2018-11-18 03:02:15 -05:00
|
|
|
#
|
|
|
|
# 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.
|
|
|
|
#
|
|
|
|
|
2018-11-18 20:22:32 -05:00
|
|
|
import unittest
|
2018-11-18 03:02:15 -05:00
|
|
|
|
|
|
|
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_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 *
|
|
|
|
|
|
|
|
try:
|
|
|
|
import xmlrunner
|
|
|
|
testRunner = xmlrunner.XMLTestRunner(output='target/test-reports')
|
|
|
|
except ImportError:
|
|
|
|
testRunner = None
|
|
|
|
unittest.main(testRunner=testRunner, verbosity=2)
|