Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/7621#discussion_r35824633
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifierSuite.scala
---
@@ -0,0 +1,89 @@
+/*
+ * 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.classification
+
+import org.apache.spark.SparkFunSuite
+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.linalg.Vectors
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
+import org.apache.spark.sql.Row
+
+class MultilayerPerceptronClassifierSuite extends SparkFunSuite with
MLlibTestSparkContext {
+
+ test("XOR function learning as binary classification problem with two
outputs.") {
+ val dataFrame = sqlContext.createDataFrame(Seq(
+ (Vectors.dense(0.0, 0.0), 0.0),
+ (Vectors.dense(0.0, 1.0), 1.0),
+ (Vectors.dense(1.0, 0.0), 1.0),
+ (Vectors.dense(1.0, 1.0), 0.0))
+ ).toDF("features", "label")
+ val layers = Array[Int](2, 5, 2)
+ val trainer = new MultilayerPerceptronClassifier()
+ .setLayers(layers)
+ .setBlockSize(1)
+ .setSeed(11L)
+ .setMaxIter(100)
+ val model = trainer.fit(dataFrame)
+ val result = model.transform(dataFrame)
+ val predictionAndLabels = result.select("prediction",
"label").collect()
+ predictionAndLabels.foreach { case Row(p: Double, l: Double) =>
+ assert(p == l) }
+ }
+
+ test("3 class classification with 2 hidden layers") {
+ val nPoints = 1000
+
+ // The following weights are taken from OneVsRestSuite.scala
+ // they represent 3-class iris dataset
+ 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)
+ val rdd = sc.parallelize(generateMultinomialLogisticInput(
+ weights, xMean, xVariance, true, nPoints, 42), 2)
+ val dataFrame = sqlContext.createDataFrame(rdd).toDF("label",
"features")
+ val numClasses = 3
+ val numIterations = 100
+ val layers = Array[Int](4, 5, 4, numClasses)
+ val trainer = new MultilayerPerceptronClassifier()
+ .setLayers(layers)
+ .setBlockSize(1)
+ .setSeed(11L)
+ .setMaxIter(numIterations)
+ val model = trainer.fit(dataFrame)
+ val mlpPredictionAndLabels =
model.transform(dataFrame).select("prediction", "label")
+ .map { case Row(p: Double, l: Double) => (p, l) }
+ // train multinomial logistic regression
+ val lr = new LogisticRegressionWithLBFGS()
+ .setIntercept(true)
+ .setNumClasses(numClasses)
+ lr.optimizer.setRegParam(0.0)
+ .setNumIterations(numIterations)
+ val lrModel = lr.run(rdd)
+ val lrPredictionAndLabels =
lrModel.predict(rdd.map(_.features)).zip(rdd.map(_.label))
+ // MLP's predictions should not differ a lot from LR's.
--- End diff --
This might not be a perfect test criterion. Shall we leave a TODO for a
more rigorous test?
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