def main(args=None):
init_log()
if args is None:
args = sys.argv[1:]
np.seterr(all='raise')
# so some option parsing
parser, ns, args = init_args(description="Predict epitope sites.", args=args)
parser = hmmer_args(parser)
parser = featsel_args(parser)
parser = feature_args(parser)
parser = mrmr_args(parser)
parser = rfe_args(parser)
parser = optstat_args(parser)
parser = filter_args(parser)
parser = svm_args(parser)
parser = cv_args(parser)
parser.add_argument('ANTIBODY', type=AntibodyTypeFactory(ns.DATA), nargs='+')
ARGS = parse_args(parser, args, namespace=ns)
# do some argument parsing
if ARGS.TEST:
test_discrete(ARGS)
finalize_args(ARGS)
return {}
# maxrel doesn't support similar
if ARGS.MRMR_METHOD == 'MAXREL':
ARGS.SIMILAR = 0.0
antibodies = tuple(ARGS.ANTIBODY)
# set the util params
set_util_params(ARGS.REFSEQ.id)
# grab the relevant antibody from the SQLITE3 data
# format as SeqRecord so we can output as FASTA
# and generate an alignment using HMMER if it doesn't already exist
seqrecords, clonal, antibodies = ARGS.DATA.seqrecords(antibodies, ARGS.CLONAL)
# if we're doing LOOCV, make sure we set CV_FOLDS appropriately
if ARGS.LOOCV:
ARGS.CV_FOLDS = len(seqrecords)
ab_basename = ''.join((
'+'.join(antibodies),
'_dna' if ARGS.ENCODER == DNAEncoder else '_amino',
'_clonal' if clonal else ''
))
alignment_basename = '_'.join((
ab_basename,
ARGS.DATA.basename_root,
__version__
))
sto_filename = alignment_basename + '.sto'
# don't capture the second variable, let it be gc'd
alignment = generate_alignment(seqrecords, sto_filename, is_refseq, ARGS)[0]
re_pngs = re_compile(r'N[^P][TS][^P]', re_I)
ylabeler = Labeler(
partial(expression, ARGS.LABEL),
partial(skipper, is_refseq, ARGS.SUBTYPES)
)
alignment, y, threshold = ylabeler(alignment)
filter = naive_filter(
max_conservation=ARGS.MAX_CONSERVATION,
min_conservation=ARGS.MIN_CONSERVATION,
max_gap_ratio=ARGS.MAX_GAP_RATIO
)
extractors = [('site_ident', MSAVectorizer(ARGS.ENCODER, filter))]
if ARGS.RADIUS:
extractors.append(('pair_ident', MSAVectorizerPairwise(ARGS.ENCODER, filter, ARGS.RADIUS)))
if ARGS.PNGS:
extractors.append(('pngs', MSAVectorizerRegex(re_pngs, 4, name='PNGS')))
if ARGS.PNGS_PAIRS:
extractors.append(
('pngs_pair', MSAVectorizerRegexPairwise(re_pngs, 4, name='PNGS'))
)
extractor = FeatureUnion(extractors, n_jobs=1) # n_jobs must be 1 for now
X = extractor.fit_transform(alignment)
assert y.shape[0] == X.shape[0], \
"number of classes doesn't match the data: %d vs %d" % (y.shape[0], X.shape[0])
scorer = Scorer(ARGS.OPTSTAT)
# do grid-search as part of the svm to avoid
# performing feature selection on every iteration
# of the grid search, which naturally takes forever
svm = GridSearchCV(
estimator=SVC(kernel='linear', class_weight='auto'),
param_grid=dict(C=list(C_range(*ARGS.LOG2C))),
scoring=scorer,
n_jobs=int(getenv('NCPU', -1)),
pre_dispatch='3 * n_jobs',
cv=ARGS.CV_FOLDS - 1
)
results = None
for n_features in ARGS.FEATURE_GRID:
results_ = Results(extractor.get_feature_names(), scorer, ARGS.SIMILAR)
for train_idxs, test_idxs in StratifiedKFold(y, ARGS.CV_FOLDS):
if train_idxs.sum() < 1 or test_idxs.sum() < 1:
y_true = y[test_idxs]
results_.add(y_true, y_true, {})
continue
X_train = X[train_idxs]
y_train = y[train_idxs]
if ARGS.RFE:
clf = RFE(
estimator=svm,
n_features_to_select=n_features,
step=ARGS.RFE_STEP
)
else:
mrmr = MRMR(
k=n_features,
method=ARGS.MRMR_METHOD,
normalize=ARGS.MRMR_NORMALIZE,
similar=ARGS.SIMILAR
)
clf = Pipeline([('mrmr', mrmr), ('svm', svm)])
clf.fit(X_train, y_train)
X_test = X[test_idxs]
y_true = y[test_idxs]
if ARGS.RFE:
selector_ = clf
svm_ = clf.estimator_.best_estimator_
else:
selector_ = clf.named_steps['mrmr']
svm_ = clf.named_steps['svm'].best_estimator_
y_pred = clf.predict(X_test)
coefs, ranks = coefs_ranks(selector_.ranking_, selector_.support_, svm_.coef_)
results_.add(y_true, y_pred, coefs, ranks)
if results is None or results_ > results:
results = results_
# the alignment reflects the number of sequences either naturally
results.metadata(antibodies, ARGS.LABEL)
print(results.dumps(), file=ARGS.OUTPUT)
finalize_args(ARGS)
return results
def main(args=None):
init_log()
if args is None:
args = sys.argv[1:]
np.seterr(all='raise')
# so some option parsing
parser, ns, args = init_args(description="Predict epitope sites.",
args=args)
parser = hmmer_args(parser)
parser = featsel_args(parser)
parser = feature_args(parser)
parser = mrmr_args(parser)
parser = rfe_args(parser)
parser = optstat_args(parser)
parser = filter_args(parser)
parser = svm_args(parser)
parser = cv_args(parser)
parser.add_argument('ANTIBODY',
type=AntibodyTypeFactory(ns.DATA),
nargs='+')
ARGS = parse_args(parser, args, namespace=ns)
# do some argument parsing
if ARGS.TEST:
test_discrete(ARGS)
finalize_args(ARGS)
return {}
# maxrel doesn't support similar
if ARGS.MRMR_METHOD == 'MAXREL':
ARGS.SIMILAR = 0.0
antibodies = tuple(ARGS.ANTIBODY)
# set the util params
set_util_params(ARGS.REFSEQ.id)
# grab the relevant antibody from the SQLITE3 data
# format as SeqRecord so we can output as FASTA
# and generate an alignment using HMMER if it doesn't already exist
seqrecords, clonal, antibodies = ARGS.DATA.seqrecords(
antibodies, ARGS.CLONAL)
# if we're doing LOOCV, make sure we set CV_FOLDS appropriately
if ARGS.LOOCV:
ARGS.CV_FOLDS = len(seqrecords)
ab_basename = ''.join(('+'.join(antibodies),
'_dna' if ARGS.ENCODER == DNAEncoder else '_amino',
'_clonal' if clonal else ''))
alignment_basename = '_'.join(
(ab_basename, ARGS.DATA.basename_root, __version__))
sto_filename = alignment_basename + '.sto'
# don't capture the second variable, let it be gc'd
alignment = generate_alignment(seqrecords, sto_filename, is_refseq,
ARGS)[0]
re_pngs = re_compile(r'N[^P][TS][^P]', re_I)
ylabeler = Labeler(partial(expression, ARGS.LABEL),
partial(skipper, is_refseq, ARGS.SUBTYPES))
alignment, y, threshold = ylabeler(alignment)
filter = naive_filter(max_conservation=ARGS.MAX_CONSERVATION,
min_conservation=ARGS.MIN_CONSERVATION,
max_gap_ratio=ARGS.MAX_GAP_RATIO)
extractors = [('site_ident', MSAVectorizer(ARGS.ENCODER, filter))]
if ARGS.RADIUS:
extractors.append(('pair_ident',
MSAVectorizerPairwise(ARGS.ENCODER, filter,
ARGS.RADIUS)))
if ARGS.PNGS:
extractors.append(('pngs', MSAVectorizerRegex(re_pngs, 4,
name='PNGS')))
if ARGS.PNGS_PAIRS:
extractors.append(
('pngs_pair', MSAVectorizerRegexPairwise(re_pngs, 4, name='PNGS')))
extractor = FeatureUnion(extractors, n_jobs=1) # n_jobs must be 1 for now
X = extractor.fit_transform(alignment)
assert y.shape[0] == X.shape[0], \
"number of classes doesn't match the data: %d vs %d" % (y.shape[0], X.shape[0])
scorer = Scorer(ARGS.OPTSTAT)
# do grid-search as part of the svm to avoid
# performing feature selection on every iteration
# of the grid search, which naturally takes forever
svm = GridSearchCV(estimator=SVC(kernel='linear', class_weight='auto'),
param_grid=dict(C=list(C_range(*ARGS.LOG2C))),
scoring=scorer,
n_jobs=int(getenv('NCPU', -1)),
pre_dispatch='3 * n_jobs',
cv=ARGS.CV_FOLDS - 1)
results = None
for n_features in ARGS.FEATURE_GRID:
results_ = Results(extractor.get_feature_names(), scorer, ARGS.SIMILAR)
for train_idxs, test_idxs in StratifiedKFold(y, ARGS.CV_FOLDS):
if train_idxs.sum() < 1 or test_idxs.sum() < 1:
y_true = y[test_idxs]
results_.add(y_true, y_true, {})
continue
X_train = X[train_idxs]
y_train = y[train_idxs]
if ARGS.RFE:
clf = RFE(estimator=svm,
n_features_to_select=n_features,
step=ARGS.RFE_STEP)
else:
mrmr = MRMR(k=n_features,
method=ARGS.MRMR_METHOD,
normalize=ARGS.MRMR_NORMALIZE,
similar=ARGS.SIMILAR)
clf = Pipeline([('mrmr', mrmr), ('svm', svm)])
clf.fit(X_train, y_train)
X_test = X[test_idxs]
y_true = y[test_idxs]
if ARGS.RFE:
selector_ = clf
svm_ = clf.estimator_.best_estimator_
else:
selector_ = clf.named_steps['mrmr']
svm_ = clf.named_steps['svm'].best_estimator_
y_pred = clf.predict(X_test)
coefs, ranks = coefs_ranks(selector_.ranking_, selector_.support_,
svm_.coef_)
results_.add(y_true, y_pred, coefs, ranks)
if results is None or results_ > results:
results = results_
# the alignment reflects the number of sequences either naturally
results.metadata(antibodies, ARGS.LABEL)
print(results.dumps(), file=ARGS.OUTPUT)
finalize_args(ARGS)
return results
def main(args=None):
if args is None:
args = sys.argv[1:]
np.seterr(all='raise')
parser, ns, args = init_args(description='learn model for labeled sequences', args=args)
parser = hmmer_args(parser)
parser = featsel_args(parser)
parser = feature_args(parser)
parser = mrmr_args(parser)
parser = rfe_args(parser)
parser = optstat_args(parser)
parser = filter_args(parser)
parser = svm_args(parser)
parser = cv_args(parser)
def GzipType(string):
try:
return gzip_open(string, 'wb')
except:
return ArgumentTypeError("cannot open '{0:s}' for writing".format(string))
parser.add_argument('--tree', dest='TREE')
parser.add_argument('ANTIBODY', type=AntibodyTypeFactory(ns.DATA), nargs='+')
parser.add_argument('MODEL', type=GzipType)
ARGS = parse_args(parser, args, namespace=ns)
antibodies = tuple(ARGS.ANTIBODY)
# do some argument parsing
if ARGS.TEST:
test_discrete(ARGS)
finalize_args(ARGS)
return {}
if ARGS.MRMR_METHOD == 'MAXREL':
ARGS.SIMILAR = 0.0
# set the util params
set_util_params(ARGS.REFSEQ.id)
# grab the relevant antibody from the SQLITE3 data
# format as SeqRecord so we can output as FASTA
# and generate an alignment using HMMER if it doesn't already exist
seqrecords, clonal, antibodies = ARGS.DATA.seqrecords(antibodies, ARGS.CLONAL)
ab_basename = ''.join((
'+'.join(antibodies),
'_dna' if ARGS.ENCODER == DNAEncoder else '_amino',
'_clonal' if clonal else ''
))
alignment_basename = '_'.join((
ab_basename,
ARGS.DATA.basename_root,
__version__
))
sto_filename = alignment_basename + '.sto'
alignment, hmm = generate_alignment(seqrecords, sto_filename, is_refseq, ARGS)
re_pngs = re_compile(r'N[^P][TS][^P]', re_I)
# compute features
ylabeler = Labeler(
partial(expression, ARGS.LABEL),
partial(skipper, is_refseq, ARGS.SUBTYPES)
)
alignment, y, threshold = ylabeler(alignment)
filter = naive_filter(
max_conservation=ARGS.MAX_CONSERVATION,
min_conservation=ARGS.MIN_CONSERVATION,
max_gap_ratio=ARGS.MAX_GAP_RATIO
)
extractors = [('site', SiteVectorizer(ARGS.ENCODER, filter))]
if ARGS.RADIUS:
extractors.append(('site_pairs', PairwiseSiteVectorizer(ARGS.ENCODER, filter, ARGS.RADIUS)))
if ARGS.PNGS:
extractors.append(('pngs', MotifVectorizer(re_pngs, 4, name='PNGS')))
if ARGS.PNGS_PAIRS:
extractors.append(
('pngs_pairs', PairwiseMotifVectorizer(re_pngs, 4, name='PNGS'))
)
extractor = FeatureUnion(extractors, n_jobs=1) # n_jobs must be one for now
X = extractor.fit_transform(alignment)
Cs = list(C_range(*ARGS.LOG2C))
scorer = Scorer(ARGS.OPTSTAT)
# we don't let GridSearchCV do its parallelization over all combinations
# of grid points, because when the length of FEATURE_GRID is short,
# it takes way longer than it should
# usually the # of Cs is larger than the # of ks
C_jobs = int(getenv('NCPU', -1))
k_jobs = 1
# if not, swap the parallelization strategy
if len(ARGS.FEATURE_GRID) > len(Cs):
C_jobs, k_jobs = k_jobs, C_jobs
mrmr = MRMR(
method=ARGS.MRMR_METHOD,
normalize=ARGS.MRMR_NORMALIZE,
similar=ARGS.SIMILAR
)
svm = GridSearchCV(
estimator=SVC(kernel='linear', class_weight='auto'),
param_grid=dict(C=Cs),
scoring=scorer,
n_jobs=C_jobs,
pre_dispatch='3 * n_jobs'
)
pipe = Pipeline([('mrmr', mrmr), ('svm', svm)])
if len(ARGS.FEATURE_GRID) == 1:
pipe.set_params(mrmr__k=ARGS.FEATURE_GRID[0], svm__cv=ARGS.CV_FOLDS)
clf = pipe.fit(X, y)
else:
pipe.set_params(svm__cv=ARGS.CV_FOLDS - 1)
clf = GridSearchCV(
estimator=pipe,
param_grid=dict(mrmr__k=ARGS.FEATURE_GRID),
scoring=scorer,
n_jobs=k_jobs,
pre_dispatch='3 * n_jobs',
cv=ARGS.CV_FOLDS
).fit(X, y).best_estimator_
pickle_dump((MODEL_VERSION, ARGS.ENCODER, ARGS.LABEL, hmm, extractor, clf), ARGS.MODEL)
ARGS.MODEL.close()
mrmr_ = clf.named_steps['mrmr']
svm_ = clf.named_steps['svm'].best_estimator_
coefs, ranks = coefs_ranks(mrmr_.ranking_, mrmr_.support_, svm_.coef_)
results = Results(extractor.get_feature_names(), scorer, ARGS.SIMILAR)
results.add(y, clf.predict(X), coefs, ranks)
results.metadata(antibodies, ARGS.LABEL)
print(results.dumps(), file=ARGS.OUTPUT)
finalize_args(ARGS)
return ARGS.MODEL
def main(args=None):
if args is None:
args = sys.argv[1:]
np.seterr(all='raise')
parser, ns, args = init_args(
description='learn model for labeled sequences', args=args)
parser = hmmer_args(parser)
parser = featsel_args(parser)
parser = feature_args(parser)
parser = mrmr_args(parser)
parser = rfe_args(parser)
parser = optstat_args(parser)
parser = filter_args(parser)
parser = svm_args(parser)
parser = cv_args(parser)
def GzipType(string):
try:
return gzip_open(string, 'wb')
except:
return ArgumentTypeError(
"cannot open '{0:s}' for writing".format(string))
parser.add_argument('--tree', dest='TREE')
parser.add_argument('ANTIBODY',
type=AntibodyTypeFactory(ns.DATA),
nargs='+')
parser.add_argument('MODEL', type=GzipType)
