arviz.plot_ppc#

arviz.plot_ppc(data, kind='kde', alpha=None, mean=True, observed=True, observed_rug=False, color=None, colors=None, grid=None, figsize=None, textsize=None, data_pairs=None, var_names=None, filter_vars=None, coords=None, flatten=None, flatten_pp=None, num_pp_samples=None, random_seed=None, jitter=None, animated=False, animation_kwargs=None, legend=True, labeller=None, ax=None, backend=None, backend_kwargs=None, group='posterior', show=None)[source]#

Plot for posterior/prior predictive checks.

Parameters
data: az.InferenceData object

arviz.InferenceData object containing the observed and posterior/prior predictive data.

kind: str

Type of plot to display (“kde”, “cumulative”, or “scatter”). Defaults to kde.

alpha: float

Opacity of posterior/prior predictive density curves. Defaults to 0.2 for kind = kde and cumulative, for scatter defaults to 0.7.

mean: bool

Whether or not to plot the mean posterior/prior predictive distribution. Defaults to True.

observed: bool, default True

Whether or not to plot the observed data.

observed_rug: bool, default False

Whether or not to plot a rug plot for the observed data. Only valid if observed is True and for kind kde or cumulative.

color: str

Valid matplotlib color. Defaults to C0.

color: list

List with valid matplotlib colors corresponding to the posterior/prior predictive distribution, observed data and mean of the posterior/prior predictive distribution. Defaults to [“C0”, “k”, “C1”].

gridtuple

Number of rows and columns. Defaults to None, the rows and columns are automatically inferred.

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.

data_pairs: dict

Dictionary containing relations between observed data and posterior/prior predictive data. Dictionary structure:

  • key = data var_name

  • value = posterior/prior predictive var_name

For example, data_pairs = {'y' : 'y_hat'} If None, it will assume that the observed data and the posterior/prior predictive data have the same variable name.

var_names: list of variable names

Variables to be plotted, if None all variable are plotted. 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: dict

Dictionary mapping dimensions to selected coordinates to be plotted. Dimensions without a mapping specified will include all coordinates for that dimension. Defaults to including all coordinates for all dimensions if None.

flatten: list

List of dimensions to flatten in observed_data. Only flattens across the coordinates specified in the coords argument. Defaults to flattening all of the dimensions.

flatten_pp: list

List of dimensions to flatten in posterior_predictive/prior_predictive. Only flattens across the coordinates specified in the coords argument. Defaults to flattening all of the dimensions. Dimensions should match flatten excluding dimensions for data_pairs parameters. If flatten is defined and flatten_pp is None, then flatten_pp = flatten.

num_pp_samples: int

The number of posterior/prior predictive samples to plot. For kind = ‘scatter’ and animation = False if defaults to a maximum of 5 samples and will set jitter to 0.7. unless defined. Otherwise it defaults to all provided samples.

random_seed: int

Random number generator seed passed to numpy.random.seed to allow reproducibility of the plot. By default, no seed will be provided and the plot will change each call if a random sample is specified by num_pp_samples.

jitter: float

If kind is “scatter”, jitter will add random uniform noise to the height of the ppc samples and observed data. By default 0.

animated: bool

Create an animation of one posterior/prior predictive sample per frame. Defaults to False. Only works with matploblib backend. To run animations inside a notebook you have to use the nbAgg matplotlib’s backend. Try with %matplotlib notebook or %matplotlib  nbAgg. You can switch back to the default matplotlib’s backend with %matplotlib  inline or %matplotlib  auto. If switching back and forth between matplotlib’s backend, you may need to run twice the cell with the animation. If you experience problems rendering the animation try setting animation_kwargs({'blit':False}) or changing the matplotlib’s backend (e.g. to TkAgg) If you run the animation from a script write ax, ani = az.plot_ppc(.)

animation_kwargsdict

Keywords passed to matplotlib.animation.FuncAnimation. Ignored with matplotlib backend.

legendbool

Add legend to figure. By default True.

labellerlabeller instance, optional

Class providing the method make_pp_label to generate the labels in the plot titles. Read the Label guide for more details and usage examples.

ax: numpy array-like of matplotlib axes or bokeh figures, optional

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).

backend: str, optional

Select plotting backend {“matplotlib”,”bokeh”}. Default to “matplotlib”.

backend_kwargs: bool, optional

These are kwargs specific to the backend being used, passed to matplotlib.pyplot.subplots() or bokeh.plotting.figure(). For additional documentation check the plotting method of the backend.

group: {“prior”, “posterior”}, optional

Specifies which InferenceData group should be plotted. Defaults to ‘posterior’. Other value can be ‘prior’.

show: bool, optional

Call backend show function.

Returns
axes: matplotlib axes or bokeh figures

See also

plot_bpv

Plot Bayesian p-value for observed data and Posterior/Prior predictive.

plot_lm

Posterior predictive and mean plots for regression-like data.

plot_ppc

plot for posterior/prior predictive checks.

plot_ts

Plot timeseries data.

Examples

Plot the observed data KDE overlaid on posterior predictive KDEs.

>>> import arviz as az
>>> data = az.load_arviz_data('radon')
>>> az.plot_ppc(data, data_pairs={"y":"y"})
../../_images/arviz-plot_ppc-1.png

Plot the overlay with empirical CDFs.

>>> az.plot_ppc(data, kind='cumulative')
../../_images/arviz-plot_ppc-2.png

Use the coords and flatten parameters to plot selected variable dimensions across multiple plots. We will now modify the dimension obs_id to contain indicate the name of the county where the measure was taken. The change has to be done on both posterior_predictive and observed_data groups, which is why we will use map() to apply the same function to both groups. Afterwards, we will select the counties to be plotted with the coords arg.

>>> obs_county = data.posterior["County"][data.constant_data["county_idx"]]
>>> data = data.assign_coords(obs_id=obs_county, groups="observed_vars")
>>> az.plot_ppc(data, coords={'obs_id': ['ANOKA', 'BELTRAMI']}, flatten=[])
../../_images/arviz-plot_ppc-3.png

Plot the overlay using a stacked scatter plot that is particularly useful when the sample sizes are small.

>>> az.plot_ppc(data, kind='scatter', flatten=[],
>>>             coords={'obs_id': ['AITKIN', 'BELTRAMI']})
../../_images/arviz-plot_ppc-4.png

Plot random posterior predictive sub-samples.

>>> az.plot_ppc(data, num_pp_samples=30, random_seed=7)
../../_images/arviz-plot_ppc-5.png