tabensemb.trainer.Trainer.plot_partial_dependence_2way#
method
- Trainer.plot_partial_dependence_2way(x_feature: str, y_feature: str, program: str, model_name: str, df: DataFrame, derived_data: Dict[str, ndarray], ax: Axes | None = None, projection: str = '3d', grid_size: int = 10, percentile: int | float = 100, figure_kwargs: Dict | None = None, imshow_kwargs: Dict | None = None, surf_kwargs: Dict | None = None, savefig_kwargs: Dict | None = None, save_show_close: bool = True, **kwargs)[source]#
Calculate and plot a 2-way partial dependence plot with bootstrapping for a pair of features.
- Parameters:
- x_feature
A continuous feature.
- y_feature
A continuous feature.
- program
The selected model base.
- model_name
The selected model in the model base.
- ax
matplotlib.axes.Axes- projection
None or “3d”. Will use
matplotlib.pyplot.imshowfor None andmatplotlib.pyplot.plot_surfacefor “3d”.- grid_size
The number of sequential values.
- percentile
The percentile of the feature used to generate sequential values.
- df
The tabular dataset.
- derived_data
The derived data calculated using
derive_unstacked().- kwargs
Other arguments for
cal_partial_dependence_2way().- figure_kwargs
Arguments for
plt.savefig- savefig_kwargs
Arguments for
plt.savefig- imshow_kwargs
Arguments for
plt.imshow- surf_kwargs
Arguments for
plt.plot_surface- save_show_close
Whether to save, show (in the notebook), and close the figure if
axis not given.- kwargs
Arguments for
cal_partial_dependence_2way().
- Returns:
- matplotlib.axes.Axes