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import csv
import glob
import json
import gzip
import logging
import functools
from pathlib import Path
import wandb
from typing import List, Tuple, Dict, Iterator, Union
from dpr.data.qa_validation import calculate_matches
logger = logging.getLogger()
logger.setLevel(logging.INFO)
if logger.hasHandlers():
logger.handlers.clear()
console = logging.StreamHandler()
logger.addHandler(console)
RECALL_FILE_NAME = "recall_at_k.csv"
RESULTS_FILE_NAME = "results.json"
def parse_qa_csv_file(location) -> Iterator[Tuple[str, List[str]]]:
with open(location) as ifile:
reader = csv.reader(ifile, delimiter="\t")
for row in reader:
question = row[0]
answers = eval(row[1])
yield question, answers
def parse_qa_json_file(path):
with open(path, "r") as f:
data = json.load(f)
for d in data:
question = d["question"]
answers = d["answers"]
if "entity" in d:
yield question, answers, d["entity"]
else:
yield question, answers
def validate(
dataset_name: str,
passages: Dict[object, Tuple[str, str]],
answers: List[List[str]],
result_ctx_ids: List[Tuple[List[object], List[float]]],
workers_num: int,
match_type: str,
out_file: str,
use_wandb: bool = True,
output_recall_at_k: bool = False,
log: bool = True
) -> Union[List[List[bool]], Tuple[object, List[float]]]:
match_stats = calculate_matches(
passages, answers, result_ctx_ids, workers_num, match_type
)
top_k_hits = match_stats.top_k_hits
# if log: logger.info("Validation results: match_stats %s", match_stats)
# if log: logger.info("Validation results: top k documents hits %s", top_k_hits)
top_k_hits = [v / len(result_ctx_ids) for v in top_k_hits]
if log: logger.info("Validation results: top k documents hits accuracy %s", top_k_hits)
with open(out_file, "w") as f:
for k, recall in enumerate(top_k_hits):
f.write(f"{k+1},{recall}\n")
if use_wandb:
wandb.log({f"eval-{dataset_name}/k": k+1, f"eval-{dataset_name}/recall": recall})
if log: logger.info(f"Saved recall@k info to {out_file}")
return match_stats.questions_doc_hits if not output_recall_at_k else (match_stats.questions_doc_hits, top_k_hits)
def load_passages(ctx_file: str) -> Dict[object, Tuple[str, str]]:
docs = {}
logger.info("Reading data from: %s", ctx_file)
if ctx_file.endswith(".gz"):
with gzip.open(ctx_file, "rt") as tsvfile:
reader = csv.reader(
tsvfile,
delimiter="\t",
)
# file format: doc_id, doc_text, title
for row in reader:
if row[0] != "id":
docs[row[0]] = (row[1], row[2])
else:
with open(ctx_file) as tsvfile:
reader = csv.reader(
tsvfile,
delimiter="\t",
)
# file format: doc_id, doc_text, title
for row in reader:
if row[0] != "id":
docs[row[0]] = (row[1], row[2])
return docs
def save_results(
passages: Dict[object, Tuple[str, str]],
questions: List[str],
answers: List[List[str]],
top_passages_and_scores: List[Tuple[List[object], List[float]]],
per_question_hits: List[List[bool]],
out_file: str,
output_no_text: bool = False,
):
# join passages text with the result ids, their questions and assigning has|no answer labels
merged_data = []
assert len(per_question_hits) == len(questions) == len(answers)
for i, q in enumerate(questions):
q_answers = answers[i]
results_and_scores = top_passages_and_scores[i]
hits = per_question_hits[i]
docs = [passages[doc_id] for doc_id in results_and_scores[0]]
scores = [str(score) for score in results_and_scores[1]]
hit_indices = [j+1 for j, is_hit in enumerate(hits) if is_hit]
hit_min_rank = hit_indices[0] if len(hit_indices) > 0 else None
ctxs_num = len(hits)
d = {
"question": q,
"answers": q_answers,
"hit_min_rank": hit_min_rank,
"all_hits": hit_indices,
"ctxs": [
{
"id": results_and_scores[0][c],
"rank": (c + 1),
"title": docs[c][1],
"text": docs[c][0] if not output_no_text else "",
"score": scores[c],
"has_answer": hits[c],
}
for c in range(ctxs_num)
],
}
merged_data.append(d)
with open(out_file, "w") as writer:
writer.write(json.dumps(merged_data, indent=4) + "\n")
logger.info("Saved results * scores to %s", out_file)
def get_datasets(qa_file_pattern):
logger.info(f"Reading datasets usign the pattern {qa_file_pattern}")
all_patterns = qa_file_pattern.split(",")
all_qa_files = functools.reduce(lambda a, b: a + b, [glob.glob(pattern) for pattern in all_patterns])
qa_file_dict = {}
for qa_file in all_qa_files:
dataset_name = Path(qa_file).stem.replace(".", "-")
dataset = list(parse_qa_csv_file(qa_file)) if qa_file.endswith(".csv") else list(parse_qa_json_file(qa_file))
questions, question_answers = [], []
for ds_item in dataset:
question, answers = ds_item
questions.append(question)
question_answers.append(answers)
qa_file_dict[dataset_name] = (questions, question_answers)
logger.info(f"{dataset_name}:{' ' * (20 - len(dataset_name))}{len(questions)} items")
return qa_file_dict
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