def __init__(self, *args):
unittest.TestCase.__init__(self, *args)
fname = "data/test_data/sequences/alignment.fa"
dl = DataLoader()
_, self.seq_x, self.ann_x, self.seq_y, self.ann_y = dl.loadSequence(fname)
self.seq_xs = Fasta.alnToSeq(self.seq_x)
self.seq_ys = Fasta.alnToSeq(self.seq_y)
def __init__(self, *args):
unittest.TestCase.__init__(self, *args)
fname = "data/test_data/sequences/alignment.fa"
dl = DataLoader()
_, self.seq_x, self.ann_x, self.seq_y, self.ann_y = dl.loadSequence(
fname)
self.seq_xs = Fasta.alnToSeq(self.seq_x)
self.seq_ys = Fasta.alnToSeq(self.seq_y)
def load_data(preparer):
dl = DataLoader()
data, target, weights = list(), list(), list()
sequences = dl.loadDirectory('data/sequences/model_train_seq/simulated')
# sequences = dl.loadDirectory('data/sequences/train_sequences')
for _, s_x, a_x, s_y, a_y in sequences:
d, t, _ = preparer.prepare_training_data(s_x, a_x, s_y, a_y)
data += d
target += t
return data, target
def load_data(preparer):
dl = DataLoader()
data, target, weights = list(), list(), list()
sequences = dl.loadDirectory('data/sequences/model_train_seq/simulated')
# sequences = dl.loadDirectory('data/sequences/train_sequences')
for _, s_x, a_x, s_y, a_y in sequences:
d, t, _ = preparer.prepare_training_data(
s_x, a_x, s_y, a_y
)
data += d
target += t
return data, target
def __init__(
self,
preparer,
filename="data/clf/randomforest.clf",
training_data_dir="data/sequences/train_sequences",
params=None,
autotrain=True,
memoization=False,
inverted=False,
use_global_classifier=False,
):
global _global_classifier
"""
@rtype : PairClassifier
"""
self._preparer = None
self.preparer = preparer
self.default_filename = filename
self.training_data_dir = training_data_dir
if params is None:
self.params = config.classifiers[config.classifier_index][2]
else:
self.params = params
self.mem = dict()
self.memoization = memoization
self.inverted = inverted
if _global_classifier is None or not use_global_classifier:
if autotrain and path.exists(self.default_filename):
if path.isfile(self.default_filename):
self.load(self.default_filename)
else:
self.classifier = self._get_classifier()
if autotrain:
sys.stderr.write('Training clasifier\n')
dl = DataLoader()
data, target, weights = list(), list(), list()
sequences = dl.loadDirectory(self.training_data_dir)
for _, s_x, a_x, s_y, a_y in sequences:
d, t, w = self.preparer.prepare_training_data(
s_x, a_x, s_y, a_y
)
data += d
target += t
weights += w
self.fit(data, target, array(weights))
self.save(self.default_filename)
if use_global_classifier:
_global_classifier = self.classifier
else:
self.classifier = _global_classifier
def __init__(
self,
preparer,
filename="data/clf/randomforest.clf",
training_data_dir="data/sequences/train_sequences",
params=None,
autotrain=True,
memoization=False,
inverted=False,
use_global_classifier=False,
):
global _global_classifier
"""
@rtype : PairClassifier
"""
self._preparer = None
self.preparer = preparer
self.default_filename = filename
self.training_data_dir = training_data_dir
if params is None:
self.params = config.classifiers[config.classifier_index][2]
else:
self.params = params
self.mem = dict()
self.memoization = memoization
self.inverted = inverted
if _global_classifier is None or not use_global_classifier:
if autotrain and path.exists(self.default_filename):
if path.isfile(self.default_filename):
self.load(self.default_filename)
else:
self.classifier = self._get_classifier()
if autotrain:
sys.stderr.write('Training clasifier\n')
dl = DataLoader()
data, target, weights = list(), list(), list()
sequences = dl.loadDirectory(self.training_data_dir)
for _, s_x, a_x, s_y, a_y in sequences:
d, t, w = self.preparer.prepare_training_data(
s_x, a_x, s_y, a_y)
data += d
target += t
weights += w
self.fit(data, target, array(weights))
self.save(self.default_filename)
if use_global_classifier:
_global_classifier = self.classifier
else:
self.classifier = _global_classifier
def compare_with_source(source_fname, realigned_fname, seq1, seq2):
d = DataLoader()
src = d.getSequences(source_fname, [seq1+'$', seq2+'$'])
realigned = d.getSequences(realigned_fname, [seq1+'$', seq2+'$'])
src_f = create_alignemnt_function(src)
realigned_f = create_alignemnt_function(realigned)
l = max(len(src[0]), len(realigned[0]))
s = 0
for i in range(min(len(src[0]), len(realigned[0]))):
if src_f(i) == realigned_f(i):
s += 1
return float(s)/l
def score(fname):
sequence_names = ['sequence1', 'sequence2', 'sequence3']
sequences = []
# fname = 'data/sequences/simulated_alignment.{}_{}.realigned.fa'
d = DataLoader()
for x in range(len(sequence_names)-1):
for y in range(x+1, len(sequence_names)):
sX = sequence_names[x]
sY = sequence_names[y]
if sX != sY:
sequences.append(d.getSequences(
fname.format(sX, sY), [sX+'$', sY+'$'])
)
return compare(*sequences)
def main(preparer_index):
path_to_data = "data/"
dp = config.preparers[preparer_index][0](constants.window_size)
clf_fname = 'data/clf/{}{}{}.clf'.format(
PairClassifier.get_name(),
config.preparers[preparer_index][2],
constants.window_size,
)
idp = config.preparers[preparer_index][1](0, constants.window_size)
iclf_fname = 'data/clf/{}{}{}{}.clf'.format(
PairClassifier.get_name(),
config.preparers[preparer_index][2],
constants.window_size,
'_indel',
)
dl = DataLoader()
# _, s_x, a_x, s_y, a_y = dl.loadSequence(
# path.join(path_to_data, 'sequences/train_sequences/simulated_alignment0.fa'),
# )
# x, y, _ = dp.prepare_training_data(s_x, a_x, s_y, a_y)
# ix, iy, _ = idp.prepare_training_data(s_x, a_x, s_y, a_y)
# c.fit(x, y)
# ic.fit(ix, iy)
_, s_x, a_x, s_y, a_y = dl.loadSequence(
path.join(
path_to_data,
'sequences/model_train_seq/simulated/simulated_alignment.fa'))
px, py, _ = dp.prepare_training_data(s_x, a_x, s_y, a_y)
ipx, ipy, _ = idp.prepare_training_data(s_x, a_x, s_y, a_y)
# plot(*compute_01graph_data(c, x, y))
# plot(*compute_01graph_data(ic, ix, iy))
# plot(*compute_01graph_data(c, px, py))
# plot(*compute_01graph_data(ic, ipx, ipy))
plot_clf(dp, px, py, clf_fname)
plt.savefig(path.splitext(clf_fname)[0] + "_test" + pic_suffix,
transparent=True,
bbox_inches='tight')
plot_clf(idp, ipx, ipy, iclf_fname)
plt.savefig(path.splitext(iclf_fname)[0] + "_test" + pic_suffix,
transparent=True,
bbox_inches='tight')
plt.show()
def main(preparer_index):
path_to_data = "data/"
dp = config.preparers[preparer_index][0](constants.window_size)
clf_fname = 'data/clf/{}{}{}.clf'.format(
PairClassifier.get_name(),
config.preparers[preparer_index][2],
constants.window_size,
)
idp = config.preparers[preparer_index][1](0, constants.window_size)
iclf_fname = 'data/clf/{}{}{}{}.clf'.format(
PairClassifier.get_name(),
config.preparers[preparer_index][2],
constants.window_size,
'_indel',
)
dl = DataLoader()
# _, s_x, a_x, s_y, a_y = dl.loadSequence(
# path.join(path_to_data, 'sequences/train_sequences/simulated_alignment0.fa'),
# )
# x, y, _ = dp.prepare_training_data(s_x, a_x, s_y, a_y)
# ix, iy, _ = idp.prepare_training_data(s_x, a_x, s_y, a_y)
# c.fit(x, y)
# ic.fit(ix, iy)
_, s_x, a_x, s_y, a_y = dl.loadSequence(
path.join(path_to_data, 'sequences/model_train_seq/simulated/simulated_alignment.fa')
)
px, py, _ = dp.prepare_training_data(s_x, a_x, s_y, a_y)
ipx, ipy, _ = idp.prepare_training_data(s_x, a_x, s_y, a_y)
# plot(*compute_01graph_data(c, x, y))
# plot(*compute_01graph_data(ic, ix, iy))
# plot(*compute_01graph_data(c, px, py))
# plot(*compute_01graph_data(ic, ipx, ipy))
plot_clf(dp, px, py, clf_fname)
plt.savefig(
path.splitext(clf_fname)[0] + "_test" + pic_suffix, transparent=True, bbox_inches='tight'
)
plot_clf(idp, ipx, ipy, iclf_fname)
plt.savefig(
path.splitext(iclf_fname)[0] + "_test" + pic_suffix, transparent=True, bbox_inches='tight'
)
plt.show()
def train(self, dirname):
dl = DataLoader()
sequences = dl.loadDirectory(dirname)
return self.train_multi(sequences)
