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489 lines
22 KiB
Python
489 lines
22 KiB
Python
import mpid as pid
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import numpy as np
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from mpi4py.futures import MPIPoolExecutor
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from smt.applications.ego import EGO, KRG
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from smt.sampling_methods import LHS
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from smt.design_space import (
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DesignSpace,
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)
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from sklearn.model_selection import KFold, LeaveOneOut
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from scipy.stats import norm
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from scipy.optimize import minimize
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import matplotlib.pyplot as plt
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import matplotlib.image as mpimg
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import matplotlib.animation as animation
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import copy
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import pickle
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import os
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import gc
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if not os.path.exists("results"):
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os.makedirs("results")
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#Space grid definition
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N = 1025
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unit_cell_pitch = 0.700 #i.e., 700 [nm]
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n0 = 1.444
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n1 = 1.0
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n2 = 1.444
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mat_refr_index = np.array([n0, n1, n2])
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ms_max_available_diameter = 122 #[um]
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output_fiber_core_diameter = 19 #[um]
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opt_max_eval = 550
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def setup_optical_system()->pid.Simulation:
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'''
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Builds the mode converter optical system using
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the specified propagation distances `d`, runs the optimization
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and returns the best objective value.
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Args:
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xdoe_points (np.ndarray): Array containing the DOE points.
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Each point consists of the propagation distances d0 and d1
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Returns:
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float: The best objective value obtained from the optimization.
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'''
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central_wavelength_um = 1.55
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wavelengths_to_test_um = [1.53, 1.55, 1.57] #[um]
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source_files = ["fiber_modes/SMF/SMF_LP01_field_distribution_lda_1530_N_1025.npy",
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"fiber_modes/SMF/SMF_LP01_field_distribution_lda_1550_N_1025.npy",
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"fiber_modes/SMF/SMF_LP01_field_distribution_lda_1570_N_1025.npy"]
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sources = [pid.Source(input_field=np.load(file),
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wavelength=wvl,
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field_norm=True) for file, wvl in zip(source_files, wavelengths_to_test_um)]
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target_files = ["fiber_modes/FMF/FMF_LP21_field_distribution_lda_1530_N_1025.npy",
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"fiber_modes/FMF/FMF_LP21_field_distribution_lda_1550_N_1025.npy",
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"fiber_modes/FMF/FMF_LP21_field_distribution_lda_1570_N_1025.npy"]
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targets = [pid.Target(input_field=np.load(file),
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associated_source=sources[i],
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wavelength=wvl,
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field_norm=True) for i, (file, wvl) in enumerate(zip(target_files, wavelengths_to_test_um))]
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#import unit cell data
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unit_cell_diameters_wvl_1530 = np.load("unit_cell/unit_cell_center_1530_h900nm_res50_wvl_diameters.npy")
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unit_cell_diameters_wvl_1550 = np.load("unit_cell/unit_cell_center_1550_h900nm_res50_wvl_diameters.npy")
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unit_cell_diameters_wvl_1570 = np.load("unit_cell/unit_cell_center_1570_h900nm_res50_wvl_diameters.npy")
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unit_cell_phases_wvl_1530 = np.load("unit_cell/unit_cell_center_1530_h900nm_res50_wvl_mode_phase.npy")
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unit_cell_phases_wvl_1550 = np.load("unit_cell/unit_cell_center_1550_h900nm_res50_wvl_mode_phase.npy")
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unit_cell_phases_wvl_1570 = np.load("unit_cell/unit_cell_center_1570_h900nm_res50_wvl_mode_phase.npy")
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#construct the UnitCell objects
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unit_cell_1530 = pid.UnitCell(wavelength=1.53, tune_param=unit_cell_diameters_wvl_1530, sim_phase=unit_cell_phases_wvl_1530)
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unit_cell_1550 = pid.UnitCell(wavelength=1.55, tune_param=unit_cell_diameters_wvl_1550, sim_phase=unit_cell_phases_wvl_1550)
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unit_cell_1570 = pid.UnitCell(wavelength=1.57, tune_param=unit_cell_diameters_wvl_1570, sim_phase=unit_cell_phases_wvl_1570)
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unit_cells_list = [unit_cell_1530, unit_cell_1550, unit_cell_1570]
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initial_space_grid = pid.SpaceGrid(
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side_pixels=N,
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unit_cell_pitch=unit_cell_pitch,
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materials_refr_index=mat_refr_index,
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xy_symmetry=True,
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)
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#setup the simulation instance
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simulation = pid.Simulation(
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sources=sources,
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targets=targets,
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central_wavelength=central_wavelength_um,
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space_grid=initial_space_grid,
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n_cpu=1
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)
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simulation.set_optimization(
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opt_param_min=0,
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opt_param_max=1,
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ms_max_avail_diameter=ms_max_available_diameter,
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phase_mask_init_size=output_fiber_core_diameter/3,
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opt_max_eval=opt_max_eval,
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conic_filter_radius=1.5,
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unit_cells=unit_cells_list,
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results_while_solving = False,
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save_results = False,
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)
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return simulation
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def EI(GP, points, f_min): #f_min is always negative
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pred = - GP.predict_values(points) #GP predicts overlaps, which are always positive
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var = GP.predict_variances(points)
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args0 = (f_min - pred) / np.sqrt(var)
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args1 = (f_min - pred) * norm.cdf(args0)
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args2 = np.sqrt(var) * norm.pdf(args0)
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if var.size == 1 and var == 0.0: # can be use only if one point is computed
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return 0.0
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ei = args1 + args2
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return ei[0,0]
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def leave_one_out(dataset, corr_function):
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full_x_doe = dataset[0]
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full_y_doe = dataset[1]
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loo = LeaveOneOut()
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z_values = []
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true_values = []
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predicted_values = []
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predicted_confidence = []
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for i, (train_index, test_index) in enumerate(loo.split(full_x_doe)):
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print(f"LOO Iteration {i}")
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train_values = full_x_doe[train_index]
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test_values = full_x_doe[test_index]
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train_outputs = full_y_doe[train_index]
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test_outputs = full_y_doe[test_index]
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sm_loo = KRG(design_space=design_space,
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n_start=40,
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print_global=False,
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eval_noise=False,
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corr=corr_function,
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)
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sm_loo.set_training_values(train_values, train_outputs)
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sm_loo.train()
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#the predicted value from the surrogate model
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test_pred_values = sm_loo.predict_values(test_values)
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#for each point in the surrogate model we can get an estimate of the prediction variance, in other
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#words, a measure of how much the model trusts the prediction at that point
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test_pred_variances = sm_loo.predict_variances(test_values)
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#standardized error: this value should roughly behave as a normal
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#distribution (Gaussian) with mean 0 and variance 1
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z = (test_outputs - test_pred_values) / np.sqrt(test_pred_variances)
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#note that we divide for the standard deviation, thus the deviation at
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#the numerator shall be within +/- 3 standard deviations, for most points
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z_values.append(z)
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true_values.append(test_outputs)
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predicted_values.append(test_pred_values)
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predicted_confidence.append(np.sqrt(test_pred_variances)) #standard deviation
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sq_err = np.mean((test_pred_values - test_outputs)**2)
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print(f" Squared error for LOO iteration {i}: {sq_err:.6e}")
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#SCATTER PLOT WITH ERROR BAR ON EACH SAMPLE loo cv
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fig, ax = plt.subplots(figsize=(10, 6))
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for i in range(len(true_values)):
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#confidence interval is set to 3 standard deviations (99.7%)
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ax.errorbar(i, predicted_values[i], yerr=3 * predicted_confidence[i], fmt='o', label='Predicted with confidence' if i == 0 else "")
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ax.scatter(i, true_values[i], marker='x', color='red', label='True value' if i == 0 else "")
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ax.set_xticks(range(len(true_values)))
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ax.set_xlabel('LOO Iteration', fontsize=20)
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ax.set_ylabel('Value', fontsize=20)
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ax.set_title('LOO Predicted vs True Values', fontsize=20)
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ax.set_xticks(range(1, len(true_values), 2))
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ax.tick_params(axis='both', labelsize=20)
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ax.grid(True, alpha=0.3)
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ax.legend()
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plt.savefig(f'{surrogate_folder_name}/loo_validation_kernel_{corr_function}.pdf')
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plt.close(fig)
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#DIAGONAL COMPARISON LINE true/predicted
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true_values_array = np.concatenate(true_values).ravel()
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predicted_values_array = np.concatenate(predicted_values).ravel()
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lower = min(true_values_array.min(), predicted_values_array.min())
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upper = max(true_values_array.max(), predicted_values_array.max())
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fig, ax = plt.subplots(figsize=(10, 6))
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ax.scatter(true_values_array, predicted_values_array, color="blue")
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ax.plot([lower, upper], [lower, upper], "r--", label="y=x")
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ax.set_xlabel('True Value', fontsize=20)
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ax.set_ylabel('Predicted Value', fontsize=20)
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ax.set_title('LOO Predicted vs True Values', fontsize=20)
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ax.tick_params(axis='both', labelsize=20)
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ax.grid(True, alpha=0.3)
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ax.legend()
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plt.savefig(f'{surrogate_folder_name}/loo_predicted_vs_true_diagonal_kernel_{corr_function}.pdf')
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plt.close(fig)
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#GAUSSIAN FIT standard normal vs. standardized residuals
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fig, ax = plt.subplots(figsize=(10, 6))
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z_values_array = np.concatenate(z_values)
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mu, std = norm.fit(z_values_array)
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x = np.linspace(-4, 4, 100)
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p = norm.pdf(x, mu, std)
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ax.plot(x, p, 'r-', label='Fitted Gaussian')
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mu_norm, std_norm = 0, 1
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ax.plot(x, norm.pdf(x, mu_norm, std_norm), 'g--', label='Standard Normal')
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ax.hist(z_values_array, bins=20, density=True, alpha=0.6, color='blue', label='Standardized residuals')
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ax.set_xlabel('Standardized Residual', fontsize=20)
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ax.set_ylabel('Density', fontsize=20)
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ax.set_title('Gaussian Fit vs Standardized Residuals', fontsize=20)
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ax.tick_params(axis='both', labelsize=20)
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ax.grid(True, alpha=0.3)
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ax.legend()
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plt.savefig(f'{surrogate_folder_name}/loo_gaussian_fit_kernel_{corr_function}.pdf')
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plt.close(fig)
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#STANDARDIZED RESIDUALS PLOT
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fig, ax = plt.subplots(figsize=(10, 6))
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z_values_array = np.concatenate(z_values)
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ax.scatter(range(len(z_values_array)), z_values_array, color='blue', label='Standardized residuals')
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ax.set_ylabel('Standardized Residual', fontsize=20)
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#ax.set_title('LOO Standardized Residuals', fontsize=20)
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ax.set_xticks(range(1, len(z_values_array), 2))
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ax.set_ylim(-np.max(np.abs(z_values_array)), np.max(np.abs(z_values_array)))
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ax.tick_params(axis='both', labelsize=20)
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ax.grid(True, alpha=0.3)
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ax.legend()
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plt.savefig(f'{surrogate_folder_name}/loo_standardized_residuals_kernel_{corr_function}.pdf')
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plt.close(fig)
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return 0
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def run_ego(initial_dataset, surrogate_model, n_ego_iter, xlimits, simulation):
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sm = surrogate_model #the surrogate is trained on overlap values (positive)
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#load the associated fold training data
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xdoe_train_loaded = copy.deepcopy(initial_dataset[:, :-1])
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ydoe_train_loaded = copy.deepcopy(initial_dataset[:, -1]) # ydoe_train_loaded > 0
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x_data = copy.deepcopy(initial_dataset[:, :-1])
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y_data = copy.deepcopy(initial_dataset[:, -1]) # y_data > 0
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n_start = 40 #number of initial random points to sample for EGO refinement
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x_et_k_tested_history = []
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EI_history = []
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for k in range(n_ego_iter):
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d0_start = np.atleast_2d(xlimits[0][0] + np.random.rand(n_start)*(xlimits[0][1]-xlimits[0][0]))
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d1_start = np.atleast_2d(xlimits[1][0] + np.random.rand(n_start)*(xlimits[1][1]-xlimits[1][0]))
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x_start = np.hstack((d0_start.T, d1_start.T))
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f_min_k = np.min(- y_data) #the most negative data point is the best point
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opt_all = np.array(#maximizes the expected improvement in a region around x_st, an element of the start point
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[
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minimize(lambda x: float(-EI(sm, np.atleast_2d(x), f_min_k)), x_st, method="SLSQP", bounds=xlimits)
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for x_st in x_start
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]
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)
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opt_success = opt_all[[opt_i["success"] for opt_i in opt_all]]
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obj_success = np.array([opt_i["fun"] for opt_i in opt_success])
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ind_min = np.argmin(obj_success) #in this case, the most negative -EI is the best (maximum EI)
