lec72-75-線形回帰.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step1: データの準備"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boston House Prices dataset\n",
      "===========================\n",
      "\n",
      "Notes\n",
      "------\n",
      "Data Set Characteristics:  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive\n",
      "    \n",
      "    :Median Value (attribute 14) is usually the target\n",
      "\n",
      "    :Attribute Information (in order):\n",
      "        - CRIM     per capita crime rate by town\n",
      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
      "        - INDUS    proportion of non-retail business acres per town\n",
      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
      "        - RM       average number of rooms per dwelling\n",
      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
      "        - DIS      weighted distances to five Boston employment centres\n",
      "        - RAD      index of accessibility to radial highways\n",
      "        - TAX      full-value property-tax rate per $10,000\n",
      "        - PTRATIO  pupil-teacher ratio by town\n",
      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
      "        - LSTAT    % lower status of the population\n",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "http://archive.ics.uci.edu/ml/datasets/Housing\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      "**References**\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
      "   - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(boston.DESCR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step2: ひとまず可視化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of houses')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(boston.target,bins=50)\n",
    "plt.xlabel('Price in $1,000s')\n",
    "plt.ylabel('Number of houses')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Number of rooms')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 5番目のRMの列が部屋の数\n",
    "plt.scatter(boston.data[:,5],boston.target)\n",
    "\n",
    "plt.ylabel('Price in $1,000s')\n",
    "plt.xlabel('Number of rooms')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</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": [
       "      CRIM    ZN  INDUS  CHAS    NOX  ...    RAD    TAX  PTRATIO       B  LSTAT\n",
       "0  0.00632  18.0   2.31   0.0  0.538  ...    1.0  296.0     15.3  396.90   4.98\n",
       "1  0.02731   0.0   7.07   0.0  0.469  ...    2.0  242.0     17.8  396.90   9.14\n",
       "2  0.02729   0.0   7.07   0.0  0.469  ...    2.0  242.0     17.8  392.83   4.03\n",
       "3  0.03237   0.0   2.18   0.0  0.458  ...    3.0  222.0     18.7  394.63   2.94\n",
       "4  0.06905   0.0   2.18   0.0  0.458  ...    3.0  222.0     18.7  396.90   5.33\n",
       "\n",
       "[5 rows x 13 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df = DataFrame(boston.data)\n",
    "\n",
    "boston_df.columns = boston.feature_names\n",
    "\n",
    "boston_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 価格列を作成する\n",
    "boston_df['Price'] = boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>Price</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",
       "      <td>24.0</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",
       "      <td>21.6</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",
       "      <td>34.7</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",
       "      <td>33.4</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",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS  ...    PTRATIO       B  LSTAT  Price\n",
       "0  0.00632  18.0   2.31   0.0  ...       15.3  396.90   4.98   24.0\n",
       "1  0.02731   0.0   7.07   0.0  ...       17.8  396.90   9.14   21.6\n",
       "2  0.02729   0.0   7.07   0.0  ...       17.8  392.83   4.03   34.7\n",
       "3  0.03237   0.0   2.18   0.0  ...       18.7  394.63   2.94   33.4\n",
       "4  0.06905   0.0   2.18   0.0  ...       18.7  396.90   5.33   36.2\n",
       "\n",
       "[5 rows x 14 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ts-hirota/.pyenv/versions/anaconda3-5.3.1/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a1afde7b8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lmplotで回帰直線を引く\n",
    "sns.lmplot('RM','Price',data=boston_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step4: Numpyを使った単回帰"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506,)\n",
      "(506, 1)\n"
     ]
    }
   ],
   "source": [
    "# 部屋数\n",
    "X = boston_df.RM\n",
    "print(X.shape)\n",
    "X = np.vstack(boston_df.RM)\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506,)\n"
     ]
    }
   ],
   "source": [
    "Y = boston_df.Price\n",
    "print(Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Xを[X 1]の形にする\n",
    "X = np.array([[value, 1] for value in X],dtype=np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ts-hirota/.pyenv/versions/anaconda3-5.3.1/lib/python3.7/site-packages/ipykernel_launcher.py:3: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "# 最小二乗法で求めた近似直線の傾きと切片の計算を実行\n",
    "a, b = np.linalg.lstsq(X,Y)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a1b42a7b8>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 元のデータをプロットする\n",
    "plt.plot(boston_df.RM,boston_df.Price,'o')\n",
