arviz_base.from_numpyro#
- arviz_base.from_numpyro(posterior=None, *, prior=None, posterior_predictive=None, predictions=None, constant_data=None, predictions_constant_data=None, log_likelihood=None, index_origin=None, coords=None, dims=None, pred_dims=None, extra_event_dims=None, num_chains=1)[source]#
Convert NumPyro data into a DataTree object.
For a usage example read Converting NumPyro objects to DataTree
If no dims are provided, this will infer batch dim names from NumPyro model plates. For event dim names, such as with the ZeroSumNormal, infer={“event_dims”:dim_names} can be provided in numpyro.sample, i.e.:
# equivalent to dims entry, {"gamma": ["groups"]} gamma = numpyro.sample( "gamma", dist.ZeroSumNormal(1, event_shape=(n_groups,)), infer={"event_dims":["groups"]} )
There is also an additional extra_event_dims input to cover any edge cases, for instance deterministic sites with event dims (which dont have an infer argument to provide metadata).
- Parameters:
- posterior
numpyro.mcmc.MCMC Fitted MCMC object from NumPyro
- prior
dict, optional Prior samples from a NumPyro model
- posterior_predictive
dict, optional Posterior predictive samples for the posterior
- predictions
dict, optional Out of sample predictions
- constant_data
dict, optional Dictionary containing constant data variables mapped to their values.
- predictions_constant_data
dict, optional Constant data used for out-of-sample predictions.
- index_origin
int, optional - coords
dict, optional Map of dimensions to coordinates
- dims
dictof {strlistofstr}, optional Map variable names to their coordinates. Will be inferred if they are not provided.
- pred_dims
dict, optional Dims for predictions data. Map variable names to their coordinates. Default behavior is to infer dims if this is not provided
- extra_event_dims
dict, optional Extra event dims for deterministic sites. Maps event dims that couldnt be inferred to their coordinates.
- num_chains
int, default 1 Number of chains used for sampling. Ignored if posterior is present.
- posterior
- Returns: