"""Forest plot code."""
from collections.abc import Mapping, Sequence
from copy import copy
from importlib import import_module
from typing import Any, Literal
import arviz_stats # pylint: disable=unused-import
import numpy as np
import xarray as xr
from arviz_base import rcParams
from arviz_base.labels import BaseLabeller
from arviz_plots.plot_collection import PlotCollection, process_facet_dims
from arviz_plots.plots.utils import filter_aes, get_contrast_colors, process_group_variables_coords
from arviz_plots.visuals import annotate_label, fill_between_y, line_x, remove_axis, scatter_x
[docs]
def plot_forest(
dt,
var_names=None,
filter_vars=None,
group="posterior",
coords=None,
sample_dims=None,
combined=False,
point_estimate=None,
ci_kind=None,
ci_probs=None,
labels=None,
shade_label=None,
plot_collection=None,
backend=None,
labeller=None,
aes_by_visuals: Mapping[
Literal[
"credible_interval",
"point_estimate",
"labels",
"shade",
],
Sequence[str],
] = None,
visuals: Mapping[
Literal[
"trunk",
"twig",
"point_estimate",
"labels",
"shade",
"ticklabels",
"remove_axis",
],
Mapping[str, Any] | Literal[False],
] = None,
stats: Mapping[
Literal["trunk", "twig", "point_estimate"], Mapping[str, Any] | xr.Dataset
] = None,
**pc_kwargs,
):
"""Plot 1D marginal credible intervals in a single plot.
Parameters
----------
dt : DataTree or dict of {str : DataTree}
Input data. In case of dictionary input, the keys are taken to be model names.
In such cases, a dimension "model" is generated and can be used to map to aesthetics.
``plot_forest`` uses the dimension "column" (creating it if necessary) to generate the grid
then adds the intervals+point estimates to its "forest" coordinate
and labels to its "labels" coordinates. The data used to plot is then the subset
``column="forest"``.
var_names : str or list of str, optional
One or more variables to be plotted.
Prefix the variables by ~ when you want to exclude them from the plot.
filter_vars : {None, “like”, “regex”}, default None
If None, 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.
group : str, default "posterior"
Group to be plotted.
coords : dict, optional
sample_dims : str or sequence of hashable, optional
Dimensions to reduce unless mapped to an aesthetic.
Defaults to ``rcParams["data.sample_dims"]``
combined : bool, default False
Whether to plot intervals for each chain or not. Ignored when the "chain" dimension
is not present.
point_estimate : {"mean", "median", "mode"}, optional
Which point estimate to plot. Defaults to rcParam :data:`stats.point_estimate`
ci_kind : {"eti", "hdi"}, optional
Which credible interval to use. Defaults to ``rcParams["stats.ci_kind"]``
ci_probs : (float, float), optional
Indicates the probabilities that should be contained within the plotted credible intervals.
It should be sorted as the elements refer to the probabilities of the "trunk" and "twig"
elements. Defaults to ``(0.5, rcParams["stats.ci_prob"])``
labels : sequence of str, optional
Sequence with the dimensions to be labelled in the plot. By default all dimensions
except "chain" and "model" (if present). The order of `labels` is ignored,
only elements being present in it matters.
It can include the special "__variable__" indicator, and does so by default.
shade_label : str, default None
Element of `labels` that should be used to add shading horizontal strips to the plot.
Note that labels and credible intervals are plotted in different :term:`plots`.
The shading is applied to both plots, and the spacing between them is set to 0
*if possible*, which is not always the case (one notable example being matplotlib's
constrained layout).
plot_collection : PlotCollection, optional
backend : {"matplotlib", "bokeh"}, optional
labeller : labeller, optional
aes_by_visuals : mapping of {str : sequence of str or False}, optional
Mapping of visuals to aesthetics that should use their mapping in `plot_collection`
when plotted. Valid keys are the same as for `visuals` except "ticklabels"
and "remove_axis" which do not apply, and "twig" and "trunk" which
take the same aesthetics through the "credible_interval" key.
By default, aesthetic mappings are generated for: y, alpha, overlay and color
(if multiple models are present). All aesthetic mappings but alpha are applied
to both the credible intervals and the point estimate; overlay is applied
to labels; and both overlay and alpha are applied to the shade.
"overlay" is a dummy aesthetic to trigger looping over variables and/or
dimensions using all aesthetics in every iteration. "alpha" gets two
values (0, 0.3) in order to trigger the alternate shading effect.
visuals : mapping of {str : mapping or False}, optional
Valid keys are:
* trunk, twig -> passed to :func:`~.visuals.line_x`
* point_estimate -> passed to :func:`~.visuals.scatter_x`
* labels -> passed to :func:`~.visuals.annotate_label`
* shade -> passed to :func:`~.visuals.fill_between_y`
* ticklabels -> passed to :func:`~.backend.xticks`
* remove_axis -> not passed anywhere, can only take ``False`` as value to skip calling
:func:`~.visuals.remove_axis`
stats : mapping, optional
Valid keys are:
* trunk, twig -> passed to eti or hdi
* point_estimate -> passed to mean, median or mode
**pc_kwargs
Passed to :class:`arviz_plots.PlotCollection.grid`
Returns
-------
PlotCollection
Notes
-----
The separation between variables and all its coordinate values is set to 1.
The only two exceptions to this are the dimensions named "chain" and "model"
in case they are present, which get a smaller spacing to give a sense of
grouping among visual elements that only differ on their chain or model id.
See Also
--------
:ref:`plots_intro` :
General introduction to batteries-included plotting functions, common use and logic overview
plot_ridge : Visual representation of marginal distributions over the y axis
Examples
--------
Single model forest plot with color mapped to the variable (mapping which is also applied
to the labels) and alternate shading per school.
Moreover, to ensure the shading looks continuous, we'll specify we don't want to use
constrained layout (set by the "arviz-variat" theme) and to avoid having the labels
too squished we'll set the ``width_ratios`` for
:func:`~arviz_plots.backend.none.create_plotting_grid` via ``pc_kwargs``.
.. plot::
:context: close-figs
>>> from arviz_plots import plot_forest, style
>>> style.use("arviz-variat")
>>> from arviz_base import load_arviz_data
>>> non_centered = load_arviz_data('non_centered_eight')
>>> pc = plot_forest(
>>> non_centered,
>>> var_names=["theta", "mu", "theta_t", "tau"],
>>> aes={"color": ["__variable__"]},
>>> figure_kwargs={"width_ratios": [1, 2], "layout": "none"},
>>> aes_by_visuals={"labels": ["color"]},
>>> shade_label="school",
>>> )
.. minigallery:: plot_forest
"""
if ci_kind not in ("hdi", "eti", None):
raise ValueError("ci_kind must be either 'hdi' or 'eti'")
if sample_dims is None:
sample_dims = rcParams["data.sample_dims"]
if isinstance(sample_dims, str):
sample_dims = [sample_dims]
if ci_probs is None:
rc_ci_prob = rcParams["stats.ci_prob"]
ci_probs = (0.5, rc_ci_prob) if rc_ci_prob > 0.5 else (0.5 * rc_ci_prob, rc_ci_prob)
if ci_probs[0] > ci_probs[1]:
raise ValueError("First element of ci_probs must be smaller than the second")
if ci_kind is None:
ci_kind = rcParams["stats.ci_kind"]
if point_estimate is None:
point_estimate = rcParams["stats.point_estimate"]
if visuals is None:
visuals = {}
if stats is None:
stats = {}
distribution = process_group_variables_coords(
dt, group=group, var_names=var_names, filter_vars=filter_vars, coords=coords
)
labellable_dims = ["__variable__"] + [
dim for dim in distribution.dims if (dim not in {"model", "column"}.union(sample_dims))
]
if labels is None:
labels = labellable_dims
if not combined and "chain" not in distribution.dims:
combined = True
labels_kwargs = copy(visuals.get("labels", {}))
if labels_kwargs is False:
raise ValueError("visuals['labels'] can't be False, use labels=[] to remove all labels")
shade_kwargs = copy(visuals.get("shade", {}))
if shade_kwargs is False:
raise ValueError("visuals['shade'] can't be False, use shade_label=None to remove shading")
if shade_label is not None and shade_label not in labels:
raise ValueError("shade_label must be one of the elements in labels argument")
if backend is None:
if plot_collection is None:
backend = rcParams["plot.backend"]
else:
backend = plot_collection.backend
plot_bknd = import_module(f".backend.{backend}", package="arviz_plots")
bg_color = plot_bknd.get_background_color()
contrast_color, contrast_gray_color = get_contrast_colors(bg_color=bg_color, gray_flag=True)
given_plotcollection = True
if plot_collection is None:
given_plotcollection = False
pc_data = distribution
if "column" not in pc_data:
pc_data = pc_data.expand_dims(column=2).assign_coords(column=["labels", "forest"])
elif ("forest" not in pc_data.column) or ("labels" not in pc_data.column):
raise ValueError(
"Found colum dimension in input data but required coordinates "
"'labels' and 'forest' are missing."