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#save the minimum EI value for this iteration to the history
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EI_history.append(- obj_success[ind_min])
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opt = opt_success[ind_min]
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#select the non-optimal points for history purposes. Later, we will plot
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#all the non-optimal points with crosses to show the search process of the n_start points
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non_optimal_points = [opt_i["x"] for i, opt_i in enumerate(opt_all) if i != ind_min]
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x_et_k = opt["x"] #this is the coordinate of the most interesting point: the one that maximizes the EI (minimize -EI)
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d0_et_k = x_et_k[0]
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d1_et_k = x_et_k[1]
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d2_et_k = d0_et_k
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d_et_k = np.round(np.array([d0_et_k, d1_et_k, d2_et_k]),0)
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print(d_et_k)
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simulation.update_propagation_distances(d_et_k)
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simulation.run()
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y_et_k = simulation.get_best_opt_val() #evaluate the objective function at the new point; y_et_k < 0
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x_et_k_tested = np.atleast_2d(x_et_k)
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x_et_k_tested_history.append(x_et_k_tested)
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d0_EI = np.linspace(xlimits[0][0], xlimits[0][1], 100)
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d1_EI = np.linspace(xlimits[1][0], xlimits[1][1], 100)
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d0_EI_grid, d1_EI_grid = np.meshgrid(d0_EI, d1_EI)
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EI_grid = np.zeros_like(d0_EI_grid)
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for i in range(d0_EI_grid.shape[0]):
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for j in range(d0_EI_grid.shape[1]):
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EI_grid[i,j] = EI(sm, np.atleast_2d([d0_EI_grid[i,j], d1_EI_grid[i,j]]), f_min_k)
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fig, ax = plt.subplots(figsize=(8,6))
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contour = ax.contourf(d0_EI_grid, d1_EI_grid, EI_grid, levels=50, cmap='viridis')
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fig.colorbar(contour, ax=ax, label='Expected Improvement')
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#scatter the full history of tested points up to the current iteration k, the current point is red, the others are blue
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x_et_k_tested_history_array = np.vstack(x_et_k_tested_history)
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#scatter last point in red, the others in blue
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ax.scatter(x_et_k_tested_history_array[:-1,0], x_et_k_tested_history_array[:-1,1], color='blue', label='Previous best')
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ax.scatter(x_et_k_tested_history_array[-1,0], x_et_k_tested_history_array[-1,1], color='red', label='Best point')
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#also plot the non optimal points with crosses for this iteration
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if len(non_optimal_points) > 0:
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non_optimal_points_array = np.vstack(non_optimal_points)
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ax.scatter(non_optimal_points_array[:,0], non_optimal_points_array[:,1], color='orange', marker='x', label='Candidate')
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ax.set_title(f'EGO Iteration {k+1}/{n_ego_iter}')
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ax.set_xlabel('d0 [um]')
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ax.set_ylabel('d1 [um]')
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ax.set_xlim(xlimits[0])
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ax.set_ylim(xlimits[1])
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ax.legend(loc='upper left', framealpha=0.5)
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fig.tight_layout()
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fig.savefig(f'{surrogate_folder_name}/optimisation_{k}.pdf')
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plt.close(fig)
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fig, ax = plt.subplots(figsize=(8,6))
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d0_grid, d1_grid = np.meshgrid(np.linspace(xlimits[0][0], xlimits[0][1], 100),
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np.linspace(xlimits[1][0], xlimits[1][1], 100))
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y_pred_grid = np.zeros_like(d0_grid)
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for i in range(d0_grid.shape[0]):
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for j in range(d0_grid.shape[1]):
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y_pred_grid[i,j] = sm.predict_values(np.atleast_2d([d0_grid[i,j], d1_grid[i,j]]))[0,0]
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contour = ax.contourf(d0_grid, d1_grid, y_pred_grid, levels=50, cmap='viridis')
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fig.colorbar(contour, ax=ax, label='predicted overlap')
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ax.scatter(xdoe_train_loaded[:,0], xdoe_train_loaded[:,1], color='blue', label='DOE Points')
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ax.scatter(x_data[len(xdoe_train_loaded):,0], x_data[len(xdoe_train_loaded):,1], color='red', label='EGO Evaluated Points')
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ax.set_xlabel('d0 [um]', fontsize=15)
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ax.set_ylabel('d1 [um]', fontsize=15)
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ax.set_xlim(xlimits[0])
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ax.set_ylim(xlimits[1])
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ax.legend(loc='upper left', framealpha=0.5, fontsize=15)
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for spine in ax.spines.values():
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spine.set_linewidth(1.5)
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ax.set_aspect('equal')
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fig.tight_layout()
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fig.savefig(f'{surrogate_folder_name}/surrogate_landscape_{k}.pdf')
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plt.close(fig)
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y_data = np.atleast_2d(np.append(y_data, y_et_k)).reshape(-1,1)
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x_data = np.atleast_2d(np.append(x_data, x_et_k_tested, axis=0))
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sm.set_training_values(x_data, y_data) #train the surrogate with positive values.
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sm.train() #train the model with the new data point
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print(f"{k+1}-th EGO refinement complete")
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ind_best = np.argmax(y_data)
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x_opt = x_data[ind_best]
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y_opt = y_data[ind_best]
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np.savetxt(f'{surrogate_folder_name}/ego_opt_results.txt', np.hstack((x_opt,y_opt)))
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#plot of the EI history
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fig, ax = plt.subplots(figsize=(8,6))
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ax.plot(range(1, n_ego_iter+1), EI_history, marker='o')
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ax.set_title('Expected Improvement History')
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ax.set_xlabel('EGO Iteration')
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ax.set_ylabel('Expected Improvement')
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ax.grid()
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fig.tight_layout()
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fig.savefig(f'{surrogate_folder_name}/EI_history.pdf')
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plt.close(fig)
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#surface plot of the refined surrogate model
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d0_grid = np.linspace(xlimits[0][0], xlimits[0][1], 100)
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d1_grid = np.linspace(xlimits[1][0], xlimits[1][1], 100)
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d0_grid_mesh, d1_grid_mesh = np.meshgrid(d0_grid, d1_grid)
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d_grid = np.column_stack((d0_grid_mesh.flatten(), d1_grid_mesh.flatten()))
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sm_predictions = sm.predict_values(d_grid).reshape(d0_grid_mesh.shape)
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fig = plt.figure(figsize=(8,6))
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ax = fig.add_subplot(111, projection='3d')
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surf = ax.plot_surface(d0_grid_mesh, d1_grid_mesh, sm_predictions, cmap='viridis', alpha=0.7, edgecolor='none')
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ax.scatter(xdoe_train_loaded[:,0], xdoe_train_loaded[:,1], ydoe_train_loaded.flatten(), color='blue', label='DOE Points')
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ax.scatter(x_data[len(xdoe_train_loaded):,0], x_data[len(xdoe_train_loaded):,1], y_data[len(xdoe_train_loaded):].flatten(), color='red', label='EGO Evaluated Points')
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ax.scatter([x_opt[0]], [x_opt[1]], [y_opt], color='green', s=100, label='Best Point', edgecolor='black')
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ax.set_xlabel('d0 [um]')
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ax.set_ylabel('d1 [um]')
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ax.legend()
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fig.tight_layout()
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fig.savefig(f'{surrogate_folder_name}/refined_surrogate_model_surface.pdf')
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plt.close(fig)
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|
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#contour plot
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fig, ax = plt.subplots(figsize=(8,6))
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cp = ax.contourf(d0_grid_mesh, d1_grid_mesh, sm_predictions, levels=50, cmap='viridis')
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fig.colorbar(cp, ax=ax, label='Surrogate Model Prediction')
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ax.scatter(xdoe_train_loaded[:,0], xdoe_train_loaded[:,1], color='blue', label='DOE Points')
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ax.scatter(x_data[len(xdoe_train_loaded):,0], x_data[len(xdoe_train_loaded):,1], color='red', label='EGO Evaluated Points')
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ax.scatter([x_opt[0]], [x_opt[1]], color='green', s=100, label='Best Point', edgecolor='black')
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ax.set_xlabel('d0 [um]')
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ax.set_ylabel('d1 [um]')
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ax.set_xlim(xlimits[0])
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ax.set_ylim(xlimits[1])
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ax.legend(framealpha=0.5)
|
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fig.tight_layout()
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fig.savefig(f'{surrogate_folder_name}/refined_surrogate_model_contour.pdf')
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plt.close(fig)
|
|
|
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pickle.dump(sm, open(f'{surrogate_folder_name}/refined_surrogate_model.pkl', 'wb'))
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pickle.dump((x_data, y_data), open(f'{surrogate_folder_name}/refined_dataset.pkl', 'wb'))
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return x_opt, y_opt, ind_best, x_data, y_data
|
|
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def train_surrogate_model(initial_dataset, corr_function):
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full_x_doe = initial_dataset[:, :2]
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full_y_doe = initial_dataset[:, 2].reshape(-1, 1)
|
|
|
|
sm = KRG(design_space = design_space,
|
|
n_start = 40,
|
|
print_global = False,
|
|
eval_noise = False,
|
|
corr = corr_function,
|
|
)
|
|
sm.set_training_values(full_x_doe, full_y_doe)
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sm.train()
|
|
|
|
return sm
|
|
|
|
def get_doe():
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if not os.path.exists(f'{doe_folder_name}/ego_doe.npy'):
|
|
#build DOE
|
|
print("Building DOE...")
