Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/6403#discussion_r31078513
--- Diff: python/pyspark/ml/classification.py ---
@@ -564,6 +564,92 @@ class GBTClassificationModel(JavaModel):
"""
+@inherit_doc
+class OneVsRest(JavaEstimator, HasFeaturesCol, HasLabelCol,
HasPredictionCol):
+ """
+ `http://en.wikipedia.org/wiki/Multiclass_classification#One-vs.-rest`
+ Reduction of Multiclass Classification to Binary Classification.
+
+ >>> from pyspark.sql import Row
+ >>> from pyspark.mllib.linalg import Vectors
+ >>> df = sc.parallelize([
+ ... Row(label=1.0, features=Vectors.dense(1.0)),
+ ... Row(label=0.0, features=Vectors.sparse(1, [], []))]).toDF()
+ >>> lr = LogisticRegression(maxIter=5, regParam=0.01)
+ >>> ovr = OneVsRest(classifier=lr).setPredictionCol("indexed")
+ >>> model = ovr.fit(df)
+ >>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0))]).toDF()
+ >>> model.transform(test0).head().indexed
+ 0.0
+ >>> test1 = sc.parallelize([Row(features=Vectors.sparse(1, [0],
[1.0]))]).toDF()
+ >>> model.transform(test1).head().indexed
+ 1.0
+ """
+
+ # a placeholder to make it appear in the generated doc
+ classifier = Param(Params._dummy(), "classifier", "base binary
classifier")
+
+ @keyword_only
+ def __init__(self, featuresCol="features", labelCol="label",
+ predictionCol="prediction", classifier=None):
+ """
+ __init__(self, featuresCol="features", labelCol="label", \
+ predictionCol="prediction", classifier=None)
+ """
+ super(OneVsRest, self).__init__()
+ self._java_obj = self._new_java_obj(
+ "org.apache.spark.ml.classification.OneVsRest", self.uid)
+ #: param for base binary classifier
+ self.classifier = Param(self, "classifier", "base binary
classifier")
+ kwargs = self.__init__._input_kwargs
+ self._set(**kwargs)
+
+ @keyword_only
+ def setParams(self, featuresCol="features", labelCol="label",
+ predictionCol="prediction", classifier=None):
+ """
+ setParams(self, featuresCol="features", labelCol="label", \
+ predictionCol="prediction", classifier=None):
+ Sets params for OneVsRest.
+ """
+ kwargs = self.setParams._input_kwargs
+ return self._set(**kwargs)
+
+ def setClassifier(self, value):
+ """
+ Sets the value of :py:attr:`estimator`.
+ """
+ self._paramMap[self.classifier] = value
+ return self
+
+ def getClassifier(self):
+ """
+ Gets the value of classifier or its default value.
+ """
+ return self.getOrDefault(self.classifier)
+
+ def _create_model(self, java_model):
+ return OneVsRestModel(java_model)
+
+ def _make_java_param_pair(self, param, value):
--- End diff --
This only works if the underlying estimator is a Java wrapper. We support
pure Python implementation of Estimator/Transformer in Pipeline and
CrossValidator. In the design doc of the Python Pipeline API:
https://issues.apache.org/jira/browse/SPARK-4586, we chose to copy the
implementations to Python for meta-algorithms. In the long term, we should
consider doing everything in Scala and wrap pure Python/R implementations as a
UDF in Scala. For this PR, we need to at least document that it only supports
estimators backed by a Java model.
---
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