'Plot GeneralizationAcrossTime object.
Plot the scores of the classifier trained at specific training time(s).
Parameters
train_time : float | list or array of float
Plots scores of the classifier trained at train_time.
title : str | None
Figure title.
xmin : float | None, optional
Min time value.
xmax : float | None, optional
Max time value.
ymin : float | None, optional
Min score value. If None, sets to min(scores).
ymax : float | None, optional
Max score value. If None, sets to max(scores).
ax : object | None
Instance of mataplotlib.axes.Axis. If None, generate new figure.
show : bool
If True, the figure will be shown. Defaults to True.
color : str or list of str
Score line color(s).
xlabel : bool
If True, the xlabel is displayed. Defaults to True.
ylabel : bool
If True, the ylabel is displayed. Defaults to True.
legend : bool
If True, a legend is displayed. Defaults to True.
chance : bool | float.
Plot chance level. If True, chance level is estimated from the type
of scorer.
label : str
Score label used in the legend. Defaults to \'Classif. score\'.
Returns
fig : instance of matplotlib.figure.Figure
The figure.'

def plot_times(self, train_time, title=None, xmin=None, xmax=None, ymin=None, ymax=None, ax=None, show=True, color=None, xlabel=True, ylabel=True, legend=True, chance=True, label='Classif.    score'):

    if (not (np.array(train_time).dtype is np.dtype('float'))):
        raise ValueError(('train_time    must    be    float    |    list    or    array    of    floats.    Got    %s.' % type(train_time)))
    return plot_gat_times(self, train_time=train_time, title=title, xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, ax=ax, show=show, color=color, xlabel=xlabel, ylabel=ylabel, legend=legend, chance=chance, label=label)
