[SPARK-7333] [MLLIB] Add BinaryClassificationEvaluator to PySpark
This PR adds `BinaryClassificationEvaluator` to Python ML Pipelines API, which is a simple wrapper of the Scala implementation. oefirouz Author: Xiangrui Meng <meng@databricks.com> Closes #5885 from mengxr/SPARK-7333 and squashes the following commits: 25d7451 [Xiangrui Meng] fix tests in python 3 babdde7 [Xiangrui Meng] fix doc cb51e6a [Xiangrui Meng] add BinaryClassificationEvaluator in PySpark
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@ -24,3 +24,19 @@ pyspark.ml.classification module
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:members:
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:undoc-members:
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:inherited-members:
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pyspark.ml.tuning module
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--------------------------------
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.. automodule:: pyspark.ml.tuning
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:members:
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:undoc-members:
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:inherited-members:
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pyspark.ml.evaluation module
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--------------------------------
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.. automodule:: pyspark.ml.evaluation
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:members:
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:undoc-members:
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:inherited-members:
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107
python/pyspark/ml/evaluation.py
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107
python/pyspark/ml/evaluation.py
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@ -0,0 +1,107 @@
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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.ml.wrapper import JavaEvaluator
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from pyspark.ml.param import Param, Params
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from pyspark.ml.param.shared import HasLabelCol, HasRawPredictionCol
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from pyspark.ml.util import keyword_only
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from pyspark.mllib.common import inherit_doc
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__all__ = ['BinaryClassificationEvaluator']
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@inherit_doc
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class BinaryClassificationEvaluator(JavaEvaluator, HasLabelCol, HasRawPredictionCol):
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"""
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Evaluator for binary classification, which expects two input
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columns: rawPrediction and label.
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>>> from pyspark.mllib.linalg import Vectors
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>>> scoreAndLabels = map(lambda x: (Vectors.dense([1.0 - x[0], x[0]]), x[1]),
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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)])
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>>> dataset = sqlContext.createDataFrame(scoreAndLabels, ["raw", "label"])
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...
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>>> evaluator = BinaryClassificationEvaluator(rawPredictionCol="raw")
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>>> evaluator.evaluate(dataset)
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0.70...
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>>> evaluator.evaluate(dataset, {evaluator.metricName: "areaUnderPR"})
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0.83...
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"""
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_java_class = "org.apache.spark.ml.evaluation.BinaryClassificationEvaluator"
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# a placeholder to make it appear in the generated doc
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metricName = Param(Params._dummy(), "metricName",
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"metric name in evaluation (areaUnderROC|areaUnderPR)")
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@keyword_only
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def __init__(self, rawPredictionCol="rawPrediction", labelCol="label",
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metricName="areaUnderROC"):
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"""
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__init__(self, rawPredictionCol="rawPrediction", labelCol="label", \
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metricName="areaUnderROC")
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"""
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super(BinaryClassificationEvaluator, self).__init__()
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#: param for metric name in evaluation (areaUnderROC|areaUnderPR)
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self.metricName = Param(self, "metricName",
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"metric name in evaluation (areaUnderROC|areaUnderPR)")
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self._setDefault(rawPredictionCol="rawPrediction", labelCol="label",
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metricName="areaUnderROC")
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kwargs = self.__init__._input_kwargs
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self._set(**kwargs)
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def setMetricName(self, value):
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"""
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Sets the value of :py:attr:`metricName`.
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"""
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self.paramMap[self.metricName] = value
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return self
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def getMetricName(self):
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"""
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Gets the value of metricName or its default value.
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"""
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return self.getOrDefault(self.metricName)
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@keyword_only
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def setParams(self, rawPredictionCol="rawPrediction", labelCol="label",
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metricName="areaUnderROC"):
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"""
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setParams(self, rawPredictionCol="rawPrediction", labelCol="label", \
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metricName="areaUnderROC")
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Sets params for binary classification evaluator.
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"""
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kwargs = self.setParams._input_kwargs
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return self._set(**kwargs)
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if __name__ == "__main__":
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import doctest
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from pyspark.context import SparkContext
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from pyspark.sql import SQLContext
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globs = globals().copy()
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# The small batch size here ensures that we see multiple batches,
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# even in these small test examples:
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sc = SparkContext("local[2]", "ml.evaluation tests")
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sqlContext = SQLContext(sc)
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globs['sc'] = sc
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globs['sqlContext'] = sqlContext
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(failure_count, test_count) = doctest.testmod(
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globs=globs, optionflags=doctest.ELLIPSIS)
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sc.stop()
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if failure_count:
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exit(-1)
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@ -93,6 +93,7 @@ if __name__ == "__main__":
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("featuresCol", "features column name", "'features'"),
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("labelCol", "label column name", "'label'"),
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("predictionCol", "prediction column name", "'prediction'"),
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("rawPredictionCol", "raw prediction column name", "'rawPrediction'"),
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("inputCol", "input column name", None),
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("outputCol", "output column name", None),
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("numFeatures", "number of features", None)]
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@ -165,6 +165,35 @@ class HasPredictionCol(Params):
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return self.getOrDefault(self.predictionCol)
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class HasRawPredictionCol(Params):
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"""
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Mixin for param rawPredictionCol: raw prediction column name.
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"""
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# a placeholder to make it appear in the generated doc
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rawPredictionCol = Param(Params._dummy(), "rawPredictionCol", "raw prediction column name")
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def __init__(self):
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super(HasRawPredictionCol, self).__init__()
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#: param for raw prediction column name
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self.rawPredictionCol = Param(self, "rawPredictionCol", "raw prediction column name")
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if 'rawPrediction' is not None:
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self._setDefault(rawPredictionCol='rawPrediction')
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def setRawPredictionCol(self, value):
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"""
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Sets the value of :py:attr:`rawPredictionCol`.
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"""
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self.paramMap[self.rawPredictionCol] = value
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return self
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def getRawPredictionCol(self):
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"""
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Gets the value of rawPredictionCol or its default value.
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"""
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return self.getOrDefault(self.rawPredictionCol)
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class HasInputCol(Params):
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"""
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Mixin for param inputCol: input column name.
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@ -22,7 +22,7 @@ from pyspark.ml.util import keyword_only
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from pyspark.mllib.common import inherit_doc
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__all__ = ['Estimator', 'Transformer', 'Pipeline', 'PipelineModel']
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__all__ = ['Estimator', 'Transformer', 'Pipeline', 'PipelineModel', 'Evaluator']
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@inherit_doc
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@ -168,3 +168,24 @@ class PipelineModel(Transformer):
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for t in self.transformers:
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dataset = t.transform(dataset, paramMap)
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return dataset
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class Evaluator(object):
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"""
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Base class for evaluators that compute metrics from predictions.
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"""
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__metaclass__ = ABCMeta
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@abstractmethod
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def evaluate(self, dataset, params={}):
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"""
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Evaluates the output.
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:param dataset: a dataset that contains labels/observations and
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predictions
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:param params: an optional param map that overrides embedded
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params
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:return: metric
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"""
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raise NotImplementedError()
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@ -20,7 +20,7 @@ from abc import ABCMeta
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from pyspark import SparkContext
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from pyspark.sql import DataFrame
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from pyspark.ml.param import Params
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from pyspark.ml.pipeline import Estimator, Transformer
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from pyspark.ml.pipeline import Estimator, Transformer, Evaluator
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from pyspark.mllib.common import inherit_doc
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@ -147,3 +147,18 @@ class JavaModel(JavaTransformer):
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def _java_obj(self):
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return self._java_model
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@inherit_doc
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class JavaEvaluator(Evaluator, JavaWrapper):
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"""
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Base class for :py:class:`Evaluator`s that wrap Java/Scala
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implementations.
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"""
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__metaclass__ = ABCMeta
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def evaluate(self, dataset, params={}):
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java_obj = self._java_obj()
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self._transfer_params_to_java(params, java_obj)
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return java_obj.evaluate(dataset._jdf, self._empty_java_param_map())
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@ -652,7 +652,7 @@ def _python_to_sql_converter(dataType):
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if isinstance(dataType, StructType):
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names, types = zip(*[(f.name, f.dataType) for f in dataType.fields])
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converters = map(_python_to_sql_converter, types)
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converters = [_python_to_sql_converter(t) for t in types]
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def converter(obj):
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if isinstance(obj, dict):
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@ -100,6 +100,7 @@ function run_ml_tests() {
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run_test "pyspark/ml/classification.py"
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run_test "pyspark/ml/tuning.py"
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run_test "pyspark/ml/tests.py"
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run_test "pyspark/ml/evaluation.py"
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}
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function run_streaming_tests() {
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