def fit_full(model_name, J, j_ind, X, y, phi_true, m0, Q0, seed):
"""Fit full model and save the results."""
print "Full model {} ...".format(model_name)
data = dict(
N=X.shape[0],
D=X.shape[1],
J=J,
X=X,
y=y,
j_ind=j_ind + 1,
mu_phi=m0,
Omega_phi=Q0.T # Q0 transposed in order to get C-contiguous
)
model = load_stan(model_name)
# Sample and extract parameters
with suppress_stdout():
fit = model.sampling(data=data,
seed=seed,
chains=4,
iter=1000,
warmup=500,
thin=2)
samp = fit.extract(pars='phi')['phi']
m_phi_full = samp.mean(axis=0)
var_phi_full = samp.var(axis=0, ddof=1)
print "Full model sampled."
if not os.path.exists('results'):
os.makedirs('results')
np.savez(
'results/res_f_{}.npz'.format(model_name),
phi_true=phi_true,
m_phi_full=m_phi_full,
var_phi_full=var_phi_full,
)
def main(model_name, conf, ret_master=False):
"""Fit requested model with given configurations.
Arg. `ret_master` can be used to prematurely exit and return the dep.Master
object, which is useful for debuging.
"""
# Ensure that the configurations class is used
if not isinstance(conf, configurations):
raise ValueError("Invalid arg. `conf`, use class fit.configurations")
print("Configurations:")
print(' ' + str(conf).replace('\n', '\n '))
# Localise few options
J = conf.J
D = conf.D
K = conf.K
# Import the model simulator module (import at runtime)
model_module = getattr(__import__('models.'+model_name), model_name)
model = model_module.model(J, D, conf.npg)
# Simulate_data
if conf.cor_input:
data = model.simulate_data(Sigma_x='rand', seed=conf.seed_data)
else:
data = model.simulate_data(seed=conf.seed_data)
# Calculate the uncertainty
uncertainty_global, uncertainty_group = data.calc_uncertainty()
# Get the prior
S0, m0, Q0, r0 = model.get_prior()
prior = {'Q':Q0, 'r':r0}
# Set init_site to N(0,A**2/K I), where A = 10 * max(diag(S0))
init_site = 10 * np.max(np.diag(S0))
# Get parameter information
pnames, pshapes, phiers = model.get_param_definitions()
# Save true values
if conf.save_true:
if not os.path.exists(RES_PATH):
os.makedirs(RES_PATH)
if conf.id:
filename = 'true_vals_{}_{}.npz'.format(model_name, conf.id)
else:
filename = 'true_vals_{}.npz'.format(model_name)
np.savez(
os.path.join(RES_PATH, filename),
J = J,
D = D,
npg = conf.npg,
seed = conf.seed_data,
pnames = pnames,
uncertainty_global = uncertainty_global,
uncertainty_group = uncertainty_group,
X_param = data.X_param,
**data.true_values
)
print("True values saved into results")
# ------------------------------------------------------
# Fit distributed model
# ------------------------------------------------------
if conf.method == 'both' or conf.method == 'distributed' or ret_master:
print("Distributed model {} ...".format(model_name))
# Options for the ep-algorithm see documentation of dep.method.Master
dep_options = dict(
prior = prior,
seed = conf.seed_mcmc,
prec_estim = conf.prec_estim,
df0 = conf.damp,
init_site = init_site,
**conf.mc_opt
)
# Temp fix for the RandomState seed problem with pystan in 32bit Python
dep_options['tmp_fix_32bit'] = TMP_FIX_32BIT
if K < 2:
raise ValueError("K should be at least 2.")
elif K < J:
# ------ Many groups per site: combine groups ------
Nk, Nj_k, j_ind_k = distribute_groups(J, K, data.Nj)
# Create the Master instance
stan_model = load_stan(os.path.join(MOD_PATH, model_name))
dep_master = Master(
stan_model,
data.X,
data.y,
A_k = {'J':Nj_k},
A_n = {'j_ind':j_ind_k+1},
site_sizes = Nk,
**dep_options
)
# Construct the map: which site contribute to which parameter
pmaps = _create_pmaps(phiers, J, K, Nj_k)
elif K == J:
# ------ One group per site ------
# Create the Master instance
dep_master = Master(
load_stan(os.path.join(MOD_PATH, model_name+'_sg')),
data.X,
data.y,
site_sizes=data.Nj,
**dep_options
)
# Construct the map: which site contribute to which parameter
pmaps = _create_pmaps(phiers, J, K, None)
elif K <= data.N:
# ------ Multiple sites per group: split groups ------
Nk, Nk_j, _ = distribute_groups(J, K, data.Nj)
# Create the Master instance
dep_master = Master(
load_stan(os.path.join(MOD_PATH, model_name+'_sg')),
data.X,
data.y,
site_sizes=Nk,
**dep_options
)
# Construct the map: which site contribute to which parameter
pmaps = _create_pmaps(phiers, J, K, Nk_j)
else:
raise ValueError("K cant be greater than number of samples")
if ret_master:
print("Returning dep.Master")
return dep_master
# Run the algorithm for `EP_ITER` iterations
print("Run distributed EP algorithm for {} iterations." \
.format(conf.iter))
m_phi_i, cov_phi_i, info = dep_master.run(
conf.iter, save_last_fits=conf.mix)
if info:
# Save results until failure
if conf.save_res:
if not os.path.exists(RES_PATH):
os.makedirs(RES_PATH)
if conf.id:
filename = 'res_d_{}_{}.npz'.format(model_name, conf.id)
else:
filename = 'res_d_{}.npz'.format(model_name)
np.savez(
os.path.join(RES_PATH, filename),
conf = conf.__dict__,
m_phi_i = m_phi_i,
cov_phi_i = cov_phi_i,
last_iter = dep_master.iter
)
print("Uncomplete distributed model results saved.")
raise RuntimeError('Dep algorithm failed with error code: {}'
.format(info))
if conf.mix:
print("Form the final approximation "
"by mixing the last samples from all the sites.")
cov_phi, m_phi = dep_master.mix_phi()
# Get mean and var of inferred variables
pms, pvars = dep_master.mix_pred(pnames, pmaps, pshapes)
# Construct a dict of from these results
presults = {}
for i in range(len(pnames)):
pname = pnames[i]
presults['m_'+pname] = pms[i]
presults['var_'+pname] = pvars[i]
# Save results
if conf.save_res:
if not os.path.exists(RES_PATH):
os.makedirs(RES_PATH)
if conf.id:
filename = 'res_d_{}_{}.npz'.format(model_name, conf.id)
else:
filename = 'res_d_{}.npz'.format(model_name)
if conf.mix:
np.savez(
os.path.join(RES_PATH, filename),
conf = conf.__dict__,
m_phi_i = m_phi_i,
cov_phi_i = cov_phi_i,
m_phi = m_phi,
cov_phi = cov_phi,
**presults
)
else:
np.savez(
os.path.join(RES_PATH, filename),
conf = conf.__dict__,
m_phi_i = m_phi_i,
cov_phi_i = cov_phi_i,
)
print("Distributed model results saved.")
# Release master object
del dep_master
# ------------------------------------------------------
# Fit full model
# ------------------------------------------------------
if conf.method == 'both' or conf.method == 'full':
print("Full model {} ...".format(model_name))
seed = np.random.RandomState(seed=conf.seed_mcmc)
# Temp fix for the RandomState seed problem with pystan in 32bit Python
seed = seed.randint(2**31-1) if TMP_FIX_32BIT else seed
data = dict(
N = data.X.shape[0],
D = data.X.shape[1],
J = J,
X = data.X,
y = data.y,
j_ind = data.j_ind+1,
mu_phi = m0,
Omega_phi = Q0.T # Q0 transposed in order to get C-contiguous
)
# Load model if not loaded already
if not 'stan_model' in locals():
stan_model = load_stan(os.path.join(MOD_PATH, model_name))
# Sample and extract parameters
with suppress_stdout():
time_full = timer()
fit = stan_model.sampling(
data = data,
seed = seed,
**conf.mc_full_opt
)
time_full = (timer() - time_full)
samp = fit.extract(pars='phi')['phi']
nsamp = samp.shape[0]
m_phi_full = samp.mean(axis=0)
samp -= m_phi_full
cov_phi_full = samp.T.dot(samp)
cov_phi_full /= nsamp -1
# Mean stepsize
steps = [np.mean(p['stepsize__'])
for p in fit.get_sampler_params()]
print(' sampling time {}'.format(time_full))
print(' mean stepsize: {:.4}'.format(np.mean(steps)))
# Max Rhat (from all but last row in the last column)
print(' max Rhat: {:.4}'.format(
np.max(fit.summary()['summary'][:-1,-1])
))
# Get mean and var of inferred variables
presults = {}
for i in range(len(pnames)):
pname = pnames[i]
samp = fit.extract(pname)[pname]
presults['m_'+pname+'_full'] = np.mean(samp, axis=0)
presults['var_'+pname+'_full'] = np.var(samp, axis=0, ddof=1)
# Save results
if conf.save_res:
if not os.path.exists(RES_PATH):
os.makedirs(RES_PATH)
if conf.id:
filename = 'res_f_{}_{}.npz'.format(model_name, conf.id)
else:
filename = 'res_f_{}.npz'.format(model_name)
np.savez(
os.path.join(RES_PATH, filename),
conf = conf.__dict__,
m_phi_full = m_phi_full,
cov_phi_full = cov_phi_full,
**presults
)
print("Full model results saved.")
