mynotebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from sklearn import linear_model\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trainfile = open('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 628,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<open file 'train.csv', mode 'r' at 0x10caa3ed0>\n"
     ]
    }
   ],
   "source": [
    "print trainfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for line in trainfile:\n",
    "    head = line.rstrip().split(',')\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Choice', 'A_follower_count', 'A_following_count', 'A_listed_count', 'A_mentions_received', 'A_retweets_received', 'A_mentions_sent', 'A_retweets_sent', 'A_posts', 'A_network_feature_1', 'A_network_feature_2', 'A_network_feature_3', 'B_follower_count', 'B_following_count', 'B_listed_count', 'B_mentions_received', 'B_retweets_received', 'B_mentions_sent', 'B_retweets_sent', 'B_posts', 'B_network_feature_1', 'B_network_feature_2', 'B_network_feature_3']\n",
      "23\n"
     ]
    }
   ],
   "source": [
    "print head\n",
    "\n",
    "print len(head)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train_A = []\n",
    "X_train_B = []\n",
    "y_train = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for line in trainfile:\n",
    "    splitted = line.rstrip().split(',')\n",
    "    label = int(splitted[0])\n",
    "    A_features = [float(item) for item in splitted[1:12]]\n",
    "    B_features = [float(item) for item in splitted[12:]]\n",
    "    y_train.append(label)\n",
    "    X_train_A.append(A_features)\n",
    "    X_train_B.append(B_features)\n",
    "#     print A_features\n",
    "trainfile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_train = np.array(y_train)\n",
    "X_train_A = np.array(X_train_A)\n",
    "X_train_B = np.array(X_train_B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def transform_features(x):\n",
    "    return np.log(1+x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(len(y_train)):\n",
    "    if y_train[i] == 0:\n",
    "        y_train[i] = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = transform_features(X_train_A) - transform_features(X_train_B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def normalize(X):\n",
    "    X_norm = X\n",
    "    cols = X.shape[1]\n",
    "    for i in range(cols):\n",
    "        m = np.mean(X[:, i])\n",
    "        std = np.std(X[:, i])\n",
    "        X_norm[:, i] = (X[:, i] - m)/std\n",
    "    return X_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.random.seed(1)      #Seed the random number generator to preserve the dev/test split\n",
    "permutation = np.random.permutation(X_train.shape[0])\n",
    "X_train = X_train[permutation,]\n",
    "y_train = y_train[permutation,]\n",
    "X_train = normalize(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5500\n"
     ]
    }
   ],
   "source": [
    "print len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "indices = np.random.permutation(X_train.shape[0])\n",
    "training_idx, test_idx = indices[:5000], indices[5000:]\n",
    "X_t = X_train[training_idx,:]\n",
    "X_test = X_train[test_idx, :]\n",
    "y_t = y_train[training_idx,]\n",
    "y_test = y_train[test_idx,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000\n",
      "500\n",
      "5000\n"
     ]
    }
   ],
   "source": [
    "print len(X_t)\n",
    "print len(X_test)\n",
    "print len(y_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = X_t\n",
    "y_train = y_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ExpLoss(X, Y, W, lmda):\n",
    "    loss = lmda * (W.dot(W))\n",
    "    yHat = X.dot(W)\n",
    "    activation = -Y * yHat\n",
    "    activationExp = np.exp(activation)\n",
    "    loss += np.sum(activationExp)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ExpLossGradient(x, y, W, lmda):\n",
    "    grad = (x.dot(W))\n",
    "    grad = -y * grad\n",
    "    grad = np.exp(grad)\n",
    "    grad = -y*x*grad\n",
    "    Wgrad = 2 * lmda * W\n",
    "    Wgrad = Wgrad + grad\n",
    "    return Wgrad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.special import expit\n",
    "\n",
    "def prediction(x, W):\n",
    "    return expit(x.dot(W))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def updateWeights(gradient, learningRate, W):\n",
    "    W = W - (learningRate * gradient)\n",
    "    return W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def logisticRegression(X, Y, maxIter, learningRate, lmda):\n",
    "    W = np.zeros(X.shape[1])\n",
    "    newLoss = ExpLoss(X, Y, W, lmda)\n",
    "    prevLoss = 0.0\n",
    "    count = 0\n",
    "    \n",
    "    while (True):\n",
    "        count += 1\n",
    "        for i in range(len(Y)):\n",
    "            #Not converged... so continue\n",
    "            d = X[i,]\n",
    "            gradient = ExpLossGradient(d, Y[i], W, lmda)\n",
    "            W = updateWeights(gradient, learningRate, W)\n",
    "        prevLoss = newLoss\n",
    "#         print W\n",
    "        newLoss = ExpLoss(X, Y, W, lmda)\n",
    "#         print \"Iteration # : \", count, \" Loss Value : \", newLoss\n",
    "        \n",
    "#         if abs(newLoss - prevLoss) < 0.0001 :\n",
    "# #             print \"Difference in old and new loss less than \", 0.0001\n",
    "# #             print \"Total Iterations till now : \", count\n",
    "# #             print \"prevLoss\", prevLoss\n",
    "# #             print \"newLoss\", newLoss\n",
    "#             break\n",
    "        \n",
    "        if count == maxIter :\n",
    "#             print \"MaxIterations reached!\"\n",
    "            break\n",
    "    return W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def runExperiments(X, Y, X_test, Y_test, lmda, learningRate, maxIter = 10):\n",
    "    W = logisticRegression(X, Y, maxIter, learningRate, lmda)\n",
    "    nCorrect = 0\n",
    "    nIncorrect = 0\n",
    "    for i in range(len(Y_test)):\n",
    "        y_hat = np.sign(X_test[i,].dot(W))\n",
    "        if y_hat == Y_test[i]:\n",
    "            nCorrect += 1\n",
    "        else:\n",
    "            nIncorrect += 1\n",
    "    return (nCorrect * 1.0/(nIncorrect + nCorrect))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "W = runExperiments(X_train, y_train, X_test, y_test, lmda=.0001, learningRate=.001, maxIter=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.81"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = linear_model.LogisticRegression(fit_intercept=False, max_iter=100, penalty='l2', verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibLinear]"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=False,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=1, warm_start=False)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_t,y_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p_train = model.predict_proba(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_pred1 = []\n",
    "y_pred2 = []\n",
    "for y in p_train:\n",
    "    if y[0] >= y[1]:\n",
    "        y_pred1.append(1)\n",
    "        y_pred2.append(0)\n",
    "    else:\n",
    "        y_pred1.append(0)\n",
    "        y_pred2.append(1)\n",
    "# p_train = p_train[:,1:2]\n",
    "\n",
    "# y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000\n"
     ]
    }
   ],
   "source": [
    "print len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "t = roc_curve(y_train, y_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([ 0.        ,  0.77226515,  1.        ]), array([ 0.        ,  0.24045651,  1.        ]), array([2, 1, 0]))\n"
     ]
    }
   ],
   "source": [
    "print t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testfile = open('test.csv')\n",
    "#ignore the test header\n",
    "testfile.next()\n",
    "\n",
    "X_test_A = []\n",
    "X_test_B = []\n",
    "Y_test = []\n",
    "for line in testfile:\n",
    "    splitted = line.rstrip().split(',')\n",
    "    label = int(splitted[0])\n",
    "    A_features = [float(item) for item in splitted[0:11]]\n",
    "    B_features = [float(item) for item in splitted[11:]]\n",
    "    Y_test.append(label)\n",
    "    X_test_A.append(A_features)\n",
    "    X_test_B.append(B_features)\n",
    "testfile.close()\n",
    "\n",
    "X_test_A = np.array(X_test_A)\n",
    "X_test_B = np.array(X_test_B)\n",
    "\n",
    "# transform features in the same way as for training to ensure consistency\n",
    "X_test = transform_features(X_test_A) - transform_features(X_test_B)\n",
    "X_test = normalize(X_test)\n",
    "# compute probabilistic predictions\n",
    "p_test = model.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_pred1 = []\n",
    "y_pred2 = []\n",
    "for y in p_test:\n",
    "    if y[0] >= y[1]:\n",
    "        y_pred1.append(1)\n",
    "        y_pred2.append(0)\n",
    "    else:\n",
    "        y_pred1.append(0)\n",
    "        y_pred2.append(1)\n",
    "# p_train = p_train[:,1:2]\n",
    "\n",
    "# y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p_test_2 = p_test[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Found input variables with inconsistent numbers of samples: [500, 5952]",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-137-2c673e168c44>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfalse_positive_rate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrue_positive_rate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroc_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp_test_2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mroc_auc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mauc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfalse_positive_rate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrue_positive_rate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/piyushghai/anaconda/lib/python2.7/site-packages/sklearn/metrics/ranking.pyc\u001b[0m in \u001b[0;36mroc_curve\u001b[0;34m(y_true, y_score, pos_label, sample_weight, drop_intermediate)\u001b[0m\n\u001b[1;32m    503\u001b[0m     \"\"\"\n\u001b[1;32m    504\u001b[0m     fps, tps, thresholds = _binary_clf_curve(\n\u001b[0;32m--> 505\u001b[0;31m         y_true, y_score, pos_label=pos_label, sample_weight=sample_weight)\n\u001b[0m\u001b[1;32m    506\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    507\u001b[0m     \u001b[0;31m# Attempt to drop thresholds corresponding to points in between and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/piyushghai/anaconda/lib/python2.7/site-packages/sklearn/metrics/ranking.pyc\u001b[0m in \u001b[0;36m_binary_clf_curve\u001b[0;34m(y_true, y_score, pos_label, sample_weight)\u001b[0m\n\u001b[1;32m    295\u001b[0m         \u001b[0mDecreasing\u001b[0m \u001b[0mscore\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    296\u001b[0m     \"\"\"\n\u001b[0;32m--> 297\u001b[0;31m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_score\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    298\u001b[0m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolumn_or_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m     \u001b[0my_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolumn_or_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_score\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/piyushghai/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.pyc\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    179\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    180\u001b[0m         raise ValueError(\"Found input variables with inconsistent numbers of\"\n\u001b[0;32m--> 181\u001b[0;31m                          \" samples: %r\" % [int(l) for l in lengths])\n\u001b[0m\u001b[1;32m    182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [500, 5952]"
     ]
    }
   ],
   "source": [
    "false_positive_rate, true_positive_rate, _ = roc_curve(y_test, p_test_2)\n",
    "roc_auc = auc(false_positive_rate, true_positive_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.69392372  0.30607628]\n",
      " [ 0.5343812   0.4656188 ]\n",
      " [ 0.90755126  0.09244874]\n",
      " ..., \n",
      " [ 0.53800552  0.46199448]\n",
      " [ 0.56811142  0.43188858]\n",
      " [ 0.60162238  0.39837762]]\n"
     ]
    }
   ],
   "source": [
    "print p_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.savetxt(\"foo_1.csv\", p_test[:,0], delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "arr = np.arange(1, len(p_test[:,0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   1    2    3 ..., 5949 5950 5951]\n"
     ]
    }
   ],
   "source": [
    "print arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'numpy.float64' object is not iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-71-a094cd616f6e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m>>\u001b[0m\u001b[0mpredfile\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0;32mprint\u001b[0m \u001b[0;34m>>\u001b[0m\u001b[0mpredfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m','\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mpredfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'numpy.float64' object is not iterable"
     ]
    }
   ],
   "source": [
    "predfile = open('foo_1.csv','w+')\n",
    "header = ['Id', 'Choice']\n",
    "print >>predfile,','.join(header)\n",
    "for line in np.concatenate((arr, p_test[:,0]),axis=0):\n",
    "    print >>predfile, ','.join([str(item) for item in line])\n",
    "\n",
    "predfile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "only integer scalar arrays can be converted to a scalar index",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-143-86c67867bddf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0marr2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: only integer scalar arrays can be converted to a scalar index"
     ]
    }
   ],
   "source": [
    "arr2 = np.concatenate(arr, p_test[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.6849748   0.52567042  0.90119837 ...,  0.52983902  0.56836426\n",
      "  0.58830821]\n"
     ]
    }
   ],
   "source": [
    "print p_test[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pt = p_test[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "axis 1 out of bounds [0, 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-150-f9814a22e22f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0marr2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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[0m",
      "\u001b[0;31mIndexError\u001b[0m: axis 1 out of bounds [0, 1)"
     ]
    }
   ],
   "source": [
    "arr2 = np.concatenate((arr, pt), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.          2.          3.         ...,  0.52983902  0.56836426\n",
      "  0.58830821]\n"
     ]
    }
   ],
   "source": [
    "print arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "all the input array dimensions except for the concatenation axis must match exactly",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-152-ecbf45320cfa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/piyushghai/anaconda/lib/python2.7/site-packages/numpy/core/shape_base.pyc\u001b[0m in \u001b[0;36mvstack\u001b[0;34m(tup)\u001b[0m\n\u001b[1;32m    232\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m     \"\"\"\n\u001b[0;32m--> 234\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_nx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0matleast_2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_m\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mhstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: all the input array dimensions except for the concatenation axis must match exactly"
     ]
    }
   ],
   "source": [
    "np.vstack((arr,pt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5951,)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5952,)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "arr = np.arange(1, len(p_test[:,0]) + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5952,)"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.00000000e+00,   2.00000000e+00,   3.00000000e+00, ...,\n",
       "          5.95000000e+03,   5.95100000e+03,   5.95200000e+03],\n",
       "       [  6.84974797e-01,   5.25670423e-01,   9.01198365e-01, ...,\n",
       "          5.29839020e-01,   5.68364264e-01,   5.88308212e-01]])"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.vstack((arr,pt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "line = 'Id,Choice'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f1 = open('foo_fin.csv', 'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f1.write(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f1.write(os.linesep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "i = 1\n",
    "for p in pt:\n",
    "    f1.write(str(i) + ',' + str(p) + os.linesep)\n",
    "    i+=1\n",
    "f1.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5952\n"
     ]
    }
   ],
   "source": [
    "print len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_pred = np.zeros(len(X_test))\n",
    "for i in range(len(X_test)):\n",
    "    y_pred[i] = expit(X_test[i,].dot(W))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "i = 1\n",
    "for p in y_pred:\n",
    "    f1.write(str(i) + ',' + str(p) + os.linesep)\n",
    "    i+=1\n",
    "f1.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p_test = model.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [ 0.33653227,  0.66346773],\n",
       "       [ 0.97086041,  0.02913959],\n",
       "       [ 0.90891867,  0.09108133],\n",
       "       [ 0.63133716,  0.36866284],\n",
       "       [ 0.25841473,  0.74158527],\n",
       "       [ 0.72547201,  0.27452799],\n",
       "       [ 0.86464328,  0.13535672],\n",
       "       [ 0.25517118,  0.74482882],\n",
       "       [ 0.96228741,  0.03771259],\n",
       "       [ 0.10945235,  0.89054765],\n",
       "       [ 0.98935099,  0.01064901],\n",
       "       [ 0.91816115,  0.08183885],\n",
       "       [ 0.60058237,  0.39941763],\n",
       "       [ 0.68349765,  0.31650235],\n",
       "       [ 0.76684109,  0.23315891],\n",
       "       [ 0.94453403,  0.05546597],\n",
       "       [ 0.48011843,  0.51988157],\n",
       "       [ 0.20586493,  0.79413507],\n",
       "       [ 0.4800463 ,  0.5199537 ],\n",
       "       [ 0.02914292,  0.97085708],\n",
       "       [ 0.9695046 ,  0.0304954 ],\n",
       "       [ 0.82168714,  0.17831286],\n",
       "       [ 0.41541707,  0.58458293],\n",
       "       [ 0.56768626,  0.43231374],\n",
       "       [ 0.92301788,  0.07698212],\n",
       "       [ 0.62264105,  0.37735895],\n",
       "       [ 0.91853644,  0.08146356],\n",
       "       [ 0.08519056,  0.91480944],\n",
       "       [ 0.98221573,  0.01778427],\n",
       "       [ 0.60327892,  0.39672108],\n",
       "       [ 0.87281023,  0.12718977],\n",
       "       [ 0.91291144,  0.08708856],\n",
       "       [ 0.03669641,  0.96330359],\n",
       "       [ 0.46002679,  0.53997321],\n",
       "       [ 0.03336128,  0.96663872],\n",
       "       [ 0.74432098,  0.25567902],\n",
       "       [ 0.04358923,  0.95641077],\n",
       "       [ 0.97178769,  0.02821231],\n",
       "       [ 0.2298888 ,  0.7701112 ],\n",
       "       [ 0.96374853,  0.03625147],\n",
       "       [ 0.11637216,  0.88362784],\n",
       "       [ 0.79679399,  0.20320601],\n",
       "       [ 0.00847384,  0.99152616],\n",
       "       [ 0.29698759,  0.70301241],\n",
       "       [ 0.91378058,  0.08621942],\n",
       "       [ 0.69755447,  0.30244553],\n",
       "       [ 0.10140205,  0.89859795],\n",
       "       [ 0.82402904,  0.17597096],\n",
       "       [ 0.50365345,  0.49634655],\n",
       "       [ 0.67963778,  0.32036222],\n",
       "       [ 0.02066389,  0.97933611],\n",
       "       [ 0.1834508 ,  0.8165492 ],\n",
       "       [ 0.16291008,  0.83708992],\n",
       "       [ 0.04531854,  0.95468146],\n",
       "       [ 0.7311061 ,  0.2688939 ],\n",
       "       [ 0.44196332,  0.55803668],\n",
       "       [ 0.40330528,  0.59669472],\n",
       "       [ 0.95738527,  0.04261473],\n",
       "       [ 0.12900981,  0.87099019],\n",
       "       [ 0.31212376,  0.68787624],\n",
       "       [ 0.97898067,  0.02101933],\n",
       "       [ 0.72310986,  0.27689014],\n",
       "       [ 0.96647301,  0.03352699],\n",
       "       [ 0.98171318,  0.01828682],\n",
       "       [ 0.84836577,  0.15163423],\n",
       "       [ 0.75781884,  0.24218116],\n",
       "       [ 0.12429225,  0.87570775],\n",
       "       [ 0.45270523,  0.54729477],\n",
       "       [ 0.71172143,  0.28827857],\n",
       "       [ 0.84833682,  0.15166318],\n",
       "       [ 0.66956375,  0.33043625],\n",
       "       [ 0.67092264,  0.32907736],\n",
       "       [ 0.47124072,  0.52875928],\n",
       "       [ 0.90818548,  0.09181452],\n",
       "       [ 0.4198349 ,  0.5801651 ],\n",
       "       [ 0.44968177,  0.55031823],\n",
       "       [ 0.23623036,  0.76376964],\n",
       "       [ 0.32744275,  0.67255725],\n",
       "       [ 0.63309703,  0.36690297],\n",
       "       [ 0.43582065,  0.56417935],\n",
       "       [ 0.6695862 ,  0.3304138 ],\n",
       "       [ 0.33919686,  0.66080314],\n",
       "       [ 0.91538931,  0.08461069],\n",
       "       [ 0.12981613,  0.87018387],\n",
       "       [ 0.20100319,  0.79899681],\n",
       "       [ 0.075709  ,  0.924291  ],\n",
       "       [ 0.13158757,  0.86841243],\n",
       "       [ 0.63727175,  0.36272825],\n",
       "       [ 0.06046511,  0.93953489],\n",
       "       [ 0.46831467,  0.53168533],\n",
       "       [ 0.7146239 ,  0.2853761 ],\n",
       "       [ 0.68818491,  0.31181509],\n",
       "       [ 0.88290225,  0.11709775],\n",
       "       [ 0.31364762,  0.68635238],\n",
       "       [ 0.1751555 ,  0.8248445 ],\n",
       "       [ 0.40361959,  0.59638041],\n",
       "       [ 0.66607803,  0.33392197],\n",
       "       [ 0.32114925,  0.67885075],\n",
       "       [ 0.12950685,  0.87049315],\n",
       "       [ 0.12315575,  0.87684425],\n",
       "       [ 0.69230281,  0.30769719],\n",
       "       [ 0.28266991,  0.71733009],\n",
       "       [ 0.49194171,  0.50805829],\n",
       "       [ 0.11583397,  0.88416603],\n",
       "       [ 0.04725743,  0.95274257],\n",
       "       [ 0.08677856,  0.91322144],\n",
       "       [ 0.26027783,  0.73972217],\n",
       "       [ 0.39446954,  0.60553046],\n",
       "       [ 0.37659052,  0.62340948],\n",
       "       [ 0.01244405,  0.98755595],\n",
       "       [ 0.721796  ,  0.278204  ],\n",
       "       [ 0.7273144 ,  0.2726856 ],\n",
       "       [ 0.09028481,  0.90971519],\n",
       "       [ 0.96828513,  0.03171487],\n",
       "       [ 0.18079101,  0.81920899],\n",
       "       [ 0.9741963 ,  0.0258037 ],\n",
       "       [ 0.08121036,  0.91878964],\n",
       "       [ 0.08505794,  0.91494206],\n",
       "       [ 0.29962371,  0.70037629],\n",
       "       [ 0.67731647,  0.32268353],\n",
       "       [ 0.2217912 ,  0.7782088 ],\n",
       "       [ 0.15536508,  0.84463492],\n",
       "       [ 0.56154266,  0.43845734],\n",
       "       [ 0.75012402,  0.24987598],\n",
       "       [ 0.18288537,  0.81711463],\n",
       "       [ 0.23030636,  0.76969364],\n",
       "       [ 0.32589696,  0.67410304],\n",
       "       [ 0.89068271,  0.10931729],\n",
       "       [ 0.5277585 ,  0.4722415 ],\n",
       "       [ 0.71976401,  0.28023599],\n",
       "       [ 0.9702365 ,  0.0297635 ],\n",
       "       [ 0.62015142,  0.37984858],\n",
       "       [ 0.7783442 ,  0.2216558 ],\n",
       "       [ 0.16526394,  0.83473606],\n",
       "       [ 0.91079512,  0.08920488],\n",
       "       [ 0.34122427,  0.65877573],\n",
       "       [ 0.29957514,  0.70042486],\n",
       "       [ 0.94493222,  0.05506778],\n",
       "       [ 0.96381266,  0.03618734],\n",
       "       [ 0.56308843,  0.43691157],\n",
       "       [ 0.728267  ,  0.271733  ]])"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_pred1 = []\n",
    "y_pred2 = []\n",
    "for y in y_pred:\n",
    "    if y >= 0.5:\n",
    "        y_pred1.append(1)\n",
    "        y_pred2.append(-1)\n",
    "    else:\n",
    "        y_pred1.append(-1)\n",
    "        y_pred2.append(1)\n",
    "# p_train = p_train[:,1:2]\n",
    "\n",
    "# y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nCount = 0\n",
    "for i in range(len(y_test)):\n",
    "    if (y_pred1[i] == y_test[i]):\n",
    "        nCount += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "403\n"
     ]
    }
   ],
   "source": [
    "print nCount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500\n"
