[SPARK-7833] [ML] Add python wrapper for RegressionEvaluator

Author: Ram Sriharsha <[email protected]>

Closes apache#6365 from harsha2010/SPARK-7833 and squashes the following commits:

923f288 [Ram Sriharsha] cleanup
7623b7d [Ram Sriharsha] python style fix
9743f83 [Ram Sriharsha] [SPARK-7833][ml] Add python wrapper for RegressionEvaluator

Author: Ram Sriharsha

mllib/src/main/scala/org/apache/spark/ml/evaluation/RegressionEvaluator.scala

@@ -31,14 +31,14 @@ import org.apache.spark.sql.types.DoubleType
  * Evaluator for regression, which expects two input columns: prediction and label.
  */
 @AlphaComponent
-class RegressionEvaluator(override val uid: String)
+final class RegressionEvaluator(override val uid: String)
   extends Evaluator with HasPredictionCol with HasLabelCol {
 
   def this() = this(Identifiable.randomUID("regEval"))
 
   /**
    * param for metric name in evaluation
-   * @group param
+   * @group param supports mse, rmse, r2, mae as valid metric names.
    */
   val metricName: Param[String] = {
     val allowedParams = ParamValidators.inArray(Array("mse", "rmse", "r2", "mae"))

mllib/src/test/scala/org/apache/spark/ml/evaluation/RegressionEvaluatorSuite.scala

@@ -39,6 +39,7 @@ class RegressionEvaluatorSuite extends FunSuite with MLlibTestSparkContext {
     val dataset = sqlContext.createDataFrame(
       sc.parallelize(LinearDataGenerator.generateLinearInput(
         6.3, Array(4.7, 7.2), Array(0.9, -1.3), Array(0.7, 1.2), 100, 42, 0.1), 2))
+    
     /**
      * Using the following R code to load the data, train the model and evaluate metrics.
      *

python/pyspark/ml/evaluation.py

@@ -19,11 +19,11 @@
 
 from pyspark.ml.wrapper import JavaWrapper
 from pyspark.ml.param import Param, Params
-from pyspark.ml.param.shared import HasLabelCol, HasRawPredictionCol
+from pyspark.ml.param.shared import HasLabelCol, HasPredictionCol, HasRawPredictionCol
 from pyspark.ml.util import keyword_only
 from pyspark.mllib.common import inherit_doc
 
-__all__ = ['Evaluator', 'BinaryClassificationEvaluator']
+__all__ = ['Evaluator', 'BinaryClassificationEvaluator', 'RegressionEvaluator']
 
 
 @inherit_doc
@@ -148,6 +148,70 @@ def setParams(self, rawPredictionCol="rawPrediction", labelCol="label",
         return self._set(**kwargs)
 
 
+@inherit_doc
+class RegressionEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol):
+    """
+    Evaluator for Regression, which expects two input
+    columns: prediction and label.
+
+    >>> scoreAndLabels = [(-28.98343821, -27.0), (20.21491975, 21.5),
+    ...   (-25.98418959, -22.0), (30.69731842, 33.0), (74.69283752, 71.0)]
+    >>> dataset = sqlContext.createDataFrame(scoreAndLabels, ["raw", "label"])
+    ...
+    >>> evaluator = RegressionEvaluator(predictionCol="raw")
+    >>> evaluator.evaluate(dataset)
+    2.842...
+    >>> evaluator.evaluate(dataset, {evaluator.metricName: "r2"})
+    0.993...
+    >>> evaluator.evaluate(dataset, {evaluator.metricName: "mae"})
+    2.649...
+    """
+    # a placeholder to make it appear in the generated doc
+    metricName = Param(Params._dummy(), "metricName",
+                       "metric name in evaluation (mse|rmse|r2|mae)")
+
+    @keyword_only
+    def __init__(self, predictionCol="prediction", labelCol="label",
+                 metricName="rmse"):
+        """
+        __init__(self, predictionCol="prediction", labelCol="label", \
+                 metricName="rmse")
+        """
+        super(RegressionEvaluator, self).__init__()
+        self._java_obj = self._new_java_obj(
+            "org.apache.spark.ml.evaluation.RegressionEvaluator", self.uid)
+        #: param for metric name in evaluation (mse|rmse|r2|mae)
+        self.metricName = Param(self, "metricName",
+                                "metric name in evaluation (mse|rmse|r2|mae)")
+        self._setDefault(predictionCol="prediction", labelCol="label",
+                         metricName="rmse")
+        kwargs = self.__init__._input_kwargs
+        self._set(**kwargs)
+
+    def setMetricName(self, value):
+        """
+        Sets the value of :py:attr:`metricName`.
+        """
+        self._paramMap[self.metricName] = value
+        return self
+
+    def getMetricName(self):
+        """
+        Gets the value of metricName or its default value.
+        """
+        return self.getOrDefault(self.metricName)
+
+    @keyword_only
+    def setParams(self, predictionCol="prediction", labelCol="label",
+                  metricName="rmse"):
+        """
+        setParams(self, predictionCol="prediction", labelCol="label",
+                  metricName="rmse")
+        Sets params for regression evaluator.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
 if __name__ == "__main__":
     import doctest
     from pyspark.context import SparkContext