#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import sys
from abc import abstractmethod, ABCMeta
from pyspark import since, keyword_only
from pyspark.ml.wrapper import JavaParams
from pyspark.ml.param import Param, Params, TypeConverters
from pyspark.ml.param.shared import HasLabelCol, HasPredictionCol, HasProbabilityCol, \
HasRawPredictionCol, HasFeaturesCol, HasWeightCol
from pyspark.ml.common import inherit_doc
from pyspark.ml.util import JavaMLReadable, JavaMLWritable
__all__ = ['Evaluator', 'BinaryClassificationEvaluator', 'RegressionEvaluator',
'MulticlassClassificationEvaluator', 'MultilabelClassificationEvaluator',
'ClusteringEvaluator', 'RankingEvaluator']
[docs]@inherit_doc
class Evaluator(Params):
"""
Base class for evaluators that compute metrics from predictions.
.. versionadded:: 1.4.0
"""
__metaclass__ = ABCMeta
@abstractmethod
def _evaluate(self, dataset):
"""
Evaluates the output.
:param dataset: a dataset that contains labels/observations and
predictions
:return: metric
"""
raise NotImplementedError()
[docs] @since("1.4.0")
def evaluate(self, dataset, params=None):
"""
Evaluates the output with optional parameters.
:param dataset: a dataset that contains labels/observations and
predictions
:param params: an optional param map that overrides embedded
params
:return: metric
"""
if params is None:
params = dict()
if isinstance(params, dict):
if params:
return self.copy(params)._evaluate(dataset)
else:
return self._evaluate(dataset)
else:
raise ValueError("Params must be a param map but got %s." % type(params))
[docs] @since("1.5.0")
def isLargerBetter(self):
"""
Indicates whether the metric returned by :py:meth:`evaluate` should be maximized
(True, default) or minimized (False).
A given evaluator may support multiple metrics which may be maximized or minimized.
"""
return True
@inherit_doc
class JavaEvaluator(JavaParams, Evaluator):
"""
Base class for :py:class:`Evaluator`s that wrap Java/Scala
implementations.
"""
__metaclass__ = ABCMeta
def _evaluate(self, dataset):
"""
Evaluates the output.
:param dataset: a dataset that contains labels/observations and predictions.
:return: evaluation metric
"""
self._transfer_params_to_java()
return self._java_obj.evaluate(dataset._jdf)
def isLargerBetter(self):
self._transfer_params_to_java()
return self._java_obj.isLargerBetter()
[docs]@inherit_doc
class BinaryClassificationEvaluator(JavaEvaluator, HasLabelCol, HasRawPredictionCol, HasWeightCol,
JavaMLReadable, JavaMLWritable):
"""
Evaluator for binary classification, which expects two input columns: rawPrediction and label.
The rawPrediction column can be of type double (binary 0/1 prediction, or probability of label
1) or of type vector (length-2 vector of raw predictions, scores, or label probabilities).
>>> from pyspark.ml.linalg import Vectors
>>> scoreAndLabels = map(lambda x: (Vectors.dense([1.0 - x[0], x[0]]), x[1]),
... [(0.1, 0.0), (0.1, 1.0), (0.4, 0.0), (0.6, 0.0), (0.6, 1.0), (0.6, 1.0), (0.8, 1.0)])
>>> dataset = spark.createDataFrame(scoreAndLabels, ["raw", "label"])
...
>>> evaluator = BinaryClassificationEvaluator()
>>> evaluator.setRawPredictionCol("raw")
BinaryClassificationEvaluator...
>>> evaluator.evaluate(dataset)
0.70...
>>> evaluator.evaluate(dataset, {evaluator.metricName: "areaUnderPR"})
0.83...
>>> bce_path = temp_path + "/bce"
>>> evaluator.save(bce_path)
>>> evaluator2 = BinaryClassificationEvaluator.load(bce_path)
>>> str(evaluator2.getRawPredictionCol())
'raw'
>>> scoreAndLabelsAndWeight = map(lambda x: (Vectors.dense([1.0 - x[0], x[0]]), x[1], x[2]),
... [(0.1, 0.0, 1.0), (0.1, 1.0, 0.9), (0.4, 0.0, 0.7), (0.6, 0.0, 0.9),
... (0.6, 1.0, 1.0), (0.6, 1.0, 0.3), (0.8, 1.0, 1.0)])
>>> dataset = spark.createDataFrame(scoreAndLabelsAndWeight, ["raw", "label", "weight"])
...
>>> evaluator = BinaryClassificationEvaluator(rawPredictionCol="raw", weightCol="weight")
>>> evaluator.evaluate(dataset)
0.70...
