From 92434524c2329f8c46d39aba97756f0019855e3d Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Tue, 11 Oct 2016 12:41:49 -0400 Subject: [PATCH 01/35] classifiers fix --- setup.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index a29bf6040..3da905580 100755 --- a/setup.py +++ b/setup.py @@ -45,9 +45,10 @@ ## Define the classifiers ## See https://pypi.python.org/pypi?%3Aaction=list_classifiers CLASSIFIERS = ( - 'Development Status :: 4 - Beta', - 'Environment :: Console', + 'Development Status :: 3 - Alpha', + 'Environment :: Other Environment', 'Intended Audience :: Developers', + 'Intended Audience :: Science/Research', 'License :: OSI Approved :: Apache Software License', 'Natural Language :: English', 'Operating System :: OS Independent', From d3b0c63c0b694ee0cd40f26894d6356829bf49ac Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Tue, 11 Oct 2016 12:48:41 -0400 Subject: [PATCH 02/35] our first feature analysis images gallery --- docs/images/pd_parallel_coordinates.png | Bin 0 -> 86199 bytes docs/images/pd_radviz.png | Bin 0 -> 47562 bytes docs/images/yb_parallel_coords.png | Bin 0 -> 93671 bytes docs/images/yb_parallel_coords_palette.png | Bin 0 -> 90743 bytes docs/images/yb_radviz.png | Bin 0 -> 50379 bytes docs/images/yb_rank2d_covariance.png | Bin 0 -> 25852 bytes docs/images/yb_rank2d_pearson.png | Bin 0 -> 25856 bytes 7 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 docs/images/pd_parallel_coordinates.png create mode 100644 docs/images/pd_radviz.png create mode 100644 docs/images/yb_parallel_coords.png create mode 100644 docs/images/yb_parallel_coords_palette.png create mode 100644 docs/images/yb_radviz.png create mode 100644 docs/images/yb_rank2d_covariance.png create mode 100644 docs/images/yb_rank2d_pearson.png diff --git a/docs/images/pd_parallel_coordinates.png b/docs/images/pd_parallel_coordinates.png new file mode 100644 index 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z73XvuW?OWEpA+m=2puf}^j}O~&0>5K9lf0xPLSJ)r{#IhMWcG0Mw$cHvp1er18F1U z`>LkZ!Iw&%X&3HVi}i1g3G^gYf@BKPTtd+xKPpuV!-?k#HHE=@gPICXZ!brPknW*M?Z_vY$d6(n%)rPp*#BYz zyG?^l?Wj6hZ4Zg=PV5V}c73_>Jm)m4{FddDsh|AOBGR}uRhP@TF6NXU035jSx zd=uE5&B;*aD*yD>ds6@B{og77c>fPy)uCy2_&=eB|1bU8ghTDUi5b#0XYsEiP$<*` MGW#R;p1AOT0kK+lw*UYD literal 0 HcmV?d00001 From 08e7ab3d93f89b1bf821ac99d72d38a54bfdaa9d Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Tue, 11 Oct 2016 15:54:20 -0400 Subject: [PATCH 03/35] fix to broken show/poof in classifier and regression score visualizers closes issue #75 --- README.md | 5 ++--- yellowbrick/classifier.py | 7 +++---- yellowbrick/regressor.py | 4 ++-- 3 files changed, 7 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 375509cc7..43e71be37 100644 --- a/README.md +++ b/README.md @@ -64,13 +64,12 @@ visualizer.poof() # Draw/show/poof the data 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 import ROCAUC +from yellowbrick.classifier import ROCAUC model = LinearSVC() model.fit(X,y) -y_pred = model.predict(X) visualizer = ROCAUC(model) -visualizer.score(y,y_pred) +visualizer.score(X,y) visualizer.poof() ``` diff --git a/yellowbrick/classifier.py b/yellowbrick/classifier.py index 40f88ae04..750e65469 100644 --- a/yellowbrick/classifier.py +++ b/yellowbrick/classifier.py @@ -124,7 +124,7 @@ def poof(self): plt.ylabel('Classes') plt.xlabel('Measures') - return self.ax + plt.show() ########################################################################## @@ -189,7 +189,7 @@ def poof(self, **kwargs): plt.xlim([-0.02,1]) plt.ylim([0,1.1]) - return self.ax + plt.show() ########################################################################## ## Class Balance Chart @@ -256,6 +256,5 @@ def poof(self): ceiling = cmax + cmax*0.1 span = cmax - cmin plt.ylim(0, ceiling) + plt.show() - - return self.ax diff --git a/yellowbrick/regressor.py b/yellowbrick/regressor.py index 6be8a221e..6ef22bdef 100644 --- a/yellowbrick/regressor.py +++ b/yellowbrick/regressor.py @@ -108,7 +108,7 @@ def poof(self): self.ax.set_title('Prediction Error for {}'.format(self.name)) self.ax.set_ylabel('Predicted') plt.xlabel('Measured') - return self.ax + plt.show() ########################################################################## ## Residuals Plots @@ -175,4 +175,4 @@ def poof(self): self.ax.set_ylabel('Residuals') plt.xlabel("Predicted Value") - return self.ax + plt.show() From aa1a4d85f30db26a7ef9d3c7dc7692bb00f7a5e5 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Thu, 13 Oct 2016 19:47:00 -0400 Subject: [PATCH 04/35] s3 downloader and signature verification for examples --- examples/download.py | 88 +++++++++++++++++---- examples/examples.ipynb | 171 +++++++++++++++++++--------------------- examples/examples.py | 153 ----------------------------------- 3 files changed, 156 insertions(+), 256 deletions(-) delete mode 100644 examples/examples.py diff --git a/examples/download.py b/examples/download.py index 5d11a86fb..aa879b6c5 100644 --- a/examples/download.py +++ b/examples/download.py @@ -20,6 +20,7 @@ import os import sys +import hashlib import zipfile try: @@ -31,31 +32,90 @@ )) sys.exit(1) + ########################################################################## -## Links to data sets +## Links and MD5 hash of datasets ########################################################################## -OCCUPANCY = ('http://bit.ly/ddl-occupancy-dataset', 'occupancy.zip') -CREDIT = ('http://bit.ly/ddl-credit-dataset', 'credit.xls') -CONCRETE = ('http://bit.ly/ddl-concrete-data', 'concrete.xls') +DATASETS = { + 'concrete': { + 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/concrete.zip', + 'signature': 'b9ea5f26a7bb272a040e2f1a993b26babbf8dc4a04ab8198bb315ca66d71f10d', + }, + 'credit': { + 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/credit.zip', + 'signature': '4a91339c69f55e18f3f48004328fbcb7868070b618208fed099920427b084e5e', + }, + 'occupancy': { + 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/occupancy.zip', + 'signature': '429cfe376dc9929a1fa528da89f0e1626e34e19695f3f555d8954025bbc522b8', + } +} + + +########################################################################## +## Download functions +########################################################################## +def sha256sum(path, blocksize=65536): + """ + Computes the SHA256 signature of a file to verify that the file has not + been modified in transit and that it is the correct version of the data. + """ + sig = hashlib.sha256() + with open(path, 'rb') as f: + buf = f.read(blocksize) + while len(buf) > 0: + sig.update(buf) + buf = f.read(blocksize) + return sig.hexdigest() -def download_data(url, name, path='data'): + +def download_data(url, path='data', signature=None, extract=True): + """ + Downloads the zipped data set specified at the given URL, saving it to + the output path specified. This function verifies the download with the + given signature (if supplied) and extracts the zip file if requested. + """ + # Create the output directory if it does not exist if not os.path.exists(path): os.mkdir(path) - response = requests.get(url) - with open(os.path.join(path, name), 'w') as f: - f.write(response.content) + # Get the name of the file from the URL + name = os.path.basename(url) + dlpath = os.path.join(path, name) + + # Fetch the response in a streaming fashion and write it to disk. + response = requests.get(url, stream=True) + with open(dlpath, 'wb') as f: + for chunk in response.iter_content(65536): + f.write(chunk) + + # If verify, compare the signature + if signature is not None: + dlsignature = sha256sum(dlpath) + if signature != dlsignature: + raise ValueError( + "Download signature does not match hardcoded signature!" + ) + + # If extract, extract the zipfile. + if extract: + zf = zipfile.ZipFile(dlpath) + zf.extractall(path) -def download_all(path='data'): - for href, name in (OCCUPANCY, CREDIT, CONCRETE): - download_data(href, name, path) +def download_all(path='data', verify=True, extract=True): + """ + Downloads all the example datasets. If verify is True then compare the + download signature with the hardcoded signature. If extract is True then + extract the contents of the zipfile to the given path. + """ + for name, meta in DATASETS.items(): + url = meta['url'] + signature = meta['signature'] if verify else None - # Extract the occupancy zip data - z = zipfile.ZipFile(os.path.join(path, 'occupancy.zip')) - z.extractall(os.path.join(path, 'occupancy')) + download_data(url, path=path, signature=signature, extract=extract) if __name__ == '__main__': diff --git a/examples/examples.ipynb b/examples/examples.ipynb index 7f7b27e11..56b014de8 100644 --- a/examples/examples.ipynb +++ b/examples/examples.ipynb @@ -50,24 +50,16 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "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+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\nL58OQ8GVMBTMR3ByjEwph7FQI/JT9jN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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -84,59 +76,60 @@ "collapsed": false }, "source": [ - "## Load Datasets for Example Code" + "## Load Datasets for Example Code\n", + "\n", + "Yellowbrick has provided several datasets wrangled from the [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/) 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 example notebook. Note that this will create a directory called `data` that contains subdirectories with the given data. " ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "collapsed": false }, "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.xls\"),\n", - " \"concrete\": os.path.join(FIXTURES, \"concrete.xls\"),\n", - " \"occupancy\": os.path.join(FIXTURES, 'occupancy', 'datatraining.txt'),\n", - "}\n", - "\n", - "## Human readable column names\n", - "columns = {\n", - " \"credit\": [\n", - " 'id', 'limit', 'sex', 'edu', 'married', 'age', 'apr_delay', 'may_delay',\n", - " 'jun_delay', 'jul_delay', 'aug_delay', 'sep_delay', 'apr_bill', 'may_bill',\n", - " 'jun_bill', 'jul_bill', 'aug_bill', 'sep_bill', 'apr_pay', 'may_pay', 'jun_pay',\n", - " 'jul_pay', 'aug_pay', 'sep_pay', 'default'\n", - " ],\n", - " \"concrete\": [\n", - " 'cement', 'slag', 'ash', 'water', 'splast',\n", - " 'coarse', 'fine', 'age', 'strength'\n", - " ],\n", - " \"occupancy\": [\n", - " 'date', 'temp', 'humid', 'light', 'co2', 'hratio', 'occupied'\n", - " ],\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", + "def load_data(name, download=True):\n", " \"\"\"\n", - " Loads and wrangles the passed in dataset.\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", + " \n", + " # Get the path from the datasets \n", " path = datasets[name]\n", - " data = {\n", - " 'credit': lambda p: pd.read_excel(p, header=1),\n", - " 'concrete': lambda p: pd.read_excel(p),\n", - " 'occupancy': lambda p: pd.read_csv(p),\n", - " }[name](path)\n", - "\n", - " data.columns = columns[name]\n", - " return data" + " \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)" ] }, { @@ -158,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -182,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "collapsed": true }, @@ -206,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -215,7 +208,7 @@ "data": { "image/png": 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Uq1fPOl31Z6dOnao0sSjP+++/rwkTJqhTp06aM2eOtdwvnfusffbZZ7rrrrvk7e1tbW/d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\nJNHUwuEYaZYjDsfA5UGCAACAAXesILB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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -233,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -242,7 +235,7 @@ "data": { "image/png": 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xcT5tsbGxcjqdfuvOmzdPF154oXr37m3J/kokCAAA1Esul0sRERE+bRERESot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\nlXJychQVFaV27dopMzNTb7zxRq2vMTV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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -271,9 +264,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -281,26 +274,26 @@ "data = load_data('occupancy') \n", "\n", "# Specify the features of interest and the classes of the target \n", - "features = ['temp', 'humid', 'light', 'co2', 'hratio']\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.occupied.as_matrix()" + "y = data.occupancy.as_matrix()" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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RM2qf3Xn44Yfx3Xff4fz586iursaHH35o8kg8e3t7lJeXm7SvVqvFF198gTlz\n5jQ5opboXve3v/0N+/btQ1lZmdClELULDHVETRCJRIZgNXjwYDz33HNYsGABoqKi4ODgAIlEAjs7\nO8O+dY+tNWrUKFy9ehXjx49vts3PP/8c8fHxsLe3t+AnIbI9IpEIc+fOxYYNGzjVCREAqdAFELVH\nfn5+SEpKQo8ePXDp0iUAQEZGBkaNGoWZM2cCqLn9um7dOnTq1AkADPvV+vjjjw1/7t27t0nP/+za\ntQvDhg2Dp6enpT4KkU2TyWSYMWOGoXeb6F7GnjoiE126dAkLFixAUVER1Go11q5di65du1psdYcz\nZ85ALpcjODjYIucjuld4e3sjPDwc33//vdClEAmKPXVEJpowYQIuX76MKVOmQKlUon///li7dm2r\nzqXRaLB7925cu3YNvXr1QlhYGM6fP4+nnnrKskUT3SMGDhyIrKws/P777wgNDRW6HCJBMNQRtcCS\nJUuwZMkSs86Rm5uLN954AzKZDM7Ozrh+/ToSExOxYcMGC1VJdG8aN24cvvjiC/j6+hpNP0R0r+Dt\nV6I29uGHH6JTp05wdnYGADg7O6Nfv35YvXq1wJURdXxPPvkktm7dCqVSKXQpRG2OoY6oDel0OuTm\n5kIsNv6nJxaLkZ2dzaWPiMwkFosxe/ZsbNq0SehSiNocQx1RO8FAR2QZLi4u6Nu3L5KTk4UuhahN\nMdQRtSGxWAwPD496AU6v18Pb25uTDRNZSEREBH777TdUVVUJXQpRm2GoI2pjzzzzDNLS0qBSqQDU\nzHeXn5+PRYsWCVwZkW2ZOXMmNm/eLHQZRG2Go1+J2pi9vT2mTp0KpVKJvLw89OjRA48//jicnJyE\nLo3Iptx9GzY8PFzocoisjqGOqA3p9Xps374dzz77bL3BEkRkeREREVi7di0GDBgABwcHocshsir+\nVCFqQzt37sSUKVMY6IjaEG/D0r3C7J8sFy9exKOPPorQ0FBMmzYN586da3C/iRMnYtCgQQgLC0No\naCgmT55sbtNEHUpmZibUajW6desmdClE9xSOhqV7hVmhrrq6GgsXLkRsbCzOnDmDmTNnYtGiRfVG\nG6lUKty4cQNHjhzB2bNn8fvvv2P37t1mFU7UkdTedo2NjRW6FKJ7EkfD0r3ArFD366+/QiKRIC4u\nDhKJBNOnT4e7uzuOHj1qtN+VK1fg6ekJNzc3s4ol6qh425VIeLwNS7bOrJ8w6enp6NGjh9G27t27\nIz093WjlCwLLAAAgAElEQVTbpUuXIJFI8Nhjj2HYsGGYPXs2rl27Zk7TRB0Gb7sStQ+8DUu2zqxQ\nV1VVVW80kYODQ4Nr7g0cOBAffvghjh49iuDgYMybNw/V1dXmNE/UIezatYu3XYnaiYiICJw5c4Yr\nuJBNMmtKk4YCXFVVFRwdHY22xcXFIS4uzvD6H//4B5KSknDp0iWEhISY3J5KpbLKP8TaSWBr/09k\nqWvi8uXL6N69O3+BsQH8nrAdo0ePxq5duzB27FizzsNrguqy9jXR3M8Ss0JdYGAgkpKSjLZlZGRg\nypQpRtu++eYb3HfffRg2bBgAQKPRQKPRQCaTtai91NRUc8ptVlpamlXPTx2PudfE9u3b8cgjj+DC\nhQsWqoiExu8J23D69Gl07twZEonE7HPxmqC6hLomzAp1Q4cORXV1NZKSkhAXF4edO3eiqKgIERER\nRvvl5eVh06ZNSExMhJubG1auXInAwEAEBQW1qL1evXrB3t7enJIbpFKpkJaWhp49e7Y4aJJtssQ1\ncfr0aUycOBHBwcEWro6EwO8J2+Lp6YkTJ05g2rRprT4Hrwmqy9rXRHV1dZMdXGaFOnt7eyQmJuKf\n//wnVq1ahYCAAKxduxZyuRxz587FkCFDMG/ePCxYsAAVFRWIjY1FVVUVhgwZgjVr1rS4PZlMZtV/\nODKZDHK53Grnp46ntdeEXq9HSkoKnnnmGStURULi94RtCAgIwMGDB6HX681eaYLXBNVlrWtCJBI1\n+b7Zy4T17t0bW7Zsqbc9MTHxr0akUixduhRLly41tzmiDuHgwYOIjo4WugwiakJsbCy2b9+OmTNn\nCl0KkUVw0iwiC9NqtUhNTW3x4wVE1LYUCgUAoLS0VOBKiCyDoY7Iwnbv3s1l8Ig6iNreOiJbwFBH\nZEEqlQr5+fno0qWL0KUQkQnkcjlcXV2RnZ0tdClEZmOoI7Kgb7/9Fo888ojQZRBRC0ydOhW7du0S\nugwiszHUEVnInTt3oFKp4OHhIXQpRNQCUqkU3bp143xz1OEx1BFZyI4dO7gcGFEHNWbMGBw8eFDo\nMojMwlBHZAFarRZqtRrOzs5Cl0JErSASieDp6YnCwkKhSyFqNYY6Igs4dOgQoqKihC6DiMwwYcIE\n7NmzR+gyiFrN7MmHiahmzeOYmBihy7BZ2WWVmLP1JDJLK+Hv6ogNccPhqzBvFQCiuhwcHKBWq6HV\nai2yJixRW2OoIzJTRkYGunXrJnQZNm3O1pP44XIWAODP7BLM3PwzZHZShjyyuKioKBw6dIi/pFGH\nxNuvRGbikmDWl1laafT69O0i/HA5C39ml+CHy1mYs/WEQJWRrenevTuuX78udBlErcJQR2SGqqoq\n2NnZ8VaNlfm7Ohpv0Bu/vF0n9BGZIyAggMGOOiSGOiIz7N27FxMmTBC6DJu3IW44xgf5YUBnN4wL\n8sOQrsZzAdYLfURmiI6O5vQm1CHxmToiMxQWFsLLy0voMmyer8IB38/96xZ3TlkV5mw9gdt3PVNH\nZCkSiQRSqRRKpRJyuVzocohMxlBH1Eq///47QkJChC7jnlQ35BFZ2oQJE7B3714u+0cdCm+/ErXS\nmTNnEB4eLnQZ1I5ll1ViYuJBDFq5GxMTDyKnrErokshEXl5enIiYOhz21BG1QklJCVxdXSESiYQu\nhdqZu+fUyy6tQkGlCkDNVCwh7+/GuZcnc/qVDmLAgAE4d+4ce+Spw2CoI2qFffv2YfLkyUKXQe3Q\n3XPq1VVQqcIz2w5jwQO5qFCVwknmihE9p8NRpmjjKskUDzzwAD7//HOGOuowGOqIWkGpVHKdV2pQ\n7Zx6/s6VeCnyJuRSHZQaMVYe64rMckd0V5xFZnE5AKCkMgfH07Yjpv8sq9XD1ThaTyQSQSwWQ6/X\ns1eeOgSGOqIWKisrg5OTk9BlUBvLKavC84dv4M7BTHTp5NRoOPJ3dcSf2SV4KfImFHItAMBeqsWb\nURl45YfeUMjVRvtXqEqtUm9tmPslPQ/l1RoA/12NI+kYZFJJgyGPAbC+4OBg/Pnnnxg4cKDQpRA1\niwMliFro6NGjGDVqlNBlUBtb+O0ZnMyuwPnc0iZXsaidU08u1Rltt5MA/xqTCje5xmi7k8zVKvXW\n3gauDXS1Tt8sbHA1juyySgx6/3uu1FHH4MGDcfbsWaHLIDIJQx1RC+Xn58Pb21voMqiNZdUZudrY\nKha1063oUH+VEQc7PRRyLe4oRShROsO/Ux+M6Dnd4rVml1Xil4y8ht+scxex9nPEJ/1iGNRR64fL\nWXho7U+NjtptaHTvH5mF8HnzGzguTYLPm98gJavI7M8jpNrbr0QdAW+/ErWARqOBWMzfhe5FfgoH\nnM/961Zpc6tYjA6ag1+u/hsScf1A4CTTw1vha7FBEnVvm6o0WpSrNPX2c5SKcX8Xdxy59lfgq/0c\np2/Wn75DD+BwWi66vb0dyf+YgIF+7vgjsxAxaw+gqKraaN8/s0vgv3yb0TaVRoXoNT8h/53HzP6M\nQurcuTMyMzPh7+8vdClETeJPJ6IWOH36NB544AGhyyABrHvkfgz3c0KwjyvGBfk1u4pFH99u+PsD\nr8LTpSd0dXKdWAQU3EnD8bTtFqmt9lZr7W3ThgIaADjK7JA0c6TRkmu1n0Nfd0Hdu6h1eoR/uBcH\nrmZi8Kq99QJdU4qq1JC8uAmSFzfB/sVNOJTa8Mjg9mzkyJH4+eefhS6DqFnsqSNqgcuXL+Opp54S\nugwSgI/CAR+NCkD//v1NXjrKUabA4O6P4/9OfgIfp+J672eXXMP+C1+Y3WOXWfdWcCMDNb2c5I2u\nxmEvFqMC2kbbUOv0GPvvQ62uEQC0AMasOwgnmRTQA0O6emDzjMgmB2O0h8Ebjo6OqKrixNHU/jHU\nEbUQpzaglpiz9SROpHth1mA1enlUwMHurx4xnV6DzOIrZk9rUjvittb9XdzhYCfFsYw8o9uw+eVK\n9H9vJwoqquHlLEeA+1+jeH1c5ChWqhs6vUXpAUNNh9NyMWfriXohs6kJnBvavy04OjqivLycUxlR\nu8ZQR2Si1NRU9OjRQ+gyqIPJLK1EWbUdPj4ZAIW9Gi+MyEaAWzlw1+1Oc6c12RA3HHO2nsDtOr1Z\nOWVVhu214ag2IBVUqnAprxQzN/+MA4vGopuHCy7n3zGrjtb44UoWotf8iKSZI7Ej5Tqe3XGmyf0b\nG6BibaNGjcLRo0cxceJEQdonMgVDHZGJTpw4gccff1zoMqgdq1CV4UTadqPVIur2ovk4VwF1nl8z\nd1qTxm6p3r190Mrd9Ua3AsDha3lwXJoEJzsphgd44E611qh3zNr0euDItbx6gywa09wAFWvx9fVF\nXl4jI4qJ2gmGOiITabVa2NnZCV0GtWMn0rYjs/gKgJrVIr77/SPMGuSMiC5a7LrSHVP65EAuNR6V\nKpXYW2Vak7rqhsu7qTQ6qDTVOH2rCNffrKmltofvYk4ptAJP6eHpKENnVwdDL6RQRCIRtFotJJL6\n09UQtQcc/UpkgqKiInTq1EnoMqidq3sbVaWpRLkqD51dCvFg16vo7lH/K1erVePnK1tQqSqzam2G\nSZEljT8TqtbpEfL+bgDA93Oj8cdLkxEZ6GXVupqjkEvxjwf7GEb2+i/fhgHv7Wx07jxrGjJkCE6f\nPt3m7RKZiqGOyAS//fYbhgwZInQZ1M41dRvV0V6FMzfrBxE99MgpS8ehi5utWZrhVuyw7k1PnF1Q\nqcLMpGOG10kzR8LTUWbV2ppSptTg9X0pRtsu5t8RZLWLvn374sqVK23eLpGpGOqITMCJR8kUI3pO\nh3+nPnBz9IVMavzsl79CBTuJBrnlbhCL6j/5UlBxExmFeZiYeBDBK76Dz5vfoP97Ow0rNVhKQUXz\nz8rdPc+dr8IB516eDDd5+3paZ9/lLCSevNymbXJ1CWrvGOqITMSpTKg5jjIFYvrPwsNhL2Bq6Aso\nV/317JVEDPTxUsJFVgIvl64NHn/g/Mfo0+kkMksKUVCpwuX8O/jhchZCVu5ucbDbfyUT8pc3Q/ri\nJshf3myY9NeUgQYarfFzf74KB1xY+jBG9/SBs70UTvYSdJLboaeHM+zExv8u2vJfyYJtvBVKdLf2\n9asXEZGNcJQp4OniDmV1vvF2u8aDj51Ei4G+5VgefQ3LDvZAWXXNwJyCChW6LN8GqRhwkdkjyFuB\nO9UaeDrJoNfrkXtHifxyFTyd7NHJ0R6/3y6E8q55hNU6PWLWHQQA9PNywege3jhxowAqja7BOjT6\n+hX6KhxwYOGYetvvnjbF39URs8IDMXPzcWh0ekjFIuydFwVPJzmi1/yEoirLz4MneXET7AGcenEi\nBvq5W/z8dSkUCpSVlUGhMH95NyJLY6gjakZeXh68vIR9WJw6Jg8nd2TWCXUAkFd2E+6O/iiqzGzw\nOIVci1mDs/DxyQDDNj0AtQ4oqqrGiRsFDR5nyjQkF/Pv4GIz89FpdHrD3HHNrd7Q0HQqsSHd6+2X\n/85jcFm6GZUay9++rAYwZt0B5PzP3yx+7rpCQkJw7tw5REZGWr0topbi7VeiZpw7dw6DBg0Sugzq\ngBqbqkQHDeylMvgqAhs9tpdHBRT21l/hoTFHruVZfDDC8ecnwMtJBplUDHcHewwP8DSsQWuuMlXb\n/F11794d6enpbdIWUUsx1BE1IzMzE35+5v/QoXuPo0wBD6cuDb5XUlmIcQPnwb9Tnwbfd7DTY1F4\nwz15beVIWo5FB2kM9HNHzv/8DZUrZiD/nTgcWzwef7w0GXvmRmNYgIdZ59Zo9RYfVNIQDpag9oyh\njsgEHCRBrRXd74l6I2EBQKmpmdOudsSsi7z+Lf5eXhV4uL8Henm6GLb16VSGdVMuIHHqBaybcgF9\nOllvfrsqjQ7d3t6OlKwiq7VRa9tTozE+yM/Qc9fS6X21ej1+uJwlyFQnRO0FQx0RkRU5yhSYGvpC\nk+/H9J+FcQPmNvj+ggdycfnVmpGnAPCPyFuwkwBiMWAnAV4aeQv+zpV4ftgNLBudhueH3bDobVu1\nTo9RCT9a7HyNqX02r7bnruEhHM07nJoF+5dqRv16vbHVKoG0drAEUXvDUEfUhPz8fHh7Nz1ZK1Fz\nHGUNj5S8exWJE2nbG9wnu/gaKlVl2Dyj5sH8ugtCiMXAS5E3MdC3HF3dVBjoW45Zg7MsU/h/lao0\nze9kYVJx63rHldqaXjs9agaVjFl3wLKF4a/BEkTtDUMdURNSUlIQEhIidBlkAyQNTDh89yoSdZcY\nq6WDBsfTtsNX4YD7u7hD28DjXHKpcb+Wm9zygwbaalmu7LJKTEw8CD8Xy6xiYY0BFN27d0dGRobF\nz0tkLoY6oiZkZ2dzkARZREMTDhdU3DT8uaklxorLC7DixxV4OvR4vZ46AFBqjL/KS5R2rS+0Ef7L\nt+GhtT9ZPdzN2XoSP1zOwo1SpUXOp9PpLV6zWCyGTtfaG8RE1sNQR9QEkUjEQRJkEfd3n9joe7eL\nriCz+Gqj7xdVlcLHqRgyqQ7iBr61CypluJDniJslMqTkOOOL36zzi8jhtFw8uvGwyfv/kVkIz9e3\nQPrSJti/tBkjP/mh2YB1o6jc3DKNqHV6Dp6gewZDHRFRG/g17bsGt98uuoIDF79AzfTCDRM3M2wg\n0L0SALD8cE98fDLAsBKFNZy4UWjS1CH7r2Ri8Kq9KFaqodfXPOd2/Ho++vxv0+vZZluhJ/B2aaXF\nzykSiTi1CbU7XFGCiMjKKlRlRrda71YT6JpmSm9xsHc1RGgqGlrOD5ez4L98m9E2iUiE8K7u2PbU\naPgqHDDps4Z79MqrNfWOd5Pb4fAzY+DlLEeJ0vLPwHk6Web5vLspFAqUl1u2V5HIXAx1RI3QarW8\n9UoW0djIVlN5u3RBUcUNNHU5SiRi/LZkIkZ9+iPKBBitqtXrcfJGYb2wZ4oSpRqhH+zB/feZNwFx\no6zQo+br64ucnByLn5fIHLz9StSIsrIyeHhY6YcM3VPKqlo3V1rts/jFlU0HOgDwdOqC1/b+Lkig\ns5Qztwqtct6CymqLn9PHxwd5eXkNvqdUKlFYaJ3PQtQUhjqiRhQWFsLX11foMqiDq1CVoVzVsh/w\nen3Nfw0NimjMyD6PIbPOs2N9vV0x3Mzlt2yBv2v9FT3M5ePjg9zc3AbfmzFjBs6fP2/xNomaw1BH\n1IjCwkJOPExmO3xxM/T6lk1/IRKh2Z65u3k5dYWjTFEvvAS4O+HY4gnYvyC6Re3bknFBftgQN9zi\n53VyckJVVcODOoqLiy3eHpEpGOqIGlFcXAwvr/rrcRK1RGHFLau3MbrfTADAhrjhRuun1oaZqF5+\nyFwWi/FBfgjycoFjSxdW7aBG9fDGnrnR8FU4tFmbzz77LLKzs/H8889j8+bN2L9/PyZPnozw8HDM\nmjUL169fBwBkZmZi6NCh2LhxI4YPH45hw4Zh27ZtWL9+PYYNG4aIiAjs2bMHAJCcnIxJkyZh+fLl\nCAsLQ0xMDPbu3dtmn4k6Dg6UIGqETqeDVMp/ImQevZXHo8b0m21Yhqx2/dSGNPSe5MVNVq1NaEkz\nR7Z5mwkJCYiKisKyZcvQqVMnzJ49G4mJiRgwYACSkpIwf/58QyArKSlBdnY2jh07hm+//RbLli3D\nE088gV9++QXffPMN3n77bUycWDO/YVpaGiIiInDq1CkkJydj4cKF6NOnD3r06NHmn5HaL/bUERFZ\nScGdTJP2eyriPUwOWdyqNvzde7XquOwyy8/d1p6six3Spj10Ddm+fTumTZuGQYMGQSKR4IknnoBG\no8GpU6cM+8yaNQsSiQRDhw6FTqczvI6IiEBpaSlUKhWAmtu9S5YsgZ2dHUaMGIHIyEjs27dPqI9G\n7RS7IYiIrGT/hQ0m7+vh4oe/DXkNR69sQW5ZuknHSND6+dfmbD3Z6mPbu8xlsYIHOqBmmcHk5GTs\n3LkTAKDX66HRaJCVlYWAgACIRCIoFDW9rOL/jopxcXExel27HJmvry/s7e0N5/b19UV+fn6bfRbq\nGBjqiIisRKUxrTdsz7m1GB00A44yBcYPnIfMolTsv9h8IHR2ULSqrgdXf49fbjT/MH/dyYzFIkAu\nlUCvB6o02la1bU1SsQj75kW1i0AHAN7e3pg9ezaee+45w7YbN27A19cXBQUFAEybWBoACgoKoNfr\nDftnZmZi0KBBli+aOjSGOiIigeXfuYFvTv+rxcc5y91btH92WSWmfnYQv2WWmLT/bRN6vA6lZiFm\n3cEW1WEJEhEQ06dmMIhQIc7JyQmVlfWDu729PcrLy/Hwww/jhRdeQHR0NPr164f9+/djyZIl2L17\nN+zs7OotM9bU69LSUqxfvx6zZ8/G8ePHcerUKbz++uvW+WDUYTHUERFZWIWqDLt/W23lVsQY0XO6\nob0TadtRoSqFk8wVI3pONwyeuNvoT39CasGdZs/s7mCPg4tiTApLUb38oP0gHtvOZSDu/35p+cdo\nJa2+Zrmyhz8/jF9fmNBm7d7N29sbRUX1J5aeNm0a3njjDcyfPx+vvfYaXnnlFWRnZ8PPzw8ff/wx\nunXrhszMzHq9dE29VigUyMnJwYgRI+Dl5YXVq1fjvvvus84How6LoY6oATqdzvBMC1FLnUjbDqXO\neuuCdnLwQ0zwU4bgdiJtOzKLrwAASipzcDxtO2L6zzLsv/aXS3h2xxmTzr0udgjmDgtqcU2xId2x\nYuIdLN1zrsXHmuP0rUK4v/41jjwzFgP9WtZzaS6ZTNZgqJs/fz7mz59veD1hQv3Q6e/vj0uXLpn8\nWiKRYNmyZVi2bJmlyicbZJGfWhcvXsSjjz6K0NBQTJs2DefONfyPeuPGjRg5ciTuv/9+vPLKK1Aq\nlZZonsji1Go1JJJ7ZDIvsrjWLgtmqqmDFxv1xFWoSo3er339R2YhJC9uMjnQaT+Ib1Wgq7X7/M1W\nH2uOUqUGoR/swaHUrDZtVyqVQqttf88W0r3L7FBXXV2NhQsXIjY2FmfOnMHMmTOxaNGiejNtHz58\nGF988QU2b96MI0eOoKSkBCtWrDC3eSKr0Gq1DHXUasrq5m9xmmP/hS+w8+xH2H/hC1SqyuAkczV6\nXypxQuDyrRi8yvQJatfFDjG7LlMGX1hTzLqDbRbsNBoNUlJSkJKS0ujKEkRtzexQ9+uvv0IikSAu\nLg4SiQTTp0+Hu7s7jh49arTfrl27EBsbi65du8LZ2RnPP/88vvvuu3oPhhK1BxqNhqGOWk2ta/gu\nhEJmmWXnMouvoKQyB5nFV7Dl1L9wu+gKdDqguFKClBxnxG+R4EaZ6YvYvx7dz6weuvYkZt1BOL20\nCSlZ1ust/eWXXzB//nz89ttvKC4uxksvvYT//Oc/VmsvPDwcJ0/a7hQ0ZDlmh7r09PR6M1p3794d\n6enpTe7XvXt3VFZWNrogMpGQGOrIGkK6jjL7HHV/DxaLa9aJFYsBV7kWH58MQFm1nUnnWhc7BNoP\n4vE/EwabXdeYte1nIlylHhjy4V7klFm+B62iogIbNmyAl5cX3NzcoFAo4OXlhX379iEtLc3i7RG1\nhNkDJaqqquDgYDxCysHBod7zcnX3q/1zS7qtVSqVVXr2amfsrv0/UWVlJSQSCa8JMjD1e+J6fkqj\n7x1L/cbsOpqa1qy5Kc8U9mq89uBVeDjWhEBoL2Dvb70Q1X+GWTUt+OYkDqYVmHUOS9Po9Jj19S/Y\n8WSkRc/71VdfwdXVtd52T09PbN26FS+++KJF26OOxdp5orq66R54s0NdYwHO0dHRaJtcLjfarzbM\n1d2vKampqWZU2jz+lkW1CgsLIRKJeE1QPU1dE2pdFS6rvm/wPU/0QwEuWqssAPV78ep6feQteDob\nb8urTMVDCXuRX6mBl6MUbw71g6eDaT19AJBwNhObLpc2v6MA0nOLceHCBYueMy0tDTJZ/ZU8xGIx\n8vLyLN4edUxC/ewwO9QFBgYiKSnJaFtGRgamTJlitK1Hjx7IyMgwvE5PT4dCoYCPj4/JbfXq1cto\nmRRLUalUSEtLQ8+ePRv8x0r3nqysLFy5coXXBBmY8j2x78+1jR4/fOB47Eqxbqi73Ui2+mTyIDw9\ntBe2nHmr3nt6ACezKwAAaaUqfHThTot6t/7vK+t+JnMEendC//79LXrOuLg4rFmzBp6enkbby8vL\nERkZafH2qGOxdp6orq5usoPL7FA3dOhQVFdXIykpCXFxcdi5cyeKiooQERFhtN+UKVPw1ltvYcyY\nMfD19cUnn3xSL/g1RyaTWfUHrEwmg1wut9r5qeNwdHSEVqvlNUH1NHVNlKoaf0Z4V8oH1ioJOh1w\nqcARn53uYrR9zpBu+PdjTQe0ur172XeUNnHNO9qJ8cXjERb/LGFhYfD390dhYSGcnWu6PZVKJcRi\nMWJjY/ksLgGwXp5oblk5s0Odvb09EhMT8c9//hOrVq1CQEAA1q5dC7lcjrlz52LIkCGYN28eRo8e\njczMTMybNw/l5eUYNWoUXn75ZXObJ7IKqVQKjUYjdBnUgVSoygRpV6cDrhXZw8Vei1mDs/DFb37w\ndHbFsecmmLQiREWdx5r9XZt+JCa7rBJztp5Eal4JrhWZtrZta0gAyOwkEItEGNLVA5tnRNb7PDll\nVZi5+Wecvl2EqmottHcl1JE9fK22fNhbb72Fb775BkeOHEFZWRkiIyPx9NNPM9CR4CyyokTv3r2x\nZcuWetsTExONXs+cORMzZ860RJNEVsVJRamlTqRtF6RdlVaEXp41D093hQqbHtNhUsh0VKjKsP/C\nFqOlw2L6zcb+ixsMx3o4dcGovnH48WYKbpdWwt/VERvihhuCW+Zd22oD0qMbj+DkjUKrfBa5VIyh\nAZ5ImjnSpEDmq3DAgUVjAdQEvDlbTxh9DmsRiUSIi4szzPTw8MMPW+XRIKKW4jJhRA1gqKOWqruq\ng6nqBi1zabQ1y5M1tnTYUxHv1Tvm+7nRRq8nJh7ED5drJvH9M7sE9/3PNuisMKWoCECn/64za+4S\nX74Kh3qfw9o4STm1Nwx1RA2QSCQMddQiTjJXlFTmtPg4f/dekIrtodGZPlmwVgdUa0XIKHZAVE9f\n5JT9NS9oubII+y98gXKl8eS7LQmdmaXGt1UtFei2PhGB2JDuljlZO8DlBKm94YrlRA1o7mFUorpG\n9Jze4mPsJDUPUrvIO5t8jE4HvLSvN579vh/GDZiDkX0eg3+nPpBKam7/aXTVyCy+AqW6wui4ukuJ\nNSa7rBI3istNrqcpYhHgKBVjdE8fZC6LtalABwAlJSVwcXERugwiA/bUERFZgKNMgQe6PYxT13ea\nfMy44HkAgC//CMBDATfgcNdjWXp9/cmE9Xpg5c/3oazaDj06ydHPpxMcZQ6I6T8LO89+ZNRTaC91\ngqfLfUbP1DX1rBwA/JFZiCEf7jWrZ87TQYpzrzxstUEK7UlBQQGCg4OFLoPIgKGOiMhC+nYZiqzi\na7hV+mez+04OWQwPFz8AQHqRFs9daNn8ZteKlZiz9YThObK6t38VDu6I6T/L6JjRa/fgzK2a27J/\nZpeg74qduLT0rwA2dt1Bs2+13iuBDgD0ej1vv1K7wtuvREQWFD3AtCW3agMd0Pw0Io25fdezbyN6\nTod/pz5wc/SFf6c+Dd4Org10tcqUGsxMOmZ4XaI0/bm+xszZesLscxBR67CnjojIgrb92vQkwzod\ncPx2f4wrqzL0aG2IG26YjqOwQomsMuOlFxX2aswanAU3uRolSjt88ZsfyqrtjMKgo0xRr2fOFIfT\ncnHwahZe2nUGGguMiLhdar2564ioaeypI2qCvrnFNInqKNfkN/qeXg98ccYLG38z7tGqnY5j37zo\neoEOAGYNzsJA33J0dVNhoG85Zg2umW6kpXOxNXajcMy/DyIlu2VTsgS4NXyLtbW9jkRkPoY6okY4\nOTmhrEyYVQLIdp3I9AbQcI/WmHX7GzzGTa6u9/r16H4tfnbthwWWmcdt/4JopL8Zi8xlsRjd0wfO\n9sdTlRIAACAASURBVFI4y6QY1cPbqpP+ElHTePuVqBEeHh7Iy8uDj4+P0KWQjdDr/7qV2tXtBvZf\nKMCIntPhKFMAAC7mNvxLRInSDl2hMnq9KnZwi9uP6uWHzGWx6Pb2dqhbeat1/4JoRPWqeR7QV+GA\nAwvHtOo8HZ1Wq4VYzH4Ral94RRI1wsPDAzk5LZ9MlqgxldVSLB6eh4G+5XCTlyOz+AqO/3d5sS1n\n0xs9zt89Bik5zrhZIkNKjjP6dp7Q6hp8FQ5I/kfzxzc0U+OwAA9DoLvX5efnw9PTU+gyiIywp46o\nEe7u7sjIyBC6DLIhfTr3QBdVKUruuvNaoSrFlrPpmJF0vNHjXox6AMADFqtjoJ87VkwMwdI95+q9\nJxGJUL1yJg6lZmH8+kOGwROD/d2w7anRFquho8vNzWUvPrU7DHVEjZDJZFAq6z+0TtSUu9dWrVSV\n4XjadqMJgI+nbTeaT85J5orpnzce6II8rTPw4KWogdh4Kg2XCoxXnhgaUNP7FNXLD6r3Z1qlbVuQ\nm5uLPn36oKCgQOhSiAwY6oiIrKShaUZqg93dQQ/4rsHj5SLg4DOtv9XanAPPjMfMzT/j9O0iQA8M\n6eqBzTMirdaeLcnLy8OIESMY6qhdYagjImpDLZlP7to/Y626OoOvwgEHFo212vltmVar5WoS1O5w\noARREzhPHQnpXlluqyPidwO1Rwx1RE1wcHBAZSVnyCfr2l9n/jiJWFRvG7UvIlFD44OJhMXbr0RN\n6NevH86fP4/w8HChSyEbFtXLD9oP4oUug0xUWloKhUIhdBlE9bCnjqgJvXr1wpUrV4Qug4jakXPn\nziEkJEToMojqYagjaoJUKoVWqxW6DCJqR9LT09G9e3ehyyCqh6GOiIiohfhMHbVHDHVEzXB0dORg\nCSIyYKCj9oqhjqgZwcHB+PPPP4Uug4jaAQ6SoPaMoY6oGb1798bVq1eFLoOI2gEOkqD2jKGOqBkc\nLEFEtTIyMjhIgtothjoiIiIT6fV6PlNH7RZDHZEJHBwcUFFRIXQZREREjWKoIzLBgAEDOFiC6B5X\nXFwMNzc3ocsgahRDHZEJ+vbti4sXLwpdBhEJ6PDhwxg5cqTQZRA1iqGOyAR8hoaISkpK4O7uLnQZ\nRI1iqCMy0X333YcbN24IXQYRCaC6uhp2dnZCl0HUJIY6IhNFRkbi2LFjQpdBRAI4efIkhg8fLnQZ\nRE1iqCMykVwuR3V1tdBlEJEA0tPT0aNHD6HLIGoSQx1RCygUCpSVlQldBhG1Ib1eL3QJRCZhqCNq\ngVGjRuHw4cNCl0FEbejSpUvo27ev0GUQNYuhjqgFPD09UVRUJHQZRNSGkpOTMWTIEKHLIGoWQx1R\nC0kkEqjVaqHLIKI2otPpIJFIhC6DqFkMdUQtNGzYMPz6669Cl0FEbSA3Nxfe3t5Cl0FkEoY6ohbq\n1asXrl27JnQZRNQGjhw5glGjRgldBpFJGOqIWkGv13NEHNE9oKKiAs7OzkKXQWQShjqiVhg0aBBO\nnz4tdBlEZEX5+flwc3MTugwikzHUEbVCaGgozp07J3QZRGRFe/fuxYQJE4Qug8hkDHVEreTu7o7C\nwkKhyyAiK9BqtdBoNJDL5UKXQmQyhjqiVpowYQL27NkjdBlEZAWHDh1CVFSU0GUQtQhDHVErOTg4\nQK1WQ6vVCl0KEVnY9evX0b17d6HLIGoRhjoiM4wePRqHDh0SugwisqD/3969R1VVJ+4ffw6Hq2Aq\niij6DRQs7waZScsUMkEzEZNRUzKx8Zo1ZZPmmqao5TQ6NlopYFiTFugytbylU4o5WY6DpmV5WUWg\no3gLFVC5H8/vj0Z+Iioilw2b92stl7DPOXs/x/Xx8PDZt4yMDPn5+RkdA6g0Sh1QBe3bt9eRI0eM\njgGgGrHrFfUVpQ6oojvvvFNHjx41OgaAapCfny9HR0duC4Z6iVIHVNHDDz+srVu3Gh0DQDXYvHkz\nlzFBvUWpA6rIarXKarWqoKDA6CgAqujs2bPy8vIyOgZwWyh1QDV45JFHtGnTJqNjAKiC7777Tt27\ndzc6BnDbKHVANWjZsqV+/fVXo2MAqILdu3erV69eRscAbhulDqgmvXv31tdff210DAC34fjx42rZ\nsqUsFovRUYDbRqkDqkmPHj30/fffy263Gx0FQCVt3LhRQ4YMMToGUCWUOqAaDRw4UJ9//rnRMQBU\nwqFDh9ShQwc5OPAjEfUbIxioRv7+/jpy5IhKSkqMjgLgFn355ZdcbBimUOVSt3TpUvXt21c9e/bU\njBkzbnhZhx9++EGdO3dWUFCQAgMDFRQUpMTExKpuHqhzhg4dqnXr1hkdA8AtSE1NVc+ePTmWDqZQ\npVL35Zdf6oMPPlBSUpK2b9+u7OxszZ0797rPPXz4sPr27au9e/dq37592rt3ryZOnFiVzQN1UuvW\nrZWdna38/HyjowC4CbvdzhmvMJUqlbr169crKipKd955pzw8PPSHP/xB69atu+6B4gcPHlSnTp2q\nsjmg3oiKitKaNWuMjgHgJrjHK8zGsaIn2Gw25eXllVtusViUnp6uAQMGlC5r166d8vLydPr0abVq\n1arM8w8dOiRnZ2f1799fdrtd4eHhmj59upycnKrhbQB1S5MmTWS325WTk6MmTZoYHQfANS5fvqyf\nfvpJ/fv3NzoKUG0qLHWpqamKiYkpd7yBj4+PHB0d5ebmVrrsytfX2+3k6empXr16adSoUcrKytKz\nzz6rhQsXavr06VV9D0CdFBUVpRUrVmj8+PFGRwFwjQ0bNnAJE5hOhaUuODhYhw8fvu5jERERZU6M\nuFLmGjVqVO658fHxpV+3bdtWkydP1oIFCypV6goLC2vkGmCFhYVl/gaqY0xYLBa5ubnp6NGj8vb2\nrq5oMAifE+ZRVFSkzMxMhYeHV+mezYwJXKumx0RRUdFNH6+w1N2Mv7+/MjIySr9PT0/XHXfcUe4H\nWG5urhYvXqxp06aVFr6CggK5uLhUans///xzVeJWKC0trUbXj/qnqmPC399fS5Ys0bBhw6opEYzG\n50T9t2XLFvXq1UsHDhyolvUxJnAto8ZElUpdRESEYmNjFRYWplatWmnhwoWKiIgo97zGjRtry5Yt\nstvteuGFF5SZmal3331Xo0aNqtT2OnToIGdn56pEvq7CwkKlpaUpICCg0kUT5lSdY+LMmTNydHTU\n3XffXU3pYAQ+J8zh/Pnz8vLy0gMPPFDldTEmcK2aHhNFRUU3neCqUqkLDQ1VZmamJk6cqIsXLyok\nJEQvvviiJOnkyZMaPHiwNm3apFatWmnx4sWaPXu2evfuLVdXV40aNUpPPPFEpbbn4uJSo/9xXFxc\n5OrqWmPrR/1THWPi0Ucf1cKFC9WlSxc5OlbpvxzqAD4n6rc1a9Zo4sSJ1TpBwJjAtWpqTFR0PcUq\n/4SJjo5WdHR0ueWtW7fW3r17S7/39/fXBx98UNXNAfXSqFGjtGLFikr/IgOg+mzdulX9+vWrkT0+\nQF3AbcKAWuDl5aUWLVrc8KQjADXr/PnzOn78uLp162Z0FKDGUOqAWjJo0CBt2bKF+8ICBkhOTtbo\n0aONjgHUKEodUIuu7IYFUHvY7YqGglIH1KIru2EPHTqkgoICHTt2rErXyQJwc+x2RUPCqXhALQsP\nD9fYsWNLz+S2WCzq0qWLnnvuuQrPbAJQOcnJyZo4caLRMYBawUwdUMvi4uLk6ekpLy+v0pm7jIwM\nLV682OhogKmw2xUNDaUOqEV2u10//PBDuVvpNWrUSN99951BqQDzYbcrGiJKHVCLbDbbDe/dV1P3\nNgYaIs52RUNEqQNqkaOjo9zd3a/7WOPGjTmmDqgGmzZtUkhICLtd0eBQ6oBaNmDAAGVnZ5dZdubM\nGYWHhxuUCDCPK4cxdO3a1eAkQO2j1AG1LDIyUpGRkSouLlZOTo6Ki4sVEBAgX19fo6MB9drp06f1\n7bff6pFHHjE6CmAILmkC1JD4+HitWrVKly5dUseOHfXnP/9ZHTp0kCQNHDhQAwcOLPP8JUuWyNvb\nWy1btjQiLlCvFRYWavny5Xr22WeNjgIYhpk6oAZ88sknWr9+vZKSkrRr1y4FBwdr0qRJN33N+PHj\nlZycfMMTKQBcn91uV2Jiop566ilZrVaj4wCGodQBtyA1NVVRUVEKDAzUkCFD9M033ygvL0+vvfaa\n+vTpoz59+ujll1/WxYsXJUnZ2dmaPHmy2rRpIwcHB40dO1YnTpzQqVOnbrgNq9Wq8ePHKzExkbNg\ngUpYuXKlBg8erDvuuMPoKIChKHVABc6dO6cpU6YoOjpae/fu1fTp0zVt2jQ999xzOnLkiDZu3KjN\nmzcrKytLr7zyiqTfZt0iIyNL15GSkqJmzZqpVatWN91WkyZNNHDgQK1atapG3xNgFl999ZXatm2r\n9u3bGx0FMBylDqjA9u3b5evrq8jISFksFoWGhioxMVE7d+7UjBkz1LRpUzVu3FgzZ87U5s2bVVhY\nWOb1u3fvVmxsrP785z/f0vYCAgLk7e2tr7/+uibeDmAav/zyi06ePKk+ffoYHQWoEyh1QAWysrLk\n7e1dZpmfn59sNptat25duqxNmzay2+06c+ZM6bK1a9dq0qRJeuWVVyp1Rl6/fv303//+VxkZGVV/\nA4AJ5ebmavPmzRoxYoTRUYA6g1IHVMDb21unT58us2z16tWyWCzKzMwsXXbs2DFZrVY1a9ZM0m/3\neJ0zZ44WL15cZlfsrXr88ce1ceNG5ebmVu0NACZjs9n0/vvva8KECVywG7gKpQ6oQL9+/ZSZmakN\nGzbo8uXL2rZtm5YuXarIyEjNmzdP58+fV05OjubNm6eQkBB5eHhozZo1+vDDD7VixQr16tXrtrZr\nsVg0YcIEvffee5wRC/zPlTNdx4wZIxcXF6PjAHUKpQ6oQNOmTZWYmKikpCTdf//9WrhwoeLj4/Xy\nyy/Lz89PQ4YMUVhYmDw9PTV37lxJUmJioi5duqThw4crKChIgYGBCgoKUnp6eqW27erqqqeeekrx\n8fEqKSmpibcH1Bt2u13/+Mc/NHDgQK7nCFwHFx8GbkGPHj20cuXKcstjY2MVGxtbbvnnn39ebdtu\n0qSJxo4dq7i4OE2bNo3rcKHB+vDDD9W3b1+1a9fO6ChAncRMHVAPeHp6avTo0YqPj9fly5eNjgPU\nuuTkZN13332ld2UBUB6lDqgnvLy8FBUVpYSEBC5OjAbl448/Vvfu3dW5c2ejowB1GqUOqEdat26t\noUOHKiEhgRk7NAirVq1SQECAunXrZnQUoM6j1AH1TNu2bRUZGam4uDjZbDaj4wA1Jjk5WXfffbeC\ngoKMjgLUC5Q6oB7y8fHRiBEjFBcXx1mxMKVly5YpMDBQ3bt3NzoKUG9Q6oB6ytvbW2PGjNGiRYtU\nXFxsdBygWly5bElwcDDH0AGVRKkD6rHmzZtr3LhxWrRokfLz842OA1TJ5cuXlZiYqNDQUN11111G\nxwHqHUodUM81bdpUEyZM0LvvvlvmtmVAfXLhwgW98847ioiI4Dp0wG2i1AEm4OHhoWeffVZbtmzR\nnj17jI4DVMqRI0f0wQcfaPLkyWrdurXRcYB6i1IHmISDg4PGjRunc+fOad26dUbHAW7Jzp07tXPn\nTj3zzDNydXU1Og5Qr1HqAJMJCwuTv7+/EhMTOTMWdZbdbtfq1atVVFSk0aNHy2KxGB0JqPcodYAJ\nde3aVY899pjeeecdZWdnGx0HKKOoqEgJCQnq0aOHQkJCjI4DmAalDjCpFi1aaNq0aVq+fLl++ukn\no+MAkqSsrCzFxcVp9OjR3McVqGaUOsDEnJ2dNXXqVO3fv1/bt283Og4auB9//FGffvqpnnnmGTVt\n2tToOIDpUOqABiAqKkrOzs5KTk6W3W43Og4aoC+++ELp6emaMGGCHB0djY4DmBKlDmggHnjgAfXp\n00cLFy7UiRMnjI6DBuLChQtKSEhQixYtFBERYXQcwNT4dQloQHx9fTVt2jR9/PHHcnV11dChQznr\nEDVmx44dOnz4sMaNGyc3Nzej4wCmx0wd0MA4ODho1KhR6t69O7N2qBFXZudcXV01YcIECh1QS5ip\nAxqo9u3bl87aubm5KSIiglk7VBmzc4BxmKkDGrArs3bdunXTwoULdfLkSaMjoZ5idg4wHjN1AJi1\nQ5UwOwfUDczUAZDErB0qj9k5oG5hpg5AGVdm7a7clzMqKoobraMMm82mDRs26OzZs8zOAXUIpQ5A\nOQ4ODhoxYoSys7O1fPlyNWnSREOHDuWisQ2c3W7X1q1blZaWpiFDhqht27ZGRwJwFT6hAdxQ06ZN\nNX78eJ08eVLvv/++fH19FR4ezvF2DdCuXbv07bffqn///howYIDRcQBcB8fUAahQ69atNWnSJHXo\n0EEJCQn65ptvjI6EWnLw4EHFxcXJarXq6aefVseOHY2OBOAGKHUAbpm/v7+mTp0qd3d3xcfHa//+\n/UZHQg05duyYEhISdOrUKU2dOlX33Xef0ZEAVIDdrwAq7Z577tE999yjHTt2KCEhQQMHDlS7du2M\njoVqkJWVpU8//VStWrXSpEmT5ODA7/5AfUGpA3DbHnzwQfXp00f//Oc/tWXLFgUHB6tbt25Gx8Jt\nOHr0qLZu3SoPDw89+eSTcnZ2NjoSgEqi1AGoEovFokGDBslut+s///mPlixZohYtWmjQoEFcCqWO\ns9ls2rZtm44cOSJfX1+NGzdOVqvV6FgAbhOlDkC1sFgs6t27t3r37q0zZ85o5cqVKikp0cMPPyxf\nX1+j4+EqWVlZ+uyzz1RSUqLQ0FDOZgVMglIHoNq1bNlSTz75pGw2m7Zu3aovvvhCfn5+euihh5gJ\nqgF2u10fffSRvv/+e5WUlMjLy0uTJk2Sl5dXmefs3r1b3333nZo3b64RI0Zw0WDAZCh1AGqM1WpV\neHi4JOmXX37R0qVL5eTkpMGDB6t58+YGpzOPv/zlLzp9+rTc3d3l5OSk3NxczZw5UwsWLJCjo6M+\n++wzZWdnq2fPnpo4caLRcQHUEEodgFrh7+8vf39/5efna9OmTcrOzlazZs0UGhqqZs2aGR2v3jp+\n/LgyMjLKzMo5ODioWbNmev755xUaGqpHH32Uf2OgAaDUAahVbm5uGj58uCTp3Llz2rZtm3JycuTk\n5KQ+ffpwaZRK2rFjhxo3blxuuZOTk7y9vfXEE08YkAqAESh1AAzj6elZWvAKCwv1zTffaPv27bLb\n7erSpYt69uzJMXg3YLfbdejQIR08eFCXLl0qd6ax3W6Xi4uLQekAGKHaSt3s2bPl7OysGTNmXPfx\noqIixcbGKiUlRU5OToqOjtbkyZOra/MA6jkXFxc99NBDkn4rJAcOHNBHH30ku90ub29v9evXT+7u\n7ganNFZxcbF27typ9PR0SVKnTp306quvaurUqbLb7WXuyXvu3DlNmTLFqKgADFDlUpedna05c+Zo\n3bp1iomJueHzFixYoFOnTmnbtm3KysrS+PHj5efnp4EDB1Y1AgCTsVgs6tq1q7p27SpJOnnypDZu\n3Kj8/HxJvxXArl27qmPHjnJycjIyao25fPmy0tLS9MMPP+jChQuSfjvxJDg4WP369Svz3D/96U+a\nN2+e8vLy5ODgIAcHBw0YMEBBQUHVmiknJ0dWq1UeHh7Vul4A1aPKpW706NG69957FRYWdtPnbdiw\nQfPnz5e7u7vc3d0VHR2tTz/9lFIHoEKtW7fWyJEjS7/Pz8/XgQMHSq+FJ/1W9Lp06aJOnTrVu6J3\nvQJnsVgUEBCgAQMG6I477rjp6319fbVo0SIdOXJEOTk56ty5c+m/QWZmpvr37699+/ZV+RIm4eHh\nSkpKUkBAgN59912lp6dr7ty5VVongOpTYamz2WzKy8srt9xiscjDw0PLli2Tl5eXZs2adcN15Obm\nKisrS/7+/qXL2rVrp+XLl99mbAANmZubm3r27KmePXuWLisoKChX9KxWq7y9veXt7a1WrVqpRYsW\nhh2jZ7fbde7cOZ06dUqnTp3SmTNnVFhYKKlyBe5m/Pz8rrv86t2yVZGdnV369aRJk6plnQCqT4Wl\nLjU1VTExMeU+FHx8fJSSklLmNPobyc/Pl8ViKXMgr6ura+mulFtVWFgou91eqdfc6nqv/htgTNRP\nXbp0UZcuXUq/Lykp0dmzZ3X69GmlpqYqKytLly9fLvc6BwcHNW/evLT4ubi4yGq1ytHRUVarVU5O\nTiopKZHdbtelS5dUUFAgm82mkpISlZSUqLi4WGfPntWZM2d05syZ0lJ5NYvFombNmsnb21u+vr66\n7777rnt/1YKCgmr9N7nyufnee+9p7dq1ysnJ0WOPPabp06dr0KBBCg4OVkpKisLDw/XCCy/ozTff\n1K5du/Trr7+qZcuWmj59ukJCQvT4449LkqKiojRnzhwdPnxYaWlpevPNN5WXl6e33npLKSkpslgs\n6tu3r6ZPn2763bR8TuBaNT0mioqKbvp4haUuODhYhw8frlKIK2WusLCw9EDngoKCSh/0/PPPP1cp\nR0XS0tJqdP2ofxgT5mC1WuXj4yMfH5/rPm6z2ZSTk6OMjAzt3btXxcXFstlsunz5skpKSmSz2WSz\n2ST9VgCvlL2r/zRp0kTNmzdXx44d5eh484/WnJwc5eTkVPv7vJ5ff/1VkvTTTz/pjTfeUGZmpl55\n5RW1a9dOxcXFSktL0zvvvKOSkhLNmzdPBw8eVGxsrFxcXLR+/Xq9/vrr8vLy0ssvv6wxY8aUfv/V\nV18pNzdXBw4c0KJFi5Sbm6s33nhDDg4OiouL0x//+Ec988wztfIejcbnBK5l1JiolUuaXPmwS09P\nl6enpyQpIyOjzO7YW9GhQ4fr/mZbVYWFhUpLS1NAQACXAIAkxgTKq69j4sSJE7JYLHrxxRfVsmVL\n9ejRQ0uXLpWzs7OcnJwUGRmpHj16SPrtM1aSPDw8dOrUKbVr107Z2dllZj+vXER6x44dunDhggIC\nArR7924lJSWpY8eOkqTY2FgNGzZMb7/9dr36t6qs+jomUHNqekwUFRXddIKr1q5TN2TIEC1atEhv\nv/22zp8/r6SkJM2cObNS63BxcanR/zguLi7lrvWEho0xgWvVtzFx5TPTy8urNLeTk5McHBxksVjk\n4+NTujwrK0uvvfaa9u/frzvvvFNt27aV3W4v836vvH9HR0c5ODioqKhIJSUl8vPzK31e+/btZbfb\nlZubq//7v/+r5Xdc++rbmEDNq6kxUdHxsQ7VvsWrBAYG6ttvv5UkPffcc/Lz89OgQYMUHR2tkSNH\nVnjGLACgZl39Q+LVV19V+/bttWvXLq1atUpjxoyp8PUtWrSQs7OzMjMzS5cdO3ZMVquVW5MBtaza\nZur++te/llu2b9++0q9dXFwUGxur2NjY6tokAOAW3OoJZhcvXpSrq6ssFotOnjypt99+W9Jvxxxe\nOWHk4sWLZV5jsVgUERGhN998U/Pnz5eDg4PmzZunkJAQ058oAdQ1NTpTBwAw3rW7bCwWS+mfq82a\nNUvbtm1TUFCQxo4dq5CQELm5uemXX36RJD322GOKiYnR2rVry73Oz89PQ4YMUVhYmDw9Pbl+HWAA\ni70mrhFSzQoLC/Xjjz+qa9euNXJM3ZXrW3Xp0oXjIiCJMYHyGBO4FmMC16rpMVFRH2KmDgAAwAQo\ndQAAACZAqQMAADABSh0AAIAJUOoAAABMgFIHAABgApQ6AAAAE6DUAQAAmAClDgAAwAQodQAAACZA\nqQMAADABSh0AAIAJUOoAAABMgFIHAABgApQ6AAAAE6DUAQAAmAClDgAAwAQodQAAACZAqQMAADAB\nSh0AAIAJUOoAAABMgFIHAABgApQ6AAAAE6DUAQAAmAClDgAAwAQodQAAACZAqQMAADABSh0AAIAJ\nUOoAAABMgFIHAABgApQ6AAAAE6DUAQAAmAClDgAAwAQodQAAACZAqQMAADABSh0AAIAJUOoAAABM\ngFIHAABgApQ6AAAAE6DUAQAAmAClDgAAwAQodQAAACZAqQMAADABSh0AAIAJUOoAAABMgFIHAABg\nApQ6AAAAE6DUAQAAmAClDgAAwAQodQAAACZAqQMAADABSh0AAIAJUOoAAABMgFIHAABgAo7VtaLZ\ns2fL2dlZM2bMuO7j58+fV3BwsBo1aiS73S6LxaKIiAjFxsZWVwQAAIAGq8qlLjs7W3PmzNG6desU\nExNzw+cdOnRIHTp00IYNG6q6SQAAAFyjyrtfR48eLScnJ4WFhd30eQcPHlSnTp2qujkAAABcR4Uz\ndTabTXl5eeWWWywWeXh4aNmyZfLy8tKsWbNuup5Dhw7p+PHjGjRokC5evKi+ffvqpZdeUuPGjW8/\nPQAAACTdQqlLTU1VTEyMLBZLmeU+Pj5KSUmRl5fXLW2ocePG6t27t37/+9+ruLhYM2bM0Kuvvqr5\n8+ffXnIAAACUqrDUBQcH6/Dhw1Xe0LUnRDz//POKjo6+pdfa7XZJ0sWLF1VYWFjlLNcqKioqXf+V\nr9GwMSZwLcYErsWYwLVqekwUFxdL+v+96FrVdvbrzdjtdi1YsEAjR45UmzZtJEkFBQVydLy1zV95\nE0eOHKmpiJKko0eP1uj6Uf8wJnAtxgSuxZjAtWp6TBQXF8vV1bXc8lopdRaLRfv27VNmZqZmz56t\nixcvasGCBRo+fPgtvd7d3V133XWXnJycyu0GBgAAaAjsdruKi4vl7u5+3cdrtNQFBgbqvffe0733\n3qu///3vev311xUSEiKLxaJHH31UL7zwwi2tx8HBgRMqAABAg3e9GborLPYb7ZgFAABAvcFtwgAA\nAEyAUgcAAGAClDoAAAAToNQBAACYAKUOAADABCh1V4mPj1doaKh69eqlsWPH6ueffzY6EuqI2bNn\n629/+5vRMWCQgwcP6ne/+50CAwM1bNgwff/990ZHQh2xf/9+Pfjgg0bHQB2wZ88ejRgxQj17VpVV\n+AAAAzBJREFU9lRYWJhWrlxZ6xkodf/zySefaP369UpKStKuXbsUHBysSZMmGR0LBsvOztZLL72k\n5ORko6PAIEVFRZoyZYqioqK0Z88eRUdHa+rUqcrPzzc6Ggy2evVqPfXUUyopKTE6CgyWm5urp59+\nWk8++aT27Nmjt956S/Pnz9e///3vWs1BqfufnJwcTZ48WW3atJGDg4PGjh2rEydO6NSpU0ZHg4FG\njx4tJycnhYWFGR0FBtm1a5esVqtGjhwpq9Wq4cOHy9PTU//617+MjgYDLV68WElJSZoyZYrRUVAH\nnDhxQiEhIRo8eLAkqXPnzrr//vu1b9++Ws1RK7cJqytsNpvy8vLKLbdYLIqJiSmzLCUlRc2aNVOr\nVq1qKx4McLMx4eHhoWXLlsnLy0uzZs0yIB3qgvT0dPn7+5dZ1q5dO6WnpxuUCHVBVFSUJk+erNTU\nVKOjoA7o2LGj5s6dW/p9Tk6O9uzZo2HDhtVqjgZV6lJTUxUTE1Pu/rE+Pj5KSUkp/X737t2KjY3V\n7NmzazsiallFY8LLy8ugZKgr8vPz5ebmVmaZm5ubCgoKDEqEuqBFixZGR0AddeHCBU2ePFndunVT\naGhorW67QZW64OBgHT58+KbPWbt2rV5//XW98soreuSRR2opGYxyK2MCDdv1Clx+fr4aNWpkUCIA\nddWxY8c0ZcoU+fr6asGCBbW+fY6pu0pcXJzmzJmjxYsXKzIy0ug4AOqA9u3bKyMjo8yyjIwMBQQE\nGJQIQF104MABjRw5Ug8++KDi4uLk7Oxc6xkodf+zZs0affjhh1qxYoV69epldBwAdUTv3r1VVFSk\n5ORklZSUaPXq1Tp37pz69OljdDQAdURWVpYmTJig8ePHa+bMmYbloNT9T2Jioi5duqThw4crKChI\ngYGBCgoK4mBooIFzdnbWkiVLtGHDBt1///1avny5EhIS5OrqanQ0AHXEmjVrdP78ecXHxyswMLC0\nQ7z11lu1msNit9vttbpFAAAAVDtm6gAAAEyAUgcAAGAClDoAAAAToNQBAACYAKUOAADABCh1AAAA\nJkCpAwAAMAFKHQAAgAlQ6gAAAEzg/wHcNIV6m0tTZAAAAABJRU5ErkJggg==\n", 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RERGRBWBSR0RERGQBmNQRERERWQAmdUREREQWgEkdERERkQVgUkdERERkAZjUEREREVkA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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -329,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "collapsed": true }, @@ -339,26 +332,26 @@ "data = load_data('occupancy') \n", "\n", "# Specify the features of interest and the classes of the target \n", - "features = ['temp', 'humid', 'light', 'co2', 'hratio']\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.occupied.as_matrix()" + "y = data.occupancy.as_matrix()" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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kuv21NPpKm/tS28YAkYiI2oyDKaudOn78ZX9cLPNtvqFZXbAnlwkY0bWsXnHJ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\ngwcZOXIk06dPx+l0Ap5/VzVfUmpqKgsWLKC0tJR9+/bxyiuve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TkpJCWVkZ9fX1vP/++173rKmpYdmyZTgcDnbs2MHu3bu1STRNJk2aRHZ2Nt988w0ul4vj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H4nOo2Ba7LXjmtLtqOviX39nW6SInOZPmQ7/VIjy7JipdNf6NLWW0unozlopTMUrUZP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XuWdSJXNimvhsvyc7q6Jd7xyqxPia+eWsQtbmBBHsYeGqhN6TZHpymUir9OT1/dGc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\nZufOnfz+++/MmjWL1atXs2nTJvbv309MTIxNeoPBwOT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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -390,7 +383,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -415,7 +408,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -436,16 +429,16 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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CCOGxtm/fzujRo21mML/44ovs3bvXboFuIYRwlce1PP7jH/9Aq9Vag2F2djbfffcdK1eu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\nb6qgKAI6zXn4h4O88ccqNFoIeOXcduNdfPGBB2P2f+/gr+K+9gSdAk3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ZAAAg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\nUYAhFOIAAAAASUVORK5CYII=\n", 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0chfovPr26z0qqVPmhpKzLZcJdAaPusgYZgE1R75g7JSUPoOkrq+ltihJHmMl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\nglQqVZ/HvuxaVlYWgoOD8c9//hPjx4/Hfffdh3PnzqGmpgYSiYSfSwM4O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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -473,7 +466,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": { "collapsed": true }, @@ -494,16 +487,16 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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EVGFDxixE0uopMCjvzAkk38AQSERERBVWo0YkJs/8UOoyqAI4HExEREQUgBgCiYiIiAIQ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\n", 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wOLxLREREFAAY+oiIiIgCAEMfERERUQBg6CMiIiIKAAx9RERERAGAoY+IiIgoADD0ERER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\nRC6AoY+IiIjIBTD0EREREbmA/weepWupxG0OdwAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -537,7 +530,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": { "collapsed": true }, @@ -564,7 +557,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": { "collapsed": true }, @@ -574,12 +567,12 @@ "data = load_data('occupancy') \n", "\n", "# Specify the features of interest and the classes of the target \n", - "features = ['temp', 'humid', 'light', 'co2', 'hratio']\n", - "classes = ['unoccupied', 'occupied']\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.occupied.as_matrix()\n", + "y = data.occupancy.as_matrix()\n", "\n", "# Create the train and test data \n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)" @@ -587,16 +580,16 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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hhBBCeJEgUQghhBBCeJEgUQghhBBCeJEgUQghhBBCeJEgUQghhBBCeJEgUQgh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\nVPF/JSvnh4w/KwwAAAAASUVORK5CYII=\n", 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JEoUQQgghhAsJEoUQQgghhAsJEoUQQgghhAsJEoUQQgghhAsJEoUQQgghhAsJ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M9//sOnn35qXKezqKgItVotXc1mSCauCPEnebhred++fQwZMoTw8HAANm3ahIODA/b29gwbNoxh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\nAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -634,12 +627,12 @@ "data = load_data('occupancy') \n", "\n", "# Specify the features of interest and the classes of the target \n", - "features = ['temp', 'humid', 'light', 'co2', 'hratio']\n", - "classes = ['unoccupied', 'occupied']\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.occupied.as_matrix()\n", + "y = data.occupancy.as_matrix()\n", "\n", "# Create the train and test data \n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)" @@ -654,9 +647,9 @@ "outputs": [ { "data": { - "image/png": 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4nU4HBweHQsefeeYZEaqhspDddieciiUiIiqZ7OxsxMXF4eOPP0ZOTo7Y5VA5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\nVCpotVqZqiZ7c8hgV1hYiN69e9sc8/f3R2Fh4V3H+fv7o6q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Ks2fPRr9+/aBSqRAWFoZWrVrhX//6F/7973/DyckJs2fPhpOT02MD6AsvvIDGjRvjtdde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\niIiIiOwEgx0RERGRnWCwIyIiIrIT/wO75/wjEdCeOgAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -694,12 +687,12 @@ "data = load_data('occupancy') \n", "\n", "# Specify the features of interest and the classes of the target \n", - "features = ['temp', 'humid', 'light', 'co2', 'hratio']\n", - "classes = ['unoccupied', 'occupied']\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.occupied.as_matrix()\n", + "y = data.occupancy.as_matrix()\n", "\n", "# Create the train and test data \n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)" @@ -714,9 +707,9 @@ "outputs": [ { "data": { - "image/png": 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lJiYGn1hB2+Lz+c77ZFejgffWW2/p2LFjWr58uV588UVJUkREhCZMmKCCggLNnz9fGRkZ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\nnBz5/X6NHj1akyZ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"text/plain": [ - "" + "" ] }, "metadata": {}, diff --git a/examples/examples.py b/examples/examples.py deleted file mode 100644 index 1c99474d7..000000000 --- a/examples/examples.py +++ /dev/null @@ -1,153 +0,0 @@ -#!