def test_sequence_table():
alphabet = Alphabet.create(b"ACGT", b"X")
seqt = SequenceTable.create(alphabet)
with pytest.raises(RuntimeError):
seqt.normalize()
seqt.add(Sequence.create(b"AGTG", alphabet), log(0.2))
seqt.add(Sequence.create(b"T", alphabet), log(1.2))
assert_allclose(seqt.lprob(Sequence.create(b"AGTG", alphabet)), log(0.2))
assert_allclose(seqt.lprob(Sequence.create(b"T", alphabet)), log(1.2))
assert lprob_is_zero(seqt.lprob(Sequence.create(b"", alphabet)))
with pytest.raises(RuntimeError):
seqt.lprob(Sequence.create(b"AT", Alphabet.create(b"AT", b"X")))
with pytest.raises(RuntimeError):
seqt.add(Sequence.create(b"AT", Alphabet.create(b"AT", b"X")), log(0.2))
seqt.normalize()
assert_allclose(seqt.lprob(Sequence.create(b"AGTG", alphabet)), log(0.2 / 1.4))
assert_allclose(
seqt.lprob(Sequence.create(b"T", alphabet)), log(1.2 / 1.4), rtol=1e-6
)
def test_alphabet():
abc = Alphabet.create(b"ACGT", b"X")
assert abc.length == 4
assert abc.has_symbol(b"A")
assert abc.has_symbol(b"C")
assert abc.has_symbol(b"G")
assert abc.has_symbol(b"T")
assert abc.symbol_idx(b"A") == 0
assert abc.symbol_idx(b"C") == 1
assert abc.symbol_idx(b"G") == 2
assert abc.symbol_idx(b"T") == 3
assert abc.symbol_id(0) == b"A"
assert abc.symbol_id(1) == b"C"
assert abc.symbol_id(2) == b"G"
assert abc.symbol_id(3) == b"T"
assert abc.symbols == b"ACGT"
assert str(abc) == "{ACGT}"
assert repr(abc) == "<Alphabet:{ACGT}>"
with pytest.raises(TypeError):
Alphabet.create("ACGTç", b"X")
with pytest.raises(RuntimeError):
Alphabet.create("ACGTç".encode(), b"X")
def imm_example():
alphabet = Alphabet.create(b"AC", b"X")
hmm = HMM.create(alphabet)
S = MuteState.create(b"S", alphabet)
hmm.add_state(S, log(1.0))
E = MuteState.create(b"E", alphabet)
hmm.add_state(E, lprob_zero())
M1 = NormalState.create(b"M1", alphabet, [log(0.8), log(0.2)])
hmm.add_state(M1, lprob_zero())
M2 = NormalState.create(b"M2", alphabet, [log(0.4), log(0.6)])
hmm.add_state(M2, lprob_zero())
hmm.set_transition(S, M1, log(1.0))
hmm.set_transition(M1, M2, log(1.0))
hmm.set_transition(M2, E, log(1.0))
hmm.set_transition(E, E, log(1.0))
hmm.normalize()
hmm.set_transition(E, E, lprob_zero())
dp = hmm.create_dp(E)
return {"hmm": hmm, "dp": dp, "alphabet": alphabet}
def test_hmm_viterbi_2():
alphabet = Alphabet.create(b"AC", b"X")
hmm = HMM.create(alphabet)
S = MuteState.create(b"S", alphabet)
hmm.add_state(S, log(1.0))
E = MuteState.create(b"E", alphabet)
hmm.add_state(E, lprob_zero())
M1 = NormalState.create(b"M1", alphabet, [log(0.8), log(0.2)])
hmm.add_state(M1, lprob_zero())
M2 = NormalState.create(b"M2", alphabet, [log(0.4), log(0.6)])
hmm.add_state(M2, lprob_zero())
hmm.set_transition(S, M1, log(1.0))
hmm.set_transition(M1, M2, log(1.0))
hmm.set_transition(M2, E, log(1.0))
hmm.set_transition(E, E, log(1.0))
hmm.normalize()
hmm.set_transition(E, E, lprob_zero())
dp = hmm.create_dp(E)
dp_task = DPTask.create(dp)
seq = Sequence.create(b"AC", alphabet)
dp_task.setup(seq)
r = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(seq, r.path), log(0.48))
seq = Sequence.create(b"AA", alphabet)
dp_task.setup(seq)
r = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(seq, r.path), log(0.32))
seq = Sequence.create(b"CA", alphabet)
dp_task.setup(seq)
r = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(seq, r.path), log(0.08))
seq = Sequence.create(b"CC", alphabet)
dp_task.setup(seq)
r = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(seq, r.path), log(0.12))
hmm.set_transition(M1, E, log(1.0))
seq = Sequence.create(b"AC", alphabet)
dp = hmm.create_dp(E)
dp_task = DPTask.create(dp)
dp_task.setup(seq)
r = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(seq, r.path), log(0.48))
seq = Sequence.create(b"AA", alphabet)
dp_task.setup(seq)
r = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(seq, r.path), log(0.32))
def test_mute_state():
alphabet = Alphabet.create(b"ACGU", b"X")
state = MuteState.create(b"S", alphabet)
assert state.name == b"S"
assert_allclose(state.lprob(Sequence.create(b"", alphabet)), log(1.0))
assert lprob_is_zero(state.lprob(Sequence.create(b"AC", alphabet)))
assert state.min_seq == 0
assert state.max_seq == 0
assert str(state) == "S"
assert repr(state) == "<MuteState:S>"
def test_table_state():
alphabet = Alphabet.create(b"ACGU", b"X")
seqt = SequenceTable.create(alphabet)
seqt.add(Sequence.create(b"AUG", alphabet), log(0.8))
seqt.add(Sequence.create(b"AUU", alphabet), log(0.4))
state = TableState.create(b"M2", seqt)
assert state.name == b"M2"
assert_allclose(state.lprob(Sequence.create(b"AUG", alphabet)), log(0.8))
assert_allclose(state.lprob(Sequence.create(b"AUU", alphabet)), log(0.4))
assert str(state) == "M2"
assert repr(state) == "<TableState:M2>"
def test_hmm_trans_prob():
alphabet = Alphabet.create(b"ACGU", b"X")
hmm = HMM.create(alphabet)
S = MuteState.create(b"S", alphabet)
with pytest.raises(RuntimeError):
hmm.set_start_lprob(S, log(0.4))
hmm.add_state(S)
E = MuteState.create(b"E", alphabet)
with pytest.raises(RuntimeError):
hmm.transition(S, E)
with pytest.raises(ValueError):
hmm.set_transition(S, E, lprob_zero())
with pytest.raises(ValueError):
hmm.set_transition(E, S, lprob_zero())
with pytest.raises(ValueError):
hmm.del_state(E)
hmm.add_state(E)
with pytest.raises(RuntimeError):
hmm.set_transition(E, S, lprob_invalid())
with pytest.raises(ValueError):
hmm.normalize()
hmm.set_transition(S, E, log(0.5))
assert_allclose(hmm.transition(S, S), lprob_zero())
assert_allclose(hmm.transition(S, E), log(0.5))
assert_allclose(hmm.transition(E, S), lprob_zero())
assert_allclose(hmm.transition(E, E), lprob_zero())
with pytest.raises(ValueError):
hmm.normalize()
with pytest.raises(ValueError):
hmm.normalize()
hmm.set_start_lprob(S, log(0.4))
hmm.set_transition(E, E, log(0.1))
hmm.normalize()
assert_allclose(hmm.transition(S, E), log(1.0))
assert_allclose(hmm.transition(E, S), lprob_zero())
assert_allclose(hmm.transition(S, S), lprob_zero())
assert_allclose(hmm.transition(E, E), log(1.0))
def test_normal_state():
alphabet = Alphabet.create(b"ACGT", b"X")
state = NormalState.create(
b"M0",
alphabet,
[log(0.1), log(0.2), log(0.3), log(0.3)],
)
assert state.name == b"M0"
assert_allclose(state.lprob(Sequence.create(b"A", alphabet)), log(0.1))
assert_allclose(state.lprob(Sequence.create(b"C", alphabet)), log(0.2))
assert_allclose(state.lprob(Sequence.create(b"G", alphabet)), log(0.3))
assert_allclose(state.lprob(Sequence.create(b"T", alphabet)), log(0.3))
assert state.min_seq == 1
assert state.max_seq == 1
with pytest.raises(RuntimeError):
state.lprob(Sequence.create(b"T", Alphabet.create(b"ACGT", b"X")))
assert lprob_is_zero(state.lprob(Sequence.create(b"AC", alphabet)))
assert str(state) == "M0"
assert repr(state) == "<NormalState:M0>"
def test_sequence():
alphabet = Alphabet.create(b"ACGT", b"X")
seq = Sequence.create(b"ACAAAGATX", alphabet)
assert len(seq) == 9
assert bytes(seq) == b"ACAAAGATX"
assert str(seq) == "ACAAAGATX"
assert repr(seq) == "<Sequence:ACAAAGATX>"
Sequence.create(b"ACGXXT", alphabet)
with pytest.raises(RuntimeError):
Sequence.create(b"ACGWT", alphabet)
with pytest.raises(RuntimeError):
Sequence.create("ACGTç".encode(), alphabet)
def test_hmm_viterbi_1():
alphabet = Alphabet.create(b"ACGU", b"X")
hmm = HMM.create(alphabet)
S = MuteState.create(b"S", alphabet)
hmm.add_state(S, log(1.0))
E = MuteState.create(b"E", alphabet)
hmm.add_state(E, lprob_zero())
M1 = NormalState.create(
b"M1",
alphabet,
[log(0.8), log(0.2), lprob_zero(), lprob_zero()],
)
hmm.add_state(M1, lprob_zero())
M2 = NormalState.create(
b"M2",
alphabet,
[log(0.4 / 1.6), log(0.6 / 1.6), lprob_zero(), log(0.6 / 1.6)],
)
hmm.add_state(M2, lprob_zero())
hmm.set_transition(S, M1, log(1.0))
hmm.set_transition(M1, M2, log(1.0))
hmm.set_transition(M2, E, log(1.0))
hmm.set_transition(E, E, log(1.0))
hmm.normalize()
hmm.set_transition(E, E, lprob_zero())
assert_allclose(hmm.transition(E, E), lprob_zero())
assert_allclose(hmm.transition(S, S), lprob_zero())
assert_allclose(hmm.transition(S, E), lprob_zero())
assert_allclose(hmm.transition(E, S), lprob_zero())
dp = hmm.create_dp(E)
dp_task = DPTask.create(dp)
seq = Sequence.create(b"AC", alphabet)
dp_task.setup(seq)
result = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(seq, result.path), log(0.3))
def test_hmm_states():
alphabet = Alphabet.create(b"ACGU", b"X")
hmm = HMM.create(alphabet)
S = MuteState.create(b"S", alphabet)
hmm.add_state(S)
seqt = SequenceTable.create(alphabet)
seqt.add(Sequence.create(b"AGU", alphabet), log(0.8))
seqt.add(Sequence.create(b"AGG", alphabet), log(0.2))
M = TableState.create(b"M", seqt)
hmm.add_state(M)
with pytest.raises(ValueError):
hmm.add_state(S)
with pytest.raises(ValueError):
hmm.add_state(M)
assert len(hmm.states()) == 2
def test_hmm_loglikelihood():
alphabet = Alphabet.create(b"ACGU", b"X")
hmm = HMM.create(alphabet)
S = MuteState.create(b"S", alphabet)
hmm.add_state(S, log(1.0))
E = MuteState.create(b"E", alphabet)
hmm.add_state(E, lprob_zero())
M1 = NormalState.create(
b"M1",
alphabet,
[log(0.8), log(0.2), lprob_zero(), lprob_zero()],
)
hmm.add_state(M1, lprob_zero())
M2 = NormalState.create(
b"M2", alphabet, [log(0.4 / 1.6), log(0.6 / 1.6), lprob_zero(), log(0.6 / 1.6)]
)
hmm.add_state(M2, lprob_zero())
hmm.set_transition(S, M1, log(1.0))
hmm.set_transition(M1, M2, log(1.0))
hmm.set_transition(M2, E, log(1.0))
hmm.set_transition(E, E, log(1.0))
hmm.normalize()
p = hmm.loglikelihood(
Sequence.create(b"AC", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, log(0.3))
p = hmm.loglikelihood(
Sequence.create(b"AA", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, log(0.2))
p = hmm.loglikelihood(
Sequence.create(b"AG", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, lprob_zero())
p = hmm.loglikelihood(
Sequence.create(b"AU", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, log(0.3))
p = hmm.loglikelihood(
Sequence.create(b"CC", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, log(0.075))
p = hmm.loglikelihood(
Sequence.create(b"CA", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, log(0.05))
p = hmm.loglikelihood(
Sequence.create(b"CG", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, lprob_zero())
p = hmm.loglikelihood(
Sequence.create(b"CG", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, lprob_zero())
p = hmm.loglikelihood(
Sequence.create(b"CU", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, log(0.075))
