tabensemb.trainer.Trainer.plot_corr_with_label#
method
- Trainer.plot_corr_with_label(imputed=False, features: List[str] | None = None, order: str = 'alphabetic', method: str = 'pearson', clr=None, ax=None, figure_kwargs: Dict | None = None, barplot_kwargs: Dict | None = None, select_by_value_kwargs: Dict | None = None, legend_kwargs: Dict | None = None, savefig_kwargs: Dict | None = None, save_show_close: bool = True) Axes[source]#
Plot correlation coefficients between the target and each feature.
- Parameters:
- imputed
Whether the imputed dataset should be considered. If False, some NaN coefficients may exist for features with missing values.
- features
A subset of continuous features to calculate correlations on.
- order
The order of features. “alphabetic”, “ascending”, or “descending”.
- method
The argument of
pd.DataFrame.corr. “pearson”, “kendall”, “spearman” or Callable.- clr
A seaborn color palette or an Iterable of colors. For example seaborn.color_palette(“deep”).
- ax
matplotlib.axes.Axes- figure_kwargs
Arguments for
plt.figure.- imshow_kwargs
Arguments for
plt.imshow.- select_by_value_kwargs
Arguments for
tabensemb.data.datamodule.DataModule.select_by_value().- legend_kwargs
Arguments for
plt.legend- savefig_kwargs
Arguments for
plt.savefig- save_show_close
Whether to save, show (in the notebook), and close the figure if
axis not given.
- Returns:
- matplotlib.axes.Axes