linear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression implementation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.optimize import minimize\n",
    "from sklearn.cross_validation import train_test_split\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "user_data = pd.read_csv('data/user_data.tsv', sep='\\t', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_count</th>\n",
       "      <th>elite</th>\n",
       "      <th>account_age</th>\n",
       "      <th>fans</th>\n",
       "      <th>profile</th>\n",
       "      <th>useful_votes</th>\n",
       "      <th>num_tips</th>\n",
       "      <th>num_likes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>---teJGnwK07UO6_oJfbRw</th>\n",
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       "      <td>2017</td>\n",
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       "    <tr>\n",
       "      <th>--0KsjlAThNWua2Pr4HStQ</th>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>1010</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>--0mI_q_0D1CdU4P_hoImQ</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>--106arHH4D3fLenTl3YZA</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>921</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "                        review_count  elite  account_age  fans  profile  \\\n",
       "user_id                                                                   \n",
       "---teJGnwK07UO6_oJfbRw             2      0         2017     0        0   \n",
       "--0HEXd4W6bJI8k7E0RxTA            34      0         1651     2        0   \n",
       "--0KsjlAThNWua2Pr4HStQ            60      2         1010     2        0   \n",
       "--0mI_q_0D1CdU4P_hoImQ            31      0         1956     1        0   \n",
       "--106arHH4D3fLenTl3YZA             1      0          921     0        0   \n",
       "\n",
       "                        useful_votes  num_tips  num_likes  \n",
       "user_id                                                    \n",
       "---teJGnwK07UO6_oJfbRw             0       0.0        0.0  \n",
       "--0HEXd4W6bJI8k7E0RxTA            15       0.0        0.0  \n",
       "--0KsjlAThNWua2Pr4HStQ           191       0.0        0.0  \n",
       "--0mI_q_0D1CdU4P_hoImQ            17       1.0        0.0  \n",
       "--106arHH4D3fLenTl3YZA             1       0.0        0.0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_age</th>\n",
       "      <th>elite</th>\n",
       "      <th>fans</th>\n",
       "      <th>num_likes</th>\n",
       "      <th>num_tips</th>\n",
       "      <th>profile</th>\n",
       "      <th>review_count</th>\n",
       "      <th>useful_votes</th>\n",
       "      <th>user_id</th>\n",
       "      <th>Active Life</th>\n",
       "      <th>...</th>\n",
       "      <th>Financial Services</th>\n",
       "      <th>Food</th>\n",
       "      <th>Health &amp; Medical</th>\n",
       "      <th>Home Services</th>\n",
       "      <th>Hotels &amp; Travel</th>\n",
       "      <th>Local Services</th>\n",
       "      <th>Nightlife</th>\n",
       "      <th>Pets</th>\n",
       "      <th>Restaurants</th>\n",
       "      <th>Shopping</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [account_age, elite, fans, num_likes, num_tips, profile, review_count, useful_votes, user_id, Active Life, Arts & Entertainment, Automotive, Beauty & Spas, Event Planning & Services, Financial Services, Food, Health & Medical, Home Services, Hotels & Travel, Local Services, Nightlife, Pets, Restaurants, Shopping]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 24 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_matrix = pd.read_json('data/feature_matrix.json')\n",
    "feature_matrix = feature_matrix[['account_age', 'elite', 'fans', 'num_likes', 'num_tips', 'profile', 'review_count', 'useful_votes']]\n",
    "feature_matrix['user_id'] = feature_matrix.index\n",
    "#feature_train, feature_test = train_test_split(feature_matrix, test_size=0.3)\n",
    "\n",
    "user_weights = pd.read_csv('user_weights.csv', index_col=0)\n",
    "\n",
    "data = pd.merge(feature_matrix, user_weights, how='inner', on=['user_id'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y = user_weights.loc[user_weights.index,:]\n",
    "y = y['Restaurants'].as_matrix()\n",
    "\n",
    "X = feature_matrix.as_matrix()\n",
    "rows, columns = X.shape\n",
    "X = np.insert(X, 0, 1, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def cost(theta, X, y):\n",
    "    \"\"\"\n",
    "    Calculates the sum of squares cost for a given set of parameters\n",
    "    \"\"\"\n",
    "    X = np.matrix( X )\n",
    "    y = np.matrix( y )\n",
    "    theta = np.matrix( theta )\n",
    "    \n",
    "    # Predicted labels\n",
    "    y_hat = X.dot( theta.T )\n",
    "    \n",
    "    # Sum of error terms in prediction\n",
    "    SOS = np.power( np.subtract( y_hat, y ), 2 ).sum()\n",
    "    \n",
    "    N = y.size\n",
    "    cost = ( 1.0 / ( 2 * N ) ) * SOS\n",
    "    return cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class LinearRegression(object):\n",
    "    def __init__(self):\n",
    "        self.theta = None\n",
    "        \n",
    "    def train(self, X, y):\n",
    "        \"\"\"\n",
    "        Estimates linear regression parameters\n",
    "        \"\"\"\n",
    "        rows, columns = X.shape\n",
    "        # Initialize random values for weights\n",
    "        self.theta = np.matrix([random.random() for _ in xrange(columns)])\n",
    "        res = minimize( cost, self.theta, (X, y) )\n",
    "        self.theta = res.x\n",
    "        \n",
    "    def predict(self, X):\n",
    "        \"\"\"\n",
    "        Predict output for a given vector X\n",
    "        \"\"\"\n",
    "        return X.dot(self.theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
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      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
