def copy_testimgs():
DST = "/tmp/testimgs"
test_genes = datautil.get_test_gene_list(size=0)
for g in test_genes:
pics = datautil.get_gene_pics(g)
dst_dir = os.path.join(DST, g)
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
for pic in pics:
src = os.path.join(c.QDATA_DIR, g, pic)
dst = os.path.join(dst_dir, pic)
shutil.copy(src, dst)
def evaluate_ilocator():
test_genes = datautil.get_test_gene_list(size=0)
pd = []
gt = []
d = datautil.load_enhanced_label()
for g in test_genes:
pics = datautil.get_gene_pics(g)
g_scores = []
for pic in [x.replace(".", "_") for x in pics]:
spath = os.path.abspath(
os.path.join("util/testimgs_ilocator_result/%s.txt" % pic))
with open(spath, 'r') as f:
score = [float(x) for x in f.readline().strip().split()]
if any([math.isnan(x) for x in score]):
pass
else:
g_scores.append(score)
g_scores = np.stack(g_scores)
g_scores = np.mean(g_scores, axis=0)
pd.append(g_scores)
gene_label = np.zeros(6)
for l in d[g]:
gene_label[l] = 1
gt.append(gene_label)
gt = np.stack(gt)
pd = np.stack(pd)
# rearrange label order
idx = np.array([5, 0, 6, 4, 2, 1])
pd = pd[:, idx]
thr = pd.max(axis=1)
zeros = np.zeros(thr.shape[0])
thr = np.min(np.stack([zeros, thr], axis=1), axis=1)
pd = np.greater_equal(pd, thr[:, np.newaxis]).astype(int)
npmetrics.write_metrics(gt, pd, "util/ilocator.txt")
def load_test_data(size=1, fv='res18-128'):
gene_list = datautil.get_test_gene_list(size)
return _load_data(gene_list, size=size, fv=fv)
def load_test_data(size=0, fv='matlab'):
gene_list = datautil.get_test_gene_list(size)
return _load_data(gene_list, size=size)
def load_test_data(size=1):
gene_list = datautil.get_test_gene_list(size)
return _load_data(gene_list, isTrain=False, size=size)