| |
| from typing import Any, Dict, List, Optional, Tuple, Union |
|
|
| import numpy as np |
| import pandas as pd |
|
|
|
|
| def transform_jsonl_to_df(dict_list: List[Dict[str, Any]]) -> pd.DataFrame: |
| """Relevant function: `io_utils.read_from_jsonl()`""" |
| data_dict: Dict[str, List[Any]] = {} |
| for i, obj in enumerate(dict_list): |
| for k, v in obj.items(): |
| if k not in data_dict: |
| data_dict[k] = [None] * i |
| data_dict[k].append(v) |
| for k in set(data_dict.keys()) - set(obj.keys()): |
| data_dict[k].append(None) |
| return pd.DataFrame.from_dict(data_dict) |
|
|
|
|
| def get_seed(random_state: Optional[np.random.RandomState] = None) -> int: |
| if random_state is None: |
| random_state = np.random.RandomState() |
| seed_max = np.iinfo(np.int32).max |
| seed = random_state.randint(0, seed_max) |
| return seed |
|
|
|
|
| def stat_array(array: Union[np.ndarray, List[int], 'torch.Tensor']) -> Tuple[Dict[str, float], str]: |
| if isinstance(array, list): |
| array = np.array(array) |
| mean = array.mean().item() |
| std = array.std().item() |
| min_ = array.min().item() |
| max_ = array.max().item() |
| size = array.shape[0] |
| string = f'{mean:.6f}±{std:.6f}, min={min_:.6f}, max={max_:.6f}, size={size}' |
| return {'mean': mean, 'std': std, 'min': min_, 'max': max_, 'size': size}, string |
|
|
