if __name__ == "__main__":
if not os.path.exists('./results'):
os.makedirs('./results')
print("Train LPMC_RR_L with LS-ABS and grad")
model = LPMC_RR(data_folder, file='12_13_14.csv')
ioa = OptAlg(alg_type='LS-ABS', direction='grad')
res = {'time': [], 'LL': [], 'epochs': []}
for i in range(20):
tmp = model.optimize(
ioa, **{
'verbose': False,
'max_epochs': 1000,
'batch': 1000
})
res['time'].append(tmp['opti_time'])
res['LL'].append(tmp['fun'])
res['epochs'].append(tmp['nep'])
with open('results/LS-ABS_grad.json', 'w') as outfile:
json.dump(res, outfile)
print("{}/20 done!".format(i + 1))
base_param = {'perc_hybrid': 30, 'thresh_upd': 1, 'count_upd': 2, 'window': 10, 'factor_upd': 2}
main_params = {'verbose': False, 'nbr_epochs': 1000, 'batch': 1000}
main_params.update(base_param)
draws = 20
res = {}
tmp_res = {'time': [], 'LL': [], 'epochs': []}
print("Start with base params")
for i in range(draws):
tmp = model.optimize(ioa, **main_params)
tmp_res['time'].append(tmp['opti_time'])
tmp_res['LL'].append(tmp['fun'])
tmp_res['epochs'].append(tmp['nep'])
res['base'] = tmp_res
with open('results/parameters_RR.json', 'w') as outfile:
json.dump(res, outfile)
print("{}/{} done!".format(i+1, draws))
print("")
print("Start with thresh_upd")