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Remove expired deprecation of using scipy matrix #393

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Merged
merged 3 commits into from
Feb 21, 2023
Merged

Remove expired deprecation of using scipy matrix #393

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0213c4b
Remove expired deprecation of using scipy matrix
eriknw Feb 21, 2023
6e4bd6e
Maybe fix complex tests on Windows
eriknw Feb 21, 2023
9c9df06
Also asinh
eriknw Feb 21, 2023
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40 changes: 0 additions & 40 deletions graphblas/io.py
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Original file line number Diff line number Diff line change
Expand Up @@ -111,20 +111,6 @@ def from_numpy(m): # pragma: no cover (deprecated)
return from_scipy_sparse(A)


def from_scipy_sparse_matrix(m, *, dup_op=None, name=None):
"""Matrix dtype is inferred from m.dtype."""
_warn(
"`from_scipy_sparse_matrix` is deprecated; please use `from_scipy_sparse` instead.",
DeprecationWarning,
)
A = m.tocoo()
nrows, ncols = A.shape
dtype = _lookup_dtype(m.dtype)
return _Matrix.from_coo(
A.row, A.col, A.data, nrows=nrows, ncols=ncols, dtype=dtype, dup_op=dup_op, name=name
)


def from_scipy_sparse(A, *, dup_op=None, name=None):
"""Create a Matrix from a scipy.sparse array or matrix.

Expand Down Expand Up @@ -361,32 +347,6 @@ def to_numpy(m): # pragma: no cover (deprecated)
return sparse.toarray()


def to_scipy_sparse_matrix(m, format="csr"): # pragma: no cover (deprecated)
"""format: str in {'bsr', 'csr', 'csc', 'coo', 'lil', 'dia', 'dok'}."""
import scipy.sparse as ss

_warn(
"`to_scipy_sparse_matrix` is deprecated; please use `to_scipy_sparse` instead.",
DeprecationWarning,
)
format = format.lower()
if _output_type(m) is _Vector:
indices, data = m.to_coo()
if format == "csc":
return ss.csc_matrix((data, indices, [0, len(data)]), shape=(m._size, 1))
rv = ss.csr_matrix((data, indices, [0, len(data)]), shape=(1, m._size))
if format == "csr":
return rv
else:
rows, cols, data = m.to_coo()
rv = ss.coo_matrix((data, (rows, cols)), shape=m.shape)
if format == "coo":
return rv
if format not in {"bsr", "csr", "csc", "coo", "lil", "dia", "dok"}:
raise _GraphblasException(f"Invalid format: {format}")
return rv.asformat(format)


def to_scipy_sparse(A, format="csr"):
"""Create a scipy.sparse array from a GraphBLAS Matrix or Vector.

Expand Down
5 changes: 0 additions & 5 deletions graphblas/tests/test_io.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,8 +43,6 @@ def test_deprecated():
with pytest.warns(DeprecationWarning):
a2 = gb.io.to_numpy(v)
np.testing.assert_array_equal(a, a2)
with pytest.warns(DeprecationWarning):
gb.io.to_scipy_sparse_matrix(v, "coo")


@pytest.mark.skipif("not ss")
Expand Down Expand Up @@ -298,9 +296,6 @@ def test_from_scipy_sparse_duplicates():
a2 = gb.io.from_scipy_sparse(a, dup_op=gb.binary.plus)
expected = gb.Matrix.from_coo([0, 1, 2], [2, 1, 0], [1, 2, 7])
assert a2.isequal(expected)
with pytest.warns(DeprecationWarning):
a3 = gb.io.from_scipy_sparse_matrix(a, dup_op=gb.binary.plus)
assert a3.isequal(expected)


@pytest.mark.skipif("not ss")
Expand Down
31 changes: 22 additions & 9 deletions graphblas/tests/test_numpyops.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
# These tests are very slow, since they force creation of all
# numpy unary, binary, monoid, and semiring objects.
import itertools
import sys

import numpy as np
import pytest
Expand All @@ -10,11 +11,14 @@
import graphblas.monoid.numpy as npmonoid
import graphblas.semiring.numpy as npsemiring
import graphblas.unary.numpy as npunary
from graphblas import Vector
from graphblas import Vector, backend
from graphblas.dtypes import _supports_complex

from .conftest import compute

is_win = sys.platform.startswith("win")
suitesparse = backend == "suitesparse"


def test_numpyops_dir():
assert "exp2" in dir(npunary)
Expand Down Expand Up @@ -61,6 +65,11 @@ def test_npunary():
"INT64": {"reciprocal"},
"FC64": {"ceil", "floor", "trunc"},
}
if suitesparse and is_win:
# asin and asinh are known to be wrong in SuiteSparse:GraphBLAS
# due to limitation of MSVC with complex
blocklist["FC64"].update({"asin", "asinh"})
blocklist["FC32"] = {"asin", "asinh"}
Comment on lines +71 to +72
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I don't know if we strictly need to exclude asinh given the input arguments we use, but asinh was called out in the documentation for not working.

isclose = gb.binary.isclose(1e-6, 0)
for gb_input, np_input in data:
for unary_name in sorted(npunary._unary_names):
Expand Down Expand Up @@ -148,18 +157,22 @@ def test_npbinary():
gb_left.dtype.name, ()
):
continue
if not _supports_complex and binary_name == "ldexp":
if is_win and binary_name == "ldexp":
# On Windows, the second argument must be int32 or less (I'm not sure why)
np_right = np_right.astype(np.int32)
with np.errstate(divide="ignore", over="ignore", under="ignore", invalid="ignore"):
gb_result = gb_left.ewise_mult(gb_right, op).new()
if gb_left.dtype == "BOOL" and gb_result.dtype == "FP32":
np_result = getattr(np, binary_name)(np_left, np_right, dtype="float32")
compare_op = isclose
else:
np_result = getattr(np, binary_name)(np_left, np_right)
compare_op = npbinary.equal

try:
if gb_left.dtype == "BOOL" and gb_result.dtype == "FP32":
np_result = getattr(np, binary_name)(np_left, np_right, dtype="float32")
compare_op = isclose
else:
np_result = getattr(np, binary_name)(np_left, np_right)
compare_op = npbinary.equal
except Exception: # pragma: no cover (debug)
print(f"Error computing numpy result for {binary_name}")
print(f"dtypes: ({gb_left.dtype}, {gb_right.dtype}) -> {gb_result.dtype}")
raise
np_result = Vector.from_coo(np.arange(np_left.size), np_result, dtype=gb_result.dtype)

assert gb_result.nvals == np_result.size
Expand Down