NLTK朴素贝叶斯,文本分类代码示例 采样 SMSSpamCollection数据集下载

https://stackoverflow.com/

文本分类代码 编译通过

 

import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import csv
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
import sklearn.metrics as metrics
from sklearn import tree
from sklearn.linear_model import SGDClassifier
from sklearn.svm import LinearSVC
from sklearn.ensemble import RandomForestClassifier


# 文本清洗预处理
def preprocessing(text):
    # text = text.encode("gbk").decode("utf8")
    # tokons to word 句子标记解析  单词标记解析
    tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]
    # print("单词标记解析：", tokens)
    # 停用词删除
    stop = stopwords.words('english')
    tokens = [token for token in tokens if token not in stop]
    # print("停用词删除", tokens)
    # 单词字数小于3删除 并转换成小写
    tokens = [word.lower() for word in tokens if len(word) >= 3]
    # print("字数小于3删除", tokens)
    # lemmatize
    # 把（文中的词）按屈折变化形式（或异体形式）进行归类
    lemter = WordNetLemmatizer()
    tokens = [lemter.lemmatize(word) for word in tokens]
    # print("词性归类", tokens)
    preprosses_text = " ".join(tokens)
    # print("处理后的文本：", preprosses_text)
    return preprosses_text


with open('SMSSpamCollection', 'r', newline='', encoding='mac_roman') as csvfile:
    smsdata_data = []
    sms_lablel = []
    csv_reader = csv.reader(csvfile, delimiter='\t')
    for line in csv_reader:
        sms_lablel.append(line[0])
        smsdata_data.append(preprocessing(line[1]))
    csvfile.close()


print('smsdata_data', smsdata_data)
print('sms_lablel', sms_lablel)


# 采样
# 训练集和样本集分开  7：3
trainset_size = int(round(len(smsdata_data)*0.70))
# i chose this threshold for 70:30 train and test split.
print('The training set size for this classifier is ' + str(trainset_size) + '\n')
x_train = np.array([''.join(el) for el in smsdata_data[0:trainset_size]])
y_train = np.array([el for el in sms_lablel[0:trainset_size]])
x_test = np.array([''.join(el) for el in smsdata_data[trainset_size+1:len(smsdata_data)]])
# or el in sms_labels[trainset_size+1:len(sms_lablel)]])
y_test = np.array([el for el in sms_lablel[trainset_size+1:len(sms_lablel)]])
print("x_train：====", x_train)
print("y_train:====", y_train)


# 术语文档矩阵 bow 词袋
with open('SMSSpamCollection', 'r', newline='', encoding='mac_roman') as csvfile:
    sms_exp = []
    csv_reader = csv.reader(csvfile, delimiter='\t')
    for line in csv_reader:
        sms_exp.append(preprocessing(line[1]))
    vectorizer = CountVectorizer(min_df=1)
    X_exp = vectorizer.fit_transform(sms_exp)
    print("||".join(vectorizer.get_feature_names()))
    print('X_exp>>>>>', X_exp.toarray())
    csvfile.close()


# TF/IDF
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)
print('x_train:.....>>>>', X_train)
print('x_test:>>>>>>>', X_test)


# 朴素贝叶斯 朴素贝叶斯分类器
clf = MultinomialNB().fit(X_train, y_train)
y_nb_predicted = clf.predict(X_test)
print("y_nb_predicted>>", y_nb_predicted)
print('\n confusion_matrix \n ')
cm = metrics.confusion_matrix(y_test, y_nb_predicted)
print(cm)
print('\n Here is the classification report:')
print(metrics.classification_report(y_test, y_nb_predicted))

# 得到前n个特征值
feature_names = vectorizer.get_feature_names()
coefs = clf.coef_
intercept = clf.intercept_
coefs_with_fns = sorted(zip(clf.coef_[0], feature_names))
n = 15
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 分类器
# clf = tree.DecisionTreeClassifier.fit(X_train.toarray(), y_train)
# y_tree_predicted = clf.predict(X_test.toarray())
# print(y_tree_predicted)
# print(' \n Here is the classification report: y_tree_predicted')
# print(metrics.classification_report(y_test, y_tree_predicted))


# 随机梯度下降
clf = SGDClassifier(alpha=0.001, max_iter=50).fit(X_train, y_train)
y_pred = clf.predict(X_test)
print('\n Here is the classification report:')
print(metrics.classification_report(y_test, y_pred))
print(' \n confusion_matrix \n ')
cm = (metrics.confusion_matrix(y_test, y_pred))
print(cm)

# 支持向量机
svm_classifier = LinearSVC().fit(X_train, y_train)
y_svm_predicted = svm_classifier.predict(X_test)
print('\n Here is the classification report:')
print(metrics.classification_report(y_test, y_svm_predicted))
print(' \n confusion_matrix \n ')
cm = (metrics.confusion_matrix(y_test, y_svm_predicted))
print(cm)


# 随机森林
# RandomForestClassifier
clf = RandomForestClassifier(n_estimators=10)
clf.fit(X_train, y_train)
y_RF_pred = clf.predict(X_test)
print('RF_confusion_matrix:')
print(metrics.confusion_matrix(y_test, y_RF_pred))
print('RF_classification_report:')
print(metrics.classification_report(y_test, y_RF_pred))

 

 

 

 

UCI垃圾邮件数据集下载

http://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection