def calib(símismo, rep, quema, extraer):
"""
Esta función sirve para llamar a las funcionalidades de calibración de PyMC.
:param rep: El número de repeticiones para la calibración.
:type rep: int
:param quema: El número de repeticiones iniciales a cortar de los resultados. Esto evita que los resultados
estén muy influenciados por los valores iniciales (y posiblemente erróneos) que toman los parámetros al
principio de la calibración.
:type quema: int
:param extraer: Cada cuántas repeticiones hay que guardar para los resultados. Por ejemplo, con `extraer`=10,
cada 10 repeticiones se guardará, así que, con `rep`=10000, `quema`=100 y `extraer`=10, quedaremos con trazas
de (10000 - 100) / 10 = 990 datos para aproximar la destribución de cada parámetro.
"""
símismo.n_iter += rep
if not usar_pymc3:
# Utilizar el algoritmo Metrópolis Adaptivo para la calibración. Sería probablemente mejor utilizar NUTS, pero
# para eso tendría que implementar pymc3 aquí y de verdad no quiero.
if símismo.método.lower() == 'metrópolis adaptivo':
símismo.MCMC.use_step_method(pm2.AdaptiveMetropolis,
símismo.MCMC.stochastics,
delay=200,
interval=200,
greedy=False,
shrink_if_necessary=True,
verbose=4)
elif símismo.método.lower() == 'metrópolis':
pass
else:
raise ValueError
# Llamar la función "sample" (muestrear) del objeto MCMC de PyMC
símismo.MCMC.sample(iter=rep,
burn=quema,
thin=extraer,
verbose=1,
tune_interval=10)
else:
if símismo.método.lower() == 'mcs':
n_trazas = 1
dir_temp = mkdtemp(prefix='TKN_MCS')
traza = mcs.sample_smc(n_steps=rep,
n_chains=n_trazas,
progressbar=False,
homepath=dir_temp,
stage=0,
random_seed=42)
def check_trace(self, step_method):
"""Tests whether the trace for step methods is exactly the same as on master.
Code changes that effect how random numbers are drawn may change this, and require
`master_samples` to be updated, but such changes should be noted and justified in the
commit.
This method may also be used to benchmark step methods across commits, by running, for
example
```
BENCHMARK=100000 ./scripts/test.sh -s pymc3/tests/test_step.py:TestStepMethods
```
on multiple commits.
"""
n_steps = 100
with Model() as model:
x = Normal('x', mu=0, sd=1)
if step_method.__name__ == 'SMC':
trace = smc.sample_smc(n_steps=n_steps,
n_chains=2,
start=[{
'x': 1.
}, {
'x': -1.
}],
random_seed=1,
n_jobs=1,
progressbar=False,
homepath=self.temp_dir)
elif step_method.__name__ == 'NUTS':
step = step_method(scaling=model.test_point)
trace = sample(0,
tune=n_steps,
discard_tuned_samples=False,
step=step,
random_seed=1,
chains=1)
else:
trace = sample(0,
tune=n_steps,
discard_tuned_samples=False,
step=step_method(),
random_seed=1,
chains=1)
assert_array_almost_equal(trace.get_values('x'),
self.master_samples[step_method],
decimal=select_by_precision(float64=6,
float32=4))
def test_sample_n_core(self, n_jobs, stage):
mtrace = smc.sample_smc(
n_steps=self.n_steps,
step=self.step,
stage=stage,
n_jobs=n_jobs,
progressbar=True,
homepath=self.test_folder,
model=self.ATMIP_test,
rm_flag=True)
x = mtrace.get_values('X')
mu1d = np.abs(x).mean(axis=0)
np.testing.assert_allclose(self.muref, mu1d, rtol=0., atol=0.03)
def test_sample_n_core(self, n_jobs, stage):
mtrace = smc.sample_smc(samples=self.samples,
n_chains=self.n_chains,
stage=stage,
n_jobs=n_jobs,
progressbar=True,
homepath=self.test_folder,
model=self.ATMIP_test,
rm_flag=True)
x = mtrace.get_values('X')
mu1d = np.abs(x).mean(axis=0)
np.testing.assert_allclose(self.muref, mu1d, rtol=0., atol=0.03)
# Scenario IV Ching, J. & Chen, Y. 2007
assert np.round(np.log(self.ATMIP_test.marginal_likelihood)) == -12.0
def test_sample_n_core(self, n_jobs, stage):
mtrace = smc.sample_smc(samples=self.samples,
chains=self.n_chains,
stage=stage,
cores=n_jobs,
progressbar=True,
homepath=self.test_folder,
model=self.ATMIP_test,
rm_flag=True)
x = mtrace.get_values('X')
mu1d = np.abs(x).mean(axis=0)
np.testing.assert_allclose(self.muref, mu1d, rtol=0., atol=0.03)
# Scenario IV Ching, J. & Chen, Y. 2007
assert np.round(np.log(self.ATMIP_test.marginal_likelihood)) == -12.0
def check_trace(self, step_method):
"""Tests whether the trace for step methods is exactly the same as on master.
