[GitHub] spark pull request #17407: [SPARK-20043][ML] DecisionTreeModel: ImpurityCalc...
Github user asfgit closed the pull request at: https://github.com/apache/spark/pull/17407 --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #17407: [SPARK-20043][ML] DecisionTreeModel: ImpurityCalc...
Github user facaiy commented on a diff in the pull request:
https://github.com/apache/spark/pull/17407#discussion_r108320537
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
---
@@ -385,6 +385,22 @@ class DecisionTreeClassifierSuite
testEstimatorAndModelReadWrite(dt, continuousData, allParamSettings ++
Map("maxDepth" -> 0),
allParamSettings ++ Map("maxDepth" -> 0), checkModelData)
}
+
+ test("SPARK-20043: " +
+ "ImpurityCalculator builder fails for uppercase impurity type Gini
in model read/write") {
+val rdd = TreeTests.getTreeReadWriteData(sc)
+
+val data: DataFrame =
+ TreeTests.setMetadata(rdd, Map.empty[Int, Int], numClasses = 2)
+
+val dt = new DecisionTreeClassifier()
+ .setImpurity("Gini")
+ .setMaxDepth(2)
+
+val model = dt.fit(data)
+
+testDefaultReadWrite(model, false)
--- End diff --
done.
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[GitHub] spark pull request #17407: [SPARK-20043][ML] DecisionTreeModel: ImpurityCalc...
Github user facaiy commented on a diff in the pull request:
https://github.com/apache/spark/pull/17407#discussion_r108320525
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
---
@@ -385,6 +385,22 @@ class DecisionTreeClassifierSuite
testEstimatorAndModelReadWrite(dt, continuousData, allParamSettings ++
Map("maxDepth" -> 0),
allParamSettings ++ Map("maxDepth" -> 0), checkModelData)
}
+
+ test("SPARK-20043: " +
+ "ImpurityCalculator builder fails for uppercase impurity type Gini
in model read/write") {
+val rdd = TreeTests.getTreeReadWriteData(sc)
+
+val data: DataFrame =
+ TreeTests.setMetadata(rdd, Map.empty[Int, Int], numClasses = 2)
+
+val dt = new DecisionTreeClassifier()
+ .setImpurity("Gini")
+ .setMaxDepth(2)
+
+val model = dt.fit(data)
+
--- End diff --
delete some blank lines to keep compact.
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[GitHub] spark pull request #17407: [SPARK-20043][ML] DecisionTreeModel: ImpurityCalc...
Github user facaiy commented on a diff in the pull request:
https://github.com/apache/spark/pull/17407#discussion_r108320481
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala
---
@@ -178,6 +178,22 @@ class DecisionTreeRegressorSuite
TreeTests.allParamSettings ++ Map("maxDepth" -> 0),
TreeTests.allParamSettings ++ Map("maxDepth" -> 0), checkModelData)
}
+
+ test("SPARK-20043: " +
+ "ImpurityCalculator builder fails for uppercase impurity type in
model read/write") {
+val rdd = TreeTests.getTreeReadWriteData(sc)
+
+val data: DataFrame =
+ TreeTests.setMetadata(rdd, Map.empty[Int, Int], numClasses = 0)
+
+val dt = new DecisionTreeRegressor()
+ .setImpurity("Variance")
+ .setMaxDepth(2)
+
+val model = dt.fit(data)
+
--- End diff --
removed.
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[GitHub] spark pull request #17407: [SPARK-20043][ML] DecisionTreeModel: ImpurityCalc...
Github user hhbyyh commented on a diff in the pull request:
https://github.com/apache/spark/pull/17407#discussion_r108094236
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
---
@@ -385,6 +385,22 @@ class DecisionTreeClassifierSuite
testEstimatorAndModelReadWrite(dt, continuousData, allParamSettings ++
Map("maxDepth" -> 0),
allParamSettings ++ Map("maxDepth" -> 0), checkModelData)
}
+
+ test("SPARK-20043: " +
+ "ImpurityCalculator builder fails for uppercase impurity type Gini
in model read/write") {
+val rdd = TreeTests.getTreeReadWriteData(sc)
+
+val data: DataFrame =
+ TreeTests.setMetadata(rdd, Map.empty[Int, Int], numClasses = 2)
+
+val dt = new DecisionTreeClassifier()
+ .setImpurity("Gini")
+ .setMaxDepth(2)
+
+val model = dt.fit(data)
+
+testDefaultReadWrite(model, false)
--- End diff --
how about setting testParams=true for this case.
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[GitHub] spark pull request #17407: [SPARK-20043][ML] DecisionTreeModel: ImpurityCalc...
Github user hhbyyh commented on a diff in the pull request:
https://github.com/apache/spark/pull/17407#discussion_r108094126
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
---
@@ -385,6 +385,22 @@ class DecisionTreeClassifierSuite
testEstimatorAndModelReadWrite(dt, continuousData, allParamSettings ++
Map("maxDepth" -> 0),
allParamSettings ++ Map("maxDepth" -> 0), checkModelData)
}
+
+ test("SPARK-20043: " +
+ "ImpurityCalculator builder fails for uppercase impurity type Gini
in model read/write") {
+val rdd = TreeTests.getTreeReadWriteData(sc)
+
+val data: DataFrame =
+ TreeTests.setMetadata(rdd, Map.empty[Int, Int], numClasses = 2)
+
+val dt = new DecisionTreeClassifier()
+ .setImpurity("Gini")
+ .setMaxDepth(2)
+
+val model = dt.fit(data)
+
--- End diff --
The blank lines kinda stand out.
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[GitHub] spark pull request #17407: [SPARK-20043][ML] DecisionTreeModel: ImpurityCalc...
Github user hhbyyh commented on a diff in the pull request:
https://github.com/apache/spark/pull/17407#discussion_r108094114
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala
---
@@ -178,6 +178,22 @@ class DecisionTreeRegressorSuite
TreeTests.allParamSettings ++ Map("maxDepth" -> 0),
TreeTests.allParamSettings ++ Map("maxDepth" -> 0), checkModelData)
}
+
+ test("SPARK-20043: " +
+ "ImpurityCalculator builder fails for uppercase impurity type in
model read/write") {
+val rdd = TreeTests.getTreeReadWriteData(sc)
+
+val data: DataFrame =
+ TreeTests.setMetadata(rdd, Map.empty[Int, Int], numClasses = 0)
+
+val dt = new DecisionTreeRegressor()
+ .setImpurity("Variance")
+ .setMaxDepth(2)
+
+val model = dt.fit(data)
+
--- End diff --
the second unit test seems redundant for this case.
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