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dask-version #1711

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dask-version #1711

Update pyproject.toml
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Azure Pipelines / scverse.anndata failed Oct 14, 2024 in 17m 43s

Build #20241014.3 had test failures

4 errors / 2 warnings

Details

Tests

  • Failed: 106 (1.08%)
  • Passed: 8,317 (84.59%)
  • Other: 1,409 (14.33%)
  • Total: 9,832
Code coverage

  • 5062 of 6026 line covered (84.00%)

Annotations

Check failure on line 19 in Build log

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@azure-pipelines azure-pipelines / scverse.anndata

Build log #L19 

Bash exited with code '4'.

Check failure on line 10 in Build log

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@azure-pipelines azure-pipelines / scverse.anndata

Build log #L10 

No code coverage results were found to publish.

Check failure on line 19 in Build log

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@azure-pipelines azure-pipelines / scverse.anndata

Build log #L19 

Bash exited with code '4'.

Check failure on line 6051 in Build log

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@azure-pipelines azure-pipelines / scverse.anndata

Build log #L6051 

Bash exited with code '1'.

Check failure on line 1 in test_concatenate_roundtrip[inner-sparse_dask_array-concat_func0-False]

See this annotation in the file changed.

@azure-pipelines azure-pipelines / scverse.anndata

test_concatenate_roundtrip[inner-sparse_dask_array-concat_func0-False] 

ValueError: zero-dimensional arrays cannot be concatenated
Raw output 
join_type = 'inner'
array_type = <function as_sparse_dask_array at 0x7fad43daf280>
concat_func = functools.partial(<function concat at 0x7fad43e0eb80>, merge='unique')
backwards_compat = False

    @mark_legacy_concatenate
    @pytest.mark.parametrize(
        ("concat_func", "backwards_compat"),
        [
            (partial(concat, merge="unique"), False),
            (lambda x, **kwargs: x[0].concatenate(x[1:], **kwargs), True),
        ],
    )
    def test_concatenate_roundtrip(join_type, array_type, concat_func, backwards_compat):
        adata = gen_adata((100, 10), X_type=array_type, **GEN_ADATA_DASK_ARGS)
    
        remaining = adata.obs_names
        subsets = []
        while len(remaining) > 0:
            n = min(len(remaining), np.random.choice(50))
            subset_idx = np.random.choice(remaining, n, replace=False)
            subsets.append(adata[subset_idx])
            remaining = remaining.difference(subset_idx)
    
        result = concat_func(subsets, join=join_type, uns_merge="same", index_unique=None)
    
        # Correcting for known differences
        orig, result = fix_known_differences(
            adata, result, backwards_compat=backwards_compat
        )
    
>       assert_equal(result[orig.obs_names].copy(), orig)

/home/vsts/work/1/s/tests/test_concatenate.py:197: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/functools.py:888: in wrapper
    return dispatch(args[0].__class__)(*args, **kw)
/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/site-packages/anndata/tests/helpers.py:764: in assert_adata_equal
    assert_equal(
/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/functools.py:888: in wrapper
    return dispatch(args[0].__class__)(*args, **kw)
/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/site-packages/anndata/tests/helpers.py:627: in assert_equal_dask_array
    assert_equal(b, a.compute(), exact, elem_name)
/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/site-packages/dask/base.py:376: in compute
    (result,) = compute(self, traverse=False, **kwargs)
/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/site-packages/dask/base.py:662: in compute
    results = schedule(dsk, keys, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

arrs = [<5x5 sparse matrix of type '<class 'numpy.float32'>'
	with 12 stored elements in Compressed Sparse Row format>, <5x5 sparse matrix of type '<class 'numpy.float32'>'
	with 11 stored elements in Compressed Sparse Row format>]
sorter = array([7, 5, 0, 9, 6, 4, 1, 2, 3, 8]), axis = 0

    def concatenate_arrays(arrs, sorter, axis):
>       return np.take(np.concatenate(arrs, axis=axis), np.argsort(sorter), axis=axis)
E       ValueError: zero-dimensional arrays cannot be concatenated

/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/site-packages/dask/array/_shuffle.py:190: ValueError

Check failure on line 1 in test_concatenate_roundtrip[inner-sparse_dask_array-<lambda>-True]

See this annotation in the file changed.

@azure-pipelines azure-pipelines / scverse.anndata

test_concatenate_roundtrip[inner-sparse_dask_array-<lambda>-True] 

ValueError: zero-dimensional arrays cannot be concatenated
Raw output 
join_type = 'inner'
array_type = <function as_sparse_dask_array at 0x7fad43daf280>
concat_func = <function <lambda> at 0x7fad40ed2b80>, backwards_compat = True

    @mark_legacy_concatenate
    @pytest.mark.parametrize(
        ("concat_func", "backwards_compat"),
        [
            (partial(concat, merge="unique"), False),
            (lambda x, **kwargs: x[0].concatenate(x[1:], **kwargs), True),
        ],
    )
    def test_concatenate_roundtrip(join_type, array_type, concat_func, backwards_compat):
        adata = gen_adata((100, 10), X_type=array_type, **GEN_ADATA_DASK_ARGS)
    
        remaining = adata.obs_names
        subsets = []
        while len(remaining) > 0:
            n = min(len(remaining), np.random.choice(50))
            subset_idx = np.random.choice(remaining, n, replace=False)
            subsets.append(adata[subset_idx])
            remaining = remaining.difference(subset_idx)
    
        result = concat_func(subsets, join=join_type, uns_merge="same", index_unique=None)
    
        # Correcting for known differences
        orig, result = fix_known_differences(
            adata, result, backwards_compat=backwards_compat
        )
    
>       assert_equal(result[orig.obs_names].copy(), orig)

/home/vsts/work/1/s/tests/test_concatenate.py:197: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/functools.py:888: in wrapper
    return dispatch(args[0].__class__)(*args, **kw)
/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/site-packages/anndata/tests/helpers.py:764: in assert_adata_equal
    assert_equal(
/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/functools.py:888: in wrapper
    return dispatch(args[0].__class__)(*args, **kw)
/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/site-packages/anndata/tests/helpers.py:627: in assert_equal_dask_array
    assert_equal(b, a.compute(), exact, elem_name)
/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/site-packages/dask/base.py:376: in compute
    (result,) = compute(self, traverse=False, **kwargs)
/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/site-packages/dask/base.py:662: in compute
    results = schedule(dsk, keys, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

arrs = [<8x5 sparse matrix of type '<class 'numpy.float32'>'
	with 16 stored elements in Compressed Sparse Row format>, <10x5 sparse matrix of type '<class 'numpy.float32'>'
	with 12 stored elements in Compressed Sparse Row format>]
sorter = array([16,  6,  7, 11,  0,  4,  3, 13,  9, 10, 14, 15,  1,  5, 12,  2,  8,
       17])
axis = 0

    def concatenate_arrays(arrs, sorter, axis):
>       return np.take(np.concatenate(arrs, axis=axis), np.argsort(sorter), axis=axis)
E       ValueError: zero-dimensional arrays cannot be concatenated

/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/site-packages/dask/array/_shuffle.py:190: ValueError

Check failure on line 1 in test_concatenate_roundtrip[outer-sparse_dask_array-<lambda>-True]

See this annotation in the file changed.

@azure-pipelines azure-pipelines / scverse.anndata

test_concatenate_roundtrip[outer-sparse_dask_array-<lambda>-True] 

ValueError: zero-dimensional arrays cannot be concatenated
Raw output 
join_type = 'outer'
array_type = <function as_sparse_dask_array at 0x7fad43daf280>
concat_func = <function <lambda> at 0x7fad40ed2b80>, backwards_compat = True

    @mark_legacy_concatenate
    @pytest.mark.parametrize(
        ("concat_func", "backwards_compat"),
        [
            (partial(concat, merge="unique"), False),
            (lambda x, **kwargs: x[0].concatenate(x[1:], **kwargs), True),
        ],
    )
    def test_concatenate_roundtrip(join_type, array_type, concat_func, backwards_compat):
        adata = gen_adata((100, 10), X_type=array_type, **GEN_ADATA_DASK_ARGS)
    
        remaining = adata.obs_names
        subsets = []
        while len(remaining) > 0:
            n = min(len(remaining), np.random.choice(50))
            subset_idx = np.random.choice(remaining, n, replace=False)
            subsets.append(adata[subset_idx])
            remaining = remaining.difference(subset_idx)
    
        result = concat_func(subsets, join=join_type, uns_merge="same", index_unique=None)
    
        # Correcting for known differences
        orig, result = fix_known_differences(
            adata, result, backwards_compat=backwards_compat
        )
    
>       assert_equal(result[orig.obs_names].copy(), orig)

