rnn_cws/test_keras_model.py at master · clayandgithub/rnn_cws · GitHub

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# -*- coding: utf-8 -*-

'''
python test_keras_model.py cws_info_file keras_model_file keras_model_weights_file test_data_file output_file
'''

import numpy as np
import json
import h5py
import codecs
import time
import sys

import pretrain as cws
import viterbi

from sklearn import model_selection

from keras.preprocessing import sequence
from keras.optimizers import SGD, RMSprop, Adagrad
from keras.utils import np_utils
from keras.models import Sequential,Graph, model_from_json
from keras.layers.core import Dense, Dropout, Activation, TimeDistributedDense
from keras.layers.embeddings import Embedding
from keras.layers.recurrent import LSTM, GRU, SimpleRNN

from gensim.models import Word2Vec

def loadModel(modelPath, weightPath):

    fd = open(modelPath, 'r')
    j = fd.read()
    fd.close()

    model = model_from_json(j)

    model.load_weights(weightPath)

    return model


# 根据输入得到标注推断
def cwsSent(sent, model, cwsInfo):
    (initProb, tranProb), (vocab, indexVocab) = cwsInfo
    vec = cws.sent2vec(sent, vocab, ctxWindows = 7)
    vec = np.array(vec)
    probs = model.predict_proba(vec)
    #classes = model.predict_classes(vec)

    prob, path = viterbi.viterbi(vec, cws.corpus_tags, initProb, tranProb, probs.transpose())

    ss = ''
    for i, t in enumerate(path):
        ss += '%s/%s '%(sent[i], cws.corpus_tags[t])
    ss = ''
    word = ''
    for i, t in enumerate(path):
        if cws.corpus_tags[t] == 'S':
            ss += sent[i] + ' '
            word = ''
        elif cws.corpus_tags[t] == 'B':
            word += sent[i]
        elif cws.corpus_tags[t] == 'E':
            word += sent[i]
            ss += word + ' '
            word = ''
        elif cws.corpus_tags[t] == 'M':
            word += sent[i]

    return ss

def cwsFile(fname, dstname, model, cwsInfo):
    fd = codecs.open(fname, 'r', 'utf-8')
    lines = fd.readlines()
    fd.close()

    fd = open(dstname, 'w')
    for line in lines:
        rst = cwsSent(line.strip(), model, cwsInfo)
        fd.write(rst.encode('utf-8') + '\n')
    fd.close()

if __name__ == '__main__':
    if len(sys.argv) < 6:
        print(globals()['__doc__'] % locals())
        sys.exit(1)
    cws_info_file, keras_model_file, keras_model_weights_file, test_data_file, output_file = sys.argv[1:6]

    cwsInfo = cws.loadCwsInfo(cws_info_file)
    print('Loading model...')
    start_time = time.time()
    model = loadModel(keras_model_file, keras_model_weights_file)
    print("Loading used time : ", time.time() - start_time)
    print('Done!')
    print('-------------start predict----------------')
    #s = u'为寂寞的夜空画上一个月亮'
    #print cwsSent(s, model, cwsInfo)
    cwsFile(test_data_file, output_file, model, cwsInfo)