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plot.py
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import tensorflow as tf
import glob
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter, ScalarFormatter
from matplotlib import rc
import numpy as np
import scipy.stats as sp
from cycler import cycler
plt.rcParams["font.family"] = "Times New Roman"
plt.rcParams['axes.prop_cycle'] = cycler('color',['#377eb8', '#ff7f00', '#4daf4a','#f781bf', '#a65628', '#984ea3','#999999', '#e41a1c', '#dede00'])
lite = False
def read_data(tf_dir, tag="return_mean", MAX_HORIZON=1000000000):
list_of_data = []
tf_files = glob.glob(tf_dir + "events.out.tfevents*")
for tf_file in tf_files:
done = False
data = []
frames = []
for e in tf.train.summary_iterator(tf_file):
for v in e.summary.value:
if v.tag == "frames":
frames.append(v.simple_value)
if v.simple_value > MAX_HORIZON:
done = True
elif v.tag == tag:
data.append(v.simple_value)
if done:
break
data = data[:min(len(frames), len(data))]
frames = frames[:min(len(frames), len(data))]
if len(data) != 0:
list_of_data.append((data, frames))
list_of_data = sorted(list_of_data, key=(lambda pair: min(pair[1])))
cleaned_frames = []
cleaned_data = []
max_frames = 0
for (data, frames) in list_of_data:
for i in range(len(data)):
if frames[i] < max_frames:
continue
else:
max_frames = frames[i]
cleaned_frames.append(frames[i])
cleaned_data.append(data[i])
# Cumulative sum smoothing
window_width = 1
cumsum_vec = np.cumsum(np.insert(cleaned_data, 0, 0))
ma_vec = (cumsum_vec[window_width:] - cumsum_vec[:-window_width]) / window_width
return cleaned_frames[:len(ma_vec)], ma_vec
# Returns (X_data, (Y_average, Y_error))
def read_data_and_average(tf_dirs, tag="return_mean", MAX_HORIZON=1000000000):
X_all = None
Ys = []
if lite:
tf_dirs = tf_dirs[:1]
for tf_dir in tf_dirs:
X, Y = read_data(tf_dir, tag, MAX_HORIZON)
Ys.append(Y)
if X_all is None:
X_all = X
else:
if len(X) < len(X_all):
X_all = X
# print(tf_dir, len(Y))
N = min([len(Ys[i]) for i in range(len(Ys))])
M = len(Ys)
print(M, N, [len(Ys[i]) for i in range(len(Ys))])
avgs = [0 for i in range(N)]
lower = [0 for i in range(N)]
upper = [0 for i in range(N)]
for n in range(N):
vals = []
for m in range(M):
vals.append(Ys[m][n])
vals = np.array(vals)
if M == 1:
lo = hi = np.mean(vals)
else:
lo, hi = sp.t.interval(0.9, len(vals)-1, loc=np.mean(vals), scale=sp.sem(vals))
avgs[n] = (lo+hi)/2
lower[n] = lo
upper[n] = hi
return X_all, avgs, lower, upper
def plot_letter_env_experiments():
until_logs = [glob.glob("storage-good/RGCN/*Until*/train/"),
glob.glob("storage-good/GRU/*Until*/train/"),
glob.glob("storage-good/LSTM/*Until*/train/"),
glob.glob("storage-good/Myopic/*Until*/train/"),
glob.glob("storage-good/GRU-no-progression/*Until*/train/"),
glob.glob("storage-good/No-LTL/*Until*/train/")
]
eventually_logs = [glob.glob("storage-good/RGCN/*Eventually*/train/"),
glob.glob("storage-good/GRU/*Eventually*/train/"),
glob.glob("storage-good/LSTM/*Eventually*/train/"),
glob.glob("storage-good/Myopic/*Eventually*/train/"),
glob.glob("storage-good/GRU-no-progression/*Eventually*/train/"),
glob.glob("storage-good/No-LTL/*Eventually*/train/")
]
labels = ["GNN+progression", "GRU+progression", "LSTM+progression", "Myopic", "GRU", "No LTL"]
horizon = 20000000000
fig, (ax1, ax2) = plt.subplots(1, 2)
plt.subplots_adjust(top = 0.92, bottom = 0.28, hspace = 0, wspace = 0.12, left=0.07, right = 0.96)
fig.set_size_inches(10,5)
ax1.set_ylabel("Discounted return", fontsize = 16)
ax1.tick_params(labelsize=12)
ax1.set_title("Avoidance Tasks", fontsize = 16)
ax1.xaxis.set_major_formatter(FormatStrFormatter('%d'))
for data_line, label in zip(until_logs, labels):
X, Y_avg, Y_lower, Y_upper = read_data_and_average(data_line, tag="average_discounted_return", MAX_HORIZON=horizon) #average_discounted_return, average_discounted_return, average_reward_per_step
X = [x / 1000000 for x in X]
ax1.plot(X, Y_avg, linewidth = 2, label=label)
ax1.fill_between(X, Y_lower, Y_upper, alpha=0.2)
ax2.tick_params(labelsize=12)
ax2.set_title("Parallel Tasks", fontsize = 16)
ax2.xaxis.set_major_formatter(FormatStrFormatter('%d'))
for data_line, label in zip(eventually_logs, labels):
X, Y_avg, Y_lower, Y_upper = read_data_and_average(data_line, tag="average_discounted_return", MAX_HORIZON=horizon) #return_mean, average_discounted_return, average_reward_per_step
X = [x / 1000000 for x in X]
ax2.plot(X, Y_avg, linewidth = 2, label=label)
ax2.fill_between(X, Y_lower, Y_upper, alpha=0.2)
fig.text(0.5, 0.2, 'Frames (millions)', ha='center', fontsize = 16)
handles, labels = ax1.get_legend_handles_labels()
legend = fig.legend(handles, labels, loc="lower center",bbox_to_anchor = (0.5, 0), markerscale=6, fontsize=16, ncol = 3)
for i in range(len(legend.get_lines())):
legend.get_lines()[i].set_linewidth(3)
plt.savefig("figs/letter-env.pdf")
plt.show()
def plot_pretraining_experiments():
until_logs = [glob.glob("storage-good/RGCN/*Until_1_3_1_2*/train/"),
glob.glob("transfer-storage/RGCN/*pretrained_Until_1_3_1_2*/train/"),
glob.glob("storage-good/GRU/*Until_1_3_1_2*/train/"),
glob.glob("transfer-storage/GRU/*pretrained_Until_1_3_1_2*/train/"),
glob.glob("storage-good/Myopic/*Until*/train/")
]
eventually_logs = [glob.glob("storage-good/RGCN/*Eventually_1_5_1_4*/train/"),
glob.glob("transfer-storage/RGCN/*pretrained_Eventually_1_5_1_4*/train/"),
glob.glob("storage-good/GRU/*Eventually_1_5_1_4*/train/"),
glob.glob("transfer-storage/GRU/*pretrained_Eventually_1_5_1_4*/train/"),
glob.glob("storage-good/Myopic/*Eventually*/train/")
]
labels = ["GNN+progression", "GNN+progression+pretraining", "GRU+progression", "GRU+progression+pretraining", "Myopic"]
horizon = 10000000
fig, (ax1, ax2) = plt.subplots(1, 2)
plt.subplots_adjust(top = 0.92, bottom = 0.28, hspace = 0, wspace = 0.12, left=0.07, right = 0.96)
fig.set_size_inches(10,5)
ax1.set_ylabel("Discounted return", fontsize = 16)
ax1.tick_params(labelsize=12)
ax1.set_title("Avoidance Tasks", fontsize = 16)
ax1.xaxis.set_major_formatter(FormatStrFormatter('%d'))
for data_line, label in zip(until_logs, labels):
X, Y_avg, Y_lower, Y_upper = read_data_and_average(data_line, tag="average_discounted_return", MAX_HORIZON=horizon) #average_discounted_return, average_discounted_return, average_reward_per_step
X = [x / 1000000 for x in X]
if label == "GNN+progression":
color = '#377eb8'
style = "-"
elif label == "GNN+progression+pretraining":
color = '#377eb8'
style = "dotted"
elif label == "GRU+progression":
color = "#ff7f00"
style = "-"
elif label == "GRU+progression+pretraining":
color = "#ff7f00"
style = "dotted"
elif label == "Myopic":
color = '#f781bf'
style = "-"
else:
raise Exception("")
ax1.plot(X, Y_avg, linewidth = 2, label=label, linestyle=style, color=color)
ax1.fill_between(X, Y_lower, Y_upper, alpha=0.2, color=color)
ax2.tick_params(labelsize=12)
ax2.set_title("Parallel Tasks", fontsize = 16)
ax2.xaxis.set_major_formatter(FormatStrFormatter('%d'))
for data_line, label in zip(eventually_logs, labels):
X, Y_avg, Y_lower, Y_upper = read_data_and_average(data_line, tag="average_discounted_return", MAX_HORIZON=horizon) #return_mean, average_discounted_return, average_reward_per_step
X = [x / 1000000 for x in X]
if label == "GNN+progression":
color = '#377eb8'
style = "-"
elif label == "GNN+progression+pretraining":
color = '#377eb8'
style = "dotted"
elif label == "GRU+progression":
color = "#ff7f00"
style = "-"
elif label == "GRU+progression+pretraining":
color = "#ff7f00"
style = "dotted"
elif label == "Myopic":
color = '#f781bf'
style = "-"
else:
raise Exception("")
ax2.plot(X, Y_avg, linewidth = 2, label=label, linestyle=style, color=color)
ax2.fill_between(X, Y_lower, Y_upper, alpha=0.2, color=color)
fig.text(0.5, 0.2, 'Frames (millions)', ha='center', fontsize = 16)
handles, labels = ax1.get_legend_handles_labels()
legend = fig.legend(handles, labels, loc="lower center",bbox_to_anchor = (0.5, 0), markerscale=6, fontsize=16, ncol = 3)
for i in range(len(legend.get_lines())):
legend.get_lines()[i].set_linewidth(3)
plt.savefig("figs/transfer.pdf")
plt.show()
def plot_safety_experiments():
# plt.figure(figsize=(4,3))
# plt.ylabel("Discounted return")
# plt.xlabel("Frames")
# plt.title(title)
# for data_line, label in lines:
# X, Y_avg, Y_lower, Y_upper = read_data_and_average(data_line, tag="return_mean") #return_mean, average_discounted_return, average_reward_per_step
# plt.plot(X, Y_avg, linewidth = 1, label=label)
# plt.fill_between(X, Y_lower, Y_upper, alpha=0.2)
# plt.legend()
# # plt.savefig("figs/" + title + ".pdf")
# plt.show()
A_logs = glob.glob("zone-good/pretrained/*/train/")
B_logs = glob.glob("zone-good/RGCN/*/train/")
C_logs = glob.glob("zone-good/myopic/*/train/")
logs = [A_logs, B_logs, C_logs]
labels = ["GNN+progression+pretraining", "GNN+progression", "Myopic"]
horizon = 20000000
fig, (ax1) = plt.subplots(1, 1)
fig.set_size_inches(7,5)
plt.ylabel("Total reward", fontsize = 16)
plt.tick_params(labelsize=12)
plt.title("Avoidance Tasks", fontsize = 16)
ax1.xaxis.set_major_formatter(FormatStrFormatter('%d'))
for data_line, label in zip(logs, labels):
X, Y_avg, Y_lower, Y_upper = read_data_and_average(data_line, tag="return_mean", MAX_HORIZON=horizon) #average_discounted_return, average_discounted_return, average_reward_per_step
X = [x / 1000000 for x in X]
if label == "GNN+progression+pretraining":
color = '#377eb8'
style = "dotted"
elif label == "Myopic":
color = '#f781bf'
style = "-"
elif label == "GNN+progression":
color = '#377eb8'
style = "-"
else:
raise Exception("")
plt.plot(X, Y_avg, linewidth = 2, label=label, color=color, linestyle=style)
plt.fill_between(X, Y_lower, Y_upper, alpha=0.2, color=color)
plt.xlabel('Frames (millions)', fontsize = 16)
handles, labels = ax1.get_legend_handles_labels()
legend = plt.legend(handles, labels, markerscale=6, fontsize=16)
for i in range(len(legend.get_lines())):
legend.get_lines()[i].set_linewidth(3)
plt.savefig("figs/safety.pdf")
plt.show()
def plot_toy_experiments():
# Toy
A_logs = glob.glob("toy-minigrid/RGCN/*/train/")
B_logs = glob.glob("toy-minigrid/myopic/*/train/")
logs = [A_logs, B_logs]
labels = ["GNN+progression", "Myopic"]
horizon = 3000000
fig, (ax1) = plt.subplots(1, 1)
fig.set_size_inches(7,5)
plt.ylabel("Total reward", fontsize = 16)
plt.tick_params(labelsize=12)
#plt.title("Avoidance Tasks", fontsize = 16)
#ax1.xaxis.set_major_formatter(ScalarFormatter())
plt.ylim([0,1.05])
for data_line, label in zip(logs, labels):
X, Y_avg, Y_lower, Y_upper = read_data_and_average(data_line, tag="return_mean", MAX_HORIZON=horizon) #average_discounted_return, average_discounted_return, average_reward_per_step
X = [x / 1000000 for x in X]
if label == "GNN+progression+pretraining":
color = '#377eb8'
style = "dotted"
elif label == "Myopic":
color = '#f781bf'
style = "-"
elif label == "GNN+progression":
color = '#377eb8'
style = "-"
else:
raise Exception("")
plt.plot(X, Y_avg, linewidth = 2, label=label, color=color, linestyle=style)
plt.fill_between(X, Y_lower, Y_upper, alpha=0.2, color=color)
plt.xlabel('Frames (millions)', fontsize = 16)
handles, labels = ax1.get_legend_handles_labels()
legend = plt.legend(handles, labels, markerscale=6, fontsize=16, loc="lower right")
for i in range(len(legend.get_lines())):
legend.get_lines()[i].set_linewidth(3)
plt.savefig("figs/toy.pdf")
plt.show()
# # # Letter-Env UNTIL_1_3_1_2
# A_logs = glob.glob("storage-good/RGCN/*Until*/train/")
# B_logs = glob.glob("storage-good/GRU/*Until*/train/")
# C_logs = glob.glob("storage-good/LSTM/*Until*/train/")
# D_logs = glob.glob("storage-good/Myopic/*Until*/train/")
# E_logs = glob.glob("storage-good/GRU-no-progression/*Until*/train/")
# F_logs = glob.glob("storage-good/No-LTL/*Until*/train/")
# lines = [(A_logs, "GNN+progression"), (B_logs, "GRU+progression"), (C_logs, "LSTM+progression"), (D_logs, "Myopic"), (E_logs, "GRU"), (F_logs, "No LTL")]
# MAX_HORIZON = 20000000
# title = "Letter-Env: Until_1_3_1_2"
# # Letter-Env EVENTUALLY_1_5_1_4
# A_logs = glob.glob("storage-good/RGCN/*Eventually*/train/")
# B_logs = glob.glob("storage-good/GRU/*Eventually*/train/")
# C_logs = glob.glob("storage-good/LSTM/*Eventually*/train/")
# D_logs = glob.glob("storage-good/Myopic/*Eventually*/train/")
# E_logs = glob.glob("storage-good/GRU-no-progression/*Eventually*/train/")
# F_logs = glob.glob("storage-good/No-LTL/*Eventually*/train/")
# lines = [(A_logs, "GNN+progression"), (B_logs, "GRU+progression"), (C_logs, "LSTM+progression"), (D_logs, "Myopic"), (E_logs, "GRU"), (F_logs, "No LTL")]
# MAX_HORIZON = 20000000
# title = "Letter-Env: Eventually_1_5_1_4"
# #Symbol-Env UNTIL_1_3_1_2
# C_logs = glob.glob("symbol-storage/RGCN/*Until_1_3_1_2*/train/")
# D_logs = glob.glob("symbol-storage/GRU/*Until_1_3_1_2*/train/")
# lines = [(C_logs, "GNN"), (D_logs, "GRU")]
# title = "Symbol Env: Until_1_3_1_2"
# #Symbol-Env EVENTUALLY_1_5_1_4
# A_logs = glob.glob("symbol-storage/RGCN/*Eventually_1_5_1_4*/train/")
# B_logs = glob.glob("symbol-storage/GRU/*Eventually_1_5_1_4*epochs:4*lr:0.003*/train/")
# lines = [(A_logs, "GNN"), (B_logs, "GRU")]
# title = "Symbol Env: Eventually_1_5_1_4"
#=============================================================================================================================================
# ## NOTE: For the transfer results you need to comment out the code at the bottom.
# # Transfer Until_1_3_1_2
# A1_logs = glob.glob("storage-good/RGCN/*Until_1_3_1_2*/train/")
# A2_logs = glob.glob("transfer-storage/RGCN/*pretrained_Until_1_3_1_2*/train/")
# A3_logs = glob.glob("transfer-storage/RGCN/*pretrained-freeze_ltl_Until_1_3_1_2*bs:64*/train/")
# B1_logs = glob.glob("storage-good/GRU/*Until_1_3_1_2*/train/")
# B2_logs = glob.glob("transfer-storage/GRU/*pretrained_Until_1_3_1_2*/train/")
# B3_logs = glob.glob("transfer-storage/GRU/*pretrained-freeze_ltl_Until_1_3_1_2*/train/")
# lines_A = [(A1_logs, "GNN", "-"), (A2_logs, "GNN+pretraining", "dotted")]
# lines_B = [(B1_logs, "GRU", "-"), (B2_logs, "GRU+pretraining", "dotted")]
# MAX_HORIZON = 10000000
# title = "Transfer - Letter-Env: Until_1_3_1_2"
# plt.ylabel("Discounted return")
# plt.xlabel("Frames")
# for data_line, label, linestyle in lines_A:
# X, Y_avg, Y_lower, Y_upper = read_data_and_average(data_line, tag="average_discounted_return") #return_mean, average_discounted_return, average_reward_per_step
# plt.plot(X, Y_avg, linewidth = 1, label=label, linestyle=linestyle, color="b")
# plt.fill_between(X, Y_lower, Y_upper, alpha=0.2, color="b")
# for data_line, label, linestyle in lines_B:
# X, Y_avg, Y_lower, Y_upper = read_data_and_average(data_line, tag="average_discounted_return") #return_mean, average_discounted_return, average_reward_per_step
# plt.plot(X, Y_avg, linewidth = 1, label=label, linestyle=linestyle, color="orange")
# plt.fill_between(X, Y_lower, Y_upper, alpha=0.2, color="orange")
# plt.legend()
# plt.show()
#=============================================================================================================================================
# #Transfer EVENTUALLY_1_5_1_4
# A1_logs = glob.glob("storage-good/RGCN/*Eventually_1_5_1_4*/train/")
# A2_logs = glob.glob("transfer-storage/RGCN/*pretrained_Eventually_1_5_1_4*/train/")
# A3_logs = glob.glob("transfer-storage/RGCN/*pretrained-freeze_ltl_Eventually_1_5_1_4*bs:64*/train/")
# B1_logs = glob.glob("storage-good/GRU/*Eventually_1_5_1_4*/train/")
# B2_logs = glob.glob("transfer-storage/GRU/*pretrained_Eventually_1_5_1_4*/train/")
# B3_logs = glob.glob("transfer-storage/GRU/*pretrained-freeze_ltl_Eventually_1_5_1_4*/train/")
# lines_A = [(A1_logs, "GNN", "-"), (A2_logs, "GNN+pretraining", "dotted")]
# lines_B = [(B1_logs, "GRU", "-"), (B2_logs, "GRU+pretraining", "dotted")]
# MAX_HORIZON = 10000000
# title = "Transfer - Letter-Env: Eventually_1_5_1_4"
# plt.ylabel("Discounted return")
# plt.xlabel("Frames")
# for data_line, label, linestyle in lines_A:
# X, Y_avg, Y_lower, Y_upper = read_data_and_average(data_line, tag="average_discounted_return") #return_mean, average_discounted_return, average_reward_per_step
# plt.plot(X, Y_avg, linewidth = 1, label=label, linestyle=linestyle, color="b")
# plt.fill_between(X, Y_lower, Y_upper, alpha=0.2, color="b")
# for data_line, label, linestyle in lines_B:
# X, Y_avg, Y_lower, Y_upper = read_data_and_average(data_line, tag="average_discounted_return") #return_mean, average_discounted_return, average_reward_per_step
# plt.plot(X, Y_avg, linewidth = 1, label=label, linestyle=linestyle, color="orange")
# plt.fill_between(X, Y_lower, Y_upper, alpha=0.2, color="orange")
# plt.legend()
# plt.show()
#=============================================================================================================================================
# #Safety
# A_logs = glob.glob("zone-good/pretrained/*/train/")
# B_logs = glob.glob("zone-good/myopic/*/train/")
# lines = [(A_logs, "Best GNN so far"),(B_logs, "Best Myopic")]
# title = "Safety"
# #=========================================
# # Toy
# # A_logs = glob.glob("toy-minigrid/RGCN/*/train/")
# # B_logs = glob.glob("toy-minigrid/myopic/*/train/")
# # lines = [(A_logs, "GNN"), (B_logs, "Myopic")]
# # title = "Toy Minigrid"
# plt.figure(figsize=(4,3))
# plt.ylabel("Discounted return")
# plt.xlabel("Frames")
# plt.title(title)
# for data_line, label in lines:
# X, Y_avg, Y_lower, Y_upper = read_data_and_average(data_line, tag="return_mean") #return_mean, average_discounted_return, average_reward_per_step
# plt.plot(X, Y_avg, linewidth = 1, label=label)
# plt.fill_between(X, Y_lower, Y_upper, alpha=0.2)
# plt.legend()
# # plt.savefig("figs/" + title + ".pdf")
# plt.show()
#plot_letter_env_experiments()
# plot_pretraining_experiments()
plot_safety_experiments()
# plot_toy_experiments()