DecisionTree.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision Treeによる機械学習を実行してみる。[参考ドキュメント](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=6, micro=2, releaselevel='final', serial=0)\n"
     ]
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.datasets import load_iris, load_boston\n",
    "from sklearn.metrics import accuracy_score, mean_squared_error\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor, export_graphviz\n",
    "import sys\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "print(sys.version_info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "目的変数がクラスであるデータに対してDecisionTreeのクラス分類器である[DecisionTreeClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier)を用いてみる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1 解析するデータの読み込み"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "iris (機械学習でよく使われるあやめのデータセット) を読み込む。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()\n",
    "X_iris = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "y_iris = iris.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "サンプルサイズは150である。  \n",
    "そして説明変数としてとしてsepal length (がくの長さ), sepal width (がくの幅), petal length (花びらの長さ), petal width (花びらの幅)がある。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<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",
       "  </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"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "目的変数はあやめの種類である。0: setosa, 1: versicolor, 2: virginica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 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, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 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, 2, 2, 2, 2])"
      ]
     },
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "サンプルサイズと説明変数の数はshapeに保存されている。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_iris.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2 機械学習"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 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, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 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, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(random_state=0)\n",
    "clf.fit(X_iris, y_iris)\n",
    "y_predicted = clf.predict(X_iris)\n",
    "y_predicted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[accuracy_score](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html#sklearn.metrics.accuracy_score)関数を用いてTraining setの正答率を求めてみる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_predicted, y_iris) *100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正答率は100%であった。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "これはデフォルトでは、完全にデータを分離されるかあるいはノードに含まれるデータが最低2つになるまで  \n",
    "ノードを伸長していくように設定されているからである。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3 決定木の視覚化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1.3.1 Graphvizがインストールされている場合"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-learnのDecusion Treeを用いてノードを視覚化するには `Graphviz` を使用する。  \n",
    "*chemowakate/tutorial-7th のDockerイメージを使用している場合は`Graphviz`およびPythonモジュール`graphviz`をインストール済み \n",
    "  \n",
    "[Graphviz | Graphviz - Graph Visualization Software](http://www.graphviz.org/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "<path fill=\"#8139e5\" fill-opacity=\"0.498039\" stroke=\"black\" d=\"M564,-306C564,-306 394,-306 394,-306 388,-306 382,-300 382,-294 382,-294 382,-235 382,-235 382,-229 388,-223 394,-223 394,-223 564,-223 564,-223 570,-223 576,-229 576,-235 576,-235 576,-294 576,-294 576,-300 570,-306 564,-306\"/>\n",
       "<text text-anchor=\"middle\" x=\"479\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal width (cm) &lt;= 1.55</text>\n",
       "<text text-anchor=\"middle\" x=\"479\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.4444</text>\n",
       "<text text-anchor=\"middle\" x=\"479\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 6</text>\n",
       "<text text-anchor=\"middle\" x=\"479\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 2, 4]</text>\n",
