| import os, json |
| import pandas as pd |
|
|
| from itertools import product |
| from easydict import EasyDict |
|
|
| from clearance_ft.utils.plot import draw_boundaryplot |
|
|
|
|
| class DictX(dict): |
| def __getattr__(self, key): |
| try: |
| return self[key] |
| except KeyError as k: |
|
|
| raise AttributeError(k) |
|
|
| def __setattr__(self, key, value): |
| self[key] = value |
|
|
| def __delattr__(self, key): |
| try: |
| del self[key] |
| except KeyError as k: |
| raise AttributeError(k) |
|
|
| def __repr__(self): |
| return '<DictX ' + dict.__repr__(self) + '>' |
|
|
|
|
| def load_hparams(file_path): |
| hparams = EasyDict() |
| with open(file_path, 'r') as f: |
| hparams = json.load(f) |
| return hparams |
|
|
|
|
| def generate_combinations(dic): |
| keys = dic.keys() |
| values = (dic[key] if isinstance(dic[key], list) else [dic[key]] for key in keys) |
| combinations = [dict(zip(keys, combination)) for combination in product(*values)] |
| return combinations |
|
|
|
|
| def save_result(config, result_dir, df_result:pd.DataFrame, output_devlog:dict, output_testlog:dict, df_train:pd.DataFrame = None, df_test:pd.DataFrame = None, df_duplData:pd.DataFrame = None): |
| result_name = f"{config.model_type}_{config.train_dataType}_seed{config.num_seed}_r2_{output_testlog['r2']:.3f}" |
| result_path = f"results/{result_dir}/{config.feature_type}_{result_name}" |
| if not os.path.exists(result_path): |
| os.makedirs(result_path) |
| |
| if df_train is not None: |
| df_train.to_csv(f'{result_path}/train.csv', index=False) |
| if df_test is not None: |
| df_test.to_csv(f'{result_path}/test.csv', index=False) |
|
|
| result_file = f'{result_path}/results.csv' |
| df_result.to_csv(result_file, index=False) |
| print('-----------------Save prediction result ----------------------') |
|
|
| save_file = os.path.join(result_path, "figure.png") |
| slop = draw_boundaryplot(df_result, save_file, df_duplData) |
|
|
| submit_config = {"model_type":config.model_type, "chem_model":config.chem_model, |
| "checkpoint_name": config.checkpoint_name, "feature_type": config.feature_type, "test_rate":config.test_rate, |
| "data_augmentation" : config.augmentation, "protainData_extended" : config.extend_protType, |
| "scale": config.scale, "chem_max" : config.chem_max, "batch_size" : config.batch_size, |
| "lr":config.lr, "dropout":config.dropout, "max_epoch": config.max_epoch, "test_sampling_type": config.sampling_type, |
| "valid_MSE": str(output_devlog['rmse']), "valid_MAE": str(output_devlog['MAE']), "valid_r2": str(output_devlog['r2']), |
| "valid_rm2": str(output_devlog['rm2']), "valid_CI": str(output_devlog['ci']), |
| "test_MSE": str(output_testlog['rmse']), "test_MAE": str(output_testlog['MAE']), "test_r2": str(output_testlog['r2']), |
| "test_rm2": str(output_testlog['rm2']), "test_CI": str(output_testlog['ci']), "slop": str(slop)} |
|
|
| with open(os.path.join(result_path,"config.json"), 'w', encoding='utf-8') as mf: |
| json.dump(submit_config, mf, indent='\t') |
|
|
| print('Done.') |
|
|
| |
| |
|
|
