# # 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 from distutils.version import LooseVersion import numpy as np import pandas as pd import pyspark.pandas as ps from pyspark.testing.pandasutils import PandasOnSparkTestCase, TestUtils from pyspark.pandas.window import Rolling class RollingTestsMixin: def test_rolling_error(self): with self.assertRaisesRegex(ValueError, "window must be >= 0"): ps.range(10).rolling(window=-1) with self.assertRaisesRegex(ValueError, "min_periods must be >= 0"): ps.range(10).rolling(window=1, min_periods=-1) with self.assertRaisesRegex( TypeError, "psdf_or_psser must be a series or dataframe; however, got:.*int" ): Rolling(1, 2) def _test_rolling_func(self, ps_func, pd_func=None): if not pd_func: pd_func = ps_func if isinstance(pd_func, str): pd_func = self.convert_str_to_lambda(pd_func) if isinstance(ps_func, str): ps_func = self.convert_str_to_lambda(ps_func) pser = pd.Series([1, 2, 3, 7, 9, 8], index=np.random.rand(6), name="a") psser = ps.from_pandas(pser) self.assert_eq(ps_func(psser.rolling(2)), pd_func(pser.rolling(2))) self.assert_eq(ps_func(psser.rolling(2)).sum(), pd_func(pser.rolling(2)).sum()) # Multiindex pser = pd.Series( [1, 2, 3], index=pd.MultiIndex.from_tuples([("a", "x"), ("a", "y"), ("b", "z")]), name="a", ) psser = ps.from_pandas(pser) self.assert_eq(ps_func(psser.rolling(2)), pd_func(pser.rolling(2))) pdf = pd.DataFrame( {"a": [1.0, 2.0, 3.0, 2.0], "b": [4.0, 2.0, 3.0, 1.0]}, index=np.random.rand(4) ) psdf = ps.from_pandas(pdf) self.assert_eq(ps_func(psdf.rolling(2)), pd_func(pdf.rolling(2))) self.assert_eq(ps_func(psdf.rolling(2)).sum(), pd_func(pdf.rolling(2)).sum()) # Multiindex column columns = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y")]) pdf.columns = columns psdf.columns = columns self.assert_eq(ps_func(psdf.rolling(2)), pd_func(pdf.rolling(2))) def test_rolling_min(self): self._test_rolling_func("min") def test_rolling_max(self): self._test_rolling_func("max") def test_rolling_mean(self): self._test_rolling_func("mean") def test_rolling_quantile(self): self._test_rolling_func(lambda x: x.quantile(0.5), lambda x: x.quantile(0.5, "lower")) def test_rolling_sum(self): self._test_rolling_func("sum") @unittest.skipIf( LooseVersion(pd.__version__) >= LooseVersion("2.0.0"), "TODO(SPARK-43451): Enable RollingTests.test_rolling_count for pandas 2.0.0.", ) def test_rolling_count(self): self._test_rolling_func("count") def test_rolling_std(self): self._test_rolling_func("std") def test_rolling_var(self): self._test_rolling_func("var") def test_rolling_skew(self): self._test_rolling_func("skew") def test_rolling_kurt(self): self._test_rolling_func("kurt") def _test_groupby_rolling_func(self, ps_func, pd_func=None): if not pd_func: pd_func = ps_func if isinstance(pd_func, str): pd_func = self.convert_str_to_lambda(pd_func) if isinstance(ps_func, str): ps_func = self.convert_str_to_lambda(ps_func) pser = pd.Series([1, 2, 3, 2], index=np.random.rand(4), name="a") psser = ps.from_pandas(pser) self.assert_eq( ps_func(psser.groupby(psser).rolling(2)).sort_index(), pd_func(pser.groupby(pser).rolling(2)).sort_index(), ) self.assert_eq( ps_func(psser.groupby(psser).rolling(2)).sum(), pd_func(pser.groupby(pser).rolling(2)).sum(), ) # Multiindex pser = pd.Series( [1, 2, 3, 2], index=pd.MultiIndex.from_tuples([("a", "x"), ("a", "y"), ("b", "z"), ("c", "z")]), name="a", ) psser = ps.from_pandas(pser) self.assert_eq( ps_func(psser.groupby(psser).rolling(2)).sort_index(), pd_func(pser.groupby(pser).rolling(2)).sort_index(), ) pdf = pd.DataFrame({"a": [1.0, 2.0, 3.0, 2.0], "b": [4.0, 2.0, 3.0, 1.0]}) psdf = ps.from_pandas(pdf) # The behavior of GroupBy.rolling is changed from pandas 1.3. if LooseVersion(pd.__version__) >= LooseVersion("1.3"): self.assert_eq( ps_func(psdf.groupby(psdf.a).rolling(2)).sort_index(), pd_func(pdf.groupby(pdf.a).rolling(2)).sort_index(), ) self.assert_eq( ps_func(psdf.groupby(psdf.a).rolling(2)).sum(), pd_func(pdf.groupby(pdf.a).rolling(2)).sum(), ) self.assert_eq( ps_func(psdf.groupby(psdf.a + 1).rolling(2)).sort_index(), pd_func(pdf.groupby(pdf.a + 1).rolling(2)).sort_index(), ) else: self.assert_eq( ps_func(psdf.groupby(psdf.a).rolling(2)).sort_index(), pd_func(pdf.groupby(pdf.a).rolling(2)).drop("a", axis=1).sort_index(), ) self.assert_eq( ps_func(psdf.groupby(psdf.a).rolling(2)).sum(), pd_func(pdf.groupby(pdf.a).rolling(2)).sum().drop("a"), ) self.assert_eq( ps_func(psdf.groupby(psdf.a + 1).rolling(2)).sort_index(), pd_func(pdf.groupby(pdf.a + 1).rolling(2)).drop("a", axis=1).sort_index(), ) self.assert_eq( ps_func(psdf.b.groupby(psdf.a).rolling(2)).sort_index(), pd_func(pdf.b.groupby(pdf.a).rolling(2)).sort_index(), ) self.assert_eq( ps_func(psdf.groupby(psdf.a)["b"].rolling(2)).sort_index(), pd_func(pdf.groupby(pdf.a)["b"].rolling(2)).sort_index(), ) self.assert_eq( ps_func(psdf.groupby(psdf.a)[["b"]].rolling(2)).sort_index(), pd_func(pdf.groupby(pdf.a)[["b"]].rolling(2)).sort_index(), ) # Multiindex column columns = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y")]) pdf.columns = columns psdf.columns = columns # The behavior of GroupBy.rolling is changed from pandas 1.3. if LooseVersion(pd.__version__) >= LooseVersion("1.3"): self.assert_eq( ps_func(psdf.groupby(("a", "x")).rolling(2)).sort_index(), pd_func(pdf.groupby(("a", "x")).rolling(2)).sort_index(), ) self.assert_eq( ps_func(psdf.groupby([("a", "x"), ("a", "y")]).rolling(2)).sort_index(), pd_func(pdf.groupby([("a", "x"), ("a", "y")]).rolling(2)).sort_index(), ) else: self.assert_eq( ps_func(psdf.groupby(("a", "x")).rolling(2)).sort_index(), pd_func(pdf.groupby(("a", "x")).rolling(2)).drop(("a", "x"), axis=1).sort_index(), ) self.assert_eq( ps_func(psdf.groupby([("a", "x"), ("a", "y")]).rolling(2)).sort_index(), pd_func(pdf.groupby([("a", "x"), ("a", "y")]).rolling(2)) .drop([("a", "x"), ("a", "y")], axis=1) .sort_index(), ) @unittest.skipIf( LooseVersion(pd.__version__) >= LooseVersion("2.0.0"), "TODO(SPARK-43452): Enable RollingTests.test_groupby_rolling_count for pandas 2.0.0.", ) def test_groupby_rolling_count(self): self._test_groupby_rolling_func("count") def test_groupby_rolling_min(self): self._test_groupby_rolling_func("min") def test_groupby_rolling_max(self): self._test_groupby_rolling_func("max") def test_groupby_rolling_mean(self): self._test_groupby_rolling_func("mean") def test_groupby_rolling_quantile(self): self._test_groupby_rolling_func( lambda x: x.quantile(0.5), lambda x: x.quantile(0.5, "lower") ) def test_groupby_rolling_sum(self): self._test_groupby_rolling_func("sum") def test_groupby_rolling_std(self): # TODO: `std` now raise error in pandas 1.0.0 self._test_groupby_rolling_func("std") def test_groupby_rolling_var(self): self._test_groupby_rolling_func("var") def test_groupby_rolling_skew(self): self._test_groupby_rolling_func("skew") def test_groupby_rolling_kurt(self): self._test_groupby_rolling_func("kurt") class RollingTests(RollingTestsMixin, PandasOnSparkTestCase, TestUtils): pass if __name__ == "__main__": import unittest from pyspark.pandas.tests.test_rolling import * # noqa: F401 try: import xmlrunner testRunner = xmlrunner.XMLTestRunner(output="target/test-reports", verbosity=2) except ImportError: testRunner = None unittest.main(testRunner=testRunner, verbosity=2)