gpytorch/models/exact_prediction_strategies.py
Killed 54 out of 88 mutantsSurvived
Survived mutation testing. These mutants show holes in your test suite.Mutant 1
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -25,7 +25,7 @@
def prediction_strategy(train_inputs, train_prior_dist, train_labels, likelihood):
- train_train_covar = train_prior_dist.lazy_covariance_matrix
+ train_train_covar = None
if isinstance(train_train_covar, LazyEvaluatedKernelTensor):
cls = train_train_covar.kernel.prediction_strategy
else:
Mutant 7
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -39,7 +39,7 @@
train_shape = train_prior_dist.event_shape
train_labels = train_labels.view(*train_labels.shape[: -len(train_shape)], train_shape.numel())
- self.train_inputs = train_inputs
+ self.train_inputs = None
self.train_prior_dist = train_prior_dist
self.train_labels = train_labels
self.likelihood = likelihood
Mutant 11
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -43,7 +43,7 @@
self.train_prior_dist = train_prior_dist
self.train_labels = train_labels
self.likelihood = likelihood
- self._last_test_train_covar = None
+ self._last_test_train_covar = ""
mvn = self.likelihood(train_prior_dist, train_inputs)
self.lik_train_train_covar = mvn.lazy_covariance_matrix
Mutant 20
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -268,7 +268,7 @@
return fant_strat
@property
- @cached(name="covar_cache")
+ @cached(name="XXcovar_cacheXX")
def covar_cache(self):
train_train_covar = self.lik_train_train_covar
train_train_covar_inv_root = delazify(train_train_covar.root_inv_decomposition().root)
Mutant 21
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -268,7 +268,7 @@
return fant_strat
@property
- @cached(name="covar_cache")
+
def covar_cache(self):
train_train_covar = self.lik_train_train_covar
train_train_covar_inv_root = delazify(train_train_covar.root_inv_decomposition().root)
Mutant 25
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -275,7 +275,7 @@
return self._exact_predictive_covar_inv_quad_form_cache(train_train_covar_inv_root, self._last_test_train_covar)
@property
- @cached(name="mean_cache")
+ @cached(name="XXmean_cacheXX")
def mean_cache(self):
mvn = self.likelihood(self.train_prior_dist, self.train_inputs)
train_mean, train_train_covar = mvn.loc, mvn.lazy_covariance_matrix
Mutant 26
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -275,7 +275,7 @@
return self._exact_predictive_covar_inv_quad_form_cache(train_train_covar_inv_root, self._last_test_train_covar)
@property
- @cached(name="mean_cache")
+
def mean_cache(self):
mvn = self.likelihood(self.train_prior_dist, self.train_inputs)
train_mean, train_train_covar = mvn.loc, mvn.lazy_covariance_matrix
Mutant 30
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -280,7 +280,7 @@
mvn = self.likelihood(self.train_prior_dist, self.train_inputs)
train_mean, train_train_covar = mvn.loc, mvn.lazy_covariance_matrix
- train_labels_offset = (self.train_labels - train_mean).unsqueeze(-1)
+ train_labels_offset = (self.train_labels - train_mean).unsqueeze(+1)
mean_cache = train_train_covar.inv_matmul(train_labels_offset).squeeze(-1)
if settings.detach_test_caches.on():
Mutant 33
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -281,7 +281,7 @@
train_mean, train_train_covar = mvn.loc, mvn.lazy_covariance_matrix
train_labels_offset = (self.train_labels - train_mean).unsqueeze(-1)
- mean_cache = train_train_covar.inv_matmul(train_labels_offset).squeeze(-1)
+ mean_cache = train_train_covar.inv_matmul(train_labels_offset).squeeze(+1)
if settings.detach_test_caches.on():
mean_cache = mean_cache.detach()
Mutant 41
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -305,7 +305,7 @@
# Find the components of the distribution that contain test data
test_mean = joint_mean[..., self.num_train :]
# For efficiency - we can make things more efficient
- if joint_covar.size(-1) <= settings.max_eager_kernel_size.value():
+ if joint_covar.size(+1) <= settings.max_eager_kernel_size.value():
test_covar = joint_covar[..., self.num_train :, :].evaluate()
test_test_covar = test_covar[..., self.num_train :]
test_train_covar = test_covar[..., : self.num_train]
Mutant 42
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -305,7 +305,7 @@
# Find the components of the distribution that contain test data
test_mean = joint_mean[..., self.num_train :]
# For efficiency - we can make things more efficient
- if joint_covar.size(-1) <= settings.max_eager_kernel_size.value():
+ if joint_covar.size(-2) <= settings.max_eager_kernel_size.value():
test_covar = joint_covar[..., self.num_train :, :].evaluate()
test_test_covar = test_covar[..., self.num_train :]
test_train_covar = test_covar[..., : self.num_train]
Mutant 43
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -305,7 +305,7 @@
# Find the components of the distribution that contain test data
test_mean = joint_mean[..., self.num_train :]
# For efficiency - we can make things more efficient
- if joint_covar.size(-1) <= settings.max_eager_kernel_size.value():
+ if joint_covar.size(-1) < settings.max_eager_kernel_size.value():
test_covar = joint_covar[..., self.num_train :, :].evaluate()
test_test_covar = test_covar[..., self.num_train :]
test_train_covar = test_covar[..., : self.num_train]
Mutant 49
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -332,7 +332,7 @@
# NOTE TO FUTURE SELF:
# You **cannot* use addmv here, because test_train_covar may not actually be a non lazy tensor even for an exact
# GP, and using addmv requires you to delazify test_train_covar, which is obviously a huge no-no!
- res = (test_train_covar @ self.mean_cache.unsqueeze(-1)).squeeze(-1)
+ res = (test_train_covar @ self.mean_cache.unsqueeze(+1)).squeeze(-1)
res = res + test_mean
return res
Mutant 51
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -332,7 +332,7 @@
# NOTE TO FUTURE SELF:
# You **cannot* use addmv here, because test_train_covar may not actually be a non lazy tensor even for an exact
# GP, and using addmv requires you to delazify test_train_covar, which is obviously a huge no-no!
- res = (test_train_covar @ self.mean_cache.unsqueeze(-1)).squeeze(-1)
+ res = (test_train_covar @ self.mean_cache.unsqueeze(-1)).squeeze(+1)
res = res + test_mean
return res
Mutant 56
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -350,7 +350,7 @@
test points
"""
if settings.fast_pred_var.on():
- self._last_test_train_covar = test_train_covar
+ self._last_test_train_covar = None
if settings.skip_posterior_variances.on():
return ZeroLazyTensor(*test_test_covar.size())
Mutant 59
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -365,7 +365,7 @@
train_train_covar = self.likelihood(dist, self.train_inputs).lazy_covariance_matrix
test_train_covar = delazify(test_train_covar)
- train_test_covar = test_train_covar.transpose(-1, -2)
+ train_test_covar = test_train_covar.transpose(+1, -2)
covar_correction_rhs = train_train_covar.inv_matmul(train_test_covar)
# For efficiency
if torch.is_tensor(test_test_covar):
Mutant 65
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -370,7 +370,7 @@
# For efficiency
if torch.is_tensor(test_test_covar):
# We can use addmm in the 2d case
- if test_test_covar.dim() == 2:
+ if test_test_covar.dim() != 2:
return lazify(
torch.addmm(test_test_covar, test_train_covar, covar_correction_rhs, beta=1, alpha=-1)
)
Mutant 66
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -370,7 +370,7 @@
# For efficiency
if torch.is_tensor(test_test_covar):
# We can use addmm in the 2d case
- if test_test_covar.dim() == 2:
+ if test_test_covar.dim() == 3:
return lazify(
torch.addmm(test_test_covar, test_train_covar, covar_correction_rhs, beta=1, alpha=-1)
)
Mutant 67
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -372,7 +372,7 @@
# We can use addmm in the 2d case
if test_test_covar.dim() == 2:
return lazify(
- torch.addmm(test_test_covar, test_train_covar, covar_correction_rhs, beta=1, alpha=-1)
+ torch.addmm(test_test_covar, test_train_covar, covar_correction_rhs, beta=2, alpha=-1)
)
else:
return lazify(test_test_covar + test_train_covar @ covar_correction_rhs.mul(-1))
Mutant 68
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -372,7 +372,7 @@
# We can use addmm in the 2d case
if test_test_covar.dim() == 2:
return lazify(
- torch.addmm(test_test_covar, test_train_covar, covar_correction_rhs, beta=1, alpha=-1)
+ torch.addmm(test_test_covar, test_train_covar, covar_correction_rhs, beta=1, alpha=+1)
)
else:
return lazify(test_test_covar + test_train_covar @ covar_correction_rhs.mul(-1))
Mutant 69
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -372,7 +372,7 @@
# We can use addmm in the 2d case
if test_test_covar.dim() == 2:
return lazify(
- torch.addmm(test_test_covar, test_train_covar, covar_correction_rhs, beta=1, alpha=-1)
+ torch.addmm(test_test_covar, test_train_covar, covar_correction_rhs, beta=1, alpha=-2)
)
else:
return lazify(test_test_covar + test_train_covar @ covar_correction_rhs.mul(-1))
Mutant 72
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -389,7 +389,7 @@
