from scipy.sparse import csr_matrix
from sklearn.linear_model import ElasticNet
import numpy as np

A = csr_matrix(np.array([[1,1,0,0,1],[0,0,1,0,1],[1,0,1,0,1],[0,1,1,1,0]]))
y = A[:, 0]
regr = ElasticNet()
regr.fit(A, y)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[34], line 4
      2 y = A[:, 0]
      3 regr = ElasticNet()
----> 4 regr.fit(A, y)

File ~/.e/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:918, in ElasticNet.fit(self, X, y, sample_weight, check_input)
    907     X_copied = self.copy_X and self.fit_intercept
    908     X, y = self._validate_data(
    909         X,
    910         y,
   (...)
    916         y_numeric=True,
    917     )
--> 918     y = check_array(
    919         y, order="F", copy=False, dtype=X.dtype.type, ensure_2d=False
    920     )
    922 n_samples, n_features = X.shape
    923 alpha = self.alpha

File ~/.e/lib/python3.9/site-packages/sklearn/utils/validation.py:845, in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)
    843 if sp.issparse(array):
    844     _ensure_no_complex_data(array)
--> 845     array = _ensure_sparse_format(
    846         array,
    847         accept_sparse=accept_sparse,
    848         dtype=dtype,
    849         copy=copy,
    850         force_all_finite=force_all_finite,
    851         accept_large_sparse=accept_large_sparse,
    852         estimator_name=estimator_name,
    853         input_name=input_name,
    854     )
    855 else:
    856     # If np.array(..) gives ComplexWarning, then we convert the warning
    857     # to an error. This is needed because specifying a non complex
    858     # dtype to the function converts complex to real dtype,
    859     # thereby passing the test made in the lines following the scope
    860     # of warnings context manager.
    861     with warnings.catch_warnings():

File ~/.e/lib/python3.9/site-packages/sklearn/utils/validation.py:522, in _ensure_sparse_format(spmatrix, accept_sparse, dtype, copy, force_all_finite, accept_large_sparse, estimator_name, input_name)
    519 _check_large_sparse(spmatrix, accept_large_sparse)
    521 if accept_sparse is False:
--> 522     raise TypeError(
    523         "A sparse matrix was passed, but dense "
    524         "data is required. Use X.toarray() to "
    525         "convert to a dense numpy array."
    526     )
    527 elif isinstance(accept_sparse, (list, tuple)):
    528     if len(accept_sparse) == 0:

TypeError: A sparse matrix was passed, but dense data is required. Use X.toarray() to convert to a dense numpy array.