Remove summary validation in Pipeline and enable the summary tests for the tree based predictors.

src/python/nimbusml/pipeline.py

@@ -2409,54 +2409,13 @@ def summary(self, verbose=0, **params):
             self._run_time = time.time() - start_time
             raise e
 
-        self._validate_model_summary(summary_data)
         self.model_summary = summary_data
 
         # stop the clock
         self._run_time = time.time() - start_time
         self._write_csv_time = graph._write_csv_time
         return self.model_summary
 
-    @trace
-    def _validate_model_summary(self, model_summary):
-        """
-        Validates model summary has correct format
-
-        :param model_summary: model summary dataframes
-
-        """
-        if not isinstance(model_summary, (DataFrame)):
-            raise TypeError(
-                "Unexpected type {0} for model_summary, type DataFrame "
-                "is expected ".format(
-                    type(model_summary)))
-
-        col_names = [
-            'Bias',
-            'ClassNames',
-            'Coefficients',
-            'PredictorName',
-            'Summary',
-            'VectorName'
-        ]
-
-        col_name_prefixes = [
-            'Weights',
-            'Gains',
-            'Support vectors.',
-            'VectorData'
-        ]
-
-        for col in model_summary.columns:
-            if col in col_names:
-                pass
-            elif any([col.startswith(pre) for pre in col_name_prefixes]):
-                pass
-            else:
-                raise TypeError(
-                    "Unsupported '{0}' column is in model_summary".format(
-                        col))
-
     @trace
     def save_model(self, dst):
         """

src/python/nimbusml/tests/model_summary/test_model_summary.py

@@ -67,25 +67,20 @@
     OrdinaryLeastSquaresRegressor(),
     PoissonRegressionRegressor(),
     OneVsRestClassifier(FastLinearBinaryClassifier()),
-    LightGbmClassifier(),
     GamRegressor(),
     GamBinaryClassifier(),
     PcaAnomalyDetector(),
     FactorizationMachineBinaryClassifier(),
     KMeansPlusPlus(),
-    NaiveBayesClassifier()
-
-    # Skipping these tests since they are throwing the following error:
-    #   *** System.NotSupportedException: 'Column has variable length
-    #   vector: CategoricalSplitFeatures. Not supported in python.
-    #   Drop column before sending to Python
-    #FastForestBinaryClassifier(),
-    #FastForestRegressor(),
-    #FastTreesBinaryClassifier(),
-    #FastTreesRegressor(),
-    #FastTreesTweedieRegressor(),
-    #LightGbmRegressor(),
-    #LightGbmBinaryClassifier(),
+    NaiveBayesClassifier(),
+    FastForestBinaryClassifier(number_of_trees=2), 
+    FastForestRegressor(number_of_trees=2),
+    FastTreesBinaryClassifier(number_of_trees=2),
+    FastTreesRegressor(number_of_trees=2),
+    FastTreesTweedieRegressor(number_of_trees=2),
+    LightGbmRegressor(number_of_iterations=2),
+    LightGbmClassifier(),
+    LightGbmBinaryClassifier(number_of_iterations=2)
 ]
 
 learners_not_supported = [