diff --git a/EIVPackage/EIVArchitectures/Networks.py b/EIVPackage/EIVArchitectures/Networks.py
index 1435f48f00a929d51a1a9cfde6414c11704ab7a2..3a26b02d4e7d1f74f86502e9eb6d76e08ebe16ea 100644
--- a/EIVPackage/EIVArchitectures/Networks.py
+++ b/EIVPackage/EIVArchitectures/Networks.py
@@ -24,6 +24,9 @@ class FNNEIV(nn.Module):
:param h: A list specifying the number of neurons in each layer.
:param fixed_std_x: If given, this value will be the output of the method
`get_std_x()`, no matter what the deming factor.
+ :param repetition: Positive integer, the default value for repeating input,
+ defaults to 1. For a single call this can also be specified in the forward
+ method.
**Note**:
- To change the deming factor afterwards, use the method `change_deming`
- To change fixed_std_x afterwards, use the method `change_fixed_std_x`
@@ -31,14 +34,14 @@ class FNNEIV(nn.Module):
LeakyReLUSlope = 1e-2
def __init__(self, p = 0.2, init_std_y=1.0, precision_prior_zeta=0.0,
deming=1.0, h=[10, 1024,1024,1024,1024, 1],
- fixed_std_x = None):
+ fixed_std_x = None, repetition = 1):
super().__init__()
# part before Bernoulli dropout
self.init_std_y = init_std_y
InverseSoftplus = lambda sigma: torch.log(torch.exp(sigma) - 1 )
self.std_y_par = nn.parameter.Parameter(
InverseSoftplus(torch.tensor([init_std_y])))
- self._repetition = 1
+ self._repetition = repetition
self.main = nn.Sequential(
EIVInput(precision_prior_zeta=precision_prior_zeta,
external_std_x=self.get_std_x),
@@ -110,10 +113,11 @@ class FNNEIV(nn.Module):
return self._repetition
def forward(self, x, repetition=1):
+ old_repetition = self._repetition
self._repetition = repetition
mu = self.main(x)
sigma = self.sigma(mu)
- self._repetition = 1
+ self._repetition = old_repetition
return mu, sigma
def regularizer(self, x, lamb):
@@ -154,8 +158,10 @@ class FNNEIV(nn.Module):
regularization += self.main[0].neg_x_evidence(x)
return regularization
-
- def predict(self, x, number_of_draws=100, remove_graph=True
+ # TODO: repetition
+ # + Test via breakpoints
+ def predict(self, x, number_of_draws=[100,5], number_of_parameter_chunks = None,
+ remove_graph=True
, take_average_of_prediction=True):
"""
Average over `number_of_draws` forward passes. If
@@ -164,19 +170,46 @@ class FNNEIV(nn.Module):
**Note**: This method does neither touch the input noise nor Dropout.
The corresponding setting is left to the user!
:param x: A torch.tensor, the input
- :param number_of_draws: Number of draws to obtain from x
+ :param number_of_draws: An integer or a list. If an integer
+ `number_of_draws`, will be converted internally to
+ `[number_of_draws,1]`.Numbers of draws to obtain from x via parameter
+ sampling (first element) and noise input sampling (second element).
+ :param number_of_parameter_chunks: An integer or None (default). If
+ None, the second element of `number_of_draws` will be taken (and will
+ thus be identical to 1 if `number_of_draws` is an integer, see above).
+ Samples in the parameter space will be divided into
+ `number_of_parameter_chunks` chunks when collected. Can be used to
+ reduced the memory usage.
:param remove_graph: If True (default) the output will
be detached to save memory
:param take_average_of_prediction: If False, no averaging will be
applied to the prediction and the second dimension of the first output
will count the number_of_draws.
