def test_cond_pos_differ(self):
"""lnL should differ when motif probs are not multiplicative"""
dinuc_probs = {'AA': 0.088506666666666664, 'AC': 0.044746666666666664,
'GT': 0.056693333333333332, 'AG': 0.070199999999999999,
'CC': 0.048653333333333333, 'TT': 0.10678666666666667,
'CG': 0.0093600000000000003, 'GG': 0.049853333333333333,
'GC': 0.040253333333333335, 'AT': 0.078880000000000006,
'GA': 0.058639999999999998, 'TG': 0.081626666666666667,
'TA': 0.068573333333333333, 'CA': 0.06661333333333333,
'TC': 0.060866666666666666, 'CT': 0.069746666666666665}
mg = Nucleotide(motif_length=2, motif_probs=dinuc_probs,
mprob_model='monomer')
mg_lf = mg.makeLikelihoodFunction(self.tree)
mg_lf.setParamRule('length', is_independent=False, init=0.4)
mg_lf.setAlignment(self.aln)
cd = Nucleotide(motif_length=2, motif_probs=dinuc_probs,
mprob_model='conditional')
cd_lf = cd.makeLikelihoodFunction(self.tree)
cd_lf.setParamRule('length', is_independent=False, init=0.4)
cd_lf.setAlignment(self.aln)
self.assertNotAlmostEqual(mg_lf.getLogLikelihood(),
cd_lf.getLogLikelihood())
def test_lf_display(self):
"""str of likelihood functions should not fail"""
for (dummy, model) in self.ordered_by_complexity:
di = Nucleotide(motif_length=2, mprob_model=model)
di.adaptMotifProbs(self.cond_root_probs, auto=True)
lf = di.makeLikelihoodFunction(self.tree)
s = str(lf)
def test_lf_display(self):
"""str of likelihood functions should not fail"""
for (dummy, model) in self.ordered_by_complexity:
di = Nucleotide(motif_length=2, mprob_model=model)
di.adaptMotifProbs(self.cond_root_probs, auto=True)
lf = di.makeLikelihoodFunction(self.tree)
s = str(lf)
def test_get_statistics(self):
"""get statistics should correctly apply arguments"""
for (mprobs, model) in self.ordered_by_complexity:
di = Nucleotide(motif_length=2, motif_probs=mprobs,
mprob_model=model)
lf = di.makeLikelihoodFunction(self.tree)
for wm, wt in [(True, True), (True, False), (False, True),
(False, False)]:
stats = lf.getStatistics(with_motif_probs=wm, with_titles=wt)
def test_sim_alignment(self):
"""should be able to simulate an alignment under all models"""
for (mprobs, model) in self.ordered_by_complexity:
di = Nucleotide(motif_length=2, motif_probs=mprobs,
mprob_model=model)
lf = di.makeLikelihoodFunction(self.tree)
lf.setParamRule('length', is_independent=False, init=0.4)
lf.setAlignment(self.aln)
sim = lf.simulateAlignment()
def test_reconstruct_ancestor(self):
"""should be able to reconstruct ancestral sequences under all
models"""
for (mprobs, model) in self.ordered_by_complexity:
di = Nucleotide(motif_length=2, mprob_model=model)
di.adaptMotifProbs(mprobs, auto=True)
lf = di.makeLikelihoodFunction(self.tree)
lf.setParamRule('length', is_independent=False, init=0.4)
lf.setAlignment(self.aln)
ancestor = lf.reconstructAncestralSeqs()
def test_get_statistics(self):
"""get statistics should correctly apply arguments"""
for (mprobs, model) in self.ordered_by_complexity:
di = Nucleotide(motif_length=2,
motif_probs=mprobs,
mprob_model=model)
lf = di.makeLikelihoodFunction(self.tree)
for wm, wt in [(True, True), (True, False), (False, True),
(False, False)]:
stats = lf.getStatistics(with_motif_probs=wm, with_titles=wt)
def test_reconstruct_ancestor(self):
"""should be able to reconstruct ancestral sequences under all
models"""
for (mprobs, model) in self.ordered_by_complexity:
di = Nucleotide(motif_length=2, mprob_model=model)
di.adaptMotifProbs(mprobs, auto=True)
lf = di.makeLikelihoodFunction(self.tree)
lf.setParamRule('length', is_independent=False, init=0.4)
lf.setAlignment(self.aln)
ancestor = lf.reconstructAncestralSeqs()
def test_sim_alignment(self):
"""should be able to simulate an alignment under all models"""
for (mprobs, model) in self.ordered_by_complexity:
di = Nucleotide(motif_length=2,
motif_probs=mprobs,
mprob_model=model)
lf = di.makeLikelihoodFunction(self.tree)
lf.setParamRule('length', is_independent=False, init=0.4)
lf.setAlignment(self.aln)
sim = lf.simulateAlignment()
def test_getting_node_mprobs(self):
"""return correct motif probability vector for tree nodes"""
tree = LoadTree(treestring='(a:.2,b:.2,(c:.1,d:.1):.1)')
aln = LoadSeqs(data={
'a': 'TGTG',
'b': 'TGTG',
'c': 'TGTG',
'd': 'TGTG',
})
motifs = ['T', 'C', 'A', 'G']
aX = MotifChange(motifs[0], motifs[3], forward_only=True).aliased('aX')
bX = MotifChange(motifs[3], motifs[0], forward_only=True).aliased('bX')
edX = MotifChange(motifs[1], motifs[2],
forward_only=True).aliased('edX')
cX = MotifChange(motifs[2], motifs[1], forward_only=True).aliased('cX')
sm = Nucleotide(predicates=[aX, bX, edX, cX], equal_motif_probs=True)
lf = sm.makeLikelihoodFunction(tree)
lf.setParamRule('aX', edge='a', value=8.0)
lf.setParamRule('bX', edge='b', value=8.0)
lf.setParamRule('edX', edge='edge.0', value=2.0)
lf.setParamRule('cX', edge='c', value=0.5)
lf.setParamRule('edX', edge='d', value=4.0)
lf.setAlignment(aln)
# we construct the hand calc variants
mprobs = ones(4, float) * .25
a = make_p(.2, (0, 3), 8)
a = dot(mprobs, a)
b = make_p(.2, (3, 0), 8)
b = dot(mprobs, b)
e = make_p(.1, (1, 2), 2)
e = dot(mprobs, e)
c = make_p(.1, (2, 1), 0.5)
c = dot(e, c)
d = make_p(.1, (1, 2), 4)
d = dot(e, d)
prob_vectors = lf.getMotifProbsByNode()
self.assertFloatEqual(prob_vectors['a'].array, a)
self.assertFloatEqual(prob_vectors['b'].array, b)
self.assertFloatEqual(prob_vectors['c'].array, c)
self.assertFloatEqual(prob_vectors['d'].array, d)
self.assertFloatEqual(prob_vectors['edge.0'].array, e)
def test_results_different(self):
for (i, (mprobs, dummy)) in enumerate(self.ordered_by_complexity):
results = []
for (dummy, model) in self.ordered_by_complexity:
di = Nucleotide(motif_length=2, motif_probs=mprobs,
mprob_model=model)
lf = di.makeLikelihoodFunction(self.tree)
lf.setParamRule('length', is_independent=False, init=0.4)
lf.setAlignment(self.aln)
lh = lf.getLogLikelihood()
for other in results[:i]:
self.failIfAlmostEqual(other, lh, places=2)
for other in results[i:]:
self.assertFloatEqual(other, lh)
results.append(lh)
def test_getting_node_mprobs(self):
"""return correct motif probability vector for tree nodes"""
tree = LoadTree(treestring='(a:.2,b:.2,(c:.1,d:.1):.1)')
aln = LoadSeqs(data={
'a': 'TGTG',
'b': 'TGTG',
'c': 'TGTG',
'd': 'TGTG',
})
motifs = ['T', 'C', 'A', 'G']
aX = MotifChange(motifs[0], motifs[3], forward_only=True).aliased('aX')
bX = MotifChange(motifs[3], motifs[0], forward_only=True).aliased('bX')
edX = MotifChange(motifs[1], motifs[2], forward_only=True).aliased('edX')
cX = MotifChange(motifs[2], motifs[1], forward_only=True).aliased('cX')
sm = Nucleotide(predicates=[aX, bX, edX, cX], equal_motif_probs=True)
lf = sm.makeLikelihoodFunction(tree)
lf.setParamRule('aX', edge='a', value=8.0)
lf.setParamRule('bX', edge='b', value=8.0)
lf.setParamRule('edX', edge='edge.0', value=2.0)
lf.setParamRule('cX', edge='c', value=0.5)
lf.setParamRule('edX', edge='d', value=4.0)
lf.setAlignment(aln)
# we construct the hand calc variants
mprobs = ones(4, float) * .25
a = make_p(.2, (0,3), 8)
a = dot(mprobs, a)
b = make_p(.2, (3, 0), 8)
b = dot(mprobs, b)
e = make_p(.1, (1, 2), 2)
e = dot(mprobs, e)
c = make_p(.1, (2, 1), 0.5)
c = dot(e, c)
d = make_p(.1, (1, 2), 4)
d = dot(e, d)
prob_vectors = lf.getMotifProbsByNode()
self.assertFloatEqual(prob_vectors['a'].array, a)
self.assertFloatEqual(prob_vectors['b'].array, b)
self.assertFloatEqual(prob_vectors['c'].array, c)
self.assertFloatEqual(prob_vectors['d'].array, d)
self.assertFloatEqual(prob_vectors['edge.0'].array, e)
def test_results_different(self):
for (i, (mprobs, dummy)) in enumerate(self.ordered_by_complexity):
results = []
for (dummy, model) in self.ordered_by_complexity:
di = Nucleotide(motif_length=2,
motif_probs=mprobs,
mprob_model=model)
lf = di.makeLikelihoodFunction(self.tree)
lf.setParamRule('length', is_independent=False, init=0.4)
lf.setAlignment(self.aln)
lh = lf.getLogLikelihood()
for other in results[:i]:
self.assertNotAlmostEqual(other, lh, places=2)
for other in results[i:]:
self.assertFloatEqual(other, lh)
results.append(lh)
def test_newQ_is_nuc_process(self):
"""newQ is an extension of an independent nucleotide process"""
nuc = Nucleotide(motif_probs = self.asymm_nuc_probs)
new_di = Nucleotide(motif_length=2, mprob_model='monomer',
motif_probs = self.asymm_root_probs)
nuc_lf = nuc.makeLikelihoodFunction(self.tree)
new_di_lf = new_di.makeLikelihoodFunction(self.tree)
# newQ branch length is exactly motif_length*nuc branch length
nuc_lf.setParamRule('length', is_independent=False, init=0.2)
new_di_lf.setParamRule('length', is_independent=False, init=0.4)
nuc_lf.setAlignment(self.aln)
new_di_lf.setAlignment(self.aln)
self.assertFloatEqual(nuc_lf.getLogLikelihood(),
new_di_lf.getLogLikelihood())
def compare_models(motif_probs, motif_length):
# if the 1st and 2nd position motifs are independent of each other
# then conditional is the same as positional
ps = Nucleotide(motif_length=motif_length, motif_probs=motif_probs,
mprob_model='monomers')
cd = Nucleotide(motif_length=motif_length,motif_probs=motif_probs,
mprob_model='conditional')
ps_lf = ps.makeLikelihoodFunction(self.tree)
ps_lf.setParamRule('length', is_independent=False, init=0.4)
ps_lf.setAlignment(self.aln)
cd_lf = cd.makeLikelihoodFunction(self.tree)
cd_lf.setParamRule('length', is_independent=False, init=0.4)
cd_lf.setAlignment(self.aln)
self.assertFloatEqual(cd_lf.getLogLikelihood(),
ps_lf.getLogLikelihood())
def test_position_specific_mprobs(self):
"""correctly compute likelihood when positions have distinct
probabilities"""
aln_len = len(self.aln)
posn1 = []
posn2 = []
for name, seq in self.aln.todict().items():
p1 = [seq[i] for i in range(0,aln_len,2)]
p2 = [seq[i] for i in range(1,aln_len,2)]
posn1.append([name, ''.join(p1)])
posn2.append([name, ''.join(p2)])
# the position specific alignments
posn1 = LoadSeqs(data=posn1)
posn2 = LoadSeqs(data=posn2)
# a newQ dinucleotide model
sm = Nucleotide(motif_length=2, mprob_model='monomer', do_scaling=False)
lf = sm.makeLikelihoodFunction(self.tree)
lf.setAlignment(posn1)
posn1_lnL = lf.getLogLikelihood()
lf.setAlignment(posn2)
posn2_lnL = lf.getLogLikelihood()
expect_lnL = posn1_lnL+posn2_lnL
# the joint model
lf.setAlignment(self.aln)
aln_lnL = lf.getLogLikelihood()
# setting the full alignment, which has different motif probs, should
# produce a different lnL
self.failIfAlmostEqual(expect_lnL, aln_lnL)
# set the arguments for taking position specific mprobs
sm = Nucleotide(motif_length=2, mprob_model='monomers',
do_scaling=False)
lf = sm.makeLikelihoodFunction(self.tree)
lf.setAlignment(self.aln)
posn12_lnL = lf.getLogLikelihood()
self.assertFloatEqual(expect_lnL, posn12_lnL)
def fit_constructed_gen(results=results):
if 'constructed_gen' in results:
return
preds = [
MotifChange(a, b, forward_only=True)
for a, b in [['A', 'C'], ['A', 'G'], ['A', 'T'], ['C', 'A'],
['C', 'G'], ['C', 'T'], ['G', 'C'], ['G', 'T'],
['T', 'A'], ['T', 'C'], ['T', 'G']]
]
nuc = Nucleotide(predicates=preds)
nuc_lf = _make_likelihood(nuc, tree, results)
nuc_lf.optimise(**opt_args)
results['constructed_gen'] = nuc_lf
def test_newQ_is_nuc_process(self):
"""newQ is an extension of an independent nucleotide process"""
nuc = Nucleotide(motif_probs=self.asymm_nuc_probs)
new_di = Nucleotide(motif_length=2,
mprob_model='monomer',
motif_probs=self.asymm_root_probs)
nuc_lf = nuc.makeLikelihoodFunction(self.tree)
new_di_lf = new_di.makeLikelihoodFunction(self.tree)
# newQ branch length is exactly motif_length*nuc branch length
nuc_lf.setParamRule('length', is_independent=False, init=0.2)
new_di_lf.setParamRule('length', is_independent=False, init=0.4)
nuc_lf.setAlignment(self.aln)
new_di_lf.setAlignment(self.aln)
self.assertFloatEqual(nuc_lf.getLogLikelihood(),
new_di_lf.getLogLikelihood())
def test_position_specific_mprobs(self):
"""correctly compute likelihood when positions have distinct
probabilities"""
aln_len = len(self.aln)
posn1 = []
posn2 = []
for name, seq in list(self.aln.todict().items()):
p1 = [seq[i] for i in range(0, aln_len, 2)]
p2 = [seq[i] for i in range(1, aln_len, 2)]
posn1.append([name, ''.join(p1)])
posn2.append([name, ''.join(p2)])
# the position specific alignments
posn1 = LoadSeqs(data=posn1)
posn2 = LoadSeqs(data=posn2)
# a newQ dinucleotide model
sm = Nucleotide(motif_length=2,
mprob_model='monomer',
do_scaling=False)
lf = sm.makeLikelihoodFunction(self.tree)
lf.setAlignment(posn1)
posn1_lnL = lf.getLogLikelihood()
lf.setAlignment(posn2)
posn2_lnL = lf.getLogLikelihood()
expect_lnL = posn1_lnL + posn2_lnL
# the joint model
lf.setAlignment(self.aln)
aln_lnL = lf.getLogLikelihood()
# setting the full alignment, which has different motif probs, should
# produce a different lnL
self.assertNotAlmostEqual(expect_lnL, aln_lnL)
# set the arguments for taking position specific mprobs
sm = Nucleotide(motif_length=2,
mprob_model='monomers',
do_scaling=False)
lf = sm.makeLikelihoodFunction(self.tree)
lf.setAlignment(self.aln)
posn12_lnL = lf.getLogLikelihood()
self.assertFloatEqual(expect_lnL, posn12_lnL)
def compare_models(motif_probs, motif_length):
# if the 1st and 2nd position motifs are independent of each other
# then conditional is the same as positional
ps = Nucleotide(motif_length=motif_length,
motif_probs=motif_probs,
mprob_model='monomers')
cd = Nucleotide(motif_length=motif_length,
motif_probs=motif_probs,
mprob_model='conditional')
ps_lf = ps.makeLikelihoodFunction(self.tree)
ps_lf.setParamRule('length', is_independent=False, init=0.4)
ps_lf.setAlignment(self.aln)
cd_lf = cd.makeLikelihoodFunction(self.tree)
cd_lf.setParamRule('length', is_independent=False, init=0.4)
cd_lf.setAlignment(self.aln)
self.assertFloatEqual(cd_lf.getLogLikelihood(),
ps_lf.getLogLikelihood())
def test_cond_pos_differ(self):
"""lnL should differ when motif probs are not multiplicative"""
dinuc_probs = {
'AA': 0.088506666666666664,
'AC': 0.044746666666666664,
'GT': 0.056693333333333332,
'AG': 0.070199999999999999,
'CC': 0.048653333333333333,
'TT': 0.10678666666666667,
'CG': 0.0093600000000000003,
'GG': 0.049853333333333333,
'GC': 0.040253333333333335,
'AT': 0.078880000000000006,
'GA': 0.058639999999999998,
'TG': 0.081626666666666667,
'TA': 0.068573333333333333,
'CA': 0.06661333333333333,
'TC': 0.060866666666666666,
'CT': 0.069746666666666665
}
mg = Nucleotide(motif_length=2,
motif_probs=dinuc_probs,
mprob_model='monomer')
mg_lf = mg.makeLikelihoodFunction(self.tree)
mg_lf.setParamRule('length', is_independent=False, init=0.4)
mg_lf.setAlignment(self.aln)
cd = Nucleotide(motif_length=2,
motif_probs=dinuc_probs,
mprob_model='conditional')
cd_lf = cd.makeLikelihoodFunction(self.tree)
cd_lf.setParamRule('length', is_independent=False, init=0.4)
cd_lf.setAlignment(self.aln)
self.assertNotAlmostEqual(mg_lf.getLogLikelihood(),
cd_lf.getLogLikelihood())