Skip to main content

base_models

Defines abstract models, mixins, and other common backend-agnostic classes.

Implementations of these abstract models should be located in bitfount.models.models or in the models subpackage of a backend.

Classes​

ClassifierMixIn​

class ClassifierMixIn(    multilabel: bool = False, param_clipping: Optional[dict[str, int]] = None,):

MixIn for classification problems.

Classification models must have this class in their inheritance hierarchy.

Arguments

  • multilabel: Whether the problem is a multi-label problem. i.e. each datapoint belongs to multiple classes
  • param_clipping: Arguments for clipping for BatchNorm parameters. Used for federated models with secure aggregation. It should contain the SecureShare variables and the number of workers in a dictionary, e.g. {"prime_q":13, "precision": 10**3,"num_workers":2}

Attributes

  • multilabel: Whether the problem is a multi-label problem
  • n_classes: Number of classes in the problem

Ancestors​

  • bitfount.models.base_models._BaseModelRegistryMixIn
  • bitfount.types._BaseSerializableObjectMixIn

Subclasses​

Variables​

Methods​

set_number_of_classes​

def set_number_of_classes(self, schema: TableSchema) ‑> None:

Sets the target number of classes for the classifier.

If the data is a multi-label problem, the number of classes is set to the number of target columns as specified in the DataStructure. Otherwise, the number of classes is set to the number of unique values in the target column as specified in the BitfountSchema. The value is stored in the n_classes attribute.

LoggerConfig​

class LoggerConfig(    name: str,    save_dir: Optional[Path] = PosixPath('bitfount_logs'),    params: Optional[_StrAnyDict] = {},):

Configuration for the logger.

The configured logger will log training events, metrics, model checkpoints, etc. to your chosen platform. If no logger configuration is provided, the default logger is a Tensorboard logger.

Arguments

  • name: The name of the logger. Should be one of the loggers supported by the chosen backend
  • save_dir: The directory to save the logs. Defaults to BITFOUNT_LOGS_DIR
  • params: A dictionary of keyword arguments to pass to the logger. Defaults to an empty dictionary

Variables​

  • static name : str - same as argument
  • static params : Optional[dict] - same as argument
  • static save_dir : Optional[pathlib.Path] - same as argument

RegressorMixIn​

class RegressorMixIn():

MixIn for regression problems.

Currently, just used for tagging purposes.

Ancestors​

  • bitfount.models.base_models._BaseModelRegistryMixIn
  • bitfount.types._BaseSerializableObjectMixIn