7583681e6b
* Added isLargerBetter() method to Pyspark Evaluator to match the Scala version. * JavaEvaluator delegates isLargerBetter() to underlying Scala object. * Added check for isLargerBetter() in CrossValidator to determine whether to use argmin or argmax. * Added test cases for where smaller is better (RMSE) and larger is better (R-Squared). (This contribution is my original work and that I license the work to the project under Sparks' open source license) Author: noelsmith <mail@noelsmith.com> Closes #8399 from noel-smith/pyspark-rmse-xval-fix.
314 lines
11 KiB
Python
314 lines
11 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from abc import abstractmethod, ABCMeta
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from pyspark.ml.wrapper import JavaWrapper
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from pyspark.ml.param import Param, Params
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from pyspark.ml.param.shared import HasLabelCol, HasPredictionCol, HasRawPredictionCol
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from pyspark.ml.util import keyword_only
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from pyspark.mllib.common import inherit_doc
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__all__ = ['Evaluator', 'BinaryClassificationEvaluator', 'RegressionEvaluator',
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'MulticlassClassificationEvaluator']
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@inherit_doc
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class Evaluator(Params):
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"""
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Base class for evaluators that compute metrics from predictions.
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"""
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__metaclass__ = ABCMeta
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@abstractmethod
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def _evaluate(self, dataset):
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"""
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Evaluates the output.
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:param dataset: a dataset that contains labels/observations and
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predictions
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:return: metric
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"""
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raise NotImplementedError()
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def evaluate(self, dataset, params=None):
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"""
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Evaluates the output with optional parameters.
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:param dataset: a dataset that contains labels/observations and
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predictions
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:param params: an optional param map that overrides embedded
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params
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:return: metric
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"""
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if params is None:
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params = dict()
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if isinstance(params, dict):
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if params:
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return self.copy(params)._evaluate(dataset)
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else:
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return self._evaluate(dataset)
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else:
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raise ValueError("Params must be a param map but got %s." % type(params))
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def isLargerBetter(self):
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"""
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Indicates whether the metric returned by :py:meth:`evaluate` should be maximized
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(True, default) or minimized (False).
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A given evaluator may support multiple metrics which may be maximized or minimized.
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"""
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return True
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@inherit_doc
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class JavaEvaluator(Evaluator, JavaWrapper):
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"""
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Base class for :py:class:`Evaluator`s that wrap Java/Scala
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implementations.
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"""
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__metaclass__ = ABCMeta
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def _evaluate(self, dataset):
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"""
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Evaluates the output.
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:param dataset: a dataset that contains labels/observations and predictions.
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:return: evaluation metric
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"""
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self._transfer_params_to_java()
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return self._java_obj.evaluate(dataset._jdf)
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def isLargerBetter(self):
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self._transfer_params_to_java()
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return self._java_obj.isLargerBetter()
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@inherit_doc
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class BinaryClassificationEvaluator(JavaEvaluator, HasLabelCol, HasRawPredictionCol):
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"""
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Evaluator for binary classification, which expects two input
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columns: rawPrediction and label.
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>>> from pyspark.mllib.linalg import Vectors
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>>> scoreAndLabels = map(lambda x: (Vectors.dense([1.0 - x[0], x[0]]), x[1]),
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... [(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)])
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>>> dataset = sqlContext.createDataFrame(scoreAndLabels, ["raw", "label"])
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...
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>>> evaluator = BinaryClassificationEvaluator(rawPredictionCol="raw")
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>>> evaluator.evaluate(dataset)
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0.70...
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>>> evaluator.evaluate(dataset, {evaluator.metricName: "areaUnderPR"})
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0.83...
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"""
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# a placeholder to make it appear in the generated doc
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metricName = Param(Params._dummy(), "metricName",
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"metric name in evaluation (areaUnderROC|areaUnderPR)")
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@keyword_only
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def __init__(self, rawPredictionCol="rawPrediction", labelCol="label",
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metricName="areaUnderROC"):
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"""
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__init__(self, rawPredictionCol="rawPrediction", labelCol="label", \
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metricName="areaUnderROC")
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"""
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super(BinaryClassificationEvaluator, self).__init__()
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self._java_obj = self._new_java_obj(
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"org.apache.spark.ml.evaluation.BinaryClassificationEvaluator", self.uid)
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#: param for metric name in evaluation (areaUnderROC|areaUnderPR)
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self.metricName = Param(self, "metricName",
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"metric name in evaluation (areaUnderROC|areaUnderPR)")
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self._setDefault(rawPredictionCol="rawPrediction", labelCol="label",
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metricName="areaUnderROC")
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kwargs = self.__init__._input_kwargs
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self._set(**kwargs)
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def setMetricName(self, value):
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"""
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Sets the value of :py:attr:`metricName`.
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"""
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self._paramMap[self.metricName] = value
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return self
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def getMetricName(self):
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"""
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Gets the value of metricName or its default value.
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"""
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return self.getOrDefault(self.metricName)
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@keyword_only
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def setParams(self, rawPredictionCol="rawPrediction", labelCol="label",
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metricName="areaUnderROC"):
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"""
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setParams(self, rawPredictionCol="rawPrediction", labelCol="label", \
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metricName="areaUnderROC")
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Sets params for binary classification evaluator.
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"""
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kwargs = self.setParams._input_kwargs
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return self._set(**kwargs)
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@inherit_doc
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class RegressionEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol):
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"""
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Evaluator for Regression, which expects two input
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columns: prediction and label.
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>>> scoreAndLabels = [(-28.98343821, -27.0), (20.21491975, 21.5),
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... (-25.98418959, -22.0), (30.69731842, 33.0), (74.69283752, 71.0)]
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>>> dataset = sqlContext.createDataFrame(scoreAndLabels, ["raw", "label"])
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...
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>>> evaluator = RegressionEvaluator(predictionCol="raw")
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>>> evaluator.evaluate(dataset)
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2.842...
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>>> evaluator.evaluate(dataset, {evaluator.metricName: "r2"})
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0.993...
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>>> evaluator.evaluate(dataset, {evaluator.metricName: "mae"})
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2.649...
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"""
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# Because we will maximize evaluation value (ref: `CrossValidator`),
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# when we evaluate a metric that is needed to minimize (e.g., `"rmse"`, `"mse"`, `"mae"`),
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# we take and output the negative of this metric.
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metricName = Param(Params._dummy(), "metricName",
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"metric name in evaluation (mse|rmse|r2|mae)")
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@keyword_only
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def __init__(self, predictionCol="prediction", labelCol="label",
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metricName="rmse"):
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"""
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__init__(self, predictionCol="prediction", labelCol="label", \
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metricName="rmse")
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"""
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super(RegressionEvaluator, self).__init__()
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self._java_obj = self._new_java_obj(
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"org.apache.spark.ml.evaluation.RegressionEvaluator", self.uid)
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#: param for metric name in evaluation (mse|rmse|r2|mae)
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self.metricName = Param(self, "metricName",
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"metric name in evaluation (mse|rmse|r2|mae)")
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self._setDefault(predictionCol="prediction", labelCol="label",
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metricName="rmse")
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kwargs = self.__init__._input_kwargs
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self._set(**kwargs)
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def setMetricName(self, value):
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"""
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Sets the value of :py:attr:`metricName`.
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"""
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self._paramMap[self.metricName] = value
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return self
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def getMetricName(self):
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"""
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Gets the value of metricName or its default value.
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"""
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return self.getOrDefault(self.metricName)
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@keyword_only
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def setParams(self, predictionCol="prediction", labelCol="label",
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metricName="rmse"):
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"""
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setParams(self, predictionCol="prediction", labelCol="label", \
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metricName="rmse")
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Sets params for regression evaluator.
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"""
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kwargs = self.setParams._input_kwargs
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return self._set(**kwargs)
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@inherit_doc
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class MulticlassClassificationEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol):
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"""
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Evaluator for Multiclass Classification, which expects two input
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columns: prediction and label.
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>>> scoreAndLabels = [(0.0, 0.0), (0.0, 1.0), (0.0, 0.0),
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... (1.0, 0.0), (1.0, 1.0), (1.0, 1.0), (1.0, 1.0), (2.0, 2.0), (2.0, 0.0)]
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>>> dataset = sqlContext.createDataFrame(scoreAndLabels, ["prediction", "label"])
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...
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>>> evaluator = MulticlassClassificationEvaluator(predictionCol="prediction")
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>>> evaluator.evaluate(dataset)
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0.66...
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>>> evaluator.evaluate(dataset, {evaluator.metricName: "precision"})
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0.66...
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>>> evaluator.evaluate(dataset, {evaluator.metricName: "recall"})
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0.66...
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"""
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# a placeholder to make it appear in the generated doc
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metricName = Param(Params._dummy(), "metricName",
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"metric name in evaluation "
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"(f1|precision|recall|weightedPrecision|weightedRecall)")
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@keyword_only
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def __init__(self, predictionCol="prediction", labelCol="label",
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metricName="f1"):
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"""
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__init__(self, predictionCol="prediction", labelCol="label", \
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metricName="f1")
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"""
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super(MulticlassClassificationEvaluator, self).__init__()
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self._java_obj = self._new_java_obj(
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"org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator", self.uid)
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# param for metric name in evaluation (f1|precision|recall|weightedPrecision|weightedRecall)
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self.metricName = Param(self, "metricName",
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"metric name in evaluation"
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" (f1|precision|recall|weightedPrecision|weightedRecall)")
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self._setDefault(predictionCol="prediction", labelCol="label",
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metricName="f1")
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kwargs = self.__init__._input_kwargs
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self._set(**kwargs)
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def setMetricName(self, value):
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"""
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Sets the value of :py:attr:`metricName`.
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"""
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self._paramMap[self.metricName] = value
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return self
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def getMetricName(self):
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"""
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Gets the value of metricName or its default value.
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"""
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return self.getOrDefault(self.metricName)
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@keyword_only
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def setParams(self, predictionCol="prediction", labelCol="label",
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metricName="f1"):
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"""
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setParams(self, predictionCol="prediction", labelCol="label", \
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metricName="f1")
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Sets params for multiclass classification evaluator.
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"""
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kwargs = self.setParams._input_kwargs
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return self._set(**kwargs)
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if __name__ == "__main__":
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import doctest
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from pyspark.context import SparkContext
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from pyspark.sql import SQLContext
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globs = globals().copy()
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# The small batch size here ensures that we see multiple batches,
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# even in these small test examples:
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sc = SparkContext("local[2]", "ml.evaluation tests")
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sqlContext = SQLContext(sc)
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globs['sc'] = sc
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globs['sqlContext'] = sqlContext
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(failure_count, test_count) = doctest.testmod(
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globs=globs, optionflags=doctest.ELLIPSIS)
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sc.stop()
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if failure_count:
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exit(-1)
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