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Commit e9217da5 authored by LysandreJik's avatar LysandreJik
Browse files

Cleanup 

Improve global visibility on the run_squad script, remove unused files and fixes related to XLNet.
parent 9ecd83da
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Showing with 45 additions and 1387 deletions
examples/run_squad.py
View file @ e9217da5
......@@ -27,8 +27,7 @@ import glob
import timeit
import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
TensorDataset)
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler, TensorDataset)
from torch.utils.data.distributed import DistributedSampler
try:
......@@ -48,14 +47,6 @@ from transformers import (WEIGHTS_NAME, BertConfig,
from transformers import AdamW, get_linear_schedule_with_warmup, squad_convert_examples_to_features
from utils_squad import (convert_examples_to_features as old_convert, read_squad_examples as old_read, RawResult, write_predictions,
RawResultExtended, write_predictions_extended)
# The follwing import is the official SQuAD evaluation script (2.0).
# You can remove it from the dependencies if you are using this script outside of the library
# We've added it here for automated tests (see examples/test_examples.py file)
from utils_squad_evaluate import EVAL_OPTS, main as evaluate_on_squad
logger = logging.getLogger(__name__)
ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) \
......@@ -98,14 +89,16 @@ def train(args, train_dataset, model, tokenizer):
optimizer_grouped_parameters = [
{'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay},
{'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
# multi-gpu training (should be after apex fp16 initialization)
......@@ -133,20 +126,26 @@ def train(args, train_dataset, model, tokenizer):
model.zero_grad()
train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
set_seed(args) # Added here for reproductibility (even between python 2 and 3)
for _ in train_iterator:
epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
for step, batch in enumerate(epoch_iterator):
model.train()
batch = tuple(t.to(args.device) for t in batch)
inputs = {'input_ids': batch[0],
'attention_mask': batch[1],
'start_positions': batch[3],
'end_positions': batch[4]}
inputs = {
'input_ids': batch[0],
'attention_mask': batch[1],
'start_positions': batch[3],
'end_positions': batch[4]
}
if args.model_type != 'distilbert':
inputs['token_type_ids'] = None if args.model_type == 'xlm' else batch[2]
if args.model_type in ['xlnet', 'xlm']:
inputs.update({'cls_index': batch[5],
'p_mask': batch[6]})
inputs.update({'cls_index': batch[5], 'p_mask': batch[6]})
outputs = model(**inputs)
loss = outputs[0] # model outputs are always tuple in transformers (see doc)
......@@ -173,8 +172,8 @@ def train(args, train_dataset, model, tokenizer):
model.zero_grad()
global_step += 1
# Log metrics
if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
# Log metrics
if args.local_rank == -1 and args.evaluate_during_training: # Only evaluate when single GPU otherwise metrics may not average well
results = evaluate(args, model, tokenizer)
for key, value in results.items():
......@@ -183,8 +182,8 @@ def train(args, train_dataset, model, tokenizer):
tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step)
logging_loss = tr_loss
# Save model checkpoint
if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
# Save model checkpoint
output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
......@@ -213,6 +212,7 @@ def evaluate(args, model, tokenizer, prefix=""):
os.makedirs(args.output_dir)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
# Note that DistributedSampler samples randomly
eval_sampler = SequentialSampler(dataset) if args.local_rank == -1 else DistributedSampler(dataset)
eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
......@@ -225,11 +225,14 @@ def evaluate(args, model, tokenizer, prefix=""):
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = %d", len(dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
all_results = []
start_time = timeit.default_timer()
for batch in tqdm(eval_dataloader, desc="Evaluating"):
model.eval()
batch = tuple(t.to(args.device) for t in batch)
with torch.no_grad():
inputs = {
'input_ids': batch[0],
......@@ -238,10 +241,13 @@ def evaluate(args, model, tokenizer, prefix=""):
if args.model_type != 'distilbert':
inputs['token_type_ids'] = None if args.model_type == 'xlm' else batch[2] # XLM don't use segment_ids
example_indices = batch[3]
# XLNet and XLM use more arguments for their predictions
if args.model_type in ['xlnet', 'xlm']:
inputs.update({'cls_index': batch[4],
'p_mask': batch[5]})
inputs.update({'cls_index': batch[4], 'p_mask': batch[5]})
outputs = model(**inputs)
for i, example_index in enumerate(example_indices):
......@@ -250,11 +256,13 @@ def evaluate(args, model, tokenizer, prefix=""):
output = [to_list(output[i]) for output in outputs]
# Some models (XLNet, XLM) use 5 arguments for their predictions, while the other "simpler"
# models only use two.
if len(output) >= 5:
start_logits = output[0]
start_top_index = output[1]
end_logits = output[2]
end_top_index = output[3],
end_top_index = output[3]
cls_logits = output[4]
result = SquadResult(
......@@ -278,16 +286,17 @@ def evaluate(args, model, tokenizer, prefix=""):
# Compute predictions
output_prediction_file = os.path.join(args.output_dir, "predictions_{}.json".format(prefix))
output_nbest_file = os.path.join(args.output_dir, "nbest_predictions_{}.json".format(prefix))
if args.version_2_with_negative:
output_null_log_odds_file = os.path.join(args.output_dir, "null_odds_{}.json".format(prefix))
else:
output_null_log_odds_file = None
# XLNet and XLM use a more complex post-processing procedure
if args.model_type in ['xlnet', 'xlm']:
# XLNet uses a more complex post-processing procedure
predictions = compute_predictions_log_probs(examples, features, all_results, args.n_best_size,
args.max_answer_length, output_prediction_file,
output_nbest_file, output_null_log_odds_file, args.predict_file,
output_nbest_file, output_null_log_odds_file,
model.config.start_n_top, model.config.end_n_top,
args.version_2_with_negative, tokenizer, args.verbose_logging)
else:
......@@ -296,6 +305,7 @@ def evaluate(args, model, tokenizer, prefix=""):
output_nbest_file, output_null_log_odds_file, args.verbose_logging,
args.version_2_with_negative, args.null_score_diff_threshold)
# Compute the F1 and exact scores.
results = squad_evaluate(examples, predictions)
return results
......@@ -308,7 +318,10 @@ def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=Fal
cached_features_file = os.path.join(input_dir, 'cached_{}_{}_{}'.format(
'dev' if evaluate else 'train',
list(filter(None, args.model_name_or_path.split('/'))).pop(),
str(args.max_seq_length)))
str(args.max_seq_length))
)
# Init features and dataset from cache if it exists
if os.path.exists(cached_features_file) and not args.overwrite_cache and not output_examples:
logger.info("Loading features from cached file %s", cached_features_file)
features_and_dataset = torch.load(cached_features_file)
......@@ -341,7 +354,6 @@ def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=Fal
return_dataset='pt'
)
if args.local_rank in [-1, 0]:
logger.info("Saving features into cached file %s", cached_features_file)
torch.save({"features": features, "dataset": dataset}, cached_features_file)
......@@ -452,6 +464,11 @@ def main():
parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
args = parser.parse_args()
args.predict_file = os.path.join(args.output_dir, 'predictions_{}_{}.txt'.format(
list(filter(None, args.model_name_or_path.split('/'))).pop(),
str(args.max_seq_length))
)
if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir:
raise ValueError("Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(args.output_dir))
......
examples/utils_squad.py deleted 100644 → 0
View file @ 9ecd83da
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Load SQuAD dataset. """
from __future__ import absolute_import, division, print_function
import json
import logging
import math
import collections
from io import open
from tqdm import tqdm
from transformers.tokenization_bert import BasicTokenizer, whitespace_tokenize
# Required by XLNet evaluation method to compute optimal threshold (see write_predictions_extended() method)
from utils_squad_evaluate import find_all_best_thresh_v2, make_qid_to_has_ans, get_raw_scores
logger = logging.getLogger(__name__)
class SquadExample(object):
"""
A single training/test example for the Squad dataset.
For examples without an answer, the start and end position are -1.
"""
def __init__(self,
qas_id,
question_text,
doc_tokens,
orig_answer_text=None,
start_position=None,
end_position=None,
is_impossible=None):
self.qas_id = qas_id
self.question_text = question_text
self.doc_tokens = doc_tokens
self.orig_answer_text = orig_answer_text
self.start_position = start_position
self.end_position = end_position
self.is_impossible = is_impossible
def __str__(self):
return self.__repr__()
def __repr__(self):
s = ""
s += "qas_id: %s" % (self.qas_id)
s += ", question_text: %s" % (
self.question_text)
s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
if self.start_position:
s += ", start_position: %d" % (self.start_position)
if self.end_position:
s += ", end_position: %d" % (self.end_position)
if self.is_impossible:
s += ", is_impossible: %r" % (self.is_impossible)
return s
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self,
unique_id,
example_index,
doc_span_index,
tokens,
token_to_orig_map,
token_is_max_context,
input_ids,
input_mask,
segment_ids,
cls_index,
p_mask,
paragraph_len,
start_position=None,
end_position=None,
is_impossible=None):
self.unique_id = unique_id
self.example_index = example_index
self.doc_span_index = doc_span_index
self.tokens = tokens
self.token_to_orig_map = token_to_orig_map
self.token_is_max_context = token_is_max_context
self.input_ids = input_ids
self.input_mask = input_mask
self.segment_ids = segment_ids
self.cls_index = cls_index
self.p_mask = p_mask
self.paragraph_len = paragraph_len
self.start_position = start_position
self.end_position = end_position
self.is_impossible = is_impossible
def read_squad_examples(input_file, is_training, version_2_with_negative):
"""Read a SQuAD json file into a list of SquadExample."""
with open(input_file, "r", encoding='utf-8') as reader:
input_data = json.load(reader)["data"]
def is_whitespace(c):
if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
return True
return False
examples = []
for entry in input_data:
for paragraph in entry["paragraphs"]:
paragraph_text = paragraph["context"]
doc_tokens = []
char_to_word_offset = []
prev_is_whitespace = True
for c in paragraph_text:
if is_whitespace(c):
prev_is_whitespace = True
else:
if prev_is_whitespace:
doc_tokens.append(c)
else:
doc_tokens[-1] += c
prev_is_whitespace = False
char_to_word_offset.append(len(doc_tokens) - 1)
for qa in paragraph["qas"]:
qas_id = qa["id"]
question_text = qa["question"]
start_position = None
end_position = None
orig_answer_text = None
is_impossible = False
if is_training:
if version_2_with_negative:
is_impossible = qa["is_impossible"]
if (len(qa["answers"]) != 1) and (not is_impossible):
raise ValueError(
"For training, each question should have exactly 1 answer.")