def main(filename='res.npz'):
# ------------------------------------------------------
# Simulate data
# ------------------------------------------------------
# Set seed
rnd_data = np.random.RandomState(seed=SEED_DATA)
# Parameters
# Number of observations for each group
if hasattr(NPG, '__getitem__') and len(NPG) == 2:
Nj = rnd_data.randint(NPG[0],NPG[1]+1, size=J)
else:
Nj = NPG*np.ones(J, dtype=np.int64)
# Total number of observations
N = np.sum(Nj)
# Observation index limits for J groups
j_lim = np.concatenate(([0], np.cumsum(Nj)))
# Group indices for each sample
j_ind = np.empty(N, dtype=np.int64)
for j in xrange(J):
j_ind[j_lim[j]:j_lim[j+1]] = j
# Assign parameters
alpha_j = MU + rnd_data.randn(J)*TAU
phi_true = np.log([MU, TAU, BETA, SIGMA])
dphi = 4 # Number of shared parameters
# Simulate data
# Truncated normal rejection sampling
X = X_MU + rnd_data.randn(N)*X_STD
xneg = X<0
while np.any(xneg):
X[xneg] = X_MU + rnd_data.randn(np.count_nonzero(xneg))*X_STD
xneg = X<0
f = alpha_j[j_ind] + X*BETA
y = f + rnd_data.randn(N)*SIGMA
yneg = y<0
while np.any(yneg):
y[yneg] = f[yneg] + rnd_data.randn(np.count_nonzero(yneg))*SIGMA
yneg = y<0
# ------------------------------------------------------
# Prior
# ------------------------------------------------------
# Moment parameters of the prior (transposed in order to get F-contiguous)
S0 = np.diag(V0).T
m0 = M0
# Natural parameters of the prior
Q0 = np.diag(np.ones(dphi)/V0).T
r0 = M0/V0
prior = {'Q':Q0, 'r':r0}
# ------------------------------------------------------
# Distributed EP
# ------------------------------------------------------
print "Distributed model..."
# Options for the ep-algorithm see documentation of dep.method.Master
options = {
'seed' : SEED_MCMC,
'init_prev' : True,
'prec_estim' : PREC_ESTIM,
'chains' : CHAINS,
'iter' : ITER,
'warmup' : WARMUP,
'thin' : THIN
}
# Temp fix for the RandomState seed problem with pystan in 32bit Python
options['tmp_fix_32bit'] = TMP_FIX_32BIT
model = load_stan('model')
if K < 2:
raise ValueError("K should be at least 2.")
elif K < J:
# ---- Many groups per site ----
# Combine smallest pairs of consecutive groups until K has been reached
Nk = Nj.tolist()
Njd = (Nj[:-1]+Nj[1:]).tolist()
Nj_k = [1]*J
for _ in xrange(J-K):
ind = Njd.index(min(Njd))
if ind+1 < len(Njd):
Njd[ind+1] += Nk[ind]
if ind > 0:
Njd[ind-1] += Nk[ind+1]
Nk[ind] = Njd[ind]
Nk.pop(ind+1)
Njd.pop(ind)
Nj_k[ind] += Nj_k[ind+1]
Nj_k.pop(ind+1)
Nk = np.array(Nk) # Number of samples per site
Nj_k = np.array(Nj_k) # Number of groups per site
j_ind_k = np.empty(N, dtype=np.int32) # Within site group index
k_lim = np.concatenate(([0], np.cumsum(Nj_k)))
for k in xrange(K):
for ji in xrange(Nj_k[k]):
ki = ji + k_lim[k]
j_ind_k[j_lim[ki]:j_lim[ki+1]] = ji
# Create the Master instance
dep_master = Master(
model,
X,
y,
A_k={'J':Nj_k},
A_n={'j_ind':j_ind_k+1},
site_sizes=Nk,
prior=prior,
**options
)
elif K == J:
# ---- One group per site ----
# Create the Master instance
dep_master = Master(
model,
X,
y,
A_k={'J': np.ones(K, dtype=np.int64)},
A_n={'j_ind': np.ones(N, dtype=np.int64)},
site_sizes=Nj,
prior=prior,
**options
)
elif K <= N:
# ---- Multiple sites per group ----
# Split biggest groups until enough sites are formed
ppg = np.ones(J, dtype=np.int64) # Parts per group
Nj2 = Nj.astype(np.float)
for _ in xrange(K-J):
cur_max = Nj2.argmax()
ppg[cur_max] += 1
Nj2[cur_max] = Nj[cur_max]/ppg[cur_max]
Nj2 = Nj//ppg
rem = Nj%ppg
# Form the number of samples for each site
Nk = np.empty(K, dtype=np.int64)
k = 0
for j in xrange(J):
for kj in xrange(ppg[j]):
if kj < rem[j]:
Nk[k] = Nj2[j] + 1
else:
Nk[k] = Nj2[j]
k += 1
# Create the Master instance
dep_master = Master(
model,
X,
y,
A_k={'J': np.ones(K, dtype=np.int64)},
A_n={'j_ind': np.ones(N, dtype=np.int64)},
site_sizes=Nk,
prior=prior,
**options
)
else:
raise ValueError("K cant be greater than number of samples")
# Run the algorithm for `EP_ITER` iterations
print "Run distributed EP algorithm for {} iterations.".format(EP_ITER)
m_phi, cov_phi, info = dep_master.run(EP_ITER)
var_phi = np.diagonal(cov_phi, axis1=1, axis2=2)
if info:
raise RuntimeError('Dep algorithm failed with error code: {}'
.format(info))
print "Form the final approximation " \
"by mixing the samples from all the sites."
S_mix, m_mix = dep_master.mix_phi()
var_mix = np.diag(S_mix)
print "Distributed model sampled:"
print " exp(phi) = {}".format(np.array2string(np.exp(m_mix), precision=1))
print "True values:"
print " exp(phi) = {}".format([MU, TAU, BETA, SIGMA])
# ------------------------------------------------------
# Save results
# ------------------------------------------------------
np.savez(filename,
seed_data=SEED_DATA,
seed_mcmc=SEED_MCMC,
J=J,
K=K,
Nj=Nj,
N=N,
dphi=dphi,
niter=EP_ITER,
m0=M0,
V0=V0,
phi_true=phi_true,
m_phi=m_phi,
var_phi=var_phi,
m_mix=m_mix,
var_mix=var_mix
)
def main(filename='res_full.npz'):
# ------------------------------------------------------
# Simulate data
# ------------------------------------------------------
# Set seed
rnd_data = np.random.RandomState(seed=SEED_DATA)
# Parameters
# Number of observations for each group
if hasattr(NPG, '__getitem__') and len(NPG) == 2:
Nj = rnd_data.randint(NPG[0],NPG[1]+1, size=J)
else:
Nj = NPG*np.ones(J, dtype=np.int64)
# Total number of observations
N = np.sum(Nj)
# Observation index limits for J groups
j_lim = np.concatenate(([0], np.cumsum(Nj)))
# Group indices for each sample
j_ind = np.empty(N, dtype=np.int64)
for j in xrange(J):
j_ind[j_lim[j]:j_lim[j+1]] = j
# Assign parameters
alpha_j = MU + rnd_data.randn(J)*TAU
phi_true = np.log([MU, TAU, BETA, SIGMA])
dphi = 4 # Number of shared parameters
# Simulate data
# Truncated normal rejection sampling
X = X_MU + rnd_data.randn(N)*X_STD
xneg = X<0
while np.any(xneg):
X[xneg] = X_MU + rnd_data.randn(np.count_nonzero(xneg))*X_STD
xneg = X<0
f = alpha_j[j_ind] + X*BETA
y = f + rnd_data.randn(N)*SIGMA
yneg = y<0
while np.any(yneg):
y[yneg] = f[yneg] + rnd_data.randn(np.count_nonzero(yneg))*SIGMA
yneg = y<0
# ------------------------------------------------------
# Prior
# ------------------------------------------------------
# Moment parameters of the prior (transposed in order to get F-contiguous)
S0 = np.diag(V0).T
m0 = M0
# Natural parameters of the prior
Q0 = np.diag(np.ones(dphi)/V0).T
r0 = M0/V0
prior = {'Q':Q0, 'r':r0}
# ------------------------------------------------------
# Full model
# ------------------------------------------------------
print "Full model..."
