if __name__ == "__main__": if not os.path.exists('./results'): os.makedirs('./results') print("Train LPMC_Full_L with LS-ABS and hybrid-inv") model = LPMC_Full(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))
'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/Full_base.json', 'w') as outfile: json.dump(res, outfile) print("{}/{} done!".format(i + 1, draws))