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import os
import warnings
from unittest import TestCase
from numpy.testing import assert_allclose, assert_almost_equal
import cogent3.evolve.parameter_controller
import cogent3.evolve.substitution_model
from cogent3 import make_aligned_seqs, make_tree
base_path = os.getcwd()
data_path = os.path.join(base_path, "data")
good_rule_sets = [
[{"par_name": "length", "is_independent": True}],
[{"par_name": "length", "is_independent": True}],
[
{
"par_name": "length",
"clade": True,
"is_independent": True,
"edges": ["a", "b"],
}
],
[{"par_name": "length", "is_independent": True, "edges": ["a", "c", "e"]}],
[{"par_name": "length", "is_independent": True, "edge": "a"}],
]
bad_rule_sets = [[{"par_name": "length", "clade": True, "edges": ["b", "f"]}]]
class test_parameter_controller(TestCase):
"""Tesing Parameter Controller"""
def setUp(self):
# length all edges 1 except c=2. b&d transitions all other
# transverions
self.al = make_aligned_seqs(
data={"a": "tata", "b": "tgtc", "c": "gcga", "d": "gaac", "e": "gagc"},
moltype="dna",
)
self.tree = make_tree(treestring="((a,b),(c,d),e);")
self.model = cogent3.evolve.substitution_model.TimeReversibleNucleotide(
equal_motif_probs=True, model_gaps=True
)
def test_scoped_local(self):
model = cogent3.evolve.substitution_model.TimeReversibleNucleotide(
equal_motif_probs=True, model_gaps=True, predicates={"kappa": "transition"}
)
lf = model.make_likelihood_function(self.tree)
lf.set_constant_lengths()
lf.set_alignment(self.al)
null = lf.get_num_free_params()
lf.set_param_rule(par_name="kappa", is_independent=True, edges=["b", "d"])
self.assertEqual(null + 2, lf.get_num_free_params())
def test_set_get_motif_probs_nstat(self):
from cogent3 import get_model
aln = make_aligned_seqs(
data=dict(
a="AACGAAGCAGAGTCACGGCA",
b="ACGGAAGTTGAGTCACCCCA",
c="TGCATCGAAAAGTCACGCTG",
),
moltype="dna",
)
bases = "ACGT"
expect = aln.get_motif_probs()
expect = [expect[b] for b in bases]
tree = make_tree("(a,b,c)")
gn = get_model("GN")
lf = gn.make_likelihood_function(tree)
lf.set_alignment(aln)
got = lf.get_motif_probs().to_dict()
got = [got[b] for b in bases]
assert_allclose(got, expect)
def test_set_motif_probs(self):
"""Mprobs supplied to the parameter controller"""
def compare_mprobs(got, exp):
# handle min val
motifs = list(got)
assert_almost_equal(
[got[m] for m in motifs], [exp[m] for m in motifs], decimal=5
)
model = cogent3.evolve.substitution_model.TimeReversibleNucleotide(
model_gaps=True, motif_probs=None
)
lf = model.make_likelihood_function(self.tree, motif_probs_from_align=False)
mprobs = {"A": 0.1, "C": 0.2, "G": 0.2, "T": 0.5, "-": 0.0}
lf.set_motif_probs(mprobs)
# node the LF adjust motif probs so they are all >= 1e-6
got = lf.get_motif_probs().to_dict()
compare_mprobs(got, mprobs)
lf.set_motif_probs_from_data(self.al[:1], is_constant=True)
assert_almost_equal(lf.get_motif_probs()["G"], 0.6, decimal=4)
lf.set_motif_probs_from_data(self.al[:1], pseudocount=1)
self.assertNotEqual(lf.get_motif_probs()["G"], 0.6)
# test with consideration of ambiguous states
al = make_aligned_seqs(
data={"seq1": "ACGTAAGNA", "seq2": "ACGTANGTC", "seq3": "ACGTACGTG"}
)
lf.set_motif_probs_from_data(al, include_ambiguity=True, is_constant=True)
motif_probs = dict(lf.get_motif_probs())
correct_probs = {
"A": 8.5 / 27,
"C": 5.5 / 27,
"-": 0.0,
"T": 5.5 / 27,
"G": 7.5 / 27,
}
compare_mprobs(motif_probs, correct_probs)
assert_allclose(sum(motif_probs.values()), 1.0)
def test_set_multilocus(self):
"""2 loci each with own mprobs"""
model = cogent3.evolve.substitution_model.TimeReversibleNucleotide(
motif_probs=None
)
lf = model.make_likelihood_function(
self.tree, motif_probs_from_align=False, loci=["a", "b"]
)
mprobs_a = dict(A=0.2, T=0.2, C=0.3, G=0.3)
mprobs_b = dict(A=0.1, T=0.2, C=0.3, G=0.4)
for is_constant in [False, True]:
lf.set_motif_probs(mprobs_a, is_constant=is_constant)
lf.set_motif_probs(mprobs_b, locus="b")
self.assertEqual(lf.get_motif_probs(locus="a"), mprobs_a)
self.assertEqual(lf.get_motif_probs(locus="b"), mprobs_b)
def test_set_param_rules(self):
lf = self.model.make_likelihood_function(self.tree)
def do_rules(rule_set):
for rule in rule_set:
lf.set_param_rule(**rule)
for rule_set in good_rule_sets:
lf.set_default_param_rules()
do_rules(rule_set)
for rule_set in bad_rule_sets:
lf.set_default_param_rules()
self.assertRaises(
(KeyError, TypeError, AssertionError, ValueError), do_rules, rule_set
)
def test_set_constant_lengths(self):
t = make_tree(treestring="((a:1,b:2):3,(c:4,d:5):6,e:7);")
lf = self.model.make_likelihood_function(t) # self.tree)
lf.set_param_rule("length", is_constant=True)
# lf.set_constant_lengths(t)
lf.set_alignment(self.al)
self.assertEqual(lf.get_param_value("length", "b"), 2)
self.assertEqual(lf.get_param_value("length", "d"), 5)
def test_pairwise_clock(self):
al = make_aligned_seqs(data={"a": "agct", "b": "ggct"}, moltype="dna")
tree = make_tree(treestring="(a,b);")
model = cogent3.evolve.substitution_model.TimeReversibleDinucleotide(
equal_motif_probs=True, model_gaps=True, mprob_model="tuple"
)
lf = model.make_likelihood_function(tree)
lf.set_local_clock("a", "b")
lf.set_alignment(al)
lf.optimise(local=True, show_progress=False)
rd = lf.get_param_value_dict(["edge"], params=["length"])
self.assertAlmostEqual(lf.get_log_likelihood(), -10.1774488956)
self.assertEqual(rd["length"]["a"], rd["length"]["b"])
def test_local_clock(self):
lf = self.model.make_likelihood_function(self.tree)
lf.set_local_clock("c", "d")
lf.set_alignment(self.al)
lf.optimise(local=True, tolerance=1e-8, max_restarts=2, show_progress=False)
rd = lf.get_param_value_dict(["edge"], params=["length"])
self.assertAlmostEqual(lf.get_log_likelihood(), -27.84254174)
self.assertEqual(rd["length"]["c"], rd["length"]["d"])
self.assertNotEqual(rd["length"]["a"], rd["length"]["e"])
def test_complex_parameter_rules(self):
# This test has many local minima and so does not cope
# with changes to optimiser details.
model = cogent3.evolve.substitution_model.TimeReversibleNucleotide(
equal_motif_probs=True, model_gaps=True, predicates={"kappa": "transition"}
)
lf = model.make_likelihood_function(self.tree)
lf.set_param_rule(par_name="kappa", is_independent=True)
lf.set_param_rule(par_name="kappa", is_independent=False, edges=["b", "d"])
lf.set_constant_lengths(make_tree(treestring="((a:1,b:1):1,(c:2,d:1):1,e:1);"))
# print self.pc
lf.set_alignment(self.al)
lf.optimise(local=True, show_progress=False)
rd = lf.get_param_value_dict(["edge"], params=["kappa"])
self.assertAlmostEqual(lf.get_log_likelihood(), -27.3252, 3)
self.assertEqual(rd["kappa"]["b"], rd["kappa"]["d"])
self.assertNotEqual(rd["kappa"]["a"], rd["kappa"]["b"])
def test_bounds(self):
"""Test setting upper and lower bounds for parameters"""
lf = self.model.make_likelihood_function(self.tree)
lf.set_param_rule("length", value=3, lower=0, upper=5)
# Out of bounds value should warn and keep bounded
with warnings.catch_warnings(record=True) as w:
lf.set_param_rule("length", lower=0, upper=2, warn=True)
self.assertTrue(len(w), "No warning issued")
self.assertEqual(lf.get_param_value("length", edge="a"), 2)
# upper < lower bounds should fail
self.assertRaises(ValueError, lf.set_param_rule, "length", lower=2, upper=0)
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