[SPARK-13505][ML] add python api for MaxAbsScaler

## What changes were proposed in this pull request?
After SPARK-13028, we should add Python API for MaxAbsScaler.

## How was this patch tested?
unit test

Author: zlpmichelle <zlpmichelle@gmail.com>

Closes #11393 from zlpmichelle/master.

1 files changed, 68 insertions, 7 deletions

diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 67bccfae7a..369f3508fd 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -28,13 +28,14 @@ from pyspark.mllib.common import inherit_doc
 from pyspark.mllib.linalg import _convert_to_vector
 
 __all__ = ['Binarizer', 'Bucketizer', 'CountVectorizer', 'CountVectorizerModel', 'DCT',
-           'ElementwiseProduct', 'HashingTF', 'IDF', 'IDFModel', 'IndexToString', 'MinMaxScaler',
-           'MinMaxScalerModel', 'NGram', 'Normalizer', 'OneHotEncoder', 'PCA', 'PCAModel',
-           'PolynomialExpansion', 'QuantileDiscretizer', 'RegexTokenizer', 'RFormula',
-           'RFormulaModel', 'SQLTransformer', 'StandardScaler', 'StandardScalerModel',
-           'StopWordsRemover', 'StringIndexer', 'StringIndexerModel', 'Tokenizer',
-           'VectorAssembler', 'VectorIndexer', 'VectorSlicer', 'Word2Vec', 'Word2VecModel',
-           'ChiSqSelector', 'ChiSqSelectorModel']
+           'ElementwiseProduct', 'HashingTF', 'IDF', 'IDFModel', 'IndexToString',
+           'MaxAbsScaler', 'MaxAbsScalerModel', 'MinMaxScaler', 'MinMaxScalerModel',
+           'NGram', 'Normalizer', 'OneHotEncoder', 'PCA', 'PCAModel', 'PolynomialExpansion',
+           'QuantileDiscretizer', 'RegexTokenizer', 'RFormula', 'RFormulaModel',
+           'SQLTransformer', 'StandardScaler', 'StandardScalerModel', 'StopWordsRemover',
+           'StringIndexer', 'StringIndexerModel', 'Tokenizer', 'VectorAssembler',
+           'VectorIndexer', 'VectorSlicer', 'Word2Vec', 'Word2VecModel', 'ChiSqSelector',
+           'ChiSqSelectorModel']
 
 
 @inherit_doc
@@ -545,6 +546,66 @@ class IDFModel(JavaModel):
 
 
 @inherit_doc
+class MaxAbsScaler(JavaEstimator, HasInputCol, HasOutputCol):
+    """
+    .. note:: Experimental
+
+    Rescale each feature individually to range [-1, 1] by dividing through the largest maximum
+    absolute value in each feature. It does not shift/center the data, and thus does not destroy
+    any sparsity.
+
+    >>> from pyspark.mllib.linalg import Vectors
+    >>> df = sqlContext.createDataFrame([(Vectors.dense([1.0]),), (Vectors.dense([2.0]),)], ["a"])
+    >>> maScaler = MaxAbsScaler(inputCol="a", outputCol="scaled")
+    >>> model = maScaler.fit(df)
+    >>> model.transform(df).show()
+    +-----+------+
+    |    a|scaled|
+    +-----+------+
+    |[1.0]| [0.5]|
+    |[2.0]| [1.0]|
+    +-----+------+
+    ...
+
+    .. versionadded:: 2.0.0
+    """
+
+    @keyword_only
+    def __init__(self, inputCol=None, outputCol=None):
+        """
+        __init__(self, inputCol=None, outputCol=None)
+        """
+        super(MaxAbsScaler, self).__init__()
+        self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.MaxAbsScaler", self.uid)
+        self._setDefault()
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    @since("2.0.0")
+    def setParams(self, inputCol=None, outputCol=None):
+        """
+        setParams(self, inputCol=None, outputCol=None)
+        Sets params for this MaxAbsScaler.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def _create_model(self, java_model):
+        return MaxAbsScalerModel(java_model)
+
+
+class MaxAbsScalerModel(JavaModel):
+    """
+    .. note:: Experimental
+
+    Model fitted by :py:class:`MaxAbsScaler`.
+
+    .. versionadded:: 2.0.0
+    """
+
+
+@inherit_doc
 class MinMaxScaler(JavaEstimator, HasInputCol, HasOutputCol):
     """
     .. note:: Experimental