[SPARK-7690] [ML] Multiclass classification Evaluator

Multiclass Classification Evaluator for ML Pipelines. F1 score, precision, recall, weighted precision and weighted recall are supported as available metrics.

Author: Ram Sriharsha <rsriharsha@hw11853.local>

Closes #7475 from harsha2010/SPARK-7690 and squashes the following commits:

9bf4ec7 [Ram Sriharsha] fix indentation
3f09a85 [Ram Sriharsha] cleanup doc
16115ae [Ram Sriharsha] code review fixes
032d2a3 [Ram Sriharsha] fix test
eec9865 [Ram Sriharsha] Fix Python Indentation
1dbeffd [Ram Sriharsha] Merge branch 'master' into SPARK-7690
68cea85 [Ram Sriharsha] Merge branch 'master' into SPARK-7690
54c03de [Ram Sriharsha] [SPARK-7690][ml][WIP] Multiclass Evaluator for ML Pipeline

1 files changed, 66 insertions, 0 deletions

diff --git a/python/pyspark/ml/evaluation.py b/python/pyspark/ml/evaluation.py
index 595593a7f2..06e8093522 100644
--- a/python/pyspark/ml/evaluation.py
+++ b/python/pyspark/ml/evaluation.py
@@ -214,6 +214,72 @@ class RegressionEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol):
         kwargs = self.setParams._input_kwargs
         return self._set(**kwargs)
 
+
+@inherit_doc
+class MulticlassClassificationEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol):
+    """
+    Evaluator for Multiclass Classification, which expects two input
+    columns: prediction and label.
+    >>> scoreAndLabels = [(0.0, 0.0), (0.0, 1.0), (0.0, 0.0),
+    ...     (1.0, 0.0), (1.0, 1.0), (1.0, 1.0), (1.0, 1.0), (2.0, 2.0), (2.0, 0.0)]
+    >>> dataset = sqlContext.createDataFrame(scoreAndLabels, ["prediction", "label"])
+    ...
+    >>> evaluator = MulticlassClassificationEvaluator(predictionCol="prediction")
+    >>> evaluator.evaluate(dataset)
+    0.66...
+    >>> evaluator.evaluate(dataset, {evaluator.metricName: "precision"})
+    0.66...
+    >>> evaluator.evaluate(dataset, {evaluator.metricName: "recall"})
+    0.66...
+    """
+    # a placeholder to make it appear in the generated doc
+    metricName = Param(Params._dummy(), "metricName",
+                       "metric name in evaluation "
+                       "(f1|precision|recall|weightedPrecision|weightedRecall)")
+
+    @keyword_only
+    def __init__(self, predictionCol="prediction", labelCol="label",
+                 metricName="f1"):
+        """
+        __init__(self, predictionCol="prediction", labelCol="label", \
+                 metricName="f1")
+        """
+        super(MulticlassClassificationEvaluator, self).__init__()
+        self._java_obj = self._new_java_obj(
+            "org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator", self.uid)
+        # param for metric name in evaluation (f1|precision|recall|weightedPrecision|weightedRecall)
+        self.metricName = Param(self, "metricName",
+                                "metric name in evaluation"
+                                " (f1|precision|recall|weightedPrecision|weightedRecall)")
+        self._setDefault(predictionCol="prediction", labelCol="label",
+                         metricName="f1")
+        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="f1"):
+        """
+        setParams(self, predictionCol="prediction", labelCol="label", \
+                  metricName="f1")
+        Sets params for multiclass classification evaluator.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
 if __name__ == "__main__":
     import doctest
     from pyspark.context import SparkContext