090962cd42
### What changes were proposed in this pull request? This PR proposes migration of `pyspark.ml` to NumPy documentation style. ### Why are the changes needed? To improve documentation style. ### Does this PR introduce _any_ user-facing change? Yes, this changes both rendered HTML docs and console representation (SPARK-33243). ### How was this patch tested? `dev/lint-python` and manual inspection. Closes #30285 from zero323/SPARK-33251. Authored-by: zero323 <mszymkiewicz@gmail.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
950 lines
33 KiB
Python
950 lines
33 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, HasProbabilityCol, \
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HasRawPredictionCol, 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', 'MultilabelClassificationEvaluator',
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'ClusteringEvaluator', 'RankingEvaluator']
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@inherit_doc
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class Evaluator(Params, metaclass=ABCMeta):
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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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pass
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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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Parameters
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----------
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dataset : :py:class:`pyspark.sql.DataFrame`
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a dataset that contains labels/observations and predictions
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Returns
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-------
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float
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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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.. versionadded:: 1.4.0
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Parameters
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----------
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dataset : :py:class:`pyspark.sql.DataFrame`
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a dataset that contains labels/observations and predictions
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params : dict, optional
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an optional param map that overrides embedded params
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Returns
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-------
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float
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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, metaclass=ABCMeta):
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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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def _evaluate(self, dataset):
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"""
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Evaluates the output.
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Parameters
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----------
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dataset : :py:class:`pyspark.sql.DataFrame`
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a dataset that contains labels/observations and predictions
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Returns
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-------
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float
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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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Evaluator for binary classification, which expects input columns rawPrediction, label
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and an optional weight column.
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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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.. versionadded:: 1.4.0
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Examples
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--------
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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()
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>>> evaluator.setRawPredictionCol("raw")
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BinaryClassificationEvaluator...
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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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>>> evaluator.getNumBins()
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1000
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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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numBins = Param(Params._dummy(), "numBins", "Number of bins to down-sample the curves "
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"(ROC curve, PR curve) in area computation. If 0, no down-sampling will "
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"occur. Must be >= 0.",
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typeConverter=TypeConverters.toInt)
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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, numBins=1000):
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"""
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__init__(self, \\*, rawPredictionCol="rawPrediction", labelCol="label", \
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metricName="areaUnderROC", weightCol=None, numBins=1000)
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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", numBins=1000)
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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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@since("3.0.0")
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def setNumBins(self, value):
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"""
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Sets the value of :py:attr:`numBins`.
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"""
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return self._set(numBins=value)
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@since("3.0.0")
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def getNumBins(self):
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"""
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Gets the value of numBins or its default value.
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"""
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return self.getOrDefault(self.numBins)
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def setLabelCol(self, value):
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"""
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Sets the value of :py:attr:`labelCol`.
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"""
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return self._set(labelCol=value)
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def setRawPredictionCol(self, value):
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"""
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Sets the value of :py:attr:`rawPredictionCol`.
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"""
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return self._set(rawPredictionCol=value)
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@since("3.0.0")
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def setWeightCol(self, value):
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"""
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Sets the value of :py:attr:`weightCol`.
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"""
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return self._set(weightCol=value)
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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, numBins=1000):
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"""
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setParams(self, \\*, rawPredictionCol="rawPrediction", labelCol="label", \
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metricName="areaUnderROC", weightCol=None, numBins=1000)
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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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Evaluator for Regression, which expects input columns prediction, label
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and an optional weight column.
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.. versionadded:: 1.4.0
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Examples
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--------
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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()
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>>> evaluator.setPredictionCol("raw")
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RegressionEvaluator...
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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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>>> evaluator.getThroughOrigin()
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False
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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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var - explained variance.""",
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typeConverter=TypeConverters.toString)
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throughOrigin = Param(Params._dummy(), "throughOrigin",
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"whether the regression is through the origin.",
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typeConverter=TypeConverters.toBoolean)
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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, throughOrigin=False):
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"""
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__init__(self, \\*, predictionCol="prediction", labelCol="label", \
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metricName="rmse", weightCol=None, throughOrigin=False)
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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", throughOrigin=False)
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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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@since("3.0.0")
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def setThroughOrigin(self, value):
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"""
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Sets the value of :py:attr:`throughOrigin`.
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"""
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return self._set(throughOrigin=value)
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@since("3.0.0")
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def getThroughOrigin(self):
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"""
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Gets the value of throughOrigin or its default value.
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"""
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return self.getOrDefault(self.throughOrigin)
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def setLabelCol(self, value):
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"""
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Sets the value of :py:attr:`labelCol`.
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"""
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return self._set(labelCol=value)
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def setPredictionCol(self, value):
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"""
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Sets the value of :py:attr:`predictionCol`.
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"""
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return self._set(predictionCol=value)
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@since("3.0.0")
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def setWeightCol(self, value):
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"""
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Sets the value of :py:attr:`weightCol`.
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"""
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return self._set(weightCol=value)
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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, throughOrigin=False):
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"""
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setParams(self, \\*, predictionCol="prediction", labelCol="label", \
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metricName="rmse", weightCol=None, throughOrigin=False)
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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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HasProbabilityCol, JavaMLReadable, JavaMLWritable):
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"""
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Evaluator for Multiclass Classification, which expects input
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columns: prediction, label, weight (optional) and probabilityCol (only for logLoss).
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.. versionadded:: 1.5.0
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Examples
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--------
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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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>>> evaluator = MulticlassClassificationEvaluator()
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>>> evaluator.setPredictionCol("prediction")
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MulticlassClassificationEvaluator...
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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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>>> evaluator.evaluate(dataset, {evaluator.metricName: "truePositiveRateByLabel",
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... evaluator.metricLabel: 1.0})
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0.75...
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>>> evaluator.setMetricName("hammingLoss")
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MulticlassClassificationEvaluator...
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>>> evaluator.evaluate(dataset)
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0.33...
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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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>>> 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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>>> predictionAndLabelsWithProbabilities = [
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... (1.0, 1.0, 1.0, [0.1, 0.8, 0.1]), (0.0, 2.0, 1.0, [0.9, 0.05, 0.05]),
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... (0.0, 0.0, 1.0, [0.8, 0.2, 0.0]), (1.0, 1.0, 1.0, [0.3, 0.65, 0.05])]
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>>> dataset = spark.createDataFrame(predictionAndLabelsWithProbabilities, ["prediction",
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... "label", "weight", "probability"])
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>>> evaluator = MulticlassClassificationEvaluator(predictionCol="prediction",
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... probabilityCol="probability")
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>>> evaluator.setMetricName("logLoss")
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MulticlassClassificationEvaluator...
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>>> evaluator.evaluate(dataset)
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0.9682...
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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|accuracy|weightedPrecision|weightedRecall|weightedTruePositiveRate| "
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"weightedFalsePositiveRate|weightedFMeasure|truePositiveRateByLabel| "
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"falsePositiveRateByLabel|precisionByLabel|recallByLabel|fMeasureByLabel| "
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"logLoss|hammingLoss)",
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typeConverter=TypeConverters.toString)
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metricLabel = Param(Params._dummy(), "metricLabel",
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"The class whose metric will be computed in truePositiveRateByLabel|"
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"falsePositiveRateByLabel|precisionByLabel|recallByLabel|fMeasureByLabel."
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" Must be >= 0. The default value is 0.",
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typeConverter=TypeConverters.toFloat)
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beta = Param(Params._dummy(), "beta",
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"The beta value used in weightedFMeasure|fMeasureByLabel."
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" Must be > 0. The default value is 1.",
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typeConverter=TypeConverters.toFloat)
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eps = Param(Params._dummy(), "eps",
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"log-loss is undefined for p=0 or p=1, so probabilities are clipped to "
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"max(eps, min(1 - eps, p)). "
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"Must be in range (0, 0.5). The default value is 1e-15.",
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typeConverter=TypeConverters.toFloat)
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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, metricLabel=0.0, beta=1.0,
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probabilityCol="probability", eps=1e-15):
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"""
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__init__(self, \\*, predictionCol="prediction", labelCol="label", \
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metricName="f1", weightCol=None, metricLabel=0.0, beta=1.0, \
|
|
probabilityCol="probability", eps=1e-15)
|
|
"""
|
|
super(MulticlassClassificationEvaluator, self).__init__()
|
|
self._java_obj = self._new_java_obj(
|
|
"org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator", self.uid)
|
|
self._setDefault(metricName="f1", metricLabel=0.0, beta=1.0, eps=1e-15)
|
|
kwargs = self._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)
|
|
|
|
@since("3.0.0")
|
|
def setMetricLabel(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`metricLabel`.
|
|
"""
|
|
return self._set(metricLabel=value)
|
|
|
|
@since("3.0.0")
|
|
def getMetricLabel(self):
|
|
"""
|
|
Gets the value of metricLabel or its default value.
|
|
"""
|
|
return self.getOrDefault(self.metricLabel)
|
|
|
|
@since("3.0.0")
|
|
def setBeta(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`beta`.
|
|
"""
|
|
return self._set(beta=value)
|
|
|
|
@since("3.0.0")
|
|
def getBeta(self):
|
|
"""
|
|
Gets the value of beta or its default value.
|
|
"""
|
|
return self.getOrDefault(self.beta)
|
|
|
|
@since("3.0.0")
|
|
def setEps(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`eps`.
|
|
"""
|
|
return self._set(eps=value)
|
|
|
|
@since("3.0.0")
|
|
def getEps(self):
|
|
"""
|
|
Gets the value of eps or its default value.
|
|
"""
|
|
return self.getOrDefault(self.eps)
|
|
|
|
def setLabelCol(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`labelCol`.
|
|
"""
|
|
return self._set(labelCol=value)
|
|
|
|
def setPredictionCol(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`predictionCol`.
|
|
"""
|
|
return self._set(predictionCol=value)
|
|
|
|
@since("3.0.0")
|
|
def setProbabilityCol(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`probabilityCol`.
|
|
"""
|
|
return self._set(probabilityCol=value)
|
|
|
|
@since("3.0.0")
|
|
def setWeightCol(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`weightCol`.
|
|
"""
|
|
return self._set(weightCol=value)
|
|
|
|
@keyword_only
|
|
@since("1.5.0")
|
|
def setParams(self, *, predictionCol="prediction", labelCol="label",
|
|
metricName="f1", weightCol=None, metricLabel=0.0, beta=1.0,
|
|
probabilityCol="probability", eps=1e-15):
|
|
"""
|
|
setParams(self, \\*, predictionCol="prediction", labelCol="label", \
|
|
metricName="f1", weightCol=None, metricLabel=0.0, beta=1.0, \
|
|
probabilityCol="probability", eps=1e-15)
|
|
Sets params for multiclass classification evaluator.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
|
|
@inherit_doc
|
|
class MultilabelClassificationEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol,
|
|
JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Evaluator for Multilabel Classification, which expects two input
|
|
columns: prediction and label.
|
|
|
|
.. versionadded:: 3.0.0
|
|
|
|
Notes
|
|
-----
|
|
Experimental
|
|
|
|
Examples
|
|
--------
|
|
>>> scoreAndLabels = [([0.0, 1.0], [0.0, 2.0]), ([0.0, 2.0], [0.0, 1.0]),
|
|
... ([], [0.0]), ([2.0], [2.0]), ([2.0, 0.0], [2.0, 0.0]),
|
|
... ([0.0, 1.0, 2.0], [0.0, 1.0]), ([1.0], [1.0, 2.0])]
|
|
>>> dataset = spark.createDataFrame(scoreAndLabels, ["prediction", "label"])
|
|
...
|
|
>>> evaluator = MultilabelClassificationEvaluator()
|
|
>>> evaluator.setPredictionCol("prediction")
|
|
MultilabelClassificationEvaluator...
|
|
>>> evaluator.evaluate(dataset)
|
|
0.63...
|
|
>>> evaluator.evaluate(dataset, {evaluator.metricName: "accuracy"})
|
|
0.54...
|
|
>>> mlce_path = temp_path + "/mlce"
|
|
>>> evaluator.save(mlce_path)
|
|
>>> evaluator2 = MultilabelClassificationEvaluator.load(mlce_path)
|
|
>>> str(evaluator2.getPredictionCol())
|
|
'prediction'
|
|
"""
|
|
metricName = Param(Params._dummy(), "metricName",
|
|
"metric name in evaluation "
|
|
"(subsetAccuracy|accuracy|hammingLoss|precision|recall|f1Measure|"
|
|
"precisionByLabel|recallByLabel|f1MeasureByLabel|microPrecision|"
|
|
"microRecall|microF1Measure)",
|
|
typeConverter=TypeConverters.toString)
|
|
metricLabel = Param(Params._dummy(), "metricLabel",
|
|
"The class whose metric will be computed in precisionByLabel|"
|
|
"recallByLabel|f1MeasureByLabel. "
|
|
"Must be >= 0. The default value is 0.",
|
|
typeConverter=TypeConverters.toFloat)
|
|
|
|
@keyword_only
|
|
def __init__(self, *, predictionCol="prediction", labelCol="label",
|
|
metricName="f1Measure", metricLabel=0.0):
|
|
"""
|
|
__init__(self, \\*, predictionCol="prediction", labelCol="label", \
|
|
metricName="f1Measure", metricLabel=0.0)
|
|
"""
|
|
super(MultilabelClassificationEvaluator, self).__init__()
|
|
self._java_obj = self._new_java_obj(
|
|
"org.apache.spark.ml.evaluation.MultilabelClassificationEvaluator", self.uid)
|
|
self._setDefault(metricName="f1Measure", metricLabel=0.0)
|
|
kwargs = self._input_kwargs
|
|
self._set(**kwargs)
|
|
|
|
@since("3.0.0")
|
|
def setMetricName(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`metricName`.
|
|
"""
|
|
return self._set(metricName=value)
|
|
|
|
@since("3.0.0")
|
|
def getMetricName(self):
|
|
"""
|
|
Gets the value of metricName or its default value.
|
|
"""
|
|
return self.getOrDefault(self.metricName)
|
|
|
|
@since("3.0.0")
|
|
def setMetricLabel(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`metricLabel`.
|
|
"""
|
|
return self._set(metricLabel=value)
|
|
|
|
@since("3.0.0")
|
|
def getMetricLabel(self):
|
|
"""
|
|
Gets the value of metricLabel or its default value.
|
|
"""
|
|
return self.getOrDefault(self.metricLabel)
|
|
|
|
@since("3.0.0")
|
|
def setLabelCol(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`labelCol`.
|
|
"""
|
|
return self._set(labelCol=value)
|
|
|
|
@since("3.0.0")
|
|
def setPredictionCol(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`predictionCol`.
|
|
"""
|
|
return self._set(predictionCol=value)
|
|
|
|
@keyword_only
|
|
@since("3.0.0")
|
|
def setParams(self, *, predictionCol="prediction", labelCol="label",
|
|
metricName="f1Measure", metricLabel=0.0):
|
|
"""
|
|
setParams(self, \\*, predictionCol="prediction", labelCol="label", \
|
|
metricName="f1Measure", metricLabel=0.0)
|
|
Sets params for multilabel classification evaluator.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
|
|
@inherit_doc
|
|
class ClusteringEvaluator(JavaEvaluator, HasPredictionCol, HasFeaturesCol, HasWeightCol,
|
|
JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Evaluator for Clustering results, which expects two input
|
|
columns: prediction and features. The metric computes the Silhouette
|
|
measure using the squared Euclidean distance.
|
|
|
|
The Silhouette is a measure for the validation of the consistency
|
|
within clusters. It ranges between 1 and -1, where a value close to
|
|
1 means that the points in a cluster are close to the other points
|
|
in the same cluster and far from the points of the other clusters.
|
|
|
|
.. versionadded:: 2.3.0
|
|
|
|
Examples
|
|
--------
|
|
>>> from pyspark.ml.linalg import Vectors
|
|
>>> featureAndPredictions = map(lambda x: (Vectors.dense(x[0]), x[1]),
|
|
... [([0.0, 0.5], 0.0), ([0.5, 0.0], 0.0), ([10.0, 11.0], 1.0),
|
|
... ([10.5, 11.5], 1.0), ([1.0, 1.0], 0.0), ([8.0, 6.0], 1.0)])
|
|
>>> dataset = spark.createDataFrame(featureAndPredictions, ["features", "prediction"])
|
|
...
|
|
>>> evaluator = ClusteringEvaluator()
|
|
>>> evaluator.setPredictionCol("prediction")
|
|
ClusteringEvaluator...
|
|
>>> evaluator.evaluate(dataset)
|
|
0.9079...
|
|
>>> featureAndPredictionsWithWeight = map(lambda x: (Vectors.dense(x[0]), x[1], x[2]),
|
|
... [([0.0, 0.5], 0.0, 2.5), ([0.5, 0.0], 0.0, 2.5), ([10.0, 11.0], 1.0, 2.5),
|
|
... ([10.5, 11.5], 1.0, 2.5), ([1.0, 1.0], 0.0, 2.5), ([8.0, 6.0], 1.0, 2.5)])
|
|
>>> dataset = spark.createDataFrame(
|
|
... featureAndPredictionsWithWeight, ["features", "prediction", "weight"])
|
|
>>> evaluator = ClusteringEvaluator()
|
|
>>> evaluator.setPredictionCol("prediction")
|
|
ClusteringEvaluator...
|
|
>>> evaluator.setWeightCol("weight")
|
|
ClusteringEvaluator...
|
|
>>> evaluator.evaluate(dataset)
|
|
0.9079...
|
|
>>> ce_path = temp_path + "/ce"
|
|
>>> evaluator.save(ce_path)
|
|
>>> evaluator2 = ClusteringEvaluator.load(ce_path)
|
|
>>> str(evaluator2.getPredictionCol())
|
|
'prediction'
|
|
"""
|
|
metricName = Param(Params._dummy(), "metricName",
|
|
"metric name in evaluation (silhouette)",
|
|
typeConverter=TypeConverters.toString)
|
|
distanceMeasure = Param(Params._dummy(), "distanceMeasure", "The distance measure. " +
|
|
"Supported options: 'squaredEuclidean' and 'cosine'.",
|
|
typeConverter=TypeConverters.toString)
|
|
|
|
@keyword_only
|
|
def __init__(self, *, predictionCol="prediction", featuresCol="features",
|
|
metricName="silhouette", distanceMeasure="squaredEuclidean", weightCol=None):
|
|
"""
|
|
__init__(self, \\*, predictionCol="prediction", featuresCol="features", \
|
|
metricName="silhouette", distanceMeasure="squaredEuclidean", weightCol=None)
|
|
"""
|
|
super(ClusteringEvaluator, self).__init__()
|
|
self._java_obj = self._new_java_obj(
|
|
"org.apache.spark.ml.evaluation.ClusteringEvaluator", self.uid)
|
|
self._setDefault(metricName="silhouette", distanceMeasure="squaredEuclidean")
|
|
kwargs = self._input_kwargs
|
|
self._set(**kwargs)
|
|
|
|
@keyword_only
|
|
@since("2.3.0")
|
|
def setParams(self, *, predictionCol="prediction", featuresCol="features",
|
|
metricName="silhouette", distanceMeasure="squaredEuclidean", weightCol=None):
|
|
"""
|
|
setParams(self, \\*, predictionCol="prediction", featuresCol="features", \
|
|
metricName="silhouette", distanceMeasure="squaredEuclidean", weightCol=None)
|
|
Sets params for clustering evaluator.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
@since("2.3.0")
|
|
def setMetricName(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`metricName`.
|
|
"""
|
|
return self._set(metricName=value)
|
|
|
|
@since("2.3.0")
|
|
def getMetricName(self):
|
|
"""
|
|
Gets the value of metricName or its default value.
|
|
"""
|
|
return self.getOrDefault(self.metricName)
|
|
|
|
@since("2.4.0")
|
|
def setDistanceMeasure(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`distanceMeasure`.
|
|
"""
|
|
return self._set(distanceMeasure=value)
|
|
|
|
@since("2.4.0")
|
|
def getDistanceMeasure(self):
|
|
"""
|
|
Gets the value of `distanceMeasure`
|
|
"""
|
|
return self.getOrDefault(self.distanceMeasure)
|
|
|
|
def setFeaturesCol(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`featuresCol`.
|
|
"""
|
|
return self._set(featuresCol=value)
|
|
|
|
def setPredictionCol(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`predictionCol`.
|
|
"""
|
|
return self._set(predictionCol=value)
|
|
|
|
@since("3.1.0")
|
|
def setWeightCol(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`weightCol`.
|
|
"""
|
|
return self._set(weightCol=value)
|
|
|
|
|
|
@inherit_doc
|
|
class RankingEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol,
|
|
JavaMLReadable, JavaMLWritable):
|
|
"""
|
|
Evaluator for Ranking, which expects two input
|
|
columns: prediction and label.
|
|
|
|
.. versionadded:: 3.0.0
|
|
|
|
Notes
|
|
-----
|
|
Experimental
|
|
|
|
Examples
|
|
--------
|
|
>>> scoreAndLabels = [([1.0, 6.0, 2.0, 7.0, 8.0, 3.0, 9.0, 10.0, 4.0, 5.0],
|
|
... [1.0, 2.0, 3.0, 4.0, 5.0]),
|
|
... ([4.0, 1.0, 5.0, 6.0, 2.0, 7.0, 3.0, 8.0, 9.0, 10.0], [1.0, 2.0, 3.0]),
|
|
... ([1.0, 2.0, 3.0, 4.0, 5.0], [])]
|
|
>>> dataset = spark.createDataFrame(scoreAndLabels, ["prediction", "label"])
|
|
...
|
|
>>> evaluator = RankingEvaluator()
|
|
>>> evaluator.setPredictionCol("prediction")
|
|
RankingEvaluator...
|
|
>>> evaluator.evaluate(dataset)
|
|
0.35...
|
|
>>> evaluator.evaluate(dataset, {evaluator.metricName: "precisionAtK", evaluator.k: 2})
|
|
0.33...
|
|
>>> ranke_path = temp_path + "/ranke"
|
|
>>> evaluator.save(ranke_path)
|
|
>>> evaluator2 = RankingEvaluator.load(ranke_path)
|
|
>>> str(evaluator2.getPredictionCol())
|
|
'prediction'
|
|
"""
|
|
metricName = Param(Params._dummy(), "metricName",
|
|
"metric name in evaluation "
|
|
"(meanAveragePrecision|meanAveragePrecisionAtK|"
|
|
"precisionAtK|ndcgAtK|recallAtK)",
|
|
typeConverter=TypeConverters.toString)
|
|
k = Param(Params._dummy(), "k",
|
|
"The ranking position value used in meanAveragePrecisionAtK|precisionAtK|"
|
|
"ndcgAtK|recallAtK. Must be > 0. The default value is 10.",
|
|
typeConverter=TypeConverters.toInt)
|
|
|
|
@keyword_only
|
|
def __init__(self, *, predictionCol="prediction", labelCol="label",
|
|
metricName="meanAveragePrecision", k=10):
|
|
"""
|
|
__init__(self, \\*, predictionCol="prediction", labelCol="label", \
|
|
metricName="meanAveragePrecision", k=10)
|
|
"""
|
|
super(RankingEvaluator, self).__init__()
|
|
self._java_obj = self._new_java_obj(
|
|
"org.apache.spark.ml.evaluation.RankingEvaluator", self.uid)
|
|
self._setDefault(metricName="meanAveragePrecision", k=10)
|
|
kwargs = self._input_kwargs
|
|
self._set(**kwargs)
|
|
|
|
@since("3.0.0")
|
|
def setMetricName(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`metricName`.
|
|
"""
|
|
return self._set(metricName=value)
|
|
|
|
@since("3.0.0")
|
|
def getMetricName(self):
|
|
"""
|
|
Gets the value of metricName or its default value.
|
|
"""
|
|
return self.getOrDefault(self.metricName)
|
|
|
|
@since("3.0.0")
|
|
def setK(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`k`.
|
|
"""
|
|
return self._set(k=value)
|
|
|
|
@since("3.0.0")
|
|
def getK(self):
|
|
"""
|
|
Gets the value of k or its default value.
|
|
"""
|
|
return self.getOrDefault(self.k)
|
|
|
|
@since("3.0.0")
|
|
def setLabelCol(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`labelCol`.
|
|
"""
|
|
return self._set(labelCol=value)
|
|
|
|
@since("3.0.0")
|
|
def setPredictionCol(self, value):
|
|
"""
|
|
Sets the value of :py:attr:`predictionCol`.
|
|
"""
|
|
return self._set(predictionCol=value)
|
|
|
|
@keyword_only
|
|
@since("3.0.0")
|
|
def setParams(self, *, predictionCol="prediction", labelCol="label",
|
|
metricName="meanAveragePrecision", k=10):
|
|
"""
|
|
setParams(self, \\*, predictionCol="prediction", labelCol="label", \
|
|
metricName="meanAveragePrecision", k=10)
|
|
Sets params for ranking evaluator.
|
|
"""
|
|
kwargs = self._input_kwargs
|
|
return self._set(**kwargs)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import doctest
|
|
import tempfile
|
|
import pyspark.ml.evaluation
|
|
from pyspark.sql import SparkSession
|
|
globs = pyspark.ml.evaluation.__dict__.copy()
|
|
# The small batch size here ensures that we see multiple batches,
|
|
# even in these small test examples:
|
|
spark = SparkSession.builder\
|
|
.master("local[2]")\
|
|
.appName("ml.evaluation tests")\
|
|
.getOrCreate()
|
|
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)
|