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111 lines
4.1 KiB
Python
111 lines
4.1 KiB
Python
# Adapted from https://github.com/openai/baselines/blob/master/baselines/results_plotter.py
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from ..component.bench import load_monitor_log
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import numpy as np
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import os
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import re
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class Plotter:
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COLORS = ['blue', 'green', 'red', 'cyan', 'magenta', 'yellow', 'black', 'purple', 'pink',
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'brown', 'orange', 'teal', 'coral', 'lightblue', 'lime', 'lavender', 'turquoise',
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'darkgreen', 'tan', 'salmon', 'gold', 'lightpurple', 'darkred', 'darkblue']
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X_TIMESTEPS = 'timesteps'
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X_EPISODES = 'episodes'
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X_WALLTIME = 'walltime_hrs'
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def __init__(self):
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pass
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def rolling_window(self, a, window):
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shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
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strides = a.strides + (a.strides[-1],)
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return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)
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def window_func(self, x, y, window, func):
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yw = self.rolling_window(y, window)
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yw_func = func(yw, axis=-1)
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return x[window - 1:], yw_func
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def ts2xy(self, ts, xaxis):
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if xaxis == Plotter.X_TIMESTEPS:
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x = np.cumsum(ts.l.values)
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y = ts.r.values
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elif xaxis == Plotter.X_EPISODES:
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x = np.arange(len(ts))
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y = ts.r.values
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elif xaxis == Plotter.X_WALLTIME:
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x = ts.t.values / 3600.
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y = ts.r.values
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else:
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raise NotImplementedError
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return x, y
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def load_results(self, dirs, max_timesteps=1e8, x_axis=X_TIMESTEPS, episode_window=100):
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tslist = []
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for dir in dirs:
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ts = load_monitor_log(dir)
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ts = ts[ts.l.cumsum() <= max_timesteps]
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tslist.append(ts)
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xy_list = [self.ts2xy(ts, x_axis) for ts in tslist]
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if episode_window:
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xy_list = [self.window_func(x, y, episode_window, np.mean) for x, y in xy_list]
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return xy_list
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def load_evaluation_episodes_results(self,
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dirs,
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evaluation_episodes_interval,
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evaluation_episodes,
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max_timesteps=1e8):
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raw_data = self.load_results(dirs, max_timesteps, episode_window=0)
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ys = []
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for x, y in raw_data:
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y = np.reshape(np.asarray(y), (-1, evaluation_episodes)).mean(-1)
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x = np.arange(y.shape[0]) * evaluation_episodes_interval
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ys.append(y)
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return x, np.stack(ys)
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def average(self, xy_list, bin, max_timesteps, top_k=0):
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if top_k:
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perf = [np.max(y) for _, y in xy_list]
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top_k_runs = np.argsort(perf)[-top_k:]
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new_xy_list = []
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for r, (x, y) in enumerate(xy_list):
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if r in top_k_runs:
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new_xy_list.append((x, y))
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xy_list = new_xy_list
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new_x = np.arange(0, max_timesteps, bin)
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new_y = []
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for x, y in xy_list:
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new_y.append(np.interp(new_x, x, y))
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return new_x, np.asarray(new_y)
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def plot_results(self, dirs, max_timesteps=1e8, x_axis=X_TIMESTEPS, episode_window=100, title=None):
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import matplotlib.pyplot as plt
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plt.ticklabel_format(axis='x', style='sci', scilimits=(1, 1))
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xy_list = self.load_results(dirs, max_timesteps, x_axis, episode_window)
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for (i, (x, y)) in enumerate(xy_list):
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color = Plotter.COLORS[i]
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plt.plot(x, y, color=color)
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plt.xlabel(x_axis)
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plt.ylabel("Episode Rewards")
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if title is not None:
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plt.title(title)
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def load_log_dirs(self, pattern, negative_pattern=' ', root='./log', **kwargs):
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dirs = [item[0] for item in os.walk(root)]
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leaf_dirs = []
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for i in range(len(dirs)):
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if i + 1 < len(dirs) and dirs[i + 1].startswith(dirs[i]):
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continue
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leaf_dirs.append(dirs[i])
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names = []
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p = re.compile(pattern)
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np = re.compile(negative_pattern)
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for dir in leaf_dirs:
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if p.match(dir) and not np.match(dir):
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names.append(dir)
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print(dir)
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return sorted(names)
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