lpspline.viz.plot_diagnostic#

lpspline.viz.plot_diagnostic(model: LpRegressor, X: DataFrame, ncols: int = 4, y: Series | None = None)[source]#

Generate a diagnostic plot rendering the individually learned spline components natively.

Dynamically sizes subplot grids according to the mathematical component complexity of the underlying optimized model.

Parameters:

model (LpRegressor) – An LPSpline regression model object which has completed the fit() cycle.
X (pl.DataFrame) – A Polars DataFrame containing the predictive feature fields.
ncols (int, default=4) – The maximum number of subplots generated per row.
y (pl.Series, default=None) – Optional true response series. If provided, calculates and overlays partial residuals.

Returns:

fig (matplotlib.figure.Figure) – The rendered Matplotlib figure window.
axes (numpy.ndarray) – Array containing the individual subplot Matplotlib Axes.