def test_should_print_class_name(self):
# setup
sumofpairs = SumOfPairs().get_name()
star = Star().get_name()
entropy = Entropy().get_name()
# check
self.assertEqual("SumOfPairs", sumofpairs)
self.assertEqual("Star", star)
self.assertEqual("Entropy", entropy)
def test_get_score_of_column_with_only_gaps(self):
# setup
column = MSA(['-', '-', '-'])
# results
result = SumOfPairs(column).get_column_score(0)
expected = 3
# check
self.assertEqual(expected, result)
def test_only_gaps_with_BLOSUM62(self):
# setup
sequences = MSA(['---', '---'])
# results
result = SumOfPairs(sequences, Blosum62()).compute()
expected = 3
# check
self.assertEqual(expected, result)
def test_basic_score_with_gaps_BLOSUM62(self):
# setup
sequences = MSA(['FA', 'A-'])
# results
result = SumOfPairs(sequences, Blosum62()).compute()
expected = -10
# check
self.assertEqual(expected, result)
def test_basic_score_of_12_with_PAM250(self):
# setup
sequences = MSA(['AA', 'AA', 'AA'])
# results
result = SumOfPairs(sequences, PAM250()).compute()
expected = 12
# check
self.assertEqual(expected, result)
def run_all_scores(msa: list) -> None:
align_sequences = list(pair[1] for pair in msa)
sequences_id = list(pair[0] for pair in msa)
# Percentage of non-gaps and totally conserved columns
non_gaps = PercentageOfNonGaps()
totally_conserved_columns = PercentageOfTotallyConservedColumns()
percentage = non_gaps.compute(align_sequences)
conserved = totally_conserved_columns.compute(align_sequences)
print("Percentage of non-gaps: {0} %".format(percentage))
print("Percentage of totally conserved columns: {0}".format(conserved))
# Entropy
value = Entropy().compute(align_sequences=align_sequences)
print("Entropy score: {0}".format(value))
# Sum of pairs
value = SumOfPairs(Blosum62()).compute(align_sequences=align_sequences)
print("SumOfPairs score (Blosum62): {0}".format(value))
value = SumOfPairs(PAM250()).compute(align_sequences=align_sequences)
print("SumOfPairs score (PAM250): {0}".format(value))
value = SumOfPairs(FileMatrix('PAM380.txt')).compute(align_sequences=align_sequences)
print("SumOfPairs score (PAM380): {0}".format(value))
# Star
value = Star(Blosum62()).compute(align_sequences=align_sequences)
print("Star score (Blosum62): {0}".format(value))
value = Star(PAM250()).compute(align_sequences=align_sequences)
print("Star score (PAM250): {0}".format(value))
# STRIKE
value = Strike().compute(align_sequences=align_sequences,
sequences_id=sequences_id,
chains=['A', 'E', 'A', 'A'])
print("STRIKE score: {0}".format(value))
def test_get_score_of_an_alignment(self):
# setup
sequences = \
MSA(['---GKGDPKKPRGKMSSYAFFVQTSREEHKKKHPDASVNFSEFSKKCSERWKTMSAKEKGKFEDMAKADKARYEREMKTYI------PPKGE----',
'------MQDRVKRPMNAFIVWSRDQRRKMALENPRMR-NS-EISKQLGYQWKMLTEAEKWPFFQEAQKLQAMHREKYPNYKYRP---RRKAKMLPK',
'MKKLKKHPDFPKKPLTPYFRFFMEKRAKYAKLHPEMS-NL-DLTKILSKKYKELPEKKKMKYIQDFQREKQEFERNLARFREDH---PDLIQNAKK',
'--------MHIKKPLNAFMLYMKEMRANVVAES-TLK-ESAAINQILGRRWHALSREEQAKYYELARKERQLHMQLYPGWSARDNYGKKKKRKREK'])
# results
result = SumOfPairs(sequences, PAM250()).compute()
expected = 24
# check
self.assertEqual(expected, result)
from sequoya.util.solution import get_representative_set
from sequoya.util.visualization import MSAPlot
if __name__ == '__main__':
# setup Dask client (web interface will be initialized at http://127.0.0.1:8787/workers)
cluster = LocalCluster(n_workers=4, processes=True)
client = Client(cluster)
ncores = sum(client.ncores().values())
