【464】文本转字符向量bag of words

利用 sklearn.feature_extraction.text 中的 CountVectorizer 来实现

• 首先获取所有的文本信息
• 然后将文本信息转化为从 0 开始的数字
• 获取转换后的字符向量

参见如下代码：

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>>> text_01 = "My name is Alex Lee." >>> text_02 = "I like singing and playing basketball." >>> text_03 = "I also like swimming during leisure time." >>> texts = [text_01, text_02, text_03]   >>> texts ['My name is Alex Lee.', 'I like singing and playing basketball.', 'I also like swimming during leisure time.']   >>> import sklearn >>> from sklearn.feature_extraction.text import CountVectorizer >>> vect = CountVectorizer().fit(texts) >>> x = vect.transform(texts)   >>> x <3x15 sparse matrix of type '<class 'numpy.int64'>'     with 16 stored elements in Compressed Sparse Row format>   >>> vect.get_feature_names() ['alex', 'also', 'and', 'basketball', 'during', 'is', 'lee', 'leisure', 'like', 'my', 'name', 'playing', 'singing', 'swimming', 'time'] >>> vect.vocabulary_ {'my': 9, 'name': 10, 'is': 5, 'alex': 0, 'lee': 6, 'like': 8, 'singing': 12, 'and': 2, 'playing': 11, 'basketball': 3, 'also': 1, 'swimming': 13, 'during': 4, 'leisure': 7, 'time': 14}   >>> x <3x15 sparse matrix of type '<class 'numpy.int64'>'     with 16 stored elements in Compressed Sparse Row format>   >>> print(x)   (0, 0)    1   (0, 5)    1   (0, 6)    1   (0, 9)    1   (0, 10)   1   (1, 2)    1   (1, 3)    1   (1, 8)    1   (1, 11)   1   (1, 12)   1   (2, 1)    1   (2, 4)    1   (2, 7)    1   (2, 8)    1   (2, 13)   1   (2, 14)   1   >>> x.toarray() array([[1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0],        [0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0],        [0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1]], dtype=int64)