forked from moreo/QuaPy
77 lines
3.1 KiB
Python
77 lines
3.1 KiB
Python
from ClassifierAccuracy.util.commons import *
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from ClassifierAccuracy.util.plotting import plot_diagonal
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PROBLEM = 'multiclass'
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ORACLE = False
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basedir = PROBLEM+('-oracle' if ORACLE else '')
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if PROBLEM == 'binary':
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qp.environ['SAMPLE_SIZE'] = 1000
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NUM_TEST = 1000
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gen_datasets = gen_bin_datasets
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elif PROBLEM == 'multiclass':
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qp.environ['SAMPLE_SIZE'] = 250
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NUM_TEST = 1000
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gen_datasets = gen_multi_datasets
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for (cls_name, h), (dataset_name, (L, V, U)) in itertools.product(gen_classifiers(), gen_datasets()):
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print(f'training {cls_name} in {dataset_name}')
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h.fit(*L.Xy)
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# test generation protocol
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test_prot = UPP(U, repeats=NUM_TEST, return_type='labelled_collection', random_state=0)
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# compute some stats of the dataset
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get_dataset_stats(f'dataset_stats/{dataset_name}.json', test_prot, L, V)
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# precompute the actual accuracy values
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true_accs = {}
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for acc_name, acc_fn in gen_acc_measure():
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true_accs[acc_name] = [true_acc(h, acc_fn, Ui) for Ui in test_prot()]
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# instances of ClassifierAccuracyPrediction are bound to the evaluation measure, so they
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# must be nested in the acc-for
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for acc_name, acc_fn in gen_acc_measure():
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print(f'\tfor measure {acc_name}')
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for (method_name, method) in gen_CAP(h, acc_fn, with_oracle=ORACLE):
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result_path = getpath(basedir, cls_name, acc_name, dataset_name, method_name)
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if os.path.exists(result_path):
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print(f'\t\t{method_name}-{acc_name} exists, skipping')
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continue
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print(f'\t\t{method_name} computing...')
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method, t_train = fit_method(method, V)
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estim_accs, t_test_ave = predictionsCAP(method, test_prot, ORACLE)
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save_json_result(result_path, true_accs[acc_name], estim_accs, t_train, t_test_ave)
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# instances of CAPContingencyTable instead are generic, and the evaluation measure can
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# be nested to the predictions to speed up things
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for (method_name, method) in gen_CAP_cont_table(h):
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if not any_missing(basedir, cls_name, dataset_name, method_name):
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print(f'\t\tmethod {method_name} has all results already computed. Skipping.')
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continue
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print(f'\t\tmethod {method_name} computing...')
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method, t_train = fit_method(method, V)
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estim_accs_dict, t_test_ave = predictionsCAPcont_table(method, test_prot, gen_acc_measure, ORACLE)
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for acc_name in estim_accs_dict.keys():
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result_path = getpath(basedir, cls_name, acc_name, dataset_name, method_name)
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save_json_result(result_path, true_accs[acc_name], estim_accs_dict[acc_name], t_train, t_test_ave)
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print()
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# generate diagonal plots
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print('generating plots')
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for (cls_name, _), (acc_name, _) in itertools.product(gen_classifiers(), gen_acc_measure()):
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plot_diagonal(basedir, cls_name, acc_name)
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for dataset_name, _ in gen_datasets(only_names=True):
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plot_diagonal(basedir, cls_name, acc_name, dataset_name=dataset_name)
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print('generating tables')
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gen_tables(basedir, datasets=[d for d,_ in gen_datasets(only_names=True)])
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