TST Extend tests for `scipy.sparse.*array` in `sklearn/cluster/tests/test_optics.py` #27104

marenwestermann · 2023-08-18T13:26:46Z

Reference Issues/PRs

What does this implement/fix? Explain your changes.

Makes use of the CSR_CONTAINERS fix to test sparse arrays.

Any other comments?

I included a NotImplementedError exception because I got the following test failure:

Details

________________________________________________________________________________ test_precomputed_dists[float64-csr_array-True] ________________________________________________________________________________

is_sparse = True, global_dtype = <class 'numpy.float64'>, csr_container = <class 'scipy.sparse._arrays.csr_array'>

    @pytest.mark.parametrize("is_sparse", [False, True])
    @pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
    def test_precomputed_dists(is_sparse, global_dtype, csr_container):
        redX = X[::2].astype(global_dtype, copy=False)
        print("redX", redX)
        dists = pairwise_distances(redX, metric="euclidean")
        print("dists", dists)
        dists = csr_container(dists) if is_sparse else dists
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", EfficiencyWarning)
>           clust1 = OPTICS(min_samples=10, algorithm="brute", metric="precomputed").fit(
                dists
            )

sklearn/cluster/tests/test_optics.py:815:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
sklearn/base.py:1215: in wrapper
    return fit_method(estimator, *args, **kwargs)
sklearn/cluster/_optics.py:348: in fit
    ) = memory.cache(compute_optics_graph)(
../../mambaforge/envs/sklearn-dev/lib/python3.10/site-packages/joblib/memory.py:349: in __call__
    return self.func(*args, **kwargs)
sklearn/utils/_param_validation.py:211: in wrapper
    return func(*args, **kwargs)
sklearn/cluster/_optics.py:617: in compute_optics_graph
    _set_reach_dist(
sklearn/cluster/_optics.py:668: in _set_reach_dist
    dists = X[point_index, unproc]
../../mambaforge/envs/sklearn-dev/lib/python3.10/site-packages/scipy/sparse/_index.py:57: in __getitem__
    self._raise_on_1d_array_slice()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = <30x30 sparse array of type '<class 'numpy.float64'>'
	with 900 stored elements in Compressed Sparse Row format>

    def _raise_on_1d_array_slice(self):
        """We do not currently support 1D sparse arrays.

        This function is called each time that a 1D array would
        result, raising an error instead.

        Once 1D sparse arrays are implemented, it should be removed.
        """
        if self._is_array:
>           raise NotImplementedError(
                'We have not yet implemented 1D sparse slices; '
                'please index using explicit indices, e.g. `x[:, [0]]`'
            )
E           NotImplementedError: We have not yet implemented 1D sparse slices; please index using explicit indices, e.g. `x[:, [0]]`

../../mambaforge/envs/sklearn-dev/lib/python3.10/site-packages/scipy/sparse/_index.py:41: NotImplementedError

Not sure if this is the best solution.

github-actions · 2023-08-18T13:28:20Z

✔️ Linting Passed

All linting checks passed. Your pull request is in excellent shape! ☀️

_{Generated for commit: c1d74d0. Link to the linter CI: here}

sklearn/cluster/tests/test_optics.py

ogrisel · 2023-09-11T08:56:34Z

@marenwestermann do you need help with this? Are the above information enough for you to investigate? Otherwise I can try to dig further to give more specific guidance.

glemaitre · 2023-09-11T09:31:18Z

One thing that I could see is that our check_array can actually fail on some other sparse matrix format than CSC and CSR. #27336 should solve the problem.

marenwestermann · 2023-09-14T21:20:42Z

Thanks @ogrisel for asking. I actually do need help. I applied the suggested change, i.e.

dists = X[[point_index], unproc]

and this resulted in the following error:

Details

============================================================================================= test session starts ==============================================================================================
platform darwin -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0
rootdir: /Users/maren/open-source/scikit-learn, configfile: setup.cfg
plugins: xdist-3.1.0, cov-4.0.0
collected 153 items / 145 deselected / 8 selected

sklearn/cluster/tests/test_optics.py ssss.F..                                                                                                                                                            [100%]

=================================================================================================== FAILURES ===================================================================================================
_______________________________________________________________________________ test_precomputed_dists[float64-csr_matrix-True] ________________________________________________________________________________

is_sparse = True, global_dtype = <class 'numpy.float64'>, csr_container = <class 'scipy.sparse._csr.csr_matrix'>

    @pytest.mark.parametrize("is_sparse", [False, True])
    @pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
    def test_precomputed_dists(is_sparse, global_dtype, csr_container):
        redX = X[::2].astype(global_dtype, copy=False)
        dists = pairwise_distances(redX, metric="euclidean")
        dists = csr_container(dists) if is_sparse else dists
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", EfficiencyWarning)
>           clust1 = OPTICS(min_samples=10, algorithm="brute", metric="precomputed").fit(
                dists
            )

sklearn/cluster/tests/test_optics.py:814:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
sklearn/base.py:1215: in wrapper
    return fit_method(estimator, *args, **kwargs)
sklearn/cluster/_optics.py:348: in fit
    ) = memory.cache(compute_optics_graph)(
../../mambaforge/envs/sklearn-dev/lib/python3.10/site-packages/joblib/memory.py:349: in __call__
    return self.func(*args, **kwargs)
sklearn/utils/_param_validation.py:211: in wrapper
    return func(*args, **kwargs)
sklearn/cluster/_optics.py:617: in compute_optics_graph
    _set_reach_dist(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

core_distances_ = array([ 4.60937947,  4.59708404,  5.52672977,  5.29246467,  4.82327335,
        2.6358904 ,  3.14641026,  3.03184371, ...006936,  2.53532733,  2.52139009,  2.37094714,
        6.59694387,  8.98309419, 10.91542663,  8.9213898 ,  7.13039835])
reachability_ = array([inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,
       inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf,
       inf, inf, inf, inf])
predecessor_ = array([-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]), point_index = 0
processed = array([ True, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False])
X = <30x30 sparse matrix of type '<class 'numpy.float64'>'
	with 900 stored elements in Compressed Sparse Row format>, nbrs = NearestNeighbors(algorithm='brute', metric='precomputed', n_neighbors=10)
metric = 'precomputed', metric_params = None, p = 2, max_eps = inf

    def _set_reach_dist(
        core_distances_,
        reachability_,
        predecessor_,
        point_index,
        processed,
        X,
        nbrs,
        metric,
        metric_params,
        p,
        max_eps,
    ):
        P = X[point_index : point_index + 1]
        # Assume that radius_neighbors is faster without distances
        # and we don't need all distances, nevertheless, this means
        # we may be doing some work twice.
        indices = nbrs.radius_neighbors(P, radius=max_eps, return_distance=False)[0]

        # Getting indices of neighbors that have not been processed
        unproc = np.compress(~np.take(processed, indices), indices)
        # Neighbors of current point are already processed.
        if not unproc.size:
            return

        # Only compute distances to unprocessed neighbors:
        if metric == "precomputed":
            dists = X[[point_index], unproc]
            if issparse(dists):
                dists.sort_indices()
                dists = dists.data
        else:
            _params = dict() if metric_params is None else metric_params.copy()
            if metric == "minkowski" and "p" not in _params:
                # the same logic as neighbors, p is ignored if explicitly set
                # in the dict params
                _params["p"] = p
            dists = pairwise_distances(P, X[unproc], metric, n_jobs=None, **_params).ravel()

        rdists = np.maximum(dists, core_distances_[point_index])
        np.around(rdists, decimals=np.finfo(rdists.dtype).precision, out=rdists)
        improved = np.where(rdists < np.take(reachability_, unproc))
>       reachability_[unproc[improved]] = rdists[improved]
E       IndexError: too many indices for array: array is 1-dimensional, but 2 were indexed

sklearn/cluster/_optics.py:683: IndexError
=========================================================================================== short test summary info ============================================================================================
FAILED sklearn/cluster/tests/test_optics.py::test_precomputed_dists[float64-csr_matrix-True] - IndexError: too many indices for array: array is 1-dimensional, but 2 were indexed
============================================================================ 1 failed, 3 passed, 4 skipped, 145 deselected in 0.46s ============================================================================

I then tried the following:

reachability_[unproc[[improved]]] = rdists[improved]

and equally

predecessor_[unproc[[improved]]] = point_index

and this fixed the index errors but resulted in the following error:

