diff --git a/.gitignore b/.gitignore index f4dc71209..244cca5b6 100644 --- a/.gitignore +++ b/.gitignore @@ -121,3 +121,5 @@ fabric.properties # modules.xml # .idea/misc.xml # *.ipr + +.idea diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 000000000..aaba59f2e --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,275 @@ +# Contributing to Yellowbrick + +**NOTE: This document is a "getting started" summary for contributing to the Yellowbrick project.** To read the full contributor's guide, please visit the [contributing page](http://www.scikit-yb.org/en/latest/about.html#contributing) in the documentation. Please make sure to read this page carefully to ensure the review process is as smooth as possible and to ensure the greatest likelihood of having your contribution be merged. + +For more on the development path, goals, and motivations behind Yellowbrick, check out our developer presentation: [Visualizing Model Selection with Scikit-Yellowbrick: An Introduction to Developing Visualizers](http://www.slideshare.net/BenjaminBengfort/visualizing-model-selection-with-scikityellowbrick-an-introduction-to-developing-visualizers). + +## How to Contribute + +Yellowbrick is an open source project that is supported by a community who will gratefully and humbly accept any contributions you might make to the project. Large or small, any contribution makes a big difference; and if you've never contributed to an open source project before, we hope you will start with Yellowbrick! + +Principally, Yellowbrick development is about the addition and creation of *visualizers* --- objects that learn from data and create a visual representation of the data or model. Visualizers integrate with Scikit-Learn estimators, transformers, and pipelines for specific purposes and as a result, can be simple to build and deploy. The most common contribution is therefore a new visualizer for a specific model or model family. We'll discuss in detail how to build visualizers later. + +Beyond creating visualizers, there are many ways to contribute: + +- Submit a bug report or feature request on [GitHub Issues](https://github.com/DistrictDataLabs/yellowbrick/issues). +- Contribute a Jupyter notebook to our examples[ gallery](https://github.com/DistrictDataLabs/yellowbrick/tree/develop/examples). +- Assist us with [user testing](http://www.scikit-yb.org/en/latest/evaluation.html). +- Add to the documentation or help with our website, [scikit-yb.org](http://www.scikit-yb.org). +- Write unit or integration tests for our project. +- Answer questions on our issues, mailing list, Stack Overflow, and elsewhere. +- Translate our documentation into another language. +- Write a blog post, tweet, or share our project with others. +- Teach someone how to use Yellowbrick. + +As you can see, there are lots of ways to get involved and we would be very happy for you to join us! The only thing we ask is that you abide by the principles of openness, respect, and consideration of others as described in the [Python Software Foundation Code of Conduct](https://www.python.org/psf/codeofconduct/). + +## Getting Started on GitHub + +Yellowbrick is hosted on GitHub at https://github.com/DistrictDataLabs/yellowbrick. + +The typical workflow for a contributor to the codebase is as follows: + +1. **Discover** a bug or a feature by using Yellowbrick. +2. **Discuss** with the core contributes by [adding an issue](https://github.com/DistrictDataLabs/yellowbrick/issues). +3. **Assign** yourself the task by pulling a card from our [Waffle Kanban](https://waffle.io/DistrictDataLabs/yellowbrick). +4. **Fork** the repository into your own GitHub account. +5. Create a **Pull Request** first thing to [connect with us](https://github.com/DistrictDataLabs/yellowbrick/pulls) about your task. +6. **Code** the feature, write the documentation, add your contribution. +7. **Review** the code with core contributors who will guide you to a high quality submission. +8. **Merge** your contribution into the Yellowbrick codebase. + +**Note**: Create a pull request as soon as possible, even before you've started coding. This will allow the core contributors to give you advice about where to add your code or utilities and discuss other style choices and implementation details as you go. Don't wait! + +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. + +### 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. + +Once forked, use the following steps to get your development environment set up on your computer: + +1. Clone the repository. + + After clicking the fork button, you should be redirected to the GitHub page of the repository in your user account. You can then clone a copy of the code to your local machine. + + `` + $ git clone https://github.com/[YOURUSERNAME]/yellowbrick + $ cd yellowbrick + `` + +2. Create a virtual environment. + + Yellowbrick developers typically use [virtualenv](https://virtualenv.pypa.io/en/stable/) (and [virtualenvwrapper](https://virtualenvwrapper.readthedocs.io/en/latest/), [pyenv](https://github.com/pyenv/pyenv-virtualenv) or [conda envs](https://conda.io/docs/using/envs.html) in order to manage their Python version and dependencies. Using the virtual environment tool of your choice, create one for Yellowbrick. Here's how with virtualenv: + + `` + $ virtualenv venv + `` + +3. Install dependencies. + + Yellowbrick's dependencies are in the `requirements.txt` document at the root of the repository. Open this file and uncomment the dependencies that are for development only. Then install the dependencies with `pip`: + + `` + $ pip install -r requirements.txt + `` + + Note that there may be other dependencies required for development and testing, you can simply install them with `pip`. + +4. Switch to the develop branch. + + The Yellowbrick repository has a `develop` branch that is the primary working branch for contributions. It is probably already the branch you're on, but you can make sure and switch to it as follows:: + + `` + $ git fetch + $ git checkout develop + `` + +At this point you're ready to get started writing code. If you're going to take on a specific task, we'd strongly encourage you to check out the issue on [Waffle](https://waffle.io/DistrictDataLabs/yellowbrick) and create a [pull request](https://github.com/DistrictDataLabs/yellowbrick/pulls) **before you start coding** to better foster communication with other contributors. + +### Branching Conventions + +The Yellowbrick repository is set up in a typical production/release/development cycle as described in "[A Successful Git Branching Model](http://nvie.com/posts/a-successful-git-branching-model/)>." The primary working branch is the `develop` branch. This should be the branch that you are working on and from, since this has all the latest code. The `master` branch contains the latest stable version and release_, which is pushed to PyPI_. No one but core contributors will generally push to master. + +**NOTE:** All pull requests should be into the `yellowbrick/develop` branch from your forked repository. + +You can work directly in your fork and create a pull request from your fork's develop branch into ours. We also recommend setting up an `upstream` remote so that you can easily pull the latest development changes from the main Yellowbrick repository (see [configuring a remote for a fork](https://help.github.com/articles/configuring-a-remote-for-a-fork/)). You can do that as follows: + +`` +$ git remote add upstream https://github.com/DistrictDataLabs/yellowbrick.git +$ git remote -v +origin https://github.com/YOUR_USERNAME/YOUR_FORK.git (fetch) +origin https://github.com/YOUR_USERNAME/YOUR_FORK.git (push) +upstream https://github.com/DistrictDataLabs/yellowbrick.git (fetch) +upstream https://github.com/DistrictDataLabs/yellowbrick.git (push) +`` + +When you're ready, request a code review for your pull request. Then, when reviewed and approved, you can merge your fork into our main branch. Make sure to use the "Squash and Merge" option in order to create a Git history that is understandable. + +**NOTE**: When merge a pull request, use the "squash and merge" option. + +Core contributors have write access to the repository. In order to reduce the number of merges (and merge conflicts) we recommend that you utilize a feature branch off of develop to do intermediate work in:: + + $ git checkout -b feature-myfeature develop + +Once you are done working (and everything is tested) merge your feature into develop.:: + + $ git checkout develop + $ git merge --no-ff feature-myfeature + $ git branch -d feature-myfeature + $ git push origin develop + +Head back to Waffle and checkout another issue! + +## Developing Visualizers + +In this section, we'll discuss the basics of developing visualizers. This of course is a big topic, but hopefully these simple tips and tricks will help make sense. + +One thing that is necessary is a good understanding of Scikit-Learn and Matplotlib. Because our API is intended to integrate with Scikit-Learn, a good start is to review ["APIs of Scikit-Learn objects"](http://scikit-learn.org/stable/developers/contributing.html#apis-of-scikit-learn-objects) and ["rolling your own estimator"](http://scikit-learn.org/stable/developers/contributing.html#rolling-your-own-estimator). In terms of matplotlib, check out [Nicolas P. Rougier's Matplotlib tutorial](https://www.labri.fr/perso/nrougier/teaching/matplotlib/). + +### Visualizer API + +There are two basic types of Visualizers: + +- **Feature Visualizers** are high dimensional data visualizations that are essentially transformers. +- **Score Visualizers** wrap a Scikit-Learn regressor, classifier, or clusterer and visualize the behavior or performance of the model on test data. + +These two basic types of visualizers map well to the two basic objects in Scikit-Learn: + +- **Transformers** take input data and return a new data set. +- **Estimators** are fit to training data and can make predictions. + +The Scikit-Learn API is object oriented, and estimators and transformers are initialized with parameters by instantiating their class. Hyperparameters can also be set using the `set_attrs()` method and retrieved with the corresponding `get_attrs()` method. All Scikit-Learn estimators have a `fit(X, y=None)` method that accepts a two dimensional data array, `X`, and optionally a vector `y` of target values. The `fit()` method trains the estimator, making it ready to transform data or make predictions. Transformers have an associated `transform(X)` method that returns a new dataset, `Xprime` and models have a `predict(X)` method that returns a vector of predictions, `yhat`. Models also have a `score(X, y)` method that evaluate the performance of the model. + +Visualizers interact with Scikit-Learn objects by intersecting with them at the methods defined above. Specifically, visualizers perform actions related to `fit()`, `transform()`, `predict()`, and `score()` then call a `draw()` method which initializes the underlying figure associated with the visualizer. The user calls the visualizer's `poof()` method, which in turn calls a `finalize()` method on the visualizer to draw legends, titles, etc. and then `poof()` renders the figure. The Visualizer API is therefore: + +- `draw()`: add visual elements to the underlying axes object +- `finalize()`: prepare the figure for rendering, adding final touches such as legends, titles, axis labels, etc. +- `poof()`: render the figure for the user. + +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:: + +```python +import matplotlib.pyplot as plot + +from yellowbrick.base import Visualizer + +class MyVisualizer(Visualizer): + + def __init__(self, ax=None, **kwargs): + super(MyVisualizer, self).__init__(ax, **kwargs) + + def fit(self, X, y=None): + self.draw(X) + return self + + def draw(self, X): + if self.ax is None: + self.ax = self.gca() + + self.ax.plot(X) + + def finalize(self): + self.set_title("My Visualizer") +``` + +This simple visualizer simply draws a line graph for some input dataset X, intersecting with the Scikit-Learn API at the `fit()` method. A user would use this visualizer in the typical style:: + +```python +visualizer = MyVisualizer() +visualizer.fit(X) +visualizer.poof() +``` + +Score visualizers work on the same principle but accept an additional required `model` argument. Score visualizers wrap the model (which can be either instantiated or uninstantiated) and then pass through all attributes and methods through to the underlying model, drawing where necessary. + +### Testing + +The test package mirrors the yellowbrick package in structure and also contains several helper methods and base functionality. To add a test to your visualizer, find the corresponding file to add the test case, or create a new test file in the same place you added your code. + +Visual tests are notoriously difficult to create --- how do you test a visualization or figure? Moreover, testing Scikit-Learn models with real data can consume a lot of memory. Therefore the primary test you should create is simply to test your visualizer from end to end and make sure that no exceptions occur. To assist with this, we have two primary helpers, `VisualTestCase` and `DatasetMixin`. Create your unittest as follows:: + +```python +from tests.base import VisualTestCase +from tests.dataset import DatasetMixin + +class MyVisualizerTests(VisualTestCase, DatasetMixin): + + def test_my_visualizer(self): + """ + Test MyVisualizer on a real dataset + """ + # Load the data from the fixture + dataset = self.load_data('occupancy') + + # Get the data + X = dataset[[ + "temperature", "relative_humidity", "light", "C02", "humidity" + ]] + y = dataset['occupancy'].astype(int) + + try: + visualizer = MyVisualizer() + visualizer.fit(X) + visualizer.poof() + except Exception as e: + self.fail("my visualizer didn't work") +``` + +Tests can be run as follows:: + +``` +$ make test +``` + +The Makefile uses the nosetest runner and testing suite as well as the coverage library, so make sure you have those dependencies installed! The `DatasetMixin` also requires requests.py to fetch data from our Amazon S3 account. + +### Documentation + +The initial documentation for your visualizer will be a well structured docstring. Yellowbrick uses Sphinx to build documentation, therefore docstrings should be written in reStructuredText in numpydoc format (similar to Scikit-Learn). The primary location of your docstring should be right under the class definition, here is an example:: + +```python +class MyVisualizer(Visualizer): + """ + This initial section should describe the visualizer and what + it's about, including how to use it. Take as many paragraphs + as needed to get as much detail as possible. + + In the next section describe the parameters to __init__. + + Parameters + ---------- + + model : a Scikit-Learn regressor + Should be an instance of a regressor, and specifically one whose name + ends with "CV" otherwise a will raise a YellowbrickTypeError exception + on instantiation. To use non-CV regressors see: + ``ManualAlphaSelection``. + + 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. + + Examples + -------- + + >>> model = MyVisualizer() + >>> model.fit(X) + >>> model.poof() + + Notes + ----- + + In the notes section specify any gotchas or other info. + """ +``` + +You should also add your example to the `examples` directory of the documentation when you have the chance, as well as create a demonstration in a notebook in the `examples` directory of the repository. + +## Throughput + +[![Throughput Graph](https://graphs.waffle.io/DistrictDataLabs/yellowbrick/throughput.svg)](https://waffle.io/DistrictDataLabs/yellowbrick/metrics/throughput) diff --git a/DESCRIPTION.txt b/DESCRIPTION.txt index 342718999..c7af7074d 100644 --- a/DESCRIPTION.txt +++ b/DESCRIPTION.txt @@ -1,15 +1,20 @@ -Yellowbrick is a suite of visual analysis and diagnostic tools designed to facilitate machine learning with Scikit-Learn. The package includes visualizations that can help users navigate the feature selection process, build intuition around model selection, diagnose common problems like bias, heteroscedasticity, underfit, and overtraining, and support hyperparameter tuning to steer predictive models toward more successful results. +Yellowbrick is a suite of visual analysis and diagnostic tools designed to facilitate machine learning with Scikit-Learn. The library implements a new core API object, the "Visualizer" that is an Scikit-Learn estimator: an object that learns from data. Like transformers or models, visualizers learn from data by creating a visual representation of the model selection workflow. + +Visualizers allow users to steer the model selection process, building intuition around feature engineering, algorithm selection, and hyperparameter tuning. For example, visualizers can help diagnose common problems surrounding model complexity and bias, heteroscedasticity, underfit and overtraining, or class balance issues. By applying visualizers to the model selection workflow, Yellowbrick allows you to steer predictive models to more successful results, faster. Some of the available tools include: -- histograms -- scatter plot matrices +- pairwise feature ranking - parallel coordinates -- jointplots +- radial visualization - ROC curves - classification heatmaps - residual plots +- prediction error plots +- alpha selection plots - validation curves - gridsearch heatmaps +- text frequency distributions +- tsne corpus visualization -For more, please see the full documentation at: http://yellowbrick.readthedocs.org/en/latest/ +And much more! Please see the full documentation at: http://scikit-yb.org/ diff --git a/MAINTAINERS.md b/MAINTAINERS.md new file mode 100644 index 000000000..9dee9b26b --- /dev/null +++ b/MAINTAINERS.md @@ -0,0 +1,27 @@ +# Maintainers and Contributors + +This file describes how the Yellowbrick project is maintained and provides contact information for key folks in the project. + +When creating a pull request, your contribution will be reviewed by one or probably two maintainers who will give you the :+1: when your extension is ready to be merged. Maintainers work hard to ensure that Yellowbrick is a high quality project and that contributors are successful. + +For more about how to develop visualizers and contribute features to Yellowbrick, see our [contributor's guide](CONTRIBUTING.md) and the documentation. + +## Maintainers + +This is a list of the primary project maintainers. Feel free to @ message them in issues and converse with them directly. + +- [bbengfort](https://github.com/bbengfort) +- [NealHumphrey](https://github.com/NealHumphrey) +- [jkeung](https://github.com/jkeung) + +## Core Contributors + +This is a list of the core-contributors of the project. Core contributors set the road map and vision of the project. Keep an eye out for them in issues and check out their work to use as inspiration! Most likely they would also be happy to chat and answer questions. + +- [rebeccabilbro](https://github.com/rebeccabilbro) +- [mattandahalfew](https://github.com/mattandahalfew) +- [pdamodaran](https://github.com/pdamodaran) +- [ndanielsen](https://github.com/ndanielsen) +- [tuulihill](https://github.com/tuulihill) +- [balavenkatesan](https://github.com/balavenkatesan) +- [morganmendis](https://github.com/morganmendis) diff --git a/README.md b/README.md index 43e71be37..e11593784 100644 --- a/README.md +++ b/README.md @@ -7,50 +7,62 @@ [![Stories in Ready](https://badge.waffle.io/DistrictDataLabs/yellowbrick.png?label=ready&title=Ready)](https://waffle.io/DistrictDataLabs/yellowbrick) -A suite of visual analysis and diagnostic tools to facilitate feature selection, model selection, and parameter tuning for machine learning. - +**Visual analysis and diagnostic tools to facilitate machine learning model selection.** ![Follow the yellow brick road](docs/images/yellowbrickroad.jpg) Image by [Quatro Cinco](https://flic.kr/p/2Yj9mj), used with permission, Flickr Creative Commons. -# What is Yellowbrick? -Yellowbrick is a suite of visual analysis and diagnostic tools to facilitate feature selection, model selection, and parameter tuning for machine learning. All visualizations are generated in Matplotlib. Custom `yellowbrick` visualization tools include: - -## Tools for feature analysis and selection - - Boxplots (box-and-whisker plots) - - Violinplots - - Histograms - - Scatter plot matrices (sploms) - - Radial visualizations (radviz) - - Parallel coordinates - - Jointplots - - Rank 1D - - Rank 2D - -## Tools for model evaluation -### Classification - - ROC-AUC curves - - Classification heatmaps - - Class balance chart - -### Regression - - Prediction error plots - - Residual plots - - Most informative features - -### Clustering - - Silhouettes - - Density measures - -## Tools for parameter tuning - - Validation curves - - Gridsearch heatmaps +This README is a guide for developers, if you're new to Yellowbrick, get started at our [documentation](http://www.scikit-yb.org/). + +## What is Yellowbrick? + +Yellowbrick is a suite of visual diagnostic tools called "Visualizers" that extend the Scikit-Learn API to allow human steering of the model selection process. In a nutshell, Yellowbrick combines Scikit-Learn with Matplotlib in the best tradition of the Scikit-Learn documentation, but to produce visualizations for _your_ models! + +![Visualizers](docs/images/visualizers.png) + +### Visualizers + +Visualizers are estimators (objects that learn from data) whose primary objective is to create visualizations that allow insight into the model selection process. In Scikit-Learn terms, they can be similar to transformers when visualizing the data space or wrap an model estimator similar to how the "ModelCV" (e.g. RidgeCV, LassoCV) methods work. The primary goal of Yellowbrick is to create a sensical API similar to Scikit-Learn. Some of our most popular visualizers include: + +#### Feature Visualization + +- Rank2D: pairwise ranking of features to detect relationships +- Parallel Coordinates: horizontal visualization of instances +- Radial Visualization: separation of instances around a circular plot + +#### Classification Visualization + +- Class Balance: see how the distribution of classes affects the model +- Classification Report: visual representation of precision, recall, and F1 +- ROC/AUC Curves: receiver operator characteristics and area under the curve +- Confusion Matrices: visual description of class decision making + +#### Regression Visualization + +- Prediction Error Plots: find model breakdowns along the domain of the target +- Residuals Plot: show the difference in residuals of training and test data +- Alpha Selection: show how the choice of alpha influences regularization + +#### Clustering Visualization + +- K-Elbow Plot: select k using the elbow method and various metrics +- Silhouette Plot: select k by visualizing silhouette coefficient values + +#### Text Visualization + +- Term Frequency: visualize the frequency distribution of terms in the corpus +- TSNE: use stochastic neighbor embedding to project documents. + +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 Visualizers! ## Using Yellowbrick + The Yellowbrick API is specifically designed to play nicely with Scikit-Learn. Here is an example of a typical workflow sequence with Scikit-Learn and Yellowbrick: ### Feature Visualization + In this example, we see how Rank2D performs pairwise comparisons of each feature in the data set with a specific metric or algorithm, then returns them ranked as a lower left triangle diagram. + ```python from yellowbrick.features import Rank2D @@ -61,7 +73,9 @@ visualizer.poof() # Draw/show/poof the data ``` ### Model Visualization + In this example, we instantiate a Scikit-Learn classifier, and then we use Yellowbrick's ROCAUC class to visualize the tradeoff between the classifier's sensitivity and specificity. + ```python from sklearn.svm import LinearSVC from yellowbrick.classifier import ROCAUC @@ -79,26 +93,22 @@ We also have a [quick start guide](https://github.com/DistrictDataLabs/yellowbri ## Contributing to Yellowbrick -Yellowbrick is an open source tool designed to enable more informed machine learning through visualizations. If you would like to contribute, you can do so in the following ways: +Yellowbrick is an open source project that is supported by a community who will gratefully and humbly accept any contributions you might make to the project. Large or small, any contribution makes a big difference; and if you've never contributed to an open source project before, we hope you will start with Yellowbrick! - - Add issues or bugs to the bug tracker: https://github.com/DistrictDataLabs/yellowbrick/issues - - Work on a card on the dev board: https://waffle.io/DistrictDataLabs/yellowbrick - - Create a pull request in Github: https://github.com/DistrictDataLabs/yellowbrick/pulls +Principally, Yellowbrick development is about the addition and creation of *visualizers* --- objects that learn from data and create a visual representation of the data or model. Visualizers integrate with Scikit-Learn estimators, transformers, and pipelines for specific purposes and as a result, can be simple to build and deploy. The most common contribution is therefore a new visualizer for a specific model or model family. We'll discuss in detail how to build visualizers later. -This repository is set up in a typical production/release/development cycle as described in [A Successful Git Branching Model](http://nvie.com/posts/a-successful-git-branching-model/). A typical workflow is as follows: +Beyond creating visualizers, there are many ways to contribute: -1. Select a card from the [dev board](https://waffle.io/districtdatalabs/yellowbrick) - preferably one that is "ready" then move it to "in-progress". -2. Create a branch off of develop called "feature-[feature name]", work and commit into that branch. - ``` - ~$ git checkout -b feature-myfeature develop - ``` +- Submit a bug report or feature request on [GitHub Issues](https://github.com/DistrictDataLabs/yellowbrick/issues). +- Contribute a Jupyter notebook to our examples[ gallery](https://github.com/DistrictDataLabs/yellowbrick/tree/develop/examples). +- Assist us with [user testing](http://www.scikit-yb.org/en/latest/evaluation.html). +- Add to the documentation or help with our website, [scikit-yb.org](http://www.scikit-yb.org). +- Write unit or integration tests for our project. +- Answer questions on our issues, mailing list, Stack Overflow, and elsewhere. +- Translate our documentation into another language. +- Write a blog post, tweet, or share our project with others. +- Teach someone how to use Yellowbrick. -3. Once you are done working (and everything is tested) merge your feature into develop. - ``` - ~$ git checkout develop - ~$ git merge --no-ff feature-myfeature - ~$ git branch -d feature-myfeature - ~$ git push origin develop - ``` +As you can see, there are lots of ways to get involved and we would be very happy for you to join us! The only thing we ask is that you abide by the principles of openness, respect, and consideration of others as described in the [Python Software Foundation Code of Conduct](https://www.python.org/psf/codeofconduct/). -4. Repeat. Releases will be routinely pushed into master via release branches, then deployed to the server. +For more information, checkout [CONTRIBUTING.md](https://github.com/DistrictDataLabs/yellowbrick/blob/develop/CONTRIBUTING.md). diff --git a/docs/_static/theme_overrides.css b/docs/_static/theme_overrides.css new file mode 100644 index 000000000..63ee6cc74 --- /dev/null +++ b/docs/_static/theme_overrides.css @@ -0,0 +1,13 @@ +/* override table width restrictions */ +@media screen and (min-width: 767px) { + + .wy-table-responsive table td { + /* !important prevents the common CSS stylesheets from overriding + this as on RTD they are loaded after this stylesheet */ + white-space: normal !important; + } + + .wy-table-responsive { + overflow: visible !important; + } +} diff --git a/docs/about.rst b/docs/about.rst index 2abb2502b..3bf781d61 100644 --- a/docs/about.rst +++ b/docs/about.rst @@ -2,13 +2,10 @@ About ===== - -About Yellowbrick ------------------ -Yellowbrick is an open source, pure Python project that extends Scikit-Learn with visual analysis and diagnostic tools. The Yellowbrick API also wraps matplotlib 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. +Yellowbrick is an open source, pure Python project that extends Scikit-Learn with visual analysis and diagnostic tools. The Yellowbrick API also wraps Matplotlib 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. The Model Selection Triple -^^^^^^^^^^^^^^^^^^^^^^^^^^ +-------------------------- Discussions of machine learning are frequently characterized by a singular focus on model selection. Be it logistic regression, random forests, Bayesian methods, or artificial neural networks, machine learning practitioners are often quick to express their preference. The reason for this is mostly historical. Though modern third-party machine learning libraries have made the deployment of multiple models appear nearly trivial, traditionally the application and tuning of even one of these algorithms required many years of study. As a result, machine learning practitioners tended to have strong preferences for particular (and likely more familiar) models over others. However, model selection is a bit more nuanced than simply picking the "right" or "wrong" algorithm. In practice, the workflow includes: @@ -23,56 +20,6 @@ Recently, much of this workflow has been automated through grid search methods, The Yellowbrick library is a diagnostic visualization platform for machine learning that allows data scientists to steer the model selection process. Yellowbrick 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. - -Contributing ------------- - -If you would like to contribute to Yellowbrick, you can do so in the following ways: - -- Add issues or bugs to the bug tracker: https://github.com/DistrictDataLabs/yellowbrick/issues -- Work on a card on the dev board: https://waffle.io/DistrictDataLabs/yellowbrick -- Create a pull request in Github: https://github.com/DistrictDataLabs/yellowbrick/pulls - -Here's how to get set up with Yellowbrick in development mode (since the project is still under development). - -1. Fork and clone the repository. After clicking fork in the upper right corner -for your own copy of yellowbrick to your github account. Clone it in a directory -of your choice.:: - - $ git clone https://github.com/[YOURUSERNAME]/yellowbrick - $ cd yellowbrick - -2. Create virtualenv and create the dependencies.:: - - $ virtualenv venv - $ pip install -r requirements.txt - -3. Fetch and switch to development.:: - - $ git fetch - $ git checkout develop - - -Git Conventions -^^^^^^^^^^^^^^^ - -The Yellowbrick repository is set up in a typical production/release/development cycle_ as described in "A Successful Git Branching Model." A typical workflow is as follows: - -1. Select a card from the dev board_, preferably one that is "ready" then move it to "in-progress". - -2. Create a branch off of develop called "feature-[feature name]", work and commit into that branch.:: - - $ git checkout -b feature-myfeature develop - -3. Once you are done working (and everything is tested) merge your feature into develop.:: - - $ git checkout develop - $ git merge --no-ff feature-myfeature - $ git branch -d feature-myfeature - $ git push origin develop - -4. Repeat. Releases will be routinely pushed into master via release branches, then deployed to the server. - 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. @@ -80,146 +27,25 @@ The Yellowbrick package gets its name from the fictional element in the 1900 chi From Wikipedia_: "The road is first introduced in the third chapter of The Wonderful Wizard of Oz. The road begins in the heart of the eastern quadrant called Munchkin Country in the Land of Oz. It functions as a guideline that leads all who follow it, to the road's ultimate destination—the imperial capital of Oz called Emerald City that is located in the exact center of the entire continent. In the book, the novel's main protagonist, Dorothy, is forced to search for the road before she can begin her quest to seek the Wizard. This is because the cyclone from Kansas did not release her farmhouse closely near it as it did in the various film adaptations. After the council with the native Munchkins and their dear friend the Good Witch of the North, Dorothy begins looking for it and sees many pathways and roads nearby, (all of which lead in various directions). Thankfully it doesn't take her too long to spot the one paved with bright yellow bricks." +Team +---- + +Yellowbrick is is developed by data scientists who believe in open source and the project enjoys contributions from Python developers all over the world. The project was started by `@rebeccabilbro`_ and `@bbengfort`_ as an attempt to better explain machine learning concepts to their students; they quickly realized, however, that the potential for visual steering could have a large impact on practical data science and developed it into a high-level Python library. + +Yellowbrick is incubated by `District Data Labs`_, an organization that is dedicated to collaboration and open source development. As part of District Data Labs, Yellowbrick was first introduced to the Python Community at `PyCon 2016 `_ in both talks and during the development sprints. The project was then carried on through DDL Research Labs (semester-long sprints where members of the DDL community contribute to various data related projects). + +License +------- + +Yellowbrick is an open source project and its `license `_ is an implementation of the FOSS `Apache 2.0 `_ license by the Apache Software Foundation. `In plain English `_ this means that you can use Yellowbrick for commercial purposes, modify and distribute the source code, and even sublicense it. We want you to use Yellowbrick, profit from it, and contribute back if you do cool things with it. + +There are, however, a couple of requirements that we ask from you. First, when you copy or distribute Yellowbrick source code, please include our copyright and license found in the `LICENSE.txt `_ at the root of our software repository. In addition, if we create a file called "NOTICE" in our project you must also include that in your source distribution. The "NOTICE" file will include attribution and thanks to those who have worked so hard on the project! Finally you can't hold District Data Labs or any Yellowbrick contributor liable for your use of our software, nor use any of our names, trademarks, or logos. -Changelog ---------- - -Version 0.3.3 -^^^^^^^^^^^^^ -Intermediate sprint to demonstrate prototype implementations of text visualizers for NLP models. Primary contributions were the ``FreqDistVisualizer`` and the ``TSNEVisualizer``. - -The ``TSNEVisualizer`` displays a projection of a vectorized corpus in two dimensions using TSNE, a nonlinear dimensionality reduction method that is particularly well suited to embedding in two or three dimensions for visualization as a scatter plot. TSNE is widely used in text analysis to show clusters or groups of documents or utterances and their relative proximities. - -The ``FreqDistVisualizer`` implements frequency distribution plot that tells us the frequency of each vocabulary item in the text. In general, it could count any kind of observable event. It is a distribution because it tells us how the total number of word tokens in the text are distributed across the vocabulary items. - -* Tag: v0.3.3_ -* Deployed: Wednesday, February 22, 2017 -* Contributors: Rebecca Bilbro, Benjamin Bengfort - -Changes: - - TSNEVisualizer for 2D projections of vectorized documents - - FreqDistVisualizer for token frequency of text in a corpus - - Added the user testing evaluation to the documentation - - Created scikit-yb.org and host documentation there with RFD - - Created a sample corpus and text examples notebook - - Created a base class for text, ``TextVisualizer`` - - Model selection tutorial using Mushroom Dataset - - Created a text examples notebook but have not added to documentation. - - -Version 0.3.2 -^^^^^^^^^^^^^ -Hardened the Yellowbrick API to elevate the idea of a Visualizer to a first principle. This included reconciling shifts in the development of the preliminary versions to the new API, formalizing Visualizer methods like `draw()` and `finalize()`, and adding utilities that revolve around Scikit-Learn. To that end we also performed administrative tasks like refreshing the documentation and preparing the repository for more and varied open source contributions. - -* Tag: v0.3.2_ -* Deployed: Friday, January 20, 2017 -* Contributors: Benjamin Bengfort, Rebecca Bilbro - -Changes: - - Converted Mkdocs documentation to Sphinx documentation - - Updated docstrings for all Visualizers and functions - - Created a DataVisualizer base class for dataset visualization - - Single call functions for simple visualizer interaction - - Added yellowbrick specific color sequences and palettes and env handling - - More robust examples with downloader from DDL host - - Better axes handling in visualizer, matplotlib/sklearn integration - - Added a finalize method to complete drawing before render - - Improved testing on real data sets from examples - - Bugfix: score visualizer renders in notebook but not in Python scripts. - - Bugfix: tests updated to support new API - -Hotfix 0.3.1 -^^^^^^^^^^^^ -Hotfix to solve pip install issues with Yellowbrick. - -* Tag: v0.3.1_ -* Deployed: Monday, October 10, 2016 -* Contributors: Benjamin Bengfort - - Changes: - - Modified packaging and wheel for Python 2.7 and 3.5 compatibility - - Modified deployment to PyPI and pip install ability - - Fixed Travis-CI tests with the backend failures. - -Version 0.3 -^^^^^^^^^^^ -This release marks a major change from the previous MVP releases as Yellowbrick moves towards direct integration with Scikit-Learn for visual diagnostics and steering of machine learning and could therefore be considered the first alpha release of the library. To that end we have created a Visualizer model which extends sklearn.base.BaseEstimator and can be used directly in the ML Pipeline. There are a number of visualizers that can be used throughout the model selection process, including for feature analysis, model selection, and hyperparameter tuning. - -In this release specifically we focused on visualizers in the data space for feature analysis and visualizers in the model space for scoring and evaluating models. Future releases will extend these base classes and add more functionality. - -* Tag: v0.3_ -* Deployed: Sunday, October 9, 2016 -* Contributors: Benjamin Bengfort, Rebecca Bilbro, Marius van Niekerk - - Enhancements: - - Created an API for visualization with machine learning: Visualizers that are BaseEstimators. - - Created a class hierarchy for Visualizers throughout the ML process particularly feature analysis and model evaluation - - Visualizer interface is draw method which can be called multiple times on data or model spaces and a poof method to finalize the figure and display or save to disk. - - ScoreVisualizers wrap Scikit-Learn estimators and implement fit and predict (pass-throughs to the estimator) and also score which calls draw in order to visually score the estimator. If the estimator isn't appropriate for the scoring method an exception is raised. - - ROCAUC is a ScoreVisualizer that plots the receiver operating characteristic curve and displays the area under the curve score. - - ClassificationReport is a ScoreVisualizer that renders the confusion matrix of a classifier as a heatmap. - - PredictionError is a ScoreVisualizer that plots the actual vs. predicted values and the 45 degree accuracy line for regressors. - - ResidualPlot is a ScoreVisualizer that plots the residuals (y - yhat) across the actual values (y) with the zero accuracy line for both train and test sets. - - ClassBalance is a ScoreVisualizer that displays the support for each class as a bar plot. - - FeatureVisualizers are Scikit-Learn Transformers that implement fit and transform and operate on the data space, calling draw to display instances. - - ParallelCoordinates plots instances with class across each feature dimension as line segments across a horizontal space. - - RadViz plots instances with class in a circular space where each feature dimension is an arc around the circumference and points are plotted relative to the weight of the feature. - - Rank2D plots pairwise scores of features as a heatmap in the space [-1, 1] to show relative importance of features. Currently implemented ranking functions are Pearson correlation and covariance. - - Coordinated and added palettes in the bgrmyck space and implemented a version of the Seaborn set_palette and set_color_codes functions as well as the ColorPalette object and other matplotlib.rc modifications. - - Inherited Seaborn's notebook context and whitegrid axes style but make them the default, don't allow user to modify (if they'd like to, they'll have to import Seaborn). This gives Yellowbrick a consistent look and feel without giving too much work to the user and prepares us for Matplotlib 2.0. - - Jupyter Notebook with Examples of all Visualizers and usage. - - Bug Fixes: - - Fixed Travis-CI test failures with matplotlib.use('Agg'). - - Fixed broken link to Quickstart on README - - Refactor of the original API to the Scikit-Learn Visualizer API - -Version 0.2 -^^^^^^^^^^^ -Intermediate steps towards a complete API for visualization. Preparatory stages for Scikit-Learn visual pipelines. - -* Tag: v0.2_ -* Deployed: Sunday, September 4, 2016 -* Contributors: Benjamin Bengfort, Rebecca Bilbro, Patrick O'Melveny, Ellen Lowy, Laura Lorenz - - Changes: - - Continued attempts to fix the Travis-CI Scipy install failure (broken tests) - - Utility function: get the name of the model - - Specified a class based API and the basic interface (render, draw, fit, predict, score) - - Added more documentation, converted to Sphinx, autodoc, docstrings for viz methods, and a quickstart - - How to contribute documentation, repo images etc. - - Prediction error plot for regressors (mvp) - - Residuals plot for regressors (mvp) - - Basic style settings a la seaborn - - ROC/AUC plot for classifiers (mvp) - - Best fit functions for "select best", linear, quadratic - - Several Jupyter notebooks for examples and demonstrations - - - -Version 0.1 -^^^^^^^^^^^ -Created the yellowbrick library MVP with two primary operations: a classification report heat map and a ROC/AUC curve model analysis for classifiers. This is the base package deployment for continuing yellowbrick development. - -* Tag: v0.1_ -* Deployed: Wednesday, May 18, 2016 -* Contributors: Benjamin Bengfort, Rebecca Bilbro - - Changes: - - Created the Anscombe quartet visualization example - - Added DDL specific color maps and a stub for more style handling - - Created crplot which visualizes the confusion matrix of a classifier - - Created rocplot_compare which compares two classifiers using ROC/AUC metrics - - Stub tests/stub documentation +We think that's a pretty fair deal, and we're big believers in open source. If you make any changes to our software, use it commercially or academically, or have any other interest, we'd love to hear about it. .. _SIGMOD: http://cseweb.ucsd.edu/~arunkk/vision/SIGMODRecord15.pdf -.. _cycle: http://nvie.com/posts/a-successful-git-branching-model/ -.. _board: https://waffle.io/districtdatalabs/yellowbrick .. _Wikipedia: https://en.wikipedia.org/wiki/Yellow_brick_road -.. _v0.3.3: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.3.3 -.. _v0.3.2: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.3.2 -.. _v0.3.1: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.3.1a2 -.. _v0.3: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.3 -.. _v0.2: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.2 -.. _v0.1: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.1 +.. _`@rebeccabilbro`: https://github.com/rebeccabilbro +.. _`@bbengfort`: https://github.com/bbengfort +.. _`District Data Labs`: http://www.districtdatalabs.com/ diff --git a/docs/api/modules.rst b/docs/api/modules.rst index 7dcf01ec0..5e6b79cd5 100644 --- a/docs/api/modules.rst +++ b/docs/api/modules.rst @@ -5,6 +5,9 @@ API Reference :maxdepth: 4 yellowbrick + yellowbrick.features + yellowbrick.regressor + yellowbrick.classifier + yellowbrick.cluster yellowbrick.text yellowbrick.style - yellowbrick.features diff --git a/docs/api/yellowbrick.classifier.rst b/docs/api/yellowbrick.classifier.rst new file mode 100644 index 000000000..89cfd05d9 --- /dev/null +++ b/docs/api/yellowbrick.classifier.rst @@ -0,0 +1,45 @@ +yellowbrick.classifier package +============================== + +Submodules +---------- + +yellowbrick.classifier.base module +---------------------------------- + +.. automodule:: yellowbrick.classifier.base + :members: + :undoc-members: + :show-inheritance: + +yellowbrick.classifier.class_balance module +------------------------------------------- + +.. automodule:: yellowbrick.classifier.class_balance + :members: + :undoc-members: + :show-inheritance: + +yellowbrick.classifier.classification_report module +--------------------------------------------------- + +.. automodule:: yellowbrick.classifier.classification_report + :members: + :undoc-members: + :show-inheritance: + +yellowbrick.classifier.confusion_matrix module +---------------------------------------------- + +.. automodule:: yellowbrick.classifier.confusion_matrix + :members: + :undoc-members: + :show-inheritance: + +yellowbrick.classifier.rocauc module +------------------------------------ + +.. automodule:: yellowbrick.classifier.rocauc + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/api/yellowbrick.cluster.rst b/docs/api/yellowbrick.cluster.rst new file mode 100644 index 000000000..44c573ce6 --- /dev/null +++ b/docs/api/yellowbrick.cluster.rst @@ -0,0 +1,29 @@ +yellowbrick.cluster package +=========================== + +Submodules +---------- + +yellowbrick.cluster.base module +------------------------------- + +.. automodule:: yellowbrick.cluster.base + :members: + :undoc-members: + :show-inheritance: + +yellowbrick.cluster.elbow module +-------------------------------- + +.. automodule:: yellowbrick.cluster.elbow + :members: + :undoc-members: + :show-inheritance: + +yellowbrick.cluster.silhouette module +------------------------------------- + +.. automodule:: yellowbrick.cluster.silhouette + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/api/yellowbrick.features.rst b/docs/api/yellowbrick.features.rst index 4dc15c809..c009821cb 100644 --- a/docs/api/yellowbrick.features.rst +++ b/docs/api/yellowbrick.features.rst @@ -35,3 +35,11 @@ yellowbrick.features.rankd module :members: :undoc-members: :show-inheritance: + +yellowbrick.features.jointplot module +------------------------------------- + +.. automodule:: yellowbrick.features.jointplot + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/api/yellowbrick.regressor.rst b/docs/api/yellowbrick.regressor.rst new file mode 100644 index 000000000..c256d7c4a --- /dev/null +++ b/docs/api/yellowbrick.regressor.rst @@ -0,0 +1,29 @@ +yellowbrick.regressor package +============================= + +Submodules +---------- + +yellowbrick.regressor.base module +--------------------------------- + +.. automodule:: yellowbrick.regressor.base + :members: + :undoc-members: + :show-inheritance: + +yellowbrick.regressor.residuals module +-------------------------------------- + +.. automodule:: yellowbrick.regressor.residuals + :members: + :undoc-members: + :show-inheritance: + +yellowbrick.regressor.alphas module +----------------------------------- + +.. automodule:: yellowbrick.regressor.alphas + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/api/yellowbrick.rst b/docs/api/yellowbrick.rst index bbdc9bd2b..4990cdffd 100644 --- a/docs/api/yellowbrick.rst +++ b/docs/api/yellowbrick.rst @@ -28,14 +28,6 @@ yellowbrick.bestfit module :undoc-members: :show-inheritance: -yellowbrick.classifier module ------------------------------ - -.. automodule:: yellowbrick.classifier - :members: - :undoc-members: - :show-inheritance: - yellowbrick.exceptions module ----------------------------- diff --git a/docs/changelog.rst b/docs/changelog.rst new file mode 100644 index 000000000..9648cba7b --- /dev/null +++ b/docs/changelog.rst @@ -0,0 +1,174 @@ +========= +Changelog +========= + +Version 0.4 +----------- +This release is the culmination of the Spring 2017 DDL Research Labs that focused on developing Yellowbrick as a community effort guided by a sprint/agile workflow. We added several more visualizers, did a lot of user testing and bug fixes, updated the documentation, and generally discovered how best to make Yellowbrick a friendly project to contribute to. + +Notable in this release is the inclusion of two new feature visualizers that use few, simple dimensions to visualize features against the target. The ``JointPlotVisualizer`` graphs a scatter plot of two dimensions in the data set and plots a best fit line across it. The ``ScatterVisualizer`` also uses two features, but also colors the graph by the target variable, adding a third dimension to the visualization. + +This release also adds support for clustering visualizations, namely the elbow method for selecting K, ``KElbowVisualizer`` and a visualization of cluster size and density using the ``SilhouetteVisualizer``. The release also adds support for regularization analysis using the ``AlphaSelection`` visualizer. Both the text and classification modules were also improved with the inclusion of the ``PosTagVisualizer`` and the ``ConfusionMatrix`` visualizer respectively. + +This release also added an Anaconda repository and distribution so that users can ``conda install`` yellowbrick. Even more notable, we got yellowbrick stickers! We've also updated the documentation to make it more friendly and a bit more visual; fixing the API rendering errors. All-in-all, this was a big release with a lot of contributions and we thank everyone that participated in the lab! + +* Tag: v0.4_ +* Deployed: Thursday, May 4, 2017 +* Contributors: Benjamin Bengfort, Rebecca Bilbro, Nathan Danielsen, Matt Andersen, Prema Roman, Neal Humphrey, Jason Keung, Bala Venkatesan, Paul Witt, Morgan Mendis, Tuuli Morril + +Changes: + - Part of speech tags visualizer -- ``PosTagVisualizer``. + - Alpha selection visualizer for regularized regression -- ``AlphaSelection`` + - Confusion Matrix Visualizer -- ``ConfusionMatrix`` + - Elbow method for selecting K vis -- ``KElbowVisualizer`` + - Silhouette score cluster visualization -- ``SilhouetteVisualizer`` + - Joint plot visualizer with best fit -- ``JointPlotVisualizer`` + - Scatter visualization of features -- ``ScatterVisualizer`` + - Added three more example datasets: mushroom, game, and bike share + - Contributor's documentation and style guide + - Maintainers listing and contacts + - Light/Dark background color selection utility + - Structured array detection utility + - Updated classification report to use colormesh + - Added anacondas packaging and distribution + - Refactoring of the regression, cluster, and classification modules + - Image based testing methodology + - Docstrings updated to a uniform style and rendering + - Submission of several more user studies + +Version 0.3.3 +------------- +Intermediate sprint to demonstrate prototype implementations of text visualizers for NLP models. Primary contributions were the ``FreqDistVisualizer`` and the ``TSNEVisualizer``. + +The ``TSNEVisualizer`` displays a projection of a vectorized corpus in two dimensions using TSNE, a nonlinear dimensionality reduction method that is particularly well suited to embedding in two or three dimensions for visualization as a scatter plot. TSNE is widely used in text analysis to show clusters or groups of documents or utterances and their relative proximities. + +The ``FreqDistVisualizer`` implements frequency distribution plot that tells us the frequency of each vocabulary item in the text. In general, it could count any kind of observable event. It is a distribution because it tells us how the total number of word tokens in the text are distributed across the vocabulary items. + +* Tag: v0.3.3_ +* Deployed: Wednesday, February 22, 2017 +* Contributors: Rebecca Bilbro, Benjamin Bengfort + +Changes: + - TSNEVisualizer for 2D projections of vectorized documents + - FreqDistVisualizer for token frequency of text in a corpus + - Added the user testing evaluation to the documentation + - Created scikit-yb.org and host documentation there with RFD + - Created a sample corpus and text examples notebook + - Created a base class for text, ``TextVisualizer`` + - Model selection tutorial using Mushroom Dataset + - Created a text examples notebook but have not added to documentation. + + +Version 0.3.2 +------------- +Hardened the Yellowbrick API to elevate the idea of a Visualizer to a first principle. This included reconciling shifts in the development of the preliminary versions to the new API, formalizing Visualizer methods like `draw()` and `finalize()`, and adding utilities that revolve around Scikit-Learn. To that end we also performed administrative tasks like refreshing the documentation and preparing the repository for more and varied open source contributions. + +* Tag: v0.3.2_ +* Deployed: Friday, January 20, 2017 +* Contributors: Benjamin Bengfort, Rebecca Bilbro + +Changes: + - Converted Mkdocs documentation to Sphinx documentation + - Updated docstrings for all Visualizers and functions + - Created a DataVisualizer base class for dataset visualization + - Single call functions for simple visualizer interaction + - Added yellowbrick specific color sequences and palettes and env handling + - More robust examples with downloader from DDL host + - Better axes handling in visualizer, matplotlib/sklearn integration + - Added a finalize method to complete drawing before render + - Improved testing on real data sets from examples + - Bugfix: score visualizer renders in notebook but not in Python scripts. + - Bugfix: tests updated to support new API + +Hotfix 0.3.1 +------------- +Hotfix to solve pip install issues with Yellowbrick. + +* Tag: v0.3.1_ +* Deployed: Monday, October 10, 2016 +* Contributors: Benjamin Bengfort + + Changes: + - Modified packaging and wheel for Python 2.7 and 3.5 compatibility + - Modified deployment to PyPI and pip install ability + - Fixed Travis-CI tests with the backend failures. + +Version 0.3 +----------- +This release marks a major change from the previous MVP releases as Yellowbrick moves towards direct integration with Scikit-Learn for visual diagnostics and steering of machine learning and could therefore be considered the first alpha release of the library. To that end we have created a Visualizer model which extends sklearn.base.BaseEstimator and can be used directly in the ML Pipeline. There are a number of visualizers that can be used throughout the model selection process, including for feature analysis, model selection, and hyperparameter tuning. + +In this release specifically we focused on visualizers in the data space for feature analysis and visualizers in the model space for scoring and evaluating models. Future releases will extend these base classes and add more functionality. + +* Tag: v0.3_ +* Deployed: Sunday, October 9, 2016 +* Contributors: Benjamin Bengfort, Rebecca Bilbro, Marius van Niekerk + + Enhancements: + - Created an API for visualization with machine learning: Visualizers that are BaseEstimators. + - Created a class hierarchy for Visualizers throughout the ML process particularly feature analysis and model evaluation + - Visualizer interface is draw method which can be called multiple times on data or model spaces and a poof method to finalize the figure and display or save to disk. + - ScoreVisualizers wrap Scikit-Learn estimators and implement fit and predict (pass-throughs to the estimator) and also score which calls draw in order to visually score the estimator. If the estimator isn't appropriate for the scoring method an exception is raised. + - ROCAUC is a ScoreVisualizer that plots the receiver operating characteristic curve and displays the area under the curve score. + - ClassificationReport is a ScoreVisualizer that renders the confusion matrix of a classifier as a heatmap. + - PredictionError is a ScoreVisualizer that plots the actual vs. predicted values and the 45 degree accuracy line for regressors. + - ResidualPlot is a ScoreVisualizer that plots the residuals (y - yhat) across the actual values (y) with the zero accuracy line for both train and test sets. + - ClassBalance is a ScoreVisualizer that displays the support for each class as a bar plot. + - FeatureVisualizers are Scikit-Learn Transformers that implement fit and transform and operate on the data space, calling draw to display instances. + - ParallelCoordinates plots instances with class across each feature dimension as line segments across a horizontal space. + - RadViz plots instances with class in a circular space where each feature dimension is an arc around the circumference and points are plotted relative to the weight of the feature. + - Rank2D plots pairwise scores of features as a heatmap in the space [-1, 1] to show relative importance of features. Currently implemented ranking functions are Pearson correlation and covariance. + - Coordinated and added palettes in the bgrmyck space and implemented a version of the Seaborn set_palette and set_color_codes functions as well as the ColorPalette object and other matplotlib.rc modifications. + - Inherited Seaborn's notebook context and whitegrid axes style but make them the default, don't allow user to modify (if they'd like to, they'll have to import Seaborn). This gives Yellowbrick a consistent look and feel without giving too much work to the user and prepares us for Matplotlib 2.0. + - Jupyter Notebook with Examples of all Visualizers and usage. + + Bug Fixes: + - Fixed Travis-CI test failures with matplotlib.use('Agg'). + - Fixed broken link to Quickstart on README + - Refactor of the original API to the Scikit-Learn Visualizer API + +Version 0.2 +----------- +Intermediate steps towards a complete API for visualization. Preparatory stages for Scikit-Learn visual pipelines. + +* Tag: v0.2_ +* Deployed: Sunday, September 4, 2016 +* Contributors: Benjamin Bengfort, Rebecca Bilbro, Patrick O'Melveny, Ellen Lowy, Laura Lorenz + + Changes: + - Continued attempts to fix the Travis-CI Scipy install failure (broken tests) + - Utility function: get the name of the model + - Specified a class based API and the basic interface (render, draw, fit, predict, score) + - Added more documentation, converted to Sphinx, autodoc, docstrings for viz methods, and a quickstart + - How to contribute documentation, repo images etc. + - Prediction error plot for regressors (mvp) + - Residuals plot for regressors (mvp) + - Basic style settings a la seaborn + - ROC/AUC plot for classifiers (mvp) + - Best fit functions for "select best", linear, quadratic + - Several Jupyter notebooks for examples and demonstrations + + + +Version 0.1 +----------- +Created the yellowbrick library MVP with two primary operations: a classification report heat map and a ROC/AUC curve model analysis for classifiers. This is the base package deployment for continuing yellowbrick development. + +* Tag: v0.1_ +* Deployed: Wednesday, May 18, 2016 +* Contributors: Benjamin Bengfort, Rebecca Bilbro + + Changes: + - Created the Anscombe quartet visualization example + - Added DDL specific color maps and a stub for more style handling + - Created crplot which visualizes the confusion matrix of a classifier + - Created rocplot_compare which compares two classifiers using ROC/AUC metrics + - Stub tests/stub documentation + + +.. _v0.4: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.4 +.. _v0.3.3: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.3.3 +.. _v0.3.2: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.3.2 +.. _v0.3.1: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.3.1a2 +.. _v0.3: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.3 +.. _v0.2: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.2 +.. _v0.1: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v0.1 diff --git a/docs/conf.py b/docs/conf.py index 6180e0c05..c18255998 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -167,6 +167,12 @@ # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] +html_context = { + 'css_files': [ + '_static/theme_overrides.css', # override wide tables in RTD theme + ], + } + # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. diff --git a/docs/contributing.rst b/docs/contributing.rst new file mode 100644 index 000000000..822d72b72 --- /dev/null +++ b/docs/contributing.rst @@ -0,0 +1,295 @@ +============ +Contributing +============ + +Yellowbrick is an open source project that is supported by a community who will gratefully and humbly accept any contributions you might make to the project. Large or small, any contribution makes a big difference; and if you've never contributed to an open source project before, we hope you will start with Yellowbrick! + +Principally, Yellowbrick development is about the addition and creation of *visualizers* --- objects that learn from data and create a visual representation of the data or model. Visualizers integrate with Scikit-Learn estimators, transformers, and pipelines for specific purposes and as a result, can be simple to build and deploy. The most common contribution is therefore a new visualizer for a specific model or model family. We'll discuss in detail how to build visualizers later. + +Beyond creating visualizers, there are many ways to contribute: + +- Submit a bug report or feature request on `GitHub Issues `_. +- Contribute an Jupyter notebook to our `examples gallery `_. +- Assist us with `user testing `_. +- Add to the documentation or help with our website, `scikit-yb.org `_ +- Write unit or integration tests for our project. +- Answer questions on our issues, mailing list, Stack Overflow, and elsewhere. +- Translate our documentation into another language. +- Write a blog post, tweet, or share our project with others. +- Teach someone how to use Yellowbrick. + +As you can see, there are lots of ways to get involved and we would be very happy for you to join us! The only thing we ask is that you abide by the principles of openness, respect, and consideration of others as described in the `Python Software Foundation Code of Conduct `_. + +Getting Started on GitHub +------------------------- + +Yellowbrick is hosted on GitHub at https://github.com/DistrictDataLabs/yellowbrick. + +The typical workflow for a contributor to the codebase is as follows: + +1. **Discover** a bug or a feature by using Yellowbrick. +2. **Discuss** with the core contributes by `adding an issue `_. +3. **Assign** yourself the task by pulling a card from our `Waffle Kanban `_ +4. **Fork** the repository into your own GitHub account. +5. Create a **Pull Request** first thing to `connect with us `_ about your task. +6. **Code** the feature, write the documentation, add your contribution. +7. **Review** the code with core contributors who will guide you to a high quality submission. +8. **Merge** your contribution into the Yellowbrick codebase. + +.. note:: Create a pull request as soon as possible, even before you've started coding. This will allow the core contributors to give you advice about where to add your code or utilities and discuss other style choices and implementation details as you go. Don't wait! + +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. + +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. + +Once forked, use the following steps to get your development environment set up on your computer: + +1. Clone the repository. + + After clicking the fork button, you should be redirected to the GitHub page of the repository in your user account. You can then clone a copy of the code to your local machine.:: + + $ git clone https://github.com/[YOURUSERNAME]/yellowbrick + $ cd yellowbrick + +2. Create a virtual environment. + + Yellowbrick developers typically use `virtualenv `_ (and `virtualenvwrapper `_), `pyenv `_ or `conda envs `_ in order to manage their Python version and dependencies. Using the virtual environment tool of your choice, create one for Yellowbrick. Here's how with virtualenv:: + + $ virtualenv venv + +3. Install dependencies. + + Yellowbrick's dependencies are in the `requirements.txt` document at the root of the repository. Open this file and uncomment the dependencies that are for development only. Then install the dependencies with ``pip``:: + + $ pip install -r requirements.txt + + Note that there may be other dependencies required for development and testing, you can simply install them with ``pip``. + +4. Switch to the develop branch. + + The Yellowbrick repository has a ``develop`` branch that is the primary working branch for contributions. It is probably already the branch you're on, but you can make sure and switch to it as follows:: + + $ git fetch + $ git checkout develop + +At this point you're ready to get started writing code. If you're going to take on a specific task, we'd strongly encourage you to check out the issue on `Waffle `_ and create a `pull request `_ *before you start coding* to better foster communication with other contributors. + +The next section is about managing GitHub branches for contributions, but if you're ready jump straight to `Developing Visualizers`_! + +Branching Convention +~~~~~~~~~~~~~~~~~~~~ + +The Yellowbrick repository is set up in a typical production/release/development cycle as described in "`A Successful Git Branching Model `_." The primary working branch is the ``develop`` branch. This should be the branch that you are working on and from, since this has all the latest code. The ``master`` branch contains the latest stable version and release_, which is pushed to PyPI_. No one but core contributors will generally push to master. + +.. note:: All pull requests should be into the ``yellowbrick/develop`` branch from your forked repository. + +You can work directly in your fork and create a pull request from your fork's develop branch into ours. We also recommend setting up an ``upstream`` remote so that you can easily pull the latest development changes from the main Yellowbrick repository (see `configuring a remote for a fork `_). You can do that as follows:: + + $ git remote add upstream https://github.com/DistrictDataLabs/yellowbrick.git + $ git remote -v + origin https://github.com/YOUR_USERNAME/YOUR_FORK.git (fetch) + origin https://github.com/YOUR_USERNAME/YOUR_FORK.git (push) + upstream https://github.com/DistrictDataLabs/yellowbrick.git (fetch) + upstream https://github.com/DistrictDataLabs/yellowbrick.git (push) + +When you're ready, request a code review for your pull request. Then, when reviewed and approved, you can merge your fork into our main branch. Make sure to use the "Squash and Merge" option in order to create a Git history that is understandable. + +.. note:: When merging a pull request, use the "squash and merge" option. + +Core contributors have write access to the repository. In order to reduce the number of merges (and merge conflicts) we recommend that you utilize a feature branch off of develop to do intermediate work in:: + + $ git checkout -b feature-myfeature develop + +Once you are done working (and everything is tested) merge your feature into develop.:: + + $ git checkout develop + $ git merge --no-ff feature-myfeature + $ git branch -d feature-myfeature + $ git push origin develop + +Head back to Waffle and checkout another issue! + +Releases +~~~~~~~~ + +When ready to create a new release we branch off of develop as follows:: + + $ git checkout -b release-x.x + +This creates a release branch for version x.x. At this point do the version bump by modifying ``version.py`` and the test version in ``tests/__init__.py``. Make sure all tests pass for the release and that the documentation is up to date. There may be style changes or deployment options that have to be done at this phase in the release branch. At this phase you'll also modify the ``changelog`` with the features and changes in the release. + +Once the release is ready for prime-time, merge into master:: + + $ git checkout master + $ git merge --no-ff --no-edit release-x.x + +Tag the release in GitHub:: + + $ git tag -a vx.x + $ git push origin vx.x + +You'll have to go to the release_ page to edit the release with similar information as added to the changelog. Once done, push the release to PyPI: + + $ make build + $ make deploy + +Check that the PyPI page is updated with the correct version and that ``pip install -U yellowbrick`` updates the version and works correctly. Also check the documentation on PyHosted, ReadTheDocs, and on our website to make sure that it was correctly updated. Finally merge the release into develop and clean up: + + $ git checkout develop + $ git merge --no-ff --no-edit release-x.x + $ git branch -d release-x.x + +Hotfixes and minor releases also follow a similar pattern; the goal is to effectively get new code to users as soon as possible! + +.. _release: https://github.com/DistrictDataLabs/yellowbrick/releases +.. _PyPI: https://pypi.python.org/pypi/yellowbrick + +Developing Visualizers +---------------------- + +In this section, we'll discuss the basics of developing visualizers. This of course is a big topic, but hopefully these simple tips and tricks will help make sense. First thing though, check out this presentation that we put together on yellowbrick development, it discusses the expected user workflow, our integration with Scikit-Learn, our plans and roadmap, etc: + +.. raw:: html + + + +One thing that is necessary is a good understanding of Scikit-Learn and Matplotlib. Because our API is intended to integrate with Scikit-Learn, a good start is to review `"APIs of Scikit-Learn objects" `_ and `"rolling your own estimator" `_. In terms of matplotlib, check out `Nicolas P. Rougier's Matplotlib tutorial `_. + +Visualizer API +~~~~~~~~~~~~~~ + +There are two basic types of Visualizers: + +- **Feature Visualizers** are high dimensional data visualizations that are essentially transformers. +- **Score Visualizers** wrap a Scikit-Learn regressor, classifier, or clusterer and visualize the behavior or performance of the model on test data. + +These two basic types of visualizers map well to the two basic objects in Scikit-Learn: + +- **Transformers** take input data and return a new data set. +- **Estimators** are fit to training data and can make predictions. + +The Scikit-Learn API is object oriented, and estimators and transformers are initialized with parameters by instantiating their class. Hyperparameters can also be set using the ``set_attrs()`` method and retrieved with the corresponding ``get_attrs()`` method. All Scikit-Learn estimators have a ``fit(X, y=None)`` method that accepts a two dimensional data array, ``X``, and optionally a vector ``y`` of target values. The ``fit()`` method trains the estimator, making it ready to transform data or make predictions. Transformers have an associated ``transform(X)`` method that returns a new dataset, ``Xprime`` and models have a ``predict(X)`` method that returns a vector of predictions, ``yhat``. Models also have a ``score(X, y)`` method that evaluate the performance of the model. + +Visualizers interact with Scikit-Learn objects by intersecting with them at the methods defined above. Specifically, visualizers perform actions related to ``fit()``, ``transform()``, ``predict()``, and ``score()`` then call a ``draw()`` method which initializes the underlying figure associated with the visualizer. The user calls the visualizer's ``poof()`` method, which in turn calls a ``finalize()`` method on the visualizer to draw legends, titles, etc. and then ``poof()`` renders the figure. The Visualizer API is therefore: + +- ``draw()``: add visual elements to the underlying axes object +- ``finalize()``: prepare the figure for rendering, adding final touches such as legends, titles, axis labels, etc. +- ``poof()``: render the figure for the user (or saves it to disk). + +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 + + from yellowbrick.base import Visualizer + + class MyVisualizer(Visualizer): + + def __init__(self, ax=None, **kwargs): + super(MyVisualizer, self).__init__(ax, **kwargs) + + def fit(self, X, y=None): + self.draw(X) + return self + + def draw(self, X): + if self.ax is None: + self.ax = self.gca() + + self.ax.plot(X) + + def finalize(self): + self.set_title("My Visualizer") + +This simple visualizer simply draws a line graph for some input dataset X, intersecting with the Scikit-Learn API at the ``fit()`` method. A user would use this visualizer in the typical style:: + + visualizer = MyVisualizer() + visualizer.fit(X) + visualizer.poof() + +Score visualizers work on the same principle but accept an additional required ``model`` argument. Score visualizers wrap the model (which can be either instantiated or uninstantiated) and then pass through all attributes and methods through to the underlying model, drawing where necessary. + +Testing +~~~~~~~ + +The test package mirrors the yellowbrick package in structure and also contains several helper methods and base functionality. To add a test to your visualizer, find the corresponding file to add the test case, or create a new test file in the same place you added your code. + +Visual tests are notoriously difficult to create --- how do you test a visualization or figure? Moreover, testing Scikit-Learn models with real data can consume a lot of memory. Therefore the primary test you should create is simply to test your visualizer from end to end and make sure that no exceptions occur. To assist with this, we have two primary helpers, ``VisualTestCase`` and ``DatasetMixin``. Create your unittest as follows:: + + from tests.base import VisualTestCase + from tests.dataset import DatasetMixin + + class MyVisualizerTests(VisualTestCase, DatasetMixin): + + def test_my_visualizer(self): + """ + Test MyVisualizer on a real dataset + """ + # Load the data from the fixture + dataset = self.load_data('occupancy') + + # Get the data + X = dataset[[ + "temperature", "relative_humidity", "light", "C02", "humidity" + ]] + y = dataset['occupancy'].astype(int) + + try: + visualizer = MyVisualizer() + visualizer.fit(X) + visualizer.poof() + except Exception as e: + self.fail("my visualizer didn't work") + +Tests can be run as follows:: + + $ make test + +The Makefile uses the nosetest runner and testing suite as well as the coverage library, so make sure you have those dependencies installed! The ``DatasetMixin`` also requires requests.py to fetch data from our Amazon S3 account. + +Documentation +~~~~~~~~~~~~~ + +The initial documentation for your visualizer will be a well structured docstring. Yellowbrick uses Sphinx to build documentation, therefore docstrings should be written in reStructuredText in numpydoc format (similar to Scikit-Learn). The primary location of your docstring should be right under the class definition, here is an example:: + + class MyVisualizer(Visualizer): + """ + This initial section should describe the visualizer and what + it's about, including how to use it. Take as many paragraphs + as needed to get as much detail as possible. + + In the next section describe the parameters to __init__. + + Parameters + ---------- + + model : a Scikit-Learn regressor + Should be an instance of a regressor, and specifically one whose name + ends with "CV" otherwise a will raise a YellowbrickTypeError exception + on instantiation. To use non-CV regressors see: + ``ManualAlphaSelection``. + + 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. + + Examples + -------- + + >>> model = MyVisualizer() + >>> model.fit(X) + >>> model.poof() + + Notes + ----- + + In the notes section specify any gotchas or other info. + """ + +You should also add your example to the ``examples`` directory of the documentation when you have the chance, as well as create a demonstration in a notebook in the ``examples`` directory of the repository. diff --git a/docs/evaluation.rst b/docs/evaluation.rst index b8dd9033f..fccd5de05 100644 --- a/docs/evaluation.rst +++ b/docs/evaluation.rst @@ -9,7 +9,16 @@ options and customize as much as possible. After you've exercised the code with your examples, respond to our `alpha testing survey `__! -Step One: Dataset +Step One: Questionaire +~~~~~~~~~~~~~~~~~~~~~~ +Please open the quesionaire, in order to familiarize yourself with the +feedback that we are looking to receive. We are very interested in +identifying any bugs in Yellowbrick. Please include al cells in your +jupyter notebook that produce errors so that we may reproduce the +problem. + + +Step Two: Dataset ~~~~~~~~~~~~~~~~~ Select a multivariate dataset of your own; the more (e.g. different) @@ -40,22 +49,22 @@ publicly available for us to review. If the data is also public (or you're willing to share it with the primary contributors) that will help us figure out bugs and required features much more easily! -Step Two: Notebook -~~~~~~~~~~~~~~~~~~ +Step Three: Notebook +~~~~~~~~~~~~~~~~~~~~ Create a notebook in a GitHub repository. We suggest the following: 1. Fork the Yellowbrick repository 2. Under the ``examples`` directory, create a directory named with your GitHub username -3. Create the notebook in examples/USERNAME/testing.ipynb +3. Create a notebook named ``testing``, i.e. examples/USERNAME/testing.ipynb Alternatively, you could just send us a notebook via Gist or your own repository. However, if you fork Yellowbrick, you can initiate a pull request to have your example added to our gallery! -Step Three: Model with Yellowbrick and Scikit-Learn -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Step Four: Model with Yellowbrick and Scikit-Learn +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Add the following to the notebook: @@ -75,9 +84,9 @@ an end-to-end model from data loading to estimator(s) with visualizers along the way. **IMPORTANT**: please make sure you record all errors that you get and -any tracebacks you receive for step four! +any tracebacks you receive for step three! -Step Four: Feedback +Step Five: Feedback ~~~~~~~~~~~~~~~~~~~ Finally, submit feedback via the Google Form we have created: @@ -90,8 +99,8 @@ the first to report a bug or feature request, we will make sure you're notified (we'll tag you using your Github username) about the created issue! -Step Five: Thanks! -~~~~~~~~~~~~~~~~~~ +Step Six: Thanks! +~~~~~~~~~~~~~~~~~ Thank you for helping us make Yellowbrick better! We'd love to see pull requests for features you think would be extend the library. We'll also diff --git a/docs/examples/images/confusionMatrix_3_0.png b/docs/examples/images/confusionMatrix_3_0.png new file mode 100644 index 000000000..83c650173 Binary files /dev/null and b/docs/examples/images/confusionMatrix_3_0.png differ diff --git a/docs/examples/methods.rst b/docs/examples/methods.rst index 4edd33f38..c100a0efa 100644 --- a/docs/examples/methods.rst +++ b/docs/examples/methods.rst @@ -185,7 +185,7 @@ 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. -Data scientists use this method to dect separability between classes. +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? @@ -225,11 +225,11 @@ Parallel Coordinates 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 +infinite horizontal space (e.g. a scrollbar), an infinite number of dimensions can be displayed! Data scientists use this method to detect clusters of instances that -have similar classes, and to note features that have high varaince or +have similar classes, and to note features that have high variance or different distributions. .. code:: python @@ -269,7 +269,7 @@ implemented two regressor evaluations: - Residuals Plot: plot the difference between the expected and actual values -- Prediction Error: plot expected vs. the actual values in model space +- Prediction Error: plot the expected vs. actual values in model space Estimator score visualizers *wrap* Scikit-Learn estimators and expose the Estimator API such that they have ``fit()``, ``predict()``, and @@ -289,7 +289,7 @@ the final step in a ``Pipeline`` or ``VisualPipeline``. Residuals Plot ~~~~~~~~~~~~~~ -A residual plot shows the residuals on the vertical axis and the +A residuals 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 @@ -327,9 +327,9 @@ appropriate. Prediction Error Plot ~~~~~~~~~~~~~~~~~~~~~ -Plots the actual targets from the dataset against the predicted values +A prediction error plot shows 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 +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. .. code:: python @@ -368,9 +368,11 @@ Classification models attempt to predict a target in a discrete space, that is assign an instance of dependent variables one or more categories. Classification score visualizers display the differences between classes as well as a number of classifier-specific visual -evaluations. We currently have implemented three classifier evaluations: +evaluations. We currently have implemented four classifier evaluations: -- ClassificationReport: Presents the confusion matrix of the classifier +- ClassificationReport: Presents the classification report of the classifier + as a heatmap +- ConfusionMatrix: Presents the confusion matrix of the classifier as a heatmap - ROCAUC: Presents the graph of receiver operating characteristics along with area under the curve @@ -398,8 +400,8 @@ Classification Report ~~~~~~~~~~~~~~~~~~~~~ The classification report visualizer displays the precision, recall, and -F1 scores for the model. Integrates numerical scores as well color-coded -heatmap in order for easy interpretation and detection. +F1 scores for the model. In order to support easier interpretation and problem detection, the report integrates numerical scores with a color-coded +heatmap. .. code:: python @@ -432,10 +434,69 @@ heatmap in order for easy interpretation and detection. .. image:: images/examples_32_0.png +Confusion Matrix Visualizer +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The ``ConfusionMatrix`` visualizer is a ScoreVisualizer that takes a +fitted Scikit-Learn classifier and a set of test X and y values and +returns a report showing how each of the test values predicted classes +compare to their actual classes. Data scientists use confusion matrices +to understand which classes are most easily confused. These provide +similar information as what is available in a ClassificationReport, but +rather than top-level scores they provide deeper insight into the +classification of individual data points. + +Below are a few examples of using the ConfusionMatrix visualizer; more +information can be found by looking at the +Scikit-Learn documentation on `confusion matrices `_. + +.. code:: python + + #First do our imports + import yellowbrick + + from sklearn.datasets import load_digits + from sklearn.cross_validation import train_test_split + from sklearn.linear_model import LogisticRegression + + from yellowbrick.classifier import ConfusionMatrix + +.. code:: python + + # We'll use the handwritten digits data set from scikit-learn. + # Each feature of this dataset is an 8x8 pixel image of a handwritten number. + # Digits.data converts these 64 pixels into a single array of features + 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) + + model = LogisticRegression() + + #The ConfusionMatrix visualizer taxes a model + cm = ConfusionMatrix(model, classes=[0,1,2,3,4,5,6,7,8,9]) + + #Fit fits the passed model. This is unnecessary if you pass the visualizer a pre-fitted model + cm.fit(X_train, y_train) + + #To create the ConfusionMatrix, we need some test data. Score runs predict() on the data + #and then creates the confusion_matrix from scikit learn. + cm.score(X_test, y_test) + + #How did we do? + cm.poof() + + + +.. image:: images/confusionMatrix_3_0.png + + + ROCAUC ~~~~~~ -Plot the ROC to visualize the tradeoff between the classifier's +A ROCAUC (Receiver Operating Characteristic/Area Under the Curve) plot allows the user to visualize the tradeoff between the classifier's sensitivity and specificity. .. code:: python @@ -472,7 +533,7 @@ sensitivity and specificity. ClassBalance ~~~~~~~~~~~~ -Class balance chart that shows the support for each class in the fitted +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 diff --git a/docs/images/alphaselect/elasticnet.png b/docs/images/alphaselect/elasticnet.png new file mode 100644 index 000000000..7ae7bbace Binary files /dev/null and b/docs/images/alphaselect/elasticnet.png differ diff --git a/docs/images/alphaselect/lasso.png b/docs/images/alphaselect/lasso.png new file mode 100644 index 000000000..64b8642d9 Binary files /dev/null and b/docs/images/alphaselect/lasso.png differ diff --git a/docs/images/alphaselect/lassolars.png b/docs/images/alphaselect/lassolars.png new file mode 100644 index 000000000..721b31ced Binary files /dev/null and b/docs/images/alphaselect/lassolars.png differ diff --git a/docs/images/alphaselect/ridge.png b/docs/images/alphaselect/ridge.png new file mode 100644 index 000000000..31f2c4ce7 Binary files /dev/null and b/docs/images/alphaselect/ridge.png differ diff --git a/docs/images/visualizers.png b/docs/images/visualizers.png new file mode 100644 index 000000000..342b6ba86 Binary files /dev/null and b/docs/images/visualizers.png differ diff --git a/docs/index.rst b/docs/index.rst index 463928436..27eb95d07 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,71 +1,91 @@ .. yellowbrick documentation master file, created by - sphinx-quickstart on Tue Jul 5 19:45:43 2016. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. + sphinx-quickstart on Tue Jul 5 19:45:43 2016. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. -======================================= -Welcome to yellowbrick's documentation! -======================================= -Yellowbrick is a suite of visual analysis and diagnostic tools to facilitate feature selection, model selection, and parameter tuning for machine learning. All visualizations are generated in Matplotlib. +=========================================== +Yellowbrick: Machine Learning Visualization +=========================================== +.. image:: images/visualizers.png -Custom ``yellowbrick`` visualization tools include: +Yellowbrick is a suite of visual diagnostic tools called "Visualizers" that extend the Scikit-Learn API to allow human steering of the model selection process. In a nutshell, Yellowbrick combines Scikit-Learn with Matplotlib in the best tradition of the Scikit-Learn documentation, but to produce visualizations for *your* models! For more on Yellowbrick, please see the :doc:`introduction`. -Tools for feature analysis and selection ----------------------------------------- +If you're new to Yellowbrick, checkout the :doc:`setup` or skip ahead to the :doc:`examples/examples`. Yellowbrick is a rich library with many Visualizers being added on a regular basis. For details on specific Visualizers and extended usage head over to the :doc:`api/modules`. If you've signed up to do user testing, checkout the :doc:`evaluation` (and thank you!). - - Boxplots ("box-and-whisker" plots) - - Violinplots - - Histograms - - Scatter plot matrices ("sploms") - - Radial visualizations ("radviz") - - Parallel coordinates - - Jointplots - - Rank 1D - - Rank 2D +Visualizers +----------- +Visualizers are estimators (objects that learn from data) whose primary objective is to create visualizations that allow insight into the model selection process. In Scikit-Learn terms, they can be similar to transformers when visualizing the data space or wrap an model estimator similar to how the "ModelCV" (e.g. `RidgeCV `_, `LassoCV `_) methods work. The primary goal of Yellowbrick is to create a sensical API similar to Scikit-Learn. Some of our most popular visualizers include: -Tools for model evaluation --------------------------- +Feature Visualization +~~~~~~~~~~~~~~~~~~~~~ -Classification -^^^^^^^^^^^^^^ - - ROC-AUC curves - - Classification heatmaps - - Class balance charts +- Rank2D: pairwise ranking of features to detect relationships +- Parallel Coordinates: horizontal visualization of instances +- Radial Visualization: separation of instances around a circular plot -Regression -^^^^^^^^^^ - - Prediction error plots - - Residual plots - - Most informative features +Classification Visualization +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Clustering -^^^^^^^^^^ - - Silhouettes - - Density measures +- Class Balance: see how the distribution of classes affects the model +- Classification Report: visual representation of precision, recall, and F1 +- ROC/AUC Curves: receiver operator characteristics and area under the curve +- Confusion Matrices: visual description of class decision making +Regression Visualization +~~~~~~~~~~~~~~~~~~~~~~~~ -Tools for parameter tuning --------------------------- +- Prediction Error Plots: find model breakdowns along the domain of the target +- Residuals Plot: show the difference in residuals of training and test data +- Alpha Selection: show how the choice of alpha influences regularization -- Validation curves -- Gridsearch heatmap +Clustering Visualization +~~~~~~~~~~~~~~~~~~~~~~~~ +- K-Elbow Plot: select k using the elbow method and various metrics +- Silhouette Plot: select k by visualizing silhouette coefficient values -========= -Contents: -========= +Text Visualization +~~~~~~~~~~~~~~~~~~ + +- Term Frequency: visualize the frequency distribution of terms in the corpus +- TSNE: use stochastic neighbor embedding to project documents. + +... 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! + +Getting Help +------------ + +Yellowbrick is a welcoming, inclusive project in the tradition of Matplotlib and Scikit-Learn. Similar to those projects, we try to follow the `Python Software Foundation Code of Conduct `_. Please don't hesitate to reach out to us for help or if you have any contributions or bugs to report! + +We're still in the initial stages of the project, and don't necessarily have a mailing list or FAQ put together (but with your help we can build one!). Ask questions on `Stack Overflow `_ and tag them with "yellowbrick". Or you can add issues on GitHub. You can also tweet or direct message us on Twitter `@DistrictDataLab `_. + +Open Source +----------- + +The Yellowbrick `license `_ is an open source `Apache 2.0 `_ license. Yellowbrick enjoys a very active developer community; please consider joining them and `contributing `_! + +Yellowbrick is hosted on `GitHub `_. The `issues `_ and `pull requests `_ are tracked there. + + +================= +Table of Contents +================= + +The following is a complete listing of the Yellowbrick documentation for this version of the library: .. toctree:: - :maxdepth: 4 + :maxdepth: 2 introduction setup examples/examples api/modules - evaluation about + evaluation + contributing + changelog ================== Indices and tables diff --git a/docs/introduction.rst b/docs/introduction.rst index 7dc263802..2bc7aee98 100644 --- a/docs/introduction.rst +++ b/docs/introduction.rst @@ -1,18 +1,31 @@ -============================ -Introduction to Yellowbrick -============================ +============ +Introduction +============ .. image:: images/yellowbrickroad.jpg + Image by QuatroCinco_, used with permission, Flickr Creative Commons. Yellowbrick is a new Python library that extends the Scikit-Learn API_ to incorporate visualizations into the machine learning workflow. -Yellowbrick is an open source, pure Python project that extends Scikit-Learn with visual analysis and diagnostic tools. The Yellowbrick API also wraps matplotlib 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. +Yellowbrick is an open source, pure Python project that extends Scikit-Learn with visual analysis and diagnostic tools. The Yellowbrick API also wraps Matplotlib 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. Recently, much of this workflow has been automated through grid search methods, standardized APIs, and GUI-based applications. In practice, however, human intuition and guidance can more effectively hone in on quality models than exhaustive search. By visualizing the model selection process, data scientists can steer towards final, explainable models and avoid pitfalls and traps. The Yellowbrick library is a diagnostic visualization platform for machine learning that allows data scientists to steer the model selection process. Yellowbrick 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. +Presentations +------------- + +Yellowbrick has enjoyed the spotlight at a few conferences and in several presentations. We hope that these videos, talks, and slides will help you understand Yellowbrick a bit better. + +Videos: + - `Visual Diagnostics for More Informed Machine Learning: Within and Beyond Scikit-Learn (PyCon 2016) `_ + - `Visual Diagnostics for More Informed Machine Learning (PyData Carolinas 2016) `_ + +Slides: + - `Visualizing the Model Selection Process `_ + - `Visualizing Model Selection with Scikit-Yellowbrick `_ .. _QuatroCinco: https://flic.kr/p/2Yj9mj .. _API: http://scikit-learn.org/stable/modules/classes.html diff --git a/examples/LCOMBS/testing.ipynb b/examples/LCOMBS/testing.ipynb new file mode 100644 index 000000000..220b70896 --- /dev/null +++ b/examples/LCOMBS/testing.ipynb @@ -0,0 +1,741 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Predicting Abalone Snail Sex Using Physical Characteristics\n", + "\n", + "### Data was found at https://archive.ics.uci.edu/ml/datasets/Abalone , University of California, Irvine's Machine Learning repository." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "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" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "os.chdir(\"/Users/lisacombs/Documents/yellowbrick/\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sexlengthdiameterheightw_weights_weightv_weightsh_weightrings
0M0.4550.3650.0950.51400.22450.10100.15015
1M0.3500.2650.0900.22550.09950.04850.0707
2F0.5300.4200.1350.67700.25650.14150.2109
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4I0.3300.2550.0800.20500.08950.03950.0557
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" + ], + "text/plain": [ + " sex length diameter height w_weight s_weight v_weight \\\n", + "0 M 0.455 0.365 0.095 0.5140 0.2245 0.1010 \n", + "1 M 0.350 0.265 0.090 0.2255 0.0995 0.0485 \n", + "2 F 0.530 0.420 0.135 0.6770 0.2565 0.1415 \n", + "3 M 0.440 0.365 0.125 0.5160 0.2155 0.1140 \n", + "4 I 0.330 0.255 0.080 0.2050 0.0895 0.0395 \n", + "\n", + " sh_weight rings \n", + "0 0.150 15 \n", + "1 0.070 7 \n", + "2 0.210 9 \n", + "3 0.155 10 \n", + "4 0.055 7 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Load the data\n", + "\n", + "data = pd.read_csv(\"./life.csv\")\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Use only M/F, no infants and make variable numeric.\n", + "data = data.loc[data['sex'].isin(['M','F'])]\n", + "data['sex'] = np.where(data['sex']=='M', 0, 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "//anaconda/lib/python3.5/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" + ] + } + ], + "source": [ + "# Feature Analysis Imports \n", + "# NOTE that all these are available for import from the `yellowbrick.features` module \n", + "from yellowbrick.features.rankd import Rank2D \n", + "from yellowbrick.features.radviz import RadViz \n", + "from yellowbrick.features.pcoords import ParallelCoordinates" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['sex',\n", + " ' length',\n", + " ' diameter',\n", + " ' height',\n", + " ' w_weight',\n", + " ' s_weight',\n", + " ' v_weight',\n", + " ' sh_weight',\n", + " ' rings']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(data) # numeric variables to be used as features" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Specify the features of interest\n", + "\n", + "features = [' length',\n", + " ' diameter',\n", + " ' height',\n", + " ' w_weight',\n", + " ' s_weight',\n", + " ' v_weight',\n", + " ' sh_weight',\n", + " ' rings']\n", + "\n", + "# Extract the numpy arrays from the data frame \n", + "X = data[features].as_matrix()\n", + "y = data.sex.as_matrix()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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pTp06mjt3rmbMmKGIiAitXbtW8+bNyzOfoLAsRlFrCTeQmZmpQ4cO6X9q15aP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\niwICArRkyRLdcccdGj9+/HW3kZaWptO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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the visualizer with the Covariance ranking algorithm \n", + "visualizer = Rank2D(features=features, algorithm='covariance')\n", + "\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" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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EDYQAAJDEEkEAAEzLjHMCzHfGAABAEpUAAAAkmbMSQBIAAIDMOSfAfGcMAAAk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\ntdKXX37p6bCK7dSpUxoxYoTCwsLUqVM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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the visualizer with the Pearson ranking algorithm \n", + "visualizer = Rank2D(features=features, algorithm='pearson')\n", + "\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" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Specify the features of interest and the classes of the target \n", + "features = [' length',\n", + " ' diameter',\n", + " ' height',\n", + " ' w_weight',\n", + " ' s_weight',\n", + " ' v_weight',\n", + " ' sh_weight',\n", + " ' rings']\n", + "\n", + "classes = ['M', 'F']\n", + "\n", + "# Extract the numpy arrays from the data frame \n", + "X = data[features].as_matrix()\n", + "y = data.sex.as_matrix()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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rMGjQIDg5Oek6LCL6P0zqiEiNUqnEkSNHkJGRgeDgYEgkEl2HRHVM79694e3t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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the visualizer\n", + "visualizer = visualizer = RadViz(classes=classes, features=features)\n", + "\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" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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XXiIrK2u/5drd9e7dm48++qjTa5s2baK9vX2PWeev40B+t9auXYvb7WbGjBnxkPfZZ59R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UFZlx+jPWil+efnMf+P92Gy2GjOjF6zmjQmFfax6irDRmVXXy/XPHs/504JIEvQG7Fy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\n+hdaURFY95FpGZJBLB1nbFiO+RsmdjOUIBoh+PqMn9XM3H6GT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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the visualizer\n", + "visualizer = visualizer = ParallelCoordinates(classes=classes, features=features)\n", + "\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" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Regression Evaluation Imports \n", + "\n", + "from sklearn.linear_model import Ridge, Lasso \n", + "from sklearn.cross_validation import train_test_split\n", + "\n", + "from yellowbrick.regressor import PredictionError, ResidualsPlot" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Load the data - without classifier\n", + "\n", + "feature_names = [' length',\n", + " ' diameter',\n", + " ' height',\n", + " ' w_weight',\n", + " ' s_weight',\n", + " ' v_weight',\n", + " ' sh_weight', \n", + " ' rings']\n", + "\n", + "target_name = ' sh_weight'\n", + "\n", + "# Get the X and y data from the DataFrame \n", + "X = data[feature_names].as_matrix()\n", + "y = data[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": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Hz6DurqsOCNHdySQfIUSrSSUb21ozSWXPL2dkBrUQosvqVAHm6dOnueuuuxgx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\nQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 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 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": 18, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "feature_names = [' length',\n", + " ' diameter',\n", + " ' height',\n", + " ' w_weight',\n", + " ' s_weight',\n", + " ' v_weight',\n", + " ' sh_weight']\n", + " \n", + "target_name = ' rings'\n", + "\n", + "# Get the X and y data from the DataFrame \n", + "X = data[feature_names].as_matrix()\n", + "y = data[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": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "//anaconda/lib/python3.5/site-packages/scipy/linalg/basic.py:884: 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", + " warnings.warn(mesg, RuntimeWarning)\n" + ] + }, + { + "data": { + "image/png": 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CIgAAAAwERAAAABgIiAAAADAQEAEAAGAgIAIAAMBAQAQAAICBgAgAAAADAREAAAAGAiIA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\nGhqqbdu2ubosVIP09HRFRkb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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 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": "code", + "execution_count": 20, + "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", + "\n", + "from yellowbrick.classifier import ClassificationReport, ROCAUC, ClassBalance" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Specify the features of interest and the classes of the target \n", + "features = [' length',\n", + " ' diameter',\n", + " ' height',\n", + " ' w_weight',\n", + " ' s_weight',\n", + " ' v_weight',\n", + " ' sh_weight',\n", + " ' rings']\n", + "\n", + "classes = ['M', 'F']\n", + "\n", + "# Extract the numpy arrays from the data frame \n", + "X = data[features].as_matrix()\n", + "y = data.sex.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": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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QQohikCRKCCGEEKIYJIkSQgghhCgGGVguhBBCiD9dxZJGtErm/1yPpYSR+IcQ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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the classification model and visualizer \n", + "bayes = GaussianNB()\n", + "visualizer = ClassificationReport(bayes, classes=classes)\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": 23, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Specify the features of interest and the classes of the target \n", + "features = [' length',\n", + " ' diameter',\n", + " ' height',\n", + " ' w_weight',\n", + " ' s_weight',\n", + " ' v_weight',\n", + " ' sh_weight',\n", + " ' rings']\n", + "\n", + "classes = ['M', 'F']\n", + "\n", + "# Extract the numpy arrays from the data frame \n", + "X = data[features].as_matrix()\n", + "y = data.sex.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": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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AAAoUKMDixYvZuHGj6XmF9erVY9asWRQrVozq1atz9OhRdu3ahb+/PwC1a9fms88+I3fu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\nOlS9CEVEROyQLsXaGWtaOg9/tJNvoi8AsKD3fTwRVMfUmg4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "logistic = LogisticRegression()\n", + "visualizer = ROCAUC(logistic)\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": 25, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Specify the features of interest and the classes of the target \n", + "features = [' length',\n", + " ' diameter',\n", + " ' height',\n", + " ' w_weight',\n", + " ' s_weight',\n", + " ' v_weight',\n", + " ' sh_weight',\n", + " ' rings']\n", + "\n", + "classes = ['M', 'F']\n", + "\n", + "# Extract the numpy arrays from the data frame \n", + "X = data[features].as_matrix()\n", + "y = data.sex.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": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ADETEAQAAGIiIAwAAMBARBwAAYCAiDgAAwEBEHAAAgIGIOAAAAAMRcQAAAAYi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\nlJWVpTfffFOffPKJ1qxZc/E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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the classification model and visualizer \n", + "forest = RandomForestClassifier()\n", + "visualizer = ClassBalance(forest, classes=classes)\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" + ] + } + ], + "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": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/NealHumphrey/.gitignore b/examples/NealHumphrey/.gitignore new file mode 100644 index 000000000..d8e8b4c85 --- /dev/null +++ b/examples/NealHumphrey/.gitignore @@ -0,0 +1,2 @@ +/data/* +!/data/readme.md \ No newline at end of file diff --git a/examples/NealHumphrey/data/readme.md b/examples/NealHumphrey/data/readme.md new file mode 100644 index 000000000..3bd5e91b5 --- /dev/null +++ b/examples/NealHumphrey/data/readme.md @@ -0,0 +1,2 @@ +Zipped data file available here: http://archive.ics.uci.edu/ml/machine-learning-databases/00315/ +Extract just the two .txt files into this folder \ No newline at end of file diff --git a/examples/NealHumphrey/feedback_notes.txt b/examples/NealHumphrey/feedback_notes.txt new file mode 100644 index 000000000..2702e60c9 --- /dev/null +++ b/examples/NealHumphrey/feedback_notes.txt @@ -0,0 +1,52 @@ +Notes for user feedback testing: + +User Research Process: +* Very vague instructions +* Don't know what feedback to collect before taking the survey, and survey won't let me see questions due to mandatory fields. I recommend changing to a one-page survey (multi-page is better when you want to encourage people to start a longish survey they might abandon. Our testers will have already invested several hours in testing) + + +Bugs etc.: +* Had to add the classifier.py classes into the __init__ to get them to load in the namespace +* Need label rotation to angled text when there are too many categorical labels on an axis (example: ClassBalance report) +* Don't we want consistent x/y min and max values for the prediction error plot? That would make the 45 degree line actually 45 degrees. +* ROCAUC needs x / y axis labels + + +# Usability: + +## General notes +* Many visualizers don't play well with KFold. I started out using KFold validation, but then abandoned it in favor of train_test_split to better make use of the visualizers functionality. + + +## Methods and the YellowBrick API +Overall my biggest difficulty in learning YellowBrick is understanding how the sklearn API is being used in the context of a visualizer. The use of some of the methods feel like a square peg in a round hole. + +### Feature analysis: +* fit() makes sense. Get the data you need. +* transform() - doesn't make sense. in sklearn, you use transform when you want data back that's been altered in some way. Here, it's not necessary to alter the data - and behind the scenes, transform() is doing stuff to the visualizer rather than to the data. In all the feature analysis examples, transform() is called immediately after fit() with the exact same data. Should the transform() functionality be rolled into fit()? For example, calculating Pearson correlations feels like 'fitting' the visualizer to the data. +* score() - isn't included in this type of analyzer. for Rank2D, 'transform()' could logically be called 'score()'. But probably don't want to do that since you don't need to pass it the X_test and y_test the way you do in the ScoreVisualizers that use the score() method. + +### ScoreVisualizer +* score() makes sense. You need the test data to be able to evaluate the score. One question is whether I'd want multiple types of visualizers to use the same predict() results in calculating their scores (for instance, prediction error plot and residuals plot). But, why isn't score() method included in the base class? +* fit() as a pass-through of fitting the associated model makes sense. But, the way the tutorials are set up don't fit with the workflow I'd expect to use. I would expect to use model.fit(), and then instantiate the visualizer with the *fitted* model, then use score() on the visualizer to get the results. After digging into the source code I can see this technically works (since all visualizer.fit() does is run the fit() method of the associated model). I would expect fitting the model outside of the visualizer to be the more common use case - many reasons e.g. you want to run multiple visualizations evaluating the fitted + +### ModelVisualizer +Not fully implemented I know - but thinking about how to create consistent methodology: +* fit() - Interesting question. Will this re-run self.model.predict() for every permutation of the model needed, saving each fitted model for later use? Or, will it save the data and let the predict() method fit the models (thus allowing it to throw away the model after it calculates the scores)? Should look into the sklearn source code to inform performance tradeoffs. +* predict() - makes sense that this would create predictions based on test data. But when we're predicting across multiple items (e.g. multiple k's), what will this return? Should it be a generator like the MultiModelMixin? +* score() - currently not included in the skeleton class. Would it make more sense for this class to behave the same as the ScoreVisualizer and be able to operate just via fit() and score() by doing predict() implicitly? This would be my suggestion. + + +## Rank2D +* the name 'features' is not immediately obvious what it means - what about feature_names? +* documentation on what Rank2D does is not clear. Took me a few times (albeit skimming quickly) to realize what it was doing. + +## ClassBalance visualizer +I would expect a standalone ClassBalance report to be based on the ClassBalance of the training data, not the test data. After digging into the source code, I saw that you were pulling this out of the precision_recall_fscore_support report; having a graph of support makes sense but I think would be more useful side-by-side with the other 3 scores. It also seems unnecessary to require a fitted model to create this report - you can calculate support from y alone. + + +# 'examples' documentation notes: +Per my usability comments, the example documentation showing how each visualizer work would be much clearer if the comment next to each of the visualizer methods was more explanatory. Examples: +* FeatureVisualizer: Instead of `#Transform the data` (this just repeats the method name), say `#for a visualizer, transform() calculates and places the data on the chart` - but I still advocate for ditching transform() altogether. +* ScoreVisualizer: Instead of `#fit the data to the visualizer`, say `#fit the sklearn model associated with the visualizer to the training data. Omit this step if your model was fit before creating the visualizer` +* Early examples have good explanations e.g. "Data scientists use this for..." but these fall off by the later sections (e.g. ROCAUC is not explained at all). \ No newline at end of file diff --git a/examples/NealHumphrey/testing_yb.ipynb b/examples/NealHumphrey/testing_yb.ipynb new file mode 100644 index 000000000..9a3e63e89 --- /dev/null +++ b/examples/NealHumphrey/testing_yb.ipynb @@ -0,0 +1,435 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "\n", + "\"\"\"\n", + "The data set in this example represents 1059 songs from various countries obtained \n", + "from the UCI Machine Learning library. Various features of the audio tracks have been \n", + "extracted, and each track has been tagged with the latitude and longitude of the capital\n", + "city of its country of origin. \n", + "\n", + "We'll treat this as a classification problem, and attempt to train a model to predict \n", + "the country of origin of each model. \n", + "\n", + "Data source did not specifify what the audio features specifically are, just\n", + " \"In the 'default_features_1059_tracks.txt' file, the first 68 columns are audio \n", + " features of the track, and the last two columns are the origin of the music, \n", + " represented by latitude and longitude. \n", + "\n", + " In the 'default_plus_chromatic_features_1059_tracks.txt' file, the first 116 \n", + " columns are audio features of the track, and the last two columns are the \n", + " origin of the music.\"\n", + "\"\"\"\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import sklearn\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.utils.multiclass import unique_labels\n", + "import sys\n", + "\n", + "#First get the data. The UCI ML Library distributes it as a zipped file;\n", + "#download the data and extract the two provided files to the 'data' folder before continuing\n", + "music_df = pd.read_csv('data\\default_plus_chromatic_features_1059_tracks.txt', header=None)\n", + "music = music_df.as_matrix()\n", + "\n", + "\n", + "#Our features are all but the last two columns\n", + "X = music[:,0:-2]\n", + "\n", + "#Since feature names were not given, we'll just assign strings with an incrementing integer\n", + "names = np.linspace(start=1, stop=116, num=116, dtype='int').tolist()\n", + "for idx, name in enumerate(names):\n", + " names[idx] = \"Feature \" + str(name)\n", + "\n", + "\n", + "#The source data said that each song as tied to the capital city of it's origin country via a lat/lon pair. \n", + "#Let's treat this as a multi-class classification problem. \n", + "#Rather than reverse-geocoding, we'll just make a string out of the unique lat/lon pairs\n", + "lats = [\"%.2f\" % lat for lat in music_df[116]]\n", + "lons = [\"%.2f\" % lon for lon in music_df[117]]\n", + "song_latlons = []\n", + "for index, value in enumerate(lats):\n", + " city_id = lats[index] + \",\" + lons[index]\n", + " song_latlons.append(city_id)\n", + "\n", + "unique_latlons = unique_labels(song_latlons)\n", + "city_options = ['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z','AA','AB','AC','AD','AE','AF','AG']\n", + "city_name_map = {}\n", + "for idx,latlon in enumerate(unique_latlons):\n", + " city_name_map[latlon] = city_options[idx]\n", + "\n", + "ylist = []\n", + "for latlon in song_latlons:\n", + " ylist.append(city_name_map[latlon])\n", + "y = np.array(ylist)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#We want yellowbrick to import from this repository, and assume this notebook is in repofolder/examples/subfolder/\n", + "sys.path.append(\"../../\")\n", + "import yellowbrick as yb\n", + "from yellowbrick.features.rankd import Rank2D \n", + "from yellowbrick.features.radviz import RadViz \n", + "from yellowbrick.features.pcoords import ParallelCoordinates " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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byEuv/EkVR2IYxrZSQcXWT5ECC973xg0i0gJcAZyhqoNMEWVTbKo6ICJnAv+D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\n7ryrs34Sf7tB72rbmHauuM4gt60tX7o8V6XIR7l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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#See how well correlated the features are\n", + "visualizer = Rank2D(features = names, algorithm = 'pearson')\n", + "visualizer.fit(X, y)\n", + "visualizer.transform(X)\n", + "visualizer.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from sklearn import metrics\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.cross_validation import train_test_split\n", + "from sklearn.svm import SVC\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import confusion_matrix\n", + "from yellowbrick.classifier import ClassificationReport\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def train_and_classification_report(model):\n", + " X_train, X_test, y_train, y_test = train_test_split(X,y, test_size =0.2, random_state=11)\n", + " \n", + " model.fit(X_train, y_train)\n", + " y_predict = model.predict(X_test)\n", + "\n", + " print(\"prec: {}\".format(metrics.precision_score(y_true = y_test, y_pred = y_predict, average=\"weighted\")))\n", + " print(\"rec: {}\".format(metrics.recall_score(y_true= y_test, y_pred = y_predict, average = \"weighted\")))\n", + "\n", + " cr_viz = ClassificationReport(model) #,classes=city_options\n", + " cr_viz.fit(X_train, y_train)\n", + " cr_viz.score(X_test, y_test)\n", + " cr_viz.poof()\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "prec: 0.430726301383904\n", + "rec: 0.4056603773584906\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\humph\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1115: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.\n", + " 'recall', 'true', average, warn_for)\n", + "C:\\Users\\humph\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1115: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n", + " 'recall', 'true', average, warn_for)\n" + ] + }, + { + "ename": "IndexError", + "evalue": "list index out of range", + "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 3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0myellowbrick\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclassifier\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mClassificationReport\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[1;31m#This produces an IndexError: list index out of range.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mtrain_and_classification_report\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mLogisticRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mtrain_and_classification_report\u001b[0;34m(model)\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mcr_viz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mClassificationReport\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#,classes=city_options\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mcr_viz\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mcr_viz\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 13\u001b[0m \u001b[0mcr_viz\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[0;32mC:\\Users\\humph\\Documents\\Github\\yellowbrick\\yellowbrick\\classifier.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscores\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscores\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 135\u001b[0;31m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 136\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[0;32mC:\\Users\\humph\\Documents\\Github\\yellowbrick\\yellowbrick\\classifier.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, y, y_pred)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mva\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'center'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'center'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 161\u001b[0m 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Adding the reloading functionality so we can edit the source code and see results here. \n", + "import importlib\n", + "importlib.reload(yb.classifier)\n", + "from yellowbrick.classifier import ClassificationReport\n", + "\n", + "#This produces an IndexError: list index out of range. \n", + "train_and_classification_report(LogisticRegression())" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#This demonstrates a version of the Seaborn confusion matrix heatmap we could replicate (and improve on). \n", + "def train_and_confusion_matrix(model):\n", + " X_train, X_test, y_train, y_test = train_test_split(X,y, test_size =0.2, random_state=11)\n", + " \n", + " model.fit(X_train, y_train)\n", + " y_predict = model.predict(X_test)\n", + "\n", + " print(\"prec: {}\".format(metrics.precision_score(y_true = y_test, y_pred = y_predict, average=\"weighted\")))\n", + " print(\"rec: {}\".format(metrics.recall_score(y_true= y_test, y_pred = y_predict, average = \"weighted\")))\n", + "\n", + " c_matrix = confusion_matrix(y_true = y_test, y_pred = y_predict)\n", + " \n", + " sns.heatmap(c_matrix, square=True, annot=True, cbar=False, xticklabels=city_options, yticklabels = city_options)\n", + " plt.xlabel('predicted value')\n", + " plt.ylabel('true value')" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "prec: 0.430726301383904\n", + "rec: 0.4056603773584906\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\humph\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1115: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.\n", + " 'recall', 'true', average, warn_for)\n" + ] + }, + { + "data": { + "image/png": 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IcLieGZ5uxfvSI6jqD3xt/rT6ti9A3759GThwIAMHDmTYsGE88cQTLF26lOjo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BAAAg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\ni7S0NNy9exf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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "train_and_confusion_matrix(LogisticRegression())" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def train_and_class_balance(model):\n", + " X_train, X_test, y_train, y_test = train_test_split(X,y, test_size =0.2, random_state=11)\n", + " \n", + " class_balance = yb.classifier.ClassBalance(model, classes=city_options)\n", + " class_balance.fit(X_train, y_train)\n", + " class_balance.score(X_test, y_test)\n", + " class_balance.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\humph\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1115: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n", + " 'recall', 'true', average, warn_for)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "train_and_class_balance(LogisticRegression())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "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": 1 +} diff --git a/examples/README.md b/examples/README.md new file mode 100644 index 000000000..d798ae2cb --- /dev/null +++ b/examples/README.md @@ -0,0 +1,66 @@ +# Yellowbrick Examples + +[![Visualizers](../docs/images/visualizers.png)](../docs/images/visualizers.png) + +Welcome to the yellowbrick examples directory! This directory contains a gallery of visualizers and their application to classification, regression, clustering, and other machine learning techniques with Scikit-Learn. Examples have been submitted both by the Yellowbrick team and also users like you! The result is a rich gallery of tools and techniques to equip your machine learning with visual diagnostics and visualizer workflows! + +## Getting Started + +The notebook to explore first is the `examples.ipynb` Jupyter notebook. This notebook contains the executable examples from the tutorial in the documentation. However, before you can successfully run this notebook, you must first download the sample datasets. To download the samples run the downloader script: + +``` +$ python download.py +``` + +This should create a directory called `examples/data`, which in turn will contain CSV or text datasets. There are two primary problems that the download script may have: first, you may get the error `"The requests module is required to download data"`. To fix this problem: + +``` +$ pip install requests +``` + +The second problem may be `"Download signature does not match hardcoded signature!"` This problem means that the file you're trying to download has changed. Either download a more recent version of Yellowbrick, or use the URLs in the `download.py` script to fetch the data manually. If there are any other problems, please notify us via [GitHub Issues](https://github.com/DistrictDataLabs/yellowbrick/issues). + +Once the example data has been downloaded, you can run the examples notebook as follows: + +``` +$ jupyter notebook examples.ipynb +``` + +If you don't have jupyter installed, or other dependencies, you may have to `pip install` them. + +## Organization + +The examples directory contains many notebooks, folders and files. At the top level you will see the following: + +- examples.ipynb: a notebook with executable versions of the tutorial visualizers +- download.py: a script to download the example data sets +- palettes.ipynb: a visualization of the Yellowbrick palettes +- data: a directory containing the example datasets. + +In addition to these files and directory, you will see many other directories, whose names are the GitHub usernames of their contributors. You can explore these user submitted examples or submit your own! + +### Contributing + +To contribute an example notebook of your own, perform the following steps: + +1. Fork the repository into your own account +2. Checkout the develop branch (see [contributing to Yellowbrick](http://www.scikit-yb.org/en/latest/about.html#contributing) for more. +3. Create a directory in the repo, `examples/username` where username is your GitHub username. +4. Create a notebook in that directory with your example. See [user testing](http://www.scikit-yb.org/en/latest/evaluation.html) for more. +5. Commit your changes back to your fork. +6. Submit a pull-request from your develop branch to the Yellowbrick develop branch. +7. Complete the code review steps with a Yellowbrick team member. + +That's it -- thank you for contributing your example! + +A couple of notes. First, please make sure that the Jupyter notebook you submit is "run" -- that is it has the output saved to the notebook and is viewable on GitHub (empty notebooks don't serve well as a gallery). Second, please do not commit datasets, but instead provide instructions for downloading the dataset. You can create a downloader utility similar to ours. + +One great tip, is to create your PR right after you fork the repo; that way we can work with you on the changes you're making and communicate about how to have a very successful contribution! + +### User Examples + +In this section we want to thank our examples contributors, and describe their notebooks so that you can find an example similar to your application! + +- [bbengfort](https://github.com/bbengfort): visualizing text classification +- [rebeccabilbro](https://github.com/rebeccabilbro): visualizing book reviews data +- [nathan](https://github.com/ndanielsen/): visualizing the Iris dataset diff --git a/examples/balavenkatesan/testing.ipynb b/examples/balavenkatesan/testing.ipynb new file mode 100644 index 000000000..59a98894d --- /dev/null +++ b/examples/balavenkatesan/testing.ipynb @@ -0,0 +1,468 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 148, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "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.pyplot as plt\n", + "from sklearn.datasets import load_boston\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Using Yellowbrick to Boston Dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "This is a user study to test YellowBrick library by exploring the Boston Dataset in sklearn. " + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "#loading the dataset, extracting the data, targets and feature names\n", + "boston = load_boston()\n", + "X = boston.data[:, None, 0]\n", + "\n", + "# target contains the price\n", + "y = boston.target\n", + "# Use only one feature\n", + "features = boston.feature_names" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "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 (boston.DESCR)" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(506,)" + ] + }, + "execution_count": 152, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Creating a LinearRegression model by splitting the data and targets into train & test datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "## from sklearn.cross_validation import train_test_split as tts\n", + "\n", + "from sklearn.linear_model import LinearRegression\n", + "from yellowbrick.regressor import ResidualsPlot\n", + "\n", + "model = LinearRegression()\n", + "\n", + "# Split the data into training/testing sets\n", + "X_train = X[:-20]\n", + "X_test = X[-20:]\n", + "\n", + "# Split the targets into training/testing sets\n", + "y_train = y[:-20]\n", + "y_test = y[-20:]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(486, 1)" + ] + }, + "execution_count": 154, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coefficients: \n", + " [-0.42249408]\n", + "Variance score: -1.39\n" + ] + }, + { + "data": { + "image/png": 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BwBChCwCGCF0AMEToAoAhQhcADBG6AGCI0AUAQ4QuABgidAHAEKELAIYIXQAwROgCgCFC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\nA1p4c6CIUAJvAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Train the model using the training sets\n", + "model.fit(X_train, y_train)\n", + "\n", + "# The coefficients\n", + "print('Coefficients: \\n', model.coef_)\n", + "\n", + "# Explained variance score: 1 is perfect prediction\n", + "print('Variance score: %.2f' % model.score(X_test, y_test))\n", + "\n", + "# Plot outputs\n", + "plt.scatter(X_test, y_test, color='green')\n", + "plt.plot(X_test, model.predict(X_test), color='blue', linewidth=3)\n", + "\n", + "plt.xticks(())\n", + "plt.yticks(())\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import cross_val_predict\n", + "y_pred = cross_val_predict(model, X, y, cv=10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(506,)" + ] + }, + "execution_count": 157, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_pred.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(506,)" + ] + }, + "execution_count": 158, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 162, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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VLV9J8TFY4uLq53GE2sdp1Dwu4vGVeXztY3HJrG4p5K4tCHittOTmL8cSSuAY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\nRK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig,ax = plt.subplots()\n", + "\n", + "ax.scatter(y, y_pred)\n", + "ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4)\n", + "ax.set_xlabel('Measured')\n", + "ax.set_ylabel('Predicted')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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1s7xmvSt0UBjfvyufXncaG289h7umDeevn3/PsrxiyqqNoNm6XXOCB+SmrGVd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\nliFoYNBa9zd1luabBTwL7MKoQrpDa/1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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from yellowbrick.regressor import PredictionError\n", + "from yellowbrick.regressor import RegressionScoreVisualizer\n", + "y = y.reshape(-1, 1)\n", + "sviz = PredictionError(model)\n", + "sviz.score(y, y_pred)\n", + "sviz.poof() " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.neighbors import KNeighborsRegressor\n", + "knn = KNeighborsRegressor()\n", + "knn.fit(X_train, y_train)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "sviz = PredictionError(knn)\n", + "sviz.score(y, y_pred)\n", + "sviz.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": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/bbengfort/.gitignore b/examples/bbengfort/.gitignore new file mode 100644 index 000000000..60baa9cb8 --- /dev/null +++ b/examples/bbengfort/.gitignore @@ -0,0 +1 @@ +data/* diff --git a/examples/bbengfort/alphas.ipynb b/examples/bbengfort/alphas.ipynb new file mode 100644 index 000000000..25f71df7b --- /dev/null +++ b/examples/bbengfort/alphas.ipynb @@ -0,0 +1,746 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Alphas Plots for Regularized Regression\n", + "\n", + "This is a development notebook to explore the use of alpha plots for regularized regression." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os \n", + "import sys\n", + "import numpy as np \n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "sys.path.append(\"../..\")\n", + "import yellowbrick as yb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "## The path to the test data sets\n", + "FIXTURES = os.path.join(os.getcwd(), \"data\")\n", + "\n", + "## Dataset loading mechanisms\n", + "datasets = {\n", + " \"energy\": os.path.join(FIXTURES, \"energy\", \"energy.csv\"),\n", + " \"concrete\": os.path.join(FIXTURES, \"..\", \"..\", \"data\", \"concrete\", \"concrete.csv\"),\n", + "}\n", + "\n", + "\n", + "def load_data(name):\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", + " 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": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Load the data\n", + "df = load_data('energy')\n", + "feature_names = ['X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7', 'X8']\n", + "target_name = 'Y1'\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() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Alphas Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Set the alphas \n", + "alphas = np.logspace(-12, -1, 400)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualizers " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.5/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" + ] + } + ], + "source": [ + "from yellowbrick.regressor import AlphaSelection \n", + "from sklearn.linear_model import RidgeCV, LassoCV, LassoLarsCV, ElasticNetCV" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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YuXIl6enpbN++nYkTJ1KjRg0AZs6cSXZ2dpG55Dc7p7T+NmzYwIoVKzh06BAp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\nU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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = AlphaSelection(RidgeCV(alphas=alphas, scoring=\"neg_mean_squared_error\"))\n", + "model.fit(X, y)\n", + "model.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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w67cXFBERkey35s8zDPl5DwDVS/nxRRvdOEJyhsOXXU9PT3x9fY2OISIiIv9z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\nuy4QERERmbB2X3brGnSYv/sMFu47C50gwEylwsJHwzArrjdUKt5Hl4i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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = AlphaSelection(LassoCV(alphas=alphas))\n", + "model.fit(X, y)\n", + "model.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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UtfXhyDkOX7pmcY6Tg4H65Yr/WX5LUau0Lw4OBiuktS0qwyIiIiL5TGamiX3n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\ncosxZs0BFFRUAwA6eLti7ZjeiA7wkLgyIiIiIuvCMGxF6hu0mPNDOv6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = AlphaSelection(LassoLarsCV())\n", + "model.fit(X, y)\n", + "model.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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JmQhPzkZUeh7KKmQ1vqe9uaFitLZbKzN0sTWBgY7WC6ya1I2gYTc+Ph6RkZG4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\nkW6++WZNmDBBJ06csCglAAAAKhNblN20tDQNHTpUx44dK7X9m2++0dG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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = AlphaSelection(ElasticNetCV(alphas=alphas))\n", + "model.fit(X, y)\n", + "model.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from yellowbrick.regressor import ManualAlphaSelection \n", + "from sklearn.linear_model import Ridge, Lasso" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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uXLijPvdKiXY+Klu2LJ6enpYOQ0RERB4wLi4u2Nvb5yo7c+YMDRo0wNnZmV27\ndvHnn39Su3Zt3N3dWbduHW3btiU+Pp4yZcrQrl07AH788UcqVKjAr7/+SpcuXahZsyZ79uzh4MGD\nuLu7s23bNiZNmkTJkiXN4zg5Od1xMu/v78/58+c5e/Ys7u7u7Ny5kwYNGlChQgUAtm/fTtmyZfHy\n8rqtPm5ubiQkJNxx3Z2oUaMGly9fvqM+90ob1oiIiIhYWJcuXXB1dWXJkiWsWbOGlStX0rdvXy5c\nuMCCBQuoXLkysbGxBAQE4OvrS3R0NC1atMDNzc38lPbXX38lOTmZVq1akZaWRr169bh48SKtWrUi\nIyODOnXq8OGHH+ZKsu+WtbU1kyZNYtasWaxcuZKffvqJoKAgc/2iRYvYtm3bbfe527rCTk+0RURE\nRCzMZDJd96ny7Nmzzd9fvHgRZ2dnwsPDzVNhfvnlF/r160dSUhLbtm3jueeeIzs7m7S0NOLi4nB0\ndCQ7O5t9+/YxaNAgTp48SenSpfMl5mbNmtGsWTMAunXrlqsuODj4jvvcbV1hpkQ7H12+kkX85XSq\n3mQdThEREZE7dezYMRo2bAhAaGgobdu2BaBMmTLs27ePTp060a5dO/bs2cPp06dxcXEhKiqK3bt3\n4+HhQbVq1di+fTu2trZUqVLFkpdSrCjRzkdJ6Vk0mLqBt1p783brujjZ21g6JBERESkCqlevTv/+\n/QHo16+fuXzIkCHm71u0aGH+PmfOdqtWrQomQLkuzdHOZ5cyMhm/KYIaH69gwuYILqZdsXRIIiIi\nImIBSrTzUTn7EnSo5Q5AUmoG7/8URs0JK/j01/2kZGRaODoRERERKUhKtPORjbUVy15swW+DOvJo\nzfIAxF9O5921odSasJLg3w6Snpll4ShFREREpCAo0c5HMTExhIaG8kh1Nzb9+zE2vd6eRzzKARB7\nMZUhK3dRe8JKvtr+N1eysi0crYiIiEjxERISwokTJwp0TCXa99GjtSqwbVBH1r32KE2quABwKjmF\nfy/dQd3Jq5i/K4pMJdwiIiIiRZIS7fvMZDLRyasSfw15nOUvtaFhhatbvB49f4mXf/iThlPW8MOe\nY2RnGxaOVERERETykxLtAmIymehavwq7hz7B931a4uX2EACH4i7Q+z+/0+jTtayIOIFhKOEWERER\nKQqUaBcwKysTz/p6ED78Kb7t1RxPl6u7M+2LTeKZb7cS8Pl61keeVsItIiIi8oBTom0h1lZW9Gni\nyYERXZnT7zKyAAAgAElEQVTdsylVnK7uJrn7VAJPff0LLaf/zC+HYywcpYiIiIjcLSXaFlbS2opX\nm9biUGA3vujuT4WH7AHYfjyOx2Ztpt3Mjfx+9JyFoxQRERGRO6VEu5CwLWHNwBZeHB7VjaAujXF1\nsAVgS9RZWs/4mce/+i+7TsRbOEoRERERiIiIYMKECaxcuZIxY8Zw6tSpe+6TkpLCm2++SWxs7D2P\nVViUsHQARYmTkxPVq1e/p3PYlyzB263r8lrTWkz/7SBBWw6QlJrBxkNn2HjoDE/Vq8yHnXzwqeic\nT1GLiIiI3L6MjAyGDBnC0qVLcXZ2xsPDg8DAQBYuXHjXfZYtW0ZMTAybNm1i5MiR9zTWjXh6epKZ\nWbA7dSvRzkf29vaULVs2X85V2rYkge0b8O/mdfh8aySfb4vkYvoV1uw/xZr9p3jGpxpjOzSkbnmn\nfBlPRESkKAsJCblueVpaGtHR0ZQvX55SpUrdsH9CQgJHjx696RhNmjS5pxgfFCEhIZQpUwZn56sP\n/Ro2bMjevXtJSEgwl91pnx49egAQHBx8z2PdiLOz803v8f2gRLuQc7K34YNOPgxu6UXQr/sJ/uMg\nKRlZLN17nGXhx3nBrzpjOjSkputDlg5VRESk0OrQocNN62fNmsWzzz57w/pt27bx8ssv3/QcCQkJ\ndxXbPy1YsAAAa2troqOjCQwMJDMzk+TkZMqVK3fP5/+n5ORkvvnmG/NxzopnJpPJfGxnZ8fAgQNz\n9Tt9+jSOjo7mYysrKxwcHDh8+DABAQHXHetu+txLv8JCifYDwsXBlolP+vFWa28m/7KPWX/+TXpm\nNot2H+PHPdG89HBNRj/WgMpODpYOVURERO7Chx9+SNWqVXnppZcAeO+991i6dCmOjo40btw438dz\ndHRk6NChd9wvMTERW1vbXGW2trZcvHgxX/vcS7/CQon2A8a9jD1Tu/rzTpt6TNgcwTc7jnAlK5s5\nfx1mQUgU/36kDiPb1adcaTtLhyoiIlJobNy48brlOVNHmjdvftP+rVq1uuE58sO+ffvYtGkTW7du\nNZdVq1aNsLAw6tati6ur622dJy0tjW+++SbPU+j8VLp06Tz7faSkpODkdOPprHfT5176FRZKtB9Q\nlRxLMaNHAMPa1OWjjeH8Z/cx0jOz+XxbJF/vOMyQlt4MbVMXJ3sbS4cqIiJicTeaP52SknJb71g5\nOzvf8ZzgO7Fz5078/f2xtrY2l5UoUYLQ0NA7eup8vakeN5KUlMTcuXNvWH+jqSOenp4sXbrUfJyR\nkcHly5epVKnSDc91N33upV9hUSgS7YyMDD744AM2bdqEnZ0dL7/8svljk2sdOHCADz74gL///pta\ntWrxwQcfUK9ePXP92rVrmTZtGnFxcbRo0YJx48bl+p8nKCiIZcuWkZ2dzTPPPMPw4cPNdd9++y2T\nJk3CZDJhGAYmk4mXXnqJd9999/5d/D2q7lKGeb2a8+6j9Rm7IYxl4Se4lJ7Jx5sjmPnHIYa3rceg\nFnVwsC1p6VBFRETkBlxcXLC3t89VdubMGRo0aICzszO7du3izz//pHbt2ri7u7Nu3Tratm1LfHw8\nZcqUoV27dkRGRrJjxw68vb0xmUxs2bIFPz8/nJyc2L17NwMGDMh1ficnp7uaOuLv78/58+c5e/Ys\n7u7u7Ny5kwYNGlChQgUAtm/fTtmyZfHy8rrtPnc7VmFXKNbRnjx5MgcOHGDhwoWMHTuW4ODg6348\nk5qaSv/+/fH392f58uX4+voyYMAA0tLSAAgPD2f06NEMHjyYxYsXk5ycTGBgoLn/3LlzWb9+PTNn\nzmT69OmsWbOGefPmmeujoqLo3bs3f/zxB3/88Qe///47gwYNuv8/gHzg7e7I4r6t2fV2Zzp5VQQg\nMTWDUev3UGviSmb8fpD0zCwLRykiIiLX06VLF1xdXVmyZAlr1qxh5cqV9O3blwsXLrBgwQIqV65M\nbGwsAQEB+Pr6Eh0dTYsWLXBzc+PChQvA1cQ5JiaGypUrU6lSJc6ePUuzZs1o0KABp0+fzrdYra2t\nmTRpErNmzWLlypX89NNPBAUFmesXLVrEtm3b7qjPunXr+OCDDzCZTHz66ad8//33t9Wv0DMsLCUl\nxWjYsKGxa9cuc9nMmTONPn365Gm7ZMkSo3379rnKOnToYKxYscIwDMN49913jZEjR5rrYmJiDC8v\nL+PUqVOGYRhGmzZtzG0NwzBWrVpltG3b1nzcq1cvY/HixXd1HeHh4cZvv/1mHsvStkXFGq2DNxhW\nQxeYvzzGLTPm7jhsXMnMsnR4D7zLly8bISEhxuXLly0dihQA3e/iRfe7eHmQ7vfAgQMNwzCMvXv3\nGjNnzjQMwzDGjRtnnDx50khKSjJSU1ONYcOGGadOnTKuXLlivPXWW4ZhGMa6deuMLVu2FJocxVLO\nnz9vbN++3QgPDy+wMS3+RPvgwYNkZWXh6+trLmvcuDHh4eF52oaHh+d569bPz489e/YAEBYWhr+/\nv7mufPnyVKhQgb1793Lu3DliYmJyzdFq3LgxZ86cIT7+6o6LUVFReHh43PW1JCUlcezYsbvun59a\nerrz6xsdWP9aOxpXvjqn7ETiZV79cTsNp6xhcVg02dnGLc4iIiIihcGxY8do2LAhAKGhobRt2xaA\nMmXKsG/fPhwdHUlKSqJy5cqcPXuWAwcO8PDDDwNXV+5IT0/Ps3pHcXP06FFzzldQLD5HOy4uDicn\nJ0qU+P9QXFxcSE9PJzExMdf86nPnzlG7du1c/V1cXDhy5Ij5XG5ubrnqXV1diY2NJS4uDpPJlKs+\n5w3e2NhYTCYTycnJLF++nBEjRmBnZ8czzzxzyzUzCzOTyURHr4p0qFOBFREnGbMhjMizyRyKu0Cv\nhb8xueI+Pnrcl87elcxrZoqIiEjhU716dfr37w9Av379zOVDhgwxf1++fPlcxzmJee/evQsmSMnD\n4ol2amoqNja5V8bIOc7IyMhVnpaWdt22Oe1uVp+amprr3NeOc/ToUXMiPnv2bA4cOMD48eOxtram\nb9++t3096enppKSk3Hb7gtKppiuPvdGOH/eeYOKvB4hOTCHsTCJdvvmVgKoujG1fj5bV83ch/KIs\n589Tzn+laNP9Ll50v4sX3e/iI+edvoJk8UTb1tY2T0Kdc3zt27c3amtnZ3fL+pyPSzIyMvIk8vb2\n9nh7e7Nr1y5Kly4NQK1atUhISOD777+/o0Q7JiaGyMjI225f0BrZwaIO1Vh1NJG5++KJT81kx4nz\ndJ67jYDyDvzbx426Lva3PpEAEB0dbekQpADpfhcvut/Fi+530RcdHV3g629bPNF2d3cnKSmJ7Oxs\nrKyuThmPj4/Hzs6Ohx56KE/buLi4XGXx8fHmLUnd3NzyzL2Jj4/Hzc0Nd3d3DMMgPj6eihWvrsqR\nM50kp39Okp3D09OTs2fP3tH1VKhQAW9v7zvqYwkN68M7nTKZs/Mon247RGJqBjtiL7Mj9hhPelfk\n/XZ1qevueOsTFVOpqalER0fj4eGR5xdCKXp0v4sX3e/iRfe7+EhNTSUpKalAx7R4ou3t7U2JEiUI\nCwvDz88PgJCQEOrXr5+nrY+PD3PmzMlVFhoayhtvvAGAr68vu3fvplu3bsDVp8uxsbH4+vri5uZG\nxYoV2b17tznRDgkJoUKFCubldL755hs2bNhgPndkZCSenp53dD22traUKlXqjvpYSqlSENjBl4Gt\n6vL51kimbo3kYvoV1kaeYd3BM/RqVJ2xHRtS0/WhW5+smLK3t39g7rfcO93v4kX3u3jR/S76cmZA\nFCSLrzpiZ2dH165dGTt2LBEREWzevJl58+aZp2vEx8eTnp4OQMeOHbl48SITJkwgKiqK8ePHk5qa\nSqdOnQDo1asXq1atYunSpRw8eJARI0bQtm1bc2L9/PPPExQUxM6dO9mxYwdTp041j9OiRQvi4+OZ\nPHkyJ06cYN26dXzzzTfmFw+KsofsbBjT0Yeo97ozrE1d7EpYYxjwXegx6k1ezetL/uJU0mVLhyki\nIiLyQLH+4IMPPrB0EM2aNSMyMpKgoCD++usv3njjDbp37w5cXb7Pw8MDLy8vbGxsePjhh/nuu++Y\nPXs2WVlZTJ06lfLlywP/v5zf9OnT+e6776hduzYff/yx+TeYRo0aERcXx9SpU9m4cSM9evTg9ddf\nB64uj9OkSROWLVtGcHAwe/fu5c0336Rr1663dQ3nzp0jOTkZOzs7qlateh9+SvdfKZsSPFanIi89\nXIO0K1mEnU4gM9sg9FQCX/55iISUDPwqO+NgY/EPQizuypUr5mlLJUtq182iTve7eNH9Ll50v4uP\nM2fOcOnSJZycnHB3dy+QMU2GYWgx5XwQERFBRkYG3t7eReajp6PnL/LRxnAW7T5G9v/+mJS2LcFb\nrbwZ2roujvY2tzhD0ZWSkkJkZGSRut9yY7rfxYvud/Gi+128hIeHYzKZaNCgQYGMZ/GpI1J4ebqU\n4dtezdk77Emebnj1Kf2l9EzGb4qgxscr+OSXfVxOv2LhKEVEREQKJyXackt1yzuxpG9rdr7VmY5e\nV+e7J6ZmELhuD7UmrmTm74fIyMyycJQiIiIihYsSbbltjau4sP61dmwZ2IEW1a/usHn2YhqDV+yk\n7uTV/Gf3UbKysy0cpYiIiEjhoERb7lhLT3e2DOzAutcepVElZwCOJVyi73d/4PfpOtbsP4mm/ouI\niEhxp0Rb7orJZKKTVyV2vtWZH15sRe1yV9fa3hebRLe5W2g5/We2Rt3ZZj8iIiIiRYkSbbknVlYm\nevpUI2L4U8zu2ZRKjlff2N5+PI5HZ26k85z/sudUgoWjFBERESl4SrQlX5SwtuLVprU4FNiVT570\nw7nU1aX/fj54hiafraPXwm0cjrtg4ShFRERECo4S7XyUmJjI0aNHLR2GRdmXLME7betxZFR3Rj/W\nwLy5zeKw49T75Oouk6eTUywcpYiIiBQ3UVFRxMfHF+iYSrTzUVpaGklJSZYOo1BwtLfhw06+HB7V\njcEtvShpbUVWtsGcvw5Te8JKRqzZzfnL6ZYOU0RERIqJxMREUlIK9mGfEm25r9zL2PN5N38OjuzK\ni008MZkgLTOLoC0HqDlhBR9vCueSNr0RERGRIkiJthQID+fSzOvVnL3DnqJr/SoAXEi7wpgNe6k1\nYSXBvx0kXZveiIiISBGiRFsKVL3yTix/qQ1/vNmJNjXcATh3KY0hK3dRd/IqFoZo0xsREREpGpRo\ni0U0rVaOzf9+jJ/6t6Nx5aub3kQnXKbf93/Q6NO1rNqnTW9ERETkwaZEWyzGZDLRoU5FdrzVmR//\nsenN/thknp63hRbTN7DlSKyFoxQRERG5O0q0xeJMJhPP/G/Tm6+ebUrl/21689fxeNp9uYnHv/ov\noafOWzhKERERkTujRDsf2dnZ4eTkZOkwHlglrK14JaAWBwO7MuWpxriUsgVg46Ez+H+2nucWbONv\nbXojIiIid6Fs2bKUKlWqQMdUop2PypYti6enp6XDeODZlyzB0DZ1OfJeN95/rKF505ule49T/3+b\n3pzRpjciIiJyB2rUqIGrq2uBjqlEWwqth+xs+KCTD0dGdePNll7Y/HPTm4krGbUulKTUDEuHKSIi\nInJdSrSl0HMrY89n3fyJHNmVPv/b9Cb1ShaTf9lPzY9XEPTrflKvZFo6TBEREZFclGjLA8PDuTTf\n9mrOnnee5Im6lQBITM1gxNpQvCauYu6OI2RmaQ1uERERKRyUaMsDp0GFsqx+5VG2DOzAIx7lADiV\nnMJri7fj++laVkSc0BrcIiIiYnFKtOWB1dLTnW2DOrLipTbUdXcEIPJsMs98u5UW0zewNeqshSMU\nERGR4kyJtjzQTCYTXepXIWzYk3zz3CNUcfr/NbgfnbmRJ7/+hfAziRaOUkRERIojJdpSJFhbWdHv\n4RocHNmNKU81xrmUDQA/RZ7Gb+paXvzud46dv2jhKEVERKQ4UaKdj2JiYggNDbV0GMWaXUnrq2tw\nj+pOYLv62Je0xjBg0e5jeE9ezVsrdxF3Kc3SYYqIiEgBCwkJ4cSJEwU6phJtKZIc7W0Y37kRh0d1\nY0Cz2lhbmbiSlc303w5Sc8IKPvp5LxfTrlg6TBERESnClGhLkVbhoVLMfCaA/e92oadPNQAupWfy\n4cZwak1cQfBvB8nIzLJwlCIiIlIUKdGWYqFWuYf44cVW7HirM+1qlQcg7lI6Q1buot4nq/ku9BjZ\n2VoSUERERPKPEm0pVppUcWHj64/x84D2NK7sDMDR85fos+h3/D9bx4aDp7UGt4iIiOQLJdpSLLWv\nXYG/hnTm+z4tqelaBoCwM4k8MecX2n+5iR3H4ywcoYiIiDzolGhLsWVlZeJZXw/2vduFGT0CKF/G\nHoAtUWd55IsNPPPtVg6eTbZwlCIiIvKgUqItxV5Jaytef6Q2fwd2ZdzjvjxkVxKAFREnaBi0hgFL\ntnMmOcXCUYqIiMiDRol2PnJycqJ69eqWDkPukoNtSUa1b8CRUd15u7U3NtZWZGUbfP3XEWpPXMn7\nP+0hOTXD0mGKiIjIXfD09MTV1bVAx1SinY/s7e0pW7aspcOQe+TiYEtQlyYcCuzGi008MZkg9UoW\nEzbvo9aElXyxLZJ0LQkoIiLyQHF2dqZUqVIFOqYSbZEbqFrWgXm9mhM69Ek6eVUE4HxKOm+vCqHJ\nFxv5OTpZSwKKiIjIDSnRFrmFhhXLsu61dmz+92P4V3EBIDoxhff/PE3r2b+w+e8YC0coIiIihZES\nbZHb1LZmebYPeZzv+7TE09kBgLAzSXScvZlOszcTdjrBwhGKiIhIYaJEW+QOmExXlwTcNbgDw5uU\nx9XBFoBNf8fQ5LN1vPjd70QnXLJwlCIiIlIYKNEWuQs2JazoWduZ8Lc78v5jDXGwKYFhwKLdx/Ce\ntIphq0M4fznd0mGKiIiIBSnRFrkHZWxL8kEnH/4O7MaAZrWxtjKRkZXNZ1sjqTVhBZP/u4/UK5mW\nDlNEREQsQIl2PkpNTSUxMdHSYYgFlH/InpnPBBAx/CmeblgVgOS0K4xav4c6E1cxd8cRsrKzLRyl\niIhI8ZWQkEBKSsFuQKdEOx8lJSVx7NgxS4chFlTHzZElfVvzx5udaOnpBsDp5BReW7ydRp+uZc3+\nkxiGlgQUEREpaEePHiU+Pr5Ax1SiLXIfNK1Wjl/f6MCqV9pSr7wjAPtjk+k2dwttZ27kr+NxFo5Q\nRERE7jcl2iL3iclk4sm6ldnzzpPMebYZlRyv7kb129FzNP9iAz3nb+XvuAsWjlJERETuFyXaIveZ\ntZUVLwfU5ODIrkzo3AhHu5IALA8/Qf1PVvPG0h3EXki1cJQiIiKS35RoixSQUjYlGNGuPodHdeft\n1t7YWFuRlW0we/vf1J64kg827OVi2hVLhykiIiL5RIm2SAFzcbAlqEsTIkd2pXfj6phMcDkjk3Gb\nwqk9cSUzfz/ElSytUCIiIvKgU6ItYiEezqVZ8EILQt5+gg51KgJw7lIag1fspP4nq1kcFq0VSkRE\nRB5gSrRFLMy3kjM/9W/HxgHt8avsDMCR+Iv0Wvgbzab9xJYjsRaOUERERO6GEu18VKFCBfz8/Cwd\nhjyg2tWuwI4hnflP7xZUdy4NwK6T52n35Sae/PoXImK0GZKIiMjdatKkCVWrVi3QMZVoixQiVlYm\nevlVZ/+ILnzerQmuDrYA/BR5mkafruWl7//gROJlC0cpIiIit0OJtkghZFvCmsEtvTk8qhuj2tfH\nvqQ1hgELQo7iNWklI9bsJjEl3dJhioiIyE0o0RYpxB6ys2Hc4434O7AbrzWthbWVifTMbIK2HKD2\nxJV8vvUA6ZlZlg5TRERErkOJtsgDoKJjKWb1bEr4sKfoUq8yAAkpGbyzejf1Jq/mhz3HyM7WCiUi\nIiKFiRJtkQeIl7sjK15uy9aBHWlazRWAYwmX6P2f32n2xU9sjTpr4QhFREQkhxJtkQdQC083fh/c\niR9fbEUNlzIAhJw8z6MzN9Llm184EJtk4QhFREREibbIA8pkMvGMTzX2vfsU07r541Lq6gol6w6c\nxidoLQOWbCfmQoqFoxQRESm+lGjno8TERI4ePWrpMKSYsSlhzaCWXhwe1Y2R7epjV8KabMPg67+O\nUHviSj7YsJeLaVcsHaaIiIhFRUVFER8fX6BjKtHOR2lpaSQl6SN7sQxHexs+7tyIQ4Fd6etfA5MJ\nUjKyGLcpnNoTV/Lln4e4kpVt6TBFREQsIjExkZSUgv2kt1Ak2hkZGYwaNQp/f39atmzJvHnzbtj2\nwIEDPPvss/j6+tKzZ0/279+fq37t2rU89thj+Pr6MmjQIBITc++mFxQURLNmzQgICGDKlCnXHSMr\nK4uuXbsSHBx87xcnUsAqOzkw9/lHCB36JB3qVATg3KU0Bi3bic+UNayMOIFhaIUSERGR+61QJNqT\nJ0/mwIEDLFy4kLFjxxIcHMzGjRvztEtNTaV///74+/uzfPlyfH19GTBgAGlpaQCEh4czevRoBg8e\nzOLFi0lOTiYwMNDcf+7cuaxfv56ZM2cyffp01qxZc92k/ptvvuHvv/++fxcsUgAaVizLT/3bsaF/\nO3wrlgXgUNwFeny7lTYzNvLX8TgLRygiIlK0WTzRTk1NZenSpYwePRovLy/at2/Pq6++yn/+8588\nbdetW4e9vT3Dhw/H09OT9957DwcHBzZs2ADAokWLePzxx+nSpQu1a9dmypQpbN26ldOnTwOwcOFC\n3nzzTRo1asTDDz/MsGHD8oxz/PhxFi5cSM2aNe//xYsUgMfqVGTX20/wba/mVHEqBcDvx87R/IsN\nPDt/K0fiL1g4QhERkaLJ4on2wYMHycrKwtfX11zWuHFjwsPD87QNDw+ncePGucr8/PzYs2cPAGFh\nYfj7+5vrypcvT4UKFdi7dy/nzp0jJiaGJk2a5BrnzJkzuSbGjx07ljfffBMnJ6d8u0YRS7OyMtGn\niScHR3Zj0hN+ONqVBGBZ+AnqTV7NkBU7ibuUZuEoRUREihaLJ9pxcXE4OTlRokQJc5mLiwvp6el5\n5lefO3cONze3XGUuLi6cPXvWfK5r611dXYmNjSUuLg6TyZSr3tXVFcMwiI2NBWDZsmVkZGTQs2fP\nfL1GkcLCrqQ1wx+tx+FR3RnSyouS1lZkZhsE/36I2hNXMum/EaRkZFo6TBERkSKhxK2b3F+pqanY\n2NjkKss5zsjIyFWelpZ23bY57W5Wn5qamuvc146TkJDAZ599xvz58+/petLT0wv8jVYpeDl/nnL+\n+6CxN8H4x+rxSuNqfLR5P0sjTnEh7QrvrQ9jxu+HeL9dXXr5VsPaymTpUAuFB/1+y53R/S5edL+L\nj5x3+gqSxRNtW1vbPAl1zrG9vf1ttbWzs7tlva2trfn4nwm2yWTC3t6e8ePH06NHD2rUqHHX12Jn\nZ8elS5eIjIy863PIgyU6OtrSIdyzdxs8xBMVqvNF2Fn2nEvhzIVU/r1iN5/+uo/Bvu40reCAyaSE\nG4rG/Zbbp/tdvOh+F33nz5/HwcGhQMe0eKLt7u5OUlIS2dnZWFldnckSHx+PnZ0dDz30UJ62cXG5\nV0qIj4+nXLlyALi5ueVZiDw+Ph43Nzfc3d0xDIP4+HgqVry65FnOucqVK8f69euxt7dnwYIFwNUn\n03v27OHnn39mzZo1t3UtZcuWpVGjRnl+QZCiJzU1lejoaDw8PIrE/fYGerQw+OlQDGM27uNQ3EWO\nJKUzZMsJ2tZwY1yHBvhULL7vLRS1+y03p/tdvOh+Fx/e3t4cPny4QMe0eKLt7e1NiRIlCAsLw8/P\nD4CQkBDq16+fp62Pjw9z5szJVRYaGsobb7wBgK+vL7t376Zbt24AxMTEEBsbi6+vL25ublSsWJHd\nu3ebE+2QkBAqVKiAq6srmzZtynXed955Bx8fH15++eU7uh57e3tKlSp1R33kwVXU7vczfjXp5uPJ\n3J1H+PDncGIvpvJr1Dlazvovvf08+aiTD9WcS1s6TIspavdbbk73u3jR/S4eCvoTWou/DGlnZ0fX\nrl0ZO3YsERERbN68mXnz5tG3b1/g6hPp9PR0ADp27MjFixeZMGECUVFRjB8/ntTUVDp16gRAr169\nWLVqFUuXLuXgwYOMGDGCtm3bmhPr559/nqCgIHbu3MmOHTuYOnWqeZwqVark+rK1tcXR0ZEKFSpY\n4KciYjklrK3o36w2hwK78kFHHxxsSmAY8J/dR/GevIoRa3aTlJpx6xOJiIgUcxZPtAECAwOpX78+\nffv2Zdy4cQwZMoT27dsD0KJFC3766ScASpcuzaxZswgJCaFHjx5EREQwZ84c8xxtX19fPvroI2bM\nmMELL7yAk5MTEyZMMI/z6quv0rlzZwYPHszbb79N9+7dzYn2tTQnVYq70rYleb9DQ/4O7MaAZrWx\ntjKRnplN0JYD1Jqwgum/RZKRmWXpMEVERAotk6G9mPNFREQEGRkZeHt766OnYiAlJYXIyMhidb8P\nnk0mcF0oq/efMpfVdC3DhCca8XSDqkX6l9PieL+LM93v4kX3u3gJDw/HZDLRoEGDAhmvUDzRFpHC\nz8vdkRUvt+XXNzrQpIoLAEfiL/Ls/G20Dv6ZHdrSXUREJBcl2iJyR1rVcGf7m4/zn94tqFb26jJJ\nf0TH8cgXG+i1cBtHz1+0cIQiIiKFgxJtEbljVlYmevlV58CIrkx+8v+3dF8cdpy6k1czbHUICSnp\nFo5SRETEspRo56OYmBhCQ0MtHYZIgbErac2wtvX4O7Abg1t6UcLKxJWsbD7bGkntCSv5fOsB0vXC\npIiIFAIhISGcOHGiQMdUoi0i98y1tB2fd/Nn37td6N6gKgCJqRm8s3o39T9ZzZK9x9F71yIiUtwo\n0RaRfFOr3EMs7dearQM78nDVqy9MHj1/iecXbKPl9J/589g5C0coIiJScJRoi0i+a+Hpxp9vPs53\n/4PT9XQAACAASURBVGpJ9f/tJLn9eBwtg3/m2flbORJ/wcIRioiI3H9KtEXkvjCZTDzXyIP9I7oQ\n1KUxTvY2ACwLP0H9T9YwdNUuzl/WC5MiIlJ0KdEWkfvKtoQ1b7euy+FR3XirlTclra24kpXNtG0H\nqT1xJVO36IVJEREpmpRoi0iBcC5ly6ddm7D/3S4841MNgKTUDIav2U3dyav4Yc8xvTApIiJFihJt\nESlQNVz/j707j6qq3P84/j7M4ASKKOaIEzgi4DxPOYPdsrRBK81u5ZDlgGV6zXLCbNDK0rJSG8xb\n4qypZc4KiqCCE86CggKiHAaB3x8Uv8u1bmIHNsPntVarxd77nOdzeFz6ZfPs71OO74Z2YueoXrSt\nVRmAczdu88TyXbT7YCM7o68anFBERMQyVGhbkLOzM3Xq1DE6hkix0K6OGztH9+K7oZ3wqJTzwOSB\nC9fp8uEWHv7iF07G6YFJERGxHA8PD1xdXQt1TBXaFuTo6IiLi4vRMUSKDZPJxCPNa3Fsoj/zA/yo\n6JTzwOTqiIs0nbuGsT8eIP5WqsEpRUSkJKhYsSJOTk6FOqYKbRExnJ2NNWM7eXFy8kBe6dwIO2sr\n7mRls3DXCerPWk3Q9mOkZuiBSRERKV5UaItIkeHiZE+Qvy/HJ/nzmHdtAG6mZhC4/hBec4L5+tBZ\nsrL0wKSIiBQPKrRFpMipU6kcXz/VkT1jetOhjhsAFxJu89SKXbT9YCM7zuiBSRERKfps7udFFy9e\n5PDhw8THx2NlZYWbmxvNmjWjevXqls4nIqVY61qV+eWlB1l99CKB6w5xOj6ZkIvX6fbRFvwbV2d2\nfx8aulUwOqaIiMgfuudC+86dO6xZs4alS5dy6tQpbG1tqVChAllZWSQlJZGVlUX9+vUZNmwYAwcO\nxNrauiBzi0gpYTKZeKhpTfp5PcCne0/x5pZwrqeksebYJdZHXmZkm/pMfbAZbuUcjY4qIiKSxz0V\n2seOHSMwMBA7OzseeughunTpQu3atbGyyll5kpWVRVRUFPv27eOLL75gyZIlzJkzh2bNmhVoeBEp\nPexsrBnV0ZMn/TyYve0oH+yMJO1OFh/vOcny0LMEdm/M2E5eONre1y/qRERELO6e/kWaPn06b7zx\nBq1atfrD81ZWVjRq1IhGjRrx7LPPsnv3bmbMmMH3339v0bBFndlsJiEhodBbx4iUJs6Odszu78M/\n2zVgyobDfHP4HMlpGby+IYxP9p7i7b4tGOxdGysrk9FRRUSkCLlx4wYpKSmUKVOm0Ma8p4chv/vu\nuz8tsv9I+/btWbly5X2HKq4SExM5e/as0TFESoXaFcuy/MmO7H+5L5088j4w2e6DjeyKvmZwQhER\nKUqio6OJj48v1DHvqdA2me6+M3TlyhV27txJamoq169fv6fXiIhYml+NSmx/8UH+/XRn6ruWA+Dg\nxet0/nAzg77cwZn4ZIMTiohIaZXv9n7p6emMGzeObt268fzzzxMXF8fUqVN55plnuHXrVkFkFBH5\nn0wmEwOb1iR8wgDeDfDDxTFnh8kfwi/QeO4axq8JISElzeCUIiJS2uS70P7444+Jioriyy+/xN7e\nHoBnnnmGixcvMm/ePIsHFBG5V3Y21ozp5MXJ1wbycicvbK2tyMjM4t0dkTSYtZoFOyPJyMwyOqaI\niJQS+S60169fzxtvvEHr1q1zj/n5+TFjxgy2bdtm0XAiIvejopM97wT4cXTiAB5qWhOAGynpvLw6\nhKZz1xB89CLZ2dphUkRECla+C+2rV69Ss2bNu467u7uTlJRkkVAiIpZQz7U8q57uzC8vPYhfjUoA\nnIpP5h9Lf6H7xz9x6NLdz5eIiIhYSr4L7bp167J37967jq9fv5569epZJJSIiCV19KjC3jF9+Orx\n9lSvkNN+c8eZq7R6bwNPf7ObS4m3DU4oIiIlUb53dhg9ejTjxo3j9OnTZGZm8uOPP3L27Fk2b97M\nu+++WxAZRUT+NisrE0/4evCPZjV5b0cks7cf5VbaHZaFRLPqyHle7dKICV0bU9be1uioIiJSQuT7\njnbXrl354IMPOHr0KNbW1nz22WdcvHiRd999l169ehVExmLD3d0dHx8fo2OIyP/gaGvD5B5NORE4\nkOfa1MfKZMKckclbP0XQcFYwn+0/RWaWHpgUESlp/Pz8/nD5c0HK9x3tzz77jP79+7NixYqCyCMi\nUiiqlndk0aA2jOrQkAlrD7HlxBVik82MXLmPhTtPEOTvS48G7kbHFBGRYuy+2vulpqYWRBYRkULX\nxN2FjSO7s/65bjSuWgGA8JgEen2ylQFLthN5VQ95i4jI/cl3od28eXO2b99eEFlERAzT2/MBDr3S\nn48faY1bWQcANkRepvm8tbyy9jAJqXcMTigiIsVNvpeOlC1blrlz57Jo0SJq166du2nN77766iuL\nhRMRKUw21laMbNuAwS1qM2f7Md7dcZy0O1ksPhDN14etmHTLjle7NcPB1troqCIiUgzku9B2cnJi\n4MCBBZFFRKRIKO9gx9t9W/B82wa8vuEwXx86y+2MLKZuOcpnB88ys18LHvOujclkMjqqiIgUYfku\ntGfNmlUQOUREipyaLmVY9kQHnmtZm5d/2MeRODPnE27zxPJdLNgZxTx/P9rWrmx0TBERKaLyXWgD\nxMTEsGLFCk6ePImNjQ3169fnscceo1q1apbOJyJiOL/qFfm0R21OZVdg6k9Hib5+i33n4+mwYBOD\nmtdiVr8W1KlUzuiYIiJSxOT7YcgTJ07g7+9PcHAwtra2ZGdn88MPP+Dv78+pU6cKImOxkZCQQHR0\ntNExRKQAmEwmAho/wNGJ/szz98XZ0Q6A74+cp9GcNUxaG0qiOd3glCIi8mfOnDlDfHx8oY6Z70J7\n7ty5tG7dmq1bt/Lhhx/y8ccfs3XrVtq2bcu8efMKImOxkZqaSmJiotExRKQA2dtYM65zI05OHsjo\njp7YWJlIz8xi3i/HaTBzNR/tOkFGpja8EREpahISEkhJSSnUMfNdaB86dIjRo0fn6TZib2/PSy+9\nRGhoqEXDiYgUVZXK2PPewJZETPTHv3F1AK6npDH6xwM0D1rLuuOXyM7ONjiliIgYKd+FdpkyZcjI\nyLjr+B8dExEp6RpULs+Pz3Zl2ws9afFARQBOxN0k4LOfeXDRVsIu3zA4oYiIGCXfhXabNm2YO3du\nniUSN27cICgoiLZt21o0nIhIcdGlXlUOvNyXpUPa8UAFJwC2n47F7931DP92D1eSCvfXlSIiYrx8\ndx0ZP348gwcPpmvXrtSuXRuAc+fOUaFCBZYvX27pfCIixYaVlYmhfnV5pFkt5u84ztztx7idfocv\nDp5h5ZFzTOjSmFe7NKKMva3RUUVEpBDku9CuWrUq69evZ82aNZw8eZLs7GweffRRBgwYQNmyZQsi\no4hIseJkZ8OUns0Y3roeUzceYenB06SkZzJ9SziL951iRp8WDPXzwMpKG96IiJRk+V46AnDs2DFq\n1arFtGnT+Ne//sWFCxeIioqydDYRkWLNvbwTix9rS+gr/ehevyoAV26aGf7dHlq+u57tp2IMTigi\nIgUp34X2+vXrefbZZ4mMjMw9dvnyZZ5++mm2bt1q0XDFjYODA87OzkbHEJEipnm1imx+vgdrhnfF\n0608AGFXEui5aCsDP/+Zk3E3DU4oIlLyubi44OTkVKhj5rvQXrRoEYGBgQwfPjz32AcffMDEiRNZ\nsGCBRcMVNy4uLnh4eBgdQ0SKIJPJRL9G1QkbP4CF/2iFa5mcFqlrj12i6dw1vBJ8kISUNINTioiU\nXHXr1sXV1bVQx8x3oX3hwgU6d+581/GuXbty7tw5S2QSESmxbK2teKF9Q05OHsiEro2xs7biTlY2\n7/8aRYNZq1m4M0ob3oiIlBD5LrTd3d05ePDgXccPHz5M5cqVLRJKRKSkq+Box+z+Phyb5M/DzWoC\ncCMlnbGrD+I9by3rteGNiEixl++uI0OGDGHGjBlcuHCB5s2bAxAREcGXX37Jiy++aPGAIiIlmUel\ncqwc1plfz1xl/JoQQi/dIOraTfw/+5keDdx5x9+XJu4uRscUEZH7kO9Ce9iwYaSnp/PVV1+xaNEi\nANzc3Bg3bhxPPvmkxQOKiJQGnepWYd/YviwLjWbKhsNcuWlm68kYWryznhFt6jG9V3PcyjkaHVNE\nRPIh34U2wHPPPcdzzz1HQkICtra26p8tImIBVlYmhrWsyyPNahL083Hm/XIMc0Ymn+49xbeHz/Fa\n96aM6eSJvY210VFFROQe3Fcf7StXrnDr1i1cXFw4evQob775JsHBwZbOJiJSKpWxt+VfvZsTOSmA\nJ3zrAHAzNYPA9YdoPGcNq46c1/ptEZFiIN+F9k8//cSDDz7IkSNHuHDhAiNGjGDv3r1MmzaNZcuW\nFURGEZFSqYZLGb56vAN7x/ahXe2ch83P3rjFY1/9StePthB68brBCUVE5H/Jd6H90UcfMXz4cNq2\nbcvatWupVq0a69evZ+bMmSxfvrwgMhYbMTExHDp0yOgYIlLCtKrpyq+jevHNUx2p5VIGgJ3R12j9\n/gae+WY3V5JSDE4oIlL0hYSEcOHChUIdM9+F9pkzZ3j00UexsrJi9+7ddO7cGSsrK7y9vYmJ0XbC\nIiIFwWQy8ah3bY5PCuDtvt6UtbchOxu+Comm4ezVzNgSTkr6HaNjiojIf8h3oV2+fHmSk5NJTk4m\nPDycdu3aATkb2VSsWNHiAUVE5P852FoT2L0pJwIHMrx1PUwmSEnP5F+bj+A1O5jlodFkZWn9tohI\nUZDvQrtz585MnTqVMWPGUK5cOdq3b8+ePXuYOnUqPXr0KIiMIiLyX6qWd+TTR9sS+ko/utWrCsCl\npBSGfb2bdh9sZM/ZawYnFBGRfBfab7zxBj4+Pjg5OfHxxx9jZ2dHWFgYbdu2ZcKECQWRUURE/kTz\nahXZ8s8e/PhMF+q7lgPg4MXrdFy4mcFf/cq5G7cMTigiUnrlu4+2g4MDgYGBeY5pR0gREeOYTCb8\nm9Sgt2c1Pt5zkje3hJNoTuf7I+dZc+wiL3fyIrB7E8o72BkdVUSkVLmvPtqWlp6ezmuvvUbLli3p\n2LEjS5cu/dNrjx8/zqOPPoq3tzeDBg3i2LFjec6vW7eOnj174u3tzahRo0hISMhzft68ebRt25bW\nrVsTFBSU51xERASDBw/G29ubPn36sHr1ast9SBGRAmZnY83YTl6cnDyQUR0aYm1lIu1OFnO2H6Ph\nrGAW7ztFZlaW0TFFREqNIlFoz5kzh+PHj7Ns2TKmTZvGwoUL2bJly13Xmc1mRo4cScuWLfnhhx/w\n9vbm+eefJzU1FYDw8HCmTJnC6NGjWblyJUlJSUyePDn39Z9//jkbNmzgo48+YsGCBaxduza3qL91\n6xYjR47Ez8+PdevW8eKLLzJlyhQOHz5cON8EERELqVTGnvcfakX4+AH09XoAgGu3Uvnn9/vwm7+B\n7afUIUpEpDAYXmibzWZWrVrFlClT8PT0pEePHowYMeIPe3KvX78eR0dHJkyYgIeHB6+//jplypRh\n06ZNAKxYsYI+ffrg7+9PgwYNCAoKYseOHVy+fBmAZcuWMWbMGFq0aEGrVq0YP3587jgxMTF07tyZ\n8ePHU716dQYMGECDBg3y1Rfb2dmZOnXqWOC7IiLy93lWqcDaEd3YOLI7jatWACA8JoGei7Yy8POf\nORl30+CEIiKFx8PDA1dX10IdM9+FdkhICBkZGRYLEBUVRWZmJt7e3rnHfH19CQ8Pv+va8PBwfH19\n8xzz8fHJvescFhZGy5Ytc89VrVoVd3d3jhw5wrVr14iJicHPzy/POFeuXCE+Pp769esze/ZsALKz\ns9m+fTtnz57N835/xdHRERcXl3u+XkSkMDzYsBqHXunPhw+3pnJZewDWHrtE07lreCX4IAkpaQYn\nFBEpeBUrVsTJyalQx8x3oT169GhOnjxpsQBxcXE4OztjY/P/z2VWqlSJtLS0u9ZXX7t2DTc3tzzH\nKlWqxNWrV3Pf67/Pu7q6EhsbS1xcHCaTKc95V1dXsrOziY2NzT2WkZFB06ZNeemllxg4cCDNmjWz\n2GcVETGKjbUV/2zXgBOBAxnfpRF21lbcycrm/V+jaDBrNQt3RpGRqfXbIiKWlO+uIxUrViQ5Odli\nAcxmM3Z2eZ+E//3r9PT0PMdTU1P/8Nrfr/tf581mc573/l/jfP/990RHRzN9+nRq1arF008/na/P\nIyXf7/Os+S4dStJ82wLTunsxtEUN3th8lODjl7mRks7Y1Qf5cFcUb/duSq8GVTGZTEZHNUxJmm/5\na5rv0iU7O7tQ/37Ld6HdqVMnnn/+eTp37kytWrWwt7fPc37UqFH5ej97e/u7Ct3fv3Z0dLynax0c\nHP7y/O8509PT7yqw/3McW1tbvLy88PLy4urVqyxbtixfhfa5c+fu+Vop/jTfpUtJm+/XvSvQp5oN\n74VeJSohlZPxyQxavofWVcvwsk8V6jo7GB3RUCVtvuV/03yXHv99U7Yg5bvQ3rx5M5UqVeLo0aMc\nPXo0zzmTyZTvQrtKlSokJiaSlZWFlVXOSpb4+HgcHBwoX778XdfGxcXlORYfH0/lypUBcHNzIz4+\n/q7zbm5uVKlShezsbOLj46lWrRpA7nKSypUrc+nSJc6dO0eHDh1yX1u3bt27lq/8ldq1a9/1A4KU\nPGazmXPnzmm+S4mSPN9eXjCkYzbfHLnA9J+OEpOcyv7Y2zyxMZqn/eowpVsjKpctXQV3SZ5vuZvm\nu3Q5depUoY6X70J7+/btFg3g5eWFjY0NYWFh+Pj4ADkPXDZp0uSua5s3b87ixYvzHDt06FDuhjne\n3t6EhoYycOBAIKeTSGxsLN7e3ri5uVGtWjVCQ0NzC+2QkBDc3d1xdXVlw4YNTJs2jd27d+f+pHP0\n6FHq1q2br8/j6OhY6AvtxTia79KlJM/3c+29eNyvHkE/H2feL8cwZ2Ty+cGzrIq4xGvdmzKmkyf2\nNtZGxyxUJXm+5W6a79KhsJfF3Vd7v+zsbH799VeWLFnCF198we7du8nMzLyvAA4ODgQEBDBt2jQi\nIiLYunUrS5cuZdiwYUDOHem0tJwn4nv16kVycjIzZ87kzJkzvPXWW5jNZnr37g3AkCFDCA4OZtWq\nVURFRTFp0iS6du2aW1gPHjyYefPmceDAAfbv38/8+fNzx+nSpQvlypVj6tSpnDt3jrVr1/L5559r\n10sRKTXK2Nvyr97NiZwUwOM+Oa1Kb6ZmELj+EE3mruHf4efJzs42OKWISPFhys7n35qJiYkMHz6c\nY8eOUa5cObKzs7l16xaNGzdm6dKldy33uBepqalMnz6dzZs3U65cOUaMGMFTTz0FgKenJ7Nnz869\nSx0REcG0adOIjo6mYcOGTJ8+HU9Pz9z3Wr16Ne+//z5JSUl06NCBGTNmUKFCTv/YrKwsgoKC+OGH\nH7C2tmbQoEGMGzcu97Vnz55lxowZhIWFUbFiRV544QUefvjhe/oMERERJCUlUadOHR544IF8fw+k\neElJSSEyMhIvLy/dASkFSut8H7gQz6vBIew59/9L9jp5uDHP3w/fGpUMTFawSut8l1aa79Ljxo0b\nnDx5kjJlytC0adNCGTPfhfbkyZMJDw/nnXfeyS1wo6KimDBhAj4+PkyfPr1AghZ1ERERnD9/Hmdn\n5zzrvKVk0l/MpUtpnu/s7GxWhp1n8vpDnE+4DYDJBE/5evB23xZUq1Dyvh+leb5LI8136RESEkJs\nbCy1atUqtEI730tHfv75Z6ZNm5bnLrKnpydTpkxh69atFg0nIiLGMplMPNaiNscm+fN2X2/K2tuQ\nnQ1fhUTTcPZqZmwJJyX9jtExRUSKpHwX2nfu3PnD7StdXV25deuWRUKJiEjR4mhrQ2D3ppwIHMjw\n1vUwmSAlPZN/bT6C1+xglodGk5Wl9dsiIv8p34V248aN+eabb+46/s033+Dl5WWRUCIiUjRVLe/I\np4+2JfSVfnSrVxWAS0kpDPt6N+0XbGTP2WsGJxQRKTry3d7v5ZdfZujQoXna8YWGhhIVFcWSJUss\nHlBERIqe5tUqsuWfPVh77BIT14ZyKj6ZAxeu03HhZgY1r8Xs/j7UrljW6JgiIobK9x3tFi1asGLF\nCqpXr86uXbvYuXMnNWrU4Ouvv6ZNmzYFkVFERIogk8mEf5MahE8YwDv+vjg75uxB8P2R8zSaE8zr\nGw6TnJphcEoREePk+442QLNmzXj33XctnUVERIohOxtrXu7ciKf86jJ98xEW7T1J2p0sZm87yhcH\nzvB23xYM9fPAyqpwN4oQETHaPd3RfuSRRwgJCbnnN92zZ889958WEZGSoVIZez74RyuOjB9Ab8+c\njcJik80M/24Pbd7fwK5ord8WkdLlnu5oT5s2jddeew0HBwf69OlD586d8fDwyLONZVRUFPv27ePf\n//43GRkZzJkzp8BCF1Xu7u56IFRESj2vKhVY/1x3NkVd5tXgEKKu3ST00g06f5izfntOfx9qaf22\niBQyPz8/wsPDC3XMeyq0mzZtyo8//khwcDBLly5l7ty52NnZUaFCBbKyskhKSiIzM5N69eoxdOhQ\nHnroIWxs7mtVioiIlBC9PR+ge313Fu05wfTN4SSY0/n+yHnWHrvEq10aMbFbY8ra2xodU0SkwNxz\nNWxjY8PDDz/Mww8/zPnz5wkLCyM+Ph4rKysqV65M8+bNqVGjRkFmFRGRYsbW2orRHb143Mcjd/12\n6p1M3t4awdIDp3m7Xwue9NH6bREpme7rtnOtWrWoVauWpbOIiEgJ9fv67efbNeDV4BB+OhnDlZtm\nnvlmDx/vPsH8gJa0rV3Z6JgiIhaV7/Z+IiIi96txVWc2juzOmuFdaVC5PAAHLlynw4JNPLF8JxcT\nbhucUETEclRoi4hIoTKZTPRrVJ0j4/szP8Avt//2t4fP4TUnmH9tOsLtNPXfFpHiT4W2iIgYws7G\nmrGdvDgRGMAL7RpgZTJhzshkxk/heM1Zw4rQaLKyso2OKSJy3/JdaK9bt46kpKSCyCIiIqWQa1kH\nFj7cmsOv9qN7/aoAXE5KYejXu+mwYBP7z8cZnFBE5P7ku9B+8803iYvTX3p/JCEhgejoaKNjiIgU\nS03cXdj8fA9WP9uFeq7lANh/IZ52H2xi6Ne7uJyUYmxAESnWzpw5Q3x8fKGOme9Cu3bt2pw8ebIg\nshR7qampJCYmGh1DRKTYMplMDGhcg4gJAwga4Et5h5w+2ytCz+I5ezUztoSTkn7H4JQiUhwlJCSQ\nklK4P7Dnu72fp6cn48ePZ8mSJdSuXRt7e/s852fNmmWxcCIiUjrZ2VjzSpdGPOlbh2mbj7Bk32lS\n0jP51+YjfLb/FLP7+/CYd+08OxSLiBQ1+b6jffbsWXx9fSlTpgxxcXFcunQpz38iIiKW4lbOkY8f\naUPoK/3oWq8KABcTU3hi+S46LdzMwQuF+2tgEZH8yPcd7WXLlhVEDhERkT/VrJoLP/2zJ8FHLzJx\n7SHOXE9mz7k42ry/kaf8PJjZtwXVKjgZHVNEJI/72hny9u3brFmzhpMnT2JjY0P9+vXp27cvZcuW\ntXQ+ERERIGf99sCmNenj9QALdkbx1k8RJKdlsCwkmh/CLxDYvQnjOnvhaHtf/7SJiFhcvpeOXLly\nhQEDBjB79mwOHz7M/v37efvtt/H39yc2NrYgMoqIiOSyt7FmfNfGnJgcwIg29TCZ4Hb6Hd7YGEbj\nOWtYGXaO7Gz13xYR4+W70J49ezZVq1Zl27ZtrF69mjVr1rBt2zaqVatGUFBQQWQUERG5S5Vyjnwy\nqC0h4/rRuW7O+u3zCbcZsmwnXT/awqFL1w1OKCKlXb4L7T179hAYGIirq2vuMVdXVyZOnMiuXbss\nGq64cXBwwNnZ2egYIiKlivcDFdn2Qk9WDutE7YplANgZfY1W721g+Ld7iL1pNjihiBQFLi4uODkV\n7rMc+S60ra2tcXR0vOu4vb096enpFglVXLm4uODh4WF0DBGRUsdkMvFws1ocmxjAzL4tKGtvQ3Y2\nfHHwDA1nr2b2tghSMzKNjikiBqpbt26eG8WFId+Fto+PDx999BEZGRm5xzIyMli0aBE+Pj4WDSci\nIpIfDrbWTOrehKjAAJ5pVReTCW6l3eH1DWE0mbuGf4ef1/ptESk0+X40e/z48QwePJiePXvSpEkT\nACIiIrh9+zbLly+3eEAREZH8ci/vxJLH2vFCu4a8EhzCrrPXOHvjFo9++Sud61ZhfoAf3g9UNDqm\niJRw+b6jXbduXYKDg+nXrx/p6emkpaUxYMAAgoOD8fT0LIiMIiIi98W3RiV+eelBvh3aiVouOeu3\nd5y5it+76xm5ci9Xk7V+W0QKTr7vaI8aNYpx48YxYcKEgsgjIiJiUSaTiUHNa9G/0QO8tyOSWduO\ncjv9Dp/tP83KsPO83qMpYzp5Ym9jbXRUESlh8n1He9++fdjb2xdEFhERkQLjaGvD5B5NiQoMYKhf\nzoPryWkZBK4/RNO5a1kdcUHrt0XEovJdaD/00EPMmzePU6dOlfouIyIiUvxUq+DE0iHt2f9yX9rV\nrgzAmevJPPzFDnou+omjsUkGJxSRkiLfS0d27NjBhQsX2Lx58x+ej4yM/NuhRERECppfjUr8OqoX\n34WdI3DdIS4mpvDz6au0P3OVgLrOvFOjDrUKueeuiJQs+S60X3jhhYLIUSLExMRgNpvp0KGD0VFE\nROQemEwmBreog3/jGszfcZw524+Skp7Jj6cT2fbeZt54sDmjOjTETuu3RYq9kJAQYmNjqVWrVqGN\nme9C+9ixYwwdOpSaNWsWRB4REZFC52Rnw5SezXi6ZV0C14TwzZEL3Ey7w4S1oXyy9yRBA3wZ0Lg6\nJpPJ6KgiUozke432jz/+iJVVvl8mIiJS5FV3LsOnj7Tk8wdr07JGTp/t0/HJPLT0F3p9spWj9Mpd\nmQAAIABJREFUMQkGJxSR4iTfFXPnzp1Zvnw5t27dKog8IiIihmvi6sS257qw7IkOVK+Qs05726lY\nWryznpf+vZ/4W6kGJxSR4iDfS0fi4uLYsGEDX375JZUqVbqr1d+2bdssFk5ERMQoJpOJx33qENC4\nOvN+OU7Qz8cwZ2SyaM9Jvj18jqkPNuOFdg20fltE/lS+C+3WrVvTunXrgsgiIiJS5JSxt2Var+Y8\n26oek9cf4pvD50g0p/NKcAiL9pxknr8vfb0e0PptEbnLfe0MKSIiUtrUcCnD8ic78lIHT8atPsjB\ni9c5GXcT/89+pmcDd+YH+NGoqrPRMUWkCLmvpxqjoqKYPHkygwcP5urVq6xYsYIDBw5YOpuIiEiR\n07Z2ZfaM6cMXQ9pTrbwjAD+djMH7nXWM+eEA12+nGZxQRIqKfBfaR48eZdCgQVy6dImjR4+Snp7O\niRMnePbZZ9mxY0dBZCw2nJ2dqVOnjtExRESkgFlZmXjKz4PIwACm9GyKg401mVnZfLj7BA1nrWbB\nzkgyMrOMjiki/8HDwwNXV9dCHTPfhfa8efN49tlnWbZsGba2tgC8+eabPPXUUyxYsMDiAYsTR0dH\nXFxcjI4hIiKFpKy9LdN7e3N8kj+PeudsgpFgTufl1SG0eGcdm6IuG5xQRH5XsWJFnAp5t9f7uqM9\ncODAu44PGTKEM2fOWCSUiIhIcVKrYlm+eaoTv47qhW/1nP7bkVeT6Ld4O/2XbOfEtSSDE4qIEfJd\naNva2v5hD+0rV67g6OhokVAiIiLFUfs6buwb25fPB7fD/bf12xsjL9MsaC3j14SQZE43OKGIFKZ8\nF9o9evTgvffe4+bNm7nHzpw5w8yZM+nSpYsls4mIiBQ7VlYmhrWsS+SkACZ3b4KdtRV3srJ5d0ck\nDWevZvG+U2Rmaf22SGmQ70J70qRJ3L59mzZt2mA2m/nHP/5B//79sba2ZuLEiQWRUUREpNgp52DL\nW31bcGySPw81rQlA3K00/vn9Plq/t5Gd0VcNTigiBS3ffbTLli3Lt99+y969ezl+/DhZWVk0aNCA\njh07YmV1X90CRURESiyPSuVY9XRntp2M4ZXgEI7GJnL48g26fLiFR71rMae/LzVdyhgdU0QKQL4L\n7d+1bduWtm3bWjKLiIhIidW9gTuhr/Rj8b5TTN0Uxo2UdFaGnWfN0UtM7NaYCV0b42R33/8si0gR\npFvQFmQ2m0lISDA6hoiIFFE21la80L4hJyYPZFSHhlhbmUi9k8mbW8JpNCeYbw+fJTs72+iYIiXS\njRs3SElJKdQxVWhbUGJiImfPnjU6hoiIFHEVnex5/6FWHH61P93rVwXgYmIKTyzfReeFmzl06brB\nCUVKnujoaOLj4wt1TBXaIiIiBmlc1ZnNz/fgx2e6ULdSOQB2n4uj1XsbGLlyL1eTzQYnFJG/Q4W2\niIiIgUwmE/5NahAxcQCz+rWgrL0N2dnw2f7TeM4OZv4vx0m/k2l0TBG5Dyq0RUREigB7G2smdmtC\nVGAAw1rWBeBmagYT1obSLGgt649f0vptkWJGhbaIiEgR4l7eic8Ht2Pf2D60qeUKwKn4ZPw/+5l+\nS7YTeVXbuYsUFyq0RUREiqCWNV3ZNbo3Xz3enmq/bee+OeoK3vPW8krwQRK1nbtIkadCW0REpIgy\nmUw84etBZGAAr/doir1Nznbu7/8aRcNZq/lk70lt5y5ShKnQFhERKeLK2tvyZh9vjk305x/NcrZz\nj7+dxour9tPy3Q3sOKPt3EWKIhXaFuTu7o6Pj4/RMUREpISqU6kc3w/rzNYXetLM3QWAI1cS6PbR\nFh776lfO3bhlcEKRosvPz4+aNWsW6pgqtEVERIqZrvWqcnBcXz58uDWVnOwBWHXkPI3nrGHapjBu\np2UYnFBEoIgU2unp6bz22mu0bNmSjh07snTp0j+99vjx4zz66KN4e3szaNAgjh07luf8unXr6Nmz\nJ97e3owaNequLdHnzZtH27Ztad26NUFBQXnORUdHM3z4cHx9fenRoweffPKJ5T6kiIiIBdlYW/HP\ndg04MTmAMR09c7dzf+unCLzmrOHrQ9rOXcRoRaLQnjNnDsePH2fZsmVMmzaNhQsXsmXLlruuM5vN\njBw5kpYtW/LDDz/g7e3N888/T2pqKgDh4eFMmTKF0aNHs3LlSpKSkpg8eXLu6z///HM2bNjARx99\nxIIFC1i7dm1uUZ+amsrIkSOpWrUq//73v5k6dSpffvklX3/9deF8E0RERO6Di5M97w5sSdir/enZ\nwB2Ay0kpPLViF50WbibkorZzFzGK4YW22Wxm1apVTJkyBU9PT3r06MGIESNYvnz5XdeuX78eR0dH\nJkyYgIeHB6+//jplypRh06ZNAKxYsYI+ffrg7+9PgwYNCAoKYseOHVy+fBmAZcuWMWbMGFq0aEGr\nVq0YP3587jgHDx7k5s2bTJ8+ndq1a9OpUyeefvpp1q1bV3jfDBERkfvUqKozG0d2J3h4V+q55mzn\nvudcHG3e38Dwb/cQe1PbuYsUNsML7aioKDIzM/H29s495uvrS3h4+F3XhoeH4+vrm+eYj48Phw8f\nBiAsLIyWLVvmnqtatSru7u4cOXKEa9euERMTg5+fX55xrly5Qnx8PF5eXnz44YfY2Njkef/k5GSL\nfE4REZGCZjKZ6N+oOuETBjCnvw/l7G3JzoYvDp7Bc3YwQduPkabt3EUKjeGFdlxcHM7OznkK3EqV\nKpGWlnbX+upr167h5uaW51ilSpW4evVq7nv993lXV1diY2OJi4vDZDLlOe/q6kp2djaxsbG4urrm\nKdLT0tJYuXIl7dq1s9hnFRERKQz2NtaM79qYqMAAnmlVF5MJktMyCFx/iGZBa1l77KLWb4sUApu/\nvqRgmc1m7Ozs8hz7/ev09Ly7XqWmpv7htb9f97/Om83mPO/9v8bJzs5m0qRJpKSkMHLkyHx/Hin5\nfp9nzXfpoPkuXUrSfJe3gQ8GePO0Ty0mbjjC/gvXOR2fzMDPf6F7PTdm92mOp1t5o2MaqiTNt/y1\n7OxsTCZToY1neKFtb29/V6H7+9eOjo73dK2Dg8Nfnre3t8/9+r8L7P8cJzMzk4kTJ7Jjxw6++OIL\nKlWqdM+fJSEhgUuXLlG9evV7fo0Ub+fOnTM6ghQizXfpUpLm2xH4oL0bW6o7sCDsKtdS7rDt9DXa\nLPyJR+pX5LlmlSlvZ210TEOVpPmWP3bp0iXKlClDlSpVCm1MwwvtKlWqkJiYSFZWFlZWOStZ4uPj\ncXBwoHz58nddGxcXl+dYfHw8lStXBsDNzY34+Pi7zru5uVGlShWys7OJj4+nWrVqALnLSX5//Z07\nd3j55ZfZs2cPixcvpnnz5vn6LKmpqZQtWxYvL698vU6KH7PZzLlz56hdu/ZdPxBKyaP5Ll1K8nw3\nagTPdb/DeztP8t6uE6TeyeK7kzf46dIt3ujeiKd962Bjbfiq0kJVkudb8jKbzSQmJhbqmIYX2l5e\nXtjY2BAWFpa7q2JISAhNmjS569rmzZuzePHiPMcOHTrEiy++CIC3tzehoaEMHDgQgJiYGGJjY/H2\n9sbNzY1q1aoRGhqaW2iHhITg7u6Oq6srAG+88QZ79+7ls88+o0WLFvf1eezt7XFycrqv10rx4+jo\nqPkuRTTfpUtJnW8nJ3h7gB8j23syad0hvj9ynhsp6YxbG8bnIed4d2BLutaranTMQldS51v+3+8r\nIAqT4T+2Ojg4EBAQwLRp04iIiGDr1q0sXbqUYcOGATl3pNPS0gDo1asXycnJzJw5kzNnzvDWW29h\nNpvp3bs3AEOGDCE4OJhVq1YRFRXFpEmT6Nq1a25hPXjwYObNm8eBAwfYv38/8+fPzx1n9+7d/Pjj\njwQGBlKjRg3i4+OJj4/nxo0bBnxXREREClatimX5dmgntr/4IM2r5WznHhGTSI+Pf+KRL3Zw9rq6\nbon8XYbf0QaYPHky06dPZ9iwYZQrV46xY8fSo0cPADp06MDs2bMZOHAgZcuWZdGiRUybNo2VK1fS\nsGFDFi9enPsTire3N2+++Sbvv/8+SUlJdOjQgRkzZuSOM2LECBISEhg9ejTW1tYMGjQot9DesmUL\nJpOJqVOn5slWrVo1tm3bVkjfCRERkcLVuW4VDo7ry2f7T/PGxjDib6fxY8QFNkRe4tUujZjUrQll\n7W2NjilSLJmy1d/HIiIiIjh//jzOzs506NDB6DhSwFJSUoiMjMTLy0u/aiwFNN+lS2me70RzOjO2\nhLNwVxR3snLKg2rlHZnV34fHW9TByqrwujUUltI836VNSEgIsbGx1KpVi6ZNmxbKmIYvHREREZGi\nwdnRjncC/DgyfgC9PHOWXV65aWbY17vpuHATBy7E/8U7iMh/UqEtIiIieXhWqcCG57qzdkQ3GlTO\n6QC273w8bd/fyDPf7CbmZorBCUWKBxXaFuTg4ICzs7PRMURERCyir9cDHBnfn6ABvpR3yFmn/VVI\nNJ6zg5mz7SipGdrOXYoPFxeXQl8epELbglxcXPDw8DA6hoiIiMXY2VjzSpdGRAUGMLx1PUwmuJV2\nh9c2HKZp0BqCj2o7dyke6tatm9vSubCo0BYREZG/VKWcI58+2pYDL/elQx03AKKv3+IfS3+h1ydb\nORqTYHBCkaJHhbaIiIjcM5/qlfjlpQf5+smO1HDO+TX8tlOx+Mxfz5gfDnAjJc3ghCJFhwptERER\nyReTycRjLWpzfFIA0x5shqOtNZlZ2Xy4+wQNZ63mo10nuJOZZXRMEcOp0BYREZH74mRnw9RezTk+\nKYBHvWsBcCMlndE/HsBn/jq2nYwxOKGIsVRoi4iIyN9S06UM3zzViV9eepAWD1QE4FhsEg9+spWH\nv/iFaG3nLqWUCm0RERGxiI4eVdj/ch8+GdSGymXtAVgdcZHGc9bw+obDJKdmGJxQpHCp0BYRERGL\nsbayYkSb+pwIHMgrnRthY2UiPTOL2duO4jUnmK9CzpCVpXaAUjqo0LagmJgYDh06ZHQMERERw1Vw\ntCPI35fwCQPo6/UAADE3zTzzzR7aL9jI/vNxBieU0iYkJIQLFy4U6pgqtEVERKTANHSrwNoR3Vg3\nohsNf9vO/cCF67T7YBPDvt7NlSRt5y4llwptERERKXB9vB7gyIQBvOPvS4XftnNfHpqznfusrRHa\nzl1KJBXaIiIiUihsra14uXPOdu7PtamPyQS30+8wZWMYTeau4ceIC9rOXUoUFdoiIiJSqNzKObJo\nUBtCxvWjk0fOdu5nb9zikS928OCirURoO3cpIVRoi4iIiCG8H6jI9hcf5NuhnajpUgaA7adj8Xln\nPaP+vZ/rt7WduxRvKrRFRETEMCaTiUHNa3F8kj/TezfH0daarOxsPt5zkoazVvPhriht5y7Flgpt\nERERMZyjrQ1TejYjclIAQ1rUBiDBnM6YHw/iO389209pO3cpflRoW5CzszN16tQxOoaIiEixVcOl\nDMuf7Mivo3rhUz1nO/ejsYn0XLSVR77YwbkbtwxOKMWVh4cHrq6uhTqmCm0LcnR0xMXFxegYIiIi\nxV77Om7sG5t3O/cfIy7QaE4w0zaFcTtN27lL/lSsWBEnJ6dCHVOFtoiIiBRJv2/nHhU4kLGdPLGx\nMpF2J4u3foqg0Zw1fHv4rNoBSpGmQltERESKNGdHO+YHtCRs/AB6NnAH4FJSCk8s30XXj7YQdvmG\nwQlF/pgKbRERESkWvKpUYOPI7vzwTBc8KpUFYGf0NVq+u4EXVu0j/laqwQlF8lKhLSIiIsWGyWQi\noEkNIib483Zfb8rY2ZCVnc2ne0/RcHYwC3ZGkqF2gFJEqNAWERGRYsfB1prA7k2JDAzgCd+cjl+J\n5nReXh2Czzvr2HpS7QDFeCq0RUREpNh6oIITXz3egZ2jeuH7WzvA41eT6PXJVh7+4hfOXk82OKGU\nZiq0LchsNpOQkGB0DBERkVKnXR039o3ty6eP/n87wNURF2k8dw1TN6odoMCNGzdISUkp1DFVaFtQ\nYmIiZ8+eNTqGiIhIqWRlZWJ465x2gC938sptB/j21px2gN8cUjvA0iw6Opr4+PhCHVOFtoiIiJQo\nzo52vBPgR9j4ATzYsBqQ0w7wyRW76PLhFg5fUjtAKRwqtEVERKRE8qpSgQ3PdWP1s12oW6kcALvO\nXqPle+t5/vu9xKkdoBQwFdoiIiJSYplMJgY0rkHExAHM7NuCMnY2ZGfDkn2naThrNR/tPcWdLC0n\nkYKhQltERERKPHsbayZ1b0JUYABP+noAkJSawaQN4TyxMZrtp68anFBKIhXaIiIiUmpUq+DEl4+3\nZ9fo3vjVqATA2aQ0Ar7cxT+W/kK02gGKBanQFhERkVKnbe3K7B3Thw8H+lLRwRqA4KMXaTJ3DVM2\nHOaW2gGKBajQFhERkVLJysrEUN/arOpfj9Ht6+e2A5y17SiN5qxhRWi02gHK36JC24Lc3d3x8fEx\nOoaIiIjkQ1k7a2b2bsaR8QPo5ZnTDvByUgpDv95N54WbOXTpusEJxRL8/PyoWbNmoY6pQltEREQE\n8KxSgfUjuhE8vGtuO8Dd5+Jo9d4GRq7cy7Vks8EJpbhRoS0iIiLyG5PJRP9G1YmYOIBZ/VpQ1j6n\nHeBn+0/jOTuY93+NJCMzy+iYUkyo0BYRERH5L/Y21kzsltMO8Cm//28H+EpwCC3eWceWE1cMTijF\ngQptERERkT/hXt6JL4a0Z/eY3rT8rR1g5NUk+ny6jYGf/8yZeLUDlD+nQltERETkL7SpVZk9Y/qw\n5LG2VCnnAMDaY5doMncNr6sdoPwJFdoiIiIi98DKysQzreoRFRjAq10aYWNlIj0zi9nbjuI1O5jl\nagco/0WFtoiIiEg+lHewY+4AX8InDKD3b+0Ar9w0M+zr3XRcsJmQi2oHKDlUaFtQQkIC0dHRRscQ\nERGRQtDQrQLrn+vOmuFdqe+a0w5w7/k42ry/gee+UzvAoubMmTPEx8cX6pgqtC0oNTWVxMREo2OI\niIhIIerXqDpHJgxgdj+f3HaAnx84TcPZwby74zjpdzKNjijk3BBNSUkp1DFVaIuIiIj8TfY21kzo\n1piowACG/tYO8GZqBuPXhOI9bx2boi4bnFCMoEJbRERExELcyzuxdEh79ozpTauaOe0AT8TdpN/i\n7QR89jOn428anFAKkwptEREREQtrXasyu0f34fPB7XLbAa47fommc9cyed0hklPVDrA0UKEtIiIi\nUgCsrEwMa1mXqMAAxndphK21FemZWcz9+Rhec4JZFhJNVpbaAZZkKrRFREREClB5Bzvm/NYOsI/X\nAwDE3DTz9De76bhwEwcvFG4nDCk8KrRFRERECkGDyuVZN6Iba0d0o0Hl8gDsOx9Pm/c3MvzbPVxV\nO8ASR4W2BTk4OODs7Gx0DBERESnC+no9wJHx/Znb34dy9rYAfHHwDJ6zg5n/i9oBFhQXFxecnJwK\ndUwV2hbk4uKCh4eH0TFERESkiLOzsebVrjntAJ9uWRfIaQc4YW1OO8CNkWoHaGl169bF1dW1UMdU\noS0iIiJikKrlHflscDv2ju1D65o5ReCJuJv0X7Id/8+2cypO7QCLMxXaIiIiIgZrVdOVXaN7s3RI\nO6qWcwRg/fHLNA1aS6DaARZbKrRFREREigArKxND/XLaAU7o2hhbaysyMrMI+vkYnrOD+fLgGbUD\nLGZUaIuIiIgUIeUcbJnd34eICQPo1yinHWBssplnv91DhwWbOKB2gMWGCm0RERGRIqh+5fKsGd6N\ndf/RDnD/hXjavr+RZ7/dQ+xNtQMs6opEoZ2ens5rr71Gy5Yt6dixI0uXLv3Ta48fP86jjz6Kt7c3\ngwYN4tixY3nOr1u3jp49e+Lt7c2oUaNISEjIc37evHm0bduW1q1bExQU9IdjJCYm0r59e65cufL3\nP5yIiIjI39Dnt3aAQQN8c9sBfvlbO8B3fj6mdoBFWJEotOfMmcPx48dZtmwZ06ZNY+HChWzZsuWu\n68xmMyNHjqRly5b88MMPeHt78/zzz5OamgpAeHg4U6ZMYfTo0axcuZKkpCQmT56c+/rPP/+cDRs2\n8NFHH7FgwQLWrl17V1GflJTEP//5T27cuFGwH1pERETkHtnZWPNKl0acmBzAM61y2gEmp2Uwcd0h\nms9bxwa1AyySDC+0zWYzq1atYsqUKXh6etKjRw9GjBjB8uXL77p2/fr1ODo6MmHCBDw8PHj99dcp\nU6YMmzZtAmDFihX06dMHf39/GjRoQFBQEDt27ODy5Zw/fMuWLWPMmDG0aNGCVq1aMX78+DzjhIaG\n8vDDD+cW7vkVExPDoUOH7uu1IiIiIn+lSjlHljzWjn1j+9CmVk47wJNxNxmwZDv9l2znpNoB/qmQ\nkBAuXLhQqGMaXmhHRUWRmZmJt7d37jFfX1/Cw8PvujY8PBxfX988x3x8fDh8+DAAYWFhtGzZMvdc\n1apVcXd358iRI1y7do2YmBj8/PzyjHPlyhXi43MeKti1axeDBg3igw8+IDtbT/WKiIhI0dSypis7\nR/XmiyHtcS+f0w5wY+RlmgWtZdLaUG6mphucUKAIFNpxcXE4OztjY2OTe6xSpUqkpaXdtb762rVr\nuLm55TlWqVIlrl69mvte/33e1dWV2NhY4uLiMJlMec67urqSnZ1NbGwsAGPHjuX555/H2toak8lk\n0c8pIiIiYklWViae8vMgclIAE7s2xu63doDzfjmO5+xgvjigdoBGs/nrSwqW2WzGzs4uz7Hfv05P\nz/vTWGpq6h9e+/t1/+u82WzO897/a5y/Iy0tjZSUFIu9nxRNv/95+v3/UrJpvksXzXfpUhLm2xp4\no5snjzevzuSN4Ww8EcPV5FSGf7eHj3dHMrevNy1rVDQ6puHud2nw32F4oW1vb39Xofv7146Ojvd0\nrYODw1+et7e3z/36vwvs/x7n74iJiSEyMtJi7ydF27lz54yOIIVI8126aL5Ll5Iy39N9Xejtbsv8\nQ7Gcv5lOyKUEun36M/3qVOAlbzdcHW2NjmiYc+fO4ezsXKhjGl5oV6lShcTERLKysrCyylnJEh8f\nj4ODA+XLl7/r2ri4uDzH4uPjqVy5MgBubm65663/87ybmxtVqlQhOzub+Ph4qlWrBpC7nOT311uC\nu7s7Xl5eFns/KZrMZjPnzp2jdu3aFv1BTYomzXfpovkuXUrifHt5wZOds/hk/2lm/xzJzbQ7rD+b\nxI7Lt5nYxZMX29bD3sba6JiFzmw2k5iYWKhjGl5oe3l5YWNjQ1hYGD4+PkDOU6FNmjS569rmzZuz\nePHiPMcOHTrEiy++CIC3tzehoaEMHDgQyLm7HBsbi7e3N25ublSrVo3Q0NDcQjskJAR3d3dcXV0t\n9nns7e1xcnKy2PtJ0ebo6Kj5LkU036WL5rt0KWnz7QRM6unN020aMmVDGEsPnuZW+h2mbjnKV4fO\nMz/Aj36Nqhsds1D9vgKiMBn+MKSDgwMBAQFMmzaNiIgItm7dytKlSxk2bBiQc0c6LS0NgF69epGc\nnMzMmTM5c+YMb731Fmazmd69ewMwZMgQgoODWbVqFVFRUUyaNImuXbvmFtaDBw9m3rx5HDhwgP37\n9zN//vzccf6buo6IiIhIcVelnCOLH2vLvrF9aVsr5zf4p+OT8f/sZ/ot3saJa0kGJyzZDC+0ASZP\nnkyTJk0YNmwYM2bMYOzYsfTo0QOADh06sHHjRgDKli3LokWLCAkJ4eGHHyYiIoLFixfn/oTi7e3N\nm2++yYcffsjjjz+Os7MzM2fOzB1nxIgR9O3bl9GjRzNu3DgeeuihPy2076friLOzM3Xq1Mn360RE\nREQKkl+NSuwc3YsvH///doCboq7QLGgtE9aUjnaAHh4eFl3FcC9M2bp1axERERGkp6fj5eVVon71\nJH8sJSWFyMhIzXcpofkuXTTfpUtpnO9baRnM2naU+b8cJz0zC4Aq5Rx4u28LhvnVxcqq5LY4Dg8P\nx2Qy0bRp00IZr0jc0RYRERGRwlHW3pa3+7bg6ER/BjTOWad9NTmVEd/tpd0HG9l3Pu4v3kHulQpt\nERERkVKorms5Vj/blY0ju+PpltPp7eDF67T/YBPDvt5NzE3tC/J3qdAWERERKcUebFiNsPEDmB/g\nR3mHnD7by0Oj8ZwdTND2Y6TdyTQ4YfGlQltERESklLO1tmJsJy9OBAYwvHU9TCa4lXaHwPWHaB60\nlg2Rl42OWCyp0BYRERERANzKOfLpo23Z/x/tAE/FJzNgyXb6L9nOybibBicsXlRoi4iIiEgevn/Q\nDnBj5GWaBa0lcN0hklMzDE5YPKjQtiCz2UxCQoLRMURERET+NpPJxJO+HkROCmBSt8bYWluRkZlF\n0M/H8JwdzFchZ8jKKj5dom/cuEFKSuE+4KlC24ISExM5e/as0TFERERELKacgy0z+/kQMWEA/Ro9\nAEBssplnvtlDx4WbCLl43eCE9yY6Opr4+PhCHVOFtoiIiIj8pfqVy7NmeDfWjuhGfddyAOw7H0+b\n9zfw3Hd7uZZsNjhh0aNCW0RERETuWV+vBwifMIA5/X0oa29DdjZ8fuA0DWcH8/6vkWT8ttukqNAW\nERERkXyys7FmfNfGRAUGMNTPA4CbqRm8EhxCi3fW8dOJKwYnLBpUaIuIiIjIfXEv78TSIe3ZPaY3\nfjUqARB5NYnen27jH0t/Ifp6ssEJjaVCW0RERET+lja1KrN3TB+WPNYWt7IOAAQfvUiTuWuYujGM\n22mlsx2gCm0RERER+dusrEw806oeUYEBjOvshY2VibQ7Wby9NYJGc9bw7eGzZGcXn3aAlqBCW0RE\nREQspoKjHfP8/QgbP4CeDdwBuJSUwhPLd9H1oy0cuXLD4ISFR4W2Bbm7u+Pj42N0DBERERHDeVWp\nwMaR3fnxmS54VCoLwM7oa/jN38BL/97P9dtphZrHz8+PmjVrFuqYKrRFREREpECYTCZDN3Z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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = ManualAlphaSelection(Ridge())\n", + "model.fit(X, y)\n", + "model.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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HixYtbnuOu3X48GG2bt3Krl27TNuqVatGZGQkderUwcPD447Ok5mZSWho6E13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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = ManualAlphaSelection(Lasso())\n", + "model.fit(X, y)\n", + "model.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ridge Regularization\n", + "\n", + "### Manual Ridge " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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JIaw4fMHKlYmIiIhYh8K1/Cv2dnZMaF+LmZ3q4mhvR1xCMi/M2czY0EO60VFE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\nG43eWc1Pf6bYt0CR26BwLSIiInZ1b5niLHm8BRuHtaOef1kA4k+epdX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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.linear_model import Ridge\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "errors = []\n", + "model = Ridge()\n", + "for alpha in alphas:\n", + " model.set_params(alpha=alpha)\n", + " score = cross_val_score(model, X, y, cv=12, scoring='neg_mean_squared_error')\n", + " errors.append(score.mean())\n", + "\n", + "plt.plot(alphas, errors, label='ridge')\n", + "plt.xlabel('alpha')\n", + "plt.ylabel('mean squared error')\n", + "plt.title(\"Manual Ridge Alpha Scoring\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ridge CV" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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zzjvvsG3bNkJDQ4mLi2PJkiWW0F7ca5cHezs73uxdsFzipcxsJn9edksZioiI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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.linear_model import RidgeCV \n", + "\n", + "# Must pass store_cv_values=True in order to store the MSE of the alphas. \n", + "ridge = RidgeCV(alphas=alphas, store_cv_values=True, scoring='neg_mean_squared_error')\n", + "ridge.fit(X, y)\n", + "\n", + "# cv_values_ : array, shape = [n_samples, n_alphas] or shape = [n_samples, n_targets, n_alphas], optional\n", + "# Cross-validation values for each alpha (if store_cv_values=True and cv=None). \n", + "# After fit() has been called, this attribute will contain the mean squared errors (by default) or the \n", + "# values of the {loss,score}_func function (if provided in the constructor).\n", + "ridge.cv_values_.mean(0)\n", + "\n", + "g = plt.plot(ridge.alphas, ridge.cv_values_.mean(0), label=\"ridge\")\n", + "plt.xlabel(\"alpha\")\n", + "plt.ylabel(\"mean squared error\")\n", + "plt.title(\"RidgeCV Alapha Error\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.00085571831449399844" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ridge.alpha_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## LASSO Regularization \n", + "\n", + "### Lasso CV" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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nLrvz58+nZs2a9OvXD4CKFSuyd+9e5s6dy/vvv3/f54mPj3dQQskMGh/npbFx\nXhob55aVxsditfJOVAL7zl4HoG+VQpQ1XSMm5prByRwjK41NduLp6bj1m5267J45c4aKFStmOFax\nYsUMKzbcj8DAQLy9vTMzmmSCpKQk4uPjNT5OSGPjvDQ2zi0rjs8nPx3ip5PpRbdd5eKMbeeaN6Rl\nxbHJLo4dO+bQ8zt12S1ZsuRtxfb48eMUL17crvN4e3vj4+OTmdEkE2l8nJfGxnlpbJxbVhmfxXtP\nMGZD+soLNYrnZ1an+vh4OnU1eGhZZWyyE0f/48opVmO4m44dO7JlyxbmzJnDyZMnmT17NlFRUXTq\n1MnoaCIiIlna7oRL9Pj2ZwCK5PZmWc9GLl90JXtyurL713ZftmxZpk2bxooVK2jVqhURERFMnz6d\nMmXKGJhQREQkazt3PYmwWRtISknDK4cb3/doSLG8utoprsnp/gkXExOT4eOQkBC+//57g9KIiIi4\nluTUNNrO2sTJK+krLUxvH0LwY4UMTiXiOE53ZVdEREQcw2q10nfxNraeOA/A0EZP0LlGaYNTiTiW\nyq6IiEg28fG6/czdeRyA5x8vxsfPVTM4kYjjqeyKiIhkA3N2xDFq7V4AqgbkY37neri7qQaI69N3\nuYiIiItbf/QMfb7bCkDxvD5EvBJKnpyOW8RfxJmo7IqIiLiwfacv0272JlItVvLk9GBV71CtvCDZ\nisquiIiIi0q4ksgLM37ienIKHu5uLOnWkMpF8xkdS+SRUtkVERFxQddumWkxYwOnrv5vibHG5Ysa\nnErk0VPZFRERcTEpaRbazd7EvjOXAfjw2ap0qaklxiR7UtkVERFxIVarlVcXbyPy2FkAetYuyztN\nKhucSsQ4KrsiIiIu5KMf9zFnRxwAz1QIYHK7YEwmk8GpRIyjsisiIuIiZkfH8cGP+wCoFpCP77o2\nwMNd/6uX7E3vABERERfw45HTvLo4fS3dEn7pa+nmzulhcCoR46nsioiIZHF7T1+i/ZzNpFqs5M3p\nwQ+9GxOgtXRFAJVdERGRLC3hSiItZmz431q63RvyRBE/o2OJOA2VXRERkSzqapKZF2b8ZFtLd8ZL\nIYSW01q6In+lsisiIpIFmVPTeHHOJvafuQLAR82r8XINraUr8ncquyIiIlmM1Wqlz1/W0u0VXJYR\njSsZnErEOansioiIZDEfrN3HvJ3HAWgWFMA3bbWWrsjdqOyKiIhkITO3x/LRuvS1dKsXy094F62l\nK/JP9O6QihEAAAAgAElEQVQQERHJItYePk3fJdsAKJkvFyt7NdJauiL3oLIrIiKSBew5dYn2czeR\n9v9r6a56JVRr6YrcB5VdERERJ3fyciItZvzEjeRUPNzdWNrjaa2lK3KfVHZFRESc2JX/X0v39LUk\nAP7zUgiNyhYxOJVI1qGyKyIi4qTMqWm8OHsTB86mr6U7+rlqdNZauiJ2UdkVERFxQlarld7fbeOn\n2PS1dHvXKcewUK2lK2IvlV0REREnNGrtXubvSl9Lt3nFYkxqU1tr6Yo8AJVdERERJ/Of7cf4eN1+\nAJ4snp9FXeqTQ2vpijwQvXNEREScyJrDp+i3ZDvwv7V0fb20lq7Ig1LZFRERcRK7Ey7x0tzNpFms\n+Hl78sMroRTNo7V0RR6Gyq6IiIgT+P1yIi3+k76Wrqe7G9/3eJrHtZauyENT2RURETFY+lq6kZz5\ncy3dDk/RsIy/walEXIPKroiIiIHMqWm0m72Rg2evAjDmuep0erKUwalEXIfKroiIiEEsFiu9wrey\nIfYcAH1CyjE09AmDU4m4FpVdERERA1itVgat3MnCX38D0tfS/TpMa+mKZDaVXREREQN8vG4/X285\nDEBwyYJaS1fEQZzqXWU2m2nRogU7duwAYMSIEQQFBVGxYkWCgoJsf7p3725sUBERkYcwacthRq3d\nC0ClIn6s6h2qtXRFHCSH0QH+ZDabGTRoELGxsbZj7777LoMHD7Z9nJCQQNeuXenatasREUVERB7a\n/F3HeXN5+kWdUvl9Wd2nMfl9vAxOJeK6nKLsxsXF8fbbb9923NfXF19fX9vHQ4cOpXnz5oSGhj7K\neCIiIpki4uBJei76BYAiub1Z+2oTAvJq0wgRR3KKaQzR0dGEhIQQHh6O1Wq943O2bt3Krl27GDhw\n4CNOJyIi8vA2xZ2jw9wttt3RVvdpTJmCuY2OJeLynOLKbseOHe/5nOnTp9OmTRv8/bXItoiIZC2/\nJlyk1X82cCs1DW8PdyJ6NaJKQD6jY4lkC05Rdu/l5MmTbNu2jffee++BPj8pKSmTE0lm+HNcND7O\nR2PjvDQ2zu1O43P0/HWa/2cT15NT8HA3sbBjHar5+3Lz5k2jYmZLeu84L6vV6tAl97JE2f3xxx+p\nWLEipUuXfqDPj4+Pz9xAkqk0Ps5LY+O8NDbO7c/xOZeYwivrfuPCzVRMwKg6ARRLvUxMzGVD82Vn\neu84J09PT4edO0uU3S1bttCkSZMH/vzAwEC8vb0zMZFkhqSkJOLj4zU+Tkhj47w0Ns7tr+Nzw+JG\n5xkbOXczFYCJLavTs9aDXbSRh6f3jvM6duyYQ8+fJcru/v376dev3wN/vre3Nz4+utvVWWl8nJfG\nxnlpbJxbiikHL86P4tiFGwCMea46/RtWMjiVgN47zsjRuwY6xWoM/+TUqVMkJiZStmxZo6OIiIjc\n061UCy8t+IVdCZcAePvpxxka+oTBqUSyL6e7svv3dn/x4kVMJhN58uQxKJGIiMj9SU2z8N7PCUSd\nSr+i27N2WT574UmHX7kSkbuz+8ruqlWruHr1qiOyABATE0OtWrVsH1epUoWYmBg8PLSNooiIOC+L\nxcpry3ex+f+LbpsqJZn6YrCKrojB7C67H374IefPn3dEFhERkSzJarXy9sqdfLvndwAalSnM/M71\ncHdz+tmCIi7P7ndhYGAgR48edUQWERGRLGn0+v18teUwAJUKeLOwYwheOdwNTiUi8ABzdoOCghg8\neDAzZswgMDAQLy+vDI9/8sknmRZORETE2X0TdZj31+wFoGLhPHxevwi+Xk53S4xItmX3u/G3336j\nRo0aAJrOICIi2dqCXccZsGwHAIH5c7GiWz2unIo3NpSIZGB32Z03b54jcoiIiGQpqw4l0GPRLwD4\n587J2lebUNQnB1dOGRxMRDJ4oN+zJCYmsnLlSo4ePUqOHDkoV64czz33HL6+vpmdT0RExOlsjjvH\nS3M2k2ax4uftyZo+TShbMA83b940OpqI/I3dZff06dO8/PLLXLx4kVKlSmGxWPjuu++YOnUqCxcu\npEiRIo7IKSIi4hR2J1yi1cwN3EpNw9vDnZW9GlElIJ/RsUTkLuxejeHTTz+lSJEiREZGsnz5clau\nXElkZCQBAQGMGzfOERlFREScwtHz12g+fT3XbqXg4e7Gku4NqVuqsNGxROQf2F12f/nlF4YPH07B\nggVtxwoWLMjQoUOJiorK1HAiIiLO4uTlRJr9ez3nbyRjMsGcjnV5NqiY0bFE5B7sLrvu7u54e3vf\ndtzLywuz2ZwpoURERJzJhRu3eHbaen6/nAjApDbBvFQ90NhQInJf7C67Tz75JJMnTyYlJcV2LCUl\nhalTp/Lkk09majgRERGjXbtl5vkZP3H4j2sAjH6uGn2fKm9wKhG5X3bfoDZ48GA6dOhA06ZNqVSp\nEgD79+8nMTGR+fPnZ3pAERERo9xKSSNs5kZ2nrwIwKCGjzMstJLBqUTEHnZf2S1TpgwrVqzg+eef\nx2w2k5ycTIsWLVixYgVBQUGOyCgiIvLIpaZZ6DBvMxvjzgHQo3YZxrZ4EpPJZHAyEbGH3Vd2+/fv\nz8CBAxkyZIgj8oiIiBjOYrHS+7utRBxMACCsckmmtqujoiuSBdl9ZXfbtm14eXk5IouIiIjhLBYr\nry3dztydxwFoXK4IC16uRw53u/+XKSJOwO53blhYGOPHj+fYsWNafUFERFyK1WplwLJopm87BkBw\nyYIs7f40XjncDU4mIg/K7mkMmzZt4vfff2ft2rV3fDwmJuahQ4mIiDxqVquVQSt2MuWXowDUKlGA\n1X0akzunh8HJRORh2F12+/Xr54gcIiIihrFarQyJ2MVXWw4D8GTx/Kzu05i83p4GJxORh2V32T14\n8CBdu3alZMmSjsgjIiLySFmtVkb8sJsvNqX/ZrJaQD7WvtqEfD66P0XEFdg9Z3fZsmW4uWmSvoiI\nZH1Wq5WRq/cwbsNBACoX9WPtq03Ir6Ir4jLsbq0NGzZk/vz53LhxwxF5REREHpkPf9zHJ5EHAHii\nSF7W9W1KQd+cBqcSkcxk9zSG8+fP89///pc5c+ZQoECB25Yhi4yMzLRwIiIijjJ63T4+/HEfAEGF\n87Cub1MKqeiKuBy7y25wcDDBwcGOyCIiIvJIfBZ5gH+t2QtA+UJ5WN+vKf65vQ1OJSKO8EA7qImI\niGRVEzYc5J3/7gagTIHcrO/XlKJ5fAxOJSKO8kB3mh0+fJgRI0bQoUMHzp07x4IFC4iOjs7sbCIi\nIpnqy80xDF31KwCl8vsS2a8pxfKq6Iq4MrvL7oEDB3jxxRdJSEjgwIEDmM1mjhw5Qs+ePdm0aZMj\nMoqIiDy0b6IOM2jFTgAey5eLyH5NKZEvl8GpRMTR7C6748ePp2fPnsybNw8Pj/RdZT788EO6dOnC\n119/nekBRUREHtaUX44wYNkOAEr4+RDZrymP5fc1OJWIPAoPdGW3devWtx3v2LEjcXFxmRJKREQk\ns0zfdoz+S9On2hXL60Nkv2coVSC3walE5FGxu+x6eHjccY3d06dP4+2tO1lFRMR5zIqOpe/ibQAU\nzeNNZL+mlCmooiuSndhddps0acLEiRO5du2a7VhcXBxjxozh6aefzsxsIiIiD2zezuP0/m4rAP65\nc7K+b1PKFcpjcCoRedTsLrvDhg0jMTGROnXqkJSURJs2bXjhhRdwd3dn6NChjsgoIiJil4W//kbP\nRb9gtUIhXy/W921KkH9eo2OJiAHsXmfX19eXRYsWsXXrVg4dOoTFYqF8+fLUr18fN7cHWslMREQk\n04Tvjqfbwp+xWK0UzJVedB8v4md0LBExiN1l908hISGEhIRkZhYREZGHsnTfCbosjMJitZLfx5Mf\n+zahUtF8RscSEQPpUqyIiLiE5ft/p9O8LaRZrPh5e/Ljq02pGpDf6FgiYjCVXRERyfJWHUqgw7wt\npFqs5M3pwdpXm1C9uIquiDhZ2TWbzbRo0YIdO3bYjp09e5Y+ffpQrVo1nnnmGf773/8amFBERJzN\n6phTvDh7EylpFnJ7ebC6T2NqlihgdCwRcRJOU3bNZjODBg0iNjbWdsxisdCnTx88PT1Zvnw5vXr1\nYujQoRmeIyIi2dePR07TdvZGzGkWfL1y8N/eoQQ/VsjoWCLiRO7rBrURI0bc9wk/+eQTu0PExcXx\n9ttv33Z806ZNnD17lkWLFuHj40NgYCBbtmxh9+7dlC1b1u7XERER1xF59AxhMzeSnGohl2cOfnil\nMU+VKmx0LBFxMvdVds+ePWv7e1paGtHR0RQuXJgnnngCDw8PDh06xJkzZ2jatOkDhYiOjiYkJIS3\n3nqLqlWrZjhep04dfHx8bMcmTZr0QK8hIiKuY2PsWVrN3MCt1DS8PdyJeCWUeqVVdEXkdvdVdmfN\nmmX7+2effYa/vz9jxozBw8MDSJ9uMGrUKFJTUx8oRMeOHe94/OTJkxQrVowJEyawYsUK8ufPT//+\n/WnSpMkDvY6IiGR9Px45TZtZG0lKSSNnDndW9mpEwzL+RscSESdl95zdxYsX07dvX1vRBXBzc6N7\n9+6sXr06U8MlJiby/fffc+3aNf7973/TqlUr3nzzTQ4ePJipryMiIlnDygMnafWfDbaiu7zn04SW\nK2p0LBFxYnZvKuHp6cmpU6coU6ZMhuNxcXHkypUr04IBuLu7ky9fPj744AMAKlasyM6dOwkPD+fD\nDz+87/MkJSVlai7JHH+Oi8bH+WhsnFd2Hpul+0/yypIdpFqs5PJ0Z/HLdalbwo+bN28aHc0mO4+P\ns9PYOC+r1YrJZHLY+e0uuy+88ALvvvsuAwcOpFKlSlgsFn799Ve+/vprOnXqlKnhChUqdNsWxKVK\nleLo0aN2nSc+Pj4TU0lm0/g4L42N88puY7Pq+BU+3n4aixV8Pdz48ukSFLx1gZiYC0ZHu6PsNj5Z\nicbGOXl6ejrs3HaX3cGDB3Pr1i3ef/99UlNTsVqteHl58fLLL9O/f/9MDVetWjWmTp2aofHHxcVR\nrFgxu84TGBiIt7d3pmaTh5eUlER8fLzGxwlpbJxXdhybGdFxfLjtNAD5fTxZ0a0e1QKccwvg7Dg+\nWYXGxnkdO3bMoed/oGkMH374IcOGDeO3337DZDJRqlSpDCsmZJbnn3+eyZMnM2rUKHr16sWWLVvY\nsmULS5Ysses83t7eDsknmUPj47w0Ns4ru4zN5xsPMSRiDwBFcnvzY98mPFHEz+BU95Zdxicr0tg4\nH0dOYYAH3FTi1q1brFu3jrVr11KsWDEOHDjA5cuXMyXQX79gX19fZs6cyfHjx2nRogXz589n4sSJ\nBAUFZcpriYiIc7JarXy8bh9DInYBUMLPh42vP5Mliq6IOBe7r+xeuHCBl156iYsXL2I2m2nfvj0z\nZ87kwIEDzJkz57Yb1+wVExOT4eMyZcowb968hzqniIhkHVarlXd+2M3YDekr75Qu4Mv6vk15LL+v\nwclEJCuy+8rup59+Srly5di6dSteXl4AjBs3jgoVKjBu3LhMDygiItmHxWLlreU7bEU3qHAeNr7e\nTEVXRB6Y3WV327ZtDBgwIMPk7ty5czNkyBB+/fXXTA0nIiLZR5rFwquLtzEp6ggAVQPyseG1ZyiW\nV/MrReTB2T2NITEx8a4Tux90BzUREcneUtIs9Pj2Z77dHQ9ArRIF+G+fxuT38TI2mIhkeXZf2a1V\nqxbffvtthmMpKSlMnjyZJ598MtOCiYhI9pCcmkaHeZttRbd+6cL82LeJiq6IZAq7r+wOGzaMzp07\nEx0dTUpKCqNGjeL48eNcv36d+fPnOyKjiIi4qKSUVNrO3sTaw+nr6DYpX5Tvuzckl5fHPT5TROT+\n2F12y5Qpw8qVK/n2228pXLgwFouF5s2b06lTJ4oXL+6IjCIi4oJuJKfQeuYGNsSeA+CFx4sT3rUB\nOT3cDU4mIq7E7rLbv39/Bg4cyJtvvumIPCIikg1cSTLzwvSf2HriPADtqj7G/M718HB/oOXfRUTu\nyu6yu23bNtuSYyIiIva6cOMWzadH8mvCJQC61CzNjPYh5FDRFREHsPsnS1hYGOPHj+fYsWOYzWZH\nZBIRERd19loSoVN+tBXdV0PKM/Olp1R0RcRh7L6yu2nTJn7//XfWrl17x8f/vgOaiIgIwMnLiTSd\nuo5jF64DMLBhRca1qJFhm3gRkcxmd9nt16+fI3KIiIgLO37xOk2mrOPE5UQA3mtamVHNqqroiojD\n2V12w8LCHJFDRERc1OFzV2k6dR2nryUBMOa56gxrXMngVCKSXdhddgEiIyM5evQoaWlptmNms5n9\n+/cza9asTAsnIiJZ277Tl3nm3+s4fyMZgImta/JG/YoGpxKR7MTusjt+/HhmzJhBwYIFuXjxIv7+\n/ly4cAE3Nzdat27tiIwiIpIF7Tx5kWf/vZ7LSWZMJpjarg6v1ClndCwRyWbsvv01IiKCd955h6io\nKAoXLszChQuJioqicuXK1KlTxxEZRUQki4k8eobGU37kcpIZdzcTczrWVdEVEUPYXXYvXrxIaGgo\nABUqVGDfvn34+fkxcOBAZsyYkekBRUQka/luTzzPz/iJG8mpeLi7sahLAzrXKG10LBHJpuyexpAn\nTx5u3rwJQMmSJYmNjQUgICDA9ncREcmeJm05zFsrdmC1gq9XDr7v/jSNyxc1OpaIZGN2X9kNDg5m\n/PjxnDt3jqpVq7JmzRouXbrE2rVrKViwoCMyioiIk7NarYxcvZs3l6cX3cK+Odnw2jMquiJiOLvL\n7tChQ/njjz9YvXo1zZo1w9PTk7p16zJ27Fi6devmiIwiIuLEUtMs9PluG2PWHwCgdAFfot54lieL\nFzA4mYjIA0xjKFq0KMuXLyc5ORlPT08WLFjAli1bKFKkCFWqVHFERhERcVI3zal0mr+FiIMJAFQv\nlp8feofin9vb4GQiIukeaJ1dAC8vLwC8vb155plnMi2QiIhkDZdvJtPqPxv4Of48AKFli7C0R0Py\n5PQ0OJmIyP/YXXaDgoL+cXvHmJiYhwokIiLOL+FKIs9Nj+Tg2asAvFj1MeZ0qotXDneDk4mIZGR3\n2R0zZkyGspuamkp8fDzLly9n6NChmRpOREScT8y5qzSftp6TV9JX5nm9bgUmtq6Fm9vdL4SIiBjF\n7rLbpk2bOx6vVKkSixcvplWrVg8dSkREnNO2E+dpMeMnLt00A/BR82qMaFzpH3/jJyJiJLtXY7ib\nKlWqsGvXrsw6nYiIOJn/xpyiyZR1XLppxs1k4t8v1uGdJpVVdEXEqT3wDWp/lZiYyPz587XOroiI\ni5qzI47e320lzWIlZw53vu1Sn5aVShgdS0TknjLtBjWTycQHH3yQKaFERMQ5WK1Wxm84xPAffgXA\nz9uTFT0bUa90YYOTiYjcn4e+QQ3Aw8ODqlWrUqKE/pUvIuIqLBYrQyJ2MXFz+io7xfL68N/eoVQq\nms/gZCIi9y/TblATERHXYU5No+eiX/h2dzwAFQrlYc2rTSiZL5exwURE7GR32Z00adJ9P7d///72\nnl5ERAx2IzmFdrM3se7oGQCCSxZkZa9GFPTNaXAyERH72V12t2/fzr59+7BYLAQGBuLh4UF8fDxJ\nSUkULVrU9jyTyaSyKyKSxZy/cYsWM35ix8mLADSvWIzwLvXJ5eVhcDIRkQdjd9mtV68eaWlpfPHF\nF/j7+wNw/fp1hg4dSrly5Rg0aFCmhxQREcf77eJ1mk+L5NiF6wB0qVma6e1D8HDPtFUqRUQeObt/\ngs2dO5eRI0faii5A7ty5GThwIOHh4ZkaTkREHo29py9R7+u1tqI7pNETzOrwlIquiGR5dl/ZNZvN\nJCUl3Xb8woULmRJIREQerY2xZwmbtZFrt1IAmNCyBm81fNzgVCIimcPuf7I3adKEkSNHsn37dhIT\nE7lx4wabNm3iX//6Fy1btnRERhERcZA5O+J4dlok126lkMPNxNxOdVV0RcSl2H1l99133+WNN96g\nW7dutvV2rVYrzz33HEOGDMn0gCIikvksFivvr93DmPUHAMjt5UF41wY0CwowOJmISOayu+z6+voy\na9Ys4uLiOHbsGACPP/44JUuWfOgwZrOZtm3b8q9//YtatWoB8PHHHzN//nxMJhNWqxWTycR7771H\n586dH/r1RESyo1spafRY9DPf7TkBQAk/HyJeCaWyNosQERdkd9kFuHXrFiVKlKBMmTLExcWxbt06\nqlatSs2aNR84iNlsZtCgQcTGxmY4fvz4cQYPHkxYWJjtmK+v7wO/johIdnb+xi3CZm5k64nzANQs\nUYDlPZ+maB4fY4OJiDiI3XN2d+zYQYMGDdi1axd//PEH7du3Z8qUKXTt2pUffvjhgULExcXRvn17\nEhIS7vjY448/ToECBWx/vLy8Huh1RESys5hzVwn5crWt6LauXIINrz2joisiLs3usvv555/TuHFj\nKleuzKpVq8iVKxc///wz7733HtOmTXugENHR0YSEhBAeHo7VarUdv3HjBufOnSMwMPCBzisiIul+\nOnaGul+t5rdLNwB4++nHWdy1IT6eD/QLPhGRLMPun3KHDh1i7Nix+Pr6EhUVxdNPP42XlxcNGzbk\n008/faAQHTt2vOPx48ePYzKZmDJlCps3b8bPz48ePXrQunXrB3odEZHsaOb2WPot2UaqxYq7m4lJ\nbWrTJ6S80bFERB4Ju8uut7c3ZrOZ5ORkdu3axYsvvgikr7ObJ0+eTA13/Phx3N3dKVu2LF26dCE6\nOpqRI0fi6+tLkyZN7vs8d1oXWIz357hofJyPxsZ52TM2FouVD9Yf5PMtRwDI45WDuR3q0LisPzdv\n3nRozuxK7x3npbFxXn8uQOAodpfd4OBgxo0bR968eXFzc6N+/frExMTw8ccfU7du3UwN17p1a5o0\naWK7Ia18+fLEx8fz7bff2lV24+PjMzWXZC6Nj/PS2Dive43NrVQLH2w7TeTv1wAomsuDzxuWICDl\nEjExlx5BwuxN7x3npbFxTp6eng47t91l9/333+f999/nyJEjjBs3Dl9fX1auXEnevHkZPnx4pgf8\n+8oLpUuXZvv27XadIzAwEG9v78yMJZkgKSmJ+Ph4jY8T0tg4r/sZmz9u3OKlBVvZmZBedGsWz0d4\n56co7JvzUUbNlvTecV4aG+f151K2jmJ32c2fPz9ff/11hmNDhgzBzS3z90//8ssv2b17N7Nnz7Yd\ni4mJoVSpUnadx9vbGx8f3W3srDQ+zktj47zuNjYHz16hxYyNnLicCEDbKiWZ3bGubkR7xPTecV4a\nG+fjyCkM8ACrMdzxJA4ougChoaHs3LmTmTNncvLkSRYuXMjKlSt55ZVXHPJ6IiJZ2bojp6n39Rpb\n0R0W+gSLujRQ0RWRbM3pfgL+td1XrlyZr776iokTJ/Lll19SokQJJkyYQJUqVQxMKCLifKZvO8br\nS7eTZrGSw83E5HbB9AouZ3QsERHDOV3ZjYmJyfBxaGgooaGhBqUREXFuFouVET/8yviNhwDIm9OD\nxd0a0rh8UYOTiYg4B6cruyIicn9umlPpsjCK5ftPAlAqvy8Rr4RS0T+vwclERJzHA5fdCxcukJKS\nkmHHM4CAgICHDiUiIv/s3PVbdPx2IztOXgQg5LFCfN+jIYVz6y5zEZG/srvs/vrrr4wYMYLff/89\nw/E/FwT++zQEERHJXLFXbjHshw2cvJq+MUT7ao8xs8NTeHvol3UiIn9n90/Gjz/+mEKFCjF06FBy\n587tiEwiInIXEYdO8cqP8dxMtQDwTpNKfNCsGm5ujl26R0Qkq7K77B47dozly5dTpkwZR+QREZE7\nsFisfPjjPj5atw8AD3cTU9uF0L22fhaLiPwTu8tu0aJFSUxMdEQWERG5g6tJZrou/JlVhxIAyJ/T\nnUUv16NxxZIGJxMRcX527wbRr18/xowZw5EjR0hJSXFEJhER+X+Hz10l5MvVtqJbs3g+5j5bmpDH\nChqcTEQka7D7yu6UKVM4ffo0rVu3vuPjukFNRCRzrDxwkq4Lf+Z6cvqFhR61yzD22cr8FnvU4GQi\nIlmH3WW3X79+jsghIiL/z2Kx8vG6fXzwY/r83BxuJr5oVYt+dcuTlJRkcDoRkazF7rIbFhbmiBwi\nIgJcu2Wm28KfWXkwfdpCYd+chHdtQIMy/gYnExHJmh5oUcbIyEiOHj1KWlqa7ZjZbGb//v3MmjUr\n08KJiGQnR89fI2zmBg7/cQ2AmiUKsKRbQ0rky2VwMhGRrMvusjt+/HhmzJhBwYIFuXjxIv7+/ly4\ncAE3N7e7zuMVEZF/9sOhBF5eEMW1W+nzc7vWLM2UdnXI6eFucDIRkazN7tUYIiIieOedd4iKiqJw\n4cIsXLiQqKgoKleuTJ06dRyRUUTEZVksVkav20ermRu4disFdzcTX7auxcwOT6noiohkArvL7sWL\nFwkNDQWgQoUK7Nu3Dz8/PwYOHMiMGTMyPaCIiKu6fiuF9nM38681e7FaoWAuL358tQn96wdhMmlH\nNBGRzGD3NIY8efJw82b6fuwlS5YkNjYWgICAANvfRUTkn8VeuEbYzI0cOncVgCeL52dp96cpqfm5\nIiKZyu4ru8HBwYwfP55z585RtWpV1qxZw6VLl1i7di0FC2qRcxGRe1lz+BTBE1fbim7nGqXY3L+Z\niq6IiAPYXXaHDh3KH3/8werVq2nWrBmenp7UrVuXsWPH0q1bN0dkFBFxCVarlc8iD/DCjJ+4kmTG\n3c3E561qMqdjXbw9HmhxHBERuQe7f7oWLVqU5cuXk5ycjKenJwsWLCAqKgp/f3+qVKniiIwiIlne\njeQUeoVvZcneEwAU8PFiUdf6hJYranCy/2vvzuOqrPP+j78PO7gjIOIGrqggW2qkubc4o2lNapaZ\nY41pabfp/KasxrrbXG5zupum1cZKrdQp0ywzzcqsFDcEFxRQxF0QXJDlAOf6/eHtKcJS9BzOxeH1\nfM6faXsAACAASURBVDx8POL7vc51PpcfxXcX1/l+AcC9XfWthJSUFGVmZmrQoEGKiIhQeHi4A8sC\nAPexL+eshr37nXYePy1Jig1rpI//3EfhgXVdXBkAuL8qh92CggLdf//92rFjhywWi3r06KE5c+bo\n0KFD+ve//60mTdjlBwAuWrrjoP6y+CedK7mwfu5dceF6e3iiAnx4bAEAqkOVn9mdO3euLBaL1qxZ\nIz8/P0nStGnT5Ovrq9mzZzu8QACoiaxl5Xr008266/31OldSKi8Pi+bclqCF9/Qk6AJANaryd9xv\nvvlGL730klq0aGEfa9WqlZ5++mlNmDDBocUBQE2UnX9eIxes18aDuZKkZg0C9NG9N+qGiBAXVwYA\ntU+Vw25eXp6Cg4MrjdetW9e+/i4A1FZfph3R6EU/6FRhiSRpQPumWnhPTwXX9XNxZQBQO1X5MYbo\n6GitWrWq0viiRYvUqVMnhxQFADVNuc2mp79M1qB563SqsEQWi/T0zV30xV/6EXQBwIWqfGd3ypQp\nGjt2rFJSUlRWVqbXX39dmZmZ2rVrl9555x1n1AgApnbyXJFGLdqgr9OPS7qw7e+Ce3rq5g5hLq4M\nAFDlO7vx8fH66KOPFBAQoFatWik5OVmhoaFatGiRunfv7owaAcC0Nuw/qYS5n9uDbmKrYG2d8keC\nLgCYxFV9JDgyMpKVFwDUaoZh6B/f7dHjn29Tuc2QJE3u1VEzB8XL27PK9xEAAE5S5bBrtVq1dOlS\n7du3T1artdL8jBkzHFIYAJjV6SKrxn70o5bvPCRJqu/nrXkjEvWnLq1cXBkA4NeqHHYfe+wxrV27\nVh07dpSvr68zagIA09p+OE/D3/9O+08VSJK6NG2kJff1Urvg+i6uDABwKVUOu+vXr9fcuXN10003\nOaMeADAlwzA0b1OG/mtZkkrKbJKksd3a6pU7usrfm00iAMCsqvwdun79+oqIiHBGLQBgSudLSvXw\nJ0lasGW/JMnf21Ov3tFdY7q1cXFlAIDLqfKnKMaPH68ZM2bo0KFDzqgHAExl78kzSnxllT3otguq\np5/+ayBBFwBqiCrf2W3fvr3mzp2rm2+++ZLze/bsueaiAMDVDMPQ+1v265FlSSooKZMk3RnTSm8P\nv171/XxcXB0A4EpVOew++eSTCg8P12233aaAgABn1AQALnWmyKqHPt6kj7ZnSZK8PT30P4PjNbFn\npCwWi2uLAwBUSZXD7uHDh7VixQqFh4c7oRwAcK2NB3N0z8LvlZV3XpLUpnE9LRrVU11bBrm4MgDA\n1ajyM7vR0dE6ePCgM2qR1WrV4MGDtXnz5kpzBQUF6tWrlz799FOnvDeA2q3cZtOLa1PV69XV9qB7\n73WttXXKHwm6AFCDVfnO7pAhQzRt2jTdeeedatGihby9vSvMDx069KoKsVqtmjJlijIyMi45P3v2\nbOXk5FzVuQHg9xw+fV6jP/hB32WekCTV8/XWa3d2193xrDwDADVdlcPu9OnTJUlvvfVWpTmLxXJV\nYTczM1NTp079zfktW7Zo06ZNCgri7goAx1qWmq2/LP5J+UUXdoTs3jJIC0f1VOvG9VxcGQDAEaoc\ndtPS0hxeRFJSkhITEzV58mTFxMRUmLNarZo+fbqefvppPfXUUw5/bwC1U6G1TFNXbNFbP6VLkiwW\naVr/KE2/OUbenlV+wgsAYFKm2PZn5MiRvzn3xhtvqHPnzrrhhhuqsSIA7izlaL7uWfi9dp84I0lq\n1iBA79/dQ33ahrq4MgCAo5ki7P6WjIwMLVmyRCtWrLim8xQVFTmoIjjSxb7QH/Nx194YhqE3N2Xq\nqdWp9i1/B3UM06tD49U4wFeFhYUurvDy3LU37oL+mBe9MS/DMJy6rKOpw+7f//53PfLIIwoMDLym\n82RlZTmmIDgF/TEvd+pNfnGZnt14VD8cLZAk+XpaNDm+ie5o20AnD+7XSRfXV1Xu1Bt3RH/Mi96Y\nk4+P8zbrsRiGYTjt7FchMjJSCxYsULNmzdSvXz8FBAToYonFxcXy8fFR9+7dL/kBuV9LTU2V1WpV\neHi4/P39nV06qqioqEhZWVn0x4TcrTfrMk5o3MdbdKKgWJLUuUl9zR/WXR2b1HdxZVXnbr1xN/TH\nvOiNeaWnp8vDw0PR0dFOOb9p7+yGhoZqzZo1FcZGjRql++67T4MGDarSufz9/dntzcToj3nV9N5Y\ny8r191XJmvPtbvvYxJ4dNGtQgvy8PV1Y2bWr6b1xd/THvOiN+Th7Z0rThl0PDw+1aNGiwpinp6cC\nAwMVEhLioqoA1BS7j5/WmA9/0NbDeZKkoDq+eueuGzSoU3MXVwYAqE6mC7u/l+7Zkx7A5ZTbbPrf\n9Wl6atV2+4fQ+rcL1bsjeyisAXdzAKC2MV3Y3bNnz2/Off3119VYCYCaZv+pcxr70Y/6fv+Fj5v5\nenno+YFxmtyrozw8+J9lAKiNTBd2AaCqDMPQWxvT9f9WbNV5a5kkKaF5oN4d2UOdQhu6uDoAgCsR\ndgHUaEfOFOovS37S6rSjkiQvD4ueHBCtaQOi2QkNAEDYBVAzGYahD7dn6ZFPkpRfZJUkdWrSQO+O\n7KGEFo1dXB0AwCwIuwBqnNyCYj308SZ9nJItSbJYpEd7ddJzA2Nr/JJiAADHIuwCqFE+23VIDy7d\nqBPnLmwQERFYV/++6wb1atPExZUBAMyIsAugRjhTZNWjy7fovc2Z9rG/XN9O/zM4QfX8vF1YGQDA\nzAi7AExvXfox3b/4J2Xnn5ckNa3vr7eHJ2pgx2YurgwAYHaEXQCmVWgt07TPt+nVDXvtY3fFheuf\nd3RTYICvCysDANQUhF0ApvRtxnGNW7JRmafOSZIaB/jqX3d217CYVi6uDABQkxB2AZjKmSKrHlu5\nTW9vTLeP/bFTM701LFGh9f1dWBkAoCYi7AIwjRU7D+nhjzfp6NkiSVJggI/mDumqUQkRsljY7hcA\nUHWEXQAud+Jckf5r2WYt3XHQPjYiNlwvD71OIfW4mwsAuHqEXQAuYxiGFmzdr6nLtyiv8MIuaM0a\nBOhff+qmwZ1buLg6AIA7IOwCcImDeQUa/59N+mrvUfvYuMR2mvnHeDXw93FhZQAAd0LYBVCtbDZD\nr/2wV098sV3nrWWSpHZB9fTm8ET1Zhc0AICDEXYBVJs9J85o3JKf9GNWjiTJ08Oiqb07afotXeTv\nzbcjAIDj8a8LAKezlpXrf77ZpefXpMpabpMkxYY10tsjEhXfvLGLqwMAuDPCLgCn2nQwR+OXblLK\nsXxJkq+Xh56+OUZT+nSSt6eHi6sDALg7wi4Ap8gvLNETX2zX2xvTZRgXxm5sHaI3h12vDiENXFsc\nAKDWIOwCcCjDMPT+lv16bOVW5RSUSJLq+3nrxT/G6cHr28vDg80hAADVh7ALwGF2HT+tiR9v0vr9\nJ+1jI+PCNee269jqFwDgEoRdANfsfEmpnluTqn98t1tltgvPLHQIrq9X/9RN/do1dXF1AIDajLAL\n4KoZhqHlOw/p0eVblJ1/XpLk5+WpJ2+K1tQ+neTr5eniCgEAtR1hF8BVycor0CPLkvT57iP2sYEd\nm+mV27uqdeN6LqwMAICfEXYBVIm1rFwvfbtbL6xNVVFpuSSpeYMAvXx7Vw2NaiGLhQ+gAQDMg7AL\n4Ip9k3FcEz/epLSTZyVd2AFtcq+Omn5zF9X19XZxdQAAVEbYBXBZ2fnn9fjKbVqcnGUf6xEerH/d\n2V3RTRu5rjAAAC6DsAvgNxVayzTn+x2a/c0u+yMLjQN8NWtwvO67rg1r5gIATI+wC6ASwzC05uAZ\nvfH5Gh06UyhJ8rBYNP6G9nrmlhg1ruPr4goBALgyhF0AFSQfydMjn2zSD1m59rG+bZvoH0O78sgC\nAKDGIewCkCTlFBRr+pfJmrcxQzbjwsYQrRoGaM6Qrro9mlUWAAA1E2EXqOVKy2167Ye9+u/VO3Sm\nuFSSFODtqfs6BerZIT0U2IA1cwEANRdhF6jFVqcd1ZTlm+1LiUnSPQkRmt6vo84cyZKfNzugAQBq\nNsIuUAvtyzmr/7diq1buPmwfu65FY708tKsSw4NVWFioM0d+5wQAANQQhF2gFjl5rkjPfpWitzam\nq9x24bncJvX89OIf4jX6utYsJQYAcDseri7gl6xWqwYPHqzNmzfbx77//nsNGTJEMTExGjp0qNav\nX+/CCoGaqdBaphfXpqr9jOV6/cd9KrcZ8vH00F/7dFLa40M0phtr5gIA3JNp7uxarVZNmTJFGRkZ\n9rHs7GxNmjRJU6ZMUb9+/bR27Vo9/PDDWr16tcLCwlxYLVAzlNtsem/zfj39ZbKOni2yj4+MC9dz\nA2MV0ZgPnwEA3Jspwm5mZqamTp1aafz48eMaMWKERo8eLUkaM2aMXn/9daWkpBB2gd9hGIa+TDuq\nx1du087jp+3jfdo00azBCbquRWMXVgcAQPUxRdhNSkpSYmKiJk+erJiYGPt4t27d1K1bN0lSWVmZ\nli1bJqvVqi5duriqVMD0th/O02Mrt+rr9OP2sU5NGmjmoHj9oWMz1ssFANQqpgi7I0eO/N357Oxs\nDRw4UDabTVOnTuWuLnAJ2fnn9fdVyVq0bb/+b08INa3vr2duidGYrm3k5WmqR/QBAKgWpgi7lxMY\nGKiPP/5Y27dv14wZM9SqVSvddNNNri4LMIVT50s0e91O/XNDmkrKbJKkOj5e+n99O2tK746q4+vt\n4goBAHCdGhF269atq8jISEVGRiojI0MLFiyoUtgtKiq6/EGodhf7Qn+uztniUv3rx3T988d0nSsp\nkyR5elg0JiFc0/p2UpN6flJ5qQoLS6t8bnpjXvTG3OiPedEb8zIMw6mP2Jk67GZkZOj06dO67rrr\n7GNt2rRRUlJSlc6TlZXl4MrgSPSnaorLbFq6L0/v7zmlMyXl9vE+zetpQkyIIhr4Ku/wAeU54L3o\njXnRG3OjP+ZFb8zJx8fHaec2ddhdt26dli1bplWrVtnHdu7cqTZt2lTpPOHh4fL393d0ebhGRUVF\nysrKoj9XyFpm03tbD2j2d2k6fq7YPt6/bYj+3r+zEpoHOuy96I150Rtzoz/mRW/MKz093annN3XY\nHTJkiN5++2299NJLuvPOO7VhwwatXLlSS5YsqdJ5/P39FRAQ4KQqca3oz+8rt9m0cOsBPfvVDmXl\nnbeP9wgP1nN/iFPvNk2c9t70xrzojbnRH/OiN+bj7FWCTBd2f3nBTZo00TvvvKMXXnhBCxcuVLNm\nzfTKK68oMjLShRUC1cNmM/Rxarae+TJZaSfP2sfjmgXquYGxujUyjGXEAAC4DNOF3T179lT4ukuX\nLlq8eLGLqgGqn2EY+mzXYT37VYq2H/n5ydvIkPr671tjdUd0S7b2BQDgCpku7AK1lc1maPmuQ3r+\nqxQlH823j4cH1tH0m2N0T3wEa+UCAFBFhF3AxWw2Q5+kZuuFNalKOfZzyG3WIEDT+kfp/u5t5ePl\n6cIKAQCouQi7gIuU22z6z45svbA2RbuOn7GPt2gYoMf6R2lst7byJeQCAHBNCLtANSu32bQ4+aBe\nXJuqPSd+DrmtGtXR4/2jNKZrG+7kAgDgIIRdoJqUltv00fYsvbg2Vftyfl5dISKwrqYNiNLo69rI\nm2dyAQBwKMIu4GSF1jLNT8rQS9/u1sH8n9fJbRtUT9P6R+uehAhCLgAATkLYBZwkv7BEr/2wV698\nn6bc8yX28fbB9fXEgGiNjAtndQUAAJyMsAs42NEzhXp5/R69+dM+FZSU2ccTmgfqsf5RGhrVQp4e\nhFwAAKoDYRdwkH05ZzXnm11asGW/rOU2+3j/dqH6W78o9W8Xyo5nAABUM8IucI02HczRS9/u1iep\n2TKMC2MWi3R7dEv9rW9ndW0Z5NoCAQCoxQi7wFUot9n06c5Devm7PfoxK8c+7u3poXsTWuuvfTup\nQ0gDF1YIAAAkwi5QJeeKSzU/KUOvfJ+mA3kF9vF6vt564Pq2erR3JzVrEODCCgEAwC8RdoErkJ1/\nXq9uSNPbG9N1trjUPt6qUR09cmOkxnZvq/p+Pi6sEAAAXAphF/gdSdm5+t/1e7R0x0GV2wz7eLeW\njfVo7066I7oly4cBAGBihF3gV0rKyrU4OUuvbdirzYdO2cctFmloVEtN6d1RieHBrKwAAEANQNgF\n/s/h0+f1xo/7NG9TunIKft4Eoo6Pl8Z2b6tJPSPVJqieCysEAABVRdhFrWYYhtbvP6lXN6Rp+c5D\nFR5VaBdUTw/37KDR17VRA3+exwUAoCYi7KJWOlts1aJtB/Tmj/uUeuy0fdxikf7QsZke7hGpm9o3\nlYcHjyoAAFCTEXZRq2w9dEpvbdynD7dl6bz15618G/r7aGy3thp/Q3seVQAAwI0QduH2CkpK9dH2\nLL310z5tPZxXYS4mrJEm9Oigu+PCVcfX20UVAgAAZyHswm3tOJqnt35K16KtB3Su5Oe1cf29PTUi\nNlzjEtupW8sgVlUAAMCNEXbhVvILS/ThtizN35yhbb+6i9s5tIHGXd9eo65rrYZ84AwAgFqBsIsa\nz2YztC7juOYnZWhZarZKymz2OV8vDw2LuXAX9wbWxgUAoNYh7KLGysor0HubM/Xe5kwdzD9fYS6u\nWaD+3K2NRsZHKDDA10UVAgAAVyPsokY5U2TV0h0HtWjrfq3ff7LCXCN/H92TEKExXdsqrnmgiyoE\nAABmQtiF6ZWW2/TV3qNasGW/Ptt1WMVl5fY5i0Ua0K6p/tytrYZEtZCft6cLKwUAAGZD2IUpGYah\nTdm5+mh7lj7afqDC9r2S1LFJA92b0Fp3x0eoRaM6LqoSAACYHWEXpmEYhrYdztOS5Cwt3XGw0nO4\nwXV9dVdchO5NaK345oF82AwAAFwWYRcuZRiGdh4/oxVpe7V0x0Fl5J6rMO/n5alBnZvr3uta65YO\nYfL29HBRpQAAoCYi7MIl0k6c0aLN6fpwa6YOnN1TYc7H00O3RIZpeGy4Bndqrnp+7GwGAACuDmEX\n1SYz95yWJGdpSfJBpRzLrzDn5WFR//ZNNSI2XEOiWrDpAwAAcAjCLpzGMAztOJqvFTsPafnOQ0o+\nWjHgelikhJA6uvf6Droroa0a12E9XAAA4FiEXThUWblN3x84qeU7D2nFzkOVPmRmsUg9I0I0PCZc\nf2gfrFOHDqhjxwgFsPEDAABwAsIurllBSalW7z2qFTsP6/Pdh5VfZK0w7+lhUa/WIbqtcwv9KaaV\nmjUIkCQVFhbqlCsKBgAAtQZhF1flyJlCfbHniFbsPKSv04+ppMxWYb6Oj5duiQzTkKgW+kPHZmzZ\nCwAAXIKwiytSWm7TDwdOanXaUX2ZdrTSB8wkqUk9Pw3u3Fy3dW6h/u2aspsZAABwOVOFXavVqj/9\n6U+aPn26unbtKklKTk7WzJkztXfvXoWGhmrs2LEaNmyYiyutHQ7ln9eqtCP6Mu2o1qUf17mS0krH\nRIbU122dW+i2qBbq3jJIHh5s9AAAAMzDNGHXarVqypQpysjIsI/l5uZq3LhxuvvuuzV79mzt3LlT\n06ZNU0hIiHr37u3Cat1TobVMPxw4qa/2HtOXaUe0+8SZSsf4eXmqT9smGhjZTLdEhqldcH0XVAoA\nAHBlTBF2MzMzNXXq1Erja9euVXBwsCZPnixJatmypTZu3KiVK1cSdh2gtNympOxcfZNxXOvSj+un\nrBxZy22VjmsfXF+3Robp1shm6tUmRP7epvhjAwAAcFmmSC1JSUlKTEzU5MmTFRMTYx/v1auXOnXq\nVOn4c+fOVRrD5dlshlKO5Wtd+nGtyziu7/efUEFJWaXjAnw81bdtqP3ubevG9VxQLQAAwLUzRdgd\nOXLkJcfDwsIUFhZm//rUqVP64osv9Mgjj1RXaTVaSVm5th46pR8O5GjDgZP6Meuk8gqtlY7z8rDo\n+lbB6ts2VP3ahap7qyD5evHhMgAAUPOZIuxeiZKSEk2aNEkhISEaMWJElV5bVFTkpKrM5UxxqTZl\nn9JPB3P1U/YpbT2cp+Kyyo8lWCxSl9CG6t06WL1bh+iGVkGq6/vzH4Vya4kukYkd7mJfakt/ahJ6\nY170xtzoj3nRG/MyDEMWi/M+4F4jwm5hYaEmTJig7Oxsffjhh/L1rdqarVlZWc4pzIUMw9CJwjKl\n5BRqR06hknMKlXG6RMYljvWwSO0b+ikmOECxIQFKaBKghhfDbXm+Du2vvIxYdXLH/rgLemNe9Mbc\n6I950Rtz8vHxcdq5TR92CwoK9MADD+jw4cN677331KJFiyqfIzw8XP7+/k6orvqcKizRtiP52no4\nX1uP5Gnr4XzlnC+55LH+3p7q2jxQia0aK7FVkLq1CFQ9X+9qrvjyioqKlJWV5Rb9cTf0xrzojbnR\nH/OiN+aVnp7u1PObOuwahqGJEyfqyJEjWrhwocLDw6/qPP7+/goICHBscU50rrhUyUfztOXQKW3O\nPqXNh3K1/1TBbx4fVMdXPSJC1DMiRD1bhyiuWaC8PT2qseJrU9P6U5vQG/OiN+ZGf8yL3piPMx9h\nkEwedpcuXaqkpCS9/vrrqlu3rnJzcyVJ3t7eatCggYuru3aGYejQ6ULtOJqnHUfzL/w6kq/MU7+9\n2oSPp4dimzVS1xZB6toySN1aNlb74PpO/4MCAABQE5ku7FosFntw++qrr2QYhsaPH1/hmK5du+r9\n9993RXnX5NjZQq1OO6aUY3naceRCuM0v+u1PglksUseQBuraMkhdWzRW15ZB6tK0oXxYKQEAAOCK\nmC7s7tmzx/7f8+bNc2EljrU5O1c3vbH2klvuShfu2HYObaiYsEaKCWukLmGNFN88UPX9nPfANgAA\ngLszXdh1R/tyzmrQvHX2oBtUx1cxYY0U2yxQXcIaKTaskTqENKhRz9kCAADUBIRdJ8spKNbAt9Yq\n93yJLBbpg1E3alhMK56xBQAAqAaEXScyDEMPfbxJWXnnJUmv3tFdw2PDXVsUAABALcLPzZ1ocXKW\nPknJliQ9mNhe429o7+KKAAAAahfCrpMcP1ukSZ8kSZLCA+to9uB4F1cEAABQ+xB2ncAwDE34z0bl\nFV5YVuydETeorgl3MAMAAHB3hF0nWLj1gFbsOixJmtizg/q0DXVxRQAAALUTYdfBjpwp1ORPN0uS\n2gbV04t/iHNxRQAAALUXYdeBym023btog04XWWWxXHh8oQ6PLwAAALgMYdeBnl+Tqu8yT0iSHusX\npZ6tQ1xcEQAAQO1G2HWQbzOO6/k1qZKkG8KD9d+3xLi4IgAAABB2HSCnoFijFm2QzTDUyN9Hi0bd\nKC+2/gUAAHA5Etk1KrfZNPqDH3TsbJEkad6IRLVsVMfFVQEAAEAi7F6z6V/u0Fd7j0q6sMzY0OiW\nLq4IAAAAFxF2r8F/dhzUzK93SpISWwVr9uAEF1cEAACAXyLsXqWdx/I19qMfJUlN6/tr6Zhe8vXy\ndHFVAAAA+CXC7lU4ea5It8//VuetZfLx9NB/xvRW0/oBri4LAAAAv0LYraJCa5mG/vtb7T9VIEn6\n5x3ddH2rYBdXBQAAgEsh7FZBuc2mUYs2aFN2riTpr3066YHr27m4KgAAAPwWwu4VMgxDjyzbrOU7\nD0mShsW00ow/xru4KgAAAPwewu4VMAxDf12xVW/8uE+S1DMiRO+O7CEPD4uLKwMAAMDvIexehmEY\nempVsl5ev0eSFNcsUJ+O7SM/b1ZeAAAAMDvC7mU8vybVvpZudNOG+nJcfzUK8HVxVQAAALgSXq4u\nwKwMw9ATn2/X7G92SZIiQ+pr9YMDFFTXz8WVAQAA4EoRdi+hrNym8f/ZqPlJmZKkdkH1tGb8TWpS\nz9/FlQEAAKAqCLu/UlBSqnsXbdCKXYclSfHNA/X5A/0UQtAFAACocQi7v7D/1DndMf9bpR47LUnq\n1zZUH/+5t+r7+bi4MgAAAFwNwu7/WbvvmEYuWK+8QqskaWRcuOaNuIFVFwAAAGqwWh92S8ttevar\nHZr59S7ZDEMeFotm/jFOU/p0ksXCOroAAAA1Wa0Ou2knzmj0Bxu09XCeJKmhv48+GHWjbokMc3Fl\nAAAAcIRaGXaLSss0e90uzVq3UyVlNklS7zZN9O7IHmrZqI6LqwMAAICj1Kqwa7MZ+iQ1W4+t3Kqs\nvPOSJG9PDz0/MFaP9u4oTw/22AAAAHAntSLs2myGPk45qOe/SlXKsXz7eL+2ofrf27uqU2hDF1YH\nAAAAZ3H7sHvOWq7Yl1frQP55+1jLRnU0e3CC7uzSkg+hAQAAuDG3D7tnreX2oNuyUR1N6x+lMV3b\nyMeLJcUAAADcnakeUrVarRo8eLA2b95caW7//v2Ki4u7qvPe0CpIC+7pqb2PD9G4xPYEXQAAgFrC\nNHd2rVarpkyZooyMjEpzR44c0YQJE1RaWlrl84YGeGv1A70VEBDgiDIBAABQg5jizm5mZqaGDx+u\nw4cPV5pbvXq17rzzTvn7+1/VuT09eCYXAACgtjJF2E1KSlJiYqIWL14swzAqzH333XeaOnWqHnvs\nMRdVBwAAgJrKFI8xjBw58jfnXnzxRUnSTz/9VF3lAAAAwE2YIuw6y8VnfNPT01lizIQu3sWnP+ZD\nb8yL3pgb/TEvemNepaWlTu2JW4fdi79xHuyMZkoWi0U+Pj6uLgOXQG/Mi96YG/0xL3pjXhaLhbB7\nta52qTIAAAC4B255AgAAwG0RdgEAAOC2TBd2eWgcAAAAjmIxfr2wLQAAAOAmTHdnFwAAAHAUwi4A\nAADcFmEXAAAAbouwCwAAALdF2AUAAIDbqnFh12q16oknnlDXrl114403av78+b957O7duzV8+HDF\nxsZq2LBh2rVrV4X5lStX6qabblJsbKwmTpyo/Px8Z5fv9hzZn7feekv9+/dXQkKC/vznPyszM9PZ\n5bs1R/bmolWrVikyMtJZJdcajuzN6tWrdeuttyouLk7333+/jh496uzy3Z6j+lNeXq5Zs2apnUUo\nMQAACvFJREFUZ8+e6tatmx599FHl5eVVxyW4rar05qItW7ZowIABlcbJBI7lyN5ccx4waphnn33W\nGDJkiLFnzx5jzZo1Rnx8vLF69epKxxUWFho9evQwZs+ebWRmZhrPP/+80aNHD6OoqMgwDMPYsWOH\nERMTYyxfvtzYu3evMWrUKOPBBx+s7stxO47qzwcffGAkJiYa3377rZGVlWU8+eSTRt++fY3i4uLq\nviS34ajeXHT27FmjR48eRmRkZHVdgttyVG+Sk5ONzp07G0uWLDEOHDhgPPjgg8aIESOq+3LcjqP6\n8+abbxp9+vQxNm/ebKSnpxt333238dBDD1X35biVK+3NRWlpaUaPHj2Mfv36VRgnEzieo3rjiDxQ\no8JuYWGh0aVLF2Pz5s32sddee8249957Kx27dOlSY8CAARXGbr75ZmPZsmWGYRjG3/72N+Pxxx+3\nzx07dsyIjIw0Dh8+7KTq3Z8j+zN8+HBj3rx59rnS0lIjNjbW+PHHH51UvXtzZG8ueuqpp4y7776b\nsHuNHNmbSZMmGdOmTbPPHTp0yOjXr5+Rn5/vpOrdnyP7M27cOGPWrFn2uXXr1hlxcXFOqtz9VaU3\nhmEYH374oREXF2cMGTKkUqAiEziWI3vjiDxQox5jSEtLU3l5uWJjY+1jCQkJSklJqXRsSkqKEhIS\nKozFx8dr+/btkqTk5GR17drVPhcaGqqmTZtqx44dTqre/TmyP4899pgGDRpkn7u4s965c+ecUbrb\nc2RvJCkpKUlJSUkaP36884quJRzZm02bNunmm2+2zzVv3lxff/21GjZs6KTq3Z8j+xMdHa3vvvtO\nJ06cUHFxsVauXKnOnTs79wLcWFV6I0kbNmzQ7Nmzdd9991WaIxM4liN744g8UKPCbk5Ojho2bCgv\nLy/7WOPGjVVSUlLp2ZqTJ08qJCSkwljjxo114sQJ+7l+PR8UFKTjx487qXr358j+xMfHq0mTJva5\nJUuWqLy8vNI/JLgyjuyN1WrV9OnT9cwzz8jX19f5xbs5R/WmoKBAZ86cUVlZme6//3717NlTDz30\nkL1vuDqO/LszYcIEhYWFqXfv3kpISNC2bds0Z84c51+Em6pKbyTp1VdfveTzoBfPRSZwHEf2xhF5\noEaF3aKiIvn4+FQYu/i11WqtMF5cXHzJYy8ed7l5VJ0j+/NLO3bs0Oz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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.linear_model import LassoCV \n", + "\n", + "lasso = LassoCV(alphas=alphas)\n", + "lasso.fit(X, y)\n", + "\n", + "# mse_path_ : array, shape (n_l1_ratio, n_alpha, n_folds)\n", + "# Mean square error for the test set on each fold, varying l1_ratio and alpha.\n", + "\n", + "g = plt.plot(lasso.alphas_, lasso.mse_path_.mean(1), label=\"lasso\")\n", + "plt.xlabel(\"alpha\")\n", + "plt.ylabel(\"mean squared error\")\n", + "plt.title(\"LassoCV Alapha Error\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "9.9999999999999998e-13" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lasso.alpha_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lasso LARS CV" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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l4Ddx/Hwob7tVNxcDr3RozAvtGuKuECzXcbgwLCIiIvJPmc1m5mw6yOjlCVzN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\nSkqKOnbsaD/WsGFDRUZGavPmzS6q3jWc2ZOdO3cqKirKpfVeC1XpiSS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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.linear_model import LassoLarsCV \n", + "\n", + "larslasso = LassoLarsCV()\n", + "larslasso.fit(X, y)\n", + "\n", + "# mse_path_ : array, shape (n_l1_ratio, n_alpha, n_folds)\n", + "# Mean square error for the test set on each fold, varying l1_ratio and alpha.\n", + "\n", + "g = plt.plot(larslasso.cv_alphas_, larslasso.cv_mse_path_.mean(1), label=\"lasso lars\")\n", + "plt.xlabel(\"alpha\")\n", + "plt.ylabel(\"mean squared error\")\n", + "plt.title(\"LassoLarsCV Alapha Error\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "4.3567817268382741e-16" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "larslasso.alpha_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ElasticNet Regularization" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+3IhINTHsEpHcUlNT37g16549e+Do6Fhip66+ffsiMTERu3btwtatW2Fra4tPP/0UhoaG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\nHIuyCwAAAMf6/0alLTLgAAH1AAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.linear_model import ElasticNetCV \n", + "\n", + "enet = ElasticNetCV(alphas=alphas)\n", + "enet.fit(X, y)\n", + "\n", + "g = plt.plot(enet.alphas_, enet.mse_path_.mean(1), label=\"elasticnet\")\n", + "plt.xlabel(\"alpha\")\n", + "plt.ylabel(\"mean squared error\")\n", + "plt.title(\"ElasticNetCV Alapha Error\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "9.9999999999999998e-13" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "enet.alpha_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot All " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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27dpMnz6dnJwcatWqxalTp9izZw/Dhw8HSubsLl26lKpVqxISEkJiYiLLly8nNDQUZ2dn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\n53I3MwABw+Ow++mnn2rXrl1avXr1Uce3bNnS7mK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2q7R2j+oaKn90/9DgMCVG9zocbJvD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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot all \n", + "plt.plot(ridge.alphas, ridge.cv_values_.mean(0), label=\"ridge\")\n", + "plt.plot(lasso.alphas_, lasso.mse_path_.mean(1), label=\"lasso\")\n", + "# plt.plot(larslasso.cv_alphas_, larslasso.cv_mse_path_.mean(1), label=\"lasso lars\")\n", + "plt.plot(enet.alphas_, enet.mse_path_.mean(1), label=\"elasticnet\")\n", + "plt.xlabel(\"alpha\")\n", + "plt.ylabel(\"mean squared error\")\n", + "plt.title(\"Regression CV Alapha Error\")\n", + "plt.legend(loc='best')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot Ridge Coefficients against Alpha \n", + "\n", + "This sample code comes from the [Scikit-Learn documentation](http://scikit-learn.org/stable/auto_examples/linear_model/plot_ridge_coeffs.html). Just an interesting different take on this to see if we can make a visualizer from it. " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+nm+++YYmTZoA0KxZMyIjI/H397fZ/3fffcc777yDyWQq4Ltmq3Hjxlgsllzx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c99TYv/uxK3ujV7uiEREREREZGqYvl+RzGmXWNfgurpbzERkd+r8MiY/Px8GjduDMC6devo2bMn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\ne4wdO5bbb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KvNxor9jr+tr4sK0t/JIiJ/wSXP2dO0aVMGDRqEn58f9evX54cffmDBggUq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\npPjobpo+/GbFOOODNv6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.linear_model import Ridge \n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn.datasets import make_regression\n", + "\n", + "clf = Ridge()\n", + "\n", + "# To do this, you need to know the true function defined by w. \n", + "X, y, w = make_regression(n_samples=10, n_features=10, coef=True,\n", + " random_state=1, bias=3.5)\n", + "\n", + "coefs = []\n", + "errors = []\n", + "\n", + "alphas = np.logspace(-6, 6, 200)\n", + "\n", + "# Train the model with different regularisation strengths\n", + "for a in alphas:\n", + " clf.set_params(alpha=a)\n", + " clf.fit(X, y)\n", + " coefs.append(clf.coef_)\n", + " errors.append(mean_squared_error(clf.coef_, w))\n", + "\n", + "# Display results\n", + "plt.figure(figsize=(20, 6))\n", + "\n", + "plt.subplot(121)\n", + "ax = plt.gca()\n", + "ax.plot(alphas, coefs)\n", + "ax.set_xscale('log')\n", + "plt.xlabel('alpha')\n", + "plt.ylabel('weights')\n", + "plt.title('Ridge coefficients as a function of the regularization')\n", + "plt.axis('tight')\n", + "\n", + "# This plot is similar to the plots we did above. \n", + "plt.subplot(122)\n", + "ax = plt.gca()\n", + "ax.plot(alphas, errors)\n", + "ax.set_xscale('log')\n", + "plt.xlabel('alpha')\n", + "plt.ylabel('error')\n", + "plt.title('Coefficient error as a function of the regularization')\n", + "plt.axis('tight')\n", + "\n", + "plt.show()" + ] + } + ], + "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": 0 +} diff --git a/examples/bbengfort/cluster.ipynb b/examples/bbengfort/cluster.ipynb new file mode 100644 index 000000000..6a719c313 --- /dev/null +++ b/examples/bbengfort/cluster.ipynb @@ -0,0 +1,237 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cluster Analysis \n", + "\n", + "This notebook prototypes the cluster analysis visualizers that I'm currently putting together.\n", + "\n", + "**NOTE**: Currently I'm using the sklearn `make_blobs` function to create test datasets with specific numbers of clusters. However, in order to add this to the documentation, we should add a real dataset. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import sys \n", + "sys.path.append(\"../..\")\n", + "\n", + "import yellowbrick as yb \n", + "import matplotlib.pyplot as plt \n", + "\n", + "from functools import partial\n", + "from sklearn.datasets import make_blobs as sk_make_blobs\n", + "from sklearn.datasets import make_circles, make_moons" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "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": [ + "## Elbow Method \n", + "\n", + "This method runs multiple clustering instances and computes the average silhoutte score for each K. Model selection works by selecting the K that is the \"elbow\" of a curve that looks like an arm. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.5/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" + ] + } + ], + "source": [ + "from sklearn.cluster import KMeans\n", + "from yellowbrick.cluster import KElbowVisualizer" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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iIiJWRyFYRERERKyOQrCIiIiIWB2FYBERERGxOgrBIiIiImJ1FIJFRERExOoo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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## Make 8 blobs dataset \n", + "X, y = make_blobs(centers=8)\n", + "visualizer = KElbowVisualizer(KMeans(), k=(4,12))\n", + "visualizer.fit(X)\n", + "visualizer.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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BTByXIiJTVUtqTxp0NBh5GGdM1XOIiIiIpAeF4EzG2sqKnM725HS2T9XjjEYT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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## Make cicles dataset \n", + "X, y = make_circles(n_samples=N_SAMPLES)\n", + "visualizer = KElbowVisualizer(KMeans(), k=(4,12))\n", + "visualizer.fit(X)\n", + "visualizer.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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yLVK/MIjcDRSCRUTkhjKNRtbszer0kLO7gouDPQMaV+P50FpUK+VpwwoLlsFg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\nSXPnztWkSZPUsmVLLVmyRH/605+0Zs2acmUpKCiQJB04cEAWi6XCnwW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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## Make moons dataset \n", + "X, y = make_moons(n_samples=N_SAMPLES)\n", + "visualizer = KElbowVisualizer(KMeans(), k=(4,12))\n", + "visualizer.fit(X)\n", + "visualizer.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Silhouette Score \n", + "\n", + "Visualizer using the silhouette score metric " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from yellowbrick.cluster import SilhouetteVisualizer" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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oKioKjRs3xvTp0/HXX3+hTp06OH78ODIyMtCtWzejnHe1f//++P7777Fhwwa8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\nANC7d2+sXLkSALBjxw5ER0dLGc/gu+++Q15enuHnU6dO4eeff5Yw0eP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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## Make 8 blobs dataset \n", + "X, y = make_blobs(centers=8)\n", + "visualizer = SilhouetteVisualizer(KMeans(6))\n", + "visualizer.fit(X)\n", + "visualizer.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": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/bbengfort/errors.md b/examples/bbengfort/errors.md new file mode 100644 index 000000000..788769cf2 --- /dev/null +++ b/examples/bbengfort/errors.md @@ -0,0 +1,223 @@ +### Error 1 + +Trying to import Yellowbrick: + +``` + +from yellowbrick.text import FreqDistVisualizer +--------------------------------------------------------------------------- +ImportError Traceback (most recent call last) + in () +----> 1 from yellowbrick.text import FreqDistVisualizer + +ImportError: No module named 'yellowbrick' +``` + +Was not in a virtualenv, tried to get a virtualenv set up; still having problems with yellowbrick. +Solved: sys.path.append + +### Error 2 + +On import, deprecation warning: + +``` +/usr/local/lib/python3.5/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. + "This module will be removed in 0.20.", DeprecationWarning) +``` + +In my user testing I'm using Scikit-Learn 0.18 + +### Error 3 + +Running `FreqDistVisualizer` in a pipeline: + +``` +from sklearn.pipeline import Pipeline +from sklearn.feature_extraction.text import CountVectorizer +from yellowbrick.text import FreqDistVisualizer + +visualizer = Pipeline([ + ('norm', TextNormalizer()), + ('count', CountVectorizer(tokenizer=lambda x: x, preprocessor=None, lowercase=False)), + ('viz', FreqDistVisualizer()) +]) + +visualizer.fit_transform(documents(), labels()) +visualizer.named_steps['viz'].poof() +``` + +I get the following error: + +``` +--------------------------------------------------------------------------- +AttributeError Traceback (most recent call last) + in () + 9 ]) + 10 +---> 11 visualizer.fit_transform(documents(), labels()) + 12 visualizer.named_steps['viz'].poof() + +/usr/local/lib/python3.5/site-packages/sklearn/pipeline.py in fit_transform(self, X, y, **fit_params) + 301 Xt, fit_params = self._fit(X, y, **fit_params) + 302 if hasattr(last_step, 'fit_transform'): +--> 303 return last_step.fit_transform(Xt, y, **fit_params) + 304 elif last_step is None: + 305 return Xt + +/usr/local/lib/python3.5/site-packages/sklearn/base.py in fit_transform(self, X, y, **fit_params) + 495 else: + 496 # fit method of arity 2 (supervised transformation) +--> 497 return self.fit(X, y, **fit_params).transform(X) + 498 + 499 + +AttributeError: 'NoneType' object has no attribute 'transform' +``` + +This is because the `fit()` method needs to return self. +Also, the features passed into fit do not match the vector y that I'm passing to it. +Instead, features needs to be added as input to the initializer, or passed to +`inverse_transform` as needed on the vectorizer. + +### Error 4 + +If I don't call poof() then I get a graphic displayed in the notebook that's not finalized. I think this is going to be a weirdness of `%matplotlib inline` + +![No poof called, no finalize](figures/nopoofnofinal.png) + + +### Error 5 + +Ran the TSNE visualizer ... but ended up with an empty figure? + +![No TSNE?](figures/notsne.png) + +### Error 6 + +I created a classifier, and I wanted to get a ClassBalance, ClassificationReport, _and_ ROCAUC for each of them. So I trained the model, then created the `ScoreVisualizer` by passing in the _trained_ model, and called score on it. + +``` +from sklearn.linear_model import LogisticRegression +from yellowbrick.classifier import ClassBalance, ClassificationReport, ROCAUC + +logit = LogisticRegression() +logit.fit(docs_train, labels_train) + +logit_balance = ClassBalance(logit) +logit_balance.score(docs_test, labels_test) +logit_balance.poof() +``` + +Because I didn't pass in a classes argument I got the following: + +``` +--------------------------------------------------------------------------- +TypeError Traceback (most recent call last) + in () + 1 logit_balance = ClassBalance(logit) +----> 2 logit_balance.score(docs_test, labels_test) + 3 logit_balance.poof() + +/Users/benjamin/Repos/tmp/yellowbrick/yellowbrick/classifier.py in score(self, X, y, **kwargs) + 488 y_pred = self.predict(X) + 489 self.scores = precision_recall_fscore_support(y, y_pred) +--> 490 self.support = dict(zip(self.classes_, self.scores[-1])) + 491 return self.draw() + 492 + +TypeError: zip argument #1 must support iteration +``` + +Passing the following fixed the problem: + +``` +logit_balance = ClassBalance(logit, classes=set(labels_test)) +``` + +### Error 7 + +Attempting to get a classification report for a multi-class classifier: + +``` +logit_balance = ClassificationReport(logit, classes=set(labels_test)) +logit_balance.score(docs_test, labels_test) +logit_balance.poof() +``` + +In the same way as before led to the following exception: + +``` +--------------------------------------------------------------------------- +IndexError Traceback (most recent call last) + in () + 1 logit_balance = ClassificationReport(logit, classes=set(labels_test)) +----> 2 logit_balance.score(docs_test, labels_test) + 3 logit_balance.poof() + +/Users/benjamin/Repos/tmp/yellowbrick/yellowbrick/classifier.py in score(self, X, y, **kwargs) + 133 self.scores = map(lambda s: dict(zip(self.classes_, s)), self.scores[0:3]) + 134 self.scores = dict(zip(keys, self.scores)) +--> 135 return self.draw(y, y_pred) + 136 + 137 def draw(self, y, y_pred): + +/Users/benjamin/Repos/tmp/yellowbrick/yellowbrick/classifier.py in draw(self, y, y_pred) + 158 for column in range(len(self.matrix)+1): + 159 for row in range(len(self.classes_)): +--> 160 self.ax.text(column,row,self.matrix[row][column],va='center',ha='center') + 161 + 162 fig = plt.imshow(self.matrix, interpolation='nearest', cmap=self.cmap, vmin=0, vmax=1) + +IndexError: list index out of range +``` + +And it displayed the following: + +![No classification report](figures/nocr.png) + +**NOTE**: Same behavior when I tried to fit and score the classifier myself. + +### Error 8 + +Running a ROCAUC on a multiclass classifier: + +``` +logit_balance = ROCAUC(logit) +logit_balance.score(docs_test, labels_test) +logit_balance.poof() +``` + +Resulted in a warning that we didn't have a binary classifier: + +``` +--------------------------------------------------------------------------- +ValueError Traceback (most recent call last) + in () + 1 logit_balance = ROCAUC(logit) +----> 2 logit_balance.score(docs_test, labels_test) + 3 logit_balance.poof() + +/Users/benjamin/Repos/tmp/yellowbrick/yellowbrick/classifier.py in score(self, X, y, **kwargs) + 311 """ + 312 y_pred = self.predict(X) +--> 313 self.fpr, self.tpr, self.thresholds = roc_curve(y, y_pred) + 314 self.roc_auc = auc(self.fpr, self.tpr) + 315 return self.draw(y, y_pred) + +/usr/local/lib/python3.5/site-packages/sklearn/metrics/ranking.py in roc_curve(y_true, y_score, pos_label, sample_weight, drop_intermediate) + 503 """ + 504 fps, tps, thresholds = _binary_clf_curve( +--> 505 y_true, y_score, pos_label=pos_label, sample_weight=sample_weight) + 506 + 507 # Attempt to drop thresholds corresponding to points in between and + +/usr/local/lib/python3.5/site-packages/sklearn/metrics/ranking.py in _binary_clf_curve(y_true, y_score, pos_label, sample_weight) + 312 array_equal(classes, [-1]) or + 313 array_equal(classes, [1]))): +--> 314 raise ValueError("Data is not binary and pos_label is not specified") + 315 elif pos_label is None: + 316 pos_label = 1. + +ValueError: Data is not binary and pos_label is not specified + +``` \ No newline at end of file diff --git a/examples/bbengfort/figures/nocr.png b/examples/bbengfort/figures/nocr.png new file mode 100644 index 000000000..c6cc3efc4 Binary files /dev/null and b/examples/bbengfort/figures/nocr.png differ diff --git a/examples/bbengfort/figures/nopoofnofinal.png b/examples/bbengfort/figures/nopoofnofinal.png new file mode 100644 index 000000000..eebd6f80e Binary files /dev/null and b/examples/bbengfort/figures/nopoofnofinal.png differ diff --git a/examples/bbengfort/figures/notsne.png b/examples/bbengfort/figures/notsne.png new file mode 100644 index 000000000..5f120cadb Binary files /dev/null and b/examples/bbengfort/figures/notsne.png differ diff --git a/examples/figures/pipeline_prototype.png b/examples/bbengfort/figures/pipeline_prototype.png similarity index 100% rename from examples/figures/pipeline_prototype.png rename to examples/bbengfort/figures/pipeline_prototype.png diff --git a/examples/pipeline.ipynb b/examples/bbengfort/pipeline.ipynb similarity index 100% rename from examples/pipeline.ipynb rename to examples/bbengfort/pipeline.ipynb diff --git a/examples/rank2d.ipynb b/examples/bbengfort/rank2d.ipynb similarity index 99% rename from examples/rank2d.ipynb rename to examples/bbengfort/rank2d.ipynb index 3d12f322c..938b3fc23 100644 --- a/examples/rank2d.ipynb +++ b/examples/bbengfort/rank2d.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 20, "metadata": { "collapsed": true }, @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 21, "metadata": { "collapsed": true }, @@ -251,19 +251,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.11" - }, - "widgets": { - "state": { - "2b2e97ac5fb04360a14b241b102d297c": { - "views": [ - { - "cell_index": 7 - } - ] - } - }, - "version": "1.2.0" + "version": "2.7.12" } }, "nbformat": 4, diff --git a/examples/bbengfort/testing.ipynb b/examples/bbengfort/testing.ipynb new file mode 100644 index 000000000..9dfd2ca34 --- /dev/null +++ b/examples/bbengfort/testing.ipynb @@ -0,0 +1,566 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "# Visual Diagnosis of Text Analysis with Baleen \n", + "\n", + "This notebook has been created as part of the [Yellowbrick user study](http://www.scikit-yb.org/en/latest/evaluation.html). I hope to explore how visual methods might improve the workflow of text classification on a small to medium sized corpus. \n", + "\n", + "## Dataset \n", + "\n", + "The dataset used in this study is a sample of the [Baleen Corpus](http://baleen.districtdatalabs.com/). The Baleen corpus has been ingesting RSS feeds on the hour from a variety of topical feeds since March 2016, including news, hobbies, and political documents and currently has over 1.2M posts from 373 feeds. [Baleen](https://github.com/bbengfort/baleen) (an open source system) has a sister library called [Minke](https://github.com/bbengfort/minke) that provides multiprocessing support for dealing with Gigabytes worth of text. \n", + "\n", + "The dataset I'll use in this study is a sample of the larger data set that contains 68,052 or roughly 6% of the total corpus. For this test, I've chosen to use the preprocessed corpus, which means I won't have to do any tokenization, but can still apply normalization techniques. The corpus is described as follows:\n", + "\n", + "Baleen corpus contains 68,052 files in 12 categories.\n", + "Structured as:\n", + "\n", + "- 1,200,378 paragraphs (17.639 mean paragraphs per file)\n", + "- 2,058,635 sentences (1.715 mean sentences per paragraph).\n", + "\n", + "Word count of 44,821,870 with a vocabulary of 303,034 (147.910 lexical diversity).\n", + "\n", + "Category Counts: \n", + "\n", + "- books: 1,700 docs\n", + "- business: 9,248 docs\n", + "- cinema: 2,072 docs\n", + "- cooking: 733 docs\n", + "- data science: 692 docs\n", + "- design: 1,259 docs\n", + "- do it yourself: 2,620 docs\n", + "- gaming: 2,884 docs\n", + "- news: 33,253 docs\n", + "- politics: 3,793 docs\n", + "- sports: 4,710 docs\n", + "- tech: 5,088 docs\n", + "\n", + "This is quite a lot of data, so for now we'll simply create a classifier for the \"hobbies\" categories: e.g. books, cinema, cooking, diy, gaming, and sports. \n", + "\n", + "Note: this data set is not currently publically available, but I am happy to provide it on request. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "%matplotlib inline " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "import os \n", + "import sys \n", + "import nltk\n", + "import pickle\n", + "\n", + "# To import yellowbrick \n", + "sys.path.append(\"../..\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Loading Data \n", + "\n", + "In order to load data, I'd typically use a `CorpusReader`. However, for the sake of simplicity, I'll load data using some simple Python generator functions. I need to create two primary methods, the first loads the documents using pickle, and the second returns the vector of targets for supervised learning. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "CORPUS_ROOT = os.path.join(os.getcwd(), \"data\") \n", + "CATEGORIES = [\"books\", \"cinema\", \"cooking\", \"diy\", \"gaming\", \"sports\"]\n", + "\n", + "def fileids(root=CORPUS_ROOT, categories=CATEGORIES): \n", + " \"\"\"\n", + " Fetch the paths, filtering on categories (pass None for all). \n", + " \"\"\"\n", + " for name in os.listdir(root):\n", + " dpath = os.path.join(root, name)\n", + " if not os.path.isdir(dpath):\n", + " continue \n", + " \n", + " if categories and name in categories: \n", + " for fname in os.listdir(dpath):\n", + " yield os.path.join(dpath, fname)\n", + "\n", + "\n", + "def documents(root=CORPUS_ROOT, categories=CATEGORIES):\n", + " \"\"\"\n", + " Load the pickled documents and yield one at a time. \n", + " \"\"\"\n", + " for path in fileids(root, categories):\n", + " with open(path, 'rb') as f:\n", + " yield pickle.load(f)\n", + "\n", + "\n", + "def labels(root=CORPUS_ROOT, categories=CATEGORIES):\n", + " \"\"\"\n", + " Return a list of the labels associated with each document. \n", + " \"\"\" \n", + " for path in fileids(root, categories):\n", + " dpath = os.path.dirname(path) \n", + " yield dpath.split(os.path.sep)[-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Feature Extraction and Normalization \n", + "\n", + "In order to conduct analyses with Scikit-Learn, I'll need some helper transformers to modify the loaded data into a form that can be used by the `sklearn.feature_extraction` text transformers. I'll be mostly using the `CountVectorizer` and `TfidfVectorizer`, so these normalizer transformers and identity functions help a lot. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from nltk.corpus import wordnet as wn\n", + "from nltk.stem import WordNetLemmatizer \n", + "from unicodedata import category as ucat\n", + "from nltk.corpus import stopwords as swcorpus\n", + "from sklearn.base import BaseEstimator, TransformerMixin \n", + "\n", + "\n", + "def identity(args):\n", + " \"\"\"\n", + " The identity function is used as the \"tokenizer\" for \n", + " pre-tokenized text. It just passes back it's arguments. \n", + " \"\"\"\n", + " return args \n", + "\n", + "\n", + "def is_punctuation(token):\n", + " \"\"\"\n", + " Returns true if all characters in the token are\n", + " unicode punctuation (works for most punct). \n", + " \"\"\"\n", + " return all(\n", + " ucat(c).startswith('P')\n", + " for c in token \n", + " )\n", + "\n", + "\n", + "def wnpos(tag):\n", + " \"\"\"\n", + " Returns the wn part of speech tag from the penn treebank tag. \n", + " \"\"\"\n", + " return {\n", + " \"N\": wn.NOUN,\n", + " \"V\": wn.VERB,\n", + " \"J\": wn.ADJ, \n", + " \"R\": wn.ADV, \n", + " }.get(tag[0], wn.NOUN)\n", + "\n", + "\n", + "class TextNormalizer(BaseEstimator, TransformerMixin):\n", + " \n", + " def __init__(self, stopwords='english', lowercase=True, lemmatize=True, depunct=True):\n", + " self.stopwords = frozenset(swcorpus.words(stopwords)) if stopwords else frozenset()\n", + " self.lowercase = lowercase \n", + " self.depunct = depunct \n", + " self.lemmatizer = WordNetLemmatizer() if lemmatize else None \n", + " \n", + " def fit(self, docs, labels=None):\n", + " return self\n", + "\n", + " def transform(self, docs): \n", + " for doc in docs: \n", + " yield list(self.normalize(doc)) \n", + " \n", + " def normalize(self, doc):\n", + " for paragraph in doc:\n", + " for sentence in paragraph:\n", + " for token, tag in sentence: \n", + " if token.lower() in self.stopwords:\n", + " continue \n", + " \n", + " if self.depunct and is_punctuation(token):\n", + " continue \n", + " \n", + " if self.lowercase:\n", + " token = token.lower() \n", + " \n", + " if self.lemmatizer:\n", + " token = self.lemmatizer.lemmatize(token, wnpos(tag))\n", + " \n", + " yield token " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Corpus Analysis \n", + "\n", + "At this stage, I'd like to get a feel for what was in my corpus, so that I can start thinking about how to best vectorize the text and do different types of counting. With the Yellowbrick 0.3.3 release, support has been added for two text visualizers, which I think I will test out at scale using this corpus. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.5/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" + ] + }, + { + "ename": "AttributeError", + "evalue": "'NoneType' object has no attribute 'transform'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\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 9\u001b[0m ])\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocuments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\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[1;32m 12\u001b[0m \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnamed_steps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'viz'\u001b[0m\u001b[0;34m]\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/usr/local/lib/python3.5/site-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m 301\u001b[0m \u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 302\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlast_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fit_transform'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 303\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mlast_step\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 304\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mlast_step\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mXt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.5/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m 495\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 496\u001b[0m \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 497\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\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 498\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'transform'" + ] + }, + { + "data": { + "image/png": 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fiBEjkiRvvfXWoNUBAACUqur3watUKknS7cVXTqipqRm0OgAAgFJV/b4Bo0ePTpK0trZ2\n2XfkyJEkSX19/aDV9cXRo0fz1FNP9amXMryyd2/7qZLJO1eSTZJXX321Q92bTXvb18qp9ry7r3PP\nqY7VWVtbW5Kc9rrtS99QH2uoz6/UsYb6/Iw1OD3GGpweYw1Oj7EGp6f0serq6k5aV/UjeJMmTUqS\n7N+/v8u+pqamjB07NiNHjhy0OgAAgFJV/QjemDFjMnny5DQ2NnbZ19jYmJkzZw5qXV/U1dVl1qxZ\nfe7n7Lf1mZ0dLnJy4mjaxIkdL3xyXu3hzJ49+7R63t3XuedUx+rsxP8I9bS/J33pG+pjDfX5lTrW\nUJ+fsQanx1iD02Oswekx1uD0lDzW008/fUp1VT+ClyQLFizI9u3bs2vXrvZtJx4vXLhw0OsAAABK\nVPUjeEly0003ZfPmzVm2bFlWrFiR1tbWrFu3LrNmzcqiRYsGvQ4AAKBEVTmC1/nqlOPGjcuGDRty\n2WWX5Z577slDDz2U+fPn54EHHsjw4cMHvQ4AAKBEZ3wE70c/+lG326dMmZI1a9actH+w6gAAAErT\nL9/BAwAAYOAJeAAAAIUQ8AAAAAoh4AEAABRCwAMAACiEgAcAAFAIAQ8AAKAQAh4AAEAhBDwAAIBC\nCHgAAACFEPAAAAAKIeABAAAUQsADAAAohIAHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAAhRDw\nAAAACiHgAQAAFELAAwAAKISABwAAUAgBDwAAoBACHgAAQCEEPAAAgEIIeAAAAIUQ8AAAAAoh4AEA\nABSidrAnAJy5hh8+lpebD7U/fmXv3iTJ1md2dqm9+Pz6LFm4YMDmBgDAwBHwoAAvNx/KG+M/0P64\npe3cJMno8RO7Fr/WNfQBAFAGp2gCAAAUQsADAAAohIAHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4\nAAAAhRDwAAAACiHgAQAAFELAAwAAKISABwAAUAgBDwAAoBACHgAAQCEEPAAAgEIIeAAAAIUQ8AAA\nAAoh4AEAABRCwAMAACiEgAcAAFAIAQ8AAKAQAh4AAEAhBDwAAIBCCHgAAACFEPAAAAAKIeABAAAU\nQsADAAAohIAHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAAhRDwAAAACiHgAQAAFELAAwAAKES/\nBrznnnsuv/3bv50rr7wyH/rQh3LzzTdn165dHWr27NmTVatWZe7cuZk7d25Wr16dAwcOdHmuatcB\nAACUpra/nnj37t25/vrrM2rUqKxatSqVSiUPPvhgrr/++mzevDnve9/70tzcnKVLl6atrS0rV65M\nW1tb1q5dmx07dqShoSG1te9Mr9p1AAAAJeq3xPOtb30rb775ZjZs2JDp06cnSebOnZslS5bkr/7q\nr3Lbbbdl/fr1aWpqyqOPPpqpU6cmSa644oosX748mzZtypIlS5Kk6nUAAAAl6rdTNHft2pULLrig\nPdwlyaxZs3L++ednx44dSZItW7Zkzpw57WEsSebNm5epU6dmy5Yt7duqXQcAAFCifgt4F110Ud54\n4428/vrr7duam5tz8ODBTJgwIS0tLdm9e3cuv/zyLr0zZszIs88+myRVrwMAAChVvwW8G264IXV1\ndfnyl7+c559/Ps8//3y+/OUvp66uLjfccEP27duX5J0g2NmECRNy8ODBHDp0qOp1AAAApeq37+Bd\ndtll+epXv5pbbrkl11577TuD1dbm61//eqZPn56f/OQnSZKRI0d26R0xYkSS5K233srhw4erWldf\nX3+mPxoAAMCQ1G8B7wc/+EHuuOOOXHXVVfl3/+7f5dixY/nOd76T//Sf/lO+8Y1v5LzzzkuS1NTU\n9PgcNTU1qVQqVa0DAAAoVU3lRDKqotbW1nzsYx/LlClT8r3vfa89WLW1teW6667La6+9lrVr12bx\n4sW58847c/3113fo/8pXvpK/+qu/ypNPPpmXXnop1157bdXqujvC15unn346R48edYuF97iNj21L\ny4RL2h8fP348STJsWMeznMc2vZB/v+D/O62ed/d17unPsbrT1taWJKe13vvSM5BjDfX5lTrWUJ+f\nsQanx1iD02Oswekx1uD0lD5WXV1dZs2a1Wtdv3wH78UXX0xLS0uuueaaDkfNamtrs2jRovziF7/I\nwYMHkyT79+/v0t/U1JSxY8dm5MiRmTRpUlXrAAAAStUvh6VOhLoTRxHe7dixY0mSMWPGZPLkyWls\nbOxS09jYmJkzZ/ZLXV+cSlKmbFuf2ZnR4ye2P3711VeTJBMnTuxQd17t4cyePfu0et7d17mnP8fq\nzlNPPZUkPe6vVs9AjjXU51fqWEN9fsYanB5jDU6PsQanx1iD01PyWE8//fQp1fXLEbxLLrkkF154\nYTZt2pSjR4+2bz9y5Eh+8IMfZNy4cbnkkkuyYMGCbN++Pbt27WqvOfF44cKF7duqXQcAAFCifjmC\nV1tbm//yX/5Lbr311lx33XW57rrrcuzYsfz3//7f84//+I/56le/mnPOOSc33XRTNm/enGXLlmXF\nihVpbW3NunXrMmvWrCxatKj9+apdBwAAUKJ+u3LINddck/POOy/3339//vzP/zxJMnPmzHzzm9/M\nRz7ykSTJuHHjsmHDhtx111255557MmrUqMyfPz+33XZbhg8f3v5c1a4DAAAoUb9eGvIjH/lIe5jr\nyZQpU7JmzZqTPle16wAAAErTL9/BAwAAYOAJeAAAAIUQ8AAAAAoh4AEAABRCwAMAACiEgAcAAFAI\nAQ8AAKAQAh4AAEAhBDwAAIBCCHgAAACFEPAAAAAKIeABAAAUQsADAAAohIAHAABQCAEPAACgEAIe\nAABAIQQ8AACAQgh4AAAAhRDwAAAACiHgAQAAFELAAwAAKISABwAAUAgBDwAAoBACHgAAQCEEPAAA\ngEIIeAAAAIWoHewJAIOj4YeP5eXmQx22vbJ3b5Jk6zM7O2y/+Pz6LFm4YMDmBgBA3wh48B71cvOh\nvDH+Ax22tbSdmyQZPX5ix+LXOgY+AACGJqdoAgAAFELAAwAAKISABwAAUAgBDwAAoBACHgAAQCFc\nRRM4LZ1vr9DTrRUSt1cAABhoAh5wWjrfXqHHWysk7bdXcM89AICBIeAB/c499wAABobv4AEAABRC\nwAMAACiEgAcAAFAIAQ8AAKAQAh4AAEAhBDwAAIBCCHgAAACFEPAAAAAKIeABAAAUQsADAAAohIAH\nAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAAhRDwAAAACiHgAQAAFKJ2sCcA0JOGHz6Wl5sPtT9+\nZe/eJMnWZ3Z2qLv4/PosWbhgQOcGADAUCXjAkPVy86G8Mf4D7Y9b2s5NkoweP7Fj4WsdAx8AwHuV\nUzQBAAAKIeABAAAUQsADAAAohIAHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAAhRDwAAAACiHg\nAQAAFKJfA96BAwfy+7//+/nIRz6SD33oQ/nc5z6XJ598skPNnj17smrVqsydOzdz587N6tWrc+DA\ngS7PVe06AACA0tT21xMfPnw4119/fV577bXceOONGTt2bL797W/nxhtvzMMPP5xLLrkkzc3NWbp0\nadra2rJy5cq0tbVl7dq12bFjRxoaGlJb+870ql0HAABQon5LPA888EBeeumlPPTQQ/nQhz6UJPmX\n//Jf5uqrr87atWvzla98JevXr09TU1MeffTRTJ06NUlyxRVXZPny5dm0aVOWLFmSJFWvAwAAKFG/\nBbwf/OAH+fjHP94e7pJk/PjxWb16dfuRtC1btmTOnDntYSxJ5s2bl6lTp2bLli3tgazadUC5Gn74\nWF5uPtT++JW9e5MkW5/Z2aX24vPrs2ThggGbGwBAf+uXgLdnz57s27cvn//859u3vfnmmxk9enR+\n67d+K0nS0tKS3bt359Of/nSX/hkzZmTbtm39UgeU7eXmQ3lj/AfaH7e0nZskGT1+Ytfi17qGPgCA\ns1m/XGTlpZdeSk1NTcaNG5evfOUr+fCHP5xf+ZVfyYIFC7J169Ykyb59+5IkF110UZf+CRMm5ODB\ngzl06FDV6wAAAErVL0fwWlpaUqlU8vWvfz3Dhw/P7//+72fYsGFZt25dvvjFL2bdunUZNWpUkmTk\nyJFd+keMGJEkeeutt3L48OGq1tXX11fhJwQAABh6+iXgHT16NEly8ODBPPbYY+2h6jd+4zdy9dVX\n52tf+1ruuOOOJElNTU2Pz1NTU5NKpVLVur44evRonnrqqT71UoZX9u5tP9UvSY4fP54kefXVVzvU\nvdm0t32tnGrPu/s69xhrYF737rS1tSXJaf3u96Wn1LGG+vyMNTg9xhqcHmMNTo+xBqen9LHq6upO\nWtcvp2iOHj06STJ//vwOR8zGjBmTT3ziE3n22Wdz7rnv/AOstbW1S/+RI0eSJPX19e3PVa06AACA\nUvXLEbwT34O78MILu+y78MILU6lU2vft37+/S01TU1PGjh2bkSNHZtKkSVWt64u6urrMmjWrT72U\nYeszOztcpOPE0aCJEzteuOO82sOZPXv2afW8u69zj7EG5nXvzon/Vetpf7V6Sh1rqM/PWIPTY6zB\n6THW4PQYa3B6Sh7r6aefPqW6fjmCd8kll6Suri4/+9nPuuzbvXt3RowYkXHjxmXy5MlpbGzsUtPY\n2JiZM2cmeeeoXzXrAAAAStUvAW/UqFH5xCc+ka1bt2bnzn+6DPnu3buzdevWfPKTn0xNTU0WLFiQ\n7du3Z9euXe01Jx4vXLiwfVu16wAAAErUbzc6v+222/L444/nhhtuyNKlS1NbW5uHHnooo0aNype+\n9KUkyU033ZTNmzdn2bJlWbFiRVpbW7Nu3brMmjUrixYtan+uatcBAACUqF+O4CXJxRdfnO9973uZ\nM2dOHnzwwaxZsyYzZszId77znUyePDlJMm7cuGzYsCGXXXZZ7rnnnjz00EOZP39+HnjggQwfPrz9\nuapdBwAAUKJ+O4KXJJMnT87dd9/da82UKVOyZs2akz5XtesAAABK029H8AAAABhYAh4AAEAhBDwA\nAIBCCHgAAACFEPAAAAAKIeABAAAUQsADAAAohIAHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAA\nhRDwAAAACiHgAQAAFELAAwAAKISABwAAUAgBDwAAoBACHgAAQCFqB3sCAIOt4YeP5eXmQx22vbJ3\nb5Jk6zM7O2y/+Pz6LFm4YMDmBgBwOgQ84D3v5eZDeWP8Bzpsa2k7N0kyevzEjsWvdQx8AABDiVM0\nAQAACiHgAQAAFELAAwAAKISABwAAUAgBDwAAoBACHgAAQCEEPAAAgEK4Dx5AH3W+QXpPN0dP3CAd\nABgYAh5AH3W+QXqPN0dP3CAdABgQTtEEAAAohIAHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAA\nhRDwAAAACiHgAQAAFMKNzgEGUMMPH8vLzYc6bHtl794kydZnOt4M/eLz67Nk4YIBmxsAcPYT8AAG\n0MvNh/LG+A902NbSdm6SZPT4iR2LX/unwNc5GPYUChPBEADeywQ8gLNA52DYYyhMOgRDAOC9xXfw\nAAAACiHgAQAAFMIpmgCFckEXAHjvEfAACtXXC7oAAGcvp2gCAAAUQsADAAAohIAHAABQCAEPAACg\nEAIeAABAIQQ8AACAQgh4AAAAhXAfPAA66HyDdDdHB4Czh4AHQAedb5Du5ugAcPZwiiYAAEAhBDwA\nAIBCCHgAAACFEPAAAAAKIeABAAAUwlU0AThjp3prhcTtFQCgPwl4AJyxU761QuL2CgDQj5yiCQAA\nUAgBDwAAoBACHgAAQCEEPAAAgEIMSMB77rnnMnPmzHzjG9/osH3Pnj1ZtWpV5s6dm7lz52b16tU5\ncOBAl/5q1wEAAJSo36+ieezYsdx+++05duxYh+3Nzc1ZunRp2trasnLlyrS1tWXt2rXZsWNHGhoa\nUltb2y91AAAAper31HP//ffnZz/7WZft69evT1NTUx599NFMnTo1SXLFFVdk+fLl2bRpU5YsWdIv\ndQAAAKXq11M0n3/++dx///354he/mEql0mHfli1bMmfOnPYwliTz5s3L1KlTs2XLln6rAwAAKFW/\nBbwTp2bUlb4jAAAgAElEQVR+9KMfzaJFizrsa2lpye7du3P55Zd36ZsxY0aeffbZfqkDAAAoWb+d\novnAAw9k9+7duf/++/P222932Ldv374kyUUXXdSlb8KECTl48GAOHTpU9br6+voz/rkAAACGqn45\ngvfCCy/kvvvuy+rVqzNhwoQu+w8fPpwkGTlyZJd9I0aMSJK89dZbVa8DAAAoWdWP4B0/fjy/93u/\nl6uuuirXXXddtzUnvo9XU1PT4/PU1NRUva6vjh49mqeeeqrP/Zz9Xtm7Ny1t57Y/Pn78eJLk1Vdf\n7VD3ZtPe9rVyqj3v7uvcYyyv+1Ae60zn1522trYkOa333L70GGtweow1OD3GGpweYw1OT+lj1dXV\nnbSu6gFv7dq1eeGFF7Jx48a8/vrrSZI33ngjSdLa2prXX389o0ePbn/c2ZEjR5Ik9fX1Va8DYGj5\nX9t/nP2HjrQ/PhEMhw3reILJ++pHZP6vzRnQuQHA2ajqAW/btm15++23uxy9q6mpydq1a7Nu3bps\n2rQpSbJ///4u/U1NTRk7dmxGjhyZSZMmVbWur+rq6jJr1qw+93P22/rMzoweP7H98YmjEhMnTuxQ\nd17t4cyePfu0et7d17nHWF73oTzWmc6vve/9HzhpX+1rO9t7OjvxP6A97e9JX/qMdWY9xhqcHmMN\nTo+xBqen5LGefvrpU6qresC7/fbb24/YnfCLX/wit956axYvXpzFixfn/e9/fyZPnpzGxsYu/Y2N\njZk5c2aSZMyYMVWtAwAAKFnVL7IyY8aMzJs3r8OfK6+8MkkyefLk/Oqv/mrq6uqyYMGCbN++Pbt2\n7WrvPfF44cKF7duqXQcAAFCqfrtNwsncdNNN2bx5c5YtW5YVK1aktbU169aty6xZszrcN6/adQAA\nAKXqtxudd1ZTU9PhSpbjxo3Lhg0bctlll+Wee+7JQw89lPnz5+eBBx7I8OHD+60OAACgVANyBO/i\niy/OT3/60y7bp0yZkjVr1py0v9p1AAAAJRqwI3gAAAD0LwEPAACgEIN2kRUA6IuGHz6Wl5sPtT9+\nZe/eJO/cU6+zi8+vz5KFC7r09NZ3ogcAzkYCHgBnlZebD+WN8f90c/SWtnOTpMtN3ZMkr+3stqfX\nvte6BkUAOFs4RRMAAKAQAh4AAEAhBDwAAIBCCHgAAACFEPAAAAAKIeABAAAUQsADAAAohIAHAABQ\nCAEPAACgEAIeAABAIQQ8AACAQgh4AAAAhRDwAAAACiHgAQAAFELAAwAAKISABwAAUAgBDwAAoBC1\ngz0BABiqGn74WF5uPtT++JW9e5MkW5/Z2aX24vPrs2ThggGbGwB0R8ADgB683Hwob4z/QPvjlrZz\nkySjx0/sWvxa19AHAAPNKZoAAACFcAQPAKqo82mdSc+ndjqtE4BqE/AAoIo6n9aZ9HJq57tO6zzV\n7/u9OxQKkwB0JuABwBBwyt/3e1co7GuYBKBcvoMHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAA\nhRDwAAAACuE2CQDwHlONe+711NO5D4CBJeABwHtMNe6512NPpz4ABpZTNAEAAAoh4AEAABRCwAMA\nACiEgAcAAFAIF1kBAPpF5ytvJqd2xU4A+k7AAwD6RecrbyandsVOt2QA6DsBDwAYUtySAaDvfAcP\nAACgEAIeAABAIQQ8AACAQgh4AAAAhRDwAAAACuEqmgDAWc899wDeIeABAGe9/r7nnlAInC0EPADg\nPeuU77nnfnvAWcJ38AAAAArhCB4AwGnwfT9gKBPwAABOg+/7AUOZgAcAMAB83w8YCL6DBwAAUAhH\n8AAAhqhTPa0z+adTO31HEN7bBDwAgCHqlE/rTNpP7ezv7wgmgiEMZQIeAAB9CpOOFsLQI+ABANAn\nA3m0UJiEUyPgAQAwoIbiqadCIaUQ8AAAKFZfbk/h+4iczQQ8AAB4F99H5Gwm4AEAwBnq6ymkUG0C\nHgAADBKng1Jt/Rbwtm3blr/8y79MY2Njampq8sEPfjC33HJLZs+e3V6zZ8+e/Nmf/Vkef/zxJMnH\nP/7xrF69OuPGjevwXNWuAwCAoaAvp4NCb/ol4P34xz/OypUrc8kll+RLX/pSjh07lo0bN+Zzn/tc\nNm7cmFmzZqW5uTlLly5NW1tbVq5cmba2tqxduzY7duxIQ0NDamvfmVq16wAA4GzW1+/7OVr43tAv\nqedP//RP80u/9Et5+OGHU1dXlyS59tprc8011+Tuu+/OunXrsn79+jQ1NeXRRx/N1KlTkyRXXHFF\nli9fnk2bNmXJkiVJUvU6AAA4m/X1+34DefGYvtyeQgCtjqoHvJaWluzYsSMrVqxoD3dJcuGFF+aq\nq67K//2//zdJsmXLlsyZM6c9jCXJvHnzMnXq1GzZsqU9kFW7DgAAODX9HibPMIDSVdUDXn19ff7m\nb/4mo0aN6rLv9ddfT21tbVpaWrJ79+58+tOf7lIzY8aMbNu2LUmqXgcAAJTFzew7qnrAGzZsWH75\nl3+5y/bnnnsuTzzxRD72sY9l3759SZKLLrqoS92ECRNy8ODBHDp0qOp19fX1Z/SzAQAAQ8tA3cz+\nbLnX4YBceeTNN9/M6tWrU1NTk89//vM5fPhwkmTkyJFdakeMGJEkeeutt6peJ+ABAAB9OR30bLnX\nYb8HvNbW1tx8883ZsWNHfud3ficf/vCH8+STTyZJampqeuyrqalJpVKpal1fHT16NE899VSf+zn7\nvbJ3b/svcJIcP348SfLqq692qHuzaW/7WjnVnnf3de4xltd9KI91pvMbyLHeK6/7QI41FF6LgRzL\n617+WEN9fqWO9V74LPlf23+c/YeOdOnZ+FjXr5G9r35E5v/anC7bk6Stra3DNU560q8B7+DBg1m5\ncmV+8pOf5Lrrrsstt9ySJBk9enSSd8JfZ0eOvPPD19fXV70OAABgIO0/dCQtEy5pf3wi4A0bNqxr\ncdMLZzxevwW8AwcOZMWKFXn++efzm7/5m/mDP/iD9n2TJk1Kkuzfv79LX1NTU8aOHZuRI0dWva6v\n6urqMmvWrD73c/bb+szODofeT/wvzcSJHQ/Hn1d7OLNnzz6tnnf3de4xltd9KI91pvMbyLHeK6/7\nQI41FF6LgRzL617+WEN9fqWO5bOk55+rs6effrrb7Z31S8A7fPhwe7i78cYbs3r16g77x4wZk8mT\nJ6exsbFLb2NjY2bOnNkvdQAAACXr5rjgmfvDP/zDPP/881m2bFmXcHfCggULsn379uzatat924nH\nCxcu7Lc6AACAUlX9CN7OnTvzyCOP5Lzzzsu0adPyyCOPdKn5zGc+k5tuuimbN2/OsmXLsmLFirS2\ntmbdunWZNWtWFi1a1F5b7ToAAIBSVT3gPf7446mpqUlLS0vuuOOObms+85nPZNy4cdmwYUPuuuuu\n3HPPPRk1alTmz5+f2267LcOHD2+vrXYdAABAqaoe8D772c/ms5/97CnVTpkyJWvWrBnwOgAAgBL1\ny3fwAAAAGHgCHgAAQCEEPAAAgEIIeAAAAIUQ8AAAAAoh4AEAABRCwAMAACiEgAcAAFAIAQ8AAKAQ\nAh4AAEAhBDwAAIBCCHgAAACFEPAAAAAKIeABAAAUQsADAAAohIAHAABQCAEPAACgEAIeAABAIQQ8\nAACAQgh4AAAAhRDwAAAACiHgAQAAFELAAwAAKISABwAAUAgBDwAAoBACHgAAQCEEPAAAgEIIeAAA\nAIUQ8AAAAAoh4AEAABRCwAMAACiEgAcAAFAIAQ8AAKAQAh4AAEAhBDwAAIBCCHgAAACFEPAAAAAK\nIeABAAAUQsADAAAohIAHAABQCAEPAACgEAIeAABAIQQ8AACAQgh4AAAAhRDwAAAACiHgAQAAFELA\nAwAAKISABwAAUAgBDwAAoBACHgAAQCEEPAAAgEIIeAAAAIUQ8AAAAAoh4AEAABRCwAMAACiEgAcA\nAFAIAQ8AAKAQAh4AAEAhBDwAAIBCCHgAAACFEPAAAAAKIeABAAAUQsADAAAohIAHAABQiCID3p49\ne7Jq1arMnTs3c+fOzerVq3PgwIHBnhYAAEC/qh3sCVRbc3Nzli5dmra2tqxcuTJtbW1Zu3ZtduzY\nkYaGhtTWFvcjAwAAJCkw4K1fvz5NTU159NFHM3Xq1CTJFVdckeXLl2fTpk1ZsmTJIM8QAACgfxR3\niuaWLVsyZ86c9nCXJPPmzcvUqVOzZcuWQZwZAABA/yoq4LW0tGT37t25/PLLu+ybMWNGnn322UGY\nFQAAwMAoKuDt27cvSXLRRRd12TdhwoQcPHgwhw4dGuhpAQAADIiiAt7hw4eTJCNHjuyyb8SIEUmS\nt956a0DnBAAAMFBqKpVKZbAnUS1PPvlkfuu3fit/8id/kn/7b/9th31333131qxZk23btmX8+PGn\n/JxPPPFECnqJ6KNDb7Xm+DnD/2nDiSVR07Fu2LG3Uz9q5Gn1vLuvS4+xvO5DeKwznd9AjvWeed0H\ncqwh8FoM5Fhe9/LHGurzK3UsnyU993WnpqYmv/Irv9Lj/qSwq2iOHj06SdLa2tpl35EjR5Ik9fX1\np/WcNTXvvPLDhw8/SSUlG1dXd4qVo/rQ8099fekxVl97/qmvrNdiIMc6s/kN5Fhe98EZy+s+OGOV\n9boP5FhDfX6ljuV3q6e+zt5+++32bNKbogLepEmTkiT79+/vsq+pqSljx47t9vTN3lx55ZVVmRsA\nAEB/K+o7eGPGjMnkyZPT2NjYZV9jY2Nmzpw5CLMCAAAYGEUFvCRZsGBBtm/fnl27drVvO/F44cKF\ngzgzAACA/lXURVaS5MCBA1m0aFHOOeecrFixIq2trVm3bl2mTJmSjRs3+i4dAABQrOICXpL84z/+\nY+666648/vjjGTVqVH791389t912Wy644ILBnhoAAEC/KTLgAQAAvBcV9x08AACA9yoBDwAAoBAC\nHgAAQCEEPAAAgEIIeAAAAIUQ8AAAAAoh4AEAABSidrAnAGebPXv25M/+7M/y+OOPJ0k+/vGPZ/Xq\n1Rk3btwp9d9555156aWX8td//de91m3bti1/+Zd/mcbGxtTU1OSDH/xgbrnllsyePbvXvr/927/N\nPffck+effz719fX59Kc/nVtuuSWjR48+pfk999xzue6663LzzTdn1apVvdZed911eeaZZ7ps/9Sn\nPpWvf/3r3fYcOHAgX/va17J169a0trbmsssuy5e//OVceeWV3da//PLL+eQnP9nrPB566KFcddVV\n3f4sX/3qV/PEE09k2LBhueqqq7J69epMnTq11+f7+7//+/z5n/95nn322YwdOzZXX311fvd3fzcX\nXHBBl9qe/j5Ptk5OZR3cfvvt2bdvXx588MFee062Vnrq622tnMr8Oq+VnnpOtk566uttrXTuOdV1\n8sgjj3Q7Vm9rpaf5dV4nM2bMyIEDB7Jjx44ef2e7Wxe//uu/nm9/+9un/Lt+55135qmnnkp9fX2v\nPZ3XxT//5/88lUolL730Uo893a2JOXPm5MEHHzzl+T333HP5N//m32TChAlpbm7usae7dVGpVHLB\nBRektbW1x77O62LSpEmpqanJz3/+8y49va2LE7cBHjFiRM4555xux+q8Lt7//vfn2LFjefHFF3uc\nX3fvH/PmzcuDDz7Y6/ty57Vx+eWX5+DBg9m5c+cpvZefWKdf+MIXTvoZ0HltTJ06NW1tbdmzZ0+P\nPZ3XxuzZs9PU1JQXXnjhlOZ34v1i4cKF+fnPf97r/DqvjRN/V+ecc07Gjx/fbU937xef+tSnsmXL\nlm7H6mltvPv20BdccEE+85nPdBmru/eLT33qU/n+97/f68/V22dLT5+9vX2WnMrndef3r556TvY5\n0lNfb58jpzK/7mp66uvts+QLX/hCtz0n+zdH57FO5bPkT/7kT/Jf/+t/7TJWb58jPf1Mp/PvjdMh\n4MFpaG5uztKlS9PW1paVK1emra0ta9euzY4dO9LQ0JDa2t5/pRoaGtLQ0JA5c+b0WvfjH/84K1eu\nzCWXXJIvfelLOXbsWDZu3JjPfe5z2bhxY2bNmtVt39/+7d/mt3/7tzNr1qzceuutefXVV/Otb30r\nzz77bDZs2HDSn+/YsWO5/fbbc+zYsZPWJsnOnTszf/78LFiwoMP2SZMmdVt/+PDhXH/99Xnttddy\n4403ZuzYsfn2t7+dG2+8MQ8//HAuueSSLj3jxo3LV7/61S7bW1tb80d/9EcZP358pk+f3mX/7t27\nc/3112fUqFFZtWpVKpVKHnzwwVx//fXZvHlz3ve+93U7x7/7u7/LTTfdlPPOOy//4T/8hxw/fjzr\n16/P3/3d3+W73/1uxowZ017b09/nydbJpk2bTroOvve972XTpk35tV/7tV7HOtlaee6557rt622t\nLF68+KTz67xWelvbva2Tnvp6WysrV67s0nMq66Sn16K3tfL5z3++257O6+TnP/95GhoaUldXl9/9\n3d9NbW1tl9/Z7tbF/fffnx/84Ae59NJLT+l3vaGhId///veTJNOmTeuxp/O62LVrV77zne+kpqYm\nN954YyZMmNClp7s1sX79+nzrW9/qdazO6+I//sf/mGPHjuXo0aO99nReFzt37sz999+fMWPG5Atf\n+EK3fZ3Xxeuvv56HHnooNTU1uemmmzJu3LgOPf/iX/yLbtfFT3/606xbty61tbVZtWpVt39fndfF\nz3/+83z3u9/NOeecky9+8YsZNWpUl57u3j8eeOCBbNiwodf35c5r48UXX8zDDz+cUaNG5dZbb01T\nU1Ov7+Unfo+mTZt20s+AzmvjxRdfzHe/+93213DYsGFdejqvjX/4h3/I//yf/zPnnnvuKX3WnHi/\naGtry+bNmzN79uxe+969Nl544YV885vfzD/7Z/8sn/jEJ1JXV9elp7v3i29+85v54z/+40ybNq3b\nsbp7zzgxVqVSydixY7N48eJ85zvf6TBWd+8Xa9asydatW3P55Zf3+HP19tmyYcOGbj97e/ss+e53\nv3vSz+vO7689fcaf7HNkxowZ3fb19jny13/91yedX3fz6e3fIT19lkycOLHbnpP9m+P9739/l76T\nfZZceOGF+da3vtVlrN4+R/7H//gf3c7vdP69cdoqwCn72te+Vrn88ssrL774Yvu27du3V6ZNm1b5\n/ve/32PfsWPHKn/xF39RmT59emX69OmVG264oddxrr322spv/MZvVI4cOdK+7bXXXqvMmTOnsmLF\nih77/vW//teVT37ykx36NmzYUJk+fXrl//yf/3PSn+8b3/hGZebMmZXp06dX/uIv/qLX2t27d1em\nTZtW2bRp00mf94Svfe1rlcsuu6zy93//9+3b9u/fX5k9e3blP//n/3zKz1OpVCp//Md/XJkxY0bl\nH/7hH7rd/0d/9EeV6dOnV37605+2b/t//+//VaZNm1b5b//tv/X4vP/qX/2rygc/+MHK7t2727c1\nNjZWLrvssspXvvKVSqVy8r/PntbJpZdeWrn55pt7XQdtbW2Vr3/96+01N954Y69j9bRWrrrqqsrV\nV1/dY193a+Xb3/52Zdq0aZVp06addJ2eWCvTpk2r3HDDDT2O09M6OZXXsPNa2bdvX2XGjBmVSy+9\n9JR+jyqVd9bJZZddVrnjjjt6HKu7tfKTn/ykcumll/b4WnReJ9dee23lox/9aGX69Ont66Tz72x3\n6+Lqq6+uXHrppZWN/397Zx5UxZX+/e+9IIuyqKhEpZQ4BmRxAUEWoyKKC8qmuCDuilucCIrBddxl\nxCBoXDKIGTMuYDmjiKMVSdRIqowV4xJroiCCCwFUghcu22Xt9w+q++3bfXrBZPL7vb7nU2VZ3NtP\nP2f5nuc53X363NOnuc9IY53fXk5OTszAgQNl44NQF2FhYcyoUaMYb29v7hihDUkTI0eOZJycnJir\nV6/Klo/l4MGDjLOzM+Pk5MSkpqZK2pB0oSbuCXXB1mvQoEFcDFETK318fBgnJyfm1q1bkr6EumD7\n2MnJiYshQhtS/JgwYQLj5OTE7N69m/tMGJeF2oiIiGCGDx/OODk5cbmFFMuF42jIkCGKOUDYzhER\nEUxAQICRNoQ2Qm1EREQwPj4+jLOzM3eMXK5h44WTkxPj5eUlWz6hNtTkNVK8CAkJYZydnZn4+HhJ\nOyERERGMp6cn4+LiwuUWoQ0pXrB9nJiYKOlLLrfMnDmTmHvl5hzLly+XzNdS8VUqxyuNPSk7ub5Z\nu3at4nyCdF4pX3JzDikbpTlHe+Y87Jxj48aNRBu5OUdUVBTRRs18422h7+BRKO3g8uXLGDZsmNES\nPz8/P7z//vu4fPky0aaxsRHh4eE4dOgQwsPD0aNHD1kfer0ejx8/RnBwMMzMzLjP7ezs4O3tjbt3\n70r6sbOzw/Tp043shg0bBoZhkJ+fL+s3Pz8fn3/+OT766COjJSpSPHnyBBqNBv369VM8liUrKwsB\nAQEYOnQo91m3bt2QkJAALy8v1efJz8/HqVOnMGXKFHh6ehKPefr0Kbp06WL0dG/gwIHo3LkzHj9+\nTLQpKSlBQUEBwsLC4ODgwH3u4uICPz8/ZGVlqepPkk6GDh0KMzMzXLt2TdLOYDAgPDwcR44cwdSp\nU9G1a1fcv39f0peUVqytrdHS0oIXL14Q7UhaaWxsxIkTJ8AwDNzc3GR1ympl6dKlYBgGP/zwg2Sd\nSDpR04ZCrTQ2NmLx4sVoaWmBh4eH4jhiy3ny5ElYWVnh3Llzkr6EWmlsbMTmzZsBAD169BDZCHXC\n9kNYWBj8/f2RlZUFQDxmhbrQ6/UoKSmBra0tcnJyuPML7fjtFRwcDKDtLrNUfNDr9cjPz+d0wZZv\n8uTJGDZsGHdevg1JE3q9Hq9fvwYAFBUVSZaP395Hjhzh/jYxMZG0KSgoMNKF2rjH1wW/XuvWreNi\niFKsvHPnDnQ6HQYMGAAfHx9JX3xd8Pu4S5cuXAzh25DiR2NjIxwcHODo6IiLFy9yvoRxma8Nti/m\nzp2Lfv36cblFaCMcR927d0eHDh1kc4CwnVlfUVFRRtrg2wi1wf4dFhbG9TupfHxdsPECANzd3WVz\nFD9mqM1rpHhhb2+PMWPGGD19l8uHjY2NsLCwQG1tLaZOncrlFqENKV44ODjA0tIShYWFRF9yuWXQ\noEG4e/cuMfdKzTl69+6Na9euEW2k4qtUjlcaez/++CPRTq5vWltb8e9//1t2PkEqj9w8RGrOIWcj\nN+dwcHBQPedh5xyBgYG4cOEC0UZqzmFtbU3sXzXzjd8CvcCjUFSi1+tRXFwMNzc30Xeurq74+eef\niXYNDQ2oq6tDamoqEhMTjSY9JKysrPDVV19h3rx5ou90Op3kMlAzMzMcPXoUS5YsMfr84cOHAKSX\nTQL/d0nEhx9+iJCQENnysRQUFAAA/vSnPwEA6uvrZY//5Zdf8OrVK27JIQDU1dUBAKKiojBt2jRV\nfgEgJSUFFhYWWLVqleQx9vb2qKqqgk6n4z6rrKxEdXW15MXBq1evAIC4VLRv377Q6XQoLi6W7U8p\nnTQ0NMDU1BQdO3aU1EF9fT0MBgM+++wz7Ny5E1qtFq2trZK+pLTS0NCApqYmWFpaEu1IWmloaEBV\nVRUAYNGiRZI65WslKCgIADBhwgTJOpF0ojQmSFrR6XSoq6vD/v37uWVySrA66dSpk+z4E2qloaEB\nNTU10Gq1GDFihMhGqBN+P7A6YY9hxyxJF6ydt7e3KH7wxzq/vfbu3Qt7e3u89957onqwNtbW1rhy\n5QqnC375hDGE/ZukCSsrK6xduxaAOH4Iz8PXhdR7m3wboS5MTEwU455QF1ZWVjh//jzmzZsniiFy\nsTItLQ2WlpZISkqSLSNfF2wbRkREiGIIa0OKH2y7Dh8+3EgX/Lgs1Aa/L/i5RRjLhePI1NQUAwYM\nkM0B1tbWRu3M98WvO99GqA32b7a8bHlIuYavi4iICGg0GqPJNsmOrw0zMzMcOHBAtk6keNHc3Iyj\nR4/i0KFDRrqQy4dmZmawsbFBx44djXKL0EYYL8zMzLB37140NTUZ6YJ9l61Xr16SuaWlpQXPnz8H\n0Hbhxkcql7S0tKC6uhomJibEfE2KrwzDSOZ4uTnHmzdv0NjYSLSTmnOw78i5uLhIzidIc47W1lbZ\neQgpl8jNXeTmHNOnT8f169dVz3lSUlJgbm6O58+fS9qQ5hwVFRXQ6/V47733RDZq5hvsMW8DvcCj\nUFTCDjR7e3vRdz169EB1dTVqampE31lbWyMnJwfjx49X5Uer1aJPnz6id8Ty8vJw9+5dySdWQkpL\nS3Hu3Dns2rULzs7OGDt2rOSxaWlpKC4uxrZt21SdG2gLtp06dUJiYiI8PT3h4eGBoKAgySeZ7MYO\nXbt2xZ49e+Dl5QVPT0+MGzcO169fV+03Ly8P3377LaKiotCtWzfJ4+bMmQMzMzOsWbMG+fn5yM/P\nx5o1a2BmZoY5c+YQbdiX4Wtra0XfVVZWAmhLKnL9KaUTa2trzJgxAwaDgagToO2F/pycHK6vtFot\nPD09JX1JaaWkpASNjY1GTyfkKC0txddff42WlhYMGDBAtVasrKwAAP3795c8nqSTKVOmIDY2VrJe\nJK2MGjUKGo3G6E6xHKxOZs2ahatXr8qOP6FWSktL4ejoCAsLC6JWhDrh9wOrk/LycqMxS9IFa9en\nTx+j+CEc61ZWVpzmtFotd0EmrC9ro9FojHTB+qmoqDA6r1xMKS0tRVZWFv72t7+JNEGyY3Wxfft2\n9O7dGxqNRrJ8QNvdeL4uhg4dikWLFnEbSZDs2Ikwq4thw4YhLCwM0dHRRjFErl55eXm4ceMGoqOj\n4ezsLFtGvi4KCgpQX1+P3bt3G8UQvo2a+PHw4UNRXFbKLXq9HqdPnxbFcqXcQsoBQm0I6+7q6qqY\nN4TndXNzk7SRyy1SOUout5Bs1OQWNflQmFukbJRyC9/OyckJY8eOldRGWloa9Ho9NBoNKioqjL6T\n0oLFcnwAABS4SURBVEVaWhp3ccNesPAh6aK0tFSyH5TmHFqtVtXcgK33li1boNVqceDAAcljSbq4\nc+eO7DyEpAs/Pz8UFRURbeR0sW7dOtVzHlYXLi4uKCsrk7Qh6SIqKgoAsHv3btHxauJFeXm5Yvmk\noJusUCgqYQehhYWF6Dtzc3MAbZN/dtLLR6v9bfdS6urqkJCQAI1Gg5iYGMXjq6qqEBgYCI1GAwsL\nC2zatElyYlxQUIDDhw9jy5Yt6NGjB0pKSlSV6cmTJ6itrUV1dTWSkpJQXV2Nf/zjH1i9ejWam5sR\nGhpqdLxerwfDMNi/fz86dOiATZs2QavV4tixY/joo49w7Ngx0R1MEhkZGTA1NcXs2bNlj3NxccHe\nvXsRGxvLLSUyNTXF/v37iZuyAG13Bjt16oRvvvnG6K5kTU0Nvv/+ewBtd0fl+lNOJ+xnck87hRNj\n4d9KsFrRarV/iFaUyielk/j4eLS2top0AqjTihJ8nSiNv/ZqRY1OqqqqkJSUxI1ZtfFDq9WKxrpG\no5FtZzXxQXiMnI2cJkh2SjGEZKMmfgjt2MmOnC4GDx4s2xZS8YNURiVdCG2UdMEwDJYvXy5qVzlt\nAG27Ou7YsYM4PqW03Z5xzdYDaNv04YcffpC0EZ43Li4O48ePJ/qR04Vc+aS0ERcXx40Fvo1SvDhw\n4ABWrlyp2BZ8bciVT04XPXv2hI+Pj8iOpA22fczNzVFfX4/Gxkaj8pB0wdr4+/sjNzcXBoOB2Kd8\nXTQ1NaG8vBw7d+5UnePr6uoQGxsLhmGwYMECRTu2vYA2vcbExKB3795EG5IuGIbB7du3sX37dklf\nQl08efIEqampYBgGt27dEj0ZltLF4cOHkZ2djYULF6pqj4yMDJiYmODBgwfYunWrpI1QF+xyzNmz\nZ8Pf319ko3a+8bbQCzwKRSXsYJWbaLV3Mq4Gg8GAZcuW4fHjx1i6dKmqd9U0Gg1SUlLQ1NSEEydO\nYP78+UhNTeWW1LG0trZi3bp18Pb2RmRkZLvKNWPGDLS0tGDWrFncZ8HBwZg8eTKSkpIQEhJi1B5s\n8qqurkZOTg53ITx69GiMHTsW+/btw9mzZ2V9NjQ04OLFiwgMDETPnj1lj83KysKGDRvg7e2N6dOn\no6WlBRkZGVi1ahUOHjyIgIAAkU2HDh0wb948HD58GJ988gliYmLQ0NCA5ORktLa2AoDiTqn/UzoB\nfl+tCHlbrajRiRA1WpGjPToBlLUiREknDMMgOTnZqB/u3bsHQL7vGxsbsWbNmnb1n5o+Fx7j7u6O\nJUuWSNpIaWLEiBEiX0q6kCqfki6CgoJEdhcuXAAgrYtPP/0UnTp1kqyXlC6kyiini3379uHkyZMi\nG6X4ERsbi169ehm1K7sSQUobGo0GmzZtQlZWlmQsJ9moyQH8us+fPx+DBg2StRGed8WKFViwYAFc\nXFyMbMaMGSOrC7nySWkjODgYer0emzdv5mxSUlIU48Xhw4cV20KoDb1eL2kjp4ukpCRJO742Fi1a\nhLi4OFhaWkKj0aC+vl6UW4S5hD/WXFxckJubq5hLWltbodPpYG1trTpuGwwGLF26FE+fPoWDgwPi\n4+MVbTQaDZKTk7Fv3z5UVlbi73//OwYPHgxXV1dReYS6YMdGr169ZMvI10VrayuOHDkCX19flJSU\nICkpCZmZmUbHk3TB7lJpamqKH3/8UbFeDQ0NyM7ORseOHTFw4EDZ8vF1ERkZidTUVFRVVeHMmTMY\nMWKEaCnm7zHfkIMu0aRQVMI+TifdMWPvspCe3v0WqqursWDBAty+fRuRkZGIjY1VZWdjY4OJEyci\nNDQUJ0+eRK9evZCYmCg6Lj09HQUFBVi9ejV0Oh10Oh33HpbBYIBOp5N8+XjGjBlGCRhoexIRFhaG\niooKPHnyxOg7tv2CgoKM2sna2hqBgYH4+eefFd/ju3XrFurq6jBhwgTZ4wwGA3bv3g13d3ccP34c\nkyZNQmhoKE6cOIH+/ftj06ZNaGpqItr++c9/xuzZs3Hp0iWEhIRg2rRpsLW15ZZk2drayvr+n9AJ\n8L9DKyTaqxNAnVbkXopXqxO27EpaISGlk+nTp4NhGDx69MioH5R0wTAMVq9e3a7+U9PnwmMWLVqk\naEPSxK5du4h2crrQ6/WYM2cO0ZecLn799VfMmjVLZCenixEjRuA///mPbL1IupBqQzldvP/++4iL\niyP6kosfGo0GEyZMEI01OW0AbZPnqVOnyo5PNX0otBPWPSEhQdFGeN7evXvjypUrIht2K3+peNHS\n0kJsC0BaGxEREaipqYGLiwtn89e//lUxXuTl5SEgIEC2XkJtSLWfwWDArl27JOPF7t27MXbsWKKv\nlStXctoIDQ1FYWEh3NzcEBkZaRTP2Hgq1AV/rFVVVYFhGC6PSeXr9PR0NDU1oU+fPqpyPF8TJiYm\n2L9/vyo7GxsblJSUoLy8HOnp6bC3t8fOnTtFNmlpaaJ4cfz4cQBtG5E9e/YMb968Ifri64Jti/j4\neIwbNw6//vor9+6fsP34ukhPT0dhYSEXL8rKymTrxeqivr5eNvcJ40VZWRnKy8tx7NgxODo6YsOG\nDdwSXL6f3zrfkINe4FEoKmFfsCatiX79+jVsbGwkl9i8DW/evMGcOXNw//59zJgxAzt27Hir85ib\nmyMgIABlZWXcum6W7777Dk1NTYiMjISfnx/8/PwwZcoUaDQapKenw9/fH2VlZe3yx/6Qt/DdAPY9\nAjs7O5GNnZ0dGIYhvk/A58aNGzA3N8eoUaNkjysqKoJer0dwcLDRHU5TU1OEhISgoqLCaFdAPuzd\n8tzcXJw6dQrffvstUlNTUVlZCRMTE9nNaoA/XifAf0cr7B1EFjVaUfv7iYC0TgB1WpG7wFOrE0Cd\nVkg3A0g6+ctf/sI9hZ42bZpRP8jpori4GFqtFg8ePFDdf01NTYp9LtRFXFxcu3Vibm4OX19flJWV\nEe2kdAEAX375JR48eIDJkyer1qS5uTl3gSz0JaWLN2/e4ObNmwCAsLAwSV9CXciNGyld6PV6VFZW\norm5GePHjxf5Uhs/+GONrZdSzJCL5XKQ7JRihhpfwmPYv0tLS3Hx4kU0Nzeryi1q68WPGaT2U5Nb\npHzJxQy+zePHj1FdXa0qtwh9abVaThuurq7QarX4/vvvkZ6eDoZhuGWkbPuwMY7VBX+snT59GgzD\nICoqSjZff/fddwDaNj5R6ge+Jrp37w6GYdo1N2DLFxUVhZKSErx8+dLIxs/PD2fPnhXFi5MnTwJo\newI2fvx4+Pv7q/YVGRnJtV9sbKyRDUkXrN3169fR2tqK0aNHy/q6ceMGtFotWlpaZNtCGC9YPzNm\nzEBBQQEqKiowffp0kZ/fOt+Qgy7RpFBUYm1tDQcHB25HLT4PHz6Eu7v77+artrYWCxcuRH5+PubP\nn8+9HyFHUVERFi9ejJiYGO7FXpaamhriBhXr16/n7kSxVFRUID4+HuHh4QgPDyduZPLq1SssWrQI\nwcHBWLFihagcAIy2/QXadooyMzMjPrEpLi6Gubk5l8CluHfvHtzd3dGpUyfZ4/hLWoSwFyFSFwiX\nLl1Cjx494O3tbZQY7ty5Azc3N8VNPv5InQD/Pa0Il/6o0cqGDRuMvn8bnQDqtCL3Xp1anQDqtEJC\nqBP2B3V1Oh26d++O7du3Gx0vpYva2lrcuHEDLS0tWLhwoar+a21tRV5eHgwGg2SfC3WxcuVKREdH\nS+pEShO1tbW4cuUKgLZ3STZu3GhkR9JFaWkpp4WJEydi165dRt9L6aK2tpZ7vzIqKgpbtmwxsiPp\ngq1nRUUFTE1NZZ9u8XWhNG5IumBt2An3smXLRD6EuigqKkJgYCBaWlpE8YMfl/na4PeFMGZIxXKg\n7aL//v37yMjIkM0B/LpHREQgNzdX1qa8vBxTpkzhtMEvn7A87O6zW7ZsEd0YefToEfbs2YMhQ4Yg\nLi7OKLew56mvr0d0dDQmTZqEFStWGPkSxgzWpl+/fiJdsHa2trai3EJqw3v37qF///4ICQmRjYts\nnfi6YH25uLgAMM4tfF98bezatYsbN1u2bIGpqSlWrFhhFE/79etnpAv+WFu/fj3s7OywYMEC2Xy9\nfv16xMTEoFu3bli3bh0Aco4XjoeQkBDFeG8wGBAYGMi1F7986enpuHnzJnbu3ImNGzciPDwcYWFh\nsLCwED2p/umnn5CSkoIPP/wQw4cPxwcffICqqiojXyYmJpg8eTIXM/i+Tp48iatXr2LLli3Ytm0b\nZ0OKF6zdoUOH8NNPPyEtLU12znPv3j0MGDAAn3zyiWxbsMtBWV3wy3fp0iX885//xOrVq7Fv3z4j\nP791viEHfYJHobSDcePG4ebNm3j69Cn3Gfv3pEmTfjc/27ZtQ35+PubNm6dqwge0batbU1ODzMxM\nNDc3c5+XlJQgJycHw4YN45YssLi6unJ3pNh/Hh4eANqSqK+vLzHA2NvbQ6/X4+zZs0Y7QJWWluL8\n+fPw9fUV3U21tLREYGAgrl+/bvRbQcXFxbh+/TrGjBkj+z5Bc3Mznjx5wiVROT744APY2dnh/Pnz\nRi+uNzQ0ICsrC126dCFuTQwAx48fx44dO4yS9OXLl/Ho0SNER0cr+gb+OJ0A/z2tCPtCjVaEvI1O\nAHVakaI9OgHUaaVDhw4iO6FOtm3bhry8PAAQTQZYSLpYuXIltw252v6rrKxEXV2dbJ8LdaGkEylN\nJCQkoKqqCj179hRd3AFkXbA7Fw4ZMgSpqamiGCKli4SEBOh0OvTs2VN0cQeQdcHWy8TEBBMmTJCM\nIUJdKLUHSResjY2NDezs7IgxRKgLdrvzly9fYubMmdxxwrjM1wbbF8eOHUNRUREXM+RiOdD2FKm5\nuVkxB/DrvmPHDsVY0K9fP6Nj2PKdOHECV65c4c7Lt/Hy8hLpgt2F8pdffoGXlxenC75dz549UV1d\nzWmD7+vcuXNczODbWFlZiXTRt29f6PV6PHr0CKNHj+Z0QWpDVhuDBw9WbIshQ4aIdNG3b19UV1cj\nNzcXnTt35nQh9MXXBjtudDodXrx4gWXLlhFzL18XrA3DMHj58iWio6MV87WrqyvMzc1ha2srm+OF\n40FNvBfGDNamT58+uH//Pnx8fLi84ODgwJ1DeF72fX0PDw8sXLgQI0aMEPkSxgzWV9++fXHr1i34\n+flh5MiRRjakeOHq6goHBwc8ePAA48aNk53zsLoYOnSoYlu4u7sb6YItn6enJ+7evYuuXbtyy3/5\nfn6P+YYU9AkehdIOFi9ejAsXLmDevHlYuHAhDAYDjh07hoEDB6r+/TglCgsLkZ2dDVtbWzg7OyM7\nO1t0DGnnQRMTE2zatAkJCQmYPXs2QkJCoNPpcPr0aZiamnI/3Px7sXnzZnz88ceYOXMmpk2bhpqa\nGpw+fRodOnSQ9LV27Vrcvn0bc+bMwdy5c2FqaooTJ07A0tIScXFxsv7KysrQ1NSkasmCqakpNm7c\niPj4eERGRiIyMhItLS3417/+hWfPnmHv3r2Sv6MWExODVatWYenSpdwW3F9++SVGjhypuo//CJ0A\n/12tqNmBUw1voxNAWStz584l2rVHJ4A6rSQnJ4vs+DoZPHgwtwEIu/W+sC9CQ0NFuigtLcXNmzdh\nYmKCiRMnquq/wsJCbkMGqT53c3Mz0kV6ejqys7NhaWmJuro6bN26VfQTAqGhoSJNFBYW4uuvv4ZG\no0FUVJTq8n3zzTcA2n5QWMpGqIsXL16o8sXXxeTJk3HhwgVux8KBAwcS2x0w1oXaccPXRUBAAC5c\nuACtVgu9Xo9Zs2bh0qVLIhtS/GDfvc3MzERdXZ3RWGPf8RRqw8/PD1999RU6duyI2tpaHDx4UDGW\nazQaODo64vHjx5LjWlj3S5cuITg4GJmZmQgODoanpyccHByMbEjxwsfHBzk5OTAxMYGHh4eq8rEx\nt6KiQjZHCbXh6+uLK1euyPoixQutVguGYfDs2TOcOnVKMh+y2nBwcFCMiyYmJsR4YWlpCb1eDxsb\nG2RmZhJ9KeWW0tJSUZsp5ZLXr18T27o90DkHmT9izvF7zDcky/SbrCmU/8/o2rUrTp06hcTERBw4\ncACWlpYICgrC2rVriXf5pZB7UnX79m1oNBro9XrRkjcWUrBlP2d/fHTPnj2wtLSEv78/YmNj0bdv\n33aVT2l3rqCgIHz22WdIS0tDcnIyLCws4OPjg9WrV8PR0ZFo07t3b5w5cwaffvopvvjiCzAMAy8v\nL6xdu5a4VI+PTqeDRqNRvUFJcHAwbG1t8fnnnyMlJQUA4O7uzv3osBTjxo1DcnIyjh49isTERHTr\n1g1Lly7FkiVLZHe546NWJ0ptTOoH/t/t0YrwPGq0oma3T2EZhTZqdSK0U6MVUvnU6ERop6SV5ORk\nkQ1fJ0eOHOHOW1BQQHwiFBoaKtIFC8MwkhMU4VhnfyeuublZss+3bt1qpAv2ncW6ujqcOXMGALj/\n+X6EmmCXwWo0GuLOqlLl02g0YBgGV69exbVr14g2Ql2w7avki68Ltg4Mw8BgMGDPnj2S5ePrQu24\n4eviiy++ANC2BEuj0SAjIwMZGRkiG1L8WL58ORwdHXH8+HHJsUaKGT4+PtDr9di7d6/qWN69e3d8\n/PHHonEdFxeHPn36IDMzU7Luz58/x/Pnz9G5c2eRL1K88PDw4JbVqi2fVqvF+PHjUVxcLNkWpJgx\nePBg1NfXIz09nWgjFS98fX2RnZ0tmw/52lATF6XixdSpU5Gbmytppya3COOpmlyiJl+T8gj7WXvz\nCP9cSu3F/pTO2+Y6/mdKuYTkS+2cg+RfKZcIbZTyCKl8bzPfUIuGkXtTnUKhUCgUCoVCoVAo/89A\n38GjUCgUCoVCoVAolHcEeoFHoVAoFAqFQqFQKO8I9AKPQqFQKBQKhUKhUN4R6AUehUKhUCgUCoVC\nobwj0As8CoVCoVAoFAqFQnlHoBd4FAqFQqFQKBQKhfKOQC/wKBQKhUKhUCgUCuUdgV7gUSgUCoVC\noVAoFMo7Ar3Ao1AoFAqFQqFQKJR3BHqBR6FQKBQKhUKhUCjvCPQCj0KhUCgUCoVCoVDeEegFHoVC\noVAoFAqFQqG8I/wf27lDuEaJvt4AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.pipeline import Pipeline \n", + "from sklearn.feature_extraction.text import CountVectorizer \n", + "from yellowbrick.text import FreqDistVisualizer\n", + "\n", + "visualizer = Pipeline([\n", + " ('norm', TextNormalizer()),\n", + " ('count', CountVectorizer(tokenizer=lambda x: x, preprocessor=None, lowercase=False)),\n", + " ('viz', FreqDistVisualizer())\n", + "])\n", + "\n", + "visualizer.fit_transform(documents(), labels())\n", + "visualizer.named_steps['viz'].poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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Aq6OsV/mU/hcMCZMAcF6pB7zatWtLko4fP+62Lz09XVWq\nVJGfn5/X6gAAwLWtJMGQIaQAcF6xV9G8nMqVK6tOnTpKTk5225ecnKymTZt6tQ4AAAAATFXqAU+S\nIiMjlZSUpAMHDji3Ob6Oioryeh0AAAAAmKjUh2hK0ogRI/TBBx8oOjpaw4cPV15enpYsWaLQ0FD1\n6dPH63UAAAAAYKJS6cG7eHXKatWqadmyZWrcuLHmzJmjf//73+revbsWLVqk8uXLe70OAAAAAEx0\nxT14n3/+eaHb69Wrp4ULF172eG/VAQAAeLr6JitvArhelMkQTQAAgOuBx6tvsvImgOtEmSyyAgAA\nAAC4+ujBAwAAKAYeqg7gWkbAAwAAKIaSPlSd+X4ArgYCHgAAwFXAfD8AVwNz8AAAAADAEAQ8AAAA\nADAEQzQBAACuUSzoAqC4CHgAAADXqJIu6ALgxsUQTQAAAAAwBD14AAAABmFYJ3BjI+ABAAAYhGGd\nwI2NIZoAAAAAYAgCHgAAAAAYgiGaAAAAYO4eYAgCHgAAAJi7BxiCIZoAAAAAYAh68AAAAFAiDOsE\nrj0EPAAAAJQIwzqBaw9DNAEAAADAEPTgAQAA4KoqydBOhoMCniHgAQAA4KoqydBOhoMCnmGIJgAA\nAAAYgh48AAAAGMvToZ0M64QpCHgAAAAwlsdDOy8Y1skcQVzPCHgAAADABZgjiOsZc/AAAAAAwBAE\nPAAAAAAwBAEPAAAAAAxBwAMAAAAAQxDwAAAAAMAQBDwAAAAAMAQBDwAAAAAMQcADAAAAAEMQ8AAA\nAADAEAS8/2fvvsOiuNq/gX8XLIhdTKxRwSQUBRVBrEBEsQAGC2LHQjCxl8QW0WjsLRo7FkAFRBQ7\nmhgTe2I3do0tUVREQWlS97x/8GNelt2F3QHN8+zz/VyXV8LunGk75dwz59yHiIiIiIjIQJT6t1eA\niIiIiOh/VdTBnxH7OkWnaetUqQAfD/d3vEb0344BHhERERHRvyT2dQreVG+o28Qv77/blSGDwACP\niIiIiOi/CN/6UWEY4BERERER/ReR+9ZPTmDIYPK/DwM8IiIiIqL/AXICw3cdTDIoLHkM8IiIiIiI\nqETpHBiyX2GJY4BHRERERET/OjYHLRkM8IiIiIiI6F/HjKIlgwOdExERERERGQgGeERERERERAaC\nTTSJiIiIiOi/lpyMnYbc348BHhERERER/deSk7FTTn8/uUHh+w4mGeAREREREREVQW4SmPedPIZ9\n8IiIiIiIiAwEAzwiIiIiIiIDwQCPiIiIiIjIQDDAIyIiIiIiMhAM8IiIiIiIiAwEAzwiIiIiIiID\nwQCPiIiIiIjIQDDAIyIiIiIiMhAM8IiIiIiIiAwEAzwiIiIiIiIDwQCPiIiIiIjIQDDAIyIiIiIi\nMhAM8IiIiIiIiAwEAzwiIiIiIiIDwQCPiIiIiIjIQDDAIyIiIiIiMhAM8IiIiIiIiAwEAzwiIiIi\nIiIDwQCPiIiIiIjIQDDAIyIiIiIiMhAM8IiIiIiIiAwEAzwiIiIiIiIDwQCPiIiIiIjIQDDAIyIi\nIiIiMhAM8IiIiIiIiAwEAzwiIiIiIiIDwQCPiIiIiIjIQDDAIyIiIiIiMhAM8IiIiIiIiAxEqXc5\n8169euH69etqn3fq1AkrVqwAADx58gQLFizA+fPnAQCurq6YPHkyqlWrplKmpKcjIiIiIiIyNO80\nwLt//z46duwId3d3lc9r164NAHj9+jUGDRqE7OxsBAQEIDs7Gxs3bsTdu3cRFRWFUqVKvZPpiIiI\niIiIDNE7i3iePHmCt2/fws3NDV5eXhqnCQ4OxosXL7B//36Ym5sDAOzs7DBkyBDs3r0bPj4+72Q6\nIiIiIiIiQ/TO+uDdu3cPCoUCFhYWWqeJiYlBixYtpGAMAFq1agVzc3PExMS8s+mIiIiIiIgM0TsL\n8P766y8AQMOGDQEAb9++Vfk+KSkJjx8/RqNGjdTK2tjY4MaNG+9kOiIiIiIiIkP1TgO88uXLY/78\n+bC3t0ezZs3QsWNH6U1aXFwcAKBGjRpqZT/88EMkJycjJSWlxKcjIiIiIiIyVO+sD969e/eQmpqK\n5ORkLFq0CMnJydiyZQsmTJiA7Oxs1KtXDwBgYmKiVrZs2bIAct/6paamluh0FSpUKIGtIyIiIiIi\n+s/zzgI8X19f5OTkoF+/ftJnXbt2haenJxYtWoQff/wRAKBQKLTOQ6FQQAhRotMREREREREZqnca\n4BVUtmxZfP7551i9ejVMTU0BAOnp6WrTZWRkAAAqVKhQ4tMREREREREZqnfWB0+bvAHH84Ku+Ph4\ntWlevHiBSpUqwcTERBozr6SmIyIiIiIiMlTvJMCLi4uDp6cn1qxZo/bdgwcPAAB169ZF3bp1cfPm\nTbVpbt68icaNGwMAKlasWKLTERERERERGap3EuDVqFEDSUlJiIqKkpKfAMDTp0+xe/dutGzZEmZm\nZhtKknEAACAASURBVHB3d8eZM2fw8OFDaZq8vz08PKTPSno6IiIiIiIiQ/TO+uAFBgZizJgx6NOn\nD3x8fJCSkoLw8HCULl0agYGBAAB/f3/s3bsXfn5+GDp0KNLT07Fp0ybY2trCy8tLmldJT0dERERE\nRGSI3lkfvI4dO2LlypUoV64cli5ditDQUNjb22P79u2wsLAAkNsfLywsDNbW1vjxxx+xdetWdOzY\nEUFBQShdurQ0r5KejoiIiIiIyBC9szd4ANChQwd06NCh0GkaNGiA9evXFzmvkp6OiIiIiIjI0Lz3\nLJpERERERET0bjDAIyIiIiIiMhAM8IiIiIiIiAwEAzwiIiIiIiIDwQCPiIiIiIjIQDDAIyIiIiIi\nMhAM8IiIiIiIiAwEAzwiIiIiIiIDwQCPiIiIiIjIQDDAIyIiIiIiMhAM8IiIiIiIiAwEAzwiIiIi\nIiIDwQCPiIiIiIjIQDDAIyIiIiIiMhAM8IiIiIiIiAwEAzwiIiIiIiIDwQCPiIiIiIjIQDDAIyIi\nIiIiMhAM8IiIiIiIiAwEAzwiIiIiIiIDwQCPiIiIiIjIQDDAIyIiIiIiMhAM8IiIiIiIiAwEAzwi\nIiIiIiIDwQCPiIiIiIjIQDDAIyIiIiIiMhAM8IiIiIiIiAwEAzwiIiIiIiIDwQCPiIiIiIjIQDDA\nIyIiIiIiMhAM8IiIiIiIiAwEAzwiIiIiIiIDwQCPiIiIiIjIQDDAIyIiIiIiMhAM8IiIiIiIiAwE\nAzwiIiIiIiIDwQCPiIiIiIjIQDDAIyIiIiIiMhAM8IiIiIiIiAwEAzwiIiIiIiIDwQCPiIiIiIjI\nQDDAIyIiIiIiMhAM8IiIiIiIiAwEAzwiIiIiIiIDwQCPiIiIiIjIQDDAIyIiIiIiMhAM8IiIiIiI\niAwEAzwiIiIiIiIDwQCPiIiIiIjIQDDAIyIiIiIiMhAM8IiIiIiIiAwEAzwiIiIiIiIDwQCPiIiI\niIjIQDDAIyIiIiIiMhAM8IiIiIiIiAwEAzwiIiIiIiIDwQCPiIiIiIjIQDDAIyIiIiIiMhAM8IiI\niIiIiAwEAzwiIiIiIiIDwQCPiIiIiIjIQDDAIyIiIiIiMhAM8IiIiIiIiAwEAzwiIiIiIiIDwQCP\niIiIiIjIQDDAIyIiIiIiMhAM8IiIiIiIiAwEAzwiIiIiIiIDwQCPiIiIiIjIQDDAIyIiIiIiMhAM\n8IiIiIiIiAwEAzwiIiIiIiIDwQCPiIiIiIjIQDDAIyIiIiIiMhAM8IiIiIiIiAwEAzwiIiIiIiID\nwQCPiIiIiIjIQBhkgPfkyROMGjUKTk5OcHJywuTJk5GQkPBvrxYREREREdE7VerfXoGS9vr1awwa\nNAjZ2dkICAhAdnY2Nm7ciLt37yIqKgqlShncJhMREREREQEwwAAvODgYL168wP79+2Fubg4AsLOz\nw5AhQ7B79274+Pj8y2tIRERERET0bhhcE82YmBi0aNFCCu4AoFWrVjA3N0dMTMy/uGZERERERETv\nlkEFeElJSXj8+DEaNWqk9p2NjQ1u3LjxL6wVERERERHR+2FQAV5cXBwAoEaNGmrfffjhh0hOTkZK\nSsr7Xi0iIiIiIqL3wqACvNTUVACAiYmJ2ndly5YFALx9+/a9rhMREREREdH7YlABnhACAKBQKLRO\nU9h3RERERERE/80UIi8qMgB37tzB559/jsDAQPTv31/lu4ULFyIkJASXL1/W+IZPm0uXLkEIgTJl\nypT06tJ/kTcpqVAaly5yOqOcLFSuUF6vMvnLySnDZf3nr5+hLqu46/c+l8X9zv1e3GVxvxv+sv7T\n189Ql8VzS3u5gjIzM6FQKGBvb1/oPAwqwEtOToajoyO+/PJLjBs3TuW7iRMn4tSpUzh79qxe87x8\n+TKEEChdWrcfhYiIiIiIqKRlZWVBoVCgWbNmhU5nUOPgVaxYEXXr1sXNmzfVvrt58yYaN26s9zyL\n2oFERERERET/KQyqDx4AuLu748yZM3j48KH0Wd7fHh4e/+KaERERERERvVsG1UQTABISEuDl5QVj\nY2MMHToU6enp2LRpExo0aIDw8HA2tSQiIiIiIoNlcAEeADx69Ajz58/H+fPnUa5cObi4uOCbb75B\n1apV/+1VIyIiIiIiemcMMsAjIiIiIiL6X2RwffCIiIiIiIj+VzHAIyIiIiIiMhAM8IiIiIiIiAwE\nAzwiIiIiIiIDwQCPiIiIiIjIQDDAIyIiIiIiMhAM8IiIiIiIiAwEAzwiIiIiIiIDwQCP6H9EZmbm\nv70K78Xr169LfJ5KpRKPHz8u8fkSEZWElJSUf3sV6H+YId0jX79+jbS0tH97NYqNAR6Rnl68eIE/\n//wTycnJyMzMhFKpfCdl9OHm5oajR49q/f7AgQNo166dymc+Pj7YsmUL4uPjS3RdCvP69WvExMRg\nw4YNCAkJwU8//VRkxSQ7OxuXL19GTEwMXr58iZSUFLx580br9N7e3li9erVe62VtbY0DBw5o/T46\nOhre3t56zfNdSUhIwIEDBxAUFIQnT54gISEB9+/fL7SMnP3+30KpVOLly5c6P8B4X/viXTxoKIw+\n58mlS5cKnVdsbCwCAgLexWr+xxk3bhyOHj2KrKysdzL//MfloEGD8Pvvv2ud9tdff4WHh4fa51u2\nbCl0GTExMejSpQuuXbuGYcOGoVmzZnB0dMTw4cNx4cIFjWX27dsHa2trHbfi3Xj69Gmh/549e4ZX\nr14hJyfnX11Pfcg9njw9PbFkyRKcP3++xOsHJfFw99+4R8q51+nq5MmT2LhxI2JiYqT9c+TIEbRv\n3x6tWrWCg4MDBg8eXGLL+zeU+rdXgOi/xcWLFzF37lzcunULALB582YIITBlyhRMmTIFXbt2LZEy\n+b148QLPnj2DhYUFypYti1KlSsHIyEjtQhcbG4tr166hUqVKavNQKpU4cuSI2kVeoVBg3rx5WLhw\nIRwdHeHl5QV3d3dUrFhRp/2RkJCAM2fO4OnTp+jatStMTU2RmJiIhg0bapw+PDwcixcvRnp6OoQQ\n0udly5bFpEmT0L9/f7Uyhw4dwty5c/Hq1SsAufsvKysLY8aMwahRo+Dv769WJjExER988EGh6x4X\nF6dS0RJC4Pz588jOzlabVqlUYv/+/VAoFGrfDR8+HK6urnBxcUHt2rULXWZ+48aNg5eXF5ydnVG6\ndGmdy23evBkrVqxARkYGFAoFbG1tkZ6ejhEjRqBPnz6YMWOG2nrqu98zMzOxYcMGnD59GvHx8Ror\nGwqFQq/tzV8uNDRU73Ka/P3331iyZAlOnTqFjIwMbNq0CUZGRliyZAkmT54MBwcHtTJyjsHMzEz8\n+OOP2L9/P16+fKl1f9y8eVPlM29vb/j4+GDkyJF6b1t2djauXbuGZ8+eoUWLFjAxMUFOTg4qV66s\ncXp9zxN/f3+sW7cOLVq0UPk8JycHmzZtwtq1a5GRkaFxWZ6entIx37x5cxgZFf2c+NKlS7C3t9f6\nfWxsLLp27Yrq1asXOa/8FAoFfvnlF72XNWvWLAQFBQHIvUb/9NNPqFixItzd3eHp6QknJyeN53tB\nbm5umDZtGtzc3DR+Hx0djfnz52Pv3r0AgHPnzqFjx46oX7++2rRKpRInTpzAkydP1L6bN28e0tPT\n1YLuJ0+eYNasWTh58iQqV66Mfv36wdTUFG3atEFiYiJOnDiBkydPIiAgAOPGjdO4zEGDBhW5nflp\nO4dPnDghnSOaAjJN5dq3b6/TfjY2Noa1tTXGjx+P1q1b6/Qb9+jRA5aWljpsUeHrqOv+yQvC5R5P\n9erVQ0REBDZt2oSKFSuidevWcHV1hbOzM6pVq6a1XFHH4IEDB/D999/j7NmzOm1HHjn3yLdv35bY\n8STnXpeftrpTWloavvjiC1y6dEm6B1haWmL69OkYN24cateujQEDBiAlJQU///wz+vXrhx07dmg8\nZwuj7fcojKbrWXEwwCPSwdWrVzFkyBDUqlULfn5+0gWpcuXKKFu2LL7++muUL18eLi4uxSqTp6jA\n0MXFBRMnTpTevikUCqxfvx7r16/XuP5CCLVgcseOHXjy5AkOHjyImJgYfPvtt5g1axbatWsHT09P\ntG/fHmXLltU4P30vvr/88gtmz54NGxsb+Pv7w8LCAkIIPHjwAMHBwZgzZw5q166Nzz77TCpz6tQp\nTJw4Efb29vD398eCBQsAAHXr1oWVlRWWLl2KDz74AJ9//rnKunl6eiIqKgrt27fXWmGsVq0a1q1b\nh0ePHkn7LzIyEpGRkRqnB4CBAweqffb8+XPMnj0bAPDxxx/D2dkZn332Gezt7Qut+MqpBOzfvx+L\nFi2Ch4cH3N3dMXbsWACAjY0NOnXqhO3bt8Pc3FzlBitnv8+dOxeRkZGoWbMm6tSpo3U7NFVEX716\nhYyMDFSuXBn169eHUqlEbGwsEhMTUblyZZibm2vdPn08evQIvXv3hkKhQLt27XDkyBEAuRXBhw8f\nYujQodiyZQuaNm1arH0BAIsWLcK2bdvQsGFDODg4oEyZMjqtoy4PGjTRN1iTc540bNgQAQEBWLly\npfRm//Lly5gxYwb++usvWFlZYebMmRrXT05FVJeAMj09Xe2hwc2bN5GamgpLS0tYWFhIzcBu3ryJ\natWqoVWrVrKWlT94PXHiBM6ePYuYmBj8/PPP2LVrF6pXr46uXbvCw8MDdnZ20rRyHqwlJSVJlb28\nh2rz5s3TuJ+EEGjTpo3a53369MEPP/yA9PR0jBkzRmVbMjMz0b9/f9y9exfPnj1DZGQkzMzMAAC3\nb9/G5MmTsX79erx69Qrff/+92rw1ncf6CgsLw5w5cwAAZmZmOp8js2fPxtKlS5GVlYVu3bpJlfFH\njx7hwIEDSE5ORv/+/ZGeno7ffvsNAQEBCAkJQUBAQJG/cVpamtq2ybk+ado/SqUSiYmJyMjIQJ06\ndfDJJ59I3+lzPOW3Zs0a6S38yZMncerUKUydOhUKhQKNGzeGi4uL9CBRzjGob+AlhEB8fLxe90hT\nU9MS2edy7nV5iqo7XblyBdevX0dgYCBatGiBmzdvYu7cuQgICICNjQ22bdsm1XtGjRoFHx8fLF++\nHD/88IPasm7duoVffvkF8fHxam9s09PToVAoVLZPzvWsOBQi/2NMItJo2LBhePbsGaKjo5GWlobW\nrVsjODgYrVq1QmpqKvr164fy5csjPDy8WGWA3MBwwIABqFWrFj777DOEhoZi8+bNqFSpEsaNG4fY\n2FisXbsW1atXx927dyGEwLRp09C7d280a9ZMbd2NjIyki0epUtqf6dy/fx8xMTH49ddfcfv2bZQr\nVw4dOnRAt27d0KZNGyn42L9/P7755huVi29wcDAsLCwwf/58/PTTT5g6darKxdfX1xdZWVnYvn27\n2s0/KysLvr6+KFeuHMLCwqTP+/bti5ycHGzfvh1v3rxBq1atpP2Xk5MDPz8/pKWlITo6WmV+gYGB\nOHDgADIzM1GvXj2YmZmpBSkKhQLz58/HkydPIISAn58fhg8frrFylbf/LCwsNO63ly9f4tSpUzh5\n8iTOnDmDxMREVKpUCW3atIGLi4vGiq8QQqUS8ObNmyIrAd27d0e1atWwadMmJCYmquwPABgxYgQe\nP36M/fv3F2u/t27dGq1bt8aSJUs0bq8258+fR0BAAGbOnIlu3bqp7PMDBw5g+vTpmDdvHrp27ap3\nZQNQfdI7ZswYXLlyBbt374ZCoVA5t168eIF+/fqhQYMG2LhxY7H2BQC0adMGzZs3x48//qjX+n77\n7be4e/eudK7q4tSpUwgICIC9vT06dOiABQsWIDg4GDVq1MC0adPw559/YsGCBSrBmpzzJC0tDSNH\njsTFixcxe/ZsXLp0CTt37kT58uUxZswY9O/fv9AHFAUrordu3VKriDZu3Fia3sfHB3/99VeRAWX+\n69fhw4cxdepUrF+/Xq0if/nyZQQEBGDMmDFqD17kLCtPTk4OTp06hUOHDuHYsWN48+YNPvroI3h4\neMDLyws1atRAly5ddG7WLoSAg4MDWrZsCSEEVq9ejY4dO2p8s5R3nfHw8NDYimL58uVYt24devTo\ngWvXruGvv/5C06ZNMXPmTFhbW6NZs2YYPXo0hg4dqlIuLS0NX375Jc6fPw9fX1989913AHKbaE6e\nPFmqCBdHp06dYGpqig0bNuj1FnbOnDk4evQoIiMj8eGHH6p89+bNG/j4+MDNzQ2TJ0/G27dv0b9/\nf1SpUgXJycl6/8b6XJ90kZOTg6NHj2L69OlYvXo1HB0dNU5T2PGk7Z6SJyEhAUePHpWaKCoUCpw/\nf17vY9DExETtHqRL4LVs2bJi3SPl7nM59zpAt7pTxYoV4evriwkTJkjloqOj8e2332LhwoXo1q2b\nyjxXrVqFrVu3qr0B/fnnnzF+/PhCmw8rFArp/JJ7PSsWQURFatasmdiwYYMQQoiEhARhaWkpzpw5\nI32/detW4eDgUOwyQggxdOhQ0aVLF/H27Vvx6tUrlXIpKSmiW7duom/fviplVq5cKe7cuVPs7czK\nyhKnT58W48aNE5aWltI/Z2dnERISIpRKpfD29hZDhw7Vul1fffWV8PT0VJmvnZ2dCAkJ0brckJAQ\n0axZM5XPmjRpIpXRtJzw8HDRtGlTtXl99tlnOv3LLzo6Wvzzzz867iXtlEqluHbtmli7dq3o0KGD\nsLKyEjY2NoWWyc7OFseOHROTJ08WTk5OwsrKSnTs2FEsX75c3L9/X5rO1tZWbNu2TQiheX9s375d\nNGnSRGXecva7o6Oj2L59u87bnMfLy0vMmTNH6/cLFy4U7u7uQgghpk+fLiwtLYWVlZVwdnbW+zdz\ndHQUa9asEUJo3hcbN24UTk5OKsuXsy+EyD0Od+zYodtOyGf69OmiadOmwsbGRnTu3Fn0799fDBw4\nUOXfoEGDVMr06dNH+Pj4iJycHLXtys7OFv379xfdu3dXWz8550lmZqYYPXq09DtMmjRJvHz5Uu/t\nFEKIV69eiR07dkjHvLW1tcr3qampYvDgwcLW1lbs3r1bBAYGCmtra+Hg4CC2bNkicnJy1Obp7u4u\nli1bpnWZK1euFK6urmqfy1mWJg8ePBDjx4+XroFWVlbC19dXbNq0SURHR4tdu3YJS0tLERgYKKKj\no9X+7dmzR5w4cUJkZWVJ85wyZYq4cuWKTsvXJDQ0VLqmFDwm8x8HBb19+1b06dNHWFlZiQULFggh\nhNi7d6+wsrKSvS752draioiICL3LOTk5iaCgIK3fb9y4UbRs2VL6OyQkRDg6Osr6jfW5Pulj0aJF\nonfv3kVOp+14OnLkiMp0ycnJ4vjx42Lp0qWib9++wtbWVlhaWgoHBwcxfPhwIYQQ169fl30MCiHE\nuXPnRNOmTcXu3bvV9tX+/ftFkyZNxMGDB1U+j4iIEDdv3tRr38jd53LudULoVneysrISYWFhKuVi\nY2OFpaWliImJUZvntm3bhK2trcZtc3V1FefPnxfp6elatzGP3OtZcbCJJpGOCmt28vbtW419c+SU\nuXz5MkaMGAETExO8fftW5bvy5cvDx8cHK1asUPl81KhRAPTvu5NX5vTp0zh8+DCOHj2K5ORkVK1a\nFf3794eXlxcUCgUiIiKwYMECPHr0CPfv30evXr20zs/FxQXz589X2w8FtyW/1NRUGBsbq3xWunRp\nje398yQkJGjsv/brr79qLaNN9+7dAeT+JuXKlQOQ28QuJiYGRkZG6NKlC6pUqVLoPO7fv48LFy5I\n/549ewaFQlHkU1pjY2PprcfDhw+xcuVKxMTEYO3atVi3bh2aNGkCf39/lC9fHsnJyVrnExsbC1NT\nU5XP5Oz3zp0748iRI/D19S10vQv6+++/Cy1Ts2ZNvHjxAgDw/fffw87ODjNmzEDr1q3VjpeiZGZm\namySlMfY2FitD5mcfQEAjRs3xvXr1+Hj46PXOp4+fRpVq1YFAGRkZODp06dFlrl16xbGjx+v8e2Z\nsbExPDw8sGjRIpXP5Z4npUuXxooVKzBz5kzs3LkTDg4OUtM+XaSkpODSpUvS8X79+nVkZmaiYsWK\naN68ucq0pqamCAoKwsSJEzFlyhQoFAp069YNkyZN0rrMFy9eFNr3yNTUVGMSGTnLynPv3j0cPnwY\nhw4dwoMHD2BsbAxXV1d4eXkBACIjI7F48WKMGjUKI0eOxNOnT+Hu7o5PP/20qN0FADof59qOlQ4d\nOiA1NRUrVqzAxYsX0bZtW6kf0ccff4xdu3ahb9++avcdExMTBAUFYeDAgQgJCYEQApaWliXWB69e\nvXp4+fKlXvMBct9wFZZQJCsrC+np6dLfZcuWhVKplPUb63N90keDBg2wbds2jd/pcjyNHj0ao0aN\nwuvXr3Hx4kXcuXMHSqUSlSpVQvPmzTFhwgS0aNEC1tbWUiuaRo0aoVGjRgCg9zEI5F5/e/XqpTEp\niqenJ27evIkVK1aovFn74Ycf0Lt3b70S88jd53LudYBudad58+Zh79696NWrl3Se1K5dG2fPnlXr\nIpGdnY39+/erNMHN8+jRI0ycOFFjX29N5F7PioMBHpEOmjRpggMHDmi8GaalpWHnzp2wtbUtdpk8\ncgJDffvunDhxAocOHcKvv/6KpKQkqUmmp6cn2rRpo1LZbdKkCZ49e4a9e/fKuvg6OjoiLCwMPXr0\nUGuKExcXh/DwcLVKYYsWLbBz504MGDBAbRkvXrxARESEWhldJSQkqFxsk5KSMH78eCQlJSEqKgop\nKSno2bMnnj17BiEE1qxZg/DwcHz00Ucq8wkJCcHFixdx8eJFJCYmAgA+/fRTuLm5wcnJCQ4ODlIl\nXxtdKwGffvopwsPD4ePjoxYA3L59G2FhYWr9x+Ts98mTJyMgIAB9+vRBhw4dYGZmprFvYMHKgbm5\nOQ4ePIg+ffqoBUoZGRnYtWuXStM0Hx8fxMXFYfXq1XB1dUWnTp0K3U/5WVlZ4ddff9WYFCXvplyw\nGZycfQHk7g9/f398+umn6NKlS6E36fzkPGiQE6zpcp5kZGQU2ulfqVRi5syZWLdunfSZtg7/c+fO\n1bkiWnDb9AkoLS0tsXPnTvj4+KhdTxISEhAWFoYmTZpoLKvPsu7fv49Dhw7hp59+wr179wAA9vb2\nmDFjhtqDHQ8PD/Tu3RshISEYOXKk9GAtv6ysLJw+fRpGRkZo3bq1WrN4XZKRaKpsFrRnzx4peQuQ\n2xRPoVCgc+fO6Nq1K/z8/FT6gFasWBGbN2/G0KFDERoaKj0gKYk+eAEBAZg7dy46deqksTKsjYOD\nA0JDQ9GpUyc0aNBA5bvY2Fhs3bpVJZnK0aNHpSRe+h5P+l6fdJGZmYl9+/apLFfu8ZR3T61Zsyb8\n/Pzg4+ODChUqFLkOmo7BosgJvIQQatfNosjd5+3atdP7XpenqLpT6dKlce3aNXTt2hW9e/eWEhcV\nfAgeERGB7du34+7duxr739WoUUOvTKnFuZ7JxT54RDq4fPkyBg4ciKZNm8LNzQ2LFi3CuHHjUK5c\nOWzduhVPnz7Fpk2b0LJly2KVAYAhQ4YgNTUVO3bsUGt/npaWhu7du6NWrVoICQmRysjpu2NlZYVS\npUqhXbt28PLyQvv27WFiYqJ1HwQGBuLVq1eoUKEC/vjjD+zevRtGRkYq63f79m30798fn332mUof\nrrt378LX1xdGRkbw9vaWbuYPHjzAvn37kJOTg4iICJWng/fv34evry/MzMzg7OyMbdu2oX///jA2\nNsbu3buRmZmpViZPREQETp48ibS0NJVgOCcnB6mpqbh37x6uX78ufT579mzs2LFDagcfEhKCBQsW\nYNKkSWjcuDG++eYbODg4YOnSpSrLsbKygkKhQI0aNeDn54cePXoU+rY0/7ZpqgR4enpqfFvYu3dv\n3L9/H6ampsjKyoKjoyN++eUXdOrUCdnZ2Th27BgqVKiAqKgolSBUzn4/ceIExo4dW+jbrvx9C/LE\nxMRgwoQJaNKkCXr06IGPPvoI6enp+PvvvxEREYGnT59i/fr1Kn04lEolvL29kZaWhp9//lmnjIwA\n8Ntvv2HEiBHw8PCAm5sbxo8fjzlz5qBq1arYtGkTLl++jOXLl6sEjXL2BQB06dIFCQkJSEpKKnR/\nFMyimZ+2jG4FjRw5Eg8ePMCePXuQlpamcm69ePECPXr0gK2tLdauXSuV0eU8sbCwQPny5XXat/lt\n3bpV7TMrKysAhVdEi8ogFxsbCyMjI9SqVUv6rGBAeebMGQQEBODDDz+Ep6enyvG0b98+ZGVlYevW\nrVICBrnLytueTz/9FJ6envDy8lKZtqAxY8bgn3/+wZ49e5CZmYk5c+bgyZMn2Lx5MzIzM+Hr64vb\nt28DyE1mExoaKgUAuiYj6d69u07ZJQtq0aIF5s2bh7t37+Lw4cOoV6+e2jSpqamYNWsW9u3bp/E8\nlmPmzJk4efIknj9/DnNzc1SrVk1t/TW9+Xv48CH69u2LlJQUODs7o379+ihTpgwePXqEEydOoFSp\nUti2bRtGjBiB58+fIzs7G2ZmZlIrizy6HE9yrk+A9iyamZmZePjwIZKSkjB69GiMGDECgPzjady4\ncTh79izOnj2L27dvw8jICDY2NnB0dESLFi3QvHlzrQFfREREkQ8N8u8Lb29vmJqaYuvWrRoDr969\ne6NcuXLYvn279Hl4eDjWrVuHadOmScF0Uceo3H0eFxeHXr166XWvA3SvOw0fPhyLFy+GmZkZNmzY\noHHd27dvj5SUFEyfPl2tXx4AhIaGIiQkRGP/UU10vZ7l77tcXAzwiHR0+vRpzJw5U+2J5wcffIDp\n06drfAMhp4ycwFBOooXIyEh07txZp4AkP7kX36tXr2LOnDm4evWqyueNGzfG9OnTVTIe5rlz5w7m\nzJmD8+fP61xmw4YNWLp0KcqUKYMKFSogMTERNWvWxOvXr/H27VvUqVMHHh4eKp2sXV1d0blz2dOl\nMAAAIABJREFUZ0yZMgUAMGDAADx8+BCnT58GAAQFBSE4OFhtDKtt27bh3LlzOHfuHN68eQMzMzM4\nOjpKN+WPP/5Y4z6UWwkICgrCsmXLpKa0AFCuXDk4Ozvj66+/VtvngP773dPTE4mJiRg5ciTMzc01\nNlsEoNZRHMjtrL506VK8evVKuvkLIVCnTh0EBgbC1dVVrUxmZiYyMjJ0Hp4j/7LmzZuH1NRU6e2F\nEAJly5bF+PHjMXjwYLUyco7BvCZgRdHU/E7fYVLkPtTQ9zx5/fp1kU2OtTl27FiRFdGvvvpK1rwL\nBpRnzpzBkiVLVIJnhUIBBwcHTJkyBY0aNZKdlCBvWcuWLYOnp6fOTdxycnKkc2LZsmUICgpCz549\nMXfuXOzYsQMzZszAoEGDYG1tjQULFqBTp05Spl25yUj0lZSUhAoVKhT6wOT+/fs4f/48+vTpo/F7\nXR9KALmVYV1oeqv97NkzrFy5EkePHpWaqJmamqJ9+/YYO3YsPvroI/Tp0wf37t3DBx98oNd+K3g8\nybk+ads2Y2NjVK9eHZ6enujXr580v+IcT3mSkpJw7tw5nD17FhcuXMDdu3cB5L4FKphUbNWqVVi1\napWUGEXbsDv594WcwKtLly549uyZ1uFTAM0PuuTscyD3+NP3Xqdv3SkzM1PrQ5YHDx6gfv360m8z\ndepUtWkOHz4MhUKB5s2bawx487Lm5tHlelaSGOAR6UEIgRs3buDx48dQKpWoU6cOGjduXGh2Sjll\n9A0MmzZtivHjx8PPz09j1qmIiAgsWrQIly9f1mt7b968CRsbG7XP5Vx887x69QqxsbHSRV6XG/br\n16/xzz//SPuvsPTzXbp0gYmJCbZu3YrExER07NgRR44cQe3atREZGYmlS5di165dKk2CbG1t8d13\n36Fnz55ITk5Gq1at0LVrV6m/U1RUFObOnYsrV65oXe7t27fxxx9/4Ny5c7h48SKSkpJQpUoVODo6\nqmVgLG4lQAiBxMRE5OTkoFq1atJ3mioLeXTd73Z2dvjmm29kV5yVSiWuX7+Op0+fQqFQ4KOPPtJ4\nDJWElJQUnD59WuXcat26dZHNYuUcg/rSNRtuwWFS5DzUyKPreeLq6orevXtLbx3k0qciWhwJCQmI\njY2FQqFAnTp1ivx9S1rBJt15OnbsCCcnJ+mt3LBhw3D16lX8/vvvKFWqFH788UdERUXh5MmTAHLP\nrWnTpmkNqv4TFHfs1uJ4/fq19JZOzhtMXbzP65M22o6n/JRKpXQsHTlyBDdv3tT4xtXV1RX16tXD\nxo0bdR6eAtA/8NIU4Gii6UFXcfa5tnudNnIequsi76GsPrS9IX9f1zP2wSPSQ14qcH1eo8sp06ZN\nG+minr/Cpi0wlNN3JysrCytWrCi0KWNKSorGC9SHH36IBQsW6BxoFDWgrUKhQJkyZWBmZgY7OzsM\nGTJEpdJdpUoVnd84xMbGYsKECahQoQIqVKiAypUr48KFC+jevTv69euHixcv4scff8SyZcukMjVq\n1MDjx48B5I6XlpOTo3KDu3TpUqFv2IDcG4CVlRU8PDxw+vRphIWF4dq1a9IYbfnlf3uoTf5KgLGx\nMX7++We4u7sDyN1fBSsIV65cwYwZM7Bv3z6N8zMzM4OZmZnUR8jY2Fjj0Bnm5uaF9rEsipGREezs\n7LSO91SSKlSooPMNO2+AbldXV9jb2+uVUATIfeNx9OhRPH36FKVLl0bt2rXh4uKiNYnOihUrULdu\nXWmYlLwm1Y0aNcKePXvQr18/rF+/Xi3As7S0xNatW/V6qAHk9j18+PChSoKlN2/eaHxDn5iYWCJB\nbaVKlaTxJvOa5d28eVPjNePp06cIDw/HF198Ia3Thg0bkJCQgC+++EJrhff169f4448/EBsbi9Kl\nS+Pp06do3bp1kf2TcnJycP36dcTGxqJMmTKoWbOm1muwvk268zx//lwKuN++fYvz58/D1dVVOqdq\n1aql0rRXbjKSzMxM/Pjjj1IzPE19sItqJqyL4ozdmkefN38F6ftWWZ/fOM/7uD7JPZ5u3bqFP/74\nA3/88QcuXLiAtLQ0mJqaolWrVujTpw+cnZ3VyiQkJGDkyJF6BXcA0KNHD3h7e+PGjRtSsFFY4KVv\nIqz89N3n7u7u8PLygpeXFxo0aKBzv2fg/9ed9H2oXpS8ZtcloVq1anptk1wM8Ih0kJmZiQ0bNuD0\n6dOIj4/XeoMtmJRAlw71BfslPHr0CA0aNIBCoVDJlpUnJSUFS5YskcYzAuQlJFm+fDk2bdqEmjVr\nolKlSrh79y4cHBwQHx+P2NhYWFhY4Ouvv1abn5xAo1WrVvjll1/w5s0bWFhYqAxoe/PmTZQtWxaN\nGjXC69evsXnzZuzduxebNm1CaGiotM81NTbQVKkpVaqUSn+j+vXr486dO9LfTk5Oan3p8t6wpKSk\n4ODBg6hcuTLat2+PuLg4bNiwAXv37tX6tuPNmzc4e/asdGN++PAhgNyAb/jw4dI4TQXpWwmYMGEC\nFixYAE9PT5X5pKSkYPHixYiKilK7yevbRwjIbQ46bdo0NGrUCO3atdNaOSvuWHbFtWfPniLPx/zL\nyhuge+PGjahUqZJOA3TnWbJkCTZv3qy2nMWLF2Pw4MGYNGmSWhk52XDzy3uoUVRADuifYMnT0xNR\nUVFScKYvfSuid+/excCBA5GSkgJPT08pwHvz5g3CwsJw4MABjUmMwsPDsXjxYqSnp6uc/2XLlsWk\nSZM0JtkBcvtozpo1C3FxcVI5hUKBDz/8EDNnzlRpdqdLk+6CY8vlqV69uhSwnTx5EpmZmSoPhu7c\nuaPSP0duMpJFixZh27ZtaNiwIRwcHPSuzOtK7kMJQN6bv+IErkX9xvn7qOuqJK5Pco8nJycnJCUl\nQQiBTz75BL6+vnBxcUHz5s0LDUw++eQT6Z6jLyMjI9SoUQNKpVK6JyuVSp0D8jxKpVIa21fft1Ga\n9nmNGjWwdu1arFmzBjY2NvDy8oKHh0eRD7nyz1Pfh+pypKSkYP/+/ejWrZtU59i5cyfS09PRq1cv\ntZwG+vaVLC4GeEQ6mDt3LiIjI1GzZk3UqVNHpwugrh3qCxowYABCQkI09t+KiYnBvHnz8OrVK5UA\nb8KECfD19UW3bt3g7OwMhUKBo0eP4tixY1LfnTFjxqjM6/Dhw2jRogVCQkIQHx8PFxcXzJgxA59+\n+imOHz+OsWPHanyaJyfQsLGxwf79+7FmzRq1Pg1XrlzB0KFD4e3tDR8fH9y5cwfDhg3DV199hadP\nn8Le3h5OTk5FNs3I07BhQ1y+fFlKa29ubq4SKL1580btbec333yDt2/fYufOnahRowa+++47mJiY\n4K+//kJ4eDi8vb2lbFv5de/eXcomWLFiRbRu3Rr+/v5o165doTcjOZWAtm3bYvLkydLNAwAOHjyI\n+fPn4+XLl3B1dcW3336rUmbVqlXYsWMHevbsCSA3KLp165ZKH6EVK1ZIfYQASL/fl19+ibJly6JK\nlSpq+16hUBSa3vxd++GHH7B+/XqULl1a40D2mqxZs0ZtgO6pU6cWOkA3kLs/Nm7cCFdXV3z11Vdo\n2LAhlEolHjx4gA0bNiA4OBiffPKJNNRGfvpmw5UTkJ86dQoTJ06Evb09/P39sWDBAgBA3bp1YWVl\nhaVLl+KDDz5QSbBkZGSEe/fuwcXFBfXq1dO4D7VVduVURJcuXYry5csjMjJSpWn0119/DV9fX/j5\n+WHJkiUqAe8vv/yC2bNnw8bGBv7+/rCwsIAQAg8ePEBwcDDmzJmD2rVrq2XTu3DhAkaPHg0zMzOM\nHz8eDRs2lMqFh4djzJgx2LJli5SdMTo6GtbW1ipNurds2aLSpLtHjx4at8vJyQmhoaEoW7YswsLC\npEzESUlJ2LVrF3bs2IG+fftK01+8eBHly5fH559/rlcykkOHDsHd3V2tqXdJk/tQQu6bP7mBqy6/\ncd6y3ze5x5ODgwNcXFzg7OyMmjVr6ry8cePGYfz48XBycir0zWpB+gbkycnJmDFjhvRQUtMD17y3\nZcW1detWxMfH49ChQ4iJicHChQuxePFitGjRAt26dUPHjh1RoUKFf/UhY2xsLAYPHownT57A1tZW\num9cunQJ0dHRiIyMRGhoqPTwUNe+kiWqREfVIzJQrVq1EhMnTtSrjLu7u/D29hbx8fF6lXNzcxNO\nTk4qg4o+fvxYDBs2TFhZWYm2bduKAwcOqJW7ffu2GDBggMoA5ZaWlqJnz57i8uXLatM3atRIbN26\nVfq7devWKgNcBwYGii+++EKt3PDhw4WNjY2IioqSPjtw4IBo06aNsLS0FMOHD1cbNNzNzU0sXrxY\n6zYvW7ZMdOjQQfp71apVwtLSUnz//fday2gTHh4uLC0txcSJE0Vqaqo4fPiwsLS0FCtXrhQHDx4U\nbdq0URso/u7du0KpVKrNKzMzs9DBn729vcWyZcvE+fPnRXZ2ts7r2LlzZ+Ht7S2Sk5PFP//8Iywt\nLcU///wjsrOzRVhYmLC3txcPHz5UKZOdnS0mTZokrK2txapVq8TQoUOFpaWlaN++vTh69KjG5XTo\n0EF8++230t9Dhw4VDg4O0sC3K1asEG3btlUpM2DAAJ3+/ZvatWsnhg0bJtLS0oo1n6IG6BZCiG7d\nuqkNSJ7foEGDRI8ePdQ+Hzx4sPDx8RFCqA/Wm5qaKtzd3YWfn59KmaVLlwpLS0sxbdo0IYQQkZGR\nwtLSUsydO1dER0eLFi1aiMDAQJUycgZH13dg+fxGjBghIiMjxbNnz7Tuk4JatGghQkNDtX6/adMm\n0apVK5XPevfuLbp37y4yMjLUps/MzBTdu3cX/fr1U/tu0KBBwt3dXSQlJal9l5ycLNzd3YW/v7/0\nma2trQgODlZZ1+joaOnvCRMmiPHjx2tc7zdv3ojBgwcLS0tL0axZM+m6fPHiRWFpaSn8/PxU1kPu\nfm/SpInawObvQrNmzaTfSdMA00FBQcLe3l6tnC4DTBe85gqRe88ZPXq03uup72/8PhXneBJCiMTE\nRHHw4EERFBQkgoODxeHDh0VycrLW6YcNGybatWsnrKysRNOmTcVnn30m2rdvr/LPzc1Npcyff/4p\nbG1thbu7u5g/f770e12/fl106NBBWFtbi2PHjqmUCQwMFJaWlsLX11e693z99ddiyJAhonHjxsLT\n01OcPHlSpUxCQoIuu6xIsbGxYuPGjaJXr17CyspK2NnZiTFjxuh8PulyXdPX+PHjhZOTk8r5kefC\nhQuiZcuWKvdeFxcXMXDgQI3Xs3eFb/CIdJCdnQ1HR0e9yjx79gzTpk3TuwnU9u3bMWTIEPj5+WHN\nmjW4dOkS1q5di6ysLPj5+WH06NEaU57r23fHxMRE5SlSvXr1pCQJQG7ikZiYGLVyq1evxrRp0zBj\nxgzExcXh0qVLOH36NOrUqaPxDR2Qm9SiRo0aWrfZzMwMcXFx0t8ffvghhBBFDhKuSd++ffH8+XOE\nhYWhVKlScHd3h6urK1atWgUgt99WwaangwcPRvfu3dU+z3tDpM3u3bul/9en74mcfoLGxsZYuHAh\nqlSpgpUrV8LY2BgjR45EQECA1ifV+vYRAjSnxv9Pk5KSgk6dOqmlS9e1rK4DdAO5qdwnT56sdX7u\n7u4qQ4LkGTNmDAYOHIgBAwbAzc0NCoUCV69exV9//SVldJs1a5ZKmUOHDqFXr17Sm/+ffvoJFStW\nxKRJk1CqVCk8fvwYUVFRKmXkDI4uZ4y+PKtXrwaQ2zcuJiZG6htXq1YttGnTRmPfOKVSqTJgdUFC\nCLXvb9++jQkTJmh8q1O6dGl8/vnnWt8mjRw5UmNW1goVKqBXr14qqdHlNOnOU6lSJQQHByMhIQEV\nKlSQ1tXGxgY7d+5Uexssd783btwY169fl1olvCtyx26V++YvNTVVaxP2wuj7G79PxTme5DRJzsjI\nQP369VG/fn2d11FOU9xjx46hY8eOWLlypZTIbeDAgbCzs8OtW7cwYMAAtWtQ9+7dSySZU+3atTFw\n4ECYm5tjx44dOHbsGH7++ecSGeJDrnPnzmHo0KFSIrv8mjdvjoEDB6oMMyG3r2RxMMAj0kHnzp1x\n5MiRQgcHLUhuh/rq1asjLCwMw4cPlzIZOjg4YMaMGVr7bchJYmJtbY0TJ05I22RhYaGSZTMuLk5r\npVHfQOPjjz/G7t274evrq7Gf2J49e1SCuRs3bqBKlSrYu3cvevfurXfn6PHjx2P06NFSuXXr1uHC\nhQt4/fo1mjVrpha0paWloW7dunotI4+cvifFqQRMnToVVatWxfLly6FUKgtthqRvHyFdacquqmtz\nmS1btui9vILatWuHP/74Q68Kr9wBusuXL4/4+Hit833x4oXG36BZs2ZYv349Zs6ciYULFwKANGDu\nBx98gGXLlqmNgSknIJeTYClPwSQVtWrV0ilVt74V0aZNmyIyMhJ9+vSRBtjOk5qaiqioKLVBfsuU\nKVPoWIypqak6N9vOT6FQqAxQLKdJd0EF+3CamJhIwZ0uGRPz0zT95MmT4e/vj08//RRdunR5Zwka\n5DyUyKNvc2Tg3QWuBX9j4P1dn+QeT3KbJMt5ICcnIE9ISJCGTahatSpq1KiBq1evws7ODtbW1ujV\nqxfWrl2L1q1bS2WKm8wpMzMTx48fx+HDh/Hbb7/h7du3qFevHkaNGgUvL69CyyYkJODp06coVaoU\n6tatq9Og8fpIS0sr9JivUKGCyrW6OH0l5WKAR6SDyZMnIyAgAH369EGHDh20pnH29vaW/l9uh3og\n98lwSEgIRo8ejdOnT2PYsGGFzkNOEpMRI0Zg9uzZ6NevH4KCguDh4YFdu3Zh6tSpsLCwQEhISKEp\n2fUJNEaNGoURI0bg888/R58+faQBbR8+fIhdu3bh1q1bWL58OQDgu+++w86dOzFy5EhcuHABnTp1\ngrOzs8Y3aQqFAiNHjtS4zFKlSqm8VbOzs9P6Vs3Pzw/BwcFo1KiRxifU2sjte6JLJeD169caB3HP\nb926dVi3bp30d8GEBPr2EQL+f+IDfbOrFkxLDeS+uUlMTERGRgbq1Kmj93mgTWBgIIYMGYKJEycW\nej7mf+ueVxEqbIBuTdq2bYtt27ahc+fOaqmyb926hW3btmkdy0nfbLhyAnI5CZYA/RKR5CenIjpq\n1CgMGDBAGvOxfv36UCgU+Oeff3Dw4EHEx8erZelzdHREWFgYevToobbNcXFxCA8P17hdTZo0wc6d\nO9GvXz+YmpqqfJeSkoKoqCiVc7xHjx6YNWsWMjMzMXv2bGn8tVWrVsHCwgKhoaGwtLTUuC8A/ZMl\nycmwmJfEZ86cOdLb3YJKIotms2bNEBQUhBkzZuj8UAKQ/+ZPbuCq728MvL/rk9zjacOGDbCxscH2\n7dtVAgdra2u4u7vD19cXGzduVAvw5NI3IC9fvrzKZwVb/HzyySfYsWOHShm5yZx++eUXHDp0CL/9\n9hvS0tJQvXp19OzZE15eXkVm4rx06RIWLVqEq1evStc1Y2NjtGnTBpMmTULDhg11Xo/C2NjYYPfu\n3ejXr5/avszKysK+fftU7hdy+0oWB8fBI9LBiRMnMHbs2EKfKBcc82TmzJk4efIknj9/XmiH+sJO\nwezsbFy6dAllypRRCbYKdhQOCwvDwoULsXz5cq1JTKZOnaqSxKRNmzZwcHBAcHAw9u/fD2NjY3z/\n/fcICwsDkNssYsOGDfDw8Cj07aD4v0GmC25XwcrGb7/9hnnz5uHx48cq4+7UqlULU6ZMQadOnZCQ\nkABnZ2d4eXmhefPmmDFjRqHJPLSNM6PvWzV/f39cvHgR6enpMDExQZUqVTQmnSiY4WrYsGF49uyZ\n1NSldevW0viDeVnFypcvj/DwcJVyERERmDVrFjw9PTF79mycPHkSY8eOxahRo2BhYYF58+ZBqVTK\nuhHkrygnJSVh7Nix+P3332Fqaorvv/8eHh4euHTpEvr164eWLVti5cqVKk2dFi9erDW76t9//w0L\nCwv4+fnp/DY7JycHR48exfTp07F69Wq9mzprcvXqVYwZMwbPnz/XeGzmHZP5jw1dBujWFPA9ffoU\nPXv2RFJSEtq2bQtzc3MAuQPhnj59GhUrVkRUVJRaBshx48bBy8sLzs7OOneonzJlCo4fP46AgACE\nhYXh1atXOH78OABg165d+OGHH9C3b1+VMankDI5+4cIFDB48GGZmZujfv79akoqXL1+qJCLJz9fX\nF1lZWWoVUSC3YuPr64ty5cpJ15H8y1y4cCGuXbum8rmVlRWmTp0KJycnlc/v3r0LX19fGBkZwdvb\nW0rO8uDBA+zbtw85OTkaB32/cOECBg0ahJo1a2LAgAEq5cLDwxEXF6cy2DGQG8SEhYXhzJkzKF26\nNL766iscO3YMQO6T+KCgII37QpdkSR4eHtKwKPpOn2fKlCk6jQtXnFT2ADB8+HC4urqiXbt2ePPm\njU4PJQD9B5jO06VLFyQkJKi9lc5P071Ezm+szbu4Psk5npo0aYIJEybAz89P4zxDQ0OxYsUKVK1a\nFdOmTYObmxsASP8tTMF715AhQ5CamoodO3aojZublpaG7t27o1atWiqZSL/44gukp6cjJCQExsbG\nmDVrFs6ePYuDBw9CoVBg8eLFiI6Oxu+//y6VCQwMxIEDB5CZmalXMicrKyuUL18eHTt2hJeXF1q1\naqVTIq2LFy9i8ODBMDExQbdu3dCgQQPk5OTg0aNH2L9/P4yMjBAREaExgZ2+jh8/ji+//BJWVlbw\n8fFReWgVHR2N69evY82aNdIDOn9/f9y9exfx8fEwMTFB1apVNdabSjKLJgM8Ih14enoiMTERI0eO\nhLm5udamQS1atJD+X9sT8JKSvz9Hhw4d0LlzZ43DGgC5N5yYmBhpTLbVq1cjIiICp06dUpkuMzMT\nMTExiIuLw9ChQ1G6dGmdKxcFaats3LlzB3///Teys7NRt25d2NraSvNXKpXIyclB6dKl0aFDB5Qq\nVQpTp04tdJ8XzNolZ4BpXQf1Ltgcxt7eHiNGjIC/v7/GAea3bduGFStWqA1aDcivVMpRsI9Qeno6\n7t27pzGNtJubG+rUqaOSXXXfvn0q2VW3bt2q15tOIDdwvHDhAiIjI4u9Pb169cKDBw/Qt29fNGjQ\nQGvFU1NmS0D/AbqfPHmCpUuX4vjx40hLSwMAlCtXDs7Ozvj666/VgjsgtxlpfHw8KlWqBHd3d3h6\nesLJyanQc6lgQD579mx4enpKAbmTkxNWrVql1vdI38HR/fz88Pz5c+zcuVNtXikpKejZsyfq1aun\nsR+TrhXRS5cuafw+b5BfpVKJWrVqFdpE+OrVq5gzZw6uXr2q03blOXr0KGbPno24uDiVh2iaBjse\nPnw4XFxc0LZtW9SrV0/6/Pz583jz5o3GJt15unTpAhMTE5WMiUeOHFHJmLhr1y4pANF3+vft888/\nl86Fjz/+GM7OznB1dUXz5s2LrGDLGWC6OIGrPr+xLkry+gTkPpzNf13K6yJgb2+v8U2lo6Mjhg0b\nhi+//FLj/NasWYPg4GBYWVlhxIgR0j1Gzr1LTkD++++/Y9iwYahduzZ27dqFv//+G71790arVq1Q\nv3597Nq1C+3bt1dp2qlrHahg39SYmBi4ubnpnQV14MCBeP78ObZv3652zr548QK+vr6wtrbGmjVr\n9JqvNgcPHsSCBQsQHx+v8tC6WrVqmDJlCrp166aybroo0T7w7y2dC9F/MVtbW7FlyxZZZbOyssTl\ny5fFwYMHxZEjR8S1a9dKeO2EaNq0aaHrFxoaKmxtbaW/d+zYIezs7ERgYKAYOnSoEEKIjIwM4e3t\nLaysrISVlZXw8PAoNIPku2ZnZyfCw8P1Lic3o5sccrPO5cnLZpnn3Llz4siRI7L3+40bN2SVy09u\ndtWi5B1zJcHOzk4EBQUVax45OTni8uXLYs2aNaJ79+7C0tJSWFlZFVnm5cuXIj4+XuTk5BQ6rVKp\nFL///rsIDAwUTk5OUgbcefPmiT///LPQsq9evVLJtpaenq7TdSMxMVH8+eef4ty5c2LPnj3i+PHj\naseYELnXiw0bNmidT1BQkHB0dNT4nYODg1i7dq3WsqtXrxYODg5FrmtBr1690vrdy5cvxZ9//imu\nXLmic1bi7Oxs8eeff4qDBw+KgwcPiitXrmjcF926dZOueZ6enmLRokXi/PnzRf6+QuifMbG4GRbf\nh/j4eLF7924xYcIE0bJlS2FpaSkcHR3FuHHjxO7duwv9nZRKpbh+/bqIiYkRBw4cEJcvX9a4z0uK\nrr+xLuRenwYOHKj3P01Zeb/66ivRtm1bERcXp/bd8+fPRZs2bcTw4cNlbZsmp06dEm5ubmpZt9u2\nbSsOHz6ssczJkyeFv7+/lHF648aNomnTpsLS0lL07t1br8y670LTpk3F5s2btX6/fv16WdemwiiV\nSnH16lVx6NAhcfDgQXHp0iWRmZlZosuQi33wiHRgbm6O5ORkvcvl7+eSX1H9XIqSk5Oj8kZLThIT\nExMTvcdIe5+sra0RGxurd7niDjCtD7l9T/IUfPNUWPMgXfrGJSUlqTV1K0rBJjJys6sWJjMzE/v2\n7Ss0I6k+atasqfdgvID+A3Tn9/btW5QrVw5mZmZITExEREQEjI2N0blzZ1SpUkVteoVCgZYtW6Jl\ny5aYOXMmTp06hUOHDmHv3r3YsmULPvroI3h4eMDLy0s6N+UkgsgbOy82NhabNm2CqalpkWPnFUVT\nkoo8cvvGyel7BuRm6zx79qyUrTMuLg6tW7fWqf+k+L+3OmXKlIGxsbHGN7179+7Fy5cvcerUKZw8\neRLR0dHYtGkTKlWqhDZt2khjk2l666JvsiRdp7e2tsaiRYukRBJWVlZFvukqiT54QG4/UG9vb3h7\ne0MIgRs3buDUqVPYtWsXDh8+DCMjI9y4cUOlzLfffotu3brByckJjRo10ilRT0nR5TeHk5/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GvWDObm5iLplc7OzqisrMTevXvFUsjKysowadIkVFVVcanVP/74Iy5fvqxUsStlI0+/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\nIIiPBArwCIIgCIIgCIIgPhL+D4YIpwprbr3zAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "vect = Pipeline([\n", + " ('norm', TextNormalizer()),\n", + " ('count', CountVectorizer(tokenizer=lambda x: x, preprocessor=None, lowercase=False)),\n", + "])\n", + "\n", + "docs = vect.fit_transform(documents(), labels())\n", + "viz = FreqDistVisualizer() \n", + "viz.fit(docs, vect.named_steps['count'].get_feature_names())\n", + "viz.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.pipeline import Pipeline \n", + "from sklearn.feature_extraction.text import TfidfVectorizer \n", + "from yellowbrick.text import TSNEVisualizer\n", + "\n", + "vect = Pipeline([\n", + " ('norm', TextNormalizer()),\n", + " ('tfidf', TfidfVectorizer(tokenizer=lambda x: x, preprocessor=None, lowercase=False)),\n", + "])\n", + "\n", + "docs = vect.fit_transform(documents(), labels())\n", + "\n", + "viz = TSNEVisualizer() \n", + "viz.fit(docs, labels())\n", + "viz.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Classification \n", + "\n", + "The primary task for this kind of corpus is classification - sentiment analysis, etc. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split as tts \n", + "\n", + "docs_train, docs_test, labels_train, labels_test = tts(docs, list(labels()), test_size=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", + " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", + " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", + " verbose=0, warm_start=False)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LogisticRegression \n", + "from yellowbrick.classifier import ClassBalance, ClassificationReport, ROCAUC\n", + "\n", + "logit = LogisticRegression()\n", + "logit.fit(docs_train, labels_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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C+SvhwSTAnwcjbgCKVGRkpL799lv16dNHlSpVUmJiouLj43X27Fm1aNFCkydP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\nvy9JCgkJ0SeffKLGjRtLksq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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "logit_balance = ClassBalance(logit, classes=set(labels_test))\n", + "logit_balance.score(docs_test, labels_test)\n", + "logit_balance.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "IndexError", + "evalue": "list index out of range", + "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[0mlogit_balance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mClassificationReport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogit\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m)\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 159\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "logit_balance = ClassificationReport(logit, classes=set(labels_test))\n", + "logit_balance.score(docs_test, labels_test)\n", + "logit_balance.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "IndexError", + "evalue": "list index out of range", + "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[0mlogit_balance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mClassificationReport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLogisticRegression\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[0mlogit_balance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocs_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mlogit_balance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocs_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels_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[0mlogit_balance\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/Users/benjamin/Repos/tmp/yellowbrick/yellowbrick/classifier.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 135\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\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[0m\n\u001b[0m\u001b[1;32m 136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0m\n", + "\u001b[0;32m/Users/benjamin/Repos/tmp/yellowbrick/yellowbrick/classifier.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, y, y_pred)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m)\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 159\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mva\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m,\u001b[0m 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "logit_balance = ClassificationReport(LogisticRegression())\n", + "logit_balance.fit(docs_train, labels_train)\n", + "logit_balance.score(docs_test, labels_test)\n", + "logit_balance.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Data is not binary and pos_label is not specified", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\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[0mlogit_balance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mROCAUC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlogit_balance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocs_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mlogit_balance\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/Users/benjamin/Repos/tmp/yellowbrick/yellowbrick/classifier.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 311\u001b[0m \"\"\"\n\u001b[1;32m 312\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\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[0;32m--> 313\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\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[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthresholds\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[0m\n\u001b[0m\u001b[1;32m 314\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroc_auc\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[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 315\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\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[0m\n", + "\u001b[0;32m/usr/local/lib/python3.5/site-packages/sklearn/metrics/ranking.py\u001b[0m in \u001b[0;36mroc_curve\u001b[0;34m(y_true, y_score, pos_label, sample_weight, drop_intermediate)\u001b[0m\n\u001b[1;32m 503\u001b[0m \"\"\"\n\u001b[1;32m 504\u001b[0m fps, tps, thresholds = _binary_clf_curve(\n\u001b[0;32m--> 505\u001b[0;31m y_true, y_score, pos_label=pos_label, sample_weight=sample_weight)\n\u001b[0m\u001b[1;32m 506\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 507\u001b[0m \u001b[0;31m# Attempt to drop thresholds corresponding to points in between and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.5/site-packages/sklearn/metrics/ranking.py\u001b[0m in \u001b[0;36m_binary_clf_curve\u001b[0;34m(y_true, y_score, pos_label, sample_weight)\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[0marray_equal\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[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 313\u001b[0m array_equal(classes, [1]))):\n\u001b[0;32m--> 314\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Data is not binary and pos_label is not specified\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 315\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mpos_label\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 316\u001b[0m \u001b[0mpos_label\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: Data is not binary and pos_label is not specified" + ] + } + ], + "source": [ + "logit_balance = ROCAUC(logit)\n", + "logit_balance.score(docs_test, labels_test)\n", + "logit_balance.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": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/download.py b/examples/download.py index a004c4502..7aa9f85e1 100644 --- a/examples/download.py +++ b/examples/download.py @@ -43,6 +43,10 @@ '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', @@ -51,10 +55,22 @@ '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': '7eb79d0be41f9d9f6373b1fa3f4789e7a9ef880720dc8ea29ec752b914d4b525', + }, } diff --git a/examples/examples.ipynb b/examples/examples.ipynb index 7431d24d8..857b19903 100644 --- a/examples/examples.ipynb +++ b/examples/examples.ipynb @@ -4,7 +4,7 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -34,6 +34,7 @@ "# 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 " @@ -55,19 +56,11 @@ "collapsed": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python2.7/site-packages/scipy/linalg/basic.py:884: 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", - " warnings.warn(mesg, RuntimeWarning)\n" - ] - }, { "data": { - "image/png": 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9e6oH28tN+N8Dx/Ft22mnsRmxYSjMM6Iw14jZUyJlSkhE3k6KI2EAcO89D2Lza8/Aph4+\nR21MORw2BMVZh7Oo1QiNAxyiDWqVBkmRMzAtLhcp0bOgDfCt82xZqBF5kfrOM9hRbkJReT3KTjiv\n+5MSGYzCXCNW5KUhd2qUIq9eIiLvIsWRMACIj08Y85E4h2jHye5jqGs7CFNHNZIzh9uviaKI/i4R\nN89bjtTYTOgDxpfLG7BQI7/R0tqKNU9tQdOAHclBGrz80C+QkBAvdyy3zrVwKiozodTU5jSWEBaI\npZnJuCzMhsJrL0NwMC8KIKJBUlxpee89D+KFV55Cv7ULQQGD54VNJHctnPq77eg4eRrmdh3uvWt8\n58tN9hWonmKhRn5jzVNb8LZmBhCmxjcOB/D0Zry16Um5Y7nUfsaMtyobsL28Hp8dc93CaUVeGq6d\nFg+rIKC6uhpqNY+gEdEwKa60jI9PwGP3PzuhHUdG38JpDjRioMdZJuMKVCmxUCO/0TRgB8LOXpKt\nVqPptF3eQN/Raz7bwqlssIWTzUULp4JcA27KSIQuYPjqJaurjRGR35Pq/LKJMN4WTmaz2ePnVvLr\n4goLNfIbyUGawSNpajXgcCA5SP5Ltc+1cCoqH2zhZLFd2MKpMNeIH85iCyciGhupzi+TUs9A21Bx\n5qqFU1pcNoyxOYgKmbiPIpX4ulwMCzXyGy8/9Avg6c1oOj18jpoc3LVw+t4lSSjINeC2zBSEBXr/\npeVEJA9Pr7SUivsWTjlIi8uetBZOSnldRouFGvmNhIR42c5Js9kd+KS2BUVlF7Zwgiji2rRY3Dkv\nA0vnsIUTEUljPFdaSqXf0ov69grUtVcoroWTnK/LeHhcqB04cAAbN25EfX094uPjsXbtWtx6661S\nZCPyag6HiC/qTqGovB7FBy9s4QRRBFQqQBQRdWwP/u3nP5QnKBGRBMzWPpg6qlDXdmELJ11AEIwx\nc5AWl40EL2vhJDePCjWHw4F169bh8ccfx0033YT9+/fjJz/5CebOnYukpCSpMhJ5DVEU8XVjB4rK\n6rHjoAknepxPUr38bAun//6f/0VlaMbgjQq8sIGIaDQEm/lsC6cKNHfXOLVw0mr0SI2ejbS4HCRG\npkOj5od44+HRq9bb24uuri5YrYMf46hUKmi1Wmg0POmZ/Icoiqg82Y2i8noUldWjrvOM0/icxEgU\n5A52CTjXwumfb4moVNiFDUSkDFL115woVruAps4jqGs/iKbOby9o4ZQSPRNpsb7Twmkkk/U+eVSo\nRUZG4kc/+hF+9atf4f7774coiti4cSMSEpSzQxFJraW1Faue3IRDvQ4ERMbDkZCGmg7nRRndtXBS\nyoUNRKQ8UvXXlJJDtONE9xE0dR9GY+dh2BzC0JhapcHUqAykxeUMtnDS+Md5tpP1PnlUqImiiMDA\nQGzZsgXXXXcdvvzyS9x3332YPXs2LrnkklFtQxAESdZF8YTFYnH6KidmcU0pWRq6+nDHM2/gsHYW\nEHv26qSzRVpyRBCWzUnB8uxU5CZFDl295Gr/jogIx/8884jTbeP9PVDKa3N+Bk+zCILg/k5eSAnz\nHeCb+4wUlJLFpu5zWufLquqTZb9xOOxoPV2HuvYKNJkPw1E7fBGUCiokhE9DanQWkiNnQhcw2BXF\nbhVht05cVqW8R4B075O7+c6jQm337t2orKzEAw88AABYuHAhFi5ciJKSEqxfv35U2zCZTJ5EkFRt\nba3cEYYwi2tyZGkfsOLjhl7sNvWisn0ACHA+YhzdfwrP3j4Pc2KDoFapgO5mHOpuHmFrE8ff3ydv\noKT5DlDW+8Qsw/q6bU7rfPV121BdXT0pzy2KIvod7ei2N6LH3gg7nIuIYHUsIjUpiNAkI8AaiIFW\noKb1+KRkO5/c7xEwee+TR4XayZMnL6gEtVottNrRfyZtMBgQGhrq/o4TyGKxoLa2Funp6dDr5T1k\nyyzKyNLRb8HbVSewo7IBX9S14bwmAdCKNlihGbxi0+HAVQHt+PGiyyc800h88X0SBAE1NTUSJlMG\nJcx3gG/uM76UZf3Pf4s//L/fwWzvQaA6HOt//lvExU1cX2JRFNHZdwINXVVo6KzGgPW003hUUCL0\n1jjkpl+DyNC4CcsxGkp5jwDp3id3851HhdqCBQvw/PPPY9euXVi6dCn27duHjz76CH/+859HvQ2d\nTjchvcPGQ6/XM4sL/pJltC2csiMD8POnN+NYVz+mRwXhlUd+qYjXx5fep8lY9FIOSprvAN/aZ6Qk\nd5aUlFQ8vv53E95fc7QtnHSqEFRXVyMyNI7v0Xmkep/czXceFWoZGRnYvHkzNm3ahI0bNyIxMRHP\nPvssMjMzPdks0aTpF2x491CTyxZOgQEa3JqZjIJcA344ayqCtMO/Ltt+9+iETqKTqaW1FWue2oKm\ngeELGxISJu5/70Qkn57+NtS1j62FkxLOq/RnHi9qsmjRIixatEiCKESTYzQtnArzjFg8O9kvWjit\neWoL3tbMAMLUg71Qn94sWwcHIpLeGXMX6torUNdWjs6+k05jgy2csmGMy0ZMyFSfPZrtzbj6HPmF\ni7VwUqtUuC49AYV5Riydk4poP2vh1DRgB8LOrhLOxXeJfEK/0DvUX/O7LZwCtaEwxs7BtLhcWVo4\n0diwUCOfdX4Lp50VJrSdcb6c+5pp8SjIMWJZTioSwoJkSim/5CDN4JE0Lr5L5NUu1sJJHxAMQ2wW\n0mJzkBCRxhZOXoSFGvmU0bRwKswzIj/bgJSoEJlSKgsX3yWSRuupVmx+9RnYNf3Q2INx7z0PTnhH\nAXctnFKiZ2NaXA6SImdAreZ/wrwRCzVy6dzq+8e6BoaubFTqCeaiKKKqpRtFZfUoKq/H8Q7nFk5Z\nUyJRmGdEQa4B6bHhMqVUroSEeJ6TRiSBza8+g+nX6qBW6yd0pXqbXUDjiC2cApASPcsvWjj5CxZq\n5NKap7bgHe1MIEGNQw4H1Ao8wfxoWy+2n+2veai1x2ksPTYMhblGFOYZkemihRMRkdTsmn6nlept\n6n43jxjDth02nOg6irr2g2jsGKGFU+zZFk4B/nWera9joUYuKfUE88bufrx9+BiKyutxoMl53Z+U\nyGAszzFiRZ4Rc5OjefUSEU0qjT3YaaV6jT3Yo+2JogMne2pxouEwTB3VsNqHl8lQQY3EyOlIi8tB\nasxs6AM8ey5SLhZq5JKSTjCvONaAOzdvwzFHGAZ0h5zG4kMDsTzHgMI8I+Yb4qBWszgjInnce8+D\n2PzaM7Cph89RGytRdKC1tx61LQdQZ66Avca5+09CuBHG2BwYY+cgSCd/lwuaeCzUyKWXH/oFHE9u\nGlp9/+VHfjmpz9/RZ8HOChO2l9fj05oWICBxaEwr2vF/51+CghwjFk5PQICGVy8Rkfzi4xPGdU6a\nKIpoP9OEuraDqG+vQL/Q6zQeG5pydiHabIToI6SKS16ChRq5lJAQP+mr7/eaBbxd1YRtZXXOLZy+\n8xFm1plv8cryn0x4HiKiieKuhVNkUAL01jhcPut6xEYkyZSSlICFGsnqXAun7eUm/O1w0wUtnG6Z\nPRUNZaX4WpU49DFsKtf5IiIvNdoWTkGaCFRXVyNUHy1TUlIKFmo06Sw2O3Z/24xtZa5bON18SSIK\nc424LTMFYYFatH5/Fn4m48ewRESeOG3uRH17BeraKtDZ1+w0FqKPhDE2G9PichAdkjR0ERT7a9I5\nLNRoUpxr4bS9vB67KhvRPXD+peUXb+Ekx8ewRESeuFgLpyBtGIyxc5AWl8MWTuQWCzWaMA6HiC/r\nT6GorB7FLlo4XWWMw4q8NL9v4UREvsFtC6eYLBjjsjElYhpbONGosVAjSYmiiP2NHSgqr8eOchOa\nvtPC6dLkaKzIS8PyHLZwIiLv566FU2pMJtJic5AUmc4WTjQuHhdqra2teOyxx/D1118jLCwMd911\nF+68804pspEXqTzZNWILp8wpEViRl8YWTkTkE6x2AU2dh1HXdhBNXd/CIQ4vCK5Ra5EcNRNpcdlI\njprJFk7kMY8LtTVr1mD+/Pl46aWXUFdXhx//+MeYM2cOcnNzpchHCnauhdP28npUt7hu4VSQa0BW\nYpRMCYmIpMEWTiQXjwq1gwcPoq2tDffddx9UKhWmT5+OoqIiREXxD7OvaujqG+yv6aKFU3JEMArz\njCjMZQsnIvIurada8cLWjRiwdSPovUj8atXDiI2NxcmeY6hrO+iihZMKUyKnIy02B4bYTLZwognj\nUaFWXV2N9PR0PPfcc3jnnXcQGhqKVatWYcmSJVLlIwVoOW3Gu18PFmd76tucxtjCiYh8weZXn0HG\noiCo1YP9Ov/3s/9EfGo4LLY+p/vFhxsxLS4Hhhi2cKLJ4VGh1tPTg6+++grz58/HZ599hsrKStx9\n991ISUnBpZdeKlVGkkFHnwXbDxzHf5fW48CpQ3AMX7yEqCAd7shORUGuEYvYwomIvJwoitBGWKBW\nD35kqdaoEZGEoSItNjQZxthspMXlsIUTTTqPCjWdTofIyEisXLkSAJCXl4ebb74ZH3/88agLNUEQ\nZF/Yz2KxOH2Vk5xZes1WvHu4GTsrG/FRTctwCycAoboA3Do7CflzUnBD+hToAgaLM5tVgM068dmU\n9B4Bysrji1kEQXB/Jy+khPkO8M19ZqxEUUTPQCtMnVVo6KyCMc/5QifzGQcuz7gRqdFZCAs82x1A\nnLyFaPkeuaakLIA0edzNdx4VamlpabDZbBBFceh8JIfDAVEU3TxymMlk8iSCpGpra+WOMGSyspht\nDnzRfAYfmXrwZfMZWOznrfujUeGqpFDcZIjAVUmhCAxQA/Yu1HzbNSnZXFHSewQoKw+zKJ+S5jtA\nWe/TZGWxOE6j296AHnsjLOJppzHzGRu6WgfQYRLwvfkFQFc0GrpOAjg5Kdlc8cf3aDSUlAWY2Dwe\nFWpXXXUVgoKC8Ic//AFr1qzBwYMH8dFHH+FPf/rTqLdhMBgQGirv5/wWiwW1tbVIT0+HXi/v1TpS\nZGk91YZ7f/cSTgyImBqkwub71yA+Pm74OWx2fFzTih2VjXjv8An0CcOXlms1KtyYPgX52Sm4cVos\nWhvrfeZ18dU8vphFEATU1NRImEwZlDDfAb65z1xMn6UbDZ1VMHVWodvS4jQWrItAanQWUqMyEayJ\nxrFjx5C+xD9eF2ZRRh53851HhZper8cbb7yBxx9/HAsWLEBoaCj+4z/+A9nZ2aPehk6nU0xLIL1e\n7xNZfvn7V/COdiagU+OAwwH1869g+++fwKe1LSgaQwsns9mMVg+zSE1JWQBl5fGlLL56xbCS5jvA\nt/aZ7xpNCydjXDbiw1KHWjid+1jTl18XTzDLyDzJ426+83gdtZSUFPzxj3/0dDMkoaYBOxB29gR/\ntRp7hDgkP1F8QQunq9PiUZBrQH6OgS2ciMjrXayFky4gCMaYwf6aCRFpbOFEXoMtpHyMKIoICQoG\nRBE4W6W36aKBs0XaZSkxKMgxYHmuEals4UREXs5tC6fo2UiLy0FiZDo0av7JI+/DvdZHOLVwCjA6\njWXEhOBfLk9HYZ6RLZyIyOvZ7AIaO4+grv0gmjq/hUO0DY1p1FqkRM9CWmw2pkZfggA1WziRd2Oh\n5sVqzrZwKhqhhVNBrgEFuUbMYQsnIvJyo27hFDMLWo38J5kTSYWFmpdp6OrDjrPF2TcuWjgV5Bqx\nIo8tnIjI+zkcdrctnKbF5iCVLZzIh7FQ8wItvQPYWWFCUVk9vnTRwik/x4AVbOFERD5AFB1o7a1H\nXVsF6tsrL2jhlBBuRFpcDgwxWQjShcmUkmjysFBTqM5+AX8rb8T28np8WtsKx3mLCEcG6XDHnFQU\n5rGFExF5P1EU0XGmCSeaj6C+vQL9Qq/T+HALp2yE6CNlSkkkDxZqCtJrFrCz3IQ/fdmAr7Yddm7h\npA/AbZkpKMg14HuXJEEXoJExKRGRZ0RRRFd/C2pOfoNaSxmqjjgfOYsKnoK0uBwYY7MRHhQjU0oi\n+bFQk9mA1Yb3Dp1AUXk9/nboBMy24S4B+gA1bpmdjIJcI26ZNRXBOr5dROTdevrbUNd+EHVtFegZ\nOOU0Fh4Yi7S4bBhjcxAVkiBTQiJl4V9+GQg2O3YfPYmisnr8tboRZyzDl5YHqFW4YkoIfnrVbCzL\nTUN4oE4L9LBJAAAgAElEQVTGpJ5raW3Fmqe2oGnAjuQgDV5+6BdISIiXOxYRTaIz5i7UtVegrq0c\nnX3OfTODdeEIdiRi7oyFSIxO40VQRN/BQm2S2OwOfHasFUVl9XirssFlC6eCXCN+mJGAk3U1yMw0\nINDLizQAWPPUFrytmQGEqfGNwwE8vRlvbXpS7lhENMEu1sIpUBuKtNhsGOOyEa6Nx6FDhxEdkjSu\nIq31VCs2v/oM7Jp+aOzBuPeeBxEfz6Nx5DtYqE0gh0PEnvo2FJXXo/igCafOmJ3GrzLGoTDPiGXZ\nBkwJH2zhZDabcdLVxrzUd9tZNZ22X/wBROS1RtfCKRsJEdOGWjid6685XptffQbTr9VBrdbDYXdg\n82vP4MmHX/Bom0RKwkJNYqIo4pumThSV1WN7eT2aevqdxucmR2NFrtFvWjglB2kGj6Sp1YDDgeQg\nXgRB5EvkbuFk1/RDrR5c4FatUcOm7nfzCCLvwkJNIlUnu1BUXo+iMhOOdZx2GpudEIEVeUYU5Box\nI86/Wji9/NAvgKc3o+n08DlqROTdrHYBTRdp4ZQcNRPT4nIwNeoSBGgmtoWTxh4Mh90BtUYNh90B\njZ0L35JvYaHmgYu1cJoeM9jCaUWeEVl+3MIpISGe56QR+YBRt3CKngVtwOS1cLr3ngex+bVnYFMP\nn6NG5EskK9Ta29tx22234emnn8bChQul2qziuGvhtDzXgBV5abiULZyIyMs5RDtOdo/UwkmNxMjp\nSIvLQWrMbNlaOMXHJ/CcNPJpkhVqDz/8MHp6etzf0Qu5a+G0LDsVK/LSsMDoWQunltZWrHpyE451\nDWB6VBBeeeSXXMqCiCbV+S2cTB2VMFsvbOFkjM2BMXYOgnShMqUk8h+SFGrbtm1DSEgIpkyZIsXm\nFKGz34K3Khou2sKpINeA69KnSNbCac1TW/COdiaQoMYhhwNqLmVBRJNAFEW0n2lCXdvBEVs4nesS\nEKKPkCklkX/yuFCrr6/Hn/70J+zYsQNLliyRIpNsTlusKK5uRlFZPT48ehJW+/DVS5PRwolLWRDR\nZBFFEd39LTjRcgR1bRU4Y3E+lYMtnIiUwaNCzW6344EHHsAjjzyC8HDvvJpxwGpDSVUT/t8Xjdiz\n/QjMtuHibLJbOHEpCyKaaD39bahp+QY1lm9Qdcj5CvVzLZzS4nIQGcxFY4mUwKPK48UXX8SsWbNw\nzTXXjHsbgiB4vODhmJ/T5sAnta3YXtGA9w4344zg3MLpxhlTkJ+dgltmJiE88Oyl5Q4bzGbbCFuU\nxqb7fgbbsy+irscMY4Qem9avm/TX5nwWi8Xpq5yUlAVQVh5fzCIIgvs7eSE55jsAOGPpQmNnNRq6\nqtDV3+I0FqyLQGpUJlKjsxAVnDh0EdRk5fTF/VcKzOKakrIA0uRxN9+pRPG8k6/G6Ac/+AHa29uH\nfrFPnz6NoKAgrF69GitXrrzoYy0WC6qqqsb71GNmc4g4cKoPu029+KyxF73C8JEzFYBLE4JxsyEC\ni1LCEKnnqiVESpCVlQW9fvKWepgokz3fAYBVHECPvQndtgYMiM4fawZAjwhNCiI0KQhWx/AKdSIF\nGGm+86gief/9952+v/766/HYY4+NaXkOg8GA0NCJuXLI4RDxVWMHiisasbOyEW19zhXvlakxWJ6d\nih9mxOF0SxPS09Nl/6NgsVhQW1vLLArOorQ8vphFEATU1NRImEwZJnK+AwCLtQ+N3YfR0FmFU6fr\nncZ0miAkR82CIToLEbpEHDt23Kf2GWZhFm/N426+k/TQ0Xj+V6bT6RAYGChZhnMtnLaXD7Zwaux2\nbieSNzV6qEvAuRZOZrMZ1S1N0Ov1kmbxBLO4pqQsgLLy+FIWXz3CI/V8B7hv4ZQSPRvT4nKQFDkD\navXgea/nPtb0pX1GSsziGrOMzJM87uY7SQu1jz/+WMrNjUl1Sze2ldWN2MKp8GxxluFnLZyIyPfY\n7AIaL9LCKSV6JtJiJ6eFExFNLK8+Gau2vRfbywcXoq1q6XYamxYTisJco9+3cCIi3+CuhVNS5AxM\ni8ud9BZORDSxvK5Qa+zqw46DJhSV12N/Y4fT2NSIYCzPGeyveVkKT5AlIu928RZOKiRGpsMYmw1D\nbKZsLZyIaGJ5RaHWenoAOw82oKi8Hl/UnXIaiw8NRH6OAQW5BlxljPeohRMRkdzYwomIzqfYQq2z\n34JdlQ0oKpu8Fk5ERHIYTQsnY+zgQrRs4UTkXxRVqJ02W/HX6kYUlddj97fOLZxCdAG4LXOwS8D3\nZiZBPwEtnIiIJosoiujqO4m69go3LZzmIDwoVqaURCQ32Qs1s82ODysGLwh479AJmG3D/S31AWr8\ncFYyCvMmp4UTEdFE6xloQ13bQdS1HUTPQJvTWFhgDKbF5cAYm4OoEN9v4dR6qhUvbN2IAVs3gt6L\nxK9WPYz4eN//uYnGQvbK5+oXP8Kx7uETZAPUKtx0SRIKc424PSsZ4YE6GdMREUnnnzV/QfuAyem2\nEH0k0mKzYYzLRkzIVL+6CGrzq88gY1EQ1OpgOOwObH7tGTz58AtyxyJSFNkLtX6rHWqVCgunx6Mw\nLw13zElFTAgvLSci39NjHjyCFqQNgzF2DtLichAXlgKVyj/Ps7Vr+qFWD873ao0aNnW/m0cQ+R/Z\nC7WHr8/E0rnpmBIeJHcUIqIJlRqVhfSkHCREpEHtp8XZ+TT2wSNpao0aDrsDGjuXGCH6LtkLtf8z\n14BwFmlE5Adykm9EeDi7o5xz7z0P4oVXnkK/tQtBAYPnqBGRM9kLNSIi8k/x8Ql47P5nUV1djczM\nTEX1biRSCh57JyIiIlIoFmpERERECsVCjYiIiEihPC7U9u/fj4KCAlx22WW4+eabUVRUJEUuIiIi\nIr/n0cUEvb29WLt2LR599FHccsstOHToEH76058iNTUV8+fPlyojERERkV/y6Ihac3MzFi1ahFtu\nuQUAMHv2bFxxxRUoKyuTJBwRERGRP/OoUJs5cyaeffbZoe97enqwf/9+zJo1y+NgRERERP5OsosJ\nTp8+jVWrVmHOnDm47rrrpNosERERkd+SZMHbxsZGrF69GgaDAS+8MLaGuoIgwGw2u7/jBLJYLE5f\n5cQsrikpC6CsPL6YRRAEKeIojhLmO8A39xkpMItrzDIyKfK4m+9UoiiK4946gOrqaqxcuRK33347\n1q9fP+rHWSwWVFVVefLUROTjsrKyoNfr5Y7hMc53ROTOSPOdR0fU2tvbsXLlSvzbv/0b7r777nFt\nw2AwIDQ01JMYHrNYLKitrUV6errsfxSYRflZlJbHF7MIgoCamhoJkymDEuY7wDf3GWZhFm/N426+\n86hQ27lzJ7q6uvDSSy/hxRdfBACoVCr867/+K/793/99VNvQ6XSK6e+m1+uZxQVmGZmS8vhSFpVK\nJWEa5VDSfAf41j4jJWZxjVlG5kked/OdR4Xaz372M/zsZz/zZBNERERENAK2kCIiIiJSKBZqRERE\nRArFQo2IiIhIoVioERERESkUCzUiIiIihWKhRkRERKRQLNSIiIiIFIqFGhEREZFCsVAjIiIiUigW\nakREREQKxUKNiIiISKFYqBEREREpFAs1IiIiIoVioUZERESkUCzUiIiIiBTK40Lt0KFDWL58OfLy\n8rB06VIcPHhQilxEREREfs+jQk0QBKxevRr5+fnYv38//uVf/gVr1qzBwMCAVPmIiIiI/JZHhdre\nvXuh0WhQWFgIjUaDZcuWITo6Gv/4xz+kykdERETktzwq1I4fP47p06c73ZaWlobjx497FIqIiIiI\ngABPHjwwMICgoCCn24KCgmA2m90+VhRFAEB/f78nESQhCAIA4MyZM0P/ZhZmuRgl5fHFLFarFcDw\nPOHtlDTfAb65zzALs8hBijzu5juPCjVXRdnAwACCg4NHHezEiROeRJCUyWSSO8IQZnFNSVkAZeXx\nxSxWqxWBgYGSbEtOSpzvAN/cZ6TALK4xy8ikyDPSfOdRoTZt2jT85S9/cbqtrq4Ot912m9vHhoSE\nICMjA1qtFiqVypMYRORjRFGE1WpFSEiI3FEkwfmOiEbibr7zqFC78sorIQgC/vKXv6CwsBAlJSXo\n7OzE1Vdf7faxarUaYWFhnjw9EfkwXziSdg7nOyK6mIvNdyrRw5NAjh49ikcffRQ1NTUwGAz47W9/\ni+zsbE82SURERESQoFAjIiIioonBFlJERERECsVCjYiIiEihWKgRERERKRQLNSIiIiKFYqFGRERE\npFAs1IiIiIgUioUaERERkUKxUCMiIiJSKBZqRERERArFQo2IiIhIoVioERERESkUCzUiIiIihWKh\nRkRERKRQLNSIiIiIFIqFGhEREZFCsVAjIiIiUigWakREREQKxUKNiIiISKFYqBEREREpFAs1IiIi\nIoVioUZERESkUCzUiIiIiBSKhRoRERGRQrFQIyIiIlIoFmpERERECsVCjYiIiEihWKgRERERKRQL\nNSIiIiKFYqFGREREpFAs1IiIiIgUKkCuJ3Y4HOjr64NWq4VKpZIrBhEpkCiKsFqtCAkJgVrt/f+f\n5HxHRCNxN9/JVqj19fXh6NGjcj09EXmBjIwMhIWFyR3DY5zviMidkeY72Qo1rVYLADAajQgNDZUr\nBgDAYrGgpqYGM2bMgF6vZxZm8ao8vphFEAQcPXp0aJ7wdkqa7wDf3GeYhVm8NY+7+U62Qu3c4X+t\nViv7iy2KIgBAp9MxC7OMipLy+HIWX/mYUEnzHeDb+wyzMMtkkjLPSPOd95/8QUREROSjWKgRERER\nKRQLNSIiIiKFYqFGREREpFBjKtQqKipwzTXXXHC7KIq488478dxzz0kWjIiIiMjfjbpQKy4uxl13\n3QWbzXbB2Ouvv44DBw5IGoyIiIjI342qUNu6dSvefPNNrF69+oKxI0eOYNeuXbjxxhslD0dERETk\nz0ZVqOXn56OkpARZWVlOtwuCgAcffBAbNmxAcHDwhAQkIiIi8lejWvA2NjbW5e3PP/88rr32Wsyd\nOxc7duwYVwBBEGA2m8f1WKlYLBanr3JiFteUlAVQVh5fzCIIghRxFEcJ8x3gm/uMFJjFNWYZmRR5\n3M134+5MUFpair1796K4uHi8mwAAmEwmjx4vpdraWrkjDGEW15SUBVBWHmZRPiXNd4Cy3idmcY1Z\nXFNSFmBi84y7UHv//ffR2NiIBQsWAAD6+/uh0Whw/PhxbN26ddTbMRgMsve+s1gsqK2tRXp6uuwt\nKZhF+VmUlscXswiCgJqaGgmTKYMS5jvAN/cZZmEWb83jbr4bd6H2xBNP4Iknnhj6/je/+Q2ioqLw\nwAMPjGk7Op0OgYGB440hKb1ezywuMMvIlJTHl7L4So/P71LSfAf41j4jJWZxjVlG5kked/MdF7wl\nIiIiUqgxHVGbN28eSktLXY49/fTTkgQiIiIiokE8okZERESkUCzUiIiIiBSKhRoRERGRQrFQIyIi\nIlIoFmpERERECsVCjYiIiEihWKgRERERKRQLNSIiIiKFYqFGREREpFDj7vVJRL7hVEsL/rhuDQKa\nm2BLSsbdL76M+IQEuWMRERFYqBH5vT+uW4MbPvgr1ADsFQfw+jrgNzvekjsWERGBH30S+b2A5qah\niUADQHOiSc44RER0HhZqRH7OlpQM+9l/289+T0REysCPPon83N0vvozX1w0eSTt3jhoRESnDmAq1\niooKrF27Fp9//jkAoLW1FU888QT2798PrVaL73//+1i/fj20Wu2EhCUi6cUnJPCcNCIihRr1R5/F\nxcW46667YLPZhm779a9/jcTERHzxxRd4++23UVlZiZdeemlCghIRERH5m1EValu3bsWbb76J1atX\nD91mtVoREhKC1atXQ6vVIiYmBosXL0ZZWdmEhSUiIiLyJ6Mq1PLz81FSUoKsrKyh27RaLbZu3YqY\nmJih2z799FPMnDlT+pREREREfmhUhVpsbKzb+zz55JOoq6vDPffc43EoIiIiIpLgqk+LxYL7778f\nNTU1ePPNNxEdHT2mxwuCALPZ7GkMj1gsFqevcmIW15SUBVBWHl/MIgiCFHEURwnzHeCb+4wUmMU1\nZhmZFHnczXcqURTF0W5s3759uPfee1FaWgoA6Onpwd13343Q0FBs3rwZYWFhow5msVhQVVU16vsT\nkf/JysqCXq+XO4bHON8RkTsjzXceHVFbt24d4uLisGXLFmg0mnFtw2AwIDQ01JMYHrNYLKitrUV6\nerrsfxSYRflZlJbHF7MIgoCamhoJkymDEuY7wDf3GWZhFm/N426+G3ehVlZWhv3790Ov1+Oyyy6D\nSqUCAGRmZuKNN94Y9XZ0Oh0CAwPHG0NSer2eWVxglpEpKY8vZTk3n/gaJc13gG/tM1JiFteYZWSe\n5HE3342pUJs3b97Qx555eXk4fPjwuEIRERERkXvs9UlERESkUCzUiIiIiBSKhRoRERGRQrFQIyIi\nIlIoFmpERERECsVCjYiIiEihWKgRERERKRQLNSIiIiKFYqFGREREpFAs1IiIiIgUioUaERERkUKx\nUCMiIiJSKBZqRKQo1vZ2nP7yn3LHICKacKLDgc5vjl/0PgGTlIWIaES2nh50v/s2OouL0PPJR0Bc\nPNRFJXLHIiKSnCiK6Pj6GExFpWjYsRdWhw3T375nxPuPqVCrqKjA2rVr8fnnnwMAent78dBDD2Hv\n3r0IDw/HmjVrkJ+f79lPQER+wd7Xh+733xsszna/D1EQhsbUgYEyJiMikpYoiuiubICpqBSmolL0\n1Z0aGgtIDL/oY0ddqBUXF+PZZ59FQMDwQx555BGEhISgtLQUhw8fxsqVK5GRkYHs7Oxx/BhE5Osc\nFgt6Pvw7OouL0P23d+Do7x8aU2m1iLjpe4jOX4Ggm76Hw8cv/nEAEZHS9X7bDNP2weKs9/AJp7HQ\n9CkwrliAxPzL0SB0jLiNURVqW7duxQcffIDVq1fjtddeAwD09/fj448/xu7du6HVapGdnY3Fixej\npKSEhRoRDXFYrTj92cfoKN6O7ndKYO/tHR7UaBC+6HrELF+ByFtvR0BkJADAYrHIlJaIyDN9prah\n4qyrrN5pLDg1FoblV8KwYgGi8oxQqVSD812Vh4Vafn4+Vq1ahX379g3dVl9fD61Wi6lTpw7dlpaW\nhg8//HCMPxIR+RrRbkfvPz9D584d6CrZCVtH+/CgSoWwBVcjOr8QUUuWQRsXJ19QIiIJDJzshund\ncpiK9qC9tMZpLDAhAqn5V8Cw4irEXpkOlXps13GOqlCLjY29MNTAAPR6vXOYwECYzeYxBSAi3yCK\nIvr374Pjj6/gyOf/gK21xWk85PJ5iF5WgOg7lkOXNHWErRAReQdzey/qtu9B439/gm8PNAGiODSm\niw5FytLLYVixAPELZ0OtGf8iG+O+6jMoKOiCjyfMZjOCg4PHtB1BEGQv7s79HEr4uIVZXFNSFkBZ\neeTMIooizFWV6NlVjO6SnbA2mAAAtrPjgVlzELE0H5G33wGdMQ0A4ADc/s4L511Y4EuUMN8B3H9H\nwiyuMcswa08/mt8tQ1PxVzj1ySGINvvQWEBYIJJunYup+fOQcH0m1LrBEkuwCoB15G26m+/GXagZ\nDAbYbDa0tLRgypQpAIC6ujpMnz59TNsxmUzjjSC52tpauSMMYRbXlJQFUFaeycwimuohfvIh8MmH\nQMN3fodTUoEbbobquhshGNPQBqCtrx+orp60fEqlpPkO8N/91x1mcc1fszjMVpz5/BhOf/gt+vYc\nhygMF2cqfQBCr56GsJtmImRBGtSBWnQC6Kz5VrLnH3ehFhISguuvvx6///3vsWHDBhw9ehTvvvsu\nXn311TFtx2AwIDQ0dLwxJGGxWFBbW4v09PQLPs5lFmZRep7JyiLU16G7ZCd6SnbCXFXpNKZNSUXE\nkmUIvvV2NOoDMWPGDI+yCIKAmpoa93f0MkqY7wD/3H+ZhVnGwm6xovWjKjTt+Aon/1YOe9/wETyV\nVoOEm+Ygedk8xNw4G/WtTR7lcTffebTg7YYNG/DYY49h4cKFCAkJwfr168d8xadOp0OgQtZM0uv1\nzOICs4xMSXkmIovQfAKdu4rRuWMb+vZ/7TSmnZKI6DuWI3rZcoTMuxIqlQpmsxlN1dUeZ1GpVJ5G\nVyQlzXeA7++/48Usrvl6FofVhpZPqtFQVIrGkq9h7Tlv+SCNGgnXZcJQOB8pSy+HLmrwP1xmsxlo\nbfIoj7v5bkyF2rx581BaWjr0fUREBDZt2jSuYESkTNa2NnSV7ETnzu04/eXnTifIBkTHIGrJMkQv\nW46wq6+FSqORMSkRkWdEhwNtX3yL+m170LjzK1jaTzuNx10zE4bC+UhddgUC4yNkycgWUkQEW3c3\nut4pQWdxEXo/+wSwD5+DoQkPR+TiJYjJL0DYohug1mplTEpE5BlRFNGx7xhMRXvQsGMvBpq7nMaj\nL58OQ8GVMBTMR3ByjEwph7FQI/JT9jN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NoVQi/L65iE7LQOTDKfAPC7vm+Ty3jIh6kytl\nl1D+03zNBoPrCKcQTX/E6LQY+VgSIidrOr0IavvqD5BSOdxxRK1J4O+rNmDtDp6q0VexUaMeIdra\n0Pj1lzi/9W9oyMuFssOvNeHnh/BZc9pHOEVFXXdfvG8ZEcldy/k6nMs54BjhVHjGZS14iGOEU4xO\ni/7Tux/hZK9sgh8cj/GDAvbKxh7LTfLHRo08RtjtuLL/W9TmZKE+L9c5wkl5dR3AyfFxeGznbihv\n4mIA3reMiOSo2xFOqYnQ6G5+hJPf8DDYDzU4z1HzG37tbxqo73C7Uauursbq1atx8OBBhIWF4de/\n/jWWLFniiWzUCwgh0HzooGNKwI4ctF4477LeGNwP4aYWAIACQFWA8qaaNCIiObE2NKMyr6hHRzg9\nu3EN3rOvgunHSwgaNQDPb1zjqfjUC7ndqK1YsQJarRYffvghysrK8Itf/AJxcXG48847PZGPZEgI\nAVPxD6jN0aM+NxuWs2Uu68ET4qBO00GdqsM7K/8Lc3b/g+eXEVGv1dZsRuXOwzinL8CF3T0/wmng\noEF4dft7/C0CAXCzUTMYDLh8+TJefPFFKBQKjBo1Cnq9HlHdnHNE8nfp4kV8tGI5bKU/Iv+2UXhy\n4/8izNjgOHKWkwXzqRMuj1eNvh3RqY7mLHj8BOd23ruMiHojm9mKC7sNKNcX4Pyuw7C1tF+h7hcY\ngKEP3omYDC2GPTKVI5yoR7nVqJWUlGD06NF4++23sXPnToSGhmL58uVYsGCBp/KRRP78zArcv2cX\n/ADYz/wbxXeOQ1iT6wmtgSNioE5Nd8zXnDyl0xNkee8yIuot7K1taC4oQ9F7BajaeRitjSbnGkc4\nkVTcatSMRiMKCwuh1Wqxb98+HDt2DEuXLsWIESMwbdq0G9qH1WqF2Wx2J4bbLD/dy8vS4Z5eUpFD\nltaqKmiOHnLeu8wPcDZpAQMHIuLRhYhYlI5+8QnOEU49nVcO70tHcsrji1msVmv3D+qF5FDvAN/8\nzNwqYbOj5rtTqMwpROUnB9Fa1377ICgU6D/zDgxPTcCw+dOg+mk6gB3o8T9Hqd+Xjpila57I0129\nUwjR4TKVm/SXv/wFf/3rX/Hdd985t/3+979HVFQUVq5ced3nWiwWFBcX3+pLk4eJhgbgm68gvvwc\nOHrY5eolAeDswCG47eU/AHdOhcLfX7qg1KdMnDgRKpVK6hhuY72TFyEEzMeq0Pj5CTTtPQlbTbPL\netDEIQifOxZh996BgAGhEqWkvqareufWEbXY2Fi0tbVBCOH8tZfdbsfN9H4ajQahodL+RbBYLDhz\n5gxGjx4t+T8Knshyuboaf3vhN1BeOI/WocPwH++ux4CBA695nK3RiMbPdqHhkxxc2fclYLM51xQh\noagIj0AV/FA7bjz+84ONne7DW+T0ZyS3PL6YxWq14vTp0x5MJg9yqHeAb35muiOEgNFwznHkLPd7\ntJyrdVmPiBuBwQumwTq1P8Ynx0v6vlyuvoQNz7wO69laBI6MxtMbVrH+yjCLp/J0V+/catTuvvtu\nBAcH44MPPsCKFStgMBjwxRdfYMuWLTe8j8DAQNlc0aJSqXwiy9YXX3CeX2YrPoJtL/o5zxOzNTej\nIf+fqMvRw7gnH6LDIVe/4GBEPPQIotMyEDH3QUwEZHfVkZz+jAB55fGlLN3dELS3klO9A3zrM9OV\nmxnhZDabUVJSIvn78tEL/42Jn12BH4JgP34FHz3/liwmE0j9vnQkpyyAe3m6q3duNWoqlQpbt27F\nmjVrkJSUhNDQULz66quYNGmSO7slNwVcqHSZjRlQcQ71u/7RPsKppcX5WIVSiYi5D0KdqnOMcOr4\nbV8G59IQEd2sK6XVjhFOWQeuHeE0cgA0Oi00jyUhclKMLL8UXCm7jK8CLyBI+MOssCG8rFXqSCQh\nt++jNmLECPz5z3/2RBbykI6zMe0A7j5RjDOPLWp/gL+/Y4RTqu6GRjgREcmdc4TT9v2o/f5Hl7Wr\nI5w0GUmIThwty+asowv11UixapyTCXbWn+/+SeSzOELKhwibDU3f/QsLo8JRpwyEf6sVfgD82toA\nhQJhSTMczdmCVCglPN+BiMgTzJcbUZFTiPKs/bj0r5Odj3DKSMKAmWNvaoST1Eaqh8Kvon3W50j1\nEIkTkZTYqMlIZzeZHdjNuCUhBJoPFqIuJwt1O7LRerEKAHD13tghdyVAnaqDemEaAodxKgAR9W7W\nhmZUfHIQ5foCVH957QinEQsTEKObjsH33voIJ6kFjlTDbmif9Rk4Ui11JJJQ7/wU+6iON5m1nfk3\n/vJMZqc3ixVCwHTMgNocPepys2EtP+uyHhw3GdFpOkQtSkdQ7G3eCU9E1ENar5hxfuchlOsLUPV/\nBtcRTv1UGJ4yFZqMJAx5YJJHRjhJ7bE1z+D1Q8vRzyjQHKHAa6+/InUkkhAbNRn5+UUA/ucrXdZN\nJ0+0j3A6fdJlLej2MY4jZ2kZCB47zjuBiYh6iM1sxYV8A8r1+x0jnEwdrlDvMMJpeMpUBITI5+o/\nT9i++gOkVA53HFFrEvj7qg2yuOqTpMFGTUY6XgRwdYC55WwZ6nKzUJuTBdMxg8vjA2M0UKemIzr9\nMQTHTZb9CbJERNdjb23DxS+KUa7fj4q8IrQ1uY5wGnxfHDQZWgyf79sjnOyVTfBD+zlq9srGbp5B\nvoyNmows3bARmzPt8D91AsNUgZh3sQI/TLzd5THKQYN/OnKmQ8hdiWzOiKhXEzY7WorO4fCmg7iQ\ndwjWuivtiwoFBiaPgyZ9OkakJSKof7h0Qb3Ib3gY7Ifaz1HzGx4mdSSSEBs1mWi9fBnYmYeFVxrQ\nXP4jIASuHugPUEcjakEq1Gk6hN09kyOciKhXE0Kg5sBplOsLUJ5VAEu10WU9evrt0OimIyZ9OvoN\n7Xsn0j+7cQ3es6+C6cdLCBo1AM9vXCN1JJIQGzUJtTU0oGHXp6jN0aPxq70uI5z8wsIRlTIf0ekZ\nCJt1L/yUSgmTEhG5RwiB+qNnHVMCsgrQXF7jsh4RNwIjH78bGp0WobF9+/ZBAwcNwqvb35PdZBiS\nBhs1L7M1N6Phs12oy9XDuGf3NSOcwh54GI3xiRj3n79Cv8hICZMSEbnPeLzSMSVAX4CmU1Uua+F3\nDMXQ1LtgnqzG1EfuYUNC1Ak2al5gN5th3LMbtTl6GPN3wW7qcIJsYKBjhNMiHSIffgStAQEoKSmB\nHwsWEfVSV0qrHb/W1O9Hw7EKl7WQkQOgydBCo9MicrIGFosFJSUlEiUlkj82aj3E3tqKxq++QF1O\nFup35sHe1NS+6O+P8Nn3to9w6nDkrJXzNYmoF2o5X4dz2QdQru9khNPQKMcIJ522V4xwIpITNmoe\nJGw2NH37jWNKwKe5sNXVtS8qFAibcY+jOZu/CMoBA6QLSkTkAeZLRlTkft/5CKf+YT+NcNJi4Myx\nUPj1nhFORHLCRs1NQgg0f38AtTlZqN+Rjdbqiy7rIQmJjttpLEpH4JChEqUkIvKM7kY4DV9wFzQZ\n2l49wolITvi36BYIIdDyw1HHlIDcbFjPlbusB8dNRnR6BtSL0qEaGStRSiIiz3AZ4bT7KOyt7Veo\nu4xwenAy/FW8Qp3IkzzWqNXU1ODRRx/FW2+9heTkZE/tVlZM/z7uaM52ZMN8+pTLWtCYsVCn6aBO\n1SH4jrESJSQi8ozrjnBSKTH0oTuhydBi2LwpPjfCiUhOPNao/eEPf4DRaOz+gb2MuazUOV/TVHLM\nZS1QMxLqNB2i0zIQPHEST5Alol7N3tqGqs+P4VxWwfVHOC24C4ER/SRMStR3eKRR2759O0JCQjB4\n8GBP7E5y1vOVqNuRjbocPZoPFbmsKYcMhXpRGtRpGQiJT2BzRkS9mt1mx6Wvj6NcX4CKHd9zhBOR\nzLjdqJ09exZbtmxBdnY2FixY4IlMkhD1daj960dozNuBK/u/dVkLiO6PqIVpiE7TITRpBq9eIqJe\nTdjtMP1wHoYtR3H+kyKYLza4rPfX3o4YnRYxaYl9coQTkZy41ajZbDasXLkSr7zyCsLDb+2bltVq\nhVmie4fZGuph/OdO1OdmQXz7DS7Y269e8guPQMS8FEQsSkfozGQoAhxvlaXDJIGeYLFYXP4rJWbp\nmpzy+GIWaw//PZOKlPVOCIGGo+WozPkelTmFMFXWuaxHTI7B8NQEDE9LRIimv3N7T+f1xc+vJzBL\n5+SUBfBMnu7qnUKIDje+uUnr169HbW0t1qxxDIydM2cOVq9efUMXE1gsFhQXF9/qS98y0dIMfPct\nxJefA98XAG1t7YtBQUDSTCjumwvcNR2KwECv5yOidhMnToRKpZI6htukqncAYCmtQdPnJ9C45yRa\nK+pd1gJHqhF2/1iEzx2LQA2PnBFJqat659YRtfz8fNTU1CA/Px8A0NTUhBdeeAGZmZlYtmzZDe1D\no9EgNDTUnRjdsptMaNq7Bw07stG0ZzdEh2+ICpUK/Wbfi5bEuzFq8RIER0lbrCwWC86cOYPRo0dL\n/g8Us/SOPL6YxWq14vTp0x5MJg/eqHcA0Fx26acjZ9/DWOw6wqmfpj+GLJyGtvhBmPCQVvL5mr74\n+WWWvpHFU3m6q3duN2od3cwRtasCAwN7pFDYrVY0fvk56nKzUb/rU5cRToqAAITPuQ/qVB0iH5mP\nNpUKJSUlCI5SS160rlKpVMzSCTllAeSVx5ey+OpFOj1V7wCgpbIW5dkHUK4vQN3B649wujpfMygo\nyGc+M57ELJ1jlq65k6e7eufRG95KXVyFzYamb/ahNjcL9Z/ugK2+w2F+hQJhM5PbRzj1bz8Ho43z\nNYmoFzJfMuJcbiHK9QW4/K8TLmuq/mGISUtEjI4jnIh6M482anv37vXk7m6IsNtx5fsDqMvWo+6T\nHLRdqnZZD52uhTo1A1ELUxE4eIjX8xEReZK1/kr7CKevSlxHOEX0w4gFdyEmQ4vBcyZwhBORD+iV\nf4uFEGg5erh9hFPlz87BuHOqY0rAonSoYjQSpSQi8ozWK2ac/8chlOv3o+r/DNeOcHp0GjQZWgx5\ngCOciHxNr2rUTMdLUJujR11uFiw/nnFZC7pjnGO+ZqoOQbePkSghEZFntJmsqMo/6hjh9M8j145w\nenAyNBlJGPYIRzgR+TLZN2rm0h+dR85+PsJJFXsb1Kk6qNN0CJ4QJ/k5ckRE7rg6wqlcX4DKT7sY\n4aTTYvhCjnAi6itk2ahZKitQvyMbtTlZaDn8sxFOQ4dBnZruGOE0NZ7NGRH1at2NcBo0axxi0rUY\nkZrAEU5EfZBsGrXW6mrUfZKDulw9rhTsd1kL6D8A6oVpUKfpEKq9m1cvEVGvJux21Bw4g3L9fpzL\nKexyhJMmfTqCh0RJlJKI5EDyRq0hLxdVWX9H4zf7gA4jnPwjIxGVsgDq9AyE3zPbOcKJiKi3MpZU\n4se8IyjPOoCWczUua1F3aqB5LAkx6VqEjhwgUUIikhvJu5/q11cBF6sAAH4hIYic9yii0zMQfu9c\n+HGEExH5kIL099BW1ej8OXzsUGgytNBkJCH8jqESJiMiuZK8UVMEBiIiZQGi0x9DxIMPw78fT5Al\nIt8VEjvQ0ZzptIicFMPzbInouiRv1Ebt/ReihvCbJBH5vun6ZzEiaQKbMyK6YZKfle8f0vMDiomI\n5CAyjkfQiOjmSN6oEREREVHn2KgRERERyRQbNSIiIiKZYqNGREREJFNuN2pFRUXQ6XSIj4/H3Llz\nodfrPZGLiIiIqM9z6/YcjY2NePrpp7Fq1SrMmzcPx48fxy9/+UvExMRAq9V6KiMRERFRn+TWEbUL\nFy5g1qxZmDdvHgBg/PjxSExMxJEjRzwSjoiIiKgvc6tRGzt2LNatW+f82Wg0oqioCOPGjXM7GBER\nEVFf57GLCZqamrB8+XLExcVh9uzZntotERERUZ/lkRFSFRUVyMzMhEajwZ/+9Kebeq7VaoXZbPZE\njFtmsVhc/islZumcnLIA8srji1msVqsn4siOHOod4JufGU9gls4xS9c8kae7eqcQQohb3juAkpIS\nLFu2DPPnz8dLL710w8+zWCwoLi5256WJyMdNnDgRKpVK6hhuY70jou50Ve/cOqJWU1ODZcuW4Ve/\n+hWWLl16S/vQaDQIDZV23qfFYsGZM2cwevRoyf9RYBb5Z5FbHl/MYrVacfr0aQ8mkwc51DvANz8z\nzMIsvTVPd/XOrUYtNzcX9fX1+PDDD7FhwwYAgEKhwBNPPIHnn3/+hvYRGBiIoKAgd2J4jEqlYpZO\nMEvX5JTHl7L46uByOdU7wLc+M57ELJ1jlq65k6e7eudWo/bUU0/hqaeecmcXRERERNQFjpAiIiIi\nkik2akREREQyxUaNiIiISKbYqBERERHJFBs1IiIiIplio0ZEREQkU2zUiIiIiGSKjRoRERGRTLFR\nIyIiIpIpNmpEREREMsVGjYiIiEim2KgRERERyRQbNSIiIiKZYqNGREREJFNuN2rHjx9Heno6pkyZ\ngoULF8JgMHgiFxEREVGf51ajZrVakZmZibS0NBQVFWHx4sVYsWIFTCaTp/IRERER9VluNWoHDhyA\nv78/MjIy4O/vj9TUVKjVanz99deeykdERETUZ7nVqJWWlmLUqFEu22JjY1FaWupWKCIiIiJys1Ez\nmUwIDg522RYcHAyz2exWKCIiIiICAtx5cmdNmclkQr9+/bp9rhACANDS0uJOBI+wWq0AgCtXrjj/\nn1mY5XrklMcXs7S2tgJorxO9nZzqHeCbnxlmYRYpeCJPd/VOIdyohN988w3Wrl2Lzz//3LktJSUF\nv/nNb3Dfffdd97lNTU04derUrb40EfUBY8aMQVhYmNQx3MZ6R0Td6areuXVEbfr06bBarfj444+R\nkZGBvLw81NXVYcaMGd0+NyQkBGPGjIFSqYRCoXAnBhH5GCEEWltbERISInUUj2C9I6KudFfv3Dqi\nBgCnTp3CqlWrcPr0aWg0Grz22muYNGmSO7skIiIiInigUSMiIiKinsERUkREREQyxUaNiIiISKbY\nqBERERHJFBs1IiIiIplio0ZEREQkU5I0asePH0d6ejqmTJmChQsXwmAwSBEDAFBUVASdTof4+HjM\nnTsXer1esixX1dTUICkpSdLh9tXV1Vi+fDmmTZuGWbNmYevWrZJlOXz4MFJTUzFt2jQ89NBD2LVr\nlyQ5fvjhB8ycOdP5c2NjI5555hnEx8djzpw5yMnJkSxLdXU1nn76aSQmJmLGjBl44403nHe79naW\nq4QQWLJkCd5++22v5JAr1rvrk0O9A1jzfk5O9a6zPH2q5gkvs1gs4p577hHbt28XbW1tIicnRyQl\nJYmWlhZvRxFGo1EkJCSIXbt2CSGEKCkpEQkJCWL//v1ez9LRk08+KcaPHy/27dsnWYZFixaJP/7x\nj8Jms4kzZ86IhIQEceTIEa/nsNlsQqvVij179gghhDh48KCYMGGCOH/+vFdzZGdni/j4eDF9+nTn\ntmeffVasXLlSWK1WYTAYREJCgjAYDJJkWbx4sVi7dq2wWq2ipqZG6HQ68e6770qS5arNmzeL8ePH\ni3Xr1vV4DrliveueHOqdEKx5Hcmp3nWVpy/VPK8fUTtw4AD8/f2RkZEBf39/pKamQq1WS/Jt6sKF\nC5g1axbmzZsHABg/fjwSExNx5MgRr2e5avv27QgJCcHgwYMly2AwGHD58mW8+OKL8PPzw6hRo6DX\n6xEbG+v1LI2Njaivr3d+U1IoFFAqlfD39/dahk2bNmHbtm3IzMx0bmtpacHevXvx3HPPQalUYtKk\nSUhJSUFeXp7Xs1y9o3VmZiaUSiWio6ORkpLS45/jzrJcdeLECXzyySfdjpLzdax31yeHegew5nUk\np3rXVZ6+VvO83qiVlpZi1KhRLttiY2NRWlrq7SgYO3Ys1q1b5/zZaDSiqKgI48aN83oWADh79iy2\nbNmC1157TdJh1CUlJRg9ejTefvttzJgxAw8++CCOHj2KiIgIr2eJjIzE448/jt/+9reYMGEClixZ\nglWrVmHQoEFey5CWloa8vDxMnDjRue3s2bNQKpUYNmyYc5s3PsedZVEqldi0aROio6Od27766iuM\nHTvW61kAx5Dil19+GWvXrkW/fv16NIPcsd51TS71DmDN60hO9a6rPH2t5nm9UTOZTAgODnbZFhwc\nDLPZ7O0oLpqamrB8+XLExcVh9uzZXn99m82GlStX4pVXXkF4eLjXX78jo9GIwsJCqNVq7Nu3D2+9\n9RbWrl2LQ4cOeT2LEAJBQUF4//33YTAYsHHjRrz55ps4efKk1zL079//mm0mkwkqlcplW1BQUI9/\njjvL8nNvvPEGysrK8OSTT0qS5Z133sE999yDqVOn9ujr9wasd52TU70DWPM6klO96yrPz/l6zfN6\no9ZZkTKZTJJ+866oqMDjjz8OtVqN999/X5IMGzZswLhx4zo9QdHbAgMDERkZiWXLliEgIABTpkzB\n3LlzsXfvXq9n2bNnD44dO4b7778fAQEBSE5ORnJyslcOuV9PcHAwLBaLyzaz2Szp59hiseC5sQwu\nVAAAArFJREFU557Dd999h23btkGtVns9Q0FBAQ4cOIDnnnvO668tR6x3nZNTvQNY87ojx3oH9J2a\nF9Bje+7Cbbfdho8//thlW1lZGR599FFvRwHgOOS9bNkyzJ8/Hy+99JIkGQAgPz8fNTU1yM/PB+D4\nxvvCCy8gMzMTy5Yt82qW2NhYtLW1QQgBhUIBALDb7ZL8eqKqqgpWq9Vlm1KphFKp9HqWjjQaDdra\n2nDx4kXn+TVlZWXX/JrLW4xGI5YuXYrQ0FBkZWUhLCxMkhz5+fmoqKhAUlISAMe5Lf7+/igtLcWm\nTZskySQl1rvOyaneAax53ZFbvQP6WM3z6KUJN+DqVVDbtm0Tra2tIjs7WyQlJQmTyeTtKOLy5ctC\nq9WKzZs3e/21uzN79mzJroIym80iOTlZrF+/XrS1tYlDhw6JqVOneu0Kn45Onjwp4uLixI4dO4QQ\nQhQWFopp06aJ4uJir2cpLCy85iqo3/3ud8JkMgmDwSASExO99h79PMvixYtFZmamaGtr88rrXy9L\nRy+//DKv+mS965aU9U4I1rzOyKnedZanL9U8rx9RCwwMxObNm7Fq1Sq888470Gg02LhxI4KCgrwd\nBbm5uaivr8eHH36IDRs2AHBcYfPEE0/g+eef93qejq5+q5OCSqXC1q1bsWbNGiQlJSE0NBSvvvoq\nJk2a5PUsY8aMwfr16/Huu+/izTffxJAhQ7Bu3TpMmDDB61l+bu3atVi9ejWSk5MREhKCl156SZL3\n6MiRIygqKoJKpUJ8fLzzszNhwgRJ7wVFrHc3Ssp6B7Dm3Qi51Dug79U8hRASX25DRERERJ3iCCki\nIiIimWKjRkRERCRTbNSIiIiIZIqNGhEREZFMsVEjIiIikik2akREREQyxUaNiIiISKbYqBERERHJ\nFBs1IiIiIpn6f7z74lOZ0H4VAAAAAElFTkSuQmCC\n", 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HOhKRvCqOlOHF17YieXkMFGUWVFXFi69txb3Im/KiOfZYx/kwRKZMaqyj1rj6E9G4mFq6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\n0cgGSLoY+6pVq7B8+XLMnz8fWq0WCxYswPz586U8BElE8PK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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -113,6 +106,7 @@ " \"credit\": os.path.join(FIXTURES, \"credit\", \"credit.csv\"),\n", " \"concrete\": os.path.join(FIXTURES, \"concrete\", \"concrete.csv\"),\n", " \"occupancy\": os.path.join(FIXTURES, \"occupancy\", \"occupancy.csv\"),\n", + " \"mushroom\": os.path.join(FIXTURES, \"mushroom\", \"mushroom.csv\"),\n", "}\n", "\n", "\n", @@ -161,9 +155,18 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "collapsed": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.5/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" + ] + } + ], "source": [ "# Feature Analysis Imports \n", "# NOTE that all these are available for import from the `yellowbrick.features` module \n", @@ -214,9 +217,9 @@ "outputs": [ { "data": { - "image/png": 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Uq1fPOl31Z6dOnao0sSjP+++/rwkTJqhTp06aM2eOtdwvnfusffbZZ7rrrrvk7e1tbW/d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\nJNHUwuEYaZYjDsfA5UGCAACAAXesILB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gNmZzYWHvlW54d9KMGTO0d+9erVixQnFxcfk+FOrKZyxY1X9PnzWlHc8KZU1p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\n5HL5tMKeOo6JiREjkuStXr0avb29/o2NGo0GXq8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xcT5tsbGxcjqdfuvOmzdPF154oXr37m3J/kokCAAA1Esul0sRERE+bRERESot\nLfVp++KLL7Rq1SpNmDDB0u1zmiMAAAYCcZpjdcmAy+VSZGSk93FZWZkmTJigKVOmKDw8XB6Px7Lt\nkyAAAGAgEJdavuCCC7R06VKftuLiYqWnp3sfb9myRd9++61ycnIkSRUVFXK5XOrRo4dWrlyp1q1b\nn/T2SRBQo3/EJVgS5+qdn1sSBwCCSUpKitxut5YuXaphw4Zp+fLlOnDggFJTU73rJCUl6fPP//s/\ndsOGDRo7dqzWrVtnevsBTxDsNkkmzsiw4nQOj82CzNDCso5ZzTo0Nx1j57s7LegJAJwZbPbTf6nl\nsLAwLViwQJMmTVJ+fr7atWunOXPmKDw8XFlZWbr00kuVnZ19yrYf8AQBAIB6LwAJgiR17NhRr7zy\nil/7ggULql2/R48ellQPJM5iAAAA1aCCAACAkQBMUgw0EgQAAAzYgvB2z8GXEgEAAENUEAAAMBKg\nSYqBRAUBAAD4oYIAAICRIKwgkCAAAGAgEJdaDrTg22MAAGDohBKEzZs3q1evXt7HW7ZsUefOnZWY\nmKiEhAQlJiZq/vz5lncSAICAsjusXRqAOg8xFBQUKC8vTyEh/31JUVGRevfurblz556SzgEAUC80\nkA91K9WpgjB37lwtWbJEubm5Pu1bt25Vp06dTknHAABA4NSpgpCRkaGcnBxt2LDBp33btm0KCwtT\nv3795PF4dNVVV+mee+5RaGjoKeksAACBwCTFGrRo0aLa9mbNmiktLU1vvfWWFi9erE8++UQzZsyw\ntIMAAAQccxBOzOzZs70/n3feecrJydG0adN0zz33mO4Yzhxvt7/EdIzf79pkQU8AAHV10gnC4cOH\nNXfuXI0ZM0aRkZGSpNLSUjVq1OiE4oQ67KoyUbqx2076pf9lO7NKR6GNo0zHaHFRMwt6Iv20dZ8l\ncQAgoBrIt34rnXSCEBMTo/fee08ej0f33nuvvvvuO82bN0833HCDlf0DACDguJvjCbDZbJo7d66K\nioqUkpKiP/7xj/r973+vm266ycr+AQCAADihCkKPHj20bt067+O4uDgtXLjQ8k4BAFCvcBYDAAAA\nN2sCAMAYkxQBAMDxbEGYIDDEAAAA/FBBAADASBBOUiRBAADAAEMMAAAAooIAAICxIKwgkCAAAGAk\nCOcgBN8eAwAAQ1QQAAAwwM2aAABAvbF161YNHTpUCQkJGjx4sDZt2lTterNnz1bfvn3Vo0cPjRgx\nQtu3bzf0nlzoAAAfbElEQVS9bSoIaBAKWl1sOkbG3i8t6AmAoBSASYput1u5ubkaPXq0MjIytHz5\nco0ePVrvv/++IiIivOu9/vrrWrlypZYsWaI2bdpo3rx5uv322/Xhhx+a2n7AE4Qwu00eR4ALGTZb\nYLdvsZDwMNMxGjVpZEFPpKYXNDUd47sv91nQEwAwIQAJwvr16+VwODRs2DBJ0pAhQ7Ro0SKtXbtW\nV199tXe9Q4cOKScnR+eee64kacSIEXrmmWe0Z88etW7d+qS3H/AEAQAA+HM6nYqLi/Npi42NldPp\n9GkbNWqUz+MPPvhAZ511lqnkQCJBAADAkC0Apzm6XC6foQRJioiIUGlpaY2v+fTTTzV58mRNmTLF\n9PZJEAAAMBKAIYbqkgGXy6XIyMhq11++fLkeeeQRTZo0SQMGDDC9fc5iAACgHrrgggtUXFzs01Zc\nXKwOHTr4rTtr1iw98cQTmjt3rv7whz9Ysn0SBAAAjNjs1i51kJKSIrfbraVLl6qiokIFBQU6cOCA\nUlNTfdZ77bXXtHjxYr388svq0aOHZbtMggAAgJEAJAhhYWFasGCBVq1apeTkZL300kuaM2eOwsPD\nlZWVpfnz50uS5s+fr19++UVDhgxRYmKiEhISlJiY6DeZ8UQxBwEAgHqqY8eOeuWVV/zaFyxY4P35\nnXfeOSXbJkEAAMCAp47f+s8kJAgAABgJwgQh+PYYAAAYooIAAICRM+yS/HVBBQEAAPihggAAgJEA\nXGo50EgQAAAwEIxnMQTfHgMAAENUEAAAMBKEFQQSBASNglYXWxInY++XlsQB0ICQIJx+drtMDXQ4\nLDjzxGMP+NtgqbCY6m8FeiLCm5qPIUlVlR7TMc5u38R0jB93HTIdAwCCyZn1yQgAwKlABQEAAByP\nsxgAAABEBQEAAGNBWEEgQQAAwAj3YgAAAKCCAACAsSAcYgi+PQYAAIaoIAAAYCAYT3MkQQAAwEgQ\n3u45+PYYAAAYooIAAIARhhgAAICfIEwQgm+PAQCAISoIAAAYCcIKAgkCcIKqvvq36Rj2jpdZ0BMA\npwunOQZAt73/qzBPxUm/3tVykOk+eMJjTMdw28NMx5CkaPcR0zFCrrvJdIxmRw6YjiFJ9ogoC4I4\nzMeoqjQfAwCCSMATBAAA6j0qCAAAwA93cwQAAPXF1q1bNXToUCUkJGjw4MHatGlTtestWrRIvXv3\nVlJSku6//36Vlpaa3jYJAgAARmx2a5c6cLvdys3NVUZGhgoLC5WZmanRo0fL5XL5rPfRRx9p4cKF\nWrJkidasWaODBw8qLy/P9C6TIAAAUA+tX79eDodDw4YNk8Ph0JAhQ9SsWTOtXbvWZ72VK1cqIyND\nbdu2VXR0tMaOHasVK1bI4/GY2j4JAgAABjw2u6VLXTidTsXFxfm0xcbGyul01rpebGysSkpKtHfv\nXlP7zCRFAACMBOAsBpfLpYiICJ+2iIgIv/kFx6937OfjhyJOFBUEAADqoZqSgcjISJ+28PBwn/WO\nJQbHr3eiSBAAADDgsdksXeriggsuUHFxsU9bcXGxOnTo4NMWFxfns57T6VTjxo3VqlUrU/tMggAA\ngAGPx9qlLlJSUuR2u7V06VJVVFSooKBABw4cUGpqqs966enpWrZsmXbs2KGjR49qxowZSk9PN73P\nJAgAANRDYWFhWrBggVatWqXk5GS99NJLmjNnjsLDw5WVlaX58+dLkvr27ausrCxlZ2crLS1NTZo0\n0bhx40xv3+Yxex7ESSorK9MXX3yhi8q+Nncvhv8xfy+GcI/bdAyr7sXQyIp7MRz4xnSMKu7FcEpx\nsybg5Bz77OjSpYsaNWp02rZ7tMTchL/jRUdGGK8UYJzFAACAgYB8kw4whhgAAIAfKggAABioCsIS\nAgkCEAAPNoozXsnAY2U7LegJAFQv8AmCzSHZTj41s1tyB07zIy2W3QjUiqt1earMx7BIlesX0zFs\noRZMAK1HkxT/3D070F0AcIICNJ8/oAKfIAAAUM8F4xADkxQBAIAfKggAABgIwgICCQIAAEYYYgAA\nABAVBAAADHEWAwAA8FN/Th4/fRhiAAAAfqggAABgIAhHGEgQAAAwwlkMAAAAOsEEYfPmzerVq5f3\n8eHDhzVmzBglJSUpLS1NBQUFlncQAIBA83g8li4NQZ2HGAoKCpSXl6eQkP++ZOLEiYqKitK6deu0\nbds2ZWVlqWPHjurWrdsp6SwAADg96lRBmDt3rpYsWaLc3FxvW0lJiT744APdddddCg0NVbdu3TRo\n0CAtX778lHUWAIBAqLJ4aQjqlCBkZGRo+fLl6tKli7dt165dCg0N1bnnnutti42NldPptL6XAAAE\nkMdj7dIQ1ClBaNGihV+by+VSo0aNfNrCw8NVWlpqTc8AAEDAnPRpjhERESorK/NpKy0tVWRkpOlO\nATD2SEQHS+JMcu2wJA5wJqtqKF/7LXTSCUK7du1UUVGhPXv2qHXr1pKk4uJixcXFnVggT+WvSwB5\nbObP9qxXh44F+2MLDbOgI5KqzP9uPWX1qCplN//etghzmI5xtKKhjGICZ4Z69T/+NDnp/3ZRUVFK\nS0vT008/rdLSUm3evFlvvvmmBg0aZGX/AABAAJj6OvToo4+qvLxcl19+ue6++26NHz+eUxwBAGec\nKo+1S0NwQkMMPXr00Lp167yPmzRpounTp1veKQAA6pMgnILApZYBAIA/btYEAICBqiCcpkiCAACA\nAYYYAAAARIIAAICh+noWw6JFi9S7d28lJSXp/vvvr/Fqxnv37tUdd9yh5ORkpaamasqUKSovL681\nNgkCAAAN0EcffaSFCxdqyZIlWrNmjQ4ePKi8vLxq173vvvvUpk0b/etf/9KKFSu0ZcsWzZ49u9b4\nJAgAABiojzdrWrlypTIyMtS2bVtFR0dr7NixWrFihTzHbaC8vFxRUVHKzc1VaGiomjdvrkGDBunz\nzz+vNT6TFAEAMBCosxgqKytVUlLi126z2eR0OnXFFVd422JjY1VSUqK9e/d6b4EgSaGhoZo7d67P\n6z/66CPFx8fXum0SBAAA6qkNGzZo1KhRstlsPu3nnHOOQkJCFBER4W079rPL5ao15pQpU1RcXKyn\nnnqq1vVIEAAAMBCo0xx79uypoqKiap9LT0/3mZR4LDGo6a7KZWVlGjdunLZv364lS5aoWbNmtW6b\nBAEAAAP18XbPcXFxKi4u9j52Op1q3LixWrVq5bfuoUOHdNtttyk6OlqvvvqqYmJiDOMzSREAgAYo\nPT1dy5Yt044dO3T06FHNmDFD6enp1a47ZswYtWzZUs8++2ydkgOJCgIQ9B6J6GA6xiTXDgt6AtRf\nlVWB7oG/vn376rvvvlN2draOHj2qPn36aNy4cZKkH374QQMHDtTq1av1ww8/qLCwUI0aNVJSUpJ3\nPsPFF1+sF198scb4gU8Qqqokz8m/8zbjVQx57A7TMWz16P6dHhuFoeN53NVfPCQQokPqz+/naEU9\n/K8H1EP1cYhBkjIzM5WZmenX3qZNG23cuFGS1Lp1a23btu2EY9ef/1QAAKDeCHwFAQCAeq6ynlYQ\nTiUSBAAADNTXIYZTiSEGAADghwoCAAAG6uNZDKcaFQQAAOCHCgIAAAaCcQ4CCQIAAAaC8SwGhhgA\nAIAfKggAABioRxfLPW1IEAAAMFAZhBkCQwwAAMAPFQQAAAxwFgMAAPBTGXz5AUMMAADAHxUEAKb9\nOTzOdIxHS3da0BPg1GCIASetPh06No/5i4Zbtj92hwVByi2IYQ2Pu9R0jOiQ+lO4c9hspmPsc1dY\n0BOgfuMsBgAAAFFBAADAUDAOMVBBAAAAfqggAABgIBhPcyRBAADAAEMMAAAAooIAAIChqiA8zZEE\nAQAAA8E4B4EhBgAA4IcKAgAABoJxkiIJAgAABiqDMEFgiAEAAPihggAAgIFgPIuBCgIAAA3UokWL\n1Lt3byUlJen+++9XaWntd5z1eDy66aab9OSTTxrGJkEAAMBApcfaxQofffSRFi5cqCVLlmjNmjU6\nePCg8vLyan3Nc889p40bN9YpPgkCAAAGqjweSxcrrFy5UhkZGWrbtq2io6M1duxYrVixQp4a4hcV\nFemNN95Q//796xSfOQgA6oUHG8VZEuexsp2WxAHqg8rKSpWUlPi122w2OZ1OXXHFFd622NhYlZSU\naO/evWrdurXP+m63Ww888IAeffRR/f3vf6/TtkkQJFmRzNnMh7CMx3aGFYbsFuyPFTEsihMdUn9+\nPw5blekY7iqH6Rg/l1eajgGcSoE6zXHDhg0aNWqUbDbfT5lzzjlHISEhioiI8LYd+9nlcvnFyc/P\nV+/evZWYmEiCAACAVSoDdBZDz549VVRUVO1z6enpPpMSjyUGkZGRPuutW7dO69evV0FBwQltmwQB\nAIAGKC4uTsXFxd7HTqdTjRs3VqtWrXzWe/vtt7V792797ne/kySVlJTI4XDI6XRq7ty5NcavP7VO\nAADqqcoqj6WLFdLT07Vs2TLt2LFDR48e1YwZM5Senu633iOPPKLPPvtMGzZs0IYNGzRo0CD98Y9/\nrDU5kKggAABgKFBDDLXp27evvvvuO2VnZ+vo0aPq06ePxo0bJ0n64YcfNHDgQK1evdpvwmJdkSAA\nANBAZWZmKjMz06+9TZs2NV7v4PHHH69TbBIEAAAM1McKwqlGggAAgIFgTBCYpAgAAPxQQQAAwAAV\nBAAAAFFBAADAUDBWEEgQAAAwEIwJAkMMAADADxUEAAAMBGMFgQQBAAADJAgA0MBV7VhvOoa9Q4oF\nPQEatoAnCI6z2yrEcfKvr7TbTPfBXWk+MwxzmO+HJHkcoaZj/NTsIgt6Yo2mYeanuVTJ/Htrs+bX\nY4kBhV1Nx6j6ZqsFPZHs4VGmY9hCzR+zCgkzH0OSLcSCvgDVqKCCAAAAjheMQwycxQAAAPxQQQAA\nwEAwVhBIEAAAMFDpCb4EgSEGAADghwoCAAAGgnGIgQoCAADwQwUBAAADwVhBIEEAAMBAMCYIDDEA\nAAA/VBAAADBQWVUV6C6cdqYThOeee07Tpk1TWFiYPB6PbDabFixYoO7du1vRPwAAAi4YhxhMJwjb\ntm3Tfffdp5EjR1rQHQAAUB+YnoOwbds2XXRR/bl7IAAAVqus8li6NASmEoTS0lLt2rVLixcvVmpq\nqgYOHKjXXnvNqr4BAFAvVFR5LF0aAlNDDPv27VNiYqKGDx+unj176v/+7/+Um5urs88+W7169bKq\njwBwWrn//aolccIuu96SOEAgmEoQzjvvPL344ovex0lJSbr22mv1/vvv1zlB8Ngd8thtZrphmhWb\nt+w+HjbzZ546bOZ3yKpfyZFy829MdFhgjw+reUIjTMewxVkzCbhy80emY4S27Wi+IyGNzMeQ5LHg\n76fy+x0W9ARnmoYyLGAlU39NW7du1fz5833aysrK1KiRNX/sAADUB/V1DsKiRYvUu3dvJSUl6f77\n71dpaWm165WXl+vRRx9VSkqKUlJSNHHiRJWXl9ca21SCEBkZqVmzZundd9+Vx+PRunXrtHr1al13\n3XVmwgIAAAMfffSRFi5cqCVLlmjNmjU6ePCg8vLyql336aef1s6dO/Xee+/p3Xff1Y4dO7Rw4cJa\n45saYmjfvr2eeeYZ5efna/z48WrdurWeeOIJxcfHmwkLAEC9Uh+HGFauXKmMjAy1bdtWkjR27Fjd\ndNNNmjRpkmy/GWquqKjQq6++qoKCAsXExEiSZsyYoYqKilrjm74OQp8+fdSnTx+zYQAAwHEqKytV\nUlLi126z2eR0OnXFFVd422JjY1VSUqK9e/eqdevW3vavv/5aVVVV3hMJSktLNXDgQN177721bptL\nLQMAYCBQFYQNGzZo1KhRPhUBSTrnnHMUEhKiiIj/Tno+9rPL5fJZ9+DBg3K73VqzZo1ee+01/fLL\nL8rOzlbjxo2Vk5NT47ZJEAAAMBCoBKFnz54qKiqq9rn09HSfSYnHEoPIyEif9Y7dCuHuu+9WdHS0\noqOjNWrUKC1ZsqTWBIG7OQIA0ADFxcWpuLjY+9jpdKpx48Zq1aqVz3rt27eX3W6X2+32tlVUVMhj\ncH4+CQIAAAY8VR5LFyukp6dr2bJl2rFjh44ePaoZM2YoPT3db72YmBj169dP06ZN05EjR7R37169\n8MILGjBgQK3xSRAAADBQVeWxdLFC3759lZWVpezsbKWlpalJkyYaN26cJOmHH35QYmKi9uzZI0l6\n4okn1KpVKw0YMEDXXnutUlNTNWrUqFrjMwcBAIAGKjMzU5mZmX7tbdq00caNG72PIyMj9cgjj5xQ\nbBIEAAAMGI3Xn4lIEAAAMGDVvIGGhDkIAADADxUEAAAMWDWxsCGhggAAAPxQQQAAwICnKtA9OP1I\nEADgFKn4/B+mY4QkXG1BT2AWZzEEgK2iTDYTYztW/M6suMZ2SIjNeKW6sCBN9XgsGDmyWbM/DgvC\nHHWbf09iQiz647aZf2/trkPm+2G3ZnTQ1vl3pmOUf1VoOoYjpqnpGJJkaxRuOoY9PMqCnkiyO6yJ\nAwRIwBMEAADqu2CcpEiCAACAAa6DAAAAICoIAAAYCsYKAgkCAAAGqoLwLAaGGAAAgB8qCAAAGGCI\nAQAA+AnGBIEhBgAA4IcKAgAABoLxQklUEAAAgB8qCAAAGOBmTQAAwE8w3u6ZIQYAAOCHCgIAAAaC\ncZIiCQIA1GOVRR+bjuGI72VBT4JbMF4HocEnCDabFTHMB7Fs/orN/KiPFfvjsOB9tYrdgr78UmnN\nDkXbKyyJY5bHguNEklRlfn8cHZPMd2Pn56ZjSNaMmdqbNLcgiiS7+X+vHrfLgo4AJ6fBJwgAAJxq\nVBAAAIAf7uYIAAAgKggAABhiiAEAAPgJxgSBIQYAABqoRYsWqXfv3kpKStL999+v0tLSatf78ccf\nlZOTox49eqhXr16aNm2aYWwSBAAADFRVeSxdrPDRRx9p4cKFWrJkidasWaODBw8qLy+v2nWnTJmi\n9u3b65NPPlFBQYHeeustrVixotb4JAgAADRAK1euVEZGhtq2bavo6GiNHTtWK1asqPbGUsXFxaqo\nqFBFRYU8Ho8cDofCw8NrjU+CAACAAY/HY+lSV5WVlTpy5IjfcvToUTmdTsXFxXnXjY2NVUlJifbu\n3esX57bbbtOrr76qhIQE9e3bV4mJibrqqqtq3TaTFAEAMBCoSYobNmzQqFGj/K6Qe8455ygkJEQR\nERHetmM/u1z+V+D0eDzKycnRbbfdpt27dysnJ0evvvqqrr/++hq3TYIAAEA91bNnTxUVFVX7XHp6\nus+kxGOJQWRkpM96P/30kyZPnqxPP/1UoaGhiouLU1ZWlpYtW0aCAACAGfXxbo5xcXEqLi72PnY6\nnWrcuLFatWrls95PP/2kiooKlZeXKzQ0VJLkcDi8P9eEOQgAABjwVFVaulghPT1dy5Yt044dO3T0\n6FHNmDFD6enpfut16NBBrVq1Ul5entxut7799lstXLhQAwYMqDU+CQIAAA1Q3759lZWVpezsbKWl\npalJkyYaN26cJOmHH35QYmKi9uzZo7CwMM2fP1/ffvutevXqpREjRuiaa67RiBEjao3PEAMAAAas\n+tZvtczMTGVmZvq1t2nTRhs3bvQ+jouL03PPPXdCsUkQAAAwUF8ThFOJBAEAznCVu7dYEsdxfldL\n4qBhCHiC4KmqkkeBnR16IhetqMnx56gGkt2CrtSn/bFCiEW74/I4TMeIseJ4q6wwHePXOOWmY3gc\ntc+Ergt7rDUfPJVffWY6hu3sthb0RLKVl5kPEtHYfAw7U82s4KmkggAAAI4TjEMMpJYAAMAPFQQA\nAAxQQQAAABAVBAAADAVjBYEEAQAAA8GYIDDEAAAA/FBBAADAQDBWEEgQAAAwUBWECQJDDAAAwA8V\nBAAADDDEAAAA/ARjgsAQAwAA8EMFAQAAA9zNEQAA+AnGIQYSBABAnVTu3mI6huP8rhb0BKdD4BOE\nqkpJnoB2wWazBXT7qJ3dgl+PVb9hjxXHiqfKghjmQ1jGgm9WNo81O2TvfJnpGLaSny3oieRxhFoS\nx7QqC443SbIH95S1YKwgBPdvHAAAVCvwFQQAAOq5YKwgkCAAAGDAY9VQTQPCEAMAAPBDBQEAAAMM\nMQAAAD/BmCAwxAAAAPxQQQAAwEBVEFYQSBAAADAQjPdiYIgBAAD4MZ0gbN26VUOHDlVCQoIGDx6s\nTZs2WdEvAADqDU9VpaWL1aZMmaInn3yyxufdbrcefPBBJScnKzU1VXPnzjWMaSpBcLvdys3NVUZG\nhgoLC5WZmanRo0fL5XKZCQsAAOrg4MGDeuCBB7R06dJa15s2bZr27NmjDz/8UEuXLtXf//53/eMf\n/6j1NaYShPXr18vhcGjYsGFyOBwaMmSImjVrprVr15oJCwBAvVJfKwjDhw9XaGiorrzyylrXW7Vq\nlXJychQVFaV27dopMzNTb7zxRq2vMTVJ0el0Ki4uzqctNjZWTqfTTFgAAOqVQF0HobKyUiUlJX7t\nNptN0dHReuGFF9SyZUtNmDChxhiHDx/Wvn37fD6vY2Nj9dJLL9W6bVMJgsvlUkREhE9bRESESktL\nzYQFAACSNmzYoFGjRsl23K3mzznnHH3wwQdq2bKlYQyXyyWbzabw8HBvW3h4uOF0AFMJQnXJgMvl\nUmRkpOFrPf///u9uj00ycQ+Mcrf75F98LEalBfeid9iM16kDe2WF6RjlHvOZrsdmzf5YwW5BV6za\nGwuOFJVZcbxZxGZBVzxWBLEihiRPhflj327RF0WPJUdLPeKxZn8cZWWmXu/+///zPRb1p64CVUHo\n2bOnioqKTMU4lhiUlZUpKipKklRaWur9uSamEoQLLrjAb2JEcXGx0tPTDV9bXl4uSdrprr2DhrZv\nN/d6IKjVl0TQqjvlHbEghlXvyZl23rxF+7P/C0vClJeX+3wjPtXcnz9/2rZltSZNmqh58+ZyOp1q\n1qyZpF8/q4+fInA8UwlCSkqK3G63li5dqmHDhmn58uU6cOCAUlNTDV8bFRWljh07KjQ01K90AgBA\ndTwej8rLyw2//cLXoEGDNHPmTD3zzDP6+eeftWTJEo0fP77W15hKEMLCwrRgwQJNmjRJ+fn5ateu\nnebMmVOnrM5utysmJsbM5gEAQeh0Vg4asoSEBD377LPq3r277r77bj3++OP6/e9/L7vdrhEjRhie\n+WDznO6BHAAAUO9xqWUAAOCHBAEAAPghQQAAAH5IEAAAgB8SBAAA4CcgCQK3iD41nnvuOXXp0kWJ\niYlKSEhQYmKiPvvss0B3q8HbvHmzevXq5X18+PBhjRkzRklJSUpLS1NBQUEAe9dwHf++btmyRZ07\nd/Y5fufPnx/AHjY8hYWFuv7665WUlKQrr7xSy5Ytk8Qxi5Nj6joIJ+PYLaJHjx6tjIwMLV++XKNH\nj9b777/vd18HnJht27bpvvvu08iRIwPdlTNGQUGB8vLyFBLy3z+ViRMnKioqSuvWrdO2bduUlZWl\njh07qlu3bgHsacNS3ftaVFSk3r171+k+9fB3+PBh3XHHHZo0aZIGDhyorVu3atSoUWrbtq1efvll\njlmcsNNeQeAW0afOtm3bdNFFFwW6G2eMuXPnasmSJcrNzfW2lZSU6IMPPtBdd92l0NBQdevWTYMG\nDdLy5csD2NOGpbr3Vfq1stipU6cA9arh+/7779WnTx8NHDhQktS5c2clJydr48aN+vDDDzlmccJO\ne4LALaJPjdLSUu3atUuLFy9WamqqBg4cqNdeey3Q3WrQjlW4unTp4m3btWuXQkNDde6553rbOH5P\nTHXvq/RrgvvZZ5+pX79+SktLU15enveeLTAWHx+vvLw87+NDhw6psLBQkhQSEsIxixN22hMEbhF9\nauzbt0+JiYkaPny41qxZo7/85S964okn9PHHHwe6aw1WixYt/NpcLpcaNWrk0xYeHs7xewKqe18l\nqVmzZkpLS9Nbb72lxYsX65NPPtGMGTNOc+/ODEeOHFFubq66du2q5ORkjlmclNOeIJi5RTRqdt55\n5+nFF19Ur169FBISoqSkJF177bV6//33A921M0pERITKjrtdbWlpKcevBWbPnq2RI0cqPDxc5513\nnnJycvTee+8FulsNzu7du3XjjTfqrLPO0owZMxQZGckxi5Ny2hOECy64QMXFxT5txcXF6tChw+nu\nyhll69atfjO+y8rK/L45wJx27dqpoqJCe/bs8bbV5bapqN3hw4f15JNPqqSkxNtWWlrK8XuCvvzy\nSw0bNky9evXSrFmzFBYWxjGLk3baE4Tf3iK6oqJCBQUFdb5FNGoWGRmpWbNm6d1335XH49G6deu0\nevVqXXfddYHu2hklKipKaWlpevrpp1VaWqrNmzfrzTff1KBBgwLdtQYtJiZG7733nmbMmKGKigp9\n/fXXmjdvnoYMGRLorjUY+/btU1ZWlm655Raf2/hyzOJkBeRujl999ZUmTZqk7du3q127dpo8eTKn\n21hgzZo1ys/P1+7du9W6dWvdc889uuKKKwLdrQZvw4YNGjt2rNatWyfp18lfDz/8sNatW6eoqCjd\neeedGjx4cIB72fAc/77u3LlTU6ZM0ZYtWxQeHq4bbrhBY8aMCXAvG4558+Zp+vTpioiI0LF/6zab\nTSNGjNAtt9yiSZMmcczihHC7ZwAA4IdLLQMAAD8kCAAAwA8JAgAA8EOCAAAA/JAgAAAAPyQIAADA\nDwkCAADwQ4IAAAD8kCAAAAA//w9u92dlCocT2AAAAABJRU5ErkJggg==\n", 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sLCyg7dRjr9cb0P7jjz9q7ty5iomJ0dy5c9W2bVsNGjRI+fn55T8Qp2E6CQAA\nB6js68SEh4fbkpVTj6tWrRrQHhISovj4eKWlpUmS4uLi9Nlnn+kf//iHUlNTjY2JSgwAADin2NhY\nHTx4UD6fz9/m8XgUERGhGjUCb60THR2txo0bB7Q1atRIe/bsMTomkhgAABygsq8TEx8fr9DQUG3Y\nsMHflpOTo5YtW9r6JiQkKC8vL6Bt+/btql+//vkfgBKQxAAAgHOKiIhQr169lJGRodzcXC1fvlzz\n589XcnKypJNVmaKiIknSnXfeqX/961+aPn26duzYoWnTpmnXrl3q2bOn0TGRxAAA4AAut7tCtrIY\nOXKkWrZsqeTkZI0fP15DhgxR9+7dJUmdOnXSu+++K0mqV6+eXn75Za1cuVK33nqrVq1apTlz5igm\nJsboMWFhLwAADlCWM4kqSkREhCZOnKiJEyfanvv59FFiYqIWLVpUoeOp/CMCAABwHqjEONTBI0eN\nxKkZWc1IHABAxSrrzRovBkGVxBT63PqosPzzZYmXmjuX3letlpE46W3rGIkjSYVWiLFYAAA4VVAl\nMQAAoGRUYuxIYgAAcICynkl0MeCIAAAAR6ISAwCAA7hCWA/5c1RiAACAI1GJAQDAAVjYa0cSAwCA\nA7hZ2GvDEQEAAI5EJQYAAAdgOsmOIwIAAByJSgwAAA5AJcaOJAYAAAfgir12HBEAAOBIVGIAAHAA\nppPsOCIAAMCRqMQAAOAAVGLsSGIAAHAAN0mMDUnMRW71r7sai9Xhgw+NxQIA4FyCKomxJB074St3\nnGKfVf7B/I/Ld8JMIKv87+sUn2Xmduz/vu23RuIAACoep1jbcUQAAIAjBVUlBgAAlIyFvXYkMQAA\nOABJjB1HBAAAOBKVGAAAHICFvXYcEQAA4EhUYgAAcAB3iJnLa1xISGIAAHAAFvbacUQAAIAjkcQA\nAOAArhB3hWxl4fV6NWrUKLVr106dO3fW/Pnzz/maXbt2KTExUV999dX5vvUzYjoJAACUytNPP63N\nmzcrMzNTu3bt0vDhw1W/fn316NHjjK8ZM2aMjh07ViHjIYkBAMABKvsU68LCQmVnZ+vll19WXFyc\n4uLilJKSoqysrDMmMW+//baOHj1aYWNiOgkAAAeo7OmkvLw8FRcXKyEhwd+WlJSkTZs2ldj/wIED\nevbZZzV+/HhZlrkbM5+OJAYAAJxTQUGBatasqdDQnyZxoqKiVFRUpAMHDtj6T5o0SX369FGTJk0q\nbExMJwFU7ATyAAAgAElEQVQA4ACVfYp1YWGhwsLCAtpOPfZ6vQHtn3/+udavX6/x48dX6JioxAAA\ngHMKDw+3JSunHletWtXfVlRUpIyMDGVkZNiSHtOoxMCY95okGolz07b1RuIAwIWkshf2xsbG6uDB\ng/L5fHL/bywej0cRERGqUaOGv9+mTZu0a9cuDR48OGAtzB//+Ef17t1bY8aMMTamoEpiXJJCXAbi\nuAwE+R/LZehLU0GLmsqjdtMoY7G2LdtmLBYAwM7lrtzbDsTHxys0NFQbNmzQNddcI0nKyclRy5Yt\nA/q1adNGy5YtC2j79a9/rSeffFIdO3Y0OqagSmIAAEBwioiIUK9evZSRkaGnnnpK+fn5mj9/viZN\nmiTpZFWmevXqCg8PV4MGDWyvj4mJUe3atY2OiTUxAAA4gTukYrYyGDlypFq2bKnk5GSNHz9eQ4YM\nUffu3SVJnTp10rvvvlvi60zOkJyOSgwAACiViIgITZw4URMnTrQ9l5eXd8bXbdmypULGQxIDAIAT\nVPLC3mBEEgMAgAO4Qip3YW8wIq0DAACORCUGAAAnqORTrIMRlRgAAOBIVGIAAHACKjE2JDEAADhA\nZd92IBhxRAAAgCOddxLj9Xp166236quvvvK3TZgwQXFxcYqPj/f/+frrrxsZKAAAF7UguGJvsDmv\n6SSv16tHHnlEW7duDWjfvn27hg0bpj59+vjbIiMjyzdCAACAEpQ5idm2bZuGDh16xudSUlIUFWXu\n7sgAAECOr5pUhDJPJ61Zs0YdO3bUggULZFmWv/3IkSPKz89Xo0aNTI4PAADo5MLeiticrMyVmP79\n+5fYvn37drlcLs2cOVMff/yxatasqYEDB6p3797lHiQAAMDPGTvFevv27QoJCVHTpk117733as2a\nNXriiScUGRnpv003AAA4T0wn2RhLYnr37q3u3bv7F/JeffXV+v777/XXv/6VJAZl8m6jNsZi3fz9\nRmOxAADBxejF7n5+JlLjxo21evXqMsWoElL++Tm3q9whfuJy9nzh2VSpcYmxWHWa1TYSp2Czx0gc\nALjgUImxMfYbetq0afr9738f0LZlyxZdeeWVpnYBAMBFyxUSUiGbkxlLYrp166acnBzNmzdPO3fu\n1BtvvKG3335bKSkppnYBAADgV67pJJfrp3mbVq1a6fnnn9dzzz2nadOmqUGDBnr22WfVunXrcg8S\nAICLnsNPh64I5UpitmzZEvC4W7du6tatW7kGBAAAUBrcxRoAACdgYa8NSQwAAA7gIomxYYINAAA4\nEpUYAACcgIW9NhwRAADgSFRiAABwANbE2JHEAADgBCQxNkwnAQAAR6ISAwCAE7Cw14YjAgAAHIlK\nDAAADuD0O05XBCoxAAA4gTukYrYy8Hq9GjVqlNq1a6fOnTtr/vz5Z+z70UcfqXfv3kpMTFSvXr20\ncuXK8h4BGyoxuKBlx7YwFqtv/jfGYgGAEz399NPavHmzMjMztWvXLg0fPlz169dXjx49Avrl5eVp\n8ODBGjFihK6//np9/PHHSk9P11tvvaVmzZoZG09QJTEul0vhIUFWHHK5KnsEFSY0IsxYrPBLw43E\nqdm4ppE4kvTDNx5jsQCg0lXyKdaFhYXKzs7Wyy+/rLi4OMXFxSklJUVZWVm2JGbJkiXq2LGj7r77\nbknS3XffrZUrV+rdd9+9cJMYAAAQnPLy8lRcXKyEhAR/W1JSkmbPnm3r26dPHx0/ftzWfuTIEaNj\nIokBAMABXJV8inVBQYFq1qyp0NCfUoeoqCgVFRXpwIEDqlWrlr+9cePGAa/997//rS+//FJ33XWX\n0TGRxAAA4ARBMJ0UFha4DOHUY6/Xe8bX7d+/X4MHD1ZSUpJuvPFGo2MKsgUoAAAgGIWHh9uSlVOP\nq1atWuJrPB6PkpOT5XK5NG3aNONjohIDAIATuCq37hAbG6uDBw/K5/PJ/b+pLY/Ho4iICNWoUcPW\nPz8/XwMGDFBISIgyMzMDpptMoRIDAADOKT4+XqGhodqwYYO/LScnRy1btrT1LSwsVEpKiqpUqaKs\nrCzVqVOnQsZEEgMAgBO43BWzlVJERIR69eqljIwM5ebmavny5Zo/f76Sk5MlnazKFBUVSZJmzZql\nXbt2aeLEifL5fPJ4PPJ4PJydBADAxciq5OkkSRo5cqTGjh2r5ORkVa9eXUOGDFH37t0lSZ06ddKk\nSZPUu3dvLVu2TMeOHdMdd9wR8PrevXtr4sSJxsZDEgMAAEolIiJCEydOLDERycvL8//93Xff/UXG\nQxIDAIATBEElJthwRAAAgCNRiQEAwAku4Hv5nS+SGAAAnKCSbzsQjDgiAADAkajEAADgAMFwinWw\n4YgAAABHohIDAIATUImxIYkBSik7toWROH3zvzESB8BFhiTGJqiSGJekEAOfUYjBs9Asd1AdIqPC\nqlczFiuipplYvmLLSBxJiml0qbFY+74/ZCwWAMCMC/c3NAAAFxIqMTYcEQAA4EhUYgAAcABOsbYj\niQEAwAlIYmw4IgAAwJGoxAAA4ATcANKGSgwAAHAkKjEAADgBa2JsSGIAAHAAzk6y44gAAABHohID\nAIATuKk7/BxHBAAAOBKVGAAAnIA1MTYkMQAAOAFJjA1HBAAAOBKVGAAAnIBKjA1JDPAL8337mbFY\n7quvMxYLAJwmqJKYsBOFav3DinLHKYy+1cBoTrIiqhuJ43WHGYkjSZHew0bihN52r5E4klT78H4j\ncdxVLzES52SwEHOxfMXmYgHAeeBid3ZBlcQAAIAzIImx4YgAAABHIokBAMAJXK6K2crA6/Vq1KhR\nateunTp37qz58+efse/mzZt1xx13KCEhQf369dM333xT3iNgQxIDAABK5emnn9bmzZuVmZmpjIwM\nTZ8+XcuWLbP1KywsVGpqqtq1a6dFixYpISFB9913n44dO2Z0PCQxAAA4gctdMVspFRYWKjs7W48/\n/rji4uLUvXt3paSkKCsry9Z3yZIlqlq1qh599FE1btxYjz32mC655BK99957Jo8ISQwAAE5gudwV\nspVWXl6eiouLlZCQ4G9LSkrSpk2bbH03bdqkpKSkgLZrrrlG69evP/8DUAKSGAAAcE4FBQWqWbOm\nQkN/OrE5KipKRUVFOnDgQEDfffv2KSYmJqAtKipK+fn5RsfEKdYAADhBJZ9iXVhYqLCwwGuenXrs\n9XoD2o8dO1Zi35/3Ky8qMQAA4JzCw8NtScipx1WrVi1V34iICKNjohIDAIADWGU8Hdq02NhYHTx4\nUD6fT273yRqIx+NRRESEatSoYetbUFAQ0ObxeBQdHW10TFRiAABwAMuqmK204uPjFRoaqg0bNvjb\ncnJy1LJlS1vfNm3a2Bbxrlu3LmBRsAkkMQAA4JwiIiLUq1cvZWRkKDc3V8uXL9f8+fOVnJws6WSl\npaioSJL0f//3fzp8+LCeeuopbdu2TRMmTFBhYaFuvvlmo2MiiQEAwAF8llUhW1mMHDlSLVu2VHJy\nssaPH68hQ4aoe/fukqROnTrp3XfflSRFRkZq1qxZysnJ0e23367c3Fy99NJLxtfEuCyrjO+gguTm\n5so69qPivDvKHaswwdxdrCMsMyupTd7FOtzUXaz3l/9Yn+LjLtaVwn31dZU9BOCikZubK0lq1apV\npez/yNHCCokbWa3quTsFKRb2AgDgAEFRcQgyJDEAADiAjyzGhjUxAADAkajEAA42KryJkThPFW0z\nEgdAxQmSJaxBJbiSGJeMLMZ0G70ekJlildEhmbr0tOUzE8cgX+GPxmK5qphbTB2MC3ufSEqt7CEA\nQKUKriQGAACUiDUxdiQxAAA4ADmMHQt7AQCAI1GJAQDAAZhOsqMSAwAAHIlKDAAADsAp1nYkMQAA\nOEDwXRSj8jGdBAAAHIlKDAAADsBskh2VGAAA4EhUYgAAcABOsbY770qM1+vVrbfeqq+++srftmvX\nLg0cOFCJiYm65ZZb9NlnnxkZJAAAFzvLsipkc7LzSmK8Xq8eeeQRbd26NaD9wQcfVExMjN566y31\n7NlTaWlp2rt3r5GBAgAAnK7MScy2bdt0xx13aNeuXQHtX3zxhXbu3Klx48apcePGSk1NVUJCgrKz\ns40NFgCAi5WvgjYnK3MSs2bNGnXs2FELFiwIKENt2rRJLVq0UHh4uL8tKSlJGzZsMDNSAACA05R5\nYW///v1LbC8oKFBMTExAW1RUlPLz889vZAAAwM/hy1cqhLGzkwoLCxUWFhbQFhYWJq/Xa2oXACrI\nuKpNjcUaXbj13J0AlJmPLMbGWBITHh6uQ4cOBbR5vV5FRESUPoglyVdsakhGWC4zl9IJyq+eofcm\nSa4qYefuVBoGP3+r6JixWMa4zR3zOmEhRuIcOeH0WXEAFytj/6LGxsaqoKAgoM3j8Sg6OtrULgAA\nuGhZFbQ5mbEkpk2bNtq8eXPA9NHatWuVkJBgahcAAAB+xpKY9u3bq27duhoxYoS2bt2qOXPmKDc3\nV3379jW1CwAALlo+q2I2JytXEuNyuX4K5HZrxowZKigo0O2336533nlHL774oi677LJyDxIAgIud\nZVXM5mTlWti7ZcuWgMcNGjRQZmZmuQYEAABQGtwAEgAAB/A5fhmueebO9wQAAPgFUYkBAMABnL5+\npSJQiQEAwAGccHbSlClT1LFjR3Xo0EGTJ08+a98NGzbozjvvVGJiom6++WYtXLiwzPujEgMAAMpt\n3rx5Wrp0qWbMmKHjx49r2LBhqlOnjgYOHGjr6/F4lJqaqrvuukvPPPOMvv76a40cOVIxMTHq0qVL\nqfdJJQYAAAcI9lOsMzMzlZ6ersTERLVv317Dhg1TVlZWiX2XL1+u6OhoPfTQQ2rYsKF+85vfqFev\nXvrnP/9Zpn1SiQEAAOWyb98+7dmzR23btvW3JSUlaffu3fJ4PKpTp05A/+uvv17Nmze3xTl8+HCZ\n9kslBgAAB/DJqpDNhIKCArlcLsXExPjb6tSpI8uytHfvXlv/evXqqXXr1v7H//3vf7V06VJde+21\nZdovlRgAABygss9OKioqUn5+fonPHT16VJIUFhbmbzv199PvqXimuIMHD1ZMTIx+97vflWlMJDEA\nAOCcNm7cqAEDBgTccuiUYcOGSTqZsPw8ealateoZYx49elR/+tOftGPHDv31r39VeHh4mcZEEgMA\ngAP4KrkU0759e+Xl5ZX43L59+zRlyhR5PB7Vq1dP0k9TTNHR0SW+5siRI0pJSdGuXbv06quvqkGD\nBmUeE2tiAABAucTExKhu3bpau3atvy0nJ0d169a1LeqVJMuylJaWph9++EFZWVlq0qTJee2XSgwA\no8ZVbWokzujCrUbiABeKYl9lj+Ds7rzzTk2ZMkWxsbGyLEtTp07VoEGD/M/v379fERERqlatmhYu\nXKg1a9Zo5syZioyMlMfjkSRVqVJFl156aan3GXxJjK/8n5J9tu78We4QI3Fcpi+LaIDlohBXWpb3\nWGUPwSYyNPg+vyMngvxfWcDBKns66VxSUlJ04MABDR48WCEhIerXr5+Sk5P9z/ft21e33Xab0tLS\ntGzZMlmWpfvvvz8gRrt27fTaa6+Vep/Bl8QAAADHcbvdGj58uIYPH17i8ytXrvT/fe7cuUb2SRID\nAIADFAd5JaYyBF89GgAAoBSoxAAA4ADBviamMpDEAADgAMF+dlJlYDoJAAA4EpUYAAAcgOkkOyox\nAADAkajEAADgAJxibUcSAwCAAwThhd8rHdNJAADAkajEAADgAMWUYmyoxAAAAEeiEgMAgANwirUd\nSQwAAA5QTA5jw3QSAABwJCoxAAA4ANNJdiQxAILSExFNjMUaf2ybsVgAggdJzC8kGPNnl2XulqjG\n3p87xFQkSccNxjLD8h4zFisyNPhmg0NcLiNxPN4TRuIAFxJOsbYjiQEAwAGYTrILvv/KAQAAlAKV\nGAAAHIBTrO2oxAAAAEeiEgMAgAOwJsaOJAYAAAfwcXaSDdNJAADAkajEAADgACzstaMSAwAAHIlK\nDAAADsDCXjuSGAAAHKCYJMaG6SQAAOBIJDEAADiAz2dVyGbSlClT1LFjR3Xo0EGTJ08u1WuOHDmi\n66+/XosXLy7z/phOAgAA5TZv3jwtXbpUM2bM0PHjxzVs2DDVqVNHAwcOPOvrnnnmGRUUFJzXPqnE\nAADgAMVWxWymZGZmKj09XYmJiWrfvr2GDRumrKyss74mJydHq1evVp06dc5rnyQxAAA4gM+yKmQz\nYd++fdqzZ4/atm3rb0tKStLu3bvl8XhKfI3X69Xo0aOVkZGhKlWqnNd+mU4CcMEbFd7ESJynirYZ\niQNcaAoKCuRyuRQTE+Nvq1OnjizL0t69e0ustMyaNUstWrTQtddee977JYk5B1NntLnMhDHKcl3g\nhTi3wfdnKpbBMUWGBt/nF+LyGYnj9YUYiSNJB44XG4sFVKbKPsW6qKhI+fn5JT539OhRSVJYWJi/\n7dTfvV6vrf/WrVv15ptv6u233y7XmEhiAADAOW3cuFEDBgyQy2X/b/mwYcMknUxYfp68VK1a1db/\niSeeUHp6umrXrl2uMZHEAADgAMWVfBfr9u3bKy8vr8Tn9u3bpylTpsjj8ahevXqSfppiio6ODui7\ne/durV+/Xv/61780ceJESdKxY8eUkZGhpUuXas6cOaUeE0kMAAAOUNlJzNnExMSobt26Wrt2rT+J\nycnJUd26dW3rYWJjY/XBBx8EtN1zzz1KTk7WLbfcUqb9ksQAAIByu/POOzVlyhTFxsbKsixNnTpV\ngwYN8j+/f/9+RUREqFq1amrQoEHAa0NCQlS7du2AhcGlQRIDAIADBHMlRpJSUlJ04MABDR48WCEh\nIerXr5+Sk5P9z/ft21e33Xab0tLSbK8taZ1NaZDEAACAcnO73Ro+fLiGDx9e4vMrV64842tXrFhx\nXvskiQEAwAGCvRJTGUhiAABwAJIYu+C7WhYAAEApUIkBAMABqMTYUYkBAACORCUGAAAHoBJjRxID\nAIADkMTYMZ0EAAAciUoMAAAOQCXGjkoMAABwJCoxAAA4AJUYO5IYACgl39YvjcVyN/2VsVi4OJwg\nibEJqiTGFRqq0HqNyx2n2H1+d8MsibfYzJcmLMTcmKyQKkbiFNRuZiSOSTXDzM1w+mTumJ/nDVYr\n1G9yWhmJ49ux2UgcSXJHXGIkjquKme+4JCk0zEgYV6jBMQEwIqiSGAAAUDKmk+xY2AsAAByJSgwA\nAA5AJcaOJAYAAAcotkhifo7pJAAA4EhUYgAAcACmk+yoxAAAAEeiEgMAgANQibEjiQEAwAFIYuyY\nTgIAAI5EJQYAAAco9vkqewhBx2glZvny5YqLi1N8fLz/zyFDhpjcBQAAgCTDlZitW7eqW7dumjBh\ngqz/XZQnPDzc5C4AALgosSbGzmgSs23bNl111VWqXbu2ybAAAFz0SGLsjE4nbdu2TVdeeaXJkAAA\nACUyWon57rvv9Mknn2jmzJny+Xy66aablJ6eripVqpjcDQAAF50TVGJsjCUxu3fv1rFjxxQREaFp\n06Zp165dmjBhgoqKijRq1ChTuwGAC4L3szeNxAm77g4jcQAnMpbE1KtXT1999ZUiIyMlSXFxcfL5\nfPrzn/+skSNHyuVylSKKS5Y7uM76dpdm2KVg9OajLjOzgCGl+kxKx9RxOnzc3IGKDDP3/oKRVaWq\nkTiuJklG4khS8aYPjcSp0vBqI3EkSaFmTi6wDP3cSVLx7q3GYuHiwZoYO6NrYk4lMKc0adJERUVF\nOnjwoMndAABw0Sn2WRWymTRlyhR17NhRHTp00OTJk8/ad+/evUpNTVVCQoJ69OihpUuXlnl/xpKY\nTz/9VB06dFBRUZG/bfPmzapZs6Zq1aplajcAACAIzZs3T0uXLtWMGTP0wgsv6J133tH8+fNL7Ovz\n+ZSamqqwsDAtXrxYgwYN0p///Gdt3Vq2KqWxuZvExERVq1ZNjz32mB588EHt2LFDkydP1h//+EdT\nuwAA4KIV7NNJmZmZGjJkiBITEyVJw4YN07Rp0zRw4EBb31WrVmnv3r3629/+pmrVqqlRo0b65JNP\ntH79ejVt2rTU+zSWxFxyySWaO3eunnzySfXt21eXXHKJ+vfvrz/84Q+mdgEAAILQvn37tGfPHrVt\n29bflpSUpN27d8vj8ahOnToB/desWaNf/epXqlatmr9t+vTpZd6v0VW0TZo00bx580yGBAAACu5K\nTEFBgVwul2JiYvxtderUkWVZ2rt3ry2J2blzp+rXr69nn31W//jHP1S7dm2lpaWpe/fuZdpvcJ0K\nBAAASlTZSUxRUZHy8/NLfO7o0aOSpLCwMH/bqb97vV5b/x9//FGLFi3Sb37zG82ePVtffvmlhgwZ\nojfffFMtWrQo9ZhIYgAAwDlt3LhRAwYMKPGSKcOGDZN0MmH5efJStar9chAhISGqVauWxo4dK0mK\nj49XTk6OFixYoHHjxpV6TCQxAAA4gFX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nLWsdO3aEi4sLfv31V+V1jh49ijFjxqCoqAjTpk3DyJEjERsbi08//RQffvgh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\nO/D/AVvwztGQIY1uAAAAAElFTkSuQmCC\n", 