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[GitHub] [spark] huaxingao opened a new pull request #28913: [SPARK-23631][ML][PySpark] Add summary to RandomForestClassificationModel
huaxingao opened a new pull request #28913:
URL: https://github.com/apache/spark/pull/28913
### What changes were proposed in this pull request?
Add summary to RandomForestClassificationModel...
### Why are the changes needed?
so user can get a summary of this classification model, and retrieve common metrics such as accuracy, weightedTruePositiveRate, roc (for binary), pr curves (for binary), etc.
### Does this PR introduce _any_ user-facing change?
Yes
```
RandomForestClassificationModel.summary
RandomForestClassificationModel.evaluate
```
### How was this patch tested?
Add new tests
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[GitHub] [spark] huaxingao commented on pull request #28913: [SPARK-23631][ML][PySpark] Add summary to RandomForestClassificationModel
Posted by GitBox <gi...@apache.org>.
huaxingao commented on pull request #28913:
URL: https://github.com/apache/spark/pull/28913#issuecomment-651250226
@zhengruifeng
Thanks for your comments.
Seems I have to add summary to each of the Classifiers one by one. Using this PR as an example, I think I need to have the following in RandomForestclassifier.scala, to be consistent with the implementation of LogisticRegressionTrainingSummary.
```
sealed trait RandomForestClassificationSummary extends ClassificationSummary
sealed trait RandomForestClassificationTrainingSummary extends RandomForestClassificationSummary
with TrainingSummary
sealed trait BinaryRandomForestClassificationSummary extends BinaryClassificationSummary
sealed trait BinaryRandomForestClassificationTrainingSummary extends
BinaryRandomForestClassificationSummary with RandomForestClassificationTrainingSummary
private class RandomForestClassificationTrainingSummaryImpl
private class RandomForestClassificationSummaryImpl
private class BinaryRandomForestClassificationTrainingSummaryImpl
private class BinaryRandomForestClassificationSummaryImpl
```
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SparkQA commented on pull request #28913:
URL: https://github.com/apache/spark/pull/28913#issuecomment-648517835
**[Test build #124434 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/124434/testReport)** for PR 28913 at commit [`1e3e3d5`](https://github.com/apache/spark/commit/1e3e3d55c04e09cf2dc91fe3df7031068d05f1ed).
* This patch passes all tests.
* This patch merges cleanly.
* This patch adds the following public classes _(experimental)_:
* `sealed trait RandomForestClassificationSummary extends ClassificationSummary `
* `class RandomForestClassificationSummary(_ClassificationSummary):`
* `class RandomForestClassificationTrainingSummary(RandomForestClassificationSummary,`
* `class BinaryRandomForestClassificationSummary(_BinaryClassificationSummary):`
* `class BinaryRandomForestClassificationTrainingSummary(BinaryRandomForestClassificationSummary,`
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[GitHub] [spark] huaxingao commented on a change in pull request #28913: [SPARK-23631][ML][PySpark] Add summary to RandomForestClassificationModel
Posted by GitBox <gi...@apache.org>.
huaxingao commented on a change in pull request #28913:
URL: https://github.com/apache/spark/pull/28913#discussion_r444571654
##########
File path: mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
##########
@@ -166,7 +166,35 @@ class RandomForestClassifier @Since("1.4.0") (
val numFeatures = trees.head.numFeatures
instr.logNumClasses(numClasses)
instr.logNumFeatures(numFeatures)
- new RandomForestClassificationModel(uid, trees, numFeatures, numClasses)
+ createModel(dataset, trees, numFeatures, numClasses)
+ }
+
+ private def createModel(
+ dataset: Dataset[_],
+ trees: Array[DecisionTreeClassificationModel],
+ numFeatures: Int,
+ numClasses: Int): RandomForestClassificationModel = {
+ val model = copyValues(new RandomForestClassificationModel(uid, trees, numFeatures, numClasses))
+ val weightColName = if (!isDefined(weightCol)) "weightCol" else $(weightCol)
+
+ val (summaryModel, probabilityColName, predictionColName) = model.findSummaryModel()
+ val rfSummary = if (numClasses <= 2) {
+ new BinaryRandomForestClassificationTrainingSummaryImpl(
+ summaryModel.transform(dataset),
+ probabilityColName,
+ predictionColName,
+ $(labelCol),
+ weightColName,
+ Array(0.0))
Review comment:
for non iterative algorithm, set objectiveHistory to Array(0.0).
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[GitHub] [spark] huaxingao commented on pull request #28913: [SPARK-23631][ML][PySpark] Add summary to RandomForestClassificationModel
Posted by GitBox <gi...@apache.org>.
huaxingao commented on pull request #28913:
URL: https://github.com/apache/spark/pull/28913#issuecomment-652551624
Thanks! @srowen @zhengruifeng
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* This patch **fails Scala style tests**.
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[GitHub] [spark] zhengruifeng commented on pull request #28913: [SPARK-23631][ML][PySpark] Add summary to RandomForestClassificationModel
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zhengruifeng commented on pull request #28913:
URL: https://github.com/apache/spark/pull/28913#issuecomment-650710662
It seems that you need to add summary in ml one by one.
Is it possible to add it as a generic function in `Classifier`/`Regressor` or even in `Predictor`?
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Merged to master
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