tabensemb.trainer.Trainer.cal_partial_dependence_2way#
method
- Trainer.cal_partial_dependence_2way(x_feature: str, y_feature: str, grid_size: int = 10, percentile: int | float = 100, x_min: int | float | None = None, x_max: int | float | None = None, y_min: int | float | None = None, y_max: int | float | None = None, df: DataFrame | None = None, **kwargs) Tuple[ndarray, ndarray, ndarray][source]#
Calculate 2-way partial dependency. See the source code of
plot_partial_dependence_2way()for its usage.- Parameters:
- x_feature
A continuous feature.
- y_feature
A continuous feature.
- grid_size
The number of sequential values.
- percentile
The percentile of the feature used to generate sequential values.
- x_min
The lower limit of the generated sequential values of the first feature. It will override the left percentile.
- x_max
The upper limit of the generated sequential values of the first feature. It will override the right percentile.
- y_min
The lower limit of the generated sequential values of the second feature. It will override the left percentile.
- y_max
The upper limit of the generated sequential values of the second feature. It will override the right percentile.
- df
The tabular dataset.
- kwargs
Other arguments for
_bootstrap_fit(). The above grid_size, percentile, y_min, y_max are passed to it for the second feature.
- Returns:
- list
The grid of the first feature
- list
The grid of the second feature
- list
pdp values of each first-feature value and each second-feature value in grids.