     ]
    }
   ],
   "source": [
    "print len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 588,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testRec = open('sample_predictions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 589,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for line in testRec:\n",
    "    head = line.rstrip().split(',')\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 590,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Id', 'Choice']\n"
     ]
    }
   ],
   "source": [
    "print head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 591,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "0.474329577\n",
      "0.098801635\n",
      "0.137932293\n",
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      "0.255949057\n",
      "0.659494016\n",
      "0.97994645\n",
      "0.021005756\n",
      "0.676974978\n",
      "0.318313857\n",
      "0.844445805\n",
      "0.647216887\n",
      "0.41657478\n",
      "0.92952245\n",
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      "0.990191596\n",
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      "0.088215603\n",
      "0.901005068\n",
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      "0.782041577\n",
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      "0.948324279\n",
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      "0.914081388\n",
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      "0.066122191\n",
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      "0.390288857\n",
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      "0.099262097\n",
      "0.234522674\n",
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      "0.277821345\n",
      "0.223375052\n",
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      "0.055059311\n",
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      "0.11710703\n",
      "0.675513198\n",
      "0.468455727\n",
      "0.241987571\n",
      "0.300438215\n",
      "0.75210497\n",
      "0.47016098\n",
      "0.431635736\n",
      "0.411691788\n"
     ]
    }
   ],
   "source": [
    "yPred = []\n",
    "for line in testRec:\n",
    "    splitted = line.rstrip().split(',')\n",
    "    val = float(splitted[1])\n",
    "    print val\n",
    "    if val >= 0.5:\n",
    "        yPred.append(1)\n",
    "    else:\n",
    "        yPred.append(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.465920689029\n",
      "5952\n",
      "5952\n"
     ]
    }
   ],
   "source": [
    "print expit(X_test[1, ].dot(W))\n",
    "print len(yPred)\n",
    "print len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
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1, 1, 1, -1, -1, 1, -1, -1, 1, 1, 1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, 1, 1, 1, 1, 1, -1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, 1, -1, -1, 1, 1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, 1, 1, -1, 1, -1, 1, 1, 1, -1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, -1, 1, -1, 1, 1, -1, 1, 1, -1, -1, -1, -1, 1, 1, -1, -1, -1, -1, 1, -1, -1, 1, -1, 1, 1, -1, 1, -1, 1, 1, -1, -1, 1, 1, 1, -1, -1, 1, -1, -1, 1, 1, -1, 1, -1, -1, 1, -1, -1, -1, -1, 1, -1, -1, -1, 1, -1, 1, -1, -1, 1, -1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, -1, 1, -1, 1, -1, 1, -1, -1, 1, -1, 1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, -1, -1, -1, -1, 1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, -1, 1, -1, 1, 1, -1, 1, -1, 1, -1, 1, 1, -1, -1, -1, 1, -1, 1, -1, -1, -1, 1, -1, -1, -1, 1, -1, -1, -1]\n"
     ]
    }
   ],
   "source": [
    "print yPred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.986055107527\n"
     ]
    }
   ],
   "source": [
    "nCorrect = 0\n",
    "nIncorrect = 0\n",
    "# X_test\n",
    "for i in range(len(yPred)):\n",
    "    y_hat = expit(X_test[i,].dot(W))\n",
    "    \n",
    "    if y_hat >= 0.5:\n",
    "        y_hat = 1\n",
    "    else :\n",
    "        y_hat = -1\n",
    "            \n",
    "    if y_hat == yPred[i]:\n",
    "        nCorrect += 1\n",
    "    else:\n",
    "        nIncorrect += 1\n",
    "print (nCorrect * 1.0 /(nCorrect + nIncorrect))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def kFoldCrossValidation(k, lmda, learningRate, maxIter = 100):\n",
    "    trainfile = open('train.csv')\n",
    "    for line in trainfile:\n",
    "        head = line.rstrip().split(',')\n",
    "        break\n",
    "    X_train_A = []\n",
    "    X_train_B = []\n",
    "    y_train = []\n",
    "    for line in trainfile:\n",
    "        splitted = line.rstrip().split(',')\n",
    "        label = int(splitted[0])\n",
    "        A_features = [float(item) for item in splitted[1:12]]\n",
    "        B_features = [float(item) for item in splitted[12:]]\n",
    "        y_train.append(label)\n",
    "        X_train_A.append(A_features)\n",
    "        X_train_B.append(B_features)\n",
    "    trainfile.close()\n",
    "    Y_train = np.array(y_train)\n",
    "    X_train_A = np.array(X_train_A)\n",
    "    X_train_B = np.array(X_train_B)\n",
    "    \n",
    "    for i in range(len(y_train)):\n",
    "        if y_train[i] == 0:\n",
    "            y_train[i] = -1\n",
    "            \n",
    "    X_train = transform_features(X_train_A) - transform_features(X_train_B)\n",
    "    np.random.seed(1)      #Seed the random number generator to preserve the dev/test split\n",
    "    permutation = np.random.permutation(X_train.shape[0])\n",
    "    X_train = X_train[permutation,]\n",
    "    Y_train = Y_train[permutation,]\n",
    "    X_train = normalize(X_train)\n",
    "    \n",
    "#     dt = DecisionTreeClassifier(splitter='best', criterion='gini', max_depth=4)\n",
    "#     dt.fit(X_train, y_train)\n",
    "    \n",
    "    \n",
    "    testfile = open('test.csv')\n",
    "    #ignore the test header\n",
    "    testfile.next()\n",
    "\n",
    "    X_test_A = []\n",
    "    X_test_B = []\n",
    "    Y_test = []\n",
    "    for line in testfile:\n",
    "        splitted = line.rstrip().split(',')\n",
    "        label = int(splitted[0])\n",
