887279cc46
## What changes were proposed in this pull request? Followup to PR https://github.com/apache/spark/pull/17085 This PR adds the weight column to the pyspark side, which was already added to the scala API. The PR also undoes a name change in the scala side corresponding to a change in another similar PR as noted here: https://github.com/apache/spark/pull/17084#discussion_r259648639 ## How was this patch tested? This patch adds python tests for the changes to the pyspark API. Please review http://spark.apache.org/contributing.html before opening a pull request. Closes #24197 from imatiach-msft/ilmat/regressor-eval-python. Authored-by: Ilya Matiach <ilmat@microsoft.com> Signed-off-by: Sean Owen <sean.owen@databricks.com>
479 lines
17 KiB
Python
479 lines
17 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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import sys
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from abc import abstractmethod, ABCMeta
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from pyspark import since, keyword_only
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from pyspark.ml.wrapper import JavaParams
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from pyspark.ml.param import Param, Params, TypeConverters
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from pyspark.ml.param.shared import HasLabelCol, HasPredictionCol, HasRawPredictionCol, \
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HasFeaturesCol, HasWeightCol
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from pyspark.ml.common import inherit_doc
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from pyspark.ml.util import JavaMLReadable, JavaMLWritable
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__all__ = ['Evaluator', 'BinaryClassificationEvaluator', 'RegressionEvaluator',
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'MulticlassClassificationEvaluator', 'ClusteringEvaluator']
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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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.. versionadded:: 1.4.0
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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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@since("1.4.0")
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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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@since("1.5.0")
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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(JavaParams, Evaluator):
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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, HasWeightCol,
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JavaMLReadable, JavaMLWritable):
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"""
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.. note:: Experimental
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Evaluator for binary classification, which expects two input columns: rawPrediction and label.
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The rawPrediction column can be of type double (binary 0/1 prediction, or probability of label
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1) or of type vector (length-2 vector of raw predictions, scores, or label probabilities).
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>>> from pyspark.ml.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 = spark.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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>>> bce_path = temp_path + "/bce"
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>>> evaluator.save(bce_path)
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>>> evaluator2 = BinaryClassificationEvaluator.load(bce_path)
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>>> str(evaluator2.getRawPredictionCol())
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'raw'
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>>> scoreAndLabelsAndWeight = map(lambda x: (Vectors.dense([1.0 - x[0], x[0]]), x[1], x[2]),
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... [(0.1, 0.0, 1.0), (0.1, 1.0, 0.9), (0.4, 0.0, 0.7), (0.6, 0.0, 0.9),
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... (0.6, 1.0, 1.0), (0.6, 1.0, 0.3), (0.8, 1.0, 1.0)])
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>>> dataset = spark.createDataFrame(scoreAndLabelsAndWeight, ["raw", "label", "weight"])
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...
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>>> evaluator = BinaryClassificationEvaluator(rawPredictionCol="raw", weightCol="weight")
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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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0.82...
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.. versionadded:: 1.4.0
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"""
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metricName = Param(Params._dummy(), "metricName",
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"metric name in evaluation (areaUnderROC|areaUnderPR)",
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typeConverter=TypeConverters.toString)
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@keyword_only
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def __init__(self, rawPredictionCol="rawPrediction", labelCol="label",
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metricName="areaUnderROC", weightCol=None):
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"""
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__init__(self, rawPredictionCol="rawPrediction", labelCol="label", \
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metricName="areaUnderROC", weightCol=None)
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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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self._setDefault(metricName="areaUnderROC")
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kwargs = self._input_kwargs
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self._set(**kwargs)
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@since("1.4.0")
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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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return self._set(metricName=value)
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@since("1.4.0")
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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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@since("1.4.0")
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def setParams(self, rawPredictionCol="rawPrediction", labelCol="label",
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metricName="areaUnderROC", weightCol=None):
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"""
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setParams(self, rawPredictionCol="rawPrediction", labelCol="label", \
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metricName="areaUnderROC", weightCol=None)
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Sets params for binary classification evaluator.
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"""
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kwargs = self._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, HasWeightCol,
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JavaMLReadable, JavaMLWritable):
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"""
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.. note:: Experimental
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Evaluator for Regression, which expects input columns prediction, label
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and an optional weight column.
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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 = spark.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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>>> re_path = temp_path + "/re"
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>>> evaluator.save(re_path)
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>>> evaluator2 = RegressionEvaluator.load(re_path)
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>>> str(evaluator2.getPredictionCol())
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'raw'
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>>> scoreAndLabelsAndWeight = [(-28.98343821, -27.0, 1.0), (20.21491975, 21.5, 0.8),
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... (-25.98418959, -22.0, 1.0), (30.69731842, 33.0, 0.6), (74.69283752, 71.0, 0.2)]
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>>> dataset = spark.createDataFrame(scoreAndLabelsAndWeight, ["raw", "label", "weight"])
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...
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>>> evaluator = RegressionEvaluator(predictionCol="raw", weightCol="weight")
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>>> evaluator.evaluate(dataset)
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2.740...
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.. versionadded:: 1.4.0
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"""
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metricName = Param(Params._dummy(), "metricName",
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"""metric name in evaluation - one of:
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rmse - root mean squared error (default)
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mse - mean squared error
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r2 - r^2 metric
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mae - mean absolute error.""",
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typeConverter=TypeConverters.toString)
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@keyword_only
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def __init__(self, predictionCol="prediction", labelCol="label",
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metricName="rmse", weightCol=None):
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"""
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__init__(self, predictionCol="prediction", labelCol="label", \
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metricName="rmse", weightCol=None)
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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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self._setDefault(metricName="rmse")
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kwargs = self._input_kwargs
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self._set(**kwargs)
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@since("1.4.0")
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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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return self._set(metricName=value)
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@since("1.4.0")
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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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@since("1.4.0")
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def setParams(self, predictionCol="prediction", labelCol="label",
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metricName="rmse", weightCol=None):
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"""
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setParams(self, predictionCol="prediction", labelCol="label", \
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metricName="rmse", weightCol=None)
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Sets params for regression evaluator.
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"""
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kwargs = self._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, HasWeightCol,
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JavaMLReadable, JavaMLWritable):
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"""
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.. note:: Experimental
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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 = spark.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: "accuracy"})
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0.66...
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>>> mce_path = temp_path + "/mce"
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>>> evaluator.save(mce_path)
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>>> evaluator2 = MulticlassClassificationEvaluator.load(mce_path)
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>>> str(evaluator2.getPredictionCol())
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'prediction'
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>>> scoreAndLabelsAndWeight = [(0.0, 0.0, 1.0), (0.0, 1.0, 1.0), (0.0, 0.0, 1.0),
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... (1.0, 0.0, 1.0), (1.0, 1.0, 1.0), (1.0, 1.0, 1.0), (1.0, 1.0, 1.0),
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... (2.0, 2.0, 1.0), (2.0, 0.0, 1.0)]
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>>> dataset = spark.createDataFrame(scoreAndLabelsAndWeight, ["prediction", "label", "weight"])
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...
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>>> evaluator = MulticlassClassificationEvaluator(predictionCol="prediction",
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... weightCol="weight")
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>>> evaluator.evaluate(dataset)
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0.66...
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>>> evaluator.evaluate(dataset, {evaluator.metricName: "accuracy"})
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0.66...
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.. versionadded:: 1.5.0
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"""
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metricName = Param(Params._dummy(), "metricName",
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"metric name in evaluation "
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"(f1|weightedPrecision|weightedRecall|accuracy)",
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typeConverter=TypeConverters.toString)
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@keyword_only
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def __init__(self, predictionCol="prediction", labelCol="label",
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metricName="f1", weightCol=None):
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"""
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__init__(self, predictionCol="prediction", labelCol="label", \
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metricName="f1", weightCol=None)
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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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self._setDefault(metricName="f1")
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kwargs = self._input_kwargs
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self._set(**kwargs)
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@since("1.5.0")
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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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return self._set(metricName=value)
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@since("1.5.0")
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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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@since("1.5.0")
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def setParams(self, predictionCol="prediction", labelCol="label",
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metricName="f1", weightCol=None):
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"""
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setParams(self, predictionCol="prediction", labelCol="label", \
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metricName="f1", weightCol=None)
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Sets params for multiclass classification evaluator.
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"""
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kwargs = self._input_kwargs
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return self._set(**kwargs)
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@inherit_doc
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class ClusteringEvaluator(JavaEvaluator, HasPredictionCol, HasFeaturesCol,
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JavaMLReadable, JavaMLWritable):
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"""
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.. note:: Experimental
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Evaluator for Clustering results, which expects two input
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columns: prediction and features. The metric computes the Silhouette
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measure using the squared Euclidean distance.
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The Silhouette is a measure for the validation of the consistency
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within clusters. It ranges between 1 and -1, where a value close to
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1 means that the points in a cluster are close to the other points
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in the same cluster and far from the points of the other clusters.
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>>> from pyspark.ml.linalg import Vectors
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>>> featureAndPredictions = map(lambda x: (Vectors.dense(x[0]), x[1]),
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... [([0.0, 0.5], 0.0), ([0.5, 0.0], 0.0), ([10.0, 11.0], 1.0),
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... ([10.5, 11.5], 1.0), ([1.0, 1.0], 0.0), ([8.0, 6.0], 1.0)])
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>>> dataset = spark.createDataFrame(featureAndPredictions, ["features", "prediction"])
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...
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>>> evaluator = ClusteringEvaluator(predictionCol="prediction")
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>>> evaluator.evaluate(dataset)
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0.9079...
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>>> ce_path = temp_path + "/ce"
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>>> evaluator.save(ce_path)
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>>> evaluator2 = ClusteringEvaluator.load(ce_path)
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>>> str(evaluator2.getPredictionCol())
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'prediction'
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.. versionadded:: 2.3.0
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"""
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metricName = Param(Params._dummy(), "metricName",
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"metric name in evaluation (silhouette)",
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typeConverter=TypeConverters.toString)
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distanceMeasure = Param(Params._dummy(), "distanceMeasure", "The distance measure. " +
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"Supported options: 'squaredEuclidean' and 'cosine'.",
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typeConverter=TypeConverters.toString)
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@keyword_only
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def __init__(self, predictionCol="prediction", featuresCol="features",
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metricName="silhouette", distanceMeasure="squaredEuclidean"):
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"""
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__init__(self, predictionCol="prediction", featuresCol="features", \
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metricName="silhouette", distanceMeasure="squaredEuclidean")
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"""
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super(ClusteringEvaluator, self).__init__()
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self._java_obj = self._new_java_obj(
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"org.apache.spark.ml.evaluation.ClusteringEvaluator", self.uid)
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self._setDefault(metricName="silhouette", distanceMeasure="squaredEuclidean")
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kwargs = self._input_kwargs
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self._set(**kwargs)
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@since("2.3.0")
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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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return self._set(metricName=value)
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@since("2.3.0")
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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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@since("2.3.0")
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def setParams(self, predictionCol="prediction", featuresCol="features",
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metricName="silhouette", distanceMeasure="squaredEuclidean"):
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"""
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setParams(self, predictionCol="prediction", featuresCol="features", \
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metricName="silhouette", distanceMeasure="squaredEuclidean")
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Sets params for clustering evaluator.
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"""
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kwargs = self._input_kwargs
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return self._set(**kwargs)
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@since("2.4.0")
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def setDistanceMeasure(self, value):
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"""
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Sets the value of :py:attr:`distanceMeasure`.
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"""
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return self._set(distanceMeasure=value)
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@since("2.4.0")
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def getDistanceMeasure(self):
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"""
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Gets the value of `distanceMeasure`
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"""
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return self.getOrDefault(self.distanceMeasure)
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if __name__ == "__main__":
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import doctest
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import tempfile
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import pyspark.ml.evaluation
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from pyspark.sql import SparkSession
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globs = pyspark.ml.evaluation.__dict__.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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spark = SparkSession.builder\
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.master("local[2]")\
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.appName("ml.evaluation tests")\
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.getOrCreate()
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globs['spark'] = spark
|
|
temp_path = tempfile.mkdtemp()
|
|
globs['temp_path'] = temp_path
|
|
try:
|
|
(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
|
|
spark.stop()
|
|
finally:
|
|
from shutil import rmtree
|
|
try:
|
|
rmtree(temp_path)
|
|
except OSError:
|
|
pass
|
|
if failure_count:
|
|
sys.exit(-1)
|