graphnet.utilities.config.training_config module

Config classes for the graphnet.training module.

class graphnet.utilities.config.training_config.TrainingConfig(*, target, early_stopping_patience, fit, dataloader)[source]

Bases: BaseConfig

Configuration for all trainings.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

  • target (str | List[str])

  • early_stopping_patience (int)

  • fit (Dict[str, Any])

  • dataloader (Dict[str, Any])

target: Union[str, List[str]]
early_stopping_patience: int
fit: Dict[str, Any]
dataloader: Dict[str, Any]
model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'dataloader': FieldInfo(annotation=Dict[str, Any], required=True), 'early_stopping_patience': FieldInfo(annotation=int, required=True), 'fit': FieldInfo(annotation=Dict[str, Any], required=True), 'target': FieldInfo(annotation=Union[str, List[str]], required=True)}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.