|
利用NLTK来进行数据处理和提取特征，再交由sklearn进行机器学习训练分类器，尝试了多个机器学习算法并评价分类性能。 训练用数据集是:https://archive.ics.uci.edu/ml/datasets/sms+spam+collection ,可下载使用。也可以用自己的数据集，但一定要做成一个样本一行，每个样本先是标签spam/ham，空一格然后是邮件内容 的形式。 上代码： import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import csv
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
from sklearn import tree
from sklearn.linear_model import SGDClassifier
from sklearn.svm import LinearSVC
from sklearn.ensemble import RandomForestClassifier
#预处理
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
#读取数据集
file_path='E:\mycode\dataset\smsspamcollection\SMSSpamCollection'
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()
#print(sms_data)
#按0.7：0.3比例分为训练集和测试集，再将其向量化
dataset_size=len(sms_data)
trainset_size=int(round(dataset_size*0.7))
print('dataset_size:',dataset_size,' trainset_size:',trainset_size)
x_train=np.array([''.join(el) for el in sms_data[0:trainset_size]])
y_train=np.array(sms_label[0:trainset_size])
x_test=np.array(sms_data[trainset_size+1:dataset_size])
y_test=np.array(sms_label[trainset_size+1:dataset_size])
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)
#朴素贝叶斯分类器
clf=MultinomialNB().fit(X_train,y_train)
y_nb_pred=clf.predict(X_test)
print(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))
#决策树
clf=tree.DecisionTreeClassifier().fit(X_train.toarray(),y_train)
y_tree_pred=clf.predict(X_test.toarray())
print('tree_confusion_matrix:')
cm=confusion_matrix(y_test,y_tree_pred)
print(cm)
print('tree_classification_report:')
print(classification_report(y_test,y_tree_pred))
#SGD
clf=SGDClassifier(alpha=0.0001,n_iter=50).fit(X_train,y_train)
y_SGD_pred=clf.predict(X_test)
print('SGD_confusion_matrix:')
cm=confusion_matrix(y_test,y_SGD_pred)
print(cm)
print('SGD_classification_report:')
print(classification_report(y_test,y_SGD_pred))
#svm
clf=LinearSVC().fit(X_train,y_train)
y_svm_pred=clf.predict(X_test)
print('svm_confusion_matrix:')
cm=confusion_matrix(y_test,y_svm_pred)
print(cm)
print('svm_classification_report:')
print(classification_report(y_test,y_svm_pred))
#RandomForestClassifier
clf=RandomForestClassifier(n_estimators=10)
clf.fit(X_train,y_train)
y_RF_pred=clf.predict(X_test)
print('RF_confusion_matrix:')
print(confusion_matrix(y_test,y_RF_pred))
print('RF_classification_report:')
print(classification_report(y_test,y_RF_pred))
|