       "<text text-anchor=\"middle\" x=\"479\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n",
       "</g>\n",
       "<!-- 3&#45;&gt;7 -->\n",
       "<g id=\"edge7\" class=\"edge\"><title>3&#45;&gt;7</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M479,-341.907C479,-333.649 479,-324.864 479,-316.302\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"482.5,-316.021 479,-306.021 475.5,-316.021 482.5,-316.021\"/>\n",
       "</g>\n",
       "<!-- 5 -->\n",
       "<g id=\"node6\" class=\"node\"><title>5</title>\n",
       "<path fill=\"#39e581\" stroke=\"black\" d=\"M128,-179.5C128,-179.5 12,-179.5 12,-179.5 6,-179.5 0,-173.5 0,-167.5 0,-167.5 0,-123.5 0,-123.5 0,-117.5 6,-111.5 12,-111.5 12,-111.5 128,-111.5 128,-111.5 134,-111.5 140,-117.5 140,-123.5 140,-123.5 140,-167.5 140,-167.5 140,-173.5 134,-179.5 128,-179.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"70\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"70\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 47</text>\n",
       "<text text-anchor=\"middle\" x=\"70\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 47, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"70\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\n",
       "</g>\n",
       "<!-- 4&#45;&gt;5 -->\n",
       "<g id=\"edge5\" class=\"edge\"><title>4&#45;&gt;5</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M170.57,-222.907C154.739,-210.88 137.437,-197.735 121.716,-185.791\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"123.736,-182.93 113.656,-179.667 119.501,-188.503 123.736,-182.93\"/>\n",
       "</g>\n",
       "<!-- 6 -->\n",
       "<g id=\"node7\" class=\"node\"><title>6</title>\n",
       "<path fill=\"#8139e5\" stroke=\"black\" d=\"M277.5,-179.5C277.5,-179.5 170.5,-179.5 170.5,-179.5 164.5,-179.5 158.5,-173.5 158.5,-167.5 158.5,-167.5 158.5,-123.5 158.5,-123.5 158.5,-117.5 164.5,-111.5 170.5,-111.5 170.5,-111.5 277.5,-111.5 277.5,-111.5 283.5,-111.5 289.5,-117.5 289.5,-123.5 289.5,-123.5 289.5,-167.5 289.5,-167.5 289.5,-173.5 283.5,-179.5 277.5,-179.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"224\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"224\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
       "<text text-anchor=\"middle\" x=\"224\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 1]</text>\n",
       "<text text-anchor=\"middle\" x=\"224\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n",
       "</g>\n",
       "<!-- 4&#45;&gt;6 -->\n",
       "<g id=\"edge6\" class=\"edge\"><title>4&#45;&gt;6</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M224,-222.907C224,-212.204 224,-200.615 224,-189.776\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"227.5,-189.667 224,-179.667 220.5,-189.667 227.5,-189.667\"/>\n",
       "</g>\n",
       "<!-- 8 -->\n",
       "<g id=\"node9\" class=\"node\"><title>8</title>\n",
       "<path fill=\"#8139e5\" stroke=\"black\" d=\"M426.5,-179.5C426.5,-179.5 319.5,-179.5 319.5,-179.5 313.5,-179.5 307.5,-173.5 307.5,-167.5 307.5,-167.5 307.5,-123.5 307.5,-123.5 307.5,-117.5 313.5,-111.5 319.5,-111.5 319.5,-111.5 426.5,-111.5 426.5,-111.5 432.5,-111.5 438.5,-117.5 438.5,-123.5 438.5,-123.5 438.5,-167.5 438.5,-167.5 438.5,-173.5 432.5,-179.5 426.5,-179.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"373\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"373\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n",
       "<text text-anchor=\"middle\" x=\"373\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 3]</text>\n",
       "<text text-anchor=\"middle\" x=\"373\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n",
       "</g>\n",
       "<!-- 7&#45;&gt;8 -->\n",
       "<g id=\"edge8\" class=\"edge\"><title>7&#45;&gt;8</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M442.224,-222.907C431.727,-211.321 420.29,-198.698 409.792,-187.111\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"412.357,-184.728 403.049,-179.667 407.169,-189.428 412.357,-184.728\"/>\n",
       "</g>\n",
       "<!-- 9 -->\n",
