def performanc_by_interpro():
pred_df = pd.read_pickle(DATA_ROOT + 'test-' + FUNCTION + '-preds.pkl')
ipro_df = load_prot_ipro()
df = pred_df.merge(ipro_df, on='proteins', how='left')
ipro = get_ipro()
def reshape(values):
values = np.hstack(values).reshape(
len(values), len(values[0]))
return values
for ipro_id in ipro:
if len(ipro[ipro_id]['parents']) > 0:
continue
labels = list()
predictions = list()
gos = list()
for i, row in df.iterrows():
if not isinstance(row['ipros'], list):
continue
if ipro_id in row['ipros']:
labels.append(row['labels'])
predictions.append(row['predictions'])
gos.append(row['gos'])
pr = 0
rc = 0
total = 0
p_total = 0
for i in range(len(labels)):
tp = np.sum(labels[i] * predictions[i])
fp = np.sum(predictions[i]) - tp
fn = np.sum(labels[i]) - tp
all_gos = set()
for go_id in gos[i]:
if go_id in all_functions:
all_gos |= get_anchestors(go, go_id)
all_gos.discard(GO_ID)
all_gos -= func_set
fn += len(all_gos)
if tp == 0 and fp == 0 and fn == 0:
continue
total += 1
if tp != 0:
p_total += 1
precision = tp / (1.0 * (tp + fp))
recall = tp / (1.0 * (tp + fn))
pr += precision
rc += recall
if total > 0 and p_total > 0:
rc /= total
pr /= p_total
if pr + rc > 0:
f = 2 * pr * rc / (pr + rc)
logging.info('%s\t%d\t%f\t%f\t%f' % (
ipro_id, len(labels), f, pr, rc))
def ipro_table():
ipro = get_ipro()
cc = get_ipro_data('ipro_cc.res')
mf = get_ipro_data('ipro_mf.res')
bp = get_ipro_data('ipro_bp.res')
inter = set(cc).intersection(set(mf)).intersection(set(bp))
res = list()
sup = 50
for key in inter:
if bp[key][0] >= sup and mf[key][0] >= sup and cc[key][0] >= sup:
res.append((key, ipro[key]['name'], bp[key][1], bp[key][2],
bp[key][3], mf[key][1], mf[key][2], mf[key][3],
cc[key][1], cc[key][2], cc[key][3]))
res = sorted(res, key=lambda x: x[2], reverse=True)
for item in res:
print(
'%s & %s & %.2f & %.2f & %.2f & %.2f & %.2f & %.2f & %.2f & %.2f & %.2f \\\\'
% item)
