Text Classifier based on Numpy,Scikit-learn,Pandas,Matplotlib
| type | Text |
|---|---|
| game | The LoL champions pro players would ban forever |
| society | In Beijing you should keep the rules |
| etc. | etc. |
>>> import TextClassifier
# cerat classifier container
>>> tc = TextClassifier.classifier_container()
# load data
# '../data/Train_data.txt' is data path
# sep Default = ',' you can change it to '\t',etc.
>>> tc.load_Data('../data/Train_data.txt',sep=',')
# train the model
>>> tc.train()
# prediction. Input list or text-String
>>> print tc.predict('Faker is the first League of Legends player to earn over $1 million in prize money')
[u'game']
>>> print tc.predict(['Faker is the first League of Legends player to earn over $1 million in prize money',
'18-year-old youth killed 88-year-old veteran',
'Take you into the real North Korea'])
[u'game',u'society',u'world']
#get X_train, X_test, y_train, y_test
>>> from sklearn import cross_validation
>>> 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)
#get TrainData Accuracy
>>> tc.Accuracy(X_train, y_train)
Accuracy:
0.917504310503 #get Confusion Matrix
>>> Y_predict = tc.predict(X_test)
>>> tc.confusion_matrix(y_test, Y_predict)
Confusion Matrix :
military baby car game food sports finance discovery regimen travel fashion history society story tech world entertainment essay
military 2831 5 3 16 9 4 8 10 0 15 8 24 9 3 6 42 6 1
baby 0 2932 3 3 26 0 1 0 10 7 10 3 16 4 3 7 20 4
car 6 10 2813 3 6 8 13 3 1 13 10 3 39 1 11 5 24 4
game 10 11 6 2843 5 9 2 4 1 11 13 3 8 4 25 3 31 3
food 0 38 0 3 2799 1 5 1 67 34 16 7 9 3 4 8 14 10
sports 2 7 6 13 6 2803 9 0 1 13 24 5 10 1 5 19 42 4
finance 12 10 13 4 15 6 2692 1 2 21 5 3 18 2 79 47 12 8
discovery 8 2 0 3 3 2 5 1155 1 5 1 1 1 0 13 9 0 1
regimen 0 59 0 0 63 0 2 0 1093 0 3 3 4 2 0 1 5 0
travel 9 19 8 8 23 4 9 8 0 2741 19 20 19 7 13 55 14 12
fashion 2 21 5 9 14 9 1 5 13 18 2772 5 7 1 6 11 77 7
history 49 9 2 3 6 3 3 6 4 28 3 2813 12 20 2 35 21 6
society 27 77 50 7 43 7 42 5 16 78 27 13 2414 29 36 36 58 15
story 3 17 1 3 7 2 2 2 2 7 5 12 19 1120 4 6 14 11
tech 16 8 19 21 6 3 52 13 3 6 5 4 14 0 2787 9 17 7
world 52 33 12 8 9 16 33 24 2 35 27 37 50 8 20 2583 30 4
entertainment 5 14 3 28 6 13 4 3 1 9 120 29 17 3 12 10 2708 8
essay 7 23 5 3 12 1 8 6 4 15 22 11 7 2 5 2 11 1010 #get sub_result and Figure
>>> tc.plot_display(y_test, Y_predict)
Plot display...
Test count: Predict count: Sub Result: Sub_Abs Result:
baby 3049 3295 246 246
car 2973 2949 -24 24
discovery 1210 1246 36 36
entertainment 2993 3104 111 111
essay 1154 1115 -39 39
fashion 2983 3090 107 107
finance 2950 2891 -59 59
food 3019 3058 39 39
game 2992 2978 -14 14
history 3025 2996 -29 29
military 3000 3039 39 39
regimen 1235 1221 -14 14
society 2980 2673 -307 307
sports 2970 2891 -79 79
story 1237 1210 -27 27
tech 2990 3031 41 41
travel 2988 3056 68 68
world 2983 2888 -95 95
- Train set: 156k news headline with 18 labels
- Test set: 36k news headline with 18 labels
- Compare with svm , naive-bayes , SGD(loss = 'perceptron') of Scikit-learn
| Classifier | Accuracy | Time cost(s) |
|---|---|---|
| scikit-learn(svm) | 71.6% | 241 |
| scikit-learn(nb) | 72.7% | 12 |
| scikit-learn(SGD) | 72.4% | 197 |
| TextClassifier | 76.8% | 8 |
$ pip install TextClassifier