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# 

# 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 pyspark import since 

from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc 

from pyspark.sql import SQLContext 

from pyspark.sql.types import ArrayType, StructField, StructType, DoubleType 

 

__all__ = ['BinaryClassificationMetrics', 'RegressionMetrics', 

'MulticlassMetrics', 'RankingMetrics'] 

 

 

class BinaryClassificationMetrics(JavaModelWrapper): 

""" 

Evaluator for binary classification. 

 

.. versionadded:: 1.4.0 

 

Parameters 

---------- 

scoreAndLabels : :py:class:`pyspark.RDD` 

an RDD of score, label and optional weight. 

 

Examples 

-------- 

>>> scoreAndLabels = sc.parallelize([ 

... (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)], 2) 

>>> metrics = BinaryClassificationMetrics(scoreAndLabels) 

>>> metrics.areaUnderROC 

0.70... 

>>> metrics.areaUnderPR 

0.83... 

>>> metrics.unpersist() 

>>> scoreAndLabelsWithOptWeight = sc.parallelize([ 

... (0.1, 0.0, 1.0), (0.1, 1.0, 0.4), (0.4, 0.0, 0.2), (0.6, 0.0, 0.6), (0.6, 1.0, 0.9), 

... (0.6, 1.0, 0.5), (0.8, 1.0, 0.7)], 2) 

>>> metrics = BinaryClassificationMetrics(scoreAndLabelsWithOptWeight) 

>>> metrics.areaUnderROC 

0.79... 

>>> metrics.areaUnderPR 

0.88... 

""" 

 

def __init__(self, scoreAndLabels): 

sc = scoreAndLabels.ctx 

sql_ctx = SQLContext.getOrCreate(sc) 

numCol = len(scoreAndLabels.first()) 

schema = StructType([ 

StructField("score", DoubleType(), nullable=False), 

StructField("label", DoubleType(), nullable=False)]) 

if numCol == 3: 

schema.add("weight", DoubleType(), False) 

df = sql_ctx.createDataFrame(scoreAndLabels, schema=schema) 

java_class = sc._jvm.org.apache.spark.mllib.evaluation.BinaryClassificationMetrics 

java_model = java_class(df._jdf) 

super(BinaryClassificationMetrics, self).__init__(java_model) 

 

@property 

@since('1.4.0') 

def areaUnderROC(self): 

""" 

Computes the area under the receiver operating characteristic 

(ROC) curve. 

""" 

return self.call("areaUnderROC") 

 

@property 

@since('1.4.0') 

def areaUnderPR(self): 

""" 

Computes the area under the precision-recall curve. 

""" 

return self.call("areaUnderPR") 

 

@since('1.4.0') 

def unpersist(self): 

""" 

Unpersists intermediate RDDs used in the computation. 

""" 

self.call("unpersist") 

 

 

class RegressionMetrics(JavaModelWrapper): 

""" 

Evaluator for regression. 

 

.. versionadded:: 1.4.0 

 

Parameters 

---------- 

predictionAndObservations : :py:class:`pyspark.RDD` 

an RDD of prediction, observation and optional weight. 

 

Examples 

-------- 

>>> predictionAndObservations = sc.parallelize([ 

... (2.5, 3.0), (0.0, -0.5), (2.0, 2.0), (8.0, 7.0)]) 

>>> metrics = RegressionMetrics(predictionAndObservations) 

>>> metrics.explainedVariance 

8.859... 

>>> metrics.meanAbsoluteError 

0.5... 

>>> metrics.meanSquaredError 

0.37... 

>>> metrics.rootMeanSquaredError 

0.61... 

>>> metrics.r2 

0.94... 

>>> predictionAndObservationsWithOptWeight = sc.parallelize([ 

... (2.5, 3.0, 0.5), (0.0, -0.5, 1.0), (2.0, 2.0, 0.3), (8.0, 7.0, 0.9)]) 

>>> metrics = RegressionMetrics(predictionAndObservationsWithOptWeight) 

>>> metrics.rootMeanSquaredError 

0.68... 

""" 

 

def __init__(self, predictionAndObservations): 

sc = predictionAndObservations.ctx 

sql_ctx = SQLContext.getOrCreate(sc) 

numCol = len(predictionAndObservations.first()) 

schema = StructType([ 

StructField("prediction", DoubleType(), nullable=False), 

StructField("observation", DoubleType(), nullable=False)]) 

if numCol == 3: 

schema.add("weight", DoubleType(), False) 

df = sql_ctx.createDataFrame(predictionAndObservations, schema=schema) 

java_class = sc._jvm.org.apache.spark.mllib.evaluation.RegressionMetrics 

java_model = java_class(df._jdf) 

super(RegressionMetrics, self).__init__(java_model) 

 

@property 

@since('1.4.0') 

def explainedVariance(self): 

r""" 

Returns the explained variance regression score. 

explainedVariance = :math:`1 - \frac{variance(y - \hat{y})}{variance(y)}` 

""" 

return self.call("explainedVariance") 

 

@property 

@since('1.4.0') 

def meanAbsoluteError(self): 

""" 

Returns the mean absolute error, which is a risk function corresponding to the 

expected value of the absolute error loss or l1-norm loss. 

""" 

return self.call("meanAbsoluteError") 

 

@property 

@since('1.4.0') 

def meanSquaredError(self): 

""" 

Returns the mean squared error, which is a risk function corresponding to the 

expected value of the squared error loss or quadratic loss. 

""" 

return self.call("meanSquaredError") 

 

@property 

@since('1.4.0') 

def rootMeanSquaredError(self): 

""" 

Returns the root mean squared error, which is defined as the square root of 

the mean squared error. 

""" 

return self.call("rootMeanSquaredError") 

 

@property 

@since('1.4.0') 

def r2(self): 

""" 

Returns R^2^, the coefficient of determination. 

""" 

return self.call("r2") 

 

 

class MulticlassMetrics(JavaModelWrapper): 

""" 

Evaluator for multiclass classification. 

 

.. versionadded:: 1.4.0 

 

Parameters 

---------- 

predictionAndLabels : :py:class:`pyspark.RDD` 

an RDD of prediction, label, optional weight and optional probability. 

 

Examples 

-------- 

>>> predictionAndLabels = sc.parallelize([(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)]) 

>>> metrics = MulticlassMetrics(predictionAndLabels) 

>>> metrics.confusionMatrix().toArray() 

array([[ 2., 1., 1.], 

[ 1., 3., 0.], 

[ 0., 0., 1.]]) 

>>> metrics.falsePositiveRate(0.0) 

0.2... 

>>> metrics.precision(1.0) 

0.75... 

>>> metrics.recall(2.0) 

1.0... 

>>> metrics.fMeasure(0.0, 2.0) 

0.52... 

>>> metrics.accuracy 

0.66... 

>>> metrics.weightedFalsePositiveRate 

0.19... 

>>> metrics.weightedPrecision 

0.68... 

>>> metrics.weightedRecall 

0.66... 

>>> metrics.weightedFMeasure() 

0.66... 

>>> metrics.weightedFMeasure(2.0) 

0.65... 

>>> predAndLabelsWithOptWeight = sc.parallelize([(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)]) 

>>> metrics = MulticlassMetrics(predAndLabelsWithOptWeight) 

>>> metrics.confusionMatrix().toArray() 

array([[ 2., 1., 1.], 

[ 1., 3., 0.], 

[ 0., 0., 1.]]) 

>>> metrics.falsePositiveRate(0.0) 

0.2... 

>>> metrics.precision(1.0) 

0.75... 

>>> metrics.recall(2.0) 

1.0... 

>>> metrics.fMeasure(0.0, 2.0) 

0.52... 

>>> metrics.accuracy 

0.66... 

>>> metrics.weightedFalsePositiveRate 

0.19... 

>>> metrics.weightedPrecision 

0.68... 

>>> metrics.weightedRecall 

0.66... 

>>> metrics.weightedFMeasure() 

0.66... 

>>> metrics.weightedFMeasure(2.0) 

0.65... 

>>> predictionAndLabelsWithProbabilities = sc.parallelize([ 

... (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])]) 

>>> metrics = MulticlassMetrics(predictionAndLabelsWithProbabilities) 

>>> metrics.logLoss() 

0.9682... 

""" 

 

def __init__(self, predictionAndLabels): 

sc = predictionAndLabels.ctx 

sql_ctx = SQLContext.getOrCreate(sc) 

numCol = len(predictionAndLabels.first()) 

schema = StructType([ 

StructField("prediction", DoubleType(), nullable=False), 

StructField("label", DoubleType(), nullable=False)]) 

if numCol >= 3: 

schema.add("weight", DoubleType(), False) 

if numCol == 4: 

schema.add("probability", ArrayType(DoubleType(), False), False) 

df = sql_ctx.createDataFrame(predictionAndLabels, schema) 

java_class = sc._jvm.org.apache.spark.mllib.evaluation.MulticlassMetrics 

java_model = java_class(df._jdf) 

super(MulticlassMetrics, self).__init__(java_model) 

 

@since('1.4.0') 

def confusionMatrix(self): 

""" 

Returns confusion matrix: predicted classes are in columns, 

they are ordered by class label ascending, as in "labels". 

""" 

return self.call("confusionMatrix") 

 

@since('1.4.0') 

def truePositiveRate(self, label): 

""" 

Returns true positive rate for a given label (category). 

""" 

return self.call("truePositiveRate", label) 

 

@since('1.4.0') 

def falsePositiveRate(self, label): 

""" 

Returns false positive rate for a given label (category). 

""" 

return self.call("falsePositiveRate", label) 

 

@since('1.4.0') 

def precision(self, label): 

""" 

Returns precision. 

""" 

return self.call("precision", float(label)) 

 

@since('1.4.0') 

def recall(self, label): 

""" 

Returns recall. 

""" 

return self.call("recall", float(label)) 

 

@since('1.4.0') 

def fMeasure(self, label, beta=None): 

""" 

Returns f-measure. 

""" 

324 ↛ 325line 324 didn't jump to line 325, because the condition on line 324 was never true if beta is None: 

return self.call("fMeasure", label) 

else: 

return self.call("fMeasure", label, beta) 

 

@property 

@since('2.0.0') 

def accuracy(self): 

""" 

Returns accuracy (equals to the total number of correctly classified instances 

out of the total number of instances). 

""" 

return self.call("accuracy") 

 

@property 

@since('1.4.0') 

def weightedTruePositiveRate(self): 

""" 

Returns weighted true positive rate. 

(equals to precision, recall and f-measure) 

""" 

return self.call("weightedTruePositiveRate") 

 

@property 

@since('1.4.0') 

def weightedFalsePositiveRate(self): 

""" 

Returns weighted false positive rate. 

""" 

return self.call("weightedFalsePositiveRate") 

 

@property 

@since('1.4.0') 

def weightedRecall(self): 

""" 

Returns weighted averaged recall. 

(equals to precision, recall and f-measure) 

""" 

return self.call("weightedRecall") 

 

@property 

@since('1.4.0') 

def weightedPrecision(self): 

""" 

Returns weighted averaged precision. 

""" 

return self.call("weightedPrecision") 

 

@since('1.4.0') 

def weightedFMeasure(self, beta=None): 

""" 

Returns weighted averaged f-measure. 

""" 

if beta is None: 

return self.call("weightedFMeasure") 

else: 

return self.call("weightedFMeasure", beta) 

 

@since('3.0.0') 

def logLoss(self, eps=1e-15): 

""" 

Returns weighted logLoss. 

""" 

return self.call("logLoss", eps) 

 

 

class RankingMetrics(JavaModelWrapper): 

""" 

Evaluator for ranking algorithms. 

 

.. versionadded:: 1.4.0 

 

Parameters 

---------- 

predictionAndLabels : :py:class:`pyspark.RDD` 

an RDD of (predicted ranking, ground truth set) pairs. 

