def test_deserialise_likelihood_function(self):
"""correctly deserialise data into likelihood function"""
# tests multiple alignments
data = load_aligned_seqs(
filename=os.path.join(os.getcwd(), "data", "brca1_5.paml")
)
half = len(data) // 2
aln1 = data[:half]
aln2 = data[half:]
loci_names = ["1st-half", "2nd-half"]
loci = [aln1, aln2]
tree = make_tree(tip_names=data.names)
model = get_model("HKY85")
lf = model.make_likelihood_function(tree, loci=loci_names)
lf.set_alignment(loci)
lf_rich_dict = lf.to_rich_dict()
got = deserialise_likelihood_function(lf_rich_dict)
self.assertEqual(str(lf.defn_for["mprobs"]), str(got.defn_for["mprobs"]))
self.assertEqual(
str(lf.defn_for["alignment"].assignments),
str(got.defn_for["alignment"].assignments),
)
# tests single alignment
model = get_model("HKY85")
lf = model.make_likelihood_function(tree)
lf.set_alignment(aln1)
lf_rich_dict = lf.to_rich_dict()
got = deserialise_likelihood_function(lf_rich_dict)
self.assertEqual(str(lf.defn_for["mprobs"]), str(got.defn_for["mprobs"]))
self.assertEqual(
str(lf.defn_for["alignment"].assignments),
str(got.defn_for["alignment"].assignments),
)
def test_get_motif_probs_by_node_mg94(self):
"""handles different statespace dimensions from process and stationary distribution"""
from cogent3.evolve.models import get_model
aln = load_aligned_seqs("data/primates_brca1.fasta", moltype="dna")
aln = aln.no_degenerates(motif_length=3)
tree = load_tree("data/primates_brca1.tree")
# root mprobs are constant
sm = get_model("MG94HKY")
lf = sm.make_likelihood_function(tree)
lf.set_alignment(aln)
mprobs = lf.get_motif_probs()
mprobs = lf.get_motif_probs_by_node()
self.assertEqual(mprobs.shape, (len(tree.get_edge_vector()), 61))
# root mprobs are variable
sm = get_model("MG94HKY", optimise_motif_probs=True)
sm = get_model("MG94HKY")
lf = sm.make_likelihood_function(tree)
lf.set_alignment(aln)
mprobs = lf.get_motif_probs_by_node()
self.assertEqual(mprobs.shape, (len(tree.get_edge_vector()), 61))
# not imlemented for monomers variant
sm = TimeReversibleCodon(mprob_model="monomers",
model_gaps=False,
recode_gaps=True)
lf = sm.make_likelihood_function(tree)
lf.set_alignment(aln)
with self.assertRaises(NotImplementedError):
_ = lf.get_motif_probs_by_node()
def test_get_model(self):
"""get_models successfully creates model instances"""
# just returns query if it's already a substitution model
for mod in (CNFGTR(), WG01(), GN()):
got = get_model(mod)
self.assertEqual(id(got), id(mod))
with self.assertRaises(ValueError):
# unknown model raises exception
_ = get_model("blah")
def test_roundtrip_discrete_time_submod(self):
"""discrete time substitution models to_json enables roundtrip"""
sm = get_model("DT")
data = sm.to_json()
got = deserialise_object(data)
self.assertEqual(got.to_rich_dict(), sm.to_rich_dict())
sm = get_model("DT", motif_length=2)
data = sm.to_json()
got = deserialise_object(data)
self.assertEqual(got.to_rich_dict(), sm.to_rich_dict())
def test_roundtrip_submod(self):
"""substitution model to_json enables roundtrip"""
sm = get_model("HKY85")
data = sm.to_json()
got = deserialise_object(data)
self.assertEqual(got.to_rich_dict(), sm.to_rich_dict())
sm = get_model("GN")
data = sm.to_json()
got = deserialise_object(data)
self.assertEqual(got.to_rich_dict(), sm.to_rich_dict())
sm = get_model("CNFGTR")
data = sm.to_json()
got = deserialise_object(data)
self.assertEqual(got.to_rich_dict(), sm.to_rich_dict())
def is_codon_model(sm):
"""True of sm, or get_model(sm), is a Codon substitution model"""
from cogent3.evolve.substitution_model import _Codon
if type(sm) == str:
sm = get_model(sm)
return isinstance(sm, _Codon)
def test_roundtrip_model_result(self):
"""mode_result.to_json enables roundtrip and lazy evaluation"""
_data = {
"Human": "ATGCGGCTCGCGGAGGCCGCGCTCGCGGAG",
"Mouse": "ATGCCCGGCGCCAAGGCAGCGCTGGCGGAG",
"Opossum": "ATGCCAGTGAAAGTGGCGGCGGTGGCTGAG",
}
aln = make_aligned_seqs(data=_data, moltype="dna")
tree = make_tree(tip_names=aln.names)
sm = get_model("HKY85")
lf = sm.make_likelihood_function(tree)
lf.set_alignment(aln)
edge_vals = zip(aln.names, (2, 3, 4))
for edge, val in edge_vals:
lf.set_param_rule("kappa", edge=edge, init=val)
result = model_result(name="test")
result[1] = lf
self.assertIs(result[1], lf)
self.assertEqual(result.nfp, lf.nfp)
self.assertEqual(result.lnL, lf.lnL)
data = result.to_json()
got_obj = deserialise_object(data)
# lazy evaluation means initially, the value is a dict
self.assertIsInstance(got_obj[1], dict)
# and properties match original
self.assertEqual(got_obj.lnL, result.lnL)
self.assertEqual(got_obj.nfp, result.nfp)
self.assertEqual(got_obj.DLC, result.DLC)
# when we ask for the lf attribute, it's no longer a dict
self.assertNotIsInstance(got_obj.lf, dict)
self.assertEqual(got_obj.lf.nfp, got_obj.nfp)
def test_model_names(self):
"""name attribute matches model name"""
for model_name in models:
model = get_model(model_name)
self.assertTrue(
model.name.startswith(model_name),
msg=f"{model.name} does not start with {model_name}",
)
def _make_model_cache(self):
# constructs all the substitution models
if hasattr(self, "_cached_models"):
return
cache = {}
for name in models:
cache[name] = get_model(name)
self._cached_models = cache
def __init__(self,
distance=None,
moltype=None,
fast_calc=None,
slow_calc=None):
super(fast_slow_dist, self).__init__(
input_types=ALIGNED_TYPE,
output_types=(PAIRWISE_DISTANCE_TYPE, SERIALISABLE_TYPE),
data_types=("ArrayAlignment", "Alignment"),
)
self._formatted_params()
self._moltype = moltype if moltype is None else get_moltype(moltype)
self._sm = None
if (fast_calc or slow_calc) and distance:
raise ValueError("cannot combine distance and fast/slow")
if distance:
fast_calc = distance
slow_calc = distance
d = set(["hamming", "paralinear", "logdet"]) & set(
[slow_calc, fast_calc])
if d and not self._moltype:
raise ValueError(f"you must provide a moltype for {d}")
try:
fast_calc = get_distance_calculator(fast_calc,
moltype=self._moltype)
except (ValueError, AttributeError):
fast_calc = None
try:
slow_calc = get_model(slow_calc)
except ValueError:
slow_calc = None
if not (fast_calc or slow_calc):
raise ValueError(f"invalid values for {slow_calc} or {fast_calc}")
self.fast_calc = fast_calc
if fast_calc and self._moltype and fast_calc.moltype != self._moltype:
raise ValueError(
f"{self._moltype} incompatible moltype with fast calculator {fast_calc.moltype}"
)
elif fast_calc:
self._moltype = fast_calc.moltype
if slow_calc and self._moltype and slow_calc.moltype != self._moltype:
raise ValueError("incompatible moltype with slow calculator")
elif slow_calc:
self._moltype = slow_calc.moltype
self._sm = slow_calc
def test_roundtrip_het_lf(self):
"""correctly round trips a site-het model"""
with open("data/site-het-param-rules.json") as infile:
rules = json.load(infile)
aln = load_aligned_seqs("data/primates_brca1.fasta", moltype="dna")
tree = load_tree("data/primates_brca1.tree")
rule_lnL = rules.pop("phylohmm-gamma-kappa")
sm = get_model("HKY85", ordered_param="rate", distribution="gamma")
lf1 = sm.make_likelihood_function(tree, bins=4, sites_independent=False)
lf1.set_alignment(aln)
lf1.apply_param_rules(rule_lnL["rules"])
data = lf1.to_json()
got_lf = deserialise_object(data)
assert_allclose(lf1.lnL, got_lf.lnL)
def _test_aln(self, seqs, model=dna_model, param_vals=None, **kw):
orig = {n: s.replace("-", "") for (n, s) in list(seqs.items())}
aln = self._make_aln(orig, model=model, param_vals=param_vals, **kw)
result = {n: s.lower() for (n, s) in list(aln.to_dict().items())}
# assert the alignment result is correct
self.assertEqual(seqs, result)
# and the moltype matches the model
model = get_model(model)
self.assertIs(aln.moltype, model.moltype)
# assert the returned alignment has the correct parameter values in the
# align.info object.
if param_vals:
for param, val in param_vals:
self.assertEqual(aln.info.align_params[param], val)
def test_zhang(self):
"""natsel_zhang correctly configured and should not fail"""
opt = dict(max_evaluations=20, limit_action="ignore")
aln = load_aligned_seqs("data/primate_brca1.fasta", moltype="dna")
natsel = evo_app.natsel_zhang(
"CNFGTR",
tree="data/primate_brca1.tree",
tip1="Human",
tip2="Chimpanzee",
opt_args=opt,
)
result = natsel(aln)
self.assertEqual(result.df, 3)
self.assertEqual(result.alt.nfp, 21)
# the naming scheme is model name followed by null/alt
self.assertTrue("CNFGTR-null" in result)
self.assertTrue("CNFGTR-alt" in result)
# result keys correct when given a model
Y98 = get_model("Y98")
natsel = evo_app.natsel_zhang(
Y98,
tree="data/primate_brca1.tree",
tip1="Human",
tip2="Chimpanzee",
opt_args=opt,
)
result = natsel(aln)
self.assertEqual(result.df, 3)
self.assertTrue("Y98-null" in result)
self.assertTrue("Y98-alt" in result)
# fails if not a codon model
with self.assertRaises(ValueError):
_ = evo_app.natsel_zhang(
"F81",
tree="data/primate_brca1.tree",
tip1="Human",
tip2="Chimpanzee",
opt_args=opt,
)
# fails if no tip names provided
with self.assertRaises(ValueError):
_ = evo_app.natsel_zhang("Y98",
tree="data/primate_brca1.tree",
opt_args=opt)
def test_roundtrip_discrete_time_likelihood_function(self):
"""discrete time likelihood function.to_json enables roundtrip"""
_data = {
"Human": "ATGCGGCTCGCGGAGGCCGCGCTCGCGGAG",
"Mouse": "ATGCCCGGCGCCAAGGCAGCGCTGGCGGAG",
"Opossum": "ATGCCAGTGAAAGTGGCGGCGGTGGCTGAG",
}
aln = make_aligned_seqs(data=_data, moltype="dna")
tree = make_tree(tip_names=aln.names)
sm = get_model("BH")
lf = sm.make_likelihood_function(tree)
lf.set_alignment(aln)
lf.optimise(max_evaluations=25, limit_action="ignore", show_progress=False)
lnL = lf.get_log_likelihood()
data = lf.to_json()
got_obj = deserialise_object(data)
assert_allclose(got_obj.get_log_likelihood(), lnL)
def test_roundtrip_likelihood_function(self):
"""likelihood function.to_json enables roundtrip"""
_data = {
"Human": "ATGCGGCTCGCGGAGGCCGCGCTCGCGGAG",
"Mouse": "ATGCCCGGCGCCAAGGCAGCGCTGGCGGAG",
"Opossum": "ATGCCAGTGAAAGTGGCGGCGGTGGCTGAG",
}
aln = make_aligned_seqs(data=_data, moltype="dna")
tree = make_tree(tip_names=aln.names)
sm = get_model("HKY85")
lf = sm.make_likelihood_function(tree)
lf.set_alignment(aln)
edge_vals = zip(aln.names, (2, 3, 4))
for edge, val in edge_vals:
lf.set_param_rule("kappa", edge=edge, init=val)
lnL = lf.get_log_likelihood()
data = lf.to_json()
got_obj = deserialise_object(data)
self.assertFloatEqual(got_obj.get_log_likelihood(), lnL)
def deserialise_substitution_model(data):
"""returns a cogent3 substitution model instance"""
from cogent3.evolve.models import get_model
data.pop("version", None)
kw = {} if "kw" not in data else data.pop("kw")
sm = None
if kw and "name" in kw:
name = kw.pop("name")
try:
sm = get_model(name, **kw)
except ValueError: # user defined sm?
pass
if sm is None:
alphabet = deserialise_alphabet(data.pop("alphabet"))
klass = _get_class(data.pop("type"))
sm = klass(alphabet, **data)
return sm
def test_roundtrip_from_file(self):
"""correctly roundtrips a likelihood function fro json file"""
_data = {
"Human": "ATGCGGCTCGCGGAGGCCGCGCTCGCGGAG",
"Mouse": "ATGCCCGGCGCCAAGGCAGCGCTGGCGGAG",
"Opossum": "ATGCCAGTGAAAGTGGCGGCGGTGGCTGAG",
}
aln = make_aligned_seqs(data=_data, moltype="dna")
tree = make_tree(tip_names=aln.names)
sm = get_model("HKY85")
lf = sm.make_likelihood_function(tree)
lf.set_alignment(aln)
edge_vals = zip(aln.names, (2, 3, 4))
for edge, val in edge_vals:
lf.set_param_rule("kappa", edge=edge, init=val)
lnL = lf.get_log_likelihood()
data = lf.to_json()
with TemporaryDirectory(dir=".") as dirname:
outpath = dirname + "/delme.json"
with open(outpath, "w") as outfile:
outfile.write(data)
got = deserialise_object(outpath)
self.assertFloatEqual(got.get_log_likelihood(), lnL)
def __init__(
self,
sm,
tree=None,
sm_args=None,
gc=1,
optimise_motif_probs=False,
lf_args=None,
opt_args=None,
show_progress=False,
verbose=False,
):
"""
Parameters
----------
sm : str or instance
substitution model, if string must be available via get_model()
(see cogent3.available_models).
tree
if None, assumes a star phylogeny (only valid for 3 taxa). Can be a
newick formatted tree, a path to a file containing one, or a Tree
instance.
sm_args
arguments to be passed to the substitution model constructor, e.g.
dict(optimise_motif_probs=True)
gc
genetic code, either name or number (see cogent3.available_codes)
optimise_motif_probs : bool
If True, motif probabilities are free parameters. If False (default)
they are estimated frokm the alignment.
lf_args
arguments to be passed to the likelihood function constructor
opt_args
arguments for the numerical optimiser, e.g.
dict(max_restarts=5, tolerance=1e-6, max_evaluations=1000,
limit_action='ignore')
show_progress : bool
show progress bars during numerical optimisation
verbose : bool
prints intermediate states to screen during fitting
"""
super(natsel_neutral, self).__init__(
input_types=(ALIGNED_TYPE, SERIALISABLE_TYPE),
output_types=(RESULT_TYPE, HYPOTHESIS_RESULT_TYPE, SERIALISABLE_TYPE),
data_types=("ArrayAlignment", "Alignment"),
)
self._formatted_params()
if not is_codon_model(sm):
raise ValueError(f"{sm} is not a codon model")
if misc.path_exists(tree):
tree = load_tree(filename=tree, underscore_unmunge=True)
elif type(tree) == str:
tree = make_tree(treestring=tree, underscore_unmunge=True)
if tree and not isinstance(tree, TreeNode):
raise TypeError(f"invalid tree type {type(tree)}")
# instantiate model, ensuring genetic code setting passed on
sm_args = sm_args or {}
sm_args["gc"] = sm_args.get("gc", gc)
sm_args["optimise_motif_probs"] = optimise_motif_probs
if type(sm) == str:
sm = get_model(sm, **sm_args)
model_name = sm.name
# defining the null model
lf_args = lf_args or {}
null = model(
sm,
tree,
name=f"{model_name}-null",
sm_args=sm_args,
opt_args=opt_args,
show_progress=show_progress,
param_rules=[dict(par_name="omega", is_constant=True, value=1.0)],
lf_args=lf_args,
verbose=verbose,
)
# defining the alternate model
alt = model(
sm,
tree,
name=f"{model_name}-alt",
sm_args=sm_args,
opt_args=opt_args,
show_progress=show_progress,
lf_args=lf_args,
verbose=verbose,
)
hyp = hypothesis(null, alt)
self.func = hyp
def __init__(self,
distance=None,
moltype=None,
fast_calc=None,
slow_calc=None):
"""
Parameters
----------
moltype : str
cogent3 moltype
distance : str
Name of a distance method available as both fast and slow calculator.
fast_calc
Name of a fast distance calculator. See cogent3.available_distances().
slow_calc
Name of a slow distance calculator. See cogent3.available_models().
Notes
-----
If you provide fast_calc or slow_calc, you must specify the moltype.
