# # 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. # from distutils.version import LooseVersion import numpy as np import pandas as pd from pyspark import pandas as ps from pyspark.pandas import set_option, reset_option from pyspark.pandas.numpy_compat import unary_np_spark_mappings, binary_np_spark_mappings from pyspark.pandas.testing.utils import ReusedSQLTestCase, SQLTestUtils class NumPyCompatTest(ReusedSQLTestCase, SQLTestUtils): blacklist = [ # Koalas does not currently support "conj", "conjugate", "isnat", "matmul", "frexp", # Values are close enough but tests failed. "arccos", "exp", "expm1", "log", # flaky "log10", # flaky "log1p", # flaky "modf", "floor_divide", # flaky # Results seem inconsistent in a different version of, I (Hyukjin) suspect, PyArrow. # From PyArrow 0.15, seems it returns the correct results via PySpark. Probably we # can enable it later when Koalas switches to PyArrow 0.15 completely. "left_shift", ] @property def pdf(self): return pd.DataFrame( {"a": [1, 2, 3, 4, 5, 6, 7, 8, 9], "b": [4, 5, 6, 3, 2, 1, 0, 0, 0]}, index=[0, 1, 3, 5, 6, 8, 9, 9, 9], ) @property def kdf(self): return ps.from_pandas(self.pdf) def test_np_add_series(self): kdf = self.kdf pdf = self.pdf if LooseVersion(pd.__version__) < LooseVersion("0.25"): self.assert_eq(np.add(kdf.a, kdf.b), np.add(pdf.a, pdf.b).rename()) else: self.assert_eq(np.add(kdf.a, kdf.b), np.add(pdf.a, pdf.b)) kdf = self.kdf pdf = self.pdf self.assert_eq(np.add(kdf.a, 1), np.add(pdf.a, 1)) def test_np_add_index(self): k_index = self.kdf.index p_index = self.pdf.index self.assert_eq(np.add(k_index, k_index), np.add(p_index, p_index)) def test_np_unsupported_series(self): kdf = self.kdf with self.assertRaisesRegex(NotImplementedError, "pandas.*not.*support.*sqrt.*"): np.sqrt(kdf.a, kdf.b) def test_np_unsupported_frame(self): kdf = self.kdf with self.assertRaisesRegex(NotImplementedError, "on-Spark.*not.*support.*sqrt.*"): np.sqrt(kdf, kdf) def test_np_spark_compat_series(self): # Use randomly generated dataFrame pdf = pd.DataFrame( np.random.randint(-100, 100, size=(np.random.randint(100), 2)), columns=["a", "b"] ) pdf2 = pd.DataFrame( np.random.randint(-100, 100, size=(len(pdf), len(pdf.columns))), columns=["a", "b"] ) kdf = ps.from_pandas(pdf) kdf2 = ps.from_pandas(pdf2) for np_name, spark_func in unary_np_spark_mappings.items(): np_func = getattr(np, np_name) if np_name not in self.blacklist: try: # unary ufunc self.assert_eq(np_func(pdf.a), np_func(kdf.a), almost=True) except Exception as e: raise AssertionError("Test in '%s' function was failed." % np_name) from e for np_name, spark_func in binary_np_spark_mappings.items(): np_func = getattr(np, np_name) if np_name not in self.blacklist: try: # binary ufunc if LooseVersion(pd.__version__) < LooseVersion("0.25"): self.assert_eq( np_func(pdf.a, pdf.b).rename(), np_func(kdf.a, kdf.b), almost=True ) else: self.assert_eq(np_func(pdf.a, pdf.b), np_func(kdf.a, kdf.b), almost=True) self.assert_eq(np_func(pdf.a, 1), np_func(kdf.a, 1), almost=True) except Exception as e: raise AssertionError("Test in '%s' function was failed." % np_name) from e # Test only top 5 for now. 'compute.ops_on_diff_frames' option increases too much time. try: set_option("compute.ops_on_diff_frames", True) for np_name, spark_func in list(binary_np_spark_mappings.items())[:5]: np_func = getattr(np, np_name) if np_name not in self.blacklist: try: # binary ufunc if LooseVersion(pd.__version__) < LooseVersion("0.25"): self.assert_eq( np_func(pdf.a, pdf2.b).sort_index().rename(), np_func(kdf.a, kdf2.b).sort_index(), almost=True, ) else: self.assert_eq( np_func(pdf.a, pdf2.b).sort_index(), np_func(kdf.a, kdf2.b).sort_index(), almost=True, ) except Exception as e: raise AssertionError("Test in '%s' function was failed." % np_name) from e finally: reset_option("compute.ops_on_diff_frames") def test_np_spark_compat_frame(self): # Use randomly generated dataFrame pdf = pd.DataFrame( np.random.randint(-100, 100, size=(np.random.randint(100), 2)), columns=["a", "b"] ) pdf2 = pd.DataFrame( np.random.randint(-100, 100, size=(len(pdf), len(pdf.columns))), columns=["a", "b"] ) kdf = ps.from_pandas(pdf) kdf2 = ps.from_pandas(pdf2) for np_name, spark_func in unary_np_spark_mappings.items(): np_func = getattr(np, np_name) if np_name not in self.blacklist: try: # unary ufunc self.assert_eq(np_func(pdf), np_func(kdf), almost=True) except Exception as e: raise AssertionError("Test in '%s' function was failed." % np_name) from e for np_name, spark_func in binary_np_spark_mappings.items(): np_func = getattr(np, np_name) if np_name not in self.blacklist: try: # binary ufunc self.assert_eq(np_func(pdf, pdf), np_func(kdf, kdf), almost=True) self.assert_eq(np_func(pdf, 1), np_func(kdf, 1), almost=True) except Exception as e: raise AssertionError("Test in '%s' function was failed." % np_name) from e # Test only top 5 for now. 'compute.ops_on_diff_frames' option increases too much time. try: set_option("compute.ops_on_diff_frames", True) for np_name, spark_func in list(binary_np_spark_mappings.items())[:5]: np_func = getattr(np, np_name) if np_name not in self.blacklist: try: # binary ufunc self.assert_eq( np_func(pdf, pdf2).sort_index(), np_func(kdf, kdf2).sort_index(), almost=True, ) except Exception as e: raise AssertionError("Test in '%s' function was failed." % np_name) from e finally: reset_option("compute.ops_on_diff_frames") if __name__ == "__main__": import unittest from pyspark.pandas.tests.test_numpy_compat import * # noqa: F401 try: import xmlrunner # type: ignore[import] testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2) except ImportError: testRunner = None unittest.main(testRunner=testRunner, verbosity=2)