sbi/inference/snre/snre_base.py
Killed 68 out of 102 mutantsTimeouts
Mutants that made the test suite take a lot longer so the tests were killed.Mutant 132
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -270,7 +270,7 @@
epoch, self._val_log_prob = 0, float("-Inf")
- while epoch <= max_num_epochs and not self._converged(epoch, stop_after_epochs):
+ while epoch <= max_num_epochs or not self._converged(epoch, stop_after_epochs):
# Train for a single epoch.
self._posterior.net.train()
Mutant 186
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -403,7 +403,7 @@
log_ratio = self.classifier(torch.cat((theta, x), dim=1).reshape(1, -1))
# Notice opposite sign to pyro potential.
- return log_ratio + self.prior.log_prob(theta)
+ return log_ratio - self.prior.log_prob(theta)
def pyro_potential(self, theta: Dict[str, Tensor]) -> Tensor:
r"""Return potential for Pyro sampler.
Survived
Survived mutation testing. These mutants show holes in your test suite.Mutant 87
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -89,7 +89,7 @@
self._build_neural_net = utils.classifier_nn(model=classifier)
else:
self._build_neural_net = classifier
- self._posterior = None
+ self._posterior = ""
self._sample_with_mcmc = True
self._mcmc_method = mcmc_method
Mutant 91
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -130,7 +130,7 @@
Posterior $p(\theta|x)$ that can be sampled and evaluated.
"""
- max_num_epochs = 2 ** 31 - 1 if max_num_epochs is None else max_num_epochs
+ max_num_epochs = 3 ** 31 - 1 if max_num_epochs is None else max_num_epochs
num_sims_per_round = self._ensure_list(num_simulations_per_round, num_rounds)
Mutant 92
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -130,7 +130,7 @@
Posterior $p(\theta|x)$ that can be sampled and evaluated.
"""
- max_num_epochs = 2 ** 31 - 1 if max_num_epochs is None else max_num_epochs
+ max_num_epochs = 2 * 31 - 1 if max_num_epochs is None else max_num_epochs
num_sims_per_round = self._ensure_list(num_simulations_per_round, num_rounds)
Mutant 93
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -130,7 +130,7 @@
Posterior $p(\theta|x)$ that can be sampled and evaluated.
"""
- max_num_epochs = 2 ** 31 - 1 if max_num_epochs is None else max_num_epochs
+ max_num_epochs = 2 ** 32 - 1 if max_num_epochs is None else max_num_epochs
num_sims_per_round = self._ensure_list(num_simulations_per_round, num_rounds)
Mutant 94
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -130,7 +130,7 @@
Posterior $p(\theta|x)$ that can be sampled and evaluated.
"""
- max_num_epochs = 2 ** 31 - 1 if max_num_epochs is None else max_num_epochs
+ max_num_epochs = 2 ** 31 + 1 if max_num_epochs is None else max_num_epochs
num_sims_per_round = self._ensure_list(num_simulations_per_round, num_rounds)
Mutant 95
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -130,7 +130,7 @@
Posterior $p(\theta|x)$ that can be sampled and evaluated.
"""
- max_num_epochs = 2 ** 31 - 1 if max_num_epochs is None else max_num_epochs
+ max_num_epochs = 2 ** 31 - 2 if max_num_epochs is None else max_num_epochs
num_sims_per_round = self._ensure_list(num_simulations_per_round, num_rounds)
Mutant 103
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -146,7 +146,7 @@
)
x = self._batched_simulator(theta)
- x_shape = x_shape_from_simulation(x)
+ x_shape = None
# First round or if retraining from scratch:
# Call the `self._build_neural_net` with the rounds' thetas and xs as
Mutant 109
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -226,7 +226,7 @@
"""
# Starting index for the training set (1 = discard round-0 samples).
- start_idx = int(discard_prior_samples and round_ > 0)
+ start_idx = int(discard_prior_samples and round_ >= 0)
# Get total number of training examples.
num_examples = sum(len(theta) for theta in self._theta_bank)
Mutant 110
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -226,7 +226,7 @@
"""
# Starting index for the training set (1 = discard round-0 samples).
- start_idx = int(discard_prior_samples and round_ > 0)
+ start_idx = int(discard_prior_samples and round_ > 1)
# Get total number of training examples.
num_examples = sum(len(theta) for theta in self._theta_bank)
Mutant 111
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -226,7 +226,7 @@
"""
# Starting index for the training set (1 = discard round-0 samples).
- start_idx = int(discard_prior_samples and round_ > 0)
+ start_idx = int(discard_prior_samples or round_ > 0)
# Get total number of training examples.
num_examples = sum(len(theta) for theta in self._theta_bank)
Mutant 112
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -226,7 +226,7 @@
"""
# Starting index for the training set (1 = discard round-0 samples).
- start_idx = int(discard_prior_samples and round_ > 0)
+ start_idx = None
# Get total number of training examples.
num_examples = sum(len(theta) for theta in self._theta_bank)
Mutant 119
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -233,7 +233,7 @@
# Select random train and validation splits from (theta, x) pairs.
permuted_indices = torch.randperm(num_examples)
num_training_examples = int((1 - validation_fraction) * num_examples)
- num_validation_examples = num_examples - num_training_examples
+ num_validation_examples = num_examples + num_training_examples
train_indices, val_indices = (
permuted_indices[:num_training_examples],
permuted_indices[num_training_examples:],
Mutant 122
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -242,7 +242,7 @@
clipped_batch_size = min(training_batch_size, num_validation_examples)
# num_atoms = theta.shape[0]
- clamp_and_warn("num_atoms", num_atoms, min_val=2, max_val=clipped_batch_size)
+ clamp_and_warn("XXnum_atomsXX", num_atoms, min_val=2, max_val=clipped_batch_size)
# Dataset is shared for training and validation loaders.
dataset = data.TensorDataset(
Mutant 123
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -242,7 +242,7 @@
clipped_batch_size = min(training_batch_size, num_validation_examples)
# num_atoms = theta.shape[0]
- clamp_and_warn("num_atoms", num_atoms, min_val=2, max_val=clipped_batch_size)
+ clamp_and_warn("num_atoms", num_atoms, min_val=3, max_val=clipped_batch_size)
# Dataset is shared for training and validation loaders.
dataset = data.TensorDataset(
Mutant 124
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -253,7 +253,7 @@
train_loader = data.DataLoader(
dataset,
batch_size=clipped_batch_size,
- drop_last=True,
+ drop_last=False,
sampler=SubsetRandomSampler(train_indices),
)
val_loader = data.DataLoader(
Mutant 126
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -260,7 +260,7 @@
dataset,
batch_size=clipped_batch_size,
shuffle=False,
- drop_last=False,
+ drop_last=True,
sampler=SubsetRandomSampler(val_indices),
)
Mutant 130
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -270,7 +270,7 @@
epoch, self._val_log_prob = 0, float("-Inf")
- while epoch <= max_num_epochs and not self._converged(epoch, stop_after_epochs):
+ while epoch < max_num_epochs and not self._converged(epoch, stop_after_epochs):
# Train for a single epoch.
self._posterior.net.train()
Mutant 137
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -288,7 +288,7 @@
)
optimizer.step()
- epoch += 1
+ epoch = 1
# Calculate validation performance.
self._posterior.net.eval()
Mutant 138
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -288,7 +288,7 @@
)
optimizer.step()
- epoch += 1
+ epoch -= 1
# Calculate validation performance.
self._posterior.net.eval()
Mutant 139
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -288,7 +288,7 @@
)
optimizer.step()
- epoch += 1
+ epoch += 2
# Calculate validation performance.
self._posterior.net.eval()
Mutant 140
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -292,7 +292,7 @@
# Calculate validation performance.
self._posterior.net.eval()
- log_prob_sum = 0
+ log_prob_sum = 1
with torch.no_grad():
for batch in val_loader:
theta_batch, x_batch = (
Mutant 147
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -301,7 +301,7 @@
)
log_prob = self._loss(theta_batch, x_batch, num_atoms)
log_prob_sum -= log_prob.sum().item()
- self._val_log_prob = log_prob_sum / num_validation_examples
+ self._val_log_prob = log_prob_sum * num_validation_examples
self._maybe_show_progress(self._show_progress_bars, epoch)
Mutant 153
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -320,7 +320,7 @@
repeated_x = utils.repeat_rows(x, num_atoms)
# Choose `1` or `num_atoms - 1` thetas from the rest of the batch for each x.
- probs = ones(batch_size, batch_size) * (1 - eye(batch_size)) / (batch_size - 1)
+ probs = ones(batch_size, batch_size) * (2 - eye(batch_size)) / (batch_size - 1)
choices = torch.multinomial(probs, num_samples=num_atoms - 1, replacement=False)
Mutant 154
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -320,7 +320,7 @@
repeated_x = utils.repeat_rows(x, num_atoms)
# Choose `1` or `num_atoms - 1` thetas from the rest of the batch for each x.
- probs = ones(batch_size, batch_size) * (1 - eye(batch_size)) / (batch_size - 1)
+ probs = ones(batch_size, batch_size) * (1 + eye(batch_size)) / (batch_size - 1)
choices = torch.multinomial(probs, num_samples=num_atoms - 1, replacement=False)
Mutant 155
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -320,7 +320,7 @@
repeated_x = utils.repeat_rows(x, num_atoms)
# Choose `1` or `num_atoms - 1` thetas from the rest of the batch for each x.
- probs = ones(batch_size, batch_size) * (1 - eye(batch_size)) / (batch_size - 1)
+ probs = ones(batch_size, batch_size) * (1 - eye(batch_size)) * (batch_size - 1)
choices = torch.multinomial(probs, num_samples=num_atoms - 1, replacement=False)
Mutant 156
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -320,7 +320,7 @@
repeated_x = utils.repeat_rows(x, num_atoms)
# Choose `1` or `num_atoms - 1` thetas from the rest of the batch for each x.
- probs = ones(batch_size, batch_size) * (1 - eye(batch_size)) / (batch_size - 1)
+ probs = ones(batch_size, batch_size) * (1 - eye(batch_size)) / (batch_size + 1)
choices = torch.multinomial(probs, num_samples=num_atoms - 1, replacement=False)
Mutant 157
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -320,7 +320,7 @@
repeated_x = utils.repeat_rows(x, num_atoms)
# Choose `1` or `num_atoms - 1` thetas from the rest of the batch for each x.
- probs = ones(batch_size, batch_size) * (1 - eye(batch_size)) / (batch_size - 1)
+ probs = ones(batch_size, batch_size) * (1 - eye(batch_size)) / (batch_size - 2)
choices = torch.multinomial(probs, num_samples=num_atoms - 1, replacement=False)
Mutant 161
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -322,7 +322,7 @@
# Choose `1` or `num_atoms - 1` thetas from the rest of the batch for each x.
probs = ones(batch_size, batch_size) * (1 - eye(batch_size)) / (batch_size - 1)
- choices = torch.multinomial(probs, num_samples=num_atoms - 1, replacement=False)
+ choices = torch.multinomial(probs, num_samples=num_atoms - 1, replacement=True)
contrasting_theta = theta[choices]
Mutant 170
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -334,7 +334,6 @@
return self._posterior.net(theta_and_x)
- @abstractmethod
def _loss(self, theta: Tensor, x: Tensor, num_atoms: int) -> Tensor:
raise NotImplementedError
Mutant 175
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -380,7 +380,7 @@
self.prior = prior
self.x = x
- if mcmc_method in ("slice", "hmc", "nuts"):
+ if mcmc_method in ("XXsliceXX", "hmc", "nuts"):
return self.pyro_potential
else:
return self.np_potential
Mutant 176
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -380,7 +380,7 @@
self.prior = prior
self.x = x
- if mcmc_method in ("slice", "hmc", "nuts"):
+ if mcmc_method in ("slice", "XXhmcXX", "nuts"):
return self.pyro_potential
else:
return self.np_potential
Mutant 177
--- sbi/inference/snre/snre_base.py
+++ sbi/inference/snre/snre_base.py
@@ -380,7 +380,7 @@
self.prior = prior
self.x = x
- if mcmc_method in ("slice", "hmc", "nuts"):
+ if mcmc_method in ("slice", "hmc", "XXnutsXX"):
return self.pyro_potential
else:
return self.np_potential