将pandas列添加到稀疏矩阵

Question

我有我想在模型中使用的X变量的其他派生值.

XAll = pd_data[['title','wordcount','sumscores','length']]
y = pd_data['sentiment']
X_train, X_test, y_train, y_test = train_test_split(XAll, y, random_state=1)

当我在标题中处理文本数据时,我首先将其分别转换为dtm:

vect = CountVectorizer(max_df=0.5)
vect.fit(X_train['title'])
X_train_dtm = vect.transform(X_train['title'])
column_index = X_train_dtm.indices

print(type(X_train_dtm))    # This is <class 'scipy.sparse.csr.csr_matrix'>
print("X_train_dtm shape",X_train_dtm.get_shape())  # This is (856, 2016)
print("column index:",column_index)     # This is column index: [ 533  754  859 ...,  633  950 1339]

现在我将文本作为文档术语矩阵,我想将其他功能添加到X_train_dtm这些数字中,例如'wordcount','sumscores','length'.我将使用新的dtm创建模型,因此我将插入附加功能更准确.

如何将pandas数据帧的其他数字列添加到稀疏csr矩阵？

Answer 1

找到了解决方案.我们可以使用sparse.hstack来做到这一点:

from scipy.sparse import hstack
X_train_dtm = hstack((X_train_dtm,np.array(X_train['wordcount'])[:,None]))