def _validate_predictive_group(data: az.InferenceData, group: str):
"""Validate the predictive groups in data.
Args:
data (arviz.InferenceData): Inference data object.
group (str): One of ['posterior', 'prior'].
Raises:
ValueError: If group is not valid.
KeyError: If predictive is not in data, gives helpful suggestion.
Returns:
xarray.Dataset: Dataset corresponding to the predictive of group.
"""
if group == "posterior":
key = "posterior_predictive"
predictive = data.get(key, None)
elif group == "prior":
key = "prior_predictive"
predictive = data.get(key, None)
else:
raise ValueError(
f"Group '{group}' is not one of ['posterior', 'prior'].")
if predictive is None:
raise KeyError(f"Group '{key}' not in data. Consider using method " +
"'Inference.{key}()' to sample the predictive.")
return predictive
def predict(
mi: MaudInput,
output_dir: str,
idata_train: az.InferenceData,
) -> az.InferenceData:
"""Call CmdStanModel.sample for out of sample predictions.
:param mi: a MaudInput object
:param output_dir: directory where output will be saved
:param idata_train: InferenceData object with posterior draws
"""
model = cmdstanpy.CmdStanModel(
stan_file=os.path.join(HERE, STAN_PROGRAM_RELATIVE_PATH_PREDICT),
cpp_options=mi.config.cpp_options,
stanc_options=mi.config.stanc_options,
)
set_up_output_dir(output_dir, mi)
kinetic_parameters = [
"keq",
"km",
"kcat",
"dissociation_constant",
"transfer_constant",
"kcat_phos",
"ki",
]
posterior = idata_train.get("posterior")
sample_stats = idata_train.get("sample_stats")
assert posterior is not None
assert sample_stats is not None
chains = sample_stats["chain"]
draws = sample_stats["draw"]
dims = {
"conc": ["experiment", "mic"],
"conc_enzyme": ["experiment", "enzyme"],
"flux": ["experiment", "reaction"],
}
for chain in chains:
for draw in draws:
inits = {
par: (
posterior[par]
.sel(chain=chain, draw=draw)
.to_series()
.values
)
for par in kinetic_parameters
if par in posterior.keys()
}
sample_args: dict = {
"data": os.path.join(output_dir, "input_data_test.json"),
"inits": inits,
"output_dir": output_dir,
"iter_warmup": 0,
"iter_sampling": 1,
"fixed_param": True,
"show_progress": False,
}
if mi.config.cmdstanpy_config_predict is not None:
sample_args = {
**sample_args,
**mi.config.cmdstanpy_config_predict,
}
mcmc_draw = model.sample(**sample_args)
idata_draw = az.from_cmdstan(
mcmc_draw.runset.csv_files,
coords={
"experiment": [
e.id for e in mi.measurements.experiments if e.is_test
],
"mic": [m.id for m in mi.kinetic_model.mics],
"enzyme": [e.id for e in mi.kinetic_model.enzymes],
"reaction": [r.id for r in mi.kinetic_model.reactions],
},
dims=dims,
).assign_coords(
coords={"chain": [chain], "draw": [draw]},
groups="posterior_groups",
)
if draw == 0:
idata_chain = idata_draw.copy()
else:
idata_chain = az.concat(
[idata_chain, idata_draw], dim="draw", reset_dim=False
)
if chain == 0:
out = idata_chain.copy()
else:
out = az.concat([out, idata_chain], dim="chain", reset_dim=False)
return out