ARGS = parse_args(parser, args, namespace=ns)
antibodies = tuple(ARGS.ANTIBODY)
# do some argument parsing
if ARGS.TEST:
test_discrete(ARGS)
finalize_args(ARGS)
return {}
if ARGS.MRMR_METHOD == 'MAXREL':
ARGS.SIMILAR = 0.0
# set the util params
set_util_params(ARGS.REFSEQ.id)
# grab the relevant antibody from the SQLITE3 data
# format as SeqRecord so we can output as FASTA
# and generate an alignment using HMMER if it doesn't already exist
seqrecords, clonal, antibodies = ARGS.DATA.seqrecords(
antibodies, ARGS.CLONAL)
ab_basename = ''.join(('+'.join(antibodies),
'_dna' if ARGS.ENCODER == DNAEncoder else '_amino',
'_clonal' if clonal else ''))
alignment_basename = '_'.join(
(ab_basename, ARGS.DATA.basename_root, __version__))
sto_filename = alignment_basename + '.sto'
alignment, hmm = generate_alignment(seqrecords, sto_filename, is_refseq,
ARGS)
re_pngs = re_compile(r'N[^P][TS][^P]', re_I)
# compute features
ylabeler = Labeler(partial(expression, ARGS.LABEL),
partial(skipper, is_refseq, ARGS.SUBTYPES))
alignment, y, threshold = ylabeler(alignment)
filter = naive_filter(max_conservation=ARGS.MAX_CONSERVATION,
min_conservation=ARGS.MIN_CONSERVATION,
max_gap_ratio=ARGS.MAX_GAP_RATIO)
extractors = [('site', MSAVectorizer(ARGS.ENCODER, filter))]
if ARGS.RADIUS:
extractors.append(('site_pairs',
MSAVectorizerPairwise(ARGS.ENCODER, filter,
ARGS.RADIUS)))
if ARGS.PNGS:
extractors.append(('pngs', MSAVectorizerRegex(re_pngs, 4,
name='PNGS')))
if ARGS.PNGS_PAIRS:
extractors.append(
('pngs_pairs', MSAVectorizerRegexPairwise(re_pngs, 4,
name='PNGS')))
extractor = FeatureUnion(extractors,
n_jobs=1) # n_jobs must be one for now
X = extractor.fit_transform(alignment)
Cs = list(C_range(*ARGS.LOG2C))
scorer = Scorer(ARGS.OPTSTAT)
# we don't let GridSearchCV do its parallelization over all combinations
# of grid points, because when the length of FEATURE_GRID is short,
# it takes way longer than it should
# usually the # of Cs is larger than the # of ks
C_jobs = int(getenv('NCPU', -1))
k_jobs = 1
# if not, swap the parallelization strategy
if len(ARGS.FEATURE_GRID) > len(Cs):
C_jobs, k_jobs = k_jobs, C_jobs
mrmr = MRMR(method=ARGS.MRMR_METHOD,
normalize=ARGS.MRMR_NORMALIZE,
similar=ARGS.SIMILAR)
svm = GridSearchCV(estimator=SVC(kernel='linear', class_weight='auto'),
param_grid=dict(C=Cs),
scoring=scorer,
n_jobs=C_jobs,
pre_dispatch='3 * n_jobs')
pipe = Pipeline([('mrmr', mrmr), ('svm', svm)])
if len(ARGS.FEATURE_GRID) == 1:
pipe.set_params(mrmr__k=ARGS.FEATURE_GRID[0], svm__cv=ARGS.CV_FOLDS)
clf = pipe.fit(X, y)
else:
pipe.set_params(svm__cv=ARGS.CV_FOLDS - 1)
clf = GridSearchCV(estimator=pipe,
param_grid=dict(mrmr__k=ARGS.FEATURE_GRID),
scoring=scorer,
n_jobs=k_jobs,
pre_dispatch='3 * n_jobs',
cv=ARGS.CV_FOLDS).fit(X, y).best_estimator_
pickle_dump((4, ARGS.ENCODER, ARGS.LABEL, hmm, extractor, clf), ARGS.MODEL)
ARGS.MODEL.close()
mrmr_ = clf.named_steps['mrmr']
svm_ = clf.named_steps['svm'].best_estimator_
coefs, ranks = coefs_ranks(mrmr_.ranking_, mrmr_.support_, svm_.coef_)
results = Results(extractor.get_feature_names(), scorer, ARGS.SIMILAR)
results.add(y, clf.predict(X), coefs, ranks)
results.metadata(antibodies, ARGS.LABEL)
print(results.dumps(), file=ARGS.OUTPUT)
finalize_args(ARGS)
return ARGS.MODEL
