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Machine Learning to identify suspects by financial data for Enron employees
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Enron_Fraud.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Student response addresses the most important characteristics of the dataset and uses these characteristics to inform their analysis. Important characteristics include:\n",
"\n",
"total number of data points\n",
"allocation across classes (POI/non-POI)\n",
"number of features used\n",
"are there features with many missing values? etc.\n",
"The rubric requires to address the most important characteristics of the dataset and to use these characteristics to inform the analysis. Please make sure your discussion considers:\n",
"\n",
"The total number of data points.\n",
"The allocation across classes (POI/non-POI).\n",
"The number of features used.\n",
"Optionally: The distribution of missing values in the features: How many missing values are there for each feature? How are you dealing with missing values? Please note that the Tester.py function automatically converts all missing values to 0."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "ImportError",
"evalue": "No module named 'sklearn.model_selection'",
"output_type": "error",
"traceback": [
"\u001b[1;31m--------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-1-6607f6d17042>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 29\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mprecision_recall_fscore_support\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprecision_score\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfeature_selection\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSelectKBest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchi2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSelectPercentile\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 31\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel_selection\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mKFold\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 32\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpreprocessing\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mMinMaxScaler\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 33\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcross_validation\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mStratifiedKFold\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mImportError\u001b[0m: No module named 'sklearn.model_selection'"
]
}
],
"source": [
"#!/usr/bin/python\n",
"from IPython.core.display import HTML\n",
"HTML(\"\"\"\n",
"<style>\n",
".output_png {\n",
" display: table-cell;\n",
" text-align: center;\n",
" vertical-align: middle;\n",
"}\n",
"</style>\n",
"\"\"\")\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"%matplotlib inline\n",
"import sys\n",
"import pickle\n",
"import seaborn as sns\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"sys.path.append(\"../tools/\")\n",
"\n",
"#utilities\n",
"from sklearn.decomposition import PCA\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.metrics import precision_recall_fscore_support, precision_score\n",
"from sklearn.feature_selection import SelectKBest, chi2, SelectPercentile\n",
"from sklearn.model_selection import train_test_split, GridSearchCV, KFold\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"from sklearn.cross_validation import StratifiedKFold\n",
"from sklearn.feature_selection import RFECV\n",
"\n",
"#classifiers\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.svm import SVC\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.cluster import KMeans\n",
"from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
"from feature_format import featureFormat, targetFeatureSplit\n",
"from tester import dump_classifier_and_data, test_classifier\n",
"from poi_email_addresses import poiEmails\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Available Features\n",
"\n",
"financial features: ['salary', 'deferral_payments', 'total_payments', 'loan_advances', 'bonus', 'restricted_stock_deferred', 'deferred_income', 'total_stock_value', 'expenses', 'exercised_stock_options', 'other', 'long_term_incentive', 'restricted_stock', 'director_fees'] (all units are in US dollars)\n",
"\n",
"email features: ['to_messages', 'email_address', 'from_poi_to_this_person', 'from_messages', 'from_this_person_to_poi', 'shared_receipt_with_poi'] (units are generally number of emails messages; notable exception is ‘email_address’, which is a text string)\n",
"\n",
"POI label: [‘poi’] (boolean, represented as integer)"
]
},
{
"cell_type": "code",
"execution_count": 249,
"metadata": {
"collapsed": true,
"scrolled": true
},
"outputs": [],
"source": [
"### Task 1: Select what features you'll use.\n",
"### features_list is a list of strings, each of which is a feature name.\n",
"### The first feature must be \"poi\".\n",
"features_list = ['poi', \n",
" 'salary', \n",
" 'deferral_payments', \n",
" 'total_payments', \n",
" 'loan_advances', \n",
" 'bonus', \n",
" 'restricted_stock_deferred', \n",
" 'deferred_income', \n",
" 'total_stock_value', \n",
" 'expenses', \n",
" 'exercised_stock_options', \n",
" 'other', \n",
" 'long_term_incentive', \n",
" 'restricted_stock', \n",
" 'director_fees',\n",
" 'from_messages', \n",
" 'to_messages', \n",
" 'from_this_person_to_poi', \n",
" 'from_poi_to_this_person', \n",
" 'shared_receipt_with_poi']\n",
"\n",
"# You will need to use more features\n",
"\n",
"### Load the dictionary containing the dataset\n",
"with open(\"final_project_dataset.pkl\", \"r\") as data_file:\n",
" data_dict = pickle.load(data_file)"
]
},
{
"cell_type": "code",
"execution_count": 250,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The total number of NaN's in this data set of 20 features is 1282.\n"
]
}
],
"source": [
"### Task 2: Remove outliers\n",
"### We drop the outliers we already know to exist from the course, like TOTAL, and others we've found by visual inspection\n",
"for outlier in [\"LOCKHART EUGENE E\", \"TOTAL\", \"THE TRAVEL AGENCY IN THE PARK\"]:\n",
" if outlier in data_dict:\n",
" data_dict.pop(outlier)\n",
"\n",
"### Conversion to pandas.dataframe for easy aggregation, cleaning, and detection of outliers\n",
"df = pd.DataFrame.from_dict(data_dict)\n",
"\n",
"### We keep only the features we've chosen\n",
"df = df.transpose()[features_list]\n",
"\n",
"### Seeing that all of the booleans and NaN's are actually strings, we convert them to appropriate\n",
"### objects\n",
"df.replace(\"NaN\", np.nan, inplace=True)\n",
"df.replace(\"False\", False, inplace=True)\n",
"df.replace(\"True\", True, inplace=True)\n",
"print \"The total number of NaN's in this data set of %d features is %d.\" \\\n",
"%(len(features_list), df.isnull().values.sum())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### More basic facets of the data set"
]
},
{
"cell_type": "code",
"execution_count": 251,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The number of \"POIs\" and \"non-POIs\" are 18 and 125, respectively, out of 143 listed individuals.\n",
"\n",
"As for the distribution of NaNs across features that have them, we can see this in the following table:\n",
"salary 49.0\n",
"deferral_payments 105.0\n",
"total_payments 20.0\n",
"loan_advances 140.0\n",
"bonus 62.0\n",
"restricted_stock_deferred 126.0\n",
"deferred_income 95.0\n",
"total_stock_value 18.0\n",
"expenses 49.0\n",
"exercised_stock_options 42.0\n",
"other 52.0\n",
"long_term_incentive 78.0\n",
"restricted_stock 34.0\n",
"director_fees 127.0\n",
"from_messages 57.0\n",
"to_messages 57.0\n",
"from_this_person_to_poi 57.0\n",
"from_poi_to_this_person 57.0\n",
"shared_receipt_with_poi 57.0\n",
"Name: count, dtype: float64\n"
]
}
],
"source": [
"print \"The number of \\\"POIs\\\" and \\\"non-POIs\\\" are %s and %s, respectively, out of %s listed individuals.\\n\" \\\n",
"%(sum(df[\"poi\"]==True), sum(df[\"poi\"]==False), df.shape[0])\n",
"\n",
"print \"As for the distribution of NaNs across features that have them, we can see this in the following table:\"\n",
"print df.shape[0]-df.describe().loc[\"count\"]"
]
},
{
"cell_type": "code",
"execution_count": 252,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Here is the distribution of filled values (not NaNs) by POI status:\n",
"\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>poi</th>\n",
" <th>False</th>\n",
" <th>True</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>salary</th>\n",
" <td>77</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>deferral_payments</th>\n",
" <td>33</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>total_payments</th>\n",
" <td>105</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>loan_advances</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bonus</th>\n",
" <td>65</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>restricted_stock_deferred</th>\n",
" <td>17</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>deferred_income</th>\n",
" <td>37</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>total_stock_value</th>\n",
" <td>107</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>expenses</th>\n",
" <td>76</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>exercised_stock_options</th>\n",
" <td>89</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>other</th>\n",
" <td>73</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>long_term_incentive</th>\n",
" <td>53</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>restricted_stock</th>\n",
" <td>92</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>director_fees</th>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>from_messages</th>\n",
" <td>72</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>to_messages</th>\n",
" <td>72</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>from_this_person_to_poi</th>\n",
" <td>72</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>from_poi_to_this_person</th>\n",
" <td>72</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>shared_receipt_with_poi</th>\n",
" <td>72</td>\n",
" <td>14</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"poi False True \n",
"salary 77 17\n",
"deferral_payments 33 5\n",
"total_payments 105 18\n",
"loan_advances 2 1\n",
"bonus 65 16\n",
"restricted_stock_deferred 17 0\n",
"deferred_income 37 11\n",
"total_stock_value 107 18\n",
"expenses 76 18\n",
"exercised_stock_options 89 12\n",
"other 73 18\n",
"long_term_incentive 53 12\n",
"restricted_stock 92 17\n",
"director_fees 16 0\n",
"from_messages 72 14\n",
"to_messages 72 14\n",
"from_this_person_to_poi 72 14\n",
"from_poi_to_this_person 72 14\n",
"shared_receipt_with_poi 72 14"
]
},
"execution_count": 252,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### Let's look at the distribution of values across the two classes\n",
"print \"Here is the distribution of filled values (not NaNs) by POI status:\\n\"\n",
"df.groupby(\"poi\").count().transpose()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the above, we see that there are features we can safely exclude as having no predictive power. These have either very few values in total (mostly NaNs), or else no values at all for actual POIs: _director fees_, _restricted stock deferred_, and _loan advances_. We may also exclude features that "
]
},
{
"cell_type": "code",
"execution_count": 253,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"What is this 'other' feature? Let's take a look:\n",
"\n",
"ALLEN PHILLIP K 152.0\n",
"BADUM JAMES P NaN\n",
"BANNANTINE JAMES M 864523.0\n",
"BAXTER JOHN C 2660303.0\n",
"BAY FRANKLIN R 69.0\n",
"Name: other, dtype: float64\n",
"\n",
"Looks like uncategorized income with a huge range.\n"
]
}
],
"source": [
"to_drop = ['director_fees', 'restricted_stock_deferred', 'loan_advances']\n",
"for col in df.columns:\n",
" if col in to_drop:\n",
" df.drop(col, axis=1,inplace=True)\n",
"\n",
"### update the features list as well\n",
"for feature in to_drop:\n",
" if feature in features_list:\n",
" features_list.remove(feature)\n",
"print \"What is this 'other' feature? Let's take a look:\\n\"\n",
"print df.other.head()\n",
"print \"\\nLooks like uncategorized income with a huge range.\""
]
},
{
"cell_type": "code",
"execution_count": 254,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"### We make a plotting function, which can further help with outlier detection\n",
"\n",
"def plot_features(df, features, logx=False, logy=False):\n",
" # initial plt figure settings\n",
" plt.figure(figsize = (9, 6))\n",
" #recasting features for indexing, to support tuple as input\n",
" features = list(features)\n",
" f1 = df[features[0]]\n",
" f2 = df[features[1]]\n",
" if logx:\n",
" plt.xscale(\"log\")\n",
" features[0] = features[0] + \" (log10)\"\n",
" if logy:\n",
" plt.yscale(\"log\")\n",
" features[1] = features[1] + \" (log10)\"\n",
" if len(features) == 2:\n",
" plt.scatter(x=f1, y=f2)\n",
" else:\n",
" f3 = df[features[2]]\n",
" for i in range(len(f3)):\n",
" if f3[i] == 0:\n",
" plt.scatter(x = f1[i], y = f2[i], color=\"b\")\n",
" else:\n",
" plt.scatter(x = f1[i], y = f2[i], marker='x')\n",
" \n",
" plt.xlabel(features[0])\n",
" plt.ylabel(features[1])\n",
"\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 255,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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AgOhFG2hYhwYAgPixDk2E7QYAAO0xKBgAAIw9Ag0AAIgegQYAAESPQAMAAKJH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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x15c89208>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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cUT/fB7htVP7/a3PfSj9+e9WPY8DVwNGjcOzmsH+lH7//Dbx/qn3o9NgNbecF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\nohheJElSUQwvkiSpKIYXSZJUlP8Phh99vFNfXqsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1be0e668>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x1be0e9b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"##Outlier detection through visualizations\n",
"plot_features(df, (\"salary\", \"bonus\", \"poi\"), True, True)\n",
"plot_features(df, (\"exercised_stock_options\", \"total_stock_value\", \"poi\"), False, False)\n",
"plot_features(df, [\"other\", \"total_stock_value\", \"poi\"], True, True) "
]
},
{
"cell_type": "code",
"execution_count": 256,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#outlier removal with a threshold function\n",
"def remove_outliers(df, feature=None, q = 0.99):\n",
" q = df[feature].quantile(q)\n",
" return df[df[feature]< q]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Feature Selection and Scaling"
]
},
{
"cell_type": "code",
"execution_count": 257,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"###select, scale, and update my_dataset with selected features\n",
"\n",
"### First, scaler can't handle NaN's, so we must impute values for them\n",
"### We try to do this with Pandas' built-in functions\n",
"### NB: I first attempted this with the sklearn.preprocessing.Imputer class, but\n",
"### the output invariably ended up with conflicting lengths--values were being dropped, \n",
"### perhaps due to use of a pandas series object rather than a numpy array.\n",
"\n",
"\n",
"for feature in df.columns:\n",
" df[feature].fillna(0, inplace=True)\n",
"\n",
"scaler = MinMaxScaler()\n",
"\n",
"df_scaled = df.copy()\n",
"df_scaled[df.columns] = scaler.fit_transform(df[df.columns])\n"
]
},
{
"cell_type": "code",
"execution_count": 258,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimal number of features : 5\n",
"[ True False False False False False True True True False False False\n",
" False True False False]\n",
"AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
" learning_rate=1.0, n_estimators=50, random_state=None)\n"
]
},
{
"data": {
"image/png": 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lvmby5JDN/dBDnnPhnEtP7Gm1knYmlAIR8IyZTUsysEI05zGMDz6AffeFq6+G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\nkVoCYpEGAFPTDiKPPwMjzex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"text/plain": [
"<matplotlib.figure.Figure at 0x1d326630>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimal number of features : 6\n",
"[False False True True False False False True True False True False\n",
" False False False True]\n",
"DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" presort=False, random_state=None, splitter='best')\n"
]
},
{
"data": {
"image/png": 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4pr8LMMLMOrxZltQdmA0cDrxCGPw3zMxmxvYZBJxpZoMl9QPGmFm/ovMcCpxn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\nZQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1b337b00>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimal number of features : 4\n",
"[ True False False True False True False True False False False False\n",
" False False False False]\n",
"RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
" max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n",
" verbose=0, warm_start=False)\n"
]
},
{
"data": {
"image/png": 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4881yR+JKbfHickewQpobuzPiPpK6B9gza31ZtC2xehiSDgbmmdlkSXWEOaya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\n/H98NOkSYrNnvwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1d9e6ba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### convert df to dictionary with appropriate keys (names of people)\n",
"my_dataset = df_scaled.to_dict(orient = \"index\")\n",
"\n",
"### Extract features and labels from dataset for local testing\n",
"data = featureFormat(my_dataset, features_list, sort_keys = True)\n",
"\n",
"labels, features = targetFeatureSplit(data)\n",
"\n",
"for i in range(len(labels)):\n",
" labels[i] = int(labels[i])\n",
"\n",
"classifiers = [AdaBoostClassifier(), \n",
" DecisionTreeClassifier(class_weight=None),\n",
" RandomForestClassifier(class_weight=None)\n",
" ]\n",
"\n",
"for clf in classifiers:\n",
" rfecv = RFECV(estimator=clf, step=1, cv=StratifiedKFold(labels, 50),\n",
" scoring='precision')\n",
" rfecv.fit(features, labels)\n",
" print \"Optimal number of features : %d\" % rfecv.n_features_\n",
" print rfecv.support_\n",
" print type(clf)\n",
" # Plot number of features VS. cross-validation scores\n",
" plt.figure()\n",
" plt.xlabel(\"Number of features selected\")\n",
" plt.ylabel(\"Cross validation score (nb of correct classifications)\")\n",
" plt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)\n",
" plt.show()\n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We take a hint from the recursive feature selection applied above, and use KBest to extract the suggested number of features for each classifier."
]
},
{
"cell_type": "code",
"execution_count": 263,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"### We create a graphing function that can evalute the effect of k in KBest on basic fit score of classifiers\n",
"\n",
"def graph_k_accuracy(kvalues = 1, clfs = []):\n",
" for clf in clfs:\n",
" plt.figure()\n",
" plt.xlabel(\"Number of KBest Features Used\")\n",
" plt.ylabel(\"Accuracy of classifier\")\n",
" scores = []\n",
" for k in range(1, kvalues + 1):\n",
" kbest = SelectKBest(k=k)\n",
" scaled_K = kbest.fit_transform(features, labels)\n",
" features_train, features_test, labels_train, labels_test = \\\n",
" train_test_split(scaled_K, labels, test_size = 0.3)\n",
" scores.append(clf.fit(features_train, labels_train).score(features_test, labels_test))\n",
" plt.plot(range(1, kvalues + 1), scores)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 264,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# split data\n",
"features_train, features_test, labels_train, labels_test = \\\n",
" train_test_split(features, labels, test_size = 0.3, random_state = 42)"
]
},
{
"cell_type": "code",
"execution_count": 268,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"GridSearchCV(cv=None, error_score='raise',\n",
" estimator=Pipeline(steps=[('skb', SelectKBest(k=10, score_func=<function f_classif at 0x000000000C5E5358>)), ('ada', AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
" learning_rate=1.0, n_estimators=50, random_state=None))]),\n",
" fit_params={}, iid=True, n_jobs=1,\n",
" param_grid=[{'skb__k': [5, 8], 'ada__n_estimators': [10, 50], 'ada__learning_rate': [0.25, 1.0]}],\n",
" pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
" scoring=None, verbose=0)\n",
"\tAccuracy: 0.86633\tPrecision: 0.49553\tRecall: 0.13850\tF1: 0.21649\tF2: 0.16182\n",
"\tTotal predictions: 15000\tTrue positives: 277\tFalse positives: 282\tFalse negatives: 1723\tTrue negatives: 12718\n",
