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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "0823043e-8451-4dc8-968c-ca066003f4a7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7958\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7958/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import gradio as gr\n",
"import torch\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from test_functions.Ackley10D import *\n",
"from test_functions.Ackley2D import *\n",
"from test_functions.Ackley6D import *\n",
"from test_functions.HeatExchanger import *\n",
"from test_functions.CantileverBeam import *\n",
"from test_functions.Car import *\n",
"from test_functions.CompressionSpring import *\n",
"from test_functions.GKXWC1 import *\n",
"from test_functions.GKXWC2 import *\n",
"from test_functions.HeatExchanger import *\n",
"from test_functions.JLH1 import *\n",
"from test_functions.JLH2 import *\n",
"from test_functions.KeaneBump import *\n",
"from test_functions.GKXWC1 import *\n",
"from test_functions.GKXWC2 import *\n",
"from test_functions.PressureVessel import *\n",
"from test_functions.ReinforcedConcreteBeam import *\n",
"from test_functions.SpeedReducer import *\n",
"from test_functions.ThreeTruss import *\n",
"from test_functions.WeldedBeam import *\n",
"# Import other objective functions as needed\n",
"import time\n",
"\n",
"from Rosen_PFN4BO import *\n",
"from PIL import Image\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"def s(input_string):\n",
" return input_string\n",
"\n",
"\n",
"\n",
"\n",
"def optimize(objective_function, iteration_input, progress=gr.Progress()):\n",
"\n",
" print(objective_function)\n",
"\n",
" # Variable setup\n",
" Current_BEST = torch.tensor( -1e10 ) # Some arbitrary very small number\n",
" Prev_BEST = torch.tensor( -1e10 )\n",
"\n",
" if objective_function==\"CantileverBeam.png\":\n",
" Current_BEST = torch.tensor( -82500 ) # Some arbitrary very small number\n",
" Prev_BEST = torch.tensor( -82500 )\n",
" elif objective_function==\"CompressionSpring.png\":\n",
" Current_BEST = torch.tensor( -8 ) # Some arbitrary very small number\n",
" Prev_BEST = torch.tensor( -8 )\n",
" elif objective_function==\"HeatExchanger.png\":\n",
" Current_BEST = torch.tensor( -30000 ) # Some arbitrary very small number\n",
" Prev_BEST = torch.tensor( -30000 )\n",
" elif objective_function==\"ThreeTruss.png\":\n",
" Current_BEST = torch.tensor( -300 ) # Some arbitrary very small number\n",
" Prev_BEST = torch.tensor( -300 )\n",
" elif objective_function==\"Reinforcement.png\":\n",
" Current_BEST = torch.tensor( -440 ) # Some arbitrary very small number\n",
" Prev_BEST = torch.tensor( -440 )\n",
" elif objective_function==\"PressureVessel.png\":\n",
" Current_BEST = torch.tensor( -40000 ) # Some arbitrary very small number\n",
" Prev_BEST = torch.tensor( -40000 ) \n",
" elif objective_function==\"SpeedReducer.png\":\n",
" Current_BEST = torch.tensor( -3200 ) # Some arbitrary very small number\n",
" Prev_BEST = torch.tensor( -3200 ) \n",
" elif objective_function==\"WeldedBeam.png\":\n",
" Current_BEST = torch.tensor( -35 ) # Some arbitrary very small number\n",
" Prev_BEST = torch.tensor( -35 )\n",
" elif objective_function==\"Car.png\":\n",
" Current_BEST = torch.tensor( -35 ) # Some arbitrary very small number\n",
" Prev_BEST = torch.tensor( -35 )\n",
