def _test_scalar_hp_inference(view,
prior_fn,
w,
grid_min,
grid_max,
grid_n,
likelihood_model,
scalar_hp_key,
burnin=1000,
nsamples=1000,
every=10,
trials=100,
places=2):
"""
view must be 1D
"""
r = rng()
hparams = {0: {scalar_hp_key: (prior_fn, w)}}
def score_fn(scalar):
d = latent.get_feature_hp(0)
prev_scalar = d[scalar_hp_key]
d[scalar_hp_key] = scalar
latent.set_feature_hp(0, d)
score = prior_fn(scalar) + latent.score_data(0, None, r)
d[scalar_hp_key] = prev_scalar
latent.set_feature_hp(0, d)
return score
defn = model_definition(len(view), [likelihood_model])
latent = initialize(defn, view, r=r)
model = bind(latent, view)
def sample_fn():
for _ in xrange(every):
slice_hp(model, r, hparams=hparams)
return latent.get_feature_hp(0)[scalar_hp_key]
for _ in xrange(burnin):
slice_hp(model, r, hparams=hparams)
print 'finished burnin of', burnin, 'iterations'
print 'grid_min', grid_min, 'grid_max', grid_max
assert_1d_cont_dist_approx_emp(sample_fn,
score_fn,
grid_min,
grid_max,
grid_n,
trials,
nsamples,
places)
def _test_scalar_hp_inference(view,
prior_fn,
w,
grid_min,
grid_max,
grid_n,
likelihood_model,
scalar_hp_key,
burnin=1000,
nsamples=1000,
every=10,
trials=100,
places=2):
"""
view must be 1D
"""
r = rng()
hparams = {0: {scalar_hp_key: (prior_fn, w)}}
def score_fn(scalar):
d = latent.get_feature_hp(0)
prev_scalar = d[scalar_hp_key]
d[scalar_hp_key] = scalar
latent.set_feature_hp(0, d)
score = prior_fn(scalar) + latent.score_data(0, None, r)
d[scalar_hp_key] = prev_scalar
latent.set_feature_hp(0, d)
return score
defn = model_definition(len(view), [likelihood_model])
latent = initialize(defn, view, r=r)
model = bind(latent, view)
def sample_fn():
for _ in xrange(every):
slice_hp(model, r, hparams=hparams)
return latent.get_feature_hp(0)[scalar_hp_key]
for _ in xrange(burnin):
slice_hp(model, r, hparams=hparams)
print 'finished burnin of', burnin, 'iterations'
print 'grid_min', grid_min, 'grid_max', grid_max
assert_1d_cont_dist_approx_emp(sample_fn, score_fn, grid_min, grid_max,
grid_n, trials, nsamples, places)
def test_kernel_slice_hp_noninform():
def init_inf_kernel_state_fn(s):
hparams = {
0: {
('alpha', 'beta'): (log_noninformative_beta_prior, 1.0),
}
}
return hparams
def prior_fn(raw):
return log_noninformative_beta_prior(raw['alpha'], raw['beta'])
kernel_fn = lambda s, arg, rng: slice_hp(s, rng, hparams=arg)
_test_kernel_slice_hp(initialize, init_inf_kernel_state_fn, prior_fn,
numpy_dataview, bind, kernel_fn,
'grid_slice_hp_noninform_samples.pdf', rng())
def test_kernel_slice_cluster_hp():
prior_fn = log_exponential(1.5)
def init_inf_kernel_state_fn(s):
cparam = {'alpha': (prior_fn, 1.)}
return cparam
kernel_fn = lambda s, arg, rng: slice_hp(s, rng, cparam=arg)
grid_min, grid_max, grid_n = 0.0, 50., 100
_test_cluster_hp_inference(initialize,
prior_fn,
grid_min,
grid_max,
grid_n,
numpy_dataview,
bind,
init_inf_kernel_state_fn,
kernel_fn,
map_actual_postprocess_fn=lambda x: x,
prng=rng())
def test_kernel_slice_hp():
indiv_prior_fn = log_exponential(1.2)
def init_inf_kernel_state_fn(s):
hparams = {
0: {
'alpha': (indiv_prior_fn, 1.5),
'beta': (indiv_prior_fn, 1.5),
}
}
return hparams
def prior_fn(raw):
return indiv_prior_fn(raw['alpha']) + indiv_prior_fn(raw['beta'])
kernel_fn = lambda s, arg, rng: slice_hp(s, rng, hparams=arg)
_test_kernel_slice_hp(initialize, init_inf_kernel_state_fn, prior_fn,
numpy_dataview, bind, kernel_fn,
'grid_slice_hp_samples.pdf', rng())
def test_kernel_slice_hp_noninform():
def init_inf_kernel_state_fn(s):
hparams = {
0: {
('alpha', 'beta'): (log_noninformative_beta_prior, 1.0),
}
}
return hparams
def prior_fn(raw):
return log_noninformative_beta_prior(raw['alpha'], raw['beta'])
kernel_fn = lambda s, arg, rng: slice_hp(s, rng, hparams=arg)
_test_kernel_slice_hp(initialize,
init_inf_kernel_state_fn,
prior_fn,
numpy_dataview,
bind,
kernel_fn,
'grid_slice_hp_noninform_samples.pdf',
rng())
def test_kernel_slice_cluster_hp():
prior_fn = log_exponential(1.5)
def init_inf_kernel_state_fn(s):
cparam = {'alpha': (prior_fn, 1.)}
return cparam
kernel_fn = lambda s, arg, rng: slice_hp(s, rng, cparam=arg)
grid_min, grid_max, grid_n = 0.0, 50., 100
_test_cluster_hp_inference(initialize,
prior_fn,
grid_min,
grid_max,
grid_n,
numpy_dataview,
bind,
init_inf_kernel_state_fn,
kernel_fn,
map_actual_postprocess_fn=lambda x: x,
prng=rng())
def test_kernel_slice_hp():
indiv_prior_fn = log_exponential(1.2)
def init_inf_kernel_state_fn(s):
hparams = {
0: {
'alpha': (indiv_prior_fn, 1.5),
'beta': (indiv_prior_fn, 1.5),
}
}
return hparams
def prior_fn(raw):
return indiv_prior_fn(raw['alpha']) + indiv_prior_fn(raw['beta'])
kernel_fn = lambda s, arg, rng: slice_hp(s, rng, hparams=arg)
_test_kernel_slice_hp(initialize,
init_inf_kernel_state_fn,
prior_fn,
numpy_dataview,
bind,
kernel_fn,
'grid_slice_hp_samples.pdf',
rng())
def sample_fn():
for _ in xrange(every):
slice_hp(model, r, hparams=hparams)
return latent.get_feature_hp(0)[scalar_hp_key]
def sample_fn():
for _ in xrange(every):
slice_hp(model, r, hparams=hparams)
return latent.get_feature_hp(0)[scalar_hp_key]