/usr/bin/env python -# Ben's scratchpad for testing - -## Imports -import os -import pandas as pd -import yellowbrick as yb -import matplotlib.pyplot as plt - -from pandas.tools.plotting import radviz, parallel_coordinates -from yellowbrick.features import ParallelCoordinates, RadViz, Rank2D - -## Module Constants - the path to the test data sets -FIXTURES = os.path.join(os.path.dirname(__file__), "examples", "data") - -## Dataset loading mechanisms -datasets = { - "credit": os.path.join(FIXTURES, "credit.xls"), - "concrete": os.path.join(FIXTURES, "concrete.xls"), - "occupancy": os.path.join(FIXTURES, 'occupancy', 'datatraining.txt'), -} - -## Human readable column names -columns = { - "credit": [ - 'id', 'limit', 'sex', 'edu', 'married', 'age', 'apr_delay', 'may_delay', - 'jun_delay', 'jul_delay', 'aug_delay', 'sep_delay', 'apr_bill', 'may_bill', - 'jun_bill', 'jul_bill', 'aug_bill', 'sep_bill', 'apr_pay', 'may_pay', 'jun_pay', - 'jul_pay', 'aug_pay', 'sep_pay', 'default' - ], - "concrete": [ - 'cement', 'slag', 'ash', 'water', 'splast', - 'coarse', 'fine', 'age', 'strength' - ], - "occupancy": [ - 'date', 'temp', 'humid', 'light', 'co2', 'hratio', 'occupied' - ], -} - - -def load_data(name): - """ - Loads and wrangls the passed in dataset. - """ - - path = datasets[name] - data = { - 'credit': lambda p: pd.read_excel(p, header=1), - 'concrete': lambda p: pd.read_excel(p), - 'occupancy': lambda p: pd.read_csv(p), - }[name](path) - - data.columns = columns[name] - return data - - -def test_parallel_coords(pandas=False, outpath=None): - """ - Runs the parallel coordinates visualizer on the dataset. - - Parameters - ---------- - pandas : bool - Run the pandas version of the function - outpath : path or None - Save the figure to disk rather than show (if None) - """ - data = load_data('occupancy') # Load the data - features = ['temp', 'humid', 'light', 'co2', 'hratio'] - classes = ['unoccupied', 'occupied'] - X = data[features].as_matrix() - y = data.occupied.as_matrix() - - if pandas: - parallel_coordinates(data[features + ['occupied']], 'occupied') - if outpath: - plt.savefig(outpath) - else: - plt.show() - - else: - visualizer = ParallelCoordinates( # Instantiate the visualizer - classes=classes, features=features - ) - visualizer.fit(X, y) # Fit the data to the visualizer - visualizer.transform(X) # Transform the data - visualizer.poof(outpath=outpath) # Draw/show/poof the data - - -def test_radviz(pandas=False, outpath=None): - """ - Runs the radviz visualizer on the dataset. - - Parameters - ---------- - pandas : bool - Run the pandas version of the function - outpath : path or None - Save the figure to disk rather than show (if None) - """ - data = load_data('occupancy') # Load the data - features = ['temp', 'humid', 'light', 'co2', 'hratio'] - classes = ['unoccupied', 'occupied'] - X = data[features].as_matrix() - y = data.occupied.as_matrix() - - if pandas: - radviz(data[features + ['occupied']], 'occupied') - if outpath: - plt.savefig(outpath) - else: - plt.show() - - else: - visualizer = RadViz( # Instantiate the visualizer - classes=classes, features=features - ) - visualizer.fit(X, y) # Fit the data to the visualizer - visualizer.transform(X) # Transform the data - visualizer.poof(outpath=outpath) # Draw/show/poof the data - - -def test_rank2d(seaborn=False, outpath=None): - """ - Runs the radviz visualizer on the dataset. - - Parameters - ---------- - pandas : bool - Run the pandas version of the function - outpath : path or None - Save the figure to disk rather than show (if None) - """ - data = load_data('occupancy') # Load the data - features = ['temp', 'humid', 'light', 'co2', 'hratio'] - classes = ['unoccupied', 'occupied'] - X = data[features].as_matrix() - y = data.occupied.as_matrix() - - if seaborn: - raise NotImplementedError("Not yet!") - - else: - visualizer = Rank2D(features=features, algorithm='covariance') - visualizer.fit(X, y) # Fit the data to the visualizer - visualizer.transform(X) # Transform the data - visualizer.poof(outpath=outpath) # Draw/show/poof the data - - -if __name__ == '__main__': - # test_parallel_coords(pandas=True) - # test_radviz(pandas=False, outpath='/Users/benjamin/Desktop/yb_radviz.png') - test_rank2d(outpath='/Users/benjamin/Desktop/yb_rank2d_covariance.png') From 8f4de77e9ef13da2d2a8c4a6c1b4ca45dec35a71 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Thu, 13 Oct 2016 20:29:50 -0400 Subject: [PATCH 05/35] tests with dataset download now possible --- .travis.yml | 3 +- examples/download.py | 1 + requirements.txt | 2 +- tests/.gitignore | 1 + tests/dataset.py | 154 +++++++++++++++++++++++++++++ tests/test_features/test_radviz.py | 27 ++++- 6 files changed, 185 insertions(+), 3 deletions(-) create mode 100644 tests/.gitignore create mode 100644 tests/dataset.py diff --git a/.travis.yml b/.travis.yml index 4147cd431..3516ae706 100644 --- a/.travis.yml +++ b/.travis.yml @@ -5,8 +5,9 @@ python: before_install: - sudo apt-get build-dep python-scipy - - pip install coveralls - pip install scipy + - pip install nose coverage mock + - pip install coveralls requests install: pip install -r requirements.txt diff --git a/examples/download.py b/examples/download.py index aa879b6c5..a82e2a341 100644 --- a/examples/download.py +++ b/examples/download.py @@ -3,6 +3,7 @@ # Downloads the example datasets for running the examples. # # Author: Rebecca Bilbro +# Author: Benjamin Bengfort # Created: Wed May 18 11:54:45 2016 -0400 # # Copyright (C) 2016 District Data Labs diff --git a/requirements.txt b/requirements.txt index af932dd2e..6944e79dc 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,11 +10,11 @@ cycler>=0.10.0 #pytz==2016.7 #python-dateutil==2.5.3 #six==1.10.0 -#requests==2.10.0 ## Testing Requirements (uncomment for development) #nose>=1.3.7 #coverage>=4.1 +#requests>=2.10.0 ## Python 2 Testing Requirements #mock>=2.0.0 diff --git a/tests/.gitignore b/tests/.gitignore new file mode 100644 index 000000000..a2ded98bb --- /dev/null +++ b/tests/.gitignore @@ -0,0 +1 @@ +fixtures/* diff --git a/tests/dataset.py b/tests/dataset.py new file mode 100644 index 000000000..45e8b7dfc --- /dev/null +++ b/tests/dataset.py @@ -0,0 +1,154 @@ +# tests.dataset +# Helper functions for tests that utilize downloadable datasets. +# +# Author: Benjamin Bengfort +# Created: Thu Oct 13 19:55:53 2016 -0400 +# +# Copyright (C) 2016 District Data Labs +# For license information, see LICENSE.txt +# +# ID: dataset.py [] benjamin@bengfort.com $ + +""" +Helper functions for tests that utilize downloadable datasets. +""" + +########################################################################## +## Imports +########################################################################## + +import os +import sys +import shutil +import hashlib +import zipfile +import numpy as np + +try: + import requests +except ImportError: + requests = None + + +########################################################################## +## Fixtures +########################################################################## + +DATASETS = { + 'concrete': { + 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/concrete.zip', + 'signature': 'b9ea5f26a7bb272a040e2f1a993b26babbf8dc4a04ab8198bb315ca66d71f10d', + }, + 'credit': { + 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/credit.zip', + 'signature': '4a91339c69f55e18f3f48004328fbcb7868070b618208fed099920427b084e5e', + }, + 'occupancy': { + 'url': 'https://s3.amazonaws.com/ddl-data-lake/yellowbrick/occupancy.zip', + 'signature': '429cfe376dc9929a1fa528da89f0e1626e34e19695f3f555d8954025bbc522b8', + } +} + +FIXTURES = os.path.join(os.path.dirname(__file__), "fixtures") + + +########################################################################## +## Test Cases that Require Download +########################################################################## + +class DatasetMixin(object): + """ + Mixin for unittest.TestCase class to download datasets from S3 for + testing real world machine learning visual diagnostics. + """ + + @staticmethod + def sha256sum(path, blocksize=65536): + """ + Computes the SHA256 signature of a file to verify that the file has not + been modified in transit and that it is the correct version of the data. + """ + sig = hashlib.sha256() + with open(path, 'rb') as f: + buf = f.read(blocksize) + while len(buf) > 0: + sig.update(buf) + buf = f.read(blocksize) + return sig.hexdigest() + + + @staticmethod + def download_data(url, path=FIXTURES, signature=None, extract=True): + """ + Downloads the zipped data set specified at the given URL, saving it to + the output path specified. This function verifies the download with the + given signature (if supplied) and extracts the zip file if requested. + """ + if requests is None: + raise ImportError( + "The requests module is required to download data --\n" + "please install it with pip install requests." + ) + + # Create the output directory if it does not exist + if not os.path.exists(path): + os.mkdir(path) + + # Get the name of the file from the URL + name = os.path.basename(url) + dlpath = os.path.join(path, name) + + # Fetch the response in a streaming fashion and write it to disk. + response = requests.get(url, stream=True) + with open(dlpath, 'wb') as f: + for chunk in response.iter_content(65536): + f.write(chunk) + + # If verify, compare the signature + if signature is not None: + dlsignature = DatasetMixin.sha256sum(dlpath) + if signature != dlsignature: + raise ValueError( + "Download signature does not match hardcoded signature!" + ) + + # If extract, extract the zipfile. + if extract: + zf = zipfile.ZipFile(dlpath) + zf.extractall(path) + + + @staticmethod + def download_all(path=FIXTURES, verify=True, extract=True): + """ + Downloads all the example datasets. If verify is True then compare the + download signature with the hardcoded signature. If extract is True then + extract the contents of the zipfile to the given path. + """ + for name, meta in DATASETS.items(): + url = meta['url'] + signature = meta['signature'] if verify else None + + DatasetMixin.download_data( + url, path=path, signature=signature, extract=extract + ) + + @staticmethod + def remove_all(fixtures=FIXTURES): + """ + Removes all the downloaded datasets as clean up + """ + shutil.rmtree(fixtures) + + @staticmethod + def load_data(name, fixtures=FIXTURES): + """ + Loads the numpy matrix from the specified data set, downloads it if + it hasn't already been downloaded. + """ + + path = os.path.join(fixtures, name, "{}.csv".format(name)) + if not os.path.exists(path): + DatasetMixin.download_all(path=fixtures) + + return np.genfromtxt(path, dtype=float, delimiter=',', names=True) diff --git a/tests/test_features/test_radviz.py b/tests/test_features/test_radviz.py index f9541657d..d2be595e1 100644 --- a/tests/test_features/test_radviz.py +++ b/tests/test_features/test_radviz.py @@ -21,13 +21,14 @@ import numpy as np import numpy.testing as npt +from tests.dataset import DatasetMixin from yellowbrick.features.radviz import * ########################################################################## ## RadViz Base Tests ########################################################################## -class RadVizTests(unittest.TestCase): +class RadVizTests(unittest.TestCase, DatasetMixin): X = np.array( [[ 2.318, 2.727, 4.260, 7.212, 4.792], @@ -40,6 +41,12 @@ class RadVizTests(unittest.TestCase): 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_normalize_x(self): """ Test the static normalization method on the RadViz class @@ -62,3 +69,21 @@ def test_radviz(self): """ visualizer = RadViz() visualizer.fit_transform(self.X, self.y) + + def test_integrated_radviz(self): + """ + Test radviz 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 + visualizer = RadViz() + visualizer.fit_transform(X, y) From 12c1ee472a6559480474f9a90d0aa8b2e0f6938a Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Sun, 16 Oct 2016 12:14:35 -0400 Subject: [PATCH 06/35] updates to docstrings for classifier score visualizers --- yellowbrick/classifier.py | 205 +++++++++++++++++++++++++++++++++++--- 1 file changed, 193 insertions(+), 12 deletions(-) diff --git a/yellowbrick/classifier.py b/yellowbrick/classifier.py index 750e65469..f03080145 100644 --- a/yellowbrick/classifier.py +++ b/yellowbrick/classifier.py @@ -57,10 +57,32 @@ 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, classes=None, **kwargs): """ - Pass in a fitted model to generate a ROC curve. + Pass in a fitted model to generate a classification report. + + Parameters + ---------- + + :param ax: the axis to plot the figure on. + + :param estimator: 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, **kwargs) @@ -73,6 +95,18 @@ def __init__(self, model, classes=None, **kwargs): 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_ @@ -81,6 +115,16 @@ def fit(self, X, y=None, **kwargs): 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') @@ -92,6 +136,15 @@ def score(self, X, y=None, **kwargs): 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 """ if self.ax is None: self.ax = plt.gca() @@ -112,6 +165,12 @@ def draw(self, y, y_pred): def poof(self): """ Plots a classification report as a heatmap. + + Returns + ---------- + + ax : the axis with the plotted figure + """ if self.ax is None: return @@ -126,6 +185,7 @@ def poof(self): plt.show() + return self.ax ########################################################################## ## Receiver Operating Characteristics @@ -138,7 +198,25 @@ class ROCAUC(ClassificationScoreVisualizer): """ def __init__(self, model, **kwargs): """ - Pass in a model to generate a ROC curve. + Pass in a fitted model to generate a ROC curve. + + Parameters + ---------- + + :param ax: the axis to plot the figure on. + + :param estimator: the Scikit-Learn estimator + Should be an instance of a classifier, else the __init__ will + return an error. + + :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, **kwargs) @@ -146,13 +224,32 @@ def __init__(self, model, **kwargs): self.name = get_model_name(self.estimator) self.ax = None - super(ROCAUC, self).__init__(model, **kwargs) + # TODO refactor to use new Yellowbrick color utils self.colors = { 'roc': kwargs.pop('roc_color', '#2B94E9'), 'diagonal': kwargs.pop('diagonal_color', '#666666'), } 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) @@ -162,6 +259,21 @@ 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 + """ if self.ax is None: self.ax = plt.gca() @@ -174,12 +286,14 @@ def draw(self, y, y_pred): def poof(self, **kwargs): """ - Called by user. + Called by user. Take in the model as input and generates a plot of + the ROC plots with AUC metrics embedded. - Only takes self. + Returns + ------ + + ax : the axis with the plotted figure - Take in the model as input and generates a plot of - the ROC plots with AUC metrics embedded. """ if self.ax is None: return @@ -191,6 +305,9 @@ def poof(self, **kwargs): plt.show() + return self.ax + + ########################################################################## ## Class Balance Chart ########################################################################## @@ -198,23 +315,61 @@ def poof(self, **kwargs): class ClassBalance(ClassificationScoreVisualizer): """ Class balance chart that shows the support for each class in the - fitted classification model. + fitted classification model displayed as a bar plot. """ def __init__(self, model, classes=None, **kwargs): """ Pass in a fitted model to generate a class balance chart. + + Parameters + ---------- + + :param ax: the axis to plot the figure on. + + :param estimator: 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 kwargs: 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. + """ + super(ClassBalance, self).__init__(model, **kwargs) # TODO: hoist self.estimator = model self.ax = None - self.name = get_model_name(self.estimator) self.colors = kwargs.pop('colors', YELLOWBRICK_PALETTES['paired']) 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_ @@ -223,6 +378,20 @@ def fit(self, X, y=None, **kwargs): 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) @@ -233,13 +402,18 @@ def draw(self): """ Renders the class balance chart across the axis. - TODO: Would rather not have to set the colors with this method. - Refactor to make better use of yb_palettes module? + Returns + ------ + + ax : the axis with the plotted figure """ 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) @@ -248,6 +422,11 @@ def draw(self): def poof(self): """ Plots a class balance chart + + Returns + ------ + + ax : the axis with the plotted figure """ if self.ax is None: return @@ -256,5 +435,7 @@ def poof(self): ceiling = cmax + cmax*0.1 span = cmax - cmin plt.ylim(0, ceiling) - + plt.show() + + return self.ax From ae2601fdbe8e08d54ab013b45c637aef83c23d4c Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Sun, 16 Oct 2016 12:30:19 -0400 Subject: [PATCH 07/35] updates to docstrings for regression score visualizers --- yellowbrick/regressor.py | 137 +++++++++++++++++++++++++++++++++++++-- 1 file changed, 131 insertions(+), 6 deletions(-) diff --git a/yellowbrick/regressor.py b/yellowbrick/regressor.py index 6ef22bdef..530ce3a4c 100644 --- a/yellowbrick/regressor.py +++ b/yellowbrick/regressor.py @@ -55,6 +55,26 @@ class PredictionError(RegressionScoreVisualizer): predicted values generated by our model(s). """ def __init__(self, model, **kwargs): + """ + Parameters + ---------- + + :param ax: the axis to plot the figure on. + + :param estimator: the Scikit-Learn estimator + Should be an instance of a regressor, else the __init__ will + return an error. + + :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. + + + These parameters can be influenced later on in the visualization + process, but can and should be set as early as possible. + """ + super(PredictionError, self).__init__(model, **kwargs) self.ax = None @@ -83,20 +103,38 @@ def score(self, X, y=None, **kwargs): y : array-like y (also y_test) is the independent actual variables to score against + Returns + ------ + + ax : the axis with the plotted figure + """ y_pred = self.predict(X) return self.draw(y, y_pred) def draw(self, y, y_pred): """ - If score is happening inside a loop, draw would get called multiple times. + 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 - But, ideally we'd want the best fit line to be drawn only once? + Returns + ------ + + ax : the axis with the plotted figure """ if self.ax is None: self.ax = plt.gca() self.ax.scatter(y, y_pred, c='#F2BE2C') + + # TODO If score is happening inside a loop, draw would get called multiple times. + # Ideally we'd want the best fit line to be drawn only once draw_best_fit(y, y_pred, self.ax, 'linear', ls='--', lw=2, c=self.colors['line']) self.ax.set_xlim(y.min()-1, y.max()+1) @@ -105,11 +143,20 @@ def draw(self, y, y_pred): return self.ax def poof(self): + """ + Returns + ------ + + ax : the axis with the plotted, labelled and formatted figure + + """ self.ax.set_title('Prediction Error for {}'.format(self.name)) self.ax.set_ylabel('Predicted') plt.xlabel('Measured') plt.show() + return self.ax + ########################################################################## ## Residuals Plots ########################################################################## @@ -124,10 +171,34 @@ class ResidualsPlot(RegressionScoreVisualizer): otherwise, a non-linear model is more appropriate. """ def __init__(self, model, **kwargs): + """ + Parameters + ---------- + + :param ax: the axis to plot the figure on. + + :param estimator: the Scikit-Learn estimator + Should be an instance of a regressor, else the __init__ will + return an error. + + :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. + + These parameters can be influenced later on in the visualization + process, but can and should be set as early as possible. + + """ + super(ResidualsPlot, self).__init__(model, **kwargs) self.ax = None self.name = get_model_name(self.estimator) + + # TODO Is there a better way to differentiate between train and test points? + # We'd like to color them differently in draw... + # Can the user pass those in as keyword arguments? self.colors = { 'train_point': kwargs.pop('train_point_color', '#2B94E9'), 'test_point': kwargs.pop('test_point_color', '#94BA65'), @@ -136,15 +207,44 @@ def __init__(self, model, **kwargs): 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 values + + kwargs: keyword arguments passed to Scikit-Learn API. + """ super(ResidualsPlot, self).fit(X, y, **kwargs) self.score(X, y, train=True) def score(self, X, y=None, train=False, **kwargs): """ - Is there a better way to differentiate between train and test points? + Generates predicted target values using the Scikit-Learn + estimator. + + Parameters + ---------- + X : array-like + X (also X_test) are the dependent variables of test set to predict + + y : array-like + y (also y_test) is the independent actual variables to score against + + train : boolean + If False, `score` assumes that the residual points being plotted + are from the test data; if True, `score` assumes the residuals + are the train data. + + Returns + ------ + + ax : the axis with the plotted figure - We'd like to color them differently in draw... - Can the user pass those in as keyword arguments? """ y_pred = self.predict(X) scores = y_pred - y @@ -152,6 +252,24 @@ def score(self, X, y=None, train=False, **kwargs): def draw(self, y_pred, residuals, train=False, **kwargs): """ + Parameters + ---------- + y_pred : ndarray or Series of length n + An array or series of predicted target values + + residuals : ndarray or Series of length n + An array or series of the difference between the predicted and the + target values + + train : boolean + If False, `draw` assumes that the residual points being plotted + are from the test data; if True, `draw` assumes the residuals + are the train data. + + Returns + ------ + + ax : the axis with the plotted figure """ if self.ax is None: @@ -166,7 +284,12 @@ def draw(self, y_pred, residuals, train=False, **kwargs): def poof(self): """ - The user calls poof + + Returns + ------ + + ax : the axis with the plotted, labelled and formatted figure + """ if self.ax is None: return @@ -176,3 +299,5 @@ def poof(self): plt.xlabel("Predicted Value") plt.show() + + return self.ax From ba14a4f068f46f0875b2470362d6145f54d89bee Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Mon, 17 Oct 2016 13:48:27 -0400 Subject: [PATCH 08/35] refactor of pcoords and radviz to DataVisualizer base; closed #63; prep for #73 --- yellowbrick/features/base.py | 119 +++++++++++++++++++++++++++++++- yellowbrick/features/pcoords.py | 68 ++---------------- yellowbrick/features/radviz.py | 74 ++------------------ yellowbrick/features/rankd.py | 6 +- 4 files changed, 129 insertions(+), 138 deletions(-) diff --git a/yellowbrick/features/base.py b/yellowbrick/features/base.py index c7b380a26..bc9205260 100644 --- a/yellowbrick/features/base.py +++ b/yellowbrick/features/base.py @@ -19,6 +19,7 @@ ########################################################################## from yellowbrick.base import Visualizer +from yellowbrick.utils import is_dataframe from sklearn.base import TransformerMixin @@ -37,9 +38,13 @@ class FeatureVisualizer(Visualizer, TransformerMixin): Accepts as input a DataFrame or Numpy array. """ - def __init__(self, **kwargs): + def __init__(self, ax=None, **kwargs): super(FeatureVisualizer, self).__init__(**kwargs) + # The figure params + # TODO: hoist to a higher level base class + self.ax = ax + def fit(self, X, y=None, **fit_params): """ This method performs preliminary computations in order to set up the @@ -81,3 +86,115 @@ def fit_transform_poof(self, X, y=None, **kwargs): Xp = self.fit_transform(X, y, **kwargs) self.poof(**kwargs) return Xp + + +########################################################################## +## Data Visualizers +########################################################################## + +class DataVisualizer(FeatureVisualizer): + """ + Data Visualizers are a subclass of Feature Visualiers which plot the + instances in feature space (also called data space, hence the name of the + visualizer). Feature space is a multi-dimensional space defined by the + columns of the instance dependent vector input, X which is passed to + `fit()` and `transform()`. Instances can also be labeled by the target + independent vector input, y which is only passed to `fit()`. For that + reason most Data Visualizers perform their drawing in `fit()`. + + This class provides helper functionality related to target identification: + 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 + ---------- + + :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(DataVisualizer, self).__init__(ax=ax, **kwargs) + + # Data Parameters + self.features_ = features + self.classes_ = classes + + # Visual Parameters + self.color = color + self.colormap = colormap + + 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 m features + + y : ndarray or Series of length n + An array or series of target or class values + + kwargs : dict + Pass generic arguments to the drawing method + + Returns + ------ + self : instance + Returns the instance of the transformer/visualizer + """ + # Get the shape of the data + nrows, ncols = X.shape + + # Store the classes for the legend if they're None. + if self.classes_ is None: + # TODO: Is this the most efficient method? + self.classes_ = [str(label) for label in set(y)] + + # Handle the feature names if they're None. + if self.features_ is None: + + # If X is a data frame, get the columns off it. + if is_dataframe(X): + self.features_ = X.columns + + # Otherwise create numeric labels for each column. + else: + self.features_ = [ + str(cdx) for cdx in range(ncols) + ] + + # Draw the instances + self.draw(X, y, **kwargs) + + # Fit always returns self. + return self diff --git a/yellowbrick/features/pcoords.py b/yellowbrick/features/pcoords.py index e2e3be389..62d665b1f 100644 --- a/yellowbrick/features/pcoords.py +++ b/yellowbrick/features/pcoords.py @@ -22,8 +22,7 @@ import numpy as np import matplotlib.pyplot as plt -from yellowbrick.utils import is_dataframe -from yellowbrick.features.base import FeatureVisualizer +from yellowbrick.features.base import DataVisualizer from yellowbrick.exceptions import YellowbrickTypeError from yellowbrick.style.colors import resolve_colors, get_color_cycle @@ -91,7 +90,7 @@ def parallel_coordinates(X, y=None, ax=None, features=None, classes=None, ## Static Parallel Coordinates Visualizer ########################################################################## -class ParallelCoordinates(FeatureVisualizer): +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 @@ -134,73 +133,16 @@ def __init__(self, ax=None, features=None, classes=None, color=None, 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__(**kwargs) - - # The figure params - # TODO: hoist to a higher level base class - self.ax = ax - - # Data Parameters - self.features_ = features - self.classes_ = classes + super(ParallelCoordinates, self).__init__( + ax, features, classes, color, colormap, **kwargs + ) # Visual Parameters - self.color = color - self.colormap = colormap self.show_vlines = vlines self.vlines_kwds = vlines_kwds or { 'linewidth': 1, 'color': 'black' } - 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 m features - - y : ndarray or Series of length n - An array or series of target or class values - - kwargs : dict - Pass generic arguments to the drawing method - - Returns - ------ - self : instance - Returns the instance of the transformer/visualizer - """ - # Get the shape of the data - nrows, ncols = X.shape - - # Store the classes for the legend if they're None. - if self.classes_ is None: - # TODO: Is this the most efficient method? - self.classes_ = [str(label) for label in set(y)] - - # Handle the feature names if they're None. - if self.features_ is None: - - # If X is a data frame, get the columns off it. - if is_dataframe(X): - self.features_ = X.columns - - # Otherwise create numeric labels for each column. - else: - self.features_ = [ - str(cdx) for cdx in range(ncols) - ] - - # Draw the instances - self.draw(X, y, **kwargs) - - # Fit always returns self. - return self - def draw(self, X, y, **kwargs): """ Called from the fit method, this method creates the parallel diff --git a/yellowbrick/features/radviz.py b/yellowbrick/features/radviz.py index 1493615fb..50b7411d0 100644 --- a/yellowbrick/features/radviz.py +++ b/yellowbrick/features/radviz.py @@ -21,8 +21,7 @@ import matplotlib.pyplot as plt import matplotlib.patches as patches -from yellowbrick.utils import is_dataframe -from yellowbrick.features.base import FeatureVisualizer +from yellowbrick.features.base import DataVisualizer from yellowbrick.exceptions import YellowbrickTypeError from yellowbrick.style.colors import resolve_colors, get_color_cycle @@ -85,7 +84,7 @@ def radviz(X, y=None, ax=None, features=None, classes=None, ## Static RadViz Visualizer ########################################################################## -class RadialVisualizer(FeatureVisualizer): +class RadialVisualizer(DataVisualizer): """ RadViz is a multivariate data visualization algorithm that plots each axis uniformely around the circumference of a circle then plots points on @@ -125,19 +124,9 @@ def __init__(self, ax=None, features=None, classes=None, color=None, 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__(**kwargs) - - # The figure params - # TODO: hoist to a higher level base class - self.ax = ax - - # Data Parameters - self.features_ = features - self.classes_ = classes - - # Visual Parameters - self.color = color - self.colormap = colormap + super(RadialVisualizer, self).__init__( + ax, features, classes, color, colormap, **kwargs + ) @staticmethod def normalize(X): @@ -148,59 +137,6 @@ def normalize(X): b = X.max(axis=0) return (X - a[np.newaxis, :]) / ((b - a)[np.newaxis, :]) - 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 m features - - y : ndarray or Series of length n - An array or series of target or class values - - kwargs : dict - Pass generic arguments to the drawing method - - Returns - ------ - self : instance - Returns the instance of the transformer/visualizer - """ - # TODO: This class is identical to the Parallel Coordinates version, - # so hoist this functionality to a higher level class that is extended - # by both RadViz and ParallelCoordinates. - - # Get the shape of the data - nrows, ncols = X.shape - - # Store the classes for the legend if they're None. - if self.classes_ is None: - # TODO: Is this the most efficient method? - self.classes_ = [str(label) for label in set(y)] - - # Handle the feature names if they're None. - if self.features_ is None: - - # If X is a data frame, get the columns off it. - if is_dataframe(X): - self.features_ = X.columns - - # Otherwise create numeric labels for each column. - else: - self.features_ = [ - str(cdx) for cdx in range(ncols) - ] - - # Draw the instances - self.draw(X, y, **kwargs) - - # Fit always returns self. - return self - def draw(self, X, y, **kwargs): """ Called from the fit method, this method creates the parallel diff --git a/yellowbrick/features/rankd.py b/yellowbrick/features/rankd.py index 91abd55ea..2c27caddb 100644 --- a/yellowbrick/features/rankd.py +++ b/yellowbrick/features/rankd.py @@ -122,11 +122,7 @@ def __init__(self, ax=None, algorithm='pearson', features=None, kwargs : dict keyword arguments passed to the super class. """ - super(Rank2D, self).__init__(**kwargs) - - # The figure params - # TODO: hoist to a higher level base class - self.ax = ax + super(Rank2D, self).__init__(ax=ax, **kwargs) # Data Parameters self.ranking_ = algorithm From 5f7ab81bb5ebf920652e589a7e6b66ad643d49de Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Sun, 30 Oct 2016 06:39:20 -0400 Subject: [PATCH 09/35] hoisted ax to Visualizer base class and model name to ScoreVisualizer base class, closes #62 --- yellowbrick/base.py | 3 +++ yellowbrick/classifier.py | 31 +++++++++++++++++++------------ yellowbrick/features/base.py | 5 +++-- yellowbrick/regressor.py | 17 ++++++++++++----- 4 files changed, 37 insertions(+), 19 deletions(-) diff --git a/yellowbrick/base.py b/yellowbrick/base.py index 485e0e9f8..c75e63cbc 100644 --- a/yellowbrick/base.py +++ b/yellowbrick/base.py @@ -38,6 +38,7 @@ class Visualizer(BaseEstimator): """ def __init__(self, **kwargs): + self.ax = None self.size = kwargs.pop('size', None) self.color = kwargs.pop('color', None) @@ -89,6 +90,8 @@ def __init__(self, model, **kwargs): self.estimator = model super(ScoreVisualizer, self).__init__(**kwargs) + self.name = get_model_name(self.estimator) + def fit(self, X, y=None, **kwargs): self.estimator.fit(X, y, **kwargs) return self diff --git a/yellowbrick/classifier.py b/yellowbrick/classifier.py index f03080145..3c85a9b91 100644 --- a/yellowbrick/classifier.py +++ b/yellowbrick/classifier.py @@ -49,6 +49,7 @@ def __init__(self, model, **kwargs): super(ClassificationScoreVisualizer, self).__init__(model, **kwargs) + ########################################################################## ## Classification Report ########################################################################## @@ -86,11 +87,13 @@ def __init__(self, model, classes=None, **kwargs): """ super(ClassificationReport, self).__init__(model, **kwargs) - # TODO: hoist - self.estimator = model - self.ax = None + ## hoisted to Visualizer base class + # self.ax = None + + ## hoisted to ScoreVisualizer base class + # self.estimator = model + # self.name = get_model_name(self.estimator) - self.name = get_model_name(self.estimator) self.cmap = kwargs.pop('cmap', ddlheatmap) self.classes_ = classes @@ -220,9 +223,11 @@ def __init__(self, model, **kwargs): """ super(ROCAUC, self).__init__(model, **kwargs) - # TODO hoist to main - self.name = get_model_name(self.estimator) - self.ax = None + ## hoisted to Visualizer base class + # self.ax = None + + ## hoisted to ScoreVisualizer base class + # self.name = get_model_name(self.estimator) # TODO refactor to use new Yellowbrick color utils self.colors = { @@ -343,11 +348,13 @@ def __init__(self, model, classes=None, **kwargs): """ super(ClassBalance, self).__init__(model, **kwargs) - # TODO: hoist - self.estimator = model - self.ax = None + ## hoisted to Visualizer base class + # self.ax = None + + ## hoisted to ScoreVisualizer base class + # self.estimator = model + # self.name = get_model_name(self.estimator) - self.name = get_model_name(self.estimator) self.colors = kwargs.pop('colors', YELLOWBRICK_PALETTES['paired']) self.classes_ = classes @@ -387,7 +394,7 @@ def score(self, X, y=None, **kwargs): y : ndarray or Series of length n An array or series of target or class values - + Returns ------ diff --git a/yellowbrick/features/base.py b/yellowbrick/features/base.py index bc9205260..2d60fa49b 100644 --- a/yellowbrick/features/base.py +++ b/yellowbrick/features/base.py @@ -42,8 +42,9 @@ def __init__(self, ax=None, **kwargs): super(FeatureVisualizer, self).__init__(**kwargs) # The figure params - # TODO: hoist to a higher level base class - self.ax = ax + + ## hoisted to Visualizer base class + # self.ax = ax def fit(self, X, y=None, **fit_params): """ diff --git a/yellowbrick/regressor.py b/yellowbrick/regressor.py index 530ce3a4c..33a0ef8d0 100644 --- a/yellowbrick/regressor.py +++ b/yellowbrick/regressor.py @@ -77,8 +77,12 @@ def __init__(self, model, **kwargs): super(PredictionError, self).__init__(model, **kwargs) - self.ax = None - self.name = get_model_name(self.estimator) + ## hoisted to Visualizer base class + # self.ax = None + + ## hoisted to ScoreVisualizer + # self.name = get_model_name(self.estimator) + self.colors = { 'point': kwargs.pop('point_color', '#F2BE2C'), 'line': kwargs.pop('line_color', '#2B94E9'), @@ -193,8 +197,11 @@ def __init__(self, model, **kwargs): super(ResidualsPlot, self).__init__(model, **kwargs) - self.ax = None - self.name = get_model_name(self.estimator) + ## hoisted to Visualizer base class + # self.ax = None + + ## hoisted to ScoreVisualizer + # self.name = get_model_name(self.estimator) # TODO Is there a better way to differentiate between train and test points? # We'd like to color them differently in draw... @@ -284,7 +291,7 @@ def draw(self, y_pred, residuals, train=False, **kwargs): def poof(self): """ - + Returns ------ From 3550f675cea0fe83e088cefef5e49012dec7a32b Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Sun, 30 Oct 2016 06:51:17 -0400 Subject: [PATCH 10/35] added experimental finalize method to ClassificationReport --- yellowbrick/classifier.py | 23 ++++++++++++++++++++--- 1 file changed, 20 insertions(+), 3 deletions(-) diff --git a/yellowbrick/classifier.py b/yellowbrick/classifier.py index 3c85a9b91..4fc795b79 100644 --- a/yellowbrick/classifier.py +++ b/yellowbrick/classifier.py @@ -177,6 +177,26 @@ def poof(self): """ if self.ax is None: return + self.finalize() + plt.show() + + 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. + + Returns + ---------- + ax : the axis with the plotted figure + + """ plt.title('{} Classification Report'.format(self.name)) plt.colorbar() x_tick_marks = np.arange(len(self.classes_)+1) @@ -186,10 +206,7 @@ def poof(self): plt.ylabel('Classes') plt.xlabel('Measures') - plt.show() - return self.ax - ########################################################################## ## Receiver Operating Characteristics ########################################################################## From 9f223e731e9e94e657ad19fbce18c07755b0131a Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Mon, 31 Oct 2016 11:51:22 -0400 Subject: [PATCH 11/35] attempt to address hoisting issue raised in commit 5f7ab81 comment 19624699 --- yellowbrick/base.py | 10 ++++++---- yellowbrick/classifier.py | 16 ++++++++-------- yellowbrick/features/base.py | 2 +- yellowbrick/features/pcoords.py | 2 +- yellowbrick/regressor.py | 13 +++++++------ 5 files changed, 23 insertions(+), 20 deletions(-) diff --git a/yellowbrick/base.py b/yellowbrick/base.py index c75e63cbc..18f9d7908 100644 --- a/yellowbrick/base.py +++ b/yellowbrick/base.py @@ -37,8 +37,8 @@ class Visualizer(BaseEstimator): primarily ensures that styling arguments are passed in. """ - def __init__(self, **kwargs): - self.ax = None + def __init__(self, ax=None, **kwargs): + self.ax = ax self.size = kwargs.pop('size', None) self.color = kwargs.pop('color', None) @@ -86,9 +86,9 @@ class ScoreVisualizer(Visualizer): Draws the score for the fitted model. """ - def __init__(self, model, **kwargs): + def __init__(self, model, ax=None, **kwargs): self.estimator = model - super(ScoreVisualizer, self).__init__(**kwargs) + super(ScoreVisualizer, self).__init__(ax=ax, **kwargs) self.name = get_model_name(self.estimator) @@ -131,6 +131,8 @@ class ModelVisualizer(Visualizer): and enables the user to visualize the performance of models across a range of hyperparameter values (e.g. using VisualGridsearch and ValidationCurve). """ + def __init__(self, ax=None, **kwargs): + super(ModelVisualizer, self).__init__(ax=ax, **kwargs) def fit(self, X, y=None, **kwargs): pass diff --git a/yellowbrick/classifier.py b/yellowbrick/classifier.py index 4fc795b79..a58033765 100644 --- a/yellowbrick/classifier.py +++ b/yellowbrick/classifier.py @@ -37,7 +37,7 @@ class ClassificationScoreVisualizer(ScoreVisualizer): - def __init__(self, model, **kwargs): + 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. @@ -47,7 +47,7 @@ def __init__(self, model, **kwargs): "This estimator is not a classifier; try a regression or clustering score visualizer instead!" ) - super(ClassificationScoreVisualizer, self).__init__(model, **kwargs) + super(ClassificationScoreVisualizer, self).__init__(model, ax=ax, **kwargs) ########################################################################## @@ -60,7 +60,7 @@ class ClassificationReport(ClassificationScoreVisualizer): for the model. Integrates numerical scores as well color-coded heatmap. """ - def __init__(self, model, classes=None, **kwargs): + def __init__(self, model, ax=None, classes=None, **kwargs): """ Pass in a fitted model to generate a classification report. @@ -85,7 +85,7 @@ def __init__(self, model, classes=None, **kwargs): 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, **kwargs) + super(ClassificationReport, self).__init__(model, ax=ax, **kwargs) ## hoisted to Visualizer base class # self.ax = None @@ -216,7 +216,7 @@ class ROCAUC(ClassificationScoreVisualizer): Plot the ROC to visualize the tradeoff between the classifier's sensitivity and specificity. """ - def __init__(self, model, **kwargs): + def __init__(self, model, ax=None, **kwargs): """ Pass in a fitted model to generate a ROC curve. @@ -238,7 +238,7 @@ def __init__(self, model, **kwargs): process, but can and should be set as early as possible. """ - super(ROCAUC, self).__init__(model, **kwargs) + super(ROCAUC, self).__init__(model, ax=ax, **kwargs) ## hoisted to Visualizer base class # self.ax = None @@ -339,7 +339,7 @@ class ClassBalance(ClassificationScoreVisualizer): Class balance chart that shows the support for each class in the fitted classification model displayed as a bar plot. """ - def __init__(self, model, classes=None, **kwargs): + def __init__(self, model, ax=None, classes=None, **kwargs): """ Pass in a fitted model to generate a class balance chart. @@ -363,7 +363,7 @@ def __init__(self, model, classes=None, **kwargs): process, but can and should be set as early as possible. """ - super(ClassBalance, self).__init__(model, **kwargs) + super(ClassBalance, self).__init__(model, ax=ax, **kwargs) ## hoisted to Visualizer base class # self.ax = None diff --git a/yellowbrick/features/base.py b/yellowbrick/features/base.py index 2d60fa49b..4394e2466 100644 --- a/yellowbrick/features/base.py +++ b/yellowbrick/features/base.py @@ -39,7 +39,7 @@ class FeatureVisualizer(Visualizer, TransformerMixin): """ def __init__(self, ax=None, **kwargs): - super(FeatureVisualizer, self).__init__(**kwargs) + super(FeatureVisualizer, self).__init__(ax=ax, **kwargs) # The figure params diff --git a/yellowbrick/features/pcoords.py b/yellowbrick/features/pcoords.py index 62d665b1f..cc2b3400b 100644 --- a/yellowbrick/features/pcoords.py +++ b/yellowbrick/features/pcoords.py @@ -134,7 +134,7 @@ def __init__(self, ax=None, features=None, classes=None, color=None, process, but can and should be set as early as possible. """ super(ParallelCoordinates, self).__init__( - ax, features, classes, color, colormap, **kwargs + ax=ax, features, classes, color, colormap, **kwargs ) # Visual Parameters diff --git a/yellowbrick/regressor.py b/yellowbrick/regressor.py index 33a0ef8d0..9a94a7516 100644 --- a/yellowbrick/regressor.py +++ b/yellowbrick/regressor.py @@ -31,7 +31,8 @@ ########################################################################## class RegressionScoreVisualizer(ScoreVisualizer): - def __init__(self, model, **kwargs): + + 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. @@ -42,7 +43,7 @@ def __init__(self, model, **kwargs): "clustering score visualizer instead!" ) - super(RegressionScoreVisualizer, self).__init__(model, **kwargs) + super(RegressionScoreVisualizer, self).__init__(model, ax=ax, **kwargs) ########################################################################## @@ -54,7 +55,7 @@ class PredictionError(RegressionScoreVisualizer): Plot the actual targets from the dataset against the predicted values generated by our model(s). """ - def __init__(self, model, **kwargs): + def __init__(self, model, ax=None, **kwargs): """ Parameters ---------- @@ -75,7 +76,7 @@ def __init__(self, model, **kwargs): process, but can and should be set as early as possible. """ - super(PredictionError, self).__init__(model, **kwargs) + super(PredictionError, self).__init__(model, ax=ax, **kwargs) ## hoisted to Visualizer base class # self.ax = None @@ -174,7 +175,7 @@ class ResidualsPlot(RegressionScoreVisualizer): a linear regression model is appropriate for the data; otherwise, a non-linear model is more appropriate. """ - def __init__(self, model, **kwargs): + def __init__(self, model, ax=None, **kwargs): """ Parameters ---------- @@ -195,7 +196,7 @@ def __init__(self, model, **kwargs): """ - super(ResidualsPlot, self).__init__(model, **kwargs) + super(ResidualsPlot, self).__init__(model, ax=ax, **kwargs) ## hoisted to Visualizer base class # self.ax = None From 51394deaa8cdec401bbc2c233a14b6ad13c11caf Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Mon, 31 Oct 2016 11:58:15 -0400 Subject: [PATCH 12/35] ax hoisting attempt 2 --- yellowbrick/base.py | 4 +++- yellowbrick/classifier.py | 18 +++++++++--------- yellowbrick/features/base.py | 4 +++- yellowbrick/features/pcoords.py | 2 ++ yellowbrick/features/radviz.py | 4 +++- yellowbrick/features/rankd.py | 2 ++ yellowbrick/regressor.py | 9 +++++---- 7 files changed, 27 insertions(+), 16 deletions(-) diff --git a/yellowbrick/base.py b/yellowbrick/base.py index 18f9d7908..2887749b7 100644 --- a/yellowbrick/base.py +++ b/yellowbrick/base.py @@ -90,6 +90,7 @@ def __init__(self, model, ax=None, **kwargs): self.estimator = model super(ScoreVisualizer, self).__init__(ax=ax, **kwargs) + self.ax = ax self.name = get_model_name(self.estimator) def fit(self, X, y=None, **kwargs): @@ -133,7 +134,8 @@ class ModelVisualizer(Visualizer): """ def __init__(self, ax=None, **kwargs): super(ModelVisualizer, self).__init__(ax=ax, **kwargs) - + self.ax = ax + def fit(self, X, y=None, **kwargs): pass diff --git a/yellowbrick/classifier.py b/yellowbrick/classifier.py index a58033765..e11c7cace 100644 --- a/yellowbrick/classifier.py +++ b/yellowbrick/classifier.py @@ -48,7 +48,7 @@ def __init__(self, model, ax=None, **kwargs): ) super(ClassificationScoreVisualizer, self).__init__(model, ax=ax, **kwargs) - + self.ax = ax ########################################################################## ## Classification Report @@ -88,11 +88,11 @@ def __init__(self, model, ax=None, classes=None, **kwargs): super(ClassificationReport, self).__init__(model, ax=ax, **kwargs) ## hoisted to Visualizer base class - # self.ax = None + self.ax = ax ## hoisted to ScoreVisualizer base class - # self.estimator = model - # self.name = get_model_name(self.estimator) + self.estimator = model + self.name = get_model_name(self.estimator) self.cmap = kwargs.pop('cmap', ddlheatmap) self.classes_ = classes @@ -241,10 +241,10 @@ def __init__(self, model, ax=None, **kwargs): super(ROCAUC, self).__init__(model, ax=ax, **kwargs) ## hoisted to Visualizer base class - # self.ax = None + self.ax = ax ## hoisted to ScoreVisualizer base class - # self.name = get_model_name(self.estimator) + self.name = get_model_name(self.estimator) # TODO refactor to use new Yellowbrick color utils self.colors = { @@ -366,11 +366,11 @@ def __init__(self, model, ax=None, classes=None, **kwargs): super(ClassBalance, self).__init__(model, ax=ax, **kwargs) ## hoisted to Visualizer base class - # self.ax = None + self.ax = ax ## hoisted to ScoreVisualizer base class - # self.estimator = model - # self.name = get_model_name(self.estimator) + self.estimator = model + self.name = get_model_name(self.estimator) self.colors = kwargs.pop('colors', YELLOWBRICK_PALETTES['paired']) self.classes_ = classes diff --git a/yellowbrick/features/base.py b/yellowbrick/features/base.py index 4394e2466..bc83cb644 100644 --- a/yellowbrick/features/base.py +++ b/yellowbrick/features/base.py @@ -44,7 +44,7 @@ def __init__(self, ax=None, **kwargs): # The figure params ## hoisted to Visualizer base class - # self.ax = ax + self.ax = ax def fit(self, X, y=None, **fit_params): """ @@ -143,6 +143,8 @@ def __init__(self, ax=None, features=None, classes=None, color=None, """ super(DataVisualizer, self).__init__(ax=ax, **kwargs) + self.ax = ax + # Data Parameters self.features_ = features self.classes_ = classes diff --git a/yellowbrick/features/pcoords.py b/yellowbrick/features/pcoords.py index cc2b3400b..f0201792c 100644 --- a/yellowbrick/features/pcoords.py +++ b/yellowbrick/features/pcoords.py @@ -137,6 +137,8 @@ def __init__(self, ax=None, features=None, classes=None, color=None, ax=ax, features, classes, color, colormap, **kwargs ) + self.ax = ax + # Visual Parameters self.show_vlines = vlines self.vlines_kwds = vlines_kwds or { diff --git a/yellowbrick/features/radviz.py b/yellowbrick/features/radviz.py index 50b7411d0..9a3d8cc78 100644 --- a/yellowbrick/features/radviz.py +++ b/yellowbrick/features/radviz.py @@ -125,9 +125,11 @@ def __init__(self, ax=None, features=None, classes=None, color=None, process, but can and should be set as early as possible. """ super(RadialVisualizer, self).__init__( - ax, features, classes, color, colormap, **kwargs + ax=ax, features, classes, color, colormap, **kwargs ) + self.ax = ax + @staticmethod def normalize(X): """ diff --git a/yellowbrick/features/rankd.py b/yellowbrick/features/rankd.py index 2c27caddb..e70c6e786 100644 --- a/yellowbrick/features/rankd.py +++ b/yellowbrick/features/rankd.py @@ -124,6 +124,8 @@ def __init__(self, ax=None, algorithm='pearson', features=None, """ super(Rank2D, self).__init__(ax=ax, **kwargs) + self.ax = ax + # Data Parameters self.ranking_ = algorithm self.features_ = features diff --git a/yellowbrick/regressor.py b/yellowbrick/regressor.py index 9a94a7516..be979ccb4 100644 --- a/yellowbrick/regressor.py +++ b/yellowbrick/regressor.py @@ -45,6 +45,7 @@ def __init__(self, model, ax=None, **kwargs): super(RegressionScoreVisualizer, self).__init__(model, ax=ax, **kwargs) + self.ax = ax ########################################################################## ## Prediction Error Plots @@ -79,10 +80,10 @@ def __init__(self, model, ax=None, **kwargs): super(PredictionError, self).__init__(model, ax=ax, **kwargs) ## hoisted to Visualizer base class - # self.ax = None + self.ax = ax ## hoisted to ScoreVisualizer - # self.name = get_model_name(self.estimator) + self.name = get_model_name(self.estimator) self.colors = { 'point': kwargs.pop('point_color', '#F2BE2C'), @@ -199,10 +200,10 @@ def __init__(self, model, ax=None, **kwargs): super(ResidualsPlot, self).__init__(model, ax=ax, **kwargs) ## hoisted to Visualizer base class - # self.ax = None + self.ax = ax ## hoisted to ScoreVisualizer - # self.name = get_model_name(self.estimator) + self.name = get_model_name(self.estimator) # TODO Is there a better way to differentiate between train and test points? # We'd like to color them differently in draw... From 24c4a39d094bb5d3330865565b0ff7405dbfdc54 Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Mon, 31 Oct 2016 13:28:56 -0400 Subject: [PATCH 13/35] ax hoist attempt 3 --- yellowbrick/base.py | 14 +++++++++----- yellowbrick/classifier.py | 12 ++++++++---- yellowbrick/features/pcoords.py | 4 +--- yellowbrick/features/radviz.py | 4 +--- yellowbrick/features/rankd.py | 2 -- yellowbrick/regressor.py | 9 +++++---- 6 files changed, 24 insertions(+), 21 deletions(-) diff --git a/yellowbrick/base.py b/yellowbrick/base.py index 2887749b7..02fd7a72b 100644 --- a/yellowbrick/base.py +++ b/yellowbrick/base.py @@ -48,8 +48,13 @@ def fit(self, X, y=None, **kwargs): """ return self + def gca(self): + if self.ax is None: + self.ax = plt.gca() + return self.ax + def draw(self, **kwargs): - pass + ax = self.gca() def poof(self, **kwargs): """ @@ -90,7 +95,6 @@ def __init__(self, model, ax=None, **kwargs): self.estimator = model super(ScoreVisualizer, self).__init__(ax=ax, **kwargs) - self.ax = ax self.name = get_model_name(self.estimator) def fit(self, X, y=None, **kwargs): @@ -133,9 +137,9 @@ class ModelVisualizer(Visualizer): of hyperparameter values (e.g. using VisualGridsearch and ValidationCurve). """ def __init__(self, ax=None, **kwargs): - super(ModelVisualizer, self).__init__(ax=ax, **kwargs) - self.ax = ax - + self.estimator = model + super(ScoreVisualizer, self).__init__(ax=ax, **kwargs) + def fit(self, X, y=None, **kwargs): pass diff --git a/yellowbrick/classifier.py b/yellowbrick/classifier.py index e11c7cace..9df92ef9e 100644 --- a/yellowbrick/classifier.py +++ b/yellowbrick/classifier.py @@ -48,7 +48,7 @@ def __init__(self, model, ax=None, **kwargs): ) super(ClassificationScoreVisualizer, self).__init__(model, ax=ax, **kwargs) - self.ax = ax + ########################################################################## ## Classification Report @@ -88,7 +88,7 @@ def __init__(self, model, ax=None, classes=None, **kwargs): super(ClassificationReport, self).__init__(model, ax=ax, **kwargs) ## hoisted to Visualizer base class - self.ax = ax + # self.ax = ax ## hoisted to ScoreVisualizer base class self.estimator = model @@ -149,6 +149,7 @@ def draw(self, y, y_pred): 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() @@ -241,7 +242,7 @@ def __init__(self, model, ax=None, **kwargs): super(ROCAUC, self).__init__(model, ax=ax, **kwargs) ## hoisted to Visualizer base class - self.ax = ax + # self.ax = ax ## hoisted to ScoreVisualizer base class self.name = get_model_name(self.estimator) @@ -297,8 +298,10 @@ def draw(self, y, y_pred): 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. @@ -366,7 +369,7 @@ def __init__(self, model, ax=None, classes=None, **kwargs): super(ClassBalance, self).__init__(model, ax=ax, **kwargs) ## hoisted to Visualizer base class - self.ax = ax + # self.ax = ax ## hoisted to ScoreVisualizer base class self.estimator = model @@ -432,6 +435,7 @@ def draw(self): ax : the axis with the plotted figure """ + # Create the axis if it doesn't exist if self.ax is None: self.ax = plt.gca() diff --git a/yellowbrick/features/pcoords.py b/yellowbrick/features/pcoords.py index f0201792c..62d665b1f 100644 --- a/yellowbrick/features/pcoords.py +++ b/yellowbrick/features/pcoords.py @@ -134,11 +134,9 @@ def __init__(self, ax=None, features=None, classes=None, color=None, process, but can and should be set as early as possible. """ super(ParallelCoordinates, self).__init__( - ax=ax, features, classes, color, colormap, **kwargs + ax, features, classes, color, colormap, **kwargs ) - self.ax = ax - # Visual Parameters self.show_vlines = vlines self.vlines_kwds = vlines_kwds or { diff --git a/yellowbrick/features/radviz.py b/yellowbrick/features/radviz.py index 9a3d8cc78..50b7411d0 100644 --- a/yellowbrick/features/radviz.py +++ b/yellowbrick/features/radviz.py @@ -125,11 +125,9 @@ def __init__(self, ax=None, features=None, classes=None, color=None, process, but can and should be set as early as possible. """ super(RadialVisualizer, self).