"\n",
" # Initial random samples\n",
" # print(objective_functions)\n",
" trained_X = torch.rand(20, objective_functions[objective_function]['dim'])\n",
"\n",
" # Scale it to the domain of interest using the selected function\n",
" # print(objective_function)\n",
" X_Scaled = objective_functions[objective_function]['scaling'](trained_X)\n",
"\n",
" # Get the constraints and objective\n",
" trained_gx, trained_Y = objective_functions[objective_function]['function'](X_Scaled)\n",
"\n",
" # Convergence list to store best values\n",
" convergence = []\n",
" time_conv = []\n",
"\n",
" START_TIME = time.time()\n",
"\n",
"\n",
"# with gr.Progress(track_tqdm=True) as progress:\n",
"\n",
"\n",
" # Optimization Loop\n",
" for ii in progress.tqdm(range(iteration_input)): # Example with 100 iterations\n",
"\n",
" # (0) Get the updated data for this iteration\n",
" X_scaled = objective_functions[objective_function]['scaling'](trained_X)\n",
" trained_gx, trained_Y = objective_functions[objective_function]['function'](X_scaled)\n",
"\n",
" # (1) Randomly sample Xpen \n",
" X_pen = torch.rand(1000,trained_X.shape[1])\n",
"\n",
" # (2) PFN inference phase with EI\n",
" default_model = 'final_models/model_hebo_morebudget_9_unused_features_3.pt'\n",
" \n",
" ei, p_feas = Rosen_PFN_Parallel(default_model,\n",
" trained_X, \n",
" trained_Y, \n",
" trained_gx,\n",
" X_pen,\n",
" 'power',\n",
" 'ei'\n",
" )\n",
"\n",
" # Calculating CEI\n",
" CEI = ei\n",
" for jj in range(p_feas.shape[1]):\n",
" CEI = CEI*p_feas[:,jj]\n",
"\n",
" # (4) Get the next search value\n",
" rec_idx = torch.argmax(CEI)\n",
" best_candidate = X_pen[rec_idx,:].unsqueeze(0)\n",
"\n",
" # (5) Append the next search point\n",
" trained_X = torch.cat([trained_X, best_candidate])\n",
"\n",
"\n",
" ################################################################################\n",
" # This is just for visualizing the best value. \n",
" # This section can be remove for pure optimization purpose\n",
" Current_X = objective_functions[objective_function]['scaling'](trained_X)\n",
" Current_GX, Current_Y = objective_functions[objective_function]['function'](Current_X)\n",
" if ((Current_GX<=0).all(dim=1)).any():\n",
" Current_BEST = torch.max(Current_Y[(Current_GX<=0).all(dim=1)])\n",
" else:\n",
" Current_BEST = Prev_BEST\n",
" ################################################################################\n",
" \n",
" # (ii) Convergence tracking (assuming the best Y is to be maximized)\n",
" # if Current_BEST != -1e10:\n",
" print(Current_BEST)\n",
" print(convergence)\n",
" convergence.append(Current_BEST.abs())\n",
" time_conv.append(time.time() - START_TIME)\n",
"\n",
" # Timing\n",
" END_TIME = time.time()\n",
" TOTAL_TIME = END_TIME - START_TIME\n",
" \n",
" # Website visualization\n",
" # (i) Radar chart for trained_X\n",
" radar_chart = None\n",
" # radar_chart = create_radar_chart(X_scaled)\n",
" # (ii) Convergence tracking (assuming the best Y is to be maximized)\n",
" convergence_plot = create_convergence_plot(objective_function, iteration_input, \n",
" time_conv, \n",
" convergence, TOTAL_TIME)\n",
"\n",
"\n",
" return convergence_plot\n",
" # return radar_chart, convergence_plot\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"def create_radar_chart(X_scaled):\n",
" fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))\n",
