{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# pipeline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2017-02-06T00:59:11.413751", "start_time": "2017-02-06T00:59:10.779398" }, "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2017-02-06T01:02:41.135896", "start_time": "2017-02-06T01:02:41.106099" }, "collapsed": false }, "outputs": [], "source": [ "from sklearn import datasets\n", "from sklearn import model_selection\n", "titanic = sns.load_dataset(\"titanic\")\n", "X_train, X_test, y_train, y_test = model_selection.train_test_split(titanic.drop(\"survived\", axis=1), titanic.survived, test_size=0.3)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2017-02-06T01:03:36.167086", "start_time": "2017-02-06T01:03:36.152178" }, "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 623 entries, 308 to 579\n", "Data columns (total 14 columns):\n", "pclass 623 non-null int64\n", "sex 623 non-null object\n", "age 511 non-null float64\n", "sibsp 623 non-null int64\n", "parch 623 non-null int64\n", "fare 623 non-null float64\n", "embarked 621 non-null object\n", "class 623 non-null category\n", "who 623 non-null object\n", "adult_male 623 non-null bool\n", "deck 146 non-null category\n", "embark_town 621 non-null object\n", "alive 623 non-null object\n", "alone 623 non-null bool\n", "dtypes: bool(2), category(2), float64(2), int64(3), object(5)\n", "memory usage: 56.1+ KB\n" ] } ], "source": [ "X_train.info()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2017-02-06T01:06:10.593096", "start_time": "2017-02-06T01:06:10.522780" }, "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " pclass sex age sibsp parch fare embarked class who \\\n", "308 2 male 30.0 1 0 24.0 C Second man \n", "417 2 female 18.0 0 2 13.0 S Second woman \n", "612 3 female NaN 1 0 15.5 Q Third woman \n", "641 1 female 24.0 0 0 69.3 C First woman \n", "853 1 female 16.0 0 1 39.4 S First woman \n", "\n", " adult_male deck embark_town alive alone \n", "308 True NaN Cherbourg no False \n", "417 False NaN Southampton yes False \n", "612 False NaN Queenstown yes False \n", "641 False B Cherbourg yes True \n", "853 False D Southampton yes False " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.head()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2017-02-06T01:05:49.111234", "start_time": "2017-02-06T01:05:49.091437" }, "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "zip argument #1 must support iteration", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m pipeline.Pipeline(\n\u001b[1;32m 4\u001b[0m [\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mpreprocessing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m ]\n\u001b[1;32m 7\u001b[0m )\n", "\u001b[0;32m/Users/knt/.pyenv/versions/anaconda3-4.2.0/lib/python3.5/site-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, steps)\u001b[0m\n\u001b[1;32m 151\u001b[0m \u001b[0;31m# shallow copy of steps\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msteps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtosequence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_steps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 155\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/knt/.pyenv/versions/anaconda3-4.2.0/lib/python3.5/site-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36m_validate_steps\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_validate_steps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 184\u001b[0;31m \u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mestimators\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;31m# validate names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: zip argument #1 must support iteration" ] } ], "source": [ "from sklearn import pipeline\n", "from sklearn import