fc251ab0b8
* add makefile * bump version * add isort and yapf * update contributing.md * update PR template * spelling check
289 lines
8.3 KiB
Python
Executable File
289 lines
8.3 KiB
Python
Executable File
#!/usr/bin/env python3
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import argparse
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import os
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import re
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import matplotlib.pyplot as plt
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import matplotlib.ticker as mticker
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import numpy as np
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from tools import csv2numpy, find_all_files, group_files
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def smooth(y, radius, mode='two_sided', valid_only=False):
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'''Smooth signal y, where radius is determines the size of the window.
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mode='twosided':
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average over the window [max(index - radius, 0), min(index + radius, len(y)-1)]
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mode='causal':
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average over the window [max(index - radius, 0), index]
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valid_only: put nan in entries where the full-sized window is not available
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'''
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assert mode in ('two_sided', 'causal')
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if len(y) < 2 * radius + 1:
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return np.ones_like(y) * y.mean()
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elif mode == 'two_sided':
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convkernel = np.ones(2 * radius + 1)
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out = np.convolve(y, convkernel, mode='same') / \
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np.convolve(np.ones_like(y), convkernel, mode='same')
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if valid_only:
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out[:radius] = out[-radius:] = np.nan
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elif mode == 'causal':
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convkernel = np.ones(radius)
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out = np.convolve(y, convkernel, mode='full') / \
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np.convolve(np.ones_like(y), convkernel, mode='full')
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out = out[:-radius + 1]
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if valid_only:
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out[:radius] = np.nan
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return out
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COLORS = (
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[
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# deepmind style
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'#0072B2',
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'#009E73',
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'#D55E00',
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'#CC79A7',
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# '#F0E442',
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'#d73027', # RED
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# built-in color
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'blue',
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'red',
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'pink',
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'cyan',
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'magenta',
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'yellow',
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'black',
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'purple',
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'brown',
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'orange',
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'teal',
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'lightblue',
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'lime',
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'lavender',
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'turquoise',
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'darkgreen',
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'tan',
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'salmon',
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'gold',
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'darkred',
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'darkblue',
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'green',
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# personal color
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'#313695', # DARK BLUE
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'#74add1', # LIGHT BLUE
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'#f46d43', # ORANGE
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'#4daf4a', # GREEN
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'#984ea3', # PURPLE
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'#f781bf', # PINK
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'#ffc832', # YELLOW
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'#000000', # BLACK
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]
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)
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def plot_ax(
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ax,
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file_lists,
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legend_pattern=".*",
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xlabel=None,
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ylabel=None,
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title=None,
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xlim=None,
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xkey='env_step',
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ykey='rew',
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smooth_radius=0,
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shaded_std=True,
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legend_outside=False,
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):
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def legend_fn(x):
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# return os.path.split(os.path.join(
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# args.root_dir, x))[0].replace('/', '_') + " (10)"
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return re.search(legend_pattern, x).group(0)
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legneds = map(legend_fn, file_lists)
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# sort filelist according to legends
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file_lists = [f for _, f in sorted(zip(legneds, file_lists))]
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legneds = list(map(legend_fn, file_lists))
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for index, csv_file in enumerate(file_lists):
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csv_dict = csv2numpy(csv_file)
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x, y = csv_dict[xkey], csv_dict[ykey]
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y = smooth(y, radius=smooth_radius)
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color = COLORS[index % len(COLORS)]
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ax.plot(x, y, color=color)
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if shaded_std and ykey + ':shaded' in csv_dict:
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y_shaded = smooth(csv_dict[ykey + ':shaded'], radius=smooth_radius)
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ax.fill_between(x, y - y_shaded, y + y_shaded, color=color, alpha=.2)
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ax.legend(
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legneds,
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loc=2 if legend_outside else None,
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bbox_to_anchor=(1, 1) if legend_outside else None
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)
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ax.xaxis.set_major_formatter(mticker.EngFormatter())
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if xlim is not None:
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ax.set_xlim(xmin=0, xmax=xlim)
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# add title
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ax.set_title(title)
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# add labels
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if xlabel is not None:
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ax.set_xlabel(xlabel)
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if ylabel is not None:
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ax.set_ylabel(ylabel)
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def plot_figure(
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file_lists,
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group_pattern=None,
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fig_length=6,
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fig_width=6,
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sharex=False,
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sharey=False,
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title=None,
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**kwargs,
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):
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if not group_pattern:
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fig, ax = plt.subplots(figsize=(fig_length, fig_width))
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plot_ax(ax, file_lists, title=title, **kwargs)
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else:
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res = group_files(file_lists, group_pattern)
