diff --git a/python/pyspark/ml/param/_shared_params_code_gen.py b/python/pyspark/ml/param/_shared_params_code_gen.py
index 5b39e5dd4e25bf31335d18608d4b3d6e289bd009..45a94e9c32962cbbb8b1933b5f8fd0f82a63a926 100644
--- a/python/pyspark/ml/param/_shared_params_code_gen.py
+++ b/python/pyspark/ml/param/_shared_params_code_gen.py
@@ -133,7 +133,10 @@ if __name__ == "__main__":
("thresholds", "Thresholds in multi-class classification to adjust the probability of " +
"predicting each class. Array must have length equal to the number of classes, with " +
"values >= 0. The class with largest value p/t is predicted, where p is the original " +
- "probability of that class and t is the class' threshold.", None)]
+ "probability of that class and t is the class' threshold.", None),
+ ("weightCol", "weight column name. If this is not set or empty, we treat " +
+ "all instance weights as 1.0.", None)]
+
code = []
for name, doc, defaultValueStr in shared:
param_code = _gen_param_header(name, doc, defaultValueStr)
diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py
index af1218128602bc5deaa025f6b0a45f364cc9b67d..8c438bc74f51ff27123654acdfe33f348cc98748 100644
--- a/python/pyspark/ml/param/shared.py
+++ b/python/pyspark/ml/param/shared.py
@@ -570,6 +570,33 @@ class HasThresholds(Params):
return self.getOrDefault(self.thresholds)
+class HasWeightCol(Params):
+ """
+ Mixin for param weightCol: weight column name. If this is not set or empty, we treat all instance weights as 1.0..
+ """
+
+ # a placeholder to make it appear in the generated doc
+ weightCol = Param(Params._dummy(), "weightCol", "weight column name. If this is not set or empty, we treat all instance weights as 1.0.")
+
+ def __init__(self):
+ super(HasWeightCol, self).__init__()
+ #: param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.
+ self.weightCol = Param(self, "weightCol", "weight column name. If this is not set or empty, we treat all instance weights as 1.0.")
+
+ def setWeightCol(self, value):
+ """
+ Sets the value of :py:attr:`weightCol`.
+ """
+ self._paramMap[self.weightCol] = value
+ return self
+
+ def getWeightCol(self):
+ """
+ Gets the value of weightCol or its default value.
+ """
+ return self.getOrDefault(self.weightCol)
+
+
class DecisionTreeParams(Params):
"""
Mixin for Decision Tree parameters.
diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py
index e12abeba019f755ebc350e869269f7be0c70893e..eb5f4bd6d70b4e6d9570a412aed412b56c0b7259 100644
--- a/python/pyspark/ml/regression.py
+++ b/python/pyspark/ml/regression.py
@@ -25,6 +25,7 @@ from pyspark.mllib.common import inherit_doc
__all__ = ['AFTSurvivalRegression', 'AFTSurvivalRegressionModel',
'DecisionTreeRegressor', 'DecisionTreeRegressionModel',
'GBTRegressor', 'GBTRegressionModel',
+ 'IsotonicRegression', 'IsotonicRegressionModel',
'LinearRegression', 'LinearRegressionModel',
'RandomForestRegressor', 'RandomForestRegressionModel']
@@ -142,6 +143,123 @@ class LinearRegressionModel(JavaModel):
return self._call_java("intercept")
+@inherit_doc
+class IsotonicRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
+ HasWeightCol):
+ """
+ .. note:: Experimental
+
+ 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
+ 0.0
+ >>> 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).")
+ featureIndex = \
+ Param(Params._dummy(), "featureIndex",
+ "The index of the feature if featuresCol is a vector column, no effect otherwise.")
+
+ @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).")
+ self.featureIndex = \
+ Param(self, "featureIndex",
+ "The index of the feature if featuresCol is a vector column, no effect " +
+ "otherwise.")
+ 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 setFeatureIndex(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):
+ """
+ .. note:: Experimental
+
+ Model fitted by IsotonicRegression.
+ """
+
+ @property
+ def boundaries(self):
+ """
+ Model boundaries.
+ """
+ return self._call_java("boundaries")
+
+ @property
+ def predictions(self):
+ """
+ Predictions associated with the boundaries at the same index, monotone because of isotonic
+ regression.
+ """
+ return self._call_java("predictions")
+
+
class TreeRegressorParams(object):
"""
Private class to track supported impurity measures.