final version of demo notebook
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Amimine committed
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@@ -25,7 +25,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 1,
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"id": "5cf6f9f8",
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"metadata": {},
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"outputs": [],
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@@ -41,7 +41,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": 2,
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"id": "54c5e14f",
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"metadata": {},
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"outputs": [],
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@@ -91,7 +91,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": 3,
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"id": "f783ba32",
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"metadata": {},
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"outputs": [],
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@@ -167,7 +167,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"execution_count": 4,
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"id": "a7789c8c",
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"metadata": {},
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"outputs": [],
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@@ -181,6 +181,10 @@
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" # Standard variation of Winter and Summer of 2025 electric prices (Euros/MWh)\n",
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" elec_std = (elec_df['Winter 2025'].std() + elec_df['Summer 2025'].std())/2\n",
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"\n",
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" elec_mean = elec_mean / 1000\n",
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" elec_std = elec_std / 1000\n",
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"\n",
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"\n",
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" print(f'Electricity prices:\\n - Mean: ${elec_mean}€/Mwh\\n - Std: ${elec_std}€/Mwh')\n",
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" return elec_mean, elec_std\n",
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"\n",
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@@ -217,7 +221,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"execution_count": 5,
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"id": "a8797755",
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"metadata": {},
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"outputs": [],
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@@ -273,7 +277,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"execution_count": 11,
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"id": "6cdd4953",
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"metadata": {},
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"outputs": [
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@@ -282,8 +286,8 @@
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"output_type": "stream",
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"text": [
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"Electricity prices:\n",
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" - Mean: $84.69424€/Mwh\n",
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" - Std: $43.11248284136333€/Mwh\n",
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" - Mean: $0.08469423999999999€/Mwh\n",
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" - Std: $0.043112482841363334€/Mwh\n",
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"Capacities of vehicles (kwh): $33 83.0\n",
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"36 20.0\n",
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"4 72.8\n",
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@@ -306,75 +310,65 @@
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"5 75.0\n",
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"dtype: float64\n",
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"\n",
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"--- Launching Scenario: No AI ---\n",
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"Finished in 0.36 seconds.\n",
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"Best f2 found: 6.9499\n",
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"Graphique sauvegardé sous : No-AI_pareto_3d.png\n"
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]
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},
