aeb3649fb9
### What changes were proposed in this pull request? This replaces deprecated API usage in PySpark tests with the preferred APIs. These have been deprecated for some time and usage is not consistent within tests. - https://docs.python.org/3/library/unittest.html#deprecated-aliases ### Why are the changes needed? For consistency and eventual removal of deprecated APIs. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Existing tests Closes #30557 from BryanCutler/replace-deprecated-apis-in-tests. Authored-by: Bryan Cutler <cutlerb@gmail.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
130 lines
5.4 KiB
Python
130 lines
5.4 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import unittest
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import py4j
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from pyspark.ml.linalg import DenseVector, Vectors
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from pyspark.ml.regression import LinearRegression
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from pyspark.ml.wrapper import ( # type: ignore[attr-defined]
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_java2py, _py2java, JavaParams, JavaWrapper
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)
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from pyspark.testing.mllibutils import MLlibTestCase
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from pyspark.testing.mlutils import SparkSessionTestCase
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from pyspark.testing.utils import eventually
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class JavaWrapperMemoryTests(SparkSessionTestCase):
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def test_java_object_gets_detached(self):
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df = self.spark.createDataFrame([(1.0, 2.0, Vectors.dense(1.0)),
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(0.0, 2.0, Vectors.sparse(1, [], []))],
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["label", "weight", "features"])
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lr = LinearRegression(maxIter=1, regParam=0.0, solver="normal", weightCol="weight",
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fitIntercept=False)
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model = lr.fit(df)
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summary = model.summary
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self.assertIsInstance(model, JavaWrapper)
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self.assertIsInstance(summary, JavaWrapper)
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self.assertIsInstance(model, JavaParams)
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self.assertNotIsInstance(summary, JavaParams)
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error_no_object = 'Target Object ID does not exist for this gateway'
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self.assertIn("LinearRegression_", model._java_obj.toString())
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self.assertIn("LinearRegressionTrainingSummary", summary._java_obj.toString())
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model.__del__()
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def condition():
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with self.assertRaisesRegex(py4j.protocol.Py4JError, error_no_object):
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model._java_obj.toString()
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self.assertIn("LinearRegressionTrainingSummary", summary._java_obj.toString())
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return True
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eventually(condition, timeout=10, catch_assertions=True)
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try:
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summary.__del__()
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except:
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pass
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def condition():
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with self.assertRaisesRegex(py4j.protocol.Py4JError, error_no_object):
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model._java_obj.toString()
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with self.assertRaisesRegex(py4j.protocol.Py4JError, error_no_object):
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summary._java_obj.toString()
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return True
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eventually(condition, timeout=10, catch_assertions=True)
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class WrapperTests(MLlibTestCase):
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def test_new_java_array(self):
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# test array of strings
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str_list = ["a", "b", "c"]
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java_class = self.sc._gateway.jvm.java.lang.String
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java_array = JavaWrapper._new_java_array(str_list, java_class)
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self.assertEqual(_java2py(self.sc, java_array), str_list)
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# test array of integers
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int_list = [1, 2, 3]
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java_class = self.sc._gateway.jvm.java.lang.Integer
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java_array = JavaWrapper._new_java_array(int_list, java_class)
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self.assertEqual(_java2py(self.sc, java_array), int_list)
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# test array of floats
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float_list = [0.1, 0.2, 0.3]
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java_class = self.sc._gateway.jvm.java.lang.Double
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java_array = JavaWrapper._new_java_array(float_list, java_class)
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self.assertEqual(_java2py(self.sc, java_array), float_list)
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# test array of bools
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bool_list = [False, True, True]
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java_class = self.sc._gateway.jvm.java.lang.Boolean
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java_array = JavaWrapper._new_java_array(bool_list, java_class)
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self.assertEqual(_java2py(self.sc, java_array), bool_list)
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# test array of Java DenseVectors
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v1 = DenseVector([0.0, 1.0])
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v2 = DenseVector([1.0, 0.0])
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vec_java_list = [_py2java(self.sc, v1), _py2java(self.sc, v2)]
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java_class = self.sc._gateway.jvm.org.apache.spark.ml.linalg.DenseVector
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java_array = JavaWrapper._new_java_array(vec_java_list, java_class)
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self.assertEqual(_java2py(self.sc, java_array), [v1, v2])
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# test empty array
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java_class = self.sc._gateway.jvm.java.lang.Integer
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java_array = JavaWrapper._new_java_array([], java_class)
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self.assertEqual(_java2py(self.sc, java_array), [])
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# test array of array of strings
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str_list = [["a", "b", "c"], ["d", "e"], ["f", "g", "h", "i"], []]
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expected_str_list = [("a", "b", "c", None), ("d", "e", None, None), ("f", "g", "h", "i"),
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(None, None, None, None)]
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java_class = self.sc._gateway.jvm.java.lang.String
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java_array = JavaWrapper._new_java_array(str_list, java_class)
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self.assertEqual(_java2py(self.sc, java_array), expected_str_list)
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if __name__ == "__main__":
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from pyspark.ml.tests.test_wrapper import * # noqa: F401
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try:
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import xmlrunner # type: ignore[import]
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testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
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except ImportError:
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testRunner = None
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unittest.main(testRunner=testRunner, verbosity=2)
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