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plot for cartpole and reacher complete

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This commit is contained in:
Niko Feith 2023-09-15 17:36:19 +02:00
parent ba31dfd7f7
commit cf70b5a24e
2 changed files with 152 additions and 55 deletions
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11
plotter/reward_plotter.py
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@ -7,8 +7,8 @@ def plot_csv(paths, x_axis, y_axis):
for path_ in paths: for path_ in paths:
data = np.genfromtxt(path_, delimiter=',', skip_header=0, dtype=float) data = np.genfromtxt(path_, delimiter=',', skip_header=0, dtype=float)
mean = np.mean(data, axis=1) mean = np.mean(data, axis=0)
std = np.std(data, axis=1) std = np.std(data, axis=0)
x = np.linspace(0, mean.shape[0], mean.shape[0]) x = np.linspace(0, mean.shape[0], mean.shape[0])
@ -35,13 +35,10 @@ def plot_csv(paths, x_axis, y_axis):
if __name__ == '__main__': if __name__ == '__main__':
filenames = ['cp-e150r10-bf15-base/cp-ei-random-1_0-15-1690282051_2959082.csv', filenames = ['franka-pei-random-1_0-6-1694787936_385925.csv',
'cp-e150r10-bf15-noshaping/cp-pei-random-0_95-15-1690276946_1944933.csv',
'cp-e150r10-bf15-noshaping/cp-pei-regular-25_0-15-1690290021_6843266.csv',
'cp-e150r10-bf15-noshaping/cp-pei-improvement-0_1-15-1690292664_0382216.csv',
] ]
home_dir = os.path.expanduser('~') home_dir = os.path.expanduser('~')
file_path = os.path.join(home_dir, 'Documents/IntRLResults/CP-Results') file_path = os.path.join(home_dir, 'Documents/IntRLResults/Franka-Results')
paths = [os.path.join(file_path, filename) for filename in filenames] paths = [os.path.join(file_path, filename) for filename in filenames]
plot_csv(paths, 'Episodes', 'Reward') plot_csv(paths, 'Episodes', 'Reward')
# #

196
plotter/reward_plotter_final.py
View File

@ -1,56 +1,129 @@
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib.ticker as ticker
import os import os
def modified_plot_v7(paths_dict, x_axis, y_axis, subplot_titles, ylims=(0, 200),offset=0):
def fixed_width_formatter(x, _):
return "{:>7}".format(x)
# Create a figure with 4 subplots
# fig, axs = plt.subplots(1, 4, figsize=(20, 6))
fig = plt.figure(figsize=(20, 6))
gs = gridspec.GridSpec(1, 4, width_ratios=[1, 1, 1, 1])
axs=[]
axs.append(fig.add_subplot(gs[0, 0]))
axs.append(fig.add_subplot(gs[0, 1]))
axs.append(fig.add_subplot(gs[0, 2]))
axs.append(fig.add_subplot(gs[0, 3]))
plt.rcParams['font.size'] = 16
label_font_size = 14
plt.tick_params(axis='y', labelsize=14)
# Plotting mean and std for the first 3 subplots
for idx, key in enumerate(['subplot1', 'subplot2', 'subplot3']):
for path_, color, plot_line in paths_dict[key]:
if plot_line:
data = np.genfromtxt(path_, delimiter=',', skip_header=0, dtype=float)
if data.shape[0] > data.shape[1]:
mean = np.mean(data, axis=1) + offset
std = np.std(data, axis=1)
else:
mean = np.mean(data, axis=0) +offset
std = np.std(data, axis=0)
x = np.linspace(0, mean.shape[0], mean.shape[0])
axs[idx].plot(x, mean, color=color)
axs[idx].fill_between(
x,
mean - 1.96 * std,
mean + 1.96 * std,
color=color,
alpha=0.5
)
axs[idx].tick_params(axis='x', labelsize=14)
axs[idx].tick_params(axis='y', labelsize=14)
axs[idx].set_xlabel(x_axis, fontsize=label_font_size)
axs[idx].text(0.5, -0.15, subplot_titles[idx], horizontalalignment='center', verticalalignment='center',
transform=axs[idx].transAxes, fontsize=label_font_size)
axs[idx].set_xlim([0, mean.shape[0]])
axs[idx].set_ylim(ylims)
axs[idx].spines['right'].set_visible(False)
axs[idx].spines['top'].set_visible(False)
axs[idx].set_xlabel(x_axis, fontsize=label_font_size, position=(0.5, -0.2))
if idx != 0:
axs[idx].set_ylabel('')
axs[idx].set_yticklabels([])
else:
axs[0].set_ylabel(y_axis, fontsize=label_font_size)
axs[0].yaxis.set_tick_params(size=14, pad=0)
axs[idx].yaxis.set_major_formatter(ticker.FuncFormatter(fixed_width_formatter))
def plot_csv_with_titles(paths_dict, x_axis, y_axis, subplot_titles, y_limits=(0, 200)): # Preparing data for the grouped bar chart in the fourth subplot
# Adjustments for dynamic subplot creation based on the dictionary size groups = [
num_subplots = len(paths_dict) [paths_dict['subplot1'][0]],
fig, axs = plt.subplots(1, num_subplots, figsize=(4*num_subplots, 4)) [paths_dict['subplot1'][1], paths_dict['subplot1'][2], paths_dict['subplot1'][3]], # Adjusted order
[paths_dict['subplot2'][1], paths_dict['subplot2'][2], paths_dict['subplot2'][3]], # Adjusted order
[paths_dict['subplot3'][1], paths_dict['subplot3'][2], paths_dict['subplot3'][3]] # Adjusted order
]
# If only one subplot, axs is not an array, so we need to convert it last_means = []
if num_subplots == 1: last_stds = []
axs = [axs] colors = []
for idx, (_, file_list) in enumerate(paths_dict.items()): for group in groups:
for path_, color in file_list: for path_, color, _ in group:
data = np.genfromtxt(path_, delimiter=',', skip_header=0, dtype=float) data = np.genfromtxt(path_, delimiter=',', skip_header=0, dtype=float)
if data.shape[0]> data.shape[1]: if data.shape[0] > data.shape[1]:
mean = np.mean(data, axis=1) mean = np.mean(data, axis=1) + offset
std = np.std(data, axis=1) std = np.std(data, axis=1)
else: else:
mean = np.mean(data, axis=0) mean = np.mean(data, axis=0) + offset
std = np.std(data, axis=0) std = np.std(data, axis=0)
x = np.linspace(0, mean.shape[0], mean.shape[0]) last_means.append(mean[-1])
last_stds.append(std[-1])
colors.append(color)
axs[idx].plot(x, mean, color=color) # Plotting the grouped bar chart in the fourth subplot
axs[idx].fill_between( bar_width = 0.2
x, r = [0] + [x * bar_width + 0.5 for x in np.arange(len(groups[1]))] + \
mean - 1.96 * std, [x * bar_width + 1.5 for x in np.arange(len(groups[2]))] + \
mean + 1.96 * std, [x * bar_width + 2.5 for x in np.arange(len(groups[3]))]
color=color,
alpha=0.5
)
axs[idx].set_title(subplot_titles[idx])
axs[idx].set_xlabel(x_axis)
axs[idx].set_xlim([0, mean.shape[0]])
axs[idx].grid(True)
axs[idx].set_ylim(y_limits)
# Only label the y-axis for the leftmost plot labels = ['BO', 'IBO-Random', 'IBO-Regular', 'IBO-Improvement', None, None, None, None, None, None]
if idx == 0: for i in range(len(r)):
axs[idx].set_ylabel(y_axis) axs[3].bar(r[i], last_means[i]-ylims[0], yerr=last_stds[i], width=bar_width,
else: color=colors[i], capsize=5, bottom=ylims[0], label=labels[i])
axs[idx].set_ylabel('')
axs[3].set_xticks([r[0], r[2], r[5], r[8]])
axs[3].set_xticklabels(['BO', 'IBO Preference', 'IBO Shaping', 'IBO Mixture'], fontsize=label_font_size, rotation=15)
axs[3].set_xlabel('')
axs[3].spines['right'].set_visible(False)
axs[3].spines['top'].set_visible(False)
axs[3].set_ylim(ylims)
axs[3].set_ylabel('')
axs[3].set_yticklabels([])
axs[3].text(0.5, -0.15, subplot_titles[3], horizontalalignment='center',
verticalalignment='center', transform=axs[3].transAxes, fontsize=label_font_size)
# Display legend for the first and second groups
handles, labels = axs[3].get_legend_handles_labels()
filtered_handles = [h for h, l in zip(handles, labels) if l]
filtered_labels = [l for l in labels if l]
axs[3].legend(filtered_handles, filtered_labels, loc='upper left', frameon=False, fontsize=12)
plt.tight_layout() plt.tight_layout()
plt.show() plt.show()
if __name__ == '__main__': if __name__ == '__main__':
home_dir = os.path.expanduser('~') home_dir = os.path.expanduser('~')
file_path = os.path.join(home_dir, 'Documents/IntRLResults/CP-Results') file_path = os.path.join(home_dir, 'Documents/IntRLResults/CP-Results')
@ -69,31 +142,58 @@ if __name__ == '__main__':
filepaths = [os.path.join(file_path, filename) for filename in filenames] filepaths = [os.path.join(file_path, filename) for filename in filenames]
# Demonstrating the adjusted function with subplot titles # Demonstrating the adjusted function with subplot titles
titles = ["Preference", "Shaping", "Combination", "Regular"] titles = ["a) Preference", "b) Shaping", "c) Combination"]
data_dict_colored = {
'subplot1': [(filepaths[0], 'C0'), (filepaths[1], 'C1'), (filepaths[2], 'C2'), (filepaths[3], 'C3')],
'subplot2': [(filepaths[0], 'C0'), (filepaths[4], 'C1'), (filepaths[5], 'C2'), (filepaths[6], 'C3')],
'subplot3': [(filepaths[0], 'C0'), (filepaths[7], 'C1'), (filepaths[8], 'C2'), (filepaths[9], 'C3')],
'subplot4': [(filepaths[0], 'C0'), (filepaths[2], 'C6'), (filepaths[5], 'C8'), (filepaths[8], 'C5')],
}
plot_csv_with_titles(data_dict_colored, 'Episodes', 'Reward', titles)
file_path_reacher = os.path.join(home_dir, 'Documents/IntRLResults/RE-Results') file_path_reacher = os.path.join(home_dir, 'Documents/IntRLResults/RE-Results')
filenames_reacher = ['base_line/re-ei-random-1_0-5-1694370994_0363934.csv', filenames_reacher = ['base_line/re-ei-random-1_0-5-1694370994_0363934.csv',
'shaping/re-ei-random-1_0-10-1694359559_616903.csv', 'preference/re-pei-random-0_95-5-1694419627_105307.csv',
'shaping/re-ei-regular-10_0-5-1694371946_5364418.csv' 'preference/re-pei-regular_outliner_free.csv',
'preference/re-pei-improvement-0_1-5-1694416723_645404.csv',
'shaping/re-ei-random-0_95-5-1694611010_6658554.csv',
'shaping/re-ei-regular-10_0-5-1694371946_5364418.csv',
'shaping/re-ei-improvement-0_1-5-1694415947_8134162.csv',
'combination/re-pei-random-0_95-5-1694423273_9474058.csv',
'combination/re-pei-regular_outliner_free.csv',
'combination/re-pei-improvement-0_1-5-1694430822_181309.csv',
] ]
filepaths_reacher = [os.path.join(file_path_reacher, filename) for filename in filenames_reacher] filepaths_reacher = [os.path.join(file_path_reacher, filename) for filename in filenames_reacher]
titles_reacher = ["Shaping"] titles_reacher = ["(a)", "(b)", "(c)", "(d)"]
data_dict_reacher = { data_dict_colored = {
'subplot1': [(filepaths_reacher[0], 'C0'), (filepaths_reacher[1], 'C1'), (filepaths_reacher[2], 'C2')] 'subplot1': [(filepaths[0], 'C0', True), (filepaths[1], 'C3', True), (filepaths[2], 'C1', False),
(filepaths[3], 'C2', False)],
'subplot2': [(filepaths[0], 'C0', True), (filepaths[4], 'C3', False), (filepaths[5], 'C1', True),
(filepaths[6], 'C2', False)],
'subplot3': [(filepaths[0], 'C0', True), (filepaths[7], 'C3', False), (filepaths[8], 'C1', True),
(filepaths[9], 'C2', False)],
# 'subplot4': [(filepaths[0], 'C0'), (filepaths[2], 'C6'), (filepaths[5], 'C8'), (filepaths[8], 'C5')],
} }
plot_csv_with_titles(data_dict_reacher, 'Episodes', 'Reward', titles_reacher, y_limits=(-150, 50)) # plot_csv_with_titles(data_dict_colored, 'Episodes', 'Reward', titles)
data_dict_reacher = {
'subplot1': [(filepaths_reacher[0], 'C0', True), (filepaths_reacher[1], 'C3', False),
(filepaths_reacher[2], 'C1', True), (filepaths_reacher[3], 'C2', False)],
'subplot2': [(filepaths_reacher[0], 'C0', True), (filepaths_reacher[4], 'C3', True),
(filepaths_reacher[5], 'C1', False), (filepaths_reacher[6], 'C2', False)],
'subplot3': [(filepaths_reacher[0], 'C0', True), (filepaths_reacher[7], 'C3', False),
(filepaths_reacher[8], 'C1', True), (filepaths_reacher[9], 'C2', False)],
}
# plot_csv_with_titles(data_dict_reacher, 'Episodes', 'Reward', titles_reacher, y_limits=(-150, 50))
# data_dict_combined = {**data_dict_colored, **data_dict_reacher}
# combined_titles = titles + titles_reacher
#
# plot_csv_with_titles(
# paths_dict=data_dict_combined,
# x_axis='Episodes',
# y_axis='Rewards',
# subplot_titles=combined_titles
# )
modified_plot_v7(paths_dict=data_dict_colored, x_axis='Episodes', y_axis='Rewards', subplot_titles=titles_reacher)
modified_plot_v7(paths_dict=data_dict_reacher, x_axis='Episodes', y_axis='Rewards with +80 offset', subplot_titles=titles_reacher,
ylims=(-80, 150), offset=80)