if __name__ == "__main__":
if not os.path.exists('./results'):
os.makedirs('./results')
print("Train LPMC_DC_L with LS-ABS and hybrid-inv")
model = LPMC_DC(data_folder, file='12_13_14.csv')
ioa = OptAlg(alg_type='LS-ABS', direction='hybrid-inv')
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_hybrid-inv.json', 'w') as outfile:
json.dump(res, outfile)
print("{}/20 done!".format(i + 1))
param_ph = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100]
res['perc_hybrid'] = {}
for ph in param_ph:
tmp_res = {'time': [], 'LL': [], 'epochs': []}
print(" Value: {}".format(ph))
main_params['perc_hybrid'] = ph
for i in range(draws):
print(main_params)
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['perc_hybrid'][ph] = tmp_res
with open('results/DC_hybrid.json', 'w') as outfile:
json.dump(res, outfile)
print("{}/{} done!".format(i + 1, draws))
print("")
