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adding tables generation

This commit is contained in:
Alejandro Moreo Fernandez 2021-08-27 13:57:26 +02:00
parent db1dbe2534
commit aeb0fcf84b
2 changed files with 15 additions and 9 deletions
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18
MultiLabel/main.py
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@ -41,8 +41,8 @@ def models():
# yield 'NaivePCC', MultilabelNaiveAggregativeQuantifier(PCC(cls()))
# yield 'NaiveACC', MultilabelNaiveAggregativeQuantifier(ACC(cls()))
# yield 'NaivePACC', MultilabelNaiveAggregativeQuantifier(PACC(cls()))
# yield 'HDy', MultilabelNaiveAggregativeQuantifier(HDy(cls()))
# yield 'EMQ', MultilabelQuantifier(EMQ(calibratedCls()))
# yield 'NaiveHDy', MultilabelNaiveAggregativeQuantifier(HDy(cls()))
# yield 'NaiveSLD', MultilabelNaiveAggregativeQuantifier(EMQ(calibratedCls()))
# yield 'StackCC', MLCC(MultilabelStackedClassifier(cls()))
# yield 'StackPCC', MLPCC(MultilabelStackedClassifier(cls()))
# yield 'StackACC', MLACC(MultilabelStackedClassifier(cls()))
@ -159,10 +159,14 @@ def load_results(result_path):
estim_prevs = [np.vstack([estim_i, 1 - estim_i]).T for estim_i in estim_prevs] # add the constrained prevalence
return true_prevs, estim_prevs
results = pickle.load(open(result_path, 'rb'))
results_npp = _unpack_result_lot(results['npp'])
results_app = _unpack_result_lot(results['app'])
return results_npp, results_app
results = {
'npp': _unpack_result_lot(results['npp']),
'app': _unpack_result_lot(results['app']),
}
return results
# results_npp = _unpack_result_lot(results['npp'])
# results_app = _unpack_result_lot(results['app'])
# return results_npp, results_app
def run_experiment(dataset_name, model_name, model):
@ -197,3 +201,5 @@ if __name__ == '__main__':

6
MultiLabel/mlevaluation.py
View File

@ -8,7 +8,7 @@ import itertools
from tqdm import tqdm
def __check_error(error_metric):
def check_error_str(error_metric):
if isinstance(error_metric, str):
error_metric = qp.error.from_name(error_metric)
@ -49,7 +49,7 @@ def ml_natural_prevalence_evaluation(model,
error_metric:Union[str,Callable]='mae',
random_seed=42):
error_metric = __check_error(error_metric)
error_metric = check_error_str(error_metric)
true_prevs, estim_prevs = ml_natural_prevalence_prediction(model, test, sample_size, repeats, random_seed)
@ -88,7 +88,7 @@ def ml_artificial_prevalence_evaluation(model,
error_metric:Union[str,Callable]='mae',
random_seed=42):
error_metric = __check_error(error_metric)
error_metric = check_error_str(error_metric)
true_prevs, estim_prevs = ml_artificial_prevalence_prediction(model, test, sample_size, n_prevalences, repeats, random_seed)