>>> evaluator.evaluate(dataset, {evaluator.metricName: "areaUnderPR"})
0.82...
>>> evaluator.getNumBins()
1000
.. versionadded:: 1.4.0
"""
metricName = Param(Params._dummy(), "metricName",
"metric name in evaluation (areaUnderROC|areaUnderPR)",
typeConverter=TypeConverters.toString)
numBins = Param(Params._dummy(), "numBins", "Number of bins to down-sample the curves "
"(ROC curve, PR curve) in area computation. If 0, no down-sampling will "
"occur. Must be >= 0.",
typeConverter=TypeConverters.toInt)
@keyword_only
def __init__(self, rawPredictionCol="rawPrediction", labelCol="label",
metricName="areaUnderROC", weightCol=None, numBins=1000):
"""
__init__(self, rawPredictionCol="rawPrediction", labelCol="label", \
metricName="areaUnderROC", weightCol=None, numBins=1000)
"""
super(BinaryClassificationEvaluator, self).__init__()
self._java_obj = self._new_java_obj(
"org.apache.spark.ml.evaluation.BinaryClassificationEvaluator", self.uid)
self._setDefault(metricName="areaUnderROC", numBins=1000)
kwargs = self._input_kwargs
self._set(**kwargs)
[docs] @since("1.4.0")
def setMetricName(self, value):
"""
Sets the value of :py:attr:`metricName`.
"""
return self._set(metricName=value)
[docs] @since("1.4.0")
def getMetricName(self):
"""
Gets the value of metricName or its default value.
"""
return self.getOrDefault(self.metricName)
[docs] @since("3.0.0")
def setNumBins(self, value):
"""
Sets the value of :py:attr:`numBins`.
"""
return self._set(numBins=value)
[docs] @since("3.0.0")
def getNumBins(self):
"""
Gets the value of numBins or its default value.
"""
return self.getOrDefault(self.numBins)
[docs] def setLabelCol(self, value):
"""
Sets the value of :py:attr:`labelCol`.
"""
return self._set(labelCol=value)
[docs] def setRawPredictionCol(self, value):
"""
Sets the value of :py:attr:`rawPredictionCol`.
"""
return self._set(rawPredictionCol=value)
[docs] @since("3.0.0")
def setWeightCol(self, value):
"""
Sets the value of :py:attr:`weightCol`.
"""
return self._set(weightCol=value)
[docs] @keyword_only
@since("1.4.0")
def setParams(self, rawPredictionCol="rawPrediction", labelCol="label",
metricName="areaUnderROC", weightCol=None, numBins=1000):
"""
setParams(self, rawPredictionCol="rawPrediction", labelCol="label", \
metricName="areaUnderROC", weightCol=None, numBins=1000)
Sets params for binary classification evaluator.
"""
kwargs = self._input_kwargs
return self._set(**kwargs)
[docs]@inherit_doc
class RegressionEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol, HasWeightCol,
JavaMLReadable, JavaMLWritable):
"""
Evaluator for Regression, which expects input columns prediction, label
and an optional weight column.
>>> scoreAndLabels = [(-28.98343821, -27.0), (20.21491975, 21.5),
... (-25.98418959, -22.0), (30.69731842, 33.0), (74.69283752, 71.0)]
>>> dataset = spark.createDataFrame(scoreAndLabels, ["raw", "label"])
...
>>> evaluator = RegressionEvaluator()
>>> evaluator.setPredictionCol("raw")
RegressionEvaluator...
>>> evaluator.evaluate(dataset)
2.842...
>>> evaluator.evaluate(dataset, {evaluator.metricName: "r2"})
0.993...
>>> evaluator.evaluate(dataset, {evaluator.metricName: "mae"})
2.649...
>>> re_path = temp_path + "/re"
>>> evaluator.save(re_path)
>>> evaluator2 = RegressionEvaluator.load(re_path)
>>> str(evaluator2.getPredictionCol())
'raw'
>>> scoreAndLabelsAndWeight = [(-28.98343821, -27.0, 1.0), (20.21491975, 21.5, 0.8),
... (-25.98418959, -22.0, 1.0), (30.69731842, 33.0, 0.6), (74.69283752, 71.0, 0.2)]
>>> dataset = spark.createDataFrame(scoreAndLabelsAndWeight, ["raw", "label", "weight"])
...
>>> evaluator = RegressionEvaluator(predictionCol="raw", weightCol="weight")
>>> evaluator.evaluate(dataset)
2.740...
