def al_exp_val(org,
ec,
k,
exp,
neg=None,
beta=1.0,
kernel='rbf',
degree=3,
gamma=0.005,
iterations=100,
batch=1,
C=1.0,
initial=2,
decf=False,
random_seed=None,
pos=None,
simfp=fptr.integer_sim):
a = bi(org, ec)
if neg is not None:
if pos:
a.add_from_sdf(pos, k, pos=True)
a.add_from_sdf(neg, k, pos=False)
suppl = pybel.readfile('sdf', os.path.join(CHEMPATH, exp))
excl = []
for mol in suppl:
smi = mol.write('can').strip()
cls = int(mol.data['label'])
a.add_from_smiles(smi, k, cls)
excl.append(smi)
a.expval_selection(k,
excl,
c=C,
gamma=gamma,
iterations=iterations,
batch=batch,
degree=degree,
kernel=kernel,
beta=beta,
decf=decf,
seed=random_seed,
simfp=simfp,
initial=initial)
else:
a.expval_selection_random(k,
exp,
c=C,
gamma=gamma,
iterations=iterations,
batch=batch,
degree=degree,
kernel=kernel,
beta=beta,
decf=decf,
seed=random_seed,
simfp=simfp,
initial=initial)
def al_xval_ins(org,
ec,
k,
neg=None,
pos=None,
beta=1.0,
kernel='rbf',
gamma=0.005,
iterations=100,
batch=1,
C=1.0,
initial=2,
decf=True,
simfp=fptr.integer_sim):
a = bi(org, ec)
if neg is not None:
if pos:
a.add_from_sdf(pos, k, pos=True)
a.add_from_sdf(neg, k, pos=False)
a.xval_selection(k,
beta=beta,
batch=batch,
kernel=kernel,
iterations=iterations,
initial=initial,
c=C,
gamma=gamma,
decf=decf,
simfp=simfp)
else:
a.xval_selection_random(k,
beta=beta,
batch=batch,
kernel=kernel,
iterations=iterations,
initial=initial,
c=C,
gamma=gamma,
decf=decf,
simfp=simfp)
def al_exp_ins(org,
ec,
k,
exp,
neg=None,
beta=1.0,
kernel='rbf',
degree=3,
gamma=0.005,
iterations=100,
batch=1,
C=1.0,
initial=2,
decf=False,
random_seed=None,
fp='FP4',
simfp=fptr.integer_sim):
a = bi(org, ec)
if neg is not None:
a.add_from_sdf(neg, k, pos=False)
else:
a.random_negatives(k)
suppl = pybel.readfile('sdf', os.path.join(CHEMPATH, exp))
excl = []
for mol in suppl:
smi = mol.write('can').strip()
cls = int(mol.data['label'])
a.add_from_smiles(smi, k, cls)
excl.append(smi)
smiles_access = [t[0] for t in a.pos[k]] + [t[0] for t in a.neg[k]]
n = max([len(str(x)) for x in smiles_access])
if fp == 'FP4':
x_pos_array = np.vstack(tuple([t[1] for t in a.pos[k]]))
x_neg_array = np.vstack(tuple([t[1] for t in a.neg[k]]))
y_obj = []
y_obj += [1] * x_pos_array.shape[0]
y_obj += [-1] * x_neg_array.shape[0]
x = np.vstack((x_pos_array, x_neg_array))
y = np.array(zip(y_obj, smiles_access),
dtype=[('label', 'i4'), ('smiles', '|S%s' % str(n))])
elif fp == 'FP2':
x_pos_array = np.vstack(
tuple([
np.array(fptr.reconstruct_fp(t[0], fptype='FP2'))
for t in a.pos[k]
]))
x_neg_array = np.vstack(
tuple([
np.array(fptr.reconstruct_fp(t[0], fptype='FP2'))
for t in a.neg[k]
]))
y_obj = []
y_obj += [1] * x_pos_array.shape[0]
y_obj += [-1] * x_neg_array.shape[0]
x = np.vstack((x_pos_array, x_neg_array))
y = np.array(zip(y_obj, smiles_access),
dtype=[('label', 'i4'), ('smiles', '|S%s' % str(n))])
else:
raise IOError("Valid values for fp are FP2 and FP4.")
outfile = "al_expins_%s_%s_beta%s_batch%s_%s_rseed%s" % (org, ec, str(
beta).replace('.', ''), str(batch), kernel, str(random_seed))
out = routines.dw_exp_ins(x,
y,
outfile,
smiles_access,
excl,
C=C,
gamma=gamma,
iterations=iterations,
batch=batch,
degree=degree,
kernel=kernel,
beta=beta,
decf=decf,
seed=random_seed,
simfp=simfp,
initial=initial)
def al_run(org,
ec,
neg,
k,
beta=1,
pos=None,
ent=False,
kernel='rbf',
degree=3,
zinc=True,
zinc_tol_l=1,
zinc_tol_r=1,
greedy=False,
vl=None,
simfp=fptr.integer_sim,
C=5,
target_bits=None,
screen=None):
#Collects isozyme data into the Isozyme class.
a = bi(org, ec)
if pos:
a.add_from_sdf(pos, k, pos=True)
a.add_from_sdf(neg, k, pos=False)
#Two branches here; one pulls potential test data from ZINC, another pulls from KEGG.
if zinc:
res_ = [(page["smiles"], fptr.integer_fp(str(page["smiles"])),
page["vendors"], page["_id"])
for page in dbq.zinc_pull(target_bits,
a.mass_avg[k],
a.mass_std[k],
zinc_tol_l=zinc_tol_l,
zinc_tol_r=zinc_tol_r)
if u'R' not in page["smiles"] and 'vendors' in page]
res_s = [rr for rr in res_ if rr[1] is not None]
if screen is not None:
patt = [pybel.Smarts(smarts) for smarts in screen.split('|')]
if len(patt) > 2:
raise IOError(
'al_run only supports OR filters for two SMARTS queries at this time.'
)
res = [
rr for rr in res_s if len(patt[0].findall(
pybel.readstring('smi', str(rr[0])))) > 0 or
len(patt[1].findall(pybel.readstring('smi', str(rr[0])))) > 0
]
else:
res = res_s
else:
res = [(page["SMILES"], np.array(fptr.integer_fp(str(page["SMILES"]))))
for page in dbq.kegg_pull(target_bits)
if u'R' not in page["SMILES"]
and np.array(fptr.integer_fp(str(page["SMILES"]))) is not None]
labels = machines.svm_clf(a.pos[k],
a.neg[k],
res,
kernel=kernel,
degree=degree,
ent=ent,
C=C)
test_a = np.vstack(
tuple([
np.array(x[1]) for x in res
if x[1] is not None and len(x[1]) == 313
]))
tc_u = dw.avg_proximity(test_a, test_a, f=simfp)
if greedy:
if ent:
xis = [
l * dw.weight(dw.entropy(p), tc_u[i], beta=beta)
for i, (l, p) in enumerate(labels)
]
else:
xis = [
l * dw.weight(dw.hyper_distance(d), tc_u[i], beta=beta)
for i, (l, d) in enumerate(labels)
]
else:
if ent:
xis = [
dw.weight(dw.entropy(p), tc_u[i], beta=beta)
for i, (l, p) in enumerate(labels)
]
else:
xis = [
dw.weight(dw.hyper_distance(d), tc_u[i], beta=beta)
for i, (l, d) in enumerate(labels)
]
if zinc:
dw.generate_report(
sorted(zip([s for s, fp, vend, z in res if fp is not None], xis,
[lab[0] for lab in labels],
[vend for s, fp, vend, z in res if fp is not None],
[z for s, fp, vend, z in res if fp is not None]),
key=lambda y: y[1],
reverse=True),
vendors_list=vl,
outfile="%s_ec%s_beta%s_%s_zinc%s%s_C%s.sdf" %
(org, ec.replace(
'.', '_'), str(beta), kernel, str(zinc_tol_l).replace(
'.', '_'), str(zinc_tol_r).replace('.', '_'), str(C)))
f = open(
"%s_ec%s_beta%s_%s_zinc%s%s_C%s.txt" % (org, ec.replace(
'.', '_'), str(beta), kernel, str(zinc_tol_l).replace(
'.', '_'), str(zinc_tol_r).replace('.', '_'), str(C)), 'w')
else:
dw.generate_report(sorted(zip([s for s, fp in res], xis,
[lab[0] for lab in labels]),
key=lambda y: y[1],
reverse=True),
outfile="%s_ec%s_beta%s_%s.sdf" %
(org, ec.replace('.', '_'), str(beta), kernel),
zinc=False)
f = open(
"%s_ec%s_beta%s_%s.txt" %
(org, ec.replace('.', '_'), str(beta), kernel), 'w')
for score in xis:
f.write(str(score) + '\n')
f.close()
def dissim_run(org,
ec,
neg,
k,
pos=None,
zinc=False,
zinc_tol_l=1,
zinc_tol_r=1,
vl=None,
simfp=fptr.integer_sim,
target_bits=None,
screen=None):
# Collects isozyme data into the Isozyme class.
a = bi(org, ec)
bits = a.analyze_reactions()
if pos:
a.add_from_sdf(pos, k, pos=True)
a.add_from_sdf(neg, k, pos=False)
#Two branches here; one pulls potential test data from ZINC, another pulls from KEGG.
res_ = [(page["smiles"], fptr.integer_fp(str(page["smiles"])),
page["vendors"], page["_id"])
for page in dbq.zinc_pull(target_bits,
a.mass_avg[k],
a.mass_std[k],
zinc_tol_l=zinc_tol_l,
zinc_tol_r=zinc_tol_r)
if u'R' not in page["smiles"] and 'vendors' in page]
res_s = [rr for rr in res_ if rr[1] is not None]
if screen is not None:
patt = [pybel.Smarts(smarts) for smarts in screen.split('|')]
if len(patt) > 2:
raise IOError(
'al_run only supports OR filters for two SMARTS queries at this time.'
)
res = [
rr for rr in res_s
if len(patt[0].findall(pybel.readstring('smi', str(rr[0])))) > 0
or len(patt[1].findall(pybel.readstring('smi', str(rr[0])))) > 0
]
else:
res = res_s
x_pos_array = np.vstack(tuple([t[1] for t in a.pos[k]]))
x_neg_array = np.vstack(tuple([t[1] for t in a.neg[k]]))
x_array = np.vstack((x_pos_array, x_neg_array))
centroid = np.mean(x_array, axis=0)
test_a = np.vstack(tuple([np.array(x[1]) for x in res
if x[1] is not None]))
test_centroid = np.mean(test_a, axis=0)
tc_u = dw.avg_proximity(test_a, test_a, f=simfp)
xis_a = [(x[0], fptr.integer_sim(centroid, x[1]), 1, x[2], x[3])
for x in res if x[1] is not None]
xis_b = [(x[0], tc_u[i] * (-math.log(fptr.integer_sim(centroid, x[1]), 2)),
1, x[2], x[3]) for i, x in enumerate(res) if x[1] is not None]
dw.generate_report(sorted(xis_a, key=lambda y: y[1]),
vendors_list=vl,
outfile="%s_ec%s_dissim_zinc%s%s.sdf" %
(org, ec.replace('.', '_'), str(zinc_tol_l).replace(
'.', '_'), str(zinc_tol_r).replace('.', '_')))
dw.generate_report(sorted(xis_b, key=lambda y: y[1]),
vendors_list=vl,
outfile="%s_ec%s_dissimcentral_zinc%s%s.sdf" %
(org, ec.replace('.', '_'), str(zinc_tol_l).replace(
'.', '_'), str(zinc_tol_r).replace('.', '_')))
np.dot(np.dot(alpha, gram_prime), alpha.T))
sensitivity = (objective - objective_prime) / (objective)
#responses.append(((a_ + 1, b_ + 1), round(sensitivity, 3)))
responses.append((c + 1, round(sensitivity, 3)))
ranks = sorted(responses, key=lambda x: x[1], reverse=True)
if draw is not None:
smarts_q = [(qq[0], patts[qq[0]].strip('\n')) for qq in ranks[:draw]]
mg = MolGrid()
for smi_ in smiles_:
mg.add_smiles(smi_)
fname = 'MolsGrid%s.svg' % '_'.join([str(sm[0]) for sm in smarts_q])
mg.generate_figures(fname, highlight=tuple([sm[1] for sm in smarts_q]))
return ranks
if __name__ == "__main__":
a = bi("Escherichi coli K12", "2.2.1.9")
a.add_from_sdf('MenD_rd1_good.sdf', 0, pos=True)
a.add_from_sdf('MenDNegatives.sdf', 0, pos=False)
ranks = svm_feature_rank(a.pos[0], a.neg[0], C=5)
f = open('feat_importance.txt', 'w')
for r in ranks:
f.write(str(r) + '\n')
f.close()