Source code for arviz.plots.forestplot

"""Forest plot."""

from import convert_to_dataset
from ..labels import BaseLabeller, NoModelLabeller
from ..rcparams import rcParams
from ..utils import _var_names, get_coords
from .plot_utils import get_plotting_function

[docs] def plot_forest( data, kind="forestplot", model_names=None, var_names=None, filter_vars=None, transform=None, coords=None, combined=False, combine_dims=None, hdi_prob=None, rope=None, quartiles=True, ess=False, r_hat=False, colors="cycle", textsize=None, linewidth=None, markersize=None, legend=True, labeller=None, ridgeplot_alpha=None, ridgeplot_overlap=2, ridgeplot_kind="auto", ridgeplot_truncate=True, ridgeplot_quantiles=None, figsize=None, ax=None, backend=None, backend_config=None, backend_kwargs=None, show=None, ): r"""Forest plot to compare HDI intervals from a number of distributions. Generate forest or ridge plots to compare distributions from a model or list of models. Additionally, the function can display effective sample sizes (ess) and Rhats to visualize convergence diagnostics alongside the distributions. Parameters ---------- data : InferenceData Any object that can be converted to an :class:`arviz.InferenceData` object Refer to documentation of :func:`arviz.convert_to_dataset` for details. kind : {"foresplot", "ridgeplot"}, default "forestplot" Specify the kind of plot: * The ``kind="forestplot"`` generates credible intervals, where the central points are the estimated posterior means, the thick lines are the central quartiles, and the thin lines represent the :math:`100\times`(`hdi_prob`)% highest density intervals. * The ``kind="ridgeplot"`` option generates density plots (kernel density estimate or histograms) in the same graph. Ridge plots can be configured to have different overlap, truncation bounds and quantile markers. model_names : list of str, optional List with names for the models in the list of data. Useful when plotting more that one dataset. var_names : list of str, optional Variables to be plotted. Prefix the variables by ``~`` when you want to exclude them from the plot. See :ref:`this section <common_var_names>` for usage examples. combine_dims : set_like of str, optional List of dimensions to reduce. Defaults to reducing only the "chain" and "draw" dimensions. See :ref:`this section <common_combine_dims>` for usage examples. filter_vars : {None, "like", "regex"}, default None If `None` (default), interpret `var_names` as the real variables names. If "like", interpret `var_names` as substrings of the real variables names. If "regex", interpret `var_names` as regular expressions on the real variables names. See :ref:`this section <common_filter_vars>` for usage examples. transform : callable, optional Function to transform data (defaults to None i.e.the identity function). coords : dict, optional Coordinates of ``var_names`` to be plotted. Passed to :meth:`xarray.Dataset.sel`. See :ref:`this section <common_coords>` for usage examples. combined : bool, default False Flag for combining multiple chains into a single chain. If False, chains will be plotted separately. See :ref:`this section <common_combine>` for usage examples. hdi_prob : float, default 0.94 Plots highest posterior density interval for chosen percentage of density. See :ref:`this section <common_ hdi_prob>` for usage examples. rope : list, tuple or dictionary of {str : tuples or lists}, optional A dictionary of tuples with the lower and upper values of the Region Of Practical Equivalence. See :ref:`this section <common_rope>` for usage examples. quartiles : bool, default True Flag for plotting the interquartile range, in addition to the ``hdi_prob`` intervals. r_hat : bool, default False Flag for plotting Split R-hat statistics. Requires 2 or more chains. ess : bool, default False Flag for plotting the effective sample size. colors : list or string, optional list with valid matplotlib colors, one color per model. Alternative a string can be passed. If the string is `cycle`, it will automatically chose a color per model from the matplotlibs cycle. If a single color is passed, eg 'k', 'C2', 'red' this color will be used for all models. Defaults to 'cycle'. textsize : float, optional Text size scaling factor for labels, titles and lines. If `None` it will be autoscaled based on ``figsize``. linewidth : int, optional Line width throughout. If `None` it will be autoscaled based on ``figsize``. markersize : int, optional Markersize throughout. If `None` it will be autoscaled based on ``figsize``. legend : bool, optional Show a legend with the color encoded model information. Defaults to True, if there are multiple models. 