import gradio as gr import torch import numpy as np import matplotlib.pyplot as plt from test_functions.Ackley10D import * from test_functions.Ackley2D import * from test_functions.Ackley6D import * from test_functions.HeatExchanger import * from test_functions.CantileverBeam import * from test_functions.Car import * from test_functions.CompressionSpring import * from test_functions.GKXWC1 import * from test_functions.GKXWC2 import * from test_functions.HeatExchanger import * from test_functions.JLH1 import * from test_functions.JLH2 import * from test_functions.KeaneBump import * from test_functions.GKXWC1 import * from test_functions.GKXWC2 import * from test_functions.PressureVessel import * from test_functions.ReinforcedConcreteBeam import * from test_functions.SpeedReducer import * from test_functions.ThreeTruss import * from test_functions.WeldedBeam import * # Import other objective functions as needed import time from Rosen_PFN4BO import * from PIL import Image def s(input_string): return input_string def optimize(objective_function, iteration_input, progress=gr.Progress()): print(objective_function) # Variable setup Current_BEST = torch.tensor( -1e10 ) # Some arbitrary very small number Prev_BEST = torch.tensor( -1e10 ) if objective_function=="CantileverBeam.png": Current_BEST = torch.tensor( -82500 ) # Some arbitrary very small number Prev_BEST = torch.tensor( -82500 ) elif objective_function=="CompressionSpring.png": Current_BEST = torch.tensor( -8 ) # Some arbitrary very small number Prev_BEST = torch.tensor( -8 ) elif objective_function=="HeatExchanger.png": Current_BEST = torch.tensor( -30000 ) # Some arbitrary very small number Prev_BEST = torch.tensor( -30000 ) elif objective_function=="ThreeTruss.png": Current_BEST = torch.tensor( -300 ) # Some arbitrary very small number Prev_BEST = torch.tensor( -300 ) elif objective_function=="Reinforcement.png": Current_BEST = torch.tensor( -440 ) # Some arbitrary very small number Prev_BEST = torch.tensor( -440 ) elif objective_function=="PressureVessel.png": Current_BEST = torch.tensor( -40000 ) # Some arbitrary very small number Prev_BEST = torch.tensor( -40000 ) elif objective_function=="SpeedReducer.png": Current_BEST = torch.tensor( -3200 ) # Some arbitrary very small number Prev_BEST = torch.tensor( -3200 ) elif objective_function=="WeldedBeam.png": Current_BEST = torch.tensor( -35 ) # Some arbitrary very small number Prev_BEST = torch.tensor( -35 ) elif objective_function=="Car.png": Current_BEST = torch.tensor( -35 ) # Some arbitrary very small number Prev_BEST = torch.tensor( -35 ) # Initial random samples # print(objective_functions) trained_X = torch.rand(20, objective_functions[objective_function]['dim']) # Scale it to the domain of interest using the selected function # print(objective_function) X_Scaled = objective_functions[objective_function]['scaling'](trained_X) # Get the constraints and objective trained_gx, trained_Y = objective_functions[objective_function]['function'](X_Scaled) # Convergence list to store best values convergence = [] time_conv = [] START_TIME = time.time() # with gr.Progress(track_tqdm=True) as progress: # Optimization Loop for ii in progress.tqdm(range(iteration_input)): # Example with 100 iterations # (0) Get the updated data for this iteration X_scaled = objective_functions[objective_function]['scaling'](trained_X) trained_gx, trained_Y = objective_functions[objective_function]['function'](X_scaled) # (1) Randomly sample Xpen X_pen = torch.rand(1000,trained_X.shape[1]) # (2) PFN inference phase with EI default_model = 'final_models/model_hebo_morebudget_9_unused_features_3.pt' ei, p_feas = Rosen_PFN_Parallel(default_model, trained_X, trained_Y, trained_gx, X_pen, 'power', 'ei' ) # Calculating CEI CEI = ei for jj in range(p_feas.shape[1]): CEI = CEI*p_feas[:,jj] # (4) Get the next search value rec_idx = torch.argmax(CEI) best_candidate = X_pen[rec_idx,:].unsqueeze(0) # (5) Append the next search point trained_X = torch.cat([trained_X, best_candidate]) ################################################################################ # This is just for visualizing the best value. # This section can be remove for pure optimization purpose Current_X = objective_functions[objective_function]['scaling'](trained_X) Current_GX, Current_Y = objective_functions[objective_function]['function'](Current_X) if ((Current_GX<=0).all(dim=1)).any(): Current_BEST = torch.max(Current_Y[(Current_GX<=0).all(dim=1)]) else: Current_BEST = Prev_BEST ################################################################################ # (ii) Convergence tracking (assuming the best Y is to be maximized) # if Current_BEST != -1e10: print(Current_BEST) print(convergence) convergence.append(Current_BEST.abs()) time_conv.append(time.time() - START_TIME) # Timing END_TIME = time.time() TOTAL_TIME = END_TIME - START_TIME # Website visualization # (i) Radar chart for trained_X radar_chart = None # radar_chart = create_radar_chart(X_scaled) # (ii) Convergence tracking (assuming the best Y is to be maximized) convergence_plot = create_convergence_plot(objective_function, iteration_input, time_conv, convergence, TOTAL_TIME) return convergence_plot # return radar_chart, convergence_plot def create_radar_chart(X_scaled): fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True)) labels = [f'x{i+1}' for i in range(X_scaled.shape[1])] values = X_scaled.mean(dim=0).numpy() num_vars = len(labels) angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist() values = np.concatenate((values, [values[0]])) angles += angles[:1] ax.fill(angles, values, color='green', alpha=0.25) ax.plot(angles, values, color='green', linewidth=2) ax.set_yticklabels([]) ax.set_xticks(angles[:-1]) # ax.set_xticklabels(labels) ax.set_xticklabels([f'{label}\n({value:.2f})' for label, value in zip(labels, values[:-1])]) # Show values ax.set_title("Selected Design", size=15, color='black', y=1.1) plt.close(fig) return fig def create_convergence_plot(objective_function, iteration_input, time_conv, convergence, TOTAL_TIME): fig, ax = plt.subplots() # Realtime optimization data ax.plot(time_conv, convergence, '^-', label='PFN-CBO (Realtime)' ) # Stored GP data if objective_function=="CantileverBeam.png": GP_TIME = torch.load('CantileverBeam_CEI_Avg_Time.pt') GP_OBJ = torch.load('CantileverBeam_CEI_Avg_Obj.pt') elif objective_function=="CompressionSpring.png": GP_TIME = torch.load('CompressionSpring_CEI_Avg_Time.pt') GP_OBJ = torch.load('CompressionSpring_CEI_Avg_Obj.pt') elif objective_function=="HeatExchanger.png": GP_TIME = torch.load('HeatExchanger_CEI_Avg_Time.pt') GP_OBJ = torch.load('HeatExchanger_CEI_Avg_Obj.pt') elif objective_function=="ThreeTruss.png": GP_TIME = torch.load('ThreeTruss_CEI_Avg_Time.pt') GP_OBJ = torch.load('ThreeTruss_CEI_Avg_Obj.pt') elif objective_function=="Reinforcement.png": GP_TIME = torch.load('ReinforcedConcreteBeam_CEI_Avg_Time.pt') GP_OBJ = torch.load('ReinforcedConcreteBeam_CEI_Avg_Obj.pt') elif objective_function=="PressureVessel.png": GP_TIME = torch.load('PressureVessel_CEI_Avg_Time.pt') GP_OBJ = torch.load('PressureVessel_CEI_Avg_Obj.pt') elif objective_function=="SpeedReducer.png": GP_TIME = torch.load('SpeedReducer_CEI_Avg_Time.pt') GP_OBJ = torch.load('SpeedReducer_CEI_Avg_Obj.pt') elif objective_function=="WeldedBeam.png": GP_TIME = torch.load('WeldedBeam_CEI_Avg_Time.pt') GP_OBJ = torch.load('WeldedBeam_CEI_Avg_Obj.pt') elif objective_function=="Car.png": GP_TIME = torch.load('Car_CEI_Avg_Time.pt') GP_OBJ = torch.load('Car_CEI_Avg_Obj.pt') # Plot GP data ax.plot(GP_TIME[:iteration_input], GP_OBJ[:iteration_input], '^-', label='GP-CBO (Data)' ) ax.set_xlabel('Time (seconds)') ax.set_ylabel('Objective Value') ax.set_title('Convergence Plot for {t} iterations'.format(t=iteration_input)) # ax.legend() if objective_function=="CantileverBeam.png": ax.axhline(y=50000, color='red', linestyle='--', label='Optimal Value') elif objective_function=="CompressionSpring.png": ax.axhline(y=0, color='red', linestyle='--', label='Optimal Value') elif objective_function=="HeatExchanger.png": ax.axhline(y=4700, color='red', linestyle='--', label='Optimal Value') elif objective_function=="ThreeTruss.png": ax.axhline(y=262, color='red', linestyle='--', label='Optimal Value') elif objective_function=="Reinforcement.png": ax.axhline(y=355, color='red', linestyle='--', label='Optimal Value') elif objective_function=="PressureVessel.png": ax.axhline(y=5000, color='red', linestyle='--', label='Optimal Value') elif objective_function=="SpeedReducer.png": ax.axhline(y=2650, color='red', linestyle='--', label='Optimal Value') elif objective_function=="WeldedBeam.png": ax.axhline(y=6, color='red', linestyle='--', label='Optimal Value') elif objective_function=="Car.png": ax.axhline(y=25, color='red', linestyle='--', label='Optimal Value') ax.legend(loc='best') # ax.legend(loc='lower left') # Add text to the top right corner of the plot if len(convergence) == 0: ax.text(0.5, 0.5, 'No Feasible Design Found', transform=ax.transAxes, fontsize=12, verticalalignment='top', horizontalalignment='right') plt.close(fig) return fig # Define available objective functions objective_functions = { # "ThreeTruss.png": {"image": "ThreeTruss.png", # "function": ThreeTruss, # "scaling": ThreeTruss_Scaling, # "dim": 2}, "CompressionSpring.png": {"image": "CompressionSpring.png", "function": CompressionSpring, "scaling": CompressionSpring_Scaling, "dim": 3}, "Reinforcement.png": {"image": "Reinforcement.png", "function": ReinforcedConcreteBeam, "scaling": ReinforcedConcreteBeam_Scaling, "dim": 3}, "PressureVessel.png": {"image": "PressureVessel.png", "function": PressureVessel, "scaling": PressureVessel_Scaling, "dim": 4}, "SpeedReducer.png": {"image": "SpeedReducer.png", "function": SpeedReducer, "scaling": SpeedReducer_Scaling, "dim": 7}, "WeldedBeam.png": {"image": "WeldedBeam.png", "function": WeldedBeam, "scaling": WeldedBeam_Scaling, "dim": 4}, "HeatExchanger.png": {"image": "HeatExchanger.png", "function": HeatExchanger, "scaling": HeatExchanger_Scaling, "dim": 8}, "CantileverBeam.png": {"image": "CantileverBeam.png", "function": CantileverBeam, "scaling": CantileverBeam_Scaling, "dim": 10}, "Car.png": {"image": "Car.png", "function": Car, "scaling": Car_Scaling, "dim": 11}, } # Extract just the image paths for the gallery image_paths = [key for key in objective_functions] def submit_action(objective_function_choices, iteration_input): # print(iteration_input) # print(len(objective_function_choices)) # print(objective_functions[objective_function_choices]['function']) if len(objective_function_choices)>0: selected_function = objective_functions[objective_function_choices]['function'] return optimize(objective_function_choices, iteration_input) return None # Function to clear the output def clear_output(): # print(gallery.selected_index) return gr.update(value=[], selected=None), None, 15, gr.Markdown(""), 'Test_formulation_default.png' def reset_gallery(): return gr.update(value=image_paths) with gr.Blocks() as demo: # Centered Title and Description using gr.HTML gr.HTML( """

Pre-trained Transformer for Constrained Bayesian Optimization

Paper: Fast and Accurate Bayesian Optimization with Pre-trained Transformers for Constrained Engineering Problems

This is a demo for Bayesian Optimization using PFN (Prior-Data Fitted Networks). Select your objective function by clicking on one of the check boxes below, then enter the iteration number to run the optimization process. The results will be visualized in the radar chart and convergence plot.

""" ) with gr.Row(): with gr.Column(variant='compact'): # gr.Markdown("# Inputs: ") with gr.Row(): gr.Markdown("## Select a problem (objective): ") img_key = gr.Markdown(value="", visible=False) gallery = gr.Gallery(value=image_paths, label="Objective Functions", # height = 450, object_fit='contain', columns=3, rows=3, elem_id="gallery") gr.Markdown("## Enter iteration Number: ") iteration_input = gr.Slider(label="Iterations:", minimum=15, maximum=50, step=1, value=15) # Row for the Clear and Submit buttons with gr.Row(): clear_button = gr.Button("Clear") submit_button = gr.Button("Submit", variant="primary") with gr.Column(): # gr.Markdown("# Outputs: ") gr.Markdown("## Problem Formulation: ") formulation = gr.Image(value='Formulation_default.png', height=150) gr.Markdown("## Results: ") gr.Markdown("The graph will plot the best observed data v.s. the time for the algorithm to run up until the iteration. The PFN-CBO shows the result of the realtime optimization running in the backend while the GP-CBO shows the stored data from our previous experiments since running GP-CBO will take longer time.") convergence_plot = gr.Plot(label="Convergence Plot") def handle_select(evt: gr.SelectData): selected_image = evt.value key = evt.value['image']['orig_name'] formulation = 'Test_formulation.png' print('here') print(key) return key, formulation gallery.select(fn=handle_select, inputs=None, outputs=[img_key, formulation]) submit_button.click( submit_action, inputs=[img_key, iteration_input], # outputs= [radar_plot, convergence_plot], outputs= convergence_plot, # progress=True # Enable progress tracking ) clear_button.click( clear_output, inputs=None, outputs=[gallery, convergence_plot, iteration_input, img_key, formulation] ).then( # Step 2: Reset the gallery to the original list reset_gallery, inputs=None, outputs=gallery ) demo.launch()