"\n"
]
}
],
"source": [
"### Task 4: Try a varity of classifiers\n",
"### Please name your classifier clf for easy export below.\n",
"### Note that if you want to do PCA or other multi-stage operations,\n",
"### you'll need to use Pipelines. For more info:\n",
"### http://scikit-learn.org/stable/modules/pipeline.htm\n",
"\n",
"###AdaBoost trial\n",
" \n",
"param_grid = [{'ada__n_estimators':[10, 50],\n",
" 'ada__learning_rate':[0.25,1.0],\n",
" 'skb__k': [5,8]\n",
" }]\n",
"\n",
"pipe = Pipeline(steps=[(\"skb\", SelectKBest()),(\"ada\", AdaBoostClassifier())])\n",
"\n",
"clf = GridSearchCV(pipe, param_grid)\n",
"test_classifier(clf, my_dataset, features_list, folds = 1000)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 269,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"GridSearchCV(cv=None, error_score='raise',\n",
" estimator=Pipeline(steps=[('skb', SelectKBest(k=10, score_func=<function f_classif at 0x000000000C5E5358>)), ('tree', DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" presort=False, random_state=None, splitter='best'))]),\n",
" fit_params={}, iid=True, n_jobs=1,\n",
" param_grid=[{'tree__max_depth': [100, 50, 25], 'skb__k': [4, 6], 'tree__max_leaf_nodes': [None, 10, 25, 50], 'tree__class_weight': ['balanced', None]}],\n",
" pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
" scoring=None, verbose=0)\n",
"\tAccuracy: 0.81013\tPrecision: 0.26600\tRecall: 0.24100\tF1: 0.25289\tF2: 0.24562\n",
"\tTotal predictions: 15000\tTrue positives: 482\tFalse positives: 1330\tFalse negatives: 1518\tTrue negatives: 11670\n",
"\n"
]
}
],
"source": [
"### DecisionTree trial\n",
"\n",
"tree_grid = [{'tree__class_weight' : ['balanced', None],\n",
" 'tree__max_depth' : [100, 50, 25],\n",
" 'tree__max_leaf_nodes' : [None, 10, 25, 50],\n",
" 'skb__k': [4,6]}]\n",
"\n",
"pipe = Pipeline(steps=[(\"skb\", SelectKBest()), (\"tree\", DecisionTreeClassifier())])\n",
"\n",
"clf = GridSearchCV(pipe, param_grid=tree_grid)\n",
"\n",
"test_classifier(clf, my_dataset, features_list, folds = 1000)"
]
},
{
"cell_type": "code",
"execution_count": 284,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pipeline(steps=[('skb', SelectKBest(k=6, score_func=<function f_classif at 0x000000000C5E5358>)), ('tree', DecisionTreeClassifier(class_weight='balanced', criterion='gini',\n",
" max_depth=50, max_features=None, max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" presort=False, random_state=None, splitter='best'))])\n",
"14.69 total_stock_value\n",
"13.71 exercised_stock_options\n",
"11.20 salary\n",
"11.13 bonus\n",
"6.58 restricted_stock\n",
"5.91 expenses\n",
"5.50 shared_receipt_with_poi\n",
"5.30 deferred_income\n",
"3.59 from_poi_to_this_person\n",
"2.77 total_payments\n",
"2.61 long_term_incentive\n",
"2.14 from_this_person_to_poi\n",
"0.68 to_messages\n",
"0.26 deferral_payments\n",
"0.17 from_messages\n",
"0.01 other\n"
]
}
],
"source": [
"K_best = clf.best_estimator_.named_steps['skb']\n",
"print clf.best_estimator_\n",
"\n",
"features_scores = zip(K_best.scores_, features_list[1:])\n",
"features_scores = sorted(features_scores, key = lambda x: x[0], reverse=True)\n",
"for i, j in features_scores:\n",
" print '%.2f' %i, j\n"
]
},
{
"cell_type": "code",
"execution_count": 265,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"GridSearchCV(cv=None, error_score='raise',\n",
" estimator=Pipeline(steps=[('skb', SelectKBest(k=10, score_func=<function f_classif at 0x000000000C5E5358>)), ('forest', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
" max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_split=1e-...mators=10, n_jobs=1, oob_score=False, random_state=None,\n",
" verbose=0, warm_start=False))]),\n",
" fit_params={}, iid=True, n_jobs=1,\n",
" param_grid=[{'forest__class_weight': [None, 'balanced'], 'skb__k': [4], 'forest__max_depth': [None], 'forest__criterion': ['gini', 'entropy']}],\n",
" pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
" scoring=None, verbose=0)\n",
"\tAccuracy: 0.85820\tPrecision: 0.42131\tRecall: 0.17000\tF1: 0.24225\tF2: 0.19303\n",
"\tTotal predictions: 15000\tTrue positives: 340\tFalse positives: 467\tFalse negatives: 1660\tTrue negatives: 12533\n",
"\n"
]
}
],
"source": [
"### RandomForest Trial\n",
"\n",
"### I choose k values reflecting the highest peaks in the above performance vs k-value graphs, and as\n",
"### the runtime on this is already enormous without testing for every possible k-value\n",
"forest_grid = [{'forest__class_weight' : [None, 'balanced'],\n",
" 'forest__criterion':['gini', 'entropy'], \n",
" 'forest__max_depth':[None],\n",
" 'skb__k': [4],\n",
" }]\n",
"\n",
"pipe = Pipeline(steps=[(\"skb\", SelectKBest()),(\"forest\", RandomForestClassifier())])\n",
"\n",
"clf = GridSearchCV(pipe, param_grid=forest_grid)\n",
"\n",
"test_classifier(clf, my_dataset, features_list, folds = 1000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Classifier performance reevaluated, with the addition of some custom features\n",
"\n",
"Now, I add a few new features, and we test the performance of the classifiers with these additions. Hopefully, they are an improvement."
]
},
{
"cell_type": "code",
"execution_count": 285,
"metadata": {
"collapsed": true,
"scrolled": false
},
"outputs": [],
"source": [
"### Task 3: Create new feature(s)\n",
"\n",
"### As suggested in the quizzes, I will make a ratio of to/from POI : total emails.\n",
"### It stands to reason that POIs communicate together with greater relative frequency.\n",
"df_scaled[\"to_poi_ratio\"] = df_scaled[\"from_poi_to_this_person\"] / df_scaled[\"to_messages\"]\n",
"df_scaled[\"from_poi_ratio\"] = df_scaled[\"from_this_person_to_poi\"]/df_scaled['from_messages']\n",
"\n",
"###I add a similar feature, extending the logic to the email POIs were CC'ed on\n",
"df_scaled[\"receipt_with_poi_ratio\"] = df_scaled[\"shared_receipt_with_poi\"]/df_scaled['from_messages']\n",
"\n",
"#add our new features to the features list\n",
"features_list.append(\"to_poi_ratio\")\n",
"features_list.append(\"from_poi_ratio\")\n",
"features_list.append(\"receipt_with_poi_ratio\")\n",
"\n",
"#NaNs introduced, and targetFeatureSplit fails to replace them with 0's as advertised\n",
"df_scaled.fillna(0, inplace=True)\n",
"df_scaled.replace(np.inf, 0, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 286,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimal number of features : 9\n",
"[ True False False False True False True True True False True False\n",
" False True False False True True False] AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
" learning_rate=1.0, n_estimators=50, random_state=None)\n"
]
},
{
"data": {
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Qz3Ssqyyl1uEN0KkT7LgjvObtbVcB4s703l3S3yS9Er3eS9LPYxzXBrgFOBKoAkZJGthg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\nzrlYPGE455yLxROGc865WP4/ZTyIJPJ5W1wAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1b1a17b8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimal number of features : 19\n",
"[ True True True True True True True True True True True True\n",
" True True True True True True True] DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" presort=False, random_state=None, splitter='best')\n"
]
},
{
"data": {
"image/png": 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DL+d6V4OUe3ebWZGqFhhZVdTUiNiCt37R57E+MLtsfQ6pEOltn7nZtiXAC5Iu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\nZq2VZkjykHwD34cjYjtgT9I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"text/plain": [
"<matplotlib.figure.Figure at 0x1973c2b0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimal number of features : 9\n",
"[False False False True True True True True True False False False\n",
" False False True False False True True] RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
" max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n",
" verbose=0, warm_start=False)\n"
]
},
{
"data": {
"image/png": 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cli6FlVdOJkaXnJx3GGZWA5wVPf/aGwvXVCxeDB99FPIc5as5DnzX1MBpp8Ezz4RuqHwb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\n4U7tjeiarQk/r+yMxPXt0xq438w+B7A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"text/plain": [
"<matplotlib.figure.Figure at 0x1dd745c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"### we recreate the data dictionary with the new features included\n",
"my_dataset = df_scaled.to_dict(orient = \"index\")\n",
"\n",
"### Extract features and labels from dataset for local testing\n",
"data = featureFormat(my_dataset, features_list, sort_keys = True)\n",
"\n",
"labels, features = targetFeatureSplit(data)\n",
"\n",
"for i in range(len(labels)):\n",
" labels[i] = int(labels[i])\n",
"\n",
"classifiers = [AdaBoostClassifier(), \n",
" DecisionTreeClassifier(class_weight=None),\n",
" RandomForestClassifier(class_weight=None)\n",
" ]\n",
"\n",
"for clf in classifiers:\n",
" rfecv = RFECV(estimator=clf, step=1, cv=StratifiedKFold(labels, 50),\n",
" scoring='precision')\n",
" rfecv.fit(features, labels)\n",
" print \"Optimal number of features : %d\" % rfecv.n_features_\n",
