leaderbot.evaluate.compare_ranks#

leaderbot.evaluate.compare_ranks(models, order=None, rank_range=None, ax=None, bg_color='none', fg_color='black', mf_color='white', save=False, latex=False)#

Compare ranking of various models.

Parameters:

modelslist: A list of models, with at least two models.
orderlist or np.array_like, default=None: A list of indices to re-order or select from the list of models. If None, the models appear are given in the models list.
rank_rangelist, default=None: A range of ranks [rank_min, rank_max] to be used in the comparison. The rank indices in rank_range are 1-based indexing and inclusive on both start and end of the range. The ranking range is with respect to the rank that is provided by the first model in the models list. If None, the full range of ranks is used.
axmatplotlib.axes._axes.Axes: If a matplotlib axis object is given, this plot is shown in the provided axis. Otherwise, a new plot is generated.
bg_colorstr or tuple, default=’none’: Color of the background canvas. The default value of 'none' means transparent.
fg_colorstr or tuple, default=’black’: Color of the axes and text.
mf_colorstr or tuple, default=None: Marker face color. If None, the ``bg_color` is used
savebool, default=False: If True, the plot will be saved. This argument is effective only if plot is True.
latexbool, default=False: If True, the plot is rendered with LaTeX engine, assuming the latex executable is available on the PATH. Enabling this option will slow the plot generation.

Raises:

RuntimeError: If any of the models are not trained before calling this method.

See also

leaderbot.evaluate.model_selection
leaderbot.evaluate.goodness_of_fit
leaderbot.evaluate.generalization

Examples

>>> import leaderbot as lb
>>> from leaderbot.models import BradleyTerry as BT
>>> from leaderbot.models import RaoKupper as RK
>>> from leaderbot.models import Davidson as DV

>>> # Load data
>>> data = lb.data.load()

>>> # Create a list of models to compare
>>> models = [
...     BT(data, k_cov=0),
...     BT(data, k_cov=3),
...     RK(data, k_cov=0, k_tie=0),
...     RK(data, k_cov=0, k_tie=1),
...     RK(data, k_cov=0, k_tie=3),
...     DV(data, k_cov=0, k_tie=0),
...     DV(data, k_cov=0, k_tie=1),
...     DV(data, k_cov=0, k_tie=3)
... ]

>>> # Train the models
>>> for model in models: model.train()

>>> # Compare ranking of the models
>>> lb.evaluate.compare_ranks(models, rank_range=[40, 70])

The above code produces plot below.