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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],) ‑> 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,) ‑> Mapping[str, bitfount.types._TensorLike]:

Defined in DistributedModelMixIn.

diff_params​

def diff_params(    self, old_params: _Weights, new_params: _Weights,) ‑> 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[Tuple[np.ndarray, np.ndarray], 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) ‑> Mapping[str, bitfount.types._TensorLike]:

Defined in DistributedModelMixIn.

initialise_model​

def initialise_model(    self, data: Optional[BaseSource] = 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[List[np.ndarray], 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) ‑> 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.

Ancestors​

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],) ‑> 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,) ‑> 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,) ‑> 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[Tuple[np.ndarray, np.ndarray], 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) ‑> Mapping[str, bitfount.types._TensorLike]:

Inherited from:

BaseDistributedModelProtocol.get_param_states :

Defined in DistributedModelMixIn.

initialise_model​

def initialise_model(    self, data: Optional[BaseSource] = 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[List[np.ndarray], 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) ‑> 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.