#
# 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 abc import abstractmethod, ABCMeta
from pyspark import since, keyword_only
from pyspark.ml.wrapper import JavaParams
from pyspark.ml.param import Param, Params, TypeConverters
from pyspark.ml.param.shared import HasLabelCol, HasPredictionCol, HasRawPredictionCol
from pyspark.mllib.common import inherit_doc
__all__ = ['Evaluator', 'BinaryClassificationEvaluator', 'RegressionEvaluator',
'MulticlassClassificationEvaluator']
@inherit_doc
class Evaluator(Params):
"""
Base class for evaluators that compute metrics from predictions.
.. versionadded:: 1.4.0
"""
__metaclass__ = ABCMeta
@abstractmethod
def _evaluate(self, dataset):
"""
Evaluates the output.
:param dataset: a dataset that contains labels/observations and
predictions
:return: metric
"""
raise NotImplementedError()
@since("1.4.0")
def evaluate(self, dataset, params=None):
"""
Evaluates the output with optional parameters.
:param dataset: a dataset that contains labels/observations and
predictions
:param params: an optional param map that overrides embedded
params
:return: metric
"""
if params is None:
params = dict()
if isinstance(params, dict):
if params:
return self.copy(params)._evaluate(dataset)
else:
return self._evaluate(dataset)
else:
raise ValueError("Params must be a param map but got %s." % type(params))
@since("1.5.0")
def isLargerBetter(self):
"""
Indicates whether the metric returned by :py:meth:`evaluate` should be maximized
(True, default) or minimized (False).
A given evaluator may support multiple metrics which may be maximized or minimized.
"""
return True
@inherit_doc
class JavaEvaluator(JavaParams, Evaluator):
"""
Base class for :py:class:`Evaluator`s that wrap Java/Scala
implementations.
"""
__metaclass__ = ABCMeta
def _evaluate(self, dataset):
"""
Evaluates the output.
:param dataset: a dataset that contains labels/observations and predictions.
:return: evaluation metric
"""
self._transfer_params_to_java()
return self._java_obj.evaluate(dataset._jdf)
def isLargerBetter(self):
self._transfer_params_to_java()
return self._java_obj.isLargerBetter()
@inherit_doc
class BinaryClassificationEvaluator(JavaEvaluator, HasLabelCol, HasRawPredictionCol):
"""
Evaluator for binary classification, which expects two input columns: rawPrediction and label.
The rawPrediction column can be of type double (binary 0/1 prediction, or probability of label
1) or of type vector (length-2 vector of raw predictions, scores, or label probabilities).
>>> 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...
.. versionadded:: 1.4.0
"""
metricName = Param(Params._dummy(), "metricName",
"metric name in evaluation (areaUnderROC|areaUnderPR)",
typeConverter=TypeConverters.toString)
@keyword_only
def __init__(self, rawPredictionCol="rawPrediction", labelCol="label",
metricName="areaUnderROC"):
"""
__init__(self, rawPredictionCol="rawPrediction", labelCol="label", \
metricName="areaUnderROC")
"""
super(BinaryClassificationEvaluator, self).__init__()
self._java_obj = self._new_java_obj(
"org.apache.spark.ml.evaluation.BinaryClassificationEvaluator", self.uid)
self._setDefault(rawPredictionCol="rawPrediction", labelCol="label",
metricName="areaUnderROC")
kwargs = self.__init__._input_kwargs
self._set(**kwargs)
@since("1.4.0")
def setMetricName(self, value):
"""
Sets the value of :py:attr:`metricName`.
"""
return self._set(metricName=value)
@since("1.4.0")
def getMetricName(self):
"""
Gets the value of metricName or its default value.
"""
return self.getOrDefault(self.metricName)
@keyword_only
@since("1.4.0")
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)
@inherit_doc
class RegressionEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol):
"""
Evaluator for Regression, which expects two input
columns: prediction and label.
>>> scoreAndLabels = [(-28.98343821, -27.0), (20.21491975, 21.5),
... (-25.98418959, -22.0), (30.69731842, 33.0), (74.69283752, 71.0)]
>>> dataset = sqlContext.createDataFrame(scoreAndLabels, ["raw", "label"])
...
>>> evaluator = RegressionEvaluator(predictionCol="raw")
>>> evaluator.evaluate(dataset)
2.842...
>>> evaluator.evaluate(dataset, {evaluator.metricName: "r2"})
0.993...
>>> evaluator.evaluate(dataset, {evaluator.metricName: "mae"})
2.649...
.. versionadded:: 1.4.0
"""
# Because we will maximize evaluation value (ref: `CrossValidator`),
# when we evaluate a metric that is needed to minimize (e.g., `"rmse"`, `"mse"`, `"mae"`),
# we take and output the negative of this metric.
metricName = Param(Params._dummy(), "metricName",
"metric name in evaluation (mse|rmse|r2|mae)",
typeConverter=TypeConverters.toString)
@keyword_only
def __init__(self, predictionCol="prediction", labelCol="label",
metricName="rmse"):
"""
__init__(self, predictionCol="prediction", labelCol="label", \
metricName="rmse")
"""
super(RegressionEvaluator, self).__init__()
self._java_obj = self._new_java_obj(
"org.apache.spark.ml.evaluation.RegressionEvaluator", self.uid)
self._setDefault(predictionCol="prediction", labelCol="label",
metricName="rmse")
kwargs = self.__init__._input_kwargs
self._set(**kwargs)
@since("1.4.0")
def setMetricName(self, value):
"""
Sets the value of :py:attr:`metricName`.
"""
return self._set(metricName=value)
@since("1.4.0")
def getMetricName(self):
"""
Gets the value of metricName or its default value.
"""
return self.getOrDefault(self.metricName)
@keyword_only
@since("1.4.0")
def setParams(self, predictionCol="prediction", labelCol="label",
metricName="rmse"):
"""
setParams(self, predictionCol="prediction", labelCol="label", \
metricName="rmse")
Sets params for regression evaluator.
"""
kwargs = self.setParams._input_kwargs
return self._set(**kwargs)
@inherit_doc
class MulticlassClassificationEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol):
"""
Evaluator for Multiclass Classification, which expects two input
columns: prediction and label.
>>> scoreAndLabels = [(0.0, 0.0), (0.0, 1.0), (0.0, 0.0),
... (1.0, 0.0), (1.0, 1.0), (1.0, 1.0), (1.0, 1.0), (2.0, 2.0), (2.0, 0.0)]
>>> dataset = sqlContext.createDataFrame(scoreAndLabels, ["prediction", "label"])
...
>>> evaluator = MulticlassClassificationEvaluator(predictionCol="prediction")
>>> evaluator.evaluate(dataset)
0.66...
>>> evaluator.evaluate(dataset, {evaluator.metricName: "precision"})
0.66...
>>> evaluator.evaluate(dataset, {evaluator.metricName: "recall"})
0.66...
.. versionadded:: 1.5.0
"""
metricName = Param(Params._dummy(), "metricName",
"metric name in evaluation "
"(f1|precision|recall|weightedPrecision|weightedRecall)",
typeConverter=TypeConverters.toString)
@keyword_only
def __init__(self, predictionCol="prediction", labelCol="label",
metricName="f1"):
"""
__init__(self, predictionCol="prediction", labelCol="label", \
metricName="f1")
"""
super(MulticlassClassificationEvaluator, self).__init__()
self._java_obj = self._new_java_obj(
"org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator", self.uid)
self._setDefault(predictionCol="prediction", labelCol="label",
metricName="f1")
kwargs = self.__init__._input_kwargs
self._set(**kwargs)
@since("1.5.0")
def setMetricName(self, value):
"""
Sets the value of :py:attr:`metricName`.
"""
return self._set(metricName=value)
@since("1.5.0")
def getMetricName(self):
"""
Gets the value of metricName or its default value.
"""
return self.getOrDefault(self.metricName)
@keyword_only
@since("1.5.0")
def setParams(self, predictionCol="prediction", labelCol="label",
metricName="f1"):
"""
setParams(self, predictionCol="prediction", labelCol="label", \
metricName="f1")
Sets params for multiclass 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)
