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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.
#
from pyspark.mllib.common import JavaModelWrapper
from pyspark.sql import SQLContext
from pyspark.sql.types import StructField, StructType, DoubleType
class BinaryClassificationMetrics(JavaModelWrapper):
"""
Evaluator for binary classification.
>>> 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):
"""
:param scoreAndLabels: an RDD of (score, label) pairs
"""
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
def areaUnderROC(self):
"""
Computes the area under the receiver operating characteristic
(ROC) curve.
"""
return self.call("areaUnderROC")
@property
def areaUnderPR(self):
"""
Computes the area under the precision-recall curve.
"""
return self.call("areaUnderPR")
def unpersist(self):
"""
Unpersists intermediate RDDs used in the computation.
"""
self.call("unpersist")
class RegressionMetrics(JavaModelWrapper):
"""
Evaluator for regression.
>>> predictionAndObservations = sc.parallelize([
... (2.5, 3.0), (0.0, -0.5), (2.0, 2.0), (8.0, 7.0)])
>>> metrics = RegressionMetrics(predictionAndObservations)
>>> metrics.explainedVariance
0.95...
>>> metrics.meanAbsoluteError
0.5...
>>> metrics.meanSquaredError
0.37...
>>> metrics.rootMeanSquaredError
0.61...
>>> metrics.r2
0.94...
"""
def __init__(self, predictionAndObservations):
"""
:param predictionAndObservations: an RDD of (prediction, observation) pairs.
"""
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
def explainedVariance(self):
"""
Returns the explained variance regression score.
explainedVariance = 1 - variance(y - \hat{y}) / variance(y)
"""
return self.call("explainedVariance")
@property
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
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
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
def r2(self):
"""
Returns R^2^, the coefficient of determination.
"""
return self.call("r2")
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()
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