with tables in pdf

KDEyAitchison/commons.py

@@ -1,11 +1,12 @@
 import numpy as np
 import pandas as pd
 
-from quapy.method.aggregative import EMQ, KDEyML
+from quapy.method.aggregative import EMQ, KDEyML, PACC
 from sklearn.linear_model import LogisticRegression
 
-METHODS = ['EMQ',
+METHODS = ['PACC',
-           'KDEy-ML',
+           'EMQ',
+           # 'KDEy-ML',
            'KDEy-MLA'
            ]
 
@@ -37,6 +38,9 @@ def new_method(method, **lr_kwargs):
     elif method == 'EMQ':
         param_grid = hyper_LR
         quantifier = EMQ(lr)
+    elif method == 'PACC':
+        param_grid = hyper_LR
+        quantifier = PACC(lr)
     else:
         raise NotImplementedError('unknown method', method)
 

KDEyAitchison/show_results.py

@@ -8,6 +8,7 @@ import quapy as qp
 from quapy.model_selection import GridSearchQ
 from quapy.protocol import UPP
 from commons import METHODS, new_method, show_results
+from new_table import LatexTable
 
 
 SEED = 1
@@ -16,6 +17,7 @@ SEED = 1
 
 if __name__ == '__main__':
     print(qp.datasets.UCI_MULTICLASS_DATASETS)
+    table = LatexTable()
     for optim in ['mae']:
         result_dir = f'results/ucimulti/{optim}'
 
@@ -29,6 +31,8 @@ if __name__ == '__main__':
                 if os.path.exists(local_result_path + '.dataframe'):
                     report = pd.read_csv(local_result_path+'.dataframe')
                     print(f'{method}\t{dataset}\t{report["mae"].mean():.5f}')
+                    table.add(benchmark=dataset, method=method, v=report["mae"].values)
                 else:
                     print(dataset, 'not found')
+    table.latexPDF('./tables/mae.pdf', landscape=False)