def plot_priors(self, var_names=None):
if not self.built:
raise ValueError("Cannot plot priors until model is built!")
with pm.Model():
# get priors for fixed fx, separately for each level of each predictor
dists = []
for fixed_term in self.fixed_terms.values():
if var_names is not None and fixed_term.name not in var_names:
continue
for i, level in enumerate(fixed_term.levels):
params = {
k: np.atleast_1d(v)[i % v.size] if isinstance(
v, np.ndarray) else v
for k, v in fixed_term.prior.args.items()
}
dists += [
getattr(pm, fixed_term.prior.name)(level, **params)
]
# get priors for random effect sigmas
for random_term in self.random_terms.values():
if var_names is not None and random_term.name not in var_names:
continue
prior = random_term.prior.args["sigma"].name
params = random_term.prior.args["sigma"].args
dists += [
getattr(pm, prior)(random_term.name + "_sigma", **params)
]
# add priors on Y params if applicable
y_priors = [(k, v) for k, v in self.y.prior.args.items()
if isinstance(v, Prior)]
if y_priors:
for y_prior in y_priors:
pm_attr = getattr(pm, y_prior[1].name)
y_prior_ = pm_attr("_".join([self.y.name, y_prior[0]]),
**y_prior[1].args)
dists.extend([y_prior_])
# make the plot!
priors_to_plot = {}
for i, dist in enumerate(dists):
dist_ = dist.distribution if isinstance(
dist, pm.model.FreeRV) else dist
priors_to_plot[dist.name] = dist_.random(size=1000).flatten()
# Probably we should replace this for something else
axes = plot_posterior(priors_to_plot,
credible_interval=None,
point_estimate=None)
return axes
def plot_priors(
self,
draws=5000,
var_names=None,
random_seed=None,
figsize=None,
textsize=None,
hdi_prob=None,
round_to=2,
point_estimate="mean",
kind="kde",
bins=None,
omit_offsets=True,
omit_group_specific=True,
ax=None,
):
"""
Samples from the prior distribution and plots its marginals.
Parameters
----------
draws : int
Number of draws to sample from the prior predictive distribution. Defaults to 5000.
var_names : str or list
A list of names of variables for which to compute the posterior predictive
distribution. Defaults to both observed and unobserved RVs.
random_seed : int
Seed for the random number generator.
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``.
hdi_prob: float
Plots highest density interval for chosen percentage of density.
Use ``'hide'`` to hide the highest density interval. Defaults to 0.94.
round_to: int
Controls formatting of floats. Defaults to 2 or the integer part, whichever is bigger.
point_estimate: str
Plot point estimate per variable. Values should be ``'mean'``, ``'median'``, ``'mode'``
or ``None``. Defaults to ``'auto'`` i.e. it falls back to default set in
ArviZ's rcParams.
kind: str
Type of plot to display (``'kde'`` or ``'hist'``) For discrete variables this argument
is ignored and a histogram is always used.
bins: integer or sequence or 'auto'
Controls the number of bins, accepts the same keywords ``matplotlib.hist()`` does.
Only works if ``kind == hist``. If ``None`` (default) it will use ``auto`` for
continuous variables and ``range(xmin, xmax + 1)`` for discrete variables.
omit_offsets: bool
Whether to omit offset terms in the plot. Defaults to ``True``.
omit_group_specific: bool
Whether to omit group specific effects in the plot. Defaults to ``True``.
ax: numpy array-like of matplotlib axes or bokeh figures
A 2D array of locations into which to plot the densities. If not supplied, ArviZ will
create its own array of plot areas (and return it).
**kwargs
Passed as-is to ``plt.hist()`` or ``plt.plot()`` function depending on the value of
``kind``.
Returns
-------
axes: matplotlib axes or bokeh figures
"""
if not self.built:
raise ValueError(
"Cannot plot priors until model is built!! "
"Call .build() to build the model or .fit() to build and sample from the posterior."
)
unobserved_rvs_names = []
flat_rvs = []
for unobserved in self.backend.model.unobserved_RVs:
if "Flat" in unobserved.__str__():
flat_rvs.append(unobserved.name)
else:
unobserved_rvs_names.append(unobserved.name)
if var_names is None:
var_names = pm.util.get_default_varnames(unobserved_rvs_names,
include_transformed=False)
else:
flat_rvs = [fv for fv in flat_rvs if fv in var_names]
var_names = [vn for vn in var_names if vn not in flat_rvs]
if flat_rvs:
_log.info(
"Variables %s have flat priors, and hence they are not plotted",
", ".join(flat_rvs))
if omit_offsets:
var_names = [
name for name in var_names if not name.endswith("_offset")
]
if omit_group_specific:
omitted = list(self.group_specific_terms)
var_names = [vn for vn in var_names if vn not in omitted]
axes = None
if var_names:
pps = self.prior_predictive(draws=draws,
var_names=var_names,
random_seed=random_seed)
axes = plot_posterior(
pps,
group="prior",
figsize=figsize,
textsize=textsize,
hdi_prob=hdi_prob,
round_to=round_to,
point_estimate=point_estimate,
kind=kind,
bins=bins,
ax=ax,
)
return axes