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OI0aMwN27dyWvZWRkYPny5ejYsSM6deqE5cuXIzs7GwCQmJiIuXPnwsnJCV26dMGmTZvK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\nlClToNVqsXPnTlRWViIhIQE+Pj5SRyM7VlFRgc8//xze3t5YuHAh1Gq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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -364,9 +367,9 @@ "outputs": [ { "data": { - "image/png": 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Fx8eH06dP884773Ds2DH8/PwYO3Ys/fr1IyIiglOnTjF37lwATp8+Tb9+/Thx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Cspii8uyg247oX9JeTXtSMg8fjCFkTJS6g+kpSoSf71Z6SuwByWzGhotdZyrGqb/+dYUxoCApIk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\nU2954IEH7FM6BoOBdu3a2Z23BAKB4G5AkiT+/Oc/ExgYyDfffFO9Y5w5hS0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A+vXrx6+//srSpUtxc3OjTZs2TJ48mUWLFtG/f38uXrzIsmXLsLS0pH///jzz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\nI06JnMrUM8I9xhOy7KJzoIlWd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7NcY68ff8z1tkkDWJHeycLkHgAO14ewo2I8hmYohJmd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\nQ7+Ru5e2UN1jxBpoe8iIcPDxxXIqYeuAjr8dv/xCM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lhdKn57KA2fOPL3dk1WDzqNSEqY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\n1Uu3XUNiqCu4v8FzeuZ4HeJYPRkKc8IH5OtVQUFhrjjduAuvX14uXJlxA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TVP24uBWhDH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wAAdqA3lqdxK9NhUzY3r45ZJKmnt1/GFrGj2naWGtc4nhsSAI8K2Z9UwK7z31yl5kX00gBc2e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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -451,9 +454,9 @@ "outputs": [ { "data": { - "image/png": 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WVpq7CKILVVZWahYONyWLGsMI0LdvX06dOsWGDRv4v//7P3788UedtYLs7e2p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\n7puXGc/Xdt8fbP2/hWRNEqvyHxuztng4ZKWl8PRCDeXXdfSnPhL0Xr5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RAAAg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OnMm6deto164djz/+ONnZ2RgMBp599lmGDBnCtm3bSEhIYObMmZw9e5YZM2bQ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\ndDWD9PR0p9uZgMt6JXl5eW4ZiXe1Xc6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pgiI5+peenVf1fHQymwdfT+SDr9bbXDOOUgUc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pwmie8GiMxxM+XnpFCSWUMD5KgW8JU4o3SnR9bGA9EdH82JAFebBreDyHHx6TFdovVoYURZGA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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -509,9 +512,9 @@ "outputs": [ { "data": { - "image/png": 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4vEtEBsdY923jsBTpg+oOCV+8eBFRUVE4d+4cAKBRo0ZIS0uDtbW1tkqslLH+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\nLl68aMRWGs+9e/cEuVwuHD58WBAEQUhKShLkcrlw+vRp1umh1NRUwdv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iIiIiEo/8/HzIZLJyd1AszaxnXJVKpVZ9BKsKpVKJvLw8jo0eOIb64fjpj2OoH46f/jiG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\nsB6WCUlXsPjLM0h16qxKEH/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KmOj5OPMubjolriEhITh//jyGDx+uOjZ//nyEhoZi7dq1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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -528,6 +531,80 @@ "g = visualizer.poof() # Draw/show/poof the data" ] }, + { + "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", + "\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": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.linear_model import RidgeCV\n", + "from yellowbrick.regressor import AlphaSelection" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "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": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ZmIzOfvsxzKIx5nZvhSqa6sV2rLIgd2yLc4zpPxxfxeMYKxbHV/E4xopV0uOr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\nYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "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 = RidgeCV(alphas=alphas, scoring=\"neg_mean_squared_error\")\n", + "visualizer = AlphaSelection(model)\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": {}, @@ -545,7 +622,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "metadata": { "collapsed": true }, @@ -572,7 +649,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "metadata": { "collapsed": true }, @@ -595,16 +672,16 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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iIuJFQaKIiIiIeNHAFREREZHfqW6VbKyWrN+djqNyNkmXIT+Xg1oSRURERMSL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tonv37owaNYro6Ohi5algV0RERERuC2dn53xBbfZrV1dXq+WLFi2iR48ePPvs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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -632,7 +709,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "metadata": { "collapsed": true }, @@ -655,16 +732,16 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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ffPABJk+eDCsrK8TGxsLKyuqZAbRTp05o0KAB3n33XQQFBcHFxQUbN27EDz/8YBj00aJF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\nRsycORPvvPMOBgwY8MjhiIiIiEg8UYUdEREREVk+2R0pRkR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tkyQVLVpUs2bNUrNmzWyuKucwPJjSMQUAANknKChIc+bM0fr16zVq1CjlzZvX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\n0aFDB722xtd1dXXNHldRUYGYmBgEBARg5MiRRn+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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -692,7 +769,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 26, "metadata": { "collapsed": false }, @@ -715,16 +792,16 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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AMJlSBbwtW7bo0UcfVfPmzdWtWzd9/PHHkqScnByNGjVK4eHhio6O1tq1a53T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\nD1qK7viOAMG2AAAAAElFTkSuQmCC\n", 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Z3NzcnNZxc3OT3W6XJJWXl592vLy83Pr4dNvXhsPhUGlp6dnsjlGq7g+gMSkv\nLz8vnn8Azk5ZWZnT/0BNHA6HbDbbGdc760Dt0qWLJGnq1KmaMmWKhg4dqqNHjzqtY7fb5eHhIUly\nd3evFpt2u10tWrSwwrSmcU9Pz1rPqbKyUnl5eXXeF9MUFBQ09BSAOisoKHC6xAfAhYmfYTiTUw9I\n1qROgfrDDz8oOztb/fv3t5YFBgaqsrJSvr6+2rt3r9P6JSUl8vX1lSS1adNGxcXF1caDg4Pl7e0t\nd3d3lZSU6IorrpAkHT9+XEeOHLG2rw1XV1cFBgbWZZeMdPKFYfsaehpAnQQEBCg4OLihpwGggZSV\nlamgoEABAQF1OriEC0t+fn6t1qtToBYWFuq+++7Tpk2brOtFc3Nz5ePjo4iICK1YsUJ2u90q4x07\ndlgvfAoNDVVWVpZ1W2VlZdq9e7cSEhJks9kUEhKiHTt2WC+kys7Olqurq4KCgmo9P5vNZr2AqzGr\nOuoMNCYeHh7nxfMPwLnx9PTkewFOqzan96U6vkgqJCREXbt21fTp07V3715t2rRJzzzzjO69915F\nRUWpXbt2mjp1qvLz85WWlqbc3FwNHTpUkhQbG6usrCylp6crPz9f06ZNk7+/vxWkd9xxh1asWKH3\n3ntPO3fuVGJiooYPH84pQwAAgAtMnQK1SZMmSklJUbNmzTRixAjNnDlTf/nLX3TXXXepSZMmSk1N\nVXFxsWJjY/Xmm28qOTlZbdu2lSS1b99eS5Ys0fr16zVs2DD9/PPPSk5Otm570KBBmjhxombNmqXx\n48crLCxMU6ZMqd+9BQAAgPFsDofD0dCTqA+5ubmSTh7lbeyysrLUa9E7Ou57eUNPBagVl+Jv9ekD\ng9S9e/eGngqABlJaWqq8vDwFBwdzih+nVdteO+v3QQUAAAB+DwQqAAAAjEKgAgAAwCgEKgAAAIxC\noAIAAMAoBCoAAACMQqACAADAKAQqAAAAjEKgAgAAwCgEKgAAAIxCoAIAAMAoBCoAAACMQqACAADA\nKAQqAAAAjEKgAgAAwCgEKgAAAIxCoAIAAMAoBCoAAACMQqACAADAKAQqAAAAjEKgAgAAwCgEKgAA\nAIxCoAIAAMAoBCoAAACMQqACAADAKAQqAAAAjEKgAgAAwCgEKgAAAIxCoAIAAMAoBCoAAACMQqAC\nAADAKAQqAAAAjEKgAgAAwCgEKgAAAIxCoAIAAMAoBCoAAACMQqACAADAKAQqAAAAjEKgAgAAwCgE\nKgAAAIw3FWkKAAAgAElEQVRCoAIAAMAoBCoAAACMQqACAADAKAQqAAAAjEKgAgAAwCgEKgAAAIxC\noAIAAMAoBCoAAACMQqACAADAKHUK1KKiIiUkJKhnz57q16+f/va3v8lut0uSnnrqKQUFBSk4ONj6\n/4UXXrC23bp1qwYPHqywsDDFxcVp//79Tre9atUqRUdHKyIiQjNmzFBFRUU97B4AAAAamzoFakJC\ngioqKrRmzRotWLBAH374oRYvXixJ2rdvn6ZMmaLNmzdry5Yt2rx5s4YOHSpJ+v777xUfH6/Y2Fit\nX79e3t7eio+Pt25348aNSklJ0ezZs7V69Wrl5OQoKSmpHncTAAAAjUWtA3Xfvn3auXOn5s6dq44d\nOyoiIkIJCQl66623JEl79+5V586d5ePjY/1zd3eXJL388ssKCQlRXFycOnbsqLlz5+rAgQPavn27\nJCkjI0OjR49Wv3791LVrVyUmJmrdunUcRQUAALgA1TpQfX19tXz5crVq1cpa5nA49PPPP+vYsWMq\nKipSQEBAjdvm5OQoKirK+tjDw0OdO3dWdna2Tpw4odzcXEVGRlrjYWFhqqys1J49e85ilwAAANCY\n1TpQL7roIvXt29f62OFw6Pnnn1efPn20b98+2Ww2paamql+/frr11lv12muvWeseOnRIfn5+TrfX\nunVrFRUV6ejRo6qoqHAad3FxUcuWLXXw4MFz2TcAAAA0Qk3PdsN58+Zpz549WrdunXbt2iUXFxcF\nBgZq1KhR2rZtm2bOnCkvLy/1799f5eXlcnNzc9rezc1Ndrtd5eXl1sc1jdeFw+FQaWnp2e6SMaru\nE6AxKS8vPy+efwDOTllZmdP/QE0cDodsNtsZ1zurQE1KSlJGRoYWLVqkwMBABQYGqn///vLy8pIk\nXXnllSooKNCLL76o/v37y93dvVps2u12tWjRwgrTmsY9PT3rNK/Kykrl5eWdzS4ZpaCgoKGnANRZ\nQUGBdd05gAsXP8NwJqcelKxJnQN19uzZyszMVFJSkvr3728tr4rTKh06dNBnn30mSWrTpo2Ki4ud\nxktKShQcHCxvb2+5u7urpKREV1xxhSTp+PHjOnLkiHx9fes0N1dXVwUGBtZ1l4xz8sVh+xp6GkCd\nBAQEKDg4uKGnAaCBlJWVqaCgQAEBAXU+wIQLR35+fq3Wq1OgLl26VJmZmVq4cKFuuOEGa/nixYuV\nnZ2tVatWWcvy8vKs4AwNDVVWVpY1VlZWpt27dyshIUE2m00hISHasWOH9UKq7Oxsubq6KigoqC7T\nk81mU7Nmzeq0jYk8PDwaegpAnXl4eJwXzz8A58bT05PvBTit2pzel+rwIqm9e/cqNTVVEydOVHh4\nuEpKSqx/MTEx+vzzz7Vy5Urt379fa9as0RtvvKHx48dLkmJjY5WVlaX09HTl5+dr2rRp8vf3t4L0\njjvu0IoVK/Tee+9p586dSkxM1PDhwzldCAAAcAGq9RHU999/XydOnFBqaqpSU1Ml/d+Frnl5eXr2\n2We1aNEiLV68WP7+/po/f766desmSWrfvr2WLFmiOXPmKCUlRd27d1dycrJ124MGDdKBAwc0a9Ys\nVVZWasCAAZoyZUo97yoAAAAaA5vD4XA09CTqQ25uriQpJCSkgWdy7rKystRr0Ts67nt5Q08FqBWX\n4m/16QOD1L1794aeCoAGUlpaqry8PAUHB3OKH6dV216r0586BQAAAH5vBCoAAACMQqACAADAKAQq\nAAAAjEKgAgAAwCgEKgAAAIxCoAIAAMAoBCoAAACMQqACAADAKAQqAAAAjEKgAgAAwCgEKgAAAIxC\noAIAAMAoBCoAAACMQqACAADAKAQqAAAAjEKgAgAAwCgEKgAAAIxCoAIAAMAoBCoAAACMQqACAADA\nKAQqAAAAjEKgAgAAwCgEKgAAAIxCoAIAAMAoBCoAAACMQqACAADAKAQqAAAAjEKgAgAAwCgEKgAA\nAIxCoAIAAMAoBCoAAACMQqACAADAKAQqAAAAjEKgAgAAwCgEKgAAAIxCoAIAAMAoBCoAAACMQqAC\nAADAKAQqAAAAjEKgAgAAwCgEKgAAAIxCoAIAAMAoBCoAAACMQqACAADAKAQqAAAAjEKgAgAAwCgE\nKgAAAIxCoAIAAMAodQrUoqIiJSQkqGfPnurXr5/+9re/yW63S5IKCws1ZswYhYeH65ZbbtGWLVuc\ntt26dasGDx6ssLAwxcXFaf/+/U7jq1atUnR0tCIiIjRjxgxVVFSc464BAACgMapToCYkJKiiokJr\n1qzRggUL9OGHH2rx4sWSpEmTJsnPz0/r16/XkCFDNHnyZB08eFCS9P333ys+Pl6xsbFav369vL29\nFR8fb93uxo0blZKSotmzZ2v16tXKyclRUlJSPe4mAAAAGotaB+q+ffu0c+dOzZ07Vx07dlRERIQS\nEhL01ltv6dNPP1VhYaGefPJJdejQQRMnTlRYWJjWrVsnSVq7dq1CQkIUFxenjh07au7cuTpw4IC2\nb98uScrIyNDo0aPVr18/de3aVYmJiVq3bh1HUQEAAC5AtQ5UX19fLV++XK1atXJa/vPPPysnJ0dd\nunSRu7u7tTwiIkJffPGFJGnnzp2Kioqyxjw8PNS5c2dlZ2frxIkTys3NVWRkpDUeFhamyspK7dmz\n56x3DAAAAI1TrQP1oosuUt++fa2PHQ6Hnn/+efXu3VvFxcXy8/NzWt/Hx0dFRUWSpEOHDlUbb926\ntYqKinT06FFVVFQ4jbu4uKhly5bWJQIAAAC4cDQ92w3nzZunvLw8rVu3Ts8995zc3Nycxt3c3KwX\nUJWXl592vLy83Pr4dNvXlsPhUGlpaV13xThV9wnQmJSXl58Xzz8AZ6esrMzpf6AmDodDNpvtjOud\nVaAmJSUpIyNDixYtUmBgoNzd3fXTTz85rWO32+Xh4SFJcnd3rxabdrtdLVq0sMK0pnFPT886zauy\nslJ5eXl13R3jFBQUNPQUgDorKChwuswHwIWJn2E4k1MPStakzoE6e/ZsZWZmKikpSf3795cktWnT\nRvn5+U7rlZSUyNfX1xovLi6uNh4cHCxvb2+5u7urpKREV1xxhSTp+PHjOnLkiLV9bbm6uiowMLCu\nu2Scky8O29fQ0wDqJCAgQMHBwQ09DQANpKysTAUFBQoICKjzASZcOE7txdOpU6AuXbpUmZmZWrhw\noW644QZreWhoqNLT02W3260q3rFjh/XCp9DQUGVlZVnrl5WVaffu3UpISJDNZlNISIh27NhhvZAq\nOztbrq6uCgoKqsv0ZLPZ1KxZszptY6KqI89AY+Lh4XFePP8AnBtPT0++F+C0anN6X6rDi6T27t2r\n1NRUTZw4UeHh4SopKbH+9ejRQ+3atdPUqVOVn5+vtLQ05ebmaujQoZKk2NhYZWVlKT09Xfn5+Zo2\nbZr8/f2tIL3jjju0YsUKvffee9q5c6cSExM1fPhwThcCAABcgGp9BPX999/XiRMnlJqaqtTUVEn/\nd6FrXl6ekpOTNWPGDMXGxuqyyy5TcnKy2rZtK0lq3769lixZojlz5iglJUXdu3dXcnKydduDBg3S\ngQMHNGvWLFVWVmrAgAGaMmVKPe8qAAAAGgObw+FwNPQk6kNubq4kKSQkpIFncu6ysrLUa9E7Ou57\neUNPBagVl+Jv9ekDg9S9e/eGngqABlJaWqq8vDwFBwdzih+nVdteq9OfOgUAAAB+bwQqAAAAjEKg\nAgAAwCgEKgAAAIxCoAIAAMAoBCoAAACMQqACAADAKAQqAAAAjEKgAgAAwCgEKgAAAIxCoAIAAMAo\nBCoAAACMQqACAADAKAQqAAAAjEKgAgAAwCgEKgAAAIxCoAIAAMAoBCoAAACMQqACAADAKAQqAAAA\njEKgAgAAwCgEKgAAAIxCoAIAAMAoBCoAAACMQqACAADAKAQqAAAAjEKgAgAAwCgEKgAAAIxCoAIA\nAMAoBCoAAACMQqACAADAKAQqAAAAjEKgAgAAwCgEKgAAAIxCoAIAAMAoBCoAAACMQqACAADAKAQq\nAAAAjEKgAgAAwCgEKgAAAIxCoAIAAMAoBCoAAACMQqACAADAKAQqAAAAjEKgAgAAwCgEKgAAAIxC\noAIAAMAoBCoAAACMQqACAADAKGcdqHa7XYMHD9b27dutZU899ZSCgoIUHBxs/f/CCy9Y41u3btXg\nwYMVFhamuLg47d+/3+k2V61apejoaEVERGjGjBmqqKg42+kBAACgkTqrQLXb7XrooYeUn5/vtHzf\nvn2aMmWKNm/erC1btmjz5s0aOnSoJOn7779XfHy8YmNjtX79enl7eys+Pt7aduPGjUpJSdHs2bO1\nevVq5eTkKCkp6Rx2DQAAAI1RnQN17969Gj58uAoLC2sc69y5s3x8fKx/7u7ukqSXX35ZISEhiouL\nU8eOHTV37lwdOHDAOgKbkZGh0aNHq1+/furatasSExO1bt06jqICAABcYOocqNu2bVPv3r2VmZkp\nh8NhLT927JiKiooUEBBQ43Y5OTmKioqyPvbw8FDnzp2VnZ2tEydOKDc3V5GRkdZ4WFiYKisrtWfP\nnrpOEQAAAI1Y07puMHLkyBqX79u3TzabTampqfroo4/UsmVLjRkzRrfddpsk6dChQ/Lz83PapnXr\n1ioqKtLRo0dVUVHhNO7i4qKWLVvq4MGDCg0Nres0AQAA0EjVOVBPZ9++fXJxcVFgYKBGjRqlbdu2\naebMmfLy8lL//v1VXl4uNzc3p23c3Nxkt9tVXl5ufVzTeG05HA6Vlpae+840sKr7A2hMysvLz4vn\nH4CzU1ZW5vQ/UBOHwyGbzXbG9eotUG+77Tb1799fXl5ekqQrr7xSBQUFevHFF9W/f3+5u7tXi027\n3a4WLVpYYVrTuKenZ63nUFlZqby8vHPck4ZXUFDQ0FMA6qygoMC65hzAhYufYTiTUw9I1qTeAlWS\nFadVOnTooM8++0yS1KZNGxUXFzuNl5SUKDg4WN7e3nJ3d1dJSYmuuOIKSdLx48d15MgR+fr61vrz\nu7q6KjAw8Bz3ouGdfGHYvoaeBlAnAQEBCg4ObuhpAGggZWVlKigoUEBAQJ0OLuHCcuo7QJ1OvQXq\n4sWLlZ2drVWrVlnL8vLyrOAMDQ1VVlaWNVZWVqbdu3crISFBNptNISEh2rFjh/VCquzsbLm6uioo\nKKjWc7DZbGrWrFn97FAD8vDwaOgpAHXm4eFxXjz/AJwbT09PvhfgtGpzel+qx78kFRMTo88//1wr\nV67U/v37tWbNGr3xxhsaP368JCk2NlZZWVlKT09Xfn6+pk2bJn9/fytI77jjDq1YsULvvfeedu7c\nqcTERA0fPpxThgAAABeYczqC+usKDgkJ0bPPPqtFixZp8eLF8vf31/z589WtWzdJUvv27bVkyRLN\nmTNHKSkp6t69u5KTk63tBw0apAMHDmjWrFmqrKzUgAEDNGXKlHOZHgAAABqhcwrUU1+QFBMTo5iY\nmNOuf80112jDhg2nHZ8wYYImTJhwLlMCAABAI1dvp/gBAACA+kCgAgAAwCgEKgAAAIxCoAIAAMAo\nBCoAAACMQqACAADAKAQqAAAAjEKgAgAAwCgEKgAAAIxCoAIAAMAoBCoAAACMQqACAADAKAQqAAAA\njEKgAgAAwCgEKgAAAIxCoAIAAMAoBCoAAACMQqACAADAKAQqAAAAjEKgAgAAwCgEKgAAAIxCoAIA\nAMAoBCoAAACMQqACAADAKAQqAAAAjEKgAgAAwCgEKgAAAIxCoAIAAMAoBCoAAACMQqACAADAKAQq\nAAAAjEKgAgAAwCgEKgAAAIxCoAIAAMAoBCoAAACMQqACAADAKAQqAAAAjEKgAgAAwCgEKgAAAIxC\noAIAAMAoBCoAAACMQqACAADAKAQqAAAAjEKgAgAAwCgEKgAAAIxCoAIAAMAoBCoAAACMQqACAADA\nKAQqAAAAjHLWgWq32zV48GBt377dWlZYWKgxY8YoPDxct9xyi7Zs2eK0zdatWzV48GCFhYUpLi5O\n+/fvdxpftWqVoqOjFRERoRkzZqiiouJspwcAAIBG6qwC1W6366GHHlJ+fr7T8vj4ePn5+Wn9+vUa\nMmSIJk+erIMHD0qSvv/+e8XHxys2Nlbr16+Xt7e34uPjrW03btyolJQUzZ49W6tXr1ZOTo6SkpLO\nYdcAAADQGNU5UPfu3avhw4ersLDQafknn3yi/fv368knn1SHDh00ceJEhYWFad26dZKktWvXKiQk\nRHFxcerYsaPmzp2rAwcOWEdgMzIyNHr0aPXr109du3ZVYmKi1q1bx1FUAACAC0ydA3Xbtm3q3bu3\nMjMz5XA4rOU7d+5Uly5d5O7ubi2LiIjQF198YY1HRUVZYx4eHurcubOys7N14sQJ5ebmKjIy0hoP\nCwtTZWWl9uzZc1Y7BgAAgMapaV03GDlyZI3Li4uL5efn57TMx8dHRUVFkqRDhw5VG2/durWKiop0\n9OhRVVRUOI27uLioZcuWOnjwoEJDQ+s6TQAAADRSdQ7U0ykrK5Obm5vTMjc3N9ntdklSeXn5acfL\ny8utj0+3fW04HA6VlpaezfSNUnV/AI1JeXn5efH8A3B2ysrKnP4HauJwOGSz2c64Xr0Fqru7u376\n6SenZXa7XR4eHtb4qbFpt9vVokULK0xrGvf09Kz1HCorK5WXl3c20zdKQUFBQ08BqLOCggKnS3wA\nXJj4GYYzOfWAZE3qLVDbtGlT7VX9JSUl8vX1tcaLi4urjQcHB8vb21vu7u4qKSnRFVdcIUk6fvy4\njhw5Ym1fG66urgoMDDzHPWl4J18Ytq+hpwHUSUBAgIKDgxt6GgAaSFlZmQoKChQQEFCng0u4sJza\niqdTb4EaGhqq9PR02e12q4x37NhhvfApNDRUWVlZ1vplZWXavXu3EhISZLPZFBISoh07dlgvpMrO\nzparq6uCgoJqPQebzaZmzZrV1y41mKqjzkBj4uHhcV48/wCcG09PT74X4LRqc3pfqse/JNWjRw+1\na9dOU6dOVX5+vtLS0pSbm6uhQ4dKkmJjY5WVlaX09HTl5+dr2rRp8vf3t4L0jjvu0IoVK/Tee+9p\n586dSkxM1PDhwzllCAAAcIE5p0D9dQU3adJEKSkpKi4uVmxsrN58800lJyerbdu2kqT27dtryZIl\nWr9+vYYNG6aff/5ZycnJ1vaDBg3SxIkTNWvWLI0fP15hYWGaMmXKuUwPAAAAjdA5neI/9QVJ/v7+\nysjIOO3611xzjTZs2HDa8QkTJmjChAnnMiUAAAA0cvV2ih8AAACoD/X2IikAABoDu92uXbt2NfQ0\nzjvl5eUqKChQRUUFL/atZ127dq3VWzOdTwhUAMAFZdeuXVr+5my1vcy7oadyXsr/sqFncH45+N8f\nNV4z1b1794aeyh+KQAUAXHDaXuatyzr5nXlFAA2Ca1ABAABgFAIVAAAARiFQAQAAYBQCFQAAAEYh\nUAEAAGAUAhUAAABGIVABAABgFAIVAAAARiFQAQAAYBQCFQAAAEYhUAEAAGAUAhUAAABGIVABAABg\nFAIVAAAARiFQAQAAYBQCFQAAAEYhUAEAAGAUAhUAAABGIVABAABgFAIVAAAARiFQAQAAYBQCFQAA\nAEYhUAEAAGAUAhUAAABGIVABAABgFAIVAAAARiFQAQAAYBQCFQAAAEYhUAEAAGAUAhUAAABGIVAB\nAABgFAIVAAAARiFQAQAAYBQCFQAAAEYhUAEAAGAUAhUAAABGIVABAABgFAIVAAAARiFQAQAAYBQC\nFQAAAEYhUAEAAGAUAhUAAABGIVABAABgFAIVAAAARiFQAQAAYJR6DdT33ntPQUFBCg4Otv6///77\nJUmFhYUaM2aMwsPDdcstt2jLli1O227dulWDBw9WWFiY4uLitH///vqcGgAAABqJeg3U/Px8xcTE\naMuWLdqyZYs2b96sOXPmSJImTZokPz8/rV+/XkOGDNHkyZN18OBBSdL333+v+Ph4xcbGav369fL2\n9lZ8fHx9Tg0AAACNRL0G6t69e9WpUye1atVKPj4+8vHxkZeXlz755BMVFhbqySefVIcOHTRx4kSF\nhYVp3bp1kqS1a9cqJCREcXFx6tixo+bOnasDBw5o+/bt9Tk9AAAANAL1HqhXXHFFteU7d+5Uly5d\n5O7ubi2LiIjQF198YY1HRUVZYx4eHurcubOys7Prc3oAAABoBOo1UL/55ht9/PHHGjBggG644QbN\nnz9flZWVKi4ulp+fn9O6Pj4+KioqkiQdOnSo2njr1q2tcQAAAFw4mtbXDX333XcqLy+Xh4eHFi9e\nrMLCQs2ZM0fl5eUqKyuTm5ub0/pubm6y2+2SpPLy8t8cry2Hw6HS0tJz2xEDlJeXN/QUgDorLy8/\nL55/OP/xPRaNzfn0/dXhcMhms51xvXoL1EsuuUTbt2+Xl5eXJCkoKEgnTpzQI488oj//+c86evSo\n0/p2u10eHh6SJHd392oxarfb1aJFizr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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -740,25 +817,586 @@ "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": [ + "## 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", + "\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", + "\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." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "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", + "\n", + "from yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "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", + "\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. " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Make 8 blobs dataset \n", + "X, y = make_blobs(centers=8)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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6duyIZ8+eITY2Fi9evMjxrP5GjRrh9u3b8Pb21tset2zZIl5Y++LFi6hZsya6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the clustering model and visualizer \n", + "model = MiniBatchKMeans(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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Elbow Method \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": 32, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Make 8 blobs dataset \n", + "X, y = make_blobs(centers=8)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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o6JgqpCY/vv82jYGBgbRr146uXbsCMG7cOFq1asW3335Lv379Hriv9M4/mnzb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\n6SuAtRGua+2KBqcSEck/73VtxMZjF4i+dIPnlu8itKovgb6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ICGbMmJG13aF+/fq88cYb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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the clustering model and visualizer \n", + "visualizer = KElbowVisualizer(MiniBatchKMeans(), k=(4,12))\n", + "\n", + "visualizer.fit(X) # Fit the training data to the visualizer\n", + "visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Text Analysis \n", + "\n", + "Yellowbrick provides the `yellowbrick.text` module for text-specific visualizers. The `TextVisualizer` class specifically deals with datasets that are corpora and not simple numeric arrays or DataFrames, providing utilities for analyzing word distribution, showing document similarity, or simply wrapping some of the other standard visualizers with text-specific display properties. \n", + "\n", + "### Loading a Text Corpus \n", + "\n", + "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](http://baleen.districtdatalabs.com/) to present the following examples. If you haven't already downloaded the data, you can do so by running:\n", + "\n", + "```\n", + "$ python download.py\n", + "```\n", + "\n", + "In the same directory as the examples notebook. Note that this will create a directory called `data` that contains subdirectories with the provided datasets.\n", + "\n", + "**NOTE**: If you've already done this from above, you do not have to do it again. " + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": true + }, + "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", + "## Corpus loading mechanisms\n", + "corpora = {\n", + " \"hobbies\": os.path.join(FIXTURES, \"hobbies\")\n", + "}\n", + "\n", + "\n", + "def load_corpus(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", + " Note: This function is slightly different to the `load_data` function \n", + " used above to load pandas dataframes into memory. \n", + " \"\"\"\n", + " \n", + " # Get the path from the datasets \n", + " path = corpora[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": "markdown", + "metadata": {}, + "source": [ + "### t-SNE: Corpus Visualization \n", + "\n", + "One very popular method for visualizing document similarity is to use t-distributed stochastic neighbor embedding, t-SNE. Scikit-Learn implements this decomposition method as the `sklearn.manifold.TSNE` transformer. By decomposing high-dimensional document vectors into 2 dimensions using probability distributions from both the original dimensionality and the decomposed dimensionality, t-SNE is able to effectively cluster similar documents. By decomposing to 2 or 3 dimensions, the documents can be visualized with a scatter plot. \n", + "\n", + "Unfortunately, `TSNE` is very expensive, so typically a simpler decomposition method such as SVD or PCA is applied ahead of time. The `TSNEVisualizer` creates an inner transformer pipeline that applies such a decomposition first (SVD with 50 components by default), then performs the t-SNE embedding. The visualizer then plots the scatter plot, coloring by cluster or by class, or neither if a structural analysis is required. " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from yellowbrick.text import TSNEVisualizer\n", + "from sklearn.feature_extraction.text import TfidfVectorizer" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Load the data and create document vectors \n", + "corpus = load_corpus('hobbies')\n", + "tfidf = TfidfVectorizer()\n", + "\n", + "docs = tfidf.fit_transform(corpus.data)\n", + "labels = corpus.target " + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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CAX7++WedYz/99FPMnj0bnp6eRqcGtceWn5+vV++1fft2iMViSCQSK+/a/H1zDYwmTJiA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\noN8XYw89nTBhycrjSKyRM+JcyXZX9ljAlXLBKklvjOfqaUkU9tDe3k5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CQuJUzYmOwsFT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mltRoqL6bCuVZRbfK+/dZMQCPwN4RLtAQzISOee3N8y961nuTf3MSHW/BR3uNqcuaYj54yfncPm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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create the visualizer and draw the vectors \n", + "tsne = TSNEVisualizer()\n", + "tsne.fit(docs, labels)\n", + "tsne.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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AXV0d7rvvPt3/tAX1tbW16NWrF9zd3bF//369137wwQeYPXs2PDw8mp0CbDi29PT0JvVc\nu3fvRlBQECIjIy1818bft6kB0fjx4/HJJ5/gk08+wa5du7Br1y68/PLLEAQBGzduxJtvvom4uDjs\n2rVL7zkbNmyAKIp45ZVXdCtNIyIiUFJSgpycHL1zHD9+HG5ubhYF+RqNBuvWrUNQUJAuE2zsc42K\nikK/fv0gimKT39+SJUt0tYCtWrVq0obk2LFjZo9Rq+EihAEDBiA7OxuxsbF619jmzZvx7bffmnXc\nxoHd7373O/ztb38DUB/sjx49Gs888wxu3bqFO3fuWDx+IpIevzaRSRYsWIBJkyZh9OjRmDRpErp3\n7466ujocOXIEmzdv1q1Oa8nYsWPx9ddf48cff2xSdyaKIk6fPq3L5jQWFhZmcJpOCk899RRSU1Mx\nY8YMvPLKKwgPD8d3332Hzz77DC+//DJatWrV5DXDhg1D//798eKLL+Kll15CTEwMMjIysGbNGjz4\n4IPw9/cHAEyePBkffvghPD09dcHBtm3b8NprrwH4tf7uP//5D+Lj45tkkp577jns3r0bv//97zFj\nxgz4+/vjs88+w9GjR7F48WKbv29D2rVr16RRcVZWFgD96ezGNYTalilhYWG6bOi4ceOQmpqKqVOn\nYsaMGejQoQN+/PFHfPjhh3j22WeNTiPn5eXpAs+amhpcuXIF27Ztw5kzZ7B27Vpds15TPtfY2Fg8\n8sgjWLJkCSorKxEbG4sDBw7g+++/12UFExMT8f777+O9995DfHw89u3bhyNHjpj0uRnSMHM2Y8YM\nTJgwAdOmTcPEiROhUqmwfft27Nu3D++++67B1zSndevWOHPmDI4ePYpevXph0KBB2LBhA9q1a4c+\nffrg6tWr+PDDDzFw4EDdtUtEysBAjfQ01wbjvvvuw+eff46NGzfin//8J27cuAE3NzddXcvTTz9t\n0nH+9re/4YknnjC4M8Err7zS7Ljmzp2rt52VqeM25bne3t7YunUrli1bhnfffRd37txBp06d9Fbk\nGTrG+++/j1WrVuG9995DcXEx2rdvj+eeew4vvfSS7nmvvvoqgoKCsG3bNnzwwQcIDw/H66+/jrFj\nxwIAfvOb32D37t2YO3cuxowZg9dff12vvYP2tcuWLcNbb72F6upqxMbGYv369UhMTNQbj6HPoKXP\nxZz3bWl7FGMaH7dNmzb45JNPsHLlSqxevRqlpaWIiorCG2+8gTFjxhg91vr167F+/XoA9VmkwMBA\n9O/fH6+//jq6d++ue66pn+s777yD1atXY8uWLSgtLUWnTp3w7rvv6rZAe+GFF1BaWooPPvgAtbW1\nSExMxKJFi3QrMJt7n6Z8Ht26dUNqaipWrFiBOXPmQBRFdOnSBevWrWvyuzd2rClTpmDx4sWYOnUq\nPvzwQ7z88svw8PDAp59+inXr1qF169ZISkrC7NmzTRonEdmOIJrydYyIcP/992PkyJFYuHChvYdC\nREQughk1IiPOnDmD48ePo6ioSK9xLhERkdwYqBEZoS1+HzZsGJ566il7D4eIiFwIpz6JiIiIFIrt\nOYiIiIgUioEaERERkUKZVKOWnp4OURT19kckIiIiZaupqYEgCAa3uyPHYFKgJoqiSU0ViYiISDl4\n73Z8JgVq2kyaoa10iIiISJkyMzPtPQSyEmvUiIiIiBSKgRoRERGRQjFQIyIiIlIoBmpERERECsVA\njYiIiEihGKgRERERKRQDNSIiIiKFYqBGREREpFAM1IiIiIgUioEaERERkUIxUCMiIiJSKAZqRERE\nRArFQI2IiIhIoRioERERESkUAzUiIiIihWKgRkRERKRQDNSIiIiIFIqBGhEREZFCMVAjIiIiUigG\nakREREQKxUCNiIiISKEYqBEREREpFAM1IiIiIoVioEZERESkUAzUiIiIiBSKgRoRERGRQjFQIyIi\nIlIoBmpERERECsVAjYiIiEihGKgRERERKRQDNSIiIiKFYqBGREREpFAM1IiIiIgUioEaERERkUIx\nUCMiIiJSKAZqRERERArFQI2IiIhIoRioERERESkUAzUiIiIihWKgRkRERKRQHvYeABFZLjsnF2u3\n78aR7CsQamowsGsUZkwcjZjoKHsPzWS5eTk4cGgvalEFD3gjMWEkoiKj7T0sIiJFYEaNyEFl5+Ri\n5sZd2Fbii4sh/XAhYjA+yb6JmWtTkZ2Ta+/hmSQ3Lwd7fkhFaJwKUT39EBqnwp4fUpGbl2PvoRER\nKQIDNSIHlbr3ILI9AiF6twIgQBAEVIfGIud2NVL3HrT38Exy4NBedIkPgSAIAABBENAlPgQHDu21\n88iIiJSBgRqRgyqqrEVNHQAIuscEQUCNKKCoqtZu4zJHLap0QZqWIAioxV07jYiISFkYqBE5qCC1\nBzzdAEDUPSaKIjwFEUHejlF+6gFviKKo95goivCAl51GRESkLAzUiBxU8shhiKkthlB1B4AIURSh\nKjiL6NYqJI8cZu/hmSQxYSTOZxTqgjVRFHE+oxCJCSPtPDIiImUQxMZfZw3IzMwEAPTs2VP2ARGR\n6bJzcrF2x24cvXAFqK3BwC6Ouerz4E/foEasgge8uOqTSEK8fzs+BmpEREROivdvx+cYhSxEpHjs\nh0ZEJD3WqBGR1dgPjYhIHgzUiMhq7IdGRCQPBmpEZDX2QyMikgcDNSKyGvuhERHJg4EaEVmN/dCI\niOTBVZ9EZLWoyGj89oFkvX5ov30gmas+iYisxECNiCQRFRmNSZHT7D0MIiKnwqlPIiIiIoVioEZE\nRESkUAzUiIiIiBSKgRoRERGRQjFQIyIiIlIoBmpERERECsX2HCSpvOxsHN6WChQXAYFBGDwhGZEx\nMfYeFrkg7bV4++JF5OXnIyQ8DG07duI1SUQOhYEaSSYvOxuHFs5Dn+pKCIIAMe8SDp06CaQs4o2R\nbEp7LcaWFqMs6yz6aDQ4fuEsggsu85okIofCqU+SzOFtqbograD6LvagHPleN7F2xevIzcux9/DI\nhWivxZuFBfCrq4MgCOhXW4MzhVfRp7qyPutLROQAmFEj6RQX6YK0o9EqdO3bCYIgIMhbjT0/pHJL\nIbKde9ciqqt1DwmCAPfqmvrHi4vtODgiItMxo0bSCQyCKIr42bMGXfuG198QAQheXugSH4IDh/ba\neYDkMu5di1CpdA+JogiNyrP+8cBAOw6OiMh0DNRIMoMnJCNdpYbY2lMXpN10c0dAaBgEQUAt7tp5\nhOQqtNeiX0gobrq5QRRFHPfwRPeQDkhXqTF4QrK9h0hEZBJOfSqMI6+ajIyJAVIWYe2K1xHkrYbg\n5YWA0DCofXwgiiI84GXvIZKL0F6LR7an4lZYBC7n56NDWDiud+yIBAf6N0VEJIiiKBp7UmZmJgCg\nZ8+esg/IlTVZNSmKSFepkeBgK9Ry83Kw54dUdIkP0b2P8xmFrFEjIrIx3r8dHzNqCtJw1SRQX/ys\nXaEWOX9hs69TWhYuKjIav30gGQd/+gY1YhU84MUgjYiIyAIM1JREu1KtAWMr1JTauywqMhqTIqe1\n+BylBZhERERKw8UESqJdqdaAsRVqLWXhlEwbYManp6H35RzEp6fh0MJ5yMvOtvfQiIiIFIOBmoLo\nVk3eC9ZEUcSPNRqUlJRix59mYcdbKU0DGQuycErgqAEmERGRLXHqU0EarlQTi4px080NNVlZGJ57\noflpzcAgiHmX9II1h+gT5aABJpmH09tERNZhoKYwkTExiJxXv3Bgx1spiFe5tbi4YPCEZBw6dVJv\npeh31XXwLK3Pwtnq5pibl4MDh/aiFlXwgDcSE0a2vHhAIQGm2eMmk+VlZ+PbP81Ep5yLEGpqIHp6\n4tujhzFi+bsM1oiITMSpTyUzIesUGRODhJRF+KXvAJyI6Ij9UZ3hLYh4MOeCzWq/tO04QuNUiOrp\nh9A4Ffb8kNri/p6Gpnlt3YjUknGT6fauX4uOmSfgd+sm2lRWwO/WTXTMPIG969fae2hERA6DgZqS\nmbi4IDImBoPGJwOBgSg8dhSdci6iqqISgG1qvw4c2qvrmaY9p7EtoxoHmBl9Bti8X5wl4ybT3Ug7\nCv9G16+/KOJG2lE7jYiIyPFw6lPBDE1rpqvUSGiUdWrYoqOs6Dr8Ku6g9M5toFt3qH19ZK/9qkUV\nBEF/1wFTtoxqOM1rD5aOm0xzF/VfLBpPb9+F0PyLiIhIDwM1BWu8uACBgQa3v2m4glKjUkEsF+FX\nV4eygnyou3SRvfbLA94Gb8hK3zLKUcftKEL6D8Tx/Dz0q63RfdE47uGJkP4D7D00IiKHwUBN4UzK\nOjWoZesREoLjt2+hX20NUF3dbBZOSokJI5vdMkrJHHXcjuKxl2bgP+ezcCQvB+41NdB4ekKMjMbj\nL82w99CIiBwGAzVn0GAFZYivD9C1G44UFKLUzx+d+wyQfRNqR90yylHH7SgiY2Lw+IpVehlhtucg\nIjIPN2V3AnnZ2fjPH2dByMuBe001NJ6q+szFilW8KRIRuTDevx0fM2pOwlsQ0UnUQBDrIIoaXBSM\nxt9ERESkcAzUnMDhbakY6ukOoWtX3WMhoqjXGFcqbBBLRERkO+yj5gxstB0TG8QSERHZFjNqzsBG\n2zG11CB2cuR0Sc9F5Cy43ykRWYOBmhMwtTGutWzZIJY3N3J0ednZ+HLdWtz55iv0dwMCwsLh7aPG\noVMnARvvwkFEjouBmhMwtTGutWzVILbhTguCIEDMu8SbGzkU7TUsnj6FpNtlEAQBpXduI6Bbd/QB\nZKkfJSLnxEDNSdhiOyZbNYhtuNMCoL9fKW9uzsHZM6baa/hgTbXuOtbuFhLSpYusW7oRkXNhoGYD\nznJTatwgtvxmFTQaN3x76At4HJJwBaiNFkeQfbhExvTeNazd0k13Pd/bLUTOLd2IyLlw1afMtDel\n+PQ09L6cg/j0NBxaOA952dn2HppFoiKjMWn8NIxIGAWPVrWIe7C99CtAA4PQuA8zb27Oo6WMqdO4\ndw33CAnBcQ9P3fUsenoiXaXGYBm3dCMi58JATWZ7169Fh1O/4Orp0yg8fx5VFZVOcVNqaQWotQZP\nSEa6Sv3rze3e4gje3AzLy87GjrdSsONPs7DjrRTlfwlwgYyp9hru4KNG167dcMQ/EF+28sf5hx9F\ngjNlDolIdpz6lFFedjau7/0a/e/crH+gsgKlt28hoFt3h78pWbMC1NhUsK0WRzgaQ82GhRqN400j\n2qidjD01voYDhz6I3967hrWBtaOXQhCRbTBQk9Hhbalo6+6mt1LSr64OpflXgKEP2nl01rF0Baip\n9UnGFkc4S92fqbTNhuuzmF4QRRF7fkiFkFmCJAdbeGGrdjL2Zugadon6PCKSFKc+5VRchB6hjWpU\nRBHH6uDw03iJCSNxPqNQ732dzyhEYsLIFl8nRX2Ss9X9maK5qeacGxcdbhoxMiYGCSmL8EvfATgR\n0REZfQYg6qUZ2J/2LT7Ytgqbt2102t0uXKI+j4gkxYyanAKD0MFHDXTthiOFhXCvroHG0xOthv/G\n4b89N14B6gEv/PaBZOOrPiWoT3LF9h3NTTW7BbSCeLPM7tOI5u4B2zDb1Fy20KTrydG4QH0eEUmL\ngZqMdFM8AEI6x/w6xfPSDHsPTRJRkdGYFDnNvBdJUZ/kgje75qaaQ2N6IL0g3a7TiNYGWi61NZkL\n1OcRkbQYqMmIRfFNSVKf5II3u+aaDY/6bTKE34y16zVmbaBly63J5GBOvaSr1OcRkXQYqMnMFjsG\nOBIpgldXvNkZm2q25zVmbaBlq63J5GDu4gBrrn9XW0BDRPUEsXFnUQMyMzMBAD179pR9QESmyMvO\n1rvZmXLT4o1OHpu3bURonKpJoFVwshqTJxjPqOlPnepvTab0GrUdb6UgPj2tyXvP6DMA4ySsl2wc\nEFbcKce312+gVZ++aNuxE69lahbv346PGTWZMTiQh7mZSrZFkI+1e8BavDBFCWxUL9lwAU1leQXK\nss4iSaPBkeNpiC8r5rVM5MQYqMmIwYFyuOJKUVuRItCyaGGKEtiqXrJBQFhWkA+/ujpAEOBeXcNr\nmcjJMVCTkRzBATN0FnLBlaK25LCBlpVsVi/ZMCCsrgZQHxBqVJ4AeC0TOTMGanKSODhghs4KLrhS\nlORnq5UI56oFAAAgAElEQVTdDQNCqFQQK8px3MMTPUJCAPBaJnJmLh+oyZqhkjg44PSd5VxxpSjZ\nhi1WdjcMCG/5ByL3RDqSgoMQ4uvDa5nIybl0oCZ3hkry4IDTdxZjTztydA0DQu2q5+u8lomcnksH\nanJnqCQPDuwwfedMNXHsaUfOgtcyketw6UDN1AyVNcGKlH9QbT19x5o4IiIi+3LtQM2EDJWSghVb\nT9+xJo6IiMi+XDpQMyVDZW6wIvdUoU2nPFgTR0REZFcuHaiZlKEyEKxUVVTiwjd7saNRMKak7Jsk\n2NLCJThTHSIRkbNx6UANMCFD1ShYqSyvQMnZ0whoE4Del3P0gjFnmypsmHG8WlGJ0wWFKNHUITi6\nM/Kys3kzdwJO9+WCiMjJuHygZkzj6dHS/Cu44OaOHqH1jSYbBmPONlWozTh+tX4tbu/9Cv3dgICI\ncHjnXMChhfN4M3cCzvblwlwtZROZaSQiJWCgZkTj6dFT14uRFBaOEF8f3XN0wZhCpgqlvMFExsQg\nICAAid266L0vV7qZOzUn+3JhjpayiQCYaSQiRWCgZoKG06M73kpBh/Q0vZ9rgzEldL+XZSrLhW/m\nTk8hXy7soaVsIgCXzjQSkXK42XsAjmbwhGSkq9T1NzNAF4xps1YJKYvwS98BOBHRERl9BiDBxt/A\njd18LBIYpHu/Wq5yM3d2LV3PTq+lLyD8ckJECsGMmpmMrRS1dcfwxtOcJZcuSn6DUUKmkOSh1K21\nsnNykbr3IIoqaxGk9kDyyGGIiY6S9iRGsomummkkImVhoGYBpWzfYmia89+XclHQvh1CfX11z7P2\nBqPUmzlJQynXM1AfoK391+f46lwhxIjuCA9uD7W7NzK3fonFzz4mabBm7AsIv5wQkRIIYuM5LQMy\nMzMBAD179pR9QI5CioJ9a4+x460UxKen6X3rr7hTjk9vFOGZ6Ej9GwyLoEnhsnNyMXfrlzh15TqK\ng7tDEAC3u+XoHhYMtdobAzX5WDh9kqTn1G5urv0C0njVZ3M/I3IUvH87PmbULCBFwb4lxzBlmtOn\nlS+iwiLwS6dODp/9YnsEx5Wbl4MDh/aiFlXwgDcSE0YiKjK6xdek7j2Iyg5dUX35hu66rvPyRf6N\nYnSJCkdRVa3k42wpm6ikTCMRuS4GahaQoveUJVtTmTrN2bpjJ4x18BsMG7E6rty8HOz5IRVd4kMg\nCF4QRRF7fkjFbx9IbjFYK6qshdBagEoQIYrivX8bAqo19f8d5M0/V0Tkerjq0xJSrAgz8xiGArsR\nwe2w73qRU67Yk2X1KtnEgUN77wVpv/7uusSH4MChvS2+LkjtAVEUEdKpG1SFZ+9d1yI83QD11Swk\njxxmg9ETESkLv6JaQoreU+Yew0Bg50zTnE2wPYJNWTJV2ZxaVEEQvPQeEwQBtbjb4uuSRw5D5tYv\ngQ5d0bULcPXiGWjKb+LRuI6YMX6U9Ks+iYgcAAM1C0jRrsLsYzQT2DnDNKdBLtiIVcpgyRyHDv+I\nf/17IwLDfAGNgK7dO5k0VdkcD3g3mLqsJ4oiPODVwquAmOgoLH72sfq2HF61GNanozxtOYiIHAgD\nNQtI0a7CnGPkZWejpKQU/z53vn6/zbBwePuorW4XoORifVfr3WZpXZcU59198CMkPN4Zglv955yV\nfh5dunTBgUN7MTlyutnHTEwY2eC91B/zfEYhfvuA8d9dTHSU5Cs7iYgcGdtzKFzDovqrFZU4XVCI\nEk0dgkc+gpEvzrA4sGpSrK/ANh6u1B5h87aNCI1TNclCFZysxuQJ9cGSHBm3zds2QtM2Hz5+nnrn\nPZd2FRHtYjFlwkyLjpubl4ODP32DGrEKHvCyWXaQiPTx/u34mFFTuIZF9SG+PgjpEgNRFJEREGBV\n0CLFylW5uVJ7BGN1XXJl3GpRBXfBHRABCL+eF+7GpypbEhUZjUmR0yx+vS0pObNMRMRAzUYsvhnI\nVVTPYn2zyVlDZqyuq6WVlMamJ1u69jzgjQD/drhRWgDfNl6AAIh1Iorzy/Hc4yMleW9KxjYwRKR0\nDNRswKqbgRlF9WYFgyYel9mGenLXkBmr67J0JaWxa0973vCuoSgpK0KdWIus9EJMfGK6S0xVOkJm\nWSnstdiFyNWxj5oNWNMTbPCEZKSr1EZ7pWlvyPHpaeh9OQfx6Wk4tHAe8rKzLT6uucd0Zpb2BjNV\nVGQ0fvtAMgpP1SA38xYKTlbrBYHajFtDpqykNHbtac9bdtEDmmJ/uJeE4cWJ85EweKgk70vxmFk2\nifaLSmicClE9/RAap8KeH1KRm5dj76EROT1m1GzBipuBqatDzc0MmHJcZht+ZWlGyxwt1XVZvJLS\nhGvPkerJJOeCbWAsYc3UOxFZh4GaLVh5MzCpqN6CYNDocZlt0LG0N5hUtJmvhispTZp2ZSDSIldr\nA2Mu7XRn5vmjEAMjERgQDLVaDUD6LypEZBgDNRuwyc3AzBuySbVnvMnrWNMbTCqWZL4YiLRMip6I\nzqphXealG164U34LJ9LOok2r1nATPNC1eyd4oLW9h0nk9NhHzUbk7glmTl80U5/rCL3WbMlRe4O5\nUj86kk7D3n5nM7ORdeEs+g3rDLFOgJfKG8cPZuOpxGmuU8/ooHj/dnwM1BTI0pWWpt6Qd7yVgvj0\ntCaZsow+AzCuUe0Zb/JErumDbasQ1dMPAPC/fUfRsWcgyssrUVtThza+fmjr3w6l2e66hsykTLx/\nOz5OfSqMNa08TG4Qa0btWXPH5FJ9agmvD8fXsC5TcAe81Cp4eatQdbsO4aH1+6/ewC07j5LI+bE9\nh8JY08rDZIFBBls9mFp7xqX61BJeH84hMWEkzmcUQhRFiJr6Rsjlt+4iMCAYgG0X0xC5MmbUlMYG\nKy2tLTB3tqX6xrI/zA6Zx9muD1fVcKVxK/cOyPj+MhKS+kCtVttlMQ2Rq2KgJjGrO/nbYKWltSvd\nbNFTzFaM7Tgg944EzsiZrg9X13ClsXYxzQ3xluntYYjIagzUJCTFvoG2aqdgzYbn9u4pZonmsmLG\nsj/MDpnPEa8PMs6lGyMT2RFr1CQkRX1ZZEwMElIW4Ze+A3AioiMy+gxQXDuMhrUrAHTTIIkJytzE\nu6WaqfrsT9OpZm32x9jPqSlHuz6IiJSMGTUpSVRfZk22yxYs7pJvJy1lxYxlf5gdMp+x64M1f+So\nrC5tIbIAAzUpuVAnf0eaBmmpZmpEwqgWdxxQwo4Ejqi564M1f+SopChtIbIEpz4lNHhCMtJVar0p\nn3SVGoPtsF1Pdk4uUjZuwayVm5CycQuyc3JtPgal0GbFGtJmxbTZn8JTNcjNvIWCk9V6QYOxn5N5\nWspuEimZTVonERnAjJqElLJvYHZOLuZu/RKVHbpCaC3goigic+uXWPzsY4iJjrLpWJTAWFbMWHbQ\nkbKHSscVoeSI8rKzceGbvWh/4yqgUsE/NAxqXx/JWycRGcJATWJKqC9L3XuwPkhr8M2vskNXpO49\niIXTJ9l1bPbgaDV1zqxxzd+Nq8U4ezoblWUabN62kfVqpDjaKc+Am2VoXVEOobICpbdvAd26w9tH\n7ZSlLaQsDNScUFFlLYTWTRc1FFXV2mlEppGzyJxZMWVomN0sulaCc+eyENE1CO0Dw+HtrWK9GimO\ndsrzamgIjt+5hX61NfCrq0Np/hVcva+X5K2TiBpjoOaEgtQeuGhgpWKQt3J/3a5YZC73CjIlrq5s\nmN38+eRpxD8QgaC27aFWqwGAPepIee6t5g/x9QG6dsORwkK4V9cg3z8IU7iQgGxAuXduFyPlTTt5\n5DBkamvU7tVkqa9mIfnZxyQetXRcrbGs3CvIlBz4arObNWIlIsL89H7GejVSnAar+UN8fRDSOQai\nKCKjzwAGaWQTXPWpANqbdnx6GnpfzkF8ehoOLZyHvOxsi44XEx2Fxc8+hoGafHS8k4OBmnzFLyRw\ntcaycq8gc4TVlS2txiVSCiWt5ifXxIyaArR0046cb9nChJjoKIdaOOByjWUlao7cHEdYXelIPeqy\nc3KRuvcgiiprEaT2QPLIYYr+4kPSUcpqfnJdDNSUoIWbtqt0wrbmpm2sFkuRn6GFzZFNrTszFvg2\nPM6tkkq4u7vB18/LprVsjrIal+1uSAmr+cl1ub/xxhtvGHvS9evXAQDt27eXezwu6dSxY2hfmN/k\nppoVEIjruz9H7/xchNy6ifaF+Uj7/nu06d0Xfm3b2nHE0vP380ewfwRO/nweJdfKUX5dwG/uf9Lo\nTVtbixXdyx/+7b3ROtgdh4/+hGD/CPj7+eumlRt+hl//dy+OlOTiZPZxZJ7MQECbIPj7+dvmjd7T\nOiwcad9/jw6aGl1gmq5SY8jMPzT7uzX2XhsKaBOEw0d/Qtv2rfQC39/c/yRu3izTHadGLEd+cTYC\nO7mjfXgAAkK9mj2mHPz9/BEf1w994wahd1x/m/8eTLF2+27ktumol/GuaRWIkvOZGNY/3s6jI2oZ\n79+OjzVqCtBcDYRGhEt1wo6KjMak8dMwZcJMTJ4w3aTMirFarMbTyoU11bgTLqJNyK0mG7TbUmRM\nDBJSFuGXvgNwIqIjMvoMQIKRhQTm1J21tKNCw+OcO52Nbn3D0MrPG8Wl1xVZy2ZvRZW1BjPeSm93\nQ0TOgVOfCtBcDcThte+aVcekhOksW08zGq3FajSt/LNnDbr17YRbNTW659prdam50ynm1p011zuu\n4XEEd+g+HxF1Ro/pihyx3Q0ROQ/+pVEIQzftw2bUMTVsx1B07Q4ul1xCeHQQgm3YSNQemxYbXYTQ\n6DMUW6vq/79KpXu+owQmUi24aHgcUVN/DAEChHsJdqdexGEBR2x3Q0TOg1OfCmbOsnAlTGfZY9Pi\nxISROJ9RqPcZnc8oRGLCSABNP0Ph9l2UCW4ICA3THcNRAhNj79WS43TrEYNzP+fjzs0qBAYEW3xM\nR5Wdk4uUjVswa+UmpGzcguyc3CbPccR2N0TkPJhRUzBzloUrYjpL5pYThhhbOdj4M1SrPHCjwguh\n9zrhK7klRGNSrZLUP44n/NARlbluuF5aAw+4KXLlpRzMWc3paO1uiMh5MFBTKL02DJ2Ckfjs71q8\neSpiOsvClhPWMraPZ+Np5dy8HMW3hGiOVHuWcu9TIHXvQd10JlD/paKyQ1ek7j3IoIyIFIOBmgJZ\nsv1Pwz5k9dNZWQiPqd/s2lZZo8ETknHo1Mlfa9TuTdUqbdNia4MUJe6hSeYrqqyF0JqrOYlI2dhH\nTYG++Go7onv5633Tb9u+FTKPZ6F3XH+Dr2nYh+xuuQihshXqbqtRXe5mck8ya/m1bYs2vfvi55IS\nXPX0wrVOnTFk5h/s31xWQub0MiNlO5aRiXyhtV4GuLz4OorOZeB49hUcy8hEeJA/2vrz90qOi/dv\nx8eMmgJZuv2PEqaznL2Dt6ttHu/MGq/mLC++jouZP6PTgERc8vWxaAcCbjVFRFJjoKZAcu97qcgt\nlRyEI+yhSabRruZM3XsQRVW1OH3hNDoNSISvrw+AlmvWDAVkALjVFBFJjoGaAsm5WbU9ep3Zktz1\nYy63eXwznCXYb7iac9bKTbh0L0jTMlSz1txq0XD3alR2iOPiBCKSFPuoKVBL2/9Yyx69zmxFWz8W\nGqeSbXuoxr3MrhcWYfe/9qPs9g1s3rbR5ltR2YM22I9PT0PvyzmIT0/DoYXzkJedbe+hWSVI7aH7\nvWoZ2oGgudWiR7NyudUUEUmOGTWFkq3ezA69zmxFivoxYxm5hj3IrhdfRdHty3h49GD4+PiYtDrX\nGbQU7EfOd9z6RFN3IGhutSg8PQ1mW7nVFBFZg39BXI2dep3ZgrX1Y6a2RdEG0Zu3bUT8gxEOtbBA\nkilLJw32G9esBXl7IPlefVnDmrTTp0+jrrMPfAODda8VRREDY8Jx5WoWt5oiIkkxUHMxtuh1Zq8+\nY9bWj5mbkXO0hQWS1SfeC/arKipRVpAPVFdD9PTEzSj5a9TkXlVpaAeCxjVpdXHBuJj2PTr17Avf\nwGBdQDbj2VEAYDDQIyKyFAM1J2bwpmbGtlSWaJyVqqiowPp/vYWg1uFo17aDrEGbtYswzA28bLmw\nQIpMmFRTloMnJOPbo4fR8exp+IsiRFHEcQ9P1GRlIS87W7ZFBeZs+SSlxjVpvr4+6DTgQbidPICO\nXj2aBGRcOEBEUmKg5qRavKnJ2OusYVaqsrISN0oLEP9gBM4du4r4uEijNVzWZONa2gvTlOOaG3jJ\nuTq3IckyYRJNWUbGxKC6czeczc2Fe00NNCpP9AgJQQeVm6x1avba8slQTZqvrw863tcDq2Y9L9t5\niYgABmoOxZwgxpybmpRTlQ2zUsWl1+HbxgsQft0ovqWpREu2zmrM0CIMU49rbuAl1SbpxkhWvC9h\nfWJbUYPeXQwEiTLWqdlry6cgtQcucpEAEdkJ23M4CHNbTxRV1prUKkDqlhbarBQAiKgDhPqbmqj5\ndQzNTSW2VCNmDVOPa0lblKjIaEwaPw1TJszE5AnT5ZnWlSgTNnhCMtJV6l9/P/fqEwdbUp8YGGSw\nlYWci1JMbZ8hteSRw6C+mqX3uamvZuma3BIRyYlfCR2EuYXupmYBpN4SSS8rBTeIdSKyTuSjW4+u\nuHG1GGdPZ6OyTIPN2zY2ydzJVZxvznHlaItidcZSokxYpIT1ibZYlNKYqe0zpNbSalAiIrkxUHMQ\n5gYxpt7UpA6OGk4H3rzlgQs55zDkoV4AgHPnshDRNQjtA8Ph7a1qMv0oV3G+PXcTkGI6V8qgSKq9\nWKUM+kxlz4DJ0GpQIiJbEMTGcwkGZGZmAgB69uwp+4DIsM3bNiI0TlVfa1ZRgdKCfIh37+L091cx\n449/NXiD1K361N7UDLQyaHhcLVEUUXCyGpMnWN8LLDcvBwd/+gY/nzyC+AciENS2PdRqtcHz6Ac1\n+jVi1kwpynVcU0j1+eZlZ+sFRY66ZZOr4mbt8ji8fz++ensxvEqLcTcgEI/OmYvBDz1k72EpCu/f\njo+BmoPQBhvhXfxQlnUObTS1yPr5CgZcuotrrfyRYOFenbYKYj7YtgpRPf2anj/zFqZMmKk3nobF\n+VK185DruMaY+r6t4Sz7bjorvRXYDbLb3KzdOof378cP05/H8Ooq3ef6ncobD2zcxGCtAd6/HR+n\nPh2Edkpx/ZIFaFdehKLbNRhY44lQL2+EWrF9j61WLpo6/SjX1lmybcllhNzTrpK17lAwbTbqYuEN\nXCkoRHh4BDq1D3CYrJS92oo4u6/eXoz/dy9IA+o/1+HVVfji7cUM1MipMFBzIFGR0eirCkTva7cB\nwQtQ1T9u7fY9tghibNVzTGkSE0Zi595/IDDKAxBEQBRQnFuLsSP/T5LjO8O+my1NC2qzUcWqAJy9\nVIrbfjE4cl2DdhU1OLxxF96d/rTigzV7tRVxdl6lxQZXQ3uVldhpRETyYKDmaBx0r05bZe6UqLqq\nBrnnSiG4i6jTAF5oLd3BW2jdIeWUqFzTq8Z2G9Bmo6788A3K2nREnXf9Z3dDo0Glmz/Wbt+N5XNe\nsXoccmIfNnncDQiEeK2gyed617+tHUdFJD3+pXAw9miLIBV7TT/a04FDezHgodgmNxPJNm5vJnAv\nEdwkmxKVc3rV2LSgNhtVdus26tr/GuDWARC9W+Fo9jmrzm8LLa3A5iIDyz06Zy6+M1Cj9uicufYe\nGpGkGKg5GHu0RVAae236bgm5N25vLnB3FyDZlKic06vGpgV12ShNNSCKwL0xuAn1/4naGovPbasg\nqbm2IgDssnepozL07/6BjZvw7yWL4Vlagrv+bbnqk5wSAzUHJFUvLDnIHURJ0ZfMlqxZTGDKZ9lc\n4H547buS7GYAQLKdEQwxNi2ozUa18fNDef5Z1IbFQhA18PJSQVVwFgO7WBbQ2HqDd0N92FI2buEi\nAxO19O/+9a++sffwiGTFLaRIMlJvR2WIXNtMySUxYSTOZxTqbT90PqMQiQkjW3ydOZ9lZEwMxs5b\niHHLV2Lc/IX1wZuUWzzJuF2Use2ZtNmox3tEor2mDP5XTqBD6UUEF2ehZ4AnZkwcbdF51/7rc5y6\nch1n035EdtoPqCy5oQuSbMXUbd7I8f7dk3PIz89HbGwsCgoK7DoOZtRIErl5OVj1/mLEPdAe+YV5\nCAwIhlqttmo7KkPknkqUmqWLKKzd2suUWkZTFwjIWRdpym4DMdFRWD53FmaY0MDZFNk5ufjqXCFK\nI/vq3s/t82fRtQtQ5GW7IImLDEznaP/uyTmEhobixx9/RNu29l2gwr8IZDVt9qdNiBt8/DwBEbhe\nko/gtmFQq9WS/jFtPJVYWVmJopJryDx12eD+oUpgySIKa29MxmoZzVkgIHddpKnbM0m1jVPq3oMQ\nI7pry90gCAKqQ2JRePEMhvXpaPXxTWWvvUsdkT23gSPXJQgCAhXQUYGBGllNm/25vv+K7o+pbxsv\nFJdeR5h3pKR/TBv2Y6uqqsK14iu4kl2EQYk9ENS+6f6hjkqKG1NLtYzGFggYyraNVWhdpLmKKmsR\nHtwet/Ovo87LF4AAQQDqym/qplxtgZu9m85V+zA6ElEUUVdXB3d3d5uc7/Lly1i4cCFOnDiByMhI\njB49Glu3bsW+ffuwc+dObNq0CZcvX0arVq3w2GOPYeHChRAEAXPnzkVgYCDy8/Oxf/9+hIeHY+nS\npfj666+RmpoKHx8fzJ07F4888gjy8/MxfPhw7Nu3D6GhoYiNjcWSJUvw/vvvIycnB7169cKSJUsQ\nFhYGADh58iRSUlJw9uxZ9OjRA0OGDEFaWho+/vhjq94ra9TIavXZHwHdesQgKz2/vt5IAOpEjUn1\nWObQTiUWnqrBD1+dwOWsUnTr1hXtOgQ6Vd2KpbVtJjPSf+3QwnmIT09D78s5iE9Pw6GF85CXnS3N\nue0sSO0Btdob3cOC4S9Wwae2An51lXg0rqPsQVJ2Ti5SNm7BrJWbkLJxCwBg4fRJWDXreSycPolB\nWjMa/rvPzbyFgpPVTvGFzBmIooh9O3fgny9Mw7ZnJuCff5iJjEOHZD2nRqPBCy+8AH9/f+zatQvT\np0/HmjVrIAgC0tLS8NZbb2H27Nn45ptv8Oabb+KTTz7Bd999p3v9li1bMHjwYPz73/+Gn58fJk+e\njNLSUuzYsQNJSUl4/fXXdc9t/HdyzZo1WLhwIT777DOUlpZi5cqVAIA7d+5g6tSp6NmzJ7744gs8\n/vjjeO+995q83hLMqJHVtNmfdh0CAXRF1rGLEN3qcKuwDrOmzm3xj6klq0S1U4mG9tG0Rd2KLdqD\nyN4guIXGyc6w20FLtFOO6NAVXaLCdVOOM8aPkvW8tl5p6mxcsQ+jI/h+9254pH6MgR738j75uchc\nuQx+wcGI7txZlnP+9NNPuHr1Knbu3AkfHx/ExMTg3Llz2LNnD3x9fbFo0SKMGDECQH2dWY8ePXD+\n/HndY/fddx/Gjx8PAHj88cexePFiLFiwACqVCs8++yy2bduGoqIiAGiykOq5557DwIEDAQATJ07E\nP//5TwDQnXv+/PkQBAHR0dH4+eefcePGDavfLzNqZLWG2Z92HQKRkNgfwX4RJgVp1qwS1QaIDcld\nt2KLla1aUZHRmDR+GqZMmInJE6ZLGgwOnpCMdJVaL2OXrlJj8IRkWdtxKIF2ynGgJh8d7+RgoCbf\n4mCpcYYsOye32ee21NyXyFHl7/8OHTz0Q4m4uhoc+3SXbOfMyspCdHQ0fHx8dI/17t0bANCjRw90\n69YNq1evxsyZM/HII4/gl19+gUaj0T03IiJC9/+9vb0RFBQElUql+28AqK6uNnjuqKhf/060atUK\ntbW1ujH16NFD72+ndkzWYkaNrGavlY32qFuxdszmkit719ICgcMOuk2ZOaRYmGBuhox7fpJTKr/T\n5CFBECDeafq4VNzd3Q23DALwv//9DzNmzMDo0aMxbNgwvPLKK3jjjTeavL7xeE3l6elp8LyGavMa\nj9FSDNRIEvZY2WiP/UNt2SZA7ua+zS02cORtymzJ2PZXjdmrHQe3qSI5uYVHQCzVz8JX1GrQuls3\n2c7ZpUsX5ObmoqKiQpdVO3nyJERRxI4dOzBmzBgsXFj/t622thZ5eXkYMmSIRecyNYjr0qUL9u/f\nr/fYqVOnLDpnYwzUyG6kWNlo67oVa8dsTobM1tk7LWfZpkzuAMXcDJk92nGwLo7k9sDz/4dvF85D\nn1ul8HZ3x63aWvwSFYPkp8fIds4hQ4YgJCQECxYswIwZM3D+/Hl8/PHH8Pf3h7+/P37++WdkZWVB\nEAS89957uHnzZrNTmYY0zISZmhX77W9/i+XLl2PRokVITk7G0aNHsWfPHvTr18/s99cYa9TIbmRf\n2SgDa8Zsbn2bdjVtQ7Zq8mlwtwMHog1QjrqH4VLraBx1D8PcrV+2WENmriC1h8Hpl+YyZFLWxpmK\ndXEkt7CICDz57lpcGTUGpwYOxZ3npuHZJe/oar3kIAgCVq9ejevXr+PJJ5/Ehg0b8PTTT8PT0xOv\nvPIKAgICMH78eEyfPh2xsbGYMmUKTp8+bdbxjf3/xnx8fLBhwwYcO3YMo0aNwhdffIFRo0Y1mSq1\nhCCaEC5mZmYCAHr27Gn1CYkays3L0Zu6lLNhrVT1XpaOefO2jQiNUzXJxhWcrMbkCU0zZOY+3xhT\ndyJwBikbt+Coe1iTz26gJl9vWtKarJtetqpBhkxJ2apZKzfhUuvoJo93vJODVbOet/2AyOac8f5d\nUlKC06dP4/7779c99sEHH+DgwYPYsmWLXcZ05coVXLt2TS+D9uabb6KyshKLFy+26tic+iS7stXU\npZT1XpaO2dz6NikXSxjaieDbo4dR3bkb2ooapwvcTJmWtHZa0BEa1nKbKnJWL774IubNm4dhw4Yh\nJycHmzdvxosvvmi38dy5cwfPPfccli5dip49e+LkyZPYvXs3li9fbvWx+a+VXIK96r0aMre+TcrF\nEg2ikmMAACAASURBVI17o1VVVKLj2dM4m5uL3l1iWtxCyt4syXqZEqCYuxjAEKm2tZILt6kiZ9S2\nbVusWrUKK1euxN///ncEBgbid7/7HSZOnGi3McXGxuIvf/kLli9fjqtXryIkJARz587Fgw8+aPWx\nGaiRQ7F0+tLUbJaczWwtyZBJlnFs1ButrCAf/qII95oaAMptamtp1suUAKWlrJuzrJR0hKwfkSWS\nkpKQlJRk72HoGTNmDMaMkX4RBQM1chjWTF+aks2Sux2GqRkyWYLFxr3RqqshiiI0ql8LXZXY1NbS\nrJcpAUpzWTf3qnKHXSnZXICp5KwfEbWMgRo5DGumL03JZtlietRYhkyuYLFJbzRPTxz38ESPkBDd\nc5TY1NaaJrHGApTmsm5wr7N6StQUUmft2IqDyDmxPQc5DGvaVZiyqbM922EA97YjWvMOqtvU4sLl\nfFRUVkq20XxkTAwSUhbhl74DcCKiI84//ChqevRCBx81gEZbSCmIuS0wzNFcuwyNurXB60DKHQTk\naB/CVhxEzokZNRdmi83FpWRts1lj2SwpGvBaSnvj1nj7oNLTB5UAbl25ju7hwfBRqyUJFhvvRJCX\nna34prZyF8MbyrrZYqWkFAsZGuMWVUTOiYGai2puiq13zDCczzmpyOBN7r097bF3qNaGHZ/i9u3r\n8LiVg7rbAuDtB3j7Iv9GMTpHhLUYLFraH625LaSkPIe17FEML3VwaGiKU46giq04iJwTG966KEPN\nVCsqKvDfzw9j1MSHmgQqcgZr5mT25G6Qm5uXg91f78Dla9morq5FVEgXPPX4BNnf/+IPFyNyYGfc\nLr6Jiuv5CO8UiGr3tvBFLVS3PJr9HTTpj6bdl1PCNhu2OIfS6IIrbXBoYf1Yc01xw92rcb5dnNGG\nvFKcizVqro33b8fHr1ouylC7ipKyGwgM87VprzFzi+dNKca3djrX3bcWDz85WHezk3LlpyEHDu1F\nrwHhuCkIaBPkDwA4f+oKblwvQKBXWyx8+c/NnrtxfzQ52mzY4hxKI9VKyeamOFF8GuqrWZJO6bIV\nB5H0Dh8+jODgYHTq1MluY+BiAhelrcdqqE7UABrbFtO3tNLSXObupSn3eExViyqEBwfBraocANAm\nyB8d+8VB5dkKc196teUAsVF/NO2YJW2zYYtzOKmiylqDn53G21eWfT+1AeaqWc9j4fRJDNKIrPT7\n3/8exXb+W8eMmosyVI+VlV6IXv266T1P7mL6WlShqqoOxaXXIaIOAtwQGBCMWtSYfSwp2muYu82T\nFDzgDbW3Ct3Dg5F/oxjVGhGebsCQLp2N32gb90eDDG02mjlHieCGHW+lONTeobZuZNtS3Rj7mxGR\nKZhRc1GG2lWMGvZ7lF2r1mXatDVqiQkjZRtH+c27uFZ8Bd6t3aBu4wHv1m64VnwF5TerzD6WFO01\nDGUa5Q5WExNG4nxGIdTe3ugSGY4e0eFAqTuefHSc0dcOnpCMdJUaFXfKUXj+PApOnsS/L+UiMuF+\no681lfYcDa+L76rroLpwDvHpaeh9OQfx6Wk4tHAe8rKzJTuv1ORoiWFM8shhUF/N0vvs1FezkDxy\nmGzntER2Ti5SNm7BrJWbkLJxi6yfCTmn2tpa3L59u8nfT7ls2bIFSUlJ6NWrF8aMGYPjx4/j6NGj\nSEpKwubNmzFo0CDcf//92LBhg97rPv30Uzz22GOIj4/HmDFjcOzYMd3PkpKS8M477+D+++/Hk08+\nqdv5YNKkSVizZg1qa2uxYMECDB48GH369MGLL76Ia9euyf5emVFzYYbqvcJCw0zeW1KKejCNpg5X\nsovQrW8YBAgQIeJKdhH80Nrs9yNFew17rPy0Zk/PyJgYFEx9AZ+9+keE1dRC461G//btkPP+BoRG\nRkqS4YqMiQFSFum18vAsLcXQnAsOVbcmR0sMYxyhboyNcskaoihi82f/wf6zV3BbdEewZx3GDu2N\n4UMHy3bOM2fOYOnSpVi7di06d+6MLVu24A9/+AOWLl2K69evY8+ePdi8eTPy8/MxZ84cBAYGYuzY\nsfj000+RkpKCv/71r+jVqxd27dqFqVOnYu/evQgODgYA/Oc//8FHH30EjUaD4OBgDBkyBKtXr8bQ\noUOxdetWHDt2DB999BG8vLzwxhtv4O9//ztWrFgh23sFGKi5FFMCK1P3lpSqg36btmp0aNcVWccu\nAm4iRA3QrUdXVNzwNP7iRqQIsqTcCN0c1uzpmXfof0iOjtQLUEMkDpoat/LY8adZstatyTFFaa8+\nY0qf4kzdexDFbSJQkJePao0IlbuA0KAIWQNYch47v/wGn+RVw619fdlMAYDV359BaLtAdO/aRZZz\n5ufnw83NDaGhoQgNDcWsWbOQmJhYvy2eRoPFixcjJiYGsbGxmDx5MrZv346xY8di69atmDx5MkaN\nGgUAmD17NtLS0vDPf/4Tf/zjHwEAo0aNQufOnfXO5+fnB7Vajfz8fHh7eyMkJAR+fn74+9//jrKy\nMlneY0MM1FyEpYFVc8GdVNstecAbQe1VaNfh15oqURRRcKPa7PcoZZBVn763TQrfavYo9pexNk6u\nDI+1fcacZaP2xi5eK8XZCg3qvHwBDwEVEHGr4AYifW/Ze2jkAL4/kwu3AP3ARtMuEl98f1S2QO3+\n++9H165d8fjjj6NHjx5ISkrCuHHjkJOTg9atWyOmwUxCXFwcNm3aBADIzs7Gyy+/rHes3r17I7tB\nyUZYWFiz5x0/fjy+/PJLDB06FIMGDcKIESPw1FNPSfzummKg5iIsCaxaCu6kKrq3NgtmKJCcNN6y\nzJT2eFI2ArbJ7g+2WFDQSJO9Q7W91STYgkquKUprGtkqeXrQ2gDyypXL0HQYAAHa60eARuWDK5dP\nyTNgcioVtXVNHhMEAeXVGtnO6e3tjZ07d+Lo0aPYv38/PvvsM2zbtg1z5syBu7u73nM1Gg3c3OrL\n8b28mpbBaDQaaDS/jtXQc7Q6d+6Mffv24cCBAzhw4ABWrFiBPXv2YOvWrRK9M8O4mMBFWFJo31Jw\nJ1XRvSl7cDZHinYcjRl6z/7tVdh98COzzyPH+AwxVOwv976djfcOzegzQLIGuM21tLB2irK5vT1N\nCWqk2EdTjoJ9KRZIhIeGQFV4Tu/6URWeQ3hYiNXjI+fXMcCnyb2gtrIccRHBsp3zp59+wvr16zFw\n4EDMmTMHX331Fe7evQt3d3eUlZXh6tWruudmZmaiW7f6admOHTsiIyND71gZGRkm90jbtm0bvvvu\nO4wcORKLFy/G+++/j+PHj6OkpES6N2cAM2ouwpJC+5ayZiMSRklWdG9pfZZU068NGXrPWWcuomsf\n888jx/gMMVTsL8W+ncaygeZsQWUOObdCsrRezNr6NrkyclJkHzuFtEOeTxiuXTqL6joAFTcBNw9k\n5ZcjZeMWp5niJXlMHT0SF9/fjmsBneDh7YPaW6XoobmOJ38zRbZzenp6Ys2aNQgKCkJCQgKOHj2K\nyspK3LlzB6IoYv78+XjttdeQnZ2NrVu3IiUlBUB9T7T58+ejU6dOiI+PxyeffIJz585hyZIlzZ5L\nrVYjKysL3bt3R0lJCdasWYOAgACEh4dj9+7dCAkJQUBAgGzvFWCg5jIsmWJsKbizV9F9Q3L0PDP0\nnuEuwk3QT6ebch5b9mSTOmiSarGIJeTeiN0S1gaPck3nSrFAQvt5+/QbisqSG8jKykJtYDgiw9vj\nqLu3YqZ4SZk6tG+PdbOn4j/7vkdBSTF6DgjHsCGjm0xBSql///54/fXX8Y9//AMpKSmIiIjAsmXL\nEBAQAHd3dwwdOhTJyclo1aoVXn31VTz2WP3fjkcffRTFxcV49913UVRUhO7du2PTpk2Ijo4GgCaZ\nfKC+NcfSpUtx+fJlvPrqqygpKcGrr76KO3fuoGfPnli/fr3B10mJe326EHP3ydS/Wcu796clm34b\n2q9UFEUUnKzG5AmWZawMvefd/9qPh0cPho+Pj1nnkWN8tmLvsUu116bJ5zFS32XtPpqzVm7CpdbR\nTR7veCcHq2Y9b/H4UzZuwVH3MKv3DNV+DnuPnEBRWDzCg4Pg46O2+HikHK50/z569Cief/55nDx5\n0t5DkRQzai7E3ClGW2XNmmz6nXcJh06dBIzUPJmbJTS1PUnj9zzxiek4kXUQXeLVZk3z2qMnm1Ts\nsUNDQ7ZoaWHOdGRL/dBMCfbkms61JPvY3HgXTp+EospaXGodofd8W7QwIaLmMVCjFlnT38tUlm76\nbU4gac5UnrWNgC0Zn9JI0TzYHPZofWHudKSh4NHUYE+u6VxzG+oaG6+c9YFEZBlOfZJRcreY2PGn\nWeh9OafJ4yciOmLc8pWSnMPeU3lysGS62FS2nPa2dlrRUlJMR5oz9Wir6Vxrxmuv3wXJh/dvx8ev\nSdQimxSV26APmL2n8qTOGFk6XWwqW2YD7bG1EyDNdKQ5xfxK2KHA2HgdYcsrIlfDQI0AyL8DQUvk\nbJ6qZeupvIbkaM1g6XSxOWwx7Q3Yb2snKaYjHW2q0JTxKiGgJKJfKfOvCUnK2NSlVDsQWDpFKlcf\nsIbsWdgvS8bIHttGyUTKYMeczKUU2SMlthJpiaONl4gYqDk9U6YuTdmBwFgmytopUrmap2rZs7Bf\nqoxRw0A4q7oY7e5WIczLW/dzubeNkosUwUN2Ti7W/utzfHWuEGJEd4QHt4fahB5g1maPHG2q0NHG\nS0RcTOAUWspkmVJE/8G2VYjq6df0uJm3WtyBoGGQ44zF+lKRotdV4+L+ivJy/P/27j24yfPeE/j3\ntWTZsg2+SFx8v4EhCZQANfFxkhLaJm6gKRvSJqw3Q1rONNCTNjTd2U2dU6ZMPYVJehpoOylxd5ZJ\nOIwPyaQ5PWkgpdsGKOA4Bkpcc7ONbNngC8YXAsYysqR3/zAyli1bsvVepe/nr9aR9T4CpPen5/ld\nqiv/Hx5tciA9JvbucfEEOWpyFh5IIZREe+/R8rkrXeiZfQ8EAYi6fQv3pM+G2RzLHmABhOuweRrG\n+7f+cUdN5wLtZAVzdCnFBAK1k/W1TIodo7G7nnHx8SgqfRR/2vsJlpuskx4Xy114IIVQdra8R8vO\ny9dG/nw8MfFou9aD+dkZ7AE2CS0PmyeiYRzKrnOTHVsCd4Ow0cYeXT5SXILG2g6focyNtR14pLgE\nwJ2k8meexz+vfxHPrd/k97hQqiHt4SiUYeBew4Gw7/FpXHw8Ch5agadf34Wn/3XrhEHXZIUH4cA7\nxN0kiKP+DQpwukVNJ/ZrgRTD5om0oqamBvfcc4/ay5AcP8F0LtBOVjBJ9FLkb8mdrC93Lze5hZoL\nFVLVahgVHvjjLUZIzVuAm40X4UxdCEEAoqOgWKK8Xo8P1aq4JZLDsmXLcPz4cbWXITkGajoX6AYe\nbBDmrxXDVIIjOZP15ejlpkbgF8rNPKRAWIE+df4oFbx4j5YxtwAF84HOpgtw3/ocjy/KxQvPfEP2\ngEnPx4d6ay9CNBmj0QiLDguqAmExgc5Nt4P81Fp2yNuZPhCpCxXUeG1SdHxvabX7BMLBBpfjctQC\nFB5IQekO92p2/ZdqMLoaOIkg/Cl1/x4cHMTNmzeRnJwMo1H+QL+1tRU/+9nPcPr0aSQnJ+M73/kO\nFixYgA0bNuDixYtoa2vDV77yFfzmN7/Ba6+9hqtXr6K4uBivvfYaZs6cCQA4deoUduzYgUuXLiE7\nOxvf//738dhjjwEAysrKYLFY0NbWhsOHDyMjIwO/+MUv8Kc//QmVlZWIi4tDWVkZvva1rwEATp8+\njV/+8pc4f/48BEFAYWEhtm/fDqvVGvJrZY6aznl3sjrODaGl7gbazzqDCtIOHKtE2iITshcnIm2R\nCQeOVaKl1T7ymEC5b0ryl58VSqGCGq9NilygYHIF/cnKz0dx+Xb8Y1khPsvMRe3SQlmDNED53Cfv\n0fKvtmzE1k0bFA0yvDlyo+nl+HA6+ZM2ewvKK/Ziy649KK/YC5u9RcEVk9aIooj3/us/sOe9nfjg\n+P9FReW/4ejxj2W9ptPpxMaNG5GQkID33nsPW7duxc6dOzEwMDDuvVhRUYGdO3di3759qKurw549\newAA165dw+bNm/HUU0/hww8/xHe/+12UlZXh9OnTI7+7d+9eFBUV4Y9//CMSExPx3HPPoa+vD+++\n+y6+/OUv46c//SkAoL+/H5s3b8bDDz+MgwcPYs+ePWhtbUVFRYUkr5f722Fgqh3kg5k2oKUqTqmn\nCqjx2tTOBZK7T91Yar9eJen9+HAq+ZN6PuYleRz88weIsvahYF7qyM+abKcw68Js3HvPIlmueezY\nMfT19WHHjh0wm83Iz8/H1q1bERU1fu/pxRdfxKJFw+t44oknRnYYKysrUVxcjNLS4fSRzMxMnD9/\nHm+//TaWL18OALjvvvvwzDPPAAC+/vWvY8eOHfjJT34Ck8mEZ599Fvv370d3dzcA4IUXXsC3v/1t\nAEBaWhoee+yxkWuFSh+fJCSpUFt2KE3qQgU1Xpveb+ZT5X29Dscg2q71wOkWER0FzEu4pfbSpixQ\nrp2S3f7VLlpQay4radeVrkbkZ/ge72XkzcLpumrZAjW73Y6cnByYzeaRnz355JOoqanxeZwgCMjO\nvvv+SEhIgMs1/GXRZrPh448/xtKlS0f+u9vtRm5u7sj/z8zMHPnfsbGxsFqtMJlMI/8fGN7dS0tL\nw9q1a/HWW2/hwoULuHTpEurr67Fs2TJJXm943iVoUsEEKmqOXBpL6kIFNV5bpI3uKS1ZieqK36Pu\ndjwcbhGiCJjaL+Azaxxs9hZJg4tQg5fJ8jWD2UFSqtu/FnazImmnlILjgWfczwRBgAfy/ZuYKAfO\nX8p9dHS038e43W6sXbsWmzdvnvC5DQaDz38be6zqdfXqVTz11FNYtGgRHnzwQTz99NM4cuQIamtr\nA7+YIDBQi0BKteyQkpQDwtV4bZE2uic/JxtzxX64Gy9AmDEbRtENQ8ZCXOq/hjf+4w94vWyLJNcJ\nNXgJVFEc7A6SVIPMJws6tbCbFWk7wxRYvCkZHrcHUYa7x479NwaQZs2T7Zo5OTlobW3F7du3ERMz\nvMHw6quv4sSJE0E/R25uLj777DOfXbO33noLTqcTzz8/tXvNX/7yFyQnJ+PNN98c+dnevXv9Bo7T\nwXdXBAqlZUe4UOO1SXUz14v6q9dhXPoookfd1J0zLahprJbsGqEGL4HyNZXcQQoUdGphNyvSdoYp\nsK8/+hT+/fe7kXlvEmYmxaOrvRf97UZs/B9fle2aDz30EKxWK7Zu3YrNmzejubkZ77zzDnbu3IlN\nm+52ApgsUCotLcW+ffuwa9cuPPnkk6irq8POnTuxffv2oNfhff7ExES0t7fjk08+QUZGBj766CMc\nOXJEsua7DNQiVDgHYaQNYnQ0xp4UCAIAY7Tfx09HqMFLoHxNJXeQAgWdWtjNirSdYQosJSUF//Lt\n/41PPj2OrkuduDd3FZZ89f4JjwmlYDAY8Nvf/hY/+9nPsG7dOlitVvz4xz+G2Wz2ue5ka0hLS8Pu\n3bvxi1/8Anv27MGcOXPw8ssvY82aNUGvw/v8q1evRk1NDX74wx8CAEpKSvDLX/4SZWVlGBoaGnf8\nOlXso0ZEsvjRq7/B/t54iLEJAAQAIoTBfqxPuYXXX/6BJNcItYdZoB59SvYZ27JrD5pn5Iz7eW6/\nHb/aspE9z2haeP/WP/ZRIyJZvPDMN7A06joSPQ7EuQaQ6HFgadR1vPDMNyS7RmnJSpg7G3zm1Jo7\nG1BasjKo3w8051aKOa3BspqNfuflenfMlFwLEWkHd9QoKHqftUnqkGNiwNiE+4cWzcPxs5emfY1g\nJz7I3RqDO2YkB96/9Y+BmozU7nkkFS2Nk6LIplYwo9R11RyFReGJ92/9Y6Amk3D6diz1rM1QtNps\nqN5fCfR0AxYritaXyjYKSclrySlcvjAA6s3V1PM8T4psvH/rH6s+ZaKFnkdS0co4qXHDxVubUXXu\nLCDD3EolryUnLTRJlZJaLSrUbI0RToE2EU0diwlk0nS1D5da23Cu6TIaW65gYMCh2w7e3kkGo6kx\nTqp6f+VI4AQM3yiXOh3Du14yXGvu9R58bGvCkfMX8LGtCXOv98hyLTkpPRxdboES7sPtut5Au8aQ\njuYZOagxpKNs30EOQieKIAzUZGCzt+BMgw19iMGAMQ7XhVhcaOvCrVsDuuzgHagyTjE93eP64giC\nAPT0SH6p3uYmNDY04IG+HhQO9OOBvh40NjSgt7lZ8mvJqdvh8vtnpscvDEDoVZ5au67N3oLyir3Y\nsmsPyiv2jgvAwi3QJqKpY6Amg8pDR2H5QjFMHfV3PtgFuE1xuFZ7QvYbihy8kww6zg2hpe4G2s86\n1SkksFj97mrAYpH8Uh1X2rDcNeRzg1zuGkLHlSuSX0tOau0EyUWtFhVyXDeY3bJwC7SJaOr0+Wmt\ncd0OF+ItGSgA0Nl8EU4PEC2IWJ5l1W1uiRYmGRStL0XVubN388ZEEWdMZhSv9z9MPZRigKz0dNxo\nPI9Ez92BwzcMBmRlpEvyWpQSjiN/1BrFJfV1g8lj1cI0AiJSF9/tMvB+uMZZZiPPMhvA8IdrnrtN\n5ZXpW1Z+PlC+HZ++UwmxuwewWFA8QfAVajHAjLw8JLUvxOcd7YDTCZhMSEpNw4xc+QYNy4Ejf7Qr\nmAKFcAy0iWhqGKgFMJ2KK364yicrPx9Zr2wN+LjJCg+y/jXw74/s3sXHBbV7p2XhPAxezxWRweyW\nMdAmIgZqk5istQGACW8Q/HDVgBALD6aye+cV7FFruPRnU5veW48E+4UunANtIgqMDW8nMVGTy/k9\n53FlyBgWzWy1QI7A5d2fl2PJmZPj/u5qlxbi6SB21KZq3FGrdwduzFFrsI+jwMKhCS0nEZDcIvX+\nHU64ozaJiXJIamxXkLzisbBoZqs2uRrLTrXwIFTBHrWGeiRLdynRhFbuo1XulhFRIAzUJjFRDgmG\nhvweq3UPunSdM6MGuQKX6RxdhiTYo1YFe8GFO7krIvV+tEpE4YF91CYxUZPLFQXZfntTGQZusov4\nVMkYuGTl5+Nbr2zF06/vwtP/ulXeo8Vge7wp2Asu3Mnd/JbNZolIC7ijNglvUcAb736AmktXgKEh\nzC/IxhNfKsTuw7XjctQQHQXHrPCY76kYixVia/P4XcsQAxelE/aDPWpV+khWDlrZNZa7aEep+Z5a\n+fMkIm1ioBaEK07jSE5aoyhi9+FafG/VEhw/e8nnBvHrP/yVXcSnSI7ARY2B6sEetSp+JCsxrR0H\njs7xkjrgUaLZrPfPs8eUjM6mZjg9An5/7BR2fm89Vj1YLNl1iEi/GKgFMNHxx/Gzl8btkrGL+NTJ\nEbiolbAfbI+3YB+nRcF001eDHAGkEv0QKw8dRY8pGY2NDXCm3gNBEHBT9GDLnj/iP9PTubNGRAzU\nApno+KOpqw/lFXt9vr2z0e30SB64MGFfNlM5DlTySE+OAFKJfojdDtfwTtqdIG147VHon7NA9eCX\niLSBgVoA/nbJbt0aQGu9Dd2pa8d9e2ejWw2QKe+NJt41Ngzc9Pni8tCieXfzOBU4IpU6n2xskPni\n2q/Ism6r2QinRxjzxUJEjDGKKRNEBIBVnwH5qyy7VnsCli8U+/327s2Z+dWWjdi6aQODNBUUrS/F\nGZPZ5+/sjMmMIh0l7GuVv/eDp74G9TfcPtXOW/b8ET2mZMUqJq1mo99q2umkHXiPUZWo3i4tWYn4\ngW6IoufOT0RE3b6FNGsKUyaICABg2LZt27ZAD+rq6gIAzJkzR+71aE5KUhKWZc1Gb2MdYgZ6Md9w\nC+YowDV3ns/jBEFAzEAvHi9aqtJKySsxJQUz71+Gv/f2ojM6Blfz5uGfXvxhSHlvrTYbDu1+A+f+\n8D7OnTqFGekZSExJkXDV+uDv/RAfJaIvfbFPUHbFGY3b7TakpN/9oiLneyTDmoSjVdUYSrD4pB28\ntO5RpCQlTem53njnA7TMzPV5PUMJFvQ21mHlF5dIuu6UpCQsmJOIY3/7G0TzTCREuZE7x4rYjgbE\ne27j4OnzOFVbhwxr0pRfBxEQ2ffvcMGvbEEY2z28vGIvulk0oGlS5r2pUUWqZWPfD1t27RmXExhj\njIJT9P2ZnO8RKfPJlGrL4bXqwWL8Z3r6yNoNjmbUu26jcdaK4XxYNtolimiMLKaBRQORRe4q0tE9\n33oFAwwCkOhxy9L/TY7+cv7y1tKsKehpOgXxzs+VeI9INY5JqertiYotyiv2Ispyr+Yqa4lIHQzU\npkGJajDSEBmrSEfv1nUOOPB5Qz3medxIWXgvYuPMku7cybUz6O+Li+XGZfzke+vH9RpU4j0SarWp\nEl/EJmsnovSOHhFpGwO1aeIwZXW02mw4+Ns30HGqBjEAZhWuQMn3XpB/PJRMVaSjd+vOd3TgAdfw\nHNnr7W1InT9f0p07uXYGJ/vionTTVin6qeXnZON7q5Zg+9vvo9cVhRSjBz96bp2kQeZk7UQMjpuw\nXTwGpyjAJIhIzVsAc8osplYQRSi+80k3Wm02fPjSFkSf/wfWegOay834S2M9vvr6r2UJ1lptNnze\n14f36i8hxRCFe9NSMTfOLN3Yp1G7dQan824w6HQCCLxzN6WjTBl3BrXyxUWKfmo2ewt2H65F7IrH\nkX5nR2334VpkSdiAdsL+jJ3X0DUooC8xE2JsAkQRuNlwEYuTm1H6AquWiSIRA7UI4O8oCIDu5gtW\n76+E0GrH8jtBGgAkiSLy7E2yTB3wHhV+yenAYGY6+tqu4OTlK0h47HGs/heJdvFG7da5TSaIt+7k\nRplMACbfuZvyUabM/eW0MLNSimNDJaYvTJQHd6WtA7ErHse9jkG0XeuB0y0iOjUbCxJuaP79SUTy\nYB+1MOevJ9SLFb/Hi29UKtInSlI93TAMOcfvCg0NyTJ1YPRRoTk+DmkFBXhiwXykpCRLtns3QRTK\noAAAGpZJREFUuufbvampOG2MxnVBQFJaesD+b5MdZQa6FiBtfzkle49NRop+at0Ol+wze/31ozN3\nNiAjIxOCICAuzoz52Rm4Ly8TBTmZcMfGS3ZtItIXBmphzt/ugM1ogf2m0++OgaZZrHBHm8bfiKOj\n5Zk6oMAoqqz8fBSXb8c/lhXi6sL74F77TdjWPYP6hfehdmkhiidL9J/i+kZf67PM3MDPPwWT7UIp\naaIAyLuLHAwpm+dOxJvXt8Ldhtx+O1a427Dj2dXIm5Ms+7WJSF/47g8zY4+fmjquQcjM8HnMkAfw\niPqrKitaX4oPP/0Up/tvjBx/XhcENOfk4atyTB1QaBTVtHu+BbE+fzls35JhILxWKhWlqMhWqv2O\nv7w+tv4horEYqIURfxVvLZcvYFZcF+Its0ceFx0FGAT9fWvPys/H13f+Ch/tfgMfnDoJkyjKWvVZ\ntL4UVefO3s0Bu3NUKEkRgQQCrU/JRr1K9R7zmiwfLtTCBjXb77D1DxGNJYhj99n9qKurAwAsXrxY\n9gXR9JVX7EWNIX3cAPmemj8j85G1Izdzj60WcN1G1IIVPt/a2fl8vFabDZ++UwmxuwewWCRvQBuq\nydb37s/LseTMyXHBU+3SQjwtceGFz5cEmf9NKXktIr3j/Vv/tL2FQlPi7/gpPj4OWQvykeduu/sN\nfdNTAMBv7UGQchSVHCZdnwI5dl5K7gQpUZVJRKQVDNTCyETHT3mzk/3ewHhTU54cI5wmpFCOnZdS\nvdS0kg9HRKQEBmphhInI2qb0cHet59hNl9L5cGrQQk86ItIGw7Zt27YFelBXVxcAYM6cOXKvh0KQ\nkpSEZVmz0dtYh5iBXsw33MJL6x7V1Qd8q82GQ7vfwLk/vI9zp05hRnoGElNS1F6WJA7tfgP3t7X4\nHNnNdQ/hdE8v7vtS8O0jgpWYkoKZ9y/D33t70Rkdg6t58/BPL/5QUzl205FhTcLRqmoMJVh8vpC8\ntO5RpCQlqb28kHlz8Fpm5uJ6TDLahBk4WlWNZVmzkZKUBJu9BW+88wHeP34ap2rrkGFNCovXTfLg\n/Vv/WExAmjFux8m7AyTTjpPS3v3RFtx/2T7u559l5uLp13cpvyAdG9lx8ubDTWHHSeu7Vf6KgkRR\nxAp3G0pLVrKQgqaE92/9C5+zAlKVFDc/uYaGa4bCOWPhbLr5cFIMbZf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+H/PNiIgmFtkJ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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Only visualize the sports, cinema, and gaming classes \n", + "tsne = TSNEVisualizer(classes=['sports', 'cinema', 'gaming'])\n", + "tsne.fit(docs, labels)\n", + "tsne.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Mi2vB1YlnMbphLyb178a5hjqkl1wNS/ZortigIgYR+hWOMjNRuAiieyTgQXl5\nOQBg9OjRijeIlOdSou3VWdrbhSzQ9y9asw57jNl9yiQltnp2lFYJj4m2OAdhOcqmHIRFRDIIJF7T\nZ82BQhJov51A+wzqYv6yKKP3fmeRNmpd66N7iSjyMQCMUoHcgAINHrTWMZz03Xmdg4qIiOSn/as/\nqS6Y4IEZDm3RelbWV4YvEudei7SMJhHpDweBkF/stK5/Wu687m9eykgbVBSOeTiJiPxhBpD8YklX\nPmpmfrSalfWX4dNz+dqTSMxoEpH+6PMKSmGn1eBBT9iXzTN/fUy1Xr4OlN4H5BBRZGAJmChMOEG2\nZ/7mpdRy+Vqq3vNoHjhwAG2WRpd/13NGk4j0iVccojBh5sczKRk+LWeg/ZX13TO/9lGZqN77d+SP\nHo/E9EzdZzTdcYALkT4wA0gUJpG2BJhc9JzhkzKgwz3zm5iYgPyJl8Fw5Gvdba8/HOBCpB/Rfech\nCiMt92VTO2ujlQxfoPtByoAOT5nfxMQEDLvwAiybf49yG6MCDnAh0g9mAInCRKuZLmZtegSzH6RM\nURNNmd/mjm50dHTicG0d/l19DIdr69DR0Rn13RyItCjyrkBEXmzbuQsvvrMJLV0GpMXa8dRdN2L6\n1ClhbYNWMl29qZm1UTvz2Fsw+0HKFDVazvzKzXi+DQdaTkLslwTECGiHiDN1JzEmrU3tphGRG2YA\nKSps27kL96z+COWDJuL4kIkoHzQR96z+CNt27lK7aapTa6JlrWUeg9kPUiZJ12rmVxF2O2IsdXAk\nPEURiLHUAXa7uu0ioj6YAaSo8OI7m9AxbCIEoeeZRxAM6Bg2AS++synsWUCtUWuiZa31Fwt2yUMp\nk6TLmfnVUtbUnS0+GSNGDETD0UOw2oFYQUTWiCLY4trVbhoRuWEASFGhpcvgDP4cBMGAlm4mwdUq\nUWptWpxg90M4y/ruU8p819aO9xetxoRcM/KzMlQPBs3xMYhPykB+eqbzNVEUYbbVq9YmIvKMdz+K\nCmmxdoiiaxlKFO1Ii2FpKpQSZe8JjhetWRdQ+VZrgyP0UKrtnTVtb+/AoeNNaMy+CHsaWlUvoQNc\nN5xITwTR/QrsQXl5OQBg9OjRijeISAmOPoAdwyZAEAwQRTvij+7D2jk3RH0JOFgu2aheGbNAgsdQ\n/j4azV+6FkeThwIADtfW4bTQD4CA5IaDKC65FKIoosRWr+pAI2eJ2lES11CJmijSBRKvsQRMUWH6\n1ClYC+AXr4TmAAAgAElEQVSldZtg6TIgLcaOp+aEfxSw1gXSvyzUPnxS+8/Rj3r3U7TaRCCmJ3CO\nFXqe47WwsowWR7oTUV8MAClqTJ86hQFfL+7B3qWjhmPVtv3O/mXVoojy97Z4zcjJ0YePwUJgevdT\nNBkFtIl2mE5UIKuwCIC25hfU8mAVImIASBSV3AcTVIsi3l/7Z2QUjUWixIyeWqOHo1nvrKk59hS+\nqahC+pgpSNDYmsKezi9fDxNaJiWQZbBLesRBIERRyFP5tnVgERqqK1ze5yujxw7/6nBkTd96dj42\nvvAYpg2wam7Qiq/uAXoiZa5Krc1nSSQVH9WJopCn8m1cjAFW0fU1Xxk9ufvwMYsSOK2W0LU2xU+w\npPRz1dp8lkRSMQAk0iglAyJP5dvB5jRYqr+G+MPrUkqKcgUgkVQypMjpHiAlkI2UYJeiD0vARBqk\ndFnJU/k2/ewxvPbg7arMgxcpJUPqOXdPnT6DI1/8BVV7v0S7pVG33QOkzFWptfksiaTiGUqkQUqX\nlXyVb5UYKe0vm8ksSmT4MZN7AbIn5aOusRn1B7/FNSOyMPfOG3SXzZWyOoxaK+kQhYoBIJFKfAVF\nUgKiUEvE4eo/JqW8Gyklw2jX+8ElISEeI4bmQszLQaqtXnfBHyCtnyvnsyS94tWVSAX+giJ/AZHj\n7y39c3G8pQXnu+14f9FqLLvnOs3NdSglm8ksinLCObgmEjO5Uh6UtDoYh8gX9gEkUoG/Pm/+plgp\n27odlv65OHS8CaeFfuiITURj9kV4dNV6zU0/0dzR7RLIAn2DAj2sw6tH4Z6ihP3hiPSDASCRCvwF\nRf4CouaObhxvboE9LhGAI4g0oC3RrLmBE1KDAkcWZdn8e/DsA7MZ/Mkg3INrODckkX7wsYxIBVL6\nvPkqK5njY3C+2w7Euv69SRAlldvCWRZkeVc94S7Jsj8ckX4wACRSQahB0awZpXh/0Wq0Z18EQTD0\nBH8nDmFQYRHM/aw+/zbcc+4xKFCPGoNr2B+OSB8E0b0240F5eTkAYPTo0Yo3iChaOLNwjqAowCzc\ntp278OiqDWhLTIdJEDEovwjp1lN+A7lFa9ZhjzG7T1BQYqvnjTvCuAT7vR402L+SKDIFEq8xA0ik\nklAzJdOnTsHG7OxeQaQVs2b4v7FH4khN8izc2Vcu50ekHwwAiXQsmCCSc+5FFy3N90hE2sFRwERR\nhiM1SQlczo9IX/jIT6RDoZTaOChD+/RYSmXXAiJ9YQBIpDNylNo4UlO79FpKNXacQ9WhL2EVBZgE\nEVn5RYhPy2DXAiKN4i+TSGekLK0WCD1mmyKZ3Mc3HKpqalFx1oZTA3Ih9kuCKALnKg9hdOpRzJo7\nS5Hv4zlLFBoGgBQwLV58q2pqseIPH2FPZS3E2FhMKsjB3NuuV71dSpCz1KblbJPU80yL52Mo9FhK\nLdu6HYaCsbigoxP1TRZYbSJis/JQlHRW9mOxbecuzF/7Z7QOLEJcTAwGm9M0c84S6QkHgVBAwr22\nqNQ2zVtRhg+qzuBI7iWozpqA9S2JmLdmo+bWxZWDnOutarXjvtTzTM7zsaqmFovWrMP8pWuxaM06\n1c4dPa6n61jaMCEhHoV5ObgwPxcjhubC1i9R1u+pqqnFo6vWozH7InTEJuK00A+HjjfB0j9X9XOW\nSG8YAFJAPAUMFlMq5ry4XLUbZ9nW7ag5Z4V1cPEP7RIg9ktCVUx6RN4UvI3ivXTU8IADGH9rEqtF\namAqRwBbVVOLX728HD99ZhnWf3MU/7YmqPpgE+wobTUD2HAFrWVbt6MtwQxBcNy6BNjjEnG8uUX1\nc5ZIbxgAUkDcA4Z2SyMOH65ElXmUahnB5o5uWEXBLZAR0GVHRN4UHKN4S2z1GNZagxJbPR6cPhar\ntu0POBOm1WyT1MA01ADWkUHc0tofpwsugSVzJCoPV6KjpUm1TKin4+uvvKl2Zj5cUws1d3TDZBDd\nzlkB57vtqp+zeqaV7DeFFwNACoh7wHCiugLWrCLExfScSmqUEM3xMTAJ7jcFEbEGROxNwTGKd9n8\ne/DsA7Ox47sjQWXCtDonoNTANNQA1pFB7LIDQM9DhDWrGCeqK1TNhLofX39929Qu5QcTtAbDHB+D\nQflFMJ041OuctSPpZIXq56xeqf3wQOqJzLsjyap3J3vj+TbYm+thKCrpuVnaBRit7cjOznS+P9w3\nzlkzSvHVkXqcO37ohzIwIHS2osBwGrNm3BS2dqgp2IEDWp0TcNaMUpR7WMN21p3XBvU+bxz7zWQU\n0A4RjiCwSxRCyoSGe2CKFgaOhGNqIcfxRuEInDx6COftQGJbM1578HbVz1m90uOoc5IHA0DyyX2U\nqJgkAqf3o7DpO9gSktGBs7APzkBCQrzzb8JdQiwYmofX587qGQV85CvAGIuSghzMve2mqLkphLK8\nmxbnBJQamIYawDr2W3ZGOs7WN8IelwhRBGKFwALJ3tQYWR3M8dfj6GmX412c03O8Z9yl+XZrVVVN\nLbZ+9S1OmmqcczcmpGdqoh8wKU8Q3esnHpSXlwMARo8erXiDSFsWrVmHPcbsPjeWEls9nn1gtuvN\nrlcGhlMyhFekHgelg5Te+62joxN1jc0Q6g7imhFZmDvzhqC+y99vRgmBHv9IPV9IOsc58K/zCThj\niIcoAqYThzCicATi0zIUPV9JOYHEa8wAkk/+SktaLSFKpccsiCd6Pw6ehCOT5rLf7N0ozYnBrHsf\nDOnz1SjHBnr8pZT9IuW3oQdq7GvHOZDT0YlzP2S/e/q/HsSF1lNBZb9JXxgARjEpFx0ppSUtlhCl\n0PIkyMHQ63HwJlx9k+Teb6GU40MRyHb4C1Ij7behZWrta8c5kJAQj5HZmc4JvFPt7TzOUYKjgKOU\n1JFfWh0lKge1R06Sb1qdo9AfPfxm/I2e5m8jfNTa173PAccE3hcMy8GMCRcy+IsSDACjVO+LTrul\nEdVf78A33zdhzovLXYLAcE3voAa9BhjRQqtzFPqjh9+MvyCVv43wUWtf6+FBhZSl7SspKcaR/m+3\nNKLycCWsWSMhCAKqutqwwK38EGmlRQe1SnUkTahTvKhJ678Zf30G+dsIHzW7DERav2EKDEcBRynH\nSMXqr3fAkjnyh4uPiBSxE8OHZEfFCDCOhNQ+Zz9Vxw2KAxHCgr+N8OG+JjkFEq8xAIxSjovON983\noTVrJAARhvNtGJmdiYSEeAxrrcGy+feo3UzFMcDwj6NBoxN/G+HDfU1yYQCoAXq4aVbV1Pb0+TOP\nQlyMAdkZ6UhIiFd8zjLSDzmzE+H6Tejht0dEpIRA4jXjwoULF/p7U2NjIwBg4MCBobUsSjhumrX9\nh+F0XCrqhWRs3/UVxg/JRFpKitrNc0pLScHU0UU4ePAg+mcPg8kU67zBP3rjVZpqK6ljxYbNqO0/\nzGWEYldSOloOl6P04rGSPydcvwm9/PaiQVVNLVZs2IxNO/bh6/3lyDGn8BgQKSyQeI2jgBWgpykU\n9DBikdQj1wjFcP0m9PTbA3qCpEVr1mH+0rVYtGZdn2mY9ErqNFPRLlKPP+kDh3QpQM6VAMJRztL6\niMVoocXSpVwjFMO1OoYaq3AEK5InWw5mEm8tnv9KiuTjT/rADKAC5Jq/jE/R0UOrx1quucLCNaef\nnuYOVCNbGa6MU6CZY62e/0rSW7aaIg8DQAXIddPkBSJ6aPVYy9VF4NJRw3Hsiz/h4O4dqNr7Jdos\njYpMOqunyW3DPQFwOIOsQANxrZ7/SuJk26Q27T0WRwC5JtjUUzmLQqPlYx1qF4Gqmlqs2rYf6SVX\n43xzCzq67Wiq2I9n7rlOke4Mofz2wlmGDPcEwOFaWxkIfBJvLZ//SuFk26Q2nmkKkaNfHS8Q0UPK\nsdZrHylH4JEoCChMTAAAiPm52PHdEUyfOkX27wv2t+evT5bc+1/OlU6ktC2UICvQbQ80EA/3tU4L\nvyU9r3RDkYHTwGhYjjkF23d9ha6kdJcLRLRM0RJN00j4O9Z6nt5k0459OB2X6vKaIAiIa2/BNZeM\nU6lVffma8ibHnCL7/k9LScH4IZloOVyOuPYWFBrb8OiNVwU1v6KUtn29vxz1QnKfIKvQ2OZzSp9g\nz720lBSUXjwW11wyDqUXj/X53nBe67TyW5Lr+BP1Fki8xgDQCy0EH9F8gdDKRTpc/B1ruebjU0Ow\ngUe4+QpUq2qPKbL/AwmSvJF6bgQbZIXj3AvntU5LvyU5jj9Rb4HEa6wlerBt5y48umo92hLMMBlE\nDMovUm14frRO0RLO/kpa4etYa72PlK+Sml5KXb7KkHLsf6XKjlLbFmz/yHCde+G61mn9t0QULgwA\n3VTV1GL+2j+jMX8qBMEAURRx7vAhoFC+4EML/U+0To8XaSWPq5b7g0qZzyzHaMWeXX8BYmJRUpCD\nuXder7lz3legWrZ1e0j7X8k53wI5N4IJsrR87gXDHB+D7yyNaKiugFUUYBJ6HvLNA/S5PUTB4jQw\nbsq2bkfrwCIIQs+uEQQB1qxiNFRXyBJ8RON8V8HQ03xugPLHVcvTm/jK1jr2y+GMUUib+lOkllyN\nui5tHkNfU96Euv+VnOZE6XND7s9Xe/WLS0cNR3X5P2HJLEbroJGwZBajuvyfuHTU8LC2g0ht7APo\nZtOOfag9b0SnEAPgx4u1sbUZk3PTQu4j8sKad7GjIwknT53FmbPnEG8yAakDddGXK5z0NgBG6X5F\nWu4PqkbfOaV465MV6v4PZiCM1H7IobRNynfIee5poW/vex9vw5mBxehub4XB1oUkgw1DCothaKnX\n5DlJFAj2AQyBOT4Gg81pOHu8Cfa4RAACRNGOxLZmzJpxV0ifXVVTi4/Lj+JMwSWAQUA7RJytb8TI\n7Ew027Vb2lSDXHMphks4StZa7Q+qdN85rQhl/wdaRg20ZBxM2wL5DrnOvbKt22Hpn4vj39fDahNh\nMgoYbM4Na9/e5o5uJCYnOKckcr7e2hj0Z7JbD+kRS8BuZs0oRfrZYygenIEUsRPxXW3IrP8Wrz14\ne8g/6LKt22FIGoAfK5sC7HGJqGts1mxpU02Om86y+ffg2Qdma/qCGmjJWu0ymJx8lQj1VspXSqBl\n1HCsjKHG6hvVJ0/h0PEmnBb6oT0mAaeFfjh0vAnVjacU+053cp+T7NZDesUA0I0j8zQtrgVXp3fj\n7jwDPnx2jiwT1jZ3dCMrvwimE4d63QgAoe6gJvpyUfACucFH2g1Dyb5zkSLQJfXCsUxYc0c3Ojo6\ncbi2Dv+uPobDtXXo6OhUNDtbV3cMNlMCHN1rAAE2UwLqjh1T7DvdyX1ORuMydhQZousxXCKp5Y5A\n0/7m+BjEJ2VgRCHQcPQQrHYgVhAxY0SWprNb5F8gJWtPNwyLKRVzXlyOCy64QJclJG+/Gb2V8pUU\nSBlVask4lNKj8XwbDrSchNgvCYjp6ZJypu4kxqS1Sd+oAOUMzkJFfQWsWcXOvr2mExVI6Z+ARWvW\nhaWEKvc5GUndHCi6MAAMUjDTOjimmcCgEchPz3Q+ec6dqa350Cg4Um/w7jeMdksjDh+uREzGKMQn\nD1F8CbJw02rfRS2TMndiyFPL2O2IsdTBOrgYgtBTjYi11AEpA4Jqs5TzND8rA98nZONkrwfglEwz\nKhrq0GnMln2KHG/kPCcjbZocih4cBRykYEZ9ankkZyTSwmounrivjPH9v/+J1kHFSDLYkZ7SX/El\nyMgzLZ0vUq4VoY4837LvAOwpg2Cvr0DMuWYktTdh6PAimOMNAS/RJ3V0b445Bbu+KUfSiHHIyMlD\n6uAhaN6/E+YJ02EymYLaDrXpbcYCimwcBRwGwab9mQ0JDyUn3g2Ve3bHahdgtLYjOzvT+R7HuRSN\nK6IEI9QsqRbPF3/XCk/XoI6OTmwt/zeal671ux8cXVLy038870RRhNlW77Ndnva11PPUU/nVPKIA\nFrcRuXoqobKbA+kVA8AgMe2vbVoOnNxvGB04C/vgDCQkxDvfE4nTqLiTq7QtR/Cm5fPFG/drUHt7\nBw7UnUSqIQFHk4f63Q/BLNHnbV8n2q0QsnNc3uvtPHUPbBetWYdmnV9L+WBPeqSfX5jG6GV902il\n9cCp9w3DeVNNCG0JMj31FfQVtAHwuh2hZJ980fr54on7NaiusRkxljpkjSgC4H8/BJO58ravT+35\nGP0GjwoqiOO1lEgdDACDxLS/tjmyIx0dnahvssBqExFrAIYnKTfCMVi+ziWpN0ctljB98RZIrPjD\nR6izmbwGhqFmn7xRM6MfbODuft601H2HfiNLkJDetyuBr88IJHPlLVDOyc5CW0NlUEEcr6VE6uAg\nkBCkpaQgx5yCqtpjaO7oRlXtMc0MNIh2OeYUbPl0G8rP2NAZmwCrEAv7iSoYRRsuGTFEc8eo9xJk\nOeYUlG3djk079qGq9hhumnQBbCdrfQ4cUnopOrl5WxrtyLd7ETNyssftKK+q9byNB/chJnt4n+Ct\n0NgmedvV6sgf6tJovc+b+pMn0Zg4KKT94I/7ACbHd4xKtOHRG68KeoCbtyX4iCgwHAQSJnrLukST\ngqF5KOpvRG3tMVhPC4gVRGSNKIIhLUPT/bo8nlPb9vs9p/RWwvSWcRNjYr1PgCxC9uyTg1pZKDn7\nHoajlOrrO9gPjkhfGACGQI8dx/UmlH5ttvhk5E8c3ed1rQZFQPDnlLeAyth+LmwT7AbCWyAxqSAH\nh32UYj1tY/6gDGdfwFCCNzUCGDkD93AEsf6+Q0/9UImiHQPAEOgt66I3oWZY9ThSO9hzylNAZa/Y\ng4qYOBgywjfBrlS9A4nqhibU1Z+AOScXgAH2ij0wFJV4zGJpMfsUStAj9zkajv3g7Tu0VhFhMErk\nG/sAhsBbfxg5+9xEs1D7telxgtZgzylPEwcnGkScyh6t2X6Bjj60H39XAxRdgo7+A2GJz4B4phkj\nbM0YIHa49CXT4kTqofbh0+M56k2ov1c5J+LufVyOt3bhq4NH8L+fbEf14UoMzxmku31LJBX7AIYJ\npy9QVqgZVj2OLgzlnHLPzMxfutZ7fzqN8FTyNhSMRaqt3uvUJVrqXhFqNxA9nqPehPJ7lTt76Dgu\nHS1NqDxcCWvWSAgDgS2tHajTSBacSG0MAEMQSRdvLZKjPKa1gMEfOc8pOcuLSpXT9N6NQo72q3GO\nKnE8Qznf5O5P7TguJ6oreoK/Hz63yw720yb6AQPAEOktwNC63jcmY8c5tNVX4WS3AVZRgEkQMTTZ\nhFlzZ6ndTEXJdU7JlaFWsm+XHvtp9qbH9gc7Cbc/oZxvcj8IOI6LVRR6HRsRJqOgqwcMIiUZ1G4A\nkYPjxrTHmI2jyUOx3zgQh5vPojMlB3ZzHmwZQ4GYOLWbqRuObGKJrR7DWmtQYqsPKmjzlZ0J1awZ\npYhvqIQoigDwY9AwozTkz/akqqYWi9asw/yla7FozTpU1dSG9Hnhbr8cvE7CvWGzy+9vjzEbC97b\nInkfhXK+meNjnPvQIZRA2nFcTIL4w+eKMJxvQ3ZGuuYDdKJwEUT3X50H5eXlAIDRo/tOqUEkl0Vr\n1mGPMdt5Y6ra+yUsmcVIxXkU5vWs9CCKIkq89A/TC0eWs/rkKdTVHUPO4CzkZ2VodpTi/KVrcTR5\naJ/Xh7XWYNn8e0L+fGfW11HyVmg/uGS+emWoQs1khqv9cvF2PFt2b0VqydV9spnh+L0pcWyqamqx\n4g8f4ePKExBzRiIn04z4+H6yHHMirQokXuNjEGmGexmop3xjgLX7x2cUvZdvHDc6S/9cHGq3wTZo\nIirqK/B9QrZmpmhx56nM2WZpxIEjBzB/6dqQ+5CFqxuFUvN26q0biLey9fnWc6j+eoezu0VWfhES\n0jPD8ntToj91wdA8LFkwH3OdAfpJmG3sp03kwACQNMP9xtRTvrHDZNRP/yp/HEHI8e/rYY9LhAAB\n1qxiNFQfRMLFl2qyc7p73642SyOqy/+J/InTcDQxQfX53qSS0s9Mq3PHydkuj3NGVu3HOdGI05nF\nEAQDRFHEucOHUAjAPCA8vzelAmm9BehE4cI+gKQZ7v2pBuUXIf7oPgw2pwHQR/8qf5o7uiEIAqw2\nEUCv+dJE7XZOd+/bZTjyNfInXobExAQA8vYJVJK/fmbufVAD7QOnFEe7vjhjwmeHjmHdvhrc9Mxi\nbNu5q8/7pPRv9NRXr6i/EdmTZ8BobQfQ8xBmzSqC5V+7dP17IyLv9JtKoYjTpww0IAbPzLkBO747\ngubWxoiYZseR5TQZBbSjJwgURRGxP3RW95fdVCtD1TuLMn/pWhz9IfhzUDt4lbJf/I1SlVoiDvcx\nKNu6HRZTKg475rMTBJwT7Zi/9s/4MDsbBUPzAh6p7WnOyMTEBIzMzkR9kwVWW885Oq6oQNe/t0iy\nbecuvPjOJrR0GZAWa8dTd92I6VOnqN0s0jEGgKQpnso1kXSRcwQhg825OHu8CTZTAkwnKjCosMjv\nlBlaWWpLa1OfSN0v/vqZSS0Rh/sYNHd0o6H6qMt8doJgQOvAImdwGmr/RscxTUiIdxlwlW9TZJMo\nQNt27sI9qz9Cx7CJEAQD6kU77ln9EdYisq6PFF4sAROFkSMImRbXgqsTz2J0w15ckZOIaQOsfoMI\nJadjCYTWpj4JZL84HjCWzb8Hzz4w22V/S5mKRI1jYI6PgdUuuATcgIi4GIMzOHV0LegtkKzspaOG\n49gXf8LB3TtQtfdLtFkadd/dIpK8+M4mdAybAEHouWULggEdwybgxXc2qdwy0jNmAInCLNhO6VpZ\nNUNrK+DItV+kTGTs7buqG5qwaM06RcrCs2aUYuOXX+OcaP8hAOiZ027w4AyY41oAhJaVraqpxapt\n+5FecjXON7ego9uOpor9eOae61j+1YiWLoMz+HMQBANaupnDoeAxACTSCS2VXrU0slKu/SIlsPU2\nJU7N981ozr5IkbJwwdA8vPbg7Zi/9s9oHViEuBgDBg/OQPrZY87gNJRVOBxZzURBQOEPfTvF/Fzs\n+O4Iy4sakRZrR73zAaCHKNqRFmNXsVWkd3x8INIJrZVetULO/eIIbOf97AoAwOsf/c1lRK2n77L8\naxcyxk5VtCw8feoUfPjsHNydZ8DV6d2YFtfiEmCGsgpHqOVjUt5Td92I+KP7IIo9AZ8o2hF/dB+e\nuutGlVtGesaVQIh0RG+rToSLnPvF36oU7t9VffIULNl9r41yrZSiNPcVeIDIWHEn0mzbuQsvrdsE\nS5cBaTEcBUyeBRKvMQDUAa1OThspuH+pt0ADIr0HUO4Bb1tbO5r278SEXLOmlygkor64FFwE0crU\nH5FKy/tXT4GpntrqT6CDSkLpf6cFvfs+VjeewvcVVcgYMwWW9Ew0h/h7iKTzgijSGBcuXLjQ35sa\nGxsBAAMHDlS6PeRmxYbNqO0/zKV/UVdSOloOl6P04rEqt07/tLp/HYFpbf9hOB2XinohGdt3fYXx\nQzKRlpKiWrs80VNbpfh6fznqheQ+Gb1CY5vHcyItJQXjh2Si5XA54tpbUGhsw6M3XhVyoFNVU4sV\nGzZj0459+Hp/OXLMKYrtz7SUFJRePBblhyrRmj8RpoQkAKH9HrR8XoRz3xKFUyDxGjOAGqeVqT8i\nlVb3b6gT+wYj2GyN3G1VO2sUTEZPyqjoQLZLrcy0nL8HNc5hKbSc9ScKJ44C1jgpk9NS8LS6f8M9\nMjOUdXDlbKsW1uMNZUStN4Ful9wTTktdJ1jO34O/80Jqm+SmlQnVidTGKEIlUrMBSvYvUjvTooRA\nsyynWk7hSMUBiLkjkZNpRnx8P0303wr3nH+hZGvkbKtWskZyz3MY6HY5MnHtlkacqK6AVRRgEkSY\ns5Ikf6fjt1B9ogn7jjUjY+xUJCYnoFoU8dWKMhSZk2CLS3T5nch5vfF1XqiZhQs0yxmJ10kigBlA\nVQSSDSgYmocHp49F556PUb/zE3Tu+RgPTh8rS/8itTMtcgtkmxzvPZwxCtkXXISYphrU7/kbCi0H\nNFEKCvecf6Fk8by19dJRwwPO8ETqnHSBbpc5PgZtlkZUHq6EJXMkWgeNhCWzGPu+b5a0H3v/FvY0\ntKIx+yIcOt6E9vYOdLQ0ofxUF7a09u/zO5Ez++nrHJYjCxdsBjGQLGdVTS3mrSjD+m+OYsvBOqz/\n5ijmrSjT9XWSyIEBoAoCufg5lmnqV3INsqf+BP1KrsGqbftDvgBFYhkkkG3q/d6E9EzkT/x/MLz0\n/0VqygDVgz9AmTKkL+b4GLS1teNwbR3+XX0Mh2vr0NbWLimL56mtD04fi1Xb9gf8gKHVknyoAt2u\nWTNKYfnXLlizin44n0UYre3IGDtV0m+09/ltFQUIggH2uETUN1l6MoqDi9H1wyIS7r8TX+slB8LX\nORxqoB/KA2wgD1cr/vARyk919QrCR6L8VBdW/OEjSe0k0jJ9X1V1yut6oo2n+qwnKkdJzFMJQ6uD\nH0IRyDbpYfvDudzapaOGY+3qj3oWnI8xoE20o3Pv3/HMnBsk/b17WxetWRfUeav3KVW8CXS7Cobm\nYdyIAnzVfR7WbhEmo4Ds7EwkJMSjubXR7/f1Pr9Nggjxh1Ks1SbCIAoQBMBk/PH8V+rc93YOh9pt\nIJTrYiBrWe+prIU19xKX77EOLsaew19JaieRljEAVIHH9UTb2vF9RRWas37m0icm0W6FkJ3j8vfB\nPCm797XJMVohJmljXVm5BHJT0dK6ulqw47sjyB89HiePVsBqB2IFEYNGjw96PdhgA+xAbs56Esx2\n5Q9MRbOHCaalnKO9z++s/CKcO3wI1qwimIwCDIIIobMV2Tk/ThMR7nM/1EA/1Ac4qQ9XYmws3BKV\nPf8dEyvpe4i0LDrvdmHmnoG7dNRwlG/b73Lxa9q/ExljpvR5oj2152P0GzxK9idlWA4gvqEyojIt\ngdxUIjXTFKzmjm4kpucgPz3T9fXWmqA+L5QAO5yZz3AKdLtCOUd7/21CeiYKAVj+tQvjivKRmpeC\nil9aSw4AACAASURBVLOnYYjvCT7VOPdDDfTD9QA3qSAHR1taIfZLAtBTihc6W1FSkOPvT4k0j0vB\nKczbuqIPTh+LHd8d+XE90RNNsOSO6/P36XXfoM2Y4PL39oo9HkfweTJ/6VocTR7a5/VhrTWY97Mr\nIm5d2UDWhA3Xurp6GEUo93Jm/tbTJWlCOUd9/a3e15RW6vzy9LD+6v99haqYdHTZgVgDUNBtwesP\n3KSr/UXRg2sBa4jUG6uv9zn6AjZ3dsPYfg4VZ20wFIyVdOHT+zqleue4UVn65+J4cwvOd9uRdLIC\ny+65TlMLuStxQ9V7kOGJHoL5aCH3+SX1YZ3HnLSMAaCG+MrALZt/j/O/pd6AAw3omIlR16I16/DF\n+TQcOt4Ee1wiAAGiaMeg6p3Y+MJjmjoGkRiwyYm/pcjGh2WKBIHEa+wDqIDeWYIDBw7APjwBib36\nVnnqqyK1T0ygnZ8jtVO9VGpnbJo7unG8pcUZ/AGAIBjQlmhWfUksd5Ha904uWpmkmpShh5kBiOTE\nAFBm7qNu7aMyUb3378gfPR6J6Zk+O1xLuQEH0/k5Wm7snvrvrHIMtlFpzU9zfAzOd9uBWNfjZRJE\n3lhkEM4AnwFCZOPMABRtOBG0zNyzBImJCcifeBkMR76WZULfcK8QoReeJoZ9dNV6WPrnqjrZ9awZ\npUg6WQFR7Jl1VxRFmE4cwqD8It5YQhTu1WwidZLqYKm1lq9SeG2laGNcuHDhQn9vamzsmXh04MCB\nft5Jm3bsw+m4VJfXTKZYjEiJxbL596D04rFIS0kJ+vPTUlIwfkgmWg6XI669BYXGNjx641VRU9L1\nZsWGzajtP8wl2Dve2Iw2QxzSU/o73ycIAuLaW3DNJX1HXCshLSUFxeYE7Pz0EwjWNiS3NyFveBHS\nrafw6I1XhXQuSFFVU4sVGzZj0459+Hp/OXLMKYp/Z7h4OuZdSeloOVyO0ovHyv59OeYUbN/1FbqS\n0l36AIbjOGqNI/iu7T8Mp+NSUS8kY/uurzB+SKZu9wWvrRQJAonXovPRVUHhKCNES0k3EJ7KcyaD\niI5uu8tramRspk+dgo3Z2b36YVoxa4byZWhvk4DLUQJXu28lEP6SbLT3p+0tUvtD8tpK0YQBoMw4\nwbB3SgYNngLvQflFaKrYDzE/V/VjIeXGIvf+UWoZQQCKBZbevtPT5wb6sCXH/vV0HLUQDIcb+0MS\n6R9LwDJjGcEzpUtGnspz/c/U49kbL4PtZK3mj4US+8dTd4RASuDe2lRx8CBOZhYrUnoNZD8EUpJV\n6vzTUylUzu4AX+8vR72Q3Cf4LjS2KVJ+JyJpAonXGAAqIC0lBaUXj8U1l4wLuc9fpFC6v5a3wHv8\nmNG6OBZK7J9Qb9Le2nTku2+QkDPc5b1y9a0MZD8E8rCl1PkX7n6IwZI7UGV/SCJtYh9A0pxwlIz0\n3H9Hif0TancEb20SursgKtTPNZh5LqUcc6XOP72UQuXusyd3f8hoLKMTqY0BIIUF59jyTYn9E+pN\n2lubSgrzUNdQqUg/V6XOE719LiBvUKREoCrXA5ccg5UYQBIFjvMAUljodY6tcM11ptT+cdykl82/\nB88+MDugm6K3Ns2deQNeuvNalNjqZZnbUsp3hrof9Pa5cs9xqOU5DH1lJ6UI93yQRJGCawFT2Oht\nrdlwr/2qxf2jRpukfKe30cm+skBKbYsSn/url5djS2t/dNkBk1FAdkY64uP7Bb0urZbXMZa6Xro3\nkbaGL7OZFIpA4jUGgEReRNqNJVJ4CmbsFXuAmDgYCsZqLsAJVFVNLX76zDKcLrgEPetHizCcb8PI\n7ExcaD8pKSjy9rlae8AAQv+dhRpAaoVjlPbH5UdhSBqArPwixKdl6PY8JnUEEq+pn/8n3YmWJ1S9\ndPCPNp5KhjXnrLBlDMaICJiYuGzrdhiSBkAUgZ7NEWCPS0RdYzNKc0LrE6rFfRHqYKVI6F/seKj5\n1/n+OFNwCbq7bTjx7X70Tx+IfimpWLFhM5Y88YjazaQIo59fCGmCkqtL9P4OLQSYkXBjUZJax8lT\nYG4VBdhdF33RbbDe3NGNrPwinDt8CNas4h+CIkCoO4hZ9z6odvNkF+pgpUiYfN/xUNN1tA420Y62\n81aI2RcCdd/hfNpgfPzdfsytqY3IB21SD+9kFBCll4AKR4ApVSTcWJSi5nHyFJibBBE2tyFteg3W\nzfExiE/KwIhCoOHoIVjtQKwgYsaILMX2rdoPXaFkJyNhiT7HQ43JKKCzwwrR0HPe2gxGiCJgSByg\ny2w2aZv+ro6kKqXLolpaYzQSbixKUfM4eQrMhyabgG4LRDFH98G6Y/swaATy0zN7jb5WZluCDebV\nDhp702p5WyrHQ012RjrqK2uB+AGAKMJot8F04hCyCovQ3NmudjMpwjAApIAoXRaVGmCG6+ajxo1F\nSzdWb9TsH+kxMJ87CwAiIlgP94NHMMG8ljL1kaB30J+ZGIem9rMw1h+COSEGQwpHID4tA2ZbvdrN\npAjDAJAConRZVEqAKfXmo4dAyp1ebqxq94/0FpjrOQvUWzgfPIIJ5rWUqY8EvYN+c2IrvqmrQvqk\nKUjslQHWYzabtI0BIAVE6eyElABTys1HK4FUoEFoOG+soQTI7B8ZOYIJ5vU2Ql4PD4O9g35ne1tr\ndJ3NJm1jAEgupFwolcxOSAkwpdx8tJChCCYIDdeNNdQAOZAHAV/nlB5uzJEumGBe7QxwILTyMBgI\nvfdpJH3Q3q+VVKOVC6W/i5+Um48WMhTBBKFy3lh9BVdyBMhSblK+zikAmjjfol0wWX09ZYC18DBI\npEVcC5icQl2TM1ykrL+qhbVPmzu6XQI5wH8QKtfasv7WR3VvW7ulEdVf78BHu7+Tdc1jX+eUXs63\naBDomtGOoFGJ9aDlFszvkCgaMAMY4QIpsWkhayaFe8bC2H4OiDXg9Y/+5txGLWQogsnmydXH0l/W\no3fb2i2NqDxcCWtWMVJxHnuM2T4zcbKdUyI0eb6FUpaOppK2XsqUeipXE4UTM4ARzF8WyJ0Wsmbu\nqmpqsWjNOsxfutYlM+W4+cz72RWos5lwOP0Cl20EoHqGIthsXqDZGE/8ZT16t+1EdQWsWUUwWtuR\nnZHuMxMn5zml1fMtkO2T629JOXJl1YkiDQPACBZoiU1rF0opN1Rf2yhHIBUKNctk/oKr3m3rZ21D\nKs5jaHp/1DdZ8O/qYzjyfT2qG0/1+Vw5zymtnW9AaN0gWNLWJj2Vq4nCiTnwCBZoSVdKaTWcF80V\nGzbjX+f7o+toHUxGAdkZ6YBb522tl63VKpNJKYH/2LZ1+OJ8Gg4db4I9LhGIEdAm2vFNRRWq3NYf\nDfWcci9pa22llVDOJ62fi9FML+VqonBiABjBgu2D9uwDs38cvZnRE0CEe4RmVU0tPi4/ijMFlwAG\nAe0Qcba+ESOzM9Fs//GGqnb/Hq32+QqkL+GsGaV4f9Fq2LIvgoCeYNF0ogLpY6b0GSkZyjkV6L+p\nIZTzSelzUavnGhHpE0vAESyUEpuncpalfy7mvLi8T388JZRt3Q5D0gD8WMUUYI9LRF1js8sNVc0y\notb7fEktgRcMzcOEXDPMTRVIbjiI9MaDGFE4AonpmX2yV1os28oplO1Tct9o/VwjIv0xLly4cKG/\nNzU2NgIABg4cqHR7SEZpKSkYPyQTLYfLEdfeAvO54+gvdOOrqnp8vb8cOeYUpKWkePzbTTv24XRc\nqvO/29s7cOh4E9o6zyMmfwzqhWRs3/UVxg/J9PoZodi0Yx86+mfhdG0FbEnmH8qYQHxdOV6692bn\nd7pvY6GxDY/eeFVYMiMrNmxGbf9hLkFyV1I6Wg6Xo/TisbJ+V1VNLVZs2IxNO/b5PXbBKK84gvbs\nC5GRk4e07DzEJiRCFEUUGttctiXQ/a10u719fiDf2/u9VbXHcNOkC2A7WRvw+aTkuRiOc03pY0VE\nygskXmMJOML1KekOklbSdS9n1TdZYDMlwCT0ZDeUnkzVHB+D+KQMjCgEGo4egtUOxAoiZozICuvK\nJL7oZdUO989yLyMCwKnTZ3Ck/FsYkgYgK78I8WkZXqfNkbq/lZ5Y3NvnPzh9LFZt2y/pez1+xrb9\nQbdRqXNR6XMtXJPAs4xNpB0sAUeJUEdvnu+2w3SiAln5Rc73KNnB3fn96CmliSIQ22rBdZdNVOT7\nghGuaUzkGl3qqYw4b0UZ5q3ZiMPpFyB70hWwZQxF/cFvUdj0Xcg3f6VHxXr7/Bff2ST5e9Ueuett\nmiN3Sp9r4dgPLGPrm9RzlfSDAWCUCHQ2fPepEwqav0Nh4QgkpGc63xPqDcjXBaVgaB4enD4WTRX7\n0ZGWh5hBQ5FecjVWbduvmQtPMH2+grmIyrWSgaebfM05K6pieub+S0iIx4ihuRg+9SqkpqWGnJlR\negUGb5/f0m2Q/L1qrhIRSECkdN/LcOwHtYNtCh6D98jEADBKBJNB6D2IYPVTjyDdekq2G5CUC8qO\n744gb/KVGFUwBIV5OUhMTNDUDaN3kJxe9w0693yMRLsVZVu3e7wwBnsRlSv74+kmbxUFdNld3yfX\njV/prJW3z0+LsUv+XjUnow4kIFJ6Lrtw7AcuyaZfDN4jEwPAKBFqBkHuG5CUC4oebhgFQ/Mwa0Yp\n2owJ6FdyDSzZo70GdsFeROXK/ni6yZsEEbFuVwG5bvxKZ628ff5Td90o+XvVHNUcTFZeqYnNw7Ef\ntLjyC0mjh2sxBY6/vCghxxqzcnZwl9KpXe05/qTyt+6uQ7Ad+eVaH9jT5NBDk01AtwWimCP7msly\ntduXHKMVe3b9BYiJRUlBDubeeT0KhuZhSHa2pO8NRxu9cT+/2y2NOFFdAYu9HYvWrAv7AAlv+1Iu\nWlifm4Kjl2sxBYZHL4poadJdKRcUvdwwpAZ2oVxE5Th2HoOdubMAQLEASKlz7sdRq6OQljkaoiii\nrqEyqO8NtI1yjWTtfX53tDShsrIS3em5yMoZiD3Gfi6jcJUcPetvX8pFzWCbQqOXazEFRhDdc/Ie\nlJeXAwBGjx6teIMoOrhPS+O4oLiXlZ03PscNQ4PTRixasw57jNl9ArsSW71LYCF1m8k/qftcbnIf\nQ8f5vXX3t2jOHoucTDMSEuIB/Lg9s2aUKnreqLkvOSWMfujhWkyBxWsMAEk1kXJBCSQoUHKbo+mG\nOn/pWhxNHtrn9WGtNVg2/x7FvlepYMnX9pj7xSgaoKmxL/kwRKSMQOI1loBJNXKXB9UKgAIpbXnb\n5lDbHq6JfOUUyjY7yukdLU04UV0BqyjAJIgYnqfsyhVSy/2Bbpuv7gFyTgLtqV1q9O+S2m/WW5u1\nek4T6QkDQIoIagdAoQSzcrQ9kBuqFoS6zbNmlOKrFWWoPNUF6+CREARA6GxFxdnTqKqpVeyYSwmW\ngtk2X32syrZuDyhA8xYw+Vo5pdyxckqY+ncFEkjr7cFGTgx+SUmcBoYigp7nqZKj7XqbpiHUbS4Y\nmocicxISsvKQaOtAitiJC3IGwlAwVtFjLmW6lGC2zdc0S4FM0eJrrklv7drx3RFF5xj0xNhxDlV7\nv8TBPTtQtfdLtFsaPQa1ev5dh4qTL5PSmAGkiBCudXmVIEfb9TZNgxzbbItLxAhzbt/PVvCYSyn3\nhzLdj6dsbSBdDHwFTL7aFc4ZAqpqalFx1oZTA3Ih9kuCKALnKg9hdOpR56h0Bz3/rkOlt6w+6Y82\n7w5EAdJbANSbHG3X2zQNcmyzWsfcX7CkaLv8DNnzFTBp5TdStnU7DAVjcUFHJ+qbLLDaRMRm5aEo\n6WyfoFYrbVajFBvNwS+FB0vAFBHUXNHBFylr/8rRdqWXCpPbpaOG49gXf8LB3T0lwDZLY8DbrNVj\nLne7AikF+lptQyv7y9FdISEhHoV5ObgwPxcjhubC1i+xz3u10Ga1SrFcOYWUZly4cOFCf29qbGwE\nAAwcOFDp9hAFJS0lBeOHZKLlcDni2ltQaGzDozdepWoA5Lhx1PYfhtNxqagXkrF91//f3t0FR3Xe\neR7/nW6pRbdeELQkEJLNiywJEWNC7DBeO7XENTXLOheZqexFJlQqU+NcxC7XhJqdq6TKu6llytmr\nHSdT7IQbdkNlybh24ni9u85SNVMOG4fyEpMJFmBJWEIKEgK9ADZIgpbUZy9Ey62WWt19Xvqc0+f7\nuaRQ6/Q5p3V+/fyf5/+8p8892qLNjZ+uVnXq2Dc3NurgU/v0/NP7dfCpfSt+h58MDo/oP771rrTr\nc5qJ1Oh+zUYtjFzSK1/5l/rcE8W3mvLjNXfjuI69/pZGGnauKAXO1yV160qvDj61b8X/bW9q1Jmz\n72m+LrliJDjz+/1wvt6/0Ksxo37VqF5ndGbV+/HDNS7l/DtpvWvp1882vFdKXqMPIHylkla9edVg\n1++KOS+VdB/YVWqfPr/31wxaD0Cvek5K/r+W8B/6AMJx5XggV1rLB6/m8Pg9PBU6L5V2H9hV6jw4\nP235uJagbQnn5TxEv19LBBtzAFFQuebAVFrLBy/m8AShdUSh81Jp94FdfpgH57RMsPnBkRf0yre+\n4dvwJ1Xm+QckAiCKUK4HctB62RXixYMjCOGp0HmptPvArqAt8Kk0nH9UKkrAKKhcpUy/tHywIl/Z\ntdylrnKXna2Umwudl9z7YHZ2TqMTU7o1elFHj5/0XUm7HIJWCvT7NIRSBe38A8Xw/5MVnitXMAta\nL7uMQnPWyvngKGeItjNXb73zkn0fzM3d1+XRm6qaHtWGngM6F20OzXzAoIYo5nACwUAJGAWVq5QZ\n1FKLn8qu5Sw7u/W+s++D+73/V5s+vqauri4lki2+LGm7IQhzOfPx0+cBQH6MACKv7BGI9uoFafKi\nFhP1rpYyg1hq8VPH/nKWnd1835n7YOq1E7qUSmh8qF+pwQHFDFOtu7o1VVPZ8wGDvA2Ynz4PAPIj\nAGJNuWUcs+5hr64//kNJCmRpyi1+m7tYrhBdjvcdnburgcFRpbb1LE8LuDvQpyc6Njr2O/woyCHK\nb58HAGujBIw15RuBOPbTNwNbmnJLWNtElOV9RyJaSLYrkyUMQ1pItkuRyv7T5VYLoWK2JrQrrJ8H\nIGjYCg5reuPd87pTs2nFvxmGoY9+9xtV9fyLsm+L5Gdeb1c1ODyiY6+/pTfePa/3L/SqvamxpK2i\nrP58Od732+9fkhq3aH7mriKL86o1FtXR2qymaErPP73fsd/jN25sA1bs1oR2ef15AMKslLzGmDzW\nlK+MY1ZV06NtDcWUXd1Y1Wl3xaXdn3e73NwUr1I8ukGd29uX/800TTUtVv6frvZoSufO/m+pqloH\nOtr18te/bOt+Kee8wiDO5QXCpvL/isKSfC1Z/qCjXVeY31OyQkHLaji08lDP/l2XL19W+vEvqtan\niw2C1hrIiZD/6b3yuDa37JVpmhq9MWD72II8rxCA8yp7Ig0sy9eS5eWvfpn5PRasF9TstPwoddeM\n3N81rAb1XZ/U7OxcUT9fbkFqDeRU6xa32qh4sTUhAP/ik4+88pVxgrSRu1+sN/pipzSXr1QfvT+j\no8dPrhqJyv1dsYipu7GExianl8usboYCqzuH+GE0shCnSqxujdQFbTQVgLsIgChZUB7IfrJeaww7\nD/y1HurpwQvqX3igSHLPqnJz7u9q3dWtu1f69aD50eVjcisUVPoOEU4FN7faqHixNSEA/yIAAmWw\n3ujLqdNnLD/w13qo326I6krzgTVHonLDRSLZok5J0cH3tXNL2tVQ4OYiBD9sm+ZUcHNzpM6rxUoA\n/IcACJTBeqMvdh/4uQ/1I6+dyDsv8Nt//IerflcydVvf/85fuP6Qd6u06dTIot3g41Rw83KkLmij\ntIRVwDrDzJ0VvIbe3l5J0t69e10/ICCMlh9kmQe+jQfZ0eMndS7atmok6sDimF751jcc/V1OHpeX\nr7si+GSFN8shco1zG4Sw4tY1clqmd+Uveq8qUrdRrbu6Fd/cbOmaAZWklLzGCCDggbXCgFMP2EIj\nUV7N4XSrtGl1ZHFVO5zHnrLdDiffuQ3KyFoQWsVkzuUHDxr0ccfTMk3p7pU+dXVK8lELI8DvCIBA\nmbkdBvw62d+t47Iy9y73Ggw2RbRwZUBdWpoXKVkLPvlG+crZhNmOUs6lVyOamXM5f3VUihgyDCnV\nulvjQx+qI9niq7AK+BkBECizcoQBv67UdmMRgpWRxdxrUFMV0Wxrt8aH+tTxMACWuoBjvWAfhJE1\nqfhz6eWIZuZcxqKGZmVKMpa2pDQN+hoCJeCTAlhkdQTEr2HAD3PUrAQLKyOLudegrTmpT8YmlDKX\n/s1KeXq9YO9WaxenFXsuc9/r3K1JDY1O6Buv/mf96yc/4+q9kzmXmWuWrqmVaUrVBn0NgVL4668P\nEBB2RkD8GAb8MkdtrRA13fCIXnz1b7Vnz568wbTUEc9V7XASce3e1qzIrUvaeW/YUnl6vWC/1upr\nv4aVYs5l9nudnZ7QwJUBpVp7dH9xTueiba7eO5lRSm3tUk9bi0YnpmSMfqhDXa16+WveT3UAgoIA\nCFhgp4zrxx0Z/DJHLTdEzc7Oqe/6pBJGg+L1O0oOpvlGNde6BslPrun737XeDme9YO/XeZlWZb/X\n8aF+pVp7ZBhSLGq4fu+sOJfpBR1sr9Lhb74UuHP5zq/P6tUfv6Fb8xFtrk7ru3/2FT337DNeH1Yg\n+aF6EUQEQMACO2VcP4YBv5Slc0PU2OS0FmMJxQxz+ZgKhYvMw2Do5m3988Cgkk88o9pk+6rw6PQ1\n8Ovqazdkv9eUubQQI/JgRm1t1hfQlCLo5/KdX5/VCz96U3M7Py/DiGjMTOuFH72pExIhsER+qV4E\nEQEQsMBuGddvDzC/lKVzQ9SDhbRik/1q7exe/j/rhYvsh8FHC9LtXc/q1pX+5dW92eHR6Wvgx2Dv\nluz3env+jqLpObW3tSiRiEvyfkqD37364zeWw58kGUZEczuf1Ks/foMAWCK/VC+CKOL1AQBBdPjQ\nQcVvDCjTR315tOfQQY+PzBq/vJ9MsDiwOKad94bVMXVRnZ1dy61ZMseWL1xkPwxSi6YMI/KwRUi/\npPKNTP3gyAvLIbNSZd7rj//dET1RM6t4fIOk4H8WyuHWfGQ5/GUYRkS3Fngkl2pqbiHvzkdYH1/R\nULHcnBdSaaM9xb6fcsy1yR6ZWx7RM5uLmi+ZXcrOtAnJtAiRGJlyQ6V9Fsphc3VaY2Z6RQg0zbQ2\nV6U9PKpg8kv1IojYCg4VKXdrr5npCU1/cFb7uzq0a8smJglbkDmn07FNujHUr1TaUO3slP7mpT91\ntWxVytZ12VuZzc7O6cOxCS3GEkpO9GnXU19gqzD4wqdzAJ+UYURkmmnFr57XiRf/hBJwiZzaxrFS\nlJLXCICoSCuCwHKbim5t0gM99mhbUX8gwrqyLN/7Pnr8pH75cUxXrgwo1br74R/btFrGfqefv/Ki\nL87NquA/M6vJC7/Wk482adfW5tBcw7WE9X72q3d+fVbfP/mGpucj2lzFKmA7vNrf3I8IgAi9I6+d\n0NX6HZKkwd/8StMtPTIMQ4mFWX1m1yMFN7gP67fK9d73D9/8J/1j37Xlc5kRn5/Rn2+P+GbCNQ+D\n1cJ6PwNhU0peo0iOipQ9L2SpTYUhyVQs+ulKsfUmCYd1ZVmh3SxSaSNnwrWpmqqIryZc+22FtR+U\nej8zWgi7uIf8jwCIipTdTiRmmDLNtKKp2eU+ZYUmCbvZF8/PfxgL7Wbxs1+9r7vLk9dNRR7MaNu2\nZjXV3PLmgG3Kdy38fI2sKOV+pq8a7OIeCgYCICpS9srEptY6nf/979S871klEvGidt5wa2WZ3/8w\nFtrN4m9e+lMdOfE/dW9Lt2qqItq2rVnJT66VZReTYkJZKcEt37V46bl9+rt3Lrh6jcodMEu5n8M6\n+g3ncA8FA02HULEypcD/8u//Sj9/5UV9seaWdt4b1oHFsYIPc7f64q33h9EPCr3v5559Rj9/5UX9\n+faI/lVyQV+suVWW8JoJa+eibbpav0Pnom36zk/e1uDwSEn/J1u+a/Hqj99w9RqVepxOKOV+pq8a\n7OIeCgZGAEOq0kpchZQ6L6yU3malnEu/l5aLed9ezLErZkSh1FGHfNfi1kJEbS4+vJwcHSn2mpdy\nP9NXDXZxDwUDVyOE/F6G9Itigs5a5/K9Y6fU3VSnxZraVQ/lIJSW/biIopjgPDQ+qaEbV5UyDcUM\nU627upVItuQNbvmuxeaqtEwXH15OfQko9ZoXe10L7WkMFMI9FAyUgEPI72XIIMk9l3O3JtV7e15v\n32tYs7y3XilucHhER4+f1JHXTujo8ZMllQQr/Zo2xauU27EqO5QNDo/o/LUpTbfs1r2tPZpu6dHA\nlQHNTE/kDW75rsV3/+wrrm6LV+i9FMuta567Hd+BxTG99Nw+nTp9xtK9ifBZ6x5igMF/GAEMITfL\nkEHiRMk091yOD/Urta1H1YtzklaX9/KV4iTZGsGr9GtaaETh1Okzat73rKavTypdU7vU/qe1W9Mf\n/FqH//qv1nzN9cqij7a1uba1mVOjI25e8zW346NigBL4sZKAlQiAIcT8DOcearnncqnnoJb7DUqr\nH8pr/WE8evykrXlh0QczGpi+pvn00u9ua04qHt/gyTV1Y35poTlsU3MLqq1PqKetRWOT00otLvV8\n3N/dse7vzveQsvLwsjsfT1q6D4o9b+X6HLOiE6hM4XniYxnzM5x7qOWey5hhyrh/T23tW5b/TzEP\nZTujOYPDI+qfuqfZ27eV2rZbs4b08ehN7U6N63ZLnY68dqJsC33cHC1aL5RlwlAiEVfn9nZJS+d9\n16KtX1k0u/PxrJy3cn2O7d6bflls5qdjAfyAOYAhxPwM59oU5J7LQ9sbtT9yR/H4BknFzx+zMy/s\n1OkzinQfUFdXl5om+1R/o091N/p1/cYNXWl+vGytRjLH4sVcRLfa9hTL7vu28vPl+hxbvTe9nqn3\nzQAAFUxJREFUaHcThGMB/IIRwJDq2LFdhw8dXP5GfOr0mVB9I3ayfLbWaE6p88fsjOZkRmgSyRbt\nSi7tdDL4m19prq2n7GU7r+YiltLmxIpCo0d237fVny/HPCur96ZfSseDwyN68dW/1bAaFBudWF4d\nThkbYUcADKmwT+x2s3xm5aFsJ8CsFWZTaUM1VSsH+MsRxLycX+p0GMqEvqHxSZ2/NqXmfc+qtj6x\n5mfF7vt26rx5Mf8yHz8sTMr8nRtselxz1bUyTVN3LpxTXVVU6Q21uj1/J1RffIFs0e9973vfK/Sf\nJiYmJElbtmwp8D8RFMdef0sjDTtXfDufr0vq1pVeHXxqn8dH577NjY363KMtunWlVzWzt9QZndFf\nfuWPyjJH7tjrb+mNd8/r/Qu9am9q1ObGxuVjOvjUPj3/9H4dfGrf8r8X0t7UqDNn39PHkYRGxm9q\nfPqOHnz0W23tfFyJRHz5/5mmqc7ojKvXN3Ms83XJFcH6L7/yR0W/Hz/IBIeRhp26OPCRplof1607\nH6sxXqNYrHrVZ8Xu+3bivGUf852aTRoz6nXm7Hv63KMtts+9lXvz/Qu9GjPqV4Vat+/BbJm/c5/c\nvaf7RpXMu7d0796s7m1+VGp+VOn6pC5evuzIOQL8oJS8RgAMqTfePa87NZtW/JthGKqZvaXnn97v\n0VGVl9XAZZVbD+jNjY1qis7r5//jLT1IpbTh/h1t3dmp8SuXVde8TbFYtaNBrFCILSZYZ17jv779\nSx37b/+gd873qrf/oxWv5aXsL0g3x65pvr5FZlW15mfuKtnYsOqzYvcLhRNfSOx8qVvvmlrlhy8D\nmb9z8Vi1pm/f0YOxQaXa9yiSXtAGI62O1mZp05bQfPFF5Sslr1ECDql8Jafo7N2SWlGgeG7OiXr3\n4kd65ItfXnE9N2xMKnrpl9q5Z49jc+KKmTpQqBSbeY3phkfUN7uoxa2fV/9Yv36faPPNNITs8mXM\nMJd3BkktfrrIJLc8a7cEbffnrZZc3ZoO4va8zGJkrw7vaWvRB7/vU1RpxTWvnrb25RHySumXCZSC\nVcAhtdaqyXT/OfV/shjalXJ2duIohpsbpK/12rXJFvXs2aMfHHlhuQm1XU6s8s28xvWpWw+bNkeU\nat2tG0P9vtm9JHvla+uubsXG+2SaacWiRtlXGBfL6mpdN1duZ0Ktk/d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GvEAJGAgwSq4I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\nRK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Don't color points with their classes \n", + "tsne = TSNEVisualizer()\n", + "tsne.fit(docs)\n", + "tsne.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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4+4tvYdjQ4Rbv09R7ZelQLiGEmIN6lojgGBtisqRHpX3PRE1NDWKHW9875UhC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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Apply clustering instead of class names. \n", + "from sklearn.cluster import KMeans \n", + "\n", + "clusters = KMeans(n_clusters=5)\n", + "clusters.fit(docs)\n", + "\n", + "tsne = TSNEVisualizer() \n", + "tsne.fit(docs, [\"c{}\".format(c) for c in clusters.labels_])\n", + "tsne.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Frequency Distribution Visualization \n", + "\n", + "A method for visualizing the frequency of tokens within and across corpora is frequency distribution. A frequency distribution tells us the frequency of each vocabulary item in the text. In general, it could count any kind of observable event. It is a distribution because it tells us how the total number of word tokens in the text are distributed across the vocabulary items." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from yellowbrick.text.freqdist import FreqDistVisualizer \n", + "from sklearn.feature_extraction.text import CountVectorizer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the `FreqDistVisualizer` does not perform any normalization or vectorization, and it expects text that has already be count vectorized.\n", + "\n", + "We first instantiate a `FreqDistVisualizer` object, and then call `fit()` on that object with the count vectorized documents and the features (i.e. the words from the corpus), which computes the frequency distribution. The visualizer then plots a bar chart of the top 50 most frequent terms in the corpus, with the terms listed along the x-axis and frequency counts depicted at y-axis values. As with other Yellowbrick visualizers, when the user invokes `poof()`, the finalized visualization is shown." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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du7YYRl0ul/z8/HTixIlma5Lk5+fn6VMAAADABcyj68B+++23mjlzpg4cOKB1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\nxf8D4NWVPdRG3icAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "vectorizer = CountVectorizer()\n", + "docs = vectorizer.fit_transform(corpus.data)\n", + "features = vectorizer.get_feature_names()\n", + "\n", + "visualizer = FreqDistVisualizer()\n", + "visualizer.fit(docs, features)\n", + "visualizer.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Visualizing Stopwords Removal\n", + "\n", + "For example, it is interesting to compare the results of the `FreqDistVisualizer` before and after stopwords have been removed from the corpus:" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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AAQAAYCkCKAAAACxFAAUAAIClCKAAAACwFAEUAAAAliKAAgAAwFIEUAAAAFiK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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "vectorizer = CountVectorizer(stop_words='english')\n", + "docs = vectorizer.fit_transform(corpus.data)\n", + "features = vectorizer.get_feature_names()\n", + "\n", + "visualizer = FreqDistVisualizer()\n", + "visualizer.fit(docs, features)\n", + "visualizer.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Visualizing tokens across corpora\n", + "\n", + "It is also interesting to explore the differences in tokens across a corpus. The hobbies corpus that comes with Yellowbrick has already been categorized (try `corpus['categories']`), so let's visually compare the differences in the frequency distributions for two of the categories: _\"cooking\"_ and _\"gaming\"_" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "hobby_types = {}\n", + "\n", + "for category in corpus['categories']:\n", + " texts = []\n", + " for idx in range(len(corpus['data'])):\n", + " if corpus['target'][idx] == category:\n", + " texts.append(corpus['data'][idx])\n", + " hobby_types[category] = texts" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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LFyRd/n6aj4+PJk2apDNnzlz3awPNmzfXd999p/Pnz9vK1q9fb9teQbp37277jmblypX1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\nxQCUiIiIiLyKASgRERERedX/ADuneoC7Pg4vAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "vectorizer = CountVectorizer(stop_words='english')\n", + "docs = vectorizer.fit_transform(text for text in hobby_types['cooking'])\n", + "features = vectorizer.get_feature_names()\n", + "\n", + "visualizer = FreqDistVisualizer()\n", + "visualizer.fit(docs, features)\n", + "visualizer.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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KTEzU6tWrXTsiAAAA1GnVmgEtKSlRcHCwnn/+eQUEBKh3797q2bOntm/fLknK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\nhJgVNaCEEEIIIcSsqAElhBBCCCFm9X8zLNaNeSPmLQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "vectorizer = CountVectorizer(stop_words='english')\n", + "docs = vectorizer.fit_transform(text for text in hobby_types['gaming'])\n", + "features = vectorizer.get_feature_names()\n", + "\n", + "visualizer = FreqDistVisualizer()\n", + "visualizer.fit(docs, features)\n", + "visualizer.poof()" + ] } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.5.2" } }, "nbformat": 4, diff --git a/examples/jkeung/testing.ipynb b/examples/jkeung/testing.ipynb new file mode 100644 index 000000000..c5c8a5ad9 --- /dev/null +++ b/examples/jkeung/testing.ipynb @@ -0,0 +1,219 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ROC Curve Example\n", + "\n", + "### Inspired by: http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html\n", + "#### This is an example of how to create an ROC Curvs in sklearn vs using the Yellowbrick libarary. The data used is the breast cancer dataset that is included in sklearn." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn import svm, datasets\n", + "from sklearn.metrics import roc_curve, auc\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import some data to play with" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "bc = datasets.load_breast_cancer()\n", + "X = bc.data\n", + "y = bc.target" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "random_state = np.random.RandomState(0)\n", + "# shuffle and split training and test sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5,\n", + " random_state=random_state)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Split the data and prepare data for ROC Curve" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Learn to predict each class against the other\n", + "classifier = svm.SVC(kernel='linear', probability=True, random_state=random_state)\n", + "y_score = classifier.fit(X_train, y_train).decision_function(X_test)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Compute ROC curve and ROC area for each class\n", + "fpr, tpr, _ = roc_curve(y_test, y_score)\n", + "roc_auc = auc(fpr, tpr)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot ROC Curve using Matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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IRERElI+TJ+/D2/sAbtx4CYkE+P77FrCzMy/VxSHAq5iJiIiIcklKyoSPzwG0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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "lw = 2\n", + "plt.plot(fpr, tpr, color='darkorange',\n", + " lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n", + "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n", + "plt.xlim([0.0, 1.0])\n", + "plt.ylim([0.0, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic example')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create ROCAUC using YellowBrick" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import yellowbrick as yb \n", + "from yellowbrick.classifier import ROCAUC" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "visualizer = ROCAUC(classifier)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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M5NZbb+W+++7DZrNRoUIF3nrrrUIVY7fbMQzDrfNyP5ak4xeSAageEkB6enqJ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\nzMxMeHp6wsHBAYmJiaivr4dOp8PFixexd+9evPTSSwAAOzs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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "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" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:py36-test]", + "language": "python", + "name": "conda-env-py36-test-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/examples/mattandahalfew/Visualizing a credit model.ipynb b/examples/mattandahalfew/Visualizing a credit model.ipynb new file mode 100644 index 000000000..4dc5d68d2 --- /dev/null +++ b/examples/mattandahalfew/Visualizing a credit model.ipynb @@ -0,0 +1,246 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Predicting credit default\n", + "This dataset includes 30000 observations and whether or not they defaulted on their credit card. Observations include data such as credit limit, age, sex, highest education reached, and marital status. The dataset was obtained from the Tests section of Yellowbrick." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.5/dist-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" + ] + } + ], + "source": [ + "import os\n", + "import zipfile\n", + "import requests\n", + "from sklearn.svm import LinearSVC\n", + "import numpy.matlib\n", + "from yellowbrick.classifier import ROCAUC" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def add_column(data_array,new_column):\n", + " nc = numpy.reshape(new_column,(-1,1))\n", + " if data_array is None:\n", + " return nc\n", + " else:\n", + " return numpy.concatenate((data_array,nc),axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def add_categorical(data_array,new_column,datatype):\n", + " nc = numpy.reshape(new_column,(-1,1))\n", + " unq = numpy.unique(new_column)\n", + " nrows = numpy.size(new_column)\n", + " new_columns = None\n", + "\n", + " for y in unq:\n", + " newcol = numpy.zeros((nrows,1),dtype=datatype)\n", + " newcol[numpy.where(nc==y)] = 1\n", + " if new_columns is None:\n", + " new_columns = newcol\n", + " else:\n", + " new_columns = numpy.concatenate((new_columns,newcol),axis=1)\n", + "\n", + " if data_array is None:\n", + " return new_columns\n", + " else:\n", + " return numpy.concatenate((data_array,new_columns),axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "creditdata_info = {\n", + " 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/credit.zip',\n", + " 'signature': '4a91339c69f55e18f3f48004328fbcb7868070b618208fed099920427b084e5e',\n", + " 'type': 'numpy',\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "path = os.getcwd()\n", + "response = requests.get(creditdata_info['url'], stream=True)\n", + "dlpath = 'creditdata.zip'\n", + "with open(dlpath, 'wb') as f:\n", + " for chunk in response.iter_content(65536):\n", + " f.write(chunk)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "zf = zipfile.ZipFile(dlpath)\n", + "zf.extractall(path)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "credittxt_path = os.path.join(path,'credit','credit.csv')\n", + "credit = np.genfromtxt(credittxt_path, dtype=float, delimiter=',', names=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "credit_keys = credit.dtype.names\n", + "datatype = credit.dtype[0]\n", + "ncols = len(credit_keys)\n", + "categorical_names = ['edu','married']\n", + "y_name = 'default'\n", + "credit_data = None\n", + "for j in range(0,ncols):\n", + " if credit_keys[j] in categorical_names:\n", + " credit_data = add_categorical(credit_data,credit[credit_keys[j]],datatype)\n", + " elif credit_keys[j] == y_name:\n", + " y = credit[y_name].astype(int)\n", + " else:\n", + " credit_data = add_column(credit_data,credit[credit_keys[j]])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#Rescale the dataset by subtracting the mean from each column and dividing by the standard deviation\n", + "datashape = credit_data.shape\n", + "nrows = datashape[0]\n", + "cmeans = np.mean(credit_data,0)\n", + "repmeans = numpy.matlib.repmat(cmeans,nrows,1)\n", + "mydata = credit_data - repmeans\n", + "sstds = np.std(mydata,0)\n", + "repstds = numpy.matlib.repmat(sstds,nrows,1)\n", + "mydata = np.divide(mydata,repstds)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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vPR+8lvNWiqKgUqnQ19fHxsaG/v37s2zZMlxcXGpxetEQSUALIcSfKCqrYM3P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\naN1X0Ddu3MDW1hZ7e3tmz56NXq/njTfeoF+/fpY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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualizer = ROCAUC(LinearSVC())\n", + "visualizer.fit(mydata,y)\n", + "visualizer.score(mydata,y)\n", + "visualizer.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": 3.0 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/examples/morganmendis/testing.ipynb b/examples/morganmendis/testing.ipynb new file mode 100644 index 000000000..9ae232819 --- /dev/null +++ b/examples/morganmendis/testing.ipynb @@ -0,0 +1,929 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Yellowbrick Examples \n", + "\n", + "Ths notebook is a sample of the examples that yellowbrick provides." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "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": "markdown", + "metadata": {}, + "source": [ + "### Load Medical Appointment Data \n", + "\n", + "The data used in this example is hosted by Kaggle at following link: https://www.kaggle.com/joniarroba/noshowappointments/downloads/medical-appointment-no-shows.zip\n", + "\n", + "The data is part of a kaggle challenge to discover if it is possible to predict if a patient will show up for an appointment.\n", + "\n", + "The data is downloaded, unzipped and stored locally within a directory named data." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "data = pd.read_csv(\"data/No-show-Issue-Comma-300k.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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AgeGenderAppointmentRegistrationApointmentDataDayOfTheWeekStatusDiabetesAlcoolismHiperTensionHandcapSmokesScholarshipTuberculosisSms_ReminderAwaitingTime
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24F2014-02-17T12:53:46Z2014-02-18T00:00:00ZTuesdayShow-Up00000000-1
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438M2015-10-21T15:20:09Z2015-10-27T00:00:00ZTuesdayShow-Up00000001-6
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" + ], + "text/plain": [ + " Age Gender AppointmentRegistration ApointmentData DayOfTheWeek \\\n", + "0 19 M 2014-12-16T14:46:25Z 2015-01-14T00:00:00Z Wednesday \n", + "1 24 F 2015-08-18T07:01:26Z 2015-08-19T00:00:00Z Wednesday \n", + "2 4 F 2014-02-17T12:53:46Z 2014-02-18T00:00:00Z Tuesday \n", + "3 5 M 2014-07-23T17:02:11Z 2014-08-07T00:00:00Z Thursday \n", + "4 38 M 2015-10-21T15:20:09Z 2015-10-27T00:00:00Z Tuesday \n", + "\n", + " Status Diabetes Alcoolism HiperTension Handcap Smokes Scholarship \\\n", + "0 Show-Up 0 0 0 0 0 0 \n", + "1 Show-Up 0 0 0 0 0 0 \n", + "2 Show-Up 0 0 0 0 0 0 \n", + "3 Show-Up 0 0 0 0 0 0 \n", + "4 Show-Up 0 0 0 0 0 0 \n", + "\n", + " Tuberculosis Sms_Reminder AwaitingTime \n", + "0 0 0 -29 \n", + "1 0 0 -1 \n", + "2 0 0 -1 \n", + "3 0 1 -15 \n", + "4 0 1 -6 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [], + "source": [ + "data.columns = ['Age','Gender','Appointment Registration','Appointment Date',\n", + " 'Day Of Week','Status','Diabetes','Alcoholism','Hypertension','Handicap',\n", + " 'Smoker','Scholarship','Tuberculosis','SMS Reminder','Awaiting Time']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Age Diabetes Alcoholism Hypertension \\\n", + "count 300000.000000 300000.000000 300000.000000 300000.000000 \n", + "mean 37.808017 0.077967 0.025010 0.215890 \n", + "std 22.809014 0.268120 0.156156 0.411439 \n", + "min -2.000000 0.000000 0.000000 0.000000 \n", + "25% 19.000000 0.000000 0.000000 0.000000 \n", + "50% 38.000000 0.000000 0.000000 0.000000 \n", + "75% 56.000000 0.000000 0.000000 0.000000 \n", + "max 113.000000 1.000000 1.000000 1.000000 \n", + "\n", + " Handicap Smoker Scholarship Tuberculosis \\\n", + "count 300000.000000 300000.000000 300000.000000 300000.000000 \n", + "mean 0.020523 0.052370 0.096897 0.000450 \n", + "std 0.155934 0.222772 0.295818 0.021208 \n", + "min 0.000000 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 0.000000 \n", + "50% 0.000000 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 0.000000 \n", + "max 4.000000 1.000000 1.000000 1.000000 \n", + "\n", + " SMS Reminder Awaiting Time \n", + "count 300000.000000 300000.000000 \n", + "mean 0.574173 -13.841813 \n", + "std 0.499826 15.687697 \n", + "min 0.000000 -398.000000 \n", + "25% 0.000000 -20.000000 \n", + "50% 1.000000 -8.000000 \n", + "75% 1.000000 -4.000000 \n", + "max 2.000000 -1.000000 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "features = ['Age','Gender','Appointment Registration','Appointment Date',\n", + " 'Day Of Week','Diabetes','Alcoholism','Hypertension','Handicap',\n", + " 'Smoker','Scholarship','Tuberculosis','SMS Reminder','Awaiting Time']\n", + "\n", + "numerical_features = data.describe().columns.values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feature Analysis \n", + "\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", + "\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", + "\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. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "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 Rank2D \n", + "from yellowbrick.features.radviz import RadViz \n", + "from yellowbrick.features.pcoords import ParallelCoordinates " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rank2D \n", + "\n", + "Rank1D and Rank2D evaluate single features or pairs of features using a variety of metrics that score the features on the scale [-1, 1] or [0, 1] allowing them to be ranked. A similar concept to SPLOMs, the scores are visualized on a lower-left triangle heatmap so that patterns between pairs of features can be easily discerned for downstream analysis. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0\n", + "0 Age\n", + "1 Diabetes\n", + "2 Alcoholism\n", + "3 Hypertension\n", + "4 Handicap\n", + "5 Smoker\n", + "6 Scholarship\n", + "7 Tuberculosis\n", + "8 SMS Reminder\n", + "9 Awaiting Time" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# To help interpret the column features being described in the visualization\n", + "pd.DataFrame(numerical_features)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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Wnd1+fn7KysrSmTNnKtQ/CABXm2rVqmnRokWKjY1VXFycEhISVFhYqBtvvFGj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\nJTM/ZrTyZ2xAcbTdbVQ/WvGE1SX4XFFRkdUl+MSVK1da3dv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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# For this visualizer numerical features are required \n", + "X = data[numerical_features].as_matrix()\n", + "y = data.Status.as_matrix()\n", + "\n", + "# Instantiate the visualizer with the Covariance ranking algorithm \n", + "visualizer = Rank2D(features=numerical_features, algorithm='covariance')\n", + "\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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Diagnostic Interpretation from Rank2D(Covariance):\n", + "\n", + "Some features share covariance with age but most of the features do not share any measureable covariance." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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SVFRURU8DAHCByymipYqrPy1/smHRvYtWrzCQmJgo0zTLtBwVAABAeTp//rwM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\nSYk6d+6s0NDQBuPdu3eXJBUXF3t8Lp800hUVFZKkDh06NJq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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the visualizer with the Pearson ranking algorithm \n", + "visualizer = Rank2D(features=numerical_features, algorithm='pearson')\n", + "\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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Diagnostic Interpretation from Rank2D(Pearson):\n", + "\n", + "Some features share a positive linear relation mostly with age and a little bit with diabetes but most of the features do not demonstrate a relationship." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "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", + "\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?" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#Need to specify the classes of interest\n", + "classes = data.Status.unique().tolist()\n", + "\n", + "# For this visualizer numerical features are required\n", + "X = data[numerical_features].as_matrix()\n", + "# Additional step here of converting categorical data 0's and 1's\n", + "y = data.Status.replace(classes,[0,1]).as_matrix() \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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WZe3atdy8+X+JaWJiIvPmzcPBwcG8ZpudnR1+fn5s2bKFs2fPUqdOHeBhopaQ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Vra7nWpFBq6JzzsZTIRF6biP/eOaXMV6dUtm9DIG4Oqdrk4VBXOmsMJXnNktWYli3PT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\n06dPx+effw5HR0cIgoB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IiIiISGQY1IiIiIiIiESGQY2IiIiIiEhkGNSIiIiIiIhEhkGN\niIiIiIhIZBjUiIiIiIiIRIZBjYiIiIiISGQY1IiIiIiIiETmfwGwXzaQcYRWXQAAAABJRU5ErkJg\ngg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the visualizer\n", + "visualizer = visualizer = RadViz(classes=classes, features=numerical_features)\n", + "\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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For regression, the RadViz visualizer should use a color sequence to display the target information, as opposed to discrete colors." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Diagnostic Interpretation from RadViz:\n", + "\n", + "It doesn't appear from this visual for there to be much differentiation between the classes. The dimensionality still interestingly shows that the other features have any interesting relations hip with Age." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parallel Coordinates \n", + "\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. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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mJCTAy8urSvWIiIiIiIjo2VVrw3pnz56N8+fPY/jw4eWCqVq3bt1w9OhRpKSk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\nc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vP153pqH8Zpwsf2DuKbPzsbS7v41qFublgxw/ePt/O65bP84rrxuu+pX2uzdCmcioiI\niIjIz11nZHX5z7eszpIvmpycCjKaKLX2bmhP1gwTMpjNOAl77G25Uyk3R6NBDANuWTV1we8sWCaG\nURpktKppzvbZjSLiT15qZ2Cm0sb7/u0j+F0Fnhtu4omBSN397YEcH9o5zEIAPTYe4vh4gPZAhqfP\nR/ilzePs7K4/OyslCqciIiIiIvJz1xVeWf7z5o7zGFj0jwZJ50oRxWlSF+T2D4XZ02uvqEJlMNIt\nq6Zxmhc+x7l/KEJfOEUi58CsqmI2qmAWLINvHOghP/+RTb48d28dBeD+53uYnHPWPbOnd5abVk6X\nrx8+3kGLN8/QrIdTUz7uueY8veF03XOicCoiIiIiIq+CjshKmA+ElpWhPWhwOBqk1V+pjNa29p6Y\n9LOpvb619/nRELG0E6+rWDfpt1bBMihYJn5XgTUt9iFKjc5/no/5+NGJSnvvDStm2NyRIFsw+e9P\nraDQIAu/Z9sofeUAavDlp1bwhjUT/NPpFqZSLn735lO2qcFSonAqIiIiIiI/dx6nn+ZAV/n6mmUO\nplJuZtIOYmkHAJs6knidhaqnDCZTblr9GdtnFSyDJ841AfCWdZO8nH1DEVZEUkym3Hgclc9f7Pzn\n373YztCsp3z9wZ3DeJ0FBme9/NXhzrr7XQ6Lj199vvzZuaLJ5/95De/dNsKD/Z0ULYM/eP1JXC9T\n5V1qFE5FRERERORVUb3v9C3rS5XE/tEwUykXUAp527vsrb37BsNc3aC19/GzTVgW9IYzLI+kLvi9\nRcsgXXAQcBVY0WS/t1Q9tVdQ80WTbxzooTj/cqs/xzs3RwF45HQrx8f8dd/RHcryvh0j5euplJuv\nPdfHXVujfOtQNxFvkd/5hdOa1ltF4VRERERERF4V1ftOQ64BXKZFfzRI0F2pZta29p6cCrChrX6n\n6VjSw4sTpZB4+7oLr5UB2DcYYV1bkvE5F03eypTfxaqnZ6b9/PRka/n61tXTrGtNAgZ/tm8ZyWx9\ntLp+eYzrls+Ur/tHgzw3FObGlTP8zdEOVjZniHjzdc8tVQqnIiIiIiLyquisqpxm8nNc1ebk9JQf\nn7NQ3iW6tStha70FGEl46A7VDxVaGIy0u2cWv6tQ9341C4N4xonXadHia3T+s8T/MT4AACAASURB\nVL6i+fCxjvI0YYAP7RrG7SiSyDr5s2eXNfyeX9k+TFdwoQ3Z4HtHOzGwaAtkePJc/cTfpUzhVERE\nREREXhU+d5CIr718fdtVXoqWwdGxINH5EOh2WGztsg9B2jcUadja+9xwmETGgcOEm15mrczC52zr\njBNLO+mxhd3G1dNc0eQvD3SXrzuDWd6xcQyAY+NBfvpSS90zHmfp/GnlfKnBl55YyY7uBOdjXtJ5\nRbIF+pcQEREREZFXTfW+01vXJAGL/mgI06hULvfUtPaemvKzrq10b7V80eSp86Vq5JvWTr7seU4L\ng6mUG0fDLNo4oJ6YDPJPp5vL17etnWRVc2ln6l8f7WR41l33zLJIpryCBhYGJK3mlzZFF/mWpUnh\nVEREREREXjXVQ5GSmXNEvAZHokHaA9nyAKKtXYm6ybYDM76Gg48eO9uMZUHIU2BzR/3amVoLu1OL\nlsGqprmad+uHI0EphE7MlYY2mQZ8eNcwTrNIwTL546eXkyvUR85bVk+zp6ey5mZyzs2fPrMMj1MT\nexconIqIiIiIyKumeihSKhtnb5+fRNbJ4KyPsfnWXq+zyJbO2tbeMNcsq2/tHY57OTXlA+DtG8df\n9vstDMaSbvJFiKUdOIzqsGjQqIKazjts7b294QxvXV/6rrGkh7882FX3DMAHdw3T5q8MX3o+GiaR\ndb7sb1wqFE5FRERERORVE/BECHkrZzXv3BoEoH80VB6KBPVTe89M+1nbkmzYurswGGlVc4oWX7bu\n/VrPDYd53bIYHpfFmtbaamzj6umx8RBPDFQGGr1l3US5kvvU+WaeGwrWPeN3FfnY3kFbAM4X1di7\nQOFUREREREReVdVTeze2TWMaFv2jQdoClSm627vjNVVNODkZYE1rbStuadDRXM7EMOBt6y+uejoc\n95DNG4zOuvE5qyf9Nq6eAnzncDczqVLl02GW2nsdhgUY/PmBPmZSjrpnVrekeOfmsZf9TUuRwqmI\niIiIiLyqqlt7x+JnWd7k5FzMS65glMOf31Vkc4d9v+mzQxGu6YtRK1sweWZ+MNJ1y2M4zZc/1/nc\ncIQbVs7gcxVZFqldU9O4eprKOfjWoUoL7/KmNG+6qrRjNZ138NVnlpXPzVZ701WTbOuKv+xvWmoU\nTkVERERE5FXVFakeijTDbVe1AAb90RAz6cqZzN0162MGZnysbErZJvsueHyg1NrrdFjcsHzmon7H\n+RkfRSyGZ9017cALldP67zk0GmHfYLh8fceG8fIO1jPTAR4+1l73DMCv7R6i2dtov+rSpXAqIiIi\nIiKvqqCnBb+7EvDetysMWPSPhgi5Ky2227vi822zFS+MB9nYbq+oQim4Dsx4AfjFDS/f2gtwYCTM\njStiuJ0WEW++5t3F23vvf76L2UyphdflsPjwruHyWdgfnWjn1KSvfK81//OD7gIf3Tt4Ub9rqVA4\nFRERERGRV5VhGHRV7Ts1GcHnhOPjAcLeHOn5wUghT4H1NUF031CYqxu09kJlMFKLL8/KujUxjZ2e\n9uF1FJlMOukNXVx7byLr4sHnK+29a1pS3LZ2cv4Jg//v2WWkc+b837Xy3Lq2Oby2861Lm8KpiIiI\niIi86qqHIkVnz7Ctx0867+DEZKC8UgZgT83U3nMxH33hVMNzpU+fj5Qn/r5rc/SifsehkTDXrZjB\nwmhwXnTx6ukzQxEOjVQm9L5j4xgdgQwAM2kX9+3vbficV3tOyxRORURERETkVVc9FGk2NcG7dywH\nSitlnGYlJe7onq1bH3N0LMTWmj2oUBpKtG+oNBhpQ/scPldtq25jL036afLmyRZN1rTUVlwbV0/B\n4JsHu0lmSxHL47T44M5Ke+/zo2H+5UykwXOyQOFURERERERedRFfO15XoHz9pnVuwOJwNEhHMFuu\nYka8Bda12QPjs4ORl23tvdi1MlAKkq9bPkMi42Ay6axZYbNQPa0PqLGMm78+2lm+3tA+x02rpsvX\n3+nvYSzhKl9bjTLuEqZwKiIiIiIirzrDMGytvdNzA3QETaIJD5NzbqLVrb01U3sHZ710BjJ4HPXn\nN09N+Rma9QBwy6ppDC6ujfaF8SA94Qwup8WallTNu4unysfONnNsrBKyf3lzlNb5yb+5osl/f3o5\nheLC3/mifsqSoXAqIiIiIiJXhOqVMqOx09y0uhWA/tEguUIluuzsmq17tj8aZkd3492hC9VTt9Ni\nb2/9s430R0Nc0zfDZNLNYMyD39YSvHj1FAy+caCH1PwAJK+ryPt3jpTvHYl7eeD5zgbPicKpiIiI\niIhcEaorpzNzUf6v6zcApXOnbf5M+b1mf561LRc/tfepcxFyhVKZ8s5NYxf9e46MhVjTOkfAXaAv\nnGlwR+PS51TKzfePV/abbu1McH3VrtV/PtvKkdFAo0eXNIVTERERERG5IjQFunA7vOXrlc1zOE2L\nE5N+TBOmU87ye7t77VXSoVkvLb4cgQZDj5I5J88Nl/aodgRzdPgbBc16R6Ihru6LMT7nYmDGW27P\nrbDqhjMteORUKy9V7Te9e+soEU9u/srgvv19xOd3o0qJwqmIiIiIiFwRTMO0Te2Nxs5wVZubfNHk\n+FiQWLoSTnd117fnHhwJs6vnwq29AO/eNnrRv+ngSJiN7XP4XQVCntrga2At0t5rYfDnz/WWV9kE\n3EXev6PS3pvMOfmTZ/ou+ncsBQqnIiIiIiJyxajdd/rObSsA6I8GCXsr4bDVn2NVs31q776hMFf3\nNm7tfXEiUB6qtK0rgdOsH57UyLGxIHt6Y0ynXYwlXCyL1A9HWmyu0VjSw49fai1f7+yJ2868vjRp\nD9xLncKpiIiIiIhcMaorp1OJYT5+3SbAon80RIsvXx40ZBiwu8dePR2Jewl580Q8te23JY+fbSo/\n+5arJi76N+0firC1M4FpGvNnV6srpYtXTwF++EI7Z6Yrrcrv2z5C0F0J2YtH26VH4VRERERERK4Y\nrcEenGapwmlhUSiMEvYYzKRdnJvxMp6s7And1VPf2vvcUJg95fOo9sD4xLkm8vNrXN60brLu/cUC\n5vHxIDu7Z0lkHczlHKxtmbuo50rvmHzjuZ7yQKaQp8B7t40sev9SpnAqIiIiIiJXDNNw0BFeUb6O\nzp7h6mUhoNTa63ZUgmB7IFfXZrtvKFI1tddelZzNuHh+pPRZXqfF5o5EzbcvXsV8ZrCJnd1xMnmT\n8aQbp1Gsec5YdDjSUNzHI6cqZ16vWTbLjgZnZpc6hVMREREREbmidEVWl/88GjvNR6/bDJRWynQE\nsxTmc6FpwJ6avaWjCQ8eZ9G2eqbaYwOVkHj31kaDkRoHzBcnAmzvipMpmJgmrK6rnnLB9t6HjnUy\nGPOUr9+/fQS/6+LOvS4VCqciIiIiInJFqR6KNJEY5G2blmEaFqenfCSzDsaS7vL7uxtUIPcNRtjb\n17gyeTQaZGKu1BrcE8oSrjufunj19MlzTeztmSWWcjAw4yPYYG2NuUg4LVgm3zzYVW4rbvLlFwnH\nS5fCqYiIiIiIXFHaQn04zNIUW8sqMpEcpC/ixMLgSDRIvlgJkJ3BLD2htO35C03ttTD42UBlMNJ7\nt0Uv+nedmAywpSsOhkHAXaA7XF+dLWKyWPX01HTQttLmhhUzOM1iw3uXIoVTERERERG5ojhMJ+2h\n5eXraOwMb97QCcDhaJB2f6XaaZr1rb1jSQ8WBt01oXXBzwaaKc7nx9JQpYsPiI+fbebq3hhTKRdn\npnx0BGoDqrXo2VOA7x7uZCReqfz6XQqnCxRORURERETkilO77/Q3b9kDWByJBnE7LaZSlf2guxq0\n9u4fCnP1Iq290ykXh6NBABwm3LZm8qJ/18mpAJs6EjgMCHgK+OrOjRpYmIu29+aLDr79fFc5HJvG\n4kF2qVE4FRERERGRK071vtPx+DlWtgTwOiGZc3Jqyk88Uwmn3aEMnUF7BfPZwch8a2/j8FfdXvvW\n9Re/8xTgn8+0cN3yaWJpFyOzblbUTAwG64K12OPjIZ46F7mk71wKFE5FREREROSK0xFajmGU4kqh\nmGciPsiWLj8A/aNBIt5c+V6HCbtrdp5OzLlJ501WNC209tpDav9oiJl0KeAGPUVWNiUv+rednvaz\nrnUOp1nE5YRUvvacaWm1jHmBiPqtQ92MVe1sFYVTERERERG5AjkdbtqCfeXr6OwZ3rdrLVAKlk3e\nAnO5UpwxjPpwCqWdp9cssvO0aBk8MT8YCeBXdlza5Nx/PNPCjSumSWadJLJOrmqwWuZCw5FyRQff\neb7rkr7z/3QKpyIiIiIickWq23f6ui2AxeCsh6k5Z3klDEBfOE2b374WZt9QmD09sUUHFD1eNRhp\nZVMaj+Pi946emfazpiWFyyxSKEI06cbVYPKu8wLDkfqjYVt78lKncCoiIiIiIlek6qFIY7MDuF0G\nbQETMOiPhvA4qsJgg+rp5JybWMbF2tb6qibAeNLNC+OB0uMGvHvrSIO7Fg+Xj5xq5eZVU2QKDiwM\nVjeonuYvUD0FKFiL71VdahRORURERETkitQRXoEx346bL2aZTAzzC6tagNK50/ZAjsJ8PnU2WCkD\npepppbW3XvVgpGuXz1IfJKvDo/29szM+VsxXXJNZk9NTfsKefN13LDa5V+wUTkVERERE5Irkdnpp\nCfaUr6OxM/zGL+wA4IXxAIWiwViysjN0WSRFsy9n+4z9Q2F29cziKK9ssQfFgyMh4hlH6fscFtct\nm2nwSxaeqa9y/vRkK7eunqJomfhdBTqCtXtPS2dPHYb2mb4chVMREREREbli1e47vWFND07TIlNw\n8OKEn0KxEhiLllHX2juVcjOedLOhfWEarz1g5osmT56rDEZ6x6axBr9i8erpuZiP3nAan7NALOPk\n1KSvbq0NWGrfvQgKpyIiIiIicsWq3ncajZ2haBVZ01qqlvaPhmgPVIKgx2mxp8HU3v22qb31Hj/b\njDWfOVv9eToCtXtL4ULV05+cbOMNayYBg6CnYD8LW37GwKnq6QUpnIqIiIiIyBWrM7yy/OdsIc1M\ncpR3bFkGUBqK5ISpucrE21XNc0Q89a2927tncZan6dqrnyMJDycn/eXrDzRcK7N49fR8zEdHMEvA\nlSeecTEc97CiqT7g5q0LD0da6hRORURERETkiuV1BWjyd5avo7Nn+ORNuwCL8aSbkbibRNZRfj9X\nNNnVE7d9xnTaxcisl62diflX6quf1YOR1rfP4TAarZVZvHr69y+18ca1kwC4HUXmso2DqKqni1M4\nFRERERGRK1pXVWvvaOwM7SEfIU8pIB6Ohmj2VSbkepxFdv8rpvbuHw7PB0owDXjHxks7ezo066XV\nnyXkzjOXczKbcXJVgxU2ecux6N7VpU7hVERERERErmid4dXlP0dnz2BZFnv6QkBppUzIU7AFy7Ut\nSYJu+0qX/cNhtnbF8ToXKqL2gJgtmDw9WBmMdPPqmbp77M/VV09/dKKdN181Ub5rJO7B47hQBVaq\nKZyKiIiIiMgVrXooUjqXJJYa59dftwmAExN+UjmTyTlX+Z5swWRnt721N5Z2MTDjY3vXwuuNWnub\nyoOR/K4i2zrqK7AXqp4Ox72EvXmavDmyBQdFy2BFU7ruEyzMqvOvskDhVERERERErmh+d4iwr618\nHY2d4a7tazGwKFgmx8YCeF2VsOdyWOzprW/h3T8U5ppl1a/XDzYamPGWr9+1tVFrb/VzDaqnL1aq\np+m8wZlpX92AJoB8UatlaimcioiIiIjIFa9236lpmvRGSoOQ+qMh2vw58vP51O2wWNeWxO+qae0d\nCrOxPUGg3PJ74cFIPaEsIXe2wa9ZvHo6kvAQcBdo9WUpWiYeZ5H2QOPPcJuNWn6XLoVTERERERG5\n4tmHIp3GsixuW9cFwOHRIIYB40l3+Z5cwWRHTWvvbMbF6Sk/uxvsQl3w9GCEdL4UkwwDfmV7o7Uy\n1eoD7g9fbOct68cBSGQdnJzy0xXM1N2XLTrqXlvKFE5FREREROSKVz0UaS47SyIzxb237AQsYhkX\nZ6e9FKuKmEXLYE+DELpvKMLVfZXXayfnZvIO9g2Gy9c7uuOYFxyMVPtniCY8OE1rvmJqEHAVcC16\nxlTDkRYonIqIiIiIyBUv6G0i4KlM0x2NnWFjZwseZ6ly2R8N0VHVPhvyFNjYnqyazlvy3FCYtS1J\nmrylc6DWy7T2Oh3whjUTDX6RscifS/7uxXZ+cX3pzGoy52Rw1suq5vrVMo2eXaoUTkVERERE5DWh\nq/rcaewMAJs6SwOM+keDuBwwOecs35MvGlXTeUviWScvTQbY02AX6oLT034GY57y9RuvmlrkzsWr\np2NJDwaU23m9ziLxjBPTUKV0MQqnIiIiIiLymtAZse87BXjvzrUAnJ32Ec84mMtWznEmc46GIXT/\nUJir+ypTe2tbe8FePW325VnZlGzwi16+evrW+eppKu9gJu1kTUuj6qmAwqmIiIiIiLxGVA9Fiqen\nSGZifPz6rYCFhcHhaJBmX2VtS6s/x6b2BG6H/bznc8NhVjSlaPOX2oAbtfY+db6JbKHy+t1bo4v8\nqgtXT/NFk75wadepaVgMz3rqWo2lROFUREREREReE0LeVnzuUPk6GjuD3+2k1V+KNf2jIYKeIsls\n6do0oAhsq2ntTWSdvDAeuGD1dC7n4LmhymCkta0pPE77apqFJxv/ueSHVdXTbMFBvmiwPJK6mL/u\nkqNwKiIiIiIirwmGYdj2nY7Ot/Zev6rUgnt0LEihCFMpV/me6ZSr4eqY/UMRWzhtdBK0urXXNOBd\nm8YW+WWLnyOdmHOTyjtY0VQKpPmiwakpf3kgk1QonIqIiIiIyGtGdWtvNHYagH9743agVO08NeXH\n56q0zbb6c2ztSNStcnluOER3KENPKD3/Sn3V88RkgNF4ZXfq65bHaBxEq5+tf/8HL7TztvnqacEy\ncTssWv3ZuvuWOoVTERERERF5zajedxpLjZPKJrjlqj4cZikU9o8GafXlyRdLgdHrtDAMi82dCdvn\nzOWcHB8P2qqnDqN+F+njA5Xqqc9V5Oq+mUV+2UIorQ+5Uyk3sYyrPAwplTc5NeWnuxyMBRRORURE\nRETkNaTJ347H6S9fL0ztXd1SqnD2j4YwDBhLVFp7x5Ju9jRo7d03GObqvlkWgmXBqo9HTww0lYMu\nwB0bGu08hZernv7di23ls6dg4HMWF713qVI4FRERERGR1wzDMOkMryxfL+w7vWNzHwBDcQ+Tcy6M\nqqwY9BTY1hWvq4weGAnT6s+xsqm6gmkPi/Gsk0MjlSFMXcEsbf7FKp4Xrp5OzLlZ31ZaSZPKOxiJ\nexvcuXQpnIqIiIiIyGtK9b7ThaFIn7xpJ6VwaNA/GqQ9UDnT2eLL4zKLbOqw7ypN5RwcHbNP7XWZ\nF955ahjw7kXXylSr/5wfvbhw9rT0nsdRaLjGZqlSOBURERERkdeU6qFI08lRMvk5uiMBgu5S0OuP\nhnCaMDFXae0diXvY09tgau9ghL19sfIqmVyxPiIdGwswkax81tauRMPzqaWK6eLV0+m0i+G4l83z\nITlTcLzcX3VJUTgVEREREZHXlOZANy6HZ/7KYix2FoCdvUEAXhgPkC0YpHNVcceA7V1xTMNe0Tw4\nEiLkLnBV61z5tdrgaWHYBiM5TXjzVY3PntojaYPq6Yk23rpurOF7S53CqYiIiIiIvKaYNedOR2fP\nAvCr12wCIFsweWE8QIuvskt0RVMGr7PAhvaa1t68g6Nj9qm9jfxsoIlCVWZ9w5qphvdZL1M9nUm7\nGIj52N6VqHtvqVM4FRERERGR15zOBvtO37vzqnJ7bv9oEL+7SCJbiTzDcU/jqb1DYfb0zpYrpo2m\n9s6kXRyOBsvXYW+B9W2NA2Z1ddZoUCH98YnS5N5G7y1lCqciIiIiIvKaU73vdDIxTC6fwek06Q6X\nznEejpYm7M6kKmdFExknO3tm60LhwZEQHmeRjVVVVa8zX/ed1YORAN65ufFgpKJlslA9bTTwKJZx\ncXIqwK6e+IX+ikuOwqmIiIiIiLzmtAZ7cJql4GlRZCw+AMAbruoAYGLOzfCsh4C7UH5mbescAVeB\ndW1zts/K5B0ciQa5elmltTfbYFhR/2iI6ZSzfL26OU3Qnau7D8BxEdXTXyzvPRVQOBURERERkdcg\nh+mkPbSifL2w7/TeW3aVX+uPBmn25ckVStVLj9NieNbD7gatvfuHwuzqjuMyS629RcvAoH4w0s8G\nmsrXhgG/tKlxwCy8TPV0NuPk+HgQl6PR1N+lSeFURERERERek6pXyizsO93S3YpnvujZP1pq7R2v\nWgMzmnCzq0E4PTQSwjQstlYNKvK76oPjzwaaKVYVQq9dFlv07Kizameq2eCevz/Rhs+pcLpA4VRE\nRERERF6TqociTcTPky+UWmw3dHgBODnpZy5nYlaFxLWtKSKePGtbalp7Cw4OR4NcUzW1N5lzUmti\nzs3x8UD52uO0uG75dMPfl6/amVpsUD2NZ53lqq4onIqIiIiIyGtUW2gZplEqkxatAuPxcwDcvbM0\nLKlgGRwbC9Dur5wLbfblGY272N3bqLU3wrauOF5n5Zyqx1Gou692MNJb1y/sPK2vjjrNSmW0dscq\nQDqvSLZA/xIiIiIiIvKa5DRdtIeWla+j862991y/FcorZUI4TJioau09Ne1reO70+dEQlgU7uitT\ndB0NEtPB4RDxTGVgUkcwR08oRaO9pvli5bWiVf9+o/OoS5XCqYiIiIiIvGbZ952WwmnQ66bFV4o6\nCytlMlXts8siWZp9OVY121t7swWT/mjI1to7l6uf2luwTJ4412R77Ze3LDZ51yjvTwUwDZ0xXYzC\nqYiIiIiIvGZ1Ve07HYufo1As7Sd93cpS6+1sxsmZaS+tVa29K5vTjCecDaun+wbDbOpIEHBX9pyG\nPfXrYh4/24xV1aW7uSNhaweuVn1faQeqNHLZ/mUSiQSf//znuemmm9iyZQu33nor/+2//Tdyufr/\nYR944AFuv/12tm/fztve9jZ+9KMfNfzMi71PRERERESWhvbwcoz5WFMo5phMDAHwb67fWr6nfzSE\n12mRqGrFPRoNNgyn/aMh8kXT9l46X189HU14ODHpL187THjT2om6+wCKmLbzpg5VTxu6bOH0E5/4\nBA8++CC33XYbv/u7v8s111zDfffdx6c+9SnbfV//+tf53Oc+x8aNG/nMZz5DV1cXn/rUp/jxj3/8\nr7pPRERERESWDpfDQ2uwt3w9Ot/a+8b1fTiMyrlTgJl0ZfpuazBPeyDHskjK9nm5osnzowtTe0vP\nZwsmTvPlByPdumaKRkORwB5IC6qeNnRZ/lUeeeQRnn76aT7zmc/w2c9+lrvvvpsvfOELfPSjH+WR\nRx7h4MGDAMTjcb761a9yxx138KUvfYm77rqL++67jz179vDFL34Ra77+fbH3iYiIiIjI0lO973Rh\nKJJpmqxoLg1BGpjxEks7CXkqrbob25NMpRwNq6f7ByOsa5ujyVvd2lsfTp8bCpPMViJV0F1kS0e8\n7j6AXNGBoerpBV2WcLpv3z4Mw+DOO++0vX777bdjWVY5nD766KOk02ne8573lO8xDIP3vve9jIyM\ncODAgUu6T0RERERElp7qoUhjs2cpWqXg97bNfUBpIu7haICIt0B2fjCS22FxYCjMngYrZfqjQTJ5\n0/beTNpVd1+uaPL0eftgpF/atDAYqb6A5qmqvhYaTO5d6i5LOL3nnnt46KGH8Hq9ttenp0vLaR2O\nUs/20aNHAdi0aZPtvk2bNmFZFkeOHLmk+0REREREZOnpCK9kYY1LrpBhKjkMwCdv2slCSDw839pb\nvVLG47LoCmbpCaVtn5cvmjw/GrK19hYtg1CDwUiP1QxGWt6UodmbpdFamXTBiVEOrYaqpzUuSzgN\nh8Ns2LCh7vXvfOc7GIbBrl27AIhGo4TDYTwej+2+9vZ2AEZGRi7pPhERERERWXo8Th8tga7y9cJK\nmWXNQQLuUkg8OhYkXwSnWUmS27sSxDMmuxtUT/cNhlndkqI9UAmkLrO+Gjo46+XMtK98bRhw5wWq\npz6XqqeLuaRwOjExccH/5ubmFn324Ycf5qc//SnXXnstW7eWJmclk0l8Pl/dvQsV14XPu9j7RERE\nRERkaeoMV1p7F4YiAezoCQKQyjs4OemnrSpsRrx5njkfZk+Dc6dHxoLM5Uz29lZ2nk6l3FWVz4ra\nwUh7+2bnq6L14XMu54Sq6qlUOF/+loobbrhh0fcMw+DjH/84v/Ebv1H33qOPPspnP/tZOjo6+MIX\nvlD33IU+81LvExERERGRpacrsprjI08CpXOnllXEMEw+tHcDT5zdD5Sm9m5on2M86SpXRDNFBz2h\nDJ2BDNFkpVMzXzQ5NFJq7f3RiTYWgmRHIGu7D+DZwTB3bx3F5yq16bodFjeumOafz7ZSCqL2vBJw\nFUjmLimKLQmX9C/y+c9//oLv154JBfjhD3/I7/zO7xAKhfja175GV1el3O73+0mn03XPLLwWDAYv\n6T4REREREVmaOiMry3/O5OeYmRujOdDFB/as56Pf24eFQX80yF1bo+QKlbC4rTNJOm+wu3eWH51o\nt33m/qEI1y2P0RvKMBSf79rM1e88zRQcPDsY4aZV0+XXbl8/MR9O6wtpyXL1VEW2apcUTt/1rndd\n0oc/+OCDfO5zn6O5uZlvfOMbrFu3zvZ+d3c3sViMXC6Hy1U5mDw2VurR7uzsvKT7RERERERkafK6\ngkR8HcRSpYwwGjtDc6ALp9OkM+RgNF5kJO5hPOmi1V9p7V3elOaRkxH2NAinR6IB5rImV/fFeOh4\nKZzGs058zjypvD1KPT7QZAunbf48K5qSDMwEGv7eoDtPIls/AXgpu2zbXx9++GF+//d/n46ODu6/\n//66YAqVabvHjx+3vX7s2DEMwyifTb3Y+0REREREZOlqtO8U4PVrF0Knm2bXZQAAIABJREFUQf9o\nEI/TIp6pVEDHk16WhdO0+bO2zytYJgdGwlxdNbUXoNWfp9aZaT/nY/Z233duXnwwUjLrbPj6UnZZ\nwumpU6f4vd/7PVpbW/nWt77FqlWrGt53880343a7uf/++8uvWZbFAw88QE9PDzt27Lik+0RERERE\nZOmq3ncajZ3Bmt/x8u9u3lV+vT9aWikTqwqnWzqTZIsGuxsMRto/FKYjmGNVc6r82niyccWzdjDS\nxrY5Aq48jdp3LQyC7kLd60vZZTmF+8d//Mdks1luvPFGDh48yMGDB23vr1+/nvXr19PU1MRHPvIR\n/uRP/oRCocC1117LT37yEw4ePMiXv/zl8qCji71PRERERESWruqJvalcnNn0BBFfO7v62nA7IFuA\nF8cDZPIG4apguKE9ydPnQuzuneUnJ9tsn3lsLEgi6+Dqvtn5lTEGmYKDDn+GsTl7pfSp8028a3MU\nj7MUik0Tbl83wV8f7aLRGdNc3kDV04rLEk7379+PYRh8//vf5/vf/37d+/fccw/r168H4BOf+ASB\nQIBvf/vbPProo6xcuZKvfOUr3HbbbbZnLvY+ERERERFZmgKeCCFvK/H0JFCqnkZ8pZbede0ejoxm\nyBVNXpgIsL0rQbZg4HZYuBwWL0wEuW7FMM2+HNOpSmW0YBkcHA6xtzf2/7N35/FNVXn/wD83e9I2\n3WibUpYKSkOBLlDKJiCroKDCw6qg8giiomz+EBS3wXGceXgYx5F5xsdxGX2UUQbFGRSXoYIbLqCM\nFNlUNil039K0SbOc3x8hN7lNCi20FNrP+/Xq63WXk9uT23tv8u35nnOwMT9JDiV1mtCgss6lxren\nzBjaLTD9zMjUCrz1QxKEIjD1BapOrxpGjbelT8Nlq1WC088//7xZ5efNm4d58+a1WDkiIiIiIuqY\nksypcnBaWHUUvSy5AIDpmT2wr9A3hs3ewkhkWmpQateis9nXz7R3Yi08QkL/ztXI+zlecczdBWYM\nT61Er061OFTqG+DolE0PteSFRyh7Sn56LFYRnJp0XmQnV+G70zEItJ4GAlWHu9WGAbrs8EwQERER\nEVG7YYnuIS8HD4p079UZ8KfQ7i309TvVqgOtlhkWG74/bUJOmH6n+0siYXOqFQMjeYWELubQ6S5/\nLIvAqWqdYtuNvUvPLDVsPUWDFtWOjcEpERERERG1G8GDItmdlahx+KZ3iTHpEGPwhT/ldTqcrNIj\n3uSG90x2rlnvwc4TcegZV4tovUtxTK+Q8N0pMwZ0roY6KJZ0eELnPAWAz44rB0ZKMTuREOE8s+ZP\nB2ZQ2hCDUyIiIiIiajci9bEw6aLl9cKqI/LyoO6B7flFkVBJQFltoH/plfF1EACyO9tCjru7wIwo\nvQe9E2vkbUU1esQY6kPK7jwRA5cnEHxKEjCld9F5v6eOgsEpERERERG1G5IkNTrf6d1D+8nL/tRe\ntzcQRGZabDhUYkJOSmhq74GSCNicagxqMOdpYoQrpGxNvQb/Ph2l2Dagcw10ai/YYto4BqdERERE\nRNSuKOc7PSYvX5/eDSrJF1j+VG6CvV6FeGOg5bNLtBPvHuyEq+LsiNS5FccUkPDtKTP6J9ugVQWC\n02K7DuGmg2k456lGLTAytUw+GoVicEpERERERO1K8Hyn1Y5S1Nb7WkJVKhW6xfjSeL1Cwg/FkdBp\ngGpnoO9ol5h6SBKQnRya2rvrpBkGrRcZlkBqb6VDi+7RdSFl95dEosSuVWybcFU5ws13Sj4MTomI\niIiIqF2JNibAoI2U14uqAqm91/dOkZf3FvrK2JyBGTazLDYcqzAgJyUwHYzfodIIVDk0ilF7AcCg\nDd8S+lmD1tMYoxtXxtnPrLH1tCEGp0RERERE1K5IkqRoPS0MCk6XX5MNf2CYXxQFrwCiDYF+o2kJ\ntdi4LwnWhFqYtB7FcX2pvVHItNhg0ASmoTlWYYBBrUwDBoDPT8TA41Vum9qn2F/L83x37ReDUyIi\nIiIiancaGxQpNT4KJq0vMKyp1+BohRGROi+cbt82jUog2uCBCgJZyaEDI+0uiIZWLc6k/fqCXKdH\njR7xoam9VQ6tPPCS31XxdTDr/YEsW0+DMTglIiIiIqJ2J3hQpMraIjhcdnk9IzlCXvan9pbXBfqH\nZibbUGDTYUDn0OD0cKkJlXJqb4DTHX7O04YDI6kk4LpebD0Nh8EpERERERG1O7GmJOg0Rnk9uN/p\nrQPT5GV/cOqb5sUnI6kGG/cmoE+iHQZNmNTeAjPSE2sQqQvsO1JuRHJkaOtpflEkyus0im3DUyvl\nUYMpgMEpERERERG1O5KkQpI5VV4PTu2dl5sGf0rtL1VGVDo0iDO64T0TL0bpPagXWqhVApmWMKP2\nFpihUeFMy6rvRQISkqNC5zwVkPD5cWXrqUEjMDClUi5BPgxOiYiIiIioXQoeFCl4vlOdRoPESF8a\nroCE/MJISBJQGjT1S5bFhpIaTdjU3h/LIlBRF5raW2TXQS15Q8p/dixGDnz9JltLzywxtdePwSkR\nEREREbVLwYMildtPod7tkNev6dlJXt5b5EvtDQ4gMy01eGt/Ivom1ShSfv12F5jRq1MtYgyBUXoL\nqg2wJthDypbX6bC/OEKxLTmqHp2jQtOAOzIGp0RERERE1C7FRXaGRq0DAAgIFFcfk/ctHZktL+8v\njoTbKyHeFEjL7Wx24kSVETq1QL+k0NTe3QXRUEnAwJQGc55qQgNZIHRgJAC4sXdJc99Su8bglIiI\niIiI2iWVpEZiVKq8Hjzf6aDuidCeiYYcbjV+LDVBqwaqHIFRdzMtdlTUaZCTEpra+1O5CeW1GuR2\nqUZwau7P5SZE60L7nu45bVYcGwCyk22Q2OdUxuCUiIiIiIjarcbmOwWAqzrp5WV/aq+9PhBAZlls\n2HKwE/ol1UCjCm0R3VUQjR5xdUiMcMLfelrp0IZN7fUKCTtPxCi2qVWArpGW1o6IwSkREREREbVb\nwfOdltachMtTL6//R2aqvPz9mSllgvuQXtXJjt0FZhg0XvRNqgk59u4CMwBgYIOW1Xpv+DDrs2Ox\nEA0aSg1h+rN2VAxOiYiIiIio3eoU2QVqlW+eUSG8KLEdl/ctHpEBf4tnUY0eRTU6mHReONy+NF2N\nCuiTVItqpxo5YUbtPVJhQqldi0FdqxCc2ru/OAI9Y0NbT4vsehwqNSm2SRysV8bglIiIiIiI2i21\nSoOEqG7yelFQv9M4kwHRBn9IJCH/TGpvRV1gSplMiw0f/RSHTIst7DQxu0+ZkWJ2IsXsgD/QdXrU\nSDE7w9bHPzBSwxZUYnBKRERERETtXPB8p8GDIgFAbtdoeXnvmdTe4BF3Myw1+NdP8TBqvUhPDG0N\n3XXS9/qGc56W1OqgU3tCyn97yoyaejVbTMNgcEpERERERO1a8KBIJbZf4PYGRtNdOKSvvHyoNAJO\nt4QYg1ue8zRC50HPOAfs9SoMCJPae6zSiBK7FrkpyuD0YEkEsiyhU9C4vSp8eSI6ZDsxOCUiIiIi\nonYuIaobVJJvFF6vcKPMdlLed2O/7lBJvkjU7VVhf0kEJAkotQdSe7OSbdhxNBbZyTa5bLBdBdFI\njHThitg6SGf2C0iI1IW2nAK+1F6m9YZicEpERERERO2aRq1Dp8gu8nph1RF5WaVSoWt0IBDdWxgF\nAIrZRzMtNrx7MAEROg+snUJTe/2j9uZ2qVIEnYfLIpAUEdr39JTNgCMVxvN9O+0Wg1MiIiIiImr3\nkhTznR5T7JvQu7O87B8UKd4USP21RNUjLsINh1vCgJTQ1N7jlUYU1eiQm1KN4K6kJ6sN6GsJnYIG\nCAyMRAEMTomIiIiIqN0LDk6Lq4/D6w2k3C4dkQV/W2lFnQ6/VOmhUQFVDo1cJtNiw1cnopGdXA0J\noTm5uwvMiDG60atTrWJUX7dXClv+65PRqHMxHAvGs0FERERERO1eYlR3SGfaNd3eepTZC+R9vRKj\nYdAE2jz9o/bWBgWPmRYb3j6QBLPeg16dakOOvysotdcjAsf69+ko9EsKbT11eVT4+iQHRgrG4JSI\niIiIiNo9ncaAuMgUeb2owZQy/SwR8rK/32mMIZDae1V8LQAJ9R4p7Ki9v1QZUWjTISelGmpJwN8S\nW+XQoltMXdg6fXqUqb3BGJwSEREREVGHYDGnyssN5zudk3OVvHykwgh7vRpGrYDD7WsFVauAfkk2\n7DkVhf5hglPA13oaqfOgT6L9TIDqU2zXIULrDil/vMoIj5cTnvoxOCUiIiIiog6h4aBIXhHoGzp/\nUG/4Wzu9QoX8Il9LakVdYCTfTIsNG/clIcbgxpVxoam9uwt8abqhqb1m5HapDFsnp4chmR/PBBER\nERERdQhJ5kBw6vI4UGEvlNcNOg0SItTyuj+116QNDJzUL6kGNfVauL3hR+09WW3AqWodspNt0KkF\nJPiC33qPCjHG8HOe1jM4lfFMEBERERFRh6DXmhBrssjrRUHznQLA8B6d5OV9xRHwCiDa4IHnTAOr\nSefFlfG1+KHIdJbU3mgYtF70S7JBkgKtpwdLIpDaSN9T8mFwSkREREREHYYytVfZ73TJiEx52V6v\nxZFyIwCgrDaQ2puVbMMb+5IRb3Thithwqb2+UXsHdamCVyiD0+zkqpZ5E+0Ug1MiIiIiIuowLEHB\naWHVMQgRGLjo6h4WaIIiJP+UMsEyLTaU2HXweIH+nW0h+0/ZDCio1iPDUgOjxgO1ytfsKiDBKyRo\nguZAJSUGp0RERERE1GEkBvU7dbrtqKorVuy/Ml4nL+8t8vU7jTcFppRJiqxHclQ9fiozhp1SBvCN\n2qtVC2Ql2yCCWk93FUQ3mg5MDE6JiIiIiKgDMemiYDYmyOsNp5SZkpEqL/9SpUd5nQZqFVBZp5G3\nZ1ps+Fu+BYkR9egaHdqPdPfJaAgRmtp7ymZAr06hqcDkw+CUiIiIiIg6FIu58X6ni4dnwD+lDKBC\n/pnU3jp3IHTKtNhwstoEr0DY1tPTNXqcrNYjPbEGkTo3dOrASL2FNTrEGupb7s20IwxOiYiIiIio\nQ1EMilR1VNHvNDHKiCh9IEzKL/IFp3HGQGrvlfG1iNC5cbxSj5wwU8oAvhRetcoXvLqCpov55mQ0\nhnUPP+dpR8fglIiIiIiIOpTgQZFq66thc5Qr9g/sapaX9xdHwuWRoNcI1Ll84ZNKAjKSarBxnwXJ\nUfXoHOUI+R27T5p9qb1dqyAgwd8aW+3UIsHEltNwGJwSEREREVGHEqGPQaQ+Vl5vON/pHYPS5WWn\nR43DpSYAQKVD2e/0x7JIX2pvmNbTIrsev1QZcFV8LWINLujVgVF695dEwppQ02Lvp71gcEpERERE\nRB1OcGpvYYN+pzOyekBCINV375nU3ghtoO9o36QaqCUvTlfrzjpqr0oCBnapgjMotfe7U2YM6sI5\nTxticEpERERERB2OYlCkqmOKfSqVCinRWnndPyiS2eCB50wDqFHrRa9Otdi4LxFdzE4kRThDfseu\nAt+ovbldqgBIUJ2Z49TlVcErAGNQsEsMTomIiIiIqAMKbjmtcZbD7lQOUjQ+LVleLrLrUVjjm/+0\nrDYQtGZZbNhXHA0gfGpviV2H45UGXBHrQGKEE1pVoDV218lo5Kaw9TQYg1MiIiIiIupwogzxMOkC\nAx81nO902cgsBKaUkbD3TOupKjBtKTKTbQAEiu3as6T2+oLX3C7VitTeg6WRGNK1UpE+3NExOCUi\nIiIiog5HkiQknWW+03RLDPSaQCTqD07jTIEpZRIiXEiJcuKfBzqhe4wDncKMwru7wByU2gvFnKeH\nyyIQbXC3zBtqBxicEhERERFRh6QYFKlByykA9Ekyycs/lZngcKugkoCKuqBRe5Nt+OpkDACgf5jW\n09JaHY5VGpBidqKLWdkv9ctfoi/4PbQnDE6JiIiIiKhDCp7vtLquBHX1NsX+m/tfKS+7vCrsL44A\nANS7A2FUpsUGQIWKOg1ywvQ7BYBvTvpTe6tQH5Tae9pmgMcrhX1NR8TglIiIiIiIOqRoYyL0mgh5\nvWFq78KhfRCu32msMZC+2yOuDlE6N7YejscVsXWINbjQUMPUXpM2kMobHKx2dDwTRERERETUIUmS\nBEt0qrzeMLXXpNMg3qSW1/PPzHeq0wC1Ll8opZKADIsNnxyNgwSgf5jW0/I6HY5UGJEQ4UKP2Dq4\nggLSeg9bTv0YnBIRERERUYelGBQpTL/T4VfEy8uVDi2OVxoAANWOoH6nFhu8UKHaqUaOot9p8NQx\nvpGBc7tUweWV5H0CDE79GJwSEREREVGHFTwoUkVtEZyuWsX+e4dnBK1JyD+T2hupC6Tm9km0Q6Py\nYttPcbgyvhZmvUsu77f7VDS8AhjYpRoSgCgdR+ltiMEpERERERF1WLERydCqDWfWBIqqjyn2j7qq\nM9RBUdPeIl8f1Ui9Fx6vb5tB60Vap1p89HMnX2pvZ+XASgBQUafFz+VGxBjcSOtkR61LHVKmo2Nw\nSkREREREHZZKUiHJnCqvNxwUCQB6xOnk5WMVRticvsCyrFYrb8+02OD2qlDrkjCgsdTeAv+ovdXw\nCBU0kreF3kX7wOCUiIiIiIg6tHPNd3pj3+7yskeosO/MwEgaVSDwzEq2ARD47FgM0jrZg9J+g1J7\nC8zwCiAnpRpqyQuTztOyb+Qyx+CUiIiIiIg6tOD5TstrCuByOxX7l4zIgGJKmSL/lDKBfqPxJhe6\nmJ3YcigRKgnITg5N7a1yaPFTmQkROg/6JNlRU68JKdORMTglIiIiIqIOLT4iBRqVL0VXQKDIdkyx\nv3O0CZG6QAvo/qIIeAUgSUBFXSDAzEq2weHWwOGWMCClsdTeM6P2plTBKyQYNGw99WNwSkRERERE\nHZpKpUaCOZC6G25Kmf5dzPJyrVuNn8pMAJTzlGZafK2lX/8Sjd4Jdpi0/sAzUObbM6m92Z1t0Km9\nijlPOzqeCSIiIiIi6vAswfOdhhkU6T9z0+Vlr1DJqb3xRpe8vUdcHcx6FzYfSIJaEsiyhEntdWpx\nuNQEg8aLDIsNHsF5Tv0YnBIRERERUYcXPChSqe0k3J56xf7Z2T0hBaXn5hf6ppTRqIHa+kBYlWGp\nQU29BvUeCQNSqoKOEG7U3uD9xOCUiIiIiIg6vE5RXaGSfP1HvcKDEtsviv0ajQrJ5kD/0kKbHuW1\nvvVqZ1C/0zOtpXsKI9En0Q69JjS197tTZni8QEZSjSLg7egYnBIRERERUYenUWmRENVVXg+X2juu\nl0VedgsV9hZFAQCi9IFRe9MTa6BRefHWPgs0qvCpvdVODQ6XRUCrFtCqGZz6MTglIiIiIiJCw/lO\nj4TsXzYyK2hNwt5CX7/TCJ0Xbq9vq14j0DvBjvI6HdxeYEDnRkbtPekbYEmn9rZY/S93DE6JiIiI\niIignO+0xHYCHq9bsb9f5zjo1YH1w6UmuM6M1lteq5W3+0ft3V8cgb5JNUEBaNCovWdSezUqtpz6\nMTglIiIiIiICkBDVHZLkC5E8XjfKagpCyvROMsrLLq8KB0t9AyMFp+f6glOBv++zQKcW6JcUmtpb\nU6+RX0s+DE6JiIiIiIgAaNU6xEemyOvhUntn9b9SXnZ7A6m9MYZAK2ucyY1u0Q6crjHAI4ABKY2l\n9ka3YO0vfwxOiYiIiIiIzrCYe8jL4QZFWjgkHYEAU5KnlJEkoLwuMGqvP7X35zIjMpJ8gyT5X+P3\n3amoFq375Y7BKRERERER0RnBgyIVVR+DV3gU+80GHWKNgY6n1U4tTtl0AHwtqX5Zyb7gdOM+C4xa\nL/om1YT8LrtLo3hNR8fglIiIiIiI6Iwkcyr8rZtuTz3Ka06HlBmaGicvu7wS8s+k9sYZXfL21FgH\nYgwuHKs0wRMyam9AvYchmR/PBBERERER0Rk6jQFxEcnyerjU3nuu7icve4UKewt96bkaFWCvD4RY\nGWdSe3+pMiDLYoNaCp02xj/aLzE4JSIiIiIiUjjXfKfje3WGOiimPFZhQJ3LF1rV1AdSfv39Tjft\nS4BJ50V6oj3kWAIMTv0YnBIREREREQWxmJX9ToVQtniqVCqkxukCGyTgh2Jfaq9ZH+ijmp5oh07t\nxYFSM7yi8dRe8mFwSkREREREFCQpOlVernfXobK2OKTM5D7d5GWHWy1PKWPUeuVUXZ1aoHeCr7X0\ndLUO2ck2qCQRcizyYXBKREREREQUxKCNRIwpUV4Pl9q7dEQ/BE8p80NRhLyvIsyUMpv3JyBS74G1\nU2hqL/kwOCUiIiIiImog6RzznXaNjUSENtBftN6jwrEKAwBArwmkAWdabJAgsKfQDCGAASlM7W0M\ng1MiIiIiIqIGlIMiHYUQoem4WSlmednlVcmpvdGGQL/TGKMb3WIcAFQosWuRnVwNCUztDYfBKRER\nERERUQPBgyI5XDWorisNKTMv1yovu7wS9hZFyevltYHU3qwzqb3/PNgJ0QYPenWqbY0qX/YYnBIR\nERERETVg0psRZYiX18Ol9t4yoGdQK6iE09VaVDt9U8l4RSDlNzPZF5x++UuML7WXo/aGxeCUiIiI\niIgoDMs55jvVaTRIigq0kGrUQH6RL7U31uiSt3ePcZxZV6HSoUZ/BqdhtVpwarfb8Zvf/AajR49G\ndnY2Zs+ejS+++CJs2Q0bNmDixInIzMzE5MmTsXXr1gsqR0REREREdKGSzOfudzrmqiR52V6vkfud\nqlVATb1a3ucftfeDw/GINbrRM46pvQ21SnAqhMCiRYvwxhtvYOLEiXjggQcAAPPnz8fnn3+uKPvi\niy9izZo16N27N1avXg2LxYLly5fj/fffP69yRERERERELSF4UKTa+irUOCtCyiwenikvC0g4WBwB\nj9f/mkC45Q9OPz4Sz9TeRrRKcLpjxw589dVXWL16NVasWIHZs2fjr3/9K5KTk/Hss8/K5Ww2G9av\nX48bbrgBv//97zFjxgw8//zzyMnJwdq1a+X/TDS1HBERERERUUuJ1MciQh8trxdVhfY7zenWCdpA\nAylUKuCnchMAwGxwy9t7J9ihU3vhhQq2ehWnlAmjVYLTqqoqpKenY/LkyfI2vV6Pfv364dChQ/K2\nvLw8OBwOzJ49W94mSRJuvvlmnD59Gt99912zyhEREREREbUUSZLOOd8pAKQlGOVllycwpYxBI+Dy\n+AZG0qoF+iTWAAA+/ikOnUwupMbUtVbVL0utEpzedNNNePvtt2EymeRtXq8XP/74I5KTk+VtP/zw\nAwAgPT1d8fr09HQIIbBv375mlSMiIiIiImpJlgbznYYzIysQwNa5A8EpAFTUBQZM8qf2fvhTAoQA\nBqZUtXR1L2utPlpvXV0d8vPzsWTJEhw9ehSLFi2S9xUVFcFsNkOv1ytek5CQAAA4ffp0s8oRERER\nERG1pOB+pzZHGezO0IBy0bC+QNCUMhV1apTatQAAo9Yrl8uw1ECCQL1XhVqXCqN7lkOr8oYcr6PS\nnLtIQGlp6MSzwUwmk6K1FADWrVuH1157DZIk4dprr8W4cePkfXa7HUajseFhYDAYAAC1tbXNKkdE\nRERERNSSzIZOMGgj4XD5UnKLqo+hR0KmokyMSYcYgwqVDl+AatAAe4siMbpHBaL0HrlctMGN1Ng6\nHK0w4Ytj0RjfqwIRKg/Ip1nB6dVXX93oPkmScNddd2HJkiWK7ePGjcPQoUPx/fff46WXXsLcuXPx\n+uuvQ6vVyq872zHDLZ+tHBERERERUUuRJAmW6CtwrDQfAFBUdSQkOAWAwd3j8MGhMgBAtVOD/EJf\ncAoAZbUaxJt8gyNlWWw4WmHClkOJGHdV6Oi/HVmzgtNf//rXZ93fsE8oAAwaNAgAMHr0aKSkpODR\nRx/Fli1bMHXqVJhMJjgcjpDX+LdFRvpytZtajoiIiIiIqKUlmXsEgtNGBkW6a1hffHDoEwCAR6jw\nY6kR9R4JOrVyZpHMZBs2H0hCrVsDh1sFqMMdrWNqVnA6bdq0C/plEyZMwKOPPor9+/dj6tSpSE5O\nRlVVFVwul9ySCgDFxcUAgKQk34S2TS1HRERERETU0oIHRaqsLYbDVQODVtlAdn3vLlBJgPdMLGrQ\neXGwJAIZlhrEGgNTynSNdiLeWI+yOh22Hu6EPv0uylu4LLTKgEhPPPEExo8fH7LdbrcDgDywkX+0\n3QMHDijK7d+/H5IkoV+/fs0qR0RERERE1NJiTInQawJj6xRVHQspo1Kp0D020JDmDppSRiUBNmeg\niTQz2Tdq79bDCb7WUwLQSsFpSkoKfvnlF2zdulWx/cUXX4QkSRg1ahQA4JprroFOp8Nrr70mlxFC\nYMOGDejcuTOysrKaVY6IiIiIiKilSZIKieZUeb2wkdTe63t3lZdt9RrsLQq0rta5AqGXf0oZAHC4\nmdfr16y03qaaM2cONm/ejNWrV2Pfvn3o2rUrPvvsM2zfvh3Tp09HTk4OACAmJgYLFizAn/70J3g8\nHgwePBgffvgh9uzZgz/84Q/yQEdNLUdERERERNQaLNFX4Jfy/QB8gyKFs3RkBtZ/8TMACYAEp1uF\ngmo9UsxOxASl9qZ1qoVB42Fg2kCrBKc6nQ6vvvoq1q1bh3/84x+orq5G9+7d8fDDD+OWW25RlL33\n3nsRERGB119/HXl5eUhNTcUzzzyjmHKmOeWIiIiIiIhaWvB8p+X2QjjdddBrlNNdXhEfBZNWQq3L\nt27SeLC3MBIpZid0aiEPkKRVC/RJtOPbU+aL+RYuea0SnAJAbGzsOUf39Zs3bx7mzZvXYuWIiIiI\niIhaUlxEMrRqPVweJwCB4urj6BpnDSmX0TkKXx33pe1WOnXYWxiJib18U8xU1mmQGOmLXDMtNgan\nDbD3LRERERER0TmoJDUSzd3l9cZSe+fmpMnL9R4JxyoMqK33hV0mrVfel2GxQYIIeX1HxuCUiIiI\niIioCZLMPeTlxgZFuj3nKkAOOiVEG9z4odg3MFKk3iNPNROl96BczAIdAAAgAElEQVRHXF0r1vby\nw+CUiIiIiIioCYLnOy2rKTiT4qtk0GmQGBnoPenxqrC3KEpeL68LTDcTPGovMTglIiIiIiJqkvjI\nFKhVvuBSCC9Kqk+ELTfqykR5ucKhRX5hhNxiqpICqbxZyQxOgzE4JSIiIiIiagK1SoPEqG7yemOp\nvYuHZ8jLAhIkAMcqfCP7xhgCU8qkmJ2KYLWjY3BKRERERETURMFTyjQ2KNLg1ERogyKtKL0L+UW+\nfqcqCah2BOY31aoYnPoxOCUiIiIiImqiJHMgOC2x/QK31xW23FUJgTlQKx06fF8YKa87PYEwTKv2\ngnwYnBIRERERETVRQlQ3qCRfy6dXeFBq+yVsuWmZgSDW7lLjVKUOVQ7fQEnRhkBAq2HLqYzBKRER\nERERURNp1Fp0iuoirxdVhe93umhYHwRPKdMpMpDaq1MDTrfUyjW9/DA4JSIiIiIiagZLE+Y77RRp\ngNkQCLe8QsLeoNTeSocm3Ms6NAanREREREREzRA8KFJJ9XF4vZ6w5QZ1jZWXy+p02F8cAfeZLqaR\nuvCv6cgYnBIRERERETVDYlR3SGdCKbfXhdKagrDl7hzaV152e1XQqT34sSwCABCh88pzn5IPg1Mi\nIiIiIqJm0Gr0iI/sLK8XNZLae1PfrpCCupbGGt2K1N6yWm2r1fFyxOCUiIiIiIiomZoy36lKpULX\n6EAAWuHQKYLTaIO79Sp4GWJwSkRERERE1EzB850WVR+DV4Sfr3Ri78DIvlUODUpqNCix+wJWnVrA\nKzhqrx+DUyIiIiIiomZKik4F4AssXR4nKuynw5ZbNjIDwVPKpJid2FsYJe9nv9MABqdERERERETN\npNeYEGtKktcLG5nv9KoEMwyaQOuoFypFaq9GxejUj8EpERERERHRebBEB+Y7bWxQJADolxwIRkvs\nOhwsNcHpZjpvQwxOiYiIiIiIzoNyUKSjECJ8K+icAWnystOjQqTOjQMlEa1ev8sNg1MiIiIiIqLz\nkGROlZed7lpU1haHLfefg3ohuN9posmlSO0lHwanRERERERE58Goi0K0MUFebyy116TTICFCI6+X\nO3TIL4oKW7YjY3BKRERERER0noJTewsbme8UAEb2TJSXy+u0qKrT4JcqfavW7XLD4JSIiIiIiOg8\nKec7bbzf6b1X95WXvUJCt5g65BcxtTcYg1MiIiIiIqLzZAlqOa2rt8HmKAtbbnhPCzSK6EvCxz/H\no5FYtkNicEpERERERHSeIvQxiNTHyeuNzXcKAFfGG+TlklodKhwaVDm1rVq/ywmDUyIiIiIiogsQ\n3Hp6tvlOp2akyss19WqkRNW2ZrUuOwxOiYiIiIiILkDD+U4bc9/wvgieUiba6G3dil1mGJwSERER\nERFdgOBBkWqcFahxVIYtlxhlRJQ+EIJV1DKlNxiDUyIiIiIiogsQZYiDSWeW18+W2pvTNUZeLq3V\nQQJHRPJjcEpERERERHQBJElq8nyndwxKl5ddXhXUKganfgxOiYiIiIiILlDD+U4bMz0jFZJii9RI\nyY6HwSkREREREdEFskT3kJer60pRW28LW06jUSElOtDX1MMxkWQMTomIiIiIiC5QtDEBBm2EvH62\nUXuvtabIy4ItpzIGp0RERERERBdIkqQmp/YuHd73YlTpssPglIiIiIiIqAU0db7T9ORY6DUXo0aX\nFwanRERERERELSC45bSithAOl73Rsn2SIi9GlS4rDE6JiIiIiIhaQGyEBTq1QV4vrj7WaNmbB1x1\nEWp0eWFwSkRERERE1AJUkgqJ5lR5vfAsqb0LBvUCwDlOgzE4JSIiIiIiaiGW6KYNihRp0KGTiR1P\ngzE4JSIiIiIiaiFJQfOdltecQr3b0WjZ69K7XYwqXTYYnBIREREREbWQ+IjO0Kh0AAABgeLq442W\n/d/pg6FVMyTz45kgIiIiIiJqISqVGonm7vL62VJ7dRoNEiMNje7vaBicEhERERERtaCmzndKSgxO\niYiIiIiIWpAlaL7T0pqTcHvq27A2lw8Gp0RERERERC2oU1RXqCTfSLxe4UGJ7UQb1+jywOCUiIiI\niIioBalVGiREdZXXzzbfKQUwOCUiIiIiImphTZ3vlAIYnBIREREREbWw4PlOS2wn4PG627A2lwcG\np0RERERERC0sIaobJMkXbnm8bpTaTrZxjS59DE6JiIiIiIhamFatQ6fILvI6U3vPjcEpERERERFR\nKwie75SDIp0bg1MiIiIiIqJWEDzfabHtGLzC04a1ufQxOCUiIiIiImoFieZUSJAAAG5PPcprTrVx\njS5tDE6JiIiIiIhagU5jQFxEZ3mdqb1nx+CUiIiIiIiolSRFp8rLHBTp7BicEhERERERtZLg+U6L\nqo5CCG8b1ubSxuCUiIiIiIiolSSZU+Xleo8DFfbCtqvMJY7BKRERERERUSsxaCMQY0qS15na2zgG\np0RERERERK3Iopjv9FjbVeQSx+CUiIiIiIioFSUFzXdaVH0UQog2rM2li8EpERERERFRK0oKajl1\nuGpQVVfShrW5dDE4JSIiIiIiakUmnRlmQyd5nf1Ow2NwSkRERERE1MoU851WMTgNh8EpERERERFR\nK7MEzXdaWHWE/U7DYHBKRERERETUyoIHRaqtr0aNs7wNa3NpYnBKRERERETUyiINsYjQx8jrhUzt\nDcHglIiIiIiI6CKwBE8pw+A0BINTIiIiIiKiiyB4ShmO2BuKwSkREREREdFFEDwoks1RDruzqg1r\nc+lhcEpERERERHQRRBniYdRGyetsPVVicEpERERERHQRSJKkSO3loEhKDE6JiIiIiIguEks0B0Vq\nDINTIiIiIiKiiyR4vtOqumII4W3D2lxaGJwSERERERFdJDGmROg1Jnnd4/W0YW0uLQxOiYiIiIiI\nLhJJUiHJnCqve4W77SpziWFwSkREREREdBEFD4rEltMABqdEREREREQXUfB8p17B4NTvogWnS5cu\nhdVqDbtvw4YNmDhxIjIzMzF58mRs3br1gsoRERERERFdqmIjkqFV69u6GpecixKcfvTRR/jggw8g\nSVLIvhdffBFr1qxB7969sXr1algsFixfvhzvv//+eZUjIiIiIiK6lKkkFRKD+p2ST6sHp5WVlfjV\nr34FnU4Xss9ms2H9+vW44YYb8Pvf/x4zZszA888/j5ycHKxduxZCiGaVIyIiIiIiuhwEz3dKPq0e\nnK5ZswZxcXEYN25cyL68vDw4HA7Mnj1b3iZJEm6++WacPn0a3333XbPKERERERERXQ6C5zsln1YN\nTrdt24YPP/wQTz75ZNiW0x9++AEAkJ6ertienp4OIQT27dvXrHJERERERESXg/jIFKhV2rauxiWl\n1YLTqqoqPP7447jtttuQkZERtkxRURHMZjP0emVn4ISEBADA6dOnm1WOiIiIiIjocqBWaZAY1b2t\nq3FJ0TSncGlp6Vn3m0wmmEwmAL503oiICCxdurTR8na7HUajMWS7wWAAANTW1jarHBERERER0eXC\nEn0FqmxtXYtLR7OC06uvvvqs+++++24sWbIEeXl5eP/99/Hqq6+GTecNFm4E33D7mlquqVwuFywW\nC4QQyM/Pb/brqWmEEHjnnXd4nlsRz/HFwfPc+niOLw6e59bHc3xx8DxfHDzPrUsSCTDqnHC5XG1d\nlUtCs4LTX//612fdn56ejurqajz++OOYNGkSevbsiYqKCggh4HQ6AQAVFRXQ6/VyK6vD4Qg5jn9b\nZGQkADS5XHNIkgStVgutlnnerUmn06FHjx7nLkjnjef44uB5bn08xxcHz3Pr4zm+OHieLw6e59Yl\nSRLUKvV5Nba1R80KTqdNm3bOMt988w1KS0uxZcsW/POf/wzZP2TIEEyZMgVPPfUUkpOTUVVVBZfL\npQgSi4uLAQBJSUkA0ORyzZGdnd3s1xAREREREVHraFZw2hS9e/fGSy+9FLL9hRdewM6dO/Hyyy/L\nAxn5R9s9cOCAYtCk/fv3Q5Ik9OvXr1nliIiIiIiI6PLU4qP1RkVFYciQISE/iYmJAIDBgwejZ8+e\nAIBrrrkGOp0Or732mvx6IQQ2bNiAzp07Iysrq1nliIiIiIiI6PLU4i2nzRETE4MFCxbgT3/6Ezwe\nDwYPHowPP/wQe/bswR/+8Ac597qp5YiIiIiIiOjydFGD03BB5L333ouIiAi8/vrryMvLQ2pqKp55\n5hmMGzfuvMoRERERERHR5UcSQoi2rgQRERERERF1bC3e55SIiIiIiIiouRicEhERERERUZtjcEpE\nRERERERtjsEpERERERERtTkGp0RERERERNTmGJwSERERERFRm2NwSkRERERERG2u3QWnX3/9NaxW\nKwYPHgy3293W1WkzDz74IKxWq+KnX79+uOaaa/DAAw/gp59+UpSfO3cuxowZ0+zfc76vOxu73Y7y\n8vIWPWZLOdf1NXr0aNx6662t8rtb+tgNj9caf8tnn30WVqsVu3btCru/oKAAVqsVDz74YIv+3pZy\nKVyLmzdvPus5vFCX6t+o4fX44IMPonfv3he1DhdTfX09nn/+edx4443Izs7GgAEDMHXqVLzwwguo\nr6+/qHVpjWdBS2jJc9TS77EtztmqVatgtVrPWW79+vXo3bs3Tp06JW8L9x0h3E9z7vvW/PxrLRda\n57NdkytXrlQ8s9avXy+f1/379zd6zHvuuQdWqzWkXkII/O1vf8PMmTMxYMAAZGdnY9KkSXj66adR\nU1Nzzrr6P0sa/vTt2xdDhw7FwoULsW/fvvM+F+frQu6dcN/JLta9GO73NPzOEO7ea03+z+tz/fjr\n9M0338BqteKdd965KPVrKk1bV6ClbdmyBSaTCVVVVfj4448xfvz4tq5Sm5EkCQ899BBiYmIAAHV1\ndThx4gQ2bdqEDz/8EC+88AIGDhwIwPcwrK2tbcvqAgB++OEH3H333Vi3bh3i4uLaujoh2vP11RrX\ngCRJkCSpRY95sVwq12JOTg7Wrl2Lnj17tsrxL5e/0axZszB06NC2rkar8Hg8uOOOO7B3717ceOON\nmDVrFjweD3bv3o1169Zh+/bt+Otf/wqtVtvWVW0zPEehmnrvjh8/Ht27d1c8xxreT7t378bGjRsx\nY8YM5OTkyNu7devWspVuR851TaalpeGpp54KeZ0kSfj444+Rnp4ess/hcGDnzp1h/64rVqzABx98\ngAkTJuCGG26ASqXCvn378OKLL+Kjjz7CG2+8gejo6LPWWZKkkL9xfX09Dh48iDfeeAO7du3CP/7x\nD3Tt2vU8zsj5uZDvHm35naxhvcN9Zwh377WmuLg4rF27VrHtN7/5jRwPCCEUZfV6PdauXYvs7OyL\nUr+malfBaX19PT766CPcdNNN2LJlC95+++12FTycjzFjxqBz586KbXPnzsXUqVOxdOlSbNu2DUaj\nEUOGDGmjGiodPnwYJSUlbV2NsNr79XWpXAOXikvlWuzatetF/aJwqcrMzERmZmZbV6NVbN26Fbt2\n7cL69esxduxYefucOXPw4osvYu3atXjrrbcwa9asNqxl2+I5On+9evVCr169FNsa3k9utxsbN25E\ndnY2Jk+efLGreFlqyjXpcDhCXtelSxfk5eXh3nvvDdn32Wefwe12w2w2K7bv2bMH7777Lh588EHc\ndtttin3Dhw/H0qVL8cILL+D+++8/Z70b+xv3798fy5Ytw8svv4xHH330nMdpKef73aOtv5M1rHe4\n7wzh7r3WZDQaQ/62Tz/9NCRJwqRJk0LKGwyGS/J+b1dpvZ988glsNhsGDRqEq6++Gp9//jnKysra\nulqXnKSkJKxcuRJlZWV466232ro6CsH/1bnU8PrqWC7la5Hal3//+9+QJAnDhg0L2XfzzTdDq9Xi\n+++/b4OaXTp4juhScz7XpCRJGDNmDA4ePIjTp0+HvO5f//oXcnNzERERodi+Z8+eRn/XhAkTkJSU\nhH//+98X9H4mTpwIo9F4wce5WC6172T8ztBy2lVwumXLFkiShIEDB2LcuHFwu934xz/+EVLuk08+\nwfTp05GdnY2xY8fi9ddfx+rVqzF69GhFuZ9//hmLFi3CwIEDkZWVhdmzZ+Pzzz+/WG+nVU2YMAE6\nnQ6fffYZgPC58++//z7mzp2LnJwc9O3bF2PGjMHatWvD9u3Zvn07Jk2ahIyMDNxwww3YsmVLSJlz\nnc/169fjoYceClufoqIiPPDAAxgyZAgyMjIwZcqUsL9j/fr1mDBhAjIyMjBs2DA88MADKCwsPL+T\n1EBTr6+Gvv/+eyxYsAADBw7EoEGDsHDhQhw+fFhRZvfu3bj99tuRnZ2N7Oxs3Hbbbdi9e3ej9Zg0\naRL69euHa6+9Fm+88UZImeYcz6/hOa+vr8eTTz6JsWPHyv2V16xZg+rqarnMqlWrMHnyZHz33XeY\nNWsWMjMzMXbsWLzzzjtwu93YuXMnvF4vFi5ciGXLlqGqqqrR3//mm2/CarXi008/Ddk3Y8YMTJ8+\nXa7nvHnzsH37dlx//fXIzMzElClT8NFHH4W8rin38Ny5czF//nz84Q9/QHZ2NoYOHYrFixdf0LW4\natUqTJw4Efn5+ZgzZw6ysrIwbNgw/PrXvw65f851zYbrc+pwOLBu3TqMHj0affv2xejRo7Fu3TrF\nf+nffvttWK1WHDx4EPfffz9yc3ORnZ2NRYsWoaCgoNG/Q1M05dngP6+fffYZ/uM//gMZGRm45ppr\nsH79+pDj7dy5E7NmzUJ2djbGjx+PTZs2hZQJ17+uuLgYDz30EK6++mpkZ2dj2rRp2LZtm6LMl19+\niQULFmDQoEHo27cvRowYgUcffRQ2m01x7PHjx+Pf//43pk6diszMTEycODHsvdUaIiIiIITAm2++\nGbLPaDRiz549cnrg3LlzcddddyEvLw833ngjMjIyMGnSJHz66aew2+149NFHkZubi6FDh+Kxxx4L\nud7O59lQW1uL6dOno3///vjuu+/k7edzfw0bNgw//vhjq54jADhy5AiWLFmCwYMHIycnB3Pnzg37\nPr/44gv5+hw1ahT+/Oc/h5Q5fPgw7rnnHgwcOBCZmZmYOXNmyHUWTnPuk4bnpynPX799+/Zh7ty5\nyMzMxNVXX42nnnpK8Tv8/cr9/d6effZZ9OnTB0ePHsXcuXORlZWF3/72txBCwOv1hhy/sX6ZjW3f\ntGkTxo4di4yMDMyYMSPs96Y9e/Zg3rx56N+/P/r37y+nxzY8/iOPPILVq1cjMzMT11xzDSorKwGc\n+3O1uXUO1pR7pLq6Gt988w28Xi8GDBiAcePG4fe//7183o1GIyZOnIjNmzcrXieEwAcffACv14vr\nr79e8bx3u93YsWMHxo0bF1In//W/cePGsEFQXl4e/u///u+s76sp9Hp9yLam3ufn+1xq+Bnb1M8O\n/+fu888/j23btsHlcuGJJ56Q90+ZMgVTpkxRvIfMzEykpaVhzJgxivdw4403Ijc3V74X+/btiz59\n+iArKwsZGRmYOHEi/vKXv4S8Z3+9G/v+Gu7ey8jIwPHjx7Fw4UL0798fubm5WLVqlXxt+xUXF2PF\nihUYMmQIcnJy8MADDyAvL69Fx6Bo2OfUv/7ll19i9erVyM3NRU5ODh588EHU1dXhk08+wU033YSs\nrCzcdNNN+OqrrxTHq6+vx9NPP40xY8agb9++GDt2LP74xz/C5XI1q17tJjitqanBJ598gqysLMTF\nxWHEiBHQ6XQhD4bt27fjnnvugcfjwfLlyzFhwgT87ne/w7Zt2xQ5/ocOHcLMmTNx5MgR3HXXXVi2\nbBk8Hg/uvPNOvP/++xf77bU4nU6Hbt264eDBg2H3//3vf8eyZctgNpuxYsUKrFq1CikpKXjxxRfx\nxz/+UVG2pKRE/hKwcuVKGAwGrFixQtHBuinnc/z48ZgxYwYA4K677pJv9OLiYkybNg1fffUVbrvt\nNqxcuRJxcXFYsWIFXnrpJfl3/PnPf8b//M//YOTIkXjssccwY8YMbNu2DXfccccF/0erqddXQ7t3\n78acOXNw5MgRLFiwAIsWLcKPP/6IuXPnyg+rvLw83HrrrSgsLMSiRYuwaNEiFBYW4vbbb8f27dsV\nx8vPz8eTTz6JCRMm4MEHH4Rer8evfvUr5OXlyWWac7yzWbNmDTZt2oRJkybh8ccfx4QJE7Bx40Ys\nW7ZMLiNJEoqLi3H33XcjJycHq1atgkajwUMPPYSFCxeioKBADui3bt2KNWvWoKKiQv4JDlYnTJgA\njUYTcn+dPHkSe/fuVaSe/PTTT1iyZAkGDRqEFStWQKVSYfHixXjvvffkMs25h7/99lu8//77WLly\nJaZOnYpFixZd0LUoSRLKy8sxf/589OzZE6tXr8aAAQPw2muvKe6fpl6zwc8ml8uF22+/HS+++CKG\nDRuGhx9+GIMGDcJf/vIX3HHHHfB4PIrX3HPPPbDZbFi+fDlmz56NHTt2KP6GfjabTfG3Cfc3Apr3\nbDh8+DCWLVuGQYMG4ZFHHkG3bt2wfv16/O1vf5PL7Ny5E3feeSfsdjuWLl2KiRMn4sknnwwZmKNh\n/7qqqipMnz4d77//PqZMmYJVq1bBaDTivvvuw8cffwwA+Pzzz3HHHXfA4XBgyZIlePjhh5GZmYmN\nGzcq0tYkSUJlZSXmz5+PK664Ag888ACSkpLw+OOP4/nnnw85Vy3thhtugFarxW9/+1tMnjwZzzzz\nDL755hv5C5xGo+yB88MPP+Chhx7CtddeixUrVqCiogJLlizBnXfeiVOnTuH+++/H0KFD8eabbyq+\nUJ3Ps8HlcsnPreeeew79+/cHcP7315QpU3DllVe26jk6fvw4pk+fjm+++QZz587F8uXLUV1djf/8\nz/9UXFclJSVYvHgxhgwZgtWrVyMlJQXPPPMMXn31VbnM3r17MXPmTOTn5+OOO+7A8uXL4XK5cO+9\n92LDhg2N1rc590m489OU5y/gC3Zuv/12XHXVVVi9ejUyMjLwyiuvYN26dXKZhveOJEkQQmDevHkw\nGo1YsWIFunTpAiHEBQ+M4v+Muv7667F8+XLYbDYsXLgQX375pVzmiy++wNy5c+V7/p577sHp06cx\nZ84cfPvtt4rjvfvuuzh8+DBWr16N6dOnIyYmpkmfq+erqffIkiVLUFRUBLVaDa/Xi8rKSvzv//4v\nFi9eLF+TarVacd537doFIQSGDRuGzp07IzY2VvG8//rrr1FTU6NIEfYbN24coqOj8eqrr2Ls2LH4\n3e9+h88++wx1dXUAQp8R5yM/Px+VlZWKvrDNuc/P97kUzrk+O2pqarB9+3YIIVBfX4/77rsParUa\nH3zwgXyPjxgxAocOHcK3334rv4du3bpBkiTYbDb5PZSWluLw4cOIjY3Ft99+iw0bNsDtdqNHjx5Y\nvXo1li9fDoPBgHXr1ik+u4KNGzcu7HeGcPee1+vFrbfeisjISKxatQrXXnst3nnnHfzqV7+Sy9nt\ndtxyyy3Ytm0bZs2ahfvuuw8HDhzAQw891OJjRIQ73qpVq1BYWIj/9//+H0aOHInNmzdj0aJFWLly\nJcaPH4/7778fpaWlWLJkiTwYl9frxZ133olXXnkFY8eOxSOPPIIhQ4bgueeew+LFi5tXKdFObNq0\nSaSlpYmXX35Z3rZw4UJhtVpFfn6+vG3s2LFiwoQJwul0ytvy8vJEWlqaGD16tLxtzpw5Yvz48cLh\ncMjbPB6PuOWWW8SwYcOEy+Vq3Td0gVatWiWsVqsoKChotMzs2bNFRkaGEML3foPf/8SJE8Xs2bMV\n5T0ejxg5cqS44YYb5G1z5swRVqtV/O1vf5O3OZ1OMXHiRDFs2DDh8Xjkck05n2+//bawWq3im2++\nkcutXLlSDBo0SJSWlirqs3z5cpGRkSHKysqEEEJcd911YuHChYoyb775prjpppvEiRMnznK2zq2p\n19eoUaPE3Llz5fVp06aJ4cOHi6qqKnnb0aNHRXp6uli7dq1wu91ixIgRYtSoUcJut8tlqqurxYgR\nI8TIkSOF2+2Wj927d29x4MABuVxBQYGwWq1i5cqVQgjR7OMF17XhNZCZmSmeeOIJxXl45plnxLRp\n00Rtba0QInCdvf7663KZHTt2yPfTM888I9LS0kRaWpro1auX6NWrl7zu/7FarWLVqlVCCCHuuusu\nkZubq7i/nnvuOZGeni5KSkrkelqtVvHqq6/KZRwOhxg/frwYMWKE4v005ZrzH2/v3r2K93oh16L/\nvLz22muKctddd52ijk25ZhvWY8OGDSItLU3x/oUQ4oUXXhBpaWliw4YN8uvS0tLE4sWLFeUee+wx\nYbVaxfHjx4UQQjz77LMhf5Oz/Y2a+2zYsWOHvM3pdIrc3FzF66dMmRJyvX799dchz2T/OfX7r//6\nL2G1WsWePXsUxx83bpyYPn26EEKI+fPnizFjxsjXvN/MmTPFgAEDFMdOS0sTTz31lLzN6/WKOXPm\niKysLFFdXS1a244dO8SwYcOE1WqVz3tWVpZYvny5OHr0qFwu3Hl9/fXXRVpampg5c6bimCNHjpTP\ndXOeDf5ngdfrFffdd5/o16+f+PTTTxXHvtD7qzXP0ZIlS0RWVpbiuV9RUSEGDhwoli5dqqjXtm3b\n5DI1NTViwIABYs6cOfK26dOni/79+4uioiJ5m9PpFFOmTBFZWVmioqJCcc78mnufNDw/zXn+vvLK\nK3IZr9crxo8fL0aNGiVve/bZZxXfB/z3/H333SeXefvtt+Xn888//6z4vQ0/KxrbPmrUKGG1WhXX\nSmVlpcjNzRVTp06V6zdmzBhxyy23KI5VV1cnxo8fL6ZMmaI4XvCz3+9cn6vNrbN/van3SFlZmUhL\nSxMvvfSS4pr0f8b5r8l7771X8cwaPHiw6NWrlygoKBDPPvus6NOnj3jllVfk5/3jjz8u38Ph6v/9\n99+LcePGKa7/vn37irvuuqtJ95f/M+G1114T5eXl8s/p05zHLBgAABwwSURBVKfFv/71LzFu3DiR\nlZUljh07Jr+mufd5c59L/tc2/P59rs+OTZs2iV69eomBAwfKf6uFCxfK3zVGjx4td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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the visualizer\n", + "visualizer = visualizer = ParallelCoordinates(classes=classes, features=numerical_features)\n", + "\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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Classifier Evaluation \n", + "\n", + "Classification models attempt to predict a target in a discrete space, that is assign an instance of dependent variables one or more categories. Classification score visualizers display the differences between classes as well as a number of classifier-specific visual evaluations. We currently have implemented three classifier evaluations:\n", + "\n", + "- ClassificationReport: Presents the confusion matrix of the classifier as a heatmap \n", + "- ROCAUC: Presents the graph of receiver operating characteristics along with area under the curve\n", + "- ClassBalance: Displays the difference between the class balances and support \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." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "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", + "\n", + "from yellowbrick.classifier import ClassificationReport, ROCAUC, ClassBalance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Classification Report \n", + "\n", + "The classification report visualizer displays the precision, recall, and F1 scores for the model. Integrates numerical scores as well color-coded heatmap in order for easy interpretation and detection. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# 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": 17, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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rrzAYDDzzzDMULFgQHx8fTp8+bTO21GQyMWXKFOu9kvWasczE6aiykJWvry+t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\nNm3KihUrCA8PZ+7cuRw4cACAi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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the classification model and visualizer \n", + "bayes = GaussianNB()\n", + "visualizer = ClassificationReport(bayes, classes=classes)\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": [ + "### ROCAUC \n", + "\n", + "Plot the ROC to visualize the tradeoff between the classifier's sensitivity and specificity." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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bIWNiYhQcHJzi7eLi4iQpVdsCqcV1B1vh2oMtcN3BFrju8LQsWLtFc7+ZLOP2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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the classification model and visualizer \n", + "logistic = LogisticRegression()\n", + "visualizer = ROCAUC(logistic)\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": [ + "### ClassBalance \n", + "\n", + "Class balance chart that shows the support for each class in the fitted classification model." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the classification model and visualizer \n", + "forest = RandomForestClassifier()\n", + "visualizer = ClassBalance(forest, classes=classes)\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" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [conda root]", + "language": "python", + "name": "conda-root-py" + }, + "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": 0 +} diff --git a/examples/ndanielsen/ScatterViz Example (Iris Dataset).ipynb b/examples/ndanielsen/ScatterViz Example (Iris Dataset).ipynb new file mode 100644 index 000000000..870a51cd3 --- /dev/null +++ b/examples/ndanielsen/ScatterViz Example (Iris Dataset).ipynb @@ -0,0 +1,180 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/var/pyenv/versions/3.5.2/envs/yb-dev/lib/python3.5/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" + ] + } + ], + "source": [ + "import yellowbrick as yb \n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "plt.rcParams['figure.figsize'] = (12, 8)\n", + "\n", + "from yellowbrick.features.rankd import Rank2D\n", + "from yellowbrick.features.radviz import RadViz\n", + "from yellowbrick.features.pcoords import ParallelCoordinates\n", + "from yellowbrick.features.scatter import ScatterViz" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n", + "col_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']\n", + "iris = pd.read_csv(url, header=None, names=col_names)\n", + "iris['species_num'] = iris.species.map({'Iris-setosa':0, 'Iris-versicolor':1, 'Iris-virginica':2})" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "all_features = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species_num']\n", + "features = ['petal_length', 'sepal_width']\n", + "classes = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']\n", + "\n", + "# Extract the numpy arrays from the data frame\n", + "X = iris[all_features]\n", + "y = iris.species_num.as_matrix()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from yellowbrick.style import palettes\n", + "from matplotlib.colors import Colormap" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "colors = palettes.PALETTES['pastel']" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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+GmNcD/wW2C/FOCRJkqRBS2WpRgjhk8DLMcZfhhDO6rF7IrCq6PVqYFKSdltb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\nnUs5JU2cfxdCWABcQ774yTH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"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/Yellowbrick in the Flower Garden.ipynb b/examples/ndanielsen/Yellowbrick in the Flower Garden.ipynb new file mode 100755 index 000000000..688cef8a3 --- /dev/null +++ b/examples/ndanielsen/Yellowbrick in the Flower Garden.ipynb @@ -0,0 +1,840 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Using Yellow Brick to Explore and Model the Famous Iris Dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exploration Notebook by:\n", + "Nathan Danielsen\n", + "Prema Damodaran" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Review of the iris dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "# read the iris data into a DataFrame\n", + "import pandas as pd\n", + "url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n", + "col_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']\n", + "iris = pd.read_csv(url, header=None, names=col_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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45.03.61.40.2Iris-setosa
\n", + "
" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "0 5.1 3.5 1.4 0.2 Iris-setosa\n", + "1 4.9 3.0 1.4 0.2 Iris-setosa\n", + "2 4.7 3.2 1.3 0.2 Iris-setosa\n", + "3 4.6 3.1 1.5 0.2 Iris-setosa\n", + "4 5.0 3.6 1.4 0.2 Iris-setosa" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Terminology\n", + "\n", + "- **150 observations** (n=150): each observation is one iris flower\n", + "- **4 features** (p=4): sepal length, sepal width, petal length, and petal width\n", + "- **Response**: iris species\n", + "- **Classification problem** since response is categorical" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lightly Preprocess the Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# map each iris species to a number\n", + "iris['species_num'] = iris.species.map({'Iris-setosa':0, 'Iris-versicolor':1, 'Iris-virginica':2})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import the Good Stuff" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import yellowbrick as yb \n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "plt.rcParams['figure.figsize'] = (12, 8)\n", + "\n", + "from yellowbrick.features.rankd import Rank2D\n", + "from yellowbrick.features.radviz import RadViz\n", + "from yellowbrick.features.pcoords import ParallelCoordinates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Feature Exploration with RadViz" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Specify the features of interest and the classes of the target\n", + "\n", + "features = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\n", + "classes = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']\n", + "\n", + "# Extract the numpy arrays from the data frame\n", + "X = iris[features].as_matrix()\n", + "y = iris.species_num.as_matrix()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ExLBq1aoG2w8ePJiwsDCuuOKK6jQHPz8/1q5dy4oVK3jppZfw8fFhypQp3H77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\nlaLa12g8/eBRV8IlSYNjcJakQeq7Wsbh1ka27Pr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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualizer = RadViz(classes=classes, features=features)\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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Setosas tend to have the largest septal-width. This can could be a great predictor.\n", + "\n", + "Then, let's remove setosa from the training set and see fi we can find any differentiation between veriscolor and virginica." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Remove Setosa from the training set" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Specify the features of interest and the classes of the target\n", + "iris_subset = iris[iris.species_num!=0]\n", + "features = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\n", + "classes = ['Iris-setosa','Iris-versicolor', 'Iris-virginica'] # but have to leave in more than two classes \n", + "\n", + "# Extract the numpy arrays from the data frame\n", + "X = iris_subset[features].as_matrix()\n", + "y = iris_subset.species_num.as_matrix()\n", + "assert y.shape[0] == X.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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p7E1PJst67Mc6zlHdGdStZes4n0/88T1gt9vZt28fQO+hQ4emVd+vGWcRERGR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\nzXWbobk5qe3k006n+uSP07D80YKvtlFWU8PcBx4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualizer = RadViz(classes=classes, features=features)\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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Try the Covariance Visualizer" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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kg5IkaVq5Atg4Ig6nDFktAGZTEie/FREHA38LvDozf9q1jUHglIi4EbgKOAB4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\nkiS1gpkeSZIaxkxPPTM9kiS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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Specify the features of interest and the classes of the target\n", + "\n", + "features = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\n", + "classes = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']\n", + "\n", + "# Extract the numpy arrays from the data frame\n", + "X = iris[features].as_matrix()\n", + "y = iris.species_num.as_matrix()\n", + "\n", + "visualizer = Rank2D(features=features, algorithm='covariance')\n", + "\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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This covariance chart is not intereptatble as they don't have labels. Also there shouldn't be half numbers in labels.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## More Feature Exploration: Look at Parallel Coodinates for all Species" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "features = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\n", + "classes = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']\n", + "\n", + "# Extract the numpy arrays from the data frame\n", + "X = iris[features].as_matrix()\n", + "y = iris.species_num.values\n", + "assert y.shape[0] == X.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Q18Z/mm1yhM7CAo9ZP1JsaAAAMTu2Edy2JWn37r2Q/gshhBBCvAjdu3encuXK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wGLvydYrQ6Wkf80TCFeZE/vS9vbBlI6eiLaI0nX1I/LTTdTfSEJsbIGq04NbRNQHT3b5LmuN7aj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\n8rjlNmXcN7VpR/CzU0l5dqL67+RwXkNcPO7db6Dg0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3aiDw7h9IA7qU2xJ1mbu3ajsl5z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5ehZHguOdjk0M8GB0U7W+f4Qvxe+8TvaC+WCzofL1o9PXqy+qman2xHGSbxrrZIFnvP1uDtw3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WsBkWFJ08iIGcXGI8soqgtckA6MPY9GWzWlDYHZpsSwHEalzWZDwWdUNR8L+BwgzBDjV0Sr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\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Y774d34sbUuQuKmD+1r9ytnkLuS/uZmXTII+89HWEx/dz8/Y3M753qgD9112BIgm43zie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\nh+cDiFYAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualizer = ParallelCoordinates(classes=classes, features=features)\n", + "\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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This clearly demonstrates the separation between features - especially petal_length and petal_width. One concern is that this demonstraction data might be obsured by the scaling of the features and add noise to the intepretation. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Feature Exploration: ParallelCoordinates with Scaling" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn import preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "features = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\n", + "classes = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']\n", + "\n", + "# Extract the numpy arrays from the data frame\n", + "X = iris[features].as_matrix()\n", + "X_scaled = preprocessing.scale(X)\n", + "y = iris.species_num.values\n", + "assert y.shape[0] == X.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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vvvqKGjVqpE4dStvXOXPmoKoqiYmJ/PTTTwCp030edRLkhXgETdul8v5foQB0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\nuqs5+sw4Inzd7Z7z2lMJPPG9aAk7rVcUXzzUr07pTKnWQL/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vz/VMHlt/iJxycdVLrdfR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\nSf1b1dBai/SiSoZ+uIaCSiM+OjVrZo+gV0zrptDe23qS+37b1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y+O69amJl6j1cacH3ex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JWu20yRaCioWivAz2tIQJiwoel53PTRtKVBd4ZvMRAr4AdjVCjgLBNh/WeIQZWXY8QhwtEET1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Y2okZNLI0YSaHXWXNwTJ9L7+F/S1/m4v5yvX/IFBsrHML3Mz5amHuKagSse4eb9L3PN9hfS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\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kN6VeU1/K72+cS74jW+Eka33fBEz0hmJ8b81+frXpOOd4GFHXdYYvbfkrk7q0xEKVZexL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XZ3NB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FHnczdd076RiuRXR5yPv537HvaAQgYjfj/O/vMPeWzzE03sXrtX9kPKDq7UszF7Oi/IPoz0Iv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\nG2jpOceh6pdQ1SA6Rc+qBdefl74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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualizer = ParallelCoordinates(classes=classes, features=features)\n", + "\n", + "visualizer.fit(X_scaled, y) # Fit the data to the visualizer\n", + "visualizer.transform(X) # Transform the data\n", + "visualizer.poof() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The scaled dataset makes it easier to see the separation between classes for each of the features.\n", + "\n", + "*TODO - Add scaling option to PararalCordinates and potentially other visualizers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Now that we have some features, Let's Evaluate Classifiers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the feature selection phase, we determined that petal_length and petal_width seem to have the best separation." + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Classifier Evaluation Imports\n", + "\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.cross_validation import train_test_split\n", + "\n", + "from yellowbrick.classifier import ClassificationReport, ClassBalance" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "features = ['petal_length', 'petal_width']\n", + "classes = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']\n", + "\n", + "# Extract the numpy arrays from the data frame\n", + "X = iris[features].as_matrix()\n", + "y = iris.species_num.as_matrix()\n", + "assert y.shape[0] == X.shape[0]\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n", + "assert y_train.shape[0] == X_train.shape[0]\n", + "assert y_test.shape[0] == X_test.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "IndexError", + "evalue": "list index out of range", + "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 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mvisualizer\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;31m# Fit the training data to the visualizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mvisualizer\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;31m# Evaluate the model on the test data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Draw/show/poof the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/var/pyenv/versions/3.5.2/envs/yellowbrick/lib/python3.5/site-packages/yellowbrick/classifier.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m,\u001b[0m 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the classification model and visualizer\n", + "bayes = GaussianNB()\n", + "visualizer = ClassificationReport(bayes, classes=classes)\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": 96, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "ClassificationReport(ax=,\n", + " classes=None, model=None)" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "visualizer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: There seems to be some sort of bug in the draw/ fit methods. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's try a naive bayes as the other didn't wrok" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Classifier Evaluation Imports\n", + "\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.cross_validation import train_test_split\n", + "\n", + "from yellowbrick.classifier import ClassificationReport, ClassBalance" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "features = ['petal_length', 'petal_width']\n", + "classes = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']\n", + "\n", + "# Extract the numpy arrays from the data frame\n", + "X = iris[features].as_matrix()\n", + "y = iris.species_num.as_matrix()\n", + "assert y.shape[0] == X.shape[0]\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n", + "assert y_train.shape[0] == X_train.shape[0]\n", + "assert y_test.shape[0] == X_test.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/var/pyenv/versions/3.5.2/envs/yellowbrick/lib/python3.5/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n", + " 'precision', 'predicted', average, warn_for)\n" + ] + }, + { + "ename": "IndexError", + "evalue": "list index out of range", + "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 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mvisualizer\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;31m# Fit the training data to the visualizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mvisualizer\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;31m# Evaluate the model on the test data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Draw/show/poof the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/var/pyenv/versions/3.5.2/envs/yellowbrick/lib/python3.5/site-packages/yellowbrick/classifier.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 134\u001b[0m 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"\u001b[0;32m/usr/local/var/pyenv/versions/3.5.2/envs/yellowbrick/lib/python3.5/site-packages/yellowbrick/classifier.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, y, y_pred)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m)\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 159\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m 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\u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'nearest'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mIndexError\u001b[0m: list index out of range" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the classification model and visualizer\n", + "bayes = MultinomialNB()\n", + "visualizer = ClassificationReport(bayes)# classes=classes)\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": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Selection: Random Forest Classification" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "features = ['petal_length', 'petal_width']\n", + "classes = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']\n", + "\n", + "# Extract the numpy arrays from the data frame\n", + "X = iris[features].as_matrix()\n", + "y = iris.species_num.as_matrix()\n", + "assert y.shape[0] == X.shape[0]\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n", + "assert y_train.shape[0] == X_train.shape[0]\n", + "assert y_test.shape[0] == X_test.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "test = pd.DataFrame(y_test, columns=['species'])" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1 13\n", + "0 11\n", + "2 6\n", + "Name: species, dtype: int64" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test.species.value_counts() # The test train split provides unbalanced classes" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Z9nAUJdyeR7nE/s5snktLCYCnUg7U5zX12CMzv7ucan+R0kv4U8qB/KWUYHFws01JCTDb\nUHplDwW+Rhmf/LjoeIxZczf9iyk9c2cAJ1B6mXcB1mjv6Z1MZo5SerouBj5FOdG4jPJUhrsy80bK\nzURXUx719sXmpU8bv+t/it7abNNrKUFjR+DVmfnRaZQxqSxfqvBJ4BlRHsc1lfd7KuV+kjIMZnfK\nMJZbaHu6R2beQQnf3wQ+SLmZbQ1g+2UEv15boX2bmcdSPs+votw492bK48H2beb/gXJyuTFlu0+i\nnBy9pK2Y91OeSHJKW7n7NnXZitJWTqEM5dklMw9eRl2+R3mE3cspNyv+N+UmtZNp3qvM/CJlzP7L\nmvoeSvmGu/c2xSyv7U5Zlud6P4Nyw+PxlLa4GfDczPzhdMuTajE0Nja2/KUkSZKkVZA9w5IkSaqW\nYViSJEnVMgxLkiSpWoZhSZIkVWtgj1a75JJLRijP+7yau78OU5IkSeqlWZRvYfzFVltt1eqcOcjn\nDD+O8p3skiRJUr89hfJ88nsYZBi+GmDzzTdneHh4gNVY9bVaLRYtWsS8efMYGVnWt5FKWhnYnqVV\ng2155oyOjvK73/0OmuzZaZBh+E6A4eFhPwR9Nv4safe1tPKzPUurBtvyQEw4LNcb6CRJklQtw7Ak\nSZKqNa0wHBFbR8RVE0xfLSK+HxEf7F3VJEmSpP6a0pjhiBgC9gI+BNwxwSJvBbYFLuld1SRJkqT+\nmmrP8EHA/sDhnTMi4lGUoPzVHtZLkiRJ6rupPk3iROAIYLv2iRExAnwO2Lv5N22tVuvfd1SqP1qt\n1j1+Slp52Z6lVYNteeaMjo5OOn9KYTgzrwaIiM5ZRwLnZeaFEbFCYXjRokUr8jKtgMWLFw+6CpJ6\nxPYsrRpsy4O3ws8ZjogdgB2ArbupwLx58/zSjT5rtVosXryYuXPn+ixDaSVne5ZWDbblmTM6Ojpp\n52s3X7qxC/BQ4O9Nj/EawF0RsUVmPmeqhYyMjPghmCEjIyPMmTNn0NWQ1AO2Z2nVYFvuv6GhoUnn\nr3AYzsx9gH3Gf4+Ik4FrM/NtK1qmJEmSNJP80g1JkiRVa1o9w5l5AbDeMubt2YP6SJIkSTPGnmFJ\nkiRVyzAsSZKkahmGJUmSVC3DsCRJkqplGJYkSVK1DMOSJEmqlmFYkiRJ1TIMS5IkqVqGYUmSJFXL\nMCxJkqRqGYYlSZJULcOwJEmSqmUYliRJUrUMw5IkSaqWYViSJEnVMgxLkiSpWoZhSZIkVcswLEmS\npGoZhiVJklQtw7AkSZKqZRiWJElStQzDkiRJqpZhWJIkSdUyDEuSJKlaswddgUGY9dbPD7oKA3L5\noCswo+48drdBV0GSJN3L2TMsSZKkahmGJUmSVC3DsCRJkqplGJYkSVK1DMOSJEmqlmFYkiRJ1TIM\nS5IkqVqGYUmSJFXLMCxJkqRqGYYlSZJULcOwJEmSqmUYliRJUrUMw5IkSaqWYViSJEnVMgxLkiSp\nWoZhSZIkVcswLEmSpGoZhiVJklQtw7AkSZKqZRiWJElStQzDkiRJqpZhWJIkSdUyDEuSJKlahmFJ\nkiRVyzAsSZKkahmGJUmSVK3Z01k4IrYGzsrMjZrfNwGOB54C3A6cDrwtM1u9rqgkSZLUa1PqGY6I\noYh4NfBtYLht1inAX4GNgUcDjwMO7nUlJUmSpH6Y6jCJg4D9gcPHJ0TEMPAv4LDMXJKZfwNOBZ7Y\n81pKkiRJfTDVYRInAkcA241PyMxR4Nkdyz0XuGw6FWi1WoyNjU3nJdKULFmyZNBVkHqu1Wrd46ek\nlZNteeaMjo5OOn9KYTgzrwaIiAnnR8QQ8BFgC+BV06ngokWLprO4NGULFy4cdBWkvlm8ePGgqyCp\nB2zLgzetG+gmEhH3AT4PPBLYLjP/Pp3Xz5s3j+Hh4eUv2FOXz/D6NAjz588fdBWknmu1WixevJi5\nc+cyMjIy6OpIWkG25ZkzOjo6aedrV2E4Ih4AnAvcAmyTmddPt4yRkRE/BOqLOXPmDLoKUt+MjIz4\nGZdWAbbl/hsaGpp0/gqH4WZoxJnA34AXZ+btK1qWJEmSNAjd9AxvQ7mhbglwQ9t44kszc9tuKyZJ\nkiT127TCcGZeAKzX/P8nwOT9zpIkSdK9mF/HLEmSpGoZhiVJklQtw7AkSZKqZRiWJElStQzDkiRJ\nqpZhWJIkSdUyDEuSJKlahmFJkiRVyzAsSZKkahmGJUmSVC3DsCRJkqplGJYkSVK1DMOSJEmqlmFY\nkiRJ1TIMS5IkqVqGYUmSJFXLMCxJkqRqGYYlSZJULcOwJEmSqmUYliRJUrUMw5IkSaqWYViSJEnV\nMgxLkiSpWoZhSZIkVWv2oCsgSZL0i7XqjCQLBl2BGfa4W+4YdBWWYs+wJEmSqmUYliRJUrUMw5Ik\nSaqWYViSJEnVMgxLkiSpWoZhSZIkVcswLEmSpGoZhiVJklQtw7AkSZKqZRiWJElStQzDkiRJqpZh\nWJIkSdUyDEuSJKlahmFJkiRVyzAsSZKkahmGJUmSVC3DsCRJkqplGJYkSVK1DMOSJEmqlmFYkiRJ\n1TIMS5IkqVqGYUmSJFXLMCxJkqRqGYYlSZJULcOwJEmSqjV7OgtHxNbAWZm5UfP7OsCJwA7ATcCh\nmfmZntdSkiRJ6oMp9QxHxFBEvBr4NjDcNuvTwC3ABsBLgGMi4gk9r6UkSZLUB1MdJnEQsD9w+PiE\niFgLeAHwnsxckpk/B04Ddu95LSVJkqQ+mGoYPhF4NPCLtmnzgNsz8w9t0xLYokd1kyRJkvpqSmOG\nM/NqgIhon7wmcFvHorcCa0ynAq1Wi7Gxsem8RJqSJUuWDLoKUs+1Wq17/JSklckgjs2jo6OTzp/W\nDXQdbgXmdExbgzKGeMoWLVrURRWkZVu4cOGgqyD1zeLFiwddBUmatnvjsbmbMLwIGI6ITTPzz820\nAC6fTiHz5s1jeHh4+Qv21LSqqJXU/PnzB10FqedarRaLFy9m7ty5jIyMDLo6Us8sGHQFNCMGcWwe\nHR2dtPN1hcNwZv4zIr4GHBkRewPzgVcCz5pOOSMjI/5BV1/MmdN54UJadYyMjPgZl7TSGcTfraGh\noUnnd/ulG3sDqwN/Bc4ADsjMi7osU5IkSZoR0+oZzswLgPXafr8eeFmP6yRJkiTNCL+OWZIkSdUy\nDEuSJKlahmFJkiRVyzAsSZKkahmGJUmSVC3DsCRJkqplGJYkSVK1DMOSJEmqlmFYkiRJ1TIMS5Ik\nqVqGYUmSJFXLMCxJkqRqGYYlSZJULcOwJEmSqmUYliRJUrUMw5IkSaqWYViSJEnVMgxLkiSpWoZh\nSZIkVcswLEmSpGoZhiVJklQtw7AkSZKqZRiWJElStQzDkiRJqpZhWJIkSdUyDEuSJKlahmFJkiRV\nyzAsSZKkahmGJUmSVC3DsCRJkqplGJYkSVK1DMOSJEmqlmFYkiRJ1TIMS5IkqVqGYUmSJFXLMCxJ\nkqRqGYYlSZJULcOwJEmSqmUYliRJUrUMw5IkSaqWYViSJEnVMgxLkiSpWoZhSZIkVcswLEmSpGoZ\nhiVJklQtw7AkSZKqZRiWJElStQzDkiRJqpZhWJIkSdUyDEuSJKlahmFJkiRVa3a3BUTEE4GPApsD\nVwOHZuZp3ZYrSZIk9VtXPcMRMQs4CzgqM9cGXgt8NiIe3IO6SZIkSX3V7TCJ+wPrA7MjYgi4CxgF\n7uy2YpIkSVK/dTVMIjOvi4iPA18ATqGE69dk5l+mWkar1WJsbKybakgTWrJkyaCrIPVcq9W6x09J\nWpkM4tg8Ojo66fyuwnBErAbcCrwUOBvYETgtIi7NzMumUsaiRYu6qYK0TAsXLhx0FaS+Wbx48aCr\nIEnTdm88Nnd7A92LgMdn5gHN79+IiK8DuwNvnUoB8+bNY3h4uMtqTNflM7w+DcL8+fMHXQWp51qt\nFosXL2bu3LmMjIwMujpSzywYdAU0IwZxbB4dHZ2087XbMLwp0PnX+I7m35SMjIz4B119MWfOnEFX\nQeqbkZERP+OSVjqD+Ls1NDQ06fxuw/B3gCMjYi/gZGBb4IXADl2WK0mSJPVdV0+TyMwFwEuA/YGb\ngI8Be2TmxT2omyRJktRXXX/pRmaeA5zTg7pIkiRJM8qvY5YkSVK1DMOSJEmqlmFYkiRJ1TIMS5Ik\nqVqGYUmSJFXLMCxJkqRqGYYlSZJULcOwJEmSqmUYliRJUrUMw5IkSaqWYViSJEnVMgxLkiSpWoZh\nSZIkVcswLEmSpGoZhiVJklQtw7AkSZKqZRiWJElStQzDkiRJqpZhWJIkSdUyDEuSJKlahmFJkiRV\nyzAsSZKkahmGJUmSVC3DsCRJkqplGJYkSVK1DMOSJEmqlmFYkiRJ1TIMS5IkqVqGYUmSJFXLMCxJ\nkqRqGYYlSZJULcOwJEmSqmUYliRJUrUMw5IkSaqWYViSJEnVMgxLkiSpWoZhSZIkVcswLEmSpGoZ\nhiVJklQtw7AkSZKqZRiWJElStQzDkiRJqpZhWJIkSdUyDEuSJKlahmFJkiRVyzAsSZKkahmGJUmS\nVC3DsCRJkqplGJYkSVK1DMOSJEmqlmFYkiRJ1ZrdbQERsQnwSWBb4GbgmMz8aLflSpIkSf3WVc9w\nRAwBZwH/B6wLPBN4b0Q8sQd1kyRJkvqq257hxwMbAQdm5p3AwojYBri265pJkiRJfdZtGN4SWAgc\nExG7UoZJHJ6Zn51qAa1Wi7GxsS6rIS1tyZIlg66C1HOtVusePyVpZTKIY/Po6Oik87sNww8Angqc\nD2wKPBY4NyL+kJk/mkoBixYt6rIK0sQWLlw46CpoBiy47fRBV2EgFiwYdA1mziPv89JBV0FSj9wb\nj83dhuEWcH1mHtn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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "from yellowbrick.classifier import ClassificationReport\n", + "\n", + "# Instantiate the classification model and visualizer\n", + "forest = RandomForestClassifier()\n", + "visualizer = ClassBalance(forest, classes=classes)\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": [ + "Note: The visualization works, but the example provides an unbalanced training/ testing set that provides a misleading for a Randomforest classifier. A more powerful example would train with balanced classes." + ] + }, + { + "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": 0 +} diff --git a/examples/nealhumphrey/confusionMatrix.ipynb b/examples/nealhumphrey/confusionMatrix.ipynb new file mode 100644 index 000000000..58aa0e521 --- /dev/null +++ b/examples/nealhumphrey/confusionMatrix.ipynb @@ -0,0 +1,313 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Confusion Matrix Visualizer\n", + "The `ConfusionMatrix` visualizer is a ScoreVisualizer that takes a fitted scikit-learn classifier and a set of test X and y values and returns a report showing how each of the test values predicted classes compare to their actual classes. Data scientists use confusion matrices to understand which classes are most easily confused. These provide similar information as what is available in a ClassificationReport, but rather than top-level scores they provide deeper insight into the classification of individual data points. \n", + "\n", + "Below are a few examples of using the ConfusionMatrix visualizer; more information can be found by looking at the sklearn.metrics.confusion_matrix documentation. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#First do our imports\n", + "#We want yellowbrick to import from this repository, and assume this notebook is in repofolder/examples/subfolder/\n", + "import sys\n", + "sys.path.append(\"../../\")\n", + "import yellowbrick\n", + "\n", + "from sklearn.datasets import load_digits\n", + "from sklearn.cross_validation import train_test_split\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "from yellowbrick.classifier.confusion_matrix import ConfusionMatrix" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ZQuXKlY0uxy4pKYn4+HhsNhve3t5Uq1aN06dPk5qaStmyZalTp459vYyMDPz8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\n0kAqIiIiIkZpIBURERERozSQioiIiIhRGkhFRERExCgNpCIiIiJilAZ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# We'll use the handwritten digits data set from scikit-learn.\n", + "# Each feature of this dataset is an 8x8 pixel image of a handwritten number.\n", + "# Digits.data converts these 64 pixels into a single array of features\n", + "digits = load_digits()\n", + "X = digits.data\n", + "y = digits.target\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size =0.2, random_state=11)\n", + "\n", + "model = LogisticRegression()\n", + "\n", + "#The ConfusionMatrix visualizer taxes a model\n", + "cm = ConfusionMatrix(model, classes=[0,1,2,3,4,5,6,7,8,9])\n", + "\n", + "#Fit fits the passed model. This is unnecessary if you pass the visualizer a pre-fitted model\n", + "cm.fit(X_train, y_train)\n", + "\n", + "#To create the ConfusionMatrix, we need some test data. Score runs predict() on the data\n", + "#and then creates the confusion_matrix from scikit learn. \n", + "cm.score(X_test, y_test)\n", + "\n", + "#How did we do?\n", + "cm.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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q3a7ffz9qOJX7Fab1Nl5IX3nlFdWpU0dOp9N0lKuy2+2y2WyuyzabTWlpaQYT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\nUiIiIiISioWUiIiIiIRiISUiIiIioVhIiYiIiEg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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#The default version reports the predicted values as a percent of each 'True' class\n", + "#Thus, the 100% values (coded in green) indicate that the predictions were 100% accurate for that class\n", + "\n", + "#We can also get total counts from the test data, instead of percent\n", + "#In this example, our heatmap tells a slightly different story, since there's a slight class imbalance\n", + "cm2 = ConfusionMatrix(model, classes=None) #classes can be auto-calculated\n", + "cm2.fit(X_train, y_train)\n", + "cm2.score(X_test, y_test, percent=False)\n", + "cm2.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example with more classes (33) and worse predictions\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#Another example\n", + "#This whole cell is just setting up X and y - it's a bit messier data set\n", + "\n", + "#Be sure to add the relevant file to the /data folder, from the UCI ML library\n", + "#available here: https://archive.ics.uci.edu/ml/datasets/Geographical+Original+of+Music\n", + "\"\"\"\n", + "The data set in this example represents 1059 songs from various countries obtained \n", + "from the UCI Machine Learning library. Various features of the audio tracks have been \n", + "extracted, and each track has been tagged with the latitude and longitude of the capital\n", + "city of its country of origin. \n", + "\n", + "We'll treat this as a classification problem, and attempt to train a model to predict \n", + "the country of origin of each model. \n", + "\n", + "Data source did not specifify what the audio features specifically are, just\n", + " \"In the 'default_features_1059_tracks.txt' file, the first 68 columns are audio \n", + " features of the track, and the last two columns are the origin of the music, \n", + " represented by latitude and longitude. \n", + "\n", + " In the 'default_plus_chromatic_features_1059_tracks.txt' file, the first 116 \n", + " columns are audio features of the track, and the last two columns are the \n", + " origin of the music.\"\n", + "\"\"\"\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import sklearn\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.utils.multiclass import unique_labels\n", + "import sys\n", + "\n", + "#First get the data. The UCI ML Library distributes it as a zipped file;\n", + "#download the data and extract the two provided files to the 'data' folder before continuing\n", + "music_df = pd.read_csv('data\\default_plus_chromatic_features_1059_tracks.txt', header=None)\n", + "music = music_df.as_matrix()\n", + "\n", + "\n", + "#Our features are all but the last two columns\n", + "X = music[:,0:-2]\n", + "\n", + "#Since feature names were not given, we'll just assign strings with an incrementing integer\n", + "names = np.linspace(start=1, stop=116, num=116, dtype='int').tolist()\n", + "for idx, name in enumerate(names):\n", + " names[idx] = \"Feature \" + str(name)\n", + "\n", + "\n", + "#The source data said that each song as tied to the capital city of it's origin country via a lat/lon pair. \n", + "#Let's treat this as a multi-class classification problem. \n", + "#Rather than reverse-geocoding, we'll just make a string out of the unique lat/lon pairs\n", + "lats = [\"%.2f\" % lat for lat in music_df[116]]\n", + "lons = [\"%.2f\" % lon for lon in music_df[117]]\n", + "song_latlons = []\n", + "for index, value in enumerate(lats):\n", + " city_id = lats[index] + \",\" + lons[index]\n", + " song_latlons.append(city_id)\n", + "\n", + "unique_latlons = unique_labels(song_latlons)\n", + "city_options = ['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z','AA','AB','AC','AD','AE','AF','AG']\n", + "city_name_map = {}\n", + "for idx,latlon in enumerate(unique_latlons):\n", + " city_name_map[latlon] = city_options[idx]\n", + "\n", + "ylist = []\n", + "for latlon in song_latlons:\n", + " ylist.append(city_name_map[latlon])\n", + "y = np.array(ylist)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "X_train_music, X_test_music, y_train_music, y_test_music = train_test_split(X,y, test_size =0.2, random_state=11)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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D4P6ZkkKhQKvVlssoXBV3yTGTKbjkmElOrvT0dAICAlg1sydjBpY+v7BGNUtm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\nMGTIEKMef+LEifueCSsoKGDGjBk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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Similar to our approach above\n", + "music_model = LogisticRegression()\n", + "music_model.fit(X_train_music, y_train_music)\n", + "\n", + "cm3 = ConfusionMatrix(music_model, classes=city_options)\n", + "\n", + "#We don't need to call fit if the model is already fit\n", + "#cm3.fit()\n", + "cm3.score(X_test_music, y_test_music, percent=True)\n", + "\n", + "#The diagonal line, not as obvious in the previous example, shows us where our correct predictions are\n", + "cm3.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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AAACGQHAFAACAIRBcAQAAYAgEVwAAABgCwRUAAACGQHAFAACAIRBcAQAAYAgE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\nEREREbEJCq4iIiIiYhP+F2OvPUJNItdyAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#We can also use the classes option to zoom in on a subset of the matrix\n", + "cm4 = ConfusionMatrix(music_model, classes=[\"AC\",\"AD\",\"AE\",\"AF\",\"AG\"])\n", + "cm4.score(X_test_music, y_test_music, percent=True)\n", + "cm4.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "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": 1 +} diff --git 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diff --git a/examples/nealhumphrey/light-dark-text.ipynb b/examples/nealhumphrey/light-dark-text.ipynb new file mode 100644 index 000000000..3d16afdfa --- /dev/null +++ b/examples/nealhumphrey/light-dark-text.ipynb @@ -0,0 +1,202 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append(\"../../\")\n", + "import yellowbrick" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from yellowbrick.style import find_text_color\n", + "from yellowbrick.style.palettes import color_sequence\n", + "\n", + "colors = ['Blues','Greens','Oranges','Purples','Reds']\n", + "cmaps = []\n", + "for c in colors:\n", + " cmaps.append(color_sequence(c))\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def show_example(coef_choice):\n", + " X = np.linspace(0,1,num=21)\n", + " Y = np.linspace(0,1,num=2)\n", + " values = np.array([np.linspace(0,1,num=20)])\n", + " fig, axarr = plt.subplots(len(cmaps),1,squeeze=False)\n", + "\n", + " for idx, cmap in enumerate(cmaps):\n", + " ax = axarr[idx,0]\n", + " ax.pcolormesh(X,Y,values,cmap=cmap)\n", + " ax.get_xaxis().set_visible(False)\n", + " ax.get_yaxis().set_visible(False)\n", + " for idx, x in enumerate(X):\n", + " if x < 1:\n", + " text_x = x + 0.025\n", + " text_y = 0.5\n", + " base_value = round(values[0][idx],1)\n", + " base_color = cmap(base_value)\n", + " color=find_text_color(base_color,coef_choice=coef_choice)\n", + " ax.text(text_x,text_y,base_value,va='center',ha='center',fontsize=12,color=color)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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EAsMCkIiIiEhgWAASERERCQwLQCIiIiKBYQFIREREJDAsAImIiIgEhgUgERER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RiQwLQCIiIiKRYQFIREREJDIsAImIiIhEhgUgERERkciwACQiIiISGRaARERE\nRCLDApCIiIhIZFgAEhEREYmMzJiNrl69CkEQIJfL/9fjISIiIqKXoNFoIJFIEBISUuK2RhWAgiBo\nvyC88uDIeEVCoamHYBIFRQWmHoJJaIo0ph6CSagLxTnv/EKR/n0XiHPemgJxPq9BXWTqEYhKRfsK\nkMuMW6wzqgCUy+UQIMA/wPeVBkYv5lH+A1MPwSTismJMPQSTiEiPMPUQTCLsgTjnfTk52dRDMIm4\nmBRTD8EkIuPE+fPG7cemHoGo7Px8FbxdPIzalucAEhEREYkMC0AiIiIikWEBSERERCQyLACJiIiI\nRIYFIBEREZHIsAAkIiIiEhkWgEREREQiwwKQiIiISGRYABIRERGJDAtAIiIiIpFhAUhEREQkMiwA\niYiIiESGBSARERGRyLAAJCIiIhIZFoBEREREIsMCkIiIiEhkWAASERERiQwLQCIiIiKRYQFIRERE\nJDIsAImIiIhEhgUgERERkciwACQiIiISGRaARERERCLDApCIiIhIZFgAEhEREYkMC0AiIiIikWEB\nSERERCQystexkyOHjmD61Jm4fes2nJyd8OHI4fhk/Njn9vnt19+xYO4CREfFwNPLE599MR7vvv9u\nqWQ/de3qNTRr9BZu3A2Dh4f7G5V9/MgJLJj5De7eDoejUwUMHD4AI8YOK3Z7tVqNlUvXYNuW7UhK\nSIaLa0X0eKc7Rn86EnK5/I3JPn/sIlbNXYvouzFwcCyHtwd3R/+R7xSfna/G2kXrcGjHEaSnZcA3\n0AdDP/8A9d+q+0K5ps4OO3UT277dicR7SbArb4tW77ZAh8FtjepbVFiEme/MhbmlEhPXf/ZGZcdc\niMapNSeRFv0QVuUsEdwzBHX71St2+/TEx/ix7xq99grejvhg3aA3JvtRaBKitlxDdkI6FHYWcG1f\nBR5dAp7bJ+1yAmK2Xkd2XDrkNko41veAd/8QSJUv9jRvyuwmnrUxrslA+FXwQlr2Y2y6tgtrL20z\nuG2PwDaY3/7TYvf1xf6F2Hnrrzciu03Vhpjd6WMEVvTBfdUjfHdyCxYfXV/s9mYSM3zeahAGN+iO\nSnZOiHgQi7mH1+KPqweNziwT2bWbYfYHXyDQ0x/30x/gu13rsHjr6mK3l8vkmDHgU7zbqgcq2Drg\ndlwEFvz+A34/vvuNyn7KtYILwlYfRrdpQ3Ay7Pxzt+3bohu+7PcxvF08EXM/HvN+/Q4bjhj+/TSW\ndPr06dNYBF1bAAAgAElEQVRL2ig1NRUAUN7RQe//zp+7gC4duuKtFs0xfeY02NvbY+b0WTA3N0ej\nxg0N7m/H9p0Y+O4H6PduP0ycPAH5+fmY/tUMVKlWBQGBz3+iedXsp27euIlunXsgS5WF0WNGwc7O\nzujc0srOLcwx2H75whX07zoAjZo3xIRpn8HW3g6LZi2GubkSdRvWMdhn8rivsH7NRgwbNRgfjh0G\nJydHLF2wAknxSWjbqbXR8y6N7Ax1usH93Lh0E5+88zlqNwnBh5OGwtrOGmvm/wyluQI169Uw2GfW\nmLk4vOMvDP18EHoN7oH7ian4Yc4a1G5aCxVdnY2ed2lkP8x7ZHA/965FYtHgbxHQoAre/qQHrGwt\nsX3Zn5Ar5fCv7Vvi2Het3Itzuy+ggmt5NOnRyOg5l1Z2ao7heSfdSMLW8X/Ao5Ynmg5vCqWNEmfW\nnoZMIYNrTTeDfeKuxCH8WDjeWdYXwd1DULNLEGp2CULV1lVhVc7K6HmXRnZyVpbB/WSEP0Do7L9Q\nrnpFePcLgcxSgejfQmGmkMK+qpPBPmmX4nFj4XE4BFWCz3u1YOVqh7hdt5CbnAnH+h5Gz7s0sjPS\nDc872KUqfuk1F+fiQ7Hk1C/IVGfhk8YDkV+gxpWkW3rbp6ge4GDEafwedkDnK6RSAPIK1Jh/fA3y\nCtRGzbs0sh9nGJ53fa+aOPTRahyNuIgpe5cjPVeFWZ1GI1eTjzPR1wz2mdlpNKa2/xBLj2/C/L9+\ngqXCAgu6jsf1pHDcuR9t1JxLLTstz3B2tVo4NHcTjoaewZRfFiA9W4VZAz9DrjofZ25eMthn61er\nMaDN2/hh93rM2bIMj1TpWDxiGjKyVbh4N9T4eZsw+yk3RxccmrcJruUrYt3hPxCXmljstj2bdMSW\nyd9h41/bMXvzMpgrlJgzaAJux0XgVmy4zrZ9G3dBORs7ODuX/Pr2yiuAs2fMRnBIMNb8pK2cW7dt\nDbVajYXzFmH0mFFQKpV6faZ/NQO9+ryNeQvnAgBatWmFhw8fYea0WejV++3/abZGo8F3y7/H7Blz\nYGFh8TJTNnn2N7OXoHpwIL5d/Q0AoHnrZtCo1Vi+6DsMGTVIL/vxo3Rs/vlXTJkzGR+OGQoAaNy8\nEQRBwNxpCzBp5gQ4lC9X5rN/XPgLqtTww1fLJgEA6r9VFwVqDdYt3Yg+w96GQqnQ2T45PgWHd/yN\nT+eNRY8BXQEAtZuEIOzCDWz/+U8EFVO4lbXsHct3wTPQA8PmDQYA1GgSiAJNAfas2oe2A1tBrih+\nFTXuTjz2rN4PO8cXe4NTFrLP/HQKTv7O6PBlRwCAV73KKNIU4fyGc6jVuzZkCv2nr9SIVFg72sA9\nxPiip6xlR/8eCpvKDqg2ujEAwCGoEoSCIsRtD4N7x6owk0v1+txbdxmODT1RdaT2zWe5wIoQigQk\n7LuDQnUhpAr9PmUte2yjAbiZeg8TDiwCAJyKvQyFmQwj6/fDuis7oS7U6GyfnqdCespdnbYBId3h\n7eCO3pvHIj1PZVSuqbNndPgIV+JvY9CmKQCAw3fOQiGVY3KboVh6fCPUBRq9PoPqd8PGS3sx55D2\n9edYxEXU8QjE6Kb9sPP6329G9oDxuHLvBgYtHK/NvnwCCpkck/uNxtIda6HW6BbQQT4B6NaoLSb/\nNB/zf/sOAHD02hnk5Odi3pBJ2HBkG1Q5hovsspQNAAPb9sbCYVOM3n7OoC/w2/Hd+Hz1bADAkSsn\nUd6mHGZ98Dn+OLHH6P0865XOAVSr1Th54hS6du+i097j7R7IzMzEmVNn9PrExsYhIjwCXbo926c7\nIu9FIioy6n+WDQAH9h/EvDnzMWHyF5j19Uyjsspa9rlT59GhSzud9k49OkKVmYULZ/TfvWSpVBgw\n7D206dhKp923ig8AIC4mrsxna9QaXD17Dc07NtVpb9GlObJVOQg9H6bXp4Jzefx0cCXavd3mnzaJ\nRAKpTAp1vnErA6bOLlAX4O6FcNRuHaLTXrddbeRm5SH88r3i+2oKsGbCT2g7oBUqehm/2lkWsgs1\nhYi/Fg+/Zn467f4t/JGfnY/E64bfLadGpMLJz/BK1ZuQXaQpRMat+6jwzMqZY0NPFORqkH4nVa+P\nKvoRcu+r4Nahqk67W4eqaLC8u9EFmCmz5WYy1HOviUMRp3Xa94efhLXSErVdA0vch4OlPcY1GYhN\n13bjxv0Io3JNni2VoblvHey4rnu4eOu1Q7A1t0YT71oG+5nLlVDlZeu0PcxOR3kr+zcjWyZH85oN\nsOP0Ad3sk3tha2mDJtX1T5Op5uEHQRCw5/wRnfaj187AytwSbwU9/8hbWcgGgJre1fDDmLn45dAf\nGLDgE0gkkudu7+HkCn83b+zUG+8++FbygreLp9HZz3qlAjA6KhpqtRp+/rpPlD6+3gCA8HD9P4S7\nt+9AIpHAz0/38JGPjzcEQUD43XC9Pq8rGwDq1K2NO5G38PmEzyCTGffkVJay46LjoFZr4O1XWafd\ny1v7SxAZoV9Au3u6Y87imfD21e2zf9dByOUyvfaymJ0YmwSNugDu3rqH39wqu2rHFhmv10eukKNK\nTX9YWVtCEATcT0zFkinLkRibhB4DuxqVa+rs1PgHKNAUomJl3SLKyVNbaKREpRTbd+eK3SgsLEKP\nj43PKyvZ6UnpKNQUopy77mkn9q7a1eJHcYYPGz+4lwp1dj42j9yEb1suxg/dvsOJlcdRVFD0RmTn\npmahqKAIli42Ou0WFbWPc5My9fpkxTwGIIGZzAzX5/2N4+9uxslBvyHil4so0hS+Ednu9i6QS2WI\nfqxbXMemJwEAvB1KPk/6k0YDUFhUiG9PrzM619TZ3uXdoZDKEZ4aq9N+L037nFLFyctgv2+PbcSA\nel3QtmojWCst0b9OJ7Sr2hjrLxp/PppJs108oJApEJ6g+5pxLzFGm+3mo9cnLeMRJBIJPJ1cddp9\nXbXj9K5o3Mq7KbMBIPZ+InwGNsYXa2YjJz8XgiA8d/unxWd44jPjTYqGRCJBFXf98RrrlQ4BZ2Ro\nnxBsbXSfMGyePM7M1H/CyHjSZmtra7iPyril85fJBgAXFxej9l9WszMztd8f62eyrW2sAQBZmcZ9\n//bvOoCtm7dj8MgPYGtnW3IHE2dnZ2rfcVrZ6J5LZWltqf1/VbZen//asHwzVn79IyQSCbq+1wl1\nm9U2KtfU2blZuQAACytznfanj3OzDZ9fE3U9Ggd+PowvN0+ATP5yf+amzFZn5QMAlFa6h9YVltrH\n6ux8/fFm5EL1QIWiwiI0H/UWbJ1tEXspFhc2nUfWAxU6ftW5zGcX5GhXh2UWuofWZeayJ/+vf0hO\nk5kHQEDYouNwbuIF9y6BUEWmIfq3UGgy8xEwpkmZz7ZRaP+2svJ1z3vOVmt/B60Vls/t72Bhhx6B\nrfHjxT+QpTZ87nRZzLaz0D53ZubpHj58usJma25tsN+So+vR0CsI+0f8AAAQIOCnczuw5DkXb5Sp\nbCvt835mju5rhipXOxZbSxu9PsdCzyIqOQ7LR89C7uI8XLwbimCfQMwbMglFQhGsLJ7/cyoL2QCQ\nkZ2JjGzDNYLh8T6pK7Kf+TnlPPk5WRr+ORnjlQrAoqLnv7M1M9NfYHyZPq8r+3V507P3/XkAHw8e\ni/pN6uHL2RPfkOznv0sqKbtpu8YIql8DoefDsPabdcjPU2Pq8kllPlsoIdvQ4QONWoM1k35G+0Ft\nULn6yx8eMGl2Ce+KJWb62XJzOXoveQfl3MrBtqL2Sd4tyB1SuRSnfzyFBgMbwsGjfJnORkmLhQay\nn64wOtZ3h8+72sN25QKdIRQJiNp8DV69a8LSxYg3WibMNivhMFiR8PzBvVOzA8wkZlh3ZWeJWWUr\n+/nPHYay5VIZTn2yHs425TH8txm4ez8GjbyD8VW7D5GtzsW47QvegOwX/54XFBag7cT++Omzb3B4\n3hZIJBIkPbyPMd9PxR9TViInL7fMZ7+Mkl5fSnpdfp5XKgDtnqzeqJ65mu3pCpihq1vtnqz8qZ5Z\n6funj61xK0Ivk/26mDL76cpp9jPZqkztYxs7/Xcv/7V6+Y+Y/eVcNG7eCGt/XQWFQvHc7ctKtrWt\n9l16TtYz79JV2sdWts+/wrNyFS8AQFD9migoKMTahb9gxKQhcKpU8jlbpsy2sNFeLJT3zKrT09U5\nSxv9i4m2LtkJCEDXkZ1QVFj0pKARIAgSFBUWwUxq3BsUU2YrrLQXE6lzdM+XVGdrHyut9C+ykill\n8KyjX3R6N/TBqTUnkRrxwKgizJTZMkvt6ltBXoFOe0GuRuf/dfpYyABIUL6W7ikK5YMrIWrzVWTF\nPDaqCDNltkqtXc2wVuj+Tj1dfVPlP39lrZ1fU5yKufxCF1+UheyMJ31szHWfQ56uvmXk6l9Y0Cu4\nLWpU8kPr74bhWMRFAMCpqCvIzMvCil6TsfrMVtxOKflcepNmZz/JttBdvXq6+lbcCll0ShxafNYb\n5W3LobxtOUQkRsPT2Q0SiQSPVIbvHlGWsl/GP+O1fObn9GTlLyPnxX/vnnqlpSpvH29IpVJE3ovU\naY+8p/0FqFq1il4f/yr+EAQBkc9c7BEZGaU9nl2tql6f15X9upgy29PbA1KpFNGRuudtxETFAAD8\nqhR/a46vPpuOmZPmoFvvrtiw42dYWhm/bG3qbFevSjCTmiEhRvc8nYRo7WMvP/0X35SE+9i9eR80\nat1DV1VqaM/dfJDysMxnO3k4wkxqhvtxuifg34/VPnbx0T+t4NLBy0iOTsHw4NEYHDgCQ6qPxN2L\nEbh7MRyDq4/AqZ2GL1IqS9n2rvYwMzPD44THOu2PE7WPHbz0i6nHCY8R+uc15D9TsBbkawsay3LG\nXXlvymyLijaQmEmQm6L7pP70sZWr/ptLCxdbAILeOXdFhdqVTDMjL8QwZXZcejIKi4rgWa6STvvT\nx5GPir9YzMnKAQHOPth797hRWWUpOzItHoVCIXwr6J5n6OuofXz7vn4x5VFO+3f37G1aTty7DAkk\nCKxY8u2ZTJ6dHIvCosJ/zqH7J7uS9vHtOP0LzJRyJfq37AFPZzc8zHyM8IQoCIKA2n41IQgCrkTc\nKPPZL+NufCQkEgl8K+meL+/r6gVBEHA71viLjp71SgWgUqlEk6aN8eeOXTrtO7btgL29PerU078v\nnLePN7wqe2HHth3P9NkJXz9fo2+K/DLZr4ups+s3rof9u3SvCNq7Yx/s7G0RXCfIYL+5U+fj55Xr\nMGLscCxfuwQy2Ysv/poyW6FUILhBEI7tPanTfnTPcVjbWSMgRP+NQ0rCfcwdvxDH9+n2OX/0ImQK\nGTx9jftdM2W2XCFHlTp+uHToik77xYOXYWlrAe+a+hfRjFv1MaZv/RLTt/375RngAa9AD8zYNgUh\nLQz/nMpStkwhg1uwGyJO6D65hR+9C3Nrc7hU0y8+sx9m4fCiQwg/qnt7jjt/3YbSSgnnKhXLfLaZ\nXAq7as54cF636HhwLg4ySwVsfCvo9bGv5gSpUobU0zE67WkX4yGRmsHO37HMZ6sLNbiYEIa2frrn\nDLb3b4rMvCyEJt8ptm+QSzXti7CB+/WV+ewCDU7cu4yeQbr3Q+0V3BbpuSpciNW/w8DTe+019dY9\nl7iJdy0IEBD1MKHsZ2vUOBF2Hj2bdNDNbtYJ6VmZuHBH/x6E6gI1VoyehWEd+//TZmZmho+7D0Jk\ncixuxBT/cyor2S8jKjkW0Slx6NWso+54m3ZCRGI04h8kvfS+X/lG0O7ublg4fxFu3rwFG2trbFy/\nCYsXLcFX06egSdMmUKlUCL12HUpzJSwttas+dna2WDBvER6kPoBUKsXSJcuweeMWLP3uW1QLqGb0\n4F8m+7+uh17Hnl17X+pG0KWRXdyNoCu5uWDFoh9w51Y4rK2t8MembfhhySp8NmU8GjSpjyxVFm5c\nvwmlUgkLSwvcCL2JT4Z/iuA6QRg+ZiiSk1J0vmxsbfTuY1ec0sgu7kbQzq5O2LBsEyLvRMPS2gL7\nfjuITd9twbAJgxHSMAjZWTmICIuAQimHuaU5nF2dcOPSTezZsh9WNlZQZWTh9zXbsP2XPzH0s0Go\n19z4Qr00sou7EXR5FwfsWb0fCeGJMLcyx6kdZ7Bv7UH0HNMNVev6IzcrD3G34yBXyKC0UMK2vC3s\nnex1vs7tuQC5QobOwztAYW78offSyC7uRtA2zjY4v/Ec0qLSoLBU4Ob+G7i45QIaD2kC92B3qHPy\nkRqeCqlCCrm5HDbOtki6nogb+25Abi6HOkeDq1sv4+r2K2g2sjncg43/xJ3SyC7uRtDmFawQt+MG\nsuLTITWXIeV4FOL/vInKfYNRLsAZBbkaqGIewUwuhVQpg5lMCjOFFPG7b0OTkQczuRnun4xG7I4b\ncO9UDRXqGj/v0sgu7kbQyaoHGFG/L/wreCFbnYsegW0wvF4fLD2zHpcSb8BKboEAJ1+oC9XIK/h3\npbVTleYIrlQN84/rfwqLsUoju7gbQcelp2BSm6GoXskPqvxsDKzXDV+0GoSp+77DqagrsFZaItit\nKvIK1MjV5CH8QQzaV2uCIQ17ICs/B9ZKS/St1QGzO43GvlsnsfTYRqPnXSrZxdwIOi41CZP6jUZ1\nrypQ5WRjYJte+OKdkZi6fhFO3bgAawsrBPtWR546H7n52n3YWFpjbI8hSM/KhK2VNRYNn4qWwY0w\ncOE43EuKMX7eJsz+L09nN3zQtg9+OfTvjaANZadnZ2Jyv4/hVK4CCooK8WmvD/F+67fx0fIvcTtO\n943qi9wI+pULQK/KXqgRVAM7t+/Eqh9WIy4uHhMnT8DYcWMAABfOXUCbFm0REFANNYNqAgBqBtWE\ni4sLtmz+FT/9+DNUKhXmfzMfvfv0KnHAr5r9X69SAJZGdnEFoIeXOwJqVMPenfuxbvUGJMYnYuyE\n0f/caPnyhSt4u+078K/mh8CaAVj/40acP30B95Pv49d1v+t9vdW6Gdw8DH+6gSmyiysAK3m4wDfA\nB8f2HMe2n3ciJeE+Phj3PvqN6ANA+2kdH3Ufi8pVvOAX6AuJRILmHZsiS5WNXRv3YueG3chW5WDk\nl8PQe2hPo+ZbmtnFFYCObhXgUcUdlw5dxl+bj+Fh0kN0HdkZ7Qdp7zEYeS0SX7+3EK5+leBR1fCL\n/akdZ2BmJnnhTwIpjeziCkA7F3s4+Toh/Fg4rm2/isz7mWg4sBHq9NXepyvpRhJ+Hb0FFbwqwMnP\nSXt7qWb+UOeocWPfDVzfFYr8rHw0/bA5grsHv9C8SyO7uALQwskaVl7lkHYuDokHw5H/IBuevWrA\no7P2U5IywtNwdeohWLvbwdpL+5xs5+8Ic0crpJ6OQeL+u8i9r4JHt0BU7mPcimtpZhdXACZk3sed\n1Ci082+K94K7wtXWCd+f24yfL28HAIRUqoYtfb9BxMM43Hnw7+HJjlWawdXWGT9dfvmPxSqN7OIK\nwJhHSQhNCkevoDYY1bQvPB0qYfah1fj22AYAQIPKQTgx9hfcSonE9STtbdJ+vbIfzjblMaJJHwxt\n2BPu9hWx7PgmfLpzUYkXMZV6djEFYMz9eIRG3kKvZp0wqusH8HR2w+xNS/Ht9h+12QG1ceKbbbgV\nG47rUbcBAMevn4NCrsBHXQZgUNs+yMxRYdi3X+Dw5RNGz9nU2f/l6eyGgW166XwSiKHs0KhbSHqY\nivda9cTwju/C1tIa41fONPgxdC9SAEoEI35bwsLCIECAf4Bxx/fp9XiU/8DUQzCJuKwYUw/BJMLT\nX/5cjjdZ2ANxzvtycrKph2AScTHF3z/y/7PIOHH+vHH7ccnb0Guz8/NV8HbxQI0aJX/a1P/ufiVE\nREREVCaxACQiIiISGRaARERERCLDApCIiIhIZFgAEhEREYkMC0AiIiIikWEBSERERCQyLACJiIiI\nRIYFIBEREZHIsAAkIiIiEhkWgEREREQiwwKQiIiISGRYABIRERGJDAtAIiIiIpFhAUhEREQkMiwA\niYiIiESGBSARERGRyLAAJCIiIhIZFoBEREREIsMCkIiIiEhkWAASERERiQwLQCIiIiKRYQFIRERE\nJDIsAImIiIhEhgUgERERkciwACQiIiISGRaARERERCLDApCIiIhIZFgAEhEREYkMC0AiIiIikZEI\ngiCUtNGVK1cgCALkCnlpjImeKBIKTT0EkygoKjD1EExCU6Qx9RBMQl0oznnnF4r077tAnPPWFIjz\neQ3qIlOPQFQq2leAXCZHrVq1StxWZswOJRKJ9l9IXm1k9EKkEqN+PP/vSKXinLdSam7qIZgG31cS\nEb0WGo3mn5qtJEatABIRERHR/x88B5CIiIhIZFgAEhEREYkMC0AiIiIikWEBSERERCQyLACJiIiI\nRIYFIBEREZHIsAAkIiIiEhkWgEREREQiwwKQiIiISGRYABIRERGJDAtAIiIiIpFhAUhEREQkMjJj\nNrp69SoEQYBcLv9fj4eIiIiIXoJGo4FEIkFISEiJ2xpVAAqCAEEQAKHolQdHL6CowNQjMI1CtalH\nYBoF+aYegWloxDnvIo3G1EMwicICcb6O5Itz2tCIdN6mYlHeGVKZcYt1RhWAcrkcEIpQw9f1lQZG\nL0bIiDf1EExCSL1n6iGYRtx5U4/ANKJDTT0Ck8i6d9vUQzCJlOhHph6CSdx9JM4zru5mSEw9BFFp\nuWQbyrlVNmpbcf5GEhEREYkYC0AiIiIikWEBSERERCQyLACJiIiIRIYFIBEREZHIsAAkIiIiEhkW\ngEREREQiwwKQiIiISGRYABIRERGJDAtAIiIiIpFhAUhEREQkMiwAiYiIiESGBSARERGRyLAAJCIi\nIhIZFoBEREREIsMCkIiIiEhkWAASERERiQwLQCIiIiKRYQFIREREJDIsAImIiIhEhgUgERERkciw\nACQiIiISGRaARERERCLDApCIiIhIZFgAEhEREYkMC0AiIiIikWEBSERERCQyLACJiIiIROa1FICH\njhxFvWZtYeXoCe/AOvhm6fcl9tny+3ZUr9MUlhU8EFC7MdZv+q3Usp+6eu06FPaVEBef8OZlHzuL\n+h3fh7VPI/g06IJvVm547vZqtQZfL1uLgGY9YePbGNWa9sSsJWug0WjerOwzYWjw7nTYNBgK306f\nYvH6/c/dPl+twZfL/oB3h/GwaTAUTQbMxKEzYS+ca/Lsa3Fo8MUfsOm3Cr4jN2Dxn1eN7ltYWIQG\nX/yB1lN3vnnZdx+jwbfXYDPpDHznXMTiY8//e4lMy4Xss1N6X8GLrrxR2X/Fq9FiWzpc1jxEzY2P\nsfxabol9Dsaq0XJbBiqueYiA9Y8x8VQ2cjTCG5V9Kk1Ar7MCgg8LaHVcwE/Rxe9jR6KAqgeK/9qZ\n+GL5psyuWL8N2v50Fr2PZaDL9nBU7T/uudtLzMxQbcAX6PTHLfQ6mo726y/CvVWvF8osC9k+jVtj\n2O9nMPlKOsYevouGgz557vZSuRytxs/GuL8j8eXVDAzfdh6BHXq/cdlP2Tq7YsL5VHjWaVLittU7\nvYOPdl3Fl1czMGpPKIK6vfdK2QAgnT59+vSSNkpNTQUgwNnBVu//zl24hLZd+6Bl8yaYM20y7O3t\nMHXWPFiYm6Nxw/oG97dt5270++BDvN+vN6ZMGI/8/HxMmTEX1ar4ITCgqtGDf5nsp27cvI0OPfpC\nlZWFT0Z9CDs7/bmZPDs/03D25eto138UWjaui9kTRsHe1gbTFv4Ac6UCjesFG+wzavI8/LDuD4wd\n1h+fjngfzo7lMWfpj4hLvI+ubZsbP+/SyM5ONZx9/R7aj1yIFnWrYdboXrC3tcS077fBQqFA4xB/\ng30GfbUGW/afxbSRPTCqbxvEpTzEl8v/QMu6AfBwKW/8vEsj+3Gi4ey7KWg/cxda1HDDrP71YW+l\nxLRfL8BCLkXjai4ljn3utsvYfDIcXk62GNDC+L+vUstOv284OzYT7VfdRAtfe8zq4Al7CxmmHYiD\nhcwMjSsb/ps5ei8D20LT8NfIGhjRyAVD6lfEkPoV8U6wI5xsFMbPuxSy1Y/SDO7nYooGPfao0MxV\ngSn1LWGvNMOci7kwl0rQwEVusM/+GDX6H1ChpbscMxpYwa+cFCtC83AvvRBdvZVGz7s0srPSDReU\n19IFDL4INHAAPvEHbOXAsghAaQbULifR276iOdDOGejtpvt1LR0wlwJfVAEspPr9TJWdnGt4LOWr\n10eLZftx//JRXF81FZqsDNQYPgNF+XlIu37GYJ8aw2eg+pApCP9tOW5vWAipuSVCPp6P9HvXkRl7\n16g5l1b2w3zD83YLqo/31+5HzPlj+HvpVORlZqDFmBkoyM9F/NWzBvv0Wfobgrq/j0tbVuHkyrnI\nTX+E9hMXIS8rE0lhl4yetymzn7Kt6Ib31+6DjbMrQneuR0ZSXLHbVmvbA72+2YjruzfjxMqvIVOY\no9UnM5EWeQcP7t3S2bZy+3dgYVsOzs7OJY5BIghCiW9VwsLCAKEINXxd9f6vXdfeyFRl4ezRf1dD\nJn41EyvXrsP96FtQKvWfAKqGNEStoJrY/Muqf9r6DhyGq6FhuHvtXImDfpVsjUaDZd+vwbQ582Fh\nYYFHjx4j+tZleLi7GZ1bWtlCRrzB9vb9PkJmVjbO7F73b/acZVi1YStSQo9AqdR9sXn0OANONVph\nwVefYPyH/75rWPDdL5g8dwXuXz+C8g72Rs27NLKF1BuGs0cugCo7F6fXT/unbdLS37Dqj6NI/ns5\nlArdF6jYpDT4dv4UyycNxIjeLbX7FgT4d/kc9Wv4YOPckUbNudSyo84bzp65C6ocNU7P+/dd9qQN\nZ7Hq4A0k/zwYSrm02HGHRqehyeRtsLdSokolexyZ2d3oOZdadnSo4exVN6DKL8TpMUH/Zu+Jwaqz\nyUieUR9Kmf4BjKn7Y7Hhciqip9Q1coaGlUZ21r3bBtt77MmESi3gSE+7f9qmncvGzzfzEfFBOSgN\nFHKOHR8AACAASURBVDUhmx+jlqMMa9vY/NO2KiwXK8PycLaPPcxlxhVCpZGdEv3IYPaQiwKyCoDf\nGv67/aK7An6NB860BBRmJc9hfYyA+XeBXxsANeyMm3NpZV9+ZPiA21tL90FuZYvDQ/9dBQr66Gv4\n9hyOHR0qoUij1uvTbXcsUi4cwflZQ/5pa73mJArzc3F0dNsSx1qa2XczDH/v3vtxL5TWtljbt+m/\n+xk/B3X6DsfCxq4ofCa7YtUgfLj9Av5aMgWn1iz8p71u/5Fo/ekcLG7mifxslVHzNmU2AAT3GIA2\nn88DAFjYOWDdwNaIvXSq2O1H7wtD8q2r2PbZgH/aen2zERUDgrGiQ3WdbVsu2YZybpVRo0aNEsfx\nSoeA1Wo1jp86ix5dOuq09+rRFZmZKpw6o/+CFhsXj/CISHTv0kG3T/cuuBcZjcio6P9ZNgDsO3gE\ns+Z/gylfjMe8mVOMyipb2RocP3cF3du30M3u3BqZqmycuqB/eC4zKxsjBvRClzbNdNqr+lYGAETF\nGV55KlPZmgKcuHwX3VvU0Wl/u3VdZGbn4tTVcL0+Lo72OL9pBt7t2PCfNolEApnUDHlq4w8/mza7\nECduJqF7fW/d7IY+yMxV49TtpGL7agoKMWj5EYzpVBP+lYwr8MtMdkERTkRloHt13ZXSt4PKIzO/\nEKeiDK+OX0vKQlAlqxfOKzPZhQJOJ2nQubLuG6lu3kpkqgWcTS7Q63M9rQDRGUX4sIa5TvuHNSxw\ntX85o4s/k2YXCbjwGGj9zKJFu4pAVgFw+XHJ+3iYL2DpPaCfx4sVf6bMNpPJ4RTSDAnHdE+RiP97\nG+RWtnAMMnxoUKo0R0GObsGhzngIpZ3DG5EtlcvhVbcZ7hzWzb51aDuU1rbwqN1Yr08Fn6oQBAHh\nx/bptMecPwaFhRW86hl3JMuU2QDgXKUmOk1bgWs71mPHhEGQSJ7/+2JXyQPlvfxx58guvfE6ePii\nnLt3MT1L9koFYFR0LNRqNfz9fHTafb21L+53I+7p9bl9JxwSiQT+vvp9BEHA3XD9Pq8rGwDq1Q5B\nzK0rmPjZWMikMqOyylR2bALUag38vT11s73ctdmRsXp9vNwrYcXXE+Hn7aHTvnP/35DLZfB/pr1M\nZv8fe/cd31T1/3H8lWZ1t1BGS+luKS2j7L1luJiCG/m6J/h1K6ggigLyBQUVRQUZgiJ7iaigDJmy\nZ+meUGihTWfaNL8/0qIhKQ3FX1PM5/l49KG5vSfvc0t788m5556kZaEvLSMiyNc8O8B01o5NyrRo\no1GraBcVjIebC0ajkdRz2bwwfQkJ6ReujMrV+ezzeejLDERcVUSF+5lGaGLTL1fZdvLy/ZQZjEy6\nt5PNeXUmO7sYvcFIREMX8+wGpsexF6xfRjySXoCu2EDPOUdwe+0P/CftZfzGJMoMts/Jsmd2Ul45\negOEe5uPrIZ6mU7VZy8bLNocu1iGQgFqpYJ7NuXh+2U2wfNzeGNXAfqbJDu1EErLIeSq+jnI1fTf\nhILqn+PjOFAq4L8RNsfaPdvNPxQntYa8FPM3kbo002uIR5D16SVnvptN8O2j8e08EJWrO0GD7sO3\ny0ASNy25KbLrNQ1FqdaQnXTWbHtOsim7QUikRZvCSxdRKBR4NTF/zagfaHodtrUQsmc2QG5GMrMH\nRvLzh69TWlxIdRdhG4aaik/L/sajUChoEGL938kWNatCKuTmmd4Je3p4mG338HAHIC/Pckj0ShvP\nKtro8v/fsgH8/Hytbr8eds2u+Pl4epifrTzcTWerPJ0NZytg9Y9bWbRiI2MfvRevq/4t6mR2vulF\n19Pd/EXZw830OK/g2hPVpy/YwIQ5K1Ao4LERfbilcwubcu2eXVhiynY1H5XxcDZdcs4rsrxEA7D/\n7HlmrjvM9ikjUKuqvkxbZ7OLTcWGp7N5ew+t6XFeseVoVHZBKel5egxGmHZnMIH1tGw9e5lpW9NI\nyy1h0f2WJ/a6lp2nLzdlqc1HBSof6/SWLxYXi4wYjTB6s46RERrGtnHhYFYZ7+8v5GJROV/2t+1v\nzJ7Z+RU/UrerXpEqHxdY/sjN5OiNrE2HR0PA3cZRx7qQrXE3vZkqu+ryYeUIm9rN+nzTM8s+okGr\nLvT5aANgml6SsH4BZ5Z9dFNkaz1M2SX55qPplZdRte6WvzdJ+37nUmoCt7/5EeuKi0g/fgDf5jH0\nf2kKxvJy1C6udT4boFiXS7Eu1+b9q+/v9d2/8Hc3VACWl5df8/tOTpYDjOXl1652nWyYa1HT7H9K\n3c6u/ue3atOvPPjcm/Tq0o5pE57/d2Qrrv0zH9y7Hd3bNmPXoVgmf7GGomI937z3ZN3PrmYQxcnK\n5YOSUgOPfPIrLwxuQ/uwRjbl1L3sas4TVrLdNEp+eqIlEQ2dCaxnuiTZM9QLjdKJtzcnM6F/AJGN\nqj9R2zf72t+39iemr2g0OFTDpC6mN2c9mqgxGGHy3kJe72ggzKv6QrwuZ1d3ZlmeCuXA6KBqdqxj\n2VRz7sDKucdJpab/vN9xrt+YfR88hS4llgatutLikQkYigs4OOulOp+tqOY10mglu7ysjMWP3s7Q\n979k9PzNKBQKdFkZ/DjlBUZ99B2lxdXfrW7v7JqoSX9tdUMFoJenqfLU5ZuP2lWOgFm7u7VyxEen\nq6KNp23VbE2y/yn2za74+eUXmmdXjL55ebpfs/2seUt49d2P6de9I6vm/w+NxvqdfXUuu2KUUXfV\naFvl6Junh4tFm7+LDjPdwNSjbSSlZQbe+Xw1740dRdPG1c9bsWt2xeib7qrRtsrRt6tH5wDe/HYP\nRiNMGNkBg6EcI6Z36SgUGAzlKJW2vUGxa7az6dSkKzG/7JhXOTrnYllUOKuduKWZ5XzD26Pr8dbm\nZI5kFNhUhNkz21NjKjfyr1pCJa/iceX3/85DrUChgIFB5n9P/QPUvLPHNE/PliLMntkeFc2vHm2r\nHJ3zqOaVast56O4D9az0sS5nlxaYRoJUbubnTlXF6Ju+wHKkKKDfXXiFtWLb2EFk/fkbABcO76S0\nII/2L88mbs1X5CVav8GormSXVIyAadzMR9sqR7OK863Ps72Ulsg3D/XH1dsHl3o+5CSdxatJEAqF\ngqJc6zcX1aXsmqi+v7aPJl7thoaqwkKDUSqVxMWb37gRV3EjR1Sk5YSIyGbhGI3GK/v8vY1CoSCq\nuW3Xs2uS/U+xa3ZQU1N2kvkdwpWPoyJCqmw77s3pvPzOLO4bNoiNS2bj5nrtwqVOZQc0Qql0Ij7V\nfImYuBTTEiJRIU0s2qRkXmT+mt/Rl5qf2ds2DwYgI8uG2d32zvb1QunkRHym+R95XMXjqKaWReSq\nPfGcybiMx/1foL17Ls53z2X7yQx+P5GO8z2fs2jb6bqf3cAZpUJB/EXzojuu4nGUlWIq7mIR83af\ns7hEW1Rqeofc0N22Nxz2zA7xUqJ0goRc8+IzIdf0PJH1LIupMC8lRiPor5qiVxFt81Io9swOdDXN\noUsxf29JcsXjsGu8tzxfbORkHtxe/apEdS47Py0eY7kBj6bhZtsrH+clWv69uPqa5qFdvUxL1qEd\npnlqIdF1PjsnJZ5yg4H6Qebz6OsHmrIvxlsWkSqNllZ33oe3fxCFl7PJTozFaDTSpGU7jEYjmSdt\nW5/Untk1cTHRdN+EZX/DMBqNXIy37ZxqzQ0VgFqtll7du7Jq3Qaz7StWr8fb24tOHdpZtAkLDSEk\nOIgVq9ebt1mznojwUJuXY6lJ9j/FvtkaenVpy+pNW82zN/yCt5cHndq0tNrujffn8OmC73npqdEs\nmvMeKtX1D/7aNVujple7SFb/ar7e0spf9uPt4UqnlpaTcJMzs3ninfms3mreZsvuY2jUKiKDbTtr\n2zVbraRXtB+r9ySYZ++Ox9tNS6cIy8us68bfwd5po9g3/e4rX+1CG9I+tBH7po9icMfgup+tcqJX\nqCerj2WbZx+9iLezik6BlvN0MvP0PLMyjh+OmK+v9/2hi3g5K2nf9Noj1HUiW6mgm5+K9Ynmo65r\n40vw0ipo38jyb6dbEzVualhxtsRs+6YkPWon6ORr29+bPbM1Tgo61DONpv3dT+dMa/K19rLeDuBo\nrukybdvrv9nc7tnlpXqyDu2gaZ/hZtsD+t2FXneZ7JP7LNrkJZle8Bu26Wm2vWGb7hiNRvIzbFtJ\nw57ZhlI9yQd2EDXAPDt60AiK8y6TfnS/1Ta3v/Ux7Ub9tfyMwsmJTg8+y6XUeLJirS8fVpeya+JS\nagKX0hKJHjTCor85yWfJzbS+XJwtbngh6MCm/nww42OOnzyFh7s7C7/9numzPmHym6/Tq0dXdLp8\nDh89hrNWi6ur6Z2zt6cn78/4iKwLF1Aplfxv9mcsXvYDcz+aTnSUbZOla5r9d0eOHmftxs01Wgi6\nVrKrWAg6oIkvUz9ZwIkz8Xi4u7Lwhw18+Nki3nnlaXp1aYcuv4DDx8/grNHg6urC4eNn+M/zb9Op\nbQtefPJB0jOzzL68PNzRamxbJLdWsqtYCDrAtz5T52/geFwaHm7OLFq/gw8XbuKdZ+6iZ/vm6AqK\nOHwmBWeNGlcXLYG+Puw+cpZv1mzH092Vy7pC5izdwtwftjLxqeH072K9YLVbdhULQQc0cGfqqoMc\nT8nGw1nNom1n+HDNId65txM9W/ijK9JzOPECzmolrlo1jbxc8avvZvb13c6zaNVKXhvRHheN7QV4\nrWRXsRB0QD0tU7emcfxcIR5aJYv2Z/Hhb2m8c2sgPcO80BWXcTi9AGeVE64aJYHeWnYl6li4/zxu\nGiW6EgOf7Mzg010ZTL0jhJ5h13glt0N2VQtBN3VXMvNgESdzDHioFSw9U8LHh4uZ0NGV7k3U6PRG\njl0sQ6tU4KpWoFEqcFYq+ORoMReLytEqFSyPLWHmwSKebu3M7SG2LwRdG9lVLQTt5wzzEiE2H9yU\nsDoDvk6EceHQsb6C/DIjp/JA42Q+svjjOThyGV5rfv2XYGszu6qFoAvPpxI95jW8w1pQVphPyB0P\nEfXgyxz/chIXDu9E5epOvWZtMOiLMZQUoUuJpUnXQYQOeZiywnxUru4EDriH1k9OJvOPH4n9brbN\nx10b2VUtBJ17LpWeT7xGo4gW6At0xAwbTY/HXmbbnEmk/LkLjZs7vlFtMJQUX5ljp3X3oMvosRTr\nctG6ezDw1emEdu7L6tcfIScl3ubjtmf233n7B9F2+BgOr/5rIWhr2cW6XHo++TpuPo0oLy+j28Mv\nEjP0QTZOHsuFq0Ysr2ch6BsuAEOCA4lp1YKVa9bz6bwFJKem8uZrL/LiONNCt3v27afXwCG0iIok\nprXpRa9N65Y08fNlyXcrmLdgMTpdPrOmvcs9I4dbPP+11CT7726kAKyV7CoKwJBAf2Kim7Fy4698\ntvAHUtIymfDfx64stLznz2P0HvEY0ZFhxLRoxtyFP7Bj7yEyzl1g/rK1Fl+D+nQlqKnlZUy7ZVdR\nAIb4N6R1swBW/XKAz5b/QkpmNhMeG8ILo01rSu45Gk+fR6cQHeZPTGQgCoWC4bd0IFdXyNerf2fe\nym3kFRQzZewonrtvgE3HW6vZVRSAIY09aR3sw6rd8Xy2+TgpF3RMGNmRF4aYPnllT+w5+ry1huim\n9YkJaWD1ORZtO42Tguv+JJBaya6iAAyp70zrJm6sOprNZ7sySblUwoQBAbzQ23SVYE+yjj6fHiXa\n15WYJu6mn3krH3QlBr7Zn8WXe85xuaiMKbcH82S367tGVxvZVRWAQZ5KWvqoWJeg58vjxaTqynml\nvQvPtTFNm9h/vozb1ubRvL6KVg1MBXUnXzWB7k6sjNMz73gxiXnljGvjwhsdbb87sbayqyoAm7oq\niPQwjcQtTYGMYng6DB4OMRUQhy/Dg3shwh2ae5oXYelF8EhIzQvA2siuqgAsyEzictwRAvqNIGLk\nM7j5BnLym/c5s+xjAHxaduGWz7eRl3iSy3FHAUj5+XucfRoTPuIJwoY+imvjpsQu/4RDH78M1X+2\nQ61mV1UAXk5P5tzpo0QPuouODzyNd5NAtn/+AXsWmrKbtunMw0u2cSH+FOfPmLKT929HqdbQ8f6n\naDtiDCX5eax7+ykS/vjF5mO2d/bfefsHETNstNkngVjLPn/6KLoLmbQecj8d7n4crZsHP017hRM/\n/mDxnLX6SSDi/09VnwTyb1fVJ4H861XxSSD/elV8Esi/XVWfBPJvV9UngfzbVfVJIP92VX0SiPj/\nUWufBCKEEEIIIW4+UgAKIYQQQjgYKQCFEEIIIRyMFIBCCCGEEA5GCkAhhBBCCAcjBaAQQgghhIOR\nAlAIIYQQwsFIASiEEEII4WCkABRCCCGEcDBSAAohhBBCOBgpAIUQQgghHIwUgEIIIYQQDkYKQCGE\nEEIIByMFoBBCCCGEg5ECUAghhBDCwUgBKIQQQgjhYKQAFEIIIYRwMFIACiGEEEI4GCkAhRBCCCEc\njBSAQgghhBAORgpAIYQQQggHIwWgEEIIIYSDkQJQCCGEEMLBSAEohBBCCOFgpAAUQgghhHAwUgAK\nIYQQQjgYKQCFEEIIIRyMFIBCCCGEEA5GCkAhhBBCCAcjBaAQQgghhINRGI1GY3U7HTx4EKPRiEat\nqo0+iUrlZfbugX0Y9PbugX2Uldi7B/ZR6pjHXV5aau8u2IWhrNzeXbCLEsc8bEod9LjtxcWnMUqV\nmnbt2lW7r00VnUKhqPgfGTCsVUqNvXtgH4563Bp3e/dA1CJHPZs66nGr7d0B4RBKS0v/qtmqYdMI\noBBCCCGE+Pdw1DdjQgghhBAOSwpAIYQQQggHIwWgEEIIIYSDkQJQCCGEEMLBSAEohBBCCOFgpAAU\nQgghhHAwUgAKIYQQQjgYKQCFEEIIIRyMFIBCCCGEEA5GCkAhhBBCCAcjBaAQQgghhINR2bLToUOH\nMBqNqNXycdZCCCGEEHVRaWkpCoWCtm3bVruvTQWg0WjEaDTecMfE9Skvd8yfucHgmMddZii3dxfs\noqzUYO8u2EVZmWP+e5frHfPf2+Cgv+cGvWP+ntuLt68LKrXSpn1tKgArR/6io1rUvFfiuunyiu3d\nBbu4kF1o7y7YRUpqrr27YBcp8Rft3QW7SEnNs3cX7CLr+Hl7d8Eusk465u/5hTM59u6CQxm/pj9+\nofVs2lfmAAohhBBCOBgpAIUQQgghHIwUgEIIIYQQDkYKQCGEEEIIByMFoBBCCCGEg5ECUAghhBDC\nwUgBKIQQQgjhYKQAFEIIIYRwMFIACiGEEEI4GCkAhRBCCCEcjBSAQgghhBAORgpAIYQQQggHIwWg\nEEIIIYSDkQJQCCGEEMLBSAEohBBCCOFgpAAUQgghhHAwUgAKIYQQQjgYKQCFEEIIIRyMFIBCCCGE\nEA5GCkAhhBBCCAcjBaAQQgghhIORAlAIIYQQwsFIASiEEEII4WCkABRCCCGEcDBSAAohhBBCOBgp\nAIUQQgghHIwUgEIIIYQQDkYKQCGEEEIIB6P6J55ky89bePvttzl58gSNGzfm6aef4cUXXrxmm+++\nW8YHUz8gISGB4OBgXn3lVUaPfqhWsisdOnSIbt27cuZ0LIGBgTdV9tZtvzBlymROnzlFw4aNeOzR\nJ3ju2eer3F+v1zPnk49ZvnwZ6RlpNPHzZ9Soe/jv8y+hVqtvmuydO7cxa9YHxMWdwcenIQ888AiP\nPvrMNbJLmDPnQzZsWEVOTjbNm7dg7NhX6NGj73Xl2jv7wIEdLJg/i+Tks3jXa8DQIQ8w6u7HbGpr\nMBgYN3YULi6uzPjfkpsq+/iJvaxe+wXpGQl4etanX5+7uHXgA1Xun5WVxhtvjbLY7t8klMkTv71p\nsuPiD7D1t4VcuJCEm1s9OnUYQreuI6/ZJvbsXn7fvoTzWUm4unoQ1bwnt/R7GI3a+abJbh7jx+D7\nY/AL8EZ3uYjtm2P5dd0pq/t27hPKg891rfK5Fs/5g32/J94U2a27B3Lf891oGu5DbnYhm5ceYcOC\ng1Xu7+SkYMij7el7VwvqN3QjM/kyq+ftZ/fmszZn1oXsDgPCePS9Wwhu0YhL5/NZ8+k+ls/8o8r9\nVWolD7/Tl/4PtMargSvJpy7y3fSdbFt+/KbKrtTQ35P5x55lwtClHN2RfM19+93bitETeuEXWo9z\nSZdZOnUHWxYfqXE2gHLSpEmTqtspKyvL1NmGjSy+t2fPHm67/Vb69unL5Hffxdvbm0mTJuLs4kL3\nbt2tPt+qVSt54MEHeOCBBxk/fgIlxSW89fZbRDVvTosWLWzufE2yKx0/fpw7B9+BTqdj3Ljn8fLy\nsjm3trL1JWVWt+/fv4+7Rg6hZ8/evDlhIl5e3rz/wbs4OzvTpbP1k9LLr77A/Plf8tRTz/Lcs8/T\nqFEj/jdzOmlpqdx26x02H3dtZBcWlVp9nsOHD/Dww3fTpUsPXnhhPJ6eXnz88VS0Wmfat+9stc1r\nr41lw4ZVjBv3Kg8++CgZGenMnPkeXbr0pEmTpjYfd21k5+aVWH2ekycP8fpr/6FNm648/MiLuLt5\nsnDhR2g0Wlq2bF9t35cuncvWX9fh69uUgYNG2HzMtZWde6nQ6vb4hOPM/Ph5oiLbM2LYU7i6erBm\n3Zdo1BoiwmOstjl1+gAH/tzGqy9+Sp/eI+jVYwi9egyhU8cBeHrWt/m4ayO7qn/v1LRTLFk6npDg\nGPr1/Q/Ozu5s+30RKpWGwADr58czsbv5bvkkwsI6MOCWx2jQIIDde1aQnZ1GdFQPm4+7NrILsgqs\nPk9wswY8N/EWzh47z/qlRygq1HPnvTGUlhpIOH3BYv/LFws4vCeVP36JM/sKiWxAqd7AmoUHKdUb\nbDru2sguuGD99zwixpe3vh7Oib1pLPv4DwrzSrh3XFf0JWWcOZRptc0947oy8pkubFp0mLVfH0Dr\nouLBV3qScuYi6QmXbDrm2souzC6y+jzRnZvy4ZYxHN6WyNdv/kr+5RIeebcf+qIyjv+RYrXNOyvu\nYeBDbVg3dz9LpmwnL6eIZ2feSkFuCaf3p9t83PbMrtSwqScfbnmIBv6e/LTwMOdTcqvct9eIaN5a\nNoqflxxl8Xvb0TireGzKLSSfukDSSfPfz573huJRz4XGjRtX24cbHgF8Z/Ik2rZtx/z5CwAYOGAg\ner2eqVM/YNzYcWi1Wos2b739FnfffTcfTv8QgAH9B5Cdk83ESRMZNeru/9fs0tJS5nwyh3femYSL\ni0tNDtnu2VOnvUdM6zZ89uk8APr17Y9er2fmrBk89eSzFtmXLuWwaNECJr8zhWefGQdAz569MRqN\nTH53IhPfnkz9+j51Pnv27Gm0aNGaadM+AaBHj77o9Xo+//wjxox5Ao3GPDs9PZUNG1YxceI07r13\nDABduvTg4MF9LF26oMrCra5lL1z4MeHhLXj1tekAdOjQk9IyPUuXzmX4iDEW2X8XH3+K75Z9Tv36\nlm/e6nr2mnVfEhQYyaMPvw1AyxadKTOUsuHHhfS/5R7Uao1Fm5S0s9Sv14jIyHY1yqwL2b/9vgg/\n33CGD30FgPCwDhgMZezYuYwunYahUllm//TzF7SI7s3QwaYrECHBMRjLy9m7fw2lZXrUVtrUtew7\n7mlNakIOiz/ZDcDpI5moVE4Muqslv204TVlZudn+Bfl6CuKyzbb1uT0SX38vZryxmYJ8vU259s6+\ne2xXEk9e4NM3tgBwdFcKKrWSEU92YtOiw5SVWhaxfUe0YMf606z6fB8AJ/amEdayMbc+EMO+X+Jv\niuz/vNOPswczmfrwagAO/ByPWqPkwfE9WfnxbosCOjzGl+5Dm/PV+F9ZOm0HAIe2JVJSWMoTUwew\nZfERCnXW31TVpWyAW8e04akPB9m8/2NTbmHb98eZ+8pPAPz5SzxePq488u4t/PbDCZuf52o3NAdQ\nr9ezfft2hg0bZrb9rhF3kZeXx85dOy3aJCcnExsby9AhQy3axMXFER9v2y9QTbIBfvxxE1OmvMf4\n8RN4f8r7NmXVtexdf+zkjjsGm20fOmQ4Ol0ee/ZYDmHrdDoeefhxbh10u9n2iIhIAJKSkm6K7H37\ndjNggPnz3HrrEPLzdRw4sNeiTcOGjVm5cguDB/91+UqhUKBSKSkpKbYp197ZpaV6jh7ZR48eA822\n9+p1G4WF+Rw//meVbcvKSpk+7VWGDx9D06bBNmfWheyyslLOxB6iXZveZts7tOtHcXEBZ+OsX/pI\nTY0lICDiuvPqTLahlKTkozRvbn4VITqqJyUlhaSkWp7sM8/Fk5OTSedO5ufUzp2GMe7Zb2wuwOyZ\nrVQ5EdGiMUf2ppptP7Q7BWcXNWFR1b+JcPfScud9MWzfHEtKfI5NufbOVqmdiO7oz96f48y2795y\nFhd3Dc3bN7HaTq1VUlRgXmTqLhfj4W37wIJ9s5W06R3EjtXml9h/X3ECV08trXoEWbQJjGqI0Qi7\nN5wx235oWyLObmra9Amu89kAYa0b88LcwWz+5hDvP7QKheLa+zcO9KJpMx92rrHsr394fZqE1rM5\n+2o3VAAmJCSg1+tpFmF+0gsPDwcg9kysRZtTp0+hUCiIiGhmtj0sLByj0ciZ2DMWbf6pbICOHTsR\nH5fAa6++hkpVswFQe2YnJSWi1+sJDzPPDg0NBeBsnOU8jMDAID6cPpOwsHCz7Rs3rkOtVhMWHm7R\npq5lp6YmU1qqJzg4zGx7UFAIAImJcRZtNBoNLVrE4O7ujtFoJDMznSlTJpCamsx99z1sU669szMz\nUykrK6Vp0xCz7f7+QRV9S6iy7aJFczAYyhjzn6rnZ9bV7AsX0jEYSmnc2Hx+bONGpkvn585bny+T\nknqW4qIC3p/2OE8+25sXXr6DFas+w2CwPp2irmVfupSJwVCGT31/s+3165tejC9mp1q0OXc+HoVC\ngVKpYul3b/HeB4OZNuMuNm+ZS5nB+nSKupbdoLE7SpUTWRl5ZtsvZOoAaOTvWe1z3HlvDOXl/fYG\nGgAAIABJREFURjYsu755UfbMbtTUC5VaSWbSZbPt55JNlwObhFh/cd+48BC9h0YR0yMIZzc1Pe6M\npE33IH5fe/KmyG4SWg+VRkla7EWz7elxpuI5INLyqlDuxUIUCmgc5G223T+8fsVz2jbFw57ZAOeT\nc7k/7CM+f3ULJYWlGI3X3j+oovhMizUfcU6Py0GhgIDIBjZnX+2GLgHn5pp+UTw8zf9APDw8AMjT\n5Vm0yato41lFG12eZZt/KhvAz8/Ppuevq9l5FT+fyqxK7u4VP78qsq+2YcM6vvt+KU8+8TRenrbN\nf7Rndn5+nllWJTc394rv667Zft682cycOQWFQsHdd4+mW7deNuXaO7ugwPTcrhVZlVxc3AAoLMy3\n2u706aOs+OFrPvr4e1Sq67vRpi5kFxblm2VVcnZ2BaCoyHI+VX5+LpcvX6C8vJy7Rz6HT31fTp7a\nz6bNi7l0KYvHH51U57NLSkzz47RaV7PtWo1LxfctswsLLmM0Gvn+h8m0atGXbl1HkZ5xhm2/LaKg\nIJe7hr9e57NdXE2/J8WF5kVjcbGpeHZ2ufbvkbunls59QvllzUmKq5hDXBezXT1MUyiK8s0vHxZX\njLC5ulsfQd248BDN2vgxfl7FVSijka0rT7Dhm0M3Rbabl+nmoIKr5sFWXkZ187S8eejwb4lkJlxi\n3JzbKSkq5fT+dMLb+PHE1AEYy404u9l2rrFnNkB+bjH5ubZfBaq+v1VPw6nODRWA5eXl1/y+k5Pl\nAGNN2vxT2f8Uu2Ybbzx7/fq1PPHUI3Tr1oNJE9+7ObJv8Gd+yy230r59Z/78cy+ffDKD4uIipk//\ntM5nG6vLVlhm6/UlfDj9VUaOfIRmzVralFPnsqt5W+xk5bqJVuvMS/+dTeNGAfj4+ALQLKINKpWa\n1WvncecdD+Pna3l552bKVlj5mVeOMEY1707/Wx4FIDioNcbycn7dtoA+vUdbjOrVtWxFNdfBqutb\n9wHhKBQKftt4utqsupTt5HTt7PJyy2yV2ol3v70bLx9Xvnj7FzISLxHZtgkjn+5ESVEZ33zwe53P\nVtQg21BWzssDF/Ha/GHM+HkMCgVkZ+iYPW4Tk364x6KAr4vZNVGT/trqhgrAyrtX83XmIyCVI0Ve\nnpZD554VbXRVtPG08W7cmmT/U+yZ7elheu78fPPRl8rRN89qRtQ+mzuHtydOoFfP3ixe9B0ajW1z\ndOyd7VGRXVBgnl05+lb5/aqEh5vmHHbo0IWysjLmzJnOiy9OwNfX+jyXupLt5mYadbx6tK3yceX3\n/27B/JkYjUYeePBZDAYDYMSIEYymZVmUSmW1ufbOdnExjToWF5uPOhUVFZh9/+/Uai3RUR0ttrdu\n1Z3Va78gNe2sTUWYPbO1WtOoo15vfudk5eib81WjcwAarYtpWk24+Y1F4WEd+GXrfM6di7epCLNn\ndlHFC+jVo22Vj6t7gW3TJZBTRzKv6+aLupBdOYrj7GZ+LnSpGH0rtPKcXQZFENjMh8mPrOLE3jQA\nTv+ZQVG+nkff6sMvy4+RZsM8RHtmF1SMgFWOQlZyrRjNKqhihCwz8RL/7bsALx9XPH1cSDubQ+Mg\nLxQK0OVYv9u4LmXXxF/9Nf93+qu/tt98crUbGqoKCwtDqVQSd9WNG3FxpjlRzaOiLNpENovEaDQS\nH28+byo+Pg6FQkFUc8s2/1T2P8We2SEhoSiVShISzbMTEkzzsZo1i6yy7Wuvv8ybb73BXSNGsfz7\n1bi5uVW5b13LDgwMRqlUkpxsvrZW5eOwMMvJ9xkZaaxY8S16vfmJLDq6FQDnz5+r89l+TQJxclKS\nkW4+7yy94nFgUJhFmx07fiItLZE772jFrYOac+ugKI4d3c/Ro/u47dYotmxZXeezGzX0x0nhxPks\n83lnWRdMLzp+fsEWbc5npfLb9jVXCrVKpaWmE6SHu22Tpe2ZXb+eH05OTuTkZJhtz7lketyggeWa\noT71/TEajRiumnNnKDfdyWjtzt26ln3xnA5juZGGfuZvKiofn0ureokMr3ouNA2pz8FdSTZl1aXs\ncym5lBvK8b1qbplvoOlxupViqkFFv84cNF+m5eSBNFAoaBpu26oK9szOiM+h3GC8MoeuUuXj5FOW\nS+9otCr6398a3yBvcrMLSY3Nxmg0Etm+CUYjxB7MsGhT17JrIvXMRRQK8L/qZ+sf7oPRaL2/trqh\nAlCr1dKzZy9WrzY/qa9ctRJvb286dexk0SYsLIyQkBBWrlpp0SYiIsLmRZFrkv1PsXd2t67dWb9h\nndn2tetW4+XlTft2Hay2e2fy23z51ec899zzfPH51zW6CcWe2RqNlg4duvLzzxvMtv/003o8Pb1o\n3dpy6Y309FQmTHiBn3/eaLZ9585tqNUaQkNtuwHF3tmtW3dk584tZtt3bN+Mu7snzZtbrkn33pR5\nfPrpKj77bPWVr4iIFkQ0a8mnn62ma9d+dT5brdbQrFlbDh76zWz7gT+34uriQWhItEWb3NxsFn87\njf1//mq2fe/+n3FxcSc4qHmdz1apNAQFtuLUafOVBE6e3IGzszv+/pbPExTYCo3GmWPHt5ltP3Pm\nD5RKFQFNLftb17LLysqJO3memC4BZtvbdg2kqEBP0tmLVbQ0reGH0Wh1vb46n11q4NSBdDoPMD8f\ndBkUQWFeCXFHLd8oVq61F9XB/ApC8/b+YDSSdY2Cta5kl+oNHNmeTM8R5r8fvUe2IP9yMaf3Wa6r\nV6o38Pwnd3DH43+tP+rkpGD42C5kxOeQeDyrzmfXREbCJTITL9N75NX9jSb9bDZZqbb9zK254YWg\nAwICmDZtKidOHMfdw4NFixcxY8aHTJr0Dj179kSn03H48GGcnZ1xdTVdQvDy8mLq1KlkZWWhVCmZ\nNXMmS75dwqeffEp0tG0njJpm/92RI0dYt35djRaCro3sqhaCburflFkfzeDUqZO4u7uz7LtvmT1n\nFuPfeJNu3Xqg0+k4duwoWq0WV1dXjh07wtPPPkH7dh149ulxZGRkmH15eHhaXbfQXtlVLQTt5+fP\nF198zNmzp3Bzc2f16u/56qtPGDfudTp27Ep+fj6nTh1Do9Hi4uJKkyZNOXRoPytXLsPd3ZO8vFwW\nLZrH0qULGDv2Fbp37201x17ZVS0M3KihH8uWfUFiUiwuLm5s2bKa77//kv/853lat+5EYWE+cXEn\n0Wg0ODu74O3tg49PI7OvrVvXo1FruO++p9Bqbf90htrIrmoh6Pr1GrNp8yLS0hNwdnZl1+5NbP7p\nW4YNeZzIZm0pKi4gJSUWtUqDVutM/fqNORt3hJ1/bESrdaa4uJBftv7A1m0rGDniGSKbtbX5uGsj\nu6p/by+vhuzY9R1ZWUloNC4cOfIzu3Yvp1+fMQQHtaKkpJBz5+NQqjRo1M4olWpUKg27966ioOAy\nKpWao8d+Zecf39Ol03CaR1b9iRX2yK5qIeici4UMGtGSJoHelBSX0rlvGAOGRbPxu6PEn7qA1llF\nQEg9SkvLzdZpa989iJDIhqxeZPtNCPbIrmoh6IvndAx/oiMBET4UF+jpPSyaoY914Ps5uzn9ZwbO\nbmqCoxpRWlKGvriMzKRLtOkZTL+7WlBcWIqzm4butzfj3ue7cej3JDZex8+hNrKrWgg6KyWXB9/o\nSUjLxhTpShg0pg33vdqDBW9v4+jOZFzcNUS08aO0uIySitcEVw8tI5/vQv7lYtw8nXl6xiDa9Qvh\ngzGrr9zFawt7Zv+db5A3t/6nDZu/+WshaGvZBZeLeWB8L+o1cqO8zMg9L3Vj4Og2fPTMBosRwOtZ\nCPqGC8CQ4BBiWrdm1apVzJ37GSkpKYx/YwIv/PcFAPbs3UPffn2Ijo4mJsY0YhAT0wY/Pz+WLlvK\nV199iS5fx/9m/I+7776n2g7faPbf3UgBWBvZVRWAQUHBtGzRinXr1/D11/NITUvl5Zdeu7LQ8oED\n+7hj8ECaR0bRsmVr5i/4ij927yLzXCZLvl1o8dWvX38CA2wbea2N7KoKwKZNA2nevAU//bSeb79d\nQEZGKs888yKPPPI0AIcP7+eBB4YQHh5JVFRLFAoFAwbcgU6Xx/LlS/j++0Xk5+t46aUJjB79uE3H\nW5vZVRUEvn4BhIY1Z+eOn1i3dglZ59N54MFnGTnKNOn+5MlDvPjC/QQFRRAWZn36wZafVuHk5MTA\ngdf3SSC1kV1VAdiwQRMCmkbw58FtbP1tJTnZ57jzjocZNOB+ABLijzN1xtM0aRJCYEAECoWCdm37\nUFJSyM5dG9m+Yw2FhfncNfxp+vYefl3HXRvZVf171/P2xbdxKCdP72D/gfVczs2iV8/76dblLgDS\n0k+xYOHLNGwQhK+v6TJ8QNNovL0acfzEb+zbv5ZLlzPp1nUUfXuPvq7jro3sKgvArALSky7Rtmsg\nvW6NxKehO5tXHGfretPNFSHNGvDCewPJTMsl/W9Ll7TtFoRPQ3e2rrf+sW22qI3sqgrAC+l5JJ2+\nQNdBEdz6QAwNm3iy8vN9bFxoKqaatfFj8pJRpMbnkHzGNBr5x6ZYvBu4MuDeVtwysiU+vh78uPgw\nC6dtr3ZZkdrOrqoAPJd0mfgj5+k9Mpphz3bGN8ibxe9t54ePTItxt+gSwMfbHyHpZBbxR88DcPT3\nZFQaFcOe6cStD7elIK+EGY+v48DPti9Abe/sv/MN8mbQmDZmnwRiLTv+yDmyM3QMeDCGwU+0x81T\ny6cvbmbbcsu1Oa+nAFQYq7vFCTh27BgA0VG2f0ybuHG6PNtvFf83uZBt/UT5b5dyA0P5N7OU+Kov\nsf2bpaTatmzSv03W8fP27oJdZJ10zN/zC2dqNjomamb8mv74hdajVatW1e77/7deiRBCCCGEqJOk\nABRCCCGEcDBSAAohhBBCOBgpAIUQQgghHIwUgEIIIYQQDkYKQCGEEEIIByMFoBBCCCGEg5ECUAgh\nhBDCwUgBKIQQQgjhYKQAFEIIIYRwMFIACiGEEEI4GCkAhRBCCCEcjBSAQgghhBAORgpAIYQQQggH\nIwWgEEIIIYSDkQJQCCGEEMLBSAEohBBCCOFgpAAUQgghhHAwUgAKIYQQQjgYKQCFEEIIIRyMFIBC\nCCGEEA5GCkAhhBBCCAcjBaAQQgghhIORAlAIIYQQwsFIASiEEEII4WCkABRCCCGEcDBSAAohhBBC\nOBgpAIUQQgghHIwUgEIIIYQQDkYKQCGEEEIIB6MwGo3G6nY6ePAgRqMRjUZTG30SFcrLq/2n+Vcy\nGBzzuMsM5fbugl2UlRrs3QW7KCtzzH/vcr1j/nsbHPT33KB3zN9ze/H2dUGlVtKuXbtq91XZ8oQK\nheKGOyWun5OTY/7cHfW41WoHHZB3tuk0JIQQohqlpaU212w2jQAKIYQQQoh/DwcdchBCCCGEcFxS\nAAohhBBCOBgpAIUQQgghHIwUgEIIIYQQDkYKQCGEEEIIByMFoBBCCCGEg5ECUAghhBDCwUgBKIQQ\nQgjhYKQAFEIIIYRwMFIACiGEEEI4GCkAhRBCCCEcjBSAQgghhBAORmXLTocOHcJoNKJWq/+/+yOE\nEEIIIWqgtLQUhUJB27Ztq93XpgLQaDRiNBrBWH7DnRPXwVBm7x7YR6ne3j2wDzluh1Je4ph/36Vl\njvk6UlputHcX7KLYQY/bXtx9G6G0cbDOpgJQrVaDsZxW4cE30i9xnYwXUuzdBbsoP3vC3l2wj1NH\n7N0DuzCePm3vLthF/hHH/PuOi8+1dxfsIrGo2N5dsIvtuY553PZyz5pvaRAabNO+MgdQCCGEEMLB\nSAEohBBCCOFgpAAUQgghhHAwUgAKIYQQQjgYKQCFEEIIIRyMFIBCCCGEEA5GCkAhhBBCCAcjBaAQ\nQgghhIORAlAIIYQQwsFIASiEEEII4WCkABRCCCGEcDBSAAohhBBCOBgpAIUQQgghHIwUgEIIIYQQ\nDkYKQCGEEEIIByMFoBBCCCGEg5ECUAghhBDCwUgBKIQQQgjhYKQAFEIIIYRwMFIACiGEEEI4GCkA\nhRBCCCEcjBSAQgghhBAORgpAIYQQQggHIwWgEEIIIYSDkQJQCCGEEMLBSAEohBBCCOFgpAAUQggh\nhHAw/0gBuOWXrXTq1Q+3hk0IbRHD/z6eU22bZctX0LJDV1wb+BHdvjOLvl1Wa9mVDh0+gsa7ISmp\nqTdf9vY/6Dz0ftyjOhHW8zb+9+XCa+6v15fy/qdfEn3LUDyiOxPVbwjvzv6C0tLSmyv7wDG6PDcJ\nz8GPEzH6ZWb+8OM19y/RlzLh6x8Ie/AlPAc/To/n32XLgWPXnWv37FNJdJnxLZ4vzybina+ZufWA\nzW0N5eV0mfEt/ef8cNNl/5xyga4rduE17yeaLdnGrMMJ19w/PrcAzWebLL7afrfjpsreeknHLUfi\n8d99gjYHzvBJ+sVq2/yUo6P/kXia7D5Bi/2neSMhk0JD+U2V7dm7J1Gb1tE2/jSt9uyk8ZOPV7mv\nz6iRtE9PqvKr/sgRN022b98+DPp1M3enJzLk8H6aP/f0NfdXODkR/d+x3HlgN6PSErl1+68EDhty\nXZl1ITtqQD9e37uNj/MzeTf+CP1ffO6a+yvVaoa9P5H3k08wu+Acbxz4nfZ3X9/Pui5kV/L2b8L/\ncpIJ79mt2n073DuSt47tYXbBOSae2Efn0ffdUDaActKkSZOq2ykrKwsw0ri+t8X39uzbz8AhI+jX\nuydTJr6Ft7cXb7/7Pi7OznTv2sXq861cs5b7/vMYo++7hzdfe4WSkhLefOc9oiIjaREdZXPna5Jd\n6fiJk9w2fBS6/Hz+++zTeHl52Zxba9mFudazDx5h0ENP0a9rJ957eSzeXp5MnPkpzloN3Tu0tdrm\n2bfeZ+6S73n+kQd56fExNG7ow5RP5pGScY4h/fvYfty1kG3MOWc9+2Qct70xg74x0Ux++C683d2Y\ntGgVzho13Vs2s9rmkQ+/5Ltte5j40HCeGdqf1KxsJsxfQd82UQQ28rH9uGsj++J569mJGdz22Sr6\nNgtg8h3d8XbRMmnTbpzVKrqH+lfb9w+27GXZn6cJru/FQ51b2HzMtZZ90XqBsefcJW5fv58+/j68\n27kZ3lo1k/bF4qJyoptffattfkvPZmXCOX4Z2pknWgbxSHQAj0QHcE9EExq5am0/7lrI1p+3/ve9\nX1fIXSeS6entxpuBjfFSKXk/JQtnJwVdPN2stvkxJ48HT6XQz9uDScGNiXDR8knGReKKSxjsY/u5\nrTaycy6VWH0et3ZtafbdEnS7dpMx7UMMeXn4v/oyxuIS8vdbvunQZ2ZweeOPXFz6ndmXW4f2GIuL\nSHt3CsbiYpuOuzays8vKrGb7dGhPv9XLOb9jF0fem4o+L4/W41/DUFzCxb37rLZpPf41Wr72Mmc+\n/5KTH89B5epC28kTuXziJHmxZ2065trKTi6xftwhnTsybssaYrdtZ+2b71J0OY8h775JaVEx8X/s\ntdrmyRWL6fzQfWyf+zU/TplBQc4lRs58n6LcPJL3H7T5uO2ZXaleU3+e37IGb/8m7F64lJyUqgeD\n2o4YwmPL5rNvyff8+N6HqJydGTrlLc6dOkPmydNm+7a89y5c63nTuHHjavugMBqNxup2OnbsGBjL\naRUebPG9QUNGkKfTsXvbz1e2vf7WJD7/ej7nE8+i1Vqe+Jq37Ui7mBiWfvPVlW33jnmEQ0eOcuaw\n7aMLNckuLS1l9mefM3HKVFxcnMnJuUTiySMEBgTYnFtb2cYLKVa33zr6KfLy8/lj9ZK/sqd+xBff\n/sC5A9vQajVm++dczqVRu95MH/8iLz720JXt0z+fz/jpszn/52/41LMs7u2VXX72kNXs217/kLzC\nInbNfvvKtje+Ws68DdvIWD4brUZttn/y+YtEPPQyc557iCcH9wPAaDQSOeYVOkeFs/iNp2w65lrL\nPnXEevZnK8kr1rPrxb/e8b2xbgfzdh0l470n0apVVfb7SPoFes5ahreLlmaN6vPL2FE2H3NtZRtP\nn7a6/fb1+8jTl7Hzrr/eHY/ffZp5J1JIf/gWtEqlRZuJe2NZciaN+If62XqIdsvOP2L97/uuE0no\nDAa2tA67sm1S0jkWnMshtlNztE6WF27a/xlLW3cXvor861wyLyObLzKz2dU2HGcrbeyVHRdvvfCN\nWLYYpbs7pwcPv7LNf/zrNHzoAY60bo9Rr6+2/40efZimE9/k9ODhFB45Wu3+tZkdW2S9GO278nvU\nHh5sGXj7lW0xE98k4pExrIpoQbmV7GEnj3Dut9/Z88y4K9sG/LQRQ3ERW4eOrLavtZm9Pdf6cY/d\nvBoXTw+md+v/13N/MIleTz3Cq43DKbsqu2lMK8Yf3MHa8e/w07RZV7b3fuZxhk+dxOv+zSnW6Ww6\nbntmA3Qdcz8jPnwPALf69ZjZ9w7idvxR5f6TTh0g5eAR5j/w6JVtjy5bQEDb1kxq3t5s33vWfEuD\n0GBatWpVbT9u6BKwXq/n9527GD74TrPtI4cPJS9Px84/9li0SU5JIfZsHMMG32HeZthQ4uITiE9I\n/H/LBtj00xbenfYhb776MlMnT7Qpq25ll/L73gMMG2T+IjPy9gHk5eez84DlO5E8XT5PPXg3g2/p\nbba9eVgIAAkpaXU/u7SM7UfPMKy7+S/7XT07kldYxM7jsRZt/Op7s+eTSdx/S9cr2xQKBSqlkmK9\n7Zef7ZpdZmB7XBrDWoebZ7eJIK+4hJ0JGVW2LTUYeGTJZsb2bkdEw3o2Z9aJbEM52zNyGBZq/i52\nRJgvefoydmVestruyMU8Yhp4XndenckuL2dXbgF31Dd/nqE+XugM5ezJK7Rocyy/iMRiPU/4mY8q\nP9HEhz/bN7O5+LNntkKtxqNLZy7/+JPZ9ksbN6H08MC9U8dqn0Pl40OTV1/iwsLF11X82TPbSa2m\nUfeupG7YZLY9de161B4eNOzS2Wo7pbOW0vx8s236Szlo61sfna5r2Uq1mma9u3N49Xqz7QdXrMXZ\n05OwHl0t2vhGRYLRyLENm822x27bjsbNjWZ9etT5bAD/1i25b+4sdn/zLd889CQoFNfcv35gAI2a\nhXNkzQaz7YdWrKFReCgNQkNszr7aDRWACYlJ6PV6mkWEmW0Pr+jQmbOWw8GnTseiUChoFm7Zxmg0\ncsbG4euaZAN0at+epJNHef3lF1Apqx69qLPZKWnoS0tpFhJsnh1kevd9JiHJok1wgD+fTB5PREiQ\n2fY1P21FrVLR7KrtdTI7Mwt9WRnNmvqaZ/s3AiA2zfKysUatol1EMB6uLhiNRlKzsnnxs29JyLxw\nZVSuzmdn56I3GGjWyLyICm9gGjWNzcqpsu3kH/dQVl7OpNstT2h1PjuvEL2hnAgv88uO4RWPz1wu\nsNruyMU88vRl9Fq1G48vNhPwza9M2H2asnLb56PZMzupuBS90Ui4i/kVhFAX08j62SLLy6fHCopR\nABonBfeeTKbJ7hOE7j3F+IRM9DdJtjYoEIVGQ3GC+TzLksQkAJzDQqt9jiavvgSGctKnzbA5197Z\n7sFBOGk06OLjzbbrEk0DIZ4R4daacWbuPELuuRu/fn1RubsRPOoufPv1JfG75TdFdoPQYJQaDedj\n48y2X4gz/Rv4RkZYtCm4mA0KBfWDAs22NwwPrXhO2wohe2YD5CSn8lZYDKtefQt9YSFUcxG2svi8\nur9ZcQmgUFjtr61qVoVUyM3LA8DTw8Nsu0fF47w8yyHRK208Pa23sXEYtSbZAH5+vla3Xw+7Zlf8\nfDw9zF+cPNxNj/N01l+crrZ6868sWrWesf+5Hy9Pj+ob2Du7oMiU5epinu1iepxXWHTN9tO/38ib\n81egUMBjt/XhlrbRNuXaPbviRdfD2fzSeuXjvGLrl6b2J59j1rYD/P78vaitXK6s89l607whT435\nKcqj4pKzTm85ryi7WE96QTEGo5Gp3ZoT6O7C1rSLTD+UQFpBMQv7t6nz2XkGgylLaf7e3L3isc7K\njRUXS8swAqNPpzCygRdj/RtwML+ID1LOc7GsjHnNbJveYs9sZcW506AzH1kyVIw0KT2ufZ5Q+dTH\nZ9RdnP/sC8qvGp2qy9nqitfB0qte98oq+qL2cLfa7vRnX9CgYwf6rDDdPGk0GklYspTTn35+U2S7\nVMx7L77qtbLyMqqzldeFM7/t4GJCEvfMmU5pURFJ+w8S0KYVw6e+g7G8HK2ba53PBijKzaUo1/o0\nCOv99ayiv/lV9tdWN1QAllfzDs/JyvB/Tdr8U9n/FPtmX/vdgpPTtYeTAVZt/oUHn3+DXp3aM+31\nF26O7GreJTlVM4w+uGtburdsxq7jsby7eA1Fej0LXn3iX5ldUlrGo9/+xH/7tqd9YPUTgW/ObMtt\nbiolm4d0ItzLjUAPU3Heo0l9NEonJu6LZXz7cCLrWX9RqzvZ1/6+tTOLvqK/g+t7MjHY9Cazu5cb\nBqORd5PP83pAI0Jdqr8Bxp7ZVHfOrOac2+CB+1AoFJz/en71WXUoW1FNttHKP4qTWs2AzetxbtiQ\nfc+/RF5cHA06daLlKy9SVljIwTfe+hdkW/7My8vKmD1wGA/N/5Tnf14LCgW5GZl8P+5VnvhhEfpq\n3ojXheyaqEl/bXVDBaBXxTsI3VXveipHwLy8LOfDXGlz1buOvIpRNS9P2+bQ1CT7n2LfbPeKbPP5\nOHn5ptE3r2rerc76ajGvfjCTft06seqLWWiuunmhzma7mV5U86+aSF05+uZVzTuw6CDTHas9Wjaj\ntMzA5MWreffhkTRtWP28FbtmO5tePPOvGm2rHH3zsvLi+ubGXRiNRiYM7IyhvByjEYwARiOG8nKU\nNr5BsWt2xeibrtRgnn1ldM7yd8dZpaRf0wYW228LasTbe2M5mq2zqQizZ7anyvTzyb9qtK1y9M1T\nZTmi6qFUogAG1jf/+7ulnjuTk89ztKDYpiLMntmGitcDpbv51YXK0beyaq4M1bvjdvLF3zE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ZQuXKlY0uxy4pKYn4+HhsNhve3t5Uq1aN06dPk5qaStmyZalTp459vYyMDPz8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\n0kAqIiIiIkZpIBURERERozSQioiIiIhRGkhFRERExCgNpCIiIiJilAZ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#See it in action with the ConfusionMatrix\n", + "\n", + "\n", + "from sklearn.datasets import load_digits\n", + "from sklearn.cross_validation import train_test_split\n", + "from sklearn.linear_model import LogisticRegression\n", + "from yellowbrick.classifier import ConfusionMatrix\n", + "\n", + "digits = load_digits()\n", + "X = digits.data\n", + "y = digits.target\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size =0.2, random_state=11)\n", + "model = LogisticRegression()\n", + "cm = ConfusionMatrix(model, classes=[0,1,2,3,4,5,6,7,8,9])\n", + "cm.fit(X_train, y_train)\n", + "cm.score(X_test, y_test)\n", + "cm.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "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": 1 +} diff --git a/examples/palettes.ipynb b/examples/palettes.ipynb index 17336a97b..69759283e 100644 --- a/examples/palettes.ipynb +++ b/examples/palettes.ipynb @@ -2314,22 +2314,23 @@ } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 2", + "display_name": "Python [conda env:bigleaf]", "language": "python", - "name": "python2" + "name": "conda-env-bigleaf-py" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": 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b/examples/pbwitt/Data/meta.json @@ -0,0 +1,63 @@ +{ + + "feature_names": [ + "Page Popularity/likes", + "Page Checkins’s", + "Page talking about", + "Page Category", + "Derived5", + "Derived6", + "Derived7", + "Derived8", + "Derived9", + "Derived10", + "Derived11", + "Derived12", + "Derived13", + "Derived14", + "Derived15", + "Derived16", + "Derived17", + "Derived18", + "Derived19", + "Derived20", + "Derived21", + "Derived22", + "Derived23", + "Derived24", + "Derived25", + "Derived26", + "Derived27", + "Derived28", + "Derived29", + "CC1", + "CC2", + "CC3", + "CC4", + "CC5", + "Base time", + "Post length", + "Post Share Count", + "Post Promotion Status", + "H Local", + "Post published weekday-Sun", + "Post published weekday-Mon", + "Post published weekday-Tues", + "Post published weekday-Weds", + "Post published weekday-Thurs", + "Post published weekday-Fri", + "Post published weekday-Sat", + "Base DateTime weekday-Sun", + "Base DateTime weekday-Mon", + "Base DateTime weekday-Tues", + "Base DateTime weekday-Wed", + "Base DateTime weekday-Thurs", + "Base DateTime weekday-Fri", + "Base DateTime weekday-Sat", + "Target_Variable" + +] + + + +} diff --git a/examples/pbwitt/Testing Paul Witt Yellowbrick .ipynb b/examples/pbwitt/Testing Paul Witt Yellowbrick .ipynb new file mode 100644 index 000000000..b61a11d76 --- /dev/null +++ b/examples/pbwitt/Testing Paul Witt Yellowbrick .ipynb @@ -0,0 +1,1020 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Yellowbrick for Machine Learning Visualizations on Facebook Data\n", + "\n", + "Paul Witt\n", + "\n", + "The dataset below was provided to the UCI Machine Learning Repository from researchers who used Neural Networks and Decision Trees to predict how many comments a given Facebook post would generate. \n", + "\n", + "There are five variants of the dataset. This notebook only uses the first. \n", + "\n", + "The full paper can be found here: \n", + "\n", + "http://uksim.info/uksim2015/data/8713a015.pdf\n", + "\n", + "\n", + "### The primary purpose of this notebook is to test Yellowbrick. \n", + "\n", + "# Attribute Information:\n", + "\n", + "\n", + "All features are integers or float values. \n", + "\n", + "\n", + "1 \n", + "Page Popularity/likes \n", + "Decimal Encoding \n", + "Page feature \n", + "Defines the popularity or support for the source of the document. \n", + "\n", + "\n", + "2 \n", + "Page Checkins’s \n", + "Decimal Encoding \n", + "Page feature \n", + "Describes how many individuals so far visited this place. This feature is only associated with the places eg:some institution, place, theater etc. \n", + "\n", + "\n", + "3 \n", + "Page talking about \n", + "Decimal Encoding \n", + "Page feature \n", + "Defines the daily interest of individuals towards source of the document/ Post. The people who actually come back to the page, after liking the page. This include activities such as comments, likes to a post, shares, etc by visitors to the page.\n", + "\n", + "\n", + "4 \n", + "Page Category \n", + "Value Encoding \n", + "Page feature \n", + "Defines the category of the source of the document eg: place, institution, brand etc. \n", + "\n", + "\n", + "5 - 29 \n", + "Derived \n", + "Decimal Encoding \n", + "Derived feature \n", + "These features are aggregated by page, by calculating min, max, average, median and standard deviation of essential features. \n", + "\n", + "\n", + "30 \n", + "CC1 \n", + "Decimal Encoding \n", + "Essential feature \n", + "The total number of comments before selected base date/time. \n", + "\n", + "\n", + "31 \n", + "CC2 \n", + "Decimal Encoding \n", + "Essential feature \n", + "The number of comments in last 24 hours, relative to base date/time. \n", + "\n", + "\n", + "32 \n", + "CC3 \n", + "Decimal Encoding \n", + "Essential feature \n", + "The number of comments in last 48 to last 24 hours relative to base date/time. \n", + "\n", + "\n", + "33 \n", + "CC4 \n", + "Decimal Encoding \n", + "Essential feature \n", + "The number of comments in the first 24 hours after the publication of post but before base date/time. \n", + "\n", + "\n", + "34 \n", + "CC5 \n", + "Decimal Encoding \n", + "Essential feature \n", + "The difference between CC2 and CC3. \n", + "\n", + "\n", + "35 \n", + "Base time \n", + "Decimal(0-71) Encoding \n", + "Other feature \n", + "Selected time in order to simulate the scenario. \n", + "\n", + "\n", + "36 \n", + "Post length \n", + "Decimal Encoding \n", + "Other feature \n", + "Character count in the post. \n", + "\n", + "\n", + "37 \n", + "Post Share Count \n", + "Decimal Encoding \n", + "Other feature \n", + "This features counts the no of shares of the post, that how many peoples had shared this post on to their timeline. \n", + "\n", + "\n", + "38 \n", + "Post Promotion Status \n", + "Binary Encoding \n", + "Other feature \n", + "To reach more people with posts in News Feed, individual promote their post and this features tells that whether the post is promoted(1) or not(0). \n", + "\n", + "\n", + "39 \n", + "H Local \n", + "Decimal(0-23) Encoding \n", + "Other feature \n", + "This describes the H hrs, for which we have the target variable/ comments received. \n", + "\n", + "\n", + "40-46 \n", + "Post published weekday \n", + "Binary Encoding \n", + "Weekdays feature \n", + "This represents the day(Sunday...Saturday) on which the post was published. \n", + "\n", + "\n", + "47-53 \n", + "Base DateTime weekday \n", + "Binary Encoding \n", + "Weekdays feature \n", + "This represents the day(Sunday...Saturday) on selected base Date/Time. \n", + "\n", + "54 \n", + "Target Variable \n", + "Decimal \n", + "Target \n", + "The no of comments in next H hrs(H is given in Feature no 39).\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "## Data Exploration \n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "import os\n", + "import json\n", + "import time\n", + "import pickle\n", + "import requests\n", + "\n", + "\n", + "import numpy as np\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": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data Shape (40948, 54)\n", + "634995 int64\n", + "0 int64\n", + "463 int64\n", + "1 int64\n", + "0.0 float64\n", + "806.0 float64\n", + "11.291044776119403 float64\n", + "1.0 float64\n", + "70.49513846124168 float64\n", + "0.0.1 float64\n", + "806.0.1 float64\n", + "7.574626865671642 float64\n", + "0.0.2 float64\n", + "69.435826365571 float64\n", + "0.0.3 float64\n", + "76.0 float64\n", + "2.6044776119402986 float64\n", + "0.0.4 float64\n", + "8.50550186882253 float64\n", + "0.0.5 float64\n", + "806.0.2 float64\n", + "10.649253731343284 float64\n", + "1.0.1 float64\n", + "70.25478763764251 float64\n", + "-69.0 float64\n", + "806.0.3 float64\n", + "4.970149253731344 float64\n", + "0.0.6 float64\n", + "69.85058043098057 float64\n", + "0.1 int64\n", + "0.2 int64\n", + "0.3 int64\n", + "0.4 int64\n", + "0.5 int64\n", + "65 int64\n", + "166 int64\n", + "2 int64\n", + "0.6 int64\n", + "24 int64\n", + "0.7 int64\n", + "0.8 int64\n", + "0.9 int64\n", + "1.1 int64\n", + "0.10 int64\n", + "0.11 int64\n", + "0.12 int64\n", + "0.13 int64\n", + "0.14 int64\n", + "0.15 int64\n", + "0.16 int64\n", + "0.17 int64\n", + "0.18 int64\n", + "1.2 int64\n", + "0.19 int64\n", + "dtype: object\n" + ] + } + ], + "source": [ + "df=pd.read_csv(\"/Users/pwitt/Documents/machine-learning/examples/pbwitt/Dataset/Training/Features_Variant_1.csv\")\n", + "\n", + "# Fetch the data if required\n", + "DATA = df\n", + "print('Data Shape ' + str(df.shape))\n", + "\n", + "print(df.dtypes)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Page Popularity/likesPage Checkins’sPage talking aboutPage CategoryDerived5Derived6Derived7Derived8Derived9Derived10...Post published weekday-FriPost published weekday-SatBase DateTime weekday-SunBase DateTime weekday-MonBase DateTime weekday-TuesBase DateTime weekday-WedBase DateTime weekday-ThursBase DateTime weekday-FriBase DateTime weekday-SatTarget_Variable
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" + ], + "text/plain": [ + " Page Popularity/likes Page Checkins’s Page talking about \\\n", + "0 634995 0 463 \n", + "1 634995 0 463 \n", + "2 634995 0 463 \n", + "3 634995 0 463 \n", + "4 634995 0 463 \n", + "\n", + " Page Category Derived5 Derived6 Derived7 Derived8 Derived9 \\\n", + "0 1 0.0 806.0 11.291045 1.0 70.495138 \n", + "1 1 0.0 806.0 11.291045 1.0 70.495138 \n", + "2 1 0.0 806.0 11.291045 1.0 70.495138 \n", + "3 1 0.0 806.0 11.291045 1.0 70.495138 \n", + "4 1 0.0 806.0 11.291045 1.0 70.495138 \n", + "\n", + " Derived10 ... Post published weekday-Fri \\\n", + "0 0.0 ... 0 \n", + "1 0.0 ... 1 \n", + "2 0.0 ... 1 \n", + "3 0.0 ... 0 \n", + "4 0.0 ... 0 \n", + "\n", + " Post published weekday-Sat Base DateTime weekday-Sun \\\n", + "0 0 0 \n", + "1 0 0 \n", + "2 0 0 \n", + "3 0 0 \n", + "4 0 0 \n", + "\n", + " Base DateTime weekday-Mon Base DateTime weekday-Tues \\\n", + "0 0 0 \n", + "1 0 0 \n", + "2 1 0 \n", + "3 0 0 \n", + "4 0 0 \n", + "\n", + " Base DateTime weekday-Wed Base DateTime weekday-Thurs \\\n", + "0 0 0 \n", + "1 0 0 \n", + "2 0 0 \n", + "3 1 0 \n", + "4 0 0 \n", + "\n", + " Base DateTime weekday-Fri Base DateTime weekday-Sat Target_Variable \n", + "0 1 0 0 \n", + "1 0 1 0 \n", + "2 0 0 0 \n", + "3 0 0 0 \n", + "4 1 0 0 \n", + "\n", + "[5 rows x 54 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "FEATURES = [\n", + " \"Page Popularity/likes\",\n", + " \"Page Checkins’s\",\n", + " \"Page talking about\", \n", + " \"Page Category\",\n", + " \"Derived5\", \n", + " \"Derived6\", \n", + " \"Derived7\", \n", + " \"Derived8\", \n", + " \"Derived9\", \n", + " \"Derived10\", \n", + " \"Derived11\", \n", + " \"Derived12\", \n", + " \"Derived13\", \n", + " \"Derived14\", \n", + " \"Derived15\", \n", + " \"Derived16\", \n", + " \"Derived17\", \n", + " \"Derived18\", \n", + " \"Derived19\", \n", + " \"Derived20\", \n", + " \"Derived21\", \n", + " \"Derived22\", \n", + " \"Derived23\", \n", + " \"Derived24\", \n", + " \"Derived25\", \n", + " \"Derived26\", \n", + " \"Derived27\", \n", + " \"Derived28\", \n", + " \"Derived29\",\n", + " \"CC1\",\n", + " \"CC2\",\n", + " \"CC3\",\n", + " 'CC4',\n", + " 'CC5',\n", + " \"Base time\",\n", + " \"Post length\",\n", + " \"Post Share Count\",\n", + " \"Post Promotion Status\",\n", + " \"H Local\",\n", + " \"Post published weekday-Sun\",\n", + " \"Post published weekday-Mon\",\n", + " \"Post published weekday-Tues\",\n", + " \"Post published weekday-Weds\",\n", + " \"Post published weekday-Thurs\",\n", + " \"Post published weekday-Fri\",\n", + " \"Post published weekday-Sat\",\n", + " \"Base DateTime weekday-Sun\",\n", + " \"Base DateTime weekday-Mon\",\n", + " \"Base DateTime weekday-Tues\",\n", + " \"Base DateTime weekday-Wed\",\n", + " \"Base DateTime weekday-Thurs\",\n", + " \"Base DateTime weekday-Fri\",\n", + " \"Base DateTime weekday-Sat\",\n", + " \"Target_Variable\"\n", + " \n", + "]\n", + "\n", + "# Read the data into a DataFrame\n", + "df.columns=FEATURES\n", + "df.head()\n", + "#Note: Dataset is sorted. There is variation in the distributions. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "40948 instances with 54 columns\n", + "\n" + ] + } + ], + "source": [ + "# Determine the shape of the data\n", + "print(\"{} instances with {} columns\\n\".format(*df.shape))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test Yellowbrick Covariance Ranking" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pwitt/anaconda/lib/python3.5/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" + ] + } + ], + "source": [ + "from yellowbrick.features.rankd import Rank2D \n", + "from yellowbrick.features.radviz import RadViz \n", + "from yellowbrick.features.pcoords import ParallelCoordinates" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Specify the features of interest\n", + "# Used all for testing purposes\n", + "features = FEATURES\n", + "\n", + "# Extract the numpy arrays from the data frame \n", + "X = df[features].as_matrix()\n", + "y = df[\"Base time\"].as_matrix()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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9e02YMEHR0dFq2rTp9Z4mANRbzZs318aNG20OPrqasl2hyp5TuZKnp6dyc3OV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\nZUgAAAAEuOqnc+zfX3srWHZ2thwOh+Li4hp87YABAyRJX3z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Xqra25LQpzhV5uSnuhj27\nTXGSVHFtJ1ugsVtiUbmtZ9hltlElSU/9yypT3PCrR5jiHA4POj92vNYUd5nb1gnsR6/8jynuT4tu\nNcVJUpDL1tksr9sQU1yHkCBT3B3GTneSdP4a2/usfTvb2pRU2N5nBY/da4qTpKVTl5viPnZ2Nl/T\n4vQ9iT69niSVu2zvx2tL7B008+5eaorbczzfFPeDcNv7bM73bV1VJemntz5miksqmWiKs34fw9vb\nf6ZUGpsG+roDYjtjB8QLg4eawuiACDS9VpVIAwAAwDts7fA9bjYEAAAAvEBFGgAAIAC4XVSkfY2K\nNAAAAOAFKtIAAAABgD3SvkdFGgAAAPACFWkAAIAAQEXa96hIAwAAAF6gIg0AANoMawdEKfC6IFKR\n9r02l0hbuxV6ojLC1rGw2Ng1cN68n5vHTk62dXO7upPth8GZ85WmuJG9ok1xkrQvbYsp7liCrRtg\nh1BbRzVJ+vs5Wzewm2IiTHHP/zDOFHe21P7DKDLE1gHtfIVtbYKN3QXvn2t/n/3bmy+Y4np3dJri\nTpXYurmNefZ5U5wk7ZvykimuaOkdpjhnsO0Pcp/nFZviJKmkwva+KCqzrXWPAf3NY2f/3dax0Kp9\nYZ4prvvv/918zeF/P+ftdOp1efsKU1yFbJ0APeGvDoiSB10Q6YAINLk2l0gDAACgLneVreAHO/ZI\nAwAAAF4gkQYAAAC8wNYOAACAAMDNhr5HRRoAAADwAhVpAACAAEBF2veoSAMAAABeoCINAAAQAKqo\nSPscFWkAAADAC1SkAQBAQDp6qtAU1+fyqCaeSfNwu6hI+5rD7Xa3it6gn332mU59eUzvTP1Js49t\nbTs+95NdTTwTeOu2zrYW6v99+nwTzwTfZv1sSb7/fPG5bpwna2Nl/V4mlew3xaVccb0p7t5je01x\nklRqrC+Fydbm3fHpDlNc4cAJpjhJsnb0Plfq26SpZ3SwT68nSSFf2damqnMvU1xZRBfz2CdLbGso\n2RLpzz77TJIUFxdnvm5zcw7/abOOV/KXtc06nj806daO48ePKyEhQbfccotuueUWJSYm6syZM005\nJAAAAOrhrnI167/m9PXXX2vw4MHau9f+i3NqaqomTJigwYMHa/LkyUpLS/N43Cbb2nHu3Dndd999\nqqys1IIFC1RZWamNGzfq0KFD2rJli9q3Z1cJAAAALk1hYaEefvhhlZeXm1+TnJysNWvWaOLEiYqP\nj9ef/vQnLVmyRA6HQxMm2P9C1GTZ7KZNm5SXl6cPPvhAffr0kSQNGjRI8fHx2rp1q6ZPn95UQwMA\nAOA72uJzpI8cOaKEhAR99dVX5tcUFhZq/fr1mjJlilavXi1Jmj59umbPnq01a9Zo/Pjxcjgcpms1\n2daOtLQ0DR06tCaJlqThw4erT58+XpXOAQAAgGpbt27V1KlTVVBQ4FGBNj09XaWlpZo5c2bNMYfD\noVmzZiknJ0eZmZnmazVJIl1QUKBjx45pwIABdc7FxsZq3759TTEsAAAAAsShQ4d0991364MPPtD1\n19tuPpZUk4fGxsbWOh4bGyu3263PP//cfK0m2dqRm5srSerWrVudc127dlVhYaGKiooUGRnZFMMD\nAADgO9ra1o7HHnvMq3vucnNzFR0drdDQ0FrHu3S58NSXnJwc87WaJJEuLi6WJIWFhdU5Vz3p8+fP\nk0gDAACgxqlTpxo973Q65XQ6JcnrB1cUFxcrPLzuY3Gr89aSkhLztZokka5+NHVjG7Wtm7irVVRU\nKLJ7Z81499eXNDdvTAyt+wtBfaLKpjXxTOCtsCDb+62nq1U8Vr3NsH62JN9/vvhcN86TtbGyfi8P\nHT5siuv/5kumuINHjpriJMn6E8D8f7DgK2zjHv3CekVZuz9Ynzdtdfgfvr2eJDkqje+z0tOmMHe7\nc+axPflx/1nOxVe8vLzc49ymubWGivSIESMaPOdwOPTQQw9p0aJFlzyOr3LUJkmkq39TKC0trXOu\nrKxMkjyuRjscDgUFB+vyq7536RP00OXNPiL8hb+RNC9/frb4XDeuNXx/onr19Pk1fZ4GhUb4flxj\ncJAn1/SXYN/+wubJ97G9jxfb4XC0+ES6POt1f0/hol544YVGz393X7M3nE5nvTlq9TFPctQmSaRj\nYmIkSSdPnqxzLi8vT9HR0fVu+2iMJ5vIAQAA0PpMm9b0fwXs0aOH8vPzVVFRoeDgf3bszMvLk1T/\nPX4NaZKndkRFRalnz57Kzs6ucy47O1sDBw5simEBAACARlU/nWP//v21jmdnZ8vhcHjU5r3JniM9\nbtw4ZWRk6OjRf+5Nq/7vSZMmNdWwAAAAQINGjhypkJAQbd68ueaY2+1WamqqYmJiNGTIEPO1mqyz\n4QMPPKD33ntPc+bM0dy5c1VaWqrk5GTFxcVp8uTJTTUsAAAAApC7gTtxd+7cKUkaM2aMJKljx46a\nP3++NmzYIJfLpWHDhmn79u3KyspSUlKS/282lKROnTopJSVFK1eu1Nq1axUeHq6xY8dq6dKltfaj\nAAAAAJeqoQR4xYoVcjgcNYm0JCUkJCgiIkIpKSlKT09X79699fLLL2vs2LGejeluKH0HAAAA0KAm\n2yMNAAAAtGUk0gAAAIAXSKQBAAAAL5BIAwAAAF4gkQYAAAC8QCINAAAAeIFEGgAAAPBCi0+kjx8/\nroSEBN1yyy265ZZblJiYqDNnzvh7WgHt2Wef1X333VfnOGvV/Hbv3q1Zs2ZpyJAhuv766xUfH6//\n/d//rRXDuvjHX/7yF82cOVM33HCD7rjjDq1YsUIlJSW1Ylgb/zpw4IAGDhyo9evX1zrOuvjHtGnT\n1K9fvzr/Fi1aVBPD2qClabLOhr5w7tw53XfffaqsrNSCBQtUWVmpjRs36tChQ9qyZYvat2/R02+T\ntmzZoi1btmjo0KG1jrNWzW/Pnj1asGCBrr32Wj366KNyuVxKTU3Vvffeq9TUVMXFxbEufvKXv/xF\n8+bNU1xcnH72s5/pH//4h9544w3t27dPKSkpkvjM+JvL5dKTTz4pl8tV6zjr4j9HjhzR2LFjNW7c\nuFrHY2JiJLE2aKHcLdivfvUr94ABA9xffvllzbGMjAx337593b///e/9OLPA43K53OvWrXP369fP\n3a9fP/fs2bNrnWetmt8999zj/v73v+8uKyurOXbq1Cn30KFD3XPnznW73ayLv/zgBz9wjx49utba\npKSkuPv16+f++OOP3W43a+Nv69evdw8cONDdr18/97p162qOsy7+cezYMXffvn3dW7dubTCGtUFL\n1KK3dqSlpWno0KHq06dPzbHhw4erT58+SktL8+PMAkt5ebmmTp2qDRs2aOrUqeratWudGNaqeRUU\nFOjQoUOaOHGiQkJCao537txZN998szIzMyWxLv5QXl6uzp0760c/+lGttRk6dKjcbrcOHjwoibXx\np4MHD+q3v/2tHn74Ybnd7lrnWBf/+OKLL+RwOHTVVVc1GMPaoCVqsYl0QUGBjh07pgEDBtQ5Fxsb\nq3379vlhVoGprKxMJSUlSkpK0sqVKxUUFFTrPGvV/CIjI7Vt2zbNmTOnzrmzZ8+qffv2rIufhISE\n6LXXXtOCBQtqHc/OzpZ04c/UrI3/VG/pGDFihCZPnlzrHOviP4cPH5YkXX311ZKk8+fP1zrP2qCl\narGJdG5uriSpW7dudc517dpVhYWFKioqau5pBaSoqCjt2LFDd911V73nWavm165dO1155ZXq0qVL\nreMHDhxQZmambrjhBtalhfjmm2/0xz/+US+++KL69u2rMWPGsDZ+9Oqrr+rYsWN6/vnn65xjXfzn\n8OHDioiI0MqVK3XDDTfo+uuv19ixY2sqzawNWqoWuzO/uLhYkhQWFlbnXGhoqKQLv7FGRkY267wC\nVbt2Df/OxVq1DCUlJUpMTJTD4dD8+fNZlxYgPz9fo0aNksPhUFhYmJ555hmFhISwNn5y+PBh/frX\nv9Zzzz2nrl276sSJE7XOsy7+88UXX6i4uFiFhYVavXq1CgsL9eabb2rJkiWqrKzUlVdeKYm1QcvT\nYhPp6n1rDoejwZjGzqH5sFb+V1paqoceekiHDh3Sgw8+qJtuuklZWVmSWBd/cjgceumll1RRUaG3\n3npL999/v5KSknT55ZfXnG/stfCdqqoqPfHEE7r55ps1bdq0emP4WeY/M2bMkMvl0qxZs2qOTZw4\nUXfffbdWr16ttWvXSmJt0PK02K0dTqdT0oUE4bvKysokid88WwjWyr8KCwsVHx+vvXv3atq0aVq8\neLEk1qUliI6O1oQJEzRlyhRt3rxZMTExWrlyJWvjBxs3btThw4e1ZMkSnT17VmfPnlV+fr6kC+tw\n9uxZ1sWPZsyYUSuJli5Umu+55x6dPn2atUGL1WIr0tXPjTx58mSdc3l5eYqOjq73TzxofqyV/5w5\nc0Zz587VwYMHNWPGDC1btqzmHOvSsoSGhmrkyJHavHlzzT5P1qb57N69WxUVFXWq0Q6HQxs3blRy\ncrK2bt0qiXVpSTp16iTpn8kya4OWpsUm0lFRUerZs2fNne7flp2drYEDB/phVqgPa+UfxcXFNUn0\n/fffr8TExFrnWRf/+PLLL/XAAw9o/vz5mjlzZq1zRUVFcjgcCgkJYW2a2ZNPPllTga52+vRp/exn\nP9PUqVM1depUXXXVVayLH+Tm5mrevHmaOHGifvKTn9Q69+WXX0qSevbsydqgRWqxWzskady4ccrI\nyNDRo0drjlX/96RJk/w4M3wXa9X8nn/+eR08eFBz5sypk0RXY12aX69evVRUVKS3335blZWVNcdP\nnDihHTt2aOjQoXI6naxNM4uNjdXw4cNr/bv++uslXUjShg0bppCQENbFD7p166aCggJt2bKl5oZP\n6cITb7Zu3aphw4apc+fOrA1aJIf7u0+jb0HOnDmjyZMnKygoSHPnzlVpaamSk5PVu3dvpaamKjg4\n2N9TDEijRo1Sz5499eabb9YcY62a15EjRzRp0iR16NBBTzzxRJ1ne0vSlClTWBc/ef/995WYmKjB\ngwdr8uTJOnv2rFJTU+VyuZSSkqJrrrmGtWkBTpw4odGjRyshIUEJCQmS+FnmLzt37tQjjzyia665\nRtOnT1dRUZFSU1NVWVmp1NRUXXXVVawNWqQWnUhL0ldffaWVK1dq7969Cg8P15133qmlS5fqsssu\n8/fUAtaoUaP0ve99T2+88Uat46xV83n77bfrfQ7ut+3fv18S6+Iv27Zt02uvvabDhw8rPDxct956\nqxYvXqxevXrVxLA2/nXixAmNGTNGCQkJevjhh2uOsy7+sWvXLr3yyis6cOCAwsLCdMstt+jRRx+t\n1cmQtUFL0+ITaQAAAKAlatF7pAEAAICWikQaAAAA8AKJNAAAAOAFEmkAAADACyTSAAAAgBdIpAEA\nAAAvkEgDAAAAXiCRBgAAALxAIg0AAAB4gUQaAAAA8AKJNAAAAOAFEmkAAADAC/8flFtiiBFTZBIA\nAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the visualizer with the Covariance ranking algorithm \n", + "visualizer = Rank2D(features=features, algorithm='covariance')\n", + "\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" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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KaWkpzp8/j3379gGAxufl4eGhduzxxx8HAI3zTbOysjB79mxUVFQgKipKZaqIFJru165d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\nAwAAACFgIA0AAACEgIE0AAAAEIL/H8exnJX2QCxjAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the visualizer with the Pearson ranking algorithm \n", + "visualizer = Rank2D(features=features, algorithm='pearson')\n", + "\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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Extraction \n", + "\n", + "Create a bunch object to store data on disk. \n", + "\n", + "- **data**: array of shape `n_samples` * `n_features`\n", + "- **target**: array of length `n_samples`\n", + "- **feature_names**: names of the features\n", + "- **filenames**: names of the files that were loaded\n", + "- **DESCR**: contents of the readme\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "- Features_Variant_1.arff\n", + "- Features_Variant_1.csv\n", + "- Features_Variant_2.arff\n", + "- Features_Variant_2.csv\n", + "- Features_Variant_3.arff\n", + "- Features_Variant_3.csv\n", + "- Features_Variant_4.arff\n", + "- Features_Variant_4.csv\n", + "- Features_Variant_5.arff\n", + "- Features_Variant_5.csv\n", + "- meta.json\n", + "- README.md\n", + "(40949, 53)\n", + "(40949,)\n" + ] + } + ], + "source": [ + "from sklearn.datasets.base import Bunch\n", + "DATA_DIR = os.path.abspath(os.path.join(\".\", \"..\", \"pbwitt\",\"data\")) \n", + " # Show the contents of the data directory\n", + "for name in os.listdir(DATA_DIR):\n", + " if name.startswith(\".\"): continue\n", + " print (\"- {}\".format(name))\n", + "\n", + "def load_data(root=DATA_DIR):\n", + " filenames = {\n", + " 'meta': os.path.join(root, 'meta.json'),\n", + " 'rdme': os.path.join(root, 'README.md'),\n", + " 'data': os.path.join(root, 'Features_Variant_1.csv'),\n", + " \n", + " }\n", + " \n", + " #Load the meta data from the meta json\n", + " with open(filenames['meta'], 'r') as f:\n", + " meta = json.load(f) \n", + " feature_names = meta['feature_names'] \n", + "\n", + " # Load the description from the README. \n", + " with open(filenames['rdme'], 'r') as f:\n", + " DESCR = f.read()\n", + "\n", + "\n", + " # Load the dataset from the data file.\n", + " dataset = pd.read_csv(filenames['data'], header=None)\n", + " #tranform to numpy\n", + " data = dataset.iloc[:,0:53] \n", + " target = dataset.iloc[:,-1] \n", + " \n", + " # Extract the target from the data\n", + " data = np.array(data) \n", + " target = np.array(target)\n", + "\n", + " # Create the bunch object\n", + " return Bunch(\n", + " data=data,\n", + " target=target,\n", + " filenames=filenames,\n", + " \n", + " feature_names=feature_names,\n", + " DESCR=DESCR\n", + " )\n", + "\n", + "# Save the dataset as a variable we can use.\n", + "dataset = load_data()\n", + "\n", + "print(dataset.data.shape)\n", + "print(dataset.target.shape) \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from yellowbrick.regressor import PredictionError, ResidualsPlot" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn import metrics\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "\n", + "from sklearn.svm import SVC\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "from sklearn import linear_model\n", + "from sklearn.cross_validation import train_test_split\n", + "from sklearn import preprocessing\n", + "from sklearn.linear_model import ElasticNet, Lasso\n", + "from sklearn.linear_model import Ridge, Lasso \n", + "from sklearn.cross_validation import KFold\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Build and Score Regression Models \n", + "\n", + "* Create function -- add parameters for Yellowbrick target visulizations \n", + "* Score models using Mean Absolute Error, Mean Squared Error, Median Absolute Error, R2" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def fit_and_evaluate(dataset, model, label,vis, **kwargs ):\n", + " \"\"\"\n", + " Because of the Scikit-Learn API, we can create a function to\n", + " do all of the fit and evaluate work on our behalf!\n", + " \"\"\"\n", + " start = time.time() # Start the clock! \n", + " scores = {'Mean Absolute Error:':[], 'Mean Squared Error:':[], 'Median Absolute Error':[], 'R2':[]}\n", + " \n", + " for train, test in KFold(dataset.data.shape[0], n_folds=12, shuffle=True):\n", + " X_train, X_test = dataset.data[train], dataset.data[test]\n", + " y_train, y_test = dataset.target[train], dataset.target[test]\n", + " \n", + " estimator = model(**kwargs)\n", + " estimator.fit(X_train, y_train)\n", + " \n", + " \n", + " expected = y_test\n", + " predicted = estimator.predict(X_test)\n", + " \n", + " #For Visulizer below \n", + " if vis=='Ridge_vis':\n", + " return [X_train,y_train,X_test,y_test]\n", + " if vis=='Lasso_vis':\n", + " return [X_train,y_train,X_test,y_test]\n", + " \n", + " \n", + " scores['Mean Absolute Error:'].append(metrics.mean_absolute_error(expected, predicted))\n", + " scores['Mean Squared Error:'].append(metrics.mean_squared_error(expected, predicted))\n", + " scores['Median Absolute Error'].append(metrics.median_absolute_error(expected, predicted ))\n", + " scores['R2'].append(metrics.r2_score(expected, predicted)) \n", + "\n", + " # Report\n", + " print(\"Build and Validation of {} took {:0.3f} seconds\".format(label, time.time()-start))\n", + " print(\"Validation scores are as follows:\\n\")\n", + " print(pd.DataFrame(scores).mean())\n", + " \n", + " # Write official estimator to disk\n", + " estimator = model(**kwargs)\n", + " estimator.fit(dataset.data, dataset.target)\n", + " \n", + " outpath = label.lower().replace(\" \", \"-\") + \".pickle\"\n", + " with open(outpath, 'wb') as f:\n", + " pickle.dump(estimator, f)\n", + "\n", + " print(\"\\nFitted model written to:\\n{}\".format(os.path.abspath(outpath)))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Lasso Scores and Visualization Below: \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pwitt/anaconda/lib/python3.5/site-packages/sklearn/linear_model/coordinate_descent.py:484: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Build and Validation of Facebook Lasso took 16.674 seconds\n", + "Validation scores are as follows:\n", + "\n", + "Mean Absolute Error: 8.270001\n", + "Mean Squared Error: 883.090939\n", + "Median Absolute Error 4.094740\n", + "R2 0.318446\n", + "dtype: float64\n", + "\n", + "Fitted model written to:\n", + "/Users/pwitt/Desktop/yellowbrick/examples/pbwitt/facebook-lasso.pickle\n" + ] + }, + { + "data": { + "image/png": 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0ymtWSAQAAABQDxAUXWE75TUrJAIAAACoB0g1LmhQesxhhcSWP37O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\nMGRmZirOs/6Rp505cwZFRUUoK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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Lasso Scores and Visualization Below: \\n\")\n", + "fit_and_evaluate(dataset, Lasso, \"Facebook Lasso\",'NA')\n", + "# Instantiate the linear model and visualizer \n", + "lasso = Lasso()\n", + "visualizer = PredictionError(lasso)\n", + "visualizer.fit(fit_and_evaluate(dataset, Lasso, \"X_train\",'Lasso_vis')[0], fit_and_evaluate(dataset, Lasso, \"y_train\",'Lasso_vis')[1]) # Fit the training data to the visualizer\n", + "visualizer.score(fit_and_evaluate(dataset, Lasso, \"X_train\",'Lasso_vis')[2], fit_and_evaluate(dataset, Lasso, \"y_train\",'Lasso_vis')[3])\n", + "g = visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ridge Scores and Target Visualization Below:\n", + "\n", + "Build and Validation of Facebook Ridge took 0.440 seconds\n", + "Validation scores are as follows:\n", + "\n", + "Mean Absolute Error: 8.367239\n", + "Mean Squared Error: 881.948637\n", + "Median Absolute Error 4.169019\n", + "R2 0.301782\n", + "dtype: float64\n", + "\n", + "Fitted model written to:\n", + "/Users/pwitt/Desktop/yellowbrick/examples/pbwitt/facebook--ridge.pickle\n" + ] + }, + { + "data": { + "image/png": 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555/fdFtVVf+GiO4fkogKIYQQQghxn7l46RJLvlvH7Ihvef+rb1kXuZWcnJx7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GEG/Nz3xDMs1kyGhobo7OwEqp8PTdP42Ptunf7/s32F/1SUz51PxiSvZ7nh1HXtVZR5Z3XPHJM2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\nma2TlZxc/6wp/MX6nx0ACCeCKAAgZrWU/ZN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NZ+plJAAAAABJRU5E\nrkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the linear model and visualizer \n", + "print(\"Ridge Scores and Target Visualization Below:\\n\")\n", + "fit_and_evaluate(dataset, Ridge, \"Facebook Ridge\", 'NA')\n", + "\n", + "ridge = Ridge()\n", + "visualizer = ResidualsPlot(ridge)\n", + "visualizer.fit(fit_and_evaluate(dataset, Ridge, \"X_train\",'Ridge_vis')[0], fit_and_evaluate(dataset, Ridge, \"y_train\",'Ridge_vis')[1]) # Fit the training data to the visualizer\n", + "visualizer.score(fit_and_evaluate(dataset, Ridge, \"X_train\",'Ridge_vis')[2], fit_and_evaluate(dataset, Ridge, \"y_train\",'Ridge_vis')[3]) # Evaluate the model on the test data \n", + "g = visualizer.poof() # Draw/show/poof the data" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pwitt/anaconda/lib/python3.5/site-packages/sklearn/linear_model/coordinate_descent.py:484: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", + " ConvergenceWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Build and Validation of Facebook ElasticNet took 15.888 seconds\n", + "Validation scores are as follows:\n", + "\n", + "Mean Absolute Error: 8.311814\n", + "Mean Squared Error: 882.415546\n", + "Median Absolute Error 4.139396\n", + "R2 0.301708\n", + "dtype: float64\n", + "\n", + "Fitted model written to:\n", + "/Users/pwitt/Desktop/yellowbrick/examples/pbwitt/facebook-elasticnet.pickle\n" + ] + } + ], + "source": [ + "fit_and_evaluate(dataset, ElasticNet, \"Facebook ElasticNet\", 'NA')" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [conda root]", + "language": "python", + "name": "conda-root-py" + }, + "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": 1 +} diff --git a/examples/pdamodaran/feature_visualizer.ipynb b/examples/pdamodaran/feature_visualizer.ipynb new file mode 100644 index 000000000..db07521ae --- /dev/null +++ b/examples/pdamodaran/feature_visualizer.ipynb @@ -0,0 +1,359 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Feature Visualizer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook provides examples of visualizations done in other data studies and modifies them using the Yellowbrick library" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "\n", + "The first set of examples comes from the [blog](https://blog.insightdatascience.com/show-me-the-data-using-graphics-for-exploratory-data-analysis-d686af4468f6#.a9knkdnu9 \"blog\") titled: Show Me The Data: Using Graphics for Exploratory Data Analysis. This blog begins with the same case outlined in the Yellowbrick examples notebook: Anscombe's quartet to stress the importance of visualization when conducting data analysis. It is worth showing again.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "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": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/Copper/anaconda/envs/py36/lib/python3.6/site-packages/scipy/linalg/basic.py:884: 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", + " warnings.warn(mesg, RuntimeWarning)\n" + ] + }, + { + "data": { + "image/png": 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hb3sWEgWi2XiKxHDwa0aDvhQN+jL0W7qdzkeGxSNNtRzpXgh+FHg8EgJLSkrw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\nNPkx+K3Kgp9sNTxjZlDwI4Twmq6tEzUHL6IqtxD3vr7CDX7urgh8YiZ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = yb.anscombe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the above example, the author explains how important it is to get a visual representation of the data. He goes on to create graphics to show the characteristics of all the features along with a correlation matrix to illustrate relationships between the features. We will do the same using the Yellowbrick API and the \"concrete\" data set. Instead of creating a correlation matrix, we will create a covariance matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from 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", + " \"credit\": os.path.join(FIXTURES, \"credit\", \"credit.csv\"),\n", + " \"concrete\": os.path.join(FIXTURES, \"concrete\", \"concrete.csv\"),\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": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Load the data\n", + "df = load_data('concrete')\n", + "features = ['cement', 'slag', 'ash', 'water', 'splast', 'coarse', 'fine', 'age']\n", + "target = 'strength'\n", + "# Get the X and y data from the DataFrame \n", + "X = df[features].as_matrix()\n", + "y = df[target].as_matrix() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's look at histograms of the features to get their characteristics." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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AtGE4SCmRdHh6emLDhg1Yv349Xn31VXTo0AFvv/02fH196x3Gh0jqDCZxHKjU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\nRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "feature_hist = df.hist(column=features)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's look at the covariance matrix of the features." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/Copper/anaconda/envs/py36/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" + ] + } + ], + "source": [ + "from yellowbrick.features.rankd import Rank2D " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ubm4YOHCgbt3Zs2cxaNAgKBQKBAUFQaVSGXw8KnwHgiBg8uTJaN26NS5cuICY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\nlT4REUmWoGGl/yImfSIikiy298XY3ic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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the visualizer with the Pearson ranking algorithm \n", + "visualizer = Rank2D(features=features, algorithm='covariance')\n", + "\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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next set of examples comes from [lecture notes](http://www.ics.uci.edu/~newman/courses/cs277/slides/lecture4_EDA_and_Visualization.pdf \"lecture notes\") from the Univeristy of California Irvine CS277 class.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are several scatterplots showing different types of relationships between features, such as linear and quadratic relationships. We will take the 'concrete' data set and use Yellowbrick's best fit curve visualization to show relationships between the the 'age' feature and the 'strength' target variable." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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EBxMfHoQgXyk61MK7zjYWlOD5nDPQDogzaulS47mcM9AC+OWEEU5z7QnxEOXb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\nREREWY6Bj4iIiCjL/QNMQ6PTLhAz+AAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_val = df['cement'].as_matrix()\n", + "fig, ax = plt.subplots()\n", + "ax.set_xlabel('age')\n", + "ax.set_ylabel('strength')\n", + "ax.set_title('Best fit curve')\n", + "ax.scatter(x_val, y)\n", + "g = yb.bestfit.draw_best_fit(x_val, y, ax, estimator='linear')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The document also shows an example using parallel coordinates as an example of multivariate visualization whereby all dimensions can be shown. Yellowbrick has an example that shows exactly how this can be achieved." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from yellowbrick.features.pcoords import ParallelCoordinates\n", + "\n", + "# 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()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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8cW27h4cH/fr144MPPuCtt97izJkzrFu3jkWLFgEwYsQI5s2bR2RkJJmZmSxb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yvN3q8k7pzV4hMOTwxzMCOuj36XitLWMO6eHZg5e6s0LiDwM2g8fHt5NapBxMK3V0ZT2CAHSBum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\nI87w1Mo2lNdmzt8bwVej453OE/e0yOGeJT9z+q1J1+vp1ZoIkMJ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QqVMn+vXrxw8//FBl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\nIAi1IgKkIAiCIAiCUCv/H6bWurpq8G/zAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the visualizer\n", + "visualizer = visualizer = ParallelCoordinates(classes=classes, features=features)\n", + "\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" + ] + } + ], + "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": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/pdamodaran/jointplot.ipynb b/examples/pdamodaran/jointplot.ipynb new file mode 100644 index 000000000..955ea685c --- /dev/null +++ b/examples/pdamodaran/jointplot.ipynb @@ -0,0 +1,442 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "\n", + "# yellowbrick.features.jointplot\n", + "# Implementations of joint plots for univariate and bivariate analysis.\n", + "#\n", + "# Author: Prema Damodaran Roman \n", + "# Created: \n", + "#\n", + "# Copyright (C) 2017 District Data Labs\n", + "# For license information, see LICENSE.txt\n", + "#\n", + "# ID: jointplot.py\n", + "\n", + "##########################################################################\n", + "## Imports\n", + "##########################################################################\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from yellowbrick.features.base import FeatureVisualizer\n", + "from yellowbrick.exceptions import YellowbrickValueError\n", + "from yellowbrick.bestfit import draw_best_fit\n", + "from yellowbrick.utils import is_dataframe\n", + "\n", + "class JointPlotVisualizer(FeatureVisualizer):\n", + " \"\"\"\n", + " JointPlotVisualizer allows for a simultaneous visualization of the relationship\n", + " between two variables and the distrbution of each individual variable. The \n", + " relationship is plotted along the joint axis and univariate distributions\n", + " are plotted on top of the x axis and to the right of the y axis.\t\n", + " \"\"\"\n", + "\n", + " def __init__(self, ax=None, feature=None, target=None, \n", + " joint_plot='scatter', joint_args=None, \n", + " xy_plot='hist', xy_args=None,\n", + " size=6, ratio=5, space=.2, **kwargs):\n", + "\n", + " \"\"\"\n", + " Initialize the visualization with many of the options required\n", + " in order to make most visualizations work.\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", + " Parameters\n", + " ----------\n", + "\n", + " ax: This is inherited from FeatureVisualizer but is defined within\n", + " JointPlotVisualizer since there are three axes objects.\n", + "\n", + " feature: The name of the X variable\n", + " If a DataFrame is passed to fit and feature is None, feature\n", + " is selected as the column of the DataFrame. There must be only\n", + " one column in the DataFrame.\n", + "\n", + " target: The name of the Y variable\n", + " If target is None and a y value is passed to fit then the target\n", + " is selected from the target vector.\n", + "\n", + " joint_plot: The type of plot to render in the joint axis. Currently,\n", + " the choices are scatter and hex. Use scatter for small datasets\n", + " and hex for large datasets\n", + "\n", + " joint_args: Keyword arguments used for customizing the joint plot.\n", + " Property Description\n", + " alpha transparency\n", + " facecolor background color of the joint axis\n", + " aspect aspect ratio\n", + " fit used if scatter is selected for joint_plot to draw a \n", + " best fit line - values can be True or False.\n", + " Uses Yellowbrick.bestfit\n", + " estimator used if scatter is selected for joint_plot to determine\n", + " the type of best fit line to use. Refer to \n", + " Yellowbrick.bestfit for types of estimators that can be used.\n", + " x_bins used if hex is selected to set the number of bins for the x value\n", + " y_bins used if hex is selected to set the number of bins for the y value\n", + " cmap string or matplotlib cmap to colorize lines\n", + " Use either color to colorize the lines on a per class basis or\n", + " colormap to color them on a continuous scale.\n", + "\n", + "\n", + " xy_plot: The type of plot to render along the x and y axes.\n", + " Currently, the choice is hist\n", + "\n", + " xy_args: Keyword arguments used for customizing the x and y plots.\n", + " Property Description\n", + " alpha transparency\n", + " facecolor_x background color of the x axis\n", + " facecolor_y background color of the y axis\n", + " bins used to set up the number of bins for the hist plot\n", + " histcolor_x used to set the color for the histogram on the x axis\n", + " histcolor_y used to set the color for the histogram on the y axis\n", + "\n", + " size: Size of each side of the figure in inches.\n", + "\n", + " ratio: Ratio of joint axis size to the x and y axes height.\n", + "\n", + " space: Space between the joint axis and the x and y axes.\n", + "\n", + " kwargs: Keyword arguments passed to the super class.\n", + "\n", + " \"\"\"\n", + " \n", + " #check matplotlib version - needs to be version 2.0.0\t\n", + " if mpl.__version__ == '2.0.0':\n", + " pass \n", + " else:\n", + " print('This Visualizer requires Matplotlib version 2.0.0. Please upgrade to continue.')\n", + "\n", + " super(JointPlotVisualizer, self).__init__(ax, **kwargs)\n", + "\n", + " self.feature = feature\n", + " self.target = target\n", + " self.joint_plot = joint_plot\n", + " self.joint_args = joint_args\n", + " self.xy_plot = xy_plot\n", + " self.xy_args = xy_args\n", + " self.size = size\n", + " self.ratio = ratio\n", + " self.space = space\n", + "\n", + " def fit(self, X, y, **kwargs):\n", + " \n", + " \"\"\"\n", + "\n", + " Sets up the X and y variables for the jointplot\n", + " and checks to ensure that X and y are of the \n", + " correct data type\n", + "\n", + " Fit calls draw \n", + "\n", + " Parameters\n", + " ----------\n", + "\n", + " X : ndarray or DataFrame of shape n x 1\n", + " A matrix of n instances with 1 feature\n", + "\n", + " y : ndarray or Series of length n\n", + " An array or series of the target value\n", + "\n", + " kwargs: dict\n", + " keyword arguments passed to Scikit-Learn API.\n", + " \"\"\"\n", + "\n", + " #throw an error if X has more than 1 column\n", + " if is_dataframe(X):\n", + " nrows, ncols = X.shape\n", + " if ncols > 1:\n", + " raise YellowbrickValueError(\n", + " \"X needs to be an ndarray or DataFrame with one feature, please select one feature from the DataFrame\"\n", + " ) \n", + "\n", + " #throw an error is y is None\n", + " if y is None:\n", + " raise YellowbrickValueError(\n", + " \"Joint plots are useful for classification and regression problems, which require a target variable\")\n", + "\n", + "\n", + " # Handle the feature name if it is None.\n", + " if self.feature is None:\n", + "\n", + " # If X is a data frame, get the columns off it.\n", + " if is_dataframe(X):\n", + " self.feature = X.columns\n", + "\n", + " else:\n", + " self.feature = ['x']\n", + "\n", + " # Handle the target name if it is None.\n", + " if self.target is None:\n", + " self.target = ['y']\n", + "\n", + " self.draw(X, y, **kwargs)\n", + " return self\n", + "\n", + "\n", + " def draw(self, X, y, **kwargs): \n", + "\n", + " \"\"\" \n", + " Sets up the layout for the joint plot\n", + " draw calls draw_joint and draw_xy\n", + " to render the visualizations\n", + "\n", + " \"\"\"\n", + "\n", + " fig = plt.figure(figsize=(self.size, self.size))\n", + " gs = plt.GridSpec(self.ratio + 1, self.ratio + 1)\n", + "\n", + " #Set up the 3 axes objects\n", + " joint_ax = fig.add_subplot(gs[1:, :-1])\n", + " x_ax = fig.add_subplot(gs[0, :-1], sharex=joint_ax)\n", + " y_ax = fig.add_subplot(gs[1:, -1], sharey=joint_ax)\n", + "\n", + " fig.tight_layout()\n", + " fig.subplots_adjust(hspace=self.space, wspace=self.space)\n", + "\n", + " self.fig = fig\n", + " self.joint_ax = joint_ax\n", + " self.x_ax = x_ax\n", + " self.y_ax = y_ax\n", + "\n", + " self.draw_joint(X, y, **kwargs)\n", + " self.draw_xy(X, y, **kwargs)\n", + " \n", + " def draw_joint(self, X, y, **kwargs):\n", + "\n", + " \"\"\" \n", + " Draws the visualization for the joint axis\n", + "\n", + " \"\"\"\n", + "\n", + " if self.joint_args is None:\n", + " self.joint_args = {}\n", + "\n", + " self.joint_args.setdefault(\"alpha\", 0.4)\n", + " facecolor = self.joint_args.pop(\"facecolor\", \"#dddddd\")\n", + " self.joint_ax.set_facecolor(facecolor)\n", + "\n", + " if self.joint_plot == \"scatter\":\n", + " aspect = self.joint_args.pop(\"aspect\", \"auto\")\n", + " self.joint_ax.set_aspect(aspect)\n", + " self.joint_ax.scatter(X, y, **self.joint_args)\n", + "\n", + " fit = self.joint_args.pop(\"fit\", True)\n", + " if fit:\n", + " estimator = self.joint_args.pop(\"estimator\", \"linear\")\n", + " draw_best_fit(X, y, self.joint_ax, estimator)\n", + "\n", + " elif self.joint_plot == \"hex\":\n", + " x_bins = self.joint_args.pop(\"x_bins\", 50)\n", + " y_bins = self.joint_args.pop(\"y_bins\", 50)\n", + " colormap = self.joint_args.pop(\"cmap\", 'Blues')\n", + " gridsize = int(np.mean([x_bins, y_bins]))\n", + "\n", + " xmin = X.min()\n", + " xmax = X.max()\n", + " ymin = y.min()\n", + " ymax = y.max()\n", + " \n", + " self.joint_ax.hexbin(X, y, gridsize=gridsize, cmap=colormap, \n", + " mincnt=1, **self.joint_args)\n", + " self.joint_ax.axis([xmin, xmax, ymin, ymax])\n", + "\n", + " def draw_xy(self, X, y, **kwargs):\n", + "\n", + " \"\"\" \n", + " Draws the visualization for the x and y axes\n", + "\n", + " \"\"\"\n", + "\n", + " if self.xy_args is None:\n", + " self.xy_args = {}\n", + "\n", + " facecolor_x = self.xy_args.pop(\"facecolor_x\", \"#dddddd\")\n", + " self.x_ax.set_facecolor(facecolor_x)\n", + " facecolor_y = self.xy_args.pop(\"facecolor_y\", \"#dddddd\")\n", + " self.y_ax.set_facecolor(facecolor_y)\n", + "\n", + " \n", + " if self.xy_plot == \"hist\":\n", + " hist_bins = self.xy_args.pop(\"bins\", 50)\n", + " self.xy_args.setdefault(\"alpha\", 0.4)\n", + " histcolor_x = self.xy_args.pop(\"histcolor_x\", \"#6897bb\")\n", + " self.x_ax.set_facecolor(facecolor_x)\n", + " histcolor_y = self.xy_args.pop(\"histcolor_y\", \"#6897bb\")\n", + " self.y_ax.set_facecolor(facecolor_y)\n", + " self.x_ax.hist(X, bins=hist_bins, color=histcolor_x, **self.xy_args)\n", + " self.y_ax.hist(y, bins=hist_bins, color=histcolor_y, \n", + " orientation='horizontal', **self.xy_args)\n", + " \n", + " def poof(self, **kwargs):\n", + "\n", + " \"\"\" \n", + " Creates the labels for the feature and target variables\n", + "\n", + " \"\"\"\n", + "\n", + " self.joint_ax.set_xlabel(self.feature)\n", + " self.joint_ax.set_ylabel(self.target)\n", + " self.finalize(**kwargs)\n", + "\n", + " def finalize(self, **kwargs):\n", + "\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", + " plt.setp(self.x_ax.get_xticklabels(), visible=False)\n", + " plt.setp(self.y_ax.get_yticklabels(), visible=False)\n", + "\n", + " plt.setp(self.x_ax.yaxis.get_majorticklines(), visible=False)\n", + " plt.setp(self.x_ax.yaxis.get_minorticklines(), visible=False)\n", + " plt.setp(self.y_ax.xaxis.get_majorticklines(), visible=False)\n", + " plt.setp(self.y_ax.xaxis.get_minorticklines(), visible=False)\n", + " plt.setp(self.x_ax.get_yticklabels(), visible=False)\n", + " plt.setp(self.y_ax.get_xticklabels(), visible=False)\n", + " self.x_ax.yaxis.grid(False)\n", + " self.y_ax.xaxis.grid(False)\n", + " self.fig.suptitle(\"Joint Plot of {} vs {}\"\n", + " .format(self.feature, self.target), y=1.05)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\n", + "import sys \n", + "import pandas as pd\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", + " \"credit\": os.path.join(FIXTURES, \"credit\", \"credit.csv\"),\n", + " \"concrete\": os.path.join(FIXTURES, \"concrete\", \"concrete.csv\"),\n", + " \"occupancy\": os.path.join(FIXTURES, \"occupancy\", \"occupancy.csv\"),\n", + "}\n", + "\n", + "\n", + "def load_data(name):\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", + " # Return the data frame\n", + " return pd.read_csv(path)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Load the data\n", + "df = load_data('concrete')\n", + "features = ['cement', 'slag', 'ash', 'water', 'splast', 'coarse', 'fine', 'age']\n", + "feature = 'cement'\n", + "target = 'strength'\n", + "# Get the X and y data from the DataFrame \n", + "X = df[feature].as_matrix()\n", + "y = df[target].as_matrix()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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98MMPRyaTwQUXXACtdcWUXi6Xw6c//elyCnWJXXfdFbZtj+ox/vKXv8QxxxyDhx56qLzN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EmpEAiUsfRdz0mAV9vIi7eOOGoDT1SKb72muDarqcZaOicVMVZ07QqtNFUzI+fM4TWFMZqh6+5I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\nFNGCp1mWst548y1Ya6naOpoyRSrFrk95fjgmNNW82Q/AD4Vi24/R5+kMI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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visualizer = JointPlotVisualizer(feature=feature, target=target, joint_plot=\"hex\")\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": 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": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/freqtext.ipynb b/examples/rebeccabilbro/freqtext.ipynb similarity index 100% rename from examples/freqtext.ipynb rename to examples/rebeccabilbro/freqtext.ipynb diff --git a/examples/rebeccabilbro/postag.ipynb b/examples/rebeccabilbro/postag.ipynb new file mode 100644 index 000000000..dc9412b0e --- /dev/null +++ b/examples/rebeccabilbro/postag.ipynb @@ -0,0 +1,314 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualizing Part-of-Speech Tagging with Yellowbrick\n", + "\n", + "This notebook is a sample of the text visualizations that yellowbrick provides, in particular a feature that enables visual part-of-speech tagging, which can be used to make decisions about text normalization and vectorization." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\n", + "import sys \n", + "\n", + "# Modify the path \n", + "sys.path.append(\"..\")\n", + "\n", + "import yellowbrick as yb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## First let's grab a few documents to experiment with" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "texts = {\n", + " 'nursery_rhyme' : '''Baa, baa, black sheep,\n", + " Have you any wool?\n", + " Yes, sir, yes, sir,\n", + " Three bags full;\n", + " One for the master,\n", + " And one for the dame,\n", + " And one for the little boy\n", + " Who lives down the lane.''',\n", + " 'algebra' : '''Algebra (from Arabic \"al-jabr\" meaning \n", + " \"reunion of broken parts\") is one of the \n", + " broad parts of mathematics, together with \n", + " number theory, geometry and analysis. In \n", + " its most general form, algebra is the study \n", + " of mathematical symbols and the rules for \n", + " manipulating these symbols; it is a unifying \n", + " thread of almost all of mathematics.''',\n", + " 'french_silk' : '''In a small saucepan, combine sugar and eggs \n", + " until well blended. Cook over low heat, stirring \n", + " constantly, until mixture reaches 160° and coats \n", + " the back of a metal spoon. Remove from the heat. \n", + " Stir in chocolate and vanilla until smooth. Cool \n", + " to lukewarm (90°), stirring occasionally. In a small \n", + " bowl, cream butter until light and fluffy. Add cooled\n", + " chocolate mixture; beat on high speed for 5 minutes \n", + " or until light and fluffy. In another large bowl, \n", + " beat cream until it begins to thicken. Add \n", + " confectioners' sugar; beat until stiff peaks form. \n", + " Fold into chocolate mixture. Pour into crust. Chill \n", + " for at least 6 hours before serving. Garnish with \n", + " whipped cream and chocolate curls if desired. '''\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/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" + ] + } + ], + "source": [ + "##########################################################################\n", + "# Imports\n", + "##########################################################################\n", + "\n", + "from yellowbrick.text.base import TextVisualizer\n", + "\n", + "##########################################################################\n", + "# PosTagVisualizer\n", + "##########################################################################\n", + "\n", + "class PosTagVisualizer(TextVisualizer):\n", + " \"\"\"\n", + " A part-of-speech tag visualizer colorizes text to enable\n", + " the user to visualize the proportions of nouns, verbs, etc.\n", + " and to use this information to make decisions about text\n", + " normalization (e.g. stemming vs lemmatization) and\n", + " vectorization.\n", + "\n", + " Parameters\n", + " ----------\n", + " kwargs : dict\n", + " Pass any additional keyword arguments to the super class.\n", + " cmap : dict\n", + " ANSII colormap\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", + " def __init__(self, ax=None, **kwargs):\n", + " \"\"\"\n", + " Initializes the base frequency distributions with many\n", + " of the options required in order to make this\n", + " visualization work.\n", + " \"\"\"\n", + " super(PosTagVisualizer, self).__init__(ax=ax, **kwargs)\n", + "\n", + " # TODO: hard-coding in the ANSII colormap for now.\n", + " # Can we let the user reset the colors here?\n", + " self.COLORS = {\n", + " 'white' : \"\\033[0;37m{}\\033[0m\",\n", + " 'yellow' : \"\\033[0;33m{}\\033[0m\",\n", + " 'green' : \"\\033[0;32m{}\\033[0m\",\n", + " 'blue' : \"\\033[0;34m{}\\033[0m\",\n", + " 'cyan' : \"\\033[0;36m{}\\033[0m\",\n", + " 'red' : \"\\033[0;31m{}\\033[0m\",\n", + " 'magenta' : \"\\033[0;35m{}\\033[0m\",\n", + " 'black' : \"\\033[0;30m{}\\033[0m\",\n", + " 'darkwhite' : \"\\033[1;37m{}\\033[0m\",\n", + " 'darkyellow' : \"\\033[1;33m{}\\033[0m\",\n", + " 'darkgreen' : \"\\033[1;32m{}\\033[0m\",\n", + " 'darkblue' : \"\\033[1;34m{}\\033[0m\",\n", + " 'darkcyan' : \"\\033[1;36m{}\\033[0m\",\n", + " 'darkred' : \"\\033[1;31m{}\\033[0m\",\n", + " 'darkmagenta': \"\\033[1;35m{}\\033[0m\",\n", + " 'darkblack' : \"\\033[1;30m{}\\033[0m\",\n", + " None : \"\\033[0;0m{}\\033[0m\"\n", + " }\n", + "\n", + " self.TAGS = {\n", + " 'NN' : 'green',\n", + " 'NNS' : 'green',\n", + " 'NNP' : 'green',\n", + " 'NNPS' : 'green',\n", + " 'VB' : 'blue',\n", + " 'VBD' : 'blue',\n", + " 'VBG' : 'blue',\n", + " 'VBN' : 'blue',\n", + " 'VBP' : 'blue',\n", + " 'VBZ' : 'blue',\n", + " 'JJ' : 'red',\n", + " 'JJR' : 'red',\n", + " 'JJS' : 'red',\n", + " 'RB' : 'cyan',\n", + " 'RBR' : 'cyan',\n", + " 'RBS' : 'cyan',\n", + " 'IN' : 'darkwhite',\n", + " 'POS' : 'darkyellow',\n", + " 'PRP$' : 'magenta',\n", + " 'PRP$' : 'magenta',\n", + " 'DT' : 'black',\n", + " 'CC' : 'black',\n", + " 'CD' : 'black',\n", + " 'WDT' : 'black',\n", + " 'WP' : 'black',\n", + " 'WP$' : 'black',\n", + " 'WRB' : 'black',\n", + " 'EX' : 'yellow',\n", + " 'FW' : 'yellow',\n", + " 'LS' : 'yellow',\n", + " 'MD' : 'yellow',\n", + " 'PDT' : 'yellow',\n", + " 'RP' : 'yellow',\n", + " 'SYM' : 'yellow',\n", + " 'TO' : 'yellow',\n", + " 'None' : 'off'\n", + " }\n", + "\n", + " def colorize(self, token, color):\n", + " \"\"\"\n", + " Colorize text\n", + "\n", + " Parameters\n", + " ----------\n", + " token : str\n", + " A str representation of\n", + "\n", + " \"\"\"\n", + " return self.COLORS[color].format(token)\n", + "\n", + " def transform(self, tagged_tuples):\n", + " \"\"\"\n", + " The transform method transforms the raw text input for the\n", + " part-of-speech tagging visualization. It requires that\n", + " documents be in the form of (tag, token) tuples.\n", + "\n", + " Parameters\n", + " ----------\n", + " tagged_token_tuples : list of tuples\n", + " A list of (tag, token) tuples\n", + "\n", + " Text documents must be tokenized and tagged before passing to fit()\n", + " \"\"\"\n", + " self.tagged = [\n", + " (self.TAGS.get(tag),tok) for tok, tag in tagged_tuples\n", + " ]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from nltk.corpus import wordnet as wn\n", + "from nltk import pos_tag, word_tokenize" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;32mBaa\u001b[0m \u001b[0;0m,\u001b[0m \u001b[0;32mbaa\u001b[0m \u001b[0;0m,\u001b[0m \u001b[0;31mblack\u001b[0m \u001b[0;32msheep\u001b[0m \u001b[0;0m,\u001b[0m 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"\n", + "\u001b[0;32mAlgebra\u001b[0m \u001b[0;0m(\u001b[0m \u001b[1;37mfrom\u001b[0m \u001b[0;32mArabic\u001b[0m \u001b[0;0m``\u001b[0m \u001b[0;31mal-jabr\u001b[0m \u001b[0;0m''\u001b[0m \u001b[0;34mmeaning\u001b[0m \u001b[0;0m``\u001b[0m \u001b[0;32mreunion\u001b[0m \u001b[1;37mof\u001b[0m \u001b[0;31mbroken\u001b[0m \u001b[0;32mparts\u001b[0m \u001b[0;0m''\u001b[0m \u001b[0;0m)\u001b[0m \u001b[0;34mis\u001b[0m \u001b[0;30mone\u001b[0m \u001b[1;37mof\u001b[0m \u001b[0;30mthe\u001b[0m \u001b[0;31mbroad\u001b[0m \u001b[0;32mparts\u001b[0m \u001b[1;37mof\u001b[0m \u001b[0;32mmathematics\u001b[0m \u001b[0;0m,\u001b[0m \u001b[0;36mtogether\u001b[0m \u001b[1;37mwith\u001b[0m \u001b[0;32mnumber\u001b[0m \u001b[0;32mtheory\u001b[0m \u001b[0;0m,\u001b[0m \u001b[0;32mgeometry\u001b[0m \u001b[0;30mand\u001b[0m \u001b[0;32manalysis\u001b[0m \u001b[0;0m.\u001b[0m \u001b[1;37mIn\u001b[0m \u001b[0;35mits\u001b[0m \u001b[0;36mmost\u001b[0m \u001b[0;31mgeneral\u001b[0m \u001b[0;32mform\u001b[0m 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"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": 1 +} diff --git a/examples/rebeccabilbro/testing.ipynb b/examples/rebeccabilbro/testing.ipynb new file mode 100644 index 000000000..82b037f68 --- /dev/null +++ b/examples/rebeccabilbro/testing.ipynb @@ -0,0 +1,521 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "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.pyplot as plt " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Yellowbrick to Explore Book Reviews \n", + "\n", + "This notebook is for the Yellowbrick user study.\n", + "\n", + "About the data:\n", + "[Amazon book reviews Data Set](http://archive.ics.uci.edu/ml/datasets/Amazon+book+reviews) \n", + "\n", + "Abstract: \n", + "213,335 book reviews for 8 different books. \n", + "\n", + "Source:\n", + "Ahmet Taspinar, info '@' ataspinar.com, http://ataspinar.com\n", + "\n", + "Data Set Information:\n", + " - Gone Girl: 41,974 \n", + " - The Girl on the Train: 37,139 \n", + " - The Fault in our Stars: 35,844 \n", + " - Fifty Shades of Grey: 32,977 \n", + " - Unbroken: 25,876 \n", + " - The hunger games: 24,027 \n", + " - The Goldfinch: 22,861 \n", + " - The Martian: 22,571 \n", + "\n", + "Attribute Information:\n", + "Each entry is separated by a newline character. Each entry contains four attributes, which are separated by a space: \n", + "1. review score \n", + "2. tail of review url\n", + "3. review title \n", + "4. HTML of review text\n", + "\n", + "After [downloading the data](http://archive.ics.uci.edu/ml/machine-learning-databases/00370/amazon_book_reviews.rar) in .rar archive form, I unpacked it with `unrar`:\n", + "\n", + "_(if you don't have unrar)_\n", + "\n", + " $ brew install unrar # or use apt-get or yum, depending on your system\n", + " $ urar e amazon_book_reviews.rar\n", + "\n", + "The result is the following 8 csv files and a metadata.txt file:\n", + "\n", + " - Andy-Weir-The-Martian.csv\n", + " - Laura-Hillenbrand-Unbroken.csv\n", + " - Donna-Tartt-The-Goldfinch.csv\n", + " - Paula_Hawkins-The-Girl-On-The-Train.csv\n", + " - EL-James-Fifty-Shades-of-Grey.csv\n", + " - Suzanne-Collins-The-Hunger-Games.csv\n", + " - Fillian_Flynn-Gone_Girl.csv\t\n", + " - John-Green-The-Fault-in-our-Stars.csv\n", + " - metadata.txt" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from sklearn.datasets.base import Bunch\n", + "\n", + "\n", + "## The path to the test data sets\n", + "FIXTURES = os.path.join(os.getcwd(), \"data\")\n", + "\n", + "\n", + "## Dataset loading mechanisms\n", + "datasets = {\n", + " \"reviews\": os.path.join(FIXTURES, \"reviews\")\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", + " # Read the files in the directory as the categories. \n", + " categories = [\n", + " os.path.splitext(f)[0] for f in os.listdir(path) \n", + " if os.path.isfile(os.path.join(path, f))\n", + " and os.path.join(path, f).endswith(\".csv\")\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", + " files.append(os.path.join(path, cat + '.csv'))\n", + "\n", + " with open(os.path.join(path, cat + '.csv'), 'r') as f:\n", + " content = f.read()\n", + " docs = [s.strip() for s in content.splitlines()]\n", + " for doc in docs[:1000]: # limited size so nb won't crash\n", + " data.append(doc)\n", + " target.append(cat)\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": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "corpus = load_data('reviews')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualizing Stopwords Removal\n", + "\n", + "How much does stopwords removal impact a corpus of book reviews?\n", + "\n", + "To visualize the transformation, we can compare the results before and after stopwords have been removed from the corpus using the Yellowbrick `FreqDistVisualizer`:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/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" + ] + } + ], + "source": [ + "from yellowbrick.text.freqdist import FreqDistVisualizer \n", + "from sklearn.feature_extraction.text import CountVectorizer" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/axes/_axes.py:531: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n", + " warnings.warn(\"No labelled objects found. \"\n" + ] + }, + { + "data": { + "image/png": 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1/Uk9PqiH/Pz8rnDPAAAwp6byEK93AwAAMBHCHwAAgIkQ/gAAAEyE8AcAAGAi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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "vectorizer = CountVectorizer()\n", + "docs = vectorizer.fit_transform(corpus.data)\n", + "features = vectorizer.get_feature_names()\n", + "\n", + "visualizer = FreqDistVisualizer()\n", + "visualizer.fit(docs, features)\n", + "visualizer.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/axes/_axes.py:531: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n", + " warnings.warn(\"No labelled objects found. \"\n" + ] + }, + { + "data": { + "image/png": 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lbm9NAQAAkH/4i4uLU9++fdWnTx+NHDnSLHdzc5O/v7+mT5+u1NRUHT16VJGR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\n2LDBLHvhhRfUp08fPf3006pfv7727dt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09MTdu3fx66+/Up+/kZERNm7cyNArrV69GgYG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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "vectorizer = CountVectorizer(stop_words='english')\n", + "docs = vectorizer.fit_transform(corpus.data)\n", + "features = vectorizer.get_feature_names()\n", + "\n", + "visualizer = FreqDistVisualizer()\n", + "visualizer.fit(docs, features)\n", + "visualizer.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualizing tokens across corpora\n", + "\n", + "It is also interesting to explore the differences in tokens across a corpus. For example, do people say different things in reviews about books by men vs. books by women?" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "male = ['Andy-Weir-The-Martian',\n", + " 'John-Green-The-Fault-in-our-Stars']\n", + "\n", + "female = ['Laura-Hillenbrand-Unbroken',\n", + " 'Paula_Hawkins-The-Girl-On-The-Train',\n", + " 'Suzanne-Collins-The-Hunger-Games',\n", + " 'Donna-Tartt-The-Goldfinch',\n", + " 'EL-James-Fifty-Shades-of-Grey',\n", + " 'Fillian_Flynn-Gone_Girl']\n", + "\n", + "male_author_reviews = []\n", + "female_author_reviews = []\n", + "\n", + "for book in male:\n", + " for idx in range(len(corpus.data)):\n", + " if corpus.target[idx] == book:\n", + " male_author_reviews.append(corpus.data[idx])\n", + "\n", + "for book in female:\n", + " for idx in range(len(corpus.data)):\n", + " if corpus.target[idx] == book:\n", + " female_author_reviews.append(corpus.data[idx]) " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/axes/_axes.py:531: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n", + " warnings.warn(\"No labelled objects found. \"\n" + ] + }, + { + "data": { + "image/png": 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ATITwBwAAYCKEPwAAABMh/AEAAJhIg8s9AJhDeXm5jh8/fs56s2bN5OLicglH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ABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "vectorizer = CountVectorizer(stop_words='english')\n", + "docs = vectorizer.fit_transform(text for text in female_author_reviews)\n", + "features = vectorizer.get_feature_names()\n", + "\n", + "visualizer = FreqDistVisualizer()\n", + "visualizer.fit(docs, features)\n", + "visualizer.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/axes/_axes.py:531: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n", + " warnings.warn(\"No labelled objects found. \"\n" + ] + }, + { + "data": { + "image/png": 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qVZKkgoICjRs3Tn5+fgoKClJKSoqxTUlJiaZOnSp/f38FBARoxYoVdvuMi4tT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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "vectorizer = CountVectorizer(stop_words='english')\n", + "docs = vectorizer.fit_transform(text for text in male_author_reviews)\n", + "features = vectorizer.get_feature_names()\n", + "\n", + "visualizer = FreqDistVisualizer()\n", + "visualizer.fit(docs, features)\n", + "visualizer.poof()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## t-SNE: Corpus Visualization \n", + "\n", + "What patterns can we see if we project the book reviews into 2 dimensional space? " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from yellowbrick.text import TSNEVisualizer\n", + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "\n", + "tfidf = TfidfVectorizer()\n", + "\n", + "docs = tfidf.fit_transform(corpus.data)\n", + "labels = corpus.target" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'Paula_Hawkins-The-Girl-On-The-Train'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\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[0;31m# Create the visualizer and draw the vectors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mtsne\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTSNEVisualizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtsne\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mtsne\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/3.5/lib/python3.5/site-packages/yellowbrick/text/tsne.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 266\u001b[0m \u001b[0;31m# Draw the vectors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 267\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvecs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 268\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 269\u001b[0m \u001b[0;31m# Fit always returns self.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/yellowbrick/text/tsne.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, points, target, **kwargs)\u001b[0m\n\u001b[1;32m 309\u001b[0m \u001b[0;31m# Plot the points\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpoints\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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[0;32m--> 311\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpoints\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'x'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpoints\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'y'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 313\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfinalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'Paula_Hawkins-The-Girl-On-The-Train'" + ] + }, + { + "data": { + "image/png": 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MgpxsTCYjldVH5Z4BFH6wisxpM3E6ncR2FREIBrEf78q2qgq1Wz/78tzPmvmV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\nW5hY1InRXjCg+tflcuEumImSPIwzx05hD2j4O0B87Sg/3vJ1/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9u1q7rq1ZI7LuBKN81g\neodATAjkgAQud/BeMOQNJCTHhbyBhJDkyCVAAiEYjLHBYGOMAWNwB/cmybIkq9ftfWbeP9a7rKRV\ncwnl9vMPrDUzzzPPzM5891cLC7RIeh/+oJWzZyvs3qsiaVX2H5CwWhXqGlR27dVgNql094rY7dl4\n/BY2bNGiqiaystOZd/YY9hzIZsuuYpoaVmE1a7jyslws5i9iiWw2G1OnTKBqXE583u29vcAXIrWr\nsw5J66K7J0BVRYAly+C0GWdi0RdzTpaJ8TU6RMGEolc5LS+D22+46rjXTrRb2Ohup/jyy0AAk+Dh\nk6WvMGHCxD4C8m9vrqChtQbB34wmbCA/+2pau7pJt+fhaUjDPvs6Vm5+ndLMoS0uyRINButokCyu\n0OEPceRANQ17djLurHkYiseQperY8P4HtOvHElBF7AZxQOeHIpsWpeNoNnAgjGyOuldFQUDnjPCd\npwZad6rXfoYQEvA3fYYq6bH7HaiFWdRPnIlWEXCoWlaZPqMqQ+S+RXf2jWdsayZLtkFYQSOIaIIK\nEYMG2Ru1uuaOKWanx8WhHN+grtVEkbRuw2dURGwUT6gcsj3b4ZZ2vOl5uDIr4gkug7nEByNRNDbu\nq6GjeRcX+nNGbQlLJrBGGitqs9lob93Jd78lIwoislLPS8/dwo9/tn5U5/J1iFNM8fUkJfJSpPin\nEe1WQPQ9h0BHH/et0aiycfNhzp7tYOWaCFdeHOTdD7QsOD9Md6+WnOweGhpa0YgCM09VeHOljjy7\ngiwLTBqn0tAsUFEC1XUSNluAay/vRBDSSLd62LBVi8WsRdAWxbtOzJjaFZ9ZomWrv8XR649aHMPB\nI7z0ai8mvUB3t5ZvXaeik4KMq5Ro7IrGFzoXOPtYQM6/53oeeub5Pu2rjsVFd83Rbgg+vYKg1zCm\nchJWbYj7jgrImHBpaK0hz1LLgvMcKIrKC68/TdA8BslkoqhqMm6PljZPJf9y69Av8GQ1+kbT0WDD\nlq3MuOVfMKZnoNEZCAX8mDNsZFkM9ITcFEV6WbTwX6OJGAmdH0KFIcwGH3K7m32lPs4xTwCGznzd\nqJcJjzuH9KPZv96d72BozyJzTLQosksbxjWlkEf+O9qPODEbOazLYNM/3maMJBLyR6j3dZInp/Oh\npp05tnz0RRn8ZNF/D3nNEkWSFFSjhRkZuu5hIKzgdXvpDu0j4OmlpXYXRXXN3PLofSO+PxJFI2EV\nMm3QPHpL2PEKrDRTN6Ig4PYobN0eQlAPDNqTeTC+DnGKKb6enFSR98c//pElS5bg9XoZP348P/nJ\nT6iqqjqZQ6ZIcdI43gbk/S1BL7zcid/jwKAPEQjqsFjPx9H7Cc2tQQx6lXBExGRQAQFJqyEc9uP3\nC8hyEKMB9Do476xoG7NQGI68oWPPARP33jWN3XvryMrw0d1rxuWx4vbA+i2VyEqAZa/ewd591UyZ\nkIZOJw2IJepvcZSVQHzeZ87K4MVXFMaUe5EkGRUQRENc8PS3gDz0zPMD2lcNZrGJtxiToba6mvKK\ncqx6KR67NWFqFZmnVCZt7xSz6kTyLKRZprFqy1JuPM/NWRPctKt5ZE6bG99PW+oZ9rol61zxSVsR\nNV0BLp3lwGLWDBl/pbNlcXDDOrydbeSUjUHSGzDLfqxBJ7eUZ7BoUVRw9e/84FC08fVzOqOCuXOY\nF7+mvJReSUZUZEJqGL0lk2y3H08EVK2AxWpm7tjJSfvrFowto77cRrvPy8G9+zk9XIRJb+bMMafR\nUmwckTUrUSSF9QJKJGp9HKruYe2mrew70g0GLbqIn1MzW/A39bLsCdOILXCJohFJgG4nkDNqoXa8\nAsvty0JWetm6PcTZcwK0dpoZUzm6TNtjzTJPkWI4TprIe+ONN3jrrbf4+9//Tn5+Pn/605+46667\n+OCDD07WkClSnFSOtQF5TBw2179Brbkbk8mIKBox6rSUF3ciacOEwhKNHQJer5HM9GhihVaj4AsY\nCEckfAENDlcIOaJh114wGgQEMVpEORgSEUUBSdJz6ozzyMwMEAzp8PvduJwt6PVaGo5korcEWXDe\nERwOP4dqDvLCiyHCsoXKsVdxw7e+qM3Xv2bbslfv6GPBKi0rZf/+ABPHdSJqjGRlj6G+PbngSda+\najBiQs3b2sr48dNo3PIpZbPPjpdeGaq9U0y4KOgQxSARKQtFcRFWcrl5wejbQiXrXJErn0V7Ww0v\nrHyVWWNzhuxooFciFE6bQzjgp373DiK9nZw3fSJ/ffSBPgLTpgbZsGkdQVFCr4SprLL3mcNIXvyz\nZo7nwzYNhBSCDhdZSojLuzJYsWcLXWkGrGEDi37xX/HtE8WRKGoYd0olc264hOxn3mJszxfiaKTW\nsESRlFM5mYCg4ZBjoHs3cU0PN7aiyOmogkJEDSP4qjFleggc6eGFh54cUZZp4v2g8XsoLE5POu5w\nHK/AWnjHy7z03C0I6gFaO80UFU8/ob1rU6Q4Hk6ayHM6ndx9990UFkaDixcuXMjTTz9NW1sbeXl5\nw+ydIsVXj2NtQB4Th8sbHeRle3G4QqRnhmlrD1JcaEBAhwrsObgBlSBL3oI0M/zl7zp0OokVaydi\n0LlwOtsZO9ZHZalMQZ7C2vVaOrpErBZAMFBYmENbWwuKksGcWeX8+fk2pkyEcETDt67xsXj5Wwjn\nn8pHH+/m0gs8tLSJ1NZ5aTmynNUrrYNaJvu7byVdEZdcfhcHa5fR0/Uxfl87haUBGhsb2fTps30s\nnf0LCQ9VFy4u1ISo+BAkXZ/SK0N1Q4gJF7u9kva2GrqavWzeOTl+TsfTRSE2L61WorBoIuKES7kh\nSZxhYqzbtMoStq16DdGaiej38Ot770gejC+KhLJL6fEGEOQIG/ccxJmkf3CyMWKC9dHvfQvD0Tp+\nnW313Jw1idpAHdN9Mj2Zfh7582/6HK+/WL7uwnk0NTURsGlYt/kzpKBKWC+QUzl5QMxgMnf7sYgk\npypRmGVGL7ajlcEVzMDb46OheT9lQSemTA/eQxb+8ViAb/3kB0nLiyS7rrEkkNfv/+0JKUUyktIm\nJSUl/Phn63ntlYcZUzl4O78UKb4MjkvkybKMz+cb8O+CIHD77X1dMmvXriUjIyMl8FJ8bTnWBuQx\ncThloo5PthhweyA9Ix1B6EFVVAQxWgfP5wO308mdt0QQRHC5FP72Kpx+xlj279vBZZecwjvv7qGq\nPAAIzDlNYMkKiTFlIOkLOOes8nhihVZow56j58w5hrgwzUwPoygqRkMIAdi9T2D+eUE6uxVKywd3\nL/V33553UbStmU6n49orio5aNmt58blbWHhTRh9L56KFPxpQSHjQdYpZmFSFcChIU81BNqoqvqZ6\nnM55Q76sE4WLXgnzk39/8oSV55CUMKFQkNaubmQVDu/dm3Q+/Vt/WUcgLp2qhEajwZiRDQi0Bnv5\n9vce5so0e1JRMVh7sZgLPObiLWjXIdot3JtElPQXR7EOJhoEKiI2kGWUiIaAoIn37R2Ju3002A0i\n2eYKOjoU/O4elCNBTOPOxaJfzXkX9hw9PzfLP1zDG08YRlxe5ESXIhnN8WLfEyXcSE1tPeXlR0Yd\nm5cixYnmuETeli1buP32278Ifj1KQUEBa9eujX/eunUrP/3pT/nFL34xquOHQiECgcDxTDHFV4Bg\nMNjnv19Xzjnvh3y45km0Ygdh2c6MOd/hlZd+hKSJfj7vogeSPswDoSwikXaMRgNnzw3xydYMzjtn\nDHUNVtZvdqLXBwkE9eQVnokkfUiv04koKmzaBpdcEKG17RPSLc28ukTEnqNn7XodWo1CdqaOkgKV\nynIt5RWldLTXUFPTxpiqPM6+6HF27zmAohxC1IAiqwTDZWz8fDy1hz9nfFUEm01AIEIkIgEqgtKc\n9Pum1+u5/KqfxD/HrqNIG6p6NJkEsBi7UNV0VFXF5ZbZuL+NZvV9CtON/PD6L0TRYN/pq+edzetr\nPkJWVLZs2cqUi6/AZDaRO2sOf1/xLndcd+Wg10av13PblZf1XffjeHY4HE6eWvwWHQGFdIJ4d7+I\ntawSJRRk5oIrks4noIqoqsLR5cCvCMPOwSwH6Oj1EtYE0KgyQncnsl9LuU+P0hbib48+jWbGWMIa\nCUkO4wqFyRxiDL1ez02PfH9U6xC7npqeIIXjyuP/XtvtpzUnjKpXUYkO2OI5Mc/kf7vhUp5cvIIm\nUcXV0MqF6dORtDpsuUefEWq01Z4lN0C41YWqGuPnHGpxDjqHcKsLd0hge8N+8Ic5tL0bV307xqIs\nrnjgtlGLrdGMHQj4iYTDtDbtIS/bz5QJZizmXlat+E+uuu6xUY17vHxTnrkpooRCoeE3GoTjEnlz\n587lwIEDQ26zfPlyHnvsMR555BEWLFgwquM3NDQcz/RSfMWora39sqdw3FRNuDH+/+++8wTnzqk9\nanU4zJLFDzP3zO8P2Kew5FLeWduNVoCGxlYKCvJ4+/1czjz3Hg7VvAGRbgJyFlPHXYPD0UNYdiIJ\nYQwGPwdrBead0YBWI+P1aZk2OUREMYAK76xRsdkMNLbZMW85jD1H4YJzTJhMm1my+GFmzn2YF19/\nBIOuh87uCOPGpnOkqRVrxmzWfPQpPb1+0tMMlJZV4XS6aO2ws3fv3hGvRUe3hNPpils227tM8c9v\nfppN7uyrCXgNBOQIDz/4nyy8/Voslr4B8R6Pl4+270I1GBECfs6dPgWLxYzP56UwN1rGJRQK0unx\njGpux8sflr1Hbfo4BElAVRUKVRdzpk0HQIWk83G2t2J2OhdhRTQAACAASURBVOMJHs72Vvbu3YvH\n7WHji+8g9gRRMvXMve3S+Dr0dHUhtnRDJIIcDuAN+NAFdbh10b/vbOtl7thrMQgCXreLza+9QmtA\nRgkFGDtlWnyME4FDCtHc28kKoZdegxGly0tBr4hLm40gRAWs3dOTdLzYOYY63NRnayk4bRKSHIlf\nz2TceOZ01vzhVcZ3HLX+HnGwr1SkVwohyiqKRkCbVUVrs4P6fTvRhQRCOhX96aWDnnOP6Ke+ppop\nzkwcQQ9laiE1m6o5rWgCLzz8JBd+/8ak+w1Gj+jH6QrHM2977BG2bN7Ezu0vYdR14w9lMXX6QswW\nCxvW/5Fz59RSam/HmhZi/adhzjqjGI+rmr179+L1uJPudzL5JjxzUxwfJzW79g9/+AMvvfQSzz77\nLLNmzRr1/qWlpQNeCim+fgSDQWpra6msrESv13/Z0zlhHD4oY7Va45/zcmQmTZoERJMtPnjvqbiV\n7/qbHk9qRZh33vl9Po8bPz5uLezorcGesR+9LgJAmkVFksBmtiHLElMm9TLtFAOhsI+3VvmZMVVC\nVTswmyqQNE62bnwce5aPhia4+foqCvMl3n1vM5Xju0lLsxAOm1n1QSaHmwP4/EEKiiwUFRUNa+2I\nXc8rr3mUDR//Pm7Z/O5d3+OzzX9GK3bQEyomP2ghLSgAOuwGO7veWIdBb0Dt8CLYzVzxwG28vuZD\nxs+/Iu5+3PP5Ju647ko27a/BbDZ/UdjYYo6v7WhxOp28vubDuDXsuguHdv0CKGu29bm27sMudm/Z\niEZnQA4FKNKJA+ZTVFTE62s+IqzRIslh7r75emw2Gy898jtmth8VMu0qNe9s5tbH/hUAbfY2sntl\nWjMqURCIuLqRqveRZklDUVXMpcb4PPZs2cSc629BK4ooqsq+dav52fe/Fz+XROuj3SDywM1XYLPZ\nhj3/2PX81k/v5Xv/9nN2ZJ+CrJWwWg2MNTmZI/XScdTd/sB93026di898jsmtph4LctH+vhJtDv9\nzL3wAvbU7BvSAvuZYsRqNcY/lyvn0CGr0XsqYueW2x7g9cf/hzTRD6KCIop4s7P7rL3T4eCtp15E\n7fBislnosWgJRUSQRUx6M0Y1jM1qJVP2j/oeKvqvB1jx5N9Qjt6ztz9wGx+89ySXX9h+9J5tZ/OO\nd7jqusfizwOfx4JOcmOxKJjNFsxpY5k0aRLLljySdL+TwTf1mfu/FY/Hc8xGr5Mm8pYuXcqLL77I\n4sWLKS8vH36HJOh0OgwGwwmeWYovC71e/424nrFs2dqDuymyu8nNq0IUNShCQfz83lr2O86YceDo\nA72Hjz/83YgycQ0GAzfd+iucTgfewGNs+XQXYysEJC0oCgSCKiaLgWCwA1UVyLCF6exykG5VsaWZ\nEcQwnZ11HDwQ5q7bAoiCgNfr5LU3HVx9mQ050o3ZqJJh06MSRtK2ct1Vp8atkSOdJ4DdbuemW3/V\n59+qqqKfuxYvwRtSEIRoT1ex18XhmjouL5yFIFhQulVWPf0yyrQKNBotkUiY5o5O6lo7+OvyFVx+\n9ums2rgl3mpMURT+/Na78WSDRKGRLBkh8e9/Xb4C+8yz4kJy+Ufrh42VyzdLdAvEE0bMeomiiVNQ\nRQ2CHEHXVDPgXjYYDPzg1oGWIrHbj0ZztNguIHT74vvmmyXCGh0WswlUFbMunWanid94D+G2pZEX\nMRKJRNDp9Ig6PZJGpLSgAADjpMnk5n4RF/r0kn+w31KBkCbQrSg8vWQlj997+7Dn73A4+cOy91As\nmewJhvAaTKh6I0FZZcPhdna+8MCQaxU7x802H+Ouvg5TVhYRZPZ+/hllWelDfuelfCt0f1GjLq3E\nzk239h3P4FEomViFOxLgtWArjQ0dNP35H/EkkFeefpnK/cTvq0abjqLSsTQfrAOXgmzQgiAi5VtH\n/fwxGAzc+asf9f03XTcazdEi1SLopW4MBgOKUAD0YM+ror2thiPNaWzdPYUFVzyIwWAYdL+TyTfl\nmfu/nS/NXTsU//M//4PX6+Xaa6OB1qqqIggCr7/+OhUVFSdr2BQpTjorlj1GVfHb2C0BtnwWJCw3\nU1y+oE9JjWPNxI2xeuWvmDe3liyTlupDEk4XSFp4a7WB00+fzsZP13DVpUHaOyIEAiEiEQ1/fSVA\ndpZAS5uG/DwjohCLx1HISA+Rbg0RCikEQwoAqqLi98vD9tNdvvQxmhvWYTSp5OTO48JLfnT0b07+\nunxFUnGV2NM10tWDIiv0Ti7h/WAXc91pWLQG5HZ3PNmiuaMTbZoNvTmNzGlzWbVx04ASJv2TDWIk\nS0a46eIL48Jvz8Fazqg6BVOatU+27lAsWnhtn4SR7NOmUVr4RRuy1q6WEV/LoYrtLlp4LZ/8+89o\nCLkxaDSMKcrH07YbLrwJqyDg9HpY+dLfmH3aNHxN9eTPjfYdVhQFxefpU2i6qdebtFxNsu4diTy1\n+C1q08dhtVrpLfSittZC+TQUVaG7s3PE56jqNAiRCCoqglYDWmlAv+L+2arn33M9Hzz7+pA16mLr\n97K/kd3jy8CssKbHR+gvr/DkA3fHizF7wn4+r9+PIvtYyQ5KTi2msfkIZWVlNBRpuGbRnSPKlh2O\nwRKw+iYoTeUH9/dNuDjWxK0UKY6HkybyVq9efbIOnSLFl0pn+0fMmuwhw6qQleFn2cqoMHI6naxe\n+QSS2M7efdVUllmwmIWkPWKHIyYSDcYMAsFOwrKKx29hyvRr0RltXHtlOrV1vShKkP3VGspL4OpL\nA/Q6QFYUFr/hwuuNoNFqAJHmVh2btxtp6Ywg75TJbTMSCOqQ9Okoijroi2fFssfobVrMpWd7ETTg\nCbn4cI2Gqgk38vqaD/tYiBLFV2JP122BHiquuoZ8VcXoVtn0zkrO8+oR7ZZ4VmxdYyt6cxrTZs8Z\nIEQSRUrA6+HzAzU889ryuLBMJmL69EnNLmDH5o2cfsH8AUWUB8Nms/XJIn128ZIBHTBGylDFdm02\nG2//5tGjgjKMXW6hqayc7oAPubUas9mMN6ywcP48nHNO5bfPP4nGZCDi9ROWymnImxLPfHU0rCQ9\nf8KAcjWJdfFCoSB1ez9g2asr4mVuOgIKghRdP0mNEOlugZAfMeAhPU3qcy6xkipNvV7q6w9TNqaS\nIquBe+65ns+f+APpqHh6OzHa0vA11XPz/ff22b9/tuoHz74+aLZqY2MDLz53K2ZrJzvtMgeFKWQW\nFqC3GBHwsrO6EYfDyZvuduSwHlfrYWa2ujFq9Og0CtJMK79e/A/gqLj85V/YvXoj09xZFFeVIh5j\n9m3/bPPYj7v+9SVHul+KFCeTVFuzFCmSMFR3C78vWvIkFPah0URQVYXZU/f0KSEyZUIai5e6MZm8\nSXvEDkfsV//hRh1nnw6dXSp6fZB1m7eTZh5D0fgxwHYEVSEc1uAL6Gnr8KKTFHRSgLwcDc+/rCU7\nW6G5RSA7C9xuLVdfNoW1HwcxW8eiU3L53hV3s3nDnwZ98bTu30hmegStCkQg5OtFiTSxYf0faQhm\nIhflYLdXotVKAyxEsfpp7teWkz+hkkgkTGttI+58Mw0GTdyKcteN1/Hs4iXxrhQep4N9e/fyzGtR\ngRLx++MiZfumDVSdfQH5hYVxYSmp6gABlij8ivJyadi2kdYdW0ZcDDl6D3zhBo74/bRtWYdgMI/o\nGIn7Kn4PSlU22mkVScVhf0H50DPP03J4J7n2THQGHQhZvLDsTQr0B/i3Gw8fFdUqtz0dwqvoEYQg\nqqpnTHEpFcEjA8rVJJaXqdv7AXdcWovNKsXL3NgNZdRFotZdvbOVUOUMNEYTgiKT5uybWBcrqVK7\nez29hbNp9wdw2At59q21/OLxh1n87hqMES1SZw+/vP/eAT9okvWJHYwXn7uV6y+rjW53vsqOXzch\nWWYiCAKqqpIhdvHES0sJz7uS9rojdKVlkeXdwa3uTNRe2PTR9vixYuKy1aHB7Ie22kaKxo8ZVfuz\nxOs10pCGE7HfcCSzTqbi8FLESIm8FCmSMFR3i8LSc/hk67sokSCKrEEjmfnL37YgiV6Wv2Xm3LOn\nkJFhZOLEsQCD9ogdDKfTgd8f4OXXmkFx0OOQyM8NI4ph8rOr2b9fpqutnnRriB6HFhENRoMECORk\ngaIq5OWJ+IOQm63lqktCCIJMR1cnaz+2c9/9b/d5+ZaUDP7icbXrMUoaFCUaRxTyCdQeauDqS7Ws\n+qwQm8VJZ8chcvPGDmrdilmStFqJwrHlGH0Tuf2m6/tskyhE9u3dy9k33IIk6VAUhbYt6+jduYmQ\noCXsdVOUG+0KEbPaLbx43oCOFq+seq9PV4dJJQXcnayA8RDuu/5u4N6dm7gryTGSkbhvfVMTLQf3\ncfoZs5K6m/tzz5UXsPuXv2XC6VcgAGa9nv2rllE6vm8IgOg6iG182VErbJDwoWoe/+nzA46XWBdv\n2asrQBB44lWVrnAasqOWx3/4HbpfXIocySDfpMWanU4YAb0oUJk3vs+xYh1M/BGZYO12OgUBTcdh\n0ouyRlR0ur/rOmDT8uziJQNc/g6Hk/V1sHPpVLK1PXzvgiNMz2kg7NxAi89CU6OHkDmXQ1t3kzsz\nm6LxFXg6HHgz0hE8AgKgU794vcXEZUQvovhU1KB8TH1qv4okq+XXv4xOiv+9pEReihRJSIypU5QI\ntdVv8+iidwgEu8jOysDrTycYVPF52hCEDq5eEEZVRezZLtau38UVl82Kuz6HisNJtBi6vDY0okBn\n+4dYzX6uvLScjZt9qEojOp2KooCqyCjhGsqLfVTXiZiNsL9GS3a2hVVrQ0yZJOPzycyaLtPeoWIw\nKAiiBoPegtWqo6qyLO5SHkn/3fzMC+jdB4tbD2M0qPjDYxg3Ox9BaOGSGb2s3vIG7Z5CpJLuQa1b\nQ7Uji5EoEJ55DSRJB0SFnGAwx8XVs4uXIIpHe6MetdolExcjGROGLnY7XCzbUCTuq2q0iAnnM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38OziJRh86VjMnRj0Ws699kIcbit5eeOOObHhn81oatslEyGJMYAOTzq7e4tZ/f/+ktRVOlKR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99PyICevLlkTMhkw7a/E03JRdM0mtxZVLzwR3x5U9CMKEV5SeytaSQQdhBtaySUOwd7cwVq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\nnRjThAUAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create the visualizer and draw the vectors \n", + "tsne = TSNEVisualizer()\n", + "tsne.fit(docs, labels)\n", + "tsne.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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V0azeqFADhtnOrhoflWs3sWLXRgKBABNGX4th8KuV5fxYSc75997Hu8EgHy1d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0xs++AJFMWG0yBSOb4YUQeaKpLsGGgOdcbUKURCUT5cs56dH9bgdNvp6wqQV5zOgU31VE4u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\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gEvOCJI0cGfK0mkMsytUyZGTwY/G61U1jtpRqN8ix2Xr61iVobVnH4rLJBPNVbMoMM2uL\nn4d3voyVSRBOm2RNF18yTLc5xImpGrJz8pTkfXQODBKxgtgBl2nxEhKxHEbWJZIzyQzlGIk2UTZ1\nKr7QABWvx9ls7uBiex5P7EqS9nUz/+zJmN50XDfFttfasCybqbMbeH3lLrKZPKm4w9TG6XT09rGh\ntZNb776XVCpFZNIUjFCEaEkZti9LIPzW6236TLJOhpIJE7FKg+zaspbq+iiWbZHL5BgZ6MTzXE69\ncCau62HbFtvXdzD3hMlESkIMd7pkhl0iVpK6ymlMmXQaEYv9TjQae/0XJ6axasV2kvUuTqaW2qZP\nEpw5DdPIkndthtq636tfexERkY8shfmjjM/NUVlVycahOI0Lj2PdQy9QX1tDIBLFtGxSaYvkYD+r\nH/4VpmGRT45w2d/8HT6fn2w2wx/uu4vZs+eM68yPjaVsDNtYbVvo6N293xGVezsFNT44SCaT4Yn2\ndWTIkwvAlFAtm5x+fK5J0nCZvJfV3vuXLSecMgg7NuVegE5zhAA2mVSWtGES8my2mAOUuwFyRhlz\nqOZpq4U+J0kEPw4eYc/G8RxW2Z1EPR8pI0+FF8IDhsw0z9mDnJWfympzN1kvT0cigZF1MQ0TfyqM\naRj09fVygTeDrJMil8qxwthBaV0Vzyd2MNiRp9qrBMcj4lgYHQ6lToC23TZ4HgYGz3U0M+2cz1M6\nHMMyTTbdfS9l/cP0LYzw/aXf4M5f/xuBAORdi+RIhuH+DNFgkC0vDTBz5jy2bNnG9Cmz6OjtwwhF\n6O0bIFS+kW2vbaVy6km4nodlQCrjI5dL43nguh6ZVA5cIOCjr7OTitoYHa2D2D6TkXia8uoo2WyO\nkvLwaJj3WaRGsgz1pYgGypk+cRGXfXm0gz62d4IDnDMw9vpH7RCnpefQGrH44me/yr/c8yD1DbML\nX9fRmzi8X3IREZGjiML8UWZsJvyM2gp2vPA0C2Z/jBcfe5SK+l5SGQs3MInUUBzbsigpjTGxYSK5\ndBqfz4/fH2D27Dl8/TPjJ6oc6ljKsSkvY1WZs264nO9ddAMVbQ6hoMXJTQt4tnsdkWiE2bk6TMNg\nxJfHnVi1x7Xc7hFmlE1kbXIbJa6fneYQc9wa0l6aCkIYpkE9UfqsJE8EWymzojRUTGBl726mZWPk\nnDyz8pW84G8j6vpxTJdSL0AeF4B+I4NjwFqzkxwOIc/HuZkpDOazdJgj7Ha6eXbLKkpLSunpHsbJ\npkl6OUI5E3vnMHnHYM7acrZ7LQzU5qjo9BF53WXAyNBpHMPPXs4R2PwGlV4vwy/eRuM5S6iprCfQ\nVMeMj8/niz/6awACdimT6kJvblatpzQztXCCK4zuhbjv0TvY0NpJb98AJ39qIZGSMJW1edq297Lt\n+d/hi5bS0RLHMmJ4a3eSjMfBMYmVTmDhOX+C6wwz1PsGs2ZPIJfJE5kbYPuGLoZ3JfG80R57Pucw\nNJBkwx82c/qiBfzpmzPsb7373sLeif3tl9jb6z82yefDOAhNRESk2CnMH2X2NhM+fvVnxh36lJox\nmYmnnIVhGLS0tbHm5Rc55exzxwWsfR0qtS87W1v5+TXXE+kfZLi0lOqFJxPDB8Cj/3QHx7SGR/vm\nKY+1bMQftFnYNJu1bMRMO/RPtPjuXsY3mjVRJs2YRm4wSWjEo9/KsL5kkCQ5dhsZzJzNcbl6hq0c\nVcEYm8qH+VjzsTzQu5KR9X2EnNFRlhnPIeTZuK7BiuMc/HU1pLu6YaPBxenpkB+tkT8e2IFtBmj1\nelno1JL0HJwul3W5dhamp2F6fnZacY5N12BgUO4GWefv4bjXJvDUhu1My5SSzFs8t9hgxmcvxv3N\nf3JZrAInm8U0c9z79J34P/t1MiX5ceMqL7tgCb98YDktuzeSSedobppHPB4vPOdjmzxvvfteQuUb\niZSEcfJ5DDdJon8XPqOWbH+MU45bTEdnF81zLh5Xh7n7sSdI9SUIBUOUxkrp7e4nncqxe3sflh9W\nP7uFSFmIfCZPZmSE736jimhka2HiTM5855jLff/Rsq9JPu/F4WMiIiJHG4V52SPg/8s92oQwnAAA\nGFFJREFUDxaC2cS6WlpffZGONSv3GEd5sCuxAD+/5nou2LEbwzBItPfw0I4e/vS4z+F2OzzRvo45\nwTLIjR42ZKYdms5bSH8wyISGWZg1Ub629Pq9vlkYW+W1qprZ0LKdOZM/RmRiFZlMhhlbbF56fRWh\nVAQn4pExTZJkaZ1n8Tc3/JSbTriGciNILudxfHYCq3wdDC2cwDFXXoZnQNAX4LU7f4n5qoljuSS9\nPBFsNtg9BMwAJYEYydwQ5RmbWifCOqsLn2cybGTBK8FjtEJjubCadlw3z++sFhq9UkL1TZQYASoy\naUy/jWeY4DhUOgn625/iYycP8uC9N8Emg9DQIG51Lc6UCs7+k1NwnDztnW3c+NNvMH/m8YUTXuOD\ngzgrN7Ojdx1VFTPBylBVW0JtbQUfO2MhbetTXHr+pfzqkTtxzdbCyMqx1z8eP4c77vsPnvzvZ6mf\nWkYuk+eEM+fywiOvEnQTuHaeZH8Pn1jQQ2lJHQCZ9C6W//JfaW3dRFv3JuaefDHBSPSwVtUP9/Ax\nERGRo5nCvOzh7XUH07SY2ziBr7yjWnMoK7EAkf7BwtfjuVjJAV5Y/yr5gMmwP03ttEl0b9uFk87R\nP9Hiazd+9aAODNrXKm88HueBZcvpbXdosKBh+mRMyyY3b3qhulI2vZ5payyMfB4MyFse02qbKTfC\n+CwfSduB2nI8N84wGUpdPz7PZoIZY2Wwg45MHDJ5erMJ+uwktV6EafkKttr9mBhkDIchMrSacU7L\nTWKiESXk+WixBgl3DGO50Gv58FwHw/WwXejJj7Cgpo+2XBXdDz7FBb0JJjXOxe3v4fY30sw6+bN0\ndLcTidlU1IeZND9UOOH1/mXLadzosCVQx0u/3kywMk00GiBcUspLz79K/+4kyWSKmSdWFk6H/eUD\ny/H7A7jm6Az2JZd+CX8Ypsx/q9LUtamTL316CNM0aG8bZP2mIDDauX9tW5azrwjTMPcYdnV288pv\nfs7c2adoVV1EROQDojAveziYusOh9pvjpVG62ttx8nmyTh6TOpoTpRhJg/4peXYfG8KZUI9ZE+W7\n+1iFH5tP7naPFLrW+wr8YyE//q3reGDZclq7hjFrAuOqK9+4cxnfPeUagp5D1nCYQgWprgEsnwWW\nQd7NQyLB45EWKjI+Qp6fhdThmBb+nEWrNUjCSBIxLRZl6/Bh8qq/g6jjY7s1yIiVxbUMypwQGAZB\n0w+uh43JjDXDbC95mGT1FO7evp4JeYdhw09+bj21J12OYRh4j21gOL0VGD1ZNjKYxHVdPBxcz8TJ\nebiuwxurV7H8yZtG5/ZPbaT6nHOZlrfZ2PIE0Mbs42oIRiMkp+RZ9cxqepKlWD4DJ+cx1Jvi/MtP\nL4T7+x69A9Mbf2JqQ90iVq7rwzY6SaQuwh+BV9cOknNrqW7w84t71zOQ91NuZzmxqZKvXHn5Ify2\niYiIyLuhMC97OJi6w6H2m8tnH8+Dr+2g3rXpNiyOzc2kNztESXU5s5pn7vM01Lcbm09uGGHcbocH\nli0/4P32d9JqY2Mjp3/uIipXJ1jbupH2nk6mbR6i55lnoKqMIWOYM+bOYXBzjuGBOLMSZXieRzqe\nJB/McEJ4Bqt9u5jjVdLnjoDnUedGmGfWkbM9Hvdv45PROazPtBNNhMkYDn7PpM3sYKDUYMGCE5iN\ny6zMjMJknzvrWxn7ACNZUY6Lj10bt+NmcsRr6tm2apCW3Z0EIhaLT1xA++adhHY4TMuEsYcreTbg\nEQiZOD4/MfsUOtbfwUh/GrwgDXWTeC65mlOPm4lpjp4C+9idr4z7hMUxUnzm/C+NOzH1s5dct883\nTRf92Z/T07wI0zTpdx2GVq3mz6894EspIiIi7xGFeTksh9pv7v7jdj7mnkiJ42c1o935ktISGmZO\npXWidcD772xt5fe3P8zWRICsH05dcAJO14HvdyBjnfsJDbOojM3FMiwqBrOYZSEuWfp1AG5+5Qbq\nBiKst3vwuQYd5gjZvMNLQ1sYsDM0mqWUTqxiZHCItnw3+Pqxq6Icf/Yn8WJlNO2qYfXWbQSSHjkr\nz4lnXMDVN359tOf+Zh1obLLLrCmL6B+MYpk5Ihd9lkfWtXFMn0s6UMKpJafRtxn+7lv/wH2P3kF3\nS5INK7ZzVmIm2NAwvYne7Fam1dXiN21C1ZVsfs4kbJQwacJkXNeluqaazMjo6r6BRU199bhVeNMN\nHdKJqWUNs2gbcfHIY2BSNmHWu35NRERE5OApzMsHIuDZeCZ4jsdMr4rn/W3UVPjJz7PGVV/2VaX5\n30uWcuJQDaU5H27K4+mX/4DVV8Ft3/7Jfus2B7K/lfsx33n4Z3zvzOsI7vCTSKc4LTuJV/0dTM3H\nSOGxtSSOVeIy5eJjOYlj8Mfz+6wBjU0B+n+PP4PPzXH+KSfizAvgLnDBDXLFqeew4sWVZA2bMjfH\nwplnMKU3xIslw7xUYRHvaONPoRC2b1v7E8L9DgCmZdMwaRK9W9aRyeTIDScoK59ONlHOjtd6Md0Q\n0xrm0lBXVgjv6YZS2tanCqvwl12w5JCev4mlQQaDjRimiee6TEzvPKT7i4iIyLujMC8fiKYzF+Ab\n2sFA/xB4UDK7gaWP/XyPsLuvKo3dnSbkhhn0UuBA3EkyKVHB7rtf5uY/rOI7D//ssAP9AXkeRHzU\neSWEcyWkjRzDZMjgYGMxP9DAJjPBVQexafe2+x+kI9GBz58jl/Vxy8+f4dNXHV/orD/5/EN8+W2r\n4ret3cmL6S4mXXQ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Only visualize the books by female authors\n", + "tsne = TSNEVisualizer(classes=female)\n", + "tsne.fit(docs, labels)\n", + "tsne.poof()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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oqsLetg4jOQ3DWQvRMPG6KjaGu7f5SbsiIjvS0u4iclhQX+7WIxCoX/l06nNz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\nEARBEISPHxG4hXdlqYp3brWK06timCbdx/sWg/U7qdBe2tOdz4MPfAufz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CRJipNzuCVJ\nkqQYGdySJElSjAxuSZIkKUYGtyRJkhQjg1uSJEmKkcEtSZIkxcjgliRJkmJkcEuSJEkxMrglSZKk\nGBnckiRJUowMbkmSJClGBrckSZIUI4NbkiRJipHBLUmSJMXI4JYkSZJiZHBLkiRJMTK4JUmSpBgZ\n3JIkSVKMDG5JkiQpRga3JEmSFCODW5IkSYqRwS1JkiTFyOCWJEmSYmRwS5IkSTEyuCVJkqQYGdyS\nJElSjAxuSZIkKUYGtyRJkhQjg1uSJEmKkcEtSZIkxcjgliRJkmJkcEuSJEkxMrglSZKkGBnckiRJ\nUowMbkmSJClGBrckSZIUI4NbkiRJilHqWm9EUQRAtVpdsMNIkvRld+V788r3qCRdM7hrtRoAR44c\nWbDDSJL0VVGr1chms4t9DEnXgUR0jZ/gYRgyOztLOp0mkUgs9LkkSfpSiqKIWq1Gc3MzQeCdm5L+\nRHBLkiRJ+vz86S1JkiTFyOCWJEmSYmRwS5IkSTEyuCVJkqQY/S8S4dAbIyddSQAAAABJRU5ErkJg\ngg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Only visualize the books by male authors\n", + "tsne = TSNEVisualizer(classes=male)\n", + "tsne.fit(docs, labels)\n", + "tsne.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": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/examples/text.ipynb b/examples/text.ipynb deleted file mode 100644 index 7c5d55c46..000000000 --- a/examples/text.ipynb +++ /dev/null @@ -1,510 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Yellowbrick Text Examples \n", - "\n", - "This notebook is a sample of the text visualizations that yellowbrick provides" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "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.pyplot as plt " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load Text Corpus for Example Code\n", - "\n", - "Yellowbrick has provided a text corpus wrangled from the [Baleen RSS Corpus](http://baleen.districtdatalabs.com/) to present the following examples. If you haven't downloaded the data, you can do so by running:\n", - "\n", - "```\n", - "$ python download.py\n", - "```\n", - "\n", - "In the same directory as the text notebook. Note that this will create a directory called `data` that contains subdirectories with the provided datasets. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "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": "markdown", - "metadata": {}, - "source": [ - "## t-SNE: Corpus Visualization \n", - "\n", - "One very popular method for visualizing document similarity is to use t-distributed stochastic neighbor embedding, t-SNE. Scikit-Learn implements this decomposition method as the `sklearn.manifold.TSNE` transformer. By decomposing high-dimensional document vectors into 2 dimensions using probability distributions from both the original dimensionality and the decomposed dimensionality, t-SNE is able to effectively cluster similar documents. By decomposing to 2 or 3 dimensions, the documents can be visualized with a scatter plot. \n", - "\n", - "Unfortunately, `TSNE` is very expensive, so typically a simpler decomposition method such as SVD or PCA is applied ahead of time. The `TSNEVisualizer` creates an inner transformer pipeline that applies such a decomposition first (SVD with 50 components by default), then performs the t-SNE embedding. The visualizer then plots the scatter plot, coloring by cluster or by class, or neither if a structural analysis is required. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/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" - ] - } - ], - "source": [ - "from yellowbrick.text import TSNEVisualizer\n", - "from sklearn.feature_extraction.text import TfidfVectorizer" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Load the data and create document vectors \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": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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jiigQsnU+h4tYaPq6juDKnmgCRkEQIN1Nw564WJvsS6rhLiJVkrQZO80q4aVd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\nHqPTjbXutNJzL1YQ3btidIxBIpDk5xdg8oUzMFA9O+fKz2h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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Create the visualizer and draw the vectors \n", - "tsne = TSNEVisualizer()\n", - "tsne.fit(docs, labels)\n", - "tsne.poof()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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D6tChQ7h8+bKjz8gXmzZtwqlTpzBw4EDHa/X0uSYnJyM7O7tS8PP9999j1KhRuHTpEqKj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\nG1MzVsBisQRhpNJxN/6q78eamf+QZe8tJaax29e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D+AAAyRA+AEAyhA8AkAzhAwAkQ/gA\nAMkQPgBAMoQPAJAM4QMAJEP4AADJED4AQDKEDwCQDOEDACRD+AAAyRA+AEAyhA8AkAzhAwAkQ/gA\nAMkQPgBAMoQPAJAM4QMAJEP4AADJED4AQDKEDwCQDOEDACRD+AAAyRA+AEAyhA8AkAzhAwAkQ/gA\nAMkQPgBAMoQPAJAM4QMAJEP4AADJED4AQDKEDwCQDOEDACRD+AAAyRA+AEAyhA8AkAzhAwAkQ/gA\nAMkQPgBAMoQPAJAM4QMAJEP4AADJED4AQDKEDwCQDOEDACRD+AAAyRA+AEAyhA8AkAzhAwAkQ/gA\nAMkY6fQXeZ5HRMTKykrPFgMAg+7R5+ajz1H6S8fwaTabERFx9erVni0GAIZFs9mM0dHRspfBY7K8\nQ5K22+24f/9+1Gq1yLKs1+sCgIGU53k0m80YGxuLSsUbJf2mY/gAAAwbKQoAJEP4AADJED4AQDKE\nDwCQjH8BKX7w4eXd22YAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Only visualize the sports, cinema, and gaming classes \n", - "tsne = TSNEVisualizer(classes=['sports', 'cinema', 'gaming'])\n", - "tsne.fit(docs, labels)\n", - "tsne.poof()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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yEiP7Ae24Fvi4DyLtOCp5mNMpdDOWIpw+BjfzylKQlxh5nIYd1wIf90GkHTNS\nHuZ0Ct2MpQinj8HNvLQs6pXsmpGskh3XAjPARNoxkCLLON0pe2Xw1cvO+re6rr2y5CT64zRELx+R\niPjSYrKM0y+AFfGFpnYGd3bWv9V1PX3JWhwpGBT7c++je7Fu3h2mfT5p4/dJCvkPX1pMjtC6FGF2\n5yzi0padmRU7l0Wtrmuns5vUnlcyhERmYCAlKD/O+MzunEUcfEUM7sxgdV0nWnLy4zUiCivbsR3n\nlW2HzOT8yEIJ+fGOK7PvGBLxjj89L6F1A6vrOppdWzfvjthDMf14jYjCynZsx3ll2yEzMSMlKK9m\nLlIxO6sh4h1/Xt3M60Rdi3yNeD3jobUda6mPjue1IXgK5VXVmuoy3feJ3HbIfRhICUrEZSmriRBk\nOP1uP1LPzGvE7PNu1x4ipwI2re1YS310PK/7Pt2D5suu11SX6b7Pj/0rWYetR1AiLktZLdHyjd2Y\n8ncPM68Rs8+7XQ+2dEt71VIfHc9rcfE5musy3ff5sX8l6zAjJaiOM77m5qDm9DZpx5S/e5iZ3TP7\nvNuV8TBabrsyWlrqo+N5La+qRrPGukz3fcwMk5mYkXIJt8w83c6rm8EpNbPPu10ZD6PltqtfMVIf\nev4tM05kJz6Q0yX4QEJj1M68nX6IKDnDrefdaLm19isibqIXsUzkPXwgpwekSlV37EjuvHEiVv15\nqykdi1c6KbWbXZnyt58Ibcyt591oubUuQYr4IE4Ry0T+wnULl0iVqu6Ynp+8cLlp6XqvLCnatfk3\nupdt+pK1KK+qRjAYtOR7vMQrbcyNtC6B2XUdaSFimchfmJFyiVQzz44bTpsiWehpUsfilc3Xdm3+\n5exYO7VtTITMlddozWiJ+NgAEctE/sIW5wEdN5z2CoRN2zhr9iZcpzI20Zl3/oEP0fy/f0Hj8RZL\nvl+U2bGbMmNq2xgzV8nZdb5F3MQtYpnIX7jZ3AM6bji988aJWL15mykbZ/VsZk2VOSivqm7L2EgS\nFFnG8FCDrRkbq7+/vKoaNblFsdlxaUs9Fs2ZZtrnayqHg/Wshdo2xhsuknPT+SZyI24297hE6Xmz\nOtF0qf9EQVOq5S2nlwqt/n4Rns4OAI3HW/DJoUaEZAU5AQk9upxypBxqqF1e4hJOck5fV0R+xkCK\nDEkUNKXq1POlMF7fux+tioRsScHgnmHN32nkLkWrB2NR7v7a9+keHC8cAykjAyE5gn2fvu90kQwT\nJUgVEYP/5BugAAAgAElEQVRMIucwkCJDEgVNKTt1WUZmUz0iyECmHAZ69NX8nR2Dt8kVy9FD5bu4\n0g3GXtnQXFx8DpoOfIIQMpCjnEZx8TlOF8kwUYJUETHIJHIOAykyJFHQlKpTD0o5GDJm/D/+fHSv\n5u/sdJeirP4uxXSDsVfuuivsmYfmfiWx81LYUu90kchCogWZXpmQEKnBQMomVnYsTnZaiYKmVJ26\nGUsQHT8jepeiGcsaXtlrwgwFOckrExIiNRhI2cTKjsXJTkvrTNiMAb7jZ9xZMbfTXYp6eWWviWgZ\nChGIliURrTxm8sqEhEgNBlI2sbJjcVOnZcYAb+VdiszkeJdoWRLRymMmr0xIiNRgIGUTKzsWUTot\nL8ywRcvkWFWnXjhXWok24RCtPGbihIT8hIGUDZqbgwidOoUDb/8FSlY2JpQMxPwZt5r2+aJ0WkZm\n2H4c2NWwKmvh5WxIMqJMOEQtj5lEm5AQWYmBlA0q129CXcEwFF5ZAkWWkRtqMDVIEKXTip9hRyIy\ntuz6BIeWrFUVGPlxYFfDqqyFl7MhyYgy4RC1PESkDwMpG3hl0EqXNYqfYdc1HEBmRi6OFAxSFRjp\nqSO7s1hOZM2sylp4ORuSjCgTjijRykPJRa/9xmMnsW/fZyg+dzAKz8hl5pwA8KXFtjD7xb9OSffS\n2PiXh3Zv+D8UDx8JQF1gpKeO7H6Jbbrvs+LFsVa9kJUveiVSL3rtv9P4Jer6j8Wbp/L44myKYUbK\nBl5J4afLGsXPsNte3tsVgLrAaP7USVi4ZiN2fFAHJSsHQ0oGIhgMppzt2Z3pS/d9VixPWpW18Eo2\nxOt764wen9frxy7Raz8kZUIKBBAKK65eXSBzMZCygVcGLS3LQVqDx/z8fOTk5qL/FTdAkiTUqQhE\n7F6eSvd9Ti3h+nmw9PreOqPH5/X6sUv02s9RTuNrOYKcgOTq1QUyF1sBqaZlOSgaPK6bdwcWzZmm\namA/3CJDUvmqF63lMUO673NqCdfuJU6RaG0zbmP0+LxeP3aJXvujCs/EkINvYWyXk1wSpxhmpEg1\nqzNrWjNMdmf60n2fU0u4XrmZQQ+vb5o3enxer594VmZmFQVQFAXZXbpg3EWlvsr6UnqSoihKoh+E\nQiHU1NSgtLQUOTk5dpeLfCgYDCZ8b5/ZvLYU1rYfrSg2WJa21GPRnGlOF8sWdrUZpxg9PjPrR/Tr\npryqum0ZU2pbdhseajBtomXlZ5M7pIqJGEh5hOidnEi81il6PZhwOzdem4nKvPjpTbZdN3rqbPqS\ntThSMCj2595H92LdvDtMKY+Vn03ukCom4tKeR3BTqXpeWwrzys0MXuXGazNRme28bvTUmR9ew0Vi\nYiDlEUY7OTfOmvVip0h2SnVtinrdJSqzndeNnv7Myj2KXnmEDVmDgZRHGO3k3DhrTiXVAMVOkeyU\n6toU9bpLVGY7rxs9/ZmVmVlmfSkVBlIeYbSTMzNtL8IsO9UAxU6R7JTq2hR1mTlRme28bjjZITdh\nIOURRjs5M9P2IsyyRR2gyH9SXZuiLjM7Pdlw+vuJtGAgRQDMnQGKEMSIOkC5hQhZRdGZUUfMvBC5\nHx9/QKYT4blGfCSAMV57RIQVWEdE/sHHH5CtRJhlc2nAGBGyiqLzUx0xQ0mUHAMpMh2DmNTcMChx\naTQ9P9WRCPseiUTFQIrIZm4YlPRkFd0QIJrJjsyrKHXqp+wbkVYMpMgXRBmQAHcMSnqyim4IEM1k\nR+ZVlDr1U/aNSCteDeQL0QHpSMEg1OQWoXL9C46VpU9u22AEwFOD0uEWGZIkdoDoNqLU6fypkzA8\n1IDeR/eitKWedxcSxWFGinxBpCyQCJvxrZAsayFSNtBtRMkEcd8jUXIMpMgXRBmQAO8OSskCRFGW\np9zIzKCbAS2RNRhIkS/MnzoJDzz5LF6t3QcpHMKQ0sEIBoOWDyR+GrySBYhWZgO9UL+pjsHMoJsB\nLZE1vLE5gyiN/Px8ZOdko/DSf0Lhld9FXc/zbNknZfXerObmIMqrqjF9yVqUV1UjGAya+vlmsHJP\nmEh73/Sy6xhE2W9F5DXMSHmQF2bpVnBin5TV3+mGLIOVe8IOt8g4jZPY98FOhKRMHDj1Je6zIdNo\nJrvapUjL20RewkDKg9wwuDrBzIFEbbBq9eAl0ib6ZKzcE9YnN4D/+b93cazfBZAAZEROuq692xXg\nePUmByKncUriQUzhJ2bmLdxql2Osvm3cq49SUGv+1EnoIoeQc/prdJdPYciA/q5r73Y9WiAa0K6b\ndwcWzZnmqqwdkciYkfIgpvATMzMzojYTZPUdesmyDH5Z3s3Pz8c1Fw1FTW6ha9u7V+/iJHfxS59h\nBQZSHsQUvvVECVaTDcJ+Wt5le7eP2wdbt5dfKy3H66c+w2wMpDzIzzNcuzpK0QdvN+ydMotI7d3r\nA7XbB1u3l18rLcfrpz7DbAykyFPs6ihFGrwTESVj5jdeGKhTBYNuHGzjj2fnx3vQb3QhMrMyXVN+\nI7ScL/YZ+jGQcoDXZ61OaW4O4qX3duNo3wByAhIGF/bzfEeZjOgZM68SNdAwa4mnT24AB05+hf01\nu9CCDBSePmbLg22NiD+elkAD6hoOoGTQQF8EC1qCI/YZ+jGQcoCos1a3B3iV6zehJSMXocwuaIWE\nuoYDGHGWtzvKZETPmHmVqLN6s5Z45k+dhBvuXoiTfYcjNyOAbmePFqb/Sib+eIovvASH3t6B3j38\nESxoCY7YZ+jHQMogPcGHqLNWqwM8qwO1wy0yioePBGp2IYQMdD/1Jeb/rMK0z3ea2wNdPxB1Vm/W\nEk9+fj6GlpyPngUDY3936KgY/Vcy8ceTmdsV14wuxaI505wuli0YHNmDgZRBeoIPNbNWJwZNtz+F\nu09uAIdzu2LImPFQZBmlLfWuDDSSnXtRM5n0D2YNXGZf/2Yu8YiadUtG1OCWvIOBlEF6gg81L9B1\nYtB0+1O4vdJhJjv3omYyyXxmX/9mLvG47Trze1aGmWzrMZAySE/wEf8CXUmSUJego3Ri0LS6g4yv\nq3CoFbtrP8L0JWvTXtxqOwKvdJjJzr3bMgGkn9nXv5nXhleuM79gJtt67IkN0vt6h3SvcXHi1R9W\nv0Iivq5OvPPfyLvoKlVvvFf7OhavSHbu7XqVCDnP76/+IfPwlWHWY0bKIL2zs3TZBbelz9WIr6vp\nS9biSF43AOkvbr8taSU798wE+IcXr39yBjPZ1mMgZZF0y1HpOkqvD5paLm6/dQSinnvutbBPtA1E\n67xszX+yzsE2qAeDcutJiqIoiX4QCoVQU1OD0tJS5OTk2F0u10h2YZdXVbetS0sSFFnG8FCDkIOj\nU4LBYKeLO1mHqOV3yTps0/Zjnbenpj4YbJEVUsVEzEgZxDus9NGSdRE1Q+M3VrVpuwc+Nw207Efa\nU1Mf3FxNdvP2GokNkm3k42ZR8hqr2rTdNxO46eYF9iPtqakPbq4mu/n7qjQB77Aiv7CqTds98Llp\noHWiH2luDqK8qhrTl6xFeVU1gsGg5d+plpr6YPBJduMeKYO4f8efjCwPuWlpyQ7lVdWoyS2K3UxQ\n2lJvySs8ovX+0nu70RLIQfGFlyAzt6tl3+dWbt+XxT6ZrJAqJmIg5RAOptazso6NDDZuH6jMZtfA\nF633yOkI6hoOoHvDB7hmdCmvvQ6mL1mLIwWDYn/ufXQv1s27w8ESETmPm80FxA2R1rOyjo1sAuYG\n4vbsupkgWu+ZWZkoGTQQvXtEmIlKwG+PGwE4sSVjGEg5hIOp9aysYyODjRUDFQeC9KwKELxW9356\n7lD03L389gf4asAIDBlQiMMZAU5sSRMGUg7x46zPblbWsZHBxoqByo8ZTq0BjFUBgtfq3k+PG4me\nu+YuB3Eqoyv2NH6OYecM4MSWNGEg5RA1nbrXZrp2i9Zxw9ET2LfvMzSeOxjlVdWm1KORwcaKgcqP\nGU6tAYxVAYIf694roucuRzmNr6EgJCuc2JJmDKQcoqZT99pM127ROi6vqkbz2dcjKElo9mg9mp19\nc0MQL0oAw+yye0XPXfHwkcAHO5EbaUFpP9nTy5lkPgZSAhNloHA7t9WjniDG7GUrM4N4q4IyUQIY\nP+0p8prYuWuJ4KLhRUJOGEh8rgyk3DBbNoMoA4Xbua0e9QQxZi9bmRl8WpVZFSWA8dOeIq/huSMz\nuDKQemDNBmzZcwQhKRM5chihNRvx6LzZThfLdKIMFG7nhnqMnxzs/HgP+o0uRGZWpmMZNDODT6sy\ngiIMgn6Z1GkVXy/5UhiQZQSlHNYReZIrA6kdNXtwrPhSSJKErxUZOz540+kiWUKEgcIL3FCP8Vmb\nlkAD6hoOoGTQQMcyaGYGn27LCGrBfYyJxdfL63v3I7OpHkPGjGcdkSe5MpBSsnIgffP/JUhQsrId\nLY9eVs5mOVO2n5E6j8/aFF94CQ69vQO9eziXQTMz+HRDRlAvt+2/s0t8vbQqEiLIAMA6Im9yZSA1\noWQgXjx2Eq2KhGxJwYSSgU4XSRc9s1m1gzVnyvYzUufxWZvM3K64ZnSp8E/dVtsW3ZAR1MvL2TYj\n4uslW1KQKYcB8CXC5E2ubNELZtyKW/oBE3uexs1nKVgw41ani6SLnrfQRwfrIwWDUJNbhMr1L5j2\n2fFEfgO8qIzUuZq32otGbVv0MjeeNzvE18t3e7bg2qF9WUfkWa7MSHllhttxNpsvhVFeVZ1yhq92\nKcHoTJkZLe2M1Lkb27Qoy1pOLmO78bzZgfVCfuLKjJRXdJzNQpbTzvD75LYN0kDqNLnRmbLRjJYf\n+S07obYtWo2ZMSKx7K+vx/hpZSi5/T6Mn1aG+vp6p4tkKVdmpLyi46xt+pK1aYMXtRt3jc4Indr7\n4eZN8iLMwu2sP1E2kWvNjJlZR25ur0RWmVKxHHWFYyFJARyTI5i8cAXeWLfU6WJZhoEUxOkM1QQv\ndg3WTg2SXFI0Jlp/kawIXm84gJfnLMQ1o0stadMiBI6A9qDfzDbG9krUWZOcBUlquw6lQAaaIt4O\nNbx9dCql6wztCrREmeEDzg2Souy7cato/e1p/BwnMvMQ6VIQW+5y0wCv5ZrTet2Y2cbYXslOokz6\n0+kVCOOYHIEUyIAiR9ArEHa6SJZiIIX0naFds05RZvhO4u3kxkTrLyQrUAIKcpTTrhzgtVxzWq8b\nM9uYE+1VhMFUhDK4qVzJaC2vWzKgGyvmYvLCFWiKZKJXIIyNFXOdLpKlOEoh/aZZbry2j982bJst\nWn8Fhz5Gz88/QvHwkUIFpGofq2HVNdfcHEQoFMKBv29F42svYsixTwy1MSfaqwib60UoQyKilisZ\nreV1y1hUVFSEN9YtRe1vK/FG9aMoKipyukiWYkYK6ZcGRM+SuG0WloqiAIqiOF0M14pmZ+4LBr9p\n04fRB+I8TVztjNqqa65y/SbU9RiKwsvOgyLLyA01GLpWnMgii7CcKEIZEhG1XMloLa/oY5FfMZBC\n+s7Q6b1L6QIlt6R71fDSsTjJzgFeSyCvduCw6ppz20CbiNbB1IqJlqgDuijlUlvnWsvr9FhEiTGQ\nUsHpvUvpggsvDA5RXjoWv9AS/KodOKy65kQZaI3QOphaMTkRdUAXpVxq61xreZ0eiygxBlIukC64\n8MLgEOWlY/ELLcGv0wOd099vBq2DqRWTE1EHdFHKpbbOO5Y3uofQC9s0/ISBlAukCy68MDhEWXks\nTu0l89IetkS0BL9OD3ROf78TODmxn94659YGd5KUJDt7Q6EQampqUFpaipycHLvLRXGCsY3DEU8O\nxHYpr6pu66QkCYosY3iowZZOyqnvtQvbp3pOBNU8P/bTW+fTl6zFkYJBsT/3ProX6+bdYWVRSaVU\nMREzUi7gx1m0FZzaf+X1fV9sn+o5kXHg+bGf3jpn9tCdGEiRIW5atnKqk2LnSFFeD6rJGC9t0/AT\nBlKkSzSAeum93WjJyEXx8JE4nNvVlhm23uDNqU6KnSNFMaimVJg9dCfukSJdovt+/m9vA0KZXdDz\n0McYMma8LWv6Xt1z5KbsHunj1/1KbNvkdtwjRaaLLlHkBCS0QkIIGbbNsL26PMI7dsRixeCfLuPg\n1YCDbZu8jIGUgNzQmUaXKAYX9kNdwwF0P/Wlbe8a8+ryiJkBohvakBXMPG4nBv9k3+n28+nVyQ8R\nwJcWC8kNL96Mvqy131f1uPksBdurKrBozjRDnbvaF9p69cXG6V6erYVZbUjtORGFmdeOEy+ITfad\nbugTUjGzbROJhhkpAblh9mbFpki1GQCvbsg0c1O6WW3IbUsy0eMOf30C+z7YiT2tp6BUVevK4DiR\n+Uz2nVb3CVZnvHjDBXkZAykBeXXpKh03BJBWMjNANKsNJTsnoi41RY973wc7caxvCc5QWlCT219X\nAOjE4J/sO63uE6wOmL06+SECGEgJya+zN78GkFYwqw0lOyeiZqqix72n9RTOUFowuLCf7qDcicE/\n2Xda3Sf4fRJDZAQDKQH5dfbm1wDSCma1oWTnRNSBN3rcSlU1anL7mxaUO52Bs7pPMHMS43RdEdmN\nz5EiIs3Kq6pRk1sUG3hLW+qxaM40p4sVY/bzmrz67LIoM+vL63VF/sTnSJFunF0mJkK9OFkGK7OH\nZhyX2RkcUTNwZjGzvtTUlQjXD5FZGEj5iJ7Oy469MPvr6zGlYjma5Cz0CoSxsWIuioqKTP0Os4mw\nR8jJMli51PTA2mfw4rEuaFUCyJYUtK59FkvvmWXJd6nF/XvqqakrEa4fIrOwN/ARPc+iseNZOlMq\nlqOucCyaB45CXf8xmLxwhe7Psuu5R048Y0jEMlhhe+1+nMjsitasPJzIyMP2j/Y5XSTPPrvMCmrq\nyqttl/yJGSkf0bM8YcdMvEnOgiQFvilXBpoi+pulXTNdETIUIpTBClI4BEVR2vbYQAHCIaeL5Nsb\nQPRQU1debbvkT2y9PqLn6cJ2zMR7BcJQ5Mg35YqgVyCs+7PsmumKkKFQWwa3PZ38qtLB6PlFLbp8\n8Ql6fv4Rriod7HSRyGQiXD9EZuFdez4i6pvn6+vrMXnhCjRFMg3vkSqvqsZ7GWfj04OH0BKJYOCh\nGvy/ZQuEOE6nuO0uKlHbqd3M3JDNzd1ExqSKiRhICYydn3bBYBA3lC1EY0YBcnEaA0svxsjAUaED\nh47MPu/Tl6zFkYJBsT/3ProX6+bdYUZRyUJmBsBuC6aJRMPHH7iUkf0+fg3C8vPzMXTY+egZFzgc\nOnrEwRJpZ/Y+L+5HcSczH7ng9cc3aOHXvpGsw0BKYEY6Pz/fXuz2wMHsQY9PjP8HNw2iqdqx1uNI\n9Fluqgu11ByTn/tGsgYDKYEZCQj8PAN1e+CQ7rxrHQB5x9k/mD2IWhmMpGrHWo8j0Wctftp7AYWa\nevFz30jW8EUg1dwcxANrNmBHzR4oWTmYUDIQC2bcKvzsy0hA4PasjBFuDxzSnXfOqPUzexC18lyk\nasdajyPRZ3kxoFBzTH7uG8kavgikKtdvwpY9R3Cs+FJIAF48dhK5Lhh8jAQEeoIwL6b63Sjdeffi\nAGil+Ha9u/Yj5F3UG9l53U0ZRJ06F2YEA14MKNQck9sz1iQeXwRSh1tkhKTM2POFWhUJh06ddrhU\n2tixnMNMhzt4cQC0Uny77jaqECfefQX9h5WYMog6dS7MCAacDiismLipOSa3Z6xJPL4IpPrkBpAj\nh/G1IkOChGxJcd3gY0eQw0yHOzDbqE18u87KycbQYSWmPf7BqWDEjGDA6YBCS5+mtv1qPSavXxde\nPz5R+CKQmj91EkJrNmLHB29CycrGhJKBmD/1VqeLpYkdQQ4zHe7g9myj3Z273natppxOByNupqVP\ns6r9inRdmCnadl9++wN8NWAEhgwoxOGMgGeOTzS+CKTy8/Px6LzZThfDEDuCHDWza85w3EmkbKPd\ng5ferJFXB1lRaOnTrGq/Il0XZoq23eYuB3Eqoyv2NH6OYecM8MzxicYXgZQX2LGEoGZ2zcGlMzcE\nlyJlG60avJKdB71ZI68OsqK488aJmLxwOZoiWegVCOPOirlJf9eq9ivSdWGmaNvNUU7jaygIyYqn\njk80rFWXiA4G6+bdgUVzpjk2UNv1UmA3iQaXRwoGoSa3CJXrX3C6SJ2I9JJYPS/PVsPs82BVOanN\nqj9vRY9Lr8eQ8degx6XXYfXmbUl/16r2K9J1YaZo2y0ePhI9v6hFwaGPPXV8omFGijTx6gzOCLsz\nF3oyYCLt5bEqu8onwruLlvNl1SZyka4LIzoe7503TsTqzdtwqCWCi4YXCZkl9xIGUqQJB5fO7A4u\n3b68atWgaPZ58MogKyorrxu3XyNadTze1Zu3efp4RcNAysOs2Lvj5sHFzPqI/6x8KYwhLbsRRLYt\nwaXf9u6oHRQZ5LuLlefLb9eI345XNAykPMxvs7J0zKyPjp81PNSAR22qW7cvr2oNaNUOEm4O8v3I\nyvPl9mtEK78dr2gYSHmYH2cpqQZpM+v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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Don't color points with their classes \n", - "tsne = TSNEVisualizer()\n", - "tsne.fit(docs)\n", - "tsne.poof()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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Porq62lqX4o0PPvgAv/32G6ZMmWL9ru7268CBA5GXl2cXiHz++ee4//77cf78ecTExKCy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\ntxRHWNR3epGyE3cXSMmRaQvWIcRQCDpJHuo7SxC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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Apply clustering instead of class names. \n", - "from sklearn.cluster import MiniBatchKMeans \n", - "\n", - "clusters = MiniBatchKMeans(n_clusters=5)\n", - "clusters.fit(docs)\n", - "\n", - "tsne = TSNEVisualizer() \n", - "tsne.fit(docs, [\"c{}\".format(c) for c in clusters.labels_])\n", - "tsne.poof()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Frequency Distribution Visualization \n", - "\n", - "A method for visualizing the frequency of tokens within and across corpora is frequency distribution. A frequency distribution tells us the frequency of each vocabulary item in the text. In general, it could count any kind of observable event. It is a distribution because it tells us how the total number of word tokens in the text are distributed across the vocabulary items." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from yellowbrick.text.freqdist import FreqDistVisualizer \n", - "from sklearn.feature_extraction.text import CountVectorizer" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that the `FreqDistVisualizer` does not perform any normalization or vectorization, and it expects text that has already be count vectorized.\n", - "\n", - "We first instantiate a `FreqDistVisualizer` object, and then call `fit()` on that object with the count vectorized documents and the features (i.e. the words from the corpus), which computes the frequency distribution. The visualizer then plots a bar chart of the top 50 most frequent terms in the corpus, with the terms listed along the x-axis and frequency counts depicted at y-axis values. As with other Yellowbrick visualizers, when the user invokes `poof()`, the finalized visualization is shown." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/axes/_axes.py:531: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n", - " warnings.warn(\"No labelled objects found. \"\n" - ] - }, - { - "data": { - "image/png": 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AACZC+AMAADARwh8AAICJEP4AAABMhPAHAABgIoQ/AAAAEyH8AQAAmAjhDwAA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\nCQ0NRbt27eDh4YEZM2YoDfnI8+GHH6JPnz7o378/oqOjERc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Dw8PzD4I3/nh4eHh4eHh4\n/kHwxh8PDw8PDw8Pzz8I3vjj4eHh4eHh4fkH8f8ADusMZsI0+ToAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "vectorizer = CountVectorizer()\n", - "docs = vectorizer.fit_transform(corpus.data)\n", - "features = vectorizer.get_feature_names()\n", - "\n", - "visualizer = FreqDistVisualizer()\n", - "visualizer.fit(docs, features)\n", - "visualizer.poof()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualizing Stopwords Removal\n", - "\n", - "For example, it is interesting to compare the results of the `FreqDistVisualizer` before and after stopwords have been removed from the corpus:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/axes/_axes.py:531: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n", - " warnings.warn(\"No labelled objects found. \"\n" - ] - }, - { - "data": { - "image/png": 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jQ+vXr9egQYPk5uYmX19fhYaGavny5ZKk+Ph49e3bVzabTV5eXurRo4eWLVt2\n+7cAAACgGCo0xNntdiUmJurvf/+7goKC1KFDB33xxRc6evSo3NzcVKVKFbNu9erVdfjwYaWlpSkl\nJUXe3t55pgEAAODWFXo4NSUlRQ0aNNALL7ygJ554Qnv27FG/fv3Uq1cvlS5d2qFumTJlZLfbdfHi\nRfP51dNyywEAAHBrCg1xVatW1aJFi8znAQEBeuaZZ/TDDz8oMzPToa7dbpe7u7sZ3jIzM2Wz2cxp\nuY8BAABwawo9nPrzzz9r3rx5DmWZmZl69NFHlZ2draSkJLP8yJEj8vb2loeHhypWrOhw+DR3GgAA\nAG5doSHO3d1ds2fP1tq1a2UYhhISErR69Wp1795dISEhmjp1qux2u/bt26eVK1cqLCxMkhQWFqbY\n2FilpqYqMTFRixcvVqdOnYp8g2AdOTk5SkpKuu5fTk7O3W4iAAD3rEIPp1arVk0zZ87UtGnTFBUV\npcqVK+vtt99W7dq1FRMTo3Hjxik4OFg2m01RUVHy8fGRJA0ZMkQTJ05Uu3btVKJECfXs2VOtW7cu\n8g2CdSQnJ2v8im/lXq58nmkZaec0OqypPD09lZycXOByPD095erqWlTNBADgnuTUOHEtWrRQixYt\n8pR7eHhoxowZ+c5TunRpRUdHKzo6+lbah/uce7nysj1U8brTCwp60v/CXuXKlYuqiQAA3JOcCnHA\n3VRY0AMAoDji3qkAAAAWRIgDAACwIEIcAACABRHiAAAALIgQBwAAYEGEOAAAAAsixAEAAFgQIQ4A\nAMCCCHEAAAAWRIgDAACwIEIcAACABRHiAAAALIgQBwAAYEGEOAAAAAsixAEAAFgQIQ4AAMCCCHEA\nAAAWRIgDAACwIEIcAACABRHiAAAALIgQBwAAYEGEOAAAAAsixAEAAFgQIQ4AAMCCCHEAAAAWRIgD\nAACwIEIcAACABRHiAAAALIgQBwAAYEGEOAAAAAsixAEAAFgQIQ4AAMCCCHEAAAAWRIgDAACwIEIc\nAACABRHiAAAALIgQBwAAYEGEOAAAAAsixAEAAFgQIQ4AAMCCCHEAAAAWRIgDAACwIEIcAACABRHi\nAAAALIgQBwAAYEGEOAAAAAtyOsSlpKSoadOm2rx5syTpxIkTeumll9SgQQO1bdtWmzZtMuumpaVp\nwIABCggIUEhIiOLi4m57wwEAAIozp0Pc66+/rtTUVPP54MGD5efnpx07dmjUqFF69dVXlZSUJEka\nPXq0bDb0NS4MAAAgAElEQVSbEhISNGPGDL3zzjvat2/f7W89AABAMeVUiPvHP/4hm82mypUrS5J+\n++03/frrr4qMjJSrq6uefPJJNWrUSKtWrVJGRobWr1+vQYMGyc3NTb6+vgoNDdXy5cuLdEMAAACK\nk0JDXGJioj788ENFR0fLMAxJ0pEjR1SlShWVKlXKrFe9enUdPnxYR48elZubm6pUqZJnGgAAAG6P\nAkNcTk6O/va3v2n06NEqV66cWZ6RkaEyZco41C1btqzsdrsyMjJUunRph2llypSR3W6/jc0GAAAo\n3goMcbNnz1bt2rXVvHlzh/KyZcsqMzPToezixYtyd3fPd5rdbpe7u/ttajIAAAAKDHFr1qzR6tWr\n1bhxYzVu3Fj//e9/NXToUB05ckQnTpzQpUuXzLpHjhyRt7e3vLy8lJ2dbV7kcPU0AAAA3B6Fhrgd\nO3bo+++/1/fff68//OEPmj59uvr06SNvb2/NnDlTWVlZ2rx5s3bs2KF27drJZrMpJCREU6dOld1u\n1759+7Ry5UqFhobeqW0CAAC4793QYL8uLi7m49jYWB04cEBNmzbV22+/rWnTpqlSpUqSpJiYGF26\ndEnBwcEaMmSIoqKi5Ovre3tbDgAAUIyVvJHK69evNx//4Q9/0IIFC/Kt5+HhoRkzZtxaywAAAHBd\n3HYLAADAgghxAAAAFkSIAwAAsCBCHAAAgAUR4gAAACyIEAcAAGBBhDgAAAALIsQBAABYECEOAADA\ngghxAAAAFkSIAwAAsCBCHAAAgAUR4gAAACyIEAcAAGBBhDgAAAALKnm3GwDcDjk5OUpOTi6wjqen\np1xdXe9QiwAAKFqEONwXkpOTNX7Ft3IvVz7f6Rlp5zQ6rKk8PT0LDHsEPQCAVRDicN9wL1detocq\nFlinoLCXG/QqV65cVE0EAOC2IcSh2HEm7AEAcK/jwgYAAAALIsQBAABYECEOAADAgghxAAAAFkSI\nAwAAsCBCHAAAgAUR4gAAACyIEAcAAGBBDPYLXIP7sAIArIAQB1zD2fuwcnsuAMDdRIgD8sGtuQAA\n9zrOiQMAALAgQhwAAIAFEeIAAAAsiHPigJvEVawAgLuJEAfcJK5iBQDcTYQ44BY4cxVrYT129NYB\nAG4GIQ4oYgX12NFbBwC4WYQ44A5g3DkAwO3G1akAAAAWRIgDAACwIEIcAACABRHiAAAALIgQBwAA\nYEGEOAAAAAtiiBHgHsAtvAAAN4oQB9wDuIUXAOBGEeKAewQDAgMAboRT58StXr1a7du3V/369RUa\nGqp169ZJktLS0jRgwAAFBAQoJCREcXFx5jxZWVkaNWqUAgMDFRQUpLlz5xbNFgAAABRDhfbEJSYm\n6vXXX9dHH30kPz8/JSQkqE+fPtq6davGjh0rm82mhIQEHThwQOHh4apRo4Z8fX01ffp0JSUlacOG\nDUpJSVHv3r1VrVo1tW3b9k5sFwAAwH2t0J64atWq6dtvv5Wfn5+ys7OVnJysBx54QCVLltT69es1\naNAgubm5ydfXV6GhoVq+fLkkKT4+Xn379pXNZpOXl5d69OihZcuWFfkGAQAAFAdOnRNXtmxZHT9+\nXG3atJFhGIqOjtaxY8fk5uamKlWqmPWqV6+ur7/+WmlpaUpJSZG3t7fDtE8++eT2bwFQjHAVKwAg\nl9MXNjz66KPat2+ffvjhB/Xt21evvPKKSpcu7VCnTJkystvtunjxovn86mm55QBuDlexAgByOR3i\nSpS4cuQ1MDBQbdq00f79+5WZmelQx263y93d3QxvmZmZstls5rTcxwBuHlexAgAkJ86J27x5s3r1\n6uVQdunSJXl5eSk7O1tJSUlm+ZEjR+Tt7S0PDw9VrFhRhw8fzjMNAAAAt67QEFe3bl399NNPWrFi\nhQzD0ObNm7VlyxZ169ZNISEhmjp1qux2u/bt26eVK1cqLCxMkhQWFqbY2FilpqYqMTFRixcvVqdO\nnYp8gwAAAIqDQkPcww8/rPfee08ff/yxGjVqpHfffVdz5sxR9erVFRMTo0uXLik4OFhDhgxRVFSU\nfHx8JElDhgxRtWrV1K5dO/Xo0UPdunVT69ati3yDAAAAigOnzolr2LChvvjiizzlHh4emjFjRr7z\nlC5dWtHR0YqOjr6lBgIAACAvbrsF3IcKG4qEYUgAwPoIccB9qKChSBiGBADuD4Q44D7FUCQAcH8j\nxAHFlLN3f5DEXSIA4B5EiAOKKWfv/iDJqXqenp6chwcAdxAhDijGnD3k6kw9zsMDgDuLEAfgtiks\n7Dl7CJceOwAoHCEOwB3j7CFceuwAoHCEOAB3FFfNAsDtQYgDcM/hsCsAFI4QB+Cew2FXACgcIQ7A\nPYnDrgBQsBJ3uwEAAAC4cYQ4AAAAC+JwKgDLKuwCCC5+AHA/I8QBsCzuEgGgOCPEAbA0LoAAUFxx\nThwAAIAFEeIAAAAsiMOpAO5r3P0BwP2KEAfgvsbdHwDcrwhxAO57XPwA4H5EiAMAcdgVgPUQ4gBA\nHHYFYD2EOAD4/zjsCsBKGGIEAADAgghxAAAAFkSIAwAAsCBCHAAAgAUR4gAAACyIEAcAAGBBDDEC\nADeAQYEB3CsIcQBwAxgUGMC9ghAHADeosEGBc3JylJSUVOAy6K0DcKsIcQBwm505c0bvJ/xKbx2A\nIkWIA4AiwC28ABQ1rk4FAACwIHriAOAu4UpXALeCEAcAd4mzV7p6enoWGvYqVKigM2fOFFiHQAjc\nXwhxAHAXOXPunDNhL+KJPzt1MYUzgZCwB1gDIQ4ALMCZsHe7AiFXzgLWQIgDgGKGK2eB+wNXpwIA\nAFgQIQ4AAMCCCHEAAAAWRIgDAACwIC5sAADkwUDEwL3PqRD3ww8/aPLkyTp8+LAqVKigl19+Wd26\ndVNaWppGjRql7du3q1y5curfv7+6dOkiScrKylJ0dLTWr18vNzc39ejRQ3379i3SjQEA3B4MRQLc\n+woNcWlpaYqMjNTYsWPVoUMH/fzzz+rVq5cee+wxffrpp7LZbEpISNCBAwcUHh6uGjVqyNfXV9On\nT1dSUpI2bNiglJQU9e7dW9WqVVPbtm3vxHYBAG5RYUOR5OTkKCkpqcBleHp6ShK9ekARKDTE/f77\n72rRooU6dOggSapTp44CAwO1a9cubdiwQV999ZXc3Nzk6+ur0NBQLV++XL6+voqPj9e0adNks9lk\ns9nUo0cPLVu2jBAHAPeJM2fOOHWXCEn06gFFoNAQV6tWLU2aNMl8npqaqh9++EE1a9ZUyZIlVaVK\nFXNa9erV9fXXXystLU0pKSny9vZ2mPbJJ5/c5uYDAO4mZwcOZoBh4Pa7oQsbzp8/r379+snHx0eB\ngYH6+9//7jC9TJkystvtunjxovn86mm55QAAXIuLKYAb43SIO3bsmPr16ycvLy9Nnz5d//73v5WZ\nmelQx263y93d3QxvmZmZstls5rTcxwAAXIuLKYAb41SI++mnnxQeHq5nnnlGUVFRkiQvLy9lZ2cr\nKSnJ/EAdOXJE3t7e8vDwUMWKFc2rWa+eBgDA9XDYFXBeoYP9pqSkKDw8XL179zYDnCTZbDaFhIRo\n6tSpstvt2rdvn1auXKmwsDBJUlhYmGJjY5WamqrExEQtXrxYnTp1KrotAQDc93KviC3oLycn5243\nE7gjCu2J++KLL3T27FnNmTNHs2fPliS5uLioZ8+eGj9+vMaOHavg4GDZbDZFRUXJx8dHkjRkyBBN\nnDhR7dq1U4kSJdSzZ0+1bt26aLcGAHBfc/aKWA65ojgoNMRFREQoIiLiutNnzJiRb3np0qUVHR2t\n6Ojom24cAADX4pArcAW33QIA3He40hXFASEOAHDf4UpXFAeEOADAfcmZw6702MHKCHEAgGKLHjtY\nGSEOAFCsFdZjlzusSUHorcPdQIgDAKAADGuCexUhDgCAQnB+He5FhDgAAG4Dzq/DnUaIAwDgNmEg\nYtxJhd47FQAAAPceQhwAAIAFEeIAAAAsiBAHAABgQVzYAADAHeTsUCSSCqzHcCUgxAEAcAc5OxSJ\npOvWY7gSSIQ4AADuOGeHInHmlmC3o1fP2Xr0/t1bCHEAAFjU7ejVc7YevX/3HkIcAAAWdrt69Zyt\ndzd6/5xdVnHrJSTEAQAAp92N3j9nl+Xp6Vmswh4hDgAA3JA73fvnbB1nA2ZhYe929iQ6W+9mwiUh\nDgAA3DduNezdzp5EZ+vd7PmGhDgAAFDs3K7ev6Ko5yzu2AAAAGBBhDgAAAALIsQBAABYECEOAADA\ngghxAAAAFkSIAwAAsCBCHAAAgAUR4gAAACyIEAcAAGBBhDgAAAALIsQBAABYECEOAADAgghxAAAA\nFkSIAwAAsCBCHAAAgAUR4gAAACyIEAcAAGBBhDgAAAALIsQBAABYECEOAADAgghxAAAAFkSIAwAA\nsCBCHAAAgAUR4gAAACyIEAcAAGBBNxTi9u3bp+bNm5vP09LSNGDAAAUEBCgkJERxcXHmtKysLI0a\nNUqBgYEKCgrS3Llzb1+rAQAAirmSzlaMi4vTpEmTVLLk/2YZPXq0bDabEhISdODAAYWHh6tGjRry\n9fXV9OnTlZSUpA0bNiglJUW9e/dWtWrV1LZt2yLZEAAAgOLEqZ64uXPnavHixerXr59ZlpGRofXr\n12vQoEFyc3OTr6+vQkNDtXz5cklSfHy8+vbtK5vNJi8vL/Xo0UPLli0rmq0AAAAoZpwKcV26dNHy\n5ctVr149sywxMVFubm6qUqWKWVa9enUdPnxYaWlpSklJkbe3d55pAAAAuHVOhbiHH344T9nFixdV\nunRph7IyZcrIbrfr4sWL5vOrp+WWAwAA4Nbc9NWpZcuWVWZmpkOZ3W6Xu7u7Gd6unm6322Wz2W52\ndQAAALjKTYc4Ly8vZWdnKykpySw7cuSIvL295eHhoYoVKzocPs2dBgAAgFt30yHOZrMpJCREU6dO\nld1u1759+7Ry5UqFhYVJksLCwhQbG6vU1FQlJiZq8eLF6tSp021rOAAAQHF2S4P9xsTE6NKlSwoO\nDtaQIUMUFRUlHx8fSdKQIUNUrVo1tWvXTj169FC3bt3UunXr29JoAACA4s7pceIkqXHjxkpISDCf\ne3h4aMaMGfnWLV26tKKjoxUdHX1LDQQAAEBe3HYLAADAgghxAAAAFkSIAwAAsCBCHAAAgAUR4gAA\nACyIEAcAAGBBhDgAAAALIsQBAABYECEOAADAgghxAAAAFkSIAwAAsCBCHAAAgAUR4gAAACyIEAcA\nAGBBhDgAAAALIsQBAABYECEOAADAgghxAAAAFkSIAwAAsCBCHAAAgAUR4gAAACyIEAcAAGBBhDgA\nAAALIsQBAABYECEOAADAgghxAAAAFkSIAwAAsCBCHAAAgAUR4gAAACyIEAcAAGBBhDgAAAALIsQB\nAABYECEOAADAgghxAAAAFkSIAwAAsCBCHAAAgAUR4gAAACyIEAcAAGBBhDgAAAALIsQBAABYECEO\nAADAgghxAAAAFkSIAwAAsCBCHAAAgAUR4gAAACyIEAcAAGBBhDgAAAALKtIQ9/PPP6tr166qX7++\nOnfurL179xbl6gAAAIqNIgtxWVlZ6tevn7p06aIffvhBPXr0UP/+/XXx4sWiWiUAAECxUWQhbvv2\n7XJ1dVW3bt3k6uqqv/71r6pQoYI2b95cVKsEAAAoNoosxB0+fFje3t4OZdWrV9fhw4eLapUAAADF\nRpGFuIsXL6ps2bIOZWXLlpXdbi+qVQIAABQbJYtqwfkFtosXL8rd3b3QeQ3DkHTlvDrcGdnZ2Sph\nPy/jvEueaSXs55WdnW0+zq/O7a7HOlmnFddpTjMeZp2ss0jrsc77c52ZmZkO5bk5KDcXXcvFuN6U\nW7RlyxbFxMTo66+/NstCQ0M1ePBgPf300wXOe/78eR06dKgomgUAAGApNWrU0IMPPpinvMh64po0\naaKsrCwtWbJE3bp10/Lly3XmzBkFBQUVOq/NZlONGjXk5uYmF5f8ky0AAMD9zDAMXbp0STabLd/p\nRdYTJ0mHDh3S2LFj9euvv8rLy0vR0dHy9fUtqtUBAAAUG0Ua4gAAAFA0uO0WAACABRHiAAAALIgQ\nBwAAYEGEOAAAAAsixAEAAFgQIQ4AAMCCCHG4rQ4ePJhv+ffff3+HWwIAwP2NEIc8nL1nbW69y5cv\n6+LFi8rIyNALL7wgu92uixcvmn+nTp1SREREnvlHjhyptWvXKj093al1HT16VIZh6PLly3mmx8fH\n57nnXEGOHj2qhIQEZWdn6/z5807Pd7MSEhIkSadPn1Z0dLRmzJjhcG/hDh06aN68eTpx4sRtW+dv\nv/2mLVu2aPPmzQ5/10pLS9Pnn3+uWbNmKT09Xdu3b893eZcvX9a///1v/frrrwWut7DX6lacOnXq\nti7vWrnvxZycHK1evdp83W7GhQsX8i3ftGmT+Xj9+vX51lm+fHmessWLF+dbd+7cuebjwj4DV38u\nr/eXa9KkSfkuY+zYsQ7PIyIitGrVqjz3yr5WSkpKgdNz5bfPT58+rUGDBl13nn379uno0aNOLT8/\nzv7zmZOTo2+//VZxcXHKzMxUYmJinnly75F5OzjbrmeffTbfeu3atctTVth30b2ssPfk77//Xujf\nzbiR74Ub/W251e80BvuFpCtfPHPnztVnn32mCxcuaMWKFRo5cqSmT58uT0/PAuu9+uqrOnTokDIz\nM2UYRr63SnvyySf1/vvvO5TNmzdPW7Zs0U8//aQGDRqoRYsWatmypapWrWrWSU9PV0xMjFasWKFS\npUpp2bJlCg8P17x58/SnP/3JrNe4cWNt27ZNbm5uBW5nSkqKhg4dqr1798rV1VVxcXF67rnnNG/e\nPNWvX1+1atUq9FZvBw4ckCRt27ZN48ePNwOLJHP7c+tI0jvvvKPVq1dr48aN6t+/v86fP6+SJUuq\ncuXKmjhxoiRpw4YNWrVqlTZs2KDatWsrNDRU7dq1U/ny5SVJO3bsKLBNktSoUSPz8cSJE7VkyRJ5\neno6bI+Li4tDcNi3b5/69OmjGjVq6Mcff1R8fLzCwsI0atQo1atXT0OGDNG0adPk7u6ufv366dix\nY5KkatWqac6cOXrsscfMZTn7Wh04cEBTp07V0aNH84S83LaNHTtWb775psO0zz77TFOmTHHYF+np\n6frXv/6l33//Pc+y/va3vykkJKTQ1zN3nStWrNAbb7yhnTt3atKkSVqxYoVcXFzUs2dP9enTx6x/\nvWW6ubnpoYce0pNPPqlXXnlFzz//vD766CM98MADkqSzZ89q/PjxWrdunb744gtJUteuXRUXF+dw\nc+sLFy6oV69e2r17t06fPq39+/dLkgYPHqyZM2c6rPPChQsaPXq0du/eLUny9/dXkyZN1LJlS7Vo\n0UKVKlVyqJ/7/r72a//qsjFjxki68oMZFRXlUC89PV3vv/++du7caZYtWrRIq1at0qFDh/TUU08p\nNDRUQUFBKlHCsY/Az89PAQEBCg0NVatWra57G6HAwEDNmDFDTzzxhCRp6dKlevvtt+Xj46MFCxZI\nuvLZGzNmjDZs2KB58+YpNjZWJUqU0Lhx49S5c+d8l3uty5cvm99ZQUFB+vbbbx32y/nz59WmTRtz\n3yYmJioiIkLZ2dk6c+aMli9fro4dO2rmzJkKCQkx52vcuLHatm2r0NBQh8+kJMXGxhbarv79+zvV\nrvj4eE2ZMkWGYWj9+vV66qmnHJZz4cIFHTp0SFu3bjXLCvouOnToUKGflbi4uEK3pVSpUtq4caM8\nPT2v+xpLV76jnPm+3bRpk9atWyep8PdkfstzdXVVTk6O2ba9e/dKkl588cVCP8dhYWFOfy8U9tvi\n7HfajSLEQdKVD8dPP/2kAQMGKDIyUlu3btXo0aOVkZGhOXPmFFrv3Llzio6O1l//+lctXbrUYdml\nSpVyCILXOn/+vLZt26YtW7ZozZo1qlq1quLj4yVJr7/+uux2u4YNG6ZOnTpp+/btmjZtmvbv36+P\nP/7YXMbIkSNVtWpVhYaG6uGHH3b4cJYtW9Z8PHDgQFWtWlVDhw5Vs2bNtGPHDi1atEjx8fH65z//\nafYyrVq1Snv27NGgQYNUpUoVnTx5UrNmzZKfn58GDhwoSerYsaOaN2+usLAwlSzpeBviP//5z+bj\n1q1ba9GiRXJ3d1eTJk305ZdfqkKFCgoJCdF3333nMF9GRoY2bNigtWvXaseOHfLz81NYWJjDhz81\nNVVlypTRI488otOnTys9PV2PPfaYvvrqK7NOo0aNtGjRItWqVeu6+12SunXrpt69e6tNmzZq1KiR\nduzYoZ07d2rkyJGqUKGCnn76ab300kuKiIhQnTp1NHjwYF2+fFkzZszQjz/+qEWLFpnLcva16tKl\nix577DG1b98+z35r0aKFWadOnTp68803dfToUY0ePVpHjhxRVFSUQkNDzfovv/yyTpw4IT8/vzyh\nYeLEiWav13fffadt27YpPDxcVapUUVJSkj744AM1a9ZMr776qiSZ4bVhw4Zq0qSJFi5cqIcffljP\nP/+8tmzZYi43NjZW8fHx6t27tx599FElJSXpo48+UqNGjVSzZk199tlnatKkiTIyMvTTTz/pww8/\n1MaNGzVx4kTVrVtXw4YN08svv6xz587l+5qUKlVKnTt31htvvCG73a7u3bvr7Nmz+u9//6s//OEP\n+dbN7ek+deqUtm7dqs2bN2v79u2qWrWqGeh8fHwK7UW9fPmyZs+erbNnz2rnzp1q2LBhnvWFhoaq\nU6dOeeY9fvy4Vq1apa+++kqnTp1S27ZtFRYWZt5qMTU1VV9++aVWrlyp/fv3q0WLFgoNDdWTTz7p\n8D5ISEjQsGHDNHz4cMXHx+vXX3/ViBEj1LFjR7NO165d9cILLygsLExBQUGaPHmyPD09NWDAAPMH\nf9++fVq+fLkOHz4su90ud3d3eXt7KzQ0VL6+vjp58qTatm0ru93u1D+fvXr1UsuWLdWzZ0/zs/L1\n119r1qxZ5veVdOV2kytXrtSqVat0+fJldejQQaGhoapZs6bZm5iWlqbvvvtOTzzxhPn9sm3bNrVs\n2VJjxoxxul1LlizR2bNnNXfuXPXt2zfPa9WiRQvVqFHDLCvou2jEiBHXeVf8z9UBOXdf+/v7m5+D\nXbt2ydfXV7///rvOnDkjf39/HThwQB07djS3Mz4+Xq1atdLEiROd+r7t37+/hg4d6tR7MiMjQ4Zh\n6OOPP9aRI0f02muvqVKlSjp9+rSmTJmiypUra/DgwZKu/JYtW7ZMzz77rNn+uLg4PfHEE6pUqZLi\n4+P1/PPPa/Xq1U59LxT22+Lsd9oNMwDDMJo3b26cOXPGMAzDaNSokWEYhpGenm40btz4purl2rt3\nr5GYmHjd9R4+fNj4xz/+YQwdOtRo1qyZ0aJFCyMqKsqc3rRpUyM9Pd1hfVlZWUZAQIDDcho2bGjU\nrFnTqFmzplGrVi2jVq1a5uOrBQYGGpmZmQ7Ly8nJMRo2bJhnO9PS0hzKzp8/bzzxxBPmc39/f+PS\npUvX3bZcuetZs2aN0bp1a8MwDCMzM9Msv9aOHTuMmJgYIyQkxGjTpo3Rq1cvo1mzZsbatWuNd955\nx5gyZYqRlZVlGIZhXLp0yZgxY4YxevRoh2W0atXKOH/+fKFtCwgIMHJychzaaRiG0aBBA6NBgwbm\n88DAQHOdhnHlNbh6umE4/1r5+/ubr8H1nD9/3ujevbvx4osvGv7+/saYMWOM1NTUPPX8/f3zvE75\neeqpp4ykpCSHslOnThnBwcHm89w2JyQkGE2bNjXL69ev7zBfhw4djP/85z8OZcePHzc6duxoGIZh\nnDx50px/4sSJRqNGjYxmzZoZK1euzNOuzp07F9r2XJGRkU7XNQzDSE1NNaZNm2b4+/ubn4OrPx/X\n+8s1YcKEG1qfYRjGiRMnjA8//NAIDQ01GjZsaLRu3dro2LGjsWvXLod6SUlJxqJFi4xnnnnGaNy4\nsTFu3Djjxx9/NKfv3r3baNSokfHiiy/m+z7O/b7ZvXu3w3s497X6/PPPjcaNGxsxMTHG4sWLjbi4\nOGPx4sVGTEyMERgYaCxdutQwDMNISUkxjh07ZjRu3Ng4fvy4cezYMeP48ePG8ePHjVOnTjmss1Gj\nRkZ2drb5ONe1n4Or7dq1y3j77bfN/TBv3jzj1KlTRkREhLFu3TqHups2bTJ69OiRb7uu/ru2XYZh\nGF999dV123DtNhiG899FBYmMjDSWL1/uULZmzRpj0KBBhmEYxsaNGw0/P788r/3evXuNZ555xqHM\nme9bw3D+PRkYGGjY7XaHMrvd7rCdXbp0Mfbv3+9Q55dffjG6du1qGIZhHDlyxAgODnb6e6Gw3xZn\nv9NuVMnCYx6KC1dXV4fnOTk5Kl269A3Vu5HDHEFBQUpPT1dISIiaNGmiwYMHy8vLy6FO6dKldf78\nebm7u5tl586d04MPPuhQ71//+pdT2+jh4aFjx47J29vbLDt+/LgqVqzoUC8zM1Pp6ekO60lOTnao\nExgYqJ07dyowMLDAddauXVtvvfWWduzYoaefflrnzp3TO++8Iz8/P7POnj17tHr1aq1Zs0aGYah9\n+/aaMWOGfHx8JElfffWVRo8erZycHG3fvt3suShZsqQiIyPVuHFjxcTEmMsbPXq0IiMj1aVLF5Ur\nV86hPcHBweZjLy8vbdq0yeFwUEJCgqpVq6aMjAzt2rVLDRo0UM3/1955R0Vxtm38ApVmQSwIGI01\nMbGxdCkK2JWlWIIlViQaQKMoIigWMEpUEBULWLAnYIFEmiKCgihCfBUjFhRFpCoqAlIWeL4/ODPf\nDrPLDkiied/5ncM5ujzMzuzOPPPMfV/3dX/9NbKysvDtt98CaNDqNE7Vcf2uvv32Wzx//pwRIWhM\nhw4dcOjQITg7O0NHR4eVhqD4+uuvUVJSwnqPxrx79w5KSkqM1wgh+PDhA/1/DQ0NxMXF4cKFCzAx\nMXMR7P8AACAASURBVAEAnDlzBn369GH8XWFhIbp06cJ4TVVVFS9fvqTfq7KyEk+ePMG0adNQVFSE\n7Oxs9O/fH0+ePAEADBgwAABYUeumCAwMRE5ODr788ktUVlbi5MmTUFNTw9SpU+lozfXr15Gamopb\nt27h0aNHGDRoEObPnw8jIyMA0jV4kvD09ERubi6KiorodF5tbS2ePn2K77//nh5XVFSE2NhYREdH\n48GDBzAzM4OTkxMsLS2hoKCAkydPYunSpUhOTgYAPHv2DNHR0bh48SKKioowfvx4aGhoYO7cuejf\nvz+dghw6dCjS0tKwa9cuWiaxevVqAEDnzp3x4MEDhIeHY8SIEZCXl0dycjI0NDQAAPv27cPBgwfp\nKKA41tbWWL58Oezs7OjrvnFEXBI9evTAvXv3oK2tTb+WmZnJio5SlJeXIycnB8+fP8fr16+hra2N\nFy9ewNraGuXl5di7dy9jvLGxMZYvXw4ArP2qqalBQUEBevfuzUqFAw0RttDQUISHh6OwsBBdu3aF\ntbU15s2bxxjX1Fw0evRoxMfHw8jISGqKU1wLduvWLezevZvx+7Fjx2LdunUAGqLqlZWV9BxG8fXX\nX+PFixeM17jMt0DDOZmVlYULFy7Qx2llZYXBgwczxrVr1w4vXrxgZEQePXrEmJuys7NZc1CfPn3o\na7RPnz4oKytDz549Oc0Lsu4tXOe05sIv4ngANEwCK1asgJubGwghePnyJXbs2MHSWcgaFxAQgKVL\nl6Kurg4hISHYu3cvneZovIibMmUKUlNTkZqaCpFIRP9QNzhqzJIlS+Ds7Iy6ujqkpqYiMDAQNjY2\njG317NkTlZWVKC0tpbVR1A2nZ8+e9Lh58+bB0dER8+bNg0gkwvnz5xESEoLZs2cztmdvb4/58+fj\n+++/R48ePZCXl4fjx49j7ty59JiOHTti0aJF0NbWZt3UxbVLvr6+2LlzJ4YMGYKlS5fi0aNHKCkp\nwebNm+kxCxcuxJgxY7BlyxaYmJiwUoPDhg2DnZ0dLl++jDt37kBPT4/+3fXr11np6vj4eKSnpyMn\nJ4exrcaaOHd3dyxZsgRGRkaorKzEmjVrkJCQgICAAJSXl2PJkiX47rvvYGBggMWLF8PGxgY1NTWI\niIigtVPN/a6++eYbzJkzB5aWllBTU2P87vz584wbSF1dHd6/fw9DQ0P6OMRvJJ6enpgzZw7Mzc1Z\nCznqhg8AEyZMwJIlS7B48WKoq6sjPz8f+/btY6QFPTw84OHhAUVFRRw+fBgpKSnYsWMHS/tjZmYG\nV1dXuLq6QkNDAwUFBQgICICZmRmqq6vp1Ih4+g8A/V7imsmUlBR4e3vjxYsXTeoqASAkJAT79u1D\nWloavL29kZGRAXl5eTx58oROhTk4OEBZWRlTp07Frl27WAtt8WtBFoGBgdi7dy+9+K2trUVtbS10\ndHQYizgLCwvo6OjAzs4OwcHBUFVVZWxn1KhRSE1NRXBwMKKjo/Hs2TOMGjUKy5Ytw6hRo+gF2n/+\n8x8kJSXR17+6ujomT54ssUDExcUF3333HZSUlHD8+HGkpaXByckJO3bsANCQrvzmm28kHtdXX33F\nKqZ6+PAhtm/fjpcvX7IKE6jrZenSpXB0dISdnR1qamqwZ88ehIWFwcPDgzE+KioK0dHRSE5OxsCB\nA2FlZQUfHx9069YNADBp0iQsXLgQx44dw8KFCwE0pLH37dvH2meuOtNDhw4hNDQUixYtgpaWFnJz\nc3HkyBFUV1czdFvic9GyZcvw8OFDei6i9K4BAQGs+UcSmpqaiIiIwJQpU+jX/vjjD6irqwNoWOAq\nKSnB19cXrq6uUFFRQWlpKXx9fRnzF8BtvgWAq1evYvny5bC0tISWlhZevnyJ2bNnw8/Pj3GvcnBw\nwLx582BjY0NvLyIigjEnCAQC+Pj4YPXq1ejQoQPKysqwbds2DBs2DPX19QgNDcXAgQOxbNkyeHp6\nypwXpN1biouLaX0nIHtOay68Jo4HQMOTkK+vL8LDw1FVVQUFBQVYWVlh7dq1DHGqpHFCoRCenp5o\n3749DA0NkZqaijt37sDR0RGpqamQl5eHjo4Obt++LfG9y8vLkZaWhtTUVPz++++Ql5fH9evXATRM\nbkeOHMG5c+dQUFCA7t27w9bWFosXL2boaEJDQ7FlyxZWZW1jrRgAxMTEsLbXWONDCMHp06cRExOD\nV69eQV1dHTY2Npg2bRo9pimRsouLi8TXa2pqoKCgwHq9qqqKFSmijl98Qr1w4QK8vLwwcuRIejGS\nkpICf39/RjRNIBDg/Pnz6Nu3r9R9pMjPz0dkZCT9eUyePJmOiGZmZiI0NBSZmZkoLS2FgoIC+vbt\nCzs7O8b7AQ3VsImJiTh79myT31Xjm5441EK/8XGLY2BgQP97zpw5eP36NYYOHcqKEFNFI0DD575z\n507ExsYyvk9nZ2d63yitDvU91NTUQE5OjlUsU15eDm9vb1y8eBHV1dVQUFCAjY0NVq9ejUePHuHA\ngQPYuHEjo0BHGlx1lUDDzX/37t344osvYGBggLNnz6Jbt26wsrJCSkoKgAbx/c2bN3Hz5k2kp6ej\nR48eMDQ0hKGhISMCywVTU1MEBARAJBLh999/x+bNm+Hr6wuRSIRNmzbR4/Ly8jgtDufOnQtra2tM\nmDCBLvYQ582bN/jzzz8xduxY+rW6ujrW90pBVVQqKSmhvLwclZWV9MOMi4sL2rdvT2usKAoLC+Hn\n5weRSISAgAD6dVtbWwwcOBDjxo1jfd+UThNo0NmJzx12dnasBcnYsWMhFAphZWXFWGhRFBcXIyQk\nBDExMaipqUG3bt1QVFQEVVVVBAUFMa5ZT09PVFdXy9SZjh07FkFBQYz3e/r0KRYuXMioSN+0aRNW\nrlwp8fOnsLGxwalTp5ocAwDp6elwcnJCz549oaGhgfz8fFrL1r59e3z//fdwdXVFREQEMjMzoaKi\ngg8fPkBXV5dVMMdlvgUa5ofly5czzuWrV69ix44dDF0i0FAQERMTg9evX0NdXR3W1taMxVRxcTFW\nrFiBP//8E+3bt8eHDx9gbGyMzZs34/nz5/D29oa/vz9rYS0SiQBAYhGdpHuLlpZWk58jwJzTmgu/\niONh8ebNG3Tu3Fnm05j4uOLiYqirq2P8+PEICAjAb7/9hrdv32L37t1ITk7Gli1bEB0dzdpGYWEh\nUlJS6DRQ586dMWrUKLi5uTVrny0sLODq6goFBQUkJiZi2bJl8PX1RZ8+fbBixQp6XHl5ucTJKTEx\nkTFZN4fq6moUFxdDTU1N4ra5VP4+e/YMBw4cQFFRESOSmJOTQy9oKR4+fIhLly7Rk9OkSZNYN4vx\n48fj3LlzMifi1kQgEGDAgAGwsbHBpEmTWNFJSUi7SXO9kQgEAqSkpDCKV2RRW1vLWjABDRNpSkqK\nxN9JQiQSobS0FF26dJF6rRQVFSE3N5eRApOTk6Nv/AKBAGlpaZze08DAALdu3UJiYiI2bdqEhIQE\n1NXVwdDQEOnp6azxFRUVOH36NA4fPozS0lJWZE8Wenp6SE9PR0lJCebNm4fIyEhUVFRgwoQJjIrH\nN2/e4OTJk6xz9+nTp81KF1OIRCLs2bMH58+fR0lJCVRVVWFtbY1Vq1YxHoBycnJw/vx5FBcXY82a\nNbh8+TKmTp0KoCESt27dOsTHx6Ndu3ZQVFREdXU1amtrYWlpCR8fH0bEUE9PD7du3eIUgaLIyMiA\nqqoqSwKyePFi+Pn5yTx3a2pqcPv2bfo61tHRYZ0HJiYmiIuLg4qKCv39i0QiGBsbMyoaJZ27NTU1\nMDU1ZdiRGBoaIikpSeKDJIWlpSXCwsLoyGFTlJaWIiEhAcXFxdDU1ISFhQU6dOiAN2/eQCQS0ZHg\n3Nxc+jibEw1ujK6uLtLS0hjfU319PfT19RkV0xRUGrpXr14AIPH7LSgoQHFxMTQ0NFiRa6DpKlw1\nNTUYGRmhV69eSE5OhomJicxq25ycHOTn50NfXx+VlZUypSCy4NOpPABkWzVQiJdJUzfpsLAwbN++\nHWlpaTLTHOJMmDABBQUF0NfXh4WFBVasWMGKXhQVFeH48eMS90s8ZVlaWgqhUIiioiIcOHAAmpqa\n2LhxI6ZMmcJYxM2fP1+i7UN8fDzu3LmDOXPm4MSJEwydUWOoMvvy8nJs2LCB1rHJy8vD3Nwcv/zy\nC2MC9/Pzw/379+Hn5wdnZ2d0794dGhoaWL9+Pfbv3w+g4YlbTU0NWlpa9AX+22+/YdasWaz3HzRo\nkMyq09mzZ2Px4sWYPn06OnfuzDiWUaNGNal7oWhuiD8lJQVxcXGIjIyEn58fDA0NYWtrS2ujKLjc\npMvKylBVVSXzRjho0CAUFBRIjHiIU1NTg8DAwCbfc/To0QgKCpJZ4VxWVoZff/1VokWKePTv0KFD\ntD2L+M1VTk6O/my56ioBoG/fvjh69CgSEhIwcuRIVFdXIzg4GF9//TU95u7du7h+/Tpu3LiBv/76\nC0OGDIGjo2OLHlA0NTWRm5uLXr164fXr16ioqEDbtm1ZqUg3NzeUlZVBTU0Nb9++xYABAxAfH48Z\nM2YAQLPPtYCAANy6dQu+vr50anDPnj3YuXMnbS9x9epVuLm5YfTo0bh48SKWL1+OXbt24fXr11i8\neDE6deqE3bt348OHD3j+/DkqKyuhrKyML7/8UqLtxbBhw5CVlcX4LBvDVe/7119/ST1eSm9F0a1b\nN3qxRHnOictJuOpMdXR0sGvXLqxYsQLy8vKor6/H7t27Gfo9oOHhztnZGePGjWPZD1HRLW1tbdjZ\n2cHIyIh1HaxevRrJyckwNTWlI3xqamq0LIJaSBUXF2P69Ok4deoU4/0zMzPpf4tLWPLz8xEUFCTx\nmjp+/Dj97z59+uDKlSsYM2YM/dqVK1dYC+ny8nJs3ryZTkNHRERg0aJFjDR0bW0tLl26hOfPn7Pe\nUzyT8vDhQ6lVuACwefNm+Pv74+eff0ZNTQ2EQiFsbGxYc7QsC5KWwkfieADItmqg4FIm3VSaQ5z4\n+HgYGxs3GUWxt7eHgoIC9PX1WfslfqFNnDgRp06dQpcuXWBoaIjk5GTIy8tDX1+fkcZdv369RNuH\nDRs2oHfv3rhw4QKEQiHCw8Ol7hM1Ya9btw6vX7+Gu7s7evbsidzcXGzfvh1dunTBli1b6PEjR47E\n77//DjU1Nfpp+sOHD7CwsKCFy9ra2rh58yZevnwJHx8fHDt2DHfu3IG3tzfOnz/f7Bth41QnBaWJ\n49JBQ9z4VRrSUnRv377FxYsXERQURKfLKbZv345bt27hp59+Ytyk9fX16Zu0q6sr0tLSpN5IxLcV\nHh4Oc3NzqKqqNjlO1nvq6enR+itqO5L0aUuWLMHz589hZmbGipyIe1iZm5tj3bp1jBtOY9zc3BAb\nGytTVwk0+Ott3rwZioqK8PPzw+PHj+kbCJV6HTFiBMzMzGBubg4zM7OPeso/fvw4goODce7cOezd\nuxdPnjxBu3bt0KZNGxw5coQeJxAIkJiYiIKCAuzYsQOHDh3C1atXERQUhNOnTyM1NVXmuSueTrKw\nsEBoaCitrQIaHubs7OzotLG1tTW8vLygr69P2308ffoUDg4ODDNlWWzbtg1AQ0o4NTUVY8aMYRUC\nUecRF1sToEEukJmZCQsLC9a85+Pjw/DkE/9cJJ1rgYGBiI+Ph7OzM9zd3bFv3z4EBgZCV1eXLoIA\ngBcvXmDBggUoLy+Huro6iouL0a1bNxw4cICOQgGy5wVq/6WxdetWWFlZITIykt6WpGPo378/goOD\nMWfOHKnvJ744mzNnDgghGDNmDCtNKb7Yu379OpycnGBsbIyePXvi5cuXSE1Nxb59+xipUi52R25u\nbkhOToZAIGA9ZIlfey4uLhg7dixD2xsbG4uYmBjs2rULiYmJ8PPzw4ULF5Cenk7b7HTt2hU2NjYQ\nCoXo0aOHTAuSlsIv4ngANEzE165dkznpU2J3eXl53Lt3D0KhEKtWraInPklO8xSSvKXCw8MRFhaG\nV69e4eTJk9i5cyfWr19PPy0LBAKkpqY2Gf4HGp7eL1++jGPHjtGRQkVFReTk5CA0NJQx1tfXF+fP\nn4eCggI8PDwwefLkJrctTmlpKZ2GMTU1RUxMDOMzKy0txbhx4xjVbiNHjkRkZCQ6depEL+LKysow\nefJk2mfI3NwcCQkJEIlEsLCwoFOo1A2KWnRx1Yo1h9raWjqto6GhAYFAADk5Odjb2yMjI0Nq9V3j\nIgmK//znP/RE1qVLF9ja2mLBggX077ncpGXdSCi4juPynk11yxBPAenp6dGTdFMYGhrixo0bTabo\nWqKrbApCCEQiEW7duoXCwkJ0794dhoaGEvWWXPjzzz8xZMgQyMnJISQkhDYiFl9wmpqaIjk5GZWV\nlRg/fjx9TlP62OZiZGSEhIQExsPdhw8fMHr0aPpBRV9fH7du3YKcnBx9TdXX18PQ0BBpaWkwMzOT\n2Tnhxo0bTZ4/FNR5xFXvy3Xh8vjxY7rKslu3bpg8eTKGDBnC+Jv6+nqEhITQOtNu3bphypQpWLx4\nMUOGEBERAZFIBDk5Obx58wb19fXo2rUrVFRUoKamhuHDhzdpvNtSUlJSEBUVhVevXqFnz56YMmUK\nhg4divDwcJkLd/H7ga6uLq5evSo18i7+QJmTk4Pw8HBUVFRAU1MT48ePR8+ePRnnC5c0tKGhIUJD\nQ1lVpo0xMDDAzZs3GddxYxlD43Ogrq4OcXFx+OWXX1BcXIz79+/DyMgI165dg4KCAuOcNTAwkCiH\n4AqfTuUBwN2qQVaZtLj5K9AQ+s/Pz4e5uTlrERccHIwLFy7AwcEBmzdvRvv27VFYWAgfHx/4+voC\naLhhPnjwgGHHIYmffvoJffv2hZKSEjZt2gR/f3+Ul5fTbVrE0xiybB+AhoWIn58fS+fz5s0b3Lt3\njx5HOYFT1NfXs54kuVT+fvPNN/D394eLiwvU1dURFxcHJSUlemKiFmhctWIAcPPmTYY9hEgkQnZ2\nNiNa9PDhQzg5OaGiooJ+gldTU0NwcDCOHz+OefPmYcKECZg/f77M99u2bRtiY2NRW1uLyZMn49Ch\nQxLTU5J0IB07dmToxsQXYE2J28XHNQWX9+Ra4dy1a1dO2ik7OzscPHgQixYtkrr/0hZq0tqVyXro\nycnJgaOjI2pqamixObUAE7c+4Iquri6t4XFwcJD4OQ4cOBCnTp3CrFmzoKKignv37kFJSYn+jJob\nRTYxMcH69euxbt06qKqq4t27d/j5558ZkZZBgwYhNDSUTtkCDaJy6nzbu3cvHBwc8OOPP7IWRuJw\nPX+A/7c1oSLjjW1NKBrPgZK4du0afvrpJ0aV5ffff8+qsszLy0NWVhZiYmLoysywsDCYmpoy7FNC\nQ0Nx584dqKurQ0NDA0VFRSgqKoKmpiaqq6shEolw4MAB6OjoNKklBP5fehAZGYnXr19DU1MTdnZ2\n+OGHHxjn/alTp7B7927Y2tqif//+9DmyceNGukWcSCRCVlYWevfuTZv4ZmdnQ1dXl3E/6NWrF96/\nfy91XqMeLCnEr9s9e/awIphc0tDt27eXqIFrDJcqXCriWllZifj4eERFReHmzZsYMWIE/aDA1d6q\nufCROB4ADSJdZ2dnqVYNjSdiqkxaVVVVZpl0eHg40tLSGClGoEGDdOLECWhpadFPJu/evcOECRPo\n/p0ZGRmYN28eBAIBa78ap5sA6UJWae2GKBpPAjY2NtDR0UGnTp3w6NEjTJgwAYcOHcKUKVNoW4BN\nmzbh+fPnWL16NTQ1NZGXl4cdO3agT58+2LBhA70tLpW/L1++hJeXF3x9ffHkyRMsXboUNTU12Lhx\nI6NCi6voeMOGDYiMjISqqipqa2uhoKCAvLw8jB07luHtZG9vj5EjR+LHH3+EvLw86urqEBgYiNu3\nb+PYsWN4/Pgx5s2bh7i4OJkLx5UrV8LW1laiRUrjcfLy8qybdF1dHfz9/QHI1s1RabCmEE+ncnlP\nrhXOwcHBuHLlCuzt7VkpUPH0srW1NR4/foy2bduyzl3qWmlOQUvjh56EhAS4uLhAU1OTfuhxcHCA\nQCCAs7Mzfb7v3bsX6enpOHr0qMzPTByuGp7MzEwsX74chw8fxt27d2m7EycnJzg5OXFK3YtHkUtK\nSuDs7Iw7d+7QFY3GxsbYsWMH/Xk/evQIDg4O6NatG548eQKBQIDs7GwcOnSIriY8e/YsTp482WR2\ngEJaRI5qwWRmZobCwkJ4eHjQet/y8nI4ODhgx44dGDduHOPvZHm2ca2yXLRoETQ1NeHt7Y2JEydi\n6tSp6NixI86ePctogbV27Vr07t2b0aP66NGjyM7Ohre3N3799VeEh4fD2dmZoSWMiYnB9OnTaQ0t\n0CA5yczMxJIlS6ChoYG8vDwEBQXB2NgYq1atordvamqK4OBg2jsSaPC7dHd3p68XDw8PDBkyhJES\nPXPmDBITExk+eQEBAYiOjoa1tTXLdmj27NmcekqLP2hJS0Pr6enRHRtOnDiBmzdvYv78+az3FH+g\n51KFu3nzZly6dAnXrl3DgAEDYGtri0mTJjG2e/r0aRw6dAjz5s1DQEAAvLy8EBISgunTp7OsVJoD\nv4jjASDbqqG5E3FjqLSgOMbGxkhISICioiL9++rqalhaWtI3sClTpqBDhw7Q1dVl7Zd4FIOLkLU5\naGtrIz09HXl5efDw8MDp06fx9OlTLFu2DFFRUQAaUjxeXl64ePEi6urq0K5dO1hbW8PDw0Nq6kJa\n5a+uri60tLRgYWEBCwsLDBkyBCKRiPEkCXDXihkaGuL06dN4+/YtTp06hZ07d+LgwYPIzMzEzp07\n6XECgQDp6emMz7a2tpYW3AMNomM1NbVmVYA2BXWTvnv3LpSVlVFZWYkRI0YwbtKyNGzNSYOJv2dT\nCwOuFc5cdEUAu0m5ONS1MnPmTKipqaFz587Iy8uDgYEBfvvtN8yYMQPOzs6Mv+Hy0GNoaIjr168z\nND4ikQhGRkYSq/eaoqUanqKiIlRUVHC67urq6vD06VOJxs+5ubkoKSmBpqamxIhJRUUFrl69ivz8\nfHTv3p3WRYpz4MABTJs2TeZDj5eXFyIiIjBu3DhavH7p0iUYGRlBRUUF165dw/jx4+monoqKCsrL\ny/Hhwweoq6szokrSPNu+++472rONa5WliYkJkpKSkJOTA6FQiLS0NCgrK7PSd0ZGRrh+/TrjOq6r\nq4OxsTFSU1Ppbffs2VOmllBfX5+WQlAUFxfD2tqaPs+ABr/E+Ph4htSlqqoKZmZm9Fyvo6ODtLQ0\n1n7p6enRPWkB7ilorjS2pqIsS8TtjqQVh0nyaORShbtz507Y2Ng0ed5zsbdqLnw6lQdAQ0VVU1YN\nw4YNg5KSkkyxe+Pfi0QiXL58WaLdhImJCTZu3Ig1a9ZATk4OtbW18PPzo93lgYZIReNFhiS2bt0K\nkUiEuLg42NraolevXhg/fjw2bdrE8FMCwMmFvkuXLqivr0fPnj2RnZ0NAOjfvz+KioroMSoqKvDz\n88OWLVvw/v17qWk2LpW/qampSE9Px9WrV+Hl5YWSkhKYm5vD3Nwc48ePp8crKirC1NQUAKT23gQa\n0g39+/fHu3fv6IqwefPmsRYgw4YNQ1xcHCZMmEC/lpaWxqhq4+JzxAXxiMiMGTMwbdo0Wr+joaGB\na9eu0RNadHQ0Q8PWr18/DBo0CHZ2dnB3d29WGgxoSIH+9ttvTS4MuFY4X7lypcn3ojz/GjvVS+LB\ngweMghZnZ2eYmJjA29ubtYirrKykUy/UuausrMy4Njp27Ihnz54xPOaePXvGye6lMWlpafDz84OC\nggL9sDB79mw6Ai7uPyaJ3NxcRqQpPj4emzdvZlx71DE09pCUFsnKzs5Gv379aAnEV199RS8AX716\nhVevXjGiKI37iUrjxYsXdLSJ4rvvvsP+/fuxc+dOZGRkYPbs2Xj06BH9+3fv3qGgoACjRo1i3IhD\nQ0NZnm2GhoZYuHAhvYjjWmUpJyeH8vJyXL58GQKBAMrKysjNzWU9JKqqquLGjRv03AA0yCmocfn5\n+ejUqRMKCgpoexvqO+3bty+j4rhDhw6saLR4NJma4+3t7eHp6QkPDw907doV79+/x44dOxgLMi0t\nLVy8eBGTJk2iXzt37hzrOLmkoJuDvLw8Fi1ahEWLFkkd8/DhQ87bq6qqwhdffEFH+6hFHtVdBABW\nrFiBrKws+Pv70+dtY53jxIkTMXHiRKl+oS2BX8TxAJBt1WBsbIzbt2+ztAkAs6qK+j31GiEEHTt2\nZKQXKdatWwc3NzeMGDEC9fX10NbWhp6eHp3eAhomv4yMDJkl2ImJibSQVU5ODm3atMHy5csZkzLA\n3YVeR0cHa9euxYYNG9C/f38cPXoUysrKjPB4bm4u9u7dC19fXzqlpKqqit27dzP0KsuWLZNa+UvR\ntm1bGBkZwcjICNOnT0dERAROnDiBiIgIxlMhV63YF198QVtMVFRU4M2bN2jbti1rkd21a1e4urri\nzJkz6N27N4qLi3Ht2jUMHz4cP/30E61p7Nmzp8Qm4s2Bmqhramrw5MmTJnUysjRsK1euhJ+fH5Yt\nWyZVb9U43S4rxdWjRw+8efMGPXr0QH5+PkQiETp16oTS0lIADWkgSbYJFHJycpg1axbnawVo0Fkp\nKiqid+/e9MJEW1ubds8Xp/FDj0gkYj30zJkzB46Ojpg/fz60tLSQl5eHY8eOMQpLuCJLwyNu+Esd\nf+PjFI9Mbtu2DTNmzED79u3x559/0gvCxvYnTXUfOHDgAG7fvs3qhiG+D831wwOA+/fvszIJAoEA\nd+7cAdDwsKOgoIBz584xxlBSEXFKS0vRu3dvxmu9evViXHuurq5SqyzFsbGxga2tLd6+fYutW7fi\n4cOHdBcVcVauXAkXFxeYmprSXURSUlKwdetWPH36FHPnzsUPP/yAy5cvN6klBICpU6fCwcEB6eFM\n5AAAGupJREFUTk5O6NWrF4qLi3Hw4EGMGDECV69eZaRsgYYOFW3btkVtbS0IIVBUVMSyZcsANFgn\nubi44NChQ/R1VVhYiAMHDrC+Ay5tw5qDLP0oAE4tvLhYBQGSu0mI6xy5+IW2BD6dygNAtlVDQUEB\nOnfuTPeHbExNTQ0GDx6MrKwsxusdO3ZEx44dIS8vjzZt2rCePu7evYtnz57h9evXqKmpoaM+1M18\n7dq1uHDhAgQCAWu/xG/SlpaW+PXXX9GjRw861fTq1SvY29szIidcXeipnoKurq4oKirCihUr6BY4\nFhYWALjrVbhU/oaFhdE9L6mKO2pRJ+6dx9UINSoqCl5eXoiMjERoaChiY2PRtm1bDBgwgPG5NVUd\nmZmZievXr+Orr76Cra0tlJSUUFVVhWfPniEyMhLu7u6sVmpc4KKTkaVhGzp0KO7duwcfHx+WnoVC\nPN3OJcUlq8LZ0dERBw8elJn6KSgooDWS0qCe6H/88UcMGDAALi4umDFjBpycnKCkpIS1a9fSVZ4U\npaWlWL16NZKSklBfX4+2bdvSDz3ikbbQ0FD88ccfKCkpgZaWFmxtbWFtbS11X6TRHA1PUlISXaWo\npaUFGxsbVicDgUCA27dvIz8/H8uXL8eZM2dQUFCAuXPnIi4ujh7HpfvAixcvWAulj2HmzJkwNTWF\nk5MT/fC5f/9+XLlyBWfPnsXVq1fh7+8vsUdzY6nIkiVLMHDgQIZnm7+/Px4/fozg4GB6XFZWFqKj\no+nvadKkSRKP6caNG1BQUICuri4KCwtx9+5dRnSegupJS6X8rKys8MUXX9BmtsOHD+ekJRSP1je2\nQyGEoLa2FnJycjh9+rTUz1Ncn1ZSUoLExETa7Nfc3Jx1zXK5PpsDF/2opEVXQkICq7iEi1UQIFvn\n+Msvv+D+/ftwcXGBk5MTkpOTsW7dOlRUVNB+oS2C8PAQQtasWSP1hxBCvv76azJo0CCpP1999RWn\ncTo6OiQoKIgQQsiWLVvI0KFDiY2NDZkyZQr9M3XqVHq/9uzZI/VHnD179hBbW1sSFxdHdHR0yM2b\nN8n3339PAgICGON0dXUJIYS8fv2aTJ48mRBCSHl5OTE1NWWMe/HiBXF3dyeEEJKYmEi0tbXJqFGj\nyN27d+kxxsbGpK6ujmRnZ5PBgweTDx8+EEIIEQgEjG3Z29uTZ8+eNfn5Dx48mGhraxMfHx/y+PFj\nqeO2bdtGpk2bRpKSksjTp09JYmIimTp1KvH19WWNffnyJRGJRKS+vp5cuHCB/Prrr6Sqqkridquq\nqsiLFy9IWVkZ/ZqFhQXjeMW5e/cusbCwaPKYpCEQCEhtbS3jtdraWqKtrc3Yvr29PRk0aBARCATk\n66+/JvPnzyclJSWEkIbv8dy5c2T48OEkMTFR4o84Y8aMIU+fPmW89uTJEzJy5Ej6//X19SQiIoKU\nl5eTt2/fEi8vL7JixQqZ311TiEQiUlhYSPLy8hg/FLm5uWT+/PmksLCQJCcnE4FAQAYPHkzOnDkj\ncXvXr18nnp6exNHRkaxZs4ZkZGS0eN+4EB0dTRwcHMikSZPIvHnzSHh4OGvM8ePHiYGBAdmyZQs5\nfPgw8fHxIfr6+iQiIoIxbuzYsaS8vJzU1dURAwMDUl9fTwhhXy/6+vpEJBIxXquurib6+vr0/42M\njBjn6seSlZVFxowZQwwNDYlQKCQGBgZk4sSJ5OHDhyQtLY3o6OiQhIQE8uHDB/qntLSUnDt3jowb\nN46xrefPnxNLS0tiYGBArKysiIGBAZk0aRLJyclptf39GMrLy0lUVBQ5ePAgiYiIIO/evWONSUlJ\nIeHh4eT8+fMkPDychIWFSZxjWgsu12dzsLS0pK8z6rx5+/YtMTQ0pMfY2tqy5onExERiZWXFeM3A\nwIDU1dXJfE8dHR3WuLq6OqKjo0MIIcTMzIy8efOGsU8VFRXEwMCgOYfGgk+n8gCQXWovyQ9MnOLi\nYpnjCCHIysqCm5sbfvjhB0RGRuLEiRNN2odw9coaOXIklJWV4efnh7q6Oqxbt47u2ykOVxf6TZs2\nQVNTE4QQbN26FU5OTujUqRO8vb3pKBtXvQqXJu1paWlIT0/HzZs34e7ujuLiYujq6sLQ0JDRtUGW\nVkwcDQ0NpKamIj8/H0KhEAUFBVBUVGSMaarrRHObiHOFi05mwYIF0NLSwrhx4+jUoLiGbeHChdi/\nfz9qampYaT2AXWTAJcUlJycHGxsb1NTU4PXr19i4cSMAya16uKR+/vjjD2zYsAFVVVWstltUys/G\nxgZaWlo4deoUbf4sqaAFaNrSgfosHzx4AD8/P4nO97Ku4cZs2rQJq1atwsSJE5scd/DgQRw5coSR\nhrK2toarqyvDINXY2BhOTk603MDX1xdKSkqsNkxcug/06tUL9+/f59TpggsDBgxATEwM7ty5Q7dg\n0tbWhry8PKqqqnDz5k0MHTqUlR6XJBX58ssvERsbi/T0dLx58waampp0U/VPzeLFi2FtbY3Ro0dL\n9Q7cuHEjLly4ILGy/e+Cy/XZHLjoR1+8eAEzMzPG35mZmcHV1ZXxGherIICbzrHx39fV1bHm5ObC\nL+J4AMjufyir3x31e1nj1NXV6YWZnJwcozxdnOa0vwIa0lIdOnSAUCiEUChkCWcppk+fjpkzZ+Lc\nuXMYN24cHB0d0a5dO1Z7mgcPHiA4OBjPnz+ntQ3Kysq07xzAXa+yfft2dOjQge7dKAllZWWYmZnB\nzMwMz58/x8WLFxESEoIrV64wFnFc/M6AhhY+ixcvpr3t9PX1IRQKsWvXLka6xNfXFxUVFYiKimJ0\nndiyZQuMjIywbt06qU3ExX27mgMXnYysQg/KvmLChAmIjY2V+Z5cFgZcK5yb0myJp378/f3h7u6O\niRMnSmyWzeU4xdm/fz9CQkIY14yVlRXc3d3pRZyXlxd69+4Nd3d3zj1gpREdHc2pCriqqopRTAA0\naGwb34A9PDxw+PBhyMnJYcOGDdiwYQPKy8vh4+PDGOfp6YkFCxYgLCyM1X2Aol27dliwYAG6d+/O\nqtIWnxdkIU3n+PbtW3qhTaX9Gy+C27Rpg65du7K+W8r8uPH1Qck8PiWmpqY4ceIEvLy8MHr0aAiF\nQpiamjIeVGJiYhAWFiaxsv3vgmvbMK5wKZrjWlySkpKCx48fY8+ePVKtggDZOkcufqEtgdfE8QBo\n8Jdq3P8wNjYWM2bMYFhXtCYHDx5EXl4efvjhB5ZG4vLlyxAKhThx4oREf7KioiJG5Vl9fT3tHn75\n8mX07dsXQqEQkyZNYpkpUi70VBl6RUUF5s6dyzDtNDU1paNe165dw4kTJ5Cbm4tZs2Yxmn9z0atw\nadIeHR1N97ysrq7GyJEjMWrUKJiamjKOn4vfGdAQybKwsMDcuXNpzU5cXBx2797N8KFqqutEXFwc\n1q1bhytXrqBt27asJuKbN29mtSjiChedDEV2djZd6FFVVdUi4TqXtkRcWvUA3DRbQEPkieoGwQVZ\nx8nF0oFrhxMurF+/HgUFBU322QSAffv2ISsrC56enujevTvKysrg5+cHFRUVLFu2jBEJoZBVnZec\nnIwNGzYgLy8PhoaGWLlyJaOQhktbPC5QOsdx48ZBVVWVFZ3ianHx8uVLrFixAoQQZGZmsh5OqXZu\nXB44/glevnxJd1UpLi7GhAkTYG1tjWHDhjHsa+zt7XHx4kXU1NTA0tISycnJf8v+NL4+i4qK0L17\nd1bbMK6UlpbSbbWk6Ue5tvCSZRXEtZtEdnY2Tp48ifDwcFRWVqJdu3YQCoUMv9CWwC/ieADI7n/4\nd6Cvr4+ysjKJTtxBQUEAGjoxNK4yLCsrg5eXF8NnSJyamhpcu3YNgYGBePLkCUaMGIHp06dj3Lhx\niIqKgo+PD11xSMREu+I3ze3btyMmJoaOsvXp0wdLlizB1KlTsXTpUnqcNId/qvgBaBBN//zzz036\nB9nZ2dERmGHDhkmNPmZkZGDLli20x9qHDx8wYsQI+Pn5McTtBgYGuHHjBtq0acOIAOjq6jJ8qExN\nTREZGYnOnTvTr719+xZCoRDJycm4ceMGbf9gaGgIFRUVuol4QEAAo39ja8K10IMrXNoSUQvXplr1\nAA2fbUpKCiPSVVNTA1NTU8aEv379ejoC+jHHSd0kDh8+jOfPn7MsHbp160ZXA86ePRsbNmyQ6LvW\nXLj64QkEAlRWVkJOTo5e6JNGYng5OTncu3ePU3UelTa2sbGhzVX/+OMPRtqYgjL37t27Ny0HaAn6\n+vpISUmRGjHlwpUrV/Du3Tts3LgR3t7e9GdQV1cHFRUV6OnpMdq+fWry8/Nx6dIlnD9/Hvn5+eja\ntSsUFBRQW1uL7du3Y8iQITA1NcUff/yBtm3bwsLCotleg81BJBLRKWh1dXXo6OjItJZqCmlFc+IP\n2U0tuiiroKZSusrKyrSRPEXjJRV1b6G8QEeNGgVzc3Po6Oi0+HxlbJ9fxPEArd//kAvSqveqq6vh\n5uaGt2/f0pV+4igoKMDOzo6ld6OicdHR0YiPj0f37t0hFAqhpaWFI0eOYMCAAbhx4wacnZ1ZKQSA\nnQqWFWXj6vDPtUk7F6iJoF+/fhK1YhRCoRA+Pj7Q1tamFySZmZlYvXo1IiMj6XFNdZ0YNGgQ/P39\n6R6BQ4cOxZ49e+gISmPD0dZkyJAhaNeuHaZOnQp7e3uG71lLmDlzpsy2RJSFhKwKZ1nVh5QEoKqq\nirZSaRyxpFJ+XI6zcbcROTk5lqXD3bt3AQCbN2/GhQsXYGlpyYpsNvdcE+9/SS3EgAY7nC5dutD9\nOLm46QPAyZMn6eo8Z2dnJCUlSazO49IJgKoUp1Lf4eHhcHR0bLG5t4eHB7744gtYW1uja9eujGu0\nuSbXGRkZOH36NHx9fekKSEnWQ5+CoqIixMbGIjo6Gg8ePICZmRmEQiEsLS2hoKCAkydPIiAgAPX1\n9TIr21uT3NxcrF69GuvWrcPgwYPxyy+/ICMjA/7+/pxaYzVm69at+PXXX9GvXz/GQlBOTg5//fUX\np0UXNb81XqRRf0ON49pNora2lpZOJCUlNSmdaA78Io4HQEP6bcyYMZg1axYmTpyI7du3Q0lJCXPn\nzpXaTuufwMXFpUkbDIr169cjLi4OCgoKmDRpEqytrRmi/IcPH2LmzJlQUlJCcnLyRz3hUXB1+Ofa\npJ0LXCeCuLg4eHp6ws7ODmFhYXBwcEBoaCg8PT0Z0QzxrhO1tbVQVFSEUCiEh4cH7OzsEBAQgG+/\n/RalpaX48ccfoaamRtuACAQCqdHQj6WyspIu9Lhx44bUQg+ucGlLtH//fnTt2rXJVj2A7NQsleqr\nqKiQmCYRlwJwOc7mtBxauXKlxDTl+/fvGW2OuMBl4Uv14+TCyJEj8fvvv0NNTY1eJH/48IEu6KDg\nkjbmmvrmip6eHp3ybLxwbW763tHRERoaGjKthz4F3377LXR0dGBlZYWJEyeyOlzk5uZi27ZtWLNm\nDXr06IE2bdogKioK5eXlsLOz+2gRvjQWLFiAwYMHw8XFBUpKSvTCPisri87KNAcTExPs27dPYtEc\n1+upOVZBzaU1JCI0H1XbyvNfQ2ZmJhk7dix58eIFiYyMJIMHDyaDBw8me/fu/dS7xok1a9aQlJQU\n2ragMWVlZSQjI4MEBQURX19fUlRUxLALoOxBmgNljVBYWEhsbW0JIYS8efOGmJubt/xAmsHTp0+J\nn58f0dbWJoMGDWL87sSJE+Tu3btk/fr1xNHRkaxdu5akpaWR/fv3S9xWVVUVKS4uZnx+lB0Lxfv3\n78nkyZNpqwFxS5C/k2fPnpEDBw4QQ0NDMmTIkBZtw9DQUKKtCVXeX1dXR4YPH04OHTpEJkyYQIYP\nH07GjBlDAgMDWXYXlOXCmTNnSFBQENm/fz8JCwsjkZGRJCYmhsTGxpKEhASJ9icXLlyQ+rm1xnEu\nXryY1NTU0P+vrq4mfn5+Ldqep6cnOXDgAOO1kJAQ4uXlRQgh5PTp02T69Omct2dmZkZKS0sJIf9v\nsfD+/XtiZmZGCCH0dbhnzx6ycuVK8vr1a0IIIaWlpcTLy4vs2rWL3paxsTGpqKh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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "vectorizer = CountVectorizer(stop_words='english')\n", - "docs = vectorizer.fit_transform(corpus.data)\n", - "features = vectorizer.get_feature_names()\n", - "\n", - "visualizer = FreqDistVisualizer()\n", - "visualizer.fit(docs, features)\n", - "visualizer.poof()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualizing tokens across corpora\n", - "\n", - "It is also interesting to explore the differences in tokens across a corpus. The hobbies corpus that comes with Yellowbrick has already been categorized (try `corpus['categories']`), so let's visually compare the differences in the frequency distributions for two of the categories: _\"cooking\"_ and _\"gaming\"_" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "hobby_types = {}\n", - "\n", - "for category in corpus['categories']:\n", - " texts = []\n", - " for idx in range(len(corpus['data'])):\n", - " if corpus['target'][idx] == category:\n", - " texts.append(corpus['data'][idx])\n", - " hobby_types[category] = texts" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/axes/_axes.py:531: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n", - " warnings.warn(\"No labelled objects found. \"\n" - ] - }, - { - "data": { - "image/png": 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kwsPD1bJlyyxHZW5Wv379LNuW07l+Y8aMMefi8/T01KuvvqpWrVqZF2fcHBzuxPXr17Vn\nzx7z8fHjxxUTE2MO0966fyVlGR7MKQzXq1dPcXFxOnjwoE2bGzZsUL169e6qz9mpWbOmHB0ds7ym\n69atk4eHxx0Nod66L+3dhuvXr5sn8Us3znmLiYlRgwYNsm1nz549Gj58uC5fvmyWnThxQqdOnTL/\nMWjYsGGWyX03b94sHx+fO+r/zp07lZiYaJbFxMToxx9/tAmYxYsXV6FChTR48GAZhmEeSXZzc1Pl\nypV15swZm89QfHy8Zs2apZSUlNv25Wbz5883JxR2d3fXyy+/rI4dO2Z5fwH3GyEOuEPNmzfX008/\nrR49emjt2rWKjIzUwIEDtWHDBnPKiD59+igmJkYDBw7Ud999p+nTp2v16tU26/H19dWmTZv05Zdf\nateuXerXr5/S09PN5dWqVZOfn5/69++viIgIff/99xo7dqyWLl2a49QOt8o8yf7//u//tGvXLn38\n8cf67LPP9Pbbb5t1KlWqpJo1a+rAgQMKCAjIcX3BwcE6ffq0Bg0apJ07d2ratGnasmXLbevXr19f\nGzZs0MyZM7V371599dVXWrt2rXkenbu7uwzD0KZNm8yLGuzh6Oio0aNHa/369ebVlzVr1lTz5s0l\n3TiB/8iRI5o/f7727NmjYcOGZZk2w93dXcePHzens7hZ8+bNVb16dQ0YMEDLly/X9u3b1bt3b505\nc0a9e/e2u5+5KVmypN544w2FhYVp7ty52r17t95//32tXLlSISEhd3RF86370t5tMAxDI0eO1KpV\nq2z25a3nUmbq2LGjXFxcFBwcrO3bt+u///2vgoODVbt2bfM8se7du+vo0aMaOnSoduzYoREjRujQ\noUPq2bNnjv2/fPmytm/froSEBL311lvmFbabNm3SunXr1LNnT5UtW1bt27fPdl/269dPy5cv1+HD\nhyVJISEhioiI0OTJkxUZGamvvvpKw4YNU0ZGht3D0/Xr19fRo0c1evRo7dmzR2vWrNHSpUtve/EP\ncL/cUYg7cuSImjZtaj5OS0vTuHHj1LBhQzVs2FChoaFKS0szl0+bNk2NGjWSj4+PJk6cyJw6eCQU\nLlxYixcvVu3atTVu3Dj17dtXZ8+e1UcffWQeBXrmmWe0aNEi/fHHHwoJCdH+/fuz3E6oX79+at26\ntSZPnqyhQ4eqevXqNlciSjeuzmvTpo1mzpyp4OBg/fDDD5o6dWquV1/e/MXv7e2tefPm6eTJk+rb\nt68+//xzDR8+3GY6D+lGqHz88cfVsGHDHNddpUoVLViwQDExMQoJCdEPP/ygoUOH3rb9tm3bKjQ0\nVN9884169uypOXPm6I033lBISIikGyfRZ07psGTJkizPv3mdN5e7urpq8ODBmjJlikaMGKGaNWtq\n/vz55tGcTp066Y033tCiRYvUv39/FS1aNMvdBbp27arLly+rV69eWeYec3R01OLFi+Xn56cpU6Zo\n0KBBSklJUXh4eK7nUdoTvG6uM2LECDNs9O7dW5GRkZo4caLNVZb2rPPWfWnvNjg4OGj48OH64IMP\nNHLkSNWuXVsLFiy4bZtlypRReHi4HnvsMQ0dOlQTJ05U48aN9dFHH5l1qlWrpjlz5uj48ePq37+/\nfv75Z4WFhWU5l/Rmbdu21dNPP63+/ftr586d+tvf/qalS5ea7YwZM0Y1a9bU559/nuXK10ydO3eW\nl5eXxowZI8Mw1KpVK33wwQfas2ePgoODNXv2bL300kuaOXOmzfbfuq03P65bt66mTp2qw4cPq0+f\nPnr//ffl7++v0aNH5/yCAPeYg2Fnssr8T6Zw4cKKjIyUJE2aNEnHjx/X7Nmzzbmz/P391atXL4WH\nh+urr77S4sWLJUm9evVS27Zt1aNHj3u3NcBDbMWKFXr33Xd1+PDhHIcrH5ROnTrJx8dHQ4YMedBd\nwX30sL8vAdyeXUfi5s2bp/DwcPXp08csS09P15dffqlRo0bpsccek7u7u2bPnm0OxaxatUpvvvmm\nSpUqpVKlSik4ONhmBnIAD4dFixZp0KBB+vnnn7McCQQAPLzsCnFBQUFauXKlzU2LT58+revXr+vQ\noUN64YUX5OfnpyVLlpiTH0ZFRZkTT0o3zrm59dY+AB687777TpGRkRozZozNFX8AgIebXVOMZDdP\nz6VLl5Samqpt27bp66+/VmJionr16iV3d3f17t1bSUlJNvPuuLi46Pr160pNTeWQPQqkzGk2HjYf\nf/zxg+4CHqCH9X0JIHd3PU+cs7OzDMPQoEGDVKxYMRUrVkzdunVTeHi4evfuLRcXFyUnJ5v1k5OT\n5ejoaFeAu379uhITE+Xk5HTH95oEAAB4FBiGobS0NLm5uWU7HdNdh7iKFSuqUKFCNjebTk9PN69A\n9fLyUnR0tDmbdVRUlLy8vOxad2Jion799de77RoAAMAjo3Llytne6eSuQ9xjjz2m559/XtOnT9fU\nqVN17do1ffLJJ+btVgIDA7Vo0SI1bNhQjo6Omj9/vrksN05OTmanGXoFAAAFUWpqqn799VczF90q\nT7fdmjRpkiZNmqS2bdsqLS1N7du3V7du3SRJr7/+us6fP6+goCClpaXp5Zdf1ltvvWXXejOHUJ2d\nnW3uSQgAAFDQ3O7UMrvnibufUlJS9OOPP6pGjRqEOAAAUCDlloe47RYAAIAFEeIAAAAsiBAHAABg\nQYQ4AAAACyLEAQAAWBAhDgAAwIIIcQAAABZEiAMAALAgQhwAAIAFEeIAAAAsiBAHAABgQYQ4AAAA\nCyLEAQAAWBAhDgAAwIIIcQAAABZEiAMAALAgQhwAAIAFEeIAAAAsiBAHAABgQYQ4AAAACyLEAQAA\nWBAhDgAAwIIIcQAAABZEiAMAALAgQhwAAIAFEeIAAAAsiBAHAABgQYQ4AAAACyLEAQAAWBAhDgAA\nwIIIcQAAABZEiAMAALCgwg+6A3g4ZGRkKC4uLsc6np6ecnR0vE89AgAAOSHEQZIUFxen8at2y9Xd\nI9vl1xIuKTSwscqWLXufewYAALJDiIPJ1d1DbiVKPehuAAAAO3BOHAAAgAUR4gAAACyIEAcAAGBB\nhDgAAAALIsQBAABY0B2FuCNHjqhp06ZZyg3DUJcuXTRlyhSzLDU1Ve+++658fHzk6+urefPm5b23\nAAAAkHQHIS4iIkI9evRQenp6lmWLFi3SgQMHbMqmT5+u2NhYbdmyRUuXLtVXX32lb775Ju89BgAA\ngH0hbt68eQoPD1efPn2yLDt+/LhWrFihFi1a2JSvXr1avXv3lpubmypUqKDOnTtrxYoV+dNrAACA\nAs6uEBcUFKSVK1eqRo0aNuWpqakaPny4xo0bJ1dXV7M8ISFB8fHx8vLyMssqVaqkqKiofOo2AABA\nwWZXiCtdunS25R988IGee+451atXz6Y8KSlJDg4OcnFxMctcXFyUlJSUh64CAAAg013fdisyMlJ7\n9uxRRERElmWZ4S0lJUVubm6SpOTkZPN3AAAA5M1dh7j169crJiZGjRs3liRdu3ZNjo6OioqK0rx5\n81SyZElFRUWpZMmSkqTo6Gib4VUAAADcvbueJ27s2LH64YcftHfvXu3du1cBAQF64403zKlEAgMD\nFRYWpsuXL+vUqVMKDw9Xu3bt8q3jAAAABdk9m+x30KBBqlixotq0aaPOnTurU6dOatWq1b1qDgAA\noEBxMAzDeNCduFVKSop+/PFH1ahRQ0WKFHnQ3SkQYmNj9cG2n+VWolS2yxMvnteQZtVUtmzZ+9wz\nAAAKptzyELfdAgAAsCBCHAAAgAUR4gAAACyIEAcAAGBBhDgAAAALIsQBAABYECEOAADAgghxAAAA\nFkSIAwAAsCBCHAAAgAUR4gAAACyIEAcAAGBBhDgAAAALIsQBAABYECEOAADAgghxAAAAFkSIAwAA\nsCBCHAAAgAUR4gAAACyIEAcAAGBBhDgAAAALIsQBAABYECEOAADAgghxAAAAFkSIAwAAsCBCHAAA\ngAUR4gAAACyIEAcAAGBBhDgAAAALIsQBAABYECEOAADAgghxAAAAFkSIAwAAsCBCHAAAgAUR4gAA\nACyIEAcAAGBBhDgAAAALIsQBAABYECEOAADAgghxAAAAFnRHIe7IkSNq2rSp+fjs2bPq16+ffHx8\n5Ovrq/HjxystLc1cPm3aNDVq1Eg+Pj6aOHGiDMPIv54DAAAUYHaHuIiICPXo0UPp6elm2TvvvKMn\nnnhCO3fu1H//+18dPXpUc+fOlSSFh4drx44dWrNmjdatW6cffvhBixcvzv8tAAAAKIDsCnHz5s1T\neHi4+vTpY5alpaXJzc1Nffr0kZOTk0qVKqWAgAAdPHhQkrRq1Sq9+eabKlWqlEqVKqXg4GAtX778\n3mwFAABAAWNXiAsKCtLKlStVo0YNs8zJyUnz5s1TqVKlzLKtW7eqatWqkqSoqCg9/fTT5rJKlSrp\n1KlT+dRtAACAgs2uEFe6dOlc64wfP17R0dHq1auXJCkpKUkuLi7mchcXF12/fl2pqal32VUAAABk\nKpzXFaSkpGjo0KE6ceKEwsPDVaJECUk3QltycrJZLzk5WY6OjnJ2ds5rkwAAAAVenqYYuXz5sjp3\n7qwrV67oyy+/1N/+9jdzmZeXl6Kjo83HUVFR8vLyyktzAAAA+P/ydCQuJCREnp6emj17thwdHW2W\nBQYGatGiRWrYsKEcHR01f/58tWvXLk+dxYOVkZGh2NjYHOt4enpKkuLi4nKtd+t7BgAA2O+uQ9zB\ngwe1f/9+FSlSRN7e3nJwcJAkVa9eXZ9++qlef/11nT9/XkFBQUpLS9PLL7+st956K7/6jQfgwoUL\n+ijyhFzdPbJdfi3hkkIDG0uSxq/anWu9smXL3rO+AgDwqLujENegQQNFRkZKkurWratjx47dtm6h\nQoU0cOBADRw4MG89xEPF1d1DbiVK5Vs9AABwd7jtFgAAgAUR4gAAACyIEAcAAGBBhDgAAAALIsQB\nAABYECEOAADAgghxAAAAFkSIAwAAsCBCHAAAgAUR4gAAACyIEAcAAGBBhDgAAAALIsQBAABYECEO\nAADAgghxAAAAFkSIAwAAsCBCHAAAgAUR4gAAACyIEAcAAGBBhDgAAAALIsQBAABYECEOAADAgghx\nAAAAFkSIAwAAsKDCD7oDKLgyMjIUFxeXYx1PT085Ojrepx4BAGAdhDg8MHFxcRq/ardc3T2yXX4t\n4ZJCAxurbNmy97lnAAA8/AhxeKBc3T3kVqLUg+4GAACWwzlxAAAAFkSIAwAAsCBCHAAAgAUR4gAA\nACyIEAcAAGBBhDgAAAALIsQBAABYEPPE4aGWkZGh2NjYHOtwVwcAQEFEiMND7cKFC/oo8gR3dQAA\n4BaEODz0uKsDAABZcU4cAACABRHiAAAALIgQBwAAYEF3FOKOHDmipk2bmo8TEhIUEhIib29v+fv7\nKyIiwlyWmpqqd999Vz4+PvL19dW8efPyr9cAAAAFnN0XNkRERGjy5MkqXPh/TwkNDZWbm5siIyN1\n7Ngx9ezZU5UrV1atWrU0ffp0xcbGasuWLYqPj1f37t1VsWJFtW7d+p5sCAAAQEFi15G4efPmKTw8\nXH369DHLrl27ps2bN2vAgAFycnJSrVq1FBAQoJUrV0qSVq9erd69e8vNzU0VKlRQ586dtWLFinuz\nFQAAAAWMXSEuKChIK1euVI0aNcyyU6dOycnJSeXKlTPLKlWqpKioKCUkJCg+Pl5eXl5ZlgEAACDv\n7ApxpUuXzlKWlJSkIkWK2JS5uLgoOTlZSUlJ5uObl2WWAwAAIG/u+urUokWLKiUlxaYsOTlZrq6u\nZni7eXlycrLc3NzutjkAAADc5K5DXIUKFZSenm5zX8vo6Gh5eXmpePHiKlWqlM3waeYyAAAA5N1d\nhzg3Nzf5+/tr2rRpSk5O1pEjR7RmzRoFBgZKkgIDAxUWFqbLly/r1KlTCg8PV7t27fKt4wAAAAVZ\nnib7HTdunNLS0uTn56dBgwZp2LBhqlmzpiRp0KBBqlixotq0aaPOnTurU6dOatWqVb50GgAAoKCz\ne544SWrQoIEiIyPNx8WLF9eMGTOyrVukSBG99957eu+99/LUQQAAAGTFbbcAAAAsiBAHAABgQYQ4\nAAAACyKI3tlHAAAgAElEQVTEAQAAWBAhDgAAwIIIcQAAABZEiAMAALAgQhwAAIAFEeIAAAAsiBAH\nAABgQYQ4AAAACyLEAQAAWBAhDgAAwIIIcQAAABZEiAMAALAgQhwAAIAFEeIAAAAsiBAHAABgQYQ4\nAAAACyLEAQAAWBAhDgAAwIIIcQAAABZEiAMAALAgQhwAAIAFFX7QHQDyQ0ZGhuLi4nKs4+npKUl2\n1XN0dMy3vgEAcC8Q4vBIiIuL0/hVu+Xq7pHt8msJlxQa2FiS7KpXtmzZe9ZXAADyAyEOjwxXdw+5\nlSiVb/UAAHiYcU4cAACABRHiAAAALIgQBwAAYEGEOAAAAAsixAEAAFgQIQ4AAMCCCHEAAAAWRIgD\nAACwIEIcAACABRHiAAAALIjbbgG3yMjIUGxsbI51PD095ejoeJ96BABAVoQ44BYXLlzQR5En5Oru\nke3yawmXFBrYWGXLlr3PPQMA4H8IcUA2XN095Fai1IPuBgAAt5Xnc+IOHDigDh066Nlnn1WbNm20\nZs0aSVJCQoJCQkLk7e0tf39/RURE5LmzAAAAuCFPR+KuX7+ukJAQjRkzRi1bttT+/fv11ltvqV69\nepo0aZLc3NwUGRmpY8eOqWfPnqpcubJq1aqVX30HAAAosPIU4hISEnTx4kWlpaVJkhwcHOTk5KRC\nhQpp8+bN2rBhg5ycnFSrVi0FBARo5cqVhDg8MjIyMhQXF5djHU9PT0myqx4XSgAA7kSeQpyHh4de\ne+01DRkyREOHDpVhGJowYYIuXrwoJycnlStXzqxbqVIlbdy4Mc8dBh4WcXFxGr9qd64XQEiyqx4X\nSgAA7kSeQpxhGHJxcdHs2bPVvHlz7dq1S//617/04YcfqkiRIjZ1XVxclJycnKfOAg8bey+A4EIJ\nAEB+y9OFDRs2bNDRo0fVsmVLFS5cWH5+fvLz89Ps2bOVkpJiUzc5OVmurq556iwAAABuyFOI++uv\nv5SammpT5uTkpOrVqys9Pd1mwtTo6Gh5eXnlpTkAAAD8f3kKcY0bN9axY8e0YsUKSdLevXu1adMm\nvfTSS/L399e0adOUnJysI0eOaM2aNQoICMiXTgMAABR0eQpxlStX1qxZs/TJJ5/I29tb48aN0+TJ\nk1W9enWNGzdOaWlp8vPz06BBgzRs2DCuTAUAAMgneb5jQ7NmzdSsWbMs5cWLF9eMGTPyunoAAABk\nI893bAAAAMD9R4gDAACwoDwPpwLIu4yMDJurubPD3R8AADcjxAEPgQsXLuijyBPc/QEAYDdCHPCQ\n4O4PAIA7wTlxAAAAFkSIAwAAsCBCHAAAgAUR4gAAACyIEAcAAGBBhDgAAAALIsQBAABYECEOAADA\ngghxAAAAFkSIAwAAsCBCHAAAgAUR4gAAACyIEAcAAGBBhDgAAAALIsQBAABYECEOAADAgghxAAAA\nFkSIAwAAsCBCHAAAgAUVftAdAJD/MjIyFBcXl2MdT09PScqxnqenpxwdHfO1bwCA/EGIAx5BcXFx\nGr9qt1zdPbJdfi3hkkIDG0vSbetl1ilbtuw97SsA4O4Q4oBHlKu7h9xKlMq3egCAhwvnxAEAAFgQ\nIQ4AAMCCCHEAAAAWRIgDAACwIC5sAJAje6crYSoSALi/CHEAcmTvdCVMRQIA9xchDkCumIYEAB4+\nnBMHAABgQYQ4AAAACyLEAQAAWBAhDgAAwIIIcQAAABZEiAMAALCgPIe4s2fPqnfv3nr22WfVrFkz\nffrpp5KkhIQEhYSEyNvbW/7+/oqIiMhzZwEAAHBDnueJ69u3rxo1aqS5c+cqOjpar7/+umrWrKnF\nixfLzc1NkZGROnbsmHr27KnKlSurVq1a+dFvAA8Z7uwAAPdXnkLc4cOHFRcXp3/9619ycHCQl5eX\nli1bJmdnZ23evFkbNmyQk5OTatWqpYCAAK1cuZIQBzyiuLMDANxfeQpxP/30k55++mlNmTJFq1ev\nVrFixdS7d28988wzcnJyUrly5cy6lSpV0saNG/PcYQAPL+7sAAD3T55C3OXLl/X999+rUaNG2rZt\nm44ePaqePXtq3rx5KlKkiE1dFxcXJScn56mzAKwvt2FXhlwBwD55CnHOzs7y8PBQz549JUl169ZV\ny5YtNXv2bKWkpNjUTU5Olqura16aA/AIyGnYlSFXALBfnkJcpUqVlJ6eLsMw5ODgIEm6fv26qlWr\nph9++EGxsbHmH+Po6Gh5eXnlvccALI9hVwDIuzxNMdKkSRMVLVpUYWFhysjI0IEDB7Rp0ya1adNG\n/v7+mjZtmpKTk3XkyBGtWbNGAQEB+dVvAACAAi1PR+KKFCmiTz/9VGPGjFHjxo1VrFgxjRw5UrVq\n1dK4ceM0evRo+fn5yc3NTcOGDePKVAAAgHyS53ninnzySS1cuDBLefHixTVjxoy8rh4AAADZ4LZb\nAAAAFkSIAwAAsCBCHAAAgAUR4gAAACwozxc2AEB+y+2uDtKNOztIsqsed4AA8CgixAF46OR0Vwfp\nf3d2kGRXPe4AAeBRRIgD8FCy964O3P0BQEHFOXEAAAAWRIgDAACwIEIcAACABRHiAAAALIgLGwA8\n8nKbsoTpSgBYESEOwCMvpylLmK4EgFUR4gAUCPZMRcJ0JQCshBAHAHfA3rtJMOwK4F4jxAHAHbD3\nbhIMuwK41whxAHCH7Bl25WIKAPcaIQ4A7gEupgBwrxHiAOAe4WIKAPcSIQ4AHnL2Xkwh5Tw8y9As\n8GghxAHAQ87eiymk2w/PMjQLPHoIcQBgAfYOuzI8CxQc3DsVAADAgghxAAAAFsRwKgAUIPl1kYS9\n9ZgPD7h3CHEAUIDkx0US9tZjPjzg3iLEAUABk98XSeTXfHj5OZVKbnXyux5HEvEgEOIAAA+F/JxK\nJac6+V2PI4l4UAhxAICHxv0++ncv6gH3C1enAgAAWBAhDgAAwIIYTgUAIB/k9/QtXCiB3BDiAADI\nB/k9fQsXSiA3hDgAAPIJF0ngfiLEAQDwEMpteJYhVxDiAAB4COU0PMuQKyRCHAAAD63chl3tvZiC\nI3aPJkIcAAAWZe/FFByxezQR4gAAsDAukii4mOwXAADAgvItxMXHx6tx48bavn27JOnMmTN66623\nVK9ePbVu3Vrbtm3Lr6YAAAAKvHwbTv33v/+ty5cvm48HDhyoJk2aaNGiRdq1a5cGDx6stWvXMi4P\nAMB9ll93k+AiiYdLvoS4L774Qm5ubmZAO3nypE6cOKHPPvtMjo6Oeu6551S/fn2tXbtWPXr0yI8m\nAQCAnfLjbhJcJPHwyXOIO3XqlJYsWaKvvvpK7dq1kyRFR0erXLlycnZ2NutVqlRJUVFReW0OAADc\nBe4m8ejJU4jLyMjQ//3f/yk0NFTu7u5m+bVr1+Ti4mJTt2jRojp37lxemgMAAA9Yfg3NZtZjePbu\n5SnEzZkzR1WrVlXTpk1tyosWLaqUlBSbsqSkJLm6uualOQAA8IDlx9DszfUYnr17eQpx69evV3x8\nvNavXy9JunLligYPHqzevXvrzJkzSktLk5OTk6QbQ6wNGzbMe48BAMADlZ9Ds/bcI1ay76ievfUe\nlaN/eQ5xN/P399fo0aPl5+enb7/9VjNnztSAAQMUGRmpffv2acyYMXnqLAAAeLTYc49Yyb6jevbW\ne1SO/uXrHRscHBzM38PCwhQaGqrGjRvL09NTH3zwgcqUKZOfzQEAgEeAPUfsuDAjq3wNcZs3bzZ/\nf+KJJ7Ro0aL8XD0AAECe2TOEa4UhV+6dCgAAChR7hnCtMORKiAMAAAVObsOu+T2VSm717uboHyEO\nAADgFvk9lUpO9e726B8hDgAAIBv5fTFFfl90USjf1gQAAID7hhAHAABgQYQ4AAAACyLEAQAAWBAh\nDgAAwIIIcQAAABZEiAMAALAgQhwAAIAFEeIAAAAsiBAHAABgQYQ4AAAACyLEAQAAWBAhDgAAwIII\ncQAAABZEiAMAALAgQhwAAIAFEeIAAAAsiBAHAABgQYQ4AAAACyLEAQAAWBAhDgAAwIIIcQAAABZE\niAMAALAgQhwAAIAFEeIAAAAsiBAHAABgQYQ4AAAACyLEAQAAWBAhDgAAwIIIcQAAABZEiAMAALAg\nQhwAAIAFEeIAAAAsiBAHAABgQYQ4AAAAC8pziNu/f79eeeUVeXt7q1WrVlq2bJkkKSEhQSEhIfL2\n9pa/v78iIiLy3FkAAADcUDgvT05ISFC/fv00atQovfjii/r555/VrVs3PfXUU/r888/l5uamyMhI\nHTt2TD179lTlypVVq1at/Oo7AABAgZWnI3F//vmnmjVrphdffFGSVK1aNfn4+OjAgQPasmWLBgwY\nICcnJ9WqVUsBAQFauXJlvnQaAACgoMtTiKtSpYomT55sPr58+bL2798vSSpcuLDKlStnLqtUqZKi\noqLy0hwAAAD+v3y7sOHKlSvq06ePatasKR8fHxUpUsRmuYuLi5KTk/OrOQAAgAItX0JcTEyMXnvt\nNZUoUUKzZ8+Wq6urUlJSbOokJyfL1dU1P5oDAAAo8PIc4n766Sd16tRJTZs21Zw5c+Ts7KwKFSoo\nPT1dsbGxZr3o6Gh5eXnltTkAAAAojyEuPj5ePXv2VPfu3TVs2DCz3M3NTf7+/po2bZqSk5N15MgR\nrVmzRgEBAXnuMAAAAPI4xcjXX3+tixcvau7cuZozZ44kycHBQV27dtX48eM1atQo+fn5yc3NTcOG\nDWN6EQAAgHySpxAXHBys4ODg2y6fMWNGXlYPAACA2+C2WwAAABZEiAMAALAgQhwAAIAFEeIAAAAs\niBAHAABgQYQ4AAAACyLEAQAAWBAhDgAAwIIIcQAAABZEiAMAALAgQhwAAIAFEeIAAAAsiBAHAABg\nQYQ4AAAACyLEAQAAWBAhDgAAwIIIcQAAABZEiAMAALAgQhwAAIAFEeIAAAAsiBAHAABgQYQ4AAAA\nCyLEAQAAWBAhDgAAwIIIcQAAABZEiAMAALAgQhwAAIAFEeIAAAAsiBAHAABgQYQ4AAAACyLEAQAA\nWBAhDgAAwIIIcQAAABZEiAMAALAgQhwAAIAFEeIAAAAsiBAHAABgQYQ4AAAACyLEAQAAWBAhDgAA\nwILuaYj7+eef1bFjR9WtW1ft27fX4cOH72VzAAAABcY9C3Gpqanq06ePgoKCtH//fnXu3Fl9+/ZV\nUlLSvWoSAACgwLhnIW7Pnj1ydHRUp06d5OjoqA4dOqhkyZLavn37vWoSAACgwLhnIS4qKkpeXl42\nZZUqVVJUVNS9ahIAAKDAuGchLikpSUWLFrUpK1q0qJKTk+9VkwAAAAVG4Xu14uwCW1JSklxdXXN9\nrmEYkm6cV4f7Iz09XYWSr8i44pDt8kLJV2QYpXOtk56e/r/6+VCPNu9dm7nVo03apE3apM3722ZK\nSopNeWYOysxFt3Iwbrckj3bs2KFx48Zp48aNZllAQIAGDhyoFi1a5PjcK1eu6Ndff70X3QIAALCU\nypUr67HHHstSfs+OxDVs2FCpqalaunSpOnXqpJUrV+rChQvy9fXN9blubm6qXLmynJyc5OCQfbIF\nAAB4lBmGobS0NLm5uWW7/J4diZOkX3/9VaNGjdKJEydUoUIFvffee6pVq9a9ag4AAKDAuKchDgAA\nAPcGt90CAACwIEIcAACABRHiAAAALIgQBwAAYEGEOAAAAAsixAEAAFgQIQ52O378eLble/fuvWdt\njh07VocOHbpn6wcAwKoIcbBx/fp1/fbbbzpx4oT5OCkpSdeuXdPrr7+u5ORkJSUlmT/nzp1TcHDw\nbdd3+vRpRUZGKj09XVeuXMmx7ezulevs7Gzeqm3GjBk6efJkjutISkpSbGys/vzzT/3555/6/fff\ntXXrVu3cuVOStH379tv+3GsZGRnavXu3IiIilJKSolOnTt31ulJTU3X69GkZhqHr168/NP3K777l\nlzFjxigxMTHHOlevXs22fNu2bdmW27Od6enpOnv2rPl+zPzJyZEjR3T69GmbssmTJ2dbd9SoUTaP\nH8Q/WvbYvHlztuUrV67Ml/Vnt88k+153e7344ouaP3++zpw5k2O9W1/rzJ+4uLjb3g88t+lab/d3\n9Oa/xbf7yS/Zrevs2bPav3+/9u3bZ/Nzp8LDw7MtnzdvXpayzNc5KSlJCxYs0FdffZXr/pOkc+fO\n3XZZbvdpt+dzPGLECG3YsCHX91t+7bNMTPZbwB0/flyDBg3SBx98IFdXV/Xp00cxMTGSpIoVK2rs\n2LHq0aOHkpOTZRhGtrdBe+655/TRRx/ZlMXHx2vw4ME6fPiwHB0dFRERoVdffVXz589X3bp1zXrp\n6emaN2+eli1bpqtXr2rVqlUaMWKEpk+fLk9PT0k3/sDt3btXa9eu1aZNm1S2bFkFBgaqbdu2evzx\nx811LVu2TBMnTszygXzqqafk5OSkNWvWyN/fP9v94ODgkOWLZsWKFVq2bJni4+MVHh6u6dOna9So\nUTa3Pzl27JimTZum06dPZ/kiv3l9p06dUnBwsNLT03XhwgWtXLlSL730kmbOnGnTp71792rZsmWK\ni4vT9OnTtXTpUvXr10+Ojo6SpMTERI0bN06rVq2Ss7OzVqxYoZ49e2r+/Pn6+9//ripVquR6q7pj\nx47Z1a/s7tN3q/r165u/59Y3f3//XPv2zTffaMmSJQoICNDjjz+uBQsWaNOmTTIMQ23btlW3bt1U\nqJDt/57p6enasGGDTp06leU1CAkJkST5+Pjou+++k7Oz823bDgoK0scff6xixYpJki5evKjx48dr\n8+bNNkeDc9vOTKtWrdLo0aPNz04mBwcHm9dg165dGjlypLZs2aL58+crLCxMhQoV0qBBg+Tk5CTp\nRogbNmyYTX8TExP10Ucfad++fUpJSZFhGPL19dXu3btt2rty5YpeeOEFHTx40Cyz532bmpqq1atX\nq0OHDjp9+rTGjx8vDw8PjRgxQiVLlsx1/6empiowMFCS1LFjR0VERNj06+rVq+rWrZtNv7p06ZLt\ne8TJyUklSpTQc889p8DAwNvus9GjR6t9+/bm83J73e0Jke3atZMkbdmyRWvXrtWWLVtUtWpVBQQE\nqE2bNvLw8LCpX716dXM/3Po3s1ChQmrQoIEGDhyoL774QpMmTdL27ds1aNAgFS9eXLNmzbK5q1Fu\nf0dz+rxntn3s2DE9//zz2rx5sxo2bHjb+pGRkZJuBKfevXvbLNu5c6dGjx5t8zdt4cKF5vdG4cL/\nu4Ong4ODuS7p9u+j4OBgMxAPHDhQM2fOtGnz6tWrCg0NtXl/LFmyRHPnztW+ffs0YsQIHTlyRIUK\nFVKTJk00fPhwSTf+sRk7dqzNupYtW6apU6fahCV7vnsk+z/H8+fP144dO/TTTz+pXr16atasmZo3\nb67y5cvf8T67E4S4Au7VV19VixYt9NZbbyk4OFjVqlXTwIEDdf36dc2YMUNHjx7VjBkzlJSUpA4d\nOmj58uU2z3d2drZ5w2fq37+/ypcvr8GDB6tJkybat2+fPv30U61evVpffvmlWW/y5Mn66aefFBIS\non79+um7775TaGiorl27prlz59qsMyUlRdu2bdOHH36oX3/9VcWKFVO9evU0fPhwVaxYUc2bN9eQ\nIUPk7Oysbdu2acCAAZo0aZIqVqyowYMH57gfUlNTbf7Qz58/X6tXr1aPHj00fvx4bd26VSEhIXri\niSc0adIks15QUJCeeuoptW3b1uZDKUnNmjUzf+/WrZuaN2+url27qn79+tq3b582btyoWbNmafXq\n1ZKk5cuXa8aMGXrllVe0ZMkSbdy4UW+++aYaN26sESNGSJL+/e9/Kzk5WUOGDFG7du20Z88effDB\nB/rxxx/1ySefmEdQ165dq0OHDmnAgAEqV66czp49q1mzZql27drq37+/Xf2Ki4sz612+fFkuLi56\n/PHHdf78eSUmJuqpp57St99+a9bJrW+ZR7S+//577dq1Sz179lS5cuUUGxurhQsXqkmTJrp48aJ+\n++03zZ49W0uWLNGuXbvUrVs3paen65NPPpGvr2+WMDN06FDt3LlTdevWzfKHMfOLYdSoUfrrr7/U\nqlUreXp62nyR+fn5mXV++uknLVmyRFu3btX777+v6tWra/To0Xrqqafs3s6bX//evXurTZs2ZhjL\n5Orqav7esWNHvf766woMDJSvr6+mTJkiT09P9evXT9WrV9fFixf1ww8/6Nlnn7VZh7OzswICAtSo\nUSO1bt36jv7Rsud9Gxoaqp9//lnLly9Xly5dVKpUKbm4uCghIcHms3m7/Z+enq4DBw7o0qVLWfqT\n2f/27dtrzJgxZtnkyZO1YsUKvfLKK/rb3/6m2NhYRUREqFGjRipTpoxWr16t1157TZs3b852n4WE\nhGjTpk3m+nJ73Tt06CDpxuc/8/aQmZ+XkydPytvbO8tRomvXrmnLli3asGGD9u3bp9q1ayswMFAt\nWrSQs7OzlixZon379mno0KEqV66c/vrrL02fPl0VKlRQYGCgFixYoB07duj555/X2LFj1aZNG3Xo\n0EGPPfaYIiIiFBERYbaV29/R3I4KSlK5cuW0f/9+eXt76/vvv79tiGvQoIEkqUWLFurQoYP69Omj\ny5cva+LEidqwYYOCg4Ntwl2zZs0UGhqqFi1a5Nj+7d5HFy9eVHx8vC5evKi//vpLTzzxhM3zMt8f\nN4/0tG3bVrNmzVL58uXVoEEDRUREqHTp0nrppZe0e/duSTfe29WqVdPYsWN1+vRphYaGKjo6WsOG\nDVNAQIC5Lnu/e+z9HGe6cuWKdu3apR07dmj9+vUqX768+Tfe3n12RwwUaPXq1TN/9/HxMVJTU83H\nqampNstvdfjwYePUqVPZLvPx8TFSUlIMwzCM+vXrG4ZhGBkZGcazzz5rU69p06bGhQsXbOolJiYa\nDRo0MAzDMFJSUoyNGzcaQ4YMMerWrWu8/PLLxsKFC43Y2FgjJSXFmDZtmtGyZUvDMAyjbt26hmEY\nRmxsrNGuXTvDMAzjwoULRrNmzcz2mjRpkm1/M9vO5O/vb5w5c8Zm2cWLFw0fHx+benXq1DG3Myf1\n69c30tPTs7R18/5t1aqVcfz4ccMwDMPb29swDMP466+/bPrcuHFjIzEx0WY9qampZv1MTZs2NRIS\nEmzKrly5YjRq1OiO+/Wf//zHmDp1qvneSEtLM2bMmGGEhobarMvevj3//PNGbGysTdm5c+cMPz8/\nw8fHx7h8+bJhGIbx3HPP2dSLjY3Nsv8NwzAaNGhgREdHZym/WfPmzbP98ff3t6n3/vvvG/Xr1zea\nNGlirFmzJtt12budt+7r28l8rx88eNDw9vY2MjIyDMP43/vZMAxjwoQJOa4jPj7eiImJMRo0aGD8\n8ccfNj/nzp3LUt+e923z5s2NhIQEIz4+3qhatapx7tw5IyUlJctn2J793759+xyXZwoKCjJ+/PFH\nm7JffvnF6Nixo2EYhhEdHW34+fnZtc8yt8Ge13348OFGeHi4TdmXX35p9O3bN9t+7tu3zxg3bpzh\n7+9vvPDCC0a3bt2MJk2aGBs2bDCaNWtmXL161aZ+YmKi+XcoKSnJeOaZZ4yMjAwjKirKqF69unHt\n2rVs+5/b39GTJ08ahmEYJ06cuO3PnYqNjTXatGljDBs2zGjcuLHRvXt34/fff89Sr0GDBuZ+z4k9\n76N+/frZ1bfMfbB161Zzf6anp9us68qVK8Ybb7xhdOnSxahTp44xcuRI82/KzXL77slk7+fYMAwj\nKirK+OKLL4zBgwcbTZo0MZo1a2YMGzbMXG7vPrsThXOPeXiUPf744zpw4IDq1aunZ555RidOnFC1\natUk3RhqLVOmjFnX3iEMSSpevLhiYmLk5eVllv3xxx8qVapUlj5kDhVmysjIUJEiRSRJjRo1UvHi\nxfXiiy9q2bJl+sc//mFTt127dub5bmXKlNGFCxdUpkwZ/fnnn0pLS5O7u7suXryojh07yjAMXbhw\nQUFBQTbrSExMtBkekm6cb5HZV+P/H6wuWrRolr5Wq1ZNp06dUuXKlbPbvaYyZcro6NGjqlOnjln2\n888/2/z3eenSJT399NOSZP63XLp0aaWlpZl1ihQpoitXrtj8B3jp0qUsQ58pKSlKTEy0Kb/5yNqd\n9OuLL77Qnj17zKMshQsXVr9+/dSgQQONGzfujvt26dIlubi42JQZhqFr166pUKFC5ra7uLjYDF27\nurpm+U9Yktzc3Gzep9nZsmXLbZf99ttv5u9BQUE6e/asoqKi5OXlZS7LfF3uZDtbtGih1atX2/z3\nnx0PDw8dO3ZMK1asUKNGjVSoUCHt3LlTZcuWNeu8++67iomJ0dmzZ833Y3p6uk6ePKnOnTub79Xv\nv/8+x7Yy2fO+TUxMlKurqzZv3qynn35anp6eunr1apbPgD37/9Yj+LcTFRWVpU8VK1Y0X4eKFSvq\nypUrKl26dK77TMr5db/Zt99+q/Hjx9uU/fOf/9TEiRPNx4cOHdK6deu0fv16c3h/xowZqlmzprmO\n0NBQOTo66sKFCzbv3fj4eF27dk3Sjb9v0o3hwk2bNqlu3boqWrSoYmJibJ4j5f53NCgoSAcOHNBL\nL72U5QibcdNwak7DqJkyh/PKlCmj8PBw9ejRQ1WqVNGiRYuyrd++fXstWLBAb7/9dpb3xM3seR+F\nhYXp9OnTqlChgpKSkhQeHq4SJUqoQ4cONv2uVKmSPv74Y23dulXPPfecUlJSNH/+fD3zzDNmnWLF\nimnhwoXq16+f6tWrl2Vo9WY5ffdksvdz7Ovrq8TERPn7+6thw4YaOHCgKlSocFf77E4Q4gq4IUOG\nqHfv3nrllVfUoEEDBQcH6+WXX1ZqaqpWrlypkSNHmnVnzJih/v37KyMjQ0uWLNGcOXPMIYxbQ9yb\nb76pnj176v+1d+ZhOeX//39Gk21sIcowZpoh67TvUZZC0mLJnpgICX0VSkoZQmWJVIylhpnILutQ\npNPameIAACAASURBVJIM1YzdZGlPqxbVXb1/f/Q753Of+9x1n7vJYDqP67qvi9O5z/0+77O9zmt5\nvuzs7CAQCHDq1CkcOnQIs2fPZqxnamqKVatWwdXVFYQQZGZmwt/fH2PGjAHQEO5buHAhOnToIHb8\n3377Lf2AMDMzw7x583DkyBHo6elh9erVaNeuHQYNGoTFixejpKQE3t7erDHIyckxcrsAwMDAAN7e\n3li7di1kZGRQW1uLgIAA6OrqMtYbPHgw5s6di9GjR6N79+6Mv7m5udH/Xr58ORwcHGBtbY2amhoE\nBQXh+PHjdJgUANTV1bF7925G6PfIkSMMA8vGxgaOjo5YtmwZ6urqkJSUhD179sDS0pLx27a2tpg/\nfz7mzJmD3r17IysrC+Hh4Zg3bx5jPS7j6tatG1JSUqCpqUkvi4+PZ4XRuY5t/PjxcHR0xOLFi6Gg\noIDs7GwEBwfDysoKdXV1WL58OTw9PbF48WK4u7tj5cqVqKmpwY4dO2BiYgJR7O3tsXr1asyfP591\nDISNr9evX+PUqVPIz8/H2rVr8fvvv2PKlCn0A5CIZJZQuVCiuS+S9pN68FRVVeH48eMICgpCly5d\nGNsWDpk5OTlh+vTpaN++PcLDw5GcnIylS5fC39+fXmfPnj3Yu3cvbfzW1taitrYW6urqmDNnDuth\nJw7h3+Ry3mpqamLVqlV4+PAhpk+fjpycHHh5ecHAwICxPpf5T0hIgI+PD968eUPPs7CRQaGmpgZf\nX1+4ubnhyy+/RFlZGbZt24YRI0agvr6efpGbPXu2xDmjaOy4C6OkpIQrV65g4sSJ9LKTJ08yHsIL\nFizA2LFjsXnzZhgYGLByM0eMGAFra2t8+eWXmD9/Puzs7KCoqIjs7GxERERgxowZePv2LVxdXTFw\n4EBYWVmhuLgYW7ZswZMnT+j7sDCS7qP3798H0PDC/ezZM5w/fx65ubno2bMnzM3NMWzYMADA7t27\nWfMiiug59P79e8THx8PS0pJ+eRI+hxISEvDs2TMEBQWxXmCE87saO4/09fXpdYRz3Xx8fOhctxcv\nXtC5bgDg7e2NTZs2oV27dli5ciVSUlJw9epVBAYGsgzVuro6vHv3Djo6OvSxEh6XpGePtNexjY0N\nkpKSkJSUBIFAQH+E70Fc50wa+Jw4Hjx69AiRkZF49OgRSktLIScnh2+++QbW1taMpHsdHR0kJSUh\nJSUFDg4OSEpKQps2baCurk7fTIS5dOkSTp48iZycHPTq1QtWVlb0g5Giuroafn5+OH36NKqqqug8\nH3d3d3Tq1Ana2tpISEhg5e2IgxCCc+fOYezYsRAIBAgMDER5eTnjjejPP/+k35yFuXv3Lp0TAjTk\ngLm6uiIuLg6EEMjKykJTUxOBgYEMr52wsSPKli1bGP9PS0tjzIe1tTXDMMrJyYGjoyNyc3NRVlaG\nPn36oH379ggNDUW/fv0ANFQLHzx4kDWvixcvZswRIQTHjh3DpUuX8PbtWygoKMDS0pLlheQyrvPn\nz8PT0xMjR46EgoICsrKykJiYiMDAQMb58ffffyM2NhZRUVFNjo0yyC5fvswY27Jly0AIQUBAAI4f\nP476+nrU1NSAEII2bdpgzJgx8PPzY+WhqKioiJ1/YQPh5s2bcHV1xZgxY3DlyhVcunQJ06ZNw+zZ\ns5usrhaH6DGgxk/t5+nTpyVuQ/Slp6qqCkCD97G8vBzv379nGMmGhobYuXMnBAIBzp49i02bNsHP\nzw8CgQAbN26U+je5nLdlZWU4ePAg2rVrh0WLFuHZs2c4ceIEVq1aRRd/ANzmf9KkSTAyMsLkyZNZ\n17Kwdz0/Px+rVq3CH3/8gU6dOqGyshL6+vrYtGkTXr16BR8fHwQGBmLw4MES5wzgftwTEhLg5OSE\nAQMG0J783NxchISE0IVYVVVVLA8y0HA+iBp0UVFROH/+PPLz86GoqAhra2tYWFjgyZMnuHXrFubP\nn48//vgDcnJy0NDQQG5uLlJTU2FmZsbaPpf76K1bt7BixQqMHj0aSkpKyMzMRExMDAICAmijhKKm\npgZ3796ljT1dXV20b99e6nOoqYpn4Xup8Hm0ePFiPH36lHUeccl1kwQ1HnHHQ9y4JD17mnMdAw0e\n1uTkZCQlJeHs2bNo06YN4uPjGWOUNDapaNHgLM9/GlNTU/Lo0SOyYcMGsnz5ckIIIXFxcWTChAms\ndcvKysRuIyYmptHtFxYW0vkCeXl5hJCGXJU9e/aQ169fk4qKClJZWUl/RImIiBC73eDgYPo7ampq\n5P3794zt5OfnE1VVVbHfffv2LUlNTSU5OTmNjrs5NJZPWFdXR1JSUsjFixdJcnIyEQgELfq7zR3X\n48ePya5du4inpycJCgqic3GEUVVVJVOnTiURERGksLCQ0++J27+EhAQSFBREwsPDSXJyMklLS6Pz\n+3bs2CHlHjVgYWFB7t69Swj5X77hixcvyKhRoxjr5ebmkuTkZHL37l36k5yczNpeWVkZqaqqIoQ0\n5GmVlJRIHENTOaSvXr0igYGBZO3ataSkpIRERUUx/k7l/BQUFBBzc3NCCCHl5eXE0NBQ4u8SQpoc\nH5UT+SFRVVWV6lzOzs4mKSkprNxJitOnTzf6EYbrcSek4VqPiooiISEh5NSpU3S+FEV6ejpxc3Mj\ndnZ2ZO7cuWTu3Llk5syZRF9fn/N+USQkJLCWFRQU0PdViri4OFJfXy9xe1ZWViQ2NpaxLDY2lkya\nNIm1D2PGjCFGRkZk+vTpxNDQkBgZGZEXL16wtpmenk4SEhLIs2fPmvztV69ekYSEBCIQCFh5uIQQ\nsffO+vp6Rg4il1w3ilOnTpEZM2aQMWPGkJycHOLm5sbIQZw8eXKjz5/GEH72SKKp6zgnJ4ecPHmS\nuLi4EAMDA2Jubk62bdvGWk/SnEkDH07l4QyXsA/F/PnzOck1CJeDUx6u48ePY/v27XSlZHl5OYKC\ngmhXOREKwxQWFuKvv/4CAPj7+9MeK4ry8nKEhIQgLCyMrt4TljihGDlyJGvZ7du3ER0djbdv30JR\nURFWVlasCsGioiL88ssvyMvLo2UFqFwl4TygpvIJe/XqBUNDQ4ZWXceOHVFRUUG/wVEVlHl5eQgP\nD0d2djZLGmLXrl1NelgohD2EXPMcv/32W6irq9Nv70pKSqztJiQk4Nq1a7hw4QICAgKgo6MDKysr\njB49mlH5W1NTgz179uDUqVMoLCxE165dMXnyZKxevRqnT59GYGAgdHR0cO/ePQwfPhxBQUH098PD\nw7Fy5UrWb7979w5XrlxBbm4uFixYgD///JMR+s7JyaG9i9R59M033zA0nbiW/8fHx2P58uU4cuQI\nhg8fTsuiBAcHM84PrnMr6i1auXIldu3ahYKCAtpbpKioiIyMDPTr1w8FBQWoqKiArKwsS5PqwYMH\nCAgIYJ2PRUVF+PPPP+n1BAIBgoKCxB4Daq4bk4QRlvv48ccf8cUXX0icfx0dHfzxxx/Q0dFhbU8Y\nLnIxABAREcH4W0lJCbKzs2FsbMzwUnE57hTdu3eHoqIine+Wk5PDCA+7u7uje/fuUFJSQnZ2NrS0\ntPDbb79h1qxZjO1wkW9ZuXIldu7cCT09PQANOYN+fn6sKMGGDRtQU1MDCwsLWFpaNur1fPPmDYyM\njBjLjIyM4OLiwli2adMmWFlZYdmyZXQKwd69e+Hr64vDhw8DaPCGOjs746+//kK3bt1QXFyMgQMH\nIiQkhJH7yFVGau7cuYiIiKDzFdPT0+Hh4YGMjAw6LMwl1w1gqwZ06tQJubm58PX1pVUDysrKUFVV\nxfAWC3P06FGxy4URTrnheh2PHz8eOTk50NLSgomJCVatWsWQF5FmzqSBD6e2criI3FIGBCA57EPB\nVa5BUjl4UyX0ffv2RVVVFWbPni2xTH3q1KlSyaSEh4djz549sLKyQp8+fZCdnY1z587Bw8ODkeO1\ncOFClJWVoXv37iguLsZ3332Hy5cvY8aMGYycuMZkJJycnNC+fXuGhp3wg5MyWKmbv62tLZ3DJxoy\ncHJyokVhc3NzERsbi0mTJtGSCefPn8e4ceMYRlxT46KkGl6+fAkHBwcIBAJ6LmRkZHDo0CFGwrUw\nxcXFuHLlCkJDQ+nwAsX27dtx9+5drFixAkpKSsjIyEBQUBC0tLRw/fp17Ny5E0OGDEFpaSmWLFmC\n7t27Y+/evQAacqaEdaOAhnDwokWLMHDgQPz55584f/48Jk+eDHd3dzp8PHfuXJibm2PGjBnQ1tam\ndQd//fVXWkKCa/m/hYUFXFxcGPl5N27cQFBQECMEw2VuAWDy5Mnw9PSElpYWLfPy999/Y+HChbQs\nS3h4OMLCwnDy5Ens3bsXL168wBdffIG2bdvi4MGD9LYsLS2hrq6OLl264OnTpxg/fjwOHDgAGxsb\nLFiwgNMxoCRc9uzZg/Pnz2PBggW03Mfhw4ehpaWFQYMGITIyErq6upg4caLE+Xd1dcXly5ehqqrK\nKiIS1gfjIhfTGKdPn0ZycjKjGIHLcQe46Tiqqqrizp07yMzMhK+vL44cOYKUlBT4+Pgw7ilc5FsS\nExPh4uICV1dXnD9/Hs+fP8fatWsxadIk1n7du3cP0dHRuHLlCnr06AFLS0tYWFgwDCpKEkT43P39\n998RHBzMGJuOjg7i4+MZ4xIIBNDV1cUff/wBoEGzrUuXLnB3d0eHDh1QUVGBLVu2oKSkBHv27KG/\nx1VGKjQ0FCdOnMDBgwdx4cIFhIaGwtLSEqtXr6ZzzB4/fkznugUEBODZs2fYtGkTAgMDGeH2MWPG\nICIiAkpKSvTxLCkpwfjx43Hnzh0ADXneycnJ0NXVRc+ePRn3Uzc3N8ydO5c1x8LIyMggPDyc/j/X\n6/j69evQ19dvNH9bmjmTin/kx+P57Jk+fTpRUVHhVIrPNYRBwUWugUs5eGVlJcnJySFZWVkkKyuL\nvH79mty4cYO1La5l6oQ0SJe8evWK1NfXi3WjGxkZsaQOUlNTyZgxYxjLVFVVSUlJCXn8+DFZuHAh\nIaQhjDFz5kzGelwlEW7dukXWrFlDFixYQNavX88K5XGVNJk5cya5f/8+a/yWlpZSj2vBggUkKCiI\nDuvU19eToKAgYmdnJ/a379+/T3x9fYmhoSGZPHkyOXjwIOPvxsbGdLicIjc3l+jp6bHCJ+/evSPm\n5ubEz8+P3n9Rpk+fTi5fvkwI+V/I7N69e7T0DCGEPHnyhBgYGBBLS0sydOhQMmfOHKKvr08ePXrE\nmAsuIRVVVVVWiKuuro4lMcL1mGtqatLbE5aREN3evXv3SFVVFamuriYhISHE39+fFbb+4YcfiEAg\nIK9evaLPwRcvXpCJEycy1mvqGFCYm5uzpCUyMzPpEF1eXh7R19fnNP9BQUGNfkTnTJJcSVOIzhmX\n404IIfPnzydHjhxhbOPq1auMcOSoUaNIfX09qa6uZoRQRX+T6zX64MEDoqWlRebOncsp/FdbW0su\nXbpEjI2NyZAhQxh/u337NhkxYgRxdHQkvr6+ZPHixURVVZUVth0zZgwrPPr06VMyduxY+v+6urp0\nqgBFZWUlaz+5ykgRQkh4eDgZOnQomThxIuu+JA16enr02KjxVFVVMY7H2rVrG/0I01iKgeg5z/U6\nJkRyqFeaOeMKH05t5YSHh8POzg7jx4/H/Pnzm1yXSwhDWrkGSeXgTXVhEK1U5FKmzlVtv6qqijFO\noCGBW7T1TKdOndC1a1fIycnh2bNnABo8l8JeOOB/MhKnTp2Crq6uWEmEiIgI2vs3cOBAZGdnY+nS\npQzvn6amJh4/fowffvgBTfHkyRNWaGbQoEF48+aN2HE1JdXw119/ITQ0lJ5DGRkZLF68GIcOHWJs\na9u2bbh8+TJqa2thbm6OAwcOsMIhQEPVm2hlVufOnUEIwbfffovo6GiYm5vTy/ft2wdbW1v06NFD\nbHgvPT0d48aNo8cGABoaGigsLGTs+5UrV3Dz5k1kZ2ejV69eMDY2RteuXel1uJb/Kysr4+zZs4yw\nXXR0NOP84Tq3QMN5FRkZiRkzZtDLLl26xJo7DQ0NvH79GtnZ2Vi4cKHYeZSXl0d9fT369u2L9PR0\nerx5eXmM9Zo6BhS5ubksr1nXrl2RmZkJoEH+pqamhtP8C4dChRENN3KRK6HGL4xAIMDvv//OGi+X\n4w4ADx8+xIEDBxj7MG7cOEZl5ODBgxEYGAgnJycoKCjg2rVraN++Pcvz0pR8y7Zt2xj/Hz58OJKT\nk7Fr1y66AlT03vH+/Xtcv34d0dHRuHPnDvT09FhpEwYGBoiKisLFixdRWFgIVVVVuLu7M6IeQINn\n0sHBAfPnz4eSkhKysrJw5MgR2Nvb0+vIycmhqKiIEdUoLi6WWv5EOMrTv39/jB07FqmpqSgsLKT/\nJhzlOX36NI4fP463b9822iGHi2qAcKShrq6u0WvZxMSEVZBXW1sLKysr2isJcL+OuYR6pZHe4gpv\nxLVy2rVrBx8fH9jZ2WHq1KmN5hEADSX3olAhDAqucg1cy8FDQkKwadMmsV0YROFSpv7TTz9BIBDg\n2rVrsLKyQv/+/WFmZoaNGzcy1Pbnz5+PtWvXwt3dHb169UJZWRkCAgJgaWnJaL/y/fff4+jRo5g1\naxY6duyIP//8E+3bt2eFOpcvXy4xn3D//v04ePAghg4dSi+bPHkyXFxcaCNu+fLlsLOzg5qaGush\nLBxuGjFiBPz8/LBq1Sp06tQJpaWl8PPzY1SdAtzyHDt37oyXL18ywhovX75kPTDz8vKwceNGsfIL\nwhgYGGDDhg1Yv349unbtipKSEvz000/Q09PDrFmzsGzZMty6dYsODffr1w/79++Hg4OD2P6NX3/9\nNWJjYxmVsomJiYxzhMpBEpaQAIAff/yRfnhzLf93c3PDkiVLEB4ejt69eyMvLw9ZWVkICwuTem6B\nBkX7hQsX4rfffkNlZSXmzp2L9PR0elwA91wadXV1eHh4wMvLC8rKyjh8+DA6dOjAkv5o6hhQUDlV\nLi4u6NOnD3JycrBz504YGRmhuroae/fuxYgRI1BaWipx/l++fImQkBBWrt7r16/pvE+Au1yMmpoa\nK+2gS5cu8PLygig1NTWorq6mqxbFnZtc9BI9PDzg6emJkpISrF69GsuXL0dNTQ28vb0Z22pKvqW4\nuJjxfwUFBZibmzfat3fFihW4desWvvvuO1hZWWHz5s2sbVJ8//33WLFihdi/UdjZ2aF9+/Y4d+4c\nCgsL0bdvX7i4uNDt0YCGlxlHR0csX74cioqKyMrKwt69e1kVsZLkT4Q7cVDIyMjQ4W7hNBEuBhDQ\ncK24urpCV1cXhBCoqqrSqgEUTeV75ufnY8aMGairq0NlZSXjfAcaKlZFX965XseRkZF0qHfz5s3o\n3Lkzdu3ahfHjx3Oes+bA58TxAGho2ty9e/cm4/mNQeXxSAOXhtza2tq0fEleXh4cHR1x+vRpFBcX\nw8bGBjExMYz1uZSpGxgY4Nq1a+jYsSOdUyEQCKCvr8/YBzU1Nbx//x4yMjJo164d3ZsSAG2kysjI\n4OTJk1i5ciV+/vlnpKWl0flES5cuxdKlS+ntnTlzhhb77NixI8rLy1FZWQkFBQX65qitrY24uDiG\n2GRNTQ1MTEzoB52NjQ2+/PJLaGhosN4whb0dWVlZWLFiBR4+fIiOHTuisrISGhoa2LlzJ3r27Mn4\nnqQ8xyNHjuDQoUNi397t7OwkHkdRCgsLsWzZMqSkpNBj09fXh7+/P+Tl5fHu3TtkZ2ezkrgLCgpw\n8uRJliRIcnIyHB0doauri1u3bsHc3BwxMTHw9PTEo0ePADR4OUVzYcrLy3HlyhVaIFea8v+ioiLE\nxsaioKAAvXv3xqhRo1g9NAHuOaQVFRVNeou45tKUlJRg+/btcHFxQV5eHlatWkV7n4U919QxSE1N\nRYcOHfD+/Xvo6enRx4CaHx8fH1y5cgXV1dWQk5ODpaUl3Nzc8PTpU4SEhMDb25uWxhGdf+HE/Zkz\nZ6J79+7o1q0bsrKyoK2tjd9++w0zZszAsmXL6HFxkSsBwMqVbdu2LXr06MESg05MTISTkxP69+9P\n6yUWFBRg//79tIYaAFy9ehUeHh6wtrbG8ePHsXDhQlovkTL8NTQ0oKSkBBMTE5iYmGDYsGEQCAQs\nyRtpZIcksWPHDlhaWrK8vP+EhIQEqKioQF5eHnFxcZCVlWUYM3V1ddi1axdt6CkpKcHKygoODg6s\nHD8u8idc4JLrJkxBQQGys7OhoKDA8ohJyvd8/Pgx3r17h0WLFmH//v2M78rJyWHQoEGs5yCX61hf\nXx8xMTFo164d/Uysrq7G6NGjGS8qLTVnFLwRx8OZxkIYoaGhjB6aFE0pzFOaS+I8KxQdOnTAhAkT\ncPToUcjLy0NHRwe3b99GmzZtoKWlxXKFUxd/bGwsNm7ciJiYGNTV1dGVjkBDxd2vv/6K3r170+u/\nffsWtra2DHV3Lj0JgYbiiri4OFy8eBFv375Fnz59YGVlxfJ4iYqLlpSUICcnB6NGjcK+ffsAAMHB\nwXj+/DnL+9exY0c4OzuDEAJ9fX3cu3ePk9p3YmIi7t+/D1lZWSgoKKC2thbp6ems3qNcxFAjIyNZ\nN3Xht/fmkJGRgcLCQigqKnIKoTVFdnY2Lly4QN8Yzc3N8fXXX2Pr1q0oKSkRq7j+xRdfYPz48VBX\nV+d0Lgr/VmOIVu1ymVvKSyiKsJeQMpDk5OTo87a+vh7a2tr0uQ00zOnevXsbbawu2vCdSuSvr6+n\nH4aiDxSBQIDS0lLIy8s36mFtbP4puBYFSENhYSF+//13umJ67NixrPPIwsICCxYsYFQRnjhxAseP\nH8eJEycY60rSS6ytrcW9e/dw8+ZNxMXFobCwEMbGxjA2Nhar79YUZWVl+PXXX8VWsIoaes+fP6dF\nfHv06MEQ8ZWWw4cP48CBAzhy5AiUlZURHR0NPz8/OlIgTFVVFfLz8yEvLy82QlNeXi52eWxsLKNv\nNADExcXhzJkz9D5QvWYpuBpAgGTVABMTE0RGRkJBQYFelpeXB2tra4bm3KVLlzBhwgTW+M+cOQMr\nKyucOHEC06ZNa7KaVdiD5urqCllZWaxduxbjxo1DQkICtm3bhsLCQgQEBNBjNzAwkCjMLQ18OJUH\naWlpOHPmDNLT01FVVYWOHTtCWVkZFhYWGDFiBL0eFcKgvFCEEHTu3FlsCEOSwry+vj7u37/PCosA\nTAmRxrowiLbfAriVqUtS209PT8e3337b5ANd2N0eHh6OvXv3wtLSEt9//73YPDaAWyh6//79eP/+\nPS5fvszy/h06dAiEEBBCkJaWJrEc3cvLCxcuXEDXrl1RW1sLOTk5ZGVl0blLFFzkLYCGqlhtbW36\ngSlu/qUhMjISp0+fZtzUm+PVo1BSUsKiRYtYyymDddCgQWJzPmtqaqCrq8vpXKQQJ70hIyODrl27\nMsKuTc2thYUFXR15/fp1Vq5UeXk5QxKEay7Nxo0baZmMLVu2YOnSpejSpQt8fHwQFRVF57XW1NTg\nxYsX6N+/P129nJ6eDg0NDdqIa8yjREmMGBkZ0UaOkpISzMzMkJ2dDU1NTdpzQdGtWze0a9cO/fv3\np3NjVVVVkZGRAaDh4SYqsyOKcP5UWloaFi5cCGVlZSgpKSE+Ph4BAQE4cOAA1NXVGXMk+rJhY2PD\nmm+gIQWBut+lpaWx8uZkZWWhq6sLXV1dTJs2DWfOnEFERATOnDmDx48fY82aNdi6dWuTIU0q5cHV\n1RWvXr2CkZFRk0LmlCEuLOI7Z84csSK+XDh06BCOHTtG58qZm5tj6NChWLBgAW3ElZeXw8vLi24v\n1qZNGxgbG2Pr1q0Mo42rjNTp06fh5+cHW1tbaGlpITMzEx4eHnQ7RICd6yYQCMR2yBFWDaDut0uW\nLGHcb5vK9ywvL0dubi6ABsmYgQMHMlJ/ysvLsXHjRlhZWeHatWuYNm0aLl++LHYuZWRkGEYcFerV\n09NDfX292FAvV8kYaeA9ca2cqKgobN++HRYWFvjmm2/Qvn17VFVV4eXLl7hw4QLWrFmDCRMmoH37\n9nj+/Dnju507d0bnzp3Rpk0btG3blqEFJklhPicnB926daOTpEWRkZHBd999x+rCEBAQgIqKCjg7\nO7Py4riUqUvqeECFb7mGdUaOHIl9+/Yx8tjS0tLg4uLCKD9vDOFQNBfvn7+/P65fvw41NTV07dqV\nYUwI58Tp6Ojg2LFjKC4uxtGjR7Fjxw7s378fjx49wo4dO+j1uMhbcNWN4sqBAwcQGRmJH3/8kQ53\nHDx4ENOnTxdriEkiISEBGzduRFZWFt2XUtT4MjQ0pHvsCqOtrY2zZ8/SuT+N0bdvX/rfVFicori4\nGGFhYfjmm28YhqKkueXiJaRaEx07dgwHDhyAnZ0ddu7cCU9PTxw6dAjTpk1jtFIzMDBAXFwcXr9+\nDQsLCyQnJ6NDhw6srirr1q3DsGHDGA+hEydOIDY2lpZz8fT0xJkzZ2BqakpLjFy9ehW6urro2LEj\nbt26hbVr19KaWE3l6y1ZsgTfffcdnJycMGPGDCxduhTt27eHh4cHbt26hUmTJjFkdkQRzp8CGsKz\nU6dOZXg1o6KicPz4cUZ42dnZGerq6ozjcvbsWcTGxjKuAy5aYMePH0dSUhLu3r0LGRkZaGtr00bd\nV199hdDQUCxevJghwyEKlfKgqalJS4Y0hbW1NVauXMkwYG/evAl/f3+cP3++ye+KQ0NDA4mJiSzd\nRiMjIzqtwNPTE2/fvsWaNWvQt29fZGRkYPv27ZCXl2fIt3CVkZo4cSL8/PwYDoHU1FS4urri6tWr\nABo65Li5uSEuLg719fWNdsjhcr/9v//7P7Rp04aV71lXVwcvLy+YmZmhpKRE7Px88cUXsLGxobug\nSPKYiXqtU1NT8fLlSxQUFKCmpob2zAuvx0UyRiqaXdfK85/AxMSEpKamiv1bamoqMTExoUupdnc6\nVQAAIABJREFUBw0aRFRUVBr9qKurk9DQUEKIZIV5SdtSUVH50LveImhpabHK8UUlCAghjA4RlZWV\npLS0lJw8eZKYmppK9XtcpRqo8vXi4mL6N6qrq4mBgQFjPS7yFs7OzmT9+vV0l4zy8nLi4eEhlaSL\nMGPHjmV1fHjx4gUZOXJks7ZnampKAgICyMOHD8mzZ8/oT1xcHJk6dSqZMmUKGTJkCJkyZQrjM378\neGJmZsbYlkAgILm5ubScDfWRRFVVFat7AlfpkEOHDnHaz4sXL5KFCxeSiRMnEjs7O7HSPgYGBqS0\ntJSEhYWROXPmEEIaJBNEx6ampsbq1FBbW8uQcJk3bx6Jj49nrHP37l1ib29PCGm4P5iamhInJyfi\n5+dHqqur6X0LDw8n06ZNo7+XkZFB5s+fT3Jzc8nt27eJmpoaGTp0KDlx4gSnfa+oqGD8X1jqQXj8\norIP8+fPJ4MGDSJmZmbEwcGBWFpakkGDBpGJEycyzoWpU6eSU6dOkdraWqKrq0tu3bpFHj9+zJAU\nGjp0KFFVVSW+vr5NdjHg0q3G1NSU1RFCHOrq6qz9rKurI+rq6hK/K47FixeTDRs20LIXZWVlZOPG\njYxr2cDAgNVFoKSkhJbaEIaLjJSGhgbrXBMIBKx9iI+PJ+7u7sTBwYGsXbuWpKWlsbbF5X6bmppK\nbG1tiYqKClFTUyODBg0i8+fPZ8nxWFtbix0vhY2NDbGxsSEWFhZERUWFmJqaEnt7ezJx4kSioqJC\nZs+ezVh/8+bNZPjw4cTS0pL+ro2NDZkyZYrY7TclGSMNfDi1lfPu3TsMHjxY7N8GDhyIiooKREdH\nAwDjTVgUQgieP38OV1dXLFq0SKLCfFPbEqYx1Xhx2+DSPaGsrAzHjh1DZmYmamtrGd8XzkUpLS2F\nr68vlixZAmVlZezZswcZGRnw9PRkhRS4VLGKC9U1FopuisakGkT56quv8Ndff2HYsGGoqKhAUVER\nZGVlWWFiLvIWVJ4hVXDRqVMneHh4iO1ywYXS0lKW9EG/fv2aDGE3RUlJCUuZHmio1qupqUFJSQm8\nvb1ZFWCUaDLFuXPn4OXlxThuANv7Ko6srCxWRTZX6ZBu3bqxctUoqDf4jRs3YvXq1WJzeISxtLTk\n1FidS8P3hw8fsgo61NTU6FDZiBEjUFBQgNLSUgQEBEBOTo4+x2fPns3wDFtaWkJJSQlHjx6FiYkJ\n3SRctCggJCQEjo6OjGXx8fHYsGED43pXUFDAgwcPGLlQDx48YIl9T548mVPupp+fH6ytrZGSkoLa\n2lq6wrqoqIheJzk5Gffu3cOdO3ewZs0a5OfnQ0NDAzo6OoyuDVzCjJQ4r62tLavKW9jrNmDAANy4\ncYORP3bjxg3GcZIGb29vODs7Q1NTky4qEg35AaA92hT19fV00Yi0MlKDBw/Gb7/9xrj+IiMjGdGO\no0ePYvfu3bCysoKysjIto+Pt7c04R7ncb+3t7aGkpARTU1O6GEvYy0XlYx89erTRe06HDh3oFJh1\n69bB1tZWrNdamAsXLiAiIkKi/BMXyRhp4MOprRwnJyd06tQJzs7OjJBRbm4uAgICIBAIxCZdi6Om\npgbHjh3D/PnzOSvMS0L0QikuLkZkZCQmTpzICCMB3LonODo6IiMjA3p6eqxKNuGEf6qTwvr16+mw\n7+7du1FXV0cnqQLcq1hFQ6uNVdM1BtW6RljzTpSoqCj639HR0fD09MSFCxcQGRmJy5cvQ1ZWFt99\n9x3j4fr06VMsXLgQPXv2xIsXL6CmpkbLW1DG/ahRo/Dbb78xHpDZ2dmYNWsW6/hwwdHREd9//z1W\nrVqFNm3aoL6+HoGBgXj27BlLpoMLzs7OmD17dpMtnf7880+Wbp4oxsbGcHR0xIQJE1jHRdjYED0G\nAoEAr169wuzZsxkaX1zmltqeMML6i1TRi46ODuLi4hhhsMagwmVNNVbn0vB95syZMDQ0xNKlS+lz\nOTg4GDExMYiKisLNmzcRGBiIqqoqBAcHQ1lZmS66ePPmDRwcHOiCJ65FAWPHjqUNnNLSUmzevBlX\nrlyBo6Mjw7i7cOECHers27cvsrKycObMGWzcuJHWGBSGqkrt06eP2LCVmZkZdu7ciV9//RXFxcUI\nCgrC7du3sXnzZly8eJG1/qtXr3DlyhUcOnQIFRUVjPxFLmFGrmHj+Ph4LF26FPr6+ujbty8yMzOR\nlJSE4OBgljyGNGRmZqKwsBC9evViFeNs3LgRr169gpubG51msH37dgwYMADe3t5QUVERKyMlvA/C\nLz1//fUX7O3toaioSFe3FxYW4sCBAxgyZAiAhnSHsLAw+v8AkJKSgjVr1jCK5rjcb4GGAo7GzjXh\nlBlx+yE6fnV1dSQnJzOKyerq6qCpqcnoHmNoaIiYmJgm7+krVqzAzZs3MXDgQFhaWmLixImNSsZw\nhTfiWjnv3r3D+vXrcePGDcjKytIXRm1tLUaPHo1NmzbRrVGk5Y8//sCwYcPoPLSKigrMmzePVRIu\nLYWFhZgzZw4uXbrEWK6mpobY2Fjk5OTA398fBw4cwM2bNxEaGopjx47R69y8eVPiPmlrayM+Pp5x\nQVZXV2PkyJF07gggXRXrP4HKm4qIiBBbEUZJsAiTlZWF3r17o23btoiOjkZ5eTmsra0ZEiZA4/IW\nlFDmrl27cOPGDZZulImJidg+ppJ48+YN7O3tUV5eDgUFBeTn56Nnz54ICQlh9b5tCmdnZ8jIyKC4\nuBgPHjyApqYm67hSBisX2Qd9fX1G9VpjCLfWAoA2bdrgm2++YeT8AKC9zpKEZhv7DeEWUhs2bEBO\nTg5MTU3Rq1cvhhEp7LmRhsLCQlomRUFBAcbGxowHyosXL7BkyRKUlZXRx0leXh47duxAWVkZnf/1\n8uVLTvl6FOnp6XRRQFVVFeOBmZeXB3t7e4wYMQJxcXFQUVGBt7e32PMiLi4O58+fpyumLS0tWVXh\nOTk5WLlyJdLS0uh8X1EpFaDBKFy3bh2tBVZeXo6FCxfC398fpqamAICLFy8iPj4eiYmJ9L1g1KhR\nMDQ0ZF2Tfn5+OHXqFOTk5LBu3TqxhmVTCHuIXr9+jdOnT6OiogKKioowMzND3759myUHlZGRATc3\nN6xfvx5Dhw7F1q1bkZaWhsDAQNq4raysxMqVK5GQkIC6ujrIyMjAysoK69evZ3lOuVJUVIQbN27Q\nIsKjRo1iXKtGRka4fv064yWlqqoKRkZGjOIvae+34s41rvnYFJMmTcLSpUsZHsHjx4/j2LFjDA/6\n/v37kZWVhUWLFrEMM+pY7dixA+bm5jh//jzy8vLQo0cPTJo0iZHjJy28EcdDS1EADW/8HTt2xNdf\nf41OnTph586dzXpQR0dHw9fXF6WlpQDAeFuSFJqSRHl5OUaPHs3S9aKS19+/fw8zMzPcunWL3ifK\n8LK0tMSRI0fEanoJM3LkSERERDDCFhkZGbCzs2NIkfwbFBYW4q+//gLQ8CYn2kOyrKwMnp6erJ6i\nXKGqdLOzs2FhYYGcnBwMGDCAnk9fX198+eWXOHv2LENiZNGiRZykTkQ5c+YMBAIBZGRkaHmLHj16\noGPHjujevTt++OEHljq8OJpKIKegws+UcDBFSUkJYmJiYGNjQ3vPNmzYAC0tLVaRAVdKS0sZBpqx\nsTEuXLjQpIB2UwgXvXD13LQ0L1++hLe3N+7fv48jR47g5cuXmDJlCqqqqtC2bVv6JUeS9pWkogBh\nioqKsHDhQsjLy+Pnn39m/I2rNBGFvb09+vXrR/fpLC4uxrZt21BWVsY4f7joOFpbW9MenREjRrA8\n4sJhRgDYu3cv0tPTsXXrVroCVdg4uHPnDkOCSSAQ0BJAlJeIQpK3iCv29vYYOnQoHWmoqKjAvn37\n8Pz5c4SGhgJo8P45OTkhLCwMAwYMQFRUFA4fPozg4GBG+JprygnAlkmhDBfqOP7888949eoV1q1b\nhx49euDdu3fw9/dHz5494ezsTKsGiM6xMNTcSjrXROdWHMJzy8VrDTRcr2VlZWJ7X1PbE1dtHBMT\n0+xqY4A34lo9kZGRCAwMpLXUhg8fjqCgIPqNSLSqjSuGhoZYtmwZDA0NWdpS0nilRKUABAIBkpKS\n0K9fP7qKjsLe3h5jx47FrFmzMGHCBGzfvh3t27fHvHnzaNXvxMREpKSkYNasWSyPiLBHY8+ePTh3\n7hzmzp2LPn36IC8vjw5nNqeC8p9QVVWF2bNno7i4GDk5Oay8Hzk5OVhbW7NEcLnQVONvNzc3uLu7\nw8fHB7t27WIY4hTN8QLNnDkTKSkptFBnXl4e8vLyoKioiOrqaggEAoSEhDCkIiTx8uVLKCgooFOn\nTkhLS8OXX34pUSD10aNH2LJlCyorKyEjI4OqqipadkPUoyccqn7w4AECAgJYuZdFRUWMsJq5uTl2\n797NkAURBxf9ReFKOerBADTIXsjLy3M2fKXh5s2bcHNzw+jRo3HlyhVcunQJ06ZNw+zZsxnnGpWv\n19TvDxs2DF988QWmTJkCW1tblkSNaIi6vLwcr169wqBBg2hDMSoqihUKE0b0gQk0eN6TkpIYHp7q\n6moYGBgw9PW46DhKQpowY1MSQLt37+bkcWqOd19TUxPJycmMuautrYWenh79wmBhYQEXFxeGOPSN\nGzcQFBTE8EJzTTlpynBZtmwZY85kZGQgKyuL2tpaEELQrl07pKamSqUaIOlca87cSvJaS9outb2W\nrjYGeCOu1WNqaoqdO3diyJAhKC0txZIlS9C9e3faQFJTU2uWh0dPTw+3b99ulqdGGNEwWJs2bTBg\nwADY2tqyHrSPHj1qtHuC8ENYHKIeDUIIfvvtN1y8eJFW5bewsGDd7P9tnJycOHmguGJvbw8TExPM\nmzeP9vxcu3YNu3fvxoQJE3D69GlkZWWJDYE31wvk4eGB/v37MwyBw4cPIz09HT4+Pvj111/pPopc\nOH/+PLy8vHDs2DGoqKjg2LFj2LVrF3766SdGQrgolFiuh4cHgIbwpzhDRDRUbWlpCXV1dXTp0gVP\nnz7F+PHjceDAAdjY2GDBggV0GNrR0RFJSUkYPHgwevbs2agcjDivC9VCigrDfQjDVxLCEilUrpuo\n/AzALV/v/fv3dFFAYmIiqyhANEQtDmtra/olhqscjI2NDdzd3Rlh1idPnmDDhg0Szy/RkHZLwlUC\nqKUZO3Ysdu7cyRALfvLkCVasWEG/MKipqeH+/fuMc7K+vh46OjqM0CbXlJOmDJeQkBCJY5bWWJV0\nrn1MNDQ0kJyczHBs1NfXQ0tLi9GvVRp4I66Vo6mpyXgjLSsrw8yZM2FkZIQ1a9Y024gLCwtDcXEx\n7O3tWcKLzcnl4AqX7gnSUlNTwymh/HNEW1sbiYmJaNu2Lf2gBhpuNtRNZfz48Y0KXjYHXV1dxMfH\nsxKF9fX1kZSUJPVNbdy4cQgMDGQULqSlpcHNzY0et6iIrEAgQExMDG34E0JoMV5hxIWqVVVVce/e\nPWRlZWHdunU4duwY/v77bzg7OyM6OpoRhm4saVm0RZow4opeWtrw5YKWlhYdkqIMfHEPc2nz9Zoq\nCqB4+fJlk8LSNTU12Lt3L6ZOnYp+/frhl19+oVuJCYvnent74+zZs7CwsED//v2Rn5+Ps2fPQkND\ng6EzKdp0XngOpG0pCDTdrQYAo7WUra0trly5gpqaGowePVqsnmFLQb3gWFtb0y8DZ86cgaurK2xs\nbAA0VJzOmTOHEQ4/f/48fvnlF0RGRtLLuKactKThIk0IF+B2rv2bUEU7wi+Xv//+O4KDg5vduYSX\nGGnlfPvtt4iOjqbf+Dt37ox9+/bB1tYWPXr0aHZ7EHl5eezatQuHDx+ml4kLd0hCmvY0XLonNCbl\nQKnQU2Gp2tpahISEIDIyEuXl5Th37hzWrVuHHTt2iO19+bnCpfF3SxpwAOjOBoaGhvSyO3fu0F6w\n7OxsqYppCgsLWTI5Q4cORWFhIf1/qhk3dT63bdsWX3/9Nby9veHj44Pi4mJUV1ezmnbLycmxCkbk\n5eVRX1+Pvn37Ij09HQCgrKyMvLw8AA3h71OnTuHkyZMso1Acffv2RU1NDZ0fWF9fj7dv3wL4Xxuv\n69evs9oPzZ07F/r6+vDx8YGtrS2rIfc/RVgihZo3cRIplNEhGnYU9tSKKwrw9vZmnAMAd2FpLy8v\n5OTk0J4VVVVVBAQEYMuWLfD09KTXq66uxvjx4yEQCPD3338D+F9+oXAz+sZC2qLyH1yQ1K0G4C4B\n1NLMmjULX331FS5evIgXL16gd+/e2LFjB6MzgpubG5YsWYLw8HD07t0beXl5yMrKYlWOT58+HQ4O\nDqyUE2FJHaBlZVI8PDzQvn17WiTZysoKu3fvhpeXFx3C5XqufQxcXFwarTZuLrwnrpVz7949LFu2\njG6rQvHw4UM4ODiguLi4WQm0enp6cHFxga6u7j/KiXN0dGy0PY1oD1Auat5UWKpXr15QVFRsNCx1\n7do1PHz4EE5OTli6dClu376N9evX04nA/xW4NP7+EL/p5uYGQ0ND9OnTBzk5OUhISMCWLVvw/fff\nY968eVi0aBHnNlyUMbNkyRJ6WVhYGBISEhgvEQkJCXTPxb59+8LGxobhveMaql69ejVkZGTg5eWF\nxYsXY9y4cejQoQMOHDiAa9euITg4WKow9NmzZ+Hr64uKiopG9enMzMzg6enJeBAJdxnIzMzE3Llz\nERMTw2nOuMBVIoVLvt6cOXOaLAqgWLFiBbp06QJ3d3d06NABFRUV2LJlC0pKShjHRldXF9evX2eE\nv8vKymBmZsapwlgUcTl2lI6jtJWlkrrVANwlgD4UkjydRUVFdA5Y7969MWrUKFYxGNeUk5aUSeES\nwpVUgPKxef78OS5evEgXiU2cOJGlmykVzZYJ5vnPUFpaSh4/fsxa/vbtWxISEtKsberp6bFUupuD\nhoYGKSgo4LQuFzVvd3d31j4dOnSIeHp6EkIIOXbsGJk2bRoxMjKiFdUptf2KigqxquWfO6mpqWTD\nhg3EwcGBuLu7k+Tk5A/+m+np6WTPnj1kw4YNZN++fSQjI4MQQkh2djZJSUmRaltPnz4lJiYmRFdX\nl1hYWBBdXV1iamrK6Arxyy+/EG1tbbJ582by888/E19fX6KlpUWio6OlHntxcTFxd3cnBQUF5OHD\nh8TU1JQYGBiQGzduMNYT7QbRGOPGjSNHjhwhpaWlpKKigvGhuHLlCvnhhx/IsmXLiK+vL1m6dClR\nVVUlly5dIi9evCD6+vrk8OHDUu+LJMrLy0l0dDTZv38/OXPmDCkpKWGtM2PGDKKiokJGjhxJpk+f\nTkaNGkVUVFSIiYkJ0dfXJ1paWuSPP/7g9Hu6urqsa7iyspLV5UJXV5e8ffuWsaygoIDVmYIQQk6d\nOkVsbW3JmDFjSE5ODnFzc6M7FlBkZmYyPjk5OaSmpobTmEWR1K1G+DdrampIfX09OXfuHPn1119Z\n+97S5OXlEVtbWzJ06FBiYGBAhgwZQqysrEhubm6L/5Zwh5rHjx+TzZs3Ew8PD7Jnzx7y/PlzugOM\nNBgZGZFXr14xlr1584aYmJi01LA/O3gjjueDcOjQIbJ582aSk5NDKioqGBe0NHBtT0MIIXv37iUr\nV64k+fn5hBBC3r17R7y8vMjWrVvJ+/fvSWVlJdHR0RHbbogyzqiWNkZGRqS0tJQQ8j8j7t27d8TI\nyEiq8fP8O9TU1JDExEQSHR1NkpOTWQ9gAwMD8vDhQ8ayBw8eSN32jJCGh8aaNWsIIYTExsYSVVVV\nMmrUqEbb10lCXGslcbSk4duScH0x4sLIkSNJdnY2Y1lWVhYZNWoUY5mvry+ZNm0auXHjBnn48CG5\nceMGsbW1JX5+foz1QkNDyaRJk8jp06eJhoYGeffuHZk3bx59/D4EkyZNIm/evCGEEKKjo0PKy8tJ\nVVUVqyVYc1u8/RNaooUe1c6Naikl7kMIu7XioEGDGJ/mtFYMCgoi48aNI+Hh4eTq1askIiKCmJqa\n0u0eWyN8OJXng6Crq4uSkpImNXO4EBYWhhs3bkhsTwNwU/MmhODnn3+WGJYaM2YMXr58CVdXV8yd\nOxenT5+Gv78/unfvLnWrrE+ZJ0+eYPv27WLbkH1I/bGWZPr06WIT+idMmEALQnMVE+XCjz/+CEVF\nRfj4+GDChAmYMmUKunTpghMnTkisghYHFVrm0h7qU6QlC1V27tyJmJgYicLSNTU12L17NyuU5+jo\nyDjGY8aMQUREBJSUlBjFBOPHj8edO3dadiL+P1y61Yi2eBPu7vJPdTSbQk9Pj9FCD2jIBxw5ciTn\n64ASHm+qotja2vqDyKSQT1Q14GPCG3E8HwSuEgCSkEbklMtN49atW9i6davEfKxu3brhzp07uHTp\nEqqqqiAnJwcLCwt4eHg0W7X8U8TKygrff/89TE1NWe1ijI2NP86gOJCZmQl/f38QQnD9+nWWUGZ5\neTmePXuGq1evApAsJioNBgYGiIuLw+vXr2FhYYHk5GR06NCh2ZqKP/74I27fvg0lJSXWi0pzjMJ/\nm5bM16urq8OuXbtw7tw5hrC0g4MDKyeWC/r6+oiJiUG7du3oStPq6mqMHj2aVSjSkkjqVsO1xVtL\n09It9D4m/2XVAGngjTieVsfLly9x8eJF5OfnQ1FREZMmTcJXX32FnJwc5Ofn44cffmAk0BYVFaFb\nt26sAo3/Apqamrh79+5nuW9Hjx5FcXGx2KbpcnJyMDY2xuTJkzmJiUqDoaEhLl68iMjISNy6dQsR\nERHIyMjArFmzEBcXJ/V+SPJofOq0dKEK0OAlpdp8CUtHrFmzBlu3bqVbrolDuDDA1dUVsrKyWLt2\nLcaNG4f4+Hhs374dhYWFDEHaloRLtxquLd5aGq6eTi58DC9+a1ENkAbeiOP55GmqPU1zeP/+PUpL\nSxlq+3///TdDoXzdunX46quvMHnyZJbUyofUufu3WbBgAdasWcOSjficuHr1Kt3fUpQPEdLZvn07\nLl26hOLiYmzZsgUDBgyAo6MjpkyZguXLl0u1rf8KXF6MuFBeXg4vLy9cunQJhBC0adOGrpz/8ssv\nERoaSvdsbQxhDb7S0lK4ubkhLi4O9fX1kJWVhaamJgIDA5slH8IFLt1q/mmLt+bSkp7Oj+HF37p1\na6tQDZAG3ojj+aSR1J5GWiIjI7F582bU1NQwlvfv359WLAcaPFTl5eUAwJJP+JA5K/8WVDuzrKws\nJCUlYezYsSxttsYEUD8VqL6+W7dubdQr86H2ITExEXJyctDQ0EBubi5SU1NhZmYm1TbGjBmD69ev\nQ1dXt9HxJyYmtsRwPxs8PT3x9u1brFmzBn379kVGRga2b98OeXn5ZndOiIuLw+HDhyEQCNCvXz9Y\nW1v/YwHwpmiqWw3VXoxri7eWhkuLNK58DC/+yJEjcfbsWXTv3p3OcaysrISJiQmjS0Rrghf75fmk\nuXz5Mo4fPy62PU1zCAkJwaZNmyAnJ4fY2Fg4OzvDz8+Pod4ONGh3/ZehhE47duwIDQ0NVFRUoK6u\nDjU1NaisrGRpQn2KUPuQn58vNjfm3bt3H+y3hfWt+vTpI1YPThKULiP1MvL+/XuWTlxrIyYmBpcu\nXaK7vCgrK2Pr1q20p3XFihUStyEcThUWAO/Tp49YAfCWxt7eHv7+/mK71VBiv021ePuQXLx4kdXK\nsLmMGDECz58//9e9+KLGcV1dHaNQo7XBG3E8nzSEECgrK6OkpIQ23Ozs7BoteJBEaWkpLCwskJeX\nh5CQECgqKsLb2xs2NjZYtWoVvV5zmkt/TlDdLuLj4+Hk5ITw8HAMHz4c+/btw+HDh+neuZ8ylHBq\nRUUF/Pz86JBOTU0N9uzZgwsXLnzM4UmE8gbl5+dj06ZNdA4V8N/y+kpLXV0d4//19fX0sRUnTNsU\nBw4cwMGDBxkC4JMnT4aLi8sHM+Ia61YDAKGhoSCEYOPGjWJbvIWGhrLyO1sSMzMzLFu2jHOLNHFQ\nXvzOnTvDzs7uX/Xim5qaYtWqVXB1dQUhhC5wEi1sak3wRhzPJ01Lt6fp3bs3ioqK0Lt3b2RnZ0Mg\nEKBLly6MB2hrws/Pj9F3dMmSJRg0aBB++uknTk3JPxWWL1+OPXv2ICUlBZ6enpCVlWU8RD9l/Pz8\nsGLFCrE5VK2NcePGYdWqVXBzc6MT77dv3063bBLOd6NoqkqxqqoK3333HWOZiorKB21vFRAQAG9v\nb1a3murqari6ukrV4q2loVqkCTeep14YuBYjfEwv/vDhw1FRUYEZM2agqqoKEydOhIWFBVxdXT/Y\nb37y/AtadDw8zebChQtETU2NZGVlkcDAQDJu3DgyceJE4uzs3Kzt7dixg5ibm5OCggLi7OxMnJ2d\niaurK5k+fXoLj/zzQFVVldTX1zOW1dXV0arznwMCgYCsWLGCmJmZEVVVVRIaGkoEAsHHHhZndHV1\nW6S7yX+BiooK4uDgQIYOHUpUVFTI4MGDybp16xjdKwhpOOZBQUHE0NCQqKqqkjdv3pDZs2ezujhw\nEQBvTueApuDSrUYacd2WJj4+nri7uxMHBwfi7e1N0tLSmrWd27dvE1VVVfr7wcHBRFtb+4N2fNHS\n0qKFvAsLCzmJZP/X4QsbeD5Jqqqq0L59e7x//x6ZmZl0I/Br166hoqIC1tbWkJWVlVoniBCCc+fO\nYezYsRAIBAgMDER5eTmcnZ1ZeXGtgalTp2LOnDmwsrKil50/fx6//PILIiMjP+LIJCPsTamrq6OT\n4oODg+nw2+dQSRwWFobi4mKxOVSfw/hbEiq8HxYWhgEDBiAqKgqHDx9GcHAwNDQ06PW4VilyEQBv\n6bD14cOHkZOTA3t7e3Tp0uWTqmw/evQodu/eDSsrKzoace7cOXh7e0vdK9nCwgIuLi6Mqv4bN24g\nKCjog3nxW4tqgDTwRhzPJwklnCramFr4Bgw0uPQXL16MRYsWSbX9rKwsFBQUoE+fPujTwqC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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "vectorizer = CountVectorizer(stop_words='english')\n", - "docs = vectorizer.fit_transform(text for text in hobby_types['cooking'])\n", - "features = vectorizer.get_feature_names()\n", - "\n", - "visualizer = FreqDistVisualizer()\n", - "visualizer.fit(docs, features)\n", - "visualizer.poof()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/matplotlib/axes/_axes.py:531: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n", - " warnings.warn(\"No labelled objects found. \"\n" - ] - }, - { - "data": { - "image/png": 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2m2JjY7Vt2za98MIL2rRpk8LDw5WVlaUTJ05o4cKFevbZZyVJQUFBWrx4sc6c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b/examples/tuulihill/Tuulitest.ipynb @@ -0,0 +1,756 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**User testing with the Wine Quality data set from the UCI repository**\n", + "Data consist of wine quality catogorization (on a scale of 1-12) with 10 features. This is the white wine data set." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "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", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "wine = pd.read_csv('Dropbox/DataScience/YellowBrick/whitewine.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/tuulimorrill/anaconda3/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" + ] + } + ], + "source": [ + "# Feature Analysis Imports \n", + "# NOTE that all these are available for import from the `yellowbrick.features` module \n", + "from yellowbrick.features.rankd import Rank2D \n", + "from yellowbrick.features.radviz import RadViz \n", + "from yellowbrick.features.pcoords import ParallelCoordinates" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " fixed acidity volatile acidity citric acid residual sugar chlorides \\\n", + "0 7.0 0.27 0.36 20.7 0.045 \n", + "1 6.3 0.30 0.34 1.6 0.049 \n", + "2 8.1 0.28 0.40 6.9 0.050 \n", + "3 7.2 0.23 0.32 8.5 0.058 \n", + "4 7.2 0.23 0.32 8.5 0.058 \n", + "\n", + " free sulfur dioxide total sulfur dioxide density pH sulphates \\\n", + "0 45.0 170.0 1.0010 3.00 0.45 \n", + "1 14.0 132.0 0.9940 3.30 0.49 \n", + "2 30.0 97.0 0.9951 3.26 0.44 \n", + "3 47.0 186.0 0.9956 3.19 0.40 \n", + "4 47.0 186.0 0.9956 3.19 0.40 \n", + "\n", + " alcohol quality qualitybin \n", + "0 8.8 6 low \n", + "1 9.5 6 low \n", + "2 10.1 6 low \n", + "3 9.9 6 low \n", + "4 9.9 6 low " + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wine.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 4898.000000\n", + "mean 5.877909\n", + "std 0.885639\n", + "min 3.000000\n", + "25% 5.000000\n", + "50% 6.000000\n", + "75% 6.000000\n", + "max 9.000000\n", + "Name: quality, dtype: float64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wine.quality.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#add a new column to create a binary class for wine quality \n", + "winemed = wine.quality.median()\n", + "wine['qualitybin'] = wine['quality'].apply(lambda x: str('high') if x > 6 else str('low'))" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 4898\n", + "unique 2\n", + "top low\n", + "freq 3838\n", + "Name: qualitybin, dtype: object" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wine.qualitybin.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Load the classification data set\n", + "data = wine\n", + "\n", + "# Specify the features of interest\n", + "features = [\n", + " 'fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide',\n", + " 'density', 'pH', 'sulphates', 'alcohol'\n", + " ]\n", + "\n", + "# Extract the numpy arrays from the data frame\n", + "X = data[features].as_matrix()\n", + "y = data['quality'].as_matrix()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([6, 6, 6, ..., 6, 7, 6])" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Rank 2D**" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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cc8+pVatW7ooPAABUEJfvaBgdHa3o6Gh3xAIAQIXhkkQjbnMMADCl62VxoDtx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\nZKIAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the visualizer with the Covariance ranking algorithm\n", + "visualizer = Rank2D(features=features, algorithm='covariance')\n", + "\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" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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qzpw5kqT58+drxIgR+vXXX5WQkKDs7Gx17NhRycnJjhZBZXO4wsdut9td/oYX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\nLVu21FtvvSV/f3/169fP8X6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Instantiate the visualizer with the Pearson ranking algorithm\n", + "visualizer = Rank2D(features=features, algorithm='pearson')\n", + "\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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**RadViz**" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Load the classification data set\n", + "data = wine\n", + "\n", + "# Specify the features of interest and the classes of the target\n", + "features = [\n", + " 'fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide',\n", + " 'density', 'pH', 'sulphates', 'alcohol'\n", + " ]\n", + "classes = ['low','high']\n", + "\n", + "# Extract the numpy arrays from the data frame\n", + "X = data[features].as_matrix()\n", + "y = data.qualitybin.as_matrix()" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['low', 'low', 'low', ..., 'low', 'high', 'low'], dtype=object)" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "list indices must be integers or slices, not str", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\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 2\u001b[0m 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\u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Draw/show/poof the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/tuulimorrill/anaconda3/lib/python3.6/site-packages/yellowbrick/features/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[0;31m# Draw the instances\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 184\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;31m# Fit always returns self.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/tuulimorrill/anaconda3/lib/python3.6/site-packages/yellowbrick/features/radviz.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 181\u001b[0m \u001b[0mrow_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\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;36m2\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[0m\n\u001b[1;32m 182\u001b[0m \u001b[0mxy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mrow_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 183\u001b[0;31m \u001b[0mkls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my\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[0m\u001b[1;32m 184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0mto_plot\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkls\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxy\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: list indices must be integers or slices, not str" + ] + } + ], + "source": [ + "# Instantiate the visualizer\n", + "visualizer = RadViz(classes=classes, features=features)\n", + "\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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Parallel Coordinates**" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "list indices must be integers or slices, not str", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\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 2\u001b[0m \u001b[0mvisualizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParallelCoordinates\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[0mfeatures\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Fit the data to the visualizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Transform the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Draw/show/poof the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/tuulimorrill/anaconda3/lib/python3.6/site-packages/yellowbrick/features/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[0;31m# Draw the instances\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 184\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;31m# Fit always returns self.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/tuulimorrill/anaconda3/lib/python3.6/site-packages/yellowbrick/features/pcoords.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 175\u001b[0m \u001b[0;31m# TODO: How to map classmap to labels?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# Get the label for the row\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 177\u001b[0;31m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 178\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 179\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mused_legends\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: list indices must be integers or slices, not str" + ] + } + ], + "source": [ + "# Instantiate the visualizer\n", + "visualizer = ParallelCoordinates(classes=classes, features=features)\n", + "\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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Regressor Evaluation**" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Regression Evaluation Imports\n", + "\n", + "from sklearn.linear_model import Ridge, Lasso\n", + "from sklearn.cross_validation import train_test_split\n", + "\n", + "from yellowbrick.regressor import PredictionError, ResidualsPlot" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Load the data\n", + "df = wine\n", + "feature_names = [\n", + " 'fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide',\n", + " 'density', 'pH', 'sulphates', 'alcohol'\n", + " ]\n", + "\n", + "target_name = 'quality'\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": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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uN4qiSFqWNmjvetQ0Db/fj8vlwuFwtPj3LISIXI214LVkVq203omerlsHfAB2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\nRETegQEfE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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 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 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**" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Load the data\n", + "df = wine\n", + "feature_names = [\n", + " 'fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide',\n", + " 'density', 'pH', 'sulphates', 'alcohol'\n", + " ]\n", + "\n", + "target_name = 'quality'\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": 50, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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7stlsmjlzpvr06aP09HR9+eWXuuWWWxQVFaXY2Fj7uNTUVKWmptqfr1y5Urm5uRo+fLgk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\n6VJj3d69e+Xp6alJkybJ29t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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 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": [ + "**Classifier Evaluation**" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "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", + "\n", + "from yellowbrick.classifier import ClassificationReport, ROCAUC, ClassBalance" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Load the classification data set\n", + "data = wine\n", + "\n", + "# Specify the features of interest and the classes of the target\n", + "features = [\n", + " 'fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide',\n", + " 'density', 'pH', 'sulphates', 'alcohol'\n", + " ]\n", + "\n", + "classes = data['qualitybin']\n", + "\n", + "# Extract the numpy arrays from the data frame\n", + "X = data[features].as_matrix()\n", + "y = data['qualitybin'].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": 81, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'high'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\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 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mvisualizer\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;31m# Fit the training data to the visualizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mvisualizer\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;31m# Evaluate the model on the test data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m 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"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": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/setup.cfg b/setup.cfg index 08259800c..c8d57ee9b 100644 --- a/setup.cfg +++ b/setup.cfg @@ -2,4 +2,8 @@ description-file = DESCRIPTION.txt [wheel] -universal=1 +universal = 1 + +[test] +local_freetype = True +tests = True diff --git a/tests/__init__.py b/tests/__init__.py index ce13cc8a4..a26de3714 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -28,7 +28,7 @@ ## Test Constants ########################################################################## -EXPECTED_VERSION = "0.3.3" +EXPECTED_VERSION = "0.4" ########################################################################## diff --git a/tests/dataset.py b/tests/dataset.py index 13427b394..eddb66ba4 100644 --- a/tests/dataset.py +++ b/tests/dataset.py @@ -42,6 +42,11 @@ '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', @@ -52,11 +57,26 @@ 'signature': '429cfe376dc9929a1fa528da89f0e1626e34e19695f3f555d8954025bbc522b8', 'type': 'numpy', }, + 'mushroom': { + 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/mushroom.zip', + 'signature': '884c43cb70db35d211c67b1cf6a3683b2b4569393d2789d5c07840da4dc85ba8', + 'type': 'numpy', + }, 'hobbies': { 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/hobbies.zip', 'signature': '415c8f68df1486d5d84a1d1757a5aa3035aef5ad63ede5013c261d622fbd29d8', 'type': 'corpus', - } + }, + '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': '7eb79d0be41f9d9f6373b1fa3f4789e7a9ef880720dc8ea29ec752b914d4b525', + 'type': 'numpy', + }, } FIXTURES = os.path.join(os.path.dirname(__file__), "fixtures") diff --git a/tests/test_classifier/__init__.py b/tests/test_classifier/__init__.py new file mode 100644 index 000000000..c82b7f039 --- /dev/null +++ b/tests/test_classifier/__init__.py @@ -0,0 +1,6 @@ +#Backend must be set before first use. +# Setting backend here allows us to run tests just in this folder, without running the whole yellowbrick.tests folder +# This command will have no effect if backend has already been set previously. +import matplotlib +matplotlib.use('Agg') + diff --git a/tests/test_classifier/test_class_balance.py b/tests/test_classifier/test_class_balance.py new file mode 100644 index 000000000..e7bdb49c6 --- /dev/null +++ b/tests/test_classifier/test_class_balance.py @@ -0,0 +1,35 @@ + +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) + diff --git a/tests/test_classifier.py b/tests/test_classifier/test_classification_report.py similarity index 51% rename from tests/test_classifier.py rename to tests/test_classifier/test_classification_report.py index 514fb236b..c63916685 100644 --- a/tests/test_classifier.py +++ b/tests/test_classifier/test_classification_report.py @@ -1,27 +1,8 @@ -# tests.test_classifier -# Tests for the classifiers module. -# -# Author: Rebecca Bilbro -# Created: Sat Oct 8 16:30:39 2016 -0400 -# -# Copyright (C) 2016 District Data Labs -# For license information, see LICENSE.txt -# -""" -Testing for the Classification Score Visualizers -""" - -########################################################################## -## Imports -########################################################################## - -import unittest +from yellowbrick.classifier.classification_report import * from tests.base import VisualTestCase -from yellowbrick.classifier import * from sklearn.svm import LinearSVC -from sklearn.metrics import * ########################################################################## ## Data @@ -38,22 +19,6 @@ y = np.array([1, 1, 0, 1, 0, 0]) -########################################################################## -## Test for ROC-AUC Curve -########################################################################## - -class ROCAUCTests(VisualTestCase): - - def test_roc_auc(self): - """ - Assert no errors occur during ROC-AUC integration - """ - model = LinearSVC() - model.fit(X,y) - visualizer = ROCAUC(model, classes=["A", "B"]) - visualizer.score(X,y) - - ########################################################################## ## Test for Classification Report ########################################################################## @@ -68,3 +33,4 @@ def test_class_report(self): model.fit(X,y) visualizer = ClassificationReport(model, classes=["A", "B"]) visualizer.score(X,y) + diff --git a/tests/test_classifier/test_confusion_matrix.py b/tests/test_classifier/test_confusion_matrix.py new file mode 100644 index 000000000..2fa1ccc42 --- /dev/null +++ b/tests/test_classifier/test_confusion_matrix.py @@ -0,0 +1,63 @@ + +from yellowbrick.classifier.confusion_matrix import * +from tests.base import VisualTestCase + + +from sklearn.datasets import load_digits +from sklearn.cross_validation import train_test_split +from sklearn.linear_model import LogisticRegression + + + + +class ConfusionMatrixTests(VisualTestCase): + def __init__(self, *args, **kwargs): + super(ConfusionMatrixTests, self).__init__(*args, **kwargs) + #Use the same data for all the tests + self.digits = load_digits() + + X = self.digits.data + y = self.digits.target + + X_train, X_test, y_train, y_test = train_test_split(X,y, test_size =0.2, random_state=11) + self.X_train = X_train + self.X_test = X_test + self.y_train = y_train + self.y_test = y_test + + def test_confusion_matrix(self): + model = LogisticRegression() + cm = ConfusionMatrix(model, classes=[0,1,2,3,4,5,6,7,8,9]) + cm.fit(self.X_train, self.y_train) + cm.score(self.X_test, self.y_test) + + def test_no_classes_provided(self): + model = LogisticRegression() + cm = ConfusionMatrix(model) + cm.fit(self.X_train, self.y_train) + cm.score(self.X_test, self.y_test) + + def test_raw_count_mode(self): + model = LogisticRegression() + cm = ConfusionMatrix(model) + cm.fit(self.X_train, self.y_train) + cm.score(self.X_test, self.y_test, percent=False) + + def test_zoomed_in(self): + model = LogisticRegression() + cm = ConfusionMatrix(model, classes=[0,1,2]) + cm.fit(self.X_train, self.y_train) + cm.score(self.X_test, self.y_test) + + def test_extra_classes(self): + model = LogisticRegression() + cm = ConfusionMatrix(model, classes=[0,1,2,11]) + cm.fit(self.X_train, self.y_train) + cm.score(self.X_test, self.y_test) + self.assertTrue(cm.selected_class_counts[3]==0) + + def test_one_class(self): + model = LogisticRegression() + cm = ConfusionMatrix(model, classes=[0]) + cm.fit(self.X_train, self.y_train) + cm.score(self.X_test, self.y_test) \ No newline at end of file diff --git a/tests/test_classifier/test_rocauc.py b/tests/test_classifier/test_rocauc.py new file mode 100644 index 000000000..6d5aa736c --- /dev/null +++ b/tests/test_classifier/test_rocauc.py @@ -0,0 +1,34 @@ + +from yellowbrick.classifier.rocauc 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 ROCAUCTests(VisualTestCase): + + def test_roc_auc(self): + """ + Assert no errors occur during ROC-AUC integration + """ + model = LinearSVC() + model.fit(X,y) + visualizer = ROCAUC(model, classes=["A", "B"]) + visualizer.score(X,y) diff --git a/tests/test_cluster/__init__.py b/tests/test_cluster/__init__.py new file mode 100644 index 000000000..ec8386ffb --- /dev/null +++ b/tests/test_cluster/__init__.py @@ -0,0 +1,18 @@ +# tests.test_cluster +# Tests for the cluster visualizers. +# +# Author: Benjamin Bengfort +# Created: Thu Mar 23 17:37:57 2017 -0400 +# +# Copyright (C) 2016 District Data Labs +# For license information, see LICENSE.txt +# +# ID: __init__.py [] benjamin@bengfort.com $ + +""" +Tests for the cluster visualizers. +""" + +########################################################################## +## Imports +########################################################################## diff --git a/tests/test_cluster/test_base.py b/tests/test_cluster/test_base.py new file mode 100644 index 000000000..27afd26ad --- /dev/null +++ b/tests/test_cluster/test_base.py @@ -0,0 +1,57 @@ +# tests.test_cluster.test_base +# Test the cluster base visualizers. +# +# Author: Rebecca Bilbro +# Created: Thu Mar 23 17:38:42 2017 -0400 +# +# Copyright (C) 2016 District Data Labs +# For license information, see LICENSE.txt +# +# ID: tests.test_cluster.test_base.py [] benjamin@bengfort.com $ + +""" +Test the cluster base visualizers. +""" + +########################################################################## +## Imports +########################################################################## + +import unittest + +from yellowbrick.exceptions import YellowbrickTypeError +from yellowbrick.cluster.base import ClusteringScoreVisualizer + +from sklearn.svm import SVC, SVR +from sklearn.linear_model import Ridge, RidgeCV, LinearRegression +from sklearn.ensemble import RandomForestClassifier +from sklearn.cluster import KMeans, MiniBatchKMeans, AffinityPropagation +from sklearn.cluster import MeanShift, DBSCAN, Birch + +########################################################################## +## Clustering Base Test Cases +########################################################################## + +class ClusterBaseTests(unittest.TestCase): + + def test_clusterer_enforcement(self): + """ + Assert that only clustering estimators can be passed to cluster viz + """ + nomodels = [ + SVC, SVR, Ridge, RidgeCV, LinearRegression, RandomForestClassifier + ] + + for nomodel in nomodels: + with self.assertRaises(YellowbrickTypeError): + visualizer = ClusteringScoreVisualizer(nomodel()) + + models = [ + KMeans, MiniBatchKMeans, AffinityPropagation, MeanShift, DBSCAN, Birch + ] + + for model in models: + try: + visualizer = ClusteringScoreVisualizer(model()) + except YellowbrickTypeError: + self.fail("could not pass clustering estimator to visualizer") diff --git a/tests/test_cluster/test_elbow.py b/tests/test_cluster/test_elbow.py new file mode 100644 index 000000000..5283aa49b --- /dev/null +++ b/tests/test_cluster/test_elbow.py @@ -0,0 +1,82 @@ +# tests.test_cluster.test_elbow +# Tests for the KElbowVisualizer +# +# Author: Benjamin Bengfort +# Created: Thu Mar 23 22:30:19 2017 -0400 +# +# Copyright (C) 2016 District Data Labs +# For license information, see LICENSE.txt +# +# ID: test_elbow.py [] benjamin@bengfort.com $ + +""" +Tests for the KElbowVisualizer +""" + +########################################################################## +## Imports +########################################################################## + +from ..base import VisualTestCase +from ..dataset import DatasetMixin + +from sklearn.datasets import make_blobs +from sklearn.cluster import KMeans, MiniBatchKMeans +from yellowbrick.cluster.elbow import KElbowVisualizer +from yellowbrick.exceptions import YellowbrickValueError + + +########################################################################## +## KElbowVisualizer Test Cases +########################################################################## + +class KElbowVisualizerTests(VisualTestCase, DatasetMixin): + + def test_integrated_kmeans_elbow(self): + """ + Test no exceptions for kmeans k-elbow visualizer on blobs dataset + + See #182: cannot use occupancy dataset because of memory usage + """ + + # Generate a blobs data set + X,y = make_blobs( + n_samples=1000, n_features=12, centers=6, shuffle=True + ) + + try: + visualizer = KElbowVisualizer(KMeans(), k=4) + visualizer.fit(X) + visualizer.poof() + except Exception as e: + self.fail("error during k-elbow: {}".format(e)) + + def test_integrated_mini_batch_kmeans_elbow(self): + """ + Test no exceptions for mini-batch kmeans k-elbow visualizer + + See #182: cannot use occupancy dataset because of memory usage + """ + + # Generate a blobs data set + X,y = make_blobs( + n_samples=1000, n_features=12, centers=6, shuffle=True + ) + + try: + visualizer = KElbowVisualizer(MiniBatchKMeans(), k=4) + visualizer.fit(X) + visualizer.poof() + except Exception as e: + self.fail("error during k-elbow: {}".format(e)) + + def test_invalid_k(self): + """ + Assert that invalid values of K raise exceptions + """ + + with self.assertRaises(YellowbrickValueError): + model = KElbowVisualizer(KMeans(), k=(1,2,3,4,5)) + + with self.assertRaises(YellowbrickValueError): + model = KElbowVisualizer(KMeans(), k="foo") diff --git a/tests/test_cluster/test_silhouette.py b/tests/test_cluster/test_silhouette.py new file mode 100644 index 000000000..1d01db777 --- /dev/null +++ b/tests/test_cluster/test_silhouette.py @@ -0,0 +1,71 @@ +# tests.test_cluster.test_silhouette +# Tests for the SilhouetteVisualizer +# +# Author: Benjamin Bengfort +# Created: Mon Mar 27 10:01:37 2017 -0400 +# +# Copyright (C) 2016 District Data Labs +# For license information, see LICENSE.txt +# +# ID: test_silhouette.py [] benjamin@bengfort.com $ + +""" +Tests for the SilhouetteVisualizer +""" + +########################################################################## +## Imports +########################################################################## + +from ..base import VisualTestCase + +from sklearn.cluster import KMeans, MiniBatchKMeans +from sklearn.datasets import make_blobs + +from yellowbrick.exceptions import YellowbrickValueError +from yellowbrick.cluster.silhouette import SilhouetteVisualizer + + +########################################################################## +## SilhouetteVisualizer Test Cases +########################################################################## + +class SilhouetteVisualizerTests(VisualTestCase): + + def test_integrated_kmeans_silhouette(self): + """ + Test no exceptions for kmeans silhouette visualizer on blobs dataset + + See #182: cannot use occupancy dataset because of memory usage + """ + + # Generate a blobs data set + X, y = make_blobs( + n_samples=1000, n_features=12, centers=8, shuffle=True, + ) + + try: + visualizer = SilhouetteVisualizer(KMeans()) + visualizer.fit(X) + visualizer.poof() + except Exception as e: + self.fail("error during silhouette: {}".format(e)) + + def test_integrated_mini_batch_kmeans_silhouette(self): + """ + Test no exceptions for mini-batch kmeans silhouette visualizer + + See #182: cannot use occupancy dataset because of memory usage + """ + + # Generate a blobs data set + X, y = make_blobs( + n_samples=1000, n_features=12, centers=8, shuffle=True, + ) + + try: + visualizer = SilhouetteVisualizer(MiniBatchKMeans()) + visualizer.fit(X) + visualizer.poof() + except Exception as e: + self.fail("error during silhouette: {}".format(e)) diff --git a/tests/test_features/test_jointplot.py b/tests/test_features/test_jointplot.py new file mode 100644 index 000000000..1f5689bd5 --- /dev/null +++ b/tests/test_features/test_jointplot.py @@ -0,0 +1,101 @@ +# tests.test_features.test_jointplot +# Test the JointPlotVisualizer +# +# Author: Prema Damodaran Roman +# Created: Mon Apr 10 21:00:54 2017 -0400 +# +# Copyright (C) 2017 District Data Labs +# For license information, see LICENSE.txt +# +# ID: test_jointplot.py [] pdamo24@gmail.com $ + +""" +Test the JointPlotVisualizer. + +These tests work differently depending on what version of matplotlib is +installed. If version 2.0.0 or greater is installed, then most tests will +execute, otherwise most will skip and only the warning will be tested. +""" + +########################################################################## +## Imports +########################################################################## + +import warnings +import unittest +import numpy as np +import matplotlib as mpl +import numpy.testing as npt + +from tests.dataset import DatasetMixin +from yellowbrick.features.jointplot import * + +########################################################################## +## JointPlotVisualizer Tests +########################################################################## + +# Determine version of matplotlib +MPL_VERS_MAJ = int(mpl.__version__.split(".")[0]) + + +class JointPlotTests(unittest.TestCase, DatasetMixin): + + X = np.array([1, 2, 3, 5, 8, 10]) + + y = np.array([1, 3, 6, 2, 9, 2]) + + def setUp(self): + self.concrete = self.load_data('concrete') + + def tearDown(self): + self.concrete = None + + @unittest.skipIf(MPL_VERS_MAJ > 1, "requires matplotlib 1.5.3 or less") + def test_warning(self): + """ + Ensure that the jointplot warns if mpl version is < 2.0.0 + """ + # Note Python 3.2+ has a self.assertWarns ... but we need to be + # Python 2.7 compatible, so we're going to do this. + with warnings.catch_warnings(record=True) as w: + # Cause all warnings to always be triggered. + warnings.simplefilter("always") + + # Trigger a warning. + visualizer = JointPlotVisualizer() + + # Ensure that a warning occurred + self.assertEqual(len(w), 1) + self.assertEqual( + str(w[-1].message), + "JointPlotVisualizer requires Matplotlib version 2.0.0.Please upgrade to continue." + ) + + + @unittest.skipIf(MPL_VERS_MAJ < 2, "requires matplotlib 2.0.0 or greater") + def test_jointplot(self): + """ + Assert no errors occur during jointplot visualizer integration + """ + + visualizer = JointPlotVisualizer() + visualizer.fit(self.X, self.y) + visualizer.poof() + + + @unittest.skipIf(MPL_VERS_MAJ < 2, "requires matplotlib 2.0.0 or greater") + def test_jointplot_integrated(self): + """ + Test jointplot on the concrete data set + """ + + # Load the data from the fixture + X = self.concrete['cement'] + y = self.concrete['strength'] + feature = 'cement' + target = 'strength' + + # Test the visualizer + visualizer = JointPlotVisualizer(feature=feature, target=target, joint_plot="hex") + visualizer.fit(X, y) # Fit the data to the visualizer + g = visualizer.poof() diff --git a/tests/test_features/test_scatter.py b/tests/test_features/test_scatter.py new file mode 100644 index 000000000..dfe9e3ac2 --- /dev/null +++ b/tests/test_features/test_scatter.py @@ -0,0 +1,211 @@ +# tests.test_features.test_scatter +# Test the ScatterViz feature analysis visualizers +# +# Author: Nathan Danielsen +# Created: Fri Feb 26 19:40:00 2017 -0400 +# +# Copyright (C) 2016 District Data Labs +# For license information, see LICENSE.txt +# +# ID: test_scatter.py [] nathan.danielsen@gmail.com $ +""" +Test the ScatterViz feature analysis visualizers +""" + +########################################################################## +# Imports +########################################################################## + +import unittest +import numpy as np +import numpy.testing as npt +import matplotlib as mptl + +from tests.dataset import DatasetMixin +from yellowbrick.features.scatter import * +from yellowbrick.exceptions import YellowbrickValueError +from yellowbrick.style import palettes + +try: + import pandas +except ImportError: + pandas = None + +########################################################################## +# ScatterViz Base Tests +########################################################################## + + +class ScatterVizTests(unittest.TestCase, DatasetMixin): + + # yapf: disable + 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, ] + ]) + # yapf: enable + y = np.array([1, 1, 0, 1, 0, 0]) + + def setUp(self): + self.occupancy = self.load_data('occupancy') + + def tearDown(self): + self.occupancy = None + + def test_init_alias(self): + features = ["temperature", "relative_humidity"] + visualizer = ScatterVisualizer(features=features, markers=['*']) + self.assertIsNotNone(visualizer.markers) + + def test_scatter(self): + """ + Assert no errors occur during scatter visualizer integration + """ + X_two_cols = self.X[:, :2] + features = ["temperature", "relative_humidity"] + visualizer = ScatterViz(features=features) + visualizer.fit_transform(X_two_cols, self.y) + + def test_color_builds(self): + """ + Assert no errors occur during scatter visualizer integration + """ + colors = palettes.PALETTES['pastel'] + X_two_cols = self.X[:, :2] + features = ["temperature", "relative_humidity"] + visualizer = ScatterViz(features=features, color=colors) + visualizer.fit_transform(X_two_cols, self.y) + + def test_scatter_no_features(self): + """Assert no errors occur during scatter visualizer integration + with no featues + """ + X_two_cols = self.X[:, :2] + visualizer = ScatterViz() + visualizer.fit_transform_poof(X_two_cols, self.y) + self.assertEquals(visualizer.features_, ['Feature One', 'Feature Two']) + + def test_scatter_only_two_features_allowed_init(self): + """ + Assert that only two features are allowed for this visualizer in init + """ + features = ["temperature", "relative_humidity", "light"] + + with self.assertRaises(YellowbrickValueError) as context: + visualizer = ScatterViz(features=features) + + + def test_scatter_xy_and_features_raise_error(self): + """ + Assert that x,y and features will raise error + """ + features = ["temperature", "relative_humidity", "light"] + + with self.assertRaises(YellowbrickValueError) as context: + visualizer = ScatterViz(features=features, x='one', y='two') + + def test_scatter_xy_changes_to_features(self): + """ + Assert that x,y and features will raise error + """ + visualizer = ScatterViz(x='one', y='two') + self.assertEquals(visualizer.features_, ['one', 'two']) + + def test_scatter_requires_two_features_in_numpy_matrix(self): + """ + Assert that only two features are allowed for this visualizer if not + set in the init + """ + visualizer = ScatterViz() + with self.assertRaises(YellowbrickValueError) as context: + visualizer.fit_transform(self.X, self.y) + self.assertTrue( + 'only accepts two features' in str(context.exception)) + + def test_integrated_scatter(self): + """ + Test scatter on the real, occupancy data set + """ + # Load the data from the fixture + X = self.occupancy[[ + "temperature", "relative_humidity", "light", "C02", "humidity" + ]] + y = self.occupancy['occupancy'].astype(int) + + # Convert X to an ndarray + X = X.view((float, len(X.dtype.names))) + + # Test the visualizer + features = ["temperature", "relative_humidity"] + visualizer = ScatterViz(features=features) + visualizer.fit_transform_poof(X[:, :2], y) + + def test_scatter_quick_method(self): + """ + Test scatter on the real, occupancy data set + """ + # Load the data from the fixture + X = self.occupancy[[ + "temperature", "relative_humidity", "light", "C02", "humidity" + ]] + y = self.occupancy['occupancy'].astype(int) + + # Convert X to an ndarray + X = X.view((float, len(X.dtype.names))) + + # Test the visualizer + features = ["temperature", "relative_humidity"] + ax = scatterviz(X[:, :2], y=y, ax=None, features=features) + + # test that is returns a matplotlib obj with axes + self.assertIn('Axes', str(ax.properties()['axes'])) + + @unittest.skipUnless(pandas is not None, + "Pandas is not installed, could not run test.") + def test_integrated_scatter_with_pandas(self): + """ + Test scatter on the real, occupancy data set with a pandas dataframe + """ + # Load the data from the fixture + X = self.occupancy[[ + "temperature", "relative_humidity", "light", "C02", "humidity" + ]] + y = self.occupancy['occupancy'].astype(int) + + # Convert X to a pandas dataframe + X = pandas.DataFrame(X) + X.columns = [ + "temperature", "relative_humidity", "light", "C02", "humidity" + ] + + # Test the visualizer + features = ["temperature", "relative_humidity"] + visualizer = ScatterViz(features=features) + visualizer.fit_transform_poof(X, y) + + def test_integrated_scatter_numpy_named_arrays(self): + dt = np.dtype({ + 'names': ['one', 'two', 'three', 'four', "five"], + 'formats': [ + np.float64, + np.float64, + np.float64, + np.float64, + np.float64, + ] + }) + + X_named = self.X.astype(dt, casting='unsafe') + visualizer = ScatterViz(features=['one', 'two']) + visualizer.fit_transform_poof(X_named, self.y) + self.assertEquals(visualizer.features_, ['one', 'two']) + + + def test_integrated_scatter_numpy_arrays_no_names(self): + visualizer = ScatterViz(features=[1, 2]) + visualizer.fit_transform_poof(self.X, self.y) + self.assertEquals(visualizer.features_, [1, 2]) diff --git a/tests/test_regressor/__init__.py b/tests/test_regressor/__init__.py new file mode 100644 index 000000000..1fac4fdd9 --- /dev/null +++ b/tests/test_regressor/__init__.py @@ -0,0 +1,18 @@ +# tests.test_regressor +# Tests for the regressor visualizers. +# +# Author: Benjamin Bengfort +# Created: Mon Mar 06 22:01:34 2017 -0500 +# +# Copyright (C) 2016 District Data Labs +# For license information, see LICENSE.txt +# +# ID: __init__.py [] benjamin@bengfort.com $ + +""" +Tests for the regressor visualizers. +""" + +########################################################################## +## Imports +########################################################################## diff --git a/tests/test_regressor/test_alphas.py b/tests/test_regressor/test_alphas.py new file mode 100644 index 000000000..51738be86 --- /dev/null +++ b/tests/test_regressor/test_alphas.py @@ -0,0 +1,122 @@ +# tests.test_regressor.test_alphas +# Tests for the alpha selection visualizations. +# +# Author: Benjamin Bengfort +# Created: Tue Mar 07 12:13:04 2017 -0500 +# +# Copyright (C) 2016 District Data Labs +# For license information, see LICENSE.txt +# +# ID: tests.test_regressor.test_alphas.py [] benjamin@bengfort.com $ + +""" +Tests for the alpha selection visualizations. +""" + +########################################################################## +## Imports +########################################################################## + +import unittest +import numpy as np + +from tests.base import VisualTestCase +from yellowbrick.regressor.alphas import * +from yellowbrick.exceptions import YellowbrickTypeError +from yellowbrick.exceptions import YellowbrickValueError + +from sklearn.svm import SVR, SVC +from sklearn.linear_model import Ridge, RidgeCV +from sklearn.linear_model import Lasso, LassoCV +from sklearn.linear_model import LassoLars, LassoLarsCV +from sklearn.linear_model import ElasticNet, ElasticNetCV +from sklearn.datasets import make_regression + + +########################################################################## +## Alpha Selection Tests +########################################################################## + +class AlphaSelectionTests(VisualTestCase): + + def test_regressor_cv(self): + """ + Ensure only "CV" regressors are allowed + """ + + for model in (SVR, Ridge, Lasso, LassoLars, ElasticNet): + with self.assertRaises(YellowbrickTypeError): + alphas = AlphaSelection(model()) + + for model in (RidgeCV, LassoCV, LassoLarsCV, ElasticNetCV): + try: + alphas = AlphaSelection(model()) + except YellowbrickTypeError: + self.fail("could not instantiate RegressorCV on alpha selection") + + def test_only_regressors(self): + """ + Assert AlphaSelection only works with regressors + """ + with self.assertRaises(YellowbrickTypeError): + model = AlphaSelection(SVC()) + + def test_store_cv_values(self): + """ + Assert that store_cv_values is true on RidgeCV + """ + + model = AlphaSelection(RidgeCV()) + self.assertTrue(model.estimator.store_cv_values) + + model = AlphaSelection(RidgeCV(store_cv_values=True)) + self.assertTrue(model.estimator.store_cv_values) + + model = AlphaSelection(RidgeCV(store_cv_values=False)) + self.assertTrue(model.estimator.store_cv_values) + + def test_get_alphas_param(self): + """ + Assert that we can get the alphas from ridge, lasso, and elasticnet + """ + alphas = np.logspace(-10, -2, 100) + + # Test original CV models + for model in (RidgeCV, LassoCV, ElasticNetCV): + try: + model = AlphaSelection(model(alphas=alphas)) + malphas = model._find_alphas_param() + self.assertTrue(np.array_equal(alphas, malphas)) + except YellowbrickValueError: + self.fail("could not find alphas on {}".format(model.name)) + + def test_get_alphas_param_lassolars(self): + """ + Assert that we can get alphas from lasso lars. + """ + X, y = make_regression() + model = AlphaSelection(LassoLarsCV()) + model.fit(X, y) + try: + malphas = model._find_alphas_param() + self.assertTrue(len(malphas) > 0) + except YellowbrickValueError: + self.fail("could not find alphas on {}".format(model.name)) + + def test_get_errors_param(self): + """ + Test known models we can get the cv errors for alpha selection + """ + + # Test original CV models + for model in (RidgeCV, LassoCV, LassoLarsCV, ElasticNetCV): + try: + model = AlphaSelection(model()) + + X, y = make_regression() + model.fit(X, y) + + errors = model._find_errors_param() + self.assertTrue(len(errors) > 0) + except YellowbrickValueError: + self.fail("could not find errors on {}".format(model.name)) diff --git a/tests/test_regressor.py b/tests/test_regressor/test_residuals.py similarity index 90% rename from tests/test_regressor.py rename to tests/test_regressor/test_residuals.py index aa673a449..51a9dc3d7 100644 --- a/tests/test_regressor.py +++ b/tests/test_regressor/test_residuals.py @@ -1,5 +1,5 @@ -# tests.test_regressor -# Ensure that the regressor visualizations work. +# tests.test_regressor.test_residuals +# Ensure that the regressor residuals visualizations work. # # Author: Rebecca Bilbro # Created: Sat Oct 8 16:30:39 2016 -0400 @@ -7,9 +7,10 @@ # Copyright (C) 2016 District Data Labs # For license information, see LICENSE.txt # +# ID: test_residuals.py [] benjamin@bengfort.com $ """ -Ensure that the regressor visualizations work. +Ensure that the regressor residuals visualizations work. """ ########################################################################## @@ -20,7 +21,7 @@ import numpy as np from tests.base import VisualTestCase -from yellowbrick.regressor import * +from yellowbrick.regressor.residuals import * from sklearn.svm import SVR from sklearn import cross_validation as cv diff --git a/tests/test_text/test_freqdist.py b/tests/test_text/test_freqdist.py new file mode 100644 index 000000000..d8c118194 --- /dev/null +++ b/tests/test_text/test_freqdist.py @@ -0,0 +1,44 @@ +# tests.test_text.test_freqdist +# Tests for the frequency distribution visualization +# +# Author: Rebecca Bilbro +# Created: 2017-03-22 15:27 +# +# Copyright (C) 2017 District Data Labs +# For license information, see LICENSE.txt +# +# ID: test_freqdist.py [] rbilbro@districtdatalabs.com $ + +""" +Tests for the frequency distribution text visualization +""" + +########################################################################## +## Imports +########################################################################## + +import unittest + +from yellowbrick.text.freqdist import * +from tests.dataset import DatasetMixin +from sklearn.feature_extraction.text import CountVectorizer + + +########################################################################## +## FreqDist Tests +########################################################################## + +class FreqDistTests(unittest.TestCase, DatasetMixin): + + def test_integrated_freqdist(self): + """ + Assert no errors occur during freqdist integration + """ + corpus = self.load_data('hobbies') + vectorizer = CountVectorizer() + + docs = vectorizer.fit_transform(corpus.data) + features = vectorizer.get_feature_names() + + visualizer = FreqDistVisualizer() + visualizer.fit(docs, features) diff --git a/tests/test_text/test_postag.py b/tests/test_text/test_postag.py new file mode 100644 index 000000000..462994c5d --- /dev/null +++ b/tests/test_text/test_postag.py @@ -0,0 +1,69 @@ +# -*- coding: utf8 -*- +# tests.test_text.test_postag +# Tests for the part-of-speech tagging visualization +# +# Author: Rebecca Bilbro +# Created: 2017-03-22 15:46 +# +# Copyright (C) 2017 District Data Labs +# For license information, see LICENSE.txt +# +# ID: test_postag.py [] rbilbro@districtdatalabs.com $ + +""" +Tests for the part-of-speech tagging visualization +""" + +########################################################################## +## Imports +########################################################################## + +import unittest + +from yellowbrick.text.postag import * + +try: + import nltk + from nltk.corpus import wordnet as wn + from nltk import pos_tag, word_tokenize +except ImportError: + nltk = None + +########################################################################## +## Data +########################################################################## + +pie = """ +In a small saucepan, combine sugar and eggs +until well blended. Cook over low heat, stirring +constantly, until mixture reaches 160° and coats +the back of a metal spoon. Remove from the heat. +Stir in chocolate and vanilla until smooth. Cool +to lukewarm (90°), stirring occasionally. In a small +bowl, cream butter until light and fluffy. Add cooled +chocolate mixture; beat on high speed for 5 minutes +or until light and fluffy. In another large bowl, +beat cream until it begins to thicken. Add +confectioners' sugar; beat until stiff peaks form. +Fold into chocolate mixture. Pour into crust. Chill +for at least 6 hours before serving. Garnish with +whipped cream and chocolate curls if desired. +""" + + +########################################################################## +## PosTag Tests +########################################################################## + +class PosTagTests(unittest.TestCase): + + @unittest.skipUnless(nltk is not None, "NLTK is not installed, could not run test.") + def test_integrated_postag(self): + """ + Assert no errors occur during postag integration + """ + tokens = word_tokenize(pie) + tagged = pos_tag(tokens) + + visualizer = PosTagVisualizer() + visualizer.transform(tagged) diff --git a/tests/test_utils.py b/tests/test_utils.py index 48ae79f65..40e7b4cb0 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -22,14 +22,15 @@ import inspect import unittest -from sklearn.cluster import KMeans from sklearn.pipeline import Pipeline from sklearn.decomposition import PCA from sklearn.neighbors import LSHForest -from sklearn.linear_model import LassoCV +from sklearn.linear_model import RidgeCV, LassoCV from sklearn.linear_model import LinearRegression from sklearn.linear_model import LogisticRegression from sklearn.ensemble import RandomForestClassifier +from sklearn.cluster import KMeans, MiniBatchKMeans +from sklearn.cluster import AffinityPropagation, Birch from yellowbrick.utils import * from yellowbrick.base import Visualizer, ScoreVisualizer, ModelVisualizer @@ -52,9 +53,11 @@ def test_real_model(self): model1 = LassoCV() model2 = LSHForest() model3 = KMeans() + model4 = RandomForestClassifier() self.assertEqual(get_model_name(model1), 'LassoCV') self.assertEqual(get_model_name(model2), 'LSHForest') self.assertEqual(get_model_name(model3), 'KMeans') + self.assertEqual(get_model_name(model4), 'RandomForestClassifier') def test_pipeline(self): """ @@ -99,6 +102,7 @@ def test_estimator_instance(self): KMeans(), LSHForest(), PCA(), + RidgeCV(), LassoCV(), RandomForestClassifier(), ) @@ -127,6 +131,7 @@ def test_estimator_class(self): KMeans, LSHForest, PCA, + RidgeCV, LassoCV, RandomForestClassifier, ) @@ -174,6 +179,7 @@ def test_regressor_instance(self): # Test regressors are identified correctly regressors = ( + RidgeCV, LassoCV, LinearRegression, ) @@ -202,6 +208,7 @@ def test_regressor_class(self): # Test regressors are identified correctly regressors = ( + RidgeCV, LassoCV, LinearRegression, ) @@ -273,6 +280,7 @@ def test_classifier_instance(self): PCA, LSHForest, LinearRegression, + RidgeCV, LassoCV, ) @@ -300,6 +308,7 @@ def test_classifier_class(self): KMeans, PCA, LSHForest, + RidgeCV, LassoCV, LinearRegression, ) @@ -326,6 +335,161 @@ def test_classifier_visualizer(self): model = ScoreVisualizer(RandomForestClassifier()) self.assertTrue(is_classifier(model)) + ##//////////////////////////////////////////////////////////////////// + ## isclusterer testing + ##//////////////////////////////////////////////////////////////////// + + def test_clusterer_alias(self): + """ + Assert is_clusterer aliases isclusterer + """ + instance = KMeans() + self.assertEqual(is_clusterer(instance), isclusterer(instance)) + + def test_clusterer_instance(self): + """ + Test that is_clusterer works for instances + """ + + # Test clusterers are identified correctly + clusterers = ( + KMeans, + MiniBatchKMeans, + AffinityPropagation, + Birch + ) + + for model in clusterers: + instance = model() + self.assertTrue(is_clusterer(instance)) + + # Test that non-clusterers are identified correctly + notclusterers = ( + RidgeCV, + LassoCV, + LinearRegression, + PCA, + LSHForest, + LogisticRegression, + RandomForestClassifier, + ) + + for model in notclusterers: + instance = model() + self.assertFalse(is_clusterer(instance)) + + def test_clusterer_class(self): + """ + Test that is_clusterer works for classes + """ + + # Test clusterers are identified correctly + clusterers = ( + KMeans, + MiniBatchKMeans, + AffinityPropagation, + Birch + ) + + for klass in clusterers: + self.assertTrue(inspect.isclass(klass)) + self.assertTrue(is_clusterer(klass)) + + # Test that non-clusterers are identified correctly + notclusterers = ( + RidgeCV, + LassoCV, + LinearRegression, + PCA, + LSHForest, + LogisticRegression, + RandomForestClassifier, + ) + + for klass in notclusterers: + self.assertTrue(inspect.isclass(klass)) + self.assertFalse(is_clusterer(klass)) + + def test_clusterer_pipeline(self): + """ + Test that is_clusterer works for pipelines + """ + model = Pipeline([ + ('reduce_dim', PCA()), + ('kmeans', KMeans()) + ]) + + self.assertTrue(is_clusterer(model)) + + def test_clusterer_visualizer(self): + """ + Test that is_clusterer works on visualizers + """ + model = ScoreVisualizer(KMeans()) + self.assertTrue(is_clusterer(model)) + +class DivSafeTests(unittest.TestCase): + + def test_div_1d_by_scalar(self): + result = div_safe( [-1, 0, 1], 0 ) + self.assertTrue(result.all() == 0) + + def test_div_1d_by_1d(self): + result =div_safe( [-1, 0 , 1], [0,0,0]) + self.assertTrue(result.all() == 0) + + def test_div_2d_by_1d(self): + numerator = np.array([[-1,0,1,2],[1,-1,0,3]]) + denominator = [0,0,0,0] + result = div_safe(numerator, denominator) + + def test_invalid_dimensions(self): + numerator = np.array([[-1,0,1,2],[1,-1,0,3]]) + denominator = [0,0] + with self.assertRaises(ValueError): + result = div_safe(numerator, denominator) + + def test_div_scalar_by_scalar(self): + with self.assertRaises(ValueError): + result = div_safe(5, 0) + +class StructuredArrayTests(unittest.TestCase): + + def test_isstructuredarray_true(self): + x = np.array([(1,2.,'Hello'), (2,3.,"World")], dtype=[('foo', 'i4'),('bar', 'f4'), ('baz', 'S10')]) + self.assertTrue(isstructuredarray(x)) + + def test_isstructuredarray_false(self): + x = np.array([[1,2,3], [1,2,3]]) + self.assertFalse(isstructuredarray(x)) + + def test_isstructuredarray_list(self): + x = [[1,2,3], [1,2,3]] + self.assertFalse(isstructuredarray(x)) + +class NarrayIntColumnsTests(unittest.TestCase): + + def test_has_ndarray_int_columns_true_int_features(self): + x = np.random.rand(3,5) + features = [0, 1] + self.assertTrue(has_ndarray_int_columns(features, x)) + + def test_has_ndarray_int_columns_true_int_strings(self): + x = np.random.rand(3,5) + features = ['0', '1'] + self.assertTrue(has_ndarray_int_columns(features, x)) + + def test_has_ndarray_int_columns_false_not_numeric(self): + x = np.random.rand(3,5) + features = ['a', '1'] + self.assertFalse(has_ndarray_int_columns(features, x)) + + def test_has_ndarray_int_columns_false_outside_column_range(self): + x = np.random.rand(3,5) + features = ['0', '10'] + self.assertFalse(has_ndarray_int_columns(features, x)) + + ########################################################################## ## Decorator Tests ########################################################################## diff --git a/yellowbrick/__init__.py b/yellowbrick/__init__.py index 8c96f503d..e6ffdb43e 100644 --- a/yellowbrick/__init__.py +++ b/yellowbrick/__init__.py @@ -31,6 +31,7 @@ # Import yellowbrick functionality to the top level from .anscombe import anscombe +from .classifier import ROCAUC, ClassBalance, ClassificationScoreVisualizer # from .classifier import crplot, rocplot # from .regressor import peplot, residuals_plot diff --git a/yellowbrick/base.py b/yellowbrick/base.py index 8272e21e3..38b288265 100644 --- a/yellowbrick/base.py +++ b/yellowbrick/base.py @@ -35,21 +35,25 @@ class Visualizer(BaseEstimator): The base class for feature visualization and model visualization primarily ensures that styling arguments are passed in. - """ - def __init__(self, ax=None, **kwargs): - """ + 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). + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Notes + ----- These parameters can be influenced later on in the visualization process, but can and should be set as early as possible. - Parameters - ---------- - ax: matplotlib axes - the axis to plot the figure on. + """ - kwargs: dict - keyword arguments passed to the super class. - """ + def __init__(self, ax=None, **kwargs): self.ax = ax self.size = kwargs.pop('size', None) self.color = kwargs.pop('color', None) @@ -84,6 +88,13 @@ def gca(self): def draw(self, **kwargs): """ Rendering function + + Parameters + ---------- + + kwargs: dict + generic keyword arguments. + """ ax = self.gca() @@ -96,10 +107,11 @@ def poof(self, outpath=None, **kwargs): Parameters ---------- - outpath: string + outpath: string, default: None path or None. Save figure to disk or if None show in window - kwargs: generic keyword arguments. + kwargs: dict + generic keyword arguments. """ if self.ax is None: return @@ -113,6 +125,11 @@ def poof(self, outpath=None, **kwargs): def set_title(self, title=None): """ Sets the title on the current axes. + + Parameters + ---------- + title: string, default: None + Add title to figure or if None leave untitled. """ title = self.title or title if title is not None: @@ -134,11 +151,39 @@ def fit_draw(self, X, y=None, **kwargs): """ Fits a transformer to X and y then returns visualization of features or fitted model. + + 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 + Generic keyword arguments. """ self.fit(X, y, **kwargs) self.draw(**kwargs) def fit_draw_poof(self, X, y=None, **kwargs): + """ + Fits a transformer to X and y then shows + the visualization of features or fitted model. + + 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 + Generic keyword arguments. + """ self.fit_draw(X, y, **kwargs) self.poof(**kwargs) @@ -164,8 +209,8 @@ def __init__(self, model, ax=None, **kwargs): models: object the Scikit-Learn models being compared with each other. - ax: matplotlib axes - the axis to plot the figure on. + ax : matplotlib Axes, default: None + The axes to plot the figure on. kwargs: dict keyword arguments. @@ -193,6 +238,14 @@ def fit(self, X, y=None, **kwargs): return self def predict(self, X): + """ + Parameters + ---------- + + X : ndarray or DataFrame of shape n x m + A matrix of n instances with m features + + """ return self.estimator.predict(X) def draw(self, X, y): @@ -253,6 +306,14 @@ def fit(self, X, y=None, **kwargs): pass def predict(self, X): + """ + Parameters + ---------- + + X : ndarray or DataFrame of shape n x m + A matrix of n instances with m features + + """ pass ########################################################################## @@ -273,7 +334,8 @@ def __init__(self, models, ax=None, **kwargs): Parameters ---------- - models: the Scikit-Learn models being compared with each other. + models: Scikit-Learn estimator + the Scikit-Learn models being compared with each other. kwargs: dict keyword arguments. @@ -299,6 +361,19 @@ def predict(self, X, y): """ Returns a generator containing the predictions for each of the internal models (using cross_val_predict and a CV=12). + + 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 Scikit-Learn API. + """ for model in self.models: yield cvp(model, X, y, cv=12) diff --git a/yellowbrick/classifier.py b/yellowbrick/classifier.py deleted file mode 100644 index 417979b15..000000000 --- a/yellowbrick/classifier.py +++ /dev/null @@ -1,578 +0,0 @@ -# yellowbrick.classifier -# Visualizations related to evaluating Scikit-Learn classification models -# -# Author: Rebecca Bilbro -# Author: Benjamin Bengfort -# Created: Wed May 18 12:39:40 2016 -0400 -# -# Copyright (C) 2016 District Data Labs -# For license information, see LICENSE.txt -# -# ID: classifier.py [5eee25b] benjamin@bengfort.com $ - -""" -Visualizations related to evaluating Scikit-Learn classification models -""" - -########################################################################## -## Imports -########################################################################## - -import numpy as np -import matplotlib.pyplot as plt - -from sklearn.pipeline import Pipeline -from sklearn.cross_validation import train_test_split -from sklearn.metrics import auc, roc_auc_score, roc_curve -from sklearn.metrics import precision_recall_fscore_support - -from .exceptions import YellowbrickTypeError -from .utils import get_model_name, isestimator, isclassifier -from .base import Visualizer, ScoreVisualizer, MultiModelMixin -from .style.palettes import color_sequence, color_palette, LINE_COLOR - - -########################################################################## -## Classification Visualization Base Object -########################################################################## - -class ClassificationScoreVisualizer(ScoreVisualizer): - - def __init__(self, model, ax=None, **kwargs): - """ - Check to see if model is an instance of a classifer. - Should return an error if it isn't. - """ - if not isclassifier(model): - raise YellowbrickTypeError( - "This estimator is not a classifier; try a regression or clustering score visualizer instead!" - ) - - super(ClassificationScoreVisualizer, self).