Source code for arviz.plots.parallelplot

"""Parallel coordinates plot showing posterior points with and without divergences marked."""
import numpy as np
from scipy.stats import rankdata

from ..data import convert_to_dataset
from ..labels import BaseLabeller
from ..sel_utils import xarray_to_ndarray
from ..rcparams import rcParams
from ..stats.stats_utils import stats_variance_2d as svar
from ..utils import _numba_var, _var_names, get_coords
from .plot_utils import get_plotting_function


[docs]def plot_parallel( data, var_names=None, filter_vars=None, coords=None, figsize=None, textsize=None, legend=True, colornd="k", colord="C1", shadend=0.025, labeller=None, ax=None, norm_method=None, backend=None, backend_config=None, backend_kwargs=None, show=None, ): """ Plot parallel coordinates plot showing posterior points with and without divergences. Described by https://arxiv.org/abs/1709.01449 Parameters ---------- data: obj Any object that can be converted to an :class:`arviz.InferenceData` object refer to documentation of :func:`arviz.convert_to_dataset` for details var_names: list of variable names Variables to be plotted, if `None` all variables are plotted. Can be used to change the order of the plotted variables. Prefix the variables by ``~`` when you want to exclude them from the plot. filter_vars: {None, "like", "regex"}, optional, 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. A la ``pandas.filter``. coords: mapping, optional Coordinates of ``var_names`` to be plotted. Passed to :meth:`xarray.Dataset.sel`. figsize: tuple Figure size. If None it will be defined automatically. textsize: float Text size scaling factor for labels, titles and lines. If None it will be autoscaled based on ``figsize``. legend: bool Flag for plotting legend (defaults to True) colornd: valid matplotlib color color for non-divergent points. Defaults to 'k' colord: valid matplotlib color color for divergent points. Defaults to 'C1' shadend: float Alpha blending value for non-divergent points, between 0 (invisible) and 1 (opaque). Defaults to .025 labeller : labeller instance, optional Class providing the method ``make_label_vert`` to generate the labels in the plot. Read the :ref:`label_guide` for more details and usage examples. ax: axes, optional Matplotlib axes or bokeh figures. norm_method: str Method for normalizing the data. Methods include normal, minmax and rank. Defaults to none. backend: str, optional Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib". backend_config: dict, optional Currently specifies the bounds to use for bokeh axes. Defaults to value set in ``rcParams``. backend_kwargs: bool, optional These are kwargs specific to the backend being used, passed to :func:`matplotlib.pyplot.subplots` or :func:`bokeh.plotting.figure`. show: bool, optional Call backend show function. Returns ------- axes: matplotlib axes or bokeh figures See Also -------- plot_pair : Plot a scatter, kde and/or hexbin matrix with (optional) marginals on the diagonal. plot_trace : Plot distribution (histogram or kernel density estimates) and sampled values or rank plot Examples -------- Plot default parallel plot .. plot:: :context: close-figs >>> import arviz as az >>> data = az.load_arviz_data('centered_eight') >>> az.plot_parallel(data, var_names=["mu", "tau"]) Plot parallel plot with normalization .. plot:: :context: close-figs >>> az.plot_parallel(data, var_names=["mu", "tau"], norm_method='normal') """ if coords is None: coords = {} if labeller is None: labeller = BaseLabeller() # Get diverging draws and combine chains divergent_data = convert_to_dataset(data, group="sample_stats") _, diverging_mask = xarray_to_ndarray( divergent_data, var_names=("diverging",), combined=True, ) diverging_mask = np.squeeze(diverging_mask) # Get posterior draws and combine chains posterior_data = convert_to_dataset(data, group="posterior") var_names = _var_names(var_names, posterior_data, filter_vars) var_names, _posterior = xarray_to_ndarray( get_coords(posterior_data, coords), var_names=var_names, combined=True, label_fun=labeller.make_label_vert, ) if len(var_names) < 2: raise ValueError("Number of variables to be plotted must be 2 or greater.") if norm_method is not None: if norm_method == "normal": mean = np.mean(_posterior, axis=1) if _posterior.ndim <= 2: standard_deviation = np.sqrt(_numba_var(svar, np.var, _posterior, axis=1)) else: standard_deviation = np.std(_posterior, axis=1) for i in range(0, np.shape(mean)[0]): _posterior[i, :] = (_posterior[i, :] - mean[i]) / standard_deviation[i] elif norm_method == "minmax": min_elem = np.min(_posterior, axis=1) max_elem = np.max(_posterior, axis=1) for i in range(0, np.shape(min_elem)[0]): _posterior[i, :] = ((_posterior[i, :]) - min_elem[i]) / (max_elem[i] - min_elem[i]) elif norm_method == "rank": _posterior = rankdata(_posterior, axis=1, method="average") else: raise ValueError(f"{norm_method} is not supported. Use normal, minmax or rank.") parallel_kwargs = dict( ax=ax, colornd=colornd, colord=colord, shadend=shadend, diverging_mask=diverging_mask, posterior=_posterior, textsize=textsize, var_names=var_names, legend=legend, figsize=figsize, 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_parallel", "parallelplot", backend) ax = plot(**parallel_kwargs) return ax