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1.1.2

Changed

- Address AnnData >= 0.10 deprecation warning for {func}`anndata.read` by replacing instances with
{func}`anndata.read_h5ad` {pr}`2531`.
- Address AnnData >= 0.10 deprecation warning for {class}`anndata._core.sparse_dataset.SparseDataset`
by replacing instances with {class}`anndata.experimental.CSCDataset` and
{class}`anndata.experimental.CSRDataset` {pr}`2531`.

1.1.1

Fixed

- Correctly apply non-default user parameters in {class}`scvi.external.POISSONVI` {pr}`2522`.

1.1

1.1.0

Added

- Add {class}`scvi.external.ContrastiveVI` for contrastiveVI {pr}`2242`.
- Add {class}`scvi.dataloaders.BatchDistributedSampler` for distributed training {pr}`2102`.
- Add `additional_val_metrics` argument to {class}`scvi.train.Trainer`, allowing to specify
additional metrics to compute and log during the validation loop using
{class}`scvi.train._callbacks.MetricsCallback` {pr}`2136`.
- Expose `accelerator` and `device` arguments in {meth}`scvi.hub.HubModel.load_model` `pr`{2166}.
- Add `load_sparse_tensor` argument in {class}`scvi.data.AnnTorchDataset` for directly loading
SciPy CSR and CSC data structures to their PyTorch counterparts, leading to faster data loading
depending on the sparsity of the data {pr}`2158`.
- Add per-group LFC information to
{meth}`scvi.criticism.PosteriorPredictiveCheck.differential_expression`. `metrics["diff_exp"]`
is now a dictionary where `summary` stores the summary dataframe, and `lfc_per_model_per_group`
stores the per-group LFC {pr}`2173`.
- Expose {meth}`torch.save` keyword arguments in {class}`scvi.model.base.BaseModelClass.save`
and {class}`scvi.external.GIMVI.save` {pr}`2200`.
- Add `model_kwargs` and `train_kwargs` arguments to {meth}`scvi.autotune.ModelTuner.fit`
{pr}`2203`.
- Add `datasplitter_kwargs` to model `train` methods {pr}`2204`.
- Add `use_posterior_mean` argument to {meth}`scvi.model.SCANVI.predict` for stochastic prediction
of celltype labels {pr}`2224`.
- Add support for Python 3.10+ type annotations in {class}`scvi.autotune.ModelTuner` {pr}`2239`.
- Add option to log device statistics in {meth}`scvi.autotune.ModelTuner.fit` with argument
`monitor_device_stats` {pr}`2260`.
- Add option to pass in a random seed to {meth}`scvi.autotune.ModelTuner.fit` with argument `seed`
{pr}`2260`.
- Automatically log the learning rate when `reduce_lr_on_plateau=True` in training plans
{pr}`2280`.
- Add {class}`scvi.external.POISSONVI` to model scATAC-seq fragment counts with a Poisson
distribution {pr}`2249`
- {class}`scvi.train.SemiSupervisedTrainingPlan` now logs the classifier calibration error
{pr}`2299`.
- Passing `enable_checkpointing=True` into `train` methods is now compatible with our model saves.
Additional options can be specified by initializing with {class}`scvi.train.SaveCheckpoint`
{pr}`2317`.
- {attr}`scvi.settings.dl_num_workers` is now correctly applied as the default `num_workers` in
{class}`scvi.dataloaders.AnnDataLoader` {pr}`2322`.
- Passing in `indices` to {class}`scvi.criticism.PosteriorPredictiveCheck` allows for running
metrics on a subset of the data {pr}`2361`.
- Add `seed` argument to {func}`scvi.model.utils.mde` for reproducibility {pr}`2373`.
- Add {meth}`scvi.hub.HubModel.save` and {meth}`scvi.hub.HubMetadata.save` {pr}`2382`.
- Add support for Optax 0.1.8 by renaming instances of {func}`optax.additive_weight_decay` to
{func}`optax.add_weight_decay` {pr}`2396`.
- Add support for hosting {class}`scvi.hub.HubModel` on AWS S3 via
{meth}`scvi.hub.HubModel.pull_from_s3` and {meth}`scvi.hub.HubModel.push_to_s3` {pr}`2378`.
- Add clearer error message for {func}`scvi.data.poisson_gene_selection` when input data does not
contain raw counts {pr}`2422`.
- Add API for using custom dataloaders with {class}`scvi.model.SCVI` by making `adata` argument
optional on initialization and adding optional argument `data_module` to
{meth}`scvi.model.base.UnsupervisedTrainingMixin.train` {pr}`2467`.
- Add support for Ray 2.8-2.9 in {class}`scvi.autotune.ModelTuner` {pr}`2478`.

