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[SPARK-44051][TESTS][PS][CONNECT] Split `pyspark.pandas.tests.connect…
….data_type_ops.test_parity_num_ops` ### What changes were proposed in this pull request? Split `pyspark.pandas.tests.connect.data_type_ops.test_parity_num_ops` ### Why are the changes needed? it is slow ``` Starting test(python3.9): pyspark.pandas.tests.connect.data_type_ops.test_parity_num_ops (temp output: /__w/spark/spark/python/target/fd59d461-ba78-4672-a164-5d0790c57fb0/python3.9__pyspark.pandas.tests.connect.data_type_ops.test_parity_num_ops__gsu_twd5.log) Finished test(python3.9): pyspark.pandas.tests.connect.data_type_ops.test_parity_num_ops (333s) ... 1 tests were skipped ``` ### Does this PR introduce _any_ user-facing change? no, test-only ### How was this patch tested? updated CI Closes apache#41591 from zhengruifeng/ps_test_split_num_ops. Authored-by: Ruifeng Zheng <ruifengz@apache.org> Signed-off-by: Ruifeng Zheng <ruifengz@apache.org>
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python/pyspark/pandas/tests/connect/data_type_ops/test_parity_num_arithmetic.py
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# | ||
# Licensed to the Apache Software Foundation (ASF) under one or more | ||
# contributor license agreements. See the NOTICE file distributed with | ||
# this work for additional information regarding copyright ownership. | ||
# The ASF licenses this file to You under the Apache License, Version 2.0 | ||
# (the "License"); you may not use this file except in compliance with | ||
# the License. You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
# | ||
import unittest | ||
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from pyspark import pandas as ps | ||
from pyspark.pandas.tests.data_type_ops.test_num_arithmetic import ArithmeticTestsMixin | ||
from pyspark.pandas.tests.connect.data_type_ops.testing_utils import OpsTestBase | ||
from pyspark.testing.pandasutils import PandasOnSparkTestUtils | ||
from pyspark.testing.connectutils import ReusedConnectTestCase | ||
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class ArithmeticParityTests( | ||
ArithmeticTestsMixin, PandasOnSparkTestUtils, OpsTestBase, ReusedConnectTestCase | ||
): | ||
@property | ||
def psdf(self): | ||
return ps.from_pandas(self.pdf) | ||
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if __name__ == "__main__": | ||
from pyspark.pandas.tests.connect.data_type_ops.test_parity_num_arithmetic import * # noqa | ||
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try: | ||
import xmlrunner # type: ignore[import] | ||
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testRunner = xmlrunner.XMLTestRunner(output="target/test-reports", verbosity=2) | ||
except ImportError: | ||
testRunner = None | ||
unittest.main(testRunner=testRunner, verbosity=2) |
43 changes: 43 additions & 0 deletions
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python/pyspark/pandas/tests/connect/data_type_ops/test_parity_num_reverse.py
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# | ||
# Licensed to the Apache Software Foundation (ASF) under one or more | ||
# contributor license agreements. See the NOTICE file distributed with | ||
# this work for additional information regarding copyright ownership. | ||
# The ASF licenses this file to You under the Apache License, Version 2.0 | ||
# (the "License"); you may not use this file except in compliance with | ||
# the License. You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
# | ||
import unittest | ||
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from pyspark import pandas as ps | ||
from pyspark.pandas.tests.data_type_ops.test_num_reverse import ReverseTestsMixin | ||
from pyspark.pandas.tests.connect.data_type_ops.testing_utils import OpsTestBase | ||
from pyspark.testing.pandasutils import PandasOnSparkTestUtils | ||
from pyspark.testing.connectutils import ReusedConnectTestCase | ||
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class ReverseParityTests( | ||
ReverseTestsMixin, PandasOnSparkTestUtils, OpsTestBase, ReusedConnectTestCase | ||
): | ||
@property | ||
def psdf(self): | ||
return ps.from_pandas(self.pdf) | ||
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if __name__ == "__main__": | ||
from pyspark.pandas.tests.connect.data_type_ops.test_parity_num_reverse import * # noqa: F401 | ||
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try: | ||
import xmlrunner # type: ignore[import] | ||
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testRunner = xmlrunner.XMLTestRunner(output="target/test-reports", verbosity=2) | ||
except ImportError: | ||
testRunner = None | ||
unittest.main(testRunner=testRunner, verbosity=2) |
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python/pyspark/pandas/tests/data_type_ops/test_num_arithmetic.py
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# | ||
# Licensed to the Apache Software Foundation (ASF) under one or more | ||
# contributor license agreements. See the NOTICE file distributed with | ||
# this work for additional information regarding copyright ownership. | ||
# The ASF licenses this file to You under the Apache License, Version 2.0 | ||
# (the "License"); you may not use this file except in compliance with | ||
# the License. You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
# | ||
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import datetime | ||
import unittest | ||
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import pandas as pd | ||
import numpy as np | ||
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from pyspark import pandas as ps | ||
from pyspark.pandas.tests.data_type_ops.testing_utils import OpsTestBase | ||
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class ArithmeticTestsMixin: | ||
"""Unit tests for arithmetic operations of numeric data types. | ||
A few test cases are disabled because pandas-on-Spark returns float64 whereas pandas | ||
returns float32. | ||
The underlying reason is the respective Spark operations return DoubleType always. | ||
""" | ||
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@property | ||
def float_pser(self): | ||
return pd.Series([1, 2, 3], dtype=float) | ||
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@property | ||
def float_psser(self): | ||
return ps.from_pandas(self.float_pser) | ||
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def test_add(self): | ||
pdf, psdf = self.pdf, self.psdf | ||
for col in self.numeric_df_cols: | ||
pser, psser = pdf[col], psdf[col] | ||
self.assert_eq(pser + pser, psser + psser) | ||
self.assert_eq(pser + 1, psser + 1) | ||
# self.assert_eq(pser + 0.1, psser + 0.1) | ||
self.assert_eq(pser + pser.astype(bool), psser + psser.astype(bool)) | ||
self.assert_eq(pser + True, psser + True) | ||
self.assert_eq(pser + False, psser + False) | ||
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for n_col in self.non_numeric_df_cols: | ||
if n_col == "bool": | ||
self.assert_eq(pser + pdf[n_col], psser + psdf[n_col]) | ||
else: | ||
self.assertRaises(TypeError, lambda: psser + psdf[n_col]) | ||
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def test_sub(self): | ||
pdf, psdf = self.pdf, self.psdf | ||
for col in self.numeric_df_cols: | ||
pser, psser = pdf[col], psdf[col] | ||
self.assert_eq(pser - pser, psser - psser) | ||
self.assert_eq(pser - 1, psser - 1) | ||
# self.assert_eq(pser - 0.1, psser - 0.1) | ||
self.assert_eq(pser - pser.astype(bool), psser - psser.astype(bool)) | ||
self.assert_eq(pser - True, psser - True) | ||
self.assert_eq(pser - False, psser - False) | ||
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for n_col in self.non_numeric_df_cols: | ||
if n_col == "bool": | ||
self.assert_eq(pser - pdf[n_col], psser - psdf[n_col]) | ||
else: | ||
self.assertRaises(TypeError, lambda: psser - psdf[n_col]) | ||
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def test_mul(self): | ||
pdf, psdf = self.pdf, self.psdf | ||
for col in self.numeric_df_cols: | ||
pser, psser = pdf[col], psdf[col] | ||
self.assert_eq(pser * pser, psser * psser) | ||
self.assert_eq(pser * pser.astype(bool), psser * psser.astype(bool)) | ||
self.assert_eq(pser * True, psser * True) | ||
self.assert_eq(pser * False, psser * False) | ||
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if psser.dtype in [int, np.int32]: | ||
self.assert_eq(pser * pdf["string"], psser * psdf["string"]) | ||
else: | ||