    "\n",
    "# 回帰直線を描く\n",
    "x = boston_df.RM\n",
    "plt.plot(x,a*x+b,'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step5: 誤差について"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ts-hirota/.pyenv/versions/anaconda3-5.3.1/lib/python3.7/site-packages/ipykernel_launcher.py:2: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# 最小二乗法結果をうける\n",
    "result = np.linalg.lstsq(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "平均二乗誤差の平方根は、6.60\n"
     ]
    }
   ],
   "source": [
    "# 2つ目の要素に、誤差の合計が入る\n",
    "error_total = result[1]\n",
    "\n",
    "# 誤差の平均値の平方根を計算する\n",
    "rmse = np.sqrt(error_total/len(X))\n",
    "\n",
    "print(f'平均二乗誤差の平方根は、{rmse[0]:0.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step6: scikit-leanを使った重回帰分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "lreg = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 説明変数\n",
    "X_multi = boston_df.drop('Price',1)\n",
    "\n",
    "# 目的変数\n",
    "Y_target = boston_df.Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# モデル作成\n",
    "lreg.fit(X_multi,Y_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "切片は36.49\n"
     ]
    }
   ],
   "source": [
    "print(f'切片は{lreg.intercept_:0.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "係数の数は13個\n"
     ]
    }
   ],
   "source": [
    "print(f'係数の数は{len(lreg.coef_)}個')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>Coefficient Estimate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CRIM</td>\n",
       "      <td>-0.107171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZN</td>\n",
       "      <td>0.046395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INDUS</td>\n",
       "      <td>0.020860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHAS</td>\n",
       "      <td>2.688561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NOX</td>\n",
       "      <td>-17.795759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RM</td>\n",
       "      <td>3.804752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AGE</td>\n",
       "      <td>0.000751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DIS</td>\n",
       "      <td>-1.475759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RAD</td>\n",
       "      <td>0.305655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TAX</td>\n",
       "      <td>-0.012329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PTRATIO</td>\n",
       "      <td>-0.953464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>B</td>\n",
       "      <td>0.009393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LSTAT</td>\n",
       "      <td>-0.525467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Price</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Features  Coefficient Estimate\n",
       "0      CRIM             -0.107171\n",
       "1        ZN              0.046395\n",
       "2     INDUS              0.020860\n",
       "3      CHAS              2.688561\n",
       "4       NOX            -17.795759\n",
       "5        RM              3.804752\n",
       "6       AGE              0.000751\n",
       "7       DIS             -1.475759\n",
       "8       RAD              0.305655\n",
       "9       TAX             -0.012329\n",
       "10  PTRATIO             -0.953464\n",
       "11        B              0.009393\n",
       "12    LSTAT             -0.525467\n",
       "13    Price                   NaN"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 全ての要素を説明変数に用いてFeaturesというカラム名にする\n",
    "coeff_df = DataFrame(boston_df.columns)\n",
    "coeff_df.columns = ['Features']\n",
    "\n",
    "coeff_df['Coefficient Estimate'] = pd.Series(lreg.coef_)\n",
    "coeff_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step7: 学習(Training)と検証(Validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 説明変数をX、目的変数をYとする\n",
    "X_train,X_test,Y_train,Y_test = sklearn.model_selection.train_test_split(X_multi,boston_df.Price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(379, 13) (127, 13) (379,) (127,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape,X_test.shape,Y_train.shape,Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step8: 価格の予測"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lreg = LinearRegression()\n",
    "\n",
    "lreg.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_train = lreg.predict(X_train)\n",
    "pred_test = lreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_trainを使ったモデルの平均二乗誤差=21.11\n",
      "X_testを使ったモデルの平均二乗誤差=24.98\n"
     ]
    }
   ],
   "source": [
    "print(f'X_trainを使ったモデルの平均二乗誤差={np.mean((Y_train - pred_train)**2):0.2f}')\n",
    "print(f'X_testを使ったモデルの平均二乗誤差={np.mean((Y_test - pred_test)**2):0.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step9: 残差プロット"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Residual Plots')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 学習用データ\n",
    "train = plt.scatter(pred_train,(pred_train - Y_train),c='b',alpha=0.5)\n",
    "# テスト用データ\n",
    "test = plt.scatter(pred_test,(pred_test - Y_test),c='r',alpha=0.5)\n",
    "# y=0の水平軸を描く\n",
    "plt.hlines(y=0,xmin=-10,xmax=50)\n",
    "\n",
    "plt.legend((train,test),('Training','Test'),loc='lower left')\n",
    "plt.title('Residual Plots')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}