renaming main

ClassifierAccuracy/main.py

@@ -1,67 +0,0 @@
-from collections import defaultdict
-from time import time
-from utils import *
-from models_multiclass import *
-from quapy.protocol import UPP
-from commons import *
-
-
-qp.environ['SAMPLE_SIZE'] = 250
-NUM_TEST = 100
-
-for cls_name, h in gen_classifiers():
-    print(cls_name)
-
-    acc_trues = defaultdict(lambda : [])  # acc_name : list of results
-    acc_predicted = defaultdict(lambda : defaultdict(lambda : [])) # acc_name : method_name : list of results
-
-    for dataset_name, (L, V, U) in gen_datasets():
-        print(dataset_name)
-
-        h.fit(*L.Xy)
-
-        # test generation protocol
-        test_prot = UPP(U, repeats=NUM_TEST, return_type='labelled_collection', random_state=0)
-
-        # compute some stats of the dataset
-        get_dataset_stats(f'dataset_stats/{dataset_name}.json', test_prot, L, V)
-
-        # precompute the actual accuracy values
-        dataset_true_accs = {}
-        for acc_name, acc_fn in gen_acc_measure():
-            dataset_true_accs[acc_name] = [true_acc(h, acc_fn, Ui) for Ui in test_prot()]
-            acc_trues[acc_name].extend(dataset_true_accs[acc_name])
-
-
-        for method_name, method in gen_CAP(h, vanilla_acc_fn):
-            print('PARCHEADO con vanilla accuracy')
-            # training
-            tinit = time()
-            method.fit(V)
-            t_train = time()-tinit
-
-            # predictions
-            dataset_method_accs, t_test_ave = get_method_predictions(method, test_prot, gen_acc_measure)
-
-            # accumulate results across datasets
-            for acc_name, _ in gen_acc_measure():
-                acc_predicted[acc_name][method_name].extend(dataset_method_accs[acc_name])
-
-                print(f'\t{method_name} took train={t_train:.2f}s test(ave)={t_test_ave:.2f}s')
-                result = {
-                    't_train': t_train,
-                    't_test_ave': t_test_ave,
-                    'true_acc': dataset_true_accs[acc_name],
-                    'estim_acc': dataset_method_accs[acc_name]
-                }
-                save_json_file(f"results/{cls_name}/{acc_name}/{dataset_name}/{method_name}.json", result)
-
-    for acc_name, _ in gen_acc_measure():
-        acc_predicted_ = list(acc_predicted[acc_name].items())
-        plot_diagonal(cls_name, acc_name, acc_trues[acc_name], acc_predicted_)
-
-gen_tables()
-
-
-
-