huaxingao commented on a change in pull request #27094:
[SPARK-30419][ML][PySpark] Make IsotonicRegression extend Regressor
URL: https://github.com/apache/spark/pull/27094#discussion_r363046502
##########
File path: project/MimaExcludes.scala
##########
@@ -465,7 +465,15 @@ object MimaExcludes {
ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.deploy.SparkHadoopUtil.appendS3AndSparkHadoopConfigurations"),
// [SPARK-29348] Add observable metrics.
-
ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.sql.streaming.StreamingQueryProgress.this")
+
ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.sql.streaming.StreamingQueryProgress.this"),
+
+ // [SPARK-30419][ML] Make IsotonicRegression extend Regressor
+
ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.ml.regression.IsotonicRegression.fit"),
+
ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.ml.regression.IsotonicRegression.setFeaturesCol"),
Review comment:
I was confused as well when I first saw the Mima errors lol
Here are the Mima errors:
```
[error] * method
fit(org.apache.spark.sql.Dataset)org.apache.spark.ml.regression.IsotonicRegressionModel
in class org.apache.spark.ml.regression.IsotonicRegression has a different
result type in current version, where it is org.apache.spark.ml.Model rather
than org.apache.spark.ml.regression.IsotonicRegressionModel
[error] filter with:
ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.ml.regression.IsotonicRegression.fit")
[error] * method
setFeaturesCol(java.lang.String)org.apache.spark.ml.regression.IsotonicRegression
in class org.apache.spark.ml.regression.IsotonicRegression has a different
result type in current version, where it is org.apache.spark.ml.Predictor
rather than org.apache.spark.ml.regression.IsotonicRegression
[error] filter with:
ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.ml.regression.IsotonicRegression.setFeaturesCol")
[error] * method
setLabelCol(java.lang.String)org.apache.spark.ml.regression.IsotonicRegression
in class org.apache.spark.ml.regression.IsotonicRegression has a different
result type in current version, where it is org.apache.spark.ml.Predictor
rather than org.apache.spark.ml.regression.IsotonicRegression
[error] filter with:
ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.ml.regression.IsotonicRegression.setLabelCol")
[error] * method
setPredictionCol(java.lang.String)org.apache.spark.ml.regression.IsotonicRegression
in class org.apache.spark.ml.regression.IsotonicRegression has a different
result type in current version, where it is org.apache.spark.ml.Predictor
rather than org.apache.spark.ml.regression.IsotonicRegression
[error] filter with:
ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.ml.regression.IsotonicRegression.setPredictionCol")
[error] * method
setFeaturesCol(java.lang.String)org.apache.spark.ml.regression.IsotonicRegressionModel
in class org.apache.spark.ml.regression.IsotonicRegressionModel has a
different result type in current version, where it is
org.apache.spark.ml.PredictionModel rather than
org.apache.spark.ml.regression.IsotonicRegressionModel
[error] filter with:
ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.ml.regression.IsotonicRegressionModel.setFeaturesCol")
[error] * method
setPredictionCol(java.lang.String)org.apache.spark.ml.regression.IsotonicRegressionModel
in class org.apache.spark.ml.regression.IsotonicRegressionModel has a
different result type in current version, where it is
org.apache.spark.ml.PredictionModel rather than
org.apache.spark.ml.regression.IsotonicRegressionModel
[error] filter with:
ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.ml.regression.IsotonicRegressionModel.setPredictionCol")
```
The APIs are still the same, but the return types are different. Before the
change, setXXX is in ```IsotonicRegression``` and the return type is
```IsotonicRegression```
```
def setFeaturesCol(value: String): this.type = set(featuresCol, value)
```
After the change, setXXX is in the super class ```Predictor``` and the
return type is ```Predictor```
```
def setFeaturesCol(value: String): Learner = set(featuresCol,
value).asInstanceOf[Learner]
```
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