvuLlbmLKMY9ZSFgsvddCteDF+GzPzNQRPuzoHry+tUCm1KbhiNZAvaERrOsQpWaDOUvF05acTbE45/zjlbRuXF6zzDJv31nA0YdfoG/rlpxa+2TJfNW2S4BGSTsXFlvvNb9XusS1B3DiR/E29aX+mp7959uwZGXqykG6E9jW63jMvoyzNBzRGqg3NIJ1HWOubHBm33e2UrEZDNQ0UEOOzYRShF8fxgpNIWMOu7beQc+lx61D85XMz//ux3DGI/Ne0q6UZWk1sNh6r9lacIVtvY7rhEC5DL5+A4G2t2RsRmbbsCjlZGXqQujoniCOfQyPuRJSOvLCe94NzB/q9fd3yy23sG3bNnp7q0s2bQTrCmK+vkyz9X2ryX4uFzM3E+HXh1MjLjXTQDoyoyReask8F8r5bCptWVppLLZpVJAt17Kt19OkY12grKzNyGwbltbgB3Jm6oa3b9rtTEepWGO2dB2i3n9/n//85+flPI1gXQEkv0yDj+4mOjSBr7uVpZecWrUv02xBrNB+dy12qRl952mtNyLBVDeDzakNxtATL9O3dUtJ7mMzr6sSP/RKbhqy1lsBo5NcJWOlXJSyQUZr0nutRO88RR4bfwzHHkago3sTwzsgczMCzLphae3cmtO1TQiB6T+TZau/BTK84Hv9ixHV+P3FYjFuuOEGhoaG6Onp4dlnn+XrX/86t99+O0opwuEwt912Gx6Ph7/5m7+hp6eHQ4cOcfnll7N37152797NJZdcwqc+9Sm2bt3KTTfdxIMPPsihQ4c4duwYR44c4cYbb2TLli088sgjfPOb36S5uZm2tjZOOeUUPv7xjxe95kawrgD23vkQe771a+zRMMpxmdo3wNjLh1FScsrH31PRcxWiN56t313rXWpy03D04ReQMQfNNDCDzQT6ulLHxEcjhF8bKkqqle+6pJQc/cULJf/QM95vKZG2g+YxyrIsheOT6AYn9mPZUUyPj6WtCe8BTRT3OaSXjJVSOPEDuHYIpRw0rYnxkR8S7Ll+XnrXqVL0+OO4ziC6sRR/28Ul9c6T5LGW9ss50n81QmvOHt85TQRL/Dv3PHDpjoIM5zZWmS55G0Yr0FCq1BuqYRkM8MADD7BixQq++c1v0t/fz3vf+1727t3LV7/6VZYuXcqOHTv4xS9+wfve9z4OHjzId7/7XWKxGG9/+9t5/PHH8fl8vPWtb+VTn/pUxut6vV7uuecennrqKb773e9y4YUX8uUvf5kHHniAzs5OPv3pT5f8XjSCdZlwLZv+ex/DGplI6Jg1gZISa2SC/nsfY+1H31HRzLVQvXG+fnc1s8RCkCTP9W3dwq6tdyAdmfVD9Hb4CazuLkqqleu6Dv3091jD4/h6l2QcW8wPPT7t9BYbGEv01m0XzaNjBptpWtZRsgNcchKdEBqa0LDsKAeP7QZgY++FRb1WesnYib+BEx9OfBeFQDPasuRO1cTowJ2MDn4b6Y4l/MRFP1Z0NyhJsKf4bAJA9/age3pm9TJP/DuIklMoaSM0D8kedPKYUmReDdQWlTKJmon+/v7U1K01a9YQDAZZunQpt9xyC36/n8HBQc455xwATjrpJFpaWvB6vXR2dtLe3g5kD+sAUhO6li1bRjweJxQK0dzcTGdnJwBvfvObGRkZKXq9MA9TtxY7okdHiR4Zy/rghBBEj4wRPTpa0fMl+745H5vDNGWuXapr2XmeWXl4W/30XHpm1t+T/XVvqz9zAtmMx9ODbL7rUq4icngUZri3QeGTwLzBZuKhKWJDEyhXIjSBciWxoQnioYmSHOCSk+hAMBULcSx8hFD4CKHIAHsGfoftZm9Q5kLH0u0E2t+J60yAUCB0DE83HvPkVClYSqvo1y0GUlqMD9+H64yAchODP5SL64wwPnxfyedPbkZmTtJSysU/TQQTwgAk0anniEWeJxZ+Hts6gFJO2jHZU82Cy65bkGz5SsFxJoiFn8dxJmq9lJwo5343G9avX89zzz0HwBtvvMHo6Chf/OIX+cpXvsKtt95Kd3c3ybEZuYJyPsw8dsmSJYTDYUKhRPXnD3/4Q0nrhUZmXQHM9UEW/kEXgnL0xtXapZaKufrrhfbf812X5tERiETp2sz04S3mh66UQqEQaZ+lQpF7BM7cSE6ii8YniNpTCCGmf+SKsDXG7sNP8icr31bUawqh07bkQ0yN/gyBlpFZwvzohp34Uez4kYz3CUAgsONHsuRWxWCurHh0cAeuM4XhCSZaANLGtUfw+jZkZc6zubadKJAyzpF9W4lNPoOUYTQtQFPL+fSuvR9NK86zupqolmXwBz/4QT73uc9x5ZVX0tvbi2mavP/97+fKK6/E5/PR2dnJ0NBQ2evXNI0vfvGLXHvttbS0tCCl5OSTTy7ptRrBukz4etrxL+8gciiUGZcV+Fe04+tpr/g5SzVNmU3aZbT6cC0b17LnjXA2l568UL15vusSukZTbztCm5lxF/5DT5y3BRl3iYemUI6LMBJlcHNJS0kbHNPw4zVMRiORrJ24rumEwkdwpVM04UwzgujGkjnLxdWCIP/WdLbHCnrtWcxPkmx4TTPQzFUY3pXTJfjEhkUp54TOnnPhyL6thMcfQxPatOd7nPD4YxzZt5UV6x+o9fIyUA3L4N27d/PBD36QzZs3c+DAAZ577jluvPHGnMf+8Ic/BMA0TX7zm9+k/v7UU08BcP/99wNkkMbWrFmT+vsrr7zCD37wA7xeL5/5zGfo6Slto9gI1mVCNz2s+sjF7PvWr4mPhVG2i/DoeNsDrPrIW6oS+Eo1Tcm5S1WKqf1DaKaHZz56T01kEXPpyQt5vHPTOg795PfoTZ7UtSlXsubqtyA0reQfujfYTNOSZgyfF7WyM9WzFrqG4feUVIbTNYNgYDkDY69lbiQUmF4/ccfCciJFW/PmnU41T7rhRG+5Fyd+OGMTopTC8C5Hr0A2mysrnsmGT0i3EtffcCLLhuNMEJ18JovIqAmN6OQzOM7ENOGuPlANy+CTTjqJT33qU9x+++04jsPf//3fV2i12QgEAnzoQx+iqamJ5cuXc9lll5X0Oo1gXQGs/+t3oWkaA4/uJj4ygbezlWXT0q1qohQLzZm7VOvYBArw9XYghJh3wlm5SLLAh3ftJXYkhB2xMAJNtJ+xgqUXH5fPlfpDn7nB0dM2AuWU4U5dvpnXj71END6JVBJNaJheP81mB7rmwTRKm2JVSxKVppm0dX+EscFvI53R6YzWQPd00Nb9kaptFrINVNIeq2MnslpZwzrWaygZnq48ZELJCI71GoZx1rytp1BU0jK4q6srlflWG1dddRVXXXVV2a8jlMrdeavE0PATDfXqsJMLrmUTPTrGc397P67lZj1u+D1ccN91dX8de+54KLNSICWuFaf3vW9i4ydK28HOxHFZWHZ2Xk714eUjOzkYehmFQhOJ/rpSkhXBDUUzwrPWXKNAkJRuhccfQzrDaEZXltNYNRAauCNnRaG54z01c3DL9xnU2hrWcSY48IczUSoHkVF4WfUnL1Qss27EkcqhkVlXEAtpWIRuetBNg/h4rG4IZ8UiJwtc09B9TRzbtQd3+zsrstmo1uSuas55rxWJqpjBGrmQHuCg8HGctawozAzKcwXjWlvDGkYrTS3np3rWqetQkkDr+XVVAm/gOBrB+gRGubOzZ0O5VYZCnj/f7PZKb8YW85z3YjcL6QHOdY7h2iGEAN1YgmbMnXmWu0koBfmCMkoyNfZQzmDc3n1NXVjD9q69nyP7thKdfAYlIwjNT6A1wQZvoD6xOO4MDZSEasgiynVIK+b51dxszCcac94zfb0TU7WGp61obTxaU8GZ53xWFHJlyJOhnyGdIQzv8oxjk8G4pf3yWZ3W5osMp2leVqx/AMeZSPSozdWNjLrO0TBFOcGxdvs7WX7ZWRh+D9KyMfwell92VsnkuKSTmBOJZzik7dvxy6Keb0/GALAnY3mfn9xsFGKc0kBhkNLCiR+tuoHKzHMms02l3OmsWiAQiSlbyHkzdsm3vpnvSb7hKeBix48AEpAoaU3/OxGMFQmntVyoBRnOMFppCpy1IAJ1Lb6bheDVV1/l2Wefrfp5Gpn1CY5K9mPL9fF1LZvBx3cTfmMkQ9PsDTYz+PjunM+vhgbzREQtSU/p0iul7GkWeXJcpT1tH2rOuwxrtvck37xtITwIIB7rR7qTKb23bgTx+jZgeHtqKq9biKg1IW8uPPzww3R2dnLuuedW9TyNYL1IUWzPuBL92HJ7yPHQFGMvHMIanczwWY8Nj6NelDmfXy3y14mGWpKe0qVXQnhS1qEwHfy0xOc535nnbO9Je/c1eaZ46SA8OPaxaYZ/wnLVjg/i9W1A08yGR3mRqMZ3c//+/dx4440YhoGUkg996EP8+Mc/RtM0hoeHueKKK7jyyivZvXs3N998M7quY5omN998M1JKPvaxj9He3s7555/Pf/zHf+DxeDjttNP49a9/zTPPPIPjOFx66aV89KMfrdj7cEIE64UkqSoXtZyqNVcPWQ+YRI+O5v0c9ICJE4nl9Fl3wjH0QP6sYyEx8esNtZ6HPdPMRfcEUz1rwwiScCGb38yzkPckd4Zso+md6IadmnwmhIHH24lSAiktNM2ckwxXK+ldvaFa382dO3dy5pln8rd/+7f87ne/o7+/n8HBQf7zP/8TKSXve9/7ePe7380XvvAFbrnlFjZu3MivfvUrbr31Vm644QaGh4f50Y9+hNfrRSlFZ2cnZ555Jp/85Cf553/+Z7q7u/n3f//3Sr0NwCIP1snANfDYbqyhCczuVpa9pXpzpusBtZyqlZew5rgo5eG3H7171g2EG7bw+E0sy8mybvUETNywBa2lmYWcSCh2czqzpJuchS2EBzVPpef0bFP39ADeBBvcs2SaqTy/mWe+MjccJ4LlypB9/jMIq0fRNF+G7akQOkqOZbyXuchw9V7ynW8U8jmU8t384Ac/yN13380111xDS0sLF110EWeffTZeb8IXfd26dbzxxhsMDQ2lJmmde+653HbbbQCsWLEidWw6vvrVr3LbbbcxMjLCli1bil7XbFjUwXrPnQ+lbECl7aLtG2Bi92GklGyo8JzpekC1Zr8Wg1w9ZKU82JMxNEOfdQPhDTbTdubJTLx8CCs0lbJuNYPNtG5cXjN2tzMxgXXgNcy+1Rit9UvEKbWqkixDSzecMQs74T62DKG3lbaeIrLDXNIrKFxnXWkU4oqWb82xyIvTJX09I8AWUsYvpOR7ImXd1XKn+/Wvf82b3vQmrr/+en7605/y9a9/nfb2dlzXJR6Ps2/fPk4++WS6u7t55ZVX2LBhA88++yx9fX2Jc6fZBAshkFISj8f5xS9+wde//nUALrvsMi6//HKWL1+eawlFY9EGa9ey2X/fY1gjkyBIjTa0RibZf99jrKvwnOl6QD1M1ZrZQ9YDJr/96LfRjBllrBwbCN30sPTiDThTMQIrOxPTsjwGSsHSi+ef3S3jcfr/civhZ59BRsJo/gCBc89nzXfvR8uxq64kXOkUrb0utqqSftP3t25mdGAHrj0yPQVMQ+GilMX48PeK6g2Wkx3OzDZr5eldjM/6zDWXSiCbq+Tb1rWN8eHvnVBZd7X87k8//XQ++9nPctdddyGlZOvWrfzHf/wH1157LWNjY3zsYx8jGAzy5S9/mZtvvhmlFLqu85WvfCXna/3jP/4ja9asoa2tLeUDftFFF9Hb21vytc/Eog3W0aNjiVnGM0f9CIgcHiN6dIzmvq6arK1aqCfdcbKHHD06WtQGIjMzdzGazZqxu/v/ciuTTzyG0DSE4UHF40w+8Rj9f7mVdf+nOpOJpJK8evRpBif2Y9lRTI+Ppa0JV7OZgxfSUUxVJVcwbWo+D03zIjUjVbo1jCAe8+Sie4PFEoLqNVMslQiW63n+5k20dPxpqmedC3OVfEeP/hORyZ01cz6rFapByFu5ciU/+MEPUv9/5plneOGFF/jGN76Rcdypp57K97///aznJydxAVxyySVccsklAGzatInrr7++5HXNhkUbrEGRiNT5hg6XOIy4jjGXyQkwK8GrGih2A1Ev7G5nYoLws89kjdcUmkb42WdwJiaqUhJ/9ejTHAq9ghAaumbguDaHQq8AzOoXXkxVJVcwDY/+DOlGaQqcNS2VOj4Pu5jeYDGEoHrvz5bqiiaETnv3NTS3Xw7KZXLsp0QnnyY8/uCs1zhbyVdorcTCzxdFtKrXTVCxqIU7XT1i0QZrX08Hvt4OIoePZY3r8y/vWLTM4Vw9466LTkFKya6P3DnvDPFSXdJqze62DryGjIQRRvb6ZCSCdeA1jDMrO5nIlQ4D4/tTGuMkhNAYGN/P+mXn5S2JF7opyhtMtaZUkBAzboTF9AaLIQSVKsmZ7yA0lyta+nqEMDI2IK59DCktPOaqOa9xtpKvr/ksopOPFfS+1vsmqFRU053u/PPP5/zzz6/Ka1cKizZY66aHNVdfzJ5v/Rp7NHzcYKMjwJqrqzNnuh6QKzPtv+c3HKkRQxwWpnGJ2bcazR9AxbODn+b3Y/atrvg5LSdC3InmDMhxJzbrjOtCN0X5zTx0hB5Auhaa7jv+/CJ7g4USgkqR5NRbEMq1HpBIdwohDITw4tiDKOkghMBjrprzGvOVfNu6tnF0mrg2EzM3U7UeFNJAdbBogzXAur9+F0LTGHz0j8SGJ2nqamHpJafVdZCoFJKZaT0wxOultF0MjNZWAueen+pZJ6GkpPnc86tSAjcNP6bHh+PaWY95jaY5Z1wXsimaLZh6fWfgb7mA6NTTRfUGZ2a6hRCCSpHk1FsQmrke6U5ihZ9D93TiMftQ0p7u/2u4dgjDuzL1nuS7xtlKvgW9rzXWzDdQPSzqYL0Qg0SlMd8M8dk0vrUubReLNd+9P40NHkHz+2meZoNXA7pmsLR1VapnnYRSkmVtq+Zkhc/1fU8GVV/LJsJjv8y66Te3byG47Dqk3F5QmTlfptvefS0wOyEo8drtSHc81R9P6rs1vS2r7F7pIDRXKb2Qx2euJ2GV6uI6ITzmSoTmQQgPKBelnOnAnTh+rtZCrpJvIUSraumSG6g9FnWwTmKhBYlKYr4Y4nbE4tV/epDR5w4QH4/Oa1+8WtC8Xtb9nwfmVWe9dumbiTsxQuEjxB2rpBnXM7/v2UG1A00PoJRAybGsm36hvcG5Mt18hCClXMaG7iEe68eOH0z5aSces/GYJzE2dE9GebtSQWiuUnqhpfZc60lapab7metGEMcemi6LH2fjlyI7KoRoVS1dcgO1xwkRrE9kVGMMZjqSRhz933uU8BsjaB4Db7AZzTTmtS9eTRitrRUjk+XTT8+UbHkNk2Vtqzh1+WY8enma7uygGk0EjLZ30tb5IdACIMPTRiiF9X4LzXRzBdDkegxvLxDHtg4iZRRN82OYJ2F4e7PK25UKQnNtMAottedaT8oq1Q6l/Mw95skoJRHCBByE1lK27Gi2zVS1dMkN1B6NYH0CoJoEr307fsnhn/6e6MAYQtdSgzcAmld1V6QvXilvd2lZOKMhjI4gmjm/N6259NMzJVuudBmaOIDXaJpVsjXneWcLqpO7AKZ71MURtkrNdGeux/CuxLWPoTQz4aFtrgREdtCvQBCaa4PR2rm14FJ7vvXonpPw+jYCWqpU3bFsO21d21DueEUZ7DNL9cn/t3VtAxqDQhYbGsH6BEC1evdJ8pqSEmm7CC1RzBRCJEZcruwsqy9eqaEkynUZunsHkzufxB0LobcHablwM93Xbkfo88Mink0/vX7ZeSVLtpLIm7HPElTj0RcTN3vdVzRhq9RMN9uHPNHnFUJL9HWny8eQHfTLNceYa4PhWK8VtQHpWLodpRzC44+gZATdWEJL++bU37NK1XplfO1ztTVATbc1RlObrp413ylog7BY9NiLHY1gXUXU27SvSvXuk9flWk6CvOY10Dw6ypWpY5TjIm0XM1h6X7xSQ0mG7t7B2MM/R+g6wmxCRiOMPZwISku3V59FPJd+ekVwQ8mSrbky9nxBVSkX6YbRmjJvzoUStkrNdGeuJ30kZvo4TMgO+uWaY8y1wTDM1QVvQJIBMzr5dCJQa358zZtSVQkh9KoRuWaW6uPRl7Hjg3i83XjMVQVvuupNCtfA7FiYzJ86h3Qle+54iJ1X3c4TH/4mO6+6nT13PIRMC2YLEcnr2vWRO9n5kbt47m//D/HQJAiBGWzOMIUTho7QRcl98bkkZ66VLW/KuWbLYnLnk1kZtNB1Jnc+ibSsotdWLJL66VyIOzFAYHp8OR+fS7KVzNgd187I2F89+jRwPKgq5WY8T8kYmuYn1y0gmUXOhY6l22nueA9C86OUhdD8NHe8Z9ZMd+Z6kn1eqST69DhMSAQSf56gr2kmhren6Cww73sxfS7DaJ318fTzJQOmkhE0zYdCER7/JaODO4paU7HILuVLXCeENi0PS39fIxNPImX+73f6NaRXVqp9DQ2UhkZmXQXsvfOhDDOWqX0DjL18GCUlpyzgaV8zM13XcnBiNlP7h2he1Q2AFZpCxh18vR2seO85JffFKyU5c0ZDuGMhhJld2nTHR3FGQ3iXVVfKMpd+2u9tKUmyVajjWa7ycXPb24hO7kLJ7E1EoYStUjPdmevxNJ063ecF6Y5Xtcc6Vym9IHlUjoCZtGittpY5q42Q1HKjZcnDiuEOJNHQY9cvGsG6wnAtm/57H8MamUjYnGoCJSXWyAT99z7G2gU67Stfptu8qpvokRBaU6LE3nJKL8Gz+zjlk5fh8Zf+Y6+U5MzoCKK3B5HR7NKm3taB0VF9KUsh+umkNGtgfD9xJ1aQZKtQx7N8QTUkjIqwhou1gcy3nvnonc61wShkA3I8YJrY1uu4Tig1/ETTWnHtYTRzRVXWn9VGSNNyp8vDoDjuQMZjDT12XaIRrCuM6NFRokfGSPcjhwTpKnpkjOjRUXw9HXXVyy4E+TJdIQRNna2c89Ur0U1Pxa6pUpIzzTRpuXBzqmd9/HVcWi7cXBAr3HJcQhGLoN/ENErr5c0VjDWhsbH3QtYvO6/g0ZjFOp7NDKrVmGZUDLLGYVbR+3mucxfzeDJgxqO7ExpqBAINlIt0x5gI/YglPZ+o2rozuQIauhGc7ll3Tv9NIl2L5ta3FcwdyHisoceuSzSCdcUxcybncSil2P/PTzC++9C8D9QoF3Nlur6ejopvPColOeu+NhF8Jnc+iTs+it7WkWKDzwZXSnbs3MMTrw2lgvWW1d1sv3A9ulbc51VoMNY1Iy+ZLNex5TieNaYZlQZNM/E1byIy8QSCzCFBhreT6OTTyKXbq/ZeztxkeX0b8Po2oJQiHvkjUobRdD+RyV2IASP3hK+GHnvBQSilcs6KtCyLl156idNPPx1znjWpCxmuZfPwRX9P5FAoM24r0Eyd9lNPQniO30SVK1l+2Vk1sUQtlq2+546Hcma6yy87q6rGJ7XSWd/x5Cs8+PJhdO34B+lKxWUbl3Pd5g0lr6MYzJXVJ9nguTL22eZfLybUQnpkW4d44+VLkc7EtJmMge4JYnj7gDi9a76HUeUqwczrPnb0fzMV+glCa0oFYKVcmjvek5MVnskGn567XWE2eCOOVA6NzLrC0E0Pqz5yMfu+9WviY2GU7SI8Op5WP7rPkxGoAYQm6P/eIww+unvebDpL1S/XanpWpSRnmmkWRCaTlkVkeJidrxzKyqB1TfBE/xDXbFpXckm8EBSa1ZdSPi8U9a6/nU/p0cz3Qvd0Yfr/BOlOpvrVyXMKbX7KyOmleiktopNPo+mBjGNmI4w1KisLC41gXQWs/+t3oWkaA4/uJj4ygbezleDZfQw+ujvr2PDrw0QHxvAGW+dtfGWp+uXFPhgl3TwlMjzC2yYcDq89k/+3+b0o7fjNfzRqEYpY9LRWxuQiF3bs3JPK6ps8OhHb4cGXDwPkzOqLKZ/PhYWivy3UGrScoR2zvRfJMrKmNWUcX4sycjmEsfnkCjRQOhrBugrIFdQAxl54I7PnKyVWaArNkzAVSaKa4ysrMTJzIQxGKSUrTDdP8fp9BEZDrN39WwB+d/Gfpo7r8JkEy2C6zwXLcXmifyij/A7zl9XX2yjKXChEeiSEUfbQjtnei1oT9NLRIIwtfpwYTa0aIRnUdNOTYjenu3xJ28GNO3iDzVnBM6klrgRcyyZ6dDTV+7VC2T/oSp+zVlDKJTRwB0f2Xc2R/qs5su9qQgN3ZBldzMRM8xRNEwQDJlJorOx/Ed1JbLJcqdiypruqwTIUsQhFc5tZJLP6amGuIDibycZ8IplJ5nxsOpOcy/Rjrsfnei+Ucgguu47etffSu+Z79K69l+Cy62pSfZjL8KVR3l74aGTW84iZPV+j1U/gpE58vdlZaiXGV+bqTXduWoe3I4Abre7IzEqjUPlUqVlhLvOUkzsS/b/x8TDa+Djm0h62rEn0jauJoD+RuUdsJ+uxamf1ucupCdMPKWN1o79NZpK5esaa3gFaoOyhHYWWlitdRi6VK1BPmX4DlUcjWM8jcpXH++/5TaJfrKdJQCo0vjJXb/roL1/EaDZRrqzKyMy5UCyzuxj5VDmuTLnMU4QQ9AWboaeTi7ZdypKO1qpm1EmYhs6W1d05mejVzuozy6lqhulHE+PHfkhw2fU1710n/cSt8POui2liAAAgAElEQVQZbGzdcxIt7ZtBhssf2jHPpeVyuQINwtjiRiNY1wDpPd9qMaxn602DYtmlZ3Bs1555Y3WXykAvhmhVFslmFvOU9s0Xs7Rrfnv0yez9if4hRqMWHT5zXrL6dP2tEz+YMv1ACXRPK+GxXyKEUZPedXrGOTZ0D9KdQvcsSW0mHDuE17cxNfWq3KEdhWqRK8WarxRXoEEYW5xYkME6PhEh/NoQgdXdeKvIyJ0PVIthPZu3tj0Wpe9DF7B++zvnjdVdCgO9WKJVuZlQMeYp1Z6opmsa123ewDWb1pXtnlYsOpZuR0mH0NFvgFIgdAxvQkMshJh37+isjFNrJx7rx/D24jH78JgrU97cTHtkzxVok0M75grEs5WWK8mab3h1NzAXFlSwduIOu7bezsjTe3HDFnrApHPTOi64/3oM74K6lCxUmmHtDTbjaffhjEfQPAaklYyTvelyz1lowCqVgZ4kWjV5sm98ueRT5boyCV1n6fbr6Np2TV7zlErN2C4UpqFXVSKWC0LotHV+iKmxnwJaRj8YyveOLjYTnZlxSnccO34QiOMxVyXWmGMGdiWGdgih0959Dc3tlyMAPW3aV2jgjjkz4UKvteHV3cBcWFARbtfW2xl8dDdCEwiPjow7DD66m11bb2fLA5+s9fLqBtKV9N/zG6b6hwgfHEH3GpjBZgInd6GkKrs3XWzAKnWCVilEq0qQbGYzT6nUjO16h2YE0Y3OivZrS8lEc2WcQvOgaV5cO4ThXZnxWPrayh3aMdt6lXJmzYTburYxPvy9wq9VC4DwTfffZ1iDNqRXDbCAgnV8IsLI03sRM0qiQhOMPL2X+ERkwZfEK4VkQPH1diDjDvHQFNGBMTSvzpptby27N11swCp1glaxRKsUY7xze1VINpXQqC8UFFOlKDR7LKUnmzvjTAyvcOIDGSMh81VQSh3aMdt6W4MfmDUTHj36T0Qmd855rekbAju6B+lOpGxLhRANr+4GUlgwwTr82hBu2ELkKIm6kTjh14bwntU3/wurM2QEFCnx97YTWBFEugpvWxNrrnlbWeXaUgJWORO0CiFazcYY16bL/5WYnFWpGdsLBXNVKYrJlEvtyebjIXjMkxMjKfU2lJyouExprvW2dm7Ny48QWiux8PMFXWv6hsDrW4ttvY5jH0MpF9N/ZkN61UAKCyZYB1Z3owdMZDy7JKr7vQRWd9dgVfWHREAJExsYwwpNIW0XzaNjBpuBjrIDSqkBq1TWeyFEq9kY49svXF+xyVmVmrG9UDBXmbiYTLnUnmz+DF/S1r2tajKludaLDOetPPiazyI6+dic15q9IRApwhzoLFv9LQyjMhayDSx8LJhg7W3107lpXapnnYSSKmH00SiBA4mAEg9NERuaAJFoEyhXEhuaQPPqZQeUUgNWuaz3dKJVOrHN0bVZGeOOq3h4z5G80q9iWN2VmrG90JCrTFxspjwbU19orYlSurRyBtzZMnwh9KoQrwpRFuRbV1vXNo5GXpyz359/Q6ChVAxkGGgE6wYSWDDBGuCC+68/zgaPxNH93hQbvIHjUEqhUJmzdlHkHoZaHMoNWOUw0HMR28w3r2bU78VMO69mu3jDFqOGzm/2ObkD+d4BLvn9fkZ37lkQk8fqDcVmyrkyZKUUtrUfTTMZ2P9XecvotTD7KLRnn29dhTy34efdQDFYUMHa8BpseeCTi0pnXWkkssQWZNwlHppCOS7CSJTBzSUtFemrVipgFatVzkVss37zR87ubGH3WzYipGTNoy/Tte8o3nAc2+/l8Opu3nj76agZJe/uh/7AwbEwTV5PY/JYgcggkhURaJLPa+vaBhzPRJ34CACGtxcQcxLO5tvso1BlQa51FfLccqWGDZxYWFDBOglvq79BJssDb7CZpiXNGD4vamVnqmctdA3D76lIX7XcgFWKVjkfsU03dE45HOKPlsPap16l56WDoAlcQ2Opx8D7ylE8us6+t52GVArblZhSseLAMN6uzBLjYps8VinkI5L5Wy5kauyhvIEm3/N61nwHaQ8x+PoNKJU5GKRcE5BKzuAuJ6Mv9LkNP+8GCsWCDNYN5MfMMrU+Hdyq0VctNWCV5GZ27Bh2fBCht4J7/GsrlWIpcOmSZqL9g0jAIwTBQBN9wQBCCGJ7jvLI6SsYjrvYrqQjHOOtYQstByexEFa3Kx0sJ4Jp+NG1xf8TykckC7S/i+aO9+QNNHNKn+Q4Qngz3MegNBOQas7gLiejn+u5DT/vBgrF4r/T1DGqZVmZq0zduWk9y//0zbiWXdOybbHSr+M34ccJXvECMtxE/I1VRJ87jwPHooQiFlFdsO/QKBdLSc/yDry6jiYSfeq+YIDRA8NEhiexm5swNEHTkhYiPg8HQuHEoA4pkbaD5jFmJclJJXn16NMMTuzHsqOYHh9LW1dxSs8mNLE4p81mE8lkKrhGJ3fSu/benIEm+bzEv2MpF7Rk5tyy5M9x7WM49mBqapZuBPGYJ5fUr10IM7hnQ8PPu4G50AjWNUC1LSvTy9TW8ASv/+gZRnbt5fCDz6fGZJ70wfNp6mqd98BdrPQr/SZstnUQs8Yx175CKGIx9PBpCAXHNp7EaMDLUUBMxOgLNqNcibRdMDRsv8n6tcuwNIFH19A1wbF1PfheOkjneIT4aELiJgyN7s0bEHl02K8efZpDoVcQQkPXDBzX5lDoFQA29l5Ylfer1jhOJDNnTODyoGmtuPYwHnNFVqBx7WGsyB+QzkTGVCzD24dyRxkfvAspLZR0EEID5eLYQygl6Vi2vajssuGr3cCJgMWZDtQ5kmVgJxLPKAPv2/HLip5HNz0c+vHvGHj4RZxIHM00GPvjIXZ/9Sc88s5b2PWRO9lzx0NIV1b0vLMhKf3K+diMrHbmTThwcidN3W2JwH3SfpQfjp5+Ev2XbER6dEbW9RCajDK5f4jR5w8w9ocDhJ4/gG27aB4NPxCYiKLZLv2XbCRq6ESPTaJsF83QMYMt2JOxnJ+DKx0GxvcnAksahNAYGN+PK7P1/4sBSSJZwqxjCJSLIBFcpTvGROhHOZ83cexHuM4EIKffM4kTH8aJH0iZhnjMVRjeLhJSJYlARwgzRUQrFMkNRc7HpkvqDTSw0NHIrOcZ82lZOfNc4QPDxIbHEUIQn4ziTMXm3de6GOlXljxIga+nHX1ZG68fGeSlv3wT46ILzXYxJyLsv2g9S145QuxICE0qhEfH19GMz3HY9O1fgybwhuPEA15GVi3FY2gEz+oDR6ZIeEDOz8FyIsSdaM4eddyJYTkR/N7SNLHVaIfkI1oVS8DSNBNf8yYiE09kSgGVwvB2Ep18Grl0e9Y5olNPY3iWHB+xSWI+uGsfozl4OrGppxCiaTpgr0xl6+Cg3HHQC1d5NCRQDZwIaATrecZ8Wlamn0u5knhoCjHdy1W2m+jTmt6qbBJmCz6FSr9SN2E3TPj1EeKhKVzbQfPoeFyTiGxm7WN/PC7V8nloCU3ScXYfmitT08Y6/3iQ+LERxk7uQnp0jLhL7wuv0xWJoy9thxll71yfg2n4MT0+HNfOuh6v0YRpFC8hLKUdMlewzUe0au++lrGhu0siYLUu+e+MDX0ro6SdHJmZiwyW3GR5zJMBskrnbZ1XEY++nAquyV42gNBaig6uDQlUAycCGsF6njGflpXp55K2i3JcmDYIER49Ecyo3Cah0OBTqPQreRMeev5+YkOTIARC01DShRe6eNMjT+KxE9ckPTqeaJy2sQixgyECKztx4y5ClzTH4kQ0DUMqLMCjawQ7AjRPWbiOizP9tyQpLdfnoGsGS1tXpXrWSSglWda2qiRWeDGs+ELZzvmIVpGJx5FuuCQClu7pwvT/CdKdTAXd48E1O3NNz3RnzpsWWjMec3nFg2tDAtXAYkejZz3PSJaB1Yw+cbWkVclzaR79OHFKkfAKnzYKqdQmodhefFL6Nds1t7b/TyZ/vwLleEFzUbYHa98Gml7ZwkmHjqELgZQKXQiWtPnx+b1EDh5L9axHn38dezKKz+fhjJVLOGt5kLOWB+lb0sKULnjx9RGePxzi+cMhDoSmUK6b93M4pWcTK4Ib0DUPrnTRNQ8rghs4pWdT0e/VXO0Q18rM4JNBWMlIRrAdHdyROiYf0QogOvlMqqqSOtc0AUtKK+v4dCQ3TYl/N2VMufLnmXLlb92MUm7yLwjNRCmVOr5j6XaaO96D0PwoZSE0P80d7yk5uCYlUL1r76V3zffoXXsvwWXXlS3baqCBekEjs64B5tOyMv1cnjY/8fEwTUtaCJzcBVRuk1CtXrw9GmXi0XPQA29Ca4ogY35wDVQ8js9RnNHVmsiqpzPj0IERnKiFZhoJD3mlEkxvBLquk7x1HwhNcaSzhWNrltH52iAqEueIruGcsoa35PkcNKGxsfdC1i87r2yddTHtkELZzvksQJWyUTI8nd1mBtZCNc3FZq5zHV8tfXFDAtXAYkUjWFcBrmUTPToGqJyZ43xaVuaScR3btafim4RyevGz9bgzSvnh4wQurQnM5TaGD8R0CE5UKxQev4lmaChHIjw65pIWlFSpkreuCUJTMUZOW8G+t51Gv30q3rBFPGDS5DPZqhS5wkZ6v7hUMlmu68p6bCYrvkAf7nxEq0SvODBtPJKJQglYxQbXQo9vBNfKoJLObQ3UJxrBuoKQrmTPnQ+x/77HiRweBcDX28Gaqy9m3V+/K4s0NJ+Wlbrpwb9iCRs/cRnu9ndWfJNQSi++kB53FntcSHxnP4N3xWt0tFjIyeeJH1pN9LnzkbaLjLs09bTT3NeNcl00j4EScPClg+wfGENZNm7A5I8ndzL+lo2JdXh0Yu0Jgtho1CIUsVITvqA67ljp16U0ge1KPLqGkCqr0lEo2zkf0QqgqeX86Z718b+V0iMuNrg2gnF1UU3ntgbqC/pNN910U64HXNdlaGiI7u5uDKMR0wvB3rseZu+OXxEbGCepcrEnIoy/fARNwJLz1tZ2gdPQDB1Piw8tj/lHqa8ZGxxnYs/RzBGmrqTnHWfQdcH6rOfsvevhRLByJZqhI22XiT1HccYjGe9Vx5tW44xHiB4N4T31cfwb9tK0tIXW9SeBstH8h5HaFFN/aMEaCyNjDtbIBMqVeNsDvD4a4bDp4Zmr38LAmSfz2rlr2NniI64U7T5vxppaTS9//qZVGGmDP0YH75omY7kIYYCyiUf3IN1xfM3nlfyetZ7dxyPPHaD/1SMMDY1zxJHEzu7jks/9acomFkAIA8ceJB7dM4Pc5hJofzv+lgtSf2sKvAnpjuPEj6LkFJreSqD97XSd9GWknMj6e+Km3qCuLFQc/27aoCTgEo/uLfu7WSk04kjl0Hj3KgTXshl4dDfxsTBpclSEENijYQYf/WNF5VH1iGJ68cX0uJOl/FV/eRFH9z6OMNakWO2Bvi4CKzsx/KNEn29G2pLY8HhqhrdSENI1Rk5bgeP34vi9CCAYMDk2ZbGyI5BigbtSsWVNN2baJqaa7ljffmYfD25cjmd9T6oMb+sa1jP7uG7zhoxjC+0Zz1Z+bnhQLy4kvpuP48QPZsjjdCNIZOLxhnPbIkMjWFcI8dAU1vBEgsw0Y36yclxiw5MV1VDXI4rpxZfS4xb6JHjCkKN3a1sjGC1xAn1dqddXjktsLMyBC9bx+iUbM47vCwZwpUQXgpjt0uEz2bKmm+0XZlYAip3bXCgsx+WJ/iF0TSC142V4HXiif4hrNq3L2DQU2zPOV35ulKUXD6QTwgq/iOuOIhApZznHHkJJp+TvZgP1iUawrhC8wWbM7la0fQNZsixh6DR1tVRUQ13PKKQXX0qPO1/vVtoO7oQXGfMjhKB5VXdqPKiUkvHNG7LmWQshOLMnyLev2EQ47hD0mxnBca5zQnnuWKGIRShq0eTJPmeuvnnqnI1g20ASWiAh5WOGJA+BlGHQctv6NrAw0WhWVQj6/2/v3nrjurIDj//3PqfOKVbxWpJ4k9rW/WIrjudhbI3casvdETrtIG9B+sERLCMdR2PHQAODwQDzEQYYIEGSjrpngIFH8QAezEweBjHSMBJYlu1I6Aa60+1uXZqSLI8lSqRUvNbl1Dln73kgi2KRRVIkq1QXrh/glyLLtUVTXmftvdbafoLBl5/B602DffS6tZZEX5qBk8+29Rb4em2k33x5/+4c5Sri8YMVV2eWB410bO/khaNfIza24j1hZHhuZy++6zDUnaoaqFf7zJV6jB9XJuWTSVV/b1/Hyl8T1RkTEJVG1+wZbysmh3JSWFv5u22tRTtpMLkGLUzUg2TWNbT/7CmMMdx69wL5O5MApHb2se/My3XpoW51G+k3r3p22/t1Cj17mI1/WXXe+NmTz4DncvHGGNl8kYf5EkrBRyP3+cXdSU7sndv+dnT1Z9d6TMfyXYcTe/v54ModnEXHJtXOzcXKytXQuakLmGgM7faT7nl5S1RDazeD1/Ecofo1cZitGAWbSD4jM9HbjLJLH8vmBUHA559/ztGjR/F9ecpfj7X6rEWljVxksbSv9FEb2PLAX24DC6KY//zRr7h4c4zEoqAeG8urR3YuK+pa6zM3KzaGc59d5+KNMSYKQcW5+UoPDqLSw9G/ZPL+jzDRxEKw0m4fvQNvsm3onUYvr+4ejv4Fs9n/C8pjrlth7u9PuvcUPdv+sOGFhBJHakeCtWgLsYkIojxunCCeLFYN/EEU8/p7n5KPll9nmXJd3n3tpYZktEEUk80HK56bi+qMCfjily8Rle5UjFKduxFsJ7t/69O2rYZ+1F99kSD3i7mzaydNInl0foqwmt8FamzftcSR2pFtcNFwmwlWxhqujV7i/vQtgrCAn+hgoHsPh7zl87o3WtRVb+Vzc7E+cWmUOLxbZea5Ig7vEpdG0cndjVlcnS2+sMXrOAAYTBygMJi4sKELW0Rzk2AtGmZhG/jm2EKwXuv8eKlro5cWbsJytEsUh3yVvQrAkeHjFd9bLurKh8szaynqaj12/h+1ytfaUfXef412fIq5n+CnjlZ8v1IOuakLdPb+Hq431La7De1ODsZEw5z77DofXLlDPoxIJhzyYcQHV+5w7rPrj/X+2ETcm7q1bAKXUpp7U7eITWVQLhd1La0M32xRVxDFfJGd5YvsDEEUr/2GNhWbiHxpetnPvV5cb4iEN4xdEpYtloQ3jNumLW7l3v+lrJm/sMU+urHNWksY3KI48wmjN85wd+QM2Xt/vay7QTQ/yazFY6vl2erioSCLOVpVHQpS/d+RpxQVqt58VYqKBFF+2YUb5aEn1Yq61is2hh98eo13f3KDO1N5QDHc3cGZF/bx1kuHtkyR2IpHEUPH0HUcZaq1T8+O15m4/yNMPPlogpfTS8+O19s2g1zxwhadQOn0QpEZQFT6gqg0jtIu2umSbfEWJsF6CykXYa33asdabFcvVYvzY99N4Sc6iOJw2dc8N4nvLn+/ozVvf/0w3zt2YNMPHuc+u84P//k6D2aDhXPTO1N5fvjZdbRSa1aXt4v1HEXUWt/gW6A0+akLxNE4jruD1HzrVruqfmHL3Jl1Mv2vsbYAzF2NGpcegLI4bobyRmotRuWKJ0+C9Raw2cynvF3taFWxXQ1sOCDV4vzY0S4D3XsWAkWZtYbBnj2rPpBstqgriGI+unGfyXxYUeCklGKiUOKj39x/rN2BVrfWUcTBwRc2fOf346jXvdjN7lHv/8cEuc8xJod2UiScPpROEQU3KAVfYeNplO5g6Qn/ZkblisbYGvt0W1w584nisCLzuTZ6ac33rrVdvdEz2lqdHx8aOsauzGEcBURZHAW7Moc5NLS8GryWsvmA8dkioTHLvhYZy3iuQDbfmGlacRCSv3uPYPbLuk/0Kh9FVFM+ingStPa3VPFU+SGlo+s4rjeEnzqKlzyAokipcIU4LuKnnsNxt6F1kigcJwxuL7x/M6NyRWNIZt3mNpv51LPdqRbnxwrLgP4ZXc5FQvuAhLOdlM6heHFDa3pcmZRPf2eSkQczyx44XK3Yke544tXlJjb85tw/MD3133B2/AanO8DvGmDH839AZujf1qXPdiNHEaI2jAkozFyaGy06z9oYE02AUjjOXkwiM3dmrRRxlCXhP4W1dt33mIvGk2Dd5jZShPXovTFBFNPb4VGskkH3dfikPZfR6fyGzn5rcX68uN/UddJYW1hXAc1Gi+Z81+HlfQP8+v5kxZm1tZa+lM/JAwNPfAt85NyHTI79CP/AdbAaW3IoPhhn/Bd/i9KqLgVFmzmKEJtT7UY4a0OsnTtasibE9XYDzI0jNQHg0Nl3qq3P9NuV/E1qcxvJfJYWlD3MBQRxzJ5MF+Xj2Sg2WNfy5v+8tOmis42eH2/mrulaFM2dPX4QY+2janALO3tSnHlh34aqyzcjDkLGPvsl6W/dBrto/UpRyubITVyoW0HRoaFjYGZ5MPFT8qYLz8sw2LOn7kcRW121qnClEijlglIonQAUCX8PrvcUKJeh/f8d163+cC6amwTrNreRzGdpQdlwTwe3Hs4yOp1nW8qnu8MDLNPFkISrN110ttHsdjN3TdeiaM7RmndOHOHNf3OQ0ekCYFe9waueStlZwsJDVLJQcfsYgAljomK2LgVFxpS4O3KaxMxl+k0OpTro8I+xc/Bv69q2JapXhSvloN3y9bSVP//O3lMSqFtYywfrWl+u0I7KGc69qVuUoiKem1wx86lWUKaUYu/2Ljyl+e2dffzszgQ/uf0Qx9FkUh5P93Wi1Oo90tUC8maz243eNV2LHu/FfNdhd4PvKvcynSQ6tmOLHahE5S6KTji4yUxdCorujpwmN3UBrTRaJYCI/MzH3B05za6D79f880SlajfC9Q68iQLyM5/V7JY40XgtG6wfDbL/BBNnGz6wvplppTkyfJyDgy+s2We9WkHZr8cmmQpKKKUwFjCWsZkiwEKwWlp0tlpA3mx2W73fdO53Y7UCmmadEb4Zjp+g//hRJm4/vXBmDYC1eJlO0n0v1/xhNoqmKcxcXpZBa6UpzFwmiqYlk6uz1VrXegfelESmjbTsPlW5sGguq0oQR1lmsn/PxP1zjV5a03K0S8rrXrXop9z/vFRsLLlSjJ9wSDga15nLSpVSZHMlzPzlbUt7pFcaKfpXF6/VpCWsb+AsnX3fQekU1gYonaKz7zurZhEr/Rmrrb/ejAmISqM1abHaf/YUvf1vEn75DCZwUF5McvsOdjz3R3XJqqLgJtbkqn7NmjxRcLPmnymqq9a6ttXa2dpdS2bW5cIi0ITBrYqL1yfDMXp2vIHjtFZm1CzK/c/ljLesGMWkPBetFKi5gDc+W0QpRWgMYWxwta7okV5tu/mfRkbJBREpf/mv4Hqy240MxVjpz7jZGeHrUY+dIe1oDr39KnFwiuDhQ5zuEonUQN3+Z+36e9E6jbWlZV9TOoXr763L5wqxFbVkZl0uLCrPvQUDKKwNKQVfMjH6541eYks7e/wgrx7ZScp1CcKYlOvy+8/s4rnh3oXv2Z1Js6MziVagFXQnPV49srOiCrq83VxNIYxJVwnUsLHsdr1ZRLU/49L119PinaHFVxnWYmfI8ROkhgfxO5+qa1blut0ku17E2MrBMMYaOrpelC1wIWqoJTNr7WZQuoc4/BUAxuSxJgQMKIfph/+bvqHvS3a9QSv1Pyc+ubqQjSql2LOtk109KU7s6+ffnXx2WUa62kjRTIfPsd3b+fD6aEOy21rOCF+vzbScNZvh/ee5O3KawszluQcPnSLd/SLD+883emlCtJXWDNbaJ5l+nsL0BawN5pr9lQIUSrmEpa+YGP1ztu/6j41eaktb2v9cdeLYoZWrt9fabj57/CAJR9fkBqyN2uyM8I3YTMtZs9HaY9fB94miaaLgJq6/VzJqIeqgJYM1QGbo+8w8/F+UitfmZ9MrlEqgdQqUQzH3c4wJWiZDaQUbyUZXGynayOy2kTbactbMXLcb132+0csQom21bLB2nBRd2/5g7nxaaRRzmbXF4roZjJluqQyllTxONrq4r3qtgNyI7LZeVur7X/r6RlrOhBBbV8sGa5jLrmez/4covD9/8byL62ZI+E+jdLolM5RWt1pfdbsE5GpWqu7u7f8TJsf+S9XXoXKYhQyuEEKspKWDteOk6Ol/g9mJvwdr5mfh6i2ZoUwXS9x8OMPebV10J726fc5ao0Hrcfd1K1h8ocji6u789MeYOLfsdWDNljOZzrdxsYnWHAAkRCtp+d/iauP2tlKGUooiTr/3KZduPyBXikh7Lsee3s75117Cc2v3n/dxRoPWeoxnq1ipuhugMHOZZPq3Kl5bWvW99KhGpvNtnLGGa6OXuD99iyAs4Cc6GOieG60rs8pFK2v5YL2RoRjt5PR7n/LRyD201iQcTSk2fDRyj9Pvfcr7r79cs895nIy5Hcd4Po6VqrutDbEmhzUhamnWvErV90pZOjzetZ9b2bXRSwuX1jjaJYpDvspeBeDI8PEGr06IjWubR82tOFpvulji0u0H6CVtU1prLt1+wHRx+WSpjVgrYy6PBm2mMZ5PUrm6e6m57oT0/PHMkvesUPW9Vg92LcaStqvYRNybulVxuxyAUpp7U7eIzfJ+fyFaRdsE663o5sMZcqXq/wPKlyJuPpypyeesNomsnDHDo77q2NiK73mSYzwboVzdbe3yeebJrhextvLnYW1MaoWainKWXk05GxfVBVGeUlSo+rVSVCSIlrfKCdEqJFi3sL3bukh71U8yUp7L3m1dNfmc9WTMjR7j2SgrXSgyvP/8mheNLL7MY6UsHVq3B/tJ8d0UfqKj6tc8N4nvtt8RjNg6Wv7MeivrTnoce3r7wpl1mTGGY3v7a1YVvp6LL7bqoJPVaidWen2lQrKOruPkJn8sPdjr5GiXge49C2fWZdYaBnv2SFW4aGmSWbe486+9xMn9g3iOJooNnqM5uX+Q86+9VNPPWW/GXB50srXZfcMAAAo8SURBVBUC9WIr1U5Ue32lyzwUrPvaTzHn0NAxdmUO4+gEsYlxdIJdmcMcGjrW6KUJsSnKLj1QmxcEAZ9//jlHjx7F9+Vpvtk1S5+1eDzGBNwdOVN15CjKZ+Dp/zS35W1yW67DoRakz7o5SBypHfktbhPdSY/nd26r++e002jQRqrW7mWtJSp9QRw+YPTGGZzE0EJ/tVgfR7ukPLlQRLQP2QYXogGqFZIt3M+uHLTTVdM7roUQrU2CtRANsLTdy9qYOMyCAsfNUP6rKf3VQgiQYC1aWByEzH4xzuwXY8RB2OjlrFtFu5fJYTG4iX4S/tMV37fR/urFLWFCiNYmZ9ai5ZjYcP0HP+bWux+TvzMBQMdwH/vOfIMDb30b7bTGM+jidq+4NMr92/8Ba4vLvm+9/dXllrDc1AVMNIZ2+0n3vCyzxYVoYRKs66AUFZkpZulKZvDc5NpvEOsycu5DRn74jwQPZmC+7Tt/5yHXf/iPKK05+Pa3G7vAddLaRyd3k+r5Rk3uuM7e+wGT93+EiSawNkKpEUqFK1hr2Db0Tj3+CEKIOpNgXUORibh47X3GZ74kiku4jseOrqc4cei7uNI+UhNxEHLvo19TmswtBGoApRThRI77H/2Kfd/7Jo6/fB53s6vFDXLGBEyNvUscPkApNT8cxBCHD5gae5e+gTelDUyIFiQRpIYuXnufe5M3UFqhtcbYiHuTN7h47X1eOfJao5fXFkrZWYLxaUwYo5ZcLGKjmOL4DKXsLB1DfQ1a4cbV4ga5uDRKHN5FqcqfjVKKqHSHIPcv+OnfloAtRItpjcO9FlCKiozPfLksgCitGJ/5klK0/CxSrJ+X6cTv70ZXuYZTuQ7JHV14mc4GrKx2NnODnJ3/p+I1azEmj4mnuX/733N35AzZe39d9eIRIURzkmBdIzPFLFFc/UrKKC4xU5TbkmrB8RMMvvwMXm+6IipZa0n0pRk4+WxLboHXiusNkfCGsYt+ONYWsCZAax/H7Zb+bSFakATrGulKZnCd6mM+XcejKym3JdXK/rOn2P+n3yK1q4+5g2tFameGg3/6LfafPdXo5TWU1j49O17HcbeDcjA2xtoQpX1cfxfSvy1Ea5Iz6xrx3CQ7up5aOLMus8ayo/cpqQqvIe1oDr/zHQ68+TsURicBS8dQ35bOqBfrG3wLlCY/dYGodIdScAs3sWPF/m3tDTVopUKIxyWZdQ2dOPRdBnv3oZWLMQatXAZ793Hi0HcbvbS25PgJOnfvoHN3vwTqRcqFasMHzrPzwHukuk6Q8HdTUT6P3I8tRCuRzLqGXO3yypHXpM9aNIVa928LIRpHgnUdeG6SbZ3DjV6GEEBt+reFEI0lwVo0VByElLKzeJlO2cquk1r0bz8JxgRzM9B1Wu7xFmIJCdaiIUxsGDn3IWMXrxJkc/iZNP0nDrP/7KmWme3darT2m6qYLDYRQZTHc3ymx/8ruamLlAq/wMZ5lE7hp58j1X1CZpoLgQRr0SAj5z7kzgc/RzkaJ5kgype488HPAVputrdYH2MN10YvcX/6FkFYoNd8QrcZwdfTxOEESilMHFAqXMHEswBkBt9u8KqFaCxJYURNxUFIYXRi1Ssr4yBk7OIV1JIMWjmasYtXWvK6S/H4ro1e4qvsVaI4xFGWROkqxXCWILi/MCZVoYijLEop6QcXAsmsRY2sZ1u7lJ0lyOZxksvPqEsT+ZaZ7R1F00TBTVx/L67b3ejltITYRNybujV/wQhom0OTx2LnArJOLXyvtSHWhFgr/eBCSLAWNbGebW0v04mfSRPll49n9fpSTT/b25gSd0dOU5y5jDE5tE6T7HqR4f3n0br6FDsxJ4jylKICzvwtdEalMSqFskUMDhaLmu8HVyqB0gmU7pR+cLHlyTZ4m3ucbelafMZ6trUdP0H/icPY2FS8bmND/4kjTV8VfnfkNLmpC1hbQqkE1pbITV3g7sjpRi+t6fluCj/R8egFlSDQBwGwunNh3rvF4rgZrLWkpB9cCMms21V5W/rex1fIPZghvb2LwW8cqUu19Ua2tfe8cZLSVJ7sz74gmi7g9aXoP3Gk6Wd7R9E0hZnLaFX5M9RKU5i5TBRNy5b4KhztMtC9h6+yVxe2wnPeScDQq+/hMIqJczg6jddxmFT3N6QfXAgkWLeta3/zYy7/j8/IFkPC2JCYyJH5fw+JreHIn32npp+1nm3tpWfbXk8H/S8f4dD3XyWRav7sKQpuYk0OpZY/mFiTnzvDdp9vwMpax6GhYwDcm7pFKSriuUl6B9/iwMC/gnhS+qyFqEKCdRuKg5BLf/dTxvMBSim0VsTWMpYPuPR3P+Xgn/xOTbeay9va5TPrsmrb2kvPtuMgYvzT63g9qZZo2XL9vWidxtrlDyZKp3D9vQ1YVWvRSnNk+DgHB18giPL4bmrhDBunvEUuuxNCLCZn1m1oZmyKybHphTaYMqUUk+PTzIxN1fwz9589xc5Xn8dNJTBBiJtKsPPV5yu2tduhZct1u0l2vYixleftxho6ul6ULfB1cLRLyut+FKiFECuSvyVtKJdMkE+6eJFZ9rW875JLJuit8WdqR3Pw7W+z73vfXHF86MLZtudgwgidcEHPBe5Watka3n+euyOnKcxcxpq5aVvp7rlqcCGEqAcJ1m1oe18nM0d2se1fbsOiu7Uxlpkju9jeV7/WKMdPrBhw3Z4UwYNpCqMTmNjgeC5+ppP00ztaomWrTGuPXQfflz5rIcQTI9vgbch3Hb72x69w99ldRAkHHcZECYe7z+7ia3/8Cr775Ocsm9hw6Y2/YfbmfYKJHNFskXCmQOH+FLO3xlqiZWsp1+0mmX5eArUQou4ks25TZ08c5pyj+fTqKPmH06S2dfPS4SHOHj/YkPVc+6t/YOyTqzgdHlgwYYQphiil0b7LnjdONmRdQgjRCiRYtylHa97++mG+d+wA2XxAJuU3JKOG+cKyf/oVNjIorXBSHo71sNaiHI2f6SSayrdE65YQQjSCBOs257sOQ92ptb+xjkrZWcJ8gE44j6aWqbnqdGKDm/Ja5rxaCCEaQc6sRd15mU6S27rwM4/GSS5wNAOvHG2582ohhHiSJLMWdVcemhJOFwAIsrPYMAZX0//1wxx853cbvEIhhGhuEqzFE1EejjJ28QpBNofb4dH/zWc59Ge/W/NZ5UII0W4kWIsn4nGGpgghhKhOgrV4olYbmiKEEKI62X8UQgghmpwEayGEEKLJSbAWQgghmpwEayGEEKLJSbAWQgghmpwEayGEEKLJSbAWQgghmpwEayGEEKLJSbAWQgghmpwEayGEEKLJSbAWQgghmtyKs8Gtnbt4uFQqPbHFCCGEaB/l+FGOJ2LjVgzWYRgCcP369Se2GCGEEO0nDEOSyWSjl9HSlF3hkccYQy6XI5FIoJR60usSQgjR4qy1hGFIOp1Gazl13YwVg7UQQgghmoM86gghhBBNToK1EEII0eQkWAshhBBNToK1EEII0eT+P/BPVRlmj9rKAAAAAElFTkSuQmCC\n", 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n+y6ztRSMjjtbfYNSajeYmXe+o4vlc8anxRCYoRbTRMtBJVJRf/zaB9j0YTdU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\n", 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\n", 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" ] }, "metadata": {}, @@ -1468,14 +1319,14 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 43, "metadata": {}, "outputs": [ { "data": { - "image/png": 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TU4eqsGx6Y+wmG8XUDc588yCvP/ePrEo1FIeNGVcsp/mhlwh39mPp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kPgXLcZZZPAWMYzc/BoKl4wbTJuBDA7jHQak089gdS7h8AS8xB6IuCMgaWTOYvCCkKdeoRMlcz2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\nRK5CYII=\n", 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\n", 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" ] }, "metadata": {}, @@ -1505,7 +1356,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 44, "metadata": { "collapsed": true }, @@ -1526,14 +1377,14 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 45, "metadata": {}, "outputs": [ { "data": { - "image/png": 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mkCxevJirV68ybtw4AFq2bEnHjh0ZOXIka9aswd3dnffeew83NzdGjx5NWFgY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\niIiI3NDvTOrXr092dvaN7FJERETqgBt6h0RERESkPAokIiI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\n", 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" ] }, "metadata": {}, @@ -1561,14 +1412,14 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 46, "metadata": {}, "outputs": [ { "data": { - "image/png": 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ZFey8AEIIIbIyUSoxt7Ckes1aVHnLhds34qjb+H1KlLTNNkX/02MwbGztaNz8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\n", 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\n", 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" ] }, "metadata": {}, @@ -1596,7 +1447,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 47, "metadata": { "collapsed": true }, @@ -1614,14 +1465,14 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 48, "metadata": {}, "outputs": [ { "data": { - "image/png": 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H98OYPLgfRkbG9Bg8gkcPwrOMoclOq07dWPbNFA7tDqV0uf9Rx9WN29f+5OqF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\njWLWrFn4+voybtw4evfuTXZ2NitWrCAoKIg9e/ZQqlQpAgMDcXJyYtq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\n", 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" ] }, "metadata": {}, @@ -1640,14 +1491,14 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 49, "metadata": {}, "outputs": [ { "data": { - "image/png": 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MHj2aRo0a0aVLF3x8fPjoo48YNmwYn332GZUrVy7w/lJaQoji8VcjPKV9yu5t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\n95EGoDZE4xgAAAAASUVORK5CYII=\n", + "image/png": 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" + ], + "text/plain": [ + " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)\n", + "0 5.1 3.5 1.4 0.2\n", + "1 4.9 3.0 1.4 0.2\n", + "2 4.7 3.2 1.3 0.2\n", + "3 4.6 3.1 1.5 0.2\n", + "4 5.0 3.6 1.4 0.2\n", + "5 5.4 3.9 1.7 0.4\n", + "6 4.6 3.4 1.4 0.3\n", + "7 5.0 3.4 1.5 0.2\n", + "8 4.4 2.9 1.4 0.2\n", + "9 4.9 3.1 1.5 0.1" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Converting the data into dataframe\n", + "feature_names = iris.feature_names\n", + "iris_dataframe = pd.DataFrame(X, columns=feature_names)\n", + "iris_dataframe.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### K-Means Algorithm\n", + "K-Means is a simple unsupervised machine learning algorithm that groups data into the number $K$ of clusters specified by the user, even if it is not the optimal number of clusters for the dataset. 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bp4TDYQwNDaG3t7fmF6RW+/pWQp+C3W43crkcxsbGNDeLvpaiGdXgjMqU++6UPhzo3Zv6+FkjEjSMiK3eRUbXNDIysqYyx5igtBFGK97pa6m1og+802BzOVohKPSLOjExobVGr3UtI7SLwOgzkUqL9Wg1eGmxnp20m1WwltZS6t4sTdDQZwDWm2Kud3kZXT+ANZfSzgSlTTBa8a6Po1A3ksvlMlzxXo+lYYZMJoNcLgdRFDE1NWXpy1SJdtm0SqlUrJdOp5FKpRCNRrXRvHoLZr3QboJidjMul6BBXWQ0xdxqDZPZ90Y/cmItwQSlDaCxEsD4ZhmLxRCNRlcF3qtRj4VitNiQ+n7n5+fhcDgwMDBgqQXGeqBcuxF9y3c6U0TvIjM7drddaBeLkWKHuFUTGH2RrN5FVulet2KhrDXrBGCC0lKsVLzT2EoikSgbeK9Go7O8ZFnG7OwsZFnG5OQkQqGQpesZOabdNjAj6Bsmls4UKTd21+v11rQ628kqANrngaAR70upwOiLZEsFhv5HY55WLBQmKAzDpFIpLRPF6I1GA+8cx2F8fNyUmACNjaGkUikEg0F0dXVhfHxcqxA2ez2jltZ6oJzA0LnutFW/KIpFFky7BmjbTdiasZ5KXRgymQxisRhyuZzWqNTsmpiFwjAEDbxHIhF4PB74/f6ax6iqikgkgkQigbGxMYRCIUvXNuO6Kj2ukjAQQrCwsIDFxUWMjo5qAc56KL3WWrRGrMBxJ6ci0lb92WxWqwQv16q/XWi3z6gVAldOYLLZLFKpFADg8OHDhjthG+ke3o4wQWkiViveg8GgVr9BLZpW1ZPooZMeAWBqampVgNkuC6XcedptA2sEHHeyk7JeYPSt+gVBgCiKSKVSLW/V304bYDtYTLRRqcPhwPLyMiYmJlZ1wtY/IOgtUFVV29YarQYTlCZRGnivVE9CofUbkUhkVf1GOwgKdb/5/f6Kkx4bVT/S6o2iVegFhrbqp+mtrW7V3w4bOKUdOhjooe6rcp2wqQVKHxCcTicef/xxDAwM4KyzzrJ1HQ8//DAeeeQRAIVC43379uE3v/lN1VERZmGC0mBUVdVqS0p7VlWreJ+dnUU+n2+LinegeEY3db/VqnuxQjsVMbY7PM/D6XRCEAQMDAzUbNXv8XgaWgnebrSToJRbi15g+vr6QEhh+B3Hcfj5z3+Oe+65BxMTE7jgggvwhje8AWeffXZd69ixYwd27NgBAPjEJz6BG264wVYxAZigNJRqFe+VNk46G6S7u1sLbpfSiFhIreOAwlNNMBiEKIqGOgRbvd56zfJqBPp7q9Wt+tt9A28VRjO2aBHszTffjOuuuw6EFCaY7t69G4cOHapbUCgvvvgiDh06hDvvvNOW8+lhgtIAKvXh0sPzvDZtEYDmvtDPBqlEK1xekiThyJEjGBgYQF9fX8PqXk6lLK9G08xW/e20ibfTWgBr6yGEwO124/TTT8d5551n63q+8Y1v4NZbb7X1nBQmKDZjpuK9tHGiw+Ew9ORfK/5i5JpGUVVV889PTk5qKZCNhFkfxjHzXjWyVX+jN3FCCFSiQuBrB6qNrmUxvYhn5p7Bxu6NOKvf3niFHis1JY2qQ1leXsbRo0dx8cUX235ugAmKbZjpw0V/TzfrSCSCwcFB+P3+hle8mzmOzlShtRBmxaSRFgoTnZNY3ciNtuo3MnK3kew8vBOPHHgEeTWPbcPbcMu5txjqc1eNQ0uH8ImnPoGElAAAvOmMN+FtZ7/N1nWbWU+5YxohKLt378Yll1xi+3kpTFBswKhVUkoymUQ2mzXcOJHSaEHRZ5ht2LABoihamvbYyBgKw34qteqnAkNnupdr1d8oC2UhvYAf7v0heK6wuf529reY7JnE6yZfV/EYI9/Dh/Y/hOX8sva6Rw8+ipvOvAkib/+WaKWmpFEWytGjRzE2Nmb7eSlMUOqkWuC9EqlUCpFIBIIgYHJy0vSN00hBodPlJEnSMsySyWTTNvl28n2vBRpZUa0fWAVAa3RZrlV/o9Ywl5yDoirghcL5RU7EQnqh6jFGvosKUYp+VqE27B638hkRQhpSh/LOd77T9nPqYYJiEdoEMZVKYWhoyNBGSAjB/Pw8lpaW4Pf7tZnTZmlUllc6nUYgEIDP58PY2Ji2NqubvB0WSiqVQigUKmpBIoois2JaQLVW/alUShulYGer/tN6T4Pf48eKtAIA4MBh6+DWqsdQQSGE4KnAUwglQ9g6tBVn9J2hvebaqWvxwvwLyCpZEELw6vFXwyE0pvOzFeuN9fI6hVBVFfl8HrIsI5fLGTqGptwKgoDp6WmtYtYKdgflCSGIRqOIRqMYGRlBd3e3oeOsXq/WMXRNtKXL4OAgVFXVKsRp4VoikWjIjBFGbfSdlLu7u7X2/G6329ZW/R6HBx+5+CN4+MDDyKt5bB/djlcOvhLPzj2L383+DiIv4vXTr8d417h2DLUI7v/j/dh5eCd4nsfPD/0c79/2flw0chEAYOvQVtz9qrvxzNwzGPIO4YpNV9j23pRiRRxYL69TgNLAu5GNnRCCeDyOcDhcFHivJ7Bs5wavHxs8NTUFp9Np2/Wsoqoqjh8/DlVVtVkqiqJorpdcLofjx49rXV5pgRjdvNZqC3grtFOKrCAI6Onpsb1V/1jXGG7bdpv28/7Yfnz3T98Fh8LffSxxDB+/9OPocBbSo+m9+l+B/zo5x11V8MTRJzRBAYDJ3klM9k7a8rdXg1kojFWUC7zXcj0pioJQKFQ28G7VyqDXtUNQaBFlT09PVbddMy2UbDYLSZLQ1dWlrYkKOIWK3vDwcNHGtbKygvn5+TXToXc9Ufo5l+ukXG+rfsre6F7k5BwWMgvocHSgy9mFQ/FDOGfwHG0tHMdB4IrPRwP7zUZVVVPWGbXAmaCsUxRF0YoQ9ZtuNVGg7dx9Pl/ZiYVW4yD02Ho2eH0sp1YRpR3XM4Le7SaKIjZs2GD4GqUbV2kDxVb1tzrVqFV3ZVer/kw+g9/N/q4QWOeAzV2bMdIxov2ebsbXb7keP/jTD6BCRaezEztO32H732wEsxYK/c6sxfuUCUoVSiveS+F5fpUo6DfrkZGRir1y6nV5WQ3Kq6qKo0ePguO4sh2CK9FIl5d+MNf4+Piq9vxmrl2ugSIVGNrfqpHtR5pNu7i8zK6D48y36qcbbHAlCJ/Lh0Q2AQ4cVKKiz9u3ai1vOO0NOLv/bBxLHMPWwa3we2uPimgEVufJr0XLmglKBWjgvdoXpVQUJElCIBCAIAg1N+t6LRQrxyaTSciyjL6+PvT39xveAOrJ8qq1TppZRgdz0ffcyHqMbGKlHV717UcikUhRdXgri/fWOvUKm/5BoFKrfjpLRMpLGPeNY9xXCMQ7BWfhnuFWr6VZcZJqmH1v6HeGWSjrADMV73oLhT5VGe11VU8MxeyxNJ1zeXkZPM9jYGDA1PUa4fIihCAWi2FhYQGjo6OaJdfozby0/Ui54j0qLiyDrHVUszQv6LkAe2b3QOVUcDyHV0+8WgvQA+1jtVHMFjYyQVknmK14p0/ggUAA2WwWmzdvNtyepFlZXrRPmNPpxOTkJA4dOtTQ6xlBURQEg0HIslw2s6yZGWWVivf0GWR6C6bdMsjapR6n0Zu43tJ8Xf/rcNrYadgV2IVesRdn+87G4cOHtUaXsiw3bB1WMBtgZzGUdUDpACwjZLNZKIoCnufLBt6r0Ywsr6WlpaJ0ZRqQb9T1jByXyWQwMzMDn89Xtj2/GTdcIzax0uK9ShlkTqezbTbzdngab7ZVMOWfwpR/SvtZ36o/lUpBlmXk8/m2iJUxl9cpRKUMrmrQortYLAYAGB0dtXRtusGbvdFrbfCKomBubg6ZTGZVunKrBIV2Fpifny9bPFntWq3aCCplkGUyGSwvL0OWZczMzLAMMrTeUtK36qd1S16vtyGt+s1iJShPa9XWGqe0oCiKgkgkgmw2a1gUJElCMBjUsqQOHDhgWRSsPmVXC3ZnMhkEAgF4vd5VVlM9jfysbhg0JlXaH2wtovfrd3Z2IhgMor+/vyiDTL9pNeOpuJ3iBe2yDtoHq1Gt+q2sx6yFslYfTE5JQdEH3s24nhKJBEKhEPr7+7UsqXpcLzSob0dzSL0FMDw8jJ6enqrHmk3xpNcwe5wsyzh8+DA6OzuL+oNVO8bI59Hs6v1Ka2AZZAUUtXxqfSsoF7NoZat+s0H5dnpIMMspJyilgfdytSSlUBdSOp1eFXi3KgqA9U2xVARrzaCv95pWbm5CCuNLU6kURkdHKwrceqJcBpl+06IZZPpNa62TlJL4wu+/gAPzB9Dl6ML7L3k/XtH/ipauSVXVmskTpa369Y0ua7XqN4vZoDyzUNYI5QLvtQQlnU4jGAyio6MDU1NTq4qNWlnxDpysyKd1HHZZAJWOM/Kloi1n0uk0Ojo6TIlJO1gedlFu06IZZLFYrMjCsZpB1uqn2X958V9waPEQZCJjMbeI+56/D//0F//UsvUA1t4Th8MBh8NhqFU/bXRp9BpW0oaZoLQxqqpqLq7SD7aSIOhbgVQLItdbT1JPxfv8/DxisVhRHYeRYxsZmM9ms5iZmUFHRwcGBwexsrJi+lp2rqedMJpBtpZ6kCVyicJ3igDggHgu3nKRs6MPVrVW/dFoVHsYqNWqn96jTFDWCbUGYJUThHw+j2AwCEJIxQ68lFZYKIqiQJIkJJNJTE9Pmyq+a5SgEHJyyiON4SwvL1tyr601obBCtd5Wpa1H6MZVbpNp9Xu1pXcLXlp46cRigM3dm1vu/7db0DiuuFU/FRiaolytVb+VjK212hgSWMeCYrTivdRKoIH3vr4+DAwMNLTi3crmubKyomWZTUxM2J5yXI1Kx6mqilAoVDZNuZG0ejO1E44r7m2lrwyPxWJVM8hauYHfeMaN4DgOewJ70OPpwa0X3dqytVAabSHpBaZWq34r2WOqqq4J67Qc61JQzFS8UwtDVVXMzc0hlUph06ZNWuZOLeqxUMy4vAghiEQiiMfjGB4eRjgctvSlsdtCyWazCAQC8Hg8ZdOUrSYA1NoUWv0UDDRW0Ep7kNEBY/q6Co/Hg3w+X7PnXCPhOA43nnEjLvdfDofDAa+j/Pcmkozgpy//FLIq47Lxy3DehvMatqZmvxflrE29wKiqimPHjhlu1U/Tntci605QzFa800390KFDWu2GmQ+zGRaKvunk9PQ0AOubWT2CUgqtxN+wYQN6e3ttu9ZaoNmbN8/zZTPI6FTLxcVF27KSrFBtE8/kM7h7192I5+IAgBfmX8CHLvpQw7LBjAjKvug+7DyyE4QQXDt9bdF44HrRC4zX60UoFMKGDRsMt+o3Oz+lnVg3gmK2Dxdw0u+vKErV2o1qWA2s02NrbbjUBadvOqkoSksEhR5nxsXVqIyy9SxWRqAZZIlEAr29vXA6nbZnkJmh2mdxYPEAFtIL2sx2FSr2RPa0TFBCKyHc8/t7IKkSAGD/4n588rJPYqhzqCFr4XneUKt+l8uFw4cPY2BgwHBPQKN84xvfwK9+9Svk83ncdNNNeNOb3mTr+SnrRlAAc+l5+sA7gIpZXLWoZ2Or5i6jLrhkMrnKBVfvNevZ5HO5HGZmZuB2uzE5OVnVmmsHt9SpQqUMMurTb0YGWaXPe6hjqGh6oqqq6HE3ri6plqA8G3lWExMAyMpZPDf/HK7pvMb2tZTL2NJ3XNALzMLCAu6//368/PLLmJiYwGWXXYaLLroIF154YV01S8888wz27NmDBx98EJlMBvfff3+9f1ZF1o2gmMmkWF5eRigUgt/vx8DAAF566aW6q92tUCsu4Xa7MT09Xbb2pVF9wKodt7y8jFgshqGhIfT29hqKTzWzdxijgF0ZZGaodi9u6NyAN7/izfjZgZ9BVmVsG96Gaybt37yNrAUAhjuGoRJVGwmsEhXDHcMtWQtwUmA2btyIb3/72zh48CDC4TAOHjyIr371q4jH47juuussr+Hpp5/Gli1bcOuttyKZTOLDH/6w5XPVYt0IihHoXJCVlRVs3LhRe+pvxXx3el29GOlTb6tt2lQ8myUodNjY0tLSqk4BrYKJTgGjG5bVDLJKLGYW8eSxJyHyIq6durbmOq6bvg7XTF4DWZXhEhuW77vKAAAgAElEQVTby63WWrYNb8NrJ16L/zz+nwAHXD1xNbYObW3IWswWNQIFa/P888/Htddea8salpaWEAqF8PWvfx3BYBC33HILdu7c2RAPwroRlFpvTiaTQTAYLPvUTzd2K24AuywUWl2ezWYNpd426+mfzlMBCl2VzYgJs1Dak3IZZLRoT59BVqlx4lJmCXc8dQcS2QQA4JnQM3jPae+peV2BFyDwjc9eMiKybzv7bXjLmW/R1tXItZi1/uyuQ+np6cHk5KQ2E8nlcmFxcRF9fX21DzbJ2qyeMQGteD927BgGBgYwPj7eFu1T9MdmMhkcPnxYm6tipI6jGZt1IpHAkSNHtKBvM2teGM3h8NJh/Hj/j/Hk7JPo8fdg06ZNmJiYQFdXFyRJwtzcHI4cOYJQKIR4PA5JkvCr478qzHM/YSkHl4PYs7CnbWJmRi33ZgicFS+C3XUo559/Pp566imt9CCTyTSst966sVCA1RtYPp/H7OwsFEWpWvFeb/sUmqZsZb3JZLJme5dKxzZKUPSuQerislL1bpXSNeZyOciyXPSkzMSqQD01FwcXD+LeP9wLlahQiYq9sb34yMUfWdWZt7QH2VJ0CdlsFqJDhCAIICBwC60bYFVKq1u/6LHSRsVuC+WKK67A7t27ceONN4IQgjvuuKNhdS7rSlD0rKysYHZ2Fr29vRgcHKx6gzUisF4LWZaRSCSgqmrN9i52XrfWcZIkYWZmBk6ns8g1aLVIsZ6qfEJOzp2nnxF11TAxqZ9ds7ugkhPTATkeBxcPIpqOYrBjsOh1pRlkG0Y24EDmAA4uHoSsyDjHfw5O6zgNmUwGbre75UV57SQoZtdCCGlIL69GBuL1rDtB0T9dj4+Po6Ojo+Yx9daSmD02mUwiGAxqmThWUgKtuumqbfLLy8uYnZ3VRgbrvwjNFBT6twUCAUiShM2bN4PneciyrD0pS5KEUCiEjo4OQ9XH65V6hNUhOIo2PIET4BZrx+46PB24+8q78ceFP8LBOTDlm9K6TMTj8ULFvI0ZZGawmv3YKKzMQgHW5vhfYJ0JSi6Xw/Hjx+Fyucqm21ai3hiKmfYp8/PzWFpawujoqNbB1ApW3XTlNnlVVRGJRLC8vFy17YyV61k95vjx4+js7MTk5CSAgttF/6QsSRJ6enqgKAoSiQTC4TCcTqcmMKfSOF6rm+cbTnsD9sX2YXZ5FhzH4dqpa9HlMta1WuAFnDt0rvazKIoYGBiA2+3WAvyLi4vIZrNNnWJJ77d2ERSzbVSYoLQR4XAY/f396OnpMXVD1RtDMXJsPp9HIBAAxxVGBzscDiwtLbW04h042dZFFMWqImzlC2rlmHg8DlmWMTg4iMHBguulnGBzXKFBHy0O06fC0nG8+lYkzZwhvlbwOrz4+KUfR3A5CJ/Thz6v9awf/UZeK4OskbPd28k6AYwN+yp9PcAEpS3YtGmTpU223hhKrWOpK6m0g3ErK97169K3dbHzemaOoa7KZDIJURSLEhSMbBClqbB0hng6ncbc3FxR/KXa/Iq1Rr0bqMiL2Nyz2Za1lFsHz/Po6OjQXM+lnwsdvWtXD7J2ExSzAXZmobQRVjfZRvXj0ruSNm7cuCqe04pkACqA4XAYiUTCcGdlq4JihHw+j5mZGYiiiKmpKRw5cqTmtWqtp1qmEp1fod/ITsX4i50YTtUt87noXWQAVk1GNLuOdtqMzQocs1DaCKtPJnZWu1NoQaDD4cDU1FRZs7cVFoqqqkgkEnC73RXXZef1ah1DExT6+vrQ39/fsKfL0kwl2oqExl9oZ9hTLf5iF1YtA/3oXUJODq4qnStiVPjNrkNWZTwdfBo8eGwf3661Y7ELsxlb9PuyVh9w1pWgWMVuS4H2SyqXLVXrWDPXNbvmlZUVLC0twe12Y9OmTaa+eHa7vAghWuv1sbExrS17uePsThGu1opkYWGhyM/f0dFhqaizWbSbi8csSSmJz+76LI7Gj6Lf04/btt2Gyd5JbXCVXvgjkUjNDDIz70deyeN//vJ/4vnI8yAguGjkInzhNV+wVVSsWihr9TNlgoL6ihP1YkTbuqfTaUM9rxrd+p5CK2Tj8biWsNDKqndFURAMBrWCUyvxDDvXo3d/9ff3F/n5Q6HQuo2/2IlVYfvanq/hhcgL4DgOK9IKvrT7S/jya78MYLXwE0I04ddnkOnbxJhZx88P/hwvzL+gVcvvntuN/zjyH3jd1OtM/x2VMGuhrOV58sA6E5R6XF71WiilkwuNmKzNcHnR7DKe5zE9PY1EIoFcLmfpmmYpt8ZMJoOZmRl0dXVhw4YNZT+zVlfBG4m/uN1uqKoKRVHWrHvCTqwKSjQdLToumolWfC3HnWz73tfXV5RBRi1Lp9MJRVGQzWZrZpBJigQOulorcMgqWdN/QzWsFDYyQVnj1NttWJZlHD16tOLkwmrHNlJQaLcA2qafWibNathIv0j0S7W4uIhIJGK6zUwlmiU65eIvKysrIITgyJEjRfEXj8fTVHdFu7i8rH4WUz1T2BvdC54rfAc3d282fGy5DLJ4PI54PI5wOAxZlqtmkF03fR0eO/wYAsuF5qeTPZO4euJqS39HJcwWNjILZR1g1fVEOwQTQrQuns24LlA7PkELKGvFJ+y4Xq3jFEVBOBw2NOHR6LVatYlSN4wgCFqvs2w2i1QqVRR/oQWW7Rx/sRsrf+c7znkHAODQ0iH0efrwnvNqdy2uhCAIcLvdcLlcGBsbq5hBRl1kPpcPX736q3jk5UfAcRxuPP1GeBz2jmawkjbMBGWNY2VjT6fTCAQCmlvErJgAjbFQ6CRKAJienl6VxdVsQSGE4NixY3C5XDUnPJYetxaoVv8Sj8fXTPxlObcMnuPR6eys/eIyWLWUBF7Au859l6Vr1lpHpQyyVCpVlDp+w8QNDUsdtxKUZ4LSJjQjhkJIoR1+NBrF6Ogourq6sLi4aOkLZXdBJU3BrdYQs976FTMsLy8DKIxXNpMSbPR17Sg65eIvqVRKs2AEQdCsFztG8dbr8iKE4PPPfB6/nvk1OHC4ZvIavHfbey2dpx2o9H7QzgpOp7MogyyTyWB5edlQBpkVWFD+FMRoDEWWZQQCARBCijoEWx3QVW/9Cz22WgquXdc0cxzNKkskEuB53tC44HLn0F+73HrWAg6HAz09PavSYGlqeSvjLwDw5LEn8ctjvyzEMEDw2OHHsG14Gy4evdj0udrhMzEqsPoMMjomuVYGmZV7mAXlT0GMWArVnv7r3aStPGVSi6FU5Gq5VBqdQUXXAwBTU1M4ePCg6XO0w8bUCCrVv+jjL/r+Y82Iv8QyseK6CwJEUhFT52inhoxWLTYjGWRme5DRtTCX1xrF6g1dTVD0NRyVnv6tuq70WVdWBEWSJBw6dMjQzBf9cY2yUNLpNGZmZorW06gKe6OvaWfsiL/U6/LaPrYdD7/8MFakFQBAr6cX28e2mzpHu2SaAfatpVwGGRUYIxlkVteiqmrbxtmMsK4ExSqVNj3aiVcQhLIBbv3xzezJRQhBKpVCMpnExo0bNX99o65X6zhCCBYXFzE/P6/FlUp/b/ZadrxmrVEaf5EkSQsiLywsQBTFIveYHUHksa4xfOpVn8LPDvwMPMfjxjNurKvrsJ0kpSS+/IcvYym7hPM2nIe/fsVf1/zczabpGkUQBHR2dmoPlPrZPDSGWiowVtbCXF7rgHIWRiKRQCgUQn9/f82Acr11LGaOlWUZwWAQkiQVbT6Nul6t42jqdC6XKzt50uqXe61bH3ZQLohcGn+hrpmOjg7L7/UW/xZ86OIPWV5noyyUD/3qQ3g2/Cw4jsNTgcJM9Lec9ZaWrKUUURTR1dVVNYOMZn6aKX5lgrIOKG2fQic+munE24yJj6lUCsFgEN3d3ejq6kIqlTJ9PTsFhXYH8Hq9mJycLPtFqKcgshankuiUi79kMhnMzs4iFoshHA43Pf5CacQmnpNz2Bfbd7I4FgTPhp9tG0HRUy6DTJIkLC8vI5VK4ejRo1oGGf2MKokGi6G0EfWkDdMsj2AwuGqeei0abaEQcnK2OnUpJRKJphco6o+jFlyt7gCNiqGsR5eXGaiPn+M4jI+PgxCiuWDo4Dba3NLr9Zoa8mSWRgi7U3Cix92DTLIw0VRVVUiKhPnU/KqZ96VraXUrHI7j4HK50NnZiUwmg/HxcS2DbGlpaVV2n767tZVs0XZiXQlKvRw9ehRDQ0Om01wbOddElmXMzs5CluUil1KzCxSBwpdVP+Nls4EGmFY41cXCCvr4i94FQ9vA6+Mv1Z6QrVL6mWXyGdz3/H2I5+K4eORiXD1prqUJx3G4/cLb8fnffx7RVBRL8hKeCT2Dm352E9659Z246cybyh7XjgkCtTLIJElCNpvFrl27sGnTJgwMDNi6jje+8Y1a7GdsbAx33323refXs+4ExeyGSWMAAMoOwWrENfVUEyNajd/V1YXx8fGiTaAVLVRUVcXRo0drjgu243qnQpaXHZTbQCsV8ZU+IVPrpd4576VrIITgw//5YbwwX+gi/Ovjv4aiKrh2+lpT590+vh2Xjl2KT/7mk/jl8V8CAGQi44E/PYAbTr8BTsG56ph2EpRK7qtyGWRzc3OYm5vDY489hrvvvhsXXXQRLrnkEuzYscOQ270SuVwOhBA88MADls9hhnUnKGbIZDIIBALo6OiAKIqW0/XstlDKubjKHWc1VdnKRkxN9sHBQdNV73bEUMptmgxjVIq/6Oe81xN/Kf18k/kkXoq+pJ1DhYpdoV2mBYWuXSXF93lOzkFSpLYXFKNrEQQBY2NjuOOOO3Do0CH4fD7s27cPu3fvxuzsLE477TTLa9i/fz8ymQz+/u//HrIs44Mf/CC2bt1q+Xy1OCUFRb9hDw8Po6enBwcOHGhapla1YxVFwezsLPL5fNmsqXqvafY42momFovB5XLZbo5Xu+5aRCUq7vn9Pdg9txse0YObt96MS8cubci16rGK9U/Isiwjk8kglUpp8RdqvRiNv+g3To/oQaejEwkpAaAwyGopu4RoOop+b7/p9V41cRV+O/tb5JQcVKLigtELKvYcaydBsRJgV1UVAwMDmJ6exl/+5V/WvQa32413vOMdeNOb3oRjx47h5ptvxs6dOxsWU1t3gmImJqHvEFzvsCs7jqWzQnw+H8bGxqrejFYTAcwICh2EJcsyRkZGEIvFGno9/TF6CCFlUy/bUXR+su8nePTgo9pn97ldn8MP/+qH6HCYd6UaxcwGGk1HsXtuNyZ7JnF63+kACimw9cRfSjdxkRdxy3m34CvPfQUziRmEkiHsj+7Hzw78DK/Z9BqcP3w+to9tx7kbzjW05kvHLsXdr7obT88+Db/bj7ecWTnTq50ExUrbFcDeefITExPadNaJiQn09PRoD9KNYN0JSjVo2m25mEQjA+u1jlVVFbFYDPPz84ZnhTTaQtEPwqJZKs2K2eiPyefzmJmZQTabhdPp1DKX2pXgSrDovoplYphPzWOiZ6KFqyrw0sJLuPOpOxHPxcFzPN7+Z29fFdwuF38pzVDStyBxu91lP99rpq7BlZuvxLnfPlerrViWlvHY4ccQy8bw65lf42N//jGcv+F8Q2s/f/h8nD9c+7XtJChWZqEA9s6T/+lPf4oDBw7grrvuQiQSQTKZbKiX4ZQQFEJONk8cHR0tWwzYrFqScsRiMRBibqZKvYJS7Yu3tLSEcDi8StyabRGkUikEAgH4/X6MjY1pfa+i0agmMADaau7Imf1n4heHf6H9PNo1ipHOkYZcq9rnkZNzuPcP92IuOYfTek/D2175Nnzvxe8hISUK9wAIHtr/UM3q82oZSjT+QqckSpJU1ILEwTsgE1mzpun/qUSFrMp4OvC0YUEx8560w30AWJuFAthrodx444346Ec/iptuugkcx+HTn/50Q1PI172g6OeDVGueWG8tiRVByWQySCaTcLvdmJiYMHUj1SMolVBVFXNzc0in06sGYdVb42OWZDKJhYUFjI2NoaOjQwsc0zTlSCRSqE2QJMt+/0Zw7dS1iGfj+M3sb+AVvXjHOe+ASzQ/K8cM33vxe9gb2wuX4MKOLTtwztA5uOvpu/Dr478Gx3F49NCj+MpzX0FezUPgBIz6RsFxHBSigIAUjcGtRbn4SzweRyKR0L5nevfYBcMX4PGjj2vHdzo7IfACCCFwi9UHrVmh3QTFioVip6A4nU588YtftO18tVh3gqL/AMuNwK1EvXGQfD5v+PWEECwtLSESicDj8aCrq8v0TWRH/zD9+yFJEmZmZioOwmpWEoCqqkgmk1BVFZOTkxWTEnieh8PhgN/vL/L7r6ysYH5+XqtM7ujoKCocawZvOestNSu6LUMI+GefBReJgExN4enob7EnswciJyIn5/DAnx7A6X2nY39sPziOQ0pKYSW3Atkho9/bj9BKCJ3OTnQ7u3Hl5iuLOw1bQBRFeDweZLNZjI6Oroq/3HX2XfCLfrwcfxkO0QGBF5BTcji973S89ay3GrpGJBXBl3Z/CXk1jzef8Wa8cuCVcApOCHzxPfqTfT/BfbvvA0Tg6smrcftFt7dUXMw2emyEhdJs1p2gANCK7xKJBMbHxw353O0edlUJWveSzWYxOTmJpaWlpqb/ljt2eXkZs7OzGBwchN/vr5i222iXFxU1juPg9/srikm5tZX6/albJhqNQpKklrUlsRvh0Uch7NoFiCL43/wGqTOyEEdOfo1X8itIZBPo8/QhkopAJjIICAReQKejExu7NmLr4FbceMaNuHLzlXWtJZ1PYy45hy6ukNZeKf5yZ/edSKfTyGazyAt5qKKKzX2b4XXUrq9I59O46Wc34fnI8wCAH/zpBxjxjaDf049bzrsF12+5HgBwNH4UX3jmC1jJrkAQBTzwpwewxb8Ff7Xlr+r6G+uhHSyUZrPuBCWXy+H48eNa8Z1R10e9xYlGjs1ms5iZmUFHRwempqbA87zl69rR7oWQk7Pna/Uta7SFsrKygmAwiMHBQUiSVPOLWO28HMdVbAu/tLQEAEVtSdq11QV34ADEhx4Cl8lAPeMMyDfdBOH55wF6T/M8zgikcWCDAyJf+LcBzwD8Hj9uv+h2fOo3n0JgOYC8mseAtxCIHeoYwsf+/GPY2L2xrrX9PvR73PHUHQithLDBuwEfPPuDGBsbK15/lfhLdCGKsFzoP0Y/h3It4H8T/A2ejzxfcNGpClJyCrFMDBzH4Uu7v4QrN18Jn9OHl2MvIy2nC9cFB3DA8cTxuv7GerEyrRFY2zVW605QVlZW0N3djb6+vqa2T6l2LCEE8Xgc4XBYq3vRH9vsFiocxyGfz2uDsIwKbyPWqU+YoJ0KIpGIrdZQubYkqVSqaPSrfixvW3yh83k4/uVfwGWzAADhmWdA+voKYiLL2ssu4DYCp12AP87/EW7RjetPux4OwYEz+s7A99/wfSiqguOJ43hw34MghGDH6TvqFhMA+OpzX8V8eh6iIGI+M4/vHfweXvdnr9N+n5SSeC7yHDZ3bdaup4+/DAwMrGoBDxTHX0RRxGjnKDyiB1klq51b4AoPAIlcAolcAj6nD9uGt2HQO4i55TkAgFtw48KRC+v+O+vBStowfchcq6w7QaE3qlnqjaFU2gBVVUUoFEImk1kV6KbHmom/UOhNZyUISQjB8ePHmzKYqxr6Ohcj0ybtQO+WoaNfSyfzUfdYR0dH2afmprCyAi6RAGjWnyCAC4ch/8VfQPy3fwMAEFFE4tJLcdXEq3DVxFVlTyPwAiZ7J3HJyCV4KfYSXpx/Eaf7T18VfzALtQYoGTmj/e/DS4fxvifeh+PLx+ERPLht223421f+7apzVGoBT+NgoihioGMA7zj7Hfjevu8hJaXgFAtNIwkhOLP/TAx3FOop+r39+PxrPo97n74XLo8L1225ztIYYzuxkja8lt1dwDoUFKtY3diByhYKbe/u8Xg0F1e5Y+t1XRm9aQkpDMJSFAXDw8Po6zM+SMnuLC/q/uvs7LTUp8wugdO7x/r7+zX3GK0aB1rkHuvqAvH7wdERBYoCdeNGqBdeCGl6Glw4jPzoKORotOapHj30KB586UEIvACVqJhNzuKDF36wruVdMnIJDi0eAjgABLhw8KQ18I0930BwJQiBEyCpEr7zx+/gb876m6oiVq3+5ebpm/H6gdeDc3A4nDqM3bHd6HJ34b3nv7fonOdvOB93bL0DExMTbeHGtJI2vJatE4AJiobdMRRay9Go9u70WKNPNfpBWE6n03RxoJ2uOZoEUOu9aQVG3WNOp9M+i02WIezcCS6TgXzllYDfD4gi8jffDPGRR4BMBuTMM6G++tWF1/v9IH5/wfVlQFBeiLygbbw8x2NvdK+2ecXSMXz/pe9DIQquP+16TPZOGlryBy74ADZ0bMDh+GGMe8bx2uHXar/LKbmi10qqBJWoEGB8ky+Nv4yOjiKdTmNzejMu8V8CWZYhxSXEpXhR/KWdNmVmoawDrN5MdrVPoS6ucrUcdl/X6Cafy+UwMzMDj8eDyclJHDlypK7qdasQQrTsu801Wt+3Q1uVau4xaunNzs7W5x5TVTjvvBPCiy8CHAdh507kPvtZoL8fZONG5N///oqHGn2PPGLx+9zhKMxRSUpJvO/J92F2eRYcV5iK+M9X/TPGusYqnOkkHMfhprMKVfbxeBy53EkRuW76OuwK7UJGzkAlKi4fvxwOoT53Js/zVUfwAgVLkrbpaYeN2ayFwgRlHWHHxk43brfbjampqYa2dweMZXrRQVhDQ0Pw+/2Wr1mvhUJHFxNCMDU1VTUJwEjacj11OFbRu8e6u7u11jR695i+uNLQ5793L4Q9e4ATKdJ8NArxsccg/93flT+AECCVAk5ksBkRsLee9VaEkiHMrszC5/LhrWcX6j+eDjyN4HJQq0VZyi7hyWNP4u/+rPy1JUWCg68tmldNXIUuVxeeDj6NIe+QLTU5z4SeQSgZwqWjl2KwY7Bs/IVOMD1+/LhWh0QTLVqxUVsNyq9lmKCcoN6NXZZlHDlyxPSALjvrSfRUG4RVzzXNfkk4joMkSTh8+DC6u7sxNDRUV0pwO8FxXJF7TJIkpNNpzT1Ge49VzR4rt4FU2lTCYTi/8pVCUWNvL+S3v10TomoMdQ7hc1d8DvFcHD6nT2v73ucpjqGpRIXPubotUVbO4uP/9XG8FH0Jnc5O3HLuLbhi0xXa78vdExeNXISLRi6quTaKrMqIpqPwe/yr2tJ//bmv4+eHfg4OHH6y7yf4xGWfwBb/Fu331JIURRHRaBRTU1Na/GVxcRHZbHZV/7FmuMWspA23Q+ynHpignMCqhUI3blVVMTU1ZXqCYSNcXjQlWBCEsoOwrFoogHlByeVySKVSGBsbM9T0cq3CcYWxry6XC729vVBVVes9RrPH/C++iN5du+AQBJDLL4fyuteBvOIVULZvh/C73wEcB3VkBPIb31j2Go4HHwS3uAg4HOCSSbgefBBcJUumBIEXVgnItuFtuG76Ovzi0C+gQsWlY5fijaevvvY3n/8mng0/C47jkMvk8M9/+GdsH9uuubHqjVvsj+3HB578AA4vHcaQdwhnDZwFn8uH84bOw44tO7DzyE7NilqRVvDwyw/jHy/5x1XnoesoV/9C3WORSASyLBelJzcqk89KYWO7xH+ssu4EpZkxlFwuh0AgoFV0WxmHa7eFkkwmEQwGq7abaUQfsFIIIQiHw0in0/D7/abEpJlZXo2C5/ni4spjxyA+/jhkQpDK54Ef/QhpjwfieefB++EPw/HMM+BSKSjbt2vurFK0jK8KP5uF4zh8+OIP42/P/ltIqoRx33jZzziejYOAFNJ2BScSuQSS+SR6BXsSKj6767PYH9sPANi/uB9HEkewxb8Fu+d2aw0l9ZT+rP17hQ28WvwlFotprsyOjg54PB7b+sCZFQhCSMt60NnF2l69jZgVFBqbGBwcRG9vL/bu3WvpSc2OLC+geBDW2NiY9uWx85pGj9NbSL29vWvejLcDZyAA0e2Gk+MAQqCoKtT5ecQSCSw/8QT6n3wSDp4HSSbBv/GNZe8j9YwzIASDgCAAigJletqWtW3o3FD19xu7NuLw0mHk1TxAgHMGz0GP62Rxbr0WSlJKaudRiAIVJyrGwWFfdB9eO/Fa/OLwL8BzPDodnbj+tOvLnsfoOkrjL6WuSjviL/R7wrK8TlGMbpaqqiIcDmNlZaUoNmE1ZdGOgkqzBYKNFJR0Oo2ZmRmtaHJ+fr4h1wHaIxPMKMrpp0PgeXCEFLK5eB7ec8+Fx+2G+6GHoKbTUBQF8re/jRDHARdeqAX4qQUs33ADiNcL7vhxYHAQ2WuuAWfh/TVLMBnEgHcASSkJnuO1ehZasV6voFw8cjH+EP5D4Tzg0Ono1M7r9/hx6/m34s8G/wyRVASXjF6CUd9o2fNYfaDTuyr19S/6+Av9LFwul6Fr6N1vRiGErPmHr3UnKI10edHmhU6nc1VsolUtVHK5HEKhUNkCQbuvWe04fQfl0dFRdHV11TymHtacr3lwEPn//t8hPvkkoKpQLrsMZHoa/FNPgUunIQgCBEGAE8D48jLiPp/mktG7z7xXX33yvsvlql7SLvJKHn6PH37PySxBhShaXYmZ7CSVqPin3/8T9kT2oNvdjdsvuB0fvPCD6HZ3Y190HwghCKVCWMmt4BX9r8At594CALhs/LKa57ajBqVa/CUcDhuOv1gd/8sslHVCLUGp1ZGXHm/2CaOe9FdZlhGJRDAyMlLUH8zINe0UFP0cldIhYVb+vnaPj1iFnHMO8uecU/yPqgruwAFwsgy1txdkZAR8OIy+738ffr8f+R07IBGCdDqNRCKBcDgMl8ulbWbNeJ+umrgKu+d2FwoUVRWXbLxkVSZWJfJKHvPpefR5+uAW3fjm89/Ed//0XW0Gy0J6AT/+qx/jnee8s+g4laimW+s3oqixUvwlleEC4LEAACAASURBVEpp8ZdyqeJWWyIxQWlD7Kyz0KffVuvIa1UYrHQNphu4JEkYHBw0JSZA/WnDeiRJQiAQgMPhKDtHpZE0ejPlXngB4m9/CzidyP+3/wZUiUtVQ/j3f4fjhz8E8nkol1+O/C23ABwHpNNw3XtvIfU3nQYfDkMeHYUQiQCHD4PfuxeO73wHzuuug+uWW7TsMX1xZT6fRygUKmrNbzeXjl2K//Oq/4NnQs9gwDOA608vjmFU2jwPLx3GbU/chgOLB7ChcwPu3H4nDiweKBrodTxxHMl8clW6spU5Lc2okjcaf7GSOcbShtcRdJPV35R0sxRFsWYxntV28mY3d73brbOz09INaFeWF80o6+vrQ39/v20ZZUazvBoJ99JLcH7xi+AUpTDUat8+5D7zmZOt442eJxyG88tfBidJhZ9/+lOomzdDufZacDMz4EIhwOcDcbvBJZPgZ2ehjo2Bf/FFcLkckMtBeO45kB//GPLf/E1Rx16fz4dwOIzOzk7tiZm6xzo6OuBxuSAQsqpWhTtwAM577wW3tAR1yxZIH/tYzXqWrUNbsXVoa9nfVdrIv/D7L2D/YiF7K5QM4Z7f34OrNl9V9PrhjmEtZlIvzW67Uin+kkqlEI/HtWmxVOxrxV+YhbKO4DiuyG1FXVwDAwOGWuE3w0KhM0PommZnZ5sat9GLbiwWQzQabVhGWasRd+0qiAkAcBy4Y8fAzc6CbNpk6jzcoUPgMplCZhZO3GczM1AAkNFRkP5+cIuLheC6ohQGZz37bCElWBRBHA6A58EtLJQ/P8etemJOpVLI/9u/wfPgg+DyeShnn43cXXfB7fMVigA/8xkIR48CAPhQCKSnB/kPfMDye1WJZC5Z9POKtIL3bXsfFtILeHHhRfS4evAPF/2DbSLQ6joOffzF6/UiGo2ip6enYvyl1JpkMZQ2xeomxvM8FEXBwsICEolEzaFTpcfW40aq9nRFSPlBWM2yivTH0YyyXC5XdURvPbRDHQrxegttTuhn4nCAnEg0MIN69tmFrsGJROG8ogiFxlF8PuT+8R/h+uQngeVlkKEhqFu2gN+3D4TnAacTZONGIBQCxsYKQXhdfKr0ntGemGUZnn/9V0CWoRICZc8erNx3H2avvx5epxObAgHw9FiOAzc3Z/2NKrMOyqXjl2JXaBdUqCCE4MLhCyHyIj796k/XdT2z62gF1NrQx19oe/7SZAs6QbRRFkosFsOOHTtw//33Y2pqyvbz61mXglIPMzMzhlxcpVi1UGhqYaUvgyzLFQdhNTuzjBCiNUOcnJxsaEZZq5FvvBH8/v3g9+4FnE7Ib3oTYLAzsvjII+Cfew7wepF/+9uRvfNOOB94AJBlyK95DdRLLtFeq27fDumjHy10FT7xfqpnnw3pxhsh/uEPEP/v/wWXToN78UXwH/pQoXFkjQJaLpEAkknA4QAvCOAFAQMch47Nm5FOp5EbGoJy5Ag4ADyA3MgIiKJY9t9Xunfffe670e3sxvPzz2O0cxTvOf89ls5f7zpaQTlryeFwoLu7G93d3UXxl127duHTn/40NmzYgMsvvxxXXHEFzjvvvKLkFqvk83nccccdNZvU2gUTlBOsrKxAlmV0d3djw4YNlupJ6ukFVs7cTafTCAQCFXtgNVNQVlZWkMvl0NfXZ+r9aVQMpeE4HJDuugvc4iKIy2U4IC/s3AnH976nWTZcMIjcl7+M3Be+UPEY5fLLwb/wAvi9ewsC5nbDsXMnlG3btLkoAMDPzsL58Y+DIwSkpwfZd7+77PnI4CDUyUnwMzOFdfA8lG3btIAy97nPQfzSl0BiMWSmphC+/npkjhxpSL+rm866SetK3GjaSVBqWRv6+Ms111yDK664Av/xH/+Bubk53HvvvTh06BAef/xxUzOLyvHZz34Wf/3Xf4377ruvrvMY5ZQXFEIKLdXj8TicTie6u7st3ZR29uQipDAIa35+vqimo9ZxZq5ndK2EnBzR63K54Dvhh28mqqoilUrB5XJpT9FNER2OK4zdNfryl16C4ytfAXf8OIjPB7jd4FZWwMXj1c/D88jfdhsc99wDbmkJ8HjAh8PgHnmk+HVzc3D88Y9ARwdACDoPH8bCJz8J7uhRiA8/DHAc8m99K9Dfj9zdd8P57W8D6TSUSy+F8qpXaachIyOQPvc5AIUNYAwoyh4r7XdFW/NXol028nYKapuN54iiiDPOOAM7duyAx+NBPp+ve4Lpww8/DL/fj8suu4wJSj0Y/SBpixCe5zE9PY2ZmZmGzyapdayqqpidndViFNXM3nrcbEbWSuMliqJgamoKwWCwYdeqdIwsy5iZmYEsy5BlWevg2xZWjJ5EAs6vfa3gnpqfBz8zAyKKIN3dEH75S8hvfnP14zmuEJTXxew4hwPq6aeDP3iwEOuQpJO/5ziI+/ZBPHoU7nvuKTSNzOfh+Na3QDZuhDo2BunDHwbZsqXCBYspN+89lUoV+fv19Rb6jbudBKUd1gFYm4UCQDvGjnHYDz30EDiOw+9+9zvs27cPH/nIR/C1r30NAwMDdZ+7EutSUIxQromiHW6reo4tHYRV64akSQRmMbIZlxvR20z3FSGFYVYzMzPo6emB3++HoihaWuby8jJkWUYoFNI2umbMpK8Ef/AguHRhzjony4X/D4AIAoRf/QrydddVbPpIIZOTwJ/+VIilEAJ140ZId94J4fHHgXwewrPPwvHzn598fXc3OnbvLogJAG5+HlwyCdXthqAocN57L3Jf+Yqlv0cUxSJ/fy6XQzqdRjwex9zcnOYe6+joaPpcmkq0m6CYbbsCwFYL6wc/+IH2v9/2trfhrrvuaqiYAKegoOgzpkpTXpsxPbHSsSsrK4hGo0WDsBp1zVrH0caXpSN6m2UVcBwHWZZx7NgxjIyMoLu7G7IsF7Ug8Xg8WFpaQkdHB9LpNBYWFiCKoiYuzR6qpG7eDOJwgMvlQDwecJIE0tkJjueBbLZgfeghBMITT4APhaC88pVQzz8f8g03AJJUsEh8Pkh/93eAKEJ5/esBAMqVV4KfnQW/dy9ITw+St94KEgwWenudqJcBxwEnhJUKTb1wHAe32w232w2/36+5x1KpFMLhMKQTNTaKorRU2NtJUKzMQgHAChvXErTQCEDZJop2jQE2AyGFaXOxWAyba4zFLcVuQaHxpEojepthodD4kSRJmJqaqpqdwvN80VM0beoXi8UgSZLW1M/yeF4z9Pcj//a3w/GlL4GfnwcRhELar9sNddu2VUF9xze/CeGJJ8DxPIR//3fk3/UuKFdeCfktVaYb+nzI3n9/YWKj2w0pmcTAn/95Ie5yAtXvB3y+goVj0N1lFr17DIA2pTSdTiMajVZ1jzWSdhIUsy4v+h1p1PofeOCBhpy3lHUpKOU+FOriol1w7arqrudYGsMBgOHhYdPzVOwUFH16cqWU6UYLiqIoCAQCkGVZeyI2cx19Uz9FUbSeS/rxvHTmhW1PgsvLcHz72+CiUZDRUeS++10ojz0G4f/9P3CJBPKvfz2UG288Wc9yAmHXroL1goJrTHjqKShXXmnsmh0dgKKg43//bzgDgcK56fm7uyFv2wYyNIT8exqbpquns7MTHo+nyD22tLS0yj1mtFuvFdotKG+m7IAG8dtFEK2yLgVFjz5LqVZVd70WSj6fN/x6fQyHxm/MYldQnsYqjIzobZTLi8ZsfD4f+vv7EYlEqr6+llAJglB2PG+5GEA9m5zza18D//LLhQ19bq6QsfXudxfcV1UgDgeKrmiypYvzf/0v8Dt3njjZyfdBHRuD9JnPmDqXnZRzj5V269VbL3a6x9a6hdIuYlgP61pQ6FM3IcTQnJBmxFAIWT0I6/jx4y1poQIAS0tLCIfDWqyi1nGNWCNtc0NjNukTwW099QhZac8lfQxgbm4Oqqpq4uL1es0VtM7NnbQOOK7Qm6scdP0nXiu/+c1wfPObhVhLb2+h+aQRVlbA/+EPEP7rv0B8PqguF/hcDiAEpLsb2XvuMbx2u6i2kVerFqdxL307kno21XYTFDNrWQ9tV4B1KigcxyGVSiEQCFR1cZU7rpExFP0gLH3bEqtCVk/rFUVREAqFkEwmMTExYci9ZLfLS289lra5aWTwvzQGkM/nkUqlkEwmMT8/D4fDURTcr2qxDQyAW1mhiwYpk0UjPvwwxIceKkxZvOIKLV6inHMO+ECgEOs4sZZqcPv3w3377eBmZ8HNzUH1+5HftAmO5WWQkRFkHnwQGB629qbUgZnNs7RavJx7zOwwKyvraDRWgvJMUNqU5eVlBAIBjI6Owufz1T7gBFbTcIHaYpTJZBAIBMoOwmp2CxWafisIAqampgzHE+zM8qo2ZdJIfMvOtTgcDvT09KCnp0dLV6ZP0Pl8vmqBn/Q//gec3/ymFkPJ//3fF6/78GGI3/1uYVIjAPHRR6Fu2QLliiuA/n6o/f2FDK18XsvOqoTzW98qNJE80VeMX1oC3G6oW7cid++9LRETwLr413KP0awxo+6xdhIUZqGsIzo7Ow25uEqxYxxvOahbaXh4uOzsEqvXtbKpptNpzM3NQRAEbNy40dRNb5eFQuttvF5vxSmTrSpa5BYX4eU4ePv70d/fv2qgErVuXC5XYY39/ZA++tGK5+OPHCl0LaZ/I8+D0xWIij/6ERz/+q+AokDevh35f/iHVQF8Df2Exp4e5IeGEP7oR9F3xRVF/b24mRkI//mfULdsKeob1kjs2MgrucdSqVSRe4wmVpTeN+0kKGYFgsVQ2hie5y0F++qNoZQeSwdhpVKpqm6lZlgo+nYuAwMDSCQSpr98dggKbcFfafIlPabpEALHZz8L8Ze/BDgO8lVXIX/77asGKlEXTSKR0Kws/ez30rUr550H0tUFLllo5U4cDijnnw8A4I4cKfT9OoH45JNQX/EKre6kFPnqqyE891zBmiEEuSuuQG7r1iIx4X//e7jf9z7wc3Mgbjek974X+dtus/vdKqJRG3k591gqlao4672dBIVZKOsIqzdVvTEU/capH4RVy63U6J5cqqoiFAohk8lgcnISqqoiHo+bvh5g3XIg5OQMlY0bN2rxC6vXqfWecaEQxO9+F/zSEtSxMeTf9S6gSpxI+NWvCmJy4kstPvEE1IsvhnLZyVnmeheNz+fTqvj1qcn64L4gCEBfH3If/zgcP/lJwQq5+mqQM88EAPCBQMHdRe8NngdXJbtNue465Hp6wO/eDXVsDCuvfa1WnU9xfuc74MPhQoJALgfHgw8i/973nrSQGkCzil3pe186650mVgAFEXK73aYSKxoBs1AYtrVeKR2EVUvgGunyosLmcrkwNTUFnueRzWYtC5iVY1RVNTVDxfR1CAE3O1soJBwcBACI3/kO+BNzPviXX4bjhz9cFd8oumYsttrVVGGolX6d1EVDC1RTqRQSiQQikQicTmdBXDZvhvuOO1ZbL+eeWxiwdUKMiMsF5aKLql5T2b4dyvbthR+Wl1e/oPQ+shgTNIv2tykKHN/6FriFBchXXw31ggsacr1y7rFAIIBcLodjx461tGsCYM1CabUI2sHa/wtsxA6XVyQSwdLSkqGn8NJjzVJLAGmtS39/f5GwNbqppB5ZlpHP5w33J6MYTcGGqsL5xS+C37MHAKC86lXIv+td4KNR/cIB/c/l1vmqV0F8+OGTm3tfX1GH3lpwHAen0wmn07lq9jvt3ks3uI6OjsLm0dWF3Kc+BcePfgTk8wXr5ayzDF+z3HuUv+km8Lt3g4/FQEQR8vXXN9Q6oevgOA7/n703D5Olrs/F36ru6r17pmdfe7azAeEYWUQOiAgGxEgksigS0RivxGsWEn0geLn6GLl6o9EQiKgPeJMryRV4xAVDDKCHHyKJeiIc9rNwzpyZ6emZnul971p/f/T51qmuqe6uqq5ezsy8z8PDw9DV9a3qqs/n+9neF5IE58c/DubRRwGKAvPggyg98EBVlNcqMAwDm82GgYEBuFwumTWBpMeIiqKZ7jEzMMo2vBn05IFN6lDMPizNOBSyQy0UChuEsBqhFRQq6lkX9XFmYNQRkdZtiqIwMTGh+7x6W7wBwPbTn4J+6SVZE932i1+Av+ACiKOjoBcWKs5EFCGNj9f/wuFhlP/3/67QwKMisIV6lPPpNPy/+EWFmv7Nb94Q3ajZe9UFZoZhKs5leBiu2283vYOW75UogjpyBOLEBIr/9//C/swzFe3697zH1PcagexQEgnYn3pKvhfU2hrsDz3UFodC1kFITNWsCVbNHRldS6s+363YlA7FLMwa9kKhgMXFRQDA9PS0YYNtZcpLEAQsLy+D47ia6aV2NAEkEglEo1GMjIxgbW3N8D3Rex6lXvvJRYLOZMB99KNw/fEfg1pdhTg2Br5GobvqnNPT4P7yLxufNBqF5667QK2twfn00xAuvhjcH/1R3UPUBWbCmhyLxcCyLNxud1Vx3xAEAc5bboH9iScAmgb/vveh/LWv1e4WaxWczso/ZC4HkB19O1ArzWSz2Takx5RzR1anxySpvqR3rWO2HUoXw4zRNGrYlZ1To6OjiEQiLZsm13Ockv5+Zmam5gPaSodCOtsKhYKc4mpEo6J1Hj2QJAn8vn2wPfFEpTgtSZVU1Xnngbn3XtCJBOBywRaPw/GVr4D9X/8L1G9+A+b73wdEEcKVV0K49FJDawMA5t/+DXQkAs+rr8ImiqBfegncNdfUj2hU10d20ACqeMeUWuPEyNX6HclvYf+XfwHz7/8uR2PM974H/t3vhvA7v2P42sxANp4+H9iPfQzOe+4B8nmIZ50F9tZb27KGqnU0gHruqFZ6zOv1anbu6V3HdpfXFocRh6IWwmIYBsvLy6bOa0WXF6EvGR4eRjAYrPswNzMUWO84IoZls9kwOzsLm80G/qQ2iBHoWZ98fYODYO+8E/af/hQSRcm6I/SJE6dqBxQFen4e1MoKnF/9aiWqAUC/8QbEoSG560ovJEmC7ZVXIOVylc6sfB7MAw+Au/32qs/Znn0W1NJSpXV4z56a36fFO0Y6x5TT41oGjqIoUJlMdTQiSaAaNBRYBfXvxN1+O/j3vAf04mKlecDAYLEVazHTCl8rPRaJREylx8ysQ5Kk7RrKZoPeGoGWEBYJc808TM2kvADIjQBq+pJ6x1kdoSjFsNRUN1a0ldalPxkbA3fzzdV/GxwEjhyRDa00NAT6hRcqkQzJ8fM8bC+9BP6kQ7E98wxsP/4xIEkQ3vOeyjS7BoS3vQ3UX/81pMrFQfJ6q5sAANi//W0wP/whQNOwP/oo2E99CuK+fbquk/COKafHlQaOGDfyzPDXXAPmoYcqNSNJgnDGGRXH2gZo0a5LZ50FwUBzgZVrabbYrk6PEVJRJS2PUpNHK6owE21sRyibEErHUOvBJOJT6kiAhLhmd0lmjC4xKPl83hAzADmf0bXW+ixh8NUimLSa/0uJep9hb70VVD4PanER0sgI2E99CiiVINntlcl1AMjnQb/yCuwPPwzhTW8Cc889oE4yRtP/8A8QR0c1Iwtpxw5w558PvPQSKJcLcLshKLVHJKlqpoXiedh/8hOwOhyKGur2WGLgstksCoUCaJrGek8P/PffD98jjwA2G7hbbgECAcPnMovTdZhQD0jnnjo9Fo/HUS6XN9S+zNqA7RpKl8OsIav1QEiShNXVVWQyGUzXEMIikYbRB8PMWgndOwCEQiHDXWWA8RdQvU7lPdFLMGkVGq67rw/lr351w5+5D38Y9sceA5VIgCqXQb/xBmzPPAOmXAbldkM6maKhOK4SvezZA+RysP/kJ5B6eip1CYpC4W/+BvyXvoQeSYJ49tng1UV5dfrCImOhNHCJRALFYrHyO3g84G66qZKecTjg4bi2KCeeztPpRqGVHiPDlalUSo4ezdz37Qhlk0LLKZChKZqma4pPAc3Nkxg5jkRJo6OjWDk5vGcUzTYP6BHkUh9jBOpjLCOlvPpqCFdfDeYb34Dt2WdBv/56RSqX5wGOA+VwAHY7xP7+StSRTML1sY/B9sYbAADuiivAfvnLkCYnEbnjDrhmZzeehKLAX3MN7N/5DihJguTzgWugj2IGFEWBYRhZJ5zneeTz+bZKIneKc00NrdRbq6GsfQHV0SPHcVhYWJDrLy6Xq25zxXaEskmhdgpklqKvrw+Dg4N1H9hm6OT1pnjUUdLq6mrbhhTJMUoxrJGREcsbAIx0eZmF5HKBWl4GvbwMUBQkmq7UVgQBcLtBMQzoQgH0gw9WnMnJNdmfeAL8zTcDdYrsAMBfey2Es88GPT8P4ZxzAA1a+2ah3pHb7XZN7qtWSyJ3Q4TSDZESiR4dDgdisRgGBwerWsOV3GPK5gryHG8X5bsYzQw3kh1DveHAWudslXEnEQFFUVURQTtmSpTgOA7z8/OyGJZemOnLr4dmjYc0MlKhWxEEQBAq38cwkIaGIE5PV86xvFylhgigQkOvs3NN2rWrurbSRqi5r2pJIpMCs1lj1g2GvJvWAZyaetdqDSfaL5Ikwev1Vr2HVkYogiDgzjvvxPz8PCiKwuc//3nsasOzuGkdilkQKV8iV6qHe0p5rBkj3SjlVU+it5moyOjMTTqdRrlcxszMjK5uMnIeM2szhWwW1MoKpMnJKgZeLdALCxDPPhvIZEDnchXHwvOQTjpqyeOB8Ja3AE4n7E89VSFylCTwb3sbxL17N3JmdTlqSSKn02msrq6alkTuFkPeLesAtNeivv+EOeHJJ5/EP/zDP2BoaAiXXXYZLrnkEpx77rnGh1tVePrppwEADz30EH71q1/h7/7u7/CNb3yjqe/Ug22HooIkSQiHwwgEAjW1OmqhFRFKI4nedkQohKadhO16nYn6XGYHxEqlEsLhMGw2W5XSonL99K9+BcfXvw4qk4E0OIjypz5Vd/ZDHB2FTRRB2e1yIV6cnAS8XggXXADuPe+BNDUFACj+0z/B/thjgMcD/vrrKwX3LnAoZo2osjVZLYmsFLZqJTWJ1egmh9KowE5Rp3jfbrjhBrz73e/G/v37sba2hrvvvhurq6vYv39/Uymwd77znbj05NBuJBJBoE1df93/pLQRyWQS5XIZAwMDGBkZMXy8lRGKKIpYXV1tKNHbaoeiFMPq7+/H2tqaqXOZ/bySuRlA1TS5KIrI5/Nwu91wfve7oMplwOkElcmAefhhsJ/7XM1zCL/3e6CXl0G/8goojoM0PAz4/eCvvhrcJz5R/eGBAfB1mIpPdzQjidwthrxb1gEYX4vdbseePXvwgQ98AH/xF39hWceX3W7H7bffjqeeegr33HNP09+n65xtOUsHYJT2gAhhkZyyGVjFycVxHBYXF2G321uqpdIIxJgPDw+jr68PhUKhJRP2taDUm3c6nRAEQRa6KhQKiEQicsF5JpmEk+Ngs9tBUxRQKtX/cooC99//O/iLLoLjvvtApdMQ9+xpyMm1FdBIEpnMXni93q4x5N2yDsCcnjxwqoZiZS3lb/7mb/DpT38aN9xwAx5//HHTtk0vNq1D0QuWZbG0tASGYTA3NydPI5tBs9ECMZR6u8rIsVan2ZQNCUoa/nY1ACjpZEgNS1DoepCCM0VRCIVCEAQB0nnnQXjySbAsCwpAYc8e4GT0suEF5Xk4PvlJ2A4dgjQ4iNIDDwAa0szdjnYYUYqi5E2WliQy+W3zte51m2CULr6VMNOAQmbgrMIPf/hDRKNR3HLLLXJU2Y7fZks7lGw2i+Xl5Sq9ELMGGmieQiUej2N9fR0TExNyb7uec1pp5NUcZeriYKvnDniex8LCAgBgamqq8ZAYz8O+tgbpwx8GPTcHWziM8swMSuecg/zJ6EW5o2YYBs6PfQzMk09Whg2PHoX7ve9F8ZlnWnpdmwVqSeRMJoN4PG4ZsaJZdNMch5kIxeq1X3HFFbjjjjtw0003ged5fOYzn2nL4PGmdSh1uZ8kCWtra0gmk5icnKwSwmpGtbFZCpVkMom5uTlDHR7NRA1q56dUd9QSwzJrHPSuUckHViqVTp0/mQS9f3+lYH7ppfLUOZ3NwvnpT4M+fhySxwPuwx8G//GPwwagH5DbZcmwXzweh81mw66XXoJEUZUWYJquzKJswzDIYCXDMJicnKwiViREqRskkVuEbkp5GXVurVi7x+PB3//931v6nXqwaR1KLfA8j3A4DEmSNPmvmhHZMnMsMeIURWFqaspwu6BVaSgywKlWd2zFubRApv9JN1sikagcs7oK2513AqkUJEGA7de/Bnv77aAAjN5zD2yHD0MKBEA5HGD++Z8hvPOdgOI3tdlsVTvqcrkMMRCAtLJSIXekKAhuN7g2UZVYiW7YlSt/VyWxImmNzefzyGQy1ZLIHo+csrRyHd3kUIzWcDv9O1qFLeVQSH1Ca56DoJmUl1GDq5TojTWQqLXqnOrjJOmUpkujVJvZcwG1U2WSJGF9fR3JZFKe/leCfvzxSiswRQE2G+gDB0AtLoLZvx/2l16qEEAmEhAnJoCeHqBcrnIo6vW7XC7w3/oWHB/8IKhYDKLHg/XbbsP64mJVt1Mn6wGnG2q9R4YlkZtANzmUbkh5dQpbwqEojeb4+HjdnmyapqsKwEag91hl0Zuk3OQduUE0m/KKRCKyGJbT6WzZubQgiiLC4bCsLqmMEORzUVT1tDpFARQF24EDKPb3w53PV/6cSEDYtw/QwWiA3btR+M1vgEIB8HgQAODXoCppSkVxi0CvIW8kiUx4xxrxXjW7jnZgO0LZhCA/qLrI3Mhokkl5s+dsZHCVEr3KlJuVtRA9IHUkUi/Rk9+2MuVFyPNcLldddUnxmmtA/epXwEmaFGHfPkgTEwDDQOjvh+DzgYrHIU5Ngb3zTmMLU7RQ1qMqIXMvyuilG9ANRtTsGqyWRO6Ge0FgtOOsG1KXVmHTOhRAWwirEVpZQ1EOCU5MTFStx+x5zTQREEPp8/kQCoUs5djSgtqhFAoF3iYufwAAIABJREFULC4uor+/HwMDA/XrNX19EL76VUg//zkEt7siUkVR4K++Grj/fiAYhBgKVaRmLYwi1FQZ6ujF6XRCFEWwLLsdvTQJimpeErmbHIpRB0G4vzYDNq1DYVkWx48f1yWJq0SzNZRaxyolevv6+jSPbUfKi1C5uN1u+P1+Qy+hFS8sEeNqlHpUQvJ6IVxxBQRBkHd/0uWXY8nrxQ6nE9KuXfpSXSZBcRwCX/kKetbWIOzbh/INNyCTyaBUKsmyBp2ovbS6hVvvGqw25LUkkcmzoyWJ3E0OxUyEsu1QuhwOh8NwCy7QXNuw1rEktZRKpepK9LbaoUhShfo+m81iZmbGVM2m2bTc6uoq0um0LjEuci5RFOVz2u12iKIIURTBcRyE/n5ws7NNFXSpw4crk/LJJMTZWbC33VaVCgMA13/7b2B+8AMAgPTww0ChAN/NNyOZTGJmZqajtZdOG9FWG3Il75hSEpkwJRBRq25KG5mJUE63DsNa2LQOhXSZGEUzKS91hCIIApaWliCKYl0RqmbOqyeiUophzSoMcLscClDRvQfqi3GpQZyJ0mjRNI1yuSzzezEMs+FzRl5mx733gl5dBQDYXn4ZzLe/De5P//TUB3getl/+Uv5PqlSC/emngZMa9kZrL91i9KxCu6OkWpLIyWQSPM+jVCpVFfc74XDNTMpvludi0zoUwJwBbLaGQs5XKpWwsLCAQCDQUITK7Fr1HFdrHWbOR4418sKwLItSqQSfz2eIvZmiKKRSKfT09FRtDNLpNKLRKEZHR+UWZ/KdJHohzqVhKkGSKmqNp05a0UhRwmaD1NMDKJQxpTrptUa1F/UkeTPohpQX0NkoibQm8yc1ajweD/L5PNbW1sBxXFXtpV1RgJm24e2U1yZFM7tw4oxIrnd0dBS9OjmimuHkqnWcUipYvY5mHZgeI0KGJRmGQV9fn+6XTBRFjIyMIJ1Oy0OfPp9P3v2HQiHNlBlN01XORelYyJptR47A+aUvgVpfhzg7WxHaWliotCOLIsQdO9QXjPLtt8P5uc+BjkYh7N2ru5usXvSSSCQsiV42e8rLyDpsNlsVuauSdywWi8n/v9XR4naEsg0ZzUQoQGVHHo1GNYf0Gp3XjIFvVLeptY5W12wSiQSi0SgmJyexvr6u67uVNRNiGCSpwna7srIi70LX19fh9Xrh8/lq7vLVLyiJXpxf/CLoQ4cqsyyxGPh3vKMyv5JIQNy9G/xNN234LuHaa1G46ipQ8Tik0VHAbgdMtJbXil4SiQTK5fKGYvPpgG5xKKIobkilqnnH2iWJvB2hbEOGWYfC8zxWVlYgiiJ27txpuFBslYEnYliCINStVzTTzVYPyuI/mfvRwwKgVS8BKvc1Go3C5XJhdHQUkiTJWh1k10ly6h6Pp6ZRINELHYtVohGgkuJKp1H44hcb1148HkgWUn9bEb10S8qrG9DIsdW636T+AlgniWzmmO0I5TSA2TqBUUNLKF38fj84jjPVdWSFlgqZc/F6vQ3rFWYZAerdU0EQ5BSVUsel0e9Qy5kUi0WEw2EEg8EqfjHlrrNUKslCUCzLykbY5/Np5szFHTtgI05FFIEzzpC7x8gayP1pJ8xGL52ODrolQjG6jlZJIpOW4e1J+W0AMJ56Iqmd8fFxeDwepNNpU+dtNkJRi2HpgZUpr3K5jIWFBfj9/g1NCPWujaS51C9hJpPB6uoqRkZGas6rKAfiBgcHwfO8HL0QOg8SvRBNCO6LX4T05S+DWl+HtGcP+D//c121l3a+8HqjF57nOx6ldJNDMfsbKVuTm5VENrOO7QhlE0NJmljvRSEqj0oeLEEQTL/gzTQDlEolLC8vV4lhtep8WseZcWbAqbqG8j5LkoR4PC5LCxipQ9nt9io6j2KxiFwuh9XVVZmM0Ov1wvf5z9c0CrVqL90WvRQKBWQyGaytrSGbzZ52tRerYaVja6ckMtlQbTuU0wBmHjCyG61XKCMSvQzDVPFgkePMPNxmOMREUUQ8HocgCNixY0fbdFTIccT4x2Kxus5MfS5l8V15n4iTZlkW09PTTbV5UtQppcGhoaEqo0Co1En0Um9eoVb0QtKF7TYGyuiFzFzQNG1p55gRdMvuupWRkloSmfCOaUkiG30eOrVJaRU2tUMxi3rGlrTCavFQNfNAGzXwREfFbrfLvfitPJ/6OHWEVu/88rkEAdLPfgawLKTLLgOlIOokOjV2ux1TU1OWv2Bqo1AoFJDL5eTuMeJcvF5vzY0EWRPLslhZWUFPT09VRKv8TLtA07Rm9KJUUGxl9NJNKa92rEOZZq0niZzL5WryjqnXDWw7lE0NrQI52Y03kuhtFN3UghEDrxTD8ng8iEQihs5F1mk2xSYIAk6cOAGbzaabqRiCAPov/xL0Cy9UWnZ/+ENw99wDOJ0olUoIh8Po6empSRZpJSiK2pDSyOVySKfTWFlZgdPplB2MuiBbKBTkKf1gMCj/Xav20upWUPXvp4xe+vr6atZeSORmhRHrdA2HoFOOTd2anE6nkUgkkEwmsbKy0lASmdiZbYeyiaF2KEoK/Eb8YGZ3/nq6vCTplK7L5OQkfD4fSqVSS+dJtNawtLSEYDCIoaEhXS8xRVGw//znFWdC0oNvvAHbj36E1FVXYWVlBcPDw+jp6TG8HivAMAyCwaBckCXRC1H2JJGLIAhYX1/H2NiYTP1BoDQI6poLqRO1wmjUu/+1SBaVxs6K6GUrRSj1QFEVSWSHw4GJiYkqSeRIJCLPVyklka2OUDiOw2c+8xksLy+DZVl84hOfwOWXX27Jd+vBpnYoZh8wZetwI511Naxo/9WCVhOAeq1Wnk8LmUwGLMtiaGgIQ0NDuo+z2+3IxOPwcRxoALaT9zCbSmF1ddVw8b2V0OKKymazMpWHy+VCuVyWDYfWM9aosN+O6EUNNcmiVdFLNxjyblqHssmkkSTywYMHsbi4iN27d+Pss8+25PyPPfYYent78ZWvfAWpVArXXHPNtkPpNEg6iHQvDQ0Noa+vT/du3GoDX6sJoNFxjaD3OEk6pTDpdDp1d5KRWktfXx/Y668H9+yzsB05AlaSwI2OYuWCCzAxMdE1zkQLDMOgXC6DpmnMzs6CZVnkcrkqShiyy69lhGsV9sl/m41emkk3GYle6k2Rd4sh76Z1aP2WFLVREpnneRw+fBgPPPAAvva1r2Hfvn1497vfjSuuuML0+d/1rnfhyiuvlNfS7o3LtkPRAEVRSCaTyOVyhlpxgeYoVLQcERGj6uvrw+Dg4IaXxuz59Doiku5jWRZzc3NyGqgRlJ1cNE3D5fcD3/wm8IMfILW2hvQ73gFnMIilpSXNeZFuAGEdoGka09PToGkaTqezygjncjkkEglEIhG43W75OoxSwpiNXqy4V3qil0YCV51GtzgUvVooNE1jz549uPXWW7G8vIyJiQk8++yzOHr0aFMOhdiqXC6HP/uzP8Ott95q+rvMYFM7FDMPmCAIKJfLGyR6jZzTqgiFiGHVE6NqpmbT6DgSGTkcjiqZ3kbH1aRRef55sE8+iQBFYWB0FOL119ecFyGGuVMcR+TavV4vhoeHNzxLSiNMhg+1KGEaGeFWRS/NwEj0YlRMqlXoFoditI2atBkPDw/juuuus2QNKysr+OQnP4kPfvCDuPrqqy35Tr3Y1A7FKEqlEhYXF0HTNAYGBkzNQVgRMUhStRhWPTGqVpE8FotFLCwsbIiMGh2nNIjKFzwfDsPx+c/Dz/OVocJvfxvs8DBwySUb5kVyuZw8KV+v46pVIJQv/f39utU+bTbbBiJC4lzK5TI8Ho98HbWeK6PRSzuMaKPoRTlP1MnopZscitHBRivvWSwWw0c/+lF89rOfxYUXXmjZ9+rFtkM5CSLROzIygkKhYPp7mtGGJ3nVpaWlDXxYtaB3sr/WcVogtPdakVEj9oANO1ZRROGf/xnCc8/Bk0yCVrTa0q+/DuGSS6q+Q0/HFdn5t8J4ZbNZWXqgVmt4Iyjbd8msghYlDIle9A5Vkn8rHUy7oY5elpaWYLPZDNderEa3OBQzTMNWPsff/OY3kclkcN999+G+++4DANx///0NFVKtwpZ3KJIkIRqNIp1OyxK9pVKpKdVGsxGDIAg4duwYenp6NNMstY4DjL9QWuuUJHO098S4adGolP/n/4R7/344bTbQ4TAkigJ6ewFRhDQ3V3eNyo4rszULI0gkEojH45Z3nakpYbQILYmDaRS9kH9nMhlwHAebzSY7mU50jtE0jUAgILdVk+glmUzKUUuray9mNlStgtGIw+oI5c4778SdOvV6WoFN7VAaPWBkOluSpCqqd7N1EMB8hJLL5cDzPCYmJnSLchE0M1NCIIoiwuEweJ5vSHuvPE5ZfFfeb0EQsHziBCb/8z/hcrsBiqpoifA8pJERCG9/O8R3vtPQNeqpWTSisa91H6LRKPL5PKamplrKh6WctCaElrlcrooShjiYWg0KyWQS6+vrsuPrJKGl8ne3qnPMLLrBoRjVh99MPF7AJnco9VAsFrG4uKgp0dvMFLlR406igmQyCZqmDTsTwNx6leskszYul0vuZtJzXK3iO8uyWFpagtfthtPvB3K5yv/o6YFwySXgP/c5g1e4EeqaRa1dv8/nq8sOS7rYRFHE9PR0+9ss7fYqSphaDQqEEiYWi8nRNJlFqld7aXVhv9ZzV6/2YnX00i2NAYDxTMG2Q9kEaCTRa4aoUXms3ghFKYY1MzODY8eOmTqnmQiFOCHSljwwMFClOVIP6hx+VfE9n8fy8rJMTcLfeCOYBx4AOK4Smdx8s7GL0wE9u34tIkiO4xAOh+F0OjExMdFxo0SMbK0GBYqqUPyPjo7WjaLaTcevt2mhVdFLt6S7AOPOzeqUV6expRyK3u6pZmSA9abL1GJYZH1mz2kmQhFFEQsLC3W5yWodJwjChhcnlUphbW0N4+Pjcj+8+IEPgN23D1hagrR3L2Cy0G0E6l0/KexHIhEIgiB3jMXjcfT19el2pO0GaVDo6emRNx5utxvRaFS+Dr2ElgRWRy9mjHmt6EWtnqg3eukmh2K2bXizYFM7FOVDxnEclpaWQNN0w+6pZmsojYy7ln6IegdpBGbSbNFoFJIkNWxLVh/ncrkQjUaRzWar2mDX1taQy+WqUjHycaEQEArpXh/9L/8C+/79gM0G/rrrIDYx6EVRp4ggh4eHwbIs4vE41tbWQFEU8vm8PPFei0qlkyBdf06nE5OTk/L6SIMCibaJomOj9mqroxcrjLne6MXj8Wj+Rt20yzeT8mpGpqHbsKkdCgFJ6+glNGy2hlJLWleSTlGYqCfwSTrDrEMxkmZbWlqSr8+IMxFFET09PfD7/VUFccKuPDIy0nRBm3ruOdgffhjkDti/8Q2we/YYckj1kM/nkc1mMTU1BZfLJV+HkkqFFPY7baRILcrv929gSXA4HOjr60NfX5/p9upuFBMzE710U4RiRg+l08+ZldjUDkWSpCqJ3lrT5mo0k/Kq5YzUFCZauxJyXqMPmN4IRZlmGxkZwWuvvabrZVQX30lB3OVyoVgswu12w+l0IhaLIRKJVBmzegVxLdDHj0O5GorjQB8+DLFJhyJJEtbX15HJZKqiKLWWiNFhxFZBOVzZSAWzVnt1MpmU26tJ9FIvCjMTvbTamOuJXhwOR9e0Dm8X5TcxSP5cyc6rB82kvLSOVXZRKSlMtI41ExnpiajI7pUQXSrPV+8FqFV8LxQKWF5e3jBN3uyku3jWWZC+/31QJ6M8yeuFuHdv45tQ7ztPMjVzHIfp6WlNJ6d3GLEdfGOk3jMyMqJ7E0Sg1V5NohcjUZiajp/8Wx29tNOIq6MXEpml02lwHIf5+XlDtZdWYDtC2cSgaRqTk5OmO6DMnlN5LDHkerqoWkWjohQGU+p4NDpOc/IdlUn6aDSqqQtSb9IdQENjJp1zDviPfxy2n/4UoGnwN9wADA/rug9aUCpBhkIh3S+vHn16s1FYPZDGhomJCXg8nqa/T0uPPpfLIR6PY3l52RChpTp64XlefkY6ARKZ0TQNQRAwPDy8IXqpJ27VCpiJUDrFV9cKbGqHAphvqW02QiHpNqUYVqvOW+saJUnCysoK8vk8ZmZmNkRpFEVh8dlFZE5kQNEUxs4bw8CZA3Un37XSRvWuR52KyWazDVNK4lVXQbzqKsP3QQ0SGQYCAU2mZr1o1M5rBd+YJFUUQVOplK57a/Y6lFGY2eFQ0lYfDofR29sLp9MpP3+dqL2QXb5W9ELa2AFjnWNmsR2hbGMDmq2hkHpJsVhsqPCohJURipITrJZMb3Y+i2KkCMZZMebhX4bhG/eB8TEbdlqiKCISiYDn+Zppo0ZrJC+8OqW0trYGhmHg9/s3zIqYRS2pXitgNd8YaWcvFouYmppqW72m1nDo+vp6XYdPanGk5VqJToiJaUUFWpuZdkQv2zWUbWxAMzUUIvtpSG9dcV6zDkW5XsKaHAgE6nKC8XkejK3aeOXjefR4eza0XJMBQCNpo3qolVJSzoo0mrGohXopOavRLN+YclJ/amqqY+kPreFQrRoSaREfHh6uORTcbjr+Rka8Vu3F6ujFTGPAdoSyBWC2hlIoFLCysiLXbozueqxIeZEZl5GRkYY7c++IF9zrHOx2OyRIgB3wjfiq1k26jUiLaivy0OqUknrGQi8JJEkbJZNJhEKhtjGsEmgVxOullAhTAsMwXTGpr4SWw08mk4jH46AoCrmTdDr1qG2sFhOrBaNGvFH0QmZ6jEYvZB3bEcomhpmXlBh2Iw8qEcMaGhpCIpEwdd5mury4IodoIYpkNimzJjeCZ8QDX78P2eNZgAJGzx2F3XnqkSA1gmao3M1APWNBZkfq5flJvahUKmF6erorhsUa8Y1JkiQ70W5yJmpQFCVHLCQlRxxlPWobNVoVvTTTaWZl9GImOgGwXZTf7DAyZKimc6EoCvF43PR5zTiU5Z8vI/56HBIk7Lx4Jzx79HcHBaYCGNxVXbAmA5ipVKojO30laJqu6lJSG2XiWNLpNGw2G6YbkFt2CsqUkt/vx+LiorxDPn78uG6j3Akkk0nEYrGqZ0GL2mZlZUW34qaV0YuVrcu1ohc1I4FW9GJGC4Wcc7Ng26HUgB7jriWGxXFcW6nv115bw/qhddgcNgwNDCF1JIXUzhR6Z+qzFhMaFWLYSL2CoiisrKyAZdmu2ekTqPP8HMchk8lgbW0NoijC7XYjkUi0Vd3RKMjOVzljojTKVtSQrIK680wr3aiktgHMzyHVEhMj/64XvbRqFsZo9GI2Qtl2KKcRzD5oxLjXepkJ/b1aDMtslGHm2GKxiOOHjoNxMJWXlaZAMzTK6XLd48hOcHR0VDYAqVQKkUgEFEXB4XBgdHQU6wfXkY/mwXgYTFw0UZUO6wbwPI9EIoHBwUH09PTIhX1lt5Xf7+8KGhXgVLOAkjwT0OYbU/N0KWtI7ZqnWFtbq0pz6UGtDjjSeKCm49eC1txLp6WQyXrqRS8OhwOiKKJcLuv6nbYjlC2EetECkcjVor+3ouVYD8gaZs+fxWvHXjv1PygguLN2MV6SJAicAIEVQDM0KJ5CMBiEx+PB4uKiXJc4+G8HkT+eh9vrhsvpAptjsev3dpm6rlaASPUqd/pa3VZkgK+TNCpkJimRSOiaMdGqIbWTb4zUo1iWbarzTGmAAW1CSz2OshEljBbzdauhFb2kUikkk8mq6IW0JmsO8W5HKFsHWq3DZNemRyLXzK5JT4RChguTyaS8htLVJay9uIbeYC9Gzh2Bw6vdCSWKIlILKURfiCI1n0L8cBz+CT8cAw749vkwMTUhG+eCswBHnwOlcgnpdBqxtRh853U+DQM0lurV6rYi+ijKFli/39/yeoUkVZidC4WCqRSiuobUar4x0sYsSZJlLeIEjRylMnrRS2hJooRgMCiTsnbi2SSDlQ6HAxMTE2BZVqaFWV1d1ay9tCpCefHFF/G3f/u3ePDBBy39Xj3Ydig1oG4dVophNZLI1VvQ1zq2ERUKIZicnZ2VDYjIimCzLEooIX4kjiSdRCldgsiLcAVcGL9wHKAq17D24hooG4XY6zFQNIV0JA2qRME/70fg7FO8UQ6vAwzDVPLmfkCiKzUXo628VoIYZ6NSvTabTXPmhRSRW1WvIMOggiBYMmOiNemu5SjN8o0RJmqGYTA2NtZSR6t2lEbnd4BTkhSBQADBYHBD5ELewXZFAGSmRLmhUab+lLWX+fl55HI5TE9PW3qf77//fjz22GOaG612YNM7lGZrKEBlMnhhYQE+nw+hUKjhd5LdhxnW4FopL47jsLCwAKfTWUUwyZd4LO1fgiAKKHAFHHviGAKTAay/ug4A6NvZh/XX13H2R84GBQoiLwIUIPIiynwZkk3CZN8kbFK1sZu4aALzT8yjEC+A8TAIXRqCt88rU4qr5yvIlHuriBOtkuptNPNC0jB+v78pR0mMs91ux+TkZEuMWi1HaYZvjOd5OeVZbxi2Fag3vxOPx0HTtHwtJM1HaHWCwaA8ra9eczulkIHatRyt2svRo0fxox/9CEeOHMHevXtxySWX4Morr0SoSVbtUCiEe++9F7fddltT32MWm96hmAVxKFpiWHqONTtPIghCpesnVgBto+Huc8t6Lv39/RgYGJAjmdUXVpE4mkApVoK9145CtHJM/FAcECsPeDlbRvxIHKVECZ4BDzyDHuTjefB2HmJJxNDMECABwR3VdRcbY8OO9+zQXKfWfEU2m5UNmdU7/lZK9dZKwywsLFQZgnrcVlrrJd1z7ZoxaYZvjBjn3t7erlCvVD9f6jSfy+VCqVTSpH5RwmoxsUbQs4kkzvPKK6/EhRdeiHA4jEKhgJ///Od48MEH8T/+x/9oag1XXnmlTMTaCWw7lDpIp9NyesUI86sR6pZiqojwf4RB0zQ8Z3kg0RJe/97ryC5nAQDuSTeYM5kNei7HnziO5PEkRF5E4lAC/tlK+qCQKKCUKoHLc2C8DPwhPyiago2pGPbBcwex/v+tY8c1O4AU4PQ7EZwNYuDMAQN3pvpaSSsvMWTZbNay1FipVJJJCFtt7BrNvCjnK2rt+EulEpaWlhoau1ZDL9+YzWbD8vIyBgYGLOc8swLqNF8+n8fS0hJcLheSySSy2WxV9FKvsK9EK6IXM23DbrcbF1xwAd7xjnc0de5uwaZ3KGYMkCiKKJVKAFBTDKse9EYo5WwZB+4+AK7EAQCk5yQMvHUA/DoPu9OOTDaD9RfWsSe4B+tL68gGshh+8zCKiSJih2NgXAxoG43+M/qRWc1gYHoANocNgYkA4ofi4Is8JFHC2PljcPY45TmHuX1zcNvdmH9qHqVECRkmg77dfaBtze/WGIap2vGTHL+Z1BhZ7/DwMHp6eppemxFocVuRa4lGo5o7fpIj78R660HdbaXc8ZdKJZktmGXZttXDzIBo8IyNjdVkH1Cm+eq9t/WiFxJpGHUwZgYbN1OHF7AFHIpRkPCfoij09fWZ6pzRG6Es/+cyuBInG9ZSpoTkoSR8bh+SyWRlWE9048S/nYBv1IdisogXvv0C+nf3I3IggqGzh+Ad9MLT74F7xo3pC6bh8rtQypQwcu4I2CyLoTcNYe5dc0gkEojFYrLOxmsPvYZioggAKGfKoGgKM78zY/ha64GmadOpMat1QZqF3W6Xp8O1dvwOhwOlUgljY2NtpakxA6fTCZZlwXEcxsfHAcAwhX27QZy1kvCzltPP5/OIRqNgGEZXk0KjqX2tz2hhqzMNA9sOpQpKMSyO40wPKOqNUGxOGyABRPNWEiX4ZnyIvxoHY2fgdXoRPxFH/65K6iSzkEE5W8bQ2UMI7ggifigOZ8AJyk6h/9x+eIe9EDgBrkCFHsPpd2L4t4dlavTp6Wl5B1qIF04NY9IUCrGCqWvVi0apMZfLBb/fD6/Xi3Q6rVtzpRNQF1lJK7nD4UAkEunozIseEGetbLtudsffSpCZI/VAqBpKp99Mk4LZ2otRsaxWOZSJiQk88sgjln+vHmw7FJwaPFOqGkaj0aYm3vVEKJMXTyL6YhTphTQgAYG5ANy73Pits34Lh759CPFYHKVMCUs/X4LdY0dmOQMbY0PscAwOrwNj549hz3V7AA8QjoSRZtPoP7cf2SOV+svg7kEk2ARomq5qW5UkCXa3HUJRAKjKfzt72mu41akxQgC5trYGoMIVRRoUumWXrAZxJrlcTm7jJh1K2Wy27dLBekBmeLScdaM0Xyf4xkhTQa2Zo1qo1aRAImQjhJYE9aSQAXM1lM1EDAlsAYeih/4gEomgWCxidnZW3sGTjisz0Buh0DYa5//J+UgcTSCbzyLvzMPpdEIIC3D3uuHp8yB+JI7l15YRmAxAKAooJorwDntRWC3AFXTBN1LZJU9NTSGbzYL1sqB/i4bb7UaqmELAWa2JUs6W8eL/eRHphTSykSwGzxzE0N4hy9NdRkDTlfXG43F4vZXW5Hw+X7WzJNFLt7yAap16si51h5J6l9wpji4yEJvNZnUPWKp3/O3mG0ulUlhfX7eEoFTZpGD2WmpJISsn9o3UoDZjyouSzG7DTyOUy9rcVqS9k2EqXVTKBykej6NcLmNsbMzw+ZaWluD3+zUFiACAK3IorBfgHfLC5rTJbMWDg4NIpVIQj4gI/0cY2eUs0otpQAQC0wGwGRalRAnBXUG4e91w9jnhG/GhnCnDO+zFzvfsBG2nkU6nsbKyAoZhwPN8VafV0e8dRfxwRdOisF4Az/E4/0/Px9DZQ4av0yqwLCvfM7VUr3JnWSwW5dRYOwcq1SBDrjabDWNjY7qNAknz5XI5+VqUhf1WgTBil0olTE5OGlbb1AKZ39G6Fiv4xkgkFQqFWp72JNeSz+dRKBRMSTor03JOp1NX5xh5R2dmOreZsxqbPkIBtCfQyWxHX1+fpt64kdZfPecjiB+J4/D3D0PgKlxagYvc9heZAAAgAElEQVQC8E/6MTc3h3K5DFEU0TPTgwP3HABQiSjykTxsTps8ZDh4xiBAA5FfR4CTQVQyncSxJ44heF4QaytrCO0Iwefzbei0iixGIBQEsHEWpfUSKIrC4jOLKKfLmLx40tT1NoNGUr3q9lctbRS/39+2dBKZzjYzAKiV5lNSjxBHaWUxXDmtHwqFLIskWsk3RqQTjLAhNAP1tdRqsfZ4PJr3j7AuKBtI9NDxb8YIZUs4FDWIGJZ6tkMJs8OJ5NhazujE/hOgaAqwVXZh7C9Z5A/ksVRYwuQ7J8G6WMSX4hh+0zByq7mKkqIESIKEwGQAbJ5FKVMC42XgGayo/vFFHna3HUd+dgTCkwK8bi8S3gQ8wx5QNIWRc0Ywds5YJcd7HoWj/3YU6dU0BFaAp98DTuSQeCPRdodiVKq31pxIu1JjSi31ZtUra3F0GZl5aQRlJNWqaX3AOr4xZVrOCMOxlain5hiJRDZEYvl8HpFIZEM3oh4xsc3oULZEyouo4xEW1Vwu17CDKJPJIJmsqB8aBQllBwY2DgseuPcAooeiSK2k4Al6kHk9A5qpPHy5RA6B3QH4/X7EXo2BZ3lwOQ6Mn8HO390Jd58bbJ7Frmt2oXemF7/+u19j+T+XIfKVuRnaTmPqwimInIjIgQgCkwH07+qHyIs488YzEZytRADh/wzjte++Vsnpz/rA8RwkRsLuG3bLO34r0iK1QHQ2kskkJicnLRHw0kqNWUGhQkAiqXbMmCiL4fl8Hg6HQ45e9KZgiFaPy+XCyMhIx5oBlDQquVyuZpOCkkQzFAq19PkzC2Uklsvl5JkVEl3rdQ6iKIJlWZw4cQLBYHA75XW6gaIoOVWhFMOqh1bQ0EuShFwmh5WXV+DxepCOppFbzSE4Faw4jyyH3GIOwXOCyIQrjsbG2FCMFVHOluHsccI35kP/rn5QNAW7xw67x45cKgdHwAEbbwNN0ygkKy3BIneSzdROI72Qlh3KxIUTCIQCOPb4MfAlHj6/DzNXzIAO0PIu2YwR0wPi1K2W6tVKjeVyOZw4caLp1BjpNNIbSTWLWsVwdQqmFisvqQ1q1aTajXo0PRzHydeRy+XA87wlJJqtgjISIyzCvb29cjSmlxWCRI4ulwsTExNtvILWY0s4lGKxiBMnTmwQw6qHZoWy1A6FZ3kcf/E4imwR/ZP9SBxOQBIkcFkOIi9C4iupLS7LIfpyFIyHgbPHCXe/uzKrAmD0/FGMXzBeSZkBECQBjhkHJr2TcDldWH1+tdICHHBCEiR4hyo9+yInwjdSbQgD4wHs/chelNMVR0XbaVA0JRMNFgoFmccMQJVYlVkDJQgClpeXQVEUplso1at88UdGRjRTY0rqkXpQFoc7IYWsFt8i6SQ1K6/f7wfDMFVpuU5Sv2hBaxaJMCXzPA+XyyUrbnabDLISmUwG0WgUoVBIbmXWIkxVRmLkWSfO3uFwYOfOnV3rPM1iSziUtbU1jIyMGEpVNBuhKFuOc/Ecnv27Z8HlOBQXiyhECnAHKw8iV+CQW8vBPeCGyItg/AwgVKbXfWM+2BgbREHEGe8/A+Pnj8vfmUqlIPQK8OV8cLldEHkRez+6F1yOg1AWMHr+KLLhbEWZ8YJRDJyxMf1mc9hg99rxwrdeQHY5C4fPgTM/cCaCO4KyEVPmxNfX11Eul+VahR6DTNBMMbsZaMkGK0kTa6XG1Pn8bqEkqcXKG4vFQNM0eJ6XazzdDpvNhlwuB5fLhdHRUfk5i0QiutUd2w3y3Kg3GFqRmPKdeeKJJ9DT04Ndu3ZhfHx8UzoTYIvVUIyAUNbv2mVcpTAej6NUKmF8fBylUgnPPfAc+JXKDEIpU8KhRw/BP+qHzWFDcGcQnn4PBt80iPB/hBE9EUVvoBelXAnlRBn+cT/mrprDme8/E8DJXPNqFOvz65jeNY38Yh75tTz8Y34M7TXe+vvK/3sFay+tyQbe4XXgojsvqvl5kt/PZrMoFAq6ahXFYhHhcNiSYraVUKbGstls1a4ymUyC53lMTEx0ZT5fjWw2i0gkAq/XC5Zlu3Z+h0AURZneX0t7Rd2WTCIxr9fbMfYE0kRiNFrleR5PPfUUHn/8cbz00ksYGxvD29/+dlx77bWYnp5u3YI7gO5/UyyAmSHFZtqGSYdYJpPB8vIyenw9KPsqszCugAsDewYQmAzA4XcAIjB7xSxG3jyC2Msx+EN+9A31gWd50HYaw3uHMXBWJboQRREnjp7AsUeOwSk6kd6fxvTl05h715ypdQIAm2WrXuZytgxJlOS0mhpqTis13bua/FFLqrdboJUay2QyCIfDEEURfr8f+XzeUCTWCSinyUmnEYnEyExSJ3Tpa4FoxTgcDoyOjmquRdnKq9ZHaYcMshpkyNIMHZAkSdi5cyduu+02zM7O4tVXX8UzzzyDV199ddM5lC0RofA8b9ih8DyPo0eP4owzzjB8vmQyiVgsJvf+F5YKOPS9Q6BoCkJZQM90D9wDbrBpFsGdQYy/pZLKmv/ZPH7z6G8Q8AeQW8ph5LdHKp0kvIjdN+xGGmms/2IdUlShJMkLeOun3yrT0xvF8SePY/6pedD2ihMMTARw3p+cZ/h7lGF+NpsFz/NgGAYsy2JiYqIuB1O3gKTl3G43+vv7ZSOmjMRaPYRoFORZq9ctp+5O6oRBJmhWyEuZgs3lci2RQVZDObFv9LcnwnibtWaixpaIUMzAbA1FFEUkk0lwHIedO3eCYRh4dnswd9UcDtx7AHyBhyiKmLhoAv07q4um05dNwzZpw+qhVRSeLmDp+SWU18qABKwuruKSv74ErJ9FKpo6dZBQKbqbdSgzvzMDUEB6Pg2H34Fd1xhP8QHVtYqBgQFEIhHk8xUqmXA4vKF43G0ol8tYWlqq0l1xOBwbusYWFxdNC29ZCdJ6rWcAsNGciBUzL3pgRfeZWh+F53n5t1lbW9PNMKwXxGFvOxN92BIOxeyDq2QX1QPywpCHXmk4V/5rBb7hSqeVyIo4/Ohh7PurfQBQNU07PjeOgaEBxPfHkVpNQRAFiJIIep1G6rUUBs4aQPJIEhRNQRIlBKYCYDzmDTRFUZj9nVnTx6uhlOol7dnq4rHdbpdTY93QzUNmTIaGhjTpchoJbxED1q7UmHJmw+gAoJZBbgcBpFIVUms+yyzsdrsuGWQzv42SSNNoU8ZWdCbAFnEoZkBRlFxH0fMwFItFWePa4/HIrLkEXIFDOVtGMV6E3WUHbEAxUQTjZUAztDw9C1Ro5wfOGMDKCysQBRH+fj96ZnqQXE1CGBYQuDgAbpmDv9+PmUu7ZyiKDNOppXq1CBNJEVkUxaqW5HZPDpMaj94ZE71dY61KjUmShEgkAo7jLJnZqEcASX6bejMvekBamfv7+1vafdaMDLIaiUQCiUSiKWfidDqxY8eOLeNMgG2HUhd66VfS6TQikQjGxsbQ09ODYrG4IV3GeBisvrAKiqLAl3gk3khA5ETYPXbsed8eDJ41KH9WFEUE3xZE32t98Dq8sDvtoG00zrrsLLj6XMiP5ZGdqZAMnlg4IRvkTu72iVRvT0+PrHuvBeVLr5yrUNJ0kOil1d1VyvqDEWp0JWoNVLYiNUaiPwAIhUKWO1+9My9GiDmJJPLg4GBNstRWQa8MstpZEhaHUChk2JmQSGwrOhNgixTlBUEAz/OGjzt8+DBmZmZqPlRKcSXlkBN5iXbu3Cl/9oX/8wIWfraAcrqM/HoeNsaGqcsqtC6Mh8GOq3cgv5pHz84eJNkkHA4HAkwA4WfDgAhMXDQB/5h/w/nJbj+Xy3Vst2+V9C3Jh2ezWbn+omxJtnJaf319HZlMxpTR0HsOkhrL5XJNp8b0dEa1EnopVJQoFotYWlrqug4/wtFFrqVUKsnOkud5WdzNaK1P6Ux27ty56Xi69GBLOBRRFMFxnOHjjh49WrN7RhRFhMNh8Dy/gXuIZVnMz89j9+7d8t8O/uNBJI8mAQCrB1fB5lhMXToFiqKQOJaAZ8ADQRSQL+bxpj98E2bfMmvYaJAdZTabNT2AaBStkupV7iiz2axlbLyE+qVcLltG5a4HJP2i1TXWyFmS7jOv14uhoaGO15yUFCqEMkVdqyCbjNHR0a6XRCbOkkTJyjqS3mdt25lUsJ3yqoNa9Cvk4XG5XJoUIlrHzbxzBqnjKQisAGePE87eSg5X4AWUM2U4gg7kC3l4PV5kDmZAXWDcaCinqEmxtdFEuFkod/mtkOpVpotI+oUoOprd7ZNNAEVRmJqaautLXy81Vs9ZKovZpPus06hFoUKeNdIuPjIy0hbus2Zhs9lQLpchSRLm5uYgCIIh5meWZbGwsACXy7WlnQmwHaHUxfHjxzE8PFw1Q0F0VAYGBmq+4OuH13HgkQMYnxjH3Lvm4ButUGGX0iWsv7oO35gPhfUC0ifScPQ4cOjHh1AqluQHtneuF+fcck5T16yEUkdEma4wW3dRqhV2YpJca7ffSHSrW9h31aiXGrPb7YhEIhgYGNDUiulGENJEj8eDUqnU0ZkXPZAkCbFYTN4YqZ9ljuNk50+Yn0ndxeVygef5bWeiwJZwKCRnahQnTpxAf3+/HLITHZWJiYmaYXwmnMEvv/pLJOIJ9PX1wea0Yd9n9sHhc2xoQSaGef6JeRReLsDO2EHZKOz9yF70724NsZ+ytTKbzRquu/A8j3A4LFNmdPoFquUslbl9sstv1DDQDVBOuBeLRTgcDvT29nbFhHsjkPQnoSapNYRI6GA6PY9EouxcLqeLMl/ZBfejH/0I3/3ud/Hbv/3buPDCC3Httdd2fWqvHdh2KHWwsLCA3t5eBAIBRKNRuYhbj8fn6L8exfxT84gn4ujv64coijjz/Wdi7K3VfEU8z+P4K8eRW8hh7py5CnV9NIeBMwY2MAO3EizLIpvNVtVdaoX3xDAHAoGO06JrQSu373a7USgU5IjydABpZR4dHQWAqgn3Vqg6WgE9kr3KIcRcLteymRc9IA01+XzelP4Ky7L45S9/iZdffhmvv/46Dh48iH379uHee+/tqt+l3dh2KHVACqGZTAaSJOkq4i79Ygk/u+1niM/HYaft8I378N5/fi+Cc6dSFqVSCa/9/DVEHovA6XACIrD72t0IvS1keI1WQi3spKy7kMikllRvNyKZTMrDehzHwe12ywa507vjWiC7fHUrM9ntE2dJcvtkt99JAkszbbbK3X4ul4MgCFV1sVZGvsSZEDEvow0rpGbidruxY8cO0DSNfD6PhYUFnHnmmS1a9emB7aJ8HZAHLxAI6G7V5FgO2ZUsIFb0SgqxAlZfWpUdCtl95g/m4XadNBg0MP/UfMcdSi3ix/n5eZkskaQyun0XlkwmZTI/t9std/KQwj7ZHfv9fksFxJpBPB6Xh+nUu3zlhLvWQKXT6ayqI7XjepqR7FXPvJA23lQqhZWVFVMzL3rXHI1GUSwWLXMmAOD1ere8MwG2iEMx83KRGoPf78fY2Jju49ZfXIe7zw1X0AVBECBJEo4+dxSBtwRAURRyuRwmJyeRcqTA4lTUJAndFSiSLqtyuQyKojA6OgqWZRGJRCAIgmy8mpmgbgVIkTWdTmN6elo2RuppffWQmzKV1O7rURpmvSqWWkN72WxW7hqzQhCt0ZpJykirmG0UtdiFlWJVZhU3lWteXV1FuVy21Jls4xS2RMoLqMxo6EUikcDa2ho8Hg9cLheGhvTpjIiiiLXX1vD4Rx+XVRYB4O1ffTuocQrRF6OIPx+vyOsO+ZF+NQ2b3QaRFzF9+TR2Xr2z9pe3GUqp3snJySojR+ouZCisndPtetZsZMaEpEPJ9bSTLJGcf3V1Vb7PzZ6vHakxpWGenJxs6TS4Fou1UnhL7/U0u2ZCH+PxeDA3N7ftTGpgyzgUPSJbxCCRQl06nYYkSRgeHm74/aIoygSPx35yDC9/52VIooTd1+2G7wIfSrESoj+OAlQlLVYqldB7Ti8ojkL/TD9mLqw9kd9uKKV6x8fH67486rqLMvXSTpp3QksiSRImJiZMv/DtvB5RFGXOrPHx8ZYYZrUgWrOpMcIlRsTH2k0tom4Z18OmQN5rlmW3nUmLse1QToLMKVAUJT90sVgMHMfJ3TYAUIgVsPbyGnxjPgzsrrCmSpIEQRA2PMyEEt3v94N9g8WJn52o+szE2yYwcN6A3GXV7HyIFWhGqleZesnlcpqCW62AkpTSSloSreuxiptLEASEw2HYbLa2tV+rr8doakzJJdZoo9EOaLEpqGdeiDPhOA6Tk5OG19xOZ8JxHP7qr/4Ky8vLoGkaX/jCFzA3d0o8b//+/fj6178Ou92Oa6+9FjfccEPL1mIWW6KG0gilUkluh1UaUbVqY+JYAgfvPwiBFyCJEqYvm8aOd+/QLFITxlZCiZ5hM4AI4OTmSBIk9IR65BdgZGREng8hu1ZlnaIdzqVZqV6lwVW28BIqcWK8rKy7tLKVWet6yAQ1x3GmJXY7NWSpvh4yI6KcCK+VGiMsAzRNY3x8vCuaGLTYFNREozzPg6IoU2Sa7Y5MnnnmGfA8j4ceegjPPfcc7r77btx7770AKs7mS1/6Er73ve/B7XbjxhtvxGWXXWapFIAV2DIOpRaNSjabRTgcxsjIyIZ2WDXb8In9JyAKFZp5iqaw+MwiZq6YAW2rftASiQRisVgVv1VgMoC5d89h6RdLgASMXziO4Oyp86mpt0kePBaLYXl5ucoYtyLNYLVUr5qeg3TxEOZaK+ouxAG2Y5K8Fm290a4kK0SmrEAtXRQ11bvf74fNZkM4HO4YMaUeaF3P0tISOI6DJElYWFgwNPNSLpexsLAAr9fbtjTXzMwMBEGAKIrykC7BsWPHEAqFZPLVc889FwcOHMBVV13V8nUZwZZxKGoQxbtYLIapqSlNYsMNqo3SqWMhARKqIxPSkpjP56s6jAjG3jKGsbfo6xgjvFwDAwOaxsvKeQoylNYMjXsjqLt4SJoiGo2aqlOQSK5T5IPqLiuS1ycCYlqpy3bpgpiBumWcpJIWFxchCEKF/bqLGIPrgRTgbTYbpqYqBKxKDR7lzIvWBq0TzgQAPB4PlpeXcdVVVyGZTOKb3/ym/P9yuVzVc+71epHL5dqyLiPYkg6FFENLpRJmZ2dr7ijVDmXq0ikkjiYAqZIDn3r7FCi6YixIIRsApqenLY0ilMZLa56CqAkaLbIqHaAZISGzsNlsssqeuuWVpDHqtYi2iuHYLGia3iAgRhweabF2OByIxWIYGRlpiuK/HSC/gdPpRC6XQyAQAMMwWF9fx/Lyclu74IxCWedRNmcoNXi0Zl7sdjtYlsXo6GhHnAkA/NM//RMuvvhifOpTn8LKygo+/OEP48c//rEcLebzefmz+Xy+K6leuutpaAN4nsfi4iLsdjtmZmbqGn51DaVvZx/O+/PzEHs1Bu+wF0NnV9qJWZaVp+qNFrKNQmuewogxJlBK9VrtAI3ASN2FoqgqHfV2dpHphTp1ybIs4vE41tbWQFEUMpmMXB/rNmOsBFEdVEr2Dg4ObpAMbpVmjRmQZ5p0J9Zai9bMy69//Wvcdddd8Hq9eOtb34r3vve9mkzirQRx3ADQ09MDnuchCAIAYG5uDgsLC0ilUvB4PPiv//ov/NEf/VHb1qYXW6bLi7wIhApcj65EoVDAysoKZmdn5ZZgNYjuQ6cpSbR4rGoNH7aqK8pqkJ1kNptFqVSSr2FiYqJlqTmrQWoSExMT8o5fyWWlTPV1y++gNzVXr8uqXY0kyrU00zRQLpdx/PhxrK6u4o033sAzzzyDSCSCp556qm2RQD6fx2c+8xmsr6+D4zjcfPPNACp26P3vf7/c5SVJEq699lrcdNNNbVmXEWwZhxKPx7G0tCTL9OoBUV6cm5ur0nwnIKmX8fHxKor7boB6+JB08DAMg0gkclow7xIoxcwYhtE9f9BpKCWG1YSiyuiS5MJbPd2uB2Yle7WYhduVGiPPB2nBNuNMSNF+bm5OPr5QKHRFSvV0wpZxKOvr62AYxtDOlmVZvPHGG/D5fAgEAvJOn9BO5HI5eefZzSDRWSqVkinRg8Gg7GC6GYSUkmEY2Viod8ZGUn3tgJL+RQ9hotIYZ7PZqhbeVqptqmGlZK/WgChxLlZGY6IoYmlpSZZTMPq9ZGRA7Uy2YQ5bxqEo85F6QCbf1WkXj8cDjuNA03TLaSesBImmyJCmUj+EFPW7Ke0CnKpN1Wux1aLmaBdrrRZIowNhsjWzM1dPt1uttqmFQqGAcDjckq455QZAGY01y51GnAnDMKZSt9vOxHpsGYciCAJ4ntf12VqT7yQdAFQeZqIU2M07faVU7+TkZFU0RTqSyKQ+0B1pF+DUvTbaYstxnOwsi8UiPB6PbLxa/RsRWhIylW3FZkPJ+twq9gHSkdaO1K1VqTFBELC0tGR6NoY4E7/fj9nZ2W1nYhG2HYoCpPCuVS8pFApYXl5Gf38/gsEgJEmqKrAyDFO10+8GGJHqVZMKchzXsZ0+MXDNpl7IvIu6CN6KuotSr75VtCRajRf15in0QNk00Il6gZnUGHEmTqfTFNPAtjNpHbYdyknUcybpdBrRaBRjY2Pw+TaqKSoLrCSnTwxXpzi5mpXq1drpt4NRmNxrqw2cJEnyTp90JFmlftjsbtks1L8RmdbXGzGTe93KgVYj0JMaEwQBi4uLcLvdplr02+1Mvv/97+MHP/gBgErx//XXX8dzzz0nb5TuuusuPP/883JkeN9993XlfIlebBmHIooiOI6r+f/Iv9WT7yRdNDExUVf6V3kM2UUSzfZ2c3LpqT0YgXKyXcnAa2VOnzAXEOW/VkZ5ymhMXXcxutMnZJper1dXK3qroNQQUdbGalGNkA60Vt9rs6ilR086Fk/HNNfnP/957NmzB+9///vlv9144434+te/3nXMCWax5R2KknZe+YCRaXpC0212V640XCzLyjvIVqWRSHG1VXMxysn2bDYLm83WdDSmLGSrtVfaAa2dvh5qG0JM2dvbi/7+/q5JnShrY7lcDqIoVtXGksmkYcneToPU1CiKgiAIhrvGOu1MXn75ZXz5y1/Ggw8+KP9NFEVcfPHFOOeccxCLxXDdddfhuuuua+u6rMaWdii1iu8cxyEcDss5WqsMf6vTSCQfXis1ZzW0ojH1ZHsjEMctCEJH9DXUUFLb1Bs+JAauHcSUzULZklwqlQBUpt57enq6elqfgLBb+Hw+DA4OAkBVNNZIcVPJJj4zM9MRx/8nf/In+IM/+AO89a1vlf+Wy+Xwne98B3/4h38IQRBw880344tf/CL27NnT9vVZhS3rUERR1KyXEAbbYDDY0l2nOo3UTMeYMl2kNUTXLiiL+uVyuSFDMqk9kLbPTutrqKE1fEgoU2KxGEZHR08rwkQyOxUMBlEoFKqK4N06IKp2Jur11VLctNlsMknn4uIienp6OuZMMpkMbrzxRjz++ONVfxcEAcViUd78ffnLX8auXbtwzTXXtH2NVqH7tycWgTxIpPCupWFCdvjtYLBVEySSXXEsFgPDMPJL3iicJ8yqxWJRtx55q2CEIZnQuPt8vo7WHuqBoih4vV54vV45px+Px5FIJEDTtNxq3SpJAauglBkmvG19fX0bWIWtbFSwAuQZCQQCNVkdKIqqeu5I19hTTz2Fu+++G2NjY9i3bx+uvfbaDlxBBQcOHMCFF1644e8nTpzArbfeih/+8IcQRRHPP/88fv/3f78DK7QOWyZCIekZQLv4HovFkEqlOrrDJ2tR1ijIS641Ba5kOG6VhKwVUKeRiEPRy6nWLSDDoUT7nUSYRuou7YZeyV4tLXpl23gnpH5JZGFGRKpYLOLYsWNYWFjA4cOH8fTTT8Nut+Nf//Vf2/77PPDAA7Db7fjIRz4CAPjHf/xHhEIhXH755XjggQfwk5/8BAzD4L3vfS9uvPHGtq7NamwZh7K6uopEIiHTqJBiJJnVYFkWExMTXWcM1N1Iyu6qcDhsSqq3k8jlclheXobb7Ua5XO462pRaUKYU1V1RWnUXvRFmK0HYdyVJqqJy1wO1djtJyTYSELMCWkzHRlAsFuVmienpaVlcLx6Pd53C4WbDlnEo5XIZa2trSKVSYFkWTqcTpVIJd911F2666SZcddVVXZfDV4PkitPpNMrlMpxOJ/r7+zuygzQDMvdAJrJr0aZYLRPcDEjreDabRSgUarjh6BbSRysle5UpWdKSTCIXqzcBxJmQGqZRaDmTbbQPW8ahEBAjduDAAdxxxx249NJLcf3116Onp6frd8nAKale0u2iTrl0q84GqT3USynWYkjulMOUJAkrKysol8tymsvo8WrSx1ZLOQOtHbSsNa1vxSaAtGETrRKj2HYmnceWcygAcPDgQfzxH/8xPvvZz+Lyyy+X+/ILhUKVfGu7NR0agUj1qvVA2jF4aBbK7iI9O3wCLYLEdvKmNZMuqgWSRlJuAoxMtusB0bpxuVymaEmMQos81Qx3GsuyWFhYMC2PvO1MugNb0qEsLy8jn89j165dVX9nWRapVArJZFIm4lNT13cCSqM8OTlZ10moBw87ySasHA5thixRK+XSymsSBKGKtqYV96zWZHsz19SoxbbVMFtLIs7E7EzPtjPpHmxJh6IHPM8jlUohlUohk8kAgJyC8fv9bUvBKKV6jQ7+abEJ1+oYsxrEKBPRI6uccasZktu9wwdO1V3ITp8M6hm5JmVXVDdM7de6JvXwIVGHNCroRUCcSTAYxNTUVMe5uR555BE89NBDsNvt+MQnPoF3vOMdLV1Pt2HboeiAIAjIZDJIJpNIp9MQRbHKubSqZmGlVG8t/qpWFMAJv1WrO9DUNYpmGZIJB1q9uYdWQzmop6y71Gvfbbb20GrUGj50uVxIJBIYGho6LZyJGmpurvX1dXz0ox/Fo48+inK5jA9+8IN49NFHO552bie6r3rbhbDZbAgGgwgGgxBFEdlsVibXW11drSetf9kAABpeSURBVCqIW/XwlMtlLC0tWSbVS1EUXC4XXC4XBgcH5dx3IpFAJBKxrABOdpzEuLXyJVdeExmmzGaz8jClEWobvTrqrYZ6UI/UXcjQrbrucjpQwGgNH5L3B6jM9wiCINPb6EGhUMDS0lLHnMnLL7+MN954A5/73Ofkv7300kt485vfDIfDAYfDgVAohEOHDmHv3r1tXVsnse1QDIKmaXnCnVCiJxIJJJNJrK2tyQVxMuti5kHP5/NYXl7G8PAwenp6WnAVgMPhkI0+KYArjZaZIT1CTNnKddcDwzDyNSkbFaLRaN1GhU6vux4YhqnazJCaSywWg81mA8dx6O/vN7XD7xSIQyGMFKTmt7i4qGsuqdPOBAC+9a1v4ZOf/GTV33K5XBXDhtfrlVvHtwq2HUoToChKTkVMTk6iWCzKHWOxWEwuSgYCAd1MvGQaux3qeQR2ux29vb3o7e2tKqwqHWSj6Is4o3auux7U1DbEaJ04caKKIZnneaysrLSNULMZ0DSNQCCAQCCAfD6PcDgMr9eLdDqNVCrVdpkEM9DSrSfvkHIuKRqNguM4OXImbdbd4EwymQzm5+eriB7JdeTzefm/8/n8aa1tYgbbDsUiUBQFj8cDj8eD8fFxlEol2bkkEomqLh6tQqtSe2VqaqpjGhU2m002WiQCy2azWFhYqCkcRtqZQ6FQR2lraoHsen0+H0ZGRuQ5inA4LNeSAGjyu3UjlJK9xBCTGkUsFsPy8nLHZ3i0oOVMlKAoCm63G263G4ODg1V8cHfccQfy+Tze/OY34/LLL++YMwFqc3Pt3bsXd999N8rlMliWxbFjxzZ0km52bBfl2wCWZZFMJpFKpZDL5WCz2aoK4izL4sCBA5icnGxKe6WVqCUcxvM8SqXSaaWtAZxygsPDw3LthTAkd4q/Sg/IYGs9RUue5+UCeLtpU2qBOBOzxKvRaBT//u//jldeeQW/+c1vMDQ0hA996EO4/vrrW7Da+qjHzfXII4/g4YcfhiRJuOWWW3DllVe2fX2dxLZDaTNIO3IymZRf+q997WsIhUL4whe+0FVcYrVAuqsikQhYlq0isOwWypRaIESg6XR6gxNUG+JuI3w0I9lba4anlpJjK0BqVGadST6fx9LSEvr7+xEKhSCKIg4ePIhSqYSLLrqoBSvehllsO5QO4vjx47jllltw/vnn433vex9ompY7k7qVQgU4NWNCeKIEQZAjF0KZQiKwbtrlK5UhQ6FQ3ftbS2irU7ohVkj2NlJybKWCqNkaldqZnA4pya2MbYfSQfzu7/4uPvShD+EDH/iA3I6cSCSQTqchCIJMYxEIBLpihwycmjFxu92ag3+dpkypBb007rWO1Ssp0AoQpmOr04rKGR4yG2Jl3YV0K5pt1Nh2Jqcfth1KB8FxnKaRlSQJuVxOrrtwHFdlmDtVsCezMXo11JVtrkQHpRPXQJh3KYrC+Ph4Uzvxdg6IkvRcJpMxxINmBrU2AmbrLlY5k4GBAUxOTm47k9ME2w6ly0F2x8S5lMvlqvRLu/PgZqea1bv8Wh1jVqOVzLvARnJEku7TM0xZD4S/LZ/PN0zPWY1adPV6fyvShVavcaDR8eFweNuZnIbYdiinGcisSyqVQrFYlOWCA4FAy9IvpLPIqlkNrY6xVjA8k/Sc1+ttizIk2eXncjnk83nTu3ylZG8oFOpoHYrUXYjTbFR3OR2dybe+9S3s37///2/v3GObLNswfnU97FTG3JExtsqAyWGCQKKCIqCGM6LZOAzYEIYSAnIK4gkN+BEQDYkHDqKI03kCxRBJAI1sBg9xAZEp4ABn3Fi3dR1dt3Vrt659vz/4nudrt26s3du+bXf//oK+2XKXlF7v+9z3fV2wWq3Izs52mhzLz8/Hl19+yd0Ttm/fjrS0NK/XFKiQoAQwra2tfGKsubmZjyMzd2Qx/jOyZnBHy3wxcTxCcswM8cSPy/F3sgU4T4KaeounDsksg8Vqtbrd6/EFrvou7InMbDbfdqS5O6QQk+LiYnz44YfYv38/zGYzDh8+jGeeeYZf37x5M5588klkZGR4vZZggAQlSLBarU7W+2yL39MvZsekwttZ5otJx8wQd/y4GGazGZWVlR472IpNR4fkrtyEmd0/c5b25/FrwLnv0tzcDEEQcMcddyAmJsbtz4tUx1x79uyBTCbD9evXYTKZsGXLFtx99938+syZMzFs2DDo9XpMmTIFq1at8kldgQoJShBis9m4uHhivd/bpEKx6Bgc1pOJMdYM9nTnwdt05ZAcGRkJo9Eout2/L2hsbER1dTXi4uL4xr47fRcmJvHx8Rg0aJBPeyZbt25FVVUV3n33XVRWVmL16tU4ffo0r2Hv3r1YvHgx1Go11q5di+zs7D5nSe8O/rnoQPQKuVyO2NhYxMbGwm63o7GxEQaDAbW1tU4uvK6+mG02G7RaLWQyGTQajaRfbB39uNgRUl1dncsjJH/zE3OFK4dkVjeLRWhoaPDrPSRHWO0ajYbb7jja21RVVcFmszn5jDl+pqQUEwCIjo5GWloaVCoV0tLSEBoaCoPBgNjYWAiCgGXLlvEbk8mTJ+PKlSskKN3g/59YoleEhIRw40dBELj1/s2bN6HT6bgdelRUFOrr63HmzBlMnTrVZ+FSPcVxKsxxYqyyshLALfdki8Xi8fm9VISEhKCxsRFRUVGIj4/3yJhTKtjmfkcPN0dProSEBP7UwqISmpubUVZWhnvvvRdms1kyMQGA8ePH4+OPP8by5ctRW1sLs9nMj0lNJhPmzJmDkydPIiIiAsXFxcjMzPR5jYEEHXn1UZjxIzsaKy8vx+7duzFr1iw8/fTTPhtH7i1s+72hoQEKhYLfDXtz+1ssWGSvqyk0R4dkFkft61Hx7jAajdDr9W5v7re3t+Pq1avYu3cvSkpKMHToUMycOROPPvooNBqNFyvumtdffx3FxcUQBAEbN26E0WhES0sLFi5ciOPHj6OgoAAqlQoTJkzAunXrJKkxUAgKQbHb7di2bRuuXr0KlUqFHTt2OH04+3os5+34/fffsWbNGixfvhwTJ06ExWJxWkL0xTa4JzjuaqSkpECpVDqlHTKzR0f7c3+BRfb2JB2yuzFrKUTTUzFhNDU1QavVIjo6GhUVFSgsLMTPP/+M/Px8pKSkeKFiwlcEhaB89913KCwsxGuvvYaLFy/i4MGDOHDgAACK5ewJOTk5yMvLw5QpUwDcGg1l1vstLS1ODVZ/ydpggwNtbW1ISUlxKRbM7JFNjDkma0rZn2CRvZ6ONHcc3fWlQ3JvPcWYmCQkJCA5OdkvPkuEeARFD+W3337DpEmTAAD33HMPLl26xK9RLOftKSgocPp7aGgoBgwYgAEDBsBqtXJxuXHjBs8WYbsuUhwp2e12aLVaCIKA1NTULmtQKBQ87dDT4DCxYVHDvYnsZXG6sbGxXDS7iggWEyYmGo3Go38zEpPgJygExWQyOW1wy+VytLe3Q6FQUCxnL1EqlUhISEBCQgK33jcajdBqtQDcG0cWA+Z0rFAoMHDgwB5/KTkGh3VMcOzp0mFvYZkgYkYNO4pmx4hgx2PL3jokGwwGGAwGEhOiW4JCUDpGb9rtdn6kQbGc4qFQKBAXF4e4uDjYbDY0Njaivr4eOp0O1dXVTuLijSMl1sSOiIhAYmKix19KHRMc2dIhmxhjI66ukjU9hXmhdZVWKAaOEcGOk3AVFRW9ckjurdsxE5PExES3bgKIwCMoBGXcuHEoKirCrFmzcPHiRafYTYrl9A5yubzTnbHBYEBdXR0/ehHzSIn1Hfr373/bJrY7OEY3JyQkcBsYnU4nmpMwW7b0ZW69TCZDZGQkIiMjkZiYyN9XTU2NW++LiYlGo/HoCI3EpG8RFE15NuV17do1CIKAnTt34uzZsxTLKQFsHNlgMHDrfdaviIqK8ujoxWKx8FwMZtLnC1w5CbubF9KTyF5f01OHZMdkS0/FhD2VJScni/kWuqU7s8fCwkLs27cPCoUCmZmZWLBggc/q6gsEhaAQ/gnzsGJNfWa9z5r6PdmnYEdFYvYdPKFjXkhP4oE9iez1NV05JLe1tXHrfE/EpLGxEVqt1udi0p3Zo9VqxaxZs/DVV18hPDwc2dnZOHjwIOLi4nxWX7ATFEdehH/ieJyUnJwMi8XCxcVgMPBx5KioKJf9CmaF7sujoq5QKBTccYAd8TlOjLEjJDZK67ir4bhF7m+4el91dXVc/I1Go9vDClKJCQD89NNPSE9Px5o1a7jZI6OsrAypqan8xmT8+PE4d+4cZs6c6dMagxkSFMJnhIWFISkpCUlJSWhra+O5LhUVFdx6n53rf//994iMjMTYsWP95qiI0bH5zcaRKyoqEBISAqVSCYvFAo1GI1m6pifIZDJYLBYAwNChQ2G1WvmxVVcOyR2RUkyAW6PNXZk90sSn9yFBISRBpVIhMTERiYmJfBy5vr4eWq0Wp0+fxokTJ/D222/7/RcyiwlQq9Ww2+3Q6XRobGyEXC5HRUVFj76E/QFXCZFKpZIPKzAHgtraWlitVqd+EmvqMzFJSkrCwIEDJXkf3Zk90sSn9yFBEYnb2b/s2LEDFy5c4C64+/fvpw/z/2DjyLGxsXjrrbdQVFSEPXv2QKFQ4Pr1607uyP7qwMvy381mM9LS0qBQKDp9CXsre763MDFpaWmBRqPpNHAgk8n4MiVzSDaZTDAajSgtLcUnn3yCsWPHYsSIERg+fLhkYgJ0b/Y4ZMgQlJeXw2g0IiIiAufPn0deXp5ktQYj1JQXie7sXwAgOzsb+/bt8+mUUqBx/vx5vP766zhw4AC33mcutQ0NDbDZbIiIiOB9F7E3wT2FGVSazeYu82PY8RGbrHIUSakjflntnsQNt7e349ixY/jhhx/w559/Ij09HY888giysrI8dgLoLd2ZPbIpL0EQkJmZiSVLlkhSY7BCgiISu3btwujRozF79mwAwKRJk/Djjz8CuPX08uCDD2LcuHGoq6tDVlYWsrKypCw34BAEASaTifddrFarU+CWVEdjPfEU64jNZuPi0tLS0qPgMG8gRnZ9Q0MDH5yIjY1FcXExzpw5g2nTpmHixIleqJrwZ/zz/CAA6c7+paWlBUuXLsXy5cths9mQm5uLjIwMDB8+XMKKAwvHTe+UlBS0tLRwcdHr9VCpVD63dxcEAVqtFna7vVtPsY7I5XKnySrH4DAx7VJuV3tNTQ1aW1tFEZOkpCQAt26kmK8e0fcgQRGJ7uxfwsPDkZuby3cR7r//fpSWlpKgeIjjFvigQYNgNpt5U//mzZtQKpX8WMxb1vt2ux2VlZWQyWS9yn/vKjiMTYx5QyQ9earqiCsxIQj/6QwGOOPGjcPZs2cBoJP9y7///ovs7GzYbDZYrVZcuHABo0aNkqrUoCM8PBxJSUkYOXIkMjIykJiYiLa2NpSXl+P69euoqqqCyWSCWKe7NpsNN27cgFwu75WYdIQJ5YABAzB06FDe3K6qqsLff/+N6urqXr8PJiZWq7XXTybJyckkJoQT1EMRidvZvxw6dAinTp2CUqnEvHnzkJ2dLXXJQY/VauVPLiaTiY/4dhx3dQebzYaKigqEhYX5NCaZeXGZTCan4DB33ocgCDzj3VMhdBSTAQMGuP3zRHBDgkL0CWw2GxeXpqYmCILgtvV+d5G9voSN7bLgMDYx1tGLyxHHfk8gickTTzzBe5ODBg3Crl27+DUaxfc/qIdC9AnkcjliY2P5ODKz3q+trUV1dbXTGK+rSSt3Inu9jVKpdAoOY+Ki0+n4xJhareYuz4EqJq2trRAEoVMAHOPy5cs4dOgQjeL7EfSEQvRpBEFAU1MTnxhrb2/nqYfMHbm6uhqVlZVIS0vzKLLXVzhOjJlMJh4c1tzcjJCQkIASEwAoKSnBli1bkJycjPb2dmzatAn33HMPABrF91dIUAjif7ApK2Zg2dbWhtraWrz66qtYv3495s6d69f2KY4wjzGWf+KYSunO5JvRaOT2+4mJiV6u2pmrV6+ipKQE8+fPx7///ounnnoKp0+f5kmsbCOejeLv3LmTJiclhqa8gpSSkhLk5OR0er2wsBCZmZlYuHAhjh49KkFl/gubsho0aBAyMjIQEhKCV199FUuWLMGwYcNQVlYGnU6HlpYW0SbGvIUgCDAYDAgLC0N6ejp/OqmpqcH169f5xJjdbu/yd0gpJgAwePBgPPbYY5DJZBg8eDCio6Oh1+sBOI/iq9VqPopPSAv1UIKQ999/H998802nDA6r1Ypdu3Y55UE8/PDDlAfhAqPRiDVr1mD79u2YNm0aWltbXVrvM28uf3pyYTsycrmcpySGhYUhLCwM8fHx3GOM2dQ7vg82nCC1mADAV199hWvXrmHbtm3Q6XQwmUyIj48HcGsUf8OGDTh+/DjsdjsuXLiAJ554QpI6if9DR15ByLfffou77roLW7ZscXoKKS0txRtvvIEPPvgAALBz506MHTuW8iBcwEZsXVmwW61W3nNpamriGfVRUVGSGz/a7XbcuHEDCoWiR5G77e3t3AamvLwcn3/+OcaPH49Ro0Zh1KhRkokJcCtZ8oUXXkBVVRVkMhk2b96MkpISGsX3Y+gJJQiZPn06KisrO71OeRA9RyaTdZnnoVQqkZCQgISEBG69bzQaodVqAcDtcWSxYGKiVCqRlJTUo6cmhULBJ8aSkpKg1Wpx9uxZ5OfnIz09HY8++iiysrIkSctUqVTYs2eP02vjxo3jf165ciVWrlzp67KIbiBB6UNQHoT4MOv9uLg42O12NDQ0oL6+HjqdDtXV1U7i4k3rfba9r1KpeiwmHWlubsaYMWMwZ84cREdH49dff8WZM2dQWlqK++67zwtVE8EGCUofgvIgvEtISAi/22dxugaDAXV1daipqeE59Mz4USyYmISGhnq8vV9fX4+amhqkpKQgISEBAPDQQw/hoYceEq1OIvghQekDnDhxgudBPP/888jLy+N5EFKekQczrmKCDQYDX6YMDQ1Fv379+K6Lp019ZgUTHh6OxMRE0cSEIDyBmvIE4UMEQYDZbOZNfYvFApVKxZv67rgKk5gQ/gYJCkFIiMVi4eLS0tLCx5GjoqK6zaEXw1eMiUlqaiofxyWI3kCCQhB+Qltbm5M7slwud5lDz8RErVYjPj4+oMSkO7PHo0eP4osvvoBCocDq1asxdepUn9VFiAP1UAjCT1CpVJ3Gkevr66HVavkWf2trK7Zu3YqXXnoJgwcPDigx6c7sUa/Xo6CgAMeOHUNraysWL16MBx54QNThBcL7kPUK4XW6soHJz8/H7NmzkZOTg5ycHPzzzz8SVOefsHHkYcOGYcyYMbjzzjvR0NCA9evXY+TIkVCr1dzM0h2kPOYqLS2F2WzGihUrkJubi4sXL/Jrf/zxB8aOHcujnFNTU8lKJQChJxTCq3RlAwMAly5dwu7du5GRkSFBZYGDXC6HzWbDyy+/jMzMTCxduhQGgwF6vR41NTWIiIjgfRdX1vsMg8EAnU4nWc8kLCwMeXl5XZo90tJt4EOCQniV1NRUvPPOO9iyZUuna5cvX8Z7770HvV6PKVOmYNWqVRJUGBjU1NTgySefxKJFiwAA/fv3hyAIMJlM3GOstraW56H069cPoaGh/OeZmGg0Gsm82wYPHgyNRtPJ7DEpKYmWboMEEhTCq3RlAwMAs2fPxuLFi6FWq7F27VoUFRVRI7YLMjIyOj3JyWQyLh4pKSncet9oNEKv1/PjI+Y8LKWYAN2bPY4ePRpvvvkmWltb0dbWhrKyMqSnp0tWK+EZNOVFeJ3Kykps2rTJyaiS3V2zu9BPP/2UO/wSvcdsNsNoNMJgMMBisfjFaPDtzB6PHj2KI0eOQBAErFq1CtOnT5e0XsJ9SFAIr+NKUJqamjBnzhycPHkSERERWL9+PTIzMzF58mQJKw1O7Ha7pA7IRN+BjrwIn+JoA7Nx40bk5uZCpVJhwoQJJCZegsSE8BX0hEIQBEGIAt26EARBEKJAgkIQBEGIAgkKQRAEIQokKARBeMzNmzcxefJklJWVOb1Otjp9E5ryIvoEVqsVL774IrRaLdra2rB69Wo88sgj/HphYSH27dsHhUKBzMxMLFiwQMJqAwOr1YpXXnkFYWFhna6RrU7fhJ5QiD7BN998g+joaHz22Wc4dOgQ/vOf//BrVqsVu3btwuHDh1FQUIAjR46grq5OwmoDg927d2PRokUug7mYrU52djYOHjwoQXWEFJCgEH2CGTNmYP369QBubenL5XJ+raysDKmpqejfvz9UKhXGjx+Pc+fOSVVqQPD1118jJiYGkyZNcnl99uzZ2LZtGz766CP89ttvKCoq8nGFhBSQoBB9gsjISKjVaphMJqxbtw4bNmzg18jp1n2OHTuGX375BTk5Ofjrr7/w3HPPQa/XA7gl2MuWLUNMTAxUKhUmT56MK1euSFwx4QtIUIg+Q3V1NXJzczFv3jzMnTuXv05Ot+7z6aef4pNPPkFBQQFGjBiB3bt3c68wk8mEOXPmoLm5GYIgoLi4mHopfQRqyhN9grq6OqxYsQKvvPIKJkyY4HRtyJAhKC8vh9FoREREBM6fP4+8vDyJKg1cyFaHIOsVok+wY8cOnDp1Cmlpafy1+fPnw2w2Y+HChXzKSxAEZGZmYsmSJRJWSxCBCQkKQRAEIQrUQyEIgiBEgQSFIAiCEAUSFIIgCEIUaMqLICTgdlYw+fn5+PLLLxETEwMA2L59u9NAAUH4IyQoBCEBzArmjTfegNFoxOOPP+4kKOSFRQQiJCgEIQEzZszA9OnTAXS2ggH+74Wl1+sxZcoUrFq1SooyCcItSFAIQgIiIyMBwKUVDHDLC2vx4sVQq9VYu3YtioqKMHXqVClKJYgeQ015gpCIrqxgyAuLCFRIUAhCApgVzLPPPousrCyna+SFRQQqtClPEBJwOyuY48ePo6CggHthrVu3TsJqCaJnkKAQBEEQokBHXgRBEIQokKAQBEEQokCCQhAEQYgCCQpBEAQhCv8FHWuA6gIHxiQAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Fitting the model with a dummy model, with 3 clusters (we already know there are 3 classes in the Iris dataset)\n", + "k_means = KMeans(n_clusters=3)\n", + "k_means.fit(X)\n", + "\n", + "# Plotting a 3d plot using matplotlib to visualize the data points\n", + "fig = plt.figure(figsize=(7,7))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "\n", + "# Setting the colors to match cluster results\n", + "colors = ['red' if label == 0 else 'purple' if label==1 else 'green' for label in k_means.labels_]\n", + "\n", + "ax.scatter(X[:,3], X[:,0], X[:,2], c=colors)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the above example plot, one of the clusters is linearly seperable and at a good seperation from other two clusters. Two of the clusters are close by and not linearly seperable.\n", + "\n", + "Also the dataset is 4-dimensional i.e. it has 4 features, but for the sake of visualization using `matplotlib`, one of dimensions has been ignored. Therefore, it can be said that just visualization of data-points is not always enough for knowing optimal number of clusters $K$. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Elbow Method \n", + "\n", + "Yellowbrick's `KElbowVisualizer` implements the “elbow” method of selecting the optimal number of clusters by fitting the K-Means model with a range of values for $K$. If the line chart looks like an arm, then the “elbow” (the point of inflection on the curve) is a good indication that the underlying model fits best at that point.\n", + "\n", + "In the following example, the `KElbowVisualizer` fits the model for a range of $K$ values from 2 to 10, which is set by the parameter `k=(2,11)`. When the model is fit with 3 clusters we can see an \"elbow\" in the graph, which in this case we know to be the optimal number since our dataset has 3 clusters of points. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the clustering model and visualizer\n", + "model = KMeans()\n", + "visualizer = KElbowVisualizer(model, k=(2,11))\n", + "\n", + "visualizer.fit(X) # Fit the data to the visualizer\n", + "visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default, the scoring parameter `metric` is set to `distortion`, which computes the sum of squared distances from each point to its assigned center. However, two other metrics can also be used with the `KElbowVisualizer`—`silhouette` and `calinski_harabaz`. The `silhouette` score is the mean silhouette coefficient for all samples, while the `calinski_harabaz` score computes the ratio of dispersion between and within clusters.\n", + " \n", + "The `KElbowVisualizer` also displays the amount of time to fit the model per $K$, which can be hidden by setting `timings=False`. In the following example, we'll use the `calinski_harabaz` score and hide the time to fit the model." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the clustering model and visualizer \n", + "model = KMeans()\n", + "visualizer = KElbowVisualizer(model, k=(2,11), metric='calinski_harabaz', timings=False)\n", + "\n", + "visualizer.fit(X) # Fit the data to the visualizer\n", + "visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is important to remember that the Elbow method does not work well if the data is not very clustered. In such cases, you might see a smooth curve and the optimal value of $K$ will be unclear.\n", + "\n", + "You can learn more about the Elbow method at Robert Grove's [Blocks](https://bl.ocks.org/rpgove/0060ff3b656618e9136b)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Silhouette Visualizer \n", + "\n", + "Silhouette analysis can be used to evaluate the density and separation between clusters. The score is calculated by averaging the silhouette coefficient for each sample, which is computed as the difference between the average intra-cluster distance and the mean nearest-cluster distance for each sample, normalized by the maximum value. This produces a score between -1 and +1, where scores near +1 indicate high separation and scores near -1 indicate that the samples may have been assigned to the wrong cluster.\n", + "\n", + "The `SilhouetteVisualizer` displays the silhouette coefficient for each sample on a per-cluster basis, allowing users to visualize the density and separation of the clusters. This is particularly useful for determining cluster imbalance or for selecting a value for $K$ by comparing multiple visualizers.\n", + "\n", + "Since we created the sample dataset for these examples, we already know that the data points are grouped into 8 clusters. So for the first `SilhouetteVisualizer` example, we'll set $K$ to 3 in order to show how the plot looks when using the optimal value of $K$. \n", + "\n", + "Notice that graph contains homogeneous and long silhouettes. In addition, the vertical red-dotted line on the plot indicates the average silhouette score for all observations." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the clustering model and visualizer \n", + "model = KMeans(3)\n", + "visualizer = SilhouetteVisualizer(model)\n", + "\n", + "visualizer.fit(X) # Fit the data to the visualizer\n", + "visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the next example, let's see what happens when using a non-optimal value for $K$, in this case, 6. \n", + "\n", + "Now we see that the width of clusters 1 to 6 have become narrow, of unequal width and their silhouette coefficient scores have dropped. This occurs because the width of each silhouette is proportional to the number of samples assigned to the cluster. The model is trying to fit our data into a larger than optimal number of clusters, making some of the clusters narrower but much less cohesive as seen from the drop in average-silhouette score." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the clustering model and visualizer \n", + "model = KMeans(6)\n", + "visualizer = SilhouetteVisualizer(model)\n", + "\n", + "visualizer.fit(X) # Fit the data to the visualizer\n", + "visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## After Gopal's improvements to Silhouette Visualizer" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from yellowbrick.style import color_palette\n", + "from yellowbrick.cluster.base import ClusteringScoreVisualizer\n", + "\n", + "from sklearn.metrics import silhouette_score, silhouette_samples\n", + "\n", + "\n", + "## Packages for export\n", + "__all__ = [\n", + " \"SilhouetteVisualizer\"\n", + "]\n", + "\n", + "\n", + "##########################################################################\n", + "## Silhouette Method for K Selection\n", + "##########################################################################\n", + "\n", + "class SilhouetteVisualizer(ClusteringScoreVisualizer):\n", + " \"\"\"\n", + " The Silhouette Visualizer displays the silhouette coefficient for each\n", + " sample on a per-cluster basis, visually evaluating the density and\n", + " separation between clusters. The score is calculated by averaging the\n", + " silhouette coefficient for each sample, computed as the difference\n", + " between the average intra-cluster distance and the mean nearest-cluster\n", + " distance for each sample, normalized by the maximum value. This produces a\n", + " score between -1 and +1, where scores near +1 indicate high separation\n", + " and scores near -1 indicate that the samples may have been assigned to\n", + " the wrong cluster.\n", + "\n", + " In SilhouetteVisualizer plots, clusters with higher scores have wider\n", + " silhouettes, but clusters that are less cohesive will fall short of the\n", + " average score across all clusters, which is plotted as a vertical dotted\n", + " red line.\n", + "\n", + " This is particularly useful for determining cluster imbalance, or for\n", + " selecting a value for K by comparing multiple visualizers.\n", + "\n", + " Parameters\n", + " ----------\n", + " model : a Scikit-Learn clusterer\n", + " Should be an instance of a centroidal clustering algorithm (``KMeans``\n", + " or ``MiniBatchKMeans``).\n", + "\n", + " ax : matplotlib Axes, default: None\n", + " The axes to plot the figure on. If None is passed in the current axes\n", + " will be used (or generated if required).\n", + "\n", + " kwargs : dict\n", + " Keyword arguments that are passed to the base class and may influence\n", + " the visualization as defined in other Visualizers.\n", + "\n", + " Attributes\n", + " ----------\n", + " silhouette_score_ : float\n", + " Mean Silhouette Coefficient for all samples. Computed via scikit-learn\n", + " `sklearn.metrics.silhouette_score`.\n", + "\n", + " silhouette_samples_ : array, shape = [n_samples]\n", + " Silhouette Coefficient for each samples. Computed via scikit-learn\n", + " `sklearn.metrics.silhouette_samples`.\n", + "\n", + " n_samples_ : integer\n", + " Number of total samples in the dataset (X.shape[0])\n", + "\n", + " n_clusters_ : integer\n", + " Number of clusters (e.g. n_clusters or k value) passed to internal\n", + " scikit-learn model.\n", + "\n", + " Examples\n", + " --------\n", + "\n", + " >>> from yellowbrick.cluster import SilhouetteVisualizer\n", + " >>> from sklearn.cluster import KMeans\n", + " >>> model = SilhouetteVisualizer(KMeans(10))\n", + " >>> model.fit(X)\n", + " >>> model.poof()\n", + " \"\"\"\n", + "\n", + " def __init__(self, model, ax=None, **kwargs):\n", + " super(SilhouetteVisualizer, self).__init__(model, ax=ax, **kwargs)\n", + "\n", + " # Visual Properties\n", + " # TODO: Fix the color handling\n", + " self.colormap = kwargs.get('colormap', 'set1')\n", + " self.color = kwargs.get('color', None)\n", + "\n", + " def fit(self, X, y=None, **kwargs):\n", + " \"\"\"\n", + " Fits the model and generates the silhouette visualization.\n", + " \"\"\"\n", + " # TODO: decide to use this method or the score method to draw.\n", + " # NOTE: Probably this would be better in score, but the standard score\n", + " # is a little different and I'm not sure how it's used.\n", + "\n", + " # Fit the wrapped estimator\n", + " self.estimator.fit(X, y, **kwargs)\n", + "\n", + " # Get the properties of the dataset\n", + " self.n_samples_ = X.shape[0]\n", + " self.n_clusters_ = self.estimator.n_clusters\n", + "\n", + " # Compute the scores of the cluster\n", + " labels = self.estimator.predict(X)\n", + " self.silhouette_score_ = silhouette_score(X, labels)\n", + " self.silhouette_samples_ = silhouette_samples(X, labels)\n", + "\n", + " # Draw the silhouette figure\n", + " self.draw(labels)\n", + "\n", + " # Return the estimator\n", + " return self\n", + "\n", + " def draw(self, labels):\n", + " \"\"\"\n", + " Draw the silhouettes for each sample and the average score.\n", + "\n", + " Parameters\n", + " ----------\n", + "\n", + " labels : array-like\n", + " An array with the cluster label for each silhouette sample,\n", + " usually computed with ``predict()``. Labels are not stored on the\n", + " visualizer so that the figure can be redrawn with new data.\n", + " \"\"\"\n", + "\n", + " # Track the positions of the lines being drawn\n", + " y_lower = 10 # The bottom of the silhouette\n", + "\n", + " # Get the colors from the various properties\n", + " # TODO: Use resolve_colors instead of this\n", + " colors = color_palette(self.colormap, self.n_clusters_)\n", + "\n", + " # For each cluster, plot the silhouette scores\n", + " for idx in range(self.n_clusters_):\n", + "\n", + " # Collect silhouette scores for samples in the current cluster .\n", + " values = self.silhouette_samples_[labels == idx]\n", + " values.sort()\n", + "\n", + " # Compute the size of the cluster and find upper limit\n", + " size = values.shape[0]\n", + " y_upper = y_lower + size\n", + "\n", + " color = colors[idx]\n", + " self.ax.fill_betweenx(\n", + " np.arange(y_lower, y_upper), 0, values,\n", + " facecolor=color, edgecolor=color, alpha=0.5\n", + " )\n", + "\n", + " # Label the silhouette plots with their cluster numbers\n", + " self.ax.text(-0.05, y_lower + 0.5 * size, str(idx))\n", + "\n", + " # Compute the new y_lower for next plot\n", + " y_lower = y_upper + 10\n", + "\n", + " # The vertical line for average silhouette score of all the values\n", + " self.ax.axvline(\n", + " x=self.silhouette_score_, color=\"red\", linestyle=\"--\"\n", + " )\n", + "\n", + " return self.ax\n", + "\n", + " def finalize(self):\n", + " \"\"\"\n", + " Prepare the figure for rendering by setting the title and adjusting\n", + " the limits on the axes, adding labels and a legend.\n", + " \"\"\"\n", + "\n", + " # Set the title\n", + " self.set_title((\n", + " \"Silhouette Plot of {} Clustering for {} Samples in {} Centers\"\n", + " ).format(\n", + " self.name, self.n_samples_, self.n_clusters_\n", + " ))\n", + "\n", + " # Set the X and Y limits\n", + " # The silhouette coefficient can range from -1, 1;\n", + " # but here we scale the plot according to our visualizations\n", + "\n", + " # l_xlim and u_xlim are lower and upper limits of the x-axis,\n", + " # set according to our calculated maximum and minimum silhouette score along with necessary padding\n", + " l_xlim = max(-1, min(-0.1, round(min(self.silhouette_samples_) - 0.1, 1)))\n", + " u_xlim = min(1, round(max(self.silhouette_samples_) + 0.1, 1))\n", + " self.ax.set_xlim([l_xlim, u_xlim])\n", + "\n", + " # The (n_clusters_+1)*10 is for inserting blank space between\n", + " # silhouette plots of individual clusters, to demarcate them clearly.\n", + " self.ax.set_ylim([0, self.n_samples_ + (self.n_clusters_ + 1) * 10])\n", + "\n", + " # Set the x and y labels\n", + " self.ax.set_xlabel(\"silhouette coefficient values\")\n", + " self.ax.set_ylabel(\"cluster label\")\n", + "\n", + " # Set the ticks on the axis object.\n", + " self.ax.set_yticks([]) # Clear the yaxis labels / ticks\n", + " self.ax.xaxis.set_major_locator(plt.MultipleLocator(0.1)) # Set the ticks at multiples of 0.1\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the clustering model and visualizer \n", + "model = KMeans(6)\n", + "visualizer = SilhouetteVisualizer(model)\n", + "\n", + "visualizer.fit(X) # Fit the data to the visualizer\n", + "visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/examples/gokriznastic/silhouette-visualizer-test.ipynb b/examples/gokriznastic/silhouette-visualizer-test.ipynb new file mode 100644 index 000000000..6ae91dcc3 --- /dev/null +++ b/examples/gokriznastic/silhouette-visualizer-test.ipynb @@ -0,0 +1,437 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys \n", + "\n", + "# Modify the path \n", + "sys.path.append(\"..\")\n", + "\n", + "import yellowbrick as yb\n", + "import matplotlib.ticker as ticker\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Setup a plot such that only the bottom spine is shown\n", + "def setup(ax):\n", + " ax.spines['right'].set_color('none')\n", + " ax.spines['left'].set_color('none')\n", + " ax.yaxis.set_major_locator(ticker.NullLocator())\n", + " ax.spines['top'].set_color('none')\n", + " ax.xaxis.set_ticks_position('bottom')\n", + " ax.tick_params(which='major', width=1.00)\n", + " ax.tick_params(which='major', length=5)\n", + " ax.tick_params(which='minor', width=0.75)\n", + " ax.tick_params(which='minor', length=2.5)\n", + " ax.set_xlim(0, 5)\n", + " ax.set_ylim(0, 1)\n", + " ax.patch.set_alpha(0.0)\n", + "\n", + "\n", + "plt.figure(figsize=(8, 6))\n", + "n = 8\n", + "\n", + "# Multiple Locator\n", + "ax_ = plt.subplot(n, 1, 2)\n", + "setup(ax_)\n", + "ax_.xaxis.set_major_locator(ticker.MultipleLocator(0.5))\n", + "#ax_.xaxis.set_minor_locator(ticker.MultipleLocator(0.1))\n", + "#ax_.text(0.0, 0.1, \"MultipleLocator(0.5)\", fontsize=14, transform=ax_.transAxes)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# yellowbrick.cluster.silhouette\n", + "# Implements visualizers using the silhouette metric for cluster evaluation.\n", + "#\n", + "# Author: Benjamin Bengfort \n", + "# Created: Mon Mar 27 10:09:24 2017 -0400\n", + "#\n", + "# Copyright (C) 2016 District Data Labs\n", + "# For license information, see LICENSE.txt\n", + "#\n", + "# ID: silhouette.py [57b563b] benjamin@bengfort.com $\n", + "\n", + "\"\"\"\n", + "Implements visualizers that use the silhouette metric for cluster evaluation.\n", + "\"\"\"\n", + "\n", + "##########################################################################\n", + "## Imports\n", + "##########################################################################\n", + "\n", + "import numpy as np\n", + "\n", + "from yellowbrick.style import color_palette\n", + "from yellowbrick.cluster.base import ClusteringScoreVisualizer\n", + "\n", + "from sklearn.metrics import silhouette_score, silhouette_samples\n", + "\n", + "\n", + "## Packages for export\n", + "__all__ = [\n", + " \"SilhouetteVisualizer\"\n", + "]\n", + "\n", + "\n", + "##########################################################################\n", + "## Silhouette Method for K Selection\n", + "##########################################################################\n", + "\n", + "class SilhouetteVisualizer(ClusteringScoreVisualizer):\n", + " \"\"\"\n", + " The Silhouette Visualizer displays the silhouette coefficient for each\n", + " sample on a per-cluster basis, visually evaluating the density and\n", + " separation between clusters. The score is calculated by averaging the\n", + " silhouette coefficient for each sample, computed as the difference\n", + " between the average intra-cluster distance and the mean nearest-cluster\n", + " distance for each sample, normalized by the maximum value. This produces a\n", + " score between -1 and +1, where scores near +1 indicate high separation\n", + " and scores near -1 indicate that the samples may have been assigned to\n", + " the wrong cluster.\n", + "\n", + " In SilhouetteVisualizer plots, clusters with higher scores have wider\n", + " silhouettes, but clusters that are less cohesive will fall short of the\n", + " average score across all clusters, which is plotted as a vertical dotted\n", + " red line.\n", + "\n", + " This is particularly useful for determining cluster imbalance, or for\n", + " selecting a value for K by comparing multiple visualizers.\n", + "\n", + " Parameters\n", + " ----------\n", + " model : a Scikit-Learn clusterer\n", + " Should be an instance of a centroidal clustering algorithm (``KMeans``\n", + " or ``MiniBatchKMeans``).\n", + "\n", + " ax : matplotlib Axes, default: None\n", + " The axes to plot the figure on. If None is passed in the current axes\n", + " will be used (or generated if required).\n", + "\n", + " kwargs : dict\n", + " Keyword arguments that are passed to the base class and may influence\n", + " the visualization as defined in other Visualizers.\n", + "\n", + " Attributes\n", + " ----------\n", + " silhouette_score_ : float\n", + " Mean Silhouette Coefficient for all samples. Computed via scikit-learn\n", + " `sklearn.metrics.silhouette_score`.\n", + "\n", + " silhouette_samples_ : array, shape = [n_samples]\n", + " Silhouette Coefficient for each samples. Computed via scikit-learn\n", + " `sklearn.metrics.silhouette_samples`.\n", + "\n", + " n_samples_ : integer\n", + " Number of total samples in the dataset (X.shape[0])\n", + "\n", + " n_clusters_ : integer\n", + " Number of clusters (e.g. n_clusters or k value) passed to internal\n", + " scikit-learn model.\n", + "\n", + " Examples\n", + " --------\n", + "\n", + " >>> from yellowbrick.cluster import SilhouetteVisualizer\n", + " >>> from sklearn.cluster import KMeans\n", + " >>> model = SilhouetteVisualizer(KMeans(10))\n", + " >>> model.fit(X)\n", + " >>> model.poof()\n", + " \"\"\"\n", + "\n", + " def __init__(self, model, ax=None, **kwargs):\n", + " super(SilhouetteVisualizer, self).__init__(model, ax=ax, **kwargs)\n", + "\n", + " # Visual Properties\n", + " # TODO: Fix the color handling\n", + " self.colormap = kwargs.get('colormap', 'set1')\n", + " self.color = kwargs.get('color', None)\n", + "\n", + " def fit(self, X, y=None, **kwargs):\n", + " \"\"\"\n", + " Fits the model and generates the silhouette visualization.\n", + " \"\"\"\n", + " # TODO: decide to use this method or the score method to draw.\n", + " # NOTE: Probably this would be better in score, but the standard score\n", + " # is a little different and I'm not sure how it's used.\n", + "\n", + " # Fit the wrapped estimator\n", + " self.estimator.fit(X, y, **kwargs)\n", + "\n", + " # Get the properties of the dataset\n", + " self.n_samples_ = X.shape[0]\n", + " self.n_clusters_ = self.estimator.n_clusters\n", + "\n", + " # Compute the scores of the cluster\n", + " labels = self.estimator.predict(X)\n", + " self.silhouette_score_ = silhouette_score(X, labels)\n", + " self.silhouette_samples_ = silhouette_samples(X, labels)\n", + "\n", + " # Draw the silhouette figure\n", + " self.draw(labels)\n", + "\n", + " # Return the estimator\n", + " return self\n", + "\n", + " def draw(self, labels):\n", + " \"\"\"\n", + " Draw the silhouettes for each sample and the average score.\n", + "\n", + " Parameters\n", + " ----------\n", + "\n", + " labels : array-like\n", + " An array with the cluster label for each silhouette sample,\n", + " usually computed with ``predict()``. Labels are not stored on the\n", + " visualizer so that the figure can be redrawn with new data.\n", + " \"\"\"\n", + "\n", + " # Track the positions of the lines being drawn\n", + " y_lower = 10 # The bottom of the silhouette\n", + "\n", + " # Get the colors from the various properties\n", + " # TODO: Use resolve_colors instead of this\n", + " colors = color_palette(self.colormap, self.n_clusters_)\n", + "\n", + " # For each cluster, plot the silhouette scores\n", + " for idx in range(self.n_clusters_):\n", + "\n", + " # Collect silhouette scores for samples in the current cluster .\n", + " values = self.silhouette_samples_[labels == idx]\n", + " values.sort()\n", + "\n", + " # Compute the size of the cluster and find upper limit\n", + " size = values.shape[0]\n", + " y_upper = y_lower + size\n", + "\n", + " color = colors[idx]\n", + " self.ax.fill_betweenx(\n", + " np.arange(y_lower, y_upper), 0, values,\n", + " facecolor=color, edgecolor=color, alpha=0.5\n", + " )\n", + "\n", + " # Label the silhouette plots with their cluster numbers\n", + " self.ax.text(-0.05, y_lower + 0.5 * size, str(idx))\n", + " \n", + " # Compute the new y_lower for next plot\n", + " y_lower = y_upper + 10\n", + "\n", + " # The vertical line for average silhouette score of all the values\n", + " self.ax.axvline(\n", + " x=self.silhouette_score_, color=\"red\", linestyle=\"--\"\n", + " )\n", + "\n", + " return self.ax\n", + "\n", + " def finalize(self):\n", + " \"\"\"\n", + " Prepare the figure for rendering by setting the title and adjusting\n", + " the limits on the axes, adding labels and a legend.\n", + " \"\"\"\n", + "\n", + " # Set the title\n", + " self.set_title((\n", + " \"Silhouette Plot of {} Clustering for {} Samples in {} Centers\"\n", + " ).format(\n", + " self.name, self.n_samples_, self.n_clusters_\n", + " ))\n", + "\n", + " # Set the X and Y limits\n", + " # The silhouette coefficient can range from -1, 1;\n", + " # but here we scale the plot according to our visualizations\n", + " \n", + " # l_xlim and u_xlim are lower and upper limits of the x-axis, \n", + " # set according to our calculated maximum and minimum silhouette score along with necessary padding\n", + " l_xlim = max(-1, min(-0.1, round(min(self.silhouette_samples_)-0.1,1)))\n", + " u_xlim = min(1, round(max(self.silhouette_samples_)+0.1,1))\n", + " self.ax.set_xlim([l_xlim, u_xlim])\n", + "\n", + " # The (n_clusters_+1)*10 is for inserting blank space between\n", + " # silhouette plots of individual clusters, to demarcate them clearly.\n", + " self.ax.set_ylim([0, self.n_samples_ + (self.n_clusters_ + 1) * 10])\n", + "\n", + " # Set the x and y labels\n", + " self.ax.set_xlabel(\"silhouette coefficient values\")\n", + " self.ax.set_ylabel(\"cluster label\")\n", + "\n", + " # Set the ticks on the axis object.\n", + " self.ax.set_yticks([]) # Clear the yaxis labels / ticks\n", + " self.ax.xaxis.set_major_locator(ticker.MultipleLocator(0.1)) # Set the ticks at multiples of 0.1\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.datasets import make_blobs\n", + "\n", + "# Make blobs dataset\n", + "X, y = make_blobs(n_samples=1000, n_features=12, centers=8, shuffle=False, random_state=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.cluster import KMeans\n", + "# Instantiate the clustering model and visualizer\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot()\n", + "\n", + "model = KMeans(random_state=0)\n", + "visualizer = SilhouetteVisualizer(model, ax=ax)\n", + "\n", + "visualizer.fit(X) # Fit the training data to the visualizer\n", + "visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = KMeans(n_clusters=2, random_state=0)\n", + "visualizer = SilhouetteVisualizer(model, ax=ax)\n", + "\n", + "visualizer.fit(X) # Fit the training data to the visualizer\n", + "visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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Bf9bXArNFZBnuS/4KMNpvdE8BY0TkblVdhmu5PCAiz+K6Qlu9pL2V5XoJd/HGdb4Vle3XuO6Mm3O89Hu4i2WW4M4pZs5V/Qv3eb/gt61D/fSx7aRyHnCRiCzB7TgvVNWVOeb7kn+/TBfJ0/7xaTnmPRvXtfgMrn8/c27pFtwO8nkRWYw76LvKT8vefo7F7byX+Ti3q+rCdpYjJ18kjgQW+GU8F7c9ftLmC7dxu23Fl4Dd/Hd8ITDXd9+9gzsXfpf/7Hb0eYLrsnsF14W2CLhRVVsWCHDbxUO4z2eFiLyIOweSOdd3AfA7v06vwxWZ9raNXHbzn+tNuAs01raYfjauq24Zbptchrsw4lncvvOfIvJP3PnRr3ci/mZU9QNcr8uVftky3+dXcsx7G66Ffrvfhp/DFba9fffeX3Dnoh/0+7hjgSOyWnWtuRe4REROpPXvZ8tc3sNtY//nvwuX4s4rtipmtwgxpncTkctxv09a43smnsVd2PFByKn1KJLH34+aLbX7o2BjTI/3Ou4INnMO7zQrVKansZaVMcaYgmdjAxpjjCl4PbIbcPHixSW4K0dW07HLcI0xxrgLfIYAixoaGja2N3Mh6ZHFCleotvZHicYYY5xZuB+p9xg9tVitBqivryeZbGvwi+6xfPlyJk7MNRxi74wbZmxb5mjEDitu4y67kCwuhifyv98OY5kbGxt58cUXYcuhugpeTy1WKYBkMklJybYMFtw5sVRAMtaZ3xNum3hzOHHDjG3LHI3YoS3zmndIliShvdgBkAqgOE6suOvyDGP/5fW40yc9tViFquaNJpo/ej3vcWvf3kjzuvzHDTO2LXM0YocVN9EcQNBM8+M5YgcQqy0nVlIERTFIJoj1L+/SYmU6zopVJwQxIJH/CymDeCyUuGHGtmWORuyw4q6fvAvl5eVbxI5VFBOv70+8OtewjyYMVqyMMZG19tTzKB0yZLPnYkVx4lJLvE+uu3GYsNjvrIwxJiMG8XFWqAqRFStjTGRV3beQ+O3XbXocH1JFvLaijVeYsFixMsZEVtWDdxO/59fuQSpNbIAVqkIV2XNWd999N7/7nbtj88aNG1mxYgVPPvkkffr0aeeVxpheJx4jMWEQ8b52QUWhimyxOuKIIzjiiCMAuPDCCznyyCOtUBkTUYnxtcQHVLY/owlN5LsBly1bxssvv8znP//5sFMxxoShKG6FqgeIfLG67rrrOOuss8JOwxgTlqLI7wZ7hEh/Sh9++CGvvvoqu+66a9ipGGPyLAgCUmVlxCrsooqeINLFatGiRcycOTPsNIwxIYgF8Nz/3gHPPRd2KqYDIl2sXnvtNerq6sJOwxiTb4kYialDCKwLsMeI7NWAAKeddlrYKRhj8i0VkNhhAPG+ZVQsWwZNTWCnAgpepIuVMSZ6YjWlxAe6q/9GX3ABJJOwcmW4SZl2WRvYGBMdqTTxUTVhZ2E6wYqVMSYaYhDfvj+xfjZKRU9k3YDGmN4vCIgPqyYxwlpVPZUVK2NMr5eYNJh4//Kw0zDbwIqVMaZ3SgfEKpPEB1daoeoFrFgZY3qfABLjBhAbUkksFmt1tld/8APGjx+fx8RMZ1mxMsb0HumAWN9S4mP6Ea9u/26/n4wfDw0NeUjMbCsrVsaYni2VhniMWG058eHVxGvsar/eyIqVMaZHCoIAUgHxkTUktutHLN56d19rJhx+OJSUwEsvdUOGpitZseqMGNCJL8a2CmJBKHHDjG3LHI3YLePG4jFIxCARh5j/Ox4jVhR385UVESstgvIksaqSThUqgFhzM8Tt56Y9gRWrTvhgRJLtGkblPe57i99nZAhxw4xtyxyN2GEus+kZrFh1QvOGj1n/wXt5j9v08QehxA0zti1zNGKHFTcepEmn02zcithBKkV5/0HdmJXJxYpVJ3z03JO8sPIfeY+7dvXbvPDGkrzHDTO2LXM0YocVVzauh+ZGXrj/5nbnjSeKqB42hpqR46xYhcCKVSfEEkUUJdu/LLarxYuTocQNM7YtczRihxU3RoxYLNZ27ABqRgoDxjdQ2qdf/pIzm7FiZYyJrNf3nEaf6uqc0+JFSSoGDKV6RD19R9TnOTPTkhUrY0xkvT57Z4YOHbrF87F4nBEzDqRq8IgQsjK5WLEyxpgsidJyhk/f1wpVgbEfGBhjImvC7fcz7JqFmx4HqRQjdtnfClUBinTL6rrrruPhhx+mqamJY445hqOOOirslIwxedT/xddJFCUASDc3MXK3Q6gcWBdyViaXyBarp59+miVLlnD77bezfv16brrpprBTMsaEqLS6luq67cJOw7QissXqiSeeoL6+nrPOOot169Zx3nnnhZ2SMSYk6eZmBo6fRsyGXipYkS1Wa9euZdWqVVx77bW8+eabnHnmmTzwwANt3vvGGNM7VQ4cRt/Rdl+rQhbZYlVTU8OYMWNIJpOMGTOGkpIS/v3vf9O/f/+wUzPG5FMQMGjHGWFnYdoR2TZvQ0MDjz/+OEEQsGbNGtavX09NTU3YaRlj8ug/dYNonjDeLqroASLbstprr71YtGgRc+bMIQgC5s+fTyKRCDstY0yeBOk0K751Dnse/YWwUzEdENliBdhFFcZEWKyoiIoROxKP20FqTxDZbkBjTHSlUylqt5vEwD8/BDffHHY6pgMi3bIyxkRTaXU/Bk/ejcaDPwfJJJx0UtgpmXZYy8oYEy2xOCNmHmw/U+lhrFgZYyIj3dxEXcNsyqrtJyo9jRUrY0wkpFPN9N9+MjWj7Me/PZGdszLG9HpBENBn6Bjqpu0ddiqmk6xlZYzp1YIgTUmfvgybtlfYqZhtYC0rY0yvFYvHGTh+F2rrp5AoKt5i+vN33MHUqVNDyMxsLStWxpheJwgCglQTw3c9kH6jd2h1vnR5OVRW5jEz01lWrIwxPZorTM0kSssoKimnqKScyoHDqBkplPbp1+Zrk2++CX37wpgxecrWdJYVq06IFRWTSJbmPW68uCSUuGHGtmWORuzOxo0XJ6kaPJJBE3elKFmy1a+vP/NM96PglSu3+rUmv6xYdUL1xFns0NCQ97jrFy8OJW6YsW2ZoxE7zGU2PYMVq04o/mQNvPtC3uOWfLQS3q3Ie9wwY9syRyN2WHFj6WZIx7bt+5xugqZPINUE1SOgZkTXJWg2sWLVCdVrHoONT+Y9br+3V0PzM3mPG2ZsW+ZoxA4rbjy1Hhob4dlbO/HqNNSMhtpxUDkYkpVQ1rfLczSOFatOCGJxSGx5GWy3ixeFEzfM2LbM0Ygd5jLD1sUOUjBwIoycDVWDui0lszkrVsYY01FB4ArVxM+HnUnkWLEyxkTWf07enf792r68fTPJCqg/tPsSMq2yYmWMiayNU4bDkKEdmzkIYNBkSJZ3b1ImJxsb0BhjOqKkEkbb+IJhiXTL6rDDDqOqqgqAuro6LrnkkpAzMsbkU/8L73U/Cr7qlLZnDNIwel8oSuYnMbOFyBarjRs3AnDrrZ25ZNUY0xsk/v0xJDa0PVMQQHktDLUBb8MU2W7AF154gfXr13PKKadwwgknsHTp0rBTMsYUonQTTPhc2FlEXmRbVqWlpZx66qkcddRRrFy5ktNPP50HHniAoqLIrhJjTEupJhg+A6oGh51J5EV2zzx69GhGjhxJLBZj9OjR1NTU8O677zJkyJCwUzPGFIJ0EwyeAvWHhJ2JIcLdgHfddReXXnopAGvWrGHdunUMGDAg5KyMMQWjrD9MmAOxWNiZGCLcspozZw7f/va3OeaYY4jFYlx88cXWBWhMxKyfsR1VuW6+GAQwcIIVqgIS2b1zMpnkiiuuCDsNY0yIPjp6Z6pa/ig4CKCkCupmhpOUySmyxcoYY7aQbob+4sb+s99UFRQrVsaYyKr6zT+gshJO39f98HfoNBh/WNhpmRwie4GFMcaU/f0VeHi5e1BcAWMPDDch0yorVsYYk26G7faH4tKwMzGtsGJljDEDJthwSgXOipUxJtpicag/OOwsTDusWBljIizmbqhYWh12IqYddjWgMSZ60s1QUs3GwcMoqu4fdjamA6xYGWOiIwigrAZG7wODJ/HCzXvT0NAQdlamA6xYGWN6p1QjJJJQVArFZVAxCCoGQt2udmv6HijUYiUie7Q1XVUfy1cuWyNVXAllFXmP21y8Dsr65j1umLFtmaMRu0vjxoqgVqByCAzcoc3x/fo8+SS88w4cdFDXxDbdJuyW1YVtTAuAvfOVyNb4YNgBEELXwbvFixkRUpdFWLFtmaMRO6y4Iy691N3WfuXKvMc2WyfUYqWqe4UZv7NKSlYDy/Iet7z8VSCc8crCim3LHI3YYcWNxZqBGO1/n5uBkUC/bs/J5BZ2ywoAERkJ/AIYBcwCbgNOUdWVIabVqsrKl4C38h63uno18O+8xw0zti1zNGKHFTcebwRSwN9amSONK2YDgUn5SsvkUCi/s7oOuBxYB6wBbgduCTUjY0zEBUBf4GTgCCARbjoRVyjFqlZV/wKgqoGq3gD0CTknY0yklQP/BZSEnYihcIrVehGpwx3KICK7AxvDTckYE10xYCdcwTKFoCDOWQFfA/4AbCciS3FnMY8KNyVjTG/3/p0nMmjQgBbPNgPTgIkhZGRaUxDFSlX/KSLTgXpca09VtTHktIwxvVxqZF8Y2rJY7QOMCyMd04aC6AYUkWrgUtxFFTcA/y0ieWl/v//+++y555688sor+QhnjCkgsY8bYd0G/yiFu+LPClUhKohiBdyI21JOAs4AqoDruztoU1MT8+fPp7TUbrhmTBQN2PvnMPEi3OnyocAuIWdkWlMQ3YDAWFWdk/X4HBH5V3cHveyyyzj66KO5/vpur4vGmIKWxA2YUyjH76alQvlkVERmZh6IyGTgpe4MePfdd9OvXz9mzZrVnWGMMT3COOzXMoUt7IFsX8O1v8uAOSLyAq47cDzdXKx++9vfEovF+Pvf/86KFSs4//zzueaaaxgwoOXJVmNM71YEzAg7CdOOsLsBZ4cVeOHChZv+njt3LgsWLLBCZUwkFeN+V2UKWdgD2b4OICIlwMFAJW6rSQCjgfnhZWeM6f1ihH/MbjqiUD6l23GDcI0FHgf2Ap7IV/Bbb701X6GMMQWjhNVfOJXhw+3Hvz1BoRSrScD2wJXATcA84I5QMzLG9HJ78M7BOzLcbmvfIxTK1YDvqGoAvABMUtXwbuhjjOnlmoHJwJiwEzFboVBaVstF5GrgGmChiAzFzngaY7pcGhhO5se/Y775TaipgbvvDjUr075CaVmdCdypqs/jLqoYAhwbbkrGmN4lwF23tT+ZY+HyF16AZ54JMynTQWH/zmqPHI//A/wWu3+0MaZLJXC/lrHh1XqisLsBL2xjWoAb/8QYY7ZRCpiOFaqeK+zfWe0VZnxjTFTMBKaEnYTZBmG3rIwxphs14e7jOijsRMw2smJljOmlmoGptFWoPpo+nZLa2rxlZDqvIK4GFJEzws7BGNMTpYGNQKP/O44btW074HhgtzZf/fr8+XDjjd2co+kKhdKy+gpwXdhJdFRzcxVudKj8amz8BAjnKDCs2LbM0YjdubiluBHaBgDluHEECuL423SDQilWb4jIw8DTwPrMk6p6UXgpte4//5kK5H+IlvffX8yoUeEMDRNWbFvmaMQOK+7A226Dxx+Hc87Je2yzdQqlWD2V9XfBj1zxydsbWLXs7bzH/XDlx6xK5j9umLFtmaMRO6y4tbfeRnNxEe/sc3SHX5NqThOPxxg2eUg3ZmZaKohipaoXikgFrqN5OVCmqh+HnFar1r21nlUfrsl73I9WfcKqxvzHDTO2LXM0YocVt6Y5TSpIsWp5+7HTzWlK+5TQf1Rfhky0qwvzrSA6eEVkb+BZ4B5gIPC6iOwfblbGGOPE4jFGzRjBxEPGMWzyEOKJgth1RkqhrPFLgN2BD1T1bWAP4PJwUzLGGIgXxdl+9mgGju1vRSpEhbLm475IAeAHtDXGmFClU2mG7zSUPoOqwk4l8grinBXwpogcAgQiUgOcBfy/kHMyxvRy6UQR8USi1ekB0G9ETf4SMq0qlGJ1Bu4uwcOBV4CHgdO7M2AqlWLevHm89tprJBIJLrnkEkaMGNGdIY0xBebhb93I0KG5r+pLNafZbreRJIp8GWg1AAAY10lEQVRbL2YmfwqlWE1W1WOynxCRI4BuuyPaI488AsBvfvMbnn76aS655BKuueaa7gpnjOlhymrKqB1jdyoqFGHfz+rzQAlwkYjMz5pUBFxANxarfffdl9mzZwOwatUqam18MGMip/rNlyjfsJZPxuyw2fNBEFAzxM5TFZKwW1ZVuMG7qoDs24U0A9/p7uBFRUWcf/75PPjgg1x11VXdHc4YU2Cm/+r7JBIJll31p82eTzelqR1rrapCEvb9rH4B/EJE9lHV/8s8LyJ9VPXDfORw2WWX8Y1vfIPPfe5z3H///ZSXl+cjrDGmgA2bMoTymrKw0zBZCuXS9XIRuUxEKkVkBfCqiJzUnQF///vfc911buzcsrIyYrEYiTauCjLG9H5BOqB2dF/qbCilglMoxWo+cBtwNPAPYBRuJPZus//++/P8889z3HHHceqpp3LBBRdQUlLSnSGNMQUsSAeU1ZQyYnpd2KmYHMI+Z7WJqj4rIguAX6vqOhEp7s545eXlXHnlld0ZwhjTg8SL4+xwYL2NUlGgCuVTWSMiVwPTgAdE5ArsR8HGmDwJ0gHb7T7KClUBK5RP5hhgEbCXH239VVyXoDHGdJvFx3+Ll8+6lL4ja6gZ2ifsdEwbCqVYHe7/nykiJwAfAUeEmI8xJgLWjhxPcq9ZjN19VNipmHYUyjmr7N9YFQOzgMeAW8JJxxgTBenmNLVj+oadhumAgihWqnpy9mMR6QfcEVI6xpgISCQT7H/NWZTfUg7PPRd2OqYdBVGscliHu3zdGGO6XBAEjNqljkTjRtzY6qbQFUSxEpFH+HSLiQFjgD+Gl5ExpjfrO7yGvnU1bAw7EdNhBVGsgAVZfwfAe3YDRmNMd0inAoZMGBh2GmYrhT3q+h7+z5bt8FoR2UNVH8t3TsaY3ikIAuKJBMMmDKCin40B2tOE3bK6sI1pAbB3vhIxxvQ+QRCQak4TT8QZWF9L3eTBFCXD3u2Zzgh71PVNl6yLyEBVfUdEyoGhqvpyiKkZY3qodMqN8ZesSFLRt4ya4X0orynLOTrFu3PmUFdnYwH2BAVxiCEiXwFOBnYCBgD3iciPVfX6cDPLLdmniKoBlXmPW/JJMVUD8x83zNi2zNGI3VVxkxXFVA/tQ78RNR2af82JJ1LX0LDNcU33K4hiBZwB7AKgqq+LSAPwNFCQxapm+yqkYbu8x123+INQ4oYZ25Y5GrHDXGbTMxRKsSqGza4ibaSAf/xQ9OKLrP/wo7zHTb70Uihxw4xtyxyN2GHFHbXguzRXV9P0ta+3PlNTE8GGDQSpFLF4jOIddqB4++3zl6QBCqdY/R54WETuxBWpI4F7wk2pdUWvvkbTqtV5j5tctYqmj0LaiYUU25Y5GrHDittn6VLiiQRNe87eYlrQ3EyspISS3XcjMWIE8aoqYqWlxGKxvOdpCqRYqer5IjIH2BNoAq5S1d+HnJYxJoKCdJpYURElu+9OyS47EysqiN1k5BXMp6CqdwF3hZ2HMSa6gliM0n32pnjiROLJZNjpmCwFU6yMMSZMQXMzpQccQMmUyWGnYnIolPtZGWNMaIKmJpI77URy8qSwUzGtiGzLqqmpiQsuuIC33nqLxsZGzjzzTPbZZ5+w0zLG5NHG/rWUlJaQGDqUsv32DTsd04bIFqt7772XmpoaLr/8ctauXcvhhx9uxcqYiHnr8MMZPHAgpZN2DDsV047IFqsDDzyQAw44YNPjRCIRYjbGmLDEqypJTpwYdhqmHZE9Z1VRUUFlZSXr1q3j7LPP5pxzzgk7JWNMnlWtWEFZY6Ndnt4DRLZYAaxevZoTTjiBz372sxx66KFhp2OMyaMgCKh96u8U/eQnYadiOiCyhxPvvfcep5xyCvPnz2fGjBlhp2OMybNYOk1QUhJ2GqaDItuyuvbaa/nwww/5+c9/zty5c5k7dy4bNmwIOy1jTJ4U1deDDZ3UY0S2ZTVv3jzmzZsXdhrGmDDEExTvNJV02HmYDotsy8oYE01BczPJqZMpHjEi7FTMVrBiZYyJlFiyhJI99gg7DbOVrFgZYyIjaG4mOWUSsbjb9a1YuBCWLAk5K9MRVqyMMZERKyqmZNasTY9TffpA374hZmQ6yoqVMSYyiurHbvYD4OI1a+DNN0PMyHRUZK8GNMZETDJJybRpmz0lp50GySSsXBlOTqbDrGVljOn9iooo3WcfEkOGhJ2J6SRrWRljeq0g1Uzx9vUkp0+jqK4u7HTMNrBiZYzpneJxKj5/HEV1w8LOxHQBK1bGmF4lABIDBlAyc4YVql7EipUxpkcLUilIp4lVVBCvqaHs4INI9O8fdlqmi1mx6oT0oMEkamvzHrcpHicxekze44YZ25Y5GrE7EzeWiBErKyde25/kuHHESku3Ou4b557L2LFjt/p1Jv+sWHVC49TJVDQ05D3uxsWLQ4kbZmxb5mjEDivuf2bPhpDWtdk6Vqw64fUNr9O0ujHvcV/55JVQ4oYZ25Y5GrF7WtxUOkVjqpGieBHF8SQ7D9mZeMx+CdSdrFh1wntN77Dhg/V5j/t202piH4Rz/52wYtsyRyN2WHFnfeW7lJSU8H83f6fDr0kHaYZX1TF7+F7E7H5YeWPFyhgTWdVvryWRSHR4/iAIGFo5lFl1e1qhyjNrtxpjTAcl4nF2HTLDuvxCYGvcGGPaEQQBzelmJg2YQnlxedjpRJJ1AxpjTDvSpDh49GeoLc//T1aME/mW1bPPPsvcuXPDTsMYU6DSQYphlXVWqEIW6ZbVDTfcwL333ktZWVnYqRhjQvDSHjtSUVHZ6vRUkGJszVhmDJ2Zx6xMLpFuWY0YMYKrr7467DSMMSH56xcPYcl5x7Y6PU6MaYOm5zEj05pIF6sDDjiAoqJINy6NMa1IpVOM6z+e4kRx2KkYIl6sjDHRNvPmv7Dj1b/NOS0eizFl4NQ8Z2RaY8XKGBNZO/xlMaPveXyL51NBikkDJpOIdfwHw6Z7WR+YMcZkiQG7DN6Z+n7jwk7FZIl8y6quro4777wz7DSMMQUgHaQY33+CFaoCFPliZYwx4EapqKsazuQBk8NOxeRgxcoYYwBiMH3QdBugtkDZOStjTGR9UlNJMukuTR9eNZyKZOs/EDbhsmJljIms237+FYYMGUpz0Mw4O09V0Kwb0BgTaekgzaCyQQwsHxR2KqYNVqyMMZE1YvFLjHha2XfUfmGnYtph3YDGmMg64Iq7KC+pIHHy98NOxbTDWlbGmEjJ3EgxBhTFiimyUSp6BGtZGWMiIR2kSQUpRlePZkz1GAaUDYTYN8NOy3SQFStjTK+VDtI0p5sYVDGY+pp6BlQMpE+yz6bpG0PMzWwdK1bGmB4rCAJSQYpUkCIei5OIJUgmkpQkkiQTJYytGUtt2QBqSmrsx749nBWrTqgtHsCQ6iF5jxu8l2Z09ei8xw0zti1zNGJ3Nm5xvJjBFYOpTFZRVlRGMp60otRLWbHqhJGlo2gY2pD3uMnVJaHEDTO2LXM0YocV96Wrr2bihAl5j2u2nhWrTtB3G1m7fHXe47761gbWluQ/bpixbZmjETu0uMWDebu5Gjr4fV7fmCZGQFmyiCkja+hfVdrNGZoMK1ad8M7HKT5+9+O8x337oxSxEOKGGduWORqxw4r7ztoNJFatJV2cbHO+5lSa7QdXsfN2/RnYp8S6GkNgxcoYE1lf/sbxJBIJfnP7ozmnBwFUlxdTP7iKqaP65Tc5sxkrVsYYk0NzKs3M+gFMHtE37FQMVqyMMWYzqXQaGVLNoD6ljB/Wp/0XmLywYmWMMVnKkkXstYONwF5oIlus0uk0CxYsQFVJJpN8//vfZ+TIkWGnZYwJ2ajairBTMDlEdiDbhx56iMbGRu644w7OPfdcLr300rBTMsaELEbA+KHW9VeIItuyWrx4MbNmzQJgypQpLF++POSMjDH59vhnj6e6umbT40HV5QysLgsxI9OayBardevWUVlZuelxIpGgubmZoqLIrhJjImfJXocyZOhQANLpgIl11SFnZFoT2W7AyspKPv740x8hptNpK1TGRNjogRWMHljZ/owmFJEtVjvttBOPPfYYAEuXLqW+vj7kjIwx+XbETy9k7++dQyqVZmR/u7CikEW2KbHffvvx5JNPcvTRRxMEARdffHHYKRlj8qzu5edJJBKMH1ZN/RC7sKKQRbZYxeNxLrroorDTMMaELB6DPcfb76oKXWS7AY0xBgIScdsN9gT2KRljIixGssh2gz2BfUrGmEhKpwNKimLE7W4fPUJkz1kZY6IrnQ7Yd+IgPpk6hbL+/cNOx3SAFStjTKQ0p9LstcMgthvUh8Xf+x79GxrCTsl0gBUrY0xkFMdjNIyqZdxQG6mip7FzVsaYXi+dDqitLOHQhjp2Gv3pHX8H3Hkn/OxnIWZmOspaVsaYXi2VStO3Isnh04dvMW3QrbdCMglnnRVCZmZrWLEyxvRaQRAwZmAV++04OOxUzDayYmWM6TWaU2kAiuIxkkUJRg2oYOb2tcRidn16T2fFyhjTY6VSaRKJOFWlRVSXJ9luYCWjB1RSbD/07XWsWHXC8D5FDA3hvjcl699h+5DutxNWbFvmaMTe2rjxeIySogRVpUWMGVhpLacI6KnFKgHQ2NgYSvBR/YqZOCL/IzQnPywJJW6YsW2ZoxF7W+Juy36gceBAKC6GjRs7/R7bYmOe42atq0ReA3eBWBAEYeew1RYvXrw78HjYeRhjTA81q6Gh4Ymwk9gaPbVltQiYBawGUiHnYowxPUUCGILbh/YoPbJlZYwxJlrskhljjDEFz4qVMcaYgmfFyhhjTMGzYmWMMabgWbEyxhhT8HrMpesiUgb8GhgIfAScqKrv5phvLPB7VZ24jfHiwM+BycBG4DRVfTlr+unAGUAz8H1V/cO2xNua2H6eAcDfgB1VdUM+4orI14Cj/cM/quqFXRG3g7HPAk4CAuCirlrfHVzXceB+4B5VvbYr4nYktohcBeyG294BPquq/8lD3IOA7/qHzwBnqeo2XzbcVlwRmQL8JGv2XYHDVPWBbY3bXmw//RvAMUAauFhVf5enuOf7uB8CP+jK/Yh//12Ay1R1dovnDwXm4/ZfN6nqDV0Ztzv0pJbVmcAyVZ0F3ALMazmDiMwFfgPUdkG8w4BSVZ0BfAu4IivOYOBs3I7kAOASESnpgpjtxvbxDwD+AgzqwphtxhWRMcBxwExgBrC/iEzKU+xa4Es+9j7ANSLSVePrtLmuve8D/XI8392xdwIOUNXZ/t82F6r24opIFXA5cIiq7gqspGu+T23GVdWlmeUEfgbc3VWFqr3YIlKD+z7PAPZn86LZnXF3BI7FFeb9gYtEpLyrAovIecAvgNIWzxcDP/Yx9wS+4PdpBa0nFavdgczG+ydg3xzzrMWt/C6Np6pPAdOypu0MPKmqG/0O5GWgK3fcbcUGd/S3L/DvLozZXtw3gANVNaWqaaAY6JIWXXuxVfU9YLKqNgGDgQ+64ki/vbgAIjIHt77/1EXxOhTbH5FvD1wvIk+KyCn5iIs7IFgGXCEijwNrcvVgdENcAESkArgQVzy6UluxPwZeByr8v3Se4o4HHlXVDb535CW6dj/yCnBEjufHAy+r6lpVbQSewA2yUNAKsliJyKkisjz7H1ANZI4sP/KPN6Oqf1DVj7sojT5Z8QBSIlLUyrSc+XRTbFT1QVV9vwvjtRtXVZtU9T0RiYnID4ElqvpiPmL7+M0i8mXgKeCufMQVkYm4I9/5XRivQ7FxO82rgeOBA4EvdWFLtq24tcBewPnAQcA5IlKfh7gZpwL/6w9QulJ7sd8Ansd1e16Vp7jLgD1EpEpE+uMOFCq6KrCq/hZo6kBOXb3/6hYFWaxU9UZVnZj9D7dyq/wsVcAH3ZzGh1nxAOKq2tzKtK7Op63Y3anNuCJSCiz083wpn7EBVPWnuKFi9hCRvfIQ9wRgGPAw7nzZ10XkwC6K217sT4ArVfUTVf3I5zA5D3HfBxap6tuqug54DJiSh7gZx+G6rrpaW7EPwm1Xo4ERwGEisnN3x1XVFcBPca32K4Cnga4u0h3JKR/7021WkMWqFU8CB/u/D6L7B7LdFE9EdsUdBWX8A5glIqUiUo1rVi/PU+zu1Gpcf47oHuBZVT1DVbt6TMa2YouI3O1zaMKdqO6qrppW46rqeaq6iz+PcjPwoy4+j9LW51wPPCEiCX+OYXfcUX93x10MTBSRWt8C2BXX4ujuuPjvUomqvtFF8Toaey2wHtjou+M+AGq6O66/SKpWVXcHvgoMp2v3I61ZAWwvIv1EJAnsAfw9D3G3SY+5GhC4BviViDwBNOK6ZxCRHwB3qeo/ujje74D9RORvQAw4WUS+juvrvddfqfU4ruB/p6uuyOtI7C6M0+G4uAEw9wRK/NViAN9W1a7ayNtb38/ivlAB8CdV/Ws+4nZRjE7FFpGFuG7PJuAWVX0uT3G/DfzZz3unqnbVDrS9dV2Pu6CjO7S3zPsCT4lIGncO58HujgvcB4wRkUW4fdo3u+EgcBMRORaoVNXrfQ5/xu2/blLVt7orblexgWyNMcYUvJ7UDWiMMSairFgZY4wpeFasjDHGFDwrVsYYYwqeFStjjDEFryddum56IBH5I3Aabhyy2ap6kois9H+v7KaYo4F5qnqq/+3Ozap6eHfEaiePm4DZwHeAccDJuHHnTlDVVn9oKyJL25rexuu6ZFlFZAGAqi7YlvcxpitZsTLdSlUzP4jMZ9iRwHb+777A1HwGz3ISbhDTRhF5FdjXD1H1o7Ze1JlC5YW5rMZ0KytWpkuISB1uKKbMQKBnq+pTmVZUjpfMF5GpQDmupfG0H4PuetwI5x/791gkIjfjBvy82ccKVDUmIpW4Ebon4n60fJmq3o4b222MiPwMNyrAUBH5naoeLiInAOfgusAX425/sdkPuv2PJ+fhfoC8CDgdN3DvDbghj9LAD1X1FhFJ4EYpn+1zuFlVfywi9+J+BPoPEXkGqAN+7997ic+/H3AjrtW1Efi6qj7c3vKJyEm48QL7AWOAv6jql/xyb1rWrOX5EfCWql7hH/8Wd7udl3DjD1bibr1ziba4BUomF//3SXzaOp6OG7m7HDdE0Bmq+pr/semJfh39Q1XPyPHZG7PV7JyV6SqnAn9Q1Wm4gV93b2f+51V1Km5n+Q3/3K+Bq1R1EvA14C5p+9Yr84DFqtqAGzLmO+JuZXI28E9VPcv/vcoXqgm4wjPTt17eyYoNgIgMw98+QVUn4IrEZ4AFwPt+nMq9gQV+YNnTAVR1J9xo/J8VkVmq+l/++SmqegqwCjhYVZdmhfsebgSF8cBc4H86uHzgBj09EjdK96HibjexaVlbvM+tuHsmZW4BMgN3f67TcPdim44bvPbyNtZ19jpK4sbvO9Yv9xXADb5wfxs3sngDkPTr05htZi0r01UeAu72raX7cQN0tuX3/v/ngCN9K2Ksqt4N7nYKIvJvoK3+w32Bcvn09hkVwAQ+vVlhS3vhbrvxlO+WTLLleHszcLd/edPnMRdARObhCjJ+9Pl7cK2pPYApIrK3f30lsCMdG7tyT/ywYaq6zMfuyPIB/M0PcIvvYuzX2nKr6hI/juVYXJG7z3dNngsc6IdX2tHn3hH1uG7We7O6d/uoasoPK7QIN47kFT1hGB/TM1ixMl1CVZ8UkR2AQ4DP487X7NfGSzIjXge47rJcrfwYbhvNzJO5cVxGAjheVZ/x0wbh7vG1WysxE7ix7s7281ey5XegycfDzzPA/9kyv0xuCeC8TJEVd6PIda3Eb6llrHFA9m1XWlu+49j8XmKb1k8bfo37XGYCl/rn7sQN4nof7qalx+R6oYjE1N0/LLPuE8CrmXNrvkWVuRHoYbjBbw8CHhCR47pwHEcTYdYNaLqEH1D4eFX9FfBl3F1uO0xVPwReFZEj/PvtirvR4nLcOZFMi+KwrJc9jLuDNCIyBPgX7hYPzXxahLL/fhQ4XEQGihvB/Rrc+atsi4Bd5dM7p/4Y+KyPdaqPVevzeNQ/f7qIFPvi9wRuZ90Rj/Fp99w43E36sgfrbG35WpO9rC0txBWrsT5HcAcT81X1HlxxyRSebO8BE/z6+i//3AtAPxHJ3LDvFOA2X9ifx93Rez7ubtZdeTNBE2FWrExXuRqYIyJLcSNNn9CJ9zgeOFtEluG6EY9QdyfTa4HZIvIvXKtptZ//QqBM3M05H8a1cF7B3QKhRkRuBdYA/09EHlHVZ/1rHsZ1Pyb4tJUBgKquwt2u4c/+fdcDvwQuwu2gl+GKzP/4Fs+1uAsVlgD/BH6pqo92cHm/i7tVw7O4YjJXN78DcmvL15pNy9pygr/txnu4OxRkYizA3YbkedydYlfi7umU7VvAH3Aj3qt/r43AUbi7Cf8Ld0HFqeruKHw9sEhEFuNup35TR1aEMe2xUdeNMcYUPGtZGWOMKXhWrIwxxhQ8K1bGGGMKnhUrY4wxBc+KlTHGmIJnxcoYY0zBs2JljDGm4P1/VkCK1DxZzK8AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.cluster import MiniBatchKMeans\n", + "# Instantiate the clustering model and visualizer\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot()\n", + "\n", + "model = MiniBatchKMeans(random_state=0)\n", + "visualizer = SilhouetteVisualizer(model, ax=ax)\n", + "\n", + "visualizer.fit(X) # Fit the training data to the visualizer\n", + "visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = MiniBatchKMeans(n_clusters=12, random_state=0)\n", + "visualizer = SilhouetteVisualizer(model, ax=ax)\n", + "\n", + "visualizer.fit(X) # Fit the training data to the visualizer\n", + "visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/examples/iguk1987/Yellowbrick_Tour.ipynb b/examples/iguk1987/Yellowbrick_Tour.ipynb new file mode 100644 index 000000000..e1afa2b6e --- /dev/null +++ b/examples/iguk1987/Yellowbrick_Tour.ipynb @@ -0,0 +1,2047 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Unlocking the Black Box: How to Visualize Data Science Project Pipeline with Yellowbrick Library" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "No matter whether you are a novice data scientist or a well-seasoned and established professional working in the field for a long time, you most likely faced a challenge of interpreting results generated at any stage of the data science pipeline, be it data ingestion or wrangling, feature selection or model evaluation. This issue becomes even more prominent when the need arises to present interim findings to a group of stakeholders, clients, etc. How do you deal in that case with the long arrays of numbers, scientific notations and formulas which tell a story of your data set? That's when visualization library like Yellowbrick becomes an essential tool in the arsenal of any data scientist and helps to undertake that endevour by providing interpretable and comprehensive visualization means for any stage of a project pipeline." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this post we will explain how to integrate visualization step into each stage of your project without a need to create customized and time-consuming charts, while getting the benefit of drawing necessary insights into the data you are working with. Because, let's agree on that, unlike computers, human eye perceives graphical represenation of information way better, than it does with bits and digits. Yellowbrick machine learning visualization library serves just that purpose - to \"create publication-ready figures and interactive data explorations while still allowing developers fine-grain control of figures. For users, Yellowbrick can help evaluate the performance, stability, and predictive value of machine learning models and assist in diagnosing problems throughout the machine learning workflow\" ( http://www.scikit-yb.org/en/latest/about.html ).\n", + "\n", + "For the purpose of this exercise we will be using a dataset from UCI Machine Learning Repository on Absenteeism at Work ( https://archive.ics.uci.edu/ml/machine-learning-databases/00445/ ). This data set contains a mix of continuous, binary and hierarchical features, along with continuous target representing a number of work hours an employee has been absent for from work. Such a variety in data makes for an interesting wrangling, feature selection and model evaluation task, results of which we will make sure to visualize along the way.\n", + "\n", + "To begin, we will need to pip install and import Yellowbrick Pyhton library. To do that, simply run the following command from your command line: \n", + "$ pip install yellowbrick\n", + "\n", + "Once that's done, let's import Yellowbrick along with other essential packages, libraries and user-preference set up into the Jupyter Notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "%matplotlib inline\n", + "from cycler import cycler\n", + "import matplotlib.style\n", + "import matplotlib as mpl\n", + "mpl.style.use('seaborn-white')\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.pyplot import figure\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.linear_model import RidgeCV\n", + "from sklearn.model_selection import KFold\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.svm import LinearSVC, NuSVC, SVC\n", + "from sklearn.tree import DecisionTreeRegressor\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.model_selection import StratifiedKFold\n", + "from sklearn.feature_selection import SelectFromModel\n", + "from sklearn.linear_model import Ridge, Lasso, ElasticNet\n", + "from sklearn.linear_model import LogisticRegressionCV, LogisticRegression, SGDClassifier\n", + "from sklearn.ensemble import BaggingClassifier, ExtraTreesClassifier, RandomForestClassifier, RandomTreesEmbedding, GradientBoostingClassifier\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "from sklearn.pipeline import make_pipeline\n", + "from sklearn.model_selection import train_test_split as tts\n", + "from sklearn.metrics import roc_curve\n", + "from sklearn.metrics import f1_score\n", + "from sklearn.metrics import recall_score\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.metrics import precision_score\n", + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.metrics import classification_report\n", + "from yellowbrick.features import Rank1D\n", + "from yellowbrick.features import Rank2D\n", + "from yellowbrick.classifier import ClassBalance\n", + "from yellowbrick.model_selection import LearningCurve\n", + "from yellowbrick.model_selection import ValidationCurve\n", + "from yellowbrick.classifier import ClassPredictionError\n", + "from yellowbrick.classifier import ClassificationReport\n", + "from yellowbrick.features.importances import FeatureImportances" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Ingestion and Wrangling" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are ready to proceed with downloading a zipped archive containing the dataset directly from the UCI Machine Learning Repository and extracting the data file. To perform this step, we will be using the urllib.request module which helps with opening URLs (mostly HTTP) in a complex world." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Beginning file download...\n" + ] + }, + { + "data": { + "text/plain": [ + "('/Users/Yara/Downloads/Absenteeism_at_work.zip',\n", + " )" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import urllib.request\n", + "\n", + "print('Beginning file download...')\n", + "\n", + "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00445/Absenteeism_at_work_AAA.zip' \n", + "\n", + "urllib.request.urlretrieve( url ## , Specify a path to folder you want the archive to be stored in, e.g. '/Users/Yara/Downloads/Absenteeism_at_work_AAA.zip') \n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unzip the archive and extract a CSV data file which we will be using. Zipfile module does that flawlessly." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import zipfile\n", + " \n", + "fantasy_zip = zipfile.ZipFile('C:\\\\Users\\\\Yara\\\\Downloads\\\\Absenteeism_at_work_AAA.zip')\n", + "fantasy_zip.extract('Absenteeism_at_work.csv', 'C:\\\\Users\\\\Yara\\\\Downloads')\n", + " \n", + "fantasy_zip.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the data and place it in the same folder as your Python code." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = pd.read_csv('C:\\\\Users\\\\Yara\\\\Downloads\\\\Absenteeism_at_work.csv', 'Absenteeism_at_work.csv', delimiter=';')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at a couple of randomly selected rows from the loaded data set." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IDReason for absenceMonth of absenceDay of the weekSeasonsTransportation expenseDistance from Residence to WorkService timeAgeWork load Average/day...Disciplinary failureEducationSonSocial drinkerSocial smokerPetWeightHeightBody mass indexAbsenteeism time in hours
208281953322526928378.884...01100269169248
5401022113436152328268.519...01110480172278
732102274136152328264.604...01110480172278
5242813105422526928284.853...01100269169241
629101932236152328222.196...01110480172278
277190931291501232294.217...11010065169230
1361122152289361333308.593...01210190172303
609252522223516832264.249...03000075178253
460222375117926930230.290...03000056171192
2943301064248251447265.017...11200186165320
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10 rows × 21 columns

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" + ], + "text/plain": [ + " ID Reason for absence Month of absence Day of the week Seasons \\\n", + "208 28 19 5 3 3 \n", + "540 10 22 11 3 4 \n", + "732 10 22 7 4 1 \n", + "524 28 13 10 5 4 \n", + "629 10 19 3 2 2 \n", + "277 19 0 9 3 1 \n", + "136 11 22 1 5 2 \n", + "609 25 25 2 2 2 \n", + "460 22 23 7 5 1 \n", + "294 33 0 10 6 4 \n", + "\n", + " Transportation expense Distance from Residence to Work Service time \\\n", + "208 225 26 9 \n", + "540 361 52 3 \n", + "732 361 52 3 \n", + "524 225 26 9 \n", + "629 361 52 3 \n", + "277 291 50 12 \n", + "136 289 36 13 \n", + "609 235 16 8 \n", + "460 179 26 9 \n", + "294 248 25 14 \n", + "\n", + " Age Work load Average/day ... \\\n", + "208 28 378.884 ... \n", + "540 28 268.519 ... \n", + "732 28 264.604 ... \n", + "524 28 284.853 ... \n", + "629 28 222.196 ... \n", + "277 32 294.217 ... \n", + "136 33 308.593 ... \n", + "609 32 264.249 ... \n", + "460 30 230.290 ... \n", + "294 47 265.017 ... \n", + "\n", + " Disciplinary failure Education Son Social drinker Social smoker Pet \\\n", + "208 0 1 1 0 0 2 \n", + "540 0 1 1 1 0 4 \n", + "732 0 1 1 1 0 4 \n", + "524 0 1 1 0 0 2 \n", + "629 0 1 1 1 0 4 \n", + "277 1 1 0 1 0 0 \n", + "136 0 1 2 1 0 1 \n", + "609 0 3 0 0 0 0 \n", + "460 0 3 0 0 0 0 \n", + "294 1 1 2 0 0 1 \n", + "\n", + " Weight Height Body mass index Absenteeism time in hours \n", + "208 69 169 24 8 \n", + "540 80 172 27 8 \n", + "732 80 172 27 8 \n", + "524 69 169 24 1 \n", + "629 80 172 27 8 \n", + "277 65 169 23 0 \n", + "136 90 172 30 3 \n", + "609 75 178 25 3 \n", + "460 56 171 19 2 \n", + "294 86 165 32 0 \n", + "\n", + "[10 rows x 21 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.sample(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "740" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.ID.count()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, selected dataset contains 740 instances, each instance representing an employed individual. Features provided in the dataset are those considered to be related to the number of hours an employee was absent from work (target). For the purpose of this exercise, we will subjectively group all instances into 3 categories, thus, converting continuous target into categorical. To identify appropriate bins for the target, let's look at the min, max and mean values." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.924324324324324\n", + "0\n", + "120\n" + ] + } + ], + "source": [ + "# Getting basic statistical information for the target\n", + "print(dataset.loc[:, 'Absenteeism time in hours'].mean())\n", + "print(dataset.loc[:, 'Absenteeism time in hours'].min())\n", + "print(dataset.loc[:, 'Absenteeism time in hours'].max())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If approximately 7 hours of absence is an average value accross our dataset, it makes sense to group records in the following manner:\n", + "\n", + "1) Low rate of absence (Low), if 'Absenteeism time in hours' value is < 6;\n", + "\n", + "2) Medium rate of absence (Medium), if 'Absenteeism time in hours' value is between 6 and 30;\n", + "\n", + "3) High rate of absence (High), if 'Absenteeism time in hours' value is > 30.\n", + "\n", + "Upon grouping, we will be further exploring data and selecting relevant features from the dataset in order to predict an absentee category for the instances in the test portion of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "dataset['Absenteeism time in hours'] = np.where(dataset['Absenteeism time in hours'] < 6, 1, dataset['Absenteeism time in hours'])\n", + "dataset['Absenteeism time in hours'] = np.where(dataset['Absenteeism time in hours'].between(6, 30), 2, dataset['Absenteeism time in hours'])\n", + "dataset['Absenteeism time in hours'] = np.where(dataset['Absenteeism time in hours'] > 30, 3, dataset['Absenteeism time in hours'])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IDReason for absenceMonth of absenceDay of the weekSeasonsTransportation expenseDistance from Residence to WorkService timeAgeWork load Average/day...Disciplinary failureEducationSonSocial drinkerSocial smokerPetWeightHeightBody mass indexAbsenteeism time in hours
01126731289361333239.554...01210190172301
1360731118131850239.554...11110098178311
2323741179511838239.554...01010089170311
37775127951439239.554...01211068168241
41123751289361333239.554...01210190172301
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5 rows × 21 columns

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" + ], + "text/plain": [ + " ID Reason for absence Month of absence Day of the week Seasons \\\n", + "0 11 26 7 3 1 \n", + "1 36 0 7 3 1 \n", + "2 3 23 7 4 1 \n", + "3 7 7 7 5 1 \n", + "4 11 23 7 5 1 \n", + "\n", + " Transportation expense Distance from Residence to Work Service time Age \\\n", + "0 289 36 13 33 \n", + "1 118 13 18 50 \n", + "2 179 51 18 38 \n", + "3 279 5 14 39 \n", + "4 289 36 13 33 \n", + "\n", + " Work load Average/day ... Disciplinary failure \\\n", + "0 239.554 ... 0 \n", + "1 239.554 ... 1 \n", + "2 239.554 ... 0 \n", + "3 239.554 ... 0 \n", + "4 239.554 ... 0 \n", + "\n", + " Education Son Social drinker Social smoker Pet Weight Height \\\n", + "0 1 2 1 0 1 90 172 \n", + "1 1 1 1 0 0 98 178 \n", + "2 1 0 1 0 0 89 170 \n", + "3 1 2 1 1 0 68 168 \n", + "4 1 2 1 0 1 90 172 \n", + "\n", + " Body mass index Absenteeism time in hours \n", + "0 30 1 \n", + "1 31 1 \n", + "2 31 1 \n", + "3 24 1 \n", + "4 30 1 \n", + "\n", + "[5 rows x 21 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Let's look at the data now!\n", + "dataset.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the target is taken care of, time to look at the features. Those of them storing unique identifiers and / or data which might 'leak' information to the model, should be dropped from the data set. For instance, 'Reason for absence' feature stores the information 'from the future' since it will not be available in the real world business scenario when running the model on a new set of data. Therefore, it is highly correlated with the target." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = dataset.drop(['ID', 'Reason for absence'], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Month of absence', 'Day of the week', 'Seasons',\n", + " 'Transportation expense', 'Distance from Residence to Work',\n", + " 'Service time', 'Age', 'Work load Average/day ', 'Hit target',\n", + " 'Disciplinary failure', 'Education', 'Son', 'Social drinker',\n", + " 'Social smoker', 'Pet', 'Weight', 'Height', 'Body mass index',\n", + " 'Absenteeism time in hours'],\n", + " dtype='object')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now left with the set of features and a target to use in a machine learning model of our choice. So, let's separate features from the target, and split our dataset into a matrix of features (X) and an array of target values (y)." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "features = ['Month of absence', 'Day of the week', 'Seasons',\n", + " 'Transportation expense', 'Distance from Residence to Work',\n", + " 'Service time', 'Age', 'Work load Average/day ', 'Hit target',\n", + " 'Disciplinary failure', 'Education', 'Son', 'Social drinker',\n", + " 'Social smoker', 'Pet', 'Weight', 'Height', 'Body mass index']\n", + "target = ['Absenteeism time in hours']" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "X = dataset.drop(['Absenteeism time in hours'], axis=1)\n", + "y = dataset.loc[:, 'Absenteeism time in hours']" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Setting up some visual preferences prior to visualizing data\n", + "class color:\n", + " PURPLE = '\\033[95m'\n", + " CYAN = '\\033[96m'\n", + " DARKCYAN = '\\033[36m'\n", + " BLUE = '\\033[94m'\n", + " GREEN = '\\033[92m'\n", + " YELLOW = '\\033[93m'\n", + " RED = '\\033[91m'\n", + " BOLD = '\\033[1m'\n", + " UNDERLINE = '\\033[4m'\n", + " END = '\\033[0m'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exploratory Analysis and Feature Selection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Whenever one deals with a categorical target, it is important to remember to test the data set for class imbalance issue. Machine learning models struggle with performing well on imbalanced data where one class is overrepresented, while the other one is underrepresented. While such data sets are representative of the real life, e.g. no company will have majority or even half of its employees missing work on a massive scale, they need to be adjusted for the machine learning purposes, to improve algorithms' ability to pick up patterns present in that data.\n", + "\n", + "And to check for the potential class imbalance in our data, we will use Class Balance Visualizer from Yellowbrick." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m Low: \u001b[0m 468\n", + "\u001b[1m Medium: \u001b[0m 244\n", + "\u001b[1m High: \u001b[0m 28\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Calculating population breakdown by target category\n", + "Target = y.value_counts()\n", + "print(color.BOLD, 'Low:', color.END, Target[1])\n", + "print(color.BOLD, 'Medium:', color.END, Target[2])\n", + "print(color.BOLD, 'High:', color.END, Target[3])\n", + "\n", + "# Creating class labels\n", + "classes = [\"Low\", \"Medium\", \"High\"]\n", + "\n", + "# Instantiate the classification model and visualizer\n", + "mpl.rcParams['axes.prop_cycle'] = cycler('color', ['red', 'limegreen', 'yellow'])\n", + "forest = RandomForestClassifier()\n", + "fig, ax = plt.subplots(figsize=(10, 7))\n", + "visualizer = ClassBalance(forest, classes=classes, ax=ax)\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['top'].set_visible(False)\n", + "ax.grid(axis='x')\n", + "\n", + "visualizer.fit(X, y) # Fit the training data to the visualizer\n", + "visualizer.score(X, y) # Evaluate the model on the test data\n", + "g = visualizer.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is an obvious class imbalance here, therefore, we can expect the model to have difficulties learning the pattern for Medium and High categories, unless data resampling is performed or class weight parameter applied within selected model if chosen algorithm allows it.\n", + "\n", + "With that being said, let's proceed with assessing feature importance and selecting those which will be used further in a model of our choice. Yellowbrick library provides a number of convenient vizualizers to perform feature analysis, and we will use a couple of them for demonstration purposes, as well as to make sure that consistent results are returned when different methods are applied." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Rank 1D visualizer utilizes Shapiro-Wilk algorithm that takes into account only a single feature at a time and assesses the normality of the distribution of instances with respect to the feature. Let's see how it works!" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Creating 1D visualizer with the Sharpiro feature ranking algorithm\n", + "fig, ax = plt.subplots(figsize=(10, 7))\n", + "visualizer = Rank1D(features=features, ax=ax, algorithm='shapiro')\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['left'].set_visible(False)\n", + "ax.spines['bottom'].set_visible(False)\n", + "\n", + "visualizer.fit(X, y)\n", + "visualizer.transform(X)\n", + "visualizer.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Rank 2D Visualizer, in its turn, utilizes a ranking algorithm that takes into account pairs of features at a time. It provides an option for a user to select ranking algorithm of their choice. We are going to experiment with covariance and Pearson, and compare the results." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate visualizer using covariance ranking algorithm\n", + "figsize=(10, 7)\n", + "fig, ax = plt.subplots(figsize=figsize)\n", + "visualizer = Rank2D(features=features, ax=ax, algorithm='covariance', colormap='summer')\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['left'].set_visible(False)\n", + "ax.spines['bottom'].set_visible(False)\n", + "\n", + "visualizer.fit(X, y)\n", + "visualizer.transform(X)\n", + "visualizer.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate visualizer using Pearson ranking algorithm\n", + "figsize=(10, 7)\n", + "fig, ax = plt.subplots(figsize=figsize)\n", + "visualizer = Rank2D(features=features, algorithm='pearson', colormap='winter')\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['left'].set_visible(False)\n", + "ax.spines['bottom'].set_visible(False)\n", + "\n", + "visualizer.fit(X, y)\n", + "visualizer.transform(X)\n", + "visualizer.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visual representation of feature correlation makes it much easier to spot pairs of features, which have high or low correlation coefficients. For instance, lighter colours on both plots indicate strong correlation between such pairs of features as 'Body mass index' and 'Weight'; 'Seasons' and 'Month of absence', etc.\n", + "\n", + "Another way of estimating feature importance relative to the model is to rank them by feature_importances_ attribute when data is fitted to the model. The Yellowbrick Feature Importances visualizer utilizes this attribute to rank and plot features' relative importances. Let's look at how this approach works with Ridge, Lasso and ElasticNet models." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualizing Ridge, Lasso and ElasticNet feature selection models side by side for comparison\n", + "\n", + "# Ridge\n", + "# Create a new figure\n", + "mpl.rcParams['axes.prop_cycle'] = cycler('color', ['red'])\n", + "fig = plt.gcf()\n", + "fig.set_size_inches(10,10)\n", + "ax = plt.subplot(311)\n", + "labels = features\n", + "viz = FeatureImportances(Ridge(alpha=0.1), ax=ax, labels=labels, relative=False)\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['top'].set_visible(False)\n", + "ax.grid(False)\n", + "\n", + "# Fit and display\n", + "viz.fit(X, y)\n", + "viz.poof()\n", + "\n", + "# ElasticNet\n", + "# Create a new figure\n", + "mpl.rcParams['axes.prop_cycle'] = cycler('color', ['salmon'])\n", + "fig = plt.gcf()\n", + "fig.set_size_inches(10,10)\n", + "ax = plt.subplot(312)\n", + "labels = features\n", + "viz = FeatureImportances(ElasticNet(alpha=0.01), ax=ax, labels=labels, relative=False)\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['top'].set_visible(False)\n", + "ax.grid(False)\n", + "\n", + "# Fit and display\n", + "viz.fit(X, y)\n", + "viz.poof()\n", + "\n", + "# Lasso\n", + "# Create a new figure\n", + "mpl.rcParams['axes.prop_cycle'] = cycler('color', ['purple'])\n", + "fig = plt.gcf()\n", + "fig.set_size_inches(10,10)\n", + "ax = plt.subplot(313)\n", + "labels = features\n", + "viz = FeatureImportances(Lasso(alpha=0.01), ax=ax, labels=labels, relative=False)\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['top'].set_visible(False)\n", + "ax.grid(False)\n", + "\n", + "# Fit and display\n", + "viz.fit(X, y)\n", + "viz.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Having analyzed the output of all utilized visualizations (Shapiro algorithm, Pearson Correlation Ranking, Covariance Ranking, Lasso, Ridge and ElasticNet), we can now select a set of features which have meaningful coefficient values (positive or negative). These are the features to be kept in the model:\n", + "\n", + "- Disciplinary failure\n", + "- Day of the week\n", + "- Seasons\n", + "- Distance from Residence to Work\n", + "- Number of children (Son)\n", + "- Social drinker\n", + "- Social smoker\n", + "- Height\n", + "- Weight\n", + "- BMI\n", + "- Pet\n", + "- Month of absence\n", + "\n", + "Graphic visualization of the feature coefficients calculated in a number of different ways significantly simplifies feature selection process, making it more obvious, as it provides an easy way to visualy compare multiple values and consider only those which are statistically significant to the model.\n", + "\n", + "Now let's drop features which didn't make it and proceed with creating models." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Dropping features from X based on visual feature importance visualization\n", + "X = X.drop(['Transportation expense', 'Age', 'Transportation expense', 'Service time', 'Hit target', 'Education','Work load Average/day '], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some of the features which are going to be further utilized in the modeling stage, might be of a hierarchical type and require encoding. Let's look at the top couple of rows to see if we have any of those." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Month of absence Day of the week Seasons \\\n", + "0 7 3 1 \n", + "1 7 3 1 \n", + "2 7 4 1 \n", + "3 7 5 1 \n", + "4 7 5 1 \n", + "\n", + " Distance from Residence to Work Disciplinary failure Son Social drinker \\\n", + "0 36 0 2 1 \n", + "1 13 1 1 1 \n", + "2 51 0 0 1 \n", + "3 5 0 2 1 \n", + "4 36 0 2 1 \n", + "\n", + " Social smoker Pet Weight Height Body mass index \n", + "0 0 1 90 172 30 \n", + "1 0 0 98 178 31 \n", + "2 0 0 89 170 31 \n", + "3 1 0 68 168 24 \n", + "4 0 1 90 172 30 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks like 'Month of absence', 'Day of week' and 'Seasons' are not binary. Therefore, we'll be using pandas get_dummies function to encode them." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# Encoding some categorical features\n", + "X = pd.get_dummies(data=X, columns=['Month of absence', 'Day of the week', 'Seasons'])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Distance from Residence to WorkDisciplinary failureSonSocial drinkerSocial smokerPetWeightHeightBody mass indexMonth of absence_0...Month of absence_12Day of the week_2Day of the week_3Day of the week_4Day of the week_5Day of the week_6Seasons_1Seasons_2Seasons_3Seasons_4
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" + ], + "text/plain": [ + " Distance from Residence to Work Disciplinary failure Son Social drinker \\\n", + "0 36 0 2 1 \n", + "1 13 1 1 1 \n", + "2 51 0 0 1 \n", + "3 5 0 2 1 \n", + "4 36 0 2 1 \n", + "\n", + " Social smoker Pet Weight Height Body mass index Month of absence_0 \\\n", + "0 0 1 90 172 30 0 \n", + "1 0 0 98 178 31 0 \n", + "2 0 0 89 170 31 0 \n", + "3 1 0 68 168 24 0 \n", + "4 0 1 90 172 30 0 \n", + "\n", + " ... Month of absence_12 Day of the week_2 Day of the week_3 \\\n", + "0 ... 0 0 1 \n", + "1 ... 0 0 1 \n", + "2 ... 0 0 0 \n", + "3 ... 0 0 0 \n", + "4 ... 0 0 0 \n", + "\n", + " Day of the week_4 Day of the week_5 Day of the week_6 Seasons_1 \\\n", + "0 0 0 0 1 \n", + "1 0 0 0 1 \n", + "2 1 0 0 1 \n", + "3 0 1 0 1 \n", + "4 0 1 0 1 \n", + "\n", + " Seasons_2 Seasons_3 Seasons_4 \n", + "0 0 0 0 \n", + "1 0 0 0 \n", + "2 0 0 0 \n", + "3 0 0 0 \n", + "4 0 0 0 \n", + "\n", + "[5 rows x 31 columns]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['Distance from Residence to Work', 'Disciplinary failure', 'Son',\n", + " 'Social drinker', 'Social smoker', 'Pet', 'Weight', 'Height',\n", + " 'Body mass index', 'Month of absence_0', 'Month of absence_1',\n", + " 'Month of absence_2', 'Month of absence_3', 'Month of absence_4',\n", + " 'Month of absence_5', 'Month of absence_6', 'Month of absence_7',\n", + " 'Month of absence_8', 'Month of absence_9', 'Month of absence_10',\n", + " 'Month of absence_11', 'Month of absence_12', 'Day of the week_2',\n", + " 'Day of the week_3', 'Day of the week_4', 'Day of the week_5',\n", + " 'Day of the week_6', 'Seasons_1', 'Seasons_2', 'Seasons_3',\n", + " 'Seasons_4'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "print(X.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model Evaluation and Selection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our matrix of features X is now ready to be fitted to a model, but first we need to split the data into train and test portions for further model validation." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Perform 80/20 training/test split\n", + "X_train, X_test, y_train, y_test = tts(X, y, test_size=0.20, random_state=42)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the purpose of model evaluation and selection we will be using Yellowbrick's Classification Report Visualizer, which displays the precision, recall, F1, and support scores for the model. In order to support easier interpretation and problem detection, the report integrates numerical scores with a color-coded heatmap. All heatmaps are normalized, i.e. in the range from 0 to 1, to facilitate easy comparison of classification models across different classification reports." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a function to visualize estimators\n", + "def visual_model_selection(X, y, estimator):\n", + " visualizer = ClassificationReport(estimator, classes=['Low', 'Medium', 'High'], cmap='PRGn')\n", + " visualizer.fit(X, y) \n", + " visualizer.score(X, y)\n", + " visualizer.poof() " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visual_model_selection(X, y, ExtraTreesClassifier(class_weight='balanced'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the purposes of this exercise we will consider F1 score when estimating models' performance and making a selection. All of the above models visualized through Yellowbrick's Classification Report Visualizer make clear that classifier algorithms performed the best. We need to pay special attention to the F1 score for the underrepresented classes, such as \"High\" and \"Medium\", as they contained significantly less instances than \"Low\" class. Therefore, high F1 score for all three classes indicate a very strong performance of the following models: Bagging Classifier, Random Forest Classifier, Extra Trees Classifier.\n", + "\n", + "We will also use Class Prediction Error visualizer for these models to confirm their strong performance." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualizaing class prediction error for Bagging Classifier model\n", + "classes = ['Low', 'Medium', 'High']\n", + "\n", + "mpl.rcParams['axes.prop_cycle'] = cycler('color', ['turquoise', 'cyan', 'teal', 'coral', 'blue', 'lime', 'lavender', 'lightblue', 'darkgreen', 'tan', 'salmon', 'gold', 'darkred', 'darkblue'])\n", + "\n", + "fig = plt.gcf()\n", + "fig.set_size_inches(10,10)\n", + "ax = plt.subplot(311)\n", + "visualizer = ClassPredictionError(BaggingClassifier(), classes=classes, ax=ax)\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['top'].set_visible(False)\n", + "ax.grid(False)\n", + "\n", + "visualizer.fit(X_train, y_train)\n", + "visualizer.score(X_test, y_test)\n", + "g = visualizer.poof()\n", + "\n", + "# Visualizaing class prediction error for Random Forest Classifier model\n", + "classes = ['Low', 'Medium', 'High']\n", + "\n", + "mpl.rcParams['axes.prop_cycle'] = cycler('color', ['coral', 'tan', 'darkred'])\n", + "\n", + "fig = plt.gcf()\n", + "fig.set_size_inches(10,10)\n", + "ax = plt.subplot(312)\n", + "visualizer = ClassPredictionError(RandomForestClassifier(class_weight='balanced'), classes=classes, ax=ax)\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['top'].set_visible(False)\n", + "ax.grid(False)\n", + "\n", + "visualizer.fit(X_train, y_train)\n", + "visualizer.score(X_test, y_test)\n", + "g = visualizer.poof()\n", + "\n", + "# Visualizaing class prediction error for Extra Trees Classifier model\n", + "classes = ['Low', 'Medium', 'High']\n", + "\n", + "mpl.rcParams['axes.prop_cycle'] = cycler('color', ['limegreen', 'yellow', 'orange'])\n", + "\n", + "fig = plt.gcf()\n", + "fig.set_size_inches(10,10)\n", + "ax = plt.subplot(313)\n", + "visualizer = ClassPredictionError(ExtraTreesClassifier(class_weight='balanced'), classes=classes, ax=ax)\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['top'].set_visible(False)\n", + "ax.grid(False)\n", + "\n", + "visualizer.fit(X_train, y_train)\n", + "visualizer.score(X_test, y_test)\n", + "g = visualizer.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model Optimization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can conclude that ExtraTreesClassifier seems to perform better as it had no instances from \"High\" class reported under the \"Low\" class.\n", + "\n", + "However, decision trees become more overfit the deeper they are because at each level of the tree the partitions are dealing with a smaller subset of data. One way to avoid overfitting is by adjusting the depth of the tree. Yellowbrick's Validation Curve visualizer explores the relationship of the \"max_depth\" parameter to the R2 score with 10 shuffle split cross-validation.\n", + "\n", + "So let's proceed with hyperparameter tuning for our selected ExtraTreesClassifier model using Validation Curve visualizer!" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Performing Hyperparameter tuning \n", + "# Validation Curve\n", + "mpl.rcParams['axes.prop_cycle'] = cycler('color', ['purple', 'darkblue'])\n", + "\n", + "fig = plt.gcf()\n", + "fig.set_size_inches(10,10)\n", + "ax = plt.subplot(411)\n", + "viz = ValidationCurve(ExtraTreesClassifier(class_weight='balanced'), ax=ax, param_name=\"max_depth\", param_range=np.arange(1, 11), cv=3, scoring=\"accuracy\")\n", + "\n", + "# Fit and poof the visualizer\n", + "viz.fit(X, y)\n", + "viz.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can observe on the above chart that even though training score keeps rising continuosly, cross validation score drops down at max_depth=7. Therefore, we will chose that parameter for our selected model to optimize its performance." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visual_model_selection(X, y, ExtraTreesClassifier(class_weight='balanced', max_depth=7))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conclusions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we demonstrated in this article, visualization techniques prove to be a useful tool in the machine learning toolkit, and Yellowbrick provides a wide selection of visualizers to meet the needs at every step and stage of the data science project pipeline. Ranging from feature analysis and selection, to model selection and optimization, Yellowbrick visualizers make it easy to make a decision as to which features to keep in the model, which model performs best, and how to tune model's hyperparameters to achieve its optimal performance for future use. Moreover, visualizing algorithmic output also makes it easy to present insights to the audience and stakeholders, and contribute to the simplified interpretability of the machine learning results." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/jlinGG/Interactive_YB_classifiers.ipynb b/examples/jlinGG/Interactive_YB_classifiers.ipynb new file mode 100644 index 000000000..2e04c817c --- /dev/null +++ b/examples/jlinGG/Interactive_YB_classifiers.ipynb @@ -0,0 +1,179 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n", + " from numpy.core.umath_tests import inner1d\n" + ] + } + ], + "source": [ + "%matplotlib inline \n", + "\n", + "import matplotlib.pyplot as plt \n", + "\n", + "import pandas as pd\n", + "\n", + "import sklearn.datasets\n", + "from sklearn.model_selection import train_test_split as tts\n", + "\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "from yellowbrick.classifier import ClassBalance\n", + "from yellowbrick.classifier import ClassPredictionError\n", + "from yellowbrick.classifier import ClassificationReport\n", + "\n", + "from ipywidgets import interact" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from yellowbrick.download import download_all\n", + "\n", + "## The path to the test data sets\n", + "FIXTURES = os.path.join(os.getcwd(), \"data\")\n", + "\n", + "## Dataset loading mechanisms\n", + "datasets = {\n", + " \"occupancy\": os.path.join(FIXTURES, \"occupancy\", \"occupancy.csv\"),\n", + "}\n", + "\n", + "\n", + "def load_data(name, download=True):\n", + " \"\"\"\n", + " Loads and wrangles the passed in dataset by name.\n", + " If download is specified, this method will download any missing files.\n", + " \"\"\"\n", + "\n", + " # Get the path from the datasets\n", + " path = datasets[name]\n", + "\n", + " # Check if the data exists, otherwise download or raise\n", + " if not os.path.exists(path):\n", + " if download:\n", + " download_all()\n", + " else:\n", + " raise ValueError((\n", + " \"'{}' dataset has not been downloaded, \"\n", + " \"use the download.py module to fetch datasets\"\n", + " ).format(name))\n", + "\n", + "\n", + " # Return the data frame\n", + " return pd.read_csv(path)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:9: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", + " if __name__ == '__main__':\n", + "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/ipykernel_launcher.py:10: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", + " # Remove the CWD from sys.path while we load stuff.\n" + ] + } + ], + "source": [ + "# Load the classification data set\n", + "data = load_data(\"occupancy\")\n", + "\n", + "# Specify the features of interest and the classes of the target\n", + "features = [\"temperature\", \"relative humidity\", \"light\", \"C02\", \"humidity\"]\n", + "classes = [\"unoccupied\", \"occupied\"]\n", + "\n", + "# Extract the numpy arrays from the data frame\n", + "X = data[features].as_matrix()\n", + "y = data.occupancy.as_matrix()\n", + "\n", + "# Create the train and test data\n", + "X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a18a92ea143746cf86c15a1137e9bc58", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(Dropdown(description='model', options=('ClassBalance', 'ClassPredictionError', 'Classifi…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "models = {\n", + " cls.__name__: cls\n", + " for cls in (ClassBalance, ClassPredictionError, ClassificationReport)\n", + "}\n", + "\n", + "@interact(model=list(models.keys()))\n", + "\n", + "def graph_classifers(model=\"ClassBalance\"):\n", + " _, axes = plt.subplots(ncols=1, figsize=(15,5)) \n", + " \n", + " viz = models[model](RandomForestClassifier(), classes=classes)\n", + " viz.fit(X_train, y_train)\n", + " viz.score(X_test, y_test)\n", + " viz.finalize()\n", + " \n", + " return axes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/ndanielsen/Missing Values Examples.ipynb b/examples/ndanielsen/Missing Values Examples.ipynb new file mode 100644 index 000000000..10570cd21 --- /dev/null +++ b/examples/ndanielsen/Missing Values Examples.ipynb @@ -0,0 +1,266 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Missing Values Visualizer Examples" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "\n", + "%autoreload 2\n", + "\n", + "import sys\n", + "sys.path.append(\"./../..\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%reload_ext yellowbrick\n", + "%matplotlib inline\n", + "# Imports\n", + "import pandas as pd \n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "from yellowbrick.contrib.missing import MissingValuesDispersion, MissingValuesBar\n", + "from sklearn.datasets import make_classification" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Use the Horse Colic Data Set \n", + "\n", + "Contains natural missing values in data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "headers = pd.read_csv(\"./horse-colic.attrs\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/horse-colic/horse-colic.data'\n", + "\n", + "# Retrieve Data Set\n", + "df = pd.read_csv(url, delim_whitespace=True)\n", + "df.columns = headers.Attribute.values\n", + "df.replace(to_replace=\"?\", value=np.nan, inplace=True,)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "X = df.drop(['cp_data'], axis=1)\n", + "y = df['cp_data']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Missing Values Dispersion Chart" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "classes=['sick', 'healthy']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### No target y passed in, produces mono-color chat" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No handles with labels found to put in legend.\n" + ] + }, + { + "data": { + "image/png": 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ccRzH6aX4nHlN+qvOPHXuZaBw8ZvjOI7TvfiTeRN0hc68ET142I1tSNmxCnZGi8h8deo4juM4PY8/mTdBV+jMW6EHb6LtnXqqbcdxHKdxXGfeJCKyOqbbnoG96dgFWx0+MhkcReQNVR0YtN5Lh3/bYDK39YA3gJUxnfqJmB78nmB3JWA+4EDgaez1+RJYnvMLVPXCsJp9pKpOSvk1BhgJvA78PrQJcLCqPikil2Or7BcEzlXVK0XkZUwjPxD4A/Zlb1ao87iIPAf8CxDgTeB7qvpZrF9cZ+44jtMUrjPvZrbANnU5AlslvnhJ+btU9ddBS760qq4vIssCz2XKjQReVtWdRGQ1bPD/GJuPv0FElscG/AtL2jsauDMM+qsBlwd9+ibY3PcsYMtMnbOwAf4mEVkH+zKwHrbRzOaq2iYi/wK+RudV+R1wnbnH0lvpS7FA34qnP8fiOvOe4/fAVEznfSD2hJ4lrfPW8PNLwP0AYbX7pEwdSZ1/TlV/gz0NbyciVwHHYrryMtYC9g5P6pcCS4UV74di8/1/BrKjbVqL/hgwKBx/W1Xbwu+uRXccx+kl+GDePMOBe1X1W8B1wJHYdqzLAYjISsBSqfIzw8+ngK+HMksCq2fsTsSefBGRVUTkT8DPgPtVdbfQ1lyUMwn4dVit/gPgKhFZDhiqqttjT/y/zOwSl9air4NNA4Dr0B3HcXol/pq9eR4G/igix2IytZ9i+5dPDZryicTnjv8ObCUi92GD5XTg09T5i4E/hF3cBmBP0osC54vITtjbgBkiUvYO+1Tg9yKyP7bn+omhvYGh7c+As1R1hogkdQ4HLhWRw7Gn/32qdobjOI7T/fhg3iSq+gKwUeTU8EjZEak/BXui/4mILI0tbns7U2aXiN01I8eGRdpKH9suUqdTCtWUpv1lbC1A9vzA1O++8t3pVs4dOxGAQzb5Ug974ji9D3/N3nO0ATuLyAPYfPuRqvpxKwyLyP4iUmU+3XEcx+kD+JN5DxH2de/09N4ijgauoONre8dxHKeP4jrzOYTwpN0hDznwY4K+XERGYvrwNuAC7Gl/eyypy/qYVv2EIDc7m/apgT+p6rlBA/8ppmufH9O6fxdYERiuqi/Ecqfn+es6c6fVXDNpMgA7D1m6pKTj9AlcZ95H6ZSHHNOdd0BVfy8ixwE7YXPlywQt+5LAYSLyGbZBzYbY9R8XErKA6dr3E5GLsDzpW4vIScB3RUSJ5E7P7EnfCdeZeyytYtw0mzMfOrQ1c+Z96bpA34qnP8fiOvO+T1EecojL1NJa9Smqelywc6+qzlLVT7FNX74cyif52KcG+wBTMD15Xu50x3Ecp4fxwXzOIZaH/H6Cnh34aqpsknM9rVVfXET+GY5tFI7Ni+U6T3afK5pzycud7jiO4/Qw/pp9zuESTPs9DttP/STgv8DvRORV4LVU2XuBfwCbAd8OdebBcpffKiLDROR+bB79WlV9JKUxz+MWYFjInb4IcGN4Q+A43YJL0hwnH18A53QJyQK4RufMD7/6dgYNsl1k73nhzQ7nNl3187P/Yz937MTZ5zdd9fMdymXrZUnslNmItZcul7WRtX3ThEksscSSs+tk24rVSxNrM0Ze+3n2s3bz+it9furUKbNjifVdcqzMt+RY0bXMuzYxn2M2Y7Gny2y08HSGDh2a20Zem7GY8vo2L5aittL2q5RJjm+08HTGTVso6l/WN4jft1l/s2T7t4g8+7HYs8dvmjCJ4UOHdOqHotjqtFV0L9Wpk/Yn+39CQhNz5rUWwHXra/YqObZF5I2i812NiCwlIrHNWsrq3VCx3CYi8pWC8wuIyL4lNl4Oi9CapmJ7lWJzHMdxegafM+/MV4Bt61ZS1R0qFt0bS1+ax0CgcHBtMaXt1YjNcRzH6QG6ZM5cRBYjknc7nD5ZRJbBZFV7AO9g88FrYAuq5g82BhPPqf08cB+WmOROLOXo+oCq6u4hzedl2HzwdEyitUBoY0HgQ2B/TCt9DabLXhUYr6o/Bo4B1g57md8aqfdfbPHX4sBCwDGqensqZ/kBwJ7YIrSHVPXgVL8MBf4H+KqIPIMtaDs09MVzwf4xwJdF5PgQ/4XB/+WAY1X1rzl9/gw2V75G6NOdgR2xLw9zAydgA3dRe+cSz32exDYGeAzbUnYxYEdVfSXmT0IjEouEtjZL0DZ16rTM8U+YMGF6+H3y7PNtbZ90KJet19m+2SmzEWsvXS5rI2vb7EyZXSfbVqxeRz87txkjr/08+1m7ef2VPZ/EEuu7dMxFviXHiq5l3rWJ+RyzGYu9Q/0hSzNhwoTcNvLajMWU17d5sRS1lbZfpczs40OWnv2ZyfqX9Q3i923W3yzZ/i0iz34s9uxx+73981/U93n9W9RW0b1Up07an+z/CWkmTJjQya9W01UL4L5Ift7tG1R1dBj0fo4NzAuo6oYisiLw/VAuL6f2YGw19evYoLUBcBDwoogsEeqdrqq3ici2wLpYopDzwuKvbwFnYIPY6lgu7+mh/kAsMclIVb1ERP4cqXcasAw2KH+OztnO9gIOUNWHROTHIjKPqs4AUNUJInIbtiHLNGwR27qq+r6I/BrTkp8KrKWqJ4vIt4GzVXWMiHwjlI8O5tgXi6tVdayI/DLYegeYoqrDw/7vD5a0dyaZ3Od03nd+vKoeKiKnYl8YzsjxB2hcZ37NpPY58xc/6TgPNmjQ52drjcdNmzj7/KBBHeexsvWyJHbKbMTaS5fL2sjafuQtmzNP6mTbitVLE2szRl77efazdvP6K30+mTPP67vkWJlvybGia5l3bWI+x2zGYu9Y3+bM89rIazMWU17f5sVS1FbafpUy7cenz/7MZP3L+gbx+zbrb5Zs/xaRZz8We/b4I29N6vD5L+r7vP4taqvoXqpTJ+1P9v+EhDldZ16Ud3ts+HkfpoNeHRgPoKqvYk/KkJ9Te7Kqvho00tNU9RlVnQW8iz3BprXVN6vq7ZiM6+jwZHk8kPT686r6vqp+hn05yM5Dd6qnqk9jGc2uAX5H5z7cC/hJyHa2EvlpSlcBnk6tCB+LPVWneR34kYhciSVGKdpv/VNVzfYttOdPr9Jep9znkXYeDT89n7njOE4voasG86K82+uHnxtjOb2foT2v9/LACuF8ozm109rqXUXkIEwjfWTQSP8o+JRnK9FoE6snImsBi6rqNtjr9PMz9ffDnuw3xd4KfCPH/kvY6+2Fw/FNgWcz7Z8CXKGquwN3U5y/fF4RWTv8/k0sC1vSHhXb65T7PNKOyx8cx3F6GV0iTRORzbBBbjK2m9ia2C5j/8TSaw4G3sMGw6nAb7FX6K8AX1fVQWHO/FJsDn1e4CBVfTiZvw3tpH9/DHv1vQj25DwAe32+GzZ3n8w9Lwgcgj31jlbVDUP9B7D59U+B/ws2bo7UexQb5D6HDYIXq+qVqXnlfbGB/31M+72fqn6U6psfAT8BfogN9odiA+rztC9EeyD01WPYm43JwL+BtVV1DRF5GRiSsfsy9kS+IvAq9oZg51DuqFBml5L2folNZyxByH2uqjdn5sw77AWvqicSoVlpWn/ezrE347H0XvpSPP05lkalaa4z7yPEBviepLt15lW0r0V62jo686Jz2WNJLC9+Ml+n9oq02lnKNL3p+Kro4mPnukJnXqZhTsda1C9ZPxrRmWf9SHTmSduJP3n3VOz3Mg1/Ub/EYsj2R7Yfi+7PCRMmVNKZpynSmWf9yNv/IOZXUr6ujj2hra2Ns3bdstK+DnnXqap+v2i/hzw7ZftczDE68+7Qi4vIUSKyfnnJ1iIi24fX/XXqjAiL7RzHcRyn2+m127mqauEq6S7kEGyx2X+qVlDVUV3mTXUfBve0D47jOE7PUDiY97BefBQm4RoIbI1Jr1YFzlTVUSKyLjYv/xnwETY3/aqIHIul/pwHuFBVLw6L4HYJ7Y9W1fOC/Y+x+fvlgBHh5zrAFSKyETb3na23A3AkNrf+H2ye/Xhsgd71wJ+xNx4LYPPLj5X1Z5iLngQMwRa5/TD8fgw2vz0QuERVLwgL8M4L5SZjOvIPsDn+QSGGm1X12BDj0uHfd4Ezc8p06IewV/s+WL70AaHsCSKyI3BY6PNxyVx8Ed2lM6+ifS3S09bRmRedyx5rL9tZm12k1e7cF8Wa3nR8VXTxsXNdoTMv0zCnYy3ql6wfjejMO8UUdOZJ24k/efdU7PcyDX9Rv8RiyPZHth/L789ynXnHfsjXmWf9yNv/IOZXUr6ujr1j/Qnk3b9F/Vv1Mx37PeZjzE7ZPhe9UWfek3rxNIur6neC9vkWYBS2OG5fVX1MRIYD5wTt81bB1gDgdBFZAxscE730HWLZwwBeUdUfich+wP6qOjIspBsZYo/V2xn4lar+RUT2wBaKJayPDbB7YAv+FqYjRf15X2j/AOBo4AZsZf+62JeDJ0XkuhD33qr6TBhwjwjHHlDVfcM2r//GFs4B3KWqvw5fqvLKdOiHsIHMUdhueB+FflwR07mvp6rTReRKEdlCVe+ggO7SmVfRvhbpaevozIvOZY8lscS02UVa7Sxlmt50fFV08bFzXaEzL9Mwp2Mt6pesH43ozDvH1D5nHtNex3wt0mhX0Zmn+yUWQ7Y/sv1YdH9OmDChks48TZHOPOtH3v4HMb+S8nV17AltbW0d9gAo2tch7zpV1e8X7feQZ6dsn4ue0JmXDeZvAoeGp9H3yNeLbwO8TUovLiJRvbiIdNCLA4jINFV9Jvye6MXTJE+3aW3z8qmn3rHY5iWCbWryGfb0+DMR+QGm974zlF0SWC38ntZMfzPT5po59Q4Dfh6e9ifScROXW0OZm7An919kbBb1513h533A8OR3Vf0YQESewt5MfAnLlEao/xz2ZehrQUXwHuGtSCDRmReVyfbDKsBTqvphOJ6sX1gW+Edoe9HgT+Fg7jiO43Q9ZQvgelIvniZW9j/SnrAk0UxPwrZKnVtE5hWRO7DB7Glgs6CfHgU8UWA30V3n1dsfk2xtivXH9qm6w4DXVXVLbCA/LWO7qD+Tr25pjfg6IjJARBbCpi+eC37tEXw6AvgbNkUwVVV3Bc4GFhKRxHaiMy8qk+2HF4AhIpJMlfwF+yLSBmwR2j4fk7Q5juM4PUyhNK2H9eJn0D5nPkRVjwqvhyep6uAwZ34uNiDOAPZR1RdF5OdYopS5sTnzUSLyv9g8+vzY24ODsNf9o8O2r/8D7KSqI0TkF6H9LbFtYLP1tgaOw3TkH2Bz1gdhX1KuCz7Pi731ODnsQFelP6dgO65NA3bHdmM7FxtEl8a2lf2j2P7uZ9O+BmGf0N6fsF3wPsbmxb+FbdWaxLhGhTLpfhiBTTfMAm5R1dNEZDfgAGwK42VgL1XtPBGH68zTeCy9k74UC/StePpzLK4zn4NJb8aSOjYsHNupp/xqhlYO5nk61yLK6uRph5u1GyufaGbz6ndFfI3aLWszHUvVOs36UGSjUftlsTR7TxTloI/ptau2VUSjn5k8vXdZ3Wbv26LPYFcN5s3cL2X18sr0ap250xhSLXd4Wb7zESLSMtlexfZcQ+84jtOL6bU68z5Kkjv8svTBMAedsDfhtTcwpht82hubGngidrI3aOgdx3GcYnww717SucPPw/Z4Xwy7Dsdi89npfOfbAjtgEre36bjYbjZhfns7bIX5Mthc/fVhBfyzwCfY/HdZexuQ0ZGLyInYeoBJmL7+E2y1+2hVPbUs4GZ05jENcFXK6uRph5u1m18+P5auiK9Ru9XarK6ZbYUPRTYatV8WS7P3RFEO+pheu2pbZTTymcnTe5fVbfa+LfsMdoU2u/n7pbHPW2/QmTutJZ07/CzgDlU9V0RWAMZhg2SS7/zf2MK3b6vqzKBx/1qB7YWBLTD52HgRuQlLOnOKqj5aob0PiOjIM22shGnP58c2zCkdzFsxZ56ncy2irE6edrhZu7HyiWY2r35XxNeo3bI207FUrdOsD0U2GrVfFkuz90RRDvqYXrtqW0U0+pnJ03uX1W32vi36DHbVnHkz90tZvbwyc3o+c6ectP7+NUwV8LnkpKrOxJ6CrxGR3wNfoDif+T2qOlNV38RWxi+bmKrSHrahTaIjH4Otsl8108aTqjpDVacBH+I4juP0CnwfiYjvAAAgAElEQVQw717SucPT+vsVsE1pJidlwqK07VT1h5j0bW6K85kPDbY+j71KfyvVZml7WL7zMh25Sx8cx3F6IS5N60aCTj7JHX4mtmf9Uliu9OOC1jvJd74zNqAm76g/xrTx85LKUR7sjsD2kZ+O7XF/bLD1cij7kYgsVdLeD7EvBB105NjGNMmc+WypXHpvgBiuM2/HY+md9KVYoG/F059jaVSa5nPm3UjINb5O6tB2kTIXY0lTwPaur8o92cQn6UxqqvpOhfYmYovk0pyY+n1Mql7uQN5q6mjGG6nT9TrzybT6/6VWa8irtlk3lqyfrdbUd1U/1NEVJzSqM+8K5gSdebO2GqErdeY9jb9mr4GIDBOR0Q3UGxlWhTfa7stBoz4q7NLWqJ2mcsw7juM4vRN/Mu8DuBbccRynf9OvBvMCPfb3sXnjebFFXttji8POxxLKzAecgOmyCYlPrgeuUtWrReR0bHHZAOAcVb1OLB/6udjK8hlkFpOJyILA5Zjcaz7gQOBh4CIs89rc2Nz3mIJ45sVeja+tqtNE5HBMI34jkRzyqXpjCNvHishIbDObUVgu9jZsz/3R2N7x6wJ/V9WjJZJLXVXfLerz7tCZN6Ld7imdeatotYa8XpuN68xbralvth+a0Zln9dmN6sxbyZykMy87PqfpzItwnXnXENNjrw5sE/TVFwPfwRaTLaOq64vIkthmKndi2u1bsBztN4vIVsDKqrpRssAtZGu7EPieqj4rIhd28sI2cXlZVXcSy9O+DTaf/raq7iMiS2NSsjXyAlHVT0XkeuB7wBXALiG2i4nnkC9jFSzBzILY6vYVQj+8guVYj+VSP6bIYHfozBvRbveUzrxVtFpDXrXNZnXmrdbUN9MPRQuT6uiKExrVmbeKOU1nXnR8TtSZ5+E6864jpsd+C/ijiFyObYoyL5Yb/X4AVZ2iqseF+ptig10yQq0FDA1Pu7eFuoOBz6vqs6HMvyJ+pO0/p6q/Cba2DrauB+YRkWVK4rkM2CPkG1dVnUwmhzyWIS2PtNztxfCkPRV4U1XfCYv2EslDkkt9DLYN7Ao4juM4PU5/HMyzeuzp2M5nO2H7pn+IDXATCTuuicjiYQc2gL9jr+FPDXnbJwF3B2325sC1WD7w10Qk+RoX27ktbX8VEflTsHVNsLUVllL1naJgVPW54O//Yk/Oie1YDvmEj4Dlwu9fTR0v0ynGcqk7juM4PUy/0pnH9NiY5vvP2NP0DOxp/T7gNGx+eF1sOuIkbKAfGV6N7wyMwPY2PxsbmBcBbgzbta4PXIDttPY+8JiqnpjyZQFsXnsFbK79UOBJbEBeCfui8TtVvTTRi2Pz6UkSlnRcuwAnA6up6qyyHPIisnXw+VXgtfBzVLC9YTpvfLCf1OuUSz319qEDrjNvx2PpnfSlWKBvxdOfY/F85hUIg3mHDVecrqEVg/m4aQvN1upW1ZAXaWYT7W9CYjetCa6qrU3bjZ1PH09iSUh8iLWX50tMr52OpcxeNt6iGIo4/OrbGTRoUGVNdZZYf1eJLet/to/yYirSuaevS9F1zPZhto2sz0mZdJ9k4y0qW7XdvBzgZXsoxD4jef7k9Uu6TlKv6LOUrVf2WSv6zKRtpPukTtxF8Wbv51icdfB85k4UERkjIkNKyrie3HEcpx/Rr1azux7bcRzH6Yv0q8F8TkNEFsNWqy8BLI/NwQOcHFa5fwzsgS2SuwSTsb1AWGkf5s476c1F5HlsXcDqmNxucUxPr6q6u4gMCvYWxNYJ7I/N61+D6dBXBcar6o/LYmhGZ97W1jZbq1tVQ16kmU20v+1lP+lwPNtO3Kd8bXGRb21tbbN/T3yItZfnS0yvnY6lzF423qIYymhra6usqc4S6+8qsWX9z/ZRXkxlOvfkuhRdx2wfZtvI+pyUSfdJNt6islXbzdNml+2hEPuM5PmT1y/pOkm9os9Stl61z1r8M5O20R53eX70mO9F1yd9LhtnXVxn7nwRW5R2Q1g5fw+2YO0GVR0tIgcAP8cG5gXC4rUVge+H+mcR15sPxlbev459EdgAy8z2oogsEeqdp6q3isi3gDMwPfnqmA59eig7UFULX+k3M2c+aNCg2VrdqhryIs1sov1NSOymNcFVtbVpu7Hz6eNJLAmJD7H28nyJ6bXTsZTZy8ZbFEMR10yyOfOqmuossf6uElvW/2wf5cVUpHNPX5ei65jtw2wbWZ+TMuk+ycZbVLZqu3na7LI9FGKfkTx/8volXSepV/RZytYr+6wVfWbSNtJ9Uifuoniz93Mszjq4ztwBeBPYTkSuwlbeJ/nMx4af92F69dWB8QCq+ir29Az5evPJqvqqqn4KTFPVZ1R1FrbD3QKY3v3ooCc/Hkg+Nc+r6vuq+hn2RWCB1ofsOI7j1MUH897Nz4D7VXU3THOebPCyfvi5MfAU8AzwdYDwBJ9s5pKnNy+TMEwCjgx68h+FtqvUcxzHcXqAfiVNm9MQkc2w/eEnY7uyrYntVqfYq/L3gD3Dud9ir9BfAb6uqoPK9OahjfTvj2G6+YWw7WgXwObND8GexEer6oah7APATqr6csx315m347H0TvpSLNC34unPsXg+8z6Iqt6NDeBV+Emk/svYXu3Z4wNzfk/nWv9OpI0NU2U3jJxvOc3mH06IaZir6MzzNNFpyjS5Gy0c1y3naVar5Gcv0u0WlY1Rp2+vmTSZcdMmluaJT/yCzhrpqvGnbcTs5NWJnY+VTV+XpK0yjXjWThVtecynvPuhrGwRybVJ26h6L5Xp6NPlquxX0Mj+Buk6q8z3Cdnxr+pnryhneizOrJ2iPQaaue+6Gn/NXhMRWSrsuFZUptt13iIyQkS2baDe9iKyvIgMFJHfdYVvjuM4TtfiT+b1+QqwLfCnnnYkTRMa+kMI6VCBA1rnkeM4jtNd9NvBPGztujf2duIEYCkszelnwDhVPUpElgX+iOm858I03ccAa4vI/thq8nMwDfYywI9V9b6c9naM2P8Vth/8McAdwdbjtM9XL4flNP+riDyJrUz/CrZA7U1gE0xrvnWw8UY4dyTwCZbSdLSqnioia2Z9BZbE0q5eISK7AVcEedsWwC+whCyTQz+tE7Nb1s/N5jNvNv9wu63OGuYqOvM8TXSaUk3ukKU76JnLdO1V8rMX6XaLysZoVmce8z3xy47F9dNl8adtxOzk1Ymdj5ZNXZekrTKNeNZOFW15zKeYjr1K2TLS8ZTprfM+EzEf0uWq7FfQyP4GHfYX+NzCnbTZVT97RTnTY3Fm7RTtMdDofec6865niqoOF5GlgHHAeiGn+ZVhQPt/wM2qepGIfANbRX4q9iR7iYj8EPiZqj4ZXr3vhQ3wHQj2T4rYPxq4F/vCMF5V/y4i3wbOVtUxoc2TgL8CiwJ/UtWfiMgk4DBVPVZE7qFzzvOVsEF/fuA/wec1sr6q6n5h0dtIbJBGRObCNozZSFVfE5FDMFnc33LsFtLsArhm8w8nxDTMVXTmeZroNOWa3Okd9MxluvYq+dmLdLtFZWM0qzOP+Z74BZ010lXjT9uI2cmrEzsfLzu9k5a5TCOetVNFWx7zKaZjr1K2iOTapG1UvZfKdPTpclX2K2hkf4OO+wt80mnRWNXPXlHO9FicWTtFeww0ct91l868vw/mGn5+Ectr/g8RARs4V8U03H8ACE/c94nIsFT914DjROTDUOe9nHai9lX1DhH5DXAF7Rrw14FjRWQfTAo2b8rOI+HnVEyOBpblLav3flJVZwAzgm91fF0GeE9VXwt/j8UyyP0tx67jOI7Tw/T3BXAzw8+XsI1Wtgja6vOBB+iYc3wTETkz1En67TzgBFXdE0tfOhdxovZFZEns6fww2nORn4K97t4duDtjs6qOMFYuz9d0PABvA4uJSJLvfFMgSXPqOkbHcZxeSL/VmWfToYY54wOwOeWXsVfmC2NP5osS8ndjc9T/B1yMDYJ7Y0/H/waWUdUt0trtVHsx+1dhW7NeJSKjsCfv/2KvtScHm2ur6hpJTnNV/Sit8RaRv2Lbrf4P7XPmI1V1p9Bukov8sBxffxHq7o/lT98wvOo/BRvop2B529eM2c3rX9eZt+Ox9E76UizQt+Lpz7F4PnOnV+GDeTseS++kL8UCfSue/hyLbxrTCxGR7YEHVfU/meO/Ac4J+6i3oo1fYq/ujy56Wi6xMwy4FpuLn4Xt/Ha1qp4f3hqMVtXbUuULn8yb4dyxE7lpwqsMn7YQ0HHjkITYhi9psnXyNvUostkoWftTp05hiSfe73Q8z4/0uTzK4sgebyTmWJ10LI30VVHMdepVsRPbACV9v2y0MJ2OpaniYxW/YpvPFPVbndhi16auz82Wq1onbzOddPnk+E0TXu0US9X7PW+Tomyd7EY2VeOseu/nbYrUVfT3OfOu5hBgsexBVT20FQN54LvYyvbzWmDrLlUdpqqbYXPlPwtZ1BzHcZxezBz/ZB7mvrfG9hNfFThTVUeJyKaYfnxuYBFgF+BV7Olz8VD+GFW9XUR2BQ7F5sOfw+aP58FWmS+PLV7bRFWXr2oXW4We1nBfj82D/yP4OzL83UHHrqrPpWIbg+3FvhSwDfA7YLXQ9rHYF4WtgfVE5O1MvZGqOklERgIDgZuwOfr1gR8AW6nqDwq6dlFMEz+joIzjOI7TC5jjB/PA4qr6HRFZDbgFGIXpqndT1f+IyNHAjpheexlswdfngNVFZGlMy72uqr4vIr/GMoUNAF5S1R1FZAjwdGirkt2gGU9ruAcCQ1X1ExHZOtg6ls469tmDeeAaVb1RRH4MvK2q+wSfx4aFcTtgr8DvD7K3KKr6qIhchn15WBkYFim2efgiMBP4FEvM8kGw+0sROSpVdqncxlI0opdMNmJINsBIbxzSXqbzhi9psnXyNvUostkoMftTp07pdDzPj/S5PMriyB5vJOa8OkksjfRVUcx16lWxE9sApcP9MmTp2RsTVbmHGvUrtvlMUb/ViS12ber63Gy5qnXyNtNJl08fz8ZS9X7P26QoWye7kU3VOKve++lNZnzTmOo8Fn620a65fg04T0Q+wFKC/ktVnxaRi4FrsCfn87DdzJ5W1WSCZiywJfakfBtAeML9b027WV5S1ez/0J107JF6iRZ+LWBjEdkg/D2PiCxT1CmBtLTtIiw/+SmpeNPclaxWj3BEds68QtsNbRozbtpEHnlr0uwNMNIbhyTENnxJk62Tt6lHkc1GydqfOnUKSyyxZKfjeX6kz+VRFkf2eCMxx+qkY2mkr4pirlOvip3YBigd75fpszcmqnIPNepXbPOZon6rE1vs2tT1udlyVevkbaaTLp8cf+StSZ1iqXq/521SlK2T3cimapxV7/2kXHdtGtNX5sxjS/IvxXY5G4HtVjaXiKwFLKqq22CpQ8/HNOBfFpGwHGa2rvop2nOEr4o9edexCx013ImmPU1Mx54lqTcJe0ofBmyF5Rh/J6c/PsK2ggX4aur4r8K/ESKySk5dx3EcZw6jrwzmMa4C7hWRf2Hzv8tjr7CHichYbDA8XlXfxubA7w767WWwvdF/DwwOZU/EBsjKdkPZ+7B597xX0qcBw8Or7ZMw7XoeFwNDwvat9wGvqGrsCwLYm4Hficg/sekCRGQ4sDpwOrYw72oRmTenvuM4jjMH4TrzHMIc9iJhgdxqwG2qumpP+zWn4DrzdjyW3klfigX6Vjz9ORbXmbeeF4FrROQEbB78Jz3hxJykVW8VWZ05UKrXTPSiedrzsuPZNmLH0sfr+JVoZvP0xmk7ee1mbcZ05mlda56feX1QRmKvrW0ypz4xJlenGzse8yNmO8/3dN20/3m2Yv0Ray/Rmafrxa5LzL+sD1XK5JHXVkwDnW0z1kaRjbx7JKaTz9PpZ+3llYldryo+p49VvZZ5n9Oy47HPSd61TCi794va70p8MM9BVd8ANutpPwj5xrH5+dmo6qEtbCPRqt8SVug7juM4cxA+mONadbpOq+44juN0Az6Yt+Na9dZo1TvQCp05tOuEy+rkac/LjmfbiB1LH6/rV1ZnntW5pnXQZXazcaT1tVk7WT/z+qCMjnrcKbk63djxmB8x23m+p+um/c+zFeuPaCxBZ56uF7suMf+yPlQpk0deWzENdLbNbBuJbj7PRt49EtPJ5+n0s/byysSuV16/xD+DbZWvZd7ntOx47HOSdy3bbRTf+7F2XGfevbhWPZ86WvUOtEJnDu064aI6SbmYbrjseLaN2LH08Tp+JZrZPL1x2k5eu1mbMZ15YrPIz7w+KCOx19bW1klnnhCLK0usrbw+iNVN+59nK9YfsfYSnXm6Xuy6xPzL+lClTB55bcU00Nk2020kC62KbOTdIzGdfJ5OP2svr0zseuX1S7b/JkyYwKBBgypfy7zPadnx2Ock71omlN372XZcZ979uFa9I65VdxzHmUPwwbwY16q7Vt1xHKfX4zrzLqQ/a9VdZ96Ox9I76UuxQN+Kpz/H4jrz3kmXaNWb1Z6HV/pLqurYnPPDsJXsefu0dymN6MxjNtJU1ZnnaZzzqKszz9O116WKBjnP3yJ9bKyNdN9l85mny1bRmWfbLNJsF/me13dV9N0xnXlZ7Nk2i3TlSYwQz6leta1GdeZ14kjbSfubZzd2TxT5lbVVZ5+DmF/Z8kX69DL/03+nbVfVmRe15zrzPkYXatWb1Z5/D3gDW6jnOI7jzOH4YJ5DX9Wei8gKwAjgExF5JNgfoqoficgZ2CK5l4HVwnz50sCFqvr78ER/XrA5GdhbVd9tRX87juM4jeODeTF9Tnuuqq+JyCjgDVUdX6ArnxfbGW4A8LiI3Iytwt9bVZ8RkX2AI7AvGLl0l848z0a6fhWdeZ7GOY+6OvM8XXtdqmiQ8/wt0sfG2sjmbk7Hki5bRWeebbNIs13ke17fVdF3x3TmZbFn2yzSlScx2vFyPX+rdeZ14kjbSfub14d590SeX1lbdfY5yOrMY+Xzrk0V/9N/p21X1ZkXtec6895HX9aex0jryR9I7IrIM8Bg4EvYCneCP9nNaTrRXTrzmI00VXXmeRrnPOrqzPN07XWpokHO87dIHxtrI9132Xzm6bJVdObZNos020W+5/VdFX13TGdeFnu2zSJdeRIjxHOqV22rUZ15nTjSdtL+5vVh7J4o8itrq+o+BzGdeax83rWp4n/677TtqjrzovZcZ9776Kva83T9j4DlRGQu7PV9wroiMk/w/0vAC9jmM3sEnfoRwN8ibTuO4zjdjD+Z1yfRiE8D3qRdI36CiPwAGySPV9W3wyr2u0VkJvA8cBT29Dsq6MlfobP2vNBuKJtoz/fP8fE04A9hTn0WsE/m/ATgVyIyEcuW9g9snnxKqsxHwK3YvPuJqvpO2A72ChGZJ8eu4ziO0wO4zryb6S/ac9eZt+Ox9E76UizQt+Lpz7G4zjxCOvNXQZk3msnfLSJHAXep6viKVV4EbhSR67HX4VHtedhJbicsmck7qnpzTrn9gctV9dO6vkds3aCqOzRrp5U0qtesk4+82XzmVXOQV2kvbTdbP0/HXTVvdlVf8uokOvPh0xaq1Fdl56pSJed71Tzzdf2pk5c8r61W+5THNZMmM27axMrXJl2mqi6+7v3fSr11I5/TVnwmirTxedfYdeZzIKp6Rs3yb4jIzliWsg0rlB9VUuRo7JV704N5bxvIHcdxnGr0icFcRBYDLsPmd5cHLlDVC8Ppk0NmsI+BPbDEIpdgUrAXgPmDjcHYCvBkPvhgVX1cRJ7H5qhXB+7ENN/rA6qquweZ12hMIlZVl55dfZ7EcSomQ2sjLIwTkROxDV6uB/4c7CyASdOGhnZHA9uJyOnAxpic7BxVvS68nXgMWBPLXb4jNicf08W/oaoDRWRdbLHdZ9jc+X6h3WuCb6sC41X1x6UXx3Ecx+ly+sRgDnwRe9K9QUSWB+7BEp0A3KCqo0XkAODn2MC8gKpuKCIrAt8P5c4CzlXVm0RkHSxJynqYJGtz4HXsi8AGwEHAiyKyRMaPqrr0q7MBiMh6wCbYKvRF6Cz7Wh/bqGUP4MvAwmEjl+OAnURkK2BlVd1IRBYAHhCRO0Ld8ap6aPiysHPwrYN+PdPWpcC+qvpYSK5yDnB4KLclMD3EPzDscpdLIxKLhGxu5jrUyUfebD7zajnI2yq1l7abbTdPx101b3Ystqr921FT3Fapr8rOVaVKzveqeebr6n/r5CXPa6uuT81Q59qky1TVxde9/5uJrUgzX2a/brtV89e3+5avU4+17zrz6rwJHCoiOwDvYRrohGTL0vuAbYC3gfEAqvqqiCQ7k3wpKRsGsUTkPDnZ61xEpqnqM+H3d2nXnidU0qXnxLA68HDIYvaeiDyZOX8rsBpwE/ZK/ReZ82sBQ8OTOKEPBoffH035NbCCfn15VU1iGQskUwnPJ7p5EXmdzvF3otkFcI1qsuvkI282n3mZj4lmtkp7abvZdvN03FXzZsdiq9q/aZ35oEGDKvVV2bmqVMn5XjXPfF39b5285Hlt1fGpGa6ZdHuta5MuU1UXX/f+bzS2Ms18mf267VbNX59QpFPP2nOdeT1+Btyvqrth6UPTm5+sH35ujGm8n6Fd5708NsCCLUbbOBxfB3u1DXGteR6VdOk5dZ8B1heRuYO2+8uZ88OA11V1S2wgPy0cTzTjk4C7gwZ8c+w1+gsxvwr06wn/EZGvhN8TfXxefI7jOE4P01eezG8BzheRnYCpwAwRSR4HtxORQ7En9j3D+S1E5EFM5/12KHc4cKmIHI49rbZKQx3Tj3civA24FXgIG/TfyhR5HBgdtN7zACeH4/diOvHNsHzo92Kv6W8M28jGmsvTryfsB/w2bCQzA9eTO47j9GpcZ+50Ca4zb8dj6Z30pVigb8XTn2NxnXk/p0gjHhb6ra2qt+ScH0yBVK5qnvSuoNU689j5RsomtFrbXVXfnqWqxr2OLzHqapmb1dvW1Xm3Wt9bN2d4rF5CnTzkjdCszryKP3W03l1NV/VjlZztdWx0Fz6Y9xFKNOKbA0Ow6YhGbFfNk+44juP0AD6Y9zJCHvXtgEUx+djJ2Lz+qZju+wUslequwN7YnPcJwNVBI34AtjZgJjb//lNsT/iFROQ+4F0KdO9BvrYZdm9cr6pnJjvpYbnNz8ZW008Hvp/KCuc4juP0ED6Y904WBrYAlsVkdJ8BG6rqWyJyCjACG1CnqOpwgNRCt72AA1T1obBYbi5MWjZEVW8Og32R7n1Xwsr50E6a7bBV8r8BtgWWBAoH896mM4+db6Rsu49VdazFOvMyv8vyrJfpiNNlGtGZd7TXfTrzujrvuu21QmdellO7va38POStohmdeRV/6mi9m6Ur9wAos2vt5+dsr2PDfrrOvL9yT9CbvxlWwa8GXBsG7AWBO7AsbBqpuxdwuIisDNxPZylcme59V2zwH4hp29OcBhyD7YT3GvBgWSC9TWceO99I2YQqflXRmZf5XZZnvUxHnC7TiM48oa6WuVkNdV2dd532WqUzL8upnZCXh7xVNKszr+JPHa13M3T1HgBldqE4Z3sdG64z798MBRCRz2MbszwPDA8a8lOBu0K5WC7z/bDkMpsC6wLfoGP+8lzde5Dz7YjtErcZMEJEVkrZ3g0YpaqbAU+Tn4LVcRzH6Ub8ybx3MlBEkn3gD8AG47+LyNyYXn4PYMWcuk9iuvb3aX96fg84RkQeoUD3rqofi8g7wAPAh8DtQHoF+3jgslB3Jj6YO47j9ApcZ97LCAvghqjqUT3tSzO4zrwdj6V30pdigb4VT3+OxXXmTpcQtrbdVlVPLi3cBTSqiS7Sg2c1wHV0xF2tM69SP/13XT9itmLn6tidk3XmVXTTdfKpN6K/LtKkl/maJk9nHuu/mJa6js487Wede6aZ+6FuPvOyuKu2U9Q/rcqn3gp8MO9lVMhf3q2EhCuPlRZ0HMdxegwfzPspOXr2uYCfYHvTzwK2x/Kgj1TVnUTkOWz1u2Dz7d9T1c+633vHcRwnjQ/m/Zusnv33wDaqOj2kSP0OtoguYRVgc1VtE5F/YbnXHyhqoJU68zp61iI9eFYDXEdH3NU68/z6ca14XT9itmLniuxWyTNd5VxVX7tSZ56+Lnk26uRTb0R/XaRJz5YrsxfTmcf6L6alrqMzT/tZ516sc32azWdeFndVH4v6p2o+ddeZO11NWs8+BXsa/2PQoA/BdOpp3lbVJP97Omd7Lq3UmdfRsxbpwbMa4Do64q7WmeeRpxWv40eerdi5PLtV80xXOVfV167UmaevS56NOvnUG9FfF2nS68SWpzOP9V9MS11HZ572s869WLVPWpHPvCzuqj4W9U+VfOrdpTP3wbx/k9azJzK4L4Rzd9B5wxmXPjiO4/RCfDDv32T17HthT+MzgCmYBv2lnnPPcRzHqYLrzPspXa1nd515Ox5L76QvxQJ9K57+HIvrzHuItA5bRN4ImcvGYCvAJ3WTD9sDvwTOB4ap6g6pTGc7AW+o6kWp8ksBGwJTReQo4C5VHd8dvnYXRXrYZjShzei7Y+01m6+9GT+yvnQFrcg53UofW6FDr1K2K3JtN6ozL6pTtUy2bEIz93AraNX1LPtMduV90yp8MG+SXqLD/i5wmKreApxXofxXgCVUdWTXuuU4juN0B/1+MBeRBYHLgZWA+YADsSQilwFLYPPGF6jqheFpdxK20nsu4Ifh95GqulPE9heAC7FV38sBx6rqX0XkKeBZLI/4IGB/VX1aRLYCvquqB6RsjAHeApYCtgF+h2VRmxs4FlgM2BpYT0TeBm5U1YElYR8DrC0i+2OJWEZjWdK+i2VlWw44FxiO6cwPV9WbRGRH4DAsJeu4OX3LWcdxnL5Cvx/MsVfRL4dNUVbDBsyPgdGqeoOILA/cgw3KAPep6siQF/xo4IYC20OAs1V1jIh8AzgJ+CuwCHCKqj4qInsBewJHAHsDp0fsXKOqN4b85DtERV4AACAASURBVG+r6j4isjQwVlXXEJEdgr/3p/KaF3Eq9gXkkuBXwqKquqWI7AT8FHsVPww4RETuDf6vF3ToV4rIFqp6R1FDzerMG6VID1tVK1rVbhXSsTSbT7wZP/Ls1Klf57q0Iud0K/Nl19X/NnJv1NVuV6VRnXl5nebu/57KZ572p9E847HjrcjZ7jrznkEIebtV9TngNyKyAnBoGCTfw3ZES0jSj96HPbkW8TpwrIjsg8m60naSXOTXAhNE5CzgC6r6SMROUnYtYGMR2SD8PY+ILFMWYA0eDT+nAhNVdVbQny8AfBHbXOYf4QvDosCqmIQtl55aAFekh62qFa1qt4xsLM3kE2/GjyI7VevXvS6tyDndynzZdfW/jdwbdbXbVWlUZ15Up2qZbNmEZu7hIqreZ3XbrvJ/QCtytns+855hIraTGSKyioj8CfgZcL+q7gZcR0e9dXJVvom9ji/iFOAKVd0duDtjZyaAqk4L587F0pPGSPKWT8Ke0ocBWwXf3inxIc9e7NoXSRtewjaK2SK0fz4lu785juM43YMP5nAxsIqI3ANcAZwD3AL8JBw7FJghIsnj5YhwfBvsdXUR1wFnichYbNvUvKfoS7Gn/Ksr+DoktH8f8ErYwa0uLwBricihVSuo6n+xvrlHRB7Evkw820DbjuM4TotxnXkNukpyJiJfAw5S1T1aabcncZ15Ox5L76QvxQJ9K57+HIvrzOdQRORAYB/gB03aeaPCKvY5mkY1v7HjCVV1stl66fpVaIW2vBV+tNKXKm0kNKqjr3t9qubjrnPd7nnhTYBczXgrc1o3E2szOvO0Jr5K/m6goRzsrb7fukL/XSefeav8agU+mNcgzBW32uZvgd+22q7jOI7Tf+iTg3nYqrRMMz37SVZERgMXAQ/SWXP+cDi2CjAAOEdV/5x+5S4iIzGd9hnY6vTFgYWAY1T19pRfw4CfY9K3QaHNzYG1gXODln1TbC7+M2xu+0fArmXxAPOHOAYBT2B7rZ+A6cgXwZ7+9wDWA5YGHlfVvUTkRGBl4HMh7p+q6j9z/Fg59MUMbL3FLqksao7jOE4P0ScH80CuZhq4KadOTHM+FPivqu4mIosCj4TkJDFWxRa5/Q82OK4eKfMFYJ1g97pQZwXgRhG5CFsMt5GqviUipwAjgE8rxLMgcKSqviIi12KDP5jE7BARWQyYoqpbiMjcwNNBggfwsapuJSJbAD8Tkdtz/JgPy3t+BLAx9qWlcDBvpc68Uc1v7Hh7G9V0stl66fpVaDafeav8SNto1Jc6+t9G/EvXr3t9qubjrqMznzp1Wvg9rhlvxf4FdeoU549vTGee1sRXyd8NNJSDvZl85nm+V7VXtXydfOZVbbjOvDmKNNNZEslYTHN+AfB/4dj7IvIMNgB3qh92cbsYuAbTlMe2Vn1KVT8VkanAC6r6ScqvZbEn72uDlntBTMf9fIV4XlXVV8Lv94VYoF2j/iHwORG5BvgAe1pPdO+J7SRHeZ4fvwCOBG4D3sU2zSmklQvgGtX8xo4nVNXJZuul65fRinzmrfAja6MRX+rqfxvxrxEf6+QOT8pV1Zm/+InNmedpxluxf0GdOnllmtGZpzXxVfJ3Aw3lYG8mn3me71Xs1SlfJ595FRuuM2+esmX684rIIiIyH7BGOBbTnE/EnkIJT+ZrYZrrj7ABD+Cr4fxa2BP0NtiubufX9Ott4N/A8DA/fyrtm9SUxfMFEUn82QhI7oZEurYVMEhVd8YG4QVp/xKTtZ3nx3DgXlX9FvZW4cgSnxzHcZxuoC8P5mX8Btv05C9A8kQb05xfAiwtIuOAMcBJqvoW9tT9OxH5JzaXDvAcMCzoyq8Djq/jUNCMHwL8XUTuw+a9q35FmwycJyL3Y/rzWzPnx4fYxoaYX8T2na/jx8PAySJyFzYlEfuy4jiO43QzrjN3ugTXmbfjsfRO+lIs0Lfi6c+xuM68lyIiN6jqDjnnhpGTca3BtpqyJyKDsYQtG7bCn0Y5d+xE2tomM3RoXG+cUCdHedHfrcpnnpcHua1tcoe5xjLdcpV2s9SJo0rsVbXMVfT8WY1u1b7P03iX1ck7V+V8zL+sD1W103n3SldprZP7rFmdeVlbZf1Rxdc690KVeyTrezP680Z05l2tp69Cf37N3i3kDeSO4ziO0yr8ybwCQbe+HZYpbBngZFW9vkATvjf2RekE4GpVHZiTCx1gNRG5FZOy3aKqJ4aFdOeFcpODvXWBM7Ec6Jdgq9N/gq1InwVsn+P7MCx/+UxMC3+Jql6Qo5Mflap3KrAZdo9cr6pnxvxS1Xfr96jjOI7TSnwwr87CWLKUZYHxInIz+ZrwKao6HCCTXzyWC30B7IvCAOBV4MRgd29VfSakTz0Ck4YtoKobBLtHA9uE3OIXA98BXsvxfQXsy8DcwJMicl2FeHfFdOyvh7jI8euYIiONSCzaNZoTonrjhDo5yov+blU+86I8yInOHCjVLVdpN0udOKrEXlXLXEXPn9XoVu37PI13WZ28c7HzMf1vLC952oeq2um8e6XV+b/T7bRCZ17WVll/VPG1yr2QXJsq90jW92b0543ozMvuCdeZ9y7uCau83wz67uXJ14Rrjo1YLvSnVPVjABGZEY59CVspD/bk/Vw4nrb7FvBHEfkAe9q/v8D3+1JtPEWOTj7DrtiOdgMJ2vsCv3JpZAHcuGkTaWtrY+jQoVG9cUKdHOVFf7cqn3leHuS2trbZOnOgVLdcpd0sdeKoEntVLXMVPX9Wo1u17/M03mV18s5lz+ctTIrlJU/7UFU7nXevtDr/d2Ivuc+a1ZmXtVXWH1V8LbsX0temyj2S9b0Z/XkjOvOiuLpLZ+6DeXWGAojI54HFMB12osV+V0S2xTZjWZF2bXfMxr/pmAs9JidQYA9VfVVEvkm7nn1m8GFx4KTQFtiXiNiAnLCOiAwA5sc09c/RrpOfhOnkZz/Vh3SvOwI7h0PPhK1i8/xyHMdxehAfzKszMGzjujhwgKp+JiKJFntu4D1s7/MVC2yMEJHDgGnA7tgGNDF+DFwhIvNgg/0+dNSEvwf8C3sanwEkbwpeyrE3L/Z0vTTwC1V9W0QSnfyrZF7Pq+rHIvIOpsP/ELgdmwKI+eU4juP0MK4zr0BYADdEVY9qwsYYuiAXeoV2h9FC+VtVXGfejsfSO+lLsUDfiqc/xzJH6MxFZAFgN1W9rDvbLUJEVgTWVtVburipZYDbROQcVY3t2V5IWEm+pKqObYUzInIUNof/BOGahC8t76jqza1oo1HydOZ1bcTqNZLPPK0zzdLd+cyz9or8qKLbzdPG18nf3FW67yq5xBuxV1ffX6STL2qzGZ15I/dIWT7zIj/r6MzTVNkroG4cVSjax6Co3UY+E62+D7uK7n7NPhDYF+g1gzmWgnQIkDuYq+qoFrQzHrixiS8N3wPeAGoN5qo6BtuGNnv8DJi9Ucy+wGUtitNxHMfpZrp7MD8G+LKIHI/l5P49No8LcLCqPikiz2OrvVcHkjnq9QFV1d1FZBS22GsQlvlrj6CVPgjYBZvLHa2q54WyS4d/38V02oOwhVs3Yzrwo4CFwh7khxHXXt+C6ar/gc0952qtRWQJ4Cpskdw8wLHBz62B9UTkbVW9P1X+dCyRywBsL/gbsQH7JOAx7Ol5a0we9omIPAL8AXgW05z/qIl+HI19SUiuydzAG6p6UdYvVb0uSOr2xBbiPaSqB+dcZ8dxHKcb6e7B/FRgLVU9WUTOBO5U1QtD7vDLsWxfg7Gn5deBd4ANgIOAF8NACZY6dE8R2Rr4ZXhl/MNQH+COkAAF4C5V/XV4An1AVfcNr/v/rarHisgZ2Hz4zWFxWoyBwNCQrvQBirXWxwJ3qOq5IV/4OGAVLG3o6MxAvhWwsqpuFHx6AFuZvgvwt9AHh4cc5aOwgXa8iCwCnKKqjzbZj9lrcmKeXyJyB7AXtvjvIRH5sYjMo6ozKKCVOvPGbDSfzzytM83S3fnM0z6V+VFFQ56njS/S1dbJM19X9509V1eTX1eTXDWfeZHOvSt05o3f9/k68yI/6+jM01TZK6CROKxO/rUp2segqN1GPhOtuA/7us58LWBzEUl2Qlsq/Jysqq8CiMg0VX0m/P4u7bm703rtXwNrAithT6AASwKrhd8TbfY7wNdEZDNsNXjZqqy01OslVU3+Jy/TWn8JuBpAVV8Tkfew3d1irAUMDYvjEnuDVfWxkKXt69iXgBhJXM30Yx5Rv7DB/HARWRlbSV8khwNaqzOvayNWr5F85mmdaZbuzmee9qnMjyoa8jxtfJ6utm6e+Tq679i5upr8OprkOvnMi3TuXaEzb+QeKctnXuRnHZ15mip7BdSNA8oXjRXtY1DUbiOfiWbvw76az3xmqs1JwK9DvuwfYK+moTxvNwTNN+16bQ0/Nwv2RmELu5I2wV5TT1XVXYGzsVfrc2V86pSjPGMD2rXWw7Cn8r9lfJtIe/7zFbAvFnnbd00C7g62NgeuBV4QkQ2xLyhjgZ+lfEhfr5kpG432Y8xurl/Aftg0xKbYjnLfqNiG4ziO04V092D+FjBfeDV8KvCD8PR3G9XzdgNsFXJqHwH8TFUfx57Kx4nIw9hTeXZr0zuB/wn5vC/EnqiXB54EhovITsRzlGdJtNbjsB3SnsicPw17Uh4L/BXYv+BV9C3AByJyLzABG4DnxubA9wb+F9hdRNYL5w8MbxbSNNOP0PGa5Pqlqu9jfXVv6Pu3gAdrtuU4juN0AXOczjxZuKWqea+fnV6A68zb8Vh6J30pFuhb8fTnWOYInbnTmbBN6h6pOfmist2liU+3uT3woKr+p7vazJKXy7uV+aDTWtUy7XW6/Sr5zbN5pvN0ykU2quYLb0bP3lV5thvJr52UKbIB8dzwVfo3m2e+lXmqG8nDntdeVdJ7M6TtJLSqzVaWrdK3deKo4kdVTXxvyE9elzluMFfVET3tQyupuTNbqSa+CzgEGAn02GDuOI7jFDPHDeY9TdglLZ2vfClMn/4ZME5VjwpJSM7G0qFOB76P6bljOdFfxgboi6iniW8Dzg/tfoQtTpubdk383dhe8auHfeTPBCao6rWpWF7BFrs9g83Tn4OtFVgGWxuwJLAOtkZgI0zT3kHL34IudRzHcZrEB/PGmKKqw0VkKUxHvl7IK36liGwBbImtAP8N/7+9cw+ToyrT+C+JIQRhVVhcQFFElhdWEEOQi0KSVQEBJYIroMsdDCAgqJgoN2EXEFRwI7cQIARNAA2KuiARMQIJUSFBlEv4VMQYxchFESSQhBD/+E4zNZ2u7uqZnumpnu/3PDzTXV11zvm6Onx1Lu95YV88KcKanujfqyq3GU38AuDoJGMbjyfiU+iuid8c2DMt6NsLOKOqvk2B7c3s6SRt+0zacOZjwBFm9nFJ9+M98y2ooeU3szy7V6BnOvMK1X7Ga37eOj/orFa1kfY6W38Rf3N/ne8B3qxHevU1eW3vjadzPYpqZuv5uxfVWNcrA2p7wxf5fqt95nvqU513TbM+7Hn1FSW7N0P2fYVW1dnKc4t4gDcTR5F2FNXE9+S+16PTdeZlppLAtsAT8w+S7nw93Cv8PHwjmR/jq+orq76rPdE3zCm3iCZ+EzO7P72+C19ZD9018VcCn8R77LfXmJd/yswq/1r+BJwh6YUUx7NV5+Zp+esm81YsgMvz8m6lH3RWq9pIe52tv4i/ebXPdJ5OuV4ZRf3Ce+rTXq+uLM0s5sn7HhvVUUQHX6GI33meNjrrM98Tn+p67W/Whz2vvqJk92bIllOhVXW28twiHuDNxFGkHUU18T2573l0qs68U6hovB/Dh7t3T5rsi/Fd3A4GppvZf+L69wnp/GpP9Cdyyj2cxpr4xyW9Pb0ei2/vmi0DM5uHP1wchQ+j58UBLsv7gpkdhkvQhmTOGUp9LX8QBEHQRqJn3gvM7ElJFwF3ShoG/B4fXh8BXCXpeTwZTsATbi1P9FpF/xi4TtIuwHK6a+JPS/uzfxy4JCX5l8j3Fp8JfMTMHmoQzgxgVhox+CM+bw6+y97X8amDipZ/BG4cU63lD4IgCNpA6XTmZaUVnug9rPez+Nau0/qz3tCZdxGxDEw6KRborHgGcyyhMw/WIG2wswm+Or7I+ePw7Vqbkcv1OX2t8ezr8qt9plvpbd6MVreoV3VffA99oXvuTXurddl55zTrf1702v74rov+vvpKZ95JlCHuSOb9RDu8wjtNkx8EQRDUJpL5ICAN8XfTuOML67YysxeT5O0RfM6/cs01+Gr9kcBkM/uGpLH4XvCrcOOVY8xsZT+GEgRBENQgkvngoZvGnXwjGSStB4wBdsY3iNkjLbS7EtjVzJ6Q9L/4qvsr61XaSp15q3Tl1fR1+V5HbZ15b+suooctWkerdea1yu4qo/e65958d9W67Lxzivp8N3ttX/3mstrsor+vvtKZ95b+0GYXJXTmwUCiWuOenfzp5ktuZs9JOhmYikvoZuAPARsD30or8EcCP2pUaSt15q3SlVfT1+VX+0z3RgteTRE9bNE6Wq0zr1V2hVbonnvz3VXrsvPOKerz3ey1ffGbq9ZmF/199ZXOvDcMtAVwoTMPBhLVGvc/ABunHvc7sidK2hjfRW4/YB/gS8AzuGRtfNKZnwvM6bfWB0EQBLlEz3zw0E3jjq9y/wE+T/63qnOXpvPn4/PjX0nbw54E3CJpKL5D3KH91fggCIIgn9CZDwLaoXEPnXkXEcvApJNigc6KZzDHEjrzfiQZrLzfzK6rc85SM9uo6ljbvcGrkXSCmV3S7nbUI8+7u9H5vfXxztMK19LxVqhVZy2dedYbPS+mInrhZn2X6+mfi/iIV2uzi3pQt1Kn2+g7b0Zn3cjfuohWvLod1X7mUOx3W7T8ZuiNf3xv9iwo0pbe/FaLlF/rswrN/h4aHW9Ubn8Qc+Y94+24G1qznITPV/crZja9Tq/89H5tTBAEQdByBnXPvKA3+YbAtcBr8VXfh+KOaNtJmoDvXd7NB9zM5teoax8aeIOnHdtW4u5kI4Ab8N3b3gSMxy1LT8P3e98ImGpml0raFjdKGYJ7mR8JjMI90Vfgq9JfAI4Hhqc690ttWF/SZbhcbasU89rAI2a2maQ7cEOY9fHFcJfhbmlDgdPN7I4efPVBEARBCxnUyTzRyJv8A8D3zWyKpHcBO+IruY81s6m1fMDxBN8NM7ulkTd4ev375CM+BXiLme0t6Ww8qd8PvAFP1EOBByTNwrXeR5rZw5KOAibisrG1zWwnAEmnAvuk2K4A9jSzcyWdaGafSA82eVxvZjdJOg63TT1K0ga49erb6n25rfYzL6L7bZWPd55WuJaOt6vNed7N3XXmWW/0vJiK6IWb9V2up38u6iOe1cw28htv1P6e0Og7b0ZnXc/PvKhWvLod1X7mUOx3W7T8elTrmXvjH9+bPQsa1VOkrCVLljT1vRXxKq9Q1GO96PFG5YbOvH9o5E0uYBpA6nHPT3uYV2jkA16LPG9wgPvS32fwXdnAV5uvnV7PN7PlAJIeTG3cGrgstXs47rKWjQ28d32tpH8AWwE/rdO+IVXvK+VsC+wmaaf0/lWS/tXMnsoNtMV+5kV0v63y8c7TCtfS8VaoVWctnXnWGz0vpiJ64WZ9l+vpn4v4iFdrsxv5jTdqf09o9J0X1VkvXLiwrp95Ua14dTuq/cyh2O+2aPl51Fpo1Rv/+N7sWdConkZlVe5NM99bEa/yCkU91oser1du6Mz7j0be5IuAdwJIGiPpArr7iuf5gOfV1cgbvJG84B2ShklaB+8V/yaVd2gqayJwczY2Sa8BzgYOAo7Gh9wr7az8fRHfFAZg+xrtBn+4uD7VsxcwC/hrg/YGQRAEfUwk84SZPYnPfd8p6ed4svo1cB4wPs0dnw1cge9Lvm3aJa3iAz4X2BLXb+dR8QZfQpc3+AK8V17UG3w4cCswFzgn9YqPw+fi5wHn0/VgUOFZ4G68Nz4XT+aVdj4saQYwG9gslXEAtUcYrgC2knRnimVx2lUuCIIgaCOhMy8RA9WitBahM+8iYhmYdFIs0FnxDOZYeqozj555DSSNk3RD1bHzGywSa6b876S/20oa00w7gFdLWilpdOa8YyWd1cs2Le3N9UEQBEH7iGTeBsxs//Tyw8B/NHHdHcCJ+BD4NZKa7/IGQRAEHUesZu8Bki6kS1Z2nZlNlrQ/MAnXiT+OLzY7E185/np8xfqJZjYv9YJH4xaiKyTdh2vJq3XgefwGl4WdC5xS1bZXdp5LvfopwGa4tG0kvshtMq5b3wY4xcy+B4xI52+Kz7l/At/g5mpgg1T8J5MEbzG+GO5hM/tU8W8uCIIg6AsimefznrTorcLmwJmSPgC8Bff6fhW+iG0O8FHgy2Z2o6RD6drpbZmZvUfS24DrgO0AzOxPaZOYpWZ2j6T3UaUDp/6iuDOAe9IGNEVYz8z2kHQQ8KnU/nH4rnTfwxP9JDNbLOlbePJ/N/BjM7tc0r8D1+APMZsC25vZ0zXq6UYrdOadQMQyMOmkWKCz4olYmiOSeT5zsgvNJJ2fXm4NzDWz1cBKST/Dh8o/DXxe0om4nO27lXIAzOwhSd32aq+iGR04ZrZc0hH4A8KVOadlZXK/SH+fARaZ2erka17Rr//BzBan1/Nxff22+EPNgen4+unvU0USObRGZ152IpaBSSfFAp0Vz2COJXTm/cci0hC7pOHAu/Bh7wnAWWY2Fk+ilWHyio/4NqzZ034ZGNpAB56Lmd2HJ/NJmcPDJa0raS26787WSLbwxuRjTorvQXwo/atJV34ALsOrtDsIgiAYIEQybxIzuxl4TNJP8U1lbkxJ9R7g5uQZvhFdG7eMSseuAj5eVdxC4AR8k5Y8HXgjzgMWZ97/X6VdVccb8TTwtRTXYjO7FZ+TPyBNN8zGE3wQBEEwwAideR+S5GJLzWxKu9vS34TOvIuIZWDSSbFAZ8UzmGMJnXnQYyRNlPTn5JYWBEEQlIxYANeHmNlZ7W5DQQ7G7VYPwveJD4IgCEpEJPNBTtoi9lFcjz4DmC5pR+BS4Dl8lf2LZnZ4WqnfzYO9Pa0OgiAIssSc+SAnmaxcn/zW5wGfAS4HDklyunNxD/UvAVOBsenSHwHHmZnVKrcyZ97X7Q+CIOhQmpozj575IEbS64C9gdenXvdr8NX1m5jZQ+m0ufjwe54He81kXiEWwEUsA5VOigU6K57BHEvozIOecDBwtZntYWbvB3YC9gBekFTZM37n9LeeB3sQBEHQRiKZD26OBr5ReWNmy4Bv44l6mqTbgR2BlWb2S3ruwR4EQRD0ITHMPogxs+1qHPuEpOOBD5rZk5LOAVakz74MfLmfmxkEQRA0IJJ5UIu/ALelfeL/DhzW5vYEAZPvWgTASWO2bnNLgmDgEck8WAMzuxHfDjYIgiAoAZHMS4ykLXFb0pfw9Q9TcRvVg9LnS81so2S1ukH6bx98z/UdgKW4nesHgVXp+pH43vATgGHA/+P7tv8EOBTY0sxWSboAWGhm3+qfaIMgCII8IpmXm91xg5eJwG64FWsec8zsq5LGAxuY2Y6SNsQd3wC+AnzNzG6V9F7gfOA03DRmtJmtkLQ5sKekHwJ74Z7qdQk/cydi6T1Lljyd6l/WsjI76b5AZ8UTsTRHJPNyczVufzobn9u+rerzrI1qRQ++NckrPS1weyQd3xY4VdKkdN3KdPwxM1uRXl8JfBIfBbg9czyX0JlHLK1i3vM+Zz56dGvmzDvpvkBnxTOYYwmd+eBkPDDXzN4LzAIOBDYGkPRmYP3MuRUP8geBXdI5rwO2TMcfASYlDfkxqbzsdZjZPOCtwFH4g0QQBEEwAIieeblZAFwr6XR8fvuzwGmSfg4sovZ2qrcAe0maj8+ZL8N74acAlyfntJHASTl1zgQ+ktkhLgiCIGgzkcxLjJk9CuxadXh8jfMOz7wV3ps/XtIG+K5uT5nZcmDPGtXsXPV+GD7cHgT9SkjSgiCfSOaDjyXABZJOxhPzpJTIG5JWxW+Cr34PgiAIBgiRzAcZZvY8NXrvBa89vLWtCYIgCFpBLIALgiAIgpITPfOgrxgGsGJFQ/VaLsuXFxr9LwURy8Ckk2KBzopnsMaS+X/msGbqGLJ69epmzg+CQixcuHBX3As9CIIgaJ7dRo8ePa/oydEzD/qKe/Fd6f6MbxUbBEEQNGYYvl/Ivc1cFD3zIAiCICg5sQAuCIIgCEpOJPMgCIIgKDmRzIMgCIKg5EQyD4IgCIKSE8k8CIIgCEpOSNOCAYWkocBlwHbAcuBoM/tte1vVHJLuA55Nbx8DrgAmAy8Bt5nZ2e1qW1Ek7QRcYGbjJG0BTAdW4xa6x5vZy5K+AOyDx3Wymd3TtgbXoSqWUcDNwG/Sx5eb2TfLEIuk4cA0YDNgBHAO8DAlvDc5sSyhhPdGUsV8Svh9OBZ4kX6+L5HMg4HGh4C1zWwXSTsDF9LDveTbQbKQHZJ84SvH7gc+DPwOuEXSKDP7RZua2BBJE4FDgOfToYuA083sDklTgPGSFgNjgZ2ATYFvA+9sR3vrUSOW0cBFZnZh5pztKUEswMHA02Z2iKT1gfvTf2W8N7Vi+R/KeW8+CGBm75Y0DjgXGEI/35cYZg8GGrsCswHM7GfADu1tTtNsB6wj6TZJcySNAUaY2aNmthr4IfC+9jaxIY8C+2fejwbuTK9vxdu/Kz7KsNrM/gC8StKG/dvMQtSKZR9Jd0m6WtJ6lCeWWcAZ6fUQvHdX1nuTF0vp7o2ZfReYkN6+GXiGNtyXSObBQONfgL9n3q+SVKYRpGXAV3Bv+GOBa9KxCs8Br2lDuwpjZt8GVmYODUkPItDV/ur7NCDjqhHLPcBnzWwMPlLyBcoTyz/M7LmU5G4ETqek9yYnljLfm5ckXQtcDMykDfclknkw0HgWWC/zfqiZvdSuxvSAXwMz0tP3r/F/vOtnerHXUAAABRRJREFUPl8Pf3IvEy9nXlfaX32fyhLXTWa2sPIaGEWJYpG0KfAT4Btmdh0lvjc1Yin1vTGzw4At8fnzkZmP+uW+RDIPBhp3A3sDpDnzB9rbnKY5Ep/nR9ImwDrA85LeKmkI3mMvmwHNL9JcIMBeePvvBvaUNFTSm/CHrqfa1cAm+KGkHdPr9wILKUkskv4NuA2YZGbT0uFS3pucWEp5byQdIunz6e0y/AFrQX/flzINXwaDg5uA3SXNx+fSjmhze5rlamC6pHn4StYj8X/cM3EDhdvM7OdtbF9P+AxwpaS1gEXAjWa2StJc4Kd4p+D4djawCY4DLpa0ElgKTDCzZ0sSy6nA64AzJFXmm08CvlbCe1Mrlk8DXy3hvfkOcI2ku4DhwMn4vejXfzNhtBIEQRAEJSeG2YMgCIKg5EQyD4IgCIKSE8k8CIIgCEpOJPMgCIIgKDmRzIMgCIKg5IQ0LQiCliJpM3zznIdxed5awOPAEWb2xybL2hfYwczOlHQ2cLuZzZV0FTDFzBb0sq2rzWxID67bATjWzI7u67qCoAiRzIMg6AseN7N3VN5I+iK+1eV+zRRiZt8Hvp/ejsV3DKOZJNoXpIeItrYhCLJEMg+CoD+4C9gXXtnZbzKwNvAUcIyZ/VbSp4HD8E127jGzYyQdDowD5uCmO1dJ2g9/MDgruVKdirtwrcJ3FZuIu1LdhNtPjgL+AnzEzP5a3TBJU4EdU1uOxEcS5gCbJdvKscDnzGyvzDXjUv3jJN2B7yu+G7AhcKKZ3ZpGKGYA6wI/y1y7LnApsA2+kdAFZna9pIuADZOT2MeAE4FdzWxVD77vYJARc+ZBEPQpybv6QODutCPWDcAJZrYdMAW4PpnpfB5P2KOBlyW9oVKGmX0dWID72z+QKXtv/CFhNJ60t8ANbsAd7C4ys23wPbD/O6eJd6ZRhO8Ak83st7gP/bj0+WG4N3U91jKzXYBP4d7cAJcA01PZd2fOPR1YaGajgTHAaZI2B04DdpD0UeCLwMGRyIOiRDIPgqAv2ETS/cnL/Vf41ryfw40o/mZm9wKY2Sw8Ab8amA/ci7tlXWpmfypQz3uA683shWTIMw3f1xvgiYxv/IN0N7yp8IKZzUyvZ9CVwKcBh0haJ5X33QbtmF2jnnHAN9PrmXS5t70PODZ9N3fhsb/NzF7Aty+eCXzJzB5tUGcQvEIMswdB0Bd0mzOvIOmNNc4dgg83fwjYGTemmC0pryedpbpDMoSu/6+9mDm+On1WTbbnO4SuhDsLOBf4L+AHZra8QTsqdWXrWZ1p32q6HM6G4b3u++AV05HK8L+AJ/GRhiAoTPTMgyDoTwzYQNI7ASQdACzGE9wi4AEzOxOf+3571bUvsWYHZA7wUUkj01D9EaRFcgVZN62YB58vvx3AzJYBtwLn0XiIPY/b8bl8gP2BEZk2HwcgaWN85OJNaVrhHGAXYFSaQgiCQkQyD4Kg30g93AOBSyQ9CJwAHGhmTwJXAPdKWog7ak2vunw2MEXSuzLl3QzcjM+nP4Q/GFzcRJOeAT4k6ZfA7vicd4UbgGd74XJ3AvBhSb/CbX2fS8fPBkam+OcAE9OQ+lTgQjP7HXAMHutre1h3MMgI17QgCIIqJA3Dh9mfMLOL2t2eIGhEzJkHQRCsyQJcqrZvoxODYCAQPfMgCIIgKDkxZx4EQRAEJSeSeRAEQRCUnEjmQRAEQVByIpkHQRAEQcmJZB4EQRAEJeefidCSL8cmRE4AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viz = MissingValuesDispersion(classes=classes)\n", + "viz.fit(X)\n", + "viz.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Target y passed in, produces Dispersion chart with elements colored by target variable" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viz = MissingValuesDispersion(classes=classes)\n", + "viz.fit(X, y=y)\n", + "viz.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Missing Values Bar Chart\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### No target y passed in, produces mono-color bar chart" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No handles with labels found to put in legend.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "oz = MissingValuesBar(classes=classes)\n", + "oz.fit(X)\n", + "oz.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Target y passed in, produces stacked bar chart" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "oz = MissingValuesBar(classes=classes)\n", + "oz.fit(X, y=y)\n", + "oz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/ndanielsen/Missing Values.ipynb b/examples/ndanielsen/Missing Values.ipynb new file mode 100644 index 000000000..13837cbe3 --- /dev/null +++ b/examples/ndanielsen/Missing Values.ipynb @@ -0,0 +1,668 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "\n", + "%autoreload 2\n", + "\n", + "import sys\n", + "sys.path.append(\"./../..\")" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "%reload_ext yellowbrick\n", + "%matplotlib inline\n", + "# Imports\n", + "import pandas as pd \n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "from sklearn.preprocessing import OneHotEncoder\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['surgery', 'Age', 'Hospital Number', 'rectal temperature', 'pulse',\n", + " 'respiratory rate', 'temperature of extremities',\n", + " 'peripheral pulse', 'mucous membranes', 'capillary refill time',\n", + " 'pain', 'peristalsis', 'abdominal distension', 'nasogastric tube',\n", + " 'nasogastric reflux', 'nasogastric reflux PH', 'rectal examination',\n", + " 'abdomen', 'packed cell volume', 'total protein',\n", + " 'abdominocentesis appearance', 'abdomcentesis total protein',\n", + " 'outcome', 'surgical lesion', 'type of lesion 1',\n", + " 'type of lesion 2', 'type of lesion 3', 'cp_data'], dtype=object)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "headers = pd.read_csv(\"./horse-colic.attrs\")\n", + "headers.Attribute.values" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(299, 28)" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/horse-colic/horse-colic.data'\n", + "\n", + "# Retrieve Data Set\n", + "df = pd.read_csv(url, delim_whitespace=True)\n", + "df.columns = headers.Attribute.values\n", + "df.replace(to_replace=\"?\", value=np.nan, inplace=True,)\n", + "df.shape\n", + "# matrix = df.as_matrix()" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [], + "source": [ + "matrix = df.as_matrix()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([['1', 1, 534817, ..., 0, 0, 2],\n", + " ['2', 1, 530334, ..., 0, 0, 1],\n", + " ['1', 9, 5290409, ..., 0, 0, 1],\n", + " ..., \n", + " ['1', 1, 529386, ..., 0, 0, 2],\n", + " ['1', 1, 530612, ..., 0, 0, 1],\n", + " ['1', 1, 534618, ..., 0, 0, 2]], dtype=object)" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [], + "source": [ + "# a = np.asarray([[320, True], [400, False], [350, True], [360, True], [340, True], [340, True], [425, False], [380, False], [365, True]])\n", + "# print (a)" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [], + "source": [ + "# matrix[matrix==np.nan] = -999999" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [], + "source": [ + "nan_matrix = matrix.astype(float)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1,\n", + " 0,\n", + " 0,\n", + " 60,\n", + " 24,\n", + " 58,\n", + " 56,\n", + " 69,\n", + " 46,\n", + " 32,\n", + " 55,\n", + " 44,\n", + " 56,\n", + " 103,\n", + " 105,\n", + " 246,\n", + " 102,\n", + " 118,\n", + " 29,\n", + " 33,\n", + " 164,\n", + " 197,\n", + " 1,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0]" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nan_col_counts = [np.count_nonzero(np.isnan(col)) for col in nan_matrix.T]\n", + "nan_col_counts" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ind = np.arange(len(nan_col_counts)) # the x locations for the groups\n", + "width = 0.5 # the width of the bars\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 6), dpi=80,)\n", + "rects1 = ax.bar(ind - width/2, nan_col_counts, width,\n", + " color='black')\n", + "# Add some text for labels, title and custom x-axis tick labels, etc.\n", + "ax.set_ylabel('Count')\n", + "ax.set_title('Missing Values by Column')\n", + "ax.set_xticks(ind)\n", + "ax.set_xticklabels(headers.Attribute.values, rotation='vertical')\n", + "ax.legend()\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": {}, + "outputs": [], + "source": [ + "# yellowbrick.features.missing\n", + "# Feature importance visualizer\n", + "#\n", + "# Author: Nathan Danielsen \n", + "# Created: Fri Mar 24 8:17:36 2018 -0500\n", + "#\n", + "# Copyright (C) 2018 District Data Labs\n", + "# For license information, see LICENSE.txt\n", + "#\n", + "# ID: missing.py [] nathan.danielsen@gmail.com.com $\n", + "\n", + "\"\"\"\n", + "Implementation of missing values visualizers\n", + "\n", + "To Include:\n", + "- Bar\n", + "- Density Matrix (by time, specifiable index)\n", + "- Heatmap\n", + "\n", + "\"\"\"\n", + "\n", + "##########################################################################\n", + "## Imports\n", + "##########################################################################\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from yellowbrick.utils import is_dataframe\n", + "from yellowbrick.utils import is_structured_array\n", + "from yellowbrick.features.base import DataVisualizer\n", + "\n", + "# from yellowbrick.style.colors import resolve_colors\n", + "\n", + "\n", + "\n", + "##########################################################################\n", + "## Feature Visualizer\n", + "##########################################################################\n", + "\n", + "\n", + "class MissingValuesBarVisualizer(DataVisualizer):\n", + " \"\"\"\n", + " \"\"\"\n", + "\n", + " def __init__(self,\n", + " ax=None,\n", + " x=None,\n", + " y=None,\n", + " features=None,\n", + " classes=None,\n", + " color=None,\n", + " colormap=None,\n", + " **kwargs):\n", + " \"\"\"\n", + " \"\"\"\n", + "\n", + " super(MissingValuesBarVisualizer, self).__init__(ax, features, classes, color,\n", + " colormap, **kwargs)\n", + "\n", + "\n", + "\n", + " def fit(self, X, y=None, **kwargs):\n", + " \"\"\"\n", + " TODO if y, then color code the missing values in the chart?\n", + "\n", + "\n", + " \"\"\"\n", + " nrows, ncols = df.shape\n", + "\n", + " # Handle the feature names if they're None.\n", + " if self.features_ is not None and is_dataframe(X):\n", + " X = X[self.features_].as_matrix()\n", + "\n", + " # handle numpy named/ structured array\n", + " elif self.features_ is not None and is_structured_array(X):\n", + " X_selected = X[self.features_]\n", + " X = X_selected.copy().view((np.float64, len(X_selected.dtype.names)))\n", + "\n", + " else:\n", + " pass\n", + "\n", + " if self.features_ is None:\n", + " self.features_ = range(nrows)\n", + "\n", + " if self.classes_ is None:\n", + " # TODO: Is this the most efficient method?\n", + " self.classes_ = [str(label) for label in np.unique(y)]\n", + "\n", + " nan_matrix = X.astype(float)\n", + " self.nan_col_counts = [np.count_nonzero(np.isnan(col)) for col in nan_matrix.T]\n", + "\n", + " # Draw the instances\n", + " self.draw(X, y, **kwargs)\n", + "\n", + " # Fit always returns self.\n", + " return self\n", + "\n", + " def draw(self, X, y, **kwargs):\n", + " \"\"\"Called from the fit method, this method creates a scatter plot that\n", + " draws each instance as a class or target colored point, whose location\n", + " is determined by the feature data set.\n", + " \"\"\"\n", + "\n", + " width = 0.5 # the width of the bars\n", + "\n", + " self.ax.bar(ind - width/2, self.nan_col_counts, width,\n", + " color='black')\n", + "\n", + " def finalize(self, **kwargs):\n", + " \"\"\"\n", + " Finalize executes any subclass-specific axes finalization steps.\n", + " The user calls poof and poof calls finalize.\n", + "\n", + " Parameters\n", + " ----------\n", + " kwargs: generic keyword arguments.\n", + "\n", + " \"\"\"\n", + " # Set the title\n", + " self.set_title(\n", + " 'Missing Values by Column'\n", + " )\n", + " ind = np.arange(len(self.features_)) # the x locations for the groups\n", + " # Remove the ticks from the graph\n", + " self.ax.set_ylabel('Count')\n", + " self.ax.set_xticks(ind)\n", + " self.ax.set_xticklabels(self.features_, rotation='vertical')\n", + " # Add the legend\n", + " self.ax.legend(loc='best')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viz = MissingValuesBarVisualizer(features=headers.Attribute.values)\n", + "viz.fit(matrix)\n", + "viz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 210, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.00000000e+00, 1.00000000e+00, 5.34817000e+05,\n", + " 3.92000000e+01, 8.80000000e+01, 2.00000000e+01,\n", + " nan, nan, 4.00000000e+00,\n", + " 1.00000000e+00, 3.00000000e+00, 4.00000000e+00,\n", + " 2.00000000e+00, nan, nan,\n", + " nan, 4.00000000e+00, 2.00000000e+00,\n", + " 5.00000000e+01, 8.50000000e+01, 2.00000000e+00,\n", + " 2.00000000e+00, 3.00000000e+00, 2.00000000e+00,\n", + " 2.20800000e+03, 0.00000000e+00, 0.00000000e+00,\n", + " 2.00000000e+00])" + ] + }, + "execution_count": 210, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "matrix\n", + "nan_matrix = matrix.astype(float)\n", + "nan_matrix[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 266, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 271, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/var/pyenv/versions/3.5.2/envs/yb-dev/lib/python3.5/site-packages/matplotlib/figure.py:418: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", + " \"matplotlib is currently using a non-GUI backend, \"\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "nrows, ncols = matrix.shape\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 290, + "metadata": {}, + "outputs": [], + "source": [ + "# yellowbrick.features.missing\n", + "# Feature importance visualizer\n", + "#\n", + "# Author: Nathan Danielsen \n", + "# Created: Fri Mar 24 8:17:36 2018 -0500\n", + "#\n", + "# Copyright (C) 2018 District Data Labs\n", + "# For license information, see LICENSE.txt\n", + "#\n", + "# ID: missing.py [] nathan.danielsen@gmail.com.com $\n", + "\n", + "\"\"\"\n", + "Implementation of missing values visualizers\n", + "\n", + "To Include:\n", + "- Bar\n", + "- Density Matrix (by time, specifiable index)\n", + "- Heatmap\n", + "\n", + "\"\"\"\n", + "\n", + "##########################################################################\n", + "## Imports\n", + "##########################################################################\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from yellowbrick.utils import is_dataframe\n", + "from yellowbrick.utils import is_structured_array\n", + "from yellowbrick.features.base import DataVisualizer\n", + "\n", + "# from yellowbrick.style.colors import resolve_colors\n", + "\n", + "\n", + "\n", + "##########################################################################\n", + "## Feature Visualizer\n", + "##########################################################################\n", + "\n", + "\n", + "class MissingValueDensity(DataVisualizer):\n", + " \"\"\"\n", + " \"\"\"\n", + "\n", + " def __init__(self,\n", + " ax=None,\n", + " x=None,\n", + " y=None,\n", + " features=None,\n", + " classes=None,\n", + " color=None,\n", + " colormap=None,\n", + " **kwargs):\n", + " \"\"\"\n", + " \"\"\"\n", + "\n", + " super(MissingValueDensity, self).__init__(ax, features, classes, color,\n", + " colormap, **kwargs)\n", + "\n", + "\n", + "\n", + " def fit(self, X, y=None, **kwargs):\n", + " \"\"\"\n", + " TODO if y, then color code the missing values in the chart?\n", + "\n", + "\n", + " \"\"\"\n", + " nrows, ncols = df.shape\n", + "\n", + " # Handle the feature names if they're None.\n", + " if self.features_ is not None and is_dataframe(X):\n", + " X = X[self.features_].as_matrix()\n", + "\n", + " # handle numpy named/ structured array\n", + " elif self.features_ is not None and is_structured_array(X):\n", + " X_selected = X[self.features_]\n", + " X = X_selected.copy().view((np.float64, len(X_selected.dtype.names)))\n", + "\n", + " else:\n", + " pass\n", + "\n", + " if self.features_ is None:\n", + " self.features_ = range(nrows)\n", + "\n", + " if self.classes_ is None:\n", + " # TODO: Is this the most efficient method?\n", + " self.classes_ = [str(label) for label in np.unique(y)]\n", + "\n", + " nan_matrix = X.astype(float)\n", + " self.nan_col_counts = [np.count_nonzero(np.isnan(col)) for col in nan_matrix.T]\n", + "\n", + " self.nan_locs = np.argwhere(np.isnan(nan_matrix))\n", + " \n", + " # Draw the instances\n", + " self.draw(X, y, **kwargs)\n", + "\n", + " # Fit always returns self.\n", + " return self\n", + "\n", + " def draw(self, X, y, **kwargs):\n", + " \"\"\"Called from the fit method, this method creates a scatter plot that\n", + " draws each instance as a class or target colored point, whose location\n", + " is determined by the feature data set.\n", + " \"\"\"\n", + "\n", + " width = 0.5 # the width of the bars\n", + "\n", + " x, y = list(zip(*self.nan_locs))\n", + "\n", + " self.ax.scatter(x, y, alpha=0.5, marker=\"|\")\n", + "\n", + " def finalize(self, **kwargs):\n", + " \"\"\"\n", + " Finalize executes any subclass-specific axes finalization steps.\n", + " The user calls poof and poof calls finalize.\n", + "\n", + " Parameters\n", + " ----------\n", + " kwargs: generic keyword arguments.\n", + "\n", + " \"\"\"\n", + " # Set the title\n", + " self.set_title(\n", + " 'Dispersion of Missing Values by Feature'\n", + " )\n", + " ind = np.arange(len(self.features_)) # the x locations for the groups\n", + " # Remove the ticks from the graph\n", + " self.ax.set_xlabel('Count')\n", + " self.ax.set_yticks(ind)\n", + " self.ax.set_yticklabels(self.features_)\n", + " # Add the legend\n", + " self.ax.legend(loc='best')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 291, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pylab import rcParams\n", + "rcParams['figure.figsize'] = 5, 10\n", + "\n", + "viz = MissingValueDensity(features=headers.Attribute.values)\n", + "viz.fit(matrix)\n", + "viz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/ndanielsen/Untitled.ipynb b/examples/ndanielsen/Untitled.ipynb deleted file mode 100644 index e14329518..000000000 --- a/examples/ndanielsen/Untitled.ipynb +++ /dev/null @@ -1,385 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from collections import OrderedDict\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "\n", - "from sklearn import neighbors\n", - "from sklearn import naive_bayes" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from sklearn.datasets import load_iris\n" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "data = load_iris()" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "metadata": {}, - "outputs": [], - "source": [ - "df = pd.DataFrame(data.data)" - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(150,)" - ] - }, - "execution_count": 130, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.to_records(index=False).shape" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "x = np.array([(1,2.,'Hello'), (2,3.,\"World\")], dtype=[('foo', 'i4'),('bar', 'f4'), ('baz', 'S10')])" - ] - }, - { - "cell_type": "code", - "execution_count": 138, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 2], dtype=int32)" - ] - }, - "execution_count": 138, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x['foo']" - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 2.318, 2.727, 4.26 , 7.212, 4.792],\n", - " [ 2.315, 2.726, 4.295, 7.14 , 4.783],\n", - " [ 2.315, 2.724, 4.26 , 7.135, 4.779],\n", - " [ 2.11 , 3.609, 4.33 , 7.985, 5.595],\n", - " [ 2.11 , 3.626, 4.33 , 8.203, 5.621],\n", - " [ 2.11 , 3.62 , 4.47 , 8.21 , 5.612]])" - ] - }, - "execution_count": 132, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X['one']" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[( 2.318,), ( 2.727,), ( 4.26 ,), ( 7.212,), ( 4.792,)],\n", - " [( 2.315,), ( 2.726,), ( 4.295,), ( 7.14 ,), ( 4.783,)],\n", - " [( 2.315,), ( 2.724,), ( 4.26 ,), ( 7.135,), ( 4.779,)],\n", - " [( 2.11 ,), ( 3.609,), ( 4.33 ,), ( 7.985,), ( 5.595,)],\n", - " [( 2.11 ,), ( 3.626,), ( 4.33 ,), ( 8.203,), ( 5.621,)],\n", - " [( 2.11 ,), ( 3.62 ,), ( 4.47 ,), ( 8.21 ,), ( 5.612,)]], \n", - " dtype=[('one', '\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;31m# raise Exception(self.yy.ravel().shape)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mZ\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mc_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mxx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/usr/local/var/pyenv/versions/3.5.2/envs/yb-dev/lib/python3.5/site-packages/sklearn/naive_bayes.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0mPredicted\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \"\"\"\n\u001b[0;32m---> 65\u001b[0;31m \u001b[0mjll\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_joint_log_likelihood\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 66\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjll\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/var/pyenv/versions/3.5.2/envs/yb-dev/lib/python3.5/site-packages/sklearn/naive_bayes.py\u001b[0m in \u001b[0;36m_joint_log_likelihood\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 705\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 706\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 707\u001b[0;31m return (safe_sparse_dot(X, self.feature_log_prob_.T) +\n\u001b[0m\u001b[1;32m 708\u001b[0m self.class_log_prior_)\n\u001b[1;32m 709\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/var/pyenv/versions/3.5.2/envs/yb-dev/lib/python3.5/site-packages/sklearn/utils/extmath.py\u001b[0m in \u001b[0;36msafe_sparse_dot\u001b[0;34m(a, b, dense_output)\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 188\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 189\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfast_dot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 191\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: shapes (160000,2) and (8,3) not aligned: 2 (dim 1) != 8 (dim 0)" - ] - } - ], - "source": [ - "X = X_two_cols\n", - "model = naive_bayes.MultinomialNB()\n", - "model.fit(X, y)\n", - "\n", - "# Plot the decision boundary. For that, we will assign a color to each\n", - "# point in the mesh [x_min, x_max]x[y_min, y_max].\n", - "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n", - "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n", - "\n", - "# set the step increment for drawing the boundary graph\n", - "x_step = (x_max - x_min) * step_size\n", - "y_step = (y_max - y_min) * step_size\n", - "\n", - "xx, yy = np.meshgrid(\n", - " np.arange(x_min, x_max, x_step), np.arange(y_min, y_max, y_step))\n", - "\n", - "# raise Exception(self.yy.ravel().shape)\n", - "Z = model.predict(np.c_[xx.ravel(), yy.ravel()])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/ndanielsen/Untitled1.ipynb b/examples/ndanielsen/Untitled1.ipynb deleted file mode 100644 index 669d625b0..000000000 --- a/examples/ndanielsen/Untitled1.ipynb +++ /dev/null @@ -1,212 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [], - "source": [ - "X = np.array([\n", - " (1.1, 9.52, 1.23, 0.86, 7.89, 0.13), \n", - " (3.4, 2.84, 8.65, 0.45, 7.43, 0.16),\n", - " (1.2, 3.22, 6.56, 0.24, 3.45, 0.17),\n", - " (3.8, 6.18, 2.45, 0.28, 2.53, 0.13), \n", - " (5.1, 9.12, 1.06, 0.19, 1.43, 0.13),\n", - " (4.4, 8.84, 4.97, 0.98, 1.35, 0.13),\n", - " (3.2, 3.22, 5.03, 0.68, 3.53, 0.32),\n", - " (7.8, 2.18, 6.87, 0.35, 3.25, 0.38), \n", - " ], dtype=[('a','" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from yellowbrick.contrib.scatter import ScatterVisualizer\n", + "\n", + "visualizer = ScatterVisualizer(\n", + " x=\"light\", y=\"C02\", alpha=0.1, classes=classes\n", + ")\n", + "\n", + "visualizer.fit(X, y)\n", + "visualizer.transform(X)\n", + "visualizer.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "import itertools\n", + "import numpy as np\n", + "\n", + "from yellowbrick.features.base import DataVisualizer\n", + "from yellowbrick.utils import is_dataframe, is_structured_array\n", + "from yellowbrick.utils import has_ndarray_int_columns\n", + "from yellowbrick.exceptions import YellowbrickValueError\n", + "from yellowbrick.style.colors import resolve_colors\n", + "\n", + "\n", + "##########################################################################\n", + "# Quick Methods\n", + "##########################################################################\n", + "\n", + "def scatterviz(X,\n", + " y=None,\n", + " ax=None,\n", + " alpha = 1,\n", + " features=None,\n", + " classes=None,\n", + " color=None,\n", + " colormap=None,\n", + " markers=None,\n", + " **kwargs):\n", + " \"\"\"Displays a bivariate scatter plot.\n", + "\n", + " This helper function is a quick wrapper to utilize the ScatterVisualizer\n", + " (Transformer) for one-off analysis.\n", + "\n", + " Parameters\n", + " ----------\n", + "\n", + " X : ndarray or DataFrame of shape n x m\n", + " A matrix of n instances with m features\n", + "\n", + " y : ndarray or Series of length n, default: None\n", + " An array or series of target or class values\n", + "\n", + " ax : matplotlib axes, default: None\n", + " The axes to plot the figure on.\n", + "\n", + " features : list of strings, default: None\n", + " The names of two features or columns.\n", + " More than that will raise an error.\n", + " \n", + " classes : list of strings, default: None\n", + " The names of the classes in the target\n", + "\n", + " color : list or tuple of colors, default: None\n", + " Specify the colors for each individual class\n", + "\n", + " colormap : string or matplotlib cmap, default: None\n", + " Sequential colormap for continuous target\n", + "\n", + " markers : iterable of strings, default: ,+o*vhd\n", + " Matplotlib style markers for points on the scatter plot points\n", + " \n", + " alpha : float, default: 1.0\n", + " Specify a transparency where 1 is completely opaque and 0 is completely\n", + " transparent. This property makes densely clustered points more visible.\n", + "\n", + " Returns\n", + " -------\n", + " ax : matplotlib axes\n", + " Returns the axes that the parallel coordinates were drawn on.\n", + " \"\"\"\n", + " # Instantiate the visualizer\n", + " visualizer = ScatterVisualizer(ax, features, classes, color, colormap,\n", + " markers, **kwargs)\n", + "\n", + " # Fit and transform the visualizer (calls draw)\n", + " visualizer.fit(X, y, **kwargs)\n", + " visualizer.transform(X)\n", + "\n", + " # Return the axes object on the visualizer\n", + " return visualizer.ax\n", + "\n", + "\n", + "##########################################################################\n", + "# Static ScatterVisualizer Visualizer\n", + "##########################################################################\n", + "\n", + "class ScatterVisualizer(DataVisualizer):\n", + " \"\"\"\n", + " ScatterVisualizer is a bivariate feature data visualization algorithm that\n", + " plots using the Cartesian coordinates of each point.\n", + "\n", + " Parameters\n", + " ----------\n", + "\n", + " ax : a matplotlib plot, default: None\n", + " The axis to plot the figure on.\n", + "\n", + " x : string, default: None\n", + " The feature name that corresponds to a column name or index postion\n", + " in the matrix that will be plotted against the x-axis\n", + "\n", + " y : string, default: None\n", + " The feature name that corresponds to a column name or index postion\n", + " in the matrix that will be plotted against the y-axis\n", + "\n", + " features : a list of two feature names to use, default: None\n", + " List of two features that correspond to the columns in the array.\n", + " The order of the two features correspond to X and Y axes on the\n", + " graph. More than two feature names or columns will raise an error.\n", + " If a DataFrame is passed to fit and features is None, feature names\n", + " are selected that are the columns of the DataFrame.\n", + "\n", + " classes : a list of class names for the legend, default: None\n", + " If classes is None and a y value is passed to fit then the classes\n", + " are selected from the target vector.\n", + "\n", + " color : optional list or tuple of colors to colorize points, default: None\n", + " Use either color to colorize the points on a per class basis or\n", + " colormap to color them on a continuous scale.\n", + "\n", + " colormap : optional string or matplotlib cmap to colorize points, default: None\n", + " Use either color to colorize the points on a per class basis or\n", + " colormap to color them on a continuous scale.\n", + "\n", + " markers : iterable of strings, default: ,+o*vhd\n", + " Matplotlib style markers for points on the scatter plot points\n", + "\n", + " kwargs : keyword arguments passed to the super class.\n", + " \n", + " alpha : float, default: 1.0\n", + " Specify a transparency where 1 is completely opaque and 0 is completely\n", + " transparent. This property makes densely clustered points more visible.\n", + "\n", + " These parameters can be influenced later on in the visualization\n", + " process, but can and should be set as early as possible.\n", + " \"\"\"\n", + "\n", + " def __init__(self,\n", + " ax=None,\n", + " x=None,\n", + " y=None,\n", + " alpha = 1,\n", + " features=None,\n", + " classes=None,\n", + " color=None,\n", + " colormap=None,\n", + " markers=None,\n", + " **kwargs):\n", + " \"\"\"\n", + " Initialize the base scatter with many of the options required in order\n", + " to make the visualization work.\n", + " \"\"\"\n", + " super(ScatterVisualizer, self).__init__(ax, features, classes, color,\n", + " colormap, **kwargs)\n", + "\n", + " self.x = x\n", + " self.y = y\n", + " \n", + " self.alpha = alpha\n", + " \n", + " self.markers = itertools.cycle(\n", + " kwargs.pop('markers', (',', '+', 'o', '*', 'v', 'h', 'd')))\n", + "\n", + " self.color = color\n", + " self.colormap = colormap\n", + "\n", + " if self.x is not None and self.y is not None and self.features_ is not None:\n", + " raise YellowbrickValueError(\n", + " 'Please specify x,y or features, not both.')\n", + "\n", + " if self.x is not None and self.y is not None and self.features_ is None:\n", + " self.features_ = [self.x, self.y]\n", + " # Ensure with init that features doesn't have more than two features\n", + " if features is not None:\n", + " if len(features) != 2:\n", + " raise YellowbrickValueError(\n", + " 'ScatterVisualizer only accepts two features.')\n", + "\n", + " def fit(self, X, y=None, **kwargs):\n", + " \"\"\"\n", + " The fit method is the primary drawing input for the parallel coords\n", + " visualization since it has both the X and y data required for the\n", + " viz and the transform method does not.\n", + "\n", + " Parameters\n", + " ----------\n", + " X : ndarray or DataFrame of shape n x m\n", + " A matrix of n instances with 2 features\n", + "\n", + " y : ndarray or Series of length n\n", + " An array or series of target or class values\n", + "\n", + " kwargs : dict\n", + " Pass generic arguments to the drawing method\n", + "\n", + " Returns\n", + " -------\n", + " self : instance\n", + " Returns the instance of the transformer/visualizer\n", + " \"\"\"\n", + " _, ncols = X.shape\n", + "\n", + " if ncols == 2:\n", + " X_two_cols = X\n", + " if self.features_ is None:\n", + " self.features_ = [\"Feature One\", \"Feature Two\"]\n", + "\n", + " # Handle the feature names if they're None.\n", + " elif self.features_ is not None and is_dataframe(X):\n", + " X_two_cols = X[self.features_].as_matrix()\n", + "\n", + " # handle numpy named/ structured array\n", + " elif self.features_ is not None and is_structured_array(X):\n", + " X_selected = X[self.features_]\n", + " X_two_cols = X_selected.copy().view((np.float64, len(X_selected.dtype.names)))\n", + "\n", + " # handle features that are numeric columns in ndarray matrix\n", + " elif self.features_ is not None and has_ndarray_int_columns(self.features_, X):\n", + " f_one, f_two = self.features_\n", + " X_two_cols = X[:, [int(f_one), int(f_two)]]\n", + "\n", + " else:\n", + " raise YellowbrickValueError(\"\"\"\n", + " ScatterVisualizer only accepts two features, please\n", + " explicitly set these two features in the init kwargs or\n", + " pass a matrix/ dataframe in with only two columns.\"\"\")\n", + "\n", + " # Store the classes for the legend if they're None.\n", + " if self.classes_ is None:\n", + " # TODO: Is this the most efficient method?\n", + " self.classes_ = [str(label) for label in np.unique(y)]\n", + "\n", + " # Draw the instances\n", + " self.draw(X_two_cols, y, **kwargs)\n", + "\n", + " # Fit always returns self.\n", + " return self\n", + "\n", + " def draw(self, X, y, **kwargs):\n", + " \"\"\"Called from the fit method, this method creates a scatter plot that\n", + " draws each instance as a class or target colored point, whose location\n", + " is determined by the feature data set.\n", + " \"\"\"\n", + " # Set the axes limits\n", + " self.ax.set_xlim([-1,1])\n", + " self.ax.set_ylim([-1,1])\n", + "\n", + " # set the colors\n", + " color_values = resolve_colors(\n", + " n_colors=len(self.classes_),\n", + " colormap=self.colormap,\n", + " colors=self.color\n", + " )\n", + "\n", + " colors = dict(zip(self.classes_, color_values))\n", + "\n", + " # Create a data structure to hold the scatter plot representations\n", + " to_plot = {}\n", + " for kls in self.classes_:\n", + " to_plot[kls] = [[], []]\n", + "\n", + " # Add each row of the data set to to_plot for plotting\n", + " # TODO: make this an independent function for override\n", + " for i, row in enumerate(X):\n", + " row_ = np.repeat(np.expand_dims(row, axis=1), 2, axis=1)\n", + " x_, y_ = row_[0], row_[1]\n", + " kls = self.classes_[y[i]]\n", + "\n", + " to_plot[kls][0].append(x_)\n", + " to_plot[kls][1].append(y_)\n", + "\n", + " # Add the scatter plots from the to_plot function\n", + " # TODO: store these plots to add more instances to later\n", + " # TODO: make this a separate function\n", + " for i, kls in enumerate(self.classes_):\n", + " self.ax.scatter(\n", + " to_plot[kls][0],\n", + " to_plot[kls][1],\n", + " marker=next(self.markers),\n", + " color=colors[kls],\n", + " label=str(kls),\n", + " alpha=self.alpha,\n", + " **kwargs)\n", + "\n", + " self.ax.axis('equal')\n", + "\n", + " def finalize(self, **kwargs):\n", + " \"\"\"\n", + " Finalize executes any subclass-specific axes finalization steps.\n", + " The user calls poof and poof calls finalize.\n", + "\n", + " Parameters\n", + " ----------\n", + " kwargs: generic keyword arguments.\n", + "\n", + " \"\"\"\n", + " # Divide out the two features\n", + " feature_one, feature_two = self.features_\n", + "\n", + " # Set the title\n", + " self.set_title('Scatter Plot: {0} vs {1}'.format(\n", + " str(feature_one), str(feature_two)))\n", + " # Add the legend\n", + " self.ax.legend(loc='best')\n", + " self.ax.set_xlabel(str(feature_one))\n", + " self.ax.set_ylabel(str(feature_two))\n", + "\n", + "\n", + "# Alias for ScatterViz\n", + "ScatterViz = ScatterVisualizer\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Now with alpha" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/peter/.pyenv/versions/3.7.0/lib/python3.7/site-packages/ipykernel_launcher.py:206: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n" + ] + }, + { + "data": { + "image/png": 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89atf5aqrruKCCy7gHe94B9/97nfZtWsXlmVxwQUXUF9fz0MPPcStt97K5MmT6e/v56ijjhr1e5HPmt7VQA3wbaXUt6Pbvgz8VCnlB14D7tZaR5RSTwHP4AysWR099mvAbfHH5rGsYpxxa3PZ+vTc7es3r8GwDHxmGaFIT9rrmoZHEtTFuObW8ELh3oTnMPRa3+9+9zsmT57MjTfeSFdXFxdddBF+v5+bb76ZBQsWsGHDBrZv3853vvMdbrrppoRt6ezdu5fbb7+dyspKPvaxjyXMtblhwwZqamq4/vrraWtr4+KLL+aSSy7hhhtu4J577mHSpEl89rOfHdJ7Ga589ul9GSfIJRvQgKu1vg64Lmnb1lTHCpFKbeWc2OO9bRrLTj1PYbrtQhSy7du3c9pppwFQUVHBggULePTRR1mwYAEAH/nIRwA4cODAgG3x4qetXLx4MZMmTQKcJYqSV05oaGiILTdkWRYtLS1UV1dTU1MDEFuhYbTJULcCFgxb9ITC9ITC9MU9Dkq6ghDj1rJZp7Ns1un4vaX4vaWx58Pp21uwYAEvvPACAF1dXWzdupXZs2ezc+dOAH75y1/yyCOPMG3atAHb/H5/bOWExsbDOa7bt2+nt7eXSCTCyy+/PGDlhPe+973ceeed3HbbbSxfvpypU6fS0dFBa2srQGyFhtEm05AVqPk1FWxYdbiinGrC6YkgU6pCquM2Nq7D7y2VKceEiPPRj36Ub3/721x66aUEg0G++MUvsmDBAq6++mpM06S2tpZVq1Yxffr0Adv8fj/f+973mDlzJtOmTYtd0+fz8eUvf5kDBw5w3nnnsXjx4ti+Sy65hGuvvZaPf/zjdHV1sWLFCvx+P9/5znf49Kc/TXV1NV7v2IQfWWWhSBTT6LuNjesyzrqSatWFYro/QyH3J7NiG725Z88e/u3f/o3/+7//y+n40fz7kVUWhBBiAhqPwa4QSJ+eKDgtnc1p95mGZxRLIkRhmj17ds61vPFGgp4oOJn68y5b8f1RLIkQYryRoCeKSvICs0KI4iJBr0AV8yoLqQaquGRUpxDFTQayFKhCWWVhKDY2rsM0PAMS0U3Dg99bOkalEkKMB1LTK2ABr0mZ30uZ30tJ3GM3EBaq3a2NKWdesewItZVzZOV0IYpYYX/6CZEk08hOIUThk6AnCsrGxnWU+CpSrqQg6QpCCOnTEwXHnZ0+mdvkKbU9IYqXBD1RENx+ulC4F9u20y4mmy4gCiGKgwS9AuasphAGoC9s4QmF47YXprbufTmvni6EKD4S9ApUoayykKv4xWSJgIEpwU8IMYAEvQLl9ZoJeXgdVYGCzcuLV1s5h92tjWkDniwiK0Rxk9GbQgghioYEPVFwsqUmpEpnEEIUBwl6oujICE4hipcEPVFQzl56mcyvKYRISwayFKhw2KKp7fCKAs0dQapaOmLP59dU4C3wOTjTkaAoRPGSoFegXmtp58JfPYbfYwDQ29dH6TP7AAhFbP54xTs55ojJeS2DmzDuphOMhvWb16RtvnRXWaitnDNq5RFCjC8S9ArU5l0H2NXek7ixuzthf76D3lgIhXvTpiW4AW80g7AQYnyRoFeg9rRlXiw12/7hiJ8SLP455L/WN6tGsbdNpw18EvCEKG7F2alTBF7cfXBY+yeqTANZ+vq7WL95jTNrixCiKElNr0Dtae8e1v7hcGtTY9GnB05wS0fSFYQobhL0ClRnb2hY+ycy0/Bk7NcTQhSvvAY9pdRy4Ada67OUUuuBGdFd84BntdaXKKX+CEwF+oFerfX5SqmFwFrABrYAq7XWMnvwILzVGRzW/onsshXfB2Dt01cN2JepFiiEKHx5C3pKqW8CnwC6AbTWl0S31wCPAV+NHroIWKa1tuNOvwm4Vmv9uFLq58CFwL35KmshCtnD2z8SxmrQyMbGdbJQrBAipXwOZNkOXJRi+/eAn2mt31JKTQcmAX9SSj2tlHpf9Jh64Ino4weBc/JYzoKUrVpcyNXmls7mjH132ebmFEIUrrzV9LTWv1dKzYvfppSaBpzN4VqeH/gxcDMwGdiklHoOMOJqfp1AdS6vuWXLlhEoeWHIVpGzgYaGhtEoyqjr608/SMdDAOzU771Q78dIkfuTmdyfzMbL/RntgSwfBu7SWrujDPYBP9dah4G3lVIvAYrEikgl0J7Lxevq6ggEAiNZ3gkrcFcjmXrtAkB9ff1oFWdUbdv8IDCw/840PMysWQBA/dLE997Q0FCw92MkyP3JTO5PZqN5f4LBYMYK0Gjn6Z2D01wZ/3wDgFKqAqgDXgNeUkqdFT3mfOCpUSxjQYjOPjbk/RPZJcuvTTvV2N42Lf19QhSx0Q56CtjhPtFaPwhsVUo9CzwMXK21PgB8DfieUuoZnCbQu0e5nBOenaV9M9v+QmTZESw7Irl6QhSxvDZvaq13AqfGPV+W4pivpNi2FViZz7IVukof9PZn3l/Izl56WcqUBSBtDp8QovBJcnqB8vi80B/OvL9Axc/1KYQQ8Qr3k6/IBbxeIH3Qc/YXpt2tjWn3HTl56SiWRAgx3hTuJ1+RK/VmHqmSbX+hkkEsQhQ3CXoFan9X5sEa2fYXKpmGTIjiJksLFaj+LFOuZNs/UeXSn1fiqxiFkgghxiMJeoUqW+tlAbdumoYHI82ftml4uGT5taNcIiHEeCHNm4Uql3nICtDZSy9j/eY1hMK9KXMRLTsSS2UwDU9sRQYhRHGQoFegyj3QmX7wJuUFOOey27RZXVrL/o6mMS6NEGI8kqBXoHqy1OSy7Z/IsgU8N21hrJY+EkKMHenTK1CRLANVsu2fiM5eepmkJAghMpKgV6DKfJnbL7Ptn4g2Nq6TeTWFEBlJ82aBqvEbtGT4/K/xF97wzb1tOusxJb4KqQ0KUcQk6BWoziyJeNn251s4bNHUlj5RfH5NBV7v4Boi/N5SQBLQhRDpSdArUId6Mge1bPvzramti4+sfYJAisAWDFtsWLWSRbVVg7qmu4aeO/emaRxuwpWVFYQQIEGvYPUMc/9oCHhNyvyp/gQz5FpkkNxs6feWDqj1pVtcVghRHCToiYKRHOBSNXNKf54QxU2CnigYbnNmpqZMNxDGN30KIYqHBD1RMNwpxQ6vmG6Qar61El+FNHMKUaQkT08ULJ/pZ+DM2gbB/h72tmnWbbpGVlkXoshITU+MmWDYItWgFWf70LlNlx6Pj4jtXF9GbwohQIKeGCPzayrYsGplxv1D5TZzrt+8BsuOUOKrwLIsQpEewCbgK6e2co7MvSlEEZKgJ8aE12sOOg9vKEzDQ23lHELhXt7u2IVNAU46KoTImfTpiYJ1yfJrmVWjACdnzzCc/r1QuFdqeUIUKanpiYLW0tlMX38XpuFhVo2SPD0hipwEvSIWDluDnt9yonBHZbp5eZYdYXdrIwYGXtPP+s1rYsdesvzaMSmjEGL0FeYnnshJpgmfJ7q9bTo2B2c8G5t+KzgGJRJCjAdS0yti21s6hrSawUSQLUVBandCFCcJekVs9T3P8ZfPnTMqoyhHS67J5vHHyaAWIYpHXoOeUmo58AOt9VlKqROA+4Ft0d23aq1/p5T6LvBenCzlr2itn1NKLQTW4swhtQVYrbWWseYjLNgfKbjaXktnc06rp+9ubaTEN/RcQCHExJS3Tzql1DeBXwEl0U31wE1a67OiP79TSp0IrASWA5cAt0SPvQm4Vmt9Bs48Uhfmq5zF7K2uPj539zMF1bdXWzlHZl8RQqSVz6/324GL4p7XA+9VSj2plLpdKVUJnA48rLW2tdbNgFcpVRs99onoeQ8C5+SxnEWtOzS0tevGq8E0VYbCvYTCvQkjOYUQhS1vzZta698rpebFbXoO+JXWukEpdQ3wXaAdOBh3TCdQDRhaaztpW1ZbtmwZdrmLTXdvmEeefZEtFf7YtpkVPrzmxGvu3BXcNKjjDdv58+/v7wcvNDQ05KNYBUPuT2ZyfzIbL/dnNAey3Ku1bncfAz8D/ghUxh1TiRMIrRTbsqqrqyMQCIxAUQvAXQOH66cSAn75egcBrzNJczBssWHVygk5uKW98VUAOlrfzOl423BqubNqFPRCfX193so20TU0NMj9yUDuT2ajeX+CwWDGCtBofp1/SCl1SvTx2UADsAk4VyllKqXmAKbW+gDwklLqrOix5wNPjWI5i4oBlPi8lPmdn8AYDmjZ2LhuWEv9nL30skE1b1p2RPr/hCgyo1nT+wLwM6VUP7AP+KzWukMp9RTwDE4AXh099mvAbUopP/AacPcolrOoDFxideIzDY8EMyFESnkNelrrncCp0ccvAitSHHMdcF3Stq04ozpFEXBrd26qgeTQCSHyZeKNVhAjygBKov15hWKwtby9bZrG3vvyVBohxHgiM7IUORuIrrgzZtzanFvDG+3anWVH8Mg/BSGKgvxLF/TE5eoFwzLxjRCicEnQE/zPx89IeD6/ZmJPz3Xk5KUAKVdZSCeCrLwgRDGQoCfGTU7eSDVrnr30siGlPmxsXCcDZ4QocDKQRQghRNGQoCdE1O7WRtZvXjOsBHkhxPgmQa/IFWr7dktn81gXQQgxDhXqZ57I0dQy31gXYcSt23TNkGdkyWUtPiHExCU1vSJXVerPftAEM9SAZxqFlaQvhBhIanpF7rgZ42Pk5ngwq0YBMvWZEIVManpF7oxFM8e6COOG9AMKUfgk6BWx2z/6Dj55wsKxLsaIc5PTB6u2co7U8oQocNK8WcRWLS+8gAfOBNKD5SEgAU+IIiA1PVFwhjKQpdyckoeSCCHGGwl6QgAd1puSlC5EEZCgJ0TU3jbN+s1rxroYQog8kqAnRJRlR+jr75LAJ0QBk6AnRJJQuJeNjeukuVOIAiSjN0XBGcp6esn2tmn83tKRKpIQYpyQoCcKTktns8yhKYRISZo3RcEJhXuHPP8mOH17fm8ptZVzRrBUQojxQIKeEElMwyOzswhRoKR5UxSUjY3rhlXLK/FVcMnya0ewREKI8SRjTU8p5VVKfVkp9WOl1BlJ+67La8mEGIKhTEHmKvFVSJOmEAUuW/PmL4ATgDeBdUqpq+P2fSBvpRJiCIZby5MmTSEKX7bmzZO01scBKKXWAX9VSvVorf8TMPJeOiEGYTi1PDAk4AlRBLLV9EylVDmA1roFuAD4slLqY4Cd78KJ/NrW0hH7CYetsS7OmJOZWIQofNlqej8DXlRKfUFr/ajWeq9S6nzgIWBatosrpZYDP9Ban6WUOj56vQgQBC7TWu9XSt0MnA50Rk+7EPABdwGlOE2rl2ute4bw/kQKq5c7i6V++NePARCMWNz7qXeyZPqksSzWsLnJ5H39XUM425bcPiGKQMaantb6l8D7gG1x214HlgHfynSuUuqbwK+Akuimm4Evaa3PAu4BroxurwfO1VqfFf05BHwHuEtrfQbwEvC5Qb4vkYM33u7gjbc72NXaxZ72Yv9OYcgMLEIUgVxSFjzAJ5VSswELp+b1YLRfL5PtwEXAndHnl2it34p73T6llAksAn6plJoO3K61vgOn5nd99NgHo49/kuN7Emm4NbwynzPY49OnOM8LJVlzODU1D34ZuSlEEcgY9JRS/wJ8FrgbeD66+QjgV0qp/9Fa/zjduVrr3yul5sU9fyt6zdOALwJnAuU4TZ434QTXx5RSLwBVwKHoqZ1AdS5vZsuWLbkcJpLYwLatW5nc9VbWY8ez4YzcjBDkzdbtNDQ0jGCJCovcm8zk/mQ2Xu5PtpreV4Djk/vTlFI3AS8CaYNeKkqpfwKuAd6rtW5RSnmAm93rK6UeBY4DOoBKoDf6//Zcrl9XV0cgEBhMkQrXXQMnW75ls1OTcWt87vMSE/5wxdHUq5mjV748eOXpDcM63zbC1NfXj1BpCktDQ4Pcmwzk/mQ2mvcnGAxmrABlC3r9OINKkpVG9+VMKfVxnL65s7TWrdHNRwO/U0qdgNPKdjrwG2ATzkjRtcD5wFODeS0xOKECGbi56vQbWL95zaAHspiGB8tOvAnJywqNZTqDW5axTqkYL+WY6OQ+jq1sQe/7wEtKqY2A2/Z1BPAunBpbTqI1up8CzcA9SimAJ7TW31VK3Qk8ixNE12mtX1VKrQF+o5T6DHAA+Ngg3pMYpAI7EpgxAAAgAElEQVSJeUNe/85pFk0cyOLm/Pm9pbH19eRDSoiJz7DtzOl2SqmZwDnATJyE9D3A41rr3fkvXm4aGhrmAU3SvHmY52t3Zj8oanKJjye/eB5LjpjYKQtrn75qSOeV+CrwR6qonjQpFuyS+wdNw8OsGgWM3jd0N4i7A3Tig/JoBuCNjes41N5OaUVgTMuRDyNV68ql+W68/D7Hwhg1b86vr6/fmbw/29ybJwANwNta6xu01v8PWAw8q5Q6Ng/lFWNg1SkLWFRbNdbFGDOXLL+WuYEV7G3TWHYk5YAYy46wt02zt02Pyorq6zeviQXgtu79tHXvz3qOrPYuRHbZmjdvBC7VWj/ubtBaX6OUehJnxOU5eSybGCWLp1Xj9U78xIUSXwUw+OT0XGuIlh3BNDyDLtdQuTWBsBXE7y0ds9rA2Usvo6GhgXbvq7HnE11yrSv+y0K+3p97XenTG1vZgl5NfMBzaa0fUkr9ID9FEqPtmZ0teEyTDy+bQ0WFf6yLM2TukkBDbebMVUtnc94+uNyp0CKRfvqtELtbnVG4ff1daT+Yx+IDXIiJKlvQ8ymlTK11wliHaFL5xP10FAl+/cIOfv3CDsIftrjiHUePdXHGNcuOjMp0Zf1WiOTpbfe2aVnRfYRIrat4ZQt6TwDfjf7EuxZ4IS8lEmNmf6fMPZmJk9rg9PftaX0dGPlaVabaqhtwk19ntD7AJTCMDLmPYytb0PsW8IBS6p9xZmQxgBOBt5H19ArOgc5in38zMzfgWXYEI4+Tt2UajGLZEdZvXiNr/wkxRBmDnta6Uyl1JvBOnMVkLeAWrbUkixegLfvaCYetCT2oJRy2mFx+NK3dW/P6OnY0u7Gls3lEr7t+85qszad9/V3DXDtQuOSLQ/HJOuG01toGHo3+iAKRPBUZwKZdB9nW0jEu8vXCYYumtvSjMOfXVKQMzk1tXTzXfIB5kwxMw8bIsNRxqn2WlX5fpmuNlFC4N6c5RFPV+OQDXIjsclllQRSJYMRmZ3vXuAh6TW1dfGTtEwRSBLZg2GLDqpVpcwtnVDpLMw4nSNn24M4fqVlbZtUoWjqbc0q76OvvYndrI+s3r4n1BQohMpOgV2SSlxdyn4NT69Nvt7NwavpEdbeGNdSa2GAEvCZl/lR/ouGs55pG5pmGBitdEHSD00jl77lBc92ma4a1aoQQIjUJeiLBph0trH2uKWsNazg1seE6d2Ejr+3Zz6LaT43cRW2wbCdJIGJB8tvKVOszDQ+1lXNitT23n28otS83T28wAU9qeULkToJekUm3vJCro9fJDyv1eVN80CfWsIZTE8unt7ur8BgGc6oPDPpcAyfgRWzwRt9/5qZOEwjwdueb+DxTKAsM/Z/UxsZ1hMK9+L2llPgqYo9rK+fEktTTnQfSpydELiToiQTP7j5AOGJzzMzJlPpGb8qtXJw19xUAvGY/Yas3rzOPpApybvCzbCBaK9yy35n6bO6kILCf/fsqOfnIGvpCB2IDTVo6m7OmGLijNi07Ql9/VywlIpdEeHckp6wEIUR2EvREAo9pYllW9gPHsXnVBwd9TnIPoNu66S5CYtuA4dQEsSFsQW/Yg2E4/aNej02p16LCf4juYB+eaNRs6WxOCFypamVuDS9+xRM3JcIg8xcPAzNhtn4hRGYS9IpUcrOmy2Mag1sdOI+CYYv4ptIHti0B4N1HvYrXTD0B8/yaCjaNUJphfLOmHa3dYUBPv49r/rqETyetWhKxwDTAsvrxeAJYVoRQxAl4+w/tTOjvg8QkdL+3NOF4d2DMrBrF2Usvix2bvM6fu18IkRsJeiKBdzSS0XIwv6aCDatWptz32p79afoSwes1E6YLG4zBvvVfvzify09sYs6kbnr7PezqLGN2VRCfB0KRxNltQpEe9rZpbNvG5ykZMOAl9jxa7OTaW/JUYyOdFC9EsZCgJxJ4TRMLm77+MMkLDDs1r+TnAwetJB83pHJ4zbSjP5tbRv/P1jAON3VmcqCnjKMmW/RHut0zGdh4atPS2RxrRt7YuC62lp/LbRJNF9zcvkIhxOBI0BMxBjCtPMDkMj8/+sBJzJtcMeCY+TUVsf+nq4nFH5cP2Zrz8p3fFvCGufyEJjCg1BshFDaxbagtC+IxDU486uv8Y+d/ARDs78HGpsRXEQtUoXAvfrOUmvLpKa9vYKTtp5OmTCGGR4JeETNJ/AOwcFrXDMNk3uSKjHl2mWpiE1G6tITk7ZYFHsNmbk0PTW1lRGzY01FCT7+H+TU9RCybPe2ZJ+5OXh4ovulyd2sjNrZMKC1EnkjQK2Jrzj+e6ZWlzKgsweNxRn/MrirD6zXzWlObqOKbOMt9EY6q6aEyEKEz6GF/dyU+02RfVwWzq8owI05Q29P6OgZmQi3PawYIhXs51NsCHE41cJsrR3N1diGKjQS9Ivb7l5sJRfI7e8pocwd6ZOt/i6+9pTs0Ve3PDXxeEyoDh5tRfR4PB3ureWTHEj55mhmrpbkzrMSPwEzOvXNXYnenNLPsCPsPNcXy/NzzhRDDJ0GviJUHvBihsZ09ZaTlMtgk3XHJQS7baE4zur8qEKG6xMkN/MRxz/Lanv00t3g5e+llaZsx4fD0Yes3r0kYlGJg0h8JYljjYyStEIVEgp4oKHOnfZDmg86UXYNJQUjIx4s+Tnd6puuWeQ9nOYbDFt1B50vFtpaO2HZ3m7t2YfJyQkb0Bfze1LmIQoihk6BXxCb6zCv5kHIwS+w/Sc2idurHrd1b6ewr567NawiGezGAF579BQ+9sRSAcxcexLJslszuStms7I7cjK8lCiFGhgS9ItY3Avl0hSKugjdAqky75APiz7UsCFpOnp4nusNjGJT5vdH5Qw0e2rGUU+bczTNbtw9IschlLT0hxNBI0Ctig10otWAZOPkaSfciXb/fwI2J56a6p3OqD/CJYx4DoDfsB6Czb2/anEIZwSlEfkjQK2IdwRAl3uH9CbiLyYbDFns6BuanzZ5Uhtc0h7So7FAXqrVsY1CLyBrRn+Sg5wYvy3KWGjI4PHgF4FDQw9f/sphb3/8qXjP3LxAe0+LchU6/Y4mvYkDNzk1kF0KMPAl6Reyqdx3DiqOmDTknLxy2eGz7Pr50z2bC4TC72vuID0EWcGR1KRgGf7zinRxzxORBXX+oC9UOJuDFartp4rG732sk1oyt6EusXt7MlrcrqZvWmRD4MtWi/WaYaeUdgH9A+oK7pJC7HJEQYmTlNegppZYDP9Ban6WUWgisJboMGbBaa20ppb4LvBdnEsevaK2fS3dsPstajGZNKhtWft62gx18dv3feLOjNzYDZ/Ivadch50O9obl10EEP8r9QrTGYyqcb0KJPy30R5kdnZvEMYqV1cBa6rSntG7DdXVJIZmQRIj9GaBGWgZRS3wR+BZREN90EXKu1PgPn4+NCpdSJwEpgOXAJcEu6Y/NVzmL24u6DPKLf5BH9Jq/tbyc8yIEte9p7eLOzd4zXSU90ZGUZpglmtNaV9WcQ144NaLGhI+ihu99DU1sZt2yewz/2VZLr7TMMOKK8nf5I74BJvd2mTQl4QuRHPmt624GLgDujz+uBJ6KPHwTeA2jgYa21DTQrpbxKqdo0x96bx7IWpe88+He8JmAaHDmpnD9dcfagan5hy8oyrDHDuTn018VzV01/fNcxac+JzV2ZoUxDHbgTn7YQtuDrf1mcsP+WzXMo8xr8+PwtuK2x7msl/x+g1B+JNWXGD1qRgCdEfuUt6Gmtf6+Umhe3yYgGN4BOoBqoAuKXuXa3pzo2qy1btgyrzMUmDNgWYNkEu7vZsmULHVWBnM/f/EZrzrW8nTubaPAeij1v7ghy1VO78ZkDo1C/ZXPDGUcC0N3dTSRoEg47r9TZ2Qk46RbJ5d3d25hyFGa8+ICYNQDGr5oOsZpcd//AkZVe4IvLd2MaBkZcn2KmlzDxAVBuTKHbOki5OYVJvctoaGjIUrCxMV7LNV7I/clsvNyf0RzIEt/4Uwm0Ax3Rx8nbUx2bVV1dHYFA7h/aBe2uxpwOcwfMh0wfi5cuZcn0STm/xEuhrfDcvpyOnTdvPvX1C2PPq1o6mPT3tpT9dT2hMHV1dQBc1LUJj2EQ8DqB5P11O51y2zZ1decl1ExfeXqD02CfQ+0zp3QNI/qlIHpJN9g1tZUNONRrgsdj0hfxUm46s7Jku75tOIG8y97PrMnjewX0hoYG6uvrx7oY45bcn8xG8/4Eg8GMFaC89eml8JJS6qzo4/OBp4BNwLlKKVMpNQcwtdYH0hwr8mhfd4idrbknRYfDFi+/2ZrHEjksyyZi20Ss6I/t/FhWYmRz57McaW7gilhOsPv6XxZzy+bEUZVeYHJZKQtnXIya9QV83vIBeXarTr8hbe6dTDcmxOgZzZre14DblFJ+4DXgbq11RCn1FPAMTgBene7YUSxn0drfMXA0YTqvtbSz9rk3cjrWC8yoLMl6XLL5NRV88rTPO6+3x/kTWDL7wwn7880wnD630sAcjp8V4frzj2d/Zw//8+JOAh4TwzDwmgZ+r8kb+37P1rdgSW0En8dPKHI4HSFVUJbpxjJz75l8IRAjKa9BT2u9Ezg1+ngrzkjN5GOuA65L2pbyWJFf4UHMxdnQ3Ep3OHs74qyqUv79vOM566gZgy5P/EK1zS3On+pYLIFkEyHY34TX8HDSzJewPOfwi2e2EUzKU+gORZg7qZuIbYGd2Mze0tmccrox0/AkrLAQTz70RTrytzF0kpwuYhr3tcVm/h8JHzt2Nle/5wQW1VaN2DVT2di4jpbOZvyeMkKRzKuWD1dLZzNTJoFpGJjRts/LT2xiTnU3NlDmi9AfMQhHgvRFfNz5j1N591GvsmjKobTXlJpeouR1B+NryaP1IS9BpXBJ0BMxa194g2NnTmH5nKmxIDWU6cNcPq+XJUckDoxxUxV2tnZxqC9EX3/i+E+/x6Q/RYVzMB8+CcsEjQDj8ERl9PV3sbflp1xxUhXzarop9Tm1N4/h9Ps5+X82Js4yQ5847ln2d1Vik3qOzVRzb46HD30xPsnfxvBJ0BMxh/osvnD3syyaWoFhGAQjFj+7aDnzJif2nc2vqcipKTRVzHGnFvN7TEq8iQM7gmGLGy+sZ97kikH115299DLWbbpmQABxVzlPZTD5ejY2YGOQWN5SXwRv3HVSzb9Z5u1nbnVbyuuahifWrycOS15sdzQ/zCWoFD4JeiKBaUDIstnd1gk2fOmezQSifVduLWzDqpXsPdSd9VoeM3UN0Z1arDzpz68nFGbe5Ioh9dv5vaUDFmMdruTm0vhrHzOjY5CzuXgwjMPXKPFVxFZOTzaWH/pifJO/jeGToCcS9EVsmg50xZLOdx3oclYWMGHp9EmxyZ93t2XvO6sqSfzzCocttrd0cKgvRDA8MDhZljXoqdDcf/zVpbW0de8f0Kc3nKWTMvUPJl82fsYW96FhHE449XpKiFiHR8dKP974JEGl8EnQEwPE97KFwPkEj0BnsJ+A18PO1i4qfNnXe1tcmziRTlNbF6vveY6dbalriX4D9nT0DOgHzFVN+fSUoyRzkmkV2RS6Ql4MDALecGz+zO5+D+VuH59bybWhL+xlWtUsekJvDr5cRUwCjsgHCXoiZ7vbezANg3+95zkurZ+X9Xi/b+Cfl88THRaSYsHWFDOSAZm/dbvb1j59FZB6fTr3+qmeJ5Qjbtoxwzw8G0v8Me551SV+bDuIjUEoLnXDnYT6uBlO87BpgGkcXjkh3ftIRT70RTrytzF0EvREzkzTGaYf8JoEzMGv7B22LEJhyxkWkmKAySDSBNNKFfBSyXXezVTn2TZYttv0adLT72FHWxm3bp7DF5Y3M7+mB8uO1vZsKPNb9ITepDwgTZoThQSVwiVBr4iVew2qS0rY19U7YB284Zpc6uOkWYnr5+1q7WLvofT9ZMmTV+cyki7VqM14uaQtJM/DGT/qM1WN1DCcAO22iF7zyBKCETvWOlrui+BJWFnBdlId2rSM1hRijEnQK2KXnbKA1acvYfPOA9zw6Cv0R2x2tnUfXjcuiZViY6oKkw18/axlLJ6R2De3r6OPTJO4TCnzEYkOZsklNzActrBsG8tK3QSZ6yCWdMel2h6/TJAB2LbFz97nTG7bEfSwq72cUMTEa1qDKoMQYnRI0CtiPmDRlCq8pkn13/wEw1YsDTs+NrnP50wqp7rUj2XZTK0I4DeNlP1wlg3HzawZELiy5fa19vTz1T+8wH1XvItFtVVZR9I98MpaDvWZVPpHup46MIimDYxxfX9VgQiVJT46QhV4zB4CnnDCtwLbtgn297Bu0zXMqlGANKMJMdok6BWx37/czL+cuQxwEsNt28YXDWSWZROKRj43TaGqxEepz0NPKMwpc6dy3xXvSnvtM+ZOS3geDlvot9NPxeUK5Dj7Szhs0d6zhwp/eMRrU8npB27gsu3EQZ6xldfjXn9etbM8pGUPLJQdbUQeqZlihBCDJ0GviFmYhC2LRVOq2LBqJeGwxZ4Op89tb3sP//HIy/g8BgGvJ9qU5ySQB8MWJV4v71Yzc36tbQc7+J+GpozHzJ5Ujt+T2wCZpjZnGrOAB4YwpialnNbYy1FfxAs2VAYsbJxV0gO+MkLhXllKSIgxJEGviO3v6mNPew9Lpk+KzYLi5siFwxYrjpqW9tzBLuuzp72Hj9Ztw4YB69G5DCAUicTW9XPn/UwXIPZ3VdIVDHPsjM6MU45lE39erCYXX7uL2x+xnOnGEsodFyj93nLCkT7m1MxnyewPM7+mgrs2fxtwUhZaOpslMV2IMSRBr4hZQFNLB+EFMwb0v8Uv6zNUGxvXYdswd9oH2dXalXVB853tTtL6hb/cCMC7F8/gXDWLo2qrOGv+DEqSZnj5k17M1v0d/NeFryY0Rw65tmakGJiT1MHZ0++hwu+MFjWTXss0PMyongsM7Kuz7Ai7WxsxMNl/qEnmdBRijEjQK3L/cu/zvFPNzNs6dT2hML/5288JWxZl0dlKVi8/vH5cqlpfMPr/+1/fx/2v7wPgO++u48qzjkkIfO9XrzP1hMHNgZlKqiDpNnW6u5xpRE3U7C9wqPMBAPpCOxLOmVWjUgYwv7c05/xBIUR+SdArcvkYU/HXV9fREwoTtnrp648wo6IzulKBxd6Owa+gDvDvj2zhtPnTM/YjDrc/Lj7VIX55Io/pIeArpbZyDmcvmc3GRuc97G1zOhPd3LtUAW/95jWxPENwBrOEIr3sbm3ENDyxY2or50iNT4hRIEFPDJu7Rp77eE97N6+/fYgyXwTLtmnrdYLEvJoeevo9afv0Mkk1pvOhN5ZiWRarlz8znOLHGNF8DSNhm0nA56zgkLzCuZt2kM7GxnUZV36I397S2czGxnUS+ITIMwl6Ytia2rq4+I7HMAwIRiyaDlQQpiLajGnEglx8s+ZI6Lcg4B3hP+EBE09b9PV3UeKrGPTcmeAExv2HmghFetMe4zZ97m5tlFqfEHkmQU8MW9iyCEUi7D7UA/bA6cRGQnL6+fyaCjasWgnAJv1Uxnk7My2g4PbdZUtXGEogik+u393amNM57nRlEvyEyI/cMoGFyGBPew+72rrpC9sEI+l7CW/ZPCenps3Vy5uz1grd0aWLaqswDU/G6c0ydfXFTyuWzcbGdQmjLnMxmIAHzghQy47Q198Va/IUQowcqemJBPH9c6m4uXPJTBgwhdlg+u7cIDeU/r7LVnyfSVfdyU0XvArkZ75Ld7LofOTYGZix2Vri+/mC/T3sbdOyoKkQI0iCXpEzSEw0b2rr4iNrn0g5HVgwbLFh1cq8pTfMr+lh9fLmnFMb4sVX9EZyZhWXW/va0/r6oHLs3P3rN6+J9d0ZmBiGEQtwdpo1LmwsbNvp6yvxVchAFyFGgAS9Ije3hAE1t4DXpMyf6k9j5Hvr3MBW5ovgNW3qpjmzq/SFTbYdLAfAaxrccvHJA+bzjBc/fVm65YDGUm3lHPa2aSw7gmEYsZGf7rZMTMMjs7gIMUIk6BW5a9930ohcx7KHn/O3t6OE6RVByn0RwtbhUZ9HVpXwyMqZA2ZkibfqxJ1pB6yMdMAb7lRi7tybufbX+b2lA9IlhBBDI0GviE0r9/PKvg5+8NdXYtsilsWBriBTyqHM780pYMyuKmPulAp2t/dgAuGIFVuhIRs3sN143usA7O8KMKuqDzhcC7zn1aOzXmfupN5hz8ySi3RNkdkkL5OUK2NU3pUQxUOCXhF7uzvEzU9vTblvb0cPx82sSdPMmcjrNfGbJgunOH2DnT297OrsH7FyRnKYSbrfmgLsG/JrJC8R5HJHU8a7ZPm1Q36d5OA3q0bR0tk8YJoyd7aW4b6eECKRBD2RUr9lEwxblPmzHzu/poLff+qdhC2LXa1d3Prkq+zqfHtQr9fUVhZ7vO1gecLAlVs/dGzKVR02Nq6LNTWW+SNDXmkhU202VX/b2qeviiWr5zKwJN3oS7fstZVzEtIaTMPDZSu+z/rNa3J8B0KIXI1q0FNKrQJWRZ+WAMcDlwI3Aruj278LPAX8N3AczvzDV2it3xjNsgroC4fpCR0e5BIMp27ac3PmtrV08KW7n2VHe/rZR9JJN2uLCcyvrUqZJhFvcmmArmDGQwYMaEk3wKXE5wTYTFOIuXl0kD3NI/513AAYCvdiWZGEvjrT8KSdtFoIMTJGNehprdcCawGUUrcAdwD1wDe11r93j1NKXQSUaK3foZQ6FfgxcOFollXA5Scv5KLj5iZsy7aOXiTFiuHDMb2ihEjEYltLR2zba3vupszvpT/SS3VpbfR12wdMFJ0sOcClC3huDS6XxPKNjeuYU/tBPrL2CT6w2OmXfOiNpQCcu7ARy7I5YVY5hgH3vXQHnX17CUf6ME0ftm1h2DY+jxfT8AxYXFaaNYUYeWPSvKmUOglYprVerZR6EDhBKfUV4DngSuB04C8AWutno8eLURa2rLTJ6OmEIkMb6OFKzsc72NPHVX9+kYD3cB/Xu486wClzpuLLsGJ6uqbO+G3xQdId+lnmn4ltH14dIVWfHji5duA0UXYH7ybgnY4nerELFr0WOwoTguEIOw52EbJ6gBKmlYeACB7D4mBPCcfOnEFtpfQ0CDEaxupf2tXA96KPHwH+ADQBPwc+D1QBh+KOjyilvFrrjIliW7ZsyUNRi9d/PraFpb5u5lQFcjq+uSNIf7hvRMtg2xAJBYlEDlfL/vDKPOqrZhMOvADAtMBJHIq000dXwnmpxAfDgauiGzzTtJ9lU0uxzX6M2D+PxKBn4qfEqMIbcTo82zp3cfa8/XgN57jqEuce7O+s4N4tR/GV0/bQEzJ4WM+PXeP8xW8wvbKLnpBBpH0xk6L3uKGhYXA3aBSN57KNB3J/Mhsv92fUg55SahKgtNaPRTfdobVuj+77I3AxTsCrjDvNzBbwAOrq6ggEcvuALnh35T7fYzqlJQHq6upynoGlqqUD3+N7gZEJfCagpldTUxpIaIr0hMLU1dXR3OJ089YvraeeetY+fdWgrm8Dtm0QjPjY11HG03tP4J/rHididkVTE9JFzghTaqbFmiLve+kOvIcOMqW8O+GwGVU9XHTcLvyBXrzeUt5ftxOAx3cdg9frxTBMvF5vzvc4fuDOaPf7NTQ0UF9fP6qvOZHI/clsNO9PMBjMWAEaiwmnzwQ2AiilDOBlpdTs6L6zgQZgE3BB9JhTgVdSXEfkmdccuxyxSSVeFkytGBDwMnEHoOTKAPoiXg70VDG9souz5r6Cx7SxiZAp1d6yIwmTQS+Z/WEeemMpEcskYpm095XT3ldOqTfEvOqDKa/x+K5jONCT25eJjY3rWL95DbtbG2OrMAghhmYsmjcVsANAa20rpa4A7lFK9QKNwG047UnvVkr9Deez6fIxKKcAdrZ2pezXix+x2NPTz/16D00Hu2jtHplaXpXfR4nXkzHgxdd2Bpv07Ya0t7ureKr5GE6f9RJeL/SFvZT6LAYuZnSYaXhSLiqbLoj5fTOAQwS8Tu7iWXMPf4d76I2lfOLUzGVNHkxj2RHWPn2VjPYUYghGPehprX+U9Pxh4OEUh35+dEok0tnV3sPH7nyS2z56KsuOmBwLfuGwxV/0Hr6w4Tk82Bzs7aNnhKflHMrUYckJ3qm4fXgRC15rqeKZvcso9SUckfUaqVZMD4atWBK9W7tz30NfaAfzqp3Hb/dUZ72+a2Pjuoy1OsuOSK1PiEGSIWMio9befj71201UlARYvUIxvbKUsGVx1f0NtPXlY7lYRyjiJMf3hAa+RnK+4GBqee5AFk+Khv2pZR34PKlreCW+CkJhJ/8wuWZ1eEFbd1Hb69O+fk+/l4feWBJ7ni73EXKbjNqyI6zbdI3U+ITIkQQ9kdWhkMWhUC9XP/h3wPmjyVe4m1zi4+vvXMZ7lhxBielNmy4Rny84mMmY42t6v3phLgumhrFtm3u3HMVFx+1iTlUrYOD3lGCanljt8ZLl16adIcVNzgcnAJf4KrAsi1CkB3ACZsRyXvik2VP4xKlnpH0vrsEEcrePUQiRnQQ9MWhDCXgXLptJdcDP3kM9/G3n27FWRJ8BZYEAfq+J4TH574tO4ZyjZw4qN3CwbOC1lkpmTSrjRx84iXmTK9iyZQt1defx3Bs3E7H6ME0nCdDAg9dTwraWDsr8MwFiifLpchhD4V7suFwIt4YIUB7w5jwadlaNynnVdVl6SIjcSNATeecBvrBiCe9WM4e8MvtIcOPQP/ZVMrnsfP50xcLY63VUBVhUW8UzW/uw7AjB/h5sIByx6QyFuPPZX8RmWoGn0i6o687kEj+JdHwfYK5NkKkWn02nxFchTZtC5EiCnhhV8U2ByeIDYjhssaejJ2H/7ElleE0nIGYNjgYJY1JiCenAL56bwy0XVzC/poK7G6535sGMRHjl6Q0YRgCIJC4hZMO0sg7OXfAqD++oi21MN7LVDXx722eCKeEAABQTSURBVPSAqcUGK76WOJxjhBAOCXoi7yLA3vYeXtnbSsOeVgBmVJUQti1e3tNGW28IgHnVz7CrtYe7X11EKGyxv6uPkmhAsbBZOKWSEp93QC3Lbdrb3dqYdSYWA/jBua+zfd9e/ufZ9th+K/ofwwgmXMMG+i0nAHb09fPKm62Y0XWIvnTPZv50xdk5N1cOhtunl20gi3vM+s1rxiRpXYiJRoKeGBVX/N8zWZMBVi/vBJxUCVdP3OjG1/d34ImmAXzwV39lemUJk0oDHHtEHbMnV2DSCGlWTnCFo4VYNKU9YXvyRNVukHSfN7WVRecFtSnzmpiGQSDVENCowayMnspgUxEkaV2I3EjQE6MiU8BzlxMq80USnkPiBNRh4HOnNMe2v37AmfZrduWztHbB8TMyvL5bABuqApG0gTE5+LlxLXki7FwMpdY1mBpevMHORiNEsZKgJya8+TVxfX9ZktoHO0bGMKC6JMKt73+VLW8708H+umHe4C4yBAZmYr9iFrIMkRC5kaAnxlzyArKpalWZaoNNbWWJgS8DyzYwDTvtArKQtPxQ9P9eE+qmOc2vlmWlzm4fAW6zaK4Bz11lXQiRGwl6YsK7ZfMcVi9v5vgZnbF18dIJRnzc/doKLl7yNBX+/pTHxDdxusHRMJzAF7bgP9/XSDDi5TcvnTKk8mZL28i1b85deFYIkTsJemJCSFcbXL28mdXLm1k0pXtAmkI8d2BKqTfEJ455jJ7+wxNuGsQtFGscjpkDaoKG2zxqU+IJc+7C13CnHhuMprYuPrz28ZSx+YJFr3H01Nz686SGJ8TgSdAT48ZQBoskyDY81Dy8ltacyfNp7d6KaXiYOUmx5+BrYDoXSJv2EP2PYYCJzaIph2jY8VMaduTepxYOW+xs7SIYjtDc1oNhwGdO2gXAvEndVAUiWDZkW9Vp1ek35PR6QohEEvTEmMvUl5eL+TU9lHhz6AOznHw804QPnPCphF33/O2ndNn7o4dlr2kZGAR8g29abGrr4kv3bGZXaxchy3nvcyf10NRWRlUgEr12dmufvooSX4UMYBFikMZiEVkxzrlNhuPRLZvnDAiOVYEIHnNwyxGt23RNQh7d3MAKLlvx/ZTLBrlKfBUcOXkpJb4KAr5yLll+LbWVcwY972XA4+T5rV7ezPyaHqoDEY6f0RnrO4TEHMF0+vq7WLfpmkG9thDFTmp6Yszkmp+X7XzLdub3zL4S3mHp8uDOXnoZ6zeviU3tFX+cOwdmKNyLZUfY2Lgu5wmhk33m5F3MndSD1xxMqYUQwyVBT8QMNwgN1awqZ7X1bQfL8RjgMw2qfHDekpkEIxCOWLR2h+jtDxOMWFQEPFT4fRw3wwk4puk0CVo4TReZpiKLt7u1MbYCeYUxnXrqE/anCowtnc2x7fGjLN0al1tTbOloJhixElZbwJhGXzjCexZ2MrPSqeGlKlcujpy8VKYcE2IIJOgVgeH2meXCAKb4Pfzz8gVMrygjYlmEQhF+/4/tNLb2pTzHLc+N570ee75oSjlHVJdz9PQw/37ByRnntVy/uQE4XANz2+rdkZq5suwIHfabrH36Ko6cvJTayjlp0wbiVzyID4ru492tjRh4iODjUG8/5dEvEN39HmAPAMFwGU1tZU6KxRCYhmfMAp7bJCwBV0xUEvRETC5J4q6jJpWyZFo156lZlJX6mV5VwrxJFSyqrcLrNdmyp5V3/eIRDvaE0l7DfR23iW/18mYq/R78Xi8PbFuS9jyXO4hj7dNX5fYGczDU5sp4lh0BO0J14HAtbpLHCX42cPyMTiJDaNU0Dc+gpycTQiSSoFfA8tVcuWhqRcbVBcJhiz827s4Y8DKJ2DbBcPbRmMOZ0Dnf0s7tiRP4PObhUZrp8unjZ40p8VWM6SoKu4KbaG98NdbXGX/vpdYnJhIJemJQ/JlWMAhb/KP5ILdu3spdL+7Ieq34mmV1iZdFM97LWYtmUuL18olTnTXzMmnpTD/CdCj9ZCMloRvPGLgtbDlJ7nZcMr1tJAZBd/vk8qMHpFcIIYZOgl4BG0xzZarzUplZXc7PLlqeMiA1tXXx/t88xv6u4KDLGrZsbt/cxHlL5uS8Pl38OnrjXXxr5pa3KzGAhVN6KfeFYwfYRINk9GDThDL/+PgnOjewgvql9dKnJya88fEvSox7R1QEWHPBiSyfN5VFU6rSrlrurmw+WB7DJDDIJRBsG/Yd2jWk1xt4MXLLCs9BqoE0kRSttQYGpmni8zhJ7v0RZ6kkv7ccgOlVczlnmQQXIUaSBD2RUYXf4OYPnsryOf+/vXsPjqs87zj+3bMrS5Ys2ZIvhJttGduPE8R4jDwWUC5mgIGGZkgTOiVMZgCHUlL/kc6kbToZmOBOOm1nSDq5kCYNxpA2niaEtGGcBNzhkkAoskdQgjB5bYNsTTDFF2RLluSVds/2j3NWXtnatSxLezu/zwzD7jlHe149e6znvJfzvgvGBqkU8qeX7SGZ8s+qz3Db7lVcPLcBJjETSq4P+vczPDqUd7mgTCaoLRXiZ3Jy3SmJ6lyaSMcWoQ0/t/tgI63NQ2OL0XrAl649wIrafuJejIWNi8dqrB+Zu0Q1KZEZoqQXAecyaCU5kiGR8Pnpm/v5cPAEhwaTHE+OEvc8UqkUh4ZGaKmfxbzZtfQPJ1kyd/JrwGXNinucSKWDJXty5K5GkPJ9fp+zonrK90mm05xIxWmoSRVcJijfPt+feF/u6unnLPystkUDdB9sHPsuLphTy9O/W8VTG65nxcImntv1g4pYCFbJWCqdkp4UNArctbXzjMdt7Ohl3iyYfZYjRRPAgWPDvHdsiGUt4//o9/Qd54lXvovnxfjZ2yvZe2QAL6yXpdI+f76uho8tmvgZwFyFEl/uMUBQ7SuwWsNkHxvI9s+N1faA73cu5pKWerxYjH/+43UsX9A01jeqZCJSHEp6UlLZpXr8lM/AiVF+8+5B9n14snaX7WybFfdIxDw8L2eMY7jUT6GElq/WVhNvYDg9hDfJycsmWrtuYeNiDg30jk1Lduo5M7FgpObbh4KBOYmaOC0Ns8lkMixf0DTpATsiMn2U9GRaTHWk6AmfoJ0R2H9smI1PvYoXi/G5tfuYXRPHY4S0Dzcv38XVi9PEYrDltdZzKqsXi7Nu+Re4fcsL3LNmB/Wz0iTCpDiQjLOvr4EX9rXx5N3r6Xr3m4ykhrmw2SasjT236we81+fG1QCz/Yi+D73HmnlhX/Cg/SXzwfczjEw0qkVEiqLoSc/MXgP6w7c9wPeAbwApYLtzbpOZecB3gNVAErjXObe32GWVmVOfiOH7QbtjIh4Pa3VB/x4EIxvjsVjBethkpxobHE3QUl9L2j9xWtPk/qMNLG0eZHYizXAqzv6jDeP2T2bpnmxC/I/Or3Ji9DheLB78Dh7cddXnueuq03/mTM8gisjMKGrSM7M6IOacW5+z7X+BTwPvAj83szVAK1DnnLvSzK4AvgbcVsyyysyKxWLBQqnh/73sCq2hx7qW0jp/Dtcv7Qbg+Z5V7PtwcKx508/k7XYbJ+XDe/1zuPFj93Og72kODfSOWwro0a4l3Nu+n48u7B+3nM9kZoSB8X1xd3Q8cNosMWrCFCkvxa7prQbqzWx7eO6HgFrn3DsAZvYscCNwPvAMgHPuVTNbW+RyyhTN5KTWE53rX297K+jey9Ovl/BgaDTNT9/czxevvZO6uuCS33MoSHLLWubwfM+lbHtrmPr6oM8umfb51qfWTak2pgEpIuWt2ElvCHgYeBRYAfwSOJqzfwBYBjQBx3K2p80s4ZxLFfrw7u7u6S2tzJjhUT8copLBS/tjz8ulPIiHA0De7+vHX5IhA/Qc6mfIPzm48mye6Fv9kQHu+9lvaRr+kKsuCmpeKd9n07qFeX+mqf893njj/an/glWoq6ur1EUoa4pPYeUSn2Invd3AXudcBthtZseAlpz9jQRJsD58neWdKeEBtLW1UVtbO53lrVxby3tqrmzjYYagqRLGD4LJvv72KTXHU5s0+5PxsXXpTpV9ZCAWfnb9og7a21dOeGxXVxft7e0T7hPF50wUn8KKGZ9kMlmwAlTspLcBuAz4CzO7gCC5DZrZJQR9ejcDm4CLgE8APw779N4scjmlQvT01dO2aIBEvPAsYq3NQ4yObOe5Xa9OSxNk7oPzE59vzhlnr6nEc0fJVOOs76e8FTvpbQYeN7OXCW7aNxDc9P8QiBOM3uw0s53ATWb2CsHfsnuKXE4potwlkC5sOsHDt/xu3Bp7Wbk1wGzfYWtzOEtLnrkzY7FgV0NNmnjsMEHL+bnr6TvOnzz+qwnnC02mfJ68+7oZG8RSynNHyVTjrO+nvBU16TnnRoA7J9h1xSnH+cD9RSmUVLSevnqAwquQh22i6UyG61Z+dtrOXZvw8qyCcMaW+Io+d5RMNc76fsqX6thSco90LuaRzsUMjcbZc6SBv3pmFXuONLDnSMPYPgj75WrS1Nek2djRe1qtL99zexmCCZ/dodqCzU4iUv2U9GRG5SanYss+d5f2g8S49Y3lJSmHiJQPTUMmFeFM05wdS8ZpqElTE0yGMq7W132wkY0dvczW4AGRyFPSkxmROzgl9z3kf4A9d/vZPOSe/exk2qMmnjOTSmz84q0nJ6sWkahS0pOqcGiwET8zwsr5wWjOlA+Do/GxRVsBLl3UxIY/mL5zBlOVnT4wYbJTmFXquaNkqnHW91O+lPRkRkx11YXJfm5WHXB4cB1rWi/m9Z6nOJY8mehya5cJpmlR2FBr8xyevPu6gvtnSinPHSVTjbO+n/KmpCdlry4eI+HFePCWy7j1o4tJeN5pD/i+/cFR3O/jBR9QT07jkj6JhFeyZ61Kee4omWqc9f2UNyU9KWvnz6nlwnkNJFM+t126NO8fkxXzm7hiyUKGR5uZ13QDm5cAXAnA5iVwXlMdS1vm6C5bJOKU9GRGnWuz5mcub+W+qwwo3CyUSHh88vLPndO5RKT6KelJSTUkIJ2C2hqYP7uGVYvmsnzRPBKJBPNmz2JjhzFvbl2piykiVUJJr0oN/v1n2Nbdy5Ydjjc+6Ofg8ZGzWo4HYEENzG2oo/foibELZYRgstQ4wfI+sfB1PAazEjC/vo5ETYLRdIYv39DGeY11HB4c4bymOhLe+OfkLppXz4r5TZp8V0SKRkmvStXVJbh97TJuX7sMgM6dO2lZanmPn8qM8SnfB5+8SUuzyYtIuVHSi4iEp5FoIiK6DRcRkchQ0hMRkchQ0hMRkchQ0hMRkchQ0hMRkchQ0hMRkciolkcW4gAjIyOlLkdZSyaTpS5CWVN8ClN8ClN8CitWfHLyQHyi/bFM7hLTFaqrq+tq4KVSl0NERMrGNe3t7S+furFaano7gWuA9+GsZ9sSEZHqEQfOJ8gLp6mKmp6IiMhkaCCLiIhEhpKeiIhEhpKeiIhEhpKeiIhERrWM3pQJmJkHfAdYDSSBe51ze0tbqtIxs9eA/vBtD/A94BtACtjunNsUxZiZWQfwT8659Wa2HHgcyADdwEbnnG9mXwFuJYjVXzrnduQ7thS/w0w6JT5rgG3AnnD3vzjnfhTV+JhZDfAYsBSoBb4K7KKMryHV9KrbJ4E659yVwN8CXytxeUrGzOqAmHNuffjfPcB3gTuBq4GO8A9apGJmZn8DPArUhZu+DjzgnLsGiAG3mdnlwHVAB3AH8Ei+Y4tZ9mKYID7twNdzrqMfRTk+wGeBI+HveAvwbcr8GlLSq25XA88AOOdeBdaWtjgltRqoN7PtZva8mV0L1Drn3nHOZYBngRuJXszeAT6V874d+FX4+pecjMl251zGOdcLJMxsYZ5jq81E8bnVzH5tZpvNrJFox+dJ4MHwdYygFlfW15CSXnVrAo7lvE+bWVSbtIeAh4GbgfuBLeG2rAFgLhGLmXPuKWA0Z1MsvAmA/DHJbp/o2KoyQXx2AH/tnLsWeBf4CtGOz3Hn3ECY/H8CPECZX0NKetWtH2jMee8551KlKkyJ7Qb+PbzT3E3wD7AlZ38jcBTFLLc/JV9MstsnOrba/adzriv7GlhDxONjZhcDLwD/5pzbSplfQ0p61e03wMcBzOwK4M3SFqekNhD2z5nZBUA9MGhml5hZjKAG+BKK2etmtj58/YecjMnNZuaZ2WKCG4HDeY6tds+a2brw9Q1AFxGOj5mdB2wHvuSceyzcXNbXUNU22wgQ3IneZGavELS331Pi8pTSZuBxM3uZYKTYBoK7zB8SzNW33TnXaWY7iXbMvgh838xmAW8DP3HOpc3sJeB/CG6UN+Y7thQFLrLPA98ys1Hg/4D7nHP9EY7Pl4Fm4EEzy/btfQH4ZrleQ5p7U0REIkPNmyIiEhlKeiIiEhlKeiIiEhlKeiIiEhlKeiIiEhlKeiIiEhlKeiIVwszWm9mLZvaomeWdE9TMlprZvjz7tpjZkpkqo0i508PpIhXGOXfvOfz49cCm6SqLSKXRw+kiFSKcrumh8O1DzrkXzewfgNuBw8D7wNPAi0AnwXyIbUAfwZJJfwb8HbAXuMY5d6SIxRcpC2reFKlQZvYJgiVbLiWYL3RNzu6FBOu+tQEfAHc45/4ROAB8XAlPokrNmyKV6ybgx865EWDEzP4rZ98B59yO8PVbwIKil06kDKmmJ1K50uT/N5y7HFKGYPJskchT0hOpXP8NfNrMZplZE/BHBAmukBRq4ZEIU9ITqVDOuV8AvwZeB35O0F83fIYf2wb8wsxaZ7h4ImVJozdFKpSZXQmsdM49YWY1BGuVbXDO/bbERRMpW0p6IhXKzFqArcD5BK02TzjnHi5tqUTKm5KeiIhEhvr0REQkMpT0REQkMpT0REQkMpT0REQkMpT0REQkMv4f/F8q8lkeATQAAAAASUVORK5CYII=\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualizer = ScatterVisualizer(x=\"light\", y=\"C02\", alpha=0.5, classes=classes, size=(500,350))\n", + "\n", + "visualizer.fit(X, y)\n", + "visualizer.transform(X)\n", + "visualizer.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/peter/.pyenv/versions/3.7.0/lib/python3.7/site-packages/ipykernel_launcher.py:206: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n" + ] + }, + { + "data": { + "image/png": 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LL7+cYcOGMWjQIK699lreeecdqqurGTNmDAMGDODpp5/m9ttvp1u3bmzevJnevXtn/L1QghCRnFXbcti4aUO9Zdj21sQDDzxAt27dmDBhAhs2bGD48OGUlJQwadIk1q5dy7Jly1i6dCnXXHMNEydOpKysjAcffJClS5emfc3333+fu+++m86dOzNy5Mh68y49+OCDdO3alfHjx/PZZ59x2mmn8eijj3L99dczbdo0dtppp6y1NpQgRGI2MexaktywdOlSDj30UAA6depEWVkZs2bNoqysjGQyyY9//GMA1qxZQ1lZGUDdulSp0xj16dOHnXbaCYD9999/i9lZk8lk3XhEZWUlq1evpkuXLnTt2hWgbhbYTNMgtYjkrMP2PpnD9j6ZDiWd6FDSqW55e8YiysrKmDdvHgAbNmxgyZIl7L777qxYsQIIBpf//ve/s8suu2yxrqSkpG521sWLF9e95tKlS9m4cSNVVVUsWLBgi9lZTzjhBKZMmcLkyZM5/vjj2XnnnVm3bh2ffvopQN0ssJmmFoRITKprggHphiqrt1wnrcdPfvITrr76akaMGEFFRQXnnXceZWVljB07li+++ILevXszatQoevTowdixYyksLKR79+6MGjWKkpISrrvuOnbbbTd22WWXutds164dF154IWvWrOH444+nT58+ddtOPfVUrrrqKk477TQ2bNjAyJEjKSkp4ZprruGMM86gS5cuFBdn56c61tlczWwAcIO7DzGzA4EngLfCzbe7+wNmdi1wAlAJjHH3V81sL+BeoAYoB85197R/VZrNNbe0hXpWVlZz36yX2Pff/i1ye79du+bVdRDZ/kwzdRbTttRz5cqVXHzxxfz1r3+NKaptl7XZXM3sUuB04ItwVQKY6O43p5Q5CDgSGADsATwMHAxMBK5y9+fM7A7gJOCRuGIVaWnFxYXs+40OJPb4RrZDaRNy6fTWXBJbC8LMfgQsAKa4+0Azux0wgqT0FjAGGA10dPfrw+e8BhwLvA7s7u41ZnYScKy7n5tuX7UtiFgqIiKS/zLbgnD3h82sV8qqV4G73D1pZlcC1wJrgU9SyqwHugAF7l7TYF2T1MWUG9pCPdXFlJ/yrZ4pXUyRMjny8Yi7r619DNwK/A3onFKmM0HSqI5YJ5Iz3lj1Gec+s4KSF97fYltldTXP/PJYdT9Jq5fJQ5inzeyQ8PF3gSQwBzjOzArN7FtAobuvAV4zsyFh2aHA7AzGKdIiCgugXVHhFv+KC/On5SD5LZMtiF8Ct5rZZuBD4Gx3X2dms4GXCZJV7TjDr4DJZlYCvAk8FPWCIiISn1gThLuvAAaGj+cDh0WUGQeMa7BuCcHZTSIikiVq64qISCQlCJGYXXxoed2U3yK5RFNtiMSkdqqNGoIztmun3dBUG5IrlCBEYtBv1678/oTllJSUUFUd/Jn9/nsr6m0Xae2UIERiUFxcSElRAe2Li9hUVQBASVFRve0irZ0ShEhMrP1QEokEf5ozFoBTB1yd5YhEto4OY0REJJJaECIx8a9m8M68ZyGcEHPavAl124b3vyRbYYk0mxKESMxK23XMdggi20QJQiQmtWMQtS0HtRok12gMQkREIilBiIhIJHUxicRMXUuSq9SCEBGRSEoQIiISSQlCREQiKUGIiEgkJQgREYmkBCEiIpGUIEREJJIShIiIRIr1QjkzGwDc4O5DzOwA4FagCqgAfuruH5nZJOBwYH34tJOAdsBUoAPwATDa3b+MM1YREakvthaEmV0K3AW0D1dNAs539yHANOCycH0COM7dh4T/PgeuAaa6+xHAa8Av4opTRESixdmCWAoMB6aEy6e6+6qU/X5lZoXA3sCdZtYDuNvd7yFoUYwPy84IH9/S1A7Ly8tbMPzsSCaT2Q4hI1TP/NNW6tpW6gkxJgh3f9jMeqUsrwIws0OB84DBwA4E3U4TgSLgWTObB+wIfB4+dT3QpTn77Nu3L6WlpS1VhYxLJpMkEolshxE71TP/tJW65ls9KyoqGj2wzuggtZmdAtwBnODuq4EvgUnu/qW7rwdmAf2AdUDn8GmdgbWZjFOkJU2bN6He3eREckXGEoSZnUbQchji7svC1fsAc8ysyMzaEXQtzQfmAMPCMkOB2ZmKU0REAhmZ7tvMioDfA+8C08wM4Hl3v9bMpgCvAJuBP7v7IjP7LfAnMzsLWAOMzEScIi2p9p7Um6o2AronteSeWBOEu68ABoaL3dKUuQm4qcG6j4Dj44xNREQapxsGicRE96SWXKcrqUVEJJIShIiIRFIXk0jM1LUkuUotCBERiaQEISIikZQgREQkkhKEiIhEUoIQEZFIShAiIhJJCUJERCIpQYiISCQlCBERiaQEISIikZQgREQkkhKEiIhEUoIQEZFIShAiIhJJCUIkZtPmTah3P2qRXKH7QYjEbH3Fp9kOQWSbKEGIxMS/msE7856FmhqAeq0I3URIckGsCcLMBgA3uPsQM9sLuBeoAcqBc9292syuBU4AKoEx7v5qurJxxirS0jbVfMHmii+pIUgQaklIroltDMLMLgXuAtqHqyYCV7n7EUABcJKZHQQcCQwATgX+kK5sXHGKxKWkYAc6l3ajIPyvc2m3un8iuSDOFsRSYDgwJVxOAM+Hj2cAxwIOzHT3GuBdMys2s+5pyj7S1A7Ly8tbLvosSSaT2Q4hI9pCPa39UAAW8hAA3y74Tt22fKx/PtYpSlupJ8SYINz9YTPrlbKqIEwEAOuBLsCOwCcpZWrXR5VtUt++fSktLd2uuLMpmUySSCSyHUbs2lo9y+c8DJDXdW5rn2m+qKioaPTAOpOD1KljCJ2BtcC68HHD9VFlRXKSupQkV2XyOojXzGxI+HgoMBuYAxxnZoVm9i2g0N3XpCkrkpOG979EZy1JTspkC+JXwGQzKwHeBB5y9yozmw28TJCszk1XNoNxiogIMScId18BDAwfLyE4Y6lhmXHAuAbrIsuKiEjmaKoNERGJpAQhIiKRlCBERCSSEoSIiERqVoIws64R677d8uGIiEhr0ehZTGa2B8FcSNPNbGj4uPZ504E+8YYnIiLZ0tRprtcB3wF2A15IWV8JPBFXUCIikn2NJgh3/zmAmV3m7jdkJiQREWkNmnuh3GQz+yWwM193M+Huv4klKhERybrmJohHgI+BRUBNE2VF2rzKymoWf7IR3vskcnu/XbtSXKyTCKV1a26C6ObumvpCpJneWPUZ5z6zgpIX3ufSIxYBcOPs/QCorK7mmV8eS2KPb2QzRJEmNfcQZqGZ5c8k6CIZUFgA7YoK6+4o166okHZFhRQXquUguaGp01yXE3QpdQROMbP3Cc5gKgBq3L13/CGK5KaxQ5ZQUFDAzh0rALj40ODGLME9qo/OYmQizdNUF9OQTAQhIiKtT1Onub4DYGY/bbCpBthoZp3dPfdvBC0Sg+47bKq3XNuSEMkVzR2kPgk4EHg0XP4e8D7QycymuvstcQQnIiLZ09zRsm8CB7n7xe5+MdA/fO4gYFRMsYmISBY1N0F0B9anLG8kOPW1El0XIbKFfrt2pQAoKIj+12/XLea/FGl1mtvF9DAwy8z+SpBUfgQ8Go5NrIorOJFcVVxcmDLnQJrtIq1cs76l7n4FcBOwD7AncIO7Xw0sAUbGF56IiGRLU9dBHOTu881sMLCOoCVRu22wu7+Q/tkibVdlZXXQ+ZqmFVFZWa1WhLR6TXUxnQOcTTDtd9RYw1FbszMzG8XXg9rtgQOAEcAE4L1w/bXAbOA2oB9QAZzp7m9vzb5Esim58pPgDybNCF1y5ScM6NU9kyGJbLWmEsTeZjYrfNzwWGirB6fd/V7gXgAz+wNwD5AALnX31NbJcKC9uw8ys4HAzQSn2orkhAUfftboH9eCDz9TgpBWr6kEMS6OnZpZf2A/dz/XzGYAB5rZGOBV4DLgcOApAHd/JSwvkjPmrljNoJ6Nbz9r4D6ZC0hkGzR1JfXzMe13LEG3FcDfCS7AWw7cQdCttSPweUr5KjMrDk+rTau8PPcv6k4mk9kOISPyvZ5LVq5qNEEsWbkq796DfKtPOm2lntD801xbjJntBJi7Pxuuusfd14bb/kZwCu3nQOeUpxU2lRwA+vbtS2lpaUuHnDHJZJJEIv8nzW0L9Sx46ePGt5fukFfvQVv4TCH/6llRUdHogXU2TqMYDDwDYGYFwAIz2z3c9l0gCcwBhoVlBgILsxCnyDbbUNH48UxT20Vag4y3IAADlgG4e42ZnQlMM7ONwGJgMlAFHGNmLxEMjo/OQpwi22xz5ebt2i7SGmQ8Qbj7TQ2WZwIzI4qek5mIRFreR+u+3K7tIq2BrtQRicGX1Y3Ms9GM7SKtgRKESAzaFzaeAJraLtIaKEGIxKCysZn6mrFdpDVQghCJQXETLYSmtou0BkoQIjHYUNH4WUpNbRdpDZQgRGJQWb1920VaAyUIkRg09YelPzzJBfqeisSgqeukdR215AIlCJEYqAUh+UDfU5EYqAUh+UAJQkREIilBiIhIJCUIERGJpAQhIiKRlCBERCSSEoSIiERSghARkUhKECIiEkkJQkREIilBiIhIJCUIERGJVJzpHZrZfGBduLgc+CMwiWB6mpnufp2ZFQK3Af2ACuBMd38707GKiLRlGU0QZtYeKHD3ISnrXgd+BCwDnjSzA4E9gfbuPsjMBgI3AydlMlYRkbYu0y2IfkBHM5sZ7nscUOruSwHM7GngaGBX4CkAd3/FzPpnOE4RkTYv0wniS2ACcBewNzADWJuyfT3QG9gR+DxlfZWZFbt7o7Mkl5eXt2y0WZBMJrMdQka0lXo2Jt/eg3yrTzptpZ6Q+QSxBHjb3WuAJWb2OdAtZXtngoTRMXxcq7Cp5ADQt29fSktLWzLejEomkyQSiWyHEbs2Uc+pi5sskk/vQZv4TMm/elZUVDR6YJ3ps5h+TjCegJntRpAIvjCzMjMrAI4DZgNzgGFhuYHAwgzHKSLS5mW6BXE3cK+ZvQjUECSMauAvQBHBWUz/NLO5wDFm9hJQAIzOcJwiIm1eRhOEu28CRkZsGtigXDVwTkaCEhGRSLpQTkREIilBiIhIJCUIERGJpAQhIiKRlCBERCSSEoSIiERSghARkUhKECIiEkkJQkREImX8hkEibUVVdfD/osL6yyK5QglCJCZFhY0vi7R2ShAiMVELQnKdEoRITNZ8WQJAj06b6i2L5AolCJGY7NxxU6PLIq2dEoRITDQGIblOCUIkJhqDkFynYxoREYmkFoRITNTFJLlOX1kREYmkBCEiIpHUxSQSEw1SS67LaIIws3bAPUAvoBT4LfAe8ATwVljsdnd/wMyuBU4AKoEx7v5qJmMV2V6/eGw/AO76waJ6yyK5ItMtiNOAT9z9dDPrBrwO/AaY6O431xYys4OAI4EBwB7Aw8DBGY5VRKRNy3SCeBB4KHxcQNA6SABmZicRtCLGAIcDM929BnjXzIrNrLu7r85wvCIibVZBTU1NxndqZp2Bx4DJBF1NC9w9aWZXAl2BtQQtjdvD8i8AP3f3t6NeL5lM9gKWZyJ2keY4ZOpi/vPooNe0Q7sqADZuLqrbfuU/9ubVkftmJTaRCHsmEokVDVdmfJDazPYAHgFuc/epZraTu68NNz8C3Ar8Deic8rTOBEmjUX379qW0tLSlQ86YZDJJIpHIdhixaxP1nLq47uGOpVsmCCCv3oM28ZmSf/WsqKigvLw87fZMD1L3AGYC57n7M+Hqp83s/HAQ+rtAEpgD3GhmE4DdgUJ3X5PJWEW215X/2Bv4epC6dlkkV2S6BTGWoAvpajO7Olx3MXCLmW0GPgTOdvd1ZjYbeJngWo1zMxynyHarTQxRy2c+qjOapPXLaIJw9wuBCyM2HRZRdhwwLuaQREQkDV0oJxKT2lZCbctBrQbJNUoQIjGpPYspalnjEZILNBeTiIhEUgtCJCa1rYTaloNaDZJr1IIQEZFIShAiIhJJXUwiMVPXkuQqtSBERCSSEoSIiERSghARkUhKECIiEkkJQiRm/3n0W1tcVS2SC5QgREQkkk5zFYlJwzvKaS4myTVqQYiISCS1IERiormYJNepBSEiIpHUghCJWY9Om7Idgsg2UYIQiVlVdbYjENkjj1woAAAHsElEQVQ2ShAiMfnjicGtRgsL6y8D/OIx3X5UWj+NQYiISKRW24Iws0LgNqAfUAGc6e5vZzcqkearbSXc9YNF9ZZFckVrbkH8AGjv7oOAy4GbsxyPiEib0mpbEMDhwFMA7v6KmfXPcjwiW6Xh/Eu6klpyTWtOEDsCn6csV5lZsbtXpntCeXl5/FHFLJlMZjuEjGgr9QRYV1EUuT7f3oN8q086baWe0LoTxDqgc8pyYWPJAaBv376UlpbGG1WMkskkiUQi22HErk3Uc+riJq+kzqf3oE18puRfPSsqKho9sG7NYxBzgGEAZjYQWJjdcERE2pbW3IJ4BDjGzF4CCoDRWY5HRKRNabUJwt2rgXOyHYfItqi6+fQtuiMuH5rFgES2QWvuYhIRkSxSghARkUhKECIiEkkJQkREIrXaQeqtVASwaVPuz7tfUVGR7RAyQvXMP22lrvlUz5TfzMirOQtqamoyF01Mksnk4cDsbMchIpKjjkgkEi82XJkvLYi5wBHAKqAqy7GIiOSKImBXgt/QLeRFC0JERFqeBqlFRCSSEoSIiERSghARkUhKECIiEkkJQkREIuXLaa45w8x+CPzY3UeGywOBSUAlMNPdrzOzQuA2oB9QAZzp7m9Hlc1KJZrJzAqAlUDtvTZfdvcrzOz7wDUE9bjH3SebWQfgf4FdgPXAz9x9dTbi3l7pPr/sRrX9zGw+wY28AJYDf6SZ391sxLu1zGwAcIO7DzGzvYB7gRqgHDjX3avN7FrgBII6j3H3V9OVzUYdWppaEBlkZpOA31H/fb8DGElwD+4BZnYg8AOgvbsPAi4Hbm6kbGtWBsx39yHhvyvMrB1wC3AscCRwtpn1AH4JLHT3I4A/A1dlLertl+7zy1lm1h4oSPksR7N1391WzcwuBe4C2oerJgJXhd/HAuAkMzuI4Ds7ADgV+EO6spmMPU5KEJn1EsEPIQBmtiNQ6u5L3b0GeBo4muAP7ikAd38F6N9I2dYsAfQ0s2fNbLqZGfBvwNvu/pm7bwJeBAaTUmdgBq2/bo3Z4vPLbjgtoh/Q0cxmmtksMxtMM7+7WYt46ywFhqcsJ4Dnw8e138fDCVpKNe7+LlBsZt3TlM0L6mKKgZmdAVzUYPVod3/AzIakrNuRr5vsEHSt9A7Xf56yvqqRsq1CmjqfC/zO3R80s8MJupAuon7d1gNdqF/n2nW5aovPz8yKm7qneiv3JTCB4Ch7b4IfwrUp29N+d3Oh7u7+sJn1SllVECY+qP8d/SSlTO36qLJ5QQkiBu5+N3B3M4quAzqnLHcm+KPr2GB9YSNlW4WoOptZR4K+Wtz9RTPbjeAPKKoeqfVrVXXbBg0/q8LW/gPZDEsIWn41wBIz+xzolrI97Xc3R+ueOoYQ9R1NXR9VNi+oiymL3H0dsMnMysIB3eMIJh2cAwyDukHshY2Ubc2uBcYAmFk/4D1gMbC3mXUzsxKC7qWXSakzMJTWX7fGbPH5ZTecFvFzwvGEMNF3BL5oznc3O+Fut9dSWvu138c5wHFmVmhm3yJIfmvSlM0LakFk3znAXwgmzZrp7v80s7nAMWb2EsGg1+h0ZbMR8Fa4HvhfM6s962OUu282s4sJ+qwLCc5iet/Mbgf+ZGYvApsIBj9z1SNEf3657G7g3vDzqSFIGNU0/7uba34FTA4PYt4EHnL3KjObTXBAU0jQhRpZNhsBx0GT9YmISCR1MYmISCQlCBERiaQEISIikZQgREQkkhKEiIhEUoKQNsXMupjZo9mOIx0z+354GrBI1uk6CGlrugIHZDuIRiSyHYBILV0HIW2KmT0GHA88SXBB2xiClnSSYJrmr8zsQ+Bx4AhgFcH01RcAuxNc7Pe8mT1HcFHUAIIZQMe4+8xwZto/AnsQXEh2hbv/w8zGAQOBbwH/DSwC/pPgiuSuwKXhullhqFcA3wZw93Fh7CuAIeG/nwE7h3FOitpnC71l0oapi0namguADwimEz8LONTdDwA+Bi4Jy/QAnnD3PuHyD8OpnMcRTh0SKnX3gwiu+v5TeCXtJIKrwxPAicAfzax2/p727r6vu98GnE9wr4SDgDOAa9x9McEU2ne4+/80UY/dgQPdfWwT+xTZZupikrbqOwSzkr4SzEJOCTA/ZfuM8P/vEExJXvu4a0qZyQDu/rqZrQL2J5jquY+Z/SYs047gvhgAqVOjnAZ8z8x+TNCy6LSV8c9PmQQv3T5f38rXFKlHCULaqiLgr+5+AYCZdSLl7yG8V0WtdLORpq4vDJeLgKPc/dPwdXcDPiK4kc7GlPKzgWeB54BngKkRr19D/VZ+u5THqa+Vbp8i20VdTNLWVBIkgueAH5rZLuFspLdTv/uoOU4FMLP+BC2LhQRjCP8/XL8vsIBgnKGOmXUD9iHoVppOcHe9ogbxAawB9g2fcwiwa5o4mtynyLZQgpC25iPgXeC/gOsIflwXEfwtXL+Vr9U7vE/zncAp7l5FMLYw0MwWAA8Ap7v7+tQnhUf6dwGLzOw1gvtwdzSzHYAXgH83s/OB+4FvmNni8HVfSxNHk/sU2RY6i0lkG4RnMY1z9+eyHIpIbNSCEBGRSGpBiIhIJLUgREQkkhKEiIhEUoIQEZFIShAiIhJJCUJERCL9HyTrCfIuTmC5AAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualizer = ScatterVisualizer(\n", + " x=\"temperature\", y=\"light\", alpha=0.7, classes=classes\n", + ")\n", + "\n", + "visualizer.fit(X, y)\n", + "visualizer.transform(X)\n", + "visualizer.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/rebeccabilbro/balanced_binning_docs.ipynb b/examples/rebeccabilbro/balanced_binning_docs.ipynb new file mode 100644 index 000000000..8d6824759 --- /dev/null +++ b/examples/rebeccabilbro/balanced_binning_docs.ipynb @@ -0,0 +1,286 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys \n", + "\n", + "# Modify the path \n", + "sys.path.append(\"..\")\n", + "\n", + "import pandas as pd\n", + "import yellowbrick as yb\n", + "import matplotlib.pyplot as plt\n", + " \n", + "from sklearn.datasets import load_diabetes" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from yellowbrick.base import Visualizer\n", + "from yellowbrick.exceptions import YellowbrickValueError\n", + "\n", + "\n", + "##########################################################################\n", + "## TargetVisualizer Base Class\n", + "##########################################################################\n", + "\n", + "class TargetVisualizer(Visualizer):\n", + " \"\"\"\n", + " The base class for target visualizers, generic enough to support any\n", + " computation on a single vector, y. This Visualizer is based on the\n", + " LabelEncoder in sklearn.preprocessing, which only accepts a target y.\n", + " \"\"\"\n", + "\n", + " def fit(self, y):\n", + " \"\"\"\n", + " Fit the visualizer to the target y. Note that this visualizer breaks\n", + " the standard estimator interface, and therefore cannot be used inside\n", + " of pipelines, but must be used separately; similar to how the\n", + " LabelEncoder is used.\n", + " \"\"\"\n", + " raise NotImplementedError(\n", + " \"target visualizers must implement a fit method\"\n", + " )\n", + "\n", + "\n", + "##########################################################################\n", + "## Balanced Binning Reference\n", + "##########################################################################\n", + "\n", + "class BalancedBinningReference(TargetVisualizer):\n", + " \"\"\"\n", + " BalancedBinningReference generates a histogram with vertical lines\n", + " showing the recommended value point to bin your data so they can be evenly\n", + " distributed in each bin.\n", + "\n", + " Parameters\n", + " ----------\n", + " ax : matplotlib Axes, default: None\n", + " This is inherited from FeatureVisualizer and is defined within\n", + " ``BalancedBinningReference``.\n", + "\n", + " target : string, default: \"Frequency\"\n", + " The name of the ``y`` variable\n", + "\n", + " bins : number of bins to generate the histogram, default: 4\n", + "\n", + " kwargs : dict\n", + " Keyword arguments that are passed to the base class and may influence\n", + " the visualization as defined in other Visualizers.\n", + " \n", + " Attributes\n", + " ----------\n", + " bin_edges : binning reference values\n", + "\n", + " Examples\n", + " --------\n", + " >>> visualizer = BalancedBinningReference()\n", + " >>> visualizer.fit(y)\n", + " >>> visualizer.poof()\n", + "\n", + "\n", + " Notes\n", + " -----\n", + " These parameters can be influenced later on in the visualization\n", + " process, but can and should be set as early as possible.\n", + " \"\"\"\n", + "\n", + " def __init__(self, ax=None, target=None, bins=4, **kwargs):\n", + "\n", + " super(BalancedBinningReference, self).__init__(ax, **kwargs)\n", + "\n", + " self.target = target\n", + " self.bins = bins\n", + "\n", + " def draw(self, y, **kwargs):\n", + " \"\"\"\n", + " Draws a histogram with the reference value for binning as vertical\n", + " lines.\n", + "\n", + " Parameters\n", + " ----------\n", + " y : an array of one dimension or a pandas Series\n", + " \"\"\"\n", + "\n", + " # draw the histogram\n", + " hist, bin_edges = np.histogram(y, bins=self.bins)\n", + " self.bin_edges_ = bin_edges\n", + " self.ax.hist(y, bins=self.bins, color=kwargs.pop(\"color\", \"#6897bb\"), **kwargs)\n", + "\n", + " # add vetical line with binning reference values\n", + " plt.vlines(bin_edges,0,max(hist),colors=kwargs.pop(\"colors\", \"r\"))\n", + "\n", + " def fit(self, y, **kwargs):\n", + " \"\"\"\n", + " Sets up y for the histogram and checks to\n", + " ensure that ``y`` is of the correct data type.\n", + " Fit calls draw.\n", + "\n", + " Parameters\n", + " ----------\n", + " y : an array of one dimension or a pandas Series\n", + "\n", + " kwargs : dict\n", + " keyword arguments passed to scikit-learn API.\n", + "\n", + " \"\"\"\n", + "\n", + " #throw an error if y has more than 1 column\n", + " if y.ndim > 1:\n", + " raise YellowbrickValueError(\"y needs to be an array or Series with one dimension\") \n", + "\n", + " # Handle the target name if it is None.\n", + " if self.target is None:\n", + " self.target = 'Frequency'\n", + "\n", + " self.draw(y)\n", + " return self\n", + "\n", + "\n", + " def poof(self, **kwargs):\n", + " \"\"\"\n", + " Creates the labels for the feature and target variables.\n", + " \"\"\"\n", + "\n", + " self.ax.set_xlabel(self.target)\n", + " self.finalize(**kwargs)\n", + "\n", + " def finalize(self, **kwargs):\n", + " \"\"\"\n", + " Finalize executes any subclass-specific axes finalization steps.\n", + " The user calls poof and poof calls finalize.\n", + "\n", + " Parameters\n", + " ----------\n", + " kwargs: generic keyword arguments.\n", + "\n", + " \"\"\"\n", + "\n", + " for tk in self.ax.get_xticklabels():\n", + " tk.set_visible(True)\n", + " \n", + " for tk in self.ax.get_yticklabels():\n", + " tk.set_visible(True)\n", + " \n", + " \n", + "##########################################################################\n", + "## Quick Method\n", + "##########################################################################\n", + " \n", + "def balanced_binning_reference(y, ax=None, target='Frequency', bins=4, **kwargs):\n", + " \n", + " \"\"\"\n", + " BalancedBinningReference generates a histogram with vertical lines\n", + " showing the recommended value point to bin your data so they can be evenly\n", + " distributed in each bin.\n", + "\n", + " Parameters\n", + " ----------\n", + " y : an array of one dimension or a pandas Series\n", + " \n", + " ax : matplotlib Axes, default: None\n", + " This is inherited from FeatureVisualizer and is defined within\n", + " ``BalancedBinningReference``.\n", + "\n", + " target : string, default: \"Frequency\"\n", + " The name of the ``y`` variable\n", + "\n", + " bins : number of bins to generate the histogram, default: 4\n", + "\n", + " kwargs : dict\n", + " Keyword arguments that are passed to the base class and may influence\n", + " the visualization as defined in other Visualizers.\n", + "\n", + " \"\"\"\n", + "\n", + " # Initialize the visualizer\n", + " visualizer = BalancedBinningReference(ax=ax, bins=bins, target=target, **kwargs)\n", + " \n", + " # Fit and poof the visualizer\n", + " visualizer.fit(y)\n", + " visualizer.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def balanced_binning_reference():\n", + " # Load a regression data set\n", + " data = load_diabetes()\n", + "\n", + " # Extract the target variable\n", + " y = data['target']\n", + "\n", + " # Instantiate and fit the visualizer\n", + " visualizer = BalancedBinningReference()\n", + " visualizer.fit(y)\n", + " return visualizer.poof()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "balanced_binning_reference()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/rebeccabilbro/conf_matrix_class_names.ipynb b/examples/rebeccabilbro/conf_matrix_class_names.ipynb new file mode 100644 index 000000000..536c86cdb --- /dev/null +++ b/examples/rebeccabilbro/conf_matrix_class_names.ipynb @@ -0,0 +1,161 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to pass class names to ConfusionMatrix\n", + "\n", + "This is a follow up to issue [#244](https://github.com/DistrictDataLabs/yellowbrick/issues/244) and PR [#253](https://github.com/DistrictDataLabs/yellowbrick/pull/253), to document how to pass class names to `ConfusionMatrix`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.datasets import load_iris\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import train_test_split as tts\n", + "\n", + "from yellowbrick.classifier import ConfusionMatrix" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "iris = load_iris()\n", + "\n", + "X = iris.data\n", + "y = iris.target\n", + "\n", + "X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['setosa', 'versicolor', 'virginica'], dtype='\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mcm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mConfusionMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m 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], + "source": [ + "model = LogisticRegression()\n", + "\n", + "cm = ConfusionMatrix(model, classes=classes)\n", + "cm.fit(X_train, y_train)\n", + "cm.score(X_test, y_test)\n", + "cm.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ":(\n", + "\n", + "Workaround:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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3alVmZtYtVbIjwXHAzsAWEdEEHB0RP+z0yszMrNtZ5PE2kgQcAfSVVAf0kLRGRGzT6dWZmVm3Ukn32vXAv0k7D0wGVgSmdGZRZmbWPVUSOvURcQpwFzCJdGaCzTq1KjMz65YqCZ33JfUCGoHh2Z5sS3duWWZm1h1Vcg61q0h7q+0HPCppJ+CfnVqVmZl1S5XsvXYBsEdEvAuMAn5H6mIzMzPrkDZbOpJ+3OJx+cP1gdM7qSYzM+um2uteq8utCjMzqwlthk5EnAYgqUdELMzuD8y62czMzDqsve61FYA/AxeRjtUBuETSQGC3iJiRQ33WCUqlEo2NjcydO5e6ujok0bu3LwZbDXos1ZNdrzib5Qevxrz35nDHkacz4+XXiy7LKrRwYROHnjSOxldnUFcHF5+2I+utNbDosnLV3o4E55OOzbmxbNg3gPuAX3VmUc0k7STpsA6+5lRJ3+6smrqDadOm0dTUxLBhwxg8eDBTp04tuiSr0LBD92T+nPe5bIu9uPPoMxl9wclFl2QdcPvf0v/aw9ftxxnHbs1J5z1UcEX5a2+bzvoRsX/5gIgoAadlJwDtdBFxVx7zqTWzZs2if//+APTr14/Zs2cXXJFVauA6X+TlO8cDML3xVQYOHVJwRdYRu22/JruMSsvs9Tff43PL9Sq4ovxVcpxOaxa2N1LSn4HzI+JBSZsApwFvA2uSWlcnRcQDWXg1AvOB3wC/AD4C3ie1qvYA1o6IEySdRNpVuwG4OCJ+K+n7wN7AAmB8RBzfoo5fAFtlD6/Jrn46Flgh+/tKRMxczM+gai1YsICGho8XfV1dHU1NTdTXV3KssBXp7ckvsNYu2/LiLfcyaLMNWXbQStTV11Nqaiq6NKtQQ0M9Bx7/V26+5yVu/PWuRZeTu/Z+ZV6TtHPLgdnBoYvameBS4IDs/hhSN9207CShuwIXZuP6AmdExN6kQLkBGAlcDCxfNs+NgdGk0+9sCqwlaX1gT2DL7G9NSbuUvWYXYA1gc1Lw7Ju9BuD+iNiyFgMHoKGhgYULP15vKJVKDpwq8dTlf2Lee3MY89A1DN19B96a+JwDpwqN/dlXiHGHcNjJ45j7/vyiy8lVey2dHwL3SxoH/J20C/UI0mUORi9iuuOAcyX1B7YmhdtWkprP2dYgaUB2P7Lbs4AfkbYZ/TObZzMBj2d70S0Evi/pm8BjEfERgKSHgHXLXjMUeCjrEvxI0mPAOi3mWZP69evHtGnTWHHFFZk1axZ9+/YtuiSr0KAR6/PKfY8y7ntns8rw9ej3hVWLLsk64MpbnuMf78zmfw/fnN7L9KS+ro76+to6OqXN1duICGAT4B+kq4aOBl4DNoqIye1NNLvuzo2kFsstwAvAtRExKpvOjUDz3m/Nq2n7A2MjYlvgOaB8B4IXgWGS6iX1lHQPqVtuM0kN2SUXtsmGNXuBrGtNUk9Sa+ilFvOsSQMGDKC+vp5JkyYxdepUhgzxdoFqMf2l19n82AM46JHr2PaMYxj3vZ8WXZJ1wNd3XJPJz7/DyP2uYaeDb+S8E7djmaV7Fl1WrtrdphMRbwE/bu857bgceIW0Hect4FJJDwLLARdFRFOLsxw8Dvxe0lxSKBxG6mojIiZLuguYQArKiyPiaUk3lA17mBRwG2av+YukUZIeJV319IaImNRinjWpeTdpqz4fTJ/JlTuMKboMW0x9ei/F9efX3naccnWlUqnoGrqkiRMnrg68ut5669GrV+3tYVLt+vfvzzEza+v4h+7klFJQajx+0U+0LmdeU2+em7sLwBrDhw9/reV4bz02M7PcVLTLtKQ+wBDgWaB3RMzt1KrMzKxbWmRLR9KXgaeBW4GVSbtS79jZhZmZWfdTSffaWaS9wP6d7VgwEji3U6syM7NuqZLQqY+It5sfRMTznViPmZl1Y5Vs0/lHdnR/SdLngCOB/+vcsszMrDuqpKVzOLAfsBrpuJuN+OSBm2ZmZhVZZEsnIv4F7JNDLWZm1s0tMnQkvQp86gjSiBjcKRWZmVm3Vck2nVFl93sCuwM+RN/MzDqsku61ltfCPVfSk8CZnVOSmZl1V5V0r21T9rCOdPmAZTqtIjMz67Yq6V47rex+CZjGxxdoMzMzq1gloXNDRFzc6ZWYmVm3V8lxOkd2ehVmZlYTKmnpvCHpftLloz9oHhgRp3daVWZm1i1VEjqPld2vrYt5m5nZEtVm6Eg6ICL+EBGntfUcMzOzjmhvm84xuVVhZmY1wZerNjOz3LS3TWddSa+0MrwOKPnca2Zm1lHthc7LwM55FWJmZt1fe6Ezv5XzrpmZmS229rbpTMitCjMzqwlthk5EHJVnIWZm1v157zUzM8uNQ8fMzHLj0DEzs9w4dMzMLDcOHTMzy41Dx8zMcuPQMTOz3FRyPR2zqnT+8u8WXYItplOAurV+VnQZthjq5s2DKVPaHO/QsW5pxowZRZdgn0H//v05ZubAosuwxbDMKgP48u2/bnO8u9fMzCw3Dh0zM8uNQ8fMzHLj0DEzs9w4dMzMLDcOHTMzy41Dx8zMcuPQMTOz3Dh0zMwsNw4dMzPLjUPHzMxy49AxM7PcOHTMzCw3Dh0zM8uNQ8fMzHLj0DEzs9w4dMzMLDcOHTMzy41Dx8zMcuPQMTOz3Dh0zMwsNw4dMzPLjUPHzMxy49AxM7PcOHTMzCw3Dh0zM8uNQ8fMzHLj0DEzs9w4dMzMLDcOHTMzy01D0QVY/kqlEo2NjcydO5e6ujok0bt376LLsgp42VW3Hkv1ZNcrzmb5wasx77053HHk6cx4+fWiy8pVoS0dSTtJOqzC564s6aJ2xm8k6cdLrrrua9q0aTQ1NTFs2DAGDx7M1KlTiy7JKuRlV92GHbon8+e8z2Vb7MWdR5/J6AtOLrqk3BXa0omIuzrw3LeBI9oZPxmYvCTq6u5mzZpF//79AejXrx+zZ88uuCKrlJdddRu4zhd5+c7xAExvfJWBQ4cUXFH+cg0dSX8Gzo+IByVtAtwHXAxcAtwOTAfuAB4ALgRmA/8CPgROBa6LiM0lPQM8CGwAlIBdgY2Bb0fE3pIOBr4D9ABui4hTJB0FfB3oA0wDdo+I+bm88S5mwYIFNDR8vOjr6upoamqivt6b+Lo6L7vq9vbkF1hrl2158ZZ7GbTZhiw7aCXq6uspNTUVXVpu8v6mXgockN0fA/yobNzKwI4RcQ4phA6MiO2A1voPlgOujYiRwD+B0c0jJK0InABsDQwDeklaDlgB2D4iNiOF7Ygl+caqSUNDAwsXLvzP41Kp5B+tKuFlV92euvxPzHtvDmMeuoahu+/AWxOfq6nAgfy718YB50rqTwqFSWXjXi1reawaEc9l9x8C9m5lWk9lt28AS5cNHwxMiYgPsscnAEiaD1wraQ7weaDnZ30z1apfv35MmzaNFVdckVmzZtG3b9+iS7IKedlVt0Ej1ueV+x5l3PfOZpXh69HvC6sWXVLucl1Fiogm4EZSl9otwMKy0eVx/4akdbL7m7cxuVIbw6cCa0vqBSDpJkkjgd0iYi/gaNL7rlu8d1H9BgwYQH19PZMmTWLq1KkMGVJ7/crVysuuuk1/6XU2P/YADnrkOrY94xjGfe+nRZeUuyJ2JLgceAVYExjVxnOOAC7PWiXzSV1oFYmIdyX9DHhQUom0regJYK6kCdnT3gJqbxUj07yrrVUfL7vq9sH0mVy5w5iiyyhU7qETEW/wcdfW2LJR5S2aTYGvZgFyJjA/Il5rfk5ErF42vRPKXvdANmxsi2kDbPdZazczs8+mqx4c+g5wd9bSmcXHOx+YmVkV65KhExE3ATcVXYeZmS1Z3tfSzMxy49AxM7PcOHTMzCw3Dh0zM8uNQ8fMzHLj0DEzs9w4dMzMLDcOHTMzy41Dx8zMcuPQMTOz3Dh0zMwsNw4dMzPLjUPHzMxy49AxM7PcOHTMzCw3Dh0zM8uNQ8fMzHLj0DEzs9w4dMzMLDcOHTMzy41Dx8zMcuPQMTOz3Dh0zMwsNw4dMzPLjUPHzMxy49AxM7PcOHTMzCw3Dh0zM8uNQ8fMzHLTUHQBXVgPgPnz5xddh1nNWWmllbhupaKrsMUxYEA9X053e7Q23qHTtlUAGhsbi67DrOZcddVVRZdgn90qwNSWAx06bXsC2Bp4C1hYcC1mZtWiBylwnmhtZF2pVMq3HDMzq1nekcDMzHLj0DEzs9w4dMzMLDcOHTMzy41Dx8zMcuPQMasSkvoVXYPZZ+XjdGqQpFWAnkAdsGpEPFpwSVaZvwJbFV2ELR5JDcAIPvm/d22xVeXPoVNjJF0GbAH0AXqTjhjevNCirFIzJB0DBNAEEBF3F1uSdcDNpMAZRDqA8k2g5kLH3Wu1Z0NgXWAcMBT4sNhyrAOmAxsBewH7AHsXW4510ICI2An4OzAcWLrgegrh0Kk90yOiBPSJiGlFF2OVi4gxwC+AO4HzI+Kggkuyjnk/u+0TER8ANXk6GJ8Gp8ZIOguYAawErAasERGbFVuVVULS0cC+pDXlLYEbIuLnxVZllZJ0JNAf+AjYDZgTEdsXW1X+HDo1SNKywAfAaODxiHin4JKsApIeBbaOiAWSegKPRMSIouuyyknqGxFzJG0DPJG1eGqKu9dqjKQNgPVIfcrHZvetOtRFxAKAiPiItMZsVULSKcCJ2cOjge8WWE5hHDq15xJgHnAS8CPglGLLsQ54WNJNko6RdBMwoeiCrEO+FhEnAkTEN4GvFVxPIRw6tedD4DlgqYh4DF8rqGpExHHAFaTdbi+PiB8UXJJ1TJOkpQCy7tGa/P31cTq1pwT8EbhD0p64i6bLk3RYi0HvAZ+XdFhE/K6ImmyxXAJMkfQssDZwTsH1FMKhU3v2AjaNiDskjcLHelSDVVo8LpGOaLcqEhGXSboNGAxMrdVDFrz3Wo2RtBxwMrAO0AicEREziq3KKiXpK6SDeyMibi26Hls0SSdFxJmSrqXFsTkRsW9BZRXGLZ3acznwIHA1MBIYS41u0Kw2ks4G1gQeBg6QtHW2nce6ttuz20sKraKLcOjUnhUi4jfZ/cmSvlFoNdYR20TElwAknQ88VnA9VoGIeDq7O4l0bFxNnv6mWU3uPVHjlpG0MoCklUgnHrTq0FNS8/9sHTV6GpUqdiupV2Fo9rd2seUUwy2d2nMy8IikWcBywKEF12OVux6YIOkxYLPssVWP+ojYv+giiuaWTu2ZGRGDgR0iYgg+TqdqRMQvSCsJE4DDI+K8gkuyjnlG0maSeklaqvmYnVrjvddqhKStSXus/Q/wy2xwPXBURPhUOFVA0qHAWhHxA0l3A1dGxJVF12WVkfQ0qXehWSlbAawp7l6rHTOBlYFefHzcRxPww8Iqso76DrBpdv8rwHjAoVMlImLDomvoChw6NSIippCOhr4UmAOsTjpAbW6hhVlHLCw/4ackd1NUAUkXRMRR2VnCWx6ns2VBZRXGoVN7tiCd7LMBuEFSKSLOLLgmq8ytkh4CHgeGAbcVXI9V5ozs1mf/wNt0ao6kCcB2wF3Z7ZMRMbzYqqxSkjYCBLxYdvyHVQFJl7cY9BHwBnBhRMwsoKRCeO+12rMwIuaRNmKWAHevdXGSDsluzwb2BDYE9squAmvVYxngTdKu7q8Dg0jbWP9QZFF5c/da7XlY0jWksxRfAjxRdEG2SG9kty8WWoV9VgMjYp/s/jhJd0fEyZLGF1pVztzSqT0XAY+Q9nraEbiq2HJsUSJiXHb3JuA+YBzw/4AHiqrJFstyktYGyG6XlbQC0LfYsvLl0Kk9V5Mu4rYh6dK5v2z/6daF3ETageAc0vYAX0unuhwJXC3pLVKX2tGkS438pNCqcubutdrTRDq+40cRcV12wKFVh96kMxYfGxHfkrR90QVZh4xsZaedJwuppEBu6dSenqQ15fGStgVq8lQcVWop4BhgoqR1gD4F12Mds7Okmj/Brls6tWcMsANwGbArcECx5VgHfB/YjdQdsz8pgKx6DATelPQq6SDRUi0eHOrjdMyqhKRravFKk92FpC+0HBYRrxdRS5Hc0jGrHr0kbUC6zHgTQETML7YkWxRJh0TE74HDWxl9Yt71FM2hY1Y91iJdCKxZCai5sxRXoebjrI4ghcwHZCsNtcjda2ZVJju2Y0bJkcWnAAAEx0lEQVR2RgmrEpI2AQ4EtgZuBi6PiP8rtKgCOHTMqoSkbUgH9/YAbgRej4jLiq3KOkrS8sDFwO4R0avoevLmXabNqseZwDbA28BZpO4aqxKStpZ0GfAg6QDtIQWXVAiHjln1KEXEjOz2Q2B20QVZhxxLaqFuGBFnRMQ/ii6oCO5eM6sSkn4PvEu6aug1wNCI8HFWVlXc0jGrHisD/wAeJl391acwsqrj0DGrHqcAQ4EvAQOAlYotx6zj3L1mVmVqfe8nq24+ONSsSkjamnScxwjSBunjCi3IbDE4dMyqx7HApcAhPjDUqpW718zMLDfekcDMzHLj0DEzs9x4m45ZOyStTrqUwPOkszovBbwJjFncI8olHQiMiogDJd1B2kbzZhvPPQ24NyIe6sD0SxFR18pwAecCa2SDngW+GxHTJJ0KEBGnduS9mHWUWzpmi/ZmRGwUERtHxLqk69r/ZklMOCJ2bitwMiNJJ/j8TCStCvwNuDQi1gc2AKaQznZslhu3dMw6bjzwNQBJrwF/BzYinbJ+J9JeZvXARODIiPhQ0n8DJwHvAa+TzijQ/PpRpJN4XghsBXwEnAH0AjYBfi9pd9J1WC4GVgDeB46OiKey1thVQF/gsTZq/g5wd0TcDhARJUk/A16V9InfAUlHAf8N9CFd92WviHhB0s9JlzpfCNwaEadJ+jJwDqkVOBPYJyKmdeTDtNrilo5ZB0jqCewFTCgbfGdECBhIOjXNlhGxEfAv4LislXEO6QzRWwDLtjLpo0mhMRTYHvgxcB2pVXVIRDwL/AH4YUQMAw7LxgNcAIzN5jmh5YQzG5PC8T8iYmFEXBsRC8re33LAbqTuv/WAW4Ajskstj46IDYEtgTUlLU0K0m9HxCbA7cCwtj89M7d0zCqxqqTJ2f1ewOPACWXjm3/MtwXWBB5Lm09YCphE+pF+JCLeAZB0FfDlFvMYCfwuIppIrZ51s+eS3fYlHRR6RfMwoG92QbdRwD7ZsKuB1q6x0wR8ajtPSxHxnqR9gb0lrUVquU0G/gl8IGkC8BfgpKwFdxtws6RbSK2fexY1D6ttDh2zRXsza0W05YPstgdwQ0R8F/4TFA2kgCnvVVjAp31U/kDSF4Hyq0r2AD4sr0PS54EZpK6t5umXaP1SyE+SuurK51EP3ETqemsethrwAKn1dCcpADeOiAWSNiOF487Ao5JGRsR5km4HdgHOkXRTRPyklfmbAe5eM1uSHgB2l7SipDrS9pdjSWeF3lzSoOyHfq9WXjse2FNSnaQVSRf66kUKqIaImAW8JGl/AEk7ZK8BuBfYP7v/9ex1Lf0O+IqknbPX1wEnAys2t8AyI4CXI+I8UgtuNNBD0sZZTeMj4jjS3nyS9Hdg2Yj4FXAe7l6zRXDomC0hEfE0cBpwP+nKkPXAT7Mf9aNJ4fA4aWeCli4C5gJPZ887OiJmA3cBl0jaEtgPOETSM8DZpA38JeAoYI9s+M60cnG3iHibFCDfl/RsVt+apO035e4G6iU9T9op4TVgjYh4CngUmCJpUjb8TuBEYKykiaTtTKd05DOz2uPT4JiZWW7c0jEzs9w4dMzMLDcOHTMzy41Dx8zMcuPQMTOz3Dh0zMwsNw4dMzPLjUPHzMxy8/8BX9rDXhh2mIMAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cm = ConfusionMatrix(\n", + " model, classes=classes, \n", + " label_encoder={0: 'setosa', 1: 'versicolor', 2: 'virginica'}\n", + ")\n", + "cm.fit(X_train, y_train)\n", + "cm.score(X_test, y_test)\n", + "cm.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/rebeccabilbro/correcting_percents_confusion_matrix.ipynb b/examples/rebeccabilbro/correcting_percents_confusion_matrix.ipynb new file mode 100644 index 000000000..9671ba0ea --- /dev/null +++ b/examples/rebeccabilbro/correcting_percents_confusion_matrix.ipynb @@ -0,0 +1,542 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys \n", + "\n", + "# Modify the path \n", + "sys.path.append(\"..\")\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import yellowbrick as yb\n", + "import matplotlib.pyplot as plt \n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import AdaBoostClassifier\n", + "\n", + "from yellowbrick.classifier import ConfusionMatrix" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Before\n", + "\n", + "Currently, the percents in rows of the `ConfusionMatrix` don't always add up to 100!" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "occupancy = pd.read_csv('data/occupancy/occupancy.csv')\n", + "features = [\n", + " \"temperature\", \"relative humidity\", \"light\", \"C02\", \"humidity\"\n", + "]\n", + "classes = [\"unoccupied\", \"occupied\"]\n", + "oX = occupancy[features]\n", + "oy = occupancy['occupancy']\n", + "\n", + "# Create the train and test data\n", + "oX_train, oX_test, oy_train, oy_test = train_test_split(oX, oy, test_size=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = AdaBoostClassifier()\n", + "cm = ConfusionMatrix(model, percent=True)\n", + "cm.fit(oX_train, oy_train)\n", + "cm.score(oX_test, oy_test)\n", + "cm.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Load multi-class classification dataset\n", + "game = pd.read_csv('data/game/game.csv')\n", + "\n", + "classes = [\"win\", \"loss\", \"draw\"]\n", + "\n", + "# Encode the non-numeric columns\n", + "game.replace({'loss':-1, 'draw':0, 'win':1, 'x':2, 'o':3, 'b':4}, inplace=True)\n", + "\n", + "# Extract the numpy arrays from the data frame\n", + "gX = game.iloc[:, game.columns != 'outcome']\n", + "gy = game['outcome']\n", + "\n", + "# Create the train and test data\n", + "gX_train, gX_test, gy_train, gy_test = train_test_split(gX, gy, test_size=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = AdaBoostClassifier()\n", + "cm = ConfusionMatrix(model, percent=True)\n", + "cm.fit(gX_train, gy_train)\n", + "cm.score(gX_test, gy_test)\n", + "cm.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(11)\n", + "rX = pd.DataFrame(np.random.rand(500,5))\n", + "ry = np.random.binomial(1, .6, size=500)\n", + "\n", + "rX_train, rX_test, ry_train, ry_test = train_test_split(rX, ry, test_size = 0.2, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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fABzt7veY2e5Af+DtjFcmIiIbrDxXy/wdGGZmfwQmAr2ABzJdmIiIbLjyDMucAJwPnA6McPcjgP0zWpWIiFRIecI9y91XAMcCL5lZEmiY2bJERKQiyhPuE8xsJlCHaFjmbWBcRqsSEZEKKc8J1SuBTkAbdy8ELnX3qzNemYiIbLB1Xq9uZgZcBDQyswSQZWa7uHv7jFcnIiIbpDzDMk8AvxKdRP0I2AaYmcmiRESkYsoT7kl3vwl4BfiQ6JOqB2S0KhERqZDyhPsyM6sLzAZy4ytn6mW2LBERqYjyfEfMCKKrY7oBk82sI/BtRqsSEZEKKc/VMvcCnd39J6AD8CDR0IyIiFRTpfbczezGYrfTb/4J6JehmkREpILKGpZJbLQqRESkUpUa7u7eF8DMsty9IP5/63h4RkREqrFSx9zNbEszexs4Ja15qJlNNLPGmS9NREQ2VFknVO8hurb9qbS2U4AJwN2ZLEpERCqmrDH3P7n7GekN7p4C+sZfJCYiItVUeT7EVJKCSq1CREQqVVnhPs/MOhVvjD/EpJOqIiLVWFnDMlcDb5jZq8BUoksjWxF9/e/RmSimeYf+zJ8/PxOrFtlgv/xyNlkvfFrVZYisJX/FilKnlXUppJtZS6An0a8wFQLTgObunpEEnv7cadRNLsvEqkU2WOPGjXl64dZVXYbIWi5ummTEiBElTivzu2Xc/XvgxrLmERGR6mdDT6iKiEg1pnAXEQlQeb7yFzNrCOwGzAAauPvSjFYlIiIVss6eu5kdBnwMvABsR3SJ5JGZLkxERDZceYZlBgLtgF/jE6wHA3dmtCoREamQ8v6G6g9FN9x9VgbrERGRSlCeMff/mtmxQMrMNgcuBr7ObFkiIlIR5em59yD6/dSdgDlAc+CCTBYlIiIVs86eu7v/CHTdCLWIiEglWWe4m9lcIFW83d13zUhFIiJSYeUZc++Q9n8OcBJQNyPViIhIpSjPsMxXxZruNLNpwIDMlCQiIhVVnmGZ9mk3E0AzoH7GKhIRkQorz7BM37T/U8AC4OzMlCMiIpWhPOH+pLvfn/FKRESk0pTnOveLM16FiIhUqvL03L8xszeIfmpveVGju/fLWFUiIlIh5Qn3KWn/JzJViIiIVJ5Sw93Mznb3R929b2nziIhI9VTWmPvlG60KERGpVPqZPRGRAJU15t7MzOaU0J4AUvpuGRGR6quscP8S6LSxChERkcpTVrivLOF7ZUREpAYoa8x90karQkREKlWp4e7ul2zMQkREpPLoahkRkQAp3EVEAqRwFxEJkMJdRCRACncRkQAp3EVEAqRwFxEJkMJdRCRACncRkQAp3EVEAqRwFxEJkMJdRCRACncRkQAp3EVEAqRwFxEJkMJdRCRACncRkQAp3EVEAqRwFxEJkMJdRCRACncRkQAp3EVEAqRwr2FWrSrgrKvG0/70URxwymOMnfDF6mmjxs2i7akjVt/uceOrtPnL4zz2/EwAFv22gjOvHL/Ra5baIZGdzd4j7yJ30mhaTBxJA9uVBnvtRot3RtHi3dHsNfxWEllZANjQvuROfoLtzjwBgKxNG7H343dWZfnByWi4m9kBZvZWJrdR24wYO4vGm9dn4qjTefmhLlza/3UAps+az8NPf0IqlQLg54XL+XHBUiaNOYPhz8wA4NYHpnDNBQdUWe0Sti07HUwiO5u8A7syt9997HpLL3Yb2Js51w/iw3Zdo3mOO4TsxptTZ9utyGt7Gtv/tTMATa7rwVe3PViV5QcnY+FuZlcDDwH1MrWN2qhLR6P/5e0ASKUgOyvJzwuX02fQRAZff9jq+erVzSK/oJCVqwqoVyebOd/8ytLlq9hnz62rqnQJ3LLZc0lkZ0EiQfamjUitymdG50v59Z1pJHJyqLPd1hQsWkLh7ytIZGeRrJND4e8rqbfLjiQb1mfpp1+seyNSbtkZXPd/gJOBxzO4jVqnUcM6APy2ZAVdLnuefpe3o3ufl/nHdYdSv+7/Hs6GDepw7CG7c/bVL3LjJW255f7JXNejNZcNeJ2sZIIBvQ6iYYM6VbUbEqCCJcuo3+QPtP78ZXK22oKPj70QCgup98cdaP76cPIXLeG3jz+ncNlyFox7k70eu4O5fe+lSZ+efHXrA+xxTx9SBYXMueFuCpctr+rdqfEy1nN392eAVZlaf232zfeLOfSsMZxxQjP2aNKYL75ayEU3v0bX3mOZ9eXP9LplAgA9TmvOE/ecQCoFu+60ORMmf0X7ljtxYIsdGTX+syreCwnNTlecw8+vvssU68j7+53A3o/eRrJuHX7/+jum7HkU3w4dzR6DrgXguwef4NNTe0EiwfI537DFYW34deI0Fk36kO1OP7aK9yQMOqFaw8xfsJSj/vokt13Vgb+esi9/3nd7Zr54Hm8+3pXRg45n79235O4+h62xzOBHPqD3uS1Z9vsqsrISJBKwZNnKKtoDCVX+wsUULPoNgFW/LCKRk82+44ZSf/edASj4bSkUFq6xzB97n8M3g4aT1aAeqYICSKXIatRgo9ceokwOy0gGDBw6hYWLVzBgyHsMGPIeAC8NO4X69XJKnH/Mi59x7CG7U79eDl06NuW0XmNJJhOMHnzcxixbaoFvBj9C04cH0mLiSBJ1cphz/WCWz/uWvR65jdTKVRQsW87n3W9YPf82p3Ziwbg3Kfx9BT8+9QrNnrgbCguZedoVVbgX4UgUXV2RCWbWBBjj7q3Lmi8vL68JMLdZw/HUTS7LWD0iG2LL1sN4eqFOREv1c3HTJCNGjADYJTc3d176tIz23N19HlBmsIuISOXTmLuISIAU7iIiAVK4i4gESOEuIhIghbuISIAU7iIiAVK4i4gESOEuIhIghbuISIAU7iIiAVK4i4gESOEuIhIghbuISIAU7iIiAVK4i4gESOEuIhIghbuISIAU7iIiAVK4i4gESOEuIhIghbuISIAU7iIiAVK4i4gESOEuIhIghbuISIAU7iIiAVK4i4gESOEuIhIghbuISIAU7iIiAVK4i4gESOEuIhIghbuISIAU7iIiAVK4i4gESOEuIhIghbuISIAU7iIiAVK4i4gESOEuIhIghbuISIAU7iIiAVK4i4gESOEuIhIghbuISIAU7iIiAVK4i4gESOEuIhKg7KouIJYFsLKwflXXIbKWbbfdlkS9xlVdhshattpqdf88q/i0RCqV2rjVlCAvL68d8E5V1yEiUkMdlJub+256Q3XpuX8AHAR8DxRUcS0iIjVFFrA9UYauoVr03EVEpHLphKqISIAU7iIiAVK4i4gESOEuIhIghbuISIAU7gExMz2eIgLoUsgaz8x2BQYBLYF8ojfsGcAV7j67KmsTkapTXT7EJBvuIeA6d59a1GBmrYHhwIFVVpWIVCmFe81XLz3YAdx9iplVVT0iazCzN4G6xZoTQMrd21ZBSbWCwr3m+9jMHgZeARYBmwCdgE+qtCqR/7kWGAacRDR0KBuBxtxrODNLACcC7YBNgcXAJOA5d9eDK9WCmV0FfOnuz1V1LbWFwl1EJEC6dE5EJEAKdxGRAOmEqlQbZtYEmA3MAlJAHeA74Fx3/+8GrvMcoIO7n2NmLwHd3f27UubtC7zu7uX+4RgzS7l7ooR2A+4EdombZgCXufsCM7sZwN1vXp99EVkf6rlLdfOduzd39/3dvRkwDfhXZazY3TuVFuyxgynh58rWl5ntALwJDHP3PwH7AjMBnUyUjUY9d6nuJgLHA5jZPGAq0Jzol7s6Ar2IOil5wMXu/ruZnQncQHTl0FfAkrTlOwA/APcRXWG0CuhPdB12S+AhMzsJWA7cD2wJLAMudffp8dHFCKARMKWUmnsCr7n7OAB3T5nZ7cBcM1vjNWdmlwBnAg2BQuBUd//MzO4CjiD6ZbIX3L2vmR0G3EF0VLMQ6OruC9bnzpTaQz13qbbMLAc4lejSziIvu7sBWwPnA23dvTnwI3Bl3Gu+A2gPtCG67r+4S4nCeS/gcOBGYAzRUUJ3d58BPApc7e4tgAvi6QD3Ao/E25xUfMWx/YnehFZz9wJ3H+3uq6/zNrNNiS5j7eDu+wDPAxeZ2c7A0e6+H9AW2MPM6hG9YV3o7i2BcUCL0u89qe3Uc5fqZgcz+yj+vy7wPtGHYIoUheYhwB5A0adx6wAfEoXhe+4+H8DMRgCHFdvGwcCD7l5I1ItvFs9L/LcR0AoYnvZJ30ZmtiVRz79r3DYS+HcJ+1BI9AnMMrn7YjM7HTjNzPYkOhL5CPgWWG5mk4DxwA3xEclY4Dkze56oN/9/69qG1F4Kd6luvot7xaVZHv/NAp5098tgdSBnEwV5+hFpSZ+IXJV+w8x2B75Oa8oCfk+vw8x2BH4hGhIpWn+KKMiLm0Y0xJO+jSTwNNGQTVHbTsBbREcDLxO90ezv7vlmdgDRm1AnYLKZHezug81sHHAscIeZPe3ut5SwfRENy0iN9RZwkpltE39K936i8fd3gdZm9oc4UE8tYdmJwF/MLGFm2wBvEx0l5APZ7r4I+MLMzgAwsyPiZQBeB86I/z+Ztb8zBeBB4Bgz6xQvnwD+DmxTdEQRa0X0qc3BREckRwNZZrZ/XNNEd7+S6OohM7OpwCbufjcwGA3LSBkU7lIjufvHQF/gDeBToufybXF4XkoUwu8TnVQtbgiwFPg4nu9Sd/+N6Pt5hppZW6Ab0N3MPgFuJTrRmQIuATrH7Z2A30qo7QeioP6bmc2I69uDaHw93WtA0sxmEZ2cnQfs4u7TgcnATDP7MG5/GbgeeMTM8ojOA9y0PveZ1C76+gERkQCp5y4iEiCFu4hIgBTuIiIBUriLiARI4S4iEiCFu4hIgBTuIiIBUriLiATo/wHHM75GFSq7lgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = AdaBoostClassifier()\n", + "cm = ConfusionMatrix(model, percent=True)\n", + "cm.fit(rX_train, ry_train)\n", + "cm.score(rX_test, ry_test)\n", + "cm.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "##########################################################################\n", + "## Imports\n", + "##########################################################################\n", + "\n", + "import warnings\n", + "import numpy as np\n", + "\n", + "from yellowbrick.utils import div_safe\n", + "from yellowbrick.style import find_text_color\n", + "from yellowbrick.style.palettes import color_sequence\n", + "from yellowbrick.classifier.base import ClassificationScoreVisualizer\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import confusion_matrix as confusion_matrix_metric\n", + "\n", + "\n", + "##########################################################################\n", + "## ConfusionMatrix\n", + "##########################################################################\n", + "\n", + "CMAP_UNDERCOLOR = 'w'\n", + "CMAP_OVERCOLOR = '#2a7d4f'\n", + "CMAP_MUTEDCOLOR = '0.75'\n", + "\n", + "\n", + "class ConfusionMatrix(ClassificationScoreVisualizer):\n", + " \"\"\"\n", + " Creates a heatmap visualization of the sklearn.metrics.confusion_matrix().\n", + " A confusion matrix shows each combination of the true and predicted\n", + " classes for a test data set.\n", + "\n", + " The default color map uses a yellow/orange/red color scale. The user can\n", + " choose between displaying values as the percent of true (cell value\n", + " divided by sum of row) or as direct counts. If percent of true mode is\n", + " selected, 100% accurate predictions are highlighted in green.\n", + "\n", + " Requires a classification model.\n", + "\n", + " Parameters\n", + " ----------\n", + " model : estimator\n", + " Must be a classifier, otherwise raises YellowbrickTypeError\n", + "\n", + " ax : matplotlib Axes, default: None\n", + " The axes to plot the figure on. If None is passed in the current axes\n", + " will be used (or generated if required).\n", + "\n", + " sample_weight: array-like of shape = [n_samples], optional\n", + " Passed to ``confusion_matrix`` to weight the samples.\n", + "\n", + " percent: bool, default: False\n", + " Determines whether or not the confusion_matrix is displayed as counts\n", + " or as a percent of true predictions. Note, if specifying a subset of\n", + " classes, percent should be set to False or inaccurate figures will be\n", + " displayed.\n", + "\n", + " classes : list, default: None\n", + " a list of class names to use in the confusion_matrix.\n", + " This is passed to the ``labels`` parameter of\n", + " ``sklearn.metrics.confusion_matrix()``, and follows the behaviour\n", + " indicated by that function. It may be used to reorder or select a\n", + " subset of labels. If None, classes that appear at least once in\n", + " ``y_true`` or ``y_pred`` are used in sorted order.\n", + "\n", + " label_encoder : dict or LabelEncoder, default: None\n", + " When specifying the ``classes`` argument, the input to ``fit()``\n", + " and ``score()`` must match the expected labels. If the ``X`` and ``y``\n", + " datasets have been encoded prior to training and the labels must be\n", + " preserved for the visualization, use this argument to provide a\n", + " mapping from the encoded class to the correct label. Because typically\n", + " a Scikit-Learn ``LabelEncoder`` is used to perform this operation, you\n", + " may provide it directly to the class to utilize its fitted encoding.\n", + "\n", + " cmap : string, default: ``'YlOrRd'``\n", + " Specify a colormap to define the heatmap of the predicted class\n", + " against the actual class in the confusion matrix.\n", + "\n", + " fontsize : int, default: None\n", + " Specify the fontsize of the text in the grid and labels to make the\n", + " matrix a bit easier to read. Uses rcParams font size by default.\n", + "\n", + " Attributes\n", + " ----------\n", + " score_ : float\n", + " Global accuracy score\n", + "\n", + " confusion_matrix_ : array, shape = [n_classes, n_classes]\n", + " The numeric scores of the confusion matrix\n", + "\n", + " class_counts_ : array, shape = [n_classes,]\n", + " The total number of each class supporting the confusion matrix\n", + "\n", + " Examples\n", + " --------\n", + " >>> from yellowbrick.classifier import ConfusionMatrix\n", + " >>> from sklearn.linear_model import LogisticRegression\n", + " >>> viz = ConfusionMatrix(LogisticRegression())\n", + " >>> viz.fit(X_train, y_train)\n", + " >>> viz.score(X_test, y_test)\n", + " >>> viz.poof()\n", + " \"\"\"\n", + "\n", + "\n", + " def __init__(self, model, ax=None, classes=None, sample_weight=None,\n", + " percent=False, label_encoder=None, cmap='YlOrRd',\n", + " fontsize=None, **kwargs):\n", + " super(ConfusionMatrix, self).__init__(\n", + " model, ax=ax, classes=classes, **kwargs\n", + " )\n", + "\n", + " # Visual parameters\n", + " self.cmap = color_sequence(cmap)\n", + " self.cmap.set_under(color=CMAP_UNDERCOLOR)\n", + " self.cmap.set_over(color=CMAP_OVERCOLOR)\n", + " self.fontsize = fontsize\n", + "\n", + " # Estimator parameters\n", + " self.label_encoder = label_encoder\n", + " self.sample_weight = sample_weight\n", + " self.percent = percent\n", + "\n", + " # Used to draw diagonal line for predicted class = true class\n", + " self._edgecolors = []\n", + "\n", + " def score(self, X, y, **kwargs):\n", + " \"\"\"\n", + " Draws a confusion matrix based on the test data supplied by comparing\n", + " predictions on instances X with the true values specified by the\n", + " target vector y.\n", + "\n", + " Parameters\n", + " ----------\n", + " X : ndarray or DataFrame of shape n x m\n", + " A matrix of n instances with m features\n", + "\n", + " y : ndarray or Series of length n\n", + " An array or series of target or class values\n", + "\n", + " Returns\n", + " -------\n", + "\n", + " score_ : float\n", + " Global accuracy score\n", + " \"\"\"\n", + " # Perform deprecation warnings for attributes to score\n", + " # TODO: remove this in v0.9\n", + " for param in (\"percent\", \"sample_weight\"):\n", + " if param in kwargs:\n", + " warnings.warn(PendingDeprecationWarning((\n", + " \"specifying '{}' in score is no longer supported, \"\n", + " \"pass to constructor of the visualizer instead.\"\n", + " ).format(param)))\n", + "\n", + " setattr(self, param, kwargs[param])\n", + "\n", + " # Create predictions from X (will raise not fitted error)\n", + " y_pred = self.predict(X)\n", + "\n", + " # Encode the target with the supplied label encoder\n", + " if self.label_encoder:\n", + " try :\n", + " y = self.label_encoder.inverse_transform(y)\n", + " y_pred = self.label_encoder.inverse_transform(y_pred)\n", + " except AttributeError:\n", + " # if a mapping is passed to class apply it here.\n", + " y = np.array([self.label_encoder[x] for x in y])\n", + " y_pred = np.array([self.label_encoder[x] for x in y_pred])\n", + "\n", + " # Compute the confusion matrix and class counts\n", + " self.confusion_matrix_ = confusion_matrix_metric(\n", + " y, y_pred, labels=self.classes_, sample_weight=self.sample_weight\n", + " )\n", + " self.class_counts_ = self.class_counts(y)\n", + "\n", + " # Make array of only the classes actually being used.\n", + " # Needed because sklearn confusion_matrix only returns counts for\n", + " # selected classes but percent should be calculated on all classes\n", + " selected_class_counts = []\n", + " for c in self.classes_:\n", + " try:\n", + " selected_class_counts.append(self.class_counts_[c])\n", + " except KeyError:\n", + " selected_class_counts.append(0)\n", + " self.class_counts_ = np.array(selected_class_counts)\n", + "\n", + " self.draw()\n", + "\n", + " # Retrieve and store the score attribute from the sklearn classifier\n", + " self.score_ = self.estimator.score(X, y)\n", + "\n", + " return self.score_\n", + "\n", + " def draw(self):\n", + " \"\"\"\n", + " Renders the classification report; must be called after score.\n", + " \"\"\"\n", + "\n", + " # Perform display related manipulations on the confusion matrix data\n", + " cm_display = self.confusion_matrix_\n", + "\n", + " # Convert confusion matrix to percent of each row, i.e. the\n", + " # predicted as a percent of true in each class.\n", + " if self.percent == True:\n", + " # Note: div_safe function returns 0 instead of NAN.\n", + " cm_display = div_safe(self.confusion_matrix_, self.class_counts_.reshape(-1,1))\n", + " cm_display = np.round(cm_display* 100, decimals=0)\n", + "\n", + " # Y axis should be sorted top to bottom in pcolormesh\n", + " cm_display = cm_display[::-1,::]\n", + "\n", + " # Set up the dimensions of the pcolormesh\n", + " n_classes = len(self.classes_)\n", + " X, Y = np.arange(n_classes+1), np.arange(n_classes+1)\n", + " self.ax.set_ylim(bottom=0, top=cm_display.shape[0])\n", + " self.ax.set_xlim(left=0, right=cm_display.shape[1])\n", + "\n", + " # Fetch the grid labels from the classes in correct order; set ticks.\n", + " xticklabels = self.classes_\n", + " yticklabels = self.classes_[::-1]\n", + " ticks = np.arange(n_classes) + 0.5\n", + "\n", + " self.ax.set(xticks=ticks, yticks=ticks)\n", + " self.ax.set_xticklabels(xticklabels, rotation=\"vertical\", fontsize=self.fontsize)\n", + " self.ax.set_yticklabels(yticklabels, fontsize=self.fontsize)\n", + "\n", + " # Set data labels in the grid enumerating over all x,y class pairs.\n", + " # NOTE: X and Y are one element longer than the confusion matrix, so\n", + " # skip the last element in the enumeration to label grids.\n", + " for x in X[:-1]:\n", + " for y in Y[:-1]:\n", + "\n", + " # Extract the value and the text label\n", + " value = cm_display[x,y]\n", + " svalue = \"{:0.0f}\".format(value)\n", + " if self.percent:\n", + " svalue += \"%\"\n", + "\n", + " # Determine the grid and text colors\n", + " base_color = self.cmap(value / cm_display.max())\n", + " text_color = find_text_color(base_color)\n", + "\n", + " # Make zero values more subtle\n", + " if cm_display[x,y] == 0:\n", + " text_color = CMAP_MUTEDCOLOR\n", + "\n", + " # Add the label to the middle of the grid\n", + " cx, cy = x+0.5, y+0.5\n", + " self.ax.text(\n", + " cy, cx, svalue, va='center', ha='center',\n", + " color=text_color, fontsize=self.fontsize,\n", + " )\n", + "\n", + " # Add a dark line on the grid with the diagonal. Note that the\n", + " # tick labels have already been reversed.\n", + " lc = 'k' if xticklabels[x] == yticklabels[y] else 'w'\n", + " self._edgecolors.append(lc)\n", + "\n", + "\n", + " # Draw the heatmap with colors bounded by vmin,vmax\n", + " vmin = 0.00001\n", + " vmax = 99.999 if self.percent == True else cm_display.max()\n", + " self.ax.pcolormesh(\n", + " X, Y, cm_display, vmin=vmin, vmax=vmax,\n", + " edgecolor=self._edgecolors, cmap=self.cmap, linewidth='0.01'\n", + " )\n", + "\n", + " # Return the axes being drawn on\n", + " return self.ax\n", + "\n", + " def finalize(self, **kwargs):\n", + " self.set_title('{} Confusion Matrix'.format(self.name))\n", + " self.ax.set_ylabel('True Class')\n", + " self.ax.set_xlabel('Predicted Class')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Let's see if they do now!" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = AdaBoostClassifier()\n", + "cm = ConfusionMatrix(model, percent=True)\n", + "cm.fit(oX_train, oy_train)\n", + "cm.score(oX_test, oy_test)\n", + "cm.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = AdaBoostClassifier()\n", + "cm = ConfusionMatrix(model, percent=True)\n", + "cm.fit(gX_train, gy_train)\n", + "cm.score(gX_test, gy_test)\n", + "cm.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = AdaBoostClassifier()\n", + "cm = ConfusionMatrix(model, percent=True)\n", + "cm.fit(rX_train, ry_train)\n", + "cm.score(rX_test, ry_test)\n", + "cm.poof()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/rebeccabilbro/cvscores_enhancements.ipynb b/examples/rebeccabilbro/cvscores_enhancements.ipynb new file mode 100644 index 000000000..af8739a5f --- /dev/null +++ b/examples/rebeccabilbro/cvscores_enhancements.ipynb @@ -0,0 +1,410 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys \n", + "\n", + "# Modify the path \n", + "sys.path.append(\"..\")\n", + "\n", + "import pandas as pd\n", + "import yellowbrick as yb\n", + "import matplotlib.pyplot as plt " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ahYeHB3369GHu3Ln23xcREZE7c9ieelpaGjabzX7bzc2NnJwc3N3dqVWrFrGx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\nwcHBlClTxml5vvjiC3Jycli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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.linear_model import RidgeClassifier\n", + "from sklearn.model_selection import StratifiedKFold\n", + "\n", + "from yellowbrick.model_selection import CVScores\n", + "\n", + "\n", + "room = pd.read_csv('data/occupancy/occupancy.csv')\n", + "\n", + "features = [\"temperature\", \"relative humidity\", \"light\", \"C02\", \"humidity\"]\n", + "\n", + "# Extract the numpy arrays from the data frame\n", + "X = room[features].values\n", + "y = room.occupancy.values\n", + "\n", + "# Create a cross-validation strategy\n", + "cv = StratifiedKFold(12)\n", + "\n", + "# Create the cv score visualizer\n", + "oz = CVScores(RidgeClassifier(), cv=cv)\n", + "\n", + "oz.fit(X, y)\n", + "oz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# yellowbrick.model_selection.cross_validation\n", + "# Implements cross-validation score plotting for model selection.\n", + "#\n", + "# Author: Prema Damodaran Roman\n", + "# Created: Wed June 6 2018 13:32:00 -0500\n", + "# Author: Rebecca Bilbro \n", + "# Updated: Fri Aug 10 13:15:43 2018 -0500\n", + "#\n", + "# ID: cross_validation.py [7f47800] pdamo24@gmail.com $\n", + "\n", + "\"\"\"\n", + "Implements cross-validation score plotting for model selection.\n", + "\"\"\"\n", + "\n", + "##########################################################################\n", + "## Imports\n", + "##########################################################################\n", + "\n", + "import numpy as np\n", + "import matplotlib.ticker as ticker\n", + "\n", + "from yellowbrick.base import ModelVisualizer\n", + "from sklearn.model_selection import cross_val_score\n", + "\n", + "\n", + "##########################################################################\n", + "## CVScores Visualizer\n", + "##########################################################################\n", + "\n", + "class CVScores(ModelVisualizer):\n", + " \"\"\"\n", + " CVScores displays cross-validated scores as a bar chart, with the\n", + " average of the scores plotted as a horizontal line.\n", + "\n", + " Parameters\n", + " ----------\n", + "\n", + " model : a scikit-learn estimator\n", + " An object that implements ``fit`` and ``predict``, can be a\n", + " classifier, regressor, or clusterer so long as there is also a valid\n", + " associated scoring metric.\n", + " Note that the object is cloned for each validation.\n", + "\n", + " ax : matplotlib.Axes object, optional\n", + " The axes object to plot the figure on.\n", + "\n", + " cv : int, cross-validation generator or an iterable, optional\n", + " Determines the cross-validation splitting strategy.\n", + " Possible inputs for cv are:\n", + "\n", + " - None, to use the default 3-fold cross-validation,\n", + " - integer, to specify the number of folds.\n", + " - An object to be used as a cross-validation generator.\n", + " - An iterable yielding train/test splits.\n", + "\n", + " See the scikit-learn `cross-validation guide `_\n", + " for more information on the possible strategies that can be used here.\n", + "\n", + " scoring : string, callable or None, optional, default: None\n", + " A string or scorer callable object / function with signature\n", + " ``scorer(estimator, X, y)``.\n", + "\n", + " See scikit-learn `cross-validation guide `_\n", + " for more information on the possible metrics that can be used.\n", + "\n", + " kwargs : dict\n", + " Keyword arguments that are passed to the base class and may influence\n", + " the visualization as defined in other Visualizers.\n", + "\n", + " Examples\n", + " --------\n", + "\n", + " >>> from sklearn import datasets, svm\n", + " >>> iris = datasets.load_iris()\n", + " >>> clf = svm.SVC(kernel='linear', C=1)\n", + " >>> X = iris.data\n", + " >>> y = iris.target\n", + " >>> visualizer = CVScores(model=clf, cv=5, scoring='f1_macro')\n", + " >>> visualizer.fit(X,y)\n", + " >>> visualizer.poof()\n", + "\n", + " Notes\n", + " -----\n", + "\n", + " This visualizer is a wrapper for\n", + " `sklearn.model_selection.cross_val_score `_.\n", + "\n", + " Refer to the scikit-learn\n", + " `cross-validation guide `_\n", + " for more details.\n", + "\n", + " \"\"\"\n", + "\n", + " def __init__(self, model, ax=None, cv=None, scoring=None, **kwargs):\n", + " super(CVScores, self).__init__(model, ax=ax, **kwargs)\n", + "\n", + " self.cv = cv\n", + " self.scoring = scoring\n", + "\n", + " def fit(self, X, y, **kwargs):\n", + " \"\"\"\n", + " Fits the learning curve with the wrapped model to the specified data.\n", + " Draws training and test score curves and saves the scores to the\n", + " estimator.\n", + "\n", + " Parameters\n", + " ----------\n", + " X : array-like, shape (n_samples, n_features)\n", + " Training vector, where n_samples is the number of samples and\n", + " n_features is the number of features.\n", + "\n", + " y : array-like, shape (n_samples) or (n_samples, n_features), optional\n", + " Target relative to X for classification or regression;\n", + " None for unsupervised learning.\n", + "\n", + " Returns\n", + " -------\n", + " self : instance\n", + "\n", + " \"\"\"\n", + "\n", + " self.cv_scores_ = cross_val_score(\n", + " self.estimator, X, y, cv=self.cv, scoring=self.scoring\n", + " )\n", + " self.cv_scores_mean_ = self.cv_scores_.mean()\n", + "\n", + " self.draw()\n", + " return self\n", + "\n", + " def draw(self, **kwargs):\n", + " \"\"\"\n", + " Creates the bar chart of the cross-validated scores generated from the\n", + " fit method and places a dashed horizontal line that represents the\n", + " average value of the scores.\n", + " \"\"\"\n", + "\n", + " color = kwargs.pop(\"color\", \"b\")\n", + " width = kwargs.pop(\"width\", 0.3)\n", + " linewidth = kwargs.pop(\"linewidth\", 1)\n", + "\n", + " xvals = np.arange(1, len(self.cv_scores_) + 1, 1)\n", + " self.ax.bar(xvals, self.cv_scores_, width=width)\n", + " self.ax.axhline(\n", + " self.cv_scores_mean_, color=color,\n", + " label=\"Mean score = {:0.3f}\".format(self.cv_scores_mean_),\n", + " linestyle='--', linewidth=linewidth\n", + " )\n", + "\n", + " return self.ax\n", + "\n", + " def finalize(self, **kwargs):\n", + " \"\"\"\n", + " Add the title, legend, and other visual final touches to the plot.\n", + " \"\"\"\n", + "\n", + " # Set the title of the figure\n", + " self.set_title('Cross Validation Scores for {}'.format(self.name))\n", + "\n", + " # Add the legend\n", + " loc = kwargs.pop(\"loc\", \"best\")\n", + " edgecolor = kwargs.pop(\"edgecolor\", \"k\")\n", + " self.ax.legend(frameon=True, loc=loc, edgecolor=edgecolor)\n", + "\n", + " # set spacing between the x ticks\n", + " self.ax.xaxis.set_major_locator(ticker.MultipleLocator(1))\n", + "\n", + " # Set the axis labels\n", + " self.ax.set_xlabel('Training Instances')\n", + " self.ax.set_ylabel('Score')\n", + "\n", + "\n", + "##########################################################################\n", + "## Quick Method\n", + "##########################################################################\n", + "\n", + "def cv_scores(model, X, y, ax=None, cv=None, scoring=None, **kwargs):\n", + " \"\"\"\n", + " Displays cross validation scores as a bar chart and the\n", + " average of the scores as a horizontal line\n", + "\n", + " This helper function is a quick wrapper to utilize the\n", + " CVScores visualizer for one-off analysis.\n", + "\n", + " Parameters\n", + " ----------\n", + "\n", + " model : a scikit-learn estimator\n", + " An object that implements ``fit`` and ``predict``, can be a\n", + " classifier, regressor, or clusterer so long as there is also a valid\n", + " associated scoring metric.\n", + " Note that the object is cloned for each validation.\n", + "\n", + " X : array-like, shape (n_samples, n_features)\n", + " Training vector, where n_samples is the number of samples and\n", + " n_features is the number of features.\n", + "\n", + " y : array-like, shape (n_samples) or (n_samples, n_features), optional\n", + " Target relative to X for classification or regression;\n", + " None for unsupervised learning.\n", + "\n", + " ax : matplotlib.Axes object, optional\n", + " The axes object to plot the figure on.\n", + "\n", + " cv : int, cross-validation generator or an iterable, optional\n", + " Determines the cross-validation splitting strategy.\n", + " Possible inputs for cv are:\n", + "\n", + " - None, to use the default 3-fold cross-validation,\n", + " - integer, to specify the number of folds.\n", + " - An object to be used as a cross-validation generator.\n", + " - An iterable yielding train/test splits.\n", + "\n", + " see the scikit-learn\n", + " `cross-validation guide `_\n", + " for more information on the possible strategies that can be used here.\n", + "\n", + " scoring : string, callable or None, optional, default: None\n", + " A string or scorer callable object / function with signature\n", + " ``scorer(estimator, X, y)``.\n", + "\n", + " See scikit-learn `cross-validation guide `_\n", + " for more information on the possible metrics that can be used.\n", + "\n", + " kwargs : dict\n", + " Keyword arguments that are passed to the base class and may influence\n", + " the visualization as defined in other Visualizers.\n", + "\n", + " Returns\n", + " -------\n", + " ax : matplotlib.Axes\n", + " The axes object that the validation curves were drawn on.\n", + "\n", + " \"\"\"\n", + "\n", + " # Initialize the visualizer\n", + " visualizer = cv_scores(model, X, y, ax=ax, cv=cv, scoring=scoring)\n", + "\n", + " # Fit and poof the visualizer\n", + " visualizer.fit(X, y)\n", + " visualizer.poof(**kwargs)\n", + " return visualizer.ax" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ERESSYXkTERFJhuVNREQkGZY3ERGRZFjeREREkmF5ExERSYblTUREJBmWNxER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\nNxERkWRY3k+5dOkS/Pz8DB0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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.model_selection import StratifiedKFold\n", + "\n", + "# Create a cross-validation strategy\n", + "cv = StratifiedKFold(12)\n", + "\n", + "# Create the cv score visualizer\n", + "oz = CVScores(\n", + " MultinomialNB(), cv=cv, scoring='f1_weighted'\n", + ")\n", + "\n", + "oz.fit(X, y)\n", + "oz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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OBoC/OkuPvLdu3Zrt/NB/hJ+fX67u788iz1UnT57U1q1b3XJsAMhvLDvyvnjxou666y4F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\nbwAALIbyBgDAYih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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.linear_model import Ridge\n", + "from sklearn.model_selection import KFold\n", + "\n", + "energy = pd.read_csv('data/energy/energy.csv')\n", + "\n", + "targets = [\"heating load\", \"cooling load\"]\n", + "features = [col for col in energy.columns if col not in targets]\n", + "\n", + "X = energy[features]\n", + "y = energy[targets[1]]\n", + "\n", + "cv = KFold(12)\n", + "\n", + "oz = CVScores(\n", + " Ridge(), cv=cv, scoring='r2'\n", + ")\n", + "\n", + "oz.fit(X, y)\n", + "oz.poof()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/rebeccabilbro/cvscores_experimentation.ipynb b/examples/rebeccabilbro/cvscores_experimentation.ipynb new file mode 100644 index 000000000..2cc8f98c8 --- /dev/null +++ b/examples/rebeccabilbro/cvscores_experimentation.ipynb @@ -0,0 +1,201 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Experimenting with CV Scores\n", + "\n", + "``CVScores`` displays cross validation scores as a bar chart with the\n", + " average of the scores as a horizontal line." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.model_selection import StratifiedKFold\n", + "\n", + "from yellowbrick.model_selection import CVScores" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from yellowbrick.download import download_all\n", + "\n", + "## The path to the test data sets\n", + "FIXTURES = os.path.join(os.getcwd(), \"data\")\n", + "\n", + "## Dataset loading mechanisms\n", + "datasets = {\n", + " \"bikeshare\": os.path.join(FIXTURES, \"bikeshare\", \"bikeshare.csv\"),\n", + " \"concrete\": os.path.join(FIXTURES, \"concrete\", \"concrete.csv\"),\n", + " \"credit\": os.path.join(FIXTURES, \"credit\", \"credit.csv\"),\n", + " \"energy\": os.path.join(FIXTURES, \"energy\", \"energy.csv\"),\n", + " \"game\": os.path.join(FIXTURES, \"game\", \"game.csv\"),\n", + " \"mushroom\": os.path.join(FIXTURES, \"mushroom\", \"mushroom.csv\"),\n", + " \"occupancy\": os.path.join(FIXTURES, \"occupancy\", \"occupancy.csv\"),\n", + " \"spam\": os.path.join(FIXTURES, \"spam\", \"spam.csv\"),\n", + "}\n", + "\n", + "\n", + "def load_data(name, download=True):\n", + " \"\"\"\n", + " Loads and wrangles the passed in dataset by name.\n", + " If download is specified, this method will download any missing files.\n", + " \"\"\"\n", + "\n", + " # Get the path from the datasets\n", + " path = datasets[name]\n", + "\n", + " # Check if the data exists, otherwise download or raise\n", + " if not os.path.exists(path):\n", + " if download:\n", + " download_all()\n", + " else:\n", + " raise ValueError((\n", + " \"'{}' dataset has not been downloaded, \"\n", + " \"use the download.py module to fetch datasets\"\n", + " ).format(name))\n", + "\n", + "\n", + " # Return the data frame\n", + " return pd.read_csv(path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Classification" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.model_selection import StratifiedKFold\n", + "\n", + "from yellowbrick.model_selection import CVScores\n", + "\n", + "room = load_data(\"occupancy\")\n", + "\n", + "features = [\"temperature\", \"relative humidity\", \"light\", \"C02\", \"humidity\"]\n", + "\n", + "# Extract the numpy arrays from the data frame\n", + "X = room[features].values\n", + "y = room.occupancy.values\n", + "\n", + "# Create a new figure and axes\n", + "_, ax = plt.subplots()\n", + "\n", + "# Create a cross-validation strategy\n", + "cv = StratifiedKFold(12)\n", + "\n", + "# Create the cv score visualizer\n", + "oz = CVScores(\n", + " MultinomialNB(), ax=ax, cv=cv, scoring='f1_weighted'\n", + ")\n", + "\n", + "oz.fit(X, y)\n", + "oz.poof()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.linear_model import Ridge\n", + "from sklearn.model_selection import KFold\n", + "\n", + "\n", + "energy = load_data(\"energy\")\n", + "\n", + "targets = [\"heating load\", \"cooling load\"]\n", + "features = [col for col in energy.columns if col not in targets]\n", + "\n", + "X = energy[features]\n", + "y = energy[targets[1]]\n", + "\n", + "# Create a new figure and axes\n", + "_, ax = plt.subplots()\n", + "\n", + "cv = KFold(12)\n", + "\n", + "oz = CVScores(\n", + " Ridge(), ax=ax, cv=cv, scoring='r2'\n", + ")\n", + "\n", + "oz.fit(X, y)\n", + "oz.poof()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/rebeccabilbro/dispersion_plots.ipynb b/examples/rebeccabilbro/dispersion_plots.ipynb new file mode 100644 index 000000000..f7cb6403e --- /dev/null +++ b/examples/rebeccabilbro/dispersion_plots.ipynb @@ -0,0 +1,436 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Dispersion Plots" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys \n", + "import json\n", + "import codecs\n", + "import requests\n", + "\n", + "# Modify the path \n", + "sys.path.append(\"..\")\n", + "\n", + "import pandas as pd\n", + "import yellowbrick as yb\n", + "import matplotlib.pyplot as plt " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# yellowbrick.text.dispersion\n", + "# Implementations of lexical dispersions for text visualization.\n", + "#\n", + "# Author: Larry Gray\n", + "# Created: 2018-06-21 10:06\n", + "#\n", + "# Copyright (C) 2018 District Data Labs\n", + "# For license information, see LICENSE.txt\n", + "#\n", + "# ID: dispersion.py [] lwgray@gmail.com $\n", + "\n", + "\"\"\"\n", + "Implementation of lexical dispersion for text visualization\n", + "\"\"\"\n", + "\n", + "\n", + "##########################################################################\n", + "## Imports\n", + "##########################################################################\n", + "\n", + "from collections import defaultdict\n", + "import itertools\n", + "\n", + "from yellowbrick.text.base import TextVisualizer\n", + "from yellowbrick.style.colors import resolve_colors\n", + "from yellowbrick.exceptions import YellowbrickValueError\n", + "\n", + "import numpy as np\n", + "\n", + "##########################################################################\n", + "## Dispersion Plot Visualizer\n", + "##########################################################################\n", + "\n", + "class DispersionPlot(TextVisualizer):\n", + " \"\"\"\n", + " DispersionPlotVisualizer allows for visualization of the lexical dispersion\n", + " of words in a corpus. Lexical dispersion is a measure of a word's\n", + " homeogeneity across the parts of a corpus. This plot notes the occurences\n", + " of a word and how many words from the beginning it appears.\n", + "\n", + " Parameters\n", + " ----------\n", + " target_words : list\n", + " A list of target words whose dispersion across a corpus passed at fit\n", + "\twill be visualized.\n", + "\n", + " ax : matplotlib axes, default: None\n", + " The axes to plot the figure on.\n", + "\n", + " labels : list of strings\n", + " The names of the classes in the target, used to create a legend.\n", + " Labels must match names of classes in sorted order.\n", + "\n", + " colors : list or tuple of colors\n", + " Specify the colors for each individual class\n", + "\n", + " colormap : string or matplotlib cmap\n", + " Qualitative colormap for discrete target\n", + "\n", + " ignore_case : boolean, default: False\n", + "\tSpecify whether input will be case-sensitive.\n", + "\n", + " annotate_docs : boolean, default: False\n", + " Specify whether document boundaries will be displayed. Vertical lines\n", + " are positioned at the end of each document.\n", + "\n", + " kwargs : dict\n", + " Pass any additional keyword arguments to the super class.\n", + "\n", + " These parameters can be influenced later on in the visualization\n", + " process, but can and should be set as early as possible.\n", + " \"\"\"\n", + "\n", + " # NOTE: cannot be np.nan\n", + " NULL_CLASS = None\n", + "\n", + " def __init__(self, target_words, ax=None, colors=None, ignore_case=False,\n", + " annotate_docs=False, labels=None, colormap=None, **kwargs):\n", + " super(DispersionPlot, self).__init__(ax=ax, **kwargs)\n", + "\n", + " self.labels = labels\n", + " self.colors = colors\n", + " self.colormap = colormap\n", + "\n", + " self.target_words = target_words\n", + " self.ignore_case = ignore_case\n", + " self.annotate_docs = annotate_docs\n", + "\n", + " def _compute_dispersion(self, text, y):\n", + " self.boundaries_ = []\n", + " offset = 0\n", + "\n", + "\n", + " if y is None:\n", + " y = itertools.repeat(None)\n", + "\n", + " for doc, target in zip(text, y):\n", + " for word in doc:\n", + " if self.ignore_case:\n", + " word = word.lower()\n", + "\n", + " # NOTE: this will find all indices if duplicate words are supplied\n", + " # In the case that word is not in target words, any empty list is\n", + " # returned and no data will be yielded\n", + " offset += 1\n", + " for y_coord in (self.indexed_words_ == word).nonzero()[0]:\n", + " y_coord = int(y_coord)\n", + " yield (offset, y_coord, target)\n", + " if self.annotate_docs:\n", + " self.boundaries_.append(offset)\n", + " self.boundaries_ = np.array(self.boundaries_, dtype=int)\n", + "\n", + " def _check_missing_words(self, points):\n", + " for index in range(len(self.indexed_words_)):\n", + " if index in points[:,1]:\n", + " pass\n", + " else:\n", + " raise YellowbrickValueError((\n", + " \"The indexed word '{}' is not found in \"\n", + " \"this corpus\"\n", + " ).format(self.indexed_words_[index]))\n", + "\n", + " def fit(self, X, y=None, **kwargs):\n", + " \"\"\"\n", + " The fit method is the primary drawing input for the dispersion\n", + " visualization.\n", + "\n", + " Parameters\n", + " ----------\n", + " X : list or generator\n", + " Should be provided as a list of documents or a generator\n", + " that yields a list of documents that contain a list of \n", + " words in the order they appear in the document.\n", + "\n", + " y : ndarray or Series of length n\n", + " An optional array or series of target or class values for\n", + " instances. If this is specified, then the points will be colored\n", + " according to their class.