Logistic_Regression.ipynb_bak

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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.linear_model import LinearRegression as lr\n",
      "import numpy as np\n",
      "import random\n",
      "'''\n",
      "training:\n",
      "banner_pos: 7\n",
      "site_category: 24\n",
      "app_category: 35\n",
      "device_model: 7291 -- skip due to memory limited\n",
      "device_type: 4\n",
      "'''\n",
      "\n",
      "'''\n",
      "target\n",
      "2. banner_pos     4       7\n",
      "3. site_category  7       24\n",
      "4. app_domain     9     440 -- skip\n",
      "5. app_category   10    35\n",
      "6. device_type    14    4\n",
      "7. device_conn_type 15   4\n",
      "8. C15            17    8\n",
      "9. C16            18    9\n",
      "10. C18            20   4\n",
      "11. C19            21   55\n",
      "'''\n",
      "\n",
      "\n",
      "pos = [0,7,7+24,7+24+35,7+24+35+4,7+24+35+4+4,7+24+35+4+4+8,7+24+35+4+4+8+9,7+24+35+4+4+8+9+4]\n",
      "all_number = 7+24+35+4+4+8+9+4+55\n",
      "\n",
      "#pos = [0,7,7+24,7+24+35]\n",
      "#all_number = 7+24+35+4\n",
      "\n",
      "data = []\n",
      "label = []\n",
      "count = 1\n",
      "input_train_file = 'Data/train_1234_new_rehashed.txt'\n",
      "with open(input_train_file,'r') as infile:\n",
      "    for line in infile:\n",
      "        \n",
      "        k = random.randint(1,10)\n",
      "        if k > 2:\n",
      "            continue\n",
      "        \n",
      "        if count % 100000 == 0:\n",
      "            print count\n",
      "        count += 1\n",
      "        \n",
      "        li = list(map(int, line.split(',')))\n",
      "\n",
      "        data.append([0 for b in xrange(all_number)])\n",
      "        \n",
      "        for i in range(1,len(li)):\n",
      "            if i == 3:\n",
      "                continue\n",
      "            elif i > 3:\n",
      "                data[len(data) - 1][pos[i-2] + li[i]] = 1\n",
      "            else:\n",
      "                data[len(data) - 1][pos[i-1] + li[i]] = 1\n",
      "\n",
      "        \n",
      "        label.append(li[0])\n",
      "        \n",
      "print \"Done\"\n",
      "\n",
      "X = np.array(data)\n",
      "Y = np.array(label)\n",
      "lr_instance = lr()\n",
      "lr_instance.fit(X, Y)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100000\n",
        "200000"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "300000"
       ]
      },
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       "stream": "stdout",
       "text": [
        "\n",
        "400000"
       ]
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       "stream": "stdout",
       "text": [
        "\n",
        "500000"
       ]
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       "text": [
        "\n",
        "600000"
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       "text": [
        "\n",
        "700000"
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        "\n",
        "800000"
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        "\n",
        "900000"
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        "\n",
        "1000000"
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        "\n",
        "1100000"
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        "\n",
        "1200000"
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        "\n",
        "1300000"
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        "\n",
        "1400000"
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       "text": [
        "\n",
        "1500000"
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        "\n",
        "1600000"
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        "\n",
        "1700000"
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        "\n",
        "1800000"
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       "text": [
        "\n",
        "1900000"
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       "stream": "stdout",
       "text": [
        "\n",
        "2000000"
       ]
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       "stream": "stdout",
       "text": [
        "\n",
        "2100000"
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       "stream": "stdout",
       "text": [
        "\n",
        "2200000"
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       "stream": "stdout",
       "text": [
        "\n",
        "2300000"
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       "stream": "stdout",
       "text": [
        "\n",
        "2400000"
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       "stream": "stdout",
       "text": [
        "\n",
        "2500000"
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        "2600000"
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        "\n",
        "2700000"
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        "\n",
        "2800000"
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        "\n",
        "2900000"
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       "text": [
        "\n",
        "3000000"
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       "text": [
        "\n",
        "3100000"
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       "stream": "stdout",
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        "\n",
        "3200000"
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        "\n",
        "3300000"
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Done"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "LinearRegression(copy_X=True, fit_intercept=True, normalize=False)"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "del data\n",
      "del label"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Validation\n",
      "\n",
      "validation_data = []\n",
      "validation_label = []\n",
      "input_validation_file = 'Data/validation_5_new_rehashed.txt'\n",
      "with open(input_validation_file,'r') as infile:\n",
      "    for line in infile:\n",
      "        \n",
      "        k = random.randint(1,10)\n",
      "        if k > 5:\n",
      "            continue\n",
      "            \n",
      "        li = list(map(int, line.split(',')))\n",
      "\n",
      "        validation_data.append([0 for b in xrange(all_number)])\n",
      "\n",
      "        for i in range(1,len(li)):\n",
      "            if i == 3:\n",
      "                continue\n",
      "            elif i > 3:\n",
      "                validation_data[len(validation_data) - 1][pos[i-2] + li[i]] = 1\n",
      "            else:\n",
      "                validation_data[len(validation_data) - 1][pos[i-1] + li[i]] = 1\n",
      "            \n",
      "        validation_label.append(li[0])\n",
      "        \n",
      "X_validation = np.array(validation_data)\n",
      "Y_validation = np.array(validation_label)\n",
      "result = lr_instance.predict(X_validation)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def ROC(label,result):\n",
      "    %matplotlib inline\n",
      "    from sklearn.utils import shuffle\n",
      "    from sklearn.metrics import roc_curve, auc\n",
      "    import pylab as pl\n",
      "    # Compute ROC curve and area the curve\n",
      "    Y = np.array(label)\n",
      "    fpr, tpr, thresholds = roc_curve(Y, result)\n",
      "    roc_auc = auc(fpr, tpr)\n",
      "    print \"Area under the ROC curve : %f\" % roc_auc\n",
      "\n",
      "    # Plot ROC curve\n",
      "    #pl.clf()\n",
      "    pl.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
      "    pl.plot([0, 1], [0, 1], 'k--')\n",
      "    pl.xlim([0.0, 1.0])\n",
      "    pl.ylim([0.0, 1.0])\n",
      "    pl.xlabel('False Positive Rate')\n",
      "    pl.ylabel('True Positive Rate')\n",
      "    pl.title('Receiver operating characteristic')\n",
      "    pl.legend(loc=\"lower right\")\n",
      "    pl.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#new test data\n",
      "ROC(Y_validation,result)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Area under the ROC curve : 0.682719\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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miUFEpnr/rsjgsbzgQjR+SU6G8eNdldETT8Drr0OJEn5HFd5SRkJdunQpY8aM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\nJTFkyBBGjRrF8OHD6dWrl41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LyHERua4g4ysoQf7/aCwi33vfi7gCDrHABPH/o5SIvCsi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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1b12acc10>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "k = np.array([1,2,3])\n",
      "k = list(k)\n",
      "k"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "[1, 2, 3]"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#old data\n",
      "ROC(Y_validation,result)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Area under the ROC curve : 0.618585\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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sPvNaYPgdhjERl91n3wbRC9G6dW7o7IULXYc2U3RYYjDFVUEnhmJV\n+azq5lZ44AFLCsYYk51ilRimTHG9nAcPzv22CQkJXHPNNRw+fLjgAzPGmChSbBJDUpLrxPb663DS\nSaFvFzjGUY8ePTgpNxsbY7K1du1aGjdu7HcYhcLEiRO59dZbI3a8YpMYHnvMzcQWFxf6Nmn9EpYt\nW0ZiYqJNtWnypX79+pQrV44KFSpQo0YN+vbty759+zKt8+OPP3LllVdSq1Yt6tatyw033MDGjRsz\nrXPkyBEeffRRGjduTMWKFTn99NN58MEH2blzZyTfTr79+9//ZsCAAX6HkS/bt2+na9euVK1alYsu\nuojFixfnuP6aNWvo1KkT1atXp2bNmrzwwgvp+7npppuoXbs2FStWpFWrVpn2dc011xAfH3/cZyFs\nVDXqHy7MvFu6VLVGDdUdO0LfZvXq1Vq9enV99913NTU1NV/HN5GR389JuNWvX19nz56tqqorVqzQ\n+vXr6+DBg9OXJyYm6imnnKIvvPCC7t+/X3ft2qWPPvqoVqtWTbdu3aqqqqmpqdqqVSvt0KGDrlq1\nSlNTU/X333/Xhx9+WKdPnx622I8ePVqg+9u2bZtWq1ZN9+/fn6ftjx07VqDx5FXbtm114MCBevjw\nYZ08ebJWqVJFk5KSgq67Y8cOrVGjhn744YeanJys+/fv11WrVqmq6u+//65jxozRrVu3alJSkj7x\nxBPHnZ9hw4bpQw89FHTf2X32vddz/52bl40i/cjPP/yxY6oXXaQ6fnzut921a1eej2sirzAlBlXV\nO++8U7t3757+vH379nr33Xcft13nzp31jjvuUFXV999/X2vUqKEHDhwI+bgrV67U9u3ba5UqVbRG\njRr61FNPqarqLbfcoo8++mj6et98843WqVMn/Xm9evX05Zdf1ksuuUTLlSunzzzzjF533XWZ9n3f\nfffpfffdp6qqe/bs0f79+2vNmjX1tNNO00cffVRTUlKCxvTxxx9ry5YtM7321FNPacOGDbVChQp6\nzjnn6JQpU9KXjR8/Xi+77DJ9/PHHNTY2Vh977DE9cuSIDhw4UGNjY/XUU0/Vu+66Sw8dOqSqqrt3\n79YuXbpo9erVtVKlStq1a1fduHFjyOcsFL///ruWLVs205d3XFycjhs3Luj6Q4YM0ZtvvjmkfR89\nelTLlSuny5YtS39t5syZesEFFwRdv6ATQ5G/lfTKK1C+PNxyS+63rVy5csEHZIo19ZoUJiYm8vnn\nn9O2bVsAUlNTmT9/PtcHmSXqhhtuYM6cOQB8++23dO7cmXLlyoV0vKSkJNq3b0+HDh3YsmULa9as\noV27doBrypjTrVER4eWXX+aZZ55h165d3HjjjcyYMYP9+/cDkJKSwqRJk+jduzcA/fr14+DBgyxY\nsIAZM2bwySef8Oabbwbd9+rVqzk9S9PA008/nXnz5rF9+3Zuv/12evfuzbZt29KXL168mKNHj7J8\n+XKGDh3Kww8/TGJiIjNmzOC7775j9erVjBgxIv183nbbbaxfv54lS5Zw9OhR7rnnnmzfa9euXalc\nuXLQx1VXXRV0m19++YUGDRpQvnz59NeaNGnC6tWrg66/aNEiKleuTLt27ahZsyZXXXUVGzZsCLpu\nYmJi+jlJ07BhQ37++eds30OByks2ifSDPF4JbtyoWq2aqlday9aff/6Zp/2b6BLK58Q1Ws7/Iy/q\n1aunFSpU0AoVKqiIpJcCVN2tFRHRn3/++bjtZs6cqeXKlVNVV3oYMmRIyMf88MMPtWrVqkGv3Pv1\n65djiaF+/fo6YsSITNu0atVK3333XVVV/eKLL7Rhw4aqqrp161YtVaqUrl+/Pn3dMWPGaJs2bYLG\n9be//U0HDRqUbdypqalat25dnTp1qqq6EsPJJ5+sR44cSV9evnx5nT9/fvo2U6ZM0QYNGgTd35df\nfqmVK1fO9nh58cEHH+jFF1+c6bVHHnlEb7vttqDrn3XWWXryySfrtGnTdPPmzdq7d2+97LLLjltv\n79692qhRI33uuecyvX7w4EEVkfTbioGy++xjJYbjPfAA3HUXnH128OVpLY6aNm3KwYMHIxuc8UVB\npYa8EBE+/fRT9u7dyyeffMJ7772XfmVYrVo1ypYty5YtW47bbsuWLcTGxgIQGxvL5s2bQz7mhg0b\naNGiBSVK5O1fvWXLlpme9+rVi48++giADz/8ML20sG7dOlJTU2ncuHH6lfawYcPYsWNH0P3Wq1fv\nuPfx7rvvcsEFF1C5cmWqVKnCli1b+PPPP9OXN2nSJH0q3B07dnDw4EG6dOmSfrx+/fqlr3/w4EHu\nvPNO6tevT8WKFbn22mvZu3dveomtIFStWvW4xgN79uyhWrVq2a7frl07unbtSq1atXjsscf47rvv\nSEpKSl/n0KFDdOvWjbi4OB588MFM22/cuJGyZctSo0aNAnsP2SmyiWHGDEhIgKFDgy8PbHG0cOHC\nkIvmxuRXiRIluOqqq7jvvvsYOHBg+muXXXYZEydOPG79iRMnEuc1p4uLi2PWrFkhX8jExsayePFi\nUlJSjltWvnz5TLdqEhISjlunVKnMw6ldd911xMfHs2nTJj755BN69eoFQN26dSlRogQ//fQTu3fv\nZvfu3ezdu5cVK1YEjatRo0b89ttv6c/XrVtH//79eeqpp9i1axe7du2idu3amb7IA2OpVq0aJ598\nMrNmzUo/3p49e9K/qEePHs3ChQtZtGgRe/fuZfLkyYF3II7TuXNnYmJigj66dOkSdJszzzyTtWvX\npt9aA/jhhx84O5sr0bPPPjtTgs4ay5EjR9LHXXvttdeO237NmjWceeaZQfdd4PJSzIj0g1yW3ffv\nV61fX/WLL45fduTIEX388cetxVERlNvPSaRlrXzesWOHlitXThcuXKiqrlVSTEyMvvDCC7pv3z7d\ntWuXPvLII1q1alXdsmVL+naXX365durUSVevXq0pKSm6bt06HTJkSNBWSUlJSVqrVi0dNGiQbtq0\nSfft26eLFi1SVdU33nhD69evr7/88ot+//332qRJk+NuJQXGm6Zz587avn17bdasWabXr776au3b\nt6/+9NNPmpKSomvWrNE5c+YEPRfbt2/XU089Nb3idsuWLVquXDldtGiR7t69W0eOHKmlS5fWt956\nS1XdraRWrVpl2sf999+vHTt21KVLl2pKSopu3LhRZ82apaqqzz33nMbFxenu3bv1xx9/1Pbt26uI\nZFsZnlft2rXTQYMG6aFDh3Ty5MlatWrVbFtaLVy4UMuVK6czZszQbdu2ad++fTUuLk5VVZOTk7Vr\n167avXv3bFtcWaukfCaGv/9d9aabgi/7448/9IYbbtBNmzblap8m+hW2xKCqOmDAAO3Ro0f68xUr\nVmjnzp21Ro0aWrt2bb3++usz3bdXdRc3jzzyiJ533nkaExOjDRs21IEDB