def test_compare():
from vsm import corpus
n = 4
c = corpus.random_corpus(1e3, 20, 1, 10, tok_name='sentences')
c = c.to_maskedcorpus()
c.mask_terms(['0'])
em = be.BeagleEnvironment()
em.train(c, n_columns=n)
env_matrix = em.matrix
psi = rand_pt_unit_sphere(n)
rand_perm = two_rand_perm(n)
print 'Training single processor model'
sm = BeagleOrderSingle()
sm.train(c, psi=psi, env_matrix=env_matrix, rand_perm=rand_perm)
print 'Training multiprocessor model'
mm = BeagleOrderMulti()
mm.train(c, psi=psi, env_matrix=env_matrix, rand_perm=rand_perm)
assert np.allclose(sm.matrix, mm.matrix, atol=1e-07)
def train(self,
corpus,
env_matrix=None,
psi=None,
rand_perm=None,
n_columns=2048,
lmda = 7,
tok_name='sentences'):
if env_matrix is None:
m = be.BeagleEnvironment()
m.train(corpus, n_columns=n_columns)
env_matrix = m.matrix[:, :]
b_conv = mk_b_conv(n_columns, rand_perm)
if psi is None:
psi = rand_pt_unit_sphere(env_matrix.shape[1])
self.matrix = np.zeros_like(env_matrix)
if isinstance(corpus, corp.MaskedCorpus):
sents = corpus.view_tokens(tok_name, unmask=True)
else:
sents = corpus.view_tokens(tok_name)
for sent in sents:
for i in xrange(sent.shape[0]):
if corpus.terms[sent[i]] is not np.ma.masked:
left = [env_matrix[term] for term in sent[:i]]
right = [env_matrix[term] for term in sent[i+1:]]
sent_vecs = np.array(left + [psi] + right)
conv_ngrams = reduce_ngrams(b_conv, sent_vecs, lmda, i)
ord_vec = np.sum(conv_ngrams, axis=0)
self.matrix[sent[i], :] += ord_vec
def train(self,
corpus,
env_matrix=None,
psi=None,
rand_perm=None,
n_columns=2048,
lmda = 7,
tok_name='sentences',
n_processes=20):
global _lmda
_lmda = lmda
del lmda
global _b_conv
_b_conv = mk_b_conv(n_columns, rand_perm)
del rand_perm
if env_matrix is None:
m = be.BeagleEnvironment()
m.train(corpus, n_columns=n_columns)
env_matrix = m.matrix[:]
del m
global _shape
_shape = env_matrix.shape
global _env_matrix
print 'Copying env matrix to shared mp array'
_env_matrix = mp.Array('f', env_matrix.size, lock=False)
_env_matrix[:] = env_matrix.ravel()[:]
del env_matrix
global _psi
_psi = mp.Array('f', _shape[1], lock=False)
if psi is None:
_psi[:] = rand_pt_unit_sphere(_shape[1])[:]
else:
_psi[:] = psi[:]
del psi
print 'Gathering tokens over which to map'
if isinstance(corpus, corp.MaskedCorpus):
sents = corpus.view_tokens(tok_name, unmask=True)
else:
sents = corpus.view_tokens(tok_name)
k = len(sents) / (n_processes - 1)
sent_lists = [sents[i * k:(i + 1) * k]
for i in xrange(n_processes - 1)]
sent_lists.append(sents[(i + 1) * k:])
tmp_dir = tempfile.mkdtemp()
tmp_files = [os.path.join(tmp_dir, 'tmp_' + str(i))
for i in xrange(len(sent_lists))]
sent_lists = [(sent_lists[i], tmp_files[i])
for i in xrange(len(sent_lists))]
del sents
global _terms
_terms = corpus.terms
del corpus
try:
# For debugging
# tmp_files = map(mpfn, sent_lists)
print 'Forking'
p = mp.Pool()
tmp_files = p.map(mpfn, sent_lists, 1)
p.close()
print 'Reducing'
self.matrix = np.zeros(_shape)
for filename in tmp_files:
result = np.memmap(filename, mode='r',
shape=_shape, dtype=np.float32)
self.matrix[:, :] += result[:, :]
self.matrix = np.float32(self.matrix)
finally:
print 'Removing', tmp_dir
shutil.rmtree(tmp_dir)
