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@metasyn
Created June 15, 2017 16:33
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Comparing Classifiers\n",
"\n",
"What makes a classifier different? Well, of course you can read about each algorithm. One way of getting a quick comparison is mapping out decision boundaries of each classifier. Below we'll look at three algorithms:\n",
"\n",
"1.) Logistic Regression\n",
"2.) Random Forest Classfier\n",
"3.) Support Vector Classifier\n",
"\n",
"Then we'll look at two datasets:\n",
"\n",
"1.) Diabetes - https://archive.ics.uci.edu/ml/datasets/diabetes\n",
"2.) Iris - http://archive.ics.uci.edu/ml/datasets/Iris\n",
"\n",
"For the sake of plotting, I've just chosen two features from each dataset."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import seaborn as sns\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.svm import SVC\n",
"from sklearn.cross_validation import train_test_split\n",
"from sklearn.metrics import f1_score\n",
"from sklearn.metrics import confusion_matrix\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# DIABETES DATA !"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data = pd.read_csv('~/data/diabetes.csv')\n",
"X = np.array(data[['BMI', 'age']])\n",
"y = data.response.ravel()\n",
"y = y.reshape(len(y), 1).ravel()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X_train, X, y_train, y = train_test_split(\n",
"X, y, test_size=0.33, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def fit_and_plot(clf):\n",
" \n",
" clf.fit(X_train, y_train)\n",
"\n",
" h = 0.02\n",
" x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
" y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
" xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
" Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
" Z = Z.reshape(xx.shape)\n",
" \n",
" fig = plt.figure(1, figsize=(16, 8))\n",
" plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)\n",
"\n",
" # Plot also the training points\n",
" plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', cmap=plt.cm.Paired)\n",
" plt.xlabel('BMI')\n",
" plt.ylabel('Age')\n",
"\n",
" plt.xlim(xx.min(), xx.max())\n",
" plt.ylim(yy.min(), yy.max())\n",
" plt.xticks(())\n",
" plt.yticks(())\n",
" \n",
" name = str(clf.__class__).split('.')[-1][:-2]\n",
"\n",
" fig.savefig(name + '.png', dpi=100)\n",
" \n",
" print('{}: {}'.format('Score', clf.score(X,y)))\n",
" print('{}: {}'.format('F1 Score', f1_score(clf.predict(X), y)))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/matplotlib/collections.py:548: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" if self._edgecolors == 'face':\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Score: 0.582677165354\n",
"F1 Score: 0.490384615385\n"
]
},
{
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Ae6BsAgAAzB5FEwAmQdkEMF2XR6J6q3tMh3qD2t8zJsvinogAShtFEwCmQNkEcD+RhKnB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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e85e9d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"lr = LogisticRegression(class_weight='balanced')\n",
"fit_and_plot(lr)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Score: 0.657480314961\n",
"F1 Score: 0.491228070175\n"
]
},
{
"data": {
"image/png": 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afXbHAICsRddZAAAAAEBa0aIJAMAoBLdtGdfPAQDIB7RoAgAAAADSihZNAACAcbrQGdXV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\nH2M0snGekIXFAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1159e3c90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"rfc = RandomForestClassifier()\n",
"fit_and_plot(rfc)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Score: 0.669291338583\n",
"F1 Score: 0.481481481481\n"
]
},
{
"data": {
"image/png": 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bmwAA4BKETeQTo+cACmm4oMnUWQAACogggHzihwwAbkHQBACgwOIbNhE4AQC+RtAEAMAh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\n8v8Dkn3o7/BmF0sAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x126108050>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"svc = SVC()\n",
"fit_and_plot(svc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# IRIS DATA !"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"iris_data = pd.read_csv('/Users/aljohnson/data/iris.csv')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sepal_length</th>\n",
" <th>sepal_width</th>\n",
" <th>petal_length</th>\n",
" <th>petal_width</th>\n",
" <th>species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>145</th>\n",
" <td>6.7</td>\n",
" <td>3.0</td>\n",
" <td>5.2</td>\n",
" <td>2.3</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
" <td>6.3</td>\n",
" <td>2.5</td>\n",
" <td>5.0</td>\n",
" <td>1.9</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147</th>\n",
" <td>6.5</td>\n",
" <td>3.0</td>\n",
" <td>5.2</td>\n",
" <td>2.0</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>6.2</td>\n",
" <td>3.4</td>\n",
" <td>5.4</td>\n",
" <td>2.3</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
" <td>5.9</td>\n",
" <td>3.0</td>\n",
" <td>5.1</td>\n",
" <td>1.8</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal_length sepal_width petal_length petal_width species\n",
"145 6.7 3.0 5.2 2.3 virginica\n",
"146 6.3 2.5 5.0 1.9 virginica\n",
"147 6.5 3.0 5.2 2.0 virginica\n",
"148 6.2 3.4 5.4 2.3 virginica\n",
"149 5.9 3.0 5.1 1.8 virginica"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris_data.tail()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def encoder(species):\n",
" if species == 'setosa':\n",
" return 0\n",
" if species == 'virginica':\n",
" return 2\n",
" else:\n",
" return 1"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 0\n",
"1 0\n",
"2 0\n",
"3 0\n",
"4 0\n",
"Name: species, dtype: int64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"response = iris_data.species.apply(encoder)\n",
"response.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def fit_and_plot_iris(clf):\n",
" \n",
" clf.fit(X_train, y_train)\n",
"\n",
" h = 0.02\n",
" x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
" y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
" xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
" Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
" Z = Z.reshape(xx.shape)\n",
" \n",
" c_map = plt.cm.Paired\n",
" \n",
" fig = plt.figure(1, figsize=(16, 8))\n",
" plt.pcolormesh(xx, yy, Z, cmap=c_map)\n",
"\n",
" # Plot also the training points\n",
" plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', cmap=c_map)\n",
" plt.xlabel('petal length')\n",
" plt.ylabel('sepal length')\n",
"\n",
" plt.xlim(xx.min(), xx.max())\n",
" plt.ylim(yy.min(), yy.max())\n",
" plt.xticks(())\n",
" plt.yticks(())\n",
" \n",
" name = str(clf.__class__).split('.')[-1][:-2]\n",
"\n",
" fig.savefig(name + '.png', dpi=100)\n",
" \n",
" print('{}: {}'.format('Score', clf.score(X,y)))\n",
" print('{}: {}'.format('F1 Score', f1_score(clf.predict(X), y)))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X = np.array(iris_data[['petal_length', 'sepal_length']])\n",
"y = response.ravel()\n",
"y = y.reshape(len(y), 1).ravel()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X_train, X, y_train, y = train_test_split(\n",
"X, y, test_size=0.33, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Library/Python/2.7/site-packages/sklearn/utils/class_weight.py:62: DeprecationWarning: The class_weight='auto' heuristic is deprecated in 0.17 in favor of a new heuristic class_weight='balanced'. 'auto' will be removed in 0.19\n",
" \" 0.19\", DeprecationWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Score: 0.94\n",
"F1 Score: 0.94012539185\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Library/Python/2.7/site-packages/sklearn/metrics/classification.py:756: DeprecationWarning: The default `weighted` averaging is deprecated, and from version 0.18, use of precision, recall or F-score with multiclass or multilabel data or pos_label=None will result in an exception. Please set an explicit value for `average`, one of (None, 'micro', 'macro', 'weighted', 'samples'). In cross validation use, for instance, scoring=\"f1_weighted\" instead of scoring=\"f1\".\n",
" sample_weight=sample_weight)\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x148770550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"lr = LogisticRegression(class_weight='auto')\n",
"fit_and_plot_iris(lr)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Score: 0.92\n",
"F1 Score: 0.917738562092\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Library/Python/2.7/site-packages/sklearn/metrics/classification.py:756: DeprecationWarning: The default `weighted` averaging is deprecated, and from version 0.18, use of precision, recall or F-score with multiclass or multilabel data or pos_label=None will result in an exception. Please set an explicit value for `average`, one of (None, 'micro', 'macro', 'weighted', 'samples'). In cross validation use, for instance, scoring=\"f1_weighted\" instead of scoring=\"f1\".\n",
" sample_weight=sample_weight)\n"
]
},
{
"data": {
"image/png": 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OdkcC+JE9eXgk9+45nEV93TkyNpnrzliVdYv62x0LABYsRXMB+cHD387Gbduz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\nt4qi2JjkmdeQ48/KsvzXr/K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"text/plain": [
"<matplotlib.figure.Figure at 0x115ace850>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"rfc = RandomForestClassifier()\n",
"fit_and_plot_iris(rfc)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Score: 0.98\n",
"F1 Score: 0.98\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Library/Python/2.7/site-packages/sklearn/metrics/classification.py:756: DeprecationWarning: The default `weighted` averaging is deprecated, and from version 0.18, use of precision, recall or F-score with multiclass or multilabel data or pos_label=None will result in an exception. Please set an explicit value for `average`, one of (None, 'micro', 'macro', 'weighted', 'samples'). In cross validation use, for instance, scoring=\"f1_weighted\" instead of scoring=\"f1\".\n",
" sample_weight=sample_weight)\n"
]
},
{
"data": {
"image/png": 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54dBEjYkWrqf2j+WKjUtmx1dvWpon9o7VmAgWBm/VAABU7M2eNLtyqDcvHZmc7aqNTzXT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\nmQDMBzqaAAAAVEqhCQAAQKU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"text/plain": [
"<matplotlib.figure.Figure at 0x115aac1d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"svc = SVC()\n",
"fit_and_plot_iris(svc)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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