def test_estep():
tstamps, Trace, previous_stamps, Count_zh, Count_sz, count_h, count_z, \
prob_topics_aux, Theta_zh, Psi_sz, hyper2id, source2id = \
dataio.initialize_trace(files.SIZE10, 2, 10)
kernel = NoopKernel()
kernel.build(Trace.shape[0], Count_zh.shape[1], np.zeros(0, dtype='d'))
alpha_zh = .1
beta_zs = .1
assert_equal(Count_zh.sum(), 10)
assert_equal(Count_sz.sum(), 20)
assert_equal(count_h[0], 4)
assert_equal(count_h[1], 4)
assert_equal(count_h[2], 2)
new_state = _learn._e_step(tstamps, Trace, previous_stamps, Count_zh, \
Count_sz, count_h, count_z, alpha_zh, beta_zs, prob_topics_aux, \
kernel)
assert_equal(count_h[0], 4)
assert_equal(count_h[1], 4)
assert_equal(count_h[2], 2)
assert_equal(Count_zh.sum(), 10)
assert_equal(Count_sz.sum(), 20)
def test_em():
tstamps, Trace, previous_stamps, Count_zh, Count_sz, count_h, count_z, \
prob_topics_aux, Theta_zh, Psi_sz, hyper2id, source2id = \
dataio.initialize_trace(files.SIZE10, 2, 10)
kernel = NoopKernel()
kernel.build(Trace.shape[0], Count_zh.shape[1], np.zeros(0, dtype='d'))
alpha_zh = .1
beta_zs = .1
assert (Theta_zh == 0).all()
assert (Psi_sz == 0).all()
old_Count_zh = Count_zh.copy()
old_Count_sz = Count_sz.copy()
old_count_h = count_h.copy()
old_count_z = count_z.copy()
_learn.em(tstamps, Trace, previous_stamps, Count_zh, Count_sz, count_h, \
count_z, alpha_zh, beta_zs, prob_topics_aux, Theta_zh, Psi_sz, \
10, 2, kernel)
assert (Theta_zh > 0).sum() > 0
assert (Psi_sz > 0).sum() > 0
assert_almost_equal(1, Theta_zh.sum(axis=0))
assert_almost_equal(1, Psi_sz.sum(axis=0))
assert (old_Count_zh != Count_zh).any()
assert (old_Count_sz != Count_sz).any()
assert (old_count_h == count_h).all() #the count_h should not change
def test_em():
tstamps, Trace, previous_stamps, Count_zh, Count_sz, count_h, count_z, \
prob_topics_aux, Theta_zh, Psi_sz, hyper2id, source2id = \
dataio.initialize_trace(files.SIZE10, 2, 10)
kernel = NoopKernel()
kernel.build(Trace.shape[0], Count_zh.shape[1], np.zeros(0, dtype='d'))
alpha_zh = .1
beta_zs = .1
assert (Theta_zh == 0).all()
assert (Psi_sz == 0).all()
old_Count_zh = Count_zh.copy()
old_Count_sz = Count_sz.copy()
old_count_h = count_h.copy()
old_count_z = count_z.copy()
_learn.em(tstamps, Trace, previous_stamps, Count_zh, Count_sz, count_h, \
count_z, alpha_zh, beta_zs, prob_topics_aux, Theta_zh, Psi_sz, \
10, 2, kernel)
assert (Theta_zh > 0).sum() > 0
assert (Psi_sz > 0).sum() > 0
assert_almost_equal(1, Theta_zh.sum(axis=0))
assert_almost_equal(1, Psi_sz.sum(axis=0))
assert (old_Count_zh != Count_zh).any()
assert (old_Count_sz != Count_sz).any()
assert (old_count_h == count_h).all() #the count_h should not change
def test_estep():
tstamps, Trace, previous_stamps, Count_zh, Count_sz, count_h, count_z, \
prob_topics_aux, Theta_zh, Psi_sz, hyper2id, source2id = \
dataio.initialize_trace(files.SIZE10, 2, 10)
kernel = NoopKernel()
kernel.build(Trace.shape[0], Count_zh.shape[1], np.zeros(0, dtype='d'))
alpha_zh = .1
beta_zs = .1
assert_equal(Count_zh.sum(), 10)
assert_equal(Count_sz.sum(), 20)
assert_equal(count_h[0], 4)
assert_equal(count_h[1], 4)
assert_equal(count_h[2], 2)
new_state = _learn._e_step(tstamps, Trace, previous_stamps, Count_zh, \
Count_sz, count_h, count_z, alpha_zh, beta_zs, prob_topics_aux, \
kernel)
assert_equal(count_h[0], 4)
assert_equal(count_h[1], 4)
assert_equal(count_h[2], 2)
assert_equal(Count_zh.sum(), 10)
assert_equal(Count_sz.sum(), 20)
def fit(trace_fpath, num_topics, alpha_zh, beta_zs, kernel, \
residency_priors, num_iter, burn_in, from_=0, to=np.inf):
'''
Learns the latent topics from a temporal hypergraph trace.
