def test_model_fit(fit, parameters, max_depth=10):
if type(parameters[0])==tuple:
fit_params = []
for data_param, fit_param in parameters:
print(fit_param)
if hasattr(data_param, '__len__') and len(data_param)!=fit[fit_param].shape[1]:
inds = len(data_param)
within_95 = 0.0
for i in range(inds):
within_95 += parameter_within_95(fit, data_param, fit_param, ind=i)
within_95 /= inds
else:
within_95 = parameter_within_95(fit, data_param, fit_param)
if within_95.mean()>.9:
c = '32'
else:
c = '31'
print("\x1b[%sm\"%.0f%% of values recovered\"\x1b[0m"%(c, within_95.mean()*100))
Rhats = _summary(fit, pars=fit_param)['summary'][:,-1]
if all(abs(Rhats-1)<.1):
c = '32'
else:
c = '31'
print("\x1b[%sm\"Maximum Rhat of %.2f\"\x1b[0m"%(c,max(Rhats)))
fit_params.append(fit_param)
stan_utility.check_treedepth(fit,max_depth=max_depth)
stan_utility.check_energy(fit)
check_div(fit, fit_param)
X=fixeff,
Z_u=raneff,
y=LMEdata.logprmt.values,
p_intercept=prior_intercept,
p_sd=prior_sd,
p_fmu=priors_mu,
p_fsigma=priors_sigma,
p_r=priors_raneff,
logT=1)
PMT_fit = LME.sampling(data=PMT_LME_data,
iter=2000,
chains=6,
n_jobs=6,
warmup=1000,
control=dict(adapt_delta=0.99))
PMT_fit = az.from_pystan(posterior=PMT_fit,
posterior_predictive='y_hat',
observed_data="y",
log_likelihood='log_lik',
coords={'b': fixeff_form.split('+')[1:]},
dims={'raw_beta': ['b']})
PMT_fit.to_netcdf("FittedModels/PMT_fit.nc")
print(PMT_fit)
stan_utility.check_treedepth(PMT_fit)
stan_utility.check_energy(PMT_fit)
stan_utility.check_div(PMT_fit)
weights_ext = bayesian_weights[:-1]
simulated_ext = full_simulated[:, 0:-1]
combine_curve(
experimental,
simulated_ext,
weights_ext,
options.iteration,
experimental_file.split(".dat")[0] + ".fit",
)
# TODO: Write uncertainities here probably to a separate file
for index, fname in enumerate(file_names):
fout.write(str(bayesian_weights[index]) + " ")
fout.write(str(jsd) + " ")
fout.write(str(crysol_chi2) + "\n")
# priors = bayesian_weights
# And soem stan utilitry stats
stan_utility.check_treedepth(fit)
stan_utility.check_energy(fit)
stan_utility.check_div(fit)
fout.close()
calculatePostChi()
# plot_intermediates(options.output)
# plot_chi2(options.output)
# #Simple svd for the comparison
# for experimental_file in open(options.experimental_files).readlines():
# experimental = read_file_safe(experimental_file.strip("\n"))
# svd_U, svd_s, svd_Vt = simple_svd(experimental)
# print(svd_U, svd_s, svd_Vt)
P=fixeff.shape[-1], #number of pop level effects
J=len(GLMEdata.participant.unique()),
n_u=raneff.shape[-1],
subj=GLMEdata.participant,
X=fixeff,
Z_u=raneff,
y=np.asarray(GLMEdata.response.values),
p_intercept=prior_intercept,
p_fmu=priors_mu,
p_fsigma=priors_sigma,
p_r=priors_raneff)
Precision_fit = GLME.sampling(data=Precision_GLME_data,
iter=2000,
chains=6,
n_jobs=6,
warmup=1000,
control=dict(adapt_delta=0.99))
stan_utility.check_treedepth(Precision_fit)
stan_utility.check_energy(Precision_fit)
stan_utility.check_div(Precision_fit)
Precision_fit = az.from_pystan(posterior=Precision_fit,
posterior_predictive='y_hat',
observed_data="y",
log_likelihood='log_lik',
coords={'b': fixeff_form.split('+')[1:]},
dims={'raw_beta': ['b']})
Precision_fit.to_netcdf("FittedModels/Precision_fit.nc")