{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# sklearnの基本" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "あらかじめ読み込んでおくモジュール" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "from matplotlib import pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Dataset" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.datasets import load_iris\n", "iris=load_iris()\n", "data=iris.data\n", "target=iris.target" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " sepal length (cm) sepal width (cm) petal length (cm) \\\n", "count 150.000000 150.000000 150.000000 \n", "mean 5.843333 3.054000 3.758667 \n", "std 0.828066 0.433594 1.764420 \n", "min 4.300000 2.000000 1.000000 \n", "25% 5.100000 2.800000 1.600000 \n", "50% 5.800000 3.000000 4.350000 \n", "75% 6.400000 3.300000 5.100000 \n", "max 7.900000 4.400000 6.900000 \n", "\n", " petal width (cm) \n", "count 150.000000 \n", "mean 1.198667 \n", "std 0.763161 \n", "min 0.100000 \n", "25% 0.300000 \n", "50% 1.300000 \n", "75% 1.800000 \n", "max 2.500000 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(data,columns=iris.feature_names).describe()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# scikit-learn使い方Template\n", "@hamukazuさんの[計算機統計学会第30回大会での資料](http://www.slideshare.net/hamukazu/python-62212519)より" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "教師あり学習\n", "```\n", "model = SomeAlgorithm(hyperparameters)\n", "model.fit(data,target)\n", "prediction = model.predict(unknown)\n", "```\n", "教師なし学習\n", "```\n", "model = SomeAlgorithm(hyperparameters)\n", "model.fit(data)\n", "prediction = model.predict(unknown)\n", "```\n", "変換(PCAやノルム化など)\n", "```\n", "model = SomeAlgorithm(hyperparameters)\n", "model.fit(data) \n", "prediction = model.transform(data)\n", "```" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "**インスタンス作ってfitして、predict or transform!**\n", "**これだけ**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## SVMの場合" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "- sklearnのSVMはSVC(support vector classfier)とSVR(support vector regression)の２つに分かれている\n", "\n", "- svcはprobabilityをTrueにしなければスコアが計算できない" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "from sklearn.svm import SVC\n", "clf_svc=SVC(probability=True) #モデルのインスタンス作成" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "before=dir(clf_svc) #モデルインスタンスが持つメソッドリスト" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "フィッティングと予測" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "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=True, random_state=None, shrinking=True,\n", " tol=0.001, verbose=False)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_svc.fit(data[:-5],target[:-5]) #最後の5レコード以外を使って学習する" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "array([2, 2, 2, 2, 2])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_svc.predict(data[-5:]) #最後の5レコードについて予測を行う\n", "# ラスト5レコードは全部2なので正解" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "after=dir(clf_svc) #学習済みのモデルインスタンスが持つメソッドリスト" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "学習前後でモデルのメソッドが増える。 \n", "増えるメソッドの殆どは最後に\"_\"がついているはず。 \n", "学習結果のCoefficientとか確認可能。" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'probA_', 'n_support_', 'probB_', '_intercept_', 'classes_', 'fit_status_', 'shape_fit_', 'intercept_', 'dual_coef_', '_dual_coef_', 'support_', 'support_vectors_', 'class_weight_', '_gamma', '_sparse'}\n" ] } ], "source": [ "print(set(after) - set(before))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "メソッドチェーンでのワンライナーも可能" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "array([2, 2, 2, 2, 2])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "SVC(probability=True).fit(data[:-5],target[:-5]).predict(data[-5:])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "予測結果のスコア \n", "0-1で正規化された値" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "scrolled": true, "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " setosa versicolor virginica\n", "0 0.011340 0.022454 0.966206\n", "1 0.012091 0.098005 0.889904\n", "2 0.010348 0.053140 0.936512\n", "3 0.012952 0.010192 0.976856\n", "4 0.011374 0.099246 0.889380" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "score_svc=clf_svc.predict_proba(data[-5:])\n", "pd.DataFrame(score_svc,columns=iris.target_names)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.38354193, 1.06620179, 1.55025628],\n", " [ 0.41971932, 0.92873543, 1.65154525],\n", " [ 0.41020748, 0.98579653, 1.60399599],\n", " [ 0.3576206 , 1.1423794 , 1.5 ],\n", " [ 0.42293297, 0.92995685, 1.64711017]])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_svc.decision_function_shape=\"ovr\"\n", "clf_svc.decision_function(data[-5:])\n", "# SVMは識別境界面からの距離もスコアリングに使える\n", "# でもちょっと使いにくいかも？" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# RFの場合" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "基本はSVMと同じ" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true, "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "# RFはensembleというモジュールの中に入っている\n", "from sklearn.ensemble import RandomForestClassifier as RFC\n", "clf_rfc=RFC()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n", " oob_score=False, random_state=None, verbose=0,\n", " warm_start=False)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rfc.fit(data[:-5],target[:-5])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "scrolled": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "array([2, 2, 2, 2, 2])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_rfc.predict(data[-5:])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0. , 0. , 1. ],\n", " [ 0. , 0. , 1. ],\n", " [ 0. , 0. , 1. ],\n", " [ 0. , 0. , 1. ],\n", " [ 0. , 0.1, 0.9]])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# スコア\n", "clf_rfc.predict_proba(data[-5:])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## sklearn準拠のモデルであれば、\n", "## 同じ操作で予測モデルが作れる \n", "sklearn準拠の話は[この辺](http://yamaguchiyuto.hatenablog.com/entry/python-advent-calendar-2014)参照" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 線形回帰の場合" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "線形回帰用のDataset(糖尿病トイデータ)の読み込み" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "from sklearn.datasets import load_diabetes\n", "diabetes=load_diabetes()\n", "diab_dat=diabetes.data\n", "diab_target=diabetes.target" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "こんな感じ" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " 0 1 2 3 4 5 6 7 8 9 ... \\\n", "0 151.0 75.0 141.0 206.0 135.0 97.0 138.0 63.0 110.0 310.0 ... \n", "\n", " 432 433 434 435 436 437 438 439 440 441 \n", "0 173.0 72.0 49.0 64.0 48.0 178.0 104.0 132.0 220.0 57.0 \n", "\n", "[1 rows x 442 columns]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(diab_target).T # 被説明変数は１年後の血糖値" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "基本は一緒" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true, "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "from sklearn.linear_model import LinearRegression" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reg=LinearRegression()\n", "reg.fit(diab_dat[:-20],diab_target[:-20]) #最後の20個以外で学習" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 197.61846908, 155.43979328, 172.88665147, 111.53537279,\n", " 164.80054784, 131.06954875, 259.12237761, 100.47935157,\n", " 117.0601052 , 124.30503555, 218.36632793, 61.19831284,\n", " 132.25046751, 120.3332925 , 52.54458691, 194.03798088,\n", " 102.57139702, 123.56604987, 211.0346317 , 52.60335674])" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reg.predict(diab_dat[-20:]) #最後の20個を予測" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false, "scrolled": true, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ATU3h5wFIUqEMAEkqlAEgSYUyACSpUAaAJBXKAJCkQjkNtGa8z4yk1WIA1Iz3mZG0WhwC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\nAEkqlAEgSYUyACSpUAaAJBXq/wBXrdndpepx2AAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# 実測値と予測値の比較\n", "plt.scatter(reg.predict(diab_dat[-20:]),diab_target[-20:])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# **回帰の場合も基本的には一緒**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# クラスタリングの場合" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "これも一緒" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false, "scrolled": true, "slideshow": { "slide_type": "-" } }, "outputs": [ { "data": { "text/plain": [ "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", " 1, 1, 1, 1, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 2,\n", " 0, 0, 0, 0, 2, 0, 2, 0, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0,\n", " 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 2], dtype=int32)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.cluster import KMeans\n", "Kcluster=KMeans(n_clusters=3).fit(data).predict(data) #メソッドチェーン\n", "Kcluster" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 次元削減" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "次元削減の時はpredictじゃなくてtransformになる" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.decomposition import PCA\n", "pc=PCA(n_components=2).fit(data).transform(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "PCAの結果の表示" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "PCA_result=pd.DataFrame(pc,columns=[\"pc1\",\"pc2\"])\n", "PCA_result.plot(kind='scatter',x=\"pc1\",y=\"pc2\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "おまけ \n", "PCAで二次元に圧縮してKmeansの結果を見る" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#実際のラベル、Kmeansのクラス分け結果、PCAの結果をconcatenateする\n", "rslt=pd.concat(\n", " [pd.DataFrame(target,columns=['target']),\n", " pd.DataFrame(Kcluster,columns=['class']),\n", " PCA_result],\n", " axis=1)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false, "scrolled": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 1)\n", "cmap = plt.get_cmap('rainbow')\n", "marker=[\"o\",\"x\",\"^\"]\n", "for i in range(3):\n", " tmp=rslt[rslt[\"class\"]==i]\n", " for j in range(3):\n", " tmp2=tmp[tmp[\"target\"]==j]\n", " if tmp2.shape[0]!=0:\n", " tmp2.plot(\n", " kind=\"scatter\",x=\"pc1\",y=\"pc2\",ax=ax,c=cmap(i/3),marker=marker[j]\n", " )" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# その他\n", "- scikit-learnはpandasのDataframeを入力に使えるが、**出力はnumpyのarray**になってしまう。\n", "- そのためpandas-mlというパッケージもあるが今回は省略。" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "df_iris=pd.DataFrame(iris.data)\n", "print(type(df_iris))" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "pca_iris=PCA().fit(df_iris).transform(df_iris)\n", "print(type(pca_iris))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# pandasについて\n", "- sklearnにも前処理の機能はありますが、pandasを使ったほうがいいことも多いです。\n", "- 長くなるのでpandasの説明も割愛します。\n", "- matplotlibのwrapper APIもあるので、pandasで簡単な描画も可能です。\n", "- pandasは今のところ、[このページ](http://sinhrks.hatenablog.com/entry/2015/04/28/235430)が一番まとまっていると思います。" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 終わり\n", "@y__sama\n" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }