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TEST: Reduce redundant numerical test matrices #24019

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d758590
TEST: Reduce covariance correlation test matrix
vyasr Sep 6, 2026
9c52a26
TEST: Remove unused extension compilation type parameter
vyasr Sep 6, 2026
8550120
TEST: Align covariance correlation test inputs
vyasr Sep 7, 2026
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3 changes: 1 addition & 2 deletions python/cudf/cudf/tests/private_objects/test_extension_compilation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -72,8 +72,7 @@ def func(x):


@pytest.mark.parametrize("op", arith_ops)
@pytest.mark.parametrize("ty", number_types, ids=number_ids)
def test_execute_masked_binary(op, ty):
def test_execute_masked_binary(op):
@cuda.jit(device=True)
def func(x, y):
return op(x, y)
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82 changes: 35 additions & 47 deletions python/cudf/cudf/tests/series/methods/test_cov_corr.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
# SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION.
# SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0


Expand All @@ -11,33 +11,46 @@
from cudf.testing import assert_eq
from cudf.testing._utils import expect_warning_if


@pytest.mark.parametrize(
"data1",
[
COV_CORR_DATA_PAIRS = [
pytest.param(
np.random.default_rng(seed=0).normal(-100, 100, 1000),
np.random.default_rng(seed=0).normal(-100, 100, 1000),
id="normal",
),
pytest.param(
np.random.default_rng(seed=0).integers(-50, 50, 1000),
np.zeros(100),
np.repeat(np.nan, 100),
np.random.default_rng(seed=0).integers(-50, 50, 1000),
id="integers",
),
pytest.param(np.zeros(100), np.zeros(100), id="constant"),
pytest.param(
np.repeat(np.nan, 100), np.repeat(np.nan, 100), id="all-null"
),
pytest.param(
np.array([1.123, 2.343, np.nan, 0.0]),
np.array([1.123, 2.343, np.nan, 0.0]),
id="nullable",
),
pytest.param(
pa.array([5, 10, 53, None, np.nan, None]),
np.array([1.0, 4.0, 9.0, np.nan, 16.0, 25.0]),
id="arrow",
),
pytest.param(
pd.Series([1.1, 2.32, 43.4], index=[0, 4, 3]),
np.array([], dtype="float64"),
pd.Series([43.4, 1.1, 2.32], index=[3, 0, 4]),
id="indexed-series",
),
pytest.param(np.array([], dtype="float64"), np.array([5]), id="empty"),
pytest.param(
np.array([-3]),
],
)
@pytest.mark.parametrize(
"data2",
[
np.random.default_rng(seed=0).normal(-100, 100, 1000),
np.random.default_rng(seed=0).integers(-50, 50, 1000),
np.zeros(100),
np.repeat(np.nan, 100),
np.array([1.123, 2.343, np.nan, 0.0]),
pd.Series([1.1, 2.32, 43.4], index=[0, 500, 4000]),
np.array([5]),
],
)
id="singleton",
),
]


@pytest.mark.parametrize(("data1", "data2"), COV_CORR_DATA_PAIRS)
def test_cov1d(data1, data2):
gs1 = cudf.Series(data1)
gs2 = cudf.Series(data2)
Expand All @@ -56,32 +69,7 @@ def test_cov1d(data1, data2):
np.testing.assert_approx_equal(got, expected, significant=8)


@pytest.mark.parametrize(
"data1",
[
np.random.default_rng(seed=0).normal(-100, 100, 1000),
np.random.default_rng(seed=0).integers(-50, 50, 1000),
np.zeros(100),
np.repeat(np.nan, 100),
np.array([1.123, 2.343, np.nan, 0.0]),
pa.array([5, 10, 53, None, np.nan, None]),
pd.Series([1.1032, 2.32, 43.4], index=[0, 4, 3]),
np.array([], dtype="float64"),
np.array([-3]),
],
)
@pytest.mark.parametrize(
"data2",
[
np.random.default_rng(seed=0).normal(-100, 100, 1000),
np.random.default_rng(seed=0).integers(-50, 50, 1000),
np.zeros(100),
np.repeat(np.nan, 100),
np.array([1.123, 2.343, np.nan, 0.0]),
pd.Series([1.1, 2.32, 43.4], index=[0, 500, 4000]),
np.array([5]),
],
)
@pytest.mark.parametrize(("data1", "data2"), COV_CORR_DATA_PAIRS)
def test_corr1d(data1, data2, corr_method):
if corr_method == "spearman":
# Pandas uses scipy.stats.spearmanr code-path
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