{ "metadata": { "name": "", "signature": "sha256:78f7e906a80e89832797a44cb1653eda3bdb2506692456aa8f3d7e6d04d18b63" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Fetch the data and load it in pandas" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import os\n", "from urllib.request import urlretrieve\n", "\n", "url = (\"https://archive.ics.uci.edu/ml/machine-learning-databases\"\n", " \"/adult/adult.data\")\n", "local_filename = os.path.basename(url)\n", "if not os.path.exists(local_filename):\n", " print(\"Downloading Adult Census datasets from UCI\")\n", " urlretrieve(url, local_filename)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "names = (\"age, workclass, fnlwgt, education, education-num, \"\n", " \"marital-status, occupation, relationship, race, sex, \"\n", " \"capital-gain, capital-loss, hours-per-week, \"\n", " \"native-country, income\").split(', ') \n", "data = pd.read_csv(local_filename, names=names)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "data.head()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipracesexcapital-gaincapital-losshours-per-weeknative-countryincome
0 39 State-gov 77516 Bachelors 13 Never-married Adm-clerical Not-in-family White Male 2174 0 40 United-States <=50K
1 50 Self-emp-not-inc 83311 Bachelors 13 Married-civ-spouse Exec-managerial Husband White Male 0 0 13 United-States <=50K
2 38 Private 215646 HS-grad 9 Divorced Handlers-cleaners Not-in-family White Male 0 0 40 United-States <=50K
3 53 Private 234721 11th 7 Married-civ-spouse Handlers-cleaners Husband Black Male 0 0 40 United-States <=50K
4 28 Private 338409 Bachelors 13 Married-civ-spouse Prof-specialty Wife Black Female 0 0 40 Cuba <=50K
\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ " age workclass fnlwgt education education-num \\\n", "0 39 State-gov 77516 Bachelors 13 \n", "1 50 Self-emp-not-inc 83311 Bachelors 13 \n", "2 38 Private 215646 HS-grad 9 \n", "3 53 Private 234721 11th 7 \n", "4 28 Private 338409 Bachelors 13 \n", "\n", " marital-status occupation relationship race sex \\\n", "0 Never-married Adm-clerical Not-in-family White Male \n", "1 Married-civ-spouse Exec-managerial Husband White Male \n", "2 Divorced Handlers-cleaners Not-in-family White Male \n", "3 Married-civ-spouse Handlers-cleaners Husband Black Male \n", "4 Married-civ-spouse Prof-specialty Wife Black Female \n", "\n", " capital-gain capital-loss hours-per-week native-country income \n", "0 2174 0 40 United-States <=50K \n", "1 0 0 13 United-States <=50K \n", "2 0 0 40 United-States <=50K \n", "3 0 0 40 United-States <=50K \n", "4 0 0 40 Cuba <=50K " ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": 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"text": [ "" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "data.hist('hours-per-week', bins=15);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "age = data['age']\n", "bins = np.linspace(age.min(), age.max(), 15)\n", "\n", "younger = data[age < 25]\n", "middle = data[(age > 25) & (age < 50)]\n", "older = data[age > 50]\n", "\n", "plt.hist(younger['hours-per-week'].values,\n", " bins=bins, label=\"younger\", alpha=0.2)\n", "plt.hist(middle['hours-per-week'].values,\n", " bins=bins, label=\"middle age\", alpha=0.2)\n", "plt.hist(older['hours-per-week'].values,\n", " bins=bins, label=\"older\", alpha=0.2)\n", "plt.legend();" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "data.plot(x='age', y='hours-per-week', kind='scatter',\n", " alpha=0.02, s=50);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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pMDXVZGlpQBhKbFtlfr5Fr7fKcOgyNWWjqipffLEMWLRaLQCWllYwTYXDh/fh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\nCkHQp9U6xXD4INPTc7iug6ouEsfzrK6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ZTyQSo1jMAANks404jsO8eQUkSWFgwA3OtrQU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dAAAg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WYBgFUCMINAxjh337XqBWq5HnGUnSB1w0LaLdzsrJdxd+m6LrGZJkousapjnBMBx03cT3PVQ1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\n5R6uW5o+qIx9R6Le5by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"text": [ "" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "data.groupby('income')['income'].count()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "income\n", " <=50K 24720\n", " >50K 7841\n", "Name: income, dtype: int64" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "np.mean(data['income'] == ' >50K')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "0.24080216202935936" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "data['income'].unique()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "array([' <=50K', ' >50K', nan], dtype=object)" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "data = data.dropna()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "data['income'].unique()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "array([' <=50K', ' >50K'], dtype=object)" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "target_names = data['income'].unique()\n", "target_names" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "array([' <=50K', ' >50K'], dtype=object)" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "low_income = data[data['income'] == ' <=50K']\n", "high_income = data[data['income'] == ' >50K']\n", "\n", "plt.scatter(low_income['age'], low_income['hours-per-week'],\n", " alpha=0.03, s=50, c='b', label='<=50K');\n", "plt.scatter(high_income['age'], high_income['hours-per-week'],\n", " alpha=0.03, s=50, c='g', label='>50K');\n", "plt.legend()\n", "plt.xlabel('age'); plt.ylabel('hours-per-week');" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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iaYIsmwZ+m48OahiShU6btcEIy26h6ZIsLc+N4jHHO22UUkRRjBDlgKqUwrYt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Ddnsk9Bc0TcGz558yWT5k6BosO6QoS+bBDIPg1e0dAoPrLxm0jRsZEDn3twVt33B/+5z3v3WO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\nPC/CGEGe91RVjTFjnh8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"text": [ "" ] } ], "prompt_number": 16 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Building predictive models" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data_encoded = data.apply(lambda x: pd.factorize(x)[0])\n", "data_encoded.head(5)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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\n", "
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 17, "text": [ " age workclass fnlwgt education education-num marital-status \\\n", "0 0 0 0 0 0 0 \n", "1 1 1 1 0 0 1 \n", "2 2 2 2 1 1 2 \n", "3 3 2 3 2 2 1 \n", "4 4 2 4 0 0 1 \n", "\n", " occupation relationship race sex capital-gain capital-loss \\\n", "0 0 0 0 0 0 0 \n", "1 1 1 0 0 1 0 \n", "2 2 0 0 0 1 0 \n", "3 2 1 1 0 1 0 \n", "4 3 2 1 1 1 0 \n", "\n", " hours-per-week native-country income \n", "0 0 0 0 \n", "1 1 0 0 \n", "2 0 0 0 \n", "3 0 0 0 \n", "4 0 1 0 " ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "features = data_encoded.drop('income', axis=1)\n", "X = features.values.astype(np.float32)\n", "y = (data['income'].values == ' >50K').astype(np.int32)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "np.mean(y)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "0.24080955744602439" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.cross_validation import train_test_split\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X, y, test_size=0.2, random_state=0)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.cross_validation import cross_val_score\n", "\n", "clf = DecisionTreeClassifier(max_depth=8)\n", "\n", "scores = cross_val_score(clf, X_train, y_train, cv=5, scoring='roc_auc')\n", "print(\"ROC AUC Decision Tree: {:.4f} +/-{:.4f}\".format(\n", " np.mean(scores), np.std(scores)))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "ROC AUC Decision Tree: 0.8811 +/-0.0042\n" ] } ], "prompt_number": 21 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Model error analysis" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.learning_curve import learning_curve\n", "\n", "\n", "def plot_learning_curve(estimator, X, y, ylim=(0, 1.1), cv=5,\n", " n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 5),\n", " scoring=None):\n", " plt.title(\"Learning curves for %s\" % type(estimator).__name__)\n", " plt.ylim(*ylim); plt.grid()\n", " plt.xlabel(\"Training examples\")\n", " plt.ylabel(\"Score\")\n", " train_sizes, train_scores, test_scores = learning_curve(\n", " estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes,\n", " scoring=scoring)\n", " train_scores_mean = np.mean(train_scores, axis=1)\n", " test_scores_mean = np.mean(test_scores, axis=1)\n", "\n", " plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n", " label=\"Training score\")\n", " plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n", " label=\"Cross-validation score\")\n", " plt.legend(loc=\"best\")\n", " print(\"Best test score: {:.4f}\".format(test_scores_mean[-1]))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "clf = DecisionTreeClassifier(max_depth=30)\n", "plot_learning_curve(clf, X_train, y_train, scoring='roc_auc')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Best test score: 0.7478\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "clf = DecisionTreeClassifier(max_depth=15)\n", "plot_learning_curve(clf, X_train, y_train, scoring='roc_auc')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Best test score: 0.8430\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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qoarGOG4FhgHbgY5Ab1UtF5HjgDmq+pNaq6SNcRhjTMTidc/xM3DOolqgqvvd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\nB7Rbt256+PBhVXXurnj66af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"text": [ "" ] } ], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "clf = DecisionTreeClassifier(max_depth=8)\n", "plot_learning_curve(clf, X_train, y_train, scoring='roc_auc')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Best test score: 0.8813\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.learning_curve import validation_curve\n", "\n", "\n", "def plot_validation_curve(estimator, X, y, param_name, param_range,\n", " ylim=(0, 1.1), cv=5, n_jobs=-1, scoring=None):\n", " estimator_name = type(estimator).