FuncPrivGP/ReduceSegExample.py at main · hkustDB/FuncPrivGP · GitHub

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from PrivPwcApprox import *
from AdaptApprox import *

SAVE_FIGS = True

plt.rc('axes', titlesize = 11)
plt.rc('axes', labelsize = 11)
plt.rc('xtick', labelsize = 11)
plt.rc('ytick', labelsize = 11)
plt.rc('legend', fontsize = 11)


def genNoise(method, scale, dim = 1):
    if method == 'Laplace':
        if dim == 1:
            noise = laplace.rvs(scale = scale)
        else:
            noise = np.random.randn(dim)
            noise = noise/np.linalg.norm(noise)
            noise *= gamma.rvs(a = dim, scale = scale)
    elif method == 'Gaussian':
        if dim == 1: noise = norm.rvs(scale = scale)
        else: noise = scale*np.random.randn(dim)
    return noise

def plotFig(t, x, y, eps, k_bar, filename):
    interval = (t[0], t[-1])
    breakpoints = np.linspace(t[0], t[-1], (2**k_bar)+1)
    func = time_series_func_2D(t, x, y)
    time_series = (t, np.column_stack((x, y)))

    solver = PrivatePiecewiseApprox(interval, breakpoints, basis_type = 'Linear-2D', degree = 1)
    solver.fit(func = func, time_series = time_series)
    solver.privatize(eps, 'Laplace')
    solver.smooth()

    smooth = solver.createPriv(); smooth_res = smooth(t)
    smooth_t_x = smooth_res[:, 0]; smooth_t_y = smooth_res[:, 1]
    err1 = solver.evalPrivLoss()
    print(len(breakpoints),err1)
    plt.figure(figsize = (4, 4))
    plt.xlim(0, 1600); plt.ylim(0, 2500)
    plt.xticks([0, 250, 500, 750, 1000, 1250, 1500])
    plt.plot(x, y, color = 'black', alpha = 0.9, ls = '--', label = "true")
    plt.plot(smooth_t_x, smooth_t_y, color = 'tab:blue', #ls = '--',
             alpha = 0.9, label = "privatized")
    plt.legend(framealpha = 0.4, frameon = False, fancybox = True)
    plt.subplots_adjust(left = 0.12, right = 0.99, top = 0.98, bottom = 0.07)
    if SAVE_FIGS: plt.savefig(f"results/figs/ReduceSeg/{filename}_without.pdf")
    else: plt.show()
    plt.close()

    eps_left = eps
    breakpoints = np.array([interval[0]])
    for i in range(4):
        pts, eps_left = reduce_seg(func, (interval[0]+(interval[1]-interval[0])*i/4, interval[0]+(interval[1]-interval[0])*(i+1)/4),
                                basis = 'Linear-2D', degree = 1, k0 = min(k_bar-2, 4), k = k_bar-2,
                                total_eps = eps_left, eps = eps/32, beta = 0.1, time_series = time_series)
        breakpoints = np.concatenate((breakpoints[:-1], pts))
    solver = PrivatePiecewiseApprox(interval, breakpoints, basis_type = 'Linear-2D', degree = 1)
    solver.fit(func = func, time_series = time_series)
    solver.privatize(eps_left, 'Laplace')
    solver.smooth()
    smooth = solver.createPriv(); smooth_res = smooth(t)
    smooth_t_x = smooth_res[:, 0]; smooth_t_y = smooth_res[:, 1]
    err2 = solver.evalPrivLoss()
    print(eps_left, len(breakpoints),err2, (err2-err1)/err1)
    plt.figure(figsize = (4, 4))
    plt.xlim(0, 1600); plt.ylim(0, 2500)
    plt.xticks([0, 250, 500, 750, 1000, 1250, 1500])
    plt.plot(x, y, color = 'black', ls = '--', label = "true")
    plt.plot(smooth_t_x, smooth_t_y, color = 'tab:blue',
             alpha = 0.9, label = "privatized")
    plt.legend(framealpha = 0.4, frameon = False, fancybox = True)
    plt.subplots_adjust(left = 0.12, right = 0.99, top = 0.98, bottom = 0.07)
    if SAVE_FIGS: plt.savefig(f"results/figs/ReduceSeg/{filename}_with.pdf")
    else: plt.show()
    plt.close()


x = 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np.random.seed(seed=2026)#
plotFig(t, x, y, eps = 1.0, k_bar = 6, filename = 'ReduceSegEg1')


x = 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np.random.seed(seed=2026)#
plotFig(t, x, y, eps = 1.0, k_bar = 5, filename = 'ReduceSegEg2')