types
Type hints, enums and protocols for the Bitfount libraries.
Classes
BaseDistributedModelProtocol
class BaseDistributedModelProtocol(*args, **kwargs):Federated Model structural type that only specifies the methods.
The reason for this protocol is that issubclass checks with Protocols can only
be performed if the Protocol only specifies methods and not attributes. We still
want to specify the attributes in another protocol though for greater type safety,
(both statically and dynamically) so we have this protocol that only specifies
methods and another protocol that specifies the attributes.
Ancestors
Subclasses
Methods
apply_weight_updates
def apply_weight_updates( self, weight_updates: Sequence[_Weights],) ‑> collections.abc.Mapping[str, bitfount.types._TensorLike]:Defined in DistributedModelMixIn.
deserialize
def deserialize(self, filename: Union[str, os.PathLike]) ‑> None:Defined in _BaseModel.
deserialize_params
def deserialize_params( self, serialized_weights: _SerializedWeights,) ‑> collections.abc.Mapping[str, bitfount.types._TensorLike]:Defined in DistributedModelMixIn.
diff_params
def diff_params( self, old_params: _Weights, new_params: _Weights,) ‑> collections.abc.Mapping[str, bitfount.types._TensorLike]:Defined in DistributedModelMixIn.
evaluate
def evaluate( self, test_dl: Optional[BitfountDataLoader] = None, pod_identifiers: Optional[list[str]] = None, **kwargs: Any,) ‑> Union[EvaluateReturnType, dict[str, float]]:Defined in _BaseModel.
fit
def fit( self, data: Optional[BaseSource] = None, metrics: Optional[dict[str, Metric]] = None, pod_identifiers: Optional[list[str]] = None, **kwargs: Any,) ‑> Optional[dict[str, str]]:Defined in DistributedModelMixIn.
get_param_states
def get_param_states(self) ‑> collections.abc.Mapping[str, bitfount.types._TensorLike]:Defined in DistributedModelMixIn.
initialise_model
def initialise_model( self, data: Optional[BaseSource] = None, data_splitter: Optional[DatasetSplitter] = None, context: Optional[TaskContext] = None,) ‑> None:Defined in _BaseModel.
log_
def log_(self, name: str, value: Any, **kwargs: Any) ‑> Any:Defined in DistributedModelMixIn.
predict
def predict( self, data: Optional[BaseSource] = None, pod_identifiers: Optional[list[str]] = None, **kwargs: Any,) ‑> Union[PredictReturnType, dict[str, list[np.ndarray]]]:Defined in _BaseModel.
reset_trainer
def reset_trainer(self) ‑> None:Defined in DistributedModelMixIn.
serialize
def serialize(self, filename: Union[str, os.PathLike]) ‑> None:Defined in _BaseModel.
serialize_params
def serialize_params( self, weights: _Weights,) ‑> collections.abc.Mapping[str, list[float]]:Defined in DistributedModelMixIn.
set_datastructure_identifier
def set_datastructure_identifier(self, datastructure_identifier: str) ‑> None:Defined in DistributedModelMixIn.
set_model_training_iterations
def set_model_training_iterations(self, iterations: int) ‑> None:Defined in DistributedModelMixIn.
tensor_precision
def tensor_precision(self) ‑> +T_DTYPE:Defined in DistributedModelMixIn.
update_params
def update_params(self, new_model_params: _Weights) ‑> None:Defined in DistributedModelMixIn.
DistributedModelProtocol
class DistributedModelProtocol(*args, **kwargs):Federated Model structural type.
This protocol should be implemented by classes that inherit from either
BitfountModel, or both of _BaseModel and DistributedModelMixIn.
Variables
- static
class_name : str
- static
datastructure : DataStructure
- static
epochs : Optional[int]
- static
fields_dict : ClassVar[T_FIELDS_DICT]
- static
metrics : Optional[MutableMapping[str, Metric]]
- static
nested_fields : ClassVar[T_NESTED_FIELDS]
- static
param_clipping : Optional[dict[str, int]]
- static
schema : BitfountSchema
- static
steps : Optional[int]
initialised : bool- Defined in _BaseModel.
