braindecode.visualization.plot_confusion_matrix#

braindecode.visualization.plot_confusion_matrix(confusion_mat, class_names=None, figsize=None, colormap=<matplotlib.colors.LinearSegmentedColormap object>, textcolor='black', vmin=None, vmax=None, fontweight='normal', rotate_row_labels=90, rotate_col_labels=0, with_f1_score=False, norm_axes=(0, 1), rotate_precision=False, class_names_fontsize=12)[source]#

Generates a confusion matrix with additional precision and sensitivity metrics as in [1].

Parameters:

confusion_mat (2d numpy array) – A confusion matrix, e.g. sklearn confusion matrix: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html
class_names (array, optional) – List of classes/targets.
figsize (tuple, optional) – Size of the generated confusion matrix figure.
colormap (matplotlib cm colormap, optional)
textcolor (str, optional) – Color of the text in the figure.
vmin (float, optional) – The data range that the colormap covers.
vmax (float, optional) – The data range that the colormap covers.
fontweight (str, optional) – Weight of the font in the figure: [ ‘normal’ | ‘bold’ | ‘heavy’ | ‘light’ | ‘ultrabold’ | ‘ultralight’]
rotate_row_labels (int, optional) – The rotation angle of the row labels
rotate_col_labels (int, optional) – The rotation angle of the column labels
with_f1_score (bool, optional)
norm_axes (tuple, optional)
rotate_precision (bool, optional)
class_names_fontsize (int, optional)

Returns:

fig

Return type:

matplotlib figure

References

[1]

Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F. & Ball, T. (2017). Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping , Aug. 2017. Online: http://dx.doi.org/10.1002/hbm.23730