84 lines
2.8 KiB
Python
84 lines
2.8 KiB
Python
|
#
|
||
|
|
# 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)
|
||
|
|
|
||
|
|
def areaUnderROC(self):
|
||
|
|
"""
|
||
|
|
Computes the area under the receiver operating characteristic
|
||
|
|
(ROC) curve.
|
||
|
|
"""
|
||
|
|
return self.call("areaUnderROC")
|
||
|
|
|
||
|
|
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")
|
||
|
|
|
||
|
|
|
||
|
|
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()
|