def _single_feature_to_inf(
fit: CmdStanMCMC,
coords: dict,
dims: dict,
vars_to_drop: Sequence[str],
posterior_predictive: str = None,
log_likelihood: str = None,
) -> az.InferenceData:
"""Convert single feature fit to InferenceData.
:param fit: Single feature fit with CmdStanPy
:type fit: cmdstanpy.CmdStanMCMC
:param coords: Coordinates to use for annotating Inference dims
:type coords: dict
:param dims: Dimensions of parameters in fitted model
:type dims: dict
:param posterior_predictive: Name of variable holding PP values
:type posterior_predictive: str
:param log_likelihood: Name of variable holding LL values
:type log_likelihood: str
:returns: InferenceData object of single feature
:rtype: az.InferenceData
"""
feat_inf = az.from_cmdstanpy(posterior=fit,
posterior_predictive=posterior_predictive,
log_likelihood=log_likelihood,
coords=coords,
dims=dims)
feat_inf.posterior = _drop_data(feat_inf.posterior, vars_to_drop)
return feat_inf
def standard_validate_arviz(fit, ll_label, pp_label, observed, variables):
inferred = az.from_cmdstanpy(fit,
log_likelihood=ll_label,
posterior_predictive=pp_label,
observed_data={'y': observed})
print("Displaying posterior plots.")
param_posterior_arviz_plots(inferred, variables)
print("Validating inference run.")
_ = run_validate_arviz(inferred)
print("Validating parameter sampling.")
param_validate_arviz(inferred, variables)
def get_inference_data4(self, data, eight_schools_params):
"""multiple vars as lists."""
return from_cmdstanpy(
posterior=data.obj,
posterior_predictive=None,
prior=data.obj,
prior_predictive=None,
observed_data={"y": eight_schools_params["y"]},
coords=None,
dims=None,
)
def full_fit_to_inference(
fit: CmdStanMCMC,
params: Sequence[str],
coords: dict,
dims: dict,
alr_params: Sequence[str] = None,
posterior_predictive: str = None,
log_likelihood: str = None,
) -> az.InferenceData:
"""Convert fitted Stan model into inference object.
:param fit: Fitted model
:type params: CmdStanMCMC
:param params: Posterior fitted parameters to include
:type params: Sequence[str]
:param coords: Mapping of entries in dims to labels
:type coords: dict
:param dims: Dimensions of parameters in the model
:type dims: dict
:param alr_params: Parameters to convert from ALR to CLR
:type alr_params: Sequence[str], optional
:param posterior_predictive: Name of posterior predictive values from
Stan model to include in ``arviz`` InferenceData object
:type posterior_predictive: str, optional
:param log_likelihood: Name of log likelihood values from Stan model
to include in ``arviz`` InferenceData object
:type log_likelihood: str, optional
:returns: ``arviz`` InferenceData object with selected values
:rtype: az.InferenceData
"""
if log_likelihood is not None and log_likelihood not in dims:
raise KeyError("Must include dimensions for log-likelihood!")
if posterior_predictive is not None and posterior_predictive not in dims:
raise KeyError("Must include dimensions for posterior predictive!")
inference = az.from_cmdstanpy(
fit,
coords=coords,
log_likelihood=log_likelihood,
posterior_predictive=posterior_predictive,
dims=dims
)
vars_to_drop = set(inference.posterior.data_vars).difference(params)
inference.posterior = _drop_data(inference.posterior, vars_to_drop)
return inference
def get_inference_data3(self, data, eight_schools_params):
"""multiple vars as lists."""
return from_cmdstanpy(
posterior=data.obj,
posterior_predictive=["y_hat", "log_lik"],
prior=data.obj,
prior_predictive=["y_hat", "log_lik"],
observed_data={"y": eight_schools_params["y"]},
coords={"school": np.arange(eight_schools_params["J"])},
dims={"theta": ["school"], "y": ["school"], "y_hat": ["school"], "eta": ["school"]},
)
def get_inference_data5(self, data, eight_schools_params):
"""multiple vars as lists."""
return from_cmdstanpy(
posterior=data.obj,
posterior_predictive=None,
prior=data.obj,
prior_predictive=None,
log_likelihood="log_lik",
observed_data={"y": eight_schools_params["y"]},
coords=None,
dims=None,
dtypes=data.model.code(),
)
def get_inference_data4(self, data, eight_schools_params):
"""multiple vars as lists."""
return from_cmdstanpy(
posterior=data.obj,
posterior_predictive=None,
prior=data.obj,
prior_predictive=None,
log_likelihood=False,
observed_data={"y": eight_schools_params["y"]},
coords=None,
dims=None,
dtypes={"eta": int, "theta": int},
)
def single_feature_fit_to_inference(
fit: CmdStanMCMC,
params: Sequence[str],
coords: dict,
dims: dict,
posterior_predictive: str = None,
log_likelihood: str = None,
) -> az.InferenceData:
"""Convert single feature fit to InferenceData.
:param fit: Single feature fit with CmdStanPy
:type fit: cmdstanpy.CmdStanMCMC
:param params: Posterior fitted parameters to include
:type params: Sequence[str]
:param coords: Coordinates to use for annotating Inference dims
:type coords: dict
:param dims: Dimensions of parameters in fitted model
:type dims: dict
:param posterior_predictive: Name of variable holding PP values
:type posterior_predictive: str
:param log_likelihood: Name of variable holding LL values
:type log_likelihood: str
:returns: InferenceData object of single feature
:rtype: az.InferenceData
"""
_coords = coords.copy()
if "feature" in coords:
_coords.pop("feature")
_dims = dims.copy()
for k, v in _dims.items():
if "feature" in v:
v.remove("feature")
feat_inf = az.from_cmdstanpy(
posterior=fit,
posterior_predictive=posterior_predictive,
log_likelihood=log_likelihood,
coords=_coords,
dims=_dims
)
vars_to_drop = set(feat_inf.posterior.data_vars).difference(params)
feat_inf.posterior = _drop_data(feat_inf.posterior, vars_to_drop)
return feat_inf
def get_inference_data(self, data, eight_schools_params):
"""vars as str."""
return from_cmdstanpy(
posterior=data.obj,
posterior_predictive="y_hat",
prior=data.obj,
prior_predictive="y_hat",
observed_data={"y": eight_schools_params["y"]},
log_likelihood="log_lik",
coords={"school": np.arange(eight_schools_params["J"])},
dims={
"theta": ["school"],
"y": ["school"],
"log_lik": ["school"],
"y_hat": ["school"],
"eta": ["school"],
},
)
def generate_samples(
study_name: str,
measurements: pd.DataFrame,
model_configurations: List[ModelConfiguration],
) -> None:
"""Run cmdstanpy.CmdStanModel.sample, do diagnostics and save results.
:param study_name: a string
"""
infds = {}
for model_config in model_configurations:
fit_name = f"{study_name}-{model_config.name}"
print(f"Fitting model {fit_name}...")
loo_file = os.path.join(LOO_DIR, f"loo_{fit_name}.pkl")
infd_file = os.path.join(INFD_DIR, f"infd_{fit_name}.ncdf")
json_file = os.path.join(JSON_DIR, f"input_data_{fit_name}.json")
stan_input = model_config.stan_input_function(measurements)
print(f"Writing input data to {json_file}")
jsondump(json_file, stan_input)
model = CmdStanModel(
model_name=fit_name, stan_file=model_config.stan_file
)
print(f"Writing csv files to {SAMPLES_DIR}...")
mcmc = model.sample(
data=stan_input,
output_dir=SAMPLES_DIR,
**model_config.sample_kwargs,
)
print(mcmc.diagnose().replace("\n\n", "\n"))
infd = az.from_cmdstanpy(
mcmc, **model_config.infd_kwargs_function(measurements)
)
print(az.summary(infd))
infds[fit_name] = infd
print(f"Writing inference data to {infd_file}")
infd.to_netcdf(infd_file)
print(f"Writing psis-loo results to {loo_file}\n")
az.loo(infd, pointwise=True).to_pickle(loo_file)
if len(infds) > 1:
comparison = az.compare(infds)
print(f"Loo comparison:\n{comparison}")
comparison.to_csv(os.path.join(LOO_DIR, "loo_comparison.csv"))
def get_inference_data3(self, data, eight_schools_params):
"""multiple vars as lists."""
return from_cmdstanpy(
posterior=data.obj,
posterior_predictive=["y_hat", "log_lik"],
prior=data.obj,
prior_predictive=["y_hat", "log_lik"],
observed_data={"y": eight_schools_params["y"]},
coords={
"school": np.arange(eight_schools_params["J"]),
"half school": ["a", "b", "c", "d"],
"extra_dim": ["x", "y"],
},
dims={
"eta": ["extra_dim", "half school"],
"y": ["school"],
"y_hat": ["school"],
"theta": ["school"],
"log_lik": ["log_lik_dim"],
},
dtypes=data.model,
)
def get_inference_data_warmup_true_is_false(self, data, eight_schools_params):
"""vars as str."""
return from_cmdstanpy(
posterior=data.obj_warmup,
posterior_predictive="y_hat",
predictions="y_hat",
prior=data.obj_warmup,
prior_predictive="y_hat",
observed_data={"y": eight_schools_params["y"]},
constant_data={"y": eight_schools_params["y"]},
predictions_constant_data={"y": eight_schools_params["y"]},
log_likelihood="log_lik",
coords={"school": np.arange(eight_schools_params["J"])},
dims={
"eta": ["extra_dim", "half school"],
"y": ["school"],
"log_lik": ["school"],
"y_hat": ["school"],
"theta": ["school"],
},
save_warmup=False,
)
def get_inference_data2(self, data, eight_schools_params):
"""vars as lists."""
return from_cmdstanpy(
posterior=data.obj,
posterior_predictive=["y_hat"],
predictions=["y_hat", "log_lik"],
prior=data.obj,
prior_predictive=["y_hat"],
observed_data={"y": eight_schools_params["y"]},
constant_data=eight_schools_params,
predictions_constant_data=eight_schools_params,
log_likelihood=["log_lik", "y_hat"],
coords={
"school": np.arange(eight_schools_params["J"]),
"log_lik_dim": np.arange(eight_schools_params["J"]),
},
dims={
"theta": ["school"],
"y": ["school"],
"y_hat": ["school"],
"eta": ["school"],
"log_lik": ["log_lik_dim"],
},
)
repo = Repo("./", search_parent_directories=True)
# repo_rootdir holds the absolute path to the top-level of our repo
repo_rootdir = repo.working_tree_dir
sm_gaussF = cmdstanpy.CmdStanModel(
stan_file=f"{repo_rootdir}/code/stan/test_gaussF.stan",
compile=True,)
# stan needs to know how many data points to generate,
# so pick a representative promoter
stan_data = dict(
a=22.35,
b=-17.3,
c=12.5,
z=-1.59,
)
stan_output = sm_gaussF.sample(
data=stan_data,
fixed_param=True,
iter_sampling=1,
)
# Convert to ArviZ InferenceData object
stan_output = az.from_cmdstanpy(
stan_output,
posterior_predictive=["output"]
)
# uncomment for running live in ipython,
stan_output.posterior_predictive.output
# Define the data dictionary
data_dict = {
'J': d['replicate'].max(),
'N_yield': len(d),
'N_calib': len(calib),
'idx': d['replicate'].values.astype(int),
'calib_conc': calib['carbon_conc_mM'].values.astype(float),
'calib_rel_areas': calib['rel_area_phosphate'].values.astype(float),
'optical_density': d['od_600nm'].values.astype(float),
'yield_rel_areas': d['rel_area_phosphate'].values.astype(float)
}
# Sample the inferrential model
samps = model.sample(data=data_dict)
samps = az.from_cmdstanpy(samps)
samps = samps.posterior.to_dataframe().reset_index()
# Tidy low-level parameters
_samps = samps[[
'yield_inter_dim_0', 'yield_slope_dim_0', 'yield_inter', 'yield_slope'
]]
_samps.drop_duplicates(inplace=True)
pairs = [['yield_inter_dim_0', 'yield_inter'],
['yield_slope_dim_0', 'yield_slope']]
dfs = []
for p in pairs:
_params = _samps[p]
_df = pd.DataFrame([])
_df['value'] = _params[p[1]]
_df['parameter'] = p[1]
# maximum likelihood estimation
optim = sm.optimize(data=mdl_data).optimized_params_pd
optim[optim.columns[~optim.columns.str.startswith("lp")]]
# variational inference
vb = sm.variational(data=mdl_data)
vb.variational_sample.columns = vb.variational_params_dict.keys()
vb_name = vb.variational_params_pd.columns[~vb.variational_params_pd.columns.
str.startswith(("lp", "log_"))]
vb.variational_params_pd[vb_name]
vb.variational_sample[vb_name]
# Markov chain Monte Carlo
fit = sm.sample(data=mdl_data,
show_progress=True,
chains=4,
iter_sampling=50000,
iter_warmup=10000,
thin=5)
fit.draws().shape # iterations, chains, parameters
fit.summary().loc[vb_name] # pandas DataFrame
print(fit.diagnose())
posterior = fit.stan_variables()
az_trace = az.from_cmdstanpy(fit)
az.summary(az_trace).loc[vb_name] # pandas DataFrame
az.plot_trace(az_trace)
# maximum likelihood estimation
optim_modif = sm_modif.optimize(data=mdl_data).optimized_params_pd
optim_modif[optim_modif.columns[~optim_modif.columns.str.startswith("lp")]]
# variational inference
vb_modif = sm_modif.variational(data=mdl_data)
vb_modif.variational_sample.columns = vb_modif.variational_params_dict.keys()
vb_name = vb_modif.variational_params_pd.columns[
~vb_modif.variational_params_pd.columns.str.startswith(("lp", "log_"))]
vb_modif.variational_params_pd[vb_name]
vb_modif.variational_sample[vb_name]
# Markov chain Monte Carlo
fit_modif = sm_modif.sample(data=mdl_data,
show_progress=True,
chains=4,
iter_sampling=50000,
iter_warmup=10000,
thin=5)
fit_modif.draws().shape # iterations, chains, parameters
fit_modif.summary().loc[vb_name] # pandas DataFrame
print(fit_modif.diagnose())
posterior = {k: fit_modif.stan_variable(k) for k in var_name}
az_trace = az.from_cmdstanpy(fit_modif)
az.summary(az_trace).loc[vb_name] # pandas DataFrame
az.plot_trace(az_trace, var_names=var_name)
# ---- data ---- #
eight_school_data = {
"J": 8,
"y": np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0]),
"sigma": np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0]),
}
# ---- model ---- #
stan_file = model_dir / "schools.stan"
stan_model = CmdStanModel(stan_file=stan_file)
stan_model.compile()
# ---- fitting ---- #
stan_fit = stan_model.sample(data=eight_school_data)
# ---- results ---- #
cmdstanpy_data = az.from_cmdstanpy(
posterior=stan_fit,
posterior_predictive="y_hat",
observed_data={"y": eight_school_data["y"]},
log_likelihood="log_lik",
coords={"school": np.arange(eight_school_data["J"])},
dims={
"theta": ["school"],
"y": ["school"],
"log_lik": ["school"],
"y_hat": ["school"],
"theta_tilde": ["school"],
},
sm_ols = CmdStanModel(stan_file=modelfile_ols)
fit_ols = sm_ols.sample(data=model_data_dict,
show_progress=True,
chains=4,
iter_sampling=50000,
iter_warmup=10000,
thin=5)
fit_ols.draws().shape # iterations, chains, parameters
fit_ols.summary().loc[var_name] # pandas DataFrame
print(fit_ols.diagnose())
posterior_ols = {k: fit_ols.stan_variable(k) for k in var_name}
az_trace_ols = az.from_cmdstanpy(fit_ols)
az.summary(az_trace_ols).loc[var_name] # pandas DataFrame
az.plot_trace(az_trace_ols, var_names=var_name)
gd = sns.jointplot(
x=posterior_ols["b0"],
y=posterior_ols["b1"],
marginal_kws={
"kde": True,
"kde_kws": {
"cut": 1
}
},
)
gd.plot_joint(sns.kdeplot, zorder=2, n_levels=10, cmap="gray_r")
gd.fig.suptitle("Posterior joint distribution (OLS)", y=1.02)
repo_rootdir = repo.working_tree_dir
# first load data using module util
df_unreg, = load_FISH_by_promoter(("unreg", ))
# pull out one specific promoter for convenience for prior pred check & SBC
df_UV5 = df_unreg[df_unreg["experiment"] == "UV5"]
sm = cmdstanpy.CmdStanModel(
stan_file=f"{repo_rootdir}/code/stan/constit_post_inf.stan",
compile=True,
)
all_samples = {}
for gene in df_unreg['experiment'].unique():
temp_df = df_unreg[df_unreg['experiment'] == gene]
stan_data = dict(
N=len(temp_df),
mRNA_counts=temp_df["mRNA_cell"].values.astype(int),
ppc=0 # if you produce ppc samples, the InferenceData obj is HUGE
)
with be_quiet_stan():
posterior_samples = sm.sample(data=stan_data, chains=6)
all_samples[gene] = az.from_cmdstanpy(
posterior_samples, posterior_predictive=["mRNA_counts_ppc"])
print(f"For promoter {gene}...")
