def check_summarize(name):
check_cm(name)
cm = ComponentModel(name)
x = []
for _ in range(COUNT):
x.append(cm.sample_data())
summarize(name, x)
def check_dpm(impl, data_count, beta0):
check_cm(impl)
data = histogram(np.random.randint(50, size=data_count))
data = dict([(str(i), obs) for i, obs in enumerate(data)])
betas = dict([(str(i), (1 - beta0) / len(data))
for i, obs in enumerate(data)])
hp = {
'gamma': 1.,
'alpha': 1.,
'beta0': beta0,
'betas': betas
}
ss = {'counts': data}
cm = ComponentModel(
impl,
ss=ss,
hp=hp)
samples = cm.sample_data(SAMPS)
counts = list(histogram([y for y in samples if y != -1]))
probs = list(np.exp([cm.pred_prob(x) for x in range(max(samples) + 1)]))
counts.append(len([y for y in samples if y == -1]))
probs.append(np.exp(cm.pred_prob(-1)))
assert_less(1 - sum(probs), THRESH)
probs, counts = zip(*sorted(zip(probs, counts), reverse=True)[:TOPN])
p = mgof(probs, counts, SAMPS, truncated=True)
assert_greater(p, THRESH)
def check_generate(name):
check_cm(name)
cm = ComponentModel(name)
cm.realize_hp()
params = cm.generate_post()
b = BasicDistribution(name, pm=params)
b.sample_data()
def check_ss_io(name):
check_cm(name)
cm = ComponentModel(name)
cm.realize_hp()
assert_equal(ComponentModel(name, ss=cm.dump_ss()).dump_ss(), cm.dump_ss())
cm.add_data(cm.sample_data())
assert_equal(ComponentModel(name, ss=cm.dump_ss()).dump_ss(), cm.dump_ss())
def check_ss_io(name):
check_cm(name)
cm = ComponentModel(name)
cm.realize_hp()
assert_equal(ComponentModel(name, ss=cm.dump_ss()).dump_ss(), cm.dump_ss())
cm.add_data(cm.sample_data())
assert_equal(ComponentModel(name, ss=cm.dump_ss()).dump_ss(), cm.dump_ss())
def check_summarize(name):
check_cm(name)
cm = ComponentModel(name)
x = []
for _ in range(COUNT):
x.append(cm.sample_data())
summarize(name, x)
def check_generate(name):
check_cm(name)
cm = ComponentModel(name)
cm.realize_hp()
params = cm.generate_post()
b = BasicDistribution(name, pm=params)
b.sample_data()
def check_dd(impl, data_count, D):
check_cm(impl)
data = histogram(np.random.randint(D, size=data_count), bin_count=D)
cm = ComponentModel(
impl,
ss={'counts': data},
p={'D': D})
cm.realize_hp()
_check_discrete(cm)
def check_gp(impl, data_count, lam):
check_cm(impl)
data = np.random.poisson(lam, size=data_count)
ss = {
'n': data_count,
'sum': np.sum(data),
'log_prod': np.sum(np.log(data))
}
cm = ComponentModel(impl, ss=ss)
_check_discrete(cm)
def check_sums(name):
check_cm(name)
cm = ComponentModel(name)
cm.realize_hp()
values = [cm.sample_data() for _ in range(COUNT)]
score = 0.
for value in values:
score += cm.pred_prob(value)
cm.add_data(value)
assert_almost_equal(score, cm.data_prob())
def check_sample_post_seed(name):
check_cm(name)
seed(0)
cm1 = ComponentModel(name)
post_values1 = [cm1.sample_post() for _ in range(COUNT)]
seed(0)
cm2 = ComponentModel(name)
post_values2 = [cm2.sample_post() for _ in range(COUNT)]
for i in range(COUNT):
assert_array_almost_equal(post_values1[i], post_values2[i])
def check_sums(name):
check_cm(name)
cm = ComponentModel(name)
cm.realize_hp()
values = [cm.sample_data() for _ in range(COUNT)]
score = 0.
