after_merge stringlengths 28 79.6k | before_merge stringlengths 20 79.6k | url stringlengths 38 71 | full_traceback stringlengths 43 922k | traceback_type stringclasses 555
values |
|---|---|---|---|---|
def lazy_covariance_matrix(self):
"""
The covariance_matrix, represented as a LazyTensor
"""
return super().lazy_covariance_matrix
| def lazy_covariance_matrix(self):
"""
The covariance_matrix, represented as a LazyTensor
"""
if self.islazy:
return self._covar
else:
return lazify(super().covariance_matrix)
| https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def add_diag(self, added_diag):
shape = _mul_broadcast_shape(self._diag.shape, added_diag.shape)
return DiagLazyTensor(self._diag.expand(shape) + added_diag.expand(shape))
| def add_diag(self, added_diag):
return DiagLazyTensor(self._diag + added_diag.expand_as(self._diag))
| https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def _size(self):
return _matmul_broadcast_shape(
self.left_lazy_tensor.shape, self.right_lazy_tensor.shape
)
| def _size(self):
return torch.Size(
(
*self.left_lazy_tensor.batch_shape,
self.left_lazy_tensor.size(-2),
self.right_lazy_tensor.size(-1),
)
)
| https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def _size(self):
return _mul_broadcast_shape(*[lt.shape for lt in self.lazy_tensors])
| def _size(self):
return self.lazy_tensors[0].size()
| https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def expected_log_prob(self, observations, function_dist, *params, **kwargs):
if torch.any(observations.eq(-1)):
warnings.warn(
"BernoulliLikelihood.expected_log_prob expects observations with labels in {0, 1}. "
"Observations with labels in {-1, 1} are deprecated.",
Depre... | def expected_log_prob(self, observations, function_dist, *params, **kwargs):
if torch.any(observations.eq(-1)):
warnings.warn(
"BernoulliLikelihood.expected_log_prob expects observations with labels in {0, 1}. "
"Observations with labels in {-1, 1} are deprecated.",
Depre... | https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def expected_log_prob(
self, target: Tensor, input: MultivariateNormal, *params: Any, **kwargs: Any
) -> Tensor:
mean, variance = input.mean, input.variance
num_event_dim = len(input.event_shape)
noise = self._shaped_noise_covar(mean.shape, *params, **kwargs).diag()
# Potentially reshape the noise ... | def expected_log_prob(
self, target: Tensor, input: MultivariateNormal, *params: Any, **kwargs: Any
) -> Tensor:
mean, variance = input.mean, input.variance
noise = self.noise_covar.noise
res = (
((target - mean) ** 2 + variance) / noise + noise.log() + math.log(2 * math.pi)
)
return re... | https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def __init__(self, max_plate_nesting=1):
super().__init__()
self._register_load_state_dict_pre_hook(self._batch_shape_state_dict_hook)
self.max_plate_nesting = max_plate_nesting
| def __init__(self):
super().__init__()
self._register_load_state_dict_pre_hook(self._batch_shape_state_dict_hook)
self.quadrature = GaussHermiteQuadrature1D()
| https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def expected_log_prob(self, observations, function_dist, *args, **kwargs):
likelihood_samples = self._draw_likelihood_samples(function_dist, *args, **kwargs)
res = likelihood_samples.log_prob(observations).mean(dim=0)
return res
| def expected_log_prob(self, observations, function_dist, *params, **kwargs):
"""
Computes the expected log likelihood (used for variational inference):
.. math::
\mathbb{E}_{f(x)} \left[ \log p \left( y \mid f(x) \right) \right]
Args:
:attr:`function_dist` (:class:`gpytorch.distributio... | https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def forward(self, function_samples, *args, **kwargs):
raise NotImplementedError
| def forward(self, function_samples, *params, **kwargs):
"""
Computes the conditional distribution p(y|f) that defines the likelihood.
Args:
:attr:`function_samples`
Samples from the function `f`
:attr:`kwargs`
Returns:
Distribution object (with same shape as :attr:`... | https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def marginal(self, function_dist, *args, **kwargs):
res = self._draw_likelihood_samples(function_dist, *args, **kwargs)
return res
| def marginal(self, function_dist, *params, **kwargs):
"""
Computes a predictive distribution :math:`p(y*|x*)` given either a posterior
distribution :math:`p(f|D,x)` or a prior distribution :math:`p(f|x)` as input.
With both exact inference and variational inference, the form of
:math:`p(f|D,x)` or ... | https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def __call__(self, input, *args, **kwargs):
# Conditional
if torch.is_tensor(input):
return super().__call__(input, *args, **kwargs)
# Marginal
elif isinstance(input, MultivariateNormal):
return self.marginal(input, *args, **kwargs)
# Error
else:
raise RuntimeError(
... | def __call__(self, input, *params, **kwargs):
# Conditional
if torch.is_tensor(input):
return super().__call__(input, *params, **kwargs)
# Marginal
elif isinstance(input, MultivariateNormal):
return self.marginal(input, *params, **kwargs)
# Error
else:
raise RuntimeError(... | https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.quadrature = GaussHermiteQuadrature1D()
| def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._max_plate_nesting = 1
| https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def get_fantasy_likelihood(self, **kwargs):
""" """
return super().get_fantasy_likelihood(**kwargs)
| def get_fantasy_likelihood(self, **kwargs):
return deepcopy(self)
| https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def marginal(self, function_dist, *args, **kwargs):
r"""
Computes a predictive distribution :math:`p(y^* | \mathbf x^*)` given either a posterior
distribution :math:`p(\mathbf f | \mathcal D, \mathbf x)` or a
prior distribution :math:`p(\mathbf f|\mathbf x)` as input.
With both exact inference and ... | def marginal(self, function_dist, *params, **kwargs):
name_prefix = kwargs.get("name_prefix", "")
num_samples = settings.num_likelihood_samples.value()
with pyro.plate(
name_prefix + ".num_particles_vectorized",
num_samples,
dim=(-self.max_plate_nesting - 1),
):
function_... | https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def __call__(self, input, *args, **kwargs):
# Conditional
if torch.is_tensor(input):
return super().__call__(input, *args, **kwargs)
# Marginal
elif any(
[
isinstance(input, MultivariateNormal),
isinstance(input, pyro.distributions.Normal),
(
... | def __call__(self, input, *params, **kwargs):
# Conditional
if torch.is_tensor(input):
return super().__call__(input, *params, **kwargs)
# Marginal
elif any(
[
isinstance(input, MultivariateNormal),
isinstance(input, pyro.distributions.Normal),
(
... | https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def forward(
self, function_samples: Tensor, *params: Any, **kwargs: Any
) -> base_distributions.Normal:
noise = self._shaped_noise_covar(function_samples.shape, *params, **kwargs).diag()
noise = noise.view(*noise.shape[:-1], *function_samples.shape[-2:])
return base_distributions.Independent(
b... | def forward(
self, function_samples: Tensor, *params: Any, **kwargs: Any
) -> base_distributions.Normal:
noise = self._shaped_noise_covar(function_samples.shape, *params, **kwargs).diag()
noise = noise.view(*noise.shape[:-1], *function_samples.shape[-2:])
return base_distributions.Normal(function_sample... | https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def __init__(self, likelihood, model):
if not isinstance(likelihood, _GaussianLikelihoodBase):
raise RuntimeError("Likelihood must be Gaussian for exact inference")
super(ExactMarginalLogLikelihood, self).__init__(likelihood, model)
| def __init__(self, likelihood, model):
"""
A special MLL designed for exact inference
Args:
- likelihood: (Likelihood) - the likelihood for the model
- model: (Module) - the exact GP model
"""
if not isinstance(likelihood, _GaussianLikelihoodBase):
raise RuntimeError("Likelihood mus... | https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def forward(self, function_dist, target, *params):
r"""
Computes the MLL given :math:`p(\mathbf f)` and :math:`\mathbf y`.
:param ~gpytorch.distributions.MultivariateNormal function_dist: :math:`p(\mathbf f)`
the outputs of the latent function (the :obj:`gpytorch.models.ExactGP`)
:param torch.T... | def forward(self, output, target, *params):
if not isinstance(output, MultivariateNormal):
raise RuntimeError(
"ExactMarginalLogLikelihood can only operate on Gaussian random variables"
)
# Get the log prob of the marginal distribution
output = self.likelihood(output, *params)
... | https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def forward(self, output, target, **kwargs):
"""
Computes the MLL given :math:`p(\mathbf f)` and `\mathbf y`
Args:
:attr:`output` (:obj:`gpytorch.distributions.MultivariateNormal`):
:math:`p(\mathbf f)` (or approximation)
the outputs of the latent function (the :obj:`gpytorc... | def forward(self, output, target):
"""
Args:
- output: (MultivariateNormal) - the outputs of the latent function
- target: (Variable) - the target values
"""
raise NotImplementedError
| https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def forward(self, variational_dist_f, target, **kwargs):
r"""
Computes the Variational ELBO given :math:`q(\mathbf f)` and :math:`\mathbf y`.
Calling this function will call the likelihood's :meth:`~gpytorch.likelihoods.Likelihood.expected_log_prob`
function.
:param ~gpytorch.distributions.Multivar... | def forward(self, variational_dist_f, target, **kwargs):
num_batch = variational_dist_f.event_shape.numel()
log_likelihood = self.likelihood.expected_log_prob(
target, variational_dist_f, **kwargs
).div(num_batch)
kl_divergence = self.model.variational_strategy.kl_divergence()
if kl_diverg... | https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def forward(self, variational_dist_f, target, **kwargs):
r"""
Computes the Variational ELBO given :math:`q(\mathbf f)` and :math:`\mathbf y`.
Calling this function will call the likelihood's :meth:`~gpytorch.likelihoods.Likelihood.expected_log_prob`
function.
:param ~gpytorch.distributions.Multivar... | def forward(self, variational_dist_f, target, **kwargs):
num_batch = variational_dist_f.event_shape[0]
variational_dist_u = self.model.variational_strategy.variational_distribution.variational_distribution
prior_dist = self.model.variational_strategy.prior_distribution
log_likelihood = self.likelihood.... | https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def __init__(self, *args, **kwargs):
warnings.warn("PyroVariationalGP has been renamed to PyroGP.", DeprecationWarning)
super().__init__(*args, **kwargs)
| def __init__(self, *args, **kwargs):
raise RuntimeError(
"Cannot use a PyroVariationalGP because you dont have Pyro installed."
)
| https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def sub_variational_strategies(self):
if not hasattr(self, "_sub_variational_strategies_memo"):
self._sub_variational_strategies_memo = [
module.variational_strategy
for module in self.model.modules()
if isinstance(module, ApproximateGP)
]
return self._sub_var... | def sub_variational_strategies(self):
if not hasattr(self, "_sub_variational_strategies_memo"):
self._sub_variational_strategies_memo = [
module.variational_strategy
for module in self.model.modules()
if isinstance(module, AbstractVariationalGP)
]
return self.... | https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def __call__(self, inputs, are_samples=False, **kwargs):
"""
Forward data through this hidden GP layer. The output is a MultitaskMultivariateNormal distribution
(or MultivariateNormal distribution is output_dims=None).
If the input is >=2 dimensional Tensor (e.g. `n x d`), we pass the input through eac... | def __call__(self, inputs, are_samples=False, **kwargs):
"""
Forward data through this hidden GP layer. The output is a MultitaskMultivariateNormal distribution
(or MultivariateNormal distribution is output_dims=None).
If the input is >=2 dimensional Tensor (e.g. `n x d`), we pass the input through eac... | https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def forward(self, x, inducing_points, inducing_values, variational_inducing_covar=None):
if x.ndimension() == 1:
x = x.unsqueeze(-1)
elif x.ndimension() != 2:
raise RuntimeError(
"AdditiveGridInterpolationVariationalStrategy expects a 2d tensor."
)
num_data, num_dim = x.... | def forward(self, x):
if x.ndimension() == 1:
x = x.unsqueeze(-1)
elif x.ndimension() != 2:
raise RuntimeError(
"AdditiveGridInterpolationVariationalStrategy expects a 2d tensor."
)
num_data, num_dim = x.size()
if num_dim != self.num_dim:
raise RuntimeError("... | https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def __init__(
self, num_inducing_points, batch_shape=torch.Size([]), mean_init_std=1e-3, **kwargs
):
super().__init__(
num_inducing_points=num_inducing_points,
batch_shape=batch_shape,
mean_init_std=mean_init_std,
)
mean_init = torch.zeros(num_inducing_points)
covar_init = to... | def __init__(self, num_inducing_points, batch_shape=torch.Size([]), **kwargs):
"""
Args:
num_inducing_points (int): Size of the variational distribution. This implies that the variational mean
should be this size, and the variational covariance matrix should have this many rows and columns.
... | https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def initialize_variational_distribution(self, prior_dist):
self.variational_mean.data.copy_(prior_dist.mean)
self.variational_mean.data.add_(
self.mean_init_std, torch.randn_like(prior_dist.mean)
)
self.chol_variational_covar.data.copy_(
prior_dist.lazy_covariance_matrix.cholesky().evalu... | def initialize_variational_distribution(self, prior_dist):
self.variational_mean.data.copy_(prior_dist.mean)
self.chol_variational_covar.data.copy_(prior_dist.scale_tril)
| https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def _compute_grid(self, inputs):
n_data, n_dimensions = inputs.size(-2), inputs.size(-1)
batch_shape = inputs.shape[:-2]
inputs = inputs.reshape(-1, n_dimensions)
interp_indices, interp_values = Interpolation().interpolate(self.grid, inputs)
interp_indices = interp_indices.view(*batch_shape, n_data... | def _compute_grid(self, inputs):
if inputs.ndimension() == 1:
inputs = inputs.unsqueeze(1)
interp_indices, interp_values = Interpolation().interpolate(self.grid, inputs)
return interp_indices, interp_values
| https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def forward(self, x, inducing_points, inducing_values, variational_inducing_covar=None):
if variational_inducing_covar is None:
raise RuntimeError(
"GridInterpolationVariationalStrategy is only compatible with Gaussian variational "
f"distributions. Got ({self.variational_distributio... | def forward(self, x):
variational_distribution = self.variational_distribution.variational_distribution
# Get interpolations
interp_indices, interp_values = self._compute_grid(x)
# Compute test mean
# Left multiply samples by interpolation matrix
predictive_mean = left_interp(
interp_i... | https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def __init__(
self,
model,
inducing_points,
variational_distribution,
learn_inducing_locations=True,
):
super().__init__(
model, inducing_points, variational_distribution, learn_inducing_locations
)
self.register_buffer("updated_strategy", torch.tensor(True))
self._register_l... | def __init__(
self,
model,
inducing_points,
variational_distribution,
learn_inducing_locations=False,
):
"""
Args:
model (:obj:`gpytorch.model.AbstractVariationalGP`): Model this strategy is applied to. Typically passed in
when the VariationalStrategy is created in the __init... | https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def prior_distribution(self):
zeros = torch.zeros_like(self.variational_distribution.mean)
ones = torch.ones_like(zeros)
res = MultivariateNormal(zeros, DiagLazyTensor(ones))
return res
| def prior_distribution(self):
"""
The :func:`~gpytorch.variational.VariationalStrategy.prior_distribution` method determines how to compute the
GP prior distribution of the inducing points, e.g. :math:`p(u) \sim N(\mu(X_u), K(X_u, X_u))`. Most commonly,
this is done simply by calling the user defined GP... | https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def forward(self, x, inducing_points, inducing_values, variational_inducing_covar=None):
# Compute full prior distribution
full_inputs = torch.cat([inducing_points, x], dim=-2)
full_output = self.model.forward(full_inputs)
full_covar = full_output.lazy_covariance_matrix
# Covariance terms
num_i... | def forward(self, x):
"""
The :func:`~gpytorch.variational.VariationalStrategy.forward` method determines how to marginalize out the
inducing point function values. Specifically, forward defines how to transform a variational distribution
over the inducing point values, :math:`q(u)`, in to a variational... | https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def __call__(self, x, prior=False):
if not self.updated_strategy.item() and not prior:
with torch.no_grad():
# Get unwhitened p(u)
prior_function_dist = self(self.inducing_points, prior=True)
prior_mean = prior_function_dist.loc
L = self._cholesky_factor(
... | def __call__(self, x):
if not self.variational_params_initialized.item():
self.initialize_variational_dist()
self.variational_params_initialized.fill_(1)
if self.training:
if hasattr(self, "_memoize_cache"):
delattr(self, "_memoize_cache")
self._memoize_cache = di... | https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def covar_trace(self):
variational_covar = self.variational_distribution.covariance_matrix
prior_covar = self.prior_distribution.covariance_matrix
batch_shape = prior_covar.shape[:-2]
return (variational_covar * prior_covar).view(*batch_shape, -1).sum(-1)
| def covar_trace(self):
variational_covar = (
self.variational_distribution.variational_distribution.covariance_matrix
)
prior_covar = self.prior_distribution.covariance_matrix
batch_shape = prior_covar.shape[:-2]
return (variational_covar * prior_covar).view(*batch_shape, -1).sum(-1)
| https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def mean_diff_inv_quad(self):
prior_mean = self.prior_distribution.mean
prior_covar = self.prior_distribution.lazy_covariance_matrix
variational_mean = self.variational_distribution.mean
return prior_covar.inv_quad(variational_mean - prior_mean)
| def mean_diff_inv_quad(self):
prior_mean = self.prior_distribution.mean
prior_covar = self.prior_distribution.lazy_covariance_matrix
variational_mean = self.variational_distribution.variational_distribution.mean
return prior_covar.inv_quad(variational_mean - prior_mean)
| https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def kl_divergence(self):
variational_dist_u = self.variational_distribution
prior_dist = self.prior_distribution
kl_divergence = 0.5 * sum(
[
# log|k| - log|S|
# = log|K| - log|K var_dist_covar K|
# = -log|K| - log|var_dist_covar|
self.prior_covar_logd... | def kl_divergence(self):
variational_dist_u = self.variational_distribution.variational_distribution
prior_dist = self.prior_distribution
kl_divergence = 0.5 * sum(
[
# log|k| - log|S|
# = log|K| - log|K var_dist_covar K|
# = -log|K| - log|var_dist_covar|
... | https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def forward(self, x):
"""
The :func:`~gpytorch.variational.VariationalStrategy.forward` method determines how to marginalize out the
inducing point function values. Specifically, forward defines how to transform a variational distribution
over the inducing point values, :math:`q(u)`, in to a variational... | def forward(self, x):
"""
The :func:`~gpytorch.variational.VariationalStrategy.forward` method determines how to marginalize out the
inducing point function values. Specifically, forward defines how to transform a variational distribution
over the inducing point values, :math:`q(u)`, in to a variational... | https://github.com/cornellius-gp/gpytorch/issues/905 | ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-6-c955f39ee560> in <module>
4 with torch.no_grad():
5 for x_batch, y_batch in test_loader:
----> 6 preds = model(x_batch)
7 means = t... | AttributeError |
def __init__(
self,
base_lazy_tensor,
left_interp_indices=None,
left_interp_values=None,
right_interp_indices=None,
right_interp_values=None,
):
base_lazy_tensor = lazify(base_lazy_tensor)
if left_interp_indices is None:
num_rows = base_lazy_tensor.size(-2)
left_interp_i... | def __init__(
self,
base_lazy_tensor,
left_interp_indices=None,
left_interp_values=None,
right_interp_indices=None,
right_interp_values=None,
):
base_lazy_tensor = lazify(base_lazy_tensor)
if left_interp_indices is None:
num_rows = base_lazy_tensor.size(-2)
left_interp_i... | https://github.com/cornellius-gp/gpytorch/issues/900 | (py37) vdhiman@dwarf:~/wrk/BayesCBF_ws/BayesCBF$ python tests/test_interpolated_lazy_tensor.py
Traceback (most recent call last):
File "tests/test_interpolated_lazy_tensor.py", line 30, in <module>
test_interpolated_lazy_tensor()
File "tests/test_interpolated_lazy_tensor.py", line 7, in test_interpolated_lazy_tensor
re... | RuntimeError |
def _sparse_left_interp_t(self, left_interp_indices_tensor, left_interp_values_tensor):
if hasattr(self, "_sparse_left_interp_t_memo"):
if torch.equal(
self._left_interp_indices_memo, left_interp_indices_tensor
) and torch.equal(self._left_interp_values_memo, left_interp_values_tensor):
... | def _sparse_left_interp_t(self, left_interp_indices_tensor, left_interp_values_tensor):
if hasattr(self, "_sparse_left_interp_t_memo"):
if torch.equal(
self._left_interp_indices_memo, left_interp_indices_tensor
) and torch.equal(self._left_interp_values_memo, left_interp_values_tensor):
... | https://github.com/cornellius-gp/gpytorch/issues/900 | (py37) vdhiman@dwarf:~/wrk/BayesCBF_ws/BayesCBF$ python tests/test_interpolated_lazy_tensor.py
Traceback (most recent call last):
File "tests/test_interpolated_lazy_tensor.py", line 30, in <module>
test_interpolated_lazy_tensor()
File "tests/test_interpolated_lazy_tensor.py", line 7, in test_interpolated_lazy_tensor
re... | RuntimeError |
def backward(ctx, inv_quad_grad_output, logdet_grad_output):
matrix_arg_grads = None
inv_quad_rhs_grad = None
# Which backward passes should we compute?
compute_inv_quad_grad = inv_quad_grad_output.abs().sum() and ctx.inv_quad
compute_logdet_grad = logdet_grad_output.abs().sum() and ctx.logdet
... | def backward(ctx, inv_quad_grad_output, logdet_grad_output):
matrix_arg_grads = None
inv_quad_rhs_grad = None
# Which backward passes should we compute?
compute_inv_quad_grad = inv_quad_grad_output.abs().sum() and ctx.inv_quad
compute_logdet_grad = logdet_grad_output.abs().sum() and ctx.logdet
... | https://github.com/cornellius-gp/gpytorch/issues/710 | ---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-46-593fbced29ac> in <module>()
3 kern = gpytorch.kernels.RBFKernel()(inp)
4 ld = logdet(kern)
----> 5 backward(ld)
<PATH SNIPPED>/lib/python3.7/site-pac... | TypeError |
def check(self, tensor):
return bool(
torch.all(tensor <= self.upper_bound) and torch.all(tensor >= self.lower_bound)
)
| def check(self, tensor):
return torch.all(tensor <= self.upper_bound) and torch.all(
tensor >= self.lower_bound
)
| https://github.com/cornellius-gp/gpytorch/issues/620 | ---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-5-dcd721edef0b> in <module>
73 model.likelihood.initialize(noise=1e-5)
74 model.likelihood.noise_covar.raw_noise.requires_grad_(False)
---> 75 train(mode... | RuntimeError |
def initialize(self, **kwargs):
"""
Set a value for a parameter
kwargs: (param_name, value) - parameter to initialize.
Can also initialize recursively by passing in the full name of a
parameter. For example if model has attribute model.likelihood,
we can initialize the noise with either
`mo... | def initialize(self, **kwargs):
"""
Set a value for a parameter
kwargs: (param_name, value) - parameter to initialize.
Can also initialize recursively by passing in the full name of a
parameter. For example if model has attribute model.likelihood,
we can initialize the noise with either
`mo... | https://github.com/cornellius-gp/gpytorch/issues/620 | ---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-5-dcd721edef0b> in <module>
73 model.likelihood.initialize(noise=1e-5)
74 model.likelihood.noise_covar.raw_noise.requires_grad_(False)
---> 75 train(mode... | RuntimeError |
def __init__(
self,
base_lazy_tensor,
left_interp_indices=None,
left_interp_values=None,
right_interp_indices=None,
right_interp_values=None,
):
base_lazy_tensor = lazify(base_lazy_tensor)
if left_interp_indices is None:
num_rows = base_lazy_tensor.size(-2)
left_interp_i... | def __init__(
self,
base_lazy_tensor,
left_interp_indices=None,
left_interp_values=None,
right_interp_indices=None,
right_interp_values=None,
):
base_lazy_tensor = lazify(base_lazy_tensor)
if left_interp_indices is None:
num_rows = base_lazy_tensor.size(-2)
left_interp_i... | https://github.com/cornellius-gp/gpytorch/issues/532 | ---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-62-28172f8b7beb> in <module>()
1 with torch.no_grad(), gpytorch.settings.fast_pred_var():
----> 2 observed_pred_y1 = likelihood(model(test_x, tast_i_... | RuntimeError |
def evaluate_kernel(self):
"""
NB: This is a meta LazyTensor, in the sense that evaluate can return
a LazyTensor if the kernel being evaluated does so.
"""
if not self.is_batch:
x1 = self.x1.unsqueeze(0)
x2 = self.x2.unsqueeze(0)
else:
x1 = self.x1
x2 = self.x2
... | def evaluate_kernel(self):
"""
NB: This is a meta LazyTensor, in the sense that evaluate can return
a LazyTensor if the kernel being evaluated does so.
"""
if not self.is_batch:
x1 = self.x1.unsqueeze(0)
x2 = self.x2.unsqueeze(0)
else:
x1 = self.x1
x2 = self.x2
... | https://github.com/cornellius-gp/gpytorch/issues/575 | ---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-67-cb234b5fe124> in <module>
15 output = model(X)
16 # Calc loss and backprop gradients
---> 17 loss = -mll(output, y)
18 loss.backward()... | RuntimeError |
def root_inv_decomposition(self, initial_vectors=None, test_vectors=None):
"""
Returns a (usually low-rank) root decomposotion lazy tensor of a PSD matrix.
This can be used for sampling from a Gaussian distribution, or for obtaining a
low-rank version of a matrix
"""
from .root_lazy_tensor impor... | def root_inv_decomposition(self, initial_vectors=None, test_vectors=None):
"""
Returns a (usually low-rank) root decomposotion lazy tensor of a PSD matrix.
This can be used for sampling from a Gaussian distribution, or for obtaining a
low-rank version of a matrix
"""
from .root_lazy_tensor impor... | https://github.com/cornellius-gp/gpytorch/issues/548 | ---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-1-05bfb3c2646a> in <module>
27 # this throws the error
28 with gpytorch.settings.fast_pred_var():
---> 29 model(torch.rand(100, 2))
~/.cache/pypoetr... | IndexError |
def __init__(self, representation_tree, has_left=False):
self.representation_tree = representation_tree
self.has_left = has_left
| def __init__(self, representation_tree, preconditioner=None, has_left=False):
self.representation_tree = representation_tree
self.preconditioner = preconditioner
self.has_left = has_left
| https://github.com/cornellius-gp/gpytorch/issues/501 | $ python test_1D_grid_gp_regression.py
E.
======================================================================
ERROR: test_grid_gp_mean_abs_error (__main__.TestGridGPRegression)
----------------------------------------------------------------------
Traceback (most recent call last):
File "test_1D_grid_gp_regression.p... | RuntimeError |
def forward(self, *args):
left_tensor = None
right_tensor = None
matrix_args = None
if self.has_left:
left_tensor, right_tensor, *matrix_args = args
else:
right_tensor, *matrix_args = args
orig_right_tensor = right_tensor
lazy_tsr = self.representation_tree(*matrix_args)
... | def forward(self, *args):
left_tensor = None
right_tensor = None
matrix_args = None
if self.has_left:
left_tensor, right_tensor, *matrix_args = args
else:
right_tensor, *matrix_args = args
orig_right_tensor = right_tensor
lazy_tsr = self.representation_tree(*matrix_args)
... | https://github.com/cornellius-gp/gpytorch/issues/501 | $ python test_1D_grid_gp_regression.py
E.
======================================================================
ERROR: test_grid_gp_mean_abs_error (__main__.TestGridGPRegression)
----------------------------------------------------------------------
Traceback (most recent call last):
File "test_1D_grid_gp_regression.p... | RuntimeError |
def __init__(
self,
representation_tree,
dtype,
device,
matrix_shape,
batch_shape=torch.Size(),
inv_quad=False,
logdet=False,
probe_vectors=None,
probe_vector_norms=None,
):
if not (inv_quad or logdet):
raise RuntimeError("Either inv_quad or logdet must be true (or bo... | def __init__(
self,
representation_tree,
dtype,
device,
matrix_shape,
batch_shape=torch.Size(),
inv_quad=False,
logdet=False,
preconditioner=None,
logdet_correction=None,
probe_vectors=None,
probe_vector_norms=None,
):
if not (inv_quad or logdet):
raise Runtim... | https://github.com/cornellius-gp/gpytorch/issues/501 | $ python test_1D_grid_gp_regression.py
E.
======================================================================
ERROR: test_grid_gp_mean_abs_error (__main__.TestGridGPRegression)
----------------------------------------------------------------------
Traceback (most recent call last):
File "test_1D_grid_gp_regression.p... | RuntimeError |
def forward(self, *args):
"""
*args - The arguments representing the PSD matrix A (or batch of PSD matrices A)
If self.inv_quad is true, the first entry in *args is inv_quad_rhs (Tensor)
- the RHS of the matrix solves.
Returns:
- (Scalar) The inverse quadratic form (or None, if self.inv_quad is... | def forward(self, *args):
"""
*args - The arguments representing the PSD matrix A (or batch of PSD matrices A)
If self.inv_quad is true, the first entry in *args is inv_quad_rhs (Tensor)
- the RHS of the matrix solves.
Returns:
- (Scalar) The inverse quadratic form (or None, if self.inv_quad is... | https://github.com/cornellius-gp/gpytorch/issues/501 | $ python test_1D_grid_gp_regression.py
E.
======================================================================
ERROR: test_grid_gp_mean_abs_error (__main__.TestGridGPRegression)
----------------------------------------------------------------------
Traceback (most recent call last):
File "test_1D_grid_gp_regression.p... | RuntimeError |
def inv_matmul(self, right_tensor, left_tensor=None):
"""
Computes a linear solve (w.r.t self = :math:`A`) with several right hand sides :math:`R`.
I.e. computes
... math::
\begin{equation}
A^{-1} R,
\end{equation}
where :math:`R` is :attr:`right_tensor` and :math:`A` ... | def inv_matmul(self, right_tensor, left_tensor=None):
"""
Computes a linear solve (w.r.t self = :math:`A`) with several right hand sides :math:`R`.
I.e. computes
... math::
\begin{equation}
A^{-1} R,
\end{equation}
where :math:`R` is :attr:`right_tensor` and :math:`A` ... | https://github.com/cornellius-gp/gpytorch/issues/501 | $ python test_1D_grid_gp_regression.py
E.
======================================================================
ERROR: test_grid_gp_mean_abs_error (__main__.TestGridGPRegression)
----------------------------------------------------------------------
Traceback (most recent call last):
File "test_1D_grid_gp_regression.p... | RuntimeError |
def inv_quad_logdet(self, inv_quad_rhs=None, logdet=False, reduce_inv_quad=True):
"""
Computes an inverse quadratic form (w.r.t self) with several right hand sides.
I.e. computes tr( tensor^T self^{-1} tensor )
In addition, computes an (approximate) log determinant of the the matrix
Args:
-... | def inv_quad_logdet(self, inv_quad_rhs=None, logdet=False, reduce_inv_quad=True):
"""
Computes an inverse quadratic form (w.r.t self) with several right hand sides.
I.e. computes tr( tensor^T self^{-1} tensor )
In addition, computes an (approximate) log determinant of the the matrix
Args:
-... | https://github.com/cornellius-gp/gpytorch/issues/501 | $ python test_1D_grid_gp_regression.py
E.
======================================================================
ERROR: test_grid_gp_mean_abs_error (__main__.TestGridGPRegression)
----------------------------------------------------------------------
Traceback (most recent call last):
File "test_1D_grid_gp_regression.p... | RuntimeError |
def __init__(
self,
active_dims=None,
batch_size=1,
period_length_prior=None,
eps=1e-6,
param_transform=softplus,
inv_param_transform=None,
**kwargs,
):
super(CosineKernel, self).__init__(
active_dims=active_dims,
param_transform=param_transform,
inv_param_tra... | def __init__(
self,
active_dims=None,
batch_size=1,
period_length_prior=None,
eps=1e-6,
param_transform=softplus,
inv_param_transform=None,
**kwargs,
):
period_length_prior = _deprecate_kwarg(
kwargs, "log_period_length_prior", "period_length_prior", period_length_prior
)... | https://github.com/cornellius-gp/gpytorch/issues/478 | import gpytorch
gl = gpytorch.likelihoods.GaussianLikelihood()
gl.initialize(noise=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../gpytorch/gpytorch/module.py", line 89, in initialize
setattr(self, name, val)
File "../lib/python3.6/site-packages/torch/nn/modules/module.py", line 579,... | TypeError |
def __init__(
self,
has_lengthscale=False,
ard_num_dims=None,
batch_size=1,
active_dims=None,
lengthscale_prior=None,
param_transform=softplus,
inv_param_transform=None,
eps=1e-6,
**kwargs,
):
super(Kernel, self).__init__()
if active_dims is not None and not torch.is_tens... | def __init__(
self,
has_lengthscale=False,
ard_num_dims=None,
batch_size=1,
active_dims=None,
lengthscale_prior=None,
param_transform=softplus,
inv_param_transform=None,
eps=1e-6,
**kwargs,
):
lengthscale_prior = _deprecate_kwarg(
kwargs, "log_lengthscale_prior", "len... | https://github.com/cornellius-gp/gpytorch/issues/478 | import gpytorch
gl = gpytorch.likelihoods.GaussianLikelihood()
gl.initialize(noise=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../gpytorch/gpytorch/module.py", line 89, in initialize
setattr(self, name, val)
File "../lib/python3.6/site-packages/torch/nn/modules/module.py", line 579,... | TypeError |
def __init__(
self,
nu=2.5,
ard_num_dims=None,
batch_size=1,
active_dims=None,
lengthscale_prior=None,
param_transform=softplus,
inv_param_transform=None,
eps=1e-6,
**kwargs,
):
if nu not in {0.5, 1.5, 2.5}:
raise RuntimeError("nu expected to be 0.5, 1.5, or 2.5")
... | def __init__(
self,
nu=2.5,
ard_num_dims=None,
batch_size=1,
active_dims=None,
lengthscale_prior=None,
param_transform=softplus,
inv_param_transform=None,
eps=1e-6,
**kwargs,
):
_deprecate_kwarg(
kwargs, "log_lengthscale_prior", "lengthscale_prior", lengthscale_prior
... | https://github.com/cornellius-gp/gpytorch/issues/478 | import gpytorch
gl = gpytorch.likelihoods.GaussianLikelihood()
gl.initialize(noise=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../gpytorch/gpytorch/module.py", line 89, in initialize
setattr(self, name, val)
File "../lib/python3.6/site-packages/torch/nn/modules/module.py", line 579,... | TypeError |
def __init__(
self,
active_dims=None,
batch_size=1,
lengthscale_prior=None,
period_length_prior=None,
param_transform=softplus,
inv_param_transform=None,
eps=1e-6,
**kwargs,
):
super(PeriodicKernel, self).__init__(
has_lengthscale=True,
active_dims=active_dims,
... | def __init__(
self,
active_dims=None,
batch_size=1,
lengthscale_prior=None,
period_length_prior=None,
param_transform=softplus,
inv_param_transform=None,
eps=1e-6,
**kwargs,
):
lengthscale_prior = _deprecate_kwarg(
kwargs, "log_lengthscale_prior", "lengthscale_prior", len... | https://github.com/cornellius-gp/gpytorch/issues/478 | import gpytorch
gl = gpytorch.likelihoods.GaussianLikelihood()
gl.initialize(noise=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../gpytorch/gpytorch/module.py", line 89, in initialize
setattr(self, name, val)
File "../lib/python3.6/site-packages/torch/nn/modules/module.py", line 579,... | TypeError |
def __init__(
self,
ard_num_dims=None,
batch_size=1,
active_dims=None,
lengthscale_prior=None,
param_transform=softplus,
inv_param_transform=None,
eps=1e-6,
**kwargs,
):
super(RBFKernel, self).__init__(
has_lengthscale=True,
ard_num_dims=ard_num_dims,
batc... | def __init__(
self,
ard_num_dims=None,
batch_size=1,
active_dims=None,
lengthscale_prior=None,
param_transform=softplus,
inv_param_transform=None,
eps=1e-6,
**kwargs,
):
_deprecate_kwarg(
kwargs, "log_lengthscale_prior", "lengthscale_prior", lengthscale_prior
)
su... | https://github.com/cornellius-gp/gpytorch/issues/478 | import gpytorch
gl = gpytorch.likelihoods.GaussianLikelihood()
gl.initialize(noise=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../gpytorch/gpytorch/module.py", line 89, in initialize
setattr(self, name, val)
File "../lib/python3.6/site-packages/torch/nn/modules/module.py", line 579,... | TypeError |
def __init__(
self,
base_kernel,
batch_size=1,
outputscale_prior=None,
param_transform=softplus,
inv_param_transform=None,
**kwargs,
):
super(ScaleKernel, self).__init__(has_lengthscale=False, batch_size=batch_size)
self.base_kernel = base_kernel
self._param_transform = param_tra... | def __init__(
self,
base_kernel,
batch_size=1,
outputscale_prior=None,
param_transform=softplus,
inv_param_transform=None,
**kwargs,
):
outputscale_prior = _deprecate_kwarg(
kwargs, "log_outputscale_prior", "outputscale_prior", outputscale_prior
)
super(ScaleKernel, self)... | https://github.com/cornellius-gp/gpytorch/issues/478 | import gpytorch
gl = gpytorch.likelihoods.GaussianLikelihood()
gl.initialize(noise=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../gpytorch/gpytorch/module.py", line 89, in initialize
setattr(self, name, val)
File "../lib/python3.6/site-packages/torch/nn/modules/module.py", line 579,... | TypeError |
def __init__(
self,
num_mixtures=None,
ard_num_dims=1,
batch_size=1,
active_dims=None,
eps=1e-6,
mixture_scales_prior=None,
mixture_means_prior=None,
mixture_weights_prior=None,
param_transform=softplus,
inv_param_transform=None,
**kwargs,
):
if num_mixtures is None:
... | def __init__(
self,
num_mixtures=None,
ard_num_dims=1,
batch_size=1,
active_dims=None,
eps=1e-6,
mixture_scales_prior=None,
mixture_means_prior=None,
mixture_weights_prior=None,
param_transform=softplus,
inv_param_transform=None,
**kwargs,
):
mixture_scales_prior = _d... | https://github.com/cornellius-gp/gpytorch/issues/478 | import gpytorch
gl = gpytorch.likelihoods.GaussianLikelihood()
gl.initialize(noise=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../gpytorch/gpytorch/module.py", line 89, in initialize
setattr(self, name, val)
File "../lib/python3.6/site-packages/torch/nn/modules/module.py", line 579,... | TypeError |
def __init__(
self,
noise_prior=None,
batch_size=1,
param_transform=softplus,
inv_param_transform=None,
**kwargs,
):
noise_covar = HomoskedasticNoise(
noise_prior=noise_prior,
batch_size=batch_size,
param_transform=param_transform,
inv_param_transform=inv_para... | def __init__(
self,
noise_prior=None,
batch_size=1,
param_transform=softplus,
inv_param_transform=None,
**kwargs,
):
noise_prior = _deprecate_kwarg(
kwargs, "log_noise_prior", "noise_prior", noise_prior
)
noise_covar = HomoskedasticNoise(
noise_prior=noise_prior,
... | https://github.com/cornellius-gp/gpytorch/issues/478 | import gpytorch
gl = gpytorch.likelihoods.GaussianLikelihood()
gl.initialize(noise=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../gpytorch/gpytorch/module.py", line 89, in initialize
setattr(self, name, val)
File "../lib/python3.6/site-packages/torch/nn/modules/module.py", line 579,... | TypeError |
def noise(self, value):
self.noise_covar.initialize(noise=value)
| def noise(self, value):
self.noise_covar.initialize(value)
| https://github.com/cornellius-gp/gpytorch/issues/478 | import gpytorch
gl = gpytorch.likelihoods.GaussianLikelihood()
gl.initialize(noise=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../gpytorch/gpytorch/module.py", line 89, in initialize
setattr(self, name, val)
File "../lib/python3.6/site-packages/torch/nn/modules/module.py", line 579,... | TypeError |
def __init__(
self,
num_tasks,
rank=0,
task_correlation_prior=None,
batch_size=1,
noise_prior=None,
param_transform=softplus,
inv_param_transform=None,
**kwargs,
):
"""
Args:
num_tasks (int): Number of tasks.
rank (int): The rank of the task noise covariance ... | def __init__(
self,
num_tasks,
rank=0,
task_correlation_prior=None,
batch_size=1,
noise_prior=None,
param_transform=softplus,
inv_param_transform=None,
**kwargs,
):
"""
Args:
num_tasks (int): Number of tasks.
rank (int): The rank of the task noise covariance ... | https://github.com/cornellius-gp/gpytorch/issues/478 | import gpytorch
gl = gpytorch.likelihoods.GaussianLikelihood()
gl.initialize(noise=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../gpytorch/gpytorch/module.py", line 89, in initialize
setattr(self, name, val)
File "../lib/python3.6/site-packages/torch/nn/modules/module.py", line 579,... | TypeError |
def __init__(
self,
num_tasks,
rank=0,
task_prior=None,
batch_size=1,
noise_prior=None,
param_transform=softplus,
inv_param_transform=None,
**kwargs,
):
"""
Args:
num_tasks (int): Number of tasks.
rank (int): The rank of the task noise covariance matrix to fi... | def __init__(
self,
num_tasks,
rank=0,
task_prior=None,
batch_size=1,
noise_prior=None,
param_transform=softplus,
inv_param_transform=None,
**kwargs,
):
"""
Args:
num_tasks (int): Number of tasks.
rank (int): The rank of the task noise covariance matrix to fi... | https://github.com/cornellius-gp/gpytorch/issues/478 | import gpytorch
gl = gpytorch.likelihoods.GaussianLikelihood()
gl.initialize(noise=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../gpytorch/gpytorch/module.py", line 89, in initialize
setattr(self, name, val)
File "../lib/python3.6/site-packages/torch/nn/modules/module.py", line 579,... | TypeError |
def initialize(self, **kwargs):
"""
Set a value for a parameter
kwargs: (param_name, value) - parameter to initialize
Value can take the form of a tensor, a float, or an int
"""
for name, val in kwargs.items():
if isinstance(val, int):
val = float(val)
if not hasatt... | def initialize(self, **kwargs):
# TODO: Change to initialize actual parameter (e.g. lengthscale) rather than untransformed parameter.
"""
Set a value for a parameter
kwargs: (param_name, value) - parameter to initialize
Value can take the form of a tensor, a float, or an int
"""
from .utils... | https://github.com/cornellius-gp/gpytorch/issues/478 | import gpytorch
gl = gpytorch.likelihoods.GaussianLikelihood()
gl.initialize(noise=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../gpytorch/gpytorch/module.py", line 89, in initialize
setattr(self, name, val)
File "../lib/python3.6/site-packages/torch/nn/modules/module.py", line 579,... | TypeError |
def __getattr__(self, name):
try:
return super().__getattr__(name)
except AttributeError as e:
try:
return super().__getattribute__(name)
except AttributeError:
raise e
| def __getattr__(self, name):
try:
return super().__getattr__(name)
except AttributeError as e:
from .utils.log_deprecation import LOG_DEPRECATION_MSG, MODULES_WITH_LOG_PARAMS
if (
any(isinstance(self, mod_type) for mod_type in MODULES_WITH_LOG_PARAMS)
and "log_" ... | https://github.com/cornellius-gp/gpytorch/issues/478 | import gpytorch
gl = gpytorch.likelihoods.GaussianLikelihood()
gl.initialize(noise=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../gpytorch/gpytorch/module.py", line 89, in initialize
setattr(self, name, val)
File "../lib/python3.6/site-packages/torch/nn/modules/module.py", line 579,... | TypeError |
def interpolate(self, x_grid, x_target, interp_points=range(-2, 2)):
# Do some boundary checking
grid_mins = x_grid.min(0)[0]
grid_maxs = x_grid.max(0)[0]
x_target_min = x_target.min(0)[0]
x_target_max = x_target.min(0)[0]
lt_min_mask = (x_target_min - grid_mins).lt(-1e-7)
gt_max_mask = (x_t... | def interpolate(self, x_grid, x_target, interp_points=range(-2, 2)):
# Do some boundary checking
grid_mins = x_grid.min(0)[0]
grid_maxs = x_grid.max(0)[0]
x_target_min = x_target.min(0)[0]
x_target_max = x_target.min(0)[0]
lt_min_mask = (x_target_min - grid_mins).lt(-1e-7)
gt_max_mask = (x_t... | https://github.com/cornellius-gp/gpytorch/issues/250 | ---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-14-dd2c9e04445d> in <module>()
1 # Does not work
2 test_x = torch.FloatTensor([-0.1]).unsqueeze(1)
----> 3 model(test_x)
~/anaconda/envs/py3/lib/python3... | IndexError |
def __init__(
self,
has_lengthscale=False,
ard_num_dims=None,
batch_size=1,
active_dims=None,
log_lengthscale_bounds=None,
log_lengthscale_prior=None,
eps=1e-6,
):
super(Kernel, self).__init__()
if active_dims is not None and not torch.is_tensor(active_dims):
active_dims ... | def __init__(
self,
has_lengthscale=False,
ard_num_dims=None,
log_lengthscale_prior=None,
active_dims=None,
batch_size=1,
log_lengthscale_bounds=None,
):
"""
The base Kernel class handles both lengthscales and ARD.
Args:
has_lengthscale (bool): If True, we will register ... | https://github.com/cornellius-gp/gpytorch/issues/249 | ---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-144-12b763efd692> in <module>()
19 output = model(train_x)
20 # TODO: Fix this view call!!
---> 21 loss = -mll(output, train_y)
22 loss.b... | RuntimeError |
def lengthscale(self):
if self.has_lengthscale:
return self.log_lengthscale.exp().clamp(self.eps, 1e5)
else:
return None
| def lengthscale(self):
if "log_lengthscale" in self.named_parameters().keys():
return self.log_lengthscale.exp()
else:
return None
| https://github.com/cornellius-gp/gpytorch/issues/249 | ---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-144-12b763efd692> in <module>()
19 output = model(train_x)
20 # TODO: Fix this view call!!
---> 21 loss = -mll(output, train_y)
22 loss.b... | RuntimeError |
def forward(self, x1, x2, **params):
"""
Computes the covariance between x1 and x2.
This method should be imlemented by all Kernel subclasses.
.. note::
All non-compositional kernels should use the :meth:`gpytorch.kernels.Kernel._create_input_grid`
method to create a meshgrid between x... | def forward(self, x1, x2, **params):
raise NotImplementedError()
| https://github.com/cornellius-gp/gpytorch/issues/249 | ---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-144-12b763efd692> in <module>()
19 output = model(train_x)
20 # TODO: Fix this view call!!
---> 21 loss = -mll(output, train_y)
22 loss.b... | RuntimeError |
def __call__(self, x1, x2=None, **params):
x1_, x2_ = x1, x2
# Select the active dimensions
if self.active_dims is not None:
x1_ = x1_.index_select(-1, self.active_dims)
if x2_ is not None:
x2_ = x2_.index_select(-1, self.active_dims)
# Give x1_ and x2_ a last dimension, if... | def __call__(self, x1_, x2_=None, **params):
x1, x2 = x1_, x2_
if self.active_dims is not None:
x1 = x1_.index_select(-1, self.active_dims)
if x2_ is not None:
x2 = x2_.index_select(-1, self.active_dims)
if x2 is None:
x2 = x1
# Give x1 and x2 a last dimension, if ... | https://github.com/cornellius-gp/gpytorch/issues/249 | ---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-144-12b763efd692> in <module>()
19 output = model(train_x)
20 # TODO: Fix this view call!!
---> 21 loss = -mll(output, train_y)
22 loss.b... | RuntimeError |
def __init__(
self,
num_dimensions,
variance_prior=None,
offset_prior=None,
active_dims=None,
variance_bounds=None,
offset_bounds=None,
):
super(LinearKernel, self).__init__(active_dims=active_dims)
variance_prior = _bounds_to_prior(
prior=variance_prior, bounds=variance_boun... | def __init__(
self,
num_dimensions,
variance_prior=None,
offset_prior=None,
active_dims=None,
variance_bounds=None,
offset_bounds=None,
):
"""
Args:
num_dimensions (int): Number of data dimensions to expect. This is necessary to create the offset parameter.
variance_p... | https://github.com/cornellius-gp/gpytorch/issues/249 | ---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-144-12b763efd692> in <module>()
19 output = model(train_x)
20 # TODO: Fix this view call!!
---> 21 loss = -mll(output, train_y)
22 loss.b... | RuntimeError |
def __init__(
self,
nu=2.5,
ard_num_dims=None,
batch_size=1,
active_dims=None,
eps=1e-6,
log_lengthscale_prior=None,
log_lengthscale_bounds=None,
):
if nu not in {0.5, 1.5, 2.5}:
raise RuntimeError("nu expected to be 0.5, 1.5, or 2.5")
super(MaternKernel, self).__init__(
... | def __init__(
self,
nu=2.5,
ard_num_dims=None,
log_lengthscale_prior=None,
active_dims=None,
eps=1e-8,
batch_size=1,
log_lengthscale_bounds=None,
):
if nu not in {0.5, 1.5, 2.5}:
raise RuntimeError("nu expected to be 0.5, 1.5, or 2.5")
super(MaternKernel, self).__init__(
... | https://github.com/cornellius-gp/gpytorch/issues/249 | ---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-144-12b763efd692> in <module>()
19 output = model(train_x)
20 # TODO: Fix this view call!!
---> 21 loss = -mll(output, train_y)
22 loss.b... | RuntimeError |
def forward_diag(self, x1, x2):
mean = x1.mean(1, keepdim=True).mean(0, keepdim=True)
x1_normed = x1 - mean.unsqueeze(0).unsqueeze(1)
x2_normed = x2 - mean.unsqueeze(0).unsqueeze(1)
diff = x1_normed - x2_normed
distance_over_rho = diff.pow_(2).sum(-1).sqrt()
exp_component = torch.exp(-math.sqrt... | def forward_diag(self, x1, x2):
lengthscale = self.log_lengthscale.exp()
mean = x1.mean(1).mean(0)
x1_normed = (x1 - mean.unsqueeze(0).unsqueeze(1)).div(lengthscale)
x2_normed = (x2 - mean.unsqueeze(0).unsqueeze(1)).div(lengthscale)
diff = x1_normed - x2_normed
distance_over_rho = diff.pow_(2).... | https://github.com/cornellius-gp/gpytorch/issues/249 | ---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-144-12b763efd692> in <module>()
19 output = model(train_x)
20 # TODO: Fix this view call!!
---> 21 loss = -mll(output, train_y)
22 loss.b... | RuntimeError |
def forward(self, x1, x2):
mean = x1.view(-1, 1, *list(x1.size())[2:]).mean(0, keepdim=True)
x1_, x2_ = self._create_input_grid(x1 - mean, x2 - mean)
x1_ = x1_.div(self.lengthscale)
x2_ = x2_.div(self.lengthscale)
distance = (x1_ - x2_).norm(2, dim=-1)
exp_component = torch.exp(-math.sqrt(self.... | def forward(self, x1, x2):
lengthscale = self.log_lengthscale.exp()
mean = x1.mean(1).mean(0)
x1_normed = (x1 - mean.unsqueeze(0).unsqueeze(1)).div(lengthscale)
x2_normed = (x2 - mean.unsqueeze(0).unsqueeze(1)).div(lengthscale)
x1_squared = x1_normed.norm(2, -1).pow(2)
x2_squared = x2_normed.no... | https://github.com/cornellius-gp/gpytorch/issues/249 | ---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-144-12b763efd692> in <module>()
19 output = model(train_x)
20 # TODO: Fix this view call!!
---> 21 loss = -mll(output, train_y)
22 loss.b... | RuntimeError |
def forward(self, x1, x2):
covar_i = self.task_covar_module.covar_matrix
covar_x = self.data_covar_module(x1, x2)
if covar_x.size(0) == 1:
covar_x = covar_x[0]
if not isinstance(covar_x, LazyTensor):
covar_x = NonLazyTensor(covar_x)
res = KroneckerProductLazyTensor(covar_i, covar_x)
... | def forward(self, x1, x2):
covar_i = self.task_covar_module.covar_matrix
covar_x = self.data_covar_module.forward(x1, x2)
if covar_x.size(0) == 1:
covar_x = covar_x[0]
if not isinstance(covar_x, LazyTensor):
covar_x = NonLazyTensor(covar_x)
res = KroneckerProductLazyTensor(covar_i, c... | https://github.com/cornellius-gp/gpytorch/issues/249 | ---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-144-12b763efd692> in <module>()
19 output = model(train_x)
20 # TODO: Fix this view call!!
---> 21 loss = -mll(output, train_y)
22 loss.b... | RuntimeError |
def __init__(
self,
active_dims=None,
batch_size=1,
eps=1e-6,
log_lengthscale_prior=None,
log_period_length_prior=None,
log_lengthscale_bounds=None,
log_period_length_bounds=None,
):
log_period_length_prior = _bounds_to_prior(
prior=log_period_length_prior, bounds=log_period_... | def __init__(
self,
log_lengthscale_prior=None,
log_period_length_prior=None,
eps=1e-5,
active_dims=None,
log_lengthscale_bounds=None,
log_period_length_bounds=None,
):
log_period_length_prior = _bounds_to_prior(
prior=log_period_length_prior, bounds=log_period_length_bounds
... | https://github.com/cornellius-gp/gpytorch/issues/249 | ---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-144-12b763efd692> in <module>()
19 output = model(train_x)
20 # TODO: Fix this view call!!
---> 21 loss = -mll(output, train_y)
22 loss.b... | RuntimeError |
def forward(self, x1, x2):
x1_, x2_ = self._create_input_grid(x1, x2)
x1_ = x1_.div(self.period_length)
x2_ = x2_.div(self.period_length)
diff = torch.sum((x1_ - x2_).abs(), -1)
res = torch.sin(diff.mul(math.pi)).pow(2).mul(-2 / self.lengthscale).exp_()
return res
| def forward(self, x1, x2):
lengthscale = (self.log_lengthscale.exp() + self.eps).sqrt_()
period_length = (self.log_period_length.exp() + self.eps).sqrt_()
diff = torch.sum((x1.unsqueeze(2) - x2.unsqueeze(1)).abs(), -1)
res = -2 * torch.sin(math.pi * diff / period_length).pow(2) / lengthscale
return ... | https://github.com/cornellius-gp/gpytorch/issues/249 | ---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-144-12b763efd692> in <module>()
19 output = model(train_x)
20 # TODO: Fix this view call!!
---> 21 loss = -mll(output, train_y)
22 loss.b... | RuntimeError |
def forward(self, x1, x2):
x1_, x2_ = self._create_input_grid(x1, x2)
x1_ = x1_.div(self.lengthscale)
x2_ = x2_.div(self.lengthscale)
diff = (x1_ - x2_).norm(2, dim=-1)
return diff.pow(2).div_(-2).exp_()
| def forward(self, x1, x2):
lengthscales = self.log_lengthscale.exp().mul(math.sqrt(2)).clamp(self.eps, 1e5)
diff = (x1.unsqueeze(2) - x2.unsqueeze(1)).div_(lengthscales.unsqueeze(1))
return diff.pow_(2).sum(-1).mul_(-1).exp_()
| https://github.com/cornellius-gp/gpytorch/issues/249 | ---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-144-12b763efd692> in <module>()
19 output = model(train_x)
20 # TODO: Fix this view call!!
---> 21 loss = -mll(output, train_y)
22 loss.b... | RuntimeError |
def __init__(
self,
num_mixtures=None,
ard_num_dims=1,
batch_size=1,
active_dims=None,
eps=1e-6,
log_mixture_scales_prior=None,
log_mixture_means_prior=None,
log_mixture_weights_prior=None,
n_mixtures=None,
n_dims=None,
):
if n_mixtures is not None:
warnings.warn(... | def __init__(
self,
n_mixtures,
n_dims=1,
log_mixture_weight_prior=None,
log_mixture_mean_prior=None,
log_mixture_scale_prior=None,
active_dims=None,
):
self.n_mixtures = n_mixtures
self.n_dims = n_dims
if (
log_mixture_mean_prior is not None
or log_mixture_scale_... | https://github.com/cornellius-gp/gpytorch/issues/249 | ---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-144-12b763efd692> in <module>()
19 output = model(train_x)
20 # TODO: Fix this view call!!
---> 21 loss = -mll(output, train_y)
22 loss.b... | RuntimeError |
def initialize_from_data(self, train_x, train_y, **kwargs):
if not torch.is_tensor(train_x) or not torch.is_tensor(train_y):
raise RuntimeError("train_x and train_y should be tensors")
if train_x.ndimension() == 1:
train_x = train_x.unsqueeze(-1)
if train_x.ndimension() == 2:
train_x... | def initialize_from_data(self, train_x, train_y, **kwargs):
if not torch.is_tensor(train_x) or not torch.is_tensor(train_y):
raise RuntimeError("train_x and train_y should be tensors")
if train_x.ndimension() == 1:
train_x = train_x.unsqueeze(-1)
if train_x.ndimension() == 2:
train_x... | https://github.com/cornellius-gp/gpytorch/issues/249 | ---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-144-12b763efd692> in <module>()
19 output = model(train_x)
20 # TODO: Fix this view call!!
---> 21 loss = -mll(output, train_y)
22 loss.b... | RuntimeError |
def forward(self, x1, x2):
batch_size, n, num_dims = x1.size()
_, m, _ = x2.size()
if not num_dims == self.ard_num_dims:
raise RuntimeError(
"The SpectralMixtureKernel expected the input to have {} dimensionality "
"(based on the ard_num_dims argument). Got {}.".format(
... | def forward(self, x1, x2):
batch_size, n, n_dims = x1.size()
_, m, _ = x2.size()
if not n_dims == self.n_dims:
raise RuntimeError("The number of dimensions doesn't match what was supplied!")
mixture_weights = self.log_mixture_weights.view(self.n_mixtures, 1, 1, 1).exp()
mixture_means = self... | https://github.com/cornellius-gp/gpytorch/issues/249 | ---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-144-12b763efd692> in <module>()
19 output = model(train_x)
20 # TODO: Fix this view call!!
---> 21 loss = -mll(output, train_y)
22 loss.b... | RuntimeError |
def __call__(self, *args, **kwargs):
train_inputs = tuple(Variable(train_input) for train_input in self.train_inputs)
# Training mode: optimizing
if self.training:
if not all(
[
torch.equal(train_input, input)
for train_input, input in zip(train_inputs, a... | def __call__(self, *args, **kwargs):
train_inputs = tuple(Variable(train_input) for train_input in self.train_inputs)
# Training mode: optimizing
if self.training:
if not all(
[
torch.equal(train_input, input)
for train_input, input in zip(train_inputs, a... | https://github.com/cornellius-gp/gpytorch/issues/67 | test_x = Variable(torch.rand(10), requires_grad=True)
output = model(test_x)
# this works just fine
sum_of_means = output.mean().sum()
sum_of_means.backward()
test_x.grad
Variable containing:
3.4206
-3.2818
1.8668
3.5644
-0.7677
0.7666
6.4394
5.1365
-5.0451
6.0161
[torch.FloatTensor of size 10]
# this fails with said... | RuntimeError |
def handle(self, *args, **options):
self.style = color_style()
self.options = options
if options["requirements"]:
req_files = options["requirements"]
elif os.path.exists("requirements.txt"):
req_files = ["requirements.txt"]
elif os.path.exists("requirements"):
req_files = [
... | def handle(self, *args, **options):
self.style = color_style()
self.options = options
if options["requirements"]:
req_files = options["requirements"]
elif os.path.exists("requirements.txt"):
req_files = ["requirements.txt"]
elif os.path.exists("requirements"):
req_files = [
... | https://github.com/django-extensions/django-extensions/issues/1265 | Package Version
----------------------------- ----------
django-extensions 2.1.3
$ cat r.txt
git+https://github.com/jmrivas86/django-json-widget
$ venv/bin/python -B manage.py pipchecker -r r.txt
Traceback (most recent call last):
File "manage.py", line 22, in <module>
execute_from_c... | TypeError |
def check_pypi(self):
"""
If the requirement is frozen to pypi, check for a new version.
"""
for dist in get_installed_distributions():
name = dist.project_name
if name in self.reqs.keys():
self.reqs[name]["dist"] = dist
pypi = ServerProxy("https://pypi.python.org/pypi")... | def check_pypi(self):
"""
If the requirement is frozen to pypi, check for a new version.
"""
for dist in get_installed_distributions():
name = dist.project_name
if name in self.reqs.keys():
self.reqs[name]["dist"] = dist
pypi = ServerProxy("https://pypi.python.org/pypi")... | https://github.com/django-extensions/django-extensions/issues/1265 | Package Version
----------------------------- ----------
django-extensions 2.1.3
$ cat r.txt
git+https://github.com/jmrivas86/django-json-widget
$ venv/bin/python -B manage.py pipchecker -r r.txt
Traceback (most recent call last):
File "manage.py", line 22, in <module>
execute_from_c... | TypeError |
def check_github(self):
"""
If the requirement is frozen to a github url, check for new commits.
API Tokens
----------
For more than 50 github api calls per hour, pipchecker requires
authentication with the github api by settings the environemnt
variable ``GITHUB_API_TOKEN`` or setting the ... | def check_github(self):
"""
If the requirement is frozen to a github url, check for new commits.
API Tokens
----------
For more than 50 github api calls per hour, pipchecker requires
authentication with the github api by settings the environemnt
variable ``GITHUB_API_TOKEN`` or setting the ... | https://github.com/django-extensions/django-extensions/issues/1265 | Package Version
----------------------------- ----------
django-extensions 2.1.3
$ cat r.txt
git+https://github.com/jmrivas86/django-json-widget
$ venv/bin/python -B manage.py pipchecker -r r.txt
Traceback (most recent call last):
File "manage.py", line 22, in <module>
execute_from_c... | TypeError |
def check_other(self):
"""
If the requirement is frozen somewhere other than pypi or github, skip.
If you have a private pypi or use --extra-index-url, consider contributing
support here.
"""
if self.reqs:
self.stdout.write(
self.style.ERROR("\nOnly pypi and github based req... | def check_other(self):
"""
If the requirement is frozen somewhere other than pypi or github, skip.
If you have a private pypi or use --extra-index-url, consider contributing
support here.
"""
if self.reqs:
print(
self.style.ERROR("\nOnly pypi and github based requirements ar... | https://github.com/django-extensions/django-extensions/issues/1265 | Package Version
----------------------------- ----------
django-extensions 2.1.3
$ cat r.txt
git+https://github.com/jmrivas86/django-json-widget
$ venv/bin/python -B manage.py pipchecker -r r.txt
Traceback (most recent call last):
File "manage.py", line 22, in <module>
execute_from_c... | TypeError |
def create_app(config):
mode = config.MODE
if mode & App.GuiMode:
from PyQt5.QtGui import QIcon, QPixmap
from PyQt5.QtWidgets import QApplication, QWidget
from feeluown.compat import QEventLoop
q_app = QApplication(sys.argv)
q_app.setQuitOnLastWindowClosed(True)
... | def create_app(config):
mode = config.MODE
if mode & App.GuiMode:
from quamash import QEventLoop
from PyQt5.QtGui import QIcon, QPixmap
from PyQt5.QtWidgets import QApplication, QWidget
q_app = QApplication(sys.argv)
q_app.setQuitOnLastWindowClosed(True)
q_app.s... | https://github.com/feeluown/FeelUOwn/issues/346 | [2020-02-15 23:44:20,386 ERROR __init__] : Task exception was never retrieved
future: <Task finished name='Task-22' coro=<fetch_album_cover_wrapper.<locals>.fetch_album_cover() done, defined at /usr/lib/python3.8/site-packages/feeluown/containers/table.py:28> exception=RuntimeError('no running event loop')>
Traceback (... | RuntimeError |
def play_mv_by_mvid(cls, mvid):
mv_model = ControllerApi.api.get_mv_detail(mvid)
if not ControllerApi.api.is_response_ok(mv_model):
return
url_high = mv_model["url_high"]
clipboard = QApplication.clipboard()
clipboard.setText(url_high)
cls.view.ui.STATUS_BAR.showMessage("程序已经将视频的播放地址复制... | def play_mv_by_mvid(cls, mvid):
mv_model = ControllerApi.api.get_mv_detail(mvid)
if not ControllerApi.api.is_response_ok(mv_model):
return
url_high = mv_model["url_high"]
clipboard = QApplication.clipboard()
clipboard.setText(url_high)
if platform.system() == "Linux":
Controlle... | https://github.com/feeluown/FeelUOwn/issues/80 | Traceback (most recent call last):
File "../feeluown/controller_api.py", line 46, in play_mv_by_mvid
subprocess.Popen(['vlc', url_high, '--play-and-exit', '-f'])
File "/usr/lib/python3.5/subprocess.py", line 950, in __init__
restore_signals, start_new_session)
File "/usr/lib/python3.5/subprocess.py", line 1544, in _exe... | FileNotFoundError |
def check_pids(curmir_incs):
"""Check PIDs in curmir markers to make sure rdiff-backup not running"""
pid_re = re.compile(r"^PID\s*([0-9]+)", re.I | re.M)
def extract_pid(curmir_rp):
"""Return process ID from a current mirror marker, if any"""
match = pid_re.search(curmir_rp.get_string())
... | def check_pids(curmir_incs):
"""Check PIDs in curmir markers to make sure rdiff-backup not running"""
pid_re = re.compile(r"^PID\s*([0-9]+)", re.I | re.M)
def extract_pid(curmir_rp):
"""Return process ID from a current mirror marker, if any"""
match = pid_re.search(curmir_rp.get_string())
... | https://github.com/rdiff-backup/rdiff-backup/issues/453 | vagrant@WIN-4SPEID5E7R8 C:\Users\vagrant\Develop\rdiff-backup>rmdir /s /q \temp\bla
vagrant@WIN-4SPEID5E7R8 C:\Users\vagrant\Develop\rdiff-backup>rdiff-backup ..\rdiff-backup_testfiles\stattest1 \temp\bla
vagrant@WIN-4SPEID5E7R8 C:\Users\vagrant\Develop\rdiff-backup>rdiff-backup ..\rdiff-backup_testfiles\stattest2 \t... | MemoryError |
def pid_running(pid):
"""Return True if we know if process with pid is currently running,
False if it isn't running, and None if we don't know for sure."""
if os.name == "nt":
import win32api
import win32con
import pywintypes
process = None
try:
process =... | def pid_running(pid):
"""Return True if we know if process with pid is currently running,
False if it isn't running, and None if we don't know for sure."""
if os.name == "nt":
import win32api
import win32con
import pywintypes
process = None
try:
process =... | https://github.com/rdiff-backup/rdiff-backup/issues/453 | vagrant@WIN-4SPEID5E7R8 C:\Users\vagrant\Develop\rdiff-backup>rmdir /s /q \temp\bla
vagrant@WIN-4SPEID5E7R8 C:\Users\vagrant\Develop\rdiff-backup>rdiff-backup ..\rdiff-backup_testfiles\stattest1 \temp\bla
vagrant@WIN-4SPEID5E7R8 C:\Users\vagrant\Develop\rdiff-backup>rdiff-backup ..\rdiff-backup_testfiles\stattest2 \t... | MemoryError |
def set_case_sensitive_readwrite(self, subdir):
"""Determine if directory at rp is case sensitive by writing"""
assert not self.read_only
upper_a = subdir.append("A")
upper_a.touch()
lower_a = subdir.append("a")
if lower_a.lstat():
lower_a.delete()
upper_a.setdata()
if up... | def set_case_sensitive_readwrite(self, subdir):
"""Determine if directory at rp is case sensitive by writing"""
assert not self.read_only
upper_a = subdir.append("A")
upper_a.touch()
lower_a = subdir.append("a")
if lower_a.lstat():
lower_a.delete()
upper_a.setdata()
asser... | https://github.com/rdiff-backup/rdiff-backup/issues/38 | Message: Found interrupted initial backup. Removing...
Exception '' raised of class '<type 'exceptions.AssertionError'>':
File "/usr/local/lib/python2.7/dist-packages/rdiff_backup/Main.py", line 306, in error_check_Main
try: Main(arglist)
File "/usr/local/lib/python2.7/dist-packages/rdiff_backup/Main.py", line 326, in ... | exceptions.AssertionError |
def log_to_file(self, message):
"""Write the message to the log file, if possible"""
if self.log_file_open:
if self.log_file_local:
tmpstr = self.format(message, self.verbosity)
self.logfp.write(_to_bytes(tmpstr))
self.logfp.flush()
else:
self.log_... | def log_to_file(self, message):
"""Write the message to the log file, if possible"""
if self.log_file_open:
if self.log_file_local:
tmpstr = self.format(message, self.verbosity)
if type(tmpstr) == str: # transform string in bytes
tmpstr = tmpstr.encode("utf-8", "... | https://github.com/rdiff-backup/rdiff-backup/issues/380 | UpdateError: 'data/some/sub/dir/changed-file/rdiff-backup.tmp.266' does not match source
Exception 'a bytes-like object is required, not 'str'' raised of class '<class 'TypeError'>':
File "/usr/lib64/python3.6/site-packages/rdiff_backup/Main.py", line 390, in error_check_Main
Main(arglist)
File "/usr/lib64/python3.6/si... | TypeError |
def log_to_term(self, message, verbosity):
"""Write message to stdout/stderr"""
if verbosity <= 2 or Globals.server:
termfp = sys.stderr.buffer
else:
termfp = sys.stdout.buffer
tmpstr = self.format(message, self.term_verbosity)
termfp.write(_to_bytes(tmpstr, encoding=sys.stdout.encod... | def log_to_term(self, message, verbosity):
"""Write message to stdout/stderr"""
if verbosity <= 2 or Globals.server:
termfp = sys.stderr.buffer
else:
termfp = sys.stdout.buffer
tmpstr = self.format(message, self.term_verbosity)
if type(tmpstr) == str: # transform string in bytes
... | https://github.com/rdiff-backup/rdiff-backup/issues/380 | UpdateError: 'data/some/sub/dir/changed-file/rdiff-backup.tmp.266' does not match source
Exception 'a bytes-like object is required, not 'str'' raised of class '<class 'TypeError'>':
File "/usr/lib64/python3.6/site-packages/rdiff_backup/Main.py", line 390, in error_check_Main
Main(arglist)
File "/usr/lib64/python3.6/si... | TypeError |
def open(cls, time_string, compress=1):
"""Open the error log, prepare for writing"""
if not Globals.isbackup_writer:
return Globals.backup_writer.log.ErrorLog.open(time_string, compress)
assert not cls._log_fileobj, "log already open"
assert Globals.isbackup_writer
base_rp = Globals.rbdir.... | def open(cls, time_string, compress=1):
"""Open the error log, prepare for writing"""
if not Globals.isbackup_writer:
return Globals.backup_writer.log.ErrorLog.open(time_string, compress)
assert not cls._log_fileobj, "log already open"
assert Globals.isbackup_writer
base_rp = Globals.rbdir.... | https://github.com/rdiff-backup/rdiff-backup/issues/380 | UpdateError: 'data/some/sub/dir/changed-file/rdiff-backup.tmp.266' does not match source
Exception 'a bytes-like object is required, not 'str'' raised of class '<class 'TypeError'>':
File "/usr/lib64/python3.6/site-packages/rdiff_backup/Main.py", line 390, in error_check_Main
Main(arglist)
File "/usr/lib64/python3.6/si... | TypeError |
def write(cls, error_type, rp, exc):
"""Add line to log file indicating error exc with file rp"""
if not Globals.isbackup_writer:
return Globals.backup_writer.log.ErrorLog.write(error_type, rp, exc)
logstr = cls.get_log_string(error_type, rp, exc)
Log(logstr, 2)
if Globals.null_separator:
... | def write(cls, error_type, rp, exc):
"""Add line to log file indicating error exc with file rp"""
if not Globals.isbackup_writer:
return Globals.backup_writer.log.ErrorLog.write(error_type, rp, exc)
logstr = cls.get_log_string(error_type, rp, exc)
Log(logstr, 2)
if isinstance(logstr, bytes):... | https://github.com/rdiff-backup/rdiff-backup/issues/380 | UpdateError: 'data/some/sub/dir/changed-file/rdiff-backup.tmp.266' does not match source
Exception 'a bytes-like object is required, not 'str'' raised of class '<class 'TypeError'>':
File "/usr/lib64/python3.6/site-packages/rdiff_backup/Main.py", line 390, in error_check_Main
Main(arglist)
File "/usr/lib64/python3.6/si... | TypeError |
def parse_file_desc(file_desc):
"""Parse file description returning pair (host_info, filename)
In other words, bescoto@folly.stanford.edu::/usr/bin/ls =>
("bescoto@folly.stanford.edu", "/usr/bin/ls"). The
complication is to allow for quoting of : by a \\. If the
string is not separated by :, then... | def parse_file_desc(file_desc):
"""Parse file description returning pair (host_info, filename)
In other words, bescoto@folly.stanford.edu::/usr/bin/ls =>
("bescoto@folly.stanford.edu", "/usr/bin/ls"). The
complication is to allow for quoting of : by a \\. If the
string is not separated by :, then... | https://github.com/rdiff-backup/rdiff-backup/issues/395 | rdiff-backup.exe a\ b\
Exception 'Unexpected end to file description b'a\\'' raised of class '<class 'rdiff_backup.SetConnections.SetConnectionsException'>':
File "rdiff_backup\Main.py", line 393, in error_check_Main
File "rdiff_backup\Main.py", line 410, in Main
File "rdiff_backup\SetConnections.py", line 66, in get_c... | rdiff_backup.SetConnections.SetConnectionsException |
def RORP2Record(rorpath):
"""From RORPath, return text record of file's metadata"""
str_list = [b"File %s\n" % quote_path(rorpath.get_indexpath())]
# Store file type, e.g. "dev", "reg", or "sym", and type-specific data
type = rorpath.gettype()
if type is None:
type = "None"
str_list.app... | def RORP2Record(rorpath):
"""From RORPath, return text record of file's metadata"""
str_list = [b"File %s\n" % quote_path(rorpath.get_indexpath())]
# Store file type, e.g. "dev", "reg", or "sym", and type-specific data
type = rorpath.gettype()
if type is None:
type = "None"
str_list.app... | https://github.com/rdiff-backup/rdiff-backup/issues/401 | # rdiff-backup /dev/ /tmp/faketarget/
Exception '[Errno 2] No such file or directory: b'/tmp/faketarget/bus/usb/003/rdiff-backup.tmp.22'' raised of class '<class 'FileNotFoundError'>':
File "/usr/lib/python3/dist-packages/rdiff_backup/Main.py", line 390, in error_check_Main
Main(arglist)
File "/usr/lib/python3/dist-pac... | FileNotFoundError |
def copy(rpin, rpout, compress=0):
"""Copy RPath rpin to rpout. Works for symlinks, dirs, etc.
Returns close value of input for regular file, which can be used
to pass hashes on.
"""
log.Log("Regular copying %s to %s" % (rpin.index, rpout.get_safepath()), 6)
if not rpin.lstat():
if rp... | def copy(rpin, rpout, compress=0):
"""Copy RPath rpin to rpout. Works for symlinks, dirs, etc.
Returns close value of input for regular file, which can be used
to pass hashes on.
"""
log.Log("Regular copying %s to %s" % (rpin.index, rpout.get_safepath()), 6)
if not rpin.lstat():
if rp... | https://github.com/rdiff-backup/rdiff-backup/issues/401 | # rdiff-backup /dev/ /tmp/faketarget/
Exception '[Errno 2] No such file or directory: b'/tmp/faketarget/bus/usb/003/rdiff-backup.tmp.22'' raised of class '<class 'FileNotFoundError'>':
File "/usr/lib/python3/dist-packages/rdiff_backup/Main.py", line 390, in error_check_Main
Main(arglist)
File "/usr/lib/python3/dist-pac... | FileNotFoundError |
def cmp(rpin, rpout):
"""True if rpin has the same data as rpout
cmp does not compare file ownership, permissions, or times, or
examine the contents of a directory.
"""
check_for_files(rpin, rpout)
if rpin.isreg():
if not rpout.isreg():
return None
fp1, fp2 = rpin.o... | def cmp(rpin, rpout):
"""True if rpin has the same data as rpout
cmp does not compare file ownership, permissions, or times, or
examine the contents of a directory.
"""
check_for_files(rpin, rpout)
if rpin.isreg():
if not rpout.isreg():
return None
fp1, fp2 = rpin.o... | https://github.com/rdiff-backup/rdiff-backup/issues/401 | # rdiff-backup /dev/ /tmp/faketarget/
Exception '[Errno 2] No such file or directory: b'/tmp/faketarget/bus/usb/003/rdiff-backup.tmp.22'' raised of class '<class 'FileNotFoundError'>':
File "/usr/lib/python3/dist-packages/rdiff_backup/Main.py", line 390, in error_check_Main
Main(arglist)
File "/usr/lib/python3/dist-pac... | FileNotFoundError |
def make_file_dict(filename):
"""Generate the data dictionary for the given RPath
This is a global function so that os.name can be called locally,
thus avoiding network lag and so that we only need to send the
filename over the network, thus avoiding the need to pickle an
(incomplete) rpath object.... | def make_file_dict(filename):
"""Generate the data dictionary for the given RPath
This is a global function so that os.name can be called locally,
thus avoiding network lag and so that we only need to send the
filename over the network, thus avoiding the need to pickle an
(incomplete) rpath object.... | https://github.com/rdiff-backup/rdiff-backup/issues/401 | # rdiff-backup /dev/ /tmp/faketarget/
Exception '[Errno 2] No such file or directory: b'/tmp/faketarget/bus/usb/003/rdiff-backup.tmp.22'' raised of class '<class 'FileNotFoundError'>':
File "/usr/lib/python3/dist-packages/rdiff_backup/Main.py", line 390, in error_check_Main
Main(arglist)
File "/usr/lib/python3/dist-pac... | FileNotFoundError |
def getdevnums(self):
"""Return a device's type and major/minor numbers from dictionary"""
return self.data["devnums"]
| def getdevnums(self):
"""Return a devices major/minor numbers from dictionary"""
return self.data["devnums"][1:]
| https://github.com/rdiff-backup/rdiff-backup/issues/401 | # rdiff-backup /dev/ /tmp/faketarget/
Exception '[Errno 2] No such file or directory: b'/tmp/faketarget/bus/usb/003/rdiff-backup.tmp.22'' raised of class '<class 'FileNotFoundError'>':
File "/usr/lib/python3/dist-packages/rdiff_backup/Main.py", line 390, in error_check_Main
Main(arglist)
File "/usr/lib/python3/dist-pac... | FileNotFoundError |
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