forked from moreo/QuaPy
88 lines
3.3 KiB
Python
88 lines
3.3 KiB
Python
import pickle
|
|
import os
|
|
|
|
from sklearn.linear_model import LogisticRegression
|
|
|
|
from distribution_matching.commons import METHODS, new_method, show_results
|
|
|
|
import quapy as qp
|
|
from quapy.model_selection import GridSearchQ
|
|
from quapy.protocol import UPP
|
|
|
|
|
|
SEED = 1
|
|
|
|
|
|
if __name__ == '__main__':
|
|
|
|
qp.environ['SAMPLE_SIZE'] = 500
|
|
qp.environ['N_JOBS'] = -1
|
|
n_bags_val = 250
|
|
n_bags_test = 1000
|
|
for optim in ['mae', 'mrae']:
|
|
result_dir = f'results/ucimulti/{optim}'
|
|
|
|
os.makedirs(result_dir, exist_ok=True)
|
|
|
|
for method in METHODS:
|
|
|
|
print('Init method', method)
|
|
|
|
global_result_path = f'{result_dir}/{method}'
|
|
|
|
if not os.path.exists(global_result_path + '.csv'):
|
|
with open(global_result_path + '.csv', 'wt') as csv:
|
|
csv.write(f'Method\tDataset\tMAE\tMRAE\tKLD\n')
|
|
|
|
with open(global_result_path + '.csv', 'at') as csv:
|
|
|
|
for dataset in qp.datasets.UCI_MULTICLASS_DATASETS:
|
|
|
|
print('init', dataset)
|
|
|
|
local_result_path = global_result_path + '_' + dataset
|
|
if os.path.exists(local_result_path + '.dataframe'):
|
|
print(f'result file {local_result_path}.dataframe already exist; skipping')
|
|
continue
|
|
|
|
with qp.util.temp_seed(SEED):
|
|
|
|
param_grid, quantifier = new_method(method, max_iter=3000)
|
|
|
|
data = qp.datasets.fetch_UCIMulticlassDataset(dataset)
|
|
|
|
# model selection
|
|
train, test = data.train_test
|
|
train, val = train.split_stratified(random_state=SEED)
|
|
|
|
protocol = UPP(val, repeats=n_bags_val)
|
|
modsel = GridSearchQ(
|
|
quantifier, param_grid, protocol, refit=True, n_jobs=-1, verbose=1, error=optim
|
|
)
|
|
|
|
try:
|
|
modsel.fit(train)
|
|
|
|
print(f'best params {modsel.best_params_}')
|
|
print(f'best score {modsel.best_score_}')
|
|
pickle.dump(
|
|
(modsel.best_params_, modsel.best_score_,),
|
|
open(f'{local_result_path}.hyper.pkl', 'wb'), pickle.HIGHEST_PROTOCOL)
|
|
|
|
quantifier = modsel.best_model()
|
|
except:
|
|
print('something went wrong... trying to fit the default model')
|
|
quantifier.fit(train)
|
|
# quantifier = qp.method.aggregative.CC(LogisticRegression()).fit(train)
|
|
|
|
|
|
protocol = UPP(test, repeats=n_bags_test)
|
|
report = qp.evaluation.evaluation_report(quantifier, protocol, error_metrics=['mae', 'mrae', 'kld'],
|
|
verbose=True, n_jobs=-1)
|
|
report.to_csv(f'{local_result_path}.dataframe')
|
|
means = report.mean()
|
|
csv.write(f'{method}\t{data.name}\t{means["mae"]:.5f}\t{means["mrae"]:.5f}\t{means["kld"]:.5f}\n')
|
|
csv.flush()
|
|
|
|
show_results(global_result_path)
|