2017-7-31 实验数据收集：短文章svm.ipynb

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      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/gensim/utils.py:1015: UserWarning: Pattern library is not installed, lemmatization won't be available.\n",
      "  warnings.warn(\"Pattern library is not installed, lemmatization won't be available.\")\n"
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      "Reading english Data: 59999\n",
      "Reading english Data: 59999\n",
      "Merging the English and Japanes news dataframe...\n",
      "Drop the null line...\n",
      "False    59987\n",
      "True        12\n",
      "Name: en_article, dtype: int64\n",
      "Using the the data where start= 0 , end 5000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "C value = 6\n",
      "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf',\n",
      "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "classification report of TRAINING data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      1.00      1.00      4000\n",
      "        1.0       1.00      1.00      1.00      4000\n",
      "\n",
      "avg / total       1.00      1.00      1.00      8000\n",
      "\n",
      "classification report of TEST data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.76      0.82      0.79      1000\n",
      "        1.0       0.81      0.73      0.77      1000\n",
      "\n",
      "avg / total       0.78      0.78      0.78      2000\n",
      "\n",
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32.0||Doc.387: 8.0||Doc.488: 25.0||Doc.887: 18.5||Doc.787: 2.5||Doc.695: 18.0||Doc.186: 2.0||Doc.289: 851.5||Doc.988: 1.0||Doc.87: 177.0||Doc.388: 6.0||Doc.588: 1.0||Doc.489: 4.0||Doc.888: 6.0||Doc.788: 2.5||Doc.696: 19.5||Doc.290: 196.0||Doc.187: 24.0||Doc.389: 1.0||Doc.989: 4.0||Doc.88: 26.5||Doc.490: 10.5||Doc.589: 1.0||Doc.889: 20.0||Doc.789: 1.0||Doc.291: 2.0||Doc.697: 20.5||Doc.188: 25.0||Doc.390: 1.0||Doc.491: 10.5||Doc.890: 31.0||Doc.990: 1.0||Doc.89: 26.5||Doc.790: 13.5||Doc.590: 190.0||Doc.292: 62.5||Doc.698: 6.0||Doc.891: 1.0||Doc.391: 152.0||Doc.189: 1.0||Doc.991: 17.0||Doc.492: 4.5||Doc.90: 17.5||Doc.791: 6.5||Doc.591: 37.5||Doc.293: 92.5||Doc.699: 2.0||Doc.392: 11.0||Doc.892: 5.5||Doc.190: 9.0||Doc.992: 27.0||Doc.493: 8.5||Doc.294: 39.5||Doc.592: 10.0||Doc.91: 17.5||Doc.792: 51.0||Doc.393: 11.0||Doc.893: 3.5||Doc.191: 2.0||Doc.494: 14.0||Doc.993: 37.5||Doc.793: 73.0||Doc.295: 20.5||Doc.593: 8.0||Doc.92: 17.5||Doc.394: 14.0||Doc.894: 23.0||Doc.192: 4.5||Doc.495: 3.0||Doc.794: 19.5||Doc.994: 3.5||Doc.395: 1.0||Doc.895: 31.0||Doc.193: 3.5||Doc.296: 5.0||Doc.594: 39.0||Doc.93: 17.5||Doc.496: 2.0||Doc.795: 3.0||Doc.396: 13.0||Doc.896: 31.0||Doc.194: 7.0||Doc.995: 57.5||Doc.497: 58.5||Doc.297: 1.5||Doc.595: 1.5||Doc.94: 6.5||Doc.796: 19.5||Doc.397: 6.0||Doc.897: 72.0||Doc.195: 5.0||Doc.498: 22.5||Doc.996: 15.5||Doc.797: 258.5||Doc.298: 1.0||Doc.596: 1.5||Doc.95: 22.0||Doc.398: 60.0||Doc.898: 2.5||Doc.196: 2.0||Doc.499: 15.0||Doc.798: 258.5||Doc.997: 27.0||Doc.597: 19.0||Doc.96: 84.0||Doc.399: 88.0||Doc.299: 8.0||Doc.899: 15.0||Doc.197: 45.0||Doc.799: 258.5||Doc.97: 67.0||Doc.598: 2.0||Doc.998: 63.0||Doc.198: 1.0||Doc.98: 67.0||Doc.599: 77.5||Doc.999: 27.0||Doc.99: 169.0||Doc.199: 3.0||225.839302063\n",
      "count    1000.000000\n",
      "mean       49.320000\n",
      "std        92.230014\n",
      "min         1.000000\n",
      "25%         4.500000\n",
      "50%        16.500000\n",
      "75%        49.625000\n",
      "max       851.500000\n",
      "dtype: float64\n",
      "TOP1 65\n",
      "TOP5 282\n",
      "TOP10 393\n"
     ]
    }
   ],
   "source": [
    "run svm_title.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading english Data: 59999\n",
      "Reading english Data: 59999\n",
      "Merging the English and Japanes news dataframe...\n",
      "Drop the null line...\n",
      "False    59987\n",
      "True        12\n",
      "Name: en_article, dtype: int64\n",
      "Using the the data where start= 0 , end 5000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "C value = 3\n",
      "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf',\n",
      "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "classification report of TRAINING data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      1.00      1.00      4000\n",
      "        1.0       1.00      1.00      1.00      4000\n",
      "\n",
      "avg / total       1.00      1.00      1.00      8000\n",
      "\n",
      "classification report of TEST data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.74      0.80      0.77      1000\n",
      "        1.0       0.78      0.71      0.75      1000\n",
      "\n",
      "avg / total       0.76      0.76      0.76      2000\n",
      "\n",
      "Doc.600: 45.5||Doc.500: 2.5||Doc.300: 53.0||Doc.400: 579.0||Doc.700: 8.0||Doc.0: 17.0||Doc.900: 6.0||Doc.100: 17.0||Doc.200: 6.0||Doc.800: 16.0||Doc.601: 49.0||Doc.1: 151.0||Doc.501: 10.5||Doc.901: 8.0||Doc.301: 53.0||Doc.401: 579.0||Doc.101: 9.0||Doc.701: 4.5||Doc.201: 5.0||Doc.801: 5.0||Doc.2: 3.0||Doc.502: 10.5||Doc.602: 49.0||Doc.902: 19.5||Doc.102: 15.5||Doc.302: 53.0||Doc.402: 39.5||Doc.702: 4.5||Doc.202: 19.0||Doc.802: 3.0||Doc.3: 6.0||Doc.603: 49.0||Doc.503: 3.5||Doc.103: 15.5||Doc.903: 5.0||Doc.403: 23.5||Doc.703: 24.5||Doc.303: 250.5||Doc.203: 9.0||Doc.803: 10.0||Doc.4: 59.0||Doc.604: 48.0||Doc.504: 3.5||Doc.104: 15.5||Doc.404: 597.0||Doc.904: 2.5||Doc.704: 24.5||Doc.304: 268.5||Doc.204: 10.5||Doc.804: 29.0||Doc.5: 6.5||Doc.605: 55.5||Doc.505: 211.5||Doc.905: 2.5||Doc.105: 15.5||Doc.405: 597.0||Doc.705: 8.5||Doc.305: 139.5||Doc.205: 15.5||Doc.805: 2.5||Doc.6: 3.5||Doc.606: 1.0||Doc.506: 67.5||Doc.906: 2.5||Doc.406: 597.0||Doc.106: 7.0||Doc.706: 73.5||Doc.306: 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      "count    1000.000000\n",
      "mean       49.451000\n",
      "std        96.478872\n",
      "min         1.000000\n",
      "25%         5.000000\n",
      "50%        16.000000\n",
      "75%        45.625000\n",
      "max       648.500000\n",
      "dtype: float64\n",
      "TOP1 63\n",
      "TOP5 267\n",
      "TOP10 400\n"
     ]
    }
   ],
   "source": [
    "run svm_title.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading english Data: 59999\n",
      "Reading english Data: 59999\n",
      "Merging the English and Japanes news dataframe...\n",
      "Drop the null line...\n",
      "False    59987\n",
      "True        12\n",
      "Name: en_article, dtype: int64\n",
      "Using the the data where start= 0 , end 5000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "C value = 5\n",
      "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf',\n",
      "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "classification report of TRAINING data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      1.00      1.00      4000\n",
      "        1.0       1.00      1.00      1.00      4000\n",
      "\n",
      "avg / total       1.00      1.00      1.00      8000\n",
      "\n",
      "classification report of TEST data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.72      0.81      0.76      1000\n",
      "        1.0       0.78      0.68      0.73      1000\n",
      "\n",
      "avg / total       0.75      0.75      0.74      2000\n",
      "\n",
      "Doc.700: 8.0||Doc.300: 48.0||Doc.0: 7.0||Doc.200: 23.0||Doc.500: 11.5||Doc.800: 12.0||Doc.100: 23.0||Doc.600: 107.5||Doc.400: 445.0||Doc.900: 2.0||Doc.301: 48.0||Doc.701: 3.5||Doc.201: 44.0||Doc.1: 76.0||Doc.601: 37.0||Doc.801: 1.0||Doc.501: 28.5||Doc.101: 8.0||Doc.401: 445.0||Doc.901: 10.0||Doc.302: 48.0||Doc.702: 3.5||Doc.802: 1.0||Doc.202: 20.0||Doc.902: 71.5||Doc.602: 37.0||Doc.502: 28.5||Doc.2: 31.0||Doc.402: 22.5||Doc.102: 21.5||Doc.303: 113.5||Doc.803: 1.0||Doc.703: 3.5||Doc.203: 10.0||Doc.903: 1.0||Doc.603: 37.0||Doc.503: 2.5||Doc.3: 7.0||Doc.103: 21.5||Doc.403: 18.5||Doc.304: 92.5||Doc.204: 39.5||Doc.704: 3.5||Doc.804: 21.0||Doc.604: 166.5||Doc.904: 2.5||Doc.504: 2.5||Doc.4: 3.0||Doc.104: 21.5||Doc.404: 497.0||Doc.805: 6.5||Doc.605: 187.5||Doc.205: 42.5||Doc.305: 39.5||Doc.705: 3.5||Doc.905: 2.5||Doc.5: 49.5||Doc.505: 57.5||Doc.105: 21.5||Doc.405: 497.0||Doc.806: 6.5||Doc.206: 6.0||Doc.606: 5.0||Doc.706: 3.5||Doc.306: 74.5||Doc.906: 2.5||Doc.6: 6.5||Doc.506: 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224.5||Doc.80: 2.5||Doc.781: 65.0||Doc.581: 249.0||Doc.883: 1.0||Doc.983: 29.0||Doc.280: 9.0||Doc.182: 15.0||Doc.682: 5.0||Doc.782: 65.0||Doc.81: 1.0||Doc.382: 40.0||Doc.482: 224.5||Doc.582: 336.0||Doc.884: 1.0||Doc.281: 8.0||Doc.984: 41.0||Doc.783: 1.0||Doc.583: 336.0||Doc.683: 4.0||Doc.183: 38.0||Doc.383: 17.0||Doc.82: 1.0||Doc.483: 224.5||Doc.885: 2.0||Doc.282: 2.5||Doc.784: 160.5||Doc.985: 5.0||Doc.184: 49.0||Doc.584: 11.5||Doc.684: 3.0||Doc.384: 115.5||Doc.83: 7.0||Doc.484: 35.5||Doc.283: 2.5||Doc.785: 160.5||Doc.886: 3.5||Doc.185: 2.0||Doc.986: 11.0||Doc.685: 2.0||Doc.84: 14.0||Doc.385: 115.5||Doc.585: 11.5||Doc.485: 8.5||Doc.786: 160.5||Doc.887: 5.5||Doc.284: 27.5||Doc.186: 1.0||Doc.987: 32.0||Doc.686: 2.0||Doc.85: 13.5||Doc.586: 6.0||Doc.386: 153.0||Doc.787: 4.5||Doc.486: 56.5||Doc.187: 27.0||Doc.285: 27.5||Doc.888: 19.0||Doc.687: 2.0||Doc.86: 2.5||Doc.988: 1.0||Doc.587: 72.0||Doc.387: 5.0||Doc.788: 4.5||Doc.487: 2.0||Doc.688: 6.0||Doc.188: 18.0||Doc.87: 55.0||Doc.588: 1.0||Doc.989: 13.0||Doc.286: 27.5||Doc.889: 8.0||Doc.388: 26.0||Doc.789: 1.0||Doc.488: 1.0||Doc.689: 1.0||Doc.589: 6.0||Doc.189: 1.0||Doc.287: 31.5||Doc.88: 11.5||Doc.990: 8.0||Doc.890: 30.0||Doc.790: 128.5||Doc.389: 2.0||Doc.489: 3.0||Doc.690: 7.5||Doc.590: 123.0||Doc.190: 36.0||Doc.288: 561.5||Doc.891: 2.0||Doc.791: 37.5||Doc.89: 11.5||Doc.991: 27.0||Doc.390: 1.0||Doc.691: 36.5||Doc.490: 8.5||Doc.591: 47.0||Doc.892: 2.5||Doc.289: 561.5||Doc.792: 98.0||Doc.191: 4.0||Doc.90: 21.5||Doc.992: 22.0||Doc.391: 150.0||Doc.692: 1.5||Doc.592: 12.0||Doc.491: 8.5||Doc.893: 6.5||Doc.793: 46.0||Doc.290: 206.0||Doc.192: 4.5||Doc.91: 21.5||Doc.392: 5.0||Doc.593: 30.0||Doc.993: 15.5||Doc.693: 32.5||Doc.492: 17.5||Doc.894: 58.0||Doc.794: 35.5||Doc.193: 3.5||Doc.291: 11.0||Doc.92: 21.5||Doc.694: 8.0||Doc.393: 5.0||Doc.594: 29.0||Doc.994: 2.5||Doc.493: 22.5||Doc.795: 14.0||Doc.895: 31.0||Doc.292: 79.5||Doc.93: 21.5||Doc.194: 7.0||Doc.695: 2.0||Doc.995: 15.5||Doc.394: 7.0||Doc.595: 1.5||Doc.494: 20.0||Doc.796: 35.5||Doc.293: 274.5||Doc.896: 31.0||Doc.94: 13.5||Doc.195: 52.0||Doc.696: 14.5||Doc.996: 8.5||Doc.495: 3.0||Doc.797: 160.5||Doc.395: 3.0||Doc.596: 1.5||Doc.897: 57.0||Doc.196: 69.0||Doc.294: 77.5||Doc.697: 8.5||Doc.95: 17.0||Doc.997: 34.0||Doc.496: 12.0||Doc.798: 160.5||Doc.396: 5.0||Doc.597: 29.0||Doc.898: 71.5||Doc.197: 8.0||Doc.998: 45.0||Doc.698: 11.0||Doc.96: 34.0||Doc.295: 41.5||Doc.799: 160.5||Doc.497: 51.5||Doc.397: 7.0||Doc.598: 5.0||Doc.899: 6.0||Doc.699: 2.0||Doc.198: 1.0||Doc.999: 34.0||Doc.97: 36.0||Doc.296: 6.0||Doc.398: 27.0||Doc.498: 22.5||Doc.199: 3.0||Doc.599: 107.5||Doc.98: 36.0||Doc.399: 445.0||Doc.499: 12.0||Doc.297: 1.5||Doc.99: 48.0||Doc.298: 17.0||Doc.299: 12.0||222.418270826\n",
      "count    1000.000000\n",
      "mean       43.529500\n",
      "std        76.439793\n",
      "min         1.000000\n",
      "25%         5.000000\n",
      "50%        17.250000\n",
      "75%        48.000000\n",
      "max       642.500000\n",
      "dtype: float64\n",
      "TOP1 69\n",
      "TOP5 279\n",
      "TOP10 399\n"
     ]
    }
   ],
   "source": [
    "run svm_title.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading english Data: 59999\n",
      "Reading english Data: 59999\n",
      "Merging the English and Japanes news dataframe...\n",
      "Drop the null line...\n",
      "False    59987\n",
      "True        12\n",
      "Name: en_article, dtype: int64\n",
      "Using the the data where start= 0 , end 5000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "C value = 6\n",
      "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf',\n",
      "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "classification report of TRAINING data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      1.00      1.00      4000\n",
      "        1.0       1.00      1.00      1.00      4000\n",
      "\n",
      "avg / total       1.00      1.00      1.00      8000\n",
      "\n",
      "classification report of TEST data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.71      0.80      0.75      1000\n",
      "        1.0       0.77      0.68      0.72      1000\n",
      "\n",
      "avg / total       0.74      0.74      0.74      2000\n",
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      "count    1000.000000\n",
      "mean       46.735000\n",
      "std        81.162668\n",
      "min         1.000000\n",
      "25%         4.500000\n",
      "50%        15.000000\n",
      "75%        54.000000\n",
      "max       627.500000\n",
      "dtype: float64\n",
      "TOP1 74\n",
      "TOP5 281\n",
      "TOP10 397\n"
     ]
    }
   ],
   "source": [
    "run svm_title.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading english Data: 5000\n",
      "Reading english Data: 5000\n",
      "Merging the English and Japanes news dataframe...\n",
      "Drop the null line...\n",
      "False    4995\n",
      "True        5\n",
      "Name: en_article, dtype: int64\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "C value = 3\n",
      "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf',\n",
      "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "classification report of TRAINING data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      1.00      1.00      4000\n",
      "        1.0       1.00      1.00      1.00      4000\n",
      "\n",
      "avg / total       1.00      1.00      1.00      8000\n",
      "\n",
      "classification report of TEST data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.71      0.81      0.76      1000\n",
      "        1.0       0.78      0.66      0.72      1000\n",
      "\n",
      "avg / total       0.74      0.74      0.74      2000\n",
      "\n",
      "Doc.200: 5.0||Doc.100: 9.0||Doc.700: 9.0||Doc.500: 62.5||Doc.800: 12.0||Doc.400: 134.0||Doc.600: 57.5||Doc.0: 47.0||Doc.300: 84.0||Doc.900: 5.0||Doc.701: 3.5||Doc.801: 21.0||Doc.101: 8.0||Doc.501: 253.5||Doc.301: 84.0||Doc.1: 285.0||Doc.901: 7.0||Doc.201: 8.0||Doc.601: 35.0||Doc.401: 134.0||Doc.702: 3.5||Doc.102: 14.5||Doc.802: 19.0||Doc.502: 253.5||Doc.2: 26.0||Doc.902: 5.5||Doc.302: 84.0||Doc.602: 35.0||Doc.202: 31.0||Doc.402: 209.5||Doc.703: 5.5||Doc.3: 6.0||Doc.103: 14.5||Doc.303: 107.5||Doc.803: 3.0||Doc.503: 4.5||Doc.603: 35.0||Doc.903: 33.0||Doc.403: 188.5||Doc.203: 7.0||Doc.4: 5.0||Doc.104: 14.5||Doc.704: 5.5||Doc.304: 127.5||Doc.604: 93.5||Doc.504: 4.5||Doc.804: 38.0||Doc.904: 3.5||Doc.404: 316.0||Doc.204: 30.5||Doc.105: 14.5||Doc.5: 41.5||Doc.305: 9.5||Doc.705: 3.5||Doc.905: 2.5||Doc.605: 99.5||Doc.505: 81.5||Doc.805: 3.5||Doc.405: 316.0||Doc.106: 1.0||Doc.205: 47.5||Doc.6: 19.5||Doc.306: 62.5||Doc.706: 7.5||Doc.506: 14.5||Doc.906: 2.5||Doc.606: 5.0||Doc.806: 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      "count    1000.000000\n",
      "mean       45.054500\n",
      "std        86.052224\n",
      "min         1.000000\n",
      "25%         4.000000\n",
      "50%        14.500000\n",
      "75%        44.000000\n",
      "max       805.500000\n",
      "dtype: float64\n",
      "TOP1 89\n",
      "TOP5 295\n",
      "TOP10 417\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading english Data: 5000\n",
      "Reading english Data: 5000\n",
      "Merging the English and Japanes news dataframe...\n",
      "Drop the null line...\n",
      "False    4995\n",
      "True        5\n",
      "Name: en_article, dtype: int64\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "C value = 10\n",
      "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf',\n",
      "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "classification report of TRAINING data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      1.00      1.00      4000\n",
      "        1.0       1.00      1.00      1.00      4000\n",
      "\n",
      "avg / total       1.00      1.00      1.00      8000\n",
      "\n",
      "classification report of TEST data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.75      0.83      0.79      1000\n",
      "        1.0       0.81      0.71      0.76      1000\n",
      "\n",
      "avg / total       0.78      0.77      0.77      2000\n",
      "\n",
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      "count    1000.000000\n",
      "mean       47.959500\n",
      "std        94.009896\n",
      "min         1.000000\n",
      "25%         4.000000\n",
      "50%        13.500000\n",
      "75%        46.000000\n",
      "max       811.500000\n",
      "dtype: float64\n",
      "TOP1 86\n",
      "TOP5 309\n",
      "TOP10 449\n"
     ]
    }
   ],
   "source": [
    "# coding: utf-8\n",
    "from __future__ import print_function\n",
    "\n",
    "\"\"\"\n",
    "created on 2017/05/11\n",
    "@author: liuenda\n",
    "\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sys\n",
    "from sklearn import cross_validation\n",
    "from sklearn.metrics import classification_report\n",
    "from gensim import corpora, models, similarities\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn import datasets\n",
    "from sklearn import linear_model\n",
    "from sklearn import svm\n",
    "from gensim.models import word2vec\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "import pickle\n",
    "import time\n",
    "import random\n",
    "from copy import deepcopy\n",
    "from multiprocessing import Process, Value, Array\n",
    "import multiprocessing\n",
    "\n",
    "base_path = \"/home/M2015eliu/cas/2017.1.1~LiuSTM/\"\n",
    "model_name_en = base_path + \"data/model-en/W2Vmodle.bin\"\n",
    "model_name_jp = base_path + \"data/model-jp/W2Vmodle.bin\"\n",
    "\n",
    "model_en = word2vec.Word2Vec.load(model_name_en)\n",
    "model_jp = word2vec.Word2Vec.load(model_name_jp)\n",
    "\n",
    "\n",
    "maxlen = 0 # Default: 0 -> infinite\n",
    "epoch = 50\n",
    "random.seed(1234)\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "Quick way to find ranks for SVM using multiprocessing\n",
    "provdie the same results as the find_ranking\n",
    "\"\"\"\n",
    "# 并行计算\n",
    "# def find_ranking_quick(projection1, projection2, clf, n):\n",
    "step = 100\n",
    "\n",
    "def wrapper_find_ranking_quick(X_test_scaled, clf):\n",
    "\tres = {}\n",
    "\tqueue = multiprocessing.Queue()\n",
    "\tqueue.put(res)\n",
    "\n",
    "\tjobs = []\n",
    "\tl_clf = []\n",
    "\n",
    "\tn_jobs = 10\n",
    "\tfor i in range(n_jobs):\n",
    "\n",
    "\t\t# # 1. A lower way but more memory\n",
    "\t\t#     l_clf.append(deepcopy(clf))\n",
    "\t\t#     jobs.append(Process(target=find_ranking_quick, args=((X_test_scaled[i*step:i*step+step,:200],\n",
    "\t\t#                                                           X_test_scaled[:100,200:],\n",
    "\t\t#                                                           l_clf[i], i, queue),)))\n",
    "\n",
    "\t\t# 2. A faster way but more memory\n",
    "\t\tjobs.append(Process(target=find_ranking_quick, args=((deepcopy(X_test_scaled[i * step:i * step + step, :200]),\n",
    "\t\t                                                      deepcopy(X_test_scaled[:1000, 200:]),\n",
    "\t\t                                                      deepcopy(clf), i, queue),)))\n",
    "\ts = time.time()\n",
    "\tfor j in jobs:\n",
    "\t\tj.start()\n",
    "\n",
    "\tfor j in jobs:\n",
    "\t\tj.join()\n",
    "\n",
    "\tprint(time.time() - s)\n",
    "\n",
    "\treturn  queue\n",
    "\n",
    "\n",
    "def find_ranking_quick(args):\n",
    "\tsim_results = []\n",
    "\trank_results = []\n",
    "\tstep = 100\n",
    "\tprojection1, projection2, clf, n, queue = args[0], args[1], args[2], args[3], args[4]\n",
    "\tres = {}\n",
    "\n",
    "\t# Iterate each of the ariticle from projection1 (999) as proj1\n",
    "\t# Calculate the simialrity of proj1 with all ariticles in projection2 (999)\n",
    "\tfor i, proj1 in enumerate(projection1):\n",
    "\t\t# print \"Find answer for doc.\", i\n",
    "\t\tproj1_tile = np.tile(proj1, (len(projection2), 1))\n",
    "\t\tfeatures_test = np.concatenate((proj1_tile, projection2), axis=1)\n",
    "\t\tsim = clf.predict_proba(features_test)[:, 1]\n",
    "\t\trank = pd.Series(sim).rank(ascending=False)[n * step + i]\n",
    "\t\tsim_results.append(sim)\n",
    "\t\trank_results.append(rank)\n",
    "\t\t# print(\"Find answer for doc.\", n * step + i, rank, end=\"||\", flush=True)\n",
    "\t\tsys.stdout.write(\"Doc.\" + str(n * step + i) + \": \" + str(rank) + \"||\")\n",
    "\t\tsys.stdout.flush()\n",
    "\t\tres[n * step + i] = rank\n",
    "\n",
    "\tres_all = queue.get()\n",
    "\tres_all.update(res)\n",
    "\tqueue.put(res_all)\n",
    "\treturn sim_results, rank_results\n",
    "\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "Find the ranking results with respect to real pairs\n",
    "Defaulty, projection1 should be JP\n",
    "Whiile, projection2 should be EN->JP\n",
    "\"\"\"\n",
    "def find_ranking(projection1, projection2, clf):\n",
    "\tsim_results = []\n",
    "\trank_results = []\n",
    "\n",
    "\t# Iterate each of the ariticle from projection1 (999) as proj1\n",
    "\t# Calculate the simialrity of proj1 with all ariticles in projection2 (999)\n",
    "\tfor i, proj1 in enumerate(projection1):\n",
    "\t\t# print \"Find answer for doc.\", i\n",
    "\t\tproj1_tile = np.tile(proj1, (len(projection2),1))\n",
    "\t\tfeatures_test = np.concatenate((proj1_tile, projection2), axis=1)\n",
    "\t\tsim = clf.predict_proba(features_test)[:,1]\n",
    "\t\trank = pd.Series(sim).rank(ascending = False)[i]\n",
    "\t\tsim_results.append(sim)\n",
    "\t\trank_results.append(rank)\n",
    "\t\tprint(\"Finish No.\", i, rank, end=\"||\")\n",
    "\n",
    "\t# sim_results contains 999*999 similairty matrix\n",
    "\treturn sim_results, rank_results\n",
    "\n",
    "\"\"\"\n",
    "rank_results should be list of (999,)\n",
    "\"\"\"\n",
    "def find_top(rank_results, top):\n",
    "\ts = pd.Series(rank_results)\n",
    "\tn_top = (s <= top).sum()\n",
    "\treturn n_top\n",
    "\n",
    "\n",
    "def average_docment(document_embedding):\n",
    "\treturn np.average(document_embedding, axis=0)\n",
    "\n",
    "def sum_docment(document_embedding):\n",
    "\treturn np.sum(document_embedding, axis=0)\n",
    "\n",
    "def doc2feature(corpus, tfidf, dictionary, w2v):\n",
    "    doc_features = []\n",
    "    for index, doc_bof in enumerate(corpus):\n",
    "\n",
    "        if index % 1000 == 0:\n",
    "            print(index)\n",
    "\n",
    "        doc_tfidf = tfidf[doc_bof]\n",
    "\n",
    "        doc_feature = np.zeros((200,))\n",
    "\n",
    "        for (token_id, token_tfidf) in doc_tfidf:\n",
    "            token = dictionary.get(token_id, \"[unknown-id]\").encode(\"utf-8\")\n",
    "            # if token in w2v:\n",
    "            if token in w2v:\n",
    "                token_w2v = w2v[token]\n",
    "            else:\n",
    "                # print \"No word:\", token\n",
    "                continue\n",
    "            doc_feature += token_w2v * token_tfidf\n",
    "\n",
    "        average = True\n",
    "        if average:\n",
    "            doc_feature = np.true_divide(doc_feature, len(doc_tfidf))\n",
    "        doc_features.append(doc_feature)\n",
    "\n",
    "    return doc_features\n",
    "#\n",
    "# def doc2vec_en(doc):\n",
    "# \t# r = [model_en[token] for token in doc.split()]\n",
    "# \tr = []\n",
    "# \tr_failed = []\n",
    "#\n",
    "# \tfor token in doc.split():\n",
    "# \t\tif token in model_en:\n",
    "# \t\t\tr.append(model_en[token])\n",
    "# \t\telse:\n",
    "# \t\t\tr_failed.append(token)\n",
    "#\n",
    "# \t# if len(r_failed) != 0:\n",
    "# \t#     print \" \".join(r_failed)\n",
    "#\n",
    "# \treturn r\n",
    "#\n",
    "# def doc2vec_jp(doc):\n",
    "# \t# r = [model_en[token] for token in doc.split()]\n",
    "# \tr = []\n",
    "# \tr_failed = []\n",
    "#\n",
    "# \tfor token in doc.split():\n",
    "# \t\tif token in model_jp:\n",
    "# \t\t\tr.append(model_jp[token])\n",
    "# \t\telse:\n",
    "# \t\t\tr_failed.append(token)\n",
    "#\n",
    "# \t# if len(r_failed) != 0:\n",
    "# \t#     print \" \".join(r_failed)\n",
    "#\n",
    "# \treturn r\n",
    "#\n",
    "\n",
    "# def prepare_train(dir_en, dir_jp):\n",
    "def prepare_train(dir_en_jp):\n",
    "\n",
    "\t# df_en_mapping = pd.read_csv(dir_en)\n",
    "\t# df_jp_mapping = pd.read_csv(dir_jp)\n",
    "\n",
    "\tdf_en_jp = pd.read_csv(dir_en_jp, names=[\"en_article\",\"jp_article\"], header=0)\n",
    "\tdf_en_mapping = df_en_jp[[\"en_article\"]].iloc[0:5000]\n",
    "\tdf_jp_mapping = df_en_jp[[\"jp_article\"]].iloc[0:5000]\n",
    "\n",
    "\tprint(\"Reading english Data:\", len(df_en_mapping))\n",
    "\tprint(\"Reading english Data:\", len(df_jp_mapping))\n",
    "\n",
    "\tsample_size = len(df_en_mapping)\n",
    "\n",
    "\tassert len(df_en_mapping) == len(df_jp_mapping)\n",
    "\n",
    "\t# Convert mapping to list type and then concat to the a list\n",
    "\tprint(\"Merging the English and Japanes news dataframe...\")\n",
    "\tdf_train_1 = pd.concat([df_en_mapping, df_jp_mapping], axis = 1)\n",
    "\tdf_train_1['similarity'] = pd.Series(np.ones(sample_size,)*5)\n",
    "\tdf_train_1['dis_similarity'] = pd.Series(np.ones(sample_size,)*1)\n",
    "\n",
    "\t# Remove null line\n",
    "\tprint(\"Drop the null line...\")\n",
    "\t# df_train_1 = df_train_1.dropna(subset=['en_article'])\n",
    "\tdf_train_1 = df_train_1[df_train_1['en_article'] != '<NULL>']\n",
    "\n",
    "\t# Expand the training data\n",
    "\ten_article_wrong = df_train_1.en_article.iloc[random.sample(range(len(df_train_1)),len(df_train_1))]\n",
    "\ten_article_wrong.index = df_train_1.index\n",
    "\tprint((en_article_wrong == df_train_1.en_article).value_counts())\n",
    "\tdf_train_1['en_article_wrong'] = en_article_wrong\n",
    "\n",
    "\t# Convert dateframe to list\n",
    "\ttrain_1 = df_train_1[['en_article','jp_article','similarity']].values.tolist()\n",
    "\ttrain_2 = df_train_1[['en_article_wrong','jp_article','dis_similarity']].values.tolist()\n",
    "\n",
    "\treturn train_1, train_2, df_train_1\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "\n",
    "\n",
    "\tinput = 2\n",
    "\tk = 10\n",
    "\n",
    "\t# --- Prepare and Loading the training data --- #\n",
    "\n",
    "\t# if input == 1:\n",
    "\t# \t# Prepare For the training data\n",
    "\t# \tsample_size = \"_1000\"\n",
    "\t# \tdir_en = \"./data/mapping/en_mapped_\"+str(k) + sample_size + \".csv\"\n",
    "\t# \tdir_jp = \"./data/mapping/jp_mapped_\" + str(k) + sample_size + \".csv\"\n",
    "\t#\n",
    "\t# \t# Prepare For the test data\n",
    "\t# \tsample_size = \"_1k2k\"\n",
    "\t# \tdir_en_test = \"./data/mapping/en_mapped_\"+str(k) + sample_size + \".csv\"\n",
    "\t# \tdir_jp_test = \"./data/mapping/jp_mapped_\" + str(k) + sample_size + \".csv\"\n",
    "\t#\n",
    "\t# \ttrain_1, train_2, df_train_1 = \tprepare_train(dir_en, dir_jp)\n",
    "\t# \ttest_1, test_2, df_test_1 = prepare_train(dir_en_test, dir_jp_test)\n",
    "\n",
    "\tif input == 2:\n",
    "\t\t# split_line = 5000\n",
    "\t\t# end_line = 6000\n",
    "\t\t# Prepare For the training data\n",
    "\t\t# dir_en = \"./data/news/en_news.csv\"\n",
    "\t\t# dir_jp = \"./data/news/jp_news.csv\"\n",
    "\t\tdir_en_jp = base_path + \"data_preparation/2014_cleaned_jp_en.csv\"\n",
    "\n",
    "\t\t# pairs_correct, pairs_wrong, df_pairs = prepare_train(dir_en, dir_jp)\n",
    "\t\tpairs_correct, pairs_wrong, df_pairs = prepare_train(dir_en_jp)\n",
    "\n",
    "\t\t# train_1 = pairs_correct[0:2000] + pairs_correct[3000:5000]\n",
    "\t\t# test_1 = pairs_correct[2000:3000]\n",
    "\t\t#\n",
    "\t\t# train_2 = pairs_wrong[0:2000] + pairs_wrong[3000:5000]\n",
    "\t# test_2 = pairs_wrong[split_line:end_line]\n",
    "\n",
    "\n",
    "\t# # Expand the training data\n",
    "\t# train = train_1 + train_2\n",
    "\n",
    "\t# --- Apply the word2vec model to the data sets --- #\n",
    "\n",
    "\tdf_pairs_sample = df_pairs.iloc[0:5000]\n",
    "\n",
    "\t# df_pairs_sample['word2vec_en'] = df_pairs_sample['en_article'].apply(doc2vec_en)\n",
    "\t# df_pairs_sample['word2vec_jp'] = df_pairs_sample['jp_article'].apply(doc2vec_jp)\n",
    "\n",
    "\n",
    "\t# Feature 1: TF-IDF + Average word2vec\n",
    "\n",
    "\t# --- Find tf-idf * word2vec features --- #\n",
    "\n",
    "    #  For English text:\n",
    "    # texts_en = [doc.split() for doc in list(df_pairs_sample[\"en_article\"])]\n",
    "\ttexts_en = [doc.split() for doc in list(df_pairs[\"en_article\"])]\n",
    "\tdictionary_en = corpora.Dictionary(texts_en)\n",
    "\tcorpus_en = [dictionary_en.doc2bow(text) for text in texts_en]\n",
    "\ttfidf_en = models.TfidfModel(corpus_en)\n",
    "\n",
    "\t#  For Japanese text:\n",
    "\t# texts_jp = [doc.split() for doc in list(df_pairs_sample[\"jp_article\"])]\n",
    "\ttexts_jp = [doc.split() for doc in list(df_pairs[\"jp_article\"])]\n",
    "\tdictionary_jp = corpora.Dictionary(texts_jp)\n",
    "\tcorpus_jp = [dictionary_jp.doc2bow(text) for text in texts_jp]\n",
    "\ttfidf_jp = models.TfidfModel(corpus_jp)\n",
    "\n",
    "\tfeatures_en = doc2feature(corpus_en[:5000], tfidf_en, dictionary_en, model_en)\n",
    "\tfeatures_jp = doc2feature(corpus_jp[:5000], tfidf_jp, dictionary_jp, model_jp)\n",
    "\n",
    "\t# --- When do not apply the tfidf re-weighting --- #\n",
    "\t# flag_NO_tfidf = False\n",
    "\t# if flag_NO_tfidf:\n",
    "\t#\n",
    "\t# \tdf_pairs_sample[\"sum_vector_en\"] = df_pairs_sample['word2vec_en'].apply(sum_docment)\n",
    "\t# \tdf_pairs_sample[\"sum_vector_jp\"] = df_pairs_sample['word2vec_jp'].apply(sum_docment)\n",
    "\t# \tdf_pairs_sample[\"average_vector_en\"] = df_pairs_sample['word2vec_en'].apply(average_docment)\n",
    "\t# \tdf_pairs_sample[\"average_vector_jp\"] = df_pairs_sample['word2vec_jp'].apply(average_docment)\n",
    "\t#\n",
    "\t# \tfeatures_en = list(df_pairs_sample[\"average_vector_en\"])\n",
    "\t# \tfeatures_jp = list(df_pairs_sample[\"average_vector_jp\"])\n",
    "\n",
    "\n",
    "\tfeatures_merge = np.concatenate((features_en,features_jp), axis = 1)\n",
    "\n",
    "\t# --- Expanding the training data (dissimilar paris)\n",
    "\tfeatures_en_wrong = np.array(features_en)\n",
    "\tnp.random.shuffle((features_en_wrong))\n",
    "\tc = np.all(features_en_wrong == features_en, axis=1)\n",
    "\tprint(\"C value =\", c.sum()) # check the duplicated amount\n",
    "\n",
    "\tfeatures_merge_wrong = np.concatenate((features_en_wrong,features_jp), axis = 1)\n",
    "\n",
    "\t# --- Prepare the final training and test data --- #\n",
    "\n",
    "\tX = np.concatenate((features_merge, features_merge_wrong), axis = 0)\n",
    "\ty = np.concatenate((np.ones(len(features_merge)), np.zeros(len(features_en_wrong))), axis = 0)\n",
    "\n",
    "\t# --- Split into test data and training data --- #\n",
    "\n",
    "\tX_train1, X_test1, X_train2, X_train3_wrong, X_test2_wrong = np.split(X, [2000, 3000, 5000, 9000])\n",
    "\ty_train1, y_test1, y_train2, y_train3_wrong, y_test2_wrong = np.split(y, [2000, 3000, 9000, 9000])\n",
    "\n",
    "\tX_train = np.concatenate((X_train1, X_train2, X_train3_wrong), axis = 0)\n",
    "\ty_train = np.concatenate((y_train1, y_train2, y_train3_wrong), axis = 0)\n",
    "\tX_train_correct = np.concatenate((X_train1, X_train2), axis = 0)\n",
    "\ty_train_correct = np.concatenate((y_train1, y_train2), axis = 0)\n",
    "\n",
    "\tX_test = np.concatenate((X_test1, X_test2_wrong), axis = 0)\n",
    "\ty_test = np.concatenate((y_test1, y_test2_wrong), axis = 0)\n",
    "\tX_test_correct = X_test1\n",
    "\ty_test_correct = y_test1\n",
    "\n",
    "\n",
    "\t# 一下被屏蔽的代码是使用了和keras_title一样的数据处理办法，目前已证明处理的结果和使用之前svm\n",
    "\t# 的时候的结果没什么区别，因此全部屏蔽掉了。\n",
    "\t# features_en_1 = doc2feature(corpus_en[:5000], tfidf_en, dictionary_en, model_en)\n",
    "\t# features_jp_1 = doc2feature(corpus_jp[:5000], tfidf_jp, dictionary_jp, model_jp)\n",
    "\t#\n",
    "\t# # Generate training data (similarity = 0)\n",
    "\t# features_en_0 = np.array(features_en_1)\n",
    "\t# np.random.shuffle((features_en_0))\n",
    "\t#\n",
    "\t# # check the duplicated amount\n",
    "\t# c = np.all(features_en_1 == features_en_0, axis=(1, ))\n",
    "\t# print \"C value =\", c.sum(), \"position:\", np.where(c == True)[0].tolist()\n",
    "\t#\n",
    "\t# # Prepare the final training and test data\n",
    "\t# X_1 = np.concatenate((features_en_1, features_en_0), axis=0)\n",
    "\t# X_2 = np.concatenate((features_jp_1, features_jp_1), axis=0)\n",
    "\t# y = np.concatenate((np.ones(len(features_en_1)), np.zeros(len(features_en_0))), axis=0)\n",
    "\t#\n",
    "\t# # --- Split into test data and training data --- #\n",
    "\t#\n",
    "\t# X1_train1, X1_test_1, X1_train2, X1_train3_wrong, X1_test_0 = np.split(X_1, [2000, 3000, 5000, 9000])\n",
    "\t# X2_train1, X2_test_1, X2_train2, X2_train3_wrong, X2_test_0 = np.split(X_2, [2000, 3000, 5000, 9000])\n",
    "\t# y_train1, y_test, y_train2, y_train3_wrong, Y_o = np.split(y, [2000, 3000, 9000, 9000])\n",
    "\t#\n",
    "\t# X1_train = np.concatenate((X1_train1, X1_train2, X1_train3_wrong), axis=0)\n",
    "\t# X2_train = np.concatenate((X2_train1, X2_train2, X2_train3_wrong), axis=0)\n",
    "\t# y_train = np.concatenate((y_train1, y_train2, y_train3_wrong), axis=0)\n",
    "\t# # X_train_correct = np.concatenate((X_train1, X_train2), axis = 0)\n",
    "\t# # y_train_correct = np.concatenate((y_train1, y_train2), axis = 0)\n",
    "\t#\n",
    "\t# X_train = np.concatenate((X1_train, X2_train), axis=1)\n",
    "\t#\n",
    "\t# # --- Generate balanced test data --- #\n",
    "\t# X1_test = np.concatenate((X1_test_1, X1_test_0), axis=0)\n",
    "\t# X2_test = np.concatenate((X2_test_1, X2_test_0), axis=0)\n",
    "\t# y_test = np.concatenate((np.ones(len(X1_test_1)), np.zeros(len(X1_test_0))), axis=0)\n",
    "\t#\n",
    "\t# X_test = np.concatenate((X1_test, X2_test), axis=1)\n",
    "\n",
    "\t# ------------------------------- SVM Training ------------------------------- #\n",
    "\n",
    "\tmethod = \"0\"\n",
    "\tif method == \"sgd\":\n",
    "\t\tclf = SGDClassifier(loss=\"hinge\", penalty=\"l2\", n_jobs=8)\n",
    "\t\tclf = SGDClassifier(loss=\"squared_hinge\", penalty=\"l2\", n_jobs=8)\n",
    "\telse:\n",
    "\t\tclf = svm.SVC(kernel=\"rbf\", gamma=0.001, C=100, probability=True)\n",
    "\t\t# clf = svm.SVC(kernel=\"linear1\", gamma=0.001, C=100, probability=True)\n",
    "\t\t# clf = svm.SVC(kernel=\"rbf\", gamma=1, C=100, probability=False)\n",
    "\t\t# clf = svm.SVC(kernel=\"rbf\", gamma=0.001, C=1000, probability=False)\n",
    "\t\t# clf = svm.SVC(kernel=\"rbf\", gamma=0.001, C=100, probability=False)\n",
    "\t\t# clf = svm.SVC(kernel=\"rbf\", gamma=0.001, C=10, probability=False)\n",
    "\t\t# clf = svm.SVC(kernel=\"rbf\", gamma=0.001, C=1, probability=False)\n",
    "\t\t# clf = svm.SVC(kernel=\"rbf\", gamma=0.001, C=0.1, probability=False)\n",
    "\t\t# clf = svm.SVC(kernel=\"rbf\", gamma=0.001, C=0.01, probability=False)\n",
    "\t\t# clf = svm.SVC(kernel=\"rbf\", gamma=0.001, C=0.001, probability=False)\n",
    "\n",
    "\t# 在使用\n",
    "\tstanderlization = 1\n",
    "\tif standerlization == 1:\n",
    "\t\tscaler = preprocessing.StandardScaler().fit(X_train)\n",
    "\t\tX_scaled = scaler.transform(X_train)\n",
    "\t\tX_test_scaled = scaler.transform(X_test)\n",
    "\t\tclf.fit(X_scaled, y_train)\n",
    "\n",
    "\t\ty_test_predict = clf.predict(X_test_scaled)\n",
    "\t\ty_train_predict = clf.predict(X_scaled)\n",
    "\t\tprint(clf)\n",
    "\t\tprint(\"classification report of TRAINING data:\")\n",
    "\t\tprint(classification_report(y_train, y_train_predict))\n",
    "\n",
    "\t\tprint(\"classification report of TEST data:\")\n",
    "\t\tprint(classification_report(y_test, y_test_predict))\n",
    "\n",
    "\n",
    "\telif standerlization == 2:\n",
    "\t\tmin_max_scaler = preprocessing.MinMaxScaler()\n",
    "\t\tX_scaled = min_max_scaler.fit_transform(X_train)\n",
    "\t\tX_test_scaled = min_max_scaler.transform(X_test)\n",
    "\t\tclf.fit(X_scaled, y_train)\n",
    "\n",
    "\t\ty_test_predict = clf.predict(X_test_scaled)\n",
    "\t\ty_train_predict = clf.predict(X_scaled)\n",
    "\n",
    "\t\tprint(\"classification report of TRAINING data:\")\n",
    "\t\tprint(classification_report(y_train, y_train_predict))\n",
    "\n",
    "\t\tprint(\"classification report of TEST data:\")\n",
    "\t\tprint(classification_report(y_test, y_test_predict))\n",
    "\n",
    "\telse:\n",
    "\t\tclf.fit(X_train, y_train)\n",
    "\t\t# clf.score(X_train, y_train)\n",
    "\t\t# clf.score(X_test, y_test)\n",
    "\t\ty_test_predict = clf.predict(X_test)\n",
    "\t\ty_train_predict = clf.predict(X_train)\n",
    "\n",
    "\t\tprint(\"classification report of TRAINING data:\")\n",
    "\t\tprint(classification_report(y_train, y_train_predict))\n",
    "\n",
    "\t\tprint(\"classification report of TEST data:\")\n",
    "\t\tprint(classification_report(y_test, y_test_predict))\n",
    "\n",
    "\n",
    "\n",
    "\t# print \"classification report of TRAINING data:\"\n",
    "\t# print(classification_report(y_train, y_train_predict))\n",
    "\t#\n",
    "\t# print \"classification report of TEST data:\"\n",
    "\t# print(classification_report(y_test, y_test_predict))\n",
    "\n",
    "\n",
    "\t# --- Evaluation for SVM --- #\n",
    "\n",
    "\n",
    "\t# sim_results_train, rank_results_train = find_ranking(projection1_train, projection2_train)\n",
    "\tif standerlization == 0:\n",
    "\t\t# sim_results_test, rank_results_test = find_ranking(X_test[:1000,:200] ,X_test[:1000,200:], clf)\n",
    "\t\tq = wrapper_find_ranking_quick(X_test, clf)\n",
    "\t\tdic_rank_results_test = q.get()\n",
    "\n",
    "\t\t# y_test_proba = clf.predict_proba(X_test)\n",
    "\t\t# y_train_proba = clf.predict_proba(X_train)\n",
    "\telse:\n",
    "\t\t# sim_results_test, rank_results_test = find_ranking(X_test[:1000,:200] ,X_test[:1000,200:], clf)\n",
    "\t\tq = wrapper_find_ranking_quick(X_test_scaled, clf)\n",
    "\t\tdic_rank_results_test = q.get()\n",
    "\t\t# y_test_proba = clf.predict_proba(X_test)\n",
    "\t\t# y_test_proba = clf.predict_proba(X_test_scaled)\n",
    "\t\t# y_train_proba = clf.predict_proba(X_scaled)\n",
    "\n",
    "\trank_results_test = [dic_rank_results_test[k] for k in sorted(dic_rank_results_test)]\n",
    "\n",
    "\tprint(pd.Series(rank_results_test).describe())\n",
    "\tprint(\"TOP1\", (pd.Series(rank_results_test)<=1).sum())\n",
    "\tprint(\"TOP5\", (pd.Series(rank_results_test)<=5).sum())\n",
    "\tprint(\"TOP10\", (pd.Series(rank_results_test)<=10).sum())\n",
    "\n",
    "\n",
    "\n",
    "\t# # --- Prepare for a new independent evaluation balanced data --- #\n",
    "\t#\n",
    "\t# df_pairs_evaluate = df_pairs.iloc[55000:60000]\n",
    "\t#\n",
    "\t# df_pairs_evaluate['word2vec_en'] = df_pairs_evaluate['en_article'].apply(doc2vec_en)\n",
    "\t# df_pairs_evaluate['word2vec_jp'] = df_pairs_evaluate['jp_article'].apply(doc2vec_jp)\n",
    "\t#\n",
    "\t# features_en_eva = doc2feature(corpus_en[60000:61000], tfidf_en, dictionary_en, model_en)\n",
    "\t# features_jp_eva = doc2feature(corpus_jp[60000:61000], tfidf_jp, dictionary_jp, model_jp)\n",
    "\t#\n",
    "\t# features_merge_eva = np.concatenate((features_en_eva,features_jp_eva), axis = 1)\n",
    "\t#\n",
    "\t# features_en_wrong_eva =  features_en[:1000]\n",
    "\t# # features_en_wrong_eva = np.array(features_en_eva)\n",
    "\t# # np.random.shuffle((features_en_wrong_eva))\n",
    "\t# # c = np.all(features_en_wrong_eva == features_en_eva, axis=1)\n",
    "\t# # print \"C value =\", c.sum() # check the duplicated amount\n",
    "\t#\n",
    "\t# features_merge_wrong = np.concatenate((features_en_wrong_eva,features_jp_eva), axis = 1)\n",
    "\t#\n",
    "\t# X_eva = np.concatenate((features_merge_eva, features_merge_wrong), axis = 0)\n",
    "\t# y_eva = np.concatenate((np.ones(len(features_merge_eva)), np.zeros(len(features_en_wrong_eva))), axis = 0)\n",
    "\t#\n",
    "\t# y_eva_predict = clf.predict(X_eva)\n",
    "\t#\n",
    "\t# print \"classification report of TRAINING data:\"\n",
    "\t# print(classification_report(y_eva, y_eva_predict))\n",
    "\n",
    "\n",
    "\t# # --- Evaluation for SVM --- #\n",
    "\t#\n",
    "\t# y_test_proba = clf.predict_proba(X_test)\n",
    "\t# y_train_proba = clf.predict_proba(X_train)\n",
    "\t#\n",
    "\t# # sim_results_train, rank_results_train = find_ranking(projection1_train, projection2_train)\n",
    "\t# sim_results_test, rank_results_test = find_ranking(X_test[:,:200] ,X_test[:,200:], clf)\n",
    "\t#\n",
    "\t#\n",
    "\t# print pd.Series(rank_results_test).describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading english Data: 5000\n",
      "Reading english Data: 5000\n",
      "Merging the English and Japanes news dataframe...\n",
      "Drop the null line...\n",
      "False    4995\n",
      "True        5\n",
      "Name: en_article, dtype: int64\n",
      "Using the the data where start= 0 , end 5000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "C value = 7\n",
      "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf',\n",
      "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "classification report of TRAINING data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      1.00      1.00      4000\n",
      "        1.0       1.00      1.00      1.00      4000\n",
      "\n",
      "avg / total       1.00      1.00      1.00      8000\n",
      "\n",
      "classification report of TEST data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.75      0.81      0.78      1000\n",
      "        1.0       0.80      0.73      0.76      1000\n",
      "\n",
      "avg / total       0.77      0.77      0.77      2000\n",
      "\n",
      "Doc.700: 6.0||Doc.100: 15.0||Doc.300: 35.0||Doc.200: 16.0||Doc.0: 3.0||Doc.400: 203.0||Doc.600: 48.5||Doc.500: 11.5||Doc.800: 23.0||Doc.900: 46.0||Doc.101: 2.0||Doc.601: 34.0||Doc.701: 19.5||Doc.401: 203.0||Doc.301: 35.0||Doc.201: 11.0||Doc.801: 2.0||Doc.1: 249.0||Doc.501: 19.5||Doc.901: 1.0||Doc.102: 9.5||Doc.302: 35.0||Doc.702: 19.5||Doc.602: 34.0||Doc.402: 217.5||Doc.802: 2.0||Doc.202: 25.0||Doc.2: 31.0||Doc.902: 5.5||Doc.502: 19.5||Doc.303: 62.5||Doc.103: 9.5||Doc.703: 16.5||Doc.603: 34.0||Doc.203: 6.0||Doc.403: 219.5||Doc.3: 4.0||Doc.803: 3.0||Doc.903: 54.0||Doc.503: 15.5||Doc.304: 60.5||Doc.104: 9.5||Doc.204: 22.5||Doc.704: 16.5||Doc.604: 52.5||Doc.404: 224.0||Doc.4: 4.0||Doc.804: 8.0||Doc.904: 2.5||Doc.504: 15.5||Doc.305: 5.5||Doc.205: 29.5||Doc.105: 9.5||Doc.605: 58.5||Doc.705: 13.5||Doc.805: 7.5||Doc.405: 224.0||Doc.5: 26.5||Doc.505: 38.5||Doc.905: 2.5||Doc.306: 52.5||Doc.606: 1.0||Doc.206: 1.0||Doc.106: 1.0||Doc.806: 7.5||Doc.706: 96.5||Doc.406: 224.0||Doc.506: 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15.0||Doc.919: 13.5||Doc.521: 3.5||Doc.221: 22.0||Doc.720: 3.5||Doc.421: 1.0||Doc.321: 1.0||Doc.20: 37.5||Doc.121: 1.5||Doc.821: 56.5||Doc.522: 148.5||Doc.621: 28.0||Doc.920: 15.0||Doc.222: 19.0||Doc.422: 7.0||Doc.721: 23.0||Doc.21: 191.5||Doc.322: 20.0||Doc.122: 46.0||Doc.622: 2.5||Doc.822: 75.5||Doc.523: 443.5||Doc.921: 3.0||Doc.223: 6.0||Doc.722: 26.0||Doc.423: 5.0||Doc.22: 191.5||Doc.323: 1.0||Doc.123: 9.5||Doc.224: 11.0||Doc.823: 75.5||Doc.623: 66.5||Doc.922: 64.0||Doc.524: 443.5||Doc.723: 23.0||Doc.424: 5.0||Doc.324: 3.5||Doc.124: 9.5||Doc.23: 1.0||Doc.225: 11.0||Doc.824: 156.5||Doc.923: 1.0||Doc.624: 73.5||Doc.724: 1.0||Doc.525: 443.5||Doc.325: 3.5||Doc.125: 9.5||Doc.24: 7.0||Doc.425: 5.0||Doc.226: 11.0||Doc.825: 41.5||Doc.924: 8.5||Doc.625: 15.0||Doc.725: 3.0||Doc.126: 9.5||Doc.526: 82.0||Doc.326: 2.0||Doc.426: 100.5||Doc.25: 1.0||Doc.925: 2.5||Doc.227: 1.0||Doc.626: 123.0||Doc.826: 30.0||Doc.726: 3.0||Doc.327: 1.0||Doc.127: 34.0||Doc.26: 21.0||Doc.527: 2.0||Doc.926: 1.5||Doc.427: 40.0||Doc.627: 2.5||Doc.228: 2.5||Doc.827: 6.0||Doc.727: 3.0||Doc.328: 101.5||Doc.128: 2.0||Doc.927: 130.0||Doc.27: 38.0||Doc.528: 33.0||Doc.428: 104.5||Doc.628: 316.0||Doc.828: 61.5||Doc.229: 10.5||Doc.728: 10.5||Doc.329: 24.5||Doc.129: 24.0||Doc.28: 2.0||Doc.928: 378.5||Doc.529: 9.0||Doc.429: 4.5||Doc.629: 383.0||Doc.829: 62.5||Doc.729: 10.5||Doc.230: 1.0||Doc.330: 29.5||Doc.130: 1.0||Doc.29: 6.0||Doc.929: 522.5||Doc.430: 9.0||Doc.530: 3.5||Doc.630: 281.0||Doc.730: 8.0||Doc.830: 1.0||Doc.231: 5.0||Doc.930: 522.5||Doc.30: 13.0||Doc.331: 26.5||Doc.131: 3.0||Doc.431: 13.0||Doc.531: 3.5||Doc.831: 35.0||Doc.631: 102.0||Doc.232: 7.0||Doc.731: 1.5||Doc.931: 160.5||Doc.31: 28.0||Doc.332: 38.5||Doc.132: 75.0||Doc.432: 163.0||Doc.532: 9.0||Doc.233: 12.0||Doc.832: 3.0||Doc.632: 1.0||Doc.732: 1.5||Doc.932: 25.0||Doc.32: 25.0||Doc.333: 42.5||Doc.133: 9.5||Doc.433: 1.0||Doc.533: 4.0||Doc.234: 7.0||Doc.833: 38.0||Doc.933: 82.5||Doc.633: 12.0||Doc.733: 46.0||Doc.334: 42.5||Doc.33: 42.0||Doc.134: 9.5||Doc.434: 12.0||Doc.534: 40.0||Doc.235: 7.0||Doc.834: 1.0||Doc.934: 82.5||Doc.734: 82.0||Doc.634: 23.0||Doc.335: 42.5||Doc.34: 39.5||Doc.135: 9.5||Doc.535: 10.0||Doc.435: 5.0||Doc.835: 34.0||Doc.236: 7.0||Doc.935: 48.5||Doc.735: 17.5||Doc.635: 2.0||Doc.336: 42.5||Doc.35: 39.5||Doc.136: 9.5||Doc.536: 100.0||Doc.836: 6.5||Doc.237: 28.5||Doc.436: 17.0||Doc.736: 57.5||Doc.936: 37.5||Doc.636: 21.0||Doc.337: 42.5||Doc.137: 4.0||Doc.36: 39.5||Doc.837: 184.0||Doc.437: 31.0||Doc.737: 2.0||Doc.238: 20.5||Doc.937: 23.0||Doc.537: 65.0||Doc.637: 163.5||Doc.338: 35.5||Doc.37: 35.5||Doc.138: 1.0||Doc.438: 13.0||Doc.838: 185.5||Doc.738: 67.0||Doc.239: 5.0||Doc.938: 3.0||Doc.638: 2.0||Doc.538: 119.0||Doc.339: 35.5||Doc.38: 1.0||Doc.139: 42.5||Doc.439: 35.0||Doc.739: 53.0||Doc.240: 2.0||Doc.839: 185.5||Doc.639: 215.5||Doc.939: 9.0||Doc.539: 119.0||Doc.340: 35.5||Doc.39: 20.0||Doc.440: 13.0||Doc.740: 19.0||Doc.840: 237.5||Doc.140: 27.5||Doc.241: 16.0||Doc.640: 281.5||Doc.940: 10.5||Doc.540: 10.0||Doc.40: 9.0||Doc.341: 35.5||Doc.741: 6.0||Doc.441: 4.5||Doc.141: 1.0||Doc.841: 237.5||Doc.641: 302.5||Doc.242: 55.0||Doc.941: 110.0||Doc.541: 15.0||Doc.41: 10.0||Doc.742: 53.0||Doc.342: 11.5||Doc.842: 3.0||Doc.442: 7.5||Doc.243: 27.0||Doc.942: 51.5||Doc.642: 50.0||Doc.142: 2.0||Doc.542: 5.5||Doc.42: 4.0||Doc.743: 7.5||Doc.443: 5.0||Doc.343: 11.5||Doc.843: 3.0||Doc.244: 4.0||Doc.143: 1.0||Doc.943: 2.0||Doc.643: 123.5||Doc.543: 3.5||Doc.43: 79.0||Doc.744: 96.5||Doc.144: 27.0||Doc.944: 2.0||Doc.444: 1.0||Doc.245: 13.0||Doc.344: 3.5||Doc.644: 392.5||Doc.844: 6.5||Doc.544: 3.5||Doc.44: 3.0||Doc.145: 7.0||Doc.945: 2.0||Doc.745: 271.5||Doc.246: 3.0||Doc.445: 41.5||Doc.645: 2.0||Doc.845: 16.0||Doc.545: 10.0||Doc.345: 3.5||Doc.45: 10.0||Doc.146: 12.0||Doc.946: 2.0||Doc.247: 3.0||Doc.846: 4.0||Doc.746: 96.5||Doc.446: 41.5||Doc.546: 14.0||Doc.646: 19.0||Doc.346: 5.0||Doc.46: 20.0||Doc.147: 1.5||Doc.947: 1.5||Doc.447: 70.0||Doc.248: 86.0||Doc.847: 7.0||Doc.747: 271.5||Doc.547: 5.5||Doc.647: 36.0||Doc.347: 13.5||Doc.47: 15.0||Doc.148: 1.5||Doc.948: 16.0||Doc.249: 29.0||Doc.448: 6.0||Doc.548: 5.5||Doc.648: 8.0||Doc.748: 67.0||Doc.848: 8.0||Doc.348: 13.5||Doc.48: 15.0||Doc.949: 1.0||Doc.250: 17.0||Doc.149: 8.0||Doc.549: 2.5||Doc.449: 92.5||Doc.649: 9.0||Doc.749: 53.0||Doc.849: 2.0||Doc.49: 24.0||Doc.349: 64.0||Doc.251: 5.0||Doc.550: 2.5||Doc.950: 2.5||Doc.150: 38.0||Doc.650: 3.0||Doc.450: 2.5||Doc.750: 27.0||Doc.50: 1.0||Doc.850: 108.5||Doc.350: 1.5||Doc.551: 12.0||Doc.252: 3.0||Doc.151: 1.0||Doc.951: 2.5||Doc.451: 72.0||Doc.51: 109.5||Doc.651: 57.0||Doc.751: 93.0||Doc.851: 108.5||Doc.351: 5.0||Doc.152: 11.0||Doc.552: 32.5||Doc.253: 137.0||Doc.952: 2.5||Doc.452: 6.0||Doc.52: 69.5||Doc.752: 1.0||Doc.652: 7.5||Doc.553: 24.5||Doc.153: 10.0||Doc.352: 1.0||Doc.852: 2.0||Doc.254: 71.5||Doc.453: 17.0||Doc.953: 2.5||Doc.53: 69.5||Doc.753: 40.0||Doc.653: 13.5||Doc.554: 1.0||Doc.154: 10.0||Doc.353: 23.0||Doc.853: 2.0||Doc.454: 147.5||Doc.255: 71.5||Doc.54: 69.5||Doc.754: 80.0||Doc.954: 4.0||Doc.555: 8.0||Doc.654: 13.5||Doc.155: 1.0||Doc.354: 579.0||Doc.854: 2.0||Doc.455: 13.5||Doc.55: 8.0||Doc.755: 32.0||Doc.256: 96.5||Doc.955: 1.0||Doc.556: 6.5||Doc.655: 7.5||Doc.855: 5.0||Doc.156: 3.5||Doc.456: 69.0||Doc.355: 388.0||Doc.56: 33.0||Doc.257: 96.5||Doc.756: 27.0||Doc.956: 12.0||Doc.856: 140.0||Doc.656: 1.0||Doc.557: 7.0||Doc.356: 388.0||Doc.157: 3.5||Doc.457: 15.0||Doc.258: 63.0||Doc.957: 13.0||Doc.57: 32.0||Doc.757: 2.0||Doc.857: 13.0||Doc.657: 31.0||Doc.558: 6.5||Doc.259: 6.5||Doc.158: 23.0||Doc.357: 8.0||Doc.958: 6.0||Doc.458: 25.0||Doc.58: 1.0||Doc.758: 2.0||Doc.658: 49.0||Doc.858: 30.5||Doc.559: 28.5||Doc.159: 23.0||Doc.260: 82.5||Doc.959: 106.5||Doc.459: 21.0||Doc.358: 12.0||Doc.59: 7.0||Doc.759: 2.0||Doc.560: 28.5||Doc.859: 42.5||Doc.659: 43.0||Doc.160: 23.0||Doc.960: 96.5||Doc.261: 8.5||Doc.460: 2.0||Doc.359: 1.0||Doc.760: 28.0||Doc.860: 52.0||Doc.60: 1.0||Doc.161: 3.5||Doc.561: 27.5||Doc.660: 9.0||Doc.262: 2.5||Doc.961: 1.0||Doc.360: 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196.0||Doc.668: 9.5||Doc.868: 19.5||Doc.768: 199.0||Doc.68: 45.5||Doc.969: 1.0||Doc.269: 2.5||Doc.569: 104.0||Doc.169: 91.0||Doc.468: 5.0||Doc.367: 2.0||Doc.669: 9.5||Doc.869: 40.5||Doc.769: 199.0||Doc.970: 27.0||Doc.69: 8.0||Doc.570: 23.0||Doc.170: 1.0||Doc.270: 15.0||Doc.368: 19.0||Doc.469: 31.0||Doc.770: 52.0||Doc.670: 9.5||Doc.870: 19.0||Doc.571: 155.0||Doc.70: 1.0||Doc.971: 10.0||Doc.470: 22.0||Doc.271: 14.0||Doc.171: 10.5||Doc.369: 24.5||Doc.671: 15.0||Doc.871: 44.0||Doc.771: 55.0||Doc.572: 131.5||Doc.972: 24.0||Doc.471: 43.0||Doc.272: 3.0||Doc.370: 24.5||Doc.71: 9.0||Doc.172: 19.0||Doc.672: 1.5||Doc.872: 40.5||Doc.573: 21.5||Doc.772: 24.0||Doc.371: 13.0||Doc.72: 8.0||Doc.273: 41.0||Doc.472: 1.5||Doc.973: 10.0||Doc.173: 8.0||Doc.673: 17.0||Doc.873: 3.0||Doc.574: 18.0||Doc.773: 5.0||Doc.274: 115.5||Doc.974: 27.0||Doc.73: 53.0||Doc.372: 2.0||Doc.473: 10.5||Doc.674: 1.5||Doc.174: 8.0||Doc.874: 1.0||Doc.774: 140.0||Doc.575: 115.5||Doc.74: 155.0||Doc.275: 115.5||Doc.975: 5.5||Doc.373: 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3.0||Doc.81: 2.0||Doc.380: 15.0||Doc.583: 102.0||Doc.282: 1.5||Doc.782: 30.0||Doc.882: 49.0||Doc.682: 12.0||Doc.983: 11.0||Doc.481: 443.5||Doc.183: 73.0||Doc.82: 1.0||Doc.584: 8.5||Doc.381: 15.0||Doc.283: 1.5||Doc.883: 28.0||Doc.783: 5.0||Doc.683: 17.0||Doc.984: 40.0||Doc.482: 443.5||Doc.184: 34.0||Doc.585: 8.5||Doc.382: 18.0||Doc.83: 1.0||Doc.884: 1.0||Doc.284: 35.5||Doc.684: 3.0||Doc.784: 125.5||Doc.985: 4.0||Doc.483: 443.5||Doc.586: 13.0||Doc.185: 13.0||Doc.383: 14.0||Doc.685: 3.0||Doc.84: 8.0||Doc.885: 8.0||Doc.285: 34.5||Doc.785: 125.5||Doc.484: 42.5||Doc.986: 5.0||Doc.587: 34.0||Doc.186: 2.0||Doc.384: 33.5||Doc.686: 5.0||Doc.85: 22.5||Doc.786: 125.5||Doc.886: 34.5||Doc.286: 35.5||Doc.987: 13.0||Doc.485: 13.5||Doc.187: 98.0||Doc.588: 1.0||Doc.385: 33.5||Doc.86: 14.5||Doc.787: 3.5||Doc.687: 1.0||Doc.887: 33.5||Doc.287: 36.5||Doc.988: 1.0||Doc.486: 24.5||Doc.188: 26.0||Doc.589: 3.0||Doc.386: 61.0||Doc.788: 3.5||Doc.87: 21.0||Doc.688: 2.0||Doc.888: 2.0||Doc.288: 599.5||Doc.989: 9.0||Doc.487: 2.0||Doc.189: 1.0||Doc.590: 224.0||Doc.88: 21.5||Doc.789: 1.0||Doc.387: 9.0||Doc.289: 599.5||Doc.689: 6.0||Doc.488: 1.0||Doc.990: 11.0||Doc.889: 12.0||Doc.190: 6.0||Doc.591: 42.0||Doc.790: 21.5||Doc.89: 21.5||Doc.388: 1.0||Doc.690: 30.5||Doc.290: 129.0||Doc.489: 2.0||Doc.890: 10.0||Doc.991: 12.0||Doc.191: 6.0||Doc.791: 6.5||Doc.90: 9.5||Doc.592: 11.0||Doc.490: 16.5||Doc.389: 1.0||Doc.291: 1.0||Doc.691: 177.5||Doc.891: 96.0||Doc.992: 3.0||Doc.192: 3.5||Doc.792: 25.0||Doc.593: 15.0||Doc.91: 9.5||Doc.491: 16.5||Doc.692: 1.5||Doc.292: 122.5||Doc.892: 171.5||Doc.390: 1.0||Doc.993: 115.5||Doc.193: 3.5||Doc.793: 33.0||Doc.594: 25.0||Doc.492: 26.5||Doc.92: 9.5||Doc.693: 105.5||Doc.391: 66.0||Doc.994: 43.5||Doc.293: 277.5||Doc.893: 241.5||Doc.194: 12.0||Doc.794: 116.5||Doc.595: 1.5||Doc.493: 38.5||Doc.93: 9.5||Doc.694: 16.0||Doc.995: 289.5||Doc.392: 15.0||Doc.294: 89.5||Doc.894: 585.0||Doc.195: 25.0||Doc.795: 18.0||Doc.94: 22.5||Doc.494: 23.0||Doc.596: 1.5||Doc.996: 140.5||Doc.695: 4.0||Doc.393: 15.0||Doc.295: 64.5||Doc.895: 37.0||Doc.196: 5.0||Doc.95: 44.0||Doc.597: 39.0||Doc.495: 3.0||Doc.796: 116.5||Doc.997: 14.0||Doc.696: 65.5||Doc.296: 6.0||Doc.394: 21.0||Doc.896: 37.0||Doc.197: 19.0||Doc.96: 89.0||Doc.598: 146.0||Doc.797: 125.5||Doc.496: 4.0||Doc.998: 24.0||Doc.697: 105.5||Doc.395: 3.0||Doc.297: 1.5||Doc.897: 95.0||Doc.198: 1.0||Doc.97: 67.0||Doc.599: 48.5||Doc.999: 14.0||Doc.698: 10.0||Doc.798: 125.5||Doc.497: 50.5||Doc.298: 1.0||Doc.396: 16.0||Doc.199: 4.0||Doc.98: 67.0||Doc.898: 5.5||Doc.699: 3.0||Doc.799: 125.5||Doc.498: 23.5||Doc.299: 29.0||Doc.99: 114.0||Doc.397: 11.0||Doc.899: 21.0||Doc.499: 45.0||Doc.398: 43.0||Doc.399: 203.0||230.37907505\n",
      "count    1000.000000\n",
      "mean       45.232000\n",
      "std        81.865873\n",
      "min         1.000000\n",
      "25%         4.000000\n",
      "50%        14.000000\n",
      "75%        42.625000\n",
      "max       599.500000\n",
      "dtype: float64\n",
      "TOP1 80\n",
      "TOP5 288\n",
      "TOP10 429\n"
     ]
    }
   ],
   "source": [
    "run svm_title.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading english Data: 59999\n",
      "Reading english Data: 59999\n",
      "Merging the English and Japanes news dataframe...\n",
      "Drop the null line...\n",
      "False    59987\n",
      "True        12\n",
      "Name: en_article, dtype: int64\n",
      "Using the the data where start= 0 , end 5000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "C value = 5\n",
      "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf',\n",
      "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "classification report of TRAINING data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      1.00      1.00      4000\n",
      "        1.0       1.00      1.00      1.00      4000\n",
      "\n",
      "avg / total       1.00      1.00      1.00      8000\n",
      "\n",
      "classification report of TEST data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.74      0.81      0.77      1000\n",
      "        1.0       0.79      0.71      0.75      1000\n",
      "\n",
      "avg / total       0.76      0.76      0.76      2000\n",
      "\n",
      "Doc.400: 399.0||Doc.200: 13.0||Doc.500: 32.5||Doc.600: 89.5||Doc.800: 31.0||Doc.0: 1.0||Doc.300: 70.0||Doc.100: 8.0||Doc.700: 8.0||Doc.900: 1.0||Doc.401: 399.0||Doc.801: 2.0||Doc.1: 171.0||Doc.501: 30.5||Doc.601: 37.0||Doc.201: 11.0||Doc.101: 14.0||Doc.301: 70.0||Doc.701: 3.5||Doc.901: 2.0||Doc.2: 53.0||Doc.402: 38.5||Doc.202: 14.0||Doc.102: 8.5||Doc.302: 70.0||Doc.802: 1.0||Doc.502: 30.5||Doc.602: 37.0||Doc.702: 3.5||Doc.902: 13.5||Doc.3: 6.0||Doc.703: 4.5||Doc.203: 3.0||Doc.403: 28.5||Doc.103: 8.5||Doc.903: 2.0||Doc.303: 113.5||Doc.803: 14.0||Doc.603: 37.0||Doc.503: 2.5||Doc.4: 2.0||Doc.704: 4.5||Doc.904: 2.5||Doc.104: 8.5||Doc.204: 10.5||Doc.804: 21.0||Doc.304: 96.5||Doc.404: 367.0||Doc.604: 14.5||Doc.504: 2.5||Doc.5: 60.5||Doc.705: 4.5||Doc.905: 2.5||Doc.105: 8.5||Doc.205: 10.5||Doc.805: 6.5||Doc.405: 367.0||Doc.605: 14.5||Doc.305: 17.5||Doc.505: 23.5||Doc.706: 10.5||Doc.6: 19.5||Doc.906: 2.5||Doc.106: 4.0||Doc.206: 1.0||Doc.606: 2.0||Doc.806: 5.5||Doc.406: 367.0||Doc.306: 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60.0||Doc.894: 6.0||Doc.996: 124.5||Doc.296: 5.0||Doc.795: 3.0||Doc.697: 37.5||Doc.196: 32.0||Doc.96: 91.0||Doc.594: 34.0||Doc.498: 16.5||Doc.997: 12.0||Doc.796: 187.5||Doc.395: 3.0||Doc.895: 10.0||Doc.698: 1.0||Doc.297: 1.5||Doc.97: 90.0||Doc.197: 36.0||Doc.595: 6.5||Doc.499: 66.0||Doc.797: 34.5||Doc.998: 18.0||Doc.396: 7.0||Doc.896: 10.0||Doc.298: 29.0||Doc.699: 2.0||Doc.98: 90.0||Doc.198: 1.0||Doc.596: 6.5||Doc.397: 53.0||Doc.897: 11.0||Doc.299: 43.0||Doc.798: 34.5||Doc.999: 12.0||Doc.99: 148.0||Doc.199: 4.0||Doc.597: 70.0||Doc.398: 21.0||Doc.898: 13.5||Doc.799: 34.5||Doc.598: 14.0||Doc.899: 1.0||Doc.399: 399.0||Doc.599: 89.5||226.832793951\n",
      "count    1000.000000\n",
      "mean       42.246000\n",
      "std        74.091761\n",
      "min         1.000000\n",
      "25%         4.000000\n",
      "50%        15.000000\n",
      "75%        42.500000\n",
      "max       614.500000\n",
      "dtype: float64\n",
      "TOP1 87\n",
      "TOP5 294\n",
      "TOP10 427\n"
     ]
    }
   ],
   "source": [
    "run svm_title.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading english Data: 59999\n",
      "Reading english Data: 59999\n",
      "Merging the English and Japanes news dataframe...\n",
      "Drop the null line...\n",
      "False    59987\n",
      "True        12\n",
      "Name: en_article, dtype: int64\n",
      "Using the the data where start= 0 , end 5000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "C value = 1\n",
      "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf',\n",
      "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "classification report of TRAINING data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      1.00      1.00      4000\n",
      "        1.0       1.00      1.00      1.00      4000\n",
      "\n",
      "avg / total       1.00      1.00      1.00      8000\n",
      "\n",
      "classification report of TEST data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.72      0.81      0.76      1000\n",
      "        1.0       0.78      0.69      0.73      1000\n",
      "\n",
      "avg / total       0.75      0.75      0.75      2000\n",
      "\n",
      "Doc.0: 95.0||Doc.400: 144.0||Doc.500: 20.5||Doc.600: 161.5||Doc.700: 5.0||Doc.300: 96.0||Doc.100: 4.0||Doc.200: 12.0||Doc.800: 20.0||Doc.900: 1.0||Doc.401: 144.0||Doc.601: 53.0||Doc.501: 54.5||Doc.301: 96.0||Doc.1: 567.0||Doc.701: 28.5||Doc.101: 2.0||Doc.201: 2.0||Doc.801: 22.0||Doc.901: 1.0||Doc.602: 53.0||Doc.402: 338.5||Doc.502: 54.5||Doc.2: 7.0||Doc.702: 28.5||Doc.102: 17.5||Doc.202: 28.0||Doc.302: 96.0||Doc.802: 1.0||Doc.902: 8.5||Doc.603: 53.0||Doc.403: 323.5||Doc.3: 7.0||Doc.503: 23.5||Doc.703: 61.5||Doc.103: 17.5||Doc.203: 4.0||Doc.303: 156.5||Doc.903: 6.0||Doc.803: 1.0||Doc.4: 84.0||Doc.604: 21.5||Doc.404: 210.0||Doc.704: 61.5||Doc.304: 153.5||Doc.204: 39.5||Doc.504: 23.5||Doc.104: 17.5||Doc.804: 33.0||Doc.904: 5.5||Doc.5: 31.5||Doc.705: 18.5||Doc.605: 18.5||Doc.305: 38.5||Doc.405: 210.0||Doc.205: 40.5||Doc.105: 17.5||Doc.505: 189.5||Doc.805: 4.5||Doc.6: 32.5||Doc.905: 4.5||Doc.706: 56.5||Doc.606: 1.0||Doc.406: 210.0||Doc.306: 97.5||Doc.206: 1.0||Doc.506: 55.5||Doc.106: 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      "count    1000.000000\n",
      "mean       43.271000\n",
      "std        75.535668\n",
      "min         1.000000\n",
      "25%         4.000000\n",
      "50%        16.000000\n",
      "75%        43.000000\n",
      "max       665.500000\n",
      "dtype: float64\n",
      "TOP1 84\n",
      "TOP5 289\n",
      "TOP10 417\n"
     ]
    }
   ],
   "source": [
    "run svm_title.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
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      "count    1000.00000\n",
      "mean       59.61350\n",
      "std       122.20792\n",
      "min         1.00000\n",
      "25%         4.00000\n",
      "50%        15.00000\n",
      "75%        52.25000\n",
      "max       903.00000\n",
      "dtype: float64\n",
      "TOP1 77\n",
      "TOP5 289\n",
      "TOP10 424\n"
     ]
    }
   ],
   "source": [
    "\tdf_pairs_evaluate = df_pairs.iloc[50000:60000:10]\n",
    "\n",
    "\ttexts_en_new = [doc.split() for doc in list(df_pairs_evaluate[\"en_article\"])]\n",
    "\tcorpus_en_new = [dictionary_en.doc2bow(text) for text in texts_en_new]\n",
    "\n",
    "\ttexts_jp_new = [doc.split() for doc in list(df_pairs_evaluate[\"jp_article\"])]\n",
    "\tcorpus_jp_new = [dictionary_jp.doc2bow(text) for text in texts_jp_new]\n",
    "\n",
    "\tfeatures_en_new = doc2feature(corpus_en_new[:1000], tfidf_en, dictionary_en, model_en)\n",
    "\tfeatures_jp_new = doc2feature(corpus_jp_new[:1000], tfidf_jp, dictionary_jp, model_jp)\n",
    "\n",
    "\tfeatures_merge_new = np.concatenate((features_en_new, features_jp_new), axis=1)\n",
    "\n",
    "\tX_test_scaled_new = scaler.transform(features_merge_new)\n",
    "\n",
    "\t# sim_results_test, rank_results_test = find_ranking(X_test[:1000,:200] ,X_test[:1000,200:], clf)\n",
    "\tq = wrapper_find_ranking_quick(X_test_scaled_new, clf)\n",
    "\tdic_rank_results_test_new = q.get()\n",
    "\n",
    "\trank_results_test_new = [dic_rank_results_test_new[k] for k in sorted(dic_rank_results_test_new)]\n",
    "\n",
    "\tprint(pd.Series(rank_results_test_new).describe())\n",
    "\tprint(\"TOP1\", (pd.Series(rank_results_test_new) <= 1).sum())\n",
    "\tprint(\"TOP5\", (pd.Series(rank_results_test_new) <= 5).sum())\n",
    "\tprint(\"TOP10\", (pd.Series(rank_results_test_new) <= 10).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading english Data: 59999\n",
      "Reading english Data: 59999\n",
      "Merging the English and Japanes news dataframe...\n",
      "Drop the null line...\n",
      "False    59987\n",
      "True        12\n",
      "Name: en_article, dtype: int64\n",
      "Using the the data where start= 0 , end 5000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "C value = 4\n",
      "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf',\n",
      "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "classification report of TRAINING data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      1.00      1.00      4000\n",
      "        1.0       1.00      1.00      1.00      4000\n",
      "\n",
      "avg / total       1.00      1.00      1.00      8000\n",
      "\n",
      "classification report of TEST data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.76      0.82      0.79      1000\n",
      "        1.0       0.81      0.74      0.77      1000\n",
      "\n",
      "avg / total       0.78      0.78      0.78      2000\n",
      "\n",
      "Doc.0: 2.0||Doc.500: 12.5||Doc.200: 18.0||Doc.400: 478.0||Doc.600: 16.5||Doc.100: 7.0||Doc.300: 39.0||Doc.700: 6.0||Doc.800: 15.0||Doc.900: 2.0||Doc.1: 437.0||Doc.501: 10.5||Doc.201: 34.0||Doc.101: 4.0||Doc.401: 478.0||Doc.601: 38.0||Doc.301: 39.0||Doc.701: 8.5||Doc.901: 8.0||Doc.801: 2.0||Doc.2: 16.0||Doc.502: 10.5||Doc.202: 15.0||Doc.102: 10.5||Doc.402: 251.5||Doc.602: 38.0||Doc.302: 39.0||Doc.702: 8.5||Doc.902: 20.5||Doc.802: 1.0||Doc.3: 13.0||Doc.503: 2.5||Doc.203: 14.0||Doc.103: 10.5||Doc.4: 17.0||Doc.403: 238.5||Doc.603: 38.0||Doc.504: 2.5||Doc.204: 13.5||Doc.303: 96.5||Doc.104: 10.5||Doc.703: 3.5||Doc.903: 7.0||Doc.803: 19.0||Doc.5: 9.5||Doc.404: 385.0||Doc.105: 10.5||Doc.505: 37.5||Doc.604: 16.5||Doc.904: 2.5||Doc.205: 13.5||Doc.704: 3.5||Doc.304: 107.5||Doc.804: 11.0||Doc.6: 6.5||Doc.106: 1.0||Doc.605: 17.5||Doc.705: 3.5||Doc.405: 385.0||Doc.305: 20.5||Doc.506: 6.5||Doc.905: 2.5||Doc.206: 3.0||Doc.805: 13.5||Doc.7: 7.0||Doc.606: 1.0||Doc.107: 3.5||Doc.706: 6.5||Doc.406: 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1.5||Doc.82: 2.0||Doc.280: 6.0||Doc.484: 31.5||Doc.980: 20.0||Doc.385: 14.5||Doc.181: 7.0||Doc.784: 249.5||Doc.681: 5.0||Doc.583: 193.0||Doc.485: 7.5||Doc.879: 1.0||Doc.83: 1.0||Doc.281: 8.0||Doc.386: 81.0||Doc.182: 10.0||Doc.981: 13.0||Doc.682: 3.0||Doc.486: 21.5||Doc.785: 249.5||Doc.584: 9.5||Doc.880: 1.0||Doc.84: 21.0||Doc.282: 443.5||Doc.387: 8.0||Doc.183: 120.0||Doc.982: 13.0||Doc.487: 2.0||Doc.683: 2.0||Doc.786: 249.5||Doc.585: 9.5||Doc.85: 7.5||Doc.881: 377.5||Doc.388: 4.0||Doc.184: 42.0||Doc.983: 29.0||Doc.283: 443.5||Doc.488: 1.0||Doc.684: 3.0||Doc.787: 5.5||Doc.586: 14.0||Doc.185: 23.0||Doc.389: 1.0||Doc.86: 1.5||Doc.882: 18.0||Doc.984: 91.0||Doc.284: 18.5||Doc.788: 5.5||Doc.489: 1.0||Doc.685: 2.0||Doc.587: 3.0||Doc.390: 1.0||Doc.186: 2.0||Doc.985: 22.0||Doc.87: 238.0||Doc.883: 1.0||Doc.285: 19.5||Doc.789: 1.0||Doc.490: 8.5||Doc.686: 2.0||Doc.588: 1.0||Doc.391: 94.0||Doc.187: 113.0||Doc.986: 27.0||Doc.88: 27.5||Doc.884: 1.0||Doc.790: 12.5||Doc.286: 18.5||Doc.687: 1.0||Doc.589: 3.0||Doc.392: 64.0||Doc.491: 8.5||Doc.791: 5.5||Doc.188: 29.0||Doc.89: 27.5||Doc.885: 3.0||Doc.987: 108.0||Doc.688: 11.0||Doc.287: 25.5||Doc.393: 64.0||Doc.590: 124.0||Doc.492: 3.5||Doc.886: 6.5||Doc.189: 1.0||Doc.90: 10.5||Doc.792: 21.0||Doc.988: 2.0||Doc.288: 828.5||Doc.689: 2.0||Doc.394: 81.0||Doc.591: 9.0||Doc.493: 2.5||Doc.887: 5.5||Doc.190: 13.0||Doc.793: 37.0||Doc.289: 828.5||Doc.91: 10.5||Doc.989: 7.0||Doc.395: 2.0||Doc.592: 20.0||Doc.690: 13.5||Doc.494: 2.0||Doc.888: 3.0||Doc.794: 45.5||Doc.191: 7.0||Doc.290: 102.0||Doc.92: 10.5||Doc.396: 7.0||Doc.990: 31.0||Doc.593: 4.0||Doc.691: 53.5||Doc.889: 3.0||Doc.495: 3.0||Doc.192: 9.5||Doc.795: 3.0||Doc.291: 1.0||Doc.93: 10.5||Doc.397: 17.0||Doc.594: 30.0||Doc.991: 5.0||Doc.692: 1.5||Doc.890: 174.0||Doc.796: 45.5||Doc.496: 2.0||Doc.292: 211.5||Doc.94: 7.5||Doc.193: 11.5||Doc.398: 18.0||Doc.992: 8.0||Doc.595: 3.5||Doc.693: 79.5||Doc.891: 5.0||Doc.293: 386.5||Doc.797: 249.5||Doc.95: 58.0||Doc.399: 478.0||Doc.194: 19.0||Doc.497: 57.5||Doc.596: 3.5||Doc.694: 24.0||Doc.993: 12.5||Doc.892: 1.5||Doc.294: 51.5||Doc.195: 3.0||Doc.798: 249.5||Doc.498: 10.5||Doc.96: 103.0||Doc.597: 126.0||Doc.695: 6.0||Doc.893: 1.5||Doc.994: 6.5||Doc.196: 6.0||Doc.295: 14.5||Doc.499: 4.0||Doc.97: 80.0||Doc.598: 8.0||Doc.696: 15.5||Doc.799: 249.5||Doc.894: 40.0||Doc.995: 18.5||Doc.197: 81.0||Doc.296: 1.0||Doc.599: 16.5||Doc.697: 8.5||Doc.895: 16.0||Doc.98: 80.0||Doc.198: 1.0||Doc.297: 1.5||Doc.698: 1.0||Doc.996: 15.5||Doc.896: 16.0||Doc.99: 189.0||Doc.199: 4.0||Doc.298: 116.0||Doc.699: 2.0||Doc.897: 23.0||Doc.997: 6.0||Doc.299: 15.0||Doc.898: 20.5||Doc.998: 8.0||Doc.899: 1.0||Doc.999: 6.0||228.158717871\n",
      "count    1000.000000\n",
      "mean       42.953000\n",
      "std        88.176002\n",
      "min         1.000000\n",
      "25%         4.000000\n",
      "50%        11.000000\n",
      "75%        35.500000\n",
      "max       828.500000\n",
      "dtype: float64\n",
      "TOP1 74\n",
      "TOP5 293\n",
      "TOP10 457\n",
      "Using the new test data to evaluate.......\n",
      "0\n",
      "0\n",
      "Doc.0: 84.0||Doc.200: 6.0||Doc.400: 2.0||Doc.100: 4.0||Doc.300: 5.0||Doc.600: 440.0||Doc.700: 1.0||Doc.500: 207.0||Doc.800: 74.0||Doc.900: 1.0||Doc.1: 2.0||Doc.201: 28.0||Doc.601: 8.0||Doc.101: 11.0||Doc.301: 923.0||Doc.401: 12.0||Doc.501: 12.0||Doc.801: 3.0||Doc.701: 7.0||Doc.901: 58.0||Doc.2: 1.5||Doc.602: 67.0||Doc.402: 18.0||Doc.202: 2.0||Doc.302: 2.5||Doc.102: 78.0||Doc.502: 6.0||Doc.702: 51.0||Doc.802: 34.0||Doc.902: 4.0||Doc.403: 2.0||Doc.3: 1.5||Doc.103: 65.0||Doc.303: 85.0||Doc.603: 52.0||Doc.503: 60.0||Doc.203: 23.0||Doc.903: 6.0||Doc.803: 94.0||Doc.703: 1.0||Doc.404: 6.0||Doc.104: 13.0||Doc.304: 14.0||Doc.204: 1.0||Doc.4: 59.0||Doc.904: 47.0||Doc.604: 75.0||Doc.504: 2.0||Doc.804: 23.0||Doc.704: 3.0||Doc.405: 41.0||Doc.105: 7.0||Doc.305: 1.0||Doc.905: 52.0||Doc.205: 5.0||Doc.505: 7.0||Doc.805: 100.0||Doc.5: 64.0||Doc.605: 6.0||Doc.705: 128.0||Doc.406: 2.0||Doc.106: 22.0||Doc.306: 83.0||Doc.906: 2.0||Doc.506: 14.0||Doc.206: 6.0||Doc.806: 8.0||Doc.6: 3.0||Doc.606: 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      "count    1000.000000\n",
      "mean       59.326000\n",
      "std       128.913261\n",
      "min         1.000000\n",
      "25%         4.000000\n",
      "50%        15.000000\n",
      "75%        51.000000\n",
      "max       981.000000\n",
      "dtype: float64\n",
      "TOP1 102\n",
      "TOP5 291\n",
      "TOP10 436\n"
     ]
    }
   ],
   "source": [
    "run svm_title.py # 50000:60000 级别的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using the new test data to evaluate.......\n",
      "0\n",
      "0\n",
      "Doc.100: 7.0||Doc.200: 4.0||Doc.300: 663.0||Doc.0: 67.0||Doc.400: 3.0||Doc.700: 9.0||Doc.500: 2.0||Doc.600: 5.0||Doc.800: 1.5||Doc.900: 70.0||Doc.201: 183.0||Doc.101: 19.0||Doc.301: 32.0||Doc.701: 13.0||Doc.1: 1.0||Doc.601: 1.0||Doc.401: 57.5||Doc.501: 10.0||Doc.901: 35.0||Doc.801: 23.0||Doc.202: 25.5||Doc.302: 33.0||Doc.102: 111.0||Doc.2: 1.5||Doc.702: 10.0||Doc.602: 916.0||Doc.502: 7.0||Doc.402: 27.5||Doc.902: 5.0||Doc.802: 14.0||Doc.203: 16.5||Doc.303: 5.0||Doc.103: 1.0||Doc.3: 39.0||Doc.603: 26.0||Doc.703: 82.5||Doc.503: 2.5||Doc.903: 100.0||Doc.403: 2.0||Doc.304: 1.0||Doc.104: 8.0||Doc.204: 84.0||Doc.803: 1.0||Doc.4: 2.0||Doc.704: 65.0||Doc.604: 1.5||Doc.904: 4.0||Doc.504: 1.5||Doc.105: 18.0||Doc.205: 70.0||Doc.305: 32.0||Doc.404: 1.0||Doc.804: 23.0||Doc.705: 131.0||Doc.5: 2.0||Doc.605: 1.0||Doc.905: 4.0||Doc.505: 3.0||Doc.106: 4.0||Doc.805: 48.0||Doc.405: 44.0||Doc.206: 76.0||Doc.306: 2.0||Doc.706: 1.0||Doc.6: 2.0||Doc.606: 59.0||Doc.906: 100.0||Doc.506: 1.0||Doc.107: 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247.0||Doc.799: 2.0||Doc.293: 7.0||Doc.995: 231.0||Doc.93: 59.0||Doc.595: 3.5||Doc.394: 20.0||Doc.497: 14.0||Doc.695: 21.0||Doc.195: 2.0||Doc.896: 434.0||Doc.94: 22.0||Doc.596: 10.0||Doc.294: 1.0||Doc.395: 76.0||Doc.996: 7.0||Doc.498: 11.0||Doc.897: 569.0||Doc.196: 15.0||Doc.696: 283.0||Doc.95: 44.0||Doc.597: 4.0||Doc.499: 17.0||Doc.295: 5.0||Doc.396: 3.0||Doc.997: 1.0||Doc.197: 4.0||Doc.898: 80.0||Doc.96: 1.0||Doc.598: 77.0||Doc.697: 23.0||Doc.198: 15.5||Doc.899: 1.0||Doc.296: 3.0||Doc.397: 4.0||Doc.998: 1.0||Doc.97: 39.0||Doc.599: 31.0||Doc.698: 11.0||Doc.199: 13.5||Doc.98: 4.0||Doc.297: 1.0||Doc.398: 537.0||Doc.999: 13.0||Doc.699: 1.0||Doc.99: 10.0||Doc.298: 17.0||Doc.399: 3.0||Doc.299: 9.5||225.198071003\n",
      "count    1000.000000\n",
      "mean       52.775000\n",
      "std       114.389754\n",
      "min         1.000000\n",
      "25%         4.000000\n",
      "50%        15.000000\n",
      "75%        48.000000\n",
      "max       916.000000\n",
      "dtype: float64\n",
      "TOP1 99\n",
      "TOP5 300\n",
      "TOP10 417\n"
     ]
    }
   ],
   "source": [
    "\tprint(\"Using the new test data to evaluate.......\")\n",
    "\tdf_pairs_evaluate = df_pairs.iloc[50000:55000:5]\n",
    "\n",
    "\ttexts_en_new = [doc.split() for doc in list(df_pairs_evaluate[\"en_article\"])]\n",
    "\tcorpus_en_new = [dictionary_en.doc2bow(text) for text in texts_en_new]\n",
    "\n",
    "\ttexts_jp_new = [doc.split() for doc in list(df_pairs_evaluate[\"jp_article\"])]\n",
    "\tcorpus_jp_new = [dictionary_jp.doc2bow(text) for text in texts_jp_new]\n",
    "\n",
    "\tfeatures_en_new = doc2feature(corpus_en_new[:1000], tfidf_en, dictionary_en, model_en)\n",
    "\tfeatures_jp_new = doc2feature(corpus_jp_new[:1000], tfidf_jp, dictionary_jp, model_jp)\n",
    "\n",
    "\tfeatures_merge_new = np.concatenate((features_en_new, features_jp_new), axis=1)\n",
    "\n",
    "\tX_test_scaled_new = scaler.transform(features_merge_new)\n",
    "\n",
    "\t# sim_results_test, rank_results_test = find_ranking(X_test[:1000,:200] ,X_test[:1000,200:], clf)\n",
    "\tq = wrapper_find_ranking_quick(X_test_scaled_new, clf)\n",
    "\tdic_rank_results_test_new = q.get()\n",
    "\n",
    "\trank_results_test_new = [dic_rank_results_test_new[k] for k in sorted(dic_rank_results_test_new)]\n",
    "\n",
    "\tprint(pd.Series(rank_results_test_new).describe())\n",
    "\tprint(\"TOP1\", (pd.Series(rank_results_test_new) <= 1).sum())\n",
    "\tprint(\"TOP5\", (pd.Series(rank_results_test_new) <= 5).sum())\n",
    "\tprint(\"TOP10\", (pd.Series(rank_results_test_new) <= 10).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading english Data: 59999\n",
      "Reading english Data: 59999\n",
      "Merging the English and Japanes news dataframe...\n",
      "Drop the null line...\n",
      "False    59987\n",
      "True        12\n",
      "Name: en_article, dtype: int64\n",
      "Using the the data where start= 0 , end 5000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "C value = 4\n",
      "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf',\n",
      "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "classification report of TRAINING data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      1.00      1.00      4000\n",
      "        1.0       1.00      1.00      1.00      4000\n",
      "\n",
      "avg / total       1.00      1.00      1.00      8000\n",
      "\n",
      "classification report of TEST data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.74      0.82      0.78      1000\n",
      "        1.0       0.80      0.71      0.75      1000\n",
      "\n",
      "avg / total       0.77      0.77      0.77      2000\n",
      "\n",
      "Doc.300: 52.0||Doc.200: 24.0||Doc.100: 2.0||Doc.600: 49.5||Doc.400: 471.0||Doc.500: 21.5||Doc.700: 2.0||Doc.800: 3.0||Doc.0: 10.0||Doc.900: 1.0||Doc.201: 7.0||Doc.301: 52.0||Doc.101: 5.0||Doc.401: 471.0||Doc.701: 3.5||Doc.601: 32.0||Doc.1: 59.0||Doc.501: 57.5||Doc.801: 5.0||Doc.901: 7.0||Doc.102: 9.5||Doc.202: 11.0||Doc.302: 52.0||Doc.402: 217.5||Doc.702: 3.5||Doc.602: 32.0||Doc.2: 10.0||Doc.502: 57.5||Doc.802: 2.0||Doc.203: 7.5||Doc.902: 12.5||Doc.103: 9.5||Doc.703: 14.5||Doc.303: 110.5||Doc.403: 200.5||Doc.603: 32.0||Doc.3: 6.0||Doc.803: 2.0||Doc.204: 12.5||Doc.503: 1.5||Doc.903: 4.0||Doc.104: 9.5||Doc.604: 108.5||Doc.304: 107.5||Doc.704: 14.5||Doc.404: 489.0||Doc.558: 1.5||Doc.356: 162.0||Doc.756: 32.0||Doc.854: 2.0||Doc.257: 11.5||Doc.657: 202.0||Doc.454: 135.5||Doc.953: 21.5||Doc.155: 5.0||Doc.61: 3.0||Doc.559: 4.5||Doc.757: 3.0||Doc.855: 1.0||Doc.357: 5.0||Doc.258: 22.0||Doc.658: 187.0||Doc.455: 16.5||Doc.954: 1.0||Doc.156: 2.5||Doc.62: 3.0||Doc.758: 3.0||Doc.560: 4.5||Doc.856: 1.0||Doc.358: 3.0||Doc.955: 2.0||Doc.456: 18.0||Doc.259: 11.5||Doc.659: 153.0||Doc.63: 63.0||Doc.157: 2.5||Doc.759: 3.0||Doc.561: 3.5||Doc.857: 63.0||Doc.956: 8.0||Doc.457: 2.0||Doc.359: 1.0||Doc.158: 2.0||Doc.260: 100.5||Doc.660: 1.0||Doc.562: 3.5||Doc.64: 1.0||Doc.760: 23.0||Doc.858: 211.5||Doc.458: 28.0||Doc.957: 31.0||Doc.159: 3.0||Doc.563: 11.0||Doc.261: 2.5||Doc.360: 5.0||Doc.661: 11.5||Doc.65: 23.0||Doc.761: 6.0||Doc.859: 222.5||Doc.459: 1.0||Doc.958: 1.0||Doc.160: 3.0||Doc.564: 5.0||Doc.262: 2.5||Doc.66: 1.0||Doc.662: 8.5||Doc.361: 14.0||Doc.860: 3.0||Doc.762: 20.5||Doc.460: 3.0||Doc.263: 12.0||Doc.959: 92.5||Doc.161: 17.5||Doc.565: 22.0||Doc.663: 2.0||Doc.67: 3.0||Doc.362: 29.0||Doc.861: 5.0||Doc.763: 163.5||Doc.264: 142.5||Doc.461: 3.0||Doc.566: 29.0||Doc.162: 17.5||Doc.664: 1.0||Doc.68: 18.5||Doc.960: 91.5||Doc.862: 80.0||Doc.764: 29.5||Doc.363: 23.0||Doc.462: 4.0||Doc.265: 142.5||Doc.665: 1.5||Doc.163: 54.0||Doc.69: 9.0||Doc.863: 3.0||Doc.765: 34.5||Doc.567: 15.0||Doc.961: 1.0||Doc.364: 32.0||Doc.463: 13.0||Doc.666: 1.5||Doc.70: 4.0||Doc.266: 31.5||Doc.864: 576.0||Doc.766: 34.5||Doc.164: 22.0||Doc.568: 12.0||Doc.365: 17.0||Doc.962: 36.0||Doc.464: 18.0||Doc.667: 16.5||Doc.267: 31.5||Doc.865: 2.5||Doc.71: 26.0||Doc.165: 133.0||Doc.767: 93.0||Doc.569: 22.0||Doc.366: 347.0||Doc.963: 5.0||Doc.668: 34.5||Doc.465: 1.0||Doc.268: 4.5||Doc.166: 168.0||Doc.866: 22.0||Doc.768: 93.0||Doc.72: 9.0||Doc.570: 33.0||Doc.367: 4.0||Doc.964: 3.5||Doc.269: 4.5||Doc.167: 193.0||Doc.867: 272.0||Doc.669: 34.5||Doc.769: 93.0||Doc.466: 45.0||Doc.571: 56.0||Doc.73: 215.0||Doc.368: 15.0||Doc.965: 13.5||Doc.270: 3.0||Doc.868: 2.5||Doc.168: 193.0||Doc.770: 3.0||Doc.670: 34.5||Doc.467: 27.0||Doc.74: 206.0||Doc.572: 8.5||Doc.369: 23.5||Doc.966: 13.5||Doc.271: 19.0||Doc.771: 3.0||Doc.869: 10.5||Doc.169: 239.0||Doc.671: 11.0||Doc.573: 2.5||Doc.468: 2.0||Doc.75: 158.0||Doc.967: 13.5||Doc.370: 23.5||Doc.272: 2.0||Doc.772: 10.0||Doc.574: 8.0||Doc.870: 25.0||Doc.672: 7.5||Doc.170: 1.0||Doc.76: 158.0||Doc.469: 5.0||Doc.371: 33.0||Doc.273: 12.0||Doc.968: 52.0||Doc.575: 81.5||Doc.871: 11.0||Doc.773: 1.0||Doc.372: 1.0||Doc.274: 132.5||Doc.171: 34.5||Doc.673: 33.0||Doc.470: 6.0||Doc.77: 318.0||Doc.969: 1.0||Doc.576: 30.5||Doc.872: 10.5||Doc.774: 109.0||Doc.373: 1.0||Doc.275: 132.5||Doc.172: 5.0||Doc.78: 2.0||Doc.471: 95.0||Doc.674: 7.5||Doc.970: 22.0||Doc.577: 53.0||Doc.775: 5.0||Doc.374: 62.0||Doc.873: 3.0||Doc.79: 1.0||Doc.173: 5.0||Doc.276: 3.0||Doc.472: 1.5||Doc.675: 3.0||Doc.776: 15.0||Doc.375: 1.0||Doc.971: 31.0||Doc.578: 16.0||Doc.80: 4.5||Doc.174: 5.0||Doc.874: 1.0||Doc.277: 110.0||Doc.676: 19.0||Doc.473: 22.5||Doc.777: 7.0||Doc.376: 196.5||Doc.972: 3.0||Doc.579: 14.5||Doc.81: 2.0||Doc.175: 5.0||Doc.278: 17.0||Doc.677: 1.5||Doc.875: 22.0||Doc.474: 86.5||Doc.82: 2.0||Doc.778: 35.0||Doc.176: 3.0||Doc.377: 196.5||Doc.973: 9.0||Doc.279: 32.0||Doc.580: 50.0||Doc.678: 7.0||Doc.876: 130.0||Doc.475: 1.0||Doc.177: 2.0||Doc.280: 17.0||Doc.83: 3.0||Doc.974: 22.0||Doc.779: 42.0||Doc.378: 63.0||Doc.581: 25.0||Doc.679: 60.0||Doc.877: 1.5||Doc.476: 5.0||Doc.281: 38.0||Doc.178: 30.0||Doc.975: 17.5||Doc.582: 23.0||Doc.84: 9.0||Doc.680: 1.5||Doc.780: 33.0||Doc.379: 33.0||Doc.878: 2.0||Doc.477: 6.0||Doc.282: 47.5||Doc.179: 4.0||Doc.583: 23.0||Doc.976: 22.0||Doc.681: 5.0||Doc.85: 43.5||Doc.380: 17.0||Doc.781: 33.0||Doc.478: 10.5||Doc.283: 47.5||Doc.879: 1.0||Doc.180: 2.0||Doc.682: 4.0||Doc.584: 2.5||Doc.977: 16.0||Doc.86: 4.5||Doc.381: 17.0||Doc.782: 33.0||Doc.284: 14.5||Doc.479: 21.5||Doc.880: 11.0||Doc.181: 13.0||Doc.683: 1.0||Doc.585: 2.5||Doc.978: 4.0||Doc.87: 7.0||Doc.382: 18.0||Doc.480: 28.5||Doc.285: 14.5||Doc.783: 1.0||Doc.684: 3.0||Doc.182: 5.0||Doc.881: 257.5||Doc.979: 17.5||Doc.586: 72.0||Doc.88: 8.5||Doc.481: 240.5||Doc.383: 8.0||Doc.685: 7.0||Doc.286: 14.5||Doc.784: 118.5||Doc.183: 102.0||Doc.980: 1.0||Doc.882: 7.0||Doc.587: 35.0||Doc.89: 8.5||Doc.482: 240.5||Doc.384: 42.5||Doc.686: 3.0||Doc.287: 20.5||Doc.785: 118.5||Doc.981: 4.0||Doc.90: 9.5||Doc.184: 3.0||Doc.883: 10.0||Doc.588: 1.0||Doc.483: 240.5||Doc.687: 1.0||Doc.385: 42.5||Doc.982: 4.0||Doc.91: 9.5||Doc.288: 337.5||Doc.786: 118.5||Doc.484: 86.5||Doc.185: 22.0||Doc.589: 6.0||Doc.884: 9.0||Doc.688: 2.0||Doc.386: 74.0||Doc.485: 1.5||Doc.186: 6.0||Doc.983: 5.0||Doc.92: 9.5||Doc.289: 337.5||Doc.787: 9.5||Doc.590: 221.0||Doc.689: 2.0||Doc.885: 11.0||Doc.387: 3.0||Doc.486: 10.5||Doc.984: 7.0||Doc.290: 87.0||Doc.187: 23.0||Doc.93: 9.5||Doc.591: 58.0||Doc.788: 9.5||Doc.886: 8.5||Doc.690: 15.5||Doc.388: 3.0||Doc.487: 2.0||Doc.985: 4.0||Doc.291: 1.0||Doc.188: 14.0||Doc.592: 9.0||Doc.94: 43.5||Doc.691: 45.5||Doc.789: 9.0||Doc.887: 8.5||Doc.292: 147.5||Doc.488: 1.0||Doc.986: 4.0||Doc.389: 5.0||Doc.189: 1.0||Doc.95: 110.0||Doc.593: 3.0||Doc.692: 1.5||Doc.888: 4.0||Doc.790: 30.5||Doc.987: 7.0||Doc.190: 126.0||Doc.489: 4.0||Doc.390: 1.0||Doc.293: 288.5||Doc.96: 175.0||Doc.594: 27.0||Doc.693: 75.5||Doc.889: 2.0||Doc.791: 15.5||Doc.988: 1.0||Doc.490: 8.5||Doc.294: 88.5||Doc.191: 3.0||Doc.97: 210.0||Doc.391: 104.0||Doc.595: 2.5||Doc.694: 16.0||Doc.890: 61.0||Doc.792: 40.0||Doc.989: 6.0||Doc.295: 51.5||Doc.491: 8.5||Doc.392: 46.0||Doc.596: 2.5||Doc.192: 5.5||Doc.695: 7.0||Doc.98: 210.0||Doc.891: 1.0||Doc.793: 237.0||Doc.296: 2.0||Doc.990: 119.0||Doc.393: 46.0||Doc.597: 82.0||Doc.492: 7.5||Doc.193: 12.5||Doc.696: 5.5||Doc.99: 286.0||Doc.794: 36.5||Doc.892: 18.5||Doc.394: 60.0||Doc.297: 1.5||Doc.991: 25.0||Doc.598: 9.0||Doc.194: 4.0||Doc.493: 8.5||Doc.795: 1.0||Doc.893: 18.5||Doc.697: 5.5||Doc.395: 1.0||Doc.599: 49.5||Doc.195: 3.0||Doc.298: 4.0||Doc.992: 67.0||Doc.796: 36.5||Doc.894: 8.0||Doc.494: 3.0||Doc.396: 1.0||Doc.698: 5.0||Doc.196: 9.0||Doc.797: 118.5||Doc.299: 2.0||Doc.895: 33.0||Doc.993: 58.5||Doc.397: 5.0||Doc.495: 3.0||Doc.197: 13.0||Doc.699: 2.0||Doc.798: 118.5||Doc.896: 33.0||Doc.994: 7.5||Doc.398: 12.0||Doc.496: 1.0||Doc.198: 1.0||Doc.799: 118.5||Doc.897: 30.0||Doc.995: 126.5||Doc.399: 471.0||Doc.497: 57.5||Doc.199: 3.0||Doc.898: 12.5||Doc.996: 43.5||Doc.498: 17.5||Doc.899: 9.0||Doc.997: 3.0||Doc.499: 4.0||Doc.998: 3.0||Doc.999: 3.0||255.707067966\n",
      "count    1000.000000\n",
      "mean       40.670500\n",
      "std        72.880548\n",
      "min         1.000000\n",
      "25%         3.500000\n",
      "50%        12.750000\n",
      "75%        40.625000\n",
      "max       593.000000\n",
      "dtype: float64\n",
      "TOP1 91\n",
      "TOP5 323\n",
      "TOP10 457\n",
      "Using the new test data to evaluate.......\n",
      "0\n",
      "0\n",
      "Doc.0: 29.0||Doc.300: 1.0||Doc.700: 4.0||Doc.400: 5.0||Doc.200: 4.0||Doc.100: 2.0||Doc.600: 2.0||Doc.900: 36.0||Doc.500: 1.0||Doc.800: 4.5||Doc.1: 1.0||Doc.301: 49.0||Doc.701: 240.0||Doc.101: 17.0||Doc.201: 107.0||Doc.401: 100.5||Doc.601: 1.0||Doc.501: 11.0||Doc.901: 53.0||Doc.801: 59.0||Doc.302: 1.0||Doc.702: 6.0||Doc.102: 65.0||Doc.2: 1.5||Doc.402: 44.5||Doc.602: 611.0||Doc.202: 36.5||Doc.502: 6.0||Doc.902: 8.0||Doc.802: 34.0||Doc.303: 8.0||Doc.3: 15.0||Doc.103: 1.0||Doc.703: 66.0||Doc.603: 28.0||Doc.503: 5.5||Doc.403: 5.0||Doc.203: 56.5||Doc.903: 85.0||Doc.803: 3.0||Doc.304: 23.0||Doc.4: 9.0||Doc.104: 46.0||Doc.704: 247.0||Doc.504: 1.5||Doc.604: 10.5||Doc.404: 3.0||Doc.204: 74.0||Doc.305: 54.0||Doc.904: 7.0||Doc.804: 1.0||Doc.5: 4.0||Doc.705: 145.0||Doc.105: 55.0||Doc.505: 2.0||Doc.605: 1.0||Doc.405: 15.0||Doc.306: 2.0||Doc.205: 2.0||Doc.905: 3.0||Doc.805: 52.0||Doc.6: 2.0||Doc.506: 3.0||Doc.706: 2.0||Doc.106: 10.0||Doc.606: 34.0||Doc.406: 30.0||Doc.206: 55.0||Doc.307: 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      "count    1000.000000\n",
      "mean       42.421000\n",
      "std        85.537989\n",
      "min         1.000000\n",
      "25%         4.000000\n",
      "50%        14.000000\n",
      "75%        40.250000\n",
      "max       901.000000\n",
      "dtype: float64\n",
      "TOP1 97\n",
      "TOP5 301\n",
      "TOP10 432\n"
     ]
    }
   ],
   "source": [
    "run svm_title.py\n",
    "# 之前都是使用了average的平均值的结果，现在不适用average了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "/home/M2015eliu/.pyenv/versions/anaconda2-4.1.1/lib/python2.7/site-packages/gensim/utils.py:1015: UserWarning: Pattern library is not installed, lemmatization won't be available.\n",
      "  warnings.warn(\"Pattern library is not installed, lemmatization won't be available.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average= False\n",
      "Reading english Data: 59999\n",
      "Reading english Data: 59999\n",
      "Merging the English and Japanes news dataframe...\n",
      "Drop the null line...\n",
      "False    59987\n",
      "True        12\n",
      "Name: en_article, dtype: int64\n",
      "Using the the data where start= 0 , end 5000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "C value = 1\n",
      "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf',\n",
      "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "classification report of TRAINING data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      1.00      1.00      4000\n",
      "        1.0       1.00      1.00      1.00      4000\n",
      "\n",
      "avg / total       1.00      1.00      1.00      8000\n",
      "\n",
      "classification report of TEST data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.73      0.81      0.77      1000\n",
      "        1.0       0.79      0.71      0.75      1000\n",
      "\n",
      "avg / total       0.76      0.76      0.76      2000\n",
      "\n",
      "Doc.500: 10.5||Doc.0: 9.0||Doc.700: 5.0||Doc.800: 5.0||Doc.400: 218.0||Doc.900: 12.0||Doc.300: 93.0||Doc.200: 7.0||Doc.100: 1.0||Doc.600: 58.5||Doc.501: 22.5||Doc.901: 31.0||Doc.1: 143.0||Doc.201: 6.0||Doc.801: 8.0||Doc.301: 93.0||Doc.701: 3.5||Doc.101: 7.0||Doc.401: 218.0||Doc.601: 27.0||Doc.102: 26.5||Doc.302: 93.0||Doc.502: 22.5||Doc.902: 4.5||Doc.2: 17.0||Doc.202: 75.0||Doc.802: 3.0||Doc.702: 3.5||Doc.402: 41.5||Doc.602: 27.0||Doc.103: 26.5||Doc.303: 83.5||Doc.503: 1.5||Doc.203: 7.0||Doc.403: 27.5||Doc.3: 4.0||Doc.903: 39.0||Doc.703: 11.5||Doc.803: 4.0||Doc.603: 27.0||Doc.104: 26.5||Doc.304: 107.5||Doc.204: 25.5||Doc.404: 395.0||Doc.904: 4.5||Doc.504: 1.5||Doc.704: 11.5||Doc.4: 22.0||Doc.804: 2.0||Doc.604: 104.5||Doc.105: 26.5||Doc.205: 23.5||Doc.405: 395.0||Doc.305: 29.5||Doc.505: 159.5||Doc.905: 6.5||Doc.5: 24.5||Doc.705: 13.5||Doc.805: 8.5||Doc.605: 112.5||Doc.106: 4.0||Doc.206: 4.0||Doc.406: 395.0||Doc.506: 64.5||Doc.306: 82.5||Doc.906: 6.5||Doc.6: 3.5||Doc.706: 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7.5||Doc.668: 4.5||Doc.966: 235.5||Doc.568: 158.0||Doc.769: 147.0||Doc.468: 4.0||Doc.167: 72.0||Doc.369: 3.5||Doc.66: 1.0||Doc.870: 68.0||Doc.270: 23.0||Doc.967: 235.5||Doc.770: 9.0||Doc.669: 4.5||Doc.569: 198.0||Doc.370: 3.5||Doc.168: 72.0||Doc.469: 310.0||Doc.67: 5.0||Doc.871: 20.0||Doc.271: 164.0||Doc.968: 121.0||Doc.771: 11.0||Doc.371: 46.0||Doc.570: 42.0||Doc.670: 4.5||Doc.68: 24.5||Doc.169: 108.0||Doc.872: 10.5||Doc.470: 8.0||Doc.272: 2.0||Doc.969: 1.0||Doc.372: 1.0||Doc.772: 9.0||Doc.69: 61.0||Doc.170: 3.0||Doc.571: 25.0||Doc.873: 7.0||Doc.671: 32.0||Doc.471: 68.0||Doc.970: 35.0||Doc.273: 19.0||Doc.373: 3.0||Doc.773: 2.0||Doc.171: 10.5||Doc.672: 3.5||Doc.472: 1.5||Doc.70: 4.0||Doc.874: 1.0||Doc.572: 20.5||Doc.971: 28.0||Doc.274: 168.5||Doc.374: 23.0||Doc.172: 16.0||Doc.473: 10.5||Doc.774: 93.0||Doc.71: 5.0||Doc.573: 15.5||Doc.673: 8.0||Doc.875: 21.0||Doc.972: 36.0||Doc.275: 168.5||Doc.375: 2.0||Doc.474: 85.5||Doc.72: 61.0||Doc.574: 35.0||Doc.173: 11.0||Doc.775: 5.0||Doc.674: 3.5||Doc.973: 6.0||Doc.876: 26.0||Doc.276: 14.0||Doc.376: 6.5||Doc.475: 1.0||Doc.174: 11.0||Doc.73: 14.0||Doc.575: 65.5||Doc.675: 4.0||Doc.776: 6.0||Doc.974: 35.0||Doc.877: 1.5||Doc.476: 7.0||Doc.377: 6.5||Doc.277: 11.0||Doc.175: 11.0||Doc.576: 6.5||Doc.676: 14.0||Doc.777: 3.0||Doc.74: 41.0||Doc.975: 21.5||Doc.477: 47.0||Doc.378: 6.0||Doc.878: 41.0||Doc.577: 2.0||Doc.176: 5.0||Doc.677: 1.5||Doc.278: 4.0||Doc.778: 7.0||Doc.75: 30.0||Doc.976: 35.0||Doc.379: 14.0||Doc.478: 30.5||Doc.879: 1.0||Doc.678: 38.0||Doc.578: 30.0||Doc.279: 6.0||Doc.177: 2.0||Doc.76: 30.0||Doc.977: 7.0||Doc.779: 19.0||Doc.380: 7.0||Doc.880: 8.0||Doc.479: 54.5||Doc.679: 57.0||Doc.178: 10.0||Doc.280: 6.0||Doc.579: 2.5||Doc.978: 3.0||Doc.77: 59.0||Doc.780: 12.0||Doc.381: 7.0||Doc.881: 350.5||Doc.179: 3.0||Doc.480: 195.5||Doc.580: 7.0||Doc.979: 21.5||Doc.680: 1.5||Doc.281: 6.0||Doc.78: 3.0||Doc.382: 9.0||Doc.781: 12.0||Doc.882: 33.0||Doc.581: 28.0||Doc.980: 2.0||Doc.180: 1.0||Doc.481: 428.5||Doc.681: 6.0||Doc.282: 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2.5||Doc.296: 6.0||Doc.495: 5.0||Doc.796: 56.5||Doc.194: 4.0||Doc.398: 9.0||Doc.93: 26.5||Doc.896: 71.0||Doc.695: 1.0||Doc.496: 1.0||Doc.297: 3.5||Doc.596: 15.5||Doc.995: 12.5||Doc.797: 450.5||Doc.195: 49.0||Doc.399: 218.0||Doc.94: 30.5||Doc.897: 100.0||Doc.497: 57.5||Doc.996: 12.5||Doc.696: 6.5||Doc.298: 2.0||Doc.597: 147.0||Doc.95: 26.0||Doc.798: 450.5||Doc.196: 1.0||Doc.498: 24.5||Doc.997: 2.0||Doc.898: 4.5||Doc.96: 69.0||Doc.697: 25.5||Doc.299: 5.0||Doc.598: 1.0||Doc.799: 450.5||Doc.499: 3.0||Doc.197: 4.0||Doc.998: 2.0||Doc.899: 4.0||Doc.97: 61.0||Doc.698: 3.0||Doc.599: 58.5||Doc.999: 2.0||Doc.198: 2.0||Doc.98: 61.0||Doc.699: 2.0||Doc.199: 6.0||Doc.99: 121.0||237.019535065\n",