       "<g id=\"node10\" class=\"node\"><title>9</title>\n",
       "<path fill=\"#39e581\" fill-opacity=\"0.498039\" stroke=\"black\" d=\"M643.5,-187C643.5,-187 468.5,-187 468.5,-187 462.5,-187 456.5,-181 456.5,-175 456.5,-175 456.5,-116 456.5,-116 456.5,-110 462.5,-104 468.5,-104 468.5,-104 643.5,-104 643.5,-104 649.5,-104 655.5,-110 655.5,-116 655.5,-116 655.5,-175 655.5,-175 655.5,-181 649.5,-187 643.5,-187\"/>\n",
       "<text text-anchor=\"middle\" x=\"556\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">petal length (cm) &lt;= 5.45</text>\n",
       "<text text-anchor=\"middle\" x=\"556\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.4444</text>\n",
       "<text text-anchor=\"middle\" x=\"556\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n",
       "<text text-anchor=\"middle\" x=\"556\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 2, 1]</text>\n",
       "<text text-anchor=\"middle\" x=\"556\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\n",
       "</g>\n",
       "<!-- 7&#45;&gt;9 -->\n",
       "<g id=\"edge9\" class=\"edge\"><title>7&#45;&gt;9</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M505.715,-222.907C511.508,-214.105 517.695,-204.703 523.678,-195.612\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"526.759,-197.298 529.332,-187.021 520.911,-193.45 526.759,-197.298\"/>\n",
       "</g>\n",
       "<!-- 10 -->\n",
       "<g id=\"node11\" class=\"node\"><title>10</title>\n",
       "<path fill=\"#39e581\" stroke=\"black\" d=\"M536,-68C536,-68 424,-68 424,-68 418,-68 412,-62 412,-56 412,-56 412,-12 412,-12 412,-6 418,-0 424,-0 424,-0 536,-0 536,-0 542,-0 548,-6 548,-12 548,-12 548,-56 548,-56 548,-62 542,-68 536,-68\"/>\n",
       "<text text-anchor=\"middle\" x=\"480\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"480\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n",
       "<text text-anchor=\"middle\" x=\"480\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 2, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"480\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\n",
       "</g>\n",
       "<!-- 9&#45;&gt;10 -->\n",
       "<g id=\"edge10\" class=\"edge\"><title>9&#45;&gt;10</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M527.7,-103.726C521.56,-94.879 515.057,-85.51 508.894,-76.6303\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"511.689,-74.5191 503.112,-68.2996 505.939,-78.5104 511.689,-74.5191\"/>\n",
       "</g>\n",
       "<!-- 11 -->\n",
       "<g id=\"node12\" class=\"node\"><title>11</title>\n",
       "<path fill=\"#8139e5\" stroke=\"black\" d=\"M685.5,-68C685.5,-68 578.5,-68 578.5,-68 572.5,-68 566.5,-62 566.5,-56 566.5,-56 566.5,-12 566.5,-12 566.5,-6 572.5,-0 578.5,-0 578.5,-0 685.5,-0 685.5,-0 691.5,-0 697.5,-6 697.5,-12 697.5,-12 697.5,-56 697.5,-56 697.5,-62 691.5,-68 685.5,-68\"/>\n",
       "<text text-anchor=\"middle\" x=\"632\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"632\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
       "<text text-anchor=\"middle\" x=\"632\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 1]</text>\n",
       "<text text-anchor=\"middle\" x=\"632\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n",
       "</g>\n",
       "<!-- 9&#45;&gt;11 -->\n",
       "<g id=\"edge11\" class=\"edge\"><title>9&#45;&gt;11</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M584.3,-103.726C590.44,-94.879 596.943,-85.51 603.106,-76.6303\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"606.061,-78.5104 608.888,-68.2996 600.311,-74.5191 606.061,-78.5104\"/>\n",
       "</g>\n",
       "<!-- 13 -->\n",
       "<g id=\"node14\" class=\"node\"><title>13</title>\n",
       "<path fill=\"#8139e5\" fill-opacity=\"0.498039\" stroke=\"black\" d=\"M820,-306C820,-306 658,-306 658,-306 652,-306 646,-300 646,-294 646,-294 646,-235 646,-235 646,-229 652,-223 658,-223 658,-223 820,-223 820,-223 826,-223 832,-229 832,-235 832,-235 832,-294 832,-294 832,-300 826,-306 820,-306\"/>\n",
