Spaces:
Sleeping
Sleeping
| # Provides methods to visualize the information of data, giving a brief overview in figure. | |
| import torch | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from typing import Optional, Union, List, Dict | |
| from pathlib import Path | |
| from lib.utils.data import to_numpy | |
| def show_distribution( | |
| data : Dict, | |
| fn : Union[str, Path], # File name of the saved figure. | |
| bins : int = 100, # Number of bins in the histogram. | |
| annotation : bool = False, | |
| title : str = 'Data Distribution', | |
| axis_names : List = ['Value', 'Frequency'], | |
| bounds : Optional[List] = None, # Left and right bounds of the histogram. | |
| ): | |
| ''' | |
| Visualize the distribution of the data using histogram. | |
| The data should be a dictionary with keys as the labels and values as the data. | |
| ''' | |
| labels = list(data.keys()) | |
| data = np.stack([ to_numpy(x) for x in data.values() ], axis=0) | |
| assert data.ndim == 2, f"Data dimension should be 2, but got {data.ndim}." | |
| assert bounds is None or len(bounds) == 2, f"Bounds should be a list of length 2, but got {bounds}." | |
| # Preparation. | |
| N, K = data.shape | |
| data = data.transpose(1, 0) # (K, N) | |
| # Plot. | |
| plt.hist(data, bins=bins, alpha=0.7, label=labels) | |
| if annotation: | |
| for i in range(K): | |
| for j in range(N): | |
| plt.text(data[i, j], 0, f'{data[i, j]:.2f}', va='bottom', fontsize=6) | |
| plt.title(title) | |
| plt.xlabel(axis_names[0]) | |
| plt.ylabel(axis_names[1]) | |
| plt.legend() | |
| if bounds: | |
| plt.xlim(bounds) | |
| # Save. | |
| plt.savefig(fn) | |
| plt.close() | |
| def show_history( | |
| data : Dict, | |
| fn : Union[str, Path], # file name of the saved figure | |
| annotation : bool = False, | |
| title : str = 'Data History', | |
| axis_names : List = ['Time', 'Value'], | |
| ex_starts : Dict[str, int] = {}, # starting points of the history if not starting from 0 | |
| ): | |
| ''' | |
| Visualize the value of changing across time. | |
| The history should be a dictionary with keys as the metric names and values as the metric values. | |
| ''' | |
| # Make sure the fn's parent exists. | |
| if isinstance(fn, str): | |
| fn = Path(fn) | |
| fn.parent.mkdir(parents=True, exist_ok=True) | |
| # Preparation. | |
| history_name = list(data.keys()) | |
| history_data = [ to_numpy(x) for x in data.values() ] | |
| N = len(history_name) | |
| Ls = [len(x) for x in history_data] | |
| Ss = [ | |
| ex_starts[history_name[i]] | |
| if (history_name[i] in ex_starts.keys()) else 0 | |
| for i in range(N) | |
| ] | |
| # Plot. | |
| for i in range(N): | |
| plt.plot(range(Ss[i], Ss[i]+Ls[i]), history_data[i], label=history_name[i]) | |
| if annotation: | |
| for i in range(N): | |
| for j in range(Ls[i]): | |
| plt.text(Ss[i]+j, history_data[i][j], f'{history_data[i][j]:.2f}', fontsize=6) | |
| plt.title(title) | |
| plt.xlabel(axis_names[0]) | |
| plt.ylabel(axis_names[1]) | |
| plt.legend() | |
| # Save. | |
| plt.savefig(fn) | |
| plt.close() |