def _test(self, ts):
self.model.eval()
total_correct = 0
total_sum = 0
total_gold_count = 0
total_guess_count = 0
total_overlap_count = 0
metrics = {}
steps = len(ts)
pg = create_progress_bar(steps)
for batch_dict in ts:
x, xch, lengths, y, ids = self.model.make_input(batch_dict)
inputs = (x, xch, lengths)
pred = self.model(inputs)
correct, count, overlaps, golds, guesses = self.process_output(
pred, y.data, lengths, ids, None, None)
total_correct += correct
total_sum += count
total_gold_count += golds
total_guess_count += guesses
total_overlap_count += overlaps
pg.update()
pg.done()
total_acc = total_correct / float(total_sum)
# Only show the fscore if requested
metrics['f1'] = f_score(total_overlap_count, total_gold_count,
total_guess_count)
metrics['acc'] = total_acc
return metrics
def test(self, ts, conll_file=None, txts=None):
total_correct = total_sum = 0
total_gold_count = total_guess_count = total_overlap_count = 0
steps = len(ts)
pg = ProgressBar(steps)
metrics = {}
# Only if they provide a file and the raw txts, we can write CONLL file
handle = None
if conll_file is not None and txts is not None:
handle = open(conll_file, "w")
for x, xch, y, lengths, id in ts:
correct, count, overlaps, golds, guesses = self.process_batch(
x, xch, y, lengths, id, handle, txts)
total_correct += correct
total_sum += count
total_gold_count += golds
total_guess_count += guesses
total_overlap_count += overlaps
pg.update()
pg.done()
total_acc = total_correct / float(total_sum)
# Only show the fscore if requested
metrics['f1'] = f_score(total_overlap_count, total_gold_count,
total_guess_count)
metrics['acc'] = total_acc
if handle is not None:
handle.close()
return metrics
def _test(self, ts, **kwargs):
self.model.train = False
total_correct = 0
total_sum = 0
total_gold_count = 0
total_guess_count = 0
total_overlap_count = 0
metrics = {}
steps = len(ts)
conll_output = kwargs.get('conll_output', None)
txts = kwargs.get('txts', None)
handle = None
if conll_output is not None and txts is not None:
handle = open(conll_output, "w")
pg = create_progress_bar(steps)
for batch_dict in pg(ts):
lengths = batch_dict[self.model.lengths_key]
ids = batch_dict['ids']
y = batch_dict['y']
pred = self.model.predict(batch_dict)
correct, count, overlaps, golds, guesses = self.process_output(pred, y, lengths, ids, handle, txts)
total_correct += correct
total_sum += count
total_gold_count += golds
total_guess_count += guesses
total_overlap_count += overlaps
total_acc = total_correct / float(total_sum)
# Only show the fscore if requested
metrics['f1'] = f_score(total_overlap_count, total_gold_count, total_guess_count)
metrics['acc'] = total_acc
return metrics
def test(self, ts, conll_output=None, txts=None):
total_correct = total_sum = 0
total_gold_count = total_guess_count = total_overlap_count = 0
steps = len(ts)
pg = create_progress_bar(steps)
metrics = {}
# Only if they provide a file and the raw txts, we can write CONLL file
handle = None
if conll_output is not None and txts is not None:
handle = open(conll_output, "w")
try:
for batch_dict in pg(ts):
correct, count, overlaps, golds, guesses = self.process_batch(
batch_dict, handle, txts)
total_correct += correct
total_sum += count
total_gold_count += golds
total_guess_count += guesses
total_overlap_count += overlaps
total_acc = total_correct / float(total_sum)
# Only show the fscore if requested
metrics['f1'] = f_score(total_overlap_count, total_gold_count,
total_guess_count)
metrics['acc'] = total_acc
finally:
if handle is not None:
handle.close()
return metrics
def test(self, ts, conll_output=None, txts=None):
total_correct = total_sum = 0
