def get_predictions(example, result, n_best_size, max_answer_length):
"""
This function has been mostly copied from run_squad.py.
Unfortunate, but I needed to return local variables from that function.
:param all_examples:
:param all_features:
:param all_results:
:param n_best_size:
:param max_answer_length:
:param null_score_diff_threshold:
:return:
"""
_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
"PrelimPrediction",
["feature_index", "start_index", "end_index", "start_logit", "end_logit"])
prelim_predictions = []
# keep track of the minimum score of null start+end of position 0
null_start_logit = 0 # the start logit at the slice with min null score
null_end_logit = 0 # the end logit at the slice with min null score
# start_indexes = run_squad._get_best_indexes(result.start_logits, n_best_size)
# end_indexes = run_squad._get_best_indexes(result.end_logits, n_best_size)
start_indexes, end_indexes = get_nbest_bounds_from_membership(result.membership, n_best_size)
for start_index in start_indexes:
for end_index in end_indexes:
# We could hypothetically create invalid predictions, e.g., predict
# that the start of the span is in the question. We throw out all
# invalid predictions.
if start_index >= len(example.tokens):
continue
if end_index >= len(example.tokens):
continue
if start_index not in example.token_to_orig_map:
continue
if end_index not in example.token_to_orig_map:
continue
if not example.token_is_max_context.get(start_index, False):
continue
if end_index < start_index:
continue
length = end_index - start_index + 1
if length > max_answer_length:
continue
prelim_predictions.append(
_PrelimPrediction(
# feature_index=feature_index,
feature_index=0,
start_index=start_index,
end_index=end_index,
start_logit=1,
end_logit=1))
# start_logit=result.start_logits[start_index],
# end_logit=result.end_logits[end_index]))
prelim_predictions = sorted(
prelim_predictions,
key=lambda x: (x.start_logit + x.end_logit),
reverse=True)
_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
"NbestPrediction", ["text", "start_logit", "end_logit", "start_offset", "end_offset"])
seen_predictions = set()
nbest = []
for pred in prelim_predictions:
if len(nbest) >= n_best_size:
break
# feature = features[pred.feature_index]
if pred.start_index > 0: # this is a non-null prediction
start_offset = example.token_to_orig_map[pred.start_index]
end_offset = example.token_to_orig_map[pred.end_index] + 1
try:
final_text = ' '.join(example.doc_tokens[start_offset:end_offset])
except AttributeError:
final_text = ''
if (start_offset, end_offset) in seen_predictions:
continue
seen_predictions.add((start_offset, end_offset))
else:
final_text = ""
start_offset = 0
end_offset = 0
seen_predictions.add((0, 0))
nbest.append(
_NbestPrediction(
text=final_text,
start_logit=pred.start_logit,
end_logit=pred.end_logit,
start_offset=start_offset,
end_offset=end_offset))
# if we didn't include the empty option in the n-best, include it
if (0, 0) not in seen_predictions:
nbest.append(
_NbestPrediction(
text="", start_logit=null_start_logit,
end_logit=null_end_logit,
start_offset=0,
end_offset=0))
# In very rare edge cases we could have no valid predictions. So we
# just create a nonce prediction in this case to avoid failure.
if not nbest:
nbest.append(
_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0, start_offset=0, end_offset=0))
assert len(nbest) >= 1
total_scores = []
best_non_null_entry = None
for entry in nbest:
total_scores.append(entry.start_logit + entry.end_logit)
if not best_non_null_entry:
if entry.text:
best_non_null_entry = entry
probs = run_squad._compute_softmax(total_scores)
nbest_json = []
for (i, entry) in enumerate(nbest):
output = collections.OrderedDict()
output["text"] = entry.text
output["start_offset"] = entry.start_offset
output["end_offset"] = entry.end_offset
output["probability"] = probs[i]
output["start_logit"] = entry.start_logit
output["end_logit"] = entry.end_logit
nbest_json.append(output)
assert len(nbest_json) >= 1
return nbest_json
def write_predictions(self, all_examples, all_features, all_results,
n_best_size, max_answer_length, do_lower_case,
output_prediction_file, output_nbest_file,
output_null_log_odds_file):
"""Write final predictions to the json file and log-odds of null if needed."""