__init__(model, ax=ax, **kwargs) - - -########################################################################## -## Classification Report -########################################################################## - -class ClassificationReport(ClassificationScoreVisualizer): - """ - Classification report that shows the precision, recall, and F1 scores - for the model. Integrates numerical scores as well color-coded heatmap. - - """ - def __init__(self, model, ax=None, classes=None, **kwargs): - """ - Pass in a fitted model to generate a classification report. - - Parameters - ---------- - - :param ax: the axis to plot the figure on. - - :param model: the Scikit-Learn estimator - Should be an instance of a classifier, else the __init__ will - return an error. - - :param classes: 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. - - :param colormap: optional string or matplotlib cmap to colorize lines - Use sequential heatmap. - - :param kwargs: keyword arguments passed 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. - """ - super(ClassificationReport, self).__init__(model, ax=ax, **kwargs) - - ## hoisted to ScoreVisualizer base class - self.estimator = model - self.name = get_model_name(self.estimator) - - self.cmap = color_sequence(kwargs.pop('cmap', 'YlOrRd')) - self.classes_ = classes - - def fit(self, X, y=None, **kwargs): - """ - 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: keyword arguments passed to Scikit-Learn API. - """ - super(ClassificationReport, self).fit(X, y, **kwargs) - if self.classes_ is None: - self.classes_ = self.estimator.classes_ - return self - - def score(self, X, y=None, **kwargs): - """ - Generates the Scikit-Learn classification_report - - 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 - - """ - y_pred = self.predict(X) - keys = ('precision', 'recall', 'f1') - self.scores = precision_recall_fscore_support(y, y_pred) - self.scores = map(lambda s: dict(zip(self.classes_, s)), self.scores[0:3]) - self.scores = dict(zip(keys, self.scores)) - return self.draw(y, y_pred) - - def draw(self, y, y_pred): - """ - Renders the classification report across each axis. - - Parameters - ---------- - - y : ndarray or Series of length n - An array or series of target or class values - - y_pred : ndarray or Series of length n - An array or series of predicted target values - """ - # Create the axis if it doesn't exist - if self.ax is None: - self.ax = plt.gca() - - self.matrix = [] - for cls in self.classes_: - self.matrix.append([self.scores['precision'][cls],self.scores['recall'][cls],self.scores['f1'][cls]]) - - for column in range(len(self.matrix)+1): - for row in range(len(self.classes_)): - self.ax.text(column,row,self.matrix[row][column],va='center',ha='center') - - fig = plt.imshow(self.matrix, interpolation='nearest', cmap=self.cmap, vmin=0, vmax=1) - - 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 of the classifiation report - self.set_title('{} Classification Report'.format(self.name)) - - # Add the color bar - plt.colorbar() - - # Compute the tick marks for both x and y - x_tick_marks = np.arange(len(self.classes_)+1) - y_tick_marks = np.arange(len(self.classes_)) - - # Set the tick marks appropriately - # TODO: make sure this goes through self.ax not plt - plt.xticks(x_tick_marks, ['precision', 'recall', 'f1-score'], rotation=45) - plt.yticks(y_tick_marks, self.classes_) - - # Set the labels for the two axes - self.ax.set_ylabel('Classes') - self.ax.set_xlabel('Measures') - - -def classification_report(model, X, y=None, ax=None, classes=None, **kwargs): - """Quick method: - - Displays precision, recall, and F1 scores for the model. - Integrates numerical scores as well color-coded heatmap. - - This helper function is a quick wrapper to utilize the ClassificationReport - 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 classification report was drawn on. - """ - # Instantiate the visualizer - visualizer = ClassificationReport(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 - -########################################################################## -## Receiver Operating Characteristics -########################################################################## - -class ROCAUC(ClassificationScoreVisualizer): - """ - Plot the ROC to visualize the tradeoff between the classifier's - sensitivity and specificity. - """ - def __init__(self, model, ax=None, **kwargs): - """ - Pass in a fitted model to generate a ROC curve. - - Parameters - ---------- - - :param ax: the axis to plot the figure on. - - :param model: the Scikit-Learn estimator - Should be an instance of a classifier, else the __init__ will - return an error. - - :param roc_color: color of the ROC curve - Specify the color as a matplotlib color: you can specify colors in - many weird and wonderful ways, including full names ('green'), hex - strings ('#008000'), RGB or RGBA tuples ((0,1,0,1)) or grayscale - intensities as a string ('0.8'). - - :param diagonal_color: color of the diagonal - Specify the color as a matplotlib color. - - :param 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. - - These parameters can be influenced later on in the visualization - process, but can and should be set as early as possible. - - """ - super(ROCAUC, self).__init__(model, ax=ax, **kwargs) - - ## hoisted to ScoreVisualizer base class - self.name = get_model_name(self.estimator) - - # Color map defaults as follows: - # ROC color is the current color in the cycle - # Diagonal color is the default LINE_COLOR - self.colors = { - 'roc': kwargs.pop('roc_color', None), - 'diagonal': kwargs.pop('diagonal_color', LINE_COLOR), - } - - def score(self, X, y=None, **kwargs): - """ - Generates the predicted target values using the Scikit-Learn - estimator. - - 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.fpr, self.tpr, self.thresholds = roc_curve(y, y_pred) - self.roc_auc = auc(self.fpr, self.tpr) - return self.draw(y, y_pred) - - def draw(self, y, y_pred): - """ - Renders ROC-AUC plot. - Called internally by score, possibly more than once - - Parameters - ---------- - - y : ndarray or Series of length n - An array or series of target or class values - - y_pred : ndarray or Series of length n - An array or series of predicted target values - - Returns - ------ - - ax : the axis with the plotted figure - - """ - # Create the axis if it doesn't exist - if self.ax is None: - self.ax = plt.gca() - - plt.plot(self.fpr, self.tpr, c=self.colors['roc'], label='AUC = {:0.2f}'.format(self.roc_auc)) - - # Plot the line of no discrimination to compare the curve to. - plt.plot([0,1],[0,1],'m--',c=self.colors['diagonal']) - - 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 and add the legend - self.set_title('ROC for {}'.format(self.name)) - self.ax.legend(loc='lower right') - - # Set the limits for the ROC/AUC (always between 0 and 1) - self.ax.set_xlim([-0.02, 1.0]) - self.ax.set_ylim([ 0.00, 1.1]) - - -def roc_auc(model, X, y=None, ax=None, **kwargs): - """Quick method: - - Displays the tradeoff between the classifier's - sensitivity and specificity. - - This helper function is a quick wrapper to utilize the ROCAUC - 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) - - Returns - ------- - ax : matplotlib axes - Returns the axes that the roc-auc curve was drawn on. - """ - # Instantiate the visualizer - visualizer = ROCAUC(model, ax, **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 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. - - These parameters can be influenced later on in the visualization - process, but can and should be set as early as possible. - """ - def __init__(self, model, ax=None, classes=None, **kwargs): - - super(ClassBalance, self).__init__(model, ax=ax, **kwargs) - - self.colors = color_palette(kwargs.pop('colors', None)) - self.classes_ = classes - - def fit(self, X, y=None, **kwargs): - """ - 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: keyword arguments passed to Scikit-Learn API. - - Returns - ------- - self : instance - Returns the instance of the classification score visualizer - - """ - super(ClassBalance, self).fit(X, y, **kwargs) - if self.classes_ is None: - self.classes_ = self.estimator.classes_ - return self - - 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 - - """ - # Create the axis if it doesn't exist - if self.ax is None: - self.ax = plt.gca() - - #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_)] - plt.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 - # TODO: change to the self.ax method rather than plt.xticks - plt.xticks(np.arange(len(self.support)), self.support.keys()) - - # Compute the ceiling for the y limit - cmax, cmin = max(self.support.values()), min(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 diff --git a/yellowbrick/classifier/__init__.py b/yellowbrick/classifier/__init__.py new file mode 100644 index 000000000..1ad7c6ed8 --- /dev/null +++ b/yellowbrick/classifier/__init__.py @@ -0,0 +1,15 @@ +# yellowbrick.classifier +# Visualizers for Classification analysis and diagnostics + +########################################################################## +## Imports +########################################################################## + +## Hoist visualizers into the classifier namespace +from ..base import ScoreVisualizer +from .base import ClassificationScoreVisualizer +from .class_balance import ClassBalance +from .classification_report import ClassificationReport, classification_report +from .confusion_matrix import ConfusionMatrix +from .rocauc import ROCAUC, roc_auc + diff --git a/yellowbrick/classifier/base.py b/yellowbrick/classifier/base.py new file mode 100644 index 000000000..d88df28a0 --- /dev/null +++ b/yellowbrick/classifier/base.py @@ -0,0 +1,25 @@ +from ..exceptions import YellowbrickTypeError +from ..utils import isclassifier +from ..base import ScoreVisualizer + +import numpy as np + + +class ClassificationScoreVisualizer(ScoreVisualizer): + + def __init__(self, model, ax=None, **kwargs): + """ + Check to see if model is an instance of a classifer. + Should return an error if it isn't. + """ + if not isclassifier(model): + raise YellowbrickTypeError( + "This estimator is not a classifier; try a regression or clustering score visualizer instead!" + ) + + super(ClassificationScoreVisualizer, self).__init__(model, ax=ax, **kwargs) + + #TODO during refactoring this can be used to generalize ClassBalance + def class_counts(self, y): + unique, counts = np.unique(y, return_counts=True) + return dict(zip(unique, counts)) diff --git a/yellowbrick/classifier/class_balance.py b/yellowbrick/classifier/class_balance.py new file mode 100644 index 000000000..9ff088997 --- /dev/null +++ b/yellowbrick/classifier/class_balance.py @@ -0,0 +1,182 @@ + +from .base import ClassificationScoreVisualizer +from ..style.palettes import color_palette + +import numpy as np +import matplotlib.pyplot as plt + +from sklearn.cross_validation import train_test_split +from sklearn.metrics import precision_recall_fscore_support + + +########################################################################## +## 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. + + These parameters can be influenced later on in the visualization + process, but can and should be set as early as possible. + """ + def __init__(self, model, ax=None, classes=None, **kwargs): + + super(ClassBalance, self).__init__(model, ax=ax, **kwargs) + + self.colors = color_palette(kwargs.pop('colors', None)) + self.classes_ = classes + + def fit(self, X, y=None, **kwargs): + """ + 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: keyword arguments passed to Scikit-Learn API. + + Returns + ------- + self : instance + Returns the instance of the classification score visualizer + + """ + super(ClassBalance, self).fit(X, y, **kwargs) + if self.classes_ is None: + self.classes_ = self.estimator.classes_ + return self + + 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 + + """ + # Create the axis if it doesn't exist + if self.ax is None: + self.ax = plt.gca() + + #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_)] + plt.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 + # TODO: change to the self.ax method rather than plt.xticks + plt.xticks(np.arange(len(self.support)), self.support.keys()) + + # Compute the ceiling for the y limit + cmax, cmin = max(self.support.values()), min(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 diff --git a/yellowbrick/classifier/classification_report.py b/yellowbrick/classifier/classification_report.py new file mode 100644 index 000000000..93907221c --- /dev/null +++ b/yellowbrick/classifier/classification_report.py @@ -0,0 +1,208 @@ +from .base import ClassificationScoreVisualizer + + +import numpy as np +import matplotlib.pyplot as plt + +from sklearn.cross_validation import train_test_split +from sklearn.metrics import precision_recall_fscore_support + +from ..utils import get_model_name +from ..style.palettes import color_sequence +from ..style import find_text_color + +########################################################################## +## Classification Report +########################################################################## + +class ClassificationReport(ClassificationScoreVisualizer): + """ + Classification report that shows the precision, recall, and F1 scores + for the model. Integrates numerical scores as well as a color-coded heatmap. + + Parameters + ---------- + + ax : The axis to plot the figure on. + + model : the Scikit-Learn estimator + Should be an instance of a classifier, else the __init__ will + return an error. + + classes : 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. + + colormap : optional string or matplotlib cmap to colorize lines + Use sequential heatmap. + + kwargs : keyword arguments passed to the super class. + + Examples + -------- + + >>> from yellowbrick.classifier import ClassificationReport + >>> from sklearn.linear_model import LogisticRegression + >>> viz = ClassificationReport(LogisticRegression()) + >>> viz.fit(X_train, y_train) + >>> viz.score(X_test, y_test) + >>> viz.poof() + + """ + def __init__(self, model, ax=None, classes=None, **kwargs): + + super(ClassificationReport, self).__init__(model, ax=ax, **kwargs) + + ## hoisted to ScoreVisualizer base class + self.estimator = model + self.name = get_model_name(self.estimator) + + self.cmap = color_sequence(kwargs.pop('cmap', 'YlOrRd')) + self.classes_ = classes + + def fit(self, X, y=None, **kwargs): + """ + 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: keyword arguments passed to Scikit-Learn API. + """ + super(ClassificationReport, self).fit(X, y, **kwargs) + if self.classes_ is None: + self.classes_ = self.estimator.classes_ + return self + + def score(self, X, y=None, **kwargs): + """ + Generates the Scikit-Learn classification_report + + 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 + + """ + y_pred = self.predict(X) + keys = ('precision', 'recall', 'f1') + self.scores = precision_recall_fscore_support(y, y_pred) + self.scores = map(lambda s: dict(zip(self.classes_, s)), self.scores[0:3]) + self.scores = dict(zip(keys, self.scores)) + return self.draw(y, y_pred) + + def draw(self, y, y_pred): + """ + Renders the classification report across each axis. + + Parameters + ---------- + + y : ndarray or Series of length n + An array or series of target or class values + + y_pred : ndarray or Series of length n + An array or series of predicted target values + """ + # Create the axis if it doesn't exist + if self.ax is None: + self.ax = plt.gca() + + self.matrix = [] + for cls in self.classes_: + self.matrix.append([self.scores['precision'][cls],self.scores['recall'][cls],self.scores['f1'][cls]]) + + for column in range(0,3): #3 columns - prec,rec,f1 + for row in range(len(self.classes_)): + current_score = self.matrix[row][column] + base_color = self.cmap(current_score) + text_color= find_text_color(base_color) + + self.ax.text(column,row,current_score,va='center',ha='center', color=text_color) + + fig = plt.imshow(self.matrix, interpolation='nearest', cmap=self.cmap, vmin=0, vmax=1,aspect='auto') + + 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 of the classifiation report + self.set_title('{} Classification Report'.format(self.name)) + + # Add the color bar + plt.colorbar() + + # Compute the tick marks for both x and y + x_tick_marks = np.arange(len(self.classes_)+1) + y_tick_marks = np.arange(len(self.classes_)) + + # Set the tick marks appropriately + self.ax.set_xticks(x_tick_marks) + self.ax.set_yticks(y_tick_marks) + + self.ax.set_xticklabels(['precision', 'recall', 'f1-score'], rotation=45) + self.ax.set_yticklabels(self.classes_) + + # Set the labels for the two axes + self.ax.set_ylabel('Classes') + self.ax.set_xlabel('Measures') + + +def classification_report(model, X, y=None, ax=None, classes=None, **kwargs): + """Quick method: + + Displays precision, recall, and F1 scores for the model. + Integrates numerical scores as well color-coded heatmap. + + This helper function is a quick wrapper to utilize the ClassificationReport + 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 classification report was drawn on. + """ + # Instantiate the visualizer + visualizer = ClassificationReport(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 \ No newline at end of file diff --git a/yellowbrick/classifier/confusion_matrix.py b/yellowbrick/classifier/confusion_matrix.py new file mode 100644 index 000000000..4a9b7198a --- /dev/null +++ b/yellowbrick/classifier/confusion_matrix.py @@ -0,0 +1,267 @@ + +from .base import ClassificationScoreVisualizer + +import numpy as np +import matplotlib.pyplot as plt +from sklearn.metrics import confusion_matrix + +from ..style.palettes import color_sequence +from ..style import find_text_color +from ..utils import div_safe + + +########################################################################## +## ConfusionMatrix +########################################################################## + +class ConfusionMatrix(ClassificationScoreVisualizer): + """ + Creates a heatmap visualization of the sklearn.metrics.confusion_matrix(). A confusion + matrix shows each combination of the true and predicted classes for a test data set. + + The default color map uses a yellow/orange/red color scale. The user can choose between + displaying values as the percent of true (cell value divided by sum of row) or as direct + counts. If percent of true mode is selected, 100% accurate predictions are highlighted in green. + + Requires a classification model + + Parameters + ---------- + model : the Scikit-Learn estimator + Should be an instance of a classifier or __init__ will return an error. + + ax : the matplotlib axis to plot the figure on (if None, a new axis will be created) + + classes : a list of class names to use in the confusion_matrix. + This is passed to the 'labels' parameter of sklearn.metrics.confusion_matrix(), and follows the behaviour + indicated by that function. It may be used to reorder or select a subset of labels. + If None, values that appear at least once in y_true or y_pred are used in sorted order. + Default: None + + Examples + -------- + + >>> from yellowbrick.classifier import ConfusionMatrix + >>> from sklearn.linear_model import LogisticRegression + >>> viz = ConfusionMatrix(LogisticRegression()) + >>> viz.fit(X_train, y_train) + >>> viz.score(X_test, y_test) + >>> viz.poof() + """ + + + def __init__(self, model, ax=None, classes=None, **kwargs): + + super(ConfusionMatrix, self).__init__(model, ax=ax, classes=None,**kwargs) + #Parameters provided by super (for reference during development only): + #self.ax + #self.size + #self.color + #self.title + #self.estimator + #self.name + + #Initialize all the other attributes we'll use (for coder clarity) + self.confusion_matrix = None + + self.cmap = color_sequence(kwargs.pop('cmap', 'YlOrRd')) + self.cmap.set_under(color='w') + self.cmap.set_over(color='#2a7d4f') + self.edgecolors=[] #used to draw diagonal line for predicted class = true class + + + #Convert list to array if necessary, since estimator.classes_ returns nparray + self._classes = None if classes == None else np.array(classes) + + #TODO hoist this to shared confusion matrix / classification report heatmap class + @property + def classes(self): + ''' + Returns a numpy array of the classes in y + Matches the user provided list if provided by the user in __init__ + If no list provided, tries to obtain it from the fitted estimator + ''' + if self._classes is None: + try: + print("trying") + return self.estimator.classes_ + except AttributeError: + return None + return self._classes + + @classes.setter + def classes(self, value): + self._classes = value + + #todo hoist + def fit(self, X, y=None, **kwargs): + """ + 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: keyword arguments passed to Scikit-Learn API. + """ + super(ConfusionMatrix, self).fit(X, y, **kwargs) + if self._classes is None: + self.classes = self.estimator.classes_ + return self + + def score(self, X, y, sample_weight=None, percent=True): + """ + Generates the Scikit-Learn confusion_matrix and applies this to the appropriate 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 + + sample_weight: optional, passed to the confusion_matrix + + percent: optional, Boolean. Determines whether or not the confusion_matrix + should be displayed as raw numbers or as a percent of the true + predictions. Note, if using a subset of classes in __init__, percent should + be set to False or inaccurate percents will be displayed. + """ + y_pred = self.predict(X) + + self.confusion_matrix = confusion_matrix(y_true = y, y_pred = y_pred, labels=self.classes, sample_weight=sample_weight) + self._class_counts = self.class_counts(y) + + #Make array of only the classes actually being used. + #Needed because sklearn confusion_matrix only returns counts for selected classes + #but percent should be calculated based on all classes + selected_class_counts = [] + for c in self.classes: + try: + selected_class_counts.append(self._class_counts[c]) + except KeyError: + selected_class_counts.append(0) + self.selected_class_counts = np.array(selected_class_counts) + + return self.draw(percent) + + def draw(self, percent=True): + """ + Renders the classification report + Should only be called internally, as it uses values calculated in Score + and score calls this method. + + Parameters + ---------- + + percent: Boolean + Whether the heatmap should represent "% of True" or raw counts + + """ + # Create the axis if it doesn't exist + if self.ax is None: + self.ax = plt.gca() + + if percent == True: + #Convert confusion matrix to percent of each row, i.e. the predicted as a percent of true in each class + #div_safe function returns 0 instead of NAN. + self._confusion_matrix_display = div_safe( + self.confusion_matrix, + self.selected_class_counts + ) + self._confusion_matrix_display =np.round(self._confusion_matrix_display* 100, decimals=0) + else: + self._confusion_matrix_display = self.confusion_matrix + + #Y axis should be sorted top to bottom in pcolormesh + self._confusion_matrix_plottable = self._confusion_matrix_display[::-1,::] + + self.max = self._confusion_matrix_plottable.max() + + #Set up the dimensions of the pcolormesh + X = np.linspace(start=0, stop=len(self.classes), num=len(self.classes)+1) + Y = np.linspace(start=0, stop=len(self.classes), num=len(self.classes)+1) + self.ax.set_ylim(bottom=0, top=self._confusion_matrix_plottable.shape[0]) + self.ax.set_xlim(left=0, right=self._confusion_matrix_plottable.shape[1]) + + #Put in custom axis labels + self.xticklabels = self.classes + self.yticklabels = self.classes[::-1] + self.xticks = np.arange(0, len(self.classes), 1) + .5 + self.yticks = np.arange(0, len(self.classes), 1) + .5 + self.ax.set(xticks=self.xticks, yticks=self.yticks) + self.ax.set_xticklabels(self.xticklabels, rotation="vertical", fontsize=8) + self.ax.set_yticklabels(self.yticklabels, fontsize=8) + + ###################### + # Add the data labels to each square + ###################### + for x_index, x in np.ndenumerate(X): + #np.ndenumerate returns a tuple for the index, must access first element using [0] + x_index = x_index[0] + for y_index, y in np.ndenumerate(Y): + #Clean up our iterators + #numpy doesn't like non integers as indexes; also np.ndenumerate returns tuple + x_int = int(x) + y_int = int(y) + y_index = y_index[0] + + #X and Y are one element longer than the confusion_matrix. Don't want to add text for the last X or Y + if x_index == X[-1] or y_index == Y[-1]: + break + + #center the text in the middle of the block + text_x = x + 0.5 + text_y = y + 0.5 + + #extract the value + grid_val = self._confusion_matrix_plottable[x_int,y_int] + + #Determine text color + scaled_grid_val = grid_val / self.max + base_color = self.cmap(scaled_grid_val) + text_color= find_text_color(base_color) + + #make zero values more subtle + if self._confusion_matrix_plottable[x_int,y_int] == 0: + text_color = "0.75" + + #Put the data labels in the middle of the heatmap square + self.ax.text(text_y, + text_x, + "{:.0f}{}".format(grid_val,"%" if percent==True else ""), + va='center', + ha='center', + fontsize=8, + color=text_color) + + #If the prediction is correct, put a bounding box around that square to better highlight it to the user + #This will be used in ax.pcolormesh, setting now since we're iterating over the matrix + #ticklabels are conveniently already reversed properly to match the _confusion_matrix_plottalbe order + if self.xticklabels[x_int] == self.yticklabels[y_int]: + self.edgecolors.append('black') + else: + self.edgecolors.append('w') + + # Draw the heatmap. vmin and vmax operate in tandem with the cmap.set_under and cmap.set_over to alter the color of 0 and 100 + highest_count = self._confusion_matrix_plottable.max() + vmax = 99.999 if percent == True else highest_count + mesh = self.ax.pcolormesh(X, + Y, + self._confusion_matrix_plottable, + vmin=0.00001, + vmax=vmax, + edgecolor=self.edgecolors, + cmap=self.cmap, + linewidth='0.01') #edgecolor='0.75', linewidth='0.01' + return self.ax + + def finalize(self, **kwargs): + self.set_title('{} Confusion Matrix'.format(self.name)) + self.ax.set_ylabel('True Class') + self.ax.set_xlabel('Predicted Class') \ No newline at end of file diff --git a/yellowbrick/classifier/rocauc.py b/yellowbrick/classifier/rocauc.py new file mode 100644 index 000000000..ee4e37945 --- /dev/null +++ b/yellowbrick/classifier/rocauc.py @@ -0,0 +1,190 @@ + +from .base import ClassificationScoreVisualizer +from ..utils import get_model_name +from ..style.palettes import LINE_COLOR + +import numpy as np +import matplotlib.pyplot as plt + +from sklearn.cross_validation import train_test_split +from sklearn.metrics import auc, roc_auc_score, roc_curve + + + +########################################################################## +## Receiver Operating Characteristics +########################################################################## + +class ROCAUC(ClassificationScoreVisualizer): + """ + Plot the ROC to visualize the tradeoff between the classifier's + sensitivity and specificity. + + Parameters + ---------- + + ax : the axis to plot the figure on. + + model : the Scikit-Learn estimator + Should be an instance of a classifier, else the __init__ will + return an error. + + roc_color : color of the ROC curve + Specify the color as a matplotlib color: you can specify colors in + many weird and wonderful ways, including full names ('green'), hex + strings ('#008000'), RGB or RGBA tuples ((0,1,0,1)) or grayscale + intensities as a string ('0.8'). + + diagonal_color : color of the diagonal + Specify the color as a matplotlib color. + + 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. + + These parameters can be influenced later on in the visualization + process, but can and should be set as early as possible. + + + Examples + -------- + + >>> from yellowbrick.classifier import ROCAUC + >>> from sklearn.linear_model import LogisticRegression + >>> logistic = LogisticRegression() + >>> viz = ROCAUC(logistic) + >>> viz.fit(X_train, y_train) + >>> viz.score(X_test, y_test) + >>> viz.poof() + + """ + def __init__(self, model, ax=None, **kwargs): + + super(ROCAUC, self).__init__(model, ax=ax, **kwargs) + + ## hoisted to ScoreVisualizer base class + self.name = get_model_name(self.estimator) + + # Color map defaults as follows: + # ROC color is the current color in the cycle + # Diagonal color is the default LINE_COLOR + self.colors = { + 'roc': kwargs.pop('roc_color', None), + 'diagonal': kwargs.pop('diagonal_color', LINE_COLOR), + } + + def score(self, X, y=None, **kwargs): + """ + Generates the predicted target values using the Scikit-Learn + estimator. + + 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.fpr, self.tpr, self.thresholds = roc_curve(y, y_pred) + self.roc_auc = auc(self.fpr, self.tpr) + return self.draw(y, y_pred) + + def draw(self, y, y_pred): + """ + Renders ROC-AUC plot. + Called internally by score, possibly more than once + + Parameters + ---------- + + y : ndarray or Series of length n + An array or series of target or class values + + y_pred : ndarray or Series of length n + An array or series of predicted target values + + Returns + ------ + + ax : the axis with the plotted figure + + """ + # Create the axis if it doesn't exist + if self.ax is None: + self.ax = plt.gca() + + plt.plot(self.fpr, self.tpr, c=self.colors['roc'], label='AUC = {:0.2f}'.format(self.roc_auc)) + + # Plot the line of no discrimination to compare the curve to. + plt.plot([0,1],[0,1],'m--',c=self.colors['diagonal']) + + 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 and add the legend + self.set_title('ROC for {}'.format(self.name)) + self.ax.legend(loc='lower right') + + # Set the limits for the ROC/AUC (always between 0 and 1) + self.ax.set_xlim([-0.02, 1.0]) + self.ax.set_ylim([ 0.00, 1.1]) + + +def roc_auc(model, X, y=None, ax=None, **kwargs): + """Quick method: + + Displays the tradeoff between the classifier's + sensitivity and specificity. + + This helper function is a quick wrapper to utilize the ROCAUC + 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) + + Returns + ------- + ax : matplotlib axes + Returns the axes that the roc-auc curve was drawn on. + """ + # Instantiate the visualizer + visualizer = ROCAUC(model, ax, **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 diff --git a/yellowbrick/cluster/__init__.py b/yellowbrick/cluster/__init__.py new file mode 100644 index 000000000..7f01709eb --- /dev/null +++ b/yellowbrick/cluster/__init__.py @@ -0,0 +1,24 @@ +# yellowbrick.cluster +# Visualizers for Cluster analysis and diagnostics +# +# Author: Benjamin Bengfort +# Created: Thu Mar 23 17:26:57 2017 -0400 +# +# Copyright (C) 2016 District Data Labs +# For license information, see LICENSE.txt +# +# ID: __init__.py [] benjamin@bengfort.com $ + +""" +Visualizers for Cluster analysis and diagnostics, particularly visualizations +related to evaluating Scikit-Learn clustering models. +""" + +########################################################################## +## Imports +########################################################################## + +## Hoist visualizers into the cluster namespace +from .base import * +from .elbow import * +from .silhouette import * diff --git a/yellowbrick/cluster/base.py b/yellowbrick/cluster/base.py new file mode 100644 index 000000000..3a5b3772f --- /dev/null +++ b/yellowbrick/cluster/base.py @@ -0,0 +1,51 @@ +# yellowbrick.cluster.base +# Base class for cluster visualizers. +# +# Author: Benjamin Bengfort +# Created: Thu Mar 23 17:28:38 2017 -0400 +# +# Copyright (C) 2016 District Data Labs +# For license information, see LICENSE.txt +# +# ID: base.py [] benjamin@bengfort.com $ + +""" +Base class for cluster visualizers. +""" + +########################################################################## +## Imports +########################################################################## + +from ..utils import isclusterer +from ..base import ScoreVisualizer +from ..exceptions import YellowbrickTypeError + + +## Packages for export +__all__ = [ + "ClusteringScoreVisualizer", +] + + +########################################################################## +## Clustering Score Visualization Base Object +########################################################################## + +class ClusteringScoreVisualizer(ScoreVisualizer): + """ + Base class for all ScoreVisualizers that evaluate a clustering estimator. + + 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. + """ + + def __init__(self, model, ax=None, **kwargs): + if not isclusterer(model): + raise YellowbrickTypeError( + "The supplied model is not a clustering estimator; try a " + "classifier or regression score visualizer instead!" + ) + + super(ClusteringScoreVisualizer, self).__init__(model, ax=ax, **kwargs) diff --git a/yellowbrick/cluster/elbow.py b/yellowbrick/cluster/elbow.py new file mode 100644 index 000000000..4bf3a1589 --- /dev/null +++ b/yellowbrick/cluster/elbow.py @@ -0,0 +1,172 @@ +# yellowbrick.cluster.elbow +# Implements the elbow method for determining the optimal number of clusters. +# +# Author: Benjamin Bengfort +# Created: Thu Mar 23 22:36:31 2017 -0400 +# +# Copyright (C) 2016 District Data Labs +# For license information, see LICENSE.txt +# +# ID: elbow.py [] benjamin@bengfort.com $ + +""" +Implements the elbow method for determining the optimal number of clusters. +https://bl.ocks.org/rpgove/0060ff3b656618e9136b +""" + +########################################################################## +## Imports +########################################################################## + +from .base import ClusteringScoreVisualizer +from ..exceptions import YellowbrickValueError + +from sklearn.metrics import silhouette_score + +## Packages for export +__all__ = [ + "KElbowVisualizer", +] + + +########################################################################## +## Elbow Method +########################################################################## + +class KElbowVisualizer(ClusteringScoreVisualizer): + """ + The K-Elbow Visualizer implements the "elbow" method of selecting the + optimal number of clusters for K-means clustering. K-means is a simple + unsupervised machine learning algorithm that groups data into a specified + number (k) of clusters. Because the user must specify in advance what k to + choose, the algorithm is somewhat naive -- it assigns all members to k + clusters even if that is not the right k for the dataset. + + 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 the + ``silhouette_score``, the mean silhouette coefficient for all samples. + The silhouette coefficient of a cluster is computed by comparing the mean + intra-cluster distance (a) and the mean nearest-cluster distance (b) for + each sample in the dataset; the silhouette is then computed as + ``(b-a) / max(a,b)``. The score is a value between -1 and 1, values near + zero indicate overlapping clusters. Negative values imply that samples + have been assigned to the wrong cluster, and positive values mean that + there are discrete clusters. + + Finally, the silhouette score for each k is plotted. If the line chart + looks like an arm, then the "elbow" (the point of inflection on the curve) + is the best value of k. The idea is that we want as small a k as possible + such that the clusters do not overlap. + + Parameters + ---------- + + model : a Scikit-Learn clusterer + Should be an instance of a clusterer, specifically ``KMeans`` or + ``MiniBatchKMeans``. 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). + + 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. + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Examples + -------- + + >>> from yellowbrick.cluster import KElbowVisualizer + >>> from sklearn.cluster import KMeans + >>> model = KElbowVisualizer(KMeans(), k=10) + >>> model.fit(X) + >>> model.poof() + + Notes + ----- + + 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. + + For a discussion on the Elbow method, read more at + `Robert Gove's Block `_. + + .. todo:: add parallelization ooption for performance + .. todo:: add different metrics for scores and silhoutte + .. todo:: add timing information about how long its taking + """ + + def __init__(self, model, ax=None, k=10, **kwargs): + super(KElbowVisualizer, self).__init__(model, ax=ax, **kwargs) + + # Convert K into a tuple argument if an integer + if isinstance(k, int): + k = (2, k) + + # Expand k in to the values we will use, capturing exceptions + try: + k = tuple(k) + self.k_values_ = list(range(*k)) + except: + raise YellowbrickValueError(( + "Specify 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 + + def fit(self, X, y=None, **kwargs): + """ + Fits n KMeans models where n is the length of ``self.k_values_``, + storing the silhoutte scores in the ``self.k_scores_`` attribute. + This method finishes up by calling draw to create the plot. + """ + + self.k_scores_ = [] + + for k in self.k_values_: + self.estimator.set_params(n_clusters=k) + self.estimator.fit(X) + self.k_scores_.append( + silhouette_score(X, self.estimator.labels_, metric='euclidean') + ) + + self.draw() + + def draw(self): + """ + Draw the elbow curve for the specified scores and values of K. + """ + + if self.ax is None: + self.ax = self.gca() + + # Plot the silhouette score against k + self.ax.plot(self.k_values_, self.k_scores_) + + return self.ax + + def finalize(self): + """ + Prepare the figure for rendering by setting the title as well as the + X and Y axis labels and adding the legend. + """ + + # Set the title + self.set_title( + 'Silhoutte for {} Clustering Behavior'.format(self.name) + ) + + # Set the x and y labels + self.ax.set_xlabel('k') + self.ax.set_ylabel('silhouette') diff --git a/yellowbrick/cluster/silhouette.py b/yellowbrick/cluster/silhouette.py new file mode 100644 index 000000000..17c298c7f --- /dev/null +++ b/yellowbrick/cluster/silhouette.py @@ -0,0 +1,163 @@ +# yellowbrick.cluster.silhouette +# Implements visualizers using the silhouette metric for cluster evaluation. +# +# Author: Benjamin Bengfort +# Created: Mon Mar 27 10:09:24 2017 -0400 +# +# Copyright (C) 2016 District Data Labs +# For license information, see LICENSE.txt +# +# ID: silhouette.py [] benjamin@bengfort.com $ + +""" +Implements visualizers that use the silhouette metric for cluster evaluation. +""" + +########################################################################## +## Imports +########################################################################## + +import numpy as np + +from .base import ClusteringScoreVisualizer +from ..exceptions import YellowbrickValueError +from ..style import resolve_colors, color_palette + +from sklearn.metrics import silhouette_score, silhouette_samples + +## Packages for export +__all__ = [ + "SilhouetteVisualizer" +] + + +########################################################################## +## Silhouette Method for K Selection +########################################################################## + +class SilhouetteVisualizer(ClusteringScoreVisualizer): + """ + TODO: Document this class! + """ + + def __init__(self, model, ax=None, **kwargs): + super(SilhouetteVisualizer, self).__init__(model, ax=ax, **kwargs) + + # Visual Properties + # TODO: Fix the color handling + 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. + """ + # 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 + + # Compute the scores of the cluster + labels = self.estimator.predict(X) + self.silhouette_score_ = silhouette_score(X, labels) + self.silhouette_samples_ = silhouette_samples(X, labels) + + # Draw the silhouette figure + self.draw(labels) + + # Return the estimator + return self + + def draw(self, labels): + """ + Draw the silhouettes for each sample and the average score. + + Parameters + ---------- + + labels : array-like + An array with the cluster label for each silhouette sample, + usually computed with ``predict()``. Labels are not stored on the + visualizer so that the figure can be redrawn with new data. + """ + + if self.ax is None: + self.ax = self.gca() + + # Track the positions of the lines being drawn + y_upper = 10 # the top of the silhouette + y_lower = 10 # The bottom of the silhouette + + # Get the colors from the various properties + # TODO: Use resolve_colors instead of this + colors = color_palette(self.colormap, self.n_clusters) + + # For each cluster, plot the silhouette scores + for idx in range(self.n_clusters): + + # Collect silhouette scores for samples in the current cluster . + values = self.silhouette_samples_[labels == idx] + values.sort() + + # Compute the size of the cluster and find upper limit + size = values.shape[0] + y_upper = y_lower + size + + color = colors[idx] + self.ax.fill_betweenx( + np.arange(y_lower, y_upper), 0, values, + facecolor=color, edgecolor=color, alpha=0.5 + ) + + # Label the silhouette plots with their cluster numbers + self.ax.text(-0.05, y_lower + 0.5 * size, str(idx)) + + # Compute the new y_lower for next plot + y_lower = y_upper + 10 + + # The vertical line for average silhouette score of all the values + self.ax.axvline( + x=self.silhouette_score_, color="red", linestyle="--" + ) + + return self.ax + + def finalize(self): + """ + Prepare the figure for rendering by setting the title and adjusting + the limits on the axes, adding labels and a legend. + """ + + # Set the title + self.set_title(( + "Silhouette Plot of {} Clustering for {} Samples in {} Centers" + ).format( + 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 + # silhouette plots of individual clusters, to demarcate them clearly. + 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") + self.ax.set_ylabel("cluster label") + + # 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)) diff --git a/yellowbrick/features/__init__.py b/yellowbrick/features/__init__.py index bee35802e..3e97568b2 100644 --- a/yellowbrick/features/__init__.py +++ b/yellowbrick/features/__init__.py @@ -21,3 +21,4 @@ from .pcoords import ParallelCoordinates, parallel_coordinates from .radviz import RadialVisualizer, RadViz, radviz from .rankd import Rank2D, rank2d +from .scatter import ScatterViz, scatterviz diff --git a/yellowbrick/features/base.py b/yellowbrick/features/base.py index 9a1c28ab4..c0e5e6bd7 100644 --- a/yellowbrick/features/base.py +++ b/yellowbrick/features/base.py @@ -92,40 +92,49 @@ class DataVisualizer(FeatureVisualizer): whether or not the target is sequential or categorical, and mapping a color sequence or color set to the targets as appropriate. It also uses the fit method to call the drawing utilities. - """ - def __init__(self, ax=None, features=None, classes=None, color=None, - colormap=None, **kwargs): - """ - Initialize the data visualization with many of the options required - in order to make most visualizations work. + Parameters + ---------- - These parameters can be influenced later on in the visualization - process, but can and should be set as early as possible. + 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). - Parameters - ---------- + features: 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 columns of the DataFrame. - ax: the axis to plot the figure on. + 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. - features: a list of feature names to use - If a DataFrame is passed to fit and features is None, feature - names are selected as the columns of the DataFrame. + color: list or tuple, default: None + optional list or tuple of colors to colorize lines + Use either color to colorize the lines on a per class basis or + colormap to color them on a continuous scale. - classes: 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. + colormap: string or cmap, default: None + optional string or matplotlib cmap to colorize lines + Use either color to colorize the lines on a per class basis or + colormap to color them on a continuous scale. - color: optional list or tuple of colors to colorize lines - Use either color to colorize the lines on a per class basis or - colormap to color them on a continuous scale. + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. - colormap: optional string or matplotlib cmap to colorize lines - Use either color to colorize the lines on a per class basis or - colormap to color them on a continuous scale. - - kwargs: keyword arguments passed to the super class. + 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, features=None, classes=None, color=None, + colormap=None, **kwargs): + """ + Initialize the data visualization with many of the options required + in order to make most visualizations work. """ super(DataVisualizer, self).__init__(ax=ax, **kwargs) diff --git a/yellowbrick/features/jointplot.py b/yellowbrick/features/jointplot.py new file mode 100644 index 000000000..0d576669f --- /dev/null +++ b/yellowbrick/features/jointplot.py @@ -0,0 +1,328 @@ + +# yellowbrick.features.jointplot +# Implementations of joint plots for univariate and bivariate analysis. +# +# Author: Prema Damodaran Roman +# Created: Mon Apr 10 21:00:54 2017 -0400 +# +# Copyright (C) 2017 District Data Labs +# For license information, see LICENSE.txt +# +# ID: jointplot.py [] pdamo24@gmail.com $ + +########################################################################## +## Imports +########################################################################## + +import warnings +import numpy as np +import matplotlib as mpl +import matplotlib.pyplot as plt + +from yellowbrick.features.base import FeatureVisualizer +from yellowbrick.exceptions import YellowbrickValueError +from yellowbrick.bestfit import draw_best_fit +from yellowbrick.utils import is_dataframe + +########################################################################## +## Joint Plot Visualizer +########################################################################## + + +class JointPlotVisualizer(FeatureVisualizer): + """ + JointPlotVisualizer allows for a simultaneous visualization of the relationship + between two variables and the distrbution of each individual variable. The + relationship is plotted along the joint axis and univariate distributions + are plotted on top of the x axis and to the right of the y axis. + + Parameters + ---------- + ax: matplotlib Axes, default: None + This is inherited from FeatureVisualizer but is defined within + JointPlotVisualizer since there are three axes objects. + + feature: string, default: None + The name of the X variable + If a DataFrame is passed to fit and feature is None, feature + is selected as the column of the DataFrame. There must be only + one column in the DataFrame. + + target: string, default: None + The name of the Y variable + If target is None and a y value is passed to fit then the target + is selected from the target vector. + + joint_plot: one of {'scatter', 'hex'}, default: 'scatter' + The type of plot to render in the joint axis + Currently, the choices are scatter and hex. + Use scatter for small datasets and hex for large datasets + + joint_args: dict, default: None + Keyword arguments used for customizing the joint plot: + + ============= ================================================================== + Property Description + ------------- ------------------------------------------------------------------ + alpha transparency + facecolor background color of the joint axis + aspect aspect ratio + fit used if scatter is selected for joint_plot to draw a + best fit line - values can be True or False. + Uses ``Yellowbrick.bestfit`` + estimator used if scatter is selected for joint_plot to determine + the type of best fit line to use. Refer to + Yellowbrick.bestfit for types of estimators that can be used. + x_bins used if hex is selected to set the number of bins for the x value + y_bins used if hex is selected to set the number of bins for the y value + cmap string or matplotlib cmap to colorize lines + Use either color to colorize the lines on a per class basis or + colormap to color them on a continuous scale. + ============= ================================================================== + + xy_plot: one of {'hist'}, default: 'hist' + The type of plot to render along the x and y axes + Currently, the choice is hist + + xy_args: dict, default: None + Keyword arguments used for customizing the x and y plots: + + ============== ===================================================== + Property Description + -------------- ----------------------------------------------------- + alpha transparency + facecolor_x background color of the x axis + facecolor_y background color of the y axis + bins used to set up the number of bins for the hist plot + histcolor_x used to set the color for the histogram on the x axis + histcolor_y used to set the color for the histogram on the y axis + ============== ===================================================== + + size: float, default: 6 + Size of each side of the figure in inches + + ratio: float, default: 5 + Ratio of joint axis size to the x and y axes height + + space: float, default: 0.2 + Space between the joint axis and the x and y axes + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Examples + -------- + + >>> visualizer = JointPlotVisualizer() + >>> visualizer.fit(X,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, feature=None, target=None, + joint_plot='scatter', joint_args=None, + xy_plot='hist', xy_args=None, + size=6, ratio=5, space=.2, **kwargs): + + #check matplotlib version - needs to be version 2.0.0 + if mpl.__version__ == "2.0.0": + pass + else: + warnings.warn(( + "{} requires Matplotlib version 2.0.0." + "Please upgrade to continue." + ).format(self.__class__.__name__)) + + super(JointPlotVisualizer, self).__init__(ax, **kwargs) + + self.feature = feature + self.target = target + self.joint_plot = joint_plot + self.joint_args = joint_args + self.xy_plot = xy_plot + self.xy_args = xy_args + self.size = size + self.ratio = ratio + self.space = space + + def fit(self, X, y, **kwargs): + """ + Sets up the X and y variables for the jointplot + and checks to ensure that X and y are of the + correct data type + + Fit calls draw + + Parameters + ---------- + + X : ndarray or DataFrame of shape n x 1 + A matrix of n instances with 1 feature + + y : ndarray or Series of length n + An array or series of the target value + + kwargs: dict + keyword arguments passed to Scikit-Learn API. + """ + + #throw an error if X has more than 1 column + if is_dataframe(X): + nrows, ncols = X.shape + + if ncols > 1: + raise YellowbrickValueError(( + "X needs to be an ndarray or DataFrame with one feature, " + "please select one feature from the DataFrame" + )) + + #throw an error is y is None + if y is None: + raise YellowbrickValueError(( + "Joint plots are useful for classification and regression " + "problems, which require a target variable" + )) + + + # Handle the feature name if it is None. + if self.feature is None: + + # If X is a data frame, get the columns off it. + if is_dataframe(X): + self.feature = X.columns + + else: + self.feature = ['x'] + + # Handle the target name if it is None. + if self.target is None: + self.target = ['y'] + + self.draw(X, y, **kwargs) + return self + + def draw(self, X, y, **kwargs): + """ + Sets up the layout for the joint plot draw calls ``draw_joint`` and + ``draw_xy`` to render the visualizations. + """ + + fig = plt.figure(figsize=(self.size, self.size)) + gs = plt.GridSpec(self.ratio + 1, self.ratio + 1) + + #Set up the 3 axes objects + joint_ax = fig.add_subplot(gs[1:, :-1]) + x_ax = fig.add_subplot(gs[0, :-1], sharex=joint_ax) + y_ax = fig.add_subplot(gs[1:, -1], sharey=joint_ax) + + fig.tight_layout() + fig.subplots_adjust(hspace=self.space, wspace=self.space) + + self.fig = fig + self.joint_ax = joint_ax + self.x_ax = x_ax + self.y_ax = y_ax + + self.draw_joint(X, y, **kwargs) + self.draw_xy(X, y, **kwargs) + + def draw_joint(self, X, y, **kwargs): + """ + Draws the visualization for the joint axis. + """ + + if self.joint_args is None: + self.joint_args = {} + + self.joint_args.setdefault("alpha", 0.4) + facecolor = self.joint_args.pop("facecolor", "#dddddd") + self.joint_ax.set_facecolor(facecolor) + + if self.joint_plot == "scatter": + aspect = self.joint_args.pop("aspect", "auto") + self.joint_ax.set_aspect(aspect) + self.joint_ax.scatter(X, y, **self.joint_args) + + fit = self.joint_args.pop("fit", True) + if fit: + estimator = self.joint_args.pop("estimator", "linear") + draw_best_fit(X, y, self.joint_ax, estimator) + + elif self.joint_plot == "hex": + x_bins = self.joint_args.pop("x_bins", 50) + y_bins = self.joint_args.pop("y_bins", 50) + colormap = self.joint_args.pop("cmap", 'Blues') + gridsize = int(np.mean([x_bins, y_bins])) + + xmin = X.min() + xmax = X.max() + ymin = y.min() + ymax = y.max() + + self.joint_ax.hexbin(X, y, + gridsize=gridsize, cmap=colormap, mincnt=1, **self.joint_args + ) + self.joint_ax.axis([xmin, xmax, ymin, ymax]) + + def draw_xy(self, X, y, **kwargs): + """ + Draws the visualization for the x and y axes + """ + + if self.xy_args is None: + self.xy_args = {} + + facecolor_x = self.xy_args.pop("facecolor_x", "#dddddd") + self.x_ax.set_facecolor(facecolor_x) + facecolor_y = self.xy_args.pop("facecolor_y", "#dddddd") + self.y_ax.set_facecolor(facecolor_y) + + + if self.xy_plot == "hist": + hist_bins = self.xy_args.pop("bins", 50) + self.xy_args.setdefault("alpha", 0.4) + histcolor_x = self.xy_args.pop("histcolor_x", "#6897bb") + self.x_ax.set_facecolor(facecolor_x) + histcolor_y = self.xy_args.pop("histcolor_y", "#6897bb") + self.y_ax.set_facecolor(facecolor_y) + self.x_ax.hist(X, bins=hist_bins, color=histcolor_x, **self.xy_args) + 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. + The user calls poof and poof calls finalize. + + Parameters + ---------- + kwargs: generic keyword arguments. + """ + + plt.setp(self.x_ax.get_xticklabels(), visible=False) + plt.setp(self.y_ax.get_yticklabels(), visible=False) + + plt.setp(self.x_ax.yaxis.get_majorticklines(), visible=False) + plt.setp(self.x_ax.yaxis.get_minorticklines(), visible=False) + plt.setp(self.y_ax.xaxis.get_majorticklines(), visible=False) + plt.setp(self.y_ax.xaxis.get_minorticklines(), visible=False) + plt.setp(self.x_ax.get_yticklabels(), visible=False) + plt.setp(self.y_ax.get_xticklabels(), visible=False) + self.x_ax.yaxis.grid(False) + self.y_ax.xaxis.grid(False) + self.fig.suptitle("Joint Plot of {} vs {}" + .format(self.feature, self.target), y=1.05) diff --git a/yellowbrick/features/pcoords.py b/yellowbrick/features/pcoords.py index be69cce8c..fb92c9389 100644 --- a/yellowbrick/features/pcoords.py +++ b/yellowbrick/features/pcoords.py @@ -40,33 +40,38 @@ def parallel_coordinates(X, y=None, ax=None, features=None, classes=None, 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 + ax : matplotlib Axes, default: None The axes to plot the figure on. - features : list of strings + features : list of strings, default: None The names of the features or columns - classes : list of strings + classes : list of strings, default: None The names of the classes in the target - color : list or tuple of colors + color : list or tuple of colors, default: None Specify the colors for each individual class - colormap : string or matplotlib cmap + colormap : string or matplotlib cmap, default: None Sequential colormap for continuous target - vlines : bool + vlines : bool, default: True Display the vertical azis lines - vlines_kwds : dict + vlines_kwds : dict, default: None Keyword arguments to draw the vlines + 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 @@ -94,44 +99,61 @@ 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. + + 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). + + features : 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 columns of the DataFrame. + + 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. + + color : list or tuple, default: None + optional list or tuple of colors to colorize lines + Use either color to colorize the lines on a per class basis or + colormap to color them on a continuous scale. + + colormap : string or cmap, default: None + optional string or matplotlib cmap to colorize lines + Use either color to colorize the lines on a per class basis or + colormap to color them on a continuous scale. + + vlines : boolean, default: True + flag to determine vertical line display + + vlines_kwds : dict, default: None + options to style or display the vertical lines, default: None + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Examples + -------- + + >>> visualizer = ParallelCoordinates() + >>> visualizer.fit(X, y) + >>> visualizer.transform(X) + >>> 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, features=None, classes=None, color=None, colormap=None, vlines=True, vlines_kwds=None, **kwargs): - """ - Initialize the base parallel coordinates with many of the options - required in order to make the visualization work. - - Parameters - ---------- - - :param ax: the axis to plot the figure on. - - :param features: a list of feature names to use - If a DataFrame is passed to fit and features is None, feature - names are selected as the columns of the DataFrame. - - :param classes: 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. - - :param color: optional list or tuple of colors to colorize lines - Use either color to colorize the lines on a per class basis or - colormap to color them on a continuous scale. - - :param colormap: optional string or matplotlib cmap to colorize lines - Use either color to colorize the lines on a per class basis or - colormap to color them on a continuous scale. - - :param vlines: flag to determine vertical line display, default True - - :param vlines_kwds: options to style or display the vertical lines - - :param kwargs: keyword arguments passed 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. - """ super(ParallelCoordinates, self).__init__( ax, features, classes, color, colormap, **kwargs ) @@ -147,6 +169,7 @@ def draw(self, X, y, **kwargs): Called from the fit method, this method creates the parallel coordinates canvas and draws each instance and vertical lines on it. """ + # Get the shape of the data nrows, ncols = X.shape diff --git a/yellowbrick/features/radviz.py b/yellowbrick/features/radviz.py index 89ada2519..0a0e652ca 100644 --- a/yellowbrick/features/radviz.py +++ b/yellowbrick/features/radviz.py @@ -32,7 +32,8 @@ def radviz(X, y=None, ax=None, features=None, classes=None, color=None, colormap=None, **kwargs): - """Displays each feature as an axis around a circle surrounding a scatter + """ + Displays each feature as an axis around a circle surrounding a scatter plot whose points are each individual instance. This helper function is a quick wrapper to utilize the RadialVisualizer @@ -47,19 +48,19 @@ def radviz(X, y=None, ax=None, features=None, classes=None, y : ndarray or Series of length n An array or series of target or class values - ax : matplotlib axes + ax : matplotlib Axes, default: None The axes to plot the figure on. - features : list of strings + features : list of strings, default: None The names of the features or columns - classes : list of strings + classes : list of strings, default: None The names of the classes in the target - color : list or tuple of colors + color : list or tuple of colors, default: None Specify the colors for each individual class - colormap : string or matplotlib cmap + colormap : string or matplotlib cmap, default: None Sequential colormap for continuous target Returns @@ -90,40 +91,54 @@ class RadialVisualizer(DataVisualizer): axis 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. + + 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). + + features : 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 columns of the DataFrame. + + 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. + + color : list or tuple, default: None + optional list or tuple of colors to colorize lines + Use either color to colorize the lines on a per class basis or + colormap to color them on a continuous scale. + + colormap : string or cmap, default: None + optional string or matplotlib cmap to colorize lines + Use either color to colorize the lines on a per class basis or + colormap to color them on a continuous scale. + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Examples + -------- + + >>> visualizer = RadViz() + >>> visualizer.fit(X, y) + >>> visualizer.transform(X) + >>> 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, features=None, classes=None, color=None, colormap=None, **kwargs): - """ - Initialize the base radviz with many of the options required in order - to make the visualization work. - - Parameters - ---------- - - :param ax: the axis to plot the figure on. - - :param features: a list of feature names to use - If a DataFrame is passed to fit and features is None, feature - names are selected as the columns of the DataFrame. - - :param classes: 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. - - :param color: optional list or tuple of colors to colorize lines - Use either color to colorize the lines on a per class basis or - colormap to color them on a continuous scale. - - :param colormap: optional string or matplotlib cmap to colorize lines - Use either color to colorize the lines on a per class basis or - colormap to color them on a continuous scale. - - :param kwargs: keyword arguments passed 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. - """ super(RadialVisualizer, self).__init__( ax, features, classes, color, colormap, **kwargs ) @@ -227,6 +242,10 @@ def finalize(self, **kwargs): 'RadViz for {} Features'.format(len(self.features_)) ) + # Remove the ticks from the graph + self.ax.set_yticks([]) + self.ax.set_xticks([]) + # Add the legend self.ax.legend(loc='best') diff --git a/yellowbrick/features/rankd.py b/yellowbrick/features/rankd.py index 238e08a5b..8e0eef9ea 100644 --- a/yellowbrick/features/rankd.py +++ b/yellowbrick/features/rankd.py @@ -88,6 +88,42 @@ class Rank2D(FeatureVisualizer): Rank2D performs pairwise comparisons of each feature in the data set with a specific metric or algorithm (e.g. Pearson correlation) then returns them ranked as a lower left triangle diagram. + + 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). + + algorithm : one of {'pearson', 'covariance'}, default: 'pearson' + The ranking algorithm to use, default is Pearson correlation. + + features : list + a list of feature names to use + If a DataFrame is passed to fit and features is None, feature + names are selected as the columns of the DataFrame. + + colormap : string or cmap, default: 'RdBu_r' + optional string or matplotlib cmap to colorize lines + Use either color to colorize the lines on a per class basis or + colormap to color them on a continuous scale. + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Examples + -------- + + >>> visualizer = Rank2D() + >>> visualizer.fit(X, y) + >>> visualizer.transform(X) + >>> visualizer.poof() + + Notes + ----- + These parameters can be influenced later on in the visualization + process, but can and should be set as early as possible. """ ranking_methods = { @@ -100,27 +136,6 @@ def __init__(self, ax=None, algorithm='pearson', features=None, """ Initialize the Rank2D class with the options required to rank and order features as well as visualize the result. - - Parameters - ---------- - ax : matplotlib axes - the axis to plot the figure on. - - algorithm : one of {pearson, covariance} - the ranking algorithm to use, default is Pearson correlation. - - features : list - a list of feature names to use - If a DataFrame is passed to fit and features is None, feature - names are selected as the columns of the DataFrame. - - colormap : string or cmap - optional string or matplotlib cmap to colorize lines - Use either color to colorize the lines on a per class basis or - colormap to color them on a continuous scale. - - kwargs : dict - keyword arguments passed to the super class. """ super(Rank2D, self).__init__(ax=ax, **kwargs) diff --git a/yellowbrick/features/scatter.py b/yellowbrick/features/scatter.py new file mode 100644 index 000000000..0b6de468d --- /dev/null +++ b/yellowbrick/features/scatter.py @@ -0,0 +1,313 @@ +# yellowbrick.features.scatter +# Implements a 2d scatter plot for feature analysis. +# +# Author: Nathan Danielsen +# Created: Fri Feb 26 19:40:00 2017 -0400 +# +# For license information, see LICENSE.txt +# +# ID: scatter.py [] nathan.danielsen@gmail.com $ +""" +Implements a 2D scatter plot for feature analysis. +""" + +########################################################################## +# Imports +########################################################################## +import itertools + +import numpy as np +import matplotlib.pyplot as plt +import matplotlib.patches as patches + +from yellowbrick.features.base import DataVisualizer +from yellowbrick.utils import is_dataframe, is_structured_array, has_ndarray_int_columns +from yellowbrick.exceptions import YellowbrickValueError +from yellowbrick.style.colors import resolve_colors, get_color_cycle + +########################################################################## +# Quick Methods +########################################################################## + + +def scatterviz(X, + y=None, + ax=None, + features=None, + classes=None, + color=None, + colormap=None, + markers=None, + **kwargs): + """Displays a bivariate scatter plot. + + This helper function is a quick wrapper to utilize the ScatterVisualizer + (Transformer) 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, default: None + An array or series of target or class values + + ax : matplotlib axes, default: None + The axes to plot the figure on. + + features : list of strings, default: None + The names of two features or columns. + More than that will raise an error. + + classes : list of strings, default: None + The names of the classes in the target + + color : list or tuple of colors, default: None + Specify the colors for each individual class + + colormap : string or matplotlib cmap, default: None + Sequential colormap for continuous target + + markers : iterable of strings, default: ,+o*vhd + Matplotlib style markers for points on the scatter plot points + + 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) + + # Fit and transform the visualizer (calls draw) + visualizer.fit(X, y, **kwargs) + visualizer.transform(X) + + # Return the axes object on the visualizer + return visualizer.ax + + +########################################################################## +# Static ScatterVisualizer Visualizer +########################################################################## + + +class ScatterVisualizer(DataVisualizer): + """ + ScatterVisualizer is a bivariate feature data visualization algorithm that + plots using the Cartesian coordinates of each point. + + Parameters + ---------- + + ax : a matplotlib plot, default: None + The axis to plot the figure on. + + x : string, default: None + The feature name that corresponds to a column name or index postion + in the matrix that will be plotted against the x-axis + + y : string, default: None + The feature name that corresponds to a column name or index postion + in the matrix that will be plotted against the y-axis + + features : a list of two feature names to use, default: None + List of two features that correspond to the columns in the array. + The order of the two features correspond to X and Y axises on the + graph. More than two feature names or columns will raise an error. + If a DataFrame is passed to fit and features is None, feature names + are selected that are the columns of the DataFrame. + + classes : a list of class names for the legend, default: None + If classes is None and a y value is passed to fit then the classes + are selected from the target vector. + + color : optional list or tuple of colors to colorize points, default: None + Use either color to colorize the points on a per class basis or + colormap to color them on a continuous scale. + + colormap : optional string or matplotlib cmap to colorize points, default: None + Use either color to colorize the points on a per class basis or + colormap to color them on a continuous scale. + + markers : iterable of strings, default: ,+o*vhd + Matplotlib style markers for points on the scatter plot points + + kwargs : keyword arguments passed 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, + ax=None, + x=None, + y=None, + features=None, + classes=None, + color=None, + colormap=None, + markers=None, + **kwargs): + """ + Initialize the base scatter with many of the options required in order + to make the visualization work. + """ + super(ScatterVisualizer, self).__init__(ax, features, classes, color, + colormap, **kwargs) + + self.x = x + self.y = y + self.markers = itertools.cycle( + kwargs.pop('markers', (',', '+', 'o', '*', 'v', 'h', 'd'))) + + + + if self.x is not None and self.y is not None and self.features_ is not None: + raise YellowbrickValueError( + 'Please specify x,y or features, not both.') + + if self.x is not None and self.y is not None and self.features_ is None: + self.features_ = [self.x, self.y] + + # Ensure with init that features doesn't have more than two features + if features is not None: + if len(features) != 2: + raise YellowbrickValueError( + 'ScatterVisualizer only accepts two features.') + + def fit(self, X, y=None, **kwargs): + """ + The fit method is the primary drawing input for the parallel coords + 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 2 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 + """ + _, ncols = X.shape + + if ncols == 2: + X_two_cols = X + if self.features_ is None: + self.features_ = ["Feature One", "Feature Two"] + + # Handle the feature names if they're None. + elif self.features_ is not None and is_dataframe(X): + X_two_cols = X[self.features_].as_matrix() + + # handle numpy named/ structured array + elif self.features_ is not None and is_structured_array(X): + X_selected = X[self.features_] + X_two_cols = X_selected.view((np.float64, len(X_selected.dtype.names))) + + # handle features that are numeric columns in ndarray matrix + elif self.features_ is not None and has_ndarray_int_columns(self.features_, X): + f_one, f_two = self.features_ + X_two_cols = X[:, [int(f_one), int(f_two)]] + + else: + raise YellowbrickValueError(""" + ScatterVisualizer only accepts two features, please + explicitly set these two features in the init kwargs or + pass a matrix/ dataframe in with only two columns.""") + + # 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 np.unique(y)] + + # Draw the instances + self.draw(X_two_cols, y, **kwargs) + + # Fit always returns self. + return self + + 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. + """ + # Create the axes if they don't exist + if self.ax is None: + self.ax = plt.gca(xlim=[-1, 1], ylim=[-1, 1]) + + # set the colors + if self.colormap is not None or self.color is not None: + color_values = resolve_colors( + num_colors=len(self.classes_), + colormap=self.colormap, + color=self.color) + else: + color_values = get_color_cycle() + + colors = dict(zip(self.classes_, color_values)) + + # Create a data structure to hold the scatter plot representations + to_plot = {} + for kls in self.classes_: + to_plot[kls] = [[], []] + + # Add each row of the data set to to_plot for plotting + # TODO: make this an independent function for override + for i, row in enumerate(X): + row_ = np.repeat(np.expand_dims(row, axis=1), 2, axis=1) + x_, y_ = row_[0], row_[1] + kls = self.classes_[y[i]] + + to_plot[kls][0].append(x_) + to_plot[kls][1].append(y_) + + # Add the scatter plots from the to_plot function + # TODO: store these plots to add more instances to later + # TODO: make this a separate function + for i, kls in enumerate(self.classes_): + self.ax.scatter( + to_plot[kls][0], + to_plot[kls][1], + marker=next(self.markers), + color=colors[kls], + label=str(kls), + **kwargs) + + self.ax.axis('equal') + + 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. + + """ + # Divide out the two features + feature_one, feature_two = self.features_ + + # Set the title + self.set_title('Scatter Plot: {0} vs {1}'.format( + str(feature_one), str(feature_two))) + # Add the legend + self.ax.legend(loc='best') + self.ax.set_xlabel(str(feature_one)) + self.ax.set_ylabel(str(feature_two)) + + +# Alias for ScatterViz +ScatterViz = ScatterVisualizer diff --git a/yellowbrick/regressor/__init__.py b/yellowbrick/regressor/__init__.py new file mode 100644 index 000000000..44853d793 --- /dev/null +++ b/yellowbrick/regressor/__init__.py @@ -0,0 +1,24 @@ +# yellowbrick.regressor +# Visualizers for Regression analysis and diagnostics +# +# Author: Benjamin Bengfort +# Created: Mon Mar 06 12:23:55 2017 -0500 +# +# Copyright (C) 2016 District Data Labs +# For license information, see LICENSE.txt +# +# ID: __init__.py [] benjamin@bengfort.com $ + +""" +Visualizers for Regression analysis and diagnostics, particularly +visualizations related to evaluating Scikit-Learn regressor models. +""" + +########################################################################## +## Imports +########################################################################## + +## Hoist visualizers into the regressor namespace +from .base import * +from .residuals import * +from .alphas import * diff --git a/yellowbrick/regressor/alphas.py b/yellowbrick/regressor/alphas.py new file mode 100644 index 000000000..958bcbeb6 --- /dev/null +++ b/yellowbrick/regressor/alphas.py @@ -0,0 +1,356 @@ +# yellowbrick.regressor.alphas +# Implements alpha selection visualizers for regularization +# +# Author: Benjamin Bengfort +# Created: Mon Mar 06 19:22:07 2017 -0500 +# +# Copyright (C) 2016 District Data Labs +# For license information, see LICENSE.txt +# +# ID: alphas.py [] benjamin@bengfort.com $ + +""" +Implements alpha selection visualizers for regularization +""" + +########################################################################## +## Imports +########################################################################## + +import numpy as np +import matplotlib.pyplot as plt + +from functools import partial + +from .base import RegressionScoreVisualizer +from ..exceptions import YellowbrickTypeError +from ..exceptions import YellowbrickValueError + +from sklearn.model_selection import cross_val_score + +## Packages for export +__all__ = [ + "AlphaSelection", "ManualAlphaSelection" +] + + +########################################################################## +## AlphaSelection Visualizer +########################################################################## + +class AlphaSelection(RegressionScoreVisualizer): + """ + The Alpha Selection 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. + + 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. + + 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, you + can 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. Use this + visualization to detect if the model is responding to regularization, e.g. + as you increase or decrease alpha, the model responds and error is + decreased. 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). + + Parameters + ---------- + + model : a Scikit-Learn regressor + Should be an instance of a regressor, and specifically one whose name + ends with "CV" otherwise a will raise a YellowbrickTypeError exception + on instantiation. To use non-CV regressors see: + ``ManualAlphaSelection``. + + 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. + + Examples + -------- + + >>> from yellowbrick.regressor import AlphaSelection + >>> from sklearn.linear_model import LassoCV + >>> model = AlphaSelection(LassoCV()) + >>> model.fit(X, y) + >>> model.poof() + + Notes + ----- + + This class expects an estimator whose name ends with "CV". If you wish to + use some other estimator, please see the ``ManualAlphaSelection`` + Visualizer for manually iterating through all alphas and selecting the + best one. + + This Visualizer hoooks 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. + + Note, each "RegressorCV" module has many different methods for storing + alphas and error. This visualizer attempts to get them all and is known + to work for RidgeCV, LassoCV, LassoLarsCV, and ElasticNetCV. If your + favorite regularization method doesn't work, please submit a bug report. + + For RidgeCV, make sure ``store_cv_values=True``. + """ + + def __init__(self, model, ax=None, **kwargs): + + # Check to make sure this is a "RegressorCV" + name = model.__class__.__name__ + if not name.endswith("CV"): + raise YellowbrickTypeError(( + "'{}' is not a CV regularization model;" + " try ManualAlphaSelection instead." + ).format(name)) + + # Set the store_cv_values parameter on RidgeCV + if 'store_cv_values' in model.get_params().keys(): + model.set_params(store_cv_values=True) + + # Call super to initialize the class + super(AlphaSelection, self).__init__(model, ax=ax, **kwargs) + + def fit(self, X, y, **kwargs): + """ + A simple pass-through method; calls fit on the estimator and then + draws the alpha-error plot. + """ + self.estimator.fit(X, y, **kwargs) + self.draw() + return self + + def draw(self): + """ + Draws the alpha plot based on the values on the estimator. + """ + if self.ax is None: + self.ax = plt.gca() + + # Search for the correct parameters on the estimator. + alphas = self._find_alphas_param() + errors = self._find_errors_param() + + + alpha = self.estimator.alpha_ # Get decision from the estimator + name = self.name[:-2].lower() # Remove the CV from the label + + # Plot the alpha against the error + self.ax.plot(alphas, errors, label=name) + + # Draw a dashed vline at the alpha + label = "$\\alpha={:0.3f}$".format(alpha) + self.ax.axvline(alpha, color='k', linestyle='dashed', label=label) + + return self.ax + + def finalize(self): + """ + Prepare the figure for rendering by setting the title as well as the + X and Y axis labels and adding the legend. + """ + # Set the title + self.set_title( + '{} Alpha Error'.format(self.name) + ) + + # Set the x and y labels + self.ax.set_xlabel("alpha") + self.ax.set_ylabel("error (or score)") + + # Set the legend + self.ax.legend(loc='best') + + def _find_alphas_param(self): + """ + Searches for the parameter on the estimator that contains the array of + alphas that was used to produce the error selection. If it cannot find + the parameter then a YellowbrickValueError is raised. + """ + + # NOTE: The order of the search is very important! + for attr in ("cv_alphas_", "alphas_", "alphas",): + try: + return getattr(self.estimator, attr) + except AttributeError: + continue + + raise YellowbrickValueError( + "could not find alphas param on {} estimator".format( + self.estimator.__class__.__name__ + ) + ) + + def _find_errors_param(self): + """ + Searches for the parameter on the estimator that contains the array of + errors that was used to determine the optimal alpha. If it cannot find + the parameter then a YellowbrickValueError is raised. + """ + + # NOTE: The order of the search is very important! + for attr in ('cv_mse_path_', 'mse_path_'): + if hasattr(self.estimator, attr): + return getattr(self.estimator, attr).mean(1) + + if hasattr(self.estimator, 'cv_values_'): + return self.estimator.cv_values_.mean(0) + + raise YellowbrickValueError( + "could not find errors param on {} estimator".format( + self.estimator.__class__.__name__ + ) + ) + +########################################################################## +## ManualAlphaSelection Visualizer +########################################################################## + +class ManualAlphaSelection(AlphaSelection): + """ + The ``AlphaSelection`` visualizer requires a "RegressorCV", that is a + specialized class that performs cross-validated alpha-selection on behalf + of the model. If the regressor you wish to use doesn't have an associated + "CV" estimator, or for some reason you would like to specify more control + over the alpha selection process, then you can use this manual alpha + selection visualizer, which is essentially a wrapper for + ``cross_val_score``, fitting a model for each alpha specified. + + Parameters + ---------- + + model : a Scikit-Learn regressor + Should be an instance of a regressor, and specifically one whose name + doesn't end with "CV". The regressor must support a call to + ``set_params(alpha=alpha)`` and be fit multiple times. If the + regressor name ends with "CV" a ``YellowbrickValueError`` 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). + + alphas : ndarray or Series, default: np.logspace(-10, 2, 200) + An array of alphas to fit each model with + + 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 in a `(Stratified)KFold`, + - An object to be used as a cross-validation generator. + - An iterable yielding train, test splits. + + This argument is passed to the + ``sklearn.model_selection.cross_val_score`` method to produce the + cross validated score for each alpha. + + scoring : string, callable or None, optional, default: None + A string (see model evaluation documentation) or + a scorer callable object / function with signature + ``scorer(estimator, X, y)``. + + This argument is passed to the + ``sklearn.model_selection.cross_val_score`` method to produce the + cross validated score for each alpha. + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Examples + -------- + + >>> from yellowbrick.regressor import ManualAlphaSelection + >>> from sklearn.linear_model import Ridge + >>> model = ManualAlphaSelection( + ... Ridge(), cv=12, scoring='neg_mean_squared_error' + ... ) + ... + >>> model.fit(X, y) + >>> model.poof() + + Notes + ----- + + This class does not take advantage of estimator-specific searching and is + therefore less optimal and more time consuming than the regular + "RegressorCV" estimators. + """ + + def __init__(self, model, ax=None, alphas=None, + cv=None, scoring=None, **kwargs): + + # Check to make sure this is not a "RegressorCV" + name = model.__class__.__name__ + if name.endswith("CV"): + raise YellowbrickTypeError(( + "'{}' is a CV regularization model;" + " try AlphaSelection instead." + ).format(name)) + + # Call super to initialize the class + super(AlphaSelection, self).__init__(model, ax=ax, **kwargs) + + # Set manual alpha selection parameters + self.alphas = alphas or np.logspace(-10, -2, 200) + self.errors = None + self.score_method = partial(cross_val_score, cv=cv, scoring=scoring) + + def fit(self, X, y, **args): + """ + The fit method is the primary entry point for the manual alpha + selection visualizer. It sets the alpha param for each alpha in the + alphas list on the wrapped estimator, then scores the model using the + passed in X and y data set. Those scores are then aggregated and + drawn using matplotlib. + """ + self.errors = [] + for alpha in self.alphas: + self.estimator.set_params(alpha=alpha) + scores = self.score_method(self.estimator, X, y) + self.errors.append(scores.mean()) + + # Convert errors to an ND array and draw + self.errors = np.array(self.errors) + self.draw() + + # Always make sure to return self from fit + return self + + def draw(self): + """ + Draws the alphas values against their associated error in a similar + fashion to the AlphaSelection visualizer. + """ + if self.ax is None: + self.ax = plt.gca() + + # Plot the alpha against the error + self.ax.plot(self.alphas, self.errors, label=self.name.lower()) + + # Draw a dashed vline at the alpha with maximal error + alpha = self.alphas[np.where(self.errors == self.errors.max())][0] + label = "$\\alpha_{{max}}={:0.3f}$".format(alpha) + self.ax.axvline(alpha, color='k', linestyle='dashed', label=label) + + # Draw a dashed vline at the alpha with minimal error + alpha = self.alphas[np.where(self.errors == self.errors.min())][0] + label = "$\\alpha_{{min}}={:0.3f}$".format(alpha) + self.ax.axvline(alpha, color='k', linestyle='dashed', label=label) + + return self.ax diff --git a/yellowbrick/regressor/base.py b/yellowbrick/regressor/base.py new file mode 100644 index 000000000..3890cc6cf --- /dev/null +++ b/yellowbrick/regressor/base.py @@ -0,0 +1,52 @@ +# yellowbrick.regressor.base +# Base classes for regressor Visualizers. +# +# Author: Rebecca Bilbro +# Author: Benjamin Bengfort +# Created: Fri Jun 03 10:30:36 2016 -0700 +# +# Copyright (C) 2016 District Data Labs +# For license information, see LICENSE.txt +# +# ID: base.py [] benjamin@bengfort.com $ + +""" +Base classes for regressor Visualizers. +""" + +########################################################################## +## Imports +########################################################################## + +from ..utils import isregressor +from ..base import ScoreVisualizer +from ..exceptions import YellowbrickTypeError + + +## Packages for export +__all__ = [ + "RegressionScoreVisualizer", +] + + +########################################################################## +## Regression Visualization Base Object +########################################################################## + +class RegressionScoreVisualizer(ScoreVisualizer): + """ + Base class for all ScoreVisualizers that evaluate a regression estimator. + + The primary functionality of this class is to perform a check to ensure + the passed in estimator is a regressor, otherwise it raises a + ``YellowbrickTypeError``. + """ + + def __init__(self, model, ax=None, **kwargs): + if not isregressor(model): + raise YellowbrickTypeError( + "This estimator is not a regressor; try a classifier or " + "clustering score visualizer instead!" + ) + + super(RegressionScoreVisualizer, self).__init__(model, ax=ax, **kwargs) diff --git a/yellowbrick/regressor.py b/yellowbrick/regressor/residuals.py similarity index 69% rename from yellowbrick/regressor.py rename to yellowbrick/regressor/residuals.py index a59f645aa..fca400537 100644 --- a/yellowbrick/regressor.py +++ b/yellowbrick/regressor/residuals.py @@ -1,90 +1,88 @@ -# yellowbrick.regressor -# Visualizations related to evaluating Scikit-Learn regressor models +# yellowbrick.regressor.residuals +# Regressor visualizers that score residuals: prediction vs. actual data. # # Author: Rebecca Bilbro # Author: Benjamin Bengfort # Created: Fri Jun 03 10:30:36 2016 -0700 # -# Copyright (C) 2016 District Data Labs +# Copyright (C) 2016 2016 District Data Labs # For license information, see LICENSE.txt # -# ID: regressor.py [4a59c49] benjamin@bengfort.com $ +# ID: residuals.py [] benjamin@bengfort.com $ """ -Visualizations related to evaluating Scikit-Learn regressor models +Regressor visualizers that score residuals: prediction vs. actual data. """ ########################################################################## ## Imports ########################################################################## -import matplotlib as mpl import matplotlib.pyplot as plt from sklearn.cross_validation import train_test_split -from .bestfit import draw_best_fit -from .style.palettes import LINE_COLOR -from .exceptions import YellowbrickTypeError -from .utils import get_model_name, isestimator, isregressor -from .base import Visualizer, ScoreVisualizer, MultiModelMixin +from ..bestfit import draw_best_fit +from ..style.palettes import LINE_COLOR +from .base import RegressionScoreVisualizer +from ..exceptions import YellowbrickTypeError +## Packages for export +__all__ = [ + "PredictionError", "prediction_error", + "ResidualsPlot", "residuals_plot" +] ########################################################################## -## Regression Visualization Base Object +## Prediction Error Plots ########################################################################## -class RegressionScoreVisualizer(ScoreVisualizer): - - def __init__(self, model, ax=None, **kwargs): - """ - Check to see if model is an instance of a regressor. - Should return an error if it isn't. - """ - if not isregressor(model): - raise YellowbrickTypeError( - "This estimator is not a regressor; try a classifier or " - "clustering score visualizer instead!" - ) +class PredictionError(RegressionScoreVisualizer): + """ + The prediction error visualizer plots the actual targets from the dataset + against the predicted values generated by our model(s). This visualizer is + used to dectect noise or heteroscedasticity along a range of the target + domain. - super(RegressionScoreVisualizer, self).__init__(model, ax=ax, **kwargs) + Parameters + ---------- + model : a Scikit-Learn regressor + Should be an instance of a regressor, otherwise a will raise a + YellowbrickTypeError exception on instantiation. -########################################################################## -## Prediction Error Plots -########################################################################## + 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). -class PredictionError(RegressionScoreVisualizer): - """ - Plot the actual targets from the dataset against the - predicted values generated by our model(s). - """ - def __init__(self, model, ax=None, **kwargs): - """ - Parameters - ---------- + point_color : color + Defines the color of the error points; can be any matplotlib color. - :param ax: the axis to plot the figure on. + line_color : color + Defines the color of the best fit line; can be any matplotlib color. - :param model: the Scikit-Learn estimator - Should be an instance of a regressor, else the __init__ will - return an error. + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. - :param point_color: color of the error points - Any matplotlib color + Examples + -------- - :param line_color: color of the best fit line - Any matplotlib color + >>> from yellowbrick.regressor import PredictionError + >>> from sklearn.linear_model import Lasso + >>> model = PredictionError(Lasso()) + >>> model.fit(X_train, y_train) + >>> model.score(X_test, y_test) + >>> model.poof() - :param kwargs: keyword arguments passed to the super class. - Currently passing in hard-coded colors for the prediction error - points and the line of best fit. - These will be refactored to a default Yellowbrick style. + Notes + ----- + PredictionError is a ScoreVisualizer, meaning that it wraps a model and + its primary entry point is the `score()` method. + """ - These parameters can be influenced later on in the visualization - process, but can and should be set as early as possible. - """ + def __init__(self, model, ax=None, **kwargs): super(PredictionError, self).__init__(model, ax=ax, **kwargs) @@ -95,13 +93,11 @@ def __init__(self, model, ax=None, **kwargs): def score(self, X, y=None, **kwargs): """ - Originally score for prediction error was conceived as generating - y_pred by calling the sklearn function cross_val_predict on the - model, X, y, and the specified number of folds, e.g.: - - y_pred = cv.cross_val_predict(model, X, y, cv=12) - - With the new API, there's not much for score to do. + 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 ---------- @@ -112,19 +108,17 @@ def score(self, X, y=None, **kwargs): y (also y_test) is the independent actual variables to score against Returns - ------ - - ax : the axis with the plotted figure - + ------- + score : float """ y_pred = self.predict(X) - return self.draw(y, y_pred) + self.draw(y, y_pred) + return self.estimator.score(X, y, **kwargs) def draw(self, y, y_pred): """ Parameters ---------- - y : ndarray or Series of length n An array or series of target or class values @@ -133,7 +127,6 @@ def draw(self, y, y_pred): Returns ------ - ax : the axis with the plotted figure """ # Create the axis if it doesn't exist @@ -159,7 +152,6 @@ def finalize(self, **kwargs): Parameters ---------- kwargs: generic keyword arguments. - """ # Set the title on the plot self.set_title('Prediction Error for {}'.format(self.name)) @@ -180,20 +172,20 @@ def prediction_error(model, X, y=None, ax=None, **kwargs): Parameters ---------- + model : the Scikit-Learn estimator (should be a regressor) + 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 + ax : matplotlib Axes The axes to plot the figure on. - model : the Scikit-Learn estimator (should be a regressor) - Returns ------- - ax : matplotlib axes + ax : matplotlib Axes Returns the axes that the prediction error plot was drawn on. """ # Instantiate the visualizer @@ -222,41 +214,53 @@ class ResidualsPlot(RegressionScoreVisualizer): 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. - """ - def __init__(self, model, ax=None, **kwargs): - """ - Parameters - ---------- - :param model: the Scikit-Learn estimator - Should be an instance of a regressor, else the __init__ will - return an error. + Parameters + ---------- - :param ax: the axis to plot the figure on. + model : a Scikit-Learn regressor + Should be an instance of a regressor, otherwise a will raise a + YellowbrickTypeError exception on instantiation. - :param train_color: color of the training data residuals - Residuals for training data are ploted with this color but also - given an opacity of 0.5 to ensure that the test data residuals - are more visible. Default color is 'b' for training data. + 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). - :param test_color: color of test data residuals - Residuals for test data are plotted with this color. In order to - create generalizable models, reserved test data residuals are of - the most analytical interest, so these points are highlighted by - hvaing full opacity. Default color is 'g' for test data. + train_color : color, default: 'b' + Residuals for training data are ploted with this color but also + given an opacity of 0.5 to ensure that the test data residuals + are more visible. Can be any matplotlib color. - :param line_color: color of the zero error line - Any matplotlib color. Default is a dark grey. + test_color : color, default: 'g' + Residuals for test data are plotted with this color. In order to + create generalizable models, reserved test data residuals are of + the most analytical interest, so these points are highlighted by + hvaing full opacity. Can be any matplotlib color. - :param kwargs: keyword arguments passed to the super class. - Currently passing in hard-coded colors for the residual train and - test points and the horizontal line. - These will be refactored to a default Yellowbrick style. + line_color : color, default: dark grey + Defines the color of the zero error line, can be any matplotlib color. - These parameters can be influenced later on in the visualization - process, but can and should be set as early as possible. + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. - """ + Examples + -------- + + >>> from yellowbrick.regressor import ResidualsPlot + >>> from sklearn.linear_model import Ridge + >>> model = PredictionError(Ridge()) + >>> model.fit(X_train, y_train) + >>> model.score(X_test, y_test) + >>> model.poof() + + Notes + ----- + + ResidualsPlot is a ScoreVisualizer, meaning that it wraps a model and + its primary entry point is the `score()` method. + """ + def __init__(self, model, ax=None, **kwargs): super(ResidualsPlot, self).__init__(model, ax=ax, **kwargs) diff --git a/yellowbrick/style/__init__.py b/yellowbrick/style/__init__.py index f00d00820..6be218913 100644 --- a/yellowbrick/style/__init__.py +++ b/yellowbrick/style/__init__.py @@ -20,3 +20,4 @@ from .colors import * from .palettes import * from .rcmod import * +from .utils import * diff --git a/yellowbrick/style/colors.py b/yellowbrick/style/colors.py index f3305ce09..96be185e4 100644 --- a/yellowbrick/style/colors.py +++ b/yellowbrick/style/colors.py @@ -66,8 +66,8 @@ def resolve_colors(num_colors=None, colormap=None, color=None): colormap : str or None the colormap used to create the sequence of colors - color : list or Non e - the list of colors to specifically use with the plot + color : list or None + the list of colors to specifically use with the plot """ diff --git a/yellowbrick/style/rcmod.py b/yellowbrick/style/rcmod.py index a4d44cb80..4e726f71b 100644 --- a/yellowbrick/style/rcmod.py +++ b/yellowbrick/style/rcmod.py @@ -323,7 +323,7 @@ def _plotting_context(context=None, font_scale=1, rc=None): } # Scale all the parameters by the same factor depending on the context - scaling = dict(paper=.8, notebook=1, talk=1.3, poster=1.6)['talk'] + scaling = dict(paper=.8, notebook=1, talk=1.3, poster=1.6)['notebook'] context_dict = {k: v * scaling for k, v in base_context.items()} # Now independently scale the fonts diff --git a/yellowbrick/style/utils.py b/yellowbrick/style/utils.py new file mode 100644 index 000000000..e1f12753e --- /dev/null +++ b/yellowbrick/style/utils.py @@ -0,0 +1,50 @@ +""" +Utility functions for styles +""" + +########################################################################## +## Imports +########################################################################## + +import math +import numpy as np + +from yellowbrick.exceptions import YellowbrickTypeError + +def find_text_color(base_color, dark_color="black", light_color="white", coef_choice=0): + """ + Takes a background color and returns the appropriate light or dark text color. + Users can specify the dark and light text color, or accept the defaults of 'black' and 'white' + + base_color: The color of the background. This must be + specified in RGBA with values between 0 and 1 (note, this is the default + return value format of a call to base_color = cmap(number) to get the + color corresponding to a desired number). Note, the value of `A` in RGBA + is not considered in determining light/dark. + + dark_color: Any valid matplotlib color value. + Function will return this value if the text should be colored dark + + light_color: Any valid matplotlib color value. + Function will return this value if thet text should be colored light. + + coef_choice: slightly different approaches to calculating brightness. Currently two options in + a list, user can enter 0 or 1 as list index. 0 is default. + """ + + #Coefficients: + # option 0: http://www.nbdtech.com/Blog/archive/2008/04/27/Calculating-the-Perceived-Brightness-of-a-Color.aspx + # option 1: http://stackoverflow.com/questions/596216/formula-to-determine-brightness-of-rgb-color + coef_options = [np.array((.241, .691, .068, 0)), + np.array((.299, .587, .114, 0)) + ] + + coefs= coef_options[coef_choice] + rgb = np.array(base_color) * 255 + brightness = np.sqrt(np.dot(coefs, rgb**2)) + + #Threshold from option 0 link; determined by trial and error. + #base is light + if brightness > 130: + return dark_color + return light_color diff --git a/yellowbrick/text/__init__.py b/yellowbrick/text/__init__.py index 2a7dcc50c..a3eb52cbb 100644 --- a/yellowbrick/text/__init__.py +++ b/yellowbrick/text/__init__.py @@ -19,3 +19,4 @@ from .tsne import TSNEVisualizer, tsne from .freqdist import FreqDistVisualizer, freqdist +from .postag import PosTagVisualizer diff --git a/yellowbrick/text/postag.py b/yellowbrick/text/postag.py new file mode 100644 index 000000000..7500d77ce --- /dev/null +++ b/yellowbrick/text/postag.py @@ -0,0 +1,145 @@ +# yellowbrick.text.postag +# Implementation of part-of-speech visualization for text. +# +# Author: Rebecca Bilbro +# Created: 2017-03-05 14:44 +# +# Copyright (C) 2017 District Data Labs +# For license information, see LICENSE.txt +# +# ID: postag.py [] rbilbro@districtdatalabs.com $ + +""" +Implementation of part-of-speech visualization for text, +enabling the user to visualize a single document or +small subset of documents. +""" + +########################################################################## +# Imports +########################################################################## + +from yellowbrick.text.base import TextVisualizer + +########################################################################## +# PosTagVisualizer +########################################################################## + +class PosTagVisualizer(TextVisualizer): + """ + A part-of-speech tag visualizer colorizes text to enable + the user to visualize the proportions of nouns, verbs, etc. + and to use this information to make decisions about + part-of-speech tagging, text normalization (e.g. stemming + vs lemmatization) and vectorization. + + Parameters + ---------- + kwargs : dict + Pass any additional keyword arguments to the super class. + cmap : dict + ANSII colormap + + 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, **kwargs): + """ + Initializes the base frequency distributions with many + of the options required in order to make this + visualization work. + """ + super(PosTagVisualizer, self).__init__(ax=ax, **kwargs) + + # TODO: hard-coding in the ANSII colormap for now. + # Can we let the user reset the colors here? + self.COLORS = { + 'white' : "\033[0;37m{}\033[0m", + 'yellow' : "\033[0;33m{}\033[0m", + 'green' : "\033[0;32m{}\033[0m", + 'blue' : "\033[0;34m{}\033[0m", + 'cyan' : "\033[0;36m{}\033[0m", + 'red' : "\033[0;31m{}\033[0m", + 'magenta' : "\033[0;35m{}\033[0m", + 'black' : "\033[0;30m{}\033[0m", + 'darkwhite' : "\033[1;37m{}\033[0m", + 'darkyellow' : "\033[1;33m{}\033[0m", + 'darkgreen' : "\033[1;32m{}\033[0m", + 'darkblue' : "\033[1;34m{}\033[0m", + 'darkcyan' : "\033[1;36m{}\033[0m", + 'darkred' : "\033[1;31m{}\033[0m", + 'darkmagenta': "\033[1;35m{}\033[0m", + 'darkblack' : "\033[1;30m{}\033[0m", + None : "\033[0;0m{}\033[0m" + } + + self.TAGS = { + 'NN' : 'green', + 'NNS' : 'green', + 'NNP' : 'green', + 'NNPS' : 'green', + 'VB' : 'blue', + 'VBD' : 'blue', + 'VBG' : 'blue', + 'VBN' : 'blue', + 'VBP' : 'blue', + 'VBZ' : 'blue', + 'JJ' : 'red', + 'JJR' : 'red', + 'JJS' : 'red', + 'RB' : 'cyan', + 'RBR' : 'cyan', + 'RBS' : 'cyan', + 'IN' : 'darkwhite', + 'POS' : 'darkyellow', + 'PRP$' : 'magenta', + 'PRP$' : 'magenta', + 'DT' : 'black', + 'CC' : 'black', + 'CD' : 'black', + 'WDT' : 'black', + 'WP' : 'black', + 'WP$' : 'black', + 'WRB' : 'black', + 'EX' : 'yellow', + 'FW' : 'yellow', + 'LS' : 'yellow', + 'MD' : 'yellow', + 'PDT' : 'yellow', + 'RP' : 'yellow', + 'SYM' : 'yellow', + 'TO' : 'yellow', + 'None' : 'off' + } + + def colorize(self, token, color): + """ + Colorize text + + Parameters + ---------- + token : str + A str representation of + + """ + return self.COLORS[color].format(token) + + def transform(self, tagged_tuples): + """ + The transform method transforms the raw text input for the + part-of-speech tagging visualization. It requires that + documents be in the form of (tag, token) tuples. + + Parameters + ---------- + tagged_token_tuples : list of tuples + A list of (tag, token) tuples + + Text documents must be tokenized and tagged before passing to fit() + """ + self.tagged = [ + (self.TAGS.get(tag),tok) for tok, tag in tagged_tuples + ] + # + # print(' '.join((colorize(token, color) for color, token in self.tagged))) + # print('\n') diff --git a/yellowbrick/utils.py b/yellowbrick/utils.py index bea7cf504..da4475122 100644 --- a/yellowbrick/utils.py +++ b/yellowbrick/utils.py @@ -21,6 +21,8 @@ ########################################################################## import inspect +import math +import numpy as np from functools import wraps from sklearn.pipeline import Pipeline @@ -66,8 +68,9 @@ def is_estimator(model): Parameters ---------- - model: class or instance - The object to test whether or not is a Scikit-Learn estimator. + estimator : class or instance + The object to test if it is a Scikit-Learn clusterer, especially a + Scikit-Learn estimator or Yellowbrick visualizer """ if inspect.isclass(model): return issubclass(model, BaseEstimator) @@ -84,8 +87,9 @@ def is_classifier(estimator): Parameters ---------- - estimator: class or instance - The object to test whether or not is a Scikit-Learn classifier. + estimator : class or instance + The object to test if it is a Scikit-Learn clusterer, especially a + Scikit-Learn estimator or Yellowbrick visualizer See also -------- @@ -112,8 +116,9 @@ def is_regressor(estimator): Parameters ---------- - model: class or instance - The object to test whether or not is a Scikit-Learn regressor. + estimator : class or instance + The object to test if it is a Scikit-Learn clusterer, especially a + Scikit-Learn estimator or Yellowbrick visualizer See also -------- @@ -134,6 +139,30 @@ def is_regressor(estimator): isregressor = is_regressor +def is_clusterer(estimator): + """ + Returns True if the given estimator is a clusterer. + + Parameters + ---------- + estimator : class or instance + The object to test if it is a Scikit-Learn clusterer, especially a + Scikit-Learn estimator or Yellowbrick visualizer + """ + # TODO: once we make ScoreVisualizer and ModelVisualizer pass through + # wrappers as in Issue #90, these three lines become unnecessary. + # NOTE: This must be imported here to avoid recursive import. + from yellowbrick.base import Visualizer + if isinstance(estimator, Visualizer): + return is_clusterer(estimator.estimator) + + # Test the _estimator_type property + return getattr(estimator, "_estimator_type", None) == "clusterer" + +# Alias for closer name to isinstance and issubclass +isclusterer = is_clusterer + + def is_dataframe(obj): """ Returns True if the given object is a Pandas Data Frame. @@ -155,6 +184,66 @@ def is_dataframe(obj): isdataframe = is_dataframe +def is_structured_array(obj): + """ + Returns True if the given object is a Numpy Structured Array. + + Parameters + ---------- + obj: instance + The object to test whether or not is a Numpy Structured Array. + """ + if isinstance(obj, np.ndarray) and hasattr(obj, 'dtype'): + if obj.dtype.names is not None: + return True + return False + + +# Alias for closer name to isinstance and issubclass +isstructuredarray = is_structured_array + + +def has_ndarray_int_columns(features, X): + """ Checks if numeric feature columns exist in ndarray """ + _, ncols = X.shape + if not all(d.isdigit() for d in features if isinstance(d, str)) or not isinstance(X, np.ndarray): + return False + ndarray_columns = np.arange(0, ncols) + feature_cols = np.unique([int(d) for d in features]) + return all(np.in1d(feature_cols, ndarray_columns)) + +# Alias for closer name to isinstance and issubclass +hasndarrayintcolumns = has_ndarray_int_columns + + +#From here: http://stackoverflow.com/questions/26248654/numpy-return-0-with-divide-by-zero +def div_safe( numerator, denominator ): + """ + Ufunc-extension that returns 0 instead of nan when dividing numpy arrays + + Parameters + ---------- + numerator: array-like + + denominator: scalar or array-like that can be validly divided by the numerator + + returns a numpy array + + example: div_safe( [-1, 0, 1], 0 ) == [0, 0, 0] + """ + #First handle scalars + if np.isscalar(numerator): + raise ValueError("div_safe should only be used with an array-like numerator") + + #Then numpy arrays + try: + with np.errstate(divide='ignore', invalid='ignore'): + result = np.true_divide( numerator, denominator ) + result[ ~ np.isfinite( result )] = 0 # -inf inf NaN + return result + except ValueError as e: + raise e + ########################################################################## ## Decorators ########################################################################## diff --git a/yellowbrick/version.py b/yellowbrick/version.py index 7e6c4031d..21aa9c456 100644 --- a/yellowbrick/version.py +++ b/yellowbrick/version.py @@ -19,8 +19,8 @@ __version_info__ = { 'major': 0, - 'minor': 3, - 'micro': 3, + 'minor': 4, + 'micro': 0, 'releaselevel': 'alpha', 'serial': 3, }