check_all_diagnostics(all_samples[gene])
# pickle the samples. ~20 separate netcdfs, only for use together? No thanks
outfile = open(f"{repo_rootdir}/data/mcmc_samples/constit_post_inf.pkl", 'wb')
pickle.dump(all_samples, outfile)
outfile.close()
def single_fit_to_inference(
fit: CmdStanMCMC,
params: Sequence[str],
coords: dict,
dims: dict,
alr_params: Sequence[str] = None,
posterior_predictive: str = None,
log_likelihood: str = None,
) -> az.InferenceData:
"""Convert fitted Stan model into inference object.
:param fit: Fitted model
:type params: CmdStanMCMC
:param params: Posterior fitted parameters to include
:type params: Sequence[str]
:param coords: Mapping of entries in dims to labels
:type coords: dict
:param dims: Dimensions of parameters in the model
:type dims: dict
:param alr_params: Parameters to convert from ALR to CLR (this will
be ignored if the model has been parallelized across features)
:type alr_params: Sequence[str], optional
:param posterior_predictive: Name of posterior predictive values from
Stan model to include in ``arviz`` InferenceData object
:type posterior_predictive: str, optional
:param log_likelihood: Name of log likelihood values from Stan model
to include in ``arviz`` InferenceData object
:type log_likelihood: str, optional
:returns: ``arviz`` InferenceData object with selected values
:rtype: az.InferenceData
"""
# remove alr params so initial dim fitting works
new_dims = {k: v for k, v in dims.items() if k not in alr_params}
if log_likelihood is not None and log_likelihood not in dims:
raise KeyError("Must include dimensions for log-likelihood!")
if posterior_predictive is not None and posterior_predictive not in dims:
raise KeyError("Must include dimensions for posterior predictive!")
inference = az.from_cmdstanpy(fit,
coords=coords,
log_likelihood=log_likelihood,
posterior_predictive=posterior_predictive,
dims=new_dims)
vars_to_drop = set(inference.posterior.data_vars).difference(params)
inference.posterior = _drop_data(inference.posterior, vars_to_drop)
# Convert each param in ALR coordinates to CLR coordinates
for param in alr_params:
# Want to run on each chain independently
all_chain_clr_coords = []
all_chain_alr_coords = np.split(fit.stan_variable(param),
fit.chains,
axis=0)
for i, chain_alr_coords in enumerate(all_chain_alr_coords):
