skactiveml.visualization.plot_stream_training_data#

skactiveml.visualization.plot_stream_training_data(ax, X, y, queried_indices, classes, feature_bound, unlabeled_color='grey', cmap='coolwarm', alpha=0.2, linewidth=3, plot_cand_highlight=True)[source]#

Plot the utility for the given query strategy.

Parameters

axmatplotlib.axes.Axes: The axis on which the utility is plotted. Only if y.ndim = 1 (single annotator).
Xarray-like of shape (n_samples, 1): Training data set, usually complete, i.e. including the labeled and unlabeled samples.
yarray-like of shape (n_samples, ): Labels of the training data set (possibly including unlabeled ones indicated by self.MISSING_LABEL).
queried_indicesarray-like of shape (n_samples,): Indicates which instances from candidates have been queried.
classesarray-like of shape (n_classes): Holds the label for each class.
feature_boundarray-like of shape [[xmin, ymin], [xmax, ymax]]: Determines the area in which the boundary is plotted. If candidates is not given, bound must not be None. Otherwise, the bound is determined based on the data.
unlabeled_color: str | matplotlib.colors.Colormap, optional
(default=’grey’): The color for the unlabled samples.
cmap: str | matplotlib.colors.Colormap, optional (default=’coolwarm_r’): The colormap for the confidence levels.
alpha: scalar: Set the alpha value used for blending - not supported on all backends.
linewidth: float: Set the line width in points.
plot_cand_highlight: bool: The indicator to higlight the current candidate.

Returns