lauching experiments

BayesianKDEy/full_experiments.py

@@ -48,14 +48,14 @@ from sklearn.base import clone
 
 def methods():
     acc_hyper = {}
-    hdy_hyper = {'n_bins': [3,4,5,8,16,32]}
+    hdy_hyper = {'nbins': [3,4,5,8,16,32]}
     kdey_hyper = {'bandwidth': [0.001, 0.005, 0.01, 0.05, 0.1, 0.2]}
     wrap_hyper = lambda dic: {f'quantifier__{k}':v for k,v in dic.items()}
     # yield 'BootstrapACC', AggregativeBootstrap(ACC(LR()), n_test_samples=1000, random_state=0), wrap_hyper(acc_hyper)
-    yield 'BootstrapHDy', AggregativeBootstrap(DMy(LR(), divergence='HD'), n_test_samples=1000, random_state=0), wrap_hyper(hdy_hyper)
+    # yield 'BootstrapHDy', AggregativeBootstrap(DMy(LR(), divergence='HD'), n_test_samples=1000, random_state=0), wrap_hyper(hdy_hyper)
-    #yield 'BootstrapKDEy', AggregativeBootstrap(KDEyML(LR()), n_test_samples=1000, random_state=0), wrap_hyper(kdey_hyper)
+    # yield 'BootstrapKDEy', AggregativeBootstrap(KDEyML(LR()), n_test_samples=1000, random_state=0), wrap_hyper(kdey_hyper)
     # yield 'BayesianACC', BayesianCC(LR(), mcmc_seed=0), acc_hyper
-    # yield 'BayesianHDy', PQ(LR(), stan_seed=0), hdy_hyper
+    yield 'BayesianHDy', PQ(LR(), stan_seed=0), hdy_hyper
     # yield 'BayesianKDEy', BayesianKDEy(LR(), mcmc_seed=0), kdey_hyper
 
 
@@ -137,6 +137,10 @@ if __name__ == '__main__':
     for setup in [binary, multiclass]:
         qp.environ['SAMPLE_SIZE'] = setup['sample_size']
         for data_name in setup['datasets']:
+            print(f'dataset={data_name}')
+            if data_name=='breast-cancer' or data_name.startswith("cmc") or data_name.startswith("ctg"):
+                print(f'skipping dataset: {data_name}')
+                continue
             data = setup['fetch_fn'](data_name)
             is_binary = data.n_classes==2
             result_subdir = result_dir / ('binary' if is_binary else 'multiclass')

quapy/method/_bayesian.py

@@ -1,6 +1,10 @@
 """
 Utility functions for `Bayesian quantification <https://arxiv.org/abs/2302.09159>`_ methods.
 """
+import contextlib
+import os
+import sys
+
 import numpy as np
 import importlib.resources
 
@@ -10,6 +14,8 @@ try:
     import numpyro
     import numpyro.distributions as dist
     import stan
+    import logging
+    import stan.common
 
     DEPENDENCIES_INSTALLED = True
 except ImportError:
@@ -86,6 +92,18 @@ def sample_posterior(
 def load_stan_file():
     return importlib.resources.files('quapy.method').joinpath('stan/pq.stan').read_text(encoding='utf-8')
 
+
+@contextlib.contextmanager
+def _suppress_stan_logging():
+    with open(os.devnull, "w") as devnull:
+        old_stderr = sys.stderr
+        sys.stderr = devnull
+        try:
+            yield
+        finally:
+            sys.stderr = old_stderr
+
+
 def pq_stan(stan_code, n_bins, pos_hist, neg_hist, test_hist, number_of_samples, num_warmup, stan_seed):
     """
     Perform Bayesian prevalence estimation using a Stan model for probabilistic quantification.
@@ -121,6 +139,8 @@ def pq_stan(stan_code, n_bins, pos_hist, neg_hist, test_hist, number_of_samples,
         Each element corresponds to one draw from the posterior distribution.
     """
 
+    logging.getLogger("stan.common").setLevel(logging.ERROR)
+
     stan_data = {
             'n_bucket': n_bins,
             'train_neg': neg_hist.tolist(),
@@ -129,6 +149,7 @@ def pq_stan(stan_code, n_bins, pos_hist, neg_hist, test_hist, number_of_samples,
             'posterior': 1
         }
 
+    with _suppress_stan_logging():
         stan_model = stan.build(stan_code, data=stan_data, random_seed=stan_seed)
         fit = stan_model.sample(num_chains=1, num_samples=number_of_samples,num_warmup=num_warmup)
 

quapy/method/confidence.py

@@ -642,6 +642,7 @@ class PQ(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
                               "Run `$ pip install quapy[bayes]` to install them.")
 
         super().__init__(classifier, fit_classifier, val_split)
+        
         self.nbins = nbins
         self.fixed_bins = fixed_bins
         self.num_warmup = num_warmup