plot_boxplot¶

plot_boxplot(results, labels, relative=False, plot_type='violin', outliers=True, title=None, y_min=None, y_max=None)[source]¶

Plot a box plot.

Each row of results is an independent experiment for each element in names.

Parameters:

results: np.array: Scores (either accuracies or errors) of dataset x strategy
labels: list of estimators: List with names of the estimators
relative: bool, default = False: If True, the results for a given dataset are divided by the median result.
plot_type: str, default = “violin”: This function can create four sort of distribution plots: “violin”, “swarm”, “boxplot” or “strip”. “violin” plot features a kernel density estimation of the underlying distribution. “swarm” draws a categorical scatterplot with points adjusted to be non-overlapping. “strip” draws a categorical scatterplot using jitter to reduce overplotting.
outliers: bool, default = True: Only applies when plot_type is “boxplot”.
title: str, default = None: Title to be shown in the top of the plot.
y_min: float, default = None: Min value for the y_axis of the plot.
y_max: float, default = None: Max value for the y_axis of the plot.

Returns:

figmatplotlib.figure.Figure
axmatplotlib.axes.Axes

Examples

>>> from aeon.visualisation import plot_boxplot
>>> from aeon.benchmarking.results_loaders import get_estimator_results_as_array
>>> methods = ["IT", "WEASEL-Dilation", "HIVECOTE2", "FreshPRINCE"]
>>> results = get_estimator_results_as_array(estimators=methods) 
>>> plot = plot_boxplot(results[0], methods) 
>>> plot.show() 
>>> plot.savefig("boxplot.pdf") 