-
Notifications
You must be signed in to change notification settings - Fork 350
/
plot_graph.py
142 lines (91 loc) · 4.89 KB
/
plot_graph.py
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
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
import os
import pandas as pd
import matplotlib.pyplot as plt
def save_graph():
print("============================================================================================")
# env_name = 'CartPole-v1'
# env_name = 'LunarLander-v2'
# env_name = 'BipedalWalker-v2'
env_name = 'RoboschoolWalker2d-v1'
fig_num = 0 #### change this to prevent overwriting figures in same env_name folder
plot_avg = True # plot average of all runs; else plot all runs separately
fig_width = 10
fig_height = 6
# smooth out rewards to get a smooth and a less smooth (var) plot lines
window_len_smooth = 20
min_window_len_smooth = 1
linewidth_smooth = 1.5
alpha_smooth = 1
window_len_var = 5
min_window_len_var = 1
linewidth_var = 2
alpha_var = 0.1
colors = ['red', 'blue', 'green', 'orange', 'purple', 'olive', 'brown', 'magenta', 'cyan', 'crimson','gray', 'black']
# make directory for saving figures
figures_dir = "PPO_figs"
if not os.path.exists(figures_dir):
os.makedirs(figures_dir)
# make environment directory for saving figures
figures_dir = figures_dir + '/' + env_name + '/'
if not os.path.exists(figures_dir):
os.makedirs(figures_dir)
fig_save_path = figures_dir + '/PPO_' + env_name + '_fig_' + str(fig_num) + '.png'
# get number of log files in directory
log_dir = "PPO_logs" + '/' + env_name + '/'
current_num_files = next(os.walk(log_dir))[2]
num_runs = len(current_num_files)
all_runs = []
for run_num in range(num_runs):
log_f_name = log_dir + '/PPO_' + env_name + "_log_" + str(run_num) + ".csv"
print("loading data from : " + log_f_name)
data = pd.read_csv(log_f_name)
data = pd.DataFrame(data)
print("data shape : ", data.shape)
all_runs.append(data)
print("--------------------------------------------------------------------------------------------")
ax = plt.gca()
if plot_avg:
# average all runs
df_concat = pd.concat(all_runs)
df_concat_groupby = df_concat.groupby(df_concat.index)
data_avg = df_concat_groupby.mean()
# smooth out rewards to get a smooth and a less smooth (var) plot lines
data_avg['reward_smooth'] = data_avg['reward'].rolling(window=window_len_smooth, win_type='triang', min_periods=min_window_len_smooth).mean()
data_avg['reward_var'] = data_avg['reward'].rolling(window=window_len_var, win_type='triang', min_periods=min_window_len_var).mean()
data_avg.plot(kind='line', x='timestep' , y='reward_smooth',ax=ax,color=colors[0], linewidth=linewidth_smooth, alpha=alpha_smooth)
data_avg.plot(kind='line', x='timestep' , y='reward_var',ax=ax,color=colors[0], linewidth=linewidth_var, alpha=alpha_var)
# keep only reward_smooth in the legend and rename it
handles, labels = ax.get_legend_handles_labels()
ax.legend([handles[0]], ["reward_avg_" + str(len(all_runs)) + "_runs"], loc=2)
else:
for i, run in enumerate(all_runs):
# smooth out rewards to get a smooth and a less smooth (var) plot lines
run['reward_smooth_' + str(i)] = run['reward'].rolling(window=window_len_smooth, win_type='triang', min_periods=min_window_len_smooth).mean()
run['reward_var_' + str(i)] = run['reward'].rolling(window=window_len_var, win_type='triang', min_periods=min_window_len_var).mean()
# plot the lines
run.plot(kind='line', x='timestep' , y='reward_smooth_' + str(i),ax=ax,color=colors[i % len(colors)], linewidth=linewidth_smooth, alpha=alpha_smooth)
run.plot(kind='line', x='timestep' , y='reward_var_' + str(i),ax=ax,color=colors[i % len(colors)], linewidth=linewidth_var, alpha=alpha_var)
# keep alternate elements (reward_smooth_i) in the legend
handles, labels = ax.get_legend_handles_labels()
new_handles = []
new_labels = []
for i in range(len(handles)):
if(i%2 == 0):
new_handles.append(handles[i])
new_labels.append(labels[i])
ax.legend(new_handles, new_labels, loc=2)
# ax.set_yticks(np.arange(0, 1800, 200))
# ax.set_xticks(np.arange(0, int(4e6), int(5e5)))
ax.grid(color='gray', linestyle='-', linewidth=1, alpha=0.2)
ax.set_xlabel("Timesteps", fontsize=12)
ax.set_ylabel("Rewards", fontsize=12)
plt.title(env_name, fontsize=14)
fig = plt.gcf()
fig.set_size_inches(fig_width, fig_height)
print("============================================================================================")
plt.savefig(fig_save_path)
print("figure saved at : ", fig_save_path)
print("============================================================================================")
plt.show()
if __name__ == '__main__':
save_graph()