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      "\u001b[1;32m<ipython-input-44-3f90269e7be5>\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m     10\u001b[0m         \u001b[1;31m# Initialize random values for weights\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtheta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mxrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mminimize\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mcost\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtheta\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtheta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/jacob/build/anaconda2/lib/python2.7/site-packages/scipy/optimize/_minimize.pyc\u001b[0m in \u001b[0;36mminimize\u001b[1;34m(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)\u001b[0m\n\u001b[0;32m    439\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0m_minimize_cg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfun\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mjac\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    440\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'bfgs'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 441\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_minimize_bfgs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfun\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mjac\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    442\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mmeth\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'newton-cg'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    443\u001b[0m         return _minimize_newtoncg(fun, x0, args, jac, hess, hessp, callback,\n",
      "\u001b[1;32m/home/jacob/build/anaconda2/lib/python2.7/site-packages/scipy/optimize/optimize.pyc\u001b[0m in \u001b[0;36m_minimize_bfgs\u001b[1;34m(fun, x0, args, jac, callback, gtol, norm, eps, maxiter, disp, return_all, **unknown_options)\u001b[0m\n\u001b[0;32m    845\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    846\u001b[0m         \u001b[0mgrad_calls\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmyfprime\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwrap_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfprime\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 847\u001b[1;33m     \u001b[0mgfk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmyfprime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    848\u001b[0m     \u001b[0mk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    849\u001b[0m     \u001b[0mN\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/jacob/build/anaconda2/lib/python2.7/site-packages/scipy/optimize/optimize.pyc\u001b[0m in \u001b[0;36mfunction_wrapper\u001b[1;34m(*wrapper_args)\u001b[0m\n\u001b[0;32m    287\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mwrapper_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    288\u001b[0m         \u001b[0mncalls\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 289\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwrapper_args\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    290\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    291\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mncalls\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/jacob/build/anaconda2/lib/python2.7/site-packages/scipy/optimize/optimize.pyc\u001b[0m in \u001b[0;36mapprox_fprime\u001b[1;34m(xk, f, epsilon, *args)\u001b[0m\n\u001b[0;32m    620\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    621\u001b[0m     \"\"\"\n\u001b[1;32m--> 622\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_approx_fprime_helper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepsilon\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    623\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    624\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/jacob/build/anaconda2/lib/python2.7/site-packages/scipy/optimize/optimize.pyc\u001b[0m in \u001b[0;36m_approx_fprime_helper\u001b[1;34m(xk, f, epsilon, args, f0)\u001b[0m\n\u001b[0;32m    554\u001b[0m     \"\"\"\n\u001b[0;32m    555\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mf0\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 556\u001b[1;33m         \u001b[0mf0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    557\u001b[0m     \u001b[0mgrad\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    558\u001b[0m     \u001b[0mei\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/jacob/build/anaconda2/lib/python2.7/site-packages/scipy/optimize/optimize.pyc\u001b[0m in \u001b[0;36mfunction_wrapper\u001b[1;34m(*wrapper_args)\u001b[0m\n\u001b[0;32m    287\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mwrapper_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    288\u001b[0m         \u001b[0mncalls\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 289\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwrapper_args\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    290\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    291\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mncalls\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-43-6f31e34a4151>\u001b[0m in \u001b[0;36mcost\u001b[1;34m(theta, X, y)\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m     \u001b[1;31m# Sum of error terms in prediction\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m     \u001b[0mSOS\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpower\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubtract\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0my_hat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m     \u001b[0mN\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMemoryError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "linReg = LinearRegression()\n",
    "\n",
    "\n",
    "linReg.train(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def unit_test():\n",
    "    \"\"\"\n",
    "    Tests functionality of linear regression class\n",
    "    \"\"\"\n",
    "    csv = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)      \n",
    "    train = csv.sample( frac = 0.8, random_state = 0 )\n",
    "    test = csv.drop( train.index )\n",
    "    \n",
    "    testReg = LinearRegression()\n",
    "    \n",
    "    X = train[['TV', 'Radio', 'Newspaper']].as_matrix()\n",
    "    \n",
    "    rows, columns = X.shape\n",
    "    X = np.insert(X, 0, 1, axis=1)\n",
    "    y = np.matrix(train['Sales']).T\n",
    "    \n",
    "    testReg.train(X, y)\n",
    "    \n",
    "    X_test = test[['TV', 'Radio', 'Newspaper']].as_matrix()\n",
    "    X_test = np.insert(X_test, 0, 1, axis=1)\n",
    "    y_test = np.matrix(test['Sales']).T\n",
    "        \n",
    "    if np.isclose( cost( testReg.theta, X_test, y_test ), 1.57881917886 ):\n",
    "        print \"Unit test passed!\"\n",
    "        \n",
    "    else:\n",
    "        print \"Unit test failed : (\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unit test passed!\n"
     ]
    }
   ],
   "source": [
    "unit_test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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