Source code for pymc.stats.log_density

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from collections.abc import Sequence
from typing import Any, Literal

from arviz import InferenceData
from xarray import Dataset

from pymc.backends.arviz import (
    apply_function_over_dataset,
    coords_and_dims_for_inferencedata,
)
from pymc.model import Model, modelcontext

__all__ = ("compute_log_likelihood", "compute_log_prior")

from pymc.model.transform.conditioning import remove_value_transforms


[docs] def compute_log_likelihood( idata: InferenceData, *, var_names: Sequence[str] | None = None, extend_inferencedata: bool = True, model: Model | None = None, sample_dims: Sequence[str] = ("chain", "draw"), progressbar=True, compile_kwargs: dict[str, Any] | None = None, ): """Compute elemwise log_likelihood of model given InferenceData with posterior group. Parameters ---------- idata : InferenceData InferenceData with posterior group var_names : sequence of str, optional List of Observed variable names for which to compute log_likelihood. Defaults to all observed variables. extend_inferencedata : bool, default True Whether to extend the original InferenceData or return a new one model : Model, optional sample_dims : sequence of str, default ("chain", "draw") progressbar : bool, default True compile_kwargs : dict[str, Any] | None Extra compilation arguments to supply to :py:func:`~pymc.stats.compute_log_density` Returns ------- idata : InferenceData InferenceData with log_likelihood group """ return compute_log_density( idata=idata, var_names=var_names, extend_inferencedata=extend_inferencedata, model=model, kind="likelihood", sample_dims=sample_dims, progressbar=progressbar, compile_kwargs=compile_kwargs, )
[docs] def compute_log_prior( idata: InferenceData, var_names: Sequence[str] | None = None, extend_inferencedata: bool = True, model: Model | None = None, sample_dims: Sequence[str] = ("chain", "draw"), progressbar=True, compile_kwargs=None, ): """Compute elemwise log_prior of model given InferenceData with posterior group. Parameters ---------- idata : InferenceData InferenceData with posterior group var_names : sequence of str, optional List of Observed variable names for which to compute log_prior. Defaults to all all free variables. extend_inferencedata : bool, default True Whether to extend the original InferenceData or return a new one model : Model, optional sample_dims : sequence of str, default ("chain", "draw") progressbar : bool, default True compile_kwargs : dict[str, Any] | None Extra compilation arguments to supply to :py:func:`~pymc.stats.compute_log_density` Returns ------- idata : InferenceData InferenceData with log_prior group """ return compute_log_density( idata=idata, var_names=var_names, extend_inferencedata=extend_inferencedata, model=model, kind="prior", sample_dims=sample_dims, progressbar=progressbar, compile_kwargs=compile_kwargs, )
def compute_log_density( idata: InferenceData, *, var_names: Sequence[str] | None = None, extend_inferencedata: bool = True, model: Model | None = None, kind: Literal["likelihood", "prior"] = "likelihood", sample_dims: Sequence[str] = ("chain", "draw"), progressbar=True, compile_kwargs=None, ) -> InferenceData | Dataset: """ Compute elemwise log_likelihood or log_prior of model given InferenceData with posterior group. Parameters ---------- idata : InferenceData InferenceData with posterior group var_names : sequence of str, optional List of Observed variable names for which to compute log_prior. Defaults to all all free variables. extend_inferencedata : bool, default True Whether to extend the original InferenceData or return a new one model : Model, optional kind: Literal["likelihood", "prior"] Whether to compute the log density of the observed random variables (likelihood) or to compute the log density of the latent random variables (prior). This parameter determines the group that gets added to the returned `~arviz.InferenceData` object. sample_dims : sequence of str, default ("chain", "draw") progressbar : bool, default True compile_kwargs : dict[str, Any] | None Extra compilation arguments to supply to :py:func:`pymc.model.core.Model.compile_fn` Returns ------- idata : InferenceData InferenceData with the ``log_likelihood`` group when ``kind == "likelihood"`` or the ``log_prior`` group when ``kind == "prior"``. """ posterior = idata["posterior"] model = modelcontext(model) if compile_kwargs is None: compile_kwargs = {} if kind not in ("likelihood", "prior"): raise ValueError("kind must be either 'likelihood' or 'prior'") # We need to disable transforms, because the InferenceData only keeps the untransformed values umodel = remove_value_transforms(model) if kind == "likelihood": target_rvs = list(umodel.observed_RVs) target_str = "observed_RVs" else: target_rvs = list(umodel.free_RVs) target_str = "free_RVs" if var_names is None: vars = target_rvs var_names = tuple(rv.name for rv in vars) else: vars = [umodel.named_vars[name] for name in var_names] if not set(vars).issubset(target_rvs): raise ValueError(f"var_names must refer to {target_str} in the model. Got: {var_names}") elemwise_logdens_fn = umodel.compile_fn( inputs=umodel.value_vars, outs=umodel.logp(vars=vars, sum=False), on_unused_input="ignore", **compile_kwargs, ) coords, dims = coords_and_dims_for_inferencedata(umodel) logdens_dataset = apply_function_over_dataset( elemwise_logdens_fn, posterior[[rv.name for rv in umodel.free_RVs]], output_var_names=var_names, sample_dims=sample_dims, dims=dims, coords=coords, progressbar=progressbar, ) if extend_inferencedata: idata.add_groups({f"log_{kind}": logdens_dataset}) return idata else: return logdens_dataset