Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/7150#discussion_r39089963
--- Diff: python/pyspark/ml/feature.py ---
@@ -1291,6 +1291,96 @@ class RFormulaModel(JavaModel):
"""
+@inherit_doc
+class MinMaxScaler(JavaEstimator, HasInputCol, HasOutputCol):
+ """
+ Rescale each feature individually to a common range [min, max]
linearly using column summary
+ statistics, which is also known as min-max normalization or Rescaling.
The rescaled value for
+ feature E is calculated as,
+
+ Rescaled(e_i) = (e_i - E_min) / (E_max - E_min) * (max - min) + min
+
+ For the case E_max == E_min, Rescaled(e_i) = 0.5 * (max + min)
+
+ Note that since zero values will probably be transformed to non-zero
values, output of the
+ transformer will be DenseVector even for sparse input.
+
+ >>> from pyspark.mllib.linalg import Vectors
+ >>> df = sqlContext.createDataFrame([(Vectors.dense([0.0]),),
(Vectors.dense([2.0]),)], ["a"])
+ >>> mmScaler = MinMaxScaler(inputCol="a", outputCol="scaled")
+ >>> model = mmScaler.fit(df)
+ >>> model.transform(df).show()
+ +-----+------+
+ | a|scaled|
+ +-----+------+
+ |[0.0]| [0.0]|
+ |[2.0]| [1.0]|
+ +-----+------+
+ ...
+ """
+
+ # a placeholder to make it appear in the generated doc
+ min = Param(Params._dummy(), "min", "Lower bound of the output feature
range")
+ max = Param(Params._dummy(), "max", "Upper bound of the output feature
range")
+
+ @keyword_only
+ def __init__(self, min=0.0, max=1.0, inputCol=None, outputCol=None):
+ """
+ __init__(self, min=0.0, max=1.0, inputCol=None, outputCol=None)
+ """
+ super(MinMaxScaler, self).__init__()
+ self._java_obj =
self._new_java_obj("org.apache.spark.ml.feature.MinMaxScaler", self.uid)
+ self.min = Param(self, "min", "Lower bound of the output feature
range")
+ self.max = Param(self, "max", "Upper bound of the output feature
range")
+ self._setDefault(min=0.0, max=1.0)
+ kwargs = self.__init__._input_kwargs
+ self.setParams(**kwargs)
+
+ @keyword_only
+ def setParams(self, min=0.0, max=1.0, inputCol=None, outputCol=None):
+ """
+ setParams(self, min=0.0, max=1.0, inputCol=None, outputCol=None)
+ Sets params for this MinMaxScaler.
+ """
+ kwargs = self.setParams._input_kwargs
+ return self._set(**kwargs)
+
+ def setMin(self, value):
+ """
+ Sets the value of :py:attr:`min`.
+ """
+ self._paramMap[self.min] = value
+ return self
+
+ def getMin(self):
+ """
+ Gets the value of min or its default value.
+ """
+ return self.getOrDefault(self.min)
+
+ def setMax(self, value):
+ """
+ Sets the value of :py:attr:`max`.
+ """
+ self._paramMap[self.max] = value
+ return self
+
+ def getMax(self):
+ """
+ Gets the value of max or its default value.
+ """
+ return self.getOrDefault(self.max)
+
+ def _create_model(self, java_model):
+ return MinMaxScalerModel(java_model)
+
+
+class MinMaxScalerModel(JavaModel):
+ """
+ Model fitted by MinMaxScaler.
--- End diff --
Nicer to write: ```:py:class:`MinMaxScaler````
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