Source code for arviz.plots.khatplot

"""Pareto tail indices plot."""

import logging

import numpy as np
from xarray import DataArray

from ..rcparams import rcParams
from ..stats import ELPDData
from ..utils import get_coords
from .plot_utils import format_coords_as_labels, get_plotting_function

_log = logging.getLogger(__name__)

[docs] def plot_khat( khats, color="C0", xlabels=False, show_hlines=False, show_bins=False, bin_format="{1:.1f}%", annotate=False, threshold=None, hover_label=False, hover_format="{1}", figsize=None, textsize=None, coords=None, legend=False, markersize=None, ax=None, hlines_kwargs=None, backend=None, backend_kwargs=None, show=None, **kwargs ): r"""Plot Pareto tail indices :math:`\hat{k}` for diagnosing convergence in PSIS-LOO. Parameters ---------- khats : ELPDData or array-like The input Pareto tail indices to be plotted. It can be an ``ELPDData`` object containing Pareto shapes or an array. In this second case, all the values in the array are interpreted as Pareto tail indices. color : str or array_like, default "C0" Colors of the scatter plot, if color is a str all dots will have the same color, if it is the size of the observations, each dot will have the specified color, otherwise, it will be interpreted as a list of the dims to be used for the color code. If Matplotlib c argument is passed, it will override the color argument. xlabels : bool, default False Use coords as xticklabels. show_hlines : bool, default False Show the horizontal lines, by default at the values [0, 0.5, 0.7, 1]. show_bins : bool, default False Show the percentage of khats falling in each bin, as delimited by hlines. bin_format : str, optional The string is used as formatting guide calling ``bin_format.format(count, pct)``. threshold : float, optional Show the labels of k values larger than `threshold`. If ``None`` (default), no observations will be highlighted. hover_label : bool, default False Show the datapoint label when hovering over it with the mouse. Requires an interactive backend. hover_format : str, default "{1}" String used to format the hover label via ``hover_format.format(idx, coord_label)`` figsize : (float, float), optional Figure size. If ``None`` it will be defined automatically. textsize : float, optional Text size scaling factor for labels, titles and lines. If ``None`` it will be autoscaled based on `figsize`. coords : mapping, optional Coordinates of points to plot. **All** values are used for computation, but only a a subset can be plotted for convenience. See :ref:`this section <common_coords>` for usage examples. legend : bool, default False Include a legend to the plot. Only taken into account when color argument is a dim name. markersize : int, optional markersize for scatter plot. Defaults to ``None`` in which case it will be chosen based on autoscaling for figsize. ax : axes, optional Matplotlib axes or bokeh figures. hlines_kwargs : dict, optional Additional keywords passed to :meth:`matplotlib.axes.Axes.hlines`. backend : {"matplotlib", "bokeh"}, default "matplotlib" Select plotting backend. 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. kwargs : Additional keywords passed to :meth:`matplotlib.axes.Axes.scatter`. Returns ------- axes : matplotlib_axes or bokeh_figures See Also -------- psislw : Pareto smoothed importance sampling (PSIS). Examples -------- Plot estimated pareto shape parameters showing how many fall in each category. .. plot:: :context: close-figs >>> import arviz as az >>> radon = az.load_arviz_data("radon") >>> loo_radon = az.loo(radon, pointwise=True) >>> az.plot_khat(loo_radon, show_bins=True) Show xlabels .. plot:: :context: close-figs >>> centered_eight = az.load_arviz_data("centered_eight") >>> khats = az.loo(centered_eight, pointwise=True).pareto_k >>> az.plot_khat(khats, xlabels=True, threshold=1) Use custom color scheme .. plot:: :context: close-figs >>> counties = radon.posterior.County[radon.constant_data.county_idx].values >>> colors = [ ... "blue" if county[-1] in ("A", "N") else "green" for county in counties ... ] >>> az.plot_khat(loo_radon, color=colors) Notes ----- The Generalized Pareto distribution (GPD) diagnoses convergence rates for importance sampling. GPD has parameters offset, scale, and shape. The shape parameter (:math:`k`) tells the distribution's number of finite moments. The pre-asymptotic convergence rate of importance sampling can be estimated based on the fractional number of finite moments of the importance ratio distribution. GPD is fitted to the largest importance ratios and interprets the estimated shape parameter :math:`k`, i.e., :math:`\hat{k}` can then be used as a diagnostic (most importantly if :math:`\hat{k} > 0.7`, then the convergence rate is impractically low). See [1]_. References ---------- .. [1] Vehtari, A., Simpson, D., Gelman, A., Yao, Y., Gabry, J., 2019. Pareto Smoothed Importance Sampling. arXiv:1507.02646 [stat]. """ if annotate: _log.warning("annotate will be deprecated, please use threshold instead") threshold = annotate if coords is None: coords = {} if color is None: color = "C0" if isinstance(khats, np.ndarray): khats = khats.flatten() xlabels = False legend = False dims = [] else: if isinstance(khats, ELPDData): khats = khats.pareto_k if not isinstance(khats, DataArray): raise ValueError("Incorrect khat data input. Check the documentation") khats = get_coords(khats, coords) dims = khats.dims n_data_points = khats.size xdata = np.arange(n_data_points) if isinstance(khats, DataArray): coord_labels = format_coords_as_labels(khats) else: coord_labels = xdata.astype(str) plot_khat_kwargs = dict( hover_label=hover_label, hover_format=hover_format, ax=ax, figsize=figsize, xdata=xdata, khats=khats, kwargs=kwargs, threshold=threshold, coord_labels=coord_labels, show_hlines=show_hlines, show_bins=show_bins, hlines_kwargs=hlines_kwargs, xlabels=xlabels, legend=legend, color=color, dims=dims, textsize=textsize, markersize=markersize, n_data_points=n_data_points, bin_format=bin_format, 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_khat", "khatplot", backend) axes = plot(**plot_khat_kwargs) return axes