[SPARK-7404] [ML] Add RegressionEvaluator to spark.ml

Author: Ram Sriharsha <rsriharsha@hw11853.local>

Closes #6344 from harsha2010/SPARK-7404 and squashes the following commits:

16b9d77 [Ram Sriharsha] consistent naming
7f100b6 [Ram Sriharsha] cleanup
c46044d [Ram Sriharsha] Merge with Master + Code Review Fixes
188fa0a [Ram Sriharsha] Merge branch 'master' into SPARK-7404
f5b6a4c [Ram Sriharsha] cleanup doc
97beca5 [Ram Sriharsha] update test to use R packages
32dd310 [Ram Sriharsha] fix indentation
f93b812 [Ram Sriharsha] fix test
1b6ebb3 [Ram Sriharsha] [SPARK-7404][ml] Add RegressionEvaluator to spark.ml

(cherry picked from commit f490b3b4c706c92aa65d000b9d885f4d160a5f39)
Signed-off-by: Xiangrui Meng <meng@databricks.com>

1 files changed, 71 insertions, 0 deletions

diff --git a/mllib/src/test/scala/org/apache/spark/ml/evaluation/RegressionEvaluatorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/evaluation/RegressionEvaluatorSuite.scala
new file mode 100644
index 0000000000..983f8b460b
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/evaluation/RegressionEvaluatorSuite.scala
@@ -0,0 +1,71 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.evaluation
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.ml.regression.LinearRegression
+import org.apache.spark.mllib.util.{LinearDataGenerator, MLlibTestSparkContext}
+import org.apache.spark.mllib.util.TestingUtils._
+
+class RegressionEvaluatorSuite extends FunSuite with MLlibTestSparkContext {
+
+  test("Regression Evaluator: default params") {
+    /**
+     * Here is the instruction describing how to export the test data into CSV format
+     * so we can validate the metrics compared with R's mmetric package.
+     *
+     * import org.apache.spark.mllib.util.LinearDataGenerator
+     * val data = 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))
+     * data.map(x=> x.label + ", " + x.features(0) + ", " + x.features(1))
+     *   .saveAsTextFile("path")
+     */
+    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.
+     *
+     * > library("glmnet")
+     * > library("rminer")
+     * > data <- read.csv("path", header=FALSE, stringsAsFactors=FALSE)
+     * > features <- as.matrix(data.frame(as.numeric(data$V2), as.numeric(data$V3)))
+     * > label <- as.numeric(data$V1)
+     * > model <- glmnet(features, label, family="gaussian", alpha = 0, lambda = 0)
+     * > rmse <- mmetric(label, predict(model, features), metric='RMSE')
+     * > mae <- mmetric(label, predict(model, features), metric='MAE')
+     * > r2 <- mmetric(label, predict(model, features), metric='R2')
+     */
+    val trainer = new LinearRegression
+    val model = trainer.fit(dataset)
+    val predictions = model.transform(dataset)
+
+    // default = rmse
+    val evaluator = new RegressionEvaluator()
+    assert(evaluator.evaluate(predictions) ~== 0.1019382 absTol 0.001)
+
+    // r2 score
+    evaluator.setMetricName("r2")
+    assert(evaluator.evaluate(predictions) ~== 0.9998196 absTol 0.001)
+
+    // mae
+    evaluator.setMetricName("mae")
+    assert(evaluator.evaluate(predictions) ~== 0.08036075 absTol 0.001)
+  }
+}