034ae305c3
## What changes were proposed in this pull request? This PR breaks down the large ml/tests.py file that contains all Python ML unit tests into several smaller test files to be easier to read and maintain. The tests are broken down as follows: ``` pyspark ├── __init__.py ... ├── ml │ ├── __init__.py ... │ ├── tests │ │ ├── __init__.py │ │ ├── test_algorithms.py │ │ ├── test_base.py │ │ ├── test_evaluation.py │ │ ├── test_feature.py │ │ ├── test_image.py │ │ ├── test_linalg.py │ │ ├── test_param.py │ │ ├── test_persistence.py │ │ ├── test_pipeline.py │ │ ├── test_stat.py │ │ ├── test_training_summary.py │ │ ├── test_tuning.py │ │ └── test_wrapper.py ... ├── testing ... │ ├── mlutils.py ... ``` ## How was this patch tested? Ran tests manually by module to ensure test count was the same, and ran `python/run-tests --modules=pyspark-ml` to verify all passing with Python 2.7 and Python 3.6. Closes #23063 from BryanCutler/python-test-breakup-ml-SPARK-26033. Authored-by: Bryan Cutler <cutlerb@gmail.com> Signed-off-by: hyukjinkwon <gurwls223@apache.org>
121 lines
4.8 KiB
Python
121 lines
4.8 KiB
Python
#
|
|
# 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 sys
|
|
if sys.version_info[:2] <= (2, 6):
|
|
try:
|
|
import unittest2 as unittest
|
|
except ImportError:
|
|
sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
|
|
sys.exit(1)
|
|
else:
|
|
import unittest
|
|
|
|
import py4j
|
|
|
|
from pyspark.ml.linalg import DenseVector, Vectors
|
|
from pyspark.ml.regression import LinearRegression
|
|
from pyspark.ml.wrapper import _java2py, _py2java, JavaParams, JavaWrapper
|
|
from pyspark.testing.mllibutils import MLlibTestCase
|
|
from pyspark.testing.mlutils import SparkSessionTestCase
|
|
|
|
|
|
class JavaWrapperMemoryTests(SparkSessionTestCase):
|
|
|
|
def test_java_object_gets_detached(self):
|
|
df = self.spark.createDataFrame([(1.0, 2.0, Vectors.dense(1.0)),
|
|
(0.0, 2.0, Vectors.sparse(1, [], []))],
|
|
["label", "weight", "features"])
|
|
lr = LinearRegression(maxIter=1, regParam=0.0, solver="normal", weightCol="weight",
|
|
fitIntercept=False)
|
|
|
|
model = lr.fit(df)
|
|
summary = model.summary
|
|
|
|
self.assertIsInstance(model, JavaWrapper)
|
|
self.assertIsInstance(summary, JavaWrapper)
|
|
self.assertIsInstance(model, JavaParams)
|
|
self.assertNotIsInstance(summary, JavaParams)
|
|
|
|
error_no_object = 'Target Object ID does not exist for this gateway'
|
|
|
|
self.assertIn("LinearRegression_", model._java_obj.toString())
|
|
self.assertIn("LinearRegressionTrainingSummary", summary._java_obj.toString())
|
|
|
|
model.__del__()
|
|
|
|
with self.assertRaisesRegexp(py4j.protocol.Py4JError, error_no_object):
|
|
model._java_obj.toString()
|
|
self.assertIn("LinearRegressionTrainingSummary", summary._java_obj.toString())
|
|
|
|
try:
|
|
summary.__del__()
|
|
except:
|
|
pass
|
|
|
|
with self.assertRaisesRegexp(py4j.protocol.Py4JError, error_no_object):
|
|
model._java_obj.toString()
|
|
with self.assertRaisesRegexp(py4j.protocol.Py4JError, error_no_object):
|
|
summary._java_obj.toString()
|
|
|
|
|
|
class WrapperTests(MLlibTestCase):
|
|
|
|
def test_new_java_array(self):
|
|
# test array of strings
|
|
str_list = ["a", "b", "c"]
|
|
java_class = self.sc._gateway.jvm.java.lang.String
|
|
java_array = JavaWrapper._new_java_array(str_list, java_class)
|
|
self.assertEqual(_java2py(self.sc, java_array), str_list)
|
|
# test array of integers
|
|
int_list = [1, 2, 3]
|
|
java_class = self.sc._gateway.jvm.java.lang.Integer
|
|
java_array = JavaWrapper._new_java_array(int_list, java_class)
|
|
self.assertEqual(_java2py(self.sc, java_array), int_list)
|
|
# test array of floats
|
|
float_list = [0.1, 0.2, 0.3]
|
|
java_class = self.sc._gateway.jvm.java.lang.Double
|
|
java_array = JavaWrapper._new_java_array(float_list, java_class)
|
|
self.assertEqual(_java2py(self.sc, java_array), float_list)
|
|
# test array of bools
|
|
bool_list = [False, True, True]
|
|
java_class = self.sc._gateway.jvm.java.lang.Boolean
|
|
java_array = JavaWrapper._new_java_array(bool_list, java_class)
|
|
self.assertEqual(_java2py(self.sc, java_array), bool_list)
|
|
# test array of Java DenseVectors
|
|
v1 = DenseVector([0.0, 1.0])
|
|
v2 = DenseVector([1.0, 0.0])
|
|
vec_java_list = [_py2java(self.sc, v1), _py2java(self.sc, v2)]
|
|
java_class = self.sc._gateway.jvm.org.apache.spark.ml.linalg.DenseVector
|
|
java_array = JavaWrapper._new_java_array(vec_java_list, java_class)
|
|
self.assertEqual(_java2py(self.sc, java_array), [v1, v2])
|
|
# test empty array
|
|
java_class = self.sc._gateway.jvm.java.lang.Integer
|
|
java_array = JavaWrapper._new_java_array([], java_class)
|
|
self.assertEqual(_java2py(self.sc, java_array), [])
|
|
|
|
|
|
if __name__ == "__main__":
|
|
from pyspark.ml.tests.test_wrapper import *
|
|
|
|
try:
|
|
import xmlrunner
|
|
testRunner = xmlrunner.XMLTestRunner(output='target/test-reports')
|
|
except ImportError:
|
|
testRunner = None
|
|
unittest.main(testRunner=testRunner, verbosity=2)
|