project2_LA_and_Opt_for_ML

import numpy as np import pandas as pd import matplotlib.pyplot as plt import scipy as sc from scipy.stats import norm from scipy.optimize import minimize import random from matplotlib import cm from numpy import linalg from sklearn.gaussian_process import GaussianProcessRegressor from sklearn.gaussian_process.kernels import RBF from sklearn.model_selection import train_test_split from sklearn.svm import SVC from sklearn.utils import shuffle from tqdm import tqdm #%% Import dataset ##################################### fields = ['age', 'sex', 'chest pain type', 'resting blood pressure', 'serum cholestoral' , 'if fasting blood sugar', 'resting electrocardiographic results', 'maximum heart rate achieved', 'exercise induced angina', 'ST depression', 'slope of the peak exercise ST segment', 'major vessels', 'thalassemia', 'heart disease'] heart_array = np.genfromtxt('heart.csv', delimiter=',') # Normalize the data, but not the last cell (values for heart disease yes or no). X_array = heart_array[:,:-1] y_array = heart_array[:,-1:] normalized_X_array = (X_array - X_array.mean(axis=0)) / X_array.std(axis=0) normalized_heart_array = np.hstack((normalized_X_array,y_array)) heart = pd.DataFrame(data = normalized_heart_array, columns= fields) # Take one nineth of the dataset out as test set. shuffled_heart = shuffle(heart) split_heart = np.split(shuffled_heart, 9) heart = pd.concat([split_heart[i] for i in range(8)]) heart_test = split_heart[8]

# Global variables, which are the same cluster_number = 5 # #-fold crossvalidation C_min, C_max= 0, 8 gamma_min, gamma_max = -9, 0 C_steps = 10 gamma_steps = 11 method = "self-made"

def gaussian(x, y, l = 1): l2_norm = np.sum(x**2,1).reshape(-1,1) + np.sum(y**2,1) - 2*np.dot(x, y.T) return np.exp(-.5 * (1/l) * l2_norm) def prediction_new(par_comb, X, Y, mthd="gpr"): par_comb = np.array([par_comb]) X = np.array(X) Y = np.array(Y) Sigma = gaussian(X, X) L = np.linalg.cholesky(Sigma) k = gaussian(X, par_comb) L_k = np.linalg.solve(L, k) mu = np.dot(L_k.T, np.linalg.solve(L, Y)).reshape((len(par_comb),)) K_par = gaussian(par_comb, par_comb) sigma = np.sqrt(np.diag(K_par) - np.sum(L_k**2, axis=0)) # else: # kernel = 1.0 * RBF(1.0) # gpr = GaussianProcessRegressor(kernel=kernel, random_state=0) # gpr = gpr.fit(X, Y) # mu, sigma = gpr.predict([par_comb], return_std=True) # mu(x) and sigma(x) # sigma = sigma.reshape(-1,1) return mu, sigma def gaussian_kernel(x, y, l = 1): """Returns the RPF kernel with sigma 1 and length 1.""" x, y = np.array(x), np.array(y) l2_norm = np.linalg.norm(x-y) return np.exp(-(l2_norm**2)/(2*(l**2))) def prediction(par_comb, X_i, Y_i, mthd="gpr"): """Predicts the mean and the standard deviation on par_comb (consisting of C and gamma) using X_i and Y_i.""" m = len(X_i) Sigma = np.zeros([m,m]) k = np.zeros(m) k_hh = 1 if (mthd == "self-made"): if par_comb in X_i: #otherwise it gives errors sigma = np.array([0]) index = np.where((np.array(X_i) == par_comb).all(axis=1))[0][0] Y_i = np.array(Y_i) mu = Y_i[index] # print(f"C,g are in the training set, sigma = 0 and mu = y(C,g).") else: for i in range(m): k[i] = gaussian_kernel(X_i[i], par_comb) for j in range(m): Sigma[i][j] = gaussian_kernel(X_i[i], X_i[j]) k_trans = np.transpose(k) Sigma_inv = np.linalg.pinv(Sigma) mu = np.matmul(k_trans, np.matmul(Sigma_inv, Y_i)) sigma = np.sqrt(max(k_hh - np.matmul(k_trans, np.matmul(Sigma_inv, k)),0)) else: kernel = 1.0 * RBF(1.0) gpr = GaussianProcessRegressor(kernel=kernel, random_state=0) gpr = gpr.fit(X_i, Y_i) mu, sigma = gpr.predict([par_comb], return_std=True) # mu(x) and sigma(x) sigma = sigma.reshape(-1,1) return mu, sigma

def validation_error(X_train, X_val, y_train, y_val, c_log = 0, g_log = -10): c = 10**c_log g = 10**g_log svclassifier = SVC(kernel='rbf', C = c, gamma = g) svclassifier.fit(X_train, y_train) y_predict = svclassifier.predict(X_val) error = sum(y_predict!=y_val) return error def crossvalidation_error(dataset, c_log = 0, g_log = -10): shuffled_dataset = shuffle(dataset) split_dataset = np.split(shuffled_dataset, cluster_number) error_list = [] error = 0 for i in range(cluster_number): # Compute validation data X_validation = split_dataset[i].drop('heart disease', axis=1) y_validation = split_dataset[i]['heart disease'] # Compute train data train_indices = [*range(0,cluster_number)] train_indices.remove(i) train_data = pd.concat([split_dataset[j] for j in train_indices]) #Data without split_dataset[i] X_train = train_data.drop('heart disease', axis=1) y_train = train_data['heart disease'] local_error = validation_error(X_train, X_validation, y_train, y_validation, c_log, g_log)/(len(heart)/cluster_number) error_list.append(local_error) error += local_error/cluster_number return error #error if mean, max(error_list) if max

from scipy.interpolate import NearestNDInterpolator def plot_heatmap_points(X_list, error_list, grid_stepsize): C_axis = np.arange(C_min , C_max + grid_stepsize[0] , grid_stepsize[0]) gamma_axis = np.arange(gamma_min, gamma_max+ grid_stepsize[1], grid_stepsize[1]) C_axis, gamma_axis = np.meshgrid(C_axis, gamma_axis) interp = NearestNDInterpolator(X_list, error_list) full_error_array = interp(C_axis, gamma_axis) plt.rcParams["figure.figsize"] = (7,7) plt.pcolormesh(C_axis, gamma_axis, full_error_array, shading='auto') plt.scatter([X[0] for X in X_list], [X[1] for X in X_list], label="evaluated point") index_min = np.argmin(error_list) plt.plot(X_list[index_min][0], X_list[index_min][1], "ro", label="found minimum") plt.legend() plt.colorbar() plt.ylabel("log10(gamma)") plt.xlabel("log10(C)") plt.show() def grid_search(C_steps, gamma_steps, zoom, num_zooms, plot = False): #Set up inital frame of size C_steps, gamma_steps = 3, 3 stepsize = ((C_max - C_min) / C_steps, (gamma_max - gamma_min) / gamma_steps) frame_center = (C_min + (C_max - C_min) / 2, gamma_min + (gamma_max - gamma_min) / 2) axis = [None, None] error_array = np.zeros((C_steps, gamma_steps), dtype=float) eps = 10**-9 X_list, error_list = [], [] for k in range(num_zooms): #Create the axis of the plot frame_stepsize = (stepsize[0] * zoom**(-k), stepsize[1] * zoom**(-k)) frame_width = frame_stepsize[0] * C_steps frame_height = frame_stepsize[1] * gamma_steps axis[0] = np.arange(frame_center[0] - frame_width / 2, frame_center[0] + frame_width / 2, frame_stepsize[0] + eps, dtype=float) + frame_stepsize[0] / 2 axis[1] = np.arange(frame_center[1] - frame_height / 2, frame_center[1] + frame_height / 2, frame_stepsize[1] + eps, dtype=float) + frame_stepsize[1] / 2 #If the point is inside the new smaller frame calculate the local value for C in axis[0]: for gamma in axis[1]: if(C > C_min and C < C_max and gamma > gamma_min and gamma < gamma_max): error_ratio = crossvalidation_error(heart, C, gamma) X_list.append((C, gamma)) error_list.append(error_ratio) #Print the erro and the C and gamma values error_element_index = np.argmin(error_list) C_minimum = X_list[error_element_index][0] gamma_minimum = X_list[error_element_index][1] error_ratio = error_list[error_element_index] print(f"({k+1}/{num_zooms}) Minima found for C = {10**C_minimum:.3E} and gamma = {10**gamma_minimum:.3E} and with error ratio: {error_ratio:.3f}") frame_center = (C_minimum, gamma_minimum) if plot: plt.title("Error ratio after cross validation of dataset for different C and gamma") plot_heatmap_points(X_list, error_list, (stepsize[0] * zoom**(-num_zooms) + eps, stepsize[1] * zoom**(-num_zooms) + eps)) return C_minimum, gamma_minimum, error_ratio

C_steps_frame, gamma_steps_frame = 4, 4 #size of each grid zoom = 1.7 #how much should the grid shrink num_zooms = 3 #how many times do you want to zoom in C_minimum, gamma_minimum, error_ratio_grid_search = grid_search(C_steps_frame, gamma_steps_frame, zoom, num_zooms, plot = True)

class GaussianPrediction: def __init__(self, cov = 1.0, xi = 0.01, alpha = 0.2): self.Xi = [] self.Yi = [] self.cov = cov self.xi = xi self.alpha = alpha self.FE_h = 0.2 def gaussian_kernel(self, x, y): """Returns the RBF kernel with sigma 1 and length 1.""" x, y = np.array(x), np.array(y) l2_norm = np.linalg.norm(x-y) return np.exp(-(l2_norm**2)/(2*(self.cov**2))) def sample_point(self,x): """Sample the cross validation error at a given point and calculate the Sigma matrix and it's inverse this is only done once for every added point for efficiency""" self.Xi.append(x) self.Yi.append(crossvalidation_error(heart, x[0], x[1])) # if x in self.Xi: # print(f"{x[0]}, {x[1]} combination of C and gamma is already in the trainingset") # return self.Sigma = np.zeros([len(self.Xi),len(self.Xi)]) for i in range(len(self.Xi)): for j in range(len(self.Xi)): self.Sigma[i][j] = self.gaussian_kernel(self.Xi[i], self.Xi[j]) self.Sigma_inv = np.linalg.pinv(self.Sigma) self.y = np.array(self.Yi) self.mu_sample_opt = np.min(self.y) return def evaluate_mu_sigma(self, x): """ Evaluate mu sigma at a given point x using Sigma calculated before""" k = np.zeros(len(self.Xi)) for i in range(len(self.Xi)): k[i] = self.gaussian_kernel(self.Xi[i], x) k_trans = np.transpose(k) K_hh = 1 mu = np.matmul(k_trans, np.matmul(self.Sigma_inv, self.y)) sigma = np.sqrt(max(K_hh - np.matmul(k_trans, np.matmul(self.Sigma_inv, k)), 0)) return mu, sigma def expected_improvement(self, x): """ Calculate the expected improvement for a given point""" mu, sigma = self.evaluate_mu_sigma(x) #take the value for which the mean is the smallest (best) if sigma != 0: imp = self.mu_sample_opt - self.xi - mu Z = imp / sigma ei = - imp * norm.cdf(Z) - sigma*norm.pdf(Z) else: ei = 0.0 return ei def gradient_sigma_mu(self, x): """Return the gradient of sigma and mu approximated using finite differences""" h = self.FE_h C = x[0] gamma = x[1] mu_C, sigma_C = self.evaluate_mu_sigma([C - 0.5*h, gamma ]) mu_gamma, sigma_gamma = self.evaluate_mu_sigma([C , gamma - 0.5*h]) mu_C_h, sigma_C_h = self.evaluate_mu_sigma([C + 0.5*h, gamma ]) mu_gamma_h, sigma_gamma_h = self.evaluate_mu_sigma([C , gamma + 0.5*h]) gradient_sigma = [(sigma_C_h - sigma_C) / h, (sigma_gamma_h - sigma_gamma) / h] #[dsigma/dC, dsigma/dgamma] gradient_mu = [(mu_C_h - mu_C) / h , (mu_gamma_h - mu_gamma) / h ] #[dmu/dC, dmu/dgamma] return gradient_sigma, gradient_mu def gradient_expected_improvement(self, x): """Return the gradients of the expected improvement function""" C, gamma = x[0], x[1] mu, sigma = self.evaluate_mu_sigma(x) gradient_sigma, gradient_mu = self.gradient_sigma_mu(x) if sigma != 0: imp = (self.mu_sample_opt - self.xi - mu) x = imp/sigma gradient_dC = gradient_mu[0]*norm.cdf(x) - gradient_sigma[0] * norm.pdf(x) gradient_dgamma = gradient_mu[1]*norm.cdf(x) - gradient_sigma[1] * norm.pdf(x) else: gradient_dC = 0 gradient_dgamma = 0 return np.concatenate((gradient_dC,gradient_dgamma), axis = None) def lower_confidence_bound(self, x): """Use lower confidence bound as new acquisition function, alpha is a parameter controlling the degree of exploration""" mu, sigma = self.evaluate_mu_sigma(x) # mu(x) and sigma(x) lcb = mu - self.alpha * sigma return lcb def gradient_lower_confidence_bound(self, x): """Return the gradients of the lower confidence bound""" C, gamma = x[0], x[1] mu, sigma = self.evaluate_mu_sigma(x) # mu(x) and sigma(x) gradient_sigma, gradient_mu = self.gradient_sigma_mu(x) gradient_dC = gradient_mu[0] - self.alpha*gradient_sigma[0] gradient_dgamma = gradient_mu[1] - self.alpha*gradient_mu[1] return np.concatenate((gradient_dC,gradient_dgamma), axis = None) def restrict_to_domain(point, domain): """Return the point to domain rectangle if it left is""" point = list(point) if point[0] < domain[0][0]: point[0] = domain[0][0] if point[0] > domain[0][1]: point[0] = domain[0][1] if point[1] < domain[1][0]: point[1] = domain[1][0] if point[1] > domain[1][1]: point[1] = domain[1][1] return list(point) def plot_surface(GaussianPrediction, ax, fig, aq_func, domain, steps): """Plot a surface of an arbitrary function in a given domain""" # Set up axis X = np.linspace(domain[0][0], domain[0][1], steps[0]) Y = np.linspace(domain[1][0], domain[1][1], steps[1]) # Fill grid with function value Z = np.zeros(steps) for j , y in enumerate(Y): for i , x in enumerate(X): Z[j,i] = aq_func([x, y]) X, Y = np.meshgrid(X, Y) surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm, linewidth=0, antialiased=False, alpha = 0.5) fig.colorbar(surf, shrink=0.5, aspect=5) def gradient_descent(GaussianPrediction, aq_func, aq_grad_func, trials, max_trial_length, h, min_step, domain, steps, plot = False): """For an arbitrary function start at 'trails' amount of random points in the domain and follow gradient descent to the lowest value until there is no improvement or the max amount of iterations is reached. If 'plot = True' the function also plots the surface of the function and the paths of the minimazation algorithm. At the end the z-axis is flipped to show the expected improvement""" # Set up the list for the optimalisation paths list_paths_xy = [0] * trials list_paths_z = [0] * trials # Choose random starting points initial_x = np.random.uniform(domain[0][0], domain[0][1], trials) initial_y = np.random.uniform(domain[1][0], domain[1][1], trials) for i in range(trials): starting_point = [initial_x[i], initial_y[i]] list_paths_xy[i] = [starting_point] list_paths_z[i] = [aq_func(starting_point)] current_point = list_paths_xy[i][0] for _ in range(max_trial_length): gradient = aq_grad_func(current_point) next_point = [current_point[0] - h * gradient[0], current_point[1] - h * gradient[1]] next_point = restrict_to_domain(next_point, domain) if (current_point[0] - next_point[0])**2 + (current_point[1] - next_point[1])**2 < min_step**2: break current_point = next_point if plot: list_paths_xy[i].append(next_point) list_paths_z[i].append(aq_func(next_point)) if not plot: list_paths_xy[i].append(next_point) list_paths_z[i].append(aq_func(next_point)) if plot: #Set up fig and plot surface of expected improvement fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) plot_surface(GaussianPrediction, ax, fig, aq_func, domain, steps) # Plot starting point ax.scatter([path[0][0] for path in list_paths_xy], [path[0][1] for path in list_paths_xy], [path[0] for path in list_paths_z], marker = 'o', label = 'Starting point', c = 'r') # Plot optimalisation path for every trial, but only give one a label to remove clutter for i in range(trials): line, = ax.plot([point[0] for point in list_paths_xy[i]], [point[1] for point in list_paths_xy[i]], [float(z) for z in list_paths_z[i]], '-ko', markersize = 2) line.set_label('Optimalisation path') # Plot starting ending point ax.scatter([path[-1][0] for path in list_paths_xy], [path[-1][1] for path in list_paths_xy], [path[-1] for path in list_paths_z], marker = 'o', label = 'Ending point', c = 'b') best_trial = np.argmin(np.array([path_z[-1] for path_z in list_paths_z])) point_min = list_paths_xy[best_trial][-1] z_min = float(list_paths_z[best_trial][-1]) return point_min, z_min

