Added catch for predictors that do not support summary()

src/python/nimbusml/pipeline.py

@@ -2536,14 +2536,20 @@ def summary(self, verbose=0, **params):
                 "Model is not fitted. Train or load a model before "
                 "summary().")
 
+        # check if Field Aware FactorizationMachine exists in the steps
+        from .decomposition.factorizationmachinebinaryclassifier import FactorizationMachineBinaryClassifier
+        for step in self.steps:
+            if isinstance(step, FactorizationMachineBinaryClassifier):
+                raise TypeError("FieldAwareFactorizationMachine doesn't have summary function")
+
         # check last step is predictor in case there are steps in pipeline
         # importing here to break cycle import cycle dependency between
         # pipeline and base_predictor
         from .base_predictor import BasePredictor
         if len(self.steps) > 0 and not isinstance(
                 self.last_node, BasePredictor):
             raise ValueError(
-                "Summary is availabe only for predictor types, instead "
+                "Summary is available only for predictor types, instead "
                 "got " +
                 self.last_node.type)
 

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

@@ -96,7 +96,23 @@ def test_model_summary(self):
                 [OneHotVectorizer() << categorical_columns, learner])
             train_stream = FileDataStream(train_file, schema=file_schema)
             pipeline.fit(train_stream, label_column)
-            pipeline.summary()
+            # If Pipeline does contain FactorizationMachineBinaryClassifier,
+            # check for TypeError thrown by pipeline as 
+            # FactorizationMachineBinaryClassifier does not support summary()
+            # Else, Pipeline does not contain FactorizationMachineBinaryClassifier,
+            # thus check for summary
+            containsFactorizationMachineBinaryClassifier = False
+            for step in pipeline.steps:
+                if isinstance(step, FactorizationMachineBinaryClassifier):
+                    containsFactorizationMachineBinaryClassifier = True
+            if containsFactorizationMachineBinaryClassifier:
+                    try:
+                        pipeline.summary()
+                        assert False
+                    except TypeError:
+                        assert True
+            else:
+                pipeline.summary()
 
     @unittest.skip("No unsupported learners")
     def test_model_summary_not_supported(self):