preprocessing\n", "pipeline.Pipeline(\n", " [\n", " preprocessing.LabelEncoder()\n", " ]\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2017-02-21T01:27:23.187255", "start_time": "2017-02-21T01:27:22.744076" }, "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "GridSearchCV(cv=3, error_score='raise',\n", " estimator=Pipeline(steps=[('scale', StandardScaler(copy=True, with_mean=True, with_std=True)), ('pca', PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,\n", " svd_solver='auto', tol=0.0, whiten=False)), ('svc', SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", " decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n", " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", " tol=0.001, verbose=False))]),\n", " fit_params={}, iid=True, n_jobs=1,\n", " param_grid={'svc__kernel': ('linear', 'rbf'), 'svc__C': (0.1, 0.5, 1.0, 1.5), 'pca__n_components': (2, 3)},\n", " pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n", " scoring='accuracy', verbose=0)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "from sklearn import datasets\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.decomposition import PCA\n", "from sklearn.svm import SVC\n", "from sklearn.model_selection import GridSearchCV\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.model_selection import cross_val_score\n", "\n", "\n", "iris = datasets.load_iris()\n", "pipepline = Pipeline([\n", " (\"scale\", StandardScaler()),\n", " (\"pca\", PCA()),\n", " (\"svc\", SVC()),\n", "])\n", "params = {\n", " \"pca__n_components\": (2,3),\n", " \"svc__C\": (0.1, 0.5, 1.0, 1.5),\n", " \"svc__kernel\": (\"linear\", \"rbf\"),\n", "}\n", "grid_cv=GridSearchCV(pipepline, params, cv=3, scoring=\"accuracy\")\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " iris.data, iris.target, test_size=0.3, random_state=np.random.RandomState(0))\n", "\n", "grid_cv.fit(X_train, y_train)\n", "#scores = cross_val_score(grid_cv, X_train, y_train, cv=5)\n", "#scores\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2017-02-21T01:27:46.854827", "start_time": "2017-02-21T01:27:46.849380" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.95238095238095233" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid_cv.best_score_\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2017-02-21T01:27:42.085874", "start_time": "2017-02-21T01:27:42.079358" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Pipeline(steps=[('scale', StandardScaler(copy=True, with_mean=True, with_std=True)), ('pca', PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,\n", " svd_solver='auto', tol=0.0, whiten=False)), ('svc', SVC(C=1.5, cache_size=200, class_weight=None, coef0=0.0,\n", " decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n", " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", " tol=0.001, verbose=False))])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid_cv.best_estimator_\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2017-02-21T01:27:44.838401", "start_time": "2017-02-21T01:27:44.831993" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'pca__n_components': 3, 'svc__C': 1.5, 'svc__kernel': 'rbf'}" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid_cv.best_params_\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2017-02-21T01:33:02.978776", "start_time": "2017-02-21T01:33:02.972692" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "15" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid_cv.best_index_" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2017-02-21T01:30:10.470858", "start_time": "2017-02-21T01:30:10.457517" }, "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/knt/.pyenv/versions/anaconda3-4.2.0/lib/python3.5/site-packages/sklearn/model_selection/_search.py:667: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", " DeprecationWarning)\n" ] }, { "data": { "text/plain": [ "[mean: 0.85714, std: 0.07686, params: {'svc__kernel': 'linear', 'svc__C': 0.1, 'pca__n_components': 2},\n", " mean: 0.83810, std: 0.02339, params: {'svc__kernel': 'rbf', 'svc__C': 0.1, 'pca__n_components': 2},\n", " mean: 0.86667, std: 0.06819, params: {'svc__kernel': 'linear', 'svc__C': 0.5, 'pca__n_components': 2},\n", " mean: 0.86667, std: 0.06819, params: {'svc__kernel': 'rbf', 'svc__C': 0.5, 'pca__n_components': 2},\n", " mean: 0.87619, std: 0.03329, params: {'svc__kernel': 'linear', 'svc__C': 1.0, 'pca__n_components': 2},\n", " mean: 0.88571, std: 0.02185, params: {'svc__kernel': 'rbf', 'svc__C': 1.0, 'pca__n_components': 2},\n", " mean: 0.88571, std: 0.02185, params: {'svc__kernel': 'linear', 'svc__C': 1.5, 'pca__n_components': 2},\n", " mean: 0.87619, std: 0.03329, params: {'svc__kernel': 'rbf', 'svc__C': 1.5, 'pca__n_components': 2},\n", " mean: 0.89524, std: 0.06433, params: {'svc__kernel': 'linear', 'svc__C': 0.1, 'pca__n_components': 3},\n", " mean: 0.85714, std: 0.00334, params: {'svc__kernel': 'rbf', 'svc__C': 0.1, 'pca__n_components': 3},\n", " mean: 0.94286, std: 0.02240, params: {'svc__kernel': 'linear', 'svc__C': 0.5, 'pca__n_components': 3},\n", " mean: 0.91429, std: 0.02212, params: {'svc__kernel': 'rbf', 'svc__C': 0.5, 'pca__n_components': 3},\n", " mean: 0.93333, std: 0.02704, params: {'svc__kernel': 'linear', 'svc__C': 1.0, 'pca__n_components': 3},\n", " mean: 0.94286, std: 0.04539, params: {'svc__kernel': 'rbf', 'svc__C': 1.0, 'pca__n_components': 3},\n", " mean: 0.93333, std: 0.02704, params: {'svc__kernel': 'linear', 'svc__C': 1.5, 'pca__n_components': 3},\n", " mean: 0.95238, std: 0.03497, params: {'svc__kernel': 'rbf', 'svc__C': 1.5, 'pca__n_components': 3}]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid_cv.grid_scores_" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2017-02-21T01:29:24.078423", "start_time": "2017-02-21T01:29:24.032985" }, "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "{'mean_fit_time': array([ 0.01003496, 0.00146937, 0.00133928, 0.0015343 , 0.00181842,\n", " 0.00131806, 0.00118907, 0.00153232, 0.00192833, 0.00210055,\n", " 0.00125098, 0.00170763, 0.00132998, 0.00135334, 0.00126402,\n", " 0.00159899]),\n", " 'mean_score_time': array([ 0.00115132, 0.00100772, 0.00045204, 0.00046635, 0.00045633,\n", " 0.00044831, 0.00042756, 0.00055734, 0.00074673, 0.00062307,\n", " 0.00050004, 0.00058532, 0.00043869, 0.00047898, 0.0004584 ,\n", " 0.00052667]),\n", " 'mean_test_score': array([ 0.85714286, 0.83809524, 0.86666667, 0.86666667, 0.87619048,\n", " 0.88571429, 0.88571429, 0.87619048, 0.8952381 , 0.85714286,\n", " 0.94285714, 0.91428571, 0.93333333, 0.94285714, 0.93333333,\n", " 0.95238095]),\n", " 'mean_train_score': array([ 0.91889987, 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" 'params': ({'pca__n_components': 2, 'svc__C': 0.1, 'svc__kernel': 'linear'},\n", " {'pca__n_components': 2, 'svc__C': 0.1, 'svc__kernel': 'rbf'},\n", " {'pca__n_components': 2, 'svc__C': 0.5, 'svc__kernel': 'linear'},\n", " {'pca__n_components': 2, 'svc__C': 0.5, 'svc__kernel': 'rbf'},\n", " {'pca__n_components': 2, 'svc__C': 1.0, 'svc__kernel': 'linear'},\n", " {'pca__n_components': 2, 'svc__C': 1.0, 'svc__kernel': 'rbf'},\n", " {'pca__n_components': 2, 'svc__C': 1.5, 'svc__kernel': 'linear'},\n", " {'pca__n_components': 2, 'svc__C': 1.5, 'svc__kernel': 'rbf'},\n", " {'pca__n_components': 3, 'svc__C': 0.1, 'svc__kernel': 'linear'},\n", " {'pca__n_components': 3, 'svc__C': 0.1, 'svc__kernel': 'rbf'},\n", " {'pca__n_components': 3, 'svc__C': 0.5, 'svc__kernel': 'linear'},\n", " {'pca__n_components': 3, 'svc__C': 0.5, 'svc__kernel': 'rbf'},\n", " {'pca__n_components': 3, 'svc__C': 1.0, 'svc__kernel': 'linear'},\n", " {'pca__n_components': 3, 'svc__C': 1.0, 'svc__kernel': 'rbf'},\n", " {'pca__n_components': 3, 'svc__C': 1.5, 'svc__kernel': 'linear'},\n", " {'pca__n_components': 3, 'svc__C': 1.5, 'svc__kernel': 'rbf'}),\n", " 'rank_test_score': array([14, 16, 12, 12, 10, 8, 8, 10, 7, 14, 2, 6, 4, 2, 4, 1], dtype=int32),\n", " 'split0_test_score': array([ 0.75 , 0.80555556, 0.77777778, 0.77777778, 0.83333333,\n", " 0.86111111, 0.86111111, 0.83333333, 0.80555556, 0.86111111,\n", " 0.91666667, 0.88888889, 0.91666667, 0.88888889, 0.91666667,\n", " 0.91666667]),\n", " 'split0_train_score': array([ 0.89855072, 0.88405797, 0.91304348, 0.91304348, 0.91304348,\n", " 0.91304348, 0.91304348, 0.91304348, 0.91304348, 0.92753623,\n", " 0.97101449, 0.97101449, 0.97101449, 0.97101449, 0.97101449,\n", " 0.97101449]),\n", " 'split1_test_score': array([ 0.91428571, 0.85714286, 0.94285714, 0.94285714, 0.91428571,\n", " 0.91428571, 0.91428571, 0.91428571, 0.94285714, 0.85714286,\n", " 0.97142857, 0.94285714, 0.97142857, 1. , 0.97142857, 1. ]),\n", " 'split1_train_score': array([ 0.92857143, 0.84285714, 0.92857143, 0.92857143, 0.92857143,\n", " 0.92857143, 0.92857143, 0.92857143, 0.95714286, 0.85714286,\n", " 0.95714286, 0.97142857, 0.97142857, 0.97142857, 0.95714286,\n", " 0.95714286]),\n", " 'split2_test_score': array([ 0.91176471, 0.85294118, 0.88235294, 0.88235294, 0.88235294,\n", " 0.88235294, 0.88235294, 0.88235294, 0.94117647, 0.85294118,\n", " 0.94117647, 0.91176471, 0.91176471, 0.94117647, 0.91176471,\n", " 0.94117647]),\n", " 'split2_train_score': array([ 0.92957746, 0.90140845, 0.94366197, 0.94366197, 0.94366197,\n", " 0.94366197, 0.94366197, 0.94366197, 0.95774648, 0.87323944,\n", " 0.98591549, 0.98591549, 0.98591549, 0.98591549, 0.98591549,\n", " 0.98591549]),\n", " 'std_fit_time': array([ 1.21561924e-02, 9.30547439e-05, 1.10284565e-04,\n", " 3.03047198e-04, 6.95946432e-04, 3.71424893e-05,\n", " 4.57914039e-05, 1.19908349e-04, 4.01103672e-04,\n", " 7.74041020e-04, 9.07622507e-05, 4.54353185e-04,\n", " 8.52150680e-05, 5.58674420e-05, 2.01641925e-05,\n", " 1.88688419e-04]),\n", " 'std_score_time': array([ 9.94021775e-04, 7.42038836e-04, 2.80585344e-05,\n", " 1.11471841e-05, 2.22151877e-05, 3.86568276e-06,\n", " 1.91065713e-06, 1.07979550e-04, 6.34805604e-05,\n", " 9.84837733e-05, 5.96352605e-05, 1.53933862e-04,\n", " 1.06214518e-05, 1.49894325e-05, 2.65036923e-05,\n", " 6.52451366e-05]),\n", " 'std_test_score': array([ 0.07739765, 0.02356551, 0.06872894, 0.06872894, 0.0335527 ,\n", " 0.02198419, 0.02198419, 0.0335527 , 0.06478264, 0.003334 ,\n", " 0.02254336, 0.02225554, 0.02701157, 0.04569405, 0.02701157,\n", " 0.03512587]),\n", " 'std_train_score': array([ 0.01439488, 0.02455561, 0.01250037, 0.01250037, 0.01250037,\n", " 0.01250037, 0.01250037, 0.01250037, 0.02093237, 0.03011546,\n", " 0.01174888, 0.00692886, 0.00692886, 0.00692886, 0.01174888,\n", " 0.01174888])}" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid_cv.cv_results_" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2017-02-21T01:32:14.046083", "start_time": "2017-02-21T01:32:14.040395" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "make_scorer(accuracy_score)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid_cv.scorer_" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2017-02-21T01:45:19.175729", "start_time": "2017-02-21T01:45:19.111310" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.90909091, 0.95454545, 1. , 0.95238095, 0.94736842])" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cross_val_score(grid_cv.best_estimator_, X_train, y_train, cv=5)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2017-02-21T01:45:59.226712", "start_time": "2017-02-21T01:45:59.216022" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Pipeline(steps=[('scale', StandardScaler(copy=True, with_mean=True, with_std=True)), ('pca', PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,\n", " svd_solver='auto', tol=0.0, whiten=False)), ('svc', SVC(C=1.5, cache_size=200, class_weight=None, coef0=0.0,\n", " decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n", " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", " tol=0.001, verbose=False))])" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid_cv.best_estimator_.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2017-02-21T01:47:10.780010", "start_time": "2017-02-21T01:47:10.772695" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([2, 1, 0, 2, 0, 2, 0, 1, 1, 1, 2, 1, 1, 1, 1, 0, 1, 1, 0, 0, 2, 1, 0,\n", " 0, 2, 0, 0, 1, 1, 0, 2, 1, 0, 2, 2, 1, 0, 2, 1, 1, 2, 0, 2, 0, 0])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_pred = grid_cv.best_estimator_.predict(X_test)\n", "y_pred" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2017-02-21T01:48:58.604483", "start_time": "2017-02-21T01:48:58.589666" }, "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 16\n", " 1 1.00 0.94 0.97 18\n", " 2 0.92 1.00 0.96 11\n", "\n", "avg / total 0.98 0.98 0.98 45\n", "\n" ] }, { "data": { "text/plain": [ "array([[16, 0, 0],\n", " [ 0, 17, 1],\n", " [ 0, 0, 11]])" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.metrics import confusion_matrix, classification_report\n", "\n", "print(classification_report(y_test, y_pred))\n", "confusion_matrix(y_test, y_pred)\n" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "ExecuteTime": { "end_time": "2017-02-21T01:56:56.297072", "start_time": "2017-02-21T01:56:56.282346" }, "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'accuracy': make_scorer(accuracy_score),\n", " 'adjusted_rand_score': make_scorer(adjusted_rand_score),\n", " 'average_precision': make_scorer(average_precision_score, needs_threshold=True),\n", " 'f1': make_scorer(f1_score),\n", " 'f1_macro': make_scorer(f1_score, average=macro, pos_label=None),\n", " 'f1_micro': make_scorer(f1_score, average=micro, pos_label=None),\n", " 'f1_samples': make_scorer(f1_score, average=samples, pos_label=None),\n", " 'f1_weighted': make_scorer(f1_score, average=weighted, pos_label=None),\n", " 'log_loss': make_scorer(log_loss, greater_is_better=False, needs_proba=True),\n", " 'mean_absolute_error': make_scorer(mean_absolute_error, greater_is_better=False),\n", " 'mean_squared_error': make_scorer(mean_squared_error, greater_is_better=False),\n", " 'median_absolute_error': make_scorer(median_absolute_error, greater_is_better=False),\n", " 'neg_log_loss': make_scorer(log_loss, greater_is_better=False, needs_proba=True),\n", " 'neg_mean_absolute_error': make_scorer(mean_absolute_error, greater_is_better=False),\n", " 'neg_mean_squared_error': make_scorer(mean_squared_error, greater_is_better=False),\n", " 'neg_median_absolute_error': make_scorer(median_absolute_error, greater_is_better=False),\n", " 'precision': make_scorer(precision_score),\n", " 'precision_macro': make_scorer(precision_score, average=macro, pos_label=None),\n", " 'precision_micro': make_scorer(precision_score, average=micro, pos_label=None),\n", " 'precision_samples': make_scorer(precision_score, average=samples, pos_label=None),\n", " 'precision_weighted': make_scorer(precision_score, average=weighted, pos_label=None),\n", " 'r2': make_scorer(r2_score),\n", " 'recall': make_scorer(recall_score),\n", " 'recall_macro': make_scorer(recall_score, average=macro, pos_label=None),\n", " 'recall_micro': make_scorer(recall_score, average=micro, pos_label=None),\n", " 'recall_samples': make_scorer(recall_score, average=samples, pos_label=None),\n", " 'recall_weighted': make_scorer(recall_score, average=weighted, pos_label=None),\n", " 'roc_auc': make_scorer(roc_auc_score, needs_threshold=True)}" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.metrics import scorer\n", "scorer.SCORERS]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]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