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plot_shap_bar

method plot_shap_bar(models=None, index=None, show=None, target=1, title=None, legend=None, figsize=None, filename=None, display=True)[source]
Plot SHAP's bar plot.

Create a bar plot of a set of SHAP values. If a single sample is passed, then the SHAP values are plotted. If many samples are passed, then the mean absolute value for each feature column is plotted. Read more about SHAP plots in the user guide.

Parametersmodels: int, str, Model or None, default=None
Model to plot. If None, all models are selected. Note that leaving the default option could raise an exception if there are multiple models. To avoid this, call the plot directly from a model, e.g. atom.lr.plot_shap_bar().

index: int, str, slice, sequence or None, default=None
Rows in the dataset to plot. If None, it selects all rows in the test set.

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

target: int or str, default=1
Class in the target column to look at (only for multi-class classification tasks).

title: str, dict or None, default=None
Title for the plot.

legend: str, dict or None, default=None
Does nothing. Implemented for continuity of the API.

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

filename: str or None, default=None
Save the plot using this name. Use "auto" for automatic naming. The type of the file depends on the provided name (.html, .png, .pdf, etc...). If filename has no file type, the plot is saved as html. If None, the plot is not saved.

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

Returnsplt.Figure or None
Plot object. Only returned if display=None.


See Also

plot_parshap

Plot the partial correlation of shap values.

plot_shap_beeswarm

Plot SHAP's beeswarm plot.

plot_shap_scatter

Plot SHAP's scatter plot.


Example

>>> from atom import ATOMClassifier
>>> from sklearn.datasets import load_breast_cancer

>>> X, y = load_breast_cancer(return_X_y=True, as_frame=True)

>>> atom = ATOMClassifier(X, y)
>>> atom.run("LR")
>>> atom.plot_shap_bar(show=10)

plot_shap_bar