Details

============================================================================================= test session starts ==============================================================================================
platform darwin -- Python 3.10.8, pytest-7.2.0, pluggy-1.0.0
rootdir: /Users/maren/open-source/scikit-learn, configfile: setup.cfg
plugins: xdist-3.1.0, cov-4.0.0
collected 153 items / 145 deselected / 8 selected

sklearn/cluster/tests/test_optics.py ssss.F..                                                                                                                                                            [100%]

=================================================================================================== FAILURES ===================================================================================================
_______________________________________________________________________________ test_precomputed_dists[float64-csr_matrix-True] ________________________________________________________________________________

is_sparse = True, global_dtype = <class 'numpy.float64'>, csr_container = <class 'scipy.sparse._csr.csr_matrix'>

    @pytest.mark.parametrize("is_sparse", [False, True])
    @pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
    def test_precomputed_dists(is_sparse, global_dtype, csr_container):
        redX = X[::2].astype(global_dtype, copy=False)
        dists = pairwise_distances(redX, metric="euclidean")
        dists = csr_container(dists) if is_sparse else dists
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", EfficiencyWarning)
            clust1 = OPTICS(min_samples=10, algorithm="brute", metric="precomputed").fit(
                dists
            )
        clust2 = OPTICS(min_samples=10, algorithm="brute", metric="euclidean").fit(redX)

>       assert_allclose(clust1.reachability_, clust2.reachability_)

sklearn/cluster/tests/test_optics.py:819:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

actual = array([       inf, 4.60937947, 4.59708404, 4.59708404, 4.59708404,
       3.30445873, 2.6358904 , 2.6358904 , 2.635890...4 , 2.6358904 , 2.52139009, 2.6358904 , 2.52139009,
       4.31006936, 8.38294501, 6.59694387, 8.51939412, 8.51939412])
desired = array([       inf, 4.60937947, 4.59708404, 4.59708404, 4.59708404,
       3.30445873, 2.6358904 , 2.6358904 , 2.635890...4 , 2.6358904 , 2.6358904 , 2.53532733, 2.52139009,
       4.31006936, 7.13039835, 6.59694387, 7.13039835, 4.89628456])
rtol = 1e-07, atol = 0.0, equal_nan = True, err_msg = '', verbose = True

    def assert_allclose(
        actual, desired, rtol=None, atol=0.0, equal_nan=True, err_msg="", verbose=True
    ):
        """dtype-aware variant of numpy.testing.assert_allclose

        This variant introspects the least precise floating point dtype
        in the input argument and automatically sets the relative tolerance
        parameter to 1e-4 float32 and use 1e-7 otherwise (typically float64
        in scikit-learn).

        `atol` is always left to 0. by default. It should be adjusted manually
        to an assertion-specific value in case there are null values expected
        in `desired`.

        The aggregate tolerance is `atol + rtol * abs(desired)`.

        Parameters
        ----------
        actual : array_like
            Array obtained.
        desired : array_like
            Array desired.
        rtol : float, optional, default=None
            Relative tolerance.
            If None, it is set based on the provided arrays' dtypes.
        atol : float, optional, default=0.
            Absolute tolerance.
        equal_nan : bool, optional, default=True
            If True, NaNs will compare equal.
        err_msg : str, optional, default=''
            The error message to be printed in case of failure.
        verbose : bool, optional, default=True
            If True, the conflicting values are appended to the error message.

        Raises
        ------
        AssertionError
            If actual and desired are not equal up to specified precision.

        See Also
        --------
        numpy.testing.assert_allclose

        Examples
        --------
        >>> import numpy as np
        >>> from sklearn.utils._testing import assert_allclose
        >>> x = [1e-5, 1e-3, 1e-1]
        >>> y = np.arccos(np.cos(x))
        >>> assert_allclose(x, y, rtol=1e-5, atol=0)
        >>> a = np.full(shape=10, fill_value=1e-5, dtype=np.float32)
        >>> assert_allclose(a, 1e-5)
        """
        dtypes = []

        actual, desired = np.asanyarray(actual), np.asanyarray(desired)
        dtypes = [actual.dtype, desired.dtype]

        if rtol is None:
            rtols = [1e-4 if dtype == np.float32 else 1e-7 for dtype in dtypes]
            rtol = max(rtols)

>       np_assert_allclose(
            actual,
            desired,
            rtol=rtol,
            atol=atol,
            equal_nan=equal_nan,
            err_msg=err_msg,
            verbose=verbose,
        )
E       AssertionError:
E       Not equal to tolerance rtol=1e-07, atol=0
E
E       Mismatched elements: 9 / 30 (30%)
E       Max absolute difference: 5.74705461
E       Max relative difference: 2.18030864
E        x: array([     inf, 4.609379, 4.597084, 4.597084, 4.597084, 3.304459,
E              2.63589 , 2.63589 , 2.63589 , 2.52139 , 4.597084, 2.63589 ,
E              2.63589 , 8.382945, 2.63589 , 5.223714, 5.223714, 4.872887,...
E        y: array([     inf, 4.609379, 4.597084, 4.597084, 4.597084, 3.304459,
E              2.63589 , 2.63589 , 2.63589 , 2.63589 , 4.597084, 2.63589 ,
E              2.63589 , 2.63589 , 2.63589 , 4.737295, 4.543899, 4.872887,...

sklearn/utils/_testing.py:291: AssertionError
=========================================================================================== short test summary info ============================================================================================
FAILED sklearn/cluster/tests/test_optics.py::test_precomputed_dists[float64-csr_matrix-True] - AssertionError:
============================================================================ 1 failed, 3 passed, 4 skipped, 145 deselected in 0.37s ============================================================================

I noticed that the shape of reachability_[unproc[[improved]]] changed from previously e.g. (29,) to e.g. (1, 2, 29). I don't know if this is actually desired.

So this is where I currently don't know how to proceed. I had a look at @glemaitre 's last comment but from my understanding that bug doesn't cause the problem described here. I pulled the recent changes from upstream main but the problem described here persists.

marenwestermann · 2023-09-14T21:24:23Z

PS: I finally have time again to be active on scikit-learn and I'm excited to be back. 🎉

glemaitre · 2023-09-15T09:26:29Z

Looking closer to the code, we need to change the following:

        dists = X[point_index, unproc]
        if issparse(dists):
            dists.sort_indices()
            dists = dists.data

to

        dists = X[[point_index], unproc]
        if isinstance(dists, np.matrix):
            dists = dists.A1

So:

we need to index as we mention previously using [point_index].
the current if issparse(dists) cannot be reach: indexing a sparse matrix returns a numpy matrix and indexing a sparse array returns a numpy array. So we never get a sparse matrix back.
The idea here is to handle the everything if it was a numpy array (it is what the code does). So we can recover a 1-D numpy array from the numpy matrix using this A1 that return the flatten data.

I tried locally and the tests are passing.

marenwestermann · 2023-09-15T19:47:04Z

I see, that makes sense. Thanks a lot for the detailed explanation @glemaitre. I applied the suggested changes.

glemaitre

A couple of comments to optimize the number of tests to the minimum.

sklearn/cluster/tests/test_optics.py

marenwestermann · 2023-09-16T10:01:02Z

Good catch, I wouldn't have seen this myself. I had a careful look through the changes and tested them and everything looks good.

glemaitre

Thanks @marenwestermann LGTM.

ogrisel

This PR fixes the code of the scikit-learn library (not just the test suite) and therefore needs a dedicated entry in sklearn/doc/whats_new/v1.4.rst to document the fact that sklearn.cluster.OPTICS now accepts scipy sparse arrays as inputs.

Take example on the log entry for NMF as a reference to write this entry for the changelog.

Other than that here is a suggestion for further consistency in the code (even if it does not change the behavior seen by the tests):

sklearn/cluster/_optics.py

marenwestermann · 2023-09-19T11:10:25Z

I don't think the CI failures are due to my changes (unless it's a problem that this PR which is mentioned in the changelog isn't merged yet).

glemaitre · 2023-09-20T09:14:26Z

Uhm. Could you solve the conflict of the changelog.

marenwestermann · 2023-09-20T21:03:54Z

Ah, I didn't see that it was a merge conflict, I fixed it. Please note that somewhere in the restructuring process of the changelog the changelog entry for #26602 seems to have gotten lost.

marenwestermann · 2023-09-21T07:51:52Z

Looks like the test test_precomputed_dists needs to be updated. I'll have a proper look at it tonight or tomorrow.

ogrisel · 2023-09-21T14:52:56Z

sklearn/cluster/_optics.py

+            dists = np.asarray(dists)
+        elif hasattr(dists, "toarray"):
+            # X is a scipy sparse array.
+            dists = dists.toarray()


@marenwestermann do you understand why this branch of the condition is not already covered by test_precomputed_dists?

