import numpyas np
from sklearn.model_selectionimport train_test_split
from sklearn.feature_extraction.textimport TfidfVectorizer
import re
import string
import jieba
from sklearn.linear_modelimport SGDClassifier
from sklearnimport metrics
def get_data():
''' 获取数据，数据的载入 :return: 文本数据，对应的labels '''
with open("data/ham_data.txt",encoding="utf8")as ham_f,open("data/spam_data.txt",encoding="utf8")as spam_f:
ham_data = ham_f.readlines()
spam_data = spam_f.readlines()
ham_label = np.ones(len(ham_data)).tolist()
spam_label = np.zeros(len(spam_data)).tolist()
corpus = ham_data + spam_data
labels = ham_label + spam_label
return corpus, labels
def prepare_datasets(corpus, labels, test_data_proportion=0.3):
''' 将数据分为训练集和测试集
:paramcorpus: 文本数据
:paramlabels: label数据
:paramtest_data_proportion:测试数据占比
:return: 训练数据,测试数据，训练label,测试label '''
train_X, test_X, train_Y, test_Y = train_test_split(corpus, labels,test_size=test_data_proportion,random_state=42)
return train_X, test_X, train_Y, test_Y
# 加载停用词
with open("dict/stop_words.utf8",encoding="utf8")as f:
stopword_list = f.readlines()
def tokenize_text(text):
tokens = jieba.cut(text)
tokens = [token.strip()for tokenin tokens]
return tokens
def remove_special_characters(text):
tokens = tokenize_text(text)
pattern = re.compile('[{}]'.format(re.escape(string.punctuation)))
filtered_tokens =filter(None, [pattern.sub('', token)for tokenin tokens])
filtered_text =' '.join(filtered_tokens)
return filtered_text
def remove_stopwords(text):
tokens = tokenize_text(text)
filtered_tokens = [tokenfor tokenin tokensif tokennot in stopword_list]
filtered_text =''.join(filtered_tokens)
return filtered_text
def normalize_corpus(corpus, tokenize=False):
normalized_corpus = []
for textin corpus:
text = remove_special_characters(text)
text = remove_stopwords(text)
normalized_corpus.append(text)
if tokenize:
text = tokenize_text(text)
normalized_corpus.append(text)
return normalized_corpus
def tfidf_extractor(corpus, ngram_range=(1,1)):
vectorizer = TfidfVectorizer(min_df=1,
norm='l2',
smooth_idf=True,
use_idf=True,
ngram_range=ngram_range)
features = vectorizer.fit_transform(corpus)
return vectorizer, features
def get_metrics(true_labels, predicted_labels):
print(
'Accuracy:', np.round(metrics.accuracy_score(true_labels,predicted_labels),2))
print('Precision:', np.round(metrics.precision_score(true_labels,predicted_labels,average='weighted'),2))
print('Recall:', np.round(metrics.recall_score(true_labels,predicted_labels,average='weighted'),2))
print('F1 Score:', np.round(metrics.f1_score(true_labels,predicted_labels,average='weighted'),2))
def train_predict_evaluate_model(classifier,train_features, train_labels,test_features, test_labels):
# build model
classifier.fit(train_features, train_labels)
# predict using model
predictions = classifier.predict(test_features)
# evaluate model prediction performance
get_metrics(true_labels=test_labels,predicted_labels=predictions)
return predictions
def main():
corpus,labels=get_data()
corpus,labels=remove_empty_docs(corpus,labels)
train_corpus,test_corpus,train_labels,test_labels=prepare_datasets(corpus,labels,test_data_proportion=0.3)
norm_train_corpus=normalize_corpus(train_corpus)
norm_test_corpus=normalize_corpus(test_corpus)
tfidf_vectorizer,tfidf_train_features=tfidf_extractor(norm_train_corpus)
tfidf_test_features=tfidf_vectorizer.transform(norm_test_corpus)
tokenized_train=[jieba.lcut(text)for textin norm_train_corpus]
tokenized_test=[jieba.lcut(text)for textin norm_test_corpus]
svm = SGDClassifier(loss='hinge',n_iter=100)
print("基于tfidf的支持向量机模型")
svm_tfidf_predictions = train_predict_evaluate_model(classifier=svm,
train_features=tfidf_train_features,
train_labels=train_labels,
test_features=tfidf_test_features,
test_labels=test_labels)
