######################################################################
-parser = argparse.ArgumentParser(description='Example of double descent with polynomial regression.')
+parser = argparse.ArgumentParser(
+ description="Example of double descent with polynomial regression."
+)
-parser.add_argument('--D-max',
- type = int, default = 16)
+parser.add_argument("--D-max", type=int, default=16)
-parser.add_argument('--nb-runs',
- type = int, default = 250)
+parser.add_argument("--nb-runs", type=int, default=250)
-parser.add_argument('--nb-train-samples',
- type = int, default = 8)
+parser.add_argument("--nb-train-samples", type=int, default=8)
-parser.add_argument('--train-noise-std',
- type = float, default = 0.)
+parser.add_argument("--train-noise-std", type=float, default=0.0)
-parser.add_argument('--seed',
- type = int, default = 0,
- help = 'Random seed (default 0, < 0 is no seeding)')
+parser.add_argument(
+ "--seed", type=int, default=0, help="Random seed (default 0, < 0 is no seeding)"
+)
args = parser.parse_args()
######################################################################
+
def pol_value(alpha, x):
x_pow = x.view(-1, 1) ** torch.arange(alpha.size(0)).view(1, -1)
return x_pow @ alpha
-def fit_alpha(x, y, D, a = 0, b = 1, rho = 1e-12):
+
+def fit_alpha(x, y, D, a=0, b=1, rho=1e-12):
M = x.view(-1, 1) ** torch.arange(D + 1).view(1, -1)
B = y
if D >= 2:
- q = torch.arange(2, D + 1, dtype = x.dtype).view(1, -1)
- r = q.view(-1, 1)
+ q = torch.arange(2, D + 1, dtype=x.dtype).view(1, -1)
+ r = q.view(-1, 1)
beta = x.new_zeros(D + 1, D + 1)
- beta[2:, 2:] = (q-1) * q * (r-1) * r * (b**(q+r-3) - a**(q+r-3))/(q+r-3)
- l, U = beta.eig(eigenvectors = True)
- Q = U @ torch.diag(l[:, 0].clamp(min = 0) ** 0.5)
+ beta[2:, 2:] = (
+ (q - 1)
+ * q
+ * (r - 1)
+ * r
+ * (b ** (q + r - 3) - a ** (q + r - 3))
+ / (q + r - 3)
+ )
+ W = torch.linalg.eig(beta)
+ l, U = W.eigenvalues.real, W.eigenvectors.real
+ Q = U @ torch.diag(l.clamp(min=0) ** 0.5) # clamp deals with ~0 negative values
B = torch.cat((B, y.new_zeros(Q.size(0))), 0)
M = torch.cat((M, math.sqrt(rho) * Q.t()), 0)
- return torch.lstsq(B, M).solution[:D+1, 0]
+ return torch.linalg.lstsq(M, B).solution[: D + 1]
+
######################################################################
+# The "ground truth"
+
+
def phi(x):
- return torch.abs(torch.abs(x - 0.4) - 0.2) + x/2 - 0.1
+ return torch.abs(torch.abs(x - 0.4) - 0.2) + x / 2 - 0.1
+
######################################################################
+
def compute_mse(nb_train_samples):
mse_train = torch.zeros(args.nb_runs, args.D_max + 1)
mse_test = torch.zeros(args.nb_runs, args.D_max + 1)
for k in range(args.nb_runs):
- x_train = torch.rand(nb_train_samples, dtype = torch.float64)
+ x_train = torch.rand(nb_train_samples, dtype=torch.float64)
y_train = phi(x_train)
if args.train_noise_std > 0:
- y_train = y_train + torch.empty_like(y_train).normal_(0, args.train_noise_std)
- x_test = torch.linspace(0, 1, 100, dtype = x_train.dtype)
+ y_train = y_train + torch.empty_like(y_train).normal_(
+ 0, args.train_noise_std
+ )
+ x_test = torch.linspace(0, 1, 100, dtype=x_train.dtype)
y_test = phi(x_test)
for D in range(args.D_max + 1):
alpha = fit_alpha(x_train, y_train, D)
- mse_train[k, D] = ((pol_value(alpha, x_train) - y_train)**2).mean()
- mse_test[k, D] = ((pol_value(alpha, x_test) - y_test)**2).mean()
+ mse_train[k, D] = ((pol_value(alpha, x_train) - y_train) ** 2).mean()
+ mse_test[k, D] = ((pol_value(alpha, x_test) - y_test) ** 2).mean()
return mse_train.median(0).values, mse_test.median(0).values
+
######################################################################
# Plot the MSE vs. degree curves
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
-ax.set_yscale('log')
+ax.set_yscale("log")
ax.set_ylim(1e-5, 1)
-ax.set_xlabel('Polynomial degree', labelpad = 10)
-ax.set_ylabel('MSE', labelpad = 10)
+ax.set_xlabel("Polynomial degree", labelpad=10)
+ax.set_ylabel("MSE", labelpad=10)
+
+ax.axvline(x=args.nb_train_samples - 1, color="gray", linewidth=0.5, linestyle="--")
-ax.axvline(x = args.nb_train_samples - 1,
- color = 'gray', linewidth = 0.5, linestyle = '--')
-ax.text(args.nb_train_samples - 1.2, 1e-4, 'Nb. params = nb. samples',
- fontsize = 10, color = 'gray',
- rotation = 90, rotation_mode='anchor')
+ax.text(
+ args.nb_train_samples - 1.2,
+ 1e-4,
+ "nb. params = nb. samples",
+ fontsize=10,
+ color="gray",
+ rotation=90,
+ rotation_mode="anchor",
+)
mse_train, mse_test = compute_mse(args.nb_train_samples)
+ax.plot(torch.arange(args.D_max + 1), mse_train, color="blue", label="Train")
+ax.plot(torch.arange(args.D_max + 1), mse_test, color="red", label="Test")
-ax.plot(torch.arange(args.D_max + 1), mse_train, color = 'blue', label = 'Train error')
-ax.plot(torch.arange(args.D_max + 1), mse_test, color = 'red', label = 'Test error')
+ax.legend(frameon=False)
-ax.legend(frameon = False)
+fig.savefig("dd-mse.pdf", bbox_inches="tight")
-fig.savefig('dd-mse.pdf', bbox_inches='tight')
+plt.close(fig)
+
+######################################################################
+# Plot multiple MSE vs. degree curves
+
+fig = plt.figure()
+
+ax = fig.add_subplot(1, 1, 1)
+ax.set_yscale("log")
+ax.set_ylim(1e-5, 1)
+ax.set_xlabel("Polynomial degree", labelpad=10)
+ax.set_ylabel("MSE", labelpad=10)
+
+nb_train_samples_min = args.nb_train_samples - 4
+nb_train_samples_max = args.nb_train_samples
+
+for nb_train_samples in range(nb_train_samples_min, nb_train_samples_max + 1, 2):
+ mse_train, mse_test = compute_mse(nb_train_samples)
+ e = float(nb_train_samples - nb_train_samples_min) / float(
+ nb_train_samples_max - nb_train_samples_min
+ )
+ e = 0.15 + 0.7 * e
+ ax.plot(
+ torch.arange(args.D_max + 1),
+ mse_train,
+ color=(e, e, 1.0),
+ label=f"Train N={nb_train_samples}",
+ )
+ ax.plot(
+ torch.arange(args.D_max + 1),
+ mse_test,
+ color=(1.0, e, e),
+ label=f"Test N={nb_train_samples}",
+ )
+
+ax.legend(frameon=False)
+
+fig.savefig("dd-multi-mse.pdf", bbox_inches="tight")
plt.close(fig)
######################################################################
# Plot some examples of train / test
-torch.manual_seed(9) # I picked that for pretty
+torch.manual_seed(9) # I picked that for pretty
-x_train = torch.rand(args.nb_train_samples, dtype = torch.float64)
+x_train = torch.rand(args.nb_train_samples, dtype=torch.float64)
y_train = phi(x_train)
if args.train_noise_std > 0:
y_train = y_train + torch.empty_like(y_train).normal_(0, args.train_noise_std)
-x_test = torch.linspace(0, 1, 100, dtype = x_train.dtype)
+x_test = torch.linspace(0, 1, 100, dtype=x_train.dtype)
y_test = phi(x_test)
for D in range(args.D_max + 1):
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
- ax.set_title(f'Degree {D}')
+ ax.set_title(f"Degree {D}")
ax.set_ylim(-0.1, 1.1)
- ax.plot(x_test, y_test, color = 'black', label = 'Test values')
- ax.scatter(x_train, y_train, color = 'blue', label = 'Train samples')
+ ax.plot(x_test, y_test, color="black", label="Test values")
+ ax.scatter(x_train, y_train, color="blue", label="Train samples")
alpha = fit_alpha(x_train, y_train, D)
- ax.plot(x_test, pol_value(alpha, x_test), color = 'red', label = 'Fitted polynomial')
+ ax.plot(x_test, pol_value(alpha, x_test), color="red", label="Fitted polynomial")
- ax.legend(frameon = False)
+ ax.legend(frameon=False)
- fig.savefig(f'dd-example-{D:02d}.pdf', bbox_inches='tight')
+ fig.savefig(f"dd-example-{D:02d}.pdf", bbox_inches="tight")
plt.close(fig)
projects / pytorch.git / blobdiff