def test_positive_parent_multi(self):
"""fetching a parent term when terms have multiple parents."""
# load an ontology in which Y7 is connected to both Y2 and Y1
Yfile = join(testdir, "Ymulti.obo")
Yobo = MinimalObo(Yfile)
Ydefaults = dict.fromkeys(Yobo.ids(), 0.0001)
Ydefaults["Y:003"] = 0.0002
Ydefaults["Y:005"] = 0.0002
# make slight variations of representations
rs = ReferenceSet(dict(refA=0.5, refB=0.5),
ids=Yobo.ids(),
row_priors=Ydefaults)
refA = Representation(name="refA")
refA.set("Y:002", 0.5).set("Y:005", 1).impute(Yobo, Ydefaults)
refB = Representation(name="refB")
refB.set("Y:001", 0.5).impute(Yobo, Ydefaults)
rs.add(refA).add(refB)
rs.learn_obo(Yobo)
self.assertEqual(
rs._positive_ancestor(rs.columns["refA"], rs.rows["Y:007"]),
rs.rows["Y:002"], "Y2 is a positive ancestor")
self.assertEqual(
rs._positive_ancestor(rs.columns["refB"], rs.rows["Y:007"]),
rs.rows["Y:001"], "Y1 is a positive immediate parent")
def test_impute_fromseeds_highfirst(self):
"""imputing values from manually-specified seeds."""
rr = Representation(dict())
# specify data for two children, DOID:4 is higher in tree, so should gain
rr.set("DOID:0014667", 0.4)
rr.set("DOID:0080015", 0.3)
rr.impute(self.obo,
self.obodef,
seeds=["DOID:0014667", "DOID:0080015"])
self.assertAlmostEqual(rr.get("DOID:0014667"),
0.4,
msg="should remain")
self.assertAlmostEqual(rr.get("DOID:0080015"),
0.3,
msg="should remain")
self.assertAlmostEqual(
rr.get("DOID:4"),
1 - ((1 - 0.4) * (1 - 0.3) * (1 - 0.2)),
msg="ancestor gains from two children (and prior)")
self.assertAlmostEqual(rr.get("DOID:655"),
0.2,
msg="remain; new DOID:4")
def get_priors_from_models(models, categories, obo, dark=1):
"""Compute cohort-wide phenotype frequencies
Arguments:
models dictionary of Entity objects
categories set, determines what models to use in the calculation
obo object of class Obo
dark integer, dark count for phenotype normalization
Returns:
dict mapping phenotypes (from obo) to values [0,1]
integer, number of models used to inform the prior
"""
# get a subset of the models that satisfy the criteria
all = [obj for _, obj in models.items()]
hits = filter_entities_cat(all, categories)
# transfer phenotypes into representations
obodefaults = dict.fromkeys(obo.ids(), 0)
freqcounts = dict.fromkeys(list(obo.ids()), dark)
for entity in hits:
# prepare concise representations
rep = Representation(name=entity.id)
for datum in entity.data:
rep.set(datum.phenotype, datum.value)
# convert to complete representation
rep.impute(obo, obodefaults)
# count phenotypes
for phenotype in obo.ids():
freqcounts[phenotype] += rep.data[phenotype]
# convert counts into frequencies
result = counts_p(freqcounts, len(hits), dark)
return result, len(hits)
def test_set_feature_float(self):
"""can set and retrieve values"""
rr = Representation()
rr.set("abc", 1)
self.assertEqual(rr.get("abc"), 1.0)
self.assertTrue(type(rr.get("abc")) is float)
def test_has(self):
"""querying whether a value has been set."""
rr = Representation(dict(xyz=0.2))
rr.set("bob", 0.4)
self.assertTrue(rr.has("xyz"), "set in constructor")
self.assertTrue(rr.has("bob"), "set manually")
self.assertFalse(rr.has("alice"), "not set")
def test_bg_nonverbose(self):
"""compute with background value leaves score the same."""
neutral = Representation(name="TP")
neutral.set("Y:004", self.priors["Y:004"])
chain = self.rs.inference_chain(neutral, "refA", verbose=False)
chain.evaluate()
self.assertEqual(chain.posterior, chain.prior)
def get_complete_null(dbpath):
"""create a complete representation for the null reference"""
result = Representation(name="null")
tab = ReferenceCompletePhenotypeTable(dbpath)
generator = DBGenerator(tab, where=dict(id="null"))
for row in generator.next():
result.set(row["phenotype"], row["value"])
return result
def test_keys(self):
"""setting and getting from a generic representation."""
rr = Representation(dict(xyz=0.2))
rr.set("bob", 0.4)
rr.set("xyz", 0.3)
rr.defaults(self.defaults)
self.assertEqual(rr.keys(), ["abc", "xyz", "bob"],
"keys should have defaults and non-defaults")
def test_impute_fromseeds_lowfirst(self):
"""imputing values from manually-specified seeds."""
rr = Representation(dict())
## specify an inconsistent set of values, DOID:4 is higher in tree, so cannot
## have a lower value than DOID:0014667
rr.set("DOID:0014667", 0.4).set("DOID:4", 0.1)
rr.impute(self.obo, self.obodef, seeds=["DOID:4", "DOID:0014667"])
self.assertEqual(rr.get("DOID:0080015"), 0.1, "child of DOID:4")
self.assertEqual(rr.get("DOID:655"), 0.1, "child of DOID:4")
def test_priors_reps(self):
"""generate priors for phenotypes from representations."""
repA = Representation(name="A")
repA.set("Y:006", 1)
repB = Representation(name="B")
repB.set("Y:001", 0.8)
reps = dict(A=repA, B=repB)
priors, num = get_priors_from_reps(reps, obo)
self.assertGreater(num, 0)
def test_impute_down_ordering(self):
"""updating values in representation via negative evidence."""
r1 = Representation(dict())
r1.set("DOID:3650", 0.01).set("DOID:0014667", 0.05)
r2 = Representation(dict())
r2.set("DOID:3650", 0.01).set("DOID:0014667", 0.05)
# imputation down should not depend on order of the seeds
r1.impute(self.obo, self.obodef, seeds=["DOID:3650", "DOID:0014667"])
r2.impute(self.obo, self.obodef, seeds=["DOID:0014667", "DOID:3650"])
self.assertEqual(r1.data, r2.data, "all values the same")
def test_copy(self):
"""can copy a representation into a new object."""
r1 = Representation(self.defaults, name="hello")
r1.set("abc", 0.5)
result = r1.copy()
self.assertEqual(r1.name, result.name)
self.assertEqual(r1.get("abc"), result.get("abc"))
result.set("abc", 0.75)
self.assertEqual(r1.get("abc"), 0.5)
self.assertEqual(result.get("abc"), 0.75)
def test_general_representation_get(self):
"""setting and getting from a generic representation."""
rr = Representation(dict(xyz=0.2))
rr.set("bob", 0.4)
rr.set("xyz", 0.3)
rr.defaults(self.defaults)
self.assertEqual(rr.get("bob"), 0.4,
"value should come from manual input")
self.assertEqual(rr.get("abc"), 0.1,
"value should come from defaults dict")
self.assertEqual(rr.get("xyz"), 0.3,
"value should come from manual override")
def test_sum_with_impute(self):
"""sum of values associated with the representation."""
rr = Representation(dict())
rr.set("DOID:0014667", 1)
sum1 = rr.sum()
rr.impute(self.obo, self.obodef)
sum2 = rr.sum()
self.assertEqual(sum1, 1, "value of one phenotype")
self.assertGreater(sum2,
2,
msg="value for one phenotype+ancestors+defaults")
def test_general_representation_get2(self):
"""setting and getting from a generic representation."""
# Similar to previous, but setting defaults before the specifics
rr = Representation(dict(abc=0.1, xyz=0.2))
rr.defaults(self.defaults)
rr.set("bob", 0.4)
rr.set("xyz", 0.3)
self.assertEqual(rr.get("bob"), 0.4,
"value should come from manual input")
self.assertEqual(rr.get("abc"), 0.1,
"value should come from defaults dict")
self.assertEqual(rr.get("xyz"), 0.3,
"value should come from manual override")
def test_impute_up_avoid_doubles(self):
"""updating values in representation via positive evidence in DAG"""
rr = Representation(dict())
# DOID:11044 in test ontology has two paths to root (DOID:4)
# one is direct (a shortcut)
# another path is through 0080015
rr.set("DOID:11044", 0.4)
rr.impute(self.obo, self.obodef)
self.assertGreater(rr.get("DOID:0080015"), 0.2,
"ancestor should increase")
self.assertAlmostEqual(
rr.get("DOID:0080015"),
rr.get("DOID:4"),
msg="4 should get bumped once, despite two paths from 11044")
def test_positive_parent(self):
"""fetching a parent term that has a positive value."""
rs = ReferenceSet(dict(refA=0.5), ids=Yobo.ids(), row_priors=Ydefaults)
refA = Representation(name="refA")
refA.set("Y:002", 0.5).set("Y:005", 1).impute(Yobo, Ydefaults)
rs.add(refA)
rs.learn_obo(Yobo)
refAindex = rs.columns["refA"]
self.assertEqual(rs._positive_ancestor(refAindex, rs.rows["Y:002"]),
rs.rows["Y:002"], "Y2 is itself is positive")
self.assertEqual(rs._positive_ancestor(refAindex, rs.rows["Y:007"]),
rs.rows["Y:002"], "Y2 is immediate parent of Y7")
self.assertEqual(rs._positive_ancestor(refAindex, rs.rows["Y:006"]),
rs.rows["Y:005"], "Y5 is immediate parent of Y6")
def test_impute_down(self):
"""updating values in representation via negative evidence."""
rr = Representation(dict())
rr.set("unrelated", 0.8)
rr.set("DOID:0014667", 0.05)
rr.impute(self.obo, self.obodef)
self.assertAlmostEqual(rr.get("unrelated"), 0.8,
"out-of-ontology terms remain")
self.assertAlmostEqual(rr.get("DOID:0014667"), 0.05,
"set value should remain")
self.assertAlmostEqual(rr.get("DOID:4"), 0.2,
"ancestors should remain")
self.assertAlmostEqual(rr.get("DOID:0080015"), 0.2,
"unrelated terms get default")
self.assertAlmostEqual(rr.get("DOID:655"), 0.05,
"children are unaffected")
def make_target_reference(reference, oomap, oo_median=None):
"""convert a single representation from one ontology to another."""
result = Representation(name=reference.name)
result.title = reference.title
for phenotype, value in reference.data.items():
for oo_phenotype, oo_score in oomap[phenotype]:
# perhaps compute a rescaled oo value
newvalue = value
if oo_median is not None:
newvalue = value * tanh(oo_score / oo_median)
# always take the larger value if previously set
if result.has(oo_phenotype):
newvalue = max(newvalue, result.get(oo_phenotype))
result.set(oo_phenotype, newvalue)
return result
def test_impute_up_always_increases(self):
"""updating values in representation via positive evidence."""
rr = Representation(dict())
rr.set("DOID:3650", 0.25)
defaults = self.obodef.copy()
defaults["DOID:0014667"] = 0.5
defaults["DOID:4"] = 1
rr.impute(self.obo, defaults)
self.assertEqual(rr.get("DOID:3650"), 0.25, "set value should remain")
self.assertGreater(rr.get("DOID:0060158"), 0.25,
"ancestors should receive greater score")
self.assertEqual(rr.get("DOID:655"), 0.2,
"unrelated should stay at default")
# ancestor that has already a higher score than what is propagated
self.assertGreater(
rr.get("DOID:0014667"), 0.5,
"ancestor should receive score greater than its prior")
def test_equality(self):
"""checking content of representations."""
r1 = Representation(self.defaults, name="hello")
r2 = Representation(self.defaults, name="hello")
r3 = Representation(self.defaults, name="bye")
r4 = Representation(self.defaults, name="hello")
r4.set("abc", 100)
r5 = Representation()
r6 = Representation(self.defaults, name="hello")
r6.set("qqq", 20)
self.assertTrue(r1.equal(r2), "all is the same")
self.assertFalse(r1.equal(5), "argument is not a Representation")
self.assertFalse(r1.equal(r3), "same content, but different name")
self.assertFalse(r1.equal(r4), "same name, but different content")
self.assertFalse(r1.equal(r5), "r5 is empty")
self.assertFalse(r1.equal(r6), "r6 has more keys")
self.assertFalse(r1.equal(range(4)), "must compare to Representation")
def test_impute_fromseeds_auto(self):
"""imputing values from automatically-ordered seeds."""
# specify an inconsistent set of values, DOID:4 is higher in tree, so cannot
# have a lower value than DOID:0014667
# However, low DOID:4 can impact on other branches
rr1 = Representation(dict())
rr1.set("DOID:0014667", 0.4).set("DOID:4", 0.1)
rr1.impute(self.obo, self.obodef)
# auto seeds
rr2 = Representation(dict())
rr2.set("DOID:0014667", 0.4).set("DOID:4", 0.1)
rr2.impute(self.obo, self.obodef)
# auto seeds, different initial ordering
rr3 = Representation(dict())
rr3.set("DOID:4", 0.1).set("DOID:0014667", 0.4)
rr3.impute(self.obo, self.obodef)
self.assertTrue(rr1.data == rr2.data,
"auto and manual should have same data")
self.assertTrue(rr2.data == rr3.data,
"should be = regardless of input order")
self.assertGreater(rr1.data["DOID:0014667"], 0.2,
"DOID:0014667 increase by direct evidence")
self.assertGreater(rr1.data["DOID:4"], 0.2,
"DOID:4 increases driven by 0014667")
self.assertEqual(rr1.data["DOID:11044"], 0.1,
"low raw DOID:4 propagates down")
def test_underflow(self):
"""attempt to get underflow in individual p."""
# let model have very sure values
model = Representation(name="underflow")
model.set("Y:007", 0.00001).set("Y:004", 1).set("Y:003", 1)
# let ref universe have two annotations and one null
refA = Representation(name="refA").set("Y:003", 1)
refB = Representation(name="refB").set("Y:003", 1)
rs = ReferenceSet(dict(null=0.98, refA=0.001, refB=0.001),
ids=self.obo.ids())
rs.add(refA).add(refB)
rs.learn_obo(self.obo)
rs.prep()
result = rs.inference(model, verbose=True)
self.assertGreaterEqual(result["refA"], 0,
msg="must always be a number, even if zero")
self.assertGreaterEqual(result["refB"], 0,
msg="must always be a number, even if zero")
self.assertGreaterEqual(result["refB"], 0,
msg="must always be a number, even if zero")
def test_impute_up(self):
"""updating values in representation via positive evidence."""
rr = Representation(dict())
rr.set("unrelated", 0.8)
rr.set("DOID:0014667", 0.4)
rr.impute(self.obo, self.obodef)
self.assertEqual(rr.get("unrelated"),
0.8,
msg="out-of-ontology terms remain")
self.assertEqual(rr.get("DOID:0014667"),
0.4,
msg="set value should remain")
self.assertGreater(rr.get("DOID:4"),
0.4,
msg="ancestors should receive greater score")
self.assertEqual(rr.get("DOID:0080015"),
0.2,
msg="unrelated terms get default")
self.assertEqual(rr.get("DOID:655"),
0.2,
msg="children are unaffected")
def test_subset(self):
"""make a refset smaller by ignoring some features."""
# create a reference set
rs = ReferenceSet(OrderedDict(refA=0.5, refB=0.5),
ids=testfeatures,
row_priors=zerovals)
# add some data to the two representations
r1 = Representation(name="refA")
r1.set("a", 0.1).set("b", 0.2).set("c", 0.3).set("d", 0.4)
r2 = Representation(name="refB")
r2.set("c", 0.6).set("d", 0.7).set("e", 0.8)
rs.add(r1).add(r2)
# manually create arrays with the reference set data
expected_raw_A = [0.1, 0.2, 0.3, 0.4, 0.0]
expected_raw_B = [0.0, 0.0, 0.6, 0.7, 0.8]
self.assertTrue(list(rs.data[0]), expected_raw_A)
self.assertTrue(list(rs.data[1]), expected_raw_B)
# subset to a smaller number of features
# myids - here c is repeated twice, z is not in the original features
myids = ["e", "c", "a", "z", "c"]
rs = ReferenceMatrix(rs, myids)
# check new shape (three features and two references)
self.assertEqual(len(rs.rows), 3)
self.assertEqual(len(rs.row_names), 3)
self.assertEqual(rs.data.shape, (3, 2))
# check that the relevant rows are present
result = set(rs.rows.keys())
expected = set(myids)
expected.remove("z")
self.assertEqual(result, expected)
# check data subset in output
output_A = [0.1, 0.3, 0.0]
output_B = [0.0, 0.6, 0.8]
self.assertEqual(sum(rs.data[:, 0]), sum(output_A))
self.assertEqual(sum(rs.data[:, 1]), sum(output_B))
def test_inference_chain(self):
"""compute an inference chain."""
# create a reference set
refA = Representation(name="refA")
refA.set("Y:002", 1).impute(Yobo, Ydefaults)
refB = Representation(name="refB")
refB.set("Y:001", 1).impute(Yobo, Ydefaults)
rs = ReferenceSet(dict(refA=0.5, refB=0.5),
ids=Yobo.ids(),
row_priors=Ydefaults)
rs.add(refA).add(refB)
rs.learn_obo(Yobo)
# compute a chain object explaining scoring steps
chain = rs.inference_chain(refA, "refB", verbose=True)
self.assertEqual(chain.__dict__["model"], "refA")
self.assertEqual(chain.__dict__["reference"], "refB")
self.assertGreater(len(chain.data), 2,
"data chain should describe multiple features")
self.assertTrue("background" in chain.data[0].__dict__,
"chain data have comparison information")
self.assertTrue("result" in chain.data[0].__dict__,
"chain data have TP/FP/etc codes")
def test_FP_with_fp_penalty(self):
"""FP increases more with lower fp_penalty"""
# make a new reference set with different priors
priors2 = self.priors.copy()
priors2["Y:003"] = 0.4
priors2["Y:002"] = 0.15
priors2["Y:007"] = 0.1
rs2 = ReferenceSet(dict(null=0.4, ref=0.3),
ids=Yobo.ids(),
row_priors=priors2)
ref = Representation(name="ref")
ref.set("Y:001", 1).impute(Yobo, priors2)
ref.set("Y:007", priors2["Y:007"] / 2)
rs2.add(self.refnull).add(ref)
rs2.learn_obo(Yobo)
FP = Representation(name="model").set("Y:007", 0.35)
chain1 = rs2.inference_chain(FP, "ref", verbose=True, fp_penalty=0.1)
chain1.evaluate_inference()
self.assertGreater(chain1.posterior, chain1.prior)
chain2 = rs2.inference_chain(FP, "ref", verbose=True, fp_penalty=1)
chain2.evaluate_inference()
self.assertLess(chain2.posterior, chain1.posterior)
class ReferenceMatrixTests(unittest.TestCase):
"""Test cases for computing average representation of neighbors"""
def setUp(self):
"""prepare a reference set with a few references."""
self.refnull = Representation(data=null_defaults, name="null")
self.refA = Representation(data=Ydefaults, name="refA")
self.refA.set("Y:004", 1)
self.refB = Representation(data=Ydefaults, name="refB")
self.refB.set("Y:004", 1).set("Y:001", 0.6).set("Y:003", 0.5)
self.refC = Representation(data=Ydefaults, name="refC")
self.refC.set("Y:004", 1).set("Y:001", 0.5)
self.refD = Representation(data=Ydefaults, name="refD")
self.refD.set("Y:004", 0.1)
ref_priors = dict(null=0.1, refA=0.1, refB=0.1, refC=0.1, refD=0.1)
rs = ReferenceSet(ref_priors, ids=Ydefaults.keys())
rs.add(self.refnull)
rs.add(self.refA).add(self.refB)
rs.add(self.refC).add(self.refD)
self.rm = ReferenceMatrix(rs, list(Ydefaults.keys()))
def test_neighbors_cosine(self):
"""identify neighboring/similar references."""
indexes = dict()
for _ in ["refA", "refB", "refC", "refD"]:
indexes[_] = self.rm.columns[_]
result = self.rm.nearest_neighbors("refC", 2)
expected = ["refB", "refA"]
self.assertEqual(result, expected)
def test_average(self):
"""compute an average of several representations."""
result = self.rm.get_average(["refB", "refC"])
self.assertAlmostEqual(result.get("Y:004"), 1.0)
self.assertAlmostEqual(result.get("Y:001"), 0.55)
self.assertAlmostEqual(result.get("Y:007"), Ydefaults["Y:007"])
def test_set_feature(self):
"""can set and retrieve values"""
rr = Representation()
rr.set("abc", 0.2)
self.assertEqual(rr.get("abc"), 0.2)
