[SPARK-15113][PYSPARK][ML] Add missing num features num classes

## What changes were proposed in this pull request?

Add missing `numFeatures` and `numClasses` to the wrapped Java models in PySpark ML pipelines. Also tag `DecisionTreeClassificationModel` as Expiremental to match Scala doc.

## How was this patch tested?

Extended doctests

Author: Holden Karau <holden@us.ibm.com>

Closes #12889 from holdenk/SPARK-15113-add-missing-numFeatures-numClasses.

1 files changed, 17 insertions, 5 deletions

diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py
index 1ae2bd4e40..56312f672f 100644
--- a/python/pyspark/ml/regression.py
+++ b/python/pyspark/ml/regression.py
@@ -88,6 +88,8 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction
     True
     >>> model.intercept == model2.intercept
     True
+    >>> model.numFeatures
+    1
 
     .. versionadded:: 1.4.0
     """
@@ -126,7 +128,7 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction
         return LinearRegressionModel(java_model)
 
 
-class LinearRegressionModel(JavaModel, JavaMLWritable, JavaMLReadable):
+class LinearRegressionModel(JavaModel, JavaPredictionModel, JavaMLWritable, JavaMLReadable):
     """
     Model fitted by :class:`LinearRegression`.
 
@@ -654,6 +656,8 @@ class DecisionTreeRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
     3
     >>> model.featureImportances
     SparseVector(1, {0: 1.0})
+    >>> model.numFeatures
+    1
     >>> test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
     >>> model.transform(test0).head().prediction
     0.0
@@ -719,7 +723,7 @@ class DecisionTreeRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
 
 
 @inherit_doc
-class DecisionTreeModel(JavaModel):
+class DecisionTreeModel(JavaModel, JavaPredictionModel):
     """
     Abstraction for Decision Tree models.
 
@@ -843,6 +847,8 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
     >>> test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
     >>> model.transform(test0).head().prediction
     0.0
+    >>> model.numFeatures
+    1
     >>> model.trees
     [DecisionTreeRegressionModel (uid=...) of depth..., DecisionTreeRegressionModel...]
     >>> model.getNumTrees
@@ -909,7 +915,8 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
         return RandomForestRegressionModel(java_model)
 
 
-class RandomForestRegressionModel(TreeEnsembleModel, JavaMLWritable, JavaMLReadable):
+class RandomForestRegressionModel(TreeEnsembleModel, JavaPredictionModel, JavaMLWritable,
+                                  JavaMLReadable):
     """
     Model fitted by :class:`RandomForestRegressor`.
 
@@ -958,6 +965,8 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
     >>> model = gbt.fit(df)
     >>> model.featureImportances
     SparseVector(1, {0: 1.0})
+    >>> model.numFeatures
+    1
     >>> allclose(model.treeWeights, [1.0, 0.1, 0.1, 0.1, 0.1])
     True
     >>> test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
@@ -1047,7 +1056,7 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
         return self.getOrDefault(self.lossType)
 
 
-class GBTRegressionModel(TreeEnsembleModel, JavaMLWritable, JavaMLReadable):
+class GBTRegressionModel(TreeEnsembleModel, JavaPredictionModel, JavaMLWritable, JavaMLReadable):
     """
     Model fitted by :class:`GBTRegressor`.
 
@@ -1307,6 +1316,8 @@ class GeneralizedLinearRegression(JavaEstimator, HasLabelCol, HasFeaturesCol, Ha
     True
     >>> model.coefficients
     DenseVector([1.5..., -1.0...])
+    >>> model.numFeatures
+    2
     >>> abs(model.intercept - 1.5) < 0.001
     True
     >>> glr_path = temp_path + "/glr"
@@ -1412,7 +1423,8 @@ class GeneralizedLinearRegression(JavaEstimator, HasLabelCol, HasFeaturesCol, Ha
         return self.getOrDefault(self.link)
 
 
-class GeneralizedLinearRegressionModel(JavaModel, JavaMLWritable, JavaMLReadable):
+class GeneralizedLinearRegressionModel(JavaModel, JavaPredictionModel, JavaMLWritable,
+                                       JavaMLReadable):
     """
     .. note:: Experimental