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. ridgeplot_alpha: float, optional Transparency for ridgeplot fill. If ``ridgeplot_alpha=0``, border is colored by model, otherwise a `black` outline is used. ridgeplot_overlap : float, default 2 Overlap height for ridgeplots. ridgeplot_kind : string, optional By default ("auto") continuous variables are plotted using KDEs and discrete ones using histograms. To override this use "hist" to plot histograms and "density" for KDEs. ridgeplot_truncate : bool, default True Whether to truncate densities according to the value of ``hdi_prob``. ridgeplot_quantiles : list, optional Quantiles in ascending order used to segment the KDE. Use [.25, .5, .75] for quartiles. figsize : (float, float), optional Figure size. If `None`, it will be defined automatically. ax : axes, optional :class:`matplotlib.axes.Axes` or :class:`bokeh.plotting.Figure`. backend : {"matplotlib", "bokeh"}, default "matplotlib" Select plotting backend. backend_config : dict, optional Currently specifies the bounds to use for bokeh axes. Defaults to value set in ``rcParams``. backend_kwargs : dict, 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 ------- 1D ndarray of matplotlib_axes or bokeh_figures See Also -------- plot_posterior : Plot Posterior densities in the style of John K. Kruschke's book. plot_density : Generate KDE plots for continuous variables and histograms for discrete ones. summary : Create a data frame with summary statistics. Examples -------- Forestplot .. plot:: :context: close-figs >>> import arviz as az >>> non_centered_data = az.load_arviz_data('non_centered_eight') >>> axes = az.plot_forest(non_centered_data, >>> kind='forestplot', >>> var_names=["^the"], >>> filter_vars="regex", >>> combined=True, >>> figsize=(9, 7)) >>> axes[0].set_title('Estimated theta for 8 schools model') Forestplot with multiple datasets .. plot:: :context: close-figs >>> centered_data = az.load_arviz_data('centered_eight') >>> axes = az.plot_forest([non_centered_data, centered_data], >>> model_names = ["non centered eight", "centered eight"], >>> kind='forestplot', >>> var_names=["^the"], >>> filter_vars="regex", >>> combined=True, >>> figsize=(9, 7)) >>> axes[0].set_title('Estimated theta for 8 schools models') Ridgeplot .. plot:: :context: close-figs >>> axes = az.plot_forest(non_centered_data, >>> kind='ridgeplot', >>> var_names=['theta'], >>> combined=True, >>> ridgeplot_overlap=3, >>> colors='white', >>> figsize=(9, 7)) >>> axes[0].set_title('Estimated theta for 8 schools model') Ridgeplot non-truncated and with quantiles .. plot:: :context: close-figs >>> axes = az.plot_forest(non_centered_data, >>> kind='ridgeplot', >>> var_names=['theta'], >>> combined=True, >>> ridgeplot_truncate=False, >>> ridgeplot_quantiles=[.25, .5, .75], >>> ridgeplot_overlap=0.7, >>> colors='white', >>> figsize=(9, 7)) >>> axes[0].set_title('Estimated theta for 8 schools model') """ if not isinstance(data, (list, tuple)): data = [data] if len(data) == 1: legend = False if coords is None: coords = {} if combine_dims is None: combine_dims = set() if labeller is None: labeller = NoModelLabeller() if legend else BaseLabeller() datasets = [convert_to_dataset(datum) for datum in reversed(data)] if transform is not None: datasets = [transform(dataset) for dataset in datasets] datasets = get_coords( datasets, list(reversed(coords)) if isinstance(coords, (list, tuple)) else coords ) var_names = _var_names(var_names, datasets, filter_vars) ncols, width_ratios = 1, [3] if ess: ncols += 1 width_ratios.append(1) if r_hat: ncols += 1 width_ratios.append(1) if hdi_prob is None: hdi_prob = rcParams["stats.ci_prob"] elif not 1 >= hdi_prob > 0: raise ValueError("The value of hdi_prob should be in the interval (0, 1]") plot_forest_kwargs = dict( ax=ax, datasets=datasets, var_names=var_names, model_names=model_names, combined=combined, combine_dims=combine_dims, colors=colors, figsize=figsize, width_ratios=width_ratios, linewidth=linewidth, markersize=markersize, kind=kind, ncols=ncols, hdi_prob=hdi_prob, quartiles=quartiles, rope=rope, ridgeplot_overlap=ridgeplot_overlap, ridgeplot_alpha=ridgeplot_alpha, ridgeplot_kind=ridgeplot_kind, ridgeplot_truncate=ridgeplot_truncate, ridgeplot_quantiles=ridgeplot_quantiles, textsize=textsize, legend=legend, labeller=labeller, ess=ess, r_hat=r_hat, backend_kwargs=backend_kwargs, backend_config=backend_config, show=show, ) if backend is None: backend = rcParams["plot.backend"] backend = backend.lower() # TODO: Add backend kwargs plot = get_plotting_function("plot_forest", "forestplot", backend) axes = plot(**plot_forest_kwargs) return axes