 

Examples 

-------- 

>>> predictionAndLabels = sc.parallelize([ 

... ([1, 6, 2, 7, 8, 3, 9, 10, 4, 5], [1, 2, 3, 4, 5]), 

... ([4, 1, 5, 6, 2, 7, 3, 8, 9, 10], [1, 2, 3]), 

... ([1, 2, 3, 4, 5], [])]) 

>>> metrics = RankingMetrics(predictionAndLabels) 

>>> metrics.precisionAt(1) 

0.33... 

>>> metrics.precisionAt(5) 

0.26... 

>>> metrics.precisionAt(15) 

0.17... 

>>> metrics.meanAveragePrecision 

0.35... 

>>> metrics.meanAveragePrecisionAt(1) 

0.3333333333333333... 

>>> metrics.meanAveragePrecisionAt(2) 

0.25... 

>>> metrics.ndcgAt(3) 

0.33... 

>>> metrics.ndcgAt(10) 

0.48... 

>>> metrics.recallAt(1) 

0.06... 

>>> metrics.recallAt(5) 

0.35... 

>>> metrics.recallAt(15) 

0.66... 

""" 

 

def __init__(self, predictionAndLabels): 

sc = predictionAndLabels.ctx 

sql_ctx = SQLContext.getOrCreate(sc) 

df = sql_ctx.createDataFrame(predictionAndLabels, 

schema=sql_ctx._inferSchema(predictionAndLabels)) 

java_model = callMLlibFunc("newRankingMetrics", df._jdf) 

super(RankingMetrics, self).__init__(java_model) 

 

@since('1.4.0') 

def precisionAt(self, k): 

""" 

Compute the average precision of all the queries, truncated at ranking position k. 

 

If for a query, the ranking algorithm returns n (n < k) results, the precision value 

will be computed as #(relevant items retrieved) / k. This formula also applies when 

the size of the ground truth set is less than k. 

 

If a query has an empty ground truth set, zero will be used as precision together 

with a log warning. 

""" 

return self.call("precisionAt", int(k)) 

 

@property 

@since('1.4.0') 

def meanAveragePrecision(self): 

""" 

Returns the mean average precision (MAP) of all the queries. 

If a query has an empty ground truth set, the average precision will be zero and 

a log warning is generated. 

""" 

return self.call("meanAveragePrecision") 

 

@since('3.0.0') 

def meanAveragePrecisionAt(self, k): 

""" 

Returns the mean average precision (MAP) at first k ranking of all the queries. 

If a query has an empty ground truth set, the average precision will be zero and 

a log warning is generated. 

""" 

return self.call("meanAveragePrecisionAt", int(k)) 

 

@since('1.4.0') 

def ndcgAt(self, k): 

""" 

Compute the average NDCG value of all the queries, truncated at ranking position k. 

The discounted cumulative gain at position k is computed as: 

sum,,i=1,,^k^ (2^{relevance of ''i''th item}^ - 1) / log(i + 1), 

and the NDCG is obtained by dividing the DCG value on the ground truth set. 

In the current implementation, the relevance value is binary. 

If a query has an empty ground truth set, zero will be used as NDCG together with 

a log warning. 

""" 

return self.call("ndcgAt", int(k)) 

 

@since('3.0.0') 

def recallAt(self, k): 

""" 

Compute the average recall of all the queries, truncated at ranking position k. 

 

If for a query, the ranking algorithm returns n results, the recall value 

will be computed as #(relevant items retrieved) / #(ground truth set). 

This formula also applies when the size of the ground truth set is less than k. 

 

If a query has an empty ground truth set, zero will be used as recall together 

with a log warning. 

""" 

return self.call("recallAt", int(k)) 

 

 

class MultilabelMetrics(JavaModelWrapper): 

""" 

Evaluator for multilabel classification. 

 

.. versionadded:: 1.4.0 

 

Parameters 

---------- 

predictionAndLabels : :py:class:`pyspark.RDD` 

an RDD of (predictions, labels) pairs, 

both are non-null Arrays, each with unique elements. 

 

Examples 

-------- 

>>> predictionAndLabels = sc.parallelize([([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])]) 

>>> metrics = MultilabelMetrics(predictionAndLabels) 

>>> metrics.precision(0.0) 

1.0 

>>> metrics.recall(1.0) 

0.66... 

>>> metrics.f1Measure(2.0) 

0.5 

>>> metrics.precision() 

0.66... 

>>> metrics.recall() 

0.64... 

>>> metrics.f1Measure() 

0.63... 

>>> metrics.microPrecision 

0.72... 

>>> metrics.microRecall 

0.66... 

>>> metrics.microF1Measure 

0.69... 

>>> metrics.hammingLoss 

0.33... 

>>> metrics.subsetAccuracy 

0.28... 

>>> metrics.accuracy 

0.54... 

""" 

 

def __init__(self, predictionAndLabels): 

sc = predictionAndLabels.ctx 

sql_ctx = SQLContext.getOrCreate(sc) 

df = sql_ctx.createDataFrame(predictionAndLabels, 

schema=sql_ctx._inferSchema(predictionAndLabels)) 

java_class = sc._jvm.org.apache.spark.mllib.evaluation.MultilabelMetrics 

java_model = java_class(df._jdf) 

super(MultilabelMetrics, self).__init__(java_model) 

 

@since('1.4.0') 

def precision(self, label=None): 

""" 

Returns precision or precision for a given label (category) if specified. 

""" 

if label is None: 

return self.call("precision") 

else: 

return self.call("precision", float(label)) 

 

@since('1.4.0') 

def recall(self, label=None): 

""" 

Returns recall or recall for a given label (category) if specified. 

""" 

if label is None: 

return self.call("recall") 

else: 

return self.call("recall", float(label)) 

 

@since('1.4.0') 

def f1Measure(self, label=None): 

""" 

Returns f1Measure or f1Measure for a given label (category) if specified. 

""" 

if label is None: 

return self.call("f1Measure") 

else: 

return self.call("f1Measure", float(label)) 

 

@property 

@since('1.4.0') 

def microPrecision(self): 

""" 

Returns micro-averaged label-based precision. 

(equals to micro-averaged document-based precision) 

""" 

return self.call("microPrecision") 

 

@property 

@since('1.4.0') 

def microRecall(self): 

""" 

Returns micro-averaged label-based recall. 

(equals to micro-averaged document-based recall) 

""" 

return self.call("microRecall") 

 

@property 

@since('1.4.0') 

def microF1Measure(self): 

""" 

Returns micro-averaged label-based f1-measure. 

(equals to micro-averaged document-based f1-measure) 

""" 

return self.call("microF1Measure") 

 

@property 

@since('1.4.0') 

def hammingLoss(self): 

""" 

Returns Hamming-loss. 

""" 

return self.call("hammingLoss") 

 

@property 

@since('1.4.0') 

def subsetAccuracy(self): 

""" 

Returns subset accuracy. 

(for equal sets of labels) 

""" 

return self.call("subsetAccuracy") 

 

@property 

@since('1.4.0') 

def accuracy(self): 

""" 

Returns accuracy. 

""" 

return self.call("accuracy") 

 

 

def _test(): 

import doctest 

import numpy 

from pyspark.sql import SparkSession 

import pyspark.mllib.evaluation 

try: 

# Numpy 1.14+ changed it's string format. 

numpy.set_printoptions(legacy='1.13') 

except TypeError: 

pass 

globs = pyspark.mllib.evaluation.__dict__.copy() 

spark = SparkSession.builder\ 

.master("local[4]")\ 

.appName("mllib.evaluation tests")\ 

.getOrCreate() 

globs['sc'] = spark.sparkContext 

(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) 

spark.stop() 

655 ↛ 656line 655 didn't jump to line 656, because the condition on line 655 was never true if failure_count: 

sys.exit(-1) 

 

 

if __name__ == "__main__": 

_test()