tf.logging.info("Writing predictions to: %s" %
(output_prediction_file))
tf.logging.info("Writing nbest to: %s" % (output_nbest_file))
example_index_to_features = collections.defaultdict(list)
for feature in all_features:
example_index_to_features[feature.example_index].append(feature)
unique_id_to_result = {}
for result in all_results:
unique_id_to_result[result.unique_id] = result
_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
"PrelimPrediction", [
"feature_index", "start_index", "end_index", "start_logit",
"end_logit"
])
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
scores_diff_json = collections.OrderedDict()
for (example_index, example) in enumerate(all_examples):
features = example_index_to_features[example_index]
prelim_predictions = []
# keep track of the minimum score of null start+end of position 0
score_null = 1000000 # large and positive
min_null_feature_index = 0 # the paragraph slice with min mull score
null_start_logit = 0 # the start logit at the slice with min null score
null_end_logit = 0 # the end logit at the slice with min null score
for (feature_index, feature) in enumerate(features):
result = unique_id_to_result[feature.unique_id]
start_indexes = run_squad._get_best_indexes(
result.start_logits, n_best_size)
end_indexes = run_squad._get_best_indexes(
result.end_logits, n_best_size)
# if we could have irrelevant answers, get the min score of irrelevant
if FLAGS.version_2_with_negative:
feature_null_score = result.start_logits[
0] + result.end_logits[0]
if feature_null_score < score_null:
score_null = feature_null_score
min_null_feature_index = feature_index
null_start_logit = result.start_logits[0]
null_end_logit = result.end_logits[0]
for start_index in start_indexes:
for end_index in end_indexes:
# We could hypothetically create invalid predictions, e.g., predict
# that the start of the span is in the question. We throw out all
# invalid predictions.
if start_index >= len(feature.tokens):
continue
if end_index >= len(feature.tokens):
continue
if start_index not in feature.token_to_orig_map:
continue
if end_index not in feature.token_to_orig_map:
continue
if not feature.token_is_max_context.get(
start_index, False):
continue
if end_index < start_index:
continue
length = end_index - start_index + 1
if length > max_answer_length:
continue
prelim_predictions.append(
_PrelimPrediction(
feature_index=feature_index,
start_index=start_index,
end_index=end_index,
start_logit=result.start_logits[start_index],
end_logit=result.end_logits[end_index]))
if FLAGS.version_2_with_negative:
prelim_predictions.append(
_PrelimPrediction(feature_index=min_null_feature_index,
start_index=0,
end_index=0,
start_logit=null_start_logit,
end_logit=null_end_logit))
prelim_predictions = sorted(prelim_predictions,
key=lambda x:
(x.start_logit + x.end_logit),
reverse=True)
_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
"NbestPrediction", ["text", "start_logit", "end_logit"])
seen_predictions = {}
nbest = []
for pred in prelim_predictions:
if len(nbest) >= n_best_size:
break
feature = features[pred.feature_index]
if pred.start_index > 0: # this is a non-null prediction
tok_tokens = feature.tokens[pred.start_index:(
pred.end_index + 1)]
orig_doc_start = feature.token_to_orig_map[
pred.start_index]
orig_doc_end = feature.token_to_orig_map[pred.end_index]
orig_tokens = example.doc_tokens[orig_doc_start:(
orig_doc_end + 1)]
tok_text = " ".join(tok_tokens)
# De-tokenize WordPieces that have been split off.
tok_text = tok_text.replace(" ##", "")
tok_text = tok_text.replace("##", "")
# Clean whitespace
tok_text = tok_text.strip()
tok_text = " ".join(tok_text.split())
orig_text = " ".join(orig_tokens)
final_text = run_squad.get_final_text(
tok_text, orig_text, do_lower_case)
if final_text in seen_predictions:
continue
seen_predictions[final_text] = True
else:
final_text = ""
seen_predictions[final_text] = True
nbest.append(
_NbestPrediction(text=final_text,
start_logit=pred.start_logit,
end_logit=pred.end_logit))
# if we didn't inlude the empty option in the n-best, inlcude it
if FLAGS.version_2_with_negative:
if "" not in seen_predictions:
nbest.append(
_NbestPrediction(text="",
start_logit=null_start_logit,
end_logit=null_end_logit))