if not is_impossible:
answer = qa["answers"][0]
orig_answer_text = answer["text"]
answer_offset = answer["answer_start"]
answer_length = len(orig_answer_text)
start_position = char_to_word_offset[answer_offset]
end_position = char_to_word_offset[answer_offset + answer_length - 1]
# Only add answers where the text can be exactly recovered from the
# document. If this CAN'T happen it's likely due to weird Unicode
# stuff so we will just skip the example.
#
# Note that this means for training mode, every example is NOT
# guaranteed to be preserved.
actual_text = " ".join(doc_tokens[start_position:(end_position + 1)])
cleaned_answer_text = " ".join(
whitespace_tokenize(orig_answer_text))
if actual_text.find(cleaned_answer_text) == -1:
logger.warning("Could not find answer: '%s' vs. '%s'",
actual_text, cleaned_answer_text)
continue
else:
start_position = -1
end_position = -1
orig_answer_text = ""
example = SquadExample(
qas_id=qas_id,
question_text=question_text,
doc_tokens=doc_tokens,
orig_answer_text=orig_answer_text,
start_position=start_position,
end_position=end_position,
is_impossible=is_impossible)
examples.append(example)
return examples
def convert_examples_to_features(examples, tokenizer, max_seq_length,
doc_stride, max_query_length, is_training,
cls_token_at_end=False,
cls_token='[CLS]', sep_token='[SEP]', pad_token=0,
sequence_a_segment_id=0, sequence_b_segment_id=1,
cls_token_segment_id=0, pad_token_segment_id=0,
mask_padding_with_zero=True,
sequence_a_is_doc=False):
"""Loads a data file into a list of `InputBatch`s."""
unique_id = 1000000000
# cnt_pos, cnt_neg = 0, 0
# max_N, max_M = 1024, 1024
# f = np.zeros((max_N, max_M), dtype=np.float32)
features = []
for (example_index, example) in enumerate(tqdm(examples)):
# if example_index % 100 == 0:
# logger.info('Converting %s/%s pos %s neg %s', example_index, len(examples), cnt_pos, cnt_neg)
query_tokens = tokenizer.tokenize(example.question_text)
if len(query_tokens) > max_query_length:
query_tokens = query_tokens[0:max_query_length]
tok_to_orig_index = []
orig_to_tok_index = []
all_doc_tokens = []
for (i, token) in enumerate(example.doc_tokens):
orig_to_tok_index.append(len(all_doc_tokens))
sub_tokens = tokenizer.tokenize(token)
for sub_token in sub_tokens:
tok_to_orig_index.append(i)
all_doc_tokens.append(sub_token)
tok_start_position = None
tok_end_position = None
if is_training and example.is_impossible:
tok_start_position = -1
tok_end_position = -1
if is_training and not example.is_impossible:
tok_start_position = orig_to_tok_index[example.start_position]
if example.end_position < len(example.doc_tokens) - 1:
tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
else:
tok_end_position = len(all_doc_tokens) - 1
(tok_start_position, tok_end_position) = _improve_answer_span(
all_doc_tokens, tok_start_position, tok_end_position, tokenizer,
example.orig_answer_text)
# The -3 accounts for [CLS], [SEP] and [SEP]
max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
assert max_tokens_for_doc > 0
# We can have documents that are longer than the maximum sequence length.
# To deal with this we do a sliding window approach, where we take chunks
# of the up to our max length with a stride of `doc_stride`.
_DocSpan = collections.namedtuple( # pylint: disable=invalid-name
"DocSpan", ["start", "length"])
doc_spans = []
start_offset = 0
while start_offset < len(all_doc_tokens):
length = len(all_doc_tokens) - start_offset
if length > max_tokens_for_doc:
length = max_tokens_for_doc
doc_spans.append(_DocSpan(start=start_offset, length=length))
if start_offset + length == len(all_doc_tokens):
break
start_offset += min(length, doc_stride)
for (doc_span_index, doc_span) in enumerate(doc_spans):
tokens = []
token_to_orig_map = {}
token_is_max_context = {}
segment_ids = []
# p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer)
# Original TF implem also keep the classification token (set to 0) (not sure why...)
p_mask = []
# CLS token at the beginning
if not cls_token_at_end:
tokens.append(cls_token)
segment_ids.append(cls_token_segment_id)
p_mask.append(0)
cls_index = 0
# XLNet: P SEP Q SEP CLS
# Others: CLS Q SEP P SEP
if not sequence_a_is_doc:
# Query
tokens += query_tokens
segment_ids += [sequence_a_segment_id] * len(query_tokens)
p_mask += [1] * len(query_tokens)
# SEP token
tokens.append(sep_token)
segment_ids.append(sequence_a_segment_id)
p_mask.append(1)
# Paragraph
for i in range(doc_span.length):
split_token_index = doc_span.start + i
token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]
is_max_context = _check_is_max_context(doc_spans, doc_span_index,
split_token_index)
token_is_max_context[len(tokens)] = is_max_context
tokens.append(all_doc_tokens[split_token_index])
if not sequence_a_is_doc:
segment_ids.append(sequence_b_segment_id)
else:
segment_ids.append(sequence_a_segment_id)
p_mask.append(0)
paragraph_len = doc_span.length
if sequence_a_is_doc:
# SEP token
tokens.append(sep_token)
segment_ids.append(sequence_a_segment_id)
p_mask.append(1)
tokens += query_tokens
segment_ids += [sequence_b_segment_id] * len(query_tokens)
p_mask += [1] * len(query_tokens)
# SEP token
tokens.append(sep_token)
segment_ids.append(sequence_b_segment_id)
p_mask.append(1)
# CLS token at the end
if cls_token_at_end:
tokens.append(cls_token)
segment_ids.append(cls_token_segment_id)
p_mask.append(0)
cls_index = len(tokens) - 1 # Index of classification token
input_ids = tokenizer.convert_tokens_to_ids(tokens)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
# Zero-pad up to the sequence length.
while len(input_ids) < max_seq_length:
input_ids.append(pad_token)
input_mask.append(0 if mask_padding_with_zero else 1)
segment_ids.append(pad_token_segment_id)
p_mask.append(1)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
span_is_impossible = example.is_impossible
start_position = None
end_position = None
if is_training and not span_is_impossible:
# For training, if our document chunk does not contain an annotation
# we throw it out, since there is nothing to predict.
doc_start = doc_span.start
doc_end = doc_span.start + doc_span.length - 1
out_of_span = False
if not (tok_start_position >= doc_start and
tok_end_position <= doc_end):
out_of_span = True
if out_of_span:
start_position = 0
end_position = 0
span_is_impossible = True
else:
if sequence_a_is_doc:
doc_offset = 0
else:
doc_offset = len(query_tokens) + 2
start_position = tok_start_position - doc_start + doc_offset
end_position = tok_end_position - doc_start + doc_offset
if is_training and span_is_impossible:
start_position = cls_index
end_position = cls_index
if example_index < 20:
logger.info("*** Example ***")
logger.info("unique_id: %s" % (unique_id))
logger.info("example_index: %s" % (example_index))
logger.info("doc_span_index: %s" % (doc_span_index))
logger.info("tokens: %s" % " ".join(tokens))
logger.info("token_to_orig_map: %s" % " ".join([
"%d:%d" % (x, y) for (x, y) in token_to_orig_map.items()]))
logger.info("token_is_max_context: %s" % " ".join([
"%d:%s" % (x, y) for (x, y) in token_is_max_context.items()
]))
logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
logger.info(
"input_mask: %s" % " ".join([str(x) for x in input_mask]))
logger.info(
"segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
if is_training and span_is_impossible:
logger.info("impossible example")
if is_training and not span_is_impossible:
answer_text = " ".join(tokens[start_position:(end_position + 1)])
logger.info("start_position: %d" % (start_position))
logger.info("end_position: %d" % (end_position))
logger.info(
"answer: %s" % (answer_text))
features.append(
InputFeatures(
unique_id=unique_id,
example_index=example_index,
doc_span_index=doc_span_index,
tokens=tokens,
token_to_orig_map=token_to_orig_map,
token_is_max_context=token_is_max_context,
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
cls_index=cls_index,
p_mask=p_mask,
paragraph_len=paragraph_len,
start_position=start_position,
end_position=end_position,
is_impossible=span_is_impossible))
unique_id += 1
return features
def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
orig_answer_text):
"""Returns tokenized answer spans that better match the annotated answer."""
# The SQuAD annotations are character based. We first project them to
# whitespace-tokenized words. But then after WordPiece tokenization, we can
# often find a "better match". For example:
#
# Question: What year was John Smith born?
# Context: The leader was John Smith (1895-1943).
# Answer: 1895
#
# The original whitespace-tokenized answer will be "(1895-1943).". However
# after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match
# the exact answer, 1895.
#
# However, this is not always possible. Consider the following:
#
# Question: What country is the top exporter of electornics?
# Context: The Japanese electronics industry is the lagest in the world.
# Answer: Japan
#
# In this case, the annotator chose "Japan" as a character sub-span of
# the word "Japanese". Since our WordPiece tokenizer does not split
# "Japanese", we just use "Japanese" as the annotation. This is fairly rare
# in SQuAD, but does happen.
tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
for new_start in range(input_start, input_end + 1):
for new_end in range(input_end, new_start - 1, -1):
text_span = " ".join(doc_tokens[new_start:(new_end + 1)])
if text_span == tok_answer_text:
return (new_start, new_end)
return (input_start, input_end)
def _check_is_max_context(doc_spans, cur_span_index, position):
"""Check if this is the 'max context' doc span for the token."""
# Because of the sliding window approach taken to scoring documents, a single
# token can appear in multiple documents. E.g.
# Doc: the man went to the store and bought a gallon of milk
# Span A: the man went to the
# Span B: to the store and bought
# Span C: and bought a gallon of
# ...
#
# Now the word 'bought' will have two scores from spans B and C. We only
# want to consider the score with "maximum context", which we define as
# the *minimum* of its left and right context (the *sum* of left and
# right context will always be the same, of course).
#
# In the example the maximum context for 'bought' would be span C since
# it has 1 left context and 3 right context, while span B has 4 left context
# and 0 right context.
best_score = None
best_span_index = None
for (span_index, doc_span) in enumerate(doc_spans):
end = doc_span.start + doc_span.length - 1
if position < doc_span.start:
continue
if position > end:
continue
num_left_context = position - doc_span.start
num_right_context = end - position
score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
if best_score is None or score > best_score:
best_score = score
best_span_index = span_index
return cur_span_index == best_span_index
RawResult = collections.namedtuple("RawResult",
["unique_id", "start_logits", "end_logits"])
def write_predictions(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, verbose_logging,
version_2_with_negative, null_score_diff_threshold):
"""Write final predictions to the json file and log-odds of null if needed."""
logger.info("Writing predictions to: %s" % (output_prediction_file))
logger.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 null 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 = _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
if 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 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 = get_final_text(tok_text, orig_text, do_lower_case, verbose_logging)
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 include the empty option in the n-best, include it
if 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 only have single null prediction.
# So we just create a nonce prediction in this case to avoid failure.
if len(nbest)==1:
nbest.insert(0,
_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
# 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))
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
if not 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 > 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
with open(output_prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
with open(output_nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
if version_2_with_negative:
with open(output_null_log_odds_file, "w") as writer:
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
return all_predictions
# For XLNet (and XLM which uses the same head)
RawResultExtended = collections.namedtuple("RawResultExtended",
["unique_id", "start_top_log_probs", "start_top_index",
"end_top_log_probs", "end_top_index", "cls_logits"])
def write_predictions_extended(all_examples, all_features, all_results, n_best_size,
max_answer_length, output_prediction_file,
output_nbest_file,
output_null_log_odds_file, orig_data_file,
start_n_top, end_n_top, version_2_with_negative,
tokenizer, verbose_logging):
""" XLNet write prediction logic (more complex than Bert's).
Write final predictions to the json file and log-odds of null if needed.
Requires utils_squad_evaluate.py
"""
_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
"PrelimPrediction",
["feature_index", "start_index", "end_index",
"start_log_prob", "end_log_prob"])
_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
"NbestPrediction", ["text", "start_log_prob", "end_log_prob"])
logger.info("Writing predictions to: %s", output_prediction_file)
# logger.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
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
for (feature_index, feature) in enumerate(features):
result = unique_id_to_result[feature.unique_id]
cur_null_score = result.cls_logits
# if we could have irrelevant answers, get the min score of irrelevant
score_null = min(score_null, cur_null_score)
for i in range(start_n_top):
for j in range(end_n_top):
start_log_prob = result.start_top_log_probs[i]
start_index = result.start_top_index[i]
j_index = i * end_n_top + j
end_log_prob = result.end_top_log_probs[j_index]
end_index = result.end_top_index[j_index]
# 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 >= feature.paragraph_len - 1:
continue
if end_index >= feature.paragraph_len - 1:
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_log_prob=start_log_prob,
end_log_prob=end_log_prob))
prelim_predictions = sorted(
prelim_predictions,
key=lambda x: (x.start_log_prob + x.end_log_prob),
reverse=True)
seen_predictions = {}
nbest = []
for pred in prelim_predictions:
if len(nbest) >= n_best_size:
break
feature = features[pred.feature_index]
# XLNet un-tokenizer
# Let's keep it simple for now and see if we need all this later.
#
# tok_start_to_orig_index = feature.tok_start_to_orig_index
# tok_end_to_orig_index = feature.tok_end_to_orig_index
# start_orig_pos = tok_start_to_orig_index[pred.start_index]
# end_orig_pos = tok_end_to_orig_index[pred.end_index]
# paragraph_text = example.paragraph_text
# final_text = paragraph_text[start_orig_pos: end_orig_pos + 1].strip()
# Previously used Bert untokenizer
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 = tokenizer.convert_tokens_to_string(tok_tokens)
# 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, tokenizer.do_lower_case,
verbose_logging)
if final_text in seen_predictions:
continue
seen_predictions[final_text] = True
nbest.append(
_NbestPrediction(
text=final_text,
start_log_prob=pred.start_log_prob,
end_log_prob=pred.end_log_prob))
# 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="", start_log_prob=-1e6,
end_log_prob=-1e6))
total_scores = []
best_non_null_entry = None
for entry in nbest:
total_scores.append(entry.start_log_prob + entry.end_log_prob)
if not best_non_null_entry:
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_log_prob"] = entry.start_log_prob
output["end_log_prob"] = entry.end_log_prob
nbest_json.append(output)
assert len(nbest_json) >= 1
assert best_non_null_entry is not None
score_diff = score_null
scores_diff_json[example.qas_id] = score_diff
# note(zhiliny): always predict best_non_null_entry
# and the evaluation script will search for the best threshold
all_predictions[example.qas_id] = best_non_null_entry.text
all_nbest_json[example.qas_id] = nbest_json
with open(output_prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
with open(output_nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
if version_2_with_negative:
with open(output_null_log_odds_file, "w") as writer:
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
with open(orig_data_file, "r", encoding='utf-8') as reader:
orig_data = json.load(reader)["data"]
qid_to_has_ans = make_qid_to_has_ans(orig_data)
has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]
no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
exact_raw, f1_raw = get_raw_scores(orig_data, all_predictions)
out_eval = {}
find_all_best_thresh_v2(out_eval, all_predictions, exact_raw, f1_raw, scores_diff_json, qid_to_has_ans)
return out_eval
def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):
"""Project the tokenized prediction back to the original text."""
# When we created the data, we kept track of the alignment between original
# (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
# now `orig_text` contains the span of our original text corresponding to the
# span that we predicted.
#
# However, `orig_text` may contain extra characters that we don't want in
# our prediction.
#
# For example, let's say:
# pred_text = steve smith
# orig_text = Steve Smith's
#
# We don't want to return `orig_text` because it contains the extra "'s".
#
# We don't want to return `pred_text` because it's already been normalized
# (the SQuAD eval script also does punctuation stripping/lower casing but
# our tokenizer does additional normalization like stripping accent
# characters).
#
# What we really want to return is "Steve Smith".
#
# Therefore, we have to apply a semi-complicated alignment heuristic between
# `pred_text` and `orig_text` to get a character-to-character alignment. This
# can fail in certain cases in which case we just return `orig_text`.
def _strip_spaces(text):
ns_chars = []
ns_to_s_map = collections.OrderedDict()
for (i, c) in enumerate(text):
if c == " ":
continue
ns_to_s_map[len(ns_chars)] = i
ns_chars.append(c)
ns_text = "".join(ns_chars)
return (ns_text, ns_to_s_map)
# We first tokenize `orig_text`, strip whitespace from the result
# and `pred_text`, and check if they are the same length. If they are
# NOT the same length, the heuristic has failed. If they are the same
# length, we assume the characters are one-to-one aligned.
tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
tok_text = " ".join(tokenizer.tokenize(orig_text))
start_position = tok_text.find(pred_text)
if start_position == -1:
if verbose_logging:
logger.info(
"Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
return orig_text
end_position = start_position + len(pred_text) - 1
(orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
(tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
if len(orig_ns_text) != len(tok_ns_text):
if verbose_logging:
logger.info("Length not equal after stripping spaces: '%s' vs '%s'",
orig_ns_text, tok_ns_text)
return orig_text
# We then project the characters in `pred_text` back to `orig_text` using
# the character-to-character alignment.
tok_s_to_ns_map = {}
for (i, tok_index) in tok_ns_to_s_map.items():
tok_s_to_ns_map[tok_index] = i
orig_start_position = None
if start_position in tok_s_to_ns_map:
ns_start_position = tok_s_to_ns_map[start_position]
if ns_start_position in orig_ns_to_s_map:
orig_start_position = orig_ns_to_s_map[ns_start_position]
if orig_start_position is None:
if verbose_logging:
logger.info("Couldn't map start position")
return orig_text
orig_end_position = None
if end_position in tok_s_to_ns_map:
ns_end_position = tok_s_to_ns_map[end_position]
if ns_end_position in orig_ns_to_s_map:
orig_end_position = orig_ns_to_s_map[ns_end_position]
if orig_end_position is None:
if verbose_logging:
logger.info("Couldn't map end position")
return orig_text
output_text = orig_text[orig_start_position:(orig_end_position + 1)]
return output_text
def _get_best_indexes(logits, n_best_size):
"""Get the n-best logits from a list."""
index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
best_indexes = []
for i in range(len(index_and_score)):
if i >= n_best_size:
break
best_indexes.append(index_and_score[i][0])
return best_indexes
def _compute_softmax(scores):
"""Compute softmax probability over raw logits."""
if not scores:
return []
max_score = None
for score in scores:
if max_score is None or score > max_score:
max_score = score
exp_scores = []
total_sum = 0.0
for score in scores:
x = math.exp(score - max_score)
exp_scores.append(x)
total_sum += x
probs = []
for score in exp_scores:
probs.append(score / total_sum)
return probs
examples/utils_squad_evaluate.py deleted 100644 → 0
View file @ 9ecd83da
""" Official evaluation script for SQuAD version 2.0.
Modified by XLNet authors to update `find_best_threshold` scripts for SQuAD V2.0
In addition to basic functionality, we also compute additional statistics and
plot precision-recall curves if an additional na_prob.json file is provided.
This file is expected to map question ID's to the model's predicted probability
that a question is unanswerable.
"""
import argparse
import collections
import json
import numpy as np
import os
import re
import string
import sys
class EVAL_OPTS():
def __init__(self, data_file, pred_file, out_file="",
na_prob_file="na_prob.json", na_prob_thresh=1.0,
out_image_dir=None, verbose=False):
self.data_file = data_file
self.pred_file = pred_file
self.out_file = out_file
self.na_prob_file = na_prob_file
self.na_prob_thresh = na_prob_thresh
self.out_image_dir = out_image_dir
self.verbose = verbose
OPTS = None
def parse_args():
parser = argparse.ArgumentParser('Official evaluation script for SQuAD version 2.0.')
parser.add_argument('data_file', metavar='data.json', help='Input data JSON file.')
parser.add_argument('pred_file', metavar='pred.json', help='Model predictions.')
parser.add_argument('--out-file', '-o', metavar='eval.json',
help='Write accuracy metrics to file (default is stdout).')
parser.add_argument('--na-prob-file', '-n', metavar='na_prob.json',
help='Model estimates of probability of no answer.')
parser.add_argument('--na-prob-thresh', '-t', type=float, default=1.0,
help='Predict "" if no-answer probability exceeds this (default = 1.0).')
parser.add_argument('--out-image-dir', '-p', metavar='out_images', default=None,
help='Save precision-recall curves to directory.')
parser.add_argument('--verbose', '-v', action='store_true')
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
return parser.parse_args()
def make_qid_to_has_ans(dataset):
qid_to_has_ans = {}
for article in dataset:
for p in article['paragraphs']:
for qa in p['qas']:
qid_to_has_ans[qa['id']] = bool(qa['answers'])
return qid_to_has_ans
def normalize_answer(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
regex = re.compile(r'\b(a|an|the)\b', re.UNICODE)
return re.sub(regex, ' ', text)
def white_space_fix(text):
return ' '.join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return ''.join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def get_tokens(s):
if not s: return []
return normalize_answer(s).split()
def compute_exact(a_gold, a_pred):
return int(normalize_answer(a_gold) == normalize_answer(a_pred))
def compute_f1(a_gold, a_pred):
gold_toks = get_tokens(a_gold)
pred_toks = get_tokens(a_pred)
common = collections.Counter(gold_toks) & collections.Counter(pred_toks)
num_same = sum(common.values())
if len(gold_toks) == 0 or len(pred_toks) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks)
if num_same == 0:
return 0
precision = 1.0 * num_same / len(pred_toks)
recall = 1.0 * num_same / len(gold_toks)
f1 = (2 * precision * recall) / (precision + recall)
return f1
def get_raw_scores(dataset, preds):
exact_scores = {}
f1_scores = {}
for article in dataset:
for p in article['paragraphs']:
for qa in p['qas']:
qid = qa['id']
gold_answers = [a['text'] for a in qa['answers']
if normalize_answer(a['text'])]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
gold_answers = ['']
if qid not in preds:
print('Missing prediction for %s' % qid)
continue
a_pred = preds[qid]
# Take max over all gold answers
exact_scores[qid] = max(compute_exact(a, a_pred) for a in gold_answers)
f1_scores[qid] = max(compute_f1(a, a_pred) for a in gold_answers)
return exact_scores, f1_scores
def apply_no_ans_threshold(scores, na_probs, qid_to_has_ans, na_prob_thresh):
new_scores = {}
for qid, s in scores.items():
pred_na = na_probs[qid] > na_prob_thresh
if pred_na:
new_scores[qid] = float(not qid_to_has_ans[qid])
else:
new_scores[qid] = s
return new_scores
def make_eval_dict(exact_scores, f1_scores, qid_list=None):
if not qid_list:
total = len(exact_scores)
return collections.OrderedDict([
('exact', 100.0 * sum(exact_scores.values()) / total),
('f1', 100.0 * sum(f1_scores.values()) / total),
('total', total),
])
else:
total = len(qid_list)
return collections.OrderedDict([
('exact', 100.0 * sum(exact_scores[k] for k in qid_list) / total),
('f1', 100.0 * sum(f1_scores[k] for k in qid_list) / total),
('total', total),
])
def merge_eval(main_eval, new_eval, prefix):
for k in new_eval:
main_eval['%s_%s' % (prefix, k)] = new_eval[k]
def plot_pr_curve(precisions, recalls, out_image, title):
plt.step(recalls, precisions, color='b', alpha=0.2, where='post')
plt.fill_between(recalls, precisions, step='post', alpha=0.2, color='b')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.xlim([0.0, 1.05])
plt.ylim([0.0, 1.05])
plt.title(title)
plt.savefig(out_image)
plt.clf()
def make_precision_recall_eval(scores, na_probs, num_true_pos, qid_to_has_ans,
out_image=None, title=None):
qid_list = sorted(na_probs, key=lambda k: na_probs[k])
true_pos = 0.0
cur_p = 1.0
cur_r = 0.0
precisions = [1.0]
recalls = [0.0]
avg_prec = 0.0
for i, qid in enumerate(qid_list):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
cur_p = true_pos / float(i+1)
cur_r = true_pos / float(num_true_pos)
if i == len(qid_list) - 1 or na_probs[qid] != na_probs[qid_list[i+1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(cur_p)
recalls.append(cur_r)
if out_image:
plot_pr_curve(precisions, recalls, out_image, title)
return {'ap': 100.0 * avg_prec}
def run_precision_recall_analysis(main_eval, exact_raw, f1_raw, na_probs,
qid_to_has_ans, out_image_dir):
if out_image_dir and not os.path.exists(out_image_dir):
os.makedirs(out_image_dir)
num_true_pos = sum(1 for v in qid_to_has_ans.values() if v)
if num_true_pos == 0:
return
pr_exact = make_precision_recall_eval(
exact_raw, na_probs, num_true_pos, qid_to_has_ans,
out_image=os.path.join(out_image_dir, 'pr_exact.png'),
title='Precision-Recall curve for Exact Match score')
pr_f1 = make_precision_recall_eval(
f1_raw, na_probs, num_true_pos, qid_to_has_ans,
out_image=os.path.join(out_image_dir, 'pr_f1.png'),
title='Precision-Recall curve for F1 score')
oracle_scores = {k: float(v) for k, v in qid_to_has_ans.items()}
pr_oracle = make_precision_recall_eval(
oracle_scores, na_probs, num_true_pos, qid_to_has_ans,
out_image=os.path.join(out_image_dir, 'pr_oracle.png'),
title='Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)')
merge_eval(main_eval, pr_exact, 'pr_exact')
merge_eval(main_eval, pr_f1, 'pr_f1')
merge_eval(main_eval, pr_oracle, 'pr_oracle')
def histogram_na_prob(na_probs, qid_list, image_dir, name):
if not qid_list:
return
x = [na_probs[k] for k in qid_list]
weights = np.ones_like(x) / float(len(x))
plt.hist(x, weights=weights, bins=20, range=(0.0, 1.0))
plt.xlabel('Model probability of no-answer')
plt.ylabel('Proportion of dataset')
plt.title('Histogram of no-answer probability: %s' % name)
plt.savefig(os.path.join(image_dir, 'na_prob_hist_%s.png' % name))
plt.clf()
def find_best_thresh(preds, scores, na_probs, qid_to_has_ans):
num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
cur_score = num_no_ans
best_score = cur_score
best_thresh = 0.0
qid_list = sorted(na_probs, key=lambda k: na_probs[k])
for i, qid in enumerate(qid_list):
if qid not in scores: continue
if qid_to_has_ans[qid]:
diff = scores[qid]
else:
if preds[qid]:
diff = -1
else:
diff = 0
cur_score += diff
if cur_score > best_score:
best_score = cur_score
best_thresh = na_probs[qid]
return 100.0 * best_score / len(scores), best_thresh
def find_best_thresh_v2(preds, scores, na_probs, qid_to_has_ans):
num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
cur_score = num_no_ans
best_score = cur_score
best_thresh = 0.0
qid_list = sorted(na_probs, key=lambda k: na_probs[k])
for i, qid in enumerate(qid_list):
if qid not in scores: continue
if qid_to_has_ans[qid]:
diff = scores[qid]
else:
if preds[qid]:
diff = -1
else:
diff = 0
cur_score += diff
if cur_score > best_score:
best_score = cur_score
best_thresh = na_probs[qid]
has_ans_score, has_ans_cnt = 0, 0
for qid in qid_list:
if not qid_to_has_ans[qid]: continue
has_ans_cnt += 1
if qid not in scores: continue
has_ans_score += scores[qid]
return 100.0 * best_score / len(scores), best_thresh, 1.0 * has_ans_score / has_ans_cnt
def find_all_best_thresh(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
best_exact, exact_thresh = find_best_thresh(preds, exact_raw, na_probs, qid_to_has_ans)
best_f1, f1_thresh = find_best_thresh(preds, f1_raw, na_probs, qid_to_has_ans)
main_eval['best_exact'] = best_exact
main_eval['best_exact_thresh'] = exact_thresh
main_eval['best_f1'] = best_f1
main_eval['best_f1_thresh'] = f1_thresh
def find_all_best_thresh_v2(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
best_exact, exact_thresh, has_ans_exact = find_best_thresh_v2(preds, exact_raw, na_probs, qid_to_has_ans)
best_f1, f1_thresh, has_ans_f1 = find_best_thresh_v2(preds, f1_raw, na_probs, qid_to_has_ans)
main_eval['best_exact'] = best_exact
main_eval['best_exact_thresh'] = exact_thresh
main_eval['best_f1'] = best_f1
main_eval['best_f1_thresh'] = f1_thresh
main_eval['has_ans_exact'] = has_ans_exact
main_eval['has_ans_f1'] = has_ans_f1
def main(OPTS):
with open(OPTS.data_file) as f:
dataset_json = json.load(f)
dataset = dataset_json['data']
with open(OPTS.pred_file) as f:
preds = json.load(f)
if OPTS.na_prob_file:
with open(OPTS.na_prob_file) as f:
na_probs = json.load(f)
else:
na_probs = {k: 0.0 for k in preds}
qid_to_has_ans = make_qid_to_has_ans(dataset) # maps qid to True/False
has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]
no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
exact_raw, f1_raw = get_raw_scores(dataset, preds)
exact_thresh = apply_no_ans_threshold(exact_raw, na_probs, qid_to_has_ans,
OPTS.na_prob_thresh)
f1_thresh = apply_no_ans_threshold(f1_raw, na_probs, qid_to_has_ans,
OPTS.na_prob_thresh)
out_eval = make_eval_dict(exact_thresh, f1_thresh)
if has_ans_qids:
has_ans_eval = make_eval_dict(exact_thresh, f1_thresh, qid_list=has_ans_qids)
merge_eval(out_eval, has_ans_eval, 'HasAns')
if no_ans_qids:
no_ans_eval = make_eval_dict(exact_thresh, f1_thresh, qid_list=no_ans_qids)
merge_eval(out_eval, no_ans_eval, 'NoAns')
if OPTS.na_prob_file:
find_all_best_thresh(out_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans)
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(out_eval, exact_raw, f1_raw, na_probs,
qid_to_has_ans, OPTS.out_image_dir)
histogram_na_prob(na_probs, has_ans_qids, OPTS.out_image_dir, 'hasAns')
histogram_na_prob(na_probs, no_ans_qids, OPTS.out_image_dir, 'noAns')
if OPTS.out_file:
with open(OPTS.out_file, 'w') as f:
json.dump(out_eval, f)
else:
print(json.dumps(out_eval, indent=2))
return out_eval
if __name__ == '__main__':
OPTS = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
main(OPTS)
transformers/data/metrics/squad_metrics.py
View file @ e9217da5
......@@ -578,7 +578,6 @@ def compute_predictions_log_probs(
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
orig_data_file,
start_n_top,
end_n_top,
version_2_with_negative,
......@@ -756,15 +755,4 @@ def compute_predictions_log_probs(
with open(output_null_log_odds_file, "w") as writer:
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
with open(orig_data_file, "r", encoding='utf-8') as reader:
orig_data = json.load(reader)["data"]
qid_to_has_ans = make_qid_to_has_ans(orig_data)
has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]
no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
exact_raw, f1_raw = get_raw_scores(orig_data, all_predictions)
out_eval = {}
find_all_best_thresh_v2(out_eval, all_predictions, exact_raw, f1_raw, scores_diff_json, qid_to_has_ans)
return out_eval
return all_predictions
transformers/data/processors/squad.py
View file @ e9217da5
......@@ -9,7 +9,7 @@ from ...tokenization_bert import BasicTokenizer, whitespace_tokenize
from .utils import DataProcessor, InputExample, InputFeatures
from ...file_utils import is_tf_available, is_torch_available
if is_torch_available:
if is_torch_available():
import torch
from torch.utils.data import TensorDataset
......
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