chinese-text-classification-keras/model.py at master · jingyuanz/chinese-text-classification-keras · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
#coding=utf-8
from config import Config
from data_util import DataUtil
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.layers import Embedding, Input
from keras.layers import Conv2D, GlobalMaxPooling1D, Reshape, TimeDistributed, Conv1D
from keras.layers import LSTM
import keras
from keras.regularizers import l2
import numpy as np
from keras.models import save_model, load_model
from keras import backend as K
from keras import Model
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split


class Classifier:
    def __init__(self):
        self.config = Config()
        self.du = DataUtil()
        self.inv_map = {v: k for k, v in self.config.class_dict.iteritems()}

    def run_trainer(self):
        #load train set
        self.raw_sent, self.data, self.raw_labels = self.du.load_data_set()
        #load test set
        self.test_sent, self.test_data, self.test_raw_labels = self.du.load_data_set('test')
        #shuffle train set
        self.raw_sent, self.data, self.raw_labels = shuffle(self.raw_sent, self.data, self.raw_labels)
        #train/test classes in integer
        self.classes = self.du.convert_raw_label_to_class(self.raw_labels, self.config.class_dict)
        self.test_classes = self.du.convert_raw_label_to_class(self.test_raw_labels, self.config.class_dict)
        #convert to one hot catagory
        self.test_labels = keras.utils.to_categorical(self.test_classes, self.du.config.n_classes)
        self.labels = keras.utils.to_categorical(self.classes, self.du.config.n_classes)

        #compile model
        self.model = self.build_model()
        self.model.compile(loss=keras.losses.categorical_crossentropy,
                           optimizer=keras.optimizers.RMSprop(lr=self.config.lr),
                           metrics=['accuracy'])
        self.train()
        self.evaluate()
        self.model.save(self.config.final_round_model_path)

    def run_prediction(self, sentence):
        emb_sent = self.du.prepare_predict_data(sentence)
        emb_sent = emb_sent.reshape([1,self.config.max_sent_len, self.config.emb_dim])
        self.config.dropout = 0
        self.model = load_model(self.config.dl_model_path)
        pred, prob = self.predict(emb_sent)
        print pred
        pred = pred[0]
        prob = prob[0]
        response = self.inv_map[pred]
        return response, prob

    def build_model(self):
        input = Input(shape=(self.config.max_sent_len, self.config.emb_dim))
        conv_output = Conv1D(self.config.n_filter, kernel_size=self.config.filter_size, strides=1, activation="relu")(input)
        lstm_output = LSTM(self.config.lstm_dim, dropout=self.config.dropout)(conv_output)
        out = Dense(self.config.n_classes, activity_regularizer=l2(self.config.l2_rate), activation="softmax")(lstm_output)

        model = Model(inputs=[input], outputs=[out])
        return model

    def train(self):

        check = keras.callbacks.ModelCheckpoint(self.du.config.dl_model_path, monitor='val_acc', verbose=1,
                                                save_best_only=True, save_weights_only=False, mode='auto', period=1)
        self.model.fit(self.data, self.labels,
                  batch_size=self.du.config.batch_size,
                  epochs=self.du.config.epochs,
                  verbose=1,
                  validation_split=0.1, callbacks=[check])

    def predict(self, test_data):
        probs = self.model.predict(test_data)
        predictions = np.argmax(probs, axis=-1)
        return predictions, probs

    def evaluate(self):
        # self.config.dropout = 0
        # predictions, _ = self.predict(self.data)
        # comparison = (predictions == self.classes)
        # acc = np.mean(comparison)
        print self.model.evaluate(self.test_data, self.test_labels)
        self.generate_prediction_results(self.test_data, self.test_raw_labels, self.test_sent)

    def evaluate_on_model(self, model_path):
        self.test_sent, self.test_data, self.test_raw_labels = self.du.load_data_set('test')
        self.test_classes = self.du.convert_raw_label_to_class(self.test_raw_labels, self.config.class_dict)
        self.test_labels = keras.utils.to_categorical(self.test_classes, self.du.config.n_classes)

        self.model = load_model(model_path)
        print self.model.evaluate(self.test_data, self.test_labels)
        self.generate_prediction_results(self.test_data, self.test_raw_labels, self.test_sent)


    def generate_prediction_results(self, data, raw_labels, raw_sents):
        with open(self.config.result_path, 'w') as f:
            predictions = self.model.predict(data)
            predictions = np.argmax(predictions, axis=-1)
            '''argsort'''
            # top_n = predictions.argsort(axis=-1, order=)
            for i in range(len(predictions)):
                sent = raw_sents[i]
                ground_truth = raw_labels[i]
                pred = self.inv_map[predictions[i]]
                res = '\t'.join([sent, ground_truth, pred]) + '\n'
                f.write(res)
        print "file written"


if __name__ == '__main__':
    model = Classifier()
    # model.run_trainer()
    # model.evaluate_on_model(Config().final_round_model_path)
    model.evaluate_on_model(Config().dl_model_path)