fix searcher python_path

examples/axolotl_interface/run_pipeline.py

@@ -13,8 +13,8 @@ parser.add_argument('--table_path', type=str, default=default_data_path,
                     help='Input the path of the input data table')
 parser.add_argument('--target_index', type=int, default=6,
                     help='Index of the ground truth (for evaluation)')
-parser.add_argument('--metric',type=str, default='F1_MACRO',
-                    help='Evaluation Metric (F1, F1_MACRO)')
+parser.add_argument('--metric',type=str, default='ALL',
+                    help='Evaluation Metric (F1, F1_MACRO, RECALL, PRECISION, ALL)')
 parser.add_argument('--pipeline_path', 
                     default=os.path.join(this_path, './example_pipelines/autoencoder_pipeline.json'),
                     help='Input the path of the pre-built pipeline description')
@@ -35,6 +35,6 @@ pipeline = load_pipeline(pipeline_path)
 
 # Run the pipeline
 pipeline_result = evaluate_pipeline(dataset, pipeline, metric)
-print(pipeline_result)
+print(pipeline_result.scores)
 #raise pipeline_result.error[0]
 

examples/axolotl_interface/run_search.py

@@ -9,7 +9,7 @@ from tods.searcher import BruteForceSearch
 #table_path = 'datasets/NAB/realTweets/labeled_Twitter_volume_GOOG.csv' # The path of the dataset
 #target_index = 2 # what column is the target
 
-table_path = 'datasets/yahoo_sub_5.csv'
+table_path = '../../datasets/anomaly/raw_data/yahoo_sub_5.csv'
 target_index = 6 # what column is the target
 #table_path = 'datasets/NAB/realTweets/labeled_Twitter_volume_IBM.csv' # The path of the dataset
 time_limit = 30 # How many seconds you wanna search

tods/detection_algorithm/PyodAE.py

@@ -160,7 +160,7 @@ class Hyperparams(Hyperparams_ODBase):
     contamination = hyperparams.Uniform(
         lower=0.,
         upper=0.5,
-        default=0.1,
+        default=0.01,
         description='The amount of contamination of the data set, i.e. the proportion of outliers in the data set. ',
         semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter']
     )

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: column_parsr
+        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')