def plot_heatmap(models, meta, base_F1, cut_noise, X_test, y_test, save=False):
X, y = torch.zeros(X_test.size()).float(), torch.zeros(
y_test.size()).float()
mid = int(X.size(0) / 2)
X[:mid, :4] = torch.tensor(
white_noise(X_test[:mid, :4].data.numpy(), cut_noise)).float()
X[mid:,
4:] = torch.tensor(white_noise(X_test[mid:, 4:].data.numpy(),
cut_noise)).float()
coldness = meta['coldness']
lambda_regul = meta['lambda_regul']
lambda_capacity = meta['lambda_capacity']
score = np.zeros((len(lambda_regul), len(lambda_capacity)))
for i, tau in enumerate(coldness):
for j, l_cap in enumerate(lambda_capacity):
best_F1 = -float("Inf")
for l_reg in lambda_regul:
F1, _, _, _ = evaluation(
models['model-with'][(tau, l_reg, l_cap)], 'model-with', X,
y_test)
if F1 > best_F1:
best_F1 = F1
score[i, j] = best_F1
score = (score - base_F1) / base_F1
ax = sns.heatmap(score,
annot=True,
xticklabels=lambda_capacity,
yticklabels=coldness,
cmap="YlGnBu")
plt.xlabel('coldness', fontsize=17)
plt.ylabel('lambda capacity', fontsize=17)
if save: plt.savefig('results/heatmap')
plt.show()
def plot_total_energy(models, best_combo, X_test, y_test, save=False):
energy_system = []
noises = np.linspace(0, 2, 50)
f1 = []
for noise in noises:
X = torch.zeros(X_test.size()).float()
bigmid = int(X.size(0) / 2)
smallmid = int(float(bigmid) / 2)
X = X_test.clone()
X = white_noise(X.data.numpy(), noise)
modes = [X[:, :4], X[:, 4:]]
energies = models['model-with'][best_combo][0].total_energy(
modes).data.numpy()
energy_system.append(np.mean(energies))
F1, _, _, _ = evaluation(models['model-with'][best_combo],
'model-with', X, y_test)
f1.append(F1)
plt.plot(noises, energy_system)
plt.xlabel(r'$\sigma$ corruption', fontsize=30)
plt.ylabel('Total Energy', fontsize=30)
plt.tick_params(axis='both', which='major', labelsize=25)
if save: plt.savefig('results/noise-vs-system-energy')
plt.show()
plt.plot(noises, f1)
plt.xlabel(r'$\sigma$ corruption', fontsize=30)
plt.ylabel('F1-score', fontsize=30)
plt.tick_params(axis='both', which='major', labelsize=25)
if save: plt.savefig('results/noise-vs-system-f1')
plt.show()
def noisy_signal(X_signal, model):
""" Extract random sample (size = 100) """
indices = np.arange(X_signal.size(0))
np.random.shuffle(indices)
X_signal = X_signal[indices[:100]]
""" Test """
measures = []
for noise_std in np.linspace(0, 2, 30):
q = []
X = X_signal.clone()
for i in range(X_signal.size(-1)):
X[:, i] = white_noise(X[:, i].data.numpy(), noise_std).unsqueeze(0)
potentials = model.potential(X)
mean_ = torch.mean(potentials).data
stddev_ = torch.std(potentials).data
measures.append([noise_std, mean_, stddev_])
return np.asarray(measures)
def plot_specific(models, X_test, y_test, save=False):
best_combo = (1e-4, 1e-2, 1e-3)
f1_with = []
f1_without = []
f1_base = []
noises = np.linspace(-20, 5, 50)
beta_ip, beta_dm = [], []
for noise in noises:
X = torch.zeros(X_test.size()).float()
X = X_test.clone()
noise = 10**(noise / 20)
X[:, 4:] = white_noise(X[:, 4:].data.numpy(), noise)
F1, _, recall, _ = evaluation(models['model-with'][best_combo],
'model-with', X, y_test)
f1_with.append(F1)
F1, _, recall, _ = evaluation(models['model-without'], 'model-without',
X, y_test)
f1_without.append(F1)
F1, _, recall, _ = evaluation(models['base-model'], 'base-model', X,
y_test)
f1_base.append(F1)
modes = [X[:, :4], X[:, 4:]]
_, betas = models['model-with'][best_combo][0].get_alpha_beta(modes)
beta_ip.append(torch.mean(betas[:, 0]).data.numpy())
beta_dm.append(torch.mean(betas[:, 1]).data.numpy())
plt.plot(noises, f1_with, label='with', c='b')
plt.plot(noises, f1_without, label='without')
plt.plot(noises, f1_base, label='base')
plt.axvline(x=20 * np.log10(0.5), ls='--', c='darkgrey')
plt.axhline(y=0.82, ls='--', c='grey')
plt.xlabel('NSR (dB)', fontsize=30)
plt.ylabel('F1-score', fontsize=30)
plt.ylim([0, 1])
plt.tick_params(axis='both', which='major', labelsize=25)
plt.legend(loc='best')
# plt.savefig('results/presentation/noise-generalisation-dm-noisy')
plt.show()
plt.plot(noises, beta_ip, label=r'$\beta$ corrupted mode', c='m')
plt.plot(noises, beta_dm, label=r'$\beta$ other mode', c='orange')
plt.xlabel('NSR (dB)', fontsize=30)
plt.ylabel(r'$\beta$', fontsize=30)
plt.ylim([-0.1, 1])
plt.tick_params(axis='both', which='major', labelsize=25)
plt.legend(loc='best')
plt.savefig('results/presentation/noise-generalisation-dm-noisy-beta')
plt.show()
h_combo = (1e-4, 1e-2, 1e-1)
m_combo = (1e-4, 1e-2, 1e-3)
l_combo = (1e-4, 1e-2, 0)
# h_combo = (1e-4, 1e-1, 1e-3)
# m_combo = (1e-4, 1e-2, 1e-3)
# l_combo = (1e-4, 0, 1e-3)
# # h_combo = (1, 1e-2, 1e-3)
# # m_combo = (1e-2, 1e-2, 1e-3)
# # l_combo = (1e-4, 1e-2, 1e-3)
f1_highcap = []
f1_midcap = []
f1_lowcap = []
noises = np.linspace(-20, 5, 50)
h_beta_ip, h_beta_dm = [], []
m_beta_ip, m_beta_dm = [], []
l_beta_ip, l_beta_dm = [], []
for noise in noises:
X = torch.zeros(X_test.size()).float()
X = X_test.clone()
noise = 10**(noise / 20)
X[:, :4] = white_noise(X[:, :4].data.numpy(), noise)
F1, _, recall, _ = evaluation(models['model-with'][h_combo],
'model-with', X, y_test)
f1_highcap.append(F1)
F1, _, recall, _ = evaluation(models['model-with'][m_combo],
'model-with', X, y_test)
f1_midcap.append(F1)
F1, _, recall, _ = evaluation(models['model-with'][l_combo],
'model-with', X, y_test)
f1_lowcap.append(F1)
modes = [X[:, :4], X[:, 4:]]
_, betas = models['model-with'][h_combo][0].get_alpha_beta(modes)
h_beta_ip.append(torch.mean(betas[:, 0]).data.numpy())
h_beta_dm.append(torch.mean(betas[:, 1]).data.numpy())
_, betas = models['model-with'][m_combo][0].get_alpha_beta(modes)
m_beta_ip.append(torch.mean(betas[:, 0]).data.numpy())
m_beta_dm.append(torch.mean(betas[:, 1]).data.numpy())
_, betas = models['model-with'][l_combo][0].get_alpha_beta(modes)
l_beta_ip.append(torch.mean(betas[:, 0]).data.numpy())
l_beta_dm.append(torch.mean(betas[:, 1]).data.numpy())
plt.plot(noises, f1_highcap, label='high energy regul.', c='purple')
plt.plot(noises, f1_midcap, label='good energy regul.', c='b')
plt.plot(noises, f1_lowcap, label='no energy regul.', c='skyblue')
plt.axvline(x=20 * np.log10(0.5), ls='--', c='darkgrey')
plt.axhline(y=0.82, ls='--', c='grey')
plt.xlabel('NSR (dB)', fontsize=30)
plt.ylabel('F1-score', fontsize=30)
plt.ylim([0, 1])
plt.tick_params(axis='both', which='major', labelsize=25)
plt.legend(loc='best')
plt.savefig('results/presentation/energy-dm-noisy')
plt.show()
plt.plot(noises, h_beta_ip, label=r'$\beta$ corrupted mode', c='m')
plt.plot(noises, h_beta_dm, label=r'$\beta$ other mode', c='orange')
plt.axvline(x=20 * np.log10(0.5), ls='--', c='darkgrey')
plt.xlabel('NSR (dB)', fontsize=30)
plt.ylabel(r'$\beta$', fontsize=30)
plt.ylim([-0.1, 1])
plt.tick_params(axis='both', which='major', labelsize=25)
plt.legend(loc='best')
plt.savefig('results/presentation/high-capacity-ip-noisy-beta')
plt.show()
plt.plot(noises, m_beta_ip, label=r'$\beta$ corrupted mode', c='m')
plt.plot(noises, m_beta_dm, label=r'$\beta$ other mode', c='orange')
plt.axvline(x=20 * np.log10(0.5), ls='--', c='darkgrey')
plt.xlabel('NSR (dB)', fontsize=30)
plt.ylabel(r'$\beta$', fontsize=30)
plt.ylim([-0.1, 1])
plt.tick_params(axis='both', which='major', labelsize=25)
plt.legend(loc='best')
plt.savefig('results/presentation/normal-ip-noisy-beta')
plt.show()
plt.plot(noises, l_beta_ip, label=r'$\beta$ corrupted mode', c='m')
plt.plot(noises, l_beta_dm, label=r'$\beta$ other mode', c='orange')
plt.axvline(x=20 * np.log10(0.5), ls='--', c='darkgrey')
plt.xlabel('NSR (dB)', fontsize=30)
plt.ylabel(r'$\beta$', fontsize=30)
plt.ylim([-0.1, 1])
plt.tick_params(axis='both', which='major', labelsize=25)
plt.legend(loc='best')
plt.savefig('results/presentation/no-capacity-ip-noisy-beta')
plt.show()
def plot_noise_generalisation(models,
best_combo,
X_test,
y_test,
save=False,
idx=None):
if idx is None:
suffix = ""
else:
suffix = "-" + str(idx)
f1_with = []
f1_without = []
f1_base = []
noises = np.linspace(0, 2, 50)
beta_ip, beta_dm = [], []
for noise in noises:
X = torch.zeros(X_test.size()).float()
X = X_test.clone()
X[:, :4] = white_noise(X[:, :4].data.numpy(), noise)
F1, _, recall, _ = evaluation(models['model-with'][best_combo],
'model-with', X, y_test)
f1_with.append(F1)
F1, _, recall, _ = evaluation(models['model-without'], 'model-without',
X, y_test)
f1_without.append(F1)
F1, _, recall, _ = evaluation(models['base-model'], 'base-model', X,
y_test)
f1_base.append(F1)
modes = [X[:, :4], X[:, 4:]]
_, betas = models['model-with'][best_combo][0].get_alpha_beta(modes)
beta_ip.append(torch.mean(betas[:, 0]).data.numpy())
beta_dm.append(torch.mean(betas[:, 1]).data.numpy())
plt.plot(noises, f1_with, label='with')
plt.plot(noises, f1_without, label='without')
plt.plot(noises, f1_base, label='base')
plt.axvline(x=0.5, ls='--', c='green')
plt.xlabel(r'$\sigma$ corruption', fontsize=30)
plt.ylabel('F1-score', fontsize=30)
plt.ylim([0, 1])
plt.tick_params(axis='both', which='major', labelsize=25)
plt.legend(loc='best')
if save: plt.savefig('results/noise-generalisation-ip-noisy' + suffix)
plt.show()
plt.plot(noises, beta_ip, label=r'$\beta$-ip')
plt.plot(noises, beta_dm, label=r'$\beta$-dm')
plt.xlabel(r'$\sigma$ corruption', fontsize=30)
plt.ylabel(r'$\beta$', fontsize=30)
plt.ylim([-0.1, 1])
plt.tick_params(axis='both', which='major', labelsize=25)
plt.legend(loc='best')
if save: plt.savefig('results/noise-generalisation-ip-noisy-beta' + suffix)
plt.show()
beta_ip, beta_dm = [], []
f1_with = []
f1_without = []
f1_base = []
noises = np.linspace(0, 2, 50)
for noise in noises:
X = torch.zeros(X_test.size()).float()
X = X_test.clone()
X[:, 4:] = white_noise(X[:, 4:].data.numpy(), noise)
F1, _, recall, _ = evaluation(models['model-with'][best_combo],
'model-with', X, y_test)
f1_with.append(F1)
F1, _, recall, _ = evaluation(models['model-without'], 'model-without',
X, y_test)
f1_without.append(F1)
F1, _, recall, _ = evaluation(models['base-model'], 'base-model', X,
y_test)
f1_base.append(F1)
modes = [X[:, :4], X[:, 4:]]
_, betas = models['model-with'][best_combo][0].get_alpha_beta(modes)
beta_ip.append(torch.mean(betas[:, 0]).data.numpy())
beta_dm.append(torch.mean(betas[:, 1]).data.numpy())
plt.plot(noises, f1_with, label='with')
plt.plot(noises, f1_without, label='without')
plt.plot(noises, f1_base, label='base')
plt.axvline(x=0.5, ls='--', c='green')
plt.xlabel(r'$\sigma$ corruption', fontsize=30)
plt.ylabel('F1-score', fontsize=30)
plt.ylim([0, 1])
plt.tick_params(axis='both', which='major', labelsize=25)
plt.legend(loc='best')
if save: plt.savefig('results/noise-generalisation-dm-noisy' + suffix)
plt.show()
plt.plot(noises, beta_ip, label=r'$\beta$-ip')
plt.plot(noises, beta_dm, label=r'$\beta$-dm')
plt.xlabel(r'$\sigma$ corruption', fontsize=30)
plt.ylabel(r'$\beta$', fontsize=30)
plt.ylim([-0.1, 1])
plt.tick_params(axis='both', which='major', labelsize=25)
plt.legend(loc='best')
if save: plt.savefig('results/noise-generalisation-dm-noisy-beta' + suffix)
plt.show()
x = np.linspace(0, 2, 20)
y = np.linspace(0, 2, 20)
lx = len(x)
ly = len(y)
beta_ip, beta_dm = np.zeros((20, 20)), np.zeros((20, 20))
for i in range(lx):
noise_ip = x[i]
X = X_test.clone()
X[:, :4] = white_noise(X[:, :4].data.numpy(), noise_ip)
for j in range(ly):
noise_dm = y[j]
X[:, 4:] = white_noise(X[:, 4:].data.numpy(), noise_dm)
modes = [X[:, :4], X[:, 4:]]
_, betas = models['model-with'][best_combo][0].get_alpha_beta(
modes)
beta_ip[i, j] = torch.mean(betas[:, 0]).data.numpy()
beta_dm[i, j] = torch.mean(betas[:, 1]).data.numpy()
fig = plt.figure()
ax = Axes3D(fig)
ax = fig.gca(projection='3d')
x, y = np.meshgrid(x, y)
surf = ax.plot_surface(x, y, beta_ip, rstride=1, cstride=1, cmap='hot')
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()
fig = plt.figure()
ax = Axes3D(fig)
ax = fig.gca(projection='3d')
surf = ax.plot_surface(x, y, beta_dm, rstride=1, cstride=1, cmap='hot')
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()