    "        A_features = [float(item) for item in splitted[0:11]]\n",
    "        B_features = [float(item) for item in splitted[11:]]\n",
    "        Y_test.append(label)\n",
    "        X_test_A.append(A_features)\n",
    "        X_test_B.append(B_features)\n",
    "    testfile.close()\n",
    "\n",
    "    X_test_A = np.array(X_test_A)\n",
    "    X_test_B = np.array(X_test_B)\n",
    "\n",
    "# # transform features in the same way as for training to ensure consistency\n",
    "    X_test = transform_features(X_test_A) - transform_features(X_test_B)\n",
    "    X_test = normalize(X_test)\n",
    "    \n",
    "    testRec = open('sample_predictions.csv')\n",
    "    Y_test = []\n",
    "    for line in testRec:\n",
    "        head = line.rstrip().split(',')\n",
    "        break\n",
    "        \n",
    "    for line in testRec:\n",
    "        splitted = line.rstrip().split(',')\n",
    "        val = float(splitted[1])\n",
    "        if val >= 0.5:\n",
    "            Y_test.append(1)\n",
    "        else:\n",
    "            Y_test.append(-1)\n",
    "    \n",
    "#     yProb = dt.predict_proba(X_test)\n",
    "    \n",
    "#     print dt.max_depth\n",
    "    \n",
    "#     f1 = open('foo3.csv', 'w')\n",
    "#     line = 'Id,Choice'\n",
    "#     f1.write(line + os.linesep)\n",
    "#     i = 1\n",
    "#     for p in yProb:\n",
    "#         f1.write(str(i) + ',' + str(p[0]) + os.linesep)\n",
    "#         i+=1\n",
    "#     f1.close()\n",
    "    \n",
    "#     yPredictions = dt.predict(X_test)\n",
    "# # compute probabilistic predictions\n",
    "#     nCorrect = 0\n",
    "#     nIncorrect = 0\n",
    "#     for i in range(len(yPredictions)):\n",
    "#         if yPredictions[i] == yPred[i]:\n",
    "#             nCorrect += 1\n",
    "#         else:\n",
    "#             nIncorrect += 1\n",
    "#     print nCorrect*1.0/(nCorrect + nIncorrect)\n",
    "            \n",
    "    splitPoint = (X_train.shape[0] - int(X_train.shape[0]/k))\n",
    "    acc = np.zeros(k)\n",
    "    for i in range(k):\n",
    "        indices = np.random.permutation(X_train.shape[0])\n",
    "        training_idx, test_idx = indices[:splitPoint], indices[splitPoint:]\n",
    "        X_t = X_train[training_idx,:]\n",
    "        X_test = X_train[test_idx, :]\n",
    "        Y_t = y_train[training_idx,]\n",
    "        Y_test = y_train[test_idx,]\n",
    "        acc[i] = runExperiments(X_train, Y_train, X_test, Y_test, lmda, learningRate, maxIter)\n",
    "    \n",
    "    print \"Average Accuracy : \", np.mean(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "list indices must be integers, not tuple",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-93-3bb8c789099a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mkFoldCrossValidation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlmda\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m.0001\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearningRate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxIter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-92-5121c08ab96b>\u001b[0m in \u001b[0;36mkFoldCrossValidation\u001b[0;34m(k, lmda, learningRate, maxIter)\u001b[0m\n\u001b[1;32m    104\u001b[0m         \u001b[0mX_t\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtraining_idx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m         \u001b[0mX_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 106\u001b[0;31m         \u001b[0mY_t\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtraining_idx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    107\u001b[0m         \u001b[0mY_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest_idx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m         \u001b[0macc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrunExperiments\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[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlmda\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearningRate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxIter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: list indices must be integers, not tuple"
     ]
    }
   ],
   "source": [
    "kFoldCrossValidation(k = 10, lmda=.0001, learningRate=0.01, maxIter=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 763,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def FeatureSelection():\n",
    "    trainfile = open('train.csv')\n",
    "    for line in trainfile:\n",
    "        head = line.rstrip().split(',')\n",
    "        break\n",
    "    X_train_A = []\n",
    "    X_train_B = []\n",
    "    y_train = []\n",
    "    for line in trainfile:\n",
    "        splitted = line.rstrip().split(',')\n",
    "        label = int(splitted[0])\n",
    "        A_features = [float(item) for item in splitted[1:12]]\n",
    "        B_features = [float(item) for item in splitted[12:]]\n",
    "        y_train.append(label)\n",
    "        X_train_A.append(A_features)\n",
    "        X_train_B.append(B_features)\n",
    "    trainfile.close()\n",
    "    y_train = np.array(y_train)\n",
    "    X_train_A = np.array(X_train_A)\n",
    "    X_train_B = np.array(X_train_B)\n",
    "    \n",
    "    for i in range(len(y_train)):\n",
    "        if y_train[i] == 0:\n",
    "            y_train[i] = -1\n",
    "            \n",
    "    X_train = transform_features(X_train_A) - transform_features(X_train_B)\n",
    "    np.random.seed(1)      #Seed the random number generator to preserve the dev/test split\n",
    "    permutation = np.random.permutation(X_train.shape[0])\n",
    "    X_train = X_train[permutation,]\n",
    "    Y_train = y_train[permutation,]\n",
    "    X_train = normalize(X_train)\n",
    "    \n",
    "    \n",
    "    testfile = open('test.csv')\n",
    "    #ignore the test header\n",
    "    testfile.next()\n",
    "\n",
    "    X_test_A = []\n",
    "    X_test_B = []\n",
    "    Y_test = []\n",
    "    for line in testfile:\n",
    "        splitted = line.rstrip().split(',')\n",
    "        label = int(splitted[0])\n",
    "        A_features = [float(item) for item in splitted[0:11]]\n",
    "        B_features = [float(item) for item in splitted[11:]]\n",
    "        X_test_A.append(A_features)\n",
    "        X_test_B.append(B_features)\n",
    "    testfile.close()\n",
    "    \n",
    "    testRec = open('sample_predictions.csv')\n",
    "    for line in testRec:\n",
    "        head = line.rstrip().split(',')\n",
    "        break\n",
    "        \n",
    "    for line in testRec:\n",
    "        splitted = line.rstrip().split(',')\n",
    "        val = float(splitted[1])\n",
    "        if val >= 0.5:\n",
    "            Y_test.append(1)\n",
    "        else:\n",
    "            Y_test.append(-1)\n",
    "    \n",
    "    X_test_A = np.array(X_test_A)\n",
    "    X_test_B = np.array(X_test_B)\n",
    "# transform features in the same way as for training to ensure consistency\n",
    "    X_test = transform_features(X_test_A) - transform_features(X_test_B)\n",
    "    X_test = normalize(X_test)\n",
    "    \n",
    "    \n",
    "    ss = set()\n",
    "    fs = [x for x in range(11)]\n",
    "    fs = set(fs)\n",
    "    bestAccuracy = 0.0\n",
    "    while (True):\n",
    "        bestFeature = None\n",
    "        for feature in fs:\n",
    "            if feature not in ss:\n",
    "                ssPrime = ss.copy()\n",
    "                ssPrime.add(feature)\n",
    "                print ssPrime\n",
    "#             accu = runExperiments(X_train,Y_train, X_test, Y_test, lmda = .001, learningRate = .01, maxIter = 10)\n",
    "                accu = getAccuracy(X_train, Y_train, ssPrime, X_train, Y_train, .001, 0.01, 10)\n",
    "                print bestAccuracy, accu\n",
    "                if accu > bestAccuracy:\n",
    "                    bestFeature = feature\n",
    "                    bestAccuracy = accu\n",
    "        if bestFeature is not None:\n",
    "            bestSet = set()\n",
    "            bestSet.add(bestFeature)\n",
    "            ss |= bestSet\n",
    "        if bestFeature == None or len(ss) == 11:\n",
    "            break\n",
    "    print ss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 764,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getAccuracy(X_train, y_train, featureSet, X_test, Y_test, lmda, learningRate, maxIter):\n",
    "    featIdx = list(featureSet)\n",
    "    X_t = X_train[:,featIdx]\n",
    "    X_test = X_test[:,featIdx]\n",
    "    return runExperiments(X_t, y_train, X_test, Y_test, lmda, learningRate, maxIter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 765,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set([0])\n",
      "0.0 0.753090909091\n",
      "set([1])\n",
      "0.753090909091 0.573272727273\n",
      "set([2])\n",
      "0.753090909091 0.76\n",
      "set([3])\n",
      "0.76 0.749636363636\n",
      "set([4])\n",
      "0.76 0.742363636364\n",
      "set([5])\n",
      "0.76 0.642363636364\n",
      "set([6])\n",
      "0.76 0.606727272727\n",
      "set([7])\n",
      "0.76 0.643636363636\n",
      "set([8])\n",
      "0.76 0.753272727273\n",
      "set([9])\n",
      "0.76 0.568\n",
      "set([10])\n",
      "0.76 0.598181818182\n",
      "set([0, 2])\n",
      "0.76 0.760363636364\n",
      "set([1, 2])\n",
      "0.760363636364 0.759818181818\n",
      "set([2, 3])\n",
      "0.760363636364 0.761454545455\n",
      "set([2, 4])\n",
      "0.761454545455 0.761090909091\n",
      "set([2, 5])\n",
      "0.761454545455 0.764727272727\n",
      "set([2, 6])\n",
      "0.764727272727 0.762545454545\n",
      "set([2, 7])\n",
      "0.764727272727 0.762545454545\n",
      "set([8, 2])\n",
      "0.764727272727 0.764727272727\n",
      "set([9, 2])\n",
      "0.764727272727 0.760909090909\n",
      "set([2, 10])\n",
      "0.764727272727 0.762909090909\n",
      "set([0, 2, 5])\n",
      "0.764727272727 0.762363636364\n",
      "set([1, 2, 5])\n",
      "0.764727272727 0.764727272727\n",
      "set([2, 3, 5])\n",
      "0.764727272727 0.761454545455\n",
      "set([2, 4, 5])\n",
      "0.764727272727 0.762\n",
      "set([2, 5, 6])\n",
      "0.764727272727 0.761818181818\n",
      "set([2, 5, 7])\n",
      "0.764727272727 0.761090909091\n",
      "set([8, 2, 5])\n",
      "0.764727272727 0.762545454545\n",
      "set([9, 2, 5])\n",
      "0.764727272727 0.762363636364\n",
      "set([2, 10, 5])\n",
      "0.764727272727 0.764363636364\n",
      "set([2, 5])\n"
     ]
    }
   ],
   "source": [
    "FeatureSelection()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "trainfile = open('train.csv')\n",
    "for line in trainfile:\n",
    "    head = line.rstrip().split(',')\n",
    "    break\n",
    "X_train_A = []\n",
    "X_train_B = []\n",
    "y_train = []\n",
    "for line in trainfile:\n",
    "    splitted = line.rstrip().split(',')\n",
    "    label = int(splitted[0])\n",
    "    A_features = [float(item) for item in splitted[1:12]]\n",
    "    B_features = [float(item) for item in splitted[12:]]\n",
    "    y_train.append(label)\n",
    "    X_train_A.append(A_features)\n",
    "    X_train_B.append(B_features)\n",
    "trainfile.close()\n",
    "y_train = np.array(y_train)\n",
    "X_train_A = np.array(X_train_A)\n",
    "X_train_B = np.array(X_train_B)\n",
    "    \n",
    "for i in range(len(y_train)):\n",
    "    if y_train[i] == 0:\n",
    "        y_train[i] = -1\n",
    "            \n",
    "X_train = transform_features(X_train_A) - transform_features(X_train_B)\n",
    "np.random.seed(1)      #Seed the random number generator to preserve the dev/test split\n",
    "permutation = np.random.permutation(X_train.shape[0])\n",
    "X_train = X_train[permutation,]\n",
    "Y_train = y_train[permutation,]\n",
    "X_train = normalize(X_train)\n",
    "X_train_com1 = X_train[:,[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=8, init = 'k-means++', n_init=15, max_iter=1000, random_state=0).fit(X_train_com1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 0, 3, ..., 5, 6, 1], dtype=int32)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5500\n"
     ]
    }
   ],
   "source": [
    "print len(kmeans.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def transformIntoClusters(X_train, clusters = 8):\n",
