Github user manishamde commented on a diff in the pull request:
https://github.com/apache/spark/pull/79#discussion_r11142399
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala ---
@@ -0,0 +1,431 @@
+/*
+ * 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.mllib.tree
+
+import org.scalatest.BeforeAndAfterAll
+import org.scalatest.FunSuite
+
+import org.apache.spark.SparkContext
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.tree.impurity.{Entropy, Gini, Variance}
+import org.apache.spark.mllib.tree.model.Filter
+import org.apache.spark.mllib.tree.configuration.Strategy
+import org.apache.spark.mllib.tree.configuration.Algo._
+import org.apache.spark.mllib.tree.configuration.FeatureType._
+
+class DecisionTreeSuite extends FunSuite with BeforeAndAfterAll {
+
+ @transient private var sc: SparkContext = _
+
+ override def beforeAll() {
+ sc = new SparkContext("local", "test")
+ }
+
+ override def afterAll() {
+ sc.stop()
+ System.clearProperty("spark.driver.port")
+ }
+
+ test("split and bin calculation"){
+ val arr = DecisionTreeSuite.generateOrderedLabeledPointsWithLabel1()
+ assert(arr.length == 1000)
+ val rdd = sc.parallelize(arr)
+ val strategy = new Strategy(Classification,Gini,3,100)
+ val (splits, bins) = DecisionTree.findSplitsBins(rdd,strategy)
+ assert(splits.length==2)
+ assert(bins.length==2)
+ assert(splits(0).length==99)
+ assert(bins(0).length==100)
+ }
+
+ test("split and bin calculation for categorical variables"){
+ val arr = DecisionTreeSuite.generateCategoricalDataPoints()
+ assert(arr.length == 1000)
+ val rdd = sc.parallelize(arr)
+ val strategy = new
Strategy(Classification,Gini,3,100,categoricalFeaturesInfo = Map(0 -> 2,
+ 1-> 2))
+ val (splits, bins) = DecisionTree.findSplitsBins(rdd,strategy)
+ assert(splits.length==2)
+ assert(bins.length==2)
+ assert(splits(0).length==99)
+ assert(bins(0).length==100)
+
+ //Checking splits
+
+ assert(splits(0)(0).feature == 0)
+ assert(splits(0)(0).threshold == Double.MinValue)
+ assert(splits(0)(0).featureType == Categorical)
+ assert(splits(0)(0).categories.length == 1)
+ assert(splits(0)(0).categories.contains(1.0))
+
+
+ assert(splits(0)(1).feature == 0)
+ assert(splits(0)(1).threshold == Double.MinValue)
+ assert(splits(0)(1).featureType == Categorical)
+ assert(splits(0)(1).categories.length == 2)
+ assert(splits(0)(1).categories.contains(1.0))
+ assert(splits(0)(1).categories.contains(0.0))
+
+ assert(splits(0)(2) == null)
+
+ assert(splits(1)(0).feature == 1)
+ assert(splits(1)(0).threshold == Double.MinValue)
+ assert(splits(1)(0).featureType == Categorical)
+ assert(splits(1)(0).categories.length == 1)
+ assert(splits(1)(0).categories.contains(0.0))
+
+
+ assert(splits(1)(1).feature == 1)
+ assert(splits(1)(1).threshold == Double.MinValue)
+ assert(splits(1)(1).featureType == Categorical)
+ assert(splits(1)(1).categories.length == 2)
+ assert(splits(1)(1).categories.contains(1.0))
+ assert(splits(1)(1).categories.contains(0.0))
+
+ assert(splits(1)(2) == null)
+
+
+ // Checks bins
+
+ assert(bins(0)(0).category == 1.0)
+ assert(bins(0)(0).lowSplit.categories.length == 0)
+ assert(bins(0)(0).highSplit.categories.length == 1)
+ assert(bins(0)(0).highSplit.categories.contains(1.0))
+
+ assert(bins(0)(1).category == 0.0)
+ assert(bins(0)(1).lowSplit.categories.length == 1)
+ assert(bins(0)(1).lowSplit.categories.contains(1.0))
+ assert(bins(0)(1).highSplit.categories.length == 2)
+ assert(bins(0)(1).highSplit.categories.contains(1.0))
+ assert(bins(0)(1).highSplit.categories.contains(0.0))
+
+ assert(bins(0)(2) == null)
+
+ assert(bins(1)(0).category == 0.0)
+ assert(bins(1)(0).lowSplit.categories.length == 0)
+ assert(bins(1)(0).highSplit.categories.length == 1)
+ assert(bins(1)(0).highSplit.categories.contains(0.0))
+
+ assert(bins(1)(1).category == 1.0)
+ assert(bins(1)(1).lowSplit.categories.length == 1)
+ assert(bins(1)(1).lowSplit.categories.contains(0.0))
+ assert(bins(1)(1).highSplit.categories.length == 2)
+ assert(bins(1)(1).highSplit.categories.contains(0.0))
+ assert(bins(1)(1).highSplit.categories.contains(1.0))
+
+ assert(bins(1)(2) == null)
+
+ }
+
+ test("split and bin calculations for categorical variables with no
sample for one category"){
+ val arr = DecisionTreeSuite.generateCategoricalDataPoints()
+ assert(arr.length == 1000)
+ val rdd = sc.parallelize(arr)
+ val strategy = new
Strategy(Classification,Gini,3,100,categoricalFeaturesInfo = Map(0 -> 3,
+ 1-> 3))
+ val (splits, bins) = DecisionTree.findSplitsBins(rdd,strategy)
+
+ //Checking splits
+
+ assert(splits(0)(0).feature == 0)
+ assert(splits(0)(0).threshold == Double.MinValue)
+ assert(splits(0)(0).featureType == Categorical)
+ assert(splits(0)(0).categories.length == 1)
+ assert(splits(0)(0).categories.contains(1.0))
+
+ assert(splits(0)(1).feature == 0)
+ assert(splits(0)(1).threshold == Double.MinValue)
+ assert(splits(0)(1).featureType == Categorical)
+ assert(splits(0)(1).categories.length == 2)
+ assert(splits(0)(1).categories.contains(1.0))
+ assert(splits(0)(1).categories.contains(0.0))
+
+ assert(splits(0)(2).feature == 0)
+ assert(splits(0)(2).threshold == Double.MinValue)
+ assert(splits(0)(2).featureType == Categorical)
+ assert(splits(0)(2).categories.length == 3)
+ assert(splits(0)(2).categories.contains(1.0))
+ assert(splits(0)(2).categories.contains(0.0))
+ assert(splits(0)(2).categories.contains(2.0))
+
+ assert(splits(0)(3) == null)
+
+ assert(splits(1)(0).feature == 1)
+ assert(splits(1)(0).threshold == Double.MinValue)
+ assert(splits(1)(0).featureType == Categorical)
+ assert(splits(1)(0).categories.length == 1)
+ assert(splits(1)(0).categories.contains(0.0))
+
+ assert(splits(1)(1).feature == 1)
+ assert(splits(1)(1).threshold == Double.MinValue)
+ assert(splits(1)(1).featureType == Categorical)
+ assert(splits(1)(1).categories.length == 2)
+ assert(splits(1)(1).categories.contains(1.0))
+ assert(splits(1)(1).categories.contains(0.0))
+
+ assert(splits(1)(2).feature == 1)
+ assert(splits(1)(2).threshold == Double.MinValue)
+ assert(splits(1)(2).featureType == Categorical)
+ assert(splits(1)(2).categories.length == 3)
+ assert(splits(1)(2).categories.contains(1.0))
+ assert(splits(1)(2).categories.contains(0.0))
+ assert(splits(1)(2).categories.contains(2.0))
+
+ assert(splits(1)(3) == null)
+
+
+ // Checks bins
+
+ assert(bins(0)(0).category == 1.0)
+ assert(bins(0)(0).lowSplit.categories.length == 0)
+ assert(bins(0)(0).highSplit.categories.length == 1)
+ assert(bins(0)(0).highSplit.categories.contains(1.0))
+
+ assert(bins(0)(1).category == 0.0)
+ assert(bins(0)(1).lowSplit.categories.length == 1)
+ assert(bins(0)(1).lowSplit.categories.contains(1.0))
+ assert(bins(0)(1).highSplit.categories.length == 2)
+ assert(bins(0)(1).highSplit.categories.contains(1.0))
+ assert(bins(0)(1).highSplit.categories.contains(0.0))
+
+ assert(bins(0)(2).category == 2.0)
+ assert(bins(0)(2).lowSplit.categories.length == 2)
+ assert(bins(0)(2).lowSplit.categories.contains(1.0))
+ assert(bins(0)(2).lowSplit.categories.contains(0.0))
+ assert(bins(0)(2).highSplit.categories.length == 3)
+ assert(bins(0)(2).highSplit.categories.contains(1.0))
+ assert(bins(0)(2).highSplit.categories.contains(0.0))
+ assert(bins(0)(2).highSplit.categories.contains(2.0))
+
+ assert(bins(0)(3) == null)
+
+ assert(bins(1)(0).category == 0.0)
+ assert(bins(1)(0).lowSplit.categories.length == 0)
+ assert(bins(1)(0).highSplit.categories.length == 1)
+ assert(bins(1)(0).highSplit.categories.contains(0.0))
+
+ assert(bins(1)(1).category == 1.0)
+ assert(bins(1)(1).lowSplit.categories.length == 1)
+ assert(bins(1)(1).lowSplit.categories.contains(0.0))
+ assert(bins(1)(1).highSplit.categories.length == 2)
+ assert(bins(1)(1).highSplit.categories.contains(0.0))
+ assert(bins(1)(1).highSplit.categories.contains(1.0))
+
+ assert(bins(1)(2).category == 2.0)
+ assert(bins(1)(2).lowSplit.categories.length == 2)
+ assert(bins(1)(2).lowSplit.categories.contains(0.0))
+ assert(bins(1)(2).lowSplit.categories.contains(1.0))
+ assert(bins(1)(2).highSplit.categories.length == 3)
+ assert(bins(1)(2).highSplit.categories.contains(0.0))
+ assert(bins(1)(2).highSplit.categories.contains(1.0))
+ assert(bins(1)(2).highSplit.categories.contains(2.0))
+
+ assert(bins(1)(3) == null)
+
+
+ }
+
+ test("classification stump with all categorical variables"){
+ val arr = DecisionTreeSuite.generateCategoricalDataPoints()
+ assert(arr.length == 1000)
+ val rdd = sc.parallelize(arr)
+ val strategy = new
Strategy(Classification,Gini,3,100,categoricalFeaturesInfo = Map(0 -> 3,
+ 1-> 3))
+ val (splits, bins) = DecisionTree.findSplitsBins(rdd,strategy)
+ strategy.numBins = 100
--- End diff --
Will do. Thanks!
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