Source code for arviz.plots.compareplot

"""Summary plot for model comparison."""
import numpy as np

from ..labels import BaseLabeller
from ..rcparams import rcParams
from .plot_utils import get_plotting_function


[docs] def plot_compare( comp_df, insample_dev=False, plot_standard_error=True, plot_ic_diff=True, order_by_rank=True, legend=True, title=True, figsize=None, textsize=None, labeller=None, plot_kwargs=None, ax=None, backend=None, backend_kwargs=None, show=None, ): r"""Summary plot for model comparison. Models are compared based on their expected log pointwise predictive density (ELPD). This plot is in the style of the one used in [2]_. Chapter 6 in the first edition or 7 in the second. Notes ----- The ELPD is estimated either by Pareto smoothed importance sampling leave-one-out cross-validation (LOO) or using the widely applicable information criterion (WAIC). We recommend LOO in line with the work presented by [1]_. Parameters ---------- comp_df : pandas.DataFrame Result of the :func:`arviz.compare` method. insample_dev : bool, default False Plot in-sample ELPD, that is the value of the information criteria without the penalization given by the effective number of parameters (p_loo or p_waic). plot_standard_error : bool, default True Plot the standard error of the ELPD. plot_ic_diff : bool, default True Plot standard error of the difference in ELPD between each model and the top-ranked model. order_by_rank : bool, default True If True ensure the best model is used as reference. legend : bool, default True Add legend to figure. figsize : (float, float), optional If `None`, size is (6, num of models) inches. title : bool, default True Show a tittle with a description of how to interpret the plot. textsize : float, optional Text size scaling factor for labels, titles and lines. If `None` it will be autoscaled based on `figsize`. labeller : Labeller, optional Class providing the method ``make_label_vert`` to generate the labels in the plot titles. Read the :ref:`label_guide` for more details and usage examples. plot_kwargs : dict, optional Optional arguments for plot elements. Currently accepts 'color_ic', 'marker_ic', 'color_insample_dev', 'marker_insample_dev', 'color_dse', 'marker_dse', 'ls_min_ic' 'color_ls_min_ic', 'fontsize' ax : matplotlib_axes or bokeh_figure, optional Matplotlib axes or bokeh figure. backend : {"matplotlib", "bokeh"}, default "matplotlib" Select plotting backend. backend_kwargs : bool, optional These are kwargs specific to the backend being used, passed to :func:`matplotlib.pyplot.subplots` or :class:`bokeh.plotting.figure`. For additional documentation check the plotting method of the backend. show : bool, optional Call backend show function. Returns ------- axes : matplotlib_axes or bokeh_figure See Also -------- plot_elpd : Plot pointwise elpd differences between two or more models. compare : Compare models based on PSIS-LOO loo or WAIC waic cross-validation. loo : Compute Pareto-smoothed importance sampling leave-one-out cross-validation (PSIS-LOO-CV). waic : Compute the widely applicable information criterion. References ---------- .. [1] Vehtari et al. (2016). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC https://arxiv.org/abs/1507.04544 .. [2] McElreath R. (2022). Statistical Rethinking A Bayesian Course with Examples in R and Stan, Second edition, CRC Press. Examples -------- Show default compare plot .. plot:: :context: close-figs >>> import arviz as az >>> model_compare = az.compare({'Centered 8 schools': az.load_arviz_data('centered_eight'), >>> 'Non-centered 8 schools': az.load_arviz_data('non_centered_eight')}) >>> az.plot_compare(model_compare) Include the in-sample ELDP .. plot:: :context: close-figs >>> az.plot_compare(model_compare, insample_dev=True) """ if plot_kwargs is None: plot_kwargs = {} if labeller is None: labeller = BaseLabeller() yticks_pos, step = np.linspace(0, -1, (comp_df.shape[0] * 2) - 1, retstep=True) yticks_pos[1::2] = yticks_pos[1::2] + step / 2 labels = [labeller.model_name_to_str(model_name) for model_name in comp_df.index] if plot_ic_diff: yticks_labels = [""] * len(yticks_pos) yticks_labels[0] = labels[0] yticks_labels[2::2] = labels[1:] else: yticks_labels = labels _information_criterion = ["elpd_loo", "elpd_waic"] column_index = [c.lower() for c in comp_df.columns] for information_criterion in _information_criterion: if information_criterion in column_index: break else: raise ValueError( "comp_df must contain one of the following " f"information criterion: {_information_criterion}" ) if order_by_rank: comp_df.sort_values(by="rank", inplace=True) compareplot_kwargs = dict( ax=ax, comp_df=comp_df, legend=legend, title=title, figsize=figsize, plot_ic_diff=plot_ic_diff, plot_standard_error=plot_standard_error, insample_dev=insample_dev, yticks_pos=yticks_pos, yticks_labels=yticks_labels, plot_kwargs=plot_kwargs, information_criterion=information_criterion, textsize=textsize, step=step, backend_kwargs=backend_kwargs, show=show, ) if backend is None: backend = rcParams["plot.backend"] backend = backend.lower() # TODO: Add backend kwargs plot = get_plotting_function("plot_compare", "compareplot", backend) ax = plot(**compareplot_kwargs) return ax