834e699524
`make clean html` under `python/doc` returns ~~~ /Users/meng/src/spark/python/pyspark/ml/evaluation.py:docstring of pyspark.ml.evaluation.RegressionEvaluator.setParams:3: WARNING: Definition list ends without a blank line; unexpected unindent. ~~~ harsha2010 Author: Xiangrui Meng <meng@databricks.com> Closes #6469 from mengxr/fix-regression-evaluator-doc and squashes the following commits: 91e2dad [Xiangrui Meng] fix RegressionEvaluator doc
231 lines
8.1 KiB
Python
231 lines
8.1 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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@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={}):
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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 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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@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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@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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# 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 (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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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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