def test_runner_multiprocessing_convergence():
domains = [4]
defn = model_definition(domains, [((0, 0), bb)])
prng = rng()
relations, posterior = data_with_posterior(defn, prng)
views = map(numpy_dataview, relations)
latents = [model.initialize(defn, views, prng)
for _ in xrange(mp.cpu_count())]
kc = [('assign', range(len(domains)))]
runners = [runner.runner(defn, views, latent, kc) for latent in latents]
r = parallel.runner(runners)
r.run(r=prng, niters=10000) # burnin
product_assignments = tuple(map(list, map(permutation_iter, domains)))
idmap = {C: i for i, C in enumerate(it.product(*product_assignments))}
def sample_iter():
r.run(r=prng, niters=10)
for latent in r.get_latents():
key = tuple(tuple(permutation_canonical(latent.assignments(i)))
for i in xrange(len(domains)))
yield idmap[key]
ref = [None]
def sample_fn():
if ref[0] is None:
ref[0] = sample_iter()
try:
return next(ref[0])
except StopIteration:
ref[0] = None
return sample_fn()
assert_discrete_dist_approx(sample_fn, posterior, ntries=100, kl_places=2)
def test_simple():
domains = [5, 6]
relations = [((0, 1), bb)]
relsize = (domains[0], domains[1])
raw_data = [
ma.array(np.random.choice([False, True], size=relsize),
mask=np.random.choice([False, True], size=relsize))
]
def csr(raw):
n, m = raw.shape
def indices():
for i, j in it.product(range(n), range(m)):
if not raw.mask[i, j]:
yield i, j
data = [raw[i, j] for i, j in indices()]
i = list(map(op.itemgetter(0), indices()))
j = list(map(op.itemgetter(1), indices()))
return coo_matrix((data, (i, j)), shape=raw.shape).tocsr()
defn = model_definition(domains, relations)
data = map(numpy_dataview, raw_data)
sparse_data = map(sparse_2d_dataview, map(csr, raw_data))
r = rng()
s = initialize(defn, data, r=r)
assert s and bind(s, 0, data) and bind(s, 1, data)
s1 = initialize(defn, sparse_data, r=r)
assert s1 and bind(s1, 0, sparse_data) and bind(s1, 1, sparse_data)
def entity_data_positions(domain, eid):
def f(domains, reln):
for pos0 in xrange(reln.shape[0]):
for pos1 in xrange(reln.shape[1]):
if reln.mask[pos0, pos1]:
continue
if (domains[0] == domain and pos0 == eid) or \
(domains[1] == domain and pos1 == eid):
yield [pos0, pos1]
return list(
it.chain.from_iterable(
f(domains, reln)
for (domains, _), reln in zip(relations, raw_data)))
def test(s):
for did, nentities in enumerate(domains):
for eid in xrange(nentities):
a = entity_data_positions(did, eid)
b = s.entity_data_positions(did, eid, data)
assert sorted(a) == sorted(b)
test(s)
test(s1)
def test_slice_theta_irm():
N = 10
defn = model_definition([N], [((0, 0), bbnc)])
data = np.random.random(size=(N, N)) < 0.8
view = numpy_dataview(data)
r = rng()
prior = {'alpha': 1.0, 'beta': 9.0}
s = initialize(
defn,
[view],
r=r,
cluster_hps=[{'alpha': 2.0}],
relation_hps=[prior],
domain_assignments=[[0] * N])
bs = bind(s, 0, [view])
params = {0: {'p': 0.05}}
heads = len([1 for y in data.flatten() if y])
tails = len([1 for y in data.flatten() if not y])
alpha1 = prior['alpha'] + heads
beta1 = prior['beta'] + tails
def sample_fn():
theta(bs, r, tparams=params)
return s.get_suffstats(0, [0, 0])['p']
rv = beta(alpha1, beta1)
assert_1d_cont_dist_approx_sps(sample_fn, rv, nsamples=50000)
def test_state_pickle():
defn = model_definition([5], [((0, 0), bb)])
r = rng()
relations = toy_dataset(defn)
views = map(numpy_dataview, relations)
s1 = model.initialize(defn, views, r)
s2 = pickle.loads(pickle.dumps(s1))
_assert_structure_equals(defn, s1, s2, views, r)
def test_state_pickle():
defn = model_definition([5], [((0, 0), bb)])
r = rng()
relations = toy_dataset(defn)
views = map(numpy_dataview, relations)
s1 = model.initialize(defn, views, r)
s2 = pickle.loads(pickle.dumps(s1))
_assert_structure_equals(defn, s1, s2, views, r)
def test_runner_default_kernel_config_grid():
defn = model_definition([10, 10], [((0, 0), bb), ((0, 1), nich)])
def kc_fn(defn):
return list(it.chain(
runner.default_assign_kernel_config(defn),
runner.default_relation_hp_kernel_config(defn)))
_test_runner_simple(defn, kc_fn)
