[SPARK-31893][ML] Add a generic ClassificationSummary trait #28710

huaxingao · 2020-06-02T22:29:30Z

What changes were proposed in this pull request?

Add a generic ClassificationSummary trait

Why are the changes needed?

Add a generic ClassificationSummary trait so all the classification models can use it to implement summary.

Currently in classification, we only have summary implemented in LogisticRegression. There are requests to implement summary for LinearSVCModel in https://issues.apache.org/jira/browse/SPARK-20249 and to implement summary for RandomForestClassificationModel in https://issues.apache.org/jira/browse/SPARK-23631. If we add a generic ClassificationSummary trait and put all the common code there, we can easily add summary to LinearSVCModel and RandomForestClassificationModel, and also add summary to all the other classification models.

We can use the same approach to add a generic RegressionSummary trait to regression package and implement summary for all the regression models.

Does this PR introduce any user-facing change?

How was this patch tested?

existing tests

huaxingao · 2020-06-02T22:35:38Z

mllib/src/main/scala/org/apache/spark/ml/classification/ClassificationSummary.scala

+   * Field in "predictions" which gives the probability or rawPrediction of each class as a vector.
+   */
+  @Since("3.1.0")
+  def scoreCol: String


Can't name it to probabilityCol because not all the classifiers have probabilityCol. If no probability, will use rawPrediction instead to calculate the binary classification metrics.

For backwards-compatibility, keep probabilityCol in the subclass as an alias? or else that's a breaking change?
Would we ever want to expose both a probability and some raw score like logits? That might argue for some mixin for probabilityCol or something but that may get too complex.

SparkQA · 2020-06-03T00:36:02Z

Test build #123450 has finished for PR 28710 at commit 91a121b.

This patch fails PySpark unit tests.
This patch merges cleanly.
This patch adds no public classes.

srowen

This mostly makes

mllib/src/main/scala/org/apache/spark/ml/classification/ClassificationSummary.scala

srowen · 2020-06-03T17:22:04Z

mllib/src/main/scala/org/apache/spark/ml/classification/ClassificationSummary.scala

+   * Field in "predictions" which gives the probability or rawPrediction of each class as a vector.
+   */
+  @Since("3.1.0")
+  def scoreCol: String


For backwards-compatibility, keep probabilityCol in the subclass as an alias? or else that's a breaking change?
Would we ever want to expose both a probability and some raw score like logits? That might argue for some mixin for probabilityCol or something but that may get too complex.

huaxingao · 2020-06-03T21:03:50Z

I put probabilityCol back in the subclass and this should fix the test failure I had yesterday :)
For the multiclass classification or binary classification metrics we need, seems to me that either one of probability or raw prediction is good enough.

huaxingao · 2020-06-03T21:04:51Z

also cc @zhengruifeng

SparkQA · 2020-06-04T00:44:21Z

Test build #123505 has finished for PR 28710 at commit a4b2891.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

SparkQA · 2020-06-04T01:38:42Z

Test build #123509 has finished for PR 28710 at commit 54124a4.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

SparkQA · 2020-06-04T21:08:34Z

Test build #123543 has finished for PR 28710 at commit f42fa8e.

This patch fails Python style tests.
This patch merges cleanly.
This patch adds the following public classes (experimental):
class _ClassificationSummary(JavaWrapper):
class _TrainingSummary(JavaWrapper):
class _BinaryClassificationSummary(_ClassificationSummary):
class LogisticRegressionSummary(_ClassificationSummary):
class LogisticRegressionTrainingSummary(LogisticRegressionSummary, _TrainingSummary):
class BinaryLogisticRegressionSummary(_BinaryClassificationSummary,

SparkQA · 2020-06-05T00:21:51Z

Test build #123545 has finished for PR 28710 at commit 7b01b63.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

srowen · 2020-06-06T18:04:12Z

project/MimaExcludes.scala

    //[SPARK-31840] Add instance weight support in LogisticRegressionSummary
    // weightCol in org.apache.spark.ml.classification.LogisticRegressionSummary is present only in current version
-    ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.weightCol")
+    ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.weightCol"),


I'm thinking through what if anything breaks here in user code. Are all the subclasses sealed traits? so could any user code have subclassed the old traits in a way that would then not compile now? I don't think so but I'm just going off reading the diff

Same general question on the Python side; I'm not as sure about the possibility of breaking something there

Before change, the code structure is like this:

sealed trait LogisticRegressionSummary extends Serializable sealed trait LogisticRegressionTrainingSummary extends LogisticRegressionSummary sealed trait BinaryLogisticRegressionSummary extends LogisticRegressionSummary sealed trait BinaryLogisticRegressionTrainingSummary extends BinaryLogisticRegressionSummary with LogisticRegressionTrainingSummary

So seems OK to me, no users can subclass the old traits.

Python seems OK to me too. I only added a new method. Applications written for old versions of Spark shouldn't be affected by this.

viirya · 2020-06-08T06:51:11Z

project/MimaExcludes.scala

+    ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary.org$apache$spark$ml$classification$ClassificationSummary$_setter_$org$apache$spark$ml$classification$ClassificationSummary$$multiclassMetrics_="),
+    ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary.org$apache$spark$ml$classification$ClassificationSummary$$multiclassMetrics"),
+    ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary.weightCol"),
+    ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.asBinary"),


Though they are sealed traits, could possibly the change of return type like this break in user code? Users get a BinaryClassificationSummary instead of BinaryLogisticRegressionSummary, for example.

@viirya Thanks for your comment. I think we are still OK, because summary is still an instance of BinaryLogisticRegressionSummary

val blorModel = lr.fit(smallBinaryDataset) assert(blorModel.summary.isInstanceOf[BinaryLogisticRegressionTrainingSummary]) assert(blorModel.summary.asBinary.isInstanceOf[BinaryLogisticRegressionSummary]) assert(blorModel.binarySummary.isInstanceOf[BinaryLogisticRegressionTrainingSummary])

Similarly, in multinomial case, summary is still an instance of LogisticRegressionTrainingSummary

val mlorModel = lr.setFamily("multinomial").fit(smallMultinomialDataset) assert(mlorModel.summary.isInstanceOf[LogisticRegressionTrainingSummary])

in multinomial case, summary is still an instance of LogisticRegressionTrainingSummary

Is it expected?
I think a reasonable behavior should be:
for any classification model, a ClassificationSummary should be attached;
if and only if numClasses=2, then this summary is also a BinaryClassificationSummary

zhengruifeng · 2020-06-09T02:58:02Z

mllib/src/main/scala/org/apache/spark/ml/classification/ClassificationSummary.scala

+  import sparkSession.implicits._
+
+  // TODO: Allow the user to vary the number of bins using a setBins method in
+  // BinaryClassificationMetrics. For now the default is set to 100.


'For now the default is set to 100.' it is wrong, the default numBins in BinaryClassificationMetrics is always 1000.

This piece of code was used in LogisticRegression and I moved it here. I'm not really sure what the comment means, but it might mean "Allow the user to vary the number of bins using a setBins method in BinaryClassificationMetrics. For now set it to 100."

OK, I suggest changing it to the default value of 1000, so that the metrics are the same with evaluator and BinaryClassificationMetrics with default numBins

zhengruifeng · 2020-06-09T02:58:30Z

mllib/src/main/scala/org/apache/spark/ml/classification/ClassificationSummary.scala

+      predictions.select(col(scoreCol), col(labelCol).cast(DoubleType),
+        lit(1.0)).rdd.map {
+        case Row(score: Vector, label: Double, weight: Double) => (score(1), label, weight)
+      }, 100


zhengruifeng · 2020-06-09T03:03:11Z

mllib/src/main/scala/org/apache/spark/ml/classification/ClassificationSummary.scala

+   * Field in "predictions" which gives the probability or rawPrediction of each class as a vector.
+   */
+  @Since("3.1.0")
+  def scoreCol: String


Since probabilityCol can be added in subclasses, so can scoreCol be removed?

still need to keep scoreCol in this generic trait because it is needed in BinaryClassificationSummary

new BinaryClassificationMetrics( predictions.select(col(scoreCol), col(labelCol).cast(DoubleType),

Is it possible to add another trait ProbabilisticClassifierSummary to avoid this scoreCol? it seems does not exist in current impl.

Why there is no rawPredictionCol?

SparkQA · 2020-06-11T02:01:09Z

Test build #123789 has finished for PR 28710 at commit c2fb2b0.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

zhengruifeng · 2020-06-11T08:23:38Z

MulticlassMetrics needs "prediction, label, weight(optional), probability (optional, only for logLoss)", probability can be ignored for now;

BinaryClassificationMetrics needs "score, label, weight", here score is the probability of class 1 in BinaryLogisticRegressionSummary;

So I guess we can add a BinaryClassificationSummary to support LinearSVC, then scoreCol seems no longer needed, several break changes maybe avoided.

huaxingao · 2020-06-11T20:21:42Z

@zhengruifeng Thanks for your comments.
I think the traits should be like this (not including weightCol etc. for simplicity):

trait ClassificationSummary {
  def predictionCol: String
  def labelCol: String
  val multiclassMetrics = 
      new MulticlassMetrics(
         predictions.select(col(predictionCol), col(labelCol).cast(DoubleType))
        . . . . . .
     )

trait BinaryClassificationSummary extends ClassificationSummary
  def rawPredictionCol: String
  val binaryMetrics = 
    new BinaryClassificationMetrics(
      predictions.select(col(rawPredictionCol), col(labelCol).cast(DoubleType))
      . . . . . . 
    )

However, currently BinaryLogisticRegressionSummary uses probabilityCol for BinaryClassificationMetrics and this probabilityCol is in LogisticRegressionSummary instead of BinaryLogisticRegressionSummary. In order not to break the existing code, I need to make several changes for the above traits

change rawPredictionCol to scoreCol
can't use rawPredictionCol since currently LogisticRegression uses probabilityCol
can't use probabilityCol since LinearSVC doesn't have probabilityCol
put scoreCol in ClassificationSummary (since currently probabilityCol is in LogisticRegressionSummary instead of BinaryLogisticRegressionSummary)
that's how I get the current traits as following:

trait ClassificationSummary {
  def scoreCol: String
  def predictionCol: String
  def labelCol: String
  val multiclassMetrics = 
      new MulticlassMetrics(
         predictions.select(col(predictionCol), col(labelCol).cast(DoubleType))
        . . . . . .
     )

trait BinaryClassificationSummary extends ClassificationSummary
  val binaryMetrics = 
    new BinaryClassificationMetrics(
      predictions.select(col(scoreCol), col(labelCol).cast(DoubleType))
      . . . . . . 
    )

To implement summary for other classifiers:

LinearSVCSummary extends BinaryClassificationSummary  // use rawPredicationCol
FMClassifierSummary extends BinaryClassificationSummary  // use ProbabilityCol

For RandomForestClassifer (also for DecisionTreeClassifier and GBTClassiifer)

RandomForestSummary extends ClassificationSummary
BinaryRandomForestSummary extends BinaryClassificationSummary  // use ProbabilityCol
if (numOfClass == 2)
   summary = BinaryRandomForestSummary
else
   sumary = RandomForestSummary

zhengruifeng · 2020-06-12T07:57:02Z

@huaxingao
since scoreCol is only used to compute BinaryClassificationMetrics, so what about putting it in BinaryClassificationSummary:

trait BinaryClassificationSummary extends ClassificationSummary {
 def scoreCol: String = null
}

Then in LogisticRegression:

LogisticRegressionSummary extends ClassificationSummary {
   def probabilityCol = "..."
}

trait BinaryLogisticRegressionSummary extends LogisticRegressionSummary with BinaryClassificationSummary {
    def scoreCol = if (probabilityCol.nonEmpty) probabilityCol else if (rawPredictionCol.nonEmpty) rawPredictionCol else throw ...
}

SparkQA · 2020-06-13T20:51:06Z

Test build #123980 has finished for PR 28710 at commit b8aeff6.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

SparkQA · 2020-06-15T04:10:09Z

Test build #124021 has finished for PR 28710 at commit 2e6f35c.

This patch fails Spark unit tests.
This patch merges cleanly.
This patch adds no public classes.

huaxingao · 2020-06-15T04:33:08Z

retest this please

SparkQA · 2020-06-15T07:05:02Z

Test build #124030 has finished for PR 28710 at commit 2e6f35c.

This patch fails due to an unknown error code, -9.
This patch merges cleanly.
This patch adds no public classes.

huaxingao · 2020-06-15T07:07:57Z

retest this please

zhengruifeng · 2020-06-15T07:04:40Z

mllib/src/main/scala/org/apache/spark/ml/classification/ClassificationSummary.scala

+/**
+ * Abstraction for binary classification results for a given model.
+ */
+trait BinaryClassificationSummary extends ClassificationSummary {


private[classification]？

zhengruifeng · 2020-06-15T07:05:50Z

mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala

-  @Since("1.5.0")
-  @transient lazy val recallByThreshold: DataFrame = {
-    binaryMetrics.recallByThreshold().toDF("threshold", "recall")
+  override def scoreCol: String = if (probabilityCol.nonEmpty) {


if probabilityCol.isEmpty use rawPredictionCol instead?

Since existing computation of binary did not use rawPredictionCol, so I think we can just let it alone for now.
In the furture, I think we can make it a little heuristic: if probabilityCol.nonEmpty use probabilityCol; then try rawPredictionCol

zhengruifeng · 2020-06-15T07:07:55Z

project/MimaExcludes.scala

+    ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.classification.BinaryLogisticRegressionSummary.org$apache$spark$ml$classification$ClassificationSummary$$multiclassMetrics"),
+    ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.classification.BinaryLogisticRegressionSummary.weightCol")
  )



Are there some MiMa execlusions can be removed after the lastest change?

MiMa exclusions have already been updated

SparkQA · 2020-06-15T11:39:47Z

Test build #124045 has finished for PR 28710 at commit 0898708.

This patch fails Spark unit tests.
This patch merges cleanly.
This patch adds no public classes.

zhengruifeng · 2020-06-16T07:11:40Z

retest this please

SparkQA · 2020-06-16T09:28:52Z

Test build #124110 has finished for PR 28710 at commit 61e93d0.

This patch fails Spark unit tests.
This patch merges cleanly.
This patch adds no public classes.

huaxingao · 2020-06-16T15:11:26Z

retest this please

SparkQA · 2020-06-16T15:18:38Z

Test build #124128 has finished for PR 28710 at commit 61e93d0.

This patch fails build dependency tests.
This patch merges cleanly.
This patch adds no public classes.

huaxingao · 2020-06-16T15:26:43Z

retest this please

SparkQA · 2020-06-16T15:35:31Z

Test build #124129 has finished for PR 28710 at commit 61e93d0.

This patch fails build dependency tests.
This patch merges cleanly.
This patch adds no public classes.

huaxingao · 2020-06-16T16:11:20Z

retest this please

SparkQA · 2020-06-16T19:14:28Z

Test build #124131 has finished for PR 28710 at commit 61e93d0.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

zhengruifeng

LGTM, since it is a big change with mima exclusions,
@viirya @srowen could you please help reviewing the latest commits?

huaxingao · 2020-06-18T05:04:08Z

I moved asBinary back to LogisticRegressionSummary to get rid of this

ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.asBinary")

All the rest of the MiMa problems are InheritedNewAbstractMethodProblem. I think those are OK.

huaxingao · 2020-06-18T05:06:24Z

retest this please

viirya · 2020-06-18T06:45:31Z

mllib/src/main/scala/org/apache/spark/ml/classification/ClassificationSummary.scala

+  /** Number of training iterations. */
+  @Since("3.1.0")
+  def totalIterations: Int = {
+    assert(objectiveHistory.length > 0, s"objectiveHistory length should be greater than 1.")


The assert looks like objectiveHistory length should be greater than 0?

And string interpolation s"" is unnecessary.

viirya · 2020-06-18T06:52:02Z

mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala

+  override def scoreCol: String = if (probabilityCol.nonEmpty) {
+    probabilityCol
+  } else {
+    throw new SparkException(s"probabilityCol is required for BinaryLogisticRegressionSummary.")


s"" is not needed.

viirya · 2020-06-18T06:59:24Z

python/pyspark/ml/classification.py

-
-    @property
-    @since("2.0.0")
+    @since("3.1.0")


Why change this since version? Like above probabilityCol is not changed?

SparkQA · 2020-06-18T07:05:01Z

Test build #124197 has finished for PR 28710 at commit e844c11.

This patch fails due to an unknown error code, -9.
This patch merges cleanly.
This patch adds no public classes.

SparkQA · 2020-06-18T07:05:02Z

Test build #124198 has finished for PR 28710 at commit e844c11.

This patch fails due to an unknown error code, -9.
This patch merges cleanly.
This patch adds no public classes.

SparkQA · 2020-06-18T10:08:15Z

Test build #124206 has finished for PR 28710 at commit c76d591.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

viirya · 2020-06-18T15:50:40Z

python/pyspark/ml/classification.py

-        .. note:: This ignores instance weights (setting all to 1.0) from
-            `LogisticRegression.weightCol`. This will change in later Spark
-            versions.


I saw you remove such note in latest commit. So now these methods use weightCol?

Yes. I added weightCol in another PR, but forgot to update python comment.

viirya · 2020-06-18T15:54:03Z

project/MimaExcludes.scala

+    //[SPARK-31893] Add a generic ClassificationSummary trait
+    ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionTrainingSummary.org$apache$spark$ml$classification$ClassificationSummary$_setter_$org$apache$spark$ml$classification$ClassificationSummary$$multiclassMetrics_="),
+    ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionTrainingSummary.org$apache$spark$ml$classification$ClassificationSummary$$multiclassMetrics"),
+    ProblemFilters.exclude[InheritedNewAbstractMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionTrainingSummary.weightCol"),


Not sure if it is for all cases. But this looks like just because weightCol is inherited from ClassificationSummary instead of LogisticRegressionSummary now.

I added weightCol in LogisticRegressionSummary in another PR (I just realized the MiMaExclude for that PR is not needed any more since I moved weightCol to ClassificationSummary)

3.0: no weightCol

after my changes:
ClassificationSummary has weightCol, LogisticRegressionSummary, LogisticRegressionTrainingSummary, BinaryLogisticRegressionSummary, BinaryLogisticRegressionTrainingSummary all have inherited weightCol, so there are four InheritedNewAbstractMethodProblem for weightCol

SparkQA · 2020-06-18T19:44:05Z

Test build #124227 has finished for PR 28710 at commit ead6da2.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

viirya

Looks okay. The InheritedNewAbstractMethodProblem mima problem looks because moving some methods to the traits created in this change.

huaxingao · 2020-06-19T15:36:45Z

@srowen Any more comments?

srowen · 2020-06-19T15:50:34Z

Just catching up here: so all the MiMa excludes don't actually represent public API changes right? they're all sealed or private?
We're going to lose the @Since annotations, which is a little unfortunate, but I can see the value in refactoring

huaxingao · 2020-06-19T16:15:27Z

Right. They are all sealed or private. No public API changes. @srowen

srowen · 2020-06-20T13:43:36Z

Merged to master

huaxingao · 2020-06-20T15:11:04Z

Thank you all!

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huaxingao commented Jun 2, 2020

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huaxingao force-pushed the summary_trait branch from b8aeff6 to 2e6f35c Compare June 15, 2020 01:21

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huaxingao added 3 commits June 15, 2020 09:24

[SPARK-31893][ML] Add a generic ClassificationSummary trait

a537c49

change probabilityCol to scoreCol

93c82bd

fix MiMa

981ae65

zhengruifeng approved these changes Jun 18, 2020

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move asBinary to LogisticRegression

e844c11

viirya reviewed Jun 18, 2020

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address comments

c76d591

viirya reviewed Jun 18, 2020

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remove outdated MiMaExclude

ead6da2

viirya approved these changes Jun 18, 2020

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srowen closed this in 297016e Jun 20, 2020

huaxingao deleted the summary_trait branch June 20, 2020 15:11

zero323 mentioned this pull request Jul 18, 2020

[SPARK-31893][ML] Add a generic ClassificationSummary trait zero323/pyspark-stubs#435

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[SPARK-31893][ML] Add a generic ClassificationSummary trait #28710

[SPARK-31893][ML] Add a generic ClassificationSummary trait #28710

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huaxingao commented Jun 2, 2020

What changes were proposed in this pull request?

Why are the changes needed?

Does this PR introduce any user-facing change?

How was this patch tested?

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