Node-Sherlock is a EM algorithm in which the E-Step is a gibbs sample update,
thus the reason we use a `num_iter` and `burn_in` approach, instead of the
usual convergence approach.
Parameters
----------
trace_fpath : str
The path of the trace. Each line should be a \
(timestamp, hypernode, source, destination) where the \
timestamp is a long (seconds or milliseconds from epoch).
num_topics : int
The number of latent spaces to learn
alpha_zh : float
The value of the alpha_zh hyperparameter
beta_zs : float
The value of the beta_zs (beta) hyperaparameter
kernel : Kernel object
The kernel to use
residency_priors : array of float
The kernel hyper parameters
num_iter : int
The number of iterations to learn the model from
burn_in : int
The burn_in of the chain
Returns
-------
A dictionary with the results.
'''
Dts, Trace, previous_stamps, Count_zh, Count_sz, \
count_h, count_z, prob_topics_aux, Theta_zh, Psi_sz, \
hyper2id, source2id = \
dataio.initialize_trace(trace_fpath, num_topics, num_iter, \
from_, to)
em(Dts, Trace, previous_stamps, Count_zh, \
Count_sz, count_h, count_z, alpha_zh, beta_zs, \
prob_topics_aux, Theta_zh, Psi_sz, num_iter, \
burn_in, kernel)
rv = prepare_results(trace_fpath, num_topics, alpha_zh, beta_zs, \
kernel, residency_priors, num_iter, burn_in, Dts, Trace, \
Count_zh, Count_sz, count_h, \
count_z, prob_topics_aux, Theta_zh, Psi_sz, hyper2id, \
source2id, from_, to)
rv['algorithm'] = np.asarray(['serial gibbs + em'])
return rv
def test_split():
tstamps, Trace, previous_stamps, Count_zh, Count_sz, count_h, count_z, \
prob_topics_aux, Theta_zh, Psi_sz, hyper2id, source2id = \
dataio.initialize_trace(files.SIZE10, 2, 10)
alpha_zh, beta_zs, a_ptz, b_ptz = [0.1] * 4
ll_per_z = np.zeros(2, dtype='f8')
Trace[:, -1] = 0
previous_stamps._clear()
tstamps = np.array([[1.0, 2.0, 3.0, 4.0, 5.0, 100, 200, 300, 400, 500]]).T
tstamps = np.array(tstamps, order='C')
previous_stamps._extend(0, tstamps[:, 0])
Count_zh = np.zeros(shape=(1, Count_zh.shape[1]), dtype='i4')
Count_sz = np.zeros(shape=(Count_sz.shape[0], 1), dtype='i4')
count_z = np.zeros(shape=(1, ), dtype='i4')
fast_populate(Trace, Count_zh, Count_sz, count_h, count_z)
kernel = ECCDFKernel()
kernel.build(Trace.shape[0], Count_zh.shape[0], \
np.array([1.0, Count_zh.shape[0] - 1]))
ll_per_z = np.zeros(1, dtype='f8')
quality_estimate(tstamps, Trace, \
previous_stamps, Count_zh, Count_sz, count_h, \
count_z, alpha_zh, beta_zs, \
ll_per_z, np.arange(Trace.shape[0], dtype='i4'), \
kernel)
Trace_new, Count_zh_new, Count_sz_new, count_z_new, \
new_stamps, _ = \
dynamic.split(tstamps, Trace, previous_stamps, Count_zh, Count_sz, \
count_h, count_z, alpha_zh, beta_zs, \
ll_per_z, kernel, .5, 0)
assert_array_equal(Trace_new[:, -1], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
assert_array_equal(new_stamps._get_all(0), [1, 2, 3, 4, 5])
