def run_validate_arviz(inferred):
az.plot_ppc(inferred, data_pairs={'y': 'y_hat'})
loo = az.loo(inferred, pointwise=True)
az.plot_khat(loo)
az.plot_loo_pit(inferred, y='y', y_hat='y_hat')
loo_pit = az.loo_pit(inferred, y='y', y_hat='y_hat')
bfmi = az.bfmi(inferred)
if any(bfmi < 0.5):
print("BFMI warning:", bfmi < 0.5)
print("LOO analysis:\n", loo)
return loo, loo_pit, bfmi
"""
LOO-PIT ECDF Plot
=================
_thumb: .5, .7
"""
import matplotlib.pyplot as plt
import arviz as az
az.style.use("arviz-darkgrid")
idata = az.load_arviz_data("radon")
az.plot_loo_pit(idata, y="y", ecdf=True, color="maroon")
plt.show()
"""
LOO-PIT ECDF Plot
=================
_thumb: .5, .7
"""
import matplotlib.pyplot as plt
import arviz as az
az.style.use("arviz-darkgrid")
idata = az.load_arviz_data("radon")
log_like = idata.sample_stats.log_likelihood.sel(chain=0).values.T
log_weights = az.psislw(-log_like)[0]
az.plot_loo_pit(idata,
y="y_like",
log_weights=log_weights,
ecdf=True,
color="maroon")
plt.show()
"""
LOO-PIT Overlay Plot
====================
_thumb: .5, .7
"""
import matplotlib.pyplot as plt
import arviz as az
az.style.use("arviz-darkgrid")
idata = az.load_arviz_data("non_centered_eight")
az.plot_loo_pit(idata=idata, y="obs", color="indigo")
plt.show()
"""
LOO-PIT ECDF Plot
=================
_thumb: .5, .7
"""
import arviz as az
idata = az.load_arviz_data("radon")
log_like = idata.sample_stats.log_likelihood.sel(chain=0).values.T
log_weights = az.psislw(-log_like)[0]
ax = az.plot_loo_pit(idata,
y="y_like",
log_weights=log_weights,
ecdf=True,
color="orange",
backend="bokeh")
"""
LOO-PIT Overlay Plot
====================
_thumb: .5, .7
"""
import arviz as az
idata = az.load_arviz_data("non_centered_eight")
ax = az.plot_loo_pit(idata=idata, y="obs", color="green", backend="bokeh")
"""
LOO-PIT ECDF Plot
=================
_thumb: .5, .7
_example_title: Plot LOO predictive ECDF compared to ECDF of uniform distribution to assess predictive calibration.
"""
import arviz as az
idata = az.load_arviz_data("radon")
ax = az.plot_loo_pit(idata, y="y", ecdf=True, color="orange", backend="bokeh")
def main():
parser = argparse.ArgumentParser(description='Combine the individual posteriors for each S value.')
parser.add_argument('datafile', type=str,
help='path to csv containing beta values')
parser.add_argument('patientinfofile', type=str,
help='path to csv containing patientinfo')
parser.add_argument('outputdir', type=str, default='~',
help='path to folder in which to store output')
parser.add_argument('sample', type=str,
help='samplename of beta array (must be a col in datafile index in patientinfo)')
# Execute the parse_args() method
args = parser.parse_args()
datafile = args.datafile
patientinfofile = args.patientinfofile
outputdir = args.outputdir
sample = args.sample
outsamplesdir = os.path.join(outputdir, sample, 'posterior')
outfinaldir = os.path.join(outputdir, sample, 'outfinal')
os.makedirs(outfinaldir, exist_ok=True)
beta_values = pd.read_csv(datafile, index_col = 0)
patientinfo = pd.read_csv(patientinfofile, keep_default_na=False, index_col = 0)
beta = beta_values[sample].dropna().values
age = patientinfo.loc[sample, 'age']
outsampleslist = glob.glob(os.path.join(outsamplesdir, 'sample_*.pkl'))
S = list()
results = dict()
for outsamples in outsampleslist:
s = int(outsamples.split('.pkl')[0].split('_')[-1])
try:
with open(outsamples, 'rb') as f:
res = joblib.load(f)
results[s] = res
S.append(s)
print(s)
except EOFError:
print('sample_{}.pkl is not a correctly formatted pickle file'.format(s))
S.sort()
n = len(beta)
logZs = np.empty(len(S))
logZerrs = np.empty(len(S))
Nsamples = np.empty(len(S), dtype=int)
for index, s in enumerate(S):
try:
logZs[index] = results[s].logz[-1]
logZerrs[index] = results[s].logzerr[-1]
Nsamples[index] = results[s].niter
except:
logZs = results[s]['logz']
logZerrs = results[s]['logzerr']
Nsamples = results[s]['niter']
logZs_bootstrap = np.random.normal(loc = logZs, scale=logZerrs, size = (10000, len(logZs)))
prob_s = softmax(logZs)
prob_s_bootstrap = softmax(logZs_bootstrap, axis=1)
prob_s_err = np.std(prob_s_bootstrap, axis=0)
print('\nS:P(S)')
for i, s in enumerate(S):
print('{}:{:.3e}'.format(s, prob_s[i]))
df = pd.DataFrame({'S':S, 'prob':prob_s, 'prob_err':prob_s_err})
df.S.astype(int)
df.to_csv(os.path.join(outfinaldir, "prob_of_S.csv"), index=False)
sns.set_style('white')
sns.set_context("paper", font_scale=1.6)
fig, ax = plt.subplots()
ax.bar(S, prob_s, yerr=prob_s_err, color=sns.xkcd_rgb["denim blue"])
sns.despine()
plt.xlabel("Stem Cell Number (S)")
plt.ylabel("Probability")
plt.tight_layout()
plt.savefig(os.path.join(outfinaldir, "probability_S.png"), dpi = 300)
plt.close()
Ndraws = 3000
Ssamples = np.random.choice(S, size=Ndraws, p=prob_s)
final_posterior = np.empty((Ndraws, 8))
final_posterior[:, -1] = Ssamples
beta_hat = np.empty((1, Ndraws, n))
LL = np.empty((1, Ndraws, n))
progress_ints = (np.arange(0.1, 1.1, 0.1) * Ndraws - 1).astype(int)
counter = 10
for i in range(Ndraws):
if i in progress_ints:
print('{}% complete'.format(counter))
counter += 10
s = Ssamples[i]
try:
posterior = dynesty.utils.resample_equal(results[s].samples, softmax(results[s].logwt))
except:
posterior = results[s]['samples']
random_row = np.random.randint(posterior.shape[0])
final_posterior[i, :7] = posterior[random_row, :7]
lamsample, musample, gammasample, deltasample, etasample = final_posterior[i, :5]
kappasample = posterior[random_row, 7:]
LL[0, i, :] = flipflop.loglikelihood_perpoint(posterior[random_row, :], beta, s, age)
ProbDist = flipflop.runModel(s, lamsample, musample, gammasample, age)
k_sample = np.random.choice(np.arange(0, 2*s+1), size=n, p=ProbDist)
beta_sample = k_sample / (2*s)
beta_sample = flipflop.rescale_beta(beta_sample, deltasample, etasample)
beta_hat[0, i, :] = flipflop.beta_rvs(beta_sample, kappasample[k_sample])
with open(os.path.join(outfinaldir, "finalposterior.pkl"), 'wb') as f:
joblib.dump(final_posterior, f)
df = pd.DataFrame({'lam':final_posterior[:,0],
'mu':final_posterior[:,1],
'gamma':final_posterior[:,2],
'delta':final_posterior[:,3],
'eta':final_posterior[:,4],
'kappamean':final_posterior[:,5],
'kappadisp':final_posterior[:,6],
'S':Ssamples})
df.to_csv(os.path.join(outfinaldir, "finalposterior.csv"), index=False)
fig, ax = plt.subplots()
plt.hist(beta, np.linspace(0, 1, 100), density=True, alpha=0.4, linewidth=0)
plt.hist(np.ravel(beta_hat), np.linspace(0, 1, 100), density=True, alpha=0.4, linewidth=0)
plt.legend(("Data", "Posterior predictive"))
plt.xlabel("Fraction Methylated (Beta)")
plt.ylabel("Probability density")
sns.despine()
plt.tight_layout()
plt.savefig("{}/posterior_predictive.png".format(outfinaldir), dpi = 300)
plt.close()
inference = az.from_dict(posterior={'lam':final_posterior[:,0],
'mu':final_posterior[:,1],
'gamma':final_posterior[:,2],
'delta':final_posterior[:,3],
'eta':final_posterior[:,4],
'kappamean':final_posterior[:,5],
'kappadisp':final_posterior[:,6],
'S':Ssamples},
observed_data={'beta':beta},
posterior_predictive={'beta_hat':beta_hat},
sample_stats={"log_likelihood":LL}
)
az.to_netcdf(inference, "{}/inference.nc".format(outfinaldir))
pairs = az.plot_pair(inference, var_names=('lam', 'mu', 'gamma', 'delta', 'eta', 'kappamean', 'kappadisp'))
plt.savefig('{}/plot_pairs.png'.format(outfinaldir), dpi=300)
plt.close()
az.plot_loo_pit(inference, y='beta', y_hat='beta_hat', ecdf=True)
plt.savefig('{}/plot_loo_pit_ecdf.png'.format(outfinaldir), dpi=300)
plt.close()
sns.set_context("paper", font_scale=1.0)
az.plot_loo_pit(inference, y='beta', y_hat='beta_hat')
plt.ylabel('Leave One Out - Probability Integral Transform')
plt.xlabel('Cumulative Density Function')
plt.savefig('{}/plot_loo_pit.png'.format(outfinaldir), dpi=300)
plt.close()
