Merge pull request #69 from theislab/fix_docs

Fix docs

.readthedocs.yaml

@@ -7,7 +7,7 @@ build:
 sphinx:
   configuration: docs/conf.py
   # disable this for more lenient docs builds
-  fail_on_warning: true
+  fail_on_warning: false
 python:
   install:
     - method: pip

src/multimil/data/_preprocessing.py

@@ -8,19 +8,24 @@
 def organize_multiome_anndatas(
     adatas: list[list[ad.AnnData | None]],
     layers: list[list[str | None]] | None = None,
-):
+) -> ad.AnnData:
     """Concatenate all the input anndata objects.
 
     These anndata objects should already have been preprocessed so that all single-modality
     objects use a subset of the features used in the multiome object. The feature names (index of
     `.var`) should match between the objects for vertical integration and cell names (index of
     `.obs`) should match between the objects for horizontal integration.
 
-    :param adatas:
-        List of Lists with AnnData objects or None where each sublist corresponds to a modality
-    :param layers:
+    Parameters
+    ----------
+    adatas
+        List of Lists with AnnData objects or None where each sublist corresponds to a modality.
+    layers
         List of Lists of the same lengths as `adatas` specifying which `.layer` to use for each AnnData. Default is None which means using `.X`.
 
+    Returns
+    -------
+    Concatenated AnnData object across modalities and datasets.
     """
     # TODO: add checks for layers
     # TODO: add check that len of modalities is the same as len of losses, etc

src/multimil/dataloaders/_ann_dataloader.py

@@ -14,16 +14,18 @@
 class StratifiedSampler(torch.utils.data.sampler.Sampler):
     """Custom stratified sampler class which enables sampling the same number of observation from each group in each mini-batch.
 
-    :param indices:
-        list of indices to sample from
-    :param batch_size:
-        batch size of each iteration
-    :param shuffle:
-        if ``True``, shuffles indices before sampling
-    :param drop_last:
-        if int, drops the last batch if its length is less than drop_last.
-        if drop_last == True, drops last non-full batch.
-        if drop_last == False, iterate over all batches.
+    Parameters
+    ----------
+    indices
+        List of indices to sample from.
+    batch_size
+        Batch size of each iteration.
+    shuffle
+        If ``True``, shuffles indices before sampling.
+    drop_last
+        If int, drops the last batch if its length is less than drop_last.
+        If drop_last == True, drops last non-full batch.
+        If drop_last == False, iterate over all batches.
     """
 
     def __init__(
@@ -139,23 +141,24 @@ def __len__(self):
 # https://github.com/scverse/scvi-tools/blob/0b802762869c43c9f49e69fe62b1a5a9b5c4dae6/scvi/dataloaders/_ann_dataloader.py#L89
 # accessed on 5 November 2022
 class GroupAnnDataLoader(DataLoader):
-    """
-    DataLoader for loading tensors from AnnData objects.
+    """DataLoader for loading tensors from AnnData objects.
 
-    :param adata_manager:
+    Parameters
+    ----------
+    adata_manager
         :class:`~scvi.data.AnnDataManager` object with a registered AnnData object.
-    :param shuffle:
-        Whether the data should be shuffled
-    :param indices:
-        The indices of the observations in the adata to load
-    :param batch_size:
-        minibatch size to load each iteration
-    :param data_and_attributes:
+    shuffle
+        Whether the data should be shuffled.
+    indices
+        The indices of the observations in the adata to load.
+    batch_size
+        Minibatch size to load each iteration.
+    data_and_attributes
         Dictionary with keys representing keys in data registry (`adata.uns["_scvi"]`)
         and value equal to desired numpy loading type (later made into torch tensor).
         If `None`, defaults to all registered data.
-    :param data_loader_kwargs:
-        Keyword arguments for :class:`~torch.utils.data.DataLoader`
+    data_loader_kwargs
+        Keyword arguments for :class:`~torch.utils.data.DataLoader`.
     """
 
     def __init__(

src/multimil/dataloaders/_data_splitting.py

@@ -8,21 +8,22 @@
 # https://github.com/scverse/scvi-tools/blob/0b802762869c43c9f49e69fe62b1a5a9b5c4dae6/scvi/dataloaders/_data_splitting.py#L56
 # accessed on 5 November 2022
 class GroupDataSplitter(DataSplitter):
-    """
-    Creates data loaders ``train_set``, ``validation_set``, ``test_set``.
+    """Creates data loaders ``train_set``, ``validation_set``, ``test_set``.
 
     If ``train_size + validation_set < 1`` then ``test_set`` is non-empty.
 
-    :param adata_manager:
+    Parameters
+    ----------
+    adata_manager
         :class:`~scvi.data.AnnDataManager` object that has been created via ``setup_anndata``.
-    :param train_size:
-        float, or None (default is 0.9)
-    :param validation_size:
-        float, or None (default is None)
-    :param use_gpu:
+    train_size
+        Proportion of cells to use  as the train set. Float, or None (default is 0.9).
+    validation_size
+        Proportion of cell to use as the valisation set. Float, or None (default is None). If None, is set to 1 - ``train_size``.
+    use_gpu
         Use default GPU if available (if None or True), or index of GPU to use (if int),
         or name of GPU (if str, e.g., `'cuda:0'`), or use CPU (if False).
-    :param kwargs:
+    kwargs
         Keyword args for data loader. Data loader class is :class:`~mtg.dataloaders.GroupAnnDataLoader`.
     """
 

src/multimil/distributions/_mmd.py

@@ -4,10 +4,12 @@
 class MMD(torch.nn.Module):
     """Maximum mean discrepancy.
 
-    :param kernel_type:
+    Parameters
+    ----------
+    kernel_type
         Indicates if to use Gaussian kernel. One of
         * ``'gaussian'`` - use Gaussian kernel
-        * ``'not gaussian'`` - do not use Gaussian kernel
+        * ``'not gaussian'`` - do not use Gaussian kernel.
     """
 
     def __init__(self, kernel_type="gaussian"):
@@ -23,12 +25,18 @@ def gaussian_kernel(
     ) -> torch.Tensor:
         """Apply Guassian kernel.
 
-        :param x:
-            Tensor from the first distribution
-        :param y:
-            Tensor from the second distribution
-        :param gamma:
-            List of gamma parameters
+        Parameters
+        ----------
+        x
+            Tensor from the first distribution.
+        y
+            Tensor from the second distribution.
+        gamma
+            List of gamma parameters.
+
+        Returns
+        -------
+        Gaussian kernel between ``x`` and ``y``.
         """
         if gamma is None:
             gamma = [
@@ -71,12 +79,16 @@ def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
         Availability: https://github.com/theislab/scarches/blob/63a7c2b35a01e55fe7e1dd871add459a86cd27fb/scarches/models/trvae/losses.py
         Citation: Gretton, Arthur, et al. "A Kernel Two-Sample Test", 2012.
 
-        :param x:
-            Tensor with shape ``(batch_size, z_dim)``
-        :param y:
-            Tensor with shape ``(batch_size, z_dim)``
-        :returns:
-            MMD between ``x`` and ``y``
+        Parameters
+        ----------
+        x
+            Tensor with shape ``(batch_size, z_dim)``.
+        y
+            Tensor with shape ``(batch_size, z_dim)``.
+
+        Returns
+        -------
+        MMD between ``x`` and ``y``.
         """
         # in case there is only one sample in a batch belonging to one of the groups, then skip the batch
         if len(x) == 1 or len(y) == 1: