def test_plot_sufficient_summary_02(self):
np.random.seed(42)
gradients = np.random.uniform(-1, 1, 200).reshape(50, 1, 4)
inputs = np.random.uniform(-1, 1, 200).reshape(50, 4)
weights = np.ones((50, 1)) / 50
ss = KernelActiveSubspaces(dim=3, n_features=8, n_boot=5)
ss.fit(inputs=inputs, gradients=gradients, weights=weights)
with self.assertRaises(ValueError):
ss.plot_sufficient_summary(10, 10)
def test_plot_sufficient_summary_03(self):
np.random.seed(42)
gradients = np.random.uniform(-1, 1, 200).reshape(50, 1, 4)
inputs = np.random.uniform(-1, 1, 200).reshape(50, 4)
weights = np.ones((50, 1)) / 50
ss = KernelActiveSubspaces(dim=2,
n_features=8,
method='exact',
n_boot=5)
ss.fit(inputs=inputs, gradients=gradients, weights=weights)
with assert_plot_figures_added():
ss.plot_sufficient_summary(
np.random.uniform(-1, 1, 100).reshape(25, 4),
np.random.uniform(-1, 1, 25).reshape(-1, 1))
def test_plot_sufficient_summary_02(self):
np.random.seed(42)
gradients = np.random.uniform(-1, 1, 200).reshape(50, 1, 4)
inputs = np.random.uniform(-1, 1, 200).reshape(50, 4)
weights = np.ones((50, 1)) / 50
ss = KernelActiveSubspaces()
ss.compute(inputs=inputs,
gradients=gradients,
weights=weights,
method='exact',
nboot=49,
n_features=8,
feature_map=None)
ss.partition(3)
with self.assertRaises(ValueError):
ss.plot_sufficient_summary(10, 10)
def test_plot_sufficient_summary_03(self):
np.random.seed(42)
gradients = np.random.uniform(-1, 1, 200).reshape(50, 1, 4)
inputs = np.random.uniform(-1, 1, 200).reshape(50, 4)
weights = np.ones((50, 1)) / 50
ss = KernelActiveSubspaces()
ss.compute(inputs=inputs,
gradients=gradients,
weights=weights,
method='exact',
nboot=49,
n_features=8,
feature_map=None)
ss.partition(2)
with assert_plot_figures_added():
ss.plot_sufficient_summary(
np.random.uniform(-1, 1, 100).reshape(25, 4),
np.random.uniform(-1, 1, 25).reshape(-1, 1))
'verbose': verbose
},
method='bso',
maxiter=10,
save_file=False)
print('The lowest rrmse is {}%'.format(best[0]))
W = np.load('opt_pr_matrix.npy')
b = np.load('bias.npy')
fm._pr_matrix = W
fm.bias = b
kss.fit(gradients=dt.reshape(N, 1, input_dim), outputs=t, inputs=y)
kss.plot_eigenvalues(n_evals=5, figsize=(6, 4))
kss.plot_sufficient_summary(xx, f, figsize=(6, 4))
# comparison with Nonlinear Level-set Learning explained in detail in tutorial 07
from athena.nll import NonlinearLevelSet
import torch
# create NonlinearLevelSet object, eventually passing an optimizer of choice
nll = NonlinearLevelSet(n_layers=10,
active_dim=1,
lr=0.01,
epochs=1000,
dh=0.25,
optimizer=torch.optim.Adam)
# convert to pytorch tensors
x_torch = torch.as_tensor(xx, dtype=torch.double)
def test_plot_sufficient_summary_01(self):
ss = KernelActiveSubspaces(dim=2)
with self.assertRaises(TypeError):
ss.plot_sufficient_summary(10, 10)