print(f'{ncores} cores available')
# creates the problem
problem = BAliBASE(
instance='BB20019',
path='../resources',
score_list=[SumOfPairs(),
PercentageOfTotallyConservedColumns()])
# creates the algorithm
max_evaluations = 200000
reference_point = [-175000, -1.35]
algorithm = DistributedNSGAII(
problem=problem,
population_size=100,
mutation=ShiftClosedGapGroups(probability=0.3),
crossover=SPXMSA(probability=0.7),
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations),
dominance_comparator=GDominanceComparator(reference_point),
number_of_cores=ncores,
def setUp(self):
self.sumofpairs_PAM250 = SumOfPairs(PAM250())
self.sumofpairs_Blosum62 = SumOfPairs(Blosum62())
class SumOfPairsTestCases(unittest.TestCase):
def setUp(self):
self.sumofpairs_PAM250 = SumOfPairs(PAM250())
self.sumofpairs_Blosum62 = SumOfPairs(Blosum62())
def test_basic_score_of_12_with_PAM250(self):
# setup
sequences = ['AA', 'AA', 'AA']
# results
result = self.sumofpairs_PAM250.compute(sequences)
expected = 12
# check
self.assertEqual(expected, result)
def test_basic_score_of_12_with_BLOSUM62(self):
# setup
sequences = ['AA', 'AA', 'AA']
# results
result = self.sumofpairs_Blosum62.compute(sequences)
expected = 24
# check
self.assertEqual(expected, result)
def test_basic_score_with_gaps_BLOSUM62(self):
# setup
sequences = ['FA', 'A-']
# results
result = self.sumofpairs_Blosum62.compute(sequences)
expected = -10
# check
self.assertEqual(expected, result)
def test_only_gaps_with_BLOSUM62(self):
# setup
sequences = ['---', '---']
# results
result = self.sumofpairs_Blosum62.compute(sequences)
expected = 3
# check
self.assertEqual(expected, result)
def test_get_score_of_column_with_only_gaps(self):
# setup
column = ['-', '-', '-']
# results
result = self.sumofpairs_Blosum62.get_score_of_k_column(column)
expected = 3
# check
self.assertEqual(expected, result)
def test_get_score_of_an_alignment(self):
# setup
sequences = \
['---GKGDPKKPRGKMSSYAFFVQTSREEHKKKHPDASVNFSEFSKKCSERWKTMSAKEKGKFEDMAKADKARYEREMKTYI------PPKGE----',
'------MQDRVKRPMNAFIVWSRDQRRKMALENPRMR-NS-EISKQLGYQWKMLTEAEKWPFFQEAQKLQAMHREKYPNYKYRP---RRKAKMLPK',
'MKKLKKHPDFPKKPLTPYFRFFMEKRAKYAKLHPEMS-NL-DLTKILSKKYKELPEKKKMKYIQDFQREKQEFERNLARFREDH---PDLIQNAKK',
'--------MHIKKPLNAFMLYMKEMRANVVAES-TLK-ESAAINQILGRRWHALSREEQAKYYELARKERQLHMQLYPGWSARDNYGKKKKRKREK']
# results
result = self.sumofpairs_PAM250.compute(sequences)
expected = 24
# check
self.assertEqual(expected, result)
from sequoya.operator import SPXMSA, ShiftClosedGapGroups
from sequoya.problem import BAliBASE
from sequoya.util.solution import get_representative_set
from sequoya.util.visualization import MSAPlot
if __name__ == '__main__':
# setup Dask client (web interface will be initialized at http://127.0.0.1:8787/workers)
cluster = LocalCluster(processes=True)
client = Client(cluster)
ncores = sum(client.ncores().values())
print(f'{ncores} cores available')
# creates the problem
problem = BAliBASE(instance='BB50011', path='../resources',
score_list=[SumOfPairs(), PercentageOfTotallyConservedColumns()])
# creates the algorithm
max_evaluations = 25000
algorithm = DistributedNSGAII(
problem=problem,
population_size=100,
mutation=ShiftClosedGapGroups(probability=0.4),
crossover=SPXMSA(probability=0.7),
termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations),
number_of_cores=ncores,
client=client
)
algorithm.observable.register(observer=ProgressBarObserver(max=max_evaluations))
