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)¶
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, 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: 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
