Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/8214#discussion_r41465598
--- Diff: python/pyspark/ml/regression.py ---
@@ -142,6 +143,122 @@ def intercept(self):
return self._call_java("intercept")
+@inherit_doc
+class IsotonicRegression(JavaEstimator, HasFeaturesCol, HasLabelCol,
HasPredictionCol,
+ HasWeightCol):
+ """
+ Currently implemented using parallelized pool adjacent violators
algorithm.
+ Only univariate (single feature) algorithm supported.
+
+ >>> from pyspark.mllib.linalg import Vectors
+ >>> df = sqlContext.createDataFrame([
+ ... (1.0, Vectors.dense(1.0)),
+ ... (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
+ >>> ir = IsotonicRegression()
+ >>> ir.getIsotonic()
+ True
+ >>> model = ir.fit(df)
+ >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)],
["features"])
+ >>> model.transform(test0).head().prediction
+ 0.0
+ >>> model.boundaries
+ DenseVector([0.0, 1.0])
+ """
+
+ # a placeholder to make it appear in the generated doc
+ isotonic = \
+ Param(Params._dummy(), "isotonic",
+ "whether the output sequence should be isotonic/increasing
(true) or" +
+ "antitonic/decreasing (false). (default true)")
+ featureIndex = \
+ Param(Params._dummy(), "featureIndex",
+ "The index of the feature if featuresCol is a vector column,
no effect otherwise. " +
+ "(default 0)")
+
+ @keyword_only
+ def __init__(self, featuresCol="features", labelCol="label",
predictionCol="prediction",
+ weightCol=None, isotonic=True, featureIndex=0):
+ """
+ __init__(self, featuresCol="features", labelCol="label",
predictionCol="prediction", \
+ weightCol=None, isotonic=True, featureIndex=0):
+ """
+ super(IsotonicRegression, self).__init__()
+ self._java_obj = self._new_java_obj(
+ "org.apache.spark.ml.regression.IsotonicRegression", self.uid)
+ self.isotonic = \
+ Param(self, "isotonic",
+ "whether the output sequence should be
isotonic/increasing (true) or" +
+ "antitonic/decreasing (false). (default true)")
+ self.featureIndex = \
+ Param(self, "featureIndex",
+ "The index of the feature if featuresCol is a vector
column, no effect " +
+ "otherwise. (default 0)")
+ self._setDefault(isotonic=True, featureIndex=0)
+ kwargs = self.__init__._input_kwargs
+ self.setParams(**kwargs)
+
+ @keyword_only
+ def setParams(self, featuresCol="features", labelCol="label",
predictionCol="prediction",
+ weightCol=None, isotonic=True, featureIndex=0):
+ """
+ setParams(self, featuresCol="features", labelCol="label",
predictionCol="prediction", \
+ weightCol=None, isotonic=True, featureIndex=0):
+ Set the params for IsotonicRegression.
+ """
+ kwargs = self.setParams._input_kwargs
+ return self._set(**kwargs)
+
+ def _create_model(self, java_model):
+ return IsotonicRegressionModel(java_model)
+
+ def setIsotonic(self, value):
+ """
+ Sets the value of :py:attr:`isotonic`.
+ """
+ self._paramMap[self.isotonic] = value
+ return self
+
+ def getIsotonic(self):
+ """
+ Gets the value of isotonic or its default value.
+ """
+ return self.getOrDefault(self.isotonic)
+
+ def setFeautreIndex(self, value):
+ """
+ Sets the value of :py:attr:`featureIndex`.
+ """
+ self._paramMap[self.featureIndex] = value
+ return self
+
+ def getFeatureIndex(self):
+ """
+ Gets the value of featureIndex or its default value.
+ """
+ return self.getOrDefault(self.featureIndex)
+
+
+class IsotonicRegressionModel(JavaModel):
--- End diff --
Done :)
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