From 8b97a36e01bbd3b302f8fc747054c759d04f8ab8 Mon Sep 17 00:00:00 2001 From: Mingxuan Che Date: Tue, 22 Oct 2024 15:12:49 +0200 Subject: [PATCH] update spider plotting script --- .../quadrotor/plotting/plot_radar.py | 190 +++++++++++++++--- .../quadrotor/plotting/plot_radar.sh | 5 + 2 files changed, 167 insertions(+), 28 deletions(-) create mode 100644 benchmarking_sim/quadrotor/plotting/plot_radar.sh diff --git a/benchmarking_sim/quadrotor/plotting/plot_radar.py b/benchmarking_sim/quadrotor/plotting/plot_radar.py index dbd775aea..a79af881a 100644 --- a/benchmarking_sim/quadrotor/plotting/plot_radar.py +++ b/benchmarking_sim/quadrotor/plotting/plot_radar.py @@ -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()} @@ -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], ) \ No newline at end of file diff --git a/benchmarking_sim/quadrotor/plotting/plot_radar.sh b/benchmarking_sim/quadrotor/plotting/plot_radar.sh new file mode 100644 index 000000000..a77fe258b --- /dev/null +++ b/benchmarking_sim/quadrotor/plotting/plot_radar.sh @@ -0,0 +1,5 @@ + +for IDX in `seq 0 1 5` +do + python3 plot_radar.py $IDX +done \ No newline at end of file