84 lines
2.8 KiB
Python
84 lines
2.8 KiB
Python
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#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from pyspark.mllib.common import JavaModelWrapper
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from pyspark.sql import SQLContext
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from pyspark.sql.types import StructField, StructType, DoubleType
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class BinaryClassificationMetrics(JavaModelWrapper):
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"""
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Evaluator for binary classification.
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>>> scoreAndLabels = sc.parallelize([
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... (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)
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>>> metrics = BinaryClassificationMetrics(scoreAndLabels)
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>>> metrics.areaUnderROC()
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0.70...
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>>> metrics.areaUnderPR()
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0.83...
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>>> metrics.unpersist()
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"""
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def __init__(self, scoreAndLabels):
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"""
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:param scoreAndLabels: an RDD of (score, label) pairs
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"""
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sc = scoreAndLabels.ctx
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sql_ctx = SQLContext(sc)
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df = sql_ctx.createDataFrame(scoreAndLabels, schema=StructType([
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StructField("score", DoubleType(), nullable=False),
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StructField("label", DoubleType(), nullable=False)]))
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java_class = sc._jvm.org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
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java_model = java_class(df._jdf)
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super(BinaryClassificationMetrics, self).__init__(java_model)
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def areaUnderROC(self):
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"""
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Computes the area under the receiver operating characteristic
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(ROC) curve.
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"""
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return self.call("areaUnderROC")
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def areaUnderPR(self):
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"""
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Computes the area under the precision-recall curve.
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"""
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return self.call("areaUnderPR")
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def unpersist(self):
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"""
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Unpersists intermediate RDDs used in the computation.
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"""
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self.call("unpersist")
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def _test():
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import doctest
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from pyspark import SparkContext
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import pyspark.mllib.evaluation
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globs = pyspark.mllib.evaluation.__dict__.copy()
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globs['sc'] = SparkContext('local[4]', 'PythonTest')
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(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
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globs['sc'].stop()
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if failure_count:
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exit(-1)
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if __name__ == "__main__":
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_test()
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