| # |
| # 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 pyspark.sql.functions import pandas_udf, PandasUDFType |
| from pyspark.sql.types import DoubleType, StructType, StructField |
| from pyspark.sql.tests.pandas.test_pandas_udf import PandasUDFTestsMixin |
| from pyspark.testing.connectutils import ReusedConnectTestCase |
| |
| |
| class PandasUDFParityTests(PandasUDFTestsMixin, ReusedConnectTestCase): |
| def test_udf_wrong_arg(self): |
| self.check_udf_wrong_arg() |
| |
| def test_pandas_udf_decorator_with_return_type_string(self): |
| @pandas_udf("v double", PandasUDFType.GROUPED_MAP) |
| def foo(x): |
| return x |
| |
| self.assertEqual(foo.returnType, StructType([StructField("v", DoubleType(), True)])) |
| self.assertEqual(foo.evalType, PandasUDFType.GROUPED_MAP) |
| |
| @pandas_udf(returnType="double", functionType=PandasUDFType.SCALAR) |
| def foo(x): |
| return x |
| |
| self.assertEqual(foo.returnType, DoubleType()) |
| self.assertEqual(foo.evalType, PandasUDFType.SCALAR) |
| |
| def test_pandas_udf_basic_with_return_type_string(self): |
| udf = pandas_udf(lambda x: x, "double", PandasUDFType.SCALAR) |
| self.assertEqual(udf.returnType, DoubleType()) |
| self.assertEqual(udf.evalType, PandasUDFType.SCALAR) |
| |
| udf = pandas_udf(lambda x: x, "v double", PandasUDFType.GROUPED_MAP) |
| self.assertEqual(udf.returnType, StructType([StructField("v", DoubleType(), True)])) |
| self.assertEqual(udf.evalType, PandasUDFType.GROUPED_MAP) |
| |
| udf = pandas_udf(lambda x: x, "v double", functionType=PandasUDFType.GROUPED_MAP) |
| self.assertEqual(udf.returnType, StructType([StructField("v", DoubleType(), True)])) |
| self.assertEqual(udf.evalType, PandasUDFType.GROUPED_MAP) |
| |
| udf = pandas_udf(lambda x: x, returnType="v double", functionType=PandasUDFType.GROUPED_MAP) |
| self.assertEqual(udf.returnType, StructType([StructField("v", DoubleType(), True)])) |
| self.assertEqual(udf.evalType, PandasUDFType.GROUPED_MAP) |
| |
| |
| if __name__ == "__main__": |
| import unittest |
| from pyspark.sql.tests.connect.test_parity_pandas_udf 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) |