assert_array_equal(new_stamps._get_all(1), [100, 200, 300, 400, 500])
assert Count_zh_new.shape[0] > Count_zh.shape[0]
assert Count_zh_new.shape[1] == Count_zh.shape[1]
assert Count_zh_new[0].sum() == 5
assert Count_zh_new[1].sum() == 5
assert Count_sz_new.shape[0] == Count_sz.shape[0]
assert Count_sz_new.shape[1] > Count_sz.shape[1]
assert Count_sz_new[:, 0].sum() == 10
assert Count_sz_new[:, 1].sum() == 10
assert count_z_new.shape[0] > count_z.shape[0]
assert count_z_new[0] == 10
assert count_z_new[1] == 10
def test_split():
tstamps, Trace, previous_stamps, Count_zh, Count_sz, count_h, count_z, \
prob_topics_aux, Theta_zh, Psi_sz, hyper2id, source2id = \
dataio.initialize_trace(files.SIZE10, 2, 10)
alpha_zh, beta_zs, a_ptz, b_ptz = [0.1] * 4
ll_per_z = np.zeros(2, dtype='f8')
Trace[:, -1] = 0
previous_stamps._clear()
tstamps = np.array([[1.0, 2.0, 3.0, 4.0, 5.0, 100, 200, 300, 400, 500]]).T
tstamps = np.array(tstamps, order='C')
previous_stamps._extend(0, tstamps[:, 0])
Count_zh = np.zeros(shape=(1, Count_zh.shape[1]), dtype='i4')
Count_sz = np.zeros(shape=(Count_sz.shape[0], 1), dtype='i4')
count_z = np.zeros(shape=(1, ), dtype='i4')
fast_populate(Trace, Count_zh, Count_sz, count_h, count_z)
kernel = ECCDFKernel()
kernel.build(Trace.shape[0], Count_zh.shape[0], \
np.array([1.0, Count_zh.shape[0] - 1]))
ll_per_z = np.zeros(1, dtype='f8')
quality_estimate(tstamps, Trace, \
previous_stamps, Count_zh, Count_sz, count_h, \
count_z, alpha_zh, beta_zs, \
ll_per_z, np.arange(Trace.shape[0], dtype='i4'), \
kernel)
Trace_new, Count_zh_new, Count_sz_new, count_z_new, \
new_stamps, _ = \
dynamic.split(tstamps, Trace, previous_stamps, Count_zh, Count_sz, \
count_h, count_z, alpha_zh, beta_zs, \
ll_per_z, kernel, .5, 0)
assert_array_equal(Trace_new[:, -1], [0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
assert_array_equal(new_stamps._get_all(0), [1, 2, 3, 4, 5])
assert_array_equal(new_stamps._get_all(1), [100, 200, 300, 400, 500])
assert Count_zh_new.shape[0] > Count_zh.shape[0]
assert Count_zh_new.shape[1] == Count_zh.shape[1]
assert Count_zh_new[0].sum() == 5
assert Count_zh_new[1].sum() == 5
assert Count_sz_new.shape[0] == Count_sz.shape[0]
assert Count_sz_new.shape[1] > Count_sz.shape[1]
assert Count_sz_new[:, 0].sum() == 10
assert Count_sz_new[:, 1].sum() == 10
assert count_z_new.shape[0] > count_z.shape[0]
assert count_z_new[0] == 10
assert count_z_new[1] == 10
def test_correlate_all():
tstamps, Trace, previous_stamps, Count_zh, Count_sz, count_h, count_z, \
prob_topics_aux, Theta_zh, Psi_sz, hyper2id, source2id = \
dataio.initialize_trace(files.SIZE10, 2, 10)
C = dynamic.correlate_counts(Count_zh, Count_sz, count_h, count_z, .1, \
.1)
assert_equal((2, 2), C.shape)
assert C[0, 1] != 0
assert (np.tril(C) == 0).all()
def test_correlate_all():
tstamps, Trace, previous_stamps, Count_zh, Count_sz, count_h, count_z, \
prob_topics_aux, Theta_zh, Psi_sz, hyper2id, source2id = \
dataio.initialize_trace(files.SIZE10, 2, 10)
C = dynamic.correlate_counts(Count_zh, Count_sz, count_h, count_z, .1, \
.1)
assert_equal((2, 2), C.shape)
assert C[0, 1] != 0
assert (np.tril(C) == 0).all()
def test_merge():
tstamps, Trace, previous_stamps, Count_zh, Count_sz, count_h, count_z, \
prob_topics_aux, Theta_zh, Psi_sz, hyper2id, source2id = \
dataio.initialize_trace(files.SIZE10, 2, 10)
kernel = ECCDFKernel()
kernel.build(Trace.shape[0], Count_zh.shape[0], \
np.array([1.0, Count_zh.shape[0] - 1]))
Trace[:, 0] = 0
Trace[:, 1] = 0
Trace[:, 2] = 0
Trace[:, 3] = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
Count_sz[:] = 0
Count_zh[:] = 0
count_z[:] = 0
count_h[:] = 0
fast_populate(Trace, Count_zh, Count_sz, count_h, count_z)
C = dynamic.correlate_counts(Count_zh, Count_sz, count_h, count_z, .1, \
.1)
alpha_zh, beta_zs, beta_zd = [0.1] * 3
a_ptz = 1.0
b_ptz = Count_zh.shape[0] - 1
ll_per_z = np.zeros(2, dtype='f8')
quality_estimate(tstamps, Trace, \
previous_stamps, Count_zh, Count_sz, count_h, \
count_z, alpha_zh, beta_zd, ll_per_z, \
np.arange(Trace.shape[0], dtype='i4'), kernel)
Trace_new, Count_zh_new, Count_sz_new, \
count_z_new, new_stamps, _ = \
dynamic.merge(tstamps, Trace, previous_stamps, Count_zh, Count_sz, \
count_h, count_z, alpha_zh, beta_zs, ll_per_z, kernel)
print(Trace_new)
print(np.array(new_stamps._get_all(0)))
assert len(new_stamps._get_all(0)) == 10
assert Count_zh_new.shape[0] < Count_zh.shape[0]
assert Count_zh_new.shape[1] == Count_zh.shape[1]
assert Count_sz_new.shape[0] == Count_sz.shape[0]
assert Count_sz_new.shape[1] < Count_sz.shape[0]
assert count_z_new.shape[0] < count_z.shape[0]
def test_merge():
tstamps, Trace, previous_stamps, Count_zh, Count_sz, count_h, count_z, \
prob_topics_aux, Theta_zh, Psi_sz, hyper2id, source2id = \
dataio.initialize_trace(files.SIZE10, 2, 10)
kernel = ECCDFKernel()
kernel.build(Trace.shape[0], Count_zh.shape[0], \
np.array([1.0, Count_zh.shape[0] - 1]))
Trace[:, 0] = 0
Trace[:, 1] = 0
Trace[:, 2] = 0
Trace[:, 3] = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
Count_sz[:] = 0
Count_zh[:] = 0
count_z[:] = 0
count_h[:] = 0
fast_populate(Trace, Count_zh, Count_sz, count_h, count_z)
C = dynamic.correlate_counts(Count_zh, Count_sz, count_h, count_z, .1, \
.1)
alpha_zh, beta_zs, beta_zd = [0.1] * 3
a_ptz = 1.0
b_ptz = Count_zh.shape[0] - 1
ll_per_z = np.zeros(2, dtype='f8')
quality_estimate(tstamps, Trace, \
previous_stamps, Count_zh, Count_sz, count_h, \
count_z, alpha_zh, beta_zd, ll_per_z, \
np.arange(Trace.shape[0], dtype='i4'), kernel)
Trace_new, Count_zh_new, Count_sz_new, \
count_z_new, new_stamps, _ = \
dynamic.merge(tstamps, Trace, previous_stamps, Count_zh, Count_sz, \
count_h, count_z, alpha_zh, beta_zs, ll_per_z, kernel)
print(Trace_new)
print(np.array(new_stamps._get_all(0)))
assert len(new_stamps._get_all(0)) == 10
assert Count_zh_new.shape[0] < Count_zh.shape[0]
assert Count_zh_new.shape[1] == Count_zh.shape[1]
assert Count_sz_new.shape[0] == Count_sz.shape[0]
assert Count_sz_new.shape[1] < Count_sz.shape[0]
assert count_z_new.shape[0] < count_z.shape[0]
def test_sample():
tstamps, Trace, previous_stamps, Count_zh, Count_sz, count_h, count_z, \
prob_topics_aux, Theta_zh, Psi_sz, hyper2id, source2id = \
dataio.initialize_trace(files.SIZE10, 2, 10)
kernel = NoopKernel()
kernel.build(Trace.shape[0], Count_zh.shape[1], np.zeros(0, dtype='d'))
tstamp_idx = 3
hyper = Trace[tstamp_idx, 0]
source = Trace[tstamp_idx, 1]
dest = Trace[tstamp_idx, 2]
old_topic = Trace[tstamp_idx, 3]
new_topic = _learn._sample(tstamp_idx, tstamps, Trace, \
previous_stamps, Count_zh, Count_sz, count_h, \
count_z, .1, .1, prob_topics_aux, kernel)
assert new_topic <= 3
def test_sample():
tstamps, Trace, previous_stamps, Count_zh, Count_sz, count_h, count_z, \
prob_topics_aux, Theta_zh, Psi_sz, hyper2id, source2id = \
dataio.initialize_trace(files.SIZE10, 2, 10)
kernel = NoopKernel()
kernel.build(Trace.shape[0], Count_zh.shape[1], np.zeros(0, dtype='d'))
tstamp_idx = 3
hyper = Trace[tstamp_idx, 0]
source = Trace[tstamp_idx, 1]
dest = Trace[tstamp_idx, 2]
old_topic = Trace[tstamp_idx, 3]
new_topic = _learn._sample(tstamp_idx, tstamps, Trace, \
previous_stamps, Count_zh, Count_sz, count_h, \
count_z, .1, .1, prob_topics_aux, kernel)
assert new_topic <= 3
def test_initialize():
tstamps, Trace, previous_stamps, Count_zh, Count_oz, count_h, count_z, \
prob_topics_aux, Theta_zh, Psi_oz, hyper2id, obj2id = \
dataio.initialize_trace(files.SIZE10, 2, 10)
assert_equal(len(hyper2id), 3)
assert_equal(len(obj2id), 6)
assert_equal(Trace.shape[0], 10)
assert_equal(Trace.shape[1], 4)
for z in [0, 1]:
assert previous_stamps._size(z) > 0
assert_equal(count_h[0], 4)
assert_equal(count_h[1], 4)
assert_equal(count_h[2], 2)
assert_equal(count_z.sum(), 20) #depends on memory
#We can only test shapes and sum, since assignments are random
assert_equal(Count_zh.shape, (2, 3))
assert_equal(Count_oz.shape, (6, 2))
assert_equal(Count_zh.sum(), 10)
assert_equal(Count_oz.sum(), 20)
assert (prob_topics_aux == 0).all()
#Simple sanity check on topic assigmnets. Check if topics have valid
#ids and if count matches count matrix
from collections import Counter
c = Counter(Trace[:, -1])
for topic in c:
assert topic in [0, 1]
assert c[topic] == count_z[topic] / 2
def test_initialize():
tstamps, Trace, previous_stamps, Count_zh, Count_oz, count_h, count_z, \
prob_topics_aux, Theta_zh, Psi_oz, hyper2id, obj2id = \
dataio.initialize_trace(files.SIZE10, 2, 10)
assert_equal(len(hyper2id), 3)
assert_equal(len(obj2id), 6)
assert_equal(Trace.shape[0], 10)
assert_equal(Trace.shape[1], 4)
for z in [0, 1]:
assert previous_stamps._size(z) > 0
