From 934b09fa663968396ad3878b64f545bdc0b9c107 Mon Sep 17 00:00:00 2001
From: Alejandro Moreo <alejandro.moreo@isti.cnr.it>
Date: Tue, 13 Jan 2026 14:36:20 +0100
Subject: [PATCH] bugfix

---
 BayesianKDEy/prior_effect.py | 45 +++++++++++++++++++++---------------
 1 file changed, 27 insertions(+), 18 deletions(-)

diff --git a/BayesianKDEy/prior_effect.py b/BayesianKDEy/prior_effect.py
index 524bdc6..8574bc4 100644
--- a/BayesianKDEy/prior_effect.py
+++ b/BayesianKDEy/prior_effect.py
@@ -1,4 +1,5 @@
 from collections import defaultdict
+import pandas as pd
 
 import model_selection
 import quapy as qp
@@ -33,15 +34,17 @@ def methods():
     kdey_hyper_clr = {'bandwidth': [0.05, 0.1, 0.5, 1., 2., 5.]}
 
     yield 'BayesianACC', ACC(LR()), acc_hyper, lambda hyper: BayesianCC(LR(), mcmc_seed=0, prior='uniform')
-    # yield f'BaKDE-Ait', KDEyCLR(LR()), kdey_hyper_clr, lambda hyper: BayesianKDEy(kernel='aitchison', mcmc_seed=0, engine='numpyro', temperature=None, prior='uniform', **hyper)
+    yield f'BaKDE-Ait', KDEyCLR(LR()), kdey_hyper_clr, lambda hyper: BayesianKDEy(kernel='aitchison', mcmc_seed=0, engine='numpyro', temperature=None, prior='uniform', **hyper)
 
 
-def run_test(test, alpha_test, alpha_train, concentration, prior_type, bay_quant, train_prev, results):
+def run_test(test, alpha_test, alpha_train, concentration, prior_type, bay_quant, train_prev, dataset_name, method_name, results):
     test_generator = DirichletProtocol(test, alpha=alpha_test, repeats=100, random_state=0)
     for i, (sample_X, true_prev) in tqdm(enumerate(test_generator()), total=test_generator.total(),
-                                               desc=f'{method_name} informative alpha with {concentration=}'):
+                                               desc=f'{method_name} {prior_type} alpha with {concentration=}'):
         estim_prev, region = bay_quant.predict_conf(sample_X)
 
+        results['dataset'].append(dataset_name)
+        results['method_name'].append(method_name)
         results['prior-type'].append(prior_type)
         results['train-prev'].append(train_prev)
         results['concentration'].append(concentration)
@@ -58,7 +61,15 @@ def run_test(test, alpha_test, alpha_train, concentration, prior_type, bay_quant
         results['samples'].append(region.samples)
 
 
-def experiment(dataset: Dataset, point_quantifier: AggregativeQuantifier, grid: dict, bay_constructor, hyper_choice_path: Path):
+
+def experiment(dataset: Dataset,
+               dataset_name: str,
+               point_quantifier: AggregativeQuantifier,
+               grid: dict,
+               bay_constructor,
+               method_name:str,
+               hyper_choice_path: Path):
+
     with qp.util.temp_seed(0):
 
         training, test = dataset.train_test
@@ -86,20 +97,14 @@ def experiment(dataset: Dataset, point_quantifier: AggregativeQuantifier, grid:
             # informative prior
             alpha_test_informative = alpha_train
             prior_type = 'informative'
-            run_test(test, alpha_test_informative, alpha_train, concentration, prior_type, bay_quant, train_prev, results)
+            run_test(test, alpha_test_informative, alpha_train, concentration, prior_type, bay_quant, train_prev, dataset_name, method_name, results)
 
             # informative prior
             alpha_test_wrong = antagonistic_prevalence(train_prev, strength=1) * concentration
             prior_type = 'wrong'
-            run_test(test, alpha_test_wrong, alpha_train, concentration, prior_type, bay_quant, train_prev, results)
+            run_test(test, alpha_test_wrong, alpha_train, concentration, prior_type, bay_quant, train_prev, dataset_name, method_name, results)
 
-        report = {
-            'optim_hyper': best_hyperparams,
-            'train-prev': train_prev,
-            'results': {k: np.asarray(v) for k, v in results.items()}
-        }
-
-        return report
+        return results
 
 
 if __name__ == '__main__':
@@ -107,6 +112,7 @@ if __name__ == '__main__':
     selected = select_imbalanced_datasets()
     print(f'selected datasets={selected}')
     qp.environ['SAMPLE_SIZE'] = multiclass['sample_size']
+    reports = []
     for data_name in selected:
         data = multiclass['fetch_fn'](data_name)
         for method_name, surrogate_quant, hyper_params, bay_constructor in methods():
@@ -114,8 +120,11 @@ if __name__ == '__main__':
             hyper_path = experiment_path(result_dir/'hyperparams', data_name, surrogate_quant.__class__.__name__)
 
             print(f'Launching {method_name} in dataset {data_name}')
-            experiment(dataset=data,
-                       point_quantifier=surrogate_quant,
-                       grid=hyper_params,
-                       bay_constructor=bay_constructor,
-                       hyper_choice_path=hyper_path)
+            report = qp.util.pickled_resource(
+                result_path, experiment, data, data_name, surrogate_quant, hyper_params, bay_constructor, method_name, hyper_path
+            )
+            reports.append(report)
+
+
+
+    # df = pd.DataFrame(results)
\ No newline at end of file