lsa
"""
将tfidf(word)特征降维为lsa特征，并将结果保存至本地，并将结果保存到本地
tfidf(article)可做类似处理
"""
from sklearn.decomposition import TruncatedSVD
import pickle
import time
t_start = time.time()
"""=====================================================================================================================
0 读取tfidf特征
"""
with open('tfidf_word.pkl', 'rb') as f:
x_train, y_train, x_test = pickle.load(f)
"""=====================================================================================================================
1 特征降维：lsa
"""
lsa = TruncatedSVD(n_components=200)
x_train = lsa.fit_transform(x_train)
x_test = lsa.transform(x_test)
"""=====================================================================================================================
2 将lsa特征保存至本地
"""
data = (x_train, y_train, x_test)
with open('lsa_tfidf(word).pkl', 'wb') as f:
pickle.dump(data, f_data)
t_end = time.time()
print("共耗时：{}min".format((t_end-t_start)/60))
lda
"""
将countvector(word)特征降维为lda特征，并将结果保存至本地
"""
from sklearn.decomposition import LatentDirichletAllocation
import pickle
import time
t_start = time.time()
"""=====================================================================================================================
1 countvector(word)特征加载
"""
with open('countvector_word.pkl', 'rb') as f:
x_train, y_train, x_test = pickle.load(f_tf)
"""=====================================================================================================================
2 特征降维：lda
"""
lda = LatentDirichletAllocation(n_components=200)
x_train = lda.fit_transform(x_train)
x_test = lda.transform(x_test)
"""=====================================================================================================================
3 将lda特征保存至本地
"""
data = (x_train, y_train, x_test)
with open('lda_countvector(word).pkl', 'wb') as f:
pickle.dump(data, f)
t_end = time.time()
print("共耗时：{}min".format((t_end-t_start)/60))
