"mode": ['regression'], "dropout": [0.0, 0.2, 0.4] } def gc_model_builder(model_params, model_dir): gc_model = GraphConvModel(**model_params, model_dir="./models") return gc_model i = 0 for train, test in ind: train_set = dataset.iloc[train] test_set = dataset.iloc[test] train_set.to_csv('train_' + str(i) + '.csv') test_set.to_csv('test_' + str(i) + '.csv') optimizer = wf.HyperparamOpt(gc_model_builder) best_model, best_hyperparams, all_results = optimizer.CVgridsearch( params_dict, train_set) file = open('opt_result_' + str(i) + '.txt', 'w') s = 'Best Hyperparameter:' + str(best_hyperparams) + '\n\nAll Results:\n' file.write(s) from operator import itemgetter for k, v in sorted(all_results.items(), key=itemgetter(1)): s = k + " : " + str(v) + "\n" file.write(s) file.close() if i == 0: break i += 1
"learning_rate":[0.005,0.0005, 0.001], "mode":['regression'] } def mpnn_model_builder(model_params , model_dir): return MPNNModel(**model_params, model_dir = "./models") def gc_model_builder(model_params , model_dir): gc_model = GraphConvModel(**model_params, model_dir = "./models") return gc_model i = 0 for train,test in ind: if i > 2 && i < 5: train_set = data.iloc[train] test_set = data.iloc[test] train_set.to_csv('train_'+str(i)+'.csv') test_set.to_csv('test_'+str(i)+'.csv') optimizer = wf.HyperparamOpt(mpnn_model_builder) best_model, best_hyperparams, all_results = optimizer.CVgridsearch(mpnn_dict,train_set) file = open('opt_result_'+str(i)+'.txt', 'w') s = 'Best Hyperparameter:' + str(best_hyperparams) + '\n\nAll Results:\n' file.write(s) from operator import itemgetter for k, v in sorted(all_results.items(), key=itemgetter(1)): s = k + " : " + str(v)+"\n" file.write(s) file.close() #if i == 2: # break i += 1