__init__( - ax=ax, features, classes, color, colormap, **kwargs + ax, features, classes, color, colormap, **kwargs ) - self.ax = ax - @staticmethod def normalize(X): """ diff --git a/yellowbrick/features/rankd.py b/yellowbrick/features/rankd.py index e70c6e786..2c27caddb 100644 --- a/yellowbrick/features/rankd.py +++ b/yellowbrick/features/rankd.py @@ -124,8 +124,6 @@ def __init__(self, ax=None, algorithm='pearson', features=None, """ super(Rank2D, self).__init__(ax=ax, **kwargs) - self.ax = ax - # Data Parameters self.ranking_ = algorithm self.features_ = features diff --git a/yellowbrick/regressor.py b/yellowbrick/regressor.py index be979ccb4..04c3549f9 100644 --- a/yellowbrick/regressor.py +++ b/yellowbrick/regressor.py @@ -44,8 +44,7 @@ def __init__(self, model, ax=None, **kwargs): ) super(RegressionScoreVisualizer, self).__init__(model, ax=ax, **kwargs) - - self.ax = ax + # self.ax = ax ########################################################################## ## Prediction Error Plots @@ -80,7 +79,7 @@ def __init__(self, model, ax=None, **kwargs): super(PredictionError, self).__init__(model, ax=ax, **kwargs) ## hoisted to Visualizer base class - self.ax = ax + # self.ax = ax ## hoisted to ScoreVisualizer self.name = get_model_name(self.estimator) @@ -134,6 +133,7 @@ def draw(self, y, y_pred): ax : the axis with the plotted figure """ + # Create the axis if it doesn't exist if self.ax is None: self.ax = plt.gca() @@ -200,7 +200,7 @@ def __init__(self, model, ax=None, **kwargs): super(ResidualsPlot, self).__init__(model, ax=ax, **kwargs) ## hoisted to Visualizer base class - self.ax = ax + # self.ax = ax ## hoisted to ScoreVisualizer self.name = get_model_name(self.estimator) @@ -281,6 +281,7 @@ def draw(self, y_pred, residuals, train=False, **kwargs): ax : the axis with the plotted figure """ + # Create the axis if it doesn't exist if self.ax is None: self.ax = plt.gca() From cbf533c4f599cfee022ffa653e9e883afa422912 Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Mon, 31 Oct 2016 20:46:01 -0400 Subject: [PATCH 14/35] implementation of finalization method to address issue #76 --- yellowbrick/base.py | 62 +++++++++++++++------------------ yellowbrick/classifier.py | 42 +++++++--------------- yellowbrick/features/base.py | 10 ------ yellowbrick/features/pcoords.py | 15 +++----- yellowbrick/features/radviz.py | 16 ++++----- yellowbrick/features/rankd.py | 17 +++------ yellowbrick/regressor.py | 30 +++++++--------- 7 files changed, 69 insertions(+), 123 deletions(-) diff --git a/yellowbrick/base.py b/yellowbrick/base.py index 02fd7a72b..699d85da1 100644 --- a/yellowbrick/base.py +++ b/yellowbrick/base.py @@ -54,18 +54,41 @@ def gca(self): return self.ax def draw(self, **kwargs): - ax = self.gca() + ax = self.gca() - def poof(self, **kwargs): + def poof(self, outpath=None, **kwargs): """ The user calls poof, which is the primary entry point for producing a visualization. Visualizes either data features or fitted model scores + + Parameters + ---------- + outpath: path or None + Save the figure to disk or if None show in a window + + kwargs: generic keyword arguments. + """ + if self.ax is None: return + + self.finalize() + + if outpath is not None: + plt.savefig(outpath, **kwargs) + else: + plt.show() + + def finalize(self, **kwargs): """ - raise NotImplementedError( - "All visualizations must specify their own poof methodology" - ) + Finalize executes any subclass-specific axes finalization steps. + The user calls poof and poof calls finalize. + + Parameters + ---------- + kwargs: generic keyword arguments. + """ + pass def fit_draw(self, X, y=None, **kwargs): """ @@ -104,27 +127,9 @@ def fit(self, X, y=None, **kwargs): def predict(self, X): return self.estimator.predict(X) - def score(self, X, y=None): - """ - Score will call draw to visualize model performance. - If y_pred is None, call fit-predict on the model to get a y_pred. - - Score calls draw - """ - y_pred = self.predict(X) - return self.draw(y,y_pred) - def draw(self, X, y): pass - def poof(self, **kwargs): - """ - The user calls poof - """ - raise NotImplementedError( - "Please specify how to render the feature visualization" - ) - ########################################################################## ## Model Visualizers @@ -146,17 +151,6 @@ def fit(self, X, y=None, **kwargs): def predict(self, X): pass - def poof(self, model=None): - """ - The user calls poof. - - A model visualization renders a model - """ - raise NotImplementedError( - "Please specify how to render the model visualization" - ) - - ########################################################################## ## Multiple Models and Mixins ########################################################################## diff --git a/yellowbrick/classifier.py b/yellowbrick/classifier.py index 9df92ef9e..a8b73df65 100644 --- a/yellowbrick/classifier.py +++ b/yellowbrick/classifier.py @@ -193,10 +193,6 @@ def finalize(self, **kwargs): ---------- kwargs: generic keyword arguments. - Returns - ---------- - ax : the axis with the plotted figure - """ plt.title('{} Classification Report'.format(self.name)) plt.colorbar() @@ -207,7 +203,7 @@ def finalize(self, **kwargs): plt.ylabel('Classes') plt.xlabel('Measures') - return self.ax + ########################################################################## ## Receiver Operating Characteristics ########################################################################## @@ -309,29 +305,22 @@ def draw(self, y, y_pred): return self.ax - def poof(self, **kwargs): + def finalize(self, **kwargs): """ - Called by user. Take in the model as input and generates a plot of - the ROC plots with AUC metrics embedded. - - Returns - ------ + Finalize executes any subclass-specific axes finalization steps. + The user calls poof and poof calls finalize. - ax : the axis with the plotted figure + Parameters + ---------- + kwargs: generic keyword arguments. """ - if self.ax is None: return - plt.title('ROC for {}'.format(self.name)) plt.legend(loc='lower right') plt.xlim([-0.02,1]) plt.ylim([0,1.1]) - plt.show() - - return self.ax - ########################################################################## ## Class Balance Chart @@ -447,23 +436,18 @@ def draw(self): return self.ax - def poof(self): + def finalize(self, **kwargs): """ - Plots a class balance chart + Finalize executes any subclass-specific axes finalization steps. + The user calls poof and poof calls finalize. - Returns - ------ + Parameters + ---------- + kwargs: generic keyword arguments. - ax : the axis with the plotted figure """ - if self.ax is None: return - plt.xticks(np.arange(len(self.support)), self.support.keys()) cmax, cmin = max(self.support.values()), min(self.support.values()) ceiling = cmax + cmax*0.1 span = cmax - cmin plt.ylim(0, ceiling) - - plt.show() - - return self.ax diff --git a/yellowbrick/features/base.py b/yellowbrick/features/base.py index bc83cb644..f284a7329 100644 --- a/yellowbrick/features/base.py +++ b/yellowbrick/features/base.py @@ -66,16 +66,6 @@ def transform(self, X): """ return X - def poof(self, **kwargs): - """ - The user calls poof in order to draw the feature visualization. - - Visualize data features individually or together - """ - raise NotImplementedError( - "Please specify how to render the feature visualization" - ) - def fit_transform_poof(self, X, y=None, **kwargs): """ Fit to data, transform it, then visualize it. diff --git a/yellowbrick/features/pcoords.py b/yellowbrick/features/pcoords.py index 62d665b1f..c01d319b0 100644 --- a/yellowbrick/features/pcoords.py +++ b/yellowbrick/features/pcoords.py @@ -194,22 +194,15 @@ def draw(self, X, y, **kwargs): self.ax.set_xticklabels(self.features_) self.ax.set_xlim(x[0], x[-1]) - def poof(self, outpath=None, **kwargs): + def finalize(self, **kwargs): """ - Display the parallel coordinates. + Finalize executes any subclass-specific axes finalization steps. + The user calls poof and poof calls finalize. Parameters ---------- - outpath: path or None - Save the figure to disk or if None show in a window + kwargs: generic keyword arguments. """ - if self.ax is None: return - self.ax.legend(loc='best') self.ax.grid() - - if outpath is not None: - plt.savefig(outpath, **kwargs) - else: - plt.show() diff --git a/yellowbrick/features/radviz.py b/yellowbrick/features/radviz.py index 50b7411d0..c8bff60f6 100644 --- a/yellowbrick/features/radviz.py +++ b/yellowbrick/features/radviz.py @@ -211,22 +211,18 @@ def draw(self, X, y, **kwargs): self.ax.axis('equal') - def poof(self, outpath=None, **kwargs): + def finalize(self, **kwargs): """ - Display the radial visualization + Finalize executes any subclass-specific axes finalization steps. + The user calls poof and poof calls finalize. Parameters ---------- - outpath: path or None - Save the figure to disk or if None show in a window + kwargs: generic keyword arguments. + """ - if self.ax is None: return self.ax.legend(loc='best') - - if outpath is not None: - plt.savefig(outpath, **kwargs) - else: - plt.show() + # Alias for RadViz RadViz = RadialVisualizer diff --git a/yellowbrick/features/rankd.py b/yellowbrick/features/rankd.py index 2c27caddb..f90ed45f2 100644 --- a/yellowbrick/features/rankd.py +++ b/yellowbrick/features/rankd.py @@ -257,26 +257,19 @@ def draw(self, X, **kwargs): cb = self.ax.figure.colorbar(mesh, None, self.ax) cb.outline.set_linewidth(0) - - def poof(self, outpath=None, **kwargs): + def finalize(self, **kwargs): """ - Display the Rank2D visualization + Finalize executes any subclass-specific axes finalization steps. + The user calls poof and poof calls finalize. Parameters ---------- - outpath: path or None - Save the figure to disk or if None show in a window - """ - if self.ax is None: return + kwargs: generic keyword arguments. + """ # Set the title self.ax.set_title( "{} Ranking of {} Features".format( self.ranking_.title(), len(self.features_) ) ) - - if outpath is not None: - plt.savefig(outpath, **kwargs) - else: - plt.show() diff --git a/yellowbrick/regressor.py b/yellowbrick/regressor.py index 04c3549f9..5f16a4c4d 100644 --- a/yellowbrick/regressor.py +++ b/yellowbrick/regressor.py @@ -148,20 +148,20 @@ def draw(self, y, y_pred): return self.ax - def poof(self): + def finalize(self, **kwargs): """ - Returns - ------ + Finalize executes any subclass-specific axes finalization steps. + The user calls poof and poof calls finalize. - ax : the axis with the plotted, labelled and formatted figure + Parameters + ---------- + kwargs: generic keyword arguments. """ self.ax.set_title('Prediction Error for {}'.format(self.name)) self.ax.set_ylabel('Predicted') plt.xlabel('Measured') - plt.show() - return self.ax ########################################################################## ## Residuals Plots @@ -292,22 +292,18 @@ def draw(self, y_pred, residuals, train=False, **kwargs): return self.ax - def poof(self): - """ - Returns - ------ + def finalize(self, **kwargs): + """ + Finalize executes any subclass-specific axes finalization steps. + The user calls poof and poof calls finalize. - ax : the axis with the plotted, labelled and formatted figure + Parameters + ---------- + kwargs: generic keyword arguments. """ - if self.ax is None: return - self.ax.hlines(y=0, xmin=0, xmax=100) self.ax.set_title('Residuals for {} Model'.format(self.name)) self.ax.set_ylabel('Residuals') plt.xlabel("Predicted Value") - - plt.show() - - return self.ax From e3016340e1ce9f1619c3b1976b552a0cd80913d0 Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Mon, 31 Oct 2016 21:08:45 -0400 Subject: [PATCH 15/35] adding docstrings for issue #36 --- yellowbrick/base.py | 93 +++++++++++++++++++++++++++++++++++++++ yellowbrick/classifier.py | 2 - 2 files changed, 93 insertions(+), 2 deletions(-) diff --git a/yellowbrick/base.py b/yellowbrick/base.py index 699d85da1..65b3bb9b3 100644 --- a/yellowbrick/base.py +++ b/yellowbrick/base.py @@ -38,6 +38,16 @@ class Visualizer(BaseEstimator): """ def __init__(self, ax=None, **kwargs): + """ + Parameters + ---------- + :param ax: the axis to plot the figure on. + + :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. + """ self.ax = ax self.size = kwargs.pop('size', None) self.color = kwargs.pop('color', None) @@ -45,15 +55,32 @@ def __init__(self, ax=None, **kwargs): def fit(self, X, y=None, **kwargs): """ Fits a transformer to X and y + + 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. """ return self def gca(self): + """ + Creates axes if they don't already exist + """ if self.ax is None: self.ax = plt.gca() return self.ax def draw(self, **kwargs): + """ + Rendering function + """ ax = self.gca() def poof(self, outpath=None, **kwargs): @@ -115,12 +142,36 @@ class ScoreVisualizer(Visualizer): """ def __init__(self, model, ax=None, **kwargs): + """ + Parameters + ---------- + :param models: the Scikit-Learn models being compared with each other. + + :param ax: the axis to plot the figure on. + + :param kwargs: keyword arguments. + + These parameters can be influenced later on in the visualization + process, but can and should be set as early as possible. + """ self.estimator = model super(ScoreVisualizer, self).__init__(ax=ax, **kwargs) self.name = get_model_name(self.estimator) 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. + """ self.estimator.fit(X, y, **kwargs) return self @@ -128,6 +179,16 @@ def predict(self, X): return self.estimator.predict(X) def draw(self, X, y): + """ + 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 + """ pass @@ -142,10 +203,32 @@ class ModelVisualizer(Visualizer): of hyperparameter values (e.g. using VisualGridsearch and ValidationCurve). """ def __init__(self, ax=None, **kwargs): + """ + Parameters + ---------- + :param ax: the axis to plot the figure on. + + :param kwargs: keyword arguments for Scikit-Learn model + + These parameters can be influenced later on in the visualization + process, but can and should be set as early as possible. + """ self.estimator = model super(ScoreVisualizer, self).__init__(ax=ax, **kwargs) 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. + """ pass def predict(self, X): @@ -163,6 +246,16 @@ class MultiModelMixin(object): def __init__(self, models, **kwargs): # Ensure models is a collection, if it's a single estimator then we # wrap it in a list so that the API doesn't break during render. + """ + Parameters + ---------- + :param models: the Scikit-Learn models being compared with each other. + + :param kwargs: keyword arguments. + + These parameters can be influenced later on in the visualization + process, but can and should be set as early as possible. + """ if isestimator(models): models = [models] diff --git a/yellowbrick/classifier.py b/yellowbrick/classifier.py index a8b73df65..ca7e9e1e3 100644 --- a/yellowbrick/classifier.py +++ b/yellowbrick/classifier.py @@ -165,7 +165,6 @@ def draw(self, y, y_pred): return self.ax - def poof(self): """ Plots a classification report as a heatmap. @@ -183,7 +182,6 @@ def poof(self): return self.ax - def finalize(self, **kwargs): """ Finalize executes any subclass-specific axes finalization steps. From 21619f51c68eb0449b40557d59ee58f4ce4b50ca Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Wed, 2 Nov 2016 12:46:01 -0400 Subject: [PATCH 16/35] removed test_base.py and test_features/test_base.py to resolve travis build failures --- tests/test_base.py | 14 +++++++------- tests/test_features/test_base.py | 16 ++++++++-------- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/tests/test_base.py b/tests/test_base.py index 336bcdf4c..fe948c74b 100644 --- a/tests/test_base.py +++ b/tests/test_base.py @@ -44,13 +44,13 @@ def test_visualizer_returns_self(self): visualizer = Visualizer() self.assertIs(visualizer.fit([]), visualizer) - def test_base_poof(self): - """ - Assert that the base visualizer implements poof interface - """ - with self.assertRaises(NotImplementedError): - visualizer = Visualizer() - visualizer.poof() + # def test_base_poof(self): + # """ + # Assert that the base visualizer implements poof interface + # """ + # with self.assertRaises(NotImplementedError): + # visualizer = Visualizer() + # visualizer.poof() def test_fit_draw(self): """ diff --git a/tests/test_features/test_base.py b/tests/test_features/test_base.py index 4f61f3548..09a2558d2 100644 --- a/tests/test_features/test_base.py +++ b/tests/test_features/test_base.py @@ -39,11 +39,11 @@ def test_subclass(self): self.assertIsInstance(visualizer, BaseEstimator) self.assertIsInstance(visualizer, Visualizer) - def test_interface(self): - """ - Test the feature visualizer interface - """ - - visualizer = FeatureVisualizer() - with self.assertRaises(NotImplementedError): - visualizer.poof() + # def test_interface(self): + # """ + # Test the feature visualizer interface + # """ + # + # visualizer = FeatureVisualizer() + # with self.assertRaises(NotImplementedError): + # visualizer.poof() From 89469e47d86e0931d9b50448e663a250d357de91 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Sat, 5 Nov 2016 10:27:09 -0400 Subject: [PATCH 17/35] finalized method finalized --- .../Getting Started with Yellowbrick.ipynb | 80 ++++++++++ examples/examples.ipynb | 140 +++++------------- yellowbrick/base.py | 9 ++ yellowbrick/classifier.py | 58 ++++---- yellowbrick/features/base.py | 7 - yellowbrick/features/pcoords.py | 8 +- yellowbrick/features/radviz.py | 8 +- yellowbrick/features/rankd.py | 1 + yellowbrick/regressor.py | 32 ++-- 9 files changed, 187 insertions(+), 156 deletions(-) create mode 100644 examples/Getting Started with Yellowbrick.ipynb diff --git a/examples/Getting Started with Yellowbrick.ipynb b/examples/Getting Started with Yellowbrick.ipynb new file mode 100644 index 000000000..fc7774b77 --- /dev/null +++ b/examples/Getting Started with Yellowbrick.ipynb @@ -0,0 +1,80 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Getting Started with Yellowbrick \n", + "\n", + "Thank you for agreeing to help alpha test the Yellowbrick library!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Alpha Testing \n", + "\n", + "We are looking for people to help us Alpha test the Yellowbrick project! Helping is simple: simply create a notebook that applies the concepts in this getting started guide to a small to medium size data set of your choice. Run through the examples with the dataset, and try to change options and customize as much as possible. After you've exercised the code with your examples, respond to our alpha testing survey!\n", + "\n", + "### Step One: Dataset \n", + "\n", + "Select a multivariate dataset of your own; the more datasets that we can run through Yellowbrick, the more likely we'll discover edge cases and exceptions! Note that the dataset must be well-suited to modeling with Scikit-Learn. In particular we recommend you choose a dataset whose target is suited to the following tasks:\n", + "\n", + "- [Regression](https://en.wikipedia.org/wiki/Regression_analysis) (target is a continuous variable)\n", + "- [Classification](https://en.wikipedia.org/wiki/Classification_in_machine_learning) (target is a discrete variable) \n", + "\n", + "There are data sets that are well suited to both types of analysis; either way you'll be utilizing the testing methodology for one, the other, or both. In order to find a dataset, we recommend you try the following places:\n", + "\n", + "- [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/)\n", + "- [MLData.org](http://mldata.org/)\n", + "- [Awesome Public Datasets](https://github.com/caesar0301/awesome-public-datasets) \n", + "\n", + "You're more than welcome to choose a dataset of your own, but we do ask that you make at least the notebook containing your testing results publically 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! \n", + "\n", + "### Step Two: Notebook \n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/examples.ipynb b/examples/examples.ipynb index 56b014de8..f5d85705a 100644 --- a/examples/examples.ipynb +++ b/examples/examples.ipynb @@ -50,16 +50,24 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "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+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\nL58OQ8GVMBTMR3ByjEwph7FQI/JT9jN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NoVQi/L65iE7LQOTDKfAPC7vm+Ty3jIh6kytl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\nNYmoFzJfMuJcbiHK9QW4/K8TLmuq/mGISUtEjI4jnIh6M48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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -89,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -151,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -175,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -199,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -208,7 +216,7 @@ "data": { "image/png": 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Uq1fPOl31Z6dOnao0sSjP+++/rwkTJqhTp06aM2eOtdwvnfusffbZZ7rrrrvk7e1tbW/d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\nJNHUwuEYaZYjDsfA5UGCAACAAXesILB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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -226,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -235,7 +243,7 @@ "data": { "image/png": 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xcT5tsbGxcjqdfuvOmzdPF154oXr37m3J/kokCAAA1Esul0sRERE+bRERESot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\nlXJychQVFaV27dopMzNTb7zxRq2vMTV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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -264,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -284,16 +292,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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bFy1axGSOzIatdkRNjy11RK2EQqHAp59+qp/ZyoSOzK1iq92GDRuQm5srdEhE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\n", 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gx44dEIvFKvtDVqds9feOHTtW2OJHHSQSCR48eIAOHTrUaQFS0l28J6g83hNUHu8JKk/T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\ngwFbtmzBnDlzOGE3URkMdURmKJVKTJkyBe+88w5++OEHGAwGFBQUYP3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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -322,7 +330,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "collapsed": true }, @@ -342,22 +350,11 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "image/png": 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VHZuOe/bd1qyw+Cs7juFySUkfcWFJACmEEEK0w+l0crL6GF+kxmjbbhpQoXVF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H4nOo2Ba7LXjmtLtqOviX39nW6SInOZPmQ7/VIjy7JipdNf6NLWW0unozlopTMUrUZP\nTGgqsWGpxISlEWNJwaA1jTLqxPLGvmcmZJy/HYnHGuLjwTmK9VJdt4E3j8UxL7mHqBAf26oj+c6y\n6hHisdOp5We704eJp4xIJ/dOV/Ini5stfFh+9ZiF60Q/Dt/pj9lzfX4VKxuNIF32ft4HGsO4ptVM\nfpxj2HGlQ00Dv+jfapZkgTdK43l8YR250U5mJvRS3Bw2yohKLuRTq04Fo5ArszrYURPByqxONlda\ncfo0eAKaYFFRcVMXHX1urKFXT6X9PwuqgFRRUZkQZFmmqL9lYaQpnqzY2cFzLT1V1NhKAHivLAGn\nT8PSrDjumZkevOZ7m4pp6HGyMrOb+FBlWzMzdibHm3YBMDfjFkKNY6+49fhdbCt7hYDkx2yI4NpJ\nn5nwiN3ZCEj+QSsdu2Kl4/TaR73WYrQSa1HEYqwllQhz/Lg761woLm8fzsDI7eTxEpDgUFM437y2\nBrM+gD8g8Ot9qQRkgdU5NooaLXx9QX2wx/UAbr/AT7Zn4g0MngjV+/nqvAZ0Gpm2Ph3PFyVd9sjc\n2Angk87nEavEHwOyeAWISIFXjiTww5WV6E5LGZ4W7yArysGpTuXLYElLKCfazeTFOLh7aitHWywE\n5JHvVavDSLnNxKRoJdKeY3XxSaWVZRndLEzpYWdtJJ+cimJpemew69CjG/bz939ZenGXqjJuxvXp\nLikp4dFHH2XnTsUGw263853vfId9+/YRFhbGI488wtq1awHwer384Ac/4NNPP0Wn07Fu3Toefvjh\n4FjPPvss69evR5IkbrvtNp588km1XF9F5SqmofMErf3+inPSVwcFkCQH2H/qHQC8UiQbj4egEYVh\nhTP7a9v59a4yIkN8fHa6YiadHDU5uPWcFDmJnLiCMc9FlmV2l79Br7sDUdCwfPL9w3IxLwRZloP5\nmHUdx0iJmoIgCLi8vYrnYn900dbbiCT7R7xeFLREW5KDhS6xllRC9KN3ALmUvHbgxxMyzuZKK3dM\naSOrv8/1m8dj6XDpmRrbR1KYUkARZlTsfIba9TyzI51u92DEWkDmKwUNWE0+vAGB3xxIweW7Ogqf\nlIprAd/I4PKYXjtURA6Ya18u2hwG3jsZyx1T2oLv1wCPzKvnG5ty++cn8PrROP+zSMgAACAASURB\nVL63vIp4i5elGZ1DPECH83xREs/cUBGMQi7L7GJPXQSrc2zsqo2g06UnOdzDwO9iQ0k9/oCEdrT+\nliqXjTELyPXr1/P000+j1Q6+5Lvf/S5ms5m9e/dy4sQJvvzlLzNp0iSmT5/Oc889R0tLC1u2bMFm\ns/HAAw+Qnp7O6tWrefnll9mxYwfvvadUY37lK1/hxRdf5MEHH5z4FaqoqFx0JHmwZWF8eCZJkbnB\nc+UtB/sLWOB/90QhI/C1xbnkJ0QC4AtIPPTGPmRZ5pH5bYiCH70mBEkK4PW70GmMLMq+a1xfMI81\n7qSu8zgA8zJvIdqSco5XjJ2S+i0c7vei3HLiJfQaI6Koxe0bvSewSR/Wn7eYRmxYKlHmxLNa6VwO\nGjrKJ2ysmm4jX52nWO002/VsrlSiSKtzbNgcWlLCByvIB+x6/nAwidqe4Vv0t+W1MbV/6/Sl4kTq\ne0YrzLgSkQnVB+jzjlZxfebXnG7rM/CzfAUU1WyqsDI/pZvEfh/HASJCAqzKtvFRpVJQU9cTwt76\ncK5J7WHN5Hb21keMKvo7nHpKW03kxylRyLwYB1urIrkmrYsZ/dvfn56ykhfdxwmbhYAs88zWY3zn\nun8M39Z/FMYk53/3u9/x8ssv89WvfjV4zOl08umnn/LYY4+h0+mYPn06t956K2+99RYA7777Lg8/\n/DBms5m0tDTWrVvHxo0bAXjnnXf4/Oc/j9VqxWq18tBDD7Fhw4aLsDwVFZVLQWVrUX+1MBSk3xgU\nex6fk8O1mwFosCdwvN1InMXI91fNCL7259uOc7RZ2b7KjFS2UNNjptHUXQEoAnA8VdMtPVUU1Shi\nNjNmJrnxCy58gf2cbN4fFI8DeAPuIeJRIMqcRF7CIpbm3svagm9z99wnWTb5fqYmLSbGknrFiUeA\nT068OCHjlLcb+ZeZypeFum4Db/YbS6eGu5gc7SDGPDIi+86JaAqbhqcmTI/v5dbJiln4tupI9tRd\nLWbhMpEhvjGJxzunKEViiRY3wyuvGeV1Az9fnr7Zfknk5SF9yIdy19Q2QvWD7+vGY3F4/AIWQ4Cb\nJ7Wfccw/HVLsfrwBZW3XpndzoCGcG3OU973BbmRRWjcDa35mS+lELEVlAhmTgFy7di1vvfUW+fmD\nbcZqamrQ6XQkJQ1Wy2VkZFBVVYXdbsdms5GVlTXiHEBVVRXZ2dnDztXU1FzoWlRUVC4D/oA32LIw\nPXr6sGjf4bqP8fidCOh4breyTfvft8wmPEQxG6602fnh5hLCDH6+MFsRoAnhmVS3K/mSyZGTyY6d\nM+a5OL29bC97FRmJ8JBYFmbfMWGpMTW2o+w99dawYzqN8bStcRlfwE1UaCJp1nxCjRFXfGrOttLX\nJmwsg07GrA/g8Io09ho43F9IcefUFgSBEUUzBxstvHMydtixaJOXL81RKn+ru4y8WnL1mIVHGX10\nBY20TxeFgyzL6OSmSYpQemJRHeFG31mvHxjtctr6nLSZ2V07UsgrBTWNwZ+73Lpge8rrsjqxmrwj\nXgPQ7dZR3ByKXqOseVpcH0eaLWRGuciOUiKTu+siiTUrr+/1+Pnw+MiKcJXLx5gEZHT0yKo3l8uF\nwTDcc8xoNOJ2u3G5XMGfh54bOO5yuUackyQJr3f0D5qKisqVy/Gm3Ti9dkRBw+y0G4LHOx3NnOzv\nd721Op5ut45F6TGsm50JKLmEX31jP25/gAfmtKEVfWhFPTICvoAbvcbIouw7xyzAJDnAjpOv4vL1\nohX1LM9bNyG+iADN3afYcfLvnP6Av2/B9/ns/O9xy4xHSYnKA6DX3cHuivVsKPoZJ5v3E5BGRt2u\nFAIBPzXdhydkLJdPJK3fJPql4gROtClWO6lhTvJjnSPEY22XgT8eTGGoKNKJEo/Or8esl+jzavjt\n/qvHLDxU76PTPbQLy+gRyBnxdu6b3hz8fVhNPp5YWIdBGxj1+sHRBETh8kQgB3i9NI4+78j3Y0ZC\nH5mRg5Xamyqi6XFr0Wlk7poysq/2AH8+lIQsD0YhF6b2UNQYxur+KGRZeyirsgdbXD664cBELUVl\nAjjvv8yQkBA8nuHdENxuNyaTKSgOh553u92Yzco39QGhOfScRqNBr7/AxqsqKiqXFLfPESwoyY2f\nT1iIEnmQZZn9p95BRsYvh/JqSSiiIPCrO+Yh9vdH+8vBKrZUtjArwc60OKXjRZo1n5aeUwDMz7oN\nk2E0K5DROVz7MS09yi7Hopy7iDDFnuMVY6Ojr5EtJ/6KJAeCx2amXgcQFLfRlhRWTvk8a2Y+Rpp1\nGiDQ5+li76mNvFn4P5xo2o0/MLI39eXmlb0TUzgDEKJTKkY+qrASY/Kyrz4cUZB5cmnNCPHY7dLy\nf/tSTqvSlVk3s5nUCDeSDH84mEzHqG3xrjw0BIb161Y4vduMYkn00NwGTq8FSY1w88i8ejRnFYgy\nkiwgXKZtbIA+r5Y3SkePCD86vyE4N7dfw1snlLzI+Sl2MiJH9zbt9Wo52BgWjELOTOilsDGM6fG9\nxIcq+uGkLZQQrfK3V9vloKqjd0LXpHL+nLeATEtLw+/309LSEjxWXV1NVlYW4eHhWK3W4Jb10HMA\nWVlZVFdXB89VVVUN2+5WUVG5OjhSvwVfwINOY2BG6org8dqOo7Talb/xFwqt+CWRhxbmMCtZ6SDT\n1uvim+8WYtIF+FKBEqGIsaRS13kMgJSoPDJjZo55HvUdx4NCdnLCQjJjZpz9BWPE7rLx8bE/DWsd\nmJewiBkpK0e9Pio0keV593P77CfIjJmJgIDT28P+qnd5s/AZSht24AtcGTstdpcNCfe5LxwDcr+m\nOdUZwsZjsfhlkYAs8p2lp9Brhwsej1/g+cJEOtzDo8NL0rtYnKbkwL59IoZjbVeLWbgU/FI0Okok\nMtbs4bGFdRi0owvA/DgHn5/VOOo5BWWLe7Co5vIIyd21EZTbRnqSRoT4uS5rsCf7rtpIGu3Ke3zP\ntFbONN+/Hk4YFoWcm2znaGsoN/RHIQsbw1gxZNwHX9s7UUtRuUDOW0CazWZWrFjBs88+i9vtpqSk\nhPfee481a9YAsGbNGn7961/T09NDTU0NL7/8Mrfffnvw3AsvvEBrays2m40//OEPwXMqKipXB73u\njuAWdX7SEow65YHvD3g5WP0+ADZnNAcazFhNBn5446Ag/Le3C+l0elk3sw2j1osoaBEEEV/Ag0Fr\nYuE4tq573Z3sLH8dgOjQFOZm3Dwh63N67WwufXFYdXVmzEzmZd5yzrlFmOJYkvtZ7pjz72THzkFA\nxOXrpbDmA9YffJqS+q14/RMj3s6XDUU/m7CxBAEcXpHfH0xmWoKdHTWR3De9kYxIRXgPCExJhl/t\nTaGxd7gpdFqEi/v6zcJLWkJ5/2QMVwcyoXoJnzTQw3o0BEL1fp5YVEeYIXCGaxSuSevh9rwzbfmO\n1v7w0otIGYGXihPwj2JRdHd+K+b+ghpJFni9VCmiyrE6mZM4euTQ5deyuy4iGIWcnWhnX304C5J7\nCDf4kBFwejXB7fudp1pxeK68aP4/IxeUXPKjH/0In8/H0qVLeeKJJ/jWt77FtGlKmf0TTzxBeno6\nN954I+vWreOee+5h1apVANx3332sXLmStWvXcsstt1BQUMAXvvCFC16MiorKpeNQzWYkOUCI3sKU\npGuDx482bMfh6QFEfrEnAhD4yc2ziDIp0YhNZY28eriGqbF9zE/uBCDVmkdbv4fk/Kw1mMboi+iX\nfGw78QregBuD1sSyyfdPSJWzx+/i49IX6fN0Bo8lR05mcc7d4zIjDwuJZvGku7mz4BtMip+HKGjw\n+B0cqv2I9YVPU1z3yRlbF15MypsPTviYLx5KosOpIznMw8wEOysyB03JB/T2nw4lEm/xYvcMbvea\n9X4emVePTiPT7tDxfOHVYhYuYzWdu+Jar5F4fGEdcaFjizzfkmtjSXrnKGfOVal96WjqNfJRxcja\niNMLakpbQznWqqSu3TW1FY0wujHm347EB6OQogAzE/qo6DRxXZbye9hdF8mcBKWVpQz8v/cOTfCK\nVM4HQZbly5uVO048Hg+lpaXk5+ePKOJRURkrgiBwlX30ryhsvQ28d+TXACzKvjPYn7rX3clbh35O\nQPJT2JTAb/dHMTfFyp7HbkQUBRweH9N/9i4tdjv/vaoai8FDpDmBPncnvoCHNOtUlk1eN+bo457K\njZS37AcErpv6BZKH+E+eL/6Aj4+PvRjcggeIDUtj1dQH0WoGc/LO5zPk8HRztGE75S0HgybjOo2B\nvIRFTElaPGFm52dDlmX+svvJCR1zc6WV147GkxrmxGzw8+/X1I/Ie/zgpJWTNjO13cZgb2gBmccX\n1jEtvg9fQOCp7RnUXRV+jzKxZi9tDgNnt+uR+dr8emadIfr2xWuf5k87vzXieECC/92XytFWyznH\nv1xCUq+R+OHKSmLMw6OBsgw/3pZBTbeyzZ0c5ub7K04hCvBqSTyfnBrdXHzdjCaWZXQhCEqk+vmD\nSayb2cw3PpqEx6/hxpx2PqyIBgT0GhHn0/dd8Q4HF5vLrYeujvI2FRWVKwZZlinsb1kYHhJDdtyg\nzU5h9QcEJD+SbOTPRYp346/uHCyc+cFHJdR0Olg7tQ2LwYOAiEbQ9m9dm1mQNXbbnVNth/rFI8xI\nWTEh4lGSA2w/+eow8RhpimfllC8ME4/ni9kQwYKs21g79/8xNelatKIOX8BDScNW1h98moPVH+D0\nXrwigYDkZ8PB5yZ0zNouI+tLlYKlecndPLFopHgsbg7lg3IrsxLtQfEIcOvkdqbFKykCLx9JuErE\nI0QaB8QjnM1+5/4ZLWcUj2dDI8LD8xpIi3CddXy4fEU13oDIK0cSRhwXBPjagvrgvBrsxqD9z5rJ\n7Zh0o2/jv3Y0HpnBKOSUOAcNdiNL05UCu521kaRFOPvvLfHCvoqLsCqV8aAKSBUVlXHR2FUerHZW\nWhYqnSaauyup7VDMfl87GoPLr+GBednMS1W2ug41dPCLHSfIsTpYnqlsTSVHTsLWVw/AgqzbCNGP\nrXCiy9HCnkqlMUFiRA4zUkcvahkPsiyzt3Ij9f0dbAAsxiiuz38Ag3ZihY1JH8bcjJtZO/dbTEte\nhk5jwC95Oda4gzcLn2b/qXf60wAmhi5HCweq3uX1A0/R622bsHEDEvzfgWQCskiY3sOSjJ4RPa4b\negz89kAKn5vZwpvH4oLH8+N6uXWyYjS9oyaCXbWREzavi4lB9NM1rABo9CjgjTntrMgcbSt6bBi1\nEo8vrCXa5B11/IF7y2cVmBeXo60WChtHOiVEhvhZkTlY+PLWiVg8fgGzPsAtuaObi/skkU8qo9CJ\nyloWpnSzsyaC67M60AgyfV4t0+McDKz1Ox8UT/yCVMaFKiBVVFTGjCRLFNV8CCjbuilRU/qPB9hf\n9S4Adk84n56yEBGi56mbZwHgD0h85fV9aIQAXylQiiXCjNE09wvR9OhpZMRMH9McvH43W8teJiD5\nMOnDWJJ7T7Dv9oVwqPYjKloLgz8btaGsyn8Qk37sVkLjxagLZU76atYWfIsZKSvRa4wEJD8nmvfw\nZuEz7KncSK/7/ESIx++irHkf7xb/mrcP/4LjTbsnPN/ytaNxdDgVMfXdZTWY9cNz3OxuDf+zK51Z\nCXYkGZw+JT/VavLylYJGRAFquoyjRrKuTCQCIx6bI8Xd/ORu7px64UI93BjgXxfVYtadyUt0aNvD\ny1OZ/WpJPK5RalrumdYanHe3W8em/pzJlVmdxJhHzwd983gcMuALCGhEyLK66PFomZesfJna1xBB\nuEG5WYfTw4HaM3e6Ubn4qAJSRUVlzFS1HQ72tS5Ivym43XyyeT/dTqV69Nf7lH7XP1o9k5hQpdr2\nVzvLONzYyW15bUSZlNZtOq0SdTPqzCzIum1M95dlmT2Vb2J32RAEkWWT7w9Wf18Ixxp3BW2AAHSi\ngVXTHsRiHD1fa6Ix6EzMSruetXO/zey0GzBoTUhygPKW/Wwo+hm7yt/A7rKdcxxZlmjqrmTHyb/z\n+oGfsO/UW3T0Kd07QnRjK0waK14/fFql/H4+N6MB62ltCr0BeHpnBgFJYN2M5mArPK0o8ci8+v6O\nNRp+e+BqMQuXMeuls8xVEW+To/v44uwmzursMw7iLV4eW1iH9gwFKJe7MrvbrWPj8ZHekBoRvjik\noGZTRTTdLi1a8czm4n5J5IOT0Wj6o5CL07rZWh0ZNBZvd+hZkDIYmf+SaulzWbka/mpVVFSuAPyS\nj8N1Sl/rNOtUYsPSAMVMfKDf9Yn2GE51mpiZGMlDi3IAqO7o5fsfFZMW4WJ1jrKtlRCRRUef8nBZ\nkHX7mEXgiabd1NiOAjA3/abgHC6EU22HOVj9XvBnUdByXf4XiTJf+qiYXmtkespy1s79FgXpN2HU\nhSLLEpVtRWwsepYdJ/8e7Dk+lD53F8V1n7C+8Bk2lz5PVXsxAcmPKGjJjJnJqvwv4fJNbG7lnw4l\nAQILkjtZmjF8u12W4Zd7UmnpM/ClOQ0UNYXhDiipDvfPaCY9st8svDAJm/NqMAuXiTb5cAQrrkee\nB4GkMDePzlcqyieSbKuLr8xtOMu9BxC4HEU1W6qiqOkyjjg+M6GPtPCBvEWRjSeUXNm5yXayokaP\nhr9dFoskK1FIrSiTGu5BRkl5AMVYXCsqeZTHWnto63VdhBWpjAVVQKqoqIyJsqY9ODw9CIjMTlsd\nPH6odjPegBsZLc8XKnlsv7pzHhpRRJZlHnnzAB6fjy8XKO3bQg2RtNlrAciInkF69LQx3b/NXsvB\n/uKd9Ohp5CVec8Fraug8ya6KN4YcEViRt464sPQLHvtC0GkM5CcvYW3Bt5iXeSsmfRgyMlXtxbx1\n6Dm2lb1Ce289Ve3FfFT6POsLn6G47hMcHsU6xxqazIKs27ln/ndYkvtZWrqrz3HH8RGQoLApnJQw\nJ18qaB5RNPNycTxlNgvZUQ6mJ/Tx96NKhGpxWhdL0pU5vlcWQ2nrxEZFLw4ycWZPv9A9U9WzQKTR\nxxMLazHpzxQpvDDmJPVy7/SWUc6cPp/L4w351+JEpNNuLQjwtYWDBTW7ayOo7xkwF29htLlKssA7\nJ2KCXXmWpHexpSqKG/ujkDXdIRQk2YPXP/zG/ouwIpWxcOGGaSoqKv/weHxOSuq3AjApfi7hJsXo\nuaOvkfIWpT/t+ydj6XbrWDcnk2sylEjDq4dr2HyyiVtzbSRYlEiBTmOkz9OFURfK/Kw1Y7q/29fH\ntrK/IcsSYSHRLMq+64ItPNrsdWwtexlZHnzgL5l0D8lRky9o3IlEq9ExJfEacuPnU9FayNGGbTg8\n3dTYjgYjsQMYtCayYmeRHVcwLHoqyRIlDZ9O6Lw+KI/GqA3wveXVI8TjtuoIttVYEZB5ZH4dW05F\n4Q1oSA13cf+MZgCOtobyTtnVYRYeZfTS6hiIro3+mTNqAzy+qJYo08Xte35dVicdTh2bK0d6MA4y\nNC/y0lHbHcKWqqigd+MAUSF+lmV0srXaiozAG6Xx/Ns1tWRFuShIslPYGD5irA/KY7h1cjuBgIBe\nIxMf6iHc4CctwkVtdwh9Hi1CfwHRu8cb8AUkdKf3h1S56Ki/cRUVlXNS0rAVb8CNVtQzo78PtCzL\n/YUzMi6/mXfLwrEYdDx9y2wAOhwe/u3tgyRZ3KzJU6IHsWHpdDkVEbEo+44x+R5KssSOk6/h9Pag\nEXUsn7wOvXbkdtl46Ha28snxPxGQBrP/52euITN27O0TLyW+gAdJ8qMTR/d6izInsjxvHfMybx2x\n9f7a7mcmfD6bKqL46fUVI3o6N/bogrmOS9K7sBgk3jgWh0kX4JH59eg1Mh1OHX+8SszCjRo/nSMq\nrocjIvPo/HpSwj0jzl0M7s5vZW7SuSr0L09RzcbjsXS5NCOOf3Z6S9C+51hbKEdblJSVtVNb0Yoj\nI7YyAm8eiwvmQi7L6GJrdWQwClnaZmGS1QGAJMv85OOSi7IelbOjCkgVFZWz0ufu4kTTHgCmJl0b\n7BJTbSsJdo/5wwGl3/UPbphOfJhiefPNd4uw9bl5YE4zoiATorME8x4zY2aRap06pvsfqfuUpm7F\n821R9h1Emkcm7I8Hh6ebzaUv4vUP5k7NTL2OvMRFFzTuRCPJEg2dJ9l64hVeP/AUB6rfo9ulFB+E\nGqJIs04LFvl0OprYdPQPfHT0+aDFktPby6fH/4KH7jPe43woaTHzzcW1hBqGP/h73Bp+vD0bGQGt\nKLF2aiubKqwEJIEvFzQQY/bhCwj8Zn9Kfy7hlY2AhE86XQyNFL1fnNPIlFjHuMc/3z4GogAPzmkM\nCqiz5WSe+fzFwe3X8GrJyNxh7WkFNW+UxiHJEGP2ndHq6ONTVvwS+CUwaGUijH5Sw13BCm6LMcDA\n2n6+/cTEL0blnFz5f8UqKiqXlcO1SstCoy6U/KQlAPgCXgr7+13XdkdS0mphanw4jy5Wtn8/LW/m\nLwdPsSq7g/RIJVneoAvB5eslRG9hfuatY7p3Q9dJjtRvAWBS/DyyYmdf0FrcPgebS1/A6R2M4ExO\nWMiMlAv3kZwo7C4bla1FVLYV4fQO5nppRR3p0dPJiSsgNiwdQRCQZIlaWykl9VvocrbQ3FNJ89HK\nfnvpiyMcnF6R6fHDo20Or8jPd6XhDSgxieuzOzBoJTYei+XmXBvT+83C/1YST0331WAWLhOik4K2\nQ2fizimtLEodv1+nLwB/L1G+CMkyI9IAzoVOI/O1BXX8dEcGzb2jReOHVmZf2ihkUVMYJS2hwfd8\ngFkJvaSGu6jrCaGx18jOmkiWZnRxS247u+siRvlSIfBaSTz3zVDyPpdndvFOWQyrsjt45UgCh5vC\niDN7aHUYcXj9vF1ax235qZdolSqgRiBVVFTOQkdfE6faFcPemakr0WmV7byjDVtxeu3IiPz+oBIF\n++Ud89BpRFw+P19dv59Ys4e7pio+bdbQ5GD18KLsOzHoTOe8d5+7m50nXwNkrOYk5o1RdJ4JX8DL\nJ8f/TI9r0DsuI2YG8zNvvewt0XwBL5WtRXxY8ns2FP2Mkv7fL0CsJY1F2Xdxz7zvsnjS3cSFZwTn\nKwoiadFTyUtYhEE7mA5wscSj3S0wP2V4Nbdfgj8VJdLQqwhDnShxQ3YH75bFkBfr5LY85X3fVRvB\njpqrwSxcJirEd07xuCyjk5smndtaaSgBSWnp+NV3puDyK9HNIy0h5xWNNOslnlhUR7hxFBPGYVzq\nz7bAK0cS8JyWDioI8PUFdQwI2rdOxOD2i5j1EreewVx8W00UvoDyGQvRSZh0AabG9hKq9xOQBdIj\n3cFrH9sw8f3dVc6OGoFUUVE5I4ppuEyYMZpJcQP9rjsobdgJwPbqaFr7DHxmZhrLs5WIyo82l1DV\nYeeb1zahFSUMWhPdDiWKkBU7m5SovHPeNyD52Vb2Ch6/E73GyLK8+9GKunO+7mzjbT3xMrbe+uCx\npMhcFufcjTABJuTngyzLtPfWUdFaSI2tBF9gMKpn1IWSHTuH7Lg5RJhiR7xWkiUaO09SXPcJHY7G\nEecvFhaDPCxaJsvw2/3JFLcMFkIsz+wkRCexpy6c7y2vRhSgrtvIy8UJXK6+zeMhJsRLu+vsPa5n\nxPdy3/SR1ednQpLhYIOFPxYmMSepjx9eV0mixctewGqSONpiZFq8e9yRyGiTjycW1vHTHel4A6dv\nt1++Ptk2p553T8ay9jQz9SiTn2XpnWyrsWL36Piw3ModU9pZntnJlqqoIe0hB1DE6L/MUvKmV2R2\n8mF5NCsyO3mnLJbiFgtmnR+HT0tDj5NKm53s6Itn/K8yHFVAqqiojEpTd0Uw93B2+g2IovKAOlj1\nPpLsxxcw8EZpFGa9lv+5VemHXdLUxc+2HWdJehe50f1b11oTHr8Tkz5szFHEg9XvB1scXjvpM1iM\nUee9DlmW2F2xnqbu8uCxWEsayyffj0a89P8FOr29VLUdoqK1cFg0VBBEUiInkxNXQFJkbvD3PYAs\nS7TZaylp2EZjVzmXemtytK3W10vjhlnxaEWJG3I6eOt4NA/Pa8RiCOD0ivzmQAq+q8AsPNwwIB5h\ndHscgYxIJw/NrR9RQDQasgzlNiPP7U4jP97J91dUjyi2SQn3IAoGjrcZmRI7fhGZGqF4T/5yTxrS\nsDmfyd7n0ojKzRVWFqZ0kxQ2vOvMvTNa2N8QgcuvYXNlNEszuogK8bM2v5Xf7B+5Bb2nPpJ7p7cg\nCoqRu1aUmZfcw6aKaDx+DVMTejjUrHyB+cLfdrPrsRsvyfpUVAGpoqIyCrIsUVittCyMsaSSZs0H\noKmrgrr+XtEvFUfj9mv46c3TSI4wE5AkHnpjL2EGL/dMU4o9Ik3xwc41i7LvGlNP6ar2I5Q1Kx0m\npiUvI8U65QLWIXOg+n2q2gf75kaa4lk59fNoNZfOwFqSAjR0lVHRWkhD50lkBgtQwkNiyYkrICt2\nFiH64b6Isixj62ugrGkv1bYjSHLgks35dE4XNjtrwtlcaWWobcy1ad2YdQGsJj+ZUUqR0h+Lkml3\nXPlm4XpNgB7P2eYpEGNWusIYtGcX77IMzXYtT+/KICPSw7eX1g7bbm3r0xHRv/UsyZAU5kHAwIl2\nA3kxnnGLyPw4B5+f3dRv7n7m+Q9WZl98ERmQRf5anMiTS2qGHdeK8IXZjfz2QKpiLn48jgfnNDIn\nsZccq4OKjtOdGQT+cjiRLxU0okFphfjpqSgWp3WxpcrKqU4TIhISIvtqbTg8PsyG89+tUBk7qoBU\nUVEZQXV7CZ2OJgAK0m9UCjakwX7XbQ4Le+rCmRQTxhNLlC3p3+w+yYE6G48vbMKgldBpjMEIW05c\nAclRuee8b7ezjT0VbwIQH57JrLTrL2gdRxu2caJpd/Bnsz6S6/MfwKA9dw7mRNDtbKOitZBTbYdw\n+waLCnQaAxkxM8iJKyA6NGVYDqYsy3Q6mqhsPURF60H80uh9gy8nNV0GqwJPtwAAIABJREFU/lqs\ndKJRENAIMqsn2TjQEMbyzC4A3iuLpqTlyjcLFzi9ReFIkWXW+fnXRbWEGc4s4mUZelwCz+5JJ9wo\n8djCBrKiBqv9bQ4d4UYfsaGDeYuioIjIxDAPgqCnvEPPJKt33CJycVo3XS4db50YmfIwfE2XTkRW\ndpjZURMRNI8fYE5iL8lhLhrsIeytC+f6rA5SI9x8Jr+Vp7ZnjLB4KmwK53P+JjSCjMUQQJIVU/qt\nVVH0eHTkRvdx0haKDDz+1kGev+fKclT4R0UVkCoqKsMISH4O1X4EQEpUHnHhGQCUNe+lx6XkNP3h\nYDQyAr+8Yy56rYa6Lgf/8UExC1J6gtWXBq2JPk8nJn04czNuOed9fQEPW0+8jF/yEqK3sCT3XkRh\npKfcWClvORBchzIfMzdM+xIm/cXNkfL63dTYSqhoLaS9t27YufjwTHLiCkiz5o+IgHY5WqhqK+Zk\n6wG8/tHbvF0JdDo1/GxXBpKsPOR1ooRPElmY2o1F52NuslL8c6zNfBYxcyUhY9DKuP1DBeRwAaMV\nJB5fVEdc6JnFvNMLvzuYii8gsm5mazCFA6DLpcWi9xNtHr3gZUBEJli8CIKeU506sqJ84xaRt+S2\n0+HUsbN2tGKly1OZvf5YHLMSerAYBu8pCPD4wjq++dEkZAReOxrHN6+tJTPKxdxkOwcaTjcXF3ih\nKJmvzm9Aj8zKrC521kZQkGTnYGM43S4dA6L45aIq/nD3QsSJakauckZUAamiojKMsua99Hm6EBCY\nk660LHR5+zhc9wkAhY1WqrtM3D4thVW5iciyzNc27EcjuLmvv9VamDEau1upUL0m565zGn/Lssze\nyo30uNoQEFmae2/Qb/J8qO04xp7KjcGftaKBG/IfJCzEet5jng1Zlmm1V/cXxBwdZlBu0oeTHTeH\n7Ng5I+7f42ynqr2YitaDwyx7rlScXoFXS+KDFcQGjR9PQOkKcvOkdjwBDWG6AB1OHX84mHwVmIXL\nhOn92L1n2/KUeXje8EjiUHwB+NuReBrsIdw+pY2pQzwhe9waQvUBIkOGlySPlk86ICLjQ720oaOq\nU0fmOEWkIMDnZjbR7dZytNXC2VovXiocXi2vlyby4JzhxV5RJj9L07vYXhNFmS2UIy2hzIjv464p\nrRxqspwWEYaSVgtuv9IfO9zox+UTWZbRycHGcFodBhIsLpp7Q/AFZH635ySPLL5yOkr9o6IKSBUV\nlSAevyvYsjA7roAIUxwAh2o/whdwE5C0vHLEilGr4dk1BQCsL6nj/eONPDy3GbM+gFbU0etRzIEn\nxc8jKXLSOe97smV/ME9xTvoNxIdnnvcaWnqq2F72NwaiLKKg4fqpXyQqNPG8xzwTDk8PlW1FVLYW\n0evuCB4XBQ2p1qnkxBWQEJGNOKTS2+7qoLr9CBVtRfQNec2VTkCC3xcmU9o6GME1aMETgHnJ3VhN\nPjQi+CWB3x5Ipu8qMAuPMPjo9pytxzXcP6OFWYm9I44HJKUjT1FTOLfntfP52YN9qns9Gky6AOHG\n4dvdA8LxTKJwQETGhvoQBB01XVoyosbXHlEjwsPzGnhmZzq15/TcvDRb2XvqwrkmtYvJMcMj6/fN\naGZ/Qzhuv4Y3SuPJj60k2uzj+qwOPqw4vdWlwO8PpARzUFdmdnKwMYzJ0X2U2UIxaAa35v9z0xFV\nQF4Crvy/cBUVlUtGacN2PH4nGlHHrFQl/9DW10BFayEAG45HY/fo+K/V+aRHhdLl9PD4xgPMSrAH\nty4NOjMOTzdmQwRz028+5z1tvfUc6M+tTI2awtR+s/LzoaOviU+P/2VIsYnA8rzPEReeft5jjsZA\nt5f1B/97mOdipDmBSXEFZMTMHNamsc/d1b+tXRRMA7ja+GNhEnb34CNjkrWP8g6lJd0901qDVcmv\nlsRT3XVpckwvBJNuQDzCmUTU6hzbiE4pkgz76i1srohmTZ6N/1xeHTzX5xUJ0UpYDOMTjkMZEJEx\nZh+ioKOuS0Nq5PiKp4xaiccX1vHU9gxsztMLg04XjZdCRAq8VJzIf62sRDsksKgV4QuzmvjdwRSa\new3sqIlkeWYXN+Xa2FkbOeJLSJnNjNMnotMEiDL56XHruC67gzJbKDXdJsINXno8erpcXvZUt7Eo\n42pIobh6ufJ9FVRUVC4JDk8Px5t2ATA1cTEmQ5hSxXxK6Xfd4zbxcWUkmdZQvrFMaUP47fcP0et2\n8LmZik9bqCESh0dJmL8mZ23QePxMuH0Otpa9giQHsBitXDPp7vM29e51d/DxsReH+SleO+kzpERN\nbCSiorWQj44+DyiG3XptCJMTFnLrzK9z26zHyUu8BqPOjMPTw7HGXbx9+JesL3yawpoPr1rxuPF4\nDMXNFup6lIhWqF55eAOsyW0LFpbsqQtnW/WVbxauEQI4fWev1J2f3M1dU1uDP8sylLUZ+eGWDLSi\nwA9WVjO7PzLp9IlIMoTqpWH2PgMG4eP9SA+ISKvJR6hBoKF7/I/qcKNS9GPSnh7BHM3q5+LnRLb0\nGfiwPHrE8YIkO4kWpUL97ROxuHwiJp3EmsmjmYsrrTC9AWXeyzM76XToSA5TXj80x/RLr+2Z+EWo\nDEMVkCoqKoDSsjAg+TFoTeQnLwWgqr2Ytt5aAF4siiYgi/z8tgKMOg07TrXy/L5K7pnWQrjRjyho\ncfS3CMyNX0BiRPZZ7yfLEjvLX8fh6UYUtCybfP+YbH5Gw+XtZXPpC8Mqnedl3kpW7KzzGm80ZFmm\npH4ruyvWB214lubey2fmfYcFWbdhDU3C5e3lRNNe3iv+X944+FMOVr9Hl6N5wuZwOdjfYOG9kzGY\ndAEGCjCWZXTQ6jAQafRxy+R2BAHqewy8VJzIlW8WLiPJZ3/05UY7+OLsJgbqMGwOgZ9sT6PLbeA/\nV1Qzrz/a7vaLyDKYdBJDazbOVzgOZUBERpn8mA0izfbxP67jLV4eW1SHRpDOctWle7/ePxlDa9/w\nqKJSUFMLyPR6tXzQLzKXZfx/9s4zMI7yXNvXzBbtale9d8kqlnu3sS0bGzCmhJpACoSENEhIoeUQ\nICf1EEhCAknOd4AQCAk4NEMggG1s495tybasLqv3rtWutu/M92NWK8kqlmzJda8f4B3NzL6zsztz\nz/M+z/10EmN0DNlHZVcgvU4lBzfK4KLNpuWadCUVpLIzEK2oPMyUtplpMQ+ft+pnYvALSD9+/NDV\n20xFax4Ac5KuQqvW4XI7OFK9AYCSthAKWoO4YVoCN81Iwu7ycP+7B5gRbSEnRYk46jSByLKEMSCM\nhWmnN/PNr9tOQ1cpgFeAnVmOotNtZ0vhq5jt/VONc5KuZnr88jPa33BIssTByv/4qrobepQ8wIc/\n6mRLaS0ljQfZcPwF3j70FAcrP6Tdcu66w0wmVZ06Xs1NBMDkUCJ2mRFWjjWFoBIkHsmpRiUqETgl\nMnSh31JkNKI8anFPfJCd7y+pRaNSVOBHJeEUt4Xw+MoaliabEAVwuAVkWZkqHk4kTlRnzD4RGaZ3\no9eKtJjHv+PMCBvfWVTPyFHGc9exxiWJrDs+1Ksy0uBmRYpi/bTlZAQdVg0qEe6Y2TJkXRD4y4Ek\n7C5lzKvSunBLEK53IiMQH9wvOr/5lj8KOZlc6L92P378nANyqzchIxOkC2dq3BUA5Ndvx+Y0I8ki\n/zgahVYl8tytSuHMM58VUNPVxdfmKV6RgdpgXxXx8swvoFGNPnXd2F3uq+rOiF5AVuyiMxq3W3Kx\nrfifdA6I8mXHXcHc5GvOaH/D4ZHc7Cp902duXtQaSptFiXJcm7aZpraXOVD5b1+k9lLB6hR4bn8K\nbklEFBSRISCzIrWL+h4dX5zVQlyQYmvzSm7CMG3oLjwC1Z5RO+KEBLiUaV+tErEra9dzfVYnK1K7\nUYn4pk4D1PKEicTT0SciQ3VudBoVbZbTb3MqCxPMfHl28wh/PTUfcnIpbDVysG6oldbdc5sIUCnn\n5/0iJXdxXpyZqZG9Q9atM+kxe6OQsUYnzZYArvbmqtb36BC8x/FpaSMuz2jRVz9ng19A+vFzmdNs\nqqS+qwSAeSlrUYlqemztFDYo/a4/LVd61D66ejoZkcEUNnfzzLYCbp/eSkSgCwERm1O5q02LW0Zc\naPqo79frMLGr9C1AJiwwlivSbzmjcUuyxO7St3wFLQBpkXNYMuXmM86jPJW+6GZ1+wkADtRFkBBk\nZm68clPTa2TES/Qq+rvdqfQ61Qyc8l2caGJ7ZThLEru5Ol25YX9SGsGxpgu//3Cgxo3VPXLdaIDK\nw0PLawgPVHIGPRJkRdpQi+DyCket6ty2j+yjT0SG6NwEaFS0n4GIvCa9k2sz2k+z1rnxiHz7RCzW\nUywx+zrUABysC6G6S7H+unNms08QDhzn8/tSsLmU7+XK1G50Gjd6jQe3JBLrnfqWZPjZxmP4mRwu\n0UufHz9+xoIsyxypUqapI4wJpEXOApRe1JLswerS8nFpJMlhBh6/ehaSJHP/uwdIDTX7BESAJhAZ\niSBdBPO9vpEjIUkedpb8C7urF40qgNXT7j6jloKyLHOg4gNqOgp9yxLCssjJugNBmJjLmtXZw8YT\nL/kE6qbycObHdRCiP3/tBM8V5e066nr6KqkF739lZsWYcXpEX+S5ukvHB8Ux52mUY0crerC6RhaP\nAjLfv6JuUJ9qxZJI+bfmPAnHgfSJyOAADwEaFZ1DA3On5Y6ZLSxKMI1hzck9XpNDw/uFcUOWL0ow\nE2e0e83FYwFIDbNzRdLQMTdbAjA5lChkQrCDxh49q9KUa1K3vc9YHP6yp2SSjsKPX0D68XMZU91+\ngnZLPdDXslCkvquUus5iANYdj8LuVvHszQsI1Kr564FyDte28PX5ioDQqQ3ewhWBnMwvoDmNGDxS\nvdE31ZuTeQfB+qFVmWPhaO0WypoP+V5HGpNYlX03KnFinMlMtjY2HH+Brt4mZFngP8URrEnvRHuZ\nGJ+9dDhpyLLFiSYO1IXyvSV1BKhlPBI8vy/Z15HmQkVAwimN3tHoGwsamD7AALxv1lM9SXfIgmYD\nf9ybDCiicKz0icigAA9ajYrucTYsEgX45oIGsiJGU5/n5nzuqAqjsnNwgwFBgAeXKQU1ZR0GjjYq\nzQRun96CRjx1Klrgub39UciclG6iAh2oRQmbW+XrNW5zeXj3WPUkH83liV9A+vFzmTKwZWFCWBZx\noRl4JDeHKj8GoM5k4EBdCNdkxXH7rGQaTFYe/ySPW6a1Eutt6ebwttybHr/M1/JwJKrbT/TbBCWs\nICVy5hmNu7hxL/l123yvQ/TRrJlx72nF61hpM9ex4fiLWBxdSLLIhvIwbsruGGTPcinTbNbQZR9q\ncTM9ysLKtC5ijU4kGf6RF4d51A4uFwKjF8yAIk6WJSsRrr7q6ck41y4P7KgK5adbM3hufyqFrYo4\neuFQEuNJ0+sTkUatB41axDTOQmONSuaBK+qI81rnDM/kT2XLXm/IUwV0pMFNTrJSmPduYYxSIBPo\nZk3GUNP9dquWTpvyVJccaqehR8fSpL5z2X8MD314ZPIO5DLmMrkk+vHj51TKmg95u6cILEhVqqaL\nG/fRY2tDluHveTGoRZE/3boIQRD40b8PE6brYW2mciEPUAciIxOsi2R+ytpR38tka2Nv+XoAooNT\nWZAy+lT3SFS2HeOg13QcwKANZe3MbxGgmRjj6vrOUj498Vcc7l5cHg17qoO4IbPznBVMXAi8kpvI\nqVGoGdEWZAQWeH0PzXaRvXUXut+jjFoYvcL4ytRObsjqzwucjPNsdoi8VxjFI5um8vqxBJrMAYTr\nnT6PybzGYJ7dk4pjHA1n+kSkQSuhUYmM163GqPXw4LJagrXD9+ZWmPwvfa1Jz9aKoe1FvzqvEa1K\nosUSwM6qcABuzGonOGCop+Uf96Zg9UYhr0jqISVUia6aHBp0aiXdpKnHRklz9+QdyGWKX0D68XMZ\n4nTbOV73GQDp0fMIN8RhdZp9y/bWhlHTrefBldPIjgnhgxO1/KegmnvnNyAKoFXpvdFHgZysO0bN\nY3R7nOwoXofL40CnMbJq6lcQxdGnFIejoauM3WXv+F4HqA2snfUtAgMmpoDjZEsunxX/A7fkotcV\nQGmbhhWppstKPDrcApVdQ704V6V1sNxr19RhVfP7Palc6H6PWlHCPYrf45xYM3fNaZq081tv0vDX\nwwk8sjGbDWXR9DrVZEdZeGBJLb9dW871mYpwvXVaC2UdBp7amY7FMfbBiIISMQ3USqjVApbRAorD\nEBno4sHltWjE0+X0Tm4k8sPiKDqtg8+T0qFGSa35T0kUVpeITiNxc/ZQI/5uu5aOXiUSPiXcRrNZ\nx9xYxREiUNMf2r3nzb2TdQiXLX4B6cfPZUhhwy7srl5EQc285GsByKvehMvjwOlRs74gmvhgPT9d\nM5seu5MfvH+IG7LafUUGTo9yt5qRsILo4JQR30eWZfZXfECXtRkBgSunfumMBF+buY5txa8jy8oN\nQS1quXbmN884h/LUMZ6o38me8neRZYkOq55um8zMWOtlJR4B3jgex8CiGYAZUWayI62IArRYNNic\nAk0W3Sh7Of9oRM+oeY9pYVbuW1Q34VPVsgzHm4w8vTONn2/L5GB9KGpRYlVaJ7+++iQ/zqlhdqwZ\ncUBbw5uy27l3fgNNPQH8cnsG7daxP1wJXhGp18ioVdA71Hd7VFJC7Xz/irphqpwHvQuTKSLtbhX/\nyh/qDbk40Uys0Y7FqeaTUqUv9pVpXcNOvf9hbzJWp3IyFyaayY5SytQ7bVqfiXpufSdm+2gRVz/j\nxS8g/fi5zLA6e3wWPdPjl2HUhdJmruNkay4A7xVGYnaq+d1NCwjSaXjik6MIchefy1YiJlqVHpAJ\n1kf5+mWPRHnLYZ9B+byUa4k7TXea4ei2trKl8FU8knLxFwUVa2bce8bG4wORZYnDVR+TW70RUPI+\nNSoHCcHOs973xYYsw/7a0P7XCKgEia8vaCRQK2FziTT2BPC/B1O4kKOPIhKuUcRjZKCTHy6tJUA9\ncaLI6YHPKsJ4bHMmfz6QwsnOQKINTr40q4lnryvjrjlNvqIOtahMP++rDfFtn5PSzY+W1WBxqvjV\n9gxqusaez9snInUaUKvANs6v7syYXl9R3OhMnog82hTEsSbjoGWCAA8tqwVktlaE096rQRSGNxc3\nO7W09vYb3bdbtWSEK/nZIbr+ae8H3js4acdwOeIXkH78XGYcq92KW3KhVeuZlbQK2dtlBaCtV8f2\nynCuTI/hS/NS2VfVykv7S/n6/EbUooxGFYDTY0NAICfzDtSqkYsoOiwNHKhQ9psYls0sb3vE8dDr\nMLG54BWc7r4kL4HV0+4+bcHOWFAMwt+mqFGZ2ippMxBj6CU44PI0Hn6/MMpXcNJX8XrPvCbC9coN\n+L3CaIID3LRZJ6ZYaXKQkUa5rRk0Sn/ovt7dZ4vJLvJmfgwPbcjmX/nxdFi1zIox8+CyGp5ac5Kl\nyd1YXSKigM+c/HizkV9uS/fmmiqfKyhC7icrq1Eh88zudE60jD2vt09EBqhBFMcvInNSurll2mh9\n2if7gUHgX8fjhuSBRhpcLEvqxi2JvFeo2EXNibUwLWqoEeaze1J8Uci5sRZmxyr5up02LX3i9+1j\n1UjjKXv3Myp+AenHz2VEt7WV8malInF24moC1IFUtB6l3VwHwD+PxoAg8qfbFuHySNy//gDXpHcw\nJVwRcC6PcmeakbiS6ODkEd/H4bayvXgdkuzGEBDKiqw7x+3P6HBZ2VzwClZnvwfciqw7SAqfNq79\nDIfTbWdr4WtUtR8HIK8hiKzI3svGpudUXB6BjeVR3lcykiywOMHka1O5qTyC5FA7LxxK4sKNPo5e\nMKNC4odLa4kNOvvocr1Jw5/3J/LIxmy2VkQiCLAmvZ3frCnnwWW1GLUuarp0GLUSkQZFFZV36Hlm\nVyp/3p9CfU9/CsCGsiheOpyIy6NMKT+5qopwvYs/709lb83Y0z0GikiVCsY7W3vT1DZfO8ER3oHJ\njEJ22LR8WDLUU/Rr85WCmkMNwVR2Kvm5d85sGTLtbnNraDIrDzfTonvpcah8huJ9xTduSeZPu4sm\n7RguN/wC0o+fy4i86k3ISBgCQsmOX4rTbSe3ehMAx5qCKWoz8sDyqcyKC+P32wtpM7dyqzcyobQn\nlAkNjB61VaAsS+wpexeLoxNRULE6++5xV0m7PU62Fr2GydYfFVmc9jnSo+eP/6BPweY0s+nEX2ky\nnQTgSL2RefFKXtrlymt5cb7oo1YlEW10+LqClLQFsq0ynCiDY1h7nwuF0W9mMvcvqScjYpzlygOQ\nZMhvNvKLbVP4+bYsjjeHEBfk4KtzG3n2ujJumdbKyQ49eY1BpIQ6SAtXcvXqTQH8eX8yz+xKo7zD\nMOy+D9WH8Ie9qVicIpEGF4+vrGJKmJVX8xL5uGRolfJI9IlIrUqxIhqPiBQEuHtuI7NizKOtxWSK\nyK0nI6gzDf6OqUX42rwGQODtAkVgJofaWZY8tKr62T39Fdkzo3tZGK88fJodat+4f735xKSN/3LD\nLyD9+LlMaOmpprZTefqen3ItalFDft02bC4zHkmZios26vjF2jmUtpp4aks+X5vXSIBaRi1qcHkc\nCIjK1LU4spAoaNjtMyJfPOUmIoMSxzVOSfKwvWQdbeZa37LZSVcxPSHnDI56MD22djbkv0BnbyOy\nDMeaDCxIsFx2xTIDKe/Qc6C+P/dRp5Z4wGsW3m1T89LhRNZmdPDiBRx9VAsS0ihju2tOM/PjRxNG\nI+P0wLaKUB7ZmMWf9qdQb9IxP76HH+dU8aurK0gP7+XD4ij21oSxOLGH+d6HkfZeDX87ksAvtqVz\nvDmI03125R0GfrNzCi0WLcYAD4/k1DA/3sy/i2N541isz6PydPSJSI1XRDrHYQ+kFuH+xfUkh4wm\ntCdPRHpkgdePDb1eLEnsIcbg4GSHgdwGxT/ztumtaFWD002ckpr6bqUn+6xYCw6PQEiACxkBvVpZ\n12R3sfPkSH3B/YwHv4D04+cyYGDLwjBDHFOi5mKytfny/zaWhdNu1fLM5+YTFKDhu+sPckVSB9lR\nSiK621vAMivxSiKDhnYp6aPZVEmeN6I5JWouU2OXjHOcEnvL19PQVepbNjV2yWmLdcZCu7meDfkv\nYLZ3IskilZ0BzIntvazFo80l8tfD/b6PatHDXXOaiQtSzMJfOKzczKMMDiwXqGm4KIxu13NdZjtX\nTekc937NdngrP4YffDyNdfkJSLLADVlt/HZtOfctqqPbruEPe1M4XB/CTdltXJPRiUYlY3aoeDM/\nlie3ZrC/LvS0RuYDabEE8JudaZR36NGqZL67uI6r0zvYXhXBC4cSx2w4PlBEiuMUkTq1xIPLaonQ\njzbVP3kisqIzkB1Vgz1GBQEeWq50qFlfGINbEgjTu4ft7f3HARXZU6NsLExULH1cUv95+PY7+ydl\n7JcbfgHpx89lQG1HoS+i19ey8HDlx0iyhx6Hlk/KoliaEsVXF0zh74dPkt9Y76t2VItKXlFoYAxz\nkq8e8T2szh52lPwLGZnQwBiWZtyOMA511idyK9qO+palRs5mSfot49rPcDR0lbHpxF+xu3pxelS0\n96pIj3Bc1uIR4N2CGG+RgcLajE4WJig33PUFMZzsMLA2o8MrMi9EZKRRxOPiRJPPsHusNPao+fP+\nJB7cOJ0tFZEkBDu4d34Dz15Xxuq0TnZUhfHYpkxOduj59sIGbp/RRqBGwu4S+bA4ip9szmRrRQRu\n6cxurxanmmf3pHKoPhhRgC/PauaOmc3kNobw7J5UXGMUg30iUi0qInKs24FSufzw8hr0qnFsNIG8\nVxiD+RRPzCiDiysSTbT2BrCtUhGY12d1EBIweJ7eJaup7lJyTOfFmVEJEgFqD25JRO31vKzosNBk\nGmcfSD9D8AtIP34ucSTZQ663ZWFcaAYJYVnUdZZQ743yvZkfjUsS+cvti2m12Pmvj3K5Z24jeo2E\nSlDjlpwIiKzIunPEXtOS7GFnyZvYXRbUKi2rsu8ad2vBgoZdFHpbHQLEhWSwIutOxHEW35xKRetR\ntha9hltyYnGqcbploo1+P7jjzUZ2Vof7XmdFWHyVuJWdOj49GYFB4yY80InNfSFWF41eNDM1spdv\neI3vT4ckQ1Grjv/ems5/fzaVE81GliSaeHxlJU9eWUlwgIv/O5TEY59m0mHV8NjKau6e20yIzo1b\nEth6MpyfbM7kPyXR2N3jN8k/FbekRIY/KVUKdK7L7OA7i+qo6NTz653p9I6xDmiIiBxH8XlskJMf\nLa9FZKSw5+RFIa0uFW+dGGrTde/8BjSixMclUfQ6RXRqiVunD60e//P+ZF8u5JQwOwvjlYci1YDv\nwtf8xuJnjV9A+vFziVPefIQeWxugRB89kpvD3n7XFZ2BHKoP5r6lWcxLDOehDw+THdnG7FjFJsMj\nKxGI2UmriTAONfvtI69mMy09VQAsz/g8oYHR4xtjyxGfFyNApDGJq6bfM6JgHSuFDbvZXfa2YhDe\nqyFA5cZ4mdr0DMTsUPFaXv8NOiTAxf2L61GJ0GlV8+f9itfjmoxOXssb+byfP0YXj3FGO99fUotG\nNbrAcXtgd3UID34ylT/sTafXpeKWaa38/vpy7prTRHlHIE9uzeRP+1OQZPjv1VV8Z1ED0UYXkgx7\na0N4YksGb56Iw+ycWJEtI/B+UQz/OBqHR1LyAB9eXkOHVcMvt2XSYR1b+LxPRKpEpXuNexwiMjPC\nxn2L6xlZKE6eiDxQF0JR6+CuSGqVUlDT61LzsddcPCelm4TgwebiLllFRYcShVyQ0INe7UYlyDg8\nKgSvIN5W3oxzPB+GnyH4BaQfP5cwLo+Do7VbACUnMcKYQFHjXnrs7cgyvHEslohAHb++fi6fFNWz\nqbiCL89WEsxV3kKZMEMcs5NWj/getR1FFNTvBGBa3DLSouaMa4xxulTOAAAgAElEQVS1HUXsK3/P\n9zpEH82aGfeOO4I5EMUg/BMOV30CQFNPAGGBLjRnHxy66JFleP1YHD0ODSCjEmTuX1xHiM6D3SWy\nrTIMs1ONXu0hTOfE4bm4PrRgrYuHltf6fBeHw+5SDPO/+9F0XjuaSFyQg+8squN3a8uZGW1mfUEM\nD2+cyvrCWIK0bn6cU81Dy2pJDlWEyrGmIH6xLZ1XcxPpmGRfzF3V4fxpfwo2l0B2lJXHr6zCIwv8\ncnsW9aax5aX2pWqovJHI8eimhQlmvjRrtKKTyRKRAm8cTxgy1iuSeogKdLCtMpxWy8jm4v97IBmb\n14MzLdzBHK8vZJ+BvAw89nHeJIz78sEvIP34uYQpbNiN3WVBFFTMS7kWq7PH1+96Z3UYtSY9T904\nD61K5PvvH+Irs5swaj2IgohHciEIStX1SJFAs73D1586MiiJhWk3jGt8LaYqdpSsQ/begAK1wVw7\n85vjtv0ZiEdys7vsHV+3nZouHbFBjsvapmcg++tCyG3s64Ii8PkZLWRFKlW3G8si2F6l2MasTuvk\n9WNn3+1nohFGiT5qRQ8P59QQETg0RUGWodMm8sLBRB74eAaflkexNKmbn62u4JHl1TjcAk/tSOOp\nnensrwslyuDkgSW1PLmqyldMVt4RyNO7UvnLgWQaes5dO8fCViNP75pCh1VNYrCDJ1dVEqZz89TO\nDIpahvYuHw1RGL+IXJPROWzBSj+TIyJbLAF8UhY1aJkgwCM5NYPMxWfFWJgRPdhc3C2rKGtXPpvF\niSbCvEVBSoqBMtaX9pchj7W83c8QLsTEFj9+/EwANqeFgoZdAGTHLSVIF87usndwe5zY3Sr+XRTN\nwqQIvrE4nR9/lEuErolF3opFydtzek7SVSO2DHRLLrYXr8PlsROgDmTV1LvGNeXc2dvE1qLXkGTl\nThagDuS6Wd/BEBBymi1HxuV2sL3kDRq7ywGo69aSHGqf8GKZBm/k5wcfZxMR6CQi0EWs0UFisIMY\no4MIvYtAreeCi3h2WtX863gcAGpRYm6smbWZHQBsPhlBu02L3a1Cq5KwOMRRq5vPD/KIVc0CMj9Y\nWufr1+7bQoY6k4ZX85KoM+kJ1zv5/IwWVqR0YXWJbKuMYG9tKDaXcrLC9U5umdbGsuRu30NHvSmA\n94piyG82cr6sjBp6dPxm5xR+uLSWlFA7j62s4v8dSOK5/Wl8c34dVySP3aZIFAARPB7FdHws3DGz\nhS6bhsMNI/0++0TkxH4+G8oiuSKpixhjf0FPlMHFksQuDtaHcrJDT0aEjTtmNlO0LX3Q9+P/DiXx\n/A1l6DUSKaF2pkZaKG03olVJOD0qHG6JN/Oq+MqCKRM65ssFv4D04+cS5XjdZ7g9TjQqHbOTVtPa\nU+vrS/1eYRQWp5o/37aIvPpOXjlQxC+vbgJAJajxyG7CDfHMThx56vpQxUd09jYCAiunfgmjLnTE\ndU/FbO9kc8EruDzKzV4tarh25jcJ1kee8fHanBa2Fv2dDotigN3coyExxDmh4lGWlZ7Hb51QRFis\n0UG7VUOdafgokEaUCA90EWNwkBxqJzHYRnyQk1C9G51aQhQ4Z5Xgkgyv5CVgc6vQiW7CDG7u9fZA\nLmsPZE9NCJ1WRRiHBDjZVRs+2u7OA6OLk28saGB6dK/vtSRBfouB1/ISMDs1ZEdZeGBJLbNizOQ3\nB/HioURK2g2+fRq1bm7IUix/+nIn23s1fFAczYG6kHHZ8UwW3XYNv92Vyn2L65kTa+HBZbX8PS+e\nl3OT6LQ1c8PUsdsViQKgGruIFAX45oIGumxqTnYOb4g+GbglkdePJfBoTs2g5ffObyS3MYR3CmJ5\n4soqkkIc5KR0s7smbMC2Kkpa9cxL6GVJkomqTj2l7Uacnr4opMAj/8n1C8gzxC8g/fi5BOmxtVPa\nfBCAWYmrCFDrff2um8w6dlSF843FGcxPjGDJ8xu4Y2YToTo3AiIe2Y0oqJQKaHH4O8vJllzKWg4B\nSpQyISxrzGOzOS1sLvgbdpcy5SQKKq6Zce+oRTqnP94OthS+itnegSyDya4iNnhiK63dHiWvqrDV\nwBdnNfMv4MlVSuGQ0yPQadXQbtXQYdXSYdXQYdPQ3quhw6blREsQ+S1D29IJyITpXaSFWUkOsZEa\nZic2yEmQ1o1GpUibiRKYn1WEU9JmBCDc4OK7ixvQaSRMdhUvHkokM7KXhh49IHujcedfMPUzuni8\nbXoLy5KVriNuD2yrDOO9wlhEQWZZiomrp3Ri1LrZXhXOuuNxdA/oqBOg8rAmo5O1me0EapTIe49D\nxcclUeysDjtjO57JwuFR8b8HkvnSrGauTu/kWwsbCNe7eK8oji6bhq/MaRnXd0Y1DhGpUSlR3qd3\nptJsGW4Kf3KikMVtRvbXBrM0uWfAWJSCmldykzhUH8zixB5und7KofrgQXm7Lx1J4vmYUnRqmeRQ\nO0nBNup69KhFGbck0GqxU9DYycz4C+2B6cLHLyD9+LkEya3+FFmWCNQGMz1+OSdbcumw1