" labels = [f'x{i+1}' for i in range(X_scaled.shape[1])]\n",
" values = X_scaled.mean(dim=0).numpy()\n",
" \n",
" num_vars = len(labels)\n",
" angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist()\n",
" values = np.concatenate((values, [values[0]]))\n",
" angles += angles[:1]\n",
"\n",
" ax.fill(angles, values, color='green', alpha=0.25)\n",
" ax.plot(angles, values, color='green', linewidth=2)\n",
" ax.set_yticklabels([])\n",
" ax.set_xticks(angles[:-1])\n",
" # ax.set_xticklabels(labels)\n",
" ax.set_xticklabels([f'{label}\\n({value:.2f})' for label, value in zip(labels, values[:-1])]) # Show values\n",
" ax.set_title(\"Selected Design\", size=15, color='black', y=1.1)\n",
" \n",
" plt.close(fig)\n",
" return fig\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"def create_convergence_plot(objective_function, iteration_input, time_conv, convergence, TOTAL_TIME):\n",
" fig, ax = plt.subplots()\n",
" \n",
" # Realtime optimization data\n",
" ax.plot(time_conv, convergence, '^-', label='PFN-CBO (Realtime)' )\n",
"\n",
" # Stored GP data\n",
" if objective_function==\"CantileverBeam.png\":\n",
" GP_TIME = torch.load('CantileverBeam_CEI_Avg_Time.pt')\n",
" GP_OBJ = torch.load('CantileverBeam_CEI_Avg_Obj.pt')\n",
" \n",
" elif objective_function==\"CompressionSpring.png\":\n",
" GP_TIME = torch.load('CompressionSpring_CEI_Avg_Time.pt')\n",
" GP_OBJ = torch.load('CompressionSpring_CEI_Avg_Obj.pt')\n",
"\n",
" elif objective_function==\"HeatExchanger.png\":\n",
" GP_TIME = torch.load('HeatExchanger_CEI_Avg_Time.pt')\n",
" GP_OBJ = torch.load('HeatExchanger_CEI_Avg_Obj.pt')\n",
" \n",
" elif objective_function==\"ThreeTruss.png\":\n",
" GP_TIME = torch.load('ThreeTruss_CEI_Avg_Time.pt')\n",
" GP_OBJ = torch.load('ThreeTruss_CEI_Avg_Obj.pt')\n",
" \n",
" elif objective_function==\"Reinforcement.png\":\n",
" GP_TIME = torch.load('ReinforcedConcreteBeam_CEI_Avg_Time.pt')\n",
" GP_OBJ = torch.load('ReinforcedConcreteBeam_CEI_Avg_Obj.pt')\n",
" \n",
" elif objective_function==\"PressureVessel.png\":\n",
" GP_TIME = torch.load('PressureVessel_CEI_Avg_Time.pt')\n",
" GP_OBJ = torch.load('PressureVessel_CEI_Avg_Obj.pt')\n",
" \n",
" elif objective_function==\"SpeedReducer.png\":\n",
" GP_TIME = torch.load('SpeedReducer_CEI_Avg_Time.pt')\n",
" GP_OBJ = torch.load('SpeedReducer_CEI_Avg_Obj.pt')\n",
" \n",
" elif objective_function==\"WeldedBeam.png\":\n",
" GP_TIME = torch.load('WeldedBeam_CEI_Avg_Time.pt')\n",
" GP_OBJ = torch.load('WeldedBeam_CEI_Avg_Obj.pt') \n",
"\n",
" elif objective_function==\"Car.png\":\n",
" GP_TIME = torch.load('Car_CEI_Avg_Time.pt')\n",
" GP_OBJ = torch.load('Car_CEI_Avg_Obj.pt') \n",
" \n",
" # Plot GP data \n",
" ax.plot(GP_TIME[:iteration_input], GP_OBJ[:iteration_input], '^-', label='GP-CBO (Data)' )\n",
"\n",
" \n",
" ax.set_xlabel('Time (seconds)')\n",
" ax.set_ylabel('Objective Value')\n",
" ax.set_title('Convergence Plot for {t} iterations'.format(t=iteration_input))\n",
" # ax.legend()\n",
"\n",
" if objective_function==\"CantileverBeam.png\":\n",
" ax.axhline(y=50000, color='red', linestyle='--', label='Optimal Value')\n",
"\n",
" elif objective_function==\"CompressionSpring.png\":\n",
" ax.axhline(y=0, color='red', linestyle='--', label='Optimal Value')\n",
"\n",
" elif objective_function==\"HeatExchanger.png\":\n",
" ax.axhline(y=4700, color='red', linestyle='--', label='Optimal Value')\n",
" \n",
" elif objective_function==\"ThreeTruss.png\":\n",
" ax.axhline(y=262, color='red', linestyle='--', label='Optimal Value')\n",
" \n",
" elif objective_function==\"Reinforcement.png\":\n",
" ax.axhline(y=355, color='red', linestyle='--', label='Optimal Value')\n",
" \n",
" elif objective_function==\"PressureVessel.png\":\n",
" ax.axhline(y=5000, color='red', linestyle='--', label='Optimal Value')\n",
" \n",
" elif objective_function==\"SpeedReducer.png\":\n",
" ax.axhline(y=2650, color='red', linestyle='--', label='Optimal Value')\n",
" \n",
" elif objective_function==\"WeldedBeam.png\":\n",
" ax.axhline(y=6, color='red', linestyle='--', label='Optimal Value') \n",
"\n",
" elif objective_function==\"Car.png\":\n",
" ax.axhline(y=25, color='red', linestyle='--', label='Optimal Value') \n",
"\n",
" \n",
" ax.legend(loc='best')\n",
" # ax.legend(loc='lower left')\n",
" \n",
"\n",
" # Add text to the top right corner of the plot\n",
" if len(convergence) == 0:\n",
" ax.text(0.5, 0.5, 'No Feasible Design Found', transform=ax.transAxes, fontsize=12,\n",
" verticalalignment='top', horizontalalignment='right')\n",
" \n",
" \n",
" plt.close(fig)\n",
" return fig\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"# Define available objective functions\n",
"objective_functions = {\n",
" # \"ThreeTruss.png\": {\"image\": \"ThreeTruss.png\", \n",
" # \"function\": ThreeTruss, \n",
" # \"scaling\": ThreeTruss_Scaling, \n",
" # \"dim\": 2},\n",
" \"CompressionSpring.png\": {\"image\": \"CompressionSpring.png\", \n",
" \"function\": CompressionSpring, \n",
" \"scaling\": CompressionSpring_Scaling, \n",
" \"dim\": 3},\n",
" \"Reinforcement.png\": {\"image\": \"Reinforcement.png\", \"function\": ReinforcedConcreteBeam, \"scaling\": ReinforcedConcreteBeam_Scaling, \"dim\": 3},\n",
" \"PressureVessel.png\": {\"image\": \"PressureVessel.png\", \"function\": PressureVessel, \"scaling\": PressureVessel_Scaling, \"dim\": 4},\n",
" \"SpeedReducer.png\": {\"image\": \"SpeedReducer.png\", \"function\": SpeedReducer, \"scaling\": SpeedReducer_Scaling, \"dim\": 7},\n",
" \"WeldedBeam.png\": {\"image\": \"WeldedBeam.png\", \"function\": WeldedBeam, \"scaling\": WeldedBeam_Scaling, \"dim\": 4},\n",
" \"HeatExchanger.png\": {\"image\": \"HeatExchanger.png\", \"function\": HeatExchanger, \"scaling\": HeatExchanger_Scaling, \"dim\": 8},\n",
" \"CantileverBeam.png\": {\"image\": \"CantileverBeam.png\", \"function\": CantileverBeam, \"scaling\": CantileverBeam_Scaling, \"dim\": 10},\n",
" \"Car.png\": {\"image\": \"Car.png\", \"function\": Car, \"scaling\": Car_Scaling, \"dim\": 11},\n",
"}\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"# Extract just the image paths for the gallery\n",
"image_paths = [key for key in objective_functions]\n",
"\n",
"\n",
"def submit_action(objective_function_choices, iteration_input):\n",
" # print(iteration_input)\n",
" # print(len(objective_function_choices))\n",
" # print(objective_functions[objective_function_choices]['function'])\n",
" if len(objective_function_choices)>0:\n",
" selected_function = objective_functions[objective_function_choices]['function']\n",
" return optimize(objective_function_choices, iteration_input)\n",
" return None\n",
"\n",
"# Function to clear the output\n",