def test_model_definition_pickle():
defn = model_definition([10, 12], [((0, 0), bb), ((1, 0), niw(3))])
bstr = pickle.dumps(defn)
defn1 = pickle.loads(bstr)
assert_list_equal(defn.domains(), defn1.domains())
assert_equals(defn.relations(), defn1.relations())
zipped_models = zip(defn.relation_models(), defn1.relation_models())
for model, model1 in zipped_models:
assert_equals(model.name(), model1.name())
def test_runner_default_kernel_config_grid():
defn = model_definition([10, 10], [((0, 0), bb), ((0, 1), nich)])
def kc_fn(defn):
return list(
it.chain(runner.default_assign_kernel_config(defn),
runner.default_relation_hp_kernel_config(defn)))
_test_runner_simple(defn, kc_fn)
def test_simple():
domains = [5, 6]
relations = [((0, 1), bb)]
relsize = (domains[0], domains[1])
raw_data = [
ma.array(np.random.choice([False, True], size=relsize), mask=np.random.choice([False, True], size=relsize))
]
def csr(raw):
n, m = raw.shape
def indices():
for i, j in it.product(range(n), range(m)):
if not raw.mask[i, j]:
yield i, j
data = [raw[i, j] for i, j in indices()]
i = list(map(op.itemgetter(0), indices()))
j = list(map(op.itemgetter(1), indices()))
return coo_matrix((data, (i, j)), shape=raw.shape).tocsr()
defn = model_definition(domains, relations)
data = map(numpy_dataview, raw_data)
sparse_data = map(sparse_2d_dataview, map(csr, raw_data))
r = rng()
s = initialize(defn, data, r=r)
assert s and bind(s, 0, data) and bind(s, 1, data)
s1 = initialize(defn, sparse_data, r=r)
assert s1 and bind(s1, 0, sparse_data) and bind(s1, 1, sparse_data)
def entity_data_positions(domain, eid):
def f(domains, reln):
for pos0 in xrange(reln.shape[0]):
for pos1 in xrange(reln.shape[1]):
if reln.mask[pos0, pos1]:
continue
if (domains[0] == domain and pos0 == eid) or (domains[1] == domain and pos1 == eid):
yield [pos0, pos1]
return list(it.chain.from_iterable(f(domains, reln) for (domains, _), reln in zip(relations, raw_data)))
def test(s):
for did, nentities in enumerate(domains):
for eid in xrange(nentities):
a = entity_data_positions(did, eid)
b = s.entity_data_positions(did, eid, data)
assert sorted(a) == sorted(b)
test(s)
test(s1)
def test_runner_multiprocessing():
defn = model_definition([10, 10], [((0, 0), bb), ((0, 1), nich)])
views = map(numpy_dataview, toy_dataset(defn))
kc = runner.default_kernel_config(defn)
prng = rng()
latents = [model.initialize(defn, views, prng)
for _ in xrange(mp.cpu_count())]
runners = [runner.runner(defn, views, latent, kc) for latent in latents]
r = parallel.runner(runners)
# check it is restartable
r.run(r=prng, niters=10)
r.run(r=prng, niters=10)
def test_state_copy():
defn = model_definition([5], [((0, 0), bb)])
r = rng()
relations = toy_dataset(defn)
views = map(numpy_dataview, relations)
s1 = model.initialize(defn, views, r)
s2 = copy.copy(s1)
assert_is_not(s1, s2)
_assert_structure_equals(defn, s1, s2, views, r)
s2 = copy.deepcopy(s1)
assert_is_not(s1, s2)
_assert_structure_equals(defn, s1, s2, views, r)
def test_runner_multiprocessing():
defn = model_definition([10, 10], [((0, 0), bb), ((0, 1), nich)])
views = map(numpy_dataview, toy_dataset(defn))
kc = runner.default_kernel_config(defn)
prng = rng()
latents = [
model.initialize(defn, views, prng) for _ in xrange(mp.cpu_count())
]
runners = [runner.runner(defn, views, latent, kc) for latent in latents]
r = parallel.runner(runners)
# check it is restartable
r.run(r=prng, niters=10)
r.run(r=prng, niters=10)
def test_state_copy():
defn = model_definition([5], [((0, 0), bb)])
r = rng()
relations = toy_dataset(defn)
views = map(numpy_dataview, relations)
s1 = model.initialize(defn, views, r)
s2 = copy.copy(s1)
assert_is_not(s1, s2)
_assert_structure_equals(defn, s1, s2, views, r)
s2 = copy.deepcopy(s1)
assert_is_not(s1, s2)
_assert_structure_equals(defn, s1, s2, views, r)
def latent(groups, entities_per_group, features, r):
N = groups * entities_per_group
defn = model_definition([N], [((0, 0), bb)] * features)
# generate fake data
views = []
for i in xrange(features):
Y = np.random.random(size=(N, N)) <= 0.5