__name__\n", " plt.title(\"Validation curves for %s on %s\"\n", " % (param_name, estimator_name))\n", " plt.ylim(*ylim); plt.grid()\n", " plt.xlim(min(param_range), max(param_range))\n", " plt.xlabel(param_name)\n", " plt.ylabel(\"Score\")\n", "\n", " train_scores, test_scores = validation_curve(\n", " estimator, X, y, param_name, param_range,\n", " cv=cv, n_jobs=n_jobs, scoring=scoring)\n", "\n", " train_scores_mean = np.mean(train_scores, axis=1)\n", " test_scores_mean = np.mean(test_scores, axis=1)\n", " plt.semilogx(param_range, train_scores_mean, 'o-', color=\"r\",\n", " label=\"Training score\")\n", " plt.semilogx(param_range, test_scores_mean, 'o-', color=\"g\",\n", " label=\"Cross-validation score\")\n", " plt.legend(loc=\"best\")\n", " print(\"Best test score: {:.4f}\".format(test_scores_mean[-1]))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "clf = DecisionTreeClassifier(max_depth=8)\n", "param_name = 'max_depth'\n", "param_range = [1, 2, 4, 8, 16, 32]\n", "\n", "plot_validation_curve(clf, X_train, y_train,\n", " param_name, param_range, scoring='roc_auc')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Best test score: 0.7426\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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ULTA0FsXF7PrkfTI+eIZ5uz7lo3YFHMlUjqaXTZryTgI5WXllJxhTB6wpKXiK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\nYDDGGOPDAoMxxhgfFhiMMcb4+P8BrWnD2OxO2M4AAAAASUVORK5CYII=\n", "text": [ "" ] } ], "prompt_number": 27 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Gradient Boosted Decision Trees" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.ensemble import RandomForestClassifier\n", "\n", "clf = RandomForestClassifier(n_estimators=30, max_features=10,\n", " max_depth=10)\n", "\n", "scores = cross_val_score(clf, X_train, y_train, cv=5, scoring='roc_auc',\n", " n_jobs=-1)\n", "print(\"ROC Random Forest: {:.4f} +/-{:.4f}\".format(\n", " np.mean(scores), np.std(scores)))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "ROC Random Forest: 0.8997 +/-0.0038\n" ] } ], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.ensemble import GradientBoostingClassifier\n", "\n", "clf = GradientBoostingClassifier(max_leaf_nodes=5, learning_rate=0.1,\n", " n_estimators=100)\n", "\n", "scores = cross_val_score(clf, X_train, y_train, cv=5, scoring='roc_auc',\n", " n_jobs=-1)\n", "print(\"ROC AUC Gradient Boosted Trees: {:.4f} +/-{:.4f}\".format(\n", " np.mean(scores), np.std(scores)))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "ROC AUC Gradient Boosted Trees: 0.8987 +/-0.0044\n" ] } ], "prompt_number": 29 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Evaluation of the best model" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%%time\n", "_ = clf.fit(X_train, y_train)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "CPU times: user 2.5 s, sys: 17.6 ms, total: 2.52 s\n", "Wall time: 2.52 s\n" ] } ], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.metrics import roc_auc_score\n", "\n", "y_pred_proba = clf.predict_proba(X_test)[:, 1]\n", "print(\"ROC AUC: %0.4f\" % roc_auc_score(y_test, y_pred_proba))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "ROC AUC: 0.8961\n" ] } ], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.metrics import classification_report\n", "\n", "y_pred = clf.predict(X_test)\n", "print(classification_report(y_test, y_pred, target_names=target_names))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " precision recall f1-score support\n", "\n", " <=50K 0.87 0.94 0.90 4918\n", " >50K 0.76 0.57 0.65 1595\n", "\n", "avg / total 0.84 0.85 0.84 6513\n", "\n" ] } ], "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.metrics import precision_recall_curve\n", "\n", "precision_gb, recall_gb, _ = precision_recall_curve(y_test, y_pred_proba)\n", "\n", "plt.plot(precision_gb, recall_gb)\n", "plt.xlabel('precision')\n", "plt.ylabel('recall')\n", "plt.title('Precision / Recall curve');" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 33 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Variable importances" ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.figure(figsize=(10, 5))\n", "\n", "ordering = np.argsort(clf.feature_importances_)[::-1]\n", "\n", "importances = clf.feature_importances_[ordering]\n", "feature_names = features.columns[ordering]\n", "\n", "x = np.arange(len(feature_names))\n", "plt.bar(x, importances)\n", "plt.xticks(x + 0.5, feature_names, rotation=90, fontsize=15);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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0w6nKqxNt1DipMjOzPuq2fDAbNZ7+MzMzM2uAkyozMzOzBjipMjMzM2uAkyozMzOzBjip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\nAEkrVZxiO67E0atY+Y0M3uakphXJmqF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"text": [ "" ] } ], "prompt_number": 34 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Using the boosted trees to extract features for a Logistic Regression model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following is an implementation of a trick found in:\n", "\n", "Practical Lessons from Predicting Clicks on Ads at Facebook\n", "Junfeng Pan, He Xinran, Ou Jin, Tianbing XU, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, Joaquin Qui\u00f1onero Candela\n", "International Workshop on Data Mining for Online Advertising (ADKDD)\n", "\n", "https://www.facebook.com/publications/329190253909587/" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.base import BaseEstimator, TransformerMixin, clone\n", "from sklearn.preprocessing import LabelBinarizer\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.linear_model import LogisticRegressionCV\n", "from sklearn.pipeline import make_pipeline\n", "from scipy.sparse import hstack\n", "\n", "\n", "class TreeTransform(BaseEstimator, TransformerMixin):\n", " \"\"\"One-hot encode samples with an ensemble of trees\n", " \n", " This transformer first fits an ensemble of trees (e.g. gradient\n", " boosted trees or a random forest) on the training set.\n", "\n", " Then each leaf of each tree in the ensembles is assigned a fixed\n", " arbitrary feature index in a new feature space. If you have 100\n", " trees in the ensemble and 2**3 leafs per tree, the new feature\n", " space has 100 * 2**3 == 800 dimensions.\n", " \n", " Each sample of the training set go through the decisions of each tree\n", " of the ensemble and ends up in one leaf per tree. The sample if encoded\n", " by setting features with those leafs to 1 and letting the other feature\n", " values to 0.\n", " \n", " The resulting transformer learn a supervised, sparse, high-dimensional\n", " categorical embedding of the data.\n", " \n", " This transformer is typically meant to be pipelined with a linear model\n", " such as logistic regression, linear support vector machines or\n", " elastic net regression.\n", " \n", " \"\"\"\n", "\n", " def __init__(self, estimator):\n", " self.estimator = estimator\n", " \n", " def fit(self, X, y):\n", " self.fit_transform(X, y)\n", " return self\n", " \n", " def fit_transform(self, X, y):\n", " self.estimator_ = clone(self.estimator)\n", " self.estimator_.fit(X, y)\n", " self.binarizers_ = []\n", " sparse_applications = []\n", " estimators = np.asarray(self.estimator_.estimators_).ravel()\n", " for t in estimators:\n", " lb = LabelBinarizer(sparse_output=True)\n", " sparse_applications.append(lb.fit_transform(t.tree_.apply(X)))\n", " self.binarizers_.append(lb)\n", " return hstack(sparse_applications)\n", " \n", " def transform(self, X, y=None):\n", " sparse_applications = []\n", " estimators = np.asarray(self.estimator_.estimators_).ravel()\n", " for t, lb in zip(estimators, self.binarizers_):\n", " sparse_applications.append(lb.transform(t.tree_.apply(X)))\n", " return hstack(sparse_applications)\n", "\n", "\n", "boosted_trees = GradientBoostingClassifier(\n", " max_leaf_nodes=5, learning_rate=0.1,\n", " n_estimators=100, random_state=0,\n", ") \n", "\n", "pipeline = make_pipeline(\n", " TreeTransform(boosted_trees),\n", " LogisticRegression(C=1)\n", ")\n", "\n", "pipeline.fit(X_train, y_train);" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "y_pred_proba = pipeline.predict_proba(X_test)[:, 1]\n", "print(\"ROC AUC: %0.4f\" % roc_auc_score(y_test, y_pred_proba))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "ROC AUC: 0.9084\n" ] } ], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "y_pred = pipeline.predict(X_test)\n", "print(classification_report(y_test, y_pred, target_names=target_names))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " precision recall f1-score support\n", "\n", " <=50K 0.89 0.94 0.91 4918\n", " >50K 0.76 0.63 0.69 1595\n", "\n", "avg / total 0.86 0.86 0.86 6513\n", "\n" ] } ], "prompt_number": 37 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.metrics import precision_recall_curve\n", "\n", "precision_gb_lr, recall_gb_lr, _ = precision_recall_curve(\n", " y_test, y_pred_proba)\n", "\n", "plt.plot(precision_gb, recall_gb, label='GBT')\n", "plt.plot(precision_gb_lr, recall_gb_lr, label='GBT + LR')\n", "plt.xlabel('precision')\n", "plt.ylabel('recall')\n", "plt.title('Precision / Recall curve')\n", "plt.legend();" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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