Bumped Runner call to updated version

examples/gridsearch_crossval.py

@@ -13,9 +13,12 @@
 from sklearn.preprocessing import MinMaxScaler
 
 from qualitylib.tools import import_python_file, read_dataset
+from qualitylib.feature_extractor import get_fex
 from qualitylib.runner import Runner
 from qualitylib.cross_validate import random_cross_validation
 
+from feature_extractors.ssim_fex import SsimFeatureExtractor  # Import feature extractor(s) to make visible to get_fex
+
 np.random.seed(0)
 
 
@@ -53,7 +56,7 @@ def print_agg_stats(stats: Dict[str, Any]) -> None:
     print('Stat,Median,LoCI,HiCI,Std')  # Not using spaces makes parsing text output as csv easier
     for stat_key in stats[max_param_key][0]:
         key_stats = np.array([stat[stat_key] for stat in stats[max_param_key]])
-        print(f'{stat_key},{np.median(key_stats)},{np.percentile(key_stats, lo_ci)},{np.percentile(key_stats, hi_ci)},{np.std(key_stats)}')
+        print(f'{stat_key},{np.median(key_stats):.4f},{np.percentile(key_stats, lo_ci):.4f},{np.percentile(key_stats, hi_ci):.4f},{np.std(key_stats):.4f}')
 
 
 def dict_to_str(d: Dict[Any, Any]) -> str:
@@ -68,7 +71,6 @@ def get_parser() -> argparse.ArgumentParser:
     parser.add_argument('--dataset', help='Path to dataset file for which to extract features', type=str)
     parser.add_argument('--fex_name', help='Name of feature extractor', type=str)
     parser.add_argument('--fex_version', help='Version of feature extractor', type=str, default=None)
-    parser.add_argument('--feat_file', help='Path to csv containing features', type=str, required=True)
     parser.add_argument('--feat_names_file', help='Path to file containing feature sets', type=str, default=None)
     parser.add_argument('--regressor', help='Regressor to use', type=str, default='RandomForest')
     parser.add_argument('--splits', help='Number of parallel processes', type=int, default=100)
@@ -85,7 +87,8 @@ def main() -> None:
 
     dataset = import_python_file(args.dataset)
     assets = read_dataset(dataset, shuffle=True)
-    runner = Runner(args.fex_name, args.fex_version, processes=args.processes, use_cache=True)  # Reads from stored results if available, else stores results.
+    FexClass = get_fex(args.fex_name, args.fex_version)
+    runner = Runner(FexClass, processes=args.processes, use_cache=True)  # Reads from stored results if available, else stores results.
 
     if args.feat_names_file is not None:
         mod = import_python_file(args.feat_names_file)
@@ -108,15 +111,15 @@ def main() -> None:
 
     res_dict = {}
     for key in feat_names_dict:
-        results = runner(assets, return_results=True, feat_names=np.array(feat_names_dict[key]))  # Extract features and return only specified features for cross-validation.
+        results = runner(assets, return_results=True, feat_names=np.array(feat_names_dict[key]) if feat_names_dict[key] is not None else None)  # Extract features and return only specified features for cross-validation.
         start_time = time.time()
         temp_res_dict = {}
         for model_param_dict in model_params:
             agg_stats = random_cross_validation(partial(ModelClass, **model_param_dict), results, splits=args.splits, test_fraction=0.2, processes=args.processes)
             temp_res_dict[dict_to_str(model_param_dict)] = agg_stats['stats']  # Metrics computed from each split.
             print(f'Tested params: {model_param_dict}. Time elapsed {((time.time() - start_time)/60):.2f} minutes.')
         print(f'Results - {key}')
-        print_agg_stats(res_dict)
+        print_agg_stats(temp_res_dict)
         res_dict[key] = temp_res_dict
 
     with open(args.out_file, 'wb') as out_file: