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- from sklearn.naive_bayes import GaussianNB
- from sklearn.model_selection import cross_val_score, train_test_split
- from sklearn.feature_extraction.text import CountVectorizer
- from datasets import Datasets
- import numpy as np
- # 数据预处理 向量化 以2-gram
- def to_voc(webshell, wordpress):
- webshell_voc = CountVectorizer(ngram_range=(2, 2), decode_error="ignore", token_pattern=r'\b\w+\b', min_df=1)
- x1 = webshell_voc.fit_transform(webshell).toarray()
- y1 = [1] * len(x1)
- wordpress_voc = CountVectorizer(ngram_range=(2, 2), decode_error="ignore", token_pattern=r'\b\w+\b', min_df=1,
- vocabulary=webshell_voc.vocabulary_)
- x2 = wordpress_voc.fit_transform(wordpress).toarray()
- y2 = [0] * len(x2)
- x = np.concatenate((x1, x2))
- y = np.concatenate((y1, y2))
- return x, y
- def main():
- webshell, wordpress = Datasets.load_php_webshell()
- x, y = to_voc(webshell, wordpress)
- x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)
- gnb = GaussianNB()
- gnb.fit(x_train, y_train)
- print(gnb.score(x_test, y_test)) # 0.7659574468085106
- scores = cross_val_score(gnb, x, y, cv=3, scoring="accuracy")
- print(scores.mean()) # 0.7872046254399195
- if __name__ == "__main__":
- main()
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