def run_and_plot_models(segmentDF, adjacencyMatrix, iters, warmup):
tobit_dict = get_tobit_dict(segmentDF)
# TOBIT MODEL:
t_c_params = {'adapt_delta': 0.95, 'max_treedepth': 15}
tobit_model, tobit_fit = run_or_load_model('tobit', tobit_dict, iters,
warmup, t_c_params)
check_hmc_diagnostics(tobit_fit)
plt.hist(tobit_fit['sigma'], bins=int(iters * 4 / 100))
plt.title('tobit')
tob_vars = ['sigma', 'beta_zero', 'theta']
az.plot_trace(tobit_fit, tob_vars)
# SPATIAL TOBIT MODEL:
c_c_params = {'adapt_delta': 0.95, 'max_treedepth': 15}
car_dict = add_car_info_to_dict(tobit_dict, adjacencyMatrix)
car_model, car_fit = run_or_load_model('car', car_dict, iters, warmup,
c_c_params)
check_hmc_diagnostics(car_fit)
plt.hist(car_fit['sigma'], bins=int(iters * 4 / 100))
plt.title('car')
car_vars = ['sigma', 'beta_zero', 'theta', 'alpha', 'tau']
az.plot_trace(car_fit, compact=False, var_names=car_vars)
az.plot_pair(car_fit, ['tau', 'alpha', 'sigma'], divergences=True)
plt.scatter(car_fit['lp__'], car_fit['sigma'])
plt.hist(car_fit['phi'].mean(axis=0), bins=50)
def get_varying_intercept_model_results():
# read in Cipriani data
df = get_model_input_df()
data_dict = {
'N': df.shape[0],
'Y_meas': df['lnSD'].values,
'X_meas': df['lnMean'].values,
'SD_Y': np.sqrt(df['var_lnSD'].values),
'SD_X': np.sqrt(df['var_lnMean'].values),
'K': len(df.scale.unique()),
'scale_group': df.scale_rank.values
}
varying_intercept_stan_model = compile_model(
os.path.join(stan_model_path, 'varying_intercept_regression.stan'),
model_name='varying_intercept_regression')
fit = varying_intercept_stan_model.sampling(data=data_dict,
iter=4000,
warmup=1000,
chains=3,
control={'adapt_delta': 0.99},
check_hmc_diagnostics=True,
seed=1)
pystan.check_hmc_diagnostics(fit)
data = az.from_pystan(
posterior=fit,
posterior_predictive=['Y_pred'],
observed_data=['X_meas', 'Y_meas'],
log_likelihood='log_lik',
)
return data
def get_model_results_dict():
df = get_model_input_df()
model_res_dict = {}
# fixed effects meta analyses (lnVR and lnCVR)
for model in ['fema', 'rema'
]: # lnVR, # random effects meta analyses (lnVR and lnCVR)
stan_model = compile_model(os.path.join(stan_model_path,
f'{model}.stan'),
model_name=model)
for effect_statistic in ['lnVR', 'lnCVR']:
data_dict = get_data_dict(df, effect_statistic)
fit = stan_model.sampling(data=data_dict,
iter=4000,
warmup=1000,
chains=3,
control={'adapt_delta': 0.99},
check_hmc_diagnostics=True,
seed=1)
data = az.from_pystan(
posterior=fit,
posterior_predictive=['Y_pred'],
observed_data=['Y'],
log_likelihood='log_lik',
)
model_res_dict[f'{model}_{effect_statistic}'] = data
model = 'remr'
stan_model = compile_model(os.path.join(stan_model_path, f'{model}.stan'),
model_name=model)
effect_statistic = 'lnVR'
data_dict = get_data_dict(df, effect_statistic)
fit = stan_model.sampling(data=data_dict,
iter=4000,
warmup=1000,
chains=3,
control={'adapt_delta': 0.99},
check_hmc_diagnostics=True,
seed=1)
pystan.check_hmc_diagnostics(fit)
data = az.from_pystan(
posterior=fit,
posterior_predictive=['Y_pred'],
observed_data=['Y_meas', 'X_meas'],
log_likelihood='log_lik',
)
model_res_dict[f'{model}_{effect_statistic}'] = data
return model_res_dict
def check_fit(row,root='.',):
output_dict = {}
sample_loc = root+'/output/posteriors/' + row['event_name'] + '_raw.p'
data_loc = root+'/data/timeseries/aggregated/' + row['incident_name'] + '_raw.csv'
samples = pickle.load(open(sample_loc, 'rb'))
dat = pd.read_csv(data_loc).iloc[row['start']:row['end']]
y = dat['total_tweets'].values
print('--------')
print(row['event_name'])
output_dict.update(pystan.check_hmc_diagnostics(samples, pars=['alpha','beta','decay',
'lambda','phi']))
observed_engagement = np.sum(y)
output_dict['observed_engagement'] = observed_engagement
output_dict['y_0'] = y[0]
output_dict['observed_engagement'] = observed_engagement
low, high = np.percentile(np.cumsum(samples['y_hat'],axis=1)[:,-1], q=[5.5,94.5])
cumulative_fit = np.cumsum(y)[-1] > low and np.cumsum(y)[-1] < high
output_dict['final_predicted' ] = cumulative_fit
output_dict.update(row.to_dict())
output_dict['sample_loc'] = sample_loc
output_dict['data_loc'] = data_loc
output_dict['lower_predicted']=low
output_dict['upper_predicted']=high
return output_dict
def get_subgroup_models():
df = get_formatted_data()
# drug class subgroup analysis
model_res_dict = {}
for drug_class in DRUG_CLASSES:
study_ids = df.query(f'drug_class == "{drug_class}"').study_id.unique()
df_sub = df[(df.study_id.isin(study_ids))
& (df.drug_class.isin([drug_class, 'placebo']))].copy()
placebo_controlled_study_ids = set(df_sub.query('is_active == 1')['study_id']) \
.intersection(df_sub.query('is_active == 0')['study_id'])
df_sub = df_sub[df_sub.study_id.isin(placebo_controlled_study_ids)]
for column in ['study_id', 'scale', 'drug_class']:
df_sub = add_rank_column(df_sub, column)
df_sub = aggregate_treatment_arms(df_sub)
df_sub = get_variability_effect_sizes(df_sub)
model = 'remr'
stan_model = compile_model(os.path.join(stan_model_path,
f'{model}.stan'),
model_name=model)
data_dict = get_data_dict(df_sub, 'lnVR')
fit = stan_model.sampling(data=data_dict,
iter=4000,
warmup=1000,
chains=3,
control={'adapt_delta': 0.99},
check_hmc_diagnostics=True,
seed=1)
pystan.check_hmc_diagnostics(fit)
data = az.from_pystan(
posterior=fit,
posterior_predictive=['Y_pred'],
observed_data=['Y_meas', 'X_meas'],
log_likelihood='log_lik',
)
model_res_dict[drug_class] = data
return model_res_dict
def get_baseline_severity_model():
df = prepare_data()
effect_statistic = 'lnVR'
data_dict = {
'N': len(df.study_id.unique()),
'Y_meas': df.groupby(['study_id']).agg({effect_statistic: 'first'}).reset_index()[effect_statistic].values,
'X_meas': df.groupby(['study_id']).agg({'lnRR': 'first'}).reset_index()['lnRR'].values,
'SD_Y': np.sqrt(df.groupby(['study_id']).agg(
{f'var_{effect_statistic}': 'first'}).reset_index()[f'var_{effect_statistic}'].values),
'SD_X': np.sqrt(df.groupby(['study_id']).agg(
{'var_lnRR': 'first'}).reset_index()['var_lnRR'].values),
'X0': df.groupby(['study_id']).apply(
lambda x: np.sum(x['baseline'] * x['N']) / np.sum(x['N'])
).reset_index()[0].values,
'run_estimation': 1
}
stan_model = compile_model(
os.path.join(stan_model_path, 'remr_bs.stan'),
model_name='remr_bs'
)
fit = stan_model.sampling(
data=data_dict,
iter=4000,
warmup=1000,
chains=3,
control={'adapt_delta': 0.99},
check_hmc_diagnostics=True,
seed=1
)
pystan.check_hmc_diagnostics(fit)
data = az.from_pystan(
posterior=fit,
posterior_predictive=['Y_pred'],
observed_data=['Y_meas', 'X_meas', 'X0'],
log_likelihood='log_lik',
)
return data
def get_simulation_results():
data_dict = {
'N': 1000,
'rho': -0.4,
'sd_te': 6.5,
'sd_m': 0.001,
'lambda': 0.2,
'theta': 0.9
}
