tabensemb.trainer.Trainer.plot_hist

method

Trainer.plot_hist(feature: str, ax=None, clr: Iterable | None = None, imputed=False, kde=False, category: str | None = None, x_values=None, legend: bool = True, figure_kwargs: Dict | None = None, hist_kwargs: Dict | None = None, bar_kwargs: Dict | None = None, select_by_value_kwargs: Dict | None = None, kde_kwargs: Dict | None = None, legend_kwargs: Dict | None = None, savefig_kwargs: Dict | None = None, save_show_close: bool = True) → Axes

Plot the histogram of a feature.

Parameters:

feature

The selected feature.

ax

matplotlib.axes.Axes

clr

A seaborn color palette or an Iterable of colors. For example seaborn.color_palette(“deep”).

imputed

Whether the imputed dataset should be considered.

kde

Plot the kernel density estimation along with each histogram of continuous features.

category

The category to classify histograms and stack them with different colors.

x_values

Unique values of the feature. If None, it will be inferred from the dataset.

legend

Show legends if category is not None.

figure_kwargs

Arguments for plt.figure.

bar_kwargs

Arguments for ax.bar (used for frequencies of categorical features).

hist_kwargs

Arguments for ax.hist (used for histograms of continuous features).

kde_kwargs

Arguments for plot_kde() when kde is True.

select_by_value_kwargs

Arguments for tabensemb.data.datamodule.DataModule.select_by_value().

legend_kwargs

Arguments for plt.legend if legend is True and category is not None.

savefig_kwargs

Arguments for plt.savefig

save_show_close

Whether to save, show (in the notebook), and close the figure if ax is not given.

Returns:

matplotlib.axes.Axes