File size: 3,218 Bytes
91f863a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | 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.')
# draw_boxplot(result_file, save_path)
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