Fixed

- Fix bug where `n_hidden` was not being passed into {class}`scvi.nn.Encoder` in
{class}`scvi.model.AmortizedLDA` {pr}`2229`
- Fix bug in {class}`scvi.module.SCANVAE` where classifier probabilities were interpreted as
logits. This is backwards compatible as loading older models will use the old code path
{pr}`2301`.
- Fix bug in {class}`scvi.external.GIMVI` where `batch_size` was not properly used in inference
methods {pr}`2366`.
- Fix error message formatting in {meth}`scvi.data.fields.LayerField.transfer_field` {pr}`2368`.
- Fix ambiguous error raised in {meth}`scvi.distributions.NegativeBinomial.log_prob` and
{meth}`scvi.distributions.ZeroInflatedNegativeBinomial.log_prob` when `scale` not passed in
and value not in support {pr}`2395`.
- Fix initialization of {class}`scvi.distributions.NegativeBinomial` and
{class}`scvi.distributions.ZeroInflatedNegativeBinomial` when `validate_args=True` and
optional parameters not passed in {pr}`2395`.
- Fix error when re-initializing {class}`scvi.external.GIMVI` with the same datasets {pr}`2446`.

Changed

- Replace `sparse` with `sparse_format` argument in {meth}`scvi.data.synthetic_iid` for increased
flexibility over dataset format {pr}`2163`.
- Revalidate `devices` when automatically switching from MPS to CPU accelerator in
{func}`scvi.model._utils.parse_device_args` {pr}`2247`.
- Refactor {class}`scvi.data.AnnTorchDataset`, now loads continuous data as {class}`numpy.float32`
and categorical data as {class}`numpy.int64` by default {pr}`2250`.
- Support fractional GPU usage in {class}`scvi.autotune.ModelTuner` `pr`{2252}.
- Tensorboard is now the default logger in {class}`scvi.autotune.ModelTuner` `pr`{2260}.
- Match `momentum` and `epsilon` in {class}`scvi.module.JaxVAE` to the default values in PyTorch
{pr}`2309`.
- Change {class}`scvi.train.SemiSupervisedTrainingPlan` and
{class}`scvi.train.ClassifierTrainingPlan` accuracy and F1 score
computations to use `"micro"` reduction rather than `"macro"` {pr}`2339`.
- Internal refactoring of {meth}`scvi.module.VAE.sample` and
{meth}`scvi.model.base.RNASeqMixin.posterior_predictive_sample` {pr}`2377`.
- Change `xarray` and `sparse` from mandatory to optional dependencies {pr}`2480`.
- Use {class}`anndata.experimental.CSCDataset` and {class}`anndata.experimental.CSRDataset`
instead of the deprecated {class}`anndata._core.sparse_dataset.SparseDataset` for type checks
{pr}`2485`.
- Make `use_observed_lib_size` argument adjustable in {class}`scvi.module.LDVAE` `pr`{2494}.

Removed

- Remove deprecated `use_gpu` argument in favor of PyTorch Lightning arguments `accelerator` and
`devices` {pr}`2114`.
- Remove deprecated `scvi._compat.Literal` class {pr}`2115`.
- Remove chex dependency {pr}`2482`.

1.0.4

Added

- Add support for AnnData 0.10.0 {pr}`2271`.

1.0.3

Changed

- Disable the default selection of MPS when `accelerator="auto"` in Lightning {pr}`2167`.
- Change JAX models to use `dict` instead of {class}`flax.core.FrozenDict` according
to the Flax migration guide <https://github.com/google/flax/discussions/3191> {pr}`2222`.

Fixed

- Fix bug in {class}`scvi.model.base.PyroSviTrainMixin` where `training_plan`
argument is ignored {pr}`2162`.
- Fix missing docstring for `unlabeled_category` in
{class}`scvi.model.SCANVI.setup_anndata` and reorder arguments {pr}`2189`.
- Fix Pandas 2.0 unpickling error in {meth}`scvi.model.base.BaseModelClas.convert_legacy_save`
by switching to {func}`pandas.read_pickle` for the setup dictionary {pr}`2212`.

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