total_gold_count = total_guess_count = total_overlap_count = 0
steps = len(ts)
pg = create_progress_bar(steps)
metrics = {}
# Only if they provide a file and the raw txts, we can write CONLL file
handle = None
if conll_output is not None and txts is not None:
handle = open(conll_output, "w")
try:
for batch_dict in pg(ts):
correct, count, overlaps, golds, guesses = self.process_batch(batch_dict, handle, txts)
total_correct += correct
total_sum += count
total_gold_count += golds
total_guess_count += guesses
total_overlap_count += overlaps
total_acc = total_correct / float(total_sum)
# Only show the fscore if requested
metrics['f1'] = f_score(total_overlap_count, total_gold_count, total_guess_count)
metrics['acc'] = total_acc
finally:
if handle is not None:
handle.close()
return metrics
def _test(self, ts):
self.model.eval()
total_loss = 0
total_correct = 0
total_sum = 0
total_gold_count = 0
total_guess_count = 0
total_overlap_count = 0
metrics = {}
steps = len(ts)
pg = ProgressBar(steps)
for x, xch, y, lengths, ids in ts:
x, xch, y = self._wrap(x, xch, y)
pred = self.model((x, xch))
loss = self.crit(pred, y)
total_loss += loss.data[0]
correct, count, overlaps, golds, guesses = self.process_output(
pred, y, lengths, ids, None, None)
total_correct += correct
total_sum += count
total_gold_count += golds
total_guess_count += guesses
total_overlap_count += overlaps
pg.update()
pg.done()
total_acc = total_correct / float(total_sum)
# Only show the fscore if requested
metrics['f1'] = f_score(total_overlap_count, total_gold_count,
total_guess_count)
metrics['acc'] = total_acc
metrics['avg_loss'] = float(total_loss) / total_sum
return metrics
def _test(self, ts, **kwargs):
self.model.eval()
total_correct = 0
total_sum = 0
total_gold_count = 0
total_guess_count = 0
total_overlap_count = 0
metrics = {}
steps = len(ts)
conll_output = kwargs.get('conll_output', None)
txts = kwargs.get('txts', None)
handle = None
if conll_output is not None and txts is not None:
handle = open(conll_output, "w")
pg = create_progress_bar(steps)
for batch_dict in pg(ts):
inputs = self.model.make_input(batch_dict)
y = inputs.pop('y')
lengths = inputs['lengths']
ids = inputs['ids']
pred = self.model(inputs)
correct, count, overlaps, golds, guesses = self.process_output(
pred, y.data, lengths, ids, handle, txts)
total_correct += correct
total_sum += count
total_gold_count += golds
total_guess_count += guesses
total_overlap_count += overlaps
total_acc = total_correct / float(total_sum)
# Only show the fscore if requested
metrics['f1'] = f_score(total_overlap_count, total_gold_count,
total_guess_count)
metrics['acc'] = total_acc
return metrics
def _test(self, ts, **kwargs):
self.model.train = False
total_correct = 0
total_sum = 0
total_gold_count = 0
total_guess_count = 0
total_overlap_count = 0
metrics = {}
steps = len(ts)
conll_output = kwargs.get('conll_output', None)
txts = kwargs.get('txts', None)
handle = None
if conll_output is not None and txts is not None:
handle = open(conll_output, "w")
pg = create_progress_bar(steps)
for batch_dict in ts:
x, xch, lengths, y, ids = self.model.make_input(batch_dict)
pred = self.model.predict((x, xch), lengths)
correct, count, overlaps, golds, guesses = self.process_output(
pred, y, lengths, ids, handle, txts)
total_correct += correct
total_sum += count
total_gold_count += golds
total_guess_count += guesses
total_overlap_count += overlaps
pg.update()
pg.done()
total_acc = total_correct / float(total_sum)
# Only show the fscore if requested
metrics['f1'] = f_score(total_overlap_count, total_gold_count,
total_guess_count)
metrics['acc'] = total_acc
return metrics
