1.Linear_Regression/Exercise-Boston_Housing_Problem(TensorFlow).ipynb

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   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "import seaborn; seaborn.set()\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn import decomposition\n",
    "\n",
    "import sys\n",
    "sys.path.append('./..')\n",
    "from tf_model.linear_regression import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "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": [
    "boston = load_boston()\n",
    "print(boston.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['data', 'target', 'DESCR', 'feature_names'])\n",
      "Header :  ['CRIM' 'ZN' 'INDUS' 'CHAS' 'NOX' 'RM' 'AGE' 'DIS' 'RAD' 'TAX' 'PTRATIO'\n",
      " 'B' 'LSTAT']\n",
      "Target :  [ 24.   21.6  34.7  33.4  36.2]\n"
     ]
    }
   ],
   "source": [
    "print(boston.keys())\n",
    "print(\"Header : \", boston.feature_names) \n",
    "print(\"Target : \", boston.target[0:5])  ## Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  \n",
       "0     15.3  396.90   4.98  \n",
       "1     17.8  396.90   9.14  \n",
       "2     17.8  392.83   4.03  \n",
       "3     18.7  394.63   2.94  \n",
       "4     18.7  396.90   5.33  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df = pd.DataFrame(boston.data, columns=boston.feature_names)\n",
    "boston_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 13)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Only Use : crim + chas + nox + rm + dis + ptratio + black + lstat\n",
    "boston_df.drop(['ZN', 'INDUS', 'NOX', 'AGE', 'RAD', 'TAX', 'B'], axis=1, inplace=True)\n",
    "boston_df.shape\n",
    "\n",
    "# Normalize\n",
    "boston_df_norm = (boston_df - boston_df.mean()) / (boston_df.max() - boston_df.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(456, 6)\n",
      "(456,)\n",
      "(50, 6)\n",
      "(50,)\n"
     ]
    }
   ],
   "source": [
    "# Split Training , Test Data\n",
    "\n",
    "train_X = boston_df_norm[:-50].astype(\"float32\")\n",
    "train_Y = boston.target[:-50].astype(\"float32\")\n",
    "test_X = boston_df_norm[-50:].astype(\"float32\")\n",
    "test_Y = boston.target[-50:].astype(\"float32\")\n",
    "\n",
    "print(train_X.shape)\n",
    "print(train_Y.shape)\n",
    "print(test_X.shape)\n",
    "print(test_Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# df => ndarray\n",
    "train_X = train_X.values\n",
    "test_X = test_X.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0050  loss=130.917191\n",
      "Epoch: 0100  loss=68.177673\n",
      "Epoch: 0150  loss=46.856190\n",
      "Epoch: 0200  loss=38.889648\n",
      "Epoch: 0250  loss=35.210922\n",
      "Epoch: 0300  loss=33.020306\n",
      "Epoch: 0350  loss=31.468092\n",
      "Epoch: 0400  loss=30.231146\n",
      "Epoch: 0450  loss=29.170765\n",
      "Epoch: 0500  loss=28.110998\n",
      "Epoch: 0550  loss=27.182676\n",
      "Epoch: 0600  loss=26.267014\n",
      "Epoch: 0650  loss=25.456444\n",
      "Epoch: 0700  loss=24.669088\n",
      "Epoch: 0750  loss=23.988594\n",
      "Epoch: 0800  loss=23.336798\n",
      "Epoch: 0850  loss=22.730820\n",
      "Epoch: 0900  loss=22.221388\n",
      "Epoch: 0950  loss=21.684364\n",
      "Epoch: 1000  loss=21.238050\n",
      "Optimization Finished!\n"
     ]
    }
   ],
   "source": [
    "lm = LinearRegression()\n",
    "lm.train(train_X, train_Y, learning_rate=0.1, num_iters=1000, batch_size=50, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pred_Y = []\n",
    "for i in range(test_X.shape[0]):\n",
    "    pred_Y.append(lm.predict(test_X[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# X Feature => 1D\n",
    "pca = decomposition.PCA(n_components=1)\n",
    "pca.fit(test_X)\n",
    "test_X_1d = pca.transform(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7BROxPV4RJSIioi41NGlRdL6+V48Z6ucONxfLlyojRozCAw/8DV988Snef38DXF1dkZOT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110fff2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(test_X_1d, test_Y, 'ro', label='Original data')\n",
    "plt.plot(test_X_1d, pred_Y, label='Fitted line')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.5",
   "language": "python",
   "name": "python3.5"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}