# Set seed
rnd_mcmc = np.random.RandomState(seed=SEED_MCMC)
data = dict(
N=N,
J=J,
X=X,
y=y,
j_ind=j_ind+1,
mu_phi=m0,
Omega_phi=Q0.T # Q0 transposed in order to get C-contiguous
)
# Sample and extract parameters
model = load_stan('model')
fit = model.sampling(
data=data,
seed=(rnd_mcmc.randint(2**31-1) if TMP_FIX_32BIT else rnd_mcmc),
chains=CHAINS,
iter=ITER,
warmup=WARMUP,
thin=THIN
)
samp = fit.extract(pars='phi')['phi']
m_phi_full = samp.mean(axis=0)
var_phi_full = samp.var(axis=0, ddof=1)
print "Full model sampled:"
print " exp(phi) = {}" \
.format(np.array2string(np.exp(m_phi_full), precision=1))
print "True values:"
print " exp(phi) = {}".format([MU, TAU, BETA, SIGMA])
# ------------------------------------------------------
# Save results
# ------------------------------------------------------
np.savez(filename,
seed_data=SEED_DATA,
seed_mcmc=SEED_MCMC,
J=J,
K=K,
Nj=Nj,
N=N,
dphi=dphi,
m0=M0,
V0=V0,
phi_true=phi_true,
m_phi_full=m_phi_full,
var_phi_full=var_phi_full
)
def fit_distributed(model_name, niter, J, K, Nj, X, y, phi_true, options):
"""Fit distributed model and save the results."""
print "Distributed model {} ...".format(model_name)
N = Nj.sum()
if K < 2:
raise ValueError("K should be at least 2.")
elif K < J:
# ---- Many groups per site ----
# Combine smallest pairs of consecutive groups until K has been reached
j_lim = np.concatenate(([0], np.cumsum(Nj)))
Nk = Nj.tolist()
Njd = (Nj[:-1] + Nj[1:]).tolist()
Nj_k = [1] * J
for _ in xrange(J - K):
ind = Njd.index(min(Njd))
if ind + 1 < len(Njd):
Njd[ind + 1] += Nk[ind]
if ind > 0:
Njd[ind - 1] += Nk[ind + 1]
Nk[ind] = Njd[ind]
Nk.pop(ind + 1)
Njd.pop(ind)
Nj_k[ind] += Nj_k[ind + 1]
Nj_k.pop(ind + 1)
Nk = np.array(Nk) # Number of samples per site
Nj_k = np.array(Nj_k) # Number of groups per site
j_ind_k = np.empty(N, dtype=np.int32) # Within site group index
k_lim = np.concatenate(([0], np.cumsum(Nj_k)))
for k in xrange(K):
for ji in xrange(Nj_k[k]):
ki = ji + k_lim[k]
j_ind_k[j_lim[ki]:j_lim[ki + 1]] = ji
# Create the Master instance
model = load_stan(model_name)
dep_master = Master(model,
X,
y,
A_k={'J': Nj_k},
A_n={'j_ind': j_ind_k + 1},
site_sizes=Nk,
**options)
elif K == J:
# ---- One group per site ----
# Create the Master instance
model_single_group = load_stan(model_name + '_sg')
dep_master = Master(model_single_group, X, y, site_sizes=Nj, **options)
elif K <= N:
# ---- Multiple sites per group ----
# Split biggest groups until enough sites are formed
ppg = np.ones(J, dtype=np.int64) # Parts per group
Nj2 = Nj.astype(np.float)
for _ in xrange(K - J):
cur_max = Nj2.argmax()
ppg[cur_max] += 1
Nj2[cur_max] = Nj[cur_max] / ppg[cur_max]
Nj2 = Nj // ppg
rem = Nj % ppg
# Form the number of samples for each site
Nk = np.empty(K, dtype=np.int64)
k = 0
for j in xrange(J):
for kj in xrange(ppg[j]):
if kj < rem[j]:
Nk[k] = Nj2[j] + 1
else:
Nk[k] = Nj2[j]
k += 1
# Create the Master instance
model_single_group = load_stan(model_name + '_sg')
dep_master = Master(model_single_group, X, y, site_sizes=Nk, **options)
else:
raise ValueError("K cant be greater than number of samples")
# Run the algorithm for `niter` iterations
print "Run distributed EP algorithm for {} iterations.".format(niter)
m_phi, var_phi = dep_master.run(niter)
print "Form the final approximation " \
"by mixing the samples from all the sites."
