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["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] +enabler = ["pytest-enabler (>=2.2)"] +test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] +type = ["pytest-mypy"] [metadata] lock-version = "2.0" python-versions = ">=3.9, <3.12" -content-hash = "1d9f55c7e2e42a6e6272b2333c27685f7308736995bfda50517013127d6450f5" +content-hash = "e7cb209edfc96883ba3f650d4c1cee533310fe51417d380e5e9f1827d0bffb43" diff --git a/pyproject.toml b/pyproject.toml index 2393671..4fb9a94 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,7 +22,7 @@ scipy = "^1.12" black = "^24" flake8 = "^6" jupyter = "^1.0.0" -matplotlib = "^3.8" +matplotlib = "^3.9" nbconvert = "^6.5.0" optbinning = "^0.15.0" pandas = "^1.5" diff --git a/sliceline/slicefinder.py b/sliceline/slicefinder.py index 6bd1155..9aa6656 100644 --- a/sliceline/slicefinder.py +++ b/sliceline/slicefinder.py @@ -378,7 +378,11 @@ def _eval_slice( slice_candidates = x_encoded @ slices.T == level slice_sizes = slice_candidates.sum(axis=0).A[0] slice_errors = errors @ slice_candidates - max_slice_errors = slice_candidates.T.multiply(errors).max(axis=1).A + # Here we can't use the .A shorthand because it is not + # implemented in all scipy versions for coo_matrix objects + max_slice_errors = ( + slice_candidates.T.multiply(errors).max(axis=1).toarray() + ) # score of relative error and relative size slice_scores = self._score( @@ -397,7 +401,11 @@ def _create_and_score_basic_slices( """Initialise 1-slices, i.e. slices with one predicate.""" slice_sizes = x_encoded.sum(axis=0).A[0] slice_errors = errors @ x_encoded - max_slice_errors = x_encoded.T.multiply(errors).max(axis=1).A[:, 0] + # Here we can't use the .A shorthand because it is not + # implemented in all scipy versions for coo_matrix objects + max_slice_errors = ( + x_encoded.T.multiply(errors).max(axis=1).toarray()[:, 0] + ) # working set of active slices (#attr x #slices) and top-k valid_slices_mask = (slice_sizes >= self.min_sup) & (slice_errors > 0) @@ -440,7 +448,9 @@ def _join_compatible_slices( ) -> np.ndarray: """Join compatible slices according to `level`.""" slices_int = slices.astype(int) - join = (slices_int @ slices_int.T).A == level - 2 + # Here we can't use the .A shorthand because it is not + # implemented in all scipy versions for coo_matrix objects + join = (slices_int @ slices_int.T).toarray() == level - 2 return np.triu(join, 1) * join @staticmethod @@ -503,7 +513,10 @@ def _prepare_deduplication_and_pruning( sub_pair_candidates = pair_candidates[:, start:end] # sub_p should not contain multiple True on the same line i = sub_pair_candidates.argmax(axis=1).T + np.any( - sub_pair_candidates.A, axis=1 + # Here we can't use the .A shorthand because it is not + # implemented in all scipy versions for coo_matrix objects + sub_pair_candidates.toarray(), + axis=1, ) ids = ids + i.A * np.prod(dom[(j + 1) : dom.shape[0]]) return ids diff --git a/tests/test_slicefinder.py b/tests/test_slicefinder.py index 1bbd901..72402d8 100644 --- a/tests/test_slicefinder.py +++ b/tests/test_slicefinder.py @@ -19,7 +19,9 @@ def test_dummify(benchmark, basic_test_data): basic_test_data["n_col_x_encoded"], ) - assert np.array_equal(computed.A, basic_test_data["slices"].A) + assert np.array_equal( + computed.toarray(), basic_test_data["slices"].toarray() + ) def test_maintain_top_k(benchmark, basic_test_data): @@ -58,7 +60,7 @@ def test_maintain_top_k(benchmark, basic_test_data): ] ) - assert np.array_equal(computed_tk.A, expected_tk.A) + assert np.array_equal(computed_tk.toarray(), expected_tk.toarray()) assert np.array_equal(computed_tkc, expected_tkc) @@ -278,7 +280,9 @@ def test_create_and_score_basic_slices(benchmark, basic_test_data): [0.42499999999999993, 3, 1, 4], ] ) - assert np.array_equal(computed_slices.A, basic_test_data["slices"].A) + assert np.array_equal( + computed_slices.toarray(), basic_test_data["slices"].toarray() + ) assert np.array_equal(computed_statistics, expected_r) @@ -308,7 +312,9 @@ def test_get_pair_candidates(benchmark, basic_test_data): basic_test_data["feature_offset_start"], basic_test_data["feature_offset_end"], ) - assert np.array_equal(computed.A, basic_test_data["candidates"].A) + assert np.array_equal( + computed.toarray(), basic_test_data["candidates"].toarray() + ) def test_get_pair_candidates_with_missing_parents_pruning( @@ -356,7 +362,7 @@ def test_get_pair_candidates_with_missing_parents_pruning( feature_domains=np.array([2, 2, 2, 2]), feature_offset_start=np.array([0, 2, 4, 6]), feature_offset_end=np.array([2, 4, 6, 8]), - ).A + ).toarray() assert np.array_equal(computed, expected)