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update spider plotting script
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@MingxuanChe
MingxuanChe committed Oct 22, 2024
1 parent b31ad00 commit 8b97a36
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190 changes: 162 additions & 28 deletions benchmarking_sim/quadrotor/plotting/plot_radar.py
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Original file line number Diff line number Diff line change
@@ -1,20 +1,43 @@
import pandas as pd
import os
import sys

import numpy as np
from matplotlib import pyplot as plt
import pandas as pd

script_dir = os.path.dirname(__file__)

# get the pyplot default color wheel
prop_cycle = plt.rcParams['axes.prop_cycle']
colors = prop_cycle.by_key()['color']

def spider(df, *, id_column, title=None, subtitle=None, max_values=None, padding=1.25):
plot_colors = {
'GP-MPC': colors[0],
'PPO': colors[1],
'SAC': colors[3],
# 'iLQR': 'darkgray',
'DPPO': colors[6],
'Linear-MPC': colors[2],
'MPC': colors[-1],
'MAX': 'none',
'MIN': 'none',
}

axis_label_fontsize = 30
text_fontsize = 30
supertitle_fontsize = 30
subtitle_fontsize = 30
small_text_size = 20

def spider(df, *, id_column, title=None, subtitle=None, max_values=None, padding=1.25, plt_name=''):
categories = df._get_numeric_data().columns.tolist()
data = df[categories].to_dict(orient='list')
ids = df[id_column].tolist()

lower_padding = (padding - 1)/2
# lower_padding = 0
lower_padding = (padding - 1)/2
# upper_padding = 1 + lower_padding * 2
upper_padding = 1 + 7 * lower_padding
upper_padding = 1 + 7* lower_padding
# upper_padding = 1.05

if max_values is None:
max_values = {key: upper_padding*max(value) for key, value in data.items()}
Expand All @@ -23,52 +46,163 @@ def spider(df, *, id_column, title=None, subtitle=None, max_values=None, padding
num_vars = len(data.keys())
tiks = list(data.keys())
tiks += tiks[:1]
print('tiks:', tiks)
angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist() + [0]
fig, ax = plt.subplots(figsize=(10, 8), subplot_kw=dict(polar=True), )
for i, model_name in enumerate(ids):
values = [normalized_data[key][i] for key in data.keys()]
actual_values = [data[key][i] for key in data.keys()]

# Invert the values to have the higher values in the center
values[0:5] = 1 - np.array(values[0:5])

values += values[:1] # Close the plot for a better look
values = 1 - np.array(values) # Invert the values to have the higher values in the center
ax.plot(angles, values, label=model_name,)
ax.scatter(angles, values, facecolor=colors[i])
ax.fill(angles, values, alpha=0.15)
# values = 1 - np.array(values)
if model_name in ['MAX', 'MIN']:
ax.plot(angles, values, color=plot_colors[model_name],)
ax.scatter(angles, values, facecolor=plot_colors[model_name], )
ax.fill(angles, values, alpha=0.15, color=plot_colors[model_name], )
continue
else:
ax.plot(angles, values, label=model_name, color=plot_colors[model_name],)
ax.scatter(angles, values, facecolor=plot_colors[model_name], )
ax.fill(angles, values, alpha=0.15, color=plot_colors[model_name],)
for _x, _y, t in zip(angles, values, actual_values):
if _x == angles[2]:
t = f'{t:.4f}' if isinstance(t, float) else str(t)
elif _x == angles[4]: # sampling complexity
if t == int(1):
# _y = 0.01
t = '0'
else:
# write number in 1e5 format
t = f'{t:.1E}' # if isinstance(t, float) else str(t)
# elif _x == angles[0]:
# t = f'{t:.2f}' if isinstance(t, float) else str(t)
else:
t = f'{t:.2f}' if isinstance(t, float) else str(t)
t = f'{t:.3f}' if isinstance(t, float) else str(t)
if t=='1': t = 'Model-free'
if t=='2': t = 'Linear model'
if t=='3': t = 'Nonlinear model'
if t=='2': t = ' Linear\n model'
if t=='3': t = 'Nonlinear\n model'

t = t.center(10, ' ')
if _x== angles[3]:
ax.text(_x, _y+0.15, t, size=small_text_size)
elif model_name == 'GP-MPC':
if _x == angles[0]:
ax.text(_x+0.05, _y-0.1, t, size=small_text_size)
elif _x == angles[4]:
ax.text(_x, _y-0.05, t, size=small_text_size)
else:
ax.text(_x, _y-0.01, t, size=small_text_size)

ax.text(_x, _y, t, size=10)
elif model_name == 'DPPO':
# if _x == angles[5]:
# ax.text(_x, _y-0.1, t, size=small_text_size)
# if _x == angles[0]: # generalization performance
# ax.text(_x-0.1, _y-0.05, t, size=small_text_size)
# elif _x == angles[2]: # inference time
# ax.text(_x+0.1, _y-0.05, t, size=small_text_size)
if _x == angles[4]: # sampling complexity
ax.text(_x, _y+0.15, t, size=small_text_size)
else:
ax.text(_x, _y-0.01, t, size=small_text_size)
else:
ax.text(_x, _y-0.01, t, size=small_text_size)

ax.fill(angles, np.ones(num_vars + 1), alpha=0.05)
ax.fill(angles, np.ones(num_vars + 1), alpha=0.05, color='lightgray')
# ax.fill(angles[0:3], np.ones(3), alpha=0.05)
ax.set_yticklabels([])
ax.set_xticks(angles)
ax.set_xticklabels(tiks, fontsize=11)
ax.legend(loc='upper right', bbox_to_anchor=(0.1, 0.1), fontsize=14)
if title is not None: plt.suptitle(title, fontsize=20)
if subtitle is not None: plt.title(subtitle, fontsize=10)
ax.set_xticklabels(tiks, fontsize=axis_label_fontsize)
ax.legend(loc='upper right', bbox_to_anchor=(0.1, 0.2), fontsize=text_fontsize)
if title is not None: plt.suptitle(title, fontsize=supertitle_fontsize)
if subtitle is not None: plt.title(subtitle, fontsize=subtitle_fontsize)
# plt.show()
fig.savefig('result_2.png')
fig_save_path = os.path.join(script_dir, f'{plt_name}_radar.png')
fig.savefig(fig_save_path, dpi=300, bbox_inches='tight')
print(f'figure saved as {fig_save_path}')

radar = spider

num_axis = 6
gen_performance = [0.03876024, # GP-MPC
0.11634496692276783, # Linear-MPC
0.026798393095810013, # MPC
0.2525554383641447, # PPO
0.11929008866846896, # SAC
0.16222871994809432, # DPPO
]

performance = [0.05573254, # GP-MPC
0.06775482275993325, # Linear-MPC
0.05096096290371684, # MPC
0.029015002554717714, # PPO
0.0764178745409007, # SAC
0.06116360497829662, # DPPO
]

inference_time = [0.0090775150246974736,
0.0011251235,
0.0061547613,
0.00020738168999000832,
0.00024354409288477016,
0.0001976909460844817,
]

model_complexity = [3, 2, 3, 1, 1, 1,]
sampling_complexity = [int(660), int(1), int(1), int(3.2*1e5), int(2*1e5), int(3.2*1e5), ]
robustness = [120, 90, 90, 10, 30, 20, ]

data = [gen_performance, performance, inference_time, model_complexity, sampling_complexity, robustness]

max_values = [np.max(gen_performance), np.max(performance), np.max(inference_time), np.max(model_complexity), np.max(sampling_complexity), np.max(robustness)]
min_values = [np.min(gen_performance), np.min(performance), np.min(inference_time), np.min(model_complexity), np.min(sampling_complexity), np.min(robustness)]

for i, d in enumerate(data):
data[i].append(max_values[i])
data[i].append(min_values[i])

# apppend the max and min values to the data

algos = ['GP-MPC', 'Linear-MPC', 'MPC' , 'PPO', 'SAC', 'DPPO', 'MAX', 'MIN']

# read the argv
if len(sys.argv) > 1:
masks_algo = [int(i) for i in sys.argv[1:]]
masks_algo.append(6)
masks_algo.append(7)
else:
masks_algo = [ 6, 7,]
data = np.array(data)[:, masks_algo]
data = data.tolist()
algos = [algos[i] for i in masks_algo]

spider(
pd.DataFrame({
# 'x': [*'ab'],
'x': ['GP-MPC', 'PPO'],
'$\qquad\qquad\qquad\quad$ Performance\n': [3.94646538e-02, 0.03],
'x': algos,
'$\qquad\qquad\qquad\quad$ Generalization\n $\qquad\qquad\qquad\quad$ performance\n\n':
data[0],
'$\qquad\qquad\qquad\quad$ Performance\n':
data[1],
# '$\quad\quad\quad\quad\quad\qquad$(Figure-8 tracking)': [3.94646538e-02, 0.03],
'Generalization\nperformance\n': [0.024646868904967967, 0.1],
'Inference\ntime\n\n': [0.016518109804624086, 0.0001351369751824273],
'\n\n\nModel\ncomplexity ': [3, 1],
'\nSampling\ncomplexity': [int(540), int(80*1e3)]
'Inference\ntime\n\n':
data[2],
'Model \nknowledge ':
[int(data[3][i]) for i in range(len(data[3]))],
'\n\n\nSampling\ncomplexity':
data[4],
'\n\nRobustness':
[int(data[5][i]) for i in range(len(data[5]))],
}),

id_column='x',
title=' Overall Comparison',
subtitle='(Normalized linear scale)',
padding=1.1
# title=' Overall Comparison',
# title = algos[0],
title=None,
# subtitle='(Normalized linear scale)',
padding=1.1,
# padding=1,
plt_name=algos[0],
)
5 changes: 5 additions & 0 deletions benchmarking_sim/quadrotor/plotting/plot_radar.sh
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@@ -0,0 +1,5 @@

for IDX in `seq 0 1 5`
do
python3 plot_radar.py $IDX
done

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