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[MNT] Small NumPy 2 related fixes #5954

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merged 10 commits into from
Jul 28, 2024
+25 −20
Merged

[MNT] Small NumPy 2 related fixes #5954

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dea0c57
MAINT: Small NumPy 2 related fixes
seberg Jul 3, 2024
3565fef
TST: `asfarray` is removed, it is the same as `asarray` here
seberg Jul 3, 2024
449466c
TST: Avoid behavior change in return_inverse of unique
seberg Jul 3, 2024
bc7f7c1
TST: Use deepcopy for copying the random state
seberg Jul 3, 2024
3064e56
STY: Fixup copyright/pre-commit
seberg Jul 3, 2024
e81b283
Ignore python/_thirdparty for style pre-commit check
seberg Jul 3, 2024
f266d0b
Simplify return_inverse fixup
seberg Jul 11, 2024
30c706c
Can't use `copy=None` on some older NumPy versions...
seberg Jul 16, 2024
f399da2
Fix copyright exclusion on thirdparty directory
jakirkham Jul 18, 2024
30cba15
Merge branch 'branch-24.08' into numpy2
dantegd Jul 28, 2024
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3 changes: 2 additions & 1 deletion .pre-commit-config.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -73,7 +73,8 @@ repos:
setup[.]cfg$
exclude: |
(?x)
cpp/src/tsne/cannylab/bh[.]cu$
cpp/src/tsne/cannylab/bh[.]cu$|
python/cuml/_thirdparty
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- id: verify-alpha-spec
- repo: https://github.com/rapidsai/dependency-file-generator
rev: v1.13.11
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2 changes: 1 addition & 1 deletion python/cuml/cuml/_thirdparty/sklearn/utils/sparsefuncs.py
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Expand Up @@ -214,7 +214,7 @@ def _sparse_min_or_max(X, axis, min_or_max):
if np.isnan(m):
if 'nan' in min_or_max:
m = 0
elif X.nnz != cpu_np.product(X.shape):
elif X.nnz != cpu_np.prod(X.shape):
if 'min' in min_or_max:
m = m if m <= 0 else 0
else:
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16 changes: 10 additions & 6 deletions python/cuml/cuml/internals/array.py
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Expand Up @@ -1172,12 +1172,16 @@ def from_input(
if (
not fail_on_order and order != arr.order and order != "K"
) or make_copy:
Comment on lines 1163 to 1165
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Suggested change
if (
not fail_on_order and order != arr.order and order != "K"
) or make_copy:
if not fail_on_order and order != arr.order and order != "K":

arr = cls(
arr.mem_type.xpy.array(
arr.to_output("array"), order=order, copy=make_copy
),
index=index,
)
if make_copy:
data = arr.mem_type.xpy.array(
arr.to_output("array"), order=order
)
else:
data = arr.mem_type.xpy.asarray(
arr.to_output("array"), order=order
)

arr = cls(data, index=index)
Comment on lines +1166 to +1175
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Suggested change
if make_copy:
data = arr.mem_type.xpy.array(
arr.to_output("array"), order=order
)
else:
data = arr.mem_type.xpy.asarray(
arr.to_output("array"), order=order
)
arr = cls(data, index=index)
arr = cls(
arr.mem_type.xpy.asarray(arr.to_output("array"), order=order),
index=index,
)
elif make_copy:
arr = cls(
arr.mem_type.xpy.array(arr.to_output("array"), order=order), index=index
)


n_rows = arr.shape[0]

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4 changes: 2 additions & 2 deletions python/cuml/cuml/tests/test_make_classification.py
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@@ -1,5 +1,5 @@
#
# Copyright (c) 2020-2023, NVIDIA CORPORATION.
# Copyright (c) 2020-2024, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
Expand Down Expand Up @@ -115,7 +115,7 @@ def test_make_classification_informative_features():

# Cluster by sign, viewed as strings to allow uniquing
signs = np.sign(cp.asnumpy(X))
signs = signs.view(dtype="|S{0}".format(signs.strides[0]))
signs = signs.view(dtype="|S{0}".format(signs.strides[0])).ravel()
unique_signs, cluster_index = np.unique(signs, return_inverse=True)

assert (
Expand Down
12 changes: 6 additions & 6 deletions python/cuml/cuml/tests/test_metrics.py
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Expand Up @@ -1065,7 +1065,7 @@ def test_pairwise_distances(metric: str, matrix_size, is_col_major):
cp.testing.assert_array_almost_equal(S, S2, decimal=compare_precision)

# Change precision of one parameter
Y = np.asfarray(Y, dtype=np.float32)
Y = np.asarray(Y, dtype=np.float32)
S = pairwise_distances(X, Y, metric=metric)
S2 = ref_dense_pairwise_dist(X, Y, metric=metric)
cp.testing.assert_array_almost_equal(S, S2, decimal=compare_precision)
Expand All @@ -1074,8 +1074,8 @@ def test_pairwise_distances(metric: str, matrix_size, is_col_major):
compare_precision = 2

# Change precision of both parameters to float
X = np.asfarray(X, dtype=np.float32)
Y = np.asfarray(Y, dtype=np.float32)
X = np.asarray(X, dtype=np.float32)
Y = np.asarray(Y, dtype=np.float32)
S = pairwise_distances(X, Y, metric=metric)
S2 = ref_dense_pairwise_dist(X, Y, metric=metric)
cp.testing.assert_array_almost_equal(S, S2, decimal=compare_precision)
Expand Down Expand Up @@ -1132,8 +1132,8 @@ def test_pairwise_distances_sklearn_comparison(metric: str, matrix_size):
# For fp32, compare at 4 decimals, (3 places less than the ~7 max)
compare_precision = 4

X = np.asfarray(X, dtype=np.float32)
Y = np.asfarray(Y, dtype=np.float32)
X = np.asarray(X, dtype=np.float32)
Y = np.asarray(Y, dtype=np.float32)

# Compare to sklearn, fp32
S = pairwise_distances(X, Y, metric=metric)
Expand Down Expand Up @@ -1228,7 +1228,7 @@ def test_pairwise_distances_exceptions():

X_int = rng.randint(10, size=(5, 4))
X_double = rng.random_sample((5, 4))
X_float = np.asfarray(X_double, dtype=np.float32)
X_float = np.asarray(X_double, dtype=np.float32)
X_bool = rng.choice([True, False], size=(5, 4))

# Test int inputs (only float/double accepted at this time)
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8 changes: 4 additions & 4 deletions python/cuml/cuml/tests/test_umap.py
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Expand Up @@ -420,9 +420,9 @@ def get_embedding(n_components, random_state):
)
return reducer.fit_transform(data, convert_dtype=True)

state = copy.copy(random_state)
state = copy.deepcopy(random_state)
cuml_embedding1 = get_embedding(n_components, state)
state = copy.copy(random_state)
state = copy.deepcopy(random_state)
cuml_embedding2 = get_embedding(n_components, state)

assert not np.isnan(cuml_embedding1).any()
Expand Down Expand Up @@ -475,9 +475,9 @@ def get_embedding(n_components, random_state):
reducer.fit(fit_data, convert_dtype=True)
return reducer.transform(transform_data, convert_dtype=True)

state = copy.copy(random_state)
state = copy.deepcopy(random_state)
cuml_embedding1 = get_embedding(n_components, state)
state = copy.copy(random_state)
state = copy.deepcopy(random_state)
cuml_embedding2 = get_embedding(n_components, state)

assert not np.isnan(cuml_embedding1).any()
Expand Down
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