2bbim7lypXarl07rVSp\nktasWVOfeeYZVVU9fPiw9uzZUytWrKiXXnqpjh07VuvWrZtjvKqq7733noqIjho1KtPre/fu1QED\nBmidOnX0lFNO0aZNm+p///vfbM/H3XffrS+//HL68//85z965plnamxsrA4bNkzbtGmTnhjefvtt\nvfzyyzNtf/jwYR06dKg2aNBAY2JitFGjRvriiy+qqmshddNNN2n16tW1WbNm+uGHH2qJEiUKPDFs\n375du3TpopUrV9aLLrooPemqqn777bdaoUKFTOt/8MEHevbZZ2ulSpX0qquuSm8pFR8fryKi5cuX\nT6+HqlChgs6bN09VXSulBg0aZNuyqqATQ5EbRG/FCmjb1v2sWTPMgZmoYoPoFS7r1q2jW7duLF++\n3O9Qot6kSZOYOXMm48aNC7rcRlfNQWoqtGoFN9/sKp1N8WKJwRRXNrpqDt5807UYueMO1+LonXfe\nsS8KY4zJpSJTYti2Dc4/H776ClJSEujXrx+xsbFMmDAhUwcUU3RZicEUV3YrKRt9+kCNGslUqGBz\nLxdXlhhMcVXQiaFIzPn81VcQH7+RypW7UL++zb1sjDH5UehLDIcPQ+PG8MwzyahOp0ePHlZKKKas\nxGCKK7uVlMWwYbByJUyeHOGgTNSxCwJTnBWaW0kiEgc87x3nDVV9Mcg6TwFdgINAP1UNPjRhEIsX\nu9FTg/TkN8VQYbjIMaYwCFtzVREpCYwDrgEuBG4TkUZZ1rkSaKKqjYH7gbdPtN+lSxO46KLOtG27\nj6uvhpdegjp1Cj7+aBUfH+93CFHDzkUGOxcZ7FzkXzj7MbQA1qjqH6p6FJgAXJ1lnauAdwBUdRFQ\nSUSCDh24a1cyV145jJYtO/Lnn73o1y+GP/6Anj3D+A6ikH3oM9i5yGDnIoOdi/wL562k2kDgLBQb\ngZYhrFMH2JZlPU49tTnVq8cycWIiPXqcht1ONsaY8AhniSHUG75Zv+KDbvf00wPZvHkq11xjScEY\nY8IpbK2SRORiYLiqdvKeDwFSVfWZgHVeA+JVdYL3fDVwhapuy7Ivq1U0xpg8iLZWSUuAM0SkPrAZ\n6AnclGWdqcA9wAQvkezJmhQgb2/MGGNM3oQtMajqMRHpD0who7nqKhG501v+uqrOEJE4EVkBHABu\nDVc8xhhjQlMoOrgZY4yJnKgadtsrPSwTkeUicm826zzlLV8oIsEnVy0CTnQuRKS3iPzgPT4UkfP8\niDMSQvlceOs1F5FjInJNJOOLlBD/P5qLyDzvcxEf4RAjJoT/j5NF5B0RSRCROSKStal8kSEi40Rk\nm3fnJbt1cve9mZdp38LxAEoCa4D6QGkgEWiUZZ0rgRne7y2BhX7H7eO5uASo6P1+S3E+FwHrfQ1M\nB671O26fPhOVgB+BOt7zan7H7eO5uAt4xfu9HvAb3h2SovYALgeaAiuyWZ7r781oKjEUaIe4Qu6E\n50JVF6jqXu/pZ7j+H0VRKJ8LgHuB/wE7IhlcBIVyHnoBk1V1I4Cq/hnhGCMllHOxF4gRkdJAFeCg\net+MRY2qzgV257BKrr83oykxBOvsVjuEdYriF2Io5yLQHcCnYY3IPyc8FyJSG/fF8Kr3UlH8Agjl\nM3EGUEVE5nq3UHpHLLrIOuG5UNWPcCWLP4F5QFE9F6HI9fdmNM3HUKAd4gq5kN+TiLQB+gCXhi8c\nX4VyLp4H/qGqKm6I1aLYvDmU81AaaA20B8oBX4rIx6p6KJyB+eCE50JE7gGOAbWA84HPRKSeqqaG\nO7golavvzWhKDJuAugHP6+IyW07r1PFeK2pCOReISGNgLNBZVfdEKLZIC+VcXIjrCwNQDegsIkdV\ndWpkQoyIUM7DBmCmqm4FEJElQBwwKyIRRk4o5yIOeEtVDwKLRGQzcCYQ8ujNRUjuvzf9rjgJqCAp\nhasgqg+U4cSVzxdTdCtcQzkXscCvQEu/4/X7XGRZfzxwjd9x+/SZOBtYjCstVAF+ASr4HbtP5+JO\n4CXc7fK/AL/6HXeYz0l9Qqt8Dul7M2pKDGod4tKFci6Ax3H//K95V8pHVbWFXzGHS4jnosgL8f9j\ntYiMx406UBYYpar7/Ys6PEL8TEwAzsGdix24Yf2LJBH5CLgCqCYiG4BhuNuKef7etA5uxhhjMomm\nVknGGGOigCUGY4wxmVhiMMYYk4klBmOMMZlYYjDGGJOJJQZjjDGZWGIwUUNEUrwxftIesTmsm+/2\n+SLytoj8LiKJIjJJRJrkYR9vpA1jLCJDsyybn98Yvf2knZdlIvKciJQ5wfpNRKRzQRzbFE/Wj8FE\nDRFJUtWYgl43h32MB6ap6sci0gforqrX5WN/+Y4pp/2KSClgMq5D1/Qc1u8HXKiq2c5dYUxOrMRg\nopaIlBeRr7wr5Rki0i6bdaZ4V9QrROQy7/UOXkngZxGZKCJlszuM9/NzoJW37YMi8ptXmrjfe01E\nZLwXywoRud57PV5ELhSRp4GTvTje85bt935OEJErA2J+W0SuEZESIvKsiPwkImu83rzZUtVjwByg\njbefFiLynXfMd0SkoVeaGAH09F6/XkTKebH/JCKrRaRLaH8BU2z5PcaHPeyR9sCNhpngPSbjhk2O\n8ZbFAhsC1k3yft4KPOn9LkAF3EB6PwCneK8/DfQMcrzxwLW4C6T7cWPWV8INtFYLN1zx78AFuC/j\n9wK2Tdv3N0CzwJiCxNgdeNv7vQywHjgJN1z6aC/u8sAyoEyQONP2UxH4EujrPY8BSnq/3wD8z/v9\nFuCFgO3/Bdzn/V4DWOT339oe0f2ImrGSjAEOqWrTwBdE5H7vars8bq6BU1V1e8AqicDDIqLAeFVd\nKyKtgdOAOd44UmVwX6L/zXI8AZ4FhgLfAU/gBhz7QlW3eMefiZshaxLQQkRG477ks51GMYjPgf94\nV/OdgTmqekREOuCGhG7rrXcKbpCzb7Nsf7KIJACnA/NV9b2014GRInKF917S/p+zDj3eAThJRNLG\nyKksIg1UdW0u3oMpRiwxmKjlfcFfDnTEDf61HTc4XDpVTRCRlrjZy6aKyBBv0UpVbXOCQygwSFU/\nDjhmSzJ/qYo7jG4VkQuA64E3RORdVX0llPehqofFzb/cEXdl/1HA4pGq+u4JdnFIVZuKyCnA1yLS\nVV0dw9+AncBFwLm4QeWyc7eqZk04xgRldQwmmtUGNqlqEnAjbjTZTLyWS/tV9VXgA6AxsBA4T0Qu\n9tYpLyJnZHOMrBOYzATai0hNETkN92U+V0Rq4RLEu8B/cLeXskoRkXLZHOe/QH9covvce20WcIuI\nVPfiPDOH7VHVfcD/Af8WVxSqDaRd9f9fwKr7gOoBz2cBd4pIjHecTKUyY7KyxGCiSdYmcp/g5qdd\nhasY/inIum2ARBFZCjQHXlM31/ENuCHJV+NuE50VyjHVTXj0urfNPOA/qvoD7pbPIhFZhpsm8t9B\n9jUKl0TSbvUE7vsL3OQxX6qrRAZ4E5fEFosbEvlVgpfi0/ejqgnAGu/9vYibd2AJbpKetPW+wc13\nnOBVkj8JJAHLRWQl7paZMdmy5qrGGGMysRKDMcaYTCwxGGOMycQSgzHGmEwsMRhjjMnEEoMxxphM\nLDEYY4zJxBKDMcaYTCwxGGOMyeT/AVpG4yYyUrYdAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1a45470d0>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#get the submission data\n",
      "\n",
      "#predict exist id\n",
      "pos = [0,7,7+24,7+24+35,7+24+35+4,7+24+35+4+4,7+24+35+4+4+8,7+24+35+4+4+8+9,7+24+35+4+4+8+9+4]\n",
      "all_number = 7+24+35+4+4+8+9+4+55\n",
      "submission_data = []\n",
      "submission_ids = []\n",
      "input_sub_file = 'Data/sub_rehashed_new.txt'\n",
      "with open(input_sub_file,'r') as infile:\n",
      "    for line in infile:\n",
      "\n",
      "        submission_data.append([0 for b in xrange(all_number)])\n",
      "\n",
      "        li = line.split(',')\n",
      "        \n",
      "        for i in range(1,len(li)):\n",
      "            if i == 3:\n",
      "                continue\n",
      "            elif i > 3:\n",
      "                submission_data[len(submission_data) - 1][pos[i-2] + int(li[i])] = 1\n",
      "            else:\n",
      "                submission_data[len(submission_data) - 1][pos[i-1] + int(li[i])] = 1\n",
      "        \n",
      "        submission_ids.append(li[0])\n",
      "        \n",
      "X_sub = np.array(submission_data)\n",
      "result = lr_instance.predict(X_sub)\n",
      "result = list(result)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#del X_sub\n",
      "#del submission_data\n",
      "\n",
      "has_id = {}\n",
      "for i in range(len(submission_ids)):\n",
      "    has_id[submission_ids[i]] = result[i]\n",
      "\n",
      "input_file = \"Data/sampleSubmission\"\n",
      "output_file = \"Data/My_submission.txt\"\n",
      "\n",
      "\n",
      "with open(output_file,'a') as outfile:\n",
      "    outfile.write('id')\n",
      "    outfile.write(',')\n",
      "    outfile.write('click')\n",
      "    outfile.write('\\n')\n",
      "\n",
      "count = 0\n",
      "file = open(input_file)\n",
      "for line in file:\n",
      "    if count == 0:\n",
      "        count += 1\n",
      "        continue\n",
      "    count += 1\n",
      "    li = line.split(',')\n",
      "        \n",
      "    with open(output_file,'a') as outfile:\n",
      "        outfile.write(str(li[0]))\n",
      "        outfile.write(',')\n",
      "        if li[0] in has_id:\n",
      "            outfile.write(str(has_id[li[0]]))\n",
      "        else:\n",
      "            outfile.write(str(0))\n",
      "        outfile.write('\\n')\n",
      "        \n",
      "print count    \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "4577465\n"
       ]
      }
     ],
     "prompt_number": 27
    }
   ],
   "metadata": {}
  }
 ]
}