S_mix, m_mix = dep_master.mix_samples()
var_mix = np.diag(S_mix)
print "Distributed model sampled."
if not os.path.exists('results'):
os.makedirs('results')
np.savez(
'results/res_d_{}.npz'.format(model_name),
phi_true=phi_true,
m_phi=m_phi,
var_phi=var_phi,
m_mix=m_mix,
var_mix=var_mix,
)
def main(model_name, conf, ret_master=False):
"""Fit requested model with given configurations.
Arg. `ret_master` can be used to prematurely exit and return the dep.Master
object, which is useful for debuging.
"""
# Ensure that the configurations class is used
if not isinstance(conf, configurations):
raise ValueError("Invalid arg. `conf`, use class fit.configurations")
print "Configurations:"
print ' ' + str(conf).replace('\n', '\n ')
# Localise few options
J = conf.J
D = conf.D
K = conf.K
# Import the model simulator module (import at runtime)
model_module = getattr(__import__('models.'+model_name), model_name)
model = model_module.model(J, D, conf.npg)
# Simulate_data
if conf.cor_input:
data = model.simulate_data(Sigma_x='rand', seed=conf.seed_data)
else:
data = model.simulate_data(seed=conf.seed_data)
# Calculate the uncertainty
uncertainty_global, uncertainty_group = data.calc_uncertainty()
# Get the prior
S0, m0, Q0, r0 = model.get_prior()
prior = {'Q':Q0, 'r':r0}
# Get parameter information
pnames, pshapes, phiers = model.get_param_definitions()
# Save true values
if conf.save_true:
if not os.path.exists(RES_PATH):
os.makedirs(RES_PATH)
if conf.id:
filename = 'true_vals_{}_{}.npz'.format(model_name, conf.id)
else:
filename = 'true_vals_{}.npz'.format(model_name)
np.savez(
os.path.join(RES_PATH, filename),
J = J,
D = D,
npg = conf.npg,
seed = conf.seed_data,
pnames = pnames,
uncertainty_global = uncertainty_global,
uncertainty_group = uncertainty_group,
X_param = data.X_param,
**data.true_values
)
print "True values saved into results"
# ------------------------------------------------------
# Fit distributed model
# ------------------------------------------------------
if conf.method == 'both' or conf.method == 'distributed' or ret_master:
print "Distributed model {} ...".format(model_name)
# Options for the ep-algorithm see documentation of dep.method.Master
dep_options = dict(
prior = prior,
seed = conf.seed_mcmc,
prec_estim = conf.prec_estim,
**conf.mc_opt
)
# Temp fix for the RandomState seed problem with pystan in 32bit Python
dep_options['tmp_fix_32bit'] = TMP_FIX_32BIT
if K < 2:
raise ValueError("K should be at least 2.")
elif K < J:
# ------ Many groups per site: combine groups ------
Nk, Nj_k, j_ind_k = distribute_groups(J, K, data.Nj)
# Create the Master instance
stan_model = load_stan(os.path.join(MOD_PATH, model_name))
dep_master = Master(
stan_model,
data.X,
data.y,
A_k = {'J':Nj_k},
A_n = {'j_ind':j_ind_k+1},
site_sizes = Nk,
**dep_options
)
# Construct the map: which site contribute to which parameter
pmaps = _create_pmaps(phiers, J, K, Nj_k)
elif K == J:
# ------ One group per site ------
# Create the Master instance
dep_master = Master(
load_stan(os.path.join(MOD_PATH, model_name+'_sg')),
data.X,
data.y,
site_sizes=data.Nj,
**dep_options
)
# Construct the map: which site contribute to which parameter
pmaps = _create_pmaps(phiers, J, K, None)
elif K <= data.N:
# ------ Multiple sites per group: split groups ------
Nk, Nk_j, _ = distribute_groups(J, K, data.Nj)
# Create the Master instance
dep_master = Master(
load_stan(os.path.join(MOD_PATH, model_name+'_sg')),
data.X,
data.y,
site_sizes=Nk,
**dep_options
)
# Construct the map: which site contribute to which parameter
pmaps = _create_pmaps(phiers, J, K, Nk_j)
else:
raise ValueError("K cant be greater than number of samples")
if ret_master:
print "Returning dep.Master"
return dep_master
# Run the algorithm for `EP_ITER` iterations
print "Run distributed EP algorithm for {} iterations." \
.format(conf.iter)
m_phi_i, cov_phi_i, info = dep_master.run(conf.iter)
if info:
# Save results until failure
if conf.save_res:
if not os.path.exists(RES_PATH):
os.makedirs(RES_PATH)
if conf.id:
filename = 'res_d_{}_{}.npz'.format(model_name, conf.id)
else:
filename = 'res_d_{}.npz'.format(model_name)
np.savez(
os.path.join(RES_PATH, filename),
conf = conf.__dict__,
m_phi_i = m_phi_i,
cov_phi_i = cov_phi_i,
last_iter = dep_master.iter
)
print "Uncomplete distributed model results saved."
raise RuntimeError('Dep algorithm failed with error code: {}'
.format(info))
print "Form the final approximation " \
"by mixing the samples from all the sites."
cov_phi, m_phi = dep_master.mix_phi()
# Get mean and var of inferred variables
pms, pvars = dep_master.mix_pred(pnames, pmaps, pshapes)
# Construct a dict of from these results
presults = {}
for i in xrange(len(pnames)):
pname = pnames[i]
presults['m_'+pname] = pms[i]
presults['var_'+pname] = pvars[i]
# Save results
if conf.save_res:
if not os.path.exists(RES_PATH):
os.makedirs(RES_PATH)
if conf.id:
filename = 'res_d_{}_{}.npz'.format(model_name, conf.id)
else:
filename = 'res_d_{}.npz'.format(model_name)
np.savez(
os.path.join(RES_PATH, filename),
conf = conf.__dict__,
m_phi_i = m_phi_i,
cov_phi_i = cov_phi_i,
m_phi = m_phi,
cov_phi = cov_phi,
**presults
)
print "Distributed model results saved."
# Release master object
del dep_master
# ------------------------------------------------------
# Fit full model
# ------------------------------------------------------
if conf.method == 'both' or conf.method == 'full':
print "Full model {} ...".format(model_name)
seed = np.random.RandomState(seed=conf.seed_mcmc)
# Temp fix for the RandomState seed problem with pystan in 32bit Python
seed = seed.randint(2**31-1) if TMP_FIX_32BIT else seed
data = dict(
N = data.X.shape[0],
D = data.X.shape[1],
J = J,
X = data.X,
y = data.y,
j_ind = data.j_ind+1,
mu_phi = m0,
Omega_phi = Q0.T # Q0 transposed in order to get C-contiguous
)
# Load model if not loaded already
if not 'stan_model' in locals():
stan_model = load_stan(os.path.join(MOD_PATH, model_name))
# Sample and extract parameters
with suppress_stdout():
fit = stan_model.sampling(
data = data,
seed = seed,
**conf.mc_full_opt
)
samp = fit.extract(pars='phi')['phi']
nsamp = samp.shape[0]
m_phi_full = samp.mean(axis=0)
samp -= m_phi_full
cov_phi_full = samp.T.dot(samp)
cov_phi_full /= nsamp -1
# Mean stepsize
steps = [np.mean(p['stepsize__'])
for p in fit.get_sampler_params()]
print ' mean stepsize: {:.4}'.format(np.mean(steps))
# Max Rhat (from all but last row in the last column)
print ' max Rhat: {:.4}'.format(
np.max(fit.summary()['summary'][:-1,-1])
)
# Get mean and var of inferred variables
presults = {}
for i in xrange(len(pnames)):
pname = pnames[i]
samp = fit.extract(pname)[pname]
presults['m_'+pname+'_full'] = np.mean(samp, axis=0)
presults['var_'+pname+'_full'] = np.var(samp, axis=0, ddof=1)
# Save results
if conf.save_res:
if not os.path.exists(RES_PATH):
os.makedirs(RES_PATH)
if conf.id:
filename = 'res_f_{}_{}.npz'.format(model_name, conf.id)
else:
filename = 'res_f_{}.npz'.format(model_name)
np.savez(
os.path.join(RES_PATH, filename),
conf = conf.__dict__,
m_phi_full = m_phi_full,
cov_phi_full = cov_phi_full,
**presults
)
print "Full model results saved."
def main(filename='res_full.npz'):
# ------------------------------------------------------
# Simulate data
# ------------------------------------------------------
# Set seed
rnd_data = np.random.RandomState(seed=SEED_DATA)
# Parameters
# Number of observations for each group
if hasattr(NPG, '__getitem__') and len(NPG) == 2:
Nj = rnd_data.randint(NPG[0], NPG[1] + 1, size=J)
else:
Nj = NPG * np.ones(J, dtype=np.int64)
# Total number of observations
N = np.sum(Nj)
# Observation index limits for J groups
j_lim = np.concatenate(([0], np.cumsum(Nj)))
# Group indices for each sample
j_ind = np.empty(N, dtype=np.int64)
for j in xrange(J):
j_ind[j_lim[j]:j_lim[j + 1]] = j
# Assign parameters
alpha_j = MU + rnd_data.randn(J) * TAU
phi_true = np.log([MU, TAU, BETA, SIGMA])
dphi = 4 # Number of shared parameters
# Simulate data
# Truncated normal rejection sampling
X = X_MU + rnd_data.randn(N) * X_STD
xneg = X < 0
while np.any(xneg):
X[xneg] = X_MU + rnd_data.randn(np.count_nonzero(xneg)) * X_STD
xneg = X < 0
f = alpha_j[j_ind] + X * BETA
y = f + rnd_data.randn(N) * SIGMA
yneg = y < 0
while np.any(yneg):
y[yneg] = f[yneg] + rnd_data.randn(np.count_nonzero(yneg)) * SIGMA
yneg = y < 0
# ------------------------------------------------------
# Prior
# ------------------------------------------------------
# Moment parameters of the prior (transposed in order to get F-contiguous)
S0 = np.diag(V0).T
m0 = M0
# Natural parameters of the prior
Q0 = np.diag(np.ones(dphi) / V0).T
r0 = M0 / V0
prior = {'Q': Q0, 'r': r0}
# ------------------------------------------------------
# Full model
# ------------------------------------------------------
print "Full model..."
# Set seed
rnd_mcmc = np.random.RandomState(seed=SEED_MCMC)
data = dict(
N=N,
J=J,
X=X,
y=y,
j_ind=j_ind + 1,
mu_phi=m0,
Omega_phi=Q0.T # Q0 transposed in order to get C-contiguous
)
# Sample and extract parameters
model = load_stan('model')
fit = model.sampling(
data=data,
seed=(rnd_mcmc.randint(2**31 - 1) if TMP_FIX_32BIT else rnd_mcmc),
chains=CHAINS,
iter=ITER,
warmup=WARMUP,
thin=THIN)
samp = fit.extract(pars='phi')['phi']
m_phi_full = samp.mean(axis=0)
var_phi_full = samp.var(axis=0, ddof=1)
print "Full model sampled:"
print " exp(phi) = {}" \
.format(np.array2string(np.exp(m_phi_full), precision=1))
print "True values:"
print " exp(phi) = {}".format([MU, TAU, BETA, SIGMA])
# ------------------------------------------------------
# Save results
# ------------------------------------------------------
np.savez(filename,
seed_data=SEED_DATA,
seed_mcmc=SEED_MCMC,
J=J,
K=K,
Nj=Nj,
N=N,
dphi=dphi,
m0=M0,
V0=V0,
phi_true=phi_true,
m_phi_full=m_phi_full,
var_phi_full=var_phi_full)