dataset_config

Config classes for the graphnet.data.dataset module.

class graphnet.utilities.config.dataset_config.DatasetConfig(*, path, pulsemaps, features, truth, node_truth, index_column, truth_table, node_truth_table, string_selection, selection, loss_weight_table, loss_weight_column, loss_weight_default_value, seed, graph_definition, data_representation, labels)

Bases: BaseConfig

Configuration for all `Dataset`s.

Construct DataConfig.

Can be used for dataset configuration as code, thereby making dataset construction more transparent and reproducible.

Examples

In one session, do:

>>> dataset = Dataset(...)
>>> dataset.config.dump()
path: (...)
pulsemaps:
    - (...)
(...)
>>> dataset.config.dump("dataset.yml")

In another session, you can then do: >>> dataset = Dataset.from_config(“dataset.yml”)

# Uniquely for DatasetConfig, you can also define and load # multiple datasets >>> dataset.config.selection = {

“train”: “event_no % 2 == 0”, “test”: “event_no % 2 == 1”,

} >>> dataset.config.dump(“dataset.yml”) >>> datasets: Dict[str, Dataset] = Dataset.from_config(

“dataset.yml”

) >>> datasets {

“train”: Dataset(…), “test”: Dataset(…),

}

# You can also combine multiple selections into a single, named # dataset >>> dataset.config.selection = {

“train”: [

“event_no % 2 == 0 & abs(pid) == 12”, “event_no % 2 == 0 & abs(pid) == 14”, “event_no % 2 == 0 & abs(pid) == 16”,

], (…)

} >>> dataset.config.dump(“dataset.yml”) >>> datasets: Dict[str, EnsembleDataset] = Dataset.from_config(

“dataset.yml”

) >>> datasets {

“train”: EnsembleDataset(…), (…)

}

# Finally, you can still reference existing selection files in CSV # or JSON formats: >>> dataset.config.selection = {

“train”: “50000 random events ~ train_selection.csv”, “test”: “test_selection.csv”,

}

Parameters:

• path (str | List[str])

• pulsemaps (str | List[str])

• features (List[str])

• truth (List[str])

• node_truth (List[str] | None)

• index_column (str)

• truth_table (str)

• node_truth_table (str | None)

• string_selection (List[int] | None)

• selection (str | List[str] | List[int | List[int]] | Dict[str, str | List[str]] | None)

• loss_weight_table (str | None)

• loss_weight_column (str | None)

• loss_weight_default_value (float | None)

• seed (int | None)

• graph_definition (Any)

• data_representation (Any)

• labels (Dict[str, Any] | None)

path: str | List[str]

pulsemaps: str | List[str]

features: List[str]

truth: List[str]

node_truth: List[str] | None

index_column: str

truth_table: str

node_truth_table: str | None

string_selection: List[int] | None

selection: str | List[str] | List[int | List[int]] | Dict[str, str | List[str]] | None

loss_weight_table: str | None

loss_weight_column: str | None

loss_weight_default_value: float | None

seed: int | None

graph_definition: Any

data_representation: Any

labels: Dict[str, Any] | None

as_dict()

Represent ModelConfig as a dict.

This builds on BaseModel.dict() but wraps the output in a single-key dictionary to make it unambiguous to identify model arguments that are themselves models.

Return type:

Dict[str, Dict[str, Any]]

model_config: ClassVar[ConfigDict] = {}

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

graphnet.utilities.config.dataset_config.save_dataset_config(init_fn)

Save the arguments to __init__ functions as member DatasetConfig.

Return type:

Callable

Parameters:

init_fn (Callable)

class graphnet.utilities.config.dataset_config.DatasetConfigSaverMeta

Bases: type

Metaclass for DatasetConfig that saves the config after __init__.

class graphnet.utilities.config.dataset_config.DatasetConfigSaverABCMeta(name, bases, namespace, /, **kwargs)

Bases: DatasetConfigSaverMeta, ABCMeta

Common interface between DatasetConfigSaver and ABC Metaclasses.