使用稀疏稀疏矩阵和numpy数组训练sklearn ML模型

Question

Just to explain some things more about my use case, A is a sparse matrix with tf-idf values and B is an array with some additional features of my data.

I have already splitted to training and test sets so A and B in my example are only about the training set. I (want to) do the same for the test set after this code.

I want to concatenate these matrices/arrays because then I want to pass them to a sklearn ML model to train it and I do not think that I can pass them separately.

So I tried to do this:

C = np.concatenate((A, B.T), axis=1)

where A is a <class 'scipy.sparse.csr.csr_matrix'> and B is a <class 'numpy.ndarray'> .

However, when I try to do this then I get the following error:

ValueError: zero-dimensional arrays cannot be concatenated

Also, I do not think that the idea of `np.concatenate` a numpy array with a sparse matrix is very good in my case because

1. it is basically impossible to covert my sparse array A to a dense array because it is too big
2. I will lose (or not actually??) information if I convert my fully dense array B to a sparse array

What is the best way to pass to an sklearn ML model a sparse and a fully dense array concatenated by rows?

Answer 1

1. You can use hstack from scipy. hstack will convert both matrices to scipy coo_matrix , merge them and return a coo_matrix by default.

2. No information is lost when converting dense array to sparse. Sparse matrices are just compact data storage format. Also, unless to specify a value for argument dtype of hstack everything is upcasted . So, there is no possibility of data loss there as well.

Further , if you plan to use Logistic Regression from sklearn, sparse matrices must be in csr format for fit method to work.

The following code should work for your use-case

from scipy.sparse import hstack

X = hstack((A, B), format='csr')