binary experiments

distribution_matching/lequa_experiments.py

@@ -18,7 +18,7 @@ if __name__ == '__main__':
     qp.environ['SAMPLE_SIZE'] = qp.datasets.LEQUA2022_SAMPLE_SIZE['T1B']
     qp.environ['N_JOBS'] = -1
     optim = 'mrae'
-    result_dir = f'results_lequa_{optim}'
+    result_dir = f'results/results_lequa_{optim}'
 
     os.makedirs(result_dir, exist_ok=True)
 

distribution_matching/show_results.py

@@ -2,7 +2,7 @@ import sys
 from pathlib import Path
 import pandas as pd
 
-result_dir = 'results_tweet_mae_redohyper'
+result_dir = 'results/results_tweet_mae_redohyper'
 #result_dir = 'results_lequa_mrae'
 
 dfs = []

distribution_matching/tweets_experiments.py

@@ -21,7 +21,7 @@ if __name__ == '__main__':
     n_bags_val = 250
     n_bags_test = 1000
     optim = 'mae'
-    result_dir = f'results_tweet_{optim}_redohyper'
+    result_dir = f'results/results_tweet_{optim}_redohyper'
 
     os.makedirs(result_dir, exist_ok=True)
 

quapy/data/base.py

@@ -319,7 +319,8 @@ class LabelledCollection:
         else:
             raise NotImplementedError('unsupported operation for collection types')
         labels = np.concatenate([lc.labels for lc in args])
-        classes = np.unique(labels).sort()
+        classes = np.unique(labels)
+        classes.sort()
         return LabelledCollection(instances, labels, classes=classes)
 
     @property

quapy/data/datasets.py

@@ -204,7 +204,7 @@ def fetch_UCIDataset(dataset_name, data_home=None, test_split=0.3, verbose=False
     :return: a :class:`quapy.data.base.Dataset` instance
     """
     data = fetch_UCILabelledCollection(dataset_name, data_home, verbose)
-    return Dataset(*data.split_stratified(1 - test_split, random_state=0))
+    return Dataset(*data.split_stratified(1 - test_split, random_state=0), name=dataset_name)
 
 
 def fetch_UCILabelledCollection(dataset_name, data_home=None, verbose=False) -> LabelledCollection: