Uhm did something about this one. Let me check.

Found it: #16346

Let me know which one you prefer. PR #16346 does not test cloning and that's what caused the issue. I recommend adding one more test.

I think this is important to make the estimator scikit-learn compliant (it will crash soon otherwise). Cloning is part of that so we should merge both PR. Could you merge my PR inside yours like this you supersede mine? I will review it then.

I just solve the conflicts in mine so you should have no so much trouble to merge them.

@sdpython do you mind editing the PR adding that you will also close #16336? Thanks for your patience.

ok :)

@sdpython I will do a review. I think that we might need to add some of the test (usually done in common test) but that require specific shape for X and dictionary.

We would need to have something like that (it is only the transformer).

def test_sparse_coder_common_transformer():
    from functools import partial
    from sklearn.utils.estimator_checks import check_transformer_data_not_an_array
    from sklearn.utils.estimator_checks import check_transformer_general
    from sklearn.utils.estimator_checks import check_transformers_unfitted

    rng = np.random.RandomState(777)
    n_components, n_features = 40, 3
    init_dict = rng.rand(n_components, n_features)
    
    sc = SparseCoder(init_dict)

    check_transformer_data_not_an_array(sc.__class__.__name__, sc)
    check_transformer_general(sc.__class__.__name__, sc)
    check_transformer_general_memmap = partial(
        check_transformer_general, readonly_memmap=True
    )
    check_transformer_general_memmap(sc.__class__.__name__, sc)
    check_transformers_unfitted(sc.__class__.__name__, sc)

I think this is wiser to merge with the current test and see how the test_common.py could be modify such that the size of the dictionary can be modified on the fly.

A few comments but otherwise LGTM. I don't think there is an easy way to re-add SparseCoder to test_common as it requires to specify the dictionary in the constructor params and the dictionary shape should be consistent with X.shape[1].

I have encountered this while working on other estimators outside sklearn, and it's partly why I wanted to have a better way of telling common tests now to generate required data for certain tests.

def test_sparse_coder_common_transformer():
    from functools import partial
    from sklearn.utils.estimator_checks import check_transformer_data_not_an_array
    from sklearn.utils.estimator_checks import check_transformer_general
    from sklearn.utils.estimator_checks import check_transformers_unfitted

    rng = np.random.RandomState(777)
    n_components, n_features = 40, 3
    init_dict = rng.rand(n_components, n_features)
    
    sc = SparseCoder(init_dict)

    check_transformer_data_not_an_array(sc.__class__.__name__, sc)
    check_transformer_general(sc.__class__.__name__, sc)
    check_transformer_general_memmap = partial(
        check_transformer_general, readonly_memmap=True
    )
    check_transformer_general_memmap(sc.__class__.__name__, sc)
    check_transformers_unfitted(sc.__class__.__name__, sc)

Added.

There is a single thing missing which is an entry in what's new because we are deprecated one parameter.
Can you this acknowledging yourself @sdpython and then we are good for merging.

actually @glemaitre had approved so go !

thx @sdpython

I will make a PR adding the deprecation in what's new :)