\n", + "\n", + " kwargs : dict\n", + " Pass generic arguments to the drawing method\n", + "\n", + " Returns\n", + " -------\n", + " self : instance\n", + " Returns the instance of the transformer/visualizer\n", + " \"\"\"\n", + "\n", + " if y is not None:\n", + " self.classes_ = np.unique(y)\n", + " elif y is None and self.labels is not None:\n", + " self.classes_ = np.array([self.labels[0]])\n", + " else:\n", + " self.classes_ = np.array([self.NULL_CLASS])\n", + "\n", + " # Create an index (e.g. the y position) for the target words\n", + " self.indexed_words_ = np.flip(self.target_words, axis=0)\n", + " if self.ignore_case:\n", + " self.indexed_words_ = np.array([w.lower() for w in self.indexed_words_])\n", + "\n", + " # Stack is used to create a 2D array from the generator\n", + " try:\n", + " points_target = np.stack(self._compute_dispersion(X, y))\n", + " except ValueError:\n", + " raise YellowbrickValueError((\n", + " \"No indexed words were found in the corpus\"\n", + " ))\n", + " points = np.stack(zip(points_target[:,0].astype(int),\n", + " points_target[:,1].astype(int)))\n", + "\n", + " self.target = points_target[:,2]\n", + "\n", + " self._check_missing_words(points)\n", + "\n", + " self.draw(points, self.target)\n", + " return self\n", + "\n", + " def draw(self, points, target=None, **kwargs):\n", + " \"\"\"\n", + " Called from the fit method, this method creates the canvas and\n", + " draws the plot on it.\n", + " Parameters\n", + " ----------\n", + " kwargs: generic keyword arguments.\n", + " \"\"\"\n", + "\n", + " # Resolve the labels with the classes\n", + " labels = self.labels if self.labels is not None else self.classes_\n", + " if len(labels) != len(self.classes_):\n", + " raise YellowbrickValueError((\n", + " \"number of supplied labels ({}) does not \"\n", + " \"match the number of classes ({})\"\n", + " ).format(len(labels), len(self.classes_)))\n", + "\n", + " # Create the color mapping for the labels.\n", + " color_values = resolve_colors(\n", + " n_colors=len(labels), colormap=self.colormap, colors=self.color)\n", + " colors = dict(zip(labels, color_values))\n", + "\n", + " # Transform labels into a map of class to label\n", + " labels = dict(zip(self.classes_, labels))\n", + "\n", + " # Define boundaries with a vertical line\n", + " if self.annotate_docs:\n", + " for xcoords in self.boundaries_:\n", + " self.ax.axvline(x=xcoords, color='lightgray', linestyle='dashed')\n", + "\n", + " series = defaultdict(lambda: {'x':[], 'y':[]})\n", + "\n", + " if target is not None:\n", + " for point, t in zip(points, target):\n", + " label = labels[t]\n", + " series[label]['x'].append(point[0])\n", + " series[label]['y'].append(point[1])\n", + " else:\n", + " label = self.classes_[0]\n", + " for x, y in points:\n", + " series[label]['x'].append(x)\n", + " series[label]['y'].append(y)\n", + "\n", + " for label, points in series.items():\n", + " self.ax.scatter(points['x'], points['y'], marker='|',\n", + " c=colors[label], zorder=100, label=label)\n", + "\n", + " self.ax.set_yticks(list(range(len(self.indexed_words_))))\n", + " self.ax.set_yticklabels(self.indexed_words_)\n", + "\n", + " def finalize(self, **kwargs):\n", + " \"\"\"\n", + " The finalize method executes any subclass-specific axes\n", + " finalization steps. The user calls poof & poof calls finalize.\n", + " Parameters\n", + " ----------\n", + " kwargs: generic keyword arguments.\n", + " \"\"\"\n", + "\n", + " self.ax.set_ylim(-1, len(self.indexed_words_))\n", + " self.ax.set_title(\"Lexical Dispersion Plot\")\n", + " self.ax.set_xlabel(\"Word Offset\")\n", + " self.ax.grid(False)\n", + "\n", + " # Add the legend outside of the figure box.\n", + " if not all(self.classes_ == np.array([self.NULL_CLASS])):\n", + " box = self.ax.get_position()\n", + " self.ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n", + " self.ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", + "\n", + "##########################################################################\n", + "## Quick Method\n", + "##########################################################################\n", + "\n", + "def dispersion(words, corpus, y=None, ax=None, colors=None, colormap=None,\n", + " labels=None, annotate_docs=False, ignore_case=False, **kwargs):\n", + " \"\"\" Displays lexical dispersion plot for words in a corpus\n", + "\n", + " This helper function is a quick wrapper to utilize the DisperstionPlot\n", + " Visualizer for one-off analysis\n", + "\n", + " Parameters\n", + " ----------\n", + "\n", + " words : list\n", + " A list of words whose dispersion will be examined within a corpus\n", + "\n", + " y : ndarray or Series of length n\n", + " An optional array or series of target or class values for\n", + " instances. If this is specified, then the points will be colored\n", + " according to their class.\n", + "\n", + " corpus : list\n", + " Should be provided as a list of documents that contain\n", + " a list of words in the order they appear in the document.\n", + "\n", + " ax : matplotlib axes, default: None\n", + " The axes to plot the figure on.\n", + "\n", + " labels : list of strings\n", + " The names of the classes in the target, used to create a legend.\n", + " Labels must match names of classes in sorted order.\n", + "\n", + " colors : list or tuple of colors\n", + " Specify the colors for each individual class\n", + "\n", + " colormap : string or matplotlib cmap\n", + " Qualitative colormap for discrete target\n", + "\n", + " annotate_docs : boolean, default: False\n", + " Specify whether document boundaries will be displayed. Vertical lines\n", + " are positioned at the end of each document.\n", + "\n", + " ignore_case : boolean, default: False\n", + "\tSpecify whether input will be case-sensitive.\n", + "\n", + " kwargs : dict\n", + " Pass any additional keyword arguments to the super class.\n", + "\n", + " Returns\n", + " -------\n", + " ax: matplotlib axes\n", + " Returns the axes that the plot was drawn on\n", + " \"\"\"\n", + "\n", + " # Instantiate the visualizer\n", + " visualizer = DispersionPlot(\n", + " words, ax=ax, colors=colors, colormap=colormap,\n", + " ignore_case=ignore_case, labels=labels,\n", + " annotate_docs=annotate_docs, **kwargs\n", + " )\n", + "\n", + " # Fit and transform the visualizer (calls draw)\n", + " visualizer.fit(corpus, y, **kwargs)\n", + "\n", + " # Return the axes object on the visualizer\n", + " return visualizer.ax\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "URL = \"https://raw.githubusercontent.com/foxbook/atap/master/snippets/ch08/data/oz.json\"\n", + "\n", + "def fetch_data(fname='oz.json'):\n", + " response = requests.get(URL)\n", + " outpath = os.path.abspath(fname)\n", + " with open(outpath, 'wb') as f:\n", + " f.write(response.content)\n", + " \n", + " return outpath\n", + "\n", + "# Defining fetching data from the URL\n", + "oz_json = fetch_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# oz.json contains a list of characters, reverse sorted by frequency\n", + "# And a dict with {chapter title: chapter text} key-value pairs\n", + "with codecs.open('oz.json', 'r', 'utf-8-sig') as data:\n", + " text = json.load(data)\n", + " chapters = text['chapters']\n", + " titles = list(chapters.keys())\n", + " \n", + " target_characters = [\"Dorothy\", \"Scarecrow\", \"Glinda\", \"Toto\", \"Witch\", \"Monkey\"]\n", + " chapter_text = [chap.split() for chap in chapters.values()]\n", + " \n", + " oz = DispersionPlot(target_characters, colormap='tab20b', labels=titles)\n", + " oz.fit(chapter_text, titles)\n", + " oz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/rebeccabilbro/prediction_error_alpha.ipynb b/examples/rebeccabilbro/prediction_error_alpha.ipynb new file mode 100644 index 000000000..54f319c33 --- /dev/null +++ b/examples/rebeccabilbro/prediction_error_alpha.ipynb @@ -0,0 +1,987 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding an alpha to Prediction Error plots" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys \n", + "\n", + "# Modify the path \n", + "sys.path.append(\"..\")\n", + "\n", + "import pandas as pd\n", + "import yellowbrick as yb\n", + "import matplotlib.pyplot as plt \n", + "\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:7: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", + " import sys\n", + "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:8: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", + " \n" + ] + } + ], + "source": [ + "# Load the data\n", + "concrete = pd.read_csv('data/concrete/concrete.csv')\n", + "feature_names = ['cement', 'slag', 'ash', 'water', 'splast', 'coarse', 'fine', 'age']\n", + "target_name = 'strength'\n", + "\n", + "# Get the X and y data from the DataFrame\n", + "X = concrete[feature_names].as_matrix()\n", + "y = concrete[target_name].as_matrix()\n", + "\n", + "# Create the train and test data\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/linear_model/base.py:509: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n", + " linalg.lstsq(X, y)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.linear_model import Lasso\n", + "\n", + "from yellowbrick.regressor import PredictionError\n", + "\n", + "# Instantiate the linear model and visualizer\n", + "lasso = Lasso()\n", + "visualizer = PredictionError(lasso)\n", + "\n", + "visualizer.fit(X_train, y_train) # Fit the training data to the visualizer\n", + "visualizer.score(X_test, y_test) # Evaluate the model on the test data\n", + "g = visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Does alpha work now?" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualizer = PredictionError(lasso, alpha=0.1)\n", + "\n", + "visualizer.fit(X_train, y_train) # Fit the training data to the visualizer\n", + "visualizer.score(X_test, y_test) # Evaluate the model on the test data\n", + "g = visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# yellowbrick.draw\n", + "# Utilities for common matplotlib drawing procedures.\n", + "#\n", + "# Author: Benjamin Bengfort \n", + "# Created: Sun Aug 19 10:35:50 2018 -0400\n", + "#\n", + "# ID: draw.py [] benjamin@bengfort.com $\n", + "\n", + "\"\"\"\n", + "Utilities for common matplotlib drawing procedures.\n", + "\"\"\"\n", + "\n", + "##########################################################################\n", + "## Imports\n", + "##########################################################################\n", + "\n", + "from yellowbrick.base import Visualizer\n", + "from yellowbrick.exceptions import YellowbrickValueError\n", + "\n", + "from matplotlib import patches\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "##########################################################################\n", + "## Legend Drawing Utilities\n", + "##########################################################################\n", + "\n", + "def manual_legend(g, labels, colors, **legend_kwargs):\n", + " \"\"\"\n", + " Adds a manual legend for a scatter plot to the visualizer where the labels\n", + " and associated colors are drawn with circle patches instead of determining\n", + " them from the labels of the artist objects on the axes. This helper is\n", + " used either when there are a lot of duplicate labels, no labeled artists,\n", + " or when the color of the legend doesn't exactly match the color in the\n", + " figure (e.g. because of the use of transparency).\n", + "\n", + " Parameters\n", + " ----------\n", + " g : Visualizer or Axes object\n", + " The graph to draw the legend on, either a Visualizer or a matplotlib\n", + " Axes object. If None, the current axes are drawn on, but this is not\n", + " recommended.\n", + "\n", + " labels : list of str\n", + " The text labels to associate with the legend. Note that the labels\n", + " will be added to the legend in the order specified.\n", + "\n", + " colors : list of colors\n", + " A list of any valid matplotlib color reference. The number of colors\n", + " specified must be equal to the number of labels.\n", + "\n", + " legend_kwargs : dict\n", + " Any additional keyword arguments to pass to the legend.\n", + "\n", + " Returns\n", + " -------\n", + " legend: Legend artist\n", + " The artist created by the ax.legend() call, returned for further\n", + " manipulation if required by the caller.\n", + "\n", + " Notes\n", + " -----\n", + " Right now this method simply draws the patches as rectangles and cannot\n", + " take into account the line or scatter plot properties (e.g. line style or\n", + " marker style). It is possible to add Line2D patches to the artist that do\n", + " add manual styles like this, which we can explore in the future.\n", + "\n", + " .. seealso:: https://matplotlib.org/gallery/text_labels_and_annotations/custom_legends.html\n", + " \"\"\"\n", + " # Get access to the matplotlib Axes\n", + " if isinstance(g, Visualizer):\n", + " g = g.ax\n", + " elif g is None:\n", + " g = plt.gca()\n", + "\n", + " # Ensure that labels and colors are the same length to prevent odd behavior.\n", + " if len(colors) != len(labels):\n", + " raise YellowbrickValueError(\n", + " \"please specify the same number of colors as labels!\"\n", + " )\n", + "\n", + " # Create the legend handles with the associated colors and labels\n", + " handles = [\n", + " patches.Patch(color=color, label=label)\n", + " for color, label in zip(colors, labels)\n", + " ]\n", + "\n", + " # Return the Legend artist\n", + " return g.legend(handles=handles, **legend_kwargs)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Now with alpha" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "try:\n", + " # Only available in Matplotlib >= 2.0.2\n", + " from mpl_toolkits.axes_grid1 import make_axes_locatable\n", + "except ImportError:\n", + " make_axes_locatable = None\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "from yellowbrick.regressor.base import RegressionScoreVisualizer\n", + "from yellowbrick.style.palettes import LINE_COLOR\n", + "from yellowbrick.utils.decorators import memoized\n", + "from yellowbrick.exceptions import YellowbrickValueError\n", + "from yellowbrick.bestfit import draw_best_fit, draw_identity_line\n", + "\n", + "\n", + "class PredictionError(RegressionScoreVisualizer):\n", + " \"\"\"\n", + " The prediction error visualizer plots the actual targets from the dataset\n", + " against the predicted values generated by our model(s). This visualizer is\n", + " used to dectect noise or heteroscedasticity along a range of the target\n", + " domain.\n", + "\n", + " Parameters\n", + " ----------\n", + "\n", + " model : a Scikit-Learn regressor\n", + " Should be an instance of a regressor, otherwise will raise a\n", + " YellowbrickTypeError exception on instantiation.\n", + "\n", + " ax : matplotlib Axes, default: None\n", + " The axes to plot the figure on. If None is passed in the current axes\n", + " will be used (or generated if required).\n", + "\n", + " shared_limits : bool, default: True\n", + " If shared_limits is True, the range of the X and Y axis limits will\n", + " be identical, creating a square graphic with a true 45 degree line.\n", + " In this form, it is easier to diagnose under- or over- prediction,\n", + " though the figure will become more sparse. To localize points, set\n", + " shared_limits to False, but note that this will distort the figure\n", + " and should be accounted for during analysis.\n", + "\n", + " bestfit : bool, default: True\n", + " Draw a linear best fit line to estimate the correlation between the\n", + " predicted and measured value of the target variable. The color of\n", + " the bestfit line is determined by the ``line_color`` argument.\n", + "\n", + " identity: bool, default: True\n", + " Draw the 45 degree identity line, y=x in order to better show the\n", + " relationship or pattern of the residuals. E.g. to estimate if the\n", + " model is over- or under- estimating the given values. The color of the\n", + " identity line is a muted version of the ``line_color`` argument.\n", + "\n", + " point_color : color\n", + " Defines the color of the error points; can be any matplotlib color.\n", + "\n", + " line_color : color\n", + " Defines the color of the best fit line; can be any matplotlib color.\n", + "\n", + " alpha : float, default: 1.0\n", + " Specify a transparency where 1 is completely opaque and 0 is completely\n", + " transparent. This property makes densely clustered points more visible.\n", + "\n", + " kwargs : dict\n", + " Keyword arguments that are passed to the base class and may influence\n", + " the visualization as defined in other Visualizers.\n", + "\n", + " Examples\n", + " --------\n", + "\n", + " >>> from yellowbrick.regressor import PredictionError\n", + " >>> from sklearn.linear_model import Lasso\n", + " >>> model = PredictionError(Lasso())\n", + " >>> model.fit(X_train, y_train)\n", + " >>> model.score(X_test, y_test)\n", + " >>> model.poof()\n", + "\n", + " Notes\n", + " -----\n", + "\n", + " PredictionError is a ScoreVisualizer, meaning that it wraps a model and\n", + " its primary entry point is the `score()` method.\n", + " \"\"\"\n", + "\n", + " def __init__(self, model, ax=None, shared_limits=True,\n", + " bestfit=True, identity=True, alpha=1.0, **kwargs):\n", + " # Initialize the visualizer\n", + " super(PredictionError, self).__init__(model, ax=ax, **kwargs)\n", + "\n", + " # Visual arguments\n", + " self.colors = {\n", + " 'point': kwargs.pop('point_color', None),\n", + " 'line': kwargs.pop('line_color', LINE_COLOR),\n", + " }\n", + " \n", + " if self.colors['point'] == None:\n", + " self.colors['point'] = 'b'\n", + " \n", + " # Drawing arguments\n", + " self.shared_limits = shared_limits\n", + " self.bestfit = bestfit\n", + " self.identity = identity\n", + " self.alpha = alpha\n", + "\n", + " def score(self, X, y=None, **kwargs):\n", + " \"\"\"\n", + " The score function is the hook for visual interaction. Pass in test\n", + " data and the visualizer will create predictions on the data and\n", + " evaluate them with respect to the test values. The evaluation will\n", + " then be passed to draw() and the result of the estimator score will\n", + " be returned.\n", + "\n", + " Parameters\n", + " ----------\n", + " X : array-like\n", + " X (also X_test) are the dependent variables of test set to predict\n", + "\n", + " y : array-like\n", + " y (also y_test) is the independent actual variables to score against\n", + "\n", + " Returns\n", + " -------\n", + " score : float\n", + " \"\"\"\n", + " self.score_ = self.estimator.score(X, y, **kwargs)\n", + "\n", + " y_pred = self.predict(X)\n", + " self.draw(y, y_pred)\n", + "\n", + " return self.score_\n", + "\n", + " def draw(self, y, y_pred):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " y : ndarray or Series of length n\n", + " An array or series of target or class values\n", + "\n", + " y_pred : ndarray or Series of length n\n", + " An array or series of predicted target values\n", + "\n", + " Returns\n", + " ------\n", + " ax : the axis with the plotted figure\n", + " \"\"\"\n", + " self.ax.scatter(\n", + " y,\n", + " y_pred,\n", + " c=self.colors['point'],\n", + " alpha=self.alpha)\n", + "\n", + " # Set the axes limits based on the range of X and Y data\n", + " # NOTE: shared_limits will be accounted for in finalize()\n", + " # TODO: do better than add one for really small residuals\n", + " self.ax.set_xlim(y.min()-1, y.max()+1)\n", + " self.ax.set_ylim(y_pred.min()-1, y_pred.max()+1)\n", + "\n", + " return self.ax\n", + "\n", + " def finalize(self, **kwargs):\n", + " \"\"\"\n", + " Finalize executes any subclass-specific axes finalization steps.\n", + " The user calls poof and poof calls finalize.\n", + "\n", + " Parameters\n", + " ----------\n", + " kwargs: generic keyword arguments.\n", + " \"\"\"\n", + " # Set the title on the plot\n", + " self.set_title(\n", + " 'Prediction Error for {}'.format(self.name)\n", + " )\n", + "\n", + " # Square the axes to ensure a 45 degree line\n", + " if self.shared_limits:\n", + " # Get the current limits\n", + " ylim = self.ax.get_ylim()\n", + " xlim = self.ax.get_xlim()\n", + "\n", + " # Find the range that captures all data\n", + " bounds = (\n", + " min(ylim[0], xlim[0]),\n", + " max(ylim[1], xlim[1]),\n", + " )\n", + "\n", + " # Reset the limits\n", + " self.ax.set_xlim(bounds)\n", + " self.ax.set_ylim(bounds)\n", + "\n", + " # Ensure the aspect ratio is square\n", + " self.ax.set_aspect('equal', adjustable='box')\n", + "\n", + " # Set the legend with full opacity patches using manual legend\n", + " label = [\"$R^2 = {:0.3f}$\".format(self.score_)]\n", + " manual_legend(\n", + " self, label, self.colors['point'], loc='best', frameon=True\n", + " )\n", + "\n", + " # TODO If score is happening inside a loop, draw would get called multiple times.\n", + " # Ideally we'd want the best fit line to be drawn only once\n", + "# if self.bestfit:\n", + "# draw_best_fit(\n", + "# y, y_pred, self.ax, 'linear', ls='--', lw=2,\n", + "# c=self.colors['line'], label='best fit'\n", + "# )\n", + " \n", + " # Draw the 45 degree line\n", + " if self.identity:\n", + " draw_identity_line(\n", + " ax=self.ax, ls='--', lw=2, c=self.colors['line'],\n", + " alpha=0.5, label=\"identity\"\n", + " )\n", + "\n", + " # Set the axes labels\n", + " self.ax.set_ylabel(r'$\\hat{y}$')\n", + " self.ax.set_xlabel(r'$y$')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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V1a8oSkxRlA1YPpN3YVksNiuQy61n+nagvb2dgYEB3v3udyOK4rIJxZdKTBQs4UjyceBfAQkrmrO/JLOyKTqXU8/07UC6s3Xbtm2pin3LgZKIiaqq38h4/AZwTSnmYrN4XE4907cDmVGb5SQksIIyYG2WPpdTz3Sls1zCv3Nhi4nNorGQeqZvB1aCkIAtJjaLSLKeqW5M31OiGyY7V1W/LZc4hmFw8uRJYHkLCaygvTk2y4Nc65m+XRBFMVVGIL1C33LEFhObRSWXeqZvB86fP09bWxuiKOJyuZa9kIC9zLEpEbPVM3070N7ezg9/+EOeeOKJJVdG4HJ4+/0lbWxKSLqzddWqVUuujMDlYIuJjc0isVKiNrNhi4mNzSKw0oUEbDGxsSk6HR0dK15IwI7m2NgUnXXr1tHa2sq6jZupatuUComvNFbeGdnYLBGSZQQkycFx3xb+6eAYwTdepcwps29NHV+5cxeyvHIWByvnTGxslhDt7e08/vjjGIbBo08d5vULI5imidchYZomr52/yKNPHS71NAuKbZnY2BSYdGfr+s0K+7tHsu6U3t89sqKWPLZlYmNTQDKjNu7aJoKz7JQOxjQG/KHFnF5RWRmSaGOzBMgW/g3FNMqcctZM1zKnTLPPu9jTLBq2ZWJjUwBmyyPxJpyt2XZK71tTt2KWOGCLiY3NZWMYBh0dHUD2PJKv3LmL69Y1IAgCobiOIAhct66Br9y5qxTTLRorRxZtbEqEKIq8733vo7Ozky1btsx4XZZFvn73HkIJH0mzz7skLZL0jgELYemdkY3NMqGzs5O1a9ciiiJOpzOrkKTjdcpsqPMt0uxyJ1vHgLpYgGtWVcz/5jRK1TfnC8C9gBP4W+AlrJahJnAc+KSqqku3YbDN256kj+SKK67g3nvvXda7f7N1DDg3EclbTBbdZ6Ioys3AO4HrgJuA1cBfAF9WVfUGQADuW+x52ZSGuG4wFooS1wv/21GssdP32rS2ts4rJMU8x8tlto4BCxHHUlgm7wKOAT/Bag36WeCjWNYJwJPAHYnXbVYoxWzGVcyx29vbefPNN2lubp53014x51GojojJjgGF8OGUQkzqgDbgbmAd8DNATDQtB6vXcGUuA9m9hpcu853jiz1+OsbCiIlfwAmgt6+Pc+fOcfPqy/MrFGvsjo4O3nzzTQBWr7Zq1s51nsWYh2GavNwb4PREhKhm4pIFNle5ubG1IvU5+aAZJhOjw0xkpMGUO/IXqFKIySjQoapqDFAVRYlgLXWS2L2GlznznWNcN3h85BitLTNfmwKu3LVjwb+2xRq7o6ODnp4empubWb16NY888khJ5vHTY91MumRWNV8SjknDpM9Vs+COiAPu7pTPJDXmxYG8xylFnsmrwJ2KogiKoqwCyoDnEr4UgLuAV0owL5tFopjNuPIZOx9fxvr161mzZg233377vFGbfOeRK/N1RFyoT+bebavZ01qTmhvAhip33uMsumWiqurjiqLcCLyJJWafBM4Df68oihM4BfznYs/LZvEoZjOuXMbOx5eRLCPgdDr51V/9VURRzGmZWoxznMu/kRSoGm/+lnq2jgFHDx/Ke5xS9Rr+gyxP37ToE7EpCclmXJmmtW6Y7GmtuSyHYi5j//RYd07N09vb2+np6eGee+5BkiREcfZ5ZTpEi3GOxe6ImOwYsFDspDWbklDMZlxzjZ1tqaAbJjFd52DvWKp5evpemx07drBhw4asnzWXlVPocyymCBcCW0xsSkIxm3HNNfZEKJa6sU3TRL3oZ2gqQkwzEDDZXO9jC2M8++yzgLXXZjYhgewJXwd7x9ANk1s2NfGera0FPcdCCVShQsvp2GJiU1Iu17TOdezkzeOWpdRNr1700+8PIQoCDkkABF58fT/Pdh1lS0PlrHkkmmEyForilqUZVo5pmpwZ9vPq+Yv8smuYCrejYLklMLdQ5iIQxcx9scXEZkWT7eaJxHUEYGgqksrNME3wjPfQ130CgP/3Aw/OEJLkWE+eGKZq8DiCAKcv+rlyVXUqY1S96GfAH0YzTGTJei7TH1MIqyBdKPMRiNksqfT5LRRbTGxWNNluHlkUCETjxDQDMHFIIk3lLoy+EQJA07ar2LRtx6xjYVoOT90wGQvHUC/62dJYiW6YDAbCCAI4ZRGnJAGXQrd3bWnhyY6+glsFuQrEfKHlpL9oodhiYrMiSeaQZDorAWRJxOd2csvGRuKGgVOSkEQBvf42Jof6qWlpmxEZyXYjSqJAc4WHfn+ITfU+YrpOXDeQRIFVFZ5px4ZiGj860sWZ4UBBrYJ8BKJYoeUktpjYrCjSTf6xUJSDPaO0VpWhNPimbV6Lajo7mqt589hJXE2rAAlJdlDZvIadq6pn/ELPdiMqDT4imk5M19ENcMoSjeVulIbp6fJuh5QSknSSN/0dyioimp730icfgSh2aNkWkxVGMbz0y4l0k7/S7USWRPoTRZu3NF7a8uV1ymzUR3n22GuMXmikZc/1lLkcs0ZGKlwOXLJEOK5hpNVzFQSBXS01fPaWbUQ0nefPDHC0f2KacOmGFSU6NjA+42Y2TZMj/eP8yS+OYCaWT/ksffIRiGKHlm0xWSEU00u/XMg0+SVRoKnCw4A/zNBUhE31Pms5Y5hUBfp48dAxtjRUcsuvXM+mbTtmFWDDMHniZC9nhv10TwSJhkJMyZMoDT4ME/a01uB1ynidMg/saEMWxRmh27u2tHBuNDBjbPWin7FQNLXUgvyWPvkKRDHze2wxWSEU00tfTAppSY2FooyFolS6nakbK7nc6JkIMRGOUVvmoirQR/TcMQThUs3WueaRvLbJsc6Eg1wYnwLg/bvXTrsR5wrdZt70umEyEAizyued4YvJxyGaj0AUM7/HFpMVQLG99MWgkJZUcqyDvWMc7BlFlkSaKjwpP8mWxko2N/j4xHUKXR0neOHQJSHZvXsPPz3WPes8Mq/tlsZKyrUgtQ0NuGSJ92xtzTrfbPkzmTe9IECNxznDvwJzO0QzhS+bQABMRGKzikUx8ntsMVkBFMJLn6+FcLkWRSEtqfSxWqvK6PeHGPCHAVIh2z2tNUz0d/PCc5cyW/fu3TvvPp1s11YUwOOQ846AZN70blniGy+cyHpsNofofALskESq3M6SLXdtMVkBXI6XPl8LwTBMfnKsmwM9I+gGlLvy/7IW0pLKHCv5Kz80FaFnIsTmBl/K5Nd1jba2NjZv3pxa2sw3j2JEQNKtgnz8HZkCrBtWz2LNMHho59qsx8DiLXdtMVkBXI6XPp8vn2GYfOnnh3i96yK6kUj2qvCgJepo5PplLWS+Q+ZYyWXNpnofE+EYn7hOoaHcjSAIiKKD97///andv7nOo5gRkFz9HenCl7mnqL13DAGBu7e2lnS5a4vJCmEhXvp8LYQfH7vALy8MIwoCYiJVPLmckCUx5y9rIX/tZxtLEgVqy1x0dZzgtZ5u7rvvvhllBHKdR+a1RaBgEZBcHaLpwpe5pyim6bxxYZhwXC9qUtp82GKyQliIlz4fCyGuG7zVM4qmmzgkAdME3TSQBJHBQJjWqrKcv6yFzHeYa6yqQB8vHDoGQFdX14zdv7nOI/PanjlxjH0FXjLM5xBNCp9umNP2FCXf63HInBnx45KlrO8vRFLafCwtF79NVvIpL5j8UhbaQghE4ykhGQlGuTA+Rfe4FSIdCoSRBPL6smYrFZjvr33yuty1pWXGWMnwL8xdRiCfeSSvrVyCvJ2k8IXjWmJPkYVpQlMidT8S19lUX5G1r3G2rN5CY1smS5hiJ6LN9cu8tclH9/hUqpVlhctBhduBrhsEIjHLB5H4dQzFNIzEhrlcSf7a36GsmtEyc75IUeZ1cckSm+or+L2bthIzDM6cODYt/DtXceti5l0Umnu3rUYzLB9JTNNxSCLNPk/K6ex1yjy8c+2MzYSFWpLNhy0mS5jF8Mxn+gPcssiJwUle6bzIX8Y7KHPK7FtTx1fu3MX2piqeO91PhdvBVEzDMEAQYE1VGV6HTFw3cr4RswnljuYqBASODswtnsnrIgrQPR5kaCrCc2cG+PnJPq52B2YkpOVCMeuqFApRFHho51oEBN64MIzHIU9LgNvTWoPLIZVMHJemBNsUrRJ5Jslf5i/etoPP/cp2IprB8FQEMPE6JEzTCj8++tRhbtrQSG2Zm/pyD80VXtZUl7FvTR23bm4mHNfzqrie2s4PKaF87FAXjx06P+25g71j/OxET+p96dcl6Yg0TROnJDIwGeT1Q8dRhyfzEpLlxnt3rOG6dQ1IojDr0iyf5W6hsC2TJUqxt4tn4pBE3LLEgZ7RrAK2v3sEhySyq6UmVTM1fT9JPg6+2eqwjgSjmKaJYpjT9tekR5aS18UlSzMckZoJ6/fdTHRsiCt37b7cSzLr3Cejel5WWKFZqkuzUjUuPwj4Ew/PA/8H+BagAb9QVfWPSzGvpUSxt4tnY8AfIhjT8DpmRgSCMY2RYCTlY/E4Ls0t3yhMNqGM6XqqWFFM1/GIl15LF8/kdUk6Ih2SQHRkAGdNoyWILhdCfUvBxTZ9WXa+Z4RXAsdKvpFyqS3NStG43A0IqqrenPjvw8DfAR8Ergf2KYpSnJ+VZUTSObqYnvlmn5eyWQSszCnT7PMWJAqTTSidkoRTtlpEJCuUJUkXz+R1kQQRpywS6j3L+NHXmDi+n8Zyd8q/VOFyFLRhePqyzJVWjjF9CfZ2pxSWyU7AqyjKLxKf/0eAS1XVcwCKojwN3Abk3wVohVHM7eLZ8Cacra+dvzhjCXLduvqUAOS7qSxJepQmM4okiQJ1ZS6ExL/TPzvT6kme/8GD7fR0HEQWRZpbW1P7cHa31PDEyd5LTmWHxKY6H+/b2YYri9U1H8txI2UpKIWYhID/DfwDsAl4kum9hQPA+lwGejs0Ll8NNNeYBOMGLskgGhjkQPvFouU63N9oMDhocGw0lGqMvaPWy/2N+ox5z9VEO/3YbMdtqnRRAZydjKaeu7rSEpOO3j7Gojo1Lokraj20RDXa24enfXawo4MNF49ilks4122nqqmZgcEBNle5OXtujNPjEQRB4II/ymhEQ9NN/uW1o9zRVpl3k+/JqM75npGURQLQ398PQFQ3eWV/lEpX/iK10iiFmJwGzqqqagKnFUWZBGrSXn/bNi6fLb9isQsf7bvaWsJk5n9kMlsT7Zd7x/m9+2+Z8zh/wuL4TJqFI5jw6FOHORsZJhAxGDMl6uVKdu/ahSxfuh7t7e10d/cw5Shj3TXvpKx1I7IkcNXqWu7Zupo/e/44rV7oGJokKolUlFvviwsCY44q+ly1OYfWk0ulNZNqyjLp7+9n1apVqWNu2LfwRutLlXx+8JKUQkz+H2AH8IlE43IvEFQUZQPQCbwLeFs5YOcTi1LsBPU6ZTbUzayzkWQu0//0RCQV7chliZB0In7h8YOpJZbPbS2fkmHpr9+9B4DTp0/zzDPPoA5P4lp/JU0br0iNebR/gkjcSEV7kpXik8Q0A900clqaZP5NuseD6KbJ1rTSj0ulk95SoRRX4R+BKkVRXgV+iCUuHwH+FauZ+SFVVfeXYF4lI1vORdK5t1j5JvmSjMhkI6qZqZyTuY5LRmmS/97fPTJrWDo5xrp161jTthbPhp2s2rR1xrHJ/SnJSvHpOGURTIG+iRBDgfCc55f5N1EafAhYZRajuuUUX6zM0uXColsmqqrGsCI3mVyz2HNZCswnFteurS/pTtDZmCt07ZKFlGM21xD3fGHp/skgG+srcTgcvOve+znyfPaiQpG4zvbmKjqG/NMsBt0wGJmK8qOxLmKawZkRP9e01fOVO6cvoSD730QQBLY2VWGYJteVu7jl2pW3tLlc7KtRYub75QYWPd8kF+YKXW+ucqdutFxD3HOFpbnYxZvPP42mWdfD53bOeU0e3rmWq9fU0lThIaYbCILAyFSUYCyOaZpUe53ApSVUJnP9TSJxHTlR1WyhFDJkvZSwM2BLzHy/3MUuznM5zBa6bolqOR2XvkSYLSwd6D5D9fAZun1+zp8/z6ZNm+YtHZDcn3LXlhZ+dKSLEwOTPDbSiSiKlLsc1JVZllz6Eir9bzDf36TMYWZ9bT5WegcBW0xKTC41NRY73yRXZkshUinKAAAgAElEQVTrzgzj5pr+/ZU7d/HoU4fZ3z1CMKbBxS6qh89wy8Ymbr/9djZt2pQ6Npdr4nJI/PpVG+gYmuTZM/2UOx1kRoSDiahVurN5vr+JHJt+frmylDsIFKJLgC0mS4D5boyluhcjSa5p3fMdJ8siX797D6GYxrOvvM6RNyZwbG7Oumkvn2uyproMn9uJac60KJKZvZnM9Tc5dCh/MVmqiW+zWUstpplXLg7YYrIkyPXGWGp7MYrFqWNHOHngdRySOE1Isv165nJNcs3sTafQAr7YGzdzZTZrqU+b4ppVFXmNZYvJEuLtIhZzYZom586dAy7VIymEryFzCVXmlLlunRXNmYtC/U1KsXFzPuayljr9UVtMbJYmua7JBUHggQceoLOzk82bNwOF8TWkL6G6x4OUu2QaKzzIi7S0KHaf34Uwl7UU0/N3MttiYlNUcrUqzpw5w7p165BlGVmWU0JSSF+DYZj8Qu2fNpftTVXctKGRSo+z6Df0UnOkz2UtOaX8o0u2mNgUlVysivb2dp555hk2btzIgw8+iJDm+CukryF9Lh6HRMfQJM+fGeAH7Z3saqmZc+mUblktlKXmSJ/LWlrvy39pZ4uJTdHIxao4evgQzzzzDGClygsZEYRC+Roy56Je9DPgDyMKApOROLphZl06ZbOsysN+du82F5wbspR8Y7nmCuWCLSY2RWM+q+LVN95k/ysvArMXfy6UryF9LrphTtsEGNMMq7qbQ56xdMpmWXWMhfnZiZ6S54YUglxzhXIaqwjzs7EB5rYq/N1n+OXLLwDzV5EvdHW3zE2ATvlSdbf0zYezWVaiUNpNlsWgEAWobcvEpmjMZlWM9F4gfO4IYkPlvHkkUBhfQ/pcnJKUer9hmqxKNLHSDRNBAHeiK95i5IYUIvN0qWCLiU1RybYmv3nPdrTyCBs3bsgrj+RyfQ3pc6n2OBkLx1jl87K5voKOoUkGAmFqPE6+8cIJdq6q5q4tLUXLDVmJ+3RsMXkbk0s1tcsl3arwR2L43FYI1tx1ydm6WHtW0ucyGY7x4rlBTgxOcrhvjLFQlFU+b6o7XvLzs1lWhnn5Rb2X8j6dhZL31VAU5YFiTMRm8dA0gy88fpC7/+F5PviDV7n7H57nC48fRNOK5wM4evgQz//8vxFM6zOSQlKK4k8OSaSu3M1DO9fy2Vu2oTT4uGlDE1saK1PzSn5+tj7GW2o8l5UbslQLXl0u84qJoiiViqL8XdpTH1EU5XFFUZanfNrw6FOHee38RUxzZte+YpDMIzl37hznz5+f9lquldiKRUTTMU3rRo5pVp2RZGPwUEwjGNemdTz84m07uHm177KWIqU+52KRi237GmmV0VRVfbeiKA8DzymK8o/AN1RV1Ys1QZvCkkt5xEIueZJCAkwrI5B0PEqCgCBYod7MOeXil7hcB2aFy4FblnhO7afHHyKumThkgdU+L7dsap7Wr6dQuSFLcZ9OIcjlW/MY8BmsWq0AqKr6I0VRngS+ChxUFOWTqqq+WqQ52hSQ+cojZtb2WChx3eDVN95MhX+vvfEWrty1O+V4PNw3xqG+MUaCUSJxS8BaK8usWquCMG8eSaEcmA5J5PjgBBfGg4iigEMSwIQL40GOD04UJcKyFPfpFIJ5xURV1T9VFOWK9OcURdkOvBPwAS3AzxVFeQz4jKqqoaLM1KYgJMsj5lPbIx8Mw+TFHj//8MaPOP3W6/gjceq27mVy1MHLzx4jEteRBHjp7CDqsJ+IZiIIVuPxsVCMSFxjV2vtvHkkhXJghhJtT8tcMuG4jpFY8vg8DsZC0YJbakmW2j6dQpDTVVJV9VTy34qiTAADwKvA81htKS5gWS//Cbw7lzEVRWkA2oHbsXoMfw8wgePAJ1VVXZ5eqCXOQmp7pDPfsuJnJ3o4NRoiONiLPxKHtm2Eq1fTPRFkU72P17suoukGJwYniGh6yuGpGyamYSKIAp+9Zduc8yjU5j/DMPn+gXN0jkwhigKiAGVOifpyN6IgEIrrBbPUMllq+3QKwUIkd5Oqqtlybf9CUZSP5DKAoigOrGblyX4DfwF8WVXVFxPO3vuAnyxgbjY5sJDaHvMtK5JFki1rQWTDvlu4INfhqmsGYDAQprXKi6abnB72E47r05YjumEyEooy5I8QiMbnFJNCJZP97EQPXWNBZNla2gCE4jpjoRh1Za6CWGrzsZT26VwueYvJLEKS5L05DvO/sZqVfyHxeC/wUuLfTwJ3YItJ0Uiv7ZFrnslsywrTBEGwzPXz585yNuqiTNCoqjMRq5tS70+FOwWIaMaMWqwIENMNNGN+g7QQDsykdeNxSqz2eemeCCIIAgKW76jS7eCG9Y1Fy79ZiRT0Sqmqqs53jKIovwkMq6r6tKIoSTEREu1Cweo1XJn1zRm8HXoNLwbz9WLVDJMnTwynfr3T+T9qFy0VDvy95+k/eZgRVzWhLVdz8GwPkahOJHmgABMj4IxHMA0dwWSa30YUQNd1vEaUro4T9M7jRC0P++kYC0+rU2qYJltqPBw9fCg172DcoMwhzujNnN4/WCk3CUyZDIXjGKaJgMCaemfW/spJltrfcClQqvagpqIotwG7gO8DDWmvv217DS9VxkJRqgaPz/iVjusGRyYHaAqMMdV7Dp+vAt8ahYuiiO4qY32Ni6FAGAGBZp+H1sZK6ht0hmLdjAQjTMU0wPJVOCQRn0vmE7+yh3271s47p927Z192Afz42AXe6hlF00Uq3I4ZkZ64bvBK4FhqvNYWa/fwVMzyX/zPd+2c1YexHP+G+bIseg2rqnpj8t+KorwIfBz4hqIoN6uq+iJwF/DCYs9ruZJLnsXl5GLEdYO4buBOCyWbpol60U/vZJAL6gkmhlQqXA72XncjTRuv4JdqF1OaTmViuSEJAmuqygB4R1sde1bX8Fj7eU5enGQqqmEClS4Hd29r5cEr23Ka12wOTMMw+dLPD/HLC8NouolTFmksd6MlllnJSE+28KxTFqkUncs6PFtKlsqC8PeAv1cUxQmcwooK2cxBLnkWueZiZBObbI27DdPkisbKVGGhcO85nL0nEZwyQtt2Jn0tNEHKjyJLIlc0VrKjuYqbNzSlSiMahoksihzsHWM0GMXrlNjXVscDO9ryzizNdGD+5Fg3r3ddRBSsnBHTNOn3W9kKsiROi/SsxPBsKSmpmKiqenPaw5tKNY/lSC55FvMdM5fYZL5XafBxcmiSjouT9E6EMcYHEbpPUFfmwlyzDW/rRgYDYXomgpwbCVHhNTnUN8bqKm9KPJLzKlZYNK4bHOgZQTdMxLQapqIgMDQVodnnpXt8ijXV5TgkcUWGZ0vJUrFMbPIglzwLYN5jnjjZy4HuUXTTwJWo4XGwdwzdMDk+ODGjcfe2piqmonHqvG4q19Xx2lgvYW81AV8rExNBwnEdORGtCU+FGQpEUC9OcmJgAlFgRv5HocOigWgc3SCrIAz6QxzQDb79GjN8KCspPFtKbDFZhuSSZ5H8t0uWiOk6kiCimwZOSSKq6YxMRfjhoS6GpiLENCPlW1AafBzoGUHTTSrcM0OspgnlbpnOsSDGxr14RREvoBkG50enCMbiaIaJLAqp8G+/P8RTHf38j5u2FvWmrXA5KHfJNFV4GPBfKss4EowSiunsaPakzmm5b/dfithisgzJJc/CMEy6x4MMBsIMBcJENAO3JNHoc9NU4eHJjj4ujE/hkqUZvoXWqjLkLK0OBs+eYmKgl7vvv5c3LowgiZcsAMOwskdHQ1EcmUkkgsBQIIyUZ7vJfEk6VTXdwDBN+iZD6KbJVDTOmiovVzReyjgodVvOlYh9FZchyZtGN6YnfujGpaI9T3b0YZgmw1MRQnEd0zQJxeMMT0XQDIOn1f7U0iZJ0rfgcUhctbp22vj9Z05y+q3XcQRH2ChHqfE4gUvJaK1VXqo8LgRmpqMIWJGSkWAq6ySVMTtX7Y5cjsnk7ita0QyT4WCUuG6CadJQ7uaWTU0zKt8v5+3+SxHbMlmmzBWJSPpUNtdbTlMBK6FLFAQEQaCtupz9F4ZZVellKBCZlo0a1XQ21VfwwI42ZFHkcN8Yr7zxJv3HDuCWJTbtfSe9go9tTRoRTU+VDvA4ZAzTSpUXTQMDS1QEwOd20lJZTrPPm9Xpm9kI63J2BD9+qheHJHL9ugZiuo4sirzSOcTp4QBbGqfnQi7n7f5LEVtMcmQxC//m8lnpkYixUBSAGq8LURSYCMUIxTQEASrdDmq9LnTTQBJEtETRZBOBtTVW7kffZAjDNHHJEmuqynh459rU+GdPHIMLx1lTXcb63dfQuGELPzzcxcBkiJFQlGja8qmuzMWWhkq6RsZxOB0IWEuyaq+Ta9rq8DplfnqsO+WvAJOTgxMzGmGZJhzqy39HcKZj2iNa713l8zIQCLOp3pd6bblv91+K2GIyD7lscCuUyOT7i2wYJk+c7J1xfLIQsm6YqTnJgvV/pyzicci0VnroHJliNBzDxPIh1HldvG9XG65Egtr+A2/x3HPP4pQl1u7aR/36LRwdGGdkKspEJI4oWL6WqVgMKQgN5W5+9+atPP7mCTqjIjHNpMxl7VL+yp27iOsGh/vGODPsZ2gqwqA/RCiuU+GSERM1TA50j9I5OtOKyMXHMZtjWmnwEdV0YrqOqWHnkxQJW0zmIZcNbpdTnCddjJ442ZtXjY658kiS2Z3pkY1kWweAdTUVdI4FME0TAZBFK+/C2uoGMU3n4IkOoprO5r3XMlGxilOdQ5wfnUIUIBjV2Nzgo77MbVk9oojSUMnJoUk+sbuRHTt30T0+RbnLkWoQPhaKcqR/nLFQFAGBcFxHAAKROLpu9bMB6JkIsb62IhV9SloT8+0Ins0xLQgCO1tq+Owt24houp1PUiRsMZmDufI5fnS4i/W1FThlcUHFeaJxnf84coEzI34icR23Q+L0RX+qOnr6Z2X7RZ4v1+Tzt24HLKdqVNMZD8fwuRxsqq/gylVVHB+YZGtTFYphEtP11E17sHcUfyTGudEpglWbiazTOGlUwWSQpD/WMK33TIRj1Je7kQWRuG6mfvkDMX1Gg/Cdq6q5bVMzkwmLRjOMaX6cSCJ8LQowFozwcucgpilMC1nP5+OYr4KZ1ynbu4CLiH1l52A2s1k3THomQrTVlJEeEJvrxk9aH4Zp8tNj3fzwUBcXxqdwyhK1Xhfrasq5MD4FMMPEz/aLPF+uyUQkxo0bGrltUzM/Od5Nx5A/EbmwLIJwTMPrki3nqWhVXusYmuStY8epbmyh3OuhsdxN65q1vNk9QoXbkfDJgGkIuBxyIkpkWWjJrniSKHBwaJIp/0yLaSqqUeV2MBaKJYTDuuFNwCNbeTCdo8GUhSSnhax10+SDe9bNa1HYKfKlwxaTOZjNbI7pOgJmqqVkOqGYxlgoikMSKXPIPNnRN+2L3dVzkeYmkcFAGH8kTiAa4txIAHV4EgGBwQxHIWSPOsw2N9O08kv+5lWVqKbTPR5EN022NlZSW2aJUceQn+6J4DTRUi/6OXPiKFMnDuAdb8W783r6/SFqvC68TpmpmGYlhTllTBMq3LLV8Ns0EBFSy6dtTZU8c6ifVWWWDyTd6jkz4mdbUzVnR/wMBsJ4HBLhuLXsqCtzIQkiA4Ew25urUtcirhs4JBFJELhrS8u8fzM7Rb502GIyC0lrYltTJUf7p6eWS4JIa1XZjCVG8kb+29dUInF92ua4pEP06EiQCXGCsyMBpqLxVPg0putUe5wM+EPWDcilzNWr19TOuCFmM+lPDU2mHKouWWIwEMYwTSRBSImHU7bGSma+6oZJZ8dx/KcPI4sC7vpVCKKIAIwnljKaYXLV6lp8LgdnRwIMBsKJsLBItcfJmmovO1dVc9OGRv77zRN0DE3OyK5dXeVlx6pqBAE21VtO0c7RAP3+EJVuB7ppUONxsqXB6l+zqd6XEqOophOMaynn8HzYKfKLjy0mGWRGVDwOmbhuIAoi4biO1ylz9ZpadrZUcaB7FI9DTt3MyZwOUbBu5KGpCIZh+QW2NFYS03UMU+DMcIBAJJaq7BU3TCKROLpuIorw8rkhJEHARGB1lZerVtda42SIV6ZJ73ZIiIKA0uBDN0wmIzGimp6ay6Z6yx8T03VW+Txc0VjJudEAZ08cY/zUQXxuB9VbduNt3Zj6DE03aSh3MRaOUel2IonWuayvrUBp8OGUBQ73jaPp1n4eE5PBYJxINJR15+5X7trNkx19HO4b58ywn/HEuJvqK9jTUoNblqY1wkqGd+2ckKWPLSYZZIuQOCSRHc3V3LKpKbV0OTno59xIgIlInEq3g+1NVUiJGxmsGzamGTikS0sXpyQhCTASiSOLIpphoJmgJ0oVaoZBucPyqzT5PGxvqkYSBQ71jSEIMx27mSZ9XDf45gsnUC9aodeopjMQCFPulKlwOjg+OM5EOE5MMxBFWFdbzk1lYbTQBcZqy1m3+xomKlZN29filEW2NFSmrJ3kcu3qNbUYpsnhvnE8DhkS9/mh3nGGwxrl5aAlclsEgVSUKDnnuG5wpG8UwzTxR+K81TNG93iI9TUVOGRxRbWAeLtgi0kac0VIjg9OcO/21anwLcDmBh+yKBLVdDbWVRDTjdSvqlOScMoipmkS1w1iuo7HIeNzSgxFdcpdlgMzGo1jmtYWfYcoIIkCbllmPBSb9vnpu4EzfQFJkz6uG/RMhOj3W1aBUxIpdzoIRGIEInFkyYqWdI9PEdV1vvqfz+HsbGdzvY933XEH/soWGhOnPhgIE9V02qrLeUdbHfduW41umqnPBvjas8dmXCvN0BmPaMSkKIGI5fD1uR1sa6qipdIqAdDs8/Ljo92Mh63ITrJcQDIb96GdbZwYnLQdqMsMW0zSmC9CMhaKcrhvHPXiJP3+EIYJTkmkqcKDW5anVSOTRIHGcjf9/hAOSUw5a9dWOtFkkfFIHM2I4hRFHLJImUPCMC1zXhC4JEAJMz8YjfPDQ12cGw3Mm9eStAIA6spcGIbBSCiKbphcmAwSiWuUuRyYNY1oY42M1bUyUtbENa01HOkfZ011GRvrKmip8vJre9ZTnthpKyKk/BBjoWhCoIRpuSBdY0EiukGrx0md18pBERDomwxxcSrCt187jSjCkf4xGhNO29S8BeidCHPD+kbu277GdqAuM2wxSWO+3biGYfJURx+DgTBmIkeiPBHujWo6D1zZxulhf+rGUhp86AnnZ1Sz/C3bar3cffV63uoe5eTQBEf6xzES4dU2nwdHYvNdugABdI0H0QyT8rQ5Zua1BKJx1lSVpXbMJlPklYZKyidD7FxVTV8ghNdpjSuIIuLGq/DWlPFmzyh/+K6d3LWlhR8d6eLMcIDzo1N886WTWSu4vXBmkIN9YykHa12ZizVVXoamIpQ5RASsEgSyIDISjOKPxNjXVk+F28FUNM5UVEMSo9SVTXeSColtgrYDdflhi0ka8yU9vdQ5yOBUGAFSy5mpxK5TSRS5f/tqnj0zMC0U/ME967hrSwvBuBVaPXr4ELu3r7GctA4JExieirC6qixVErFvMkRLpRdJtJyXxwcnUC/66Z+0rJymCg9Kg29GXkuZQ6Z7IshwMJrycdSXu1HqfVb6uygQ7e1EnBzEobwDQZKRJRFJEAlG45wamqBrLMiZ4cC8FdyO9I+nLK/hqQidI37ae0RiukGtQ2JVpYeLU1ErChPTKHPIrEvsBfI4ZCo9lqjUel0p/4xhmrRWldkiskyxxSSD2ZKe7trSwv98+jBeSSYUj0/bzh6IxCyfiWHMmuOQHtJMd5xOhmO8dG6I44MThGIaSoOPtTXluGQpVXtV1w0q3ZfGGvBbvcu2NFZOS2hLlh0wDKvdJsBQwDp235o6hs52QPdxDMCYGEasbaY8UYMkGNP4/oHzHBscT2WcpkdVslVwUxp89E4EGQqEiekGpglOCcZ0q83E9esamIzEONA9iksWcclyarxtjVUc6R9HNw1M41Km6/t2tdnLmmWKLSYZzJb0NBaKoukmjT43I0GYimkYBogieGWZzfUVKcdkria6QxKpK3fz4M427t2+etrnJWt5/O1rKqYJr56/mOozIwikIkTJkGnSeXxFYyWiMDPh6546je88fwyfy0GgaTNy3SrKnDKYJv5IjLbqMjxOkZhmTBOrJJkV3LxOGcO08lBcsoRLFpmMxAnFDCZ1k6c7+hn0h7h5QxNO2bKm0q29LY1WLsn62goC0ThVHmdejtbF3MVtkxu2mMxCpiBUuBxUuB00VXgwTJMarytVywPgHWvqL+tLnfl5DknEIYlEErktySVFMgU9rhuE4xrXrWtIiV3yJt/SWDkt4atHPcHTz3SxpaGSjz38Xn40AIf7xwnHNIaDUdqqy7hlUxMmQioClZmJmy5aggCabnB8cIK+yRAmVh0UXTfxOKz3a7pVxlFkiOvWN85ogmWY8P7da/POVL2cWic2xWXRxURRFAn4e0DB2pbxcSDCEm9cnl4SEGBoKoJuWKHga9vqeW8RaommO4ST+SuXskolrmmrT/2SZzqPkwlfg2dPMXjiLTatb+CWX7mNTdt28I2rLVE4NTTBv7Z3Uem5lAyWFK1kKrxHtDJ3d7fUpModqBf9nBn2oxlmam9OXDcRAd0EWRQQRYnWqjJESeRLt+2Y4UtKWiGiKOTlI8mlKr9NaSiFZXIPgKqq1ymKcjPwVayM8iXfuDx548oJJ6iBybVr63h457qi/CpmOoSTFkc4rrGvrY4Hd7bNeixY6f0TQwM0lrtxb7iSp8Yc/Pi549PqnlR6+qZ9ZrpoJasl7mmtwTBNDvVavpLtTVWcTex2NjGt5R7Wkk83TQRMXA4Jj0MmHNcZmgoXZL9MLlX57SVP6ShFR7+fKoryeOJhG1Yr0NtYQOPybL2G5+ovWwhaTJPTQT9dY2F0E16YHKXvwgVubK2Y1vd2LvJpvdhimpyLBjg9ESGqmbhkgc1Vbtri0N4+Ou+xe3btYOziEKemBMRgP2Bd8N6+Ps6dO0c5zOjZW26a7G5yc1WdTpnDhMhF/imt13BUN5H1OLUOE9EQMHSDQc2qXG+aJrIsUCEZTE0FQIDBcyoTFy7/Jk/vD5xJVDd5ZX+USldue3cuF7vX8ExK4jNRVVVTFOWfgfcCDwG3L6RxeXqv4cVaS//0WDdTHpm1qy+NOWmY9LlqcjKzF9Kn9uqr5nY4pr929VUiJ051UNvcQm2FFYr92rPHaM0y7hTw+Vu3z9jZnHndMnsN64bJhegQAB7doKHczVu9o0yEokiSSI1DYHWdVfD6unUNXLdvT17nOxuZ/YEzuWHfjkWxTOxew9kpmQNWVdUPKYryOWA/kJ4KmXPj8nSyraUPdI8yFdV4/+61C/6Spd+oMH9jq2J9mbNFiLIJaMVkL9Fzx1i7to0HHnofvZMhApF41h44oZhGMK7NuwTJ5o9JVnBzyhLbm6vZ2ljJM2cGCMY0hLi1ifG6dfV85c5dBb0Gc+UB2Uuc0lIKB+yvA62qqn4dCAEG8NblNC7PXEsnG2sPBsK8fG6Q08P+aQ6/XMh2o26orSAYjVOWZffqfCUFZ5v35fgQMgV08OwpXj/0Bk0VHlwbfPzZ8yeYimoc7BtL5Y4YJqkoT/pO3LnC2dluYmssM1XJzeuU+cKv7OC2Tc08+8Zb3PHOq4tS1cwufrR0KYVl8mPgu4qivIy11/QzWM3KF9y4PHNPTbKxdrIKe0zX8/b4Z7N0Tg1N0jMRmlEJDfLbIl+IJVmmgA6ePUXX4f1Wjkn1emqdDZQbJuUuK6zcOxmkZ2IKQRAwDJAlgXeubci5MVa2m/gDGdm9SUFcXeEsWnlEu/jR0qUUDtgg8HCWlxbcuDzdDNcNK0cifQt9ciNarkuR2aIGmUWFkuRrZieFCqywqm6YeYtduoAmhcQ0TVizDZVqJruG8Tgkmio8bKqr4Ej/OAOBEF5ZxuWQaPV5kARrLrl85lw3ca4FiwqJvXdn6bEiktbSzfCYrqcyP5PV2HOtbp4k/UbNLD24pqqMLY0+Oken8jaz47rBWFijfWgsVbowOdemCg+iIOTsd0kK6PhAL12H9wMgtG1n0rcKczKMO7FhcMAfpmciSCiuUeF00OzzpHY3nxudwiFLefl67JvYZjZWhJjAJTP8UN8YYFU8X5XYEJck16VIhcuBxyHTMTQ544ZXGir51d3rgJl1RWYjfVlzqmuYo+MaoiBQV+aatt8mqumpMecbOymgb2kaNS1tlNc1ckasRdYNfAlnq2YYiIJAz0QQ0wRJElLV2MDKJWmp9Obt67GxycaKEZN0M3xjXQUdQ/4FL0UcklXwqG8yhCQKqff0TYZYW1OeepzrDfjjYxfYf2EEj0OmTBaJaAaGYaWl15W5AWu5Mx6O8azaz6mL/ll9KUmnbZlDSkuiu5mxUIxY7xitVR5M06RrLJDq+RuMaXhkiXKnAwGrzYQkWPt/ZEmwyyHaFIQVIyZJHJLII3vWz3Bw5ruJzCWLtFR6pxVFbqn04JLFlKUyH4Zh8pNj3fzta6cv+VkiUZySQMQUmIpp1HitiIhVE8XgQM9o1pol925bnTqnrlPHCV3s5X0PPcQDu9anWoT+7Wsqpy/6EQXBqhsS09B1S0jLnRKGYdI9EUztKfI6JTukalMwVpyYwOV7/APROOG4PmPDXLIGarZlQbYw789O9PDGhWFimo5DsjbATUY1HJIThyThj8SJxA3KXDJN5W4uTkWseqppJB3HumFypH+c4c4OBk+8BcCLB48jOxzcv2MNjRUermyu5sWzg4iiQF2ZmxqvVTJSxMNENI5pmImdx1aZgiq3G4e4+M5Tm5XJihST9Bt7Ib6A9OhQeoV0mOl3mS3Me9eWFo70W8WW04XM8lcIVHuc1Ja5uHp1bWIPi0ZcN2ZEkMBqn3mgZ4TJC2dSzta1u/ZRv3rttAjVTRsa+UF7J5OReMoSWob1IVcAABLoSURBVOXzsqG2nB8f66a2zE2VaSIK0OzzsrWxkqMD49y9zd7TYnP5rCgxKVRKfT6ZlrPtYh0PRxkNRqnyOKf1+wWodDuo9TpxO6yG3ZIosK+tjjLnTN+FbpjEDYOLnacZPmmlOK/dtY+mjVcA0yNUlR4nu1pqZkSgwnGNWq+LG9c35t2/18YmV1aUmBRye3oumZbZ8lFM0+TMsJ+XO4cSvheJhnIXTRVuayeuCU5Z4pGr1nPP1tVMRKwq9DVeF7J4qXF5Mot3IBBGvNjFwPG3KHfK7L3uRpo2XpESDLdDmpbFmhTB9OVSsmmY5ZCeboHY/WhsCsWKEZNCb0/Pxe+SrZq9etGfqgfSVOFheCpC93iQ1VVl3LC+ka5ejfveofDeHWtmWFE7mqvY3VLD0YFxjvSPMxaK0ljuxjRDTDplzDXbmPC1MJkIWSdbUTxxsjdlfWUTwavX1LJ3dQ2H+8btPS02RWPFiMl8bSoWasrPlaRV4XLgdkipotJOyeqcJwoCgghSItzrj8TpT1Qku7rOkxKSTCvqcN84e1pr+Owt2/iTp49wfmyKi1MRYjWbcW6pxlHdyInBCcpdDjwOibbqcpQG3zTrazYRTHYWtPe02BSLFSMm87WpyMeUD8U0Bvwhmn3eWcc0DJP/PtHDC6cH6BoPIggCZYkWEvXlbkzDZGgqQl2Zi1qvi4im01rpRWQK3TTntKKuXVvPoWMnCHtrkRwyTocMdc3ohomm67xjTVOqVSdYopVpfWWKoL2nxabYrBgxmc9pClZdjrluIk0zePSpw+zvHrHaMzhl9q2p4yt37kKWp7/nZyd6eOxQF4IoUOlxEkxYPzHNoNrrSlWHByshzeOQKHc5OD0UmVavNZNQTOPgwXa621/BWVVP9a4bEERrLBOTUNyg3OmYIURJ62u27NnLjXDZ2MzHihETyO403d1ilRz82rPH5o3wPPrUYV7pHLJuflnCNE1eO3+RR586zNfvvlTgJ64bHOwdYyQYTaXF13pd6KbBeCiGYZrEdCMlKKYJzT5rj1BUs/JSZ7N4/N1neH30LG5ZQq5flRISsDr1lTkkdNMg05Hqccg8f2ZgWlvNnauqufuKVh4/1WsXYLYpOitKTLKZ8k+c7E3VLp0rwjMVifP4yV6CMe1ShqhDosrj5I0Lw9MsiUA0zkQ4lmpMDqS611W6HVR5nPgTjcQdkkiz79IeIZdsFVDOZkX1nzlJtPMonnof+264iUlfy4wM3FU+D5IwXUh0w0pOO9o/MeM8918Ysdpd2AWYbYrMihKTJOmNvHON8PygvZOxUBRnorl3MKYxHooy5A8jSSLfeUPl09dvRRStvSxVHmeqLUQ6TlniqjV1bGus5EDPKB6HnPp83TDZXOXGIYkzrCh/9xminUdR6iu5447b6XHWc7B3bFoGLsCuluoZjtSdq6o5NjA+rTFYkv3dI1y/rmHe87exuVxWpJgkyTXCE9cNeidDVh6GCaG4TjSuEdGMVK/gv3zhFK+cG+aHv34jDllkT2sNHRcnGQpcKsZsmtBY4WZ3ixUlccrSjOhJS1QDpltRxzpO89Tx84gNldx+++3s3buX3YYlUkf6xzE1SwDSlyfp1lcgGufN7pEZ5xmOa0yGY4TjGuUZDmg7Wc2m0KxoMck1whNILElW+7xcGA8S03RLSAwTA/DIVhPut7pH+N2fvcU377uKe7etxjBN/uPwBXongpgIrK7y8vCutakbPlv0pL19eNo8HNL/3979x8ZZ3wccfz/Pc3f+kdhOAvGvLLGjUr4xZ5JAJmiBbIE1JESEVVVHqylMVSWkSkxCU9V1q8ofm7pKrapKpQhtAkXbIqJWXSfEYLSwtnRGQg0cYSQ4+YKwExxik8RO7Di278fzPPvjubvc2Xf2+XjO53vu8/rr/Jw5P19y97nvz8/HZGffzZz7MMqmTZuyiYqXWn3JXa2Z387cDW+XrsV5c2ScrnQ6hkzvRTarCb8FOpiUui0+82G897OdvKJHuTJ2haTjegXK8YqUX5pJ4Lou//nuGS8L/JYb+GL/Fg5GNzMxEwe8Xazzhw2L7VNxHAfTNDFNk4MHDxYcppSSjGh+OzMb5wwMtqxfAy6cn5oBvLKcsllNVELg300PRTdnl4ZnEt4QY/5mrcyH0cVgr+rm5o2tRCwIGdAYtrBdsB0H23GYTTpMJ5K8fW6CF94bIWyZdLQ00dHStKwPZywW4+jRo8TjXiAqFEjKaaftuJy7cg3TMOhqbeK+mzrpavWyuJ1Lpx+QzWqiEgLZM5mfDqCUzVq5E6IdrY2c/MQgFDIJmwbxlFfaLmRZuEBTKPSpJjFjsRivvvoqAMPDw2zbtq3stmVkhkWf793I5Fwib1NbJpXC5FyCx+5RdLQ0FXt5IcpWjVIXYeAw0As0AN8DBvGh1vBip4aXGi7kzlF8cHGKg8/+lvGZOE66Sp1pGlgGNIctMLzJ0XImMU+fPs3IyAgAe/fuLTmQlHoiekNzQ8H7sdI1fWXCVVRKNYY5h4BxrfVuYD/wFPBjvFrDu/GmKf68nBfOzfqeu6fihfdGCv5+0naYmImTTBfVdRyXlwbP8bPjZ+hqaaS1IQy42K5LIuUQTzkkbZuh8au4rrvsScxYLMaxY8dwXJc7d+9h+87bfG9bZshmO/lL1rbjsqN7vcyTiIqpxjDnF1yvi2MAKWAXZdQahuvd/sb0Mmwpe0qKfcs7rss7H18GoL2licvpZVXXzSQ1cnExGBybBAz+8vatJX84//DmW7z08q8YujJH4uatTE6E+d//OVHSbtTlnoiWQlWiGqpRN2caQCnVghdUvgv8qJxaw8++8gbvjs8ST7kYuJydSnDTuoYFk5nzi1q/NjKVV6z7CvDRuY8ZuRrHwGB8LkXScRmdijNne5nJwpaJC5iOzeXpGS5esrnhWtOCpd75HNfl9yNT/G7gdYY+HMLqvYVEqpGeSxfyCojv2dxa9DXKKdi9GejakCni7hJKXOT48cXv1U9BL+wd9PaVoyoTsEqpzXg9j6e11keVUj/MebrkWsNTkTa6u9YDXjd+aG6Mq6HmghX3MkWtk7bDi5dO8Eeb8p+fTaZ448IIN65tpKXFy+DenDCw5xKELQPV0UbINLk4Pcd0IsVVIrx0yeJzPRsX7Vk8f+IjphpD3P5nD3Jl/Uni4WbiVhNXrabsfU4D23cWL7q9Wgp2lyrohb2D3j4oL1iu+DtQKdUBvAJ8W2t9OH35uFJqT/rxA8BAKa+V2+23TIPu1mZGr87mzRdk5grAOzWcObG74LUMk2tJGzddIMIyTMKWgWkZ2K7388RMgtmkTcg0WdsYJmJZi87JnHhvkNjZT7BMg6TrYrR529oNA8Zy7jMzkVuMzIOIWlCNnsl3gPXAE0qpJ9LXHgeeLLfWcIZqbyWesknYNm6KgqeGG8MWH12+lrcbFMB2HdZELAyuH9xb2xDmWiKFi/fBnY4ncYE1YYuunEqBheYtYrEYL/z3rzgxBTvvO0DEsoiETObSzydth4Rt02SGSprIlXkQsdpVY87kcbzgMV/ZtYYzDMNgxyYvU9lcyi56athxXQY/mSTaue76f+wabGtvI2yZXnazlENbY5jWhrB3AtixSdku65ojRDvb8ioFzl8izuwjaQiZdPVuxTBNLKBjbSOTk5OA19uIWFbJu1EluZFY7Wp605rX7V+4Tb454n3bF1sF6etoQ1+YwnZc5pIpRq7MZJ/zhh9etT0Xg5BlcKBvE4/drfjp66fzTgFn5PYscjek7bv//uzpX8s0UO2tTExMEAc2NEXyDu+VSmr9itWqpoNJf9c63h6dKtrtL3Zq2DC8MyuP3aN4VY8STg9BXNflNx+Mce7KDM3hMJ1tTXSsbSRimbz98QR3b23P7vXIyO1Z5AaSQqd/Z5M2vesa2HubYs9nOmlrikjvQgRGTQeT+1U3+6M9Rbv9pZwa/nD8arYmseNCyDTo3dCC7Tp8vmdj9rn/O3+Zv7uvP/t4fgAbHh5eEEhg4fDkg/dOcOeO3kr87xCiqmo6mMDi3f6lTg3Ppey8nkvCtrPZ0VzHyEuPOJNIcS2ZKjpv0dPTQzQapbu7u+CyYeY+Q5IqUQRUzQeTpSy2CmKnt8RnRCwrGxwiITOb3Qzy50VyA1huGoEHH3zwU5/+FaJWBT6YLLYKYtsun7mhhVOfTBIJeXlSO1ua+Hhyhk1tTXnpFgutuMRiMQYHB3n44YdpaFi481aIehL4YJKR35u4fjbnWjyZXc3Zsm4Nqr2N3g1raQiZi+7nyJ1sHRoaoq+vr2h6ACHqQd0Ek1y51fTWNITZ1tFGIuWwraOVr962NbvtvlhgmL9qo9Q2nj/xkZSTEHWt7r4+i+09iYRMhsansz9nejJLBZJdu3YtO/WBEEFUd8Eks/ekkKXOyBQKJEulB8jkShEi6OoumDSGLAyDBYfmYPGM7a7rMjo6CuTvI/k0wUmIIKmbOZPcSVd9YYqJmTjdrc3ZA39LnZExDIMDBw4QjUbZunVr9rqfBdOFqGV10zPJndfY0b0+m67g3fNeZrViJ3BPnTrF3Jx31tc0zbxAApIeQIiMuuiZzJ/XMAwjm7E9Ydt8695owd5FZo6ku7ubQ4cOYZqFA4OkBxCiToJJsQN/lmngpmAuZS94LneyNRqNFg0kIOkBhIA6CSbLndcotGpTCkkPIOpZXXx9Lmdeo9xAIkS9q4tgAqWVCT1z5owEEiHKVBfDHChtXqOnp4ft27fT0dEhgUSIZaqbYJJRaF7Dtm0sy8IwDB544AE5/StEGepmmFNMLBbjueeey+4lkUAiRHmq1jNRSt0J/EBrvUcpdRM+FC5frvlpBG655ZZK/0khAqsqPROl1N8CzwKN6Uu+FC5fjvmrNhJIhPh0qtUz+RD4EnAk/XNZhctPnjxZ1h8/ffo0x44dA+COO+4AVrZ2bD3UqQ16G4PevnJUJZhorX+plOrNuWSUU7i8v7+fhoblbRKLxWKMjIzQ1dVVleXfeqlTG+Q2Br19UCO1hovInR8puXD5crmuy4ULFwDZRyKE31bL0vBxpdQerfVreIXLf1eJP2IYBvv376evr4/e3t5K/Akh6tZq6Zl8E/gHpdQbQIQyC5cXMzg4yOzsLOAFFAkkQvivaj0TrfUZ4HPpx+/jQ+HyQjKrNp2dnTzyyCNYObVwhBD+WS09k4rIXf699dZbJZAIUUGBDSZy+leIlRXIYCKBRIiVF7hgcvbsWQkkQlTBalka9s2WLVvYuXMnGzdulEAixAoKTDDJTSOwb98+Of0rxAoLxDAnFotx5MiRvL0kQoiVVfPBJDPZOjY2xvDwcLVvR4i6VdPB5OTJk5JGQIhVoqaDycDAACCrNkKsBjUdTEACiRCrRa2u5lgAu3fvpr+/n3g8Xu37WZZau99yBL2NQW9fjpLPoBiu6y79W6tMLBa7Bxio9n0IUQd279q16/VSfrFWeyZvAruBUcCu8r0IEUQW0IX3WStJTfZMhBCrT81PwAohVgcJJkIIX0gwEUL4QoKJEMIXEkyEEL6o1aXhVU8pFQYOA71AA/A9YJAq1FSuNKVUOxAD9gIpAtZGpdTfAw/hVU54Gq/65L8SkDam36v/hvdetYFHKePfUXomlXMIGE/XT94PPEUVaipXWvqN+C/AbPpSoNqolNoD3AXcjVdBYTMBayNwAAhpre8C/hH4J8poowSTyvkF8ET6sYEX6efXVP5CFe7Lbz8C/hk4n/45aG3cB5zAq339X8CLBK+N7wMhpZQJtAJJymijBJMK0VpPa62vKqVa8IqKfZcyayqvVkqprwEXtda/zrkcqDYCNwJ/DPwF8A3gOcAMWBun8YY4p4FngCcp499RgkkFKaU245U6PaK1PsoK1VReQV8H9iqlXgN2Av8OtOc8H4Q2jgO/1lontNYamCP/gxWENv4NXhtvBnbgzZ9Ecp4vqY0STCpEKdUBvAJ8W2t9OH35eHoMDl5N5Zo+rKi1/hOt9Z9qrfcA7wB/BbwcpDYCrwP7lVKGUqobWAP8JmBtvAxMph9PAGHKeK/K2ZwKUUr9BPgKXtcx43G8LmQEOAU8qrUOxEHFdO/kG3i9r2cIUBuVUj8E7sX78v0OMEyA2qiUWou38tiF16afAG+xzDZKMBFC+EKGOUIIX0gwEUL4QoKJEMIXEkyEEL6QYCKE8IUEEyGELySYCCF8IcFEVIxSql0pNZk+QJa59rJS6svVvC9RGRJMRMVorS8AY0A/gFLqYcDVWv9HVW9MVIQkRxKVNgDcpZQ6A3wfL4GSCCAJJqLSBoD7gChwWGs9XOX7ERUiwURU2gBe1q7zwO1VvhdRQTJnIirtLN7J07/WWierfTOiciSYiEp7HPi51vr3S/6mqGkyzBEVoZTahpc39SwgS8F1QPKZCCF8IcMcIYQvJJgIIXwhwUQI4QsJJkIIX0gwEUL4QoKJEMIXEkyEEL74f+Llg/fzQPLcAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualizer = PredictionError(lasso, alpha=0.5)\n", + "\n", + "visualizer.fit(X_train, y_train) # Fit the training data to the visualizer\n", + "visualizer.score(X_test, y_test) # Evaluate the model on the test data\n", + "g = visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "visualizer = PredictionError(lasso, alpha=0.475)\n", + "\n", + "visualizer.fit(X_train, y_train) # Fit the training data to the visualizer\n", + "visualizer.score(X_test, y_test) # Evaluate the model on the test data\n", + "g = visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "visualizer = PredictionError(lasso, alpha=0.9)\n", + "\n", + "visualizer.fit(X_train, y_train) # Fit the training data to the visualizer\n", + "visualizer.score(X_test, y_test) # Evaluate the model on the test data\n", + "g = visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "visualizer = PredictionError(lasso, alpha=0.3)\n", + "\n", + "visualizer.fit(X_train, y_train) # Fit the training data to the visualizer\n", + "visualizer.score(X_test, y_test) # Evaluate the model on the test data\n", + "g = visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How about Residuals?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import Ridge\n", + "from yellowbrick.regressor import ResidualsPlot\n", + "\n", + "# Instantiate the linear model and visualizer\n", + "ridge = Ridge()\n", + "visualizer = ResidualsPlot(ridge)\n", + "\n", + "visualizer.fit(X_train, y_train) # Fit the training data to the model\n", + "visualizer.score(X_test, y_test) # Evaluate the model on the test data\n", + "visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ridge = Ridge()\n", + "visualizer = ResidualsPlot(ridge, alpha=0.3)\n", + "\n", + "visualizer.fit(X_train, y_train) # Fit the training data to the model\n", + "visualizer.score(X_test, y_test) # Evaluate the model on the test data\n", + "visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "##########################################################################\n", + "## Residuals Plots\n", + "##########################################################################\n", + "\n", + "class ResidualsPlot(RegressionScoreVisualizer):\n", + " \"\"\"\n", + " A residual plot shows the residuals on the vertical axis and the\n", + " independent variable on the horizontal axis.\n", + "\n", + " If the points are randomly dispersed around the horizontal axis, a linear\n", + " regression model is appropriate for the data; otherwise, a non-linear\n", + " model is more appropriate.\n", + "\n", + " Parameters\n", + " ----------\n", + " model : a Scikit-Learn regressor\n", + " Should be an instance of a regressor, otherwise will raise a\n", + " YellowbrickTypeError exception on instantiation.\n", + "\n", + " ax : matplotlib Axes, default: None\n", + " The axes to plot the figure on. If None is passed in the current axes\n", + " will be used (or generated if required).\n", + "\n", + " hist : {True, False, None, 'density', 'frequency'}, default: True\n", + " Draw a histogram showing the distribution of the residuals on the\n", + " right side of the figure. Requires Matplotlib >= 2.0.2.\n", + " If set to 'density', the probability density function will be plotted.\n", + " If set to True or 'frequency' then the frequency will be plotted.\n", + "\n", + " train_color : color, default: 'b'\n", + " Residuals for training data are ploted with this color but also\n", + " given an opacity of 0.5 to ensure that the test data residuals\n", + " are more visible. Can be any matplotlib color.\n", + "\n", + " test_color : color, default: 'g'\n", + " Residuals for test data are plotted with this color. In order to\n", + " create generalizable models, reserved test data residuals are of\n", + " the most analytical interest, so these points are highlighted by\n", + " having full opacity. Can be any matplotlib color.\n", + "\n", + " line_color : color, default: dark grey\n", + " Defines the color of the zero error line, can be any matplotlib color.\n", + "\n", + " alpha : float, default: 1.0\n", + " Specify a transparency where 1 is completely opaque and 0 is completely\n", + " transparent. This property makes densely clustered points more visible.\n", + "\n", + " kwargs : dict\n", + " Keyword arguments that are passed to the base class and may influence\n", + " the visualization as defined in other Visualizers.\n", + "\n", + " Examples\n", + " --------\n", + "\n", + " >>> from yellowbrick.regressor import ResidualsPlot\n", + " >>> from sklearn.linear_model import Ridge\n", + " >>> model = ResidualsPlot(Ridge())\n", + " >>> model.fit(X_train, y_train)\n", + " >>> model.score(X_test, y_test)\n", + " >>> model.poof()\n", + "\n", + " Notes\n", + " -----\n", + " ResidualsPlot is a ScoreVisualizer, meaning that it wraps a model and\n", + " its primary entry point is the ``score()`` method.\n", + "\n", + " The residuals histogram feature requires matplotlib 2.0.2 or greater.\n", + " \"\"\"\n", + " def __init__(self, model, ax=None, hist=True, train_color='b',\n", + " test_color='g', line_color=LINE_COLOR, alpha=1.0,\n", + " **kwargs):\n", + "\n", + " super(ResidualsPlot, self).__init__(model, ax=ax, **kwargs)\n", + "\n", + " # TODO: allow more scatter plot arguments for train and test points\n", + " # See #475 (RE: ScatterPlotMixin)\n", + " self.colors = {\n", + " 'train_point': train_color,\n", + " 'test_point': test_color,\n", + " 'line': line_color,\n", + " }\n", + "\n", + " self.hist = hist\n", + " if self.hist not in {True, 'density', 'frequency', None, False}:\n", + " raise YellowbrickValueError(\n", + " \"'{}' is an invalid argument for hist, use None, True, \" \\\n", + " \"False, 'density', or 'frequency'\".format(hist)\n", + " )\n", + "\n", + " if self.hist in {True, 'density', 'frequency'}:\n", + " self.hax # If hist is True, test the version availability\n", + "\n", + " # Store labels and colors for the legend ordered by call\n", + " self._labels, self._colors = [], []\n", + "\n", + " self.alpha = alpha\n", + "\n", + " @memoized\n", + " def hax(self):\n", + " \"\"\"\n", + " Returns the histogram axes, creating it only on demand.\n", + " \"\"\"\n", + " if make_axes_locatable is None:\n", + " raise YellowbrickValueError((\n", + " \"residuals histogram requires matplotlib 2.0.2 or greater \"\n", + " \"please upgrade matplotlib or set hist=False on the visualizer\"\n", + " ))\n", + "\n", + " divider = make_axes_locatable(self.ax)\n", + "\n", + " hax = divider.append_axes(\"right\", size=1, pad=0.1, sharey=self.ax)\n", + " hax.yaxis.tick_right()\n", + " hax.grid(False, axis='x')\n", + "\n", + " return hax\n", + "\n", + " def fit(self, X, y, **kwargs):\n", + " \"\"\"\n", + " Parameters\n", + " ----------\n", + " X : ndarray or DataFrame of shape n x m\n", + " A matrix of n instances with m features\n", + "\n", + " y : ndarray or Series of length n\n", + " An array or series of target values\n", + "\n", + " kwargs: keyword arguments passed to Scikit-Learn API.\n", + "\n", + " Returns\n", + " -------\n", + " self : visualizer instance\n", + " \"\"\"\n", + " super(ResidualsPlot, self).fit(X, y, **kwargs)\n", + " self.score(X, y, train=True)\n", + " return self\n", + "\n", + " def score(self, X, y=None, train=False, **kwargs):\n", + " \"\"\"\n", + " Generates predicted target values using the Scikit-Learn\n", + " estimator.\n", + "\n", + " Parameters\n", + " ----------\n", + " X : array-like\n", + " X (also X_test) are the dependent variables of test set to predict\n", + "\n", + " y : array-like\n", + " y (also y_test) is the independent actual variables to score against\n", + "\n", + " train : boolean\n", + " If False, `score` assumes that the residual points being plotted\n", + " are from the test data; if True, `score` assumes the residuals\n", + " are the train data.\n", + "\n", + " Returns\n", + " ------\n", + " score : float\n", + " The score of the underlying estimator, usually the R-squared score\n", + " for regression estimators.\n", + " \"\"\"\n", + " score = self.estimator.score(X, y, **kwargs)\n", + " if train:\n", + " self.train_score_ = score\n", + " else:\n", + " self.test_score_ = score\n", + "\n", + " y_pred = self.predict(X)\n", + " scores = y_pred - y\n", + " self.draw(y_pred, scores, train=train)\n", + "\n", + " return score\n", + "\n", + " def draw(self, y_pred, residuals, train=False, **kwargs):\n", + " \"\"\"\n", + " Draw the residuals against the predicted value for the specified split.\n", + " It is best to draw the training split first, then the test split so\n", + " that the test split (usually smaller) is above the training split;\n", + " particularly if the histogram is turned on.\n", + "\n", + " Parameters\n", + " ----------\n", + " y_pred : ndarray or Series of length n\n", + " An array or series of predicted target values\n", + "\n", + " residuals : ndarray or Series of length n\n", + " An array or series of the difference between the predicted and the\n", + " target values\n", + "\n", + " train : boolean, default: False\n", + " If False, `draw` assumes that the residual points being plotted\n", + " are from the test data; if True, `draw` assumes the residuals\n", + " are the train data.\n", + "\n", + " Returns\n", + " ------\n", + " ax : the axis with the plotted figure\n", + " \"\"\"\n", + "\n", + " if train:\n", + " color = self.colors['train_point']\n", + " label = \"Train $R^2 = {:0.3f}$\".format(self.train_score_)\n", + " else:\n", + " color = self.colors['test_point']\n", + " label = \"Test $R^2 = {:0.3f}$\".format(self.test_score_)\n", + "\n", + " # Update the legend information\n", + " self._labels.append(label)\n", + " self._colors.append(color)\n", + "\n", + " # Draw the residuals scatter plot\n", + " self.ax.scatter(\n", + " y_pred, residuals, c=color, alpha=self.alpha, label=label\n", + " )\n", + "\n", + " # Add residuals histogram\n", + " if self.hist in {True, 'frequency'}:\n", + " self.hax.hist(residuals, bins=50, orientation=\"horizontal\")\n", + " elif self.hist == 'density':\n", + " self.hax.hist(\n", + " residuals, bins=50, orientation=\"horizontal\", density=True\n", + " )\n", + "\n", + " # Ensure the current axes is always the main residuals axes\n", + " plt.sca(self.ax)\n", + " return self.ax\n", + "\n", + " def finalize(self, **kwargs):\n", + " \"\"\"\n", + " Finalize executes any subclass-specific axes finalization steps.\n", + " The user calls poof and poof calls finalize.\n", + "\n", + " Parameters\n", + " ----------\n", + " kwargs: generic keyword arguments.\n", + " \"\"\"\n", + " # Add the title to the plot\n", + " self.set_title('Residuals for {} Model'.format(self.name))\n", + "\n", + " # Set the legend with full opacity patches using manual legend\n", + " manual_legend(\n", + " self, self._labels, self._colors, loc='best', frameon=True\n", + " )\n", + "\n", + " # Create a full line across the figure at zero error.\n", + " self.ax.axhline(y=0, c=self.colors['line'])\n", + "\n", + " # Set the axes labels\n", + " self.ax.set_ylabel('Residuals')\n", + " self.ax.set_xlabel(\"Predicted Value\")\n", + "\n", + " # Finalize the histogram axes\n", + " if self.hist:\n", + " self.hax.axhline(y=0, c=self.colors['line'])\n", + " self.hax.set_xlabel(\"Distribution\")\n", + "\n", + "\n", + "def residuals_plot(model,\n", + " X,\n", + " y,\n", + " ax=None,\n", + " hist=True,\n", + " test_size=0.25,\n", + " train_color='b',\n", + " test_color='g',\n", + " line_color=LINE_COLOR,\n", + " random_state=None,\n", + " alpha=1.0,\n", + " **kwargs):\n", + " \"\"\"Quick method:\n", + "\n", + " Divides the dataset X, y into a train and test split (the size of the\n", + " splits determined by test_size) then plots the training and test residuals\n", + " agains the predicted value for the given model.\n", + "\n", + " This helper function is a quick wrapper to utilize the ResidualsPlot\n", + " ScoreVisualizer for one-off analysis.\n", + "\n", + " Parameters\n", + " ----------\n", + " model : a Scikit-Learn regressor\n", + " Should be an instance of a regressor, otherwise will raise a\n", + " YellowbrickTypeError exception on instantiation.\n", + "\n", + " X : ndarray or DataFrame of shape n x m\n", + " A matrix of n instances with m features.\n", + "\n", + " y : ndarray or Series of length n\n", + " An array or series of target or class values.\n", + "\n", + " ax : matplotlib Axes, default: None\n", + " The axes to plot the figure on. If None is passed in the current axes\n", + " will be used (or generated if required).\n", + "\n", + " hist : {True, False, None, 'density', 'frequency'}, default: True\n", + " Draw a histogram showing the distribution of the residuals on the\n", + " right side of the figure. Requires Matplotlib >= 2.0.2.\n", + " If set to 'density', the probability density function will be plotted.\n", + " If set to True or 'frequency' then the frequency will be plotted.\n", + "\n", + " test_size : float, int default: 0.25\n", + " If float, should be between 0.0 and 1.0 and represent the proportion\n", + " of the dataset to include in the test split. If int, represents the\n", + " absolute number of test samples.\n", + "\n", + " train_color : color, default: 'b'\n", + " Residuals for training data are ploted with this color but also\n", + " given an opacity of 0.5 to ensure that the test data residuals\n", + " are more visible. Can be any matplotlib color.\n", + "\n", + " test_color : color, default: 'g'\n", + " Residuals for test data are plotted with this color. In order to\n", + " create generalizable models, reserved test data residuals are of\n", + " the most analytical interest, so these points are highlighted by\n", + " having full opacity. Can be any matplotlib color.\n", + "\n", + " line_color : color, default: dark grey\n", + " Defines the color of the zero error line, can be any matplotlib color.\n", + "\n", + " random_state : int, RandomState instance or None, optional\n", + " Passed to the train_test_split function.\n", + "\n", + " alpha : float, default: 1.0\n", + " Specify a transparency where 1 is completely opaque and 0 is completely\n", + " transparent. This property makes densely clustered points more visible.\n", + "\n", + " kwargs : dict\n", + " Keyword arguments that are passed to the base class and may influence\n", + " the visualization as defined in other Visualizers.\n", + "\n", + " Returns\n", + " -------\n", + " ax : matplotlib axes\n", + " Returns the axes that the residuals plot was drawn on.\n", + " \"\"\"\n", + " # Instantiate the visualizer\n", + " visualizer = ResidualsPlot(\n", + " model=model, ax=ax, hist=hist, train_color=train_color,\n", + " test_color=test_color, line_color=line_color, alpha=alpha,\n", + " **kwargs\n", + " )\n", + "\n", + " # Create the train and test splits\n", + " X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size=test_size, random_state=random_state\n", + " )\n", + "\n", + " # Fit and transform the visualizer (calls draw)\n", + " visualizer.fit(X_train, y_train, **kwargs)\n", + " visualizer.score(X_test, y_test)\n", + " visualizer.finalize()\n", + "\n", + " # Return the axes object on the visualizer\n", + " return visualizer.ax\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.linear_model import Ridge\n", + "\n", + "ridge = Ridge()\n", + "visualizer = ResidualsPlot(ridge, alpha=0.9)\n", + "\n", + "visualizer.fit(X_train, y_train) # Fit the training data to the model\n", + "visualizer.score(X_test, y_test) # Evaluate the model on the test data\n", + "visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(visualizer.ax.scatter())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/rebeccabilbro/rocauc_bug_fix.ipynb b/examples/rebeccabilbro/rocauc_bug_fix.ipynb new file mode 100644 index 000000000..319e64b27 --- /dev/null +++ b/examples/rebeccabilbro/rocauc_bug_fix.ipynb @@ -0,0 +1,738 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys \n", + "\n", + "# Modify the path \n", + "sys.path.append(\"..\")\n", + "\n", + "import pandas as pd\n", + "import yellowbrick as yb\n", + "import matplotlib.pyplot as plt \n", + "\n", + "from yellowbrick.classifier import ROCAUC\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "from yellowbrick.exceptions import ModelError\n", + "from yellowbrick.exceptions import YellowbrickValueError\n", + "from yellowbrick.style.palettes import LINE_COLOR\n", + "from yellowbrick.classifier.base import ClassificationScoreVisualizer\n", + "\n", + "from scipy import interp\n", + "from sklearn.preprocessing import label_binarize\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import auc, roc_curve\n", + "\n", + "\n", + "# Dictionary keys for ROCAUC\n", + "MACRO = \"macro\"\n", + "MICRO = \"micro\"\n", + "\n", + "\n", + "##########################################################################\n", + "## ROCAUC Visualizer\n", + "##########################################################################\n", + "\n", + "class ROCAUC(ClassificationScoreVisualizer):\n", + " \"\"\"\n", + " Receiver Operating Characteristic (ROC) curves are a measure of a\n", + " classifier's predictive quality that compares and visualizes the tradeoff\n", + " between the models' sensitivity and specificity. The ROC curve displays\n", + " the true positive rate on the Y axis and the false positive rate on the\n", + " X axis on both a global average and per-class basis. The ideal point is\n", + " therefore the top-left corner of the plot: false positives are zero and\n", + " true positives are one.\n", + "\n", + " This leads to another metric, area under the curve (AUC), a computation\n", + " of the relationship between false positives and true positives. The higher\n", + " the AUC, the better the model generally is. However, it is also important\n", + " to inspect the \"steepness\" of the curve, as this describes the\n", + " maximization of the true positive rate while minimizing the false positive\n", + " rate. Generalizing \"steepness\" usually leads to discussions about\n", + " convexity, which we do not get into here.\n", + "\n", + " Parameters\n", + " ----------\n", + " model : estimator\n", + " Must be a classifier, otherwise raises YellowbrickTypeError\n", + "\n", + " ax : matplotlib Axes, default: None\n", + " The axes to plot the figure on. If None is passed in the current axes\n", + " will be used (or generated if required).\n", + "\n", + " classes : list\n", + " A list of class names for the legend. If classes is None and a y value\n", + " is passed to fit then the classes are selected from the target vector.\n", + " Note that the curves must be computed based on what is in the target\n", + " vector passed to the ``score()`` method. Class names are used for\n", + " labeling only and must be in the correct order to prevent confusion.\n", + "\n", + " micro : bool, default = True\n", + " Plot the micro-averages ROC curve, computed from the sum of all true\n", + " positives and false positives across all classes.\n", + "\n", + " macro : bool, default = True\n", + " Plot the macro-averages ROC curve, which simply takes the average of\n", + " curves across all classes.\n", + "\n", + " per_class : bool, default = True\n", + " Plot the ROC curves for each individual class. This should be set\n", + " to false if only the macro or micro average curves are required.\n", + "\n", + " kwargs : keyword arguments passed to the super class.\n", + " Currently passing in hard-coded colors for the Receiver Operating\n", + " Characteristic curve and the diagonal.\n", + " These will be refactored to a default Yellowbrick style.\n", + "\n", + " Attributes\n", + " ----------\n", + " score_ : float\n", + " Global accuracy score, unless micro or macro scores are requested\n", + "\n", + " Notes\n", + " -----\n", + " ROC curves are typically used in binary classification, and in fact the\n", + " Scikit-Learn ``roc_curve`` metric is only able to perform metrics for\n", + " binary classifiers. As a result it is necessary to binarize the output or\n", + " to use one-vs-rest or one-vs-all strategies of classification. The\n", + " visualizer does its best to handle multiple situations, but exceptions can\n", + " arise from unexpected models or outputs.\n", + "\n", + " Another important point is the relationship of class labels specified on\n", + " initialization to those drawn on the curves. The classes are not used to\n", + " constrain ordering or filter curves; the ROC computation happens on the\n", + " unique values specified in the target vector to the ``score`` method. To\n", + " ensure the best quality visualization, do not use a LabelEncoder for this\n", + " and do not pass in class labels.\n", + "\n", + " .. seealso:: http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html\n", + " .. todo:: Allow the class list to filter the curves on the visualization.\n", + "\n", + " Examples\n", + " --------\n", + " >>> from sklearn.datasets import load_breast_cancer\n", + " >>> from yellowbrick.classifier import ROCAUC\n", + " >>> from sklearn.linear_model import LogisticRegression\n", + " >>> from sklearn.model_selection import train_test_split\n", + " >>> data = load_breast_cancer()\n", + " >>> X = data['data']\n", + " >>> y = data['target']\n", + " >>> X_train, X_test, y_train, y_test = train_test_split(X, y)\n", + " >>> viz = ROCAUC(LogisticRegression())\n", + " >>> viz.fit(X_train, y_train)\n", + " >>> viz.score(X_test, y_test)\n", + " >>> viz.poof()\n", + " \"\"\"\n", + "\n", + " def __init__(self, model, ax=None, classes=None,\n", + " micro=True, macro=True, per_class=True, **kwargs):\n", + " super(ROCAUC, self).__init__(model, ax=ax, classes=classes, **kwargs)\n", + "\n", + " # Set the visual parameters for ROCAUC\n", + " self.micro = micro\n", + " self.macro = macro\n", + " self.per_class = per_class\n", + "\n", + " def score(self, X, y=None, **kwargs):\n", + " \"\"\"\n", + " Generates the predicted target values using the Scikit-Learn\n", + " estimator.\n", + "\n", + " Parameters\n", + " ----------\n", + " X : ndarray or DataFrame of shape n x m\n", + " A matrix of n instances with m features\n", + "\n", + " y : ndarray or Series of length n\n", + " An array or series of target or class values\n", + "\n", + " Returns\n", + " -------\n", + " score_ : float\n", + " Global accuracy unless micro or macro scores are requested.\n", + " \"\"\"\n", + "\n", + " # Compute the predictions for the test data\n", + " y_pred = self._get_y_scores(X)\n", + " \n", + " if len(y_pred.shape) == 1:\n", + " self._binary_decision = True\n", + " \n", + " if self.micro or self.macro or self.per_class:\n", + " raise ModelError(\n", + " \"Micro, macro, and per-class scores are not defined for \"\n", + " \"binary classification for estimators with only \" \n", + " \"decision_function methods; set micro, macro, and \"\n", + " \"per-class params to False.\"\n", + " )\n", + " else:\n", + " self._binary_decision = False\n", + " if not self.micro and not self.macro and not self.per_class:\n", + " raise YellowbrickValueError(\n", + " \"no curves will be drawn; specify micro, macro, or per_class\"\n", + " )\n", + " \n", + " # Classes may be label encoded so only use what's in y to compute.\n", + " # The self.classes_ attribute will be used as names for labels.\n", + " classes = np.unique(y)\n", + " n_classes = len(classes)\n", + "\n", + " # Store the false positive rate, true positive rate and curve info.\n", + " self.fpr = dict()\n", + " self.tpr = dict()\n", + " self.roc_auc = dict()\n", + "\n", + " # Compute ROC curve and ROC area for each class\n", + " if self._binary_decision == True:\n", + " self.fpr[0], self.tpr[0], _ = roc_curve(y, y_pred)\n", + " self.roc_auc[0] = auc(self.fpr[0], self.tpr[0])\n", + " else: \n", + " for i, c in enumerate(classes):\n", + " self.fpr[i], self.tpr[i], _ = roc_curve(y, y_pred[:,i], pos_label=c)\n", + " self.roc_auc[i] = auc(self.fpr[i], self.tpr[i])\n", + "\n", + " # Compute micro average\n", + " if self.micro:\n", + " self._score_micro_average(y, y_pred, classes, n_classes)\n", + "\n", + " # Compute macro average\n", + " if self.macro:\n", + " self._score_macro_average(y_pred, n_classes)\n", + "\n", + " # Draw the Curves\n", + " self.draw()\n", + "\n", + " # Set score to micro average if specified\n", + " if self.micro:\n", + " self.score_ = self.roc_auc[MICRO]\n", + "\n", + " # Set score to macro average if not micro\n", + " if self.macro:\n", + " self.score_ = self.roc_auc[MACRO]\n", + "\n", + " # Set score to the base score if neither macro nor micro\n", + " self.score_ = self.estimator.score(X, y)\n", + "\n", + " return self.score_\n", + "\n", + " def draw(self):\n", + " \"\"\"\n", + " Renders ROC-AUC plot.\n", + " Called internally by score, possibly more than once\n", + "\n", + " Returns\n", + " -------\n", + " ax : the axis with the plotted figure\n", + " \"\"\"\n", + " colors = self.colors[0:len(self.classes_)]\n", + " n_classes = len(colors)\n", + "\n", + " # If binary decision, plot the ROC curve\n", + " if self._binary_decision == True:\n", + " self.ax.plot(\n", + " self.fpr[0], self.tpr[0],\n", + " label='ROC for binary decision, AUC = {:0.2f}'.format(\n", + " self.roc_auc[0]\n", + " )\n", + " )\n", + " # Otherwise plot the ROC curves for each class\n", + " if self.per_class:\n", + " for i, color in zip(range(n_classes), colors):\n", + " self.ax.plot(\n", + " self.fpr[i], self.tpr[i], color=color,\n", + " label='ROC of class {}, AUC = {:0.2f}'.format(\n", + " self.classes_[i], self.roc_auc[i],\n", + " )\n", + " )\n", + "\n", + " # Plot the ROC curve for the micro average\n", + " if self.micro:\n", + " self.ax.plot(\n", + " self.fpr[MICRO], self.tpr[MICRO], linestyle=\"--\",\n", + " color= self.colors[len(self.classes_)-1],\n", + " label='micro-average ROC curve, AUC = {:0.2f}'.format(\n", + " self.roc_auc[\"micro\"],\n", + " )\n", + " )\n", + "\n", + " # Plot the ROC curve for the macro average\n", + " if self.macro:\n", + " self.ax.plot(\n", + " self.fpr[MACRO], self.tpr[MACRO], linestyle=\"--\",\n", + " color= self.colors[len(self.classes_)-1],\n", + " label='macro-average ROC curve, AUC = {:0.2f}'.format(\n", + " self.roc_auc[\"macro\"],\n", + " )\n", + " )\n", + "\n", + " # Plot the line of no discrimination to compare the curve to.\n", + " self.ax.plot([0,1], [0,1], linestyle=':', c=LINE_COLOR)\n", + " return self.ax\n", + "\n", + " def finalize(self, **kwargs):\n", + " \"\"\"\n", + " Finalize executes any subclass-specific axes finalization steps.\n", + " The user calls poof and poof calls finalize.\n", + "\n", + " Parameters\n", + " ----------\n", + " kwargs: generic keyword arguments.\n", + "\n", + " \"\"\"\n", + " # Set the title and add the legend\n", + " self.set_title('ROC Curves for {}'.format(self.name))\n", + " self.ax.legend(loc='lower right', frameon=True)\n", + "\n", + " # Set the limits for the ROC/AUC (always between 0 and 1)\n", + " self.ax.set_xlim([0.0, 1.0])\n", + " self.ax.set_ylim([0.0, 1.0])\n", + "\n", + " # Set x and y axis labels\n", + " self.ax.set_ylabel('True Postive Rate')\n", + " self.ax.set_xlabel('False Positive Rate')\n", + "\n", + " def _get_y_scores(self, X):\n", + " \"\"\"\n", + " The ``roc_curve`` metric requires target scores that can either be the\n", + " probability estimates of the positive class, confidence values or non-\n", + " thresholded measure of decisions (as returned by \"decision_function\").\n", + "\n", + " This method computes the scores by resolving the estimator methods\n", + " that retreive these values.\n", + "\n", + " .. todo:: implement confidence values metric.\n", + "\n", + " Parameters\n", + " ----------\n", + " X : ndarray or DataFrame of shape n x m\n", + " A matrix of n instances with m features -- generally the test data\n", + " that is associated with y_true values.\n", + " \"\"\"\n", + " # The resolution order of scoring functions\n", + " attrs = (\n", + " 'predict_proba',\n", + " 'decision_function',\n", + " )\n", + "\n", + " # Return the first resolved function\n", + " for attr in attrs:\n", + " try:\n", + " method = getattr(self.estimator, attr, None)\n", + " if method:\n", + " return method(X)\n", + " except AttributeError:\n", + " # Some Scikit-Learn estimators have both probability and\n", + " # decision functions but override __getattr__ and raise an\n", + " # AttributeError on access.\n", + " continue\n", + "\n", + " # If we've gotten this far, raise an error\n", + " raise ModelError(\n", + " \"ROCAUC requires estimators with predict_proba or \"\n", + " \"decision_function methods.\"\n", + " )\n", + "\n", + " def _score_micro_average(self, y, y_pred, classes, n_classes):\n", + " \"\"\"\n", + " Compute the micro average scores for the ROCAUC curves.\n", + " \"\"\"\n", + " # Convert y to binarized array for micro and macro scores\n", + " y = label_binarize(y, classes=classes)\n", + " if n_classes == 2:\n", + " y = np.hstack((1-y, y))\n", + "\n", + " # Compute micro-average\n", + " self.fpr[MICRO], self.tpr[MICRO], _ = roc_curve(y.ravel(), y_pred.ravel())\n", + " self.roc_auc[MICRO] = auc(self.fpr[MICRO], self.tpr[MICRO])\n", + "\n", + " def _score_macro_average(self, y_pred, n_classes):\n", + " \"\"\"\n", + " Compute the macro average scores for the ROCAUC curves.\n", + " \"\"\" \n", + " # Gather all FPRs\n", + " all_fpr = np.unique(np.concatenate([self.fpr[i] for i in range(n_classes)]))\n", + " avg_tpr = np.zeros_like(all_fpr)\n", + "\n", + " # Compute the averages per class\n", + " for i in range(n_classes):\n", + " avg_tpr += interp(all_fpr, self.fpr[i], self.tpr[i])\n", + "\n", + " # Finalize the average\n", + " avg_tpr /= n_classes\n", + "\n", + " # Store the macro averages\n", + " self.fpr[MACRO] = all_fpr\n", + " self.tpr[MACRO] = avg_tpr\n", + " self.roc_auc[MACRO] = auc(self.fpr[MACRO], self.tpr[MACRO])\n", + "\n", + "\n", + "##########################################################################\n", + "## Quick method for ROCAUC\n", + "##########################################################################\n", + "\n", + "def roc_auc(model, X, y=None, ax=None, **kwargs):\n", + " \"\"\"ROCAUC Quick method:\n", + "\n", + " Receiver Operating Characteristic (ROC) curves are a measure of a\n", + " classifier's predictive quality that compares and visualizes the tradeoff\n", + " between the models' sensitivity and specificity. The ROC curve displays\n", + " the true positive rate on the Y axis and the false positive rate on the\n", + " X axis on both a global average and per-class basis. The ideal point is\n", + " therefore the top-left corner of the plot: false positives are zero and\n", + " true positives are one.\n", + "\n", + " This leads to another metric, area under the curve (AUC), a computation\n", + " of the relationship between false positives and true positives. The higher\n", + " the AUC, the better the model generally is. However, it is also important\n", + " to inspect the \"steepness\" of the curve, as this describes the\n", + " maximization of the true positive rate while minimizing the false positive\n", + " rate. Generalizing \"steepness\" usually leads to discussions about\n", + " convexity, which we do not get into here.\n", + "\n", + " Parameters\n", + " ----------\n", + " model : the Scikit-Learn estimator\n", + " Should be an instance of a classifier, else the __init__ will\n", + " return an error.\n", + "\n", + " X : ndarray or DataFrame of shape n x m\n", + " A matrix of n instances with m features\n", + "\n", + " y : ndarray or Series of length n\n", + " An array or series of target or class values\n", + "\n", + " ax : the axis to plot the figure on.\n", + "\n", + " classes : list\n", + " A list of class names for the legend. If classes is None and a y value\n", + " is passed to fit then the classes are selected from the target vector.\n", + " Note that the curves must be computed based on what is in the target\n", + " vector passed to the ``score()`` method. Class names are used for\n", + " labeling only and must be in the correct order to prevent confusion.\n", + "\n", + " micro : bool, default = True\n", + " Plot the micro-averages ROC curve, computed from the sum of all true\n", + " positives and false positives across all classes.\n", + "\n", + " macro : bool, default = True\n", + " Plot the macro-averages ROC curve, which simply takes the average of\n", + " curves across all classes.\n", + "\n", + " per_class : bool, default = True\n", + " Plot the ROC curves for each individual class. Primarily this is set\n", + " to false if only the macro or micro average curves are required.\n", + "\n", + " Notes\n", + " -----\n", + " ROC curves are typically used in binary classification, and in fact the\n", + " Scikit-Learn ``roc_curve`` metric is only able to perform metrics for\n", + " binary classifiers. As a result it is necessary to binarize the output or\n", + " to use one-vs-rest or one-vs-all strategies of classification. The\n", + " visualizer does its best to handle multiple situations, but exceptions can\n", + " arise from unexpected models or outputs.\n", + "\n", + " Another important point is the relationship of class labels specified on\n", + " initialization to those drawn on the curves. The classes are not used to\n", + " constrain ordering or filter curves; the ROC computation happens on the\n", + " unique values specified in the target vector to the ``score`` method. To\n", + " ensure the best quality visualization, do not use a LabelEncoder for this\n", + " and do not pass in class labels.\n", + "\n", + " .. seealso:: http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html\n", + " .. todo:: Allow the class list to filter the curves on the visualization.\n", + "\n", + " Examples\n", + " --------\n", + " >>> from sklearn.datasets import load_breast_cancer\n", + " >>> from yellowbrick.classifier import roc_auc\n", + " >>> from sklearn.linear_model import LogisticRegression\n", + " >>> data = load_breast_cancer()\n", + " >>> roc_auc(LogisticRegression(), data.data, data.target)\n", + "\n", + " Returns\n", + " -------\n", + " ax : matplotlib axes\n", + " Returns the axes that the roc-auc curve was drawn on.\n", + " \"\"\"\n", + " # Instantiate the visualizer\n", + " visualizer = ROCAUC(model, ax, **kwargs)\n", + "\n", + " # Create the train and test splits\n", + " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n", + "\n", + " # Fit and transform the visualizer (calls draw)\n", + " visualizer.fit(X_train, y_train, **kwargs)\n", + " visualizer.score(X_test, y_test)\n", + " visualizer.finalize()\n", + "\n", + " # Return the axes object on the visualizer\n", + " return visualizer.ax\n" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "occupancy = pd.read_csv('data/occupancy/occupancy.csv')\n", + "features = [\n", + " \"temperature\", \"relative humidity\", \"light\", \"C02\", \"humidity\"\n", + "]\n", + "classes = [\"unoccupied\", \"occupied\"]\n", + "X = occupancy[features]\n", + "y = occupancy['occupancy']\n", + "\n", + "# Create the train and test data\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n", + "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n", + "from sklearn.svm import LinearSVC, NuSVC, SVC\n", + "from sklearn.linear_model import SGDClassifier\n", + "from sklearn.naive_bayes import BernoulliNB, MultinomialNB\n", + "from sklearn.linear_model import PassiveAggressiveClassifier\n", + "from sklearn.linear_model import RidgeClassifier, RidgeClassifierCV\n", + "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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x7hd/6fRE3oFJMweUg9vrxOtzE2dLICkuIzCpp8QfdyIoSBXE2xK7mWasllgsJuNBJzE2jaXTb+/386Un5Id8L3w+ncPNbeyvd/B+RSOrG7ZRUd9CRZ2xCthf76DN3fO+bHKslQnpiRSlHp/wg80+afG2EbHvt3LlSm655RZWrFjBL3/5S4CIKAZQymHEUlVfDoz8/Qanu5VdRzbS2HaUhZOMEsPT8k+jIHUyJRknqw3mPvDpvm5P6gmxKST6n5x3Hv6EesfhoEnd8J5Jt+exqPRKALZXfcimA+8G+ty343j/pTnz/K6l7oBN3mK2YTPHEW9Lwhafjcl0fJNzeuFZfrON/0nebGy22mOOK/bTxLJ+P1dyfCbJ8Zn9thsoXp+PqsY29gdN+BV1Dvb7J/4D9Q5c3mDX2ONbmenxMUzJTg6a8P1mH7/tPyVu5LrI67oeUExLlizhsssu46qrror4uOo/dwTi8bqobtxDSnz2iH3irm2porx6PXtrPsfr82A2WZlVfC5xtkTSE/IH9MQ3WvHpXlyedtxeJ4mxaQC0upqorJOBidwdNKnPHbeEpLgMfLqP5zf8Ere3e8DqrOJzOaXwTAD2126jqn5np/NWcwyJ/s1RgLSEfCZkzsRmiaO2pp7iwvH+ST0Oo54PpMbncNW8n2Izx3ZSBl3pGDdauL0+KhschtknaNLvMP8cbHDg8fWc0SErIZYZ+amByd/UUsei6VMCq4CEmNFlmu3g0KFD3HXXXVxzzTUsXbqU+Ph4nnjiif4vDANKOYxAqhv34vV5RuSqoaH1KGt3vURN8wHAMA+InPlMyp5DjDU+ytINjOAnsnrHYX/AVWcf94SYNEpz5gJQXr2B8ur1gaf8Dh93i8nK10/9FQDNbbWs2/3PHsc7KW9RIFAsJT47sLka/KSenVQSaD+nZAmzis8NtLGaYzF12aspTJtMod/0WNZQxtT87r79JpOZWFNPVTyHF6fHywG/vT/Yzt+hCKoa2/D1kM5H0yA3MY4vFWUEzD5FqXZK/IqgKNVOvK3zVFZWVsbskwqG66NFjJaWFt5//30SExNZunTpsI6tlMMIpLLOb1Ly23mjjcPZQKw1AbPJQpwtgXrHYQpSBZNzF5CfWhq1zWWf7sWtt9PYWtNtUi9JP5kYazxen4ePdq3s5CrZYW+fVXwuJ+UvAmD9nn9xtKmi2xh5KZMCysHtddLqajI2TeMyOk3quu5D00wkx2eyaNKVXUwzhmuk1XzcdLFk+m39fr6BBAOOBFpdHr9XT2ezT4cSqG5q6/E6k6ZRkBLPwnGZgSf/4jT/5J9mpzDFTkwvPv4nInv37sVqtVJYWEhpaSmrV69m6tSpwy6HUg4jDF3Xqawvx2aOJSupOKpyVDfuobx6PQdrt7Oo9KtMyJpJjCWeK+f+cMirhJ583K3mmEDMQ1X9Tqob9nTxnGnDpJm44JRbAThUv5vy9n9T/ln3/tPt+cRY4zFpJvbVbAF0NEyBSTs+PslvejGYmDWb/JRJ3Sb1ONvxYP6TC04P+Nb3Rqw1gYnZJ2aUcnO725j46x0cqOvs47+/3sHRlvYer7OYNApT7Jw5MTto8j++2ZufHI91ED7+JyI7d+7kjDPOYN68ebz88stomsa0adOiIotSDiOMhtajOJwNlGScgkkb/qcll6edPUfLKK/eQGObkY8n3Z7faSKNscb7fdzduDxtxFkTMJnMeH0eKo5t9dvhO0/qE7PnBHzX39r6Z4427e/m416UPpWzpiwH4EjTPrZV/bfTeZNmJs6WEHifEJtCsrmA7Iy8Tu6QVnNsIBeQppn46twfBTxlevM46VgdjGUa2lyB4K79dS1Bk7+xGqhr7dnHP8ZiBHidkpcamPCDzT55yXGYTWryD4VJkyZx6aWXhiXCeago5TDCqAx4KQ2fScnhbPDnimnjk32raG6vRUMjNT6XBRMvJTOxkMONe/n353/oZLrR/UnTLpn1XVLis9B1Hx/ufLHHMTISCgOfyR6TQlqCu5uPe5o9N9B+UvZcClInd3KH7OrjnhKfTZFtAbMn9f2kHh+TNNRbNOrRdZ1ah9Pv43/czh9s9mlq7+7GChBnNVOSlsDcoozAhN8R4VuSZic7ITIBXmMBp9PJAw88gM1m43vf+x6apvHHP/4x2mIBSjmMOIz9Bq3f/QZd13F7nf6n87ZO7o6ZSUUk+7N2llW8RWNbTeApviNStTBtCqdOvIwDdV9QXr2Bw417O/ePTqurkaykIsCw7ze0HsVmiSXW2tnHvcNd1WyysmDCpd2Cmjred/Dl0q/2ex8SY9MCHkCK/tF1nWNtHj7eX9Npwg9eBbS6evbxT4yxdrbzdzH7ZNhjRoSP/4mI0+nk+eefx2q1cscddxAbGxttkQIo5TCCcHpaOdq0359bpiOVdBNlFW8ZT/bedhZPvR6AdreDFz/5dY/9LJh4aUA5HGrYRW2L4e9tMdmwWWKJscbT1HaMlzbeS6urCYvJyrT804i12rtM6scn9LyUSSz3e+T0hqZpiNx5Q74Piu74fDrVzW1U1LX04OljKIN2jxfY2e3a1DgbpRlJnSb8gNknzU5q3MgI8BorOBwODhw4wJQpU0hKSuL555+nqKhoRCkGUMphRHGofjc6PgqDXFg/2ftvKo5tBQwfd7fH8I23WWIpTJuCzWxEngZvoga7Q5495VpMJjM2cyxN7cf4/MAa9tduo0k/htUcw5TcUxG580mJz+pTNjV5RBaP10dVY2s3s09g8m9w4PZ2rX1gkGGPYVpuCkm4mDG+8PgqwK8MkmJHboDXWMPpdHLmmWfS3t7O2rVrSUxMjNqGc38o5TCCCOw3+P3WjzRVUHFsK5mJRZx/yi1BG9QHMJssnH3Stf32abPGBVIWeLwuKo5tISU+m8m5C5iQNTNQeEURWVweLwcbWrvZ+Ts8fSobW/H2EuCVkxjH7IK0wNN+UbD5J9WO3R/gVVZWxuzZJ6an1IlCTEwMl156KS6XC4tlZE+/I1u6MYSu+6iql8RZE0mz56LrPj7dayQvmztu6YA9lxrbaiiv3sDuI2Wcf8otpNlzyUgsZOn020lPKFArgTDT5vZ0D/AKmH8cHGpqpady7ZoG+UnxzC/KCJh9Opl//EVcFKOXVatW8e677/LQQw+haRo/+tGPoi1SSCjlMEI41lJJu9vBpOw5aJoJn89LccY00ux5gU3h/vDpPir9RU0ONewCIM6WiMPZEPAEykgsjNhnOJFx+Iu49Gb2Odzcc4CX2aRRkBzPaeOzO9n5Oyb/voq4KEY/uq7z2GOP8cknn3DbbbeFPa12JFHKYYQQyMLq328wmcycXHBGyNfruo/XNv2OhtYjAGQnjWNy7gKK06f2mU9HYdDU7jqexM0/4QebfY45uudBguNFXM6elNNjaof85PhBFXFRjF50XUdKyeTJk9E0jd///vc4nc5RpRhAKYcRQ2V9OSbNTG7KRI42HSA9Ia/PjKYd1dV8uoec5PFomon8VBEouamqqx1H13XquwR47e9i9umviMvM/LTjqZyDzD6RKuKiGL3ccccdvPLKK3zwwQdMnDiRkpKSaIs0KJRyGAG0upqobakiN3kCHq+Ld7Y9Tqo9lwtOubXb3oDH66bOs4/XN6+jtqWKdHs+S2fcgaZpzB13QZQ+QXTpKOLSYfY5UNfSxfzjoNnZc4CX3WahONUo4lLcg9lnOIu4KE4MvvKVr1BdXU1MzOh29lDKYQRQFVTY57P9b+PxuZmYPbvTpNTcXoes3sCujupqbo3CtJOYkte9CtaJhs+nc6SlrZPZpyPASx46xpGVstciLkmxVsaldZ7wg80+6SrASzFEdu/ezYMPPsgDDzxAXFwcF110ERdddNGo/14p5TAC6HBhTYhJ5dN9b5ASn82k7Dmd2lQ37GFb1QfEWu1kWiZz+oxLRmyth4Hi9fk41NjWLZFbh9lnf7ciLsdJspmZkp3cacIPNvuM5CIuihODp59+mhdeeIHTTz+dZcuWjXql0IFSDlHG6/NwqGE3CTFp7KheB+jMKDqH7VVr2VuzifNOvgWbJZbxmdMxmyyUZJzM55s2jyrF0FHExZjwOwq3H0/p3F8Rl+l5qRSnJXSq3dtRxnHnF1uUb79i2Dl48CCFhYbn349+9CMWLlzI+eefH2WpwotSDlHmaFMFbq+T7KRxVNaXE29L5sOdL/qrq1k41lJJXspELGYbE7JmRlvcHnF6vBxsMCb+4NQOHb7+vRVxAchLimNuYcbxCb/jqT81occiLgpFtPnrX//KXXfdxbPPPst5552H3W4/4RQDKOUQdToK+9Q7DgPQ6moMVFebmD2bWGv0K3i1uT2d7Pz76zqncq5ubusxwMukaeQnx7FwXObx6F6/uadkDBZxUZwYzJkzh+LiYuLjR1flw4GilEOUcDgb0HWdg/USi8mKPTaZ5PgsTso7ddirq3UUcQme8CsGUMTljAnZRrH21OCi7XYKUuyqiIti1NPS0sK9997LzTffTGFhIVOmTGHDhg2YzSf2g41SDsOIUV1tN+WH1nOwbgeF6VNpaqshL2UiZ590XZ9xDUOhaxGX/V3MPr0VcbGZTRSn2jk5N6W7j39qgiriohgTvP322/zxj3/E4XDw4IMPApzwigGUchg2DtRuZ2PFmzQFVVez+hPiHW7cR21L1aDKgnYUcek64QevAhr7KOJSnGpnblFGFx9/VcRFMbZpaGggPj4em83GZZddRmtrK1deeWW0xRpWlHIYBg7Ubuf9HX9D0zTGZ85kcu4CMhMLeXPLY4CR/yg9Ib/Ha3Vd52hLe6cJf+POahyb3gusAhwuT4/XJsRYKElNYFHHhJ9qpyjg9WMnUwV4KRTd2LRpE1dffTU33nhjoDrb8uXLoy3WsKOUwzCQZs8j1Z7DgomXkJloJNFze10cbd4PwITsxWzYX9erj79RxKUr9aTE2ZiUkRjY5O3q46+KuCgUA2f8+PHExcURFxfXf+MTGKUcIojH6wbM1LZaSE78Km9KBwfqt1BR10KieRvTc3TqWq3M/d0XwPZu12fYY5iak9zJzl+cZsdRfYDzF84lWQV4KRRDRtf1QDW2RYsWkZyczIYNG7DZxvb/l1IOQ8Tl8VLZ2NrJ7FNR34LurWBWjuTBdUVUNnbOsaKh89AFxqph69FxfHVGSVBwV4fXj50EfxGXrpS1H1WKQaEIE7t37+bOO+9kypQp/Pe//0XTtDGvGEAph35pd3s50ODo5u3T8buqhyIuM3KbuPVLB/H6TMwtSOTL4/OO5/RJSyAv0cRn+3YCZp685toBF/JRKBRDw+fz4XA4SExMZNKkSfz+979n0aJFygwbxJhXDh1FXALePnXBXj/9F3H58rgsf2oHY/LPij9CbeMqzCYbi6dezy1njOt2bZ2jmo26h5KMU5RiUCiGmbq6Oq655hqysrJ45plnAPja174WZalGHie8cmhqdwU2eIOLuHS4fPZXxOWsiTkBc0+w2aenIi4H63bw/o43MJvMnDP1OrKTuyuGVmdTICq6MG1K+D+wQqHok5SUFDRNQ9M0nE7nqE+tHSlGtXLoKOKyv64nH3/jd30vRVxiLCaKUxOYmZ9GcVrX1A4J5CTGDijAy+N1s373K5g0E+dMvZ6c5PHd2jicDbxc9gA2SyygkZ86uipDKRSjlS1btrBr1y4uv/xyTCYTK1euxG6PfmqakcyoUg5PrN+JrGvrZPbprYhLvM1MSWoC84OKuBiTv/H0n5UQG9YAL4vZyjlTr8fpbu1RMQB8VvE2Xp+bNpebzMSiEZE3SaE40XE6nSxbtozm5mbOOOMM0tPTlWIIgYgpByGECXgUmA44gW9IKXcHnb8LuBrwAf8rpXylvz5/+94XVDsMZdBRxCV4wg82+wxXEZfqht0kx2URH5NEmj2313bHmg+yp2YTCTGptDjrKUibHHHZFIqxTFtbG3FxccTExPDggw8SFxdHenp6tMUaNURy5XAJECulXCCEmA88AFwMIIRIAe4EJgJ24HOgX+VwxyLBeVOLR0wRl6r6nazZ/izJ8ZlcOGMFpl6S5em6zif7VgGQGJtmKIdUpRwUikig6zqPPPIIUkref/99YmJiTsiU2pEmklnTFgFqAhu5AAAgAElEQVRvAUgpNwDBpc0cwH4MxWDHWD30yym5qczITxsRiuFQ/S7e2/EsmgZzx13Qq2IA2F+7jaNNFRSmnUSdo5p4W9+rDIVCMXg0TUPXdXw+H4cPH462OKOWSK4ckoDGoPdeIYRFStmRCOggRliwGfhtKB3u37+fMldteKUcBC3eI1S4PgKg2LaQ6j1NVFPWa/tjnl2YsEBzEk5PK6nmcXz22WdDkqGsrPfxxhrqXhxnrN6LpqYmPvroIy644AIAbrrpJkwmE8eOHePYsWNRlm50Eknl0AQkBr03BSmG84FcoMPX820hxFop5Sd9dVhSUsLsaQPPXBpOqht2s3r7v9A0jbNO+h8KUkUIV83G7XGyteo/HDwIM0oXUZw+ddAylJWVqdKYftS9OM5YvhfLli3j3Xff5ayzzmLhwoVj+l4E43Q62bZt26CujaRZaS1wAYB/z2Fr0Ll6oA1wSinbgQZglBRF1jCbLJw1ZXm/isHtdeHTjaR5VksMlXXlmDQzeSkTh0NQheKExu0+7qn405/+lJ///OfMmzcvihKdWERSObwCtAsh1gEPAd8RQnxXCHGRlPJD4FNggxBiPbATeLe/DqMZ2K77c2Tkpkzgijk/CMnbqKziDV7b9Hua2+twOBupc1STkzweq1kF3SgUQ2HlypXMnj2bqqoqAKZNm8add96JxTKqvPNHNBG7k1JKH/DNLofLg87/HPh5pMYPJ4cb97L54HucOfnr2Cyx/iC2vmloPYKs/oTEuDTstmR2HzVswaGZoRQKRV+0tbXR0NDA9u3byc/vuRaKYmiMrhqPUVg6HGncx+ovnuZI4z5qWypDvm7jvjfQ8TG35AJMJnMgZYaKb1AoBo7X6+WFF17A4zG2LZcvX86nn37K4sWLoyzZicvoUg7DzJHGCt794i94dQ+nT76a3BD3Cqrqd1JZL8lNnkBB2hS8Pg+HGnaTFJtBUlxGhKVWKE48Hn74YW677Tb+9Kc/AYa7anZ2dpSlOrEZVQa64Vw4HG3az7vbn8KrezhDXB2yd5FP9/LpvlWAxtxxS9A0jSMN+/D4XBSkKZOSQhEqPp8Pkz+/2Y033khVVRXLli2LslRjh1G1chiuVOtOTxurtz+N1+tXDBnTQr62pb0Bt9fJpOzZpCXkAVBZr0xKCsVA2Lp1K2eccQZr164FjEyqDz74IJmZmVGWbOwwqlYOw0WMJY4FEy5B00wDUgwASXHpXDrrLrz6cTe7yjqJxWQjO6l7Cm+FQtEdp9PJjh07WLt2LQsXLoy2OGOSUaUctAgblhpaj5AYm47ZZGFc5vQBX+/1eTCbLFjMViwYJT6b2o7R1H6MorSTMJtG1e1WKIaVtWvXUlpaSmZmJnPmzOHTTz+lpKQk2mKNWUaVWSmSHGs+yKrNj/Jf+XwgpmEgNLfX8dKn97LryMZOxw8qLyWFol8++OADLrzwQn784x8HjinFEF1G1aNspPYcjjVX8va2J/F4XZRknDyoVN+fVbxNm7u5W9nPqnoJoLKwKhQ9oOs6mqaxcOFCrr76aq677rpoi6TwM+ZXDrUtVbyz7Qk8XidfLl3G+MwZA+7jaNN+9h3bTEZCAeODzFFur5PDjXtJs+cRH5MUTrEVilFNXV0dt9xyC48//jgAZrOZRx55hDlz5vRzpWK4GFXKIdwLh9qWKt7e9gQur5NFpV9lfNbAFYNRq+F1AOaOX4oWlLr7UMNufLpXubAqFF3weDysXr2aVatWDcqMq4g8o8qsFG6ONh3A5Wln0aQrmJA1c1B97Du2mWPNBynJOJnspJJO5wJR0cqkpFBw6NAhmpqamDx5MllZWbz++uuUlpYOS8VGxcAZVcoh3F+iKXkLyE0ZT0r84CMtq+p3YtLMzC7pXGlK13Wq6iUxFjsZiYVDFVWhGNUcO3aMhQsXkpeXx/vvv4/NZmPKlCnRFkvRB6NKOYSDesdh9hzdxOySc9E005AUA8CiSVdycsEZJMamdTpe56im1dXE+MyZfVaJUyjGAhkZGSxfvpwJEyZgtVqjLY4iBEaVchjquqHecZi3tj6O0+OgMG0y2cmDD0rr8LLQNI2U+Kxu549HRav9BsXYw+v18qc//YkDBw5w7733AnDPPfdEWSrFQBgzj7T1jsO8vc1QDKdOvGxIigFg/Z5/sXbXS7g87T2er6wrR0MjP6V0SOMoFKMRn8/HP/7xD1555RVqa6Nf2lcxcEbVymGwS4d6xxHe3vY47W4HCyZeSmnOl4YkRl3LIXYe/oSU+Ews5u5L5Ha3g5rmg2QlFRNjjR/SWArFaMHlcrFjxw6mT5+O1WrlySefJCUlhfT09GiLphgEo0s5DIJWV1NAMcyfcAkiZ2hlBHVd59N9bwA6c8Yt6Rb0BsYmNejKS0kxZtB1nYsuuojy8nLWr19Pbm4uEyeqcrijmVGlHAbjrRRnTWR85gyS4tKZnDt/yDJU1pdT3bibvJTSXqu6qf0GxVhD0zSuuuoqtmzZgt1uj7Y4ijDQr3IQQtiAuwEB3AF8G/j/pJSuCMs2JJzuVmKs8WiaxpfGLw1Lnz6fl0/3vYHmr9XQYxvdS1X9TuJtyaTG54RlXIViJPLhhx/y9NNP89hjj2GxWFTqixOMUDak/wjYgVmAB5gIPBlJoXoj1HVDY1sN/9r0EJsPrAnr+Eeb99PcXktpzpdItffsAlvTdBCXp42CtMkquEdxQvPcc8/x6quv8vHHH0dbFEUECEU5zJZS/hhwSylbgWuBwYUTDwNNbcd4e+vjtLmasZpjwtp3TvJ4Lpn5HWYW9163tsOkVNiLyUmhGM3s2LEj8Po3v/kN7777rqq3cIISinLQ/aaljgQoGUGvh5X+6jk0tR3jra1/ptXVxNxxSzgpf1HYZUiOzyTWmtDr+cq6ckyahZwQ600rFKOFX/3qVyxatIgNGzYAkJ6ezsyZI/Y5UTFEQlEOvwNWAzlCiIeBjcDDEZVqEDS11fLW1scDimFq/pfD2Pcx3tzyJ2qaD/bZzuFsoL71MDnJ47GabWEbX6EYCSxevJi5c+eSmpoabVEUw0C/ykFK+SzwTeA3wF7gQinliNtz2HFoLa2uRuaUXBBWxQCwseJNjjRV4HDW99muss6o3VCovJQUJwBVVVXceuutgSC2+fPn8+abbyKE+n6PBULxVvqnlPJyYHvQsTVSyrMjKtkAmTtuCbkpEyhKnxrWfg837uVA7RdkJRVTnH5yn22Pu7Cq+AbF6OfVV1/lxRdfRAjBt7/9bSD8yS8VI5delYMQ4hVgOpAnhNjb5Zq+7SsRouv3srm9jprmg4zPnI7JZA67YtB1H5/uXQXA3HFL+/zH8PjcVDfsJjkuk8RYFRGqGJ0cOHCAgoICTCYTN998M/n5+Vx00UXRFksRBfoyK10LnAW8DZwZ9LMAOD3yovVNS3s9b299nA/kC9S1HIrIGHuObqLWUcX4zBlk9pN2+0jjPjw+d6+BcQrFSOeNN95g/vz5PPPMMwBYLBYuvvhitVoYo/S6cpBSNgFNwMVCiJlAAobZ3wycCzw1LBIG0eGt1NLewFtbH6fFWc/MosWkJeSFfSxd19lxaB1mk4XZJef12z5Q2EeZlBSjlFmzZlFYWEhmZma0RVGMAELZc3gGOBVIA3YAM4C1REE5gOER9Pa2P9PirGNG0TlML4rM1oemaZx78k0caz6IPSalz7a6rlNZX47VHENWl2pwCsVIxel08sADD7BkyRKmT59OTk4O69evx2QaM8maFX0QyrfgNOAkYCVwMzAPiIqfpld38NbWx2lur2N64dnMKDonouPZLLHkpU7qt11T2zGa2+vIS5mE2TSq0lUpxjBlZWXcf//9gXoLgFIMigChfBMOSSndGKuGU6SUXwCJkRWrZ8zYiLclMb3wrIgqhk/3vcHOw5/g030htQ94Kan9BsUIx+Fw0NzcDMCpp57KY489xmOPPRZlqRQjkVCUQ5UQ4kfAOuAWIcRVGPsPw45msvKVaTcyo2hxxDbJjrVU8kXVB+yoXh/yNR37Dflqv0Exgtm7dy+LFi3iZz/7WeDYlVdeSWJiVJ71FCOcUJTDjcA+KeWnwMvA1zCC4qKAjtlkiZhi0HU94Lr6pXFLQqr97PK0c7hpH+kJ+cTb1D+ZYuRSUFBAUlISaWlp6HpUMuAoRhF9GsiFEAlAu5TyBQAp5R+EEI8D3wX+E3nxuhKamWewHKj9giNN+yhMm0JuiLmRqht2o+s+VdhHMSJZtWoVXq+Xiy66CJvNxurVq7Fau1cvVCi60uujsRDiFqAOOCKEmOU/tgwoB64ZHvG6EEF3a6/Pw8aKN9E0E3NKLgj5OhUVrRip1NTU8M1vfpMf/vCHOJ1OAKUYFCHT18rh+8BcYBzwQyFEK3Ae8HPgiWGQbVjZV7OZ5vZapuSeSnJ8aH7euu6jsk4Sa7WTkZAfYQkViv7RdZ2mpiaSk5PJzMzk0UcfRQhBTEx409crTnz6Ug4OKeVmYLPflLQGKPUHx/WLEMIEPIqRgsMJfENKuTvo/PkYikYDyoDbpZR9GkIjGac5IWsmJpOZ/JTSkK+pdRyizd3MhKxZaCHsTygUkcTlcvHVr36VpqYm3njjDcxmMxdeeGG0xVKMUvqa0bxBr+uB5aEqBj+XALFSygXAD4EHOk4IIRKB/wcslVLOAyow6kREDU0zMT5zBjHW+JCvCURFq/0GxQjAZrORlJSE3W4PuKsqFIOlr5VD8FN8iz/WYSAsAt4CkFJuEELMCTp3KrAVeEAIMR54QkpZ01+H+/buw9ZwdIBi9I3T10ytZw9Z1pOwaAOL7dvd/hmgUbPfQd2BsrDK1R9lZcM73khmLN+LyspKPv/8c5YuNeqk33DDDcTExLBnz54oSxZ9xvL3Ihz0pRwmCSHe6+E1AFLKs/rpOwloDHrvFUJYpJQejFXCmRipOFqAD4UQ66WUO/vqcOLEUmaPy+ln2IGx+ounqa3fxSml8ykeQFbXdncLWz9+ieykEr50yoKwytQfZWVlzJ49e1jHHKmM5Xuh6zp33nkn5eXlXH311TQ1NamSnX7G8vciGKfTybZt2wZ1bV/KYengxAnQROdIapNfMQDUAp9KKQ8DCCE+wFAUfSqHcO85HGrYRWV9OTnJ4ylKO2lA1xqFfXTlpaQYdlpbW4mPj0fTNO6//36OHj3KpEmT1JOyIqz0lZX1v0Psey1wIfAPIcR8DDNSB58B04QQGUADMB94vL8Owxm44wvUatCYO27JgAPrKuuNqm9qv0ExnNx33308++yzfPTRR6SkpDB//vxoi6Q4QYlklrhXgMVCiHUYD/3XCyG+C+yWUr7mT8nxtr/tP6SU/a99tPAph91HNlLfepiJWbNJH6Abqk/3UlW/E3tMCinxWWGTSaHoD5vNhtls5uDBg6Sk9J0tWKEYChFTDlJKH93TbJQHnX8BeCFS4/eF1+dh04F3sZiszCo+d8DXH23aj9vbzoSsGaoQiiKitLS08Pe//52bbroJTdO4/fbb+cY3vkFCQlTSmynGECEpByFECTAVw/uoSEq5L5JC9Ua4pmGzycI5J11HQ+tR4mOSBny9sd8A+cqkpIgwP/nJT3j22WdJTU3lyiuvxGq1qihnxbAQSrGfZcBPgHiMEqHrhRDfk1L+LdLCRZL0hPwBm5M6qKwvx2yykJs8PsxSKRTgdrsDCuDuu+8mKytLBbMphp1Qwnp/gBGX0CSlPArMBH4UUal6IRwWnG2V/x1SzemW9noaWo+QmzwBizkqNY8UJzDvvvsus2bNCrgf5ufn8+Mf/5jY2NgoS6YYa4SiHLxSykC4pZSymkinR40QNc0H2FjxJut2vzJoz6eAl5JyYVVEiNraWrZv3x5tMRRjnFD2HL4QQtwBWIUQM4DbgM8jK1bPaCHpsp7RdZ1P9r4OwNxxFwx6IzlQ2EdVfVOEAV3XeemllzjvvPNITExk8eLFbNq0iezs7GiLphjjhDLb3g7kA23AUxjBbbdFUqjeGIpnUMWxLdQ0H6A4fRrZyeMG1YfH66a6cQ8p8VkkxqYNWhaFooO///3v3HLLLfz2t78NHFOKQTESCGXlcBPwsJQyKvsMwQxWNXh8bjZWvIlJMzO75PxBj3+4cQ9en1sFvimGhM/nQ9M0NE3jiiuuYNOmTdx6663RFkuh6EQoK4d8YIMQ4i0hxNeFEKGnLQ0z+iC3OsoPrcPhbGBK3qkkxaUPeny136AYKnv37mXJkiW8/PLLAMTExHD//fdTWFgYZckUis70qxyklHdLKccBv8FIc/G5EOKvEZesB7RBrh0mZs9hav5pnFLYX67A3tF1ncq6cqzmWLISiwfdj2Jso2kaW7ZsYd26ddEWRaHok1CD4DTACtgwPJWckRQq3MRa7cwdF3rpz55obDtKi7OekoyTMZnMYZJMMRbYvHkzCQkJTJgwgXHjxrF27VpKSkqiLZZC0Sf9rhyEEH8ADgDfxqgGN0NK+Y1IC9YTA1031DuOsLdmc1gS9qnCPorBsGPHDs455xy+9a1vBb6HSjEoRgOhrBx2ArNCKcYzktB1nU/3vc6hhl3E2xLJGWI0s7HfoCkXVkVI6LqOpmlMmTKFb3zjG5x77rkqD5diVNGrchBC3Cyl/DOQBtwqROdJUUp5T4Rl68ZA/req6ndyqGEXeSmTyE4anOtqBy5PO0eaKshIKCDOphKeKXqnpaWFX/7ylyQmJvKzn/0MoJObqkIxWujLrKR1eR38ExVC3ZD2+bx8um8V2iBrNXTlUMNOdN1HQZpaNSj6RtM01qxZwzvvvIPL5Yq2OArFoOmr2M9j/pcVUspngs8JIW6PqFS9EOokv/PIJzS2HaU050uk2odeVvSg2m9Q9EFdXR0VFRXMmjULu93OypUrKSgowGZTubcUo5e+zErfxqgD/U0hRLDvpgW4BvhjhGXrTgjKwelpY9P+1VjMNmYWLR7ykLruo6p+J7HWBNIT8obcn+LEor29ndNPPx2v18v69etJTk5mwoQJ0RZLoRgyfW1I7wZm092U5ASui6BMvaP3HwRnNduYVfIVdN1HnC2x3/b9UdtSRbu7hYlZs9G0wed2UpyYxMbGcvPNNwNgt9ujLI1CET76Miu9DrwuhPiHlHIHgBAiCSiUUn4xXAIOFJNmRuTMC1t/AZOSiopWYHgh/e1vf+P999/nySefRNM0VqxYEW2xFIqwE8qj8KlCiKeEEJnAduAlIcSvIyzXoNh79HPcnvDG51XWSzTNRF7KpLD2qxi9vPbaa6xevZo9e/ZEWxSFImKEohxuA74HfA14FTgZOC+SQvVGXxvSRxr38cHOF/hg54thG6/V1UxtSyXZSSXYLKrYyljF6/WyceNGwPgOPvzww6xbt46JEydGWTKFInKEZESXUtYBFwCrpJQeIC6iUg0QXffxyb5VAJxccEbY+q3qSLSnvJTGNDfeeCNLliyhvNxfyyM/n4KCgihLpVBEllCL/bwOjAdWCyH+AXwaWbF6prd1w96azdS2VDIuczpZSUVhG6+yTmVhVcDXvvY1zGYz6emDz+irUIw2Qlk53ADcB8yTUrqAvwJRya3UEx6vi7KKtzCbLMwuDp+1y+fzcqhhJwkxaSTHZYatX8XIZ9OmTVxzzTU4HA4Azj33XJ588kkyM9X3QDF2CEU52IClwLtCiM+Bs4CYiErVCyZTd3G/qPqQVlcjU/O+TEJsatjGOtJUgdvrpCBNqJw4Y4x///vfvPnmm7zzzjvRFkWhiBqhKIdHgHiMFcS1GKm7/xRJoQZCbspEClInh3WvAVRhn7HG9u3bA1lT7777blatWsWll14aZakUiugRinKYLaW8Q0q5RUq5WUp5B0Zw3IggK6mYc6Zeh9US3sVMZV05ZpN1yNlcFSOfxx57jEWLFvHaa68BEBcXx4IFC6IslUIRXUJRDiYhRErHG/9rT+RECo3G1hqa2o5FpO/m9joa246SmzwBi8kakTEUI4ezzz6bmTNnqlKdCkUQoSiHB4FPhRAPCCEewPBUejiyYvVMh+Vf13XW73mFf332UEQURKWKij6hqa2t5fbbb2ffvn0ATJw4kdWrVzNr1qwoS6ZQjBxCqSH9F+BSYC9QAVwmpXwqwnL1ycG6HRxu3EteyiSS4jLC3n9lvcrCeiLz4Ycf8vzzz/PYY48FjimnA4WiM31lZTUBtwOlwEdSyuHPwtoFTQOvz8PGfW+gYWLOEOtC94TH66K6YS+p8TkkxKb0f4FiVHDo0CHS0tKIjY3l4osv5plnnuGCC8L//VEoThT6Wjk8ClwJOIAfCyF+Njwi9Y2s3kBT+zFE7jxS4rPC3n914x58uod8VdjnhGHDhg0sWLCABx54ADBWCRdeeCFmsznKkikUI5e+lMPpwOlSyh9ixDZcPjwi9Y7H6+Tzg2uwmmOZUXRORMbo2G8oVCalE4Zp06ZRVFREcXFx/40VCgXQd/qMdimlDiClrBVC6MMkU+/oPorSTiLVnkOsNfy583Vdp7K+HJsljswwpuFQDC9er5f/+7//Y/LkyZxzzjkkJCTw3//+t8cgSoVC0TN9KYeuyqD/SjsRxma1s6jwyoj139B6BIezkXEZ0zFpyuQwWtmzZw/33HMPU6dO5eyzz0bTNKUYFIoB0pdyKBZCPNXbeynlDZETq2f0ECrBDYWAl5Labxh1uFwuWlpaSEtLo7S0lCeffJJTTz1VeSEpFIOkL+Xw3S7v/zuQjv3eTo8C0zFKi35DSrm7hzargFellP2m5Kiq20Bp7tKBiDEgjP0GjfzU0oiNoQg/NTU1XHzxxRQVFfH8888HNpwVCsXg6atM6DND7PsSIFZKuUAIMR94ALi4S5tfAyFny4uPiVxWTKenlaNNB8hMLCTWmhCxcRThJyMjg5ycHAoKCnC73dhstmiLpFCMekKp5zBYFgFvAUgpNwgh5gSfFEJcgbGP8VaoHZpNkRP3UP0udHwUpCqT0mjggw8+YN++fUybNg1N03jxxRexWlWqE4UiXERSOSQBjUHvvUIIi5TSI4SYBlwNXAGEHD9RVVmFVh9mKf0cdH0CQPMRnbKassgMEkbKyka+jJHC5XJxww030NraynPPPTem70VX1L04jroXQyMk5SCEsAMTgK1AvJTSEcJlTUBi0HuTv8QowP8A+cB7QAngEkJUSCn7XEUUFBQwe3z489/4dB87P36DOFsiC+ecPeI3McvKypg9e8Qkxh02GhoaSEkxotafeOIJkpOT8fl8Y/Je9MRY/V70hLoXBk6nk23btg3q2n79+4QQZwObgVeBHKBCCPGVEPpei1F3Gv+ew9aOE1LK70sp50kpzwCeBh7sTzEARv6MCHCsuRKnx0FBqirsMxLRdZ2bb76Zc845h9bWVgDOOOMMZs6cGWXJFIoTl1Ccv/8XY/+gQUpZjRE5/f9CuO4VoF0IsQ54CPiOEOK7QoiLBitsQkz402WASrQ30tE0jezsbNLS0qivj5BdUaFQdCIUs5JJSnlYCGOjVkq5veN1X0gpfcA3uxwu76HdL0KQAYAYS3yoTQdEVZ3EpJnJS5kUkf4VA6eyspJXXnmFFStWAPCTn/wEi8Wi8iEpFMNEKMqhUgixFND9hX5uBw5EVqzho9XVRK2jitzkiWGvJqcYPN/5zndYs2YNs2bNYuHChcTEqL+NQjGchKIcbgF+BxRi1HRYA9wcSaF6o6mtigGERYREZV1HrWjlwhptHA4HdruRM+s3v/kNF198MaeeemqUpVIoxib9Kgcp5VHga8MgS7/4IpDe6XjKDLXfEE2eeuopfvvb37JmzRqKioooLS2ltFRFqisU0aJf5SCE2Ef3JHxIKcdHRKI+0AivJ5HX5+FQwy4SY9NJjotc9LWifxISEjCZTBw4cICiIpURV6GINqGYlc4Iem3FKBl6QhiAjzRV4PG6KMhSJqXhxul08tRTT3HjjTdis9m48sorOe+880hKSoq2aAqFgtDMSvu7HPp/QoiNGHmRhpVwRyB0FPZRJqXh5+GHH+bee+/F7XbzrW99C03TlGJQKEYQoZiVTgt6qwFTgbiISdQHYVcO9eVYTDZykofdQjYmcblcgaR4t956Ky6Xi+uvvz7KUikUip4IJQjul0E/P8cwM10bQZl6JSaM2VKb2o7R1HaM3JSJEU3opzD4+OOPmT9/Pu+99x4ASUlJ/PSnPyUxMbGfKxUKRTQIZVb8h5Ty/yIuSQjExYTPjbWy3nBhLVQmpWEhLi6Ow4cPU15ezllnnRVtcRQKRT+EsnK4PeJShEg4vZU69hvyVYruiLFq1Sqqq6sBOOWUU/j888+57bbboiyVQqEIhVBWDgeFEO8BHwNtHQellPdETKpeaHU1AMlD7sftdXK4cS+p9lzsMUPvT9GdNWvWsHz5ci6++GL+8pe/AJCVFZncWAqFIvyEohw2BL2OaspSt6c1LP1UN+zGp3spVIn2woqu6+i6jslk4swzz+TWW2/l2mujsj2lUCiGSK/KQQhxrZTyGSnlL4dToL4IVzbtjv0G5cIaPg4fPsyKFSs47bTTWLFiBSaTid/85jfRFkuhUAySvvYc7hw2KUIkHHsOuq5TWSeJscSTkVgYBqkUADabjS1btvDxxx+j690C6hUKxShjzPlw1juqaXU1Mj5zBiYtlP14RW/s2rULh8PBjBkzSEtL491336WwsFAVTFIoTgD6Ug5ThRB7eziuAXpUciuFYTIPmJTUfsOQOHr0KGeeeSZ5eXl89NFH2Gw2lRNJoTiB6Es57MZf5nOkEI4n/cr6cjQ08lJVYZ/BoOs6mqaRlZXFnXfeiRAiEPWsUChOHPpSDq4e8ipFlaS4oblCtrsd1DQdIDOxiFirPUxSjQ1cLhf33Xcf1dXV/PGPfwTg7rvvHnK/Ho8Hn29oqdhdLteQ5ThRUPfiOGPpXphMJiyW8O4S9NXb2rCONAI4VL8LHV15KQ0Cs3Gs0iMAACAASURBVNnMf/7zH2pqaqivryc1dejR6s3NzZjN5iF9qSdMmDBkOU4U1L04zli7Fy6Xi7a2trCmo+n1v1JKeUfYRgkTbq9zSNcHCvuoqOiQaGlpYevWrSxYsACz2cxf/vIXUlNTSUgYeo4rj8eD2WwmPn5odcHdbrcya/lR9+I4Y+1e2Gw2Wltb8Xg8YVtBjCp3nTZ346Cv9ek+qup3Em9LJtWeG0apTkx0XWfp0qUsW7aMyspKAAoLC8OiGAB8Pl/Yl8EKxVjGbDYP2UQbzKj67zQNwUXyWPNBnJ5WSnO+pFwtQ0DTNG699VZ2795NRkZGtMVRKBT9EO55bVQph6EQKOyjTEq98tprr/HMM8/wwgsvYLVaWbZsWbRFUigUUWJUmZWGEiF9sL4ck2YmN2ViGCU6sVizZg3r1q3js88+i7Yow8LHH3/MggULWL58OcuXL+eyyy7jW9/6VsDLpa6ujh/84AcsX76cq6++mrvuuouamprA9Rs3buT6669n+fLlXH755Tz33HMhj/23v/2N888/nzfeeKPPdi+//DL333//4D5gL7S3t7NixQquvvpqbrrpJurq6vq95he/+AWXXHJJp2PLly9nz549gfdOp7NTOvYXX3yRa665huXLl3PVVVfx8ccfD0reRx55hCuuuIKrrrqKLVu2dDv/r3/9iwsvvJCrr76alStXDvozKjozqlYOg1UODmcj9Y5q8lImYTWfEOWvw4Ku64EiPAD33HMPd9xxB5MmDX8MyPf/XcZLmwfuOd0Rd9ETV0wv5r4LZ/d5/fz583nooYcC7++66y7ee+89zj33XO644w5uuOEGzjnnHADWrVvHLbfcwsqVKzl06BC//vWveeKJJ8jIyKC9vZ3/+Z//obCwkNNOO6234QK88847PPzwwwgx/CvZ559/ntLSUlasWMGqVat49NFH+clPftJr+7a2NsrKyigtLeXjjz9m3rx5/Y6xatUq1q5dy9NPP43VauXgwYN8/etf55VXXiEtLS1kWb/44gs++eQTVq5cSXV1NStWrOCf//xn4HxdXR2///3vefnll0lKSuK6665jwYIFrFq1akCfUdGdUaUcBkuVSrTXIz/60Y/485//zKuvvsqXv/xlkpOTSU4euynMXa7/v73zjq/xbOP492QPErHFCsFD1WhobG2JVkVbgtiqRmnL+5bWihiNvVvVKhVFqBKixkttRSkSrdG0T4kVM8iQIeuc8/7x5DxyZJN15P5+PvlwnnE/13Pn5L7udf2uZCIiInB0dOTixYuULl1adQwArVu3pkaNGpw5c4bg4GC6deumrsfY2Njg7++fYffVzZs38fHxQavVotFo8PX15dy5c4SGhjJ58mSWLFlC9eqKxldiYiKTJk3i9u3bpKSkMGXKFKOyFi1axMWLF4mOjqZ+/frMmTOHkJAQ5s2bh4WFBZaWlnzzzTfcv3+fSZMmYWFhgU6nY9GiRVSp8mQTRkhICMOGDQOgffv2fPvtt9nWy549e2jVqhXt27dnw4YNuXIOP/30E5MmTcLS0hJQNjP8/PPPGbZAjxgxgoSEJ2rLrq6uTJ8+3cjWtm3botFocHZ2RqvVEhkZqTqYmzdvIkkSZcqUAaBRo0acO3eOP/74g5EjR+b6HQUZMSnnYP+MmeDCxXpDpnh7e3P9+nVq1apV1KYw/51mOfbyMyM+Ph57+2cPaPz9998ZOHAgDx8+xMzMDG9vb1q1asXu3bvVRjs91atX5/bt20RERFC/vnFnI7M95vPnz2fQoEF4eHjw999/4+PjQ1BQELt27WL69OlGz/jpp5+oWrUqS5Ys4dq1axw5cgQHBwdA2Vbs4ODADz/8gE6nw9PTk3v37nHgwAHefvtt3n//fXbv3s2jR484ceIEjRs3Zty4cQQHBxMbG2vkHOLi4lRb7e3tiY2NzbaOAgMD8fPzUxvue/fuUalSpWzviYiIyFB/mcXGrFixItty4uLi1IY/vb0G51CzZk0uX77MgwcPsLe35+TJk7i4uBAfH5+ndxRkxKTWHJ4l17NWl8qd6Ms42JbHwbZk77qRZZk+ffoQEREBgJubGxs3bqRatWpFbFnR0bJlSwICAtiwYQOWlpZqXVSqVIlbt25luP769etUqVIFZ2dn7t69a3Tun3/+ITQ01OhYWFgYr776KgANGjTIcE96rly5QtOmTQFwcXFh8ODB6jlra2siIyMZO3YsU6dOJSEhgZSUFEaOHElERATvv/8+Bw4cwMLCgp49e+Lg4MCwYcPYsGED5ubmRs8pVaoU8fHxgOJcDQ4oM8LCwrh06RJz585l+PDhaDQaNm7cqNqUkpKiXhsfH4+NjQ0AVatWVbMAGjh27Jj63TMwYsQIdc1n4MCBRqOGp201PCO9E3Z0dGTSpEmMHj2asWPH0rBhQ5ycnLC3t8/1Owoyx6ScA+R9D+/dmCuk6pKF0B7KH+e+ffsICgoqalOKHU5OTixYsABfX18iIiJwc3PjwYMHHDp0SL3m6NGjXL9+HXd3d7p27UpgYKC60BkfH8/UqVONFqxBmSYJDg4G4O+//852W7CrqysXLlwAIDw8nM8++8zo2Xfu3GHx4sWMHTuWxMRE9Ho9O3bsoHv37gQEBODq6srmzZs5ePAgzZo1Y+3atXTu3JlVq1YZPcfNzY1ff/1VLbdZs6xHbIGBgYwZMwZ/f3/8/f1Zu3YtW7duJTk5mYYNG7J3714jGxs1agRAjx49+Pbbb0lNTQXg6tWr+Pr6ZnBUK1asICAgQP152jm4ublx/PhxdDodt2/fRqfTGa1ZpKamEhoayo8//shXX33FlStXcHNzo2nTprl+R0HmmNS0UnxSFOXJWw+gpCf2CQ0NRZIkzM3NGTJkCPXq1cvVgmlJpE6dOgwcOJCZM2eydOlSvvvuO2bPnq1OfVSuXJmVK1dibm5OtWrVGDduHKNGjcLc3Jz4+Hh69uzJa6+9ZlTm+PHjmTJlCqtXryY1NTXbBEh9+vTBx8eHAQMGoNVq8fHx4dKlS4CSg/vbb7+lf//+aDQaqlevTkREBI0bN8bX1xdbW1v0ej2zZs1Cr9czYcIEli9fjk6nY9KkSUbP6du3LxMmTKBv375YWlqyaNEiAGbNmoWXlxcNGjQAlDWYXbt2sWPHDvVeZ2dn6tevz969exk+fDhTp06le/fuWFtbU6ZMGWbMmAGAp6cn9+/fp1+/flhaWqLValmwYAHlypXL0+/k5Zdfpnnz5vTu3RudTsfUqVMB2LlzJwkJCep2a4MNH3zwAWXLlqVnz574+flleEdB7tGYQmKWkJAQF+Bq+eoVqFkxb7LQW4MX8Dgllr4tpj7TtFRxJCQkJFc9oaCgIEaMGIGfnx8fffRRIViWewzbRZ9X4uB51xxeJJ63LgICAmjfvj01a9bMR6uKhpL4vcjsbyopKYmLFy8C1GrWrNm1vJRnUq1lXgMAYx7fJzbxITXKNXxhHENeaN++PU2aNOGll14qalMEJkDHjh1xdnYuajMExQSTWnPIa3j4k6jokjGl9OjRI8aNG8eZM2cAKF++PPv3788w1SEQZIZwDIL0mJRzII9BcE/WG0rGFtbQ0FD8/f1ZtmyZekzoSAkEgmfBpOZa8uLJUlKTuBdzlXL2VbGzenG3sUVGRmJmZkaZMmVo2bIlP/74I2+88UZRmyUQCEwckxo5WJnnXvv/dvQldHotVV/gUYMsy7Rq1QofHx/1WOfOnbG2FhIhAoHg+SiwkYMkSWbAt0ATIAkYJsvy5XTnxwB90j7ulmX5i5zKtLS0yfXznyT2eXHXG1xdXXF1daVBgwbZagwJBAJBXinIkUM3wEaW5VbAREDdaCxJUm2gP9AaaAm8KUlS4/x6sF6v52aUjLWFPeVLvzjRv3q9noCAAPbv3w+AhYUFu3btYvTo0cIxPAMlVZXVwP79+40C7bLj+++/p23btiQlPcnGOHHiRI4ePWp0XZs2bdT/HzhwQK3bXr168csvvzyTnZs3b8bLywtvb28OHz6c4fzx48fp1q0bffv2VTWUDDERvXv3ZuDAgVy/nndRx5JOQa45tAV+AZBl+XdJkpqnOxcOdJZlWQsgSZIlkJhTgecvhmBvmfPU0mNdFI+TYyljXpM/zv7xTMYXR6Kiopg0aRL29va8/vrrqqiZqeLq6qrKL5y7dYCb0aE53JE3qpV5iSZVPbI8n5iYSPPmzZk7d656zMfHhz179tCxY0c++ugjBg0axOuvvw4ozmT48OGsW7eOO3fu4Ofnx7JlyyhXrhyJiYl8+OGHVKhQwaiBzIo9e/YwZ84c6tatayQP8TRJSUmkpKRke016cnvdggULOHnyJPXq1cvVPT///DOdOnUiKCiId999F1CikxMTE43u1+v1xMfHc+7cOfz9/Vm6dCl2dnZER0fz/vvvU7VqVWrXrp0rGwEePHjA2rVrWb9+PUlJSQwdOpSmTZuqe/l1Oh2TJ09m5cqVVKtWjcmTJ3P8+HEiIyNJSEhg9erVnD9/nlmzZhmp776IpKSkGEmoPy8F6RwcgPR5PbWSJFnIspwqy3IK8ECSJA2wAPhDluV/cyqwnlSHyk45p/g8d+Mg3IDGdVpTu0KTZ7W/WKDVann48CEVK1YEYO3ataSkpKgy26bK0wE7lpaWzzT6yW46zdLSMttAKBsbGywsLNRrkpOTiYyMpGLFily9epUyZcrg6empXt+hQwd27NhBaGgowcHBeHl5UaOGEpRpb2/PmjVrsLOzM5KIyEqVVZZlZs6cmaMqq7W1tfoe+aXKCuDu7k7nzp3ZtGlTjsFip06dwsXFhYEDBzJu3Dj69u0LKCNXGxsbo/s1Gg329vbs3LmTIUOGUKFCBbV+tm7dioODg9Hva/Lkydy4cUP97OjoaLTb7vfff6dZs2aqaJ+Liwvh4eE0bqxMNDx8+BBHR0dV+tzd3Z3Q0FDu3r3LG2+8gb29Pa1atWL8+PEvfFBccnIyjRo1yioILs8UpHN4BKSXqTSTZTnV8EGSJBtgNRALfJy7InPXeNyMktFgRlWnws9LkJ88fvyY9957j9TUVPbt24eFhQUdO3YkJCSkqE3Ld16t1YVXa3XJ831ClTXvqqwAXbp0yXXyncDAQHr16kXt2rWxsrLi3LlzNGmSeafL0PBnpsqamRx8dnIihvdOX6/29vbExcWpn8uWLUtiYiJhYWG4uLhw9OhR6tevT3x8vFG+c3Nzc1JTU0Xe8jxQkDX1G/AOsFmSpJbABcOJtBHDduCQLMvz8vOhiSlx3I8Np5JDTawtcr+7qThia2tL3bp1SUpKIiEhQShLFgCGZD9RUVEMGTIkV6qsrVu3JiIiIlNVVp1OZxSRnldVVoPulUGV1SCSmF6V1c7OzkiV9bvvvuP999+nXLlyuLu707NnT77//nuGDRtG6dKlGTNmzDPXT0xMDEePHiUyMpKAgADi4uJYv349TZo0wdraWh0BGjAI7Tk7O3Pnzh0jBxoSEkL58uWN5DlyGjnkpMqq0WiYP38+06dPx8rKinr16uHk5MSjR4+M7tPpdMIx5JGCXJDeBiRKknQCWAKMkSRprCRJ76IsVr8GvC1J0pG0n1Y5G5vzyOFW1CVAb7JCe2fPnmXp0qXq5y+//JJVq1YJx1DAlCRV1rywY8cOevTowerVq/H392fz5s389ttvREZG0rBhQ3VzBCgL9HXqKGl4vby88Pf3VxP5PHz4EB8fHx4/fmxU/qxZs4xUWdM7BlAEB0NCQkhKSiI2NpawsDDq1atndM3x48fx9/dn1apV3Lhxg9atW9O0aVN1sfzPP//McI8gZwrMlcqyrANGPnX4n3T/z/2+VAO5mFUyZckMnU7Hf//7X0JDQ/H09MTV1dXkF51NiZKiypoVK1eupH79+kaqvYGBgcyfP1/9bGtry5tvvsnmzZsZOnQof//9N++99x729vZYWlri5+cHwCuvvIK3tzdDhgzBwsKCxMRExo4dm2EqLicqVKig7hbT6/WMGTMGa2trTp48SUhICKNGjaJixYr06tULGxsb3nnnHerWrUvlypUJDg6mT58+6PV6Zs+enafnCkxMlbVWnVqUdcw6/6xOr+Wn32dgaWFNz+YTTWZ7Z1RUlLrg9scffxAXF0e7du2yvD63qqzFGaHKmv88b10cPHgQOzs7WrXKcRBf7CmJ34v8VmU1rQjpHILg7j+6QbI2kWpO9U3GMfj6+tKiRQsePHgAKD2u7ByDQFBQNGjQ4IVwDIL8waScgyaHeaVwQ1S0Ca03VK1alQoVKvDw4cOiNkVQwhGqrIL0mJRzSNElZ3v+VqSMmcaCKo6uhWRR3nnw4AHz589Hp1NSnn744YccPnxY3actEAgExQGTcg56XWqW5+ISo4lKuEuVMrWxMH++eeyCxM/Pj7lz57Jt2zZA2X/9vPPuAoFAkN+8MBt/i7PQXlxcnBqQ4+vrS8OGDenWrVsRWyUQCARZY1Ijh+zyhKpbWIvZesO2bdto3LgxZ8+eBaBixYqMGDHCSGJBIBAIihsm5RyyWpBO1aVwJyYMR9uKlLbJeqtrUVC+fHn0en2m0baC4s+oUaOK2oRCoSgVaguLkSNHMmLECKNjHTp0MFKaDQsLY+DAgYASd/Tdd9/Rr18/tV5kWc7zc3OjELty5Uree+89+vfvn0F59vTp00WS6tekppWyGjfcjbmCVpdSLNKBpqamsnr1anr37o2joyPt2rXj3LlzIsI5FwSemZvp8ZervkYDZ2WL5VF5E/ceXVXPGYT3KpSuwev1+wHw793TnAs/RK9XJz63TU9H7L7IGKREDHz22WccOnSIt956i1GjRjFkyBA8PBSV2xMnTjBixAgCAwO5ffs2M2fOZNWqVZQvX57ExEQGDRpE9erVjQLqipLbt2+TkJBAamoq4eHhmepmPc2qVauIiopi/fr1mJmZcf78eT7++GN++eWXPAWnHjhwgOTkZDZt2sSff/7J3LlzWb58uXpelmV27dpFYGAgoARDtmzZEltbW+7cucMPP/ygypIUJqblHMwydw/FKSp6w4YNTJw4katXrzJnzhwA4RiKKUFBQRw+fJjExETu37/PoEGDOHjwIJcuXWL8+PF4eHjQpk0bfvvtN86dO8fs2bPR6XRUqlSJhQsXMnz4cMqWLUtMTAwrV67Ex8eHmzdvotVq+eCDD+jSxVhIMC4ujsmTJxMbG0tERAT9+vWjc+fO9O/fn927d6PRaPDz86NVq1bUqFGDmTNnAlCmTBlmz55NaGgoCxcuxNLSEm9vb2xsbNiwYQOpqamqxpCdnR1ffPEFFy9epHz58ty6dYvly5djbm7OlClTSEpKwtramhkzZmQQ40tPcnIyERERODo6cvHiRUqXLq06BoDWrVtTo0YNzpw5Q3BwMN26dVOlQWxsbPD398fOzljb7Nq1a/j6+pKSkoKNjQ1Llixh/vz5dOnShfbt23P06FF2797N3LlzeeONN6hduzaurq4cPnyY7du3Y2dnh7+/P+bm5rz11lt5ep+tW7fSsWNHbGxs+PHHH5kwYUKO349NmzYRFBSEmZkywdK4cWO2bNli5Bji4+MZOdJYCKJFixZGI86QkBA1dqlp06YZVFLDwsJwd3dXMzjWrFkTWZZp0KAB06ZNY8aMGXh5eeVob35jUs7ByjxjEJxer+dmpIyluTWVHFwK3yiUPySD5HS/fv0IDw/no48+KhJbTJnc9PTbS72NPmcWCVuvsjv1Krvn6pnx8fGsXr2a//3vf6xZs4bNmzdz6tQp1q1bZ9QYTp06lcWLF+Pq6kpgYKCqm9+1a1c6derE+vXrKVu2LAsXLiQuLg4vLy9atmxJ2bJPpjmvX7+Op6cnb775Jvfu3VOnaCRJIjg4mCZNmnDq1Cl8fHzo168fs2fPpk6dOgQGBrJq1Spat25NUlKS2sP87rvvWLlyJba2tkydOpWTJ0/i5OREdHQ0W7ZsITIykjfffBOAefPmMXDgQF577TVOnjzJwoULWbRoEekpaIXaefPm8eGHH9K+fXsOHjxIaGjW+Tvu3LlDUFAQTk5OWFpasm/fPrp168auXbtYvXo1X3zxRY7vY0Cn07Fr1y42bdqEhYUFnp6e/Pe//8XGJvug2sTExAxKsgYlAwP29vYEBARkW076DSmQUSFWkiRWrlxJXFwcKSkp/PHHH/Tu3Rs/Pz+GDBlCpUqVsi2/oDAp55BZ1HPM4/vEJUVSs1wjzMwKf5E3NDSUYcOGMWrUKPr164elpSW+vr6Fbofg2WjQoAGgNGaurq5oNBocHR2N5qFBiU9xdVXiZ3r16qUer1WrFqD0/lq3bg0oSqKurq5cvnyZr7/+GlB62l5eXqxdu5Z9+/ZRqlQpdarA29ubbdu2cf/+fTp06ICFhQVhYWF88YWSOTclJQUXFxej5wGUK1eOCRMmYG9vz5UrV2jQoAFXrlyhadOmgCJnbUis8++//7JixQpWrVqFXq/PVKG0oBVqr169yiuvvAJAx44dAdi1a5d6Pr2Uj5OTk9oQ9+rVi+nTp1O7dm1q1aqFk5NTrt7HwLFjx4iPj1eFDHU6HTt37qRXr16qsqyh156QkKA6DQcHhwwN+/79+2nVqpV6LDcjh6eVZZ9WiHV1daV///4MGzYMZ2dnmjRpgrm5OcHBwdy4cYNvvvmGmJgYxowZU6gJi0zKOej0ugzHnuxSKpr1BgcHB27dusXly5dzvlhQ7MitzErFihW5du0aLi4urFy5Um2kDfcb1Fc7depEXFwc//77L66urka9yjlz5tC0aVP69evH77//zq+//gpAq1atWLBgAffu3WPatGmA4gTmzZuHs7MzISEh6uKvYYojNjaWpUuXcuTIEQA++OAD9Ho9devWZfv27YAit33t2jUAateuzZAhQ3BzcyMsLIwzZ85k+a4GhdpBgwbx888/GynUdujQATBWqK1evTqffPIJXbp0oWzZsqpC7SeffGJUrkF1tnXr1uzYsYOYmBisrKzUd0s/kjC8Jyjy5Xq9nlWrVqmJhvLyPlu2bGHmzJlqRr+QkBBmzpxJr169eOmll9i7dy89e/ZU36tRo0YAdO/enWXLljFhwgQ0Gg1nz55lzpw5RulOczNycHNz4/Dhw3Tp0iVThdjIyEji4+P56aefiI2NZciQITRr1oy9e/eq17Rp06bQM9mZlnPIJAjOEN9Q1anwnMPRo0epUKECDRo0oFq1aqpOveDF5YsvvsDHxwczMzMqVKjA4MGDWbdunXre29ubKVOm0LdvX5KSkhg1ahTlypUzKuONN95g5syZ7N69m9KlS2Nubk5ycjJWVla89dZbnDhxQs0sN336dCZMmKCuJ8yaNYuIiAi1rFKlSuHm5kbv3r2xsLDAwcGB+/fv07dvX44ePUqfPn0oX748NjY2WFpaMmHCBKZPn05SUhKJiYlMnjw52/ctKIXaqVOnsnz5cmxsbFiwYAHh4eH4+Piwc+dOdXSUGT179mTp0qVqBsSs3mfMmDH4+Pio6x0PHjzg3LlzRg1rs2bNSEpK4uzZs6pq7saNG7GwsKB69erqiG3o0KF89dVXah1bWFiwfPnyPAetdurUid9++y2DQuwPP/xAjRo16NChA1euXKFHjx5YWloyfvz4YrHV3aRUWes3kLC3ezLES05NZOMpP8raO/NO08LZchgaGkrbtm159dVX+eWXX4pE4E+osj6hJKpvZkV8fDx3797ln3/+wdPTk6ioKLp27crhw4dLTBT+4sWLGTlyJHq9vsR9L/JbldWkRg5PN8O3oy+h1+uoVgijBq1Wi7m5OS+99BLjxo2jc+fOJqP8Kig5VKlShYULF7J27Vq0Wi2ff/55iXEMoGwDtbOzM5rjFzwbJuUcnnYPhvWG6gUYFR0TE8PYsWOpWLGiujU1t8lTBILCxs7OzmgPfUlDKMvmH6blHNL5Br1ex80oGRvLUpQrVbXAHmltbc2FCxcoW7YsKSkpIjObQCAoEZiWc0jnHR7G3SYxJQ7Xim5oNPmrAnLz5k2uX79OmzZtsLGxYdu2bVSuXLlYLBIJBAJBYWBSzsEs3Ry/YZdSfk8pPX78GA8PD/R6PadPn8bR0ZGqVQtuZCIQCATFEZNyDulHCDcjZTSY4VymXjZ35B6DRo+trS3jx4/H2tpayF4IBIISi0k5BwOPk+N4EHeTSg4uWFlkHwKfEzqdjmXLlnHs2DE2bdqEmZkZQ4YMySdLBQLTpEOHDlSpUgUzMzO0Wi0JCQnMmDGDRo0aodfr+fHHH9m1a5ca6Tts2DA1riEmJoZ58+Zx48YNUlNTqVKlCn5+fplKahQVu3fvxsfHh71796ryFF9//TXly5dXA+1AiV9ZvHgx1apVIzg4mG+++YbU1FQSEhLw8vKif//+eX72oUOH+Oabb7CwsKBHjx54e3sbnf/rr7+YNm0aVlZWNGjQgMmTJ2NmZsayZcs4cuQIFhYW+Pj40Lhx4+erhBwwKedgiJC+FSUD+nyZUtJoNJw+fZrz589z48aNbANxBAVLcVRlLcmsXr1alZU4duwYy5YtY8WKFWzatImzZ8+yZs0arK2tiYqK4sMPP8TR0ZGmTZsyduxY+vTpQ6dOnQBYs2YNU6dOLfQI3+wIDAxk4MCBbN68mdGjR+d4fXh4eL4oz6akpDBnzhy2bNmCra0tffv2pUOHDkZBtFOmTMHX1xc3NzeWLFnCzp07qVOnDqdPnyYwMJA7d+4wevRotm7d+kzvnltMyjkYVhzUrG/P6BySkpI4efIkr7/+OhqNhiVLlmBubm4kkiZ48cmNKuv69evZt28fjx8/xsnJiWXLlqHT6Zg0aRK3b98mJSWFKVOmcPXqVbZu3YpOp+M///kP9+/fZ+3atVhZWeHi4oKfn1+GnW6ZlT127FgGDRqEu7s7Fy5c4Ntvv2Xp0qVMmzaN69evo9Pp+PTTT2nRogVdHHMgAgAAEYFJREFUu3bFxcVFjYD29fVFq9Vy//59Pv30Uzw8PDh8+DBLly6lVKlSODo6IkkSo0ePZtGiRQQHB6PT6Rg8eDBvv/12tnV1+/ZtdZp1/fr1rFu3TnUcTk5OjBo1io0bN1KhQgUePHigOgZAzfGQHr1ez4wZMzh//jwpKSmMHj2a0qVL89NPP6lOxKCIO3HiRKKjo4mOjqZWrVq4u7vTvXt37t+/z4gRIwgKCsrwPtk12OHh4cTExDB8+HC8vLwYOXJkjrsQt2/fnivl2SVLlqiJvQz4+/ursSZhYWHUqFFDFfRr1qwZZ86cMar/e/fu4ebmBijSGwcPHiQmJoa2bdui0WhwdnZGq9USGRlZoG2WSTkH0KDTabkVdYlS1k442lZ8plIGDhzI4cOHOXToEI0aNaJChQr5bKfgWShuqqwdOnQgOjqaNWvWYGZmxtChQ7lw4QIXLlygatWqLFmyhGvXrnHkyBEcHBxwcHBg+fLlREVFMXXqVLZt20apUqWYPXs2mzZtYsCAAepzdTpdpmX36tWLbdu24e7uTlBQEN7e3gQGBuLk5MTs2bOJiopiwIAB/O9//yMhIYGPP/6Yl156iRMnTqgqpWfPnuXrr79W5To2bdpE+fLlVeG5X3/9lZs3b7Jx40aSkpLw9vamTZs2GdbYhgwZQlJSEhEREbRr106VuY6KisrQKKVXaDUI9hkwNzfPMKV04MABoqKi2LJlCzExMfzwww+0atUqy99Ty5YtGTx4MJcvX8bPz4/u3buzfft2vLy8Mn2fV155JcsI6S1bttCjRw8cHBxo2rQp+/fvzyCvnh6NRpNr5dkxY8ZkWQ4oCq3p77O3tycuLs7omurVq3P69Gnc3d05fPgwjx8/Ji4ujjJlyhjdFxsbK5yDAY1GQ0TsdVK0ibhWbPrMEcojR46kVq1aYgpJkK0qq5mZGZaWlowdOxY7Ozvu3r1LamoqV65cUXumLi4uDB48mKCgIFWMLzw8nDp16qjKna+++irHjx836lWuWbMm07LbtWvHggULiI6OJjg4GF9fX2bMmEFISAjnz58HlIRSkZGRwBOV1goVKvD111+za9cuNBqNek2pUqXU3m7z5s158OAB//77L3/99Zea8Sw1NZVbt25lcA6GaaXFixdz8+ZNVSuqVKlSREdHGzVW169fp0qVKjg7O2dQaE1JSWHPnj28++676rGrV6+q6rGOjo58+umnnDp1yui+9NI+hvesU6cOWq2WW7dusXv3btasWcOmTZsyvM/t27epXLlyht+3Vqtl586dVK1alUOHDhETE8P69evp0qWLqtCaHoNKa2bvlZnybE4jh6cVWuPj4zM4mdmzZzNr1iy++eYbmjdvjpWVVa7uy29MKk0oPFtinyNHjuDp6cmjR48AZbFt3rx5xWqBTFA0ZNfB+Oeffzhw4ABffvklU6ZMQafTodfrVXVRUByBoUduUBKtVq0aYWFhJCQkAEqax1q1ajFmzBgCAgIICAjg0qVLmZZtZmZG586dmT59Oh4eHpibm1O7dm08PT0JCAjg+++/p3PnzmrDbHjmV199RdeuXVmwYAEtWrRAr9dTrlw54uPjVUdy7tw5QFE0bdGiBQEBAaxdu5a3334728xon376KREREfz4448ADBgwgJkzZ6oN6cOHD1m2bBl9+vShUqVKODk5ceDAAfX+devWcfDgQaMya9eurdZhbGwsQ4cOxdraWlVovXXrFjExMZn+nnr27MmCBQuoU6cODg4Omb7P06MXA7/++isvv/wyAQEB+Pv7s2XLFh4+fMg///xDw4YNOXTokCqlfuPGDZKTkylXrhxdu3YlMDBQrUuD8mz6VKmA0e/Y8JNevsTV1ZXr168THR1NcnIywcHBqox5ehsNEijR0dG0adMGNzc3jh8/jk6n4/bt2+h0ugKfBjepkQMo6w3mZpZUdnTN9T2nTp3i9OnTHD9+PNvho0CQnpo1a2Jra0ufPn0ApXceERFBnz598PHxYcCAAWi1Wnx8fLh06ZJ6X9myZRk9ejSDBg3CzMyMGjVq8Pnnn+eqbIAePXrg4eGhSjb36dMHX19fBgwYQFxcHP369TOStAbo3LkzS5YsYe3atVSuXJmoqCjMzMyYMmUKw4cPp3Tp0uh0OmrWrEmHDh04ffo0/fr1IyEhAQ8PD6OcBU9jZmbGzJkzGTBgAB4eHgwcOBCtVkv//v2xsLBAo9Hw8ccfq/Pk8+fPx8/Pj9WrV5OSkmKU1c5Ax44dOXnyJH379kWr1fLJJ5/w8ssvU7p0aXr16oWrq2uWDXznzp2ZNWuWKhOS2fvY29sTFBQEYJRFbfPmzUb5OEBxNhs2bFBHaF5eXpQqVQq9Xs+8efMAcq08mxOWlpZMnDiRoUOHotfr6dGjB5UqVeLy5cusX7+e6dOnU7NmTQYPHoytrS0tWrRQn9G8eXN69+6t5qQuaExKlbVW3WrsOL+Eak718Wg4ONt7Tp48ScuWLdFoNCQnJ3Pp0iUaNmxYGOYWOEKV9QlClfUJmdXFihUr+OCDD7CysuLzzz+nbdu2dOvWrYgsLDzi4+MJDw/n4sWLaq6GF538VmU1qWmluzFXgJx3KX355Zd4enqq6RStrKxeGMcgEOQFe3t7vL291VwCJWnkXKZMmQy7pAS5x6Smle5GK3l7c5Lo9vLy4tixY+qCl0BQUhkwYIDRLqmSRGYL0i8yhpif/MKkRg4RsTcoY1eJUjbGSb7Dw8Pp378/siwDUKNGDbZu3ZohHZ+g+GBmZqYu/AkEgudHq9VmWIt6Hkxq5KDVp2a6S+n8+fPs2bOHunXrMn369MI3TJBnLCwsePz4MQkJCZibmz9zjyclJSXD9sOSiqiLJ5SkutDr9Wi1WrRarSpnkh+Y1MgBoFpZZUrp0qVL6lZBT09Ptm/friZnF5gGpUuXxsrK6rmGwmFhYflokWkj6uIJJakuNBoNVlZW+b4136RGDpZm1lQsXZNjx47h7e3NsGHDmDFjBgDt2rUrYusEz0J+9HRKUhrMnBB18QRRF89HgTkHSZLMgG+BJkASMEyW5cvpzg8HRgCpwExZlnflVGYlx1qYmZnTrFkz3NzccHfPnUSCQCAQCPJGQU4rdQNsZFluBUwEFhlOSJJUGfgP0AZ4C5gjSZJ1TgVevxgFKHlyd+3axTvvvFMQdgsEAkGJpyCnldoCvwDIsvy7JEnN051zB36TZTkJSJIk6TLQGDiTRVnmAEf3ncH73ffzdbuWqZKUlFTUJhQbRF08QdTFE0RdkH5RPs85jgvSOTgAMek+ayVJspBlOTWTc7GAYzZlVQH46KOP+Ouvv/LdUFMkLepRgKiL9Ii6eIKoCyOqAHlapS9I5/AISL98bpbmGDI7VxqIzqasM0A74A6gzU8jBQKB4AXGHMUxZDUrkyUF6Rx+A94BNkuS1BK4kO7caWCWJEk2gDXQAMjSzTdr1iwJOF6AtgoEAsGLyjPt6y0w4b10u5UaoyRx+wDoAlyWZXlH2m6lD1EWxWfLslywOe8EAoFAkGtMQpVVIBAIBIWLyUVICwQCgaDgEc5BIBAIBBkQzkEgEAgEGSh22koFIbthiuSiHsYAfdI+7pZl+YvCt7JwyKku0l3zP2C7LMvfFb6VhUMuvhdvA9NQNoGEAJ/IsvxCLizmoi4+A/oBOpRNL9uKxNBCRJKkFsA8WZZff+r4O8BUlHZztSzL3+dUVnEcOeS77IaJkl091Ab6A62BlsCbkiQ1LhIrC4cs6yIdMwGnTI6/aGT3vSgNLAC6yrLcArgGlC8KIwuJ7OqiDPBfoBXwJvBlkVhYiEiSNB5YBdg8ddwSWIJSD68BH0qSVCmn8oqjczCS3QAyld2QZTkGMMhuvIhkVw/hQGdZlrVpvUJLILHwTSw0sqsLJEnqidI7/KXwTSt0squL1ijxRIskSToG3JNl+X7hm1hoZFcX8cB1wD7tR1fo1hU+YYBXJscboIQQRMmynIwSM9Y+p8KKo3PIVHYji3M5yW6YMlnWgyzLKbIsP5AkSSNJ0kLgD1mW/y0SKwuHLOtCkqSXUaYOphaFYUVAdn8f5YE3gAnA28CnkiS9yOkQs6sLUDpRocBZYGlhGlYUpMWKpWRy6pnazeLoHPJTdsOUya4eSIsu35B2zceFbFthk11dDAKqAoeAwcBYSZI6F655hUp2dfEQOCPL8l1ZluOAo8CLnEg9u7p4G0U2ohZQA+gmSVJJ1fh/pnazODqH31AiqclCdqOdJEk2kiQ5koPshomTZT1IkqQBtgPnZFkeIcvyi643lWVdyLI8XpblFmkLcGuAxbIsv8jTS9n9fZwFXpYkqXxaD7olSs/5RSW7uogCHgNJsiwnojSGZQrdwuLB30BdSZLKSpJkhTKldDKnm4rdbiVgG9BJkqQTpMluSJI0lieyG0uBYyiObXLaL/5FJMt6QBHTeg2wTtudAjBJluUcf+EmSrbfiaI1rdDJ6e9jErA37drNsiy/qJ0nyLkuPIDfJUnSocyz7y9CWwsdSZL6AaVkWV6ZVi97UdrN1bIs38rpfiGfIRAIBIIMFMdpJYFAIBAUMcI5CAQCgSADwjkIBAKBIAPCOQgEAoEgA8I5CAQCgSADxXErq6AEIkmSC/AvGfflvyPLcngW90wHkGV5+nM8dzCwGLiRdsgW+BX4OH3QYS7L8gOC07ZRHpZl+Y2043/KsvxcwWiSJB0BqgFxaYccgCtAf1mW72Vz34dArCzLG5/n+YKSh3AOguLE7edtRJ+RHbIsDwaQJMkcOAJ8AnyVl0JkWU4v4fF6uuP59U7DZFk+Aqoi6RZgLIpcRla0RnkfgSBPCOcgKPak6Sd9DZQCKgKLZFlemu68JbAaeDnt0LeyLH+fpjy5AqiOIrw2SZblA9k9S5ZlbVpQVb20sj8APgP0KBLYo1DkoTN73hqUhtgt7d5Tsiy3kCTJII54A3hFluV7kiSVRYnurwl0BPzSrrkKDJdl+WEO1WKPoqV0Ku1ZvdLstE37GQZYAe8CHSRJugP8mdf6EJRcxJqDoDjhLEnSn+l+xqUdH4aSu+NVFGG5WU/d1xooK8vyK4AHiqQ7KD3/1bIsN0NpJFekyVpniSRJ5VB0eX6TJKkRMBl4TZblRihKn9OyeR4Asiz/J+3fFumOpQKBQK+0Qz2An1EkHeYCb6WVtxeYl4V5qyRJOpfW0P+OEvG7JG0UMRJFqrtJWnnj0hr+HcBUWZb3Pkt9CEouYuQgKE5kNa30GdA5TRqiMcoIIj0XAUmSpL3Abp5Ms3gA9dPWAkDpmbui9KDT864kSX+iSDCYAUHARpSppZ3pevErgR9QGt/MnpcTASh5BZYBfQFfoAWKMNxhSZJAkUaJzOL+YbIsH5EkqTWwFSXJUzKKMd2BdySlkNeBzPS2clsfAoFwDgKTYDOKkNpO4CeeZMADQJblh5IkNQQ6oQixnU37bA50kGU5EkCSJGcgs8Vbdc0hPWk98vRoAItsnpctsiwHp4mfvQpUk2X5hCRJ7wHHZVl+N+2ZNhgraGZWzok0jbF1kiQ1QUnucgbF+RwFzqNMfz1NbutDIBDTSgKToBPK1Mh2FMFBw8Ixaf9/F1iPkib0Pyg7eqqjyHh/nHbNSyiNpl0ennsEZVRRNu3zcJQeflbPS8/TuQUMbECZ9/8p7fMpoFW6vAtTULK55cRilHWHkSjrIzpgNso7v43iCEBJC2mw43nrQ1CCEM5BYApMB45LknQWJT3sNRSdfgN7UOSZ/0KRdQ+SZfkCMBpoKUnSeWATMFCW5djcPlSW5fPAHOBXSZL+QVkf8M3meenZDpxLGwmkZz1KjoX1ac+4CwwBNkuSdAFlMfuzXNiWhLIeMg0lA9ifwD8ost1xKAvdAAcAn7Rsec9VH4KShVBlFQgEAkEGxMhBIBAIBBkQzkEgEAgEGRDOQSAQCAQZEM5BIBAIBBkQzkEgEAgEGRDOQSAQCAQZEM5BIBAIBBn4Pwz7A55WSnJhAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n", + " \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n", + " \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "multi_classifiers = [\n", + " BernoulliNB(),\n", + " MultinomialNB(),\n", + " LogisticRegression(),\n", + " LogisticRegressionCV(),\n", + " MLPClassifier(),\n", + " DecisionTreeClassifier(),\n", + " RandomForestClassifier()\n", + "]\n", + "\n", + "binary_classifiers = [\n", + " LinearSVC(), \n", + " SVC(), \n", + " SGDClassifier(), \n", + " PassiveAggressiveClassifier(), \n", + " RidgeClassifier(), \n", + " RidgeClassifierCV()\n", + "]\n", + "\n", + "for classifier in multi_classifiers:\n", + " oz = ROCAUC(classifier)\n", + " oz.fit(X_train, y_train)\n", + " oz.score(X_test, y_test)\n", + " g = oz.poof()\n", + " \n", + "for classifier in binary_classifiers:\n", + " oz = ROCAUC(classifier, micro=False, macro=False, per_class=False)\n", + " oz.fit(X_train, y_train)\n", + " oz.score(X_test, y_test)\n", + " g = oz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/rebeccabilbro/rocauc_bug_research.ipynb b/examples/rebeccabilbro/rocauc_bug_research.ipynb new file mode 100644 index 000000000..70a365615 --- /dev/null +++ b/examples/rebeccabilbro/rocauc_bug_research.ipynb @@ -0,0 +1,710 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# What's going on with ROCAUC for Binary Classification?\n", + "\n", + "We've identified [a bug in ROCAUC](https://github.com/DistrictDataLabs/yellowbrick/issues/252):\n", + "\n", + "```\n", + "======================================================================\n", + "ERROR: Test ROCAUC with a binary classifier\n", + "----------------------------------------------------------------------\n", + "Traceback (most recent call last):\n", + " File \"/Users/benjamin/Repos/ddl/yellowbrick/tests/test_classifier/test_rocauc.py\", line 110, in test_binary_rocauc\n", + " s = visualizer.score(X_test, y_test)\n", + " File \"/Users/benjamin/Repos/ddl/yellowbrick/yellowbrick/classifier/rocauc.py\", line 171, in score\n", + " self.fpr[i], self.tpr[i], _ = roc_curve(y, y_pred[:,i], pos_label=c)\n", + "IndexError: too many indices for array\n", + "```\n", + "\n", + "Let's see if we can figure out where it's getting triggered." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys \n", + "\n", + "# Modify the path \n", + "sys.path.append(\"..\")\n", + "\n", + "import pandas as pd\n", + "import yellowbrick as yb\n", + "import matplotlib.pyplot as plt \n", + "\n", + "from yellowbrick.classifier import ROCAUC\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "occupancy = pd.read_csv('data/occupancy/occupancy.csv')\n", + "features = [\n", + " \"temperature\", \"relative humidity\", \"light\", \"C02\", \"humidity\"\n", + "]\n", + "classes = [\"unoccupied\", \"occupied\"]\n", + "X = occupancy[features]\n", + "y = occupancy['occupancy']\n", + "\n", + "# Create the train and test data\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Binary Classification with 1D Coefficients or Feature Importances\n", + "\n", + "When the function has 1D coefficients, we don't seem to have a problem" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n", + "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "classifiers = [\n", + " AdaBoostClassifier(),\n", + " MLPClassifier(),\n", + " DecisionTreeClassifier(),\n", + " QuadraticDiscriminantAnalysis(),\n", + " DecisionTreeClassifier(),\n", + " RandomForestClassifier(),\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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8+CeezkbbG+Xhm+Z6nDthN1K3X65gDC96x9nefIqiwWLVYLTo2P/gA9bTOYWGpa7zUsG7tgyKAu4GC79GFWTb7y/irjcx6OW0k2o8dODPohx7UED6Bobj5WJMt86V2HxsOJv6wejtkkxB9weF75E/UICLMfkBDQadhWKeqR8uj35oKGi4meBK0oMPjmKe99FpFNsfnwL8dT+J2CQ97s6pH+jersm46c22P6yHklIs3DWmniJxcTLj5ZLyyL7+znU70QWrokWjUfBxTf77GD0SLt6kJ+XBB4enc0qaTA9XM1m0tg88Z50Zg5PV9vOPfmAlPFhHq7Hi4mRJs6+HHxwpFh3Kgw96Z53ZtvzRTBZFg/XBB4dWY03zYZa6mibNNh/51HiujEZjpiaBcXRvVC3JzNZPHl8+q4SFhdkd2178O3KMnx+LxYJOpwNg2bJlVK5cmUqVKhEeHk6lSpVwdnZ+qu1lW4t969atJCYm0qVLF0aPHk3fvn1RFIWOHTs+sag/Kup+Mk5OqR/yOo2CosDJv/6+6G53RAm72zh/uwDnbxewPW/y4p9UKJQ6hGa1F25xJ9GF1afLcTHGi9LecWl+NjZZR7Ip9ZAlmPR8dNj+m/rLXyvZXedusgt3k12euI7RoiPibn6724qMe1zvRz5617T/ISt/qNlDjrMQAlK/5E+YMIHIyEhWrlyJRqOxTSj0b4YlztLCXqxYMVtr/dHZkho1akSjRo2eensv5HPh6LupE0ssP3gcUIgY3zHTPx96eQcGnTNVS6TOEX300hbORf0FgLtz6vCXFQr7MrlNh6fOJoQQQjwNJycnzp8/T1RUFHfu3Hlud4c58FjxmesmDb28gyu3U7vME1Ji0Wp0tsIOqQW9VMEqvFy6RVYFFUIIIYDU+RLCwsKoX78+Wq2WRYsWkS9fvnS3hv8bDlvYFVIvxLDnzPX9QGoBf1jEH6rl38Z2kZsQQgiRlRRFoVOnTvz666/s27ePgIAAChUq9Nz347CF3dnp8d9uHrbQ6wd0pnD+F/F2f4EiXmWlRS6EEEJVGo2GIUOGcOTIEYoXL55l+3HYseKddAacdOmvLL5y+zSJKX9f9Na2+n+lqAshhFBFaGgo3bt3Jykp9c6nli1bMmXKlOfa9f5PDlvYHyf08g4SUmJxc/akcP4X1Y4jhBAij9u8eTM//PADe/fuzbZ9OmRhj0u6TaIxDqP573uoQy/vsJ1Pf/Q8uhBCCJGdIiIibI/HjRvH9u3badmyZbbt3yELu9GcjKJY04ywVqZQICV9KlOxaAPpehdCCKGKZcuWUbNmTbZt2waAm5sbtWvXztYMDnnxnNmaOpLboxfFe7sX5tXyPdQJJIQQQgC1a9emTJky+Pj4qJbBIQu7xWp+8Ehj64J3d/ai08ujVc0lhBAib0lJSeHjjz+mR48elChRgnLlynH48OGnnpHteXLIrnizNXXiEYvVJOfVhRBCqOaHH35g1qxZhISE2JapWdTBUVvsltTCblVSJwSR8+pCCCGyi9FoRKPRoNfrad26NbNmzaJz585qx7JxyBa7u7MXOq3e9liKuhBCiOxw+fJlGjduzJw5c4DUQWf69u371HOmZyWHbLH75S+Fi94dkyWFSkUbqh1HCCFEHuHj40NsbCy3b99GUZQcOQe8Qxb2h/Q6Z8oXqaN2DCGEELlYeHg4CQkJ1KpVC09PTw4ePEj+/Pan0VaLQxb2H8KXPJipzSHjCyGEcBC3b9/mtddeo2DBghw7dgxnZ+ccXdTBQQt7dNxVALQah7xEQAghRA73sJu9YMGCjBs3joCAAJydndWOlSkOWdgfMji5qB1BCCFELmI2m/nkk084e/YsS5Yssc3I5kikySuEEEI8oNVq+fnnn/nll1+4efOm2nGeiUO32IUQQoh/y2w2c/r0aQIDA9FqtSxcuBA3Nze8vLzUjvZMHLLF7uyUdfPYCiGEyFvefPNNWrVqxcWLFwEoUqSIwxZ1cNAWewmfCvwedUztGEIIIXKBbt264eXlperELc+Tw7XYL/x1jALuRXBz9kQjV8ULIYR4ShcuXGDQoEEkJycD0LZtW7744gu8vb1VTvZ8OFxlPHVtL6eu7VU7hhBCCAe1bNky1q1bZ5szPbdxyK54k8UIKOh1jnFPoRBCCHXdvHkTPz8/AMaNG0fDhg157bXXVE6VNRyuxQ5gNCdhNCepHUMIIYQD2LBhA9WrV+eHH34AwN3dPdcWdXDQwg4KIHOwCyGEsK98+fIUKFBA7RjZxkELO2jQyHStQggh0rFarSxatIjIyEgAKlSowK+//pqrW+mPctjCDjlvqjwhhBDq27lzJ6NHjyY4ONi2zGAwqBcomzlcYe8QNBINGqnrQgghbKxWK2azGYDmzZvzwQcfMH36dJVTqcPhCrtO6/TgDLsQQggB169fp23btsydOxcAjUbDO++8g6+vr8rJ1OFQt7vVLX6Dfb9/g4uTO1qtTu04QgghcgB3d3ciIiLw9fW1TbealzlUYS9bMJbLt66i1zmj0zhUdCGEEM/R1atXiYmJoXr16nh5ebFnzx78/PzyfFEHByvsAG6G/FgVC1bFonYUIYQQKoiNjaVhw4Z4e3tz6NAh3NzcKFy4sNqxcgyHK+wKCsmmeJy0erWjCCGEUIGXlxfDhw+naNGiuLrKbJ//5HCFHdulc9LdIoQQeYGiKCxfvpzjx48zf/58AEaMGKFyqpzL4a6Kl0vihRAib1EUxTZpy8NBZ0TGHKqw74koTrWSTdSOIYQQIospisL58+cB0Gq1fPHFFxw+fJhixYqpnCznc6jCfu1ePgp6FE99Ij3xQgiRaw0aNIgmTZoQEREBQIkSJShSpIjKqRyDw51jVxTrg0dS2YUQIrdq1qwZd+7cwdlZpud+Wg7VYu9S8Q8OX9yIi94dvTbvjPsrhBC53Y0bN3j//fdJSUkBoEOHDqxfv56iRYuqnMzxOFRhdzWYSDEnotPqZeQ5IYTIRT777DMWL17MunXrgNRhYWWwmWfjcF3xQgghcoe4uDg8PT0BGDNmDJUqVaJr164qp3J8DtViBzBbjCSkxGKyGNWOIoQQ4hnt2rWLatWq8eOPPwLg4eFBt27dpJX+HDhcYVce3Mgu//RCCOG4Hl7hfvfuXZWT5D7SFS+EECLLKYrChg0beOWVVyhSpAiVKlXi9OnTeHh4qB0t13GoFvu5WwXwcX94haS02YUQwlHs3r2bAQMGMGbMGNsyKepZw6EK+9HIwhTOXxoAOQ0jhBA5m6IoWK2pY480adKE//73v3z44Ycqp8r9HKqwA1hs07VKZRdCiJzqzp079OnTh3nz5gGpt69NnDiRUqVKqRssD3Cowl6vxA3uJUaj1zmj0ThUdCGEyFO0Wi3Hjh3j559/trXaRfZwqIvnyvjEcjv+HgYnmX9XCCFymtu3bxMdHU2FChXw9vZmx44dlCxZEq1WGmLZyaEKuxBCiJwpLi6O+vXr4+7uzv79+3Fzc6N06dJqx8qTsqywW61WgoOD+f333zEYDEyZMoWSJUvaXv/qq6/Ytm0bGo2GQYMG0bRp00xt12ROxqJYcJZWuxBC5Bienp68+eabeHl5ycQtKsuywr57926MRiNr167l5MmThISEsGDBAiD1m92KFSvYtWsXSUlJtGvXLtOF3aJYsFhNKEhhF0IINW3bto2DBw8SEhICwLhx41ROJCALL54LCwujfv36AFSrVo3w8HDba66urhQpUoSkpCSSkpKecghBGXlOCCHUZrVamTdvHsuXL+fy5ctqxxGPyLIWe3x8fJrBB3Q6HWazGSen1F2+8MILtGzZEovFwsCBAzO1zbhkZ/K7GbFYzUhpF0KI7BcZGUmxYsXQarUsWLAAs9ks59JzmCwr7B4eHiQkJNieW61WW1Hfv38/0dHR7NmzB4C+ffsSGBhIlSpVnrjNtadKMKbhVYwkYTQaCQsLy6r4eZYc0+whxznryTF+/j777DN27tzJokWLeOGFF2zjvMuxzlmyrLAHBgby008/0aJFC06ePElAQIDttfz58+Pi4oLBYECj0ZAvXz7i4uLsbjOgUBJu7q4k3AdnZ2eCgoKyKn6eFBYWJsc0G8hxznpyjLPG66+/ztWrVylXrhz37t2TY5yFUlJS0pzCfhpZdo69adOmGAwGunbtyvTp0xkzZgxLly5lz5491KhRg8qVK9O5c2e6dOlCqVKlqFu3rt1tNiwdiadrQbQaXVbFFkII8cC9e/eYOnUqRmPqNNmdOnVi165dlClTRuVk4kmyrMWu1WqZNGlSmmX+/v62x8OHD2f48OFPvd36AZ35617Ev84nhBDiyT766CPmz59PoUKF6N+/PxqNBp1OGlY5nQxQI4QQwiY5ORkXFxcARo4cSeHChenTp4+6ocRTcbhx/v64eRyzxah2DCGEyHUOHz5MjRo1+Omnn4DUQWeGDBmCXq9XOZl4Gg5X2I9f3oHRkqx2DCGEyHU8PDyIjY3l0qVLakcR/4LDdcVbFDMauYddCCGei59++ony5ctTuHBhqlSpwqlTp/Dx8VE7lvgXHKrFvv5M2QeD0wghhPi3Dhw4QMeOHRk1apRtmRR1x+dQLfZEox5FsaLROFRsIYTIkerWrctbb71Fr1691I4iniOHqpCuehMgg8kKIcSziI+PJzg4mFKlSjF06FC0Wi2zZ89WO5Z4zhyqK75LpT9SHzzVpDFCCCEgdTSzrVu3snnzZiwWi9pxRBZxqMKuAN1rB+Ps5KZ2FCGEcAjx8fFcvHgRSD1/vmnTJrZv3y4DzeRiDtUVD2BwcnnKaV6FECJvSkhIoGHDhuh0Ovbt24erqysVKlRQO5bIYg5X2OOSbj+4gM6hOhuEECLbubu706JFC/R6PVqtfGbmFQ5V2P+678bGsNnodc4YnFzVjiOEEDnOkSNH2L17Nx988AEAkyZNkl7OPMahvsKdiX54f6W8SYUQ4p8URWHChAl88sknnD9/HkCKeh7kUC12eX8KIUR6MTExFChQAI1Gw/z587l37x7lypVTO5ZQiUO12DUoakcQQogcJSQkhMDAQK5duwbASy+9RM2aNVVOJdTkWIX9QYtdGu5CCJGqRIkS+Pr6Ehsbq3YUkUM4VmFXO4AQQqgsKSmJ+fPnYzKljsTZrVs3Dhw4QOXKlVVOJnIKhyrs0QmuNHypOzqtzA0shMibZs+ezYQJE1i8eDGQenGci4uLyqlETuJQF88lmPSU9q3C8Ss71I4ihBDZxmKx2EaKGz58OFqtlj59+qgbSuRYDtViF0KIvOb06dPUq1eP/fv3A5A/f37GjRuHm5sMrS0ez6EK+4vecXwbOgOzxah2FCGEyBZms5lLly4RGhqqdhThIByqK96gsxCfcldGnRNC5GonTpygaNGiFCpUiMDAQMLCwihevLjasYSDcKgWu0Yj97ELIXK3Y8eO0axZM0aOHGlbJkVdPA2HarFrbYVdbnwTQuRONWrUoGPHjnTr1k3tKMJBOVRh1/zj/0II4eiMRiOzZ8/G19eX/v37o9Vq+eKLL9SOJRyYY3XFy5CyQohc5t69eyxdupQlS5ZgNpvVjiNyAYcq7HeTXSjrV0PmYhdCODSTyURkZCQAvr6+rFmzhh9//BEnJ4fqRBU5lENVyMg4D+qWfQOdVt78QgjHlJycTLNmzXjjjTdITk4GICgoCE9PT5WTidzC4Spk6OUdJKTE4u7spXYUIYR4ai4uLtSsWZPExETpehdZwqEKe8n8cfwe9QcApQpWUTmNEEJkztmzZ9m5cycjRowAYPr06Wi1DtVhKhyIQxV2X/dkzFYjznp3Xi7dQu04Qghhl6IovPPOO4SGhtK0aVMqV64sRV1kKYcq7A8HqJHb3YQQOV1CQgLu7u5oNBrmzJlDZGSkTK0qsoVDfW2Ugi6EcAQLFy4kMDCQ69evA1ChQgWaNWumciqRVzhUYZeR54QQjsDV1RWtVsu1a9fUjiLyIIcq7DJWvBAiJ7JYLKxatcp2lXvPnj05evQotWvXVjmZyIscqrAbzTo00loXQuQwH330EcOHD2fBggUAaDQauS9dqMahLp4LiyrEq2XvqB1DCCFQFAWNJrWhMWDAAG7dukX37t1VTiWEg7XYhRC2CDQeAAAgAElEQVQiJ7h06RItW7bk0KFDAHh5eTFr1ix8fHxUTiaEgxX24p73MVtMKIqcaxdCqCcmJoZjx46xc+dOtaMIkY5DdcXXLn6TFHMCbob8akcRQuQxEREReHp6UrBgQV5++WX2799PhQoV1I4lRDoO1WI36CyAxnZeSwghssPp06epX78+o0aNsi2Toi5yKocr7HJVvBAiu1WsWJFXX32VVq1aqR1FCLscqiveoLPK2DRCiCxntVr58ssvMRgM9O7dG51Ox6pVq9SOJUSm2G2xG41GFixYwKhRo4iPj2f+/PkYjcbsyJaOVqNIi10IkeViYmIICQlh9uzZpKSkqB1HiKdit7BPmjSJpKQkzp49i06n488//2TcuHHZkU0IIbKN1WolOjoagIIFC7JixQp2796Ns7OzysmEeDp2C/uZM2cYMWIETk5OuLq6MmPGDM6dO5cd2dL5+rcAnPXuquxbCJF7mUwmOnbsSPv27W0t9Lp16+Ln56dyMiGent1z7BqNBqPRaLsS/e7du6pdlX4v2RmtxqGu9xNCOAC9Xs+LL76Iq6sriYmJ0koXDs1uYe/Vqxf/93//x61bt5g6dSq7d+9myJAh2ZEtHb3WmmYYRyGEeFbXrl1jx44dDBw4EIDp06ej1+vl80U4PLuFvV27dlSqVImjR49isVhYsGAB5cqVy45s6fQPCifZlIyrIZ8q+xdC5B6DBg3iyJEj1KhRg6CgIAwGg9qRhHgu7Bb2YcOGMW/ePMqUKWNb1rt3b5YvX56lwR5HvkgLIf4Nk8mEXq8HYMaMGZw+fZrAwECVUwnxfGVY2N9++23Onz9PdHQ0jRs3ti23WCwULlw4W8L9k06rYFFlz0IIR7du3TqmTp3K999/T5EiRahUqRKVKlVSO5YQz12GhX3GjBnExsYydepUxo8f//cPODnJDEZCCIeTkJBAbGws58+fp0iRImrHESLLZHiJuYeHB8WKFWPBggXcu3ePqKgobty4weXLl9m8ebPdDVutViZMmECXLl3o2bMnV69eTfP6vn376Ny5M506dSI4ODjTM7ZpNbpMrSeEyNsURWH79u2YzWYA+vTpw7Fjx2jUqJHKyYTIWnbPsb///vucOHGCe/fu8eKLL3L+/HkCAwN54403nvhzu3fvxmg0snbtWk6ePElISAgLFiwAID4+nlmzZrFixQoKFCjA4sWLuXv3LgUKFLAbWK5YFUJkxmeffcaECRP48MMPGTZsGBqNRu5LF3mC3ZvCQ0ND2b59O6+99hqTJ09m3bp1mRpSNiwsjPr16wNQrVo1wsPDba+dOHGCgIAAZsyYQffu3SlYsGCmivqOC6Vw0sqVq0II+7p3724bdEaIvMRui71QoULo9Xr8/f35/fffadmyJQkJCXY3HB8fj4eHh+25TqfDbDbj5OTE3bt3OXr0KJs3b8bNzY0ePXpQrVo1Spcu/cRtnvvLjRYBFsBCWFiY/d9OPDU5rtlDjvPzd/v2bebNm0enTp2oVKkSly9fZtCgQdy8eZObN2+qHS9XkvdxzmS3sPv5+bFw4ULq1KnDrFmzAEhMTLS7YQ8PjzRfAKxWK05Oqbvz8vKicuXK+Pr6AlCjRg3OnTtnt7DrDXrbvaZBQUF2M4inExYWJsc1G8hxzhq//PILR44cwd/fn0qVKskxzmLyPs5aKSkpaXq6n4bdrvipU6dSrFgxqlSpQrNmzdi2bRsffvih3Q0HBgayf/9+AE6ePElAQIDttYoVK3LhwgViYmIwm82cOnUqzX3yGelT/Sxmizozywkhcp6bN29y9+5dAGrXrs2OHTuYM2eOyqmEUNcTC3tCQgLOzs60bNkSgJ49ezJ37lxOnDhhd8NNmzbFYDDQtWtXpk+fzpgxY1i6dCl79uzBx8eHd999l379+tG5c2eaNm2apvBnxNPZlMlfSwiR250/f55XXnmFUaNG2ZbVqlULrVbmkxB5W4Zd8WvWrGHKlCm4ubmxdOlSKlasyI4dO5g9ezaurq4MGjToiRvWarVMmjQpzTJ/f3/b45YtW9q+MAghxNMqW7YsVapUoVatWjKHhBCPyLCwL1myhG+//ZbIyEgWLVqEq6srBw4cYNiwYXTq1Ck7M/6D/PEKkRcpisKmTZswGo107doVnU7Hxo0bpaAL8Q8ZFnZXV1fKlStHuXLlGD9+PHXq1OGHH35Ic6W7GuRvWIi8KTY2lnfffReDwUDbtm1xdXWVoi7EY2RY2HW6v0d4y58/PzNnzrRNnqAu+UMWIi+Ji4vD09MTb29vlixZQunSpXF1dVU7lhA5VoZXmTz6TdjNzS1HFPXwmz7yDV2IPMJsNtO3b19atWplGxSrcePGvPjiiyonEyJny7DFfuXKFXr16pXu8UMrVqzI2mSPsfdyMQKL38/2/Qohsp+TkxMeHh64ublx9+5dGQ5WiEzKsLAvXLgwO3MIIQQxMTF8//339OjRA4CQkBAMBkOaU4NCiCfLsLDXrFkzO3NkyqulI+W2FiFysb59+7Jv3z7KlClDrVq15Fy6EM/A7pCyOUllvzukRpbCLkRuYbVabYPKBAcHs2/fPmrUqKFyKiEclwMO0SRFXYjcYteuXdSrV882SUvVqlUZPny4dL0L8S9kqrBHRkby888/Y7FYuHbtWlZnEkLkEVevXiUiIoLQ0FC1owiRa9gt7Dt27GDw4MFMmTKF2NhYunbtynfffZcd2dKxWDVyfl0IB3fo0CEsFguQek798OHDtGrVSuVUQuQedgv74sWL+eabb/Dw8MDHx4dNmzaxaNGi7MiWjtnqgGcOhBA2y5Yto3Xr1nzxxRdA6pwScl+6EM+X3Uqp1WrTDCNbqFAh1WZPik02qLJfIcTz0bp1axo3bsyrr76qdhQhci27Fbps2bKsWrUKs9nMuXPn+OCDDyhXrlx2ZEtnbbj9qV2FEDnHvXv3GD58uO0cuo+PD+vXr6dChQoqJxMi97Jb2CdMmMDNmzdxdnZm7NixeHh4MHHixOzIJoRwcGfPnmXVqlUsWLBA7ShC5Bl272Nft24dvXv35t13382OPE9U1DNe7QhCCDvi4uIA8PT0pE6dOqxbt46GDRuqnEqIvMNui/3mzZt07tyZvn378t1335GUlJQduR6rTrG/VNu3EMK+y5cvU7duXd5//33bsiZNmuSISaSEyCvsFvb333+fvXv3MnjwYE6dOkW7du147733siNbOmar3OomRE5WvHhxihQpQokSJVAURe04QuRJmRpSVlEUTCYTJpMJjUaDwaDO1elyu5sQOc++ffu4c+cOHTp0wMnJie3bt+Pk5FCjVQuRq9j965s8eTK7d++mfPnytGnThvHjx+Ps7Jwd2dKRwi5EzhIXF0efPn3QaDQ0a9YMDw8PKepCqMzuX2CpUqXYtGkTBQoUyI48T5RilvGjhcgJkpOTcXFxwdPTk/nz51OkSJE0410IIdSTYWFfu3YtXbp04d69e3z99dfpXh86dGiWBnucZIu0BIRQk9VqZfTo0Rw/fpwffvgBvV5Py5Yt1Y4lhHhEhn3bOfHCl+PXC6kdQYg8TavVkpCQQGJiom1GNiFEzpJhE7hr164AFC1alPbt26d5bfXq1VmbKgMmOccuRLZLSEhgz549tGnTBoCQkBD0ej0uLi4qJxNCPE6GhX3ZsmXEx8ezZs0arl+/bltusVjYunUrPXr0yJaAj9Jpcl4vghC5Xf/+/dm5cyfbt2+nTp065MuXT+1IQognyLCwlyxZkjNnzqRbbjAYCAkJydJQGangG6PKfoXIy0aOHEnZsmWpVq2a2lGEEJmQYWF/9dVXefXVV2nevDn+/v4AxMfHExUVRdmyZbMtoBAiex09epSJEyeycuVKfH19CQwMJDAwUO1YQohMsnvS+tdff2XMmDHExMTQokULhg8fzpw5c7IjmxBCBSdOnCA0NJS9e/eqHUUI8QzsFvZvvvmG999/n23bttG4cWO2bt3KgQMHsiObECKbhIeHY7VaARgwYAB79+6lS5cuKqcSQjyLTF1m7uXlxb59+/jPf/6Dk5MTKSkpWZ1LCJFN1q9fz3/+8x8WL14MpN7SVrVqVZVTCSGeld3CXqZMGQYOHEhkZCR16tThv//9L5UrV86ObEKIbNCwYUOqV69OlSpV1I4ihHgO7A7lNm3aNE6cOEFAQAAGg4G2bdvSoEGD7MiWTmScBxCtyr6FyC1SUlKYMWMGrVq1IjAwkEKFCrFr1y40Gpk9UYjcwG6L3WQy8dNPP/F///d/tG3bll9++QWj0Zgd2dK5m6zO5DNC5CYnTpxg7ty5zJw507ZMiroQuYfdwj5p0iSSk5OZNm0aM2bMwGw2M3HixOzIlk7d4jdISIlVZd9COLKUlBTi4+MBqF27Nl999RVLlixROZUQIivY7Yo/c+YMW7ZssT2fMGECLVq0yNJQGangexeAUgXlXKAQmRUZGUnnzp0JDAxk/vz5ALRr107lVEKIrGK3xa4oCnFxcbbncXFx6HQqTZ+qUXB39uLl0up8sRDCEfn5+eHs7IyzszMWi0XtOEKILGa3xd6nTx/eeOMNGjVqBMDevXsZMGBAlgcTQjy706dPc/36dZo3b45er2fHjh24urqqHUsIkQ3sFvaOHTtSuXJlQkNDsVqtzJs3j5deeik7sgkhnkFCQgLt27fHarVy6tQpPD09pagLkYdkWNitViurV6/mypUrBAUFqTKbmxAi8ywWCzqdDnd3d2bNmoW3tzeenp5qxxJCZLMMz7EHBwezc+dOXF1d+eKLL2wX3QghchZFUZg5cyYtW7bEbDYD0KFDB1599VWVkwkh1JBhYQ8NDWXVqlWMHDmS5cuXs2vXruzM9ViKIvfaCvFPGo2GiIgIIiMj+fPPP9WOI4RQWYZd8c7OzrZBK7y9vXPEABZmKexCAKkDRx04cMB2UeuMGTMAyJ8/v5qxhBA5QIYt9n8Wcq02U/PFCCGywZAhQ+jUqRNHjhwBUgu6FHUhBDyhxX7jxg3GjBmT4fPp06dnbbLH0GkUQFrtQvTv3x+DwUD58uXVjiKEyGEyLOyjR49O87xmzZpZHsYerUZRO4IQqjh//jwTJ07k888/x8fHh5o1a+aIv0khRM6TYWFv3759duYQQjzBnj17+PHHH/nuu+9466231I4jhMjB7A5QI4RQx5UrVyhRogRarZZBgwZRpUoV6tevr3YsIUQOJ1fECZED7dixgzp16rB06VIAdDqdFHUhRKZkqrAnJiZy/vx5FEUhMTExqzMJkecFBgZSsmRJihQponYUIYSDsVvYjxw5Qtu2bRkyZAi3bt2iUaNGHDx4MDuyCZFnWCwWPvvsM06dOgVA4cKFOXz4MM2bN1c5mRDC0dgt7B9//DFff/01np6eFCpUiFWrVjFz5szsyJaOySpnDkTuFBYWxgcffEBwcLBtmYwdIYR4FnYvnrNarfj6+tqelylTJksDCZFXWK1WUlJScHV1pWbNmnzyySfSQhdC/Gt2mwSFCxfmp59+QqPREBcXx4IFCzJ13s9qtTJhwgS6dOlCz549uXr16mPX6devH998802mwsrQNCK3iI6OplWrVmkGferZsycFCxZUMZUQIjewW9gnTZrE1q1biYqKokmTJpw7d45JkybZ3fDu3bsxGo2sXbuWd999l5CQkHTrzJ07l7i4uEyHddJaM72uEDmZl5cX9+/fJzY21jYjmxBCPA92u+J9fHz4+OOPn3rDYWFhtttzqlWrRnh4eJrXd+7ciUajkVt4RJ5x5coVIiIiyJ8/PwaDgW3btuHp6ZkjJlgSQuQedgt7o0aNHvvBs2fPnif+XHx8PB4eHrbnOp0Os9mMk5MTFy5cYNu2bXz66ad89tlnmU+rgNFoJCwsLPM/I56KHNusYTQaefPNNzGbzSxbtkyOczaQY5z15BjnTHYL+8qVK22PzWYzP/74I0aj0e6GPTw8SEhIsD23Wq04OaXubvPmzdy8eZPevXtz/fp19Ho9RYsWpUGDBk/eqAYMBgNBQUF29y+eXlhYmBzb50xRFNsX4ylTpqDX68mXL58c5ywm7+WsJ8c4a6WkpKTr6c4su4W9aNGiaZ7369ePDh06MGTIkCf+XGBgID/99BMtWrTg5MmTBAQE2F4bNWqU7fG8efMoWLCg/aIuhANRFIWlS5eydetWvv32W3Q6Hd26dQOklSOEyFp2C3toaKjtsaIo/PHHH6SkpNjdcNOmTTl06BBdu3ZFURSmTZvG0qVLKVGiBI0bN/53qYXI4TQaDUeOHOHUqVNcuHBBplcVQmQbu4X9008/tT3WaDR4e3s/9gr3f9Jqtemunvf390+33rBhwzKTEwCzDFAjcjBFUTh+/Dgvv/wyADNmzCAlJYUXXnhB5WRCiLzEbmFv3rw53bt3z44sdsls7CInGzFiBCtWrGDHjh3UqlWLAgUKqB1JCJEH2W0Cf/3119mRQwiH16VLF5o1a0bx4sXVjiKEyMPsttgLFy5Mr169qFq1Ks7OzrblQ4cOzdJgj6PXWgFdtu9XiMeJjIxk8uTJhISE4O3tTe3ataldu7basYQQeZzdwl6tWrXsyCGEw9m0aRPr16+nYsWKDB8+XO04QggBPKGwb9q0ifbt26vSMhcip4qOjsbX1xeNRsPgwYMpWbIkrVu3VjuWEELYZHiOfcWKFdmZQ4gc7+eff6ZmzZosX74cACcnJ9q0aSNDwgohchS5f0yITAoICCBfvnxprjURQoicJsOu+D/++OOxA8k8HCLT3ljxQjg6RVFYt24dFStWpFKlShQpUoSwsDAMBoPa0YQQIkMZFvaSJUuyaNGi7Mxil1WRLk+RfU6cOMHgwYOpU6cO27dvB5CiLoTI8TIs7A8nZslJLFLYRRZTFAWz2YxerycwMJBp06bRokULtWMJIUSmZXiOPTAwMDtzCKG62NhYevXqxZgxY2zLBg0aRIkSJVRMJYQQTyfDFvuECROyM0emOGkUQFrtImu4uroSERHBvXv3MBqN0u0uhHBIdgeoyUk0GhktXjxf0dHRXLx4kVdeeQVnZ2c2btyIr68vWq3cMCKEcEwOVdiFeJ6MRiNNmjQhISGBX375BV9fX/z8/NSOJYQQ/4oUdpFnGQwGRowYQXJyMj4+PmrHEUKI50IKu8hTvvvuOzZs2MDSpUvR6XT06dNH7UhCCPFcyYlEkads3ryZ3bt3Ex4ernYUIYTIEg5V2BW5j108g/Pnz9sez5o1i3379lG1alUVEwkhRNZxqMJulsIunlJwcDD16tXj+PHjABQsWJCyZcuqnEoIIbKOQxV2IZ5W06ZNCQoKIn/+/GpHEUKIbOFQhV0r97ELO+7evcvo0aOJjY0FoG7duuzcuVNa6UKIPMOhCrtOCruwY/Xq1SxatIiFCxfalsl86UKIvERudxMOLy4ujnz58qHRaBg0aBCenp50795d7VhCCKEKh2qxC/FPx44do06dOqxatQoAJycnevXqhZOTfGcVQuRNUtiFQytSpAgmk4n79++rHUUIIXIEadYIh7N7926KFi1K+fLlKVasGL/++iseHh5qxxJCiBxBWuzCoYSHh9O5c2f++9//oiipF1NKURdCiL85VIvdZJXvIXmV1WpFq9VSqVIlxo4dy+uvvy5XuwshxGM4VGEXeU98fDzjx4/HxcWFkJAQAEaOHKlyKiGEyLmksIsczcnJiV9++QVnZ2eSk5NxcXFRO5IQQuRoDlXY9VoroFM7hshi9+/f548//iAwMBAXFxfWr1+Pn58fBoNB7WhCCJHjOVRhF7mf2WymadOm3Llzh8OHD+Pr60vx4sXVjiWEEA5DCrvIUZycnHjrrbe4desWnp6eascRQgiHI4VdqO7gwYN8/fXXzJ8/H61Wy4ABA9SOJIQQDkvuHxOqW7RoEevWrbPNmS6EEOLZSWEXqoiMjLQ9nj17Nj/88AM1a9ZUMZEQQuQODlXYLYpDxRUZmDt3LkFBQZw4cQKAQoUKERQUpHIqIYTIHRyqUlplOvZcITAwkFKlSsnIcUIIkQUcqrALx5SYmEhISAhxcXEANGjQgEOHDlGtWjWVkwkhRO7jUIVdr7GqHUE8g+XLlzNz5kzmzp1rWybzpQshRNZwrE9X6bl1GCkpKRgMBjQaDf369cNkMtGvXz+1YwkhRK7nUC124RjOnDlDgwYNWLNmDQB6vZ7hw4fj5uamcjIhhMj9pLCL587T05O//vqLy5cvqx1FCCHyHIfqijfo5HtIThUWFka+fPkICAigePHihIWFUbBgQbVjCSFEnuNQldJFLzO75UQXLlzgtdde4+2330ZRUu9JlKIuhBDqcKgWu1ajQ0GujM9pAgICGDZsGI0bN5Z704UQQmUOVdh1Wh1mTGrHyPOMRiOzZs3CaDTy4YcfAjBx4kSVUwkhhAAH64oXOYPFYuG7775j69atJCQkqB1HCCHEIxyqxW6xWuSriEqMRiOXLl2ifPnyuLq6snr1agoXLoy7u7va0YQQQjzCoQq7VbHIGDUqsFqttGzZkj///JPDhw/j4+ND2bJl1Y4lhBDiMRyqsAt1aLVa2rRpw6VLl9Dr9WrHETmI2WzGas2ZF7QajUa1I+R6coz/Pa1W+9yH2JbCLh4rPDyclStXMn36dLRaLUOHDpUr3kUa9+/fR6fT5chx//39/dWOkOvJMX4+jEYjSUlJ5MuX77ltM8v+Iq1WK8HBwfz+++8YDAamTJlCyZIlba8vW7aM7du3A9CwYUOGDh2aVVHEM5g5cybbtm2jZcuWNGjQQIq6SMNsNqPT6XLsMMEmkwmDwaB2jFxNjvHzYTAYSExMxGw2P7cvyVl2Kdru3bsxGo2sXbuWd999l5CQENtr165dY8uWLaxZs4Z169Zx8OBBzp8/b3ebJktyVsUVYJtWFVIL+7p162jQoIGKiUROZbVac2RLXQhHpNPpnusprSwr7GFhYdSvXx+AatWqER4ebnutcOHCLFmyBJ1Oh0ajwWw24+zsnKntlipYJUvy5nVfffUVPXr04PTp00Dqv1GTJk1UTiWEELnf8+4RzbKv3PHx8Xh4eNie63Q6W1eDXq+nQIECKIrCzJkzqVChAqVLl7a7Tb3GFW2MH2ExYVkVO88ymUx4eHhw/PhxTCYZBCirhYU5/nvY398/R79XZIyFrCfH+PkwmUxcunTpuW0vywq7h4dHmn/0f3bdpaSkMHbsWNzd3TM9aplerycoKOi5Z82LzGYzS5YsoXv37nh6ehIUFETlypV55ZVX1I6W64WFhTn8+/jh1dBqnmM9evQo//vf/yhTpgyQWmSKFSvG7NmzMZlMpKSkMGPGDG7cuIHFYuGFF15g9OjR+Pr6AnD8+HE+++wzzGYziYmJdOjQgR49emRq36tWrWL16tUMGzaMFi1aZLjexo0biYiIYOTIkf/+F34gOTmZ9957jzt37uDu7s6MGTMoUKDAE38mODiYkydPsnnzZtuynj17EhwcbLsILiUlhebNm7N3714A1q5dy5YtW9BqtZhMJt555x1q1apl+/mEhIRMjWMxf/58fv75Z5ycnBg7dixVqqTtdd28eTNffvkl+fLlo3379nTq1InY2Fjee+894uPj8fLyYsqUKfj4+GT6GDkao9FI5cqV0/w9paSkpOnpfhpZVtgDAwP56aefaNGiBSdPniQgIMD2mqIoDBkyhFq1ajFgwIBMb9Oq5MzbahzRsmXLGDt2LNevX2fy5MkAmT4dIsQ/jdoaxrenrj7Xbb5RtSQzWz/5C1Dt2rWZM2eO7fm7777L3r17qVevHkOHDuWtt96ynVI6fPgwAwcOZP369dy4cYMpU6awZMkSChYsSHJyMr169aJ48eKZuq5k165dzJ07l5deeunf/ZLP4JtvvrHNz7B9+3Y+//xzxo8fn+H6SUlJhIWFERAQwNGjR9MU54xs376dQ4cOsWzZMvR6PdeuXePNN99k06ZNdr9EPOrMmTMcO3aM9evXExUVxbBhw9iwYYPt9ZiYGD799FM2btyIp6cnffr0oU6dOqxevZqgoCAGDRrE4cOH+fjjj5k6dWqm95vXZVlhb9q0KYcOHaJr164oisK0adNYunQpJUqUwGq1cuzYMYxGIwcOHABgxIgRVK9e/YnbtFjNWRU3T7BYLGi1WjQaDb169SIqKkruRhC5htFoJDo6mvz583Pu3Dny5cuX5jqRV155hRIlShAaGsrx48dp166dbRZCFxcXvvzyy3RX+UdGRjJ27FgsFgsajYbx48dz6tQpzp49y7hx45gzZw7FixcHUlvSY8aM4caNG5hMJj744IM02/roo48IDw8nNjaWcuXKMX36dMLCwpgxYwZOTk64urryySefcOvWLcaMGYOTkxNWq5WPPvqIF154wbadsLAw+vXrB0CDBg34/PPPn3hcvv/+e+rUqUODBg1YvXp1pgr7mjVrGDNmjG3ciuLFi7N582a8vb3TrDdw4EASExNtz/39/QkODk6TtV69emg0GooUKYLFYiEmJsb25SAyMpKXXnoJLy8vACpXrsypU6e4ePEi77zzDpDaSJw0aZLdzOJvWVbYtVptun+MR+97/O2337Jq1+IxIiIiGDhwIAMGDKBTp04YDIZ0HzxCPKuZrYPstq6zwi+//ELPnj25c+cOWq2Wzp07U6dOHTZt2mQruI8qXrw4N27cIDo6mnLlyqV57XH3Ec+cOZNevXrRpEkTzp07x9ixY9m4cSPbtm0jODg4zT7WrFlD0aJFmTNnDleuXOHnn3/G09MTSL3myNPTk6VLl9pGcrx58ya7d++mefPm9O7dm7179xIXF8fhw4epUqUK7733HsePH+f+/ftpCnt8fLwtq7u7O/fv33/iMVq/fj2TJk2yFd2bN2/i5+f3xJ+Jjo5Od/z+WdQBFi5c+MTtPOxKf+hh3oeFvWTJkly8eJHbt2/j7u7OkSNHKFWqFOXLly11AdIAACAASURBVGfv3r1UqFCBvXv3kpwsd0Q9DRl5PY/QarWcP3+e0NBQtaMI8dzUrl2blStXsnr1avR6PcWKFQOgUKFCXL9+Pd36V69e5YUXXqBIkSL89ddfaV47f/48Z8+eTbPs0qVLvPzyywCUL18+3c88KiIigmrVqgFQqlQp+vTpY3vN2dmZmJgYRowYwYQJE0hMTMRkMjFo0CCio6Pp3bs3O3fuxMnJiTfeeANPT0/69evH6tWr0el0afbz6PVLCQkJti8Pj3Pp0iX++OMPQkJC6N+/PxqNhm+++caW6dGLHxMSEnBxcQGgaNGiREVFpdnWgQMHiI6OTrNs4MCB9OzZ0/bfo631f2Z9uI9Hv0Dlz5+fMWPGMGzYMEaMGEHFihXx9vZmwIABXL9+nR49ehAZGUnhwoUz/B1FelLYc7GLFy8SEREBpH7QHD58mJkzZ6qcSojnz9vbm1mzZjF+/Hiio6OpWrUqt2/ftl0IBrB//36uXr1KzZo1adWqFevXrycmJgZILTgTJkzg1q1babbr7+/P8ePHATh37pyt6/5x/P39bT2R165d4913302z76ioKD7++GNGjBhBcnIyiqKwZcsW2rdvz8qVKylbtizr1q1jz549BAUFsXz5cl5//XWWLFmSZj+BgYHs27fPtt0nXYi5fv163nnnHb788ku+/PJLli9fzoYNGzAajVSsWJEffvghTcbKlSsD0LFjRz7//HPM5tTTn5cvX2b8+PHpvmQsXLiQlStX2v77Z2EPDAzk4MGDWK1Wbty4gdVqTXOO3mw2c/bsWb7++ms++eQTIiIiCAwM5Pjx43Tq1InVq1dTsmRJAgMDM/wdRXoywkQudeXKFRo0aEDlypX5/vvv0Wq1j+2aFCK3KFOmDD179mTKlClMnz6dL774gmnTptm6iwsXLsyiRYvQ6XQUK1aM9957j6FDh6LT6UhISOCNN96gYcOGabY5atQoPvjgA7766ivMZvMTL+Dq2rUrY8f+f3t3HlZVuf5//L2ZZ4REU5NUHFLMlBxxTDERKBIEFME0nL6WhsMJQ8QJydnjlEPihBOgOKCmoXAktVSkUDs5kbMlmEAiuBn2+v3Bj3UgRg0ENs/rurouXHvvtZ71SN57TffHHy8vL/Ly8vD39+fGjRsAdOjQga+//poRI0agUCho2rQpycnJdOjQgYCAAPT19eXLl5Ik4efnx7p161CpVHz55ZdFtjN8+HD8/PwYPnw42traLFu2DIAFCxbg4uJC27Ztgfx7Dg4fPsyhQ4fkzzZu3Ji33nqL48ePM3bsWAIDAxkyZAi6urrUq1dPvpHW0dGRlJQUPD090dbWJi8vjyVLlrzwnent27enc+fOeHh4oFKpCAwMBCAqKorMzEw8PDwA5DGMHj0ac3Nzmjdvjp+fH5B/9iU4OPiFtlvXKSRJkqp7EOUpuO0/Keck7t2/qO7h1BpffPEFPXv2xNnZuULvV4fHsGoDdZjnmvC4W1kq+iiWOgkNDaVPnz5FWndXpbo4x1WlpP+fCupe+/btX/iJpVp1xK6lIZLFSqNSqdiwYQOpqan4+/sDiNPuglCHDBgwgMaNG1f3MIQaoFZdYxdBJKV7/vw5mzZtYtu2bUV6vguCUDeIoi4UqFVH7EJRKpWKe/fu8eabb2JgYMD27duxsLAo8y5ZQRAEQb3VqiP2vEpMv6ntJEnC3d0de3t7UlNTAbC2tqZBgwbVPDJBEAShOtWqI3ZxIv5/FAoFvXr1Ql9fn7y8vOoejiAIglBD1KrCXtcr+927d9m2bRsBAQEoFAomT56MQqEQ9x4IgiAIslp1Kr6ul6/AwEBWrFjBiRMnAOS+74JQF507d44ePXrIXc9cXFyYPHmy/OjQkydP8PPzw9vbG09PT6ZNm1akAU18fDyjR4/G29sbV1dXdu7cWeFt79ixg8GDB3P06NEy3xcZGcnSpUtfbgfLER0dXaQJTlm++eYbevXqhVKplJfNmDGDuLi4Iu/r2bOn/POJEyfkuXVzc+PYsWMvNc7w8HBcXFxwd3cnNja22OunT5/mo48+Yvjw4XLf++zsbKZNm4a7uzuffPIJt2/ffqlt11W164i9Dpb2ws+KBgcH4+DgUCTYQhBqggu3jnL78aVKXWez+h3o0rz0SFSom+luAEFBQZw+fVpuRlOeQ4cO4eDgwJEjR3BxcSn3/QkJCWzdupUNGzZgaGhIamoqHh4etGzZUo7JrYiUlBRCQ0PZt28fSqUST09PevbsKT+vrVKpCAgIIDQ0lKZNmzJ9+nTi4+O5evUqBgYGhIeH89tvvzF//nxCQkIqvN26rpYV9rpl3759+Pn5cfDgQaytrWncuDHu7u7VPSxBqJHqSrob5LdqtbOzIywsrNx5OXfuHJaWlgwbNox//etfFSrsERERfPzxx/JBhZmZGREREcWeuJk5cyZ3796V/2xqasqaNWvkP1+6dIlOnTqho6ODjo4OlpaWXL16Vc5kT01NxcTERJ5DGxsbEhISePjwofwFq0WLFiQlJZU7ZuF/alVhr2unnU1MTMjLy+PevXtYW1tX93AEoVRdmjuUe3RdFepiuhuAg4MD586dq9AcRURE4ObmRosWLdDR0SExMZF33nmnxPcW/BtbUrqbqalpsfeXl5FeOIkO8tPdMjIy5D+bm5vz/PlzkpKSaNasGXFxcbz11lu0bduW2NhY7OzsSExM5NGjR+Tl5RXrVS+UrFZdY1d3kiQRHh4u/+IPHDiQn3/+GXt7+2oemSDUTHUx3e1FpKenExcXx/bt2/Hx8SEjI4MdO3bIYyq4H6FAQehL48aNi6W7Xbx4kTt37hRZNnPmzCLpbp999lmR18tLd1MoFCxevJg5c+Ywbtw4mjdvjpmZGa6urhgZGeHp6Ul0dDTW1taiqL8AUdhrkJ07dzJhwoQigQclfUsWBKGoupTu9iIOHTqEq6srmzdvJiQkhPDwcM6cOcOTJ0+wtrYmOjpafm98fLx8/dzFxYWQkBAyMzMB+PPPP/H39ycrK6vI+hcsWFAk3a3waXjID7+5ePEiSqWSp0+fkpSUROvWrYu85/Tp04SEhLBp0ybu3r2Lra0tly9fpkePHuzevRt7e3sRYPWCatWpeHVUkMGjUChwc3MjMTGRiRMnVvOoBKH2qSvpbqXZuHEjb731VpGb/yIiIopkRujr6/P+++8THh6Oj48Pv/76K87OzhgaGqKtrc28efMA6NSpk3xHupaWFs+fP2fq1KnFLl+Ux8LCQn4qQZIkpkyZgq6uLj/88AMXL17ks88+o0GDBri5uaGnp8cHH3xAq1atePLkCStXrmT9+vUYGxuXe8pfKKpWpbvdVX3PkC6+1T2cSvP777/j6+uLh4dHhW5oqWrqkDpWG6jDPIt0t5rn5MmTGBgY0KNHj1eyvbo4x1WlTqe7qZusrCxOnz6NsbFxjSjsgiDUXm3bthVBMAIgCvsr9/vvv5Obm0vTpk1p0aIF0dHRFX4WVRAEoTSiqAsFxM1zr9D9+/fp2bMn48ePR/X/A23atWtX5x7jEwRBEKqOOGJ/hZo0acLgwYNr/fVVQRAEoeYShb0KSZJEZGQk9+7dw9fXF4VCwdq1a6t7WIIgCIIaE6fiq9Dz588JCgpixYoVcma6IAivzt8bpqir6gzEeVUmTJjA+PHjiyzr379/kWCbpKQkvL29gfw+9OvXr8fT01Oel2vXrr309hMTE+V1/11MTAyurq54eHgQHh4O5P/7P2nSJDw9PRk7dqzcM+FVEEfslUySJJKTk2nYsCH6+vqEhIRgZmaGmZlZdQ9NEOqcvzdMUWelBeIMGjSoSgNxXoWHDx+SmZlJbm4u9+7dq1DDmk2bNpGamsqOHTvQ0NDg0qVLTJw4kWPHjqGtrf1C2//mm284dOgQ+vr6xV7Lycnhq6++Yu/evejr6zN8+HD69+9PVFQUrVu3ZtKkSRw5coSvv/6agICAF9ruyxKFvRJJksT48eM5c+YMZ8+exdTUFBsbm+oeliC8EhEXFpa4vH2TvrRtnP9sddy1MB79davYeyyMLen3licA1/84T+K9GNy6zChze5GRkcTGxvL8+XNSUlIYOXIkJ0+e5MaNG3z++ec4OjrSs2dPzpw5Q2JiIsHBwahUKho2bMjSpUsZO3Ys5ubmpKens3HjRvz9/bl//z55eXmMHj0aB4eive8zMjKYOXMmT58+JTk5GU9PT+zt7RkxYgRHjx5FoVAwb948evTogaWlJUFBQQDUq1eP4OBg/vvf/7J06VK0tbVxd3dHT0+PnTt3kpubi0KhYM2aNZiZmTF37lyuXLlC/fr1efDgAevWrUNTU5NZs2ahVCrR1dVl/vz5xfrHF1Y4EOfKlSuVEohz+/ZtAgICyMnJQU9PjwULFjB//nwcHBzo06cPcXFxHD16lIULF/Lee+/RokULrKysiI2N5eDBgxgYGBASEoKmpiaDBg16of3Zt28fAwYMQE9Pj127duHn51fm7wZAWFgYkZGRaGjkn5ju0KEDe/fuLVLUnz17xoQJE4p8rlu3bsXO9FhaWrJ69Wq++OKLYttJSkrC0tJS7hL67rvvcuHCBS5evMiYMWMA6NOnjxxJ+yqIwl6JFAoFrVq14v79+2RkZIh2sIJQxZ49e8bmzZs5cuQIW7duJTw8nHPnzrFlyxYcHR3l9wUGBrJ8+XKsrKyIiIiQ08KcnJwYOHAgO3bswNzcnKVLl5KRkYGLiwvdu3fH3NxcXsedO3dwdHTk/fff59GjR/Jp7TZt2hAfH88777zDuXPn8Pf3x9PTk+DgYFq2bElERASbNm3C1tYWpVJJREQEAOvXr2fjxo3o6+sTGBjI6dOnMTAwIC0tjb179/LkyRPef/99ABYtWoS3tzd9+/blhx9+YOnSpSxbtqzIXJQWiHP06NFKCcRZtGgR48aNo0+fPpw8eZKrV6+W+vfy+++/ExkZiZmZGdra2nz33Xd89NFHHD58mM2bNzN37txy96eASqXi8OHDhIWFoaWlhaOjI59//jl6enqlbh/yT4X//d/gv585NTQ0JDQ0tMz1AAwaNIj79++X+FppQTeFlxsaGvL06dNyt1NZRGH/hx4/fszOnTuZPHkyCoWCKVOmMG3aNPlboiDUFeUdYQP0aeNR7ntav96V1q93rdA2C3pAGBsbY2VlhUKhwNTUtMh1V8j//9TKyip/nG5u8vLmzZsD+Uddtra2QH5wiZWVFTdv3mT16tVA/hGui4sL27Zt47vvvsPIyEgOTHF3d2f//v2kpKTQv39/tLS0SEpKYu7cuUD+qdpmzZoV2R7Aa6+9hp+fH4aGhnKATOEgGXNzc1q0aAHA9evX2bBhA5s2bUKSJLS0iv/TXXAqPjU1lU8++UQOxGnYsGGpgTi2trYkJyeXGIijUqlo166dvOzWrVt06tQJgAEDBvDs2TNOnDghv164iWnhy49ubm7MmTOHFi1ayCEvFdmfAt9//z3Pnj2Te++rVCqioqJwc3OTg2wKOrNlZmbKBd/ExISMjAyMjIzkdUVHR9OjRw95WUWP2MtSWtBN4eXPnj0rFnlblURh/4e+/PJL9u3bR8uWLXF0dCzzF1QQhMpV0R4QDRo04Pbt2zRr1oyNGzfKBbbg8wVhLwMHDiQjI4Pr169jZWVV5Gjuq6++omPHjnh6evLjjz9y6tQpAHr06MGSJUt49OgRs2fPBvIL+KJFi2jcuDEXL16Ub1Qr+ML/9OlTVq1axX/+8x8ARo8ejSRJtGrVioMHDwL5yWy3b98G8jPJP/nkE2xsbEhKSuLChQul7mtBIM7IkSM5cOAANjY2ciBO//79gaKBOE2bNuXTTz/FwcEBc3NzORDn008/LbLegpAbW1tbDh06REpKCjo6OvK+FU7GK3xg06xZMyRJYtOmTQwfPvyF92fv3r0EBQXRr18/IL8lc1BQEG5ubrRr147jx48zdOhQeb/efvttAIYMGcKaNWvw8/NDoVCQkJDAV199xbFjx+R1V/SIvSxWVlbcuXOHtLQ0DAwMiI+Px8fHh4cPH3Lq1Ck6dOhAXFzcK33MWVShl5CTkyNfp5k9ezY2NjYiWlUQarC5c+fi7++PhoYGFhYWjBo1iu3bt8uvu7u7M2vWLIYPH45SqeSzzz7jtddeK7KO9957j6CgII4ePYqxsTGamppkZ2ejo6PDoEGDOHv2LJaWlgDMmTMHPz8/+fr5ggULSE5OltdlZGSEjY0NHh4eaGlpYWJiQnJyMi4uLsTFxTFs2DDq16+Pnp4e2tra+Pn5MWfOHJRKJc+fP2fmzJll7m/hQJxVq1ZVWiBOYGAg69atQ09Pj7lz58qpb1FRUfJZiZIMHTqUVatW0b17d4BS92fKlCn4+/tjYWEB5J9pSUxMLHJT4LvvvotSqSQhIUEO6dm9ezdaWlo0bdpUPlPi4+PDypUr5TnW0tJi3bp1lZZvEBUVRWZmJh4eHsyYMQMfHx8kScLV1ZWGDRsyfPhw/Pz8GD58ONra2qVeaqgKIgTmBUVHRzNt2jTCwsLUrhWsOoST1AbqMM8iBKZqJCUlcfXqVRwdHUlNTcXJyYnY2NgaOc9VMcfLly9nwoQJxW7cU3d1OgTmDbMXiwysCnl5eaSkpHDlyhW1K+yCIFSvRo0asXTpUrZt20ZeXh7Tp0+vkUW9qgwbNqzOFfWqUKsKe4em71XLdqOjo7G1tcXQ0BB7e3sSEhLKfDRDEAThZRgYGLBu3brqHka1EUE2lUPcul2Offv24eHhwYIFC+RloqgLgiAINZUo7OVwdHRk6NChjBw5srqHIgiCIAjlEoX9b9LS0vj000/lR0709PTYuHFjsQYOgiAIglATicL+N48fP2b//v3s2LGjuociCIIgCC+sVt08V1XS09N59uwZjRs3pmXLlhw6dIh33nmnuoclCILwUvr370+jRo3Q0NAgLy+PzMxM5s+fz9tvv40kSezatYvDhw/LDbXGjBkjP7eenp7OokWLuHv3Lrm5uTRq1Ih58+aV2Ga2uhw9ehR/f3+OHz9Ow4YNAVi9ejX169eXm+BAfn+C5cuX88YbbxAfH8/atWvJzc0lMzMTFxcXRowY8cLbjomJYe3atWhpaeHq6oq7u3uR13/55Rdmz56Njo4Obdu2ZebMmWhoaBAUFERCQgKGhoZMnz69SmtMnS/sjx49YsCAAbRo0YIDBw6goaFB586dq3tYgiAI/8jmzZvl55+///571qxZw4YNGwgLCyMhIYGtW7eiq6tLamoq48aNw9TUlI4dOzJ16lSGDRvGwIEDAdi6dSuBgYFFmsRUt4iICLy9vQkPD2fSpEnlvv/evXuVkmBXWpJbQYAOwKxZswgICMDGxoYVK1YQFRWFiYkJt27dYu/evaSlpTFmzBgiIyNfat8ros4X9gYNGtClSxfatm2LSqUSPd4F4SXVxHS3HTt28N1335GVlYWZmRlr1qxBpVLx5Zdf8vDhQ3Jycpg1axa3bt1i3759qFQqJk+eTEpKCtu2bUNHR4dmzZoxb968YlGfJa176tSpjBw5kq5du3L58mW+/vprVq1axezZs7lz5w4qlQpfX1+6deuGk5MTzZo1K9ZZLiUlBV9fX+zs7IiNjWXVqlUYGRlhampKmzZtmDRpEsuWLSM+Ph6VSsWoUaMYPHhwmXP18OFDuVf5jh072L59u1z0zczM+Oyzz9i9ezcWFhY8fvxYLuqAnNFemCRJLFy4kF9//ZWcnBwmTZqEsbExe/bskb8AFCTrzZgxg7S0NNLS0mjevDldu3ZlyJAhpKSkMH78eCIjI19of+7du0d6ejpjx47FxcWFCRMmlBvDevDgwQol2K1YsYKEhIQiy0JCQuReAqUluRUe76NHj+RUTxsbG06ePEnTpk3p3bs3GhoamJubo6mpSUpKitxhr7LVycIeGxvLtWvXmDBhAgqFgs2bN1e457QgCDVHWelugwcPJi0tja1bt6KhoYGPjw+XL1/m8uXLNGnShBUrVnD79m3+85//YGJigomJCevWrSM1NZXAwED279+PkZERwcHBhIWF4eXlJW9XpVKVuG43Nzf2799P165diYyMxN3dnYiICMzMzAgODiY1NRUvLy+OHDlCZmYmEydOpF27dpw9e5bRo0fTrVs3EhISWL16tdzCNiwsjPr168shKKdOneL+/fvs3r0bpVKJu7s7PXv2LBYy8sknn6BUKklOTqZ3795y1GlqamqR1DoomvRWEB5TQFNTs9hp+BMnTsgpdOnp6WzZsoUePXqU+vfUvXt3Ro0axc2bN5k3bx5Dhgzh4MGDuLi4VHh/CuzduxdXV1dMTEzo2LEj0dHRxSJ2C1MoFBVOsJsyZUqp64HSk9wKa9q0KefPn6dr167ExsaSlZVF27Zt2bJlCyNGjOCPP/7g5s2bZGVllbmtf6LOFfbs7GymTJlCcnIyrq6uWFhYiKIuCJWgpqW7aWhooK2tzdSpUzEwMOCPP/4gNzeX3377TT792qxZM0aNGkVkZKQcDHPv3j1atmwpJ4B16dKF06dPFzma27p1a4nr7t27N0uWLCEtLY34+HgCAgKYP38+Fy9e5NKlSwDk5uby5MkT4H9pbxYWFqxbt469e/eiUCjk9xgZGclHmZ07d+bx48dcv36dX375BW9vb3l9Dx48KFYIC07FL1++nPv378u9742MjEhLS6NevXrye+/cuUOjRo1o3LhxsaS3nJwcvv32Wz788EN52a1bt+jQoQMApqam+Pr6cu7cuSKfK9ytvGA/W7ZsSV5eHg8ePODo0aNs3bqVsLCwCu0P5Hf+jIqKokmTJsTExJCens6OHTtwcHCQk94KK0h7K2m/SkqwK++IvbQkt8KCg4NZsGABa9eupXPnzujo6NCrVy8uX76Mt7c3rVq1wtrausj8V7Y6c975r7/+AvJ78a5fv55vv/22yk6DCILwapT1pfzq1aucOHGCf//738yaNQuVSoUkSXJKGeQX8YIj4YLLcG+88QZJSUlkZmYCcP78eZo3b86UKVMIDQ0lNDSUGzdulLhuDQ0N7O3tmTNnDnZ2dmhqatKiRQscHR0JDQ3lm2++wd7eXv5HvWCbK1euxNnZmSVLltCtWzckSeK1117j2bNn8peAxMREID8ZrVu3boSGhrJt2zYGDx5cYt56AV9fX5KTk9m1axcAXl5eBAUFyUXwzz//ZM2aNQwbNoyGDRtiZmZWJI51+/btnDx5ssg6W7RowS+//ALkJ9X5+Pigq6srJ709ePCA9PT0Ev+ehg4dypIlS2jZsiUmJiYvtD+nTp2iffv2hIaGEhISwt69e/nzzz+5evUq1tbWxMTEyHG6d+/eJTs7m9deew0nJyciIiLkuSxIsCsYb4HCf8cF/xVu6Vs4yS07O5v4+Hg5yrbwGAvaAqelpdGzZ09u3bpFo0aN2LNnDxMnTkShUFRpjGudOGKfMWMGR48e5fTp05iYmMgJQ4IgqK8333wTfX19hg0bBuQfFScnJzNs2DD8/f3x8vIiLy8Pf39/bty4IX/O3NycSZMmMXLkSDQ0NLC0tGT69OkVWjeAq6srdnZ2HD9+HMjvfx4QEICXlxcZGRl4enoWu5fH3t6exYsXs3HjRl5//XVSU1PR0NBg1qxZjB07FmNjY1QqFW+++Sb9+/fn/PnzeHp6kpmZiZ2dXZHM8b8ruCPby8sLOzs7vL29ycvLY8SIEWhpaaFQKJg4caJ8XXjx4sXMmzePzZs3k5OTg6WlJUFBQUXWOWDAAOLi4hg+fDh5eXl8+umntG/fHmNjY9zc3LCysip2Sr/wvi5YsEBunVva/hTcXObi4iJ/Njw8HDc3tyLrGzp0KDt37pTPjLi4uGBkZIQkSSxatAigwgl25dHW1i4xye3mzZvs2LGDOXPm8OabbzJq1Cj09fXp1q0bffv2RalUsnz5cnbt2oWuri6BgYEvtN0XVavS3V4m5QZg4cKFREVFsX37dqysrKpghOpBHVLHagN1mGeR7vZqbNiwgdGjR6Ojo8P06dPp1asXH330UXUPC6j6Ob569SpXrlyRs9bVWWWnu6nlqfiMjAy2bNkiX+OZNm0aMTExoqgLglCrGBoa4u7uzrBhw5AkqcybxNRNvXr1it2NL1SMWp6K9/PzY/fu3Zibm+Ps7FzuoxCCIAg1kZeXV5G78euS119/vbqHUGupTWEv/Ay6n58fjRs3ZtCgQdU8KkEQBEEomyRJlfp0llqciv/xxx/p3bs3165dA8DS0pKZM2eip6dXzSMTBPWkoaEh330sCMI/k5eXV6nN0dTiiP3x48dcu3aNs2fP0qZNm+oejiCoPS0tLbKyssjMzERTU7PG9YLIyckp9kyzULnEHP9zkiSRl5dHXl6e3Le/MtTaI/YLFy7InXucnJz48ccfGT16dDWPShDqDmNjY3R0dGpcUYf81p9C1RJz/M8pFAp0dHQqPWCnVh6xf/vtt3h5efF///d/8vOVLVu2rOZRCULdU5lHGZWtpj6Kp07EHNdMVXbErlKpCAwMxMPDA29vb+7cuVPk9fDwcFxcXHB3dyc2NvaF1t23b18GDhyIk5NTZQ5ZEARBEGq9Kvu6feLECbKzswkLC+Pnn39m4cKFcqehlJQUQkND2bdvH0qlEk9PT3r27Fnut7/Y2Fjs7e0xMDBgz549VTV0QRAEQai1qqywX7x4kd69ewPQsWNHrly5Ir926dIlOnXqhI6ODjo6OlhaWnL16lU5VODvChrN7N+/n379+tXIa3rqQqlUVvcQ6gQxz1VPzHHVE3NcdQpuTHyZ5rBVVtgzMjKK9C/W1NQkNzcXLS2tCkXfFZaTkwPAuHHj5OABoWoU/gImVB0xz1VPzHHVE3Nc9XJycl740e0qK+x/j7dTqVTyjTYVib4rzNDQkNatW6OtrS2O1gVBEAS1J0kSOTk5L9WPv8oKu42NDbGxGRpzsAAACjZJREFUsTg4OPDzzz/TunVr+bUOHTrw73//G6VSSXZ2NklJSUVe/zsNDY1KfxxAEARBEGqyl22yVmXpbiqVijlz5nD9+nUkSSI4OJi4uDgsLS0ZMGAA4eHhhIWFIUkS48ePF+1fBUEQBKES1IrYVkEQBEEQKqbWdp4TBEEQBKE4UdgFQRAEQY3UuMJelR3rhHzlzfHWrVtxc3PDzc2NNWvWVNMoa7fy5rjgPWPGjGH37t3VMMLar7w5PnXqFO7u7ri5uTFnzpyXeh5YKH+eN2/ejIuLC66urkRHR1fTKNVDYmIi3t7exZbHxMTg6uqKh4cH4eHh5a9IqmGOHz8u+fn5SZIkST/99JM0YcIE+bXk5GTJyclJUiqV0l9//SX/LLyYsub47t270pAhQ6Tc3FxJpVJJHh4e0q+//lpdQ621yprjAsuWLZPc3NykXbt2verhqYWy5vjp06eSo6Oj9Oeff0qSJEkbN26UfxZeTFnznJ6eLvXt21dSKpVSWlqa1K9fv+oaZq23ceNGycnJSXJzcyuyPDs7W7Kzs5PS0tIkpVIpubi4SCkpKWWuq8YdsVe0Y52xsbHcsU54MWXN8euvv86mTZvkKM7c3Fx0dXWra6i1VllzDHDs2DEUCoX8HuHFlTXHP/30E61bt2bRokV4enpSv359zM3Nq2uotVpZ86yvr0/jxo3JysoiKytL9Bn5BywtLVm9enWx5UlJSVhaWmJqaoqOjg7vvvsuFy5cKHNdNS6aqTI71gklK2uOtbW1MTc3R5IkFi9eTLt27WjevHk1jrZ2KmuOr1+/zuHDh1m1ahVr166txlHWbmXNcWpqKufOnePAgQMYGBgwYsQIOnbsKH6XX0JZ8wzQqFEjHB0dycvLY/z48dU1zFpv0KBB3L9/v9jyl6l7Na6wV2bHOqFkZc0x5Pd/9vf3x9DQkNmzZ1fHEGu9sub4wIEDPHr0iI8//pgHDx6gra1NkyZN6NOnT3UNt1Yqa47r1avH22+/jYWFBQCdO3fm119/FYX9JZQ1z3FxcSQnJ3Py5EkAfHx8sLGxKTX3Q3hxL1P3atypeBsbG+Li4gBK7Fh38eJFlEolT58+LbdjnVCysuZYkiQmTpxImzZtmDdvHpqamtU1zFqtrDn+4osviIiIIDQ0lCFDhjBq1ChR1F9CWXNsbW3N9evXefLkCbm5uSQmJtKyZcvqGmqtVtY8m5qaoqenh46ODrq6uhgbG/PXX39V11DVkpWVFXfu3CEtLY3s7Gzi4+Pp1KlTmZ+pcUfsAwcO5MyZMwwbNkzuWLdlyxa5Y523tzeenp5IksSUKVPE9d+XUNYcq1Qqzp8/T3Z2Nt9//z0AU6dOLfcXSSiqvN9j4Z8rb46nTZvGmDFjALC3txcHAS+pvHk+e/Ys7u7uaGhoYGNjQ8+ePat7yGohKiqKzMxMPDw8mDFjBj4+PkiShKurKw0bNizzs6LznCAIgiCokRp3Kl4QBEEQhJcnCrsgCIIgqBFR2AVBEARBjYjCLgiCIAhqRBR2QRAEQVAjNe5xN0FQR/fv38fe3h4rK6siy9evX0+jRo1K/ExBe8lJkya99HYjIyNZuHChvI3nz5/TtWtXZs+eXaQpUUWsXLmS9u3by4+dhoaGAuDs7MzBgwdfeowA3t7e/PHHHxgYGAD53baaNm3K0qVLqV+/fqmfCwsLw9DQECcnp3+0fUFQJ6KwC8Ir0qBBg39cAF9G//79WbhwIQB5eXl4e3uzc+dOPv744xdaz+effy7/fP78efnnytqnoKAgunXrBuR3N5s8eTJbtmzhX//6V6mf+emnn+jatWulbF8Q1IUo7IJQza5fv878+fPJzMzkyZMnjB49mpEjR8qv5+Tk4O/vz40bNwDw9PTE3d2dx48fExgYyB9//IFCoWDatGnY2tqWuS1NTU06derE7du3Adi3bx9btmxBoVBgbW3NrFmz0NHRKXF7M2bMoGvXrvz3v/8FwM3NjYiICNq0acMvv/xCv379OHDgAPXr1yctLQ0nJydiY2P54YcfWLVqFbm5ubzxxhvMnz8fMzOzMseZmZlJamqq3Jr022+/ZcuWLTx//hylUklQUBA5OTnExMTw448/YmFhQdu2bV94PgRBHYlr7ILwiiQnJ+Ps7Cz/t2nTJgAiIiKYOHEi+/btY/v27axYsaLI53766SfS09M5cOAAW7ZsISEhAYAFCxbg6upKZGQk69atIzAwsNxwiNTUVOLi4rCxseHatWusX7+e0NBQoqKi0NfXZ82aNaVur0BAQIA87gJaWlrY29tz7NgxAL777jvs7Ox4+vQpy5YtIyQkhAMHDtCrVy+WLl1a4tgCAgL48MMP6dWrFx4eHtja2jJq1ChUKhV79uxh/fr1HDp0iLFjxxISEoKtrS39+/dn8uTJ9O7d+6XmQxDUkThiF4RXpLRT8TNmzOD7779nw4YNXLt2jczMzCKvt2rVilu3buHj40OfPn2YPn06AGfPnuW3335j1apVAOTm5nLv3j3atm1b5PMxMTE4OzsjSRKSJDFw4ECcnJzYuXMn7733nnz07OHhwZdffsm4ceNK3F55nJ2dCQ4OxsvLi8OHD+Pr60tiYiK///67fAZCpVJhampa4ucLTsUnJCQwefJk+vbti46ODgBr164lJiaGW7ducf78eTQ0ih+TVHQ+BEHdicIuCNXM19cXExMT3nvvPRwcHDhy5EiR183MzDhy5Ahnzpzh1KlTDBkyhCNHjqBSqdi2bRv16tUD4NGjRyXeaFb4GnthKpWqyJ8lSSI3N7fU7ZXn7bffJj09nUuXLvHo0SNsbGw4ceIENjY2rF+/HshPDiycVFUSGxsbvL298fPz4+DBgyiVSlxdXXF2dqZLly60adOGnTt3lrg/FZkPQVB34lS8IFSzM2fOMHnyZOzs7Lhw4QKQf5NbgZMnTzJ9+nT69etHQEAABgYG/P7773Tv3p1du3YBcPPmTT788EOysrIqvN2uXbsSExNDWloaAOHh4XTr1q3U7RVWkMn9dx988AGzZ8/GwcEBgHfeeYeff/6ZW7duAfD111+zePHicsc2evRosrKy2LNnD7dv30ZDQ4MJEybQvXt34uLi5PnR1NSUf/6n8yEI6kIcsQtCNZs0aRKenp6YmJjQvHlzmjRpwv379+XX+/Tpw/Hjx3F0dERXV5f333+fNm3aEBAQQGBgIB988AEAixcvxsjIqMLbfeuttxg/fjze3t7k5ORgbW3N3Llz0dXVLXF7hQ0YMABnZ2ciIyOLLP/www9ZuXIly5cvB8DCwoLg4GB8fX1RqVQ0bNiQJUuWlDs2HR0dfH19CQ4OJjo6mrZt2zJ48GD09PTo0qULDx8+BMDW1pbly5djbGz8j+dDENSFSHcTBEEQBDUiTsULgiAIghoRhV0QBEEQ1Igo7IIgCIKgRkRhFwRBEAQ1Igq7IAiCIKgRUdgFQRAEQY2Iwi4IgiAIakQUdkEQBEFQI/8PUB/Q1YOdK8QAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for classifier in classifiers:\n", + " oz = ROCAUC(classifier)\n", + " oz.fit(X_train, y_train)\n", + " oz.score(X_test, y_test)\n", + " g = oz.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks good; everything works!\n", + "\n", + "## Binary Classification with Multidimensional Coefficients or Feature Importances\n", + "\n", + "What about classification with estimators that have multidimensional coefficients? Thanks to ZJ Poh for identifying these in [this PR](https://github.com/DistrictDataLabs/yellowbrick/pull/510)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.svm import LinearSVC, NuSVC, SVC\n", + "from sklearn.linear_model import SGDClassifier\n", + "from sklearn.naive_bayes import BernoulliNB, MultinomialNB\n", + "from sklearn.linear_model import PassiveAggressiveClassifier\n", + "from sklearn.linear_model import RidgeClassifier, RidgeClassifierCV\n", + "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some of these generate the `IndexError: too many indices for array` error, but not all!\n", + "\n", + "These are the ones that seem to work: `BernoulliNB()`, `MultinomialNB()`, `LogisticRegression()`, and `LogisticRegressionCV()`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "classifiers = [\n", + " BernoulliNB(),\n", + " MultinomialNB(),\n", + " LogisticRegression(),\n", + " LogisticRegressionCV()\n", + "]\n", + "\n", + "for classifier in classifiers:\n", + " oz = ROCAUC(classifier)\n", + " oz.fit(X_train, y_train)\n", + " oz.score(X_test, y_test)\n", + " g = oz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "ename": "IndexError", + "evalue": "too many indices for array", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0moz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mROCAUC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLinearSVC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/yellowbrick/classifier/rocauc.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[0;31m# Compute ROC curve and ROC area for each class\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroc_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 179\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroc_auc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mauc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mIndexError\u001b[0m: too many indices for array" + ] + } + ], + "source": [ + "oz = ROCAUC(LinearSVC())\n", + "oz.fit(X_train, y_train)\n", + "oz.score(X_test, y_test)\n", + "oz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "ename": "IndexError", + "evalue": "too many indices for array", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0moz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mROCAUC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSVC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/yellowbrick/classifier/rocauc.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[0;31m# Compute ROC curve and ROC area for each class\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroc_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 179\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroc_auc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mauc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mIndexError\u001b[0m: too many indices for array" + ] + } + ], + "source": [ + "oz = ROCAUC(SVC())\n", + "oz.fit(X_train, y_train)\n", + "oz.score(X_test, y_test)\n", + "oz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n", + " \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n" + ] + }, + { + "ename": "IndexError", + "evalue": "too many indices for array", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0moz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mROCAUC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSGDClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/yellowbrick/classifier/rocauc.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[0;31m# Compute ROC curve and ROC area for each class\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroc_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 179\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroc_auc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mauc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mIndexError\u001b[0m: too many indices for array" + ] + } + ], + "source": [ + "oz = ROCAUC(SGDClassifier())\n", + "oz.fit(X_train, y_train)\n", + "oz.score(X_test, y_test)\n", + "oz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n", + " \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n" + ] + }, + { + "ename": "IndexError", + "evalue": "too many indices for array", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0moz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mROCAUC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPassiveAggressiveClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/yellowbrick/classifier/rocauc.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[0;31m# Compute ROC curve and ROC area for each class\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroc_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 179\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroc_auc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mauc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mIndexError\u001b[0m: too many indices for array" + ] + } + ], + "source": [ + "oz = ROCAUC(PassiveAggressiveClassifier())\n", + "oz.fit(X_train, y_train)\n", + "oz.score(X_test, y_test)\n", + "oz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "ename": "IndexError", + "evalue": "too many indices for array", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0moz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mROCAUC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRidgeClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroc_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 179\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroc_auc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mauc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mIndexError\u001b[0m: too many indices for array" + ] + } + ], + "source": [ + "oz = ROCAUC(RidgeClassifier())\n", + "oz.fit(X_train, y_train)\n", + "oz.score(X_test, y_test)\n", + "oz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "ename": "IndexError", + "evalue": "too many indices for array", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0moz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mROCAUC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRidgeClassifierCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/yellowbrick/classifier/rocauc.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[0;31m# Compute ROC curve and ROC area for each class\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroc_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 179\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroc_auc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mauc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mIndexError\u001b[0m: too many indices for array" + ] + } + ], + "source": [ + "oz = ROCAUC(RidgeClassifierCV())\n", + "oz.fit(X_train, y_train)\n", + "oz.score(X_test, y_test)\n", + "oz.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "so what's going on here?\n", + "\n", + "### The Shape of `y_pred`\n", + "\n", + "It looks like all of the classifiers that trigger the `IndexError` during binary classification with `ROCAUC` are ones that have only a `decision_function` and for which `y_pred.shape` is (n_samples,).\n", + "\n", + "#### Classifiers that Raise the IndexError with Binary Classification & `ROCAUC`\n", + "\n", + " - [LinearSVC()](http://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC.decision_function)\n", + " - [SVC()](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC.decision_function)\n", + " - [SGDClassifier()](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier.decision_function)\n", + " - [PassiveAggressiveClassifier()](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html#sklearn.linear_model.PassiveAggressiveClassifier.decision_function)\n", + " - [RidgeClassifier()](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeClassifier.html#sklearn.linear_model.RidgeClassifier.decision_function)\n", + " - [RidgeClassifierCV()](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeClassifierCV.html#sklearn.linear_model.RidgeClassifierCV.decision_function)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "attrs = (\n", + " 'predict_proba',\n", + " 'decision_function',\n", + ")\n", + "\n", + "failing_classifiers = [\n", + " LinearSVC(), \n", + " SVC(), \n", + " SGDClassifier(), \n", + " PassiveAggressiveClassifier(), \n", + " RidgeClassifier(), \n", + " RidgeClassifierCV()\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y_pred shape for LinearSVC is (4112,).\n", + "[-2.07818848 -1.10295589 -2.65880378 ... -1.5675887 -2.22618865\n", + " -2.21660691]\n", + "y_pred shape for SVC is (4112,).\n", + "[-1.0042836 -1.04618017 -1.01206048 ... -0.50206514 -0.9921702\n", + " -0.75343404]\n", + "y_pred shape for SGDClassifier is (4112,).\n", + "[-119987.75965156 -72882.79935914 -107597.19061436 ... -68765.52714843\n", + " -113531.54391398 -36124.36901115]\n", + "y_pred shape for PassiveAggressiveClassifier is (4112,).\n", + "[-2.29768419 -1.21982115 -2.12816317 ... -1.21089063 -2.20835107\n", + " -1.04096801]\n", + "y_pred shape for RidgeClassifier is (4112,).\n", + "[-0.97824702 -0.72364345 -1.14475694 ... -0.79729413 -0.91117693\n", + " -0.8346179 ]\n", + "y_pred shape for RidgeClassifierCV is (4112,).\n", + "[-0.97826157 -0.72362511 -1.14477574 ... -0.7972869 -0.91116793\n", + " -0.83472035]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n", + " \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n", + "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n", + " \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n" + ] + } + ], + "source": [ + "def profile(classifiers):\n", + " for classifier in classifiers:\n", + " classifier.fit(X_train, y_train)\n", + " # Return the first resolved function\n", + " for attr in attrs:\n", + " try:\n", + " method = getattr(classifier, attr, None)\n", + " if method:\n", + " y_pred = method(X_test)\n", + " except AttributeError:\n", + " continue\n", + " print(\"y_pred shape for {} is {}.\".format(\n", + " classifier.__class__.__name__, y_pred.shape)\n", + " ) \n", + " print(y_pred)\n", + "\n", + "profile(failing_classifiers)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Classifiers that Currently Work with Binary Classification & `ROCAUC`\n", + "\n", + "The classifiers with decision functions and `y_pred.shape` (n_samples, ) that *do* work with ROCAUC for binary classification seem to work because they also have `predict_proba`:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y_pred shape for AdaBoostClassifier is (4112,).\n", + "[-0.89443802 -0.75144235 -0.92373965 ... -0.74635979 -0.93102191\n", + " -0.25120968]\n", + "y_pred shape for QuadraticDiscriminantAnalysis is (4112,).\n", + "[-25.52942121 -20.63607659 -21.32444067 ... -17.48076674 -15.83153313\n", + " -27.96407765]\n", + "y_pred shape for LogisticRegression is (4112,).\n", + "[-7.21761368 -4.18943292 -9.03165307 ... -5.61892766 -7.64128122\n", + " -7.31392629]\n", + "y_pred shape for LogisticRegressionCV is (4112,).\n", + "[-8.12155049 -4.86393333 -8.83520012 ... -5.89258174 -8.74651903\n", + " -5.72819053]\n" + ] + } + ], + "source": [ + "working_classifiers_decision = [\n", + " AdaBoostClassifier(),\n", + " QuadraticDiscriminantAnalysis(),\n", + " LogisticRegression(),\n", + " LogisticRegressionCV()\n", + "]\n", + "\n", + "profile(working_classifiers_decision)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Sklearn Documentation\n", + "\n", + " - [AdaBoostClassifier()](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier.decision_function)\n", + " - [QuadraticDiscriminantAnalysis()](http://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.decision_function)\n", + " - [LogisticRegression()](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression.decision_function)\n", + " - [LogisticRegressionCV()](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV.decision_function)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y_pred shape for MLPClassifier is (4112, 2).\n", + "[[9.99999821e-01 1.78587267e-07]\n", + " [9.99999822e-01 1.77711818e-07]\n", + " [9.99999416e-01 5.84020242e-07]\n", + " ...\n", + " [9.99999441e-01 5.59388516e-07]\n", + " [9.99999211e-01 7.88712395e-07]\n", + " [9.99264783e-01 7.35217361e-04]]\n", + "y_pred shape for DecisionTreeClassifier is (4112, 2).\n", + "[[1. 0.]\n", + " [1. 0.]\n", + " [1. 0.]\n", + " ...\n", + " [1. 0.]\n", + " [1. 0.]\n", + " [1. 0.]]\n", + "y_pred shape for RandomForestClassifier is (4112, 2).\n", + "[[1. 0.]\n", + " [1. 0.]\n", + " [1. 0.]\n", + " ...\n", + " [1. 0.]\n", + " [1. 0.]\n", + " [1. 0.]]\n", + "y_pred shape for BernoulliNB is (4112, 2).\n", + "[[9.99902348e-01 9.76522911e-05]\n", + " [9.99902348e-01 9.76522911e-05]\n", + " [9.99902348e-01 9.76522911e-05]\n", + " ...\n", + " [9.99902348e-01 9.76522911e-05]\n", + " [9.99902348e-01 9.76522911e-05]\n", + " [3.93484879e-01 6.06515121e-01]]\n", + "y_pred shape for MultinomialNB is (4112, 2).\n", + "[[1.00000000e+000 1.08610234e-097]\n", + " [1.00000000e+000 1.29784432e-231]\n", + " [1.00000000e+000 3.25319193e-078]\n", + " ...\n", + " [1.00000000e+000 5.25817964e-196]\n", + " [1.00000000e+000 1.04606462e-075]\n", + " [2.93149511e-065 1.00000000e+000]]\n" + ] + } + ], + "source": [ + "working_classifiers_proba = [\n", + " MLPClassifier(),\n", + " DecisionTreeClassifier(),\n", + " RandomForestClassifier(),\n", + " BernoulliNB(),\n", + " MultinomialNB()\n", + "]\n", + "\n", + "profile(working_classifiers_proba)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Sklearn Documentation\n", + " \n", + " - [MLPClassifier()](http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html#sklearn.neural_network.MLPClassifier.predict_proba)\n", + " - [DecisionTreeClassifier()](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier.predict_proba)\n", + " - [RandomForestClassifier()](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier.predict_proba)\n", + " - [BernoulliNB()](http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.BernoulliNB.html#sklearn.naive_bayes.BernoulliNB.predict_proba)\n", + " - [MultinomialNB()](http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.MultinomialNB.html#sklearn.naive_bayes.MultinomialNB.predict_proba)\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/rebeccabilbro/tsne_alpha_manual_legend.ipynb b/examples/rebeccabilbro/tsne_alpha_manual_legend.ipynb new file mode 100644 index 000000000..2f5de8116 --- /dev/null +++ b/examples/rebeccabilbro/tsne_alpha_manual_legend.ipynb @@ -0,0 +1,665 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import sys \n", + "\n", + "# Modify the path \n", + "sys.path.append(\"..\")\n", + "\n", + "import pandas as pd\n", + "import yellowbrick as yb\n", + "import matplotlib.pyplot as plt \n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from yellowbrick.base import Visualizer\n", + "from yellowbrick.exceptions import YellowbrickValueError\n", + "\n", + "from matplotlib import patches\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "##########################################################################\n", + "## Legend Drawing Utilities\n", + "##########################################################################\n", + "\n", + "def manual_legend(g, labels, colors, **legend_kwargs):\n", + " \"\"\"\n", + " Adds a manual legend for a scatter plot to the visualizer where the labels\n", + " and associated colors are drawn with circle patches instead of determining\n", + " them from the labels of the artist objects on the axes. This helper is\n", + " used either when there are a lot of duplicate labels, no labeled artists,\n", + " or when the color of the legend doesn't exactly match the color in the\n", + " figure (e.g. because of the use of transparency).\n", + "\n", + " Parameters\n", + " ----------\n", + " g : Visualizer or Axes object\n", + " The graph to draw the legend on, either a Visualizer or a matplotlib\n", + " Axes object. If None, the current axes are drawn on, but this is not\n", + " recommended.\n", + "\n", + " labels : list of str\n", + " The text labels to associate with the legend. Note that the labels\n", + " will be added to the legend in the order specified.\n", + "\n", + " colors : list of colors\n", + " A list of any valid matplotlib color reference. The number of colors\n", + " specified must be equal to the number of labels.\n", + "\n", + " legend_kwargs : dict\n", + " Any additional keyword arguments to pass to the legend.\n", + "\n", + " Returns\n", + " -------\n", + " legend: Legend artist\n", + " The artist created by the ax.legend() call, returned for further\n", + " manipulation if required by the caller.\n", + "\n", + " Notes\n", + " -----\n", + " Right now this method simply draws the patches as rectangles and cannot\n", + " take into account the line or scatter plot properties (e.g. line style or\n", + " marker style). It is possible to add Line2D patches to the artist that do\n", + " add manual styles like this, which we can explore in the future.\n", + "\n", + " .. seealso:: https://matplotlib.org/gallery/text_labels_and_annotations/custom_legends.html\n", + " \"\"\"\n", + " # Get access to the matplotlib Axes\n", + " if isinstance(g, Visualizer):\n", + " g = g.ax\n", + " elif g is None:\n", + " g = plt.gca()\n", + "\n", + " # Ensure that labels and colors are the same length to prevent odd behavior.\n", + " if len(colors) != len(labels):\n", + " raise YellowbrickValueError(\n", + " \"please specify the same number of colors as labels!\"\n", + " )\n", + "\n", + " # Create the legend handles with the associated colors and labels\n", + " handles = [\n", + " patches.Patch(color=color, label=label)\n", + " for color, label in zip(colors, labels)\n", + " ]\n", + "\n", + " # Return the Legend artist\n", + " return g.legend(handles=handles, **legend_kwargs)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# yellowbrick.text.tsne\n", + "# Implements TSNE visualizations of documents in 2D space.\n", + "#\n", + "# Author: Benjamin Bengfort \n", + "# Author: Rebecca Bilbro \n", + "# Created: Mon Feb 20 06:33:29 2017 -0500\n", + "#\n", + "# Copyright (C) 2016 Bengfort.com\n", + "# For license information, see LICENSE.txt\n", + "#\n", + "# ID: tsne.py [6aa9198] benjamin@bengfort.com $\n", + "\n", + "\"\"\"\n", + "Implements TSNE visualizations of documents in 2D space.\n", + "\"\"\"\n", + "\n", + "##########################################################################\n", + "## Imports\n", + "##########################################################################\n", + "\n", + "import numpy as np\n", + "\n", + "from collections import defaultdict\n", + "\n", + "from yellowbrick.text.base import TextVisualizer\n", + "from yellowbrick.style.colors import resolve_colors\n", + "from yellowbrick.exceptions import YellowbrickValueError\n", + "\n", + "from sklearn.manifold import TSNE\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.decomposition import TruncatedSVD, PCA\n", + "\n", + "##########################################################################\n", + "## Quick Methods\n", + "##########################################################################\n", + "\n", + "def tsne(X, y=None, ax=None, decompose='svd', decompose_by=50, classes=None,\n", + " colors=None, colormap=None, alpha=0.7, **kwargs):\n", + " \"\"\"\n", + " Display a projection of a vectorized corpus in two dimensions using TSNE,\n", + " a nonlinear dimensionality reduction method that is particularly well\n", + " suited to embedding in two or three dimensions for visualization as a\n", + " scatter plot. TSNE is widely used in text analysis to show clusters or\n", + " groups of documents or utterances and their relative proximities.\n", + "\n", + " Parameters\n", + " ----------\n", + "\n", + " X : ndarray or DataFrame of shape n x m\n", + " A matrix of n instances with m features representing the corpus of\n", + " vectorized documents to visualize with tsne.\n", + "\n", + " y : ndarray or Series of length n\n", + " An optional array or series of target or class values for instances.\n", + " If this is specified, then the points will be colored according to\n", + " their class. Often cluster labels are passed in to color the documents\n", + " in cluster space, so this method is used both for classification and\n", + " clustering methods.\n", + "\n", + " ax : matplotlib axes\n", + " The axes to plot the figure on.\n", + "\n", + " decompose : string or None\n", + " A preliminary decomposition is often used prior to TSNE to make the\n", + " projection faster. Specify `\"svd\"` for sparse data or `\"pca\"` for\n", + " dense data. If decompose is None, the original data set will be used.\n", + "\n", + " decompose_by : int\n", + " Specify the number of components for preliminary decomposition, by\n", + " default this is 50; the more components, the slower TSNE will be.\n", + "\n", + " classes : list of strings\n", + " The names of the classes in the target, used to create a legend.\n", + "\n", + " colors : list or tuple of colors\n", + " Specify the colors for each individual class\n", + "\n", + " colormap : string or matplotlib cmap\n", + " Sequential colormap for continuous target\n", + "\n", + " alpha : float, default: 0.7\n", + " Specify a transparency where 1 is completely opaque and 0 is completely\n", + " transparent. This property makes densely clustered points more visible.\n", + "\n", + " kwargs : dict\n", + " Pass any additional keyword arguments to the TSNE transformer.\n", + "\n", + " Returns\n", + " -------\n", + " ax : matplotlib axes\n", + " Returns the axes that the parallel coordinates were drawn on.\n", + " \"\"\"\n", + " # Instantiate the visualizer\n", + " visualizer = TSNEVisualizer(\n", + " ax, decompose, decompose_by, classes, colors, colormap, alpha, **kwargs\n", + " )\n", + "\n", + " # Fit and transform the visualizer (calls draw)\n", + " visualizer.fit(X, y, **kwargs)\n", + " visualizer.transform(X)\n", + "\n", + " # Return the axes object on the visualizer\n", + " return visualizer.ax\n", + "\n", + "\n", + "##########################################################################\n", + "## TSNEVisualizer\n", + "##########################################################################\n", + "\n", + "class TSNEVisualizer(TextVisualizer):\n", + " \"\"\"\n", + " Display a projection of a vectorized corpus in two dimensions using TSNE,\n", + " a nonlinear dimensionality reduction method that is particularly well\n", + " suited to embedding in two or three dimensions for visualization as a\n", + " scatter plot. TSNE is widely used in text analysis to show clusters or\n", + " groups of documents or utterances and their relative proximities.\n", + "\n", + " TSNE will return a scatter plot of the vectorized corpus, such that each\n", + " point represents a document or utterance. The distance between two points\n", + " in the visual space is embedded using the probability distribution of\n", + " pairwise similarities in the higher dimensionality; thus TSNE shows\n", + " clusters of similar documents and the relationships between groups of\n", + " documents as a scatter plot.\n", + "\n", + " TSNE can be used with either clustering or classification; by specifying\n", + " the ``classes`` argument, points will be colored based on their similar\n", + " traits. For example, by passing ``cluster.labels_`` as ``y`` in ``fit()``, all\n", + " points in the same cluster will be grouped together. This extends the\n", + " neighbor embedding with more information about similarity, and can allow\n", + " better interpretation of both clusters and classes.\n", + "\n", + " For more, see https://lvdmaaten.github.io/tsne/\n", + "\n", + " Parameters\n", + " ----------\n", + "\n", + " ax : matplotlib axes\n", + " The axes to plot the figure on.\n", + "\n", + " decompose : string or None, default: ``'svd'``\n", + " A preliminary decomposition is often used prior to TSNE to make the\n", + " projection faster. Specify ``\"svd\"`` for sparse data or ``\"pca\"`` for\n", + " dense data. If None, the original data set will be used.\n", + "\n", + " decompose_by : int, default: 50\n", + " Specify the number of components for preliminary decomposition, by\n", + " default this is 50; the more components, the slower TSNE will be.\n", + "\n", + " labels : list of strings\n", + " The names of the classes in the target, used to create a legend.\n", + " Labels must match names of classes in sorted order.\n", + "\n", + " colors : list or tuple of colors\n", + " Specify the colors for each individual class\n", + "\n", + " colormap : string or matplotlib cmap\n", + " Sequential colormap for continuous target\n", + "\n", + " random_state : int, RandomState instance or None, optional, default: None\n", + " If int, random_state is the seed used by the random number generator;\n", + " If RandomState instance, random_state is the random number generator;\n", + " If None, the random number generator is the RandomState instance used\n", + " by np.random. The random state is applied to the preliminary\n", + " decomposition as well as tSNE.\n", + "\n", + " alpha : float, default: 0.7\n", + " Specify a transparency where 1 is completely opaque and 0 is completely\n", + " transparent. This property makes densely clustered points more visible.\n", + "\n", + " kwargs : dict\n", + " Pass any additional keyword arguments to the TSNE transformer.\n", + " \"\"\"\n", + "\n", + " # NOTE: cannot be np.nan\n", + " NULL_CLASS = None\n", + "\n", + " def __init__(self, ax=None, decompose='svd', decompose_by=50,\n", + " labels=None, classes=None, colors=None, colormap=None,\n", + " random_state=None, alpha=0.7, **kwargs):\n", + "\n", + " # Visual Parameters\n", + " self.alpha = alpha\n", + " self.labels = labels\n", + " self.colors = colors\n", + " self.colormap = colormap\n", + " self.random_state = random_state\n", + "\n", + " # Fetch TSNE kwargs from kwargs by popping only keys belonging to TSNE params\n", + " tsne_kwargs = {\n", + " key: kwargs.pop(key)\n", + " for key in TSNE().get_params()\n", + " if key in kwargs\n", + " }\n", + " self.transformer_ = self.make_transformer(decompose, decompose_by, tsne_kwargs)\n", + "\n", + " # Call super at the end so that size and title are set correctly\n", + " super(TSNEVisualizer, self).__init__(ax=ax, **kwargs)\n", + "\n", + " def make_transformer(self, decompose='svd', decompose_by=50, tsne_kwargs={}):\n", + " \"\"\"\n", + " Creates an internal transformer pipeline to project the data set into\n", + " 2D space using TSNE, applying an pre-decomposition technique ahead of\n", + " embedding if necessary. This method will reset the transformer on the\n", + " class, and can be used to explore different decompositions.\n", + "\n", + " Parameters\n", + " ----------\n", + "\n", + " decompose : string or None, default: ``'svd'``\n", + " A preliminary decomposition is often used prior to TSNE to make\n", + " the projection faster. Specify ``\"svd\"`` for sparse data or ``\"pca\"``\n", + " for dense data. If decompose is None, the original data set will\n", + " be used.\n", + "\n", + " decompose_by : int, default: 50\n", + " Specify the number of components for preliminary decomposition, by\n", + " default this is 50; the more components, the slower TSNE will be.\n", + "\n", + " Returns\n", + " -------\n", + "\n", + " transformer : Pipeline\n", + " Pipelined transformer for TSNE projections\n", + " \"\"\"\n", + "\n", + " # TODO: detect decompose by inferring from sparse matrix or dense or\n", + " # If number of features > 50 etc.\n", + " decompositions = {\n", + " 'svd': TruncatedSVD,\n", + " 'pca': PCA,\n", + " }\n", + "\n", + " if decompose and decompose.lower() not in decompositions:\n", + " raise YellowbrickValueError(\n", + " \"'{}' is not a valid decomposition, use {}, or None\".format(\n", + " decompose, \", \".join(decompositions.keys())\n", + " )\n", + " )\n", + "\n", + " # Create the pipeline steps\n", + " steps = []\n", + "\n", + " # Add the pre-decomposition\n", + " if decompose:\n", + " klass = decompositions[decompose]\n", + " steps.append((decompose, klass(\n", + " n_components=decompose_by, random_state=self.random_state)))\n", + "\n", + " # Add the TSNE manifold\n", + " steps.append(('tsne', TSNE(\n", + " n_components=2, random_state=self.random_state, **tsne_kwargs)))\n", + "\n", + " # return the pipeline\n", + " return Pipeline(steps)\n", + "\n", + " def fit(self, X, y=None, **kwargs):\n", + " \"\"\"\n", + " The fit method is the primary drawing input for the TSNE projection\n", + " since the visualization requires both X and an optional y value. The\n", + " fit method expects an array of numeric vectors, so text documents must\n", + " be vectorized before passing them to this method.\n", + "\n", + " Parameters\n", + " ----------\n", + " X : ndarray or DataFrame of shape n x m\n", + " A matrix of n instances with m features representing the corpus of\n", + " vectorized documents to visualize with tsne.\n", + "\n", + " y : ndarray or Series of length n\n", + " An optional array or series of target or class values for\n", + " instances. If this is specified, then the points will be colored\n", + " according to their class. Often cluster labels are passed in to\n", + " color the documents in cluster space, so this method is used both\n", + " for classification and clustering methods.\n", + "\n", + " kwargs : dict\n", + " Pass generic arguments to the drawing method\n", + "\n", + " Returns\n", + " -------\n", + " self : instance\n", + " Returns the instance of the transformer/visualizer\n", + " \"\"\"\n", + "\n", + " # Store the classes we observed in y\n", + " if y is not None:\n", + " self.classes_ = np.unique(y)\n", + " elif y is None and self.labels is not None:\n", + " self.classes_ = np.array([self.labels[0]])\n", + " else:\n", + " self.classes_ = np.array([self.NULL_CLASS])\n", + "\n", + " # Fit our internal transformer and transform the data.\n", + " vecs = self.transformer_.fit_transform(X)\n", + " self.n_instances_ = vecs.shape[0]\n", + "\n", + " # Draw the vectors\n", + " self.draw(vecs, y, **kwargs)\n", + "\n", + " # Fit always returns self.\n", + " return self\n", + "\n", + " def draw(self, points, target=None, **kwargs):\n", + " \"\"\"\n", + " Called from the fit method, this method draws the TSNE scatter plot,\n", + " from a set of decomposed points in 2 dimensions. This method also\n", + " accepts a third dimension, target, which is used to specify the colors\n", + " of each of the points. If the target is not specified, then the points\n", + " are plotted as a single cloud to show similar documents.\n", + " \"\"\"\n", + " # Resolve the labels with the classes\n", + " labels = self.labels if self.labels is not None else self.classes_\n", + " if len(labels) != len(self.classes_):\n", + " raise YellowbrickValueError((\n", + " \"number of supplied labels ({}) does not \"\n", + " \"match the number of classes ({})\"\n", + " ).format(len(labels), len(self.classes_)))\n", + "\n", + "\n", + " # Create the color mapping for the labels.\n", + " self.color_values = resolve_colors(\n", + " n_colors=len(labels), colormap=self.colormap, colors=self.color)\n", + " colors = dict(zip(labels, self.color_values))\n", + "\n", + " # Transform labels into a map of class to label\n", + " labels = dict(zip(self.classes_, labels))\n", + "\n", + " # Expand the points into vectors of x and y for scatter plotting,\n", + " # assigning them to their label if the label has been passed in.\n", + " # Additionally, filter classes not specified directly by the user.\n", + " series = defaultdict(lambda: {'x':[], 'y':[]})\n", + "\n", + " if target is not None:\n", + " for t, point in zip(target, points):\n", + " label = labels[t]\n", + " series[label]['x'].append(point[0])\n", + " series[label]['y'].append(point[1])\n", + " else:\n", + " label = self.classes_[0]\n", + " for x,y in points:\n", + " series[label]['x'].append(x)\n", + " series[label]['y'].append(y)\n", + "\n", + " # Plot the points\n", + " for label, points in series.items():\n", + " self.ax.scatter(\n", + " points['x'], points['y'], c=colors[label],\n", + " alpha=self.alpha, label=label\n", + " )\n", + "\n", + " def finalize(self, **kwargs):\n", + " \"\"\"\n", + " Finalize the drawing by adding a title and legend, and removing the\n", + " axes objects that do not convey information about TNSE.\n", + " \"\"\"\n", + " self.set_title(\n", + " \"TSNE Projection of {} Documents\".format(self.n_instances_)\n", + " )\n", + "\n", + " # Remove the ticks\n", + " self.ax.set_yticks([])\n", + " self.ax.set_xticks([])\n", + " \n", + " # Add the legend outside of the figure box.\n", + " if not all(self.classes_ == np.array([self.NULL_CLASS])):\n", + " box = self.ax.get_position()\n", + " self.ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n", + " manual_legend(\n", + " self, self.classes_, self.color_values, \n", + " loc='center left', bbox_to_anchor=(1, 0.5)\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "from download import download_all \n", + "from sklearn.datasets.base import Bunch\n", + "\n", + "## The path to the test data sets\n", + "FIXTURES = os.path.join(os.getcwd(), \"data\")\n", + "\n", + "## Dataset loading mechanisms\n", + "datasets = {\n", + " \"hobbies\": os.path.join(FIXTURES, \"hobbies\")\n", + "}\n", + "\n", + "\n", + "def load_data(name, download=True):\n", + " \"\"\"\n", + " Loads and wrangles the passed in text corpus by name.\n", + " If download is specified, this method will download any missing files. \n", + " \"\"\"\n", + " \n", + " # Get the path from the datasets \n", + " path = datasets[name]\n", + " \n", + " # Check if the data exists, otherwise download or raise \n", + " if not os.path.exists(path):\n", + " if download:\n", + " download_all() \n", + " else:\n", + " raise ValueError((\n", + " \"'{}' dataset has not been downloaded, \"\n", + " \"use the download.py module to fetch datasets\"\n", + " ).format(name))\n", + " \n", + " # Read the directories in the directory as the categories. \n", + " categories = [\n", + " cat for cat in os.listdir(path) \n", + " if os.path.isdir(os.path.join(path, cat))\n", + " ]\n", + " \n", + " \n", + " files = [] # holds the file names relative to the root \n", + " data = [] # holds the text read from the file \n", + " target = [] # holds the string of the category \n", + " \n", + " # Load the data from the files in the corpus \n", + " for cat in categories:\n", + " for name in os.listdir(os.path.join(path, cat)):\n", + " files.append(os.path.join(path, cat, name))\n", + " target.append(cat)\n", + " \n", + " with open(os.path.join(path, cat, name), 'r') as f:\n", + " data.append(f.read())\n", + " \n", + " \n", + " # Return the data bunch for use similar to the newsgroups example\n", + " return Bunch(\n", + " categories=categories,\n", + " files=files,\n", + " data=data,\n", + " target=target,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "\n", + "corpus = load_data('hobbies')\n", + "tfidf = TfidfVectorizer()\n", + "\n", + "docs = tfidf.fit_transform(corpus.data)\n", + "labels = corpus.target" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.00784313725490196, 0.4470588235294118, 0.6352941176470588)\n", + "(0.6235294117647059, 0.7647058823529411, 0.4666666666666667)\n", + "(0.792156862745098, 0.043137254901960784, 0.011764705882352941)\n", + "(0.6470588235294118, 0.00784313725490196, 0.34509803921568627)\n", + "(0.8431372549019608, 0.7803921568627451, 0.011764705882352941)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tsne = TSNEVisualizer()\n", + "tsne.fit(docs, labels)\n", + "tsne.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.00784313725490196, 0.4470588235294118, 0.6352941176470588)\n", + "(0.6235294117647059, 0.7647058823529411, 0.4666666666666667)\n", + "(0.792156862745098, 0.043137254901960784, 0.011764705882352941)\n", + "(0.6470588235294118, 0.00784313725490196, 0.34509803921568627)\n", + "(0.8431372549019608, 0.7803921568627451, 0.011764705882352941)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tsne = TSNEVisualizer(alpha=0.5)\n", + "tsne.fit(docs, labels)\n", + "tsne.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/regression.ipynb b/examples/regression.ipynb new file mode 100644 index 000000000..180153532 --- /dev/null +++ b/examples/regression.ipynb @@ -0,0 +1,257 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np \n", + "import pandas as pd\n", + "import matplotlib as mpl \n", + "import matplotlib.pyplot as plt \n", + "\n", + "from sklearn.linear_model import Lasso, LassoCV, Ridge, RidgeCV\n", + "from sklearn.model_selection import cross_val_predict, train_test_split\n", + "\n", + "from yellowbrick.datasets import load_concrete\n", + "from yellowbrick.regressor import AlphaSelection, PredictionError, ResidualsPlot\n", + "\n", + "mpl.rcParams['figure.figsize'] = (9,6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Yellowbrick - Regression Examples \n", + "\n", + "The Yellowbrick library is a diagnostic visualization platform for machine learning that allows data scientists to steer the model selection process. It extends the scikit-learn API with a new core object: the Visualizer. Visualizers allow visual models to be fit and transformed as part of the scikit-learn pipeline process, providing visual diagnostics throughout the transformation of high-dimensional data.\n", + "\n", + "Estimator score visualizers *wrap* scikit-learn estimators and expose the Estimator API such that they have `fit()`, `predict()`, and `score()` methods that call the appropriate estimator methods under the hood. Score visualizers can wrap an estimator and be passed in as the final step in a `Pipeline` or `VisualPipeline`.\n", + "\n", + "In machine learning, regression models attempt to predict a target in a continuous space. Yellowbrick has implemented the following regressor score visualizers that display the instances in model space to better understand how the model is making predictions:\n", + "- `AlphaSelection` visual tuning of regularization hyperparameters\n", + "- `PredictionError` plot the expected vs. the actual values in model space \n", + "- `Residuals Plot` plot the difference between the expected and actual values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Load Data\n", + "\n", + "Yellowbrick provides several datasets wrangled from the [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/). For the following examples, we'll use the `concrete` dataset, since it is well-suited for regression tasks.\n", + "\n", + "The `concrete` dataset contains 1030 instances and 9 attributes. Eight of the attributes are explanatory variables, including the age of the concrete and the materials used to create it, while the target variable `strength` is a measure of the concrete's compressive strength (MPa)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Use Yellowbrick to load the concrete dataset\n", + "data = load_concrete()\n", + "\n", + "# Save the data in a Pandas DataFrame\n", + "df = pd.DataFrame(data['data'], columns=data['feature_names'], dtype='float')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Save feature names as a list and target variable as a string\n", + "feature_names = ['cement', 'slag', 'ash', 'water', 'splast', 'coarse', 'fine', 'age']\n", + "target_name = 'strength'\n", + "\n", + "# Get the X and y data from the DataFrame \n", + "X = df[feature_names]\n", + "y = df[target_name]\n", + "\n", + "# Create the train and test data \n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Residuals Plot \n", + "\n", + "A residual is the difference between the observed value of the target variable (y) and the predicted value (ŷ), i.e. the error of the prediction. The `ResidualsPlot` Visualizer shows the difference between residuals on the vertical axis and the dependent variable on the horizontal axis, allowing you to detect regions within the target that may be susceptible to more or less error. \n", + "\n", + "If the points are randomly dispersed around the horizontal axis, a linear regression model is usually well-suited for the data; otherwise, a non-linear model is more appropriate. The following example shows a fairly random, uniform distribution of the residuals against the target in two dimensions. This seems to indicate that our linear model is performing well." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the linear model and visualizer \n", + "model = Ridge()\n", + "visualizer = ResidualsPlot(model)\n", + "\n", + "visualizer.fit(X_train, y_train) # Fit the training data to the visualizer\n", + "visualizer.score(X_test, y_test) # Evaluate the model on the test data \n", + "g = visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Yellowbrick's `ResidualsPlot` Visualizer also displays a histogram of the error values along the right-hand side. In the example above, the error is normally distributed around zero, which also generally indicates a well-fitted model. If the histogram is not desired, it can be turned off with the `hist=False` flag." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the linear model and visualizer \n", + "model = Ridge()\n", + "visualizer = ResidualsPlot(model, hist=False)\n", + "\n", + "visualizer.fit(X_train, y_train) # Fit the training data to the visualizer\n", + "visualizer.score(X_test, y_test) # Evaluate the model on the test data \n", + "g = visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prediction Error Plot \n", + "\n", + "Yellowbrick's `PredictionError` Visualizer plots the actual targets from the dataset against the predicted values generated by the model. This allows us to see how much variance is in the model. Data scientists can diagnose regression models using this plot by comparing against the 45-degree line, where the prediction exactly matches the model." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the linear model and visualizer \n", + "model = Lasso()\n", + "visualizer = PredictionError(model)\n", + "\n", + "visualizer.fit(X_train, y_train) # Fit the training data to the visualizer\n", + "visualizer.score(X_test, y_test) # Evaluate the model on the test data \n", + "g = visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Alpha Selection Visualizer\n", + "\n", + "The `AlphaSelection` Visualizer demonstrates how different values of alpha influence model selection during the regularization of linear models. Since regularization is designed to penalize model complexity, the higher the alpha, the less complex the model, decreasing the error due to variance (overfit). However, alphas that are too high increase the error due to bias (underfit). Therefore, it is important to choose an optimal alpha such that the error is minimized in both directions.\n", + "\n", + "To do this, typically you would you use one of the \"RegressionCV” models in scikit-learn. E.g. instead of using the `Ridge` (L2) regularizer, use `RidgeCV` and pass a list of alphas, which will be selected based on the cross-validation score of each alpha. This visualizer wraps a “RegressionCV” model and visualizes the alpha/error curve. If the visualization shows a jagged or random plot, then potentially the model is not sensitive to that type of regularization and another is required (e.g. L1 or Lasso regularization)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a list of alphas to cross-validate against \n", + "alphas = np.logspace(-10, 1, 400)\n", + "\n", + "# Instantiate the linear model and visualizer \n", + "model = LassoCV(alphas=alphas)\n", + "visualizer = AlphaSelection(model)\n", + "\n", + "visualizer.fit(X, y) # Fit the data to the visualizer\n", + "g = visualizer.poof() # Draw/show/poof the data" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/examples/zjpoh/correlated_var.ipynb b/examples/zjpoh/correlated_var.ipynb new file mode 100644 index 000000000..90e0e08c1 --- /dev/null +++ b/examples/zjpoh/correlated_var.ipynb @@ -0,0 +1,574 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-22T00:08:17.272555Z", + "start_time": "2018-08-22T00:08:17.084357Z" + }, + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-22T00:08:17.580155Z", + "start_time": "2018-08-22T00:08:17.274030Z" + }, + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import datasets\n", + "\n", + "import yellowbrick\n", + "from yellowbrick.target import FeatureCorrelation" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-22T00:12:00.556727Z", + "start_time": "2018-08-22T00:12:00.535224Z" + }, + "collapsed": true + }, + "outputs": [], + "source": [ + "data = datasets.load_diabetes()\n", + "X, y = data['data'], data['target']\n", + "feature_names = np.array(data['feature_names'])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-22T00:12:01.011381Z", + "start_time": "2018-08-22T00:12:01.006531Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Diabetes dataset\n", + "================\n", + "\n", + "Notes\n", + "-----\n", + "\n", + "Ten baseline variables, age, sex, body mass index, average blood\n", + "pressure, and six blood serum measurements were obtained for each of n =\n", + "442 diabetes patients, as well as the response of interest, a\n", + "quantitative measure of disease progression one year after baseline.\n", + "\n", + "Data Set Characteristics:\n", + "\n", + " :Number of Instances: 442\n", + "\n", + " :Number of Attributes: First 10 columns are numeric predictive values\n", + "\n", + " :Target: Column 11 is a quantitative measure of disease progression one year after baseline\n", + "\n", + " :Attributes:\n", + " :Age:\n", + " :Sex:\n", + " :Body mass index:\n", + " :Average blood pressure:\n", + " :S1:\n", + " :S2:\n", + " :S3:\n", + " :S4:\n", + " :S5:\n", + " :S6:\n", + "\n", + "Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1).\n", + "\n", + "Source URL:\n", + "http://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\n", + "\n", + "For more information see:\n", + "Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\n", + "(http://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)\n", + "\n" + ] + } + ], + "source": [ + "print(data['DESCR'])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-22T00:12:01.616021Z", + "start_time": "2018-08-22T00:12:01.486901Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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AQHl3+SQ5VatWlavr1fPhMgvk8+fPO3yAPgAA5ZWfn1+Rs8VdVmaB7O7uLulSYR4eHn9q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\nSfY6161bV+R2SXXs27fPGGN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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fea_corr = FeatureCorrelation(labels=feature_names)\n", + "fea_corr.fit(X, y)\n", + "fea_corr.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-22T00:12:02.340185Z", + "start_time": "2018-08-22T00:12:02.335165Z" + }, + "collapsed": true + }, + "outputs": [], + "source": [ + "discrete_features = [False for _ in range(len(feature_names))]\n", + "discrete_features[1] = True" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-22T00:12:14.445077Z", + "start_time": "2018-08-22T00:12:14.271593Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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gWjivQN65c6cWL16so0ePqk+fPrLZbGrZsqXuv/9+de3aVT169FBqaqq6du2q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\nAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fea_corr = FeatureCorrelation(method='mutual_info-regression',\n", + " labels=feature_names)\n", + "fea_corr.fit(X, y, discrete_features=discrete_features, random_state=0)\n", + "fea_corr.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-22T00:08:17.922387Z", + "start_time": "2018-08-22T00:08:17.915159Z" + }, + "collapsed": true + }, + "outputs": [], + "source": [ + "data = datasets.load_boston()\n", + "X, y = data['data'], data['target']\n", + "feature_names = np.array(data['feature_names'])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-22T00:08:17.926549Z", + "start_time": "2018-08-22T00:08:17.923791Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Boston House Prices dataset\n", + "===========================\n", + "\n", + "Notes\n", + "------\n", + "Data Set Characteristics: \n", + "\n", + " :Number of Instances: 506 \n", + "\n", + " :Number of Attributes: 13 numeric/categorical predictive\n", + " \n", + " :Median Value (attribute 14) is usually the target\n", + "\n", + " :Attribute Information (in order):\n", + " - CRIM per capita crime rate by town\n", + " - ZN proportion of residential land zoned for lots over 25,000 sq.ft.\n", + " - INDUS proportion of non-retail business acres per town\n", + " - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n", + " - NOX nitric oxides concentration (parts per 10 million)\n", + " - RM average number of rooms per dwelling\n", + " - AGE proportion of owner-occupied units built prior to 1940\n", + " - DIS weighted distances to five Boston employment centres\n", + " - RAD index of accessibility to radial highways\n", + " - TAX full-value property-tax rate per $10,000\n", + " - PTRATIO pupil-teacher ratio by town\n", + " - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n", + " - LSTAT % lower status of the population\n", + " - MEDV Median value of owner-occupied homes in $1000's\n", + "\n", + " :Missing Attribute Values: None\n", + "\n", + " :Creator: Harrison, D. and Rubinfeld, D.L.\n", + "\n", + "This is a copy of UCI ML housing dataset.\n", + "http://archive.ics.uci.edu/ml/datasets/Housing\n", + "\n", + "\n", + "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n", + "\n", + "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n", + "prices and the demand for clean air', J. Environ. Economics & Management,\n", + "vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n", + "...', Wiley, 1980. N.B. Various transformations are used in the table on\n", + "pages 244-261 of the latter.\n", + "\n", + "The Boston house-price data has been used in many machine learning papers that address regression\n", + "problems. \n", + " \n", + "**References**\n", + "\n", + " - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n", + " - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n", + " - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)\n", + "\n" + ] + } + ], + "source": [ + "print(data['DESCR'])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-22T00:08:17.930099Z", + "start_time": "2018-08-22T00:08:17.927745Z" + }, + "collapsed": true + }, + "outputs": [], + "source": [ + "discrete_features = [False for _ in range(len(feature_names))]\n", + "discrete_features[3] = True" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-22T00:08:18.102400Z", + "start_time": "2018-08-22T00:08:17.931239Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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pKUleq90csX/2z/7Zv7nJz8/Hb7/9Jn//lYZZBIOi0wc2NjY6N6kxJ+badxH2z/7NGfs3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\n18rtuHvRHAMAyMvLw/z582F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nRxrs7OywdOlShISEwMbGBhUrVsTSpUvLqCsi08K7KxIR\nEZHEUwlEREQkMRgQERGRxGBAREREEoMBERERSQwGREREJDEYEBERkcRgQERERBKDAREREUn/D7ma\nqo8b+eJvAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fea_corr = FeatureCorrelation(method='mutual_info-regression', \n", + " labels=feature_names, \n", + " sort=True)\n", + "fea_corr.fit(X, y, discrete_features=discrete_features, random_state=0)\n", + "fea_corr.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-22T00:08:18.109299Z", + "start_time": "2018-08-22T00:08:18.103858Z" + }, + "collapsed": true + }, + "outputs": [], + "source": [ + "data = datasets.load_wine()\n", + "X, y = data['data'], data['target']\n", + "feature_names = np.array(data['feature_names'])\n", + "X_pd = pd.DataFrame(X, columns=feature_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-22T00:08:18.113874Z", + "start_time": "2018-08-22T00:08:18.110603Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wine Data Database\n", + "====================\n", + "\n", + "Notes\n", + "-----\n", + "Data Set Characteristics:\n", + " :Number of Instances: 178 (50 in each of three classes)\n", + " :Number of Attributes: 13 numeric, predictive attributes and the class\n", + " :Attribute Information:\n", + " \t\t- 1) Alcohol\n", + " \t\t- 2) Malic acid\n", + " \t\t- 3) Ash\n", + "\t\t- 4) Alcalinity of ash \n", + " \t\t- 5) Magnesium\n", + "\t\t- 6) Total phenols\n", + " \t\t- 7) Flavanoids\n", + " \t\t- 8) Nonflavanoid phenols\n", + " \t\t- 9) Proanthocyanins\n", + "\t\t- 10)Color intensity\n", + " \t\t- 11)Hue\n", + " \t\t- 12)OD280/OD315 of diluted wines\n", + " \t\t- 13)Proline\n", + " \t- class:\n", + " - class_0\n", + " - class_1\n", + " - class_2\n", + "\t\t\n", + " :Summary Statistics:\n", + " \n", + " ============================= ==== ===== ======= =====\n", + " Min Max Mean SD\n", + " ============================= ==== ===== ======= =====\n", + " Alcohol: 11.0 14.8 13.0 0.8\n", + " Malic Acid: 0.74 5.80 2.34 1.12\n", + " Ash: 1.36 3.23 2.36 0.27\n", + " Alcalinity of Ash: 10.6 30.0 19.5 3.3\n", + " Magnesium: 70.0 162.0 99.7 14.3\n", + " Total Phenols: 0.98 3.88 2.29 0.63\n", + " Flavanoids: 0.34 5.08 2.03 1.00\n", + " Nonflavanoid Phenols: 0.13 0.66 0.36 0.12\n", + " Proanthocyanins: 0.41 3.58 1.59 0.57\n", + " Colour Intensity: 1.3 13.0 5.1 2.3\n", + " Hue: 0.48 1.71 0.96 0.23\n", + " OD280/OD315 of diluted wines: 1.27 4.00 2.61 0.71\n", + " Proline: 278 1680 746 315\n", + " ============================= ==== ===== ======= =====\n", + "\n", + " :Missing Attribute Values: None\n", + " :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\n", + " :Creator: R.A. Fisher\n", + " :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n", + " :Date: July, 1988\n", + "\n", + "This is a copy of UCI ML Wine recognition datasets.\n", + "https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\n", + "\n", + "The data is the results of a chemical analysis of wines grown in the same\n", + "region in Italy by three different cultivators. There are thirteen different\n", + "measurements taken for different constituents found in the three types of\n", + "wine.\n", + "\n", + "Original Owners: \n", + "\n", + "Forina, M. et al, PARVUS - \n", + "An Extendible Package for Data Exploration, Classification and Correlation. \n", + "Institute of Pharmaceutical and Food Analysis and Technologies,\n", + "Via Brigata Salerno, 16147 Genoa, Italy.\n", + "\n", + "Citation:\n", + "\n", + "Lichman, M. (2013). UCI Machine Learning Repository\n", + "[http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\n", + "School of Information and Computer Science. \n", + "\n", + "References\n", + "----------\n", + "(1) \n", + "S. Aeberhard, D. Coomans and O. de Vel, \n", + "Comparison of Classifiers in High Dimensional Settings, \n", + "Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of \n", + "Mathematics and Statistics, James Cook University of North Queensland. \n", + "(Also submitted to Technometrics). \n", + "\n", + "The data was used with many others for comparing various \n", + "classifiers. The classes are separable, though only RDA \n", + "has achieved 100% correct classification. \n", + "(RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \n", + "(All results using the leave-one-out technique) \n", + "\n", + "(2) \n", + "S. Aeberhard, D. Coomans and O. de Vel, \n", + "\"THE CLASSIFICATION PERFORMANCE OF RDA\" \n", + "Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \n", + "Mathematics and Statistics, James Cook University of North Queensland. \n", + "(Also submitted to Journal of Chemometrics). \n", + "\n" + ] + } + ], + "source": [ + "print(data['DESCR'])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-22T00:08:18.234879Z", + "start_time": "2018-08-22T00:08:18.115389Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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coQEAAMbje2gMlZGRoaSkJLm6ut7xUwcAADwMbn3J5KOPPionp8zHZAg0hkpKSrrrL7cC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\nSZNUoUIFBQcHa8yYMdq/f7+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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fea_corr = FeatureCorrelation(method='mutual_info-classification',\n", + " feature_index=[1, 3, 5, 7, 9])\n", + "fea_corr.fit(X_pd, y, random_state=0)\n", + "fea_corr.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-22T00:08:18.238550Z", + "start_time": "2018-08-22T00:08:18.236395Z" + }, + "collapsed": true + }, + "outputs": [], + "source": [ + "feature_to_plot = ['alcohol', 'ash', 'hue', 'proline', 'total_phenols']" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "ExecuteTime": { + "end_time": "2018-08-22T00:08:18.401591Z", + "start_time": "2018-08-22T00:08:18.239976Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fea_corr = FeatureCorrelation(method='mutual_info-classification',\n", + " feature_names=feature_to_plot)\n", + "fea_corr.fit(X_pd, y, random_state=0)\n", + "fea_corr.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/zjpoh/stacked_feature_importance.ipynb b/examples/zjpoh/stacked_feature_importance.ipynb new file mode 100644 index 000000000..1abf34978 --- /dev/null +++ b/examples/zjpoh/stacked_feature_importance.ipynb @@ -0,0 +1,419 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Yellowbrick Feature Importance Examples \n", + "\n", + "This notebook is a sample of the feature importance examples that yellowbrick provides." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "sys.path.insert(0, \"../..\")\n", + "\n", + "import importlib\n", + "import numpy as np\n", + "import pandas as pd\n", + "import yellowbrick\n", + "import yellowbrick as yb\n", + "from yellowbrick.features.importances import FeatureImportances\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn import manifold, datasets\n", + "from sklearn.linear_model import LogisticRegression, LinearRegression\n", + "\n", + "mpl.rcParams[\"figure.figsize\"] = (9,6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Iris Datasets for Example Code" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X_iris, y_iris = datasets.load_iris(True)\n", + "X_iris_pd = pd.DataFrame(X_iris, columns=['f1', 'f2', 'f3', 'f4'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Logistic Regression with Mean of Feature Importances\n", + "\n", + "*Should we normalize relative to maximum value or maximum absolute value?*" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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1CDQAAMB6BBoAAGA9Ag0AALAegQYAAFiPQAMAAKxHoAEAANYrUaCJjo6Wv7+/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\ngQYAAFjv/wG0dG2BMAIrUgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viz = FeatureImportances(LogisticRegression())\n", + "viz.fit(X_iris, y_iris)\n", + "viz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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lqch1UBQ/Pz/n3oWTJ0+qe/fucjgc8vPzc67ra9euadasWfrmm2+0b98+zZgxQ9nZ2apS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\nAGA9ggYAAFiPoAEAANb7P47tpRr/nulTAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viz = FeatureImportances(LogisticRegression(), relative=False)\n", + "viz.fit(X_iris, y_iris)\n", + "viz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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OHFG/fv2Um5srf39/Z1/n5ORo+vTp+vHHH/XNN99o8uTJcjgcqlatmm6++Wa5uLhc0u49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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viz = FeatureImportances(LogisticRegression(), absolute=True)\n", + "viz.fit(X_iris, y_iris)\n", + "viz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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AKJFiA80XX3whLy8vjRw5UhcuXNDw4cMJNAAA4Fel2EDTs2dPx8+xsbGqX79+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\n/wfyj6bxcE5ooAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viz = FeatureImportances(LogisticRegression(), relative=False, absolute=True)\n", + "viz.fit(X_iris, y_iris)\n", + "viz.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Logistic Regression with Stacked Feature Importances\n", + "\n", + "*Need to decide how to scale scale feature importance when `relative=True`*" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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kFbgNCuLr6+s8+3T8+HH17t1bmZmZ8vX1dW7rjIwMTZ8+XYcPH9ZXX32lyZMnKysrSxUq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\nRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viz = FeatureImportances(LogisticRegression(), stack=True)\n", + "viz.fit(X_iris, y_iris)\n", + "viz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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EPAAAAMsQ8AAAACzjKM5OU6dO1d69e+Xh4aHRo0crMDDwRtcFAACAEioy4O3a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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viz = FeatureImportances(LogisticRegression(), stack=True, relative=False)\n", + "viz.fit(X_iris, y_iris)\n", + "viz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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S0tIkqcRlUJKAgADn0af9+/erd+/eys3NVUBAgHNZ5+TkaOrUqdq9e7e++uorTZw4UXl5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\n8vLyFBgYeMbv2a5fv15vv/2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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viz = FeatureImportances(LogisticRegression(), stack=True, absolute=True)\n", + "viz.fit(X_iris, y_iris)\n", + "viz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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pp5/me3nN4XDo0qVLklTgGBQkODjYNft04sQJde3aVU6nU8HBwa6xzs7O1owZM3TkyBHt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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viz = FeatureImportances(LogisticRegression(), stack=True, relative=False, absolute=True)\n", + "viz.fit(X_iris, y_iris)\n", + "viz.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Digits Datasets for Example Code\n", + "\n", + "*Should we add an option to show only top n features?*" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X_digits, y_digits = datasets.load_digits(return_X_y=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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GXbt2AShaFD9//nw8ffoUL168QOvWrWFsbIy0tDQcPHhQ8ctbX18fWVlZEAQBxsbG0NPT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\ntUyagC67OL4AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viz = FeatureImportances(LogisticRegression(), stack=True, relative=True)\n", + "viz.fit(X_digits, y_digits)\n", + "viz.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Linear Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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SzHX8PRFoUGwzZszQrl27FBQUpP79++vaa6/VVVddpT59+ig+Pl7du3fX2LFjFRoaqhMn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\nrkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viz = FeatureImportances(LinearRegression())\n", + "viz.fit(X_iris, y_iris)\n", + "viz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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SzHX8PRFoUGwzZszQrl27FBQUpP79++vaa6/VVVddpT59+ig+Pl7du3fX2LFjFRoaqhMn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\nrkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viz = FeatureImportances(LinearRegression(), stack=True)\n", + "viz.fit(X_iris, y_iris)\n", + "viz.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Playground" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "importlib.reload(yellowbrick.features.importances)\n", + "from yellowbrick.features.importances import FeatureImportances" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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AAGAZAh4AAIBlCHgAAACWIeABAABYhoAHAABgGQIeAACAZQh4AAAAliHgAQAA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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viz = FeatureImportances(LogisticRegression(), relative=False, absolute=False, stack=True)\n", + "viz.fit(X_pd, y)\n", + "viz.poof()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/paper/codemeta.json b/paper/codemeta.json new file mode 100644 index 000000000..9eb754b77 --- /dev/null +++ b/paper/codemeta.json @@ -0,0 +1,30 @@ +{ + "@context": "https://raw.githubusercontent.com/codemeta/codemeta/master/codemeta.jsonld", + "@type": "Code", + "author": [ + { + "@id": "https://orcid.org/0000-0003-0660-7682", + "@type": "Person", + "email": "benjamin@bengfort.com", + "name": "Benjamin Bengfort", + "affiliation": "Georgetown University" + }, + { + "@id": "https://orcid.org/0000-0002-1143-044X", + "@type": "Person", + "email": "bilbro@gmail.com", + "name": "Rebecca Bilbro", + "affiliation": "Georgetown University" + } + ], + "identifier": "https://doi.org/10.5281/zenodo.1249057", + "codeRepository": "https://github.com/DistrictDataLabs/yellowbrick", + "datePublished": "2018-07-12", + "dateModified": "2018-07-23", + "dateCreated": "2016-05-18", + "description": "Visual analysis and diagnostic tools to facilitate machine learning model selection.", + "keywords": "machine learning, visual analysis, model selection, python, scikit-learn, matplotlib", + "license": "Apache 2.0", + "title": "Yellowbrick", + "version": "v0.8" +} diff --git a/paper/figures/classification.png b/paper/figures/classification.png new file mode 100644 index 000000000..7fb193340 Binary files /dev/null and b/paper/figures/classification.png differ diff --git a/paper/figures/clustering.png b/paper/figures/clustering.png new file mode 100644 index 000000000..490a49575 Binary files /dev/null and b/paper/figures/clustering.png differ diff --git a/paper/figures/feature_analysis.png b/paper/figures/feature_analysis.png new file mode 100644 index 000000000..f9d189316 Binary files /dev/null and b/paper/figures/feature_analysis.png differ diff --git a/paper/figures/figures.py b/paper/figures/figures.py new file mode 100644 index 000000000..6279dc18b --- /dev/null +++ b/paper/figures/figures.py @@ -0,0 +1,211 @@ +#!