tabensemb.trainer.Trainer.plot_truth_pred#
method
- Trainer.plot_truth_pred(program: str, model_name: str, kde_color: bool = False, train_val_test: str = 'all', log_trans: bool = True, central_line: bool = True, upper_lim=9, ax=None, clr: Iterable | None = None, select_by_value_kwargs: Dict | None = None, figure_kwargs: Dict | None = None, scatter_kwargs: Dict | None = None, legend_kwargs: Dict | None = None, savefig_kwargs: Dict | None = None, save_show_close: bool = True) Axes[source]#
Compare ground truth and prediction for one model.
- Parameters:
- program
The selected model base.
- model_name
The selected model in the model base
- kde_color
Whether the scatters are colored by their KDE density. Ignored if
train_val_testis “all”.- train_val_test
Which subset to be plotted. Choose from “Training”, “Validation”, “Testing”, and “all”.
- log_trans
Whether the label data is in log scale.
- central_line
Whether to plot a 45-degree diagonal line.
- upper_lim
The upper limit of x/y-axis.
- ax
matplotlib.axes.Axes- clr
A seaborn color palette or an Iterable of colors. For example seaborn.color_palette(“deep”).
- select_by_value_kwargs
Arguments for
tabensemb.data.datamodule.DataModule.select_by_value().- figure_kwargs
Arguments for
plt.figure()- scatter_kwargs
Arguments for
plt.scatter()- 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