File size: 4,329 Bytes
a89d35f | 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 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | from typing import Callable, List, Optional, Tuple
import numpy as np
import pandas as pd
# import matplotlib
# matplotlib.use('TkAgg') # Can change to 'Agg' for non-interactive mode
from matplotlib import pyplot as plt
from stable_baselines3.common.monitor import load_results
X_TIMESTEPS = "timesteps"
X_EPISODES = "episodes"
X_WALLTIME = "walltime_hrs"
POSSIBLE_X_AXES = [X_TIMESTEPS, X_EPISODES, X_WALLTIME]
EPISODES_WINDOW = 100
def rolling_window(array: np.ndarray, window: int) -> np.ndarray:
"""
Apply a rolling window to a np.ndarray
:param array: the input Array
:param window: length of the rolling window
:return: rolling window on the input array
"""
shape = array.shape[:-1] + (array.shape[-1] - window + 1, window)
strides = array.strides + (array.strides[-1],)
return np.lib.stride_tricks.as_strided(array, shape=shape, strides=strides)
def window_func(var_1: np.ndarray, var_2: np.ndarray, window: int, func: Callable) -> Tuple[np.ndarray, np.ndarray]:
"""
Apply a function to the rolling window of 2 arrays
:param var_1: variable 1
:param var_2: variable 2
:param window: length of the rolling window
:param func: function to apply on the rolling window on variable 2 (such as np.mean)
:return: the rolling output with applied function
"""
var_2_window = rolling_window(var_2, window)
function_on_var2 = func(var_2_window, axis=-1)
return var_1[window - 1 :], function_on_var2
def ts2xy(data_frame: pd.DataFrame, x_axis: str) -> Tuple[np.ndarray, np.ndarray]:
"""
Decompose a data frame variable to x ans ys
:param data_frame: the input data
:param x_axis: the axis for the x and y output
(can be X_TIMESTEPS='timesteps', X_EPISODES='episodes' or X_WALLTIME='walltime_hrs')
:return: the x and y output
"""
if x_axis == X_TIMESTEPS:
x_var = np.cumsum(data_frame.l.values)
y_var = data_frame.r.values
elif x_axis == X_EPISODES:
x_var = np.arange(len(data_frame))
y_var = data_frame.r.values
elif x_axis == X_WALLTIME:
# Convert to hours
x_var = data_frame.t.values / 3600.0
y_var = data_frame.r.values
else:
raise NotImplementedError
return x_var, y_var
def plot_curves(
xy_list: List[Tuple[np.ndarray, np.ndarray]], x_axis: str, title: str, figsize: Tuple[int, int] = (8, 2)
) -> None:
"""
plot the curves
:param xy_list: the x and y coordinates to plot
:param x_axis: the axis for the x and y output
(can be X_TIMESTEPS='timesteps', X_EPISODES='episodes' or X_WALLTIME='walltime_hrs')
:param title: the title of the plot
:param figsize: Size of the figure (width, height)
"""
plt.figure(title, figsize=figsize)
max_x = max(xy[0][-1] for xy in xy_list)
min_x = 0
for (i, (x, y)) in enumerate(xy_list):
plt.scatter(x, y, s=2)
# Do not plot the smoothed curve at all if the timeseries is shorter than window size.
if x.shape[0] >= EPISODES_WINDOW:
# Compute and plot rolling mean with window of size EPISODE_WINDOW
x, y_mean = window_func(x, y, EPISODES_WINDOW, np.mean)
plt.plot(x, y_mean)
plt.xlim(min_x, max_x)
plt.title(title)
plt.xlabel(x_axis)
plt.ylabel("Episode Rewards")
plt.tight_layout()
def plot_results(
dirs: List[str], num_timesteps: Optional[int], x_axis: str, task_name: str, figsize: Tuple[int, int] = (8, 2)
) -> None:
"""
Plot the results using csv files from ``Monitor`` wrapper.
:param dirs: the save location of the results to plot
:param num_timesteps: only plot the points below this value
:param x_axis: the axis for the x and y output
(can be X_TIMESTEPS='timesteps', X_EPISODES='episodes' or X_WALLTIME='walltime_hrs')
:param task_name: the title of the task to plot
:param figsize: Size of the figure (width, height)
"""
data_frames = []
for folder in dirs:
data_frame = load_results(folder)
if num_timesteps is not None:
data_frame = data_frame[data_frame.l.cumsum() <= num_timesteps]
data_frames.append(data_frame)
xy_list = [ts2xy(data_frame, x_axis) for data_frame in data_frames]
plot_curves(xy_list, x_axis, task_name, figsize)
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