I am a bit confused.

@ogrisel The issue is that previously dists was a sparse matrix for the test case "is_sparse"=True. Now, when we want to test for the case csr_matrix, dists becomes a numpy matrix (which is currently covered in the code) and in the case where we want to test for csr_array, dists becomes a numpy array. So we never test for the case sparse array.

Now the question is should dists be sparse when X is sparse (as it was preciously the case) or is the current implementation ok? I don't have a deep enough understanding of the OPTICS estimator to make a judgement.

Just a minimal example to understand what happens here:

In [5]: from scipy import sparse In [6]: X = sparse.random(10, 10, format="csr") In [7]: X[0, :] Out[7]: <1x10 sparse matrix of type '<class 'numpy.float64'>' with 0 stored elements in Compressed Sparse Row format> In [8]: X[0, [1, 2]] Out[8]: <1x2 sparse matrix of type '<class 'numpy.float64'>' with 0 stored elements in Compressed Sparse Row format> In [9]: X[[0], [1, 2]] Out[9]: matrix([[0., 0.]])

Since we index both dimension, we get this np.matrix that is the difference with the previous code.

For me, we don't need to cover anymore the case where we would eventually have a sparse matrix because we will get already a np.matrix or a np.array.

@marenwestermann do you think that you can reduce to only having np.matrix or np.array (not anymore sparse matrices) for the condition. Once done, I think that we good for merging.

glemaitre · 2023-10-30T10:47:56Z

This one was waiting for merge. Thanks @marenwestermann

…test_optics.py` (scikit-learn#27104)

use CSR_CONTAINERS fix

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TST Extend tests for scipy.sparse.*array #27090

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glemaitre self-requested a review August 18, 2023 14:26

remove print statement

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TST Extend tests for scipy.sparse.*array in sklearn/cluster/tests/test_hierarchical.py #27101

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jjerphan changed the title ~~TST Extend tests for scipy.sparse.*array in sklearn/cluster/tests/test_optics.py~~ TST Extend tests for scipy.sparse.*array in sklearn/cluster/tests/test_optics.py Aug 24, 2023

Merge remote-tracking branch 'upstream/main' into test-optics

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Merge remote-tracking branch 'upstream/main' into test-optics

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glemaitre self-requested a review September 15, 2023 09:05

glemaitre requested review from adrinjalali and removed request for glemaitre September 15, 2023 11:38

rewrite code so that we get a 1D numpy array

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glemaitre self-requested a review September 15, 2023 19:53

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optimise tests

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marenwestermann added 2 commits September 19, 2023 10:29

improve code consistency

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update changelog

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marenwestermann added 2 commits September 20, 2023 22:23

Merge remote-tracking branch 'upstream/main' into test-optics

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adapt to new chagelog structure

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marenwestermann added 3 commits October 2, 2023 16:43

remove conditional statement

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Merge remote-tracking branch 'upstream/main' into test-optics

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correct changelog

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glemaitre merged commit 38d499b into scikit-learn:main Oct 30, 2023

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TST Extend tests for scipy.sparse.*array in `sklearn/cluster/tests/…

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…test_optics.py` (scikit-learn#27104)

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TST Extend tests for scipy.sparse.*array in `sklearn/cluster/tests/…

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…test_optics.py` (scikit-learn#27104)

marenwestermann deleted the test-optics branch October 31, 2023 17:17

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…test_optics.py` (scikit-learn#27104)

Uh oh!

TST Extend tests for scipy.sparse.*array in sklearn/cluster/tests/test_optics.py #27104

TST Extend tests for scipy.sparse.*array in sklearn/cluster/tests/test_optics.py #27104

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Conversation

marenwestermann commented Aug 18, 2023

Reference Issues/PRs

What does this implement/fix? Explain your changes.

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TST Extend tests for `scipy.sparse.*array` in `sklearn/cluster/tests/test_optics.py` #27104

TST Extend tests for `scipy.sparse.*array` in `sklearn/cluster/tests/test_optics.py` #27104

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