Source code for pyspark.mllib.evaluation
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from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc
from pyspark.sql import SQLContext
from pyspark.sql.types import StructField, StructType, DoubleType, IntegerType, ArrayType
__all__ = ['BinaryClassificationMetrics', 'RegressionMetrics',
'MulticlassMetrics', 'RankingMetrics']
[docs]class BinaryClassificationMetrics(JavaModelWrapper):
"""
Evaluator for binary classification.
:param scoreAndLabels: an RDD of (score, label) pairs
>>> 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()
"""
def __init__(self, scoreAndLabels):
sc = scoreAndLabels.ctx
sql_ctx = SQLContext(sc)
df = sql_ctx.createDataFrame(scoreAndLabels, schema=StructType([
StructField("score", DoubleType(), nullable=False),
StructField("label", DoubleType(), nullable=False)]))
java_class = sc._jvm.org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
java_model = java_class(df._jdf)
super(BinaryClassificationMetrics, self).__init__(java_model)
@property
[docs] def areaUnderROC(self):
"""
Computes the area under the receiver operating characteristic
(ROC) curve.
"""
return self.call("areaUnderROC")
@property
[docs] def areaUnderPR(self):
"""
Computes the area under the precision-recall curve.
"""
return self.call("areaUnderPR")
[docs] def unpersist(self):
"""
Unpersists intermediate RDDs used in the computation.
"""
self.call("unpersist")
[docs]class RegressionMetrics(JavaModelWrapper):
"""
Evaluator for regression.
:param predictionAndObservations: an RDD of (prediction,
observation) pairs.
>>> 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...
"""
def __init__(self, predictionAndObservations):
sc = predictionAndObservations.ctx
sql_ctx = SQLContext(sc)
df = sql_ctx.createDataFrame(predictionAndObservations, schema=StructType([
StructField("prediction", DoubleType(), nullable=False),
StructField("observation", DoubleType(), nullable=False)]))
java_class = sc._jvm.org.apache.spark.mllib.evaluation.RegressionMetrics
java_model = java_class(df._jdf)
super(RegressionMetrics, self).__init__(java_model)
@property
[docs] def explainedVariance(self):
"""
Returns the explained variance regression score.
explainedVariance = 1 - variance(y - \hat{y}) / variance(y)
"""
return self.call("explainedVariance")
@property
[docs] 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
[docs] 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
[docs] 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
[docs]class MulticlassMetrics(JavaModelWrapper):
"""
Evaluator for multiclass classification.
:param predictionAndLabels: an RDD of (prediction, label) pairs.
>>> 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.precision()
0.66...
>>> metrics.recall()
0.66...
>>> metrics.weightedFalsePositiveRate
0.19...
>>> metrics.weightedPrecision
0.68...
>>> metrics.weightedRecall
0.66...
>>> metrics.weightedFMeasure()
0.66...
>>> metrics.weightedFMeasure(2.0)
0.65...
"""
def __init__(self, predictionAndLabels):
sc = predictionAndLabels.ctx
sql_ctx = SQLContext(sc)
df = sql_ctx.createDataFrame(predictionAndLabels, schema=StructType([
StructField("prediction", DoubleType(), nullable=False),
StructField("label", DoubleType(), nullable=False)]))
java_class = sc._jvm.org.apache.spark.mllib.evaluation.MulticlassMetrics
java_model = java_class(df._jdf)
super(MulticlassMetrics, self).__init__(java_model)
[docs] 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")
[docs] def truePositiveRate(self, label):
"""
Returns true positive rate for a given label (category).
"""
return self.call("truePositiveRate", label)
[docs] def falsePositiveRate(self, label):
"""
Returns false positive rate for a given label (category).
"""
return self.call("falsePositiveRate", label)
[docs] 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))
[docs] 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))
[docs] def fMeasure(self, label=None, beta=None):
"""
Returns f-measure or f-measure for a given label (category) if specified.
"""
if beta is None:
if label is None:
return self.call("fMeasure")
else:
return self.call("fMeasure", label)
else:
if label is None:
raise Exception("If the beta parameter is specified, label can not be none")
else:
return self.call("fMeasure", label, beta)
@property
[docs] def weightedTruePositiveRate(self):
"""
Returns weighted true positive rate.
(equals to precision, recall and f-measure)
"""
return self.call("weightedTruePositiveRate")
@property
[docs] def weightedFalsePositiveRate(self):
"""
Returns weighted false positive rate.
"""
return self.call("weightedFalsePositiveRate")
@property
[docs] def weightedRecall(self):
"""
Returns weighted averaged recall.
(equals to precision, recall and f-measure)
"""
return self.call("weightedRecall")
@property
[docs] def weightedPrecision(self):
"""
Returns weighted averaged precision.
"""
return self.call("weightedPrecision")
[docs] def weightedFMeasure(self, beta=None):
"""
Returns weighted averaged f-measure.
"""
if beta is None:
return self.call("weightedFMeasure")
else:
return self.call("weightedFMeasure", beta)
[docs]class RankingMetrics(JavaModelWrapper):
"""
Evaluator for ranking algorithms.
:param predictionAndLabels: an RDD of (predicted ranking,
ground truth set) pairs.
>>> 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.ndcgAt(3)
0.33...
>>> metrics.ndcgAt(10)
0.48...
"""
def __init__(self, predictionAndLabels):
sc = predictionAndLabels.ctx
sql_ctx = SQLContext(sc)
df = sql_ctx.createDataFrame(predictionAndLabels,
schema=sql_ctx._inferSchema(predictionAndLabels))
java_model = callMLlibFunc("newRankingMetrics", df._jdf)
super(RankingMetrics, self).__init__(java_model)
[docs] 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
[docs] 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 warining is generated.
"""
return self.call("meanAveragePrecision")
[docs] 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))
class MultilabelMetrics(JavaModelWrapper):
"""
Evaluator for multilabel classification.
:param predictionAndLabels: an RDD of (predictions, labels) pairs,
both are non-null Arrays, each with
unique elements.
>>> 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(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)
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))
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))
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
def microPrecision(self):
"""
Returns micro-averaged label-based precision.
(equals to micro-averaged document-based precision)
"""
return self.call("microPrecision")
@property
def microRecall(self):
"""
Returns micro-averaged label-based recall.
(equals to micro-averaged document-based recall)
"""
return self.call("microRecall")
@property
def microF1Measure(self):
"""
Returns micro-averaged label-based f1-measure.
(equals to micro-averaged document-based f1-measure)
"""
return self.call("microF1Measure")
@property
def hammingLoss(self):
"""
Returns Hamming-loss.
"""
return self.call("hammingLoss")
@property
def subsetAccuracy(self):
"""
Returns subset accuracy.
(for equal sets of labels)
"""
return self.call("subsetAccuracy")
@property
def accuracy(self):
"""
Returns accuracy.
"""
return self.call("accuracy")
def _test():
import doctest
from pyspark import SparkContext
import pyspark.mllib.evaluation
globs = pyspark.mllib.evaluation.__dict__.copy()
globs['sc'] = SparkContext('local[4]', 'PythonTest')
(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
globs['sc'].stop()
if failure_count:
exit(-1)
if __name__ == "__main__":
_test()
