fit_transform、transform 和 TfidfVectorizer 的工作原理

Question

我正在做一个模糊匹配项目，我发现了一个非常有趣的方法：awesome_cossim_top

我总体上理解了这个定义，但不明白当我们这样做时发生了什么fit_transform

import pandas as pd
import sqlite3 as sql
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
from scipy.sparse import csr_matrix
import sparse_dot_topn.sparse_dot_topn as ct
import re

def ngrams(string, n=3):
    string = re.sub(r'[,-./]|\sBD',r'', re.sub(' +', ' ',str(string)))
    ngrams = zip(*[string[i:] for i in range(n)])
    return [''.join(ngram) for ngram in ngrams]

def awesome_cossim_top(A, B, ntop, lower_bound=0):
    # force A and B as a CSR matrix.
    # If they have already been CSR, there is no overhead
    A = A.tocsr()
    B = B.tocsr()
    M, _ = A.shape
    _, N = B.shape

    idx_dtype = np.int32

    nnz_max = M*ntop

    indptr = np.zeros(M+1, dtype=idx_dtype)
    indices = np.zeros(nnz_max, dtype=idx_dtype)
    data = np.zeros(nnz_max, dtype=A.dtype)

    ct.sparse_dot_topn(
            M, N, np.asarray(A.indptr, dtype=idx_dtype),
            np.asarray(A.indices, dtype=idx_dtype),
            A.data,
            np.asarray(B.indptr, dtype=idx_dtype),
            np.asarray(B.indices, dtype=idx_dtype),
            B.data,
            ntop,
            lower_bound,
            indptr, indices, data)

    print('ct.sparse_dot_topn: ', ct.sparse_dot_topn)
    return csr_matrix((data,indices,indptr),shape=(M,N))

    def get_matches_df(sparse_matrix, A, B, top=100):
        non_zeros = sparse_matrix.nonzero()

        sparserows = non_zeros[0]
        sparsecols = non_zeros[1]

        if top:
            nr_matches = top
        else:
            nr_matches = sparsecols.size

        left_side = np.empty([nr_matches], dtype=object)
        right_side = np.empty([nr_matches], dtype=object)
        similairity = np.zeros(nr_matches)

        for index in range(0, nr_matches):
            left_side[index] = A[sparserows[index]]
            right_side[index] = B[sparsecols[index]]
            similairity[index] = sparse_matrix.data[index]

        return pd.DataFrame({'left_side': left_side,
                             'right_side': right_side,
                             'similairity': similairity})

这是我遇到困惑的脚本：为什么我们应该先使用 fit_transform 然后只使用 SAME 向量化器进行转换。我试图从矢量化器和矩阵打印一些输出，如 print(vectorizer.get_feature_names()) 但不理解其中的逻辑。

有谁能帮我解释一下吗？

非常感谢！！

Col_clean = 'fruits_normalized'
Col_dirty = 'fruits'

#read table
data_dirty={f'{Col_dirty}':['I am an apple', 'You are an apple', 'Aple', 'Appls', 'Apples']}
data_clean= {f'{Col_clean}':['apple', 'pear', 'banana', 'apricot', 'pineapple']}

df_clean = pd.DataFrame(data_clean)
df_dirty = pd.DataFrame(data_dirty)

Name_clean = df_clean[f'{Col_clean}'].unique()
Name_dirty= df_dirty[f'{Col_dirty}'].unique()

vectorizer = TfidfVectorizer(min_df=1, analyzer=ngrams)
clean_idf_matrix = vectorizer.fit_transform(Name_clean)
dirty_idf_matrix = vectorizer.transform(Name_dirty)

matches = awesome_cossim_top(dirty_idf_matrix, clean_idf_matrix.transpose(),1,0)
matches_df = get_matches_df(matches, Name_dirty, Name_clean, top = 0)

with pd.option_context('display.max_rows', None, 'display.max_columns', None):
    matches_df.to_excel("output_apple.xlsx")

print('done')

Answer 1

TfidfVectorizer.fit_transform 用于从训练数据集创建词汇表，TfidfVectorizer.transform 用于将该词汇表映射到测试数据集，以便测试数据中的特征数量与训练数据保持相同。以下示例可能有所帮助：

import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer

创建虚拟训练数据：

train = pd.DataFrame({'Text' :['I am a data scientist','Cricket is my favorite sport', 'I work on Python regularly', 'Python is very fast for data mining', 'I love playing cricket'],
                      'Category' :['Data_Science','Cricket','Data_Science','Data_Science','Cricket']})

还有一个小测试数据：

test = pd.DataFrame({'Text' :['I am new to data science field', 'I play cricket on weekends', 'I like writing Python codes'],
                         'Category' :['Data_Science','Cricket','Data_Science']})

创建一个名为 vectorizer

的 TfidfVectorizer() 对象

vectorizer = TfidfVectorizer()

将其拟合到训练数据上

X_train = vectorizer.fit_transform(train['Text'])
print(vectorizer.get_feature_names())

#['am', 'cricket', 'data', 'fast', 'favorite', 'for', 'is', 'love', 'mining', 'my', 'on', 'playing', 'python', 'regularly', 'scientist', 'sport', 'very', 'work']

feature_names = vectorizer.get_feature_names()
df= pd.DataFrame(X.toarray(),columns=feature_names)

现在看看如果你在测试数据集上做同样的事情会发生什么：

vectorizer_test = TfidfVectorizer()
X_test = vectorizer_test.fit_transform(test['Text'])
print(vectorizer_test.get_feature_names())

#['am', 'codes', 'cricket', 'data', 'field', 'like', 'new', 'on', 'play', 'python', 'science', 'to', 'weekends', 'writing']
feature_names_test = vectorizer_test.get_feature_names()
df_test= pd.DataFrame(X_test.toarray(),columns = feature_names_test)

它用测试数据集创建了另一个词汇表，它有 14 个独特的词（列），而训练数据有 18 个词（列）。

现在，如果您在 text-classification 的训练数据上训练机器学习算法，并尝试根据测试数据对您的矩阵进行预测，它将失败并生成一个错误，即训练和训练之间的特征不同测试数据.

为了克服这个错误，我们在 text-classification:

中做了类似的事情

X_test_from_train = vectorizer.transform(test['Text'])
feature_names_test_from_train = vectorizer.get_feature_names()
df_test_from_train = pd.DataFrame(X_test_from_train.toarray(),columns = feature_names_test_from_train)

在这里你会注意到我们没有使用 fit_transform 命令而是我们在测试数据上使用了 transform ，原因是一样的，在对测试数据进行预测时，我们只想要使用训练数据和测试数据中相似的特征，这样我们就不会出现特征不匹配错误。

希望对您有所帮助！！

fit_transform、transform 和 TfidfVectorizer 的工作原理

How fit_transform, transform and TfidfVectorizer works

python

machine-learning

sparse-matrix

fuzzy-logic

scikit-learn