Github user yanboliang commented on a diff in the pull request: https://github.com/apache/spark/pull/5941#discussion_r29867547 --- Diff: python/pyspark/mllib/evaluation.py --- @@ -67,6 +67,73 @@ def unpersist(self): self.call("unpersist") +class RegressionMetrics(JavaModelWrapper): + """ + Evaluator for regression. + + >>> predictionAndObservations = sc.parallelize([ + ... (2.5, 3.0), (0.0, -0.5), (2.0, 2.0), (8.0, 7.0)]) + >>> metrics = RegressionMetrics(predictionAndObservations) + >>> metrics.explainedVariance() + 0.95... + >>> metrics.meanAbsoluteError() + 0.5... + >>> metrics.meanSquaredError() + 0.37... + >>> metrics.rootMeanSquaredError() + 0.61... + >>> metrics.r2() + 0.94... + """ + + def __init__(self, predictionAndObservations): + """ + :param predictionAndObservations: an RDD of (prediction, observation) pairs. + """ + sc = predictionAndObservations.ctx + sql_ctx = SQLContext(sc) + df = sql_ctx.createDataFrame(predictionAndObservations, schema=StructType([ + StructField("prediction", DoubleType(), nullable=False), + StructField("observation", DoubleType(), nullable=False)])) + java_class = sc._jvm.org.apache.spark.mllib.evaluation.RegressionMetrics + java_model = java_class(df._jdf) + super(RegressionMetrics, self).__init__(java_model) + + def explainedVariance(self): --- End diff -- Yes, I agree. I will do it for both.
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