plot_boxplot_median¶

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

Plot a box plot of distributions from the median.

Each row of results is an independent experiment for each element in names. This function works out the deviation from the median for each row, then plots a boxplot variant of each column.

Parameters:

results: np.array: Scores (either accuracies or errors) of dataset x strategy
labels: list of estimators: List with names of the estimators
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_median
>>> 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_median(results[0], methods) 
>>> plot.show() 
>>> plot.savefig("boxplot.pdf") 