from sklearn.linear_model import LassoLarsIC
if torch.cuda.is_available():
torch.set_default_tensor_type('torch.cuda.FloatTensor')
np.random.seed(42)
torch.manual_seed(42)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
v = 0.1
A = 1.0
# Making grid
x = np.linspace(-3, 4, 100)
t = np.linspace(0.5, 5.0, 50)
x_grid, t_grid = np.meshgrid(x, t, indexing='ij')
dataset = Dataset(BurgersDelta, v=v, A=A)
noise_range = np.arange(0.0, 1.01, 0.10)
n_runs = 5
for noise_level in noise_range:
for run in np.arange(n_runs):
X_train, y_train = dataset.create_dataset(x_grid.reshape(-1, 1), t_grid.reshape(-1, 1), n_samples=1000, noise=noise_level, random=True, return_idx=False, random_state=run)
estimator = Clustering(estimator=LassoLarsIC(fit_intercept=False))
config = {'n_in': 2, 'hidden_dims': [30, 30, 30, 30, 30], 'n_out': 1, 'library_function':library_1D_in, 'library_args':{'poly_order':2, 'diff_order': 3}, 'sparsity_estimator': estimator}
model = DeepModDynamic(**config)
optimizer = torch.optim.Adam(model.parameters(), betas=(0.99, 0.999), amsgrad=True)
train_dynamic(model, X_train, y_train, optimizer, 10000, log_dir=f'runs/cluster_{noise_level:.2f}_run_{run}/')
t_grid.reshape(-1, 1),
n_samples=1000,
noise=noise_level,
random=True,
return_idx=False,
random_state=run
) # use the same dataset for every run; only diff is in the network
#estimator = Clustering(estimator=LassoLarsIC(fit_intercept=False))
estimator = Threshold(0.1, LassoCV(cv=5, fit_intercept=False))
config = {
'n_in': 2,
'hidden_dims': [30, 30, 30, 30, 30],
'n_out': 1,
'library_function': library_1D_in,
'library_args': {
'poly_order': 2,
'diff_order': 3
},
'sparsity_estimator': estimator
}
model = DeepModDynamic(**config)
optimizer = torch.optim.Adam(model.parameters(),
betas=(0.99, 0.999),
amsgrad=True)
train_dynamic(model,
X_train,
y_train,
optimizer,
6000,
log_dir=f'testruns/old_hyperparams_low_noise/')
random=True,
return_idx=False,
random_state=run
) # use the same dataset for every run; only diff is in the network
estimator = PDEFIND(lam=1e-3, dtol=0.1)
config = {
'n_in': 3,
'hidden_dims': [30, 30, 30, 30, 30],
'n_out': 1,
'library_function': library_2Din_1Dout,
'library_args': {
'poly_order': 1,
'diff_order': 2
},
'sparsity_estimator': estimator
}
model = DeepModDynamic(**config)
optimizer = torch.optim.Adam(model.parameters(),
betas=(0.99, 0.999),
amsgrad=True)
train_dynamic(model,
X_train,
y_train,
optimizer,
25000,
stopper_kwargs={
'patience': 100,
'initial_epoch': 5000
},
log_dir=f'runs/pdefind_{noise_level:.2f}_run_{run}/')
from DeePyMoD_SBL.deepymod_torch.estimators import Threshold
from sklearn.linear_model import LassoCV
if torch.cuda.is_available():
torch.set_default_tensor_type('torch.cuda.FloatTensor')
np.random.seed(42)
torch.manual_seed(42)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
v = 0.1
A = 1.0
# Making grid
x = np.linspace(-3, 4, 100)
t = np.linspace(0.5, 5.0, 50)
x_grid, t_grid = np.meshgrid(x, t, indexing='ij')
dataset = Dataset(BurgersDelta, v=v, A=A)
noise_range = np.arange(0.0, 1.61, 0.20)#np.arange(0.0, 0.51, 0.05)
n_runs = 5
for noise_level in noise_range:
for run in np.arange(n_runs):
X_train, y_train = dataset.create_dataset(x_grid.reshape(-1, 1), t_grid.reshape(-1, 1), n_samples=1000, noise=noise_level, random=True, return_idx=False, random_state=run) # use the same dataset for every run; only diff is in the network
estimator = Threshold(threshold=0.1, estimator=LassoCV(cv=5, fit_intercept=False))
config = {'n_in': 2, 'hidden_dims': [30, 30, 30, 30, 30], 'n_out': 1, 'library_function':library_1D_in, 'library_args':{'poly_order':2, 'diff_order': 3}, 'sparsity_estimator': estimator}
model = DeepModDynamic(**config)
optimizer = torch.optim.Adam(model.parameters(), betas=(0.99, 0.999), amsgrad=True)
train_dynamic(model, X_train, y_train, optimizer, 15000, log_dir=f'runs_final/threshold_{noise_level:.2f}_run_{run}/')