       "<text text-anchor=\"middle\" x=\"739\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">sepal width (cm) &lt;= 3.1</text>\n",
       "<text text-anchor=\"middle\" x=\"739\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.4444</text>\n",
       "<text text-anchor=\"middle\" x=\"739\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n",
       "<text text-anchor=\"middle\" x=\"739\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1, 2]</text>\n",
       "<text text-anchor=\"middle\" x=\"739\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n",
       "</g>\n",
       "<!-- 12&#45;&gt;13 -->\n",
       "<g id=\"edge13\" class=\"edge\"><title>12&#45;&gt;13</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M739,-341.907C739,-333.649 739,-324.864 739,-316.302\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"742.5,-316.021 739,-306.021 735.5,-316.021 742.5,-316.021\"/>\n",
       "</g>\n",
       "<!-- 16 -->\n",
       "<g id=\"node17\" class=\"node\"><title>16</title>\n",
       "<path fill=\"#8139e5\" stroke=\"black\" d=\"M978,-298.5C978,-298.5 862,-298.5 862,-298.5 856,-298.5 850,-292.5 850,-286.5 850,-286.5 850,-242.5 850,-242.5 850,-236.5 856,-230.5 862,-230.5 862,-230.5 978,-230.5 978,-230.5 984,-230.5 990,-236.5 990,-242.5 990,-242.5 990,-286.5 990,-286.5 990,-292.5 984,-298.5 978,-298.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"920\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"920\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 43</text>\n",
       "<text text-anchor=\"middle\" x=\"920\" y=\"-253.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 43]</text>\n",
       "<text text-anchor=\"middle\" x=\"920\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n",
       "</g>\n",
       "<!-- 12&#45;&gt;16 -->\n",
       "<g id=\"edge16\" class=\"edge\"><title>12&#45;&gt;16</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M801.797,-341.907C820.745,-329.659 841.487,-316.252 860.232,-304.135\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"862.192,-307.035 868.69,-298.667 858.392,-301.156 862.192,-307.035\"/>\n",
       "</g>\n",
       "<!-- 14 -->\n",
       "<g id=\"node15\" class=\"node\"><title>14</title>\n",
       "<path fill=\"#8139e5\" stroke=\"black\" d=\"M792.5,-179.5C792.5,-179.5 685.5,-179.5 685.5,-179.5 679.5,-179.5 673.5,-173.5 673.5,-167.5 673.5,-167.5 673.5,-123.5 673.5,-123.5 673.5,-117.5 679.5,-111.5 685.5,-111.5 685.5,-111.5 792.5,-111.5 792.5,-111.5 798.5,-111.5 804.5,-117.5 804.5,-123.5 804.5,-123.5 804.5,-167.5 804.5,-167.5 804.5,-173.5 798.5,-179.5 792.5,-179.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"739\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"739\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\n",
       "<text text-anchor=\"middle\" x=\"739\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 2]</text>\n",
       "<text text-anchor=\"middle\" x=\"739\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = virginica</text>\n",
       "</g>\n",
       "<!-- 13&#45;&gt;14 -->\n",
       "<g id=\"edge14\" class=\"edge\"><title>13&#45;&gt;14</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M739,-222.907C739,-212.204 739,-200.615 739,-189.776\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"742.5,-189.667 739,-179.667 735.5,-189.667 742.5,-189.667\"/>\n",
       "</g>\n",
       "<!-- 15 -->\n",
       "<g id=\"node16\" class=\"node\"><title>15</title>\n",
       "<path fill=\"#39e581\" stroke=\"black\" d=\"M947,-179.5C947,-179.5 835,-179.5 835,-179.5 829,-179.5 823,-173.5 823,-167.5 823,-167.5 823,-123.5 823,-123.5 823,-117.5 829,-111.5 835,-111.5 835,-111.5 947,-111.5 947,-111.5 953,-111.5 959,-117.5 959,-123.5 959,-123.5 959,-167.5 959,-167.5 959,-173.5 953,-179.5 947,-179.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"891\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"891\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
       "<text text-anchor=\"middle\" x=\"891\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1, 0]</text>\n",
       "<text text-anchor=\"middle\" x=\"891\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = versicolor</text>\n",
