leaderbot.models.RaoKupper.match_matrix

RaoKupper.match_matrix(max_rank=None, density=True, source='both', win_range=None, tie_range=None, horizontal=False, bg_color='none', fg_color='black', save=False, latex=False)

Plot match matrices of win and tie counts of mutual matches.

Parameters:

max_rankint, default=None

The maximum number of agents to be displayed. If None, all agents in the input dataset will be ranked and shown.

densitybool, default=True

If False, the frequency (count) of win and tie are plotted. If True, the probability of the win and tie are plotted.

source{'observed', 'predicted', 'both'}, default= 'both'

The source of data to be used:

• 'observed': The observed win and tie counts based on the input training data to the model.

• 'predicted': The prediction of win and tie counts by the trained model.

• 'both': Plots both of the observed and predicted data.

win_rangetuple, default=None

The tuple of two float numbers (vmin, vmax) determining the range of the heatmap plot for win matrix. If None, the minimum and maximum range of data is used.

tie_rangetuple, default=None

The tuple of two float numbers (vmin, vmax) determining the range of the heatmap plot for tie matrix. If None, the minimum and maximum range of data is used.

horizontalbool, default=False

If True, the subplots for win and tie are placed row-wise. If False, they are plotted in column-wise.

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.

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 the model is not trained before calling this method.

See also

map_distance

Examples

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

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

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

>>> # Plot match matrices for win and tie
>>> model.match_matrix(max_rank=20, density=True, source='both',
...                    latex=True, save=True, horizontal=True,
...                    win_range=[0.2, 0.6], tie_range=[0.15, 0.4])

The above code provides the text output and plot below.

Similarly, plots for win and tie frequencies can be obtained as follows:

>>> # Plot match matrices for win and tie
>>> model.match_matrix(max_rank=20, density=False, source='both',
...                    latex=True, save=True, horizontal=True,
...                    win_range=[0, 3000], tie_range=[0, 1500])