def __call__(self, xs, ts, ys):
counts = DFCounts()
for x, t, y in zip(xs, ts, ys):
generator_seg = self.generate_lines_seg(x, t, y)
generator_tag = self.generate_lines(x, t, y)
counts.l1 = conlleval.evaluate(generator_seg, counts=counts.l1)
counts.l2 = conlleval.evaluate(generator_tag, counts=counts.l2)
return counts
def __call__(self, xs, ts, ys):
counts = FCounts()
for x, t, y in zip(xs, ts, ys):
generator = self.generate_lines(x, t, y)
counts = conlleval.evaluate(generator, counts=counts)
return counts
def evaluate(model, data, batch_size, log):
was_training = model.training
model.eval()
pred = []
gold = []
with torch.no_grad():
for pa in data_generator(data, batch_size):
x = pa[0]
y = pa[1]
slot = pa[2]
_x, _y, p = model(x, y, slot, 'test')
gold += _y
pred += p
if was_training:
model.train()
_gold = []
_pred = []
for i in gold:
for j in i:
_gold.append(j)
for i in pred:
for j in i:
_pred.append(j)
return conlleval.evaluate(_gold, _pred, log, verbose=True)
def __call__(self, xs, t1s, t2s, y1s, y2s):
counts = FACounts()
if not t2s or not y2s:
t2s = [None] * len(xs)
y2s = [None] * len(xs)
for x, t1, t2, y1, y2 in zip(xs, t1s, t2s, y1s, y2s):
generator_seg = self.generate_lines_seg(x, t1, y1)
counts.l1 = conlleval.evaluate(generator_seg, counts=counts.l1)
if t2 is not None:
for ti, yi in zip(t2, y2):
counts.l2.total += 1
if ti == yi:
counts.l2.correct += 1
return counts