"def clear_output():\n",
" # print(gallery.selected_index)\n",
" \n",
" return gr.update(value=[], selected=None), None, 15, gr.Markdown(\"\"), 'Test_formulation_default.png'\n",
"\n",
"def reset_gallery():\n",
" return gr.update(value=image_paths)\n",
"\n",
"\n",
"with gr.Blocks() as demo:\n",
" # Centered Title and Description using gr.HTML\n",
" gr.HTML(\n",
" \"\"\"\n",
" <div style=\"text-align: center;\">\n",
" <h1>Pre-trained Transformer for Constrained Bayesian Optimization</h1>\n",
" <h4>Paper: <a href=\"https://arxiv.org/abs/2404.04495\">\n",
" Fast and Accurate Bayesian Optimization with Pre-trained Transformers for Constrained Engineering Problems</a> \n",
" </h4>\n",
"\n",
" <p style=\"text-align: left;\">This is a demo for Bayesian Optimization using PFN (Prior-Data Fitted Networks). \n",
" Select your objective function by clicking on one of the check boxes below, then enter the iteration number to run the optimization process. \n",
" The results will be visualized in the radar chart and convergence plot.</p>\n",
" \n",
" \n",
" \n",
"\n",
" </div>\n",
" \"\"\"\n",
" )\n",
"\n",
" \n",
" with gr.Row():\n",
" \n",
" \n",
" with gr.Column(variant='compact'):\n",
" # gr.Markdown(\"# Inputs: \")\n",
" \n",
" with gr.Row():\n",
" gr.Markdown(\"## Select a problem (objective): \")\n",
" img_key = gr.Markdown(value=\"\", visible=False)\n",
" \n",
" gallery = gr.Gallery(value=image_paths, label=\"Objective Functions\", \n",
" # height = 450, \n",
" object_fit='contain',\n",
" columns=3, rows=3, elem_id=\"gallery\")\n",
" \n",
" gr.Markdown(\"## Enter iteration Number: \")\n",
" iteration_input = gr.Slider(label=\"Iterations:\", minimum=15, maximum=50, step=1, value=15)\n",
" \n",
"\n",
" # Row for the Clear and Submit buttons\n",
" with gr.Row():\n",
" clear_button = gr.Button(\"Clear\")\n",
" submit_button = gr.Button(\"Submit\", variant=\"primary\")\n",
"\n",
" with gr.Column():\n",
" # gr.Markdown(\"# Outputs: \")\n",
" gr.Markdown(\"## Problem Formulation: \")\n",
" formulation = gr.Image(value='Formulation_default.png', height=150)\n",
" gr.Markdown(\"## Results: \")\n",
" 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.\")\n",
" convergence_plot = gr.Plot(label=\"Convergence Plot\")\n",
"\n",
"\n",
"\n",
" def handle_select(evt: gr.SelectData):\n",
" selected_image = evt.value\n",
" key = evt.value['image']['orig_name']\n",
" formulation = 'Test_formulation.png'\n",
" print('here')\n",
" print(key)\n",
"\n",
" return key, formulation\n",
" \n",
" gallery.select(fn=handle_select, inputs=None, outputs=[img_key, formulation])\n",
"\n",
"\n",
" \n",
" submit_button.click(\n",
" submit_action,\n",
" inputs=[img_key, iteration_input],\n",
" # outputs= [radar_plot, convergence_plot],\n",
" outputs= convergence_plot,\n",
" \n",
" # progress=True # Enable progress tracking\n",
" \n",
" )\n",
"\n",
" clear_button.click(\n",
" clear_output,\n",
" inputs=None,\n",
" outputs=[gallery, convergence_plot, iteration_input, img_key, formulation]\n",
" ).then(\n",
" # Step 2: Reset the gallery to the original list\n",
" reset_gallery,\n",
" inputs=None,\n",
" outputs=gallery\n",
" )\n",
"\n",
" \n",
"\n",
"demo.launch()\n"
]
},
{
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"id": "776c7ab2-96a1-4e22-9b4b-daf69960e3c4",
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