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@pmusau17
pmusau17 committed Apr 6, 2022
1 parent 6d03996 commit d9b6f7c
Showing 1 changed file with 34 additions and 35 deletions.
69 changes: 34 additions & 35 deletions src/mpc/logs/IFM.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -24,7 +24,7 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -65,7 +65,10 @@
" new_df = df.groupby(\"racecar_name\").median()\n",
" new_df['efficiency'] = (new_df['laps_completed'] / new_df['race_time']) * track_lengths[track]\n",
" new_df = new_df.reset_index()\n",
" row = [track_names[track],controller_names[controller],target_controllers[opp],target_controllers[target],new_df[new_df['racecar_name'] =='racecar2']['efficiency'].values[0],new_df[new_df['racecar_name'] =='racecar']['efficiency'].values[0],new_df['race_time'][0],val]\n",
" if(controller=='mpc_hype' or controller == \"mpcc\"):\n",
" row = [track_names[track],controller_names[controller],target_controllers[opp],target_controllers[target],new_df[new_df['racecar_name'] =='racecar2']['efficiency'].values[0],new_df[new_df['racecar_name'] =='racecar']['efficiency'].values[0],new_df['race_time'][0],val]\n",
" else:\n",
" row = [track_names[track],controller_names[controller],target_controllers[opp],controller_names[controller],new_df[new_df['racecar_name'] =='racecar2']['efficiency'].values[0],new_df[new_df['racecar_name'] =='racecar']['efficiency'].values[0],new_df['race_time'][0],val]\n",
" rows.append(row)\n",
"\n",
" if(new_df['efficiency'].max()>6):\n",
Expand All @@ -86,7 +89,7 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 15,
"metadata": {},
"outputs": [
{
Expand All @@ -98,24 +101,19 @@
"{} & Approach & Opponent Controller & Local Planner & Ego Efficiency & Opponent Efficiency & Race Duration (s) & Safety \\\\\n",
"Track & & & & & & & \\\\\n",
"\\midrule\n",
"Barca & DE & DE & DE & 5.12 & 4.50 & 7.63 & 0.00 \\\\\n",
"Barca & DE & DE & Pure Pursuit & 5.16 & 4.47 & 7.69 & 0.00 \\\\\n",
"Barca & DE & DE & DE & 5.14 & 4.48 & 7.66 & 0.00 \\\\\n",
"Barca & MPC Hype & DE & DE & 0.00 & 5.85 & 5.47 & 0.00 \\\\\n",
"Barca & MPC Hype & DE & Pure Pursuit & 0.06 & 5.74 & 5.49 & 0.00 \\\\\n",
"Barca & MPCC & DE & DE & 3.18 & 3.01 & 11.26 & 0.00 \\\\\n",
"Barca & MPCC & DE & Pure Pursuit & 3.01 & 3.09 & 10.90 & 3.33 \\\\\n",
"Barca & Pure Pursuit & DE & DE & 5.25 & 4.63 & 7.34 & 0.00 \\\\\n",
"Barca & Pure Pursuit & DE & Pure Pursuit & 5.24 & 4.83 & 7.38 & 0.00 \\\\\n",
"Porto & DE & DE & DE & 5.27 & 4.20 & 46.58 & 26.67 \\\\\n",
"Porto & DE & DE & Pure Pursuit & 5.31 & 5.11 & 56.55 & 50.00 \\\\\n",
"Barca & Pure Pursuit & DE & Pure Pursuit & 5.25 & 4.73 & 7.36 & 0.00 \\\\\n",
"Porto & DE & DE & DE & 5.29 & 4.65 & 51.57 & 38.33 \\\\\n",
"Porto & MPC Hype & DE & DE & 0.00 & 5.27 & 5.53 & 0.00 \\\\\n",
"Porto & MPC Hype & DE & Pure Pursuit & 3.06 & 5.18 & 25.74 & 13.33 \\\\\n",
"Porto & MPCC & DE & DE & 3.00 & 4.97 & 7.12 & 20.00 \\\\\n",
"Porto & MPCC & DE & Pure Pursuit & 3.00 & 5.34 & 55.14 & 46.67 \\\\\n",
"Porto & Pure Pursuit & DE & DE & 4.70 & 5.34 & 57.49 & 100.00 \\\\\n",
"Porto & Pure Pursuit & DE & Pure Pursuit & 4.70 & 5.33 & 57.51 & 100.00 \\\\\n",
"Walker & DE & DE & DE & 4.49 & 4.97 & 7.02 & 0.00 \\\\\n",
"Walker & DE & DE & Pure Pursuit & 4.51 & 4.98 & 6.94 & 50.00 \\\\\n",
"Porto & Pure Pursuit & DE & Pure Pursuit & 4.70 & 5.33 & 57.50 & 100.00 \\\\\n",
"Walker & DE & DE & DE & 4.50 & 4.98 & 6.98 & 25.00 \\\\\n",
"Walker & MPC Hype & DE & DE & 0.06 & 4.72 & 5.51 & 3.33 \\\\\n",
"Walker & MPC Hype & DE & Pure Pursuit & 1.34 & 4.71 & 5.54 & 0.00 \\\\\n",
"Walker & MPCC & DE & DE & 3.24 & 5.10 & 38.94 & 10.00 \\\\\n",
Expand All @@ -130,6 +128,7 @@
"source": [
"rows = summarize_directory('two_vehicle_experiments')\n",
"df = pd.DataFrame(rows, columns =['Track', 'Approach','Opponent Controller','Local Planner','Ego Efficiency','Opponent Efficiency','Race Duration (s)','Safety'])\n",
"df = df.groupby(['Track', 'Approach','Opponent Controller','Local Planner']).mean().reset_index()\n",
"df = round(df,2)\n",
"df = df.set_index('Track').sort_values(by=[\"Track\",\"Approach\"])\n",
"print(df.to_latex())"
Expand All @@ -156,30 +155,24 @@
"{} & Approach & Opponent Controller & Local Planner & Ego Efficiency & Opponent Efficiency & Race Duration (s) & Safety \\\\\n",
"Track & & & & & & & \\\\\n",
"\\midrule\n",
"Barca & DE & DE & DE & 5.06 & 4.60 & 6.09 & 0.00 \\\\\n",
"Barca & DE & DE & Pure Pursuit & 5.04 & 4.57 & 6.05 & 0.00 \\\\\n",
"Barca & DE & DE & DE & 5.05 & 4.59 & 6.07 & 0.00 \\\\\n",
"Barca & MPC Hype & DE & DE & 0.69 & 5.11 & 5.37 & 0.00 \\\\\n",
"Barca & MPC Hype & DE & Pure Pursuit & 0.09 & 4.64 & 5.36 & 0.00 \\\\\n",
"Barca & MPCC & DE & DE & 3.30 & 2.70 & 10.60 & 0.00 \\\\\n",
"Barca & MPCC & DE & Pure Pursuit & 3.37 & 3.10 & 9.97 & 0.00 \\\\\n",
"Barca & Pure Pursuit & DE & DE & 5.41 & 3.97 & 7.15 & 0.00 \\\\\n",
"Barca & Pure Pursuit & DE & Pure Pursuit & 5.40 & 3.93 & 7.14 & 0.00 \\\\\n",
"Porto & DE & DE & DE & 5.38 & 4.38 & 33.76 & 30.00 \\\\\n",
"Porto & DE & DE & Pure Pursuit & 5.39 & 3.82 & 33.81 & 26.67 \\\\\n",
"Barca & Pure Pursuit & DE & Pure Pursuit & 5.41 & 3.95 & 7.14 & 0.00 \\\\\n",
"Porto & DE & DE & DE & 5.38 & 4.10 & 33.78 & 28.33 \\\\\n",
"Porto & MPC Hype & DE & DE & 1.19 & 4.50 & 5.40 & 0.00 \\\\\n",
"Porto & MPC Hype & DE & Pure Pursuit & 2.75 & 2.96 & 43.26 & 30.00 \\\\\n",
"Porto & MPCC & DE & DE & 1.66 & 4.23 & 5.39 & 3.33 \\\\\n",
"Porto & MPCC & DE & Pure Pursuit & 1.83 & 4.00 & 5.37 & 16.67 \\\\\n",
"Porto & Pure Pursuit & DE & DE & 4.70 & 3.75 & 57.31 & 73.33 \\\\\n",
"Porto & Pure Pursuit & DE & Pure Pursuit & 4.70 & 3.71 & 57.30 & 66.67 \\\\\n",
"Walker & DE & DE & DE & 4.69 & 5.22 & 5.45 & 3.33 \\\\\n",
"Walker & DE & DE & Pure Pursuit & 4.67 & 5.24 & 5.43 & 0.00 \\\\\n",
"Porto & Pure Pursuit & DE & Pure Pursuit & 4.70 & 3.73 & 57.30 & 70.00 \\\\\n",
"Walker & DE & DE & DE & 4.68 & 5.23 & 5.44 & 1.67 \\\\\n",
"Walker & MPC Hype & DE & DE & 0.99 & 5.27 & 5.36 & 0.00 \\\\\n",
"Walker & MPC Hype & DE & Pure Pursuit & 2.15 & 5.27 & 5.37 & 0.00 \\\\\n",
"Walker & MPCC & DE & DE & 3.29 & 3.27 & 54.99 & 50.00 \\\\\n",
"Walker & MPCC & DE & Pure Pursuit & 4.64 & 3.54 & 20.78 & 13.33 \\\\\n",
"Walker & Pure Pursuit & DE & DE & 6.35 & 5.21 & 57.30 & 83.33 \\\\\n",
"Walker & Pure Pursuit & DE & Pure Pursuit & 6.35 & 5.20 & 57.31 & 93.33 \\\\\n",
"Walker & Pure Pursuit & DE & Pure Pursuit & 6.35 & 5.21 & 57.31 & 88.33 \\\\\n",
"\\bottomrule\n",
"\\end{tabular}\n",
"\n"
Expand All @@ -189,6 +182,7 @@
"source": [
"rows = summarize_directory('three_vehicle_experiments')\n",
"df2 = pd.DataFrame(rows, columns =['Track', 'Approach','Opponent Controller','Local Planner','Ego Efficiency','Opponent Efficiency','Race Duration (s)','Safety'])\n",
"df2 = df2.groupby(['Track', 'Approach','Opponent Controller','Local Planner']).mean().reset_index()\n",
"df2 = round(df2,2)\n",
"df2 = df2.set_index('Track').sort_values(by=[\"Track\",\"Approach\"])\n",
"print(df2.to_latex())"
Expand All @@ -205,7 +199,7 @@
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 17,
"metadata": {},
"outputs": [
{
Expand All @@ -217,14 +211,12 @@
"{} & Approach & Opponent Controller & Local Planner & Ego Efficiency & Opponent Efficiency & Race Duration (s) & Safety \\\\\n",
"Track & & & & & & & \\\\\n",
"\\midrule\n",
"Porto & DE & DE & DE & 4.88 & 3.78 & 24.64 & 16.67 \\\\\n",
"Porto & DE & DE & Pure Pursuit & 5.03 & 4.08 & 21.21 & 10.00 \\\\\n",
"Porto & DE & DE & DE & 4.96 & 3.93 & 22.93 & 13.33 \\\\\n",
"Porto & MPC Hype & DE & DE & 1.08 & 4.77 & 5.39 & 0.00 \\\\\n",
"Porto & MPC Hype & DE & Pure Pursuit & 2.16 & 4.42 & 9.05 & 0.00 \\\\\n",
"Porto & MPCC & DE & DE & 2.78 & 5.20 & 11.32 & 0.00 \\\\\n",
"Porto & MPCC & DE & Pure Pursuit & 1.32 & 4.61 & 5.40 & 0.00 \\\\\n",
"Porto & Pure Pursuit & DE & DE & 4.43 & 4.82 & 15.46 & 0.00 \\\\\n",
"Porto & Pure Pursuit & DE & Pure Pursuit & 4.47 & 4.84 & 15.37 & 3.33 \\\\\n",
"Porto & Pure Pursuit & DE & Pure Pursuit & 4.45 & 4.83 & 15.42 & 1.67 \\\\\n",
"\\bottomrule\n",
"\\end{tabular}\n",
"\n"
Expand All @@ -234,6 +226,7 @@
"source": [
"rows = summarize_directory('dynamic_obstacle_experiments',obstacle=\"true\")\n",
"df3 = pd.DataFrame(rows, columns =['Track', 'Approach','Opponent Controller','Local Planner','Ego Efficiency','Opponent Efficiency','Race Duration (s)','Safety'])\n",
"df3 = df3.groupby(['Track', 'Approach','Opponent Controller','Local Planner']).mean().reset_index()\n",
"df3 = round(df3,2)\n",
"df3 = df3.set_index('Track').sort_values(by=[\"Track\",\"Approach\"])\n",
"print(df3.to_latex())"
Expand All @@ -248,7 +241,7 @@
},
{
"cell_type": "code",
"execution_count": 28,
"execution_count": 18,
"metadata": {},
"outputs": [
{
Expand All @@ -260,14 +253,12 @@
"{} & Approach & Opponent Controller & Local Planner & Ego Efficiency & Opponent Efficiency & Race Duration (s) & Safety \\\\\n",
"Track & & & & & & & \\\\\n",
"\\midrule\n",
"Porto & DE & DE & DE & 0.41 & 1.77 & 5.51 & 0.00 \\\\\n",
"Porto & DE & DE & Pure Pursuit & 1.05 & 1.74 & 5.43 & 0.00 \\\\\n",
"Porto & DE & DE & DE & 0.73 & 1.76 & 5.47 & 0.00 \\\\\n",
"Porto & MPC Hype & DE & DE & 0.92 & 2.13 & 5.57 & 0.00 \\\\\n",
"Porto & MPC Hype & DE & Pure Pursuit & 2.21 & 0.55 & 12.27 & 0.00 \\\\\n",
"Porto & MPCC & DE & DE & 1.24 & 1.46 & 7.66 & 6.67 \\\\\n",
"Porto & MPCC & DE & Pure Pursuit & 2.77 & 1.90 & 9.91 & 0.00 \\\\\n",
"Porto & Pure Pursuit & DE & DE & 3.01 & 1.15 & 6.47 & 0.00 \\\\\n",
"Porto & Pure Pursuit & DE & Pure Pursuit & 2.98 & 1.27 & 6.49 & 3.33 \\\\\n",
"Porto & Pure Pursuit & DE & Pure Pursuit & 3.00 & 1.21 & 6.48 & 1.67 \\\\\n",
"\\bottomrule\n",
"\\end{tabular}\n",
"\n"
Expand All @@ -277,10 +268,18 @@
"source": [
"rows = summarize_directory('static_obstacle_experiments',False,obstacle=\"false\")\n",
"df4 = pd.DataFrame(rows, columns =['Track', 'Approach','Opponent Controller','Local Planner','Ego Efficiency','Opponent Efficiency','Race Duration (s)','Safety'])\n",
"df4 = df4.groupby(['Track', 'Approach','Opponent Controller','Local Planner']).mean().reset_index()\n",
"df4 = round(df4,2)\n",
"df4 = df4.set_index('Track').sort_values(by=[\"Track\",\"Approach\"])\n",
"print(df4.to_latex())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
Expand Down

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