Movatterモバイル変換

[0]ホーム
URL:

Skip to content

Navigation Menu

Sign in
Appearance settings

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly

 Sign up
Appearance settings

Remove expired deprecation of using scipy matrix#393

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to ourterms of service andprivacy statement. We’ll occasionally send you account related emails.

Already on GitHub?Sign in to your account

Merged
eriknw merged 3 commits intopython-graphblas:mainfromeriknw:remove_scipy_matrix
Feb 21, 2023
Merged
Show file tree
Hide file tree
Changes fromall commits
Commits
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
40 changes: 0 additions & 40 deletionsgraphblas/io.py
View file
Open in desktop
Original file line numberDiff line numberDiff 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 DownExpand 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 deletionsgraphblas/tests/test_io.py
View file
Open in desktop
Original file line numberDiff line numberDiff 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 DownExpand 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 deletionsgraphblas/tests/test_numpyops.py
View file
Open in desktop
Original file line numberDiff line numberDiff 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 DownExpand 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
Copy link
MemberAuthor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others.Learn more.

I don't know if we strictly need to excludeasinh given the input arguments we use, butasinh 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 DownExpand Up@@ -148,18 +157,22 @@ def test_npbinary():
gb_left.dtype.name, ()
):
continue
ifnot _supports_complex and binary_name == "ldexp":
ifis_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
[8]ページ先頭
©2009-2025 Movatter.jp