matchcake.ml.visualisation package¶

Submodules¶

matchcake.ml.visualisation.classification_visualizer module¶

class matchcake.ml.visualisation.classification_visualizer.ClassificationVisualizer(*, x: ndarray | None = None, x_reduced: ndarray | None = None, x_mesh: ndarray | None = None, reducer: Any | None = None, transform: Callable | None = None, inverse_transform: Callable | None = None, n_pts: int = 1000, seed: int | None = 0, **kwargs)¶

Bases: Visualizer

__init__(*, x: ndarray | None = None, x_reduced: ndarray | None = None, x_mesh: ndarray | None = None, reducer: Any | None = None, transform: Callable | None = None, inverse_transform: Callable | None = None, n_pts: int = 1000, seed: int | None = 0, **kwargs)¶

compute_x_mesh(**kwargs)¶

compute_x_reduced(**kwargs)¶

gather_transforms(**kwargs)¶

If a transform and an inverse_transform functions are given, they will be returned. Otherwise, the transform and inverse_transform will be inferred from the given reducer. If the reducer is None, then it will be initialized as a PCA with 2 components.

plot_2d_decision_boundaries(*, y: ndarray | None = None, model: Any | None = None, y_pred: ndarray | None = None, **kwargs)¶

matchcake.ml.visualisation.cross_validation_visualizer module¶

class matchcake.ml.visualisation.cross_validation_visualizer.CrossValidationVisualizer(cross_validation_output: CrossValidationOutput)¶

Bases: Visualizer

__init__(cross_validation_output: CrossValidationOutput)¶

plot(*, ax: Axes | None = None, score_name: str = 'Score', score_split_name: str = 'Splits', score_name_map: Dict[str, str] | None = None, palette: str | list | Dict | None = 'colorblind', estimator_name_key: str | None = None)¶

matchcake.ml.visualisation.mpl_rcparams module¶

matchcake.ml.visualisation.visualizer module¶

class matchcake.ml.visualisation.visualizer.Visualizer¶

Bases: object

Module contents¶