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TASK OBJECTIVE

scikit-learn/scikit-learn

How do you make scikit-learn Isomap and LocallyLinearEmbedding handle sparse matrix input correctly?

TypeErrorPython traceback repairs

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01

TASK STATEMENT

verbatim agent-facing issue statement

Isomap and LocallyLinearEmbedding do not accept sparse matrix input (contrary to documentation)
The [documentation](http://scikit-learn.org/stable/modules/generated/sklearn.manifold.locally_linear_embedding.html) mentions that `sklearn.manifold.LocallyLinearEmbedding` should support sparse matrix.

The error comes from the 5 [occurences](https://github.com/scikit-learn/scikit-learn/blob/14031f6/sklearn/manifold/locally_linear.py#L629) of `check_array` from `sklearn.utils.validation`.

If documentation is correct `check_array` should be called with `accept_sparse=True`
[`Check array input`](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/validation.py#L269)

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
```bash
/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
```python
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.
```

Match evidence

Primary terms

TypeErrorLocallyLinearEmbeddingsklearn/manifold/isomap.pyscikit-learn/scikit-learnsklearn.manifold.LocallyLinearEmbeddingIsomapSpectralEmbeddingsklearn.manifold.locally_linear_embeddingcheck_arrayaccept_sparse_ensure_sparse_formatFLOAT_DTYPES

Technical objective

Reconcile the input validation of the sklearn.manifold estimators with their documented support for sparse matrix input. The deliverable is a change that makes the documented contract and the runtime behaviour agree for Isomap and LocallyLinearEmbedding (and any sibling estimator in the same module found to be in the same position).

Success condition

passing a sparse matrix to the affected estimators behaves the way the project's contract says it should — either the call is accepted end to end and produces a correct embedding, or it is rejected with an accurate, intentional error and documentation that matches — with dense-input results and performance unchanged.

Search fingerprints

  • Error signature: TypeError
  • Traceback path: sklearn/manifold/isomap.py
  • Repository: scikit-learn/scikit-learn
  • Issue title: Isomap and LocallyLinearEmbedding do not accept sparse matrix input (contrary to documentation).
  • Symbols: sklearn.manifold.LocallyLinearEmbedding, Isomap, SpectralEmbedding, sklearn.manifold.locally_linear_embedding, check_array, accept_sparse, _ensure_sparse_format, FLOAT_DTYPES, fit_transform, _fit_transform, arpack, smoothed_aggregation_solver.
  • Files: sklearn/manifold/isomap.py, sklearn/manifold/locally_linear.py, sklearn/manifold/mds.py, sklearn/manifold/spectral_embedding_.py, sklearn/manifold/t_sne.py, sklearn/utils/validation.py, sklearn/pipeline.py.
  • Verbatim error string: TypeError: A sparse matrix was passed, but dense data is required. Use X.toarray() to convert to a dense numpy array.
  • Cross-references: http://scikit-learn.org/stable/modules/generated/sklearn.manifold.locally_linear_embedding.html, https://github.com/scikit-learn/scikit-learn/blob/14031f6/sklearn/manifold/locally_linear.py#L629.
  • Shapes quoted in the report: Input training data has shape: (49352, 15), Input test data has shape: (74659, 14).

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