p = hmm.loglikelihood(
Sequence.create(b"GC", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, lprob_zero())
p = hmm.loglikelihood(
Sequence.create(b"GA", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, lprob_zero())
p = hmm.loglikelihood(
Sequence.create(b"GG", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, lprob_zero())
p = hmm.loglikelihood(
Sequence.create(b"GU", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, lprob_zero())
p = hmm.loglikelihood(
Sequence.create(b"UC", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, lprob_zero())
p = hmm.loglikelihood(
Sequence.create(b"UA", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, lprob_zero())
p = hmm.loglikelihood(
Sequence.create(b"UG", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, lprob_zero())
p = hmm.loglikelihood(
Sequence.create(b"UU", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M2, 1),
Step.create(E, 0),
]
),
)
assert_allclose(p, lprob_zero())
M3 = NormalState.create(
b"M2",
alphabet,
[log(0.4), log(0.6), lprob_zero(), log(0.6)],
)
with pytest.raises(ValueError):
hmm.loglikelihood(
Sequence.create(b"UU", alphabet),
Path.create(
[
Step.create(S, 0),
Step.create(M1, 1),
Step.create(M3, 1),
Step.create(E, 0),
]
),
)
def test_hmm_viterbi_3():
alphabet = Alphabet.create(b"AC", b"X")
hmm = HMM.create(alphabet)
S = MuteState.create(b"S", alphabet)
hmm.add_state(S, log(1.0))
E = MuteState.create(b"E", alphabet)
hmm.add_state(E, lprob_zero())
M1 = NormalState.create(b"M1", alphabet, [log(0.8), log(0.2)])
hmm.add_state(M1, lprob_zero())
D1 = MuteState.create(b"D1", alphabet)
hmm.add_state(D1, lprob_zero())
M2 = NormalState.create(b"M2", alphabet, [log(0.4), log(0.6)])
hmm.add_state(M2, lprob_zero())
D2 = MuteState.create(b"D2", alphabet)
hmm.add_state(D2, lprob_zero())
hmm.set_transition(S, M1, log(0.8))
hmm.set_transition(S, D1, log(0.2))
hmm.set_transition(M1, M2, log(0.8))
hmm.set_transition(M1, D2, log(0.2))
hmm.set_transition(D1, D2, log(0.2))
hmm.set_transition(D1, M2, log(0.8))
hmm.set_transition(D2, E, log(1.0))
hmm.set_transition(M2, E, log(1.0))
hmm.set_transition(E, E, log(1.0))
hmm.normalize()
hmm.set_transition(E, E, lprob_zero())
dp = hmm.create_dp(E)
dp_task = DPTask.create(dp)
seq = Sequence.create(b"AC", alphabet)
dp_task.setup(seq)
result = dp.viterbi(dp_task)
score = hmm.loglikelihood(seq, result.path)
assert bytes(result.sequence) == b"AC"
path = result.path
steps = list(path)
assert steps[0].seq_len == 0
assert steps[1].seq_len == 1
assert steps[2].seq_len == 1
assert steps[3].seq_len == 0
assert_allclose(score, log(0.3072))
seq = Sequence.create(b"AA", alphabet)
dp_task.setup(seq)
r = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(seq, r.path), log(0.2048))
seq = Sequence.create(b"A", alphabet)
dp_task.setup(seq)
r = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(seq, r.path), log(0.128))
seq = Sequence.create(b"AC", alphabet)
dp_task.setup(seq)
r = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(seq, r.path), log(0.3072))
dp = hmm.create_dp(M2)
dp_task = DPTask.create(dp)
seq = Sequence.create(b"AC", alphabet)
dp_task.setup(seq)
r = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(seq, r.path), log(0.3072))
hmm.del_state(E)
dp = hmm.create_dp(M2)
dp_task = DPTask.create(dp)
seq = Sequence.create(b"AC", alphabet)
dp_task.setup(seq)
result = dp.viterbi(dp_task)
assert_allclose(hmm.loglikelihood(seq, result.path), log(0.3072))