APwxrEY\ngnUB/ObGeTy3swi3u87X81j2VijOSb6aMEPssPvu7G1if8UHAMSHZo7qDXkqTredLYWvYLb3RUoE\nVmXfRWzImUcAOiwNbCn8O3aXBY+kiLlQ/cRWV7b3qnh6VzoqQeaJVVXoNcr+S9sDiQh0EaZzEWN0\nevss9w7Z3i0JdNnUdFg1tPcJzAH/PtYUQm7j4AiuViXxmzVlhE3AsTT2aH35YgviuliYaCE+2IFH\nghcPJSEIUNCsdPiIDHTSYb3QTMNHFiRXpnZyY1Y7NpfiXbmjOoJog5MvzGxheUo3dd06PiiO5lhT\nEB65fz8qQWJlahc3ZbcTolOmR+0ukU9PRrD5ZMSE2PFMFpIs8K/8WFp7tXxxVjNfmNlKeKCbdcdj\nMTnU3L94bPZFfahU4JGUIpvTYdR6eGh5Lf+zLQ2za7jvyeSIyHcK4pgb14N+wFsuTerhgyIH7xXG\nMC/OTKjOzXWZHXxY0u8C4ZJUFLQYWZhgZmmyiYouPXU9+kEPBl/9116OPnrThI73csAvIP34ucRo\nM9dS06H0e52XvGaQD+SRhmBK2o385ba59NhdPLP1KE+uUqYxRUGFJHuIMCYwK/HKYfftdNvZUbwO\nj+QiUBvCyqlfGrNPo0dys634n3T2NvmW5WR+geSI6Wd8rI3d5Wwrfh23x4nDAypAr5nYpPh9tUG8\nlpfEnFgzX53bwMH6UN7x9uT93e40AERkQvUuog1OogwuIg1OIgNdRAY6iTS4CAlwE2VwEWVwAUMN\njCUZum1qOqxaJYpp05AWZp0Q8eiW4G+5ibgkkTCdi9mxvSz25rq+VxRDWYeBIK0Lu0eFgMzCBBOb\nysdnwzS5jCxGZseYuSGrmT/uTaaozcisGGVaNz28l301ofxm5xSazAGDthGQWZxo4tZprT4/UJdH\nYEdVGB+XKqkdFwcCWysi6LBq+PbCOq6a0kmozsVLhxL5/W4NDy+vHlcOrkpUpv3FMfycIwNdPJRT\ny9M7U3FJw73JxIvIHoeadwviuGde//VDEODRnBoe35LFtspw1mZ2sDaznZ3VYYOizK/mJjArppQA\ntUxqqJ2SNiftVi2iICHJIvlN3fTYnQTrJrei/lLjYvml+PHjZwwo3VwUP8XQwBjSYxaQW7UBu8uC\nWxJ5pyCGOfFhfPuKDG782zZunNrkrVgVkGQPoqAiJ/NORGHoTUGWZfaWr6fH3o4giKzKvgudZmy5\nUJIssav0LZpNlb5li9JuJCNmwRkfa2XbcfaUvYMke+h1igRqpAnNJ5QkePlIPEcaQrhzVjML4038\n9UgixW1BQ9dFoNOmpdOmpWSYYlWVIBGmcxNtdBBtdBHlFZaRgYrQNGg9hAe6CQ90kzlxhwDAx6VR\n1HQrOZrfW9JvRXOkIYhPyyMA2edhuDSpm03lUSPt6jwwsgiJM9pptmj4+baprEjp4u65zbgl2HIy\nkv87mITTc6oSkpkZY+Hz01t9n4EkK1XpHxZHT7odz2RxtCmY3+9J4wdXKL2zf7yimj/tS+Z/dqTz\nkxUV6MdxWOI4RGRKqJ3vX1HHc/sUz9ChTLyI3FUdxvLkTtIj+guloo0uFiV083FpFMtTujFqPdw2\nvZW/D/AvdXhU5DcbWJRoYXlKNyc79LRbtcgDItLffmc/b98z/IOzn+HxC0g/fi4h6rtKfIbeC1Kv\np8fWTlHTPgA2lEbQYdXy/r2LefNYNbWdFXxlZd9UshK1m5eyhjBDzLD7LmrcQ01HAaCIv+jg5DGN\nSZZlDlZ86NsWYHbiamYkrDiTQwSgsGEPh6sUM3STTUWwzjOh4rHHLvLUzinIssDjV1bT61Txs20Z\nWF3KJTMl1EYVsDjBRKddTZdNQ7dNjWeEqmWPLNJu09Ju01LUNvTvGlEiVOciyuAkPsjBLdNaCdSe\nfSS1slPPJ17D5RuzWokPdqAWocms5R958YBAoMaN1aVGQCZA7eHCyX0cTXzIeCRYm9XJogQTJ1qM\n/O1IApVd+mG3SQ+38vkZLUyN7I/+HmsK4v3CaBrM586OZ7Ko6grkNzvT+dHSGjIibDy5qoo/7k3h\n59syefzKk4Tpx/5dGo+InBnTy73zGweJtcFM7HdJRuCfxxL52eqKQdPt31zQyPc/zuajkii+PLuZ\nZcndbK0IH1Tc9o+jCcyOLSNALZMWZuNESxC9LjV937N/n6hDkmREv9/XmPELSD9+LhEk2ePr5hIT\nnEZCaBZbi/6OLEt02TRsLI/k7gVTmBodzJ3/2MYPlypT14IgIssSkUFJI4q6lp5qjnj3nRo5m2lx\ny8Y8ruN1n/kKegAyYxYzL+XaMzpGWZbJrd5EQYNiXN5tUxOic0+oeDzWZOCFQ8nMirFw1+xGNpVH\nsbVS8UZUixJfmd3EytRuvgHeLh392N0CvU4VJrsyHd3aq6XZoqXDqqXLpqbbrhkmMgYuSaTNGkCb\nNYD0COuEiEeHW+BvuQlIskB8kJ1bp7Uhisry/zuYiNXtFY0qCasLFib0+Dwgzz8ji0cBiW8saGBh\nfA8byyJ5YkvmiNPO8UF2bp/eyrz4foubsvZA3iuM4WTnmXuNXoi0W7U8vWsK31tSx7SoXp5cVcXz\n+5L5+bYsfrKigvjgsfe1Ho+IzEnpptOm4cPikdIeJjYKWd+jY0tFONdl9leca1Qy98xt4B9HE7hq\nSicxRid3zmzhD3v7o6M2t5rjTUYWJ5nJSTVR1m4gtykEUVAi0R5J5nfbCvjJNbMmbKyXOn4B6cfP\nJcLJljy6rUpf2IVpN1DfVeLzQ3zrRAwB6gCe+dw8HvnPEVak1BFrVIx1ZVlCFNTkZN4x7NS1zWlh\nZ8m/kGWJYH0UyzM+jzBGxVbSdIBjtVt9r1MiZrI049Yxbz8QSfKw9+R7PiuiHrtqQsWjJMPrR2PY\nUxPOnbNamB5l4Q/7UmnyRqjig2w8tKyW8MCRb8Q6tYxO7SYi0M2UcPuw67g8AlaXiNmhptuupt2q\npdmspdEcgCjI3Jw9mmHz2FlfGEOLRdnnIznVPjHw2tEEGr3HlBlhpaxDSUNQixdK9HFkwSEicefM\nZgwaDw9+koVDGr7YJ0Lv5OYL0MtxsrG6VDy3N5mvzWtieUo3j62s5oVDifx6RyYPLqtiauTw38nh\nEEXlNzGWgNxNU9votGoGWej0M/FT2R8Wx3BFomlQsdyy5B4+LI5hfWEMDyypY3p0L7NiLJxo6U85\neeN4PPPiy9CpJTIireS3BuHyiL7xPeMXkOPCLyD9+LkEcHucHPO2LEyNnEWYIZZdpW8CUNIWyJGG\nYJ69eTYnmrrZU1HET1d1DNp+fsq1w/av7stdtDp7UIsaVmffjUYdMGS94ahuz+eAt1obIC4kfVxF\nNwNxeRzsKFlHQ1cZAFanQLBu4iqtrU54etcUHG4VP1lZTV2Pjl9uT8cjiwjI3Di1jVumtY25slWW\nR7bf0ahkQlQeQnQeEkMcDFe1fbYUtBjY5o2afnthPaHez2prRTiH6oPpu5l32hQBNje2h/11w938\nzwcjT1sHaj2caA2isMDAcI3UjFo3N05tZ3Vav5djm9fL8eAF4uU42XhkkVfz4mnt1XDb9DZ+cEUt\n/zwazx/2TOHbC+tYlDg+w/GxiEhBgLvnNtJpU1PYOjRHeKJFpNMj8s9jCfxwae2gMTyaU83jWzIp\naw8kK9LKHTNbKGw1InlzHXtdanIbg7giqYcVKd0UthooaAn2HafZ4WZLaSNrpl54HZguRC4sgys/\nfvycEUWNe7E6exAEkfkpaylq2IPZ3okkw5v5cUyPCeXexRl8/7393DtfsfgQvBfzqKBkpifkDLvf\nY7VbaTKdBGBpxu0j5keeSmN3OTtL3vK9DjckcNX0e8bVqaYPu8vCpyde9olHp1uYkCnePsrb9Ty6\nKZsYo4tHllfxSVkkrx+LxyOLhOpc/Gx1BbdNH7t4hLF7N05GF7Vep+jLSZsfZ2JRglJxfbJDzzsn\nYny38JRQG+3ewhFRmHjblTNjtA9EwC2JXoEy+NYVoPLwuamtPHNtOddmdKBRyfQ4VKw7HstPt2Zw\noC70shCP/Qh8XBrNy0eU78G98xu5cWobLx5O4tOy8T0o9Imr06EW4XtL6kkKto04polsd3i8OYij\njYOL+KKNLhbGm3wuCQnBDlakdA1a5838WFweAb1GIjvSioDsFZjK2O5798CEjfFSxy8g/fi5yLG7\nejlRvwOAqbFLUIka8uu2A7CjKoz6Hh1/um0RT289wbTIal+rNxkZlagmJ+uOYaOC9Z2l5Ndt8+03\nPXremMbTbq7ns6J/+jwlg/WRXDvzG2hUY4tcDsRs72TD8Rdp93pYeiTQqifmJiTLsL4git/tTuG2\n6a1cPaWDp3elc7xZMfxeltTFM9eWkxzqOM2ezpzJ6ELTZ5Rt0Lj5zqIGBEExVn/hUBIeWfQJKZtL\nOefToszkNQ01OT/3nF7EDuxjDEp1+1VTOnj62nJum96GXiNhd4l8UBTF45sz2VYZccEZgZ9LDtSF\n8oc9qVhdIrdMa+Pr8xp5tzCON/Ojx/XwMlYRqVNLPLS8ljCdc4Q1JlZErstPwHlKRsm3FjZS263j\nQJ3ScvGWaa3o1P2zFRanhtxGJUq6MrWbzIhe38gAarp6qeua+FmBS5HL95flx88lwvG6bbg8DtQq\nLXOSria3eiNuyUmvU8UHxdHcMSeFMH0Abx09yk2n5NfNT7mOEP1Q2xaLvYvdZW8DEGFMYNGUz41p\nLCZbG5sLX8EjKf56em0Qa2d+e8x2PwPptDSy4fgL9Njb8UiK4BuL0fFYcLrhqZ1pHKoP5b9W1NBj\nV/Ps3lTMTjV6tYeHllXzzYWNvmnQi4VD9cEcrA8FZH6ysgqNSkaS4aXDiV5fPOV4Yo0OWnsVQS/I\ncP6jj2ONgCrrCcgsSezmqTUnuWtOMyE6Dy6PwOaT4Ty2OZOPSqMvaCPwc0lZh4Gndk6h1aJmRWo3\nP1xay86qCF48lDAmUdiHKIwtYh6ic/NITg0BqpFyhSfuu9Zl0/B+0eBZEaWgppH3i6JxeQRCdB6u\nzxx83XvnRAxuCQxaDzNjlI5YAyPUd6/bPWFjvJTxC0g/fi5izPYOSpuUKZdZCVfSY2+nsu0YAP8u\nikZGKZy5/9193DO3AbXYf6OODk5levzQamqP5GZHyTocbitatZ5V2XehFk/fmcTq6OHTEy/jdCtT\nWFq1nutmfQdDQMi4j6upu4KNJ17C5jLjdDOhPaPrurU8simbUJ2b7y2pZd3xODaURwEC2ZEWfndd\nGTNjLr4IRJdNzevHlB7dX5jRQnywEgXKawyitN3oXUv5EPuEQEZ4L0W+v50vTi8eA9Vu+sTvrBgz\nP7+qgu8saiDK4EKSYW9NKE9uzeDtE3EXkRH4uaPFEsBTO9Op6NQxO9bCT1ZWUdpu5Nk9KbjHkUos\njFFExgU5eWhZHaJ3FmIoE/dg9lllBA2mwWaXy1NMyJKS8wtwbWYHYfr+1qYmh5YjDUrUfWVqN8kh\nffZOyrj2VrXhcI29av1yxS8g/fi5iMmr2Ywke9BrgsiOX86hio8AqDMFsLM6jJ9eM5sPTtQRrisn\nPbwvN0lGJWrIyfwCwjBT14erPvZNGa/I+iJButP3iHW4rXxa8DJWp5JvpxI1rJ35rWGjm6ejuj2f\nLYWv4vI4cLiUnrcTIR5lGTaWhfGr7VO4ObuV+XE9PLNrircvrsTX5zXwaE6Nrx/yxYQsw2t58Vhd\narIiLKzJUCxOJBlfHlzfzTFM56LFG32U5Qsh93G095eZHmnG6laRHm7jsRXVPLis1peGcbQpiF98\nls6reQkXrRH4ucLiVPP73WkcaQgmNczOk6sq6bJq+PWOdOzjaBs/VhGZGWn12lwNt/LETWVLssDf\njw6OpgoC/HhFNZ+URmF2qNCqZG6b3jJou/WFMXgkCArwDLB58j5gAQ99eGRCxncp4xeQfvxcpLRb\n6qlqOw7A3JRrqGnPp6O3AYB/HY8jIzKE22Yl8addB7l1WuugbRemXkewPnLIPitbj1HijWjOTlxN\nUnj2acfh9jjZUvh3TDbFIVtA5JrpXyfCOJK58MgUN+5jR8mbSLIHh1tAq54Y8eiW4Nm9yXxWGcnD\ny2s52annlTylvV9CkI1nri1nRWr3pOQkngt2VIVR0BpEkFbJe1QizXC4Pgj3Ka3mtGpFIKeEWqno\nGn9qwblDRi246Xao+f4VdTxxZRVZXiPwsvZAnt6Zxv8eSL4kjMDPFS5J5MVDiWwsiyDK4OKJVVVo\nVRI/25aJaewOP2MWkQsTzHxpVvNIe2GiRGRVVyA7qgbPdEQbXUyLsvBhsfIQuzzZRHJIf4FPl03L\nkYb+XMhoQ98HoIzp74cqvA9YfkbCLyD9+LkIkWWZXG/LwhB9FCkRM8itVvpdH6oPpqzDwHO3LOTB\nDw/zxZl1BKj7I00xwWlkxy0dss9uawv7Tr4HQGzIFOamXHPacUiyh+0lb9BurvMtWz3tLuJC08d9\nPHnVn3Kw8j+AjNMNAWp5QgRdm0XNoxunEqCS+frcBl4+ksiRhlAEZG7ObuUXV1cSpr94p6taLFre\nKYhFFGTuX1w36FjezO+zI1HOf6DGQ4tFiT62WTSc3+jjyDdnteDhu4tquWdeC7+8upJ5cUqEqM4U\nwPP7kvnt7tRLzgj8XCEjsL4wln8ejSNQ4+HRnGqSQ+z87LOpNJvHnjc6VhG5JqOTNekjeZtOnIh8\nvygOi3OwpPnOogb21oTRZFai01+c1Tzo/d4vikGSIVTn9vWH78PpkXjtUMWEjO1SxS8g/fi5CGno\nLqPJpFzc5qdex/G67TjcvTg9Au8WxHDLzCS67U7s9kKyo/rze9SilpysoVPXLo+D7cVv4JZc6LVB\nXDn1y8Oaig9ElmX2lr3ns9cBWJ75BZIjZozrWCTZw97y9eTXK5Xjbg9oJyCNTZZhZ1UIT27J4Pqs\nNlJDbDy/PxWTQ0OYzsUvrqoYl7fjhYhHghcPJeL0iNw+vWXAuYZ6kxazc/A5NGoVcakVPVjdp89r\nnTxGnjoPVLv579WVzInrZXmKCVFQvBxfPpLAL7ele42hL+KTdoGwszqcPx9IRpbhe0vqWJTQwy+3\nZ1HRMfZUgLGKyDtntbAg3jTSXsb8fqNhc6lYd2ywf6NGJXPXnEbe9dr6ZEdZmRNr8f293RpA7oAo\nZEiAi4Gi9rGP8yZkbJcqfgHpx89FhiRLvuhjdFAKQboIShr3A7ChNBKrS8/Prp3NLzbu446Zg/N+\nFqZdT5BucLs6WZbZV/4+JlsbAiKrpn4FvXY4M+DB5FZvpKKt/wK7MPUGMmMWjutY3B4n24pe52Rr\nrvfYQD0BxbMeCf7fwUQ+Ko3me0vqOdIQwoelMcgI5CR38czaMq+J98XN68diqTXpmRvXw/VZg83h\nXz6SSP/NWUAjSr7Ka6d0PiuURxaPc2N7eO7GUhJDnIO8HJ/ccjl6OU4+BS1BPL1rCia7irvmNHFT\ndhu/3Z1BXsPYC6vGIiJFAb69sIEpYZNbnHaoIZiSVv2gZctTTNR0B1DSpkSs75jZjEroH/C/ixVL\no4hAF0uTuwdt22G1c7R+8O/KTz9+AenHz0VGZetRuqxKXtGC1Os5XPkxMhLtVg2byiP5ydUz+d89\nxdyQVYV+QEFIXEg6U2OXDNlfSdMBqtqPe/d3HTEhaacdQ0H9Lgoadvlez0y4kpmJK8d1HHZXL58W\n/I36rhJAuQlNRDSwxy7y2KeZSLLAbdNaeelIIpVdgejVHh5eXs29CxpRX+RXPlmGt/Kj2V0TTrTB\nwX2LBvfktrkE6nv6cgOVm2Vf9HHgsvPD8Cf5hqw2frC0DrWoeFQO9HL0yBf5CbuAqe/R8dSOdOpM\nAdyQ1c698xt44VAiWyvGbjg+FhGpUcn8aFkd0YGT+eAm8I9jCXgG1MEJAjy2ooZ3CmIBpUJ8ZWq/\nuXiLRUee1xdyVVoXevXAlp4C96zbO4njvbjx/yr9+LmIcEsujnpbFiZHzMDusvg6xbxzIpbE0GAW\nJUdQ3JTH7AFTNWqVlmWZnx8ydd1mruVw1ce+/c1IWHHaMZxsyeVI9Qbf68zohSxIvW5cx2Gxd7Eh\n/0XazEorstFa/42H3AYj/7Upi6vTOzFo3Lyal4jTo2J6lJnfX1fGjOiLz57nVGQZXjqUwI6qCLQq\nmR8urUV7il+l0omm/yYoINNl75uavBAqrwezIqWT26e3er0cI/iJ38vxnNJl1/Db3WnkNxtYmmzi\noeV1vF8YwzsnosZsOD4WEWnUengkpwajZqSy77N/sGntDeA/JYPdH6KNLsL1TvbV9puL6weYi/cV\n2kQZXAM61yhjKWo10W29+GcrJgO/gPTj5yKipHE/vY5uBETmJl3N4apPAChuNZDbGMQzn5vH4x/v\n5cuzB1c+Lkq7cYgdj93Vy46SdUiyhyBdBDmZdyCcRsXVdSV0B+cAACAASURBVJawp3y973VS+HSW\nZt5+2u0G0tXbzIb8F+ixtfkMws9WPMoyvHIkjjdPxHHP/Ca2V4azry4ctSjxjfn1PLy8dlA09mLF\nLcEvtk+hyaLDJQl8dW4jcUGDu35IMhxt7OssIw/47+B/XSjMiDJz15wmDtYF8+SWDN4+Eev3cjwP\n2N0q/nIghe2VYUyP7uXxK6vYXxfGXw/Hj9lwfCwiMtLg4uGcWlTCcAaUE1NUs6k8kvbewd+h+xY1\n8GFxNE6PQFCAhxum9hf2NJj1HGtSpu2vTOtCLUgMfMj6+lv7znpMlyJ+AenHz0WCw2X1tRbMjF1E\nXWcxFkcXHknp73r9tESONnSxLPEkRm3/xTk+NJOsmMWD9iXLErvL3qbXYUIlqlmdfRda9eh2KK09\nNWwvfp2+C3xMUBqrsr8ybBvEkWg2VbIh/0Wszh480sQYhFud8MSWDCxONStTung1N54Om5aEIBu/\nvbac5Smmi9aeZyAWh8B/bcpkVoyF+h4dV6Z2sSx5aGHChtIIpAHRx4H/N2jcXEjRx6RgG2sy2/nj\n3iRezk2iw+b3cjyfSLLAG8fjeOdEDEkhDn66qoo6k55n96QMmhYejbGIyJRQOz9cWsdkeUS6JZFX\n8xIHLdOoZD6X1crmk0oO+Jr0DiL0/Q9ffVHI2CAnK9IGRyE/KapHGk/bnssEv4D04+ciIb9+B06P\nHbWoIStmEfne/tfbq8Jpswby3WVZbC3Zz6IBdhQaVQDLMj4/JEKYX7fdVz19RfqthBsHVy+eSldv\nC5sLXkGSFWEaFhjLNTO+jkoce6Sopr2AzQWv4vLY8UhKW8KzFXYlrToe2ZjNsqRu7G6RD0tiAIFb\np7Xwi6srCb2I7XkGUtUVwG92pvHdJfUcrAshLczKV+Y0DbvuhrK+6bvBN7wgrYte1/mI7A1/4zVq\nXUQZnLx5PJayjguhF7cfBYFPT0byfwcTCQ5w8fiVVciywK+2pw/pOz3iHsYgImfG9PL1eY0jjuFs\nKW03cKB28PcqJ9XE3poQTHYVGpXM7TP6/XFrTYHkNyu+qKvTuhAGpHpIMvxq8/GzHtOlhl9A+vFz\nEWCxd1PcqEyjzEhYSWHDbjySC4tDxYfF0Ty8ahq/35bHl2cPviAvSvscRl3ooGWNXeUcrd0KQGbM\nwtNWTlvsXXxa8FfckvK0bgwIZ+2sb6NRB4x5/CVNB9hesg5JdiNJZ9/TWpbhzfxo/paXxG3TW/n0\nZCRlHUbCdE5+dfVJbspuv6jtefqQZdhRFcJHJdH8dFU1u6rCcXpEvre4bthCoMIWAw5PX95g3weg\n3MndksC5jz6OlG8pISLTYVXT0qsf5u9+zje5jSH8bncakgwPL68h1ujgp59l0jvGdMCxiMgVqd3c\nnN06+kpnwZsn4rC7+79/ggAPLqvlw+JoAK5IMpEa2m8u3pc7mRDsYPkpuZB/3FE0aeO8WPELSD9+\nLgKO1m5Gkt3oNEaigpJ9VdPvF0UTYQgiIjCAjLAiQnX9IYKEsKwh4rDXYWJn6VuATJghjiVTbhn1\nfe0uCxtP/BW7Syk+0WmMXD/7PnSasXUwkWWZozWbOVDxASArldZnedVxuuEX26bQYglgepSFtwvi\nsLlVrEjp5Jm15b4e0Bc7SnvCOCxODd+/oo7iNgP760L4zqJ6wgOHDwW9mneKD56o5HIZNG5s56Ug\nZTjxKKNVycQZHdSYLuROOH4quwL5nx3pdFjV3Le4ngXxPTyxdSodvWN7EBmLiLw5u43lyV2jr3SG\nWJxq3syPG7Qsxuiix66isUd5AB5oLl7VZaCwRbH7uWpKFwMfgHpdHj4pGux2cLnjF5B+/FzgdFoa\nqWg9CsDsxKvIrdkEQG23jl3VYTx+zUzezttDTkq/h5lGpRsyde2R3OwoWYfD3YtGFcDq7LtQq0Y2\nk3Z5HHx64m/0OpSLu0bUcf2s+zAEhIy4zUAk2cP+k//muDdvcyKKZWq7NTy8IZtZMRZazFr21oaj\n13j4cU4VX5/fdNHb8/ThkeDZPUksSuzhc1PbMTvU/PNYPLdMax2xkryoJZBu++Dz6fHevF2e8xF9\nHB5RkIkPslHaOXavQT/nj3arlt/snEJZu54vzmrhc1Pb+OlnU6npGpsR/elEpCDAPfMamRZlHmGN\ns8s93FsTSnXX4NmS+xY3sL5QiUJmRVp9nY4APipVopApoXaWJPblGCtj+O76A2c1lkuNS+Ry68fP\npcuR6k2ATJAuAkGArl4l921dfiyrM2LZUlLLnTMHPxkvmXLTEKGXW73RZ5uTk3XnsL2w+/BIbrYW\nvubzmxQFFWtnf4uQwKgRtxmI2+Nie/EblLUcAs5ePMoyfFQUwfP7UliZ2smn5RG0WnXMiDbz7HWl\ngzqwXOzYXPDc3hTuXdDMzJheZBn+cTSeKeE2bsoeviVcSVsgf9yfwkCRqFN7kGQRndqDU7pQLvUy\nkixQ3W3kQhG0fk5Pr0vNc/tS2V8bzJqMTr65oJFndqVzvGls6QenE5FqEb5/RT0JQbZh/np2RTUy\nAq/kJiINKALSqGTmxPZQ1KpEwO+Y2YJKUFYo7zBS6jUdvya9g4FRyAaTleqOkYTu5cdZX1VeeeUV\nZs6cyfz585k3bx7z588nNzeXnp4eHnjgARYuXMhVV13F+vX91h9Op5MnnniCJUuWkJOTw4svvni2\nw/Dj55Kksfskjd1KscvspFUc8+YuHqgLobrLyE0zktCLR4kI7PdVSwzLJj16/qD9VLfnU9SoGOLO\nSFhJyijtBmVZYmfpW7T0VAEgILJmxjeINCaOuM1AHC4rmwv+Rl1nsXd/Zyce3RI8vSuF8i4DcUFO\nPq2IQhThWwvqeWhZLTr1pVMd2dSjYX1BDD9aVkuk95zurgmloSeAby0YfvrsQF0Iz+5JQfaZbSuf\nhxJ1BPc5jz6Odj4unEion/HhlkT+lpvIf4qjWJjQw0PLa/nrkSS2V45tRuJ0IlKnlngkp5aQgOFS\nUM7uO9No1rGpfLCN2cpUE5+URiDJEGN0siqtfxq9Lwo5Jdw+KDoJAl9+Y89ZjeVS4qwFZHFxMY8+\n+ih5eXkcPXqUvLw8FixYwE9/+lOMRiP79+/n+eef5/e//z35+fkAPPfcczQ3N7Nt2zbWrVvHu+++\ny6ZNm876YPz4uZSQB7QsjDQm0WFpxOG24nALrC+I4f6lWbyVe4Cr0zt922hVOpZlDPZlNFnbfN6N\nMcGpLEhZO8p7yhyo+JDajgLfslXT7iIuNH1MY+51dLPxxIu0mmu8+zs78dhmUfHIhqkkhzio7tJT\n0h5EcoiN360tY2nypWHPA8rndLjOSEWXga/Oa0HjNQZv69XwXmE0Dyypw6CVBt2AZRk+KY3k5SOJ\nyIMu5UrbQo8sohU9uOVzLR4vkZPiZxgEPiyJ5pXcBNLDrTx5ZTWflEWzvnBshuOnE5EhOjc/XlGD\nRph494SPSmPocfTnAStT503sq1WKDG+e1oZeo7hMFLcZONmhRFevzWhH+V4rAz9U2459rOXolzgT\nIiCnTp06aJnVauWzzz7jhz/8IRqNhtmzZ3PTTTfxwQcfAPDRRx9x//33YzAYSElJ4e677+bf//73\n2Q7Fj59Liqr2fDp6GwCYGreE0iYl/+aT0ih02mBsTgc3Z1cN2mZJ+i0EBvRbV7g8TraXvIHb40Sn\nMXLl1K8giiMXUxyv/YzS5oO+18syPj9qtHIgXb0tfHL8Bbqtrb6bxJkKPFmG7ZXBPL0rjenRZrZX\nhWN1qbh9ejM/W11JiG44E+KLE1mG/5SEEx3kHpTHKsnwyv9n7zzj46jOvn3NFu1Kq957b+4Nd2Mb\nA8bGxoUSOnkSeBIIJZAEeAkhIRAIEEweQq9JgIQEAgFX3DvuTbZ6772tpO07834YaWVZxbJlW8Vz\nfdFvZmfnnNnVnvnPfc79v4+Ec8vYGqJ9LV3EuFOET4+H8XVGSMdZuvzt+PztlzzzWhGPlwPfl/jy\n2t4Y/Nzt/GZ+IWlVXnxwOPyCiMgwLxu/nFMKXFjjf5tTxcdHIrrsC/G0k1XrjtUh4OnmZGlKbUcv\nXVHI5EAzY4JbOf1/+8GvD17Qvg1XBiQgLRYLRUVFfPLJJ8yZM4clS5bw1VdfUVxcjFarJSKi88uK\ni4ujoKAAo9FIXV0dCQkJ3V5TUFCQcYoOjhZtBCDCN4X8mqNISNS2admYF8C90xNpMh0g1MvmGoyj\n/EcTHzTRdQ5Jktif91+aTNUICMxLub2LuDyT7Mr9HC/d4tqeErOY5NCp/epvtbGIDWnvYLI1Dzjq\nKIrw5++jOFTmi7tW4mC5H37udp6/Jo8lKfUjJuoI8rV+cTKIqxOaifG1dHltY24AYV42l6jsuG6L\nQ8Ub+6PZWSRPyUX5mDjdMFwlSDgkFWpBRFIEncJFIqvOkxd2JuAU4am5RTRbtby6J7rLWsPeOJuI\nTAo08cC0ci505aST1V4ur8cOfjip0mUufnV8A4Ee8hT6qWpPihrl4gqLkjrWHsv9+exIAVJ/azyO\nYAYkIOvq6pg8eTJ33HEHO3bs4Pe//z0vvfQS27dvR6frmvWk1+uxWCyYzWbX9umvdexXUFCQfRNb\nrY0ICIT7JlLVLD9g/ftkKDNiQtidl851ifKgJgig03gwM3Fll6nrnOqD5NfK2duTYhb2OQ1dVHuS\nffnfuLZHR1zJuKh5/eprSX06m059iM1pGbB4NJrhlxuSMGhFcusNVLXqmRdbz8sLc7uV7Bvu2Byw\nKd+PW8bVdqkcBFDarONYpRd3nFGSstmi4ZXdsZys9gLkahoNptNrXHf+lTOwFQGpcPGobNHxhx0J\nVLe68disYvzcHTy3Iw5HPyYIziYir4gwcuvYql5ePX/x9vdjEV36p1WDr95Gs0WDVi1x05jqjh6y\ntj0KOTrYRJJ/ZxTSIUq8szf7vPswUhiQgIyMjOTTTz/lyiuvRKPRcMUVV7B8+XIOHz6M1drVb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XuWdSJXNimvhsvyc7q6Jd7xyqxPia+eWsQtbmBBHsYeGqhN6TZHpymUir9OT1/dGc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\nglAj/x+VTai2p4E1MwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Instantiate the visualizer\n", "visualizer = visualizer = ParallelCoordinates(classes=classes, features=features)\n", @@ -383,7 +380,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "collapsed": false }, @@ -408,7 +405,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "collapsed": false }, @@ -429,22 +426,11 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "image/png": 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0chfovPr26z0qqVPmhpKzLZcJdAaPusgYZgE1R75g7JSUPoOkrq+ltihJHmMl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\nI9DiEYpqlxHYe74apRXXzd20O2LMWpzT4+egIL1B41hBegOmx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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Instantiate the linear model and visualizer \n", "ridge = Ridge()\n", @@ -466,7 +452,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "collapsed": true }, @@ -487,22 +473,11 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "image/png": 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8pTH0EREFAF4HmNyl9MrdAks9DP/kBk5eK95+pW29ILyQGIyujZUerjTwMPQR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\nmD17NgCgbdu20Gg0yM7ORvPmzQEA6enpCAwMNOm5O3XqBHd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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Instantiate the linear model and visualizer \n", "lasso = Lasso()\n", @@ -530,7 +505,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "collapsed": true }, @@ -557,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "collapsed": true }, @@ -580,22 +555,11 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "image/png": 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JEoUQQgghhAsJEoUQQgghhAsJEoUQQgghhAsJEoUQQgghhAsJEoUQQgghhAsJ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M9//sOnn35qXKezqKgItVotXc1mSCauCPEnebhred++fQwZMoTw8HAANm3ahIODA/b29gwbNoxh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\nAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Instantiate the classification model and visualizer \n", "bayes = GaussianNB()\n", @@ -617,7 +581,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "collapsed": true }, @@ -640,22 +604,11 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "image/png": 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nzhwsWbIEP/30k9jlyIpshmL1PMeOiIjIoRmNRmzfvh2bNm2ydOquXr1a5ALE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\nnbTn/6mhoaEIDg7GnDlzUFZWZuVKSTKCDaqrqxNCQ0OF9PR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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Instantiate the classification model and visualizer \n", "logistic = LogisticRegression()\n", @@ -677,7 +630,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "collapsed": true }, @@ -700,22 +653,11 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "image/png": 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lJiYGn1hB2+Lz+c77ZFejgffWW2/p2LFjWr58uV588UVJUkREhCZMmKCCggLNnz9fGRkZ\niomJ0dixYzV+/HhJ0rhx41RdXa2cnBz5/X6NHj1akyZNkiTdfPPNqqys1JQpU3Ty5EkNGzZMjz32\n2AUdmN1ub5UParvdrrpwDwGo/rF45nWyAFoffo7broiIiPOvB1rZhWyWZWnPnj3q379/qwy8nTt3\nqm7bs0pxOcM9CtqwnZ9XKzLtUaWkpIR7FADN4PV6VVpaqqSkJAKvjWqsh/hVZQAAAIYh8AAAAAxD\n4AEAABiGwAMAADAMgQcAAGAYAg8AAMAwBB4AAIBhCDwAAADDEHgAAACGIfAAAAAMQ+ABAAAYhsAD\nAAAwDIEHAABgGAIPAADAMAQeAACAYQg8AAAAwxB4AAAAhiHwAAAADEPgAQAAGIbAAwAAMAyBBwAA\nYBgCDwAAwDAEHgAAgGEIPAAAAMMQeAAAAIYh8AAAAAxD4AEAABiGwAMAADAMgQcAAGAYAg8AAMAw\nBB4AAIBhCDwAAADDEHgAAACGIfAAAAAMQ+ABAAAYhsADAAAwDIEHAABgGAIPAADAMAQeAACAYQg8\nAAAAwxB4AAAAhiHwAAAADEPgAQAAGIbAAwAAMAyBBwAAYBgCDwAAwDAEHgAAgGGaFHjbtm3TT37y\nE6WlpWnkyJH6/e9/L0mqqanR9OnTlZaWpszMTK1evTp4G5/Pp9mzZys9PV2DBw9WQUFByNfMz8/X\noEGDlJ6ergULFigQCFzEwwIAAGi72jW2Q01NjR588EE99dRTuv3227V371799Kc/Vffu3fXGG2/I\n4XBo8+bN2rdvnyZPnqzevXsrOTlZixcv1uHDh7Vx40ZVVVXp3nvvVUJCgm655RYVFRWppKRExcXF\nkqQpU6Zo5cqVuu+++y75AQMAAJiu0WfwvvrqKw0bNky33367JKlfv35KT0/Xjh07tHHjRs2YMUNR\nUVFKTk5WVlaW1q5dK0l69913lZubK4fDofj4eN1zzz1as2aNJGndunWaOHGinE6nnE6npk6dqrff\nfvsSHiYAAEDb0Wjg9enTR4sWLQp+fuLECW3btk2S1K5dO3Xr1i241qNHD7ndbtXU1Kiqqkoul+us\nNUlyu91KTEwMWauoqGjxwQAAAOAC32TxzTffaNq0aRowYIDS09Nlt9tD1mNiYuT1euXxeIKfn7nW\nsN3j8Zy1VldXJ5/P1+wDAQAAQL1Gr8FrcOjQIU2bNk3x8fFavHixysrKZFlWyD5er1exsbHBeLMs\nSw6HI7jW8HFDCJ55O5vNpujo6CYPbllWq3xjhmVZigr3EIDqH4tn/hwCaD0a/v79x7+H0XY09qRY\nkwKvtLRUkydP1ujRo5WXlydJio+P1+nTp3X48GHFxcVJksrLy+VyudSxY0c5nU653W516tQpZE2S\nXC6XysvLlZycLKn+JdszX85tioMHD17Q/pcLt9ut68I9BKD6x6LNZgv3GABaoKysLNwj4DLVaOBV\nVVVp8uTJuvfee3X//fcHtzscDmVmZio/P1/z5s3TgQMHVFxcrJdfflmSlJ2drWXLlmnJkiU6duyY\nioqKgnGYnZ2tFStWKCMjQzabTYWFhRozZswFDd6rV68LesbvclFbWyvtDvcUgNSzZ08lJSWFewwA\nzWBZlsrKypSYmHjW5VJoG3w+33mf7Go08N566y0dO3ZMy5cv14svvihJioiI0IQJEzR//nw99dRT\nGjp0qBwOh/Ly8jRgwABJ0syZM7Vw4ULdeuutioyM1IQJEzRy5EhJ0rhx41RdXa2cnBz5/X6NHj1a\nkyZNuqADs9vtrfJBbbfbVRfuIQDVPxbPvBYWQOvDz3HbFRERcf71QCu7kM2yLO3Zs0f9+/dvlYG3\nc+dO1W17VikuZ7hHQRu28/NqRaY9qpSUlHCPAqAZvF6vSktLlZSUROC1UY31EL+qDAAAwDAEHgAA\ngGEIPAAAAMMQeAAAAIYh8AAAAAxD4AEAABiGwAMAADAMgQcAAGAYAg8AAMAwBB4AAIBhCDwAAADD\nEHgAAACGIfAAAAAMQ+ABAAAYhsADAAAwDIEHAABgGAIPAADAMAQeAACAYQg8AAAAwxB4AAAAhiHw\nAAAADEPgAQAAGIbAAwAAMAyBBwAAYBgCDwAAwDAEHgAAgGEIPAAAAMMQeAAAAIYh8AAAAAxD4AEA\nABiGwAMAADAMgQcAAGAYAg8AAMAwBB4AAIBhCDwAAADDEHgAAACGIfAAAAAMQ+ABAAAYhsADAAAw\nDIEHAABgGAIPAADAMAQeAACAYQg8AAAAwxB4AAAAhiHwAAAADEPgAQAAGIbAAwAAMMwFBd6uXbt0\n0003BT/fvXu3+vXrp9TUVA0cOFCpqakqLCwMrufn52vQoEFKT0/XggULFAgEgmurVq3SkCFDlJaW\npscff1xer/ciHA4AAACaHHirV6/Wfffdp9OnTwe37d+/X0OGDNGOHTv02WefaceOHZoyZYokqaio\nSCUlJSouLtb69eu1fft2rVy5UpL00Ucf6ZVXXlFRUZE+/vhjHT9+XIsWLbrIhwYAANA2NSnwCgoK\nVFRUpGnTpoVs37t3r/r27XvO26xbt04TJ06U0+mU0+nU1KlTtWbNmuBaTk6Ounfvrg4dOuihhx7S\nO++8E/IMHwAAAJqnSYGXk5OjtWvXqn///iHb9+3bp+3bt2v48OHKzMzUokWL5Pf7JUlut1uJiYnB\nfXv06KHy8vLgmsvlClk7deqUjhw50uIDAgAAaOuaFHidO3c+5/ZOnTopMzNT7733nl599VVt2bJF\nS5culSR5PB7FxMQE942JiVFdXZ18Pp88Ho/at28fXGv42OPxNPtAAAAAUK9dS268fPny4MfXXHON\ncnNztXjxYs2aNUsxMTEhb5zwer2y2WyKjo4+a60h7GJjY5t835ZltcqXdC3LUlS4hwBU/1jkzU1A\n62RZVsifaHt8Pt9515sdeDU1NSooKND06dODYeb1emW32yVJLpdL5eXlSk5OlhT6smzDWgO3260r\nr7xSXbp0afL9Hzx4sLmjh5Xb7dZ14R4CUP1j0WazhXsMAC1QVlYW7hFwmWp24F1xxRX64IMPFAgE\n9Mgjj6iyslIvvfSSxo4dK0nKzs7WihUrlJGRIZvNpsLCQo0ZMya4NnfuXI0cOVJxcXFaunSpsrOz\nL+j+e/Xqpejo6OaOHza1tbXS7nBPAUg9e/ZUUlJSuMcA0AyWZamsrEyJiYnBJ1bQtvh8vvM+2dXs\nwIuIiFBBQYHmz5+vjIwMxcTEaOzYsRo/frwkady4caqurlZOTo78fr9Gjx6tSZMmSZJuvvlmVVZW\nasqUKTp58qSGDRumxx577ILu3263t8oHtd1uV124hwBU/1g88zpZAK0PP8dtV0RExPnXA63sQjbL\nsrRnzx7179+/VQbezp07VbftWaW4nOEeBW3Yzs+rFZn2qFJSUsI9CoBm8Hq9Ki0tVVJSEoHXRjXW\nQ/yqMgAAAMMQeAAAAIYh8AAAAAxD4AEAABiGwAMAADAMgQcAAGAYAg8AAMAwBB4AAIBhCDwAAADD\nEHgAAACGIfAAAAAMQ+ABAAAYhsADAAAwDIEHAABgGAIPAADAMAQeAACAYQg8AAAAwxB4AAAAhiHw\nAAAADEPgAQAAGIbAAwAAMAyBBwAAYBgCDwAAwDAEHgAAgGEIPAAAAMMQeAAAAIYh8AAAAAxD4AEA\nABiGwAMAADAMgQcAAGAYAg8AAMAwBB4AAIBhCDwAAADDEHgAAACGIfAAAAAMQ+ABAAAYhsADAAAw\nDIEHAABgGAIPAADAMAQeAACAYQg8AAAAwxB4AAAAhiHwAAAADEPgAQAAGIbAAwAAMAyBBwAAYBgC\nDwAAwDAXFHi7du3STTfdFPy8pqZG06dPV1pamjIzM7V69ergms/n0+zZs5Wenq7BgweroKAg5Gvl\n5+dr0KBBSk9P14IFCxQIBFp4KAAAAJAuIPBWr16t++67T6dPnw5u+8UvfiGHw6HNmzfr+eef1zPP\nPKNdu3ZJkhYvXqzDhw9r48aNev311/Vf//Vf+sMf/iBJKioqUklJiYqLi7V+/Xpt375dK1euvMiH\nBgAA0DY1KfAKCgpUVFSkadOmBbedOnVKGzZs0IwZMxQVFaXk5GRlZWVp7dq1kqR3331Xubm5cjgc\nio+P1z333KM1a9ZIktatW6eJEyfK6XTK6XRq6tSpevvtty/B4QEAALQ9TQq8nJwcrV27Vv379w9u\nq6ioUFRUlLp16xbc1qNHD7ndbtXU1Kiqqkoul+usNUlyu91KTEwMWauoqGjpsQAAAEBNDLzOnTuf\ntc3j8chut4dsi4mJkdfrlcfjCX5+5lrDdo/Hc9ZaXV2dfD7fhR8BAAAAQrRr7g3bt28vy7JCtnm9\nXsXGxgbjzbIsORyO4FrDxw0heObtbDaboqOjm3z/lmW1yjdmWJalqHAPAaj+sXjmzyGA1qPh799/\n/HsYbUdjT4o1O/Di4+N1+vRpHT58WHFxcZKk8vJyuVwudezYUU6nU263W506dQpZkySXy6Xy8nIl\nJydLqn/J9syXc5vi4MGDzR09rNxut64L9xCA6h+LNpst3GMAaIGysrJwj4DLVLMDz+FwKDMzU/n5\n+Zo3b54OHDig4uJivfzyy5Kk7OxsLVu2TEuWLNGxY8dUVFSkvLy84NqKFSuUkZEhm82mwsJCjRkz\n5oLuv1evXhf0jN/lora2Vtod7ikAqWfPnkpKSgr3GACawbIslZWVKTEx8azLpdA2+Hy+8z7Z1ezA\nk6R58+Zpzpw5Gjp0qBwOh/Ly8jRgwABJ0syZM7Vw4ULdeuutioyM1IQJEzRy5EhJ0rhx41RdXa2c\nnBz5/X6NHj1akyZNuqD7ttvtrfJBbbfbVRfuIQDVPxbPvBYWQOvDz3HbFRERcf71QCu7kM2yLO3Z\ns0f9+/dvlYG3c+dO1W17VikuZ7hHQRu28/NqRaY9qpSUlHCPAqAZvF6vSktLlZSUROC1UY31EL+q\nDAAAwDAEHgAAgGEIPAAAAMO06E0WAAA0h9/v1969e8M9RqtlWZbcbrdqa2tb5fXol4t+/fopKsrM\nf52WwAMAfOf27t2rAwtvUO9OteEepVWyS+orSSVhHqQVO/B3m/Tzrca+2YzAAwCERe9OtUr5PoEH\nXApcgwcAAGAYAg8AAMAwBB4AAIBhCDwAAADDEHgAAACGIfAAAAAMQ+ABAAAYhsADAAAwDIEHAABg\nGAIPAADAMAQeAACAYQg8AAAAwxB4AAAAhiHwAAAADEPgAQAAGIbAAwAAMAyBBwAAYBgCDwAAwDAE\nHgAAgGEIPAAAAMMQeAAAAIYh8AAAAAxD4AEAABiGwAMAADAMgQcAAGAYAg8AAMAwBB4AAIBhCDwA\nAADDEHgAAACGIfAAAAAMQ+ABAAAYhsADAAAwDIEHAABgGAIPAADAMAQeAACAYQg8AAAAwxB4AAAA\nhiHwAAAADEPgAQAAGIbAAwAAMAyBBwAAYJgWB96KFSvUv39/paamauDAgUpNTdX27dtVU1OjBx98\nUGlpacrMzNTq1auDt/H5fJo9e7bS09M1ePBgFRQUtHQMAAAA/J92Lf0C+/bt06OPPqpJkyaFbJ8x\nY4Y6dOigzZs3a9++fZo8ebJ69+6t5ORkLV68WIcPH9bGjRtVVVWle++9VwkJCbrllltaOg4AAECb\n1+Jn8Pbt26frrrsuZNupU6e0YcMGzZgxQ1FRUUpOTlZWVpbWrl0rSXr33XeVm5srh8Oh+Ph43XPP\nPVqzZk1LRwEAAIBaGHher1cVFRV69dVXNXjwYN1+++1666239MUXXygqKkrdunUL7tujRw+53W7V\n1NSoqqpKLpfrrDUAAAC0XIteoq2qqlJqaqrGjRunQYMG6S9/+YumTZumn/70p7Lb7SH7xsTEyOv1\nyuPxBD8/c61hOwAAAFqmRYF3zTXX6LXXXgt+npaWptGjR2vbtm2yLCtkX6/Xq9jY2GDYWZYlh8MR\nXGv4uKksy1IgEGjJ+GFhWZaiwj0EoPrHotfrDfcYaKMsy5K98d2AS6o1nwd9Pt9511sUeHv37tV/\n//d/a8qUKcFtlmWpa9eu2rp1qw4fPqy4uDhJUnl5uVwulzp27Cin0ym3261OnTqFrF2IgwcPtmT0\nsHG73bqLbly4AAAM5klEQVSu8d2AS87tdstms4V7DLRRbrdbfcM9BNo8k8+DLQq82NhYvfjii0pI\nSNCIESP06aefav369SoqKlJNTY3y8/M1b948HThwQMXFxXr55ZclSdnZ2Vq2bJmWLFmiY8eOqaio\nSHl5eRd037169VJ0dHRLxg+L2tpaaXe4pwCknj17KikpKdxjoI2qra2VSsI9Bdq61nwe9Pl8532y\nq0WBl5CQoCVLlui5555TXl6e4uLi9Otf/1p9+/bVvHnzNGfOHA0dOlQOh0N5eXkaMGCAJGnmzJla\nuHChbr31VkVGRmrChAkaOXLkBd233W4/6zq/1sBut6su3EMAqn8snnktLPBdao3nb5inNZ8HIyIi\nzrve4n8Hb9iwYRo2bNhZ2zt27Kjnn3/+nLex2+2aO3eu5s6d29K7BwAAwD/gV5UBAAAYhsADAAAw\nDIEHAABgGAIPAADAMAQeAACAYQg8AAAAwxB4AAAAhiHwAAAADEPgAQAAGIbAAwAAMAyBBwAAYBgC\nDwAAwDAEHgAAgGEIPAAAAMMQeAAAAIYh8AAAAAxD4AEAABiGwAMAADAMgQcAAGAYAg8AAMAwBB4A\nAIBhCDwAAADDEHgAAACGIfAAAAAMQ+ABAAAYhsADAAAwDIEHAABgGAIPAADAMAQeAACAYQg8AAAA\nwxB4AAAAhiHwAAAADEPgAQAAGIbAAwAAMAyBBwAAYBgCDwAAwDAEHgAAgGEIPAAAAMMQeAAAAIYh\n8AAAAAxD4AEAABiGwAMAADAMgQcAAGAYAg8AAMAwBB4AAIBhCDwAAADDEHgAAACGCVvg7d27Vz/+\n8Y81cOBA/ehHP9LOnTvDNQoAAIBRwhJ4Pp9P06ZNU05OjrZt26Z77rlHDzzwgDweTzjGAQAAMEpY\nAu/TTz+VzWbTXXfdJZvNpjvvvFOdOnXSpk2bwjEOAACAUcISeG63Wy6XK2Rbjx495Ha7wzEOAACA\nUcISeB6PR+3btw/Z1r59e3m93nCMAwAAYJR24bjTc8Wcx+NRbGxso7cNBAKSpJMnT8qyrEsy36Vk\nWZYOVUuBaH+4R0EbVl4tXWtZqqmpCfcoaKMsy1Ll6W4KnKoL9yhoo8pPR6pbKz4P+v31HdHQRf8o\nLIHXs2dPvf766yHbysvLlZ2d3ehtGw6ooqLiUox2ydlsNiUMn6lz/+cAvhsJ/er/PHjwYHgHQZtl\ns9nUferbqg33IGizuv/fn639POj3+xUTE3PW9rAEXkZGhnw+n15//XXdddddWrt2rf7+979r8ODB\njd7W4XCod+/eioqKUkRExHcwLQAAwOUlEAjI7/fL4XCccz0i8M+e27vEDhw4oKeeekoHDx5UfHy8\n5s6dq+Tk5HCMAgAAYJSwBR4AAAAuDX5VGQAAgGEIPAAAAMMQeAAAAIYh8AAAAAxD4AEAABiGwEOb\nlZqa2qzff7xo0SL9/Oc/vwQTAcDF9dJLLykvL++Cb3fq1Cn16dNHX3311SWYCt+FsPxDx8DlYMeO\nHeEeAQAuqalTpzbrdoFAgF8m0MrxDB6apbKyUn369JHH4wluu/POO7VmzRplZmaqsLBQo0aNUlpa\nmnJzc/XNN99Iqv+/wqefflqDBw/W4MGD9eSTT+rkyZOS6k8oy5Yt05AhQ3TDDTdo+vTpOn78uKT6\nXyUzfvx4paamasSIEXr33XclScuWLdOMGTOCMxw8eFB9+vSRJG3dulV33HGHnn766eDt1q9fH9y3\nT58+KisrkyT99a9/1fjx4/WDH/xA2dnZ2rRpU8ixTpo0SampqRo3bpwOHz58Kb6lAAzzpz/9SXfe\neaeuv/56/ehHPwqeVw4cOHDO89m5zoEnTpyQFHq+kqQZM2Zo2bJlkqTx48frmWee0ahRo3T99ddr\nxowZwd+veuY5sq6uTsuWLVNmZqZuvPFGPfHEE8HzryStWrVKN910kzIyMvQf//Efl/4bhEuKwEOz\nnev/7hq2bdy4UW+++ab++Mc/qqKiQm+88YYk6cknn1RFRYWKi4v1/vvv6+jRo5ozZ44k6c0339S6\ndev02muv6U9/+pNiYmI0f/58+f1+TZ06VT/84Q+1detWPffcc5ozZ47Ky8vPOceZn5eVlclut2vL\nli2aO3eufvazn+nzzz8P2f/bb7/Vfffdp9tvv11bt27Vk08+qccee0xffPGFJOmhhx5SYmKitm7d\nqscee0wff/zxxfkGAjDWwYMH9cADD2jatGn6n//5H82cOVMzZ87U/v37lZube87z2bnOgfPmzWvS\n/b3zzjtatmyZPvnkE/l8Pj399NPBtYZz4sqVK7Vhwwa98cYb+uCDD+T1ejV//nxJ0scff6zCwkKt\nXLlSmzZtCp5f0XoReLioGn4xyl133aXvfe97cjqduummm/TFF1/Isiz98Y9/1OOPP66rrrpKV1xx\nhfLy8vT+++/LsiytX79e48ePV3x8vKKiovSLX/xC06ZN044dO+TxeDRt2jS1a9dOAwYM0O9+9zt9\n//vfb3Qeh8OhWbNmKSoqSjfeeKNuuukmvf/++yH7bNq0SZ07d9bYsWMVERGhH/zgBxo+fLjefvtt\nHTp0SKWlpZo1a5batWungQMH6o477rgk3zsA5li/fr1++MMf6t/+7d8UGRmpoUOHKjMzU0899dQ5\nz2dXX331Pz0HNsX48ePVq1cvxcbGaubMmfrggw/k9/tD9nnrrbf04IMPqkuXLoqNjdWsWbO0bt06\n+Xw+vf/++xo9erR69eolu92uRx999FJ8W/Ad4ho8XBKdOnUKftyuXTvV1dWppqZGp0+f1r/8y78E\n17p166ZAIKC//e1vqqqqUlxcXHDtqquu0lVXXaW//vWvuvrqq0O+fsPLsI2Ji4tTdHR0yOdHjx4N\n2eerr75SWVmZbrjhBkn1kVpbW6tRo0apqqpKsbGxio2NDZm54dk9ADiX6upqdevWLWRb165d9Yc/\n/EEulytke8P57J+dA5uie/fuwY+7dOkiv98ffHm3wddff628vDzZbDZJ9ee66Ohoff3116qqqlLf\nvn1DvkbDfmidCDw0S8MPvt/vV/v27SVJx48fP+9FuZ07d1Z0dLQqKyuDJ61Dhw4pMjJS3/ve99Sl\nSxcdOXIkuP+XX36ptWvXKiMjQ3/7299Cvtbvfvc79e/fX5GRkSH/l3rs2LGQ/aqqqkIuFq6srNTA\ngQND9rn66qs1cOBAvfbaa8FtR44cUUxMjE6ePKlTp07pxIkT6tixY3ANAM6na9eu+stf/hKy7csv\nv9QNN9yg/fv3h2xvOJ/FxcWd8xw4ffr0s851DdcnNzjzHFlZWamYmJiz4vDqq6/W/PnzlZ6eLkmq\nra3VoUOHdO211+r73/++Kisrg/tWV1ertra2mUePywEv0aJZnE6nrrjiCn344YeSpDVr1ujrr78O\nvkR7LhEREcrOztazzz6rY8eO6cSJE3rmmWc0bNgwdejQQVlZWXrttdd06NAhWZalF154Qf/7v/+r\nlJQUdezYUYWFhaqtrdWuXbu0ZMkSdejQQQkJCdq1a5eOHj2qkydPnnVh8IkTJ1RYWKjTp09r06ZN\n2rJli2677baQfYYNGya3263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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Instantiate the classification model and visualizer \n", "forest = RandomForestClassifier()\n", diff --git a/yellowbrick/base.py b/yellowbrick/base.py index 65b3bb9b3..bad97bca0 100644 --- a/yellowbrick/base.py +++ b/yellowbrick/base.py @@ -51,6 +51,7 @@ def __init__(self, ax=None, **kwargs): self.ax = ax self.size = kwargs.pop('size', None) self.color = kwargs.pop('color', None) + self.title = kwargs.pop('title', None) def fit(self, X, y=None, **kwargs): """ @@ -106,6 +107,14 @@ def poof(self, outpath=None, **kwargs): else: plt.show() + def set_title(self, title=None): + """ + Sets the title on the current axes. + """ + title = self.title or title + if title is not None: + self.ax.set_title(title) + def finalize(self, **kwargs): """ Finalize executes any subclass-specific axes finalization steps. diff --git a/yellowbrick/classifier.py b/yellowbrick/classifier.py index ca7e9e1e3..65fb53fcf 100644 --- a/yellowbrick/classifier.py +++ b/yellowbrick/classifier.py @@ -165,23 +165,6 @@ def draw(self, y, y_pred): return self.ax - def poof(self): - """ - Plots a classification report as a heatmap. - - Returns - ---------- - - ax : the axis with the plotted figure - - """ - if self.ax is None: return - - self.finalize() - plt.show() - - return self.ax - def finalize(self, **kwargs): """ Finalize executes any subclass-specific axes finalization steps. @@ -192,14 +175,24 @@ def finalize(self, **kwargs): kwargs: generic keyword arguments. """ - plt.title('{} Classification Report'.format(self.name)) + # 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_) - plt.ylabel('Classes') - plt.xlabel('Measures') + + # Set the labels for the two axes + self.ax.set_ylabel('Classes') + self.ax.set_xlabel('Measures') ########################################################################## @@ -313,11 +306,13 @@ def finalize(self, **kwargs): kwargs: generic keyword arguments. """ - plt.title('ROC for {}'.format(self.name)) - plt.legend(loc='lower right') + # Set the title and add the legend + self.set_title('ROC for {}'.format(self.name)) + self.ax.legend(loc='lower right') - plt.xlim([-0.02,1]) - plt.ylim([0,1.1]) + # 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]) ########################################################################## @@ -355,9 +350,6 @@ def __init__(self, model, ax=None, classes=None, **kwargs): """ super(ClassBalance, self).__init__(model, ax=ax, **kwargs) - ## hoisted to Visualizer base class - # self.ax = ax - ## hoisted to ScoreVisualizer base class self.estimator = model self.name = get_model_name(self.estimator) @@ -444,8 +436,14 @@ def finalize(self, **kwargs): 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()) - ceiling = cmax + cmax*0.1 - span = cmax - cmin - plt.ylim(0, ceiling) + self.ax.set_ylim(0, cmax + cmax* 0.1) diff --git a/yellowbrick/features/base.py b/yellowbrick/features/base.py index f284a7329..dadbd6ba8 100644 --- a/yellowbrick/features/base.py +++ b/yellowbrick/features/base.py @@ -41,11 +41,6 @@ class FeatureVisualizer(Visualizer, TransformerMixin): def __init__(self, ax=None, **kwargs): super(FeatureVisualizer, self).__init__(ax=ax, **kwargs) - # The figure params - - ## hoisted to Visualizer base class - self.ax = ax - def fit(self, X, y=None, **fit_params): """ This method performs preliminary computations in order to set up the @@ -133,8 +128,6 @@ def __init__(self, ax=None, features=None, classes=None, color=None, """ super(DataVisualizer, self).__init__(ax=ax, **kwargs) - self.ax = ax - # Data Parameters self.features_ = features self.classes_ = classes diff --git a/yellowbrick/features/pcoords.py b/yellowbrick/features/pcoords.py index c01d319b0..5175e2992 100644 --- a/yellowbrick/features/pcoords.py +++ b/yellowbrick/features/pcoords.py @@ -189,7 +189,7 @@ def draw(self, X, y, **kwargs): for idx in x: self.ax.axvline(idx, **self.vlines_kwds) - # Finalize the plot + # Set the limits self.ax.set_xticks(x) self.ax.set_xticklabels(self.features_) self.ax.set_xlim(x[0], x[-1]) @@ -204,5 +204,11 @@ def finalize(self, **kwargs): kwargs: generic keyword arguments. """ + # Set the title + self.set_title( + 'Parallel Coordinates for {} Features'.format(len(self.features_)) + ) + + # Set the legend and the grid self.ax.legend(loc='best') self.ax.grid() diff --git a/yellowbrick/features/radviz.py b/yellowbrick/features/radviz.py index c8bff60f6..5453b384d 100644 --- a/yellowbrick/features/radviz.py +++ b/yellowbrick/features/radviz.py @@ -221,8 +221,14 @@ def finalize(self, **kwargs): kwargs: generic keyword arguments. """ + # Set the title + self.set_title( + 'RadViz for {} Features'.format(len(self.features_)) + ) + + # Add the legend self.ax.legend(loc='best') - + # Alias for RadViz RadViz = RadialVisualizer diff --git a/yellowbrick/features/rankd.py b/yellowbrick/features/rankd.py index f90ed45f2..314dc479d 100644 --- a/yellowbrick/features/rankd.py +++ b/yellowbrick/features/rankd.py @@ -245,6 +245,7 @@ def draw(self, X, **kwargs): mask = mask[::-1] # Draw the heatmap + # TODO: Move mesh to a property so the colorbar can be finalized data = np.ma.masked_where(mask, X) mesh = self.ax.pcolormesh(data, cmap=self.colormap, vmin=-1, vmax=1) diff --git a/yellowbrick/regressor.py b/yellowbrick/regressor.py index 5f16a4c4d..56045f075 100644 --- a/yellowbrick/regressor.py +++ b/yellowbrick/regressor.py @@ -44,7 +44,7 @@ def __init__(self, model, ax=None, **kwargs): ) super(RegressionScoreVisualizer, self).__init__(model, ax=ax, **kwargs) - # self.ax = ax + ########################################################################## ## Prediction Error Plots @@ -78,17 +78,11 @@ def __init__(self, model, ax=None, **kwargs): super(PredictionError, self).__init__(model, ax=ax, **kwargs) - ## hoisted to Visualizer base class - # self.ax = ax - - ## hoisted to ScoreVisualizer - self.name = get_model_name(self.estimator) - self.colors = { 'point': kwargs.pop('point_color', '#F2BE2C'), 'line': kwargs.pop('line_color', '#2B94E9'), } - # self.fig, self.ax = plt.subplots() + def score(self, X, y=None, **kwargs): """ @@ -158,9 +152,12 @@ def finalize(self, **kwargs): kwargs: generic keyword arguments. """ - self.ax.set_title('Prediction Error for {}'.format(self.name)) + # Set the title on the plot + self.set_title('Prediction Error for {}'.format(self.name)) + + # Set the axes labels self.ax.set_ylabel('Predicted') - plt.xlabel('Measured') + self.ax.set_xlabel('Measured') ########################################################################## @@ -199,12 +196,6 @@ def __init__(self, model, ax=None, **kwargs): super(ResidualsPlot, self).__init__(model, ax=ax, **kwargs) - ## hoisted to Visualizer base class - # self.ax = ax - - ## hoisted to ScoreVisualizer - self.name = get_model_name(self.estimator) - # TODO Is there a better way to differentiate between train and test points? # We'd like to color them differently in draw... # Can the user pass those in as keyword arguments? @@ -303,7 +294,12 @@ def finalize(self, **kwargs): kwargs: generic keyword arguments. """ + # Add the title to the plot + self.set_title('Residuals for {} Model'.format(self.name)) + + # Create horizontal lines self.ax.hlines(y=0, xmin=0, xmax=100) - self.ax.set_title('Residuals for {} Model'.format(self.name)) + + # Set the axes labels self.ax.set_ylabel('Residuals') - plt.xlabel("Predicted Value") + self.ax.set_xlabel("Predicted Value") From 1af97f3d4cf6d9de3aec0093ee4ab6e723fea788 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Sat, 5 Nov 2016 13:13:35 -0400 Subject: [PATCH 18/35] closed #62 --- yellowbrick/base.py | 12 ++++++++---- yellowbrick/classifier.py | 10 ---------- 2 files changed, 8 insertions(+), 14 deletions(-) diff --git a/yellowbrick/base.py b/yellowbrick/base.py index bad97bca0..9eb6c54b4 100644 --- a/yellowbrick/base.py +++ b/yellowbrick/base.py @@ -163,9 +163,9 @@ def __init__(self, model, ax=None, **kwargs): These parameters can be influenced later on in the visualization process, but can and should be set as early as possible. """ - self.estimator = model super(ScoreVisualizer, self).__init__(ax=ax, **kwargs) + self.estimator = model self.name = get_model_name(self.estimator) def fit(self, X, y=None, **kwargs): @@ -222,8 +222,9 @@ def __init__(self, ax=None, **kwargs): These parameters can be influenced later on in the visualization process, but can and should be set as early as possible. """ - self.estimator = model super(ScoreVisualizer, self).__init__(ax=ax, **kwargs) + self.estimator = model + def fit(self, X, y=None, **kwargs): """ @@ -252,7 +253,7 @@ class MultiModelMixin(object): Does predict for each of the models and generates subplots. """ - def __init__(self, models, **kwargs): + def __init__(self, models, ax=None, **kwargs): # Ensure models is a collection, if it's a single estimator then we # wrap it in a list so that the API doesn't break during render. """ @@ -265,7 +266,10 @@ def __init__(self, models, **kwargs): These parameters can be influenced later on in the visualization process, but can and should be set as early as possible. """ - if isestimator(models): + # TODO: How to handle the axes in this mixin? + self.ax = ax + + if all(isestimator, models): models = [models] # Keep track of the models diff --git a/yellowbrick/classifier.py b/yellowbrick/classifier.py index 65fb53fcf..b4096896c 100644 --- a/yellowbrick/classifier.py +++ b/yellowbrick/classifier.py @@ -87,9 +87,6 @@ def __init__(self, model, ax=None, classes=None, **kwargs): """ super(ClassificationReport, self).__init__(model, ax=ax, **kwargs) - ## hoisted to Visualizer base class - # self.ax = ax - ## hoisted to ScoreVisualizer base class self.estimator = model self.name = get_model_name(self.estimator) @@ -228,9 +225,6 @@ def __init__(self, model, ax=None, **kwargs): """ super(ROCAUC, self).__init__(model, ax=ax, **kwargs) - ## hoisted to Visualizer base class - # self.ax = ax - ## hoisted to ScoreVisualizer base class self.name = get_model_name(self.estimator) @@ -350,10 +344,6 @@ def __init__(self, model, ax=None, classes=None, **kwargs): """ super(ClassBalance, self).__init__(model, ax=ax, **kwargs) - ## hoisted to ScoreVisualizer base class - self.estimator = model - self.name = get_model_name(self.estimator) - self.colors = kwargs.pop('colors', YELLOWBRICK_PALETTES['paired']) self.classes_ = classes From a317ceab33821ca02ea1552e9810cb635d5e1d5d Mon Sep 17 00:00:00 2001 From: Ameet Rawtani Date: Sat, 5 Nov 2016 13:26:21 -0400 Subject: [PATCH 19/35] Add legend to Residual Plot Class in regressor.py --- examples/examples.ipynb | 66 ++++++++++++++++++++++++++-------------- yellowbrick/regressor.py | 5 +++ 2 files changed, 49 insertions(+), 22 deletions(-) diff --git a/examples/examples.ipynb b/examples/examples.ipynb index f5d85705a..357bd248c 100644 --- a/examples/examples.ipynb +++ b/examples/examples.ipynb @@ -97,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -159,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -183,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -207,16 +207,16 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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Uq1fPOl31Z6dOnao0sSjP+++/rwkTJqhTp06aM2eOtdwvnfusffbZZ7rrrrvk7e1tbW/d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\nJNHUwuEYaZYjDsfA5UGCAACAAXesILB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k3LoJEgQAAAyYvEwu3UrCbDYrNDTUri0kJERms9muLS0tTdWqVdOoUaMUGRmp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\nZ45Onjypbdu2aeXKlXr//ff16aefFvs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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -234,16 +234,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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xcT5tsbGxcjqdfuvOmzdPF154oXr37m3J/kokCAAA1Esul0sRERE+bRERESot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\nDwkCAADwQ4IAAAD8kCAAAAA//w9u92dlCocT2AAAAABJRU5ErkJggg==\n", 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/MwYAAIaoIAAAYMDuh5MUSRAAADDAEAMAAICoIAAAYMgfKwgkCAAAGOBGSQAA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\nAAAvJAgAAMALCQIAAPBCggAAALz8P8pneRhJHYAzAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -272,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -292,16 +292,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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gx44dEIvFKvtDVqds9feOHTtW2OJHHSQSCR48eIAOHTrUaQFS0l28J6g83hNUHu8JKk/T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fflFd5+jRo3j11VdRWFiI9957D6NGjUJUVBQ+/fRTfPzxx3j48GGFcefm5sLC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\nvfewceNGttgR1QCTOqIKJCU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mJZ6OxSs7Hq8sY0MhXu7k0Oi2g2vVqhUSExPRunVrXYdC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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -330,7 +330,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "collapsed": true }, @@ -350,11 +350,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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/+99cvHjR5viMjAyGDBlC165d2b9/PxMmTOC9997j8uXLAPzrX/+iXr167N+/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\nyE9bxl1F32LllIdJi56MgIDdZaGs+QBvnXiG7aV/oq7rHKJ4+Wknr40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3ywLVyAXpfRxv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ovO4zW5KqONUL8z/RgSeLHQOxsZG2jHLqpo7dWSHmYhM1z2M/2kJIIbpvQrv189UDGq95/CxYcS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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Instantiate the visualizer\n", "visualizer = visualizer = ParallelCoordinates(classes=classes, features=features)\n", @@ -380,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -405,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -426,11 +437,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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8vLzQaDQ91EIhhBCid+jqIKI71NfXk5OTw8CBAzu1N+GVwtvb+8oLHl1cmnJ4\nUlJSmD59OhkZGWi1WrM6Go2m3RuearVaCTg7qb6+3uz/ouOkLx1H+tJxpC8dx5n60vi92lsZ2+/i\n4tLrX0t3sfW5axlPdZTTBI87d+5k9erV/POf/zSVNTQ0EBMTw+7duykuLiY8PByAs2fPMmjQoHZd\nPzc316HtvZKdOXOmp5vQZ0hfOo70peNIXzqO9KXjSF86D6cJHhMTE8nMzOS///0vM2fOZNeuXeza\ntYvPP/+cwsJCli9fztKlSzl16hQbN27kgw8+aNf1Y2Ji8PX17aLWXxnq6+s5c+YMgwcPlqmDTpK+\ndBzpS8eRvnQc6UvHkb50nOrqaocMpjlN8BgcHMy7777LsmXLeOmll4iNjWXlypXExcWxdOlSXnzx\nRSZPnoxSqWTRokXtzrT28PCQs1odxNPTU/rSQaQvHUf60nGkLx1H+tJxpC87r89NWwNcddVVrFu3\nzqI8ICCAN998swdaJIQQQgghmpOVp0IIIYQQwm5ONfIohBBCpB/Yy6Yda6nXV+Gh8OWWqfeQMja1\np5slhPiFBI9CCNHNzuUXsjfzPFU6F/zc9KQmDiA2KqKnm+UU0g/sZd3OFcSMUQIKoIZ1O1cASAAp\nhJOQ4FEIIbrRufxC1h0uwyssCQA1sO5wNrNAAkhg0461vwSOl8WMVLJpx1rC+sdK0C2EE5DgUQgh\nutHezPOmwNHIK2wQezMziI2KuOKnbOv1VTSNOJqr1JRL0C2Ek5DgUQghulGVznqeYpXORaZsAU8X\nP6DaoryyUoFXmPnhEM2DbiFE95FsayGE6Cbn8gs5fjKb/T8d4cihn1CXFZse83PTN03ZjrQ+ZXul\nmDHlbnKP1piV5R6toX/sJKv1bQXjQoiuIyOPQgjRDYxrHUOTr+diUQ0NviqO52eTAHjra0gdPYCf\nD1mfsm2ayr0yGEdYm0/dz5o6m+xyF9RW6vu56bu1fUIICR6FEKJbGNc6egHDgbzSMlyUAZSdPMAf\nfzWZ2KgIm1O2TeVXjpSxqRbT9GH5haw7nG02da0pySZ19IDubp4QVzwJHoUQohs0n14N9Pcl0N8X\nADfVJdOavRlT7m5a89hs6jr3aA2zpsy2ed0rZduf2KgIZgF7MzMuv9bRffO1CuHsJHgUQohu4Oem\nb3Pa1daUra1kmd6w7Y8jg9vYqAineV1CXMkkeBRCiG6QmjjArmlXa1O2trS17U9P6w3BrRCi/SR4\nFEL0Cr3jhsPvAAAgAElEQVR9erYrpl1b2/bHGdgT3Pb291WIK5EEj0IIp9faCFZ4cFCPtq09HD3t\nas9UeE9qK7iVkUkheifn+PVUCCFa0TSCZW2D6PM91CLnkJo4AE1JtlmZpiSb1ETnyEC2FcQay+V9\nFaJ3kpFHIYTTc/bpWXudyy9kw55jZBZWY9A3kBjhz23XjupUAokzZyC3tc6zr7yvQlxpJHgUQjg9\nZ5+etec86nP5hfxjZw45+sG4xakASCvNpnRbFnOmdXya1pkzkNsKbtt6X2U9pBDOSYJHIYRDdOUX\nvb2Zyj3B3vOo92aep1QRgpuvylTmETqIkpJj7M0832eDotaC29beV1kPKYTzkrkBIUSnGb/o1aok\ndCEJqFVJrDtcxrn8QodcPzYqglmjQ1CpM3ArO45KncGs0SFOEUTYex51lc4Frd7y6EGtwfWKnaZt\n7X2V9ZBCOC8ZeRRCdFp37DforNOzTedOt30etZ+bHg8XA5oW9TwUjU4z/d4TbL2vsh5SCOclfwuF\nEJ12JX/R2zp3umV5auIAQg1l6Kovr/LTlmYT5qVzmuxoZ9JWprYQoufIyKMQotOcPaGlK9l7HnVs\nVAS/n0xTtvXZpmzrMTayre1JwOmLmr9uXb0bhqBUBo6/0/S4s6xzFeJKJ8GjEKLTnDmhpau15zzq\n2KgI/nBf61Pv1hJwPt78OhvSTpAQn9BnM44tX3cj2Ye+oeKgln4xI5xuGyIhrmQSPAohOs3Z9xvs\nau05j7otm3as/SWAumx4ShDp24/QP+VXfTbj2NrrHjTGn9JDmcy75aEeapUQwhoJHoUQDuGsCS29\nja0EHFfXGsDxiUjOwt7EIyFEz5PgUQghnEhTok21RXlj4+VROUcnIhn36FTX6aksy8c3IIihg+Mc\neo+22HrdthKShBA9R4JHIYRwItYScI4fqCIq8V7Tz45MRGq+GbfOT0cNIaz/Wc29Xp7dmshjb+KR\nEKLnSfAohBB2an6KzsXcY6hLf8LVs8GhgVTzBJxKTTklaldir5pD5NAUoO1EpPae9GN1j87wgezN\nPGH2PHtP0umo9iQeCSF6lgSPQghhh+YjdAUn06m4tI2ECX50VSBlvI4pGCw73mYiUkeO9LN3j05r\nCS3Gk3Rsveb2BrKOTDwSQnQdCR6FEMIOzUfo8jO/JmWq+Vq8tgKpjmpPIlJHTvqxd4/O9ia0yNnU\nQvRdff/4ByGEcIDmI3GurpcTOwqyy0nffIaD28+Seeow6Qf29kTzgI6d9JOaOABNSbZZmaY4x+LU\nG3tP0jGSs6mF6Ltk5FEIIezQfISusdEX0FCQXU7FxTpSpg821XPk9HVn2tiy3Jbme3Sq6/Qoy/K5\n4/qxFqOD7U1ouZKPrBSir5O/xUIIYYfmI3RRibdz/EAV+WfKSRgXaVbPOH3d0rn8QtZsTuP9b/az\nZnMa5/ILu7SNRpqS7DbPzo6NiuD+6eOZltQfgG0ZRRZtTBmbyqzJj1F6yJe8gwZKflIya/JjNoNk\nOZtaiL5LRh6FEO3S3iSIvqL5CJ1bkB9ni66htGq91bot1wF21/q/zpz0cy6/kPU/q6kJGY1PaAhq\nNzeLNrYnoeVKPrJSiL5OgkchhN2u9CSIkqJznDyylkrNRUrUbuAeabVey3WAHUlk6aiOnvSzN/M8\nXuHDqCotM5W11sa2fom40o+sFKIvk+BRCGG37gyCnE3zfQ5D8WQw8OM3Fzm2z4cR1wSZ6llbB9gb\n1v+1p432/hIhR1YK0TdJ8CjEFaYz0869IQhylJb9dHjff4idYL7P4YRbotj6RTW1271wda2hsVbP\nY/dbrgPsSCJLd/Nz06OxUd7SlfxLhBBCgkchriidnXbuDUGQI1jrp9K6CmJRWtRVhXhz9e3Lmv6s\nziBl7HiLOr1h/V9q4gA+258DzabcrbXxXH4hO4+dQ6PU4aFoJCY6ElVIONA3f4kQQliSv+lCXEE6\nu/deR7N5extr/WRQ9LNat7GxKaBsrR9ioyKYNToElToDt7LjqNQZzBod4lSjdLFREdyRrEJZdhi3\nUuttNAbViojRKMITaAgbwfF8NeqyYqDv/RIhhLBORh6FuIJ0dtr5SkmCsNYfUYm3czz9PRJSVKay\nrPRyBkTOQKXOaLMfOrv+b+2mbfx7RxY1Ch+UhloenDKcu2dM6/D1rImJ6s+NYwaRmJiIl5eXxePG\n6epo72qyitS4+arwCB1Ebt4xvPU1TjWSKoToOk4VPB48eJBXX32VnJwcgoKCeOihh7j33nuprKzk\n2WefJS0tDX9/fx599FHuvvvunm6uEL2OcdpZXVZMbl4BWoMrHopGRvjX2H2NKyEJwtr0fOTQFHyr\nzlF6KJN6fRUeCl8emD67WzYDX7tpG2/8WIXPiPtwAeqAN37cA2xzeADZGmNQHejvy3Agr7QMrV6B\na+0FZo0e0uc/F0KIJk4TPFZWVvLYY4/x4osvMmPGDI4fP87/+3//jwEDBvDpp5+iVCrZt28fWVlZ\nzJ07l/j4eJKTk3u62UL0KqmJA/hwWzo59f54hI1AAdRVq7lgKOJcfqF8+f/C1hrF+2+7mdioh7q9\nPf/ekYXPiPvMynziJ/LvHf/XZcGjtcSq5kF1oL8vgf6+AKhCyuWzI8QVxGmCx8LCQq677jpmzJgB\nQEJCAikpKRw6dIgdO3awefNm3N3dSU5OZubMmaxfv16CRyHaKTYqgjCvY1zw8EdbcwEPFwPR/QMI\n9B8lmbLNONv0fI3Cx+oC9RqFT5fcLze/iI1ZlRaJVSnhXhQVO3fijxCi6zlN8Dhs2DBeeeUV088V\nFRUcPHiQoUOH4ubmRmTk5c144+Li2Lp1a080U4hez8NPxYgQy82tJVPWnLNMz5/LL6ROXYq+rgEX\nDHh6uOHm2vReKQ21XXLPtJMFeIWNMivzChvE+fIMZo0e4DRBtRCiZzhN8NhcVVUV8+fPZ8SIEaSk\npPDvf//b7HEvLy80Gms7ktmm1Wrb/Rxhrr6+3uz/ouN6si+90HJJp7Mo96V3/h1xls9lbn4RaScL\nqG5wwdddz/ihkcRE9e/0Ndf/rCYxMYH07DS8h0ykpl6Hj4cBbfZefj9xiEPfM2MfXtLowcpnRF2n\nJzw4iLsmB5mV98bPTVdzls9lXyB96ThardYh13G64DEvL4/58+cTExPDG2+8wZkzZyxerEajwcen\nfdM1ubm5jmzmFe3MmTM93YQ+oyf6sp9rLRkZ6XiGDjSV1ZfmMGiAgszMzG5vj6P05OeyqOwiO88b\nLvdpA2TsOMXkAafpH2J9ix97bDmUTU3IaMLiEhhemc6p9NU0uvvTWFvEfSlRxEfHdMl7VlteTI2r\nZeCrLMsnM9PT4ffry+TfS8eRvnQeThU8ZmZmMnfuXG6//XYWLVoEQExMDA0NDRQXFxMe3rQR7dmz\nZxk0aFBrl7IQExODr6+vw9t8Jamvr+fMmTMMHjwYT0/5AumMnuzLRGBIy1GyKfGdHiUz6ooRuNa0\n7Mvuvj9AxvaDRCWZn7hCaAgX1RlMS0zs8HXT8urxCQ0BIOSGWzHmdbuVHuehm8Z0+Lq2GPvy1gkj\n2XSiCq/wy79gaIpzuOP6sV3el32F/HvpONKXjlNdXe2QwTSnCR4vXLjA3Llz+f3vf8+cOXNM5Uql\nkilTprB8+XKWLl3KqVOn2LhxIx988EG7ru/h4WF13zLRfp6entKXDtJTfTl0cBxDB8c5/Lrn8gt/\nSbRoWi9XDWzMymaWl2eXr4vz9PSk+EJ5t9y/ZSZy0aV6/EIs/znV0Ll/d1TeLqjdLK+r8nbBy8ur\nU0dNtmbIwBju9S9nb+aJy9ceJ2sbO0L+vXQc6cvO63PT1uvWrUOtVrNy5UpWrFgBgEKh4MEHH+Tl\nl1/mhRdeYPLkySiVShYtWiSZ1kJ0k/YEKD195nF33N/a0YUnfv6OIWHVpq1rjDp74kprxxp29qjJ\ntjhLwpAQwvk4TfA4b9485s2bZ/PxN998sxtbI0Tv5qgRqeYBirqsmINnC/jm54OMj/Zk5sQRFtfs\n7Ak2ndUd97cWoA5OGMWZjENcnXqtqcwRW9i0tmXQms1pPRqoCyGuXE4TPAohHMORI1LGQEldVszx\nfDUeYSNwD4PMujK0h8ssrmntZBZjeXfojvtbC0RVIeHEXzyDSu34LWxsjQD2dKAuhLhySfAoRB/j\nyKlbYyCSm1eAPnAYJeVVNBoUuNZUEh0azd7Mc2bXbG2atTu09/4dGaFtGaBeqqwmr7QCQ+klYsIC\nmT4yqltG/no6UBdCXLnkV1Qh+hhHjkgZA5FKTSNlVVp0bj4Y3L0xuPuQVVTD+QvmZ2LHRkUwa3QI\nKnUGbmXHUakzmDU6pNumUdtzf+MIrVqVhC4kAbUqiXWHyziXX9jqPVITB6ApyQaaAsesohoqqyqJ\nHn613ddwhObtMNKUZJOaKKe9CCG6low8CtHHOHJEyjiSV1VVhYuqKcuxsUZNkL8/bkolRWf3Wzyn\npxMt7L1/R0dom69DPJZZgLdPMDHRkahCwu2+BnR+XaqzHaEohLhySPAoRB/jyKljY4By6GA6BWfT\n8QqNI8jfH2+lEm1pNuHBKge2vHt1ZoTWGKBW6fajC0lo9zUctS61pwN1IcSVSYJHIfoYR49IxUZF\ncEtqMjm6YHLzCtBWqXGvbmRwdCQxbo2ObXw3csQIbUev0dNbGgkhRGdI8ChEH+ToEanUxAEUHS5j\n1JirTGW9fX2dI0ZoO3oNyZQWQvRmEjwKIVqVfmAvm3aspVJzkYoKBRFx15IQn9Dr19c5YoS2o9eQ\nTGkhRG8mwaMQwqb0A3tZt3MFMWOUhNJ0pmzu0e0MChrWqwNHI0eM0HbkGj29pRFYT9gJDw7qtvsL\nIXovCR6FEDZt2rGWmDFKs7KYkUo27VhLythUU5lxdLJeX4WHwpdbpt5j9rgw19OZ0rYSdm4dXt8t\n9xdC9G4SPArRS9iztYujjiU0qtdXAQqL8kpNOWs2p1Glc+Fi7jEuVG1l0Bj/X+rWsG5n0/n0EkDa\n1pOZ0rYSdtJOHiEp3NtU1hOfOSGE85PgUYhewJ6tXZrXuVRZzeHSCr754igpYUe47dpRHfpC93Tx\nA6otyksvuZJRWkN+5tfUVZ7Ay1uHV7aOyEFN054xI5V88H+r2JFVSfEFNf3DQhkQrJTAwgEcEazZ\nSsypbrhc3t7PnK06Qoi+R4JHIXoBe7Z2MdY5l5PDTyfPo/Dyw8Xgyb68ShqsnENtjxlT7m5a8zjy\n8tR1Vno5PiHXU5H3ISlT/YBYAI7vLwDKTQGk1q2RY3X98Igbx8VqNY2uSooOl/TJwKK7Rt/aCtZa\ntmNAkBfnyzUW7bKVsOPrfjlhpz2fudbqCCH6HgkehegF7NnapUrngrqsmMNnL+Aam2IqLzqbTp2L\nkr2Z5zv0hV53wZ19/81FU6slOCCS4LgZnC84wqipfmb1EsZFkr75jCl4rKwEVWhTQoibr4q80jJG\nDO57gYW9I3SOCC5bC9YAs3bklBXz1e4cRiUnE6jyNWuXrYSdacMjqa4oB+z/zLVVRwjR90jwKEQv\nYM/WLn5ueg6eLcAlZDCGZnW8QuPIzSsgJNrb8gKtMGZaD5uqBGKAplFHdWkR9ZoSIMDiOa5urgAc\nP1CFS/D1Zo9p9U1rJ/taYNHW6Jsjp3ZbC9ZatiM3rwBl3NXklZYR6O9r1q77p4+3SNgZEO5F2skC\nzuSXkVFch7aqBpcQy3u1/MzJlkNCXHkkeBSiF7Bna5fUxAF88/NBXIMM6H4pM55Dra1St/sL3Vqm\n9fCUIP77aSYuXv5Wn3OhsJ5tn18i+qrfEYwvDc0e83BpCmnbakdvSsA4l1/IzmPn0Ch1eCgazc64\nNgZ6nZ3abZ7JXlrWQPjoB4gcmmJWx89NbxFYag2uKLgctBsZ6zVP2DEGuG79kjA0lHFJFUJZ2U+4\nnD5C2JBRpuda+8z19JZDQojuJ8GjEL1AW1u7GAMuRU0ZekUe9Xo3fHz9TOdQU1RMamL7Mp9tZVoH\n9PNC6zuJo/s2MvKaQFP58QNVjLjhRXyCYjh6MofYCBUFpdl4hA5CV60mun9Am4FFb0rAMLZVETEa\nhXcIDcDx/GwSAFVIuClIbh7Ufbt6KXVlP+KvcqOyXMvBXRN5/a+v2rxH8302QUE0HmSmvw9gCiCN\nfbo387zZKKCHopEGLgftAJcqqyk9mc37YBaYGwNcnU5nqts/IYXGM7tQqW1vJ9TTWw4JIXqGBI9C\n9BK2tnZpHnANGxuMPl+N1tUXH0U9btRhyD3GQ9cNbvcXuq1M67oGJSGDxhNsiCR9+9fUN6ipqXYh\neeIcU0AzEig7eYARwSqKz55pyrZuVLYZWPSmBAxjW6O9q8kqUuPmq8IjdBC5ecfw1teYgmTj1O63\nq5fSP/goo+5MMF3jyO6feXLx0zYDSGujv4kpgRz67j/EBPlZBGvNRwFjoiM5evIgQ5KTgabA8cjP\nPzNy6Fh0IeFmgbmt6XDPgGDunz6u1X7oyS2HhBA9Q4JHIXq55gGXKiScBJrWu7nWXuDaxEhSr7O9\nTc+eA0f4YncWlXoP/F203DNpOBPHNk1TzphyN//6djmJ1/Qz1T+y5wLuMfPwcDEQOTiFyKEp/HSq\nEP/qIiKHXj73WhUSTgiDmHdL64FHS62t6TOOruaWXDLb/ueqQWHtuoejGNsa6O/LcCCvtAytXoFr\n7QVmjR5i6nPj1G5d2Y9mgSPAqEkDWL/qR5v3sDX6GxLsZtG3LUcBB7rpuW5SKOfLz1FV5kLpyWxG\nDh1rmlaHy4G5nxuydlEIYTcJHoXo5VoGXKqQcFQh4biVHW911GjPgSO8tbsUZdx0ACqAt3YfBI4w\ncewowvrHonefzrdf/4SPdwO1Wh+0btfh6a4hOrQpWeZSZTUFWQfx9/PlyKGfzNb8dSTwsJWAoa1S\ns+6wJ3UuwRyvczXb/ifv50ISfS6S2O67dU7ztgb6+5qSUlQh5VandvdscLV+nUB3m/ewNfrbVG6p\ntVHA9wFds8DRqErnwvSRUaw7nI1bvxhTuaxdFELY0rfSHoW4AtkK0toK3r7YnYUy7mqzMmXc1Xyx\nOwtoGtEcMfV+rrnjLwSNXkTo2IUMvGo6Y/vVEdd4jqrT6Zw+vIe4qHAu1ruQUwE79v7E2RPHmgKP\nxPYHHqmJA9CUZJuVaUqyMegb8QobRG5eAR5m2/9U4BU+kGN5l9p9r84ytvVSZTXHzhTw06lCDu7d\nxYAgL4u6sVERVF/SWbkKVF1qsFoOTaO/uUdrzMpyj9YwY8rd7W5va5+T2KgIZo0OIVCdgaIkg0B1\nBrNGh8h0tBDCKhl5FKKX62jGa6Xew2p5cVUjazansfVYAW6+9cRERzJicKTpcbeyCu6fPo41m9PQ\nBcZwPF9N6NBEKmo0NPaL49jJPdxxR0KHAg9bCRibj9aj43IGsZExk7hWb3v0rqvERkWQUlTKhz/s\nweAb3pRtPSSe9OIaovILLV7/iCETOLL7Z0ZNuvy+HNl9nhFDJtjMMDce79j83PBZU2d36NjHtj4n\nsVERhAcHkZmZSWJiIl5elkGwEEKABI9COLXm27R4KHy5Zeo9FoFDRzNe/V20VLQoq6upoeRiOd+f\nradQ441e10hZZTZXJ2IxHV2lc2kaCQwbAYCXZ1MA5+J1NefLyzv8mq1Nvfr9kklszCA2MmYS+7jY\nHr3rSufLNYydfJNFubUEn9f/+ipPLn6a9at+xC/QnapLDYwYMoGF859oNcM8ZWyqQ84I742Z0fZ8\n/oUQ3U+CRyGcVMttWqCGdTtXAFgNINsbBNwzaThv7T5oNnV99sAW3HzCyHWLQx/cQAOuNFQVk5mR\nwcTrw81Gqvzc9BYjgdAU0Dl6I3DjqFlMdCTH81ts/1Ocw4jowLYv0gXae8KKtazqNZvTrGaYb9jz\nI/1Ujt3vsjdlRlv7/H+8+XVKyi5y24yZPd08Ia5oEjwK4aSsbdMSM1LJph1r2z36Ym1atCmr+ghf\n7N5Mpd4Dlzo1itqLKMfchgFQuHtDbRXu/iFcPHUYlTrDbKQqNXEAW37eC1zOIDYGdH6N1tJeOu7y\nqNl5XL0vmW3/c1VyGNUVhjav0RUcccKKtUDzUmU1x/PqSR3o/PtddhVbm9R/tmk9yclX2d0PvWnT\neSF6Cwkehehhtr7cbG3TkldezJrNaXZ/Cba28fbEsaNMW/Os2ZzG+fpcs+d6+PjhpqslJjKU+6eP\nN3ssNiqCOdcN5qMf01AED8bDxUB0/wC86kra3Ai8I1/mtkbNNBoNmRUdnybvDEecsGItAM0rrcDH\nz/z4R2fd77Kr2Pr8e/hg9zntvWnTeSF6E8m2FqIH5eYXse5wGWpVErqQBNSqJNYdLuNcfqHN7Vh0\nimCzem1p2gdykFlZUyBy3qysSudCaKAPjTXmoYy2Rk1ihK/Va08cO4oX7x7FtJByRgdUENd4rtUs\nXeOXubXX2xsZs5RV6gzcyo6j6kCWsrUM86qSHGKiIy3q9rVzwVtj6/Pf2Ki0ux/s/ewLIdpHRh6F\n6EFpJwvwChtlVmYcYZox5e6mNV8jL0/dHT9QRVTivWb12gpUWn7RqsuKyc0rQFd9Abg8gunnpmf4\n8OHUnsqjjgYaDS64KvT0q8lh5sTx1i9O6+voWo4yXlRX4DVwgtXX21tHgjqyjrBlv6SEe3G+/HIi\nS0qYAVcrezJeSZt2z5hyNx9vfp3hKUGmMuPn395+aO+aVCGEfSR4FMKB2jslW93gwqXKavJKK9Dq\nFU1Tv6EBuOlcSBnbFLBt2rGWvPJiGhTBRCfeazoCENr+EjyXX8jxk9lcOq/FQ9FIgI8HhXVueISN\nwMu/DLUq0jSNl5o4gKLDZVwVH01uXgGXLlVwqTSf2PhI00iNvUGicY/HllOGaT/vICG42rShtr2v\no7X7hAcHtf3ELtKRKXhrU6lFxdnManFWeWenw3u7lLGplJRd5LNN6/HwAV2jkujEe+kXGExqYohd\n13DEmlQhhCUJHoVwkI6sr9JWqcmqqsHNt+nLUANkFalJcW/6yjNu07JmcxpqVZLF81v7EjS2JzT5\nei4W1dDgq+LQwa2EDE81JbbA5ZG/+6ePv5yU4lPHKY0bo2bcRaC/b6uvxdbrdq8qwGvwtWbBsVpj\n4NT5EsYlmQeP9nyZ27rPrcPr23xuV+joejp7zu/ujdvqdIXbZswkOfmqFgG6/csCHLEmVQhhSYJH\nIRzEnqCgJb2hEX1dOfiqLpfVlmPwazSrZ+1LsOh4OnovHe9/s9/qqJexPV5gOntZ4eVH7cUCxifG\nmo3+GUf+jFOwazan4T+s9ddiHHXbeewciojRRHtfHlH0ChvEzyezGBBaTVbR5eDYN3IIOWcyiR8Q\nZqpr75e5rf5NO3mEpHDvNp/vaB15v8H+qdTetK1OV+pMP0gQLkTXkOBRCAexFRScv1DDms1pVqda\nvfyDSeinIjfvGFqDKx6KRgZHR+KJ+dYzLb8E6ysu4OLuj8vAFC5UVnO4tIJvvjhKStgRbrt2FLFR\nEWbtMZ693FhZTKNvkMW0ccuRv7YCnOajbhqlDoV3CFlFaob/ci8AhYs7eaUVpsARwEcVTlTAKUp/\n/p7goYPa9WVuq03VDd27fs0YNG89VoBnhIro0ACrgbgtMpXavSQIF8LxJHgUwkGsBQWXKqs5XajG\nb0jTOsWWU62+7noICTed3mK6lvqCxfWbfwmu2ZyGqyqJS5WXR/bcY0M4VnKMhsNlzLLRnpjoSLJP\nn4L4y1+m1kb+2gpwmo+6GU99aTpruswUSCVG+JJWlIN77OXgUVuaTWJSEiGUM++WcaZye9YO2mqT\nr3v3BV3Ng2Y333o0VoLmtoJAmUoVQvR2knImhINY23LlTMYhBidYZlOnnSwAYPzQSIvnaEqyTQkn\nthhHt5pG9lRUVNZwJr+Mw+cu8kNOHf/6Zq/V9njra3hoQkSbW8tYe27zdjUfXYuJjkRb2lTXeNa0\npiSbmRNHkBJmwL3kGIbi47iXHCMhqml6/vjJbN7/Zj9rNqexdtM2lny6l805Og7m1ZGjC7a6fY+t\nNo0farmlTVdpvvWL8XU3Bc0Vpva09d45YnsfIYToSTLyKISDWFtfFR/kgr+VLVeMU60xUf2Z5eXZ\n7jVZxlE4rV5B2cVL5F+qx8XDC50OqrzC2ZqTzdSiUmaNDmVvZgbHTx2n6NxuvLy0ZNa5ExF3LQnx\nCTazg9taK9Z8FFAVEk4CkJt3DNfaC6hCyk11b7sWGg6XmQIudVkxR0/mMCr5enT+vuSUFbM9rZDQ\noSl4K5U0AMfzs0mIUllsBG2rTeHBQd22SXjzoLn5666vvoCqn5oB4V7szTzP5qP5cpqJEKLPkuBR\nCAdqub5qzeY0U5Bl3F9Ra3DFr+YcMV6hJFp5jj2MU5+NWiiq9MJF2Y+G0mw8w4ZQU1ePj6cPX+zO\n4q0nR/HD999w4uf/4NfPg0pNI9HxQajLt5JRGkORtsxmdnBr7Wo59aoKCcdbX0NKeADnyzVNwVPm\neVITBzBrdIgp4Cs7mc2o5OubMrjLitnz4z6q3MKozTxAzJBh+KjC8QgdRG7eMUKiLZNgrLVJo9G0\nq+86wxg0F5xMJz/za1xdq9E1+jJ8wEhSE6+xK/taTj0RQvR2EjwK0YWMQVadi5Lj+Wo8wkbQWK0m\nNC6WneczGZJfxNDBce2+rnEU7vg/N1F3qoAgl9ME+huo0gZQ4TUab98gKvUepB/Yy95TX3L9r4aa\nnntsbx5BYUryM78m8q5lbWYH21qP2HIUcEC4F+nFCsugaHSI6WjD9wHdL4Hj8Xw1jZFX4+Hqj76x\ngdKLxYTSlFSjNbg6ZQJJauIA/v7lV2iqN5Ey1Y+mf0I1ZB/aypr/QsDV95rVt5Z93dEsbSGEcBYS\nPArhYNZOD/li9wG8Qq/Co66M6P4B+Pp4oQ0dSNrJgg4Fj9AUQPq61TM+9ihXXxv8S6mOA3u2cvLS\ndZXcsTEAACAASURBVPj3U/GfdR8QHKskffMZXN1c0TU0Ej0kiNNHi/ENatrnsbXs4LZGyVqOsrYV\nFBlH7nLzCvAIG4FrURGeHm7UVdfgHjoIddExfFThKKqLSU1M7VC/dKXYqAhcyveRMMH86LxBY/zZ\nv2kP41oEj2DZv3LqiRCit5PgUQgHsnV6SP+wUOKHXA60dDod0LT2sSOnlBidz9nJtF8Fm5WNnRjI\n2X9u5Z7fvshrf19JlJ8/KdMHmx4/vr+Amsp6vAOajj1sbYSvPaNk9gRFxpFYrcEVBRDg70/JxRL6\nB6rQ6GrRNmhQ5Kbx0HWDnWYUruX7o1PUAJ4W9Tw8rU+ft+xf2apHCNHbya+6QjhQ82xcI6+wQRSV\nlFqtr61Ss+5wGWpVErqQBNSqJKuZxrb4+VsvDw3Ucb5cg0ZTT8I482zkhHGR6LR6ohJvbzM7uD2j\nZLaCn+blxkzjwLo8XGouoHKpJWWAkjD3Wvp71pOkrODFu0cxcewoq9fqbsZfBpq/P6WXrP/OHeil\nsitzvq1MdiGEcHZOOfL4888/89hjj7F7924AKisrefbZZ0lLS8Pf359HH32Uu+++u4dbKYQlW8FW\neLCKktNHKFWEoNUrcKMRlwsnUAW5Wg02W47s2RqddEdp9X4anT9qVRLKQF+rjyv9lSSFKts86q09\no2TNk2iMCSV6bQk0uHH81HGz7O4//mryLyO05nsdzrpustOMOIL1kdeYMfeTlf4uw1Mun6mde7SG\nSePuI7eymGN7zmDQN5AY4c+sXzZsb66rTj1JP7CXTTvWUq+vwkPhyy1T7yFlrPNN/Qshej+nCx7X\nrl3LK6+8gpvb5aY999xzKJVK9u3bR1ZWFnPnziU+Pp7k5OQebKnoazozfWxkK9hSuump0dbTqCmm\n0eCKwtCAS0M9dTpfvKzUr9K5mIKBSs1FStRuxF51P5FDU8zWHd4+9S6+S/+YxJRA03OP7LlA3JiH\nAHD1sL4HYlTIQFMSS2vs3dDa2HcNlTUc2b0BX9/DpEztBzStqzx+wDK721oABVicxtOTwaS1XwYi\nh6bgcimX0kMZpkBtYvJMTjcOwGvgIEYMbKrXcnSxOUefepJ+YC/rdq4gZowSUAA1rNu5AkACSCGE\nwzlV8Pjee+/x3XffMX/+fFatWgVAbW0t27dvZ8uWLbi7u5OcnMzMmTNZv369BI/CYRy1fYqtYMvD\nxZX+CSn0p+nUmdxiNRfwoyDzR1LChlmcMHMx9xjrsnYQM0ZJKJ4MBo7ue5/qGg1Dx0w2jU7eP2Mm\nAF9/9yUGt3po8MAvZCrxYyYDMCRlNsf2vcuIay6Pkp0+UM5vZz1p1+uxZ5Rsz4EjfPRjIYrgwXi4\nqGjQF5J0TT+z6ySM9SN9u3l2d8sAyhm3sLH1y0BCfAL3T/+96ec1m9PwCm57BLmrbNqx9pfA8bKY\nkUo27Vjbq4NHR/xCJ4RwPKcKHu+++24eeeQR9u/fbyo7d+4c7u7uREZeHkGJi4tj69atPdFE0UdZ\nm57UeIfxxuffk2A8g9mOLy5bwdbmo/XowHScoItPCAa9kkZXF35cv4SQIBcMikCiE2+nX2Awhef3\nMnyqeTAw8ppAvv36v4QNvopAf1/TqFhy8lVUu4aY7nfh4uUNsyOHNh2LmL79azwpo39gOL++YXa7\nAgprQZ5xdFBbpWZ3VhE+o27DAGiARkM1WBlPdXWtAWxP7Rvfg+b7YXooGvlvVQEL7++ZgMGekddz\n+YXsyixAGxqEh4vB7Kzr7sqgrtdX0TTiaK28d3LGXyaEEE2cKngMDg62KKurq8PT0zyz0cvLq90b\nA2u12m7dTLgvqq+vN/t/X6Ku06Pz05l+rqis5kSJBlfPSDSqeDTAZ/tzuENTT0xU/1avFR4cRLgy\nn71pp6hq9GTvsTMEebvRTxVPbrEaF58Q9HoDNXmHiFbuZNQNxlFBDVnp7zKgYQaVjRqwsp7R06OG\nPT+mERwcREDdeXYEwKc//ERp8SE8PevQ1HvjH5BIdJ0b4cObzo4OG3QVAUoVdySrTG3v6N+F3Pwi\n1v+sxit8GACZ6iKKG6sJq6rCW6mkrqaGS9WWmcgADTofdDodvlj/u6iu01OmySersALP0EQA6oG0\ns+lMP3PWZr935ecyPDiIW4fXk3byCNUNLvi665k2PJLw4CA0Go2pPxq8gtB5B6EDMgsuMUynI8Df\n1+ZrdTQ3gxKotVrenvs709/xnUdzcOuXZNqZAMCtXww7j2YQHhzUyjOdgzP1ZW8nfek4Wq3WIddx\nquDRGm9vb4sXq9Fo8PHx+f/svXl8k/ed7/vWLmu1bEvesfECeCEYYzBhTyD72iZN2rTN9LS5zXTO\nTE+3uZnOnGl6Xu1tJ2em7Uw79+Smk7RNs6cJAZIQSCAJYAzGYMxis3i35U2yLduSrF26fwjJli2D\nN5JAnvdf9qNHP/2en5bf5/musxqno6NjIaf1uaa5ufnTnsKCM2o148QY/b+l14ZXnYbEMYzVYg0f\nFGvZ/lEtt5bnTzNKmFPnm3mjWUbC4k0AOIDGM/vJGn2PMX0hoWBYFIbat1P2UOwmWFSZxIHte/Er\n9HHHHhqWEMwvwS4eIynjBn7+4lssTj3DrfclAeHvxPFDhzh1qAldcJixoAyV2Mfy7EQcI6F5t/F7\nv64Fp3El9ktrMjTqJCRNwDo4iMblYtjlJ5B+L8erX6Bi3Xgc5pmaYdRpX8J8tob8RSIaGhqmjD1q\nNdM4NEAgvQy/c1wIydXG6Lr3Wgc50xV7XenGsIt8Np/Ly40Tj9K08W43jpGh6DpG1kOtdNPWdga5\nKR9Ecs619ZAlsU17rQvN0pyVnKh9h8LVhuixplobq3LWz+n1r8Z3fLZr3my2EvJZpxwf6Ld+Imu6\nUFyPv5efFsJafnb4zIvHnJwcfD4ffX19pKWF48La2trIz7/8Bh5vHI0mfuapwMzweDw0NzdTUFAw\nxRp8raPRJ12yqIWzHcyjAYLOXoqWLcFgHBeVUrIoKSm57Fh//OgcKaVbY46p196D+PTrpMnOMxIa\nQxryIdf6iOfeVWrFmIofpP7Is5TdOC7Ajh+04E9/GJ1vgPJl2eh1Gho/bqd8Q6wArdiYzGt/6qOy\nKJd1FQsbF3y0y4PKNL4eTedOoLHsR2YdxuXXI8r6IurcSprquxh7tx6Z3IVjyE1xya0sXZLO2qWZ\n01oQNfokTr1xCqV6/MbQ7xhm2eIMdG4/Gn0SDR0iVCVriJzR0NdKToIar8sx489lh7k37jiFesMV\nrcrTrYfRZCTJ2kdXd7iGpdJh5rEHbpz1eHOlpKSE3BO57D3wFr6QA5lIzQObv8rqVZWzGudqfcfn\nsuZn+1wMG4xTjifKjFf8Dn4WuJ5/Lz9phLVcOBwOx4IY0z7z4lGtVnPzzTfzq1/9ip/97GdcvHiR\nd955h9///vezGkcul6NUxstrFZgtCoXiulvLpQWLeVipoLrhPHa/GP1ACwVLV09JZDEkiK947U4S\nEIvDsW6DbWfp62ojIFESsli5qVRJcGwYsVqL2x3/R1AcULKoeB0ut4/3du5CleBjzKti1F1OzoY1\nFKWrozF1mgQ3IJsyhlYHO4+1cPOGNXNYjekxJIixXaqE0H2hhqTgDjZ9QQeELV4nqv7AxePnuXHD\nNnLzHgVAam3k8buuPI+lBYtZn3uRBs8g3qAoHD+YGY4fNNjMnGjpR5MVG5eqyVpCXVs9pWkJUz6X\n0yVbTDfOiZazs+72M3E9jOlZGNOzwsdt8jl3DprITBNGamqr2Ve1E7/IiUKsnXeZnoX+js9lzTev\nyOPNkx1T4k03r8y7pn5/rsffy08LYS3nz+fGbQ3ws5/9jCeffJLNmzejVqt54oknhExrgQVnYmJI\n+4os3jwZ6zKLV6ImHjqxlxHCwtE84EBRdAchjwO/cSnH/cOUpinxy/XYxUtpOFJLyY3jsb4dp5zc\nt/XLNPW3sLR8M6kFq+iyjCDrb8U02kn+BOEI4PPFD9+wO+X0jbii/+/a/TY7949nZN+/7QHuvZSp\nPRsmJpCYG3ZSuTW2SvmqDUmM7W0mN+/b0WOz6Zxyz4bleOPUfwwnHZnjPsfhm5qUcrlki4VsDzib\nUkZPv/ga51uOolT5kQWVPHTHly/7Hsw0YeRaKNMzlzW/WvUwBQQE5s9nUjyuWbOGI0eORP/X6/X8\n+7//+6c4I4HPG/PZuL60sYj/OHScvq5eFEV3AOAdHSTRmIlCkUl7ZzW3bV2GSfMgiq5kLHXno/UC\nH9gazoQOW5zOIvWLWZwcZN2mFcAK3jzZDxPEY1Z2JbUf7WL1TePzqq0aJlDwDVyj4TqDu3a/zZ66\nFyi/PZGIlfLtqj+y/1QnleWrZlX+ZOK6SEMDwNRQkKB/iGMn6pGLAqQq/dy1rWhGY08ef/K6axs6\n45bN0cimitPLtVXUSpl3e8CJFkGZfYCgsw+51hD3c9Ju7uGXf3yLYLCaWx8aj0ncfeR5gGkF5Exb\nQ14LZXrm2pJxoethCggILAyfSfEoIPBZYK4bV7i1Xj0/+ONFcA8jFYXQq+QoFGHh5vSJON7QhnnI\ngdQK95Rv495JnUj6e9u5UD/eLSQ/KeyGfAB4u+owDT2OcBeTnCw+PrKS11/rRqcJ4nAn4M34BjJ9\nFiZ/I08+9QNONhzntq8vi5lj2YYU3t91iiVb/2rW5U8i63Kh/nXC6UCxeAI6NFoTEnEIcWhqwsNM\nx5/MdFa+bUWZOCYlAl3O0nXbiqwZWQunY7JFUGIMP/+eFeH4vOqGTvaeMkfdzNUNnfT11HDrFw0x\n4yy/MYmde7ZPKx5naq27Fsr0zNRCKyAgcG0giEcBgSswl0LFG1aXceOhc4xkpzDaXov9whuoVX7s\nLiWj3my6s1YSTEolFPRy1K7Dsu8cj20LP/flXe/RPbSH4koDETfkn/f+hv2159i0ehVedRrLN4xv\nwrruMdIzHqJzwIksJEEjCmAKNqGUH8VUnkSybZrYSm8b3RdqyFxaOadi1nfe/GDYXbpi3OrVWGun\nZM1jZC6JjJW5YIWyp7NKpqUkTckiv5yla77dbaazCO46eBCfNnOKm9lrG0ah8BIvOSoknb70yEyt\ndQqxlngiPnz8s4HgghYQuL4QxKPAdcdC9vidT6HiL20s4hd/eZFM3RG2PqAl7DIOcLyqjub+MgJi\nLcbUVOTJGfT3n+HpF1/D3H+W0aFWbv9aYcxYxZUGavafosmbTnKSgZHuE9Ei2impmQxau7h93abo\n+dWv/Bfld4azsAP+QNz5abUBRrqeBUCaNHuhEVnTyFr3WPxklT8WLUw+POqgyzKCp6cbOLog3UHi\nWSXj1TG8kqVrPt1t7H7xlELmOdmZdPWMcsOmTTHnKlPz6WxqxOORx70ekX/6zNGZWuviifiOU04e\nuPkb0479aSC4oAUErh8E8ShwXbEQyQMTLY2NF1ow3XBTjM1oJm3n2s09dA65UQ1XUXZLYsxjFRuS\n6Hjxz2hX/QiVIZzNPdB9AZH4EOvuTOH4/vhfS4nEiUuqp651gEUr1iMCfECPpYVs8QgG27hVx5To\nByQAZBUk0Xism+I1412aGo91k1WQRGa+lkN738SZsZFnYFYt4NrNPbQMicko/TJaaZCMwSEkBePC\n8VyvE6nGiEyTgs1Q+ol2B8nNykD/0T7e3fX/IEsI4HNJuGvT3eRmPRr3/JnGFwJ4RgZotIuQpy6P\nvgeN5hYk7vhZjOmpJtzOSuqrP6Zs3bjr+syRIe7b+leXvYaZWOsmi/iJsbPTMZ01vcPcy/t1LRzt\n8mBIEAvtAAUEBOIiiEeB64r5Jg9MtkANd3oZ7HVSBDFZzpfLEp04htYYv7aoRici0jfD7fHh7P6Y\nbV8JZ11PZykMBNSM2gaQGJcw2l6Lr+MdEmQO7C4lFpeWvPzxNooX6pOJuDIz85OAIWr2NuNy+lCq\nZGQXJl06Dh53P8alq/Eb02ZsWY1nqRsYqCfYWEN6cSVdlhGkGiNeSwsF2WHRGhFjwFXvV7xr99vU\ndbzDtofGhXtdzTvs2m2IG2M4m2xgkViCWBVbW1OsSkKBf8q5AItS1Hx58xd4+kUv779+FKUqgCyo\n4KE7/uqKGe8ztdZVrl43q5ujeFbWyl4Lh7uCOI0rUZmM2KRSoR3gZRD6bgt8nhHEo8B1xXyTByZb\noOSiAD6NgS6LNUY8Xi5L9PldH3PEHMArvoDEbKcCw5RznP5EvEMWNCmZmFvOkaL0EMlcjmsprLWT\nVfIwnkERvZ1HyVG9x8q7ItnTAeqqmukYspO5tJI3T7awavlNVJ16NerKzMxPwto9dsnaGCt8vB5F\nTD3LmVhW41nqUgvLCLYexmA7i6enG5kmhYLszJixT11o44NzQ4hSCqJ9oHtP9i+4QNm5f/ul7PJx\nSioTp01QmU02sFxroEihpstiHa9Hma5HHMrA3R/fzZyblcFT//D9eV/XQjCdlfUvh94mb+0d0e5B\nkeMLFbN6PSH03Rb4vCOIR4HriislD1wpHnKypSknO5NGcwti9Xi7wMlxZxMtEH3mTnY3edCV3Y0E\n8Ci11B78Pas3jXfKOHtsFG3qnQwNtTFw5CJiZQp+xgXdREuhfQTkmgLyL8US9hzYQ5L9ICu3xQqj\n8g0pHNr9OplLK1Gm5uOwuXhg83+PuVZxoIKRoSYyJzRnOlk9jFN1Iycu9kTFXKJOc8Wah9M9Ltca\neOS2NcBRbIZYgWKz9nHS7CTjxtsIAW7gXK+NovRUqhvaF3TTDSeiTC2ePl2CymyygbXSIH6dJuZm\nAsAQSGRdifEznxQy3Xs3GowflzmX+pfXO7MJcxAQuB4RxKPAdcXlkgdmEg852QJlMKZRDFgv1CI1\nDE8RBJMtEMebgwSTvHiG+1AkpqHIruD0sRY6Xj2OKTGAXGYgu+Rh0o1LObHztzidLaQgZWQ0QN3h\nEOXrw71+M/OTGB2ATSvuRWVagt0vxtt6mMVqL3UhG2Cacu0uj4Wd732ISCLFFLTws2/eyv964tfR\nx1/ee5SzFic1+3eG4yfdCnqkd6BIzyGoThkXc8DiK9Tfu5KlLp4Ya26sx5CRF3O+9JJVN0W/sAJl\nukSU6Y7PJhv4ckLzWkgKme6904njx2zOpv7l54WFLDQvIHAtIohHgeuKyyUPPPnUD64YDxlPGCQE\nnXz/oc0zKtviCUnQZC5ltLUORWLYXatZshlbWyLb7thGok7D8KiDqj2vk5XaTNn68e4yH+1o483n\ng2gNCsaG3fzdV74VdbGGRaqClLz1uGt3xb32wWERNtEgIZkS85iDn72wj3/++rbovNeVLKLXayXz\ni78A4ExzN57+HkK+cdEg1RhoPnuUrz5YNmX8iRZWr32E/t5wfGOEydnMk8XYsgwDLXYJvknjeoOi\nBRco9239IntqXqCkctxC21AzzH1bvz7tc2Yq/K71sjPTid8vbSzicFcrTCjxI9RijM9ci54LCFwv\nCOJR4LpjuuSBmcRDzlYYTLY0KEQBQhIxaqUCqX+MQEiEWqMjUznI4kA7dqsYy4UWVGMnKLstJea5\nN92/mDf/EsRe9H+TpRijKSCi3dxDblZGVKTarH04FTdw4sgZVt04vsnXHhrCEixCXXoXoaAfKSFq\n2mr4/m9e4Uu3ro8G80+8tlBPK6uKKgDo6DoTLTuTlySecr2TLaxiI4ib6gk0H0ShT4m7TpPF2Mt7\nj+JPTKHR3ILcNC5cQgPNrNsyVazOh4jo3rlnYkvGr8+pJSPET4545La1CznlT4zLfcaNhja2f1SL\nlKxwtvU1JIo/SYSi5wKfdwTxKHBdM3HTtw74yY4TBze5mPJsXI+TLRDFi4zUdPaikEJqkpYxWx+d\nx/ehTlRysKGbkgwNaSkGejvdwFQXqlo6jFzqYWlOGkqdJhpDFRGpHV3dZKy5n/ONqXTuPIlGbsfu\nVNI5mIdi1YOIgl4kiPAjQpa3DvPprillciLX9rI0iO1SqaCJSS0G29kp85ouQcZgO3spxvHKrCtZ\nRO9JK8VZhqhYFTn6+NaWAnKzMhY8e/XeO++Zs1icSLu5h99t34+1pxaJxIE/oOHAsaWUFhaMtyS8\nxjJtp/uM52Slc2t5PiUlJSiVU4uaT+TznG18rVufBQTmiyAeBa5bJlvLUsu+RkPNMzGuzPkWU55s\ngViUmYrHUs3ocBvO+nZ6hlxIjAWMZpfiFIUYcIwhtZ7Dbo8fG+V2K1ibZ4y6t880dGP3H6PxQgtG\nkvCGJKgMaWQV34jNshiX343C4EM8dh5Ncjg72+vzIhLLEIUCBGVhN328YP7ZWE8WIsZrfMPtxJid\ncElwrIsKx89q9urLu97D7dhH5VYt4Z9MN/XVH3O43cjGm9fTau3j/VeqWZZhYFGKmkVJSjqH3Asi\nqj6rAu2z/H5Nx0Kv5bUQ3yogcLUQxKPAdctka1mk80ndnhcxpkhnVEz5SsSzQHzvrlJys27ld6/u\n5a0LPiS5qwkBfsDmFZOsz8YRyKbuSAflN45ncZ88OoohazMhj4Mj1acxjwZRusfIWpqE6YZF1J8+\njX90EFfIQCAkBqWWspwcdFo1vc1nCfrcBIN+vI4REEkRBb3og+OZ55OF3mysJwsV4zXdhvt21Rma\ngrl4RyZkfccRvO3mHt6uOjPe2ztDx21rimY1h9lyoamKNXfGWqfL1hnYs3MfNms5jWYb8oKbaXdZ\nCUj0vPiX19H5GkhQ+fAHNJy4sJq/++LWWQuNiQLNZu3jeFs3754+ztpsBfdsWP6pCpdrLdv4WhS7\nAgKfZQTxKHDdEs8qlrm0kpwkLY/fNTNX60yI1+ru5b1HeeekGbvUhMbnQyoLu8vFciX2MVhbUcmZ\nC3osOztQKVz4/SpcinUUl5bRaLZh0y5FLPeg15XQaO6jOAtyM9Ko7WxFJO9HmZqPPjmJbocDxVgz\n3962hOfrD+A1LUeiMiCWSAlZzqNJXYTN2ofBmBZX6M3UenI1Y7zazT0c7fIgKwiXM5qY9S2d8B62\nm3v4w4FWWoMFSBeHa2cetbTQe6CJ9ZlQMu+ZxEemcBOv7I9K4aKjqxt56nIgnPhz5vhBMnVHKKsc\nt1I21u7m5V1e/vFvvjWr150Y59potiFPXY4sFRpcVrwnrZ+q8LnWso2vNbErIPBZRxCPAtctc7GW\nzbUvdsQlVlN3gtbWQxiS5AQsPgKq9Tg9K1BDVEASClK6OJXNBVoGA5txI8drtzE0NMjRUydgUSVe\nTyfGjFwS1GpQ53O+6ThDdhfy3FWY5B6wX0DiVYV7W+ucfOfrX8LqeJWjAx04nZ2MuV2kLyogKbOc\njq4zJASdLEpT8vLeo3Hddldy6V3NGK/qhk5UWn1MFnakhM/i5GDMeRaREalmvOi63JSPpfcUZ7qs\nbJv3TOJjUBogTveYQEBF54CTkNSBRBTCJHLhNO9j432xVsri1VqO7a4CZiceJ8a5RgQqhEXqpy18\nrrVs42tN7AoIfNYRxKPAdctsrWVz7YsdcYkNDjtx+Q5we7QlnpLjhz6i/qIS95KtaGQy3EM9hDpO\nscPswuLwIRcFMGiUKJKzKa24GZOoGVFaPj1dneNz9vjoGw0S8IZwmT8iEDiLRulCKtKxdM0jKPTh\nrO3URXnctaoYm7WP802tWIf76RnsxsQAlavKqekTxXXbAbx50kpH9yAdDTtRyF288kaQ+zbdxmNf\nH+8FPVMr5Wxjy+x+cbQY+8QsbHt/K+s2rYg5zxucmi3vDUkYC061DC4UD971NV778Lfkl+uix+oP\nD2BR3IPL6UXqCSALOHEoxSTInYB6yhguTwc/+Zfvz/hmBMYFmjckiakR0HX4eXp2V6HWinjlBbh5\n7T18/29/OL+LnCXXWrbxtSZ2BQQ+6wjiUeC6ZbK1zDMygFwsYe8pD9qGzimiZq59sSMuMfPh56jc\nGtv5pWJjMh0v7mPkrBuvWo3bPoIuNYfBtBW4/SK8AS82cz1aaQ6+dhtKzxhSIMmUzmBfJ5nqfEac\nbkJ+F8Ndp6lY3EDF+kQgnAlbX/00radXopUG8dpHGOGSizOngvRLcxB1HOVkSz/KvPUxc5vYa7qj\ne5Ces79Dr/QikUpQ6ALsPfpnTMnhXtC7dr/Nzv0Ty948MCWTud3cw66D9dT0i9Cm5pFt0uPXaa4Y\nW6aVBvEbwsXYJ5YMKk8NxTxHKw0iF4dwT3q+XBRAJZ5cPXLhmFw71O+R0ju0EkVGDiHHKGPm00hN\nuUhVSTjd8TOUlSpIXTWzm5EIEYEmFwWiVtn2Pb+hMK2Osi8ujZ53qmofv/lPZiQgFypp5FrLNo4n\ndluPvoV46Ain656blZdBQEBAEI8C1zkRa1mkyLYyNT+cuMLUgPnZ9MWeuAmfvNBN9g25SCQO4n2l\njCkyTOIRPMMHManAZjnHqNtDKGMNYrGMgCYNt8OG05CCUq7Ca2lBaconRaNE6bLibqnHP9RFirzj\nknAcp2xdEu8+v5+zlrWIA1KaLx5FufzO6ON+h42i0nIaTh9meWxzl2g2tycILcefZ/lyCcVrCqKP\nNx7r5rlXnwFgT90Ll3pFhy18e2peAJhUxNzK+VE1stzlMXGL8RJfJhLZ2A2p+dGSQe7+Fu5dWTjl\nvPMHWml1SKOua6+lhRyFj+WZiVPGXUgm1g59ee9RhqxJBNVhi++YrQ+bpY+h8+dJVufRVHuKwtXj\n82k81k1WQbj95ExuRiJEBNouezc17TVoU/OQjx6k7MGlMeet2JDN3j+/c0XxuNBJI9dStvFksTvY\ncYaQ6wNy1+uYjZdBQEAgzIzFo8/n49VXX+Wmm24iKyuLX//61+zevZvS0lJ++tOfkph4dX+8BQTm\nw0wC5q/UFzvC5E040OXiXK8Tv0dJvNg416CTgqIz5N8a+Y6EqK3eQZ1VB1kVBEVSfO4RAiERcp2J\nPIOYjq4zKMcG2JSdSXGplrcbs1GPNcS9ttQsCSNdzyJJ+hrWIRs0NeBw+wlYGtG7j+HR+Rmz3Afx\ntgAAIABJREFUeUgyJkYzzodHHZzrdZKgSkEKiNy92G1aju9vw+8LkF2YRPGaTJpOn+bZV37NbY/G\nCrmSykR27tkeFY+R9fWa66PyOxK3eKVe2TO1YuVmZfDNzeHM7Ia2cLZ1+aVsa8fI0JT36GqVuLH7\nxTEWUJUhDZUhjVBfI7fl5ZCfdBO7P3yDZvNpxPIg2YVJl/qVh4l3MzIduVkZfPeR8RqYVt3UmxsA\npSb+8Yl83pNGJordJ596NSYMAWYn7AUEPu/MWDw+9dRT7Nmzh4qKCvbv388f/vAHvvvd73Lw4EF+\n9rOf8atf/epqzlNAYF7MJGD+cn2xJzJ5Ew7H6w1ByhYaa9+iePW42Dx92AqiAPnlsd1kVq/T0frG\nu9izKpAoVXi66pAUrkAuDmEwppEQdPLAysKo1XTH8bdxeNXA1P7DAX+A4tVa3tu5C79iNSGlAf9I\nMzcYD1GxTke4GLmaM0eeBsIZ512WEYJjo+RkZ3K26m0KSpIo35ITHbPxWDcwRGKyHLUu/s+Ej7Ep\n6zjRxQpEYxSvFFsWz4o1nQD8uy/Hnud2u2mYIB5nY2GbS4KUVhok26TnXK8tJnlH5OiL1q1MTc/l\nfz7196y9fWr84+SbkZkQWZ9XX4wvEt2O0BXHEJJGxpmNl0FAQGAqMxaP7733Hr/73e8oKiriueee\nY926dXz7299my5YtPPLII1dzjgIC82YmAfMTY9tG3UOMjEDG4q3UNA1zrGUvcq0Br93GR8fPYVd2\nQNBHil5DSWkpxVkGuuwtGBS3ULfnEEqlF5tTScqyx1DYX4s/J6WDYecQYucAGrUaffchcvPTMdhs\nMZa33KwMthTq+eBiMXXVByhfN14bcqJLVJXgw+n14hjsQzP0ARVfjLWsLL8xiSO7/khOkpZQTyvF\nRRUYjGkM9lSz8Ws5MecWr8mkZm8zSpWUgD8Qd/720XHBElnfnOxMquuPYBMbCIikyP0OdM52vndX\nadwxpmMuLtYOcy/vH7/InpMdeCUqUvRtlJSWYjCmxbWwzTVBKtwpp5+i9FS6LFa8QRGhgeaYTjnP\n7jvHSNI26mvev1S2J0xT7TCmxEqeeffYnCyiN1XezamqfazYkB09dqqqi5sq777ic6/npJHZWppn\n6mUQEBCIz4zF49jYGKmpqQSDQQ4dOsR3v/tdACQSCWLx5+/OVeDaIhJXNzg8gLlhJxKJA49TxJdv\nvT/mvMrV60hNz+UPB1rp7eqh7uxu5HIXHr8Ow6It9ErzsAQykblBl72CXq8TV0MLFSX5bF6ee6nf\n8Td5ee9RbIZSas40M+yc2oYQYNhiR+T9GEPmYvJN8D+/cXNMm769p8zRjfDRezbh3XeOi60idv7x\nNTIXyaLu5YhL1OmAlJwsvH4pGpEVmBpKIlGFuG1FVlhIXGpNqFLHFw/uMT8FN5iAsEgtXpMZfaz2\n4ABKzbggXFeyiGf31XDs1DnaBlyINMlIJBIMOi3NfT7MvZZZiaR4LlaXWM1vXj9A8dL8KQKh1zpI\n9VkHneICfIVFIEug19Jy6b0Jt1+cbGGba4LUuJu9nRT9JbGypSw6l+qGTvrdUpLL7mOwPYM9775D\ngsyJczSAMvFGCrc9PG3c7ZX4/t/+kN/8J+z98zsoNSJc9uCMs62vtQzpmTKXG42ZehkEBATiM2Px\nWFJSwtNPP01iYiJ2u52tW7fS3d3Nv/7rv7Jy5cqrOUcBgXmTm5VB4ekTtHU8T+XWJCIf/b01f+Zs\nu4VH7r0jutG8XXWGUx0OTMG32PigFpADcLzqLZyhR1Ev28RYRz1OuwO1VoPTNUZzYz1f+8q44LD7\nxQyPOuh2gi/rixyvfv6SCznMiaohtKWPUpKzFJO/h1RlJntPmfFWnaHfLSUhq4SuwRG8QREfvFHP\nt9Zn8Ni2Iqob1DQaFQyP7KaocjyO7vRhK3nFX8Ut09Pt0OAMmYjn4h5yhgXYlzYW0dsXFhJjzvg3\nf0MW74RYvSFq9jYjkUroaPPiWvxtjJLYOMPe3m46nFISVn8ZCCGTiBDbu1CZEvnLoVNsWF0W93Xi\nuY7t/tifpkihbKVpFX5jRoxASEtJ4kzXMAMJy5GqDUi8dvyAzJSPs/cMHV3dcYukz8d1eblkEbtf\nHC2vo8tdDbmrARg+XUVq/jKGRx10WcLvrVys5O2qM1Nc8Zfj+3/7wzmV5plJ9YG0lKQrjvNZYy6x\nnJMz6Bei25SAwOeJGYvHJ598kh/96Ed0d3fzox/9iLS0NH7xi19gtVr5j//4j6s5RwGBedNu7mHn\n/u2U3x67ORZXGqjZf4o3T1ZELRUNPQ6Clo8puyvWhVWxIYm27XtwZ61FLpchlikZvFCDSiEnzRAr\nTLTSICctI8jVBsT6TM62+GnbvheNxIrLKSUgL8WQJ8d+/gBpRaWI8yrxAw0j3VhGB+BcO7qsJeHB\n1Ck8+/GH/PQrprBl87a11NQuixVca+6hKbAIl1jNuWPnGdVt4tih11izMTk6pxPVI8jyv8mwUk1N\nn4jKtBCdQ2dZlLGCE4fqWLVxPC7z5OFB1pffQcepi+SsUJOZH7Zwnjhix7/8URKS8kmwHouev+tg\nPW0OBYpFS5HIwpbWQNCPT5eNzdLEWNc5vvXD/zal1M90ruNE1c3ojcXR8SOFsuUua/RYRCB8cXMS\nY0FZNL5Sr1ZitbsRy5UERRK8IUlcC1vAIyNegtN8XZdaaXBK7CeASCIl4HVxrleCVDPeTedo8wXu\nMfd8IkkrV6o+cHeR56rPYaGZayznxAx6AQGB2TFj8VhYWMjOnTtjjv3oRz9CLpcv+KQEBBaSiFsr\nqJwm2cBt4bTZTmdTI1/aWERHdy+KgAXQTzlXqxjDDQTHhhFJE9AU3ohBAepEKW9OaBm3rmQR7/7l\nFPr0cqx2N7KMcuzyFIJaDSJrEzetLsYfkjDcPERq4bhFzhsU4VJn4be2MDFiMaRJo7qhMyow4m18\nEXd3rthCt9dLfUcuHd1DaJRj2D0axgx3kWzMIsHZijK1jJMth0k26Fmz4VZOHwmx95VTKDXgc4m5\na9N9PPb1R6mpreZffv/viDQKhkbF2PW3IDfqMciCpGkl0ddu6BlFpEsHl5uAf9zi6Q0FsHfVsyj5\nDOUbjURK/bx9+E8cPt1M0N1E7vqpruP2wydQ9FdEXazekISAw0Z2eux7EhEIKrEPuTiEF1AqZBiB\nEecYAfcIiTInD6zcPKWFZDDpRhprd8ckOM03JhHC7uHG3nO0WsaLnvsdNgzBIUKhjJgkGwCVVh/z\n3s6F2cb8TddL/OiFekrTEuY8j0+D6zmWU0Dgs8plxeNrr8UP9I/Hww8/PO/JCAhcDSJurUBAA1NK\nTIMLI6q0Yvq7RfzHIQvavAoGz5yOO5bTo8TbdwGCAWSmfIJDbajVKrJNepS6zKirLDcrg8rUes4M\nnyfkDjA0NIREpkMl05GcKMNgTMN8tgaRe4ia7f+IROLAH9DgS9xAIGUtQZEk5nXlogB2/9QbtUhh\n7oaeUURiGSUZGv76znA3GZd4GycudmH2JSLWpqJRSHAPtuEMyWlvbaWny8O6vFIu1B3APNKFXCtG\nHpDxtS98OVqCp3L1Or5qHeM3h+2oyjYQsdsOXKzi6+uN0XmIxDL8o0OIZen47FYk+nB8ZMAfQDb4\nMeX3p8XMu2x9Mu9vP0miLkBunI4sEoWPB1Yaoy7WRFcXpsICEnWamPMiAmF5diL2fgudDjlSjQGl\nQoZ4pJO8HDWPbauYIqSqGzrJW/sFui9kULN/JxKJk7ExCX75/GISuXTuY9vC1tiGlpbo+7LyjlKe\nrupFnDRuAfVaWijIzsTuH7rMiJdntjF/k3uJ25xOWs52kaJRohnuIVmSeNX6hF8NrtdYTgGBzzKX\nFY/PPPPMjAYRiUSCeBT4zBKxTmWV3Edj7bMxlqb6GjuynHCTvlHbAKbSjYRsXchSN1NXvZPydeNJ\nJ3VVA2h1W0jWeOkYkxAabCVXByvyjFFR0zngjPaPFoklpCr9lJWP11ZsPlvHEpOMRNtZQv4LNHNw\nQgymm7PHttPb4kauz4q+bkRgaKUDMdcVyept9RiR568FoMZhw3qxl1uXaOgcGuDccBMylxap14ZU\nCgZTGirDEhrOHiLTmMiFugO4LH/m1nv1RBJsJhcA90o0rFtuoqGpGg8yFPhYtzwfr2S8VE+6OsTR\n1gH8ycmIZQkELBdBLEHUf46UxPhlZKQyN8Oj8TuyKMTamLjC9hVZvHmyHyaIx4hA6DD3cqZrGJVE\nhaLzI3wiJQlKKeUZOu7dVBZXQEU+E5lLK6O1L+vrThDQpMecN9c6iJH6jJOpa+rhTP94F52C7Mxw\nPKZtIM4oM2O2MX8Te4m7nE6soy4khlyG/WOoVEYOdHopNPeytGDxnOcUj6tVe/OT6nZzNWuHCghc\na1xWPH744Yef1DwEBBacSCJGt60XD0ayS+5Dn/0Yh/a+iT/kYHjAha7sMXS5q/E7bGi1YVEpkSew\nomwtJ2rcdP7lMBrJEJn6ZO7b9N9wSIzY/WIasWC6ITfGEjY86qCpx4a2MCxGxEYQN9UTaD6IQp/C\nYmmQrz4YFjNut5sn/tcfKLo5NgazdI2O/u3vI5XdTKhPHhUYCUEn60piLSmRrF75BIuLVGPA4vLT\nOWTjkdvWYveLKVYs4lyvM8Zd6nSMklNeQu2e/31JOI4zuQC43S8mNy+P3LzYFjV2ayMQ3lR9CgNq\nTQivSot7pA9xKIQWJ+s3VXBx/96478/wiAh92VepP/IcZTdO6MhSYyMzqZL2CXGA0wkEgB2nbTiN\nKzGajJSXSnH3t/DAyrBV7e2qMzT0HCEU9FEyQUzGc3V6QxISxFOF7nzrIE4UHZEbivTiVdHH52sl\nizc/m7WP0+fa4wqdib3ERwIqJGpTdB7ZJRn4QxKOXuheUPG40N1tJnO1u91c7fkLCFxrzKo9YX9/\nP62trQQC4bpvoVAIr9dLQ0NDtHSPgMBngYmJGCa0gJvG2mfRZz/Gxof+N/1N9ZibG8C4GLnLSna6\nnnanBB8Q8LroDmnIqvwC8AVk/WdYnJnIDTcYL2sJaz5bR0FxbEZxamEZBttZHrltzZQ5BiUe4n0F\nc9Kk/I9v3X5JcMjRSgdiNv+IGPngTDfmIRcGuRGVYdwt7A2KooJCKw3i12kogmhNQrk4RIHGg8GY\nhkxiB6YmiHj8Np586gd4gnYsVh9pK78etdBFiLiMqxs6SS+uJHfgIwZVcgKaRUhEQXQhB/aggpC8\ngPrD5ylbPyF5p2oAZf6X0OWuxn5hhEN7P8YfcuB1Qun6v0a/tHLK5hxPILy89yjKtGXYLbGJNG9X\nHcYaUNMaLMCfoWHE6eZiewuHWz7m724vjuvqFDn6yC4sYDLziZ2bLDom31AshJVsshCOZKbrMlbi\nN2ZOEToTe4kfOHyUoD4bcShAZqIcgzENq8WKw7ew5deu9e421/r8BQQWmhmLx5deeolf/OIXBAIB\nRCIRoVD4Dl0kErFixQpBPAp8pohXw694tZZju1+k1KTmrs15sDnv0sYeFhA52ZmcunCcBL0ppndy\nQXYmSmNazEYRzxK2JEmMzhgb2wexlqGI8LO5ggwMTs30BdApk6a1pEwUI4oMAxKVH8vgKCaICki5\nOBQVPBGRlJiaH7WS9jfVI0lWsW/P2wwNxxcJDvsApnITICIbOQ014RCWiICcaC2LXF9RURGNZhty\nUz4jo046zENYrE2kL1qN37+Yd1/eQ4I6hM+vxq9ah2njbfgdNpZXbKLLsgJ3gpFQXyOZS8MCfCab\nc+PFRtrNL0FohDb05Cy/n8yllTT0OCAtF79Mg9XuRSxXIc1czlAvPPtxMz/9iikmplIrDfLYlgJq\n+uK7xudKPNFxuRuKuTBZCHd0dSNWZ5FtGv/8T1zLib3Ecxdl40tdHu6Bnj5+vka2sMkm13p3m2t9\n/gICC82MxeNzzz3Hd77zHR5//HFuuukm3njjDRwOB3//93/PLbfccjXnKCAwa6ar4ZdukvLIbWuj\nIs5rG6azqZH0VBN5KWq2bDTxwoHzDFk7sY+OoEtKoaMrHNsnJXajmCzwfvfqXs40d0ete9kmPYk6\nTVTITRR+fq0f3ZIHaah5nZLKcXfyxELFVbX1/GlvHV32EGK/i7X5SSQlJaPMWw9AtkmP1WHFq07C\nZjGjMqThd9gwia2sK8mjqraevxw6R589gMt+kOLF6WQmawn6pKRW3Ivq4EdY0++gtmoHqzeMu8/r\nPu6ieE2sCC6pTKRuz4vkJGmnWMsili+DMWzNOt90nK4+FyLPMKblq1EY0vBYWlhXtgXdyEVEYgnv\nHW+lv6MJozbcOjFSZkcuiu1mE9mc48Wb9fe2Mzyymxu3JQFKwENj7bMAhII+fEERIy43YrkqOl5Q\nJIlmrj9y29opwjTL3DPn2Ll4c5yv6JhJnN3kGxnJ2ABFBUunJBdFXnPi+XkqFxc7jlJUWk6iToPf\n78djaWXtzUtmNL+Zcq1nRF/r8xcQWGhmLB4tFgv33XcfMpmMoqIi6uvruf322/nxj3/MT37yE771\nrW9dzXkKCMyKy7UfmyjidAbQccnCVBJ2S9c19XDUbiRt6QYAfECjuYW12nBSQ7wNHaDfLWXUM4rc\nlI8bONdrI6+/OWzlZKoVyri4DN+wnro9r2BMkcYUKq6qredf95lxpN6COFtJENjXfgJtp5n1KQ4S\ndRoSdRrK8+BCRx99gy0o22yUZGhI8PXzxM9/i0scxBVKQbH4XlJuuJOWtuMkDA2QXnEvAFK1gWR1\nLifrLbT8+Rh6rR+XR4XM66d8y9Ri0cYUKY/fNdVatihJyQeHDyJKKUAuDiHRpaIdG8SYVjhuDTXl\n09F1hjyVGLkhjZKKdE62DdCnSaevsY9EqQ+ftRuld4RjJ+qRiwLkZGeSJw1OG2/Wd+JFitZPqtu5\nWkv17pepXP0AjYQIhGJvIMShAHIRcTPXYe6xc9PNUWYfQGKcev5MRMds4uwmzvtlaRDbJOE4+TVj\nkpHMPVQ3tGO3itHgJX+RiJyscOLQXHp/x+Naz4i+1ucvILDQzFg8RjrLACxevJgLFy5w++23k5mZ\nSV9f31WboIDAXLhc+7ErxS+JxBKcg53Yz/2ZBJmDMa8aSepNiPSay4iEbtKLN6G09tHRFc6mTRAF\nSNE5YxIVJpOxZA15xsQpouyZv7yDy9GN1vomDo8Kb/rdKHMrGDxyni7LSIxVSZWQwOJULZtKMhmz\nXKSu4x02PRBJQAlyovp5LE2QUriWo1Uvc3dF+BH7QDftPi/iGx7G4bkLqU5LyNaJ4kL8ov/xime3\nm3uo6RORX7iEjq5uXCEJXU0NpGQVxMRhQjghpW/AhlyewYkLnfh8ftyWj1Dqk3F0nUKTloO+4k4g\nLNgPVu+mITjEn/adQaozUVIoiibtKFPzsblt5MaJ1zQlBrh3UxkDB1ppHdPApbJBPksLiWIfOdmL\npmSuz5fpPlNBZx/u/rmJjrnG2c1W6EwUkm63m4aGBmDuvb+ne41PIiP6anGtz19AYKGZsXi86aab\n+MlPfsLPf/5z1q5dy89//nM2btzI+++/T3p6+pUHEBD4BLlc+7H6d4/FfU5E3PVbu8kIvU/pXTrC\nRa29nD32F/qtt1LdoI+7oZ++cI4bCsKuW8OEuEfppYxkmLnrq6a2GmVCHffdknLp9X2cOPJnztVd\nQO04hPVIFe9XGwgYt+DRL0ETcrCqqAKbIY09b/w7tz8U29N61To9b76xA/eiVQSl4QLQw6MOrI4g\nGNNALAOJHKfHj1KhQWqooLHmKMXTuNMnEhE4Sohet1wUwCPT43fYYjK8RY4+fH4Xh4+eJGRYhFga\nICE3H7HXiUhjIC2vFKUrnNRjt3ThkKciNq4kFBLj06RQ0xoWNREBOeaM//MViRn95maQvFvNwbMn\nEMsUpOs1lJSWxs1cny/TuaHlWgP3rDDOSXTM1eV9JaEz05Izc+39fbl5Xcti61qfv4DAQjJj8fgP\n//AP/PKXv+T8+fPcf//97N27l0ceeQSNRsOvfvWrqzlHAYE5MV37sSuJuN72Q5Tfrot5rHSNjro9\nh8jOK43zzHCR7HhMFIZxLUJ9raxbEytkdn/4BmUbUmKOpZl84PuAVXdnRo/VVW2nqXMr6rTx8RKm\n1tsGQJ3gxWr3YpJ6cPe30GVXIk/KQCGT4h5oRa5QIA7IkIo8GPIqyQwlYqlrmCK8JwuPjv5hdLEN\nU8jJzuTcxSaKl6dFM7xDA83cVZLI/zkwRjB/MyGxlBAh7EPtaHV6XN4gUoWK5QXhzfndtnOoc9cR\ncAwgEQUJAIrMEhqaqsnNy2N41IFbUUJj7amYup0tdaM8fEnk5mZl8OTjD06a88BVqc93uc/UXEXH\nfOLsZpJwBZd3hc+n97eAgMD1zYzFo1qt5uc//3n0/6eeeoof//jHaDQapNJZVfwREPhUuZJbT6eL\nvznrdKFpN/SSDM0V3ZMTLUI2VxC11cz9N62e0aZtbh6i8rbYMjLlG5KxvHuOxMIv09F1BgCbLTbh\nJILDo8J1ajtB/2HMdTX0drnwSUrRltyP0mBAob0UO+gYQORo45Gv3EFuVmwcc1VtPc8d7onGNmab\n9DQP1ZFv7YuxthqMaVSOXCQl0E6K/pJ1a0sZb1edwZ+8hFAwCFIZISCUlMdox1H0Gi3yCTUWPcgQ\nAxJREL1Oh3XUhkRtwHOpvWHz2TrKb3qQsaHVHNm3A0IjhNBTknvLlBuGT8JiFPlMuRNS6bKMRAXz\nt9bP/XWvRpzdbFzh08UNtza3U1NbLfSFFhD4HDNj1bdjx47LPn7//ffPezICAp8EV3Lr6ZTJxNs0\ndcqkaTf0BzYsh8uMOfG1c7MyuNDcxvaPzOw728uJlv4Ya1i8TVsijW1XGCFB5gRgxDHG8YYWHAkV\nHD9YQ8Wmcctl7UErY0MmykoOccONYZFYgILjB+uoP+bBVH4vYr+SQEiEqOckj91fPGXe7eYenv24\nGQpuJgTRhKDMnKU0N55k9ebbY9djQmeXiOXv7ROd+FPLSZCJGfOMIhJLEIXC5b5SE0KYQlYgbFlV\n4MPttJGk05GgVod7VY9akI10YLCdjZZFMhjTSM1fhdVixWgyorRdjLtO8VjIjiG5WRlU9lp49uMq\nQpq0cMJP4RJq+pxkTSh2PtsxZxpnNzGxxe+RkmSqIDlnedwC4fGIdzxe3HDjsW4KVuujsY+p6blC\n1xUBgc8hMxaP//Zv/xbzv9/vZ3R0FLlczrJlywTxKHBNcTlr1OWSba60oc9k42w390S7oqhMRmxS\naYzrMN7r2we9cccac8lQAdbuDkIZZahWPYK5M4vW599Ca1DgcUkpqniMpb69UeEYoWKTic6BQSzd\nrWSNtJLg7CBD2sQ7+z7i/f2xmbXVDZ04A3Ksp2twev0QDKDRGRCb9BRnGDDYpo+vi7hJQ7Jm5Npk\nHA4HOq0BfzBEEBH+0W6+++AGstJN0XVdm+KidvAcCekbAUhQqwlazvE/Hr6RDavLeHnv0XmVTrka\nHUM6h9wxIjrCZKvebETrTKymUxNbAjTW7sOhzSVzUrH12bjCI+/9r5/5KaqUEH5fgOzCJDLzw5+j\nN959kbRVjwtdVwQEPofMWDxWVVVNOTYyMsI///M/U15evqCTEhD4NLlcsg3M3w1a3dAZtytKRGSk\npueSqLqZY7urkCvc6JWJ3L3pazSe+jjWClRrJ6/0a9g7jhL02FElpaNXy1GW38HY4pVYBkdJkIlY\nWl7C8a43ifd1V8udhAorcdX+fxQu6SS/PBLrGZtZe+pCG829HgLpZYgTJEgT1IyO9NPWO8DqggCP\n3LZ2+mu9JC5S9Bp6R/vQ6NLwjvaiTlARsDazeXkSG1aXRdc2zJpLdSr3MhqUoxN7eWxjUfS8mcaP\nXvY9WMCOIe3mHg6cacet9kfLDEVc+ZOLxL950oo7IZeuwbB7+4M36vnWekv02mbLdAXxa/bvJHNp\nZdwC4TN1hVeuXsfifTlkV0yNfRx228idMA4IXVcEBD4vzCtYUa/X873vfY9HH32Ub3zjGws0JQGB\nT5/pkm0Wgsu5DiPiQl/xMGsqHgbCm/uWlUaKekvY/eEbjLqHGBmBjMW3ULw4mXUli/iX1124k8YT\nR1SGNEyAo/EAUqsIsXtqz2aAsYAekUSGPNROfnlslnbOCjW/fuanpOwwcrbVgT/ryyBXg0KDzzWC\nTJ+Kq+UQobyphQwntlCUajzkZGdSUlqKq6GFoQt1JAwfJknnJ+iVs3LNQ3HntmF12bSCaqbxo9Ox\nkB1DIu+ZKGMlogRjtC5oMeH4z4lWveqGTtwJuZd6jV9aN3UKz378IVnppjmJrukSWyQSZ/TveAXC\nZ5r9PV3so9ejjHu+0HVFQOD6Z96ZLmazGZfLtRBzuSKNjY08+eSTNDc3k5uby09/+lNWrFjxiby2\ngMBCoZUGcU9zPJ5FbHB4gB/95EkQjaJUSTGo03n8K4/HiNuSDA01DhtjA834Ot4hQebA6ZJzw6IS\nHr9rDWUm/xRX+PHDwwxrH0LqDaCWOIFY8QigSglRvEVN8RY1tYd3cGJQjd+wFIlEim/QTE6KAadf\nzMt7j0bFyIilix3nfYSMhYy5DRiNeTjNfRRnGchOsGPUf0jZHeMu9KpTr5JqTJ5T7cDcrIxLtQkV\n0cLWM2Gumcy7dr/Nzv3bCUk94JNz/7YHcEiMKFNLyU5wcK43XJ4oUhQ9IeiMserZ/WK6BkfGheMl\nIl1v5iIepxN31kE/9XUnosXWI8zWcj5dGMfSwpvjni90XREQuP6ZsXj84Q9/OOWYw+Hg2LFj3H33\n3Qs6qXh4vV6+853v8Dd/8zc8+OCD7Nixg+985zvs37+fhISEq/76AtcnC5k0MdNx15Us4rVjrTCh\n6HbEdbj3lDlmnO4LNbSf+BWp+SKK14xnW7/ywa+BcRf7PRuW0/DCO6h8+1hxVyLh+pDjLuh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j1PjrIFuS4jbg0Dkixk9blKesbSi5XSwnw+ed8m9jRuoarehNsRSLE8th3vJxIGQ6YqLioHeoPY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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Instantiate the linear model and visualizer \n", "ridge = Ridge()\n", @@ -438,7 +460,7 @@ "\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" + "g = visualizer.poof() # Draw/show/poof the data\n" ] }, { @@ -685,7 +707,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.11" + "version": "2.7.12" } }, "nbformat": 4, diff --git a/yellowbrick/regressor.py b/yellowbrick/regressor.py index 56045f075..60a0e0ead 100644 --- a/yellowbrick/regressor.py +++ b/yellowbrick/regressor.py @@ -303,3 +303,8 @@ def finalize(self, **kwargs): # Set the axes labels self.ax.set_ylabel('Residuals') self.ax.set_xlabel("Predicted Value") + + # Set the legend + # Assumes that the first set of points are training data, and the next are test + # Assumes that you want a box around legend + self.ax.legend(['Training Data', 'Test Data'], loc = 'best', frameon = True) From 9d4df09e622b8548e94780e051a723c5eb170258 Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Sat, 5 Nov 2016 14:03:01 -0400 Subject: [PATCH 20/35] added single call wrapper for ClassificationReport for issue #78 --- yellowbrick/classifier.py | 50 +++++++++++++++++++++++++++++++-- yellowbrick/features/pcoords.py | 2 +- yellowbrick/regressor.py | 4 +-- 3 files changed, 50 insertions(+), 6 deletions(-) diff --git a/yellowbrick/classifier.py b/yellowbrick/classifier.py index b4096896c..c65c9041c 100644 --- a/yellowbrick/classifier.py +++ b/yellowbrick/classifier.py @@ -22,6 +22,7 @@ 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 @@ -69,7 +70,7 @@ def __init__(self, model, ax=None, classes=None, **kwargs): :param ax: the axis to plot the figure on. - :param estimator: the Scikit-Learn estimator + :param model: the Scikit-Learn estimator Should be an instance of a classifier, else the __init__ will return an error. @@ -192,6 +193,49 @@ def finalize(self, **kwargs): 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 + + 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 ########################################################################## @@ -210,7 +254,7 @@ def __init__(self, model, ax=None, **kwargs): :param ax: the axis to plot the figure on. - :param estimator: the Scikit-Learn estimator + :param model: the Scikit-Learn estimator Should be an instance of a classifier, else the __init__ will return an error. @@ -327,7 +371,7 @@ def __init__(self, model, ax=None, classes=None, **kwargs): :param ax: the axis to plot the figure on. - :param estimator: the Scikit-Learn estimator + :param model: the Scikit-Learn estimator Should be an instance of a classifier, else the __init__ will return an error. diff --git a/yellowbrick/features/pcoords.py b/yellowbrick/features/pcoords.py index 5175e2992..abc63e88b 100644 --- a/yellowbrick/features/pcoords.py +++ b/yellowbrick/features/pcoords.py @@ -73,7 +73,7 @@ def parallel_coordinates(X, y=None, ax=None, features=None, classes=None, ax : matplotlib axes Returns the axes that the parallel coordinates were drawn on. """ - # Insantiate the visualizer + # Instantiate the visualizer visualizer = ParallelCoordinates( ax, features, classes, color, colormap, vlines, vlines_kwds, **kwargs ) diff --git a/yellowbrick/regressor.py b/yellowbrick/regressor.py index 56045f075..d2ab02b7c 100644 --- a/yellowbrick/regressor.py +++ b/yellowbrick/regressor.py @@ -62,7 +62,7 @@ def __init__(self, model, ax=None, **kwargs): :param ax: the axis to plot the figure on. - :param estimator: the Scikit-Learn estimator + :param model: the Scikit-Learn estimator Should be an instance of a regressor, else the __init__ will return an error. @@ -180,7 +180,7 @@ def __init__(self, model, ax=None, **kwargs): :param ax: the axis to plot the figure on. - :param estimator: the Scikit-Learn estimator + :param model: the Scikit-Learn estimator Should be an instance of a regressor, else the __init__ will return an error. From 7c3bcec90d3201f4a13ab3d402e747318e46b5a7 Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Sat, 5 Nov 2016 14:24:19 -0400 Subject: [PATCH 21/35] added single call wrapper for ROCAUC for issue #78 --- yellowbrick/classifier.py | 41 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) diff --git a/yellowbrick/classifier.py b/yellowbrick/classifier.py index c65c9041c..8043b33db 100644 --- a/yellowbrick/classifier.py +++ b/yellowbrick/classifier.py @@ -353,6 +353,47 @@ def finalize(self, **kwargs): 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 + + 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 ########################################################################## From 0e246409e964a94680dd75a18d2cd4be287ff403 Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Sat, 5 Nov 2016 14:28:28 -0400 Subject: [PATCH 22/35] added single call wrapper for ClassBalance for issue #78 --- yellowbrick/classifier.py | 43 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 43 insertions(+) diff --git a/yellowbrick/classifier.py b/yellowbrick/classifier.py index 8043b33db..0b496cb12 100644 --- a/yellowbrick/classifier.py +++ b/yellowbrick/classifier.py @@ -522,3 +522,46 @@ def finalize(self, **kwargs): # 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 + + 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 From 7af61d72624b36c5f2eced4bda8b968da7bfc2e8 Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Sat, 5 Nov 2016 17:50:52 -0400 Subject: [PATCH 23/35] added single call wrappers for PredictionError and ResidualsPlot, closes issue #78 --- yellowbrick/classifier.py | 6 +-- yellowbrick/regressor.py | 87 +++++++++++++++++++++++++++++++++++++-- 2 files changed, 87 insertions(+), 6 deletions(-) diff --git a/yellowbrick/classifier.py b/yellowbrick/classifier.py index 0b496cb12..46dfc2c2e 100644 --- a/yellowbrick/classifier.py +++ b/yellowbrick/classifier.py @@ -213,7 +213,7 @@ def classification_report(model, X, y=None, ax=None, classes=None, **kwargs): ax : matplotlib axes The axes to plot the figure on. - model : the Scikit-Learn estimator + model : the Scikit-Learn estimator (should be a classifier) classes : list of strings The names of the classes in the target @@ -373,7 +373,7 @@ def roc_auc(model, X, y=None, ax=None, **kwargs): ax : matplotlib axes The axes to plot the figure on. - model : the Scikit-Learn estimator + model : the Scikit-Learn estimator (should be a classifier) Returns ------- @@ -543,7 +543,7 @@ def class_balance(model, X, y=None, ax=None, classes=None, **kwargs): ax : matplotlib axes The axes to plot the figure on. - model : the Scikit-Learn estimator + model : the Scikit-Learn estimator (should be a classifier) classes : list of strings The names of the classes in the target diff --git a/yellowbrick/regressor.py b/yellowbrick/regressor.py index d2ab02b7c..ff5b4269b 100644 --- a/yellowbrick/regressor.py +++ b/yellowbrick/regressor.py @@ -21,6 +21,8 @@ import matplotlib as mpl import matplotlib.pyplot as plt +from sklearn.cross_validation import train_test_split + from .bestfit import draw_best_fit from .exceptions import YellowbrickTypeError from .utils import get_model_name, isestimator, isregressor @@ -83,7 +85,6 @@ def __init__(self, model, ax=None, **kwargs): 'line': kwargs.pop('line_color', '#2B94E9'), } - def score(self, X, y=None, **kwargs): """ Originally score for prediction error was conceived as generating @@ -160,6 +161,47 @@ def finalize(self, **kwargs): self.ax.set_xlabel('Measured') +def prediction_error(model, X, y=None, ax=None, **kwargs): + """Quick method: + + Plot the actual targets from the dataset against the + predicted values generated by our model(s). + + This helper function is a quick wrapper to utilize the PredictionError + 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 regressor) + + Returns + ------- + ax : matplotlib axes + Returns the axes that the prediction error plot was drawn on. + """ + # Instantiate the visualizer + visualizer = PredictionError(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 + + ########################################################################## ## Residuals Plots ########################################################################## @@ -205,7 +247,6 @@ def __init__(self, model, ax=None, **kwargs): 'line': kwargs.pop('line_color', '#333333'), } - def fit(self, X, y=None, **kwargs): """ Parameters @@ -283,7 +324,6 @@ def draw(self, y_pred, residuals, train=False, **kwargs): return self.ax - def finalize(self, **kwargs): """ Finalize executes any subclass-specific axes finalization steps. @@ -303,3 +343,44 @@ def finalize(self, **kwargs): # Set the axes labels self.ax.set_ylabel('Residuals') self.ax.set_xlabel("Predicted Value") + + +def residuals_plot(model, X, y=None, ax=None, **kwargs): + """Quick method: + + Plot the residuals on the vertical axis and the + independent variable on the horizontal axis. + + This helper function is a quick wrapper to utilize the ResidualsPlot + 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 regressor) + + Returns + ------- + ax : matplotlib axes + Returns the axes that the residuals plot was drawn on. + """ + # Instantiate the visualizer + visualizer = ResidualsPlot(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 From 5861cf12fd51a5ca2841b50940263506e273dc4f Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Mon, 7 Nov 2016 07:48:52 -0500 Subject: [PATCH 24/35] more utility tests and some new decorators, closed #89 --- tests/test_utils.py | 306 +++++++++++++++++++++++++++++++- yellowbrick/base.py | 4 +- yellowbrick/features/pcoords.py | 1 - yellowbrick/utils.py | 140 ++++++++++++++- 4 files changed, 433 insertions(+), 18 deletions(-) diff --git a/tests/test_utils.py b/tests/test_utils.py index 1b041de86..23da40417 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -17,17 +17,31 @@ ## Imports ########################################################################## +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 LinearRegression -from sklearn.neighbors import LSHForest -from sklearn.pipeline import Pipeline -import unittest +from sklearn.linear_model import LogisticRegression +from sklearn.ensemble import RandomForestClassifier -from yellowbrick.utils import get_model_name, isestimator +from yellowbrick.utils import * +from yellowbrick.base import Visualizer, ScoreVisualizer, ModelVisualizer -class ModelNameTests(unittest.TestCase): +########################################################################## +## Model Utility Tests +########################################################################## + +class ModelUtilityTests(unittest.TestCase): + + ##//////////////////////////////////////////////////////////////////// + ## get_model_name testing + ##//////////////////////////////////////////////////////////////////// def test_real_model(self): """ @@ -35,8 +49,10 @@ def test_real_model(self): """ model1 = LassoCV() model2 = LSHForest() + model3 = KMeans() self.assertEqual(get_model_name(model1), 'LassoCV') self.assertEqual(get_model_name(model2), 'LSHForest') + self.assertEqual(get_model_name(model3), 'KMeans') def test_pipeline(self): """ @@ -58,12 +74,35 @@ def test_str_input(self): """ self.assertRaises(TypeError, get_model_name, 'helloworld') + ##//////////////////////////////////////////////////////////////////// + ## isestimator testing + ##//////////////////////////////////////////////////////////////////// + + def test_estimator_alias(self): + """ + Assert is_estimator aliases isestimator + """ + self.assertEqual( + is_estimator(LinearRegression), isestimator(LinearRegression) + ) + def test_estimator_instance(self): """ Test that isestimator works for instances """ - model = LinearRegression() - self.assertTrue(isestimator(model)) + + models = ( + LinearRegression(), + LogisticRegression(), + KMeans(), + LSHForest(), + PCA(), + LassoCV(), + RandomForestClassifier(), + ) + + for model in models: + self.assertTrue(isestimator(model)) def test_pipeline_instance(self): """ @@ -80,7 +119,19 @@ def test_estimator_class(self): """ Test that isestimator works for classes """ - self.assertTrue(LinearRegression) + models = ( + LinearRegression, + LogisticRegression, + KMeans, + LSHForest, + PCA, + LassoCV, + RandomForestClassifier, + ) + + for model in models: + self.assertTrue(inspect.isclass(model)) + self.assertTrue(isestimator(model)) def test_collection_not_estimator(self): """ @@ -92,6 +143,245 @@ def test_collection_not_estimator(self): things = ['pepper', 'sauce', 'queen'] self.assertFalse(isestimator(things)) + def test_visualizer_is_estimator(self): + """ + Assert that a Visualizer is an estimator + """ + self.assertTrue(is_estimator(Visualizer)) + self.assertTrue(is_estimator(Visualizer())) + self.assertTrue(is_estimator(ScoreVisualizer)) + self.assertTrue(is_estimator(ScoreVisualizer(LinearRegression()))) + self.assertTrue(is_estimator(ModelVisualizer)) + self.assertTrue(is_estimator(ModelVisualizer(LogisticRegression()))) + + ##//////////////////////////////////////////////////////////////////// + ## isregressor testing + ##//////////////////////////////////////////////////////////////////// + + def test_regressor_alias(self): + """ + Assert is_regressor aliases isregressor + """ + instance = LinearRegression() + self.assertEqual(is_regressor(instance), isregressor(instance)) + + def test_regressor_instance(self): + """ + Test that is_regressor works for instances + """ + + # Test regressors are identified correctly + regressors = ( + LassoCV, + LinearRegression, + ) + + for model in regressors: + instance = model() + self.assertTrue(is_regressor(instance)) + + # Test that non-regressors are identified correctly + notregressors = ( + KMeans, + PCA, + LSHForest, + LogisticRegression, + RandomForestClassifier, + ) + + for model in notregressors: + instance = model() + self.assertFalse(is_regressor(instance)) + + def test_regressor_class(self): + """ + Test that is_regressor works for classes + """ + + # Test regressors are identified correctly + regressors = ( + LassoCV, + LinearRegression, + ) + + for klass in regressors: + self.assertTrue(inspect.isclass(klass)) + self.assertTrue(is_regressor(klass)) + + # Test that non-regressors are identified correctly + notregressors = ( + KMeans, + PCA, + LSHForest, + LogisticRegression, + RandomForestClassifier, + ) + + for klass in notregressors: + self.assertTrue(inspect.isclass(klass)) + self.assertFalse(is_regressor(klass)) + + def test_regressor_pipeline(self): + """ + Test that is_regressor works for pipelines + """ + model = Pipeline([ + ('reduce_dim', PCA()), + ('linreg', LinearRegression()) + ]) + + self.assertTrue(is_regressor(model)) + + def test_regressor_visualizer(self): + """ + Test that is_regressor works on visualizers + """ + model = ScoreVisualizer(LinearRegression()) + self.assertTrue(is_regressor(model)) + + ##//////////////////////////////////////////////////////////////////// + ## isclassifier testing + ##//////////////////////////////////////////////////////////////////// + + def test_classifier_alias(self): + """ + Assert is_classifier aliases isclassifier + """ + instance = LogisticRegression() + self.assertEqual(is_classifier(instance), isclassifier(instance)) + + def test_classifier_instance(self): + """ + Test that is_classifier works for instances + """ + + # Test classifiers are identified correctly + classifiers = ( + LogisticRegression, + RandomForestClassifier, + ) + + for model in classifiers: + instance = model() + self.assertTrue(is_classifier(instance)) + + # Test that non-classifiers are identified correctly + notclassifiers = ( + KMeans, + PCA, + LSHForest, + LinearRegression, + LassoCV, + ) + + for model in notclassifiers: + instance = model() + self.assertFalse(is_classifier(instance)) + + def test_classifier_class(self): + """ + Test that is_classifier works for classes + """ + + # Test classifiers are identified correctly + classifiers = ( + RandomForestClassifier, + LogisticRegression, + ) + + for klass in classifiers: + self.assertTrue(inspect.isclass(klass)) + self.assertTrue(is_classifier(klass)) + + # Test that non-regressors are identified correctly + notclassifiers = ( + KMeans, + PCA, + LSHForest, + LassoCV, + LinearRegression, + ) + + for klass in notclassifiers: + self.assertTrue(inspect.isclass(klass)) + self.assertFalse(is_classifier(klass)) + + def test_classifier_pipeline(self): + """ + Test that is_regressor works for pipelines + """ + model = Pipeline([ + ('reduce_dim', PCA()), + ('linreg', LogisticRegression()) + ]) + + self.assertTrue(is_classifier(model)) + + def test_classifier_visualizer(self): + """ + Test that is_classifier works on visualizers + """ + model = ScoreVisualizer(RandomForestClassifier()) + self.assertTrue(is_classifier(model)) + +########################################################################## +## Decorator Tests +########################################################################## + +class DecoratorTests(unittest.TestCase): + """ + Tests for the decorator utilities. + """ + + def test_docutil(self): + """ + Test the docutil docstring copying methodology. + """ + + class Visualizer(object): + + def __init__(self): + """ + This is the correct docstring. + """ + pass + + + def undecorated(*args, **kwargs): + """ + This is an undecorated function string. + """ + pass + + # Test the undecorated string to protect from magic + self.assertEqual( + undecorated.__doc__.strip(), "This is an undecorated function string." + ) + + # Decorate manually and test the newly decorated return function. + decorated = docutil(Visualizer.__init__)(undecorated) + self.assertEqual( + decorated.__doc__.strip(), "This is the correct docstring." + ) + + # Assert that decoration modifies the original function. + self.assertEqual( + undecorated.__doc__.strip(), "This is the correct docstring." + ) + + @docutil(Visualizer.__init__) + def sugar(*args, **kwargs): + pass + + # Assert that syntactic sugar works as expected. + self.assertEqual( + sugar.__doc__.strip(), "This is the correct docstring." + ) + + +########################################################################## +## Execute Tests +########################################################################## if __name__ == "__main__": unittest.main() diff --git a/yellowbrick/base.py b/yellowbrick/base.py index 9eb6c54b4..4e806f8a5 100644 --- a/yellowbrick/base.py +++ b/yellowbrick/base.py @@ -211,7 +211,7 @@ class ModelVisualizer(Visualizer): and enables the user to visualize the performance of models across a range of hyperparameter values (e.g. using VisualGridsearch and ValidationCurve). """ - def __init__(self, ax=None, **kwargs): + def __init__(self, model, ax=None, **kwargs): """ Parameters ---------- @@ -222,7 +222,7 @@ def __init__(self, ax=None, **kwargs): These parameters can be influenced later on in the visualization process, but can and should be set as early as possible. """ - super(ScoreVisualizer, self).__init__(ax=ax, **kwargs) + super(ModelVisualizer, self).__init__(ax=ax, **kwargs) self.estimator = model diff --git a/yellowbrick/features/pcoords.py b/yellowbrick/features/pcoords.py index abc63e88b..be69cce8c 100644 --- a/yellowbrick/features/pcoords.py +++ b/yellowbrick/features/pcoords.py @@ -26,7 +26,6 @@ from yellowbrick.exceptions import YellowbrickTypeError from yellowbrick.style.colors import resolve_colors, get_color_cycle - ########################################################################## ## Quick Methods ########################################################################## diff --git a/yellowbrick/utils.py b/yellowbrick/utils.py index 4f873d8ea..04748ff5a 100644 --- a/yellowbrick/utils.py +++ b/yellowbrick/utils.py @@ -1,24 +1,34 @@ -# utils +# yellowbrick.utils +# Utility functions and helpers for the Yellowbrick library. # # Author: Jason Keung -# Patrick O'Melveny -# Created: Thurs Jun 2 15:33:18 2016 -0500 +# Author: Patrick O'Melveny +# Author: Benjamin Bengfort +# Author: Rebecca Bilbro +# Created: Thu Jun 02 15:33:18 2016 -0500 # +# Copyright (C) 2016 District Data LAbs # For license information, see LICENSE.txt +# +# ID: utils.py [] jason.s.keung@gmail.com $ """ -Utility functions for yellowbrick +Utility functions and helpers for the Yellowbrick library. """ ########################################################################## ## Imports ########################################################################## +import inspect + +from functools import wraps from sklearn.pipeline import Pipeline from sklearn.base import BaseEstimator from yellowbrick.exceptions import YellowbrickTypeError + ########################################################################## ## Model detection utilities ########################################################################## @@ -26,6 +36,11 @@ def get_model_name(model): """ Detects the model name for a Scikit-Learn model or pipeline + + Parameters + ---------- + model: class or instance + The object to determine the name for """ if not is_estimator(model): raise YellowbrickTypeError( @@ -48,8 +63,13 @@ def get_model_name(model): def is_estimator(model): """ Determines if a model is an estimator using issubclass and isinstance. + + Parameters + ---------- + model: class or instance + The object to test whether or not is a Scikit-Learn estimator. """ - if type(model) == type: + if inspect.isclass(model): return issubclass(model, BaseEstimator) return isinstance(model, BaseEstimator) @@ -61,8 +81,23 @@ def is_estimator(model): def is_classifier(estimator): """ Returns True if the given estimator is (probably) a classifier. - From: https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py#L526 + + Parameters + ---------- + estimator: class or instance + The object to test whether or not is a Scikit-Learn classifier. + + See Also: + https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py#L526 """ + # 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_classifier(estimator.estimator) + + # Test the _estimator_type property return getattr(estimator, "_estimator_type", None) == "classifier" # Alias for closer name to isinstance and issubclass @@ -72,11 +107,23 @@ def is_classifier(estimator): def is_regressor(estimator): """ Returns True if the given estimator is (probably) a regressor. - From: https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py#L531 + + Parameters + ---------- + model: class or instance + The object to test whether or not is a Scikit-Learn regressor. + + See Also: + https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py#L531 """ + # 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_regressor(estimator.estimator) + + # Test the _estimator_type property return getattr(estimator, "_estimator_type", None) == "regressor" # Alias for closer name to isinstance and issubclass @@ -86,6 +133,11 @@ def is_regressor(estimator): def is_dataframe(obj): """ Returns True if the given object is a Pandas Data Frame. + + Parameters + ---------- + obj: instance + The object to test whether or not is a Pandas DataFrame. """ try: # This is the best method of type checking @@ -97,3 +149,77 @@ def is_dataframe(obj): # Alias for closer name to isinstance and issubclass isdataframe = is_dataframe + + +########################################################################## +## Decorators +########################################################################## + +def memoized(fget): + """ + Return a property attribute for new-style classes that only calls its + getter on the first access. The result is stored and on subsequent + accesses is returned, preventing the need to call the getter any more. + + See Also: + https://github.com/estebistec/python-memoized-property + + Parameters + ---------- + fget: function + The getter method to memoize for subsequent access. + """ + attr_name = '_{0}'.format(fget.__name__) + + @wraps(fget) + def fget_memoized(self): + if not hasattr(self, attr_name): + setattr(self, attr_name, fget(self)) + return getattr(self, attr_name) + + return property(fget_memoized) + + +class docutil(object): + """ + This decorator can be used to apply the doc string from another function + to the decorated function. This is used for our single call wrapper + functions who implement the visualizer API without forcing the user to + jump through all the hoops. The docstring of both the visualizer and the + single call wrapper should be identical, this decorator ensures that we + only have to edit one doc string. + + Usage: + + @docutil(Visualizer.__init__) + def visualize(*args, **kwargs): + pass + + The basic usage is that you instantiate the decorator with the function + whose docstring you want to copy, then apply that decorator to the the + function whose docstring you would like modified. + + Note that this decorator performs no wrapping of the target function. + """ + + def __init__(self, func): + """Create a decorator to document other functions with the specified + function's doc string. + + Parameters + ---------- + func : function + The function whose doc string we should decorate with + """ + self.doc = func.__doc__ + + def __call__(self, func): + """Modify the decorated function with the stored doc string. + + Parameters + ---------- + func: function + The function to apply the saved doc string to. + """ + func.__doc__ = self.doc + return func From 0f5966a8a233d3b0a700f23e36024e1f246a0be0 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Thu, 29 Dec 2016 08:40:42 -0500 Subject: [PATCH 25/35] documentation fixes --- Makefile | 1 + docs/api/yellowbrick.rst | 51 +++++++++++-------- docs/conf.py | 6 ++- .../Getting Started with Yellowbrick.ipynb | 51 ++++++++++++++----- tests/test_utils.py | 12 +++-- 5 files changed, 81 insertions(+), 40 deletions(-) diff --git a/Makefile b/Makefile index e0c7a5b86..c7334eef0 100644 --- a/Makefile +++ b/Makefile @@ -22,6 +22,7 @@ clean: -rm -rf site -rm -rf classes_$(PROJECT).png -rm -rf packages_$(PROJECT).png + -rm -rf docs/_build # Targets for testing test: diff --git a/docs/api/yellowbrick.rst b/docs/api/yellowbrick.rst index c7b063df2..a6763a4f3 100644 --- a/docs/api/yellowbrick.rst +++ b/docs/api/yellowbrick.rst @@ -20,18 +20,18 @@ yellowbrick.base module :undoc-members: :show-inheritance: -yellowbrick.classifier module ------------------------------ +yellowbrick.bestfit module +----------------------- -.. automodule:: yellowbrick.classifier +.. automodule:: yellowbrick.bestfit :members: :undoc-members: :show-inheritance: -yellowbrick.color_utils module ------------------------------- +yellowbrick.classifier module +----------------------------- -.. automodule:: yellowbrick.color_utils +.. automodule:: yellowbrick.classifier :members: :undoc-members: :show-inheritance: @@ -44,42 +44,51 @@ yellowbrick.exceptions module :undoc-members: :show-inheritance: -yellowbrick.regressor module ----------------------------- +yellowbrick.features module +--------------------------- -.. automodule:: yellowbrick.regressor +.. automodule:: yellowbrick.features :members: :undoc-members: :show-inheritance: + -yellowbrick.utils module ------------------------- +yellowbrick.pipeline module +--------------------------- -.. automodule:: yellowbrick.utils +.. automodule:: yellowbrick.pipeline :members: :undoc-members: :show-inheritance: -yellowbrick.version module --------------------------- +yellowbrick.regressor module +---------------------------- -.. automodule:: yellowbrick.version +.. automodule:: yellowbrick.regressor + :members: + :undoc-members: + :show-inheritance: + +yellowbrick.style module +--------------------------- + +.. automodule:: yellowbrick.style :members: :undoc-members: :show-inheritance: -yellowbrick.yb_palettes module ------------------------------- +yellowbrick.utils module +------------------------ -.. automodule:: yellowbrick.yb_palettes +.. automodule:: yellowbrick.utils :members: :undoc-members: :show-inheritance: -yellowbrick.yb_rcmod module ---------------------------- +yellowbrick.version module +-------------------------- -.. automodule:: yellowbrick.yb_rcmod +.. automodule:: yellowbrick.version :members: :undoc-members: :show-inheritance: diff --git a/docs/conf.py b/docs/conf.py index 4f14dbc3f..4e3d8853f 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -21,6 +21,8 @@ import sys sys.path.insert(0, os.path.abspath('..')) +import yellowbrick as yb + # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. @@ -64,9 +66,9 @@ # built documents. # # The short X.Y version. -version = '0.1' +version = yb.__version__ # The full version, including alpha/beta/rc tags. -release = '0.1' +release = yb.__version__ # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. diff --git a/examples/Getting Started with Yellowbrick.ipynb b/examples/Getting Started with Yellowbrick.ipynb index fc7774b77..c9706f3e0 100644 --- a/examples/Getting Started with Yellowbrick.ipynb +++ b/examples/Getting Started with Yellowbrick.ipynb @@ -16,19 +16,9 @@ "metadata": {}, "source": [ "# Getting Started with Yellowbrick \n", - "\n", - "Thank you for agreeing to help alpha test the Yellowbrick library!" + "\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -52,7 +42,44 @@ "\n", "You're more than welcome to choose a dataset of your own, but we do ask that you make at least the notebook containing your testing results publically 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! \n", "\n", - "### Step Two: Notebook \n" + "### Step Two: Notebook \n", + "\n", + "Create a notebook in a GitHub repository. We suggest the following:\n", + "\n", + "1. Fork the Yellowbrick repository \n", + "2. Under the examples directory, create a directory with your GitHub username \n", + "3. Create the notebook in examples/username/testing.ipynb \n", + "\n", + "You don't have to fork Yellowbrick, and can send us the notebook via Gist or your own repository, but if you do, you can initiate a pull request to have your example added to our gallery! \n", + "\n", + "### Step Three: Model with Yellowbrick and Scikit-Learn\n", + "\n", + "Add the following to the notebook:\n", + "\n", + "- A title in markdown \n", + "- A description of the dataset and where it was obtained \n", + "- A section that loads the data into a DataFrame or numpy matrix \n", + "\n", + "Then conduct the following modeling activities:\n", + "\n", + "- Feature analysis using Scikit-Learn and Yellowbrick \n", + "- Estimator fitting using Scikit-Learn and Yellowbrick \n", + "\n", + "You can follow along with the guide above, the examples directory, or even create your own custom visualizers! The goal is that you create an end-to-end model from data loading to estimator(s) with visualizers along the way. \n", + "\n", + "**IMPORTANT**: please make sure you record all errors that you get and any tracebacks you receive for step four! \n", + "\n", + "### Step Four: Feedback \n", + "\n", + "Finally, submit feedback via the Google Form we have created:\n", + "\n", + "[https://goo.gl/forms/naoPUMFa1xNcafY83](https://goo.gl/forms/naoPUMFa1xNcafY83)\n", + "\n", + "This form is allowing us to aggregate multiple submissions and bugs so that we can coordinate the creation and management of issues. If you are the first to report a bug or feature request, we will make sure you're notified about the created issue! \n", + "\n", + "### Step Five: Thanks! \n", + "\n", + "Thank you for helping us make Yellowbrick better! We'd love to see pull requests for features you thought would be neat. We'll also be doing a user study, that we'd love for you to participate in. Stay tuned for more great things from Yellowbrick!" ] } ], diff --git a/tests/test_utils.py b/tests/test_utils.py index 23da40417..48ae79f65 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -1,16 +1,18 @@ # tests.test_utils -# Test the export module - to generate a corpus for machine learning. +# Test the yellowbrick utilities module. # # Author: Jason Keung -# Patrick O'Melveny -# Created: Thurs Jun 2 15:33:18 2016 -0500 +# Author: Patrick O'Melveny +# Author: Benjamin Bengfort +# Created: Thu Jun 02 15:33:18 2016 -0500 # +# Copyright (C) 2016 District Data LAbs # For license information, see LICENSE.txt # - +# ID: test_utils.py [] jason.s.keung@gmail.com $ """ -Test the utils module - to generate a corpus for machine learning. +Test the yellowbrick utilities module. """ ########################################################################## From c63c0298970a390f1a1dc514056adf025f30ad7e Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Wed, 4 Jan 2017 17:19:56 -0500 Subject: [PATCH 26/35] updates to documentation and additional rst docs towards issue #36 --- docs/api/modules.rst | 2 + docs/api/yellowbrick.features.rst | 37 ++++++++++++++++++ docs/api/yellowbrick.style.rst | 29 ++++++++++++++ .../yellowbrick_classhierarchy.png | Bin docs/index.rst | 3 +- 5 files changed, 69 insertions(+), 2 deletions(-) create mode 100644 docs/api/yellowbrick.features.rst create mode 100644 docs/api/yellowbrick.style.rst rename docs/{api => images}/yellowbrick_classhierarchy.png (100%) diff --git a/docs/api/modules.rst b/docs/api/modules.rst index 6814db03e..be66dabad 100644 --- a/docs/api/modules.rst +++ b/docs/api/modules.rst @@ -5,3 +5,5 @@ API Reference :maxdepth: 4 yellowbrick + yellowbrick.style + yellowbrick.features diff --git a/docs/api/yellowbrick.features.rst b/docs/api/yellowbrick.features.rst new file mode 100644 index 000000000..178f92dd0 --- /dev/null +++ b/docs/api/yellowbrick.features.rst @@ -0,0 +1,37 @@ +yellowbrick.features package +=================== + +Submodules +---------- + +yellowbrick.features.base module +--------------------------- + +.. automodule:: yellowbrick.features.base + :members: + :undoc-members: + :show-inheritance: + +yellowbrick.features.pcoords module +--------------------------- + +.. automodule:: yellowbrick.features.pcoords + :members: + :undoc-members: + :show-inheritance: + +yellowbrick.features.radviz module +--------------------------- + +.. automodule:: yellowbrick.features.radviz + :members: + :undoc-members: + :show-inheritance: + +yellowbrick.features.rankd module +--------------------------- + +.. automodule:: yellowbrick.features.rankd + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/api/yellowbrick.style.rst b/docs/api/yellowbrick.style.rst new file mode 100644 index 000000000..e64a315e5 --- /dev/null +++ b/docs/api/yellowbrick.style.rst @@ -0,0 +1,29 @@ +yellowbrick.style package +=================== + +Submodules +---------- + +yellowbrick.style.colors module +--------------------------- + +.. automodule:: yellowbrick.style.colors + :members: + :undoc-members: + :show-inheritance: + +yellowbrick.style.palettes module +--------------------------- + +.. automodule:: yellowbrick.style.palettes + :members: + :undoc-members: + :show-inheritance: + +yellowbrick.style.rcmod module +--------------------------- + +.. automodule:: yellowbrick.style.rcmod + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/api/yellowbrick_classhierarchy.png b/docs/images/yellowbrick_classhierarchy.png similarity index 100% rename from docs/api/yellowbrick_classhierarchy.png rename to docs/images/yellowbrick_classhierarchy.png diff --git a/docs/index.rst b/docs/index.rst index 67aa52988..f5ae1a2c5 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -52,7 +52,7 @@ Contents: ========== .. toctree:: - :maxdepth: 2 + :maxdepth: 4 setup api/modules @@ -65,4 +65,3 @@ Indices and tables * :ref:`genindex` * :ref:`modindex` * :ref:`search` - From 2bf471e43f60f6a3427f1257448d9de18032b57d Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Fri, 13 Jan 2017 15:08:58 -0500 Subject: [PATCH 27/35] Added color sequences Closed #57 --- examples/palettes.ipynb | 2156 ++++++++++++++++++++++++++++- tests/test_style/test_palettes.py | 33 +- yellowbrick/style/palettes.py | 416 +++++- 3 files changed, 2538 insertions(+), 67 deletions(-) diff --git a/examples/palettes.ipynb b/examples/palettes.ipynb index e8faf5ce7..17336a97b 100644 --- a/examples/palettes.ipynb +++ b/examples/palettes.ipynb @@ -20,7 +20,7 @@ "%matplotlib inline \n", "\n", "import matplotlib.pyplot as plt\n", - "from yellowbrick.style.palettes import PALETTES, color_palette" + "from yellowbrick.style.palettes import PALETTES, SEQUENCES, color_palette" ] }, { @@ -34,7 +34,7 @@ "data": { "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ['blue', 'green', 'red', 'maroon', 'yellow', 'cyan']\n", + "for palette in PALETTES.keys():\n", + " color_palette(palette).plot()\n", + " plt.title(palette, loc='left')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python2.7/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n", + " max_open_warning, RuntimeWarning)\n" + ] + }, + { + "data": { + "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -212,10 +2306,10 @@ } ], "source": [ - "# ['blue', 'green', 'red', 'maroon', 'yellow', 'cyan']\n", - "for palette in PALETTES.keys():\n", - " color_palette(palette).plot()\n", - " plt.title(palette, loc='left')" + "for name, maps in SEQUENCES.items():\n", + " for num, palette in maps.items():\n", + " color_palette(palette).plot()\n", + " plt.title(\"{} - {}\".format(name, num), loc='left')" ] } ], @@ -235,7 +2329,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.11" + "version": "2.7.12" } }, "nbformat": 4, diff --git a/tests/test_style/test_palettes.py b/tests/test_style/test_palettes.py index 0419790cd..3b7447ba3 100644 --- a/tests/test_style/test_palettes.py +++ b/tests/test_style/test_palettes.py @@ -26,7 +26,8 @@ from yellowbrick.style.palettes import * from yellowbrick.style.colors import get_color_cycle from yellowbrick.style.rcmod import set_aesthetic, set_palette -from yellowbrick.style.palettes import ColorPalette, PALETTES +from yellowbrick.style.palettes import color_sequence, color_palette +from yellowbrick.style.palettes import ColorPalette, PALETTES, SEQUENCES from tests.base import VisualTestCase @@ -278,6 +279,36 @@ def test_preserved_palette_length(self): pal_out = color_palette(pal_in) self.assertEqual(pal_in, pal_out) + def test_color_sequence(self): + """ + Ensure the color sequence returns listed colors. + """ + for name, ncols in SEQUENCES.items(): + for n in ncols.keys(): + cmap = color_sequence(name, n) + + def test_color_sequence_default(self): + """ + Assert the default color sequence is RdBu + """ + cmap = color_sequence() + + def test_color_sequence_unrecocognized(self): + """ + Test value errors for unrecognized sequences + """ + with self.assertRaises(YellowbrickValueError): + cmap = color_sequence('PepperBucks', 3) + + def test_color_sequence_bounds(self): + """ + Test color sequence out of bounds value error + """ + with self.assertRaises(YellowbrickValueError): + cmap = color_sequence('RdBu', 18) + + with self.assertRaises(YellowbrickValueError): + cmap = color_sequence('RdBu', 2) if __name__ == "__main__": unittest.main() diff --git a/yellowbrick/style/palettes.py b/yellowbrick/style/palettes.py index 26b488256..7700087a1 100644 --- a/yellowbrick/style/palettes.py +++ b/yellowbrick/style/palettes.py @@ -1,27 +1,6 @@ -# yellowbrick.style.palettes -# Implements the variety of colors that yellowbrick allows access to by name. -# -# Author: Patrick O'Melveny -# Author: Benjamin Bengfort -# Created: Tue Oct 04 15:30:15 2016 -0400 -# -# Copyright (C) 2016 District Data Labs -# For license information, see LICENSE.txt -# -# ID: palettes.py [] pvomelveny@gmail.com $ - -""" -Implements the variety of colors that yellowbrick allows access to by name. -This code was originally based on Seaborn's rcmody.py but has since been -cleaned up to be Yellowbrick-specific and to dereference tools we don't use. - -Note that these functions alter the matplotlib rc dictionary on the fly. -""" - -########################################################################## -## Imports -########################################################################## - +# yellowbrick.style.palettes # Implements the variety of colors that yellowbrick allows access to by nam # +# Author: Patrick O'Melveny # Copyright (C) 2016 District Data Lab# For license information, see LICENSE.txt +# D: palettes.py [] pvomelveny@gmail.com "" Implements the variety of colors that yellowbrick allows access to by name This code was originally based on Seaborn's rcmody.py but has since beecleaned up to be Yellowbrick-specific and to dereference tools we don't use. Note that these functions alter the matplotlib rc dictionary on the fly. ######################################################################### ## Import ######################################################################### from __future__ import division from itertools import cycle @@ -62,8 +41,8 @@ "yellowbrick": ['#0272a2', '#9fc377', '#ca0b03', '#a50258', '#d7c703', '#88cada'], # The following are from ColorBrewer - "accent": ['#7fc97f', '#beaed4', '#fdc086', '#ffff99', '#386cb0', '#f0027f'], - "dark": ['#1b9e77', '#d95f02', '#7570b3', '#e7298a', '#66a61e', '#e6ab02'], + "accent": ['#386cb0', '#7fc97f', '#f0027f', '#beaed4', '#ffff99', '#fdc086'], + "dark": ['#7570b3', '#66a61e', '#d95f02', '#e7298a', '#e6ab02', '#1b9e77'], "pastel": ['#cbd5e8', '#b3e2cd', '#fdcdac', '#f4cae4', '#fff2ae', '#e6f5c9'], "bold": ['#377eb8', '#4daf4a', '#e41a1c', '#984ea3', '#ffff33', '#ff7f00'], "muted": ['#80b1d3', '#8dd3c7', '#fb8072', '#bebada', '#ffffb3', '#fdb462'], @@ -85,19 +64,287 @@ # Other palettes "flatui": ["#34495e", "#2ecc71", "#e74c3c", "#9b59b6", "#f4d03f", "#3498db"], - # Longer palettes that do not map to bgrmyck color space. - "ddl_heat": ['#DBDBDB', '#DCD5CC', '#DCCEBE', '#DDC8AF', '#DEC2A0', '#DEBB91', - '#DFB583', '#DFAE74', '#E0A865', '#E1A256', '#E19B48', '#E29539'], - - "paired": ["#fdbf6f", "#ff7f00", "#cab2d6", "#6a3d9a", "#ffff99", "#b15928", - "#b2df8a", "#33a02c", "#fb9a99", "#e31a1c", "#a6cee3", "#1f78b4"], + "paired": ["#a6cee3", "#1f78b4", "#b2df8a", "#33a02c", "#fb9a99", "#e31a1c", + "#cab2d6", "#6a3d9a", "#ffff99", "#b15928", "#fdbf6f", "#ff7f00"], - "set1": ["#e41a1c", "#377eb8", "#4daf4a", "#984ea3", "#ff7f00", "#ffff33", + "set1": ["#377eb8", "#4daf4a", "#e41a1c", "#984ea3", "#ffff33", "#ff7f00", "#a65628", "#f781bf", "#999999"], } + +SEQUENCES = { + "ddl_heat": { + 12: ['#DBDBDB', '#DCD5CC', '#DCCEBE', '#DDC8AF', '#DEC2A0', '#DEBB91', '#DFB583', '#DFAE74', '#E0A865', '#E1A256', '#E19B48', '#E29539'], + }, + "YlGn": { + 3: ["#f7fcb9", "#addd8e", "#31a354"], + 4: ["#ffffcc", "#c2e699", "#78c679", "#238443"], + 5: ["#ffffcc", "#c2e699", "#78c679", "#31a354", "#006837"], + 6: ["#ffffcc", "#d9f0a3", "#addd8e", "#78c679", "#31a354", "#006837"], + 7: ["#ffffcc", "#d9f0a3", "#addd8e", "#78c679", "#41ab5d", "#238443", "#005a32"], + + 8: ["#ffffe5", "#f7fcb9", "#d9f0a3", "#addd8e", "#78c679", "#41ab5d", "#238443", "#005a32"], + 9: ["#ffffe5", "#f7fcb9", "#d9f0a3", "#addd8e", "#78c679", "#41ab5d", "#238443", "#006837", "#004529"], + }, + "YlGnBu": { + 3: ["#edf8b1", "#7fcdbb", "#2c7fb8"], + 4: ["#ffffcc", "#a1dab4", "#41b6c4", "#225ea8"], + 5: ["#ffffcc", "#a1dab4", "#41b6c4", "#2c7fb8", "#253494"], + 6: ["#ffffcc", "#c7e9b4", "#7fcdbb", "#41b6c4", "#2c7fb8", "#253494"], + 7: ["#ffffcc", "#c7e9b4", "#7fcdbb", "#41b6c4", "#1d91c0", "#225ea8", "#0c2c84"], + 8: ["#ffffd9", "#edf8b1", "#c7e9b4", "#7fcdbb", "#41b6c4", "#1d91c0", "#225ea8", "#0c2c84"], + 9: ["#ffffd9", "#edf8b1", "#c7e9b4", "#7fcdbb", "#41b6c4", "#1d91c0", "#225ea8", "#253494", "#081d58"], + }, + "GnBu": { + 3: ["#e0f3db", "#a8ddb5", "#43a2ca"], + 4: ["#f0f9e8", "#bae4bc", "#7bccc4", "#2b8cbe"], + 5: ["#f0f9e8", "#bae4bc", "#7bccc4", "#43a2ca", "#0868ac"], + 6: ["#f0f9e8", "#ccebc5", "#a8ddb5", "#7bccc4", "#43a2ca", "#0868ac"], + 7: ["#f0f9e8", "#ccebc5", "#a8ddb5", "#7bccc4", "#4eb3d3", "#2b8cbe", "#08589e"], + 8: ["#f7fcf0", "#e0f3db", "#ccebc5", "#a8ddb5", "#7bccc4", "#4eb3d3", "#2b8cbe", "#08589e"], + 9: ["#f7fcf0", "#e0f3db", "#ccebc5", "#a8ddb5", "#7bccc4", "#4eb3d3", "#2b8cbe", "#0868ac", "#084081"], + }, + "BuGn": { + 3: ["#e5f5f9", "#99d8c9", "#2ca25f"], + 4: ["#edf8fb", "#b2e2e2", "#66c2a4", "#238b45"], + 5: ["#edf8fb", "#b2e2e2", "#66c2a4", "#2ca25f", "#006d2c"], + 6: ["#edf8fb", "#ccece6", "#99d8c9", "#66c2a4", "#2ca25f", "#006d2c"], + 7: ["#edf8fb", "#ccece6", "#99d8c9", "#66c2a4", "#41ae76", "#238b45", "#005824"], + 8: ["#f7fcfd", "#e5f5f9", "#ccece6", "#99d8c9", "#66c2a4", "#41ae76", "#238b45", "#005824"], + 9: ["#f7fcfd", "#e5f5f9", "#ccece6", "#99d8c9", "#66c2a4", "#41ae76", "#238b45", "#006d2c", "#00441b"], + }, + "PuBuGn": { + 3: ["#ece2f0", "#a6bddb", "#1c9099"], + 4: ["#f6eff7", "#bdc9e1", "#67a9cf", "#02818a"], + 5: ["#f6eff7", "#bdc9e1", "#67a9cf", "#1c9099", "#016c59"], + 6: ["#f6eff7", "#d0d1e6", "#a6bddb", "#67a9cf", "#1c9099", "#016c59"], + 7: ["#f6eff7", "#d0d1e6", "#a6bddb", "#67a9cf", "#3690c0", "#02818a", "#016450"], + 8: ["#fff7fb", "#ece2f0", "#d0d1e6", "#a6bddb", "#67a9cf", "#3690c0", "#02818a", "#016450"], + 9: ["#fff7fb", "#ece2f0", "#d0d1e6", "#a6bddb", "#67a9cf", "#3690c0", "#02818a", "#016c59", "#014636"], + }, + "PuBu": { + 3: ["#ece7f2", "#a6bddb", "#2b8cbe"], + 4: ["#f1eef6", "#bdc9e1", "#74a9cf", "#0570b0"], + 5: ["#f1eef6", "#bdc9e1", "#74a9cf", "#2b8cbe", "#045a8d"], + 6: ["#f1eef6", "#d0d1e6", "#a6bddb", "#74a9cf", "#2b8cbe", "#045a8d"], + 7: ["#f1eef6", "#d0d1e6", "#a6bddb", "#74a9cf", "#3690c0", "#0570b0", "#034e7b"], + 8: ["#fff7fb", "#ece7f2", "#d0d1e6", "#a6bddb", "#74a9cf", "#3690c0", "#0570b0", "#034e7b"], + 9: ["#fff7fb", "#ece7f2", "#d0d1e6", "#a6bddb", "#74a9cf", "#3690c0", "#0570b0", "#045a8d", "#023858"], + }, + "BuPu": { + 3: ["#e0ecf4", "#9ebcda", "#8856a7"], + 4: ["#edf8fb", "#b3cde3", "#8c96c6", "#88419d"], + 5: ["#edf8fb", "#b3cde3", "#8c96c6", "#8856a7", "#810f7c"], + 6: ["#edf8fb", "#bfd3e6", "#9ebcda", "#8c96c6", "#8856a7", "#810f7c"], + 7: ["#edf8fb", "#bfd3e6", "#9ebcda", "#8c96c6", "#8c6bb1", "#88419d", "#6e016b"], + 8: ["#f7fcfd", "#e0ecf4", "#bfd3e6", "#9ebcda", "#8c96c6", "#8c6bb1", "#88419d", "#6e016b"], + 9: ["#f7fcfd", "#e0ecf4", "#bfd3e6", "#9ebcda", "#8c96c6", "#8c6bb1", "#88419d", "#810f7c", "#4d004b"], + }, + "RdPu": { + 3: ["#fde0dd", "#fa9fb5", "#c51b8a"], + 4: ["#feebe2", "#fbb4b9", "#f768a1", "#ae017e"], + 5: ["#feebe2", "#fbb4b9", "#f768a1", "#c51b8a", "#7a0177"], + 6: ["#feebe2", "#fcc5c0", "#fa9fb5", "#f768a1", "#c51b8a", "#7a0177"], + 7: ["#feebe2", "#fcc5c0", "#fa9fb5", "#f768a1", "#dd3497", "#ae017e", "#7a0177"], + 8: ["#fff7f3", "#fde0dd", "#fcc5c0", "#fa9fb5", "#f768a1", "#dd3497", "#ae017e", "#7a0177"], + 9: ["#fff7f3", "#fde0dd", "#fcc5c0", "#fa9fb5", "#f768a1", "#dd3497", "#ae017e", "#7a0177", "#49006a"], + }, + "PuRd": { + 3: ["#e7e1ef", "#c994c7", "#dd1c77"], + 4: ["#f1eef6", "#d7b5d8", "#df65b0", "#ce1256"], + 5: ["#f1eef6", "#d7b5d8", "#df65b0", "#dd1c77", "#980043"], + 6: ["#f1eef6", "#d4b9da", "#c994c7", "#df65b0", "#dd1c77", "#980043"], + 7: ["#f1eef6", "#d4b9da", "#c994c7", "#df65b0", "#e7298a", "#ce1256", "#91003f"], + 8: ["#f7f4f9", "#e7e1ef", "#d4b9da", "#c994c7", "#df65b0", "#e7298a", "#ce1256", "#91003f"], + 9: ["#f7f4f9", "#e7e1ef", "#d4b9da", "#c994c7", "#df65b0", "#e7298a", "#ce1256", "#980043", "#67001f"], + }, + "OrRd": { + 3: ["#fee8c8", "#fdbb84", "#e34a33"], + 4: ["#fef0d9", "#fdcc8a", "#fc8d59", "#d7301f"], + 5: ["#fef0d9", "#fdcc8a", "#fc8d59", "#e34a33", "#b30000"], + 6: ["#fef0d9", "#fdd49e", "#fdbb84", "#fc8d59", "#e34a33", "#b30000"], + 7: ["#fef0d9", "#fdd49e", "#fdbb84", "#fc8d59", "#ef6548", "#d7301f", "#990000"], + 8: ["#fff7ec", "#fee8c8", "#fdd49e", "#fdbb84", "#fc8d59", "#ef6548", "#d7301f", "#990000"], + 9: ["#fff7ec", "#fee8c8", "#fdd49e", "#fdbb84", "#fc8d59", "#ef6548", "#d7301f", "#b30000", "#7f0000"], + }, + "YlOrRd": { + 3: ["#ffeda0", "#feb24c", "#f03b20"], + 4: ["#ffffb2", "#fecc5c", "#fd8d3c", "#e31a1c"], + 5: ["#ffffb2", "#fecc5c", "#fd8d3c", "#f03b20", "#bd0026"], + 6: ["#ffffb2", "#fed976", "#feb24c", "#fd8d3c", "#f03b20", "#bd0026"], + 7: ["#ffffb2", "#fed976", "#feb24c", "#fd8d3c", "#fc4e2a", "#e31a1c", "#b10026"], + 8: ["#ffffcc", "#ffeda0", "#fed976", "#feb24c", "#fd8d3c", "#fc4e2a", "#e31a1c", "#b10026"], + 9: ["#ffffcc", "#ffeda0", "#fed976", "#feb24c", "#fd8d3c", "#fc4e2a", "#e31a1c", "#bd0026", "#800026"], + }, + "YlOrBr": { + 3: ["#fff7bc", "#fec44f", "#d95f0e"], + 4: ["#ffffd4", "#fed98e", "#fe9929", "#cc4c02"], + 5: ["#ffffd4", "#fed98e", "#fe9929", "#d95f0e", "#993404"], + 6: ["#ffffd4", "#fee391", "#fec44f", "#fe9929", "#d95f0e", "#993404"], + 7: ["#ffffd4", "#fee391", "#fec44f", "#fe9929", "#ec7014", "#cc4c02", "#8c2d04"], + 8: ["#ffffe5", "#fff7bc", "#fee391", "#fec44f", "#fe9929", "#ec7014", "#cc4c02", "#8c2d04"], + 9: ["#ffffe5", "#fff7bc", "#fee391", "#fec44f", "#fe9929", "#ec7014", "#cc4c02", "#993404", "#662506"], + }, + "Purples": { + 3: ["#efedf5", "#bcbddc", "#756bb1"], + 4: ["#f2f0f7", "#cbc9e2", "#9e9ac8", "#6a51a3"], + 5: ["#f2f0f7", "#cbc9e2", "#9e9ac8", "#756bb1", "#54278f"], + 6: ["#f2f0f7", "#dadaeb", "#bcbddc", "#9e9ac8", "#756bb1", "#54278f"], + 7: ["#f2f0f7", "#dadaeb", "#bcbddc", "#9e9ac8", "#807dba", "#6a51a3", "#4a1486"], + 8: ["#fcfbfd", "#efedf5", "#dadaeb", "#bcbddc", "#9e9ac8", "#807dba", "#6a51a3", "#4a1486"], + 9: ["#fcfbfd", "#efedf5", "#dadaeb", "#bcbddc", "#9e9ac8", "#807dba", "#6a51a3", "#54278f", "#3f007d"], + }, + "Blues": { + 3: ["#deebf7", "#9ecae1", "#3182bd"], + 4: ["#eff3ff", "#bdd7e7", "#6baed6", "#2171b5"], + 5: ["#eff3ff", "#bdd7e7", "#6baed6", "#3182bd", "#08519c"], + 6: ["#eff3ff", "#c6dbef", "#9ecae1", "#6baed6", "#3182bd", "#08519c"], + 7: ["#eff3ff", "#c6dbef", "#9ecae1", "#6baed6", "#4292c6", "#2171b5", "#084594"], + 8: ["#f7fbff", "#deebf7", "#c6dbef", "#9ecae1", "#6baed6", "#4292c6", "#2171b5", "#084594"], + 9: ["#f7fbff", "#deebf7", "#c6dbef", "#9ecae1", "#6baed6", "#4292c6", "#2171b5", "#08519c", "#08306b"], + }, + "Greens": { + 3: ["#e5f5e0", "#a1d99b", "#31a354"], + 4: ["#edf8e9", "#bae4b3", "#74c476", "#238b45"], + 5: ["#edf8e9", "#bae4b3", "#74c476", "#31a354", "#006d2c"], + 6: ["#edf8e9", "#c7e9c0", "#a1d99b", "#74c476", "#31a354", "#006d2c"], + 7: ["#edf8e9", "#c7e9c0", "#a1d99b", "#74c476", "#41ab5d", "#238b45", "#005a32"], + 8: ["#f7fcf5", "#e5f5e0", "#c7e9c0", "#a1d99b", "#74c476", "#41ab5d", "#238b45", "#005a32"], + 9: ["#f7fcf5", "#e5f5e0", "#c7e9c0", "#a1d99b", "#74c476", "#41ab5d", "#238b45", "#006d2c", "#00441b"], + }, + "Oranges": { + 3: ["#fee6ce", "#fdae6b", "#e6550d"], + 4: ["#feedde", "#fdbe85", "#fd8d3c", "#d94701"], + 5: ["#feedde", "#fdbe85", "#fd8d3c", "#e6550d", "#a63603"], + 6: ["#feedde", "#fdd0a2", "#fdae6b", "#fd8d3c", "#e6550d", "#a63603"], + 7: ["#feedde", "#fdd0a2", "#fdae6b", "#fd8d3c", "#f16913", "#d94801", "#8c2d04"], + 8: ["#fff5eb", "#fee6ce", "#fdd0a2", "#fdae6b", "#fd8d3c", "#f16913", "#d94801", "#8c2d04"], + 9: ["#fff5eb", "#fee6ce", "#fdd0a2", "#fdae6b", "#fd8d3c", "#f16913", "#d94801", "#a63603", "#7f2704"], + }, + "Reds": { + 3: ["#fee0d2", "#fc9272", "#de2d26"], + 4: ["#fee5d9", "#fcae91", "#fb6a4a", "#cb181d"], + 5: ["#fee5d9", "#fcae91", "#fb6a4a", "#de2d26", "#a50f15"], + 6: ["#fee5d9", "#fcbba1", "#fc9272", "#fb6a4a", "#de2d26", "#a50f15"], + 7: ["#fee5d9", "#fcbba1", "#fc9272", "#fb6a4a", "#ef3b2c", "#cb181d", "#99000d"], + 8: ["#fff5f0", "#fee0d2", "#fcbba1", "#fc9272", "#fb6a4a", "#ef3b2c", "#cb181d", "#99000d"], + 9: ["#fff5f0", "#fee0d2", "#fcbba1", "#fc9272", "#fb6a4a", "#ef3b2c", "#cb181d", "#a50f15", "#67000d"], + }, + "Greys": { + 3: ["#f0f0f0", "#bdbdbd", "#636363"], + 4: ["#f7f7f7", "#cccccc", "#969696", "#525252"], + 5: ["#f7f7f7", "#cccccc", "#969696", "#636363", "#252525"], + 6: ["#f7f7f7", "#d9d9d9", "#bdbdbd", "#969696", "#636363", "#252525"], + 7: ["#f7f7f7", "#d9d9d9", "#bdbdbd", "#969696", "#737373", "#525252", "#252525"], + 8: ["#ffffff", "#f0f0f0", "#d9d9d9", "#bdbdbd", "#969696", "#737373", "#525252", "#252525"], + 9: ["#ffffff", "#f0f0f0", "#d9d9d9", "#bdbdbd", "#969696", "#737373", "#525252", "#252525", "#000000"], + }, + "PuOr": { + 3: ["#f1a340", "#f7f7f7", "#998ec3"], + 4: ["#e66101", "#fdb863", "#b2abd2", "#5e3c99"], + 5: ["#e66101", "#fdb863", "#f7f7f7", "#b2abd2", "#5e3c99"], + 6: ["#b35806", "#f1a340", "#fee0b6", "#d8daeb", "#998ec3", "#542788"], + 7: ["#b35806", "#f1a340", "#fee0b6", "#f7f7f7", "#d8daeb", "#998ec3", "#542788"], + 8: ["#b35806", "#e08214", "#fdb863", "#fee0b6", "#d8daeb", "#b2abd2", "#8073ac", "#542788"], + 9: ["#b35806", "#e08214", "#fdb863", "#fee0b6", "#f7f7f7", "#d8daeb", "#b2abd2", "#8073ac", "#542788"], + 10: ["#7f3b08", "#b35806", "#e08214", "#fdb863", "#fee0b6", "#d8daeb", "#b2abd2", "#8073ac", "#542788", "#2d004b"], + 11: ["#7f3b08", "#b35806", "#e08214", "#fdb863", "#fee0b6", "#f7f7f7", "#d8daeb", "#b2abd2", "#8073ac", "#542788", "#2d004b"], + }, + "BrBG": { + 3: ["#d8b365", "#f5f5f5", "#5ab4ac"], + 4: ["#a6611a", "#dfc27d", "#80cdc1", "#018571"], + 5: ["#a6611a", "#dfc27d", "#f5f5f5", "#80cdc1", "#018571"], + 6: ["#8c510a", "#d8b365", "#f6e8c3", "#c7eae5", "#5ab4ac", "#01665e"], + 7: ["#8c510a", "#d8b365", "#f6e8c3", "#f5f5f5", "#c7eae5", "#5ab4ac", "#01665e"], + 8: ["#8c510a", "#bf812d", "#dfc27d", "#f6e8c3", "#c7eae5", "#80cdc1", "#35978f", "#01665e"], + 9: ["#8c510a", "#bf812d", "#dfc27d", "#f6e8c3", "#f5f5f5", "#c7eae5", "#80cdc1", "#35978f", "#01665e"], + 10: ["#543005", "#8c510a", "#bf812d", "#dfc27d", "#f6e8c3", "#c7eae5", "#80cdc1", "#35978f", "#01665e", "#003c30"], + 11: ["#543005", "#8c510a", "#bf812d", "#dfc27d", "#f6e8c3", "#f5f5f5", "#c7eae5", "#80cdc1", "#35978f", "#01665e", "#003c30"], + }, + "PRGn": { + 3: ["#af8dc3", "#f7f7f7", "#7fbf7b"], + 4: ["#7b3294", "#c2a5cf", "#a6dba0", "#008837"], + 5: ["#7b3294", "#c2a5cf", "#f7f7f7", "#a6dba0", "#008837"], + 6: ["#762a83", "#af8dc3", "#e7d4e8", "#d9f0d3", "#7fbf7b", "#1b7837"], + 7: ["#762a83", "#af8dc3", "#e7d4e8", "#f7f7f7", "#d9f0d3", "#7fbf7b", "#1b7837"], + 8: ["#762a83", "#9970ab", "#c2a5cf", "#e7d4e8", "#d9f0d3", "#a6dba0", "#5aae61", "#1b7837"], + 9: ["#762a83", "#9970ab", "#c2a5cf", "#e7d4e8", "#f7f7f7", "#d9f0d3", "#a6dba0", "#5aae61", "#1b7837"], + 10: ["#40004b", "#762a83", "#9970ab", "#c2a5cf", "#e7d4e8", "#d9f0d3", "#a6dba0", "#5aae61", "#1b7837", "#00441b"], + 11: ["#40004b", "#762a83", "#9970ab", "#c2a5cf", "#e7d4e8", "#f7f7f7", "#d9f0d3", "#a6dba0", "#5aae61", "#1b7837", "#00441b"], + }, + "PiYG": { + 3: ["#e9a3c9", "#f7f7f7", "#a1d76a"], + 4: ["#d01c8b", "#f1b6da", "#b8e186", "#4dac26"], + 5: ["#d01c8b", "#f1b6da", "#f7f7f7", "#b8e186", "#4dac26"], + 6: ["#c51b7d", "#e9a3c9", "#fde0ef", "#e6f5d0", "#a1d76a", "#4d9221"], + 7: ["#c51b7d", "#e9a3c9", "#fde0ef", "#f7f7f7", "#e6f5d0", "#a1d76a", "#4d9221"], + 8: ["#c51b7d", "#de77ae", "#f1b6da", "#fde0ef", "#e6f5d0", "#b8e186", "#7fbc41", "#4d9221"], + 9: ["#c51b7d", "#de77ae", "#f1b6da", "#fde0ef", "#f7f7f7", "#e6f5d0", "#b8e186", "#7fbc41", "#4d9221"], + 10: ["#8e0152", "#c51b7d", "#de77ae", "#f1b6da", "#fde0ef", "#e6f5d0", "#b8e186", "#7fbc41", "#4d9221", "#276419"], + 11: ["#8e0152", "#c51b7d", "#de77ae", "#f1b6da", "#fde0ef", "#f7f7f7", "#e6f5d0", "#b8e186", "#7fbc41", "#4d9221", "#276419"], + }, + "RdBu": { + 3: ["#ef8a62", "#f7f7f7", "#67a9cf"], + 4: ["#ca0020", "#f4a582", "#92c5de", "#0571b0"], + 5: ["#ca0020", "#f4a582", "#f7f7f7", "#92c5de", "#0571b0"], + 6: ["#b2182b", "#ef8a62", "#fddbc7", "#d1e5f0", "#67a9cf", "#2166ac"], + 7: ["#b2182b", "#ef8a62", "#fddbc7", "#f7f7f7", "#d1e5f0", "#67a9cf", "#2166ac"], + 8: ["#b2182b", "#d6604d", "#f4a582", "#fddbc7", "#d1e5f0", "#92c5de", "#4393c3", "#2166ac"], + 9: ["#b2182b", "#d6604d", "#f4a582", "#fddbc7", "#f7f7f7", "#d1e5f0", "#92c5de", "#4393c3", "#2166ac"], + 10: ["#67001f", "#b2182b", "#d6604d", "#f4a582", "#fddbc7", "#d1e5f0", "#92c5de", "#4393c3", "#2166ac", "#053061"], + 11: ["#67001f", "#b2182b", "#d6604d", "#f4a582", "#fddbc7", "#f7f7f7", "#d1e5f0", "#92c5de", "#4393c3", "#2166ac", "#053061"], + }, + "RdGy": { + 3: ["#ef8a62", "#ffffff", "#999999"], + 4: ["#ca0020", "#f4a582", "#bababa", "#404040"], + 5: ["#ca0020", "#f4a582", "#ffffff", "#bababa", "#404040"], + 6: ["#b2182b", "#ef8a62", "#fddbc7", "#e0e0e0", "#999999", "#4d4d4d"], + 7: ["#b2182b", "#ef8a62", "#fddbc7", "#ffffff", "#e0e0e0", "#999999", "#4d4d4d"], + 8: ["#b2182b", "#d6604d", "#f4a582", "#fddbc7", "#e0e0e0", "#bababa", "#878787", "#4d4d4d"], + 9: ["#b2182b", "#d6604d", "#f4a582", "#fddbc7", "#ffffff", "#e0e0e0", "#bababa", "#878787", "#4d4d4d"], + 10: ["#67001f", "#b2182b", "#d6604d", "#f4a582", "#fddbc7", "#e0e0e0", "#bababa", "#878787", "#4d4d4d", "#1a1a1a"], + 11: ["#67001f", "#b2182b", "#d6604d", "#f4a582", "#fddbc7", "#ffffff", "#e0e0e0", "#bababa", "#878787", "#4d4d4d", "#1a1a1a"], + }, + "RdYlBu": { + 3: ["#fc8d59", "#ffffbf", "#91bfdb"], + 4: ["#d7191c", "#fdae61", "#abd9e9", "#2c7bb6"], + 5: ["#d7191c", "#fdae61", "#ffffbf", "#abd9e9", "#2c7bb6"], + 6: ["#d73027", "#fc8d59", "#fee090", "#e0f3f8", "#91bfdb", "#4575b4"], + 7: ["#d73027", "#fc8d59", "#fee090", "#ffffbf", "#e0f3f8", "#91bfdb", "#4575b4"], + 8: ["#d73027", "#f46d43", "#fdae61", "#fee090", "#e0f3f8", "#abd9e9", "#74add1", "#4575b4"], + 9: ["#d73027", "#f46d43", "#fdae61", "#fee090", "#ffffbf", "#e0f3f8", "#abd9e9", "#74add1", "#4575b4"], + 10: ["#a50026", "#d73027", "#f46d43", "#fdae61", "#fee090", "#e0f3f8", "#abd9e9", "#74add1", "#4575b4", "#313695"], + 11: ["#a50026", "#d73027", "#f46d43", "#fdae61", "#fee090", "#ffffbf", "#e0f3f8", "#abd9e9", "#74add1", "#4575b4", "#313695"], + }, + "Spectral": { + 3: ["#fc8d59", "#ffffbf", "#99d594"], + 4: ["#d7191c", "#fdae61", "#abdda4", "#2b83ba"], + 5: ["#d7191c", "#fdae61", "#ffffbf", "#abdda4", "#2b83ba"], + 6: ["#d53e4f", "#fc8d59", "#fee08b", "#e6f598", "#99d594", "#3288bd"], + 7: ["#d53e4f", "#fc8d59", "#fee08b", "#ffffbf", "#e6f598", "#99d594", "#3288bd"], + 8: ["#d53e4f", "#f46d43", "#fdae61", "#fee08b", "#e6f598", "#abdda4", "#66c2a5", "#3288bd"], + 9: ["#d53e4f", "#f46d43", "#fdae61", "#fee08b", "#ffffbf", "#e6f598", "#abdda4", "#66c2a5", "#3288bd"], + 10: ["#9e0142", "#d53e4f", "#f46d43", "#fdae61", "#fee08b", "#e6f598", "#abdda4", "#66c2a5", "#3288bd", "#5e4fa2"], + 11: ["#9e0142", "#d53e4f", "#f46d43", "#fdae61", "#fee08b", "#ffffbf", "#e6f598", "#abdda4", "#66c2a5", "#3288bd", "#5e4fa2"], + }, + "RdYlGn": { + 3: ["#fc8d59", "#ffffbf", "#91cf60"], + 4: ["#d7191c", "#fdae61", "#a6d96a", "#1a9641"], + 5: ["#d7191c", "#fdae61", "#ffffbf", "#a6d96a", "#1a9641"], + 6: ["#d73027", "#fc8d59", "#fee08b", "#d9ef8b", "#91cf60", "#1a9850"], + 7: ["#d73027", "#fc8d59", "#fee08b", "#ffffbf", "#d9ef8b", "#91cf60", "#1a9850"], + 8: ["#d73027", "#f46d43", "#fdae61", "#fee08b", "#d9ef8b", "#a6d96a", "#66bd63", "#1a9850"], + 9: ["#d73027", "#f46d43", "#fdae61", "#fee08b", "#ffffbf", "#d9ef8b", "#a6d96a", "#66bd63", "#1a9850"], + 10: ["#a50026", "#d73027", "#f46d43", "#fdae61", "#fee08b", "#d9ef8b", "#a6d96a", "#66bd63", "#1a9850", "#006837"], + 11: ["#a50026", "#d73027", "#f46d43", "#fdae61", "#fee08b", "#ffffbf", "#d9ef8b", "#a6d96a", "#66bd63", "#1a9850", "#006837"], + }, +} + ## Special, backward compatible color map. -ddlheatmap = mplcol.ListedColormap(PALETTES["ddl_heat"]) +ddlheatmap = mplcol.ListedColormap(SEQUENCES["ddl_heat"]) + +## Default Color Sequence +DEFAULT_SEQUENCE = "RdBu" ########################################################################## @@ -163,7 +410,7 @@ def as_rgb(self): def plot(self, size=1): """ - Plot the values in the color palatte as a horizontal array. + Plot the values in the color palette as a horizontal array. See Seaborn's palplot function for inspiration. Parameters @@ -324,3 +571,102 @@ def set_color_codes(palette="accent"): rgb = mpl.colors.colorConverter.to_rgb(color) mpl.colors.colorConverter.colors[code] = rgb mpl.colors.colorConverter.cache[code] = rgb + + +########################################################################## +## Sequence Functions +########################################################################## + +def color_sequence(palette=None, n_colors=None): + """ + Return a `ListedColormap` object from a named sequence palette. Useful + for continuous color scheme values and color maps. + + Calling this function with ``palette=None`` will return the default + color sequence: Color Brewer RdBu. + + Parameters + ---------- + + palette : None or str or sequence + Name of a palette or ``None`` to return the default palette. If a + sequence the input colors are used to create a ListedColormap. + + The currently implemented color sequences are from Color Brewer. + + Available palette names from :py:mod:`yellowbrick.colors.palettes` are: + + .. hlist:: + :columns: 3 + + * :py:const: Blues + * :py:const: BrBG + * :py:const: BuGn + * :py:const: BuPu + * :py:const: GnBu + * :py:const: Greens + * :py:const: Greys + * :py:const: OrRd + * :py:const: Oranges + * :py:const: PRGn + * :py:const: PiYG + * :py:const: PuBu + * :py:const: PuBuGn + * :py:const: PuOr + * :py:const: PuRd + * :py:const: Purples + * :py:const: RdBu + * :py:const: RdGy + * :py:const: RdPu + * :py:const: RdYlBu + * :py:const: RdYlGn + * :py:const: Reds + * :py:const: Spectral + * :py:const: YlGn + * :py:const: YlGnBu + * :py:const: YlOrBr + * :py:const: YlOrRd + * :py:const: ddl_heat + + n_colors : None or int + Number of colors in the palette. If ``None``, the default will depend + on how ``palette`` is specified - selecting the largest sequence for + that palette name. Note that sequences have a minimum lenght of 3 - if + a number of colors is specified that is not available for the sequence + a ``ValueError`` is raised. + + Returns + ------- + colormap + Returns a ListedColormap object, an artist object from the matplotlib + library that can be used wherever a colormap is necessary. + """ + # Select the default colormap if None is passed in. + palette = palette or DEFAULT_SEQUENCE + + # Create a listed color map from the sequence + if not isinstance(palette, string_types): + return mplcol.ListedColormap(palette) + + # Otherwise perform a case-insensitive lookup + sequences = {key.lower(): key for key in SEQUENCES.keys()} + if palette.lower() not in sequences: + raise YellowbrickValueError( + "'{}' is not a recognized palette!".format(palette) + ) + + # Collect the palette into the dictionary of lists. + n_palettes = SEQUENCES[sequences[palette.lower()]] + + # If no definitive color is passed in, maximize it. + if n_colors is None: + n_colors = max(n_palettes.keys()) + + else: + if n_colors not in n_palettes.keys(): + raise YellowbrickValueError( + "No {} palette of length {}".format(palette, n_colors) + ) + + # Return the color map from the sequence + return mplcol.ListedColormap(n_palettes[n_colors]) From 1ac371bbdc85af2c7f6a097c9e77eb8de6a7571f Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Sat, 14 Jan 2017 11:51:17 -0500 Subject: [PATCH 28/35] Color handling in regressor and classifier visualizers Closed #70 --- examples/examples.ipynb | 41 ++++++++++++++---------- tests/test_style/test_palettes.py | 4 +++ yellowbrick/bestfit.py | 7 ++++- yellowbrick/classifier.py | 25 ++++++++++----- yellowbrick/features/radviz.py | 5 +-- yellowbrick/regressor.py | 52 ++++++++++++++++++++++--------- yellowbrick/style/palettes.py | 5 +-- 7 files changed, 95 insertions(+), 44 deletions(-) diff --git a/examples/examples.ipynb b/examples/examples.ipynb index 2fd2795e1..7431d24d8 100644 --- a/examples/examples.ipynb +++ b/examples/examples.ipynb @@ -67,7 +67,7 @@ "data": { "image/png": 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9e6oH28tN+N8Dx/Ft22mnsRmxYSjMM6Iw14jZUyJlSkhE3k6KI2EAcO89D2Lza8/Aph4+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\nL58OQ8GVMBTMR3ByjEwph7FQI/JT9jN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NoVQi/L65iE7LQOTDKfAPC7vm+Ty3jIh6kytl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\nNYmoFzJfMuJcbiHK9QW4/K8TLmuq/mGISUtEjI4jnIh6M48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Uq1fPOl31Z6dOnao0sSjP+++/rwkTJqhTp06aM2eOtdwvnfusffbZZ7rrrrvk7e1tbW/d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\nJNHUwuEYaZYjDsfA5UGCAACAAXesILB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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\nlXJychQVFaV27dopMzNTb7zxRq2vMTV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gx44dEIvFKvtDVqds9feOHTtW2OJHHSQSCR48eIAOHTrUaQFS0l28J6g83hNUHu8JKk/T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\nO/D/AVvwztGQIY1uAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -317,6 +317,13 @@ "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": {}, @@ -359,7 +366,7 @@ "data": { "image/png": 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Fx8eH06dP884773Ds2DH8/PwYO3Ys/fr1IyIiglOnTjF37lwATp8+Tb9+/Thx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Cspii8uyg247oX9JeTXtSMg8fjCFkTJS6g+kpSoSf71Z6SuwByWzGhotdZyrGqb/+dYUxoCApIk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\naezhODVtlW2xGY+W5JpOLdAhYjMKrsXJFwfRNjjQ2c0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lFw4dOsTs2bN59NFHG9UiUiqVqNXqNmqhEEII0TG0dBDRGjQaDRkZGfTu3fuG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fLKY0mVM6mFKSAgcHMzq/aEjzzyCI888sj1WouAwA3jSt14L+8+jaLwtqRjivzFNJ75iMVZU2PU\nJhO3enpk6TQe3Udw4GNkUg+hsB5l3l2o8hfjOvD/sP7ejKTrlq3N4LVmF8f2dFK9fkJnl8O95JWY\nCJ/zo1fsYcH9eiCWBNF+rIna0luTXmdRXg7ffyyH+3r6+Pkfd9Dr8uBynsOsUxDvIBMXMUcu9GIu\nqcLmtCPRxASdMquYYx+/Rrr+CJ+7d7yf9kfvOGjYF2T52vF1nzg4iqF0vMez/Xw9H578HQpFgKAn\nQrboqyxatJS65i46B0c5e3ovMrkfmCoIo1J/Yv2x13OIjy/4uSDaQMO+91i+1pQ498i+YTLnPgSM\ni9SOnj627m3k9Hkn+cumJuRMdEtfTbH4+PvrFWvo7O6NWRndAzx1S0li7bFzbCj1uYnrprN8J2Io\nsxbg0KkYDKqIREfQSQL4nIMM9LUik9p5+KnP3zRi7KmvPgEbYdsb7xORBvCF08grf5Tq1XcmzhF6\nYwsI/HVzSfH47LPP8u1vfxuVSsWzzz57yYEuVWtRQODTgkekRpSiNGpArEAnjfDixpfZtvd9ZKow\nQa+Ee9bdG3uYXqQoL4fafiv/67WNZOkaqP5SBhBzMx8/uA3bgAazOXVm5tzSPDo6XQR3tiGRSggF\nw4kYw8YD7ay6K1m8FlfreXPbK2RlF02Jz+zpt+IMihmzdSPXpuHT6zjd72HuYBv3rJ8LwLaTR1Bl\naTADDqeVYMBPpl6Cx9VI9Z2WpLk2PFjAS//RRVvbMOlpEZxuKfrstcybN59+tx3bqZ3kqeuoviOd\nuDh8f//v+c9NvyOr0EworEWqW4RtcIxUfbZFoeQMy1WVBbT0n6YdJWfFT9D+2p/Q6yK4vQqUBU9Q\noI+5snXSCPsbGvn3HS1YJRbIe4zjhzazZMV4D/PZuKUvR/z9/f2Bs4gySlCJo+SXllA/MJhowzgb\ny/fEGEr3mJeoxow/KsPTc4L0/FIseaWkW/ffNMIxzlNffYKnvvoEm3Yewm6c2lXoZnGxCwgIXBmX\nFI/Hjx8nGAyiUqk4fvz4tOeJRFPdQAICn0Y00THCGi2eMTsipYFQOEQUEUHreZqPtjIYOMLtXxp3\n1x068C5sjD1M4+7Hvc29RMZOU/35ZOvikpUGdmz7BEc4tXh0eGQUVX8ZiWszi1eOx/m1NLhAmrq/\ne//oQMJyBXCqtZ5XNn6LaNSOUqdGEUwnkH0fVt8Y2aoQGfnyhBBZka+g2WtDjAhjmpT8zHTS9Fr2\nn0idrZeeLqFTcS/Fy6qZb9Ti8Ibo6z9PmdKBw7qL6seTX+/iNZn4PG0suyUb8NFYtxOJuZaj+xtZ\numbckthcP8rKqnvZtPNQkgB+6vZyOnt2YdMU4p7zFTxqA9m5BSgVMgIDLYkEkxc/aWNEZkGaHUtS\nOXNBTN/WD9HJnWiiEr715e9e07aUXSM+lq1al3xQr01y1c7U8t015KFjuJdARIRzzI8rOIJcn0lE\naSCsyWRwuJ85M6x1+5dA6I0tIPDp5JLicePGjSn/LyDwWeWJ28r59YHjKLIX4LL3gVROoKuRpfPm\nUH/qFR76WnIf6CWr03lj4zt4ZVkM+qRkV9QSyDSh6H8/5fgqmYdI1l2crn+D8tpxAdVY7yKc/1W6\nJTmIh5cxtvUE5gwZkqiK3MzbORvennI863AEr1jDmWNHGeptRWZ/iznlUipqKhLnHDvwFnbDf0er\n0qEwjIvQ+9YsJHDcNuXBL4okJ5zEcY3JWbB0ATULJlhAS3Ix2k/R0ZE6NlEiHY+DrFplZGDbINbQ\nHXz49n7kiiAhT5h71t2LI20x/oulaxI9rpeY+fzyedh087FZbciVKvpGRgl4ROi83Ty8ZD11zV1E\ntRYiznAiblM1ZxXR/Co0Cj+1xlFql9ekXNuVMp1Ldrau2o6ePs702aFkCQBhbRRRMErEO4o45EUa\nGiM9KwtvX/NVr/l6IfTGFhD4dDKrmMfW1lYsFgsGg4E9e/awc+dOFixYwOOPP3691icgcFPxxbtv\nB3bx7NtvItHmIo94WVlRysKlNXzYtSnlNWqjkuZIEU6/E6VtALk4ijegAaZa8LxBDRkqLXfM+xrv\n7nibiFLM2JgEn3wlbo0YWWQEY848Vt76HXyDMQFVlJdDfUsnR/e3JFnsju4fYTRUQEuPHXnWQkJn\n3kSnDFJRU5g0Z/XqdN790zs4su9DPdZHx+K8hGWs9ORR3t38b7GYw6CcB29/GH/5LZw6/GcW1Iy7\nfo/VOQgrylk8L5/JuELiKW7nOOFQclkXlcyDqXI9C0tinylSWwtqaSQhHOPEky5WVRaw+fB5EOsw\n6LWkm9Ji+3LLeorycth5oge5KBzLFJ84b1SEXBy9Lu7Ta1EKCmIu65KKKlp62pFnFiOXSgmIZYR7\njlNcWoraqCNgbceSYbz8YH9BbmSZIAEBgRvDjMXj5s2befrpp/njH/+IVqvlu9/9LitWrOC5557D\narXyt3/7t9dznQICNw1fvPt2hqN6QuaKpONjntSWJa9XQSAiQp5ZTGd3E0UlZfSab+FY3RaqV43H\n9x3fP4xMupwnV+ewZnkV/dGsxBxNbb0YVTGrZnSgBUjOWjVbijgTWkrXu39GK/fg9qvxmb+KSHkS\neWZMeKlkbiSS1GVU8PZD20vYLQr+8X/t4rE7HiTLnM7+ltepvksDxDq67D/xOmsqHmM4dA/7dh4k\nGnUx5lch16zgrpX5SdnDAKNON9bWdrSmKo7u28PSCQk18WSfiQyORHD0WTGopBTkZqGTRi5pySvK\ny+FBn5+3P25ASh5GlTjJsqWTRijMz+Xcyc1ET7+IPk2Ma0xJMG0lmVVlrKqcm3o/roJr5ap1hcQY\nzRYqgM7uJmTDHtRhERGfFZU/HdmgjZL8XAqlqesqCggICFwvZiwe/+u//otnnnmGmpoafv7znzN/\n/nxefPFFDh8+zI9+9CNBPAp8KklVDLwoLyeldSlv3p0c3bc9SSDFS5TIxFF8xMrVpOm1LF+2gsMH\nnGzftB29Pko0pGRR5d38X/fHsma3bn+PHe9sRKZX4PcrCWesI2PxPUCs6HeczsHRWCxgWI5Mn4so\n+4eMSWTIRFEUo52MavR0d3YgkyvAI0epnio0ettHyM3xs+yW8VZ1H9a/RNiuYsFdU2siHj32Md//\nyo+pay4dr0FoUnLsXB91B/ahy5pLfmZMFDeePMnisuUYzRY+2Bzg7U3HMerDeBwBDIYwFTUTs6RH\nCBV8jYh5PvuamzAf2cvKigL6B62YMWE0JyfpxC15hXnZ3FFdTGVl5ZSmAasqC9jzhz9RnHmc6tXj\ncx3b9x4FERlFeTPrUDVT4vdLwD5K17kWsrMyY2V6LgrHyXGbMynrZDRbMJotFDndnO73oPKoqaqu\nAi6K0kohflBAQODGMmPx2N/fT01NLDbok08+4aGHYqUwcnJycLundmQQEPhrJS4AuoY8nOmzU1JR\nhdFsGY+1I7V1acGi5UQ6fex6Yy9RqR9v2JgoUTLqdHO6347qovAz6LWU5+fy1Nd+RVnJnKT5t25/\njx3HNnL7l8aTYj5+51X63v8AvR4kaOnVhFCbCmkfiaCfvwB9lh+0WYwMDZCuVSLxOxiTSTBkFaG3\n5DLscDOkqEXr7aTlcC8VNeNlYpoO9XPXVyqT1lBea2LHxjMsYGq7O3/ElRA98X368GQPJRVVlBti\nHVRaeltR+qwsrr4ltne2AXSZpbh15TikYrLmSPEMd/Cn3/8JmdiJUq/B409DYuzGdy6MPyzCa5qL\nrnQpdvUguw4cwaxXIRVFEEskEI2yIl9BR08flgzTlDXGKcrLwWM9yMr7k8+pXpvJwR0fJ2XCz5bJ\nXywKTLEi3sqsBeiNoCcu7mIW44mJSxPvpfhephqvf2D8HkvTa5k72EaG3oPU1nLZ+MHLZf7fzEz3\npU1AQODmYMbiMT8/n71795KZmUlPTw+33RardffWW28xd+61d/0ICPwlmFhXr2O4F0qW0NLTTgUx\nC1DcVfz4nStSJwLc9z3gexPGSf3g1xKguEBEYV52Yt74w3LbWy9x55fHxU5v+whZORJurRl3Cbc0\nvMiZE4tZetfXASjMz6WlZ4DcwmKUXhuuUTsuqQW1Ws/QhWYwFpJRdR9D55SMtGyhs7UVmVKKy5eG\nwTguJCciUchSHleIddPvU56RquqlABzcvpnO7l5ONLUw7BNhLlmKznqBvv5BItpi9LJBShcaWFAz\nHifZWL+P1pG1mBY/AvYORp1uet1gWXQL1hN/JqrLQaYysSRfhzg3i7eOt3NvuZ9+2zCnPjqCD/kU\nsSFXp441jNeOTHUPXE64THz9EBODfz6wl+LSeUlVKqd07HG66bY6YmEMYiXv7W/i+4/lpByvf6Cd\nWkuUY+f20tznRCSWUZmj5b41VZcVUi9ufJlD7e9Om/l/M5NqL4SuNAICNxczFo8/+MEP+J//838S\nDofZsGED5eXl/PKXv2TLli0899xz13ONAgI3jIl19QKRWAmqeKxi3G0aj8G7VCJAqizTe9bPTZzv\n8/lobo5lyU58WHb1DhLVJIu2nraRKT2tK5brsG9tTsQYToyNs/e3M+SXYq6qQamQobAP0H7mOBqd\nAUUkwvrHf4XRbOHw0UY8g1aGzr2a8jU4wxaO1o2ydNW4BTReE/Fy+zTqdNPvk5GftZDR0UYkcxZi\nc9rx9fciVRnA78LT8QELvqxPmrOqVsf5zYeAR1CIwnRbHUi1ZqSARCIjtzzmrnV4bUBMnG0/uIc+\nr5S8BQuQSqVTxMZ0yTqpjs9UuKTqYS3KKKGzu3eKez1+v4xedDtLtTFLpA841NbKfRfFaqqe2MfO\n7SWoy2XRuvHSPzMRUtv2vp8kHCGW+b/tjfdnJR73NzSyZd9pnBE5enGAR9aWs2Z51YyvvxKm2wuh\nK42AwM3DjMXjHXfcwd69exkcHKS8vByAxx57jG9961ukp6df5moBgb8OJiZnyC/GKUJya72ZZs3O\nJMu0s6ef57YewpW5lLCtnfa+YcSeZFEzsZzNRORy/yRLVpSikjKsY33o1TkEL1oO1UYLmWUaon43\n+WJ5QtyM9rYzFEpDVfYkR+peZtmqcSF39KCL0NwnaAM6X/0DBtkQ4kgUndLMR+rTnBtLQ6r1U5if\nm3Kfuq0Osi2xtocRkQQxEAr6cYTEZBYtRSwRox5MbdnUqfyMjvZTXWDG5pkQoxkZb4EYF6wALYMe\n1DnzaT7fTwgJcnGU/Mws6po7KMrL4YENX2BH/UYqa8dFcHP9KA9s+OqUuWcqXFIl8cjFUbzRqe9V\n/H45flEIT0StM7B1byMtA14CmaaLax9vW9jc50wSjtOtZzIy1XRJNEP8wzPfRC7Scs+GR1LWt9x9\n6Bj/vGk/tjFw+iPkL1hFdnEpDuA3+44AjddVQF6rUkcCAgLXj1mV6tFqtRw4cIA///nPPPHEE9hs\nNvR6/eUvFBD4K2FiIkx+poHT/XakWmMiSeVSbeRmG6PVbxumuVPEqCofkSaDC9YO7CEFovS7adj/\nJsvXxMTO5HI2cbxjYhpPnkQzZ1lsbUDjySMYRntxd39CqNWFL2JCXnQfhuwqetsPk7aojKa2WNHp\n7t5e1MvWotLqaO8WcWHL26gVYzhHo4jLnkCRvwx/9xEKCkIsWztu+Ww6+AFe+ZdQZi2kpaedXIMC\ntzt5n1yD5ymfV4pIoWGkY5RQ0It/uBuNpZiwy4rbPkjAnvp1BRw+qpQDFOSuwtHWG0s0sraTYRh3\n28vF411+xtxubI4o+kwzYrEYH3C6345E7AHg/rvvA+DdHW9PKDn01cTxicxUuKRKmMrPNHCutynp\n2MT7ZduWE8iKxsVjwNpOhlpO/eAYanUGEU1GYu3lxEIdROLUAvtyQiroTf2FQ2eA/GUiwMNbe/4D\nIElA/mnHJ2zp0KNf+AD+/i4UaQX09JwEzpE9pxTNnGVs2bfzuorHa1XqSEBA4PoxY/HY3d3N17/+\ndUKhEENDQzz00EO8+uqr1NfX84c//IGKiorLDyIgcJMzMREmTa+lHGg7dYi5JiUqTGEAACAASURB\nVDFG+6mUCQpXGqPV1D2KurIGufUETo8Hh9uL2GAhml5EQ7eIs79/jTSjlKEeETLVANXrxt2hLQ0u\nRLolLC6bS2d3E4GoBLkojEXmJCA+wNq7zYAWCNBY9wIO+waWpymwdZ8lqrUgF4XRZOQjFgM+J0pL\nJW5lJm6JFlfrfvQhGZ6OU2han2fZNzKT1r1wpYldb7+LNHcp8sxiRgebMNHNhX2bUEqdvPlJFLuo\nnLbWVqoqSllVXUlLzyBhjYZAZAzXiBO5pYKQ+HGO1r3M0gkWz+N7u/H5RQR7GogYohRFpZxpa6Ki\nogryjLT0tCNWm8jPNjDqdHNw28s4ez7GMLyL3uMqVHPuJ2P+WqRaI/0XDifGvf/u+1KKxcnMVLik\nSphSegd56pYSukZSF8SuzWqkaXD8vSrJz2X/7g/pc4URSRWEQ6fInb+M7DmlnD7bgtQzQNjrpqmt\nN8kamWo9k1m1eB1H9+2+ZGmkwsUatu9+M0k8btrXimr+IwAJi7E8bxH9bZ+QPacUAGckdQeka4XQ\nlUZA4OZnxuLxF7/4BatXr+af//mfWbo0FhD/7LPP8k//9E8888wzQgcagU8F8d7EW/a9l4jzevIy\ncV5XGqM1FpGhJpbssruhEZQFRMUyokEfIWU2o4ufxh5wE3QfoMkaZnBLE0ajGERp5Fc+SkhuS5Rx\niVP/9j9SuyHZNVq1ysSerYcomv8oxSXjLtDuri7Cah0R9xCysA+5VIJ3tJ9oYAyJ3oJYJCJN4wKS\nxSOAVGSjPFtDt9WGrf0Q2YYG7nrEBMRK+xzZd57j7oUc6vYDrVQUl2FrbUCqsZCeuxiRyIsoZwHN\n579Ax8YtZOhsKJRi8ktNLFln4sTBE3RYC/j+FzYk9tgVErNCN4RIPIJnUMr2D95hbmEnn//v4wk3\nRw68iu0M6E05V1Q8e6bCZXJMa8BlRxYJ0yzOQCeFOy8WWp/I/euqCE5Iojp5aA8dAQP6pbcjFokI\n+d10na3D13EEqdbEbbfdStTvpqXHzumwNGGNjK9nOmt3R08f4sK1MJbGu69tR6UO4RvuZen6bHKL\nk7PO/RFX0s8e0bhAnVhYPSIeD6XQi1O3p7xWCF1pBARufmYsHo8ePcrmzZsRi8fdJVKplG9/+9uJ\nsj0CAn/tdPT0UT8gYt6acStV/UA7eT190z68rjRGSy2OxfAZzRbSlS0Mdh4mYiom7B5CojUT7dqP\nybMPoyGMN2jAk/swekseFXlGVBEPFsnUbGGJxE2qP2uJ2E1DmxWxoxG5KExhfi6FFhMHj34A5nmo\nNVqUmjQi3a0Y80rQhEeRy2QEvaGUa/d5gqTptaTptXy8+yDldyQLzGVr02l/bTfesh/QeGIH68vS\nmX/7PH67t48IIgKBIHIpoCtArjGy/sFkobd4ZRr1HzVQ11zK43eumLL3Tz+3CcQ2lt+SnJyybHUa\n77zyChVfeuaKimfPRrjEY1pjlmcFyqxiQkxveZ48dtPJkxgWP4pIFIvflCq0GBbewcjBP7J6xe0X\nLY3ai4lQPXSfHGJOZW5CyE5n7Y5/mVmaVczSWx4EYl8qcot9TEYh1iX9rIm6ccb/rzPgdFmR6DIR\nR2L3mufCEZ5aWz7rfZ0tQlcaAYGbmxmLR7lcjtPpnHK8p6cHjSZ1r1sBgb82rsSKeKUxWgvz02ge\nOI82bx5paQYyZDk4HE4oWEzU3kZVxi5qvjCejHbswCYcI49g87Txwy+tB8xTrGSBMVGKmcAZMOAf\nC6Nq3YxK5mZfQwAyVmMwl+B0DxIKOnF39ZOlFJGzpJaovRuVUkmbyEzL4V4M6Sp62kaQSCXYrW78\n/pgFq+mjTUiUo6SyThoMIrzeYbSmzIsWPRsKcS+9Az1EpQpEwTE0KgUKhRcY76nd2z5CT9sIvjER\nO3Y8T7EpMiWx41D7CAZTavdpmlGKKuK54uLZsxUus7lnJo79xidNRFRKPH4fYtl4gZ+gWJUosg7j\nRcKlthYevzNWa3fTzkPTzpnqS0te5QO01P8nFbXjIj2eOT+Rx9eW8esD+5BV3opKlwaMMtL0Hnly\nF4YLO3nqBmRbCwgI3PzMWDzef//9/OxnP+Ppp58GwOFwcP78eZ5++mnuueee67ZAAYEbyZVYEVdV\nFvDirnoGfdJEPFuWMsQ9t1/aQpNtTqfUYORo+ynmqr10dfdiyC5jsLcZ5cA71DyeXMWgerWRDza+\ngG75lxMCZLKV7Nbq9dQfeJeq1ePXHvmkH4c1QI54Y8w1XGwi9w4Th/d8SENrH7qcRagNOcgK5uM8\n+QEapwenM0y+0Yyi9GHOn36OvDmhpHJBJ/cP4jiyGXffHhTK1Hvj6e/A7H0O72iA377iJJyzgn5n\niHBaAInegkhkwu0eQjrkIy4ee9tHcAx7k+ZKldgRkapwu9XAVIEe8oQTPb9vBFdqedZEx/DKZGgA\nn99DFBEiougj9iltHntb6xlsfIULJ19ALtIS0Swku3bBlDHj98HkLzO5ZbVoXR1YjzXjj7iQi7Q8\nvOHrU0T5Q3fdQk/vRvY2vcaYWIspOsbf3lt+sae7gICAQIwZi8cf/ehHPPvsszz++OMEAgG++MUv\nIpVK+fKXv8yPf/zj67lGAYEbxpVaEcUyBRK5GUlEhEQcRRy1zWi+wrzsRIeZtQ2NvPhJG8p0DX6H\nB1BOOd+cAyP2bdQ3zKd2+aopVrJNO8HV+yg7tu1DygjDAw7SjT6+/DfjhfxbDvcCI9Ssz6S7/SDW\nYSUK61Z0Bgg6QnTU+zDNvxWAgDyLsDSHqnWGpHUsWpNFx4GjBEU+iktMHHj/LFKZGIlUQigYxu8L\nsuKWrESM3bH9+zixaxCqnyIy0kug+xRihRpJJIhPXc2RvfUsW5eZsqZlqsSOfJ2IZvkdHKnbzLIJ\n/cGP7h3kGw89NSPheK26mKS6Z+I9vZ8H/I4hRGIJcp0xaZ4nbivn1wf2o563Bq0sllU9dnY/T9xW\nztjguEW5t7We0c7nqb4rXmrIw+n67URaC8ktq52yluniNh+///MU5T152ddz24pqvv/k1FaPAgIC\nAnFmJB5bW1uRSqX83d/9HT/4wQ/o6uoiHA5TUFBAZ2cnTzzxBK+99tr1XquAwHXnSjI965q7yCqt\nIivpaO6sixqvWV5FXnYmdc1dvNehSnlOOBSmcmU6b257JUlM1TfUsX33m3SPDEAoDbd0AaOa+ejG\nNnP7F6JJY1TU5FK/s43cYhOFhWKKFQcwZ2sSQu/I3k841+wgKrsN3DZM6alFhNXrwBdNB0bQm1Qs\nXDWeuNJU1510bvWaDFrb2hiTaxCrjciz5kM4hMg7glKvwSst5uDWdwlOE6Y4ObHj63dW86tdPbR5\nv0THlp1oVWO47UHurvncjLKqr2UXk8n3zKjTnejpbQNaXCLEahPlCg0hvTYxT8yat4uXd7+OR6RG\nEx3jW7fFrHwxYRuzKA82vjJBOMYorzVRt31TkniM36dCwomAgMD15pLisb29ne985zt0dXUBUFpa\nygsvvEBpaSlut5tf/epXvPHGG+Tl5d2QxQoIXG+u5ME7W7dlR08fe06cp63HxqkBL+sXj3eeiVsS\ni00RNu/+3xRXj5exmVhqZdQ3buuqb6jjrT3/QWG1hky0QIgjez/kyEAIjWQYmNr7OV54PBwKs2zD\nnISYBFi2zszQuz3MKy3BNjSMY0QNTFV1/rCBwsp7aD78L9zx5WQX/cJV+UljAui0YVxOG76Bc4hd\nwxAOIJGrcPvsFK5cytyFRfSdeh1wT5lrcmJHPO5uy77TjGo+R9g1yA8fqeG2NTUp93wy17KLyeR7\nxtrazuKy5RjNFhqPHUWetRCAbquNNL02aZ4v3n17SpfwRIvyhZMvpJw3My2M0Z76PhUSTgQEBK4n\nlxSPv/jFL9Bqtbz66qtIpVJ+85vf8LOf/Ywf/vCHfPOb32RkZITvfve7PPXUUzdqvQIC153ZPnhn\n4+qOW7yk6QuIBm2MGs28dbxzisWrdvkqPmo4zdY/vkpOfswVnF9qSoixgH/cGrh995sUVicnrS1b\nl8n5N0/jCqUDyZZHiInGiWJ0chcbhWiE0/0etLml9DiGOHJgJ8tWj1u/ju8fwmv6ClklS7GdSc54\njjN5zDG3GP/QBRT5ixEpNET7j5F2/jnMxghnPtqBoWIFSxfemrIbzF3VU62Ja5ZXsWZ5VaLVY2Vl\nZcp1pOJadzGZeM88D4Qulk8KRCXEU5gmdsWZPM+lXOgx4TxVUOuVJh6/c8UVrVdAQEDgarikeDx5\n8iS/+93vqK6uBuCXv/wld955J2fPniUvL4+XXnqJ/Pz8Sw0hIPCpZzau7rjFKxQaL4EzncUrvXAh\nRuffIA39iWW145a3xroRyko/n/g55tKdmmWtkbvoVH+Bwwc2U7N6PC6wcW8nzhEveSXjYnRyFxtJ\nUIzK00PAPYYi6OdoSw7n2/tI0wVRKsxEdZ/DWH4r3VYbMmU2MLUMzMQxj+y14pUvQpdfgc89SqRz\nH8uM26n52nj2b/Oh7TicawiK1/PBxndQayN43CJy5j+EW2KeMn6czp5+PjzWzqFuP0aVeEaxi9ez\ni8nEseWiMPGmihO74kyc53Iu9Ltv+2LMsrx4/AtCqkxpAQEBgRvFJcWjx+OhoGD8AZiVlUU0GmXJ\nkiX8y7/8S6I+mYDAZ5nJbsvhzibs1qP856nglB7Cl7J4TbY+BVwOFi5bx+HDEnZs+wSVzMNYQI1c\ntox/enJcPE5nmXK6ZYjnr+JIj5T2LR+gFQ3gHA2SZQzzuS8vTJzXuK8rqfNI86FhSivvQZmfS0uP\nncJbagkf+RCZYz+RUA9DQ0N4XFbGul9AqzeSpV9A4/4dVK0Z72ZydO8gF7q0OLY68YzJCEXKMOcU\nYw86EatVKD37qLkvubZj5Yp0tm78BKPRjykzjEQqQaEK4+jbSn00lNLK1tHTxzsn7XjMS1BnmrFL\npTOKXbyeXUziY3vFGsbcTnp6PkEik7O0rCDlPJdzocfvne2737xkpvRkrlVCkICAgMBkLikeo9Ho\nFIEoFot58sknBeEoIDCB+EN509YP6B3ZQcVqIzFrYHIP4eksXgGXnRd3uZLK/ah8LsTDh9Gpddgy\nPo8/HMIodvP9uyoS83X09KHQVdJSvyOpht+xA3YcitVE7T1ENTkMKb7KQP8ZFBXzGevaw3uvn0Al\nHcI3FmJkJIK+1U3bSTset4i8vFVU1FTw8YVe5FkLcXY0UCp/m+oH0ojXczz8yTEOdi4hrFuOWhVm\nxPUg3Vt2o1X7cfgNeDT3k3fXStRGC0ZANthExHaODJOUHqcfjcYL6Kbsgyg4THqmgYqa8YzrlsO9\nNDf9mY6eR6aIn7rmLpSW+bis49ntM4ldnCj4u4Y89A9asWQYqWv2Jr2fV0K8S9HvD5xFYSmj0DiG\nSCSir7+X/EgPD6+rShp/Ji702uWrUorFS3WZuVYJQTCekBUXrxO/EAkICHz2mHGpnomoVKkzQQUE\nPqvEH9Znuk5QuyHZojax1EzcKiVNL0z83jfYjntkmPOSUuRZxYiAIGA71wD2YSxlZcgzRMjFUTKj\nNvKyM5PmNCx7lP56A9vfjrWiiwZkWKW3Is+rAqWCqMqIf7AdaeYcgoPtyGUK8nKiLFk5N1FXsaIm\nN7Ge0/UtaMPLGXNFkWVBsPN9qu9JzvatuSWLtk0duLVfYDjgYF7lrag09xLqPIJdnInGkIu9vwm1\n0ULA2k5Jfi4StRe5EbQKGaeaUmdwR8KBpLVALDu8o7WVX7+xh4qy4iSRdDWxi3ER1X/cxrzSWNby\n1YqsOF0jPpatWjfpaDFG+1RRe6Uu9EsJxGuZEDQxISvVFyIBAYHPHpcVj++++25SB5lIJML777+P\nyZScwfnoo49e+9UJCPyVEH9YT9ceMF5qJm7x2nPiFEODNtJkZtYvmcszrWLkxcVJ13giMqTGYhaW\nTBRT4yWA4nOOOt20j8rwlHyHIYcVjS6EWeKlb+QcYx4XgWCIqFyPNOrG4jtImtyK3xGltz2csq5i\nea2Jo8c+ZkXhnTR7bagko0zsABMnzSBixNGPMiMfh9OJSqNBolCTKwerx0rAPYRssImS/NxYlxTp\nEKsqzfyPf3uLcFoNR/buZNm68c40xw6MoE3TTpkHQK6SM6rKJ2SuSBJJOmkkRbTlzIRXXXMXe5t7\nCaszKBQPJHqEX6nImshsRO2VutAvJRCvpAJAXXMXdm8Ep60HrcGUqD+aKiErVe1NAQGBzw6XFI85\nOTm88sorScfS09PZsmVL0jGRSCSIR4HPNPGHcjisJVXyyMRSM0V5OVgyTIkMYaVSiUgsm3JNRCQB\n0dSHfXwuV0jMqNPN6X4PLn+UMVEQWd4yPD4nmZk6cqxtiPoa6bF5iehMLNTsZ+nqNOLu4pbDvYRD\nqUVWW89JPF4vGtMqxmTGlK/J6dUQVUsIB4OMBP1AP+ljdipKS4j0e1DlZVFVvRS7bYCGPTuYn2OE\n5i5UGg0lVQ9ibUrj7Tc+RKcDt19NUHsPGWxPuR5vNIsM0XgCTlwkraosYPPh8zBhfy8nvCZa7AKZ\nJiKaDFp62qmAhIC80qzrOLOxJl5pXcZLCcQrqQCgzFpASBfCg5l3Ttp5VKmgKC9n2oSsybU3BQQE\nPjtcUjzu3r37Rq1DQOAvytUmF8Qf1nmVD9DS8CIVy8fFzOn6EdIMK9i089C041bmaKl325Fqx13e\nUZ+LTHPalHPjAkAnjXDc6kCqNePzOJAVxKxAYokEh8dHVn45hlAXyxfOZ//BN1h6R/JYFTW5fPDy\niZSvR6KIMGd1mPZj20iTlnJsbxfVE6yEDQccjGgfIuL3oNWXEvKM4PO48ETlRP1u5ooHyNB7cJ7Z\nz6neMRRZpZyNqugYjmIdE2G2D2BeeCdjeYuxWweIiiQYbCf52oNPsKv+D5TVjiffHN8/hCz7Pgrz\nk93ZrpCYorwcHvT5efvjBqTkxbKtLyO8Jlrs5OIoPkCeWUxnd1NCPF7KcjmTe2W21sTLlYdKFXOo\nk4qnFYhXUgFgIkrLXOqaz1CUlzNtQtbk2psCAgKfHa4o5lFA4NPEtUguiD+s4x0/6j96l0jQht8r\npXTlt8kuq00a15KRHPZx35qF2Pacx+oNEYjE4hsXZUTQK5KtOxMFwKrKArb81x7c8iwCEYgOnkem\nMaDXGwhHQ4TcdrKzMinI0GDS+gH5lHUrVDJaDvcmxRlOrP9YXK3HeszDopwvsOW1HcjVEexuOXb9\nnQRDPnTyALK+o1iUUdSmLOT6OdhaG9gwX8sHH71Bj7UDiVqO7Uw6vpwHiaaXETYUc2bfdso3PIra\naEFttOC5cIQfPLqWvOxMTnVY2f/uxyDxQliNVF1N7fq1CWEXJy7wCvOyuaO6OGHFvRwTLXb5mQZO\n98dEeyAqmbLHk+no6ePf3/4IW18DEombUFjL0dblfP8LG5LulSuxJk4nSqeLOVxT8Rj9g6kF4mzm\nv5yLWygVJCAgMBlBPAp85rnS5ILJD/tai5KukVNITToW3Po4Q8MjSEqSkybi435hfbJ4LMrL4f9Y\nT7J4WLfs4vqmFwAKmQKvPgvpyChhdRpBjx23z4FJHqK8qoSCsIZVlQVs25FcsDuOKCwnPGzkzEcB\nRjw9iOWRpGLkEHNP3n7r7ZxypnFqMMCYMgxjLsIuO0GdFrkyg9LKMgpyYw0a++37effjV1Gaotx1\n13jnmYZ9b3Lc/nVEhjlIZVIGDr2JCDEajYqauSYgM5EAtGZZLAzGN9hOrSVK/YAnad1XU1Znoks3\nTa+lnFj3l6inB6NdekmRt2nrB/jcu6jdoCP28emjpWE7m7YG+MfvJPeNnk2x+UuJ0uliDo8e+5hv\nfOXH094fM53/ci7uKy0VJCAg8OlFEI8Cn3muJGM3lbWyf6Cdhyc8vJ/fdphQimunG3e6h/10AqCu\nuYuFy1dxvMOOJ7OQMa8dZVYxkZELaPUG/INtrFofa3346Oce5MP6lyivHReFnSc8/N3f/Jys7CLq\nmrvYueN5au6aGnsZ9sv49Rt7sOoXEJD1IZM48WkykSlNBMMBQjlLqe8aBAYpyM2i4+xuxIoxqmuS\nE3GWr83g/Fsf4sr7KW63m8Llq8nKykkkBP3+wF6KS+cx0XaozCqma+QUDy8puGa9mgtMSj78ZAdR\nrQW5KExhfi6lOh8PP7busmO2nttPzd3J7tqK5ToOb98PPJn6ohlwKVF6qZjDa9GGMKWLe+A8q2rG\nxfl0pYIEBAQ+mwjiUeAzz5WUSpmJtfJqu5hcKrauo6ePvc29BDJNDHWfZWzMTzgSxd/diEYaxZRW\nSYbIkzj//rvvI8ucPsV6lJVdlBDBudX/jVP1z7NgQmvAproh+tyr8IbE+Px2xkR6Aun5yDVGotEw\n3rMHcNv6SLPk0dLVjmKsH2TBKa0JE69d6WHE2o5YpsCryMTh8dHU1ksgImKAdAKnT7Nqkns6Htt4\nLQpcd/T0UT8gonTJGrqtDgIREe3nzvLk6pmNL1P4gKkCW65IlfM9c6YTpQe2foJKLiU/xZzXKuZw\noovb7o2gsfXw4K3LhYLiAgIC0yKIR4HPPFdSKmUm1sqr6WJyqThMgLeO2wirM3AHQrikJiRFhegV\nUqQyGeELhyjMz0XBSNKYqaxHm3YeSsyhNhXS1PZ5erYdQ8EQXp8Sl+E+cpbdjbejGbttCFVxLfi8\nRCJhJFI5iqxiXGcPELGlg6MXc56OUDQNcchGKpz2ENL0MFJtGj6fn+GQGEXGxdaDfgU9F3qx2waS\n4huvRctAiCWd/O71F4goRYTCWvIrH4jFqM7LoWvk1IzGMCqNkMKenKaMJTpdaeLVdKI0xBhZVd+Z\nkoR1rWMO4+I81idcQWFe9jUbW0BA4NOHIB4FPvNcSXLDTKyKlxrX57u0pepSls3Y/xdQKB5gd91R\nxPp5ePvO4iWMPOInNzeXzu5e5s5RXPa1TxS7nd29pFc9ADxA38n95CxaQ6BvEIfHhzHTQt+AjVAw\ngIhYj+aIZwSpQklEZ8a08DaUo+2QbsJzTg6jnVMScRr2WnGYHkLisaNSa/F5nFhKx2MiDRolfpmc\nzu7ehHj0DbZTYFGyaeehq2qzF086qb4rHjvoo6XhRQByy2pnXJrni/f8Nzbv/t8UV+sTx9qPOXn0\nnv9xVYlX04lSsTQjKQlLIvEg8ob51pe/O2M3stCmUEBA4FojiEcBAWaX3AAztypebtzOnn6Otg9O\nebDHxcyo051wr8rFUYqiHoxpMQuU0WxBI40w6hlCYp6LKBJEr9fjtHchcXWy6t7bL/s6/I4hmrqP\nEohK6Ovuxig3ozZaCEaiDI64cI15EUckyKVhJL4Rxi4cRgRERVJ0c5cQ9PhQ6k0ER/upLjDj8IZI\nr3qAseM+ejv30XX2HDK5iKFRGcGcB1AZzCgNmYR6m9D7elEqFiXWInZ0sbSsAFffOaS2FnTSCAUW\nJfUDoqtus/fmtlcoWp2cdFKxXMeOdzczIsmjUpzqq8BUUiWPPLohljwy0YobZ6YFx1OJ0lMHh5lb\n/R0gJnDjIlJqa6F2ec2M1nut2xQKCAgIgCAeBQSuiCst7DyRftswzZ0itHmxLjHHrQ62bTlBbVYj\nIrEEpyJWAFyqjbl1fcCZtiaqRX4kFz29wWCI9LIqQuEIQc8oMkJITNmk+1suu5aOnj6Gonq8mmyk\nWiOSqBHrsBNxxxnsQ1YkYj3RwBgBr5sxvQV52W3gGUWqTgPracS9xxF5XJiyMqku0FCQm3WxaLkd\nVfZC1t/7VQAa9uygOH8eo6P2i327PRRuWMv5Ix8hGWxK9PIuyc9FpNAS6GtOrPF4+yDKuauT1j3b\nDjAdPX1YvQ6K0Ez5nVTuxzo0RIZeSkdP34zGnC555GpaJU4WpSG/FPfYEsbcCmzHjlJ4sUsPzM6N\nfy3bFAoICAjEEcSjgMAVcrVJHE3do6graxJdYqRaM7IiM02DTWQpQ7QfqUNRdkvi/IC1nYqKKkSu\ndnwX6/vpTRkMeeyIZSryzGkoFTJCbjslcwqnn/gidc1dZJVWoXC66bbaMMpCDIhgdNRB2uK78Hj9\nhMJhvKd2IdPmIBruQycLoxPLwFLAHHcjixZZksoRxUvfWE92I7XJ0UkjGEKDDDbuRSJxEwlrMVc+\ngNFsoXROIXJjWsJ6O+p003jyJIvLlhMyW7ADh07upiLDTZo+uW3hbDrA1DV3ERWlk6pLzuhIEKfM\nij2Qzcl/e4u/f3Qta5ZXzXjsiVxtglRclMathUZVFqf7PQS1xkQHHFXEM6OY2XhR8e6RAYKijPH4\nzotcbQedmcw9saC5kKktIPDpQhCPAgJ/IcYiMtRA98UuMXECUQnZFUvp6HwD2STLnNFsQcoISzKj\nbNn3HqP9o0gtatI0atQhA/JIlPxsAwXhqVa2ycQFRJpemxBnn+yvx+b34epqJugeIahIR5JRglim\nQqbPIBLyoNOrSDOmIb1wgfvXVU1x3yu9g/zwS+sTBa73B3dTuyGdeAmaU/XPA7AgK41VleaE9dba\n2s7isuVJyTJqnYFuq2OKeJyN9c0VEqfs/HNkv53RjPtRFK/GHwkiNvv5zT4r0HhFAvJqEqQmErcW\nKiFRg1KsMWBrbUjs60QmxzRqwzb2t7xOYbWGTGLtMifGd8K1S0KazHQFzQFBQAoIfIoQxKOAwA1g\nf0MjW/adxhmRoxcHeKCmGLU4CEAgklzDT36xh7NGo2ZR9dIpYwVcduojFuatuQ+zbYCWHjtitYli\ni4Y0vXbGgmWypcxuG2DIJ0JfWoPGXMCwfZSIfZCA7TxSYy5RRIg16VhHekkzphGNBC/rvn9z2ytU\nrk5PmndBbRqfvPNH7v76j5JEjyXDiH5SmZ7C/FxOnz2H3SDhzLnz2Nwhgo4+1s9R09GTHCYwXfyo\nThpJiKYd725GInLgjmRg4xZUi+8EIIIIcTSMZs4ytuzbmVI8Xi7x5FJ7408GewAAIABJREFUMZuk\nlYlWwYnCXmocTSkcJ8c0bnvtB6y6e2p8Z/1H75JbVntVBdYvx3QFzbfvflMQjwICnyJuSvH485//\nHLlczt///d8njtXV1fHMM8/Q09NDZWUlP//5zykqKvrLLVJAYIbsb2jkN/usaObEhIoDeO5gA7Vq\nB76B88jFmoRDNWBtp+RiD+fKHH3CPR3HN9iOLBJOHDOaLVQAnd09dJ8cYk5l7oxjLydbyjq7e5Fp\nTRi0elwBH+FIFFHGHEQj3QRt7UhVVYzZBxAFfHSdOMD6zJj16lLue7vPThFT6xEqZGNTEmHOnt5L\n8aQyPUazhXmdh2luOolVWYTMpMBctJBWzwAv7jrNU7fHWj1OjB+NjxdPDJnYOnJEkseIL4y/uwup\nOCZygo5BfN2NyExGelsb8Xq9U9Y708STVHsx26SV2bi/U8U0ytXRFFeDNDqE0X7qqgqsX45LFTQX\nEBD49HD9Al+ugNHRUf7hH/6BV199Nen48PAw3//+9/nxj39MQ0MDK1as4Hvf+95faJUCArNjy77T\naOYsSzqmnruUE/1+HlxkpFLcQbBtP7LBJiryjBjNFnyD7dy/roqHl5gx2k8htbVgtJ/i4SVmFIaM\npLGMZgtV1UtZUpbP43eumLEwKMrLSRpfMjbEknwdWlkUs05ONOBFJBYjjkYQ+1342usJe+yERrqQ\nB6w0n97JD5/+Gj/9lx9S31A3Zfz6hjoGegZSzt0zGKDfPpZ0rGRBNW0tjYmf7bYBGvbsoG0ogFuV\nQ0aGGUt2NiqNBnlmMYM+KXXNXUAsflRpmZs0XiwxpCvxOsNte/G0HWT49EEyIjY4X8fYuf34Bs5h\nKluNck4tZC9kNKSko6cvaayYSCtOOf7lmHit3TZA47GjnOxx8es39kyZB2Ji1zfYnnTMN9jOqsqp\n1sJUsYvhsHbKMYB8k2VW98eVMF3h8mtV0FxAQODm4KayPD7++OMsXbqUO+64I+n4hx9+SEVFBevX\nrwfgO9/5Di+//DJNTU0sXLjwL7FUAYEZ44zIUx4fQ0NhXjbfL5nDfQm35gg6+9CUHsUT0TV3XVVi\nxkQmWso2SSPYjVnonW66raMovAM47X2EnQOIiKIoWo5EoUHjOs0czV4W3BovKzM1ri0e+7ZoTfqU\neo9HD7rw5D5C/QUH0MSc+bG/4ajfTdjrpmn/TlyOYTxoMRRW0tfbi1dkxDHiIBtQaWIWw0BUkhBP\n8fjRycR/f/LkUeob3kGiCKPySBiVrEFhyMA3eIbMW55EKhETCkfw2LrJtczl12/sSYovvJpM6vg5\n9oshBvKshYgAlyfWyzuV9XKmmfyprJR5lQ9wuv63U1pRXsui4tNx921fjMU8Lh53Xd+ouQUEBG4c\nN1Q8hsNhxsbGphwXiURotVpeeuklzGYzP/nJT5J+f/78eYqLx7/1i8Vi8vPzOX/+vCAeBW569OIA\njhTH1XgS/59N5vZsEjNmk/kaHzcq1mDrOseYJ4DIN4Y8uwyJPpuw00qmKoo+0siCGn3StZPj2sZj\n3zTACPU725BIJXR1RBib8yT6OUsQazM4dWYHc+YvTAgrfclKFpbkcrCujlGRBYXciFg2DHItvnCY\noSEb+RfFo1wUTgjmePzoZHTSCPUNdXx47CVW3W0CYm0TG+s/ZDjr60RDfmTDZwiKFHi9PrItFgwZ\nmYwOhJKE3dVkUsev7ezuRZ41/nklF0enLZsz0/sh1b2QnpbBivSvcfTYx0mtKG9EzGGqOpg3am4B\nAYEbxw0Vj4cPH+Yb3/gGIlFyTExOTg4fffQRZrM55XVerxedLtntoVKpLtulYyKBQGBW5wtMxe/3\nJ/0rMDMeqCnmuYMNqOeOJ7+42xu4t9x8RXtpyTBxb7mfQ62NuINitLIIt5fnYskwJd3jDUfr2Vr3\nPEXVWuKZr29+8v8RCARZvrQ25bhLjD283NBNf1iH2pJGRKom6h6CkBNZ8VJcZ3eTp/YDU7vXDDn6\nEvN7Q07iUTG5xSZyi2NWsKH3x/CbK9AoZLj9PkIiOaFQiAud3aDJJ8ekJBQKYXUFkeZbGHVYUah1\nDPU2E00vZnjUicnkQuwZIFvuZ2lxAX6/n4X5aZzoPYs2d15iPb6B89y+KIuNb/wmyQoHUFWr44P3\n30Otq6UoOzN2vqoEgEgkgiwaRJpeyJ4Tp7BkmFhanMU7J88mucbj41/ucyV+rS8sQhSJic2Qe5S5\nWRpCoRB2b+SKP5umuxcK8z7HHbd9LuncmcxxLf7GFy+sZvHC6lnP/WlD+Ly8dgh7ee0IBALXZJwb\nKh5XrlzJmTNnZn2dUqmc8uHj9XpRq1M5qlLT2dk563kFUtPW1vaXXsJfFQaVhLtzRtl9/HXG0KDG\nw73lZhbPL7mqvVxgUSX+73aM0OxI7mX95vsvMe+25Pi3oiotb77/Empl6ri4+tMd5M1Zwvkzg8gM\nZsLhCAFZLv4LhxGp+pBJpfh8qdse9g/2sOWtzVTMX4DfHSFVSLUnoCci09Df34tcpcPX3cL5/V7c\n7hCW+ekEfFFsPi++MTeiYAin3U4IMWGRlGDnMaLOAXqHTlGVHmT1ykrcjhHaHCNkm9OBYZqaP4y5\nsMVBFuan4XZEcYzZyE5RIFwhdSGSRBlob0Junks0ctEdbm1njkmBzWpjaNBGc3OsaHmlepim5rYp\n40/e91RUqodp6j+LV6RGJoqQaVAmXqvG1kNz8+VbSV6Ky90Ls0X4G792CHt57RD28ubhpop5nI7i\n4mJ27NiR+DkSidDV1UVJScmMxygsLESrTf3AFJgZfr+ftrY2SkpKUCiu7mH3WaOyspLHJ/x8ub3s\n7OnnUGtvwpq0oiyXwrzsWc2p2JE6Hk+pFVNZWZnyd4e6/agzzeg6hvDLpMhkoATCGRlkWIz4Bnsp\nLVxO4963qVqXn7iu5XAv85db2H1kN05NKRk5K2g/+hHFSw2Jc47sH8FrfpxQKILYVIjYfoH1dz5A\nlgoK3D1Ii8bd7nm52XQMtOCLqpBmzEUShai4Ha0szB2rq5gjHeL2W5Yl7eXalbXcfsvUvTTsNANT\nw2W8HjE162rQOc5ywXoGR3QMuShMfml2IuM7TWZO7FUlcPmGj6nfu9srKyktKeWdk3aUlvHPLd/A\neR68dfms39vrxZX+jV+L+/XThvB5ee0Q9vLa4Xa7r4kx7a9CPH7uc5/jX//1X9m1axfr16/n+eef\nx2KxUF5ePuMx5HI5SqXyOq7ys4NCoRD28hqRai87evp4/7QTZVas1qAbeP90Ow8rFbPKlFVJ9Rev\nTkYp1SfNOTEu0moLYlnyVRYUFlHfNYAsLSYApERgqJX/87H1rFlexde+934ijjEUDJNfGnNNd1/w\nYZdl/v/t3Xlc1HXiP/DXDMMw3PcNgkIpjBcoIKShlNmaR2bmtquZubVqVo/db2XHtrlbtrVt2W5q\n2lr9Nt0utTTNPCs3Q4FSFEFN7lMOQe5hmJnP74+JgYEZGGBgBng9H48exXs+8/m85x3Cy/eJQtux\nKK+qQ/bHp+Hq7QiI3GDrcTeE4koIKICN4jqmRcsROkY7DKzJrYTyeoFu7p583M24uvsjKARH4Fo2\nIJZALJEAfqORnVcI/5vcurSbse/L+bPv67KIIz25GmHyX8NZ3ILFSdoQuuP4JZQrJMgrrUJJWTl8\nZSosvD3C6Pe6ob0bARj9fzc2fDSWyuyQnHm5/T2xA7dtTn/05s/4qbR0vPdDKURe4ZCKBQT7uOLg\npfJef78OV/x5aT5sy/4bksPWfeXl5YWtW7di48aNWL9+PSIiIrB582ZLV4toQJjrPGJTVr5+eegA\njp79zy9zAkUIhhSZKdvhFvJ7xI0KRVZhDhrKCzDa7gYeui1Ot3m2xM4D0XN8uzyztKwZxee1W9O4\nTh0Hu6bZaLpRjkljx8Dd2w/qn9KhdvJHhP9EvVNjpM7umD+p/bQZl/oa2CgbYeMZBtvgybARi2Fr\nawtlVR6Kr1XBOcKly7ON6biIo05RjdpaIGD0XETeHIQEubduE2+xrR1spN6w0YhgIxYgFiqN3tPY\n3o3SxmvdnsXd3yMtrU1+cSl2fJcNhCdBgPYAyEtlNYjw90VyZv6w+qxE1M4qw+Pf/va3LmWxsbHY\nv3+/BWpDNLj6sy1MRz2tfD2Vlo5/7/sEdy7RX0wij3PD2cO7EJu0BpPGAQmLZyA0KACn0tLxxJsf\no04jRUFdAGxTsjEhrn1Y+qfkWlSLx8MrcAJUACrrFfARiTF54kRUXPgW3giDW3MRfG4Kh5uLE2oq\nr6GgqARKwQZuzUWYMykIv5kz7ZdgZge1ow+cxkyFsrUVKgEQWlshdg/GjZz/IUE+s1dt0cZOaosg\nHyfcNW0c4mKm6crbzvnWj8OBRgO7sYCfcSobE8Z0uXxAz5LuqDcn2RhSUFyGo2dzcKaoBe724h7f\nn5xZCMHJT29bcImTO4oqKuHlalXbCBORGVlleCQayfqzLUxncTEJBrdJaesxsnc1vAelt5cEv78r\nVvd151NybFTuOFsWiIJPTsHJWUDVdTUUgQsh9moPDGKpDA03GuHmMhpeY8Pw+7tikT8pCHvPlaOm\npUG356G6oQY+N4Vj77ly3HThJ+w/8Tk0MhFc6htRkSmg1TEYGpENoGqBrcwe9hIbHDiVAalzMZwl\nGkwJ69oD2pEp5y33NrAbKxc0xrcM6m+w60lvT7Ix9P59F2rQ6B0FBx9v1EgkPb6/XiWGVKRG50+t\n1IgG7PzsznqzHRURmQfDI5GV6c0+jn3V1mPUXOIEoOv2F51PBNGekjNH97VMKkGVYyjqw2+Db5A3\nyksqcSM7HTaV6VDVn4SLYzMaGu0g+M8AMF4XJNo2wN702UnIfKZA2lyJYH9XuLk4oaQsE3kF/0H0\nndqe0KlwRer3x/BTy28h8o8CVC0Q1RTCxs4RmZpQTPAORA2AfRd+htzhOgwvATLtvOXeBnZj1xs7\nUnKUn6xfwc4U3U13aHu9u+CanFkImd841FdUdnm/sTo6SzQICQ5EVnEOpD7tn1moykbCzK7ng5ub\nKX8xICLzY3gksjK9OWGkr9p6jGxD5iE95f9hclx7WLyUUo3lcx7Uu77zKTkKpQquIZFQVOajxV4J\nZe0NuMg0iBidhZhEX7TtA3n+9AHkngEeu+c2vc/n7+uDFpEApUaEogrtFurFmfsRd5v+EHrsDE/k\n7T2O+pAEiKCC2MUDMptGKDXtA6UyvzH4Pvksrqt/hALSLuHIlPOWexvYjV2/+FZtYOr8/64/81hN\n7bE01htaUH4DZUr7HoNrX6ZLJMhHoexcJSKD3FFQlAGlYANRwzWsmhk+KPMdTfmLARGZH8MjkRUa\n6IUVbT1GjcU1uI4Hcfirg7C3bUTzjRY8vGhFl1+8nU/J0YhsYCtzhLOzDK4OUoSG3YzKkx8h5h79\nIeRJ8W7I/+E0QoOW68ryi0uRnlOG2sBwtLSqoWxtxaWyYvgorwMG9mN0tatDc10x7CUCmtVqNMIR\nldercaPOBW4uTijIy0XKNVuU+3hBJhEh2McVZefKdeFI24vaddV5x97V3gb2nq7v/L4j54sN3qen\nuZC9GYo21ht6raoGN4+brldmKLg6SzQwtJV3d8PP7e1QCO9g+1/CbcKgLZQx5S8GRGR+DI9EI5B+\nj1ETlGOXQtRwDY/fF65bUd3RkhkR+Of3P8JxtHZbG7GgRuuNMkSP8kZloxoye1u4OzYD6DqH8nrz\nDXx05Iyux+zAqQwo3cNRX1mCVtdQiOycoBTb4/p1tcG6NjdLEejjgXq1LZT1tWitr4CDZwQulTUi\nsL4R6VdLYBs4ARpHLyjE4i6rfU1Zdd6X+Yi9Cfh9ncdqao9lfnEpqq5XIyXrezj7jkGwj3YqgKI8\nB/6+Pgbv3Tm4JshH4dPUXKBDqDZlukR//qLT33mgpvzFgIjMj+GRaIAM1ET+Lw8dwP4Tn0OQtKDp\nRgscJBL4BLmY9IyOv6xt66sgFdtgarB7jz1G2kCZjt3fH0GdRorgliq4uKswKjABtdklUEC76bYh\nKpEXatzH63rMMksb4DI6BjW5OWgpz4JGZAOxoEazKAxZabmIjGn/xZ+ZcgOR0SuQV18BscQVkqos\nuNlLYO+oPTc7q/BnqFRKuHQ4barzat+4mASUV17H/sPaNkOrFHff/mtdO/V3oYkp+jqP1ZShZF39\nw29FhKt2BXtWyRVMC7bD4ukTkJzZbFJwDQ0KwN2KFnz+bRokCNKutjbzdImOzNHupvzFgIjMj+GR\naAAM1ET+Lw8dwOGzOxF9pxsAWwBOyEotgdhdDd8wUbfP6PzL2sZbG2DmT/I26Zf19JjJer2S2iB6\nEaFCIy5nZ2BM5Fxkpe3VC39ZafUIki8F0N5j1rYi2dbVF25eoe0PUF6Da3ASUg/tgr+PBFKRE+6M\nno8GG0/UZ5RA2VoJjasUlSVXkL9nF1xcRairboXMOx52N8fo1bXjat/84lJcVY9C9NK3da9fLc9B\nfnEpQoMCzLavZk+kjdeQcSobgqYV8gAXLL51co/3N6XHsmP93b39dKfjuNe019/U4BoS5I87osMg\nl8sHfDNmc7R7T9tREdHAYHgkGgADNZF//4nPfwmO7SJjA5FyJBuBYR7dPsPcIanjcGVbj+bJkzfh\nyIffw84JaGq0QcC4RQgcG6d7T71KDHmAC85U5MBG4gPVL+WtFTnwd3VC4Ng4jPdxxG/mTNN7VtX1\nQzhT74362kqM8jyLKfO9dK+d/eEn5JxzguP0+3VlHVf7GvvcB079AE/3QhzLKIFdgLtuqLdjXTt+\ntr4OrepC+5hbdHtAKspzTHqvKT2WPfVODsYCrL4w536mDItEg4vhkWgADNREfkHSAm2Poz4biU2P\nzzDXL2tDQoMCcOHCT7B1yMKcB27SlWelnULJlXG6AOks0UB+UwBOHUxHc8vPqGtUwMnFDR6uTpCP\nH2+0R0wktoHYwQPK8+9jyiJXvdd8/YD8zE9Rd+IbNDRKYec0Ds8+uEgXjgx9vht1DcgqakHCmPGQ\nOLVAYe+tnSsJ6DYwr7ySg1dv1ONyaQ3CIyfD3duvT0Or/Q3ttvUluHDlEkRiW8gDnLB4+oQuC116\n6p00Ni+xczDuac9MczLnfqZENLgYHokGwEBN5Bep7AyWq1Xti02MPWMgf1nnF5fi02P7kDBXf6ud\nyBhnpJzYj8Cxcbr9DlOuiTBpxp0oqqhFbZMS1XkZGCtTYozEVa9Xr+Oc0dIKFTzG3otG2xsA2j9f\nSU41aq83455HInVlGaczUV05FcBko5+7qKIWDs7aENpxn8KiikoILQ04fyUXkyfOQn5FLRAehazi\nHERCOyzc297avob2jnMZJ4Zrywz1WPZ1PqWhOYc97ZlpToOxnykRDQyeH0U0AOYm3YuC8416ZQXn\nGzE36d5+3XfhbfcgM+WGXllWagmCwj16fEaCfFSX8KEoz0GCXP+XdUpaMl587Y945m8P48+v/gEp\nack91is5sxBSB8HgaxKhCu41F7E4yhuF1QrIfMPg5uKECeGBmD5xNBYsXIBJY0fjN3Om6QXHvSe3\nwCe6AcFTRYibawt19S401OifZVKcXY3I2EC9sgnxHth/4vNuP3d9eS5CgrXvc/f2Q2SQO2zLM9BS\nehGVV9IweaL27O22/SSlPmEoKCppf38vemu722i8O9oeyzC9Mm1wLdQrCw0KwOIob7jXXISkMkvX\n1j2FW4P39xuDjKIbRt5hXn2tNxFZHnseiQbAQE3kXzB3PgDoVg5rV1s7wVnsgvKfHLt9hilz3/q6\n0KdeJYZa7QQY2Ckw2MNPN4fR1P0ODc0ZjYxxRmlRI9JP12JyvLbXsONwfUfa4X3jnzvOV4DNLwtL\ngPaFJu412mtUv8x9lIoF3SdSCu3P6k1vbX9XWpdcSUFx5n7Y2DRApXZCSOBkoNOc0L5sl2MsADdp\nuk6LGCgDvZ8pEQ0MhkeiATJQE/kXzJ2vC5G91dMv674u9HGWaBAkX4istB16q607n1bTeQj5Rl0D\niipqUX8lHVlXtHsSjvJyRJ3iOnzQdYjey1UDB9tbsX/nCbh42qP2WiumGqhP5+H9zp9bO2RrONAl\nZxbq6hjs44pLZTWQOLlDKlLrXWeqvi5YcZZocPFKCmqLdiDuNmdof1wrcCnlEFLSxvX7e8vYNAYH\nseHzuYmI2jA8EllYf1fzmlNfF/okyEehTFkJ4HdIObEfNjaNaGkEfn2H/mk1HXvhbtQ14FJZIxrK\n8iFyuwm1oZNwvaEGahtHVNyQINzAcwLc/fGX9Y/ioyNTUOM+HlfOnkT66f9gcnz7CvTMlBtYeNty\nA+9u11Oga6ujm4sTIgBkXzyDMR5iuNdc7NNK5b70sCXIR+Grwy8gYa7+HNaIOA+zHL9nsEf0Wi4m\nBLt18y4iIoZHIosqKC7Du4dOobI0TTcs+dOVGDx2z20WCZB9XejTHsaaEeKxzGgI7hjaMjJLYO/g\nBUGshG1oDJobG1Fbr8Cp6mq4+s5B5plPIZ/mqXtvx82f24LP2OhEZP2oxld7v4CDtBEyjS3um7vc\npJ5ZY4Guc7AcLdHgt/f2vCejuYUGBcDHTQWg69C8OY7fMxSgb5/oi4Zaw3NXiYjaMDwSWdBnXx+D\nouFbvWHJrLRD+OhLJZ5bu2rQ69OfEztM7V1ru65elQqVdyRSf0pHc2MjKuuaoaouhF3pl9A4KpCV\n14iKAjVcPB26nArTMfhEBnghyPZXuGdWDMaGj+7rR+/TZxloLjJPDOTxe50/p0KhQGZttVnuTUTD\nF8MjkQVdzUlG3F36QSAyxhmph04BGPzwOJgndrTNuZOK1Civq4OquhDhmg8xZZEztHtZjsL50zfg\nNXoVAsfG6Z0KA7QHH4VCgcxMO4QE+Rt8zkAdEzkYePweEVkjhkciC5LaKQBIjZRbxmCd2NE29BwS\nHIiraZchrTqMKQv1g/SkeDfdPpG93V/xVFo6tu8+CJn9WUye7gVzHhNpyECEVEscv1dWeR0XT/wI\nBaQWn4NLRNaJ4ZHIglzt3AB03fbFTeY++JUZZO1Dz4UIFErRhGsAPLpcZ2PTvl+mqfsrnkpLxz+/\nr4BCWY47Z3vpvWaOYyI7G6izzPOLS5FTLUbA+F8PSpArKC7DyUIBQePHQyKR9OlEHSIa/hgeiSzo\n7jn344tT7yAs2kVXlnO2DkvvetyCtRo8bUPPCfJReP6NFIPXqNWOqKm8hoKiEtg0VeEjE0LU7u8v\nwXH0HIjKPoah4xzbFpyYq7dwIM4yP5WWjh3fZUNw8oNUpEZIcCDKzlUOaJA7c6UEdj5j9Mr6c/45\nEQ1PDI9EFhQzJQ5Sqa1egFl628AOSwLWNw8wNCgAS2ffjaMp/0FEXHvvY1ZaPVwD70BWcQ3EjkGI\nCB+LGhcnXW9YeVk+Dhz7DJW1pVAq7RE4JhGRN0fiWr0a9gCaW50AtHR5np3Y2ay9heY+yzy/uBTv\n/VAKhCdBBKAV0B6RGOSO5MzCAQtyDa0Dd/45EQ0fDI9EFjZYcwzbDNQQa38tmDsfvt6eulCrapEg\n0Od2FDSp4OLpgmAfR7j9cvKLzDcMH335KW40fYOQGEf4Q7s3YVbaMVysCEFNowJobIRtyDykp/w/\nTI5rn0vZtuDEWG/hnq92Iada3Kt9N819lnlyZiFEXuHouGmO9ojEDHgH2/fpnqZwstVok2on5jj/\nnIiGD4ZHomHM0AbkAzHEai6GgvT2r1Kh8g7scu2Vq6cQO7frEYYpJ/ZjUvTvkHYhGaExs3E9Hzj8\n1UFIhXL42ztj+eJHEReTgC+OfwBDvYUVzbWocR8PACbP+TP3quh6lVjvaMQ2SsFmQIPctLGBuPjN\nz4CPt66styfqENHwx/BINExpj+CrhMxXPwgZO/qvuyFWSw5zGztGTyRuBND1NBRBuIHQMWNgc/1n\nNOcdgUgjhcuoBCyZEYHpMZN11xnrLRREnnpfmzLnz9yrop0lGr2jEduIGq4hQT5w7R4S5I/EUVdx\nveZi+2rrPpyoQ0TDG8Mj0TCVnFmoC45tZL5hqKszPH/N2BCrpYe5DR6jV54DlbLrFkcAUFen/ffE\n8CD8Zs40o/c11FuY8UMlCmsmo/jLQ5BqWjB+XDhGj5tg0pw/c04/SJCPQtm5ckT4+6KoohJKjQhC\nVTZWzQwf8CDn7+2J2+VyyGSyAX0OEQ1dDI9Ew5SxwOMfOgMF50+YPMQ6UMPcnYfUR3nIcC6nHJml\nDRA0rZAHuGDBrZONnkNdk5+ErLRjiIxpD71nk29A5ZRg0lBr597ChloNMuri4ZCwEgCgBpCS9xOA\nDET7dh3eNsdnNjafsv0z58PL9ZdrZw7+EYlERIYwPBINU8aGeyNvjoSyQo2vP90Nib0GrU0izJ15\nn9EgaO6VxEDXIfW8ugZ8evxH2Dh5wWV0DADgTEUOKo5fwu9uN3xcYOTNkTiT54tD+7+EVNqMhmZ7\nKP1+AztRFeL8BJOCVsfewife/BjBk5NQWa+AWKrtdZOFTsH59N1Y98e7u9TflBBo6DM3i71QUFQC\npWCDoxeS8buZ4XrD6W2s5YhEIqLOGB6Jhiljw71NN35GesER3Lo0VFeedf47pKTJDQZIc64kbgtd\nJzPyIQqIQrB9A9xcnFBUUQulzwSoKnPQtuOl1CcM5eUZRremSZCPwrFL1fCctRGNjU3wd3SAqioP\nkUHjUVhd1eu61WmkkNnZwhtAbWMT1IIINiIBru5ues83Npe0p0U1yZmFaBZ7Iau4BlLfCb/E8Ui8\n98MZBPn7MCgS0ZDB8Eg0TBka7h3lJ8M735xEwlz9k1y6G4Y210rijqFL4aiCyN4bl8pqEAFAqRFB\nLYigEdnovUcp2Bgdfg8NCsDNHheQXZ4BTX0zbJ3tcXNIMNy9/VBfWd2rugGAi1iJWgAyO1vI7No3\nFnet1e91NTaXtKdFNfUqMQqKSiD1naBXLvIK73Hvxnd27sbOk9ll7oi0AAAgAElEQVRQ2LpA1lqH\n5YnhWLN8SZfr+tIjSkTUWwyPRMNY56HPj46cgdRBMHitsWFoc60k7hi6pCI1WgFInNxRVFEJqRiw\nEQkQBLXee6QitdGtafKLS5FTWIoSp/FoEcRwtHWGyE67D2RftrNZMiMC//z+RziOnqora8z7Eb+b\nEaF3nbEw29OiGmeJBkrBpssEAKlY6Pa97+zcjXfSBTjEroIU2sMs30n/DsBuvQDZ1x5RIqLeYngk\nGkHqVWKo1U5Alx0Eux+GNsdK4o4BKSQ4EFnFOZD6hEGpESHMzwVlaT/C3qn9HGplRQ6CZCokyG/q\ncq/84lK8fzIX1a6RaG7WAJ43oURZj+bcSsgds3FX4pgu7+mJdt5hOnZ/fwR1GilcxEr8rtP2PoDx\nuaQ9BdYE+SgcvZAMIFJXpmqoQbC/K5zVhu6otfNkNhxiV+mVOYydiZ0n38Oa5e1lfe0RJSLqLYZH\nohHEWaJBkHwhstJ26K1SvpRSjeVzHhzwZ7dFJHdvP0QCuHz1R9SU5cP2mguiXTWAjQJleSUQNK2I\n7rDaurPkzEJUiLzhEuQN8fVSlBenQyxzQGNdEbzGe/Y5LE2PmWxw8UpHxuaS9rS6OzQoAL+bGY73\nfjgDkVc4pGIBwf6ukDWXd/teha0LDG1KpLB10fu6rz2iPXnq+T/gwtVkOLlL0VDdgok334LXN27q\n1z2JaGhjeCQaQRLko3C5KhfXxYtweP9xONg1Q2gRY9ncFQO+Z2Pn0CWyc0KLBoi/NQnu3n4AtCHs\nmSjvHsNfvUoMpUY7AOzg7gcfqQscHR0gqpDAztXw/o/mYmzrIFMC6/SYyQjy92mfl6iu6fG9stY6\nGOrTlLXW6X3d1x7R7jz1/B9wwyYLCx6epCtL/z4LTz3/BwZIohGM4ZFohNG0tsDZzRd2rg9AKlLD\nV6bCxIkRPb+xnzqHroorOZg0NkYXHAHTh1mdJRqDx/d1N0fSnPqzjU5v37s8MRzvpH8Hh7EzdWVN\nV77DmsRwvev62iPanQtXk/WCIwBMnhGCL/+d3Od7EtHQx/BINIIkZxbCPzIO/l3KB2deXNszkjML\nUaeRoqmoBAD0AqQpw6wJ8lG4fDIXuQ0SiB1cAQAtFTkYZW94jqS1MmV1tHZRzG7sPPmebrX1GgOr\nrfvTI2qMk7vhXlwnt4Ht3SUi68bwSDSCDNS8OFN1XBFsF+AOhb03sopzEIn2AGlKz2FoUAAeSgQO\nnMrAhbxatNRUI3ZsABYnTTUpLFnyrO42vVkdvWb5Er3FMcb01KvZ2618GmqUhstvGC4nopGB4ZFo\nBBmIeXG90XFFcLCPKy6V1UDqE4aCogy4e/v1apg1NCgAj/06AAqFApmZmZCbeB5zf8/qNtdeir1d\nHd3f5/ZlK5+JNyUg/fssTJ4RoitL/74AE28a3KBNRNaF4ZFoBBmIeXG90bGH083FCREAiioq0dJQ\nBfeai/0eZjVFf87qbtsiqELkDaVGBKlYQOpXFzHGOR12rl69CnW96QU2xx6OfdnK5/WNm/DU83/A\nl/9OhpObFA01XG1NRAyPRCPKQMyL643OPZ9uLk5wc3GCu2cNfjNnmlme0dOQdH/O6j5wKgO5mnBI\nnNwBADWNjShX+KGq+RriwyN7Fep60wtsKPg1ix2x6bOTiBwbZlJo7euUBQZFIupscCY6mWjr1q2Y\nNWsWYmNj8cADD+Dq1au615KTkzF//nxERUVh2bJlyM/Pt1xFiYaolLRkfPDff+DC2fdQkvExwjw0\nRodIPzpyBtu/SsVHR84gv7jULM9PkI+CojxHr0xRnoMEuXl6PtuGpH2iGxA8VQTfKdoh6ZS09tXB\nxjZDN+Ws7szSBl1wBIDaujrYuvmjskGlK9P25hX2eK/etEXngFdTeQ1ZxTWo95kClXckatzHY++5\nym7/PxmbmjBYUxaIaPiwmvD4+eef48svv8SuXbtw5swZxMfH4/e//z0AoKqqCo899hiefPJJpKWl\nYdq0aVi3bp2Fa0w0tJgSrID2IdIa9/EmBxNThQYFYHGUN9xrLkJSmQX3motYbMK+jqY69M0evTO4\ngfYh6TZzk+5FwflGvWsKzjdibtK9Pd5f0LTqfa0WtD9CBbVKr9yUBUi9aYvOAa+gqARSnzBIxe1H\nTfYUWgc6uBPRyGE1w9a1tbVYvXo1AgMDAQAPPPAA/vWvf+HatWv49ttvERkZicTERADA2rVr8eGH\nHyIjIwMTJkywZLWJhgxT5/oZGiJV2Pti02ffmjxE2p3+7JHYE1OGpPtzVrc8wAVnKrTHKgKAjUgD\nRUUOgrw99K4ztTfP1LboPFdVKdhA/cvRhh11Dq3v7NyNnSezdVv83DXJB/7SZotMWSCi4WNQw6Na\nrUZTU1OXcpFIhJUrV+qVnThxAm5ubvDz80Nubi7Cwton+IvFYgQHByM3N5fhkchEps716xxAbtQ1\n4FJZI2zsg7U9kej9Yo3Boh16bjBS3q6vZ3UvuHUyKo5fQnl5BpSCDTybalDb1ISIqETdNQOxAKnz\nXFW35iL43BQONxcnves6htZ3du7GO+kCHGJXQQpAA+DTK99hjVtRlz0iiYh6Y1DDY2pqKlauXAmR\nSP8XWEBAAE6cOKH7Oi0tDRs2bMDLL78MAGhuboazs/4Pf3t7eygUnc+XICJjTA1WnRdyFFXUQuLk\nDdvGYl2ZqSfBDLa5Sfdqt+HpMHRdcL4Ri5Me7Nd9Oy7CUbVIMNpnKjxDJsBZ4o1RHjIUVuejvrLv\nvXmm7DvZsZcyf1IQ9p4rBzqEx86hdefJbDjErtK7h8PYmdh58j2T9owkIjJmUMNjfHw8Ll++3O01\n+/btw1//+lf8+c9/xty5cwEAMpmsS1Bsbm6Gg4ODyc9WKpUMm/3U0tKi92/qO0u05e3TF+LL5O0I\nndweOPLTG7Bg+m/1/mxMCfPFvgs/Q+Y3BgCgUAlovnYVoQG+UKna5/bVNGus4s9Ux7acNCEaSuUj\nOHLyC7QKDbAVOWJ+4m8xaUJ0n+ua9lOKtt2inaDtuVUjP/0Ebm51RyN8cL6kCU62GswcG4iQIO3Z\nPb15Vtf7N2LPd5uhVLYiZkqc7rqC4jKcuVKChlYxnGw1iHKXoqgyXff17RGB8PPy0D1bIXGBrYHn\nKSQuRuvHP+Pmw7Y0H7al+SiV5tng32rmPALAli1bsHPnTmzbtg2xsbG68rCwMBw+fFj3tUajQWFh\nIcLDww3dxqCCggKz1nUky87OtnQVho3BbEsHmRMmBszBj9+chMamBSKVHWImzIGDzAmZmZl618od\nriMjMxtNGluoikrgOyYaKsEGlRWVumscK4uRmWk3aPXvSVtbOsicsGiOftda58/XG3sO/gc3J+kP\nD4dOdsLHn3+OiDkvagtagYvf/IzEUVfh7+1plvvvOfgfOMi05WWV13GyUICdz5j252XlInGUCOP9\ntM9rqK1GZm217h6ipgpo1F3nXoqaKnpsD/4ZNx+2pfmwLa2H1YTHvXv34sMPP8Qnn3yC0aNH6702\ne/ZsvPHGGzh+/DgSExOxfft2+Pn5ISIiwuT7h4SEwMnJqecLyaiWlhZkZ2cjPDwcdnbWExqGIku1\npVwux5LFS3u+DsDtv/x3QXEZ9l2ogczHW/e64lou7p4Vo+tps6SBbku7w4ZXTju7S+HdoU3g443r\nNRdxu1ze4z079iI2apoAuHS5RuYkhvyXe1088SOCxusvYurpeQ/O/Bn/vngS9uNm6cqaL3+Lh2eO\n0923M/4ZNx+2pfmwLc2noaHBLJ1pVhMe3333XTQ2NmLx4sUAAEEQIBKJsGfPHowZMwZbt27Fxo0b\nsX79ekRERGDz5s29ur9UKjXp6DLqmZ2dHdvSTCzZlqYedzc2fDSWyuyQnHm5/dpY61ulO1BtaS9x\ngaG5ohqNEyQS/R+hCvT8cya/uBQHL9VB5jsZAKAU+QDoOowsk7jo7qWAtMuzenreYw/dD8nO3dh5\n8j3daus1ieFYs7znvzzwz7j5sC3Nh23Zf8Nu2PrIkSPdvh4bG4v9+/cPUm2IhrfeHnc3kNvrWDtD\ni3AupVQjSL6my7WmbNHTeSukIPlCZKXtQGRM+8Kljot88otLkXUlB/U1bpCKBQT7uOpWWff0vDXL\nl3BxDBGZndWERyIaPH0553ikMrQv5B3R83FV7aV3nalb9HTeCilwrHZRTOqhXfD3kejtO9kW8r3H\nxqCyuAYanzBcKqtBBABZc/mgnUlORNQRwyPRCNTXc45HKkP7QmqH/Xt/RrihM60Dx8ZhvI9jl/O9\n20K+DEAkgIKiDAiCDSouFOEP9yUy6BORRTA8Eg0wQ3ML/bw8en7jAOoYYEqupKA4cz9sbBogatZg\nso+qTxtojzR9HcrvfFoMYLzXsmOYd/f2g7u3HwBAUillcCQii2F4JBpAxuYWzouw7H5lbQHm+o0q\n1BbtQNxtzmj7cbD35BYAYIAcIJ1Pi+mu19JQL2VbORGRpTA8Eg0gY3MLz1xJx3g/ewvVqj3AvLTp\nLUTfqX/CjKHzrsm8+nqmNTAwxx8SEfUGwyPRADI2h7Ch1fJzC0ODAuDtZfhHQOfzrskyetNLSUQ0\nWBgeiQaQsWFHJ1vrGHY09bxrspz+bpNk6n6eRESmsnz3B9EwliAfBUV5jl6ZojwH08YGWqhG+uYm\n3YuC8416ZQXnGzE36V4L1YjMqW3ObY37eKi8I1HjPh57z1Uiv7jU0lUjoiGMPY9EA8jYsKOfl4fe\nOcSWYmgPw7Y9Bmno436eRDQQGB6JBpihYUeFoutxdJZiaA9DGh64nycRDQT+BCEiGqaMbenDrX6I\nqD/Y80hEAIbWwoqUtGTdULtEcMDYkGjI5XJLV8vqcKsfIhoIDI9EZHQz88WA1QXIlLRk7D25BSHR\njgBEAJrxU9pBhP4Uihm3JFq6elaFW/0Q0UBgeCSiIbWw4tA3e34Jju1uinHHkZNfdBseh1LPqjn1\nd6sfIqLOGB6JaEgtrNBuYC7qUt4qdN2vss1Q6lklIrJ21vebgYgG3VBaWGFsA3NbkZPR92h7VsP0\nyrQ9q4VmrRsR0UjA8EhERjczT5Bb38IKQxubX02rwZzERUbfM5R6VomIrB2HrYloSC2s6LyxuURw\nwJSQWxAzJc7oe4wdE2nJntWROgeTiIY+hkciAjC0FlZ03NhcoVAgMzOz2+utbcsazsEkoqGM4ZGI\nhj1r61kdSqvbiYg6Y3gkohHBmnpWOQeTiIYyhkciCygoLsPRszk4U9QCd3sx57uNMNY4B5OIyFT8\nay7RIMsvLsW+CzVo9I6CyicSNe7jsfdcJfKLSy1dNRokQ2l1OxFRZwyPRIMsObMQMr8xemXcc3Bk\nCQ0KwOIob7jXXISkMgvuNRexOMqbvc9ENCRw2JpokHG+GwHWNQeTiKg3+NuKaJANpdNciIiIOmN4\nJBpkCfJRUFzL1SvjfDciIhoqOGxNNMhCgwJwt6IFn3+bBgmCtKutrfQ0FyIios7Y80hkASFB/rgj\nOgy3j/cHABw5X4yPjpzhimsiIrJ67HkkspCyyuvILBDBKYhH1FG7lLRk3bndUpET7rptie4oRiIi\na8DwSGQhGUU34CCP1SvjEXUjW0paMvae3IKQaEcAIgCN2HtyCwAwQBKR1eCwNZGFNGlsDZZzy56R\n69A3exAyyVGvLGSSIw59s8dCNSIi6oq/pYgsxEHcarCcW/aMXC2a+l6VExFZAsMjkYVMCHbjlj2k\nx07s3KtyIiJLYHgkshB/b0/cPdGdR9SRztyke1FwvlGvrOB8I+Ym3WuhGhERdcUFM0QWFBLkj7Hh\noy1dDbISbYtiOq62Xnzbg1wsQ0RWheGRiMiKxMUkMCwSkVXjsDURERERmYzhkYiIiIhMxvBIRERE\nRCazmvCoVCqxYcMGxMfHIyYmBo8++ijKy8t1rycnJ2P+/PmIiorCsmXLkJ+fb7nKEhEREY1QVhMe\nt27ditzcXBw9ehSnT5+Gq6srNm7cCACoqqrCY489hieffBJpaWmYNm0a1q1bZ+EaExEREY08VhMe\nn3jiCezYsQPOzs6or69HQ0MD3N3dAQDHjh1DZGQkEhMTIZFIsHbtWlRUVCAjI8PCtSYiIiIaWQY1\nPKrVatTX13f5p6GhASKRCFKpFJs3b0ZCQgIuXLiAhx9+GACQm5uLsLCw9kqLxQgODkZubq6xRxER\nERHRABjU8JiamoqYmBjExsbq/bNw4ULdNY888gjOnz+P2bNnY9WqVVCr1Whuboa9vb3evezt7aFQ\nKAaz+kREREQj3qBuEh4fH4/Lly93e41UKgUAPP300/j444/x888/QyaTdQmKzc3NcHBwMPnZSqWS\nYbOfWlpa9P5Nfce2NB+2pfmwLc2HbWk+bEvzUSqVZrmP1Zww89xzz2HChAm4//77AQAqlQoA4Ozs\njLCwMBw+fFh3rUajQWFhIcLDw02+f0FBgXkrPIJlZ2dbugrDBtvSfNiW5sO2NB+2pfmwLa2H1YTH\niRMn4v3338ett94KDw8PbNy4EVOnTkVQUBBmz56NN954A8ePH0diYiK2b98OPz8/REREmHz/kJAQ\nODk5DeAnGP5aWlqQnZ2N8PBw2NnZWbo6Qxrb0nzYlubDtjQftqX5sC3Np6GhwSydaVYTHn/961+j\nuroa999/P1QqFW655Ra89dZbAAAvLy9s3boVGzduxPr16xEREYHNmzf36v5SqRQymWwgqj7i2NnZ\nsS3NhG1pPmxL82Fbmg/b0nzYlv037IatAWDt2rVYu3atwddiY2Oxf//+Qa4REREREXVkNfs8EhER\nEZH1Y3gkIiIiIpMxPBIRERGRyRgeiYiIiMhkDI9EREREZDKGRyIiIiIymVVt1UNEZG3yi0uRnFmI\nepUYzhINEuSjEBoUYOlqERFZDMMjEZER+cWl2HuuEjLf8QCAGgB7z+VgMcAASUQjFoetiYiMSM4s\nhMw3TK9M5huG5MxCC9WIiMjyGB6JiIyoVxn+EWmsnIhoJOBPQCIiI5wlml6VExGNBAyPRERGJMhH\nQVGeo1emKM9BgnyUhWpERGR5XDBDRGREaFAAFgNIzrzYvto6iqutiWhkY3gkIupGaFAAwyIRUQcc\ntiYiIiIikzE8EhEREZHJGB6JiIiIyGQMj0RERERkMi6YISIyEc+5JiJieCQiMgnPuSYi0uKwNRGR\nCXjONRGRFsMjEZEJeM41EZEWf+oREZmA51wTEWkxPBIRmYDnXBMRaXHBDBGRCXjONRGRFsMjEZGJ\neM41ERGHrYmIiIioFxgeiYiIiMhkDI9EREREZDKGRyIiIiIyGcMjEREREZmM4ZGIiIiITMbwSERE\nREQmY3gkIiIiIpMxPBIRERGRyRgeiYiIiMhkDI9EREREZDKGRyIiIiIyGcMjEREREZmM4ZGIiIiI\nTGaV4XHPnj2YNm2aXtnBgwdx++23Izo6GqtXr8b169ctVDsiIiKikcvqwmNRURFee+01iEQiXdnl\ny5exYcMGbNq0CWfOnIGXlxeeffZZC9aSiIiIaGSyqvCo0Wiwfv16LF26VK+8rddxwoQJkEqlePLJ\nJ/H999+jurraQjUlIiIiGpkGNTyq1WrU19d3+aehoQEAsH37dtx000249dZb9d6Xm5uLsLAw3ddu\nbm5wdXVFbm7uYFafiIiIaMSTDObDUlNTsXLlSr0haQAICAjAv/71Lxw4cACff/45Lly4oPd6c3Mz\n7O3t9crs7e2hUCgGvM5ERERE1G5Qw2N8fDwuX77cpbylpQX33nsvXn75ZchkMgiCoPe6TCbrEhSb\nm5vh4ODQ4zPb7tXU1NSPmhMAKJVKAEBDQ4Puv6lv2Jbmw7Y0H7al+bAtzYdtaT5tWahzzuqtQQ2P\nxmRkZKC4uBirV68GAKhUKjQ3NyM2NhZffvklwsLCkJeXp7u+uroadXV1ekPZxrS2tgIASkpKBqby\nI1BBQYGlqzBssC3Nh21pPmxL82Fbmg/b0nxaW1shk8n6/H6R0N/4OQBSU1PxxBNP4PTp0wC0q62X\nL1+O7du3Qy6X46WXXkJVVRW2bdvW4700Gg0aGxtha2vbZbiciIiIaKQQBAGtra1wdHSEWNz3ZS9W\n0fPYk3HjxuGll17Cs88+i+vXr2Pq1Kl45ZVXTHqvWCyGs7PzANeQiIiIyPr1p8exjVX2PBIRERGR\ndbKqfR6JiIiIyLoxPBIRERGRyRgeiYiIiMhkDI9EREREZDKGRyIiIiIy2bAOj1u3bsWsWbMQGxuL\nBx54AFevXtW9lpycjPnz5yMqKgrLli1Dfn6+5So6BGRlZWHJkiWIiorCokWLcP78eUtXacj48ccf\ncd9992Hq1Km444478OmnnwIA6urqsG7dOkydOhVJSUnYs2ePhWs6dFRVVSEhIQEnT54EoD0E4MEH\nH0R0dDTuvPNOfPfdd5at4BBQXl6O1atXY8qUKZg5cyZ27twJgN+XfXH27FksXrwYU6ZMwa9+9Ssc\nPHgQANuyNy5cuIAZM2bovu6u7ZRKJZ577jnExcVh+vTpJu35PJJ0bsvy8nI8+uijuvZ6+eWXdQeo\nAMAbb7yB+Ph4xMXF4ZVXXjHt9BlhmNq7d68wZ84cobi4WFCr1cLWrVuFWbNmCYIgCJWVlUJ0dLTw\n3XffCa2trcLbb78t3HXXXRausfVqaWkRbr31VuGTTz4RVCqVsGfPHiEhIUFoamqydNWsXm1trRAb\nGyscPHhQEARByMzMFGJjY4Xk5GThscceE55++mlBqVQK58+fF2JjY4Xz589buMZDwyOPPCJERkYK\n3333nSAIgrB48WLhzTffFFQqlXDy5EkhOjpaKCsrs3Atrds999wjvP7664JarRays7OF2NhY4dy5\nc/y+7CW1Wi3Ex8cLR48eFQRBENLS0gS5XC6UlJSwLU20e/duYerUqcK0adN0Zd213auvviqsXLlS\naGhoEPLz84WkpCTh66+/tlT1rYqhtly2bJnw0ksvCUqlUqiqqhLuu+8+4a233hIEQRB27twpLFiw\nQKiqqhKqqqqEe+65R9ixY0ePzxm2PY+1tbVYvXo1AgMDIRaL8cADD6CsrAzXrl3DsWPHEBkZicTE\nREgkEqxduxYVFRXIyMiwdLWt0pkzZ2BjY4OlS5fCxsYGixcvhoeHh67Xh4wrLS3FzJkzcddddwEA\nIiMjERcXh7Nnz+Kbb77B448/DltbW0ycOBHz58/Hvn37LFxj6/fJJ5/A0dERfn5+AICcnBxcvXoV\njz76KGxsbHDrrbciJiYGX331lYVrar3Onz+PyspK/N///R/EYjHCwsLw6aefwsfHBydOnOD3ZS/U\n1dWhpqZG15MjEolga2sLsVjMtjTBtm3bsGvXLqxZs0ZX1tTU1G3bHThwAKtXr4ajoyNCQkKwbNky\nfPHFF5b6CFbDUFu2nSazZs0a2NrawtPTE/Pnz8e5c+cAAF9++SVWrFgBT09PeHp64ve//z0+//zz\nHp81pMOjWq1GfX19l38aGhqwcuVK3H333bprT5w4ATc3N/j5+SE3N1fvXGyxWIzg4GDk5uZa4mNY\nvc7tBQCjR49me5lg3LhxeO2113Rf19bW4scffwQASCQSBAYG6l5jm/YsPz8fH3zwATZs2KAbWsnL\ny0NgYCCkUqnuOrZl9zIzMxEeHo6///3vmD59Ou68806kp6ejtrYWtra2/L7sBTc3N9x///344x//\nCLlcjuXLl+PPf/4zampq2JYmuPfee7Fv3z6MHz9eV5afn2+07erq6lBVVaX3O4ntqmWoLW1tbbFt\n2zZ4enrqyr799ltEREQA0P5+Dw8P1702evRok6bxDenwmJqaipiYGMTGxur9s3DhQr3r0tLSsGHD\nBrzwwgsAgObmZtjb2+tdY29vD4VCMWh1H0rYXuZRX1+PNWvWYMKECYiLi4OdnZ3e6zKZjG3aDbVa\njaeffhp/+tOf4OLioitvamrqctwWvz+7V1tbi5SUFHh4eOC7777D3/72N7z88stobGzk92UvCYIA\nmUyGt99+G+fPn8c777yDjRs3oqGhgW1pAi8vry5lzc3NRtuuublZ93XH19rKRzJDbdnZyy+/jLy8\nPDzyyCMAtG3duS01Gg2USmW39xkSZ1sbEx8fj8uXL3d7zb59+/DXv/4Vf/7znzF37lwAhv8ANzc3\nw8HBYcDqOpQZ+kXM9uqdoqIirFmzBiEhIdi0aROys7PR0tKid41CoWCbdmPLli2IiIjQmwgOaL8/\nO7clvz+7J5VK4ebmhocffhgAEBUVhdmzZ+Ptt9/m92UvHT16FBkZGXj66acBAImJiUhMTGRb9oOh\nP9NtbdcWdFpaWuDo6Kh7re2/ybCWlhY89dRTuHr1Knbt2gV3d3cAXfOQQqGAjY2N3kiOIUO657En\nW7Zswauvvopt27bpDWGHhYXpdXFrNBoUFhbqdd1SuzFjxiAvL0+vLC8vj+1loszMTCxduhQzZszA\nli1bIJVKERISApVKhWvXrumuy8vL6zI9gNp9/fXXOHTokG6EoaysDH/4wx+Ql5eHkpISvdWDbMvu\njR49GiqVSm9VpUajQWRkJL8ve6msrKxLL42trS3kcjnbso+6+/no6uoKT09Pvd/hbNfu1dbWYtmy\nZaivr8dnn32GgIAA3WthYWF6v98NTVMzZNiGx7179+LDDz/Exx9/jNjYWL3XZs+ejczMTBw/fhyt\nra3YunUr/Pz8dHMASN+0adOgVCrx3//+FyqVCnv27EF1dTWmT59u6apZvaqqKjz88MN46KGHsH79\nel25o6MjkpKS8MYbb0ChUODChQs4ePAg5s+fb8HaWrevv/4aaWlpSE1NRWpqKvz9/bFp0yY88sgj\nCAsLwz//+U8olUqcPHkSaWlp+NWvfmXpKlutW265Bfb29ti8eTPUajXOnj2L48eP41e/+hW/L3sp\nISEBly5d0i3YSE1NxfHjxzFv3jy2ZR8Z+/m4YMECAMCCBaRn2rsAAAoZSURBVAuwefNm1NbWIj8/\nH7t27dLrICJ969atg7e3N3bs2AFnZ2e91xYsWID33nsP5eXlqKqqwrvvvmtaWw7MY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WWFyG8T//+Q9OTk6aYPD8+fOkpqayZMkSrK2tmTRpEiNHjuS7774zc0mFEMKy7d69W5M1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\n7puXGc/Xdt8fbP2/hWRNEqvyHxuztng4ZKWl8PRCDeXXdfSnPhL0Xr5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RAAAg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\n21DACWgC8K/M63jezk4rM5QN8RzokqG2P8DfDwH+Dyb2ZWSl40jWHvhPc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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -502,9 +509,9 @@ "outputs": [ { "data": { - "image/png": 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ei9didvdgu/v4auADGPqIiIh8nj/PQBesIr7MNCEl1YDDlywAABmAPKNY9QN9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\nixYtsi3l0rJlSwQG8nIyROQ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r1lCr1W6txdOxraRjW0lXm7aKnLEWWzbPRaAiF9eFCDw+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\n", 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V2EaqYTtVjW2kGrZT1dhGqtF0OxUUFCA5OfmFU90ECX2//fYbRo4cqfjZ1tYWffr0werV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\nLl68aMRWGs+9e/cEuVwuHD58WBAEQUhKShLkcrlw+vRp1umh1NRUwdv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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -595,9 +602,9 @@ "outputs": [ { "data": { - "image/png": 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hhBBOJEgUQgghhBBOJEgUQgghhBBOJEgUQgghhBBOJEgUQgghhBBOJEgUQggh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\n/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iIuJFQaKIiIiIeNHAFREREZHfqW6VbKyWrN+djqNyNkmXIT+Xg1oSRURERMSL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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -655,9 +662,9 @@ "outputs": [ { "data": { - "image/png": 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GvHnzsGzZMvz0009ilyMr8hmKfdCxUzHgExER2SWDwYCdO3diy5Yt5k7dtWvX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\nfrxQXl4uGI1G4cMPPxTCwsIkqppuJ7aPjf766y9Bo9EIISE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ffPABJk+eDCsrK8TGxsLKyuqZAbRTp05o0KAB3n33XQQFBcHFxQUbN27EDz/8YBj00aJF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\nRsycORPvvPMOBgwY8MjhiIiIiEg8UYUdEREREVk+2R0pRkR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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -687,7 +694,7 @@ "cell_type": "code", "execution_count": 23, "metadata": { - "collapsed": true + "collapsed": false }, "outputs": [], "source": [ @@ -715,9 +722,9 @@ "outputs": [ { "data": { - "image/png": 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LF+vBBx+8phXz8vIqlzu1l5eXClxdBKDCffHyWyUAlD8cxxWXm5vbVdtLDHjV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\nZf4CzFapcCez9WIAAAAASUVORK5CYII=\n", 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AMJlSBbwtW7bo0UcfVfPmzdWtWzd9/PHHkqScnByNGjVK4eHhio6O1tq1a53T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\nD1qK7viOAMG2AAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -751,7 +758,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.11" + "version": "2.7.12" } }, "nbformat": 4, diff --git a/tests/test_style/test_palettes.py b/tests/test_style/test_palettes.py index 3b7447ba3..acfb045eb 100644 --- a/tests/test_style/test_palettes.py +++ b/tests/test_style/test_palettes.py @@ -286,12 +286,16 @@ def test_color_sequence(self): for name, ncols in SEQUENCES.items(): for n in ncols.keys(): cmap = color_sequence(name, n) + self.assertEqual(name, cmap.name) + self.assertEqual(n, cmap.N) def test_color_sequence_default(self): """ Assert the default color sequence is RdBu """ cmap = color_sequence() + self.assertEqual(cmap.name, "RdBu") + self.assertEqual(cmap.N, 11) def test_color_sequence_unrecocognized(self): """ diff --git a/yellowbrick/bestfit.py b/yellowbrick/bestfit.py index bbc027bbf..f98c0b899 100644 --- a/yellowbrick/bestfit.py +++ b/yellowbrick/bestfit.py @@ -25,6 +25,7 @@ from sklearn.metrics import mean_squared_error as mse from operator import itemgetter +from yellowbrick.style.palettes import LINE_COLOR from yellowbrick.exceptions import YellowbrickValueError @@ -112,8 +113,12 @@ def draw_best_fit(X, y, ax, estimator='linear', **kwargs): # Uses the estimator to fit the data and get the model back. model = estimator(X, y) + # Set the color if not passed in. + if 'c' not in kwargs and 'color' not in kwargs: + kwargs['color'] = LINE_COLOR + # Plot line of best fit onto the axes that were passed in. - # TODO: determin if xlim or X.min(), X.max() are better params + # TODO: determine if xlim or X.min(), X.max() are better params xr = np.linspace(*ax.get_xlim(), num=100) ax.plot(xr, model.predict(xr[:,np.newaxis]), **kwargs) return ax diff --git a/yellowbrick/classifier.py b/yellowbrick/classifier.py index 46dfc2c2e..3ab940e00 100644 --- a/yellowbrick/classifier.py +++ b/yellowbrick/classifier.py @@ -26,11 +26,11 @@ from sklearn.metrics import auc, roc_auc_score, roc_curve from sklearn.metrics import precision_recall_fscore_support -from .style.palettes import ddlheatmap from .exceptions import YellowbrickTypeError -from .style.palettes import PALETTES as YELLOWBRICK_PALETTES 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 @@ -92,7 +92,7 @@ def __init__(self, model, ax=None, classes=None, **kwargs): self.estimator = model self.name = get_model_name(self.estimator) - self.cmap = kwargs.pop('cmap', ddlheatmap) + self.cmap = color_sequence(kwargs.pop('cmap', 'YlOrRd')) self.classes_ = classes def fit(self, X, y=None, **kwargs): @@ -258,6 +258,15 @@ def __init__(self, model, ax=None, **kwargs): 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. @@ -272,10 +281,12 @@ def __init__(self, model, ax=None, **kwargs): ## hoisted to ScoreVisualizer base class self.name = get_model_name(self.estimator) - # TODO refactor to use new Yellowbrick color utils + # 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', '#2B94E9'), - 'diagonal': kwargs.pop('diagonal_color', '#666666'), + 'roc': kwargs.pop('roc_color', None), + 'diagonal': kwargs.pop('diagonal_color', LINE_COLOR), } def score(self, X, y=None, **kwargs): @@ -429,7 +440,7 @@ def __init__(self, model, ax=None, classes=None, **kwargs): """ super(ClassBalance, self).__init__(model, ax=ax, **kwargs) - self.colors = kwargs.pop('colors', YELLOWBRICK_PALETTES['paired']) + self.colors = color_palette(kwargs.pop('colors', None)) self.classes_ = classes def fit(self, X, y=None, **kwargs): diff --git a/yellowbrick/features/radviz.py b/yellowbrick/features/radviz.py index 5453b384d..89ada2519 100644 --- a/yellowbrick/features/radviz.py +++ b/yellowbrick/features/radviz.py @@ -139,8 +139,9 @@ def normalize(X): 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. + Called from the fit method, this method creates the radviz canvas and + draws each instance as a class or target colored point, whose location + is determined by the feature data set. """ # Get the shape of the data diff --git a/yellowbrick/regressor.py b/yellowbrick/regressor.py index 9b1e76bd5..a59f645aa 100644 --- a/yellowbrick/regressor.py +++ b/yellowbrick/regressor.py @@ -24,10 +24,12 @@ 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 + ########################################################################## ## Regression Visualization Base Object ########################################################################## @@ -68,6 +70,12 @@ def __init__(self, model, ax=None, **kwargs): Should be an instance of a regressor, else the __init__ will return an error. + :param point_color: color of the error points + Any matplotlib color + + :param line_color: color of the best fit line + Any matplotlib color + :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. @@ -81,8 +89,8 @@ def __init__(self, model, ax=None, **kwargs): super(PredictionError, self).