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Posted 2021-01-24 yan668

from sklearn.metrics import confusion_matrix, classification_report
from sklearn.naive_bayes import MultinomialNB
import csv
file_path=r‘F:duymaismsspamcollectionsms.txt‘
sms=open(file_path,‘r‘,encoding=‘utf-8‘)
sms_data=[]
sms_label=[]
csv_reader=csv.reader(sms,delimiter=‘	‘)
for line in csv_reader:
    sms_label.append(line[0])
    sms_data.append(preprocessing(line[1]))
sms.close()
def preprocessing(text):
    preprocessed_text = text
    return preprocessed_text
#按0.7：0.3比例分为训练集和测试集
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)
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer=TfidfVectorizer(min_df=2,ngram_range=(1,2),stop_words=‘english‘,strip_accents=‘unicode‘,norm=‘l2‘)
X_train=vectorizer.fit_transform(x_train)
X_test=vectorizer.transform(x_test)　
clf = MultinomialNB().fit(X_train,y_train)
y_nb_pred = clf.predict(X_test)
 
# 分类结果显示
print(y_nb_pred.shape,y_nb_pred) # x-test预测结果
print(‘nb_confusion_matrix:‘)
cm = confusion_matrix(y_test,y_nb_pred) #混淆矩阵
print(cm)
print(‘nb_classification_repert:‘)
cr = classification_report(y_test,y_nb_pred) # 主要分类指标的文本报告
print(cr)
 
feature_names=vectorizer.get_feature_names() # 出现过的单词列表
coefs=clf.coef_ # 先验概率 p(x_ily),6034 feature_log_preb
intercept = clf.intercept_ # P(y),class_log_prior : array,shape(n...
coefs_with_fns=sorted(zip(coefs[0],feature_names)) #对数概率P(x_i|y)与单词x_i映射
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(‘	%.4f	%-15s		%.4f	%-15s‘ % (coef_1,fn_1,coef_2,fn_2))