PCA + SVM with GridSearch using Scikit-learn

pca+svm_gridsearch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (2クラス問題)PCA+SVMのパラメータをGridSearchで探す"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# -*-coding:utf-8-*-\n",
    "# pca+svm with grid search by yuma\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.datasets import fetch_mldata\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn import svm\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix,  roc_curve, auc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作図用の関数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def show_cf(confusions, n_labels):\n",
    "    plt.clf()\n",
    "    plt.xlabel('Actual')\n",
    "    plt.ylabel('Predicted')\n",
    "    plt.grid(False)\n",
    "    plt.xticks(np.arange(n_labels))\n",
    "    plt.yticks(np.arange(n_labels))\n",
    "    plt.imshow(confusions, cmap=plt.cm.jet, interpolation='nearest');\n",
    "\n",
    "    plt.xlim([-0.5, 1.5])\n",
    "    plt.ylim([1.5, -0.5])\n",
    "\n",
    "    for i, cas in enumerate(confusions):\n",
    "        for j, count in enumerate(cas):\n",
    "            if count > 0:\n",
    "                xoff = .07 * len(str(count))\n",
    "                plt.text(j-xoff, i+.2, int(count), fontsize=32, color='white')\n",
    "    return 0\n",
    "\n",
    "def show_roc(test_labels, test_pred):\n",
    "    false_positive_rate, true_positive_rate, thresholds = roc_curve(test_labels, test_pred)\n",
    "    roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "    plt.clf()\n",
    "    plt.figure()\n",
    "    lw = 2\n",
    "    plt.plot(false_positive_rate, true_positive_rate, color='darkorange',\n",
    "         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('MNIST label=0 and 1')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "    return 0\n",
    "                \n",
    "def show_scatter(data, target, d_name='hoge', n_plot=None):\n",
    "    plt.clf()\n",
    "    c = ['#ff0000','#0000ff','#d16b16','#00984b','#0074bf',\n",
    "            '#c30068','#6d1782', '#546474', '#244765', '#8f253b']\n",
    "    c_markers = ['s', 'v', 'o', 'd', '*', '+', 'H', 'p', 'x', 'D']\n",
    "    for i in range(len(list(set(target)))):\n",
    "        feat = data[np.where(target == i)]\n",
    "        if n_plot != None:\n",
    "            feat = feat[:n_plot]\n",
    "        plt.plot(feat[:, 0], feat[:, 1], c_markers[i], markeredgecolor=c[i], markerfacecolor='#ffffff', markeredgewidth=2, markersize=10)\n",
    "    plt.legend(['0', '1','2','3','4','5','6','7','8','9'], numpoints=1, borderaxespad=0, bbox_to_anchor=(1.17, 1))\n",
    "    plt.subplots_adjust(left=0.1, right=0.85)\n",
    "    plt.savefig('{}_vector.png'.format(d_name))\n",
    "    return 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## データセットの取得とPCA適用の関数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "def get_dataset(n_labels=10):\n",
    "    dataset = fetch_mldata('MNIST original')\n",
    "    n_train = 60000\n",
    "    train_dataset = {'data': dataset['data'][:n_train] /255.,\n",
    "                     'target': dataset['target'][:n_train]}\n",
    "    test_dataset = {'data': dataset['data'][n_train:] /255.,\n",
    "                    'target': dataset['target'][n_train:]}\n",
    "    if n_labels != 10:\n",
    "        rdm = np.random.permutation(10)[:n_labels]\n",
    "        # flatten()したい多次元numpy.ndarrayが1次元に変形できず，動かない\n",
    "        train_indexes = np.asarray([np.where(train_dataset['target'] == i)[0] for i in rdm]).flatten()\n",
    "        test_indexes = np.asarray([np.where(test_dataset['target'] == i)[0] for i in rdm]).flatten()\n",
    "        train_dataset = {'data': train_dataset['data'][train_indexes],\n",
    "                      'target': train_dataset['target'][train_indexes]}\n",
    "        test_dataset = {'data': test_dataset['data'][test_indexes],\n",
    "                        'target': test_dataset['target'][test_indexes]}\n",
    "    return train_dataset, test_dataset\n",
    "\"\"\"\n",
    "\n",
    "def get_dataset(n_labels=None):\n",
    "    # 上の関数が動かないのでこちらで代用\n",
    "    dataset = fetch_mldata('MNIST original')\n",
    "    dataset['data'] = dataset['data'].reshape((len(dataset['data']), dataset['data'][0].size))\n",
    "    n_train = 60000\n",
    "    train_dataset = {'data': dataset['data'][:n_train] /255.,\n",
    "                     'target': dataset['target'][:n_train]}\n",
    "    test_dataset = {'data': dataset['data'][n_train:] /255.,\n",
    "                    'target': dataset['target'][n_train:]}\n",
