Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/8214#discussion_r39471687
--- Diff: python/pyspark/ml/regression.py ---
@@ -117,6 +118,110 @@ 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()
+ >>> model = ir.fit(df)
+ >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)],
["features"])
+ >>> model.transform(test0).head().prediction
+ 0.5
+ >>> 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)")
+ 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, istonic=False, featureIndex=0):
+ """
+ __init__(self, featuresCol="features", labelCol="label",
predictionCol="prediction",
+ weightCol=None, istonic=False, 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)")
+ self.featureIndex = \
+ Param(self, "featureIndex",
+ "The index of the feature if featuresCol is a vector
column, no effect " +
+ "otherwise. (default 0)")
+ self._setDefault(isotonic=False, featureIndex=0)
+ kwargs = self.__init__._input_kwargs
+ self.setParams(**kwargs)
+
+ @keyword_only
+ def setParams(self, featuresCol="features", labelCol="label",
predictionCol="prediction",
+ weightCol=None, istonic=False, featureIndex=0):
--- End diff --
```istonic=False``` -> ```isotonic=True```
---
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