#initialise the Gaussian predict surface variables GP_ei = GaussianPrediction(cov= 2.0, xi = 0.001) #initialse variables for the gradient descent trials = 20 max_trial_length = 200 h = 0.5 min_step = 0.02 epsilon = 10**(-3) domain = [(C_min + epsilon, C_max), (gamma_min + epsilon, gamma_max)] steps = (C_steps, gamma_steps) #Choose a random point in the domain and evaluate the error X_init = [random.uniform(C_min, C_max), random.uniform(gamma_min, gamma_max)] GP_ei.sample_point(X_init) best_so_far_error = GP_ei.Yi.copy() n = 50 for i in tqdm(range(n)): point_min, ei_min = gradient_descent(GP_ei, GP_ei.expected_improvement, GP_ei.gradient_expected_improvement, \ trials, max_trial_length, h, min_step, domain, steps, plot = False) GP_ei.sample_point(point_min) best_so_far_error.append(min(best_so_far_error[-1], GP_ei.Yi[-1]))

plt.plot(best_so_far_error) plt.axhline(y = error_ratio_grid_search, color = 'r', linestyle = '-') plt.show() plt.title("Error ratio after cross validation of dataset for different C and gamma") plot_heatmap_points(GP_ei.Xi, GP_ei.Yi, (0.1,0.1)) plt.show()

#Choose n random points in the domain and evaluate the error n = 15 #initialise the Gaussian predict surface variables GP_ei_plot = GaussianPrediction(cov= 2.0, xi = 0.001) X_init = np.array([[random.uniform(C_min, C_max), random.uniform(gamma_min, gamma_max)] for i in range(n)]) for x in X_init: GP_ei_plot.sample_point(x) trials = 30 max_trial_length = 200 h = 0.5 min_step = 0.01 domain = [(C_min, C_max), (gamma_min, gamma_max)] steps = (50, 50) #Do the gradient descent en plot the paths gradient_descent(GP_ei_plot, GP_ei_plot.expected_improvement, GP_ei_plot.gradient_expected_improvement, trials, max_trial_length, h, min_step, domain, steps, plot = True) plt.legend() plt.show()

#initialise the Gaussian predict surface variables GP_lcb = GaussianPrediction(cov = 2.0, alpha= 0.001) #initialse variables for the gradient descent trials = 20 max_trial_length = 200 h = 0.5 min_step = 0.02 epsilon = 10**(-3) domain = [(C_min + epsilon, C_max), (gamma_min + epsilon, gamma_max)] steps = (C_steps, gamma_steps) #Choose a random point in the domain and evaluate the error X_init = [random.uniform(C_min, C_max), random.uniform(gamma_min, gamma_max)] GP_lcb.sample_point(X_init) best_so_far_error = GP_lcb.Yi.copy() n = 50 for i in tqdm(range(n)): point_min, lcb_min = gradient_descent(GP_lcb, GP_lcb.lower_confidence_bound, GP_lcb.gradient_lower_confidence_bound, \ trials, max_trial_length, h, min_step, domain, steps, plot = False) GP_lcb.sample_point(point_min) best_so_far_error.append(min(best_so_far_error[-1], GP_lcb.Yi[-1]))

plt.plot(best_so_far_error) plt.axhline(y = error_ratio_grid_search, color = 'r', linestyle = '-') plt.show() plt.title("Error ratio after cross validation of dataset for different C and gamma") plot_heatmap_points(GP_lcb.Xi, GP_lcb.Yi, (0.1,0.1)) plt.show()

#Choose n random points in the domain and evaluate the error n = 15 #initialise the Gaussian predict surface variables GP_lcb_plot = GaussianPrediction(cov= 2.0, alpha = 0.001) X_init = np.array([[random.uniform(C_min, C_max), random.uniform(gamma_min, gamma_max)] for i in range(n)]) for x in X_init: GP_lcb_plot.sample_point(x) trials = 30 max_trial_length = 200 h = 0.5 min_step = 0.01 domain = [(C_min, C_max), (gamma_min, gamma_max)] steps = (50, 50) #Do the gradient descent en plot the paths gradient_descent(GP_lcb_plot, GP_lcb_plot.lower_confidence_bound, GP_lcb_plot.gradient_lower_confidence_bound, trials, max_trial_length, h, min_step, domain, steps, plot = True) plt.legend() plt.show()