       "</g>\n",
       "<!-- 13&#45;&gt;15 -->\n",
       "<g id=\"edge15\" class=\"edge\"><title>13&#45;&gt;15</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M791.736,-222.907C807.361,-210.88 824.439,-197.735 839.955,-185.791\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"842.122,-188.54 847.911,-179.667 837.852,-182.993 842.122,-188.54\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x7f1814bc0860>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import graphviz\n",
    "dot_data = export_graphviz(clf, out_file=None,  filled=True, rounded=True,\n",
    "                                                  feature_names=iris.feature_names, class_names=iris.target_names)\n",
    "graph = graphviz.Source(dot_data)\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1.3.2 Graphvizがインストールされていない場合"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Graphviz`がインストールされていない場合はDOTフォーマットデータをテキスト出力し、    \n",
    "出力されたものをコピーし[Webgraphviz](http://www.webgraphviz.com/)にペーストしGenerate Graphをクリックすると以下のような図が表示される。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "digraph Tree {\n",
      "node [shape=box, style=\"filled, rounded\", color=\"black\", fontname=helvetica] ;\n",
      "edge [fontname=helvetica] ;\n",
      "0 [label=\"petal width (cm) <= 0.8\\ngini = 0.6667\\nsamples = 150\\nvalue = [50, 50, 50]\\nclass = setosa\", fillcolor=\"#e5813900\"] ;\n",
      "1 [label=\"gini = 0.0\\nsamples = 50\\nvalue = [50, 0, 0]\\nclass = setosa\", fillcolor=\"#e58139ff\"] ;\n",
      "0 -> 1 [labeldistance=2.5, labelangle=45, headlabel=\"True\"] ;\n",
      "2 [label=\"petal width (cm) <= 1.75\\ngini = 0.5\\nsamples = 100\\nvalue = [0, 50, 50]\\nclass = versicolor\", fillcolor=\"#39e58100\"] ;\n",
      "0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel=\"False\"] ;\n",
      "3 [label=\"petal length (cm) <= 4.95\\ngini = 0.168\\nsamples = 54\\nvalue = [0, 49, 5]\\nclass = versicolor\", fillcolor=\"#39e581e5\"] ;\n",
      "2 -> 3 ;\n",
      "4 [label=\"petal width (cm) <= 1.65\\ngini = 0.0408\\nsamples = 48\\nvalue = [0, 47, 1]\\nclass = versicolor\", fillcolor=\"#39e581fa\"] ;\n",
      "3 -> 4 ;\n",
      "5 [label=\"gini = 0.0\\nsamples = 47\\nvalue = [0, 47, 0]\\nclass = versicolor\", fillcolor=\"#39e581ff\"] ;\n",
      "4 -> 5 ;\n",
      "6 [label=\"gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]\\nclass = virginica\", fillcolor=\"#8139e5ff\"] ;\n",
      "4 -> 6 ;\n",
      "7 [label=\"petal width (cm) <= 1.55\\ngini = 0.4444\\nsamples = 6\\nvalue = [0, 2, 4]\\nclass = virginica\", fillcolor=\"#8139e57f\"] ;\n",
      "3 -> 7 ;\n",
      "8 [label=\"gini = 0.0\\nsamples = 3\\nvalue = [0, 0, 3]\\nclass = virginica\", fillcolor=\"#8139e5ff\"] ;\n",
      "7 -> 8 ;\n",
      "9 [label=\"petal length (cm) <= 5.45\\ngini = 0.4444\\nsamples = 3\\nvalue = [0, 2, 1]\\nclass = versicolor\", fillcolor=\"#39e5817f\"] ;\n",
      "7 -> 9 ;\n",
      "10 [label=\"gini = 0.0\\nsamples = 2\\nvalue = [0, 2, 0]\\nclass = versicolor\", fillcolor=\"#39e581ff\"] ;\n",
      "9 -> 10 ;\n",
      "11 [label=\"gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]\\nclass = virginica\", fillcolor=\"#8139e5ff\"] ;\n",
      "9 -> 11 ;\n",
      "12 [label=\"petal length (cm) <= 4.85\\ngini = 0.0425\\nsamples = 46\\nvalue = [0, 1, 45]\\nclass = virginica\", fillcolor=\"#8139e5f9\"] ;\n",
      "2 -> 12 ;\n",
      "13 [label=\"sepal width (cm) <= 3.1\\ngini = 0.4444\\nsamples = 3\\nvalue = [0, 1, 2]\\nclass = virginica\", fillcolor=\"#8139e57f\"] ;\n",
      "12 -> 13 ;\n",
      "14 [label=\"gini = 0.0\\nsamples = 2\\nvalue = [0, 0, 2]\\nclass = virginica\", fillcolor=\"#8139e5ff\"] ;\n",
      "13 -> 14 ;\n",
      "15 [label=\"gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]\\nclass = versicolor\", fillcolor=\"#39e581ff\"] ;\n",
      "13 -> 15 ;\n",
      "16 [label=\"gini = 0.0\\nsamples = 43\\nvalue = [0, 0, 43]\\nclass = virginica\", fillcolor=\"#8139e5ff\"] ;\n",
      "12 -> 16 ;\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "dot_data = export_graphviz(clf, out_file=None,  filled=True, rounded=True,\n",
    "                                                  feature_names=iris.feature_names, class_names=iris.target_names)\n",
    "print(dot_data)"
   ]
  },
  {
   "attachments": {
    "Pasted%20image%20at%202017_10_30%2003_14%20PM.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Pasted%20image%20at%202017_10_30%2003_14%20PM.png](attachment:Pasted%20image%20at%202017_10_30%2003_14%20PM.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.4 K-fold cross validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10-fold cross validationを行い、モデルの性能評価を行う時は以下のようにスクリプトを書けば良い。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  0.93333333,  1.        ,  0.93333333,  0.93333333,\n",
       "        0.86666667,  0.93333333,  0.93333333,  1.        ,  1.        ])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(random_state=None)\n",
    "cross_val_score(clf, X_iris, y_iris, cv=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "このようにcross validationを行う時に、以下のように`max_depth`の値を変化させて性能の差を比較し、  \n",
    "最もよい性能の時のmax_depthを採用するという判断もできる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_depth=1, score=0.6666666666666667\n",
      "max_depth=2, score=0.9533333333333334\n",
      "max_depth=3, score=0.96\n",
      "max_depth=4, score=0.96\n",
      "max_depth=5, score=0.9533333333333334\n"
     ]
    }
   ],
   "source": [
    "for max_depth in range(1, 6):\n",
    "    clf = DecisionTreeClassifier(random_state=None, max_depth=max_depth)\n",
    "    score = cross_val_score(clf, X_iris, y_iris, cv=10)\n",
    "    print(f'max_depth={max_depth}, score={score.mean()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "今回の例では、`max_depth=1`の時は少しスコアが低いが、それ以降はほぼ同性能である。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数値だけでなくグラフにして`max_depth`の変化に伴う正答率の変化を見たい場合は以下のように書くと良い。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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weNoMRt1+FwGhWnKlrfAkWTxB9SzpeBE5ARwG2tbyaa3cxaKBvT0bobLmty9R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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1850ae5a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_scores = []\n",
    "\n",
    "for max_depth in range(1, 6):\n",
    "    clf = DecisionTreeClassifier(random_state=None, max_depth=max_depth)\n",
    "    score = cross_val_score(clf, X_iris, y_iris, cv=10)\n",
    "    accuracy_scores.append(score)\n",
    "\n",
    "\n",
    "X = list(range(1, 6))\n",
    "plt.plot(X, accuracy_scores)\n",
    "\n",
    "plt.xlabel('max depth')\n",
    "plt.ylabel('accuracy rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "目的変数が数値情報の場合は[DecisionTressClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.tree.ExtraTreeClassifier.html#sklearn.tree.ExtraTreeClassifier)の代わりに[DecisionTreeRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.tree.ExtraTreeRegressor.html#sklearn.tree.ExtraTreeRegressor)を用いれば同様の解析ができる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 解析するデータの読み込み"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston = load_boston()\n",
    "X_boston = pd.DataFrame(boston.data)\n",
    "y_boston = boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0     1     2    3      4      5     6       7    8      9     10  \\\n",
       "0  0.00632  18.0  2.31  0.0  0.538  6.575  65.2  4.0900  1.0  296.0  15.3   \n",
       "1  0.02731   0.0  7.07  0.0  0.469  6.421  78.9  4.9671  2.0  242.0  17.8   \n",
       "2  0.02729   0.0  7.07  0.0  0.469  7.185  61.1  4.9671  2.0  242.0  17.8   \n",
       "3  0.03237   0.0  2.18  0.0  0.458  6.998  45.8  6.0622  3.0  222.0  18.7   \n",
       "4  0.06905   0.0  2.18  0.0  0.458  7.147  54.2  6.0622  3.0  222.0  18.7   \n",
       "\n",
       "       11    12  \n",
       "0  396.90  4.98  \n",
       "1  396.90  9.14  \n",
       "2  392.83  4.03  \n",
       "3  394.63  2.94  \n",
       "4  396.90  5.33  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_boston.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 24. ,  21.6,  34.7,  33.4,  36.2])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_boston[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 13)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_boston.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 機械学習"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 24. ,  21.6,  34.7,  33.4,  36.2])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = DecisionTreeRegressor()\n",
    "regressor.fit(X_boston, y_boston)\n",
    "y_predicted = regressor.predict(X_boston)\n",
    "y_predicted[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[mean_squared_error](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html)関数を用いてRoot Mean Square Error (RMSE) を求める。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_boston, y_predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "性能が良すぎてmean_squared_errorなどが正しく動いているかわかりにくい。  \n",
    "これはデフォルトでは、完全にデータを分離されるかあるいはノードに含まれるデータが最低2つになるまで  \n",
    "ノードを伸長していくように設定されているからである。  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "そこで`max_depth=3`とし決定木の最大の深さを3に設定してみる。  \n",
    "max_depthの値を小さくするほどより不正確になるが、過学習しなくなったり、決定木は簡単になり解釈がしやすくなることもある。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 22.9052    ,  22.9052    ,  33.34883721,  33.34883721,  33.34883721])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = DecisionTreeRegressor(max_depth=3)\n",
    "regressor.fit(X_boston, y_boston)\n",
    "y_predicted = regressor.predict(X_boston)\n",
    "y_predicted[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[mean_squared_error](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html)関数を用いてRoot Mean Square Error (RMSE) を求める。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.423306191629861"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_boston, y_predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3 決定木の視覚化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Graphvizがインストールされている場合"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.3.1と同様にスクリプトを実行することで決定木を視覚化できる。  \n",
    "`max_depth=3`としているので一番上のノードから最大で３個までしかノードが発生していないのがわかる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "<graphviz.files.Source at 0x7f1814216f28>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import graphviz\n",
    "dot_data = export_graphviz(regressor, out_file=None,  filled=True, rounded=True, class_names=boston)\n",
    "graph = graphviz.Source(dot_data)\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Graphvizがインストールされていない場合については1.3.2と同様なので省略する。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.4 K-fold cross validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10-fold cross validationを行い、モデルの性能評価を行う時は以下のようにスクリプトを書けば良い。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  1.        ,  1.        ,  0.99792244,  0.        ,\n",
       "        0.        ,  0.99792244,  0.        ,  0.        ,  0.        ])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = DecisionTreeRegressor(max_depth=3)\n",
    "cross_val_score(regressor, X_iris, y_iris, cv=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "このようにcross validationを行う時に、以下のように`max_depth`の値を変化させて性能の差を比較し、  \n",
    "最もよい性能の時のmax_depthを採用するという判断もできる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_depth=1, score=0.2901063693451237\n",
      "max_depth=2, score=0.49216283351605944\n",
      "max_depth=3, score=0.49958448753462603\n",
      "max_depth=4, score=0.6\n",
      "max_depth=5, score=0.5700000000000001\n",
      "max_depth=6, score=0.6\n",
      "max_depth=7, score=0.6\n",
      "max_depth=8, score=0.6\n",
      "max_depth=9, score=0.5700000000000001\n"
     ]
    }
   ],
   "source": [
    "for max_depth in range(1, 10):\n",
    "    regressor = DecisionTreeRegressor(random_state=None, max_depth=max_depth)\n",
    "    score = cross_val_score(regressor, X_iris, y_iris, cv=10)\n",
    "    print(f'max_depth={max_depth}, score={score.mean()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "今回の例では、`max_depth=1, 2, 3`の時はスコアが低いが、それ以降はほぼ同性能である。"
   ]
  }
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