/home/vsts/work/1/s/tests/test_concatenate.py:197: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/functools.py:888: in wrapper
    return dispatch(args[0].__class__)(*args, **kw)
/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/site-packages/anndata/tests/helpers.py:764: in assert_adata_equal
    assert_equal(
/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/functools.py:888: in wrapper
    return dispatch(args[0].__class__)(*args, **kw)
/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/site-packages/anndata/tests/helpers.py:627: in assert_equal_dask_array
    assert_equal(b, a.compute(), exact, elem_name)
/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/site-packages/dask/base.py:376: in compute
    (result,) = compute(self, traverse=False, **kwargs)
/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/site-packages/dask/base.py:662: in compute
    results = schedule(dsk, keys, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

arrs = [<20x5 sparse matrix of type '<class 'numpy.float32'>'
	with 40 stored elements in Compressed Sparse Row format>, <11x5 sparse matrix of type '<class 'numpy.float32'>'
	with 22 stored elements in Compressed Sparse Row format>]
sorter = array([ 2, 13, 30,  0, 12,  8, 22, 24, 10, 23,  1, 26, 18, 17, 19, 25, 15,
       29, 21,  6,  7,  9, 27, 14, 11, 28, 20,  5, 16,  4,  3])
axis = 0

    def concatenate_arrays(arrs, sorter, axis):
>       return np.take(np.concatenate(arrs, axis=axis), np.argsort(sorter), axis=axis)
E       ValueError: zero-dimensional arrays cannot be concatenated

/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/site-packages/dask/array/_shuffle.py:190: ValueError

Check failure on line 1 in test_concatenate_roundtrip[outer-sparse_dask_array-concat_func0-False]

See this annotation in the file changed.

@azure-pipelines azure-pipelines / scverse.anndata

test_concatenate_roundtrip[outer-sparse_dask_array-concat_func0-False] 

ValueError: zero-dimensional arrays cannot be concatenated
Raw output 
join_type = 'outer'
array_type = <function as_sparse_dask_array at 0x7fad43daf280>
concat_func = functools.partial(<function concat at 0x7fad43e0eb80>, merge='unique')
backwards_compat = False

    @mark_legacy_concatenate
    @pytest.mark.parametrize(
        ("concat_func", "backwards_compat"),
        [
            (partial(concat, merge="unique"), False),
            (lambda x, **kwargs: x[0].concatenate(x[1:], **kwargs), True),
        ],
    )
    def test_concatenate_roundtrip(join_type, array_type, concat_func, backwards_compat):
        adata = gen_adata((100, 10), X_type=array_type, **GEN_ADATA_DASK_ARGS)
    
        remaining = adata.obs_names
        subsets = []
        while len(remaining) > 0:
            n = min(len(remaining), np.random.choice(50))
            subset_idx = np.random.choice(remaining, n, replace=False)
            subsets.append(adata[subset_idx])
            remaining = remaining.difference(subset_idx)
    
        result = concat_func(subsets, join=join_type, uns_merge="same", index_unique=None)
    
        # Correcting for known differences
        orig, result = fix_known_differences(
            adata, result, backwards_compat=backwards_compat
        )
    
>       assert_equal(result[orig.obs_names].copy(), orig)

/home/vsts/work/1/s/tests/test_concatenate.py:197: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/functools.py:888: in wrapper
    return dispatch(args[0].__class__)(*args, **kw)
/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/site-packages/anndata/tests/helpers.py:764: in assert_adata_equal
    assert_equal(
/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/functools.py:888: in wrapper
    return dispatch(args[0].__class__)(*args, **kw)
/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/site-packages/anndata/tests/helpers.py:627: in assert_equal_dask_array
    assert_equal(b, a.compute(), exact, elem_name)
/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/site-packages/dask/base.py:376: in compute
    (result,) = compute(self, traverse=False, **kwargs)
/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/site-packages/dask/base.py:662: in compute
    results = schedule(dsk, keys, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

arrs = [<19x5 sparse matrix of type '<class 'numpy.float32'>'
	with 38 stored elements in Compressed Sparse Row format>, <15x5 sparse matrix of type '<class 'numpy.float32'>'
	with 28 stored elements in Compressed Sparse Row format>]
sorter = array([33,  5,  2, 27, 30, 11, 14, 15, 20, 18, 22, 16, 21, 25,  9,  7,  1,
       28,  6, 26, 32, 29,  3, 19, 31,  0,  4, 17, 23, 10, 12, 13, 24,  8])
axis = 0

    def concatenate_arrays(arrs, sorter, axis):
>       return np.take(np.concatenate(arrs, axis=axis), np.argsort(sorter), axis=axis)
E       ValueError: zero-dimensional arrays cannot be concatenated

/opt/hostedtoolcache/Python/3.9.20/x64/lib/python3.9/site-packages/dask/array/_shuffle.py:190: ValueError
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