for value in values:
score += cm.pred_prob(value)
cm.add_data(value)
assert_almost_equal(score, cm.data_prob())
def check_gp(impl, data_count, lam):
check_cm(impl)
data = np.random.poisson(lam, size=data_count)
ss = {
'n': data_count,
'sum': np.sum(data),
'log_prod': np.sum(np.log(data))
}
cm = ComponentModel(impl, ss=ss)
_check_discrete(cm)
def check_sample_post_seed(name):
check_cm(name)
seed(0)
cm1 = ComponentModel(name)
post_values1 = [cm1.sample_post() for _ in range(COUNT)]
seed(0)
cm2 = ComponentModel(name)
post_values2 = [cm2.sample_post() for _ in range(COUNT)]
for i in range(COUNT):
assert_array_almost_equal(post_values1[i], post_values2[i])
def check_probs(a, b):
check_cm(a)
check_cm(b)
a = ComponentModel(a)
a.realize_hp()
b = ComponentModel(b, hp=a.dump_hp())
dps = [a.sample_data() for _ in range(DPS)]
for y in dps:
assert_almost_equal(a.data_prob(), b.data_prob())
assert_almost_equal(a.pred_prob(y), b.pred_prob(y))
a.add_data(y)
b.add_data(y)
def check_sample_data_seed(name):
check_cm(name)
n = 10
seed(0)
cm1 = ComponentModel(name)
cm1.realize_hp()
data_values1 = [cm1.sample_data() for _ in range(n)]
seed(0)
cm2 = ComponentModel(name)
cm2.realize_hp()
data_values2 = [cm2.sample_data() for _ in range(n)]
for i in range(n):
assert_almost_equal(data_values1[i], data_values2[i])
def check_sample_data_seed(name):
check_cm(name)
n = 10
seed(0)
cm1 = ComponentModel(name)
cm1.realize_hp()
data_values1 = [cm1.sample_data() for _ in range(n)]
seed(0)
cm2 = ComponentModel(name)
cm2.realize_hp()
data_values2 = [cm2.sample_data() for _ in range(n)]
for i in range(n):
assert_almost_equal(data_values1[i], data_values2[i])
def check_ss(a, b):
check_cm(a)
check_cm(b)
a = ComponentModel(a)
a.realize_hp()
b = ComponentModel(b, hp=a.dump_hp())
dps = [a.sample_data() for _ in range(DPS)]
assert_equal(a.dump_ss(), b.dump_ss())
for y in dps:
a.add_data(y)
b.add_data(y)
assert_close(a.dump_ss(), b.dump_ss())
for y in dps:
a.remove_data(y)
b.remove_data(y)
assert_close(a.dump_ss(), b.dump_ss())
def check_exchangeable(name):
check_cm(name)
cm = ComponentModel(name)
cm.realize_hp()
values = [cm.sample_data() for _ in range(COUNT)]
p1 = permutation(COUNT)
p2 = permutation(COUNT)
for i in range(COUNT):
cm.add_data(values[p1[i]])
prob1 = cm.data_prob()
for i in range(COUNT):
cm.remove_data(values[p1[i]])
assert_almost_equal(cm.data_prob(), 0.)
for i in range(COUNT):
cm.add_data(values[p2[i]])
prob2 = cm.data_prob()
assert_almost_equal(prob1, prob2)
def check_exchangeable(name):
check_cm(name)
cm = ComponentModel(name)
cm.realize_hp()
values = [cm.sample_data() for _ in range(COUNT)]
p1 = permutation(COUNT)
p2 = permutation(COUNT)
for i in range(COUNT):
cm.add_data(values[p1[i]])
prob1 = cm.data_prob()
for i in range(COUNT):
cm.remove_data(values[p1[i]])
assert_almost_equal(cm.data_prob(), 0.)
for i in range(COUNT):
cm.add_data(values[p2[i]])
prob2 = cm.data_prob()
assert_almost_equal(prob1, prob2)
def check_nich(impl, data_count, mean, std):
check_cm(impl)
ss = None
if data_count:
data = np.random.normal(mean, std, size=data_count)
ss = {'count': data_count, 'mean': data.mean(), 'variance': data.var()}
cm = ComponentModel(impl, ss=ss)
samples = cm.sample_data(SAMPS)
counts, bin_ranges = bin_samples(samples)
#use of quadrature is unfortunate but for now
#it's the easiest way to score bins and seems to work
pdf = lambda x: np.exp(cm.pred_prob(x))
probs = [quad(pdf, m, M, epsabs=0., epsrel=1e-6)[0] for m, M in bin_ranges]
assert_less(1 - sum(probs), THRESH)
probs, counts = zip(*sorted(zip(probs, counts), reverse=True)[:TOPN])
p = mgof(probs, counts, SAMPS, truncated=True)
assert_greater(p, THRESH)
def check_dpm(impl, data_count, beta0):
check_cm(impl)
data = histogram(np.random.randint(50, size=data_count))
data = dict([(str(i), obs) for i, obs in enumerate(data)])
betas = dict([(str(i), (1 - beta0) / len(data))
for i, obs in enumerate(data)])
hp = {'gamma': 1., 'alpha': 1., 'beta0': beta0, 'betas': betas}
ss = {'counts': data}
cm = ComponentModel(impl, ss=ss, hp=hp)
samples = cm.sample_data(SAMPS)
counts = list(histogram([y for y in samples if y != -1]))
probs = list(np.exp([cm.pred_prob(x) for x in range(max(samples) + 1)]))
counts.append(len([y for y in samples if y == -1]))
probs.append(np.exp(cm.pred_prob(-1)))
assert_less(1 - sum(probs), THRESH)
probs, counts = zip(*sorted(zip(probs, counts), reverse=True)[:TOPN])
p = mgof(probs, counts, SAMPS, truncated=True)
assert_greater(p, THRESH)
def test_vectorize():
for name in MODELS:
check_cm(name)
cm0 = ComponentModel(name)
cm0.realize_hp()
hp0 = cm0.dump_hp()
cms = [ComponentModel(name, hp=hp0) for _ in range(COMPS)]
for cm in cms:
dps = [cm.sample_data() for _ in range(DPS)]
for dp in dps:
cm.add_data(dp)
mod = cms[0].mod
hp = cms[0].hp
ss = [cm.ss for cm in cms]
for cm in cms:
y = cm.sample_data()
scores = numpy.zeros(COMPS)
mod.add_pred_probs(hp, ss, y, scores)
for cm, score in zip(cms, scores):
assert_almost_equal(score, cm.pred_prob(y))
def test_vectorize():
for name in MODELS:
check_cm(name)
cm0 = ComponentModel(name)
cm0.realize_hp()
hp0 = cm0.dump_hp()
cms = [ComponentModel(name, hp=hp0) for _ in range(COMPS)]
for cm in cms:
dps = [cm.sample_data() for _ in range(DPS)]
for dp in dps:
cm.add_data(dp)
mod = cms[0].mod
hp = cms[0].hp
ss = [cm.ss for cm in cms]
for cm in cms:
y = cm.sample_data()
scores = numpy.zeros(COMPS)
mod.add_pred_probs(hp, ss, y, scores)
for cm, score in zip(cms, scores):
assert_almost_equal(score, cm.pred_prob(y))
def check_nich(impl, data_count, mean, std):
check_cm(impl)
ss = None
if data_count:
data = np.random.normal(mean, std, size=data_count)
ss = {
'count': data_count,
'mean': data.mean(),
'variance': data.var()
}
cm = ComponentModel(impl, ss=ss)
samples = cm.sample_data(SAMPS)
counts, bin_ranges = bin_samples(samples)
#use of quadrature is unfortunate but for now
#it's the easiest way to score bins and seems to work
pdf = lambda x: np.exp(cm.pred_prob(x))
probs = [quad(pdf, m, M, epsabs=0., epsrel=1e-6)[0] for m, M in bin_ranges]
assert_less(1 - sum(probs), THRESH)
probs, counts = zip(*sorted(zip(probs, counts), reverse=True)[:TOPN])
p = mgof(probs, counts, SAMPS, truncated=True)
assert_greater(p, THRESH)
def check_hp_io(name):
check_cm(name)
cm = ComponentModel(name)
cm.realize_hp()
assert_equal(ComponentModel(name, hp=cm.dump_hp()).dump_hp(), cm.dump_hp())
def check_dd(impl, data_count, D):
check_cm(impl)
data = histogram(np.random.randint(D, size=data_count), bin_count=D)
cm = ComponentModel(impl, ss={'counts': data}, p={'D': D})
cm.realize_hp()
_check_discrete(cm)
def check_summarize_N(name):
check_cm(name)
cm = ComponentModel(name)
x = cm.sample_data(COUNT)
summarize(name, x)
def check_hp_io(name):
check_cm(name)
cm = ComponentModel(name)
cm.realize_hp()
assert_equal(ComponentModel(name, hp=cm.dump_hp()).dump_hp(), cm.dump_hp())
def check_summarize_N(name):
check_cm(name)
cm = ComponentModel(name)
x = cm.sample_data(COUNT)
summarize(name, x)
def check_hp(a, b):
check_cm(a)
check_cm(b)
a = ComponentModel(a)
b = ComponentModel(b)
assert_equal(a.dump_hp(), b.dump_hp())