linearsvm
"""
用linearsvm从tfidf(word)中挑选特征，并将结果保存到本地
tfidf(article)可做类似处理
"""
import time
import pickle
from sklearn.feature_selection import SelectFromModel
from sklearn.svm import LinearSVC
t_start = time.time()
"""读取特征"""
with open('tfidf_word.pkl', 'rb') as f:
x_train, y_train, x_test = pickle.load(f)
"""进行特征选择"""
lsvc = LinearSVC(C=0.5, dual=False).fit(x_train, y_train)
slt = SelectFromModel(lsvc, prefit=True)
x_train_s = slt.transform(x_train)
x_test_s = slt.transform(x_test)
"""保存选择后的特征至本地"""
num_features = x_train_s.shape[1]
with open('linearsvm-tfidf(word).pkl', 'wb') as f:
pickle.dump((x_train_s, y_train, x_test_s), data_f)
t_end = time.time()
print("特征选择完成，选择{}个特征，共耗时{}min".format(num_features, (t_end-t_start)/60))
# 特征选择完成，选择888357个特征，共耗时11.78min
lr
'''
用lr从tfidf(word)中挑选特征，并将结果保存到本地
tfidf(article)可做类似处理
'''
import time
import pickle
from sklearn.feature_selection import SelectFromModel
from sklearn.linear_model import LogisticRegression
t_start = time.time()
"""读取tfidf(word)特征"""
with open('tfidf_word.pkl', 'rb') as fp:
x_train, y_train, x_test = pickle.load(fp)
"""进行特征选择"""
LR = LogisticRegression(C=120, dual=False).fit(x_train, y_train)
slt = SelectFromModel(LR, prefit=True)
x_train_s = slt.transform(x_train)
x_test_s = slt.transform(x_test)
"""保存选择后的特征至本地"""
num_features = x_train_s.shape[1]
with open('lr-tfidf(word).pkl', 'wb') as data_f:
pickle.dump((x_train_s, y_train, x_test_s), data_f)
t_end = time.time()
print("特征选择完成，选择{}个特征，共耗时{}min".format(num_features, (t_end-t_start)/60))
