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SVM的方法

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"""
@author = 'XXY'
@contact = '529379497@qq.com'
@researchFie1d = 'NLP DL ML'
@date= '2017/12/21 10:18'
"""
from sklearn.feature_extraction.text import TfidfVectorizer as TFIDF
import json
from sklearn.metrics import classification_report
import numpy as np
import pandas as pd
# from predictor import data
from sklearn.svm import LinearSVC
from sklearn.externals import joblib
import pickle
from data_helper import get_data
from gensim.models.word2vec import Word2Vec
import thulac
from sklearn.model_selection import train_test_split
dim = 5000
def cut_text(alltext):
# 分词
count = 0
cut = thulac.thulac(seg_only=True)
train_text = []
for text in alltext:
count += 1
if count % 2000 == 0:
print(count)
train_text.append(cut.cut(text, text=True))
return train_text
def train_tfidf(train_data):
tfidf = TFIDF(
min_df=5,
max_features=dim,
ngram_range=(1, 2),
use_idf=1,
smooth_idf=1
)
tfidf.fit(train_data)
return tfidf
def train_word2vec(train_data):
model = Word2Vec(train_data, size=128, window=5, min_count=5, workers=4)
return model
def train_SVC(vec, label):
SVC = LinearSVC(C=100)
SVC.fit(vec, label)
return SVC
def get_word2vec(content):
word2vec = Word2Vec.load('predictor/model/wiki.zh.seg_200d.model')
res = np.zeros([200])
count = 0
# word_list = content.split()
for word in content:
if word in word2vec:
res += word2vec[word]
count += 1
return pd.Series(res / count)
if __name__ == '__main__':
print('reading...')
all_text, y, label, label_to_int, int_tolabel = get_data(file='./data/train_data.csv')
print('cut text...')
train_data = cut_text(all_text)
# train_data = [line.split() for line in train_data]
print('get tfidf...')
tfidf = train_tfidf(train_data)
print('saving tfidf model')
joblib.dump(tfidf, 'model/tfidf_5000.model')
X_train, X_dev, y_train, y_dev = train_test_split(train_data, y, random_state=10, test_size=0.1)
train_vec = tfidf.transform(X_train)
test_vec = tfidf.transform(X_dev)
print('training SVC')
svm = train_SVC(train_vec, y_train)
y_pre = svm.predict(test_vec)
print(classification_report(y_dev, y_pre))
print("saving svm model")
joblib.dump(svm, 'model/svm_5000.model')