"""
- x, = repeat_tensors(x, number_of_draws=number_of_draws)
- pred, sigma = self.forward(x)
- if remove_graph:
- pred, sigma = pred.detach(), sigma.detach()
- pred, sigma = reshape_to_chunks(pred, sigma,
- number_of_draws=number_of_draws)
+ # x, = repeat_tensors(x, number_of_draws=number_of_draws)
+ if type(number_of_draws) is int:
+ number_of_draws = [number_of_draws, 1]
+ if number_of_parameter_chunks is None:
+ number_of_parameter_chunks = number_of_draws[1]
+ chunk_size = number_of_draws[0] // number_of_parameter_chunks
+ pred_collection, sigma_collection = [], []
+ remaining_draws = number_of_draws[0]
+ while remaining_draws > 0:
+ if remaining_draws < chunk_size:
+ parameter_sample_size = remaining_draws
+ else:
+ parameter_sample_size = chunk_size
+ repeated_x, = repeat_tensors(x, number_of_draws=parameter_sample_size * number_of_draws[1])
+ pred, sigma = self.forward(repeated_x, repetition=number_of_draws[1])
+ if remove_graph:
+ pred, sigma = pred.detach(), sigma.detach()
+ pred, sigma = reshape_to_chunks(pred, sigma,
+ number_of_draws=parameter_sample_size * number_of_draws[1])
+ pred_collection.append(pred)
+ sigma_collection.append(pred)
+ remaining_draws -= parameter_sample_size
+ pred = torch.cat(pred_collection, dim=1)
+ sigma = torch.cat(sigma_collection, dim=1)
# reduce along the draws (actually redundant for sigma)
if take_average_of_prediction:
pred, sigma = torch.mean(pred, dim=1), torch.mean(sigma, dim=1)
@@ -184,7 +217,10 @@ class FNNEIV(nn.Module):
sigma = torch.mean(sigma, dim=1)
return pred, sigma
- def predictive_logdensity(self, x, y, number_of_draws=100, remove_graph=True,
+ # TODO: repetition
+ # + Implement
+ # + Test via breakpoints
+ def predictive_logdensity(self, x, y, number_of_draws=[100, 5], number_of_parameter_chunks = None, remove_graph=True,
average_batch_dimension=True, scale_labels=None,
decouple_dimensions=False):
"""
@@ -193,7 +229,16 @@ class FNNEIV(nn.Module):
the batch dimension.
:param x: A torch.tensor, the input
:param y: A torch.tensor, labels on which to evaluate the density
- :param number_of_draws: Number of draws to obtain from x
+ :param number_of_draws: An integer or a list. If an integer
+ `number_of_draws`, will be converted internally to
+ `[number_of_draws,1]`.Numbers of draws to obtain from x via parameter
+ sampling (first element) and noise input sampling (second element).
+ :param number_of_parameter_chunks: An integer or None (default). If
+ None, the second element of `number_of_draws` will be taken (and will
+ thus be identical to 1 if `number_of_draws` is an integer, see above).
+ Samples in the parameter space will be divided into
+ `number_of_parameter_chunks` chunks when collected. Can be used to
+ reduced the memory usage.
:param remove_graph: If True (default) the output will
be detached to save memory
:param average_batch_dimension: Boolean. If True (default) the values
@@ -201,7 +246,9 @@ class FNNEIV(nn.Module):
dimension will be left untouched and all values will be returned.
"""
out, sigmas = self.predict(x, number_of_draws=number_of_draws,
- take_average_of_prediction=False, remove_graph=remove_graph)
+ number_of_parameter_chunks=number_of_parameter_chunks,
+ remove_graph=remove_graph,
+ take_average_of_prediction=False)
# Add "repetition" dimension to y and out
y = y[:,None,...]
sigmas = sigmas[:,None,...]
diff --git a/Experiments/evaluate_tabular.py b/Experiments/evaluate_tabular.py
index d97622dfe48a41b86eb95df92d7ecec5f0c2e3fb..90b972f92148183d73cfdbb7e95cbe0391fc57f3 100644
--- a/Experiments/evaluate_tabular.py
+++ b/Experiments/evaluate_tabular.py
@@ -20,13 +20,13 @@ train_eiv = importlib.import_module(f'train_eiv_{short_dataname}')
train_data, test_data = load_data()
test_dataloader = DataLoader(test_data, batch_size=int(np.max((len(test_data),
- 800))))
+ 64))), shuffle=True)
input_dim = train_data[0][0].numel()
output_dim = train_data[0][1].numel()
def collect_metrics(x,y, seed=0,
- noneiv_number_of_draws=500, eiv_number_of_draws=500,
- decouple_dimensions=False):
+ noneiv_number_of_draws=100, eiv_number_of_draws=100,
+ decouple_dimensions=False, device=torch.device('cuda:1')):
"""
:param x: A torch.tensor, taken as input
:param y: A torch.tensor, taken as output
@@ -34,13 +34,14 @@ def collect_metrics(x,y, seed=0,
:param noneiv_number_of_draws: Number of draws for non-EiV model
for sampling from the posterior predictive. Defaults to 100.
:param noneiv_number_of_draws: Number of draws for EiV model
- for sampling from the posterior predictive. Defaults to 500.
+ for sampling from the posterior predictive. Defaults to 100.
