beeswarm_plot

method beeswarm_plot(models=None, index=None, show=None, target=1, title=None, figsize=None, filename=None, display=True, **kwargs) [source]

Plot SHAP's beeswarm plot. The plot is colored by feature values. Read more about SHAP plots in the user guide.

Parameters:

models: str, sequence or None, optional (default=None)
Name of the model to plot. If None, all models in the pipeline are selected. Note that leaving the default option could raise an exception if there are multiple models in the pipeline. To avoid this, call the plot from a model, e.g. atom.xgb.beeswarm_plot().

index: tuple, slice or None, optional (default=None)
Indices of the rows in the dataset to plot. If tuple (n, m), it selects rows n until m. If None, it selects all rows in the test set. The beeswarm plot does not support plotting a single sample.

show: int or None, optional (default=None)
Number of features (ordered by importance) to show. None to show all.

target: int or str, optional (default=1)
Index or name of the class in the target column to look at. Only for multi-class classification tasks.

title: str or None, optional (default=None)
Plot's title. If None, the title is left empty.

figsize: tuple or None, optional (default=None)
Figure's size, format as (x, y). If None, it adapts the size to the number of features shown.

filename: str or None, optional (default=None)
Name of the file. Use "auto" for automatic naming. If None, the figure is not saved.

display: bool or None, optional (default=True)
Whether to render the plot. If None, it returns the matplotlib figure.

**kwargs
Additional keyword arguments for SHAP's beeswarm plot.

Returns: fig: matplotlib.figure.Figure
Plot object. Only returned if display=None.

Example

from atom import ATOMRegressor

atom = ATOMRegressor(X, y)
atom.run("RF")
atom.beeswarm_plot()