view = numpy_dataview(Y)
views.append(view)
# assign entities to their respective groups
assignment = [[g] * entities_per_group for g in xrange(groups)]
assignment = list(it.chain.from_iterable(assignment))
latent = bind(
initialize(defn, views, r, domain_assignments=[assignment]), 0, views)
latent.create_group(r) # perftest() doesnt modify group assignments
return latent
def latent(groups, entities_per_group, features, r):
N = groups * entities_per_group
defn = model_definition([N], [((0, 0), bb)] * features)
# generate fake data
views = []
for i in xrange(features):
Y = np.random.random(size=(N, N)) <= 0.5
view = numpy_dataview(Y)
views.append(view)
# assign entities to their respective groups
assignment = [[g] * entities_per_group for g in xrange(groups)]
assignment = list(it.chain.from_iterable(assignment))
latent = bind(initialize(defn, views, r, domain_assignments=[assignment]),
0, views)
latent.create_group(r) # perftest() doesnt modify group assignments
return latent
def test_runner_default_kernel_config_convergence():
domains = [4]
defn = model_definition(domains, [((0, 0), bb)])
prng = rng()
relations, posterior = data_with_posterior(defn, prng)
views = map(numpy_dataview, relations)
latent = model.initialize(defn, views, prng)
r = runner.runner(defn, views, latent, [('assign', range(len(domains)))])
r.run(r=prng, niters=1000) # burnin
product_assignments = tuple(map(list, map(permutation_iter, domains)))
idmap = {C: i for i, C in enumerate(it.product(*product_assignments))}
def sample_fn():
r.run(r=prng, niters=10)
new_latent = r.get_latent()
key = tuple(tuple(permutation_canonical(new_latent.assignments(i)))
for i in xrange(len(domains)))
return idmap[key]
assert_discrete_dist_approx(sample_fn, posterior, ntries=100)
def test_runner_default_kernel_config_convergence():
domains = [4]
defn = model_definition(domains, [((0, 0), bb)])
prng = rng()
relations, posterior = data_with_posterior(defn, prng)
views = map(numpy_dataview, relations)
latent = model.initialize(defn, views, prng)
r = runner.runner(defn, views, latent, [('assign', range(len(domains)))])
r.run(r=prng, niters=1000) # burnin
product_assignments = tuple(map(list, map(permutation_iter, domains)))
idmap = {C: i for i, C in enumerate(it.product(*product_assignments))}
def sample_fn():
r.run(r=prng, niters=10)
new_latent = r.get_latent()
key = tuple(
tuple(permutation_canonical(new_latent.assignments(i)))
for i in xrange(len(domains)))
return idmap[key]
assert_discrete_dist_approx(sample_fn, posterior, ntries=100)
def test_runner_multiprocessing_convergence():
domains = [4]
defn = model_definition(domains, [((0, 0), bb)])
prng = rng()
relations, posterior = data_with_posterior(defn, prng)
views = map(numpy_dataview, relations)
latents = [
model.initialize(defn, views, prng) for _ in xrange(mp.cpu_count())
]
kc = [('assign', range(len(domains)))]
runners = [runner.runner(defn, views, latent, kc) for latent in latents]
r = parallel.runner(runners)
r.run(r=prng, niters=10000) # burnin
product_assignments = tuple(map(list, map(permutation_iter, domains)))
idmap = {C: i for i, C in enumerate(it.product(*product_assignments))}
def sample_iter():
r.run(r=prng, niters=10)
for latent in r.get_latents():
key = tuple(
tuple(permutation_canonical(latent.assignments(i)))
for i in xrange(len(domains)))
yield idmap[key]
ref = [None]
def sample_fn():
if ref[0] is None:
ref[0] = sample_iter()
try:
return next(ref[0])
except StopIteration:
ref[0] = None
return sample_fn()
assert_discrete_dist_approx(sample_fn, posterior, ntries=100, kl_places=2)
def test_runner_default_kernel_config_nonconj():
defn = model_definition([10, 10], [((0, 0), bbnc), ((0, 1), nich)])
kc_fn = runner.default_kernel_config
_test_runner_simple(defn, kc_fn)
# 2. load the data
# 3. initialize the model
# 4. define the runners (MCMC chains)
# 5. run the runners
# In[5]:
from microscopes.common.rng import rng
from microscopes.common.relation.dataview import numpy_dataview
from microscopes.models import bb as beta_bernoulli
