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jblitzar
/
github-python-corpus

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allgos = set()
total_score = 0.0
for p_id, score in sim_prots.items():
allgos |= annotations[prot_index[p_id]]
total_score += score
allgos = list(sorted(allgos))
sim = np.zeros(len(allgos), dtype=np.float32)
for j, go_id in enumerate(allgos):
s = 0.0
for p_id, score in sim_prots.items():
if go_id in annotations[prot_index[p_id]]:
s += score
sim[j] = s / total_score
ind = np.argsort(-sim)
for go_id, score in zip(allgos, sim):
annots[go_id] = score
blast_preds.append(annots)
# DeepGOPlus
go_set = go_rels.get_namespace_terms(NAMESPACES[ont])
go_set.remove(FUNC_DICT[ont])
labels = valid_df['annotations'].values
labels = list(map(lambda x: set(filter(lambda y: y in go_set, x)), labels))
print(len(go_set))
best_fmax = 0.0
best_alpha = 0.0
for alpha in range(40, 70):
alpha /= 100.0
deep_preds = []
for i, row in enumerate(valid_df.itertuples()):
annots_dict = blast_preds[i].copy()
for go_id in annots_dict:
annots_dict[go_id] *= alpha
for j, score in enumerate(row.preds):
go_id = terms[j]
score *= 1 - alpha
if go_id in annots_dict:
annots_dict[go_id] += score
else:
annots_dict[go_id] = score
deep_preds.append(annots_dict)
fmax = 0.0
tmax = 0.0
precisions = []
recalls = []
smin = 1000000.0
rus = []
mis = []
for t in range(10, 30):
threshold = t / 100.0
preds = []
for i, row in enumerate(valid_df.itertuples()):
annots = set()
for go_id, score in deep_preds[i].items():
if score >= threshold:
annots.add(go_id)
new_annots = set()
for go_id in annots:
new_annots |= go_rels.get_anchestors(go_id)
preds.append(new_annots)
# Filter classes
preds = list(map(lambda x: set(filter(lambda y: y in go_set, x)), preds))
fscore, prec, rec, s, ru, mi, fps, fns = evaluate_annotations(go_rels, labels, preds)
avg_fp = sum(map(lambda x: len(x), fps)) / len(fps)
avg_ic = sum(map(lambda x: sum(map(lambda go_id: go_rels.get_ic(go_id), x)), fps)) / len(fps)
print(f'Fscore: {fscore}, Precision: {prec}, Recall: {rec} S: {s}, RU: {ru}, MI: {mi} threshold: {threshold}')
if fmax < fscore:
fmax = fscore
tmax = threshold
if smin > s:
smin = s
if best_fmax < fmax:
best_fmax = fmax
best_alpha = alpha
print(f'Alpha: {alpha} Fmax: {fmax:0.3f}, Smin: {smin:0.3f}, threshold: {tmax}')
print(f'{best_alpha} {best_fmax}')
def compute_roc(labels, preds):
# Compute ROC curve and ROC area for each class
fpr, tpr, _ = roc_curve(labels.flatten(), preds.flatten())
roc_auc = auc(fpr, tpr)
return roc_auc
def compute_mcc(labels, preds):
# Compute ROC curve and ROC area for each class
mcc = matthews_corrcoef(labels.flatten(), preds.flatten())
return mcc
def evaluate_annotations(go, real_annots, pred_annots):
total = 0
p = 0.0
r = 0.0
p_total= 0
ru = 0.0
mi = 0.0
fps = []