样本数量不一致的Python Sklearn变量

Question

我正在学习情感分析，我有一个评价数据框架，必须对给定的单词列表进行评估，并获得分配给这些单词的权重。不幸的是，当我尝试拟合回归时，出现以下错误： “ ValueError：找到样本数量不一致的输入变量：[11，133401]”

我想念什么？ CSV file

import pandas
import sklearn
import numpy as np 

products = pandas.read_csv('amazon_baby.csv')
selected_words=["awesome", "great", "fantastic", "amazing", "love", "horrible", "bad", "terrible", "awful", "wow", "hate"]

#ignore all 3* reviews
products = products[products['rating'] != 3]

#positive sentiment = 4* or 5* reviews
products['sentiment'] = products['rating'] >=4


#create a separate column for each word
for word in selected_words:
   products[word]=[len(re.findall(word,x)) for x in products['review'].tolist()]

# Define X and y
X = products[selected_words]
y = products['sentiment']

from sklearn.feature_extraction.text import CountVectorizer

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

vect = CountVectorizer()

vect.fit(X_train)
X_train_dtm = vect.transform(X_train)
X_test_dtm = vect.transform(X_test)


from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression()
logreg.fit(X_train_dtm, y_train) #here is where I get the error

Answer 1

CountVectorizer()期望字符串可迭代，并返回表示单词计数的向量。您已经使用for循环实现了此功能，现在尝试使CountVectorizer()适应所选单词的计数。

假设您只想将所选单词用作功能

logreg.fit(X_train, y_train)

没有转换就可以了。

或者，如果您想使用所有单词作为功能，则可以更改X以包括完整的评论

X = products['review'].astype(str)

然后适合CountVectorizer()然后使用

logreg.fit(X_train_dtm, y_train)