"""
super(fast_slow_dist, self).__init__(
input_types=self._input_types,
output_types=self._output_types,
data_types=self._data_types,
)
self._formatted_params()
self._moltype = moltype if moltype is None else get_moltype(moltype)
self._sm = None
if (fast_calc or slow_calc) and distance:
raise ValueError("cannot combine distance and fast/slow")
if distance:
fast_calc = distance
slow_calc = distance
d = {"hamming", "percent", "paralinear", "logdet"
} & {slow_calc, fast_calc}
if d and not self._moltype:
raise ValueError(f"you must provide a moltype for {d}")
try:
fast_calc = get_distance_calculator(fast_calc,
moltype=self._moltype)
except (ValueError, AttributeError):
fast_calc = None
try:
slow_calc = get_model(slow_calc)
except ValueError:
slow_calc = None
if not (fast_calc or slow_calc):
raise ValueError(f"invalid values for {slow_calc} or {fast_calc}")
self.fast_calc = fast_calc
if fast_calc and self._moltype and fast_calc.moltype != self._moltype:
raise ValueError(
f"{self._moltype} incompatible moltype with fast calculator {fast_calc.moltype}"
)
elif fast_calc:
self._moltype = fast_calc.moltype
if slow_calc and self._moltype and slow_calc.moltype != self._moltype:
raise ValueError("incompatible moltype with slow calculator")
elif slow_calc:
self._moltype = slow_calc.moltype
self._sm = slow_calc
self.func = self.calc_distance
def __init__(
self,
sm,
tree=None,
name=None,
sm_args=None,
lf_args=None,
time_het=None,
param_rules=None,
opt_args=None,
split_codons=False,
show_progress=False,
verbose=False,
):
"""
Parameters
----------
sm : str or instance
substitution model if string must be available via get_model()
tree
if None, assumes a star phylogeny (only valid for 3 taxa). Can be a
newick formatted tree, a path to a file containing one, or a Tree
instance.
name
name of the model
sm_args
arguments to be passed to the substitution model constructor, e.g.
dict(optimise_motif_probs=True)
lf_args
arguments to be passed to the likelihood function constructor
time_het
'max' or a list of dicts corresponding to edge_sets, e.g.
[dict(edges=['Human', 'Chimp'], is_independent=False, upper=10)].
Passed to the likelihood function .set_time_heterogeneity()
method.
param_rules
other parameter rules, passed to the likelihood function
set_param_rule() method
opt_args
arguments for the numerical optimiser, e.g.
dict(max_restarts=5, tolerance=1e-6, max_evaluations=1000,
limit_action='ignore')
split_codons : bool
if True, incoming alignments are split into the 3 frames and each
frame is fit separately
show_progress : bool
show progress bars during numerical optimisation
verbose : bool
prints intermediate states to screen during fitting
Returns
-------
Calling an instance with an alignment returns a model_result instance
with the optimised likelihood function. In the case of split_codons,
the result object has a separate entry for each.
"""
super(model, self).__init__(
input_types=("aligned", "serialisable"),
output_types=("result", "model_result", "serialisable"),
data_types=("ArrayAlignment", "Alignment"),
)
self._verbose = verbose
self._formatted_params()
sm_args = sm_args or {}
if type(sm) == str:
sm = get_model(sm, **sm_args)
self._sm = sm
if len(sm.get_motifs()[0]) > 1:
split_codons = False
if misc.path_exists(tree):
tree = load_tree(filename=tree, underscore_unmunge=True)
elif type(tree) == str:
tree = make_tree(treestring=tree, underscore_unmunge=True)
if tree and not isinstance(tree, TreeNode):
raise TypeError(f"invalid tree type {type(tree)}")
self._tree = tree
self._lf_args = lf_args or {}
if not name:
name = sm.name or "unnamed model"
self.name = name
self._opt_args = opt_args or dict(max_restarts=5,
show_progress=show_progress)
self._opt_args["show_progress"] = self._opt_args.get(
"show_progress", show_progress)
param_rules = param_rules or {}
if param_rules:
for rule in param_rules:
if rule.get("is_constant"):
continue
rule["upper"] = rule.get("upper", 50) # default upper bound
self._param_rules = param_rules
self._time_het = time_het
self._split_codons = split_codons
self.func = self.fit
def __init__(
self,
sm,
tree=None,
sm_args=None,
gc=1,
optimise_motif_probs=False,
upper_omega=20.0,
lf_args=None,
opt_args=None,
show_progress=False,
verbose=False,
):
"""
Parameters
----------
sm : str or instance
substitution model, if string must be available via get_model()
(see cogent3.available_models).
tree
if None, assumes a star phylogeny (only valid for 3 taxa). Can be a
newick formatted tree, a path to a file containing one, or a Tree
instance.
sm_args
arguments to be passed to the substitution model constructor, e.g.
dict(optimise_motif_probs=True)
gc
genetic code, either name or number (see cogent3.available_codes)
optimise_motif_probs : bool
If True, motif probabilities are free parameters. If False (default)
they are estimated from the alignment.
upper_omega : float
upper bound for positive selection omega
lf_args
arguments to be passed to the likelihood function constructor
opt_args
arguments for the numerical optimiser, e.g.
dict(max_restarts=5, tolerance=1e-6, max_evaluations=1000,
limit_action='ignore')
show_progress : bool
show progress bars during numerical optimisation
verbose : bool
prints intermediate states to screen during fitting
"""
super(natsel_sitehet, self).__init__(
input_types=("aligned", "serialisable"),
output_types=("result", "hypothesis_result", "serialisable"),
data_types=("ArrayAlignment", "Alignment"),
)
self._formatted_params()
if not is_codon_model(sm):
raise ValueError(f"{sm} is not a codon model")
if misc.path_exists(tree):
tree = load_tree(filename=tree, underscore_unmunge=True)
elif type(tree) == str:
tree = make_tree(treestring=tree, underscore_unmunge=True)
if tree and not isinstance(tree, TreeNode):
raise TypeError(f"invalid tree type {type(tree)}")
# instantiate model, ensuring genetic code setting passed on
sm_args = sm_args or {}
sm_args["gc"] = sm_args.get("gc", gc)
sm_args["optimise_motif_probs"] = optimise_motif_probs
if type(sm) == str:
sm = get_model(sm, **sm_args)
model_name = sm.name
# defining the null model
epsilon = 1e-6
null_param_rules = [
dict(par_name="omega",
bins="-ve",
upper=1 - epsilon,
init=1 - epsilon),
dict(par_name="omega", bins="neutral", is_constant=True,
value=1.0),
]
lf_args = lf_args or {}
null_lf_args = lf_args.copy()
null_lf_args.update(dict(bins=("-ve", "neutral")))
self.null = model(
sm,
tree,
name=f"{model_name}-null",
sm_args=sm_args,
param_rules=null_param_rules,
lf_args=null_lf_args,
opt_args=opt_args,
show_progress=show_progress,
verbose=verbose,
)
# defining the alternate model, param rules to be completed each call
alt_lf_args = lf_args.copy()
alt_lf_args.update(dict(bins=("-ve", "neutral", "+ve")))
self.alt_args = dict(
sm=sm,
tree=tree,
name=f"{model_name}-alt",
sm_args=sm_args,
lf_args=alt_lf_args,
opt_args=opt_args,
show_progress=show_progress,
verbose=verbose,
upper_omega=upper_omega,
)
self.func = self.test_hypothesis
def __init__(
self,
sm,
tree=None,
sm_args=None,
gc=1,
optimise_motif_probs=False,
tip1=None,
tip2=None,
outgroup=None,
stem=False,
clade=True,
is_independent=False,
lf_args=None,
upper_omega=20,
opt_args=None,
show_progress=False,
verbose=False,
):
"""
Parameters
----------
sm : str or instance
substitution model, if string must be available via get_model()
(see cogent3.available_models).
tree
if None, assumes a star phylogeny (only valid for 3 taxa). Can be a
newick formatted tree, a path to a file containing one, or a Tree
instance.
sm_args
arguments to be passed to the substitution model constructor, e.g.
dict(optimise_motif_probs=True)
gc
genetic code, either name or number (see cogent3.available_codes)
optimise_motif_probs : bool
If True, motif probabilities are free parameters. If False (default)
they are estimated frokm the alignment.
tip1 : str
name of tip 1
tip2 : str
name of tip 1
outgroup : str
name of tip outside clade of interest
stem : bool
include name of stem to clade defined by tip1, tip2, outgroup
clade : bool
include names of edges within clade defined by tip1, tip2, outgroup
is_independent : bool
if True, all edges specified by the scoping info get their own
value of omega, if False, only a single omega
lf_args
arguments to be passed to the likelihood function constructor
upper_omega : float
upper bound for omega
param_rules
other parameter rules, passed to the likelihood function
set_param_rule() method
opt_args
arguments for the numerical optimiser, e.g.
dict(max_restarts=5, tolerance=1e-6, max_evaluations=1000,
limit_action='ignore')
show_progress : bool
show progress bars during numerical optimisation
verbose : bool
prints intermediate states to screen during fitting
"""
super(natsel_timehet, self).__init__(
input_types=("aligned", "serialisable"),
output_types=("result", "hypothesis_result", "serialisable"),
data_types=("ArrayAlignment", "Alignment"),
)
self._formatted_params()
if not is_codon_model(sm):
raise ValueError(f"{sm} is not a codon model")
if not any([tip1, tip2]):
raise ValueError("must provide at least a single tip name")
if misc.path_exists(tree):
tree = load_tree(filename=tree, underscore_unmunge=True)
elif type(tree) == str:
tree = make_tree(treestring=tree, underscore_unmunge=True)
if tree and not isinstance(tree, TreeNode):
raise TypeError(f"invalid tree type {type(tree)}")
if all([tip1, tip2]) and tree:
edges = tree.get_edge_names(tip1,
tip2,
stem=stem,
clade=clade,
outgroup_name=outgroup)
elif all([tip1, tip2]):
edges = [tip1, tip2]
elif tip1:
edges = [tip1]
elif tip2:
edges = [tip2]
assert edges, "No edges"
# instantiate model, ensuring genetic code setting passed on
sm_args = sm_args or {}
sm_args["gc"] = sm_args.get("gc", gc)
sm_args["optimise_motif_probs"] = optimise_motif_probs
if type(sm) == str:
sm = get_model(sm, **sm_args)
model_name = sm.name
# defining the null model
lf_args = lf_args or {}
null_lf_args = lf_args.copy()
null = model(
sm,
tree,
name=f"{model_name}-null",
sm_args=sm_args,
lf_args=null_lf_args,
opt_args=opt_args,
show_progress=show_progress,
verbose=verbose,
)
# defining the alternate model
param_rules = [
dict(
par_name="omega",
edges=edges,
upper=upper_omega,
is_independent=is_independent,
)
]
alt = model(
sm,
tree,
name=f"{model_name}-alt",
sm_args=sm_args,
opt_args=opt_args,
show_progress=show_progress,
param_rules=param_rules,
lf_args=lf_args,
verbose=verbose,
)
hyp = hypothesis(null, alt)
self.func = hyp
def __init__(
self,
model,
gc=None,
param_vals=None,
guide_tree=None,
unique_guides=False,
indel_length=1e-1,
indel_rate=1e-10,
distance="percent",
):
"""
Parameters
----------
model
substitution model instance or name. If 'codon'
(uses MG94HKY), 'nucleotide' (uses HKY85), 'protein'
(uses WG01). These choices provide also provide default
settings for param_vals.
gc : int or string
the genetic code for a codon alignment, defaults to the standard
genetic code
param_vals : dict
param name, values for parameters in model. Overrides
default choices.
guide_tree
newick string, tree instance (must have branch lengths), or a
callable that will build a tree from unaligned collection. If not
provided, estimated ONCE via constructing a crude alignment. In the
case of callable, or not provided, the computed guide tree is stored
in the returned alignment.info['guide_tree'].
unique_guides : bool
whether each alignment requires a new guide tree
indel_rate : float
probability of gap insertion
indel_length : float
probability of gap extension
distance : string
the distance measure for building a guide tree. Default is 'percent',
the proportion of differences. This is applicable for any moltype,
and sequences with very high percent identity. For more diverged
sequences we recommend 'paralinear'.
"""
super(progressive_align, self).__init__(
input_types=self._input_types,
output_types=self._output_types,
data_types=self._data_types,
)
self._param_vals = {
"codon": dict(omega=0.4, kappa=3),
"nucleotide": dict(kappa=3),
}.get(model, param_vals)
sm = {"codon": "MG94HKY", "nucleotide": "HKY85", "protein": "JTT92"}.get(
model, model
)
self._formatted_params()
kwargs = {} if gc is None else dict(gc=gc)
sm = get_model(sm, **kwargs)
moltype = sm.alphabet.moltype
self._model = sm
self._scalar = sm.word_length
self._indel_length = indel_length
self._indel_rate = indel_rate
self._moltype = moltype
self._unique_guides = unique_guides
self._distance = distance
if callable(guide_tree):
self._make_tree = guide_tree
guide_tree = None # callback takes precedence
else:
al_to_ref = align_to_ref(moltype=self._moltype)
dist_calc = dist.fast_slow_dist(
distance=self._distance, moltype=self._moltype
)
est_tree = quick_tree()
self._make_tree = al_to_ref + dist_calc + est_tree
if guide_tree is not None:
if type(guide_tree) == str:
guide_tree = make_tree(treestring=guide_tree, underscore_unmunge=True)
if guide_tree.children[0].length is None:
raise ValueError("Guide tree must have branch lengths")
# make sure no zero lengths
guide_tree = scale_branches()(guide_tree)
self._guide_tree = guide_tree
self._kwargs = dict(
indel_length=self._indel_length,
indel_rate=self._indel_rate,
tree=self._guide_tree,
param_vals=self._param_vals,
show_progress=False,
)
self.func = self.multiple_align
def __init__(
self,
sm,
tree=None,
sm_args=None,
gc=1,
optimise_motif_probs=False,
tip1=None,
tip2=None,
outgroup=None,
stem=False,
clade=True,
lf_args=None,
upper_omega=20,
opt_args=None,
show_progress=False,
verbose=False,
):
"""
Parameters
----------
sm : str or instance
substitution model, if string must be available via get_model()
(see cogent3.available_models).
tree
if None, assumes a star phylogeny (only valid for 3 taxa). Can be a
newick formatted tree, a path to a file containing one, or a Tree
instance.
sm_args
arguments to be passed to the substitution model constructor, e.g.
dict(optimise_motif_probs=True)
gc
genetic code, either name or number (see cogent3.available_codes)
optimise_motif_probs : bool
If True, motif probabilities are free parameters. If False (default)
they are estimated frokm the alignment.
tip1 : str
name of tip 1
tip2 : str
name of tip 1
outgroup : str
name of tip outside clade of interest
stem : bool
include name of stem to clade defined by tip1, tip2, outgroup
clade : bool
include names of edges within clade defined by tip1, tip2, outgroup
lf_args
arguments to be passed to the likelihood function constructor
upper_omega : float
upper bound for positive selection omega
param_rules
other parameter rules, passed to the likelihood function
set_param_rule() method
opt_args
arguments for the numerical optimiser, e.g.
dict(max_restarts=5, tolerance=1e-6, max_evaluations=1000,
limit_action='ignore')
show_progress : bool
show progress bars during numerical optimisation
verbose : bool
prints intermediate states to screen during fitting
Notes
-----
The scoping parameters (tip1, tip2, outgroup, stem, clade) define the
foreground edges.
"""
super(natsel_zhang, self).__init__(
input_types=(ALIGNED_TYPE, SERIALISABLE_TYPE),
output_types=(RESULT_TYPE, HYPOTHESIS_RESULT_TYPE, SERIALISABLE_TYPE),
data_types=("ArrayAlignment", "Alignment"),
)
self._formatted_params()
if not is_codon_model(sm):
raise ValueError(f"{sm} is not a codon model")
if not any([tip1, tip2]):
raise ValueError("must provide at least a single tip name")
if misc.path_exists(tree):
tree = load_tree(filename=tree, underscore_unmunge=True)
elif type(tree) == str:
tree = make_tree(treestring=tree, underscore_unmunge=True)
if tree and not isinstance(tree, TreeNode):
raise TypeError(f"invalid tree type {type(tree)}")
if all([tip1, tip2]) and tree:
edges = tree.get_edge_names(
tip1, tip2, stem=stem, clade=clade, outgroup_name=outgroup
)
elif all([tip1, tip2]):
edges = [tip1, tip2]
elif tip1:
edges = [tip1]
elif tip2:
edges = [tip2]
assert edges, "No edges"
# instantiate model, ensuring genetic code setting passed on
sm_args = sm_args or {}
sm_args["gc"] = sm_args.get("gc", gc)
sm_args["optimise_motif_probs"] = optimise_motif_probs
if type(sm) == str:
sm = get_model(sm, **sm_args)
model_name = sm.name
# defining the null model
epsilon = 1e-6
null_param_rules = [
dict(par_name="omega", bins="0", upper=1 - epsilon, init=1 - epsilon),
dict(par_name="omega", bins="1", is_constant=True, value=1.0),
]
lf_args = lf_args or {}
null_lf_args = lf_args.copy()
null_lf_args.update(dict(bins=("0", "1")))
self.null = model(
sm,
tree,
name=f"{model_name}-null",
sm_args=sm_args,
param_rules=null_param_rules,
lf_args=null_lf_args,
opt_args=opt_args,
show_progress=show_progress,
verbose=verbose,
)
# defining the alternate model, param rules to be completed each call
alt_lf_args = lf_args.copy()
alt_lf_args.update(dict(bins=("0", "1", "2a", "2b")))
self.alt_args = dict(
sm=sm,
tree=tree,
name=f"{model_name}-alt",
sm_args=sm_args,
edges=edges,
lf_args=alt_lf_args,
opt_args=opt_args,
show_progress=show_progress,
verbose=verbose,
upper_omega=upper_omega,
)
self.func = self.test_hypothesis
