test_predicted = fp.L1_test(dataset, f_preprocess, f_predict_model, class_weights) # Save files directory = '../submissions/' if not os.path.exists(directory): os.makedirs(directory) # Submission submission = pd.concat((pd.DataFrame(rows), pd.DataFrame(test_predicted)), axis=1) submission.columns = ['record_id'] + ['start', 'end'] + activity_names submission.to_csv('{}{}_submission.csv'.format(directory, name_to_save), index=False) if validate: # Train & Predict L2_train valid_predicted, scores = fp.L1_train(dataset, f_preprocess, f_predict_model, class_weights, verbose=1) # L2_data valid_predicted = pd.DataFrame(valid_predicted) valid_predicted.columns = activity_names valid_predicted.to_csv('{}{}_valid.csv'.format(directory, name_to_save), index=False) np.savetxt('{}{}_score.csv'.format(directory, name_to_save), scores, delimiter=",")
return(test_predicted / 2) # Set Parameters name_to_save = 'pl_L1_ET_vA1' random_state = 1 prepbd_params = {'imputer_strategy':None} f_preprocess = partial(fd.batch_preprocess, params=prepbd_params) f_predict_model = partial(predict_model, random_state=random_state, class_weights=None) # Train & Predict L1_train valid_predicted, scores = fp.L1_train(dataset, f_preprocess, f_predict_model) # Train & Predict L1_test test_predicted = fp.L1_test(dataset, f_preprocess, f_predict_model, class_weights) # Save files directory = '../predict_location/' if not os.path.exists(directory): os.makedirs(directory) # Submission room_names = json.load(open('../public_data/rooms.json', 'r')) submission = pd.concat((pd.DataFrame(rows), pd.DataFrame(test_predicted)), axis=1) submission.columns = ['record_id'] + ['start', 'end'] + room_names submission.to_csv('{}{}_submission.csv'.format(directory, name_to_save), index=False)