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我需要从存储在巨大数据框列中的文本中获取 TF-IDF 特征矩阵，从CSV 文件（无法放入内存）加载。我正在尝试使用块迭代数据帧，但它返回的生成器对象不是TfidfVectorizer方法的预期变量类型。ChunkIterator我想我在编写如下所示的生成器方法时做错了。

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


#Will work only for small Dataset
csvfilename = 'data_elements.csv'
df = pd.read_csv(csvfilename)
vectorizer = TfidfVectorizer()
corpus  = df['text_column'].values
vectorizer.fit_transform(corpus)
print(vectorizer.get_feature_names())



#Trying to use a generator to parse over a huge dataframe
def ChunkIterator(filename):
    for chunk in pd.read_csv(csvfilename, chunksize=1):
       yield chunk['text_column'].values

corpus  = ChunkIterator(csvfilename)
vectorizer.fit_transform(corpus)
print(vectorizer.get_feature_names())

任何人都可以建议如何修改上述方法，或使用dataframeChunkIterator的任何其他方法。我想避免为数据框中的每一行创建单独的文本文件。以下是一些用于重新创建场景的虚拟 csv 文件数据。

id,text_column,tags
001, This is the first document .,['sports','entertainment']
002, This document is the second document .,"['politics', 'asia']"
003, And this is the third one .,['europe','nato']
004, Is this the first document ?,"['sports', 'soccer']"
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1 回答 1

3

该方法接受生成器就好了。但它需要一个可迭代的原始文档，即字符串。您的生成器是对象的可迭代numpy.ndarray对象。所以尝试类似：

def ChunkIterator(filename):
    for chunk in pd.read_csv(csvfilename, chunksize=1):
        for document in chunk['text_column'].values:
            yield document

注意，我真的不明白你为什么在这里使用熊猫。只需使用常规csv模块，例如：

import csv
def doc_generator(filepath, textcol=0, skipheader=True):
    with open(filepath) as f:
        reader = csv.reader(f)
        if skipheader:
            next(reader, None)
        for row in reader:
            yield row[textcol]

因此，在您的情况下，传递1给 textcol，例如：

In [1]: from sklearn.feature_extraction.text import TfidfVectorizer

In [2]: import csv
   ...: def doc_generator(filepath, textcol=0, skipheader=True):
   ...:     with open(filepath) as f:
   ...:         reader = csv.reader(f)
   ...:         if skipheader:
   ...:             next(reader, None)
   ...:         for row in reader:
   ...:             yield row[textcol]
   ...:

In [3]: vectorizer = TfidfVectorizer()

In [4]: result = vectorizer.fit_transform(doc_generator('testing.csv', textcol=1))

In [5]: result
Out[5]:
<4x9 sparse matrix of type '<class 'numpy.float64'>'
    with 21 stored elements in Compressed Sparse Row format>

In [6]: result.todense()
Out[6]:
matrix([[ 0.        ,  0.46979139,  0.58028582,  0.38408524,  0.        ,
          0.        ,  0.38408524,  0.        ,  0.38408524],
        [ 0.        ,  0.6876236 ,  0.        ,  0.28108867,  0.        ,
          0.53864762,  0.28108867,  0.        ,  0.28108867],
        [ 0.51184851,  0.        ,  0.        ,  0.26710379,  0.51184851,
          0.        ,  0.26710379,  0.51184851,  0.26710379],
        [ 0.        ,  0.46979139,  0.58028582,  0.38408524,  0.        ,
          0.        ,  0.38408524,  0.        ,  0.38408524]])
于 2018-12-13T02:51:07.990 回答