self.assertRaises(TypeError, lambda: psser * psdf["string"]) | ||
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self.assert_eq(pser * pdf["bool"], psser * psdf["bool"]) | ||
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self.assertRaises(TypeError, lambda: psser * psdf["datetime"]) | ||
self.assertRaises(TypeError, lambda: psser * psdf["date"]) | ||
self.assertRaises(TypeError, lambda: psser * psdf["categorical"]) | ||
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def test_truediv(self): | ||
pdf, psdf = self.pdf, self.psdf | ||
for col in self.numeric_df_cols: | ||
pser, psser = pdf[col], psdf[col] | ||
if psser.dtype in [float, int, np.int32]: | ||
self.assert_eq(pser / pser, psser / psser) | ||
self.assert_eq(pser / pser.astype(bool), psser / psser.astype(bool)) | ||
self.assert_eq(pser / True, psser / True) | ||
self.assert_eq(pser / False, psser / False) | ||
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for n_col in self.non_numeric_df_cols: | ||
if n_col == "bool": | ||
self.assert_eq(pdf["float"] / pdf[n_col], psdf["float"] / psdf[n_col]) | ||
else: | ||
self.assertRaises(TypeError, lambda: psser / psdf[n_col]) | ||
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def test_floordiv(self): | ||
pdf, psdf = self.pdf, self.psdf | ||
pser, psser = pdf["float"], psdf["float"] | ||
self.assert_eq(pser // pser, psser // psser) | ||
self.assert_eq(pser // pser.astype(bool), psser // psser.astype(bool)) | ||
self.assert_eq(pser // True, psser // True) | ||
self.assert_eq(pser // False, psser // False) | ||
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for n_col in self.non_numeric_df_cols: | ||
if n_col == "bool": | ||
self.assert_eq(pdf["float"] // pdf["bool"], psdf["float"] // psdf["bool"]) | ||
else: | ||
for col in self.numeric_df_cols: | ||
psser = psdf[col] | ||
self.assertRaises(TypeError, lambda: psser // psdf[n_col]) | ||
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def test_mod(self): | ||
pdf, psdf = self.pdf, self.psdf | ||
for col in self.numeric_df_cols: | ||
pser, psser = pdf[col], psdf[col] | ||
self.assert_eq(pser % pser, psser % psser) | ||
self.assert_eq(pser % pser.astype(bool), psser % psser.astype(bool)) | ||
self.assert_eq(pser % True, psser % True) | ||
if col in ["int", "int32"]: | ||
self.assert_eq( | ||
pd.Series([np.nan, np.nan, np.nan], dtype=float, name=col), psser % False | ||
) | ||
else: | ||
self.assert_eq( | ||
pd.Series([np.nan, np.nan, np.nan], dtype=pser.dtype, name=col), psser % False | ||
) | ||
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for n_col in self.non_numeric_df_cols: | ||
if n_col == "bool": | ||
self.assert_eq(pdf["float"] % pdf[n_col], psdf["float"] % psdf[n_col]) | ||
else: | ||
self.assertRaises(TypeError, lambda: psser % psdf[n_col]) | ||
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def test_pow(self): | ||
pdf, psdf = self.pdf, self.psdf | ||
for col in self.numeric_df_cols: | ||
pser, psser = pdf[col], psdf[col] | ||
if col in ["float", "float_w_nan"]: | ||
self.assert_eq(pser**pser, psser**psser) | ||
self.assert_eq(pser ** pser.astype(bool), psser ** psser.astype(bool)) | ||
self.assert_eq(pser**True, psser**True) | ||
self.assert_eq(pser**False, psser**False) | ||
self.assert_eq(pser**1, psser**1) | ||
self.assert_eq(pser**0, psser**0) | ||
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for n_col in self.non_numeric_df_cols: | ||
if n_col == "bool": | ||
self.assert_eq(pdf["float"] ** pdf[n_col], psdf["float"] ** psdf[n_col]) | ||
else: | ||
self.assertRaises(TypeError, lambda: psser ** psdf[n_col]) | ||
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class ArithmeticTests(ArithmeticTestsMixin, OpsTestBase): | ||
pass | ||
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if __name__ == "__main__": | ||
from pyspark.pandas.tests.data_type_ops.test_num_arithmetic import * # noqa: F401 | ||
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try: | ||
import xmlrunner | ||
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testRunner = xmlrunner.XMLTestRunner(output="target/test-reports", verbosity=2) | ||
except ImportError: | ||
testRunner = None | ||
unittest.main(testRunner=testRunner, verbosity=2) |
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