if __name__ == '__main__':
pos, neg, test_pos = (scipy.io.mmread(
os.path.join(folder, 'data.swissprot.%s.mtx' % d))
for d in mtx_filenames)
numpy.save(os.path.join(folder, 'data.pos.swissprot.npy'), pos.todense())
numpy.save(os.path.join(folder, 'data.neg.swissprot.npy'), neg.todense())
numpy.save(os.path.join(folder, 'data.test_pos.swissprot.npy'),
test_pos.todense())
print 'read data...'
table = []
for cp in [1.0, 0.5, 0.1, 0.7, 0.6, 0.4, 0.3, 0.2, 0.9, 0.8]:
# split out the validation set separately
split = lambda a: logistic.sample_split(a, int(0.8 * a.shape[0]))
half_pos, v_pos = split(pos)
half_neg, v_neg = split(neg)
half_test_pos, v_test_pos = split(test_pos)
# figure out the subset to sample (c)
u = logistic.vstack([half_neg, half_test_pos])
pos_sample, unlabeled = logistic.sample_positive(cp, half_pos, u)
# create validation set the same way
u = logistic.vstack([v_neg, v_test_pos])
v_p, v_u = logistic.sample_positive(cp, v_pos, u)
print 'set up data...'
data = (pos_sample, unlabeled, v_p, v_u)
max_key = 24081
mtx_filenames = 'pos', 'neg', 'test_pos'
if __name__=='__main__':
pos, neg, test_pos = (scipy.io.mmread(os.path.join(folder, 'data.swissprot.%s.mtx' % d)) for d in mtx_filenames)
numpy.save(os.path.join(folder, 'data.pos.swissprot.npy'), pos.todense())
numpy.save(os.path.join(folder, 'data.neg.swissprot.npy'), neg.todense())
numpy.save(os.path.join(folder, 'data.test_pos.swissprot.npy'), test_pos.todense())
print 'read data...'
table = []
for cp in [1.0, 0.5, 0.1, 0.7, 0.6, 0.4, 0.3, 0.2, 0.9, 0.8]:
# split out the validation set separately
split = lambda a: logistic.sample_split(a, int(0.8 * a.shape[0]))
half_pos, v_pos = split(pos)
half_neg, v_neg = split(neg)
half_test_pos, v_test_pos = split(test_pos)
# figure out the subset to sample (c)
u = logistic.vstack([half_neg, half_test_pos])
pos_sample, unlabeled = logistic.sample_positive(cp, half_pos, u)
# create validation set the same way
u = logistic.vstack([v_neg, v_test_pos])
v_p, v_u = logistic.sample_positive(cp, v_pos, u)
print 'set up data...'
data = (pos_sample, unlabeled, v_p, v_u)
filenames = ["P", "N", "Q"]
max_key = 24081
npy_filenames = "pos", "neg", "test_pos"
if __name__ == "__main__":
pos, neg, test_pos = (np.load(os.path.join(folder, "data.%s.swissprot.npy" % d)) for d in npy_filenames)
print "read data..."
# set up data
table = []
for cp in [1.0, 0.5, 0.1, 0.7, 0.6, 0.4, 0.3, 0.2, 0.9, 0.8]:
# split out the validation set separately
split_half = lambda a: logistic.sample_split(a, len(a) / 2)
half_pos, v_pos = split_half(pos)
half_neg, v_neg = split_half(neg)
half_test_pos, v_test_pos = split_half(test_pos)
# figure out the subset to sample (c)
u = logistic.vstack([half_neg, half_test_pos])
pos_sample, unlabeled = logistic.sample_positive(cp, half_pos, u)
# create validation set the same way
u = logistic.vstack([v_neg, v_test_pos])
v_p, v_u = logistic.sample_positive(cp, v_pos, u)
print "set up data..."
_, estimators = logistic.calculate_estimators(pos_sample, unlabeled, v_p, v_u)
npy_filenames = 'pos', 'neg', 'test_pos'
if __name__ == '__main__':
pos, neg, test_pos = (np.load(
os.path.join(folder, 'data.%s.swissprot.npy' % d))
for d in npy_filenames)
print 'read data...'
# set up data
table = []
for cp in [1.0, 0.5, 0.1, 0.7, 0.6, 0.4, 0.3, 0.2, 0.9, 0.8]:
# split out the validation set separately
split_half = lambda a: logistic.sample_split(a, len(a) / 2)
half_pos, v_pos = split_half(pos)
half_neg, v_neg = split_half(neg)
half_test_pos, v_test_pos = split_half(test_pos)
# figure out the subset to sample (c)
u = logistic.vstack([half_neg, half_test_pos])
pos_sample, unlabeled = logistic.sample_positive(cp, half_pos, u)
# create validation set the same way
u = logistic.vstack([v_neg, v_test_pos])
v_p, v_u = logistic.sample_positive(cp, v_pos, u)
print 'set up data...'
_, estimators = logistic.calculate_estimators(pos_sample, unlabeled,
