[SPARK-28985][PYTHON][ML][FOLLOW-UP] Add _IsotonicRegressionBase

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

Adds

```python
class _IsotonicRegressionBase(HasFeaturesCol, HasLabelCol, HasPredictionCol, HasWeightCol): ...
```

with related `Params` and uses it to replace `JavaPredictor` and `HasWeightCol` in `IsotonicRegression` base classes and `JavaPredictionModel,` in `IsotonicRegressionModel` base classes.

### Why are the changes needed?

Previous work (apache#25776) on [SPARK-28985](https://issues.apache.org/jira/browse/SPARK-28985) replaced `JavaEstimator`, `HasFeaturesCol`, `HasLabelCol`, `HasPredictionCol` in `IsotonicRegression` and `JavaModel` in `IsotonicRegressionModel` with newly added `JavaPredictor`:

https://github.com/apache/spark/blob/e97b55d32285052a1f76cca35377c4b21eb2e7d7/python/pyspark/ml/wrapper.py#L377

and `JavaPredictionModel`

https://github.com/apache/spark/blob/e97b55d32285052a1f76cca35377c4b21eb2e7d7/python/pyspark/ml/wrapper.py#L405

respectively.

This however is inconsistent with Scala counterpart where both  classes extend private `IsotonicRegressionBase`

https://github.com/apache/spark/blob/3cb1b57809d0b4a93223669f5c10cea8fc53eff6/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala#L42-L43

This preserves some of the existing inconsistencies (`model` as defined in [the official example](https://github.com/apache/spark/blob/master/examples/src/main/python/ml/isotonic_regression_example.py)), i.e.

```python
from pyspark.ml.regression impor IsotonicRegressionMode
from pyspark.ml.param.shared import HasWeightCol

issubclass(IsotonicRegressionModel, HasWeightCol)
# False

hasattr(model, "weightCol")
# True
```

as well as introduces a bug, by adding unsupported `predict` method:

```python
import inspect

hasattr(model, "predict")
# True

inspect.getfullargspec(IsotonicRegressionModel.predict)
# FullArgSpec(args=['self', 'value'], varargs=None, varkw=None, defaults=None, kwonlyargs=[], kwonlydefaults=None, annotations={})

IsotonicRegressionModel.predict.__doc__
# Predict label for the given features.\n\n        .. versionadded:: 3.0.0'

model.predict(dataset.first().features)

# Py4JError: An error occurred while calling o49.predict. Trace:
# py4j.Py4JException: Method predict([class org.apache.spark.ml.linalg.SparseVector]) does not exist
# ...

```

Furthermore existing implementation can cause further problems in the future, if `Predictor` / `PredictionModel` API changes.

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

Yes. It:

- Removes invalid `IsotonicRegressionModel.predict` method.
- Adds `HasWeightColumn` to `IsotonicRegressionModel`.

however the faulty implementation hasn't been released yet, and proposed additions have negligible potential for breaking existing code (and none, compared to changes already made in apache#25776).

### How was this patch tested?

- Existing unit tests.
- Manual testing.

CC huaxingao, zhengruifeng

Closes apache#26023 from zero323/SPARK-28985-FOLLOW-UP-isotonic-regression.

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

Author: zero323

Diff for: python/pyspark/ml/regression.py

@@ -465,8 +465,38 @@ def totalIterations(self):
         return self._call_java("totalIterations")
 
 
+class _IsotonicRegressionBase(HasFeaturesCol, HasLabelCol, HasPredictionCol, HasWeightCol):
+    """
+    Params for :py:class:`IsotonicRegression` and :py:class:`IsotonicRegressionModel`.
+
+    .. versionadded:: 3.0.0
+    """
+
+    isotonic = Param(
+        Params._dummy(), "isotonic",
+        "whether the output sequence should be isotonic/increasing (true) or" +
+        "antitonic/decreasing (false).", typeConverter=TypeConverters.toBoolean)
+    featureIndex = Param(
+        Params._dummy(), "featureIndex",
+        "The index of the feature if featuresCol is a vector column, no effect otherwise.",
+        typeConverter=TypeConverters.toInt)
+
+    def getIsotonic(self):
+        """
+        Gets the value of isotonic or its default value.
+        """
+        return self.getOrDefault(self.isotonic)
+
+    def getFeatureIndex(self):
+        """
+        Gets the value of featureIndex or its default value.
+        """
+        return self.getOrDefault(self.featureIndex)
+
+
 @inherit_doc
-class IsotonicRegression(JavaPredictor, HasWeightCol, JavaMLWritable, JavaMLReadable):
+class IsotonicRegression(JavaEstimator, _IsotonicRegressionBase, HasWeightCol,
+                         JavaMLWritable, JavaMLReadable):
     """
     Currently implemented using parallelized pool adjacent violators algorithm.
     Only univariate (single feature) algorithm supported.
@@ -503,16 +533,6 @@ class IsotonicRegression(JavaPredictor, HasWeightCol, JavaMLWritable, JavaMLRead
 
     .. versionadded:: 1.6.0
     """
-
-    isotonic = \
-        Param(Params._dummy(), "isotonic",
-              "whether the output sequence should be isotonic/increasing (true) or" +
-              "antitonic/decreasing (false).", typeConverter=TypeConverters.toBoolean)
-    featureIndex = \
-        Param(Params._dummy(), "featureIndex",
-              "The index of the feature if featuresCol is a vector column, no effect otherwise.",
-              typeConverter=TypeConverters.toInt)
-
     @keyword_only
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  weightCol=None, isotonic=True, featureIndex=0):
@@ -547,26 +567,15 @@ def setIsotonic(self, value):
         """
         return self._set(isotonic=value)
 
-    def getIsotonic(self):
-        """
-        Gets the value of isotonic or its default value.
-        """
-        return self.getOrDefault(self.isotonic)
-
     def setFeatureIndex(self, value):
         """
         Sets the value of :py:attr:`featureIndex`.
         """
         return self._set(featureIndex=value)
 
-    def getFeatureIndex(self):
-        """
-        Gets the value of featureIndex or its default value.
-        """
-        return self.getOrDefault(self.featureIndex)
-
 
-class IsotonicRegressionModel(JavaPredictionModel, JavaMLWritable, JavaMLReadable):
+class IsotonicRegressionModel(JavaModel, _IsotonicRegressionBase,
+                              JavaMLWritable, JavaMLReadable):
     """
     Model fitted by :class:`IsotonicRegression`.