Methods
apply_weight_updates
def apply_weight_updates( self, weight_updates: Sequence[_Weights],) ‑> collections.abc.Mapping[str, bitfount.types._TensorLike]:Inherited from:
BaseDistributedModelProtocol.apply_weight_updates :
Defined in DistributedModelMixIn.
deserialize
def deserialize(self, filename: Union[str, os.PathLike]) ‑> None:Inherited from:
BaseDistributedModelProtocol.deserialize :
Defined in _BaseModel.
deserialize_params
def deserialize_params( self, serialized_weights: _SerializedWeights,) ‑> collections.abc.Mapping[str, bitfount.types._TensorLike]:Inherited from:
BaseDistributedModelProtocol.deserialize_params :
Defined in DistributedModelMixIn.
diff_params
def diff_params( self, old_params: _Weights, new_params: _Weights,) ‑> collections.abc.Mapping[str, bitfount.types._TensorLike]:Inherited from:
BaseDistributedModelProtocol.diff_params :
Defined in DistributedModelMixIn.
evaluate
def evaluate( self, test_dl: Optional[BitfountDataLoader] = None, pod_identifiers: Optional[list[str]] = None, **kwargs: Any,) ‑> Union[EvaluateReturnType, dict[str, float]]:Inherited from:
BaseDistributedModelProtocol.evaluate :
Defined in _BaseModel.
fit
def fit( self, data: Optional[BaseSource] = None, metrics: Optional[dict[str, Metric]] = None, pod_identifiers: Optional[list[str]] = None, **kwargs: Any,) ‑> Optional[dict[str, str]]:Inherited from:
BaseDistributedModelProtocol.fit :
Defined in DistributedModelMixIn.
get_param_states
def get_param_states(self) ‑> collections.abc.Mapping[str, bitfount.types._TensorLike]:Inherited from:
BaseDistributedModelProtocol.get_param_states :
Defined in DistributedModelMixIn.
initialise_model
def initialise_model( self, data: Optional[BaseSource] = None, data_splitter: Optional[DatasetSplitter] = None, context: Optional[TaskContext] = None,) ‑> None:Inherited from:
BaseDistributedModelProtocol.initialise_model :
Defined in _BaseModel.
log_
def log_(self, name: str, value: Any, **kwargs: Any) ‑> Any:Inherited from:
BaseDistributedModelProtocol.log_ :
Defined in DistributedModelMixIn.
predict
def predict( self, data: Optional[BaseSource] = None, pod_identifiers: Optional[list[str]] = None, **kwargs: Any,) ‑> Union[PredictReturnType, dict[str, list[np.ndarray]]]:Inherited from:
BaseDistributedModelProtocol.predict :
Defined in _BaseModel.
reset_trainer
def reset_trainer(self) ‑> None:Inherited from:
BaseDistributedModelProtocol.reset_trainer :
Defined in DistributedModelMixIn.
serialize
def serialize(self, filename: Union[str, os.PathLike]) ‑> None:Inherited from:
BaseDistributedModelProtocol.serialize :
Defined in _BaseModel.
serialize_params
def serialize_params( self, weights: _Weights,) ‑> collections.abc.Mapping[str, list[float]]:Inherited from:
BaseDistributedModelProtocol.serialize_params :
Defined in DistributedModelMixIn.
set_datastructure_identifier
def set_datastructure_identifier(self, datastructure_identifier: str) ‑> None:Inherited from:
BaseDistributedModelProtocol.set_datastructure_identifier :
Defined in DistributedModelMixIn.
set_model_training_iterations
def set_model_training_iterations(self, iterations: int) ‑> None:Inherited from:
BaseDistributedModelProtocol.set_model_training_iterations :
Defined in DistributedModelMixIn.
tensor_precision
def tensor_precision(self) ‑> +T_DTYPE:Inherited from:
BaseDistributedModelProtocol.tensor_precision :
Defined in DistributedModelMixIn.
update_params
def update_params(self, new_model_params: _Weights) ‑> None:Inherited from:
BaseDistributedModelProtocol.update_params :
Defined in DistributedModelMixIn.