sfaira.estimators.EstimatorKerasCelltype¶

class sfaira.estimators.EstimatorKerasCelltype(data: typing.Union[anndata._core.anndata.AnnData, sfaira.data.store.stores.single.StoreSingleFeatureSpace], model_dir: typing.Optional[str], model_id: typing.Optional[str], model_topology: sfaira.versions.topologies.class_interface.TopologyContainer, weights_md5: typing.Optional[str] = None, cache_path: str = 'cache/', celltype_ontology: typing.Optional[sfaira.versions.metadata.base.OntologyObo] = None, max_class_weight: float = 1000.0, remove_unlabeled_cells: bool = True, adata_ids: sfaira.consts.adata_fields.AdataIds = <sfaira.consts.adata_fields.AdataIdsSfaira object>)¶

Estimator class for the cell type model.

Attributes

`model_type`
`ntypes`
`obs`
`obs_eval`
`obs_test`
`obs_train`
`ontology_ids`
`ontology_names`
`organism`
`using_store`
`celltype_universe`
`model`

Methods

`compute_gradients_input`([test_data, ...])
`evaluate`([batch_size, max_steps, weighted])	Evaluate the custom model on local data.
`evaluate_any`(idx[, batch_size, max_steps, ...])	Evaluate the custom model on any local data.
`get_one_time_tf_dataset`(idx, mode[, ...])
`init_model`([clear_weight_cache, ...])	instantiate the model :return:
`load_pretrained_weights`()	Loads model weights from local directory or zenodo.
`load_weights_from_cache`()
`predict`([batch_size, max_steps])	Return the prediction of the model
`save_weights_to_cache`()
`split_train_val_test`(val_split, test_split)	Split indices in store into train, valiation and test split.
`train`(optimizer, lr[, epochs, ...])	Train model.
`ytrue`([batch_size, max_steps])	Return the true labels of the test set.