__init__(model, ax=ax, **kwargs) self.colors = { - 'point': kwargs.pop('point_color', '#F2BE2C'), - 'line': kwargs.pop('line_color', '#2B94E9'), + 'point': kwargs.pop('point_color', None), + 'line': kwargs.pop('line_color', LINE_COLOR), } def score(self, X, y=None, **kwargs): @@ -132,7 +140,7 @@ def draw(self, y, y_pred): if self.ax is None: self.ax = plt.gca() - self.ax.scatter(y, y_pred, c='#F2BE2C') + self.ax.scatter(y, y_pred, c=self.colors['point']) # TODO If score is happening inside a loop, draw would get called multiple times. # Ideally we'd want the best fit line to be drawn only once @@ -220,12 +228,26 @@ def __init__(self, model, ax=None, **kwargs): Parameters ---------- - :param ax: the axis to plot the figure on. - :param model: the Scikit-Learn estimator Should be an instance of a regressor, else the __init__ will return an error. + :param ax: the axis to plot the figure on. + + :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. + + :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. + + :param line_color: color of the zero error line + Any matplotlib color. Default is a dark grey. + :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. @@ -242,9 +264,9 @@ def __init__(self, model, ax=None, **kwargs): # We'd like to color them differently in draw... # Can the user pass those in as keyword arguments? self.colors = { - 'train_point': kwargs.pop('train_point_color', '#2B94E9'), - 'test_point': kwargs.pop('test_point_color', '#94BA65'), - 'line': kwargs.pop('line_color', '#333333'), + 'train_point': kwargs.pop('train_color', 'b'), + 'test_point': kwargs.pop('test_color', 'g'), + 'line': kwargs.pop('line_color', LINE_COLOR), } def fit(self, X, y=None, **kwargs): @@ -337,18 +359,18 @@ def finalize(self, **kwargs): # Add the title to the plot self.set_title('Residuals for {} Model'.format(self.name)) - # Create horizontal lines - self.ax.hlines(y=0, xmin=0, xmax=100) - - # Set the axes labels - self.ax.set_ylabel('Residuals') - self.ax.set_xlabel("Predicted Value") - # Set the legend # Assumes that the first set of points are training data, and the next are test # Assumes that you want a box around legend self.ax.legend(['Training Data', 'Test Data'], loc = 'best', frameon = True) + # Create a full line across the figure at zero error. + self.ax.axhline(y=0, c=self.colors['line']) + + # Set the axes labels + self.ax.set_ylabel('Residuals') + self.ax.set_xlabel("Predicted Value") + def residuals_plot(model, X, y=None, ax=None, **kwargs): """Quick method: diff --git a/yellowbrick/style/palettes.py b/yellowbrick/style/palettes.py index 7700087a1..7008a9bd9 100644 --- a/yellowbrick/style/palettes.py +++ b/yellowbrick/style/palettes.py @@ -27,6 +27,7 @@ ########################################################################## YB_KEY = '#111111' # The yellowbrick key (black) color is very dark grey +LINE_COLOR = YB_KEY # Colors for best fit lines, diagonals, etc. ########################################################################## @@ -341,7 +342,7 @@ } ## Special, backward compatible color map. -ddlheatmap = mplcol.ListedColormap(SEQUENCES["ddl_heat"]) +ddlheatmap = mplcol.ListedColormap(SEQUENCES["ddl_heat"][12], "DDL Heat", 12) ## Default Color Sequence DEFAULT_SEQUENCE = "RdBu" @@ -669,4 +670,4 @@ def color_sequence(palette=None, n_colors=None): ) # Return the color map from the sequence - return mplcol.ListedColormap(n_palettes[n_colors]) + return mplcol.ListedColormap(n_palettes[n_colors], name=palette, N=n_colors) From c4d3f71e8f249a7a405126fc48b4b1b2972adba7 Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Sat, 14 Jan 2017 15:20:23 -0500 Subject: [PATCH 29/35] documentation refresh towards issue #81 --- docs/about.rst | 178 +++++++++ docs/api/yellowbrick.rst | 18 +- docs/conf.py | 2 +- docs/examples/examples.rst | 10 + docs/examples/images/examples_11_0.png | Bin 0 -> 16586 bytes docs/examples/images/examples_12_0.png | Bin 0 -> 16342 bytes docs/examples/images/examples_15_0.png | Bin 0 -> 31374 bytes docs/examples/images/examples_18_0.png | Bin 0 -> 98744 bytes docs/examples/images/examples_23_0.png | Bin 0 -> 68311 bytes docs/examples/images/examples_26_0.png | Bin 0 -> 28660 bytes docs/examples/images/examples_31_0.png | Bin 0 -> 37857 bytes docs/examples/images/examples_34_0.png | Bin 0 -> 23098 bytes docs/examples/images/examples_37_0.png | Bin 0 -> 16610 bytes docs/examples/images/examples_4_1.png | Bin 0 -> 25395 bytes docs/examples/images/palettes_2_0.png | Bin 0 -> 1913 bytes docs/examples/images/palettes_2_1.png | Bin 0 -> 1985 bytes docs/examples/images/palettes_2_10.png | Bin 0 -> 1688 bytes docs/examples/images/palettes_2_11.png | Bin 0 -> 2431 bytes docs/examples/images/palettes_2_12.png | Bin 0 -> 2386 bytes docs/examples/images/palettes_2_13.png | Bin 0 -> 2841 bytes docs/examples/images/palettes_2_14.png | Bin 0 -> 2587 bytes docs/examples/images/palettes_2_15.png | Bin 0 -> 1788 bytes docs/examples/images/palettes_2_16.png | Bin 0 -> 2631 bytes docs/examples/images/palettes_2_17.png | Bin 0 -> 2396 bytes docs/examples/images/palettes_2_2.png | Bin 0 -> 2237 bytes docs/examples/images/palettes_2_3.png | Bin 0 -> 1593 bytes docs/examples/images/palettes_2_4.png | Bin 0 -> 1595 bytes docs/examples/images/palettes_2_5.png | Bin 0 -> 1732 bytes docs/examples/images/palettes_2_6.png | Bin 0 -> 2512 bytes docs/examples/images/palettes_2_7.png | Bin 0 -> 2509 bytes docs/examples/images/palettes_2_8.png | Bin 0 -> 2094 bytes docs/examples/images/palettes_2_9.png | Bin 0 -> 2355 bytes docs/examples/methods.rst | 503 +++++++++++++++++++++++++ docs/examples/palettes.rst | 93 +++++ docs/index.rst | 64 ++-- docs/introduction.rst | 18 + docs/setup.rst | 46 ++- 37 files changed, 872 insertions(+), 60 deletions(-) create mode 100644 docs/about.rst create mode 100644 docs/examples/examples.rst create mode 100644 docs/examples/images/examples_11_0.png create mode 100644 docs/examples/images/examples_12_0.png create mode 100644 docs/examples/images/examples_15_0.png create mode 100644 docs/examples/images/examples_18_0.png create mode 100644 docs/examples/images/examples_23_0.png create mode 100644 docs/examples/images/examples_26_0.png create mode 100644 docs/examples/images/examples_31_0.png create mode 100644 docs/examples/images/examples_34_0.png create mode 100644 docs/examples/images/examples_37_0.png create mode 100644 docs/examples/images/examples_4_1.png create mode 100644 docs/examples/images/palettes_2_0.png create mode 100644 docs/examples/images/palettes_2_1.png create mode 100644 docs/examples/images/palettes_2_10.png create mode 100644 docs/examples/images/palettes_2_11.png create mode 100644 docs/examples/images/palettes_2_12.png create mode 100644 docs/examples/images/palettes_2_13.png create mode 100644 docs/examples/images/palettes_2_14.png create mode 100644 docs/examples/images/palettes_2_15.png create mode 100644 docs/examples/images/palettes_2_16.png create mode 100644 docs/examples/images/palettes_2_17.png create mode 100644 docs/examples/images/palettes_2_2.png create mode 100644 docs/examples/images/palettes_2_3.png create mode 100644 docs/examples/images/palettes_2_4.png create mode 100644 docs/examples/images/palettes_2_5.png create mode 100644 docs/examples/images/palettes_2_6.png create mode 100644 docs/examples/images/palettes_2_7.png create mode 100644 docs/examples/images/palettes_2_8.png create mode 100644 docs/examples/images/palettes_2_9.png create mode 100644 docs/examples/methods.rst create mode 100644 docs/examples/palettes.rst create mode 100644 docs/introduction.rst diff --git a/docs/about.rst b/docs/about.rst new file mode 100644 index 000000000..a457108c3 --- /dev/null +++ b/docs/about.rst @@ -0,0 +1,178 @@ +===== +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. + +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: + + 1. selecting and/or engineering the smallest and most predictive feature set + 2. choosing a set of algorithms from a model family, and + 3. tuning the algorithm hyperparameters to optimize performance. + +The **model selection triple** was first described in a 2015 SIGMOD_ paper by Kumar et al. In their paper, which concerns the development of next-generation database systems built to anticipate predictive modeling, the authors cogently express that such systems are badly needed due to the highly experimental nature of machine learning in practice. "Model selection," they explain, "is iterative and exploratory because the space of [model selection triples] is usually infinite, and it is generally impossible for analysts to know a priori which [combination] will yield satisfactory accuracy and/or insights." + +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. + + +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. + +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." + + +Changelog +--------- + +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 + + + + + +.. _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 diff --git a/docs/api/yellowbrick.rst b/docs/api/yellowbrick.rst index a6763a4f3..63fcc2ab7 100644 --- a/docs/api/yellowbrick.rst +++ b/docs/api/yellowbrick.rst @@ -44,16 +44,8 @@ yellowbrick.exceptions module :undoc-members: :show-inheritance: -yellowbrick.features module ---------------------------- - -.. automodule:: yellowbrick.features - :members: - :undoc-members: - :show-inheritance: - -yellowbrick.pipeline module +yellowbrick.pipeline module --------------------------- .. automodule:: yellowbrick.pipeline @@ -69,14 +61,6 @@ yellowbrick.regressor module :undoc-members: :show-inheritance: -yellowbrick.style module ---------------------------- - -.. automodule:: yellowbrick.style - :members: - :undoc-members: - :show-inheritance: - yellowbrick.utils module ------------------------ diff --git a/docs/conf.py b/docs/conf.py index 4e3d8853f..60f0b8a3b 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -347,4 +347,4 @@ 'scipy': ('http://scipy.github.io/devdocs/', None), 'numpy': ('https://docs.scipy.org/doc/numpy-dev/', None), 'cycler': ('http://matplotlib.org/cycler/', None), - 'seaborn': ('https://web.stanford.edu/~mwaskom/software/seaborn/', None)} + 'seaborn': ('http://seaborn.pydata.org/index.html', 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sys.path.append("..") + + import pandas as pd + import yellowbrick as yb + import matplotlib.pyplot as plt + +Anscombe's Quartet +------------------ + +Yellowbrick has learned Anscombe's lesson - which is why we believe that +visual diagnostics are vital to machine learning. + +.. code:: python + + g = yb.anscombe() + + +.. image:: images/examples_4_1.png + + +Load Datasets for Example Code +------------------------------ + +Yellowbrick has provided several datasets wrangled from the `UCI Machine +Learning Repository `__ to present the +following examples. If you haven't downloaded the data, you can do so by +running: + +:: + + $ python download.py + +In the same directory as the example notebook. Note that this will +create a directory called ``data`` that contains subdirectories with the +given data. + +.. code:: python + + from download import download_all + + ## The path to the test data sets + FIXTURES = os.path.join(os.getcwd(), "data") + + ## Dataset loading mechanisms + datasets = { + "credit": os.path.join(FIXTURES, "credit", "credit.csv"), + "concrete": os.path.join(FIXTURES, "concrete", "concrete.csv"), + "occupancy": os.path.join(FIXTURES, "occupancy", "occupancy.csv"), + } + + + def load_data(name, download=True): + """ + Loads and wrangles the passed in dataset by name. + If download is specified, this method will download any missing files. + """ + + # Get the path from the datasets + path = datasets[name] + + # Check if the data exists, otherwise download or raise + if not os.path.exists(path): + if download: + download_all() + else: + raise ValueError(( + "'{}' dataset has not been downloaded, " + "use the download.py module to fetch datasets" + ).format(name)) + + + # Return the data frame + return pd.read_csv(path) + +Feature Analysis +---------------- + +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. + +At the moment we have three feature analysis visualizers implemented: + +- Rank2D: rank pairs of features to detect covariance +- RadViz: plot data points along axes ordered around a circle to detect + separability +- Parallel Coordinates: plot instances as lines along vertical axes to + detect clusters + +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. + +.. code:: python + + # Feature Analysis Imports + # NOTE that all these are available for import from the `yellowbrick.features` module + from yellowbrick.features.rankd import Rank2D + from yellowbrick.features.radviz import RadViz + from yellowbrick.features.pcoords import ParallelCoordinates + +Rank2D +~~~~~~ + +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. + +.. code:: python + + # Load the classification data set + data = load_data('credit') + + # Specify the features of interest + features = [ + 'limit', 'sex', 'edu', 'married', 'age', 'apr_delay', 'may_delay', + 'jun_delay', 'jul_delay', 'aug_delay', 'sep_delay', 'apr_bill', 'may_bill', + 'jun_bill', 'jul_bill', 'aug_bill', 'sep_bill', 'apr_pay', 'may_pay', 'jun_pay', + 'jul_pay', 'aug_pay', 'sep_pay', + ] + + # Extract the numpy arrays from the data frame + X = data[features].as_matrix() + y = data.default.as_matrix() + +.. code:: python + + # Instantiate the visualizer with the Covariance ranking algorithm + visualizer = Rank2D(features=features, algorithm='covariance') + + visualizer.fit(X, y) # Fit the data to the visualizer + visualizer.transform(X) # Transform the data + visualizer.poof() # Draw/show/poof the data + + + +.. image:: images/examples_11_0.png + + +.. code:: python + + # Instantiate the visualizer with the Pearson ranking algorithm + visualizer = Rank2D(features=features, algorithm='pearson') + + visualizer.fit(X, y) # Fit the data to the visualizer + visualizer.transform(X) # Transform the data + visualizer.poof() # Draw/show/poof the data + + + +.. image:: images/examples_12_0.png + + +RadViz +~~~~~~ + +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. + +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? + +.. code:: python + + # Load the classification data set + data = load_data('occupancy') + + # Specify the features of interest and the classes of the target + features = ["temperature", "relative humidity", "light", "C02", "humidity"] + classes = ['unoccupied', 'occupied'] + + # Extract the numpy arrays from the data frame + X = data[features].as_matrix() + y = data.occupancy.as_matrix() + +.. code:: python + + # Instantiate the visualizer + visualizer = visualizer = RadViz(classes=classes, features=features) + + visualizer.fit(X, y) # Fit the data to the visualizer + visualizer.transform(X) # Transform the data + visualizer.poof() # Draw/show/poof the data + + + +.. image:: images/examples_15_0.png + + +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 +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 +different distributions. + +.. code:: python + + # Load the classification data set + data = load_data('occupancy') + + # Specify the features of interest and the classes of the target + features = ["temperature", "relative humidity", "light", "C02", "humidity"] + classes = ['unoccupied', 'occupied'] + + # Extract the numpy arrays from the data frame + X = data[features].as_matrix() + y = data.occupancy.as_matrix() + +.. code:: python + + # Instantiate the visualizer + visualizer = visualizer = ParallelCoordinates(classes=classes, features=features) + + visualizer.fit(X, y) # Fit the data to the visualizer + visualizer.transform(X) # Transform the data + visualizer.poof() # Draw/show/poof the data + + + +.. image:: images/examples_18_0.png + + +Regressor Evaluation +-------------------- + +Regression models attempt to predict a target in a continuous space. +Regressor score visualizers display the instances in model space to +better understand how the model is making predictions. We currently have +implemented two regressor evaluations: + +- Residuals Plot: plot the difference between the expected and actual + values +- Prediction Error: plot expected vs. the actual values in model space + +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``. + +.. code:: python + + # Regression Evaluation Imports + + from sklearn.linear_model import Ridge, Lasso + from sklearn.cross_validation import train_test_split + + from yellowbrick.regressor import PredictionError, ResidualsPlot + +Residuals Plot +~~~~~~~~~~~~~~ + +A residual plot shows the residuals on the vertical axis and the +independent variable on the horizontal axis. If the points are randomly +dispersed around the horizontal axis, a linear regression model is +appropriate for the data; otherwise, a non-linear model is more +appropriate. + +.. code:: python + + # Load the data + df = load_data('concrete') + feature_names = ['cement', 'slag', 'ash', 'water', 'splast', 'coarse', 'fine', 'age'] + target_name = 'strength' + + # Get the X and y data from the DataFrame + X = df[feature_names].as_matrix() + y = df[target_name].as_matrix() + + # Create the train and test data + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + +.. code:: python + + # Instantiate the linear model and visualizer + ridge = Ridge() + visualizer = ResidualsPlot(ridge) + + visualizer.fit(X_train, y_train) # Fit the training data to the visualizer + visualizer.score(X_test, y_test) # Evaluate the model on the test data + g = visualizer.poof() # Draw/show/poof the data + + + +.. image:: images/examples_23_0.png + + +Prediction Error Plot +~~~~~~~~~~~~~~~~~~~~~ + +Plots the actual targets from the dataset against the predicted values +generated by our model. This allows us to see how much variance is in +the model. Data scientists diagnose this plot by comparing against the +45 degree line, where the prediction exactly matches the model. + +.. code:: python + + # Load the data + df = load_data('concrete') + feature_names = ['cement', 'slag', 'ash', 'water', 'splast', 'coarse', 'fine', 'age'] + target_name = 'strength' + + # Get the X and y data from the DataFrame + X = df[feature_names].as_matrix() + y = df[target_name].as_matrix() + + # Create the train and test data + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + +.. code:: python + + # Instantiate the linear model and visualizer + lasso = Lasso() + visualizer = PredictionError(lasso) + + visualizer.fit(X_train, y_train) # Fit the training data to the visualizer + visualizer.score(X_test, y_test) # Evaluate the model on the test data + g = visualizer.poof() # Draw/show/poof the data + + + +.. image:: images/examples_26_0.png + + +Classifier Evaluation +--------------------- + +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: + +- ClassificationReport: Presents the confusion matrix of the classifier + as a heatmap +- ROCAUC: Presents the graph of receiver operating characteristics + along with area under the curve +- ClassBalance: Displays the difference between the class balances and + support + +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. + +.. code:: python + + # Classifier Evaluation Imports + + from sklearn.naive_bayes import GaussianNB + from sklearn.linear_model import LogisticRegression + from sklearn.ensemble import RandomForestClassifier + from sklearn.cross_validation import train_test_split + + from yellowbrick.classifier import ClassificationReport, ROCAUC, ClassBalance + +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. + +.. code:: python + + # Load the classification data set + data = load_data('occupancy') + + # Specify the features of interest and the classes of the target + features = ["temperature", "relative humidity", "light", "C02", "humidity"] + classes = ['unoccupied', 'occupied'] + + # Extract the numpy arrays from the data frame + X = data[features].as_matrix() + y = data.occupancy.as_matrix() + + # Create the train and test data + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + +.. code:: python + + # Instantiate the classification model and visualizer + bayes = GaussianNB() + visualizer = ClassificationReport(bayes, classes=classes) + + visualizer.fit(X_train, y_train) # Fit the training data to the visualizer + visualizer.score(X_test, y_test) # Evaluate the model on the test data + g = visualizer.poof() # Draw/show/poof the data + + + +.. image:: images/examples_31_0.png + + +ROCAUC +~~~~~~ + +Plot the ROC to visualize the tradeoff between the classifier's +sensitivity and specificity. + +.. code:: python + + # Load the classification data set + data = load_data('occupancy') + + # Specify the features of interest and the classes of the target + features = ["temperature", "relative humidity", "light", "C02", "humidity"] + classes = ['unoccupied', 'occupied'] + + # Extract the numpy arrays from the data frame + X = data[features].as_matrix() + y = data.occupancy.as_matrix() + + # Create the train and test data + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + +.. code:: python + + # Instantiate the classification model and visualizer + logistic = LogisticRegression() + visualizer = ROCAUC(logistic) + + visualizer.fit(X_train, y_train) # Fit the training data to the visualizer + visualizer.score(X_test, y_test) # Evaluate the model on the test data + g = visualizer.poof() # Draw/show/poof the data + + + +.. image:: images/examples_34_0.png + + +ClassBalance +~~~~~~~~~~~~ + +Class balance chart that shows the support for each class in the fitted +classification model. + +.. code:: python + + # Load the classification data set + data = load_data('occupancy') + + # Specify the features of interest and the classes of the target + features = ["temperature", "relative humidity", "light", "C02", "humidity"] + classes = ['unoccupied', 'occupied'] + + # Extract the numpy arrays from the data frame + X = data[features].as_matrix() + y = data.occupancy.as_matrix() + + # Create the train and test data + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + +.. code:: python + + # Instantiate the classification model and visualizer + forest = RandomForestClassifier() + visualizer = ClassBalance(forest, classes=classes) + + visualizer.fit(X_train, y_train) # Fit the training data to the visualizer + visualizer.score(X_test, y_test) # Evaluate the model on the test data + g = visualizer.poof() # Draw/show/poof the data + + + +.. image:: images/examples_37_0.png diff --git a/docs/examples/palettes.rst b/docs/examples/palettes.rst new file mode 100644 index 000000000..803e44522 --- /dev/null +++ b/docs/examples/palettes.rst @@ -0,0 +1,93 @@ +.. _examples/yellowbrick-palettes: + +========= +Palettes +========= + +Yellowbrick includes custom palettes as well as familiar ones from Matplotlib and Seaborn. + +.. code:: python + + %matplotlib inline + + import matplotlib.pyplot as plt + from yellowbrick.style.palettes import PALETTES, color_palette + +.. code:: python + + # ['blue', 'green', 'red', 'maroon', 'yellow', 'cyan'] + for palette in PALETTES.keys(): + color_palette(palette).plot() + plt.title(palette, loc='left') + + + +.. image:: images/palettes_2_0.png + + + +.. image:: images/palettes_2_1.png + + + +.. image:: images/palettes_2_2.png + + + +.. image:: images/palettes_2_3.png + + + +.. image:: images/palettes_2_4.png + + + +.. image:: images/palettes_2_5.png + + + +.. image:: images/palettes_2_6.png + + + +.. image:: images/palettes_2_7.png + + + +.. image:: images/palettes_2_8.png + + + +.. image:: images/palettes_2_9.png + + + +.. image:: images/palettes_2_10.png + + + +.. image:: images/palettes_2_11.png + + + +.. image:: images/palettes_2_12.png + + + +.. image:: images/palettes_2_13.png + + + +.. image:: images/palettes_2_14.png + + + +.. image:: images/palettes_2_15.png + + + +.. image:: images/palettes_2_16.png + + + +.. image:: images/palettes_2_17.png diff --git a/docs/index.rst b/docs/index.rst index f5ae1a2c5..f3eb647a2 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -3,62 +3,70 @@ 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.** +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 -- diagonal correlation matrix + - 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 curves -- classification heatmaps +^^^^^^^^^^^^^^ + - ROC-AUC curves + - Classification heatmaps + - Class balance charts Regression -^^^^^^^^^^^^ +^^^^^^^^^^ + - Prediction error plots + - Residual plots + - Most informative features -- prediction error plots -- residual plots +Clustering +^^^^^^^^^^ + - Silhouettes + - Density measures Tools for parameter tuning ----------------------------- - -- validation curves -- gridsearch heatmap - - - +-------------------------- +- Validation curves +- Gridsearch heatmap +========= Contents: -========== +========= .. toctree:: :maxdepth: 4 + introduction setup + examples/examples api/modules + about - - +================== Indices and tables ================== diff --git a/docs/introduction.rst b/docs/introduction.rst new file mode 100644 index 000000000..7dc263802 --- /dev/null +++ b/docs/introduction.rst @@ -0,0 +1,18 @@ +============================ +Introduction to Yellowbrick +============================ + +.. 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. + +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. + + +.. _QuatroCinco: https://flic.kr/p/2Yj9mj +.. _API: http://scikit-learn.org/stable/modules/classes.html diff --git a/docs/setup.rst b/docs/setup.rst index 0f8738742..9dcb2423c 100644 --- a/docs/setup.rst +++ b/docs/setup.rst @@ -1,22 +1,40 @@ +=========== Quick Start -============== +=========== -This quick start is intended to get you setup with Yellowbrick in development -mode (since the project is still under development). +Installation +------------ -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.:: +To install the Yellowbrick library, the simplest thing to do is use ``pip`` as follows.:: - $ git clone https://github.com/[YOURUSERNAME]/yellowbrick - $ cd yellowbrick + $ pip install yellowbrick -2. Create virtualenv and create the dependencies:: +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: - $ virtualenv venv - $ pip install -r requirements.txt +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.:: -3. Fetch and switch to development.:: + from yellowbrick.features import Rank2D + visualizer = Rank2D(features=features, algorithm='covariance') + visualizer.fit(X, y) # Fit the data to the visualizer + visualizer.transform(X) # Transform the data + visualizer.poof() # Draw/show/poof the data - $ git fetch - $ git checkout develop + +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.:: + + from sklearn.svm import LinearSVC + from yellowbrick.classifier import ROCAUC + model = LinearSVC() + model.fit(X,y) + visualizer = ROCAUC(model) + visualizer.score(X,y) + visualizer.poof() + + +For additional information on getting started with Yellowbrick, check out our :ref:`examples `. 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@@ Yellowbrick includes custom palettes as well as familiar ones from Matplotlib an .. image:: images/palettes_2_16.png +.. code:: python + + for name, maps in SEQUENCES.items(): + for num, palette in maps.items(): + color_palette(palette).plot() + plt.title("{} - {}".format(name, num), loc='left') + + +.. parsed-literal:: + + /usr/local/lib/python2.7/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + max_open_warning, RuntimeWarning) + + + +.. image:: images/palettes_3_1.png + + + +.. image:: images/palettes_3_2.png + + + +.. image:: images/palettes_3_3.png + + + +.. image:: images/palettes_3_4.png + + + +.. image:: images/palettes_3_5.png + + + +.. image:: images/palettes_3_6.png + + + +.. image:: images/palettes_3_7.png + + + +.. image:: images/palettes_3_8.png + + + +.. image:: images/palettes_3_9.png + + + +.. image:: images/palettes_3_10.png + + + +.. image:: images/palettes_3_11.png + + + +.. image:: images/palettes_3_12.png + + + +.. image:: images/palettes_3_13.png + + + +.. image:: images/palettes_3_14.png + + + +.. image:: images/palettes_3_15.png + + + +.. image:: images/palettes_3_16.png + + + +.. image:: images/palettes_3_17.png + + + +.. image:: images/palettes_3_18.png + + + +.. image:: images/palettes_3_19.png + + + +.. image:: images/palettes_3_20.png + + + +.. image:: images/palettes_3_21.png + + + +.. image:: images/palettes_3_22.png + + + +.. image:: images/palettes_3_23.png + + + +.. image:: images/palettes_3_24.png + + + +.. image:: images/palettes_3_25.png + + + +.. image:: images/palettes_3_26.png + + + +.. image:: images/palettes_3_27.png + + + +.. image:: images/palettes_3_28.png + + + +.. image:: images/palettes_3_29.png + + + +.. image:: images/palettes_3_30.png + + + +.. image:: images/palettes_3_31.png + + + +.. image:: images/palettes_3_32.png + + + +.. image:: images/palettes_3_33.png + + + +.. image:: images/palettes_3_34.png + + + +.. image:: images/palettes_3_35.png + + + +.. image:: images/palettes_3_36.png + + + +.. image:: images/palettes_3_37.png + + + +.. image:: images/palettes_3_38.png + + + +.. image:: images/palettes_3_39.png + + + +.. image:: images/palettes_3_40.png + + + +.. image:: images/palettes_3_41.png + + + +.. image:: images/palettes_3_42.png + + + +.. image:: images/palettes_3_43.png + + + +.. image:: images/palettes_3_44.png + + + +.. image:: images/palettes_3_45.png + + + +.. image:: images/palettes_3_46.png + + + +.. image:: images/palettes_3_47.png + + + +.. image:: images/palettes_3_48.png + + + +.. image:: images/palettes_3_49.png + + + +.. image:: images/palettes_3_50.png + + + +.. image:: images/palettes_3_51.png + + + +.. image:: images/palettes_3_52.png + + + +.. image:: images/palettes_3_53.png + + + +.. image:: images/palettes_3_54.png + + + +.. image:: images/palettes_3_55.png + + + +.. image:: images/palettes_3_56.png + + + +.. image:: images/palettes_3_57.png + + + +.. image:: images/palettes_3_58.png + + + +.. image:: images/palettes_3_59.png + + + +.. image:: images/palettes_3_60.png + + + +.. image:: images/palettes_3_61.png + + + +.. image:: images/palettes_3_62.png + + + +.. image:: images/palettes_3_63.png + + + +.. image:: images/palettes_3_64.png + + + +.. image:: images/palettes_3_65.png + + + +.. image:: images/palettes_3_66.png + + + +.. image:: images/palettes_3_67.png + + + +.. image:: images/palettes_3_68.png + + + +.. image:: images/palettes_3_69.png + + + +.. image:: images/palettes_3_70.png + + + +.. image:: images/palettes_3_71.png + + + +.. image:: images/palettes_3_72.png + + + +.. image:: images/palettes_3_73.png + + + +.. image:: images/palettes_3_74.png + + + +.. image:: images/palettes_3_75.png + + + +.. image:: images/palettes_3_76.png + + + +.. image:: images/palettes_3_77.png + + + +.. image:: images/palettes_3_78.png + + + +.. image:: images/palettes_3_79.png + + + +.. image:: images/palettes_3_80.png + + + +.. image:: images/palettes_3_81.png + + + +.. image:: images/palettes_3_82.png + + + +.. image:: images/palettes_3_83.png + + + +.. image:: images/palettes_3_84.png + + + +.. image:: images/palettes_3_85.png + + + +.. image:: images/palettes_3_86.png + + + +.. image:: images/palettes_3_87.png + + + +.. image:: images/palettes_3_88.png + + + +.. image:: images/palettes_3_89.png + + + +.. image:: images/palettes_3_90.png + + + +.. image:: images/palettes_3_91.png + + + +.. image:: images/palettes_3_92.png + + + +.. image:: images/palettes_3_93.png + + + +.. image:: images/palettes_3_94.png + + + +.. image:: images/palettes_3_95.png + + + +.. image:: images/palettes_3_96.png + + + +.. image:: images/palettes_3_97.png + + + +.. image:: images/palettes_3_98.png + + + +.. image:: images/palettes_3_99.png + + + +.. image:: images/palettes_3_100.png + + + +.. image:: images/palettes_3_101.png + + + +.. image:: images/palettes_3_102.png + + + +.. image:: images/palettes_3_103.png + + + +.. image:: images/palettes_3_104.png + + + +.. image:: images/palettes_3_105.png + + + +.. image:: images/palettes_3_106.png + + + +.. image:: images/palettes_3_107.png + + + +.. image:: images/palettes_3_108.png + + + +.. image:: images/palettes_3_109.png + + + +.. image:: images/palettes_3_110.png + + + +.. image:: images/palettes_3_111.png + + + +.. image:: images/palettes_3_112.png + + + +.. image:: images/palettes_3_113.png + + + +.. image:: images/palettes_3_114.png + + + +.. image:: images/palettes_3_115.png + + + +.. image:: images/palettes_3_116.png + + + +.. image:: images/palettes_3_117.png + + + +.. image:: images/palettes_3_118.png + + + +.. image:: images/palettes_3_119.png + + + +.. image:: images/palettes_3_120.png + + + +.. image:: images/palettes_3_121.png + + + +.. image:: images/palettes_3_122.png + + + +.. image:: images/palettes_3_123.png + + + +.. image:: images/palettes_3_124.png + + + +.. image:: images/palettes_3_125.png + + + +.. image:: images/palettes_3_126.png + + + +.. image:: images/palettes_3_127.png + + + +.. image:: images/palettes_3_128.png + + + +.. image:: images/palettes_3_129.png + + + +.. image:: images/palettes_3_130.png + + + +.. image:: images/palettes_3_131.png + + + +.. image:: images/palettes_3_132.png + + + +.. image:: images/palettes_3_133.png + + + +.. image:: images/palettes_3_134.png + + + +.. image:: images/palettes_3_135.png + + + +.. image:: images/palettes_3_136.png + + + +.. image:: images/palettes_3_137.png + + + +.. image:: images/palettes_3_138.png + + + +.. image:: images/palettes_3_139.png + + + +.. image:: images/palettes_3_140.png + + + +.. image:: images/palettes_3_141.png + + + +.. image:: images/palettes_3_142.png + + + +.. image:: images/palettes_3_143.png + + + +.. image:: images/palettes_3_144.png + + + +.. image:: images/palettes_3_145.png + + + +.. image:: images/palettes_3_146.png + + + +.. image:: images/palettes_3_147.png + + + +.. image:: images/palettes_3_148.png + + + +.. image:: images/palettes_3_149.png + + + +.. image:: images/palettes_3_150.png + + + +.. image:: images/palettes_3_151.png + + + +.. image:: images/palettes_3_152.png + + + +.. image:: images/palettes_3_153.png + + + +.. image:: images/palettes_3_154.png + + + +.. image:: images/palettes_3_155.png + + + +.. image:: images/palettes_3_156.png + + + +.. image:: images/palettes_3_157.png + + + +.. image:: images/palettes_3_158.png + + + +.. image:: images/palettes_3_159.png + + + +.. image:: images/palettes_3_160.png + + + +.. image:: images/palettes_3_161.png + + + +.. image:: images/palettes_3_162.png + + + +.. image:: images/palettes_3_163.png + + + +.. image:: images/palettes_3_164.png + + + +.. image:: images/palettes_3_165.png + + + +.. image:: images/palettes_3_166.png + + + +.. image:: images/palettes_3_167.png + + + +.. image:: images/palettes_3_168.png + + + +.. image:: images/palettes_3_169.png + + + +.. image:: images/palettes_3_170.png + + + +.. image:: images/palettes_3_171.png + + + +.. image:: images/palettes_3_172.png + + + +.. image:: images/palettes_3_173.png + + + +.. image:: images/palettes_3_174.png + + + +.. image:: images/palettes_3_175.png + + + +.. image:: images/palettes_3_176.png + + + +.. image:: images/palettes_3_177.png + + + +.. image:: images/palettes_3_178.png + + + +.. image:: images/palettes_3_179.png + + + +.. image:: images/palettes_3_180.png + + + +.. image:: images/palettes_3_181.png + + + +.. image:: images/palettes_3_182.png + + + +.. image:: images/palettes_3_183.png + + + +.. image:: images/palettes_3_184.png + + + +.. image:: images/palettes_3_185.png + + + +.. image:: images/palettes_3_186.png + + + +.. image:: images/palettes_3_187.png + + + +.. image:: images/palettes_3_188.png + + + +.. image:: images/palettes_3_189.png + + + +.. image:: images/palettes_3_190.png + + + +.. image:: images/palettes_3_191.png + + + +.. image:: images/palettes_3_192.png + + + +.. image:: images/palettes_3_193.png + + + +.. image:: images/palettes_3_194.png + + + +.. image:: images/palettes_3_195.png + + + +.. image:: images/palettes_3_196.png + + + +.. image:: images/palettes_3_197.png + + + +.. image:: images/palettes_3_198.png + + + +.. image:: images/palettes_3_199.png + + + +.. image:: images/palettes_3_200.png + + + +.. image:: images/palettes_3_201.png + + + +.. image:: images/palettes_3_202.png + + + +.. image:: images/palettes_3_203.png + + + +.. image:: images/palettes_3_204.png + + + +.. image:: images/palettes_3_205.png + + + +.. image:: images/palettes_3_206.png + + + +.. image:: images/palettes_3_207.png + + -.. image:: images/palettes_2_17.png +.. image:: images/palettes_3_208.png From 15e97908e6385515675b65c2f9f858242207abf2 Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Sat, 14 Jan 2017 15:34:39 -0500 Subject: [PATCH 31/35] updating examples documentation post ben examples refresh --- docs/examples/images/examples_19_0.png | Bin 0 -> 98744 bytes docs/examples/images/examples_24_0.png | Bin 0 -> 69798 bytes docs/examples/images/examples_27_0.png | Bin 0 -> 28734 bytes docs/examples/images/examples_32_0.png | Bin 0 -> 36101 bytes docs/examples/images/examples_35_0.png | Bin 0 -> 23352 bytes docs/examples/images/examples_38_0.png | Bin 0 -> 17292 bytes docs/examples/methods.rst | 15 +++++++++------ docs/examples/palettes.rst | 7 ------- 8 files changed, 9 insertions(+), 13 deletions(-) create mode 100644 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Data scientists diagnose this plot by comparing against the -.. image:: images/examples_26_0.png +.. image:: images/examples_27_0.png Classifier Evaluation @@ -426,7 +429,7 @@ heatmap in order for easy interpretation and detection. -.. image:: images/examples_31_0.png +.. image:: images/examples_32_0.png ROCAUC @@ -463,7 +466,7 @@ sensitivity and specificity. -.. image:: images/examples_34_0.png +.. image:: images/examples_35_0.png ClassBalance @@ -500,4 +503,4 @@ classification model. -.. image:: images/examples_37_0.png +.. image:: images/examples_38_0.png diff --git a/docs/examples/palettes.rst b/docs/examples/palettes.rst index 2490b1d2a..cf215c788 100644 --- a/docs/examples/palettes.rst +++ b/docs/examples/palettes.rst @@ -97,13 +97,6 @@ Yellowbrick includes custom palettes as well as familiar ones from Matplotlib an plt.title("{} - {}".format(name, num), loc='left') -.. parsed-literal:: - - /usr/local/lib/python2.7/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). - max_open_warning, RuntimeWarning) - - - .. image:: images/palettes_3_1.png From a1c4834dc5766bc45862c3e077e626c38df75cae Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Sat, 14 Jan 2017 15:36:29 -0500 Subject: [PATCH 32/35] search not implemented --- docs/index.rst | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/index.rst b/docs/index.rst index f3eb647a2..9e6a6d4d6 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -72,4 +72,3 @@ Indices and tables * :ref:`genindex` * :ref:`modindex` -* :ref:`search` From 8fc8e40bd8599429917b1f0b567e1c93ea8ed3a7 Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Mon, 16 Jan 2017 11:30:20 -0500 Subject: [PATCH 33/35] minor cleanup and added links to alpha test notebook for issue #82 --- .../Getting Started with Yellowbrick.ipynb | 24 +++++++++---------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/examples/Getting Started with Yellowbrick.ipynb b/examples/Getting Started with Yellowbrick.ipynb index c9706f3e0..c525b8ec7 100644 --- a/examples/Getting Started with Yellowbrick.ipynb +++ b/examples/Getting Started with Yellowbrick.ipynb @@ -25,32 +25,32 @@ "source": [ "## Alpha Testing \n", "\n", - "We are looking for people to help us Alpha test the Yellowbrick project! Helping is simple: simply create a notebook that applies the concepts in this getting started guide to a small to medium size data set of your choice. Run through the examples with the dataset, and try to change options and customize as much as possible. After you've exercised the code with your examples, respond to our alpha testing survey!\n", + "We are looking for people to help us Alpha test the Yellowbrick project! Helping is simple: simply create a notebook that applies the concepts in this *Getting Started* guide to a small-to-medium size dataset of your choice. Run through the examples with the dataset, and try to change options and customize as much as possible. After you've exercised the code with your examples, respond to our [alpha testing survey](https://goo.gl/forms/naoPUMFa1xNcafY83)!\n", "\n", "### Step One: Dataset \n", "\n", - "Select a multivariate dataset of your own; the more datasets that we can run through Yellowbrick, the more likely we'll discover edge cases and exceptions! Note that the dataset must be well-suited to modeling with Scikit-Learn. In particular we recommend you choose a dataset whose target is suited to the following tasks:\n", + "Select a multivariate dataset of your own; the more (e.g. different) datasets that we can run through Yellowbrick, the more likely we'll discover edge cases and exceptions! Note that your dataset must be well-suited to modeling with Scikit-Learn. In particular we recommend you choose a dataset whose target is suited to the following supervised learning tasks:\n", "\n", "- [Regression](https://en.wikipedia.org/wiki/Regression_analysis) (target is a continuous variable)\n", "- [Classification](https://en.wikipedia.org/wiki/Classification_in_machine_learning) (target is a discrete variable) \n", "\n", - "There are data sets that are well suited to both types of analysis; either way you'll be utilizing the testing methodology for one, the other, or both. In order to find a dataset, we recommend you try the following places:\n", + "There are datasets that are well suited to both types of analysis; either way you can use the testing methodology from this notebook for either type of task (or both). In order to find a dataset, we recommend you try the following places:\n", "\n", "- [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/)\n", "- [MLData.org](http://mldata.org/)\n", "- [Awesome Public Datasets](https://github.com/caesar0301/awesome-public-datasets) \n", "\n", - "You're more than welcome to choose a dataset of your own, but we do ask that you make at least the notebook containing your testing results publically 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! \n", + "You're more than welcome to choose a dataset of your own, but we do ask that you make at least the notebook containing your testing results 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! \n", "\n", "### Step Two: Notebook \n", "\n", "Create a notebook in a GitHub repository. We suggest the following:\n", "\n", "1. Fork the Yellowbrick repository \n", - "2. Under the examples directory, create a directory with your GitHub username \n", - "3. Create the notebook in examples/username/testing.ipynb \n", + "2. Under the `examples` directory, create a directory named with your GitHub username \n", + "3. Create the notebook in examples/USERNAME/testing.ipynb \n", "\n", - "You don't have to fork Yellowbrick, and can send us the notebook via Gist or your own repository, but if you do, you can initiate a pull request to have your example added to our gallery! \n", + "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! \n", "\n", "### Step Three: Model with Yellowbrick and Scikit-Learn\n", "\n", @@ -58,14 +58,14 @@ "\n", "- A title in markdown \n", "- A description of the dataset and where it was obtained \n", - "- A section that loads the data into a DataFrame or numpy matrix \n", + "- A section that loads the data into a Pandas dataframe or NumPy matrix \n", "\n", "Then conduct the following modeling activities:\n", "\n", "- Feature analysis using Scikit-Learn and Yellowbrick \n", "- Estimator fitting using Scikit-Learn and Yellowbrick \n", "\n", - "You can follow along with the guide above, the examples directory, or even create your own custom visualizers! The goal is that you create an end-to-end model from data loading to estimator(s) with visualizers along the way. \n", + "You can follow along with our `examples` directory (check out [examples.ipynb](https://github.com/DistrictDataLabs/yellowbrick/blob/master/examples/examples.ipynb)) or even create your own custom visualizers! The goal is that you create an end-to-end model from data loading to estimator(s) with visualizers along the way. \n", "\n", "**IMPORTANT**: please make sure you record all errors that you get and any tracebacks you receive for step four! \n", "\n", @@ -75,11 +75,11 @@ "\n", "[https://goo.gl/forms/naoPUMFa1xNcafY83](https://goo.gl/forms/naoPUMFa1xNcafY83)\n", "\n", - "This form is allowing us to aggregate multiple submissions and bugs so that we can coordinate the creation and management of issues. If you are the first to report a bug or feature request, we will make sure you're notified about the created issue! \n", + "This form is allowing us to aggregate multiple submissions and bugs so that we can coordinate the creation and management of issues. If you are 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! \n", "\n", "### Step Five: Thanks! \n", "\n", - "Thank you for helping us make Yellowbrick better! We'd love to see pull requests for features you thought would be neat. We'll also be doing a user study, that we'd love for you to participate in. Stay tuned for more great things from Yellowbrick!" + "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 be doing a user study that we would love for you to participate in. Stay tuned for more great things from Yellowbrick!" ] } ], @@ -99,7 +99,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.5.2" } }, "nbformat": 4, From dc02e593950416e159f72a37314ee9b15e0a66c2 Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Mon, 16 Jan 2017 11:31:38 -0500 Subject: [PATCH 34/35] removed mpl inline invokation from rst version of palettes notebook --- docs/examples/palettes.rst | 2 -- 1 file changed, 2 deletions(-) diff --git a/docs/examples/palettes.rst b/docs/examples/palettes.rst index cf215c788..19c62872f 100644 --- a/docs/examples/palettes.rst +++ b/docs/examples/palettes.rst @@ -8,8 +8,6 @@ Yellowbrick includes custom palettes as well as familiar ones from Matplotlib an .. code:: python - %matplotlib inline - import matplotlib.pyplot as plt from yellowbrick.style.palettes import PALETTES, SEQUENCES, color_palette From 1ad9776565d0b57fba4c590ad905a16806bc84bd Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Fri, 20 Jan 2017 12:03:54 -0500 Subject: [PATCH 35/35] version bump to 0.3.2 --- docs/about.rst | 37 +++++++++++++++++++++----- docs/api/modules.rst | 8 +++--- docs/api/yellowbrick.features.rst | 10 +++---- docs/api/yellowbrick.rst | 4 +-- docs/conf.py | 9 ++++++- requirements.txt | 1 + tests/__init__.py | 2 +- yellowbrick/classifier.py | 44 +++++++++++++++---------------- yellowbrick/utils.py | 22 ++++++++++------ yellowbrick/version.py | 4 +-- 10 files changed, 89 insertions(+), 52 deletions(-) diff --git a/docs/about.rst b/docs/about.rst index a457108c3..de2dcf4af 100644 --- a/docs/about.rst +++ b/docs/about.rst @@ -83,12 +83,33 @@ From Wikipedia_: Changelog --------- +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. + +Version 0.3.2 +^^^^^^^^^^^^^ + +* 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 +* Tag: v0.3.1_ * Deployed: Monday, October 10, 2016 * Contributors: Benjamin Bengfort @@ -103,7 +124,7 @@ This release marks a major change from the previous MVP releases as Yellowbrick 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 +* Tag: v0.3_ * Deployed: Sunday, October 9, 2016 * Contributors: Benjamin Bengfort, Rebecca Bilbro, Marius van Niekerk @@ -134,7 +155,7 @@ Version 0.2 ^^^^^^^^^^^ Intermediate steps towards a complete API for visualization. Preparatory stages for Scikit-Learn visual pipelines. -* Tag: v0.2 +* Tag: v0.2_ * Deployed: Sunday, September 4, 2016 * Contributors: Benjamin Bengfort, Rebecca Bilbro, Patrick O'Melveny, Ellen Lowy, Laura Lorenz @@ -157,7 +178,7 @@ 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 +* Tag: v0.1_ * Deployed: Wednesday, May 18, 2016 * Contributors: Benjamin Bengfort, Rebecca Bilbro @@ -169,10 +190,12 @@ Created the yellowbrick library MVP with two primary operations: a classificatio - Stub tests/stub documentation - - - .. _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 +.. _v0.3.2: 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/api/modules.rst b/docs/api/modules.rst index be66dabad..798061270 100644 --- a/docs/api/modules.rst +++ b/docs/api/modules.rst @@ -2,8 +2,8 @@ API Reference ============== .. toctree:: - :maxdepth: 4 + :maxdepth: 4 - yellowbrick - yellowbrick.style - yellowbrick.features + yellowbrick + yellowbrick.style + yellowbrick.features diff --git a/docs/api/yellowbrick.features.rst b/docs/api/yellowbrick.features.rst index 178f92dd0..4dc15c809 100644 --- a/docs/api/yellowbrick.features.rst +++ b/docs/api/yellowbrick.features.rst @@ -1,11 +1,11 @@ yellowbrick.features package -=================== +============================ Submodules ---------- yellowbrick.features.base module ---------------------------- +-------------------------------- .. automodule:: yellowbrick.features.base :members: @@ -13,7 +13,7 @@ yellowbrick.features.base module :show-inheritance: yellowbrick.features.pcoords module ---------------------------- +----------------------------------- .. automodule:: yellowbrick.features.pcoords :members: @@ -21,7 +21,7 @@ yellowbrick.features.pcoords module :show-inheritance: yellowbrick.features.radviz module ---------------------------- +---------------------------------- .. automodule:: yellowbrick.features.radviz :members: @@ -29,7 +29,7 @@ yellowbrick.features.radviz module :show-inheritance: yellowbrick.features.rankd module ---------------------------- +--------------------------------- .. automodule:: yellowbrick.features.rankd :members: diff --git a/docs/api/yellowbrick.rst b/docs/api/yellowbrick.rst index 63fcc2ab7..bbdc9bd2b 100644 --- a/docs/api/yellowbrick.rst +++ b/docs/api/yellowbrick.rst @@ -21,7 +21,7 @@ yellowbrick.base module :show-inheritance: yellowbrick.bestfit module ------------------------ +-------------------------- .. automodule:: yellowbrick.bestfit :members: @@ -60,7 +60,7 @@ yellowbrick.regressor module :members: :undoc-members: :show-inheritance: - + yellowbrick.utils module ------------------------ diff --git a/docs/conf.py b/docs/conf.py index 60f0b8a3b..6180e0c05 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -34,10 +34,13 @@ # ones. extensions = [ 'sphinx.ext.autodoc', + 'sphinx.ext.autosummary', 'sphinx.ext.intersphinx', 'sphinx.ext.coverage', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', + 'sphinx.ext.todo', + 'numpydoc', ] # Add any paths that contain templates here, relative to this directory. @@ -340,6 +343,10 @@ # # texinfo_no_detailmenu = False +# Autodoc requires numpy to skip class members otherwise we get an exception: +# toctree contains reference to nonexisting document +# See: https://github.com/phn/pytpm/issues/3#issuecomment-12133978 +numpydoc_show_class_members = False # Locations of objects.inv files for intersphinx extension that auto links to external api docs. intersphinx_mapping = {'python': ('https://docs.python.org/3', None), @@ -347,4 +354,4 @@ 'scipy': ('http://scipy.github.io/devdocs/', None), 'numpy': ('https://docs.scipy.org/doc/numpy-dev/', None), 'cycler': ('http://matplotlib.org/cycler/', None), - 'seaborn': ('http://seaborn.pydata.org/index.html', None)} + 'sklearn': ('http://scikit-learn.org/stable/', None)} diff --git a/requirements.txt b/requirements.txt index 6944e79dc..964d7e1c6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -25,6 +25,7 @@ cycler>=0.10.0 ## Documentation (uncomment to build documentation) #Sphinx>=1.4.4 #sphinx-rtd-theme>=0.1.9 +#numpydoc>=0.6.0 ## Documentation Dependencies (installed from above) #alabaster==0.7.9 diff --git a/tests/__init__.py b/tests/__init__.py index 5013fc95e..241a2df75 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -28,7 +28,7 @@ ## Test Constants ########################################################################## -EXPECTED_VERSION = "0.3.1" +EXPECTED_VERSION = "0.3.2" ########################################################################## diff --git a/yellowbrick/classifier.py b/yellowbrick/classifier.py index 3ab940e00..417979b15 100644 --- a/yellowbrick/classifier.py +++ b/yellowbrick/classifier.py @@ -412,32 +412,32 @@ def roc_auc(model, X, y=None, ax=None, **kwargs): class ClassBalance(ClassificationScoreVisualizer): """ Class balance chart that shows the support for each class in the - fitted classification model displayed as a bar plot. - """ - def __init__(self, model, ax=None, classes=None, **kwargs): - """ - Pass in a fitted model to generate a class balance chart. + fitted classification model displayed as a bar plot. It is initialized + with a fitted model and generates a class balance chart on draw. - Parameters - ---------- + Parameters + ---------- - :param ax: the axis to plot the figure on. + ax: axes + 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. + model: estimator + Scikit-Learn estimator object. Should be an instance of a classifier, + else ``__init__()`` will raise an exception. - :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. + 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. - :param kwargs: keyword arguments passed to the super class. Here, used - to colorize the bars in the histogram. + 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. + 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)) @@ -457,7 +457,7 @@ def fit(self, X, y=None, **kwargs): kwargs: keyword arguments passed to Scikit-Learn API. Returns - ------ + ------- self : instance Returns the instance of the classification score visualizer @@ -481,7 +481,7 @@ def score(self, X, y=None, **kwargs): An array or series of target or class values Returns - ------ + ------- ax : the axis with the plotted figure """ @@ -495,7 +495,7 @@ def draw(self): Renders the class balance chart across the axis. Returns - ------ + ------- ax : the axis with the plotted figure diff --git a/yellowbrick/utils.py b/yellowbrick/utils.py index 04748ff5a..bea7cf504 100644 --- a/yellowbrick/utils.py +++ b/yellowbrick/utils.py @@ -87,8 +87,10 @@ def is_classifier(estimator): estimator: class or instance The object to test whether or not is a Scikit-Learn classifier. - See Also: - https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py#L526 + See also + -------- + is_classifier + `sklearn.is_classifier() `_ """ # TODO: once we make ScoreVisualizer and ModelVisualizer pass through # wrappers as in Issue #90, these three lines become unnecessary. @@ -113,8 +115,10 @@ def is_regressor(estimator): model: class or instance The object to test whether or not is a Scikit-Learn regressor. - See Also: - https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/base.py#L531 + See also + -------- + is_regressor + `sklearn.is_regressor() `_ """ # TODO: once we make ScoreVisualizer and ModelVisualizer pass through # wrappers as in Issue #90, these three lines become unnecessary. @@ -161,13 +165,15 @@ def memoized(fget): getter on the first access. The result is stored and on subsequent accesses is returned, preventing the need to call the getter any more. - See Also: - https://github.com/estebistec/python-memoized-property - Parameters ---------- fget: function The getter method to memoize for subsequent access. + + See also + -------- + python-memoized-property + `python-memoized-property `_ """ attr_name = '_{0}'.format(fget.__name__) @@ -189,7 +195,7 @@ class docutil(object): single call wrapper should be identical, this decorator ensures that we only have to edit one doc string. - Usage: + Usage:: @docutil(Visualizer.__init__) def visualize(*args, **kwargs): diff --git a/yellowbrick/version.py b/yellowbrick/version.py index 7c1cbad93..876e75fdc 100644 --- a/yellowbrick/version.py +++ b/yellowbrick/version.py @@ -20,9 +20,9 @@ __version_info__ = { 'major': 0, 'minor': 3, - 'micro': 1, + 'micro': 2, 'releaselevel': 'alpha', - 'serial': 2, + 'serial': 3, } ##########################################################################

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