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From yanboliang <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-9774][ML][PYSPARK] Add python api for m...
Date Tue, 15 Sep 2015 03:43:08 GMT
Github user yanboliang commented on a diff in the pull request:

    https://github.com/apache/spark/pull/8214#discussion_r39471938
  
    --- 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):
    +        """
    +        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
    +                 weightCol=None, istonic=false, 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 setIsotonicParam(self, value):
    +        """
    +        Sets the value of :py:attr:`isotonicParam`.
    +        """
    +        self._paramMap[self.isotonicParam] = value
    +        return self
    +
    +    def getIsotonicParam(self):
    +        """
    +        Gets the value of isotonicParam or its default value.
    +        """
    +        return self.getOrDefault(self.isotonicParam)
    +
    +    def setFeautreIndexParam(self, value):
    +        """
    +        Sets the value of :py:attr:`featureIndexParam`.
    +        """
    +        self._paramMap[self.featureIndexParam] = value
    +        return self
    +
    +    def getFeatureIndexParam(self):
    --- End diff --
    
    ```getFeatureIndexParam``` -> ```getFeatureIndex```


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