Lint

baal/active/heuristics/heuristics.py

@@ -74,9 +74,7 @@ def requireprobs(fn):
  def wrapper(self, probabilities, target_predictions=None):
  # Expected shape : [n_sample, n_classes, ..., n_iterations]
  probabilities = to_prob(probabilities)
- target_predictions = (
- to_prob(target_predictions) if target_predictions is not None else None
- )
+ target_predictions = to_prob(target_predictions) if target_predictions is not None else None
  return fn(self, probabilities, target_predictions=target_predictions)
 
  return wrapper
@@ -784,21 +782,23 @@ def marginal_entropy_from_probs(self, probs):
  scores (Tensor[float], [N,]): H[p(y|x_i)] for i in [1, N].
  """
  probs = torch.mean(probs, dim=-1) # [N, C]
- scores = -torch.sum(torch.xlogy(probs, probs), dim=-1) # [N,]
+ scores = -torch.sum(torch.xlogy(probs, probs), dim=-1) # [N,]
  return scores # [N,]
 
  @requireprobs
  def compute_score(self, predictions, target_predictions):
  """
  Compute the expected predictive information gain for each candidate input, x_i:
  EPIG(x_i) = E_{p_*(x_*)}[I(y;y_*|x_i,x_*)]
- = H[p(y|x_i)] + E_{p_*(x_*)}[H[p(y_*|x_*)]] - E_{p_*(x_*)}[H[p(y,y_*|x_i,x_*)]]
+ = H[p(y|x_i)] + E_{p_*(x_*)}[H[p(y_*|x_*)]]
+ - E_{p_*(x_*)}[H[p(y,y_*|x_i,x_*)]]
  where x_* ~ p_*(x_*) is a target input with unknown label y_*.
 
 
  Args:
  predictions (ndarray, [N_p, C, K]): p(y|x_i,θ_j) for i in [1, N_p] and j in [1, K].
- target_predictions (ndarray, [N_t, C, K]): p(y|x_*^i,θ_j) for i in [1, N_t] and j in [1, K].
+ target_predictions (ndarray, [N_t, C, K]): p(y|x_*^i,θ_j)
+ for i in [1, N_t] and j in [1, K].
 
  Returns:
  scores (ndarray, [N,]): EPIG(x_i) for i in [1, N_p].
@@ -818,7 +818,9 @@ def compute_score(self, predictions, target_predictions):
  probs_targ = probs_targ.reshape(K, N_t * C) # [K, N_t * C]
  probs_joint = torch.matmul(probs_pool, probs_targ) / K # [N_p, C, N_t * C]
 
- entropy_joint = -torch.sum(torch.xlogy(probs_joint, probs_joint), dim=(-2, -1)) / N_t # [N_p,]
+ entropy_joint = (
+ -torch.sum(torch.xlogy(probs_joint, probs_joint), dim=(-2, -1)) / N_t
+ ) # [N_p,]
  entropy_joint = torch.nan_to_num(entropy_joint, nan=0.0) # [N_p,]
 
  scores = entropy_pool + torch.mean(entropy_targ) - entropy_joint # [N_p,]

baal/active/heuristics/heuristics_gpu.py

@@ -78,9 +78,7 @@ def __init__(
  self.threshold = threshold
  self.reversed = reverse
  assert reduction in available_reductions or callable(reduction)
- self.reduction = (
- reduction if callable(reduction) else available_reductions[reduction]
- )
+ self.reduction = reduction if callable(reduction) else available_reductions[reduction]
 
  def compute_score(self, predictions, target_predictions=None):
  """
@@ -129,9 +127,7 @@ def predict_on_dataset(
 
  def predict_on_batch(self, data, iterations=1, use_cuda=False):
  """Rank the predictions according to their uncertainties."""
- return self.get_uncertainties(
- self.model.predict_on_batch(data, iterations, cuda=use_cuda)
- )
+ return self.get_uncertainties(self.model.predict_on_batch(data, iterations, cuda=use_cuda))
 
 
 class BALDGPUWrapper(AbstractGPUHeuristic):