TfidfVectorizer.fit_transfrom 和 tfidf.transform 之间有什么区别？

在 Tfidf.fit_transform 中，我们仅使用参数 X 而没有使用 y 来拟合数据集。 这是正确的吗？ 我们仅为训练集的参数生成 tfidf 矩阵。我们没有使用 ytrain 来拟合模型。 那我们如何对测试数据集进行预测呢

https://datascience.stackexchange.com/a/12346/122 https://datascience.stackexchange.com/a/12346/122对为什么调用它有一个很好的解释fit(), transform() and fit_transform().

In gist,

• fit()：将矢量化器/模型拟合到训练数据并将矢量化器/模型保存到变量中（返回sklearn.feature_extraction.text.TfidfVectorizer)

• transform()：使用变量输出fit()至变压器验证/测试数据（返回scipy.sparse.csr.csr_matrix)

• fit_transform()：有时你要直接转换训练数据，所以你使用fit() + transform()在一起，因此fit_transform()。 （返回scipy.sparse.csr.csr_matrix)

E.g.

from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
from scipy.sparse.csr import csr_matrix


# The *TfidfVectorizer* from sklearn expects list of strings as input.
sent0 = "The quick brown fox jumps over the lazy brown dog .".lower()
sent1 = "Mr brown jumps over the lazy fox .".lower()
sent2 = "Roses are red , the chocolates are brown .".lower()
sent3 = "The frank dog jumps through the red roses .".lower()

dataset = [sent0, sent1, sent2, sent3]

# Initialize the parameters of the vectorizer
vectorizer = TfidfVectorizer(input=dataset, analyzer='word', ngram_range=(1,1),
                     min_df = 0, stop_words=None)

[out]:

# Learns the vocabulary of vectorizer based on the initialized parameter.
>>> vectorizer =  vectorizer.fit(dataset)

# Apply the vectorizer to new sentence.
>>> vectorizer.transform(["The brown roses jumps through the chocholate dog ."])
<1x15 sparse matrix of type '<class 'numpy.float64'>'
    with 6 stored elements in Compressed Sparse Row format>

# Output to array form.
>>> vectorizer.transform(["The brown roses jumps through the chocholate dog ."]).toarray()
array([[0.        , 0.31342551, 0.        , 0.38714286, 0.        ,
        0.        , 0.31342551, 0.        , 0.        , 0.        ,
        0.        , 0.        , 0.38714286, 0.51249178, 0.49104163]])

# When you don't need to save the vectorizer for re-using.
>>> vectorizer.fit_transform(dataset)
<4x15 sparse matrix of type '<class 'numpy.float64'>'
    with 28 stored elements in Compressed Sparse Row format>

>>> vectorizer.fit_transform(dataset).toarray()
array([[0.        , 0.49642852, 0.        , 0.30659399, 0.30659399,
        0.        , 0.24821426, 0.30659399, 0.        , 0.30659399,
        0.38887561, 0.        , 0.        , 0.40586285, 0.        ],
       [0.        , 0.32107915, 0.        , 0.        , 0.39659663,
        0.        , 0.32107915, 0.39659663, 0.50303254, 0.39659663,
        0.        , 0.        , 0.        , 0.26250325, 0.        ],
       [0.76012588, 0.24258925, 0.38006294, 0.        , 0.        ,
        0.        , 0.        , 0.        , 0.        , 0.        ,
        0.        , 0.29964599, 0.29964599, 0.19833261, 0.        ],
       [0.        , 0.        , 0.        , 0.34049544, 0.        ,
        0.4318753 , 0.27566041, 0.        , 0.        , 0.        ,
        0.        , 0.34049544, 0.34049544, 0.45074089, 0.4318753 ]])


>>> type(vectorizer)
<class 'sklearn.feature_extraction.text.TfidfVectorizer'>

>>> type(vectorizer.fit_transform(dataset))
<class 'scipy.sparse.csr.csr_matrix'>

>>> type(vectorizer.transform(dataset))
<class 'scipy.sparse.csr.csr_matrix'>