" print rfecv.support_, clf\n",
" # features=features[:,rfecv.support_]\n",
" # Plot number of features VS. cross-validation scores\n",
" plt.figure()\n",
" plt.xlabel(\"Number of features selected\")\n",
" plt.ylabel(\"Cross validation score (nb of correct classifications)\")\n",
" plt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 287,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#resplit the data with new features included\n",
"features_train, features_test, labels_train, labels_test = \\\n",
" train_test_split(features, labels, test_size = 0.3, random_state = 42)"
]
},
{
"cell_type": "code",
"execution_count": 288,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"GridSearchCV(cv=None, error_score='raise',\n",
" estimator=Pipeline(steps=[('skb', SelectKBest(k=10, score_func=<function f_classif at 0x000000000C5E5358>)), ('ada', AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
" learning_rate=1.0, n_estimators=50, random_state=None))]),\n",
" fit_params={}, iid=True, n_jobs=1,\n",
" param_grid=[{'skb__k': [6, 9, 14], 'ada__n_estimators': [10, 50], 'ada__learning_rate': [0.25, 1.0]}],\n",
" pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
" scoring=None, verbose=0)\n",
"\tAccuracy: 0.85973\tPrecision: 0.43580\tRecall: 0.17650\tF1: 0.25125\tF2: 0.20034\n",
"\tTotal predictions: 15000\tTrue positives: 353\tFalse positives: 457\tFalse negatives: 1647\tTrue negatives: 12543\n",
"\n"
]
}
],
"source": [
"### Task 4 Trial 2: Try a varity of classifiers\n",
"\n",
"###AdaBoost Re-Trial\n",
"ada_grid = [{'ada__n_estimators':[10, 50],\n",
" 'ada__learning_rate':[0.25,1.0],\n",
" 'skb__k': [6, 9, 14]\n",
" }]\n",
"\n",
"pipe = Pipeline(steps=[(\"skb\", SelectKBest()),(\"ada\", AdaBoostClassifier())])\n",
"\n",
"clf = GridSearchCV(pipe, ada_grid)\n",
"\n",
"test_classifier(clf, my_dataset, features_list, folds = 1000)"
]
},
{
"cell_type": "code",
"execution_count": 295,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"GridSearchCV(cv=None, error_score='raise',\n",
" estimator=Pipeline(steps=[('skb', SelectKBest(k=10, score_func=<function f_classif at 0x000000000C5E5358>)), ('tree', DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" presort=False, random_state=None, splitter='best'))]),\n",
" fit_params={}, iid=True, n_jobs=1,\n",
" param_grid=[{'tree__max_depth': [50], 'skb__k': [8, 13, 19], 'tree__max_leaf_nodes': [None], 'tree__class_weight': [None, 'balanced']}],\n",
" pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
" scoring=None, verbose=0)\n",
"\tAccuracy: 0.82773\tPrecision: 0.33778\tRecall: 0.30400\tF1: 0.32000\tF2: 0.31020\n",
"\tTotal predictions: 15000\tTrue positives: 608\tFalse positives: 1192\tFalse negatives: 1392\tTrue negatives: 11808\n",
"\n"
]
}
],
"source": [
"### DT Re-Trial with new k value\n",
"tree_grid = [{'tree__class_weight' : [None, 'balanced'],\n",
" 'tree__max_depth' : [50],\n",
" 'tree__max_leaf_nodes' : [None],\n",
" 'skb__k': [8, 13, 19]}]\n",
"\n",
"pipe = Pipeline(steps=[(\"skb\", SelectKBest()), (\"tree\", DecisionTreeClassifier())])\n",
"\n",
"clf = GridSearchCV(pipe, tree_grid)\n",
"\n",
"test_classifier(clf, my_dataset, features_list, folds = 1000)"
]
},
{
"cell_type": "code",
"execution_count": 296,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pipeline(steps=[('skb', SelectKBest(k=8, score_func=<function f_classif at 0x000000000C5E5358>)), ('tree', DecisionTreeClassifier(class_weight='balanced', criterion='gini',\n",
" max_depth=50, max_features=None, max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" presort=False, random_state=None, splitter='best'))])\n",
"14.69 total_stock_value\n",
"13.71 exercised_stock_options\n",
"11.20 salary\n",
"11.13 bonus\n",
"8.24 from_poi_ratio\n",
"6.58 restricted_stock\n",
"5.91 expenses\n",
"5.50 shared_receipt_with_poi\n",
"5.30 deferred_income\n",
"3.59 from_poi_to_this_person\n",
"2.77 total_payments\n",
"2.61 long_term_incentive\n",
"2.43 receipt_with_poi_ratio\n",
"2.35 to_poi_ratio\n",
"2.14 from_this_person_to_poi\n",
"0.68 to_messages\n",
"0.26 deferral_payments\n",
"0.17 from_messages\n",
"0.01 other\n"
]
}
],
"source": [
"K_best = clf.best_estimator_.named_steps['skb']\n",
"print clf.best_estimator_\n",
"\n",
"features_scores = zip(K_best.scores_, features_list[1:])\n",
"features_scores = sorted(features_scores, key = lambda x: x[0], reverse=True)\n",
"for i, j in features_scores:\n",
" print '%.2f' %i, j"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"### RandomForest Re-Trial with new k-value and new features\n",
"### We learn from the previous iteration some of the parameters that seem to work best\n",
"\n",
"forest_grid = [{'forest__class_weight' : [None],\n",
" 'forest__criterion':['gini'], \n",
" 'forest__max_depth':[None],\n",
" 'forest__n_estimators': [10],\n",
" 'skb__k': [9],\n",
" }]\n",
"\n",
"pipe = Pipeline(steps=[(\"skb\", SelectKBest()),(\"forest\", RandomForestClassifier())])\n",
"\n",
"clf = GridSearchCV(pipe, param_grid=forest_grid)\n",
"\n",
"test_classifier(clf, my_dataset, features_list, folds = 1000)"
]
},
{
"cell_type": "code",
"execution_count": 312,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"GridSearchCV(cv=None, error_score='raise',\n",
" estimator=Pipeline(steps=[('skb', SelectKBest(k=10, score_func=<function f_classif at 0x000000000C5E5358>)), ('tree', DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" presort=False, random_state=None, splitter='best'))]),\n",
" fit_params={}, iid=True, n_jobs=1,\n",
" param_grid=[{'tree__max_depth': [50], 'skb__k': [19], 'tree__max_leaf_nodes': [None], 'tree__class_weight': ['balanced']}],\n",
" pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
" scoring=None, verbose=0)\n",
"\tAccuracy: 0.83773\tPrecision: 0.39139\tRecall: 0.39100\tF1: 0.39120\tF2: 0.39108\n",
"\tTotal predictions: 15000\tTrue positives: 782\tFalse positives: 1216\tFalse negatives: 1218\tTrue negatives: 11784\n",
"\n"
]
}
],
"source": [
"### Task 5: Tune your classifier to achieve better than .3 precision and recall \n",
"### using our testing script. Check the tester.py script in the final project\n",
"### folder for details on the evaluation method, especially the test_classifier\n",
"### function. Because of the small size of the dataset, the script uses\n",
"### stratified shuffle split cross validation. For more info: \n",
"### http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.StratifiedShuffleSplit.html\n",
"\n",
"tree_grid = [{'tree__class_weight' : ['balanced'],\n",
" 'tree__max_depth' : [50],\n",
" 'tree__max_leaf_nodes' : [None],\n",
" 'skb__k': [19]\n",
" }]\n",
"\n",
"pipe = Pipeline(steps = [('skb', SelectKBest()), ('tree', DecisionTreeClassifier())])\n",
"\n",
"clf = GridSearchCV(pipe, param_grid = tree_grid)\n",
"\n",
"test_classifier(clf, my_dataset, features_list, folds = 1000)"
]
},
{
"cell_type": "code",
"execution_count": 308,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pipeline(steps=[('skb', SelectKBest(k=8, score_func=<function f_classif at 0x000000000C5E5358>)), ('tree', DecisionTreeClassifier(class_weight='balanced', criterion='gini',\n",
" max_depth=50, max_features=None, max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" presort=False, random_state=None, splitter='best'))])\n"
]
}
],
"source": [
"print clf.best_estimator_"
]
},
{
"cell_type": "code",
"execution_count": 313,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"### Task 6: Dump your classifier, dataset, and features_list so anyone can\n",
"### check your results. You do not need to change anything below, but make sure\n",
"### that the version of poi_id.py that you submit can be run on its own and\n",
"### generates the necessary .pkl files for validating your results.\n",
"dump_classifier_and_data(clf, my_dataset, features_list)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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