def test_peculiar_blobs(self, data):
sampler = emcee.EnsembleSampler(6, 1, lambda x: (-(x ** 2), (np.random.normal(x), 3)))
sampler.run_mcmc(np.random.normal(size=(6, 1)), 20)
inference_data = from_emcee(sampler, blob_names=["normal", "threes"])
fails = check_multiple_attrs({"sample_stats": ["normal", "threes"]}, inference_data)
assert not fails
inference_data = from_emcee(data.obj, blob_names=["mix"])
fails = check_multiple_attrs({"sample_stats": ["mix"]}, inference_data)
assert not fails
def test_single_blob(self):
sampler = emcee.EnsembleSampler(6, 1, lambda x: (-(x**2), 3))
sampler.run_mcmc(np.random.normal(size=(6, 1)), 20)
inference_data = from_emcee(sampler, blob_names=["blob"])
fails = check_multiple_attrs({"sample_stats": ["blob"]},
inference_data)
assert not fails
def fit(self,
num_samples=500,
num_burnin=200,
num_walkers=30,
num_cores=None):
# figure out a good place to begin our sampling
p0 = self.gen_start_point(num_walkers)
# instantiate context appropriate for the number of cores requested
if num_cores is not None:
poolholder = Pool(processes=num_cores)
else:
poolholder = nullcontext()
with poolholder as pool:
# generate sampler
print('number of priors is ' + str(len(self.prior)))
print('number of parameters is ' + str(p0.shape[1]))
sampler = emcee.EnsembleSampler(num_walkers,
p0.shape[1],
self.logp,
moves=emcee.moves.DESnookerMove(),
pool=pool)
if num_burnin > 0:
p0 = sampler.run_mcmc(p0, num_burnin, progress=True)
sampler.reset()
sampler.run_mcmc(p0, num_samples, progress=True)
print("Mean acceptance fraction: {0:.3f}".format(
np.mean(sampler.acceptance_fraction)))
#assert(False)
# define variable names, it cannot be inferred from emcee
var_names = self.gather_parameter_names()
return arviz.from_emcee(sampler, var_names=var_names)
def get_inference_data_reader(self, **kwargs):
from emcee import backends # pylint: disable=no-name-in-module
here = os.path.dirname(os.path.abspath(__file__))
data_directory = os.path.join(here, "..", "saved_models")
filepath = os.path.join(data_directory, "reader_testfile.h5")
assert os.path.exists(filepath)
assert os.path.getsize(filepath)
reader = backends.HDFBackend(filepath, read_only=True)
return from_emcee(reader, **kwargs)
def posterior_samples(self):
dd = az.from_emcee(self.sampler, var_names=self.ps.names)
ds = xa.Dataset()
pst = dd.posterior
c = pst.rho.coords
DA = xa.DataArray
for i in range(1, self.nplanets + 1):
p = pst[f'p_{i}'].values
ds[f'k_{i}'] = k = DA(sqrt(pst[f'k2_{i}']), coords=c)
ds[f'a_{i}'] = a = DA(as_from_rhop(pst.rho.values, p), coords=c)
ds[f'e_{i}'] = e = DA(pst[f'secw_{i}']**2 + pst[f'sesw_{i}']**2,
coords=c)
ds[f'w_{i}'] = w = DA(arctan2(pst[f'sesw_{i}'], pst[f'secw_{i}']),
coords=c)
ds[f'i_{i}'] = inc = DA(i_from_baew(pst[f'b_{i}'].values, a.values,
e.values, w.values),
coords=c)
ds[f't14_{i}'] = DA(d_from_pkaiews(p,
k.values,
a.values,
inc.values,
0.,
0.,
1,
kind=14),
coords=c)
ds[f't23_{i}'] = DA(d_from_pkaiews(p,
k.values,
a.values,
inc.values,
0.,
0.,
1,
kind=23),
coords=c)
dd.add_groups({'derived_parameters': ds})
return dd
def fit(self,
num_samples=1000,
num_burnin=200,
num_walkers=30,
num_cores=None):
try:
import emcee
except ModuleNotFoundError as e:
raise ModuleNotFoundError(
"Bayesian estimation requires emcee to be installed.") from e
# figure out a good place to begin our sampling
p0 = self.gen_start_point(num_walkers)
# instantiate context appropriate for the number of cores requested
if num_cores is not None:
poolholder = Pool(processes=num_cores)
else:
poolholder = nullcontext()
with poolholder as pool:
# generate sampler
print('number of priors is ' + str(len(self.prior)))
sampler = emcee.EnsembleSampler(num_walkers,
len(p0.shape[0]),
self.logp,
moves=emcee.moves.DESnookerMove(),
pool=pool)
if num_burnin > 0:
p0 = sampler.run_mcmc(p0, num_burnin, progress=True)
sampler.reset()
sampler.run_mcmc(p0, nsamples, progress=True)
# define variable names, it cannot be inferred from emcee
var_names = self.gather_parameter_names()
return arviz.from_emcee(sampler, var_names=var_names)
def test_no_blobs_error(self):
sampler = emcee.EnsembleSampler(6, 1, lambda x: -(x**2))
sampler.run_mcmc(np.random.normal(size=(6, 1)), 20)
with pytest.raises(ValueError):
from_emcee(sampler, blob_names=["inexistent"])
def test_bad_blobs(self, data, blob_args):
error, names, groups = blob_args
with pytest.raises(error):
from_emcee(data.obj, blob_names=names, blob_groups=groups)
def test_verify_arg_names(self, data):
with pytest.raises(ValueError):
from_emcee(data.obj, arg_names=["not enough"])
def test_slices_warning(self, data, slices):
with pytest.warns(UserWarning):
from_emcee(data.obj, slices=slices)
plt.plot(x0, np.dot(np.vander(x0, 2), w), "--k", label="LS")
plt.plot(x0, np.dot(np.vander(x0, 2), [m_ml, b_ml]), ":k", label="ML")
plt.legend(fontsize=14)
plt.xlim(0, 10)
plt.xlabel("x")
plt.ylabel("y")
#plt.show()
plt.savefig('test_emcee_2_fig1.svg', format='svg', dpi=1200)
plt.close()
# now arviz plots
import arviz as az
var_names = ['m', 'b', 'f']
emcee_data = az.from_emcee(sampler, var_names=var_names)
az.plot_posterior(emcee_data, var_names=var_names[:])
#plt.show()
plt.savefig('test_emcee_2_fig2.svg', format='svg', dpi=1200)
plt.close()
# now trace plot
az.plot_trace(emcee_data, var_names=var_names)
#plt.show()
plt.savefig('test_emcee_2_fig3.svg', format='svg', dpi=1200)
plt.close()
az.plot_pair(emcee_data, var_names=var_names, kind='kde')
#plt.show()
plt.savefig('test_emcee_2_fig4.svg', format='svg', dpi=1200)
plt.close()
def test_inference_data(self, data, test_args):
kwargs, test_dict = test_args
inference_data = from_emcee(data.obj, **kwargs)
fails = check_multiple_attrs(test_dict, inference_data)
assert not fails
def test__verify_arg_names(self):
with pytest.raises(ValueError):
from_emcee(self.obj, arg_names=['not', 'enough'])
def test__verify_arg_names(self, obj):
with pytest.raises(ValueError):
from_emcee(obj, arg_names=["not", "enough"])
def test__verify_var_names(self):
with pytest.raises(ValueError):
from_emcee(self.obj, var_names=["not", "enough"])
return like + prior
nwalkers, draws = 50, 700
ndim = J + 2
pos = np.random.normal(size=(nwalkers, ndim))
pos[:, 1] = np.absolute(pos[:, 1])
sampler = emcee.EnsembleSampler(nwalkers,
ndim,
lnprob_8school,
args=(y_obs, sigma))
sampler.run_mcmc(pos, draws)
var_names = ['mu', 'tau'] + ['eta{}'.format(i) for i in range(J)]
emcee_data = az.from_emcee(sampler,
var_names=var_names).sel(draw=slice(100, None))
az.plot_posterior(emcee_data, var_names=var_names[:3])
#plt.show()
plt.savefig('test_arviz_fig1.svg', format='svg', dpi=1200)
plt.close()
emcee_data = az.from_emcee(sampler, slices=[0, 1, slice(2, None)])
az.plot_trace(emcee_data, var_names=["var_2"], coords={"var_2_dim_0": 4})
#plt.show()
plt.savefig('test_arviz_fig2.svg', format='svg', dpi=1200)
plt.close()
def lnprob_8school_blobs(theta, y, s):
prior = log_prior_8school(theta)
like_vect = log_likelihood_8school(theta, y, s)
break
old_tau = tau
#%%
# gather 100 more samples to ensure clean chains
sampler.run_mcmc(backend.get_last_sample(), 100, progress=True)
#%%
import arviz as az
import corner
# plot chains
reader = emcee.backends.HDFBackend("example_chain.hdf5")
full_data = az.from_emcee(reader, var_names=model.labels)
az.plot_trace(full_data)
#%%
# remove burn-in data and thin and replot
tau = reader.get_autocorr_time(tol=0)
burnin = int(tau.max())
thin = int(0.3 * np.min(tau))
burn_samples = reader.get_chain(discard=burnin, thin=thin)
log_prob_samples = reader.get_log_prob(discard=burnin, thin=thin)
log_prior_samples = reader.get_blobs(discard=burnin, thin=thin)
dd = dict(zip(model.labels, burn_samples.T))
burn_data = az.from_dict(dd)
def get_inference_data(self, obj):
return from_emcee(obj, var_names=["ln(f)", "b", "m"])
#%matplotlib qt5
analisis = Graficador(reader, ['omega_m', 'b', 'H0'], 'HS CC+H0')
analisis.graficar_contornos(sol, discard=burnin, thin=thin, poster=True)
#%%
analisis.graficar_cadenas()
analisis.reportar_intervalos(sol)
#%%
analisis.graficar_taus_vs_n(num_param=None)
# analisis.graficar_taus_vs_n(num_param=1)
# analisis.graficar_taus_vs_n(num_param=2)
#%% Forma alternativa de graficar, seguir investigando!
#%matplotlib qt5
reader = emcee.backends.HDFBackend(filename)
samples = reader.get_chain(discard=burnin, flat=True, thin=thin)
emcee_data = az.from_emcee(reader,
var_names=['$\Omega_{m}$', 'b', '$H_{0}$'])
emcee_data
az.plot_pair(
emcee_data,
kind='kde',
#kde_kwargs={"fill_last": False, 'bw':'scott','levels':[1-0.95, 1-0.68]},
contour=True,
divergences=True,
marginals=True,
point_estimate='mean',
textsize=18)
#%%
az.plot_posterior(emcee_data)
plt.show()
#%%
def get_inference_data(self):
return from_emcee(self.obj, var_names=['ln(f)', 'b', 'm'])
sampler = emcee.EnsembleSampler(nwalkers,
ndim,
log_probability,
args=(Data_x, Data_y))
sampler.run_mcmc(pos, NSAMPLES, progress=True)
flat_samples = sampler.get_chain(discard=100, thin=15, flat=True)
blobs = sampler.get_blobs(discard=100, thin=15, flat=True)
inds = np.random.randint(len(flat_samples), size=100)
if plot:
print("Plot 3")
# now plotting
# now arviz plots
var_names = ['m', 'b', 's']
emcee_data = az.from_emcee(sampler,
var_names=var_names,
blob_names=["silly"])
postplot = az.plot_posterior(emcee_data,
var_names=var_names[:],
textsize=60,
hdi_prob=0.66)
postplot[0].set_xlabel("Intercept", fontsize=60)
postplot[1].set_xlabel("Slope", fontsize=60)
postplot[2].set_xlabel("Error", fontsize=60)
#plt.show()
plt.savefig('test_full_analysis_fig3.pdf', format='pdf', dpi=1200)
plt.close()
# now trace plot
print("Plot 4")
plottrace = az.plot_trace(emcee_data,
# -
# ## Comparison
#
# Finally, let's compare the results of these different inference methods a bit more quantitaively.
# First, let's look at the posterior constraint on the period of the underdamped harmonic oscillator, the effective period of the oscillatory signal.
# +
import arviz as az
emcee_data = az.from_emcee(
sampler,
var_names=[
"mean",
"log_sigma1",
"log_rho1",
"log_tau",
"log_sigma2",
"log_rho2",
"log_jitter",
],
)
for k in emcee_data.posterior.data_vars:
if k.startswith("log_"):
emcee_data.posterior[k[4:]] = np.exp(emcee_data.posterior[k])
with model:
pm_data = az.from_pymc3(trace)
numpyro_data = az.from_numpyro(mcmc)
bins = np.linspace(1.5, 2.75, 25)
def posterior_samples(self,
burn: int = 0,
thin: int = 1,
derived_parameters: bool = True,
arviz: bool = False):
if not arviz:
df = super().posterior_samples(burn=burn, thin=thin)
if derived_parameters:
for k2c in df.columns[self._sl_k2]:
df[k2c.replace('k2', 'k')] = sqrt(df[k2c])
df['a'] = as_from_rhop(df.rho.values, df.p.values)
df['inc'] = i_from_baew(df.b.values, df.a.values, 0., 0.)
average_ks = sqrt(df.iloc[:, self._sl_k2]).mean(1).values
df['t14'] = d_from_pkaiews(df.p.values,
average_ks,
df.a.values,
df.inc.values,
0.,
0.,
1,
kind=14)
df['t23'] = d_from_pkaiews(df.p.values,
average_ks,
df.a.values,
df.inc.values,
0.,
0.,
1,
kind=23)
return df
else:
dd = az.from_emcee(self.sampler, var_names=self.ps.names)
ds = xa.Dataset()
pst = dd.posterior
ds['k'] = sqrt(pst.k2)
ds['a'] = xa.DataArray(as_from_rhop(pst.rho.values, pst.p.values),
coords=pst.k2.coords)
ds['inc'] = xa.DataArray(i_from_baew(pst.b.values, ds.a.values, 0.,
0.),
coords=pst.k2.coords)
ds['t14'] = xa.DataArray(d_from_pkaiews(pst.p.values,
ds.k.values,
ds.a.values,
ds.inc.values,
0.,
0.,
1,
kind=14),
coords=pst.k2.coords)
ds['t23'] = xa.DataArray(d_from_pkaiews(pst.p.values,
ds.k.values,
ds.a.values,
ds.inc.values,
0.,
0.,
1,
kind=23),
coords=pst.k2.coords)
dd.add_groups({'derived_parameters': ds})
return dd
