Datasets:
jblitzar
/
github-python-corpus

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test_df = pd.read_pickle(test_data_file)
test_annotations = test_df['prop_annotations'].values
test_annotations = list(map(lambda x: set(x), test_annotations))
go_rels.calculate_ic(annotations + test_annotations)
prot_index = {}
for i, row in enumerate(train_df.itertuples()):
prot_index[row.proteins] = i
# BLAST Similarity (Diamond)
diamond_scores = {}
with open(diamond_scores_file) as f:
for line in f:
it = line.strip().split()
if it[0] not in diamond_scores:
diamond_scores[it[0]] = {}
diamond_scores[it[0]][it[1]] = float(it[2])
blast_preds = []
for i, row in enumerate(test_df.itertuples()):
annots = {}
prot_id = row.proteins
# BlastKNN
if prot_id in diamond_scores:
sim_prots = diamond_scores[prot_id]
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
for go_id, score in zip(allgos, sim):
annots[go_id] = score
blast_preds.append(annots)
go_set = go_rels.get_namespace_terms(NAMESPACES[ont])
go_set.remove(FUNC_DICT[ont])
labels = test_annotations
labels = list(map(lambda x: set(filter(lambda y: y in go_set, x)), labels))
print(len(go_set))
fmax = 0.0
tmax = 0.0
smin = 1000.0
precisions = []
recalls = []
for t in range(101):
threshold = t / 100.0
preds = []
for i, row in enumerate(test_df.itertuples()):
annots = set()
for go_id, score in blast_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 = evaluate_annotations(go_rels, labels, preds)
precisions.append(prec)
recalls.append(rec)
print(f'Fscore: {fscore}, S: {s}, threshold: {threshold}')
if fmax < fscore:
fmax = fscore
tmax = threshold
if smin > s:
smin = s
print(f'Fmax: {fmax:0.3f}, Smin: {smin:0.3f}, threshold: {tmax}')
precisions = np.array(precisions)
recalls = np.array(recalls)
sorted_index = np.argsort(recalls)
recalls = recalls[sorted_index]
precisions = precisions[sorted_index]
aupr = np.trapz(precisions, recalls)
print(f'AUPR: {aupr:0.3f}')
plt.figure()
lw = 2
plt.plot(recalls, precisions, color='darkorange',
lw=lw, label=f'AUPR curve (area = {aupr:0.3f})')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title('Area Under the Precision-Recall curve')
plt.legend(loc="lower right")