Code changes that effect how random numbers are drawn may change this, and require
`master_samples` to be updated, but such changes should be noted and justified in the
commit.
This method may also be used to benchmark step methods across commits, by running, for
example
```
BENCHMARK=100000 ./scripts/test.sh -s pymc3/tests/test_step.py:TestStepMethods
```
on multiple commits.
"""
n_steps = 100
with Model() as model:
x = Normal('x', mu=0, sd=1)
if step_method.__name__ == 'SMC':
trace = smc.sample_smc(n_steps=n_steps,
n_chains=2,
start=[{'x':1.}, {'x':-1.}],
random_seed=1,
n_jobs=1, progressbar=False,
homepath=self.temp_dir)
elif step_method.__name__ == 'NUTS':
step = step_method(scaling=model.test_point)
trace = sample(0, tune=n_steps,
discard_tuned_samples=False,
step=step, random_seed=1, chains=1)
else:
trace = sample(0, tune=n_steps,
discard_tuned_samples=False,
step=step_method(), random_seed=1, chains=1)
assert_array_almost_equal(
trace.get_values('x'),
self.master_samples[step_method],
decimal=select_by_precision(float64=6, float32=4))
niter = 10000
start = pm.find_MAP(model=basic_model)
print("Maximum a-posteriori estimate:")
print(start)
n_chains = 500
n_steps = 50
tune_interval = 10
n_jobs = 1
trace = smc.sample_smc(
n_steps=n_steps,
n_chains=n_chains,
tune_interval=tune_interval,
n_jobs=n_jobs,
#start=start,
progressbar=True,
stage=0,
homepath=test_folder,
model=basic_model)
#t = trace[niter//2:] # discard 50% of values
#print t
#x0_ = trace.get_values('x0',burn=niter//2, combine=True)
#step = pm.NUTS(scaling=start)
#trace = pm.sample(niter, start = start, step = step)
#step = pm.NUTS()
#trace = pm.sample(niter, step = step, tune=2000)
# transform need to be None for SMC to work (transformed doesnt have random method)
h2 = pm.Uniform('h2', transform=None)
sigma2 = pm.HalfCauchy('sigma2', 5, transform=None)
#beta_0 = pm.Uniform('beta_0', lower=-1000, upper=1000) # a replacement for improper prior
w = pm.Normal('w', mu=0, sd=100, shape=M)
z = pm.Normal('z', mu=0, sd=(h2 * sigma2)**0.5, shape=N)
g = T.dot(L, z)
y = pm.Normal('y',
mu=g + T.dot(X, w),
sd=((1 - h2) * sigma2)**0.5,
observed=Pheno)
mtrace = smc.sample_smc(n_steps=n_steps,
n_chains=n_chains,
tune_interval=tune_interval,
n_jobs=n_jobs,
progressbar=False,
stage=0,
homepath=test_folder,
model=mixedEffect2)
#%%
pm.traceplot(mtrace, lines={
'w': w0,
'z': z0
})
#%% plot advi and NUTS (copy from pymc3 example)
burnin = 1000
from scipy import stats
import seaborn as sns
gbij = approx.bij
means = gbij.rmap(approx.mean.eval())
