weight_fitting¶

Classes for fitting per-event weights for training.

class graphnet.training.weight_fitting.WeightFitter(database_path, truth_table, index_column)[source]¶

Bases: ABC, Logger

Produces per-event weights.

Weights are returned by the public method fit_weights(), and the weights can be saved as a table in the database.

Construct UniformWeightFitter.

Parameters:

fit(bins, variable, weight_name, add_to_database, selection, transform, **kwargs)[source]¶

Fit weights.

Calls private _fit_weights method. Output is returned as a pandas.DataFrame and optionally saved to sql.

Parameters:

bins (ndarray) – Desired bins used for fitting.
variable (str) – the name of the variable. Must match corresponding column name in the truth table.
weight_name (Optional[str], default: None) – Name of the weights.
add_to_database (bool, default: False) – If True, the weights are saved to sql in a table named weight_name.
selection (Optional[List[int]], default: None) – a list of event_no’s. If given, only events in the selection is used for fitting.
transform (Optional[Callable], default: None) – A callable method that transform the variable into a desired space. E.g. np.log10 for energy. If given, fitting will happen in this space.
**kwargs (Any) – Additional arguments passed to _fit_weights.

Return type:

DataFrame

Returns:

DataFrame that contains weights, event_nos.

class graphnet.training.weight_fitting.Uniform(database_path, truth_table, index_column)[source]¶

Produces per-event weights making variable distribution uniform.

Construct UniformWeightFitter.

Parameters:

class graphnet.training.weight_fitting.BjoernLow(database_path, truth_table, index_column)[source]¶

Produces per-event weights.

Events below x_low are weighted to be uniform, whereas events above x_low are weighted to follow a 1/(1+a*(x_low -x)) curve.

Construct UniformWeightFitter.

Parameters: