01_SKlearn_Basic.ipynb

{
 "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": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2\n",
       "5                5.4               3.9                1.7               0.4\n",
       "6                4.6               3.4                1.4               0.3\n",
       "7                5.0               3.4                1.5               0.2\n",
       "8                4.4               2.9                1.4               0.2\n",
       "9                4.9               3.1                1.5               0.1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data,columns=iris.feature_names).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>setosa</th>\n",
       "      <th>versicolor</th>\n",
       "      <th>virginica</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.011340</td>\n",
       "      <td>0.022454</td>\n",
       "      <td>0.966206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.012091</td>\n",
       "      <td>0.098005</td>\n",
       "      <td>0.889904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.010348</td>\n",
       "      <td>0.053140</td>\n",
       "      <td>0.936512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.012952</td>\n",
       "      <td>0.010192</td>\n",
       "      <td>0.976856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.011374</td>\n",
       "      <td>0.099246</td>\n",
       "      <td>0.889380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": [
    {
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       "\n",
       "          7         8         9  \n",
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       "3  0.034309  0.022692 -0.009362  \n",
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      ]
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     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(diab_dat).head() # 記述子は10個の検査値"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "[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": [
       "<matplotlib.collections.PathCollection at 0x7f293ca92a58>"
      ]
     },
     "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": [
       "<matplotlib.figure.Figure at 0x7f293f76d198>"
      ]
     },
     "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": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2936c4c780>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qr4U+6Gt0iYjUgyqOmKpFua3RJSJSD6o4mpjmdIhIPShxNDGtpisi9aCmqiam\n0SUiUg+RJQ4z+xhwNXAi8A53f7zCdh8GVpOtjm539xsaFqSIiIwSZVPVk8BHgX+ttIGZTQK+B3wI\nOAk428wWNSY8EREpJ7KKw90dwMzGWsZ3MfCcu2/KbXsPcAawof4RiohIOXHvHJ8LbC64vSV3n4iI\nRKSuFYeZPQLMLrgrBWSAK939gXq+NhDLNfgVUzCKKbg4xqWYgoljTEHUNXG4+wcm+BR9wHEFt4/J\n3RdIDK+upZgCUEzBxTEuxRRMXGMKIi7DcSv1czwGHG9m84CtwCcATUYQEYlQZH0cZnammW0G3gk8\naGY/y91/lJk9CODuw8AlwMPAU8A97v5MVDGLiEi0o6ruA+4rc/9W4PSC2/8CWANDixWtcCsicRP3\nUVUtTyvcikjcKHHEXHpfdmVb0Aq3IhIPShwxpxVuRSRulDhiTivcikjcxGU4rlSgFW5FoqdBKsWU\nOGJKO6pIfOQHqaSmpdiXybDqlrtb+oROTVUxpdFUIvGhQSrFlDhiSjuqSHxokEoxJY6Y0o4qEh8a\npFJMfRwxtezCc1h1S66PYxotv6OKREmDVIopccSUdlQRiSs1VYmISChKHCIiEooSh4iIhKLEISIi\noahzPOE0w1xEGk0VR8JphrmINJoqjoRL74PUNM0wFxkvVe3hqeJIOM0wF5kYVe3hKXEknJZCEJkY\nrQsXnpqqEk4zzEUmpqsjw75MhlQqpao9IFUcItLSVLWHp4pDRFqaqvbwIkscZvYx4GrgROAd7v54\nhe1eBHYBB4Ehd1/coBBFRKSMKCuOJ4GPArdW2e4gsMTd0/UPSUREqokscbi7A5hZqsqmKdQXIyIS\nG0k4IGeAR8zsMTP7dNTBiIi0urpWHGb2CDC74K4U2URwpbs/EPBp3uPuW82sh2wCecbdfx3kgT09\nneECbgDFFIxiCi6OcSmmYOIYUxB1TRzu/oEaPMfW3P/9ZvZjYDEQKHH09++Z6MvXVE9Pp2IKQDEF\nF8e4FFMwcY0piLg0VZXt5zCzaWY2Pffz4cAHgT80MjARESkWWeIwszPNbDPwTuBBM/tZ7v6jzOzB\n3GazgV+b2X8AvwUecPeHo4lYREQg2lFV9wH3lbl/K3B67ucXgJMbHJqIiIwhLk1VIiKSEEocIiIS\nihKHiIiEosQhIiKhKHGIiEgoShwiIhKKEoeIiISixCEiIqEocYiISChKHCIiEooSh4iIhKLEISIi\noShxiIiBQjgVAAAGHUlEQVRIKEocIiISihKHiIiEosQhIiKhKHGIiEgoShwiIhKKEoeIiISixCEi\nIqEocYiISChKHCIiEsrkqF7YzL4J/DXwOvBH4Dx3311muw8Dq8kmudvd/YaGBioiIkWirDgeBk5y\n95OB54CvlG5gZpOA7wEfAk4CzjazRQ2NUkREikRWcbj7zwtu/hb4r2U2Www85+6bAMzsHuAMYEP9\nIxQRkXLi0sdxPvCzMvfPBTYX3N6Su09ERCJS14rDzB4BZhfclQIywJXu/kBumyuBIXe/q56xiIhI\nbdQ1cbj7B8b6vZmdC5wG/OcKm/QBxxXcPiZ3XxCpnp7OgJs2jmIKRjEFF8e4FFMwcYwpiChHVX0Y\n+CLwPnd/vcJmjwHHm9k8YCvwCeDsBoUoIiJlRNnHcTMwHXjEzB43s7UAZnaUmT0I4O7DwCVkR2A9\nBdzj7s9EFbCIiEAqk8lEHYOIiCRIXEZViYhIQihxiIhIKEocIiISSmSjqhrFzJYB3wKOcPcdMYjn\nWrKz3w8C24Bz3f2ViGMKtG5Yg2P6GHA1cCLwDnd/PMJYYrdempndDpwObHP3t8UgnmOAH5Cdt3UQ\nuM3dvxttVGBmhwGPAu1kj3c/cvdroo1qZDml/wNscfePRB0PgJm9COwi+/kNufviSts2dcWR25k/\nAGyKOpYC33T3P3P3U4CHgOVRB0SAdcMi8CTwUeBfowwixuul3UE2prg4AFzu7icB7wIujsP7lBvq\n/5e579vJwKlmVvGA2ECfB56OOogSB4El7n7KWEkDmjxxAN8hO1ckNtx9b8HNw8l+WJFy95+7ez6O\n35KdaBkpz3qO7GoDURpZL83dh4D8emmRcvdfA+mo48hz91fc/Yncz3uBZ4jJ8kDuvi/342Fkq45I\nh5LmTmhPA/5nlHGUkSJgTmjapioz+wiw2d2fNLOowyliZiuATwE7gb+MOJxS55M9OEpWufXS4nDG\nGltmNp/s2f3vIg4FGKkafw8sBNa4+2MRh5Q/oX1DxHGUypCdVzcMrHP32yptmOjEMcZaWL3AV8k2\nUxX+Luq4rnT3B9y9F+g1sy8Bl5Jty480ptw2DV03LEhMkixmNh34EfD5kuo6Mrlq+hQzmwHcZ2Zv\ndvdImonMbCnZfqknzGwJ0VfUhd7j7lvNrIdsAnkmV9mOkujEUWktLDN7CzAf+L9mliLb9PJ7M1vs\n7q9GFVcZdwE/pQGJowbrhtVciPcpShNZL62lmNlksknjn9z9/qjjKeXuu83sl8CHia5/4T3AR8zs\nNKAD6DSzH7j7pyKKZ4S7b839329mPyZbWTdf4qjE3f8AzMnfNrMXgD9398jbhM3seHffmLt5Jtm2\n4EgFXDcsSlGelcV5vbQU8Tpj/UfgaXe/KepA8szsCLJV9C4z6yDbCrEyqnjc/atkW0Mws/cDy+KQ\nNMxsGjDJ3fea2eHAB4GKo8+aMnGUkSE+X7CVZvYmsp3im4DPRhwPZNcNaydbngL81t0vijIgMzsz\nF9cRwINm9oS7n9roONx92Mzy66Xlh+PGIdnfBSwBZpnZS8Byd78jwnjeA3wSeNLM/oPsd+6r7v4v\nUcWUcxTw/Vw/xyTgn939pxHHFEezgR+bWYZsXvihuz9caWOtVSUiIqE0+3BcERGpMSUOEREJRYlD\nRERCUeIQEZFQlDhERCQUJQ4REQmlVeZxiMRWbun/TwMnAH+teQYSd6o4RKL3K+BUIl5CXiQoVRwi\nNWRmB4H8xbqmkl2w8d7c794FfBPoJDuz+ou5Je1/n/t9XFY3EBmTEodI7Q25+ym5pWX+zcweBYaB\ne4Ez3f13uSQxI9IoRcZJiUOk9m4HcPdnzez3wDvJrk32lLv/Lve7DNnLdIokjvo4RGovFeBnkcRS\n4hCpvfMAzOwEslfC+y3wG+BEM/uL3O8mmdnM6EIUGT81VYnU3mQze5zshXo+4+6vAZjZWcB3ctc7\nGAauAH5hZlcAnye7hPydZrYfeHNcrqAnUkrLqovUUG5U1eHuPhB1LCL1oqYqkdqK00XDROpCFYeI\niISiikNEREJR4hARkVCUOEREJBQlDhERCUWJQ0REQlHiEBGRUP4/qB1GCfwlc1UAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2936c37c88>"
      ]
     },
     "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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qJX+54vKCjrOVoSPM3o5iyDVKSH+6L3Qvw2D2PpRr34QOn0oKlTjcvehHzJrZ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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f293f76dba8>"
      ]
     },
     "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": [
      "<class 'pandas.core.frame.DataFrame'>\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": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "pca_iris=PCA().fit(df_iris).transform(df_iris)\n",
    "print(type(pca_iris))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
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   "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"
   ]
  }
 ],
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