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[SPARK-3443][MLLIB] update default values of tree: #2322

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[SPARK-3443][MLLIB] update default values of tree: #2322

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8c81831
update default values of tree:
mengxr Sep 8, 2014
5900445
update comments
mengxr Sep 8, 2014
cda453a
fix tests
mengxr Sep 8, 2014
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fix tests
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@mengxr
mengxr committed Sep 8, 2014
commit cda453a237fc8a93b5764e09ef689af1fadf8063
18 changes: 4 additions & 14 deletions mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala
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Expand Up @@ -31,7 +31,6 @@ import org.apache.spark.mllib.tree.impurity.{Entropy, Gini, Variance}
import org.apache.spark.mllib.tree.model.{DecisionTreeModel, Node}
import org.apache.spark.mllib.util.LocalSparkContext


class DecisionTreeSuite extends FunSuite with LocalSparkContext {

def validateClassifier(
Expand Down Expand Up @@ -353,8 +352,6 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
assert(splits(0).length === 99)
assert(bins.length === 2)
assert(bins(0).length === 100)
assert(splits(0).length === 99)
assert(bins(0).length === 100)

val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(8), metadata, 0,
Expand All @@ -381,8 +378,6 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
assert(splits(0).length === 99)
assert(bins.length === 2)
assert(bins(0).length === 100)
assert(splits(0).length === 99)
assert(bins(0).length === 100)

val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(2), metadata, 0,
Expand Down Expand Up @@ -410,8 +405,6 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
assert(splits(0).length === 99)
assert(bins.length === 2)
assert(bins(0).length === 100)
assert(splits(0).length === 99)
assert(bins(0).length === 100)

val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(2), metadata, 0,
Expand Down Expand Up @@ -439,8 +432,6 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
assert(splits(0).length === 99)
assert(bins.length === 2)
assert(bins(0).length === 100)
assert(splits(0).length === 99)
assert(bins(0).length === 100)

val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata)
val bestSplits = DecisionTree.findBestSplits(treeInput, new Array(2), metadata, 0,
Expand All @@ -464,8 +455,6 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
assert(splits(0).length === 99)
assert(bins.length === 2)
assert(bins(0).length === 100)
assert(splits(0).length === 99)
assert(bins(0).length === 100)

// Train a 1-node model
val strategyOneNode = new Strategy(Classification, Entropy, 1, 2, 100)
Expand Down Expand Up @@ -600,7 +589,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
val arr = DecisionTreeSuite.generateContinuousDataPointsForMulticlass()
val rdd = sc.parallelize(arr)
val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
numClassesForClassification = 3)
numClassesForClassification = 3, maxBins = 100)
assert(strategy.isMulticlassClassification)
val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)

Expand All @@ -626,7 +615,7 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
val arr = DecisionTreeSuite.generateContinuousDataPointsForMulticlass()
val rdd = sc.parallelize(arr)
val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
numClassesForClassification = 3, categoricalFeaturesInfo = Map(0 -> 3))
numClassesForClassification = 3, maxBins = 100, categoricalFeaturesInfo = Map(0 -> 3))
assert(strategy.isMulticlassClassification)
val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
assert(metadata.isUnordered(featureIndex = 0))
Expand All @@ -652,7 +641,8 @@ class DecisionTreeSuite extends FunSuite with LocalSparkContext {
val arr = DecisionTreeSuite.generateCategoricalDataPointsForMulticlassForOrderedFeatures()
val rdd = sc.parallelize(arr)
val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4,
numClassesForClassification = 3, categoricalFeaturesInfo = Map(0 -> 10, 1 -> 10))
numClassesForClassification = 3, maxBins = 100,
categoricalFeaturesInfo = Map(0 -> 10, 1 -> 10))
assert(strategy.isMulticlassClassification)
val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy)
assert(!metadata.isUnordered(featureIndex = 0))
Expand Down