machine-learning - 找到样本数量不一致的输入变量:[2，144]

我有一个包含144个反馈的训练数据集，分别有72个正反馈和72个负反馈。有两个目标标签分别是正数和负数。考虑以下代码段：

import pandas as pd
feedback_data = pd.read_csv('output.csv')
print(feedback_data)
                     data    target
0      facilitates good student teacher communication.  positive
1                           lectures are very lengthy.  negative
2             the teacher is very good at interaction.  positive
3                       good at clearing the concepts.  positive
4                       good at clearing the concepts.  positive
5                                    good at teaching.  positive
6                          does not shows test copies.  negative
7                           good subjective knowledge.  positive

from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer(binary = True)
cv.fit(feedback_data)
X = cv.transform(feedback_data)
X_test = cv.transform(feedback_data_test)

from sklearn import svm
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

target = [1 if i<72 else 0 for i in range(144)]
# the below line gives error
X_train, X_val, y_train, y_val = train_test_split(X, target, train_size = 0.50)

我不明白问题是什么。请帮忙。

最佳答案

您没有正确使用计数向量化器。这是您现在拥有的：

from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer(binary = True)
cv.fit(df)
X = cv.transform(df)
X
<2x2 sparse matrix of type '<class 'numpy.int64'>'
    with 2 stored elements in Compressed Sparse Row format>

因此，您会发现自己没有达到想要的目标。您没有正确变换每一行。您甚至没有正确地训练计数矢量化器，因为您使用了整个DataFrame而不只是注释的语料库。
要解决此问题，我们需要确保计数工作良好：
如果您这样做（使用正确的语料库）：

cv = CountVectorizer(binary = True)
cv.fit(df['data'].values)
X = cv.transform(df)
X
<2x23 sparse matrix of type '<class 'numpy.int64'>'
    with 0 stored elements in Compressed Sparse Row format>

您会看到我们正在接近我们想要的。我们只需要对它进行正确的转换（转换每一行）：

cv = CountVectorizer(binary = True)
cv.fit(df['data'].values)
X = df['data'].apply(lambda x: cv.transform([x])).values
X
array([<1x23 sparse matrix of type '<class 'numpy.int64'>'
with 5 stored elements in Compressed Sparse Row format>,
   ...
       <1x23 sparse matrix of type '<class 'numpy.int64'>'
    with 3 stored elements in Compressed Sparse Row format>], dtype=object)

我们有一个更合适的X！现在我们只需要检查是否可以拆分：

target = [1 if i<72 else 0 for i in range(8)] # The dataset is here of size 8
# the below line gives error
X_train, X_val, y_train, y_val = train_test_split(X, target, train_size = 0.50)

而且有效！

您需要确定您了解CountVectorizer如何正确使用它

关于machine-learning - 找到样本数量不一致的输入变量:[2，144]，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/54863302/