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        "author": "Arnold_Gaius,ChiangClaire",
        "author_email": "jiangcmd@qq.com",
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        "description": "TextClassifier\r\n---------------\r\n\r\nA simple, efficient text classification tool based on Numpy,scikit-learn,Pandas,Matplotlib.\r\n\r\nTrain Data Format\r\n````````````````````\r\n\r\n==========  \t\t=====================================================\r\n   type                                      Text\r\n==========  \t\t=====================================================\r\n   game                  The LoL champions pro players would ban forever\r\n  society                  In Beijing you should keep the rules\r\n   etc.                                      etc.\r\n==========  \t\t=====================================================\r\n\r\nSample Usage\r\n````````````\r\n.. code:: python\r\n\r\n    >>> import TextClassifier\r\n\t\r\n\t# cerat classifier container\r\n    >>> tc = TextClassifier.classifier_container() \r\n\t\r\n\t# load data.sep Default = ',' you can change it to '\\t',etc.\r\n    >>> tc.load_Data('../data/Train_data.txt',sep=',')\r\n\t\r\n\t# train the model\r\n    >>> tc.train() \r\n\t\r\n\t# prediction. Input list or text-String\r\n    >>> print tc.predict('Faker is the first League of Legends player to earn over $1 million in prize money') \r\n\t[u'game'] \r\n    >>> print tc.predict(['Faker is the first League of Legends player to earn over $1 million in prize money',\r\n\t\t\t'18-year-old youth killed 88-year-old veteran',\r\n\t\t\t'Take you into the real North Korea']) \r\n\t[u'game',u'society',u'world'] \r\n\t\r\n\t#get X_train, X_test, y_train, y_test\r\n    >>> from sklearn import cross_validation\r\n    >>> X_train, X_test, y_train, y_test = cross_validation.train_test_split(original_data['Text'], original_data['Categorization'], test_size=0.3, random_state=0)\r\n\t\r\n\t#get TrainData Accuracy\r\n    >>> tc.Accuracy(X_train, y_train)\r\n\tAccuracy:\r\n        0.917504310503\r\n\t\r\n\t#get Confusion Matrix\r\n    >>> Y_predict = tc.predict(X_test)\r\n    >>> tc.confusion_matrix(y_test, Y_predict)\r\n\t\r\n\tConfusion Matrix :\r\n                   military  baby   car  game  food  sports  finance  discovery  regimen  travel  fashion  history  society  story  tech  world  entertainment  essay\r\n    military           2831     5     3    16     9       4        8         10        0      15        8       24        9      3     6     42              6      1\r\n    baby                  0  2932     3     3    26       0        1          0       10       7       10        3       16      4     3      7             20      4\r\n    car                   6    10  2813     3     6       8       13          3        1      13       10        3       39      1    11      5             24      4\r\n    game                 10    11     6  2843     5       9        2          4        1      11       13        3        8      4    25      3             31      3\r\n    food                  0    38     0     3  2799       1        5          1       67      34       16        7        9      3     4      8             14     10\r\n    sports                2     7     6    13     6    2803        9          0        1      13       24        5       10      1     5     19             42      4\r\n    finance              12    10    13     4    15       6     2692          1        2      21        5        3       18      2    79     47             12      8\r\n    discovery             8     2     0     3     3       2        5       1155        1       5        1        1        1      0    13      9              0      1\r\n    regimen               0    59     0     0    63       0        2          0     1093       0        3        3        4      2     0      1              5      0\r\n    travel                9    19     8     8    23       4        9          8        0    2741       19       20       19      7    13     55             14     12\r\n    fashion               2    21     5     9    14       9        1          5       13      18     2772        5        7      1     6     11             77      7\r\n    history              49     9     2     3     6       3        3          6        4      28        3     2813       12     20     2     35             21      6\r\n    society              27    77    50     7    43       7       42          5       16      78       27       13     2414     29    36     36             58     15\r\n    story                 3    17     1     3     7       2        2          2        2       7        5       12       19   1120     4      6             14     11\r\n    tech                 16     8    19    21     6       3       52         13        3       6        5        4       14      0  2787      9             17      7\r\n    world                52    33    12     8     9      16       33         24        2      35       27       37       50      8    20   2583             30      4\r\n    entertainment         5    14     3    28     6      13        4          3        1       9      120       29       17      3    12     10           2708      8\r\n    essay                 7    23     5     3    12       1        8          6        4      15       22       11        7      2     5      2             11   1010\r\n\t\r\n\t#get sub_result and Figure\r\n    >>> tc.plot_display(y_test, Y_predict)\r\n\tPlot display...\r\n                   Test count:  Predict count:  Sub Result:  Sub_Abs Result:\r\n    baby                  3049            3295          246              246\r\n    car                   2973            2949          -24               24\r\n    discovery             1210            1246           36               36\r\n    entertainment         2993            3104          111              111\r\n    essay                 1154            1115          -39               39\r\n    fashion               2983            3090          107              107\r\n    finance               2950            2891          -59               59\r\n    food                  3019            3058           39               39\r\n    game                  2992            2978          -14               14\r\n    history               3025            2996          -29               29\r\n    military              3000            3039           39               39\r\n    regimen               1235            1221          -14               14\r\n    society               2980            2673         -307              307\r\n    sports                2970            2891          -79               79\r\n    story                 1237            1210          -27               27\r\n    tech                  2990            3031           41               41\r\n    travel                2988            3056           68               68\r\n    world                 2983            2888          -95               95\r\n\r\n\r\nInstallation \r\n```````````` \r\n.. code:: bash \r\n\r\n    $ pip install TextClassifier \r\n\r\nLinks \r\n````` \r\n\r\n* `Code on Github <https://github.com/ArnoldGaius/Text_Classifier>`_",
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