diff --git a/main.py b/main.py index 21ea6d2..373959c 100644 --- a/main.py +++ b/main.py @@ -6,40 +6,6 @@ from mopso import MOPSO from surrogate_handler import SurrogateHandler import pandas as pd - -import matplotlib.pyplot as plt -from mpl_toolkits.mplot3d import Axes3D # Nécessaire pour la 3D - -def plot_pareto_3d(archive, model_type:str): - fig = plt.figure(figsize=(12, 8)) - ax = fig.add_subplot(111, projection='3d') - - # Extraction des scores depuis l'archive - # f_best[0] = Coût, f_best[1] = Insatisfaction, f_best[2] = Stress Réseau - f1 = [p.f_best[0] for p in archive] - f2 = [p.f_best[1] for p in archive] - f3 = [p.f_best[2] for p in archive] - - # Création du nuage de points - img = ax.scatter(f1, f2, f3, c=f3, cmap='viridis', s=60, edgecolors='black') - - ax.set_xlabel('Coût (€)') - ax.set_ylabel('Insatisfaction (SoC manquant)') - ax.set_zlabel('Pic Réseau (kW)') - ax.set_title(f'Front de Pareto des Solutions Non-Dominées ({model_type})') - - # Barre de couleur - cbar = fig.colorbar(img, ax=ax, pad=0.1) - cbar.set_label('Intensité du Pic Réseau (kW)') - - # Sauvegarde et affichage - filename = f"{model_type}_pareto_3d.png" - plt.savefig(filename) - print(f"Graphique sauvegardé sous : {filename}") - plt.show() - - - class SmartMOPSO(MOPSO): def __init__(self, model_type=None, **kwargs): super().__init__(**kwargs) @@ -53,18 +19,7 @@ class SmartMOPSO(MOPSO): for p in self.particles: self.surrogate_handler.add_data(p.x, p.f_current[1]) - def iterate(self): - train_freq = 10 # Retrain every 10 iterations - - # Check if retraining is needed - if self.use_surrogate and (self.t % train_freq == 0): - self.surrogate_handler.train() - - # Determine if AI prediction should be used - use_ai = (self.use_surrogate and - self.surrogate_handler.is_trained and - self.t % train_freq != 0) - + def iterate(self, prediction_freq:int=10): # Main loop (overriding original logic to manage control flow) for t in range(self.t): self.select_leader() @@ -75,7 +30,7 @@ class SmartMOPSO(MOPSO): self.particles[i].update_position() self.particles[i].keep_boudaries(self.A_max) - if use_ai: + if (t % (prediction_freq) != 0) and self.use_surrogate: # Fast exact calculation (f1, f3) f1 = self.particles[i].f1(self.prices) f3 = self.particles[i].f3() @@ -90,16 +45,35 @@ class SmartMOPSO(MOPSO): # Standard Calculation (Slow and Exact) self.particles[i].updating_socs(self.socs, self.capacities) self.particles[i].evaluate(self.prices, self.socs, self.socs_req, self.times) - - # Capture data for AI training - if self.use_surrogate: - self.surrogate_handler.add_data(self.particles[i].x, self.particles[i].f_current[1]) self.particles[i].update_best() self.update_archive() + # Run Classic MOPSO, collect data and run training for the model + def train_surrogate_model(self): + # Generation of data + for t in range(self.t): + self.select_leader() + + for i in range(self.n): + # Movement + self.particles[i].update_velocity(self.leader.x, self.c1, self.c2, self.w) + self.particles[i].update_position() + self.particles[i].keep_boudaries(self.A_max) + # Standard Calculation (Slow and Exact) + self.particles[i].updating_socs(self.socs, self.capacities) + self.particles[i].evaluate(self.prices, self.socs, self.socs_req, self.times) + + # Capture data for AI training + self.surrogate_handler.add_data(self.particles[i].x, self.particles[i].f_current[1]) + + # End of dataset generation (based on classic MOPSO) + self.surrogate_handler.train() + + + def calculate_elec_prices(csv_file:str, sep:str=';'): elec_df = pd.read_csv(filepath_or_buffer=csv_file, sep=sep, skipinitialspace=True) @@ -153,8 +127,11 @@ def run_scenario(scenario_name, capacities:list, price_mean:float, price_std:flo 'x_min':X_MIN, 'x_max':X_MAX } - # Instantiate extended class +# Instantiate extended class optimizer = SmartMOPSO(model_type=model_type, **params) + + if(model_type is not None): + optimizer.train_surrogate_model() start_time = time.time() @@ -174,6 +151,79 @@ def run_scenario(scenario_name, capacities:list, price_mean:float, price_std:flo + + +# CSV files +elec_price_csv = 'data/elec_prices.csv' +capacity_csv = 'data/vehicle_capacity.csv' + +# Global Simulation parameters +T = 30 # Number of iterations (for the particles) +W = 0.4 # Inertia (for exploration) +C1 = 0.3 # Individual trust +C2 = 0.2 # Social trust +ARC_SIZE = 10 # Archive size +nb_vehicle = 20 + +P_MEAN, P_STD = calculate_elec_prices(elec_price_csv) +CAPACITIES = generate_capacities(capacity_csv, nb_vehicles=nb_vehicle) + +NB_TICKS = 48 +DELTA = 60 + +results = { + 'MOPSO':[], + 'MLP': [], + 'RF': [] +} +nb_particles = [20,50,100,500] + +for k in range(len(nb_particles)): + # 1. Without Surrogate (Baseline) + d1, f1_score, _ = run_scenario( + "Only MOPSO", + capacities=CAPACITIES, + price_mean=P_MEAN, + price_std=P_STD, + nb_vehicles=nb_vehicle, # Important pour la cohérence + model_type=None, + n=nb_particles[k] + ) + results['MOPSO'].append((d1, f1_score)) + + # 2. With MLP + d2, f2_score, _ = run_scenario( + "With MLP", + capacities=CAPACITIES, + price_mean=P_MEAN, + price_std=P_STD, + nb_vehicles=nb_vehicle, + model_type='mlp', + n=nb_particles[k] + ) + results['MLP'].append((d2, f2_score)) + + # 3. With Random Forest + d3, f3_score, _ = run_scenario( + "With Random Forest", + capacities=CAPACITIES, + price_mean=P_MEAN, + price_std=P_STD, + nb_vehicles=nb_vehicle, + model_type='rf', + n=nb_particles[k] + ) + results['RF'].append((d3, f3_score)) + + +# --- DISPLAY RESULTS --- +print("\n=== SUMMARY ===") +print(f"{'Mode':<15} | {'Time (s)':<10} | {'Best f2':<10}") +print("-" * 45) +for k, v in results.items(): + for i in range(len(nb_particles)): + print(f"{k:<15}_{nb_particles[i]:<15} | {v[i][0]:<10.2f} | {v[i][1]:<10.4f}") + import matplotlib.pyplot as plt import numpy as np @@ -200,6 +250,10 @@ def plot_time_benchmark(nb_particles_list, results_dict): plt.tight_layout() plt.show() + +plot_time_benchmark(nb_particles, results) + + import matplotlib.pyplot as plt @@ -226,87 +280,4 @@ def plot_f2_benchmark(nb_particles_list, results_dict): plt.tight_layout() plt.show() - - - - - -def main(): - - # CSV files - elec_price_csv = 'data/elec_prices.csv' - capacity_csv = 'data/vehicle_capacity.csv' - - # Global Simulation parameters - T = 30 # Number of iterations (for the particles) - W = 0.4 # Inertia (for exploration) - C1 = 0.3 # Individual trust - C2 = 0.2 # Social trust - ARC_SIZE = 10 # Archive size - nb_vehicle = 20 - - P_MEAN, P_STD = calculate_elec_prices(elec_price_csv) - CAPACITIES = generate_capacities(capacity_csv, nb_vehicles=nb_vehicle) - - NB_TICKS = 48 - DELTA = 60 - - results = { - 'MOPSO':[], - 'MLP': [], - 'RF': [] - } - nb_particles = [20,50,500,1000,10000] - - for k in range(len(nb_particles)): - # 1. Without Surrogate (Baseline) - d1, f1_score, _ = run_scenario( - "Only MOPSO", - capacities=CAPACITIES, - price_mean=P_MEAN, - price_std=P_STD, - nb_vehicles=nb_vehicle, # Important pour la cohérence - model_type=None, - n=nb_particles[k] - ) - results['MOPSO'].append((d1, f1_score)) - - # 2. With MLP - d2, f2_score, _ = run_scenario( - "With MLP", - capacities=CAPACITIES, - price_mean=P_MEAN, - price_std=P_STD, - nb_vehicles=nb_vehicle, - model_type='mlp', - n=nb_particles[k] - ) - results['MLP'].append((d2, f2_score)) - - # 3. With Random Forest - d3, f3_score, _ = run_scenario( - "With Random Forest", - capacities=CAPACITIES, - price_mean=P_MEAN, - price_std=P_STD, - nb_vehicles=nb_vehicle, - model_type='rf', - n=nb_particles[k] - ) - results['RF'].append((d3, f3_score)) - # --- DISPLAY RESULTS --- - - - print("\n=== SUMMARY ===") - print(f"{'Mode':<15} | {'Time (s)':<10} | {'Best f2':<10}") - print("-" * 45) - for k, v in results.items(): - for i in range(len(nb_particles)): - print(f"{k:<15}_{nb_particles[i]:<15} | {v[i][0]:<10.2f} | {v[i][1]:<10.4f}") - - plot_time_benchmark(nb_particles, results) - plot_f2_benchmark(nb_particles, results) - - - -main() \ No newline at end of file +plot_f2_benchmark(nb_particles, results) \ No newline at end of file