def test_init_W2(self):
ss = ActiveSubspaces(dim=1)
self.assertIsNone(ss.W2)
def test_init_evals_br(self):
ss = ActiveSubspaces(dim=1)
self.assertIsNone(ss.evals_br)
def test_plot_sufficient_summary_01(self):
ss = ActiveSubspaces()
with self.assertRaises(ValueError):
ss.plot_sufficient_summary(10, 10)
def test_activity_scores_03(self):
np.random.seed(42)
gradients = np.random.uniform(-1, 1, 180).reshape(15, 3, 4)
ss = ActiveSubspaces(dim=2, method='exact', n_boot=150)
with self.assertRaises(TypeError):
ss.activity_scores
def test_plot_eigenvectors_01(self):
ss = ActiveSubspaces()
with self.assertRaises(ValueError):
ss.plot_eigenvectors(figsize=(7, 7), title='Eigenvalues')
def test_compute_01(self):
ss = ActiveSubspaces()
with self.assertRaises(ValueError):
ss.compute()
def test_init_dim(self):
ss = ActiveSubspaces()
self.assertIsNone(ss.dim)
def test_init_cov_matrix(self):
ss = ActiveSubspaces()
self.assertIsNone(ss.cov_matrix)
def test_init_evects(self):
ss = ActiveSubspaces()
self.assertIsNone(ss.evects)
def test_init_subs_br(self):
ss = ActiveSubspaces()
self.assertIsNone(ss.subs_br)
def test_init_W1(self):
ss = ActiveSubspaces()
self.assertIsNone(ss.W1)
nor = Normalizer(lb, ub)
x = nor.fit_transform(X)
# Define the output of interest and compute the gradients
func = sin_2d
dfunc = egrad(func)
f = func(x)
df_exact = dfunc(x) # exact gradients
df_gp = eval_gp_grad(
x, f, n_samples,
input_dim) # gradients approximated with Gaussian process regression
title = "sin"
# Compute the active subspace with approximated gradients
asub1 = ActiveSubspaces(dim=1, method='exact', n_boot=100)
asub1.fit(gradients=df_gp)
asub1.plot_eigenvalues(figsize=(6, 4), title=title)
asub1.plot_eigenvectors(figsize=(6, 4), title=title)
asub1.plot_sufficient_summary(x, f, figsize=(6, 4), title=title)
asub2 = ActiveSubspaces(dim=2, method='exact', n_boot=100)
asub2.fit(gradients=df_gp)
asub2.plot_sufficient_summary(x, f, figsize=(6, 4), title=title)
# Compute the active subspace with exact gradients
asub1_ = ActiveSubspaces(dim=1, method='exact', n_boot=100)
asub1_.fit(gradients=df_exact)
asub1_.plot_eigenvalues(figsize=(6, 4), title=title)
asub1_.plot_eigenvectors(figsize=(6, 4), title=title)
asub1_.plot_sufficient_summary(x, f, figsize=(6, 4), title=title)
def test_fit_01(self):
ss = ActiveSubspaces(dim=1)
with self.assertRaises(ValueError):
ss.fit()
def test_init_dim(self):
ss = ActiveSubspaces(dim=1)
self.assertEqual(ss.dim, 1)
torch.set_default_tensor_type(torch.DoubleTensor)
np.random.seed(42)
# global parameters
n_train = 300
n_params = 2
x_np = np.random.uniform(size=(n_train, n_params))
f = x_np[:, 0]**3 + x_np[:, 1]**3 + 0.2 * x_np[:, 0] + 0.6 * x_np[:, 1]
df_np = np.empty((n_train, n_params))
df_np[:, 0] = 3.0 * x_np[:, 0]**2 + 0.2
df_np[:, 1] = 3.0 * x_np[:, 1]**2 + 0.6
ss = ActiveSubspaces(1)
ss.fit(inputs=x_np, gradients=df_np)
ss.plot_eigenvalues(figsize=(6, 4))
ss.plot_sufficient_summary(x_np, f, figsize=(6, 4))
nll = NonlinearLevelSet(
n_layers=10,
active_dim=1,
lr=0.008,
epochs=1000,
dh=0.25,
optimizer=torch.optim.SGD,
scheduler=torch.optim.lr_scheduler.StepLR,
)
x_torch = torch.as_tensor(x_np, dtype=torch.double)
df_torch = torch.as_tensor(df_np, dtype=torch.double)
