朴素贝叶斯应用:垃圾邮件分类
import nltk from nltk.corpus import stopwords from nltk.stem import WordNetLemmatizer def preprocessing(text): tokens=[word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)] stops=stopwords.words('english') tokens=[token for token in tokens if token not in stops] tokens=[token.lower() for token in tokens if len(tokens)>=3] lmtzr=WordNetLemmatizer() tokens=[lmtzr.lemmatize(token) for token in tokens] preprocessed_text=' '.join(tokens) return preprocessed_text
import csv file_path=r'F:\duym\ai\sms.txt' sms=open(file_path,'r',encoding='utf-8') sms_data=[] sms_label=[] csv_reader=csv.reader(sms,delimiter='\t') for line in csv_reader: sms_label.append(line[0]) sms_data.append(preprocessing(line[1])) sms.close()
from sklearn.model_selection import train_test_split x_train,x_test,y_train,y_test=train_test_split(sms_data,sms_label,test_size=0.3,random_state=0,stratify=sms_label) print(len(sms_data),len(x_train),len(x_test))
# 将其向量化 from sklearn.feature_extraction.text import TfidfVectorizer vectorizer=TfidfVectorizer(min_df=2,ngram_range=(1,2),stop_words='english',strip_accents='unicode')#,,norm='12' X_train=vectorizer.fit_transform(x_train) X_test=vectorizer.transform(x_test)
# 朴素贝叶斯分类器 from sklearn.naive_bayes import MultinomialNB clf=MultinomialNB().fit(X_train,y_train) y_nb_pred=clf.predict(X_test)
# 分类结果显示 from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report print(y_nb_pred.shape,y_nb_pred) print('nb_confusion_matrix:') cm=confusion_matrix(y_test,y_nb_pred) print(cm) print('nb_classification_report') cr=classification_report(y_test,y_nb_pred) print(cr)
feature_names=vectorizer.get_feature_names() coefs=clf.coef_ intercept=clf.intercept_ coefs_with_fns=sorted(zip(coefs[0],feature_names)) n=10 top=zip(coefs_with_fns[:n],coefs_with_fns[:-(n+1):-1]) for (coef_1,fn_1),(coef_2,fn_2) in top: print('\t%.4f\t%-15s\t\t%.4f\t%-15s'%(coef_1,fn_1,coef_2,fn_2))