Question

我想使用python来集群文档。首先，我使用tf-idf得分生成文档x术语矩阵，如下所示：

tfidf_vectorizer_desc = TfidfVectorizer(min_df=1, max_df=0.9,use_idf=True, tokenizer=tokenize_and_stem)
%time tfidf_matrix_desc = tfidf_vectorizer_desc.fit_transform(descriptions) #fit the vectorizer to text
desc_feature_names = tfidf_vectorizer_desc.get_feature_names()

矩阵形状为（1510,6862）

第一份文件的每个条款的分数：

dense = tfidf_matrix_desc.todense()
print(len(dense[0].tolist()[0]))
dataset0 = dense[0].tolist()[0] 
phrase_scores = [pair for pair in zip(range(0, len(dataset0)), dataset0) if pair[1] > 0]
print(len(phrase_scores))

输出：

print（len（密集[0] .tolist（）[0]）） - ＆gt; 6862
print（len（phrase_scores）） - ＆gt; 48 *第一份文件只有48个术语超过0.0。

现在，我想确定矩阵中给定数据集的0 tfidf得分的所有要素（术语）。我怎样才能做到这一点？

for col in tfidf_matrix_desc.nonzero()[1]:
    print(feature_names[col], ' - ', tfidf_matrix[0, col])

Answer 1

如果有人需要类似的东西，我使用的是以下内容：

# Xtr is the output sparse matrix from TfidfVectorizer
# min_tfidf is a threshold for defining the "new" 0
def remove_zero_tf_idf(Xtr, min_tfidf=0.04):
    D = Xtr.toarray() # convert to dense if you want
    D[D < min_tfidf] = 0
    tfidf_means = np.mean(D, axis=0) # find features that are 0 in all documents
    D = np.delete(D, np.where(tfidf_means == 0)[0], axis=1) # delete them from the matrix
    return D

TfidfVectorizer删除零tf-idf分数

1 个答案: