cinnamon.plot.drift.plot_drift.plot_target_drift

cinnamon.plot.drift.plot_drift.plot_target_drift(drift_explainer: AbstractDriftExplainer, max_n_cat: int = 20, figsize: Tuple[int, int] = (7, 5), bins=10, legend_labels: Tuple[str, str] = ('Dataset 1', 'Dataset 2'))

Plot distributions of labels in order to visualize a potential drift of the target labels.

Parameters

drift_explainer: AbstractDriftExplainer

A AbstractDriftExplainer object.

max_n_catint (default=20)

For multiclass classification only. Maximum number of classes to represent on the plot.

binsint or sequence of scalars or str, optional (default=10)

For regression only. ‘two_heads’ corresponds to a number of bins which is the minimum of of the optimal number of bins for dataset 1 and dataset 2 taken separately. Other value of “bins” parameter passed to matplotlib.pyplot.hist function are also accepted.

figsizeTuple[int, int] (default=(7, 5))

Graphic size passed to matplotlib.

legend_labelsTuple[str, str] (default=(‘Dataset 1’, ‘Dataset 2’))

Legend labels used for dataset 1 and dataset 2

Returns

None