Isomap and LocallyLinearEmbedding do not accept sparse matrix input (contrary to documentation) #8416
Description
The documentation mentions that sklearn.manifold.LocallyLinearEmbedding should support sparse matrix.
The error comes from the 5 occurences of check_array from sklearn.utils.validation.
If documentation is correct check_array should be called with accept_sparse=True
Check array input
I can submit a PR.
Isomap also accepts sparse matrix according to documentation on fit and fit_transform methods.
Given that SpectralEmbedding also uses the arpack solver, I guess that it also should accept sparse matrices.
- Check of check_array calls in the manifold subfolder
/usr/lib/python3.6/site-packages/sklearn/manifold $ grep 'check_array' *.py -n
isomap.py:9:from ..utils import check_array
isomap.py:103: X = check_array(X)
isomap.py:202: X = check_array(X)
locally_linear.py:11:from ..utils import check_random_state, check_array
locally_linear.py:42: X = check_array(X, dtype=FLOAT_DTYPES)
locally_linear.py:43: Z = check_array(Z, dtype=FLOAT_DTYPES, allow_nd=True)
locally_linear.py:629: X = check_array(X, dtype=float)
locally_linear.py:688: X = check_array(X)
mds.py:14:from ..utils import check_random_state, check_array, check_symmetric
mds.py:229: similarities = check_array(similarities)
mds.py:394: X = check_array(X)
spectral_embedding_.py:14:from ..utils import check_random_state, check_array, check_symmetric
spectral_embedding_.py:280: laplacian = check_array(laplacian, dtype=np.float64,
spectral_embedding_.py:283: ml = smoothed_aggregation_solver(check_array(laplacian, 'csr'))
spectral_embedding_.py:295: laplacian = check_array(laplacian, dtype=np.float64,
spectral_embedding_.py:472: X = check_array(X, ensure_min_samples=2, estimator=self)
t_sne.py:18:from ..utils import check_array
t_sne.py:706: X = check_array(X, accept_sparse=['csr', 'csc', 'coo'],- For reference, my backtrace
Input training data has shape: (49352, 15)
Input test data has shape: (74659, 14)
....
....
Traceback (most recent call last):
File "main.py", line 108, in <module>
X, X_test, y, tr_pipeline, select_feat, cache_file)
File "/home/ml/machinelearning_projects/Kaggle_Compet/Renthop_Apartment_interest/src/preprocessing.py", line 13, in preprocessing
x_trn, x_val, x_test = feat_selection(select_feat, x_trn, x_val, X_test)
File "/home/ml/machinelearning_projects/Kaggle_Compet/Renthop_Apartment_interest/src/star_command.py", line 61, in feat_selection
trn, val, tst = zip_with(_concat_col, tuples_trn_val_test)
File "/home/ml/machinelearning_projects/Kaggle_Compet/Renthop_Apartment_interest/src/star_command.py", line 23, in zip_with
return starmap(f, zip(*list_of_tuple))
File "/home/ml/machinelearning_projects/Kaggle_Compet/Renthop_Apartment_interest/src/star_command.py", line 76, in _feat_transfo
trn = Transformer.fit_transform(train[sCol])
File "/usr/lib/python3.6/site-packages/sklearn/pipeline.py", line 303, in fit_transform
return last_step.fit_transform(Xt, y, **fit_params)
File "/usr/lib/python3.6/site-packages/sklearn/manifold/locally_linear.py", line 666, in fit_transform
self._fit_transform(X)
File "/usr/lib/python3.6/site-packages/sklearn/manifold/locally_linear.py", line 629, in _fit_transform
X = check_array(X, dtype=float)
File "/usr/lib/python3.6/site-packages/sklearn/utils/validation.py", line 380, in check_array
force_all_finite)
File "/usr/lib/python3.6/site-packages/sklearn/utils/validation.py", line 243, in _ensure_sparse_format
raise TypeError('A sparse matrix was passed, but dense '
TypeError: A sparse matrix was passed, but dense data is required. Use X.toarray() to convert to a dense numpy array.