added ypacarai data

cog-imperial · May 19, 2022 · c786acd · c786acd
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diff --git a/ypacarai_array.npy b/ypacarai_array.npy
diff --git a/ypacarai_lake.png b/ypacarai_lake.png
diff --git a/ypacarai_lake.py b/ypacarai_lake.py
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+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.ticker import FormatStrFormatter
+from gp_utils import BoTorchGP
+from temperature_env import MultiObjectiveNormalDropletFunctionEnv
+from snake import MultiObjectiveSnAKe
+from bayes_op import TruncatedExpectedImprovement, EIperUnitCost, MultiObjectiveEIpu, MultiObjectiveTrEI
+from functions import MultiSchekel2D, YpacaraiLake, YpacaraiLakeSingleObjective
+import torch
+import os
+
+
+'''
+This script creates:
+(1) Ypacarai Lake Figure is purpose is set to 'plot'
+(2) Runs the experiments if purpose is set to 'experiment'
+(3) Analyses the results if purpose is set to 'process_results'
+'''
+
+purpose = 'plot'
+cost_change = 3.3
+save_plot = True
+
+if purpose == 'plot':
+
+    func = YpacaraiLake()
+    grid_to_search = func.grid_to_search
+    initial_idx = np.random.random_integers(len(grid_to_search))
+    initial_point = grid_to_search[initial_idx, :].numpy().reshape(1, -1)
+
+    env = MultiObjectiveNormalDropletFunctionEnv(func, max_batch_size = 1, budget = 100)
+    model = MultiObjectiveSnAKe(env, initial_temp = initial_point, max_change = 0.2, exploration_constant = 0, merge_constant = 'lengthscale', objective_weights = [1, 1, 1])
+    # model = TruncatedExpectedImprovement(env, initial_temp = initial_point)
+    model.gp_hyperparams[0] = (0.12, torch.tensor([0.1, 0.1]), 1e-5, 0.5)
+    model.gp_hyperparams[1] = (0.12, torch.tensor([0.1, 0.1]), 1e-5, 0.8)
+    model.gp_hyperparams[2] = (0.12, torch.tensor([0.1, 0.1]), 1e-5, 0.5)
+
+    X, Y = model.run_optim(verbose = True)
+
+    grid_len_x = 2006
+    grid_len_y = 2825
+
+    func = MultiSchekel2D(n_optims = [2, 3, 2])
+
+    x1 = np.linspace(0, 1, grid_len_x)
+    x2 = np.linspace(0, 1, grid_len_y)
+
+    xv, yv = np.meshgrid(x1, x2)
+
+    zv1 = np.zeros_like(xv)
+    zv2 = np.zeros_like(xv)
+    zv3 = np.zeros_like(xv)
+
+    for i in range(grid_len_x):
+        x = x1[i] * np.ones_like(x2)
+        y = x2
+
+        x = x.reshape(-1, 1)
+        y = np.flip(y.reshape(-1, 1))
+
+        xy = np.concatenate((x, y), axis = 1)
+
+        y1, y2, y3 = func.query_function(xy)
+
+        zv1[:, i] = y1
+        zv2[:, i] = y2
+        zv3[:, i] = y3
+
+    array_lake = np.load('ypacarai_array.npy')
+
+    zv1 = zv1 * (array_lake - 1) * (-1)
+    zv2 = zv2 * (array_lake - 1) * (-1)
+    zv3 = zv3 * (array_lake - 1) * (-1)
+
+    fig, ax = plt.subplots(nrows = 1, ncols = 3)
+    fig.set_figheight(6)
+    fig.set_figwidth(18)
+
+    ax[0].yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
+    ax[0].xaxis.set_major_formatter(FormatStrFormatter('%.1f'))
+    ax[1].yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
+    ax[1].xaxis.set_major_formatter(FormatStrFormatter('%.1f'))
+    ax[2].yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
+    ax[2].xaxis.set_major_formatter(FormatStrFormatter('%.1f'))
+
+    ax[0].tick_params(axis='both', labelsize=15)
+    ax[1].tick_params(axis='x', labelsize=15)
+    ax[1].tick_params(axis='y', labelsize=0)
+    ax[2].tick_params(axis='x', labelsize=15)
+    ax[2].tick_params(axis='y', labelsize=0)
+
+    contour_obj1 = ax[0].contourf(x1, np.flip(x2), zv1, levels = 50)
+    cbar1 = fig.colorbar(contour_obj1, ax = ax[0], format = FormatStrFormatter('%.1f'))
+    cbar1.ax.tick_params(labelsize = 15)
+    ax[0].set_title('Objective 1', fontsize = 20)
+    ax[0].set_xlabel('x1', fontsize = 15)
+    ax[0].set_ylabel('x2', fontsize = 15)
+    ax[0].scatter(X[:, 0], X[:, 1], color = 'k')
+    ax[0].plot(X[:, 0], X[:, 1], color = 'k', linewidth = 0.5, markersize = 0.001, linestyle = '--')
+    contour_obj2 = ax[1].contourf(x1, np.flip(x2), zv2, levels = 50, cmap = 'RdBu_r')
+    cbar2 = fig.colorbar(contour_obj2, ax = ax[1])
+    cbar2.ax.tick_params(labelsize = 15)
+    ax[1].set_title('Objective 2', fontsize = 20)
+    ax[1].set_xlabel('x1', fontsize = 15)
+    ax[1].scatter(X[:, 0], X[:, 1], color = 'k')
+    ax[1].plot(X[:, 0], X[:, 1], color = 'k', linewidth = 0.5, markersize = 0.001, linestyle = '--')
+    contour_obj3 = ax[2].contourf(x1, np.flip(x2), zv3, levels = 50, cmap = 'ocean')
+    cbar3 = fig.colorbar(contour_obj3, ax = ax[2])
+    cbar3.ax.tick_params(labelsize = 15)
+    ax[2].set_title('Objective 3', fontsize = 20)
+    ax[2].set_xlabel('x1', fontsize = 15)
+    ax[2].scatter(X[:, 0], X[:, 1], color = 'k')
+    ax[2].plot(X[:, 0], X[:, 1], color = 'k', linewidth = 0.5, markersize = 0.001, linestyle = '--')
+
+    if save_plot == True:
+        fig_name = 'YpacaraiExampleRun'
+        save_name = fig_name + '.pdf'
+        fig.savefig(save_name, bbox_inches = 'tight')
+
+    plt.show()
+
+if purpose == 'experiment':
+    for method in ['TrEI', 'EIpu', 'SnAKe']:
+    # for method in ['TrEI', 'EIpu']:
+        for obj_number in [0, 1, 2, 3]:
+            if obj_number == 0:
+                func = YpacaraiLake()
+            else:
+                func = YpacaraiLakeSingleObjective(obj_to_query = obj_number - 1)
+
+            for run_num in range(1, 11):
+                # set random seed
+                seed = run_num * 505
+                np.random.seed(seed)
+                torch.manual_seed(seed)
+                grid_to_search = func.grid_to_search
+                initial_idx = np.random.random_integers(len(grid_to_search))
+                initial_point = grid_to_search[initial_idx, :].numpy().reshape(1, -1)
+
+                if method == 'SnAKe':
+                    budget = 200
+                else:
+                    budget = 200
+
+                env = MultiObjectiveNormalDropletFunctionEnv(func, max_batch_size = 1, budget = budget)
+                if method == 'SnAKe':
+                    model = MultiObjectiveSnAKe(env, initial_temp = initial_point, exploration_constant = 0, merge_constant = 'lengthscale', objective_weights = [1, 1, 1])
+                elif method == 'TrEI':
+                    model = MultiObjectiveTrEI(env, initial_temp = initial_point, cost_switch = cost_change)
+                elif method == 'EIpu':
+                    model = MultiObjectiveEIpu(env, initial_temp = initial_point, cost_switch = cost_change)
+
+                if obj_number == 0:
+                    model.gp_hyperparams[0] = (0.12, torch.tensor([0.1, 0.1]), 1e-5, 0.5)
+                    model.gp_hyperparams[1] = (0.12, torch.tensor([0.1, 0.1]), 1e-5, 0.8)
+                    model.gp_hyperparams[2] = (0.12, torch.tensor([0.1, 0.1]), 1e-5, 0.5)
+                else:
+                    model.gp_hyperparams[0] = (0.12, torch.tensor([0.1, 0.1]), 1e-5, 0.5)
+
+                X, Y = model.run_optim(verbose = True)
+
+                if method in ['TrEI', 'EIpu']:
+                    folder_inputs =  'ypacarai_results/' + f'objective{obj_number}' + str(cost_change) + '-' + method + '/inputs/'
+                    folder_outputs = 'ypacarai_results/' + f'objective{obj_number}' + str(cost_change) + '-' + method + '/outputs/'
+                    file_name = f'run_{run_num}'
+                else:
+                    folder_inputs =  'ypacarai_results/' + f'objective{obj_number}' + method + '/inputs/'
+                    folder_outputs = 'ypacarai_results/' + f'objective{obj_number}' + method + '/outputs/'
+                    file_name = f'run_{run_num}'
+                # create directories if they exist
+                os.makedirs(folder_inputs, exist_ok = True)
+                os.makedirs(folder_outputs, exist_ok = True)
+                # save the following ones
+                np.save(folder_inputs + file_name, X)
+                np.save(folder_outputs + file_name, np.array(Y))
+
+if purpose == 'process_results':
+
+    final_cost = 10
+    mid_cost = 3.3
+    mse_grid = np.load('mse_ypacarai_grid.npy')
+    func = YpacaraiLake()
+    for obj_number in [0, 1, 2, 3]:
+        for method in ['SnAKe', 'EIpu', 'TrEI']:
+            Regret1 = []
+            Regret2 = []
+            Regret3 = []
+            Regret4 = []
+            Costs = []
+            num_of_samples = []
+            for run_num in range(1, 11):
+                if method in ['TrEI', 'EIpu']:
+                    folder_inputs =  'ypacarai_results/' + f'objective{obj_number}' + str(cost_change) + '-' + method + '/inputs/'
+                    folder_outputs = 'ypacarai_results/' + f'objective{obj_number}' + str(cost_change) + '-' + method + '/outputs/'
+                    file_name = f'run_{run_num}.npy'
+                else:
+                    folder_inputs =  'ypacarai_results/' + f'objective{obj_number}' + method + '/inputs/'
+                    folder_outputs = 'ypacarai_results/' + f'objective{obj_number}' + method + '/outputs/'
+                    file_name = f'run_{run_num}.npy'
+
+                X = np.load(folder_inputs + file_name)
+                Y = np.load(folder_outputs + file_name)
+
+                C = 0
+                for i in range(len(X) - 1):
+                    C = C + np.linalg.norm(X[i+1] - X[i])
+                    if C > final_cost:
+                        break
+
+
+                x_optim1 = np.array([0.20916560292243958, 0.6638457775115967])
+                x_optim2 = np.array([0.8908343315124512, 0.20615419745445251])
+                x_optim3 = np.array([0.4012976884841919, 00.9626452326774597])
+                x_optim4 = np.array([0.4275386929512024, 0.30612045526504517])
+                optim_list = [x_optim1, x_optim2, x_optim3, x_optim4]
+
+                regrets = []
+                for j, optim in enumerate(optim_list):
+                    distances = np.linalg.norm(X[:i+1, :] - optim, axis = 1)
+                    closest_point = np.argmin(distances)
+                    if j < 2:
+                        y_star, _, _ = func.query_function(optim.reshape(1, -1))
+                        y_best, _, _ = func.query_function(X[closest_point, :].reshape(1, -1))
+                    elif j < 3:
+                        _, y_star, _ = func.query_function(optim.reshape(1, -1))
+                        _, y_best, _ = func.query_function(X[closest_point, :].reshape(1, -1))
+                    else:
+                        _, _, y_star = func.query_function(optim.reshape(1, -1))
+                        _, _, y_best = func.query_function(X[closest_point, :].reshape(1, -1))
+                    regrets.append(y_star - y_best)
+
+                Regret1.append(regrets[0])
+                Regret2.append(regrets[1])
+                Regret3.append(regrets[2])
+                Regret4.append(regrets[3])
+                Costs.append(C)
+                num_of_samples.append(i+1)
+
+
+            Regret1 = np.array(Regret1)
+            Regret2 = np.array(Regret2)
+            Regret3 = np.array(Regret3)
+            Regret4 = np.array(Regret4)
+
+            print(method,f'Objective: {obj_number}',' : Reg1 : ', np.mean(Regret1) * 1000)
+            print(method,f'Objective: {obj_number}',' : Std1 : ', np.std(Regret1) * 1000)
+            print(method,f'Objective: {obj_number}',' : Reg2 : ', np.mean(Regret2) * 1000)
+            print(method,f'Objective: {obj_number}',' : Std2 : ', np.std(Regret2) * 1000)
+            print(method,f'Objective: {obj_number}',' : Reg3 : ', np.mean(Regret3) * 1000)
+            print(method,f'Objective: {obj_number}',' : Std3 : ', np.std(Regret3) * 1000)
+            print(method,f'Objective: {obj_number}',' : Reg4 : ', np.mean(Regret4) * 1000)
+            print(method,f'Objective: {obj_number}',' : Std4 : ', np.std(Regret4) * 1000)
+            print(method, ' : Cost : ', np.mean(Costs) )
+            print(method, ' : Samples : ', np.mean(num_of_samples) )