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row_n = int(np.ceil(len(res) / 3))
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col_n = min(len(res), 3)
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fig, axes = plt.subplots(
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row_n,
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col_n,
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sharex=sharex,
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sharey=sharey,
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figsize=(fig_length * col_n, fig_width * row_n),
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squeeze=False
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)
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axes = axes.flatten()
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for i, (k, v) in enumerate(res.items()):
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plot_ax(axes[i], v, title=k, **kwargs)
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if title: # add title
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fig.suptitle(title, fontsize=20)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description='plotter')
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parser.add_argument(
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'--fig-length',
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type=int,
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default=6,
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help='matplotlib figure length (default: 6)'
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)
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parser.add_argument(
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'--fig-width',
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type=int,
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default=6,
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help='matplotlib figure width (default: 6)'
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)
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parser.add_argument(
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'--style',
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default='seaborn',
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help='matplotlib figure style (default: seaborn)'
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)
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parser.add_argument(
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'--title', default=None, help='matplotlib figure title (default: None)'
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)
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parser.add_argument(
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'--xkey',
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default='env_step',
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help='x-axis key in csv file (default: env_step)'
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)
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parser.add_argument(
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'--ykey', default='rew', help='y-axis key in csv file (default: rew)'
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)
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parser.add_argument(
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'--smooth', type=int, default=0, help='smooth radius of y axis (default: 0)'
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)
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parser.add_argument(
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'--xlabel', default='Timesteps', help='matplotlib figure xlabel'
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)
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parser.add_argument(
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'--ylabel', default='Episode Reward', help='matplotlib figure ylabel'
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)
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parser.add_argument(
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'--shaded-std',
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action='store_true',
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help='shaded region corresponding to standard deviation of the group'
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)
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parser.add_argument(
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'--sharex',
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action='store_true',
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help='whether to share x axis within multiple sub-figures'
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)
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parser.add_argument(
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'--sharey',
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action='store_true',
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help='whether to share y axis within multiple sub-figures'
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)
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parser.add_argument(
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'--legend-outside',
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action='store_true',
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help='place the legend outside of the figure'
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)
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parser.add_argument(
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'--xlim', type=int, default=None, help='x-axis limitation (default: None)'
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)
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parser.add_argument('--root-dir', default='./', help='root dir (default: ./)')
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parser.add_argument(
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'--file-pattern',
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type=str,
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default=r".*/test_rew_\d+seeds.csv$",
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help='regular expression to determine whether or not to include target csv '
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'file, default to including all test_rew_{num}seeds.csv file under rootdir'
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)
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parser.add_argument(
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'--group-pattern',
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type=str,
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default=r"(/|^)\w*?\-v(\d|$)",
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help='regular expression to group files in sub-figure, default to grouping '
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'according to env_name dir, "" means no grouping'
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)
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parser.add_argument(
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'--legend-pattern',
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type=str,
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default=r".*",
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help='regular expression to extract legend from csv file path, default to '
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'using file path as legend name.'
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)
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parser.add_argument('--show', action='store_true', help='show figure')
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parser.add_argument(
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'--output-path', type=str, help='figure save path', default="./figure.png"
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)
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parser.add_argument(
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'--dpi', type=int, default=200, help='figure dpi (default: 200)'
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)
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args = parser.parse_args()
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file_lists = find_all_files(args.root_dir, re.compile(args.file_pattern))
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file_lists = [os.path.relpath(f, args.root_dir) for f in file_lists]
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if args.style:
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plt.style.use(args.style)
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os.chdir(args.root_dir)
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plot_figure(
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file_lists,
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group_pattern=args.group_pattern,
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legend_pattern=args.legend_pattern,
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fig_length=args.fig_length,
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fig_width=args.fig_width,
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title=args.title,
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xlabel=args.xlabel,
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ylabel=args.ylabel,
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xkey=args.xkey,
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ykey=args.ykey,
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xlim=args.xlim,
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sharex=args.sharex,
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sharey=args.sharey,
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smooth_radius=args.smooth,
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shaded_std=args.shaded_std,
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legend_outside=args.legend_outside
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)
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if args.output_path:
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plt.savefig(args.output_path, dpi=args.dpi, bbox_inches='tight')
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if args.show:
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plt.show()
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