leaderbot.models.RaoKupper.infer#

RaoKupper.infer(x=None)#

Infer the probabilities of win, loss, and tie outcomes.

Parameters:

xnp.ndarray, list, zip, or leaderbot.data.DataType: A 2D array (or equivalent list to zip) of integers with the shape (n_pairs, 2) where each row consists of indices [i, j] representing a match between a pair of agents with the indice i and j. Alternatively, a dictionary of the type leaderbot.data.DataType can be provided. If None, the X variable from the input data is used.

Returns:

probnp.array: An array of the shape (n_pairs, 3) where the columns represent the win, loss, and tie probabilities for the model i against model j in order that appears in the input x.

Raises:

RuntimeError: If the model is not trained before calling this method.

See also

train: train model parameters.
predict: Predict win, loss, or tie between agents.

Examples

>>> from leaderbot.data import load
>>> from leaderbot.models import Davidson

>>> # Create a model
>>> data = load()
>>> model = Davidson(data)

>>> # Train the model
>>> model.train()

>>> # Make inference
>>> prob = model.infer()