chore: update lifecycle tags

api/python/cellxgene_census/src/cellxgene_census/_experiment.py

@@ -31,7 +31,7 @@ def _get_experiment(census: soma.Collection, organism: str) -> soma.Experiment:
         ValueError: if unable to find the specified organism.
 
     Lifecycle:
-        Experimental.
+        maturing
 
     Examples:
 

api/python/cellxgene_census/src/cellxgene_census/_get_anndata.py

@@ -63,7 +63,7 @@ def get_anndata(
         An :class:`anndata.AnnData` object containing the census slice.
 
     Lifecycle:
-        Experimental.
+        maturing
 
     Examples:
         >>> get_anndata(census, "Mus musculus", obs_value_filter="tissue_general in ['brain', 'lung']")

api/python/cellxgene_census/src/cellxgene_census/_open.py

@@ -89,7 +89,7 @@ def open_soma(
             or a version are specified.
 
     Lifecycle:
-        Experimental.
+        maturing
 
     Examples:
         Open the default Census version, using a context manager which will automatically
@@ -169,7 +169,7 @@ def get_source_h5ad_uri(dataset_id: str, *, census_version: str = "latest") -> C
         KeyError: if either `dataset_id` or `census_version` do not exist.
 
     Lifecycle:
-        Experimental.
+        maturing
 
     Examples:
         >>> cellxgene_census.get_source_h5ad_uri("cb5efdb0-f91c-4cbd-9ad4-9d4fa41c572d")
@@ -206,7 +206,7 @@ def download_source_h5ad(dataset_id: str, to_path: str, *, census_version: str =
             an existing file), or is not a file.
 
     Lifecycle:
-        Experimental.
+        maturing
 
     See Also:
         :func:`get_source_h5ad_uri`: Look up the location of the source H5AD.

api/python/cellxgene_census/src/cellxgene_census/_presence_matrix.py

@@ -38,7 +38,7 @@ def get_presence_matrix(
         ValueError: if the organism cannot be found.
 
     Lifecycle:
-        Experimental.
+        maturing
 
     Examples:
         >>> get_presence_matrix(census, "Homo sapiens", "RNA")

api/python/cellxgene_census/src/cellxgene_census/_release_directory.py

@@ -54,7 +54,7 @@ def get_census_version_description(census_version: str) -> CensusVersionDescript
         KeyError: if unknown census_version value.
 
     Lifecycle:
-        Experimental.
+        maturing
 
     See Also:
         :func:`get_census_version_directory`: returns the entire directory as a dict.
@@ -83,7 +83,7 @@ def get_census_version_directory() -> Dict[CensusVersionName, CensusVersionDescr
         A dictionary that contains release names and their corresponding release description.
 
     Lifecycle:
-        Experimental.
+        maturing
 
     See Also:
         :func:`get_census_version_description`: get description by census_version.

api/python/cellxgene_census/src/cellxgene_census/experimental/ml/__init__.py

@@ -0,0 +1,7 @@
+"""
+An API to facilitate use of PyTorch ML training with data from the CZI Science CELLxGENE Census.
+"""
+
+from .pytorch import ExperimentDataPipe, Stats, experiment_dataloader
+
+__all__ = ["Stats", "ExperimentDataPipe", "experiment_dataloader"]

api/python/cellxgene_census/src/cellxgene_census/experimental/ml/pytorch.py

@@ -34,6 +34,13 @@
 
 @attrs
 class Stats:
+    """
+    Statistics about the data retrieved by ``ExperimentDataPipe`` via SOMA API.
+
+    Lifecycle:
+        experimental
+    """
+
     n_obs: int = 0
     """The total number of obs rows retrieved"""
 
@@ -71,21 +78,21 @@ def _open_experiment(
 
 class _ObsAndXIterator(Iterator[ObsDatum]):
     """
-    Iterates through a set of obs and related X rows, specified as `soma_joinid`s. Encapsulates the batch-based data
-    fetching from TileDB-SOMA objects, providing row-based iteration.
+    Iterates through a set of obs and related X rows, specified as ``soma_joinid``s. Encapsulates the batch-based data
+    fetching from SOMA objects, providing row-based iteration.
     """
 
     obs_tables_iter: somacore.ReadIter[pa.Table]
-    """Iterates the TileDB-SOMA batches (tables) of obs data"""
+    """Iterates the SOMA batches (tables) of obs data"""
 
     obs_batch_: pd.DataFrame = pd.DataFrame()
-    """The current TileDB-SOMA batch of obs data"""
+    """The current SOMA batch of obs data"""
 
     X_batch: scipy.matrix = None
-    """All X data for the soma_joinids of the current obs - batch"""
+    """All X data for the ``soma_joinid``s of the current obs - batch"""
 
     i: int = -1
-    """Index into current obs TileDB-SOMA batch"""
+    """Index into current obs ``SOMA`` batch"""
 
     def __init__(
         self,
@@ -177,7 +184,7 @@ def obs_batch(self) -> pd.DataFrame:
         Returns the current SOMA batch of obs rows.
         If the current SOMA batch has been fully iterated, loads the next SOMA batch of both obs and X data and returns
         the new obs batch (only).
-        Raises StopIteration if there are no more SOMA batches to retrieve.
+        Raises ``StopIteration`` if there are no more SOMA batches to retrieve.
         """
         if 0 <= self.i < len(self.obs_batch_):
             return self.obs_batch_
@@ -203,30 +210,33 @@ def obs_batch(self) -> pd.DataFrame:
 
 class ExperimentDataPipe(pipes.IterDataPipe[Dataset[ObsDatum]]):  # type: ignore
     """
-    An iterable-style PyTorch data pipe that reads obs and X data from a SOMA Experiment, and returns an iterator of
-    tuples of torch tensors:
+    An iterable-style PyTorch ``DataPipe`` that reads obs and X data from a SOMA Experiment, and returns an iterator of
+    tuples of PyTorch ``Tensor``s:
 
-    (tensor([0., 0., 0., 0., 0., 1., 0., 0., 0.]),  # X data
-     tensor([2415,    0,    0], dtype=torch.int32)) # obs data, encoded
+    >>> (tensor([0., 0., 0., 0., 0., 1., 0., 0., 0.]),  # X data
+        tensor([2415,    0,    0], dtype=torch.int32)) # obs data, encoded
 
     Supports batching via `batch_size` param:
-    DataLoader(..., batch_size=3, ...):
 
-    (tensor([[0., 0., 0., 0., 0., 1., 0., 0., 0.],     # X batch
-             [0., 0., 0., 0., 0., 0., 0., 0., 0.],
-             [0., 0., 0., 0., 0., 0., 0., 0., 0.]]),
-     tensor([[2415,    0,    0],                       # obs batch
-             [2416,    0,    4],
-             [2417,    0,    3]], dtype=torch.int32))
+    >>> DataLoader(..., batch_size=3, ...):
+        (tensor([[0., 0., 0., 0., 0., 1., 0., 0., 0.],     # X batch
+                 [0., 0., 0., 0., 0., 0., 0., 0., 0.],
+                 [0., 0., 0., 0., 0., 0., 0., 0., 0.]]),
+         tensor([[2415,    0,    0],                       # obs batch
+                 [2416,    0,    4],
+                 [2417,    0,    3]], dtype=torch.int32))
 
     Obs attribute values are encoded as categoricals. Values can be decoded by obtaining the encoder for a given
     attribute:
 
-    exp_data_pipe.obs_encoders()["<obs_attr_name>"].inverse_transform(encoded_values)
+    >>> exp_data_pipe.obs_encoders()["<obs_attr_name>"].inverse_transform(encoded_values)
+
+    Lifecycle:
+        experimental
     """
 
     _query: Optional[soma.ExperimentAxisQuery]
-    """In multi-processing mode (i.e. num_workers > 0), this ExperimentAxisQuery object will *not* be pickled; 
+    """In multi-processing mode (i.e. num_workers > 0), this ``ExperimentAxisQuery`` object will *not* be pickled; 
     each worker will instantiate a new query"""
 
