def custom_evaluation(
self,
teacher_action: Message,
labels: Optional[Tuple[str]],
model_response: Message,
) -> None:
if ((teacher_action[CONST.SELECTED_SENTENCES][0]
== CONST.NO_SELECTED_SENTENCES_TOKEN)
or (model_response.is_padding())
or ('text' not in model_response)):
# Has NOT selected knowledge or a is batch padding message
return
resp = model_response['text']
self.metrics.add(
'knowledge_f1_docs',
F1Metric.compute(resp,
[' '.join(teacher_action[CONST.SELECTED_DOCS])]),
)
self.metrics.add('knowledge_f1_max_docs',
F1Metric.compute(resp, CONST.SELECTED_DOCS))
self.metrics.add(
'knowledge_f1_sentences',
F1Metric.compute(
resp, [' '.join(teacher_action[CONST.SELECTED_SENTENCES])]),
)
self.metrics.add(
'knowledge_f1_max_sentences',
F1Metric.compute(resp, CONST.SELECTED_SENTENCES),
)
def custom_evaluation(
self,
teacher_action: Message,
labels: Optional[Tuple[str]],
model_response: Message,
) -> None:
if model_response.is_padding() or (not model_response.get(
'text', None)):
return
expected_graph = break_knowledge_graph(labels[0].lower())
predicted_graph = break_knowledge_graph(model_response['text'].lower())
# Encoding the graph edges/mutation operations into ints for readily use of F1Metric
expected_graph_enc, predicted_graph_enc = encode_set_elements(
expected_graph, predicted_graph)
self.metrics.add(
'response_elements_f1',
F1Metric.compute(
guess=' '.join(predicted_graph_enc),
answers=[' '.join(expected_graph_enc)],
),
)
# Subject, Relation F1
# Changind "(MUT) < you , in , house >" --into--> "(MUT) < you , in "
# This is to check F1 for the predicted subject and relation overlap.
ekg_sub_rel = set([e.rsplit(',', 1)[0] for e in expected_graph])
pkg_sub_rel = set([e.rsplit(',', 1)[0] for e in predicted_graph])
ekg_sub_rel_ids, pkg_sub_rel_ids = encode_set_elements(
ekg_sub_rel, pkg_sub_rel)
self.metrics.add(
'graph_subject_relation_f1',
F1Metric.compute(guess=' '.join(pkg_sub_rel_ids),
answers=[' '.join(ekg_sub_rel_ids)]),
)
# Subject F1
# Changind "(MUT) < you , in " (produced above) --into--> "(MUT) < you "
# This is to check F1 for the predicted subject overlap.
ekg_sub = set([e.split(',')[0] for e in ekg_sub_rel])
pkg_sub = set([e.split(',')[0] for e in pkg_sub_rel])
ekg_sub_ids, pkg_sub_ids = encode_set_elements(ekg_sub, pkg_sub)
self.metrics.add(
'graph_subject_f1',
F1Metric.compute(guess=' '.join(pkg_sub_ids),
answers=[' '.join(ekg_sub_ids)]),
)
def compute(self, guess: str, answers: Iterable[str]) -> F1Metric:
if guess is None or answers is None:
return F1Metric(0, 0)
guess = RareWordF1Calculator._filter(self._freq_dist, self._cutoff_count, guess)
answers = [
RareWordF1Calculator._filter(self._freq_dist, self._cutoff_count, a)
for a in answers
]
if not any(len(a) for a in answers):
# no rare words in labels, set denominator to zero
return F1Metric(0, 0)
return F1Metric.compute(guess, answers)
def __init__(
self,
guess: str,
labels: Optional[List[str]],
prefixes: Optional[List[str]] = None,
shared: Dict[str, Any] = None,
) -> None:
super().__init__(shared=shared)
self.prefixes = prefixes if prefixes else []
bleu = BleuMetric.compute(guess, labels)
f1 = F1Metric.compute(guess, labels)
self.add_with_prefixes("nlg_bleu", bleu)
self.add_with_prefixes("nlg_f1", f1)
def custom_evaluation(
self,
teacher_action: Message,
labels: Optional[Tuple[str]],
model_response: Message,
):
if 'text' in model_response and 'checked_sentence' in teacher_action:
self.metrics.add(
'knowledge_f1',
F1Metric.compute(model_response['text'],
[teacher_action['checked_sentence']]),
)
if 'text' in model_response and labels:
self.metrics.add(
'rare_word_f1',
self.rare_word_f1.compute(model_response['text'], labels),
)
def passes_filters(self, xturn: Dict[str, Any], yturn: Dict[str,
Any]) -> bool:
"""
Subject example to various filters.
Return whether the example passes all filters.
:param xturn:
context turn
:param yturn:
target/knowledge turn
:return passes_filters:
return whether the example passes the filters.
"""
passes = True
# Example filters
knowledge = (yturn['knowledge'].replace(TOKEN_KNOWLEDGE, '').replace(
TOKEN_END_KNOWLEDGE, '').strip())
if passes and self.opt['skip_empty_context']:
doc_context_sentences = [
s for s in xturn['text'].split(TOKEN_KNOWLEDGE)[0].split('\n')
if s
]
passes &= (len(doc_context_sentences)
) > 1 # All docs have <doc> token as their first line
if passes and self.opt['min_knowledge_length'] > 0:
passes &= len(
knowledge.split(' ')) >= self.opt['min_knowledge_length']
if passes and self.opt['min_knowledge_overlap'] > 0:
assert 0 < self.opt['min_knowledge_overlap'] <= 1
f1 = F1Metric.compute(yturn['text'].strip(), [knowledge])
passes &= f1.value() >= self.opt['min_knowledge_overlap']
if passes and self.opt['shared_knowledge_entity']:
knol_ent = extract_entities(knowledge)
if len(knol_ent) == 0:
passes &= False
label_ent = extract_entities(yturn.get('text'))
ents = set(knol_ent).intersection(label_ent)
if len(ents) == 0:
passes &= False
return passes
def get_best_doc(
self, all_docs: List[Document], labels: List[str]
) -> Tuple[Optional[float], Optional[Document], Optional[int]]:
"""
Given a set of all retrieved docs, determine best fitting document.
:param all_docs:
list of all retrieved Documents
:param labels:
labels for the current example
:return (best_f1, best_doc, best_doc_idx):
return the best document, along with the f1 overlap and index into all_docs
"""
docs = []
for i, d in enumerate(all_docs):
if d.startswith('.'):
d = d[2:]
try:
docs += [(i, s) for s in nltk.sent_tokenize(d)]
except IndexError:
# Something's up with the NLTK Sentence tokenizer here.
docs += [(i, s) for s in d.split('.')]
f1s, inds = torch.FloatTensor(
[F1Metric.compute(labels[0], [d]).value() for _, d in docs]
).topk(len(docs))
best_doc = None
best_doc_idx = None
best_f1 = None
for f1, ind in zip(f1s, inds):
if self.threshold < f1 < 1.0 and labels[0] not in docs[ind][1]:
best_doc = docs[ind][1]
best_doc_idx = docs[ind][0]
best_f1 = f1.item()
break
return best_f1, best_doc, best_doc_idx
def _record_retrieval_metrics(self, batch: Batch,
encoder_state: Tuple[Any, ...]):
"""
Compute retrieval metrics, given retrieved documents.
Only works when `--debug` is set.
If there is knowledge in the Batch, we compute the following metrics:
A) Doc Level:
1. recall @ 1 --> is the correct document the first document?
2. recall @ N --> is the correct document in the first N docs?
B) Passage Level:
1. recall @ 1 --> is the correct passage in the first document?
2. recall @ N --> is the correct passage in the first N docs?
Only works in debug mode.
:param batch:
training/eval batch
:param encoder_state:
encoder states from RagEncoder
"""
if batch.valid_indices is None or batch.observations is None:
return
docs: List[List[Document]] = []
_, _, input_turns_cnt, docs, _ = encoder_state
if input_turns_cnt is not None:
new_docs = []
offset = 0
for it in input_turns_cnt:
docs_it = [dd for d in docs[offset:offset + it] for dd in d]
new_docs.append(docs_it)
offset += it
docs = new_docs
title_key = self.opt['gold_knowledge_title_key']
passage_key = self.opt['gold_knowledge_passage_key']
batchsize = len(batch.valid_indices)
n_docs = self.opt['n_docs']
metrics = {
k: [0] * batchsize
for k in [
'doc_r@1',
f'doc_r@{n_docs}',
'passage_r@1',
f'passage_r@{n_docs}',
'title@1_f1',
'passage@1_f1',
]
}
for i in range(batchsize):
ex = batch.observations[i]
label_title = normalize_answer(ex.get(title_key, ''))
label_passage = normalize_answer(ex.get(passage_key, ''))
for rank, doc in enumerate(docs[i]):
model_title = normalize_answer(doc.get_title())
model_passage = normalize_answer(doc.get_text())
title_exact_match = model_title == label_title
passage_match = (model_passage in label_passage
or label_passage in model_passage)
if rank == 0:
metrics['doc_r@1'][i] = int(title_exact_match)
metrics['passage_r@1'][i] = int(passage_match)
metrics['title@1_f1'][i] = F1Metric.compute(
guess=model_title, answers=[label_title]).value()
metrics['passage@1_f1'][i] = F1Metric.compute(
guess=model_passage, answers=[label_passage]).value()
metrics[f'doc_r@{n_docs}'][i] = int(
metrics[f'doc_r@{n_docs}'][i] or title_exact_match)
metrics[f'passage_r@{n_docs}'][i] = int(
metrics[f'passage_r@{n_docs}'][i] or passage_match)
for m in metrics:
self.record_local_metric(
m, AverageMetric.many(metrics[m], [1] * batchsize))
def custom_evaluation(
self,
teacher_action: Message,
labels: Optional[Tuple[str]],
model_response: Message,
) -> None:
"""
Various F1 metrics for the generated model response.
"""
if not model_response.get('text'):
# No response generated by model.
return
resp = model_response['text']
# F1 metric over the *selected* knowledge.
self.metrics.add(
'knowledge_f1_docs',
F1Metric.compute(resp, teacher_action[CONST.SELECTED_DOCS]),
)
self.metrics.add(
'knowledge_f1_sentences',
F1Metric.compute(resp, teacher_action[CONST.SELECTED_SENTENCES]),
)
# F1 Metrics over the *retrieved* docs.
self.metrics.add(
'f1_retrieved_docs',
F1Metric.compute(resp,
' '.join(teacher_action[CONST.RETRIEVED_DOCS])),
)
self.metrics.add(
'max_f1_retrieved_docs',
F1Metric.compute(resp, teacher_action[CONST.RETRIEVED_DOCS]),
)
selected_doc_senetences = teacher_action[CONST.SELECTED_DOCS][0].split(
'\n')
all_doc_senetences = []
for doc in teacher_action[CONST.RETRIEVED_DOCS]:
all_doc_senetences.extend(doc.split('\n'))
self.metrics.add('exact_copied_sentences',
ExactMatchMetric.compute(resp, all_doc_senetences))
self.metrics.add(
'max_substring_copied_sentences',
CopiedSubstringMetric.compute(resp, all_doc_senetences),
)
self.metrics.add(
'max_substring_copied_docs',
CopiedSubstringMetric.compute(
resp, teacher_action[CONST.RETRIEVED_DOCS]),
)
self.metrics.add(
'substring_copied_docs',
CopiedSubstringMetric.compute(
resp, [''.join(teacher_action[CONST.RETRIEVED_DOCS])]),
)
self.metrics.add(
'max_f1_selected_docs_senetences',
F1Metric.compute(resp, selected_doc_senetences),
)
self.metrics.add('max_f1_docs_senetences',
F1Metric.compute(resp, all_doc_senetences))
# N-gram matching metrics
for k in range(1, 5): # 1..4
self.metrics.add(
f'max_bleu_selected_docs_senetences-{k}',
BleuMetric.compute(resp, selected_doc_senetences, k),
)
r1, r2, rL = RougeMetric.compute_many(resp, selected_doc_senetences)
self.metrics.add('max_rouge_selected_docs_senetences_1', r1)
self.metrics.add('max_rouge_selected_docs_senetences_2', r2)
self.metrics.add('max_rouge_selected_docs_senetences_L', rL)
def custom_evaluation(
self,
teacher_action: Message,
labels: Optional[Tuple[str]],
model_response: Message,
):
"""
Custom Evaluations for Wizard of Wikipedia.
When the label is `chosen_sent`, evaluate whether the model response...
1) Is the correct document (title)
2) _contains_ the correct chosen sentence (even if it's not wholly the answer)
When the label is `response`, we compute F1 of model generation w.r.t checked sentence.
:param teacher_action:
The message last sent from this teacher.
:param labels:
The previous correct labels, if there were any.
:param model_response:
The raw response from the model. Generally you want to rely on the
text field, but others may be necessary in specific situations.
"""
if (self.label_type == 'response' and 'text' in model_response
and 'checked_sentence' in teacher_action):
self.metrics.add(
'knowledge_f1',
F1Metric.compute(model_response['text'],
[teacher_action['checked_sentence']]),
)
if labels:
self.metrics.add(
'rare_word_f1',
self.rare_word_f1.compute(model_response['text'], labels),
)
elif (self.label_type == 'chosen_sent'
and TOKEN_KNOWLEDGE in model_response['text']):
try:
correct_title, correct_passage = [
normalize_answer(a)
for a in labels[0].split(TOKEN_KNOWLEDGE)
]
except ValueError:
# Knowledge not chosen
correct_title, correct_passage = TOKEN_NOCHOSEN, TOKEN_NOCHOSEN
title, passage = [
normalize_answer(a)
for a in model_response['text'].split(TOKEN_KNOWLEDGE)
]
self.metrics.add('title_r@1',
AverageMetric(int(correct_title == title)))
self.metrics.add('passage_r@1',
AverageMetric(int(correct_passage in passage)))
if 'title_candidates' in model_response:
title_candidates = [
normalize_answer(t)
for t in model_response['title_candidates']
][:5]
self.metrics.add(
'title_r@5',
AverageMetric(
int(any(correct_title == t
for t in title_candidates))),
)
if 'text_candidates' in model_response:
text_candidates = [
normalize_answer(t)
for t in model_response['text_candidates']
][:5]
self.metrics.add(
'passage_r@5',
AverageMetric(
int(any(correct_passage in t
for t in text_candidates))),
)
def custom_evaluation(
self,
teacher_action: Message,
labels: Optional[Tuple[str]],
model_response: Message,
):
if 'metrics' in model_response and 'type' in teacher_action:
# keep copies of metrics across both api calls/responses
prefix = teacher_action['type']
keys = list(model_response['metrics'].keys())
for k in keys:
self.metrics.add(f'{prefix}_{k}', model_response['metrics'][k])
if 'text' not in model_response or not labels or 'type' not in teacher_action:
return
domain = teacher_action['domain']
if teacher_action['type'] == 'apicall':
# also count slot accuracy
text = model_response['text']
slot_guesses = set(
text.replace(
CALL_TOKEN + " ",
"").split(' ; ')) # prevent cheating via repeated guesses
correct = 0
for slot_guess in slot_guesses:
if ' = ' not in slot_guess:
continue
try:
slot, guess = slot_guess.split(' = ')
except ValueError:
continue
if teacher_action['slots'].get(slot) == guess:
self.metrics.add('slot_p', AverageMetric(1))
self.metrics.add(f'{domain}_slot_p', AverageMetric(1))
correct += 1
else:
self.metrics.add('slot_p', AverageMetric(0))
self.metrics.add(f'{domain}_slot_p', AverageMetric(0))
logging.debug(
f"Bad slot guess '{slot_guess}' != {teacher_action['slots']}"
)
if teacher_action['slots']:
self.metrics.add(
'slot_r',
AverageMetric(correct, len(teacher_action['slots'])))
self.metrics.add(
f'{domain}_slot_r',
AverageMetric(correct, len(teacher_action['slots'])),
)
self.metrics.add(
'jga',
AverageMetric(correct == len(teacher_action['slots'])))
elif teacher_action['type'] == 'apiresp':
# keep track of statistics by domain
f1_metric = F1Metric.compute(model_response['text'], labels)
bleu_metric = BleuMetric.compute(model_response['text'], labels)
self.metrics.add(f'{domain}_lex_f1', f1_metric)
self.metrics.add(f'{domain}_lex_bleu', bleu_metric)
delex_text = model_response['text']
delex_label = labels[0]
# compute delexicalized string metrics
for slot, value in teacher_action['slots'].items():
delex_text = delex_text.replace(value, slot)
delex_label = delex_label.replace(value, slot)
f1_metric = F1Metric.compute(delex_text, (delex_label, ))
self.metrics.add('delex_f1', f1_metric)
self.metrics.add(f'{domain}_delex_f1', f1_metric)
bleu_metric = BleuMetric.compute(delex_text, [delex_label])
self.metrics.add('delex_bleu', bleu_metric)
self.metrics.add(f'{domain}_delex_bleu', bleu_metric)
