LinearRegression_with_sklearn线性回归.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pizza price problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fjIjqzrm2oNKF+rGNrDey/uUfkvSBtB1J2jPNfp6s6Wm4ci2VjnwelXRIimVj\nSZvWWGQJ0CmpK5WfJGm3dCXX9Ii4HjiVrB6nNDl8G4WcFKxdbDJwSSrwK+CXwBmDlPsSME/S7xjZ\nke6HgeMl/R5YTOURr5cBn0snpneuUa4MRwMnSbqT7HzBq4YqGNmjaw8HvphiX0TWFDYBuETSXcDv\ngK9HxKqmR26jjntJNTOznI8UzMws56RgZmY5JwUzM8s5KZiZWc5JwczMck4KZmaWc1IwM7Pc/wfH\nePrj52CyfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x204090a6208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "X = [[6],[8],[10],[14],[18]]\n",
    "y = [[7],[9],[13],[17.5],[18]]\n",
    "plt.figure()\n",
    "plt.title('Pizza price plotted against diameter')\n",
    "plt.xlabel('Diameter in inches')\n",
    "plt.ylabel('Price in dollars')\n",
    "plt.plot(X,y,'k.')\n",
    "plt.axis([0,25,0,25])\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A 12\" pizza should cost: $13.68\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Kismet\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "X = [[6],[8],[10],[14],[18]]\n",
    "y = [[7],[9],[13],[17.5],[18]]\n",
    "model = LinearRegression()\n",
    "model.fit(X,y)\n",
    "print('A 12\" pizza should cost: $%.2f' % model.predict([12])[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VDMQqbM4cGD4czj8f1lwTrr8evvUtPwXNzBax1M9FsC7g3nvTsw5efhmOOip1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\ngpmZFfx/i5JAhbWG3poAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2040a0f6828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.title('Pizza price plotted against diameter')\n",
    "plt.xlabel('Diameter in inches')\n",
    "plt.ylabel('Price in dollars')\n",
    "plt.plot(X,model.predict(X).reshape(-1,1),color='red')\n",
    "plt.plot(X,y,'k.')\n",
    "plt.axis([0,25,0,25])\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Residual sum of squares: 1.75\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "print('Residual sum of squares: %.2f'% np.mean((model.predict(X)-y) **2)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.199999999999999"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var([6,8,10,14,18],ddof=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.650000000000002"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cov([6,8,10,14,18],[7,9,13,17.5,18])[0][1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## R squared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.6620\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "X = [[6],[8],[10],[14],[18]]\n",
    "y = [[7],[9],[13],[17.5],[18]]\n",
    "X_test = [[8],[9],[11],[16],[12]]\n",
    "y_test = [[11],[8.5],[15],[18],[11]]\n",
    "model = LinearRegression()\n",
    "model.fit(X,y)\n",
    "print('R-squared: %.4f'% model.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.1875    ]\n",
      " [ 1.01041667]\n",
      " [ 0.39583333]]\n"
     ]
    }
   ],
   "source": [
    "from numpy.linalg import lstsq   #lstsq 是 LeaST SQuare （最小二乘）的意思。我们常用最小二乘法来求解超定线性方程组\n",
    "X = [[1,6,2],[1,8,1],[1,10,0],[1,14,2],[1,18,0]]\n",
    "y = [[7],[9],[13],[17.5],[18]]\n",
    "print(lstsq(X,y)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: [ 10.0625], Target: [11]\n",
      "Predicted: [ 10.28125], Target: [8.5]\n",
      "Predicted: [ 13.09375], Target: [15]\n",
      "Predicted: [ 18.14583333], Target: [18]\n",
      "Predicted: [ 13.3125], Target: [11]\n",
      "R-squared: 0.77\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "X = [[6,2],[8,1],[10,0],[14,2],[18,0]]\n",
    "y = [[7],[9],[13],[17.5],[18]]\n",
    "model = LinearRegression()\n",
    "model.fit(X,y)\n",
    "X_test = [[8,2],[9,0],[11,2],[16,2],[12,0]]\n",
    "y_test = [[11],[8.5],[15],[18],[11]]\n",
    "predictions = model.predict(X_test)\n",
    "for i,prediction in enumerate(predictions):\n",
    "    print('Predicted: %s, Target: %s' % (prediction,y_test[i]))\n",
    "print('R-squared: %.2f'% model.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Polynomial regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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c/JNKtoSEBEK18g5wriz8kpPp/MADGBHW/u9/OIILqMB1kUMJDiZHpbI9zkGz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\nvD9Bj4tsvXr1WmeMKdpQe8a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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11520f358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6], [8], [10], [14], [18]]\n",