# In very rare edge cases we could have no valid predictions. So we
# just create a nonce prediction in this case to avoid failure.
if not nbest:
nbest.append(
_NbestPrediction(text="empty",
start_logit=0.0,
end_logit=0.0))
assert len(nbest) >= 1
total_scores = []
best_non_null_entry = None
for entry in nbest:
total_scores.append(entry.start_logit + entry.end_logit)
if not best_non_null_entry:
if entry.text:
best_non_null_entry = entry
probs = run_squad._compute_softmax(total_scores)
nbest_json = []
for (i, entry) in enumerate(nbest):
output = collections.OrderedDict()
output["text"] = entry.text
output["probability"] = probs[i]
output["start_logit"] = entry.start_logit
output["end_logit"] = entry.end_logit
nbest_json.append(output)
assert len(nbest_json) >= 1
if not FLAGS.version_2_with_negative:
all_predictions[example.qas_id] = nbest_json[0]["text"]
else:
# predict "" iff the null score - the score of best non-null > threshold
score_diff = score_null - best_non_null_entry.start_logit - (
best_non_null_entry.end_logit)
scores_diff_json[example.qas_id] = score_diff
if score_diff > FLAGS.null_score_diff_threshold:
all_predictions[example.qas_id] = ""
else:
all_predictions[example.qas_id] = best_non_null_entry.text
all_nbest_json[example.qas_id] = nbest_json
if FLAGS.version_2_with_negative:
return all_predictions, all_nbest_json, scores_diff_json
return all_predictions, all_nbest_json
def get_answer(doc_tokens, tokens_for_postprocessing, start_logits, end_logits,
args):
result = RawResult(start_logits=start_logits, end_logits=end_logits)
predictions = []
Prediction = collections.namedtuple('Prediction',
['text', 'start_logit', 'end_logit'])
if args.version_2_with_negative:
null_val = (float("inf"), 0, 0)
start_indices = _get_best_indices(result.start_logits, args.n_best_size)
end_indices = _get_best_indices(result.end_logits, args.n_best_size)
prelim_predictions = get_valid_prelim_predictions(
start_indices, end_indices, tokens_for_postprocessing, result, args)
prelim_predictions = sorted(prelim_predictions,
key=lambda x: (x.start_logit + x.end_logit),
reverse=True)
if args.version_2_with_negative:
score = result.start_logits[0] + result.end_logits[0]
if score < null_val[0]:
null_val = (score, result.start_logits[0], result.end_logits[0])
doc_tokens_obj = {
'doc_tokens': doc_tokens,
}
doc_tokens_obj = SimpleNamespace(**doc_tokens_obj)
curr_predictions = []
seen_predictions = []
for pred in prelim_predictions:
if len(curr_predictions) == args.n_best_size:
break
if pred.end_index > 0: # this is a non-null prediction
final_text = get_answer_text(doc_tokens_obj,
tokens_for_postprocessing, pred, args)
if final_text in seen_predictions:
continue
else:
final_text = ""
seen_predictions.append(final_text)
curr_predictions.append(
Prediction(final_text, pred.start_logit, pred.end_logit))
predictions += curr_predictions
# add empty prediction
if args.version_2_with_negative:
predictions.append(Prediction('', null_val[1], null_val[2]))
nbest_answers = []
answer = None
nbest = sorted(predictions,
key=lambda x: (x.start_logit + x.end_logit),
reverse=True)[:args.n_best_size]
total_scores = []
best_non_null_entry = None
for entry in nbest:
total_scores.append(entry.start_logit + entry.end_logit)
if not best_non_null_entry and entry.text:
best_non_null_entry = entry
probs = _compute_softmax(total_scores)
for (i, entry) in enumerate(nbest):
output = collections.OrderedDict()
output["text"] = entry.text
output["probability"] = probs[i]
output["start_logit"] = entry.start_logit
output["end_logit"] = entry.end_logit
nbest_answers.append(output)
if args.version_2_with_negative:
score_diff = null_val[
0] - best_non_null_entry.start_logit - best_non_null_entry.end_logit
if score_diff > args.null_score_diff_threshold:
answer = ""
else:
answer = best_non_null_entry.text
else:
answer = nbest_answers[0]['text']
return answer, nbest_answers
def predict(self, all_examples, all_features, all_results, n_best_size,
max_answer_length, do_lower_case):
example_index_to_features = collections.defaultdict(list)
for feature in all_features:
example_index_to_features[feature.example_index].append(feature)
unique_id_to_result = {}
for result in all_results:
unique_id_to_result[result.unique_id] = result
_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
"PrelimPrediction", [
"feature_index", "start_index", "end_index", "start_logit",
"end_logit"
])
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
for (example_index, example) in enumerate(all_examples):
features = example_index_to_features[example_index]
prelim_predictions = []
for (feature_index, feature) in enumerate(features):
result = unique_id_to_result[feature.unique_id]
start_indexes = _get_best_indexes(result.start_logits,
n_best_size)
end_indexes = _get_best_indexes(result.end_logits, n_best_size)
# if we could have irrelevant answers, get the min score of irrelevant
for start_index in start_indexes:
for end_index in end_indexes:
# We could hypothetically create invalid predictions, e.g., predict
# that the start of the span is in the question. We throw out all
# invalid predictions.
if (start_index >= len(feature.tokens)
or end_index >= len(feature.tokens)
or start_index not in feature.token_to_orig_map
or end_index not in feature.token_to_orig_map
or not feature.token_is_max_context.get(
start_index, False)
or end_index < start_index):
continue
length = end_index - start_index + 1
if length > max_answer_length:
continue
prelim_predictions.append(
_PrelimPrediction(
feature_index=feature_index,
start_index=start_index,
end_index=end_index,
start_logit=result.start_logits[start_index],
end_logit=result.end_logits[end_index]))
prelim_predictions = sorted(prelim_predictions,
key=lambda x:
(x.start_logit + x.end_logit),
reverse=True)
_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
"NbestPrediction", ["text", "start_logit", "end_logit"])
seen_predictions = {}
nbest = []
for pred in prelim_predictions:
if len(nbest) >= n_best_size:
break
feature = features[pred.feature_index]
if pred.start_index > 0: # this is a non-null prediction
tok_tokens = feature.tokens[pred.start_index:(
pred.end_index + 1)]
orig_doc_start = feature.token_to_orig_map[
pred.start_index]
orig_doc_end = feature.token_to_orig_map[pred.end_index]
orig_tokens = example.doc_tokens[orig_doc_start:(
orig_doc_end + 1)]
tok_text = " ".join(tok_tokens)
# De-tokenize WordPieces that have been split off.
tok_text = tok_text.replace(" ##", "")
tok_text = tok_text.replace("##", "")
# Clean whitespace
tok_text = tok_text.strip()
tok_text = " ".join(tok_text.split())
orig_text = " ".join(orig_tokens)
final_text = get_final_text(tok_text, orig_text,
do_lower_case)
if final_text in seen_predictions:
continue
seen_predictions[final_text] = True
else:
final_text = ""
seen_predictions[final_text] = True
nbest.append(
_NbestPrediction(text=final_text,
start_logit=pred.start_logit,
end_logit=pred.end_logit))
# In very rare edge cases we could have no valid predictions. So we
# just create a nonce prediction in this case to avoid failure.
if not nbest:
nbest.append(
_NbestPrediction(text="empty",
start_logit=0.0,
end_logit=0.0))
assert len(nbest) >= 1
total_scores = []
best_non_null_entry = None
for entry in nbest:
total_scores.append(entry.start_logit + entry.end_logit)
if not best_non_null_entry:
if entry.text:
best_non_null_entry = entry
probs = _compute_softmax(total_scores)
nbest_json = []
for (i, entry) in enumerate(nbest):
output = collections.OrderedDict()
output["text"] = entry.text
output["probability"] = probs[i]
output["start_logit"] = entry.start_logit
output["end_logit"] = entry.end_logit
nbest_json.append(output)
assert len(nbest_json) >= 1
all_predictions[example.qas_id] = nbest_json[0]["text"]
all_nbest_json[example.qas_id] = nbest_json
return all_nbest_json
start_logit=pred.start_logit,
end_logit=pred.end_logit))
# In very rare edge cases we could have no valid predictions. So we
# just create a nonce prediction in this case to avoid failure.
if not nbest:
nbest.append(_NbestPrediction(text="empty", start_logit=0.0,
end_logit=0.0))
assert len(nbest) >= 1
total_scores = []
for entry in nbest:
total_scores.append(entry.start_logit + entry.end_logit)
probs = run_squad._compute_softmax(total_scores)
print(nbest[0].text)
#END SECTION TRYING A NEW APPROACH
#END SECTION TRYING A NEW APPROACH
#END SECTION TRYING A NEW APPROACH
#END SECTION TRYING A NEW APPROACH
i = 0
example_index_routes = example_indices_routes[0]
#start_logits and end_logits are both lists of len 384
start_logits_routes = batch_start_logits_routes[i].detach().cpu().tolist()
end_logits_routes = batch_end_logits_routes[i].detach().cpu().tolist()
#remember from above, eval_features is list of objects, each w/ fields for tokens, input_mask, input_ids, segment_ids, etc.
eval_feature_routes = eval_features_routes[