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#
# 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
import numpy as np
from pyspark.ml.evaluation import ClusteringEvaluator, RegressionEvaluator
from pyspark.ml.linalg import Vectors
from pyspark.sql import Row
from pyspark.testing.mlutils import SparkSessionTestCase
class EvaluatorTests(SparkSessionTestCase):
def test_evaluate_invalid_type(self):
evaluator = RegressionEvaluator(metricName="r2")
df = self.spark.createDataFrame([Row(label=1.0, prediction=1.1)])
invalid_type = ""
self.assertRaises(TypeError, evaluator.evaluate, df, invalid_type)
def test_java_params(self):
"""
This tests a bug fixed by SPARK-18274 which causes multiple copies
of a Params instance in Python to be linked to the same Java instance.
"""
evaluator = RegressionEvaluator(metricName="r2")
df = self.spark.createDataFrame([Row(label=1.0, prediction=1.1)])
evaluator.evaluate(df)
self.assertEqual(evaluator._java_obj.getMetricName(), "r2")
evaluatorCopy = evaluator.copy({evaluator.metricName: "mae"})
evaluator.evaluate(df)
evaluatorCopy.evaluate(df)
self.assertEqual(evaluator._java_obj.getMetricName(), "r2")
self.assertEqual(evaluatorCopy._java_obj.getMetricName(), "mae")
def test_clustering_evaluator_with_cosine_distance(self):
featureAndPredictions = map(lambda x: (Vectors.dense(x[0]), x[1]),
[([1.0, 1.0], 1.0), ([10.0, 10.0], 1.0), ([1.0, 0.5], 2.0),
([10.0, 4.4], 2.0), ([-1.0, 1.0], 3.0), ([-100.0, 90.0], 3.0)])
dataset = self.spark.createDataFrame(featureAndPredictions, ["features", "prediction"])
evaluator = ClusteringEvaluator(predictionCol="prediction", distanceMeasure="cosine")
self.assertEqual(evaluator.getDistanceMeasure(), "cosine")
self.assertTrue(np.isclose(evaluator.evaluate(dataset), 0.992671213, atol=1e-5))
if __name__ == "__main__":
from pyspark.ml.tests.test_evaluation 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)
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