TfidfVectorizer和SelectKBest错误

问题描述

我正在尝试按照本教程进行情感分析，并且我很确定到目前为止，我的代码是完全相同的。但是，我的BOW值出现了严重的差异。

https://www.tensorscience.com/nlp/sentiment-analysis-tutorial-in-python-classifying-reviews-on-movies-and-products

这是到目前为止的代码。

import nltk
import pandas as pd
import string
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_selection import SelectKBest,chi2


def openFile(path):
    #param path: path/to/file.ext (str)
    #Returns contents of file (str)
    with open(path) as file:
        data = file.read()
    return data

imdb_data = openFile('C:/Users/Flengo/Desktop/sentiment/data/imdb_labelled.txt')
amzn_data = openFile('C:/Users/Flengo/Desktop/sentiment/data/amazon_cells_labelled.txt')
yelp_data = openFile('C:/Users/Flengo/Desktop/sentiment/data/yelp_labelled.txt')


datasets = [imdb_data,amzn_data,yelp_data]

combined_dataset = []
# separate samples from each other
for dataset in datasets:
    combined_dataset.extend(dataset.split('\n'))

# separate each label from each sample
dataset = [sample.split('\t') for sample in combined_dataset]


df = pd.DataFrame(data=dataset,columns=['Reviews','Labels'])
df = df[df["Labels"].notnull()]
df = df.sample(frac=1)


labels = df['Labels']
vectorizer = TfidfVectorizer(min_df=15)
bow = vectorizer.fit_transform(df['Reviews'])
len(vectorizer.get_feature_names())

selected_features = SelectKBest(chi2,k=200).fit(bow,labels).get_support(indices=True)
vectorizer = TfidfVectorizer(min_df=15,vocabulary=selected_features)
bow = vectorizer.fit_transform(df['Reviews'])

bow

这是我的结果。

这是本教程的结果。

我一直在试图找出可能的问题，但是我什么都没做。

解决方法

问题在于您正在提供索引，请尝试提供一个真正的vocab。

尝试一下：

selected_features = SelectKBest(chi2,k=200).fit(bow,labels).get_support(indices=True)
vocabulary = np.array(vectorizer.get_feature_names())[selected_features]

vectorizer = TfidfVectorizer(min_df=15,vocabulary=vocabulary) # you need to supply a real vocab here

bow = vectorizer.fit_transform(df['Reviews'])
bow
<3000x200 sparse matrix of type '<class 'numpy.float64'>'
    with 12916 stored elements in Compressed Sparse Row format>