Communicators

Visualising and summarising Bayesian model results

Communicators help you communicate your findings. They cover both tables and plots. Again there are a set of default communicators built into hibayes which you can select from.

insert table

What makes up a communicator?

Communicators simply take in an AnalysisState and add a plot or a table. Here we see the implementation for a forest_plot, noting that you can define your own communicators using the same methodology

@communicate
def forest_plot(
    vars: list[str] | None = None,
    vertical_line: float | None = None,
    best_model: bool = True,
    figsize: tuple[int, int] = (10, 5),
    transform: bool = False,
    *args,
    **kwargs,
):
    def communicate(
        state: AnalysisState,
        display: ModellingDisplay | None = None,
    ) -> Tuple[AnalysisState, CommunicateResult]:
        """
        Communicate the results of a model analysis.
        """
        nonlocal vars
        if best_model:
            best_model_analysis = state.get_best_model()
            if best_model_analysis is None:
                raise ValueError("No best model found.")
            models_to_run = [best_model_analysis]
        else:
            models_to_run = state.models

        for model_analysis in models_to_run:
            if model_analysis.is_fitted:
                vars, dropped = (
                    drop_not_present_vars(vars, model_analysis.inference_data)
                    if vars
                    else (None, None)
                )
                if dropped and display:
                    display.logger.warning(
                        f"Variables {dropped} were not found in the model {model_analysis.model_name} inference data."
                    )
                if vars is None:
                    vars = model_analysis.model_config.get_plot_params()

                ax = az.plot_forest(
                    model_analysis.inference_data,
                    var_names=vars,
                    figsize=figsize,
                    transform=model_analysis.link_function if transform else None,
                    *args,
                    **kwargs,
                )

                if vertical_line is not None:
                    ax[0].axvline(
                        x=vertical_line,
                        color="red",
                        linestyle="--",
                    )
                fig = plt.gcf()
                
                state.add_plot(
                    plot=fig,
                    plot_name=f"model_{model_analysis.model_name}_{'-'.join(vars) if vars else ''}_forest",
                )
        return state, "pass"

    return communicate

1

here we register the communicator and enforce an agreed upon interface.

2

very useful to have kwargs here, as the user often have their own plotting args they want passed on to the plt functions.

3

if you only want to create plots for the model which fitted the model best according the information criterion specified. Otherwise plot for every model.

4

add the plot to the analysis state. Check output dir for plots

Here is an example forest plot with default configs from skewed score example

forest plot