[SPARK-30533][ML][PYSPARK] Add classes to represent Java Regressors and RegressionModels

### What changes were proposed in this pull request?

This PR adds:

- `pyspark.ml.regression.JavaRegressor`
- `pyspark.ml.regression.JavaRegressionModel`

classes and replaces `JavaPredictor` and `JavaPredictionModel` in

- `LinearRegression` / `LinearRegressionModel`
- `DecisionTreeRegressor` / `DecisionTreeRegressionModel` (just addition as `JavaPredictionModel` hasn't been used)
- `RandomForestRegressor` / `RandomForestRegressionModel`  (just addition as `JavaPredictionModel` hasn't been used)
- `GBTRegressor` / `GBTRegressionModel` (just addition as `JavaPredictionModel` hasn't been used)
- `AFTSurvivalRegression` / `AFTSurvivalRegressionModel`
- `GeneralizedLinearRegression` / `GeneralizedLinearRegressionModel`
- `FMRegressor` / `FMRegressionModel`

### Why are the changes needed?

- Internal PySpark consistency.
- Feature parity with Scala.
- Intermediate step towards implementing [SPARK-29212](https://issues.apache.org/jira/browse/SPARK-29212)

### Does this PR introduce any user-facing change?

It adds new base classes, so it will affect `mro`. Otherwise interfaces should stay intact.

### How was this patch tested?

Existing tests.

Closes #27241 from zero323/SPARK-30533.

Authored-by: zero323 <[email protected]>
Signed-off-by: Sean Owen <[email protected]>

Author: zero323

python/pyspark/ml/regression.py

@@ -41,6 +41,25 @@
            'FMRegressor', 'FMRegressionModel']
 
 
+class JavaRegressor(JavaPredictor, _JavaPredictorParams):
+    """
+    Java Regressor for regression tasks.
+
+    .. versionadded:: 3.0.0
+    """
+    pass
+
+
+class JavaRegressionModel(JavaPredictionModel, _JavaPredictorParams):
+    """
+    Java Model produced by a ``_JavaRegressor``.
+    To be mixed in with class:`pyspark.ml.JavaModel`
+
+    .. versionadded:: 3.0.0
+    """
+    pass
+
+
 class _LinearRegressionParams(_JavaPredictorParams, HasRegParam, HasElasticNetParam, HasMaxIter,
                               HasTol, HasFitIntercept, HasStandardization, HasWeightCol, HasSolver,
                               HasAggregationDepth, HasLoss):
@@ -69,7 +88,7 @@ def getEpsilon(self):
 
 
 @inherit_doc
-class LinearRegression(JavaPredictor, _LinearRegressionParams, JavaMLWritable, JavaMLReadable):
+class LinearRegression(JavaRegressor, _LinearRegressionParams, JavaMLWritable, JavaMLReadable):
     """
     Linear regression.
 
@@ -251,7 +270,7 @@ def setLoss(self, value):
         return self._set(lossType=value)
 
 
-class LinearRegressionModel(JavaPredictionModel, _LinearRegressionParams, GeneralJavaMLWritable,
+class LinearRegressionModel(JavaRegressionModel, _LinearRegressionParams, GeneralJavaMLWritable,
                             JavaMLReadable, HasTrainingSummary):
     """
     Model fitted by :class:`LinearRegression`.
@@ -758,7 +777,7 @@ class _DecisionTreeRegressorParams(_DecisionTreeParams, _TreeRegressorParams, Ha
 
 
 @inherit_doc
-class DecisionTreeRegressor(JavaPredictor, _DecisionTreeRegressorParams, JavaMLWritable,
+class DecisionTreeRegressor(JavaRegressor, _DecisionTreeRegressorParams, JavaMLWritable,
                             JavaMLReadable):
     """
     `Decision tree <http://en.wikipedia.org/wiki/Decision_tree_learning>`_
@@ -953,8 +972,10 @@ def setVarianceCol(self, value):
 
 
 @inherit_doc
-class DecisionTreeRegressionModel(_DecisionTreeModel, _DecisionTreeRegressorParams,
-                                  JavaMLWritable, JavaMLReadable):
+class DecisionTreeRegressionModel(
+    JavaRegressionModel, _DecisionTreeModel, _DecisionTreeRegressorParams,
+    JavaMLWritable, JavaMLReadable
+):
     """
     Model fitted by :class:`DecisionTreeRegressor`.
 
@@ -1000,7 +1021,7 @@ class _RandomForestRegressorParams(_RandomForestParams, _TreeRegressorParams):
 
 
 @inherit_doc
-class RandomForestRegressor(JavaPredictor, _RandomForestRegressorParams, JavaMLWritable,
+class RandomForestRegressor(JavaRegressor, _RandomForestRegressorParams, JavaMLWritable,
                             JavaMLReadable):
     """
     `Random Forest <http://en.wikipedia.org/wiki/Random_forest>`_
@@ -1198,8 +1219,10 @@ def setMinWeightFractionPerNode(self, value):
         return self._set(minWeightFractionPerNode=value)
 
 
-class RandomForestRegressionModel(_TreeEnsembleModel, _RandomForestRegressorParams,
-                                  JavaMLWritable, JavaMLReadable):
+class RandomForestRegressionModel(
+    JavaRegressionModel, _TreeEnsembleModel, _RandomForestRegressorParams,
+    JavaMLWritable, JavaMLReadable
+):
     """
     Model fitted by :class:`RandomForestRegressor`.
 
@@ -1251,7 +1274,7 @@ def getLossType(self):
 
 
 @inherit_doc
-class GBTRegressor(JavaPredictor, _GBTRegressorParams, JavaMLWritable, JavaMLReadable):
+class GBTRegressor(JavaRegressor, _GBTRegressorParams, JavaMLWritable, JavaMLReadable):
     """
     `Gradient-Boosted Trees (GBTs) <http://en.wikipedia.org/wiki/Gradient_boosting>`_
     learning algorithm for regression.
@@ -1492,7 +1515,10 @@ def setMinWeightFractionPerNode(self, value):
         return self._set(minWeightFractionPerNode=value)
 
 
-class GBTRegressionModel(_TreeEnsembleModel, _GBTRegressorParams, JavaMLWritable, JavaMLReadable):
+class GBTRegressionModel(
+    JavaRegressionModel, _TreeEnsembleModel, _GBTRegressorParams,
+    JavaMLWritable, JavaMLReadable
+):
     """
     Model fitted by :class:`GBTRegressor`.
 
@@ -1582,7 +1608,7 @@ def getQuantilesCol(self):
 
 
 @inherit_doc
-class AFTSurvivalRegression(JavaPredictor, _AFTSurvivalRegressionParams,
+class AFTSurvivalRegression(JavaRegressor, _AFTSurvivalRegressionParams,
                             JavaMLWritable, JavaMLReadable):
     """
     Accelerated Failure Time (AFT) Model Survival Regression
@@ -1723,7 +1749,7 @@ def setAggregationDepth(self, value):
         return self._set(aggregationDepth=value)
 
 
-class AFTSurvivalRegressionModel(JavaPredictionModel, _AFTSurvivalRegressionParams,
+class AFTSurvivalRegressionModel(JavaRegressionModel, _AFTSurvivalRegressionParams,
                                  JavaMLWritable, JavaMLReadable):
     """
     Model fitted by :class:`AFTSurvivalRegression`.
@@ -1855,7 +1881,7 @@ def getOffsetCol(self):
 
 
 @inherit_doc
-class GeneralizedLinearRegression(JavaPredictor, _GeneralizedLinearRegressionParams,
+class GeneralizedLinearRegression(JavaRegressor, _GeneralizedLinearRegressionParams,
                                   JavaMLWritable, JavaMLReadable):
     """
     Generalized Linear Regression.
@@ -2060,7 +2086,7 @@ def setAggregationDepth(self, value):
         return self._set(aggregationDepth=value)
 
 
-class GeneralizedLinearRegressionModel(JavaPredictionModel, _GeneralizedLinearRegressionParams,
+class GeneralizedLinearRegressionModel(JavaRegressionModel, _GeneralizedLinearRegressionParams,
                                        JavaMLWritable, JavaMLReadable, HasTrainingSummary):
     """
     Model fitted by :class:`GeneralizedLinearRegression`.
@@ -2348,7 +2374,7 @@ def getInitStd(self):
 
 
 @inherit_doc
-class FMRegressor(JavaPredictor, _FactorizationMachinesParams, JavaMLWritable, JavaMLReadable):
+class FMRegressor(JavaRegressor, _FactorizationMachinesParams, JavaMLWritable, JavaMLReadable):
     """
     Factorization Machines learning algorithm for regression.
 
@@ -2512,7 +2538,7 @@ def setRegParam(self, value):
         return self._set(regParam=value)
 
 
-class FMRegressionModel(JavaPredictionModel, _FactorizationMachinesParams, JavaMLWritable,
+class FMRegressionModel(JavaRegressionModel, _FactorizationMachinesParams, JavaMLWritable,
                         JavaMLReadable):
     """
     Model fitted by :class:`FMRegressor`.