      "count    1000.000000\n",
      "mean       43.223000\n",
      "std        80.316471\n",
      "min         1.000000\n",
      "25%         4.000000\n",
      "50%        14.000000\n",
      "75%        46.000000\n",
      "max       674.000000\n",
      "dtype: float64\n",
      "TOP1 85\n",
      "TOP5 303\n",
      "TOP10 443\n",
      "Using the new test data to evaluate.......\n",
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      "count    1000.000000\n",
      "mean       40.665000\n",
      "std        77.657414\n",
      "min         1.000000\n",
      "25%         4.000000\n",
      "50%        13.000000\n",
      "75%        39.000000\n",
      "max       852.000000\n",
      "dtype: float64\n",
      "TOP1 108\n",
      "TOP5 312\n",
      "TOP10 440\n"
     ]
    }
   ],
   "source": [
    "run svm_title.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average= False\n",
      "Reading english Data: 59999\n",
      "Reading english Data: 59999\n",
      "Merging the English and Japanes news dataframe...\n",
      "Drop the null line...\n",
      "False    59987\n",
      "True        12\n",
      "Name: en_article, dtype: int64\n",
      "Using the the data where start= 0 , end 5000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "C value = 9\n",
      "SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf',\n",
      "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "classification report of TRAINING data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      1.00      1.00      4000\n",
      "        1.0       1.00      1.00      1.00      4000\n",
      "\n",
      "avg / total       1.00      1.00      1.00      8000\n",
      "\n",
      "classification report of TEST data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.72      0.78      0.75      1000\n",
      "        1.0       0.76      0.69      0.72      1000\n",
      "\n",
      "avg / total       0.74      0.74      0.74      2000\n",
      "\n",
      "Doc.700: 5.0||Doc.500: 5.5||Doc.100: 5.0||Doc.600: 24.5||Doc.800: 3.0||Doc.200: 19.0||Doc.0: 16.0||Doc.400: 244.0||Doc.300: 60.0||Doc.900: 1.0||Doc.701: 3.5||Doc.501: 13.5||Doc.1: 9.0||Doc.601: 48.0||Doc.101: 7.0||Doc.801: 28.0||Doc.201: 3.0||Doc.301: 60.0||Doc.401: 244.0||Doc.901: 2.0||Doc.702: 3.5||Doc.2: 26.0||Doc.602: 48.0||Doc.502: 13.5||Doc.102: 54.5||Doc.202: 8.0||Doc.302: 60.0||Doc.802: 7.0||Doc.402: 68.5||Doc.902: 1.5||Doc.503: 4.5||Doc.603: 48.0||Doc.703: 13.5||Doc.3: 5.0||Doc.303: 27.5||Doc.103: 54.5||Doc.203: 13.0||Doc.803: 1.0||Doc.403: 67.5||Doc.504: 4.5||Doc.903: 4.0||Doc.704: 13.5||Doc.604: 139.5||Doc.4: 45.0||Doc.304: 28.5||Doc.804: 11.0||Doc.104: 54.5||Doc.204: 55.5||Doc.404: 256.0||Doc.505: 92.5||Doc.904: 2.5||Doc.705: 4.5||Doc.305: 11.5||Doc.605: 151.5||Doc.5: 11.5||Doc.805: 6.5||Doc.105: 54.5||Doc.205: 79.5||Doc.306: 24.5||Doc.506: 37.5||Doc.405: 256.0||Doc.706: 5.5||Doc.606: 2.0||Doc.6: 3.5||Doc.905: 2.5||Doc.806: 4.5||Doc.106: 3.0||Doc.307: 76.0||Doc.507: 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28.0||Doc.476: 7.0||Doc.175: 115.0||Doc.575: 113.5||Doc.675: 2.0||Doc.975: 15.5||Doc.876: 12.0||Doc.274: 157.5||Doc.76: 28.0||Doc.775: 9.0||Doc.375: 1.0||Doc.477: 41.0||Doc.176: 19.0||Doc.576: 2.5||Doc.676: 9.0||Doc.877: 1.5||Doc.275: 157.5||Doc.976: 25.0||Doc.77: 81.0||Doc.776: 38.0||Doc.376: 1.5||Doc.478: 11.5||Doc.577: 1.0||Doc.177: 1.0||Doc.878: 13.0||Doc.276: 1.0||Doc.677: 1.5||Doc.78: 2.0||Doc.777: 1.0||Doc.977: 11.0||Doc.377: 1.5||Doc.578: 1.0||Doc.178: 23.0||Doc.479: 17.5||Doc.879: 1.0||Doc.277: 4.0||Doc.678: 13.0||Doc.79: 1.0||Doc.778: 25.0||Doc.978: 1.0||Doc.378: 97.0||Doc.480: 97.5||Doc.179: 7.0||Doc.579: 7.5||Doc.278: 3.0||Doc.880: 4.0||Doc.80: 9.5||Doc.679: 57.0||Doc.779: 7.0||Doc.379: 5.0||Doc.481: 486.5||Doc.979: 15.5||Doc.180: 1.0||Doc.580: 5.0||Doc.81: 2.0||Doc.279: 3.0||Doc.881: 242.5||Doc.780: 43.0||Doc.680: 1.5||Doc.380: 29.0||Doc.482: 486.5||Doc.980: 1.0||Doc.181: 14.0||Doc.82: 1.0||Doc.280: 3.0||Doc.581: 93.0||Doc.781: 43.0||Doc.882: 10.0||Doc.681: 6.0||Doc.981: 11.0||Doc.483: 486.5||Doc.381: 29.0||Doc.83: 1.0||Doc.182: 7.0||Doc.281: 4.0||Doc.582: 40.0||Doc.782: 43.0||Doc.883: 36.0||Doc.682: 2.0||Doc.484: 62.5||Doc.982: 11.0||Doc.84: 18.0||Doc.282: 1.5||Doc.382: 29.0||Doc.183: 18.0||Doc.583: 40.0||Doc.783: 6.0||Doc.85: 7.5||Doc.884: 1.0||Doc.485: 36.5||Doc.283: 1.5||Doc.983: 7.0||Doc.683: 14.0||Doc.383: 5.0||Doc.584: 4.5||Doc.184: 5.0||Doc.86: 9.5||Doc.486: 48.5||Doc.284: 13.5||Doc.784: 564.5||Doc.885: 15.0||Doc.984: 37.0||Doc.684: 3.0||Doc.585: 4.5||Doc.384: 22.5||Doc.185: 16.0||Doc.487: 2.0||Doc.87: 1.0||Doc.785: 564.5||Doc.285: 17.5||Doc.886: 4.5||Doc.586: 6.0||Doc.985: 5.0||Doc.685: 5.0||Doc.488: 1.0||Doc.385: 22.5||Doc.88: 14.5||Doc.186: 1.0||Doc.786: 564.5||Doc.887: 6.5||Doc.286: 13.5||Doc.587: 28.0||Doc.986: 8.0||Doc.686: 1.0||Doc.489: 1.0||Doc.386: 41.0||Doc.89: 14.5||Doc.787: 6.5||Doc.187: 12.0||Doc.888: 5.0||Doc.588: 3.0||Doc.287: 16.5||Doc.987: 33.0||Doc.387: 2.0||Doc.490: 11.5||Doc.788: 6.5||Doc.687: 1.0||Doc.188: 19.0||Doc.90: 54.5||Doc.889: 1.0||Doc.589: 18.0||Doc.388: 6.0||Doc.288: 531.5||Doc.789: 4.0||Doc.491: 11.5||Doc.688: 15.0||Doc.988: 1.0||Doc.91: 54.5||Doc.189: 1.0||Doc.890: 18.0||Doc.590: 81.0||Doc.389: 2.0||Doc.790: 329.5||Doc.492: 19.5||Doc.689: 2.0||Doc.289: 531.5||Doc.989: 13.0||Doc.92: 54.5||Doc.891: 3.0||Doc.190: 2.0||Doc.591: 260.0||Doc.791: 225.5||Doc.493: 21.5||Doc.690: 8.5||Doc.390: 1.0||Doc.990: 7.0||Doc.290: 169.0||Doc.892: 19.5||Doc.93: 54.5||Doc.592: 5.0||Doc.191: 5.0||Doc.792: 69.0||Doc.494: 5.0||Doc.691: 34.5||Doc.391: 78.0||Doc.991: 10.0||Doc.291: 3.0||Doc.893: 48.5||Doc.94: 7.5||Doc.593: 3.0||Doc.192: 4.5||Doc.793: 18.0||Doc.495: 3.0||Doc.392: 25.0||Doc.692: 4.5||Doc.292: 125.5||Doc.992: 76.0||Doc.894: 1.0||Doc.594: 49.0||Doc.794: 27.5||Doc.193: 6.5||Doc.95: 66.0||Doc.496: 4.0||Doc.693: 162.5||Doc.393: 25.0||Doc.293: 216.5||Doc.993: 16.5||Doc.895: 95.0||Doc.595: 4.5||Doc.795: 15.0||Doc.194: 6.0||Doc.96: 91.0||Doc.694: 2.0||Doc.497: 62.5||Doc.294: 52.5||Doc.394: 26.0||Doc.994: 2.5||Doc.896: 95.0||Doc.596: 4.5||Doc.796: 27.5||Doc.195: 1.0||Doc.498: 27.5||Doc.97: 110.0||Doc.695: 2.0||Doc.995: 8.5||Doc.395: 1.0||Doc.295: 34.5||Doc.597: 7.0||Doc.797: 564.5||Doc.897: 45.0||Doc.499: 7.0||Doc.98: 110.0||Doc.196: 2.0||Doc.996: 7.5||Doc.696: 17.5||Doc.598: 1.0||Doc.396: 2.0||Doc.798: 564.5||Doc.296: 2.0||Doc.898: 1.5||Doc.99: 137.0||Doc.997: 2.0||Doc.599: 24.5||Doc.397: 1.0||Doc.197: 18.0||Doc.799: 564.5||Doc.697: 31.5||Doc.297: 3.5||Doc.899: 1.0||Doc.998: 2.0||Doc.398: 30.0||Doc.198: 1.0||Doc.698: 12.0||Doc.999: 2.0||Doc.298: 6.0||Doc.399: 244.0||Doc.199: 3.0||Doc.699: 2.0||Doc.299: 6.0||239.692256212\n",
      "count    1000.000000\n",
      "mean       40.923000\n",
      "std        78.510847\n",
      "min         1.000000\n",
      "25%         3.500000\n",
      "50%        12.500000\n",
      "75%        41.125000\n",
      "max       564.500000\n",
      "dtype: float64\n",
      "TOP1 101\n",
      "TOP5 324\n",
      "TOP10 453\n",
      "Using the new test data to evaluate.......\n",
      "0\n",
      "0\n",
      "Doc.0: 27.0||Doc.600: 3.0||Doc.700: 11.0||Doc.300: 1.0||Doc.800: 1.5||Doc.400: 1.0||Doc.500: 1.0||Doc.200: 6.0||Doc.100: 4.0||Doc.900: 53.0||Doc.701: 131.0||Doc.301: 11.0||Doc.1: 1.0||Doc.801: 17.0||Doc.601: 1.0||Doc.501: 11.0||Doc.201: 25.0||Doc.401: 9.5||Doc.901: 88.0||Doc.101: 27.0||Doc.702: 17.0||Doc.2: 1.5||Doc.302: 1.0||Doc.602: 308.0||Doc.502: 5.0||Doc.802: 17.0||Doc.902: 28.0||Doc.402: 8.5||Doc.202: 33.5||Doc.102: 28.0||Doc.703: 116.0||Doc.3: 36.0||Doc.303: 17.0||Doc.603: 11.0||Doc.503: 1.5||Doc.903: 211.0||Doc.403: 5.0||Doc.803: 4.0||Doc.203: 28.5||Doc.103: 1.0||Doc.4: 7.0||Doc.304: 4.0||Doc.704: 170.0||Doc.604: 14.5||Doc.904: 2.0||Doc.504: 1.5||Doc.404: 1.0||Doc.204: 91.0||Doc.804: 25.0||Doc.104: 37.0||Doc.5: 6.0||Doc.305: 29.0||Doc.905: 5.0||Doc.605: 1.0||Doc.705: 14.0||Doc.405: 40.0||Doc.505: 1.0||Doc.805: 89.0||Doc.6: 3.0||Doc.205: 5.0||Doc.105: 38.0||Doc.306: 5.0||Doc.906: 63.0||Doc.406: 12.0||Doc.606: 29.0||Doc.706: 3.0||Doc.506: 2.0||Doc.7: 3.0||Doc.106: 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      "count    1000.000000\n",
      "mean       40.974000\n",
      "std        77.065614\n",
      "min         1.000000\n",
      "25%         4.000000\n",
      "50%        14.000000\n",
      "75%        40.000000\n",
      "max       786.000000\n",
      "dtype: float64\n",
      "TOP1 96\n",
      "TOP5 295\n",
      "TOP10 422\n"
     ]
    }
   ],
   "source": [
    "run svm_title.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3Y9M4urq05vT002HZMk2w7rlHbwdnNF1FRiI/WzZE5L9F5KMisl1EvptmogLM\nRBvlu/hF8HtszHAzU06XRxvB20N4eGSD22/XvzfcoLXGZrcNc91jKjrNP1je6OqC974XfvhD+Nd/\nhY98pD2IFiQkW0rjPKXUDUqpnUqpucH1l4YYrXt4JEZvb2+rk+ARAV82xUUryubII6u3Cevq0udH\nHpl7UgoNt2z8gp3GMTmpt8zr6YFVq+Atb9FTiu2AQ+MGVEodi3Zk+mLg18CRaPcNB4A/Q2uSLk8x\nbWUq2isbM6375ndOjXC/CLkHwLp165g3bx4AXV1dLFy48GDDMKpff+7P/Xnxz9/3vlFOPRXOPbdy\n/+GHQaleVq5sLP6774ZLLumlq6tyf+HCXvbtgxkzivX9rTifMQO6uqbebzS/0zrfswdERjnyyMr9\n4eFRvvUtePvb80+Pez45CRs2jLJrF2zb1suWLbB/f+vS0y7nDz8Mt9zSyz33wLvfrcv3llt6Wb4c\n3vWuUZ71rPrxmf+lUoncEddHBNrf1X8DL0GTNNux6TrgO434nkC7bHgcx88WMAjcFxJ+J9pGy5xf\nAzwKHO6Euxb4LfDkiPfWd8LhkTu8L6fs0ahfnyzLpllfQ1n4XeokX07TCVHl5vpONPey9mdll007\n1amiYXhYZOPGasfi5bL297Z5c2NxUlCnpg8CFwT/XS/yLwN+1VACosnW+cH1Jda1o4GHgButawuD\ntKy1rh0CfBf41xrvbax0PKqQtkM+32lkj0YFftE3O87CeW27OMT17aYaYeXWKqJjl413YFosFJVs\n/RZ4hYSTrRXArxO9GF4THDcFcb01OD8ruK+Afehpyj7gHPRejA8Bs524dgE/D4jby4DPoD3Jv6DG\n+9MprQKhFQ3Zj9TaE0UkEWmkKYu98sLi9J1m8RFWbkWs90XFdNhJpahkaz/wAQknW1uBryZ6ceCR\nPuT4khXGbNfzEHq7njuI3q7nBirb9dxNE9v1tCtaRXy8AGtPFHET32bSlKdmyw8yio1adaGI9b6I\niNoj2N6iqd1RVLL1FrTz0E1Ad0CWXgZciN7f8I15JTqVD+9AsiXSOuKTlgDz0yH5oJF6MjIyctBu\n4oorKrYTxm7iiisat51oNE3us3nabGXZ1pJqzqLazXTUwNUqt1bIx3aWaYZg9ffro5OIlkhByZZO\nF+9Hb8/zuKWZ+j2wJa8Ep/bhHUq2RPIfuaUpwNpZMLULmrHZKpe10J0/X6SnR2R8vPrcNl7NI00G\nSUlFnPC9W7ksAAAgAElEQVRxwmTV1pLmR70N3KeTBi6q3IaGWm+z1Y4wdbwTtYGFJVs6bcxFu3p4\nB3Ax8Jy8Epvqh7cp2arXAeQ9cpuOwjwr5KWFSGPlX3+/yLx5IkcdJTJnTnNEK400JUVRDfKziN9P\n82tMRy1fs/CarRaSrU452pVsxVGR50l8vABLD+1EXO3RLoiMjbU6RclRtGnLMKSlOfN2Sh5J4W22\nWky2gGcBfwyc5R55JTqVD29TsiUS3Ul0AvFpd5V7syiyFsKUTRaarVahURKSR1tLUhdqtZsi16np\nALdswurOxo3a7tFGqdScDWSz8KsRW0S2gNnAl6xVg09YdltPAI/nlehUPryNyZZI545UpzvZEilu\n2WZps9UKFJmEFN1mqxMGdnnBLZuwMunvF1mwoNJ+SiWRU05pr/bUjigq2fo3tGPTtwHLgJe6R16J\nTuXD25hsFbmT8GgORS/bLFcj5omiT9mmRWayIkVFz7+iI6ydG4I1NuaJVl4oKtkqY3lpb/ejXcmW\nF3KdC1+2+cFrZppH0QcGRUeYBntsTNrWBrIdkSfZehLx8Vvg/xKE73js2aN3IbcxOamvZ4V9+2DL\nlspO8V1d+nzfvuzemSfsDUOhNXncKhS9bN2yaWesXFnJZ4OuLn29UbSyrraibLq64KqroLtb/7r5\n6aERVjaTk7BtG0xM6N/JSThwAC65BMbG9O+BA/mn1SM7JCFbHwXWZpWQdsTDD8OVV1YE7OSkPn/4\n4ehnmhXIWXQSYSgKyVm8GDZtqs7jTZv09U5DXmXrkQ2KXFezaM9hhMGjPky92LIF5s3Tv5ddBsuX\nw/AwnHmm/j33XE+4OgpxVWBoD/I/Ab4IXAm82T3yUselcdDANKI79VAui6xdK7JsmVYFx1kW2y5T\nRUVKZ1bTFX4qKV34/Czu1Fra7blI8qHd0C6rEacDKKjN1hN1jtRXIwKLgduBnwG/Au4BLnTCmP0T\nH0Tvn3gnIfsnhsSduGDCBMz69SJ9fTon+/vjCZuiCmQXRUpnFiv0fIeRLnx+ahR1NWma7bkTiXWr\nvqkT87JdUFSyNbfekWrC4FT0ptJfBF4FvBy4KSB2F1vh7gLuA9agV0mOBsRrVp34GyocV2CVSppk\nJSFbIsUVyC7yTmfYEvYsSV+RCGXREcctx3TPz1Z9f1yXKe0id1qBrAYL9crGD1Jah0KSrbwP4Hrg\nUeCpzvWvAvuC/+ej/XydZd0/Gvg5cGOd+BspGxGpCKzx8crUYdxpRJH26ZBakc44PmnSTovvgOLB\nd+i10cpO0xPhdJBFHvmyKS482dJkaBvwq5DrtwF3B/8HgftDwnwMmKgTf9JyEZHqRrF0qbbZcqcV\nh4bqP+8KZNdTr7nXKlVyUUZbWavY7fJcsUJkx46pdnmd5DE5a0znTiPLuprGfpZFaM/tgFYNFgYH\np77XTydmi8KQrcAg/gXB/4ngPOr4r1QTBs8Dfg1sB54NHIPeAPsx4E+DMHcDt4U8e1Wg8TqiRvyJ\nC8YVUENDUzVZ9RpH0Xakj8J0sF9w87hU0l6cDYE25LmT9gLLEr5Dzw7N5q23C4qHVg0WjKyxN3tu\nl/bTznWrSGRrJ9AtFW3RzlpH6omDRcD9VIzwH7UN5IEfAJ8OeW59QLZm14g7ccHkqWWZro1s9+6R\n3DrssPSXSnp1aafuct8M6k2HtLPQbQfUkg9hZePLIxmKYLMVRrqKjnYeZBWGbLXyAE4EDgB7geXA\n2cCNwO+A10sLyFYeaCd7lywamdl/r5Wk05RBu5RDmqjVQft9K1uPKPlQa2FJO3aCrUBW5DTpIMWU\nsesKoshotcxuFHmSrUNJAUqpVwB/KyLPTyO+AO9DE6vzROQPwbURpdTTgb8HdqG3EDo25NmZwW+5\n1gvWrVvHvHnzAOjq6mLhwoX09vYCFa+/eZ4//DDcdlsvExOwYcMoF10E557buvTEOd+ypZdNm2DJ\nklGGhuBjH+ulq6v5+JcsGaW7GyYm0okv7vnChb1cdx284hX6/LrrernhBti/P5/3t/p88WJdnsuX\nj3LkkTo/zLmNoqR3Op3Xkg8mTF7tsxPPZ8yArq6p91eubC7+3t7emvft+Bcu7GXbNti1S5fXq1/d\nHuW1f/8oS5ZAd7eun0WVl+Z/qVQid6TB2IDXkLKfLeB7wD+HXL8crbV6BtpA/r6QMDvJyEA+K7Tz\nKDRtbVytUVKWUyOujVZaNlvtNp3TrqPUTkYz8qGdtOXTGe3cBxRZZtSSv7TbNGJGZGsE+BFwqHP9\n08BvgEOpuH5YYt0/GniIDF0/ZIF265AN0m5k9Wy2shRIw8NTV4WaxQvNlEM7CtGwDtpPI7YO9eRD\nVNkUuROcLojbbtq9DyiqfKuVPk+2rDiBzwPnAUuBDwbXtgVhFLAPbdvVB5yDdmr6EDXstaSAZKsd\nkUUju/76kboCp906kOFhbXjvOsMtqhCNyl9PtoqLNGy22rWzLzo6vd20Q72JkmmebFXiPQf4Enq7\nnl8C3wQuBpQVxmzX8xB6u547iLldTztUkiKjlfnXTlMjpqGPj8tBZ7hFJYlFH6VmjU6SCUm/pdPK\nPkxTPTSkDc/dwVs7lq9HMoT1GYUhW8BzYh5/ngXZyvTDoeOEy3RBu2m2RLQm65RTRMbG9G+plO37\nGiUNnUQ2GsF0lwlhbatd60SYDWZ/v/alZ9rfdCvf6YrCa7YINpiOcWSyEXWmHx5MI7Zjx93J6MR9\nxFqh2coinzp9OsQgT5mQFpFJs2xcDUA7tjkDQ7hsv3mlkt4twm2HWRHI6dJuioq2sNkCLkhy5JXo\nVD7cstlKc0qqXUeBRUEnOs5slc1W2qRhOnUaeU1Tp0Vk0iqbqDrTzoPSML959sBHJFsCOZ3aTRHR\nUasR2/HISrPVzqNAj2zQyjrRTrZtRUHexKIoRKZePW3HuhSl2TKa5lNOKbYNpUe28GQrJ7KVVSdY\nFOHpUQy0Shvn62FytIoYF4HI1Kqn7ViX4thsuRquRtGOGnePApEt4K+AwxNFCC8EXpnXBzT84Rmu\nRhweFrn44mrhWS7rVTCbNzcXd6fDq9zTQZqkwbQTu2w6tSNpRaeZBpHJst20q7a+3mpE25Zy5crm\nvqdWHnmZVlwUiWz9J/C/wPuBF9QIdyywNnC78AiwJq8PaPjDM/SzVSqJ9PSInHiiHkmVSlNHVB7h\n8IIpHaRJGsplbVC8a9fIwfOi+wprFxTNZisMnai1yYJARpHmIsi0TizDNFAksqWANwHjwYrDSWAM\n+Cx6b8LPAz8MViQ+gt4mZ15eiW/qwzMiW3ZH1N+vCVd3tyZfnmh5tCuM6wpj32LsXoqu3Sg6fCfY\nGJrNt6zyvQjTwWFoV+1k1igM2aoKCC8GNgN7gf3A99He2weD1YhdeSU6lQ/PiGzZjdheBbN1a7px\nG3jB7JEXjH3L2JgX1B6tRRHJQ9Ht2oqUvqL0ZYUkW512ZDmNKFIxxuzpEVm1Kp0pxCIKmLRRBJW7\nx1SUyyLnnz8iY2OSikGxR7qYju2mSOShXWy2iqJ5K0pflifZehIeqWNyEi67DO65B+64A3buhNNP\nh+XL4cCBxuPt6oItW2DTJiiV9O+WLfq6h0dWmJzUde11r4Ndu2B8HN74xubqsodHHOzZo+ufjclJ\nfb2rC666Crq79W8r5eC+fdWy2MjqfftalyYXk5OwbRtMTOhfN1/zxLTsy/JidY0ewArgy8Cv0fsj\nfg3ote6bvREfRO+NeCcx90bMCsPDIhs3Vmuy0lyNWJTRicf0gO2U1Yw8S6XmV3B5eNRDLQ1IkTRb\nRUdRNEkuWt2X4acRDxKii4HfATcALweWAlcBK6wwdwH3AWuAZcBoQLxm1Ym7weJpLbyA8WgFimJj\nUS89mzcnu+7tHYuPMJlXVPKQJtJsc0Vrv+b9re7LPNnSZGgueoXjZTXCnI9eCXmWde1o4OfAjXXi\nT14yLcZ0EDBFsm/wqEaRyiaqLbgauHrXO6XtFKlssoCrASkieYhCo2XTyfK+KN/myZYmQ+8Jpg6f\nUiPMzcD9Idc/BkzUiT9hsbQe7SRgGkWndxrtjKRlk3V9jRoZJ73eCejkdtPu5dZM2bT7t0ehKH1Z\nIckWcEyd+yenmjD4IvB1tFuJHwO/B34EXGqFuRu4LeTZqwKN1xE14m+weNofRanonYrNm0V27KjO\n41JJ5Oqrp1ce5zF6jbL5SHrdo5goigYkK8SRxb7OZoc8yVaS1YjDSqnDwm4opRYE5ChNzAJ6gA8A\n16Ptte4APqiUuiwIMxMohzz7i+D32JTT1BFYvFiv/jCrUcxqs8WLW5uuTsGFF+rVPpdfrvP2wAG9\nEvWBB6ZXHme94ihqdVXS6x7FRTus8msG9WRxJ9fZWitNOxJxWRlaq/QvgHKuPxe9pc8X0mSBwA/Q\n2qnznet7gQesMJ8OeXZ98OzsGvE3zIY7AUVVT3fKdEippH2rrV6tfa2tXVucPG4UYWUTR4uXxcjc\n22xVo1PaTSeiXtnUm/bu1DpbhO8jR83WoQl42Tloj/H/ALwFQCnVA3wpID2vapTwReDnwInAF5zr\ndwDnKKWeidZqhWmvZga/YVqvg1i3bh3z5s0DoKuri4ULF9Lb2wvA6OgoQMee798/ypIl0N3dy8SE\nPi9C+gxanT/Nnk9MjHLJJXD55fr83e8eZf/+4qSvkfP9+/dPuX/hhb0sXw67do1y+eVw6qn6/Pjj\nRznzTJic7GXbNn1/wwb42Md66epqLj179sB//Mcoy5dDV5e+f9ddozztabBzZy9btlTX5y1bYMOG\nUV772kr4/fv18/v29bJyZTHyt5nz/fv3Fyo95vw3v+ll8eLq8pichJtuGuUlL2l9+opw3tUFS5aM\n0t0NExOV9nH33bBlS3V72bKll337YMaM4qS/mfMtW3rZtEl//9BQOvKh1rn5XyqVyB1JmBlwGtrX\n1XvRROh/0HslzkibBQIfRWunZjjXNwTXn4neKui+kGd30oEG8mmiqJqtTkEnaraiEPWtWY1cizAi\n9ogHX1b10cmyuOg2aRTRQP7gA9rf1aNou6h9WRCt4D0rAlL1auf67cCB4L9x/bDEun808BAd6Poh\nLUQJwKGh1hvOd4LxviEfhnS4550Is41PXsvzO6GD6oS6HgedUFZZoR4Zbfc6Uu/7Wl03CkO2gJdF\nHB8G/g9YZV9PPXHa6P5BtHPTpZa2a21wXwWE7wDQh57qHA3IVqS9lrQZ2dq8WeSKK6o90pdKIued\nJ7J4cbXNTLmsPdW7K9/sOEwD3rFD5PTTdTylkiZb69eLDAzo54eG8m8Au3ePtP1IuFNXI0bZnrRK\ni9fuq7TS1PoU3War3cuqGdQqm3pkqhM0g0W2SSsS2XoiIDdPWMfjzn9z/njqiYMjgQG0Af6jwH6g\nzwljtut5CL1dzx20eLuetFEq6U6sp0f/N+fz51d3cKVSZfNrt8MLi2P+fJGjjhLp7q48v2aNyDHH\n6HjXr8+/UY+MjLR8tOMRjrBOI64WL+0ReqfUkbS+o1my5bWQ2aHZsumE/Asj20XQ2hWJbL00yZFX\nolP58DYiWyIVstTdrQ9DmsplTbDmz9fX58+P1iyExTE+PvX51aunNoy80W4j4SIIjlYgrhYvzVFs\nEUbEaaIIdd3b1xUbRagjjaLIZLEwZKuTj3YjWyLVdjFjY5XrpiG6NjNx43Cf7+9vbcPIunFmQYx8\np1IfaZVrJxHbInVEWaSlk8qqVShSHUmKostFT7Y82ZqCPDRbc+aIHH20SF+ffr4VDSMPm62sR/Ht\nKBTjIA27oHYeoaeNItps+fJJH2ls11NUslIPRSfbhSVb6K1zPg98F/iJc/xXXolO5cPbiGzlZbO1\nY4e22XKX79sNY3h46qrFcllfS6MBXX/9SC6NMyti1Mmdlbc9SRdpdkRpkC1fPtmgmbIpOllpdxSS\nbAHXBIbw9wKfRvuyqjrySnQqH95GZCvt1YgG9mrEes8blMvacN4Yz7vn7YS0iZHvrKLR7iP0oiCr\nzrcdy8cTEY9mUVSyVQL+Lq+EZf7hbUS2igZDsPr79dGORCttYtTKzsrWNpoOyNY2FqEDyrNj7ORO\n2K1XGzeK9PZOHYgldTPSjnnWjgSxXdCO9aERFJVs/ToLX1qtOjzZag62UX2aU2Z5+AvKQki3UjjZ\n2sVSqUKEzXlaHVDRfTkZdHonbA8UjNnA0qV6+j0rB7pF7XzbQZvcLu3GRqe3IYOikq1h4C/zSljm\nH+7JVsPIUrOVhmCq1zEUteNoBnaZrFmjO9zx8XQFZDt1Gu3QCTcDewq8VBKZM2ckU6eyRe58i24n\n2U7txkantyGR4pKtEwN7rTcBTwee5B55JTqVD/dkqyG0g81WkTuGLGFrG42Lj6J2QHmg6J1wowjr\nBMO2S0oD9sDEvHd8XGTlymK0p+lACFqJTm1DBkUlW67XePf4Q16JTuXDHbLV7pqNvJD1asS0MN2E\ncB6arXZCp5Z/2EDCTCVmodmy37d5s8jevbrXGB/X90slfb0VmK6DqrxQLousWDFVjnRSX1lUsnUt\nsLnWkXlitduJJ4D3ONfNlj0PBlv23Flvyx7AN9ICopl9xFx0+qjMwNtsVaOTO2G3DRiXLgsXZmez\nZfJv716Rww7Tv0bDdcop1cb5eaJdzAGStJuifJMpcyM/3N9OaEsiBSVbrT6A1wMPBFo0l2zdBdwH\nrAGWoTejfhCYVSO+jhz5tjtqCaYknWinajbCkNdqxFaTrbgdUVE6rDxgtkvavXvk4LUsNj0fH9e9\nxd69mmDt3Ssyc2ZFwzVd0Mgm80najZFbpj3bMi7POlz06eO04MnWVGJ0LHoz6j5XswWcHxCws6xr\nRwM/B26sEee00Xx0EuKQqE7WbHQaNm+eqhmJmpry5doauNNJZipx7972JLDNkPG4G683A9skwLaN\nTVrX0xx0dGpfWViyBTwlIDcbgXc5xzWZJRI+Atwe/HfJ1s3A/SHPfAyYqBHntNF8xEHShtlK7UG9\nhj+dNBtpoJX5VSpVT0W55y5csu3aD5owvqzTgdvJj49rjZbRcLVqCrEZNEvaDcHKcvWnkXHN7FOb\n1uCkk2cJCkm2gFnobXmMkfwUg/lMEghnAo8AJ0o42bobuC3kuauCdB0REa8fIVtI2jCz0jLUU7l3\ncsNvFeKWZVbTiIZgjY3F68Btst2u2q447kmSLETJqmzsdJpyGh/X1+sR4yKjWTmSZPWnWzb1yt5O\nW39/vHdEodnvjNu+2mXhlIuikq1PAf8BHB8QnhcB84D3AD8A5qaeOHgy8G3g3dY1l2z9APh0yLPr\nA7I1OyLuqkz3o+HkDTML4pOWzZZHMsQpyyxttkznNTZWO1xYOtuRgNery2YqKa6LlTzs6ZJM+bYD\nGp0aS6rZcsumVtm7/93pxEbQzBRgXK130vpaFBSVbBkD9CcFhOd0694W4HOpJw7eCfwYOMy6lgnZ\n6mQkmSZK2jDznMv304PZIuuyjCq/wcF4mq1anVQ72pQMDenOyCWOtoaj3bfFKioaJehp2WxFvd9e\n4GLXiaGh4k8Bmvra26vdz9j1tahyOk+ydSjx8TTgf0XkCaXUb9BG6wZfAv4iQVx1oZQ6AXgHmjQd\nrpQ6HFDB7cOUUsegtxAqO2kxmBn8lqPesW7dOubNmwdAV1cXCxcupLe3F4DR0VGAXM737AGRUY48\nsnJ/eHiUb30L3v72+PHdfTdcckkvXV2V+wsX9vLww7Bu3SgXXQTnntvL5GTlHCrPP/ww3HZbLxMT\nsGFDJXzU+5KGb/Z8xgzo6pp6f+XKfMurE8+Hh0e5+WaYmOhl2zZYvry6PqbxPhHYtKmXLVtg/35d\nf265pZd77oF3vWuUP/wBhod7Ofdc2LRplGc9a2r93rKlun5v2dLL7bfDrl2j7NoF27ZV4q+Xnqj2\nsm8fzJiRff7PmAGPPdZLd7dO/1136fa0ZUsl/Dvfqe+D/r6w+u/Pk53b8m/ePJ3ftnwMe/597xvl\n1FPhG9/o5aqr4LjjdHkp1cttt8Hb3z7KTTfFl9f794+yZAl0d2v5aerrypWV9y1fXinvZz5Tn+/b\nF1/eGfls2sPy5dXtL4v8tevr5ZcDVOf35GR+7Svs3PwvlUrkjrisDK1Bek3wfz/wAeve24GfpckC\ngZdSsQd7gmobMfP7fGAQuC/k+Z3UMZAvCtI2ZKylno4a4bSLzZZH+sjTZsuthxs3xpuaitKKuSN+\noy1ybUfCRtWtnpaup7lKotny7SY+GtGQN1NXwsomD41T3jMBdn1ds0ZPsfb1NbeishEk+W4KOo34\nYeDvg/+XBITnDmAP8AfgQ6kmTLtvOCvkeAL4R2AJcAQV1w9LnGcfoo7rhyIhrcZXK55aUy1FWY3o\nO438Ebcs0yqbRqb8ojq7MKPc9ev19bDnouLN294rbAq0v7/YNlsG03U6v9G6ksRmqxVIozzD6uua\nNbpe9/Xl276S5G9RydbTgR7r/DK0M9FvAtcDh+eS4Kk2WwrYBxxA++E6B+3U9CEi7LWkgGRLJD27\nk7B4WtWpFBXtunqm3dFMPYz7bNJ3tMLeyxib22ldu1Y7xxQpTv10O+Lh4anOLUul4jm7zIoQplFX\nikZW0yB/bn015Gv7dpGzz86/fcWVAYUkW0U5Ai3Wu51rZrueh9Db9dxBjO16ioQsNVtFG0kVAUk1\nB/VQNAFaRKRRD+N2dnHDtVKz1Q6rt9wyst0/XHpp41v2ZN1e0iIQYVrTwcHOk59ptgM7r+2pxbzr\ndhwZ0HZkCzgMuCKvRKeU5ugSyBlpkaG4Uy3mXhGJQJ7TIbYgaFYYTAdC22zZNNvBpq3ZanWZ1VuN\nmARZths3P83+eMZdRyNb9uSR980SiDDS0Ij9UbuYRqSl4U17RWUjiNu2Ckm2gmlE5Vx7KvA2gj0L\n80p0Kh9eILKV1iivE7QreQsmI2DSEDJZa0ka8XOU5nRUKzuNuJ1zkk68CO0lrQ4u67Jx02n2Shwb\na7yu56FVbDZ/TRoHB+MvvHDRDmQri7JoVftKojUuDNkKNFZ/j3ax8DjwC+CS4F4/er/CJ9DOTpfm\nlehUPrxAZMujNUhTs2WQVue5ebPIFVdUb3o7NibytKeJvPWt8T14t8t0VT0UcRPqZolsq6Ywk8JN\np5k6NFOJRtPVSPpNe3G1Y2mUWVr5244+3JKg1RretOFq1iYmqhfN2CgS2dpirTp8P3AL8DtgILj+\nfeBVeSU21Q+fpmSrCKP5rGF/o93wzDcODYlcfnk1wSqVtCHnsmXNTyWm0XmWSnrp9Pz52nB6fFyf\nn3CCyDHHJNubLgtS6dEckW2XDs5NV6kkMnduhRyZ+6VSchlinnXtvtLIi7TyN0tCnIdH/jgDgk7u\nE+oR5SKRrR8DH3SuvTkgWrcDT8kroal/eIeTrbh+iYom5NP05WR3gEuXai1RqVTxcrxkicjAQGV+\nv79fTxc0ImSy6DwN4ZozR+Soo0S6uzXx2rtXYm1tY6OR6VK3Do2MjHSMEE4LjRLZtDu4kZGRTDrN\nsNWILrFq5B1hJM7WljUrj9JyZ5BGm46SaUk3YW8EYQOCpUur3YyYcJ3WruMQ5SKRrceAlzvXugKy\ndU5eiczkw2OSrXZl/bUERZGnL9Kyb7C/celS7fNlzRq9tUappIX67Nlac+RuLdEIsqon9oa3hmAl\n2bTZpMPdRmNoSDsUNZsKm3CDg5WRtVuHdu8eyby+tGN7S9Pur1EYIlzkgZSNsHI2dmBFma5Lqy7W\nkmlJN2FvJF3ugMAdFBS5njSKuG2hSGTrCeCPnWuHBNdfmFciM/nwGmTLrsyumjytiulOdQ0NVY8Y\nk9h+RKEWqep0OwSRanuQ/n79f80a7WSvp0dvJJtlJ9mssHY1W8cfr6cQzRROUpsto9UzAnf1apFj\nj9Xxlcv6miGj9vN5EvN2Igwi+UzRJqlHeZVX2qS4iAPAZr8x7vNxN2G346jVRmoRWSPr8szvVmxg\nHjfvi0a2VgPPsY4/Cq6/yrn+nLwSncqH1yBbWau43Xe4HZ2r+m0G09XBqf2Ndme4alVF6KxZk10n\naaZb3HoU1/ljmM3WzJnaZsve9DbpakRTt7Zv1/ZpZluNVaumEi2DvIl5VvUzC4JgpqjddtuIDVOt\n9yQhoHmUV5qkuKgEO2m6otqaMU0Ie74RzZadlrA2EtZ/LVigzSZsLb6pJ4ODSXIlOfKYLm0URSNb\nj4ccodfzSnQqH26RrTCB61bmLFTcUYQgrc4/rEEWVbAZGJV7Mx2jS2RNB2hGkHPmiMybp0lG2uTW\nTYMhXHGdP5rvtlcjmo67VNLXrr66uY7c7oztKTAzsg7T7I6Pi5xxxkgm9SSvKaW0676tkbbr2+Bg\n+h7V6xFQ027yHEil9a4iTx0n+UZXjphB9Jw5IwcH6jYZswfxcVcX26hFqu12u2CBHqC5mu21a/Nb\nMNMoqcwaRSJbFyQ58kp0Kh8ekK1aAteehspKgEV1fGn5fHI7lqI7OHU7jUY6xrDViOPjWivU16dt\nly6+uFrQlMvpb4Vi0pzE+ePQUEVTYuKwR8dppckQ/DDNlksUze+uXdnYbOWhSXbflXZbzoPk1Opc\nW2WzNZ3MEeJ8o2mv9qD55ptHpkzfmQGVW8dLJZHXv76+jI5T30y6Bwaq60R/v8iZZ1ZWXuc14E46\nXZoHCkO2OvkAalZUe2SQ9rJk9x1ZaLaaGS0WZaSZVgdWLousWFHxWWWTiVoEK418GBioCBhb+1HL\nmNVMKY+Pp7vVhavxW7Nmqs3W8cfrNJdKOs9MGuypkCzqQR7tzSArgpAl8QhrC64tjNm30J5WbqS8\n4tb7vLRorbD5MbC/ccWKqekIyxd70Bw2UK+Xb0ZeuW0gym44rI3Uesfg4NR6mrV8T1uzZcsio0Aw\n50kGzp5saTL0WuBfgPuARwKfXtcDRzrhzL6ID6L3RbyTOvsiBs/VVcGawnNtb9KomG7Hl5XNVrNp\nC3MTXYcAACAASURBVDvPE2l0YI2SpmbzwdhK9PVVG6jXK1dDhEBrnNKqB67GL2w14sCAyMKF+p1m\nKi+N1ZpxkKWDSzuudtNsRdVDl5imZQuTpDPPUkaY+mp/V7msyUIeU1FRGtdaAwFbs2WvfnbD15Jr\nhjS7e1Caqel68qxW2eRFkG1kYbNlE9D16/WUaBIZa+DJliZDdwP/BLwBOAu4HCgDX3XC3RUQsjXA\nMmA0IF6z6sQfWeHy0OzksRqxGTTbKBvNQ3uZdB6CIa7gSpoGe3RqC+C1a8M9GbvPmtWTtaYf06in\nYU4PSyU9zTB7dvX0YprbjrhpL5f19OnAQPoDG/sdWRCErIlHrXI2Hdf27SOpEpB69T4PGWnnY6mk\nta6LFlVsLU2YrORk2DeaRS5Rhul2Rz80pNvxihUjVXXD+DqspdmyzQ/CHBjXyv9W+lgMe/fGjVON\n8NPQTNqDDjOoTToL4MmWJkNPC7m2NjDG7w3Ozw/Oz7LCHA38HLixTvxVBdYKrU3R0YxWqdEOKA2b\nLYM4HUKc9zSSD+674678sYlZ2OjYDdtsHrkdhCF68+fLlKmQ3btH4kcc473uIgYjKF1bsWbaZpj9\nnkumm+2oWz3trjvlkdRtYYpgj2WTvhUrdHqMhsetuyKNl0WS56LyJWzgUi6LXHPNSCytk50GQ6J3\n7pRQO6dG2n7eBDluupqBKQvbH2GS+urJVjRBem6wEvKNwfnNwP0h4T4GTNSJ62CG5ykY2wVpaJWa\niSMNwRC34ddKZ975YEbDLvmJ2kYorfTZBM84P7XtB9N0ZeCm3d7k1x6tprGir6idUlrIapVXGvUq\nLZgOtadHa3m6urSPuDAtRqOdfRqyIg5q1S0TtxlkmJ0ibr21UrZRK4XTXv3aDLKoO7W0jF6zlQ3Z\nemugyTotOL8buC0k3FVBuCNqxDUl46NGJq2czmsF0hydtHp0HLfh1/JH1qzmKEkcjdTBNPLYNuq1\nbbTCtAdpIizt7rVmyU9S4Z/V6Dxt+ZKV/6K8tRP10tLfr4mW2WKmnhaj0c6+3nN55Eu5rDV4e/dq\nv3pmYY0hVK6219hVuqYGtgG5+d2xo1L/khqSJ0Xact/Na3vFsrfZSp9ozQZ+BnzeuvYD4NMhYdcH\nZGt2jfhCmfKyZck7mnYaCcdBs99jT9cY4bV+vZ67D4t38+bKdXsaMa38q9fwo4RsWjZRzcZhfG7Z\nnajtc8ueZnE72qGhqTZi7vttzVZvrz5cL/JDQyLXXz+S6reF5bvpbOwl8SZ95juSdHImjXYdiJPG\nLOqEK0+aJbL2Kj3TbtKwhSmKPDNlsHHjVNvH1atFTj01Ou8a7ezrGa43ki9J9600BMpMHdoaL1sD\n5hrRh2n4bD9/SRbrNPKtUXK/no1qXNjxGlnnVyOmT7RmAN8A7rcN35slW2GjFNfxWxxB2MiIxx15\nmOfMuU1A7PcUmcDZjc32pL1x49T8NA3C7VhHRkZCPa03antThNFqszDe5I1hsOtd3h3tuT66au2D\nloQAuAbyzeRdrXS4Nlt2x9CItsJ+Ngm5SVPbaS+CsVernX12vM6oXueX5uKFoiBsEYU9pR5VV+u1\n+Sg0+lxUek2c118/ErvehA02zHVbvrkarbD4XFI2NhZvui1sBX6c3S/SHkyE5Wfazo492aomRYcD\nI8BDwMnOvX9vZhrx9a+/QBYt2ixXXLFZFi/+u4MGwFrIjgiMHCzUkZGRKoHmnu/ePSLnnz9ysKHu\n3h0vvO0wMuzcVDYT3pzXS08rzu2Niq+5ZkRe8YqRgyRrxYoR2bVrRIaGdON7xStGZMWKqd9rBMOu\nXeH5VdF+jBxc5VMui5x//kiVAbebnrBze8Rpf48RkFHfOzw8tXx37x6p0vyknb+7do3I8cePSHe3\nSHe3yPHHj8jGjVPrgxGKdv0x+WM7JbW/f2io+ntM+cX5HiPQ3fpa73uuv35qeV1zzchB4mHK20yf\n7N6t028EbdzyMALf1D8j/OO2z7D2XCs/a8W3YsWI5VJDy5c1a+I/327yIO55nPZ0/fWV+mG3Z6O5\nNe3V1EcTn3selR6TnyZ+V97UkgfmvJ68MecTE43LKxF9/uIXj8jNN08Nb5PvkZGRgwOG7dtHDi6i\n0FOwuv6GfY9bv8fHRebNiw5vnxv57sqfOPlXLz937Ro5qMkLy7848Y+MjMjmzZvlggsukAsuuMCT\nrYOJg0OBPcAvgReF3B8E7gu5vjOugbw7ejXC2dbExPW6fvnlMoV111Lrm1GMMW41oxlXXVwEI9W4\nsNNs3BfYnpPd6/Yz9Tyt23HE0XS4IyMzYnOn0JJqC025RTkdzAq2rUqtlWdx7KDSRFb+sdypv3qL\nGMK0Bo1MI8bRQsT5ZtcZZ7lc2fy7u3uqZrIe4sqDrOp9VoiT36YTdzWhrlaw2dWI9rvL5anuEuJ+\nS1QZ1ZumrGfTl0Sz2qhmy342ye4Xcb4xKdxvaNTZcVS98GRLkyEF3Ar8xrh6CAljXD8ssa4dHWjB\n6rp+MA12cHDqdIWtBnUrZpTK1jXkjGOwatSiZuWJqz5utYG5SHLDXpPm/v6K0BkaqnyruW4LS3Nv\n+/aRmo3Izo+keZNEUNWDu6VMGm4K6r2vp0cOarZsX0M2woR9WqTdHim67wsThq4Nmdvp1yIBYcS6\nXvurR8bifn+9DjtuB+C2//FxkaOPrhBmY9ybZJolqs7bZVMvHc3U+6xQr4yMLK41HZyWp/lm24tb\nRrbGqF689WRUXDJpDwAbsdkSqbYdS2oekKaCoNbAxpjbRJnj1COpnmxpMnRT4ObhPcCLnWO2VAjZ\nPuAA0Aecg3Zq+lAte63g2SnEyt7SxS5Qo6qOM7I2XsONM0g3PrcymNHDzJlTndeVy9qJn1vZ89qq\nwk5v3Ln4MIJqE9Gwxm4betpTqWFxx9VsRQmleg4Fk6ARYdQIomy2XMIVJlDiEJW4cMlWVKceRUDr\nrZ5zOwjznKvVMGHdDiYtG6s4y/Ojvtl9znbNcPzxIi95ScWthmkHcY15a3VkUWVj3GqYPE3baLkR\nROVv2DYybhijFTcDWhtprs5sdJBr6qpdRma6M6lGqhkZlXQ1olsmph8zTobjDCZrtbU0bG7DFgAZ\ns4kwO884gy5PtjQZmgi0VmHHu6xwZrueh9Db9dxBzO164ghwG1EN0NX8GNXr5s3Vamjb8NAecaxc\nWb1Jp3vf3bahFbum2yQqSgVtKrO9OsTeX+/yy/Xv2JjI3LkV4rBoUf0pOTtud1QWZrxZq+GnoS20\niXJSNXtS1FqNaG9aGybE4qxGbBRxDFhdAWd3+mHnrmCOm+aoDqqZuOp1GlHf7MLUkZUrK0QnTIve\n7NSmC1vDbNf7MKKSNVzNg93G7UGBrQkPI1Nnn137G9LwO9Yo2ak1KI1rimKQ94yGXZ/KZb2bg8nj\nKJnsIu4gJSyfag2uowZr7uCs1upMAzdfPdnKh8zVrchhAiLKgZx9/5RTKn5SzCa/LlEyxMteeWL7\nUrFXI5pKtndvhaS0AqaiJlka7ZIpWxvkjqhqjXjse/Y2OIbMhgmCMKGZxqhxaEgLI7vczCgwDzsY\nd7mz+X5bA5t3ZypSKZuLL67uDM1m3Fdcoc9NPTLe9JvtWJIQkbhT4nHrST2SZ9ru1q0is2bpvLEH\nXENDOh/qrfRKShrdgY5x67Fqlchpp1V3WO6US7MIW2ltayCM7LP9JJmO12hgXM34wIDICSdoG7d6\nq0rtgWtSNEJq3e+260QjWsRmZFTcehIlq1eurHYyXCuOJLAVDSZfzj679vZctdLo5o2RIabsJybC\nV7PaZkPlsidbuZGtuGpRWziazsT2RVQu6wK88EKRI4+sdkRnyJU97xw2sq/XuJsRIGnAVNRamq16\nU3cDA5qUuD6UbGJlr7ByNQqmkzSq+vFxvQzcVo27CDOytuMPU03XEyxDQ9Xb6JTL+vyEE9IhwnFs\nhlzN3oIF2W8YHWazZafP1GfbdnF8XBuCH320nkI2Hak9jeYKwKTEIkn4WtoHN45ammx3RG7q5aJF\nWj588IOVOjI2JvLWt4qcfLLeb3Lt2kq7N23CnQ5O2rGF2WzZI/6eHv0ts2ZVzBUGBirtyN4CJ+zd\nSfM4bEonTEtvSLc9eFi/XufRwIBu32vW6HT39VW33TAi06xmq1EDexd23anVblw0Q/biPm8TH5cM\np+1awU2X7bJizZrK/6i0hsGdbjZ1YWAg2owhrN1feqknW7mRLVdYRgkOW/tkOpP58zUztzuXU0+t\nGLqbilAqVTuetCtcGPs277TTkoZqvBnYFbXWFEithm6+2QjMqE7OXh7txmX7Yerr0/GtWRNtT2De\nPzioNVG2/ZzpHF2tQtwGb7bVqbelTdrTWG6Z2HsoZj3tUKvTcLXA/f3akP+Zz9RtY3y80m5s27MF\nC8KnK5JM40VNlb7+9eE2HldfrfOut1dvuG1r4Uy9iGOj6WoXTT2fPVt/5/Oep+tWd3eFbI6PV+SB\nGUD19YXHlaSTt8vGNhQvl3XdX7RIDwaMLenevTqdL3pRZZBQq+5HfbORB65W0ORfFMGqNeg09aen\nR+cf6I40rpxMw2arGZi8MXZGtssEcz+NgUOc99eSi1EkOKvV73Y/tmCBJtRxpv7ctNs2waWSrt9r\n1lQ2srcHEGZAbkxY3HbsyVaOZMsWDFEFHeYewnQc9hLuMPsTV4DVM6h1UQQBkmTaKqyh24TVXVEU\npmKP0iiYhrZmjc7z+fOjtTlup9XfX62JChvlJBUy9aZV3XSEnUchTprs99srP9MWko3ATpvRxpoR\nqZk6GBzU6bbrgEvcw0h3FAl3w7j2jXbbsdPnDgDirj6OKh+zgGHOHJEZM/Q7Vq+uDmNPp4dpjOPW\nkzC4dd8MTFav1nJq1Sp9buyfVq+OV3fC8ifKjqZcnjq1E2V36nb+ExMVQ/gkC1DSWo3YDKLkvRno\nNUqmk6Le1LxJh2srnFROxYXdJk2bM3UoasuhqDjstmr2cTVmHevX67iXLasQrVoD0TzJltLvm35Q\nSk3TL/fw8PDw8PBQgIioPN71pDxe4uERF6OtToBHJEZbnQCPSIy2OgEekRhtdQI8CoFpTbaWLRWW\nnCms7RcOlIQ/v1S4d1w4Z5lwy5CwZ1iYLGtttvl/oCS87GzhpB7hdX3CjkH9/Ek9wpvWVsLbz5hz\nRJ/vGa4+//NLhdKE/p0s62PlCp2Wi9brozQhLFuq31crvms36zSaeytXCP90q/CM4yrXD5SEc1dO\nTVvSNN87Lpx6SnW89rkJZ+Krd37tZuGnu0aq3vO6PuG0hfpdJ/Xow+TL2n79e6BUya+1/cLJC/Q1\nU1YnL6h+xjxn8vrPL62Uv/nduaMSxs2HPcO6frj3TJ1x89TOr5MX6LyfcYSuZyatdvy18unazTpt\n9vsPlIS/vrryfhPGTt+948ILT9P1x4470TEyEnnPpHvPsHD1Rp0mc37LkPDBAZ0u8y32N9j13sRj\n6r257p6HtRn3ml0n7xrTv27eTZZ1fVhypq4r5h17hit1wYTduUPn4b3jleumjp2zTJ/fOy4cc7Tw\nrGfqunvvuHDifH3t1at13th1y3zbBwd0HK/rE2bP0r9hda9e2Zj8c2XILUP6WL1KePrT9O/afv2e\nc5bp77hlaGrdCGvrJs12+zNhTuoRZj1by6qjjtT5bsucA6XwOm7KyZSPnfe12pb55iTtMeow39W3\nptIubxmqPq8nP8OOh3ePHKyba/sFhf6ftP0dKOn8fU63Pk7qqaQpqfxeuWKq3L5rLNl31Xqvyctb\nhvT5LUNT5ampk7XSGlVOrnw08sHI2LOWVPp2U4fO7hVeckalrU6WJV/Ckdd8ZdEO4KCdxNq1Ff9P\nIAf3KwubLx4a0hsrG0M8M388OKgNX6N8Q9Warw+bX7fnsc191z4pzNnj+HjFPcTwsMitt+pnb721\nMkdfy32EO1dfb5sM2wYtzGO4caRnzl0njnY+hOWXmYMHvZrL9s1j7O1MvGZpeX+/yIknVjytu8vL\nbTsf2w2AbVewbFk6zkDd50y5mmPv3qnhahnJxrHhM4bnZisYc7569dR6lhbi2qbVWsnnfr9tF7V+\nvW53rvH8jh3a9sguK3tllZ03O3ZUr/wztnxhGxuHraQzHriXLq22/TMyZHCwshrRXoxRKunViKed\nVllWb7s6MAtOTD0fG6uuf2F5WWvlr1vXjQFxf38l7aWSTk9/f7WBe5gdkb2S2i23wcGpXvLtuh3H\nUDvKYWVcm6YwG8x63tGjYMtas/DErKC2t1ZzXWeEwV31Vy7reuLW1ySIu2VXLTTiK64eatlSprVz\ngx3OldtmNeJpp1W2I1q7tmLTNThYsd1avbrSDjUFyolz5PWioh3AQXcA69frApg9u/by+TCj2LjL\n9KOMX8PitDt9dy8r11dJLcPgW28VOfRQkZ07K+dGCMZ1EOm+w/iYsjuTylY7U8lmlBA0HacNe6WY\n/b2mkYSR4LB0uwIprm8lkXBXEUkMz10j3eFhnT/Gb9r69ZoIHHaYFrrHHlvtyDZO5xJndapNsGzP\n/Uk97je7CsqN1ybgdn01RMSNz17mXS5P3ZPS+Osxz5o4S6WpqxFNZ3f11frc1GU3jKkXbn0aG6tu\nz2vWVIhaHF9hterWjh3V5WMIWNQqqloeyV0v5mbF1uWXV8sXM/CxffrZ7whbqOC2I0MoTFgz0Dnj\njIrbC0PUwuJPy3mnXS719v2rFUeYgb4x7DcrzbdureTjokUVEmraijHKN/V1x47qFdylUjzfai7M\nwot6W3YlhfnuZv1rhdWVMCfHtXzDxTXSt2WB7bNy61bdJk3d7usTefaz9apjo1A55hidJl3GiHiy\nlT3ZMgVpd862IDQFaXcY9iivnrsDA9tzc62VimHkxIx2Xeeo7jV3dZ8RDB/6kO6Qd+7U5x/8YDzH\nqLU6BldwmBWCrr8g8032yNqk0dWwGY3crl0jVaNLe2QZNlp1iZTJm+OOqwj8uALJNGBbS2c74Iwj\neKI0Tyb9tu+lBQu0YACRa69NJuji+F2z67Xr3sAWVDYRtn2ZufU4yvVDkvbhat1Mea1aNZXUG02M\n7XbFEAmbhNjtIqwDs9Nn+2gzYY22OozQGIes9lZa9kpGtw3W6hzcMK7vvbA0mrIzHXiY76DBwWr/\ndHY52J1cVBrD5JDt7sYuuyiCYIiW63vu2GMreeeuyDOyw/hYs9txHAfH7vsNMTffHeZctVyu5KMd\nV6lUcb5cKmkC39OjByvLllXa0t691XsMmgFx1H64Oi9HpLe32sWJyV93cGqfu05he3o0kR0crJzP\nnDlVBiRZgWmXvZHVST3H27DrnbvyPspFT1h64gxwo+K3By6GNJ94YmU2ymj49SAeEU+2sidbRkDM\nmqWFSE9PxVmpLVjsKSoj1OfOneqMLUzYh42WjAAMW6q8Y0f1yNvtGMKmSGwyZ8LMnas1WTNnasIF\nIh/4QLXT1bCK7Hr6NZXXdDq2/zAjkIw2MKox2VMLExOV6Ryjmdm7VwvlgYFqP1uLF+tnjI8dlxAY\nYWRImHGgedRR+tf4dqpHuOxvNiOkvXu1/6G+Pl0nVq+O73k7TPO0ebP+blujt3WrLp+rrxZ52tMq\n7kPCSEq9+MPCGM2W6xDS3Hf3FDPOJKM0Ay7Zcqf7TBzbt9duHzZB7+mp+Lqy651xEuu6CxgYqGg6\n7U5h+/YKcXFhk8tyWWTJkkrHadpYX5+uz6Zu2cTY9t1j8nT+fB1PEjcRYYOqsCm0clmXw2mnVb7X\nJuivfGWF/BuZsn37yMEONqrDCtMiRU3ZDg5O1SLW6niHh3XdNlpLU8YDA7p+Gw/w9t6obsdu6p9p\nI/bgypbHUfkZ5vfO7oRt+W3ajXnWkHrj38v4gFq2TA8EbA207R9tYkLLh56e6vZoy4WrrhqZMth2\n+4p69Wbz5qk7M+zYIfKGN+iBpTtorbUnr11mriwdH9flZOp8LWfDw8N6VwhbY7p+vZZrp55a0TC5\neV4PYXtkRg08bRcqNmm099AcGNDt1TbFMWXryVZy4nQ88BlgEvgl8M/ACXWekbEx3UjOPrtS0YxD\nQpfdhznnC7NVsIW9O2qoNZ0YFr6W1kuk0qHa04zj47rxmdHO3r0iSols2FDpYGoJzTDNjNn02B1B\nGAJWS01sd1yGmNl2cmZq0+747VGWrREz18OmUAxpPumkakenRmVfa6Rnd8amLLu6dGM84YQKGTTa\nF1d4u6NxkeqRcC0Njxll2cSwlg+2KM2ZLcRc7ZF7bn+3W7fdAUEt2N9k3mELs1rtwybgttbSXO/t\nndrWxsZ0W7U1nWvXVms/4xBU4wPLOBotlao3SjcddphtlnGyaerY0qXVu0mYfLGnqKO2DTFa6DC7\nyP5+/a2m3RjfWKtWafl04omVvDYb2bs2VXYZuQOoMJnjDtqSaBncb3PzzqTTxG98rNnfvWOH7qRN\nmzdyYuvWqdp4lxy6MsK1vXPlt50fNvE15WmHM84ybZtJUxdN/TXt2Ew1h8l922N6rTIJa2O1NJLm\n28K2iItL5sLKydU2h8mwnh5dH/v6Km2xp6cyELHrlavJs2GIn73DhKknUVOuJj3uVLVpP25/s3hx\n9dRruezJVrIPgKcCPwLuBV4VHPcG155a47kp8+9GowG6IdlTc8b2xtXuLFpUmRJzbW+M00+3Um/c\nWH8KJI6n7HJ56jTjokUVVffAgP6mbdt0GGO7Va/S22pY06ENDFRr1ExnV29japsgmvwzHZXpQBYu\n1ETJ1WCZbTxso98oweROIZj3x/HobjzJ20Lt2mvl4MICEFm3Tmtgtm6tfP/atSLnnVcpE7vxz51b\nLfzs+0abYfLQeDLv66vW0ITZlMVx3Gi0aPZ3lkpTF3CITHU8mdQ5qvkm2wDVbh9unbE7MJtIl0rV\nHt2NXZ8pB5O+1au1sbmJ14xY3Tx2R+DlcjUBNnWvu7uaIJo6ZDRHdh0580y9SMPW5Jq6s2hR+DRZ\nWBlGkS8zlelOI550kk7nGWeIHH98RUbNmFHd1t2pOLvsa2lRag1swrQMYXCn6mxtockrU+dtQm/S\nZpMmI9P6+nS+2tPlto2YqbvXXls9DWzqusl7t46bb7E1fVH/3e9yp7MNiTJTjkYrZrR79mCkp2eq\naUNSWz8XJk32Qij7Xj3tlB3GfE9vry4rt10Z7eWpp2o5aMp19WqRpz5VD16OPbbaSbDJJzsNNoys\nMgOeWbN0mb/hDVrx0d1dmQK0By92fbJl7tiYrk+2vVhPj37Hjh3V6fBkKznZugL4PdBtXZsXXNtQ\n47lIbZUtEOqN8mwBHjaCsgmXqShGhWk6aRPHwEB8Q26RaqFstFkmfH+/Ngw87zxtjG2nL8ruwp5S\n6O2Vg2TUVNB16/S197+/slpqfLxi1F7Lnsrk38CAFsBbt4occYQcHEG95CW6oW7fPiIbN4ZPz4QJ\nfkMOzaoTe2VYWL6FdTSuVsVMB/z1X1fK1mw/A1p1DpURXV9fhcjs3Vud365tmi34jTbNvLerSwuu\n7dsrdiC17CXCpgHs+uKSTXcAYL79/e/XpPCkkyr13dShwcFKHLt3j4SmxdaIuEa25tsGBvS5q2Wz\nR/62lq9c1vXiKU/R02k9PbpTPeYYPWo2hGPJksqUh60pcgclZlrOPDd/fuW/Sd+pp+p32gTQaEVN\nZ+nao9gaRtuWx0yDmcUk7uopk8aohTT2VOu8eZV0Gi3rGWdU7P1WrRK55pqRg6Tbfo9d5m47MDZL\ng4PVeW9MBAxRqke+N2+eqlU0JNmu60Yb9LznVQYXz3lOpRwGBvS7588Xee5zNbE0Wm97OtF0qitW\nVBYB3XprRbbOnVstS43cW7BAty1jGrFwYWXKa80aHebMM0We/3x9fdGicDsym7yaaTezD6jp3G27\nz927R2T9ek3U9ZRvhbStXavlSS3Nlms/Z2SeIZhbt+p43/AGbSZiBq2mTrsDUJuUGy2QKSejrbOn\nRQcGKvV4fLxSH087Tb/7mc+slLWRA2Hbupl8s2WQ3W4uvljn+aGHijz5yZW0n3FGtaZ0aKiiGDHm\nCkbWHndctWbPaFBNH2Y8CZhzT7aSka0vAGMh10eBkRrPiUhlKsxm6v39umE84xn61zXQNWpNw663\nbq3YVBiNxrJlFduDpUtFXvWqSsd63HE6zNln682rZ87U56edphv9woUVzYZZ8TQ+XhGkYdNSY2NT\nSdWcOVI12jENd2BA5DWvqXQUQ0N63t0ImFJJv/foo3Vnt3q1fmbePG0PddJJOrxNJEy+2KMPu5Mx\nAuPyy7VAmzlTf+dJJ+l4zzxT5+nhh/+dnHRSZWrXFXBh2kajyl67Vqdp9myRZz1rqp1SuVxtZGwb\n/Jsl7EYbZTQGq1dXtAnz52uC+KQn6fhnz566z9dxx1VGmf+fvfePs+O46kS/N2RJHNvR2Gs+a/xD\nGiHvgLJy4mzMgp/kZZxEMpacWMJhhMMMdjJ5ydpLIvPW1pgnnDsQy8ZxeA5SsmZhR4IFnsZefiyr\nGZuQwB18I8LuZxd8DcsL4cHcyXuLHcInd3g4CQ7Y9f6oOb51a7r7VnfXr+57vp9Pf2a6b3fVqXNO\nVZ06dapKNeKbzb5xfPKkTLPdlg3Kt3+71JHXvlaOEG+8UXZIaUvE9aB2MhTf/ObB+CfiOa04071H\nhw/L/6+6StJw6aWyvLOzciHFpZfKho5k+F3f9eiAjImnZCSoR2bQ/zfcIOsQTc3QSHZhQdYPSvtD\nH+oHIlNnunNn38C65BKpe4cOSeNiyxapq3Q+qTptRAdC06icGvQDByRf6ainw4elsTY+3r+ok7n6\n6v49pblnj7wo2Fn1XFHnRt43MpqosVeNKJoi1GVLgwnV4Dt5UsrgW79V0rNjh0z/DW8YXJV2zz2P\nvjLdpccnZXnMez3Jg8svl/WfpizVs1+zYgipDqrbv5w+LcQ3fZM0QsbHJe3z8zLtsTH5/MABHVgi\nVAAAIABJREFUmeeBA1KuV13V9w6Rt5JmGGgApU4rUh2lxT+NhvTgU8A4edbe/OZBLzxNk115ZT8v\nmlLeu1f+vfLKzVvGqMaJGv9HK17V9olWvNK73/d9jw54wKampGGxZ8/mI8T0QYJ+Lq8eT3rokKT5\n4oslz/fskXVj3z75l7Y5UA01qi80QL3hBjnguuIKWW/27JHPaXDyj/+xbB9oYPnd390fKAPy//PP\nl4betm399kVfFKB7BUn/T56U7d6VV/b7LLpe/3rZDrXbkq8UB3byZL8fVQ1Cau/IoKZBN+U9NSX1\nkeo1G1v5jK3nADyW8PyTAL6U8d2Au/vqq/ujExrBjo9Ljwt1jGqFVRuxXq/v+qaOlqbKaHRK5yhO\nTEgF2bJFCl33iu3cKSvUli1SedTVPProRm/4aIT4xBMyn8suG1xFJYT8S+dFUZzL9LQs65YtfSPj\n4EGp/DRdsXWrLINOW9r0jerBIJcuxfTs3t2fFhkflxXqwAF5DzRfmUJQR15J5xpS43PyZN8zsmWL\nEDffLCs8LctX6VM7obR4IXXE1OvJdM8/X9JI0xpAv3HvdAY9VknB6zrd09OSn5dfLkdx3/It0sii\ntPft6/NOh8pr4s/ERF82ZDARz8kwUGPnrr1W8v07v7Pf8NAo/dAhyT/SCdL9q69uJsZwqKNU0lO1\nUet0pDxJX5K8OKps1JWW5C0kvszNyTLdcUe/cybd0D0O1PFSB0VLvtttSQ8F4JNBODXV16EbbpAy\nJy+BPsWlxzDq8S7kTVC/Vc/0TIqfI/lcemmfTjVe8gMfkM/37+97Wdtt2aFccknzFV6k6WBWPT18\nWOa9bVt/8KnHJOqeUjU9MrgoZOGtb5XfHz4sjakLL5Q6Nz7ez+PQISmv886TxtZ998m6cMMNsgPf\nu1eWneo3GV+0mpnq6q5dQvz4j8v7T35y81S12ulT6Ad1ypOTkqbLL5eDKFr0o24lok4ZJsXbJnnL\n6V62Kc1X2rNOR9aDa66Reqa2n7qxpS/cuesuWV5a5DQ9LY3XCy6QfLzySsm/7/xOsckjmNb+bdki\n9Ynq0mWX9QcLNNW+f3+/PbjwQtkXkI7StX17X19pQJAVlqDXC+prSMavfnX/fuvW/hnEpKfqtiLq\n/pjEQ2pj1X6DvPXk2ZY0QiTZBy4uL5k4LQDwIoAHE55/BMA3Mr7bFBw4NTWobHQl7cGVFKNA7x08\nuHm7Auowtm0bNGQojko1hsiVS40HdRpZrnyqTGTsqVNX+pQH0UseAlqJSSvvaL7+8OHBjm7/flmu\n06flvR50TjToW0Toq4T01SFUqb/lW4S46KLmQOwIBa0mbZuRtBeS3tElTf2qQa60JF31dunLssnj\nQF647dv7jc0TT/RHTmogt+rZUmlVOydKY+9e+ffyy2XDeckl/e/ToJZN3ROI5KXqsBp/p+oiXWo+\natySKpuDB4V485ubA/qXZPCTwUNTG7RiS43h0TstffSuemTIYDx0aNBTq3YYhw8nx4SQHJ54Qg6Y\niBZ17yN1ReuJE4N1jwZBNDWq6jPVYbWO0++HD/djd/SpGlU/dVmSMUB7KV16qfQ6UWdBZSbvgepR\neuMbm2Jurs8T1buapTv6FifUSefZeJPSo3bhve8dHMip5Sbd275dGhzUWZKsaKUohUTQQOLQocH4\nL7XOU74f/eigfvR66dvuENRy05W0nUpWOmlbUVC7cNNNzYEFHMSD+XmzBQh6G0d9AdUxlW76/1/9\nq82rsFXP1q5d8nuaEpyf7xsw+/YNer7puW5gXXSR9KaRgUSyoc1Fyaur63sS76+8Ug5uXv1qOUNz\nxRWyvl94oeyTAWkkU3tEPEiLIdYHcvpCCEqHja08BShhbCVVuuuu6yvHsEaSFJg6CTXOS+1w9QaQ\nGhuqdPqyVQo03rNnsDFJC6LUR5fvfrf0WqgNrR5ErTcy7fbgM7Wzvvnm/nOi5Yknkpfy6sGcappq\n8Km61wn9Dwjxvd97+4Dxq3+XxAPiLxmNqlGsf6fyiuSjx5sljVrbbclT6jD0FW3UkCUFg6cFpVO5\naRsB4oE6etP5m5SOvtu1akzpvKNyk1Fx882Dhg8Z4WRgkScHEOId77g9nZgEedDoW6dDl6O6Wknt\ndDodOXKnFYMzM32DS92dPKlB1wcfw5bZEw0HD/an0vRNOfUOnPhE+x7p8SkU10jTF1Tv9Y2Tkwxx\n4hN5JfWBIX1P777jHbe/8j3Jn3iU1ImrOqEaNbS3HeVtYmwJ0c+TBo+q0U/e1P37+1so0ICOvE80\nZUxB/6rxSe2gGitH8njiCemRonypfXryyeQBia4j5GG+4grpFdq3L9lIyUonCaqO3X777QNec3Wg\npwftp0GtU6rhRW3ewYN9TzTF2ybJX9exw4f7ekwy0xcx0Luq5/11r5O6TGEW1JYk6WrSAiq1raGp\nQjXOj2YW6Nkb39j/TZ/pUb1/an+t90Gqd5MGND6NrYaQhkdl0Wg0ngfw60KIO7XnnwTwLiHEP0n5\nrtoFZzAYDAaDUQpCiIaPfF7tIxPH+B8A/lnC8zcA+JO0j3wxmMFgMBgMxmjjVaEJsID/DOC7G43G\nOD3Y+H83gN8IQhGDwWAwGAzGBuowjfg6AM8A+DqA+zce/wSA8wG8SQjxtVC0MRgMBoPBYFTes7Vh\nTL0VwBcA/AcAvwjgzwG8jQ0tBoPBYDAYoVF5zxaDwWAwGAxGzKi8Z4vBYDAYDAYjZrCxxWAwGAwG\ng+EQbGwxGAwGg8FgOAQbWwwGg8FgMBgOwcYWg8FgMBgMhkOwscVgMBgMBoPhEGxsMRgMBoPBYDgE\nG1sMBoPBYDAYDsHGFoPBYDAYDIZDsLHFYDAYDAaD4RBsbDEYDAaDwWA4BBtbDAaDwWAwGA7BxhaD\nwWAwGAyGQ7CxxWAwGAwGg+EQbGwxGAwGg8FgOEStjK1Go/FtjUaj3Wg0/rTRaPz3RqPxltA0MRgM\nBoPBGG3UytgC8DMATgshvh3AHIBfDkwPg8FgMBiMEUdDCBGaBitoNBqXAPhzABcLIV7aePZ5AO8W\nQvxBwvv1KDiDwWAwGIxCEEI0fOQTzLPVaDQubzQaJxuNxu81Go2vNhqNlxuNxtaUd69oNBq/0mg0\n1huNxt80Go1fbTQaV2qvbQXwHBlaG1jbeJ4IIQRfkV3NZjM4DXyxbKp2sWzivVg28V4+EXIa8SoA\n7wLwFQBPA0gseaPROA9AC8AEgBkA0wD+KYDf2fiNUSN0u93QJDBSwLKJFyybeMGyYQDAq0NlLIT4\nXQDfCgCNRmMWwL6UV98PYBzAhBBideP9PwLwZwA+AODjG+99EcC3NhqNbxJ979b4xnMGg8FgMBiM\nIKhCgPw7APw+GVoAIIToAjgH4Bbl2V8D+K8A3gMAjUZj78bzTfFaKpaXgfX1/l9A/p2fB06fBubm\nBp8//ri8lpc3p6H+T2nQN+96F7C21n++tgacOiX/V/NUf19eHvxLv6t558XysqRnbk6mCcj7I0eA\nT3xikJ65uX751XJR+VVaVB7Q/enT8l26X1uT6dEzlQ5K513vumPgHTVfeof+1/M0+Y14mPRNFs/y\nfpOVjpre3JzkvcqDtTXgvvvS088q2+OPD+ri2pp8puoi8ValRecZ6YkuG1330+jS64GeFukX0aKW\nIyn9pDKrZdG/pzqkgurQsHTT9ESt+2qboNcFk/TT3lXlR+8m8VxP94477sjkhwk9RfQ8qd6rctbT\nMJXLMFpN3y1S35NQtA1YXpb1pihdefhrQmNSHVX1hv7X+y/qB1R9z1N/s5Ck948/3u8f6Tc176Q+\nOC2NNF30jtBzphvzprMAXgKwNeG35wA8lvD8kwC+pD27CtII+1MAfwDgLRl5CiGE6PWEuOsuIbrd\nwb+djhA7dwpx+LAQs7Py+eysENPT8m+vJ14BpdHryWt2dvM3U1NCTEzI/zsd+f/EhHxHp6HTEWLX\nLiHa7cG/nU4/n6Ig+qamZPk6HUnT+LgQW7bIe3pnerpfXr38RCvRovJACPn7zp1CzMzIZ3Q/NTWY\n3uHD8jnxQeexyk+6V3mdRkPab8PoTuNZ3m+y0lF1bXpaiB07+rqg8y0PPcRT4hWlNT2dLj+VHzr/\npqc3y0bX/TS69HqQlJZel7L4mlRmnR71nW5X1pluV/6m35vwMi0/nXf6uybpp72r63oaz/Pyw4Se\nInqeVO9VPg/7PU0uRXhnq76blNM0nbJ05eGvCY1JdVRv42dmNrf509PJz03r7zAe6XpP7UWnk0xT\nUh+clkaaLgohxIYd4MfO8ZVRJhHZxtaLAB5MeP4RAN8okeeAoFQjh4waEiwZSmQoZFX21dW+0NX/\nycC66iohDh6U/8/MyHeSKki7LcTFFwvxxBPyb7td3tBSaZ2dlXRs2SLEgQOSnsOHBw0svUKqBlpW\nw0Bl0isrpavyWf99//7WQMOh8zOt8cjzW9Y3WTzL+01WOioPpqcl/w8d6uuFaSOeVLYknuu6mGa8\n63xPko0pXXo90GnS5WzasavvZn1PHRMNVpI69GG8TMsvi4em6ae9qw900t5X073lls31Zphhkrc+\nmZaPDIm0NEzlkpcem/W9LC0qzp5tlaIrD3/zyj+pTUjrv8j5ULT+ZiFJ75McH1l1IiuNNPrY2PJk\nbN1+++2i2WyKZrMp7r//UQG0RLstuXLmTEu0Wi2xuirvgZYA5L0QQrRa8ndCq9USZ860BCAFK/+X\n78s0WuLEidZGWkKcODH4vpoevd9syt+feELenzkzmJ+ef557oi+JHrX8Kj1J/NHTJ34l8e/Mmf69\n5MUgf9QyZvFTLw99n8QfnR69/FnyzJJvGf7r+rC62udHnvSH6Y/O3zT+Z/FTld/99z+aq3zq92fO\nDNcvE/1OkmfW96SvJ06Y1QedH1n5qfIzrW8m+qbLz4Qf99//aC5+Fq1PpukNKy/pX7ttzo+i+mFC\nj+l9WvpZ37deqatm+pWUXh7+5pV/kj4P67+K1t+s+6T2QW+vhtU3vb/R6Wu1WqLZbIrbb79d3H77\n7Wxsab89bzqNmDPPVwTFnq14PFtJIyb2bJnT49KzleVlSfuePVt+PFsm/DChhz1bdmixSRd7ttiz\n5cvY+m0ATyc8bwFolcjzFQGpCswxW+lKmxTzozccdM8xW+npcMxW/piPpDJnxShxzFZyXkXqTJ66\nwTFbdunKw18TGpPqKMdssbFFvx0B8A0A48qz8Y1nd5fIUwghxNKSZD79JaE0m0KcOiXE0aODzxcX\n5bW01BeY+i39T2nQN7fe2u9omk35/8KC/F/NU/19aWnwL/2u5p0XS0uSnqNH+0q4uCjEhz4kxMmT\ng/QcPdovv1ouKr9Ki8oDuj91Sr5L992uTI+eqXRQOmfPtgbeUfOld+h/PU+T34iHSd9k8SzvN1np\nqOkdPSp5r/Kg2xVibi49/ayyLS4O6mK3K5+puki8VWnReUZ6ostG1/00uvR6oKdF+kW0qOVISj+p\nzGpZ9O+pDqmgOjQs3TQ9Ueu+2ibodcEk/bR3VfnRu0k819NttVqZ/DChp4ieJ9V7Vc56GqZyGUar\n6btF6nsSirYBS0uy3hSlKw9/TWhMqqOq3tD/ev9F/YCq73nqbxaS9H5xsd8/0m9q3kl9cFoaaboo\nhF9jK+hxPY1G49aNf98OuWfWXQC+DODLQoinN955HYBnAHwdwP0b7/8EgPMBvEkI8bWCeYuQZWck\nY2VlBZOTk6HJYCSAZRMvWDbxgmUTLxqNBoSn43pCG1svI3nn+N8VQrxVee8KAI8C2AugAeAzAH5E\nCFF4w1I2thgMBoPBGF2MjLEVEmxsMRgMBoMxuvBpbFVhB3nGCGFlZSU0CYwUsGziBcsmXrBsGAAb\nWwwGg8FgMBhOwdOIDAaDwWAwRg4+pxFf7SOTWLGy4oXHDAaDwWAwRhg8jciICs88E5oCRhpYNvGC\nZRMvWDYMYMQ9W5OTPI0YH3hPmnjBsokXLJt4wbKJF/5mtzhmi8FgMBgMxsiBt35gMBgMBoPBqAnY\n2GJEBd6TJl6wbOIFyyZesGwYABtbDAaDwWAwGE7BMVsMBoPBYDBGDhyzxWAwGAwGg1ETsLHFiAoc\n3xAvWDbxgmUTL1g2DICNLQaDwWAwGAynYGOrJpifB9bWBp+trcnnRbC8DDz+OLC+3n82Nwd84hPy\nNxt5JCHv5n/Ly4M0AvJepdEmbPDZFs1F0kmS6/q6fDYs/1g2ZvQt87Iow3NThJRNWp1497vdl7sK\nINkUbTt0fV9elt+pPMyr/3nqEL2rfqPej5IsS0EIMZKXLHp90O0KsWuX/Jt0nxe9nhCzs/Lq9eQ1\nNSXERRcJ0enYycMGej0h7rpL/k26tw0bfLZFc5F0kuSq3lcBvmVeFnXgeRbS6kSnU+9y50XRtkPX\nb/27Ivqfpw7Rb91u8t8qy3LDDvBjc/jKKLarbsaWEP1K2G7bMYKocZyeltfsrGxAbeaho9VqFaLz\nrruEWF31U/lt8NkWzUXSSZKryXdFZOMKvmVeFkV5borQskmrE67LXQWosinaduj6ToZOGf3PU4fo\nXWr/O51q1LthYGOLja3CaLelVNttO+mtrsr0APm/izxUFO00iE6i0TVs8MAWzUXSSZLrMITu0HX4\nlnlZFOG5KWKQTVqdcFnuKkCXTdG2Q9d3G/qfJw16l+ivgyzZ2GJjqxDq4NkqSid7ttx7tmICe7bi\nAnu2zMCerbjAxhYbW7nBMVvJ97bBMVvhwTFbcYFjtszAMVvxgY0tNrZyo9ncXGm7Xfm8CJaWhFhc\nHKxIR48KcfKk/M1GHknIOx2ytLS5svd6gzTahA0+26K5SDpJcu315LNh+ccwVSWEf5mXRRmemyKk\nbNLqxG23uS93FUCyKdp26Pq+tCS/U3mYV//z1CF6V/1Gva+yLH0aW3xcDyMqrKysRLPFAGMQLJt4\nwbKJFyybeOHzuB42thgMBoPBYIwc+GxEBoPBYDAYjJqAjS1GVOBzxOIFyyZesGziBcuGAbCxxWAw\nGAwGg+EUHLPFYDAYDAZj5MAxWwwGg8FgMBg1ARtbjKjA8Q3xgmUTL1g28YJlwwDY2GIwGAwGg8Fw\nCo7ZYjAYDAaDMXLgmC0Gg8FgMBiMmoCNLcdYXgbW1wfv19bkX8L6urxfXgYef3zw/fV1+Ux93xet\nKm0u8qD/1TyWllZy5+eKb3n5Ufb9LN0YRqOalnqvfptFn85DokXloYlsXOiQaznQN3NzsszqN6dO\nAfPzhcguTEsRPPTQirEe6PT5qDuqbqv1/t3v7uvZ44/Ld9fWJM/T6NZ1Xk2b7tX01HJReiTv06f7\ncid533dfX+425OciZkuVG5Xl2Wf7crOhu3p+edqqPG0N/abr3LC+okg9mp+XMtdp8wJfhzDGdsHT\nQdR5Tmzv9YSYnZVX0r1vWoucJp8nj6Ty3nJLK3d+rviWlx9l38/SjWE0drvJf/VDgNPo03nW7Qqx\nc6cQ09P5ZONCh1zLgd6ZnpZl7nY33/uivSjOnm0Z64FOn4+6o+q2mkenI3k8NdW/37VL/k2jW9d5\n/Ztud3MZkuQwPS3ExIQQhw7JvwcP9u9VPSgrPxeHhKs87HYl/7ZskX+7XTu6q+eXp63K09ak6dyw\nb4rUI2rXZmbkt/B4EHVwoyfU5cvYEqKvGKurg40E3esKNjsrK8v0tD9DK41WF3mreVAFKpufK77l\n5UfZ97N0Y1gaeToqE90jPuaVjQsdci0H+oY64IMH7XVWPuqUmo+JHujf+ag7qm6r9Z4MgyefFOLi\ni4Vot80HGVTWdntzmYfxneS9Y4cQV1whxD/6R0Ls27dZ7r7klxeq3KamZDmuusqu7ur55Wmr8rQ1\naTrnoq8gg+vQITa2vBlbzWbTyagjCaurkturq8n3Se+m/e4aWbS5yMNWfq74lpe+su8X4Qd9024P\n/zaP7hWVjQsdci0H9RvipS34qFNqPiZ6kPSda5ml1Xui94knzGnQy5pU5mF8V8udJXdf8ssLXV9d\n6G5SfqZtlY1+zkVfceJESwBNNra8FJw9W0a0+vZsnT3bKpwme7bcerZMZcOerfK05EWr1WLPVqSe\nLVcDevZsFSsHe7Zqamzlmes2ncv2RWvReXHTPJLKyzFbHLNVNs0iNFDHyzFbHLPFMVvJ+eVpq/K0\nNWk6N+ybIvWIY7ZqbGwtLQ0qxNKSFPjSUv9Zryfvl5aEWFzcrHCLi4Pv+6JVpc1FHvS/mkeR/Fzx\nLS8/yr6fpRvDaFTTUu/Vb7Po03lItKg8NJGNCx1yLQf65ujRzR6NhQUhms1CZBempUw+Jnqgf+ej\n7qi6rdb7227r69niony325U8T6NbL6uaNt2r6anlovRI3qdO9eVO8p6b68vdl/zyQpUblaXT6cvN\nhu7q+eVpq/K0NfSbrnMu+opmU8qc0vVpbPGmpgwGg8FgMEYOvKkpY2TB54jFC5ZNvGDZxAuWDQNg\nY4vBYDAYDAbDKXgakcFgMBgMxsiBpxEZDAaDwWAwagI2thhRgeMb4gXLJl6wbOIFy4YBsLHFYHg7\nLDgr/5AHkDPKw4YMQ+thGVSZdoY55ueBu+8ePMx5bU0+u+++8vL2dTA6pesVvvaYiO2Cxx3kGXHD\n18aTWfmH3MyWUR42ZBhaD8ugyrQzzNHtyo1fd+yQG4N2OoP3ZeXta5Ndugfvs+UeHCDPULG+Dhw7\nBtx7L/DII8Dx48DYmN/877kHePFFef+a1wAf+5hfGhjlYEOGofWwDKpMO8Mca2vAvn3A3/0d0OsB\nl1wC7NkDnDhhR96u2sIk/bzoIn8B8mxsMaLCysoKJicng+Td7QLbtwOrq8D4eLj8gXA0ZCGkbKoC\nGzIsooexyCZ0HYoRscjGJj77WeD66/v3tuXtqi3U9ZNXIzIYnrG+Lkc7q6vyr+/5/PV14IEHgOlp\neT3wQICYAkYp2JBhaD0sgyrTzjDH2howOwts3QpceKE0Xj78YXvydtUWBtdPX/OVsV3gmC3GBkLH\nm3DMVvXBMVvVpZ1hDo7Z4pit3OBpRAZheRnYvXswJmB9HTh3DjhwwE/+L7wA3Hhjn4b1deBTnwIu\nuMAPDYxysCHD0HpYBlWmnWGO+Xkp1ze9CTh0SMp7bQ149FHgta+VU4tl5O2qLUzTT47Z8gA2tuJE\nHeMb6gKWTbxg2cQLlk284JgtBoPBYDAYjJqAPVsMBoPBYDBGDuzZYjAYDAaDwagJ2NhSUOTICfpG\n/Va9T/rW9tEWsadnkh8d0UDniLk6rqZo2ebnB4+oAGRg6Pd/v6Qzb3pVhOsz3nTZzM8Dn/jEIH/X\n1vIfC2JDn0MeR6PmTf+rea+vAw89tGItD4Lt8pVpX9X7tbX+N/PzwLPPDqaxtiafh4RKt9qmudQX\nE/7aeqcMDSrS2tWsOl6kz40CvpY9xnYhYeuHIsuX6Z1uN/lv0re2l0nHnp5JfrS89+zZltOtD4qW\nrdsVYufO/vJmuj98eJDOOi95b7VaTtPXedfpCDE2Jnms8jzvEnMb+hxyawM1r6Sl8XfdJeuNrTyS\n7m2gTPtK73S7QuzaJf8KIXXk4ovl36TfQ0Glu9VqedEXE/7aeqcMDSrS2tWsOl6kz00DPG79ENzo\nCXUlGVtC9AW5uppfwTodWdE7HfNGJE8+JjTEmp5JfrOzQkxPy8vlHlNFy0YNwaFDcm8ZahB886rO\n0HnZ6STzvGy6odIoCjVvMrZs0+GjfGXaV/qGOlVVR3btEqLdjsPQIoTQF5M8bb1ThgYVae2qSR55\n+twksLHlydhqNpuJo/XVVcmZ1dUUCSWAvmm3zb8tkk+V0zPNz0eeRctG8tW/9c2rOkPnZRrPy6Yb\nKg0bebuiw0f5yrSv9E2ajrTbNiktjxD6YpKnrXdsfVukjhfpcwmtVks0m002trwUnD1bXtMzyW92\nVoi3v73Fnq1I4XoaUQj2bJnkneTZsiEb9mzZBdF95kyLPVsZYM9WzS+O2fKXnkl+HLMVPzhmK4yc\n1bz0ukG/ccxW8u+hoNLNMVvp33LM1ghcScbW0tJmQfV68nka6Bv1W/U+6dsi+WQh9vRM8ltc3Fzh\nFxft51m0bM2mEKdODX7b7QrxrndJOvOmx9gMXTbNphAnTw7yt9sVYm4uH39t6LPvOpGWN/2v5m2D\nDh/lK9O+qvfdbv+bZlMaWmoa3a58HhIh9MUkT1vvlKFBRVq7mlXHi/S5afBpbPGmpgwGg8FgMEYO\nvKkpY2Thei8nRnGwbOIFyyZesGwYABtbDAaDwWAwGE4x0sZWqB2hXWN+Xu7CqyKGXZVNMDk5GZqE\nwgi5y7gPVFk2LlFW7jb0pg6yUU+SILg6ScIn6iAb1yDZq/In2Vdd/oSRNraOHRsU7LFjwO7dYWmy\ngfe8B7j55r7BtbYm79/znrB01R27d9dXpxjpKCt31huJ3buBT38auOee/pFE99wjn40aL0YNJPul\nJSnztTX5d2mpRvL3FYkf2wXglSWkddwfiZZAx7b3zDD42MvJJeqsU1WXjUuUlXvZ7+siG9rawsdJ\nEr5QF9m4Bsl+akrutzU15V7+8Lga8dWBbb2gGBsD7r0X2L4dWF2V93XBtm3AY48B118PtNvynuEe\nddYpRjrKyp31RmJsDPixH5N8AEabF6MGXfZf+EK95D/S04jr68Ajj0iBPvLI5riJKmNtDbjzTmlo\n3Xnn5hiuWFH1+IY661TVZeMSZeVe9vu6yGZ9HXjgAWB6Wl4PPFD9OlQX2bgGyX5qCpiYkH/rIH/C\nSBtbx44Bx48D4+Pyrxo3UWVQjNbSErBnj/yrxnAx3IBibeqoU4x0lJU7640ExWgBwMmT8gL6MVyM\n+oJk/+KLwIUXAr/1W/Lviy/WR/4jvalprycGXJTr68C5c8CBA+HosoH5eRkMr04drq0Bp0/HvyJx\nZWWlsiPB5WUZyFlHnQKqLRuXKCt3G3pTB9ksLwMvvADceGOfF+vrwKc+BVxwQXXrUB1uFD1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lrjdYnZMoXt+IYY9naqC/LKpqqjfEKVdGeYbGIsi22aYmor1bJVKWbL58rA6el8eSWl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb331a78710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "fig = plt.figure(figsize=(8.5, 10)) \n",
    "gs = gridspec.GridSpec(2, 1, height_ratios=[1, 2.5]) \n",
    "ax1 = fig.add_subplot(gs[0])\n",
    "\n",
    "\n",
    "plt.plot(rank_results_test,'bx')\n",
    "ax1.set_title('Ranks for TEST-1S, SVM, short\\n', fontsize=16)\n",
    "ax1.set_ylabel('Rank (Linear)', fontsize=16)\n",
    "# ax1.set_ylim([100,0])\n",
    "ax1.set_ylim([0,150])\n",
    "ax1.plot([0, 1000], [11, 11], 'r-', lw=2, label = \"TOP-10 line\")\n",
    "# ax.plot([0, 1000], [6, 6], 'g-', lw=2, label =\"TOP-5 line\")\n",
    "# ax.plot([0, 1000], [1.2, 1.2], 'y:', lw=2, label = \"TOP-1 line\")\n",
    "ax1.tick_params(axis='y', labelsize=16)\n",
    "# plt.ylim([30,0.5])\n",
    "ax1.grid(True)\n",
    "# plt.show()\n",
    "ax1.xaxis.set_ticklabels([])\n",
    "\n",
    "\n",
    "# fig = plt.figure(figsize=(10, 17))\n",
    "ax2 = fig.add_subplot(gs[1])\n",
    "ax2.plot(rank_results_test,'bx')\n",
    "\n",
    "ax2.set_xlabel('Document Number', fontsize=16)\n",
    "ax2.set_ylabel('Rank (Log)', fontsize=16)\n",
    "ax2.set_ylim([1000,0.9])\n",
    "# plt.ylim([0.9,1000])\n",
    "ax2.set_yscale('log')\n",
    "ax2.grid()\n",
    "# plt.ylim([30,0.5])\n",
    "ax2.plot([0, 1000], [11, 11], 'r-', lw=2, label = \"TOP-10 line\")\n",
    "ax2.plot([0, 1000], [6, 6], 'g-', lw=2, label =\"TOP-5 line\")\n",
    "ax2.plot([0, 1000], [1.2, 1.2], 'y-', lw=2, label = \"TOP-1 line\")\n",
    "lgd = plt.legend(loc='upper center', ncol=3, fontsize=14, bbox_to_anchor=(0.5, -0.1))\n",
    "ax2.tick_params(axis='x', labelsize=16)\n",
    "ax2.tick_params(axis='y', labelsize=16)\n",
    "plt.tight_layout()\n",
    "plt.savefig('short-svm-test1.pdf', format='pdf', dpi=1000, bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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6.0||Doc.191: 53.5||Doc.587: 25.0||Doc.988: 1.0||Doc.690: 4.0||Doc.88: 1.0||Doc.790: 17.0||Doc.485: 42.0||Doc.288: 362.0||Doc.890: 2.0||Doc.389: 13.0||Doc.588: 193.0||Doc.691: 56.0||Doc.192: 3.0||Doc.989: 5.0||Doc.791: 14.0||Doc.89: 35.0||Doc.486: 1.0||Doc.390: 31.0||Doc.289: 88.0||Doc.891: 10.0||Doc.589: 7.5||Doc.990: 21.0||Doc.692: 43.5||Doc.193: 40.5||Doc.792: 294.0||Doc.391: 6.0||Doc.487: 23.0||Doc.90: 59.0||Doc.590: 95.5||Doc.290: 3.0||Doc.892: 15.0||Doc.693: 36.0||Doc.991: 5.0||Doc.793: 34.5||Doc.194: 39.5||Doc.392: 8.0||Doc.488: 9.0||Doc.591: 90.5||Doc.91: 76.0||Doc.291: 40.0||Doc.694: 67.0||Doc.893: 3.0||Doc.992: 7.0||Doc.794: 37.0||Doc.195: 7.0||Doc.489: 1.0||Doc.393: 40.0||Doc.592: 1.5||Doc.92: 134.0||Doc.292: 3.5||Doc.894: 3.0||Doc.695: 17.0||Doc.993: 118.0||Doc.196: 36.0||Doc.490: 9.0||Doc.795: 1.0||Doc.394: 1.0||Doc.593: 27.0||Doc.93: 16.0||Doc.696: 66.0||Doc.895: 248.0||Doc.994: 1.0||Doc.293: 77.0||Doc.491: 13.0||Doc.796: 9.5||Doc.197: 3.0||Doc.395: 80.0||Doc.594: 73.0||Doc.94: 121.0||Doc.697: 23.0||Doc.995: 131.5||Doc.896: 5.0||Doc.294: 17.0||Doc.492: 60.0||Doc.396: 19.0||Doc.198: 22.5||Doc.797: 50.0||Doc.95: 40.0||Doc.595: 13.5||Doc.698: 3.0||Doc.996: 3.0||Doc.295: 10.0||Doc.493: 48.0||Doc.897: 131.0||Doc.397: 3.0||Doc.798: 278.0||Doc.96: 1.0||Doc.596: 20.0||Doc.199: 15.5||Doc.699: 5.0||Doc.296: 6.0||Doc.494: 6.0||Doc.997: 37.0||Doc.398: 291.0||Doc.898: 40.0||Doc.799: 5.0||Doc.97: 6.0||Doc.597: 19.0||Doc.297: 1.0||Doc.495: 83.0||Doc.399: 12.0||Doc.899: 9.0||Doc.998: 5.0||Doc.98: 11.0||Doc.298: 34.0||Doc.496: 61.0||Doc.598: 34.0||Doc.99: 14.0||Doc.999: 14.0||Doc.299: 3.5||Doc.497: 9.0||Doc.599: 29.0||Doc.498: 14.0||Doc.499: 2.0||239.916135073\n",
      "count    1000.000000\n",
      "mean       40.974000\n",
      "std        77.065614\n",
      "min         1.000000\n",
      "25%         4.000000\n",
      "50%        14.000000\n",
      "75%        40.000000\n",
      "max       786.000000\n",
      "dtype: float64\n",
      "('TOP1', 96)\n",
      "('TOP5', 295)\n",
      "('TOP10', 422)\n",
      "C value = 3\n",
      "classification report of TEST data:\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.89      0.91      0.90      1000\n",
      "        1.0       0.91      0.88      0.89      1000\n",
      "\n",
      "avg / total       0.90      0.90      0.90      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\tX_test_scaled_1_new = scaler.transform(features_merge_1_new)\n",
    "\t# X_test_scaled_1_new = features_merge_1_new\n",
    "\n",
    "\t# sim_results_test, rank_results_test = find_ranking(X_test[:1000,:200] ,X_test[:1000,200:], clf)\n",
    "\tq = wrapper_find_ranking_quick(X_test_scaled_1_new, clf)\n",
    "\tdic_rank_results_test_new = q.get()\n",
    "\n",
    "\trank_results_test_new = [dic_rank_results_test_new[k] for k in sorted(dic_rank_results_test_new)]\n",
    "\n",
    "\tprint(pd.Series(rank_results_test_new).describe())\n",
    "\tprint(\"TOP1\", (pd.Series(rank_results_test_new) <= 1).sum())\n",
    "\tprint(\"TOP5\", (pd.Series(rank_results_test_new) <= 5).sum())\n",
    "\tprint(\"TOP10\", (pd.Series(rank_results_test_new) <= 10).sum())\n",
    "\n",
    "\n",
    "\n",
    "\t# --- Expanding the training data (dissimilar paris)\n",
    "\tfeatures_en_wrong_new = np.array(features_en_new)\n",
    "\tnp.random.shuffle((features_en_wrong_new))\n",
    "\tc = np.all(features_en_wrong_new == features_en_new, axis=1)\n",
    "\tprint \"C value =\", c.sum() # check the duplicated amount\n",
    "\n",
    "\tfeatures_merge_wrong_new = np.concatenate((features_en_wrong_new,features_jp_new), axis = 1)\n",
    "\n",
    "\t# --- Prepare the final training and test data --- #\n",
    "\tX_test_new = np.concatenate((features_merge_1_new, features_merge_wrong_new), axis = 0)\n",
    "\tX_test_scaled_new = scaler.transform(X_test_new)\n",
    "\n",
    "\ty_test_predict_new = clf.predict(X_test_scaled_new)\n",
    "\tprint \"classification report of TEST data:\"\n",
    "\tprint(classification_report(y_test, y_test_predict_new))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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BAX2/d29n4+XRnegI2VJKzVJKbVdK3aGUekgp9bhS6sSEbz4cuPu45d3BSqmrlFI/U0o9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb332701890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %matplotlib inline\n",
    "from matplotlib import gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "fig = plt.figure(figsize=(8.5, 10)) \n",
    "gs = gridspec.GridSpec(2, 1, height_ratios=[1, 2.5]) \n",
    "ax1 = fig.add_subplot(gs[0])\n",
    "\n",
    "\n",
    "plt.plot(rank_results_test_new,'bx')\n",
    "ax1.set_title('Ranks for TEST-2S, SVM, Short\\n', fontsize=16)\n",
    "ax1.set_ylabel('Rank (Linear)', fontsize=16)\n",
    "# ax1.set_ylim([100,0])\n",
    "ax1.set_ylim([0,150])\n",
    "ax1.plot([0, 1000], [11, 11], 'r-', lw=2, label = \"TOP-10 line\")\n",
    "# ax.plot([0, 1000], [6, 6], 'g-', lw=2, label =\"TOP-5 line\")\n",
    "# ax.plot([0, 1000], [1.2, 1.2], 'y:', lw=2, label = \"TOP-1 line\")\n",
    "ax1.tick_params(axis='y', labelsize=16)\n",
    "# plt.ylim([30,0.5])\n",
    "ax1.grid(True)\n",
    "# plt.show()\n",
    "ax1.xaxis.set_ticklabels([])\n",
    "\n",
    "\n",
    "# fig = plt.figure(figsize=(10, 17))\n",
    "ax2 = fig.add_subplot(gs[1])\n",
    "ax2.plot(rank_results_test_new,'bx')\n",
    "\n",
    "ax2.set_xlabel('Document Number', fontsize=16)\n",
    "ax2.set_ylabel('Rank (Log)', fontsize=16)\n",
    "ax2.set_ylim([1000,0.9])\n",
    "# plt.ylim([0.9,1000])\n",
    "ax2.set_yscale('log')\n",
    "ax2.grid()\n",
    "# plt.ylim([30,0.5])\n",
    "ax2.plot([0, 1000], [11, 11], 'r-', lw=2, label = \"TOP-10 line\")\n",
    "ax2.plot([0, 1000], [6, 6], 'g-', lw=2, label =\"TOP-5 line\")\n",
    "ax2.plot([0, 1000], [1.2, 1.2], 'y-', lw=2, label = \"TOP-1 line\")\n",
    "ax2.tick_params(axis='x', labelsize=16)\n",
    "ax2.tick_params(axis='y', labelsize=16)\n",
    "lgd = plt.legend(loc='upper center', ncol=3, fontsize=14, bbox_to_anchor=(0.5, -0.1))\n",
    "plt.tight_layout()\n",
    "plt.savefig('short-svm-test2.pdf', format='pdf', dpi=1000, bbox_extra_artists=(lgd,), bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
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