ehrapy.plot.rank_features_supervised

ehrapy.plot.rank_features_supervised(edata, key='feature_importances', n_features=10, ax=None, show=True, save=None, **kwargs)

Plot features with greatest absolute importances as a barplot.

Parameters:

• edata (EHRData) – Central data object. A key in edata.var should contain the feature importances, calculated beforehand.

• key (str, default: 'feature_importances') – The key in edata.var to use for feature importances.

• n_features (int, default: 10) – The number of features to plot.

• ax (Axes | None, default: None) – A matplotlib axes object to plot on. If None, a new figure will be created.

• show (bool, default: True) – If True, show the figure. If False, return the axes object.

• save (str | None, default: None) – Path to save the figure. If None, the figure will not be saved.

• **kwargs – Additional arguments passed to seaborn.barplot.

Return type:

Axes | None

Returns:

If show == False a matplotlib.axes.Axes object, else None.

Examples

>>> import ehrdata as ed
>>> import ehrapy as ep
>>> edata = ed.dt.mimic_2()
>>> ep.pp.knn_impute(edata, n_neighbors=5)
>>> input_features = [
...     feat for feat in edata.var_names if feat not in {"service_unit", "day_icu_intime", "tco2_first"}
... ]
>>> ep.tl.rank_features_supervised(edata, "tco2_first", "rf", input_features=input_features)
>>> ep.pl.rank_features_supervised(edata)