    "    for i in range(X_train.shape[1]):\n",
    "        colToTransform = X_train[:,[i]]\n",
    "        kmeans = KMeans(n_clusters=clusters, init = 'k-means++', n_init=15, max_iter=5000, random_state=0)\n",
    "        model = kmeans.fit(colToTransform)\n",
    "        X_train[:,i] = model.labels_\n",
    "    return X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def TrainNaiveBayes():\n",
    "    trainfile = open('train.csv')\n",
    "    for line in trainfile:\n",
    "        head = line.rstrip().split(',')\n",
    "        break\n",
    "    X_train_A = []\n",
    "    X_train_B = []\n",
    "    y_train = []\n",
    "    X_train = []\n",
    "    for line in trainfile:\n",
    "        splitted = line.rstrip().split(',')\n",
    "        label = int(splitted[0])\n",
    "        A_features = [float(item) for item in splitted[1:]]\n",
    "        B_features = [float(item) for item in splitted[12:]]\n",
    "        y_train.append(label)\n",
    "        X_train_A.append(A_features)\n",
    "        X_train_B.append(B_features)\n",
    "        X_train.append(A_features)\n",
    "    trainfile.close()\n",
    "    Y_train = np.array(y_train)\n",
    "    X_train_A = np.array(X_train_A)\n",
    "    X_train_B = np.array(X_train_B)\n",
    "    X_train = np.array(X_train)\n",
    "    \n",
    "    \n",
    "#     print X_train.shape[1]\n",
    "    \n",
    "#     prob0 = 0.0\n",
    "#     zeroCount = 0\n",
    "#     for i in range(len(Y_train)):\n",
    "#         if y_train[i] == 0:\n",
    "#             y_train[i] = -1\n",
    "# #             zeroCount += 1\n",
    "    \n",
    "#     prob0 = zeroCount * 1.0 / (len(y_train))\n",
    "#     prob1 = 1 - prob0\n",
    "    \n",
    "#     Prior = [prob0, prob1]\n",
    "    \n",
    "#     X_train = transform_features(X_train)\n",
    "    np.random.seed(1)      #Seed the random number generator to preserve the dev/test split\n",
    "    permutation = np.random.permutation(X_train.shape[0])\n",
    "    X_train = X_train[permutation,]\n",
    "    Y_train = Y_train[permutation,]\n",
    "    X_train = normalize(X_train)\n",
    "    X_train = transformIntoClusters(X_train, clusters=7)\n",
    "    X_train = normalize(X_train)\n",
    "#     Probab = {'1' : {}, '-1' : {}}\n",
    "    \n",
    "#     for i in range(X_train.shape[0]):\n",
    "#         for j in range(X_train.shape[1]):\n",
    "#             cluster = X_train[i,j]\n",
    "#             if y_train[i] == 1:\n",
    "#                 if j not in Probab['1']:\n",
    "#                     Probab['1'][j] = {}\n",
    "#                     if cluster not in Probab['1'][j]:\n",
    "#                         Probab['1'][j][cluster] = 1.0\n",
    "#                 else:\n",
    "#                     if cluster not in Probab['1'][j]:\n",
    "#                         Probab['1'][j][cluster] = 1.0\n",
    "#                     else:\n",
    "#                         Probab['1'][j][cluster] += 1.0\n",
    "#             else :\n",
    "#                 if j not in Probab['-1']:\n",
    "#                     Probab['-1'][j] = {}\n",
    "#                     if cluster not in Probab['-1'][j]:\n",
    "#                         Probab['-1'][j][cluster] = 1.0\n",
    "#                 else:\n",
    "#                     if cluster not in Probab['-1'][j]:\n",
    "#                         Probab['-1'][j][cluster] = 1.0\n",
    "#                     else:\n",
    "#                         Probab['-1'][j][cluster] += 1.0\n",
    "#     for j in range(X_train.shape[1]):\n",
    "#         sum1 = sum(Probab['1'][j].values())\n",
    "#         sum0 = sum(Probab['-1'][j].values())\n",
    "#         for key in Probab['1'][j]:\n",
    "#             Probab['1'][j][key] /= sum1\n",
    "#         for key in Probab['-1'][j]:\n",
    "#             Probab['-1'][j][key] /= sum0\n",
    "          \n",
    "    return Y_train, X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Y_train, X_train = TrainNaiveBayes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def PredictBayes(x, Probab, Prior):\n",
    "    Prob0 = Prior[0]\n",
    "    Prob1 = Prior[1]\n",
    "    for i in range(len(x)):\n",
    "        cluster = x[i]\n",
    "        Prob0 *= Probab['-1'][i][cluster]\n",
    "        Prob1 *= Probab['1'][i][cluster]\n",
    "    \n",
    "    if Prob0 >= Prob1:\n",
    "        return -1\n",
    "    return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Probab' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-103-8123b91bc055>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mYHat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mYHat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPredictBayes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mProbab\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPrior\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'Probab' is not defined"
     ]
    }
   ],
   "source": [
    "YHat = []\n",
    "for i in range(len(X_train)):\n",
    "    YHat.append(PredictBayes(X_train[i,], Probab, Prior))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.475272727273\n"
     ]
    }
   ],
   "source": [
    "nCorrect = 0\n",
    "nIncorrect = 0\n",
    "for i in range(len(Y_train)):\n",
    "    if YHat[i] == Y_train[i]:\n",
    "        nCorrect += 1\n",
    "    else:\n",
    "        nIncorrect += 1\n",
    "\n",
    "print nCorrect * 1.0 / (nCorrect + nIncorrect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 1 ..., 1 0 1]\n"
     ]
    }
   ],
   "source": [
    "yHat = clf.predict(X_train)\n",
    "\n",
    "print yHat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.718\n"
     ]
    }
   ],
   "source": [
    "nCorrect = 0\n",
    "nIncorrect = 0\n",
    "for i in range(len(Y_train)):\n",
    "    if yHat[i] == Y_train[i]:\n",
    "        nCorrect += 1\n",
    "    else:\n",
    "        nIncorrect += 1\n",
    "\n",
    "print nCorrect * 1.0 / (nCorrect + nIncorrect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
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   "file_extension": ".py",
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