Merge pull request #57 from outbrain/silencio

Verbosity levels

outrank/__main__.py

@@ -183,13 +183,6 @@ def main():
         help="Which ';'-separated features should be one-hot encoded into n new features (coverage analysis)",
     )
 
-    parser.add_argument(
-        '--silent',
-        type=str,
-        default='False',
-        help='Suppress the logo and tips.',
-    )
-
     parser.add_argument(
         '--subfeature_mapping',
         type=str,
@@ -225,6 +218,14 @@ def main():
         help='Relevant for task data_generator -- name of the folder that contains generated data.',
     )
 
+    parser.add_argument(
+        '--disable_tqdm',
+        default='False',
+        choices=['False', 'True'],
+        help='Either True or False.',
+    )
+
+
     args = parser.parse_args()
 
     if args.task == 'selftest':

outrank/core_ranking.py

@@ -432,6 +432,7 @@ def compute_cardinalities(input_dataframe: pd.DataFrame, pbar: Any) -> None:
                 GLOBAL_CARDINALITY_STORAGE[column].add(
                     internal_hash(unique_value),
                 )
+
         pbar.set_description(
             f'Computing cardinality (Hyperloglog update) {enx}/{input_dataframe.shape[1]}',
         )
@@ -498,6 +499,7 @@ def compute_batch_ranking(
         input_dataframe = input_dataframe[list(focus_set)]
 
     if args.transformers != 'none':
+
         pbar.set_description('Adding transformations')
         input_dataframe = enrich_with_transformations(
             input_dataframe, numeric_column_types, logger, args,
@@ -628,7 +630,7 @@ def estimate_importances_minibatches(
 
     local_coverage_object = defaultdict(list)
     local_pbar = tqdm.tqdm(
-        total=get_num_of_instances(input_file) - 1, position=0,
+        total=get_num_of_instances(input_file) - 1, position=0, disable=args.disable_tqdm == 'True',
     )
 
     file_name, file_extension = os.path.splitext(input_file)

outrank/task_ranking.py

@@ -32,14 +32,12 @@
     )
 
 
-def outrank_task_conduct_ranking(args: Any):
+def outrank_task_conduct_ranking(args: Any) -> None:
     # Data source = folder structure + relevant file specifications
-
-    # No need for full-blown ranking in this case
     if args.task in ['identify_rare_values', 'feature_summary_transformers']:
         args.heuristic = 'Constant'
 
-    if args.silent != 'True':
+    if args.disable_tqdm == 'False':
         display_tool_name()
         display_random_tip()
 

setup.py

@@ -23,7 +23,7 @@ def _read_description():
 packages = [x for x in setuptools.find_packages() if x != 'test']
 setuptools.setup(
     name='outrank',
-    version='0.95.3',
+    version='0.95.4',
     description='OutRank: Feature ranking for massive sparse data sets.',
     long_description=_read_description(),
     long_description_content_type='text/markdown',

tests/ranking_module_test.py

@@ -33,6 +33,7 @@ class args:
     target_ranking_only: str = 'True'
     interaction_order: int = 3
     combination_number_upper_bound: int = 1024
+    disable_tqdm: bool = False
 
 
 class CompareStrategiesTest(unittest.TestCase):