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{
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"data": {
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"image/png": 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truncated
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"text/plain": [
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"<Figure size 1200x800 with 2 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--- Launching Scenario: Only MOPSO ---\n",
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"Finished in 0.40 seconds.\n",
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"Best f2 found: 5.3493\n",
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"\n",
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"--- Launching Scenario: With MLP ---\n",
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"Finished in 0.56 seconds.\n",
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"Best f2 found: 5.1094\n",
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"Graphique sauvegardé sous : No-AI_pareto_3d.png\n"
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]
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},
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{
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"data": {
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"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1200x800 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Finished in 0.68 seconds.\n",
|
||||
"Best f2 found: 6.3994\n",
|
||||
"\n",
|
||||
"--- Launching Scenario: With Random Forest ---\n",
|
||||
"Finished in 0.47 seconds.\n",
|
||||
"Best f2 found: 4.0999\n",
|
||||
"Graphique sauvegardé sous : No-AI_pareto_3d.png\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1200x800 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Finished in 0.52 seconds.\n",
|
||||
"Best f2 found: 5.2397\n",
|
||||
"\n",
|
||||
"=== SUMMARY ===\n",
|
||||
"Mode | Time (s) | Best f2 \n",
|
||||
"---------------------------------------------\n",
|
||||
"No-AI | 0.36 | 6.9499 \n",
|
||||
"MLP | 0.56 | 5.1094 \n",
|
||||
"RF | 0.47 | 4.0999 \n"
|
||||
"--- Launching Scenario: Only MOPSO ---\n",
|
||||
"Finished in 0.96 seconds.\n",
|
||||
"Best f2 found: 4.5495\n",
|
||||
"\n",
|
||||
"--- Launching Scenario: With MLP ---\n",
|
||||
"Finished in 1.30 seconds.\n",
|
||||
"Best f2 found: 5.3998\n",
|
||||
"\n",
|
||||
"--- Launching Scenario: With Random Forest ---\n",
|
||||
"Finished in 1.12 seconds.\n",
|
||||
"Best f2 found: 4.4896\n",
|
||||
"\n",
|
||||
"--- Launching Scenario: Only MOPSO ---\n",
|
||||
"Finished in 9.63 seconds.\n",
|
||||
"Best f2 found: 5.4597\n",
|
||||
"\n",
|
||||
"--- Launching Scenario: With MLP ---\n",
|
||||
"Finished in 10.31 seconds.\n",
|
||||
"Best f2 found: 6.9999\n",
|
||||
"\n",
|
||||
"--- Launching Scenario: With Random Forest ---\n",
|
||||
"Finished in 9.77 seconds.\n",
|
||||
"Best f2 found: 5.7496\n",
|
||||
"\n",
|
||||
"--- Launching Scenario: Only MOPSO ---\n",
|
||||
"Finished in 18.79 seconds.\n",
|
||||
"Best f2 found: 6.6090\n",
|
||||
"\n",
|
||||
"--- Launching Scenario: With MLP ---\n",
|
||||
"Finished in 20.60 seconds.\n",
|
||||
"Best f2 found: 5.8495\n",
|
||||
"\n",
|
||||
"--- Launching Scenario: With Random Forest ---\n",
|
||||
"Finished in 22.21 seconds.\n",
|
||||
"Best f2 found: 3.4493\n",
|
||||
"\n",
|
||||
"--- Launching Scenario: Only MOPSO ---\n",
|
||||
"Finished in 196.01 seconds.\n",
|
||||
"Best f2 found: 5.0795\n",
|
||||
"\n",
|
||||
"--- Launching Scenario: With MLP ---\n",
|
||||
"Finished in 206.84 seconds.\n",
|
||||
"Best f2 found: 5.2493\n",
|
||||
"\n",
|
||||
"--- Launching Scenario: With Random Forest ---\n",
|
||||
"Finished in 202.25 seconds.\n",
|
||||
"Best f2 found: 5.4393\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -398,155 +392,57 @@
|
||||
"NB_TICKS = 48\n",
|
||||
"DELTA = 60\n",
|
||||
"\n",
|
||||
"results = {}\n",
|
||||
"results = {\n",
|
||||
" 'MOPSO':[],\n",
|
||||
" 'MLP': [],\n",
|
||||
" 'RF': []\n",
|
||||
"}\n",
|
||||
"nb_particles = [20,50,500,1000,10000]\n",
|
||||
"\n",
|
||||
"# 1. Without Surrogate (Baseline)\n",
|
||||
"d1, f1_score, archive1 = run_scenario(\n",
|
||||
" \"No AI\", \n",
|
||||
"for k in range(len(nb_particles)):\n",
|
||||
" # 1. Without Surrogate (Baseline)\n",
|
||||
" d1, f1_score, _ = run_scenario(\n",
|
||||
" \"Only MOPSO\", \n",
|
||||
" capacities=CAPACITIES, \n",
|
||||
" price_mean=P_MEAN, \n",
|
||||
" price_std=P_STD, \n",
|
||||
" nb_vehicles=nb_vehicle, # Important pour la cohérence\n",
|
||||
" model_type=None\n",
|
||||
")\n",
|
||||
"results['No-AI'] = (d1, f1_score)\n",
|
||||
"plot_pareto_3d(archive1, \"No-AI\")\n",
|
||||
" model_type=None,\n",
|
||||
" n=nb_particles[k]\n",
|
||||
" )\n",
|
||||
" results['MOPSO'].append((d1, f1_score))\n",
|
||||
"\n",
|
||||
"# 2. With MLP\n",
|
||||
"d2, f2_score, archive2 = run_scenario(\n",
|
||||
" # 2. With MLP\n",
|
||||
" d2, f2_score, _ = run_scenario(\n",
|
||||
" \"With MLP\", \n",
|
||||
" capacities=CAPACITIES, \n",
|
||||
" price_mean=P_MEAN, \n",
|
||||
" price_std=P_STD, \n",
|
||||
" nb_vehicles=nb_vehicle,\n",
|
||||
" model_type='mlp'\n",
|
||||
")\n",
|
||||
"results['MLP'] = (d2, f2_score)\n",
|
||||
"plot_pareto_3d(archive2, \"No-AI\")\n",
|
||||
" model_type='mlp',\n",
|
||||
" n=nb_particles[k]\n",
|
||||
" )\n",
|
||||
" results['MLP'].append((d2, f2_score))\n",
|
||||
"\n",
|
||||
"# 3. With Random Forest\n",
|
||||
"d3, f3_score, archive3 = run_scenario(\n",
|
||||
" # 3. With Random Forest\n",
|
||||
" d3, f3_score, _ = run_scenario(\n",
|
||||
" \"With Random Forest\", \n",
|
||||
" capacities=CAPACITIES, \n",
|
||||
" price_mean=P_MEAN, \n",
|
||||
" price_std=P_STD, \n",
|
||||
" nb_vehicles=nb_vehicle,\n",
|
||||
" model_type='rf'\n",
|
||||
")\n",
|
||||
"results['RF'] = (d3, f3_score)\n",
|
||||
"plot_pareto_3d(archive3, \"No-AI\")\n",
|
||||
"\n",
|
||||
"# --- DISPLAY RESULTS ---\n",
|
||||
"print(\"\\n=== SUMMARY ===\")\n",
|
||||
"print(f\"{'Mode':<15} | {'Time (s)':<10} | {'Best f2':<10}\")\n",
|
||||
"print(\"-\" * 45)\n",
|
||||
"for k, v in results.items():\n",
|
||||
" print(f\"{k:<15} | {v[0]:<10.2f} | {v[1]:<10.4f}\")"
|
||||
" model_type='rf',\n",
|
||||
" n=nb_particles[k]\n",
|
||||
" )\n",
|
||||
" results['RF'].append((d3, f3_score))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"id": "11b2a243",
|
||||
"execution_count": 16,
|
||||
"id": "165b48cd",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Electricity prices:\n",
|
||||
" - Mean: $84.69424€/Mwh\n",
|
||||
" - Std: $43.11248284136333€/Mwh\n",
|
||||
"Capacities of vehicles (kwh): $33 83.00\n",
|
||||
"36 20.00\n",
|
||||
"4 72.80\n",
|
||||
"13 35.80\n",
|
||||
"30 77.40\n",
|
||||
"26 17.30\n",
|
||||
"6 65.00\n",
|
||||
"27 11.40\n",
|
||||
"24 82.00\n",
|
||||
"15 87.00\n",
|
||||
"17 93.40\n",
|
||||
"8 65.50\n",
|
||||
"16 79.20\n",
|
||||
"12 58.00\n",
|
||||
"19 26.00\n",
|
||||
"9 39.20\n",
|
||||
"32 90.00\n",
|
||||
"0 95.00\n",
|
||||
"25 200.00\n",
|
||||
"5 75.00\n",
|
||||
"11 67.50\n",
|
||||
"1 11.60\n",
|
||||
"37 16.00\n",
|
||||
"21 30.20\n",
|
||||
"2 13.80\n",
|
||||
"29 212.00\n",
|
||||
"35 89.00\n",
|
||||
"3 1.49\n",
|
||||
"34 98.00\n",
|
||||
"23 60.00\n",
|
||||
"dtype: float64\n",
|
||||
"\n",
|
||||
"--- Launching Scenario: No AI ---\n",
|
||||
"Finished in 1.40 seconds.\n",
|
||||
"Best f2 found: 7.4381\n",
|
||||
"Graphique sauvegardé sous : No-AI_pareto_3d.png\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1200x800 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"--- Launching Scenario: With MLP ---\n",
|
||||
"Finished in 1.87 seconds.\n",
|
||||
"Best f2 found: 7.6076\n",
|
||||
"Graphique sauvegardé sous : No-AI_pareto_3d.png\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA0UAAAKSCAYAAAAdy3CWAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzsnXl4G+W1/79abEu2JXnfHe/ZQxZns0OAQCBhDzQsoW0WaOCWsqZAWRMaaLkEKPslQG8pW37QUMpSIJAmtCwJIZDI+x4v8e5Y8ibZ2ub9/ZE7gyTLtmRrG/t8nscPZDTSvJoZzbzfOed8j4QxxkAQBEEQBEEQBDFFkQZ6AARBEARBEARBEIGERBFBEARBEARBEFMaEkUEQRAEQRAEQUxpSBQRBEEQBEEQBDGlIVFEEARBEARBEMSUhkQRQRAEQRAEQRBTGhJFBEEQBEEQBEFMaUgUEQRBEARBEAQxpSFRRBAEQUxJLBYL/vu//xsff/xxoIdCEARBBBgSRYRf+etf/wqJRIKGhoZAD2VS8O9//xsSiQT//ve/Az2UCXHOOefgnHPO8epnNjQ0QCKR4K9//atXP5cILJs3b0ZmZqZXPuvee+/Fn//8Zyxfvtwrn+cpJ0+ehEKhwLfffhuQ7XuD7u5uRERE4NNPPw30UAiCICYEiaIghhcQrv7uvfdev47FaDTi4YcfDrrJNy8K+L+QkBBkZ2dj48aNOHHihN/HE6z7yduUlJRg/fr1yMjIgEKhQGpqKs4//3w8//zzfh/Lnj178Mwzz/h9uxMlMzMTEokEt95667DX+PP6vffeC8DIfhqbRCKBVCpFVFQU5s2bhxtvvBFHjhwJyJi8zYcffoi33noL+/btQ3x8fEDGsHPnTixbtgwrVqwQlm3evBkSiQRnnHEGGGPD3iORSHDLLbf4ZDyffvopJBIJUlJSwHGcy3UyMzNxySWXCP+OjY3Fr371Kzz00EM+GRNBEIS/kAd6AMTY7Ny5E1lZWQ7L5s6d69cxGI1G/P73vwcArz/R9wa33XYblixZAovFgmPHjuGVV17BJ598gpKSEqSkpPhtHMG+n7zBoUOHsGrVKkybNg1bt25FUlISTp48ie+++w7PPvusy0m+L9mzZw9KS0txxx13OCzPyMjA4OAgQkJC/DoeT3n11Vdx3333+fU8dYcFCxbgt7/9LQCgv78fFRUV2Lt3L1599VXceeed+NOf/hSQcb366qsjTtg9oaGhAZ999hlyc3O9MCrP6erqwuuvv47XX3/d5eslJSV4//338bOf/cxvY3r77beRmZmJhoYGHDx4EKtXr3brff/1X/+F5557DgcPHsS5557r41ESBEH4BhJFIuDCCy/E4sWL3Vp3aGgIoaGhkEqnVhBw5cqVWL9+PQBgy5YtmD59Om677Ta8/vrruO+++8b9uYwxDA0NQalUemuooucPf/gDNBoNjh49iqioKIfXOjs7AzMoF0gkEigUikAPY1TmzJmDqqoq/Pd//zeee+65QA/HgdTUVPziF79wWPb444/juuuuw9NPP428vDz8+te/9vu4vCVyb7/9dq98znh56623IJfLcemllw57TalUIj09HTt37sSVV14JiUTi8/EYDAZ8+OGHeOyxx/Daa6/h7bffdlsUzZo1C3PnzsVf//pXEkUEQYiWqTVznmTwKTbvvPMOHnzwQaSmpiI8PBx9fX0AgL179yI/Px9KpRJxcXH4xS9+gZaWFofP2Lx5MyIjI9HS0oJ169YhMjIS8fHxuOuuu2Cz2QCcfqLKp5f8/ve/F9JqHn744VHHV1ZWhnPPPRdKpRJpaWl49NFHR3zC+9lnn2HlypWIiIiASqXCxRdfjLKysnHvG/7GXF9fDwB47bXXcO655yIhIQFhYWGYPXs2XnrppWHv41NDPv/8cyxevBhKpRIvv/wyAKCnpwd33HEH0tPTERYWhtzcXDz++OPCd3JnPx08eFD4nlFRUbj88stRUVHh1ndqbm7GunXrEBERgYSEBNx5550wmUwu1z1y5AjWrl0LjUaD8PBwnH322cPqFvr7+3HHHXcgMzMTYWFhSEhIwPnnn49jx46NOo66ujrMmTNnmCACgISEBId/W61WPPLII8jJyUFYWBgyMzNx//33jzhunpFqz5xrqM455xx88sknaGxsFPY3X28yUk2RO8fg4YcfhkQiQW1tLTZv3oyoqChoNBps2bIFRqPRYd39+/fjzDPPRFRUFCIjIzFjxgzcf//9o34/nszMTGzcuBGvvvoqWltbx1z/+PHjuPDCC6FWqxEZGYnzzjsP3333ncM6/L779ttvsW3bNsTHxyMiIgJXXHEFurq63BrXSCiVSrz55puIiYnBH/7wB4f0LoPBgN/+9rfC72PGjBl48sknh6WA8elfe/fuxezZs6FUKlFQUICSkhIAwMsvv4zc3FwoFAqcc845w84B55oi/jg/+eSTeOWVV4RzbcmSJTh69Oiw71BZWYn169cjJiYGCoUCixcvxkcffTRsvbF+7zzvvPMO8vPzoVKpoFarMW/ePDz77LNj7ssPPvgAy5YtQ2Rk5LDXpFIpHnzwQRQXF+Mf//jHmJ/V2dmJG264AYmJiVAoFJg/f/6IEaiR+Mc//oHBwUFcddVVuPbaa/H+++9jaGjI7feff/75+Pjjj12m/BEEQYgBihSJgN7eXpw6dcphWVxcnPD/jzzyCEJDQ3HXXXfBZDIhNDQUf/3rX7FlyxYsWbIEjz32GDo6OvDss8/i22+/xfHjxx0mtDabDWvWrMGyZcvw5JNP4l//+heeeuop5OTk4Ne//jXi4+Px0ksv4de//jWuuOIKXHnllQCAM844Y8Qxt7e3Y9WqVbBarbj33nsRERGBV155xWXE5c0338SmTZuwZs0aPP744zAajXjppZdw5pln4vjx4+Mqqq6rqwNwOt8dAF566SXMmTMHl112GeRyOT7++GPcfPPN4DgOv/nNbxzeW1VVhQ0bNuCmm27C1q1bMWPGDBiNRpx99tloaWnBTTfdhGnTpuHQoUO477770NbWhmeeeWbM/fSvf/0LF154IbKzs/Hwww9jcHAQzz//PFasWIFjx46N+j0HBwdx3nnnoampCbfddhtSUlLw5ptv4uDBg8PWPXjwIC688ELk5+djx44dkEqlgij8+uuvsXTpUgCnU17ee+893HLLLZg9eza6u7vxzTffoKKiAosWLRpxLBkZGTh8+DBKS0vHTOP81a9+hddffx3r16/Hb3/7Wxw5cgSPPfYYKioq3JrsjcUDDzyA3t5eNDc34+mnnwYAl5NMHk+PwdVXX42srCw89thjOHbsGP785z8jISEBjz/+OIDTwv+SSy7BGWecgZ07dyIsLAy1tbUeFc4/8MADeOONN8aMFpWVlWHlypVQq9W45557EBISgpdffhnnnHMO/vOf/2DZsmUO6996662Ijo7Gjh070NDQgGeeeQa33HIL3n33XbfH5orIyEhcccUV+N///V+Ul5djzpw5YIzhsssuw5dffokbbrgBCxYswOeff467774bLS0twrHh+frrr/HRRx8Jv73HHnsMl1xyCe655x78z//8D26++Wbo9Xrs2rUL119/vcvz3Jk9e/agv78fN910EyQSCXbt2oUrr7wSJ06cEKJLZWVlWLFiBVJTU4Xr0t/+9jesW7cOf//733HFFVcAgFu/d+C0IN6wYQPOO+884ZyoqKjAt99+O2okymKx4OjRo6NG2q677jo88sgj2LlzJ6644ooRo0WDg4M455xzUFtbi1tuuQVZWVnYu3cvNm/ejJ6eHrcjYm+//TZWrVqFpKQkXHvttbj33nvx8ccf46qrrnLr/fn5+Xj66adRVlbm9/RugiAIr8CIoOW1115jAFz+McbYl19+yQCw7OxsZjQahfeZzWaWkJDA5s6dywYHB4Xl//znPxkAtn37dmHZpk2bGAC2c+dOh20vXLiQ5efnC//u6upiANiOHTvcGvsdd9zBALAjR44Iyzo7O5lGo2EAWH19PWOMsf7+fhYVFcW2bt3q8P729nam0WiGLXeG3wd/+ctfWFdXF2ttbWWffPIJy8zMZBKJhB09epQxxhz2D8+aNWtYdna2w7KMjAwGgO3bt89h+SOPPMIiIiJYdXW1w/J7772XyWQy1tTUxBgbfT8tWLCAJSQksO7ubmFZUVERk0qlbOPGjaN+z2eeeYYBYH/729+EZQaDgeXm5jIA7Msvv2SMMcZxHMvLy2Nr1qxhHMcJ6xqNRpaVlcXOP/98YZlGo2G/+c1vRt2uK7744gsmk8mYTCZjBQUF7J577mGff/45M5vNDutptVoGgP3qV79yWH7XXXcxAOzgwYPCsrPPPpudffbZwr/5c58/T3j4481/X8YYu/jii1lGRsawcdbX1zMA7LXXXhOWuXsMduzYwQCw66+/3uEzr7jiChYbGyv8++mnn2YAWFdX17Dtj0VGRga7+OKLGWOMbdmyhSkUCtba2urwPffu3Susv27dOhYaGsrq6uqEZa2trUylUrGzzjpLWMbvu9WrVzucA3feeSeTyWSsp6fHo7G5gv/eH374IWOMsQ8++IABYI8++qjDeuvXr2cSiYTV1tYKywCwsLAwh2P78ssvMwAsKSmJ9fX1Ccvvu+++YefBpk2bHI43f5xjY2OZTqcTln/44YcMAPv444+FZeeddx6bN28eGxoaEpZxHMcKCwtZXl6esMzd3/vtt9/O1Go1s1qtI+4rV9TW1jIA7Pnnnx/22qZNm1hERARjjLHXX3+dAWDvv/++8DoAh98tf2146623hGVms5kVFBSwyMhIh/05Eh0dHUwul7NXX31VWFZYWMguv/zyYeuOdG4cOnSIAWDvvvvumNsjCIIIRih9TgS8+OKL2L9/v8OfPZs2bXKIwPzwww/o7OzEzTff7FBTcfHFF2PmzJn45JNPhm3jv/7rvxz+vXLlygm5t3366adLine truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1200x800 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"--- Launching Scenario: With Random Forest ---\n",
|
||||
"Finished in 1.57 seconds.\n",
|
||||
"Best f2 found: 9.5195\n",
|
||||
"Graphique sauvegardé sous : No-AI_pareto_3d.png\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA04AAAKSCAYAAADlK4thAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzsnXl8E3X+/19J2vRuetGT0pa2QLmhQGkoIooWxQNdPNcF8dqfrgeieHIouLqKB7ruCuoXdV38iriKNwuiri6nSm+gtKUXLT3onbRNmszn9wffGZM2Z5tkMu37+Xj0oUwm83nnk8nM5zXvS8YYYyAIgiAIgiAIgiCsIhfbAIIgCIIgCIIgCG+HhBNBEARBEARBEIQdSDgRBEEQBEEQBEHYgYQTQRAEQRAEQRCEHUg4EQRBEARBEARB2IGEE0EQBEEQBEEQhB1IOBEEQRAEQRAEQdiBhBNBEARBEARBEIQdSDgRBEEQI5K+vj785S9/wRdffCG2KQRBEIQEIOFEeJR3330XMpkMVVVVYpsyLPjhhx8gk8nwww8/iG3KkLjwwgtx4YUXuvSYVVVVkMlkePfdd116XEJcbr31ViQnJ7vkWI899hjefvttzJ071yXHc5ba2lr4+/vjwIEDoozvClpaWhAUFISvv/5abFMIgiDcDgknL4YXGZb+HnvsMY/a0t3djaeeesrrFui8cOD/fH19MXbsWCxfvhynT5/2uD3eOk+upqioCMuWLUNSUhL8/f2RkJCASy65BH/96189bssHH3yALVu2eHzcoZKcnAyZTIb77rtvwGv8ef3xxx+LYNlvtslkMsjlcoSFhWHKlCm46667cOTIEVFscjWfffYZ/vnPf2LPnj0YNWqUKDZs3LgRWVlZmDdvnrDt1ltvhUwmw9SpU8EYG/AemUyGe++91y32fP3115DJZIiPjwfHcRb3SU5OxhVXXCH8OzIyEnfccQfWrVvnFpsIgiC8CR+xDSDss3HjRqSkpJhtmzx5skdt6O7uxtNPPw0ALvcMuIL7778fs2fPRl9fH44dO4Y333wTX331FYqKihAfH+8xO7x9nlzBwYMHsXDhQowZMwZ33nknYmNjUVtbi8OHD+PVV1+1KATcyQcffIDi4mKsWrXKbHtSUhJ6enrg6+vrUXuc5a233sLjjz/u0fPUEaZPn46HHnoIANDV1YUTJ05g165deOutt/Dggw/i5ZdfFsWut956y+qi3hmqqqrwzTffIC0tzQVWOU9zczPee+89vPfeexZfLyoqwieffILf/e53HrNpx44dSE5ORlVVFb777jssWrTIoff9v//3//Daa6/hu+++w0UXXeRmKwmCIMSDhJMEuOyyyzBr1iyH9u3t7YVSqYRcPrKcifPnz8eyZcsAACtXrsS4ceNw//3347333sPjjz8+6OMyxtDb24uAgABXmSp5/vznP0OlUuHnn39GWFiY2WtNTU3iGGUBmUwGf39/sc2wyaRJk1BaWoq//OUveO2118Q2x4yEhATccsstZtuef/553HzzzXjllVeQnp6Ou+++2+N2uUoIP/DAAy45zmD55z//CR8fH1x55ZUDXgsICEBiYiI2btyIa6+9FjKZzO32aLVafPbZZ3juuefwzjvvYMeOHQ4Lp4yMDEyePBnvvvsuCSeCIIY1I2t1Pczgw3k+/PBDrF27FgkJCQgMDERnZycAYNeuXcjMzERAQACioqJwyy23oK6uzuwYt956K4KDg1FXV4elS5ciODgYo0aNwsMPPwyj0Qjg/JNZPpTl6aefFkJ4nnrqKZv2lZSU4KKLLkJAQABGjx6NZ555xuqT4m+++Qbz589HUFAQQkJCsGTJEpSUlAx6bvibd2VlJQDgnXfewUUXXYTo6Gj4+flh4sSJeOONNwa8jw9D+fe//41Zs2YhICAA27ZtAwC0t7dj1apVSExMhJ+fH9LS0vD8888Ln8mRefruu++EzxkWFoarr74aJ06ccOgznTlzBkuXLkVQUBCio6Px4IMPQqfTWdz3yJEjWLx4MVQqFQIDA7FgwYIBeRRdXV1YtWoVkpOT4efnh+joaFxyySU4duyYTTsqKiowadKkAaIJAKKjo83+bTAYsGnTJqSmpsLPzw/Jycl44oknrNrNYy0Xrn9O14UXXoivvvoK1dXVwnzz+S/Wcpwc+Q6eeuopyGQylJeX49Zbb0VYWBhUKhVWrlyJ7u5us3337duHnJwchIWFITg4GOPHj8cTTzxh8/PxJCcnY/ny5XjrrbdQX19vd/+8vDxcdtllCA0NRXBwMC6++GIcPnzYbB9+7g4cOIDVq1dj1KhRCAoKwjXXXIPm5maH7LJGQEAA3n//fURERODPf/6zWSiZVqvFQw89JPw+xo8fjxdffHFAuBkfarZr1y5MnDgRAQEByM7ORlFREQBg27ZtSEtLg7+/Py688MIB50D/HCf+e37xxRfx5ptvCufa7Nmz8fPPPw/4DCdPnsSyZcsQEREBf39/zJo1C59//vmA/ez93nk+/PBDZGZmIiQkBKGhoZgyZQpeffVVu3O5e/duZGVlITg4eMBrcrkca9euRWFhIT799FO7x2pqasLtt9+OmJgY+Pv7Y9q0aVY9Wdb49NNP0dPTg+uuuw433ngjPvnkE/T29jr8/ksuuQRffPGFxfBCgiCI4QJ5nCRAR0cHzp07Z7YtKipK+P9NmzZBqVTi4Ycfhk6ng1KpxLvvvouVK1di9uzZeO6559DY2IhXX30VBw4cQF5entmi12g0Ijc3F1lZWXjxxRfx7bff4qWXXkJqairuvvtujBo1Cm+88QbuvvtuXHPNNbj22msBAFOnTrVqc0NDAxYuXAiDwYDHHnsMQUFBePPNNy16bt5//32sWLECubm5eP7559Hd3Y033ngDOTk5yMvLG1QieEVFBYDz8fcA8MYbb2DSpEm46qqr4OPjgy+++AL33HMPOI7Dn/70J7P3lpaW4qabbsIf//hH3HnnnRg/fjy6u7uxYMEC1NXV4Y9//CPGjBmDgwcP4vHHH8fZs2exZcsWu/P07bff4rLLLsPYsWPx1FNPoaenB3/9618xb948HDt2zObn7OnpwcUXX4yamhrcf//9iI+Px/vvv4/vvvtuwL7fffcdLrvsMmRmZmLDhg2Qy+WCcPzpp58wZ84cAOfDaz7++GPce++9mDhxIlpaWvDf//4XJ06cwMyZM63akpSUhEOHDqG4uNhuyOgdd9yB9957D8uWLcNDDz2EI0eO4LnnnsOJEyccWhDa48knn0RHRwfOnDmDV155BQAsLkR5nP0Orr/+eqSkpOC5557DsWPH8PbbbyM6OhrPP/88gPMPB6644gpMnToVGzduhJ+fH8rLy51K9n/yySfxj3/8w67XqaSkBPPnz0doaCgeeeQR+Pr6Ytu2bbjwwgvxn//8B1lZWWb733fffQgPD8eGDRtQVVWFLVu24N5778XOnTsdts0SwcHBuOaaa/A///M/OH78OCZNmgTGGK666ip8//33uP322zF9+nT8+9//xpo1a1BXVyd8Nzw//fQTPv/8c+G399xzz+GKK67AI488gr///e+455570NbWhhdeeAG33XabxfO8Px988AG6urrwxz/+ETKZDC+88AKuvfZanD59WvBSlZSUYN68eUhISBCuSx999BGWLl2Kf/3rX7jmmmsAwKHfO3BeNN900024+OKLhXPixIkTOHDggE2PVl9fH37++WebHrubb74ZmzZtwsaNG3HNNddY9Tr19PTgwgsvRHl5Oe69916kpKRg165duPXWW9He3u6wZ23Hjh1YuHAhYmNjceONN+Kxxx7DF198geuuu86h92dmZuKVV15BSUmJx0PJCYIgPAYjvJZ33nmHAbD4xxhj33//PQPAxo4dy7q7u4X36fV6Fh0dzSZPnsx6enqE7V9++SUDwNavXy9sW7FiBQPANm7caDb2jBkzWGZmpvDv5uZmBoBt2LDBIdtXrVrFALAjR44I25qamphKpWIAWGVlJWOMsa6uLhYWFsbuvPNOs/c3NDQwlUo1YHt/+DnYvn07a25uZvX19eyrr75iycnJTCaTsZ9//pkxxszmhyc3N5eNHTvWbFtSUhIDwPbs2WO2fdOmTSwoKIidOnXKbPtjjz3GFAoFq6mpYYzZnqfp06ez6Oho1tLSImwrKChgcrmcLV++3Obn3LJlCwPAPvroI2GbVqtlaWlpDAD7/vvvGWOMcRzH0tPTWW5uLuM4Tti3u7ubpaSksEsuuUTYplKp2J/+9Ceb41pi7969TKFQMIVCwbKzs9kjjzzC/v3vfzO9Xm+2X35+PgPA7rjjDrPtDz/8MAPAvvvuO2HbggUL2IIFC4R/8+c+f57w8N83/3kZY2zJkiUsKSlpgJ2VlZUMAHvnnXeEbY5+Bxs2bGAA2G233WZ2zGuuuYZFRkYK/37llVcYANbc3DxgfHskJSWxJUuWMMYYW7lyJfP392f19fVmn3PXrl3C/kuXLmVKpZJVVFQI2+rr61lISAi74IILhG383C1atMjsHHjwwQeZQqFg7e3tTtlmCf5zf/bZZ4wxxnbv3s0AsGeeecZsv2XLljGZTMbKy8uFbQCYn5+f2Xe7bds2BoDFxsayzs5OYfvjjz8+4DxYsWKF2ffNf8+RkZGstbVV2P7ZZ58xAOyLL74Qtl188cVsypQprLe3V9jGcRxTq9UsPT1d2Obo7/2BBx5goaGhzGAwWJ0rS5SXlzMA7K9//euA11asWMGCgoIYY4y99957DAD75JNPhNcBmP1u+WvDP//5T2GbXq9n2dnZLDg42Gw+rdHY2Mh8fHzYW2+9JWxTq9Xs6quvHrCvtXPj4MGDDADbuXOn3fEIgiCkCoXqSYC//e1v2Ldvn9mfKStWrDDz5Pzyyy9oamrCPffcY5bjsWTJEkyYMAFfffXVgDH+3//7f2b/nj9//pCq0n399deYO3eu4N0AgFGjRuHLine truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1200x800 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
@@ -555,458 +451,126 @@
|
||||
"=== SUMMARY ===\n",
|
||||
"Mode | Time (s) | Best f2 \n",
|
||||
"---------------------------------------------\n",
|
||||
"No-AI | 1.40 | 7.4381 \n",
|
||||
"MLP | 1.87 | 7.6076 \n",
|
||||
"RF | 1.57 | 9.5195 \n"
|
||||
"MOPSO _20 | 0.40 | 5.3493 \n",
|
||||
"MOPSO _50 | 0.96 | 4.5495 \n",
|
||||
"MOPSO _500 | 9.63 | 5.4597 \n",
|
||||
"MOPSO _1000 | 18.79 | 6.6090 \n",
|
||||
"MOPSO _10000 | 196.01 | 5.0795 \n",
|
||||
"MLP _20 | 0.68 | 6.3994 \n",
|
||||
"MLP _50 | 1.30 | 5.3998 \n",
|
||||
"MLP _500 | 10.31 | 6.9999 \n",
|
||||
"MLP _1000 | 20.60 | 5.8495 \n",
|
||||
"MLP _10000 | 206.84 | 5.2493 \n",
|
||||
"RF _20 | 0.52 | 5.2397 \n",
|
||||
"RF _50 | 1.12 | 4.4896 \n",
|
||||
"RF _500 | 9.77 | 5.7496 \n",
|
||||
"RF _1000 | 22.21 | 3.4493 \n",
|
||||
"RF _10000 | 202.25 | 5.4393 \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# CSV files\n",
|
||||
"elec_price_csv = 'data/elec_prices.csv'\n",
|
||||
"capacity_csv = 'data/vehicle_capacity.csv'\n",
|
||||
"\n",
|
||||
"# Global Simulation parameters\n",
|
||||
"T = 100 # Number of iterations (for the particles)\n",
|
||||
"W = 0.4 # Inertia (for exploration)\n",
|
||||
"C1 = 0.3 # Individual trust\n",
|
||||
"C2 = 0.2 # Social trust\n",
|
||||
"ARC_SIZE = 10 # Archive size\n",
|
||||
"nb_vehicle = 30\n",
|
||||
"\n",
|
||||
"P_MEAN, P_STD = calculate_elec_prices(elec_price_csv)\n",
|
||||
"CAPACITIES = generate_capacities(capacity_csv, nb_vehicles=nb_vehicle)\n",
|
||||
"\n",
|
||||
"NB_TICKS = 48\n",
|
||||
"DELTA = 60\n",
|
||||
"\n",
|
||||
"results = {}\n",
|
||||
"\n",
|
||||
"# 1. Without Surrogate (Baseline)\n",
|
||||
"d1, f1_score, archive1 = run_scenario(\n",
|
||||
" \"No AI\", \n",
|
||||
" capacities=CAPACITIES, \n",
|
||||
" price_mean=P_MEAN, \n",
|
||||
" price_std=P_STD, \n",
|
||||
" nb_vehicles=nb_vehicle, # Important pour la cohérence\n",
|
||||
" model_type=None,\n",
|
||||
" n=50\n",
|
||||
")\n",
|
||||
"results['No-AI'] = (d1, f1_score)\n",
|
||||
"plot_pareto_3d(archive1, \"No-AI\")\n",
|
||||
"\n",
|
||||
"# 2. With MLP\n",
|
||||
"d2, f2_score, archive2 = run_scenario(\n",
|
||||
" \"With MLP\", \n",
|
||||
" capacities=CAPACITIES, \n",
|
||||
" price_mean=P_MEAN, \n",
|
||||
" price_std=P_STD, \n",
|
||||
" nb_vehicles=nb_vehicle,\n",
|
||||
" model_type='mlp',\n",
|
||||
" n=50\n",
|
||||
")\n",
|
||||
"results['MLP'] = (d2, f2_score)\n",
|
||||
"plot_pareto_3d(archive2, \"No-AI\")\n",
|
||||
"\n",
|
||||
"# 3. With Random Forest\n",
|
||||
"d3, f3_score, archive3 = run_scenario(\n",
|
||||
" \"With Random Forest\", \n",
|
||||
" capacities=CAPACITIES, \n",
|
||||
" price_mean=P_MEAN, \n",
|
||||
" price_std=P_STD, \n",
|
||||
" nb_vehicles=nb_vehicle,\n",
|
||||
" model_type='rf',\n",
|
||||
" n=50\n",
|
||||
")\n",
|
||||
"results['RF'] = (d3, f3_score)\n",
|
||||
"plot_pareto_3d(archive3, \"No-AI\")\n",
|
||||
"\n",
|
||||
"# --- DISPLAY RESULTS ---\n",
|
||||
"print(\"\\n=== SUMMARY ===\")\n",
|
||||
"print(f\"{'Mode':<15} | {'Time (s)':<10} | {'Best f2':<10}\")\n",
|
||||
"print(\"-\" * 45)\n",
|
||||
"for k, v in results.items():\n",
|
||||
" print(f\"{k:<15} | {v[0]:<10.2f} | {v[1]:<10.4f}\")"
|
||||
" for i in range(len(nb_particles)):\n",
|
||||
" print(f\"{k:<15}_{nb_particles[i]:<15} | {v[i][0]:<10.2f} | {v[i][1]:<10.4f}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"id": "c7112c5a",
|
||||
"execution_count": 13,
|
||||
"id": "314f8d5a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Electricity prices:\n",
|
||||
" - Mean: $84.69424€/Mwh\n",
|
||||
" - Std: $43.11248284136333€/Mwh\n",
|
||||
"Capacities of vehicles (kwh): $33 83.00\n",
|
||||
"36 20.00\n",
|
||||
"4 72.80\n",
|
||||
"13 35.80\n",
|
||||
"30 77.40\n",
|
||||
"26 17.30\n",
|
||||
"6 65.00\n",
|
||||
"27 11.40\n",
|
||||
"24 82.00\n",
|
||||
"15 87.00\n",
|
||||
"17 93.40\n",
|
||||
"8 65.50\n",
|
||||
"16 79.20\n",
|
||||
"12 58.00\n",
|
||||
"19 26.00\n",
|
||||
"9 39.20\n",
|
||||
"32 90.00\n",
|
||||
"0 95.00\n",
|
||||
"25 200.00\n",
|
||||
"5 75.00\n",
|
||||
"11 67.50\n",
|
||||
"1 11.60\n",
|
||||
"37 16.00\n",
|
||||
"21 30.20\n",
|
||||
"2 13.80\n",
|
||||
"29 212.00\n",
|
||||
"35 89.00\n",
|
||||
"3 1.49\n",
|
||||
"34 98.00\n",
|
||||
"23 60.00\n",
|
||||
"31 50.00\n",
|
||||
"10 77.00\n",
|
||||
"22 100.00\n",
|
||||
"18 24.00\n",
|
||||
"20 40.50\n",
|
||||
"dtype: float64\n",
|
||||
"\n",
|
||||
"--- Launching Scenario: No AI ---\n",
|
||||
"Finished in 3.35 seconds.\n",
|
||||
"Best f2 found: 12.2882\n",
|
||||
"Graphique sauvegardé sous : No-AI_pareto_3d.png\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1200x800 with 2 Axes>"
|
||||
"<Figure size 1000x600 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"--- Launching Scenario: With MLP ---\n",
|
||||
"Finished in 3.73 seconds.\n",
|
||||
"Best f2 found: 7.8492\n",
|
||||
"Graphique sauvegardé sous : No-AI_pareto_3d.png\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1200x800 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"--- Launching Scenario: With Random Forest ---\n",
|
||||
"Finished in 3.94 seconds.\n",
|
||||
"Best f2 found: 6.6387\n",
|
||||
"Graphique sauvegardé sous : No-AI_pareto_3d.png\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1200x800 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"=== SUMMARY ===\n",
|
||||
"Mode | Time (s) | Best f2 \n",
|
||||
"---------------------------------------------\n",
|
||||
"No-AI | 3.35 | 12.2882 \n",
|
||||
"MLP | 3.73 | 7.8492 \n",
|
||||
"RF | 3.94 | 6.6387 \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# CSV files\n",
|
||||
"elec_price_csv = 'data/elec_prices.csv'\n",
|
||||
"capacity_csv = 'data/vehicle_capacity.csv'\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"# Global Simulation parameters\n",
|
||||
"T = 1000 # Number of iterations (for the particles)\n",
|
||||
"W = 0.4 # Inertia (for exploration)\n",
|
||||
"C1 = 0.3 # Individual trust\n",
|
||||
"C2 = 0.2 # Social trust\n",
|
||||
"ARC_SIZE = 10 # Archive size\n",
|
||||
"nb_vehicle = 35\n",
|
||||
"def plot_time_benchmark(nb_particles_list, results_dict):\n",
|
||||
" \n",
|
||||
" t_mopso = [item[0] for item in results_dict['MOPSO']]\n",
|
||||
" t_mlp = [item[0] for item in results_dict['MLP']]\n",
|
||||
" t_rf = [item[0] for item in results_dict['RF']]\n",
|
||||
"\n",
|
||||
"P_MEAN, P_STD = calculate_elec_prices(elec_price_csv)\n",
|
||||
"CAPACITIES = generate_capacities(capacity_csv, nb_vehicles=nb_vehicle)\n",
|
||||
" plt.figure(figsize=(10, 6))\n",
|
||||
" \n",
|
||||
" plt.plot(nb_particles_list, t_mopso, 'o-', label='Sans IA (MOPSO)', color='#1f77b4', linewidth=2)\n",
|
||||
" plt.plot(nb_particles_list, t_mlp, 's--', label='Avec MLP', color='#ff7f0e', linewidth=2)\n",
|
||||
" plt.plot(nb_particles_list, t_rf, '^-.', label='Avec Random Forest', color='#2ca02c', linewidth=2)\n",
|
||||
" \n",
|
||||
" plt.title(\"Temps d'exécution selon le nombre de particules\", fontsize=14, fontweight='bold')\n",
|
||||
" plt.xlabel(\"Nombre de Particules\", fontsize=12)\n",
|
||||
" plt.ylabel(\"Temps (s)\", fontsize=12)\n",
|
||||
" plt.grid(True, linestyle=':', alpha=0.7)\n",
|
||||
" plt.legend(fontsize=11)\n",
|
||||
" \n",
|
||||
"\n",
|
||||
"NB_TICKS = 72\n",
|
||||
"DELTA = 60\n",
|
||||
" \n",
|
||||
" plt.tight_layout()\n",
|
||||
" plt.show()\n",
|
||||
"\n",
|
||||
"results = {}\n",
|
||||
"\n",
|
||||
"# 1. Without Surrogate (Baseline)\n",
|
||||
"d1, f1_score, archive1 = run_scenario(\n",
|
||||
" \"No AI\", \n",
|
||||
" capacities=CAPACITIES, \n",
|
||||
" price_mean=P_MEAN, \n",
|
||||
" price_std=P_STD, \n",
|
||||
" nb_vehicles=nb_vehicle, # Important pour la cohérence\n",
|
||||
" model_type=None,\n",
|
||||
" n=100\n",
|
||||
")\n",
|
||||
"results['No-AI'] = (d1, f1_score)\n",
|
||||
"plot_pareto_3d(archive1, \"No-AI\")\n",
|
||||
"\n",
|
||||
"# 2. With MLP\n",
|
||||
"d2, f2_score, archive2 = run_scenario(\n",
|
||||
" \"With MLP\", \n",
|
||||
" capacities=CAPACITIES, \n",
|
||||
" price_mean=P_MEAN, \n",
|
||||
" price_std=P_STD, \n",
|
||||
" nb_vehicles=nb_vehicle,\n",
|
||||
" model_type='mlp',\n",
|
||||
" n=100\n",
|
||||
")\n",
|
||||
"results['MLP'] = (d2, f2_score)\n",
|
||||
"plot_pareto_3d(archive2, \"No-AI\")\n",
|
||||
"\n",
|
||||
"# 3. With Random Forest\n",
|
||||
"d3, f3_score, archive3 = run_scenario(\n",
|
||||
" \"With Random Forest\", \n",
|
||||
" capacities=CAPACITIES, \n",
|
||||
" price_mean=P_MEAN, \n",
|
||||
" price_std=P_STD, \n",
|
||||
" nb_vehicles=nb_vehicle,\n",
|
||||
" model_type='rf',\n",
|
||||
" n=100\n",
|
||||
")\n",
|
||||
"results['RF'] = (d3, f3_score)\n",
|
||||
"plot_pareto_3d(archive3, \"No-AI\")\n",
|
||||
"\n",
|
||||
"# --- DISPLAY RESULTS ---\n",
|
||||
"print(\"\\n=== SUMMARY ===\")\n",
|
||||
"print(f\"{'Mode':<15} | {'Time (s)':<10} | {'Best f2':<10}\")\n",
|
||||
"print(\"-\" * 45)\n",
|
||||
"for k, v in results.items():\n",
|
||||
" print(f\"{k:<15} | {v[0]:<10.2f} | {v[1]:<10.4f}\")"
|
||||
" \n",
|
||||
"plot_time_benchmark(nb_particles, results)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"id": "3e79471b",
|
||||
"execution_count": 14,
|
||||
"id": "1e3fa2ff",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Electricity prices:\n",
|
||||
" - Mean: $84.69424€/Mwh\n",
|
||||
" - Std: $43.11248284136333€/Mwh\n",
|
||||
"Capacities of vehicles (kwh): $33 83.00\n",
|
||||
"36 20.00\n",
|
||||
"4 72.80\n",
|
||||
"13 35.80\n",
|
||||
"30 77.40\n",
|
||||
"26 17.30\n",
|
||||
"6 65.00\n",
|
||||
"27 11.40\n",
|
||||
"24 82.00\n",
|
||||
"15 87.00\n",
|
||||
"17 93.40\n",
|
||||
"8 65.50\n",
|
||||
"16 79.20\n",
|
||||
"12 58.00\n",
|
||||
"19 26.00\n",
|
||||
"9 39.20\n",
|
||||
"32 90.00\n",
|
||||
"0 95.00\n",
|
||||
"25 200.00\n",
|
||||
"5 75.00\n",
|
||||
"11 67.50\n",
|
||||
"1 11.60\n",
|
||||
"37 16.00\n",
|
||||
"21 30.20\n",
|
||||
"2 13.80\n",
|
||||
"29 212.00\n",
|
||||
"35 89.00\n",
|
||||
"3 1.49\n",
|
||||
"34 98.00\n",
|
||||
"23 60.00\n",
|
||||
"31 50.00\n",
|
||||
"10 77.00\n",
|
||||
"22 100.00\n",
|
||||
"18 24.00\n",
|
||||
"20 40.50\n",
|
||||
"dtype: float64\n",
|
||||
"\n",
|
||||
"--- Launching Scenario: No AI ---\n",
|
||||
"Finished in 9.66 seconds.\n",
|
||||
"Best f2 found: 8.2081\n",
|
||||
"Graphique sauvegardé sous : No-AI_pareto_3d.png\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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||||
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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1200x800 with 2 Axes>"
|
||||
"<Figure size 1000x600 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"--- Launching Scenario: With MLP ---\n",
|
||||
"Finished in 11.01 seconds.\n",
|
||||
"Best f2 found: 7.5877\n",
|
||||
"Graphique sauvegardé sous : No-AI_pareto_3d.png\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1200x800 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"--- Launching Scenario: With Random Forest ---\n",
|
||||
"Finished in 11.94 seconds.\n",
|
||||
"Best f2 found: 8.3061\n",
|
||||
"Graphique sauvegardé sous : No-AI_pareto_3d.png\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1200x800 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"=== SUMMARY ===\n",
|
||||
"Mode | Time (s) | Best f2 \n",
|
||||
"---------------------------------------------\n",
|
||||
"No-AI | 9.66 | 8.2081 \n",
|
||||
"MLP | 11.01 | 7.5877 \n",
|
||||
"RF | 11.94 | 8.3061 \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# CSV files\n",
|
||||
"elec_price_csv = 'data/elec_prices.csv'\n",
|
||||
"capacity_csv = 'data/vehicle_capacity.csv'\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"# Global Simulation parameters\n",
|
||||
"T = 5000 # Number of iterations (for the particles)\n",
|
||||
"W = 0.4 # Inertia (for exploration)\n",
|
||||
"C1 = 0.3 # Individual trust\n",
|
||||
"C2 = 0.2 # Social trust\n",
|
||||
"ARC_SIZE = 10 # Archive size\n",
|
||||
"nb_vehicle = 35\n",
|
||||
"def plot_f2_benchmark(nb_particles_list, results_dict):\n",
|
||||
" s_mopso = [item[1] for item in results_dict['MOPSO']]\n",
|
||||
" s_mlp = [item[1] for item in results_dict['MLP']]\n",
|
||||
" s_rf = [item[1] for item in results_dict['RF']]\n",
|
||||
"\n",
|
||||
"P_MEAN, P_STD = calculate_elec_prices(elec_price_csv)\n",
|
||||
"CAPACITIES = generate_capacities(capacity_csv, nb_vehicles=nb_vehicle)\n",
|
||||
"\n",
|
||||
"NB_TICKS = 72\n",
|
||||
"DELTA = 60\n",
|
||||
" plt.figure(figsize=(10, 6))\n",
|
||||
" \n",
|
||||
" plt.plot(nb_particles_list, s_mopso, 'o-', label='Sans IA (MOPSO)', color='#1f77b4', linewidth=2)\n",
|
||||
" plt.plot(nb_particles_list, s_mlp, 's--', label='Avec MLP', color='#ff7f0e', linewidth=2)\n",
|
||||
" plt.plot(nb_particles_list, s_rf, '^-.', label='Avec Random Forest', color='#2ca02c', linewidth=2)\n",
|
||||
" \n",
|
||||
" plt.title(\"Meilleur Score F2 (Convergence) selon le nombre de particules\", fontsize=14, fontweight='bold')\n",
|
||||
" plt.xlabel(\"Nombre de Particules (log scale)\", fontsize=12)\n",
|
||||
" plt.ylabel(\"Meilleur F2 Score\", fontsize=12)\n",
|
||||
" plt.grid(True, linestyle=':', alpha=0.7)\n",
|
||||
" plt.legend(fontsize=11)\n",
|
||||
" \n",
|
||||
" plt.xscale('log') \n",
|
||||
" \n",
|
||||
" plt.tight_layout()\n",
|
||||
" plt.show()\n",
|
||||
"\n",
|
||||
"results = {}\n",
|
||||
"\n",
|
||||
"# 1. Without Surrogate (Baseline)\n",
|
||||
"d1, f1_score, archive1 = run_scenario(\n",
|
||||
" \"No AI\", \n",
|
||||
" capacities=CAPACITIES, \n",
|
||||
" price_mean=P_MEAN, \n",
|
||||
" price_std=P_STD, \n",
|
||||
" nb_vehicles=nb_vehicle, # Important pour la cohérence\n",
|
||||
" model_type=None,\n",
|
||||
" n=300\n",
|
||||
")\n",
|
||||
"results['No-AI'] = (d1, f1_score)\n",
|
||||
"plot_pareto_3d(archive1, \"No-AI\")\n",
|
||||
"\n",
|
||||
"# 2. With MLP\n",
|
||||
"d2, f2_score, archive2 = run_scenario(\n",
|
||||
" \"With MLP\", \n",
|
||||
" capacities=CAPACITIES, \n",
|
||||
" price_mean=P_MEAN, \n",
|
||||
" price_std=P_STD, \n",
|
||||
" nb_vehicles=nb_vehicle,\n",
|
||||
" model_type='mlp',\n",
|
||||
" n=300\n",
|
||||
")\n",
|
||||
"results['MLP'] = (d2, f2_score)\n",
|
||||
"plot_pareto_3d(archive2, \"No-AI\")\n",
|
||||
"\n",
|
||||
"# 3. With Random Forest\n",
|
||||
"d3, f3_score, archive3 = run_scenario(\n",
|
||||
" \"With Random Forest\", \n",
|
||||
" capacities=CAPACITIES, \n",
|
||||
" price_mean=P_MEAN, \n",
|
||||
" price_std=P_STD, \n",
|
||||
" nb_vehicles=nb_vehicle,\n",
|
||||
" model_type='rf',\n",
|
||||
" n=300\n",
|
||||
")\n",
|
||||
"results['RF'] = (d3, f3_score)\n",
|
||||
"plot_pareto_3d(archive3, \"No-AI\")\n",
|
||||
"\n",
|
||||
"# --- DISPLAY RESULTS ---\n",
|
||||
"print(\"\\n=== SUMMARY ===\")\n",
|
||||
"print(f\"{'Mode':<15} | {'Time (s)':<10} | {'Best f2':<10}\")\n",
|
||||
"print(\"-\" * 45)\n",
|
||||
"for k, v in results.items():\n",
|
||||
" print(f\"{k:<15} | {v[0]:<10.2f} | {v[1]:<10.4f}\")"
|
||||
"plot_f2_benchmark(nb_particles, results)"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
Reference in new issue
Block a user