     _obs_joinids_partitioned: Optional[pa.Array]
@@ -252,6 +262,16 @@ def __init__(
         num_workers: int = 0,
         soma_buffer_bytes: Optional[int] = None,
     ) -> None:
+        """
+        Construct a new ``ExperimentDataPipe``.
+
+        Returns:
+            ``ExperimentDataPipe``.
+
+        Lifecycle:
+            experimental
+        """
+
         self.exp_uri = experiment.uri
         self.aws_region = experiment.context.tiledb_ctx.config().get("vfs.s3.region")
         self.measurement_name = measurement_name
@@ -356,6 +376,15 @@ def __setstate__(self, state: Dict[str, Any]) -> None:
         self._query = None
 
     def obs_encoders(self) -> Encoders:
+        """
+        Returns the encoders that were used to encode obs column values and that are needed to decode them.
+
+        Returns:
+            ``Dict[str, LabelEncoder]`` mapping column names to ``LabelEncoder``s.
+
+        Lifecycle:
+            experimental
+        """
         if self._encoders is not None:
             return self._encoders
 
@@ -373,18 +402,41 @@ def obs_encoders(self) -> Encoders:
         return self._encoders
 
     def stats(self) -> Stats:
+        """
+        Get data loading stats for this ``ExperimentDataPipe``.
+
+        Args: None.
+
+        Returns:
+            ``Stats`` object.
+
+        Lifecycle:
+            experimental
+        """
         return self._stats
 
     @property
     def shape(self) -> Tuple[int, int]:
+        """
+        Get the shape of the data that will be returned by this ExperimentDataPipe. This is the number of
+        obs (cell) and var (feature) counts in the returned data. If used in multiprocessing mode
+        (i.e. DataLoader instantiated with num_workers > 0), the obs (cell) count will reflect the size of the
+        partition of the data assigned to the active process.
+
+        Returns:
+            2-tuple of ``int``s, for obs and var counts, respectively.
+
+        Lifecycle:
+            experimental
+        """
         self._init()
         assert self._query is not None
 
         return self._query.n_obs, self._query.n_vars
 
 
 # Note: must be a top-level function (and not a lambda), to play nice with multiprocessing pickling
-def collate_noop(x: Any) -> Any:
+def _collate_noop(x: Any) -> Any:
     return x
 
 
@@ -395,9 +447,15 @@ def experiment_dataloader(
     **dataloader_kwargs: Any,
 ) -> DataLoader:
     """
-    Factory method for PyTorch DataLoader. Provides a safer, more convenient interface for instantiating a DataLoader
-    that works with the ExperimentDataPipe, since not all of DataLoader's params can be used (batch_size, sampler,
-    batch_sampler, collate_fn).
+    Factory method for PyTorch ``DataLoader``. Provides a safer, more convenient interface for instantiating a
+    ``DataLoader`` that works with the ``ExperimentDataPipe``, since not all of ``DataLoader``'s params can be
+    used (``batch_size``, ``sampler``, ``batch_sampler``, ``collate_fn``).
+
+    Returns:
+        PyTorch ``DataLoader``.
+
+    Lifecycle:
+        experimental
     """
 
     unsupported_dataloader_args = ["batch_size", "sampler", "batch_sampler", "collate_fn"]
@@ -409,7 +467,7 @@ def experiment_dataloader(
         batch_size=None,  # batching is handled by our ExperimentDataPipe
         num_workers=num_workers,
         # avoid use of default collator, which adds an extra (3rd) dimension to the tensor batches
-        collate_fn=collate_noop,
+        collate_fn=_collate_noop,
         **dataloader_kwargs,
     )
 

api/python/notebooks/ml_demo/pytorch_lr_classifier.py

@@ -3,7 +3,7 @@
 import torch
 
 import cellxgene_census
-from cellxgene_census.experimental.ml.pytorch import experiment_dataloader, ExperimentDataPipe
+import cellxgene_census.experimental.ml as census_ml
 
 # TODO: Convert this to a notebook
 
@@ -82,7 +82,7 @@ def run():
     obs_value_filter = "tissue_general == 'tongue' and is_primary_data == True"
     var_value_filter = ""
 
-    exp_dp = ExperimentDataPipe(
+    exp_dp = census_ml.ExperimentDataPipe(
         census["census_data"]["homo_sapiens"],
         measurement_name="RNA",
         X_name="raw",
@@ -95,7 +95,7 @@ def run():
     dp = exp_dp.shuffle(buffer_size=len(exp_dp))
     dp_train, dp_test = dp.random_split(weights={"train": 0.7, "test": 0.3}, seed=RANDOM_SEED)
 
-    dl_train = experiment_dataloader(
+    dl_train = census_ml.experiment_dataloader(
         dp_train,
         # >= 1 uses multiprocessing to load data
         num_workers=0,