sklearn——CountVectorizer详解

关于sklearn——CountVectorizer的一篇详细讲解

https://blog.csdn.net/weixin_38278334/article/details/82320307

使用Keras进行设计全连接层进行文本分类

#搭建一个全连接层神经网络进行文本情感分类的demo
import pandas as pd
df = pd.read_csv('train_comment_small_50.csv',sep=',')
import re
def clean_comment(text):
    text = re.sub('<[^<]+?>',' ',text)#只要是”^”这个字符是在中括号”[]”中被使用的话就是表示字符类的否定，如果不是的话就是表示限定开头。
    text = text.replace('\"','')
    text = text.replace('"','')
    return text
df['cleaned_comment'] = df['comment_text'].apply(clean_comment)
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(df['cleaned_comment'],df['toxic'],test_size=0.2)
import nltk
from sklearn.feature_extraction.text import CountVectorizer
from nltk.corpus import stopwords
vectorizer = CountVectorizer(binary=True,stop_words= stopwords.words('english'),lowercase=True,min_df=3,max_df=0.9,max_features=5000)
X_train_onehot = vectorizer.fit_transform(X_train)
import numpy as np
np.set_printoptions(threshold=np.inf)
print(X_train_onehot)
#print(X_train_onehot.t
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
nn = Sequential()
print(vectorizer.get_feature_names())
nn.add(Dense(units=500,activation='relu',input_dim=len(vectorizer.get_feature_names())))
nn.add(Dense(units=1,activation='sigmoid'))
nn.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
nn.summary()
nn.fit(X_train_onehot[:-20],y_train[:-20],epochs=5,batch_size=128,verbose=1,validation_data=(X_train_onehot[-100:],y_train[-100:]))
socres = nn.evaluate(vectorizer.transform(X_test),y_test,verbose=1)
print('accuracy:',socres[1])
nn.save('nn.hd5')

使用CNN对文本进行分类

#简单的CNN对路透社新闻主题的分类应用

import numpy as np
import keras
from keras.datasets import reuters
from keras.preprocessing.text import Tokenizer
from tensorflow.keras.models import Sequential
from tensorflow.keras import layers
batch_size = 32
epochs = 12
maxlen =10000
embedding_dim = 128
num_filters = 64
kernel_size  =5
(x_train,y_train),(x_test,y_test)= reuters.load_data(num_words=None,test_split=0.2)
print(x_train.shape,y_train.shape,x_test.shape,y_test.shape)
word_index = reuters.get_word_index(path="return_word_index.json")
print(np.array(word_index))
num_classes = max(y_train)+1
index_to_word = {}
for key, value in word_index.items():
    print(key,value)
    index_to_word[value] = key
tokenizer = Tokenizer(num_words=maxlen)
x_train = tokenizer.sequences_to_matrix(x_train,mode='binary')
print(x_train)
x_test = tokenizer.sequences_to_matrix(x_test,mode='binary')
y_train = keras.utils.to_categorical(y_train,num_classes)#将整型的类别标签转为onehot编码
y_test = keras.utils.to_categorical(y_test,num_classes)

model = Sequential()
model.add(layers.Embedding(512,embedding_dim,input_length=maxlen))
model.add(layers.Conv1D(num_filters,kernel_size,activation='relu'))
model.add(layers.GlobalAveragePooling1D)
model.add(layers.Dense(10,activation='relu'))
model.add(layers.Dense(num_classes,activation='softmax'))
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
history = model.fit(x_train,y_train,batch_size=batch_size,epochs=epochs,verbose=1,validation_split=0.1)
score = model.evaluate(x_test,y_test,batch_size=batch_size,verbose=1)
print("loss=",  score[0])
print("accuracy=", score[1])