)
)
else:
- return test_test_covar + MatmulLazyTensor(
+ return test_test_covar - MatmulLazyTensor(
covar_inv_quad_form_root, covar_inv_quad_form_root.transpose(-1, -2).mul(-1)
)
Mutant 73
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -390,7 +390,7 @@
)
else:
return test_test_covar + MatmulLazyTensor(
- covar_inv_quad_form_root, covar_inv_quad_form_root.transpose(-1, -2).mul(-1)
+ covar_inv_quad_form_root, covar_inv_quad_form_root.transpose(+1, -2).mul(-1)
)
Mutant 77
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -390,7 +390,7 @@
)
else:
return test_test_covar + MatmulLazyTensor(
- covar_inv_quad_form_root, covar_inv_quad_form_root.transpose(-1, -2).mul(-1)
+ covar_inv_quad_form_root, covar_inv_quad_form_root.transpose(-1, -2).mul(+1)
)
Mutant 78
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -390,7 +390,7 @@
)
else:
return test_test_covar + MatmulLazyTensor(
- covar_inv_quad_form_root, covar_inv_quad_form_root.transpose(-1, -2).mul(-1)
+ covar_inv_quad_form_root, covar_inv_quad_form_root.transpose(-1, -2).mul(-2)
)
Mutant 79
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -424,7 +424,6 @@
"Fantasy observation updates not yet supported for models using InterpolatedLazyTensors"
)
- @property
@cached(name="mean_cache")
def mean_cache(self):
train_train_covar = self.train_prior_dist.lazy_covariance_matrix
Mutant 80
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -425,7 +425,7 @@
)
@property
- @cached(name="mean_cache")
+ @cached(name="XXmean_cacheXX")
def mean_cache(self):
train_train_covar = self.train_prior_dist.lazy_covariance_matrix
train_interp_indices = train_train_covar.left_interp_indices
Mutant 81
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -425,7 +425,7 @@
)
@property
- @cached(name="mean_cache")
+
def mean_cache(self):
train_train_covar = self.train_prior_dist.lazy_covariance_matrix
train_interp_indices = train_train_covar.left_interp_indices
Mutant 82
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -449,7 +449,6 @@
else:
return mean_cache
- @property
@cached(name="covar_cache")
def covar_cache(self):
# Get inverse root
Mutant 83
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -450,7 +450,7 @@
return mean_cache
@property
- @cached(name="covar_cache")
+ @cached(name="XXcovar_cacheXX")
def covar_cache(self):
# Get inverse root
train_train_covar = self.train_prior_dist.lazy_covariance_matrix
Mutant 84
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -450,7 +450,7 @@
return mean_cache
@property
- @cached(name="covar_cache")
+
def covar_cache(self):
# Get inverse root
train_train_covar = self.train_prior_dist.lazy_covariance_matrix
Mutant 85
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -560,7 +560,7 @@
class SumPredictionStrategy(DefaultPredictionStrategy):
- @property
+
def _sub_strategies(self):
sub_strategies = []
for lazy_tensor in self.train_prior_dist.lazy_covariance_matrix.evaluate_kernel().lazy_tensors:
Mutant 86
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -613,7 +613,6 @@
def get_fantasy_strategy(self, inputs, targets, full_inputs, full_targets, full_output, **kwargs):
raise NotImplementedError("Fantasy observation updates not yet supported for models using RFFs")
- @property
@cached(name="covar_cache")
def covar_cache(self):
lt = self.train_prior_dist.lazy_covariance_matrix
Mutant 87
--- gpytorch/models/exact_prediction_strategies.py
+++ gpytorch/models/exact_prediction_strategies.py
@@ -614,7 +614,7 @@
raise NotImplementedError("Fantasy observation updates not yet supported for models using RFFs")
@property
- @cached(name="covar_cache")
+ @cached(name="XXcovar_cacheXX")
def covar_cache(self):
lt = self.train_prior_dist.lazy_covariance_matrix
if isinstance(lt, ConstantMulLazyTensor):