def _test_cluster_hp_inference(initialize_fn,
prior_fn,
grid_min,
grid_max,
grid_n,
dataview,
bind_fn,
init_inf_kernel_state_fn,
inf_kernel_fn,
map_actual_postprocess_fn,
prng,
burnin=1000,
nsamples=1000,
skip=10,
trials=100,
places=2):
print '_test_cluster_hp_inference: burnin', burnin, 'nsamples', nsamples, \
'skip', skip, 'trials', trials, 'places', places
N = 1000
D = 5
# create random binary data, doesn't really matter what the values are
Y = np.random.random(size=(N, D)) < 0.5
Y = np.array([tuple(y) for y in Y], dtype=[('', np.bool)] * D)
view = dataview(Y)
defn = model_definition(N, [bb] * D)
latent = initialize_fn(defn, view, r=prng)
model = bind_fn(latent, view)
def score_alpha(alpha):
prev_alpha = latent.get_cluster_hp()['alpha']
latent.set_cluster_hp({'alpha': alpha})
score = prior_fn(alpha) + latent.score_assignment()
latent.set_cluster_hp({'alpha': prev_alpha})
return score
def sample_fn():
for _ in xrange(skip - 1):
inf_kernel_fn(model, opaque, prng)
inf_kernel_fn(model, opaque, prng)
return latent.get_cluster_hp()['alpha']
alpha0 = np.random.uniform(grid_min, grid_max)
print 'start alpha:', alpha0
latent.set_cluster_hp({'alpha': alpha0})
opaque = init_inf_kernel_state_fn(latent)
for _ in xrange(burnin):
inf_kernel_fn(model, opaque, prng)
print 'finished burnin of', burnin, 'iterations'
print 'grid_min', grid_min, 'grid_max', grid_max
assert_1d_cont_dist_approx_emp(sample_fn, score_alpha, 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_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_cluster_hp_inference(initialize_fn,
prior_fn,
grid_min,
grid_max,
grid_n,
dataview,
bind_fn,
init_inf_kernel_state_fn,
inf_kernel_fn,
map_actual_postprocess_fn,
prng,
burnin=1000,
nsamples=1000,
skip=10,
trials=100,
places=2):
print '_test_cluster_hp_inference: burnin', burnin, 'nsamples', nsamples, \
'skip', skip, 'trials', trials, 'places', places
N = 1000
D = 5
# create random binary data, doesn't really matter what the values are
Y = np.random.random(size=(N, D)) < 0.5
Y = np.array([tuple(y) for y in Y], dtype=[('', np.bool)] * D)
view = dataview(Y)
defn = model_definition(N, [bb] * D)
latent = initialize_fn(defn, view, r=prng)
model = bind_fn(latent, view)
def score_alpha(alpha):
prev_alpha = latent.get_cluster_hp()['alpha']
latent.set_cluster_hp({'alpha': alpha})
score = prior_fn(alpha) + latent.score_assignment()
latent.set_cluster_hp({'alpha': prev_alpha})
return score
def sample_fn():
for _ in xrange(skip - 1):
inf_kernel_fn(model, opaque, prng)
inf_kernel_fn(model, opaque, prng)
return latent.get_cluster_hp()['alpha']
alpha0 = np.random.uniform(grid_min, grid_max)
print 'start alpha:', alpha0
latent.set_cluster_hp({'alpha': alpha0})
opaque = init_inf_kernel_state_fn(latent)
for _ in xrange(burnin):
inf_kernel_fn(model, opaque, prng)
print 'finished burnin of', burnin, 'iterations'
print 'grid_min', grid_min, 'grid_max', grid_max
assert_1d_cont_dist_approx_emp(sample_fn,
score_alpha,
grid_min,
grid_max,
grid_n,
trials,
nsamples,
places)