/usr/bin/env python3 +# Script to create visualizations for the JOSS paper + +import os +import argparse +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt + +from yellowbrick.features import Rank2D, RadViz +from yellowbrick.model_selection import LearningCurve +from yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer +from yellowbrick.classifier import ClassificationReport, DiscriminationThreshold +from yellowbrick.regressor import ResidualsPlot, PredictionError, AlphaSelection + +from collections import namedtuple +from sklearn.datasets import make_blobs +from sklearn.naive_bayes import MultinomialNB +from sklearn.ensemble import RandomForestRegressor +from sklearn.cluster import MiniBatchKMeans, Birch +from sklearn.model_selection import train_test_split as tts +from sklearn.linear_model import LassoCV, RidgeCV, LogisticRegression + + +# Store figures alongside the script that generates them +FIGURES = os.path.dirname(__file__) + +# Path to datasets downloaded from S3 +DATA = os.path.join( + os.path.dirname(__file__), "..", "..", "yellowbrick", "datasets", "fixtures" +) + +# Quick reference dataset objects +Dataset = namedtuple('Dataset', 'X,y') +Split = namedtuple('Split', 'train,test') + + +def _make_dataset(X, y, split=False): + if split: + X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2) + return Dataset(Split(X_train, X_test), Split(y_train, y_test)) + return Dataset(X, y) + + +def load_occupancy(split=False): + """ + Create a dataset for the specified yb dataset + """ + path = os.path.join(DATA, "occupancy", "occupancy.csv") + data = pd.read_csv(path) + + X = data[["temperature", "relative humidity", "light", "C02", "humidity"]] + y = data["occupancy"] + return _make_dataset(X, y, split) + + +def load_concrete(split=False): + path = os.path.join(DATA, "concrete", "concrete.csv") + data = pd.read_csv(path) + + X = data[['cement', 'slag', 'ash', 'water', 'splast', 'coarse', 'fine', 'age']] + y = data['strength'] + return _make_dataset(X, y, split) + + +def load_spam(split=False): + path = os.path.join(DATA, "spam", "spam.csv") + data = pd.read_csv(path) + + target = "is_spam" + features = [col for col in data.columns if col != target] + + X = data[features] + y = data[target] + return _make_dataset(X, y, split) + + +def feature_analysis(fname="feature_analysis.png"): + """ + Create figures for feature analysis + """ + + # Create side-by-side axes grid + _, axes = plt.subplots(ncols=2, figsize=(18,6)) + + # Draw RadViz on the left + data = load_occupancy(split=False) + oz = RadViz(ax=axes[0], classes=["unoccupied", "occupied"]) + oz.fit(data.X, data.y) + oz.finalize() + + # Draw Rank2D on the right + data = load_concrete(split=False) + oz = Rank2D(ax=axes[1]) + oz.fit_transform(data.X, data.y) + oz.finalize() + + # Save figure + path = os.path.join(FIGURES, fname) + plt.tight_layout() + plt.savefig(path) + + +def regression(fname="regression.png"): + """ + Create figures for regression models + """ + _, axes = plt.subplots(ncols=2, figsize=(18, 6)) + alphas = np.logspace(-10, 1, 300) + data = load_concrete(split=True) + + # Plot prediction error in the middle + oz = PredictionError(LassoCV(alphas=alphas), ax=axes[0]) + oz.fit(data.X.train, data.y.train) + oz.score(data.X.test, data.y.test) + oz.finalize() + + # Plot residuals on the right + oz = ResidualsPlot(RidgeCV(alphas=alphas), ax=axes[1]) + oz.fit(data.X.train, data.y.train) + oz.score(data.X.test, data.y.test) + oz.finalize() + + # Save figure + path = os.path.join(FIGURES, fname) + plt.tight_layout() + plt.savefig(path) + + +def classification(fname="classification.png"): + + # Create side-by-side axes grid + _, axes = plt.subplots(ncols=2, figsize=(18,6)) + + # Add ClassificationReport to the reft + data = load_spam(split=True) + oz = ClassificationReport(MultinomialNB(), classes=["ham", "spam"], ax=axes[0]) + oz.fit(data.X.train, data.y.train) + oz.score(data.X.test, data.y.test) + oz.finalize() + + # Add DiscriminationThreshold to the right + data = load_spam(split=False) + oz = DiscriminationThreshold(LogisticRegression(), ax=axes[1]) + oz.fit(data.X, data.y) + oz.finalize() + + # Save figure + path = os.path.join(FIGURES, fname) + plt.tight_layout() + plt.savefig(path) + + +def clustering(fname="clustering.png"): + # Create side-by-side axes grid + _, axes = plt.subplots(ncols=2, figsize=(18,6)) + X, y = make_blobs(centers=7) + + # Add K-Elbow to the left + oz = KElbowVisualizer(MiniBatchKMeans(), k=(3,12), ax=axes[0]) + oz.fit(X, y) + oz.finalize() + + # Add SilhouetteVisualizer to the right + oz = SilhouetteVisualizer(Birch(n_clusters=5), ax=axes[1]) + oz.fit(X, y) + oz.finalize() + + # Save figure + path = os.path.join(FIGURES, fname) + plt.tight_layout() + plt.savefig(path) + +def hyperparameter_tuning(fname="hyperparameter_tuning.png"): + # Create side-by-side axes grid + _, axes = plt.subplots(ncols=2, figsize=(18,6)) + + # Load the concrete dataset + data = load_concrete(split=False) + + # Create a list of alphas to cross-validate against + alphas = np.logspace(-10, 1, 400) + + # Add AlphaSelection to the left + oz = AlphaSelection(LassoCV(alphas=alphas), ax=axes[0]) + oz.fit(data.X, data.y) + oz.finalize() + + # Add LearningCurve to the right + oz = LearningCurve(RandomForestRegressor(), scoring='r2', ax=axes[1]) + oz.fit(data.X, data.y) + oz.finalize() + + # Save figure + path = os.path.join(FIGURES, fname) + plt.tight_layout() + plt.savefig(path) + + + +if __name__ == '__main__': + parser = argparse.ArgumentParser( + description="generate visualizations for JOSS paper" + ) + + args = parser.parse_args() + feature_analysis() + regression() + classification() + clustering() + hyperparameter_tuning() diff --git a/paper/figures/hyperparameter_tuning.png b/paper/figures/hyperparameter_tuning.png new file mode 100644 index 000000000..64fd2eccb Binary files /dev/null and b/paper/figures/hyperparameter_tuning.png differ diff --git a/paper/figures/regression.png b/paper/figures/regression.png new file mode 100644 index 000000000..643623a46 Binary files /dev/null and b/paper/figures/regression.png differ diff --git a/paper/paper.bib b/paper/paper.bib new file mode 100644 index 000000000..cd1ed1f5c --- /dev/null +++ b/paper/paper.bib @@ -0,0 +1,101 @@ +@article{zenodo, + author = {Bengfort, Benjamin and + Bilbro, Rebecca and + Danielsen, Nathan and + Gray, Larry and + others}, + title = {Yellowbrick}, + month = Jul, + year = 2018, + doi = {10.5281/zenodo.1206239}, + url = {https://doi.org/10.5281/zenodo.1206239} +} + +@article{sklearn, + title = {Scikit-learn: Machine Learning in {P}ython}, + author = {Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. + and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. + and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and + Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, + journal = {Journal of Machine Learning Research}, + volume = {12}, + pages = {2825--2830}, + year = {2011} +} + +@article{matplotlib, + author = {Hunter, J. D.}, + title = {Matplotlib: A 2D graphics environment}, + journal = {Computing In Science \& Engineering}, + volume = {9}, + number = {3}, + pages = {90--95}, + abstract = {Matplotlib is a 2D graphics package used for Python + for application development, interactive scripting, and + publication-quality image generation across user + interfaces and operating systems.}, + publisher = {IEEE COMPUTER SOC}, + doi = {10.1109/MCSE.2007.55}, + year = 2007 +} + +@misc{scipy, + author = {Eric Jones and Travis Oliphant and Pearu Peterson and others}, + title = {{SciPy}: Open source scientific tools for {Python}}, + year = {2001--}, + url = "http://www.scipy.org/", + note = {[Online; accessed ]} +} + +@article{kumar2016model, + title = {Model selection management systems: The next frontier of advanced analytics}, + author = {Kumar, Arun and McCann, Robert and Naughton, Jeffrey and Patel, Jignesh M}, + journal = {ACM SIGMOD Record}, + volume = {44}, + number = {4}, + pages = {17--22}, + year = {2016}, + publisher = {ACM} +} + +@article{liu_wang_liu_zhu_2017, + title = {Towards better analysis of machine learning models: A visual analytics perspective}, + volume = {1}, + url = {https://www.sciencedirect.com/science/article/pii/S2468502X17300086}, + doi = {10.1016/j.visinf.2017.01.006}, + number = {1}, + journal = {Visual Informatics}, + author = {Liu, Shixia and Wang, Xiting and Liu, Mengchen and Zhu, Jun}, + year = {2017}, + month = {Mar}, + pages = {48–56} +} + +@article{wickham_visualizing_2015, + title = {Visualizing Statistical Models: {{Removing}} the Blindfold}, + volume = {8}, + timestamp = {2016-09-07T00:14:28Z}, + doi = {10.1002/sam.11271}, + number = {4}, + urldate = {2015-10-26}, + journal = {Statistical Analysis and Data Mining: The ASA Data Science Journal}, + author = {Wickham, Hadley and Cook, Dianne and Hofmann, Heike}, + year = {2015}, + pages = {203--225} +} + +@inproceedings{kapoor2010interactive, + title = {Interactive optimization for steering machine classification}, + author = {Kapoor, Ashish and Lee, Bongshin and Tan, Desney and Horvitz, Eric}, + booktitle = {Proceedings of the SIGCHI Conference on Human Factors in Computing Systems}, + pages = {1343--1352}, + year = {2010}, + organization = {ACM} +} + +@article{rajaraman2008more, + title = {More data usually beats better algorithms}, + author = {Rajaraman, Anand}, + journal = {Datawocky Blog}, + year = {2008} +} diff --git a/paper/paper.md b/paper/paper.md new file mode 100644 index 000000000..e684e8899 --- /dev/null +++ b/paper/paper.md @@ -0,0 +1,64 @@ +--- +title: 'Yellowbrick: Visualizing the Scikit-Learn Model Selection Process' +tags: + - machine learning + - visual analysis + - model selection + - python + - scikit-learn + - matplotlib +authors: + - name: Benjamin Bengfort + orcid: 0000-0003-0660-7682 + affiliation: 1 + - name: Rebecca Bilbro + orcid: 0000-0002-1143-044X + affiliation: 1 +affiliations: + - name: Georgetown University + index: 1 +date: 30 July 2018 +bibliography: paper.bib +--- + +# Summary + +Discussions of machine learning are frequently characterized by a singular focus on algorithmic behavior. Be it logistic regression, random forests, Bayesian methods, or artificial neural networks, practitioners are often quick to express their preference. However, model selection is more nuanced than simply picking the “right” or “wrong” algorithm. In practice, the workflow includes multiple iterations through feature engineering, algorithm selection, and hyperparameter tuning — summarized by Kumar et al. as a search for the maximally performing model selection triple [@kumar2016model]. “Model selection,” they explain, “is iterative and exploratory because the space of [model selection triples] is usually infinite, and it is generally impossible for analysts to know a priori which [combination] will yield satisfactory accuracy and/or insights.” + +Treating model selection as search has led to automation through grid search methods, standardized APIs, drag and drop GUIs, and specialized database systems. However, the search problem is computationally intractable and research in both machine learning [@wickham_visualizing_2015] and visual analytics [@liu_wang_liu_zhu_2017] suggests human intuition and guidance can more effectively hone in on quality models than exhaustive optimization methods. By visualizing the model selection process, data scientists can interactively steer towards final, interpretable models and avoid pitfalls and traps [@kapoor2010interactive]. + +Yellowbrick is a response to the call for open source visual steering tools. For data scientists, Yellowbrick helps evaluate the stability and predictive value of machine learning models and improves the speed of the experimental workflow. For data engineers, Yellowbrick provides visual tools for monitoring model performance in real world applications. For users of models, Yellowbrick provides visual interpretation of the behavior of the model in high dimensional feature space. Finally, for students, Yellowbrick is a framework for understanding a large variety of algorithms and methods. + +Implemented in Python, the Yellowbrick visualization package achieves steering by extending both scikit-learn [@sklearn] and Matplotlib [@matplotlib]. Like Yellowbrick, both scikit-learn and Matplotlib are extensions of SciPy [@scipy], libraries intended to facilitate scientific computing. Scikit-learn provides a generalized API for machine learning by exposing the concept of an `Estimator`, an object that learns from data. Yellowbrick in turn extends this concept with the idea of a `Visualizer`, an object that both learns from data and visualizes the result. Visualizers wrap Matplotlib procedures to produce publication-ready figures and rich visual analytics. + +Because Yellowbrick is part of a rich visual and machine learning ecosystem, it provides visualizations for feature and target analysis, classification, regression, and clustering model visualization, hyperparameter tuning, and text analysis. A few selected examples of visual diagnostics for model selection and their interpretations follow. + +![Feature Analysis](figures/feature_analysis.png) + +Because “more data beats better algorithms” [@rajaraman2008more], the first step to creating valid, predictive models is to find the minimum set of features that predicts the dependent variable. Generally, this means finding features that describe data in high dimensional space that are *separable* (i.e., by a hyperplane). Tools like `RadViz`, `ParallelCoordinates`, and `Manifold` help visualize high dimensional data for quick diagnostics. Bayesian models and regressions suffer when independent variables are collinear (i.e., exhibit pairwise correlation). `Rank2D` visualizations show pairwise correlations among features and can facilitate feature elimination. + +![Regression Model Tuning](figures/regression.png) + +Regression models hypothesize some underlying function influenced by noise whose central tendency can be inferred. The `PredictionError` visualizer shows the relationship of actual to predicted values, giving a sense of heteroskedasticity in the target, or regions of more or less error as predictions deviate from the 45 degree line. The `ResidualsPlot` shows the relationship of error in the training and test data and can also show regions of increased variability in the predictive model. + +![Classification Model Tuning](figures/classification.png) + +Classification analysis focuses on the precision and recall of the model's prediction of individual classes. The `ClassificationReport` visualizer allows for rapid comparison between models as a visual heatmap of these metrics. The `DiscriminationThreshold` visualizer for binary classifiers shows how adjusting the threshold for positive classification may influence precision and recall globally, as well as the number of points that may require manual checking for stricter determination. + +![Clustering Model Tuning](figures/clustering.png) + +Searching for structure in unlabelled data can be challenging because evaluation is largely qualitative. When using K-Means models, choosing K has a large impact on the quality of the analysis; the `KElbowVisualizer` can help select the best K given computational constraints. The `SilhouetteVisualizer` shows the relationship of points in each cluster relative to other clusters and gives an overview of the composition and size of each cluster which may hint at how models group similar data points. + +![Hyperparameter Tuning](figures/hyperparameter_tuning.png) + +Yellowbrick also offers several other techniques for hyperparameter tuning. Model and regression-specific `AlphaSelection` visualizers help identify the impact of regularization on linear models and the influence of complexity on the trade-off between error due to bias or variance. More generally, the `LearningCurve` visualizer shows how sensitive models are to the amount of data the model is trained on. + +Yellowbrick includes many more visualizations, intended to fit directly into the machine learning workflow, and many more are being added in each new release. From text analysis-specific visualizations to missing data analysis, to a `contrib` module that focuses on other machine learning libraries, Yellowbrick has tools to facilitate all parts of hypothesis driven development. The source code for Yellowbrick has been archived to Zenodo and the most recent version can be obtained with the linked DOI: [@zenodo]. + +# Acknowledgements + +Since we first introduced the idea of Yellowbrick at PyCon 2016, many people have joined us and stuck with us through 12 releases, ensuring the success of the project. Nathan Danielsen joined very early on and was one of our first maintainers, bringing an engineering perspective to our work and giving us much needed stability in testing. Larry Gray, Neal Humphrey, Jason Keung, Prema Roman, Kristen McIntyre, Jessica D'Amico and Adam Morris have also all joined our project as maintainers and core contributors, and we can't thank them enough. + +Yellowbrick would not be possible without the invaluable contributions of those in the Python and Data Science communities. At the time of this writing, GitHub reports that 46 contributors have submitted pull requests that have been merged and released, and we expect this number to continue to grow. Every week, users submit feature requests, bug reports, suggestions and questions that allow us to make the software better and more robust. Others write blog posts about using Yellowbrick, encouraging both newcomers and seasoned practitioners to more fully understand the models they are fitting. Our sincere thanks to the community for their ongoing support and participation. + +# References diff --git a/requirements.txt b/requirements.txt index 798d34ee9..db10367cc 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,7 @@ ## Dependencies -matplotlib>=1.5.1 -scipy>=0.19 -scikit-learn>=0.19 +matplotlib>=1.5.1,<3.0 +scipy>=1.0.0 +scikit-learn>=0.20 numpy>=1.13.0 cycler>=0.10.0 diff --git a/setup.cfg b/setup.cfg index 46ae23dce..3308f57b3 100644 --- a/setup.cfg +++ b/setup.cfg @@ -25,3 +25,4 @@ spec_header_format = {class_name} ({path}) filterwarnings = once::DeprecationWarning once::PendingDeprecationWarning + ignore::FutureWarning diff --git a/tests/__init__.py b/tests/__init__.py index 3e4b34b38..134c73e0f 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -28,7 +28,7 @@ ## Test Constants ########################################################################## -EXPECTED_VERSION = "0.8" +EXPECTED_VERSION = "0.9" 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b/tests/baseline_images/test_text/test_tsne/test_visualizer_with_pandas.png differ diff --git a/tests/conftest.py b/tests/conftest.py index 7964dee21..8e39b0261 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -19,6 +19,7 @@ import os +from pytest_flakes import FlakesItem ########################################################################## ## PyTest Hooks @@ -37,7 +38,7 @@ def pytest_itemcollected(item): """ # Ignore Session and PyFlake tests that are generated automatically - if not hasattr(item.parent, 'obj'): + if not hasattr(item.parent, 'obj') or isinstance(item, FlakesItem): return # Collect test objects to inspect diff --git a/tests/dataset.py b/tests/dataset.py index d846fd3d0..5354d912a 100644 --- a/tests/dataset.py +++ b/tests/dataset.py @@ -17,6 +17,7 @@ ## Imports ########################################################################## +import io import os import shutil import hashlib @@ -226,7 +227,7 @@ def load_corpus(name, fixtures=FIXTURES): files.append(os.path.join(path, cat, name)) target.append(cat) - with open(os.path.join(path, cat, name), 'r') as f: + with io.open(os.path.join(path, cat, name), 'r', encoding='UTF-8', errors='ignore') as f: data.append(f.read()) # Return the data bunch for use similar to the newsgroups example diff --git a/tests/random.py b/tests/rand.py similarity index 100% rename from tests/random.py rename to tests/rand.py diff --git a/tests/requirements.txt b/tests/requirements.txt index a1f4133d9..bc71ca7a1 100644 --- a/tests/requirements.txt +++ b/tests/requirements.txt @@ -1,5 +1,5 @@ # Library Dependencies -matplotlib>=1.5.1 +matplotlib>=1.5.1,<3.0 scipy>=0.19 scikit-learn>=0.19 numpy>=1.13.0 diff --git a/tests/test_base.py b/tests/test_base.py index 33b382764..c599ee2e4 100644 --- a/tests/test_base.py +++ b/tests/test_base.py @@ -25,7 +25,7 @@ from yellowbrick.exceptions import YellowbrickValueError from tests.base import VisualTestCase -from tests.random import RandomVisualizer +from tests.rand import RandomVisualizer from sklearn.datasets import make_classification diff --git a/tests/test_classifier/test_class_balance.py b/tests/test_classifier/test_class_balance.py deleted file mode 100644 index c58d2f1e9..000000000 --- a/tests/test_classifier/test_class_balance.py +++ /dev/null @@ -1,35 +0,0 @@ - -from yellowbrick.classifier.class_balance import * -from tests.base import VisualTestCase -from sklearn.svm import LinearSVC - -########################################################################## -## Data -########################################################################## - -X = np.array( - [[ 2.318, 2.727, 4.260, 7.212, 4.792], - [ 2.315, 2.726, 4.295, 7.140, 4.783,], - [ 2.315, 2.724, 4.260, 7.135, 4.779,], - [ 2.110, 3.609, 4.330, 7.985, 5.595,], - [ 2.110, 3.626, 4.330, 8.203, 5.621,], - [ 2.110, 3.620, 4.470, 8.210, 5.612,]] - ) - -y = np.array([1, 1, 0, 1, 0, 0]) - -########################################################################## -## Tests -########################################################################## - -class ClassBalanceTests(VisualTestCase): - - def test_class_report(self): - """ - Assert no errors occur during classification report integration - """ - model = LinearSVC() - model.fit(X,y) - visualizer = ClassBalance(model, classes=["A", "B"]) - visualizer.score(X,y) - self.assert_images_similar(visualizer) diff --git a/tests/test_classifier/test_class_prediction_error.py b/tests/test_classifier/test_class_prediction_error.py index 897653ba9..ae5743aa5 100644 --- a/tests/test_classifier/test_class_prediction_error.py +++ b/tests/test_classifier/test_class_prediction_error.py @@ -22,7 +22,8 @@ import pytest import matplotlib.pyplot as plt -from yellowbrick.classifier.class_balance import * +from tests.dataset import DatasetMixin +from yellowbrick.classifier.class_prediction_error import * from yellowbrick.exceptions import ModelError from sklearn.svm import LinearSVC @@ -44,7 +45,7 @@ ########################################################################## -class ClassPredictionErrorTests(VisualTestCase): +class ClassPredictionErrorTests(VisualTestCase, DatasetMixin): def test_integration_class_prediction_error_(self): """ @@ -64,7 +65,7 @@ def test_class_prediction_error_quickmethod(self): ax = fig.add_subplot() clf = LinearSVC(random_state=42) - g = class_prediction_error(clf, X, y, ax) + g = class_prediction_error(clf, X, y, ax, random_state=42) self.assert_images_similar(ax=g) @@ -108,3 +109,15 @@ def test_class_type(self): with self.assertRaises(YellowbrickValueError): visualizer = ClassPredictionError(model) visualizer.score(X, y) + + def test_score_returns_score(self): + """ + Test that ClassPredictionError score() returns a score between 0 and 1 + """ + # Create and fit the visualizer + visualizer = ClassPredictionError(LinearSVC()) + visualizer.fit(X, y) + + # Score the visualizer + s = visualizer.score(X, y) + assert 0 <= s <= 1 diff --git a/tests/test_classifier/test_classification_report.py b/tests/test_classifier/test_classification_report.py index 7b788bde7..1badee928 100644 --- a/tests/test_classifier/test_classification_report.py +++ b/tests/test_classifier/test_classification_report.py @@ -221,3 +221,14 @@ def test_invalid_support(self): match="'foo' is an invalid argument for support, use None, " \ "True, False, 'percent', or 'count'"): ClassificationReport(LinearSVC(), support="foo") + + def test_score_returns_score(self): + """ + Test that ClassificationReport score() returns a score between 0 and 1 + """ + viz = ClassificationReport(LinearSVC(random_state=42)) + + viz.fit(self.binary.X.train, self.binary.y.train) + s = viz.score(self.binary.X.test, self.binary.y.test) + + assert 0 <= s <= 1 diff --git a/tests/test_classifier/test_confusion_matrix.py b/tests/test_classifier/test_confusion_matrix.py index 055ad65b3..2d2c1ebea 100644 --- a/tests/test_classifier/test_confusion_matrix.py +++ b/tests/test_classifier/test_confusion_matrix.py @@ -173,23 +173,6 @@ def test_percent_mode(self): [ 0, 0, 0, 0, 0, 32, 0, 0, 2, 0], [ 0, 0, 0, 0, 0, 34, 0, 0, 0, 3]])) - def test_deprecated_fit_kwargs(self): - """ - Test that passing percent or sample_weight is deprecated - """ - if yb.__version_info__['minor'] >= 9: - pytest.fail("deprecation warnings should be removed after 0.9") - - args = (self.digits.X.test, self.digits.y.test) - cm = ConfusionMatrix(LogisticRegression()) - cm.fit(self.digits.X.train, self.digits.y.train) - - # Deprecated percent in score - pytest.deprecated_call(cm.score, *args, percent=True) - - # Deprecated sample_weight in score - pytest.deprecated_call(cm.score, *args, sample_weight=np.arange(360)) - def test_class_filter_eg_zoom_in(self): """ Test filtering classes zooms in on the confusion matrix. @@ -350,3 +333,27 @@ def test_isclassifier(self): with pytest.raises(yb.exceptions.YellowbrickError, match=message): ConfusionMatrix(model) + + def test_score_returns_score(self): + """ + Test that ConfusionMatrix score() returns a score between 0 and 1 + """ + data = self.load_data("occupancy") + X = data[[ + "temperature", "relative_humidity", "light", "C02", "humidity" + ]] + + y = data['occupancy'] + + # Convert X to an ndarray + X = X.copy().view((float, len(X.dtype.names))) + + X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2, random_state=42) + # Create and fit the visualizer + visualizer = ConfusionMatrix(LogisticRegression()) + visualizer.fit(X_train, y_train) + + # Score the visualizer + s = visualizer.score(X_test, y_test) + + assert 0 <= s <= 1 diff --git a/tests/test_classifier/test_prcurve.py b/tests/test_classifier/test_prcurve.py new file mode 100644 index 000000000..d4ebbc2da --- /dev/null +++ b/tests/test_classifier/test_prcurve.py @@ -0,0 +1,264 @@ +# tests.test_classifier.test_prcurve +# Tests for the Precision-Recall curves visualizer +# +# Author: Benjamin Bengfort +# Created: Tue Sep 04 16:48:09 2018 -0400 +# +# ID: test_prcurve.py [] benjamin@bengfort.com $ + +""" +Tests for the Precision-Recall curves visualizer +""" + +########################################################################## +## Imports +########################################################################## + +import sys +import pytest + +from yellowbrick.exceptions import * +from yellowbrick.classifier.prcurve import * + +from tests.base import VisualTestCase +from .test_rocauc import FakeClassifier + +from sklearn.svm import LinearSVC +from sklearn.datasets import load_iris +from sklearn.naive_bayes import GaussianNB +from sklearn.datasets import make_regression +from sklearn.tree import DecisionTreeClassifier +from sklearn.linear_model import RidgeClassifier +from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier + + +########################################################################## +## Assertion Helpers +########################################################################## + +LEARNED_FIELDS = ( + 'target_type_', 'score_', 'precision_', 'recall_' +) + + +def assert_not_fitted(oz): + for field in LEARNED_FIELDS: + assert not hasattr(oz, field) + + +def assert_fitted(oz): + for field in LEARNED_FIELDS: + assert hasattr(oz, field) + + + +########################################################################## +## PrecisionRecallCurve Tests +########################################################################## + +@pytest.mark.usefixtures("binary", "multiclass") +class TestPrecisionRecallCurve(VisualTestCase): + """ + Test the PrecisionRecallCurve visualizer + """ + + def test_fit_continuous(self): + """ + Should not allow any target type other than binary or multiclass + """ + X, y = make_regression() + with pytest.raises(YellowbrickValueError, match="does not support target type"): + oz = PrecisionRecallCurve(LinearSVC()) + oz.fit(X, y) + + def test_ensure_fit(self): + """ + Requires visualizer to be fit + """ + with pytest.raises(NotFitted, match="cannot wrap an already fitted estimator"): + oz = PrecisionRecallCurve(RidgeClassifier()) + oz.score(self.binary.X.test, self.binary.y.test) + + def test_binary_probability(self): + """ + Visual similarity of binary classifier with predict_proba function + """ + # Create and fit the visualizer + oz = PrecisionRecallCurve(RandomForestClassifier(random_state=12)) + assert_not_fitted(oz) + + # Fit returns self + assert oz.fit(self.binary.X.train, self.binary.y.train) is oz + + # Score the visualizer + s = oz.score(self.binary.X.test, self.binary.y.test) + assert_fitted(oz) + + # Score should be between 0 and 1 + assert 0.0 <= s <= 1.0 + + # Check the binary classification properties + assert oz.target_type_ == BINARY + assert isinstance(oz.score_, float) + assert oz.score_ == s + assert isinstance(oz.precision_, np.ndarray) + assert isinstance(oz.recall_, np.ndarray) + + # Compare the images + oz.finalize() + tol = 1.5 if sys.platform == 'win32' else 1.0 # fails with RMSE 1.409 on AppVeyor + self.assert_images_similar(oz, tol=tol) + + def test_binary_probability_decision(self): + """ + Visual similarity of binary classifier with both predict_proba & decision + """ + # Create and fit the visualizer + oz = PrecisionRecallCurve(AdaBoostClassifier(), iso_f1_curves=True) + assert_not_fitted(oz) + + # Fit returns self + assert oz.fit(self.binary.X.train, self.binary.y.train) is oz + + # Score the visualizer + s = oz.score(self.binary.X.test, self.binary.y.test) + assert_fitted(oz) + + # Score should be between 0 and 1 + assert 0.0 <= s <= 1.0 + + # Check the binary classification properties + assert oz.target_type_ == BINARY + assert isinstance(oz.score_, float) + assert oz.score_ == s + assert isinstance(oz.precision_, np.ndarray) + assert isinstance(oz.recall_, np.ndarray) + + # Compare the images + oz.finalize() + tol = 4.6 if sys.platform == 'win32' else 1.0 # fails with RMSE 4.522 on AppVeyor + self.assert_images_similar(oz, tol=tol) + + def test_binary_decision(self): + """ + Visual similarity of binary classifier with a decision function + """ + # Create and fit the visualizer + oz = PrecisionRecallCurve(LinearSVC(random_state=232)) + assert_not_fitted(oz) + + # Fit returns self + assert oz.fit(self.binary.X.train, self.binary.y.train) is oz + + # Score the visualizer + s = oz.score(self.binary.X.test, self.binary.y.test) + assert_fitted(oz) + + # Score should be between 0 and 1 + assert 0.0 <= s <= 1.0 + + # Check the binary classification properties + assert oz.target_type_ == BINARY + assert isinstance(oz.score_, float) + assert oz.score_ == s + assert isinstance(oz.precision_, np.ndarray) + assert isinstance(oz.recall_, np.ndarray) + + # Compare the images + # NOTE: do not finalize image to ensure tests pass on Travis + # Fails with 3.083 on Travis-CI (passes on AppVeyor) + self.assert_images_similar(oz, tol=3.5) + + def test_multiclass_decision(self): + """ + Visual similarity of multiclass classifier with a decision function + """ + # Create and fit the visualizer + oz = PrecisionRecallCurve(RidgeClassifier(random_state=993)) + assert_not_fitted(oz) + + # Fit returns self + assert oz.fit(self.multiclass.X.train, self.multiclass.y.train) is oz + + # Score the visualizer + s = oz.score(self.multiclass.X.test, self.multiclass.y.test) + assert_fitted(oz) + + # Score should be between 0 and 1 + assert 0.0 <= s <= 1.0 + + # Check the multiclass classification properties + assert oz.target_type_ == MULTICLASS + assert isinstance(oz.score_, dict) + assert oz.score_[MICRO] == s + assert isinstance(oz.precision_, dict) + assert isinstance(oz.recall_, dict) + assert len(oz.score_) == len(oz.classes_) + 1 + assert len(oz.precision_) == len(oz.classes_) + 1 + assert len(oz.recall_) == len(oz.classes_) + 1 + + # Compare the images + oz.finalize() + tol = 1.25 if sys.platform == 'win32' else 1.0 # fails with RMSE 1.118 on AppVeyor + self.assert_images_similar(oz, tol=tol) + + def test_multiclass_probability(self): + """ + Visual similarity of multiclass classifier with predict_proba function + """ + # Create and fit the visualizer + oz = PrecisionRecallCurve( + GaussianNB(), per_class=True, micro=False, fill_area=False, + iso_f1_curves=True, ap_score=False + ) + assert_not_fitted(oz) + + # Fit returns self + assert oz.fit(self.multiclass.X.train, self.multiclass.y.train) is oz + + # Score the visualizer + s = oz.score(self.multiclass.X.test, self.multiclass.y.test) + assert_fitted(oz) + + # Score should be between 0 and 1 + assert 0.0 <= s <= 1.0 + + # Check the multiclass classification properties + assert oz.target_type_ == MULTICLASS + assert isinstance(oz.score_, dict) + assert oz.score_[MICRO] == s + assert isinstance(oz.precision_, dict) + assert isinstance(oz.recall_, dict) + assert len(oz.score_) == len(oz.classes_) + 1 + assert len(oz.precision_) == len(oz.classes_) + 1 + assert len(oz.recall_) == len(oz.classes_) + 1 + + # Compare the images + oz.finalize() + tol = 6.6 if sys.platform == 'win32' else 1.0 # fails with RMSE 6.583 on AppVeyor + self.assert_images_similar(oz, tol=tol) + + @pytest.mark.filterwarnings("ignore:From version 0.21") + def test_quick_method(self): + """ + Test the precision_recall_curve quick method. + """ + data = load_iris() + model = DecisionTreeClassifier(random_state=14) + + oz = precision_recall_curve( + model, data.data, data.target, per_class=True, micro=True, + fill_area=False, iso_f1_curves=True, ap_score=False, + random_state=2) + assert isinstance(oz, PrecisionRecallCurve) + + tol = 5.8 if sys.platform == 'win32' else 1.0 # fails with RMSE 5.740 on AppVeyor + self.assert_images_similar(oz, tol=tol) + + def test_no_scoring_function(self): + """ + Test get y scores with classifiers that have no scoring method + """ + oz = PrecisionRecallCurve(FakeClassifier()) + with pytest.raises(ModelError, match="requires .* predict_proba or decision_function"): + oz._get_y_scores(self.binary.X.train) diff --git a/tests/test_classifier/test_rocauc.py b/tests/test_classifier/test_rocauc.py index df13dd378..dccf2666a 100644 --- a/tests/test_classifier/test_rocauc.py +++ b/tests/test_classifier/test_rocauc.py @@ -18,7 +18,7 @@ ## Imports ########################################################################## -import sys +import os import pytest import numpy as np import numpy.testing as npt @@ -29,34 +29,21 @@ from yellowbrick.exceptions import ModelError, YellowbrickValueError from sklearn.svm import LinearSVC -from sklearn.naive_bayes import MultinomialNB +from sklearn.naive_bayes import GaussianNB from sklearn.tree import DecisionTreeClassifier from sklearn.datasets import load_breast_cancer from sklearn.linear_model import LogisticRegression from sklearn.base import BaseEstimator, ClassifierMixin -from sklearn.model_selection import train_test_split as tts +from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier ########################################################################## -## Data +## Fixtures ########################################################################## -X = np.array( - [[ 2.318, 2.727, 4.260, 7.212, 4.792], - [ 2.315, 2.726, 4.295, 7.140, 4.783,], - [ 2.315, 2.724, 4.260, 7.135, 4.779,], - [ 2.110, 3.609, 4.330, 7.985, 5.595,], - [ 2.110, 3.626, 4.330, 8.203, 5.621,], - [ 2.110, 3.620, 4.470, 8.210, 5.612,]] - ) - -yb = np.array([1, 1, 0, 1, 0, 0]) -ym = np.array([1, 0, 2, 1, 2, 0]) - +# Increased tolerance for AppVeyor tests +TOL = 10 if os.name == 'nt' else 0.1 -########################################################################## -## Fixtures -########################################################################## class FakeClassifier(BaseEstimator, ClassifierMixin): """ @@ -69,47 +56,53 @@ class FakeClassifier(BaseEstimator, ClassifierMixin): ## Tests ########################################################################## +@pytest.mark.usefixtures("binary", "multiclass") class ROCAUCTests(VisualTestCase, DatasetMixin): - def load_binary_data(self): + def test_binary_probability(self): """ - Returns the binary test data set. + Test ROCAUC with a binary classifier with a predict_proba function """ - # Load the Data - data = self.load_data("occupancy") + # Create and fit the visualizer + visualizer = ROCAUC(RandomForestClassifier(random_state=42)) + visualizer.fit(self.binary.X.train, self.binary.y.train) - X = data[[ - "temperature", "relative_humidity", "light", "C02", "humidity" - ]] + # Score the visualizer + s = visualizer.score(self.binary.X.test, self.binary.y.test) - y = data['occupancy'].astype(int) + # Test that score method successfully returns a value between 0 and 1 + assert 0 <= s <= 1 - # Convert X to an ndarray - X = X.copy().view((float, len(X.dtype.names))) + # Check the scores + self.assertEqual(len(visualizer.fpr.keys()), 4) + self.assertEqual(len(visualizer.tpr.keys()), 4) + self.assertEqual(len(visualizer.roc_auc.keys()), 4) - # Return train/test splits - return tts(X, y, test_size=0.2, random_state=42) + for k in (0, 1, "micro", "macro"): + self.assertIn(k, visualizer.fpr) + self.assertIn(k, visualizer.tpr) + self.assertIn(k, visualizer.roc_auc) + self.assertEqual(len(visualizer.fpr[k]), len(visualizer.tpr[k])) + self.assertGreater(visualizer.roc_auc[k], 0.0) + self.assertLess(visualizer.roc_auc[k], 1.0) - def load_multiclass_data(self): - """ - Returns the multiclass test data set. - """ - raise NotImplementedError("Need to add multiclass data soon!") + # Compare the images + visualizer.poof() + self.assert_images_similar(visualizer, tol=TOL) - @pytest.mark.skip(reason="binary classifiers don't currently work as expected") - def test_binary_rocauc(self): + def test_binary_probability_decision(self): """ - Test ROCAUC with a binary classifier + Test ROCAUC with a binary classifier with both decision & predict_proba """ - X_train, X_test, y_train, y_test = self.load_binary_data() - # Create and fit the visualizer - visualizer = ROCAUC(LinearSVC()) - visualizer.fit(X_train, y_train) + visualizer = ROCAUC(AdaBoostClassifier()) + visualizer.fit(self.binary.X.train, self.binary.y.train) # Score the visualizer - s = visualizer.score(X_test, y_test) - self.assertAlmostEqual(s, 0.93230159261495249) + s = visualizer.score(self.binary.X.test, self.binary.y.test) + + # Test that score method successfully returns a value between 0 and 1 + assert 0 <= s <= 1 # Check the scores self.assertEqual(len(visualizer.fpr.keys()), 4) @@ -126,29 +119,86 @@ def test_binary_rocauc(self): # Compare the images visualizer.poof() - self.assert_images_similar(visualizer) + self.assert_images_similar(visualizer, tol=TOL) + + def test_binary_decision(self): + """ + Test ROCAUC with a binary classifier with a decision_function + """ + # Create and fit the visualizer + visualizer = ROCAUC(LinearSVC(random_state=42), micro=False, macro=False, per_class=False) + visualizer.fit(self.binary.X.train, self.binary.y.train) + + # Score the visualizer + s = visualizer.score(self.binary.X.test, self.binary.y.test) + + # Test that score method successfully returns a value between 0 and 1 + assert 0 <= s <= 1 + + # Check the scores + self.assertEqual(len(visualizer.fpr.keys()), 1) + self.assertEqual(len(visualizer.tpr.keys()), 1) + self.assertEqual(len(visualizer.roc_auc.keys()), 1) + + # Compare the images + # NOTE: increased tolerance for both AppVeyor and Travis CI tests + visualizer.poof() + self.assert_images_similar(visualizer, tol=10) + + def test_binary_micro_error(self): + """ + Test ROCAUC to see if _binary_decision with micro = True raises an error + """ + # Create visualizer with a linear model to force a binary decision + visualizer = ROCAUC(LinearSVC(random_state=42), micro=True) + visualizer.fit(self.binary.X.train, self.binary.y.train) + + # Ensure score raises error (micro curves aren't defined for binary decisions) + with self.assertRaises(ModelError): + visualizer.score(self.binary.X.test, self.binary.y.test) + + def test_binary_macro_error(self): + """ + Test ROCAUC to see if _binary_decision with macro = True raises an error + """ + # Create visualizer with a linear model to force a binary decision + visualizer = ROCAUC(LinearSVC(random_state=42), macro=True) + visualizer.fit(self.binary.X.train, self.binary.y.train) + + # Ensure score raises error (macro curves aren't defined for binary decisions) + with self.assertRaises(ModelError): + visualizer.score(self.binary.X.test, self.binary.y.test) + + def test_binary_per_class_error(self): + """ + Test ROCAUC to see if _binary_decision with per_class = True raises an error + """ + # Create visualizer with a linear model to force a binary decision + visualizer = ROCAUC(LinearSVC(random_state=42), per_class=True) + visualizer.fit(self.binary.X.train, self.binary.y.train) + + # Ensure score raises error (per_class curves not defined for binary decisions) + with self.assertRaises(ModelError): + visualizer.score(self.binary.X.test, self.binary.y.test) - @pytest.mark.xfail(reason="see issue #315") def test_multiclass_rocauc(self): """ Test ROCAUC with a multiclass classifier """ - # Load the Data - # TODO: Switch to a true multi-class dataset - X_train, X_test, y_train, y_test = self.load_binary_data() - # Create and fit the visualizer - visualizer = ROCAUC(MultinomialNB()) - visualizer.fit(X_train, y_train) + visualizer = ROCAUC(GaussianNB()) + visualizer.fit(self.multiclass.X.train, self.multiclass.y.train) # Score the visualizer - s = visualizer.score(X_test, y_test) - self.assertAlmostEqual(s, 0.93230159261495249) + s = visualizer.score(self.multiclass.X.test, self.multiclass.y.test) + + # Test that score method successfully returns a value between 0 and 1 + assert 0 <= s <= 1 # Check the scores - self.assertEqual(len(visualizer.fpr.keys()), 4) - self.assertEqual(len(visualizer.tpr.keys()), 4) - self.assertEqual(len(visualizer.roc_auc.keys()), 4) + self.assertEqual(len(visualizer.fpr.keys()), 8) + self.assertEqual(len(visualizer.tpr.keys()), 8) + self.assertEqual(len(visualizer.roc_auc.keys()), 8) for k in (0, 1, "micro", "macro"): self.assertIn(k, visualizer.fpr) @@ -160,7 +210,7 @@ def test_multiclass_rocauc(self): # Compare the images visualizer.poof() - self.assert_images_similar(visualizer, tol=0.071) + self.assert_images_similar(visualizer, tol=TOL) def test_rocauc_quickmethod(self): """ @@ -169,24 +219,20 @@ def test_rocauc_quickmethod(self): data = load_breast_cancer() model = DecisionTreeClassifier() - # TODO: impage comparison of the quick method + # TODO: image comparison of the quick method roc_auc(model, data.data, data.target) - @pytest.mark.xfail(reason="see issue #315") def test_rocauc_no_micro(self): """ Test ROCAUC without a micro average """ - # Load the Data - X_train, X_test, y_train, y_test = self.load_binary_data() - # Create and fit the visualizer visualizer = ROCAUC(LogisticRegression(), micro=False) - visualizer.fit(X_train, y_train) + visualizer.fit(self.binary.X.train, self.binary.y.train) # Score the visualizer (should be the macro average) - s = visualizer.score(X_test, y_test) - self.assertAlmostEqual(s, 0.99578564759755916) + s = visualizer.score(self.binary.X.test, self.binary.y.test) + self.assertAlmostEqual(s, 0.8) # Assert that there is no micro score self.assertNotIn("micro", visualizer.fpr) @@ -195,23 +241,19 @@ def test_rocauc_no_micro(self): # Compare the images visualizer.poof() - self.assert_images_similar(visualizer) + self.assert_images_similar(visualizer, tol=TOL) - @pytest.mark.xfail(reason="see issue #315") def test_rocauc_no_macro(self): """ Test ROCAUC without a macro average """ - # Load the Data - X_train, X_test, y_train, y_test = self.load_binary_data() - # Create and fit the visualizer visualizer = ROCAUC(LogisticRegression(), macro=False) - visualizer.fit(X_train, y_train) + visualizer.fit(self.binary.X.train, self.binary.y.train) # Score the visualizer (should be the micro average) - s = visualizer.score(X_test, y_test) - self.assertAlmostEqual(s, 0.99766508576965574) + s = visualizer.score(self.binary.X.test, self.binary.y.test) + self.assertAlmostEqual(s, 0.8) # Assert that there is no macro score self.assertNotIn("macro", visualizer.fpr) @@ -220,25 +262,19 @@ def test_rocauc_no_macro(self): # Compare the images visualizer.poof() - self.assert_images_similar(visualizer) + self.assert_images_similar(visualizer, tol=TOL) - @pytest.mark.xfail( - sys.platform == 'win32', reason="images not close on windows" - ) def test_rocauc_no_macro_no_micro(self): """ Test ROCAUC without a macro or micro average """ - # Load the Data - X_train, X_test, y_train, y_test = self.load_binary_data() - # Create and fit the visualizer visualizer = ROCAUC(LogisticRegression(), macro=False, micro=False) - visualizer.fit(X_train, y_train) + visualizer.fit(self.binary.X.train, self.binary.y.train) # Score the visualizer (should be the F1 score) - s = visualizer.score(X_test, y_test) - self.assertAlmostEqual(s, 0.98978599221789887) + s = visualizer.score(self.binary.X.test, self.binary.y.test) + self.assertAlmostEqual(s, 0.8) # Assert that there is no macro score self.assertNotIn("macro", visualizer.fpr) @@ -252,23 +288,19 @@ def test_rocauc_no_macro_no_micro(self): # Compare the images visualizer.poof() - self.assert_images_similar(visualizer) + self.assert_images_similar(visualizer, tol=TOL) - @pytest.mark.xfail(reason="see issue #315") def test_rocauc_no_classes(self): """ Test ROCAUC without per-class curves """ - # Load the Data - X_train, X_test, y_train, y_test = self.load_binary_data() - # Create and fit the visualizer visualizer = ROCAUC(LogisticRegression(), per_class=False) - visualizer.fit(X_train, y_train) + visualizer.fit(self.binary.X.train, self.binary.y.train) # Score the visualizer (should be the micro average) - s = visualizer.score(X_test, y_test) - self.assertAlmostEqual(s, 0.99766508576965574) + s = visualizer.score(self.binary.X.test, self.binary.y.test) + self.assertAlmostEqual(s, 0.8) # Assert that there still are per-class scores for c in (0, 1): @@ -278,36 +310,53 @@ def test_rocauc_no_classes(self): # Compare the images visualizer.poof() - self.assert_images_similar(visualizer) + self.assert_images_similar(visualizer, tol=TOL) def test_rocauc_no_curves(self): """ Test ROCAUC with no curves specified at all """ # Create and fit the visualizer + visualizer = ROCAUC(LogisticRegression(), per_class=False, macro=False, micro=False) + visualizer.fit(self.binary.X.train, self.binary.y.train) + + # Attempt to score the visualizer with pytest.raises(YellowbrickValueError, match="no curves will be drawn"): - ROCAUC( - LogisticRegression(), per_class=False, macro=False, micro=False - ) + visualizer.score(self.binary.X.test, self.binary.y.test) - @pytest.mark.skip(reason="not implemented yet") def test_rocauc_label_encoded(self): """ - Test ROCAUC with label encoding before scoring + Test ROCAUC with a target specifying a list of classes as strings """ - pass + class_labels = ['a', 'b', 'c', 'd', 'e', 'f'] + + # Create and fit the visualizer + visualizer = ROCAUC(LogisticRegression(), classes=class_labels) + visualizer.fit(self.multiclass.X.train, self.multiclass.y.train) + + # Score the visualizer + visualizer.score(self.multiclass.X.test, self.multiclass.y.test) + self.assertEqual(list(visualizer.classes_), class_labels) - @pytest.mark.skip(reason="not implemented yet") def test_rocauc_not_label_encoded(self): """ - Test ROCAUC without label encoding before scoring + Test ROCAUC with a target whose classes are unencoded strings before scoring """ - pass + # Map numeric targets to strings + classes = {0: 'a', 1: 'b', 2: 'c', 3: 'd', 4: 'e', 5: 'f'} + y_train = np.array([classes[yi] for yi in self.multiclass.y.train]) + y_test = np.array([classes[yi] for yi in self.multiclass.y.test]) + + # Create and fit the visualizer + visualizer = ROCAUC(LogisticRegression()) + visualizer.fit(self.multiclass.X.train, y_train) + + # Confirm that y_train and y_test have the same targets before calling score + self.assertEqual(set(y_train), set(y_test)) - @pytest.mark.xfail(reason="not working with expected precision") - def test_decision_function_rocauc(self): + def test_binary_decision_function_rocauc(self): """ - Test ROCAUC with classifiers that have a decision function + Test ROCAUC with binary classifiers that have a decision function """ # Load the model and assert there is no predict_proba method. model = LinearSVC() @@ -316,41 +365,79 @@ def test_decision_function_rocauc(self): # Fit model and visualizer visualizer = ROCAUC(model) - visualizer.fit(X, yb) + visualizer.fit(self.binary.X.train, self.binary.y.train) - expected = np.asarray([ - 0.204348, 0.228593, 0.219908, -0.211756, -0.26155 , -0.221405 + # First 10 expected values in the y_scores + first_ten_expected = np.asarray([ + -0.092, 0.019, -0.751, -0.838, 0.183, -0.344, -1.019, 2.203, 1.415, -0.529 ]) # Get the predict_proba scores and evaluate - y_scores = visualizer._get_y_scores(X) - npt.assert_array_almost_equal(y_scores, expected, decimal=1) + y_scores = visualizer._get_y_scores(self.binary.X.train) + + # Check to see if the first 10 y_scores match the expected + npt.assert_array_almost_equal(y_scores[:10], first_ten_expected, decimal=1) + + def test_multi_decision_function_rocauc(self): + """ + Test ROCAUC with multiclass classifiers that have a decision function + """ + # Load the model and assert there is no predict_proba method. + model = LinearSVC() + with self.assertRaises(AttributeError): + model.predict_proba + + # Fit model and visualizer + visualizer = ROCAUC(model) + visualizer.fit(self.multiclass.X.train, self.multiclass.y.train) + + # First 5 expected arrays in the y_scores + first_five_expected = [ + [-0.370, -0.543, -1.059, -0.466, -0.743, -1.156], + [-0.445, -0.693, -0.362, -1.002, -0.815, -0.878], + [-1.058, -0.808, -0.291, -0.767, -0.651, -0.586], + [-0.446, -1.255, -0.489, -0.961, -0.807, -0.126], + [-1.066, -0.493, -0.639, -0.442, -0.639, -1.017] + ] + + # Get the predict_proba scores and evaluate + y_scores = visualizer._get_y_scores(self.multiclass.X.train) + + # Check to see if the first 5 y_score arrays match the expected + npt.assert_array_almost_equal(y_scores[:5], first_five_expected, decimal=1) def test_predict_proba_rocauc(self): """ Test ROCAUC with classifiers that utilize predict_proba """ # Load the model and assert there is no decision_function method. - model = MultinomialNB() + model = GaussianNB() with self.assertRaises(AttributeError): model.decision_function # Fit model and visualizer visualizer = ROCAUC(model) - visualizer.fit(X, yb) - - expected = np.asarray([ - [0.493788, 0.506212], - [0.493375, 0.506625], - [0.493572, 0.506428], - [0.511063, 0.488936], - [0.511887, 0.488112], - [0.510841, 0.489158], + visualizer.fit(self.binary.X.train, self.binary.y.train) + + # First 10 expected arrays in the y_scores + first_ten_expected = np.asarray([ + [0.595, 0.405], + [0.161, 0.839], + [0.990, 0.010], + [0.833, 0.167], + [0.766, 0.234], + [0.996, 0.004], + [0.592, 0.408], + [0.007, 0.993], + [0.035, 0.965], + [0.764, 0.236] ]) # Get the predict_proba scores and evaluate - y_scores = visualizer._get_y_scores(X) - npt.assert_array_almost_equal(y_scores, expected) + y_scores = visualizer._get_y_scores(self.binary.X.train) + + # Check to see if the first 10 y_score arrays match the expected + npt.assert_array_almost_equal(y_scores[:10], first_ten_expected, decimal=1) def test_no_scoring_function(self): """ @@ -358,4 +445,4 @@ def test_no_scoring_function(self): """ visualizer = ROCAUC(FakeClassifier()) with self.assertRaises(ModelError): - visualizer._get_y_scores(X) + visualizer._get_y_scores(self.binary.X.train) diff --git a/tests/test_cluster/test_elbow.py b/tests/test_cluster/test_elbow.py index 444a3a9fa..3bd16793e 100644 --- a/tests/test_cluster/test_elbow.py +++ b/tests/test_cluster/test_elbow.py @@ -184,11 +184,31 @@ def test_invalid_k(self): """ with pytest.raises(YellowbrickValueError): - KElbowVisualizer(KMeans(), k=(1,2,3,4,5)) + KElbowVisualizer(KMeans(), k=(1, 2, 3, 'foo', 5)) with pytest.raises(YellowbrickValueError): KElbowVisualizer(KMeans(), k="foo") + def test_valid_k(self): + """ + Assert that valid values of K generate correct k_values_: + if k is an int, k_values_ = range(2, k+1) + if k is a tuple of 2 ints, k_values = range(k[0], k[1]) + if k is an iterable, k_values_ = list(k) + """ + visualizer = KElbowVisualizer(KMeans(), k=8) + assert visualizer.k_values_ == list(np.arange(2, 8+1)) + + visualizer = KElbowVisualizer(KMeans(), k=(4, 12)) + assert visualizer.k_values_ == list(np.arange(4, 12)) + + visualizer = KElbowVisualizer(KMeans(), k=np.arange(10, 100, 10)) + assert visualizer.k_values_ == list(np.arange(10, 100, 10)) + + visualizer = KElbowVisualizer(KMeans(), + k=[10, 20, 30, 40, 50, 60, 70, 80, 90]) + assert visualizer.k_values_ == list(np.arange(10, 100, 10)) + @pytest.mark.xfail( sys.platform == 'win32', reason="images not close on windows" ) diff --git a/tests/test_cluster/test_icdm.py b/tests/test_cluster/test_icdm.py new file mode 100644 index 000000000..9228b66f5 --- /dev/null +++ b/tests/test_cluster/test_icdm.py @@ -0,0 +1,277 @@ +# tests.test_cluster.test_icdm +# Tests for the intercluster distance map visualizer. +# +# Author: Benjamin Bengfort +# Created: Tue Aug 21 11:57:44 2018 -0400 +# +# ID: test_icdm.py [] benjamin@bengfort.com $ + +""" +Tests for the intercluster distance map visualizer. +""" + +########################################################################## +## Imports +########################################################################## + +import sys +import pytest +import matplotlib as mpl + +from yellowbrick.cluster.icdm import * +from yellowbrick.exceptions import YellowbrickValueError + +from tests.base import VisualTestCase +from tests.dataset import DatasetMixin, Dataset + +from sklearn.datasets import make_blobs +from sklearn.cluster import Birch, AgglomerativeClustering +from sklearn.cluster import KMeans, AffinityPropagation, MiniBatchKMeans +from sklearn.decomposition import LatentDirichletAllocation as LDA + +try: + import pandas as pd +except ImportError: + pd = None + + +# Determine version of matplotlib +MPL_VERS_MAJ = int(mpl.__version__.split(".")[0]) + + +########################################################################## +## Fixtures +########################################################################## + +@pytest.fixture(scope='class') +def blobs12(request): + """ + Creates a fixture of 1000 instances in 12 clusters with 16 features. + """ + X, y = make_blobs( + centers=12, n_samples=1000, n_features=16, shuffle=True, random_state=2121 + ) + request.cls.blobs12 = Dataset(X, y) + + +@pytest.fixture(scope='class') +def blobs4(request): + """ + Creates a fixture of 400 instances in 4 clusters with 16 features. + """ + X, y = make_blobs( + centers=4, n_samples=400, n_features=16, shuffle=True, random_state=1212 + ) + request.cls.blobs4 = Dataset(X, y) + + +def assert_fitted(oz): + for param in ('cluster_centers_', 'embedded_centers_', 'scores_', 'fit_time_'): + assert hasattr(oz, param) + + +def assert_not_fitted(oz): + for param in ('embedded_centers_', 'scores_', 'fit_time_'): + assert not hasattr(oz, param) + + + +########################################################################## +## InterclusterDistance Test Cases +########################################################################## + +@pytest.mark.usefixtures("blobs12", "blobs4") +class TestInterclusterDistance(VisualTestCase, DatasetMixin): + """ + Test the InterclusterDistance visualizer + """ + + def test_only_valid_embeddings(self): + """ + Should raise an exception on invalid embedding + """ + # On init + with pytest.raises(YellowbrickValueError, match="unknown embedding 'foo'"): + InterclusterDistance(KMeans(), embedding='foo') + + # After init + icdm = InterclusterDistance(KMeans()) + icdm.embedding = 'foo' + with pytest.raises(YellowbrickValueError, match="unknown embedding 'foo'"): + icdm.transformer + + def test_only_valid_scoring(self): + """ + Should raise an exception on invalid scoring + """ + # On init + with pytest.raises(YellowbrickValueError, match="unknown scoring 'foo'"): + InterclusterDistance(KMeans(), scoring='foo') + + # After init + icdm = InterclusterDistance(KMeans()) + icdm.scoring = 'foo' + with pytest.raises(YellowbrickValueError, match="unknown scoring method 'foo'"): + icdm._score_clusters(None) + + def test_kmeans_mds(self): + """ + Visual similarity with KMeans and MDS scaling + """ + model = KMeans(9, random_state=38) + oz = InterclusterDistance(model, random_state=83, embedding='mds') + + # Prefit assertions + assert_not_fitted(oz) + + assert oz.fit(self.blobs12.X) is oz # Fit returns self + + # Postfit assertions + assert_fitted(oz) + assert oz.embedded_centers_.shape[0] == oz.scores_.shape[0] + assert oz.embedded_centers_.shape[0] == oz.cluster_centers_.shape[0] + assert len(oz._score_clusters(self.blobs12.X)) == 9 + assert len(oz._get_cluster_sizes()) == 9 + + # Image similarity + oz.finalize() + tol = 4.9 if sys.platform == 'win32' else 1.0 # fails with RMSE 4.740 on AppVeyor + self.assert_images_similar(oz, tol=tol) + + @pytest.mark.filterwarnings("ignore:the matrix subclass is not the recommended way") + def test_affinity_tsne_no_legend(self): + """ + Visual similarity with AffinityPropagation, TSNE scaling, and no legend + """ + model = AffinityPropagation() + oz = InterclusterDistance( + model, random_state=763, embedding='tsne', legend=False + ) + + # Prefit assertions + assert_not_fitted(oz) + + assert oz.fit(self.blobs4.X) is oz # Fit returns self + + # Postfit assertions + assert_fitted(oz) + assert oz.embedded_centers_.shape[0] == oz.scores_.shape[0] + assert oz.embedded_centers_.shape[0] == oz.cluster_centers_.shape[0] + + # Image similarity + oz.finalize() + tol = 2.75 if sys.platform == 'win32' else 1.0 # fails with RMSE 2.687 on AppVeyor + self.assert_images_similar(oz, tol=tol) + + + @pytest.mark.skip(reason="LDA not implemented yet") + def test_lda_mds(self): + """ + Visual similarity with LDA and MDS scaling + """ + model = LDA(9, random_state=6667) + oz = InterclusterDistance(model, random_state=2332, embedding='mds') + + # Prefit assertions + assert_not_fitted(oz) + + assert oz.fit(self.blobs12.X) is oz # Fit returns self + + # Postfit assertions + assert_fitted(oz) + assert oz.embedded_centers_.shape[0] == oz.scores_.shape[0] + assert oz.embedded_centers_.shape[0] == oz.cluster_centers_.shape[0] + assert len(oz._score_clusters(self.blobs12.X)) == 9 + assert len(oz._get_cluster_sizes()) == 9 + + # Image similarity + oz.finalize() + self.assert_images_similar(oz, tol=1.0) + + @pytest.mark.skip(reason="agglomerative not implemented yet") + @pytest.mark.filterwarnings("ignore:Using a non-tuple sequence") + @pytest.mark.filterwarnings("ignore:the matrix subclass is not the recommended way") + def test_birch_tsne(self): + """ + Visual similarity with Birch and MDS scaling + """ + oz = InterclusterDistance(Birch(n_clusters=9), random_state=83, embedding='mds') + + # Prefit assertions + assert_not_fitted(oz) + + assert oz.fit(self.blobs12.X) is oz # Fit returns self + + # Postfit assertions + assert_fitted(oz) + assert oz.embedded_centers_.shape[0] == oz.scores_.shape[0] + assert oz.embedded_centers_.shape[0] == oz.cluster_centers_.shape[0] + assert len(oz._score_clusters(self.blobs12.X)) == 9 + assert len(oz._get_cluster_sizes()) == 9 + + # Image similarity + oz.finalize() + self.assert_images_similar(oz, tol=1.0) + + @pytest.mark.skip(reason="agglomerative not implemented yet") + def test_ward_mds_no_legend(self): + """ + Visual similarity with Ward, TSNE scaling, and no legend + """ + model = AgglomerativeClustering(n_clusters=9) + oz = InterclusterDistance( + model, random_state=83, embedding='tsne', legend=False + ) + + # Prefit assertions + assert_not_fitted(oz) + + assert oz.fit(self.blobs12.X) is oz # Fit returns self + + # Postfit assertions + assert_fitted(oz) + assert oz.embedded_centers_.shape[0] == oz.scores_.shape[0] + assert oz.embedded_centers_.shape[0] == oz.cluster_centers_.shape[0] + assert len(oz._score_clusters(self.blobs12.X)) == 9 + assert len(oz._get_cluster_sizes()) == 9 + + # Image similarity + oz.finalize() + self.assert_images_similar(oz, tol=1.0) + + def test_quick_method(self): + """ + Test the quick method producing a valid visualization + """ + model = MiniBatchKMeans(3, random_state=343) + oz = intercluster_distance(model, self.blobs4.X, random_state=93, legend=False) + assert isinstance(oz, InterclusterDistance) + + tol = 2.75 if sys.platform == 'win32' else 1.0 # fails with RMSE 2.631 on AppVeyor + self.assert_images_similar(oz, tol=tol) + + @pytest.mark.skipif(MPL_VERS_MAJ >= 2, reason="test requires mpl earlier than 2.0.2") + def test_legend_matplotlib_version(self, mock_toolkit): + """ + ValueError is raised when matplotlib version is incorrect and legend=True + """ + with pytst.raises(ImportError): + from mpl_toolkits.axes_grid1 import inset_locator + assert not inset_locator + + with pytest.raises(YellowbrickValueError, match="requires matplotlib 2.0.2"): + InterclusterDistance(KMeans(), legend=True) + + @pytest.mark.skipif(MPL_VERS_MAJ >= 2, reason="test requires mpl earlier than 2.0.2") + def test_no_legend_matplotlib_version(self, mock_toolkit): + """ + No error is raised when matplotlib version is incorrect and legend=False + """ + with pytst.raises(ImportError): + from mpl_toolkits.axes_grid1 import inset_locator + assert not inset_locator + + try: + InterclusterDistance(KMeans(), legend=False) + except YellowbrickValueError as e: + self.fail(e) diff --git a/tests/test_cluster/test_silhouette.py b/tests/test_cluster/test_silhouette.py index b0136f691..6daa64a29 100644 --- a/tests/test_cluster/test_silhouette.py +++ b/tests/test_cluster/test_silhouette.py @@ -90,3 +90,12 @@ def test_integrated_mini_batch_kmeans_silhouette(self): self.assert_images_similar(visualizer) except Exception as e: self.fail("error during silhouette: {}".format(e)) + + @pytest.mark.skip( + reason="no negative silhouette example available yet" + ) + def test_negative_silhouette_score(self): + """ + Ensure negative silhouette scores are correctly displayed by the visualizer. + """ + raise NotImplementedError("no negative silhouette example available") \ No newline at end of file diff --git a/tests/test_contrib/test_missing/test_bar.py b/tests/test_contrib/test_missing/test_bar.py new file mode 100644 index 000000000..0ab1ee3d4 --- /dev/null +++ b/tests/test_contrib/test_missing/test_bar.py @@ -0,0 +1,123 @@ +# tests.test_contrib.test_missing.test_bar +# Tests for the alpha selection visualizations. +# +# Author: Nathan Danielsen +# Created: Thu Mar 29 12:13:04 2018 -0500 +# +# Copyright (C) 2018 District Data Labs +# For license information, see LICENSE.txt +# +# ID: test_bar.py [7d3f5e6] nathan.danielsen@gmail.com $ + +""" +Tests for the MissingValuesBar visualizations. +""" + +########################################################################## +## Imports +########################################################################## + +import os +from tests.base import VisualTestCase +from sklearn.datasets import make_classification +from yellowbrick.contrib.missing.bar import * + +try: + import pandas as pd +except ImportError: + pd = None + +########################################################################## +## Feature Importances Tests +########################################################################## + +class TestMissingBarVisualizer(VisualTestCase): + """ + FeatureImportances visualizer + """ + + def setUp(self): + super(TestMissingBarVisualizer, self).setUp() + self.tol = 0.01 + if os.name == 'nt': # Windows + self.tol = 0.5 + + def test_missingvaluesbar_pandas(self): + """ + Integration test of visualizer with pandas + """ + X, y = make_classification( + n_samples=400, n_features=20, n_informative=8, n_redundant=8, + n_classes=2, n_clusters_per_class=4, random_state=854 + ) + + # add nan values to a range of values in the matrix + X[X > 1.5] = np.nan + X_ = pd.DataFrame(X) + + features = [str(n) for n in range(20)] + viz = MissingValuesBar(features=features) + viz.fit(X_) + viz.poof() + + self.assert_images_similar(viz, tol=self.tol) + + + def test_missingvaluesbar_numpy(self): + """ + Integration test of visualizer with numpy without target y passed in + """ + X, y = make_classification( + n_samples=400, n_features=20, n_informative=8, n_redundant=8, + n_classes=2, n_clusters_per_class=4, random_state=856 + ) + + # add nan values to a range of values in the matrix + X[X > 1.5] = np.nan + + features = [str(n) for n in range(20)] + viz = MissingValuesBar(features=features) + viz.fit(X) + viz.poof() + + self.assert_images_similar(viz, tol=self.tol) + + def test_missingvaluesbar_numpy_with_y_target(self): + """ + Integration test of visualizer with numpy without target y passed in + but no class labels + """ + X, y = make_classification( + n_samples=400, n_features=20, n_informative=8, n_redundant=8, + n_classes=2, n_clusters_per_class=4, random_state=856 + ) + + # add nan values to a range of values in the matrix + X[X > 1.5] = np.nan + + features = [str(n) for n in range(20)] + viz = MissingValuesBar(features=features) + viz.fit(X, y) + viz.poof() + + self.assert_images_similar(viz, tol=self.tol) + + def test_missingvaluesbar_numpy_with_y_target_with_labels(self): + """ + Integration test of visualizer with numpy without target y passed in + but no class labels + """ + X, y = make_classification( + n_samples=400, n_features=20, n_informative=8, n_redundant=8, + n_classes=2, n_clusters_per_class=4, random_state=856 + ) + + # add nan values to a range of values in the matrix + X[X > 1.5] = np.nan + + features = [str(n) for n in range(20)] + viz = MissingValuesBar(features=features, classes=['class A', 'class B']) + viz.fit(X, y) + viz.poof() + + self.assert_images_similar(viz, tol=self.tol) diff --git a/tests/test_contrib/test_missing/test_dispersion.py b/tests/test_contrib/test_missing/test_dispersion.py new file mode 100644 index 000000000..0636f7a70 --- /dev/null +++ b/tests/test_contrib/test_missing/test_dispersion.py @@ -0,0 +1,124 @@ +# tests.test_contrib.test_missing.test_dispersion +# Tests for the alpha selection visualizations. +# +# Author: Nathan Danielsen +# Created: Thu Mar 29 12:13:04 2018 -0500 +# +# Copyright (C) 2018 District Data Labs +# For license information, see LICENSE.txt +# +# ID: test_dispersion.py [7d3f5e6] nathan.danielsen@gmail.com $ + +""" +Tests for the MissingValuesDispersion visualizations. +""" + +########################################################################## +## Imports +########################################################################## +import os +from sklearn.datasets import make_classification +from tests.base import VisualTestCase + +from yellowbrick.contrib.missing.dispersion import * + +try: + import pandas as pd +except ImportError: + pd = None + +########################################################################## +## Feature Importances Tests +########################################################################## + +class MissingValuesDispersionTestCase(VisualTestCase): + """ + MissingValuesDispersion visualizer + """ + def setUp(self): + super(MissingValuesDispersionTestCase, self).setUp() + self.tol = 0.01 + if os.name == 'nt': # Windows + self.tol = 5.0 + + + def test_missingvaluesdispersion_with_pandas(self): + """ + Integration test of visualizer with pandas + """ + X, y = make_classification( + n_samples=400, n_features=20, n_informative=8, n_redundant=8, + n_classes=2, n_clusters_per_class=4, random_state=854 + ) + + # add nan values to a range of values in the matrix + X[X > 1.5] = np.nan + + X_ = pd.DataFrame(X) + features = [str(n) for n in range(20)] + viz = MissingValuesDispersion(features=features) + viz.fit(X_) + viz.poof() + + self.assert_images_similar(viz, tol=self.tol) + + def test_missingvaluesdispersion_with_pandas_with_y_targets(self): + """ + Integration test of visualizer with pandas with y targets + """ + X, y = make_classification( + n_samples=400, n_features=20, n_informative=8, n_redundant=8, + n_classes=2, n_clusters_per_class=4, random_state=854 + ) + + # add nan values to a range of values in the matrix + X[X > 1.5] = np.nan + + X_ = pd.DataFrame(X) + features = [str(n) for n in range(20)] + classes = ['Class A', 'Class B'] + viz = MissingValuesDispersion(features=features, classes=classes) + viz.fit(X_, y=y) + viz.poof() + + self.assert_images_similar(viz, tol=self.tol) + + + def test_missingvaluesdispersion_with_numpy(self): + """ + Integration test of visualizer with numpy + """ + X, y = make_classification( + n_samples=400, n_features=20, n_informative=8, n_redundant=8, + n_classes=2, n_clusters_per_class=4, random_state=852 + ) + + # add nan values to a range of values in the matrix + X[X > 1.5] = np.nan + + features = [str(n) for n in range(20)] + viz = MissingValuesDispersion(features=features) + viz.fit(X) + viz.poof() + + self.assert_images_similar(viz, tol=self.tol) + + def test_missingvaluesdispersion_with_numpy_with_y_targets(self): + """ + Integration test of visualizer with numpy with y targets + """ + X, y = make_classification( + n_samples=400, n_features=20, n_informative=8, n_redundant=8, + n_classes=2, n_clusters_per_class=4, random_state=852 + ) + + # add nan values to a range of values in the matrix + X[X > 1.5] = np.nan + + features = [str(n) for n in range(20)] + classes = ['Class A', 'Class B'] + viz = MissingValuesDispersion(features=features, classes=classes) + viz.fit(X, y=y) + viz.poof() + + self.assert_images_similar(viz, tol=self.tol) diff --git a/tests/test_contrib/test_scatter.py b/tests/test_contrib/test_scatter.py index db2cb3eb3..b6cb279e6 100644 --- a/tests/test_contrib/test_scatter.py +++ b/tests/test_contrib/test_scatter.py @@ -17,7 +17,6 @@ ########################################################################## import pytest -import unittest import numpy as np import matplotlib as mptl @@ -30,9 +29,15 @@ from yellowbrick.exceptions import ImageComparisonFailure try: - import pandas + import pandas as pd except ImportError: - pandas = None + pd = None + +try: + from unittest import mock +except ImportError: + import mock + ########################################################################## # ScatterViz Base Tests @@ -62,6 +67,9 @@ def tearDown(self): super(ScatterVizTests, self).tearDown() def test_init_alias(self): + """ + Test alias for ScatterViz + """ features = ["temperature", "relative_humidity"] visualizer = ScatterVisualizer(features=features, markers=['*']) self.assertIsNotNone(visualizer.markers) @@ -147,6 +155,26 @@ def test_integrated_scatter(self): visualizer = ScatterViz(features=features) visualizer.fit_transform_poof(X[:, :2], y) + def test_alpha_param(self): + """ + Test that the user can supply an alpha param on instantiation + """ + # Instantiate a scatter plot and provide a custom alpha + visualizer = ScatterVisualizer(alpha=0.7, features=["a", "b"]) + + # Test param gets set correctly + assert visualizer.alpha == 0.7 + + # Mock ax and fit the visualizer + visualizer.ax = mock.MagicMock(autospec=True) + visualizer.fit(self.X[:, :2], self.y) + + # Test that alpha was passed to the scatter plot + _, scatter_kwargs = visualizer.ax.scatter.call_args + assert "alpha" in scatter_kwargs + assert scatter_kwargs["alpha"] == 0.7 + + def test_scatter_quick_method(self): """ Test scatter quick method on the real, occupancy data set @@ -167,8 +195,7 @@ def test_scatter_quick_method(self): # test that is returns a matplotlib obj with axes self.assertIsInstance(ax, mptl.axes.Axes) - @unittest.skipUnless(pandas is not None, - "Pandas is not installed, could not run test.") + @pytest.mark.skipif(pd is None, reason="pandas is required for this test") def test_integrated_scatter_with_pandas(self): """ Test scatterviz on the real, occupancy data set with pandas @@ -180,7 +207,7 @@ def test_integrated_scatter_with_pandas(self): y = self.occupancy['occupancy'].astype(int) # Convert X to a pandas dataframe - X = pandas.DataFrame(X) + X = pd.DataFrame(X) X.columns = [ "temperature", "relative_humidity", "light", "C02", "humidity" ] @@ -210,7 +237,6 @@ def test_integrated_scatter_numpy_named_arrays(self): visualizer.fit_transform_poof(X_named, self.y) self.assertEquals(visualizer.features_, ['one', 'two']) - def test_integrated_scatter_numpy_arrays_no_names(self): """ Test scaterviz on regular numpy arrays diff --git a/tests/test_datasets/test_download.py b/tests/test_datasets/test_download.py new file mode 100644 index 000000000..a2fc2978a --- /dev/null +++ b/tests/test_datasets/test_download.py @@ -0,0 +1,44 @@ +import unittest + +import numpy as np +from sklearn.utils import Bunch + +from yellowbrick.datasets import * + + +class TestDataDownloaders(unittest.TestCase): + """ + Test the dataset loading functions + """ + + def test_load_concrete(self): + data = load_concrete() + self.assertIsInstance(data, np.ndarray) + + def test_load_energy(self): + data = load_energy() + self.assertIsInstance(data, np.ndarray) + + def test_load_occupancy(self): + data = load_occupancy() + self.assertIsInstance(data, np.ndarray) + + def test_load_mushroom(self): + data = load_mushroom() + self.assertIsInstance(data, np.ndarray) + + def test_load_hobbies(self): + data = load_hobbies() + self.assertIsInstance(data, Bunch) + + def test_load_game(self): + data = load_game() + self.assertIsInstance(data, np.ndarray) + + def test_load_bikeshare(self): + data = load_bikeshare() + self.assertIsInstance(data, np.ndarray) + + def test_load_spam(self): + data = load_spam() + self.assertIsInstance(data, np.ndarray) diff --git a/tests/test_draw.py b/tests/test_draw.py new file mode 100644 index 000000000..675371846 --- /dev/null +++ b/tests/test_draw.py @@ -0,0 +1,69 @@ +# tests.test_draw +# Tests for the high-level drawing utility functions +# +# Author: Benjamin Bengfort +# Created: Sun Aug 19 11:21:04 2018 -0400 +# +# ID: test_draw.py [] benjamin@bengfort.com $ + +""" +Tests for the high-level drawing utility functions +""" + +########################################################################## +## Imports +########################################################################## + +import pytest +import numpy as np +import matplotlib.pyplot as plt + +from yellowbrick.draw import * +from .base import VisualTestCase + + +########################################################################## +## Simple tests for high-level drawing utilities +########################################################################## + +def test_manual_legend_uneven_colors(): + """ + Raise exception when colors and labels are mismatched in manual_legend + """ + with pytest.raises(YellowbrickValueError, match="same number of colors as labels"): + manual_legend(None, ('a', 'b', 'c'), ('r', 'g')) + + +########################################################################## +## Visual test cases for high-level drawing utilities +########################################################################## + +class TestDraw(VisualTestCase): + """ + Visual tests for the high-level drawing utilities + """ + + def test_manual_legend(self): + """ + Check that the manual legend is drawn without axes artists + """ + # Draw a random scatter plot + random = np.random.RandomState(42) + + Ax, Ay = random.normal(50, 2, 100), random.normal(50, 3, 100) + Bx, By = random.normal(42, 3, 100), random.normal(44, 1, 100) + Cx, Cy = random.normal(20, 10, 100), random.normal(30, 1, 100) + + + _, ax = plt.subplots() + ax.scatter(Ax, Ay, c='r', alpha=0.35, label='a') + ax.scatter(Bx, By, c='g', alpha=0.35, label='b') + ax.scatter(Cx, Cy, c='b', alpha=0.35, label='c') + + # Add the manual legend + manual_legend( + ax, ('a', 'b', 'c'), ('r', 'g', 'b'), frameon=True, loc='upper left' + ) + + # Assert image similarity + self.assert_images_similar(ax=ax) diff --git a/tests/test_features/test_importances.py b/tests/test_features/test_importances.py index f1466f117..f01905d35 100644 --- a/tests/test_features/test_importances.py +++ b/tests/test_features/test_importances.py @@ -28,8 +28,9 @@ from yellowbrick.exceptions import NotFitted from yellowbrick.features.importances import * -from sklearn.base import BaseEstimator -from sklearn.linear_model import Lasso +from sklearn.datasets import load_iris +from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.linear_model import LogisticRegression, Lasso from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import GradientBoostingClassifier @@ -248,6 +249,24 @@ def test_multi_coefs(self): npt.assert_equal(visualizer.feature_importances_.ndim, 1) + @pytest.mark.xfail( + sys.platform == 'win32', reason="images not close on windows" + ) + def test_multi_coefs_stacked(self): + """ + Test stack plot with multidimensional coefficients + """ + X_iris, y_iris = load_iris(True) + X_iris_pd = pd.DataFrame(X_iris, columns=['f1', 'f2', 'f3', 'f4']) + + viz = FeatureImportances(LogisticRegression(), stack=True) + viz.fit(X_iris_pd, y_iris) + viz.poof() + + npt.assert_equal(viz.feature_importances_.shape, (3, 4)) + self.assert_images_similar(viz) + + @pytest.mark.skipif(pd is None, reason="pandas is required for this test") def test_fit_dataframe(self): """ @@ -350,6 +369,21 @@ def test_find_importances_param_not_found(self): with pytest.raises(YellowbrickTypeError): visualizer._find_importances_param() + def test_find_classes_param_not_found(self): + """ + Raises an exception when classes param not found + """ + model = MockClassifier() + visualizer = FeatureImportances(model) + + assert not hasattr(model, 'classes_') + + e = 'could not find classes_ param on {}'.format( + visualizer.estimator.__class__.__name__ + ) + with pytest.raises(YellowbrickTypeError, match=e): + visualizer._find_classes_param() + def test_xlabel(self): """ Check the various xlabels are sensical @@ -421,3 +455,10 @@ def make_importance_param(self, name='feature_importances_', value=None): def fit(self, X, y=None, **kwargs): return self + + +class MockClassifier(BaseEstimator, ClassifierMixin): + """ + Creates empty classifier. + """ + pass diff --git a/tests/test_features/test_jointplot.py b/tests/test_features/test_jointplot.py index 2ac8dde4a..4182a33af 100644 --- a/tests/test_features/test_jointplot.py +++ b/tests/test_features/test_jointplot.py @@ -24,7 +24,6 @@ import sys import pytest import warnings -import unittest import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt @@ -53,7 +52,7 @@ def setUp(self): def tearDown(self): self.concrete = None - @unittest.skipIf(MPL_VERS_MAJ > 1, "requires matplotlib 1.5.3 or less") + @pytest.mark.skipif(MPL_VERS_MAJ > 1, reason="requires matplotlib 1.5.3 or less") def test_warning(self): """ Ensure that the jointplot warns if mpl version is < 2.0.0 @@ -78,7 +77,8 @@ def test_warning(self): @pytest.mark.xfail( sys.platform == 'win32', reason="images not close on windows" ) - @unittest.skipIf(MPL_VERS_MAJ < 2, "requires matplotlib 2.0.0 or greater") + @pytest.mark.skipif(MPL_VERS_MAJ < 2, reason="requires matplotlib 2.0.0 or greater") + @pytest.mark.filterwarnings("ignore:internal gelsd driver") def test_jointplot_has_no_errors(self): """ Assert no errors occur during jointplot visualizer integration @@ -88,14 +88,13 @@ def test_jointplot_has_no_errors(self): visualizer = JointPlotVisualizer(ax=ax) visualizer.fit(self.X, self.y) - visualizer.poof() - self.assert_images_similar(visualizer) + self.assert_images_similar(visualizer, tol=10) @pytest.mark.xfail( sys.platform == 'win32', reason="images not close on windows" ) - @unittest.skipIf(MPL_VERS_MAJ < 2, "requires matplotlib 2.0.0 or greater") + @pytest.mark.skipif(MPL_VERS_MAJ < 2, reason="requires matplotlib 2.0.0 or greater") def test_jointplot_integrated_has_no_errors(self): """ Test jointplot on the concrete data set @@ -114,12 +113,11 @@ def test_jointplot_integrated_has_no_errors(self): visualizer = JointPlotVisualizer( feature=feature, target=target, joint_plot="hex", ax=ax) visualizer.fit(X, y) - visualizer.poof() - self.assert_images_similar(visualizer) + self.assert_images_similar(visualizer, tol=15) - @unittest.skipIf(MPL_VERS_MAJ < 2, "requires matplotlib 2.0.0 or greater") + @pytest.mark.skipif(MPL_VERS_MAJ < 2, reason="requires matplotlib 2.0.0 or greater") def test_jointplot_no_matplotlib2_warning(self): """ Assert no UserWarning occurs if matplotlib major version >= 2 @@ -129,7 +127,7 @@ def test_jointplot_no_matplotlib2_warning(self): warnings.filterwarnings("always", category=UserWarning) visualizer = JointPlotVisualizer() visualizer.fit(self.X, self.y) - visualizer.poof() + visualizer.finalize() # Filter out user warnings not related to matplotlib version ver_warn_msg = "requires matplotlib major version 2 or greater" diff --git a/tests/test_features/test_manifold.py b/tests/test_features/test_manifold.py index 09cab3e99..f9850c39e 100644 --- a/tests/test_features/test_manifold.py +++ b/tests/test_features/test_manifold.py @@ -92,16 +92,30 @@ def test_manifold_instance_construction(self): oz = Manifold(manifold=manifold) assert oz.manifold is manifold - @patch('yellowbrick.features.manifold.Manifold.draw', spec=True) - def test_manifold_fit(self, mock_draw): + @patch('yellowbrick.features.manifold.Manifold.fit_transform', spec=True) + def test_manifold_fit(self, mock_fit_transform): """ Test manifold fit method """ X, y = make_s_curve(1000, random_state=888) manifold = Manifold(target="auto") - assert not hasattr(manifold, 'fit_time_') assert manifold.fit(X, y) is manifold, "fit did not return self" + mock_fit_transform.assert_called_once() + + @patch('yellowbrick.features.manifold.Manifold.draw', spec=True) + def test_manifold_fit_transform(self, mock_draw): + """ + Test manifold fit_transform method + """ + X, y = make_s_curve(1000, random_state=888) + manifold = Manifold(target="auto") + + assert not hasattr(manifold, 'fit_time_') + + Xp = manifold.fit_transform(X, y) + assert Xp.shape == (X.shape[0], 2) + mock_draw.assert_called_once() assert hasattr(manifold, 'fit_time_') assert manifold._target_color_type == CONTINUOUS @@ -117,8 +131,12 @@ def test_manifold_classification(self): ) oz = Manifold(manifold="spectral", target="discrete", random_state=108) + assert not hasattr(oz, 'classes_') + oz.fit(X, y) + assert hasattr(oz, 'classes_') + assert not hasattr(oz, 'range_') self.assert_images_similar(oz, tol=0.5) def test_manifold_regression(self): @@ -129,9 +147,13 @@ def test_manifold_regression(self): n_samples=300, n_features=7, n_informative=4, random_state=87 ) - oz = Manifold(manifold="lle", target="continuous", random_state=1) + oz = Manifold(manifold="tsne", target="continuous", random_state=1) + assert not hasattr(oz, 'range_') + oz.fit(X, y) + assert not hasattr(oz, 'classes_') + assert hasattr(oz, 'range_') self.assert_images_similar(oz, tol=1.5) def test_manifold_single(self): @@ -142,7 +164,7 @@ def test_manifold_single(self): n_samples=300, n_features=7, centers=3, random_state=1112, ) - oz = Manifold(manifold="modified", random_state=139973) + oz = Manifold(manifold="mds", random_state=139973) oz.fit(X) self.assert_images_similar(oz, tol=5.0) @@ -233,3 +255,15 @@ def test_determine_target_color_type(self): msg = "could not determine target color type" with pytest.raises(YellowbrickValueError, match=msg): manifold._determine_target_color_type([]) + + def test_manifold_no_transform(self): + """ + Test the exception when manifold doesn't implement transform. + """ + X, _ = make_s_curve(1000, random_state=888) + manifold = Manifold(manifold='mds', target="auto") + + assert not hasattr(manifold._manifold, 'transform') + + with pytest.raises(AttributeError, match="try using fit_transform instead"): + manifold.transform(X) diff --git a/tests/test_features/test_pca.py b/tests/test_features/test_pca.py index 232e8e6f8..ed56753f4 100644 --- a/tests/test_features/test_pca.py +++ b/tests/test_features/test_pca.py @@ -184,6 +184,7 @@ def test_scale_true_3d_execption(self): X = np.random.normal(loc=2, size=(100, 2)) params = {'scale': True, 'proj_dim': 3} - with pytest.raises(ValueError, match="n_components=3 must be between 0 and n_features"): + e = r'n_components=3 must be between 0 and min\(n_samples, n_features\)=2' + with pytest.raises(ValueError, match=e): pca = PCADecomposition(**params) pca.fit(X) diff --git a/tests/test_meta.py b/tests/test_meta.py index e93516ec1..8318fc945 100644 --- a/tests/test_meta.py +++ b/tests/test_meta.py @@ -18,7 +18,7 @@ import pytest import inspect -from tests.random import RandomVisualizer +from tests.rand import RandomVisualizer from tests.base import ACTUAL_IMAGES, BASELINE_IMAGES from tests.base import VisualTestCase, ImageComparison diff --git a/tests/test_model_selection/test_cross_validation.py b/tests/test_model_selection/test_cross_validation.py new file mode 100644 index 000000000..516215178 --- /dev/null +++ b/tests/test_model_selection/test_cross_validation.py @@ -0,0 +1,187 @@ +# tests.test_model_selection.test_cross_validation +# Tests for the CVScores visualizer +# +# Author: Rebecca Bilbro +# Created: Fri Aug 10 13:45:11 2018 -0400 +# +# ID: test_cross_validation.py [] bilbro@gmail.com $ + +""" +Tests for the CVScores visualizer +""" + +########################################################################## +## Imports +########################################################################## + +import pytest +import numpy.testing as npt + +from tests.base import VisualTestCase +from tests.dataset import DatasetMixin + +from sklearn.svm import SVC +from sklearn.naive_bayes import BernoulliNB +from sklearn.tree import DecisionTreeRegressor +from sklearn.neighbors import KNeighborsClassifier +from sklearn.model_selection import ShuffleSplit, StratifiedKFold +from sklearn.linear_model import RidgeCV, LogisticRegressionCV + +from yellowbrick.model_selection.cross_validation import * + + +try: + import pandas as pd +except ImportError: + pd = None + +try: + from unittest.mock import patch +except ImportError: + from mock import patch + + +########################################################################## +## Test Cases +########################################################################## + +@pytest.mark.usefixtures("classification", "regression") +class TestCrossValidation(VisualTestCase, DatasetMixin): + """ + Test the CVScores visualizer + """ + + @patch.object(CVScores, 'draw') + def test_fit(self, mock_draw): + """ + Assert that fit returns self and creates expected properties + """ + X, y = self.classification + + params = ("cv_scores_", "cv_scores_mean_") + + oz = CVScores(SVC()) + + for param in params: + assert not hasattr(oz, param) + + assert oz.fit(X, y) is oz + mock_draw.assert_called_once() + + for param in params: + assert hasattr(oz, param) + + def test_classifier(self): + """ + Test image closeness on a classification dataset with kNN + """ + X, y = self.classification + + cv = ShuffleSplit(3, random_state=288) + + oz = CVScores( + KNeighborsClassifier(), cv=cv, scoring='f1_weighted', + ) + + oz.fit(X, y) + oz.poof() + + self.assert_images_similar(oz, tol=2.0) + + def test_classifier_with_cv(self): + """ + Test that CVScores passes cv param to internal sklearn classifier with CV + """ + X, y = self.classification + + cv = ShuffleSplit(3, random_state=288) + + oz_external_cv = CVScores( + LogisticRegressionCV(), cv=cv + ) + + oz_internal_cv = CVScores( + LogisticRegressionCV(cv=cv) + ) + + oz_external_cv.fit(X,y) + oz_internal_cv.fit(X,y) + + npt.assert_array_almost_equal( + oz_external_cv.cv_scores_, oz_internal_cv.cv_scores_, decimal=1 + ) + + def test_regression(self): + """ + Test image closeness on a regression dataset with a DecisionTree + """ + X, y = self.regression + + cv = ShuffleSplit(3, random_state=938) + + oz = CVScores( + DecisionTreeRegressor(random_state=23), cv=cv, scoring='r2', + ) + + oz.fit(X, y) + oz.poof() + + self.assert_images_similar(oz, tol=36.0) + + def test_regressor_with_cv(self): + """ + Test that CVScores passes cv param to internal sklearn regressor with CV + """ + X, y = self.regression + + cv = ShuffleSplit(3, random_state=288) + + oz_external_cv = CVScores( + RidgeCV(), cv=cv + ) + + oz_internal_cv = CVScores( + RidgeCV(cv=cv) + ) + + oz_external_cv.fit(X,y) + oz_internal_cv.fit(X,y) + + npt.assert_array_almost_equal( + oz_external_cv.cv_scores_, oz_internal_cv.cv_scores_ + ) + + def test_quick_method(self): + """ + Test cross validation quick method with image closeness on SVC + """ + X, y = self.classification + + cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=321) + ax = cv_scores(SVC(), X, y, cv=cv) + + self.assert_images_similar(ax=ax, tol=2.0) + + @pytest.mark.skipif(pd is None, reason="test requires pandas") + def test_pandas_integration(self): + """ + Test on mushroom dataset with pandas DataFrame and Series and NB + """ + df = self.load_pandas("mushroom") + + target = "target" + features = [col for col in df.columns if col != target] + + X = pd.get_dummies(df[features]) + y = df[target] + + assert isinstance(X, pd.DataFrame) + assert isinstance(y, pd.Series) + + cv = StratifiedKFold(n_splits=2, random_state=11) + oz = CVScores(BernoulliNB(), cv=cv) + + oz.fit(X, y) + oz.poof() + + self.assert_images_similar(oz, tol=2.0) diff --git a/tests/test_regressor/test_residuals.py b/tests/test_regressor/test_residuals.py index c4afb53ce..4ab36e8c0 100644 --- a/tests/test_regressor/test_residuals.py +++ b/tests/test_regressor/test_residuals.py @@ -42,6 +42,10 @@ except ImportError: pd = None +try: + from unittest import mock +except ImportError: + import mock # Determine version of matplotlib MPL_VERS_MAJ = int(mpl.__version__.split(".")[0]) @@ -188,6 +192,30 @@ def test_peplot_no_lines(self): self.assert_images_similar(visualizer, tol=1.0, remove_legend=True) + def test_alpha_param(self): + """ + Test that the user can supply an alpha param on instantiation + """ + # Instantiate a sklearn regressor + model = Lasso(random_state=23, alpha=10) + # Instantiate a prediction error plot, provide custom alpha + visualizer = PredictionError( + model, bestfit=False, identity=False, alpha=0.7 + ) + + # Test param gets set correctly + assert visualizer.alpha == 0.7 + + # Mock ax and fit the visualizer + visualizer.ax = mock.MagicMock(autospec=True) + visualizer.fit(self.data.X.train, self.data.y.train) + visualizer.score(self.data.X.test, self.data.y.test) + + # Test that alpha was passed to internal matplotlib scatterplot + _, scatter_kwargs = visualizer.ax.scatter.call_args + assert "alpha" in scatter_kwargs + assert scatter_kwargs["alpha"] == 0.7 + ########################################################################## ## Residuals Plot Test Cases @@ -323,3 +351,25 @@ def test_score(self): assert score == pytest.approx(0.9999888484, rel=1e-4) assert visualizer.train_score_ == pytest.approx(0.9999906, rel=1e-4) assert visualizer.test_score_ == score + + @mock.patch('yellowbrick.regressor.residuals.plt.sca', autospec=True) + def test_alpha_param(self, mock_sca): + """ + Test that the user can supply an alpha param on instantiation + """ + # Instantiate a prediction error plot, provide custom alpha + visualizer = ResidualsPlot( + Ridge(random_state=8893), alpha=0.3, hist=False + ) + + # Test param gets set correctly + assert visualizer.alpha == 0.3 + + visualizer.ax = mock.MagicMock() + visualizer.fit(self.data.X.train, self.data.y.train) + visualizer.score(self.data.X.test, self.data.y.test) + + # Test that alpha was passed to internal matplotlib scatterplot + _, scatter_kwargs = visualizer.ax.scatter.call_args + assert "alpha" in scatter_kwargs + assert scatter_kwargs["alpha"] == 0.3 diff --git a/tests/test_target/__init__.py b/tests/test_target/__init__.py new file mode 100644 index 000000000..6a1059b01 --- /dev/null +++ b/tests/test_target/__init__.py @@ -0,0 +1,15 @@ +# tests.test_target +# Tests for the target module. +# +# Author: Benjamin Bengfort +# Created: Thu Jul 19 09:09:07 2018 -0400 +# +# ID: __init__.py [] benjamin@bengfort.com $ + +""" +Tests for the target module. +""" + +########################################################################## +## Imports +########################################################################## diff --git a/tests/test_target/test_binning.py b/tests/test_target/test_binning.py new file mode 100644 index 000000000..94d33c5b1 --- /dev/null +++ b/tests/test_target/test_binning.py @@ -0,0 +1,39 @@ +# tests.test_target.test_binning +# Tests for the BalancedBinningReference visualizer +# +# Author: Juan L. Kehoe (juanluo2008@gmail.com) +# Author: Prema Damodaran Roman (pdamo24@gmail.com) +# Created: Thu Jul 20 10:21:49 2018 -0400 +# +# ID: test_binning.py + +from tests.base import VisualTestCase +from tests.dataset import DatasetMixin +from yellowbrick.target.binning import * + +########################################################################## +## BalancedBinningReference Tests +########################################################################## + +class TestBalancedBinningReference(VisualTestCase, DatasetMixin): + """ + Test the BalancedBinningReference visualizer + """ + + def test_balancedbinningreference(self): + """ + Test Histogram on a real dataset + """ + # Load the data from the fixture + dataset = self.load_data('occupancy') + + # Get the data + y = dataset["temperature"] + + + visualizer = BalancedBinningReference() + visualizer.fit(y) + visualizer.poof() + self.assert_images_similar(visualizer, tol=0.5) + + \ No newline at end of file diff --git a/tests/test_target/test_class_balance.py b/tests/test_target/test_class_balance.py new file mode 100644 index 000000000..43a2e76d2 --- /dev/null +++ b/tests/test_target/test_class_balance.py @@ -0,0 +1,211 @@ +# tests.test_target.test_class_balance +# Tests for the ClassBalance visualizer +# +# Author: Benjamin Bengfort +# Created: Thu Jul 19 10:21:49 2018 -0400 +# +# ID: test_class_balance.py [] benjamin@bengfort.com $ + +""" +Tests for the ClassBalance visualizer +""" + +########################################################################## +## Imports +########################################################################## + +import pytest +import numpy as np + +from yellowbrick.target.class_balance import * +from yellowbrick.exceptions import YellowbrickValueError + +from tests.base import VisualTestCase +from tests.dataset import DatasetMixin, Dataset, Split + +from sklearn.datasets import make_classification +from sklearn.model_selection import train_test_split as tts + +try: + import pandas as pd +except ImportError: + pd = None + + +########################################################################## +## Data Fixtures +########################################################################## + +def make_fixture(binary=False, balanced=False, split=False): + """ + Make a dataset for testing ClassBalance based on the specified params. + """ + kwargs = { + "n_samples":100, "n_features":20, "n_informative":8, "n_redundant":2, + "n_clusters_per_class":1, "random_state":89092, + } + + if binary: + kwargs['n_classes'] = 2 + kwargs['weights'] = None if balanced else [0.3, 0.7] + else: + kwargs['n_classes'] = 5 + kwargs['weights'] = None if balanced else [0.1, 0.2, 0.4, 0.2, .01] + + X, y = make_classification(**kwargs) + + if split: + X_train, X_test, y_train, y_test = tts( + X, y, test_size=0.2, random_state=101 + ) + return Dataset(Split(X_train, X_test), Split(y_train, y_test)) + + return Dataset(X, y) + + +########################################################################## +## Tests +########################################################################## + +class ClassBalanceTests(VisualTestCase, DatasetMixin): + """ + Test ClassBalance visualizer + """ + + def test_signature_exception(self): + """ + An exception is raised if X and y are put into the visualizer + """ + oz = ClassBalance() + dataset = make_fixture(split=False) + + message = "fit has changed to only require a 1D array, y" + with pytest.raises(YellowbrickValueError, match=message): + oz.fit(dataset.X, dataset.y) + + def test_invalid_target(self): + """ + A value error should be raised on invalid train or test target + """ + y_valid = np.random.randint(2, size=100) + y_invalid = np.random.uniform(size=100) + + oz = ClassBalance() + + with pytest.raises(YellowbrickValueError): + oz.fit(y_invalid) + + with pytest.raises(YellowbrickValueError): + oz.fit(y_valid, y_invalid) + + def test_class_names_must_match(self): + """ + Assert error raised when more classes are in data than specified + """ + oz = ClassBalance(labels=["a", "b", "c"]) + dataset = make_fixture(binary=False, split=False) + + with pytest.raises(YellowbrickValueError): + oz.fit(dataset.y) + + def test_binary_balance(self): + """ + Test binary classification in balance mode + """ + dataset = make_fixture(binary=True, split=False) + + oz = ClassBalance() + assert oz.fit(dataset.y) is oz + assert oz._mode == BALANCE + + #oz.finalize() + self.assert_images_similar(oz) + + def test_binary_compare(self): + """ + Test binary classification in compare mode + """ + dataset = make_fixture(binary=True, split=True) + + oz = ClassBalance() + assert oz.fit(dataset.y.train, dataset.y.test) is oz + assert oz._mode == COMPARE + + #oz.finalize() + self.assert_images_similar(oz) + + def test_multiclass_balance(self): + """ + Test multiclass classification in balance mode + """ + dataset = make_fixture(binary=False, split=False) + + oz = ClassBalance() + assert oz.fit(dataset.y) is oz + assert oz._mode == BALANCE + + #oz.finalize() + self.assert_images_similar(oz) + + def test_multiclass_compare(self): + """ + Test multiclass classification in compare mode + """ + dataset = make_fixture(binary=False, split=True) + + oz = ClassBalance() + assert oz.fit(dataset.y.train, dataset.y.test) is oz + assert oz._mode == COMPARE + + #oz.finalize() + self.assert_images_similar(oz) + + @pytest.mark.skipif(pd is None, reason="test requires pandas") + def test_pandas_occupancy_balance(self): + """ + Test pandas data frame with string target in balance mode + """ + data = self.load_data("occupancy") + y = pd.Series([ + "occupied" if yi else "unoccupied" for yi in data['occupancy'] + ]) + + # Create and fit the visualizer + oz = ClassBalance() + assert oz.fit(y) is oz + + #oz.finalize() + self.assert_images_similar(oz) + + @pytest.mark.skipif(pd is None, reason="test requires pandas") + def test_pandas_occupancy_compare(self): + """ + Test pandas data frame with string target in compare mode + """ + data = self.load_data("occupancy") + features = [ + "temperature", "relative_humidity", "light", "C02", "humidity" + ] + + X = pd.DataFrame(data[features]) + y = pd.Series([ + "occupied" if yi else "unoccupied" for yi in data['occupancy'] + ]) + + _, _, y_train, y_test = tts(X, y, test_size=0.4, random_state=2242) + + # Create and fit the visualizer + oz = ClassBalance() + assert oz.fit(y_train, y_test) is oz + + #oz.finalize() + self.assert_images_similar(oz) + + def test_quick_method(self): + """ + Test the quick method with + """ + dataset = make_fixture(binary=False, split=False) + + ax = class_balance(dataset.y) + self.assert_images_similar(ax=ax, tol=0.5) diff --git a/tests/test_target/test_feature_correlation.py b/tests/test_target/test_feature_correlation.py new file mode 100644 index 000000000..b9ba6af7a --- /dev/null +++ b/tests/test_target/test_feature_correlation.py @@ -0,0 +1,210 @@ +# tests.test_features.test_feature_correlation +# Test the feature correlation visualizers +# +# Author: Zijie (ZJ) Poh +# Created: Tue Jul 31 20:21:32 2018 -0700 +# +# Copyright (C) 2018 District Data Labs +# For license information, see LICENSE.txt +# +# ID: test_feature_correlation.py [] poh.zijie@gmail.com $ + +""" +Test the feature correlation to dependent variable visualizer. +""" + +########################################################################## +## Imports +########################################################################## + +import sys +import pytest +import numpy as np +try: + import pandas as pd +except ImportError: + pd = None +import numpy.testing as npt +import matplotlib.pyplot as plt + +from yellowbrick.target import FeatureCorrelation, feature_correlation +from yellowbrick.exceptions import YellowbrickValueError, YellowbrickWarning + +from sklearn import datasets + +from tests.base import VisualTestCase + + +########################################################################## +## Feature Correlation Tests +########################################################################## + +class TestFeatureCorrelationVisualizer(VisualTestCase): + """ + FeatureCorrelation visualizer + """ + + data = datasets.load_diabetes() + X, y = data['data'], data['target'] + labels = data['feature_names'] + + @pytest.mark.xfail( + sys.platform == 'win32', reason="images not close on windows" + ) + def test_feature_correlation_integrated_pearson(self): + """ + Test FeatureCorrelation visualizer with pearson correlation + coefficient + """ + viz = FeatureCorrelation() + viz.fit(self.X, self.y) + viz.poof() + + self.assert_images_similar(viz) + + @pytest.mark.xfail( + sys.platform == 'win32', reason="images not close on windows" + ) + def test_feature_correlation_integrated_mutual_info_regression(self): + """ + Test FeatureCorrelation visualizer with mutual information regression + """ + viz = FeatureCorrelation(method='mutual_info-regression') + viz.fit(self.X, self.y, random_state=23456) + viz.poof() + + self.assert_images_similar(viz) + + @pytest.mark.xfail( + sys.platform == 'win32', reason="images not close on windows" + ) + def test_feature_correlation_integrated_mutual_info_classification(self): + """ + Test FeatureCorrelation visualizer with mutual information + on wine dataset (classification) + """ + data = datasets.load_wine() + X, y = data['data'], data['target'] + + viz = FeatureCorrelation(method='mutual_info-classification') + viz.fit(X, y, random_state=12345) + viz.poof() + + self.assert_images_similar(viz) + + def test_feature_correlation_method_not_implemented(self): + """ + Test FeatureCorrelation visualizer with unknown method + """ + method = 'foo' + e = ('Method foo not implement; choose from *') + with pytest.raises(YellowbrickValueError, match=e): + FeatureCorrelation(method=method) + + def test_feature_correlation_labels_from_index(self): + """ + Test getting feature labels from index + """ + viz = FeatureCorrelation() + viz.fit(self.X, self.y) + + npt.assert_array_equal(viz.features_, np.arange(self.X.shape[1])) + + def test_feature_correlation_labels(self): + """ + Test labels as feature labels + """ + viz = FeatureCorrelation(labels=self.labels) + viz.fit(self.X, self.y) + + npt.assert_array_equal(viz.features_, self.labels) + + @pytest.mark.skipif(pd is None, reason="requires pandas") + def test_feature_correlation_labels_from_dataframe(self): + """ + Test getting feature labels from DataFrame + """ + X_pd = pd.DataFrame(self.X, columns=self.labels) + + viz = FeatureCorrelation() + viz.fit(X_pd, self.y) + + npt.assert_array_equal(viz.features_, self.labels) + + def test_feature_correlation_select_feature_by_index_out_of_range(self): + """ + Test selecting feature by feature index but index is out of range + """ + e = 'Feature index is out of range' + with pytest.raises(YellowbrickValueError, match=e): + viz = FeatureCorrelation(feature_index=[0, 2, 10]) + viz.fit(self.X, self.y) + + def test_feature_correlation_select_feature_by_index(self): + """ + Test selecting feature by index + """ + viz = FeatureCorrelation(feature_index=[0, 2, 3]) + viz.fit(self.X, self.y) + + assert viz.scores_.shape[0] == 3 + + def test_feature_correlation_select_feature_by_index_and_name(self): + """ + Test selecting feature warning when both index and names are provided + """ + feature_index = [0, 2, 3] + feature_names = ['age'] + + e = ('Both feature_index and feature_names are specified. ' + 'feature_names is ignored') + with pytest.raises(YellowbrickWarning, match=e): + viz = FeatureCorrelation(feature_index=feature_index, + feature_names=feature_names) + viz.fit(self.X, self.y) + assert viz.scores_.shape[0] == 3 + + def test_feature_correlation_select_feature_by_name_no_labels(self): + """ + Test selecting feature by feature names with labels is not supplied + """ + feature_names = ['age'] + + e = 'age not in labels' + with pytest.raises(YellowbrickValueError, match=e): + viz = FeatureCorrelation(feature_names=feature_names) + viz.fit(self.X, self.y) + + def test_feature_correlation_select_feature_by_name(self): + """ + Test selecting feature by feature names + """ + feature_names = ['age', 'sex', 'bp', 's5'] + + viz = FeatureCorrelation(labels=self.labels, + feature_names=feature_names) + viz.fit(self.X, self.y) + + npt.assert_array_equal(viz.features_, feature_names) + + def test_feature_correlation_sort(self): + """ + Test sorting of correlation + """ + viz = FeatureCorrelation(sort=True) + viz.fit(self.X, self.y) + + assert np.all(viz.scores_[:-1] <= viz.scores_[1:]) + + @pytest.mark.xfail( + sys.platform == 'win32', reason="images not close on windows" + ) + def test_feature_correlation_quick_method(self): + """ + Test sorting of correlation + """ + fig = plt.figure() + ax = fig.add_subplot() + g = feature_correlation.feature_correlation(self.X, self.y, ax) + + self.assert_images_similar(ax=g) diff --git a/tests/test_text/test_dispersion.py b/tests/test_text/test_dispersion.py index 7ae7ac9c8..ec94eceb7 100644 --- a/tests/test_text/test_dispersion.py +++ b/tests/test_text/test_dispersion.py @@ -18,19 +18,17 @@ ## Imports ########################################################################## -import sys import pytest +from yellowbrick.exceptions import YellowbrickValueError from yellowbrick.text.dispersion import * from tests.dataset import DatasetMixin from tests.base import VisualTestCase -from itertools import chain ########################################################################## ## DispersionPlot Tests ########################################################################## -@pytest.mark.xfail(sys.platform == "win32", reason="Issue #491") class DispersionPlotTests(VisualTestCase, DatasetMixin): def test_integrated_dispersionplot(self): @@ -38,8 +36,8 @@ def test_integrated_dispersionplot(self): Assert no errors occur during DispersionPlot integration """ corpus = self.load_data('hobbies') - - text = [word for doc in corpus.data for word in doc.split()] + + text = [doc.split() for doc in corpus.data] target_words = ['Game', 'player', 'score', 'oil', 'Man'] visualizer = DispersionPlot(target_words) @@ -54,8 +52,8 @@ def test_dispersionplot_ignore_case(self): with ignore_case parameter turned on """ corpus = self.load_data('hobbies') - - text = [word for doc in corpus.data for word in doc.split()] + + text = [doc.split() for doc in corpus.data] target_words = ['Game', 'player', 'score', 'oil', 'Man'] visualizer = DispersionPlot(target_words, ignore_case=True) @@ -71,7 +69,7 @@ def test_dispersionplot_generator_input(self): """ corpus = self.load_data('hobbies') - text = chain(*map(str.split, corpus.data)) + text = (doc.split() for doc in corpus.data) target_words = ['Game', 'player', 'score', 'oil', 'Man'] visualizer = DispersionPlot(target_words, ignore_case=True) @@ -79,4 +77,59 @@ def test_dispersionplot_generator_input(self): visualizer.ax.grid(False) self.assert_images_similar(visualizer, tol=25) - + + def test_dispersionplot_annotate_docs(self): + """ + Assert no errors occur during DispersionPlot integration + with annotate_docs parameter turned on + """ + corpus = self.load_data('hobbies') + + text = [doc.split() for doc in corpus.data] + target_words = ['girl', 'she', 'boy', 'he', 'man'] + + visualizer = DispersionPlot(target_words, annotate_docs=True) + visualizer.fit(text) + visualizer.ax.grid(False) + + self.assert_images_similar(visualizer, tol=25) + + def test_dispersionplot_color_words_by_class(self): + """ + Assert no errors occur during DispersionPlot integration + when target values are specified + """ + corpus = self.load_data('hobbies') + + text = (doc.split() for doc in corpus.data) + target_words = ['girl', 'she', 'boy', 'he', 'man'] + + target_values = corpus.target + + visualizer = DispersionPlot(target_words) + visualizer.fit(text, target_values) + visualizer.ax.grid(False) + + self.assert_images_similar(visualizer, tol=25) + + def test_dispersionplot_mismatched_labels(self): + """ + Assert exception is raised when number of labels doesn't match + """ + corpus = self.load_data('hobbies') + + text = (doc.split() for doc in corpus.data) + target_words = ['girl', 'she', 'boy', 'he', 'man'] + + target_values = corpus.target + + visualizer = DispersionPlot(target_words, annotate_docs=True, + labels=['a', 'b']) + + msg = ( + r'number of supplied labels \(\d\) ' + r'does not match the number of classes \(\d\)' + ) + + with pytest.raises(YellowbrickValueError, match=msg): + visualizer.fit(text, target_values) diff --git a/tests/test_text/test_freqdist.py b/tests/test_text/test_freqdist.py index a7364f670..e687d8b28 100644 --- a/tests/test_text/test_freqdist.py +++ b/tests/test_text/test_freqdist.py @@ -50,4 +50,4 @@ def test_integrated_freqdist(self): visualizer.fit(docs) visualizer.poof() - self.assert_images_similar(visualizer) + self.assert_images_similar(visualizer, tol=1) diff --git a/tests/test_text/test_tsne.py b/tests/test_text/test_tsne.py index 44f4de7f7..9cde779e5 100644 --- a/tests/test_text/test_tsne.py +++ b/tests/test_text/test_tsne.py @@ -17,7 +17,6 @@ ## Imports ########################################################################## -import sys import six import pytest @@ -26,6 +25,7 @@ from tests.dataset import DatasetMixin from yellowbrick.exceptions import YellowbrickValueError +from sklearn.manifold import TSNE from sklearn.datasets import make_classification from sklearn.feature_extraction.text import TfidfVectorizer @@ -34,6 +34,10 @@ except ImportError: pandas = None +try: + from unittest import mock +except ImportError: + import mock ########################################################################## ## TSNE Tests @@ -68,9 +72,6 @@ def test_make_pipeline(self): none = tsne.make_transformer(None) assert len(none.steps) == 1 - @pytest.mark.xfail( - sys.platform == 'win32', reason="unicode decode error" - ) def test_integrated_tsne(self): """ Check tSNE integrated visualization on the hobbies corpus @@ -81,12 +82,50 @@ def test_integrated_tsne(self): docs = tfidf.fit_transform(corpus.data) labels = corpus.target - tsne = TSNEVisualizer(random_state=8392, colormap='Set1') + tsne = TSNEVisualizer(random_state=8392, colormap='Set1', alpha=1.0) tsne.fit_transform(docs, labels) - tol = 40 if six.PY3 else 55 + tol = 50 if six.PY3 else 55 self.assert_images_similar(tsne, tol=tol) + def test_sklearn_tsne_size(self): + """ + Check to make sure sklearn's TSNE doesn't use the size param + """ + # In TSNEVisualizer, the internal sklearn TSNE transform consumes + # some but not all kwargs passed in by user. Those not in get_params(), + # like size, are passed through to YB's finalize method. This test should + # notify us if TSNE's params change on the sklearn side. + with pytest.raises(TypeError): + TSNE(size=(100,100)) + + def test_sklearn_tsne_title(self): + """ + Check to make sure sklearn's TSNE doesn't use the title param + """ + # In TSNEVisualizer, the internal sklearn TSNE transform consumes + # some but not all kwargs passed in by user. Those not in get_params(), + # like title, are passed through to YB's finalize method. This test should + # notify us if TSNE's params change on the sklearn side. + with pytest.raises(TypeError): + TSNE(title="custom_title") + + def test_custom_title_tsne(self): + """ + Check tSNE can accept a custom title (string) from the user + """ + tsne = TSNEVisualizer(title="custom_title") + + assert tsne.title == "custom_title" + + def test_custom_size_tsne(self): + """ + Check tSNE can accept a custom size (tuple of pixels) from the user + """ + tsne = TSNEVisualizer(size=(100, 50)) + + assert tsne._size == (100, 50) + def test_make_classification_tsne(self): """ Test tSNE integrated visualization on a sklearn classifier dataset @@ -140,7 +179,6 @@ def test_tsne_mismtached_labels(self): with pytest.raises(YellowbrickValueError): tsne.fit(X,y) - def test_no_target_tsne(self): """ Test tSNE when no target or classes are specified @@ -174,3 +212,27 @@ def test_visualizer_with_pandas(self): tol = 0.1 if six.PY3 else 40 self.assert_images_similar(tsne, tol=tol) + + def test_alpha_param(self): + """ + Test that the user can supply an alpha param on instantiation + """ + ## produce random data + X, y = make_classification(n_samples=200, n_features=100, + n_informative=20, n_redundant=10, + n_classes=3, random_state=42) + + ## Instantiate a TSNEVisualizer, provide custom alpha + tsne = TSNEVisualizer(random_state=64, alpha=0.5) + + # Test param gets set correctly + assert tsne.alpha == 0.5 + + # Mock ax and fit the visualizer + tsne.ax = mock.MagicMock(autospec=True) + tsne.fit(X, y) + + # Test that alpha was passed to internal matplotlib scatterplot + _, scatter_kwargs = tsne.ax.scatter.call_args + assert "alpha" in scatter_kwargs + assert scatter_kwargs["alpha"] == 0.5 diff --git a/tests/test_utils/test_helpers.py b/tests/test_utils/test_helpers.py index b1a0fc103..6c4fe9f62 100644 --- a/tests/test_utils/test_helpers.py +++ b/tests/test_utils/test_helpers.py @@ -47,7 +47,7 @@ class TestHelpers(object): (KNeighborsClassifier, 'KNeighborsClassifier'), (KMeans, 'KMeans'), (RandomForestClassifier, 'RandomForestClassifier'), - ], ids=lambda i: i[0]) + ], ids=["LassoCV", "KNeighborsClassifier", "KMeans", "RandomForestClassifier"]) def test_real_model(self, model, name): """ Test getting model name for sklearn estimators @@ -128,6 +128,55 @@ def test_div_scalar_by_scalar(self): with pytest.raises(ValueError): div_safe(5, 0) + def test_prop_to_size_list(self): + """ + Test prop to size correctly returns scaled values for a list + """ + # Hieghts (in cm) of U.S. Presidents in order of term until Lincoln + heights = [188, 170, 189, 163, 183, 171, 185, 168, 173, 183, 173, 173, 175, 178, 183, 193] + sizes = prop_to_size(heights, mi=1, ma=10, log=False, power=0.33) + + npt.assert_array_almost_equal(sizes, np.array([ + 9.47447296, 6.56768746, 9.58486955, 1. , 8.87285756, + 6.81851544, 9.12441277, 5.98256068, 7.26314542, 8.87285756, + 7.26314542, 7.26314542, 7.65154152, 8.15982835, 8.87285756, + 10. + ])) + + def test_prop_to_size_log(self): + """ + Test prop to size returns natural log scaled values + """ + # Hieghts (in cm) of U.S. Presidents in order of term until Lincoln + heights = [188, 170, 189, 163, 183, 171, 185, 168, 173, 183, 173, 173, 175, 178, 183, 193] + sizes = prop_to_size(heights, mi=1, ma=10, log=True, power=0.5) + + npt.assert_array_almost_equal(sizes, np.array([ + 9.271337, 5.49004 , 9.423692, 1. , 8.449214, 5.792968, + 8.791172, 4.806088, 6.343007, 8.449214, 6.343007, 6.343007, + 6.835994, 7.496806, 8.449214, 10. + ])) + + def test_prop_to_size_default(self): + """ + Test the default values of prop to size are correct + """ + vals = np.random.normal(50, 23, 500) + sizes = prop_to_size(vals) + + assert sizes.ndim == vals.ndim + assert sizes.shape == vals.shape + assert sizes.max() <= 5.0 + assert sizes.min() >= 0.0 + + def test_prop_to_size_zero_division(self): + """ + Ensure that prop to size does not cause division by zero errors + """ + vals = [8]*8 + sizes = prop_to_size(vals) + npt.assert_array_equal(sizes, [0]*8) + ########################################################################## ## Features/Array Tests diff --git a/tests/test_utils/test_timer.py b/tests/test_utils/test_timer.py new file mode 100644 index 000000000..31763b84c --- /dev/null +++ b/tests/test_utils/test_timer.py @@ -0,0 +1,49 @@ +# tests.test_utils.test_timer +# Tests for the stand alone timer functions in Yellowbrick utils. +# +# Author: ZJ Poh +# Created: Tue Jul 17 21:11:11 2018 -0700 +# +# Copyright (C) 2017 District Data Labs +# For license information, see LICENSE.txt +""" +Tests for the stand alone timer functions in Yellowbrick utils. +""" + +########################################################################## +## Imports +########################################################################## + +import pytest +try: + from unittest import mock +except ImportError: + import mock + +from yellowbrick.utils.timer import * + +########################################################################## +## Helper Function Tests +########################################################################## + +class TestTimer(object): + """ + Timer functions and utilities + """ + + @mock.patch('time.time', mock.Mock(side_effect=[1234.2, 1242.8])) + def test_timer(self): + with Timer() as timer: + pass + assert isinstance(timer.interval, float) + assert timer.interval == pytest.approx(8.6) + + +@pytest.mark.parametrize('s,expected', [ + (1.01, '00:00:01.0100'), + (61.01, '00:01:01.0100'), + (3661.01, '01:01:01.0100'), + (360061.01, '100:01:01.0100') +]) +def test_human_readable_time(s, expected): + assert human_readable_time(s) == expected diff --git a/tests/test_utils/test_types.py b/tests/test_utils/test_types.py index 7bed945aa..17d5ff033 100644 --- a/tests/test_utils/test_types.py +++ b/tests/test_utils/test_types.py @@ -77,13 +77,14 @@ ] # Import Transformers +from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.feature_extraction import DictVectorizer from sklearn.preprocessing import QuantileTransformer -from sklearn.preprocessing import StandardScaler, Imputer -from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.preprocessing import StandardScaler +from sklearn.impute import SimpleImputer TRANSFORMERS = [ - DictVectorizer, QuantileTransformer, StandardScaler, Imputer, + DictVectorizer, QuantileTransformer, StandardScaler, SimpleImputer, TfidfVectorizer, ] @@ -186,7 +187,7 @@ def test_is_estimator_search(self): (Visualizer, {}), (ScoreVisualizer, {'model': LinearRegression()}), (ModelVisualizer, {'model': LogisticRegression()}) - ], ids=lambda i: obj_name(i[0])) + ], ids=["Visualizer", "ScoreVisualizer", "ModelVisualizer"]) def test_is_estimator_visualizer(self, viz, params): """ Test that is_estimator works for Visualizers @@ -259,7 +260,7 @@ def test_is_regressor_search(self): @pytest.mark.parametrize("viz,params", [ (ScoreVisualizer, {'model': LinearRegression()}), (ModelVisualizer, {'model': Ridge()}) - ], ids=lambda i: obj_name(i[0])) + ], ids=["ScoreVisualizer", "ModelVisualizer"]) def test_is_regressor_visualizer(self, viz, params): """ Test that is_regressor works on visualizers @@ -332,7 +333,7 @@ def test_is_classifier_search(self): @pytest.mark.parametrize("viz,params", [ (ScoreVisualizer, {'model': MultinomialNB()}), (ModelVisualizer, {'model': MLPClassifier()}) - ], ids=lambda i: obj_name(i[0])) + ], ids=["ScoreVisualizer", "ModelVisualizer"]) def test_is_classifier_visualizer(self, viz, params): """ Test that is_classifier works on visualizers @@ -393,7 +394,7 @@ def test_clusterer_pipeline(self): @pytest.mark.parametrize("viz,params", [ (ModelVisualizer, {'model': KMeans()}) - ], ids=lambda i: obj_name(i[0])) + ], ids=["ModelVisualizer"]) def test_is_clusterer_visualizer(self, viz, params): """ Test that is_clusterer works on visualizers @@ -426,7 +427,7 @@ def test_is_gridsearch(self, model): assert is_gridsearch(obj) @pytest.mark.parametrize("model", - [MLPRegressor, MLPClassifier, Imputer], ids=obj_name) + [MLPRegressor, MLPClassifier, SimpleImputer], ids=obj_name) def test_not_is_gridsearch(self, model): """ Test that is_gridsearch does not match non grid searches @@ -462,7 +463,7 @@ def test_is_probabilistic(self, model): assert is_probabilistic(obj) @pytest.mark.parametrize("model", [ - MLPRegressor, Imputer, StandardScaler, KMeans, + MLPRegressor, SimpleImputer, StandardScaler, KMeans, RandomForestRegressor, ], ids=obj_name) def test_not_is_probabilistic(self, model): diff --git a/yellowbrick/anscombe.py b/yellowbrick/anscombe.py index 95389fe27..3406aeba7 100644 --- a/yellowbrick/anscombe.py +++ b/yellowbrick/anscombe.py @@ -52,8 +52,9 @@ def anscombe(): """ Creates 2x2 grid plot of the 4 anscombe datasets for illustration. """ - fig, ((axa, axb), (axc, axd)) = plt.subplots(2, 2, sharex='col', sharey='row') + _, ((axa, axb), (axc, axd)) = plt.subplots(2, 2, sharex='col', sharey='row') colors = get_color_cycle() + for arr, ax, color in zip(ANSCOMBE, (axa, axb, axc, axd), colors): x = arr[0] y = arr[1] diff --git a/yellowbrick/base.py b/yellowbrick/base.py index 28579486f..4652b1d47 100644 --- a/yellowbrick/base.py +++ b/yellowbrick/base.py @@ -181,7 +181,7 @@ def finalize(self, **kwargs): """ return self.ax - def poof(self, outpath=None, **kwargs): + def poof(self, outpath=None, clear_figure=False, **kwargs): """ Poof makes the magic happen and a visualizer appear! You can pass in a path to save the figure to disk with various backends, or you can @@ -191,7 +191,11 @@ def poof(self, outpath=None, **kwargs): Parameters ---------- outpath: string, default: None - path or None. Save figure to disk or if None show in window + path or None. Save figure to disk or if None show in window + + clear_figure: boolean, default: False + When True, this flag clears the figure after saving to file or + showing on screen. This is useful when making consecutive plots. kwargs: dict generic keyword arguments. @@ -212,6 +216,9 @@ def poof(self, outpath=None, **kwargs): else: plt.show() + if clear_figure: + plt.gcf().clear() + ##//////////////////////////////////////////////////////////////////// ## Helper Functions ##//////////////////////////////////////////////////////////////////// @@ -533,7 +540,7 @@ def score(self,X,y): return self - def poof(self, outpath=None, **kwargs): + def poof(self, outpath=None, clear_figure=False, **kwargs): if self.axarr is None: return @@ -552,3 +559,6 @@ def poof(self, outpath=None, **kwargs): plt.savefig(outpath, **kwargs) else: plt.show() + + if clear_figure: + plt.gcf().clear() diff --git a/yellowbrick/classifier/__init__.py b/yellowbrick/classifier/__init__.py index 8e7e8f029..ad7e932ac 100644 --- a/yellowbrick/classifier/__init__.py +++ b/yellowbrick/classifier/__init__.py @@ -23,8 +23,12 @@ ## Hoist visualizers into the classifier namespace from ..base import ScoreVisualizer from .base import ClassificationScoreVisualizer -from .class_balance import ClassBalance, ClassPredictionError +from .class_prediction_error import ClassPredictionError, class_prediction_error from .classification_report import ClassificationReport, classification_report from .confusion_matrix import ConfusionMatrix, confusion_matrix from .rocauc import ROCAUC, roc_auc from .threshold import DiscriminationThreshold, discrimination_threshold +from .prcurve import PrecisionRecallCurve, PRCurve, precision_recall_curve + +## Import from target for backward compatibility and classifier association +from ..target.class_balance import ClassBalance, class_balance diff --git a/yellowbrick/classifier/base.py b/yellowbrick/classifier/base.py index 5755593f6..d581b2f48 100644 --- a/yellowbrick/classifier/base.py +++ b/yellowbrick/classifier/base.py @@ -110,6 +110,28 @@ def fit(self, X, y=None, **kwargs): # Always return self from fit return self + + def score(self, X, y, **kwargs): + """ + The score function is the hook for visual interaction. Pass in test + data and the visualizer will create predictions on the data and + evaluate them with respect to the test values. The evaluation will + then be passed to draw() and the result of the estimator score will + be returned. + Parameters + ---------- + X : array-like + X (also X_test) are the dependent variables of test set to predict + y : array-like + y (also y_test) is the independent actual variables to score against + Returns + ------- + score : float + """ + self.score_ = self.estimator.score(X, y, **kwargs) + + return self.score_ + #TODO during refactoring this can be used to generalize ClassBalance def class_counts(self, y): unique, counts = np.unique(y, return_counts=True) diff --git a/yellowbrick/classifier/class_balance.py b/yellowbrick/classifier/class_prediction_error.py similarity index 51% rename from yellowbrick/classifier/class_balance.py rename to yellowbrick/classifier/class_prediction_error.py index 1e13ebac0..b9919becb 100644 --- a/yellowbrick/classifier/class_balance.py +++ b/yellowbrick/classifier/class_prediction_error.py @@ -1,182 +1,33 @@ -# yellowbrick.classifier.class_balance -# Class balance visualizer for showing per-class support. +# yellowbrick.classifier.class_prediction_error +# Shows the balance of classes and their associated predictions. # -# Author: Rebecca Bilbro -# Author: Benjamin Bengfort -# Author: Neal Humphrey # Author: Larry Gray +# Author: Benjamin Bengfort # Created: Wed May 18 12:39:40 2016 -0400 # -# Copyright (C) 2017 District Data Labs -# For license information, see LICENSE.txt -# -# ID: class_balance.py [5388065] neal@nhumphrey.com $ +# ID: class_prediction_error.py [] lwgray@gmail.com $ """ -Class balance visualizer for showing per-class support. +Shows the balance of classes and their associated predictions. """ ########################################################################## ## Imports ########################################################################## -import matplotlib.pyplot as plt import numpy as np +import matplotlib.pyplot as plt from .base import ClassificationScoreVisualizer -from sklearn.model_selection import train_test_split -from sklearn.metrics import precision_recall_fscore_support from sklearn.utils.multiclass import unique_labels from sklearn.metrics.classification import _check_targets +from sklearn.model_selection import train_test_split as tts from ..exceptions import ModelError, YellowbrickValueError from ..style.colors import resolve_colors -########################################################################## -## Class Balance Chart -########################################################################## - -class ClassBalance(ClassificationScoreVisualizer): - """ - Class balance chart that shows the support for each class in the - fitted classification model displayed as a bar plot. It is initialized - with a fitted model and generates a class balance chart on draw. - - Parameters - ---------- - - ax: axes - the axis to plot the figure on. - - model: estimator - Scikit-Learn estimator object. Should be an instance of a classifier, - else ``__init__()`` will raise an exception. - - classes: list - A list of class names for the legend. If classes is None and a y value - is passed to fit then the classes are selected from the target vector. - - kwargs: dict - Keyword arguments passed to the super class. Here, used - to colorize the bars in the histogram. - - Notes - ----- - These parameters can be influenced later on in the visualization - process, but can and should be set as early as possible. - """ - - def score(self, X, y=None, **kwargs): - """ - Generates the Scikit-Learn precision_recall_fscore_support - - Parameters - ---------- - - X : ndarray or DataFrame of shape n x m - A matrix of n instances with m features - - y : ndarray or Series of length n - An array or series of target or class values - - Returns - ------- - - ax : the axis with the plotted figure - """ - y_pred = self.predict(X) - self.scores = precision_recall_fscore_support(y, y_pred) - self.support = dict(zip(self.classes_, self.scores[-1])) - return self.draw() - - def draw(self): - """ - Renders the class balance chart across the axis. - - Returns - ------- - ax : the axis with the plotted figure - - """ - #TODO: Would rather not have to set the colors with this method. - # Refactor to make better use of yb_palettes module? - - colors = self.colors[0:len(self.classes_)] - self.ax.bar( - np.arange(len(self.support)), self.support.values(), - color=colors, align='center', width=0.5 - ) - - return self.ax - - def finalize(self, **kwargs): - """ - Finalize executes any subclass-specific axes finalization steps. - The user calls poof and poof calls finalize. - - Parameters - ---------- - kwargs: generic keyword arguments. - - """ - # Set the title - self.set_title('Class Balance for {}'.format(self.name)) - - # Set the x ticks with the class names - self.ax.set_xticks(np.arange(len(self.support))) - self.ax.set_xticklabels(self.support.keys()) - - # Compute the ceiling for the y limit - cmax = max(self.support.values()) - self.ax.set_ylim(0, cmax + cmax* 0.1) - - -def class_balance(model, X, y=None, ax=None, classes=None, **kwargs): - """Quick method: - - Displays the support for each class in the - fitted classification model displayed as a bar plot. - - This helper function is a quick wrapper to utilize the ClassBalance - ScoreVisualizer for one-off analysis. - - Parameters - ---------- - X : ndarray or DataFrame of shape n x m - A matrix of n instances with m features. - - y : ndarray or Series of length n - An array or series of target or class values. - - ax : matplotlib axes - The axes to plot the figure on. - - model : the Scikit-Learn estimator (should be a classifier) - - classes : list of strings - The names of the classes in the target - - Returns - ------- - ax : matplotlib axes - Returns the axes that the class balance plot was drawn on. - """ - # Instantiate the visualizer - visualizer = ClassBalance(model, ax, classes, **kwargs) - - # Create the train and test splits - X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) - - # Fit and transform the visualizer (calls draw) - visualizer.fit(X_train, y_train, **kwargs) - visualizer.score(X_test, y_test) - - # Return the axes object on the visualizer - return visualizer.ax - - ########################################################################## ## Class Prediction Error Chart ########################################################################## @@ -193,15 +44,28 @@ class prediction error chart on draw. ---------- ax: axes the axis to plot the figure on. + model: estimator Scikit-Learn estimator object. Should be an instance of a classifier, else ``__init__()`` will raise an exception. + classes: list A list of class names for the legend. If classes is None and a y value is passed to fit then the classes are selected from the target vector. + kwargs: dict Keyword arguments passed to the super class. Here, used to colorize the bars in the histogram. + + Attributes + ---------- + score_ : float + Global accuracy score + + predictions_ : ndarray + An ndarray of predictions whose rows are the true classes and + whose columns are the predicted classes + Notes ----- These parameters can be influenced later on in the visualization @@ -217,16 +81,17 @@ def score(self, X, y, **kwargs): ---------- X : ndarray or DataFrame of shape n x m A matrix of n instances with m features + y : ndarray or Series of length n An array or series of target or class values Returns ------- - - ax : the axis with the plotted figure + score_ : float + Global accuracy score """ - # We're replying on predict to raise NotFitted + # We're relying on predict to raise NotFitted y_pred = self.predict(X) y_type, y_true, y_pred = _check_targets(y, y_pred) @@ -243,11 +108,11 @@ def score(self, X, y, **kwargs): raise NotImplementedError("filtering classes is " "currently not supported") - # Create a table of scores whose rows are the true classes + # Create a table of predictions whose rows are the true classes # and whose columns are the predicted classes; each element # is the count of predictions for that class that match the true # value of that class. - self.scores_ = np.array([ + self.predictions_ = np.array([ [ (y_pred[y == label_t] == label_p).sum() for label_p in indices @@ -255,14 +120,14 @@ def score(self, X, y, **kwargs): for label_t in indices ]) - return self.draw() + self.draw() + self.score_ = self.estimator.score(X, y) + + return self.score_ def draw(self): """ Renders the class prediction error across the axis. - Returns - ------- - ax : the axis with the plotted figure """ indices = np.arange(len(self.classes_)) @@ -272,7 +137,7 @@ def draw(self): colors=self.colors, n_colors=len(self.classes_)) - for idx, row in enumerate(self.scores_): + for idx, row in enumerate(self.predictions_): self.ax.bar(indices, row, label=self.classes_[idx], bottom=prev, color=colors[idx]) prev += row @@ -283,10 +148,6 @@ def finalize(self, **kwargs): """ Finalize executes any subclass-specific axes finalization steps. The user calls poof and poof calls finalize. - Parameters - ---------- - kwargs: generic keyword arguments. - """ indices = np.arange(len(self.classes_)) @@ -303,7 +164,7 @@ def finalize(self, **kwargs): self.ax.set_ylabel("number of predicted class") # Compute the ceiling for the y limit - cmax = max([sum(scores) for scores in self.scores_]) + cmax = max([sum(predictions) for predictions in self.predictions_]) self.ax.set_ylim(0, cmax + cmax * 0.1) # Put the legend outside of the graph @@ -311,27 +172,51 @@ def finalize(self, **kwargs): plt.tight_layout(rect=[0, 0, 0.85, 1]) -def class_prediction_error(model, X, y=None, ax=None, classes=None, - test_size=0.2, **kwargs): +########################################################################## +## Quick Method +########################################################################## + +def class_prediction_error( + model, + X, + y=None, + ax=None, + classes=None, + test_size=0.2, + random_state=None, + **kwargs): """Quick method: - Displays the support for each class in the - fitted classification model displayed as a stacked bar plot. - Each bar is segmented to show the distribution of predicted - classes for each class. + Divides the dataset X and y into train and test splits, fits the model on + the train split, then scores the model on the test split. The visualizer + displays the support for each class in the fitted classification model + displayed as a stacked bar plot Each bar is segmented to show the + distribution of predicted classes for each class. This helper function is a quick wrapper to utilize the ClassPredictionError ScoreVisualizer for one-off analysis. + Parameters ---------- + model : the Scikit-Learn estimator (should be a classifier) + X : ndarray or DataFrame of shape n x m A matrix of n instances with m features. + y : ndarray or Series of length n An array or series of target or class values. + ax : matplotlib axes The axes to plot the figure on. - model : the Scikit-Learn estimator (should be a classifier) + classes : list of strings The names of the classes in the target + + test_size : float, default=0.2 + The percentage of the data to reserve as test data. + + random_state : int or None, default=None + The value to seed the random number generator for shuffling data. + Returns ------- ax : matplotlib axes @@ -341,9 +226,9 @@ def class_prediction_error(model, X, y=None, ax=None, classes=None, visualizer = ClassPredictionError(model, ax, classes, **kwargs) # Create the train and test splits - X_train, X_test, y_train, y_test = train_test_split(X, y, - test_size=test_size, - random_state=42) + X_train, X_test, y_train, y_test = tts( + X, y, test_size=test_size, random_state=random_state + ) # Fit and transform the visualizer (calls draw) visualizer.fit(X_train, y_train, **kwargs) diff --git a/yellowbrick/classifier/classification_report.py b/yellowbrick/classifier/classification_report.py index b8a278d42..8ffe545e9 100644 --- a/yellowbrick/classifier/classification_report.py +++ b/yellowbrick/classifier/classification_report.py @@ -62,7 +62,7 @@ class ClassificationReport(ClassificationScoreVisualizer): cmap : string, default: ``'YlOrRd'`` Specify a colormap to define the heatmap of the predicted class - against the actual class in the confusion matrix. + against the actual class in the classification report. support: {True, False, None, 'percent', 'count'}, default: None Specify if support will be displayed. It can be further defined by @@ -81,6 +81,9 @@ class ClassificationReport(ClassificationScoreVisualizer): Attributes ---------- + score_ : float + Global accuracy score + scores_ : dict of dicts Outer dictionary composed of precision, recall, f1, and support scores with inner dictionaries specifiying the values for each class listed. @@ -117,7 +120,13 @@ def score(self, X, y=None, **kwargs): y : ndarray or Series of length n An array or series of target or class values - """ + + Returns + ------- + + score_ : float + Global accuracy score + """ y_pred = self.predict(X) scores = precision_recall_fscore_support(y, y_pred) @@ -129,7 +138,7 @@ def score(self, X, y=None, **kwargs): scores = list(scores) scores[-1] = scores[-1] / scores[-1].sum() - # Create a mapping composed of precision,recall, presion, and support + # Create a mapping composed of precision, recall, F1, and support # to their respective values scores = map(lambda s: dict(zip(self.classes_, s)), scores) self.scores_ = dict(zip(SCORES_KEYS, scores)) @@ -138,7 +147,12 @@ def score(self, X, y=None, **kwargs): if not self.support: self.scores_.pop('support') - return self.draw() + self.draw() + + # Retrieve and store the score attribute from the sklearn classifier + self.score_ = self.estimator.score(X, y) + + return self.score_ def draw(self): """ diff --git a/yellowbrick/classifier/confusion_matrix.py b/yellowbrick/classifier/confusion_matrix.py index 0fc3c661c..708e197eb 100644 --- a/yellowbrick/classifier/confusion_matrix.py +++ b/yellowbrick/classifier/confusion_matrix.py @@ -17,7 +17,6 @@ ## Imports ########################################################################## -import warnings import numpy as np from ..utils import div_safe @@ -96,6 +95,9 @@ class ConfusionMatrix(ClassificationScoreVisualizer): Attributes ---------- + score_ : float + Global accuracy score + confusion_matrix_ : array, shape = [n_classes, n_classes] The numeric scores of the confusion matrix @@ -134,7 +136,7 @@ def __init__(self, model, ax=None, classes=None, sample_weight=None, # Used to draw diagonal line for predicted class = true class self._edgecolors = [] - def score(self, X, y, **kwargs): + def score(self, X, y): """ Draws a confusion matrix based on the test data supplied by comparing predictions on instances X with the true values specified by the @@ -147,18 +149,13 @@ def score(self, X, y, **kwargs): y : ndarray or Series of length n An array or series of target or class values - """ - # Perform deprecation warnings for attributes to score - # TODO: remove this in v0.9 - for param in ("percent", "sample_weight"): - if param in kwargs: - warnings.warn(PendingDeprecationWarning(( - "specifying '{}' in score is no longer supported, " - "pass to constructor of the visualizer instead." - ).format(param))) - setattr(self, param, kwargs[param]) + Returns + ------- + score_ : float + Global accuracy score + """ # Create predictions from X (will raise not fitted error) y_pred = self.predict(X) @@ -189,7 +186,12 @@ def score(self, X, y, **kwargs): selected_class_counts.append(0) self.class_counts_ = np.array(selected_class_counts) - return self.draw() + self.draw() + + # Retrieve and store the score attribute from the sklearn classifier + self.score_ = self.estimator.score(X, y) + + return self.score_ def draw(self): """ @@ -203,7 +205,7 @@ def draw(self): # predicted as a percent of true in each class. if self.percent == True: # Note: div_safe function returns 0 instead of NAN. - cm_display = div_safe(self.confusion_matrix_, self.class_counts_) + cm_display = div_safe(self.confusion_matrix_, self.class_counts_.reshape(-1,1)) cm_display = np.round(cm_display* 100, decimals=0) # Y axis should be sorted top to bottom in pcolormesh diff --git a/yellowbrick/classifier/prcurve.py b/yellowbrick/classifier/prcurve.py new file mode 100644 index 000000000..2199dbe6a --- /dev/null +++ b/yellowbrick/classifier/prcurve.py @@ -0,0 +1,472 @@ +# yellowbrick.classifier.prcurve +# Implements Precision-Recall curves for classification models. +# +# Author: Benjamin Bengfort +# Created: Tue Sep 04 16:47:19 2018 -0400 +# +# ID: prcurve.py [] benjamin@bengfort.com $ + +""" +Implements Precision-Recall curves for classification models. +""" + +########################################################################## +## Imports +########################################################################## + +import numpy as np + +from ..exceptions import ModelError, NotFitted +from ..exceptions import YellowbrickValueError +from .base import ClassificationScoreVisualizer + +from sklearn.preprocessing import label_binarize +from sklearn.multiclass import OneVsRestClassifier +from sklearn.utils.multiclass import type_of_target +from sklearn.metrics import average_precision_score +from sklearn.model_selection import train_test_split as tts +from sklearn.metrics import precision_recall_curve as sk_precision_recall_curve + + +# Target Type Constants +BINARY = "binary" +MULTICLASS = "multiclass" + +# Average Metric Constants +MICRO = "micro" + + +########################################################################## +## PrecisionRecallCurve Visualizer +########################################################################## + +class PrecisionRecallCurve(ClassificationScoreVisualizer): + """ + Precision-Recall curves are a metric used to evaluate a classifier's quality, + particularly when classes are very imbalanced. The precision-recall curve + shows the tradeoff between precision, a measure of result relevancy, and + recall, a measure of how many relevant results are returned. A large area + under the curve represents both high recall and precision, the best case + scenario for a classifier, showing a model that returns accurate results + for the majority of classes it selects. + + .. todo:: extend docstring + + Parameters + ---------- + model : the Scikit-Learn estimator + A classification model to score the precision-recall curve on. + + ax : matplotlib Axes, default: None + The axes to plot the figure on. If None is passed in the current axes + will be used (or generated if required). + + classes : list + A list of class names for the legend. If classes is None and a y value + is passed to fit then the classes are selected from the target vector. + Note that the curves must be computed based on what is in the target + vector passed to the ``score()`` method. Class names are used for + labeling only and must be in the correct order to prevent confusion. + + fill_area : bool, default=True + Fill the area under the curve (or curves) with the curve color. + + ap_score : bool, default=True + Annotate the graph with the average precision score, a summary of the + plot that is computed as the weighted mean of precisions at each + threshold, with the increase in recall from the previous threshold used + as the weight. + + micro : bool, default=True + If multi-class classification, draw the precision-recall curve for the + micro-average of all classes. In the multi-class case, either micro or + per-class must be set to True. Ignored in the binary case. + + iso_f1_curves : bool, default=False + Draw ISO F1-Curves on the plot to show how close the precision-recall + curves are to different F1 scores. + + per_class : bool, default=False + If multi-class classification, draw the precision-recall curve for + each class using a OneVsRestClassifier to compute the recall on a + per-class basis. In the multi-class case, either micro or per-class + must be set to True. Ignored in the binary case. + + fill_opacity : float, default=0.2 + Specify the alpha or opacity of the fill area (0 being transparent, + and 1.0 being completly opaque). + + line_opacity : float, default=0.8 + Specify the alpha or opacity of the lines (0 being transparent, and + 1.0 being completly opaque). + + kwargs : dict + Keyword arguments passed to the visualization base class. + + Attributes + ---------- + target_type_ : str + Either ``"binary"`` or ``"multiclass"`` depending on the type of target + fit to the visualizer. If ``"multiclass"`` then the estimator is + wrapped in a OneVsRestClassifier classification strategy. + + score_ : float or dict of floats + Average precision, a summary of the plot as a weighted mean of + precision at each threshold, weighted by the increase in recall from + the previous threshold. In the multiclass case, a mapping of class/metric + to the average precision score. + + precision_ : array or dict of array with shape=[n_thresholds + 1] + Precision values such that element i is the precision of predictions + with score >= thresholds[i] and the last element is 1. In the multiclass + case, a mapping of class/metric to precision array. + + recall_ : array or dict of array with shape=[n_thresholds + 1] + Decreasing recall values such that element i is the recall of + predictions with score >= thresholds[i] and the last element is 0. + In the multiclass case, a mapping of class/metric to recall array. + + + Example + ------- + >>> from yellowbrick.classifier import PrecisionRecallCurve + >>> from sklearn.model_selection import train_test_split + >>> from sklearn.svm import LinearSVC + >>> X_train, X_test, y_train, y_test = train_test_split(X, y) + >>> viz = PrecisionRecallCurve(LinearSVC()) + >>> viz.fit(X_train, y_train) + >>> viz.score(X_test, y_test) + >>> viz.poof() + + Notes + ----- + + .. seealso:: http://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html + """ + + def __init__(self, model, ax=None, classes=None, fill_area=True, ap_score=True, + micro=True, iso_f1_curves=False, per_class=False, fill_opacity=0.2, + line_opacity=0.8, **kwargs): + super(PrecisionRecallCurve, self).__init__(model, ax=ax, classes=classes, **kwargs) + + # Set visual params + self.set_params( + fill_area=fill_area, + ap_score=ap_score, + micro=micro, + iso_f1_curves=iso_f1_curves, + per_class=per_class, + fill_opacity=fill_opacity, + line_opacity=line_opacity, + ) + + def fit(self, X, y=None): + """ + Fit the classification model; if y is multi-class, then the estimator + is adapted with a OneVsRestClassifier strategy, otherwise the estimator + is fit directly. + """ + # The target determines what kind of estimator is fit + ttype = type_of_target(y) + if ttype.startswith(MULTICLASS): + self.target_type_ = MULTICLASS + self.estimator = OneVsRestClassifier(self.estimator) + + # Use label_binarize to create multi-label ouptut for OneVsRestClassifier + Y = label_binarize(y, classes=np.unique(y)) + elif ttype.startswith(BINARY): + self.target_type_ = BINARY + + # Different variable is used here to prevent transformation + Y = y + else: + raise YellowbrickValueError(( + "{} does not support target type '{}', " + "please provide a binary or multiclass single-output target" + ).format( + self.__class__.__name__, ttype + )) + + # Fit the model and return self + return super(PrecisionRecallCurve, self).fit(X, Y) + + def score(self, X, y=None): + """ + Generates the Precision-Recall curve on the specified test data. + + Returns + ------- + score_ : float + Average precision, a summary of the plot as a weighted mean of + precision at each threshold, weighted by the increase in recall from + the previous threshold. + """ + # If we don't do this check, then it is possible that OneVsRestClassifier + # has not correctly been fitted for multi-class targets. + if not hasattr(self, "target_type_"): + raise NotFitted(( + "{} cannot wrap an already fitted estimator" + ).format( + self.__class__.__name__ + )) + + # Compute the prediction/threshold scores + y_scores = self._get_y_scores(X) + + # Handle binary and multiclass cases to create correct data structure + if self.target_type_ == BINARY: + self.precision_, self.recall_, _ = sk_precision_recall_curve(y, y_scores) + self.score_ = average_precision_score(y, y_scores) + else: + # Use label_binarize to create multi-label ouptut for OneVsRestClassifier + Y = label_binarize(y, classes=self.classes_) + + self.precision_, self.recall_, self.score_ = {}, {}, {} + + # Compute PRCurve for all classes + for i, class_i in enumerate(self.classes_): + self.precision_[class_i], self.recall_[class_i], _ = sk_precision_recall_curve(Y[:,i], y_scores[:,i]) + self.score_[class_i] = average_precision_score(Y[:,i], y_scores[:,i]) + + # Compute micro average PR curve + self.precision_[MICRO], self.recall_[MICRO], _ = sk_precision_recall_curve( + Y.ravel(), y_scores.ravel() + ) + self.score_[MICRO] = average_precision_score(Y, y_scores, average=MICRO) + + # Draw the figure + self.draw() + + # Return a score between 0 and 1 + if self.target_type_ == BINARY: + return self.score_ + return self.score_[MICRO] + + def draw(self): + """ + Draws the precision-recall curves computed in score on the axes. + """ + if self.iso_f1_curves: + for f1 in np.linspace(0.2, 0.8, num=4): + x = np.linspace(0.01, 1) + y = f1 * x / (2 * x - f1) + self.ax.plot(x[y>=0], y[y>=0], color='#333333', alpha=0.2) + self.ax.annotate('$f_1={:0.1f}$'.format(f1), xy=(0.9, y[45]+0.02)) + + if self.target_type_ == BINARY: + return self._draw_binary() + return self._draw_multiclass() + + def _draw_binary(self): + """ + Draw the precision-recall curves in the binary case + """ + self._draw_pr_curve(self.recall_, self.precision_, label="binary PR curve") + self._draw_ap_score(self.score_) + + + def _draw_multiclass(self): + """ + Draw the precision-recall curves in the multiclass case + """ + # TODO: handle colors better with a mapping and user input + if self.per_class: + for cls in self.classes_: + precision = self.precision_[cls] + recall = self.recall_[cls] + + label = "PR for class {} (area={:0.2f})".format(cls, self.score_[cls]) + self._draw_pr_curve(recall, precision, label=label) + + if self.micro: + precision = self.precision_[MICRO] + recall = self.recall_[MICRO] + self._draw_pr_curve(recall, precision) + + self._draw_ap_score(self.score_[MICRO]) + + def _draw_pr_curve(self, recall, precision, label=None): + """ + Helper function to draw a precision-recall curve with specified settings + """ + self.ax.step( + recall, precision, alpha=self.line_opacity, where='post', label=label + ) + if self.fill_area: + self.ax.fill_between(recall, precision, step='post', alpha=self.fill_opacity) + + def _draw_ap_score(self, score, label=None): + """ + Helper function to draw the AP score annotation + """ + label = label or "Avg Precision={:0.2f}".format(score) + if self.ap_score: + self.ax.axhline( + y=score, color="r", ls="--", label=label + ) + + def finalize(self): + """ + Finalize the figure by adding titles, labels, and limits. + """ + self.set_title("Precision-Recall Curve for {}".format(self.name)) + self.ax.legend(loc='lower left', frameon=True) + + self.ax.set_xlim([0.0, 1.0]) + self.ax.set_ylim([0.0, 1.0]) + + self.ax.set_ylabel("Precision") + self.ax.set_xlabel("Recall") + + def _get_y_scores(self, X): + """ + The ``precision_recall_curve`` metric requires target scores that + can either be the probability estimates of the positive class, + confidence values, or non-thresholded measures of decisions (as + returned by a "decision function"). + """ + # TODO refactor shared method with ROCAUC + + # Resolution order of scoring functions + attrs = ( + 'decision_function', + 'predict_proba', + ) + + # Return the first resolved function + for attr in attrs: + try: + method = getattr(self.estimator, attr, None) + if method: + # Compute the scores from the decision function + y_scores = method(X) + + # Return only the positive class for binary predict_proba + if self.target_type_ == BINARY and y_scores.ndim == 2: + return y_scores[:,1] + return y_scores + + except AttributeError: + # Some Scikit-Learn estimators have both probability and + # decision functions but override __getattr__ and raise an + # AttributeError on access. + continue + + # If we've gotten this far, we can't do anything + raise ModelError(( + "{} requires estimators with predict_proba or decision_function methods." + ).format(self.__class__.__name__)) + + +# Alias for PrecisionRecallCurve +PRCurve = PrecisionRecallCurve + + +########################################################################## +## Quick Method +########################################################################## + +def precision_recall_curve(model, X, y, ax=None, train_size=0.8, + random_state=None, shuffle=True, **kwargs): + """Precision-Recall Curve quick method: + + Parameters + ---------- + model : the Scikit-Learn estimator + A classification model to score the precision-recall curve on. + + X : ndarray or DataFrame of shape n x m + A feature array of n instances with m features the model is trained on. + This array will be split into train and test splits. + + y : ndarray or Series of length n + An array or series of target or class values. This vector will be split + into train and test splits. + + ax : matplotlib Axes, default: None + The axes to plot the figure on. If None is passed in the current axes + will be used (or generated if required). + + train_size : float or int, default=0.8 + If float, should be between 0.0 and 1.0 and represent the proportion + of the dataset to include in the train split. If int, represents the + absolute number of train samples. + + random_state : int, RandomState, or None, optional + If int, random_state is the seed used by the random number generator; + If RandomState instance, random_state is the random number generator; + If None, the random number generator is the RandomState instance used + by np.random. + + shuffle : bool, default=True + Whether or not to shuffle the data before splitting. + + classes : list + A list of class names for the legend. If classes is None and a y value + is passed to fit then the classes are selected from the target vector. + Note that the curves must be computed based on what is in the target + vector passed to the ``score()`` method. Class names are used for + labeling only and must be in the correct order to prevent confusion. + + fill_area : bool, default=True + Fill the area under the curve (or curves) with the curve color. + + ap_score : bool, default=True + Annotate the graph with the average precision score, a summary of the + plot that is computed as the weighted mean of precisions at each + threshold, with the increase in recall from the previous threshold used + as the weight. + + micro : bool, default=True + If multi-class classification, draw the precision-recall curve for the + micro-average of all classes. In the multi-class case, either micro or + per-class must be set to True. Ignored in the binary case. + + iso_f1_curves : bool, default=False + Draw ISO F1-Curves on the plot to show how close the precision-recall + curves are to different F1 scores. + + per_class : bool, default=False + If multi-class classification, draw the precision-recall curve for + each class using a OneVsRestClassifier to compute the recall on a + per-class basis. In the multi-class case, either micro or per-class + must be set to True. Ignored in the binary case. + + fill_opacity : float, default=0.2 + Specify the alpha or opacity of the fill area (0 being transparent, + and 1.0 being completly opaque). + + line_opacity : float, default=0.8 + Specify the alpha or opacity of the lines (0 being transparent, and + 1.0 being completly opaque). + + kwargs : dict + Keyword arguments passed to the visualization base class. + + Returns + ------- + viz : PrecisionRecallCurve + Returns the visualizer that generates the curve visualization. + + Notes + ----- + Data is split using ``sklearn.model_selection.train_test_split`` before + computing the Precision-Recall curve. Splitting options such as train_size, + random_state, and shuffle are specified. Note that splits are not stratified, + if required, it is recommended to use the base class. + """ + # Instantiate the visualizer + viz = PRCurve(model, ax=ax, **kwargs) + + # Create train and test splits to validate the model + X_train, X_test, y_train, y_test = tts( + X, y, train_size=train_size, random_state=random_state, shuffle=shuffle + ) + + # Fit and transform the visualizer + viz.fit(X_train, y_train) + viz.score(X_test, y_test) + viz.finalize() + + # Return the visualizer + return viz diff --git a/yellowbrick/classifier/rocauc.py b/yellowbrick/classifier/rocauc.py index 1081d1030..45eced42b 100644 --- a/yellowbrick/classifier/rocauc.py +++ b/yellowbrick/classifier/rocauc.py @@ -77,21 +77,32 @@ class ROCAUC(ClassificationScoreVisualizer): micro : bool, default = True Plot the micro-averages ROC curve, computed from the sum of all true - positives and false positives across all classes. + positives and false positives across all classes. Micro is not defined + for binary classification problems with estimators with only a + decision_function method. macro : bool, default = True Plot the macro-averages ROC curve, which simply takes the average of - curves across all classes. + curves across all classes. Macro is not defined for binary + classification problems with estimators with only a decision_function + method. per_class : bool, default = True - Plot the ROC curves for each individual class. Primarily this is set - to false if only the macro or micro average curves are required. + Plot the ROC curves for each individual class. This should be set + to false if only the macro or micro average curves are required. Per- + class classification is not defined for binary classification problems + with estimators with only a decision_function method. kwargs : keyword arguments passed to the super class. Currently passing in hard-coded colors for the Receiver Operating Characteristic curve and the diagonal. These will be refactored to a default Yellowbrick style. + Attributes + ---------- + score_ : float + Global accuracy score, unless micro or macro scores are requested + Notes ----- ROC curves are typically used in binary classification, and in fact the @@ -113,29 +124,22 @@ class ROCAUC(ClassificationScoreVisualizer): Examples -------- - >>> from sklearn.datasets import load_breast_cancer >>> from yellowbrick.classifier import ROCAUC >>> from sklearn.linear_model import LogisticRegression >>> from sklearn.model_selection import train_test_split - >>> data = load_breast_cancer() - >>> X = data['data'] - >>> y = data['target'] + >>> data = load_data("occupancy") + >>> features = ["temp", "relative humidity", "light", "C02", "humidity"] >>> X_train, X_test, y_train, y_test = train_test_split(X, y) - >>> viz = ROCAUC(LogisticRegression()) - >>> viz.fit(X_train, y_train) - >>> viz.score(X_test, y_test) - >>> viz.poof() + >>> oz = ROCAUC(LogisticRegression()) + >>> oz.fit(X_train, y_train) + >>> oz.score(X_test, y_test) + >>> oz.poof() """ def __init__(self, model, ax=None, classes=None, micro=True, macro=True, per_class=True, **kwargs): super(ROCAUC, self).__init__(model, ax=ax, classes=classes, **kwargs) - if not micro and not macro and not per_class: - raise YellowbrickValueError( - "no curves will be drawn; specify micro, macro, or per_clss" - ) - # Set the visual parameters for ROCAUC self.micro = micro self.macro = macro @@ -156,15 +160,42 @@ def score(self, X, y=None, **kwargs): Returns ------- - score : float - The micro-average area under the curve of all classes. + score_ : float + Global accuracy unless micro or macro scores are requested. """ # Compute the predictions for the test data y_pred = self._get_y_scores(X) - # # Classes may be label encoded so only use what's in y to compute. - # # The self.classes_ attribute will be used as names for labels. + # Note: In the above, _get_y_scores calls either a decision_function or + # predict_proba, which should return a 2D array. But in a binary + # classification using an estimator with only a decision_function, y_pred + # will instead be 1D, meaning only one curve can be plotted. In this case, + # we set the _binary_decision attribute to True to ensure only one curve is + # computed and plotted later on. + if y_pred.ndim == 1: + self._binary_decision = True + + # Raise an error if it's a binary decision and user has set micro, + # macro, or per_class to True + if self.micro or self.macro or self.per_class: + raise ModelError( + "Micro, macro, and per-class scores are not defined for " + "binary classification for estimators with only " + "decision_function methods; set micro, macro, and " + "per-class params to False." + ) + else: + self._binary_decision = False + # If it's not a binary decision, at least one of micro, macro, or + # per_class must be True + if not self.micro and not self.macro and not self.per_class: + raise YellowbrickValueError( + "no curves will be drawn; specify micro, macro, or per_class" + ) + + # Classes may be label encoded so only use what's in y to compute. + # The self.classes_ attribute will be used as names for labels. classes = np.unique(y) n_classes = len(classes) @@ -173,10 +204,15 @@ def score(self, X, y=None, **kwargs): self.tpr = dict() self.roc_auc = dict() - # Compute ROC curve and ROC area for each class - for i, c in enumerate(classes): - self.fpr[i], self.tpr[i], _ = roc_curve(y, y_pred[:,i], pos_label=c) - self.roc_auc[i] = auc(self.fpr[i], self.tpr[i]) + # If the decision is binary, compute the ROC curve and ROC area + if self._binary_decision == True: + self.fpr[0], self.tpr[0], _ = roc_curve(y, y_pred) + self.roc_auc[0] = auc(self.fpr[0], self.tpr[0]) + else: + # Otherwise compute the ROC curve and ROC area for each class + for i, c in enumerate(classes): + self.fpr[i], self.tpr[i], _ = roc_curve(y, y_pred[:,i], pos_label=c) + self.roc_auc[i] = auc(self.fpr[i], self.tpr[i]) # Compute micro average if self.micro: @@ -189,16 +225,18 @@ def score(self, X, y=None, **kwargs): # Draw the Curves self.draw() - # Return micro average if specified + # Set score to micro average if specified if self.micro: - return self.roc_auc[MICRO] + self.score_ = self.roc_auc[MICRO] - # Return macro average if not micro + # Set score to macro average if not micro if self.macro: - return self.roc_auc[MACRO] + self.score_ = self.roc_auc[MACRO] - # Return the base score if neither macro nor micro - return self.estimator.score(X, y) + # Set score to the base score if neither macro nor micro + self.score_ = self.estimator.score(X, y) + + return self.score_ def draw(self): """ @@ -212,7 +250,16 @@ def draw(self): colors = self.colors[0:len(self.classes_)] n_classes = len(colors) - # Plot the ROC curves for each class + # If it's a binary decision, plot the single ROC curve + if self._binary_decision == True: + self.ax.plot( + self.fpr[0], self.tpr[0], + label='ROC for binary decision, AUC = {:0.2f}'.format( + self.roc_auc[0] + ) + ) + + # If per-class plotting is requested, plot ROC curves for each class if self.per_class: for i, color in zip(range(n_classes), colors): self.ax.plot( @@ -222,7 +269,7 @@ def draw(self): ) ) - # Plot the ROC curve for the micro average + # If requested, plot the ROC curve for the micro average if self.micro: self.ax.plot( self.fpr[MICRO], self.tpr[MICRO], linestyle="--", @@ -232,7 +279,7 @@ def draw(self): ) ) - # Plot the ROC curve for the macro average + # If requested, plot the ROC curve for the macro average if self.macro: self.ax.plot( self.fpr[MACRO], self.tpr[MACRO], linestyle="--", @@ -301,6 +348,8 @@ def _get_y_scores(self, X): # Some Scikit-Learn estimators have both probability and # decision functions but override __getattr__ and raise an # AttributeError on access. + # Note that because of the ordering of our attrs above, + # estimators with both will *only* ever use probability. continue # If we've gotten this far, raise an error @@ -389,15 +438,21 @@ def roc_auc(model, X, y=None, ax=None, **kwargs): micro : bool, default = True Plot the micro-averages ROC curve, computed from the sum of all true - positives and false positives across all classes. + positives and false positives across all classes. Micro is not defined + for binary classification problems with estimators with only a + decision_function method. macro : bool, default = True Plot the macro-averages ROC curve, which simply takes the average of - curves across all classes. + curves across all classes. Macro is not defined for binary + classification problems with estimators with only a decision_function + method. per_class : bool, default = True - Plot the ROC curves for each individual class. Primarily this is set - to false if only the macro or micro average curves are required. + Plot the ROC curves for each individual class. This should be set + to false if only the macro or micro average curves are required. Per- + class classification is not defined for binary classification problems + with estimators with only a decision_function method. Notes ----- @@ -420,11 +475,13 @@ def roc_auc(model, X, y=None, ax=None, **kwargs): Examples -------- - >>> from sklearn.datasets import load_breast_cancer - >>> from yellowbrick.classifier import roc_auc + >>> from yellowbrick.classifier import ROCAUC >>> from sklearn.linear_model import LogisticRegression - >>> data = load_breast_cancer() - >>> roc_auc(LogisticRegression(), data.data, data.target) + >>> data = load_data("occupancy") + >>> features = ["temp", "relative humidity", "light", "C02", "humidity"] + >>> X = data[features].values + >>> y = data.occupancy.values + >>> roc_auc(LogisticRegression(), X, y) Returns ------- diff --git a/yellowbrick/cluster/__init__.py b/yellowbrick/cluster/__init__.py index 4dab8c2e3..e783eb6ad 100644 --- a/yellowbrick/cluster/__init__.py +++ b/yellowbrick/cluster/__init__.py @@ -22,3 +22,4 @@ from .base import * from .elbow import * from .silhouette import * +from .icdm import * diff --git a/yellowbrick/cluster/base.py b/yellowbrick/cluster/base.py index 1067a3b1c..1ae1dce6a 100644 --- a/yellowbrick/cluster/base.py +++ b/yellowbrick/cluster/base.py @@ -38,7 +38,7 @@ class ClusteringScoreVisualizer(ScoreVisualizer): The primary functionality of this class is to perform a check to ensure that the wrapped estimator is a cluster estimator, otherwise a - ``YewllowbrickTypeError`` exception is raised. + ``YellowbrickTypeError`` exception is raised. """ def __init__(self, model, ax=None, **kwargs): diff --git a/yellowbrick/cluster/elbow.py b/yellowbrick/cluster/elbow.py index c495f7eac..d08de7c29 100644 --- a/yellowbrick/cluster/elbow.py +++ b/yellowbrick/cluster/elbow.py @@ -18,6 +18,7 @@ ## Imports ########################################################################## +import collections import time import numpy as np import scipy.sparse as sp @@ -125,11 +126,11 @@ class KElbowVisualizer(ClusteringScoreVisualizer): The elbow method runs k-means clustering on the dataset for a range of values for k (say from 1-10) and then for each value of k computes an - average score for all clusters. By default, the ``distortion_score`` is + average score for all clusters. By default, the ``distortion`` score is computed, the sum of square distances from each point to its assigned - center. Other metrics can also be used such as the ``silhouette_score``, + center. Other metrics can also be used such as the ``silhouette`` score, the mean silhouette coefficient for all samples or the - ``calinski_harabaz_score``, which computes the ratio of dispersion between + ``calinski_harabaz`` score, which computes the ratio of dispersion between and within clusters. When these overall metrics for each model are plotted, it is possible to @@ -150,10 +151,11 @@ class KElbowVisualizer(ClusteringScoreVisualizer): The axes to plot the figure on. If None is passed in the current axes will be used (or generated if required). - k : integer or tuple - The range of k to compute silhouette scores for. If a single integer - is specified, then will compute the range (2,k) otherwise the - specified range in the tuple is used. + k : integer, tuple, or iterable + The k values to compute silhouette scores for. If a single integer + is specified, then will compute the range (2,k). If a tuple of 2 + integers is specified, then k will be in np.arange(k[0], k[1]). + Otherwise, specify an iterable of integers to use as values for k. metric : string, default: ``"distortion"`` Select the scoring metric to evaluate the clusters. The default is the @@ -186,16 +188,21 @@ class KElbowVisualizer(ClusteringScoreVisualizer): If you get a visualizer that doesn't have an elbow or inflection point, then this method may not be working. The elbow method does not work well - if the data is not very clustered; in this case you might see a smooth - curve and the value of k is unclear. Other scoring methods such as BIC or - SSE also can be used to explore if clustering is a correct choice. + if the data is not very clustered; in this case, you might see a smooth + curve and the value of k is unclear. Other scoring methods, such as BIC or + SSE, also can be used to explore if clustering is a correct choice. For a discussion on the Elbow method, read more at `Robert Gove's Block `_. + + .. seealso:: The scikit-learn documentation for the `silhouette_score + `_ and `calinski_harabaz_score + `_. The default, `distortion_score`, is + implemented in`yellowbrick.cluster.elbow`. .. todo:: add parallelization option for performance - .. todo:: add different metrics for scores and silhoutte - .. todo:: add timing information about how long its taking + .. todo:: add different metrics for scores and silhouette + .. todo:: add timing information about how long it's taking """ def __init__(self, model, ax=None, k=10, @@ -215,19 +222,19 @@ def __init__(self, model, ax=None, k=10, # Convert K into a tuple argument if an integer if isinstance(k, int): - k = (2, k+1) - - # Expand k in to the values we will use, capturing exceptions - try: - k = tuple(k) + self.k_values_ = list(range(2, k+1)) + elif isinstance(k, tuple) and len(k) == 2 and \ + all(isinstance(x, (int, np.integer)) for x in k): self.k_values_ = list(range(*k)) - except: + elif isinstance(k, collections.Iterable) and \ + all(isinstance(x, (int, np.integer)) for x in k): + self.k_values_ = list(k) + else: raise YellowbrickValueError(( - "Specify a range or maximal K value, the value '{}' " - "is not a valid argument for K.".format(k) + "Specify an iterable of integers, a range, or maximal K value," + " the value '{}' is not a valid argument for K.".format(k) )) - # Holds the values of the silhoutte scores self.k_scores_ = None diff --git a/yellowbrick/cluster/icdm.py b/yellowbrick/cluster/icdm.py new file mode 100644 index 000000000..f24d580ef --- /dev/null +++ b/yellowbrick/cluster/icdm.py @@ -0,0 +1,519 @@ +# yellowbrick.cluster.icdm +# Implements Intercluster Distance Map visualizations. +# +# Author: Benjamin Bengfort +# Created: Tue Aug 21 11:56:53 2018 -0400 +# +# ID: icdm.py [] benjamin@bengfort.com $ + +""" +Implements Intercluster Distance Map visualizations. +""" + +########################################################################## +## Imports +########################################################################## + +import numpy as np +import matplotlib.pyplot as plt + +from matplotlib.patches import Circle +from sklearn.manifold import MDS, TSNE + +from .base import ClusteringScoreVisualizer + +from ..utils.timer import Timer +from ..utils.decorators import memoized +from ..utils.helpers import prop_to_size +from ..exceptions import YellowbrickValueError + +try: + # Only available in Matplotlib >= 2.0.2 + from mpl_toolkits.axes_grid1 import inset_locator +except ImportError: + inset_locator = None + + +## Packages for export +__all__ = [ + "InterclusterDistance", "intercluster_distance", + "VALID_EMBEDDING", "VALID_SCORING", +] + + +# Valid strings to use for embedding names +VALID_EMBEDDING = {'mds', 'tsne'} + +# Valid strings to use for scoring names +VALID_SCORING = {'membership',} + + +########################################################################## +## InterclusterDistance Visualizer +########################################################################## + +class InterclusterDistance(ClusteringScoreVisualizer): + """ + Intercluster distance maps display an embedding of the cluster centers in + 2 dimensions with the distance to other centers preserved. E.g. the closer + to centers are in the visualization, the closer they are in the original + feature space. The clusters are sized according to a scoring metric. By + default, they are sized by membership, e.g. the number of instances that + belong to each center. This gives a sense of the relative importance of + clusters. Note however, that because two clusters overlap in the 2D space, + it does not imply that they overlap in the original feature space. + + Parameters + ---------- + model : a Scikit-Learn clusterer + Should be an instance of a centroidal clustering algorithm (or a + hierarchical algorithm with a specified number of clusters). Also + accepts some other models like LDA for text clustering. + If it is not a clusterer, an exception is raised. + + ax : matplotlib Axes, default: None + The axes to plot the figure on. If None is passed in the current axes + will be used (or generated if required). + + min_size : int, default: 400 + The size, in points, of the smallest cluster drawn on the graph. + Cluster sizes will be scaled between the min and max sizes. + + max_size : int, default: 25000 + The size, in points, of the largest cluster drawn on the graph. + Cluster sizes will be scaled between the min and max sizes. + + embedding : default: 'mds' + The algorithm used to embed the cluster centers in 2 dimensional space + so that the distance between clusters is represented equivalently to + their relationship in feature spaceself. + Embedding algorithm options include: + + - **mds**: multidimensional scaling + - **tsne**: stochastic neighbor embedding + + scoring : default: 'membership' + The scoring method used to determine the size of the clusters drawn on + the graph so that the relative importance of clusters can be viewed. + Scoring method options include: + + - **membership**: number of instances belonging to each cluster + + legend : bool, default: True + Whether or not to draw the size legend onto the graph, omit the legend + to more easily see clusters that overlap. + + legend_loc : str, default: "lower left" + The location of the legend on the graph, used to move the legend out + of the way of clusters into open space. The same legend location + options for matplotlib are used here. + + .. seealso:: https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.legend + + legend_size : float, default: 1.5 + The size, in inches, of the size legend to inset into the graph. + + random_state : int or RandomState, default: None + Fixes the random state for stochastic embedding algorithms. + + kwargs : dict + Keyword arguments passed to the base class and may influence the + feature visualization properties. + + Attributes + ---------- + cluster_centers_ : array of shape (n_clusters, n_features) + The computed cluster centers from the underlying model. + + embedded_centers_ : array of shape (n_clusters, 2) + The positions of all the cluster centers on the graph. + + scores_ : array of shape (n_clusters,) + The scores of each cluster that determine its size on the graph. + + fit_time_ : Timer + The time it took to fit the clustering model and perform the embedding. + + Notes + ----- + Currently the only two embeddings supportted are MDS and TSNE. Soon to + follow will be PCoA and a customized version of PCoA for LDA. The only + supported scoring metric is membership, but in the future, silhouette + scores and cluster diameter will be added. + + In terms of algorithm support, right now any clustering algorithm that has + a learned ``cluster_centers_`` and ``labels_`` attribute will work with + the visualizer. In the future, we will update this to work with hierarchical + clusterers that have ``n_components`` and LDA. + """ + + + def __init__(self, model, ax=None, min_size=400, max_size=25000, + embedding='mds', scoring='membership', + legend=True, legend_loc="lower left", legend_size=1.5, + random_state=None, **kwargs): + # Initialize the visualizer bases + super(InterclusterDistance, self).__init__(model, ax=ax, **kwargs) + + # Ensure that a valid embedding and scoring is passed in + validate_embedding(embedding) + validate_scoring(scoring) + + # Set decomposition properties + self.embedding = embedding + self.scoring = scoring + self.random_state = random_state + + # Set visual properties + self.min_size = min_size + self.max_size = max_size + self.legend = legend + self.legend_loc = legend_loc + self.legend_size = legend_size + + # Colors are currently hardcoded, need to compute face and edge color + # from this color based on the alpha of the cluster center. The user + # can "hack" these properties before drawing, however. + self.facecolor = "#2e719344" + self.edgecolor = "#2e719399" + + if self.legend: + self.lax # If legend True, test the version availability + + @memoized + def lax(self): + """ + Returns the legend axes, creating it only on demand by creating a 2" + by 2" inset axes that has no grid, ticks, spines or face frame (e.g + is mostly invisible). The legend can then be drawn on this axes. + """ + if inset_locator is None: + raise YellowbrickValueError(( + "intercluster distance map legend requires matplotlib 2.0.2 or greater " + "please upgrade matplotlib or set legend=False on the visualizer" + )) + + lax = inset_locator.inset_axes( + self.ax, width=self.legend_size, height=self.legend_size, loc=self.legend_loc + ) + + lax.set_frame_on(False) + lax.set_facecolor("none") + lax.grid(False) + lax.set_xlim(-1.4,1.4) + lax.set_ylim(-1.4,1.4) + lax.set_xticks([]) + lax.set_yticks([]) + + for name in lax.spines: + lax.spines[name].set_visible(False) + + return lax + + @memoized + def transformer(self): + """ + Creates the internal transformer that maps the cluster center's high + dimensional space to its two dimensional space. + """ + ttype = self.embedding.lower() # transformer method type + + if ttype == 'mds': + return MDS(n_components=2, random_state=self.random_state) + + if ttype == 'tsne': + return TSNE(n_components=2, random_state=self.random_state) + + raise YellowbrickValueError("unknown embedding '{}'".format(ttype)) + + @property + def cluster_centers_(self): + """ + Searches for or creates cluster centers for the specified clustering + algorithm. This algorithm ensures that that the centers are + appropriately drawn and scaled so that distance between clusters are + maintained. + """ + # TODO: Handle agglomerative clustering and LDA + for attr in ('cluster_centers_',): + try: + return getattr(self.estimator, attr) + except AttributeError: + continue + + raise AttributeError( + "could not find or make cluster_centers_ for {}".format( + self.estimator.__class__.__name__ + )) + + def fit(self, X, y=None): + """ + Fit the clustering model, computing the centers then embeds the centers + into 2D space using the embedding method specified. + """ + with Timer() as self.fit_time_: + # Fit the underlying estimator + self.estimator.fit(X, y) + + # Get the centers + # TODO: is this how sklearn stores all centers in the model? + C = self.cluster_centers_ + + # Embed the centers in 2D space and get the cluster scores + self.embedded_centers_ = self.transformer.fit_transform(C) + self.scores_ = self._score_clusters(X, y) + + # Draw the clusters and fit returns self + self.draw() + return self + + def draw(self): + """ + Draw the embedded centers with their sizes on the visualization. + """ + # Compute the sizes of the markers from their score + sizes = self._get_cluster_sizes() + + # Draw the scatter plots with associated sizes on the graph + self.ax.scatter( + self.embedded_centers_[:,0], self.embedded_centers_[:,1], + s=sizes, c=self.facecolor, edgecolor=self.edgecolor, linewidth=1, + ) + + # Annotate the clusters with their labels + for i, pt in enumerate(self.embedded_centers_): + self.ax.text( + s=str(i), x=pt[0], y=pt[1], va="center", ha="center", fontweight="bold" + ) + + # Ensure the current axes is always the main residuals axes + plt.sca(self.ax) + return self.ax + + def finalize(self): + """ + Finalize the visualization to create an "origin grid" feel instead of + the default matplotlib feel. Set the title, remove spines, and label + the grid with components. This function also adds a legend from the + sizes if required. + """ + # Set the default title if a user hasn't supplied one + self.set_title("{} Intercluster Distance Map (via {})".format( + self.estimator.__class__.__name__, self.embedding.upper() + )) + + # Create the origin grid and minimalist display + self.ax.set_xticks([0]) + self.ax.set_yticks([0]) + self.ax.set_xticklabels([]) + self.ax.set_yticklabels([]) + self.ax.set_xlabel("PC2") + self.ax.set_ylabel("PC1") + + # Make the legend by creating an inset axes that shows relative sizing + # based on the scoring metric supplied by the user. + if self.legend: + self._make_size_legend() + + return self.ax + + def _score_clusters(self, X, y=None): + """ + Determines the "scores" of the cluster, the metric that determines the + size of the cluster visualized on the visualization. + """ + stype = self.scoring.lower() # scoring method name + + if stype == "membership": + return np.bincount(self.estimator.labels_) + + raise YellowbrickValueError("unknown scoring method '{}'".format(stype)) + + def _get_cluster_sizes(self): + """ + Returns the marker size (in points, e.g. area of the circle) based on + the scores, using the prop_to_size scaling mechanism. + """ + # NOTE: log and power are hardcoded, should we allow the user to specify? + return prop_to_size( + self.scores_, mi=self.min_size, ma=self.max_size, log=False, power=0.5 + ) + + def _make_size_legend(self): + """ + Draw a legend that shows relative sizes of the clusters at the 25th, + 50th, and 75th percentile based on the current scoring metric. + """ + # Compute the size of the markers and scale them to our figure size + # NOTE: the marker size is the area of the plot, we need to compute the + # radius of the markers. + areas = self._get_cluster_sizes() + radii = np.sqrt(areas / np.pi) + scaled = np.interp(radii, (radii.min(), radii.max()), (.1, 1)) + + # Compute the locations of the 25th, 50th, and 75th percentile scores + indices = np.array([ + percentile_index(self.scores_, p) for p in (25, 50, 75) + ]) + + # Draw size circles annotated with the percentile score as the legend. + for idx in indices: + # TODO: should the size circle's center be hard coded like this? + center = (-0.30, 1-scaled[idx]) + c = Circle( + center, scaled[idx], facecolor="none", edgecolor="#2e7193", + linewidth=1.5, linestyle="--" + ) + self.lax.add_patch(c) + + # Add annotation to the size circle with the value of the score + self.lax.annotate( + self.scores_[idx], (-0.30, 1-(2*scaled[idx])), xytext=(1, 1-(2*scaled[idx])), + arrowprops=dict(arrowstyle="wedge", color="#2e7193"), va='center', ha='center', + ) + + # Draw size legend title + self.lax.text(s="membership", x=0, y=1.2, va='center', ha='center') + + # Ensure the current axes is always the main axes after modifying the + # inset axes and while drawing. + plt.sca(self.ax) + + +########################################################################## +## Helper Methods +########################################################################## + +def percentile_index(a, q): + """ + Returns the index of the value at the Qth percentile in array a. + """ + return np.where( + a==np.percentile(a, q, interpolation='nearest') + )[0][0] + + +def validate_string_param(s, valid, param_name="param"): + """ + Raises a well formatted exception if s is not in valid, otherwise does not + raise an exception. Uses ``param_name`` to identify the parameter. + """ + if s.lower() not in valid: + raise YellowbrickValueError( + "unknown {} '{}', chose from '{}'".format( + param_name, s, ", ".join(valid) + ) + ) + + +def validate_embedding(param): + """ + Raises an exception if the param is not in VALID_EMBEDDING + """ + validate_string_param(param, VALID_EMBEDDING, "embedding") + + +def validate_scoring(param): + """ + Raises an exception if the param is not in VALID_SCORING + """ + validate_string_param(param, VALID_SCORING, "scoring") + + +########################################################################## +## Quick Method +########################################################################## + +def intercluster_distance(model, X, y=None, ax=None, + min_size=400, max_size=25000, + embedding='mds', scoring='membership', + legend=True, legend_loc="lower left", legend_size=1.5, + random_state=None, **kwargs): + """Quick Method: + + Intercluster distance maps display an embedding of the cluster centers in + 2 dimensions with the distance to other centers preserved. E.g. the closer + to centers are in the visualization, the closer they are in the original + feature space. The clusters are sized according to a scoring metric. By + default, they are sized by membership, e.g. the number of instances that + belong to each center. This gives a sense of the relative importance of + clusters. Note however, that because two clusters overlap in the 2D space, + it does not imply that they overlap in the original feature space. + + Parameters + ---------- + model : a Scikit-Learn clusterer + Should be an instance of a centroidal clustering algorithm (or a + hierarchical algorithm with a specified number of clusters). Also + accepts some other models like LDA for text clustering. + If it is not a clusterer, an exception is raised. + + X : array-like of shape (n, m) + A matrix or data frame with n instances and m features + + y : array-like of shape (n,), optional + A vector or series representing the target for each instance + + ax : matplotlib Axes, default: None + The axes to plot the figure on. If None is passed in the current axes + will be used (or generated if required). + + min_size : int, default: 400 + The size, in points, of the smallest cluster drawn on the graph. + Cluster sizes will be scaled between the min and max sizes. + + max_size : int, default: 25000 + The size, in points, of the largest cluster drawn on the graph. + Cluster sizes will be scaled between the min and max sizes. + + embedding : default: 'mds' + The algorithm used to embed the cluster centers in 2 dimensional space + so that the distance between clusters is represented equivalently to + their relationship in feature spaceself. + Embedding algorithm options include: + + - **mds**: multidimensional scaling + - **tsne**: stochastic neighbor embedding + + scoring : default: 'membership' + The scoring method used to determine the size of the clusters drawn on + the graph so that the relative importance of clusters can be viewed. + Scoring method options include: + + - **membership**: number of instances belonging to each cluster + + legend : bool, default: True + Whether or not to draw the size legend onto the graph, omit the legend + to more easily see clusters that overlap. + + legend_loc : str, default: "lower left" + The location of the legend on the graph, used to move the legend out + of the way of clusters into open space. The same legend location + options for matplotlib are used here. + + .. seealso:: https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.legend + + legend_size : float, default: 1.5 + The size, in inches, of the size legend to inset into the graph. + + random_state : int or RandomState, default: None + Fixes the random state for stochastic embedding algorithms. + + kwargs : dict + Keyword arguments passed to the base class and may influence the + feature visualization properties. + + Returns + ------- + viz : InterclusterDistance + The intercluster distance visualizer, fitted and finalized. + """ + oz = InterclusterDistance( + model, ax=ax, min_size=min_size, max_size=max_size, embedding=embedding, + scoring=scoring, legend=legend, legend_loc=legend_loc, legend_size=legend_size, + random_state=random_state, **kwargs + ) + + oz.fit(X, y) + oz.poof() + return oz diff --git a/yellowbrick/cluster/silhouette.py b/yellowbrick/cluster/silhouette.py index f247670d1..b8d7199e7 100644 --- a/yellowbrick/cluster/silhouette.py +++ b/yellowbrick/cluster/silhouette.py @@ -18,6 +18,7 @@ ########################################################################## import numpy as np +import matplotlib.ticker as ticker from ..style import color_palette from .base import ClusteringScoreVisualizer @@ -37,7 +38,63 @@ class SilhouetteVisualizer(ClusteringScoreVisualizer): """ - TODO: Document this class! + The Silhouette Visualizer displays the silhouette coefficient for each + sample on a per-cluster basis, visually evaluating the density and + separation between clusters. The score is calculated by averaging the + silhouette coefficient for each sample, computed as the difference + between the average intra-cluster distance and the mean nearest-cluster + distance for each sample, normalized by the maximum value. This produces a + score between -1 and +1, where scores near +1 indicate high separation + and scores near -1 indicate that the samples may have been assigned to + the wrong cluster. + + In SilhouetteVisualizer plots, clusters with higher scores have wider + silhouettes, but clusters that are less cohesive will fall short of the + average score across all clusters, which is plotted as a vertical dotted + red line. + + This is particularly useful for determining cluster imbalance, or for + selecting a value for K by comparing multiple visualizers. + + Parameters + ---------- + model : a Scikit-Learn clusterer + Should be an instance of a centroidal clustering algorithm (``KMeans`` + or ``MiniBatchKMeans``). + + ax : matplotlib Axes, default: None + The axes to plot the figure on. If None is passed in the current axes + will be used (or generated if required). + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Attributes + ---------- + silhouette_score_ : float + Mean Silhouette Coefficient for all samples. Computed via scikit-learn + `sklearn.metrics.silhouette_score`. + + silhouette_samples_ : array, shape = [n_samples] + Silhouette Coefficient for each samples. Computed via scikit-learn + `sklearn.metrics.silhouette_samples`. + + n_samples_ : integer + Number of total samples in the dataset (X.shape[0]) + + n_clusters_ : integer + Number of clusters (e.g. n_clusters or k value) passed to internal + scikit-learn model. + + Examples + -------- + + >>> from yellowbrick.cluster import SilhouetteVisualizer + >>> from sklearn.cluster import KMeans + >>> model = SilhouetteVisualizer(KMeans(10)) + >>> model.fit(X) + >>> model.poof() """ def __init__(self, model, ax=None, **kwargs): @@ -48,26 +105,20 @@ def __init__(self, model, ax=None, **kwargs): self.colormap = kwargs.get('colormap', 'set1') self.color = kwargs.get('color', None) - # Required internal properties - self.silhouette_score_ = None - self.silhouette_samples_ = None - self.n_samples = None - self.n_clusters = None - def fit(self, X, y=None, **kwargs): """ - Fits the model and generates the the silhouette visualization. - - TODO: decide to use this method or the score method to draw. - NOTE: Probably this would be better in score, but the standard score - is a little different and I'm not sure how it's used. + Fits the model and generates the silhouette visualization. """ + # TODO: decide to use this method or the score method to draw. + # NOTE: Probably this would be better in score, but the standard score + # is a little different and I'm not sure how it's used. + # Fit the wrapped estimator self.estimator.fit(X, y, **kwargs) # Get the properties of the dataset - self.n_samples = X.shape[0] - self.n_clusters = self.estimator.n_clusters + self.n_samples_ = X.shape[0] + self.n_clusters_ = self.estimator.n_clusters # Compute the scores of the cluster labels = self.estimator.predict(X) @@ -98,10 +149,10 @@ def draw(self, labels): # Get the colors from the various properties # TODO: Use resolve_colors instead of this - colors = color_palette(self.colormap, self.n_clusters) + colors = color_palette(self.colormap, self.n_clusters_) # For each cluster, plot the silhouette scores - for idx in range(self.n_clusters): + for idx in range(self.n_clusters_): # Collect silhouette scores for samples in the current cluster . values = self.silhouette_samples_[labels == idx] @@ -140,15 +191,22 @@ def finalize(self): self.set_title(( "Silhouette Plot of {} Clustering for {} Samples in {} Centers" ).format( - self.name, self.n_samples, self.n_clusters + self.name, self.n_samples_, self.n_clusters_ )) # Set the X and Y limits - # The silhouette coefficient can range from -1, 1 - self.ax.set_xlim([-1, 1]) - # The (n_clusters+1)*10 is for inserting blank space between + # The silhouette coefficient can range from -1, 1; + # but here we scale the plot according to our visualizations + + # l_xlim and u_xlim are lower and upper limits of the x-axis, + # set according to our calculated maximum and minimum silhouette score along with necessary padding + l_xlim = max(-1, min(-0.1, round(min(self.silhouette_samples_) - 0.1, 1))) + u_xlim = min(1, round(max(self.silhouette_samples_) + 0.1, 1)) + self.ax.set_xlim([l_xlim, u_xlim]) + + # The (n_clusters_+1)*10 is for inserting blank space between # silhouette plots of individual clusters, to demarcate them clearly. - self.ax.set_ylim([0, self.n_samples + (self.n_clusters + 1) * 10]) + self.ax.set_ylim([0, self.n_samples_ + (self.n_clusters_ + 1) * 10]) # Set the x and y labels self.ax.set_xlabel("silhouette coefficient values") @@ -156,4 +214,4 @@ def finalize(self): # Set the ticks on the axis object. self.ax.set_yticks([]) # Clear the yaxis labels / ticks - self.ax.set_xticks(np.linspace(-1,1,11)) + self.ax.xaxis.set_major_locator(ticker.MultipleLocator(0.1)) # Set the ticks at multiples of 0.1 diff --git a/yellowbrick/contrib/__init__.py b/yellowbrick/contrib/__init__.py index e69de29bb..363954ba8 100644 --- a/yellowbrick/contrib/__init__.py +++ b/yellowbrick/contrib/__init__.py @@ -0,0 +1,11 @@ +# yellowbrick.contrib +# +# Contains a variety of extra tools and experimental visualizers outside of +# core support or still in development. +# +# +# ID: __init__.py [] bilbro@gmail.com $ + + + +from .scatter import ScatterViz, ScatterVisualizer, scatterviz diff --git a/yellowbrick/contrib/missing/__init__.py b/yellowbrick/contrib/missing/__init__.py new file mode 100644 index 000000000..96ab3aecc --- /dev/null +++ b/yellowbrick/contrib/missing/__init__.py @@ -0,0 +1,13 @@ +# yellowbrick.contrib.missing +# Visualizations related to missing values +# +# Author: Nathan Danielsen +# Created: Fri Mar 29 5:17:36 2018 -0500 +# +# Copyright (C) 2018 District Data Labs +# For license information, see LICENSE.txt +# +# ID: __init__.py [5eee25b] nathan.danielsen@gmail.com $ + +from .bar import MissingValuesBar, missing_bar +from .dispersion import MissingValuesDispersion, missing_dispersion diff --git a/yellowbrick/contrib/missing/bar.py b/yellowbrick/contrib/missing/bar.py new file mode 100644 index 000000000..7fd1a7d1e --- /dev/null +++ b/yellowbrick/contrib/missing/bar.py @@ -0,0 +1,247 @@ +# yellowbrick.contrib.missing.bar +# Missing Values Bar Visualizer +# +# Author: Nathan Danielsen +# Created: Fri Mar 29 5:17:36 2018 -0500 +# +# Copyright (C) 2018 District Data Labs +# For license information, see LICENSE.txt +# +# ID: bar.py [] nathan.danielsen@gmail.com.com $ + +""" +Bar visualizer of missing values by column. +""" + +########################################################################## +## Imports +########################################################################## + +import numpy as np + +from yellowbrick.style.palettes import color_palette +from .base import MissingDataVisualizer + + +########################################################################## +## MissingValues Visualizer +########################################################################## + + +class MissingValuesBar(MissingDataVisualizer): + """The MissingValues Bar visualizer creates a bar graph that lists the total + count of missing values for each selected feature column. + + When y targets are supplied to fit, the output is a stacked bar chart where + each color corresponds to the total NaNs for the feature in that column. + + Parameters + ---------- + alpha : float, default: 0.5 + A value for bending elments with the background. + + marker : matplotlib marker, default: | + The marker used for each element coordinate in the plot + + color : string, default: black + The color for drawing the bar chart when the y targets are not passed to + fit. + + colors : list, default: None + The color pallette for drawing a stack bar chart when the y targets + are passed to fit. + + classes : list, default: None + A list of class names for the legend. + If classes is None and a y value is passed to fit then the classes + are selected from the target vector. + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Attributes + ---------- + features_ : np.array + The feature labels ranked according to their importance + + classes_ : np.array + The class labels for each of the target values + + Examples + -------- + + >>> from yellowbrick.contrib.missing import MissingValuesBar + >>> visualizer = MissingValuesBar() + >>> visualizer.fit(X, y=y) + >>> visualizer.poof() + """ + + def __init__(self, width=0.5, color='black', colors=None, classes=None, **kwargs): + super(MissingValuesBar, self).__init__(**kwargs) + self.width = width # the width of the bars + self.classes_ = classes + self.ind = None + + # Convert to array if necessary to match estimator.classes_ + if self.classes_ is not None: + self.classes_ = np.array(classes) + + # Set up classifier score visualization properties + if self.classes_ is not None: + n_colors = len(self.classes_) + else: + n_colors = None + + self.colors = color_palette(kwargs.pop('colors', None), n_colors) + + def get_nan_col_counts(self, **kwargs): + # where matrix contains strings, handle them + if np.issubdtype(self.X.dtype, np.string_) or np.issubdtype(self.X.dtype, np.unicode_): + mask = np.where( self.X == '' ) + nan_matrix = np.zeros(self.X.shape) + nan_matrix[mask] = np.nan + + else: + nan_matrix = self.X.astype(np.float) + + if self.y is None: + nan_col_counts = [np.count_nonzero(np.isnan(col)) for col in nan_matrix.T] + return nan_col_counts + + else: + # add in counting of np.nan per target y by column + nan_counts = [] + for target_value in np.unique(self.y): + + indices = np.argwhere(self.y == target_value) + target_matrix = nan_matrix[indices.flatten()] + nan_col_counts = np.array([np.count_nonzero(np.isnan(col)) for col in target_matrix.T]) + nan_counts.append((target_value, nan_col_counts)) + + return nan_counts + + def draw(self, X, y, **kwargs): + """Called from the fit method, this method generated a horizontal bar plot. + + If y is none, then draws a simple horizontal bar chart. + If y is not none, then draws a stacked horizontal bar chart for each nan count per + target values. + """ + nan_col_counts = self.get_nan_col_counts() + + # the x locations for the groups + self.ind = np.arange(len(self.features_)) + + if y is None: + self.ax.barh(self.ind - self.width / 2, nan_col_counts, self.width, + color=self.color, label=None) + else: + self.draw_stacked_bar(nan_col_counts) + + def draw_stacked_bar(self, nan_col_counts): + """Draws a horizontal stacked bar chart with different colors + for each count of nan values per label. + """ + for index, nan_values in enumerate(nan_col_counts): + label, nan_col_counts = nan_values + + if index == 0: + # first draw should be at zero + bottom_chart = np.zeros(nan_col_counts.shape) + + # if features passed in then, label as such + if self.classes_ is not None: + label = self.classes_[index] + + color = self.colors[index] + + self.ax.barh(self.ind - self.width / 2, nan_col_counts, self.width, + color=color, label=label, left=bottom_chart) + + # keep track of counts to build on stacked + bottom_chart = nan_col_counts + + def finalize(self, **kwargs): + """ + Finalize executes any subclass-specific axes finalization steps. + The user calls poof and poof calls finalize. + + Parameters + ---------- + kwargs: generic keyword arguments. + + """ + # Set the title + self.set_title( + 'Count of Missing Values by Column' + ) + tick_locations = np.arange(len(self.features_)) # the x locations for the groups + self.ax.set_yticks(tick_locations) + self.ax.set_yticklabels(self.get_feature_names()) + # Remove the ticks from the graph + self.ax.set_xlabel('Count') + + self.ax.legend(loc='best') + +########################################################################## +## Quick Method +########################################################################## + +def missing_bar(X, y=None, ax=None, classes=None, width=0.5, color='black', **kwargs): + """The MissingValues Bar visualizer creates a bar graph that lists the total + count of missing values for each selected feature column. + + When y targets are supplied to fit, the output is a stacked bar chart where + each color corresponds to the total NaNs for the feature in that column. + + Parameters + ---------- + alpha : float, default: 0.5 + A value for bending elments with the background. + + marker : matplotlib marker, default: | + The marker used for each element coordinate in the plot + + color : string, default: black + The color for drawing the bar chart when the y targets are not passed to + fit. + + colors : list, default: None + The color pallette for drawing a stack bar chart when the y targets + are passed to fit. + + classes : list, default: None + A list of class names for the legend. + If classes is None and a y value is passed to fit then the classes + are selected from the target vector. + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Attributes + ---------- + features_ : np.array + The feature labels ranked according to their importance + + classes_ : np.array + The class labels for each of the target values + + Examples + -------- + + >>> from yellowbrick.contrib.missing import missing_bar + >>> visualizer = missing_bar(X, y=y) + """ + # Instantiate the visualizer + visualizer = MissingValuesBar( + ax=ax, classes=classes, width=width, color=color, **kwargs + ) + + # Fit and transform the visualizer (calls draw) + visualizer.fit(X, y) + visualizer.poof() + + # Return the axes object on the visualizer + return visualizer.ax diff --git a/yellowbrick/contrib/missing/base.py b/yellowbrick/contrib/missing/base.py new file mode 100644 index 000000000..1c7310b8a --- /dev/null +++ b/yellowbrick/contrib/missing/base.py @@ -0,0 +1,69 @@ +# yellowbrick.contrib.missing.base +# Base Visualizer for missing values +# +# Author: Nathan Danielsen +# Created: Fri Mar 29 5:17:36 2018 -0500 +# +# Copyright (C) 2018 District Data Labs +# For license information, see LICENSE.txt +# +# ID: base.py [] nathan.danielsen@gmail.com.com $ + +""" +Base classes for missing values visualizers. +""" + +########################################################################## +## Imports +########################################################################## +import numpy as np + +from yellowbrick.features.base import DataVisualizer +from yellowbrick.utils import is_dataframe + +########################################################################## +## Feature Visualizers +########################################################################## + +class MissingDataVisualizer(DataVisualizer): + """Base class for MissingDataVisualizers. + """ + + def fit(self, X, y=None, **kwargs): + """ + The fit method is the primary drawing input for the + visualization since it has both the X and y data required for the + viz and the transform method does not. + + Parameters + ---------- + X : ndarray or DataFrame of shape n x m + A matrix of n instances with m features + + y : ndarray or Series of length n + An array or series of target or class values + + kwargs : dict + Pass generic arguments to the drawing method + + Returns + ------- + self : instance + Returns the instance of the transformer/visualizer + """ + if is_dataframe(X): + self.X = X.values + if self.features_ is None: + self.features_ = X.columns + else: + self.X = X + + self.y = y + + super(MissingDataVisualizer, self).fit(X, y, **kwargs) + + + def get_feature_names(self): + if self.features_ is None: + return ["Feature {}".format(str(n)) for n in np.arange(len(self.features_))] + return self.features_ diff --git a/yellowbrick/contrib/missing/dispersion.py b/yellowbrick/contrib/missing/dispersion.py new file mode 100644 index 000000000..e51760762 --- /dev/null +++ b/yellowbrick/contrib/missing/dispersion.py @@ -0,0 +1,226 @@ +# yellowbrick.contrib.missing.dispersion +# Missing Values Dispersion Visualizer +# +# Author: Nathan Danielsen +# Created: Fri Mar 29 5:17:36 2018 -0500 +# +# Copyright (C) 2018 District Data Labs +# For license information, see LICENSE.txt +# +# ID: dispersion.py [] nathan.danielsen@gmail.com.com $ + +""" +Dispersion visualizer for locations of missing values by column against index position. +""" + +########################################################################## +## Imports +########################################################################## + +import numpy as np + +from yellowbrick.style.palettes import color_palette +from .base import MissingDataVisualizer + + +########################################################################## +## MissingValues Visualizer +########################################################################## + +class MissingValuesDispersion(MissingDataVisualizer): + """ + The Missing Values Dispersion visualizer shows the locations of missing (nan) + values in the feature dataset by the order of the index. + + When y targets are supplied to fit, the output dispersion plot is color + coded according to the target y that the element refers to. + + Parameters + ---------- + alpha : float, default: 0.5 + A value for bending elments with the background. + + marker : matplotlib marker, default: | + The marker used for each element coordinate in the plot + + classes : list, default: None + A list of class names for the legend. + If classes is None and a y value is passed to fit then the classes + are selected from the target vector. + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Attributes + ---------- + features_ : np.array + The feature labels ranked according to their importance + + classes_ : np.array + The class labels for each of the target values + + Examples + -------- + + >>> from yellowbrick.contrib.missing import MissingValuesDispersion + >>> visualizer = MissingValuesDispersion() + >>> visualizer.fit(X, y=y) + >>> visualizer.poof() + """ + + def __init__(self, alpha=0.5, marker="|", classes=None, **kwargs): + + super(MissingValuesDispersion, self).__init__(**kwargs) + self.alpha = alpha + self.marker = marker + + self.classes_ = classes + + # Convert to array if necessary to match estimator.classes_ + if self.classes_ is not None: + self.classes_ = np.array(classes) + + # Set up classifier score visualization properties + if self.classes_ is not None: + n_colors = len(self.classes_) + else: + n_colors = None + + self.colors = color_palette(kwargs.pop('colors', None), n_colors) + + + def get_nan_locs(self, **kwargs): + """Gets the locations of nans in feature data and returns + the coordinates in the matrix + """ + if np.issubdtype(self.X.dtype, np.string_) or np.issubdtype(self.X.dtype, np.unicode_): + mask = np.where( self.X == '' ) + nan_matrix = np.zeros(self.X.shape) + nan_matrix[mask] = np.nan + + else: + nan_matrix = self.X.astype(float) + + if self.y is None: + return np.argwhere(np.isnan(nan_matrix)) + else: + nan_locs = [] + for target_value in np.unique(self.y): + indices = np.argwhere(self.y == target_value) + target_matrix = nan_matrix[indices.flatten()] + nan_target_locs = np.argwhere(np.isnan(target_matrix)) + nan_locs.append((target_value, nan_target_locs)) + + return nan_locs + + def draw(self, X, y, **kwargs): + """Called from the fit method, this method creates a scatter plot that + draws each instance as a class or target colored point, whose location + is determined by the feature data set. + + If y is not None, then it draws a scatter plot where each class is in a + different color. + """ + nan_locs = self.get_nan_locs() + if y is None: + x_, y_ = list(zip(*nan_locs)) + self.ax.scatter(x_, y_, alpha=self.alpha, marker=self.marker, label=None) + else: + self.draw_multi_dispersion_chart(nan_locs) + + def draw_multi_dispersion_chart(self, nan_locs): + """Draws a multi dimensional dispersion chart, each color corresponds + to a different target variable. + """ + for index, nan_values in enumerate(nan_locs): + label, nan_locations = nan_values + + # if features passed in then, label as such + if self.classes_ is not None: + label = self.classes_[index] + + color = self.colors[index] + + x_, y_ = list(zip(*nan_locations)) + self.ax.scatter(x_, y_, alpha=self.alpha, marker=self.marker, color=color, label=label) + + def finalize(self, **kwargs): + """ + Finalize executes any subclass-specific axes finalization steps. + The user calls poof and poof calls finalize. + + Parameters + ---------- + kwargs: generic keyword arguments. + + """ + # Set the title + self.set_title( + 'Dispersion of Missing Values by Feature' + ) + # the x locations for the groups + tick_locations = np.arange(len(self.features_)) + + self.ax.set_xlabel('Position by index') + self.ax.set_yticks(tick_locations) + self.ax.set_yticklabels(self.get_feature_names()) + self.ax.legend(loc='upper left', prop={'size':5}, bbox_to_anchor=(1,1)) + + + +########################################################################## +## Quick Method +########################################################################## + +def missing_dispersion(X, y=None, ax=None, classes=None, alpha=0.5, marker="|", **kwargs): + """ + The Missing Values Dispersion visualizer shows the locations of missing (nan) + values in the feature dataset by the order of the index. + + When y targets are supplied to fit, the output dispersion plot is color + coded according to the target y that the element refers to. + + Parameters + ---------- + alpha : float, default: 0.5 + A value for bending elments with the background. + + marker : matplotlib marker, default: | + The marker used for each element coordinate in the plot + + classes : list, default: None + A list of class names for the legend. + If classes is None and a y value is passed to fit then the classes + are selected from the target vector. + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Attributes + ---------- + features_ : np.array + The feature labels ranked according to their importance + + classes_ : np.array + The class labels for each of the target values + + Examples + -------- + + >>> from yellowbrick.contrib.missing import missing_dispersion + >>> visualizer = missing_dispersion(X, y=y) + + """ + # Instantiate the visualizer + visualizer = MissingValuesDispersion( + ax=ax, classes=classes, alpha=alpha, marker=marker, **kwargs + ) + + # Fit and transform the visualizer (calls draw) + visualizer.fit(X, y) + visualizer.poof() + + # Return the axes object on the visualizer + return visualizer.ax diff --git a/yellowbrick/contrib/scatter.py b/yellowbrick/contrib/scatter.py index bf23b7d09..5206df57b 100644 --- a/yellowbrick/contrib/scatter.py +++ b/yellowbrick/contrib/scatter.py @@ -37,6 +37,7 @@ def scatterviz(X, color=None, colormap=None, markers=None, + alpha=1.0, **kwargs): """Displays a bivariate scatter plot. @@ -71,14 +72,19 @@ def scatterviz(X, markers : iterable of strings, default: ,+o*vhd Matplotlib style markers for points on the scatter plot points + alpha : float, default: 1.0 + Specify a transparency where 1 is completely opaque and 0 is completely + transparent. This property makes densely clustered points more visible. + Returns ------- ax : matplotlib axes Returns the axes that the parallel coordinates were drawn on. """ # Instantiate the visualizer - visualizer = ScatterVisualizer(ax, features, classes, color, colormap, - markers, **kwargs) + visualizer = ScatterVisualizer(ax=ax, features=features, classes=classes, + color=color, colormap=colormap, + markers=markers, alpha=alpha, **kwargs) # Fit and transform the visualizer (calls draw) visualizer.fit(X, y, **kwargs) @@ -133,6 +139,10 @@ class ScatterVisualizer(DataVisualizer): markers : iterable of strings, default: ,+o*vhd Matplotlib style markers for points on the scatter plot points + alpha : float, default: 1.0 + Specify a transparency where 1 is completely opaque and 0 is completely + transparent. This property makes densely clustered points more visible. + kwargs : keyword arguments passed to the super class. These parameters can be influenced later on in the visualization @@ -148,6 +158,7 @@ def __init__(self, color=None, colormap=None, markers=None, + alpha=1.0, **kwargs): """ Initialize the base scatter with many of the options required in order @@ -158,6 +169,9 @@ def __init__(self, self.x = x self.y = y + + self.alpha = alpha + self.markers = itertools.cycle( kwargs.pop('markers', (',', '+', 'o', '*', 'v', 'h', 'd'))) @@ -280,6 +294,7 @@ def draw(self, X, y, **kwargs): marker=next(self.markers), color=colors[kls], label=str(kls), + alpha=self.alpha, **kwargs) self.ax.axis('equal') diff --git a/yellowbrick/datasaurus.py b/yellowbrick/datasaurus.py index 1051e5a3b..b65d31ccf 100644 --- a/yellowbrick/datasaurus.py +++ b/yellowbrick/datasaurus.py @@ -250,7 +250,7 @@ def datasaurus(): """ Creates 2x2 grid plot of 4 from the Datasaurus Dozen datasets for illustration. - + Citation: Justin Matejka, George Fitzmaurice (2017) Same Stats, Different Graphs: Generating Datasets with Varied Appearance and @@ -258,7 +258,7 @@ def datasaurus(): CHI 2017 Conference proceedings: ACM SIGCHI Conference on Human Factors in Computing Systems """ - fig, ((axa, axb), (axc, axd)) = plt.subplots(2, 2, sharex='col', sharey='row') + _, ((axa, axb), (axc, axd)) = plt.subplots(2, 2, sharex='col', sharey='row') colors = get_color_cycle() for arr, ax, color in zip(DATASAURUS, (axa, axb, axc, axd), colors): x = arr[0] @@ -266,7 +266,7 @@ def datasaurus(): # Draw the points in the scatter plot ax.scatter(x, y, c=color) - + # Set the X and Y limits ax.set_xlim(0, 100) ax.set_ylim(0, 110) diff --git a/yellowbrick/datasets/__init__.py b/yellowbrick/datasets/__init__.py index f72df1d4a..0535e081a 100644 --- a/yellowbrick/datasets/__init__.py +++ b/yellowbrick/datasets/__init__.py @@ -7,17 +7,3 @@ from .download import load_game from .download import load_bikeshare from .download import load_spam - - - - - - - - - - - - - - diff --git a/yellowbrick/datasets/download.py b/yellowbrick/datasets/download.py index 7f6fe652a..831c7da60 100644 --- a/yellowbrick/datasets/download.py +++ b/yellowbrick/datasets/download.py @@ -21,254 +21,123 @@ ########################################################################## import os -import sys -import hashlib -import zipfile -import json -import csv import numpy as np -try: - import requests -except ImportError: - print(( - "The requests module is required to download data --\n" - "please install it with pip install requests." - )) - sys.exit(1) - - -########################################################################## -## Links and MD5 hash of datasets -########################################################################## - -DATASETS = { - 'concrete': { - 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/concrete.zip', - 'signature': 'b9ea5f26a7bb272a040e2f1a993b26babbf8dc4a04ab8198bb315ca66d71f10d', - }, - 'energy': { - 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/energy.zip', - 'signature': '19fb86f3bcdde208eed46944172cb643ef6a7d58da103fb568fae43205ed89d3', - }, - 'credit': { - 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/credit.zip', - 'signature': '4a91339c69f55e18f3f48004328fbcb7868070b618208fed099920427b084e5e', - }, - 'occupancy': { - 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/occupancy.zip', - 'signature': '429cfe376dc9929a1fa528da89f0e1626e34e19695f3f555d8954025bbc522b8', - }, - 'mushroom': { - 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/mushroom.zip', - 'signature': '884c43cb70db35d211c67b1cf6a3683b2b4569393d2789d5c07840da4dc85ba8', - }, - 'hobbies': { - 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/hobbies.zip', - 'signature': '415c8f68df1486d5d84a1d1757a5aa3035aef5ad63ede5013c261d622fbd29d8', - }, - 'game': { - 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/game.zip', - 'signature': 'b1bd85789a014a898daa34cb5f89ceab6d2cd6488a2e572187e34aa4ec21a43b', - }, - 'bikeshare': { - 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/bikeshare.zip', - 'signature': 'a9b440f65549746dff680c92ff8bdca3c7265f09db1cf09e708e6e26fc8aba44', - }, - 'spam': { - 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/spam.zip', - 'signature': '65be21196ba3d8448847409b70a67d761f873f30719c807600eb516d7aef1de1', - }, -} - +from .utils import load_numpy, load_corpus, download_data, DATASETS +from .utils import _lookup_path ########################################################################## -## Download functions +## Functions ########################################################################## -def sha256sum(path, blocksize=65536): - """ - Computes the SHA256 signature of a file to verify that the file has not - been modified in transit and that it is the correct version of the data. - """ - sig = hashlib.sha256() - with open(path, 'rb') as f: - buf = f.read(blocksize) - while len(buf) > 0: - sig.update(buf) - buf = f.read(blocksize) - return sig.hexdigest() - - -def download_data(url, path='data', signature=None, extract=True): - """ - Downloads the zipped data set specified at the given URL, saving it to - the output path specified. This function verifies the download with the - given signature (if supplied) and extracts the zip file if requested. - """ - # Create the output directory if it does not exist - if not os.path.exists(path): - os.mkdir(path) - - # Get the name of the file from the URL - name = os.path.basename(url) - dlpath = os.path.join(path, name) - - # Fetch the response in a streaming fashion and write it to disk. - response = requests.get(url, stream=True) - with open(dlpath, 'wb') as f: - for chunk in response.iter_content(65536): - f.write(chunk) - - # If verify, compare the signature - if signature is not None: - dlsignature = sha256sum(dlpath) - if signature != dlsignature: - raise ValueError( - "Download signature does not match hardcoded signature!" - ) +FIXTURES = os.path.join(os.path.dirname(__file__), "fixtures") - # If extract, extract the zipfile. - if extract: - zf = zipfile.ZipFile(dlpath) - zf.extractall(path) - -def download_all(path='data', verify=True, extract=True): +def download_all(data_path=FIXTURES, verify=True): """ Downloads all the example datasets. If verify is True then compare the download signature with the hardcoded signature. If extract is True then extract the contents of the zipfile to the given path. """ for name, meta in DATASETS.items(): - url = meta['url'] - signature = meta['signature'] if verify else None - - download_data(url, path=path, signature=signature, extract=extract) - - -def _load_file_data(name, path='data', extract=True): - """ - Returns the information of the specified dataset. - """ - url = DATASETS[name]['url'] - signature = DATASETS[name]['signature'] - download_data(url, path=path, signature=signature, extract=extract) - with open(os.path.join(path, name, 'meta.json')) as meta_file: - feature_names = json.load(meta_file) - with open(os.path.join(path, name, 'README.md')) as readme_file: - description = readme_file.read() - with open(os.path.join(path, name, '{0}.csv'.format(name))) as csv_file: - data_file = csv.reader(csv_file) - # removing columns name - next(data_file) - data = np.asarray([line for line in data_file]) - result = {'data': data, 'DESCR': description} - for k, v in feature_names.items(): - result[k] = v - return result + download_data(name, data_dir=data_path) -def load_concrete(path='data', extract=True): +def load_concrete(data_path=FIXTURES): """ Downloads the 'concrete' dataset, saving it to the output path specified and returns the data. """ # name of the dataset name = 'concrete' - data = _load_file_data(name, path, extract) + data = load_numpy(name, data_path=data_path) return data -def load_energy(path='data', extract=True): +def load_energy(data_path=FIXTURES): """ Downloads the 'energy' dataset, saving it to the output path specified and returns the data. """ # name of the dataset name = 'energy' - data = _load_file_data(name, path, extract) + data = load_numpy(name, data_path=data_path) return data -def load_credit(path='data', extract=True): +def load_credit(data_path=FIXTURES): """ Downloads the 'credit' dataset, saving it to the output path specified and returns the data. """ # name of the dataset name = 'credit' - data = _load_file_data(name, path, extract) + data = load_numpy(name, data_path=data_path) return data -def load_occupancy(path='data', extract=True): +def load_occupancy(data_path=FIXTURES): """ Downloads the 'occupancy' dataset, saving it to the output path specified and returns the data. """ # name of the dataset name = 'occupancy' - data = _load_file_data(name, path, extract) + data = load_numpy(name, data_path=data_path) return data -def load_mushroom(path='data', extract=True): +def load_mushroom(data_path=FIXTURES): """ Downloads the 'mushroom' dataset, saving it to the output path specified and returns the data. """ # name of the dataset name = 'mushroom' - data = _load_file_data(name, path, extract) + data = load_numpy(name, data_path=data_path) return data -def load_hobbies(path='data', extract=True): +def load_hobbies(data_path=FIXTURES): """ Downloads the 'hobbies' dataset, saving it to the output path specified and returns the data. """ # name of the dataset name = 'hobbies' - data = _load_file_data(name, path, extract) + data = load_corpus(name, data_path=data_path) return data -def load_game(path='data', extract=True): +def load_game(data_path=FIXTURES): """ Downloads the 'game' dataset, saving it to the output path specified and returns the data. """ # name of the dataset name = 'game' - data = _load_file_data(name, path, extract) - return data + path = _lookup_path(name, data_path=data_path) + dtype = np.array(['S1']*42+['|S4']) + return np.genfromtxt(path, dtype=dtype, delimiter=',', names=True) -def load_bikeshare(path='data', extract=True): +def load_bikeshare(data_path=FIXTURES): """ Downloads the 'bikeshare' dataset, saving it to the output path specified and returns the data. """ # name of the dataset name = 'bikeshare' - data = _load_file_data(name, path, extract) + data = load_numpy(name, data_path=data_path) return data -def load_spam(path='data', extract=True): +def load_spam(data_path=FIXTURES): """ Downloads the 'spam' dataset, saving it to the output path specified and returns the data. """ # name of the dataset name = 'spam' - data = _load_file_data(name, path, extract) + data = load_numpy(name, skip_header=True, data_path=data_path) return data - - -if __name__ == '__main__': - path = 'data' - download_all(path) - print("Downloaded datasets to {}".format(os.path.abspath(path))) diff --git a/yellowbrick/datasets/utils.py b/yellowbrick/datasets/utils.py new file mode 100644 index 000000000..5abd0002d --- /dev/null +++ b/yellowbrick/datasets/utils.py @@ -0,0 +1,207 @@ +#!/usr/bin/env python +""" +Utils for downloading datasets for running the examples. +""" + +########################################################################## +## Imports +########################################################################## + +import os +import six +import hashlib +import zipfile +import numpy as np + +from sklearn.datasets.base import Bunch + +if six.PY2: + # backport for encoding in open for python2 + from io import open + +try: + from urllib.request import urlopen +except ImportError: + # python 2 + from urllib2 import urlopen + +try: + import pandas as pd +except ImportError: + pd = None + +########################################################################## +## Links and MD5 hash of datasets +########################################################################## + +DATASETS = { + 'concrete': { + 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/concrete.zip', + 'signature': 'b9ea5f26a7bb272a040e2f1a993b26babbf8dc4a04ab8198bb315ca66d71f10d', + 'type': 'numpy', + }, + 'energy': { + 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/energy.zip', + 'signature': '19fb86f3bcdde208eed46944172cb643ef6a7d58da103fb568fae43205ed89d3', + 'type': 'numpy', + }, + 'credit': { + 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/credit.zip', + 'signature': '4a91339c69f55e18f3f48004328fbcb7868070b618208fed099920427b084e5e', + 'type': 'numpy', + }, + 'occupancy': { + 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/occupancy.zip', + 'signature': '429cfe376dc9929a1fa528da89f0e1626e34e19695f3f555d8954025bbc522b8', + 'type': 'numpy', + }, + 'mushroom': { + 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/mushroom.zip', + 'signature': '884c43cb70db35d211c67b1cf6a3683b2b4569393d2789d5c07840da4dc85ba8', + 'type': 'numpy', + }, + 'game': { + 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/game.zip', + 'signature': 'b1bd85789a014a898daa34cb5f89ceab6d2cd6488a2e572187e34aa4ec21a43b', + 'type': 'numpy', + }, + 'bikeshare': { + 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/bikeshare.zip', + 'signature': 'a9b440f65549746dff680c92ff8bdca3c7265f09db1cf09e708e6e26fc8aba44', + 'type': 'numpy', + }, + 'spam': { + 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/spam.zip', + 'signature': '65be21196ba3d8448847409b70a67d761f873f30719c807600eb516d7aef1de1', + 'type': 'numpy', + }, + 'hobbies': { + 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/hobbies.zip', + 'signature': '415c8f68df1486d5d84a1d1757a5aa3035aef5ad63ede5013c261d622fbd29d8', + 'type': 'corpus', + }, +} + + +########################################################################## +## Download functions +########################################################################## + +def sha256sum(path, blocksize=65536): + """ + Computes the SHA256 signature of a file to verify that the file has not + been modified in transit and that it is the correct version of the data. + """ + sig = hashlib.sha256() + with open(path, 'rb') as f: + buf = f.read(blocksize) + while len(buf) > 0: + sig.update(buf) + buf = f.read(blocksize) + return sig.hexdigest() + + +def download_data(name, data_dir=None, signature=None, extract=True): + """ + Downloads the zipped data set specified at the given URL, saving it to + the output path specified. This function verifies the download with the + given signature (if supplied) and extracts the zip file if requested. + """ + + # Create the fixture directory + if not os.path.exists(data_dir): + os.mkdir(data_dir) + + dataset = DATASETS[name] + url = dataset['url'] + + # Get the name of the file from the URL + filename = os.path.basename(url) + dlpath = os.path.join(data_dir, filename) + dataset_path = os.path.join(data_dir, name) + + #Create the output directory if it does not exist + if not os.path.exists(dataset_path): + os.mkdir(dataset_path) + + # Fetch the response in a streaming fashion and write it to disk. + response = urlopen(url) + CHUNK = 16 * 1024 + with open(dlpath, 'wb') as f: + + while True: + chunk = response.read(CHUNK) + if not chunk: + break + f.write(chunk) + + # If verify, compare the signature + if signature is not None: + dlsignature = sha256sum(dlpath) + if signature != dlsignature: + raise ValueError( + "Download signature does not match hardcoded signature!" + ) + + # If extract, extract the zipfile. + if extract: + zf = zipfile.ZipFile(dlpath) + zf.extractall(path=data_dir) + +def load_numpy(name, data_path=None, **kwargs): + """ + Loads the numpy matrix from the specified data set, downloads it if + it hasn't already been downloaded. + """ + + path = _lookup_path(name, data_path=data_path) + return np.genfromtxt(path, dtype=float, delimiter=',', names=True, **kwargs) + + +def load_corpus(name, data_path=None): + """ + Loads a sklearn Bunch with the corpus and downloads it if it hasn't + already been downloaded. Used to test text visualizers. + """ + path = _lookup_path(name, data_path=data_path, ext=None) + + # Read the directories in the directory as the categories. + categories = [ + cat for cat in os.listdir(path) + if os.path.isdir(os.path.join(path, cat)) + ] + + files = [] # holds the file names relative to the root + data = [] # holds the text read from the file + target = [] # holds the string of the category + + # Load the data from the files in the corpus + for cat in categories: + for name in os.listdir(os.path.join(path, cat)): + files.append(os.path.join(path, cat, name)) + target.append(cat) + + with open(os.path.join(path, cat, name), 'r', encoding='UTF-8') as f: + data.append(f.read()) + + # Return the data bunch for use similar to the newsgroups example + return Bunch( + categories=categories, + files=files, + data=data, + target=target, + ) + +def _lookup_path(name, data_path=None, ext=".csv"): + """ + Looks up the path to the dataset, downloading it if necessary + """ + if ext is None: + path = os.path.join(data_path, name) + else: + path = os.path.join(data_path, name, "{}{}".format(name, ext)) + + if not os.path.exists(path): + download_data(name, signature=None, extract=True, data_dir=data_path) + + return path diff --git a/yellowbrick/draw.py b/yellowbrick/draw.py new file mode 100644 index 000000000..e7b4f80c2 --- /dev/null +++ b/yellowbrick/draw.py @@ -0,0 +1,90 @@ +# yellowbrick.draw +# Utilities for common matplotlib drawing procedures. +# +# Author: Benjamin Bengfort +# Created: Sun Aug 19 10:35:50 2018 -0400 +# +# ID: draw.py [] benjamin@bengfort.com $ + +""" +Utilities for common matplotlib drawing procedures. +""" + +########################################################################## +## Imports +########################################################################## + +from .base import Visualizer +from .exceptions import YellowbrickValueError + +from matplotlib import patches + +import matplotlib.pyplot as plt + + +########################################################################## +## Legend Drawing Utilities +########################################################################## + +def manual_legend(g, labels, colors, **legend_kwargs): + """ + Adds a manual legend for a scatter plot to the visualizer where the labels + and associated colors are drawn with circle patches instead of determining + them from the labels of the artist objects on the axes. This helper is + used either when there are a lot of duplicate labels, no labeled artists, + or when the color of the legend doesn't exactly match the color in the + figure (e.g. because of the use of transparency). + + Parameters + ---------- + g : Visualizer or Axes object + The graph to draw the legend on, either a Visualizer or a matplotlib + Axes object. If None, the current axes are drawn on, but this is not + recommended. + + labels : list of str + The text labels to associate with the legend. Note that the labels + will be added to the legend in the order specified. + + colors : list of colors + A list of any valid matplotlib color reference. The number of colors + specified must be equal to the number of labels. + + legend_kwargs : dict + Any additional keyword arguments to pass to the legend. + + Returns + ------- + legend: Legend artist + The artist created by the ax.legend() call, returned for further + manipulation if required by the caller. + + Notes + ----- + Right now this method simply draws the patches as rectangles and cannot + take into account the line or scatter plot properties (e.g. line style or + marker style). It is possible to add Line2D patches to the artist that do + add manual styles like this, which we can explore in the future. + + .. seealso:: https://matplotlib.org/gallery/text_labels_and_annotations/custom_legends.html + """ + # Get access to the matplotlib Axes + if isinstance(g, Visualizer): + g = g.ax + elif g is None: + g = plt.gca() + + # Ensure that labels and colors are the same length to prevent odd behavior. + if len(colors) != len(labels): + raise YellowbrickValueError( + "please specify the same number of colors as labels!" + ) + + # Create the legend handles with the associated colors and labels + handles = [ + patches.Patch(color=color, label=label) + for color, label in zip(colors, labels) + ] + + # Return the Legend artist + return g.legend(handles=handles, **legend_kwargs) diff --git a/yellowbrick/features/__init__.py b/yellowbrick/features/__init__.py index 3f8e6be0a..c42917e8e 100644 --- a/yellowbrick/features/__init__.py +++ b/yellowbrick/features/__init__.py @@ -25,3 +25,4 @@ from .pca import PCADecomposition, pca_decomposition from .importances import FeatureImportances, feature_importances from .rfecv import RFECV, rfecv +from .manifold import Manifold, manifold_embedding diff --git a/yellowbrick/features/base.py b/yellowbrick/features/base.py index 50078994b..5623480eb 100644 --- a/yellowbrick/features/base.py +++ b/yellowbrick/features/base.py @@ -218,7 +218,7 @@ def fit(self, X, y=None, **kwargs): # Store the classes for the legend if they're None. if self.classes_ is None: # TODO: Is this the most efficient method? - self.classes_ = [str(label) for label in set(y)] + self.classes_ = [str(label) for label in np.unique(y)] # Draw the instances self.draw(X, y, **kwargs) diff --git a/yellowbrick/features/importances.py b/yellowbrick/features/importances.py index f9280bcb1..a82671748 100644 --- a/yellowbrick/features/importances.py +++ b/yellowbrick/features/importances.py @@ -24,9 +24,10 @@ import numpy as np import matplotlib.pyplot as plt -from yellowbrick.utils import is_dataframe +from yellowbrick.utils import is_dataframe, is_classifier from yellowbrick.base import ModelVisualizer from yellowbrick.exceptions import YellowbrickTypeError, NotFitted +from ..style.palettes import color_palette ########################################################################## @@ -72,6 +73,11 @@ class FeatureImportances(ModelVisualizer): The label for the X-axis. If None is automatically determined by the underlying model and options provided. + stack : bool, default: False + If true and the classifier returns multi-class feature importance, + then a stacked bar plot is plotted; otherwise the mean of the + feature importance across classes are plotted. + kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. @@ -84,6 +90,9 @@ class FeatureImportances(ModelVisualizer): feature_importances_ : np.array The numeric value of the feature importance computed by the model + classes_ : np.array + The classees labeled. Is not None only for classifier. + Examples -------- @@ -94,13 +103,13 @@ class FeatureImportances(ModelVisualizer): """ def __init__(self, model, ax=None, labels=None, relative=True, - absolute=False, xlabel=None, **kwargs): + absolute=False, xlabel=None, stack=False, **kwargs): super(FeatureImportances, self).__init__(model, ax, **kwargs) # Data Parameters self.set_params( labels=labels, relative=relative, absolute=absolute, - xlabel=xlabel, + xlabel=xlabel, stack=stack ) def fit(self, X, y=None, **kwargs): @@ -129,15 +138,20 @@ def fit(self, X, y=None, **kwargs): # Get the feature importances from the model self.feature_importances_ = self._find_importances_param() - # If feature importances is a multidim array, we're expecting a shape of - # (n_classes, n_features) therefore we flatten by taking the average by - # column to get shape (n_features,) (see LogisticRegression) - if self.feature_importances_.ndim > 1: - self.feature_importances_ = np.mean(self.feature_importances_, axis=0) + # Get the classes from the model + if is_classifier(self): + self.classes_ = self._find_classes_param() + else: + self.classes_ = None + self.stack = False - # TODO - as an alternative to the above flattening approach, explore an - # alternative visualize that uses the array shape to create a stacked bar chart - # of feature importances for each class/feature combination + # If self.stack = True and feature importances is a multidim array, + # we're expecting a shape of (n_classes, n_features) + # therefore we flatten by taking the average by + # column to get shape (n_features,) (see LogisticRegression) + if not self.stack and self.feature_importances_.ndim > 1: + self.feature_importances_ = np.mean(self.feature_importances_, + axis=0) # Apply absolute value filter before normalization if self.absolute: @@ -145,7 +159,7 @@ def fit(self, X, y=None, **kwargs): # Normalize features relative to the maximum if self.relative: - maxv = self.feature_importances_.max() + maxv = np.abs(self.feature_importances_).max() self.feature_importances_ /= maxv self.feature_importances_ *= 100.0 @@ -164,9 +178,14 @@ def fit(self, X, y=None, **kwargs): self.features_ = np.array(self.labels) # Sort the features and their importances - sort_idx = np.argsort(self.feature_importances_) - self.features_ = self.features_[sort_idx] - self.feature_importances_ = self.feature_importances_[sort_idx] + if self.stack: + sort_idx = np.argsort(np.mean(self.feature_importances_, 0)) + self.features_ = self.features_[sort_idx] + self.feature_importances_ = self.feature_importances_[:, sort_idx] + else: + sort_idx = np.argsort(self.feature_importances_) + self.features_ = self.features_[sort_idx] + self.feature_importances_ = self.feature_importances_[sort_idx] # Draw the feature importances self.draw() @@ -185,7 +204,27 @@ def draw(self, **kwargs): pos = np.arange(self.features_.shape[0]) + 0.5 # Plot the bar chart - self.ax.barh(pos, self.feature_importances_, align='center') + if self.stack: + colors = color_palette(kwargs.pop('colors', None), + len(self.classes_)) + zeros = np.zeros(self.feature_importances_.shape[1]) + left_arr = np.zeros((self.feature_importances_.shape[1], 2)) + + for idx in range(len(self.feature_importances_)): + left = [ + left_arr[j, int(self.feature_importances_[idx][j] > 0)] + for j in range(len(self.feature_importances_[idx])) + ] + + self.ax.barh(pos, self.feature_importances_[idx], left=left, + color=colors[idx], label=self.classes_[idx]) + + left_arr[:, 0] += np.minimum(self.feature_importances_[idx], + zeros) + left_arr[:, 1] += np.maximum(self.feature_importances_[idx], + zeros) + else: + self.ax.barh(pos, self.feature_importances_, align='center') # Set the labels for the bars self.ax.set_yticks(pos) @@ -207,9 +246,27 @@ def finalize(self, **kwargs): # Remove the ygrid self.ax.grid(False, axis='y') + if self.stack: + plt.legend(bbox_to_anchor=(1.04, 0.5), loc="center left") # Ensure we have a tight fit plt.tight_layout() + def _find_classes_param(self): + """ + Searches the wrapped model for the classes_ parameter. + """ + for attr in ["classes_"]: + try: + return getattr(self.estimator, attr) + except AttributeError: + continue + + raise YellowbrickTypeError( + "could not find classes_ param on {}".format( + self.estimator.__class__.__name__ + ) + ) + def _find_importances_param(self): """ Searches the wrapped model for the feature importances parameter. @@ -257,7 +314,8 @@ def _is_fitted(self): ########################################################################## def feature_importances(model, X, y=None, ax=None, labels=None, - relative=True, absolute=False, xlabel=None, **kwargs): + relative=True, absolute=False, xlabel=None, + stack=False, **kwargs): """ Displays the most informative features in a model by showing a bar chart of features ranked by their importances. Although primarily a feature @@ -297,6 +355,11 @@ def feature_importances(model, X, y=None, ax=None, labels=None, The label for the X-axis. If None is automatically determined by the underlying model and options provided. + stack : bool, default: False + If true and the classifier returns multi-class feature importance, + then a stacked bar plot is plotted; otherwise the mean of the + feature importance across classes are plotted. + kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. @@ -308,7 +371,7 @@ def feature_importances(model, X, y=None, ax=None, labels=None, """ # Instantiate the visualizer visualizer = FeatureImportances( - model, ax, labels, relative, absolute, xlabel, **kwargs) + model, ax, labels, relative, absolute, xlabel, stack, **kwargs) # Fit and transform the visualizer (calls draw) visualizer.fit(X, y) diff --git a/yellowbrick/features/jointplot.py b/yellowbrick/features/jointplot.py index fc8443c30..4403745c2 100644 --- a/yellowbrick/features/jointplot.py +++ b/yellowbrick/features/jointplot.py @@ -292,15 +292,6 @@ def draw_xy(self, X, y, **kwargs): self.y_ax.hist(y, bins=hist_bins, color=histcolor_y, orientation='horizontal', **self.xy_args) - def poof(self, **kwargs): - """ - Creates the labels for the feature and target variables - """ - - self.joint_ax.set_xlabel(self.feature) - self.joint_ax.set_ylabel(self.target) - self.finalize(**kwargs) - def finalize(self, **kwargs): """ Finalize executes any subclass-specific axes finalization steps. @@ -310,6 +301,8 @@ def finalize(self, **kwargs): ---------- kwargs: generic keyword arguments. """ + self.joint_ax.set_xlabel(self.feature) + self.joint_ax.set_ylabel(self.target) plt.setp(self.x_ax.get_xticklabels(), visible=False) plt.setp(self.y_ax.get_yticklabels(), visible=False) diff --git a/yellowbrick/features/manifold.py b/yellowbrick/features/manifold.py index 5798f93f0..8a6577040 100644 --- a/yellowbrick/features/manifold.py +++ b/yellowbrick/features/manifold.py @@ -14,13 +14,13 @@ ## Imports ########################################################################## -import time import numpy as np import matplotlib.pyplot as plt from six import string_types -from matplotlib import patches +from yellowbrick.utils.timer import Timer +from yellowbrick.draw import manual_legend from yellowbrick.utils.types import is_estimator from yellowbrick.style import palettes, resolve_colors from yellowbrick.features.base import FeatureVisualizer @@ -151,7 +151,7 @@ class in the target. Attributes ---------- - fit_time_ : float + fit_time_ : yellowbrick.utils.timer.Timer The amount of time in seconds it took to fit the Manifold. classes_ : np.ndarray, optional @@ -262,6 +262,14 @@ def manifold(self, transformer): self._name = self._manifold.__class__.__name__ def fit(self, X, y=None): + """ + Fits the manifold on X and transforms the data to plot it on the axes. + See fit_transform() for more details. + """ + self.fit_transform(X, y) + return self + + def fit_transform(self, X, y=None): """ Fits the manifold on X and transforms the data to plot it on the axes. The optional y specified can be used to declare discrete colors. If @@ -306,12 +314,11 @@ def fit(self, X, y=None): y = np.asarray(y) self.range_ = (y.min(), y.max()) - start = time.time() - Xp = self.manifold.fit_transform(X) - self.fit_time_ = time.time() - start + with Timer() as self.fit_time_: + Xp = self.manifold.fit_transform(X) self.draw(Xp, y) - return self + return Xp def transform(self, X): """ @@ -327,7 +334,10 @@ def transform(self, X): Xprime : array-like of shape (n, 2) Returns the 2-dimensional embedding of the instances. """ - return self.manifold.transform(X) + try: + return self.manifold.transform(X) + except AttributeError as e: + raise AttributeError(str(e) + " try using fit_transform instead.") def draw(self, X, y=None): """ @@ -384,7 +394,7 @@ def finalize(self): """ self.set_title( '{} Manifold (fit in {:0.2f} seconds)'.format( - self._name, self.fit_time_ + self._name, self.fit_time_.interval ) ) self.ax.set_xticklabels([]) @@ -392,11 +402,7 @@ def finalize(self): if self._target_color_type == DISCRETE: # Add the legend - handles = [ - patches.Patch(color=self._colors[idx], label=self.classes_[idx]) - for idx in range(len(self.classes_)) - ] - self.ax.legend(handles=handles) + manual_legend(self, self.classes_, self._colors) elif self._target_color_type == CONTINUOUS: # Add the color bar diff --git a/yellowbrick/features/pcoords.py b/yellowbrick/features/pcoords.py index 642f7676e..f5c742ed4 100644 --- a/yellowbrick/features/pcoords.py +++ b/yellowbrick/features/pcoords.py @@ -21,12 +21,11 @@ import numpy as np from six import string_types -from matplotlib import patches -from operator import itemgetter from numpy.random import RandomState from sklearn.preprocessing import MinMaxScaler, MaxAbsScaler from sklearn.preprocessing import Normalizer, StandardScaler +from yellowbrick.draw import manual_legend from yellowbrick.utils import is_dataframe, is_series from yellowbrick.features.base import DataVisualizer from yellowbrick.exceptions import YellowbrickTypeError, YellowbrickValueError @@ -138,7 +137,7 @@ class ParallelCoordinates(DataVisualizer): Parallel coordinates displays each feature as a vertical axis spaced evenly along the horizontal, and each instance as a line drawn between each individual axis. This allows you to detect braids of similar instances - and separability that suggests a good classification problem. + and separability that suggests a good classification problem. Parameters ---------- @@ -341,9 +340,9 @@ def fit(self, X, y=None, **kwargs): if self.features_ is None: self.features_ = np.array(X.columns) - X = X.as_matrix() + X = X.values if is_series(y): - y = y.as_matrix() + y = y.values # Assign integer labels to the feature columns from the input if self.features_ is None: @@ -513,12 +512,10 @@ def finalize(self, **kwargs): self.ax.set_xticklabels(self.features_) self.ax.set_xlim(self._increments[0], self._increments[-1]) - # Add the legend - handles = [ - patches.Patch(color=color, label=label) - for label, color in sorted(self._colors.items(), key=itemgetter(0)) - ] - self.ax.legend(handles=handles, loc='best', frameon=True) + # Add the legend sorting classes by name + labels = sorted(list(self._colors.keys())) + colors = [self._colors[lbl] for lbl in labels] + manual_legend(self, labels, colors, loc='best', frameon=True) # Add the grid view self.ax.grid() diff --git a/yellowbrick/features/radviz.py b/yellowbrick/features/radviz.py index bfb19fd8c..714beed63 100644 --- a/yellowbrick/features/radviz.py +++ b/yellowbrick/features/radviz.py @@ -20,9 +20,10 @@ import numpy as np import matplotlib.patches as patches +from yellowbrick.draw import manual_legend from yellowbrick.utils import is_dataframe +from yellowbrick.utils import nan_warnings from yellowbrick.features.base import DataVisualizer -import yellowbrick.utils.nan_warnings as nan_warnings from yellowbrick.style.colors import resolve_colors @@ -169,7 +170,7 @@ def draw(self, X, y, **kwargs): """ # Convert from dataframe if is_dataframe(X): - X = X.as_matrix() + X = X.values # Clean out nans and warn that the user they aren't plotted nan_warnings.warn_if_nans_exist(X) @@ -188,7 +189,7 @@ def draw(self, X, y, **kwargs): color_values = resolve_colors( n_colors=len(self.classes_), colormap=self.colormap, colors=self.color ) - colors = dict(zip(self.classes_, color_values)) + self._colors = dict(zip(self.classes_, color_values)) # Create a data structure to hold scatter plot representations to_plot = {} @@ -220,7 +221,7 @@ def draw(self, X, y, **kwargs): # TODO: make this a separate function for i, kls in enumerate(self.classes_): self.ax.scatter( - to_plot[kls][0], to_plot[kls][1], color=colors[kls], + to_plot[kls][0], to_plot[kls][1], color=self._colors[kls], label=str(kls), alpha=self.alpha, **kwargs ) @@ -267,7 +268,8 @@ def finalize(self, **kwargs): self.ax.set_xticks([]) # Add the legend - self.ax.legend(loc='best') + colors = [self._colors[c] for c in self.classes_] + manual_legend(self, self.classes_, colors, loc='best') # Alias for RadViz diff --git a/yellowbrick/features/rfecv.py b/yellowbrick/features/rfecv.py index e53bf89e5..0b5a65a14 100644 --- a/yellowbrick/features/rfecv.py +++ b/yellowbrick/features/rfecv.py @@ -32,7 +32,7 @@ class RFECV(ModelVisualizer): """ Recursive Feature Elimination, Cross-Validated (RFECV) feature selection. - Selects the best subset of features for the suplied estimator by removing + Selects the best subset of features for the supplied estimator by removing 0 to N features (where N is the number of features) using recursive feature elimination, then selecting the best subset based on the cross-validation score of the model. Recursive feature elimination diff --git a/yellowbrick/model_selection/__init__.py b/yellowbrick/model_selection/__init__.py index 759e51615..78c9c245a 100644 --- a/yellowbrick/model_selection/__init__.py +++ b/yellowbrick/model_selection/__init__.py @@ -16,4 +16,4 @@ from .learning_curve import LearningCurve, learning_curve from .validation_curve import ValidationCurve, validation_curve -from .cv import CVScores, cv_scores +from .cross_validation import CVScores, cv_scores diff --git a/yellowbrick/model_selection/cross_validation.py b/yellowbrick/model_selection/cross_validation.py new file mode 100644 index 000000000..dca1b5c02 --- /dev/null +++ b/yellowbrick/model_selection/cross_validation.py @@ -0,0 +1,241 @@ +# yellowbrick.model_selection.cross_validation +# Implements cross-validation score plotting for model selection. +# +# Author: Prema Damodaran Roman +# Created: Wed June 6 2018 13:32:00 -0500 +# Author: Rebecca Bilbro +# Updated: Fri Aug 10 13:15:43 2018 -0500 +# +# ID: cross_validation.py [7f47800] pdamo24@gmail.com $ + +""" +Implements cross-validation score plotting for model selection. +""" + +########################################################################## +## Imports +########################################################################## + +import numpy as np +import matplotlib.ticker as ticker + +from yellowbrick.base import ModelVisualizer +from sklearn.model_selection import cross_val_score + + +########################################################################## +## CVScores Visualizer +########################################################################## + +class CVScores(ModelVisualizer): + """ + CVScores displays cross-validated scores as a bar chart, with the + average of the scores plotted as a horizontal line. + + Parameters + ---------- + + model : a scikit-learn estimator + An object that implements ``fit`` and ``predict``, can be a + classifier, regressor, or clusterer so long as there is also a valid + associated scoring metric. + Note that the object is cloned for each validation. + + ax : matplotlib.Axes object, optional + The axes object to plot the figure on. + + cv : int, cross-validation generator or an iterable, optional + Determines the cross-validation splitting strategy. + Possible inputs for cv are: + + - None, to use the default 3-fold cross-validation, + - integer, to specify the number of folds. + - An object to be used as a cross-validation generator. + - An iterable yielding train/test splits. + + See the scikit-learn `cross-validation guide `_ + for more information on the possible strategies that can be used here. + + scoring : string, callable or None, optional, default: None + A string or scorer callable object / function with signature + ``scorer(estimator, X, y)``. + + See scikit-learn `cross-validation guide `_ + for more information on the possible metrics that can be used. + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Examples + -------- + + >>> from sklearn import datasets, svm + >>> iris = datasets.load_iris() + >>> clf = svm.SVC(kernel='linear', C=1) + >>> X = iris.data + >>> y = iris.target + >>> visualizer = CVScores(model=clf, cv=5, scoring='f1_macro') + >>> visualizer.fit(X,y) + >>> visualizer.poof() + + Notes + ----- + + This visualizer is a wrapper for + `sklearn.model_selection.cross_val_score `_. + + Refer to the scikit-learn + `cross-validation guide `_ + for more details. + + """ + + def __init__(self, model, ax=None, cv=None, scoring=None, **kwargs): + super(CVScores, self).__init__(model, ax=ax, **kwargs) + + self.cv = cv + self.scoring = scoring + + def fit(self, X, y, **kwargs): + """ + Fits the learning curve with the wrapped model to the specified data. + Draws training and test score curves and saves the scores to the + estimator. + + Parameters + ---------- + X : array-like, shape (n_samples, n_features) + Training vector, where n_samples is the number of samples and + n_features is the number of features. + + y : array-like, shape (n_samples) or (n_samples, n_features), optional + Target relative to X for classification or regression; + None for unsupervised learning. + + Returns + ------- + self : instance + + """ + + self.cv_scores_ = cross_val_score( + self.estimator, X, y, cv=self.cv, scoring=self.scoring + ) + self.cv_scores_mean_ = self.cv_scores_.mean() + + self.draw() + return self + + def draw(self, **kwargs): + """ + Creates the bar chart of the cross-validated scores generated from the + fit method and places a dashed horizontal line that represents the + average value of the scores. + """ + + color = kwargs.pop("color", "b") + width = kwargs.pop("width", 0.3) + linewidth = kwargs.pop("linewidth", 1) + + xvals = np.arange(1, len(self.cv_scores_) + 1, 1) + self.ax.bar(xvals, self.cv_scores_, width=width) + self.ax.axhline( + self.cv_scores_mean_, color=color, + label="Mean score = {:0.3f}".format(self.cv_scores_mean_), + linestyle='--', linewidth=linewidth + ) + + return self.ax + + def finalize(self, **kwargs): + """ + Add the title, legend, and other visual final touches to the plot. + """ + + # Set the title of the figure + self.set_title('Cross Validation Scores for {}'.format(self.name)) + + # Add the legend + loc = kwargs.pop("loc", "best") + edgecolor = kwargs.pop("edgecolor", "k") + self.ax.legend(frameon=True, loc=loc, edgecolor=edgecolor) + + # set spacing between the x ticks + self.ax.xaxis.set_major_locator(ticker.MultipleLocator(1)) + + # Set the axis labels + self.ax.set_xlabel('Training Instances') + self.ax.set_ylabel('Score') + + +########################################################################## +## Quick Method +########################################################################## + +def cv_scores(model, X, y, ax=None, cv=None, scoring=None, **kwargs): + """ + Displays cross validation scores as a bar chart and the + average of the scores as a horizontal line + + This helper function is a quick wrapper to utilize the + CVScores visualizer for one-off analysis. + + Parameters + ---------- + + model : a scikit-learn estimator + An object that implements ``fit`` and ``predict``, can be a + classifier, regressor, or clusterer so long as there is also a valid + associated scoring metric. + Note that the object is cloned for each validation. + + X : array-like, shape (n_samples, n_features) + Training vector, where n_samples is the number of samples and + n_features is the number of features. + + y : array-like, shape (n_samples) or (n_samples, n_features), optional + Target relative to X for classification or regression; + None for unsupervised learning. + + ax : matplotlib.Axes object, optional + The axes object to plot the figure on. + + cv : int, cross-validation generator or an iterable, optional + Determines the cross-validation splitting strategy. + Possible inputs for cv are: + + - None, to use the default 3-fold cross-validation, + - integer, to specify the number of folds. + - An object to be used as a cross-validation generator. + - An iterable yielding train/test splits. + + see the scikit-learn + `cross-validation guide `_ + for more information on the possible strategies that can be used here. + + scoring : string, callable or None, optional, default: None + A string or scorer callable object / function with signature + ``scorer(estimator, X, y)``. + + See scikit-learn `cross-validation guide `_ + for more information on the possible metrics that can be used. + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Returns + ------- + ax : matplotlib.Axes + The axes object that the validation curves were drawn on. + + """ + + # Initialize the visualizer + visualizer = CVScores(model, ax=ax, cv=cv, scoring=scoring) + + # Fit and poof the visualizer + visualizer.fit(X, y) + visualizer.poof(**kwargs) + return visualizer.ax diff --git a/yellowbrick/model_selection/cv.py b/yellowbrick/model_selection/cv.py deleted file mode 100644 index 96749a0c0..000000000 --- a/yellowbrick/model_selection/cv.py +++ /dev/null @@ -1,237 +0,0 @@ - -# coding: utf-8 - -# In[ ]: - -# yellowbrick.model_selection.cv -# -# -# Author: Prema Damodaran Roman - -# -# Copyright (C) 2018 District Data Labs -# For license information, see LICENSE.txt -# -# ID: cv.py [7f47800] pdamo24@gmail.com $ - -########################################################################## -## Imports -########################################################################## - -import numpy as np -import matplotlib.ticker as ticker - -from yellowbrick.base import ModelVisualizer -from sklearn.model_selection import cross_val_score - -########################################################################## -## CVScores Visualizer -########################################################################## - -class CVScores(ModelVisualizer): - """ - CVScores displays cross validation scores as a bar chart and the - average of the scores as a horizontal line - - Parameters - ---------- - - model : a scikit-learn estimator - An object that implements ``fit`` and ``predict``, can be a - classifier, regressor, or clusterer so long as there is also a valid - associated scoring metric. - Note that the object is cloned for each validation. - - ax : matplotlib.Axes object, optional - The axes object to plot the figure on. - - cv : int, cross-validation generator or an iterable, optional - Determines the cross-validation splitting strategy. - Possible inputs for cv are: - - None, to use the default 3-fold cross-validation, - - integer, to specify the number of folds. - - An object to be used as a cross-validation generator. - - An iterable yielding train/test splits. - - see the scikit-learn - `cross-validation guide `_ - for more information on the possible strategies that can be used here. - - scoring : string, callable or None, optional, default: None - A string or scorer callable object / function with signature - ``scorer(estimator, X, y)``. - - See scikit-learn `cross-validation guide `_ - for more information on the possible metrics that can be used. - - kwargs : dict - Keyword arguments that are passed to the base class and may influence - the visualization as defined in other Visualizers. - - Examples - -------- - - >>> from sklearn.model_selection import KFold, cross_val_score, - >>> ShuffleSplit, StratifiedKFold - >>> from sklearn import datasets, svm, linear_model - - >>> iris = datasets.load_iris() - >>> clf = svm.SVC(kernel='linear', C=1) - - >>> X = iris.data - >>> y = iris.target - - >>> visualizer = CVScores(model=clf, cv=5, scoring='f1_macro') - >>> visualizer.fit(X,y) - >>> visualizer.poof() - - Notes - ----- - - This visualizer is a wrapper around for the ``sklearn.model_selection.cross_val_score`` - <> - - Refer to the scikit-learn - `cross-validation guide ` - for more details - - """ - - def __init__(self, model, ax=None, cv=None, scoring=None, **kwargs): - - super(CVScores, self).__init__(model, ax=ax, **kwargs) - - self.cv = cv - self.scoring = scoring - - def fit(self, X, y, **kwargs): - """ - Fits the learning curve with the wrapped model to the specified data. - Draws training and test score curves and saves the scores to the - estimator. - - Parameters - ---------- - X : array-like, shape (n_samples, n_features) - Training vector, where n_samples is the number of samples and - n_features is the number of features. - - y : array-like, shape (n_samples) or (n_samples, n_features), optional - Target relative to X for classification or regression; - None for unsupervised learning. - - Returns - ------- - self : instance - - """ - - self.cv_scores_ = cross_val_score(self.estimator, X, y, cv=self.cv, scoring=self.scoring) - self.cv_scores_mean_ = self.cv_scores_.mean() - - self.draw() - return self - - def draw(self, **kwargs): - """ - creates the bar chart of the CV scores generated from the fit method and places - a dashed horizontal line that represents the average value of the CV scores - """ - minimum = self.cv_scores_.min() - #update minimum if it is greater than 0.05 to remove whitespace in the bottom of the chart - #for easier comparison of values - if minimum > 0.05: - minimum = minimum - 0.05 - self.ax.set_ylim(minimum, 1) - xvals = np.arange(1, len(self.cv_scores_) + 1, 1) - width = kwargs.pop("width", 0.3) - self.ax.bar(xvals, self.cv_scores_, width = width) - color = kwargs.pop("color", "b") - linewidth = kwargs.pop("linewidth", 1) - self.ax.axhline(self.cv_scores_mean_, color=color, label='Average', linestyle='--', linewidth=linewidth) - - return self.ax - - def finalize(self, **kwargs): - """ - Add the title, legend, and other visual final touches to the plot. - """ - # Set the title of the figure - self.set_title('Cross Validation Scores for {}'.format(self.name)) - - # Add the legend - loc = kwargs.pop("loc", "best") - edgecolor = kwargs.pop("edgecolor", "k") - self.ax.legend(frameon=True, loc=loc, edgecolor=edgecolor) - - #set spacing between the x ticks - self.ax.xaxis.set_major_locator(ticker.MultipleLocator(1)) - - # Set the axis labels - self.ax.set_xlabel('Training Instances') - self.ax.set_ylabel('Score') - - -########################################################################## -## Quick Method -########################################################################## - -def cv_scores(model, X, y, ax=None, cv=None, scoring=None, **kwargs): - - """ - Displays cross validation scores as a bar chart and the - average of the scores as a horizontal line - - This helper function is a quick wrapper to utilize the - CVScores visualizer for one-off analysis. - - Parameters - ---------- - - model : a scikit-learn estimator - An object that implements ``fit`` and ``predict``, can be a - classifier, regressor, or clusterer so long as there is also a valid - associated scoring metric. - Note that the object is cloned for each validation. - - ax : matplotlib.Axes object, optional - The axes object to plot the figure on. - - cv : int, cross-validation generator or an iterable, optional - Determines the cross-validation splitting strategy. - Possible inputs for cv are: - - None, to use the default 3-fold cross-validation, - - integer, to specify the number of folds. - - An object to be used as a cross-validation generator. - - An iterable yielding train/test splits. - - see the scikit-learn - `cross-validation guide `_ - for more information on the possible strategies that can be used here. - - scoring : string, callable or None, optional, default: None - A string or scorer callable object / function with signature - ``scorer(estimator, X, y)``. - - See scikit-learn `cross-validation guide `_ - for more information on the possible metrics that can be used. - - kwargs : dict - Keyword arguments that are passed to the base class and may influence - the visualization as defined in other Visualizers. - - Returns - ------- - ax : matplotlib.Axes - The axes object that the validation curves were drawn on. - - """ - # Initialize the visualizer - visualizer = cv_scores(model, X, y, ax=ax, cv=cv, scoring=scoring) - - # Fit and poof the visualizer - visualizer.fit(X, y) - visualizer.poof(**kwargs) - return visualizer.ax - - diff --git a/yellowbrick/regressor/alphas.py b/yellowbrick/regressor/alphas.py index dc3a441d3..e299d6e27 100644 --- a/yellowbrick/regressor/alphas.py +++ b/yellowbrick/regressor/alphas.py @@ -96,7 +96,7 @@ class AlphaSelection(RegressionScoreVisualizer): Visualizer for manually iterating through all alphas and selecting the best one. - This Visualizer hoooks into the Scikit-Learn API during ``fit()``. In + This Visualizer hooks into the Scikit-Learn API during ``fit()``. In order to pass a fitted model to the Visualizer, call the ``draw()`` method directly after instantiating the visualizer with the fitted model. diff --git a/yellowbrick/regressor/residuals.py b/yellowbrick/regressor/residuals.py index 777e6585f..4fa85c4c4 100644 --- a/yellowbrick/regressor/residuals.py +++ b/yellowbrick/regressor/residuals.py @@ -30,7 +30,7 @@ from sklearn.model_selection import train_test_split from .base import RegressionScoreVisualizer - +from ..draw import manual_legend from ..style.palettes import LINE_COLOR from ..utils.decorators import memoized from ..exceptions import YellowbrickValueError @@ -91,6 +91,10 @@ class PredictionError(RegressionScoreVisualizer): line_color : color Defines the color of the best fit line; can be any matplotlib color. + alpha : float, default: 0.75 + Specify a transparency where 1 is completely opaque and 0 is completely + transparent. This property makes densely clustered points more visible. + kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. @@ -113,7 +117,7 @@ class PredictionError(RegressionScoreVisualizer): """ def __init__(self, model, ax=None, shared_limits=True, - bestfit=True, identity=True, **kwargs): + bestfit=True, identity=True, alpha=0.75, **kwargs): # Initialize the visualizer super(PredictionError, self).__init__(model, ax=ax, **kwargs) @@ -127,6 +131,7 @@ def __init__(self, model, ax=None, shared_limits=True, self.shared_limits = shared_limits self.bestfit = bestfit self.identity = identity + self.alpha = alpha def score(self, X, y=None, **kwargs): """ @@ -170,7 +175,12 @@ def draw(self, y, y_pred): ax : the axis with the plotted figure """ label = "$R^2 = {:0.3f}$".format(self.score_) - self.ax.scatter(y, y_pred, c=self.colors['point'], alpha=0.75, label=label) + self.ax.scatter( + y, + y_pred, + c=self.colors['point'], + alpha=self.alpha, + label=label) # TODO If score is happening inside a loop, draw would get called multiple times. # Ideally we'd want the best fit line to be drawn only once @@ -232,16 +242,18 @@ def finalize(self, **kwargs): self.ax.set_ylabel(r'$\hat{y}$') self.ax.set_xlabel(r'$y$') - # Annotate the score - # NOTE: Couldn't get this to work so added to title instead (for now) - # self.ax.annotate('$r^2={:0.3f}$'.format(self.score_), xy=(0,0), xytext=(0,0)) - # Set the legend + # Note: it would be nice to be able to use the manual_legend utility + # here, since if the user sets a low alpha value, the R2 color in the + # legend will also become more translucent. Unfortunately this is a + # bit tricky because adding a manual legend here would override the + # best fit and 45 degree line legend components. In particular, the + # best fit is plotted in draw because it depends on y and y_pred. self.ax.legend(loc='best', frameon=True) - -def prediction_error(model, X, y=None, ax=None, **kwargs): - """Quick method: +def prediction_error(model, X, y=None, ax=None, alpha=0.75, **kwargs): + """ + Quick method: Plot the actual targets from the dataset against the predicted values generated by our model(s). @@ -287,6 +299,10 @@ def prediction_error(model, X, y=None, ax=None, **kwargs): line_color : color Defines the color of the best fit line; can be any matplotlib color. + alpha : float, default: 0.75 + Specify a transparency where 1 is completely opaque and 0 is completely + transparent. This property makes densely clustered points more visible. + kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. @@ -297,7 +313,7 @@ def prediction_error(model, X, y=None, ax=None, **kwargs): Returns the axes that the prediction error plot was drawn on. """ # Instantiate the visualizer - visualizer = PredictionError(model, ax, **kwargs) + visualizer = PredictionError(model, ax, alpha=alpha, **kwargs) # Create the train and test splits X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) @@ -334,9 +350,11 @@ class ResidualsPlot(RegressionScoreVisualizer): The axes to plot the figure on. If None is passed in the current axes will be used (or generated if required). - hist : bool, default: True + hist : {True, False, None, 'density', 'frequency'}, default: True Draw a histogram showing the distribution of the residuals on the right side of the figure. Requires Matplotlib >= 2.0.2. + If set to 'density', the probability density function will be plotted. + If set to True or 'frequency' then the frequency will be plotted. train_color : color, default: 'b' Residuals for training data are ploted with this color but also @@ -352,6 +370,10 @@ class ResidualsPlot(RegressionScoreVisualizer): line_color : color, default: dark grey Defines the color of the zero error line, can be any matplotlib color. + alpha : float, default: 0.75 + Specify a transparency where 1 is completely opaque and 0 is completely + transparent. This property makes densely clustered points more visible. + kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. @@ -374,7 +396,8 @@ class ResidualsPlot(RegressionScoreVisualizer): The residuals histogram feature requires matplotlib 2.0.2 or greater. """ def __init__(self, model, ax=None, hist=True, train_color='b', - test_color='g', line_color=LINE_COLOR, **kwargs): + test_color='g', line_color=LINE_COLOR, alpha=0.75, + **kwargs): super(ResidualsPlot, self).__init__(model, ax=ax, **kwargs) @@ -387,9 +410,20 @@ def __init__(self, model, ax=None, hist=True, train_color='b', } self.hist = hist - if self.hist: + if self.hist not in {True, 'density', 'frequency', None, False}: + raise YellowbrickValueError( + "'{}' is an invalid argument for hist, use None, True, " \ + "False, 'density', or 'frequency'".format(hist) + ) + + if self.hist in {True, 'density', 'frequency'}: self.hax # If hist is True, test the version availability + # Store labels and colors for the legend ordered by call + self._labels, self._colors = [], [] + + self.alpha = alpha + @memoized def hax(self): """ @@ -493,19 +527,27 @@ def draw(self, y_pred, residuals, train=False, **kwargs): if train: color = self.colors['train_point'] - alpha = 0.5 label = "Train $R^2 = {:0.3f}$".format(self.train_score_) else: color = self.colors['test_point'] - alpha = 0.9 label = "Test $R^2 = {:0.3f}$".format(self.test_score_) + # Update the legend information + self._labels.append(label) + self._colors.append(color) + # Draw the residuals scatter plot - self.ax.scatter(y_pred, residuals, c=color, alpha=alpha, label=label) + self.ax.scatter( + y_pred, residuals, c=color, alpha=self.alpha, label=label + ) - # Add residuals histogram histogram - if self.hist: - self.hax.hist(residuals, bins=50, orientation="horizontal") + # Add residuals histogram + if self.hist in {True, 'frequency'}: + self.hax.hist(residuals, bins=50, orientation="horizontal", color=color) + elif self.hist == 'density': + self.hax.hist( + residuals, bins=50, orientation="horizontal", density=True, color=color + ) # Ensure the current axes is always the main residuals axes plt.sca(self.ax) @@ -523,8 +565,10 @@ def finalize(self, **kwargs): # Add the title to the plot self.set_title('Residuals for {} Model'.format(self.name)) - # Set the legend - self.ax.legend(loc='best', frameon=True) + # Set the legend with full opacity patches using manual legend + manual_legend( + self, self._labels, self._colors, loc='best', frameon=True + ) # Create a full line across the figure at zero error. self.ax.axhline(y=0, c=self.colors['line']) @@ -549,6 +593,7 @@ def residuals_plot(model, test_color='g', line_color=LINE_COLOR, random_state=None, + alpha=0.75, **kwargs): """Quick method: @@ -575,9 +620,11 @@ def residuals_plot(model, The axes to plot the figure on. If None is passed in the current axes will be used (or generated if required). - hist : bool, default: True + hist : {True, False, None, 'density', 'frequency'}, default: True Draw a histogram showing the distribution of the residuals on the right side of the figure. Requires Matplotlib >= 2.0.2. + If set to 'density', the probability density function will be plotted. + If set to True or 'frequency' then the frequency will be plotted. test_size : float, int default: 0.25 If float, should be between 0.0 and 1.0 and represent the proportion @@ -601,6 +648,10 @@ def residuals_plot(model, random_state : int, RandomState instance or None, optional Passed to the train_test_split function. + alpha : float, default: 0.75 + Specify a transparency where 1 is completely opaque and 0 is completely + transparent. This property makes densely clustered points more visible. + kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. @@ -613,7 +664,8 @@ def residuals_plot(model, # Instantiate the visualizer visualizer = ResidualsPlot( model=model, ax=ax, hist=hist, train_color=train_color, - test_color=train_color, line_color=line_color, **kwargs + test_color=test_color, line_color=line_color, alpha=alpha, + **kwargs ) # Create the train and test splits @@ -622,7 +674,7 @@ def residuals_plot(model, ) # Fit and transform the visualizer (calls draw) - visualizer.fit(X_train, y_train) + visualizer.fit(X_train, y_train, **kwargs) visualizer.score(X_test, y_test) visualizer.finalize() diff --git a/yellowbrick/target/__init__.py b/yellowbrick/target/__init__.py new file mode 100644 index 000000000..9051abf6a --- /dev/null +++ b/yellowbrick/target/__init__.py @@ -0,0 +1,24 @@ +# yellowbrick.target +# Implements visualizers related to the dependent (target) variable, y. +# +# Author: Benjamin Bengfort +# Created: Thu Jul 19 08:57:05 2018 -0400 +# +# ID: __init__.py [] benjamin@bengfort.com $ + +""" +Implements visualizers related to the dependent (target) variable, y. For +example, the ClassBalance visualizer shows how many of each class are +represented in the target. Other utilities include detection of sequential vs +discrete classes, binarization and thresholding visualization, and feature +correlation visualizations. +""" + +########################################################################## +## Imports +########################################################################## + +# Hoist visualizers into the top level of the target package +from .class_balance import ClassBalance, class_balance +from .binning import BalancedBinningReference, balanced_binning_reference +from .feature_correlation import FeatureCorrelation diff --git a/yellowbrick/target/base.py b/yellowbrick/target/base.py new file mode 100644 index 000000000..0ee0f6c7b --- /dev/null +++ b/yellowbrick/target/base.py @@ -0,0 +1,40 @@ +# yellowbrick.target.base +# Base classes for target visualizers +# +# Author: Benjamin Bengfort +# Created: Thu Jul 19 09:25:53 2018 -0400 +# +# ID: base.py [] benjamin@bengfort.com $ + +""" +Base classes for target visualizers +""" + +########################################################################## +## Imports +########################################################################## + +from ..base import Visualizer + + +########################################################################## +## TargetVisualizer Base Class +########################################################################## + +class TargetVisualizer(Visualizer): + """ + The base class for target visualizers, generic enough to support any + computation on a single vector, y. This Visualizer is based on the + LabelEncoder in sklearn.preprocessing, which only accepts a target y. + """ + + def fit(self, y): + """ + Fit the visualizer to the target y. Note that this visualizer breaks + the standard estimator interface, and therefore cannot be used inside + of pipelines, but must be used separately; similar to how the + LabelEncoder is used. + """ + raise NotImplementedError( + "target visualizers must implement a fit method" + ) diff --git a/yellowbrick/target/binning.py b/yellowbrick/target/binning.py new file mode 100644 index 000000000..05327bef4 --- /dev/null +++ b/yellowbrick/target/binning.py @@ -0,0 +1,190 @@ + +# yellowbrick.target.binning +# Implementations of histogram with vertical lines to help with balanced binning. +# +# Author: Juan L. Kehoe (juanluo2008@gmail.com) +# Author: Prema Damodaran Roman (pdamo24@gmail.com) + +# Created: Tue Mar 13 19:50:54 2018 -0400 +# +# Copyright (C) 2018 District Data Labs +# For license information, see LICENSE.txt +# +# ID: binning.py + +""" +Implements histogram with vertical lines to help with balanced binning. +""" + +########################################################################## +## Imports +########################################################################## +import matplotlib.pyplot as plt +import numpy as np + +from .base import TargetVisualizer +from yellowbrick.exceptions import YellowbrickValueError + +########################################################################## +## Balanced Binning Reference +########################################################################## + +class BalancedBinningReference(TargetVisualizer): + """ + BalancedBinningReference generates a histogram with vertical lines + showing the recommended value point to bin your data so they can be evenly + distributed in each bin. + + Parameters + ---------- + ax : matplotlib Axes, default: None + This is inherited from FeatureVisualizer and is defined within + ``BalancedBinningReference``. + + target : string, default: "Frequency" + The name of the ``y`` variable + + bins : number of bins to generate the histogram, default: 4 + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Attributes + ---------- + bin_edges : binning reference values + + Examples + -------- + >>> visualizer = BalancedBinningReference() + >>> visualizer.fit(y) + >>> visualizer.poof() + + + Notes + ----- + These parameters can be influenced later on in the visualization + process, but can and should be set as early as possible. + """ + + def __init__(self, ax=None, target=None, bins=4, **kwargs): + + super(BalancedBinningReference, self).__init__(ax, **kwargs) + + self.target = target + self.bins = bins + + def draw(self, y, **kwargs): + """ + Draws a histogram with the reference value for binning as vertical + lines. + + Parameters + ---------- + y : an array of one dimension or a pandas Series + """ + + # draw the histogram + hist, bin_edges = np.histogram(y, bins=self.bins) + self.bin_edges_ = bin_edges + self.ax.hist(y, bins=self.bins, color=kwargs.pop("color", "#6897bb"), **kwargs) + + # add vetical line with binning reference values + plt.vlines(bin_edges,0,max(hist),colors=kwargs.pop("colors", "r")) + + def fit(self, y, **kwargs): + """ + Sets up y for the histogram and checks to + ensure that ``y`` is of the correct data type. + Fit calls draw. + + Parameters + ---------- + y : an array of one dimension or a pandas Series + + kwargs : dict + keyword arguments passed to scikit-learn API. + + """ + + #throw an error if y has more than 1 column + if y.ndim > 1: + raise YellowbrickValueError("y needs to be an array or Series with one dimension") + + # Handle the target name if it is None. + if self.target is None: + self.target = 'Frequency' + + self.draw(y) + return self + + + def poof(self, **kwargs): + """ + Creates the labels for the feature and target variables. + """ + + self.ax.set_xlabel(self.target) + self.finalize(**kwargs) + + def finalize(self, **kwargs): + """ + Finalize executes any subclass-specific axes finalization steps. + The user calls poof and poof calls finalize. + + Parameters + ---------- + kwargs: generic keyword arguments. + + """ + + for tk in self.ax.get_xticklabels(): + tk.set_visible(True) + + for tk in self.ax.get_yticklabels(): + tk.set_visible(True) + + +########################################################################## +## Quick Method +########################################################################## + +def balanced_binning_reference(y, ax=None, target='Frequency', bins=4, **kwargs): + + """ + BalancedBinningReference generates a histogram with vertical lines + showing the recommended value point to bin your data so they can be evenly + distributed in each bin. + + Parameters + ---------- + y : an array of one dimension or a pandas Series + + ax : matplotlib Axes, default: None + This is inherited from FeatureVisualizer and is defined within + ``BalancedBinningReference``. + + target : string, default: "Frequency" + The name of the ``y`` variable + + bins : number of bins to generate the histogram, default: 4 + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + """ + + # Initialize the visualizer + visualizer = BalancedBinningReference(ax=ax, bins=bins, target=target, **kwargs) + + # Fit and poof the visualizer + visualizer.fit(y) + visualizer.poof() + + + + + + + diff --git a/yellowbrick/target/class_balance.py b/yellowbrick/target/class_balance.py new file mode 100644 index 000000000..8da018634 --- /dev/null +++ b/yellowbrick/target/class_balance.py @@ -0,0 +1,302 @@ +# yellowbrick.classifier.class_balance +# Class balance visualizer for showing per-class support. +# +# Author: Rebecca Bilbro +# Author: Benjamin Bengfort +# Author: Neal Humphrey +# Created: Wed May 18 12:39:40 2016 -0400 +# +# ID: class_balance.py [5388065] neal@nhumphrey.com $ + +""" +Class balance visualizer for showing per-class support. +""" + +########################################################################## +## Imports +########################################################################## + +import numpy as np + +from .base import TargetVisualizer +from ..style.colors import resolve_colors +from ..exceptions import YellowbrickValueError + +from sklearn.utils.multiclass import unique_labels, type_of_target + + +# Class Balance Modes +BALANCE = "balance" +COMPARE = "compare" + + +########################################################################## +## Class Balance Chart +########################################################################## + +class ClassBalance(TargetVisualizer): + """ + One of the biggest challenges for classification models is an imbalance of + classes in the training data. The ClassBalance visualizer shows the + relationship of the support for each class in both the training and test + data by displaying how frequently each class occurs as a bar graph. + + The ClassBalance visualizer can be displayed in two modes: + + 1. Balance mode: show the frequency of each class in the dataset. + 2. Compare mode: show the relationship of support in train and test data. + + These modes are determined by what is passed to the ``fit()`` method. + + Parameters + ---------- + ax : matplotlib Axes, default: None + The axis to plot the figure on. If None is passed in the current axes + will be used (or generated if required). + + labels: list, optional + A list of class names for the x-axis if the target is already encoded. + Ensure that the labels are ordered lexicographically with respect to + the values in the target. A common use case is to pass + LabelEncoder.classes\_ as this parameter. If not specified, the labels + in the data will be used. + + kwargs: dict, optional + Keyword arguments passed to the super class. Here, used + to colorize the bars in the histogram. + + Attributes + ---------- + classes_ : array-like + The actual unique classes discovered in the target. + + support_ : array of shape (n_classes,) or (2, n_classes) + A table representing the support of each class in the target. It is a + vector when in balance mode, or a table with two rows in compare mode. + + Example + ------- + To simply observe the balance of classes in the target: + + >>> viz = ClassBalance().fit(y) + >>> viz.poof() + + To compare the relationship between training and test data: + + >>> _, _, y_train, y_test = train_test_split(X, y, test_size=0.2) + >>> viz = ClassBalance() + >>> viz.fit(y_train, y_test) + >>> viz.poof() + """ + + def __init__(self, ax=None, labels=None, **kwargs): + self.labels = labels + super(ClassBalance, self).__init__(ax, **kwargs) + + def fit(self, y_train, y_test=None): + """ + Fit the visualizer to the the target variables, which must be 1D + vectors containing discrete (classification) data. Fit has two modes: + + 1. Balance mode: if only y_train is specified + 2. Compare mode: if both train and test are specified + + In balance mode, the bar chart is displayed with each class as its own + color. In compare mode, a side-by-side bar chart is displayed colored + by train or test respectively. + + Parameters + ---------- + y_train : array-like + Array or list of shape (n,) that containes discrete data. + + y_test : array-like, optional + Array or list of shape (m,) that contains discrete data. If + specified, the bar chart will be drawn in compare mode. + """ + + # check to make sure that y_train is not a 2D array, e.g. X + if y_train.ndim == 2: + raise YellowbrickValueError(( + "fit has changed to only require a 1D array, y " + "since version 0.9; please see the docs for more info" + )) + + # Check the target types for the y variables + self._validate_target(y_train) + self._validate_target(y_test) + + # Get the unique values from the dataset + targets = (y_train,) if y_test is None else (y_train, y_test) + self.classes_ = unique_labels(*targets) + + # Validate the classes with the class names + if self.labels is not None: + if len(self.labels) != len(self.classes_): + raise YellowbrickValueError(( + "discovered {} classes in the data, does not match " + "the {} labels specified." + ).format(len(self.classes_), len(self.labels))) + + # Determine if we're in compare or balance mode + self._mode = BALANCE if y_test is None else COMPARE + + # Compute the support values + if self._mode == BALANCE: + self.support_ = np.array([ + (y_train == idx).sum() for idx in self.classes_ + ]) + + else: + self.support_ = np.array([ + [ + (y == idx).sum() for idx in self.classes_ + ] + for y in targets + ]) + + # Draw the bar chart + self.draw() + + # Fit returns self + return self + + def draw(self): + """ + Renders the class balance chart on the specified axes from support. + """ + # Number of colors is either number of classes or 2 + colors = resolve_colors(len(self.support_)) + + if self._mode == BALANCE: + self.ax.bar( + np.arange(len(self.support_)), self.support_, + color=colors, align='center', width=0.5 + ) + + # Compare mode + else: + bar_width = 0.35 + labels = ["train", "test"] + + for idx, support in enumerate(self.support_): + index = np.arange(len(self.classes_)) + if idx > 0: + index = index + bar_width + + self.ax.bar( + index, support, bar_width, + color=colors[idx], label=labels[idx] + ) + + return self.ax + + def finalize(self, **kwargs): + """ + Finalize executes any subclass-specific axes finalization steps. + The user calls poof and poof calls finalize. + + Parameters + ---------- + kwargs: generic keyword arguments. + + """ + # Set the title + self.set_title( + 'Class Balance for {:,} Instances'.format(self.support_.sum()) + ) + + # Set the x ticks with the class names or labels if specified + labels = self.labels if self.labels is not None else self.classes_ + xticks = np.arange(len(labels)) + if self._mode == COMPARE: + xticks = xticks + (0.35/2) + + self.ax.set_xticks(xticks) + self.ax.set_xticklabels(labels) + + # Compute the ceiling for the y limit + cmax = self.support_.max() + self.ax.set_ylim(0, cmax + cmax* 0.1) + self.ax.set_ylabel("support") + + # Remove the vertical grid + self.ax.grid(False, axis="x") + + # Add the legend if in compare mode: + if self._mode == COMPARE: + self.ax.legend(frameon=True) + + def _validate_target(self, y): + """ + Raises a value error if the target is not a classification target. + """ + # Ignore None values + if y is None: + return + + y_type = type_of_target(y) + if y_type not in ("binary", "multiclass"): + raise YellowbrickValueError(( + "'{}' target type not supported, only binary and multiclass" + ).format(y_type)) + + +########################################################################## +## Quick Method +########################################################################## + +def class_balance(y_train, y_test=None, ax=None, labels=None, **kwargs): + """Quick method: + + One of the biggest challenges for classification models is an imbalance of + classes in the training data. This function vizualizes the relationship of + the support for each class in both the training and test data by + displaying how frequently each class occurs as a bar graph. + + The figure can be displayed in two modes: + + 1. Balance mode: show the frequency of each class in the dataset. + 2. Compare mode: show the relationship of support in train and test data. + + Balance mode is the default if only y_train is specified. Compare mode + happens when both y_train and y_test are specified. + + Parameters + ---------- + y_train : array-like + Array or list of shape (n,) that containes discrete data. + + y_test : array-like, optional + Array or list of shape (m,) that contains discrete data. If + specified, the bar chart will be drawn in compare mode. + + ax : matplotlib Axes, default: None + The axis to plot the figure on. If None is passed in the current axes + will be used (or generated if required). + + labels: list, optional + A list of class names for the x-axis if the target is already encoded. + Ensure that the labels are ordered lexicographically with respect to + the values in the target. A common use case is to pass + LabelEncoder.classes\_ as this parameter. If not specified, the labels + in the data will be used. + + kwargs: dict, optional + Keyword arguments passed to the super class. Here, used + to colorize the bars in the histogram. + + Returns + ------- + ax : matplotlib axes + Returns the axes that the class balance plot was drawn on. + """ + # Instantiate the visualizer + visualizer = ClassBalance(ax=ax, labels=labels, **kwargs) + + # Fit and transform the visualizer (calls draw) + visualizer.fit(y_train, y_test) + visualizer.finalize() + + # Return the axes object on the visualizer + return visualizer.ax diff --git a/yellowbrick/target/feature_correlation.py b/yellowbrick/target/feature_correlation.py new file mode 100644 index 000000000..ffc5579d0 --- /dev/null +++ b/yellowbrick/target/feature_correlation.py @@ -0,0 +1,326 @@ +# yellowbrick.classifier.feature_correlation +# Feature correlation to dependent variable visualizer. +# +# Author Zijie (ZJ) Poh +# Created: Wed Jul 29 15:30:40 2018 -0700 +# +# ID: feature_correlation.py [] poh.zijie@gmail.com $ + +""" +Feature Correlation to Dependent Variable Visualizer. +""" + +########################################################################## +## Imports +########################################################################## + +import numpy as np + +from yellowbrick.target.base import TargetVisualizer +from yellowbrick.utils import is_dataframe +from yellowbrick.exceptions import YellowbrickValueError, YellowbrickWarning + +from sklearn.feature_selection import mutual_info_classif +from sklearn.feature_selection import mutual_info_regression + +from scipy.stats import pearsonr + +########################################################################## +## Supported Correlation Computations +########################################################################## + +CORRELATION_LABELS = { + 'pearson': 'Pearson Correlation', + 'mutual_info-regression': 'Mutual Information', + 'mutual_info-classification': 'Mutual Information' +} + +CORRELATION_METHODS = { + 'mutual_info-regression': mutual_info_regression, + 'mutual_info-classification': mutual_info_classif +} + +########################################################################## +## Class Feature Correlation +########################################################################## + +class FeatureCorrelation(TargetVisualizer): + """ + Displays the correlation between features and dependent variables. + + This visualizer can be used side-by-side with + ``yellowbrick.features.JointPlotVisualizer`` that plots a feature + against the target and shows the distribution of each via a + histogram on each axis. + + Parameters + ---------- + ax : matplotlib Axes, default: None + The axis to plot the figure on. If None is passed in the current axes + will be used (or generated if required). + + method : str, default: 'pearson' + The method to calculate correlation between features and target. + Options include: + + - 'pearson', which uses ``scipy.stats.pearsonr`` + - 'mutual_info-regression', which uses ``mutual_info-regression`` + from ``sklearn.feature_selection`` + - 'mutual_info-classification', which uses ``mutual_info_classif`` + from ``sklearn.feature_selection`` + + labels : list, default: None + A list of feature names to use. If a DataFrame is passed to fit and + features is None, feature names are selected as the column names. + + sort : boolean, default: False + If false, the features are are not sorted in the plot; otherwise + features are sorted in ascending order of correlation. + + feature_index : list, + A list of feature index to include in the plot. + + feature_names : list of feature names + A list of feature names to include in the plot. + Must have labels or the fitted data is a DataFrame with column names. + If feature_index is provided, feature_names will be ignored. + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Attributes + ---------- + features_ : np.array + The feature labels + + scores_ : np.array + Correlation between features and dependent variable. + + Examples + -------- + + >>> viz = FeatureCorrelation() + >>> viz.fit(X, y) + >>> viz.poof() + """ + + def __init__(self, ax=None, method='pearson', + labels=None, sort=False, feature_index=None, + feature_names=None, **kwargs): + super(FeatureCorrelation, self).__init__(ax=None, **kwargs) + + self.correlation_labels = CORRELATION_LABELS + self.correlation_methods = CORRELATION_METHODS + + if method not in self.correlation_labels: + raise YellowbrickValueError( + 'Method {} not implement; choose from {}'.format( + method, ", ".join(self.correlation_labels) + ) + ) + + # Parameters + self.set_params( + method=method, + labels=labels, + sort=sort, + feature_index=feature_index, + feature_names=feature_names + ) + + def fit(self, X, y, **kwargs): + """ + Fits the estimator to calculate feature correlation to + dependent variable. + + Parameters + ---------- + X : ndarray or DataFrame of shape n x m + A matrix of n instances with m features + + y : ndarray or Series of length n + An array or series of target or class values + + kwargs : dict + Keyword arguments passed to the fit method of the estimator. + + Returns + ------- + self : visualizer + The fit method must always return self to support pipelines. + """ + self._create_labels_for_features(X) + + self._select_features_to_plot(X) + + # Calculate Features correlation with target variable + if self.method == "pearson": + self.scores_ = np.array( + [pearsonr(x, y, **kwargs)[0] for x in np.asarray(X).T] + ) + else: + self.scores_ = np.array( + self.correlation_methods[self.method](X, y, **kwargs) + ) + + # If feature indices are given, plot only the given features + if self.feature_index: + self.scores_ = self.scores_[self.feature_index] + self.features_ = self.features_[self.feature_index] + + # Sort features by correlation + if self.sort: + sort_idx = np.argsort(self.scores_) + self.scores_ = self.scores_[sort_idx] + self.features_ = self.features_[sort_idx] + + self.draw() + return self + + def draw(self): + """ + Draws the feature correlation to dependent variable, called from fit. + """ + pos = np.arange(self.scores_.shape[0]) + 0.5 + + self.ax.barh(pos, self.scores_) + + # Set the labels for the bars + self.ax.set_yticks(pos) + self.ax.set_yticklabels(self.features_) + + return self.ax + + def finalize(self): + """ + Finalize the drawing setting labels and title. + """ + self.set_title('Features correlation with dependent variable') + + self.ax.set_xlabel(self.correlation_labels[self.method]) + + self.ax.grid(False, axis='y') + + def _create_labels_for_features(self, X): + """ + Create labels for the features + + NOTE: this code is duplicated from MultiFeatureVisualizer + """ + if self.labels is None: + # Use column names if a dataframe + if is_dataframe(X): + self.features_ = np.array(X.columns) + # Otherwise use the column index as the labels + else: + _, ncols = X.shape + self.features_ = np.arange(0, ncols) + else: + self.features_ = np.array(self.labels) + + def _select_features_to_plot(self, X): + """ + Select features to plot. + + feature_index is always used as the filter and + if filter_names is supplied, a new feature_index + is computed from those names. + """ + if self.feature_index: + if self.feature_names: + raise YellowbrickWarning( + 'Both feature_index and feature_names ' + 'are specified. feature_names is ignored' + ) + if (min(self.feature_index) < 0 + or max(self.feature_index) >= X.shape[1]): + raise YellowbrickValueError('Feature index is out of range') + elif self.feature_names: + self.feature_index = [] + features_list = self.features_.tolist() + for feature_name in self.feature_names: + try: + self.feature_index.append( + features_list.index(feature_name) + ) + except ValueError: + raise YellowbrickValueError( + '{} not in labels'.format(feature_name) + ) + + +########################################################################## +## Quick Method +########################################################################## + +def feature_correlation(X, y, ax=None, method='pearson', + labels=None, sort=False, feature_index=None, + feature_names=None, **kwargs): + """ + Displays the correlation between features and dependent variables. + + This visualizer can be used side-by-side with + yellowbrick.features.JointPlotVisualizer that plots a feature + against the target and shows the distribution of each via a + histogram on each axis. + + Parameters + ---------- + X : ndarray or DataFrame of shape n x m + A matrix of n instances with m features + + y : ndarray or Series of length n + An array or series of target or class values + + ax : matplotlib Axes, default: None + The axis to plot the figure on. If None is passed in the current axes + will be used (or generated if required). + + method : str, default: 'pearson' + The method to calculate correlation between features and target. + Options include: + + - 'pearson', which uses ``scipy.stats.pearsonr`` + - 'mutual_info-regression', which uses ``mutual_info-regression`` + from ``sklearn.feature_selection`` + - 'mutual_info-classification', which uses ``mutual_info_classif`` + from ``sklearn.feature_selection`` + 'mutual_info-classification'], default: 'pearson' + + labels : list, default: None + A list of feature names to use. If a DataFrame is passed to fit and + features is None, feature names are selected as the column names. + + sort : boolean, default: False + If false, the features are are not sorted in the plot; otherwise + features are sorted in ascending order of correlation. + + feature_index : list, + A list of feature index to include in the plot. + + feature_names : list of feature names + A list of feature names to include in the plot. + Must have labels or the fitted data is a DataFrame with column names. + If feature_index is provided, feature_names will be ignored. + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Returns + ------- + ax : matplotlib axes + Returns the axes that the parallel coordinates were drawn on. + """ + + # Instantiate the visualizer + viz = FeatureCorrelation(ax, method, labels, sort, + feature_index, feature_names, **kwargs) + + # Fit and transform the visualizer (calls draw) + viz.fit(X, y, **kwargs) + viz.finalize() + + # Return the axes object on the visualizer + return viz.ax diff --git a/yellowbrick/text/dispersion.py b/yellowbrick/text/dispersion.py index 4edbc157a..55d945636 100644 --- a/yellowbrick/text/dispersion.py +++ b/yellowbrick/text/dispersion.py @@ -18,7 +18,13 @@ ## Imports ########################################################################## +from collections import defaultdict +import itertools + from yellowbrick.text.base import TextVisualizer +from yellowbrick.style.colors import resolve_colors +from yellowbrick.exceptions import YellowbrickValueError + import numpy as np ########################################################################## @@ -31,82 +37,194 @@ class DispersionPlot(TextVisualizer): of words in a corpus. Lexical dispersion is a measure of a word's homeogeneity across the parts of a corpus. This plot notes the occurences of a word and how many words from the beginning it appears. - + Parameters ---------- - words : list + target_words : list A list of target words whose dispersion across a corpus passed at fit - will be visualized. - + will be visualized. + ax : matplotlib axes, default: None The axes to plot the figure on. - - color : list or tuple of colors - Specify color for bars + + labels : list of strings + The names of the classes in the target, used to create a legend. + Labels must match names of classes in sorted order. + + colors : list or tuple of colors + Specify the colors for each individual class + + colormap : string or matplotlib cmap + Qualitative colormap for discrete target ignore_case : boolean, default: False Specify whether input will be case-sensitive. - + + annotate_docs : boolean, default: False + Specify whether document boundaries will be displayed. Vertical lines + are positioned at the end of each document. + kwargs : dict Pass any additional keyword arguments to the super class. - + These parameters can be influenced later on in the visualization process, but can and should be set as early as possible. """ - - def __init__(self, words, ax=None, color=None, ignore_case=False, **kwargs): + + # NOTE: cannot be np.nan + NULL_CLASS = None + + def __init__(self, target_words, ax=None, colors=None, ignore_case=False, + annotate_docs=False, labels=None, colormap=None, **kwargs): super(DispersionPlot, self).__init__(ax=ax, **kwargs) - - self.color = color - self.words = words + + self.labels = labels + self.colors = colors + self.colormap = colormap + + self.target_words = target_words self.ignore_case = ignore_case - - - def _compute_dispersion(self, text): - for x, word in enumerate(text): - if self.ignore_case: - word = word.lower() - - # NOTE: this will find all indices if duplicate words are supplied - # In the case that word is not in target words, any empty list is - # returned and no data will be yielded - for y in (self.target_words_ == word).nonzero()[0]: - yield (x, y) - - def fit(self, text): + self.annotate_docs = annotate_docs + + def _compute_dispersion(self, text, y): + self.boundaries_ = [] + offset = 0 + + + if y is None: + y = itertools.repeat(None) + + for doc, target in zip(text, y): + for word in doc: + if self.ignore_case: + word = word.lower() + + # NOTE: this will find all indices if duplicate words are supplied + # In the case that word is not in target words, any empty list is + # returned and no data will be yielded + offset += 1 + for y_coord in (self.indexed_words_ == word).nonzero()[0]: + y_coord = int(y_coord) + yield (offset, y_coord, target) + if self.annotate_docs: + self.boundaries_.append(offset) + self.boundaries_ = np.array(self.boundaries_, dtype=int) + + def _check_missing_words(self, points): + for index in range(len(self.indexed_words_)): + if index in points[:,1]: + pass + else: + raise YellowbrickValueError(( + "The indexed word '{}' is not found in " + "this corpus" + ).format(self.indexed_words_[index])) + + def fit(self, X, y=None, **kwargs): """ The fit method is the primary drawing input for the dispersion - visualization. It requires the corpus as a list of words. - + visualization. + Parameters ---------- - text : list - A list of words in the order they appear in the corpus. + X : list or generator + Should be provided as a list of documents or a generator + that yields a list of documents that contain a list of + words in the order they appear in the document. + + y : ndarray or Series of length n + An optional array or series of target or class values for + instances. If this is specified, then the points will be colored + according to their class. + + kwargs : dict + Pass generic arguments to the drawing method + + Returns + ------- + self : instance + Returns the instance of the transformer/visualizer """ - + + if y is not None: + self.classes_ = np.unique(y) + elif y is None and self.labels is not None: + self.classes_ = np.array([self.labels[0]]) + else: + self.classes_ = np.array([self.NULL_CLASS]) + # Create an index (e.g. the y position) for the target words - self.target_words_ = np.flip(self.words, axis=0) + self.indexed_words_ = np.flip(self.target_words, axis=0) if self.ignore_case: - self.target_words_ = np.array([w.lower() for w in self.target_words_]) - + self.indexed_words_ = np.array([w.lower() for w in self.indexed_words_]) + # Stack is used to create a 2D array from the generator - points = np.stack(self._compute_dispersion(text)) - self.draw(points) - return self - - def draw(self, points, **kwargs): + try: + points_target = np.stack(self._compute_dispersion(X, y)) + except ValueError: + raise YellowbrickValueError(( + "No indexed words were found in the corpus" + )) + points = np.stack(zip(points_target[:,0].astype(int), + points_target[:,1].astype(int))) + + self.target = points_target[:,2] + + self._check_missing_words(points) + + self.draw(points, self.target) + return self + + def draw(self, points, target=None, **kwargs): """ Called from the fit method, this method creates the canvas and - draws the distribution plot on it. + draws the plot on it. Parameters ---------- kwargs: generic keyword arguments. """ - - self.ax.scatter(points[:,0], points[:,1], marker='|', color=self.color) - self.ax.set_yticks(list(range(len(self.target_words_)))) - self.ax.set_yticklabels(self.target_words_) - + + # Resolve the labels with the classes + labels = self.labels if self.labels is not None else self.classes_ + if len(labels) != len(self.classes_): + raise YellowbrickValueError(( + "number of supplied labels ({}) does not " + "match the number of classes ({})" + ).format(len(labels), len(self.classes_))) + + # Create the color mapping for the labels. + color_values = resolve_colors( + n_colors=len(labels), colormap=self.colormap, colors=self.color) + colors = dict(zip(labels, color_values)) + + # Transform labels into a map of class to label + labels = dict(zip(self.classes_, labels)) + + # Define boundaries with a vertical line + if self.annotate_docs: + for xcoords in self.boundaries_: + self.ax.axvline(x=xcoords, color='lightgray', linestyle='dashed') + + series = defaultdict(lambda: {'x':[], 'y':[]}) + + if target is not None: + for point, t in zip(points, target): + label = labels[t] + series[label]['x'].append(point[0]) + series[label]['y'].append(point[1]) + else: + label = self.classes_[0] + for x, y in points: + series[label]['x'].append(x) + series[label]['y'].append(y) + + for label, points in series.items(): + self.ax.scatter(points['x'], points['y'], marker='|', + c=colors[label], zorder=100, label=label) + + self.ax.set_yticks(list(range(len(self.indexed_words_)))) + self.ax.set_yticklabels(self.indexed_words_) + def finalize(self, **kwargs): """ The finalize method executes any subclass-specific axes @@ -115,57 +233,82 @@ def finalize(self, **kwargs): ---------- kwargs: generic keyword arguments. """ - - self.ax.set_ylim(-1, len(self.target_words_)) + + self.ax.set_ylim(-1, len(self.indexed_words_)) self.ax.set_title("Lexical Dispersion Plot") self.ax.set_xlabel("Word Offset") self.ax.grid(False) + # Add the legend outside of the figure box. + if not all(self.classes_ == np.array([self.NULL_CLASS])): + box = self.ax.get_position() + self.ax.set_position([box.x0, box.y0, box.width * 0.8, box.height]) + self.ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) ########################################################################## ## Quick Method ########################################################################## -def dispersion(words, corpus, ax=None, color=None, ignore_case=False, **kwargs): +def dispersion(words, corpus, y=None, ax=None, colors=None, colormap=None, + labels=None, annotate_docs=False, ignore_case=False, **kwargs): """ Displays lexical dispersion plot for words in a corpus - + This helper function is a quick wrapper to utilize the DisperstionPlot Visualizer for one-off analysis - + Parameters ---------- - + words : list A list of words whose dispersion will be examined within a corpus - + + y : ndarray or Series of length n + An optional array or series of target or class values for + instances. If this is specified, then the points will be colored + according to their class. + corpus : list - A list of words in the order they appear in the corpus + Should be provided as a list of documents that contain + a list of words in the order they appear in the document. ax : matplotlib axes, default: None The axes to plot the figure on. - color : list or tuple of colors - Specify color for bars + labels : list of strings + The names of the classes in the target, used to create a legend. + Labels must match names of classes in sorted order. + + colors : list or tuple of colors + Specify the colors for each individual class + + colormap : string or matplotlib cmap + Qualitative colormap for discrete target + + annotate_docs : boolean, default: False + Specify whether document boundaries will be displayed. Vertical lines + are positioned at the end of each document. ignore_case : boolean, default: False Specify whether input will be case-sensitive. - + kwargs : dict Pass any additional keyword arguments to the super class. - + Returns ------- ax: matplotlib axes Returns the axes that the plot was drawn on """ - + # Instantiate the visualizer visualizer = DispersionPlot( - words, ax=ax, color=color, ignore_case=ignore_case, **kwargs + words, ax=ax, colors=colors, colormap=colormap, + ignore_case=ignore_case, labels=labels, + annotate_docs=annotate_docs, **kwargs ) # Fit and transform the visualizer (calls draw) - visualizer.fit(corpus) + visualizer.fit(corpus, y, **kwargs) # Return the axes object on the visualizer return visualizer.ax diff --git a/yellowbrick/text/tsne.py b/yellowbrick/text/tsne.py index fbdefc397..d60f7a24b 100644 --- a/yellowbrick/text/tsne.py +++ b/yellowbrick/text/tsne.py @@ -2,6 +2,7 @@ # Implements TSNE visualizations of documents in 2D space. # # Author: Benjamin Bengfort +# Author: Rebecca Bilbro # Created: Mon Feb 20 06:33:29 2017 -0500 # # Copyright (C) 2016 Bengfort.com @@ -21,6 +22,7 @@ from collections import defaultdict +from yellowbrick.draw import manual_legend from yellowbrick.text.base import TextVisualizer from yellowbrick.style.colors import resolve_colors from yellowbrick.exceptions import YellowbrickValueError @@ -34,7 +36,7 @@ ########################################################################## def tsne(X, y=None, ax=None, decompose='svd', decompose_by=50, classes=None, - colors=None, colormap=None, **kwargs): + colors=None, colormap=None, alpha=0.7, **kwargs): """ Display a projection of a vectorized corpus in two dimensions using TSNE, a nonlinear dimensionality reduction method that is particularly well @@ -77,6 +79,10 @@ def tsne(X, y=None, ax=None, decompose='svd', decompose_by=50, classes=None, colormap : string or matplotlib cmap Sequential colormap for continuous target + alpha : float, default: 0.7 + Specify a transparency where 1 is completely opaque and 0 is completely + transparent. This property makes densely clustered points more visible. + kwargs : dict Pass any additional keyword arguments to the TSNE transformer. @@ -87,7 +93,7 @@ def tsne(X, y=None, ax=None, decompose='svd', decompose_by=50, classes=None, """ # Instantiate the visualizer visualizer = TSNEVisualizer( - ax, decompose, decompose_by, classes, colors, colormap, **kwargs + ax, decompose, decompose_by, classes, colors, colormap, alpha, **kwargs ) # Fit and transform the visualizer (calls draw) @@ -158,6 +164,10 @@ class TSNEVisualizer(TextVisualizer): by np.random. The random state is applied to the preliminary decomposition as well as tSNE. + alpha : float, default: 0.7 + Specify a transparency where 1 is completely opaque and 0 is completely + transparent. This property makes densely clustered points more visible. + kwargs : dict Pass any additional keyword arguments to the TSNE transformer. """ @@ -165,21 +175,27 @@ class TSNEVisualizer(TextVisualizer): # NOTE: cannot be np.nan NULL_CLASS = None - def __init__(self, ax=None, decompose='svd', decompose_by=50, labels=None, - classes=None, colors=None, colormap=None, random_state=None, **kwargs): - """ - Initialize the TSNE visualizer with visual hyperparameters. - """ - super(TSNEVisualizer, self).__init__(ax=ax, **kwargs) + def __init__(self, ax=None, decompose='svd', decompose_by=50, + labels=None, classes=None, colors=None, colormap=None, + random_state=None, alpha=0.7, **kwargs): # Visual Parameters + self.alpha = alpha self.labels = labels self.colors = colors self.colormap = colormap self.random_state = random_state - # TSNE Parameters - self.transformer_ = self.make_transformer(decompose, decompose_by, kwargs) + # Fetch TSNE kwargs from kwargs by popping only keys belonging to TSNE params + tsne_kwargs = { + key: kwargs.pop(key) + for key in TSNE().get_params() + if key in kwargs + } + self.transformer_ = self.make_transformer(decompose, decompose_by, tsne_kwargs) + + # Call super at the end so that size and title are set correctly + super(TSNEVisualizer, self).__init__(ax=ax, **kwargs) def make_transformer(self, decompose='svd', decompose_by=50, tsne_kwargs={}): """ @@ -303,9 +319,9 @@ def draw(self, points, target=None, **kwargs): # Create the color mapping for the labels. - color_values = resolve_colors( + self.color_values_ = resolve_colors( n_colors=len(labels), colormap=self.colormap, colors=self.color) - colors = dict(zip(labels, color_values)) + colors = dict(zip(labels, self.color_values_)) # Transform labels into a map of class to label labels = dict(zip(self.classes_, labels)) @@ -330,7 +346,7 @@ def draw(self, points, target=None, **kwargs): for label, points in series.items(): self.ax.scatter( points['x'], points['y'], c=colors[label], - alpha=0.7, label=label + alpha=self.alpha, label=label ) def finalize(self, **kwargs): @@ -338,8 +354,6 @@ def finalize(self, **kwargs): Finalize the drawing by adding a title and legend, and removing the axes objects that do not convey information about TNSE. """ - - # Add a title self.set_title( "TSNE Projection of {} Documents".format(self.n_instances_) ) @@ -352,4 +366,7 @@ def finalize(self, **kwargs): if not all(self.classes_ == np.array([self.NULL_CLASS])): box = self.ax.get_position() self.ax.set_position([box.x0, box.y0, box.width * 0.8, box.height]) - self.ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) + manual_legend( + self, self.classes_, self.color_values_, + loc='center left', bbox_to_anchor=(1, 0.5) + ) diff --git a/yellowbrick/utils/helpers.py b/yellowbrick/utils/helpers.py index 0f6c18f66..8882cbc85 100644 --- a/yellowbrick/utils/helpers.py +++ b/yellowbrick/utils/helpers.py @@ -17,6 +17,8 @@ ## Imports ########################################################################## +from __future__ import division + import re import numpy as np @@ -86,7 +88,7 @@ def is_monotonic(a, increasing=True): Test if the array is montonically increasing, otherwise test if the array is montonically decreasing. """ - a = np.asarray(a) # ensure a is array-like + a = np.asarray(a) # ensure a is array-like if a.ndim > 1: raise ValueError("not supported for multi-dimensonal arrays") @@ -132,6 +134,54 @@ def div_safe( numerator, denominator ): raise e +def prop_to_size(vals, mi=0.0, ma=5.0, power=0.5, log=False): + """ + Converts an array of property values (e.g. a metric or score) to values + that are more useful for marker sizes, line widths, or other visual + sizes. The new sizes are computed as: + + y = mi + (ma -mi)(\frac{x_i - min(x){max(x) - min(x)})^{power} + + If ``log=True``, the natural logarithm of the property values is used instead. + + Parameters + ---------- + prop : array-like, 1D + An array of values of the property to scale between the size range. + + mi : float, default: 0.0 + The size to assign the smallest property (minimum size value). + + ma : float, default: 5.0 + The size to assign the largest property (maximum size value). + + power : float, default: 0.5 + Used to control how rapidly the size increases from smallest to largest. + + log : bool, default: False + Use the natural logarithm to compute the property sizes + + Returns + ------- + sizes : array, 1D + The new size values, in the same shape as the input prop array + """ + # ensure that prop is an array + vals = np.asarray(vals) + + # apply natural log if specified + if log: + vals = np.log(vals) + + # avoid division by zero error + delta = vals.max() - vals.min() + if delta == 0.0: + delta = 1.0 + + return mi + (ma-mi) * ((vals -vals.min()) / delta) ** power + + + ########################################################################## ## String Computations ########################################################################## diff --git a/yellowbrick/utils/timer.py b/yellowbrick/utils/timer.py new file mode 100644 index 000000000..6b63816d2 --- /dev/null +++ b/yellowbrick/utils/timer.py @@ -0,0 +1,49 @@ +# yellowbrick.utils.timer +# Timer utilities +# +# Author: ZJ Poh +# Created: Mon Jul 16 10:51:13 2017 -0700 +# +# Copyright (C) 2017 District Data Labs +# For license information, see LICENSE.txt +""" +Timer utilities +""" + +########################################################################## +## Imports +########################################################################## + +import time + +########################################################################## +## Timer Class +########################################################################## + + +def human_readable_time(s): + h, s = divmod(s, 3600) + m, s = divmod(s, 60) + return "{:>02.0f}:{:02.0f}:{:>07.4f}".format(h, m, s) + + +class Timer: + """ + A context object timer. Usage: + >>> with Timer() as timer: + ... do_something() + >>> print timer.interval + """ + def __init__(self): + self.time = time.time + + def __enter__(self): + self.start = self.time() + return self + + def __exit__(self, *exc): + self.finish = self.time() + self.interval = self.finish - self.start + + def __str__(self): + return human_readable_time(self.interval) diff --git a/yellowbrick/version.py b/yellowbrick/version.py index 84a7dc55c..b50b7df0a 100644 --- a/yellowbrick/version.py +++ b/yellowbrick/version.py @@ -19,10 +19,10 @@ __version_info__ = { 'major': 0, - 'minor': 8, + 'minor': 9, 'micro': 0, 'releaselevel': 'final', - 'serial': 12, + 'serial': 13, } ##########################################################################