def test_pickling(self):
model_builder = hmm.pssm.ModelBuilder(3)
model = model_builder.create_background_mosaic_model(3, .01, 1.0)
model_builder.dump_background_mosaic_model(model, 'model.pickle')
model_copy = model_builder.load_background_mosaic_model('model.pickle')
converted_model = hmm.model_states_2_model(model)
converted_model_copy = hmm.model_states_2_model(model_copy)
assert converted_model.A.all() == converted_model_copy.A.all()
assert converted_model.B.all() == converted_model_copy.B.all()
assert converted_model.pi.all() == converted_model_copy.pi.all()
def test_pickling(self):
model_builder = hmm.pssm.ModelBuilder(3)
model = model_builder.create_background_mosaic_model(3, .01, 1.0)
model_builder.dump_background_mosaic_model(model, 'model.pickle')
model_copy = model_builder.load_background_mosaic_model('model.pickle')
converted_model = hmm.model_states_2_model(model)
converted_model_copy = hmm.model_states_2_model(model_copy)
assert converted_model.A.all() == converted_model_copy.A.all()
assert converted_model.B.all() == converted_model_copy.B.all()
assert converted_model.pi.all() == converted_model_copy.pi.all()
def test_order_0_states(self):
model_builder = hmm.pssm.ModelBuilder(2)
model = model_builder.create_background_mosaic_model(3, .01, 1.0)
positive = model_builder.add_order_0_parameterised_state(model, emission_dist=[.1,.2,.3,.4])
negative = model_builder.add_order_0_rev_comp_state(model, positive)
B = hmm.model_states_2_model(model).B[3:]
assert infpy.check_is_close_2(B[0,0], B[1,3])
assert infpy.check_is_close_2(B[0,1], B[1,2])
assert infpy.check_is_close_2(B[0,2], B[1,1])
assert infpy.check_is_close_2(B[0,3], B[1,0])
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_order_0_states(self):
model_builder = hmm.pssm.ModelBuilder(2)
model = model_builder.create_background_mosaic_model(3, .01, 1.0)
positive = model_builder.add_order_0_parameterised_state(
model, emission_dist=[.1, .2, .3, .4])
negative = model_builder.add_order_0_rev_comp_state(model, positive)
B = hmm.model_states_2_model(model).B[3:]
assert infpy.check_is_close_2(B[0, 0], B[1, 3])
assert infpy.check_is_close_2(B[0, 1], B[1, 2])
assert infpy.check_is_close_2(B[0, 2], B[1, 1])
assert infpy.check_is_close_2(B[0, 3], B[1, 0])
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 learn_model(self):
"""
Creates several models, trains them using Baum-Welch and returns the best
"""
self.logger.info('Learning initial model')
# work out the parameters for baum-welch and run it
self.known_bases = sum(self.converter.num_known_bases_order_n(seq) for seq in self.order_n_seqs)
self.default_tolerance = 1e-6 * self.known_bases
# create models
models = [
hmm.model_states_2_model(self.pssm_traits.new_model())
for i in xrange(self.num_models)
]
LLs = [ (model,self.train_model(model)[0]) for model in models ]
# which had the best log likelihood?
best = max(LLs, key=lambda x:x[1])
# print best, LLs
return best
def learn_model(self):
"""
Creates several models, trains them using Baum-Welch and returns the best
"""
self.logger.info('Learning initial model')
# work out the parameters for baum-welch and run it
self.known_bases = sum(
self.converter.num_known_bases_order_n(seq)
for seq in self.order_n_seqs)
self.default_tolerance = 1e-6 * self.known_bases
# create models
models = [
hmm.model_states_2_model(self.pssm_traits.new_model())
for i in xrange(self.num_models)
]
LLs = [(model, self.train_model(model)[0]) for model in models]
# which had the best log likelihood?
best = max(LLs, key=lambda x: x[1])
# print best, LLs
return best
TRANSITION:4;5;1
TRANSITION:5;6;1
TRANSITION:6;7;1
TRANSITION:7;8;1
# EMISSIONS:i;b1,b2,b3,b4 - where p(state i emits base i) = bi
EMISSIONS:0;.5,.5,0,0
EMISSIONS:1;0,0,.5,.5
EMISSIONS:2;1,0,0,0
EMISSIONS:3;0,0,0,1
EMISSIONS:4;0,0,1,0
EMISSIONS:5;0,1,0,0
EMISSIONS:6;1,0,0,0
EMISSIONS:7;1,0,0,0
EMISSIONS:8;0,0,1,0
"""
model = build_model( to_parse.split('\n') )
m = hmm.model_states_2_model(model)
if False:
hmm.graph_as_svg(
m,
'test',
'model_io_test',
graphing_keywords = {
'show_dists' : lambda l: True,
'state_labels' : None
},
neato_properties = { '-Elen' : '2' }
)
with open('model.mdl', 'w') as f: write_model(model, f)
[ 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 [0, 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
except:
print 'Could not set process priority'
def bw_callback(LL): pass; #print 'LL: %.3f' % LL
order = 0
num_mosaics = 1
cache = BackgroundModelCache()
print ' fragment order # mosaics LL/base'
for fragment in all_fragments:
seqs = seqs_for_fragment(fragment)
model_builder = hmm.pssm.ModelBuilder(order)
training_sequences = [ model_builder.converter.to_order_n(s) for s in seqs ]
known_bases = sum(model_builder.converter.num_known_bases_order_n(s) for s in training_sequences)
print '%10s %10d %10d' % (fragment, order, num_mosaics),
try:
model = cache.get_model(order, num_mosaics, fragment)
except:
model_by_states = model_builder.create_background_mosaic_model(num_mosaics, 0.01, 100.0)
model = hmm.model_states_2_model(model_by_states)
tolerance = 1e-4 * known_bases
model.baum_welch(
training_sequences,
tolerance = tolerance,
callback = bw_callback
)
cache.save_model(model, order, num_mosaics, fragment)
LL = sum(model.forward(s)[0] for s in training_sequences)
print LL/known_bases
TRANSITION:3;5;.9
TRANSITION:4;5;1
TRANSITION:5;6;1
TRANSITION:6;7;1
TRANSITION:7;8;1
# EMISSIONS:i;b1,b2,b3,b4 - where p(state i emits base i) = bi
EMISSIONS:0;.5,.5,0,0
EMISSIONS:1;0,0,.5,.5
EMISSIONS:2;1,0,0,0
EMISSIONS:3;0,0,0,1
EMISSIONS:4;0,0,1,0
EMISSIONS:5;0,1,0,0
EMISSIONS:6;1,0,0,0
EMISSIONS:7;1,0,0,0
EMISSIONS:8;0,0,1,0
"""
model = build_model(to_parse.split('\n'))
m = hmm.model_states_2_model(model)
if False:
hmm.graph_as_svg(m,
'test',
'model_io_test',
graphing_keywords={
'show_dists': lambda l: True,
'state_labels': None
},
neato_properties={'-Elen': '2'})
with open('model.mdl', 'w') as f:
write_model(model, f)