from microscopes.irm.definition import model_definition
from microscopes.irm import model, runner, query
from microscopes.kernels import parallel
from microscopes.common.query import groups, zmatrix_heuristic_block_ordering, zmatrix_reorder
defn = model_definition([N], [((0, 0), beta_bernoulli)])
views = [numpy_dataview(communications_relation)]
prng = rng()
nchains = 1
latents = [model.initialize(defn, views, r=prng, cluster_hps=[{'alpha':1}]) for _ in xrange(nchains)]
kc = runner.default_assign_kernel_config(defn)
print kc
r = runner.runner(defn, views, latents[0], kc)
# ##From here, we can finally run each chain of the sampler 1000 times
# In[ ]:
start = time.time()
def infinite_relational_model(corr_matrix, lag_matrix, threshold, sampled_coords, window_size):
import numpy as np
import math
import json
import time
import itertools as it
from multiprocessing import cpu_count
from microscopes.common.rng import rng
from microscopes.common.relation.dataview import numpy_dataview
from microscopes.models import bb as beta_bernoulli
from microscopes.irm.definition import model_definition
from microscopes.irm import model, runner, query
from microscopes.kernels import parallel
from microscopes.common.query import groups, zmatrix_heuristic_block_ordering, zmatrix_reorder
cluster_matrix = []
graph = []
# calculate graph
for row in corr_matrix:
graph_row = []
for corr in row:
if corr < threshold:
graph_row.append(False)
else:
graph_row.append(True)
graph.append(graph_row)
graph = np.array(graph, dtype=np.bool)
graph_size = len(graph)
# conduct Infinite Relational Model
defn = model_definition([graph_size], [((0, 0), beta_bernoulli)])
views = [numpy_dataview(graph)]
prng = rng()
nchains = cpu_count()
latents = [model.initialize(defn, views, r=prng, cluster_hps=[{'alpha':1e-3}]) for _ in xrange(nchains)]
kc = runner.default_assign_kernel_config(defn)
runners = [runner.runner(defn, views, latent, kc) for latent in latents]
r = parallel.runner(runners)
start = time.time()
# r.run(r=prng, niters=1000)
# r.run(r=prng, niters=100)
r.run(r=prng, niters=20)
print ("inference took", time.time() - start, "seconds")
infers = r.get_latents()
clusters = groups(infers[0].assignments(0), sort=True)
ordering = list(it.chain.from_iterable(clusters))
z = graph.copy()
z = z[ordering]
z = z[:,ordering]
corr_matrix = corr_matrix[ordering]
corr_matrix = corr_matrix[:,ordering]
lag_matrix = lag_matrix[ordering]
lag_matrix = lag_matrix[:,ordering]
cluster_sampled_coords = np.array(sampled_coords)
cluster_sampled_coords = cluster_sampled_coords[ordering]
response_msg = {
'corrMatrix': corr_matrix.tolist(),
'lagMatrix': lag_matrix.tolist(),
'clusterMatrix': z.tolist(),
'clusterSampledCoords': cluster_sampled_coords.tolist(),
'nClusterList': [len(cluster) for cluster in clusters],
'ordering': ordering,
}
f = open("./expdata/clustermatrix-" + str(window_size) + ".json", "w")
json.dump(response_msg, f)
f.close()
return response_msg
# In[5]:
from microscopes.common.rng import rng
from microscopes.common.relation.dataview import numpy_dataview
from microscopes.models import bb as beta_bernoulli
from microscopes.irm.definition import model_definition
from microscopes.irm import model, runner, query
from microscopes.kernels import parallel
from microscopes.common.query import groups, zmatrix_heuristic_block_ordering, zmatrix_reorder
# ##Let's start by defining the model and loading the data
# In[6]:
defn = model_definition([N], [((0, 0), beta_bernoulli)])
views = [numpy_dataview(communications_relation)]
prng = rng()
# ##Next, let's initialize the model and define the runners.
#
# ##These runners are our MCMC chains. We'll use `cpu_count` to define our number of chains.
# In[ ]:
nchains = cpu_count()
latents = [
model.initialize(defn, views, r=prng, cluster_hps=[{
'alpha': 1e-3
}]) for _ in xrange(nchains)
]
def test_runner_default_kernel_config_nonconj():
defn = model_definition([10, 10], [((0, 0), bbnc), ((0, 1), nich)])
kc_fn = runner.default_kernel_config
_test_runner_simple(defn, kc_fn)