      "[[  1.00000000e+00   6.00000000e+00   3.60000000e+01   2.16000000e+02\n",
      "    1.29600000e+03   7.77600000e+03   4.66560000e+04   2.79936000e+05\n",
      "    1.67961600e+06   1.00776960e+07]\n",
      " [  1.00000000e+00   8.00000000e+00   6.40000000e+01   5.12000000e+02\n",
      "    4.09600000e+03   3.27680000e+04   2.62144000e+05   2.09715200e+06\n",
      "    1.67772160e+07   1.34217728e+08]\n",
      " [  1.00000000e+00   1.00000000e+01   1.00000000e+02   1.00000000e+03\n",
      "    1.00000000e+04   1.00000000e+05   1.00000000e+06   1.00000000e+07\n",
      "    1.00000000e+08   1.00000000e+09]\n",
      " [  1.00000000e+00   1.40000000e+01   1.96000000e+02   2.74400000e+03\n",
      "    3.84160000e+04   5.37824000e+05   7.52953600e+06   1.05413504e+08\n",
      "    1.47578906e+09   2.06610468e+10]\n",
      " [  1.00000000e+00   1.80000000e+01   3.24000000e+02   5.83200000e+03\n",
      "    1.04976000e+05   1.88956800e+06   3.40122240e+07   6.12220032e+08\n",
      "    1.10199606e+10   1.98359290e+11]]\n",
      "[[6], [8], [11], [16]]\n",
      "[[  1.00000000e+00   6.00000000e+00   3.60000000e+01   2.16000000e+02\n",
      "    1.29600000e+03   7.77600000e+03   4.66560000e+04   2.79936000e+05\n",
      "    1.67961600e+06   1.00776960e+07]\n",
      " [  1.00000000e+00   8.00000000e+00   6.40000000e+01   5.12000000e+02\n",
      "    4.09600000e+03   3.27680000e+04   2.62144000e+05   2.09715200e+06\n",
      "    1.67772160e+07   1.34217728e+08]\n",
      " [  1.00000000e+00   1.10000000e+01   1.21000000e+02   1.33100000e+03\n",
      "    1.46410000e+04   1.61051000e+05   1.77156100e+06   1.94871710e+07\n",
      "    2.14358881e+08   2.35794769e+09]\n",
      " [  1.00000000e+00   1.60000000e+01   2.56000000e+02   4.09600000e+03\n",
      "    6.55360000e+04   1.04857600e+06   1.67772160e+07   2.68435456e+08\n",
      "    4.29496730e+09   6.87194767e+10]]\n",
      "Simple linear regression r-squared 0.809726797708\n",
      "Quadrastic regression r-squared -0.094356667043\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "X_train = [[6],[8],[10],[14],[18]]\n",
    "y_train = [[7],[9],[13],[17.5],[18]]\n",
    "X_test = [[6],[8],[11],[16]]\n",
    "y_test = [[8],[12],[15],[18]]\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train,y_train)\n",
    "xx = np.linspace(0,26,100)\n",
    "yy = regressor.predict(xx.reshape(xx.shape[0],1))\n",
    "plt.plot(xx,yy)\n",
    "\n",
    "quadratic_featurizer = PolynomialFeatures(degree=2)        #更改阶数\n",
    "X_train_quadratic = quadratic_featurizer.fit_transform(X_train)\n",
    "X_test_quadratic = quadratic_featurizer.transform(X_test)\n",
    "regressor_quadratic = LinearRegression()\n",
    "regressor_quadratic.fit(X_train_quadratic,y_train)\n",
    "xx_quadratic = quadratic_featurizer.transform(xx.reshape(xx.shape[0],1))\n",
    "plt.plot(xx,regressor_quadratic.predict(xx_quadratic),c='r',linestyle='--')\n",
    "plt.title('Pizza price regressed on diameter')\n",
    "plt.xlabel('Diameter in inches')\n",
    "plt.ylabel('Price in dollars')\n",
    "plt.axis([0,25,0,25])\n",
    "plt.grid(True)\n",
    "plt.scatter(X_train,y_train)\n",
    "plt.show()\n",
    "\n",
    "print(X_train)\n",
    "print(X_train_quadratic)\n",
    "print(X_test)\n",
    "print(X_test_quadratic)\n",
    "print('Simple linear regression r-squared',regressor.score(X_test,y_test))\n",
    "print('Quadrastic regression r-squared',regressor_quadratic.score(X_test_quadratic,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wine datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "datasets : https://archive.ics.uci.edu/ml/datasets/Wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0      1     2     3     4    5     6     7     8     9     10    11    12  \\\n",
       "0   1  14.23  1.71  2.43  15.6  127  2.80  3.06  0.28  2.29  5.64  1.04  3.92   \n",
       "1   1  13.20  1.78  2.14  11.2  100  2.65  2.76  0.26  1.28  4.38  1.05  3.40   \n",
       "2   1  13.16  2.36  2.67  18.6  101  2.80  3.24  0.30  2.81  5.68  1.03  3.17   \n",
       "3   1  14.37  1.95  2.50  16.8  113  3.85  3.49  0.24  2.18  7.80  0.86  3.45   \n",
       "4   1  13.24  2.59  2.87  21.0  118  2.80  2.69  0.39  1.82  4.32  1.04  2.93   \n",
       "\n",
       "     13  \n",
       "0  1065  \n",
       "1  1050  \n",
       "2  1185  \n",
       "3  1480  \n",
       "4   735  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "data = pd.read_csv('./some datasets/wine.csv',header=None)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.columns = ['class','Alcohol','Malic acid','Ash','Alcalinity of ash','Magnesium','Total phenols','Flavanoids','Nonflavanoid phenols','Proanthocyanins','Color intensity','Hue','OD280/OD315 of diluted wines','Proline']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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mlQ17Im9fLclPRTIzq7EiH9G6C/hkRKySNBZYKemuiFhf1ueXwJsi4llJZwGLgVllw8+I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\nmJlZkv8GtAYNXz5jQI8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fb35a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['Flavanoids'],data['class'])\n",
    "plt.xlabel('Alcohol')\n",
    "plt.ylabel('class')\n",
    "plt.title('Alcohol Against class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xueji/anaconda/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "import pandas as pd\n",
    "import matplotlib.pylab as plt\n",
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r-squared:  0.917084159351\n"
     ]
    }
   ],
   "source": [
    "X = data[list(data.columns)[1:]]\n",
    "y = data[list(data.columns)[0]]\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y)\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train,y_train)\n",
    "y_predictions = regressor.predict(X_test)\n",
    "print('r-squared: ',regressor.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 交叉检验的实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.183425405594 [ 0.          0.51460008  0.          0.40252695  0.        ]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import cross_val_score\n",
    "regressor = LinearRegression()\n",
    "scores = cross_val_score(regressor,X,y,cv=5)\n",
    "print(scores.mean(),scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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m3RVDRPwWeHScRY4GLojM9UCvpN2AA4BVEXF3RDwD/CgtW3hMEXFdRDyWJq8H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\nAydmHbuq0uzGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f83e160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,y_predictions)\n",
    "plt.xlabel('true_class')\n",
    "plt.ylabel('predicted_class')\n",
    "plt.title('true_class & predicted_class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted:0.917604209662, True:1\n",
      "Predicted:2.00955127963, True:2\n",
      "Predicted:2.8821892583, True:3\n",
      "Predicted:3.21490417233, True:3\n",
      "Predicted:1.05632080008, True:1\n",
      "Predicted:2.90999366897, True:3\n",
      "Predicted:2.11890229528, True:2\n",
      "Predicted:2.0004470338, True:2\n",
      "Predicted:1.49633974169, True:1\n",
      "Predicted:2.80264401812, True:3\n",
      "Predicted:0.966399401063, True:1\n",
      "Predicted:1.05065329403, True:1\n",
      "Predicted:3.10969072623, True:3\n",
      "Predicted:1.38290660651, True:1\n",
      "Predicted:2.43016814305, True:3\n",
      "Predicted:2.02278177001, True:2\n",
      "Predicted:1.28631019527, True:1\n",
      "Predicted:1.95084690492, True:2\n",
      "Predicted:2.19229493799, True:2\n",
      "Predicted:1.7488666495, True:2\n",
      "Predicted:1.30154203668, True:1\n",
      "Predicted:1.52353218958, True:2\n",
      "Predicted:3.01595003076, True:3\n",
      "Predicted:1.06093469479, True:1\n",
      "Predicted:2.06911448043, True:2\n",
      "Predicted:0.837995284724, True:1\n",
      "Predicted:0.728661394454, True:1\n",
      "Predicted:2.44807251127, True:2\n",
      "Predicted:1.57400393993, True:2\n",
      "Predicted:2.02611264125, True:2\n",
      "Predicted:1.18454071887, True:1\n",
      "Predicted:1.14747735768, True:1\n",
      "Predicted:2.98899793587, True:3\n",
      "Predicted:2.84767743702, True:3\n",
      "Predicted:1.90426358747, True:2\n",
      "Predicted:1.76026189783, True:2\n",
      "Predicted:1.22698476024, True:1\n",
      "Predicted:1.1459622308, True:1\n",
      "Predicted:2.90010512555, True:3\n",
      "Predicted:2.80419489852, True:3\n",
      "Predicted:1.98232244559, True:2\n",
      "Predicted:3.04184602356, True:3\n",
      "Predicted:3.24805578398, True:3\n",
      "Predicted:1.66832343889, True:2\n",
      "Predicted:2.1208486548, True:2\n"
     ]
    }
   ],
   "source": [
    "for t,p in zip(y_test,y_predictions):\n",
    "    print('Predicted:%s, True:%s' % (p,t))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boston Housing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据来源：https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
    "\n",
    "数据解释：https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cross_validation import train_test_split\n",
    "data = load_boston()\n",
    "X_train,X_test,y_train,y_test = train_test_split(data.data,data.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#归一化处理\n",
    "X_scaler = StandardScaler()\n",
    "y_scaler = StandardScaler()\n",
    "X_train = X_scaler.fit_transform(X_train.reshape(-1,1))\n",
    "y_train = y_scaler.fit_transform(y_train.reshape(-1,1))\n",
    "X_test = X_scaler.transform(X_test.reshape(-1,1))\n",
    "y_test = y_scaler.transform(y_test.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xueji/anaconda/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:84: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDRegressor'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Found input variables with inconsistent numbers of samples: [4927, 379]",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-55-0ebcc2e37c74>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mregressor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSGDRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'squared_loss'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mregressor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'交叉验证R方值：'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'交叉验证R方均值：'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mregressor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/xueji/anaconda/lib/python3.6/site-packages/sklearn/cross_validation.py\u001b[0m in \u001b[0;36mcross_val_score\u001b[0;34m(estimator, X, y, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch)\u001b[0m\n\u001b[1;32m   1568\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1569\u001b[0m     \"\"\"\n\u001b[0;32m-> 1570\u001b[0;31m     \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1571\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1572\u001b[0m     \u001b[0mcv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_cv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mis_classifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/xueji/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mindexable\u001b[0;34m(*iterables)\u001b[0m\n\u001b[1;32m    196\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m             \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 198\u001b[0;31m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    199\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/xueji/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    171\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m         raise ValueError(\"Found input variables with inconsistent numbers of\"\n\u001b[0;32m--> 173\u001b[0;31m                          \" samples: %r\" % [int(l) for l in lengths])\n\u001b[0m\u001b[1;32m    174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [4927, 379]"
     ]
    }
   ],
   "source": [
    "regressor = SGDRegressor(loss='squared_loss')\n",
    "scores = cross_val_score(regressor,X_train,y_train,cv=5)\n",
    "print('交叉验证R方值：',score)\n",
    "print('交叉验证R方均值：',np.mean(scores))\n",
    "regressor.fit_transform(X_train,y_train)\n",
    "print('测试集R方值：',regressor.score(X_test,y_test))"
   ]
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