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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.
#
from pyspark.ml.wrapper import JavaEvaluator
from pyspark.ml.param import Param, Params
from pyspark.ml.param.shared import HasLabelCol, HasRawPredictionCol
from pyspark.ml.util import keyword_only
from pyspark.mllib.common import inherit_doc
__all__ = ['BinaryClassificationEvaluator']
@inherit_doc
class BinaryClassificationEvaluator(JavaEvaluator, HasLabelCol, HasRawPredictionCol):
"""
Evaluator for binary classification, which expects two input
columns: rawPrediction and label.
>>> from pyspark.mllib.linalg import Vectors
>>> scoreAndLabels = map(lambda x: (Vectors.dense([1.0 - x[0], x[0]]), x[1]),
... [(0.1, 0.0), (0.1, 1.0), (0.4, 0.0), (0.6, 0.0), (0.6, 1.0), (0.6, 1.0), (0.8, 1.0)])
>>> dataset = sqlContext.createDataFrame(scoreAndLabels, ["raw", "label"])
...
>>> evaluator = BinaryClassificationEvaluator(rawPredictionCol="raw")
>>> evaluator.evaluate(dataset)
0.70...
>>> evaluator.evaluate(dataset, {evaluator.metricName: "areaUnderPR"})
0.83...
"""
_java_class = "org.apache.spark.ml.evaluation.BinaryClassificationEvaluator"
# a placeholder to make it appear in the generated doc
metricName = Param(Params._dummy(), "metricName",
"metric name in evaluation (areaUnderROC|areaUnderPR)")
@keyword_only
def __init__(self, rawPredictionCol="rawPrediction", labelCol="label",
metricName="areaUnderROC"):
"""
__init__(self, rawPredictionCol="rawPrediction", labelCol="label", \
metricName="areaUnderROC")
"""
super(BinaryClassificationEvaluator, self).__init__()
#: param for metric name in evaluation (areaUnderROC|areaUnderPR)
self.metricName = Param(self, "metricName",
"metric name in evaluation (areaUnderROC|areaUnderPR)")
self._setDefault(rawPredictionCol="rawPrediction", labelCol="label",
metricName="areaUnderROC")
kwargs = self.__init__._input_kwargs
self._set(**kwargs)
def setMetricName(self, value):
"""
Sets the value of :py:attr:`metricName`.
"""
self.paramMap[self.metricName] = value
return self
def getMetricName(self):
"""
Gets the value of metricName or its default value.
"""
return self.getOrDefault(self.metricName)
@keyword_only
def setParams(self, rawPredictionCol="rawPrediction", labelCol="label",
metricName="areaUnderROC"):
"""
setParams(self, rawPredictionCol="rawPrediction", labelCol="label", \
metricName="areaUnderROC")
Sets params for binary classification evaluator.
"""
kwargs = self.setParams._input_kwargs
return self._set(**kwargs)
if __name__ == "__main__":
import doctest
from pyspark.context import SparkContext
from pyspark.sql import SQLContext
globs = globals().copy()
# The small batch size here ensures that we see multiple batches,
# even in these small test examples:
sc = SparkContext("local[2]", "ml.evaluation tests")
sqlContext = SQLContext(sc)
globs['sc'] = sc
globs['sqlContext'] = sqlContext
(failure_count, test_count) = doctest.testmod(
globs=globs, optionflags=doctest.ELLIPSIS)
sc.stop()
if failure_count:
exit(-1)
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