assert_equal(count_h[0], 4)
assert_equal(count_h[1], 4)
assert_equal(count_h[2], 2)
assert_equal(count_z.sum(), 20) #depends on memory
#We can only test shapes and sum, since assignments are random
assert_equal(Count_zh.shape, (2, 3))
assert_equal(Count_oz.shape, (6, 2))
assert_equal(Count_zh.sum(), 10)
assert_equal(Count_oz.sum(), 20)
assert (prob_topics_aux == 0).all()
#Simple sanity check on topic assigmnets. Check if topics have valid
#ids and if count matches count matrix
from collections import Counter
c = Counter(Trace[:, -1])
for topic in c:
assert topic in [0, 1]
assert c[topic] == count_z[topic] / 2
def fit(trace_fpath, num_topics, alpha_zh, beta_zs, kernel, \
residency_priors, num_iter, num_batches, mpi_mode, from_=0, to=np.inf):
'''
Learns the latent topics from a temporal hypergraph trace. Here we do a
asynchronous learning of the topics similar to AD-LDA, as well as the
dynamic topic expansion/pruing.
Parameters
----------
trace_fpath : str
The path of the trace. Each line should be a \
(timestamp, hypernode, source, destination) where the \
timestamp is a long (seconds or milliseconds from epoch).
num_topics : int
The number of latent spaces to learn
alpha_zh : float
The value of the alpha_zh hyperparameter
beta_zs : float
The value of the beta_zs (beta) hyperaparameter
kernel : Kernel object
The kernel to use
residency_priors : array of float
The kernel hyper parameters
num_iter : int
The number of iterations to learn the model from
num_batches : int
Defines the number of batches of size num_iter
Returns
-------
A dictionary with the results.
'''
assert num_batches >= 2
comm = MPI.COMM_WORLD
num_workers = comm.size - 1
Dts, Trace, previous_stamps, Count_zh, Count_sz, \
count_h, count_z, prob_topics_aux, Theta_zh, Psi_sz, \
hyper2id, source2id = \
dataio.initialize_trace(trace_fpath, num_topics, num_iter, \
from_, to)
if mpi_mode:
workloads = generate_workload(Count_zh.shape[1], num_workers, Trace)
all_idx = np.arange(Trace.shape[0], dtype='i4')
for batch in xrange(num_batches):
print('Now at batch', batch)
if mpi_mode:
for worker_id in xrange(1, num_workers + 1):
comm.send(num_iter, dest=worker_id, tag=Msg.LEARN.value)
dispatch_jobs(Dts, Trace, Count_zh, Count_sz, \
count_h, count_z, alpha_zh, beta_zs, kernel, \
residency_priors, workloads, num_workers, comm)
manage(comm, num_workers)
fetch_results(comm, num_workers, workloads, Dts, Trace, \
previous_stamps, Count_zh, Count_sz, count_h, \
count_z, alpha_zh, beta_zs, Theta_zh, Psi_sz, \
kernel)
else:
prob_topics_aux = np.zeros(Count_zh.shape[0], dtype='f8')
_learn.em(Dts, Trace, previous_stamps, Count_zh, Count_sz, \
count_h, count_z, alpha_zh, beta_zs, \
prob_topics_aux, Theta_zh, Psi_sz, num_iter, \
num_iter * 2, kernel, False)
print('Split')
ll_per_z = np.zeros(count_z.shape[0], dtype='f8')
_eval.quality_estimate(Dts, Trace, previous_stamps, \