# arviz 0.11.2 seems to flatten for some reason even though
# the PR was specifically supposed to do the opposite.
# Not sure what's going on but just going to go through cmdstanpy.
chain_clr_coords = convert_beta_coordinates(chain_alr_coords)
all_chain_clr_coords.append(chain_clr_coords)
all_chain_clr_coords = np.array(all_chain_clr_coords)
tmp_dims = ["chain", "draw"] + dims[param]
mcmc_coords = {
"chain": np.arange(fit.chains),
"draw": np.arange(fit.num_draws_sampling)
}
# restrict param DataArray to only required dims/coords
tmp_coords = {k: coords[k] for k in dims[param]}
param_da = xr.DataArray(all_chain_clr_coords,
dims=tmp_dims,
coords={
**tmp_coords,
**mcmc_coords
})
inference.posterior[param] = param_da
# TODO: Clean this up
all_dims = list(inference.posterior.dims)
dims_to_drop = []
for dim in all_dims:
if re.match(f"{param}_dim_\\d", dim):
dims_to_drop.append(dim)
inference.posterior = inference.posterior.drop_dims(dims_to_drop)
return inference
iter_sampling=200,
chains=4)
# save to arviz
var_name = ["slack_comments", "github_commits"]
idata_stan = az.from_cmdstanpy( # DOES NOT WORK, problems everywhere, idk why
posterior=posterior,
prior=prior,
posterior_predictive=[i + "_hat" for i in var_name],
prior_predictive=[i + "_hat" for i in var_name],
observed_data=var_name,
constant_data=["time_since_joined"],
log_likelihood={i: "log_likelihood_" + i
for i in var_name},
predictions=[i + "_pred" for i in var_name],
predictions_constant_data=["time_since_joined_pred"],
coords={
"developer": names,
"candidate developer": candidate_devs
},
dims={
"slack_comments": ["developer"],
"github_commits": ["developer"],
"slack_comments_hat": ["developer"],
"github_commits_hat": ["developer"],
"time_since_joined": ["developer"],
"slack_comments_pred": ["candidate developer"],
"github_commits_pred": ["candidate developer"],
"time_since_joined_pred": ["candidate developer"],
})
def _batch_func(counts: np.array,
replicates: np.array,
batches: np.array,
depth: int,
mc_samples: int = 1000,
chains: int = 4,
disp_scale: float = 1,
sigma_scale: float = 1,
reference_loc: float = -5,
reference_scale: float = 5) -> dict:
replicate_encoder = LabelEncoder()
replicate_encoder.fit(replicates)
replicate_ids = replicate_encoder.transform(replicates)
# identify reference replicates - these will be the
# first sample for each replicate group
ref_ids, lookup = np.zeros(len(replicate_ids)), {}
for i, c in enumerate(replicate_ids):
if c not in lookup:
lookup[c] = i
for i, c in enumerate(replicate_ids):
ref_ids[i] = lookup[c]
batch_encoder = LabelEncoder()
batch_encoder.fit(batches)
batch_ids = batch_encoder.transform(batches)
batch_ids = batch_ids.astype(np.int64) + 1
ref_ids = ref_ids.astype(np.int64) + 1
code = os.path.join(os.path.dirname(__file__),
'assets/batch_pln_single.stan')
sm = CmdStanModel(stan_file=code)
dat = {
'N': counts.shape[0],
'R': int(max(ref_ids) + 1),
'B': int(max(batch_ids) + 1),
'depth': list(np.log(depth)),
'y': list(map(int, counts.astype(np.int64))),
'ref_ids': list(map(int, ref_ids)),
'batch_ids': list(map(int, batch_ids)),
'sigma_scale': sigma_scale,
'disp_scale': disp_scale,
'reference_loc': reference_loc,
'reference_scale': reference_scale
}
with tempfile.TemporaryDirectory() as temp_dir_name:
data_path = os.path.join(temp_dir_name, 'data.json')
with open(data_path, 'w') as f:
json.dump(dat, f)
# Obtain an initial guess with MLE
# guess = sm.optimize(data=data_path, inits=0)
# see https://mattocci27.github.io/assets/poilog.html
# for recommended parameters for poisson log normal
fit = sm.sample(
data=data_path,
iter_sampling=mc_samples,
# inits=guess.optimized_params_dict,
chains=chains,
iter_warmup=1000,
adapt_delta=0.9,
max_treedepth=20)
fit.diagnose()
inf = az.from_cmdstanpy(
fit,
posterior_predictive='y_predict',
log_likelihood='log_lhood',
)
return inf
def from_cmdstanpy(cls, fit):
inference_data = av.from_cmdstanpy(fit)
return cls(inference_data)
import numpy as np
import matplotlib.pyplot as plt
from typing import Dict
import arviz as az
model_dir = Path("stan_code")
data_dir = Path("data")
# ---- data ---- #
discharge_data: Dict = {"T": len(Q), "Q": Q, "P": precip}
# ---- model ---- #
stan_file = model_dir / "abcmodel.stan"
stan_model = CmdStanModel(stan_file=stan_file)
stan_model.compile()
# ---- fit parameters ---- #
abcmodel_fit: CmdStanMCMC = stan_model.sample(
data=discharge_data,
chains=4,
cores=1,
seed=1111,
show_progress=True,
)
# ---- get simulations ---- #
posterior = az.from_cmdstanpy(
posterior=bern_fit,
posterior_predictive="y",
)
df_unreg, = load_FISH_by_promoter(("unreg", ))
# pull out one specific promoter for convenience for prior pred check & SBC
df_UV5 = df_unreg[df_unreg["experiment"] == "UV5"]
# ############################################################################
# PRIOR PREDICTIVE CHECK
# ############################################################################
sm_prior_pred = cmdstanpy.CmdStanModel(
stan_file=f"{repo_rootdir}/code/stan/constit_prior_pred.stan",
compile=True,
)
# stan needs to know how many data points to generate,
# so pick a representative promoter
data_prior_pred = dict(N=len(df_UV5))
prior_pred_samples = sm_prior_pred.sample(
data=data_prior_pred,
fixed_param=True,
iter_sampling=1000,
)
# Convert to ArviZ InferenceData object
prior_pred_samples = az.from_cmdstanpy(
posterior=prior_pred_samples, # this line b/c of arviz bug, PR#979
prior=prior_pred_samples,
prior_predictive=['mRNA_counts'])
prior_pred_samples.to_netcdf(
f"{repo_rootdir}/data/stan_samples/constit_prior_pred.nc")
percs = [(2.5, 97.5), (12.5, 87.5), (25, 75), (37.5, 62.5), (47.5, 52.5)]
perc_labels = [95, 75, 50, 25, 5]
# %%
# Iterate through each carbon source and strain and perform the inference
samples, summary, fits = [], [], []
for g, d in data.groupby(['strain', 'carbon_source', 'medium_base']):
data_dict = {
'J': d['replicate'].max(),
'N': len(d),
'idx': d['replicate'].values.astype(int),
'time': d['elapsed_time_hr'].values.astype(float),
'OD': d['od_600nm'].values.astype(float)
}
_samples = model.sample(data=data_dict)
_samples = az.from_cmdstanpy(_samples)
_samples = _samples.posterior.to_dataframe().reset_index()
# Get the low level parameters
params = _samples[['lam_dim_0', 'OD_init', 'lam']]
params.rename(columns={'lam_dim_0': 'level'}, inplace=True)
params['level'] = [f'replicate {i+1}' for i in params['level'].values]
params = params.melt('level', var_name='parameter')
# hyper_params
hyper_params = _samples[[
'OD_init_mu', 'lam_mu', 'OD_init_sigma', 'lam_sigma', 'sigma'
]]
hyper_params['level'] = 'hyperparameter'
hyper_params = hyper_params.melt('level', var_name='parameter')
hyper_params.loc[hyper_params['parameter'] == 'lam_mu',
bern_fit: CmdStanMCMC = bernoulli_model.sample(
data=bernoulli_data,
chains=4,
cores=1,
seed=1111,
show_progress=True,
)
# ---- results ---- #
"""samples = multi-dimensional array
all draws from all chains arranged as dimensions:
(draws, chains, columns).
"""
posterior = az.from_cmdstanpy(
posterior=bern_fit,
posterior_predictive="y",
# observed_data={"y": np.array(bernoulli_data["y"])},
)
# POSTERIOR PREDICTIVE CHECKS
# bernoulli_path =
bernoulli_ppc = CmdStanModel(stan_file=model_dir / "bernoulli_ppc.stan")
# fit the model to the data
bern_fit = bernoulli_ppc.sample(data=bernoulli_data)
# PRIOR PREDICTIVE CHECKS
# https://cmdstanpy.readthedocs.io/en/latest/sample.html
# generate data - fixed_param=True
datagen_stan = model_dir / "bernoulli.stan"