:param decouple_dimensions: Boolean. If True, the unsual convention
of Gal et al. is followed where, in the evaluation of the
log-posterior-predictive, each dimension is treated independently and then
averaged. If False (default), a multivariate distribution is used.
:returns: noneiv_rmse, noneiv_logdens,eiv_rmse, eiv_logdens
"""
+ x,y = x.to(device), y.to(device)
init_std_y = train_noneiv.init_std_y_list[0]
unscaled_reg = train_noneiv.unscaled_reg
p = train_noneiv.p
@@ -50,9 +51,9 @@ def collect_metrics(x,y, seed=0,
f'_init_std_y_{init_std_y:.3f}_ureg_{unscaled_reg:.1f}'\
f'_p_{p:.2f}_seed_{seed}.pkl')
net = Networks.FNNBer(p=p, init_std_y=init_std_y,
- h=[input_dim, *hidden_layers, output_dim])
+ h=[input_dim, *hidden_layers, output_dim]).to(device)
train_and_store.open_stored_training(saved_file=saved_file,
- net=net)
+ net=net, device=device)
# RMSE
@@ -64,10 +65,10 @@ def collect_metrics(x,y, seed=0,
y = y.view((-1,1))
assert y.shape == out.shape
res = y-out
- scale = train_data.dataset.std_labels
+ scale = train_data.dataset.std_labels.to(device)
scaled_res = res * scale.view((1,-1))
scaled_res = scaled_res.detach().cpu().numpy().flatten()
- noneiv_rmse = np.sqrt(np.mean(scaled_res**2))
+ noneiv_rmse = np.sqrt(np.mean(scaled_res**2))
# NLL
@@ -75,7 +76,9 @@ def collect_metrics(x,y, seed=0,
net.train()
noneiv_logdens = net.predictive_logdensity(x, y, number_of_draws=100,
decouple_dimensions=decouple_dimensions,
- scale_labels=train_data.dataset.std_labels.view((-1,))).mean()
+ scale_labels=\
+ train_data.dataset.std_labels.view((-1,)).to(device)\
+ ).mean().detach().cpu().numpy()
if training_state:
net.train()
else:
@@ -88,11 +91,13 @@ def collect_metrics(x,y, seed=0,
hidden_layers = train_eiv.hidden_layers
fixed_std_x = train_eiv.fixed_std_x
saved_file = os.path.join('saved_networks',
- f'eiv_{short_dataname}'\
- f'_init_std_y_{init_std_y:.3f}_ureg_{unscaled_reg:.1f}'\
- f'_p_{p:.2f}_seed_{seed}.pkl')
+ f'eiv_energy'\
+ f'_init_std_y_{init_std_y:.3f}_ureg_{unscaled_reg:.1f}'\
+ f'_p_{p:.2f}_fixed_std_x_{fixed_std_x:.3f}'\
+ f'_seed_{seed}.pkl')
net = Networks.FNNEIV(p=p, init_std_y=init_std_y,
- h=[input_dim, *hidden_layers, output_dim], fixed_std_x=fixed_std_x)
+ h=[input_dim, *hidden_layers, output_dim],
+ fixed_std_x=fixed_std_x).to(device)
train_and_store.open_stored_training(saved_file=saved_file,
net=net)
# RMSE
@@ -106,10 +111,10 @@ def collect_metrics(x,y, seed=0,
y = y.view((-1,1))
assert y.shape == out.shape
res = y-out
- scale = train_data.dataset.std_labels
+ scale = train_data.dataset.std_labels.to(device)
scaled_res = res * scale.view((1,-1))
scaled_res = scaled_res.detach().cpu().numpy().flatten()
- eiv_rmse = np.sqrt(np.mean(scaled_res**2))
+ eiv_rmse = np.sqrt(np.mean(scaled_res**2))
if training_state:
net.train()
else:
@@ -125,7 +130,9 @@ def collect_metrics(x,y, seed=0,
net.train()
eiv_logdens = net.predictive_logdensity(x, y, number_of_draws=100,
decouple_dimensions=decouple_dimensions,
- scale_labels=train_data.dataset.std_labels.view((-1,))).mean()
+ scale_labels=\
+ train_data.dataset.std_labels.view((-1,)).to(device)\
+ ).mean().detach().cpu().numpy()
if training_state:
net.train()
else:
@@ -136,19 +143,32 @@ noneiv_rmse_collection = []
noneiv_logdens_collection = []
eiv_rmse_collection = []
eiv_logdens_collection = []
-number_of_samples = 20
-for _ in tqdm(range(number_of_samples)):
- x,y = next(iter(test_dataloader))
- noneiv_rmse, noneiv_logdens, eiv_rmse, eiv_logdens = collect_metrics(x,y)
- noneiv_rmse_collection.append(noneiv_rmse)
- noneiv_logdens_collection.append(noneiv_logdens)
- eiv_rmse_collection.append(eiv_rmse)
- eiv_logdens_collection.append(eiv_logdens)
-
-
+num_test_epochs = 20
+assert train_noneiv.seed_list == train_eiv.seed_list
+seed_list = train_noneiv.seed_list
+for seed in tqdm(seed_list):
+ train_data, test_data = load_data(seed=seed)
+ test_dataloader = DataLoader(test_data,
+ batch_size=int(np.max((len(test_data),
+ 800))), shuffle=True)
+ for i in tqdm(range(num_test_epochs)):
+ for x,y in test_dataloader:
+ noneiv_rmse, noneiv_logdens, eiv_rmse, eiv_logdens =\
+ collect_metrics(x,y, seed=seed)
+ noneiv_rmse_collection.append(noneiv_rmse)
+ noneiv_logdens_collection.append(noneiv_logdens)
+ eiv_rmse_collection.append(eiv_rmse)
+ eiv_logdens_collection.append(eiv_logdens)
+
+
+# TODO: Despite statistics, the fluctuations seem to be large
print('Non-EiV')
-print(f'RMSE {np.mean(noneiv_rmse_collection):.3f} ({np.std(noneiv_rmse_collection)/np.sqrt(number_of_samples):.3f})')
-print(f'LogDens {np.mean(noneiv_logdens_collection):.3f} ({np.std(noneiv_logdens_collection)/np.sqrt(number_of_samples):.3f})')
+print(f'RMSE {np.mean(noneiv_rmse_collection):.3f}'\
+ f'({np.std(noneiv_rmse_collection)/np.sqrt(num_test_epochs):.3f})')
+print(f'LogDens {np.mean(noneiv_logdens_collection):.3f}'\
+ f'({np.std(noneiv_logdens_collection)/np.sqrt(num_test_epochs):.3f})')
print('EiV')
-print(f'RMSE {np.mean(eiv_rmse_collection):.3f} ({np.std(eiv_rmse_collection)/np.sqrt(number_of_samples):.3f})')
-print(f'LogDens {np.mean(eiv_logdens_collection):.3f} ({np.std(eiv_logdens_collection)/np.sqrt(number_of_samples):.3f})')
+print(f'RMSE {np.mean(eiv_rmse_collection):.3f}'\
+ f'({np.std(eiv_rmse_collection)/np.sqrt(num_test_epochs):.3f})')
+print(f'LogDens {np.mean(eiv_logdens_collection):.3f}'\
+ f'({np.std(eiv_logdens_collection)/np.sqrt(num_test_epochs):.3f})')
diff --git a/Experiments/train_eiv_california.py b/Experiments/train_eiv_california.py
index 40a1c60d447c801e88c555a53306a8d712d8faf2..39c95fbe0cad3b7f839a8c230e9c54e5bfcde8f3 100644
--- a/Experiments/train_eiv_california.py
+++ b/Experiments/train_eiv_california.py
@@ -118,7 +118,7 @@ def train_on_data(init_std_y, seed):
net.apply(initialize_weights.glorot_init)
net = net.to(device)
net.std_y_par.requires_grad = False
- std_x_map = lambda: 0.0
+ std_x_map = lambda: net.get_std_x().detach().cpu().item()
std_y_map = lambda: net.get_std_y().detach().cpu().item()
# regularization
reg = unscaled_reg/len(train_data)
diff --git a/Experiments/train_eiv_energy.py b/Experiments/train_eiv_energy.py
index a29a4ed1f7ce193c1b3700295e1ba6b30630e3d0..40be4b283ed12849418e6c760618f79a70122bda 100644
--- a/Experiments/train_eiv_energy.py
+++ b/Experiments/train_eiv_energy.py
@@ -118,7 +118,7 @@ def train_on_data(init_std_y, seed):
net.apply(initialize_weights.glorot_init)
net = net.to(device)
net.std_y_par.requires_grad = False
- std_x_map = lambda: 0.0
+ std_x_map = lambda: net.get_std_x().detach().cpu().item()
std_y_map = lambda: net.get_std_y().detach().cpu().item()
# regularization
reg = unscaled_reg/len(train_data)