|
|
sampling = LHS(xlimits=xlimits, seed=seed)
|
|
xdoe = np.round(sampling(ndoe),0)
|
|
ydoe = []
|
|
|
|
ydoe = np.zeros((len(xdoe), 1), dtype=float)
|
|
|
|
for i, point in enumerate(xdoe):
|
|
print(f"DOE evaluation {i+1}/{len(xdoe)} for point {point}")
|
|
d0 = point[0]
|
|
d1 = point[1]
|
|
d2 = d0
|
|
d = np.round(np.array([d0, d1, d2]),0)
|
|
simulation.update_propagation_distances(d)
|
|
simulation.run()
|
|
ydoe[i, 0] = simulation.get_best_opt_val()
|
|
|
|
with open(f'{surrogate_folder_name}/ego_evaluations.txt', 'a') as f:
|
|
for xd, yd in zip(xdoe, ydoe):
|
|
f.write(f"{xd[0]:.6e} {xd[1]:.6e} {yd[0]:.6e}\n")
|
|
else:
|
|
print("Loading existing DOE...")
|
|
initial_dataset = np.load(f'{doe_folder_name}/ego_doe.npy')
|
|
xdoe = initial_dataset[:, :xlimits.shape[0]]
|
|
ydoe = initial_dataset[:, xlimits.shape[0]:]
|
|
|
|
initial_dataset = np.hstack((xdoe,ydoe))
|
|
#save the doe
|
|
np.save(f'{doe_folder_name}/ego_doe.npy', initial_dataset)
|
|
np.savetxt(f'{doe_folder_name}/ego_doe.txt', initial_dataset)
|
|
|
|
return initial_dataset
|
|
|
|
##############################
|
|
#EGO setup
|
|
##############################
|
|
if __name__ == "__main__":
|
|
|
|
xlimits = np.array([[100,700], [100,700]]) # d0, d1 in [um]
|
|
seed = 777
|
|
np.random.seed(seed)
|
|
|
|
design_space = DesignSpace(xlimits, seed=seed)
|
|
|
|
ndoe = 20 #number of DOE samples
|
|
n_ego_iter = 15 #number of EGO refinement iterations
|
|
|
|
corr_function_name = 'matern32'
|
|
doe_folder_name = 'DOE_1_5'
|
|
surrogate_folder_name = corr_function_name + '_surrogate_filtered_1_5'
|
|
if not os.path.exists(doe_folder_name):
|
|
os.makedirs(doe_folder_name)
|
|
if not os.path.exists(surrogate_folder_name):
|
|
os.makedirs(surrogate_folder_name)
|
|
|
|
simulation = setup_optical_system()
|
|
|
|
#DEFINE AN INITIAL DESIGN OF EXPERIMENT (DOE)
|
|
initial_dataset = get_doe()
|
|
|
|
#TRAIN THE SURROGATE ON THE DOE using specified correlation function
|
|
trained_surrogate_model = train_surrogate_model(initial_dataset,
|
|
corr_function=corr_function_name)
|
|
|
|
#RUN EGO REFINEMENT for n_ego_iter iterations
|
|
print("Starting EGO optimization...")
|
|
x_opt, y_opt, ind_best, x_data, y_data = run_ego(
|
|
initial_dataset=initial_dataset,
|
|
surrogate_model=trained_surrogate_model,
|
|
n_ego_iter=n_ego_iter,
|
|
xlimits=xlimits,
|
|
simulation=simulation)
|
|
|
|
#EVALUATE THE MODEL PERFORMANCE BY RUNNING A LOO CROSS-VALIDATION
|
|
refined_dataset = pickle.load(open(f'{surrogate_folder_name}/refined_dataset.pkl', 'rb'))
|
|
leave_one_out(refined_dataset, corr_function_name)
|