update create_model to allow user to specify included or excluded col… (googleapis#16)

* update create_model to allow user to specify included or excluded columns

* made minor changes stylistically and with added ValueError outputs

Author: jonathan1920

automl/google/cloud/automl_v1beta1/helper/tables.py

@@ -1043,8 +1043,8 @@ def list_models(self, project=None, region=None):
     def create_model(self, model_display_name, dataset=None,
             dataset_display_name=None, dataset_name=None,
             train_budget_milli_node_hours=None, project=None,
-            region=None):
-        """Create a model. This will train your model on the given dataset.
+            region=None, input_feature_column_specs_included=None, input_feature_column_specs_excluded=None):
+    	"""Create a model. This will train your model on the given dataset.
 
         Example:
             >>> from google.cloud import automl_v1beta1
@@ -1057,7 +1057,6 @@ def create_model(self, model_display_name, dataset=None,
             >>>
             >>> m.result() # blocks on result
             >>>
-
         Args:
             project (Optional[string]):
                 If you have initialized the client with a value for `project`
@@ -1085,11 +1084,15 @@ def create_model(self, model_display_name, dataset=None,
                 The `Dataset` instance you want to train your model on. This
                 must be supplied if `dataset_display_name` or `dataset_name`
                 are not supplied.
-
+            input_feature_column_specs_included(Optional[string]):
+            	The list of the names of the columns you want to include to train 
+            	your model on.
+            input_feature_column_specs_excluded(Optional[string]):
+            	The list of the names of the columns you want to exclude and 
+            	not train your model on.
         Returns:
             A :class:`~google.cloud.automl_v1beta1.types._OperationFuture`
             instance.
-
         Raises:
             google.api_core.exceptions.GoogleAPICallError: If the request
                 failed for any reason.
@@ -1101,26 +1104,56 @@ def create_model(self, model_display_name, dataset=None,
             raise ValueError('\'train_budget_milli_node_hours\' must be a '
                     'value between 1,000 and 72,000 inclusive')
 
+        if input_feature_column_specs_excluded not in [None, []] and input_feature_column_specs_included not in [None, []]:
+            raise ValueError('\'cannot set both input_feature_column_specs_excluded\' and '
+                    '\'input_feature_column_specs_included\'')
+
+
         dataset_name = self.__dataset_name_from_args(dataset=dataset,
                 dataset_name=dataset_name,
                 dataset_display_name=dataset_display_name,
                 project=project,
                 region=region)
-
+        tables_model_metadata = {
+                'train_budget_milli_node_hours': train_budget_milli_node_hours
+        }
         dataset_id = dataset_name.rsplit('/', 1)[-1]
+        columns = [s for s in self.list_column_specs(dataset=dataset, dataset_name = dataset_name, dataset_display_name=dataset_display_name)]
+
+        final_columns = []
+        if input_feature_column_specs_included:
+        	column_names = [a.display_name for a in columns]
+        	if not (all (name in column_names for name in input_feature_column_specs_included)):
+        		raise ValueError('invalid name in the list' '\'input_feature_column_specs_included\'')
+            for a in columns:
+                if a.display_name in input_feature_column_specs_included:
+                    final_columns.append(a)
+
+            tables_model_metadata.update(
+                {'input_feature_column_specs': final_columns}
+            )
+        elif input_feature_column_specs_excluded:
+            for a in columns:
+                if a.display_name not in input_feature_column_specs_excluded:
+                    final_columns.append(a)
+
+            tables_model_metadata.update(
+                 {'input_feature_column_specs': final_columns}
+            )
+
         request = {
                 'display_name': model_display_name,
                 'dataset_id': dataset_id,
-                'tables_model_metadata': {
-                    'train_budget_milli_node_hours': train_budget_milli_node_hours
-                }
+                'tables_model_metadata': tables_model_metadata
         }
 
+
         return self.client.create_model(
                 self.__location_path(project=project, region=region),
                 request
         )
 
+
     def delete_model(self, model=None, model_display_name=None,
             model_name=None, project=None, region=None):
         """Deletes a model. Note this will not delete any datasets associated