382bbb606ab6d60ae1e15c7a5e78eb7b9e855275,tests/model_selection/test_incremental.py,,_test_search_basic,#Any#Any#Any#Any#Any#Any#Any#,237

Before Change

        search = InverseDecaySearchCV(model, params, **kwargs)
    else:
        raise ValueError()
    if memory == "distributed" and input_type == "dataframe":
        with pytest.raises(TypeError, match=r"to_dask_array\(lengths=True\)"):
            await search.fit(X, y, classes=[0, 1])

        // Dask-ML raised a type error; let"s implement the suggestion
        X = await c.compute(c.submit(X.to_dask_array, lengths=True))
        y = await c.compute(c.submit(y.to_dask_array, lengths=True))
    await search.fit(X, y, classes=[0, 1])

    assert search.history_
    for d in search.history_:

After Change

    assert proba.shape[1] == 2
    assert proba.shape[0] == 1000 or math.isnan(proba.shape[0])
    assert log_proba.shape[1] == 2
    assert log_proba.shape[0] == 1000 or math.isnan(proba.shape[0])

    assert isinstance(proba, da.Array)
    assert isinstance(log_proba, da.Array)

Instances