Count_zh, Count_sz, count_h, count_z, alpha_zh, \
beta_zs, ll_per_z, all_idx, kernel)
Trace, Count_zh, Count_sz, count_z, previous_stamps, \
P = split(Dts, Trace, previous_stamps, Count_zh, \
Count_sz, count_h, count_z, alpha_zh, beta_zs, \
ll_per_z, kernel)
kernel = kernel.__class__()
kernel.build(Trace.shape[0], Count_zh.shape[0], residency_priors)
if residency_priors.shape[0] > 0:
kernel.update_state(P)
print('Merge')
ll_per_z = np.zeros(count_z.shape[0], dtype='f8')
_eval.quality_estimate(Dts, Trace, previous_stamps, \
Count_zh, Count_sz, count_h, count_z, alpha_zh, \
beta_zs, ll_per_z, all_idx, kernel)
Trace, Count_zh, Count_sz, count_z, previous_stamps, \
P = merge(Dts, Trace, previous_stamps, Count_zh, \
Count_sz, count_h, count_z, alpha_zh, beta_zs, \
ll_per_z, kernel)
kernel = kernel.__class__()
kernel.build(Trace.shape[0], Count_zh.shape[0], residency_priors)
if residency_priors.shape[0] > 0:
kernel.update_state(P)
Theta_zh = np.zeros(shape=Count_zh.shape, dtype='f8')
Psi_sz = np.zeros(shape=Count_sz.shape, dtype='f8')
if batch == num_batches - 1:
print('Computing probs')
_learn._aggregate(Count_zh, Count_sz, count_h, count_z, \
alpha_zh, beta_zs, Theta_zh, Psi_sz)
print('New nz', Count_zh.shape[0])
if mpi_mode:
for worker_id in xrange(1, num_workers + 1):
comm.send(num_iter, dest=worker_id, tag=Msg.STOP.value)
rv = prepare_results(trace_fpath, num_topics, alpha_zh, beta_zs, \
kernel, residency_priors, num_iter, -1, Dts, Trace, \
Count_zh, Count_sz, count_h, count_z, prob_topics_aux, Theta_zh, \
Psi_sz, hyper2id, source2id, from_, to)
rv['num_workers'] = np.asarray([num_workers])
rv['num_batches'] = np.asarray([num_batches])
rv['algorithm'] = np.asarray(['parallel dynamic'])
return rv
def test_initialize_limits():
tstamps, Trace, previous_stamps, Count_zh, Count_oz, count_h, count_z, \
prob_topics_aux, Theta_zh, Psi_oz, hyper2id, obj2id = \
dataio.initialize_trace(files.SIZE10, 2, 10, 2, 5)
assert len(tstamps) == 3
def fit(trace_fpath, num_topics, alpha_zh, beta_zs, kernel, residency_priors, \
num_iter, from_=0, to=np.inf):
'''
Learns the latent topics from a temporal hypergraph trace. Here we do a
asynchronous learning of the topics similar to AD-LDA. An even number of
threads is required.
Parameters
----------
trace_fpath : str
The path of the trace. Each line should be a
(timestamp, hypernode, source, destination) where the
timestamp is a long (seconds or milliseconds from epoch).
num_topics : int
The number of latent spaces to learn
alpha_zh : float
The value of the alpha_zh hyperparameter
beta_zs : float
The value of the beta_zs (beta) hyperaparameter
kernel : Kernel object
The kernel to use
residency_priors : array of float
The kernel hyper parameters
num_iter : int
The number of iterations to learn the model from
Returns
-------
A dictionary with the results.
'''
comm = MPI.COMM_WORLD
num_workers = comm.size - 1
Dts, Trace, previous_stamps, Count_zh, Count_sz, \
count_h, count_z, prob_topics_aux, Theta_zh, Psi_sz, \
hyper2id, source2id = \
dataio.initialize_trace(trace_fpath, num_topics, num_iter, \
from_, to)
for worker_id in xrange(1, num_workers + 1):
comm.send(num_iter, dest=worker_id, tag=Msg.LEARN.value)
workloads = generate_workload(Count_zh.shape[1], num_workers, Trace)
dispatch_jobs(Dts, Trace, Count_zh, Count_sz, count_h, \
count_z, alpha_zh, beta_zs, kernel, residency_priors, \
workloads, num_workers, comm)
manage(comm, num_workers)
fetch_results(comm, num_workers, workloads, Dts, Trace, previous_stamps, \
Count_zh, Count_sz, count_h, count_z, alpha_zh, \
beta_zs, Theta_zh, Psi_sz, kernel)
for worker_id in xrange(1, num_workers + 1):
comm.send(worker_id, dest=worker_id, tag=Msg.STOP.value)
rv = prepare_results(trace_fpath, num_topics, alpha_zh, beta_zs, \
kernel, residency_priors, num_iter, -1, Dts, \
Trace, Count_zh, Count_sz, count_h, \
count_z, prob_topics_aux, Theta_zh, Psi_sz, hyper2id, \
source2id, from_, to)
rv['num_workers'] = np.asarray([num_workers])
rv['algorithm'] = np.asarray(['parallel gibbs + em'])
return rv
def fit(trace_fpath, num_topics, alpha_zh, beta_zs, kernel, residency_priors, \
num_iter, from_=0, to=np.inf):
'''
Learns the latent topics from a temporal hypergraph trace. Here we do a
asynchronous learning of the topics similar to AD-LDA. An even number of
threads is required.
Parameters
----------
trace_fpath : str
The path of the trace. Each line should be a
(timestamp, hypernode, source, destination) where the
timestamp is a long (seconds or milliseconds from epoch).
num_topics : int
The number of latent spaces to learn
alpha_zh : float
The value of the alpha_zh hyperparameter
beta_zs : float
The value of the beta_zs (beta) hyperaparameter
kernel : Kernel object
The kernel to use
residency_priors : array of float
The kernel hyper parameters
num_iter : int
The number of iterations to learn the model from
Returns
-------
A dictionary with the results.
'''
comm = MPI.COMM_WORLD
num_workers = comm.size - 1
Dts, Trace, previous_stamps, Count_zh, Count_sz, \
count_h, count_z, prob_topics_aux, Theta_zh, Psi_sz, \
hyper2id, source2id = \
dataio.initialize_trace(trace_fpath, num_topics, num_iter, \
from_, to)
for worker_id in xrange(1, num_workers + 1):
comm.send(num_iter, dest=worker_id, tag=Msg.LEARN.value)
workloads = generate_workload(Count_zh.shape[1], num_workers, Trace)
dispatch_jobs(Dts, Trace, Count_zh, Count_sz, count_h, \
count_z, alpha_zh, beta_zs, kernel, residency_priors, \
workloads, num_workers, comm)
manage(comm, num_workers)
fetch_results(comm, num_workers, workloads, Dts, Trace, previous_stamps, \
Count_zh, Count_sz, count_h, count_z, alpha_zh, \
beta_zs, Theta_zh, Psi_sz, kernel)
for worker_id in xrange(1, num_workers + 1):
comm.send(worker_id, dest=worker_id, tag=Msg.STOP.value)
rv = prepare_results(trace_fpath, num_topics, alpha_zh, beta_zs, \
kernel, residency_priors, num_iter, -1, Dts, \
Trace, Count_zh, Count_sz, count_h, \
count_z, prob_topics_aux, Theta_zh, Psi_sz, hyper2id, \
source2id, from_, to)
rv['num_workers'] = np.asarray([num_workers])
rv['algorithm'] = np.asarray(['parallel gibbs + em'])
return rv
def test_initialize_limits():
tstamps, Trace, previous_stamps, Count_zh, Count_oz, count_h, count_z, \
prob_topics_aux, Theta_zh, Psi_oz, hyper2id, obj2id = \
dataio.initialize_trace(files.SIZE10, 2, 10, 2, 5)
assert len(tstamps) == 3