>>> evaluator.getThroughOrigin()
False
.. versionadded:: 1.4.0
"""
metricName = Param(Params._dummy(), "metricName",
"""metric name in evaluation - one of:
rmse - root mean squared error (default)
mse - mean squared error
r2 - r^2 metric
mae - mean absolute error
var - explained variance.""",
typeConverter=TypeConverters.toString)
throughOrigin = Param(Params._dummy(), "throughOrigin",
"whether the regression is through the origin.",
typeConverter=TypeConverters.toBoolean)
@keyword_only
def __init__(self, predictionCol="prediction", labelCol="label",
metricName="rmse", weightCol=None, throughOrigin=False):
"""
__init__(self, predictionCol="prediction", labelCol="label", \
metricName="rmse", weightCol=None, throughOrigin=False)
"""
super(RegressionEvaluator, self).__init__()
self._java_obj = self._new_java_obj(
"org.apache.spark.ml.evaluation.RegressionEvaluator", self.uid)
self._setDefault(metricName="rmse", throughOrigin=False)
kwargs = self._input_kwargs
self._set(**kwargs)
[docs] @since("1.4.0")
def setMetricName(self, value):
"""
Sets the value of :py:attr:`metricName`.
"""
return self._set(metricName=value)
[docs] @since("1.4.0")
def getMetricName(self):
"""
Gets the value of metricName or its default value.
"""
return self.getOrDefault(self.metricName)
[docs] @since("3.0.0")
def setThroughOrigin(self, value):
"""
Sets the value of :py:attr:`throughOrigin`.
"""
return self._set(throughOrigin=value)
[docs] @since("3.0.0")
def getThroughOrigin(self):
"""
Gets the value of throughOrigin or its default value.
"""
return self.getOrDefault(self.throughOrigin)
[docs] def setLabelCol(self, value):
"""
Sets the value of :py:attr:`labelCol`.
"""
return self._set(labelCol=value)
[docs] def setPredictionCol(self, value):
"""
Sets the value of :py:attr:`predictionCol`.
"""
return self._set(predictionCol=value)
[docs] @since("3.0.0")
def setWeightCol(self, value):
"""
Sets the value of :py:attr:`weightCol`.
"""
return self._set(weightCol=value)
[docs] @keyword_only
@since("1.4.0")
def setParams(self, predictionCol="prediction", labelCol="label",
metricName="rmse", weightCol=None, throughOrigin=False):
"""
setParams(self, predictionCol="prediction", labelCol="label", \
metricName="rmse", weightCol=None, throughOrigin=False)
Sets params for regression evaluator.
"""
kwargs = self._input_kwargs
return self._set(**kwargs)
[docs]@inherit_doc
class MulticlassClassificationEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol, HasWeightCol,
HasProbabilityCol, JavaMLReadable, JavaMLWritable):
"""
Evaluator for Multiclass Classification, which expects input
columns: prediction, label, weight (optional) and probabilityCol (only for logLoss).
>>> scoreAndLabels = [(0.0, 0.0), (0.0, 1.0), (0.0, 0.0),
... (1.0, 0.0), (1.0, 1.0), (1.0, 1.0), (1.0, 1.0), (2.0, 2.0), (2.0, 0.0)]
>>> dataset = spark.createDataFrame(scoreAndLabels, ["prediction", "label"])
>>> evaluator = MulticlassClassificationEvaluator()
>>> evaluator.setPredictionCol("prediction")
MulticlassClassificationEvaluator...
>>> evaluator.evaluate(dataset)
0.66...
>>> evaluator.evaluate(dataset, {evaluator.metricName: "accuracy"})
0.66...
>>> evaluator.evaluate(dataset, {evaluator.metricName: "truePositiveRateByLabel",
... evaluator.metricLabel: 1.0})
0.75...
>>> evaluator.setMetricName("hammingLoss")
MulticlassClassificationEvaluator...
>>> evaluator.evaluate(dataset)
0.33...
>>> mce_path = temp_path + "/mce"
>>> evaluator.save(mce_path)
>>> evaluator2 = MulticlassClassificationEvaluator.load(mce_path)
>>> str(evaluator2.getPredictionCol())
'prediction'
>>> scoreAndLabelsAndWeight = [(0.0, 0.0, 1.0), (0.0, 1.0, 1.0), (0.0, 0.0, 1.0),
... (1.0, 0.0, 1.0), (1.0, 1.0, 1.0), (1.0, 1.0, 1.0), (1.0, 1.0, 1.0),
... (2.0, 2.0, 1.0), (2.0, 0.0, 1.0)]
>>> dataset = spark.createDataFrame(scoreAndLabelsAndWeight, ["prediction", "label", "weight"])
>>> evaluator = MulticlassClassificationEvaluator(predictionCol="prediction",
... weightCol="weight")
>>> evaluator.evaluate(dataset)
0.66...
>>> evaluator.evaluate(dataset, {evaluator.metricName: "accuracy"})
0.66...
>>> predictionAndLabelsWithProbabilities = [
... (1.0, 1.0, 1.0, [0.1, 0.8, 0.1]), (0.0, 2.0, 1.0, [0.9, 0.05, 0.05]),
... (0.0, 0.0, 1.0, [0.8, 0.2, 0.0]), (1.0, 1.0, 1.0, [0.3, 0.65, 0.05])]
>>> dataset = spark.createDataFrame(predictionAndLabelsWithProbabilities, ["prediction",
... "label", "weight", "probability"])
>>> evaluator = MulticlassClassificationEvaluator(predictionCol="prediction",
... probabilityCol="probability")
>>> evaluator.setMetricName("logLoss")
MulticlassClassificationEvaluator...
>>> evaluator.evaluate(dataset)
0.9682...
.. versionadded:: 1.5.0
"""
metricName = Param(Params._dummy(), "metricName",
"metric name in evaluation "
"(f1|accuracy|weightedPrecision|weightedRecall|weightedTruePositiveRate|"
"weightedFalsePositiveRate|weightedFMeasure|truePositiveRateByLabel|"
"falsePositiveRateByLabel|precisionByLabel|recallByLabel|fMeasureByLabel|"
"logLoss|hammingLoss)",
typeConverter=TypeConverters.toString)
metricLabel = Param(Params._dummy(), "metricLabel",
"The class whose metric will be computed in truePositiveRateByLabel|"
"falsePositiveRateByLabel|precisionByLabel|recallByLabel|fMeasureByLabel."
" Must be >= 0. The default value is 0.",
typeConverter=TypeConverters.toFloat)
beta = Param(Params._dummy(), "beta",
"The beta value used in weightedFMeasure|fMeasureByLabel."
" Must be > 0. The default value is 1.",
typeConverter=TypeConverters.toFloat)
eps = Param(Params._dummy(), "eps",
"log-loss is undefined for p=0 or p=1, so probabilities are clipped to "
"max(eps, min(1 - eps, p)). "
"Must be in range (0, 0.5). The default value is 1e-15.",
typeConverter=TypeConverters.toFloat)
@keyword_only
def __init__(self, predictionCol="prediction", labelCol="label",
metricName="f1", weightCol=None, metricLabel=0.0, beta=1.0,
probabilityCol="probability", eps=1e-15):
"""
__init__(self, predictionCol="prediction", labelCol="label", \
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)
[docs] @since("1.5.0")
def setMetricName(self, value):
"""
Sets the value of :py:attr:`metricName`.
"""
return self._set(metricName=value)
[docs] @since("1.5.0")
def getMetricName(self):
"""
Gets the value of metricName or its default value.
"""
return self.getOrDefault(self.metricName)
[docs] @since("3.0.0")
def setMetricLabel(self, value):
"""
Sets the value of :py:attr:`metricLabel`.
"""
return self._set(metricLabel=value)
[docs] @since("3.0.0")
def getMetricLabel(self):
"""
Gets the value of metricLabel or its default value.
"""
return self.getOrDefault(self.metricLabel)
[docs] @since("3.0.0")
def setBeta(self, value):
"""
Sets the value of :py:attr:`beta`.
"""
return self._set(beta=value)
[docs] @since("3.0.0")
def getBeta(self):
"""
Gets the value of beta or its default value.
"""
return self.getOrDefault(self.beta)
[docs] @since("3.0.0")
def setEps(self, value):
"""
Sets the value of :py:attr:`eps`.
"""
return self._set(eps=value)
[docs] @since("3.0.0")
def getEps(self):
"""
Gets the value of eps or its default value.
"""
return self.getOrDefault(self.eps)
[docs] def setLabelCol(self, value):
"""
Sets the value of :py:attr:`labelCol`.
"""
return self._set(labelCol=value)
[docs] def setPredictionCol(self, value):
"""
Sets the value of :py:attr:`predictionCol`.
"""
return self._set(predictionCol=value)
[docs] @since("3.0.0")
def setProbabilityCol(self, value):
"""
Sets the value of :py:attr:`probabilityCol`.
"""
return self._set(probabilityCol=value)
[docs] @since("3.0.0")
def setWeightCol(self, value):
"""
Sets the value of :py:attr:`weightCol`.
"""
return self._set(weightCol=value)
[docs] @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)
[docs]@inherit_doc
class MultilabelClassificationEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol,
JavaMLReadable, JavaMLWritable):
"""
.. note:: Experimental
Evaluator for Multilabel Classification, which expects two input
columns: prediction and label.
>>> 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'
.. versionadded:: 3.0.0
"""
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)
[docs] @since("3.0.0")
def setMetricName(self, value):
"""
Sets the value of :py:attr:`metricName`.
"""
return self._set(metricName=value)
[docs] @since("3.0.0")
def getMetricName(self):
"""
Gets the value of metricName or its default value.
"""
return self.getOrDefault(self.metricName)
[docs] @since("3.0.0")
def setMetricLabel(self, value):
"""
Sets the value of :py:attr:`metricLabel`.
"""
return self._set(metricLabel=value)
[docs] @since("3.0.0")
def getMetricLabel(self):
"""
Gets the value of metricLabel or its default value.
"""
return self.getOrDefault(self.metricLabel)
[docs] @since("3.0.0")
def setLabelCol(self, value):
"""
Sets the value of :py:attr:`labelCol`.
"""
return self._set(labelCol=value)
[docs] @since("3.0.0")
def setPredictionCol(self, value):
"""
Sets the value of :py:attr:`predictionCol`.
"""
return self._set(predictionCol=value)
[docs] @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)
[docs]@inherit_doc
class ClusteringEvaluator(JavaEvaluator, HasPredictionCol, HasFeaturesCol,
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.
>>> 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...
>>> ce_path = temp_path + "/ce"
>>> evaluator.save(ce_path)
>>> evaluator2 = ClusteringEvaluator.load(ce_path)
>>> str(evaluator2.getPredictionCol())
'prediction'
.. versionadded:: 2.3.0
"""
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"):
"""
__init__(self, predictionCol="prediction", featuresCol="features", \
metricName="silhouette", distanceMeasure="squaredEuclidean")
"""
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)
[docs] @keyword_only
@since("2.3.0")
def setParams(self, predictionCol="prediction", featuresCol="features",
metricName="silhouette", distanceMeasure="squaredEuclidean"):
"""
setParams(self, predictionCol="prediction", featuresCol="features", \
metricName="silhouette", distanceMeasure="squaredEuclidean")
Sets params for clustering evaluator.
"""
kwargs = self._input_kwargs
return self._set(**kwargs)
[docs] @since("2.3.0")
def setMetricName(self, value):
"""
Sets the value of :py:attr:`metricName`.
"""
return self._set(metricName=value)
[docs] @since("2.3.0")
def getMetricName(self):
"""
Gets the value of metricName or its default value.
"""
return self.getOrDefault(self.metricName)
[docs] @since("2.4.0")
def setDistanceMeasure(self, value):
"""
Sets the value of :py:attr:`distanceMeasure`.
"""
return self._set(distanceMeasure=value)
[docs] @since("2.4.0")
def getDistanceMeasure(self):
"""
Gets the value of `distanceMeasure`
"""
return self.getOrDefault(self.distanceMeasure)
[docs] def setFeaturesCol(self, value):
"""
Sets the value of :py:attr:`featuresCol`.
"""
return self._set(featuresCol=value)
[docs] def setPredictionCol(self, value):
"""
Sets the value of :py:attr:`predictionCol`.
"""
return self._set(predictionCol=value)
[docs]@inherit_doc
class RankingEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol,
JavaMLReadable, JavaMLWritable):
"""
.. note:: Experimental
Evaluator for Ranking, which expects two input
columns: prediction and label.
>>> 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'
.. versionadded:: 3.0.0
"""
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)
[docs] @since("3.0.0")
def setMetricName(self, value):
"""
Sets the value of :py:attr:`metricName`.
"""
return self._set(metricName=value)
[docs] @since("3.0.0")
def getMetricName(self):
"""
Gets the value of metricName or its default value.
"""
return self.getOrDefault(self.metricName)
[docs] @since("3.0.0")
def setK(self, value):
"""
Sets the value of :py:attr:`k`.
"""
return self._set(k=value)
[docs] @since("3.0.0")
def getK(self):
"""
Gets the value of k or its default value.
"""
return self.getOrDefault(self.k)
[docs] @since("3.0.0")
def setLabelCol(self, value):
"""
Sets the value of :py:attr:`labelCol`.
"""
return self._set(labelCol=value)
[docs] @since("3.0.0")
def setPredictionCol(self, value):
"""
Sets the value of :py:attr:`predictionCol`.
"""
return self._set(predictionCol=value)
[docs] @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)