)
pc_kwargs.setdefault("cols", ["column"])
pc_kwargs["figure_kwargs"] = pc_kwargs.get("figure_kwargs", {}).copy()
pc_kwargs["figure_kwargs"].setdefault("sharey", True)
width_ratios = xr.ones_like(pc_data.column, dtype=float)
width_ratios.loc[{"column": "forest"}] = 3 if len(labels) < 3 else 2
pc_kwargs["figure_kwargs"].setdefault("width_ratios", width_ratios.values)
pc_kwargs["aes"] = pc_kwargs.get("aes", {}).copy()
alpha_aes_dims = False
if shade_label is not None:
pc_kwargs["figure_kwargs"].setdefault("plot_hspace", 0)
alpha_aes_dims = pc_kwargs["aes"].get("alpha", [shade_label])
pc_kwargs["aes"]["alpha"] = alpha_aes_dims
pc_kwargs["aes"].setdefault("y", labellable_dims)
pc_kwargs["aes"].setdefault("overlay", labellable_dims)
if alpha_aes_dims is not False:
if ("__variable__" in pc_kwargs["aes"]["alpha"]) or all(
shade_label in da.dims for da in distribution.values()
):
pc_kwargs.setdefault("alpha", [0, 0.3])
else:
# trigger inclusion of neutral element in aes cycle
pc_kwargs.setdefault("alpha", [0, 0, 0.3])
if "model" in distribution.dims:
pc_kwargs["aes"].setdefault("color", ["model"])
figsize = pc_kwargs.get("figure_kwargs", {}).get("figsize", None)
figsize_units = pc_kwargs.get("figure_kwargs", {}).get("figsize_units", "inches")
if figsize is None:
coeff = 0.2
n_blocks = process_facet_dims(
pc_data, [dim for dim in pc_kwargs["aes"]["y"] if dim not in ("chain", "model")]
)[0]
if not combined and "chain" in distribution.dims:
coeff += 0.1
if "model" in distribution.dims:
coeff += 0.1 * distribution.sizes["model"]
figsize = plot_bknd.scale_fig_size(
figsize,
rows=1 + coeff * n_blocks,
cols=process_facet_dims(pc_data, pc_kwargs["cols"])[0],
figsize_units=figsize_units,
)
figsize_units = "dots"
pc_kwargs["figure_kwargs"]["figsize"] = figsize
pc_kwargs["figure_kwargs"]["figsize_units"] = figsize_units
plot_collection = PlotCollection.grid(
pc_data,
backend=backend,
**pc_kwargs,
)
if "column" in distribution.dims:
distribution = distribution.sel(column="forest")
if combined:
chain_mapped_to_aes = set(
aes_key
for var_name, child in plot_collection.aes.children.items()
for aes_key, aes_vals in child.items()
if "chain" in aes_vals.dims
)
if chain_mapped_to_aes:
raise ValueError(
f"Found properties {chain_mapped_to_aes} mapped to the chain dimension, "
"but combined=True. Set combined=False or modify the aesthetic mappings"
)
# fine tune y position for model and chain
add_factor = 0.2 if (not combined) or ("model" in distribution.dims) else 0
y_ds = plot_collection.get_aes_as_dataset("y")
if not given_plotcollection:
shift = 0
if combined and "model" in distribution.dims:
shift = xr.DataArray(
np.linspace(-0.2, 0.2, distribution.sizes["model"]),
coords={"model": distribution.model},
)
elif (not combined) and ("model" in distribution.dims):
model_spacing = xr.DataArray(
np.linspace(-0.2, 0.2, distribution.sizes["model"]),
coords={"model": distribution.model},
)
chain_lim = 0.4 * (model_spacing[1] - model_spacing[0]).item()
chain_spacing = xr.DataArray(
np.linspace(-chain_lim, chain_lim, distribution.sizes["chain"]),
coords={"chain": distribution.chain},
)
shift = model_spacing + chain_spacing
elif not combined:
shift = xr.DataArray(
np.linspace(-0.2, 0.2, distribution.sizes["chain"]),
coords={"chain": distribution.chain},
)
y_ds = y_ds.max().to_array().max() - add_factor - y_ds - shift
plot_collection.update_aes_from_dataset("y", y_ds)
if aes_by_visuals is None:
aes_by_visuals = {}
else:
aes_by_visuals = aes_by_visuals.copy()
aes_by_visuals.setdefault("credible_interval", plot_collection.aes_set.difference({"alpha"}))
aes_by_visuals.setdefault("point_estimate", plot_collection.aes_set.difference({"alpha"}))
aes_by_visuals["labels"] = {"overlay"}.union(aes_by_visuals.get("labels", {}))
aes_by_visuals["shade"] = {"overlay", "alpha"}.union(aes_by_visuals.get("shade", {}))
if labeller is None:
labeller = BaseLabeller()
# compute credible interval
ci_dims, ci_aes, ci_ignore = filter_aes(
plot_collection, aes_by_visuals, "credible_interval", sample_dims
)
twig_kwargs = copy(visuals.get("twig", {}))
trunk_kwargs = copy(visuals.get("trunk", {}))
if ci_kind == "eti":
ci_fun = distribution.azstats.eti
elif ci_kind == "hdi":
ci_fun = distribution.azstats.hdi
if twig_kwargs is not False:
twig_stats = stats.get("twig", {})
if isinstance(twig_stats, xr.Dataset):
ci_twig = twig_stats
else:
ci_twig = ci_fun(prob=ci_probs[1], dim=ci_dims, **twig_stats)
if trunk_kwargs is not False:
trunk_stats = stats.get("trunk", {})
if isinstance(trunk_stats, xr.Dataset):
ci_trunk = trunk_stats
else:
ci_trunk = ci_fun(prob=ci_probs[0], dim=ci_dims, **trunk_stats)
# compute point estimate
pe_kwargs = copy(visuals.get("point_estimate", {}))
if pe_kwargs is not None:
pe_dims, pe_aes, pe_ignore = filter_aes(
plot_collection, aes_by_visuals, "point_estimate", sample_dims
)
pe_stats = stats.get("point_estimate", {})
if isinstance(pe_stats, xr.Dataset):
point = pe_stats
elif point_estimate == "median":
point = distribution.median(dim=pe_dims, **pe_stats)
elif point_estimate == "mean":
point = distribution.mean(dim=pe_dims, **pe_stats)
else:
raise NotImplementedError("coming soon")
if twig_kwargs is not False:
x_range = ci_twig
elif trunk_kwargs is not False:
x_range = ci_trunk
elif pe_kwargs is not False:
x_range = point
else:
x_range = xr.ones_like(distribution)
# add labels and shading first, so forest plot is rendered on top
cumulative_label = []
x = 0
for label in labellable_dims:
cumulative_label.append(label)
# each variable+coord combination has the space between i.5 and (i+1).5 "reserved"
# if there multiple models/combined all lines are plotted in the central region
# of .4 width, if single model+combined, the line is at the center
# shade extend is the value to add to the max/substract to the min to shade the
# whole unit regions reserved to variables/coords
shade_extend = 0.5 if add_factor == 0 else 0.3
if label not in labels:
continue
if label == "__variable__":
y_max = y_ds.max() + shade_extend
y_min = y_ds.min() - shade_extend
else:
reduce_dims = [dim for dim in y_ds.dims if dim not in cumulative_label]
y_max = y_ds.max(reduce_dims) + shade_extend
y_min = y_ds.min(reduce_dims) - shade_extend
y = (y_max + y_min) / 2
if shade_label == label:
_, shade_aes, shade_ignore = filter_aes(
plot_collection, aes_by_visuals, "shade", sample_dims
)
if "color" not in shade_aes:
shade_kwargs.setdefault("color", contrast_gray_color)
shade_data = xr.concat((y_min, y_max), "kwarg").assign_coords(
