Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/5830#discussion_r30092237
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/reduction/OneVsRestSuite.scala ---
@@ -0,0 +1,105 @@
+/*
+ * 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.reduction
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.ml.attribute.NominalAttribute
+import org.apache.spark.ml.classification.{LogisticRegressionModel,
LogisticRegression}
+import org.apache.spark.ml.util.MetadataUtils
+import org.apache.spark.mllib.classification.LogisticRegressionSuite._
+import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
+import org.apache.spark.mllib.evaluation.MulticlassMetrics
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.{DataFrame, SQLContext}
+
+class OneVsRestSuite extends FunSuite with MLlibTestSparkContext {
+
+ @transient var sqlContext: SQLContext = _
+ @transient var dataset: DataFrame = _
+ @transient var rdd: RDD[LabeledPoint] = _
+
+ override def beforeAll(): Unit = {
+ super.beforeAll()
+ sqlContext = new SQLContext(sc)
+ val nPoints = 10000
+
+ /**
+ * The following weights and xMean/xVariance are computed from iris
dataset with lambda = 0.2.
+ * As a result, we are actually drawing samples from probability
distribution of built model.
+ */
+ val weights = Array(
+ -0.57997, 0.912083, -0.371077, -0.819866, 2.688191,
+ -0.16624, -0.84355, -0.048509, -0.301789, 4.170682)
+
+ val xMean = Array(5.843, 3.057, 3.758, 1.199)
+ val xVariance = Array(0.6856, 0.1899, 3.116, 0.581)
+ rdd = sc.parallelize(generateMultinomialLogisticInput(
+ weights, xMean, xVariance, true, nPoints, 42), 2)
+ dataset = sqlContext.createDataFrame(rdd)
+ }
+
+ test("one-vs-rest: default params") {
+ val numClasses = 3
+ val ova = new OneVsRest()
+ ova.setClassifier(new LogisticRegression)
+ assert(ova.getLabelCol == "label")
+ assert(ova.getPredictionCol == "prediction")
+ val ovaModel = ova.fit(dataset)
+ assert(ovaModel.models.size == numClasses)
+ val ovaResults = ovaModel.transform(dataset)
+ .select("prediction", "label")
+ .map(row => (row(0).asInstanceOf[Double],
row(1).asInstanceOf[Double]))
+
+ val lr = new
LogisticRegressionWithLBFGS().setIntercept(true).setNumClasses(numClasses)
+ lr.optimizer.setRegParam(0.1).setNumIterations(100)
+
+ val model = lr.run(rdd)
+ val results = model.predict(rdd.map(_.features)).zip(rdd.map(_.label))
+ // determine the #confusion matrix in each class.
+ // bound how much error we allow compared to multinomial logistic
regression.
+ val expectedMetrics = new MulticlassMetrics(results)
+ val ovaMetrics = new MulticlassMetrics(ovaResults)
+ assert(expectedMetrics.confusionMatrix ~== ovaMetrics.confusionMatrix
absTol 400)
+ }
+
+ test("one-vs-rest: pass label metadata correctly during train") {
+ val numClasses = 3
+ val ova = new OneVsRest()
+ ova.setClassifier(new MockLogisticRegression)
--- End diff --
Set logreg maxIter=1 to make this faster
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