def test_traits(self):
from hmm.pssm import create_background_model, PssmTraits, seq_to_numpy
from infpy import check_is_close_2
p_binding_site = .01
num_background_states = 2
emission_dists = [
[1., 0., 0., 0.],
[0., 1., 0., 0.],
[0., 1., 0., 0.],
[1., 0., 0., 0.],
[0., 0., 1., 0.],
[0., 0., 0., 1.],
[0., 0., 0., 1.],
[0., 0., 0., 1.],
[0., 0., 1., 0.],
[0., 1., 0., 0.],
[1., 0., 0., 0.],
[0., 1., 0., 0.],
[0., 0., 0., 1.],
]
K = len(emission_dists)
test_seq = 'accagtttgcact' # matches dist above
test_seq_order_0 = seq_to_numpy(test_seq)
# for various different orders
for order in [1, 2]:
# build a model of distribution above
traits = PssmTraits(K,
p_binding_site,
order,
num_background_states,
create_background_model,
emission_dists=emission_dists)
model = traits.new_model()
converted = hmm.model_states_2_model(model)
B = converted.B
# check the reverse complement states are correct
for n in xrange(model.N):
for o in xrange(model.M):
rev_comp_state, rev_comp_obs = traits.get_non_reverse_complement(
n, o)
assert check_is_close_2(
B[rev_comp_state, rev_comp_obs],
B[n, o]), ('%d,%d %d,%d: %f %f' %
(rev_comp_state, rev_comp_obs, n, o,
B[rev_comp_state, rev_comp_obs], B[n, o]))
# check viterbi gives correct result
test_seq_order_n = converted.converter.to_order_n(test_seq_order_0)
LL, states = converted.viterbi(test_seq_order_n)
for i, state in enumerate(states):
assert state == num_background_states + i
def test_traits(self):
from hmm.pssm import create_background_model, PssmTraits, seq_to_numpy
from infpy import check_is_close_2
p_binding_site = .01
num_background_states = 2
emission_dists = [
[ 1., 0., 0., 0. ],
[ 0., 1., 0., 0. ],
[ 0., 1., 0., 0. ],
[ 1., 0., 0., 0. ],
[ 0., 0., 1., 0. ],
[ 0., 0., 0., 1. ],
[ 0., 0., 0., 1. ],
[ 0., 0., 0., 1. ],
[ 0., 0., 1., 0. ],
[ 0., 1., 0., 0. ],
[ 1., 0., 0., 0. ],
[ 0., 1., 0., 0. ],
[ 0., 0., 0., 1. ],
]
K = len(emission_dists)
test_seq = 'accagtttgcact' # matches dist above
test_seq_order_0 = seq_to_numpy(test_seq)
# for various different orders
for order in [1, 2]:
# build a model of distribution above
traits = PssmTraits(K, p_binding_site, order, num_background_states, create_background_model, emission_dists=emission_dists)
model = traits.new_model()
converted = hmm.model_states_2_model(model)
B = converted.B
# check the reverse complement states are correct
for n in xrange(model.N):
for o in xrange(model.M):
rev_comp_state, rev_comp_obs = traits.get_non_reverse_complement(n,o)
assert check_is_close_2(B[rev_comp_state,rev_comp_obs], B[n,o]), ('%d,%d %d,%d: %f %f' % (rev_comp_state,rev_comp_obs,n,o,B[rev_comp_state,rev_comp_obs],B[n,o]))
# check viterbi gives correct result
test_seq_order_n = converted.converter.to_order_n(test_seq_order_0)
LL, states = converted.viterbi(test_seq_order_n)
for i, state in enumerate(states):
assert state == num_background_states+i
if random() > .5:
seq[start:start + K] = site
else:
seq[start:start + K] = rev_comp_site
return seq
p_binding_site = .01
order = 1
num_background_states = 2
# try to learn pssm for these sequences
seqs = [gen_sequence() for i in xrange(num_seqs)]
def bw_callback(LL):
print LL
traits = PssmTraits(K, p_binding_site, order, num_background_states,
create_background_model)
pssm_learner = PssmLearner(seqs, traits)
#pssm_learner.bw_callback = bw_callback
multiple_learner = MultiplePssmLearner(pssm_learner, 'test_multiple')
found, discarded = multiple_learner.learn_pssms(max_to_find=2,
max_attempts=2)
print 'Found %d models, discarded %d' % (len(found), len(discarded))
for model, sites, counts in found:
print '# sites: %d' % len(sites)
dist = traits.pssm_dist(model)
IC = information_content(dist)
print 'IC: %f' % IC
#image.show()
def gen_sequence():
seq = random_sequence(seq_length)
if random() > .5:
seq[start:start+K] = site
else:
seq[start:start+K] = rev_comp_site
return seq
p_binding_site = .01
order = 1
num_background_states = 2
# try to learn pssm for these sequences
seqs = [ gen_sequence() for i in xrange(num_seqs) ]
def bw_callback(LL):
print LL
traits = PssmTraits(K, p_binding_site, order, num_background_states, create_background_model)
pssm_learner = PssmLearner(seqs, traits)
#pssm_learner.bw_callback = bw_callback
multiple_learner = MultiplePssmLearner(pssm_learner, 'test_multiple')
found, discarded = multiple_learner.learn_pssms(max_to_find = 2, max_attempts = 2)
print 'Found %d models, discarded %d' % (len(found), len(discarded))
for model, sites, counts in found:
print '# sites: %d' % len(sites)
dist = traits.pssm_dist(model)
IC = information_content(dist)
print 'IC: %f' % IC
#image.show()