kwarg=["y_bottom", "y_top"]
)
shade_start = -0.1 if x == 0 else x - 0.6
xlim_labels = [-0.1, len(labels) - 0.9]
plot_collection.map(
fill_between_y,
"shade",
data=shade_data,
x=[shade_start, xlim_labels[1]],
coords={"column": "labels"},
ignore_aes=shade_ignore,
**shade_kwargs,
)
ci_global_min = x_range.min().to_array().min().item()
ci_global_max = x_range.max().to_array().max().item()
ci_range_extend = 0.1 * (ci_global_max - ci_global_min)
xlim_forest = [ci_global_min - ci_range_extend, ci_global_max + ci_range_extend]
plot_collection.map(
fill_between_y,
"shade",
data=shade_data,
x=xlim_forest,
coords={"column": "forest"},
ignore_aes=shade_ignore,
**shade_kwargs,
)
_, lab_aes, lab_ignore = filter_aes(plot_collection, aes_by_visuals, "labels", sample_dims)
extra_ignore_aes = []
for aes_key in lab_aes:
if aes_key == "overlay":
continue
aes_ds = plot_collection.get_aes_as_dataset(aes_key)
if set(aes_ds.dims).difference(cumulative_label):
extra_ignore_aes.append(aes_key)
lab_aes = set(lab_aes).difference(extra_ignore_aes)
lab_ignore = set(lab_ignore).union(extra_ignore_aes)
lab_kwargs = labels_kwargs.copy()
if "color" not in lab_aes:
lab_kwargs.setdefault("color", contrast_color)
if x == 0:
lab_kwargs.setdefault("horizontal_align", "left")
if x == len(labels) - 1:
lab_kwargs.setdefault("horizontal_align", "right")
plot_collection.map(
annotate_label,
f"{label.strip('_')}_label",
data=y,
x=x,
dim=None if label == "__variable__" else label,
subset_info=True,
coords={"column": "labels"},
ignore_aes=lab_ignore,
**lab_kwargs,
)
x += 1
ticklabel_kwargs = copy(visuals.get("ticklabels", {}))
if ticklabel_kwargs is not False:
plot_bknd.xticks(
np.arange(len(labels)),
[label.strip("_") for label in labellable_dims if label in labels],
plot_collection.get_target(None, {"column": "labels"}),
**ticklabel_kwargs,
)
# plot credible interval
default_color = plot_bknd.get_default_aes("color", 1, {})[0]
if twig_kwargs is not False:
twig_kwargs.setdefault("width", 0.7)
if "color" not in ci_aes:
twig_kwargs.setdefault("color", default_color)
plot_collection.map(
line_x,
"twig",
data=ci_twig,
ignore_aes=ci_ignore,
coords={"column": "forest"},
**twig_kwargs,
)
if trunk_kwargs is not False:
trunk_kwargs.setdefault("width", 2)
if "color" not in ci_aes:
trunk_kwargs.setdefault("color", default_color)
plot_collection.map(
line_x,
"trunk",
data=ci_trunk,
ignore_aes=ci_ignore,
coords={"column": "forest"},
**trunk_kwargs,
)
# point estimate
if pe_kwargs is not False:
if "color" not in pe_aes:
pe_kwargs.setdefault("color", default_color)
if "facecolor" not in pe_aes:
pe_kwargs.setdefault("facecolor", "white")
if "width" not in pe_aes:
pe_kwargs.setdefault("width", 1)
plot_collection.map(
scatter_x,
"point_estimate",
data=point,
ignore_aes=pe_ignore,
coords={"column": "forest"},
**pe_kwargs,
)
if shade_label is not None:
plot_bknd.xlim(xlim_labels, plot_collection.get_target(None, {"column": "labels"}))
plot_bknd.xlim(xlim_forest, plot_collection.get_target(None, {"column": "forest"}))
if visuals.get("remove_axis", True) is not False:
plot_collection.map(
remove_axis,
store_artist=backend == "none",
axis="y",
ignore_aes=plot_collection.aes_set,
)
return plot_collection
