data_representation¶
Base model for defining data representations.
- class graphnet.models.data_representation.data_representation.DataRepresentation(*args, **kwargs)[source]¶
Bases:
Model
An Abstract class to create data representations from.
Construct´DataRepresentation´. The ´detector´ holds.
´Detector´-specific code. E.g. scaling/standardization and geometry tables.
- Parameters:
detector (
Detector
) – The corresponding ´Detector´ representing the data.input_feature_names (
Optional
[List
[str
]], default:None
) – Names of each column in expected input data that will be built into a the data. If not provided, it is automatically assumed that all features in Detector is used.dtype (
Optional
[dtype
], default:torch.float32
) – data type used for features. e.g. ´torch.float´perturbation_dict (
Optional
[Dict
[str
,float
]], default:None
) – Dictionary mapping a feature name to a standard deviation according to which the values for this feature should be randomly perturbed. Defaults to None.seed (
Union
[int
,Generator
,None
], default:None
) – seed or Generator used to randomly sample perturbations. Defaults to None.add_inactive_sensors (
bool
, default:False
) – If True, inactive sensors will be appended to the data with padded pulse information. Defaults to False.sensor_mask (
Optional
[List
[int
]], default:None
) –A list of sensor id’s to be masked from the data. Any sensor listed here will be removed from the data.
Defaults to None.
string_mask (
Optional
[List
[int
]], default:None
) – A list of string id’s to be masked from the data. Defaults to None.repeat_labels (
bool
, default:False
) – If True, labels will be repeated to match the the number of rows in the output of the GraphDefinition. Defaults to False.args (Any)
kwargs (Any)
- Return type:
object
- property nb_outputs: int¶
Return the number of output features.
- property output_feature_names: List[str]¶
Initialize / return the names of output features.
- forward(input_features, input_feature_names, truth_dicts, custom_label_functions, loss_weight_column, loss_weight, loss_weight_default_value, data_path)[source]¶
Construct data as ´Data´ object.
- Parameters:
input_features (
ndarray
) – Input features for data construction. Shape ´[num_rows, d]´input_feature_names (
List
[str
]) – name of each column. Shape ´[,d]´.truth_dicts (
Optional
[List
[Dict
[str
,Any
]]], default:None
) – Dictionary containing truth labels.custom_label_functions (
Optional
[Dict
[str
,Callable
[...
,Any
]]], default:None
) – Custom label functions.loss_weight_column (
Optional
[str
], default:None
) – Name of column that holds loss weight. Defaults to None.loss_weight (
Optional
[float
], default:None
) – Loss weight associated with event. Defaults to None.loss_weight_default_value (
Optional
[float
], default:None
) – default value for loss weight. Used in instances where some events have no pre-defined loss weight. Defaults to None.data_path (
Optional
[str
], default:None
) – Path to dataset data files. Defaults to None.
- Return type:
Data
- Returns:
data