fixed searcher

tods/searcher/brute_force_search.py

@@ -191,19 +191,19 @@ def _generate_pipline(combinations): # pragma: no cover
         
         # The first three steps are fixed
         # Step 0: dataset_to_dataframe
-        step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.dataset_to_dataframe.Common'))
+        step_0 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.dataset_to_dataframe'))
         step_0.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='inputs.0')
         step_0.add_output('produce')
         pipeline_description.add_step(step_0)
 
         # Step 1: column_parser
-        step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.column_parser.Common'))
+        step_1 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.column_parser'))
         step_1.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
         step_1.add_output('produce')
         pipeline_description.add_step(step_1)
 
         # Step 2: extract_columns_by_semantic_types(attributes)
-        step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common'))
+        step_2 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
         step_2.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
         step_2.add_output('produce')
         step_2.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
@@ -211,7 +211,7 @@ def _generate_pipline(combinations): # pragma: no cover
         pipeline_description.add_step(step_2)
 
         # Step 3: extract_columns_by_semantic_types(targets)
-        step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.extract_columns_by_semantic_types.Common'))
+        step_3 = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.extract_columns_by_semantic_types'))
         step_3.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.0.produce')
         step_3.add_output('produce')
         step_3.add_hyperparameter(name='semantic_types', argument_type=ArgumentType.VALUE,
@@ -243,7 +243,7 @@ def _generate_pipline(combinations): # pragma: no cover
         #pipeline_description.add_step(tods_step_7)
 
         # Finalize the pipeline
-        final_step = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.data_transformation.construct_predictions.Common'))
+        final_step = PrimitiveStep(primitive=index.get_primitive('d3m.primitives.tods.data_processing.construct_predictions'))
         final_step.add_argument(name='inputs', argument_type=ArgumentType.CONTAINER, data_reference='steps.6.produce')
         final_step.add_argument(name='reference', argument_type=ArgumentType.CONTAINER, data_reference='steps.1.produce')
         final_step.add_output('produce')
@@ -291,4 +291,4 @@ def _generate_pipelines(primitive_python_paths, cpu_count=40): # pragma: no cove
     #for p in results:
     #    piplines.extend(p.get())
 
-    return piplines
+    return piplines