Fix minor linting issues

cebra/__init__.py

@@ -92,11 +92,11 @@ def __getattr__(key):
 
  return CEBRA
  elif key == "KNNDecoder":
- from cebra.integrations.sklearn.decoder import KNNDecoder
+ from cebra.integrations.sklearn.decoder import KNNDecoder # noqa: F811
 
  return KNNDecoder
  elif key == "L1LinearRegressor":
- from cebra.integrations.sklearn.decoder import L1LinearRegressor
+ from cebra.integrations.sklearn.decoder import L1LinearRegressor # noqa: F811
 
  return L1LinearRegressor
  elif not key.startswith("_"):

cebra/data/helper.py

@@ -94,10 +94,15 @@ class OrthogonalProcrustesAlignment:
 
  For each dataset, the data and labels to align the data on is provided.
 
- 1. The ``top_k`` indexes of the labels to align (``label``) that are the closest to the labels of the reference dataset (``ref_label``) are selected and used to sample from the dataset to align (``data``).
- 2. ``data`` and ``ref_data`` (the reference dataset) are subsampled to the same number of samples ``subsample``.
- 3. The orthogonal mapping is computed, using :py:func:`scipy.linalg.orthogonal_procrustes`, on those subsampled datasets.
- 4. The resulting orthongonal matrix ``_transform`` can be used to map the original ``data`` to the ``ref_data``.
+ 1. The ``top_k`` indexes of the labels to align (``label``) that are the closest to
+ the labels of the reference dataset (``ref_label``) are selected and used to sample
+ from the dataset to align (``data``).
+ 2. ``data`` and ``ref_data`` (the reference dataset) are subsampled to the same number
+ of samples ``subsample``.
+ 3. The orthogonal mapping is computed, using :py:func:`scipy.linalg.orthogonal_procrustes`,
+ on those subsampled datasets.
+ 4. The resulting orthongonal matrix ``_transform`` can be used to map the original ``data``
+ to the ``ref_data``.
 
  Note:
  ``data`` and ``ref_data`` can be of different sample size (axis 0) but **must** have the same number

cebra/data/load.py

@@ -663,7 +663,8 @@ def load(
  - if no key is provided, the first data structure found upon iteration of the collection will be loaded;
  - if a key is provided, it needs to correspond to an existing item of the collection;
  - if a key is provided, the data value accessed needs to be a data structure;
- - the function loads data for only one data structure, even if the file contains more. The function can be called again with the corresponding key to get the other ones.
+ - the function loads data for only one data structure, even if the file contains more. The function can be
+ called again with the corresponding key to get the other ones.
 
  Args:
  file: The path to the given file to load, in a supported format.

cebra/data/single_session.py

@@ -350,7 +350,6 @@ def __post_init__(self):
  # here might be sub-optimal. The final behavior should be determined after
  # e.g. integrating the FAISS dataloader back in.
  super().__post_init__()
- index = self.index.to(self.device)
 
  if self.conditional != "time_delta":
  raise NotImplementedError(
@@ -360,8 +359,7 @@ def __post_init__(self):
  self.time_distribution = cebra.distributions.TimeContrastive(
  time_offset=self.time_offset,
  num_samples=len(self.dataset.neural),
- device=self.device,
- )
+ device=self.device)
  self.behavior_distribution = cebra.distributions.TimedeltaDistribution(
  self.dataset.continuous_index, self.time_offset, device=self.device)
 

cebra/grid_search.py

@@ -138,7 +138,8 @@ def fit_models(self,
  to fit the CEBRA models on. The models are then trained using temporal contrastive learning
  (CEBRA-Time).
  An example of a valid ``datasets`` value could be:
- ``datasets={"dataset1": neural_data, "dataset2": (neurald_data, continuous_data, discrete_data), "dataset3": (neural_data2, continuous_data2)}``.
+ ``datasets={"dataset1": neural_data, "dataset2": (neurald_data, continuous_data, discrete_data),
+ "dataset3": (neural_data2, continuous_data2)}``.
  params: Dict of parameter values provided by the user, either as a single value, for
  fixed hyperparameter values, or with a list of values for hyperparameters to optimize.
  If the value is a list of a single element, the hyperparameter is considered as fixed.

cebra/helper.py

@@ -152,7 +152,7 @@ def _requires_package_version(function):
 
  @wraps(function)
  def wrapper(*args, patched_version=None, **kwargs):
- if patched_version != None:
+ if patched_version is not None:
  installed_version = pkg_resources.parse_version(
  patched_version) # Use the patched version if provided
  else:

cebra/models/criterions.py

@@ -36,6 +36,7 @@
 from typing import Optional, Tuple
 
 import torch
+import torch.nn.functional as F
 from torch import nn
 
 
@@ -212,7 +213,6 @@ def __init__(self,
  self.max_inverse_temperature = math.inf
  else:
  self.max_inverse_temperature = 1.0 / min_temperature
- start_tempearture = float(temperature)
  log_inverse_temperature = torch.tensor(
  math.log(1.0 / float(temperature)))
  self.log_inverse_temperature = nn.Parameter(log_inverse_temperature)

cebra/models/multiobjective.py

@@ -94,7 +94,7 @@ def is_valid(self, mode):
  Returns:
  ``True`` for a valid representation, ``False`` otherwise.
  """
- return mode in _ALL
+ return mode in _ALL # noqa: F821
 
  def __init__(
  self,

cebra/registry.py

@@ -115,7 +115,9 @@ def get_options(
  ):
  instance = cls.get_instance(module)
  if expand_parametrized:
- filter_ = lambda k, v: True
+
+ def filter_(k, v):
+ return True
  else:
 
  class _Filter(set):

cebra/solver/supervised.py

@@ -61,7 +61,7 @@ def fit(self,
  step_idx = 0
  while True:
  for _, batch in enumerate(loader):
- stats = self.step(batch)
+ _ = self.step(batch)
  self._log_checkpoint(num_steps, loader, valid_loader)
  step_idx += 1
  if step_idx >= num_steps: