tabensemb.trainer.Trainer.plot_pca_2d_visual#

method

Trainer.plot_pca_2d_visual(ax=None, category: str | None = None, clr: Iterable | None = None, features: List[str] | None = None, pca_kwargs: Dict | None = None, figure_kwargs: Dict | None = None, scatter_kwargs: Dict | None = None, legend_kwargs: Dict | None = None, savefig_kwargs: Dict | None = None, select_by_value_kwargs: Dict | None = None, save_show_close: bool = True) → Axes[source]#

Fit a sklearn.decomposition.PCA on a set of features, and plot its first two principal components as scatters.

Parameters:
ax

matplotlib.axes.Axes

category

The category to classify data points with different colors and markers.

clr

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

features

A subset of continuous features to fit the PCA.

pca_kwargs

Arguments for sklearn.decomposition.PCA.fit

figure_kwargs

Arguments for plt.figure.

scatter_kwargs

Arguments for plt.scatter

legend_kwargs

Arguments for plt.legend

savefig_kwargs

Arguments for plt.savefig

select_by_value_kwargs

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

save_show_close

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

Returns:
matplotlib.axes.Axes