与 TfidfVectorizer.fit_transform 的 return 结果混淆

Question

我想了解更多有关 NLP 的知识。我遇到了这段代码。但是在打印结果的时候，我对 TfidfVectorizer.fit_transform 的结果感到困惑。我很熟悉 tfidf 是什么，但我不明白这些数字是什么意思。

import tensorflow as tf
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
import os
import io
import string
import requests
import csv
import nltk
from zipfile import ZipFile

sess = tf.Session()

batch_size = 100
max_features = 1000

save_file_name = os.path.join('smsspamcollection', 'SMSSpamCollection.csv')
if os.path.isfile(save_file_name):
    text_data = []
    with open(save_file_name, 'r') as temp_output_file:
        reader = csv.reader(temp_output_file)
        for row in reader:
            text_data.append(row)

else:
    zip_url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip'
    r = requests.get(zip_url)
    z = ZipFile(io.BytesIO(r.content))
    file = z.read('SMSSpamCollection')

    # Format data 
    text_data = file.decode()
    text_data = text_data.encode('ascii', errors='ignore')
    text_data = text_data.decode().split('\n')
    text_data = [x.split('\t') for x in text_data if len(x) >= 1]

    # And write to csv 
    with open(save_file_name, 'w') as temp_output_file:
        writer = csv.writer(temp_output_file)
        writer.writerows(text_data)

texts = [x[1] for x in text_data]
target = [x[0] for x in text_data]
target = [1 if x == 'spam' else 0 for x in target]

# Normalize the text
texts = [x.lower() for x in texts]  # lower
texts = [''.join(c for c in x if c not in string.punctuation) for x in texts]  # remove punctuation
texts = [''.join(c for c in x if c not in '0123456789') for x in texts]  # remove numbers
texts = [' '.join(x.split()) for x in texts]  # trim extra whitespace


def tokenizer(text):
    words = nltk.word_tokenize(text)
    return words


tfidf = TfidfVectorizer(tokenizer=tokenizer, stop_words='english', max_features=max_features)
sparse_tfidf_texts = tfidf.fit_transform(texts)
print(sparse_tfidf_texts)

输出为：

(0, 630) 0.37172623140154337 (0, 160) 0.36805562944957004 (0, 38) 0.3613966215413548 (0, 545) 0.2561101665717327 (0, 326) 0.2645280991765623 (0, 967) 0.3277447602873963 (0, 421) 0.3896274380321477 (0, 227) 0.28102915589024796 (0, 323) 0.22032541100275282 (0, 922) 0.2709848154866997 (1, 577) 0.4007895093299793 (1, 425) 0.5970064521899725 (1, 943) 0.6310763941180291 (1, 878) 0.29102173465492637 (2, 282) 0.1771481430848552 (2, 243) 0.5517018054305785 (2, 955) 0.2920174942032025 (2, 138) 0.30143666813167863 (2, 946) 0.2269933441326121 (2, 165) 0.3051095293405041 (2, 268) 0.2820392223588522 (2, 780) 0.24119626642264894 (2, 823) 0.1890454397278538 (2, 674) 0.256251970757827 (2, 874) 0.19343834015314287 : : (5569, 648) 0.24171652492226922
(5569, 123) 0.23011909339432202 (5569, 957) 0.24817919217662862
(5569, 549) 0.28583789844730134 (5569, 863) 0.3026729783085827
(5569, 844) 0.20228305447951195 (5569, 146) 0.2514415602877767
(5569, 595) 0.2463259875380789 (5569, 511) 0.3091904754885042
(5569, 230) 0.2872728684768659 (5569, 638) 0.34151390143548765
(5569, 83) 0.3464271621701711 (5570, 370) 0.4199910200421362
(5570, 46) 0.48234172093857797 (5570, 317) 0.4171646676697801
(5570, 281) 0.6456993475093024 (5572, 282) 0.25540827228532487
(5572, 385) 0.36945842040023935 (5572, 448) 0.25540827228532487
(5572, 931) 0.3031800542518209 (5572, 192) 0.29866989620926737
(5572, 303) 0.43990016711221736 (5572, 87) 0.45211284173737176
(5572, 332) 0.3924202767503492 (5573, 866) 1.0

如果有人可以解释输出，我将非常高兴。

Answer 1

请注意，您正在打印稀疏矩阵，因此与打印标准密集矩阵相比，输出看起来有所不同。请参阅下面的主要组件：

元组表示：(document_id, token_id)
元组后面的值表示给定文档中给定标记的 tf-idf 分数
不存在的元组的 tf-idf 分数为 0

如果你想找到token_id对应的token，查看get_feature_names方法。

与 TfidfVectorizer.fit_transform 的 return 结果混淆

Confused with the return result of TfidfVectorizer.fit_transform

python

scikit-learn

nlp

tf-idf

tfidfvectorizer