[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

CHANGELOG.md

@@ -5,13 +5,13 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## \[Unreleased\]
+## [Unreleased]
 
 ## Notes
 
 Format:
 
-- Title: \[version\] - YYYY-MM-DD
+- Title: [version] - YYYY-MM-DD
 - Subtitle: tags (see below)
 - Items: Description of the change with link to issue/pr if available
 

CODE_OF_CONDUCT.md

@@ -106,7 +106,7 @@ Violating these terms may lead to a permanent ban.
 ### 4. Permanent Ban
 
 **Community Impact**: Demonstrating a pattern of violation of community
-standards, including sustained inappropriate behavior,  harassment of an
+standards, including sustained inappropriate behavior, harassment of an
 individual, or aggression toward or disparagement of classes of individuals.
 
 **Consequence**: A permanent ban from any sort of public interaction within

README.md

@@ -302,12 +302,8 @@ def copy(self):
     def set_split_idx(self, split_name: str, split_idx: Sequence[int]):
         """Set cell indices for a particular split.
 
-        Parameters
-        ----------
-        split_name
-            Name of the split to set.
-        split_idx
-            Indices to be used in this split.
+        Parameters ---------- split_name     Name of the split to set. split_idx
+        Indices to be used in this split.
 
         """
         self._split_idx_dict[split_name] = split_idx
@@ -339,12 +335,8 @@ def get_split_idx(self, split_name: str, error_on_miss: bool = False):
     def get_split_mask(self, split_name: str, return_type: FeatType = "numpy") -> Union[np.ndarray, torch.Tensor]:
         """Obtain mask representation of a particular split.
 
-        Parameters
-        ----------
-        split_name
-            Name of the split to retrieve.
-        return_type
-            Return numpy array if set to 'numpy', or torch Tensor if set to 'torch'.
+        Parameters ---------- split_name     Name of the split to retrieve. return_type
+        Return numpy array if set to 'numpy', or torch Tensor if set to 'torch'.
 
         """
         split_idx = self.get_split_idx(split_name, error_on_miss=True)
@@ -360,10 +352,7 @@ def get_split_mask(self, split_name: str, return_type: FeatType = "numpy") -> Un
     def get_split_data(self, split_name: str) -> Union[anndata.AnnData, mudata.MuData]:
         """Obtain the underlying data of a particular split.
 
-        Parameters
-        ----------
-        split_name
-            Name of the split to retrieve.
+        Parameters ---------- split_name     Name of the split to retrieve.
 
         """
         split_idx = self.get_split_idx(split_name, error_on_miss=True)
@@ -627,13 +616,10 @@ def get_data(
     ) -> Tuple[Any, Any]:
         """Retrieve cell features and labels from a particular split.
 
-        Parameters
-        ----------
-        split_name
-            Name of the split to retrieve. If not set, return all.
-        return_type
-            How should the features be returned. **numpy**: return as a numpy array; **torch**: return as a torch
-            tensor; **anndata**: return as an anndata object.
+        Parameters ---------- split_name     Name of the split to retrieve. If not set,
+        return all. return_type     How should the features be returned. **numpy**:
+        return as a numpy array; **torch**: return as a torch     tensor; **anndata**:
+        return as an anndata object.
 
         """
         x = self.get_x(split_name, return_type, **x_kwargs)

dance/data/base.py

@@ -305,16 +305,10 @@ def _load_dfs(paths: List[str], *, index_col: Optional[int] = 0, transpose: bool
     def get_map_dict(map_file_path: str, tissue: str) -> Dict[str, Set[str]]:
         """Load cell-type mappings.
 
-        Parameters
-        ----------
-        map_file_path
-            Path to the mapping file.
-        tissue
-            Tissue of interest.
+        Parameters ---------- map_file_path     Path to the mapping file. tissue
+        Tissue of interest.
 
-        Notes
-        -----
-        Merge mapping across all test sets for the required tissue.
+        Notes ----- Merge mapping across all test sets for the required tissue.
 
         """
         map_df = pd.read_excel(osp.join(map_file_path, "map.xlsx"))
@@ -329,12 +323,9 @@ def get_map_dict(map_file_path: str, tissue: str) -> Dict[str, Set[str]]:
 class ClusteringDataset(BaseDataset):
     """Data downloading and loading for clustering.
 
-    Parameters
-    ----------
-    data_dir
-        Path to store datasets.
-    dataset
-        Choice of dataset. Available options are '10X_PBMC', 'mouse_bladder_cell', 'mouse_ES_cell', 'worm_neuron_cell'.
+    Parameters ---------- data_dir     Path to store datasets. dataset     Choice of
+    dataset. Available options are '10X_PBMC', 'mouse_bladder_cell', 'mouse_ES_cell',
+    'worm_neuron_cell'.
 
     """
 

dance/datasets/singlemodality.py

@@ -161,11 +161,9 @@ def _raw_to_dance(self, raw_data):
 class CellTypeDeconvoDataset(BaseDataset):
     """Load raw data.
 
-    Parameters
-    ----------
-    subset_common_celltypes
-        If set to True, then subset both the reference and the real data to contain only cell types that are
-        present in both reference and real.
+    Parameters ---------- subset_common_celltypes     If set to True, then subset both
+    the reference and the real data to contain only cell types that are     present in
+    both reference and real.
 
     """
 

dance/datasets/spatial.py

@@ -1,2 +1,3 @@
 class DevError(Exception):
     """Raised when action needed by dev to fix unexpected behavior."""
+

dance/exceptions.py

@@ -10,18 +10,9 @@
 class VanillaMLP(nn.Module):
     """Vanilla multilayer perceptron with ReLU activation.
 
-    Parameters
-    ----------
-    input_dim
-        Input feature dimension.
-    output_dim
-        Output dimension.
-    hidden_dims
-        Hidden layer dimensions.
-    device
-        Computation device.
-    random_seed
-        Random seed controlling the model weights initialization.
+    Parameters ---------- input_dim     Input feature dimension. output_dim     Output
+    dimension. hidden_dims     Hidden layer dimensions. device     Computation device.
+    random_seed     Random seed controlling the model weights initialization.
 
     """
 

dance/models/nn/mlp.py

@@ -62,9 +62,8 @@ def fit_score(self, x, y, *, score_func: Optional[Union[str, Mapping[Any, float]
                   return_pred: bool = False, **fit_kwargs) -> Union[float, Tuple[float, Any]]:
         """Shortcut for fitting data using the input feature and return eval.
 
-        Note
-        ----
-        Only work for models where the fitting does not require labeled data, i.e. unsupervised methods.
+        Note ---- Only work for models where the fitting does not require labeled data,
+        i.e. unsupervised methods.
 
         """
         self.fit(x, **fit_kwargs)

dance/modules/base.py

@@ -748,7 +748,6 @@ def fit(self, train_loader, test_loader, total_loader, first="RNA"):
         None.
 
         """
-
         used_cycle = 0
 
         if self.ground_truth1 is not None:
@@ -844,7 +843,6 @@ def score(self, dataloader, metric='clustering'):
             Metric eval score for VAE2.
 
         """
-
         if metric == 'clustering':
             self.model1.eval()
             self.model2.eval()
@@ -915,7 +913,6 @@ def _encodeBatch(self, total_loader):
             Reconstruction result of modality 2.
 
         """
-
         # processing large-scale datasets
         latent_z1 = []
         latent_z2 = []
@@ -953,30 +950,30 @@ def _encodeBatch(self, total_loader):
         return latent_z1, latent_z2, norm_x1, recon_x1, norm_x2, recon_x2
 
     def forward(self, total_loader):
-        """Forward function for torch.nn.Module. An alias of encode_Batch function.
-
-        Parameters
-        ----------
-        total_loader : torch.utils.data.DataLoader
-            Dataloader for dataset.
-
-        Returns
-        -------
-        latent_z1 : numpy.ndarray
-            Latent representation of modality 1.
-        latent_z2 : numpy.ndarray
-            Latent representation of modality 2.
-        norm_x1 : numpy.ndarray
-            Normalized representation of modality 1.
-        recon_x1 : numpy.ndarray
-            Reconstruction result of modality 1.
-        norm_x2 : numpy.ndarray
-            Normalized representation of modality 2.
-        recon_x2 : numpy.ndarray
-            Reconstruction result of modality 2.
+        """Forward function for torch.nn.Module.
+
+        An alias of encode_Batch function.
+                Parameters
+                ----------
+                total_loader : torch.utils.data.DataLoader
+                    Dataloader for dataset.
+
+                Returns
+                -------
+                latent_z1 : numpy.ndarray
+                    Latent representation of modality 1.
+                latent_z2 : numpy.ndarray
+                    Latent representation of modality 2.
+                norm_x1 : numpy.ndarray
+                    Normalized representation of modality 1.
+                recon_x1 : numpy.ndarray
+                    Reconstruction result of modality 1.
+                norm_x2 : numpy.ndarray
+                    Normalized representation of modality 2.
+                recon_x2 : numpy.ndarray
+                    Reconstruction result of modality 2.
 
         """
-
         latent_z1, latent_z2, norm_x1, recon_x1, norm_x2, recon_x2 = self._encodeBatch(total_loader)
 
         return latent_z1, latent_z2, norm_x1, recon_x1, norm_x2, recon_x2