    "    \n",
    "    # 0, 1の２クラス分のデータのみ取り出している\n",
    "    train_indexes = np.where(train_dataset['target'] < 2)[0]\n",
    "    test_indexes = np.where(test_dataset['target'] < 2)[0]\n",
    "    train_dataset = {'data': train_dataset['data'][train_indexes],\n",
    "                    'target': train_dataset['target'][train_indexes]}\n",
    "    test_dataset = {'data': test_dataset['data'][test_indexes],\n",
    "                    'target': test_dataset['target'][test_indexes]}\n",
    "    return train_dataset, test_dataset\n",
    "\n",
    "def app_pca(dataset, data_dim=2):\n",
    "    #data = dataset['data'].reshape((len(dataset['data']), dataset['data'][0].size))\n",
    "    data = dataset['data']\n",
    "    pca = PCA(n_components = data_dim)\n",
    "    app_pca = pca.fit(data).transform(data)\n",
    "    app_dataset = {'data': app_pca, 'target': dataset['target']}\n",
    "    return app_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVMを実行する関数\n",
    "### http://may46onez.hatenablog.com/entry/2016/02/19/152532"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def svm_gds(train_d, test_d, parameters = {'kernel':('rbf', ), 'C':[1,], 'gamma':[0.1,]}):\n",
    "    clf = GridSearchCV(svm.SVC(), parameters, n_jobs = -1)\n",
    "    clf.fit(train_d['data'], train_d['target'])\n",
    "\n",
    "    print(clf.best_estimator_)\n",
    "    test_pred = clf.predict(test_d['data'])\n",
    "    return test_pred\n",
    "\n",
    "def svm_param(train_d, test_d, parameters={'kernel':'rbf', 'C':1, 'gamma':0.1}):\n",
    "    clf = svm.SVC(kernel=parameters['kernel'], C=parameters['C'], gamma=parameters['gamma'])\n",
    "    clf.fit(train_d['data'], train_d['target'])\n",
    "    test_pred = clf.predict(test_d['data'])\n",
    "    return test_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ハイパーパラメータ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_dim = 2\n",
    "n_labels = 2 # dummy\n",
    "use_gds = False\n",
    "pca = False\n",
    "\n",
    "parameters = {'kernel':('rbf', ), 'C':[0.001, 0.1, 1, 5, 10, 100], 'gamma':[0.001, 0.1, 1, 5, 10, 100]}\\\n",
    "                        if use_gds == True else {'kernel':'rbf', 'C':5, 'gamma':0.001}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 処理を実行してみる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datasetの読み込み\n",
      "train.shape, test.shape 12665 2115\n",
      "train_dataset.shape:(12665, 784), test:(2115, 784)\n"
     ]
    }
   ],
   "source": [
    "print(\"datasetの読み込み\")\n",
    "train_dataset, test_dataset = get_dataset(n_labels=n_labels)\n",
    "print(\"train.shape, test.shape\", len(train_dataset['target']), len(test_dataset['target']))\n",
    "if pca == True:\n",
    "    train_dataset = app_pca(train_dataset, data_dim)\n",
    "    test_dataset = app_pca(test_dataset, data_dim)\n",
    "print('train_dataset.shape:{}, test:{}'.format(train_dataset['data'].shape, test_dataset['data'].shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCAで次元圧縮したデータ空間を可視化してみる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "if data_dim < 4 and pca == True:\n",
    "    show_scatter(train_dataset['data'], train_dataset['target'], n_plot=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVMを実行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "app_svm = (svm_gds)if(use_gds == True)else(svm_param)\n",
    "test_pred = app_svm(train_dataset, test_dataset, parameters)\n",
    "test_labels = test_dataset['target']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 評価してみる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      1.00      1.00       980\n",
      "        1.0       1.00      1.00      1.00      1135\n",
      "\n",
      "avg / total       1.00      1.00      1.00      2115\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_labels, test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.999527186761\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(test_labels, test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fbb8c6dc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cfmx = confusion_matrix(test_labels, test_pred)\n",
    "show_cf(cfmx, n_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fbb8c6da58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2xrihwMpdJxksVNVs5+VycrBvleHu8OHj3Hnnxzz99Fduh2KMKYQ/bRDfi8gf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\nMcYnSxDGGGN8+n9+tSSYaO8w6AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fbb9cf1f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_roc(test_labels, test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}