[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, 31 insertions, 6 deletions

diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 6468007045..33ada27454 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -44,6 +44,23 @@ __all__ = ['LogisticRegression', 'LogisticRegressionModel',
 
 
 @inherit_doc
+class JavaClassificationModel(JavaPredictionModel):
+    """
+    (Private) Java Model produced by a ``Classifier``.
+    Classes are indexed {0, 1, ..., numClasses - 1}.
+    To be mixed in with class:`pyspark.ml.JavaModel`
+    """
+
+    @property
+    @since("2.1.0")
+    def numClasses(self):
+        """
+        Number of classes (values which the label can take).
+        """
+        return self._call_java("numClasses")
+
+
+@inherit_doc
 class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
                          HasRegParam, HasTol, HasProbabilityCol, HasRawPredictionCol,
                          HasElasticNetParam, HasFitIntercept, HasStandardization, HasThresholds,
@@ -212,7 +229,7 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
                                  " threshold (%g) and thresholds (equivalent to %g)" % (t2, t))
 
 
-class LogisticRegressionModel(JavaModel, JavaMLWritable, JavaMLReadable):
+class LogisticRegressionModel(JavaModel, JavaClassificationModel, JavaMLWritable, JavaMLReadable):
     """
     Model fitted by LogisticRegression.
 
@@ -522,6 +539,10 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
     1
     >>> model.featureImportances
     SparseVector(1, {0: 1.0})
+    >>> model.numFeatures
+    1
+    >>> model.numClasses
+    2
     >>> print(model.toDebugString)
     DecisionTreeClassificationModel (uid=...) of depth 1 with 3 nodes...
     >>> test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
@@ -595,7 +616,8 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
 
 
 @inherit_doc
-class DecisionTreeClassificationModel(DecisionTreeModel, JavaMLWritable, JavaMLReadable):
+class DecisionTreeClassificationModel(DecisionTreeModel, JavaClassificationModel, JavaMLWritable,
+                                      JavaMLReadable):
     """
     Model fitted by DecisionTreeClassifier.
 
@@ -722,7 +744,8 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
         return RandomForestClassificationModel(java_model)
 
 
-class RandomForestClassificationModel(TreeEnsembleModel, JavaMLWritable, JavaMLReadable):
+class RandomForestClassificationModel(TreeEnsembleModel, JavaClassificationModel, JavaMLWritable,
+                                      JavaMLReadable):
     """
     Model fitted by RandomForestClassifier.
 
@@ -873,7 +896,8 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol
         return self.getOrDefault(self.lossType)
 
 
-class GBTClassificationModel(TreeEnsembleModel, JavaMLWritable, JavaMLReadable):
+class GBTClassificationModel(TreeEnsembleModel, JavaPredictionModel, JavaMLWritable,
+                             JavaMLReadable):
     """
     Model fitted by GBTClassifier.
 
@@ -1027,7 +1051,7 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, H
         return self.getOrDefault(self.modelType)
 
 
-class NaiveBayesModel(JavaModel, JavaMLWritable, JavaMLReadable):
+class NaiveBayesModel(JavaModel, JavaClassificationModel, JavaMLWritable, JavaMLReadable):
     """
     Model fitted by NaiveBayes.
 
@@ -1226,7 +1250,8 @@ class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol,
         return self.getOrDefault(self.initialWeights)
 
 
-class MultilayerPerceptronClassificationModel(JavaModel, JavaMLWritable, JavaMLReadable):
+class MultilayerPerceptronClassificationModel(JavaModel, JavaPredictionModel, JavaMLWritable,
+                                              JavaMLReadable):
     """
     .. note:: Experimental