simulation_stan_model = compile_model(os.path.join(stan_model_path,
'simulation.stan'),
model_name='simulation')
fit = simulation_stan_model.sampling(data=data_dict,
warmup=500,
iter=2500,
chains=2,
check_hmc_diagnostics=True,
seed=1)
pystan.check_hmc_diagnostics(fit)
data = az.from_pystan(posterior=fit)
return data
def get_prior_comparison():
df = get_model_input_df()
model_res_dict = {}
model = 'remr_prior'
stan_model = compile_model(os.path.join(stan_model_path, f'{model}.stan'),
model_name=model)
effect_statistic = 'lnVR'
data_dict = get_data_dict(df, effect_statistic)
prior_dict = {'reference': (0, 1), 'optimisitc': (np.log(2), 0.43)}
# from scipy import stats
# stats.norm.cdf(0, loc=np.log(2), scale=0.43)
# 0.05348421366569122
for prior, (mu_prior_loc, mu_prior_scale) in prior_dict.items():
data_dict_prior = data_dict.copy()
data_dict_prior['mu_prior_loc'] = mu_prior_loc
data_dict_prior['mu_prior_scale'] = mu_prior_scale
fit = stan_model.sampling(data=data_dict_prior,
iter=4000,
warmup=1000,
chains=3,
control={'adapt_delta': 0.99},
check_hmc_diagnostics=True,
seed=1)
pystan.check_hmc_diagnostics(fit)
data = az.from_pystan(
posterior=fit,
posterior_predictive=['Y_pred'],
observed_data=['Y_meas', 'X_meas'],
log_likelihood='log_lik',
)
model_res_dict[f'{model}_{effect_statistic}_{prior}'] = data
return model_res_dict
def fit_model(row, model, keep=True,root='.'):
if keep and os.path.isfile(root + '/output/posteriors/' + row['event_name'] + '_raw.p'):
pass
else:
raw_df = pd.read_csv(root
+ '/data/timeseries/aggregated/'
+ row['incident_name'] + '_raw.csv')
raw_df = raw_df.iloc[row['start']:row['end']]
#We add one here because it avoids a divide by zero error in log. Should not impact inference
stan_data = dict(y=raw_df['total_tweets'].values.astype('int')[1:],
N=raw_df.shape[0],
x=np.log(raw_df['user_followers_count'].values.astype('int')+1))
samples = model.sampling(data=stan_data,
check_hmc_diagnostics=False,
refresh = 0,
control={'adapt_delta':.99})
pickle.dump(samples.extract(inc_warmup=False),
open(root + '/output/posteriors/' + row['event_name'] + '_extracted.p', 'wb'))
pickle.dump(samples,
open(root + '/output/posteriors/' + row['event_name'] + '_raw.p', 'wb'))
return pystan.check_hmc_diagnostics(samples, pars={'alpha','beta','lambda','decay','phi'})
for d, nm in [(logistic_data_synth, 'synth'), (logistic_data_ds1, 'ds1'),
(logistic_data_phish, 'phish')]:
if not os.path.exists('results/logistic_samples_' + nm + '.npy'):
t0 = time.process_time()
fit = sm.sampling(data=d,
iter=N_samples * 2,
chains=1,
control={
'adapt_delta': 0.9,
'max_treedepth': 15
},
verbose=True)
#fit.extract has 3 dims: iterations, chains, parametrs
f = open('results/logistic_params_' + nm + '.log', 'w')
f.write(str(pystan.check_hmc_diagnostics(fit)) + '\n')
f.write(str(fit.model_pars) + '\n')
f.write(str(fit.par_dims) + '\n')
f.close()
np.save('results/logistic_samples_' + nm + '.npy',
fit.extract(permuted=False))
tf = time.process_time()
np.save('results/' + nm + '_mcmc_time.npy', tf - t0)
############################## (U)BVI / ADVI Params ######################################
N = 10
diag = True
n_samples = 2000
n_logfg_samples = 10000
adam_learning_rate = lambda itr: 1. / np.sqrt(itr + 1)
def fit_stan(fname_fitres_comb: str,
dir_output: str,
sample: str,
screen=False,
lowz=True,
bias=True,
plot=False):
"""
Fit Stan model for w.
Parameters
----------
fname_fitres_comb: str
Complete path to fitres (with lowz, if requested).
dir_output: str
Complete path to output root folder.
sample: str
Sample to be fitted.
screen: bool (optional)
If True, print Stan results to screen. Default is False.
lowz: bool (optional)
If True, add low-z sample. Default is True.
bias: bool (optional)
If True, add bias correction. Default is True.
plot: bool (optional)
If True, generate chains plot. Default is False.
"""
# read data for Bayesian model
fitres_final = pd.read_csv(fname_fitres_comb,
index_col=False,
comment="#",
skip_blank_lines=True,
delim_whitespace=True)
# set initial conditions
z0 = 0
E0 = 0
c = 3e5
H0 = 70
# add small offset to duplicate redshifts
fitres_final = remove_duplicated_z(fitres_final)
# order data according to redshift
indx = np.argsort(fitres_final['SIM_ZCMB'].values)
# create input for stan model
stan_input = {}
stan_input['nobs'] = fitres_final.shape[0]
stan_input['z'] = fitres_final['SIM_ZCMB'].values[indx]
stan_input['mu'] = fitres_final['MU'].values[indx]
stan_input['muerr'] = fitres_final['MUERR'].values[indx]
stan_input['z0'] = z0
stan_input['H0'] = H0
stan_input['c'] = c
stan_input['E0'] = np.array([E0])
stan_input['ompri'] = om_pri[0]
stan_input['dompri'] = om_pri[1]
stan_input['wmin'] = w_pri[0]
stan_input['wmax'] = w_pri[1]
# save only stan input to file
fname_stan_input = dir_output + 'stan_input/stan_input_salt2mu_lowz_withbias_' + sample + '.csv'
stan_input2 = {}
stan_input2['z'] = stan_input['z']
stan_input2['mu'] = stan_input['mu']
stan_input2['muerr'] = stan_input['muerr']
stan_input_tofile = pd.DataFrame(stan_input2)
stan_input_tofile.to_csv(fname_stan_input, index=False)
# fit Bayesian model
model = pystan.StanModel(file=dir_input + '/cosmo.stan')
fit = model.sampling(data=stan_input,
iter=12000,
chains=5,
warmup=10000,
control={'adapt_delta': 0.99})
# get summary
res = fit.stansummary(pars=["om", "w"])
check = str(pystan.check_hmc_diagnostics(fit))
if screen:
print(res)
print(' ******* ')
print(check)
if lowz and bias:
summ_fname = dir_output + 'stan_summary/stan_summary_' + sample + '_lowz_withbias.dat'
chains_fname = dir_output + 'posteriors/pkl/chains_' + sample + '_lowz_withbias.pkl'
trace_fname = dir_output + 'posteriors/trace/trace_plot_' + sample + '_lowz_withbias.png'
elif lowz and not bias:
summ_fname = dir_output + 'stan_summary/stan_summary_' + sample + '_lowz_nobias.dat'
chains_fname = dir_output + 'posteriors/pkl/chains_' + sample + '_lowz_nobias.pkl'
trace_fname = dir_output + 'posteriors/trace/trace_plot_' + sample + '_lowz_nobias.png'
else:
summ_fname = dir_output + 'stan_summary/stan_summary_' + sample + '.dat'
chains_fname = dir_output + 'posteriors/pkl/chains_' + sample + '.pkl'
trace_fname = dir_output + 'posteriors/trace/trace_plot_' + sample + '.png'
op2 = open(summ_fname, 'w')
op2.write(res)
op2.write('\n ************* \n')
op2.write(check)
op2.close()
samples = fit.extract(pars=['om', 'w'], permuted=True, inc_warmup=False)
pickle.dump(samples, open(chains_fname, "wb"))
pystan.check_hmc_diagnostics(fit)
### plot chains
if plot:
arviz.plot_trace(fit, ['om', 'w'])
plt.savefig(trace_fname)
if lowz and bias:
data = pd.read_pickle(chains_fname)
data2 = pd.DataFrame(data)
data2.to_csv(dir_output + 'posteriors/csv/' + \
'chains_' + sample + '_lowz_withbias.csv.gz', index=False)
distanceToParent.get(x, 10) for x in hiddenLanguages + observedLanguages
]
dat["prior_only"] = 0
dat["Components"] = 2
print(dat)
sm = pystan.StanModel(file=f'{__file__[:-3]}.stan')
fit = sm.sampling(data=dat, iter=2000, chains=4)
la = fit.extract(permuted=True) # return a dictionary of arrays
with open(f"fits/{__file__}.txt", "w") as outFile:
print(fit, file=outFile)
import sys
with open(f"fits/{__file__}_diagnostic.txt", "w") as outFile:
sys.stdout = outFile
sys.stderr = outFile
print(pystan.check_hmc_diagnostics(fit), file=outFile)
# print(la, file=outFile)
#print("Inferred logits", la["LogitsAll"].mean(axis=0))
#print("Inferred hidden traits", la["TraitHidden"].mean(axis=0))
#print("alpha", la["alpha"].mean(axis=0))
#print("corr_Sigma", la["corr_Sigma"].mean(axis=0))
#print("sigma_B", la["sigma_B"].mean(axis=0))
#print("Lrescor_B", la["Lrescor_B"].mean(axis=0))
#
dlt = DLTFull(
response_col='pct_chg_in_sales_from_prev_mnth',
regressor_col=regressors,
date_col='date',
seasonality=1,
seed=2020,
level_sm_input=0.3, # recommend for higher frequency data
regressor_sigma_prior=[0.5] * len(regressors),
regression_penalty='lasso',
period=365,
prediction_percentiles=[5, 95])
dlt.fit(df=train_df)
pystan.check_hmc_diagnostics(dlt)
density_plot = plot_posterior_params(dlt,
kind='density',
incl_trend_params=True,
incl_smooth_params=True)
trace_plot = plot_posterior_params(dlt,
kind='trace',
incl_trend_params=True,
incl_smooth_params=True)
pair_plot = plot_posterior_params(dlt,
kind='pair',
pair_type='reg',
incl_trend_params=False,
# all int? data = df[['i1', 'i2', 'i3', 'Response']].values s = df[['s1', 's2', 's3']].values stimulus = np.unique(s.flatten()) # how to call the functions? fit = bds.bds(data, lapses=False) # gives warning # WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat. # To run all diagnostics call pystan.check_hmc_diagnostics(fit) pystan.check_hmc_diagnostics(fit.stanfit) # how to read out results? scale = fit.get_scale_values() CIl, CIh = fit.get_scale_credible_interval() # how to plot results? import matplotlib.pyplot as plt plt.plot(stimulus, scale, 'o') yerr = [CIh - scale, scale - CIl] plt.errorbar(stimulus, scale, yerr=yerr, fmt='none', ecolor='b', capsize=0) # others:
stan_input['H0'] = H0
stan_input['c'] = c
stan_input['E0'] = np.array([E0])
stan_input['ompri'] = om_pri[0]
stan_input['dompri'] = om_pri[1]
stan_input['wmin'] = w_pri[0]
stan_input['wmax'] = w_pri[1]
# save only stan input to file
fname_stan_input = dir_output + 'stan_input/stan_input_salt2mu_lowz_withbias_' + case + '.csv'
stan_input2 = {}
stan_input2['z'] = stan_input['z']
stan_input2['mu'] = stan_input['mu']
stan_input2['muerr'] = stan_input['muerr']
stan_input_tofile = pd.DataFrame(stan_input2)
stan_input_tofile.to_csv(fname_stan_input, index=False)
# fit Bayesian model
model = pystan.StanModel(file = dir_input + '/cosmo.stan')
fit = model.sampling(data=stan_input, iter=12000, chains=5,
warmup=10000, control={'adapt_delta':0.99})
# get summary
res = fit.stansummary(pars=["om", "w"])
check = str(pystan.check_hmc_diagnostics(fit))
