import csv
from sklearn.model_selection import train_test_split
import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sklearn.naive_bayes import MultinomialNB
def preprocessing(text):
# text = text.decode("utf-8")
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(token) >= 3]
lmtzr = WordNetLemmatizer()
tokens = [lmtzr.lemmatize(token) for token in tokens]
preprocessed_text = ' '.join(tokens)
return preprocessed_text
def read_data():
sms=open(r'd:/SMSSpamCollectionjsn.txt','r',encoding='utf-8')
sms_data = []
sms_label = []
csv_reader=csv.reader(sms,delimiter='\t')
nltk.download('punkt')
nltk.download('wordnet')
for line in csv_reader:
print(line)
sms_label.append(line[0])
sms_data.append(preprocessing(line[1]))
sms.close()
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))
print(x_train)
return sms_data,sms_label,x_train,x_test,y_train,y_test
def xiangliang(x_train,x_test):
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)
return x_train,x_test,vectorizer
def beiNB(x_train, y_train,x_test):
clf = MultinomialNB().fit(x_train, y_train)
y_nb_pred = clf.predict(x_test)
return y_nb_pred,clf
def result(vectorizer,clf):
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)
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))
if __name__ == '__main__':
sms_data,sms_lable,x_train,x_test,y_train,y_test = read_data()
X_train,X_test,vectorizer = xiangliang(x_train,x_test)
y_nb_pred,clf = beiNB(X_train, y_train,X_test)
result(vectorizer,clf)
