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decsiontree_sentiment.py
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#!/usr/bin/env python3
# coding: utf-8
# File: decsiontree_sentiment.py
# Author: lhy<[email protected],https://huangyong.github.io>
# Date: 18-3-20
import gensim
import numpy as np
from sklearn.externals import joblib
VECTOR_DIR = './embedding/word_vector.bin' # 词向量模型文件
model = gensim.models.KeyedVectors.load_word2vec_format(VECTOR_DIR, binary=False)
'''基于wordvector，通过lookup table的方式找到句子的wordvector的表示，向量求和做平均'''
def rep_sentencevector(sentence):
'''通过向量求和的方式标识sentence vector'''
word_list = [word for word in sentence.split(' ')]
embedding_dim = 200
embedding_matrix = np.zeros(embedding_dim)
for index, word in enumerate(word_list):
try:
embedding_matrix += model[word]
except:
pass
return embedding_matrix/len(word_list)
'''构造训练数据'''
def build_traindata():
X_train = list()
Y_train = list()
X_test = list()
Y_test = list()
for line in open('./data/train.txt'):
line = line.strip().strip().split('\t')
sent_vector = rep_sentencevector(line[-1])
X_train.append(sent_vector)
if line[0] == '1':
Y_train.append(1)
else:
Y_train.append(0)
for line in open('./data/test.txt'):
line = line.strip().strip().split('\t')
sent_vector = rep_sentencevector(line[-1])
X_test.append(sent_vector)
if line[0] == '1':
Y_test.append(1)
else:
Y_test.append(0)
return np.array(X_train), np.array(Y_train), np.array(X_test), np.array(Y_test),
'''基于decisiontree分类器算法, 使用SVC算法，使用默认参数'''
def train_decisiontree(X_train, Y_train):
from sklearn import tree
model = tree.DecisionTreeClassifier()
model.fit(X_train, Y_train)
joblib.dump(model, './model/sentiment_decisiontree_model.m')
'''基于decisiontree分类器的预测'''
def evaluate_decisiontree(model_filepath, X_test, Y_test):
model = joblib.load(model_filepath)
Y_predict = list()
Y_test = list(Y_test)
right = 0
for sent in X_test:
Y_predict.append(model.predict(sent.reshape(1, -1))[0])
for index in range(len(Y_predict)):
if int(Y_predict[index]) == int(Y_test[index]):
right += 1
score = right / len(Y_predict)
print('model accuray is :{0}'.format(score)) #0.6907302434144715
return score
'''实际应用测试'''
def predict_decisiontree(model_filepath):
model = joblib.load(model_filepath)
sentence1 = '这个 电视 真 尼玛 垃圾 ， 老子 再也 不买 了'
sentence2 = '这件 衣服 真的 太 好看 了 ！ 好想 买 啊 '
rep_sen1 = np.array(rep_sentencevector(sentence1)).reshape(1, -1)
rep_sen2 = np.array(rep_sentencevector(sentence2)).reshape(1, -1)
print('sentence1', model.predict(rep_sen1)) #sentence1 [0]
print('sentence2', model.predict(rep_sen2)) #sentence2 [0]
if __name__ == '__main__':
X_train, Y_train, X_test, Y_test = build_traindata()
model_filepath = './model/sentiment_decisiontree_model.m'
print(X_train.shape, Y_train.shape)
print(X_test.shape, Y_test.shape)
train_decisiontree(X_train, Y_train)
evaluate_decisiontree(model_filepath, X_test, Y_test)
predict_decisiontree(model_filepath)