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#
# Pyserini: Reproducible IR research with sparse and dense representations
#
# 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.
#
import argparse
import math
import os
import time
from collections import defaultdict
from string import Template
import yaml
from defs_odqa import models, evaluate_dpr_retrieval_metric_definitions
from utils import run_dpr_retrieval_eval_and_return_metric, convert_trec_run_to_dpr_retrieval_json, run_fusion, ok_str, fail_str
GARRRF_LS = ['answers','titles','sentences']
HITS_1K = set(['GarT5-RRF', 'DPR-DKRR', 'DPR-Hybrid'])
def print_results(metric, topics):
print(f'Metric = {metric}, Topics = {topics}')
for model in models['models']:
print(' ' * 32, end='')
print(f'{model:30}', end='')
key = f'{model}'
print(f'{table[key][metric]:7.2f}', end='\n')
print('')
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Generate regression matrix for GarDKRR')
parser.add_argument('--skip-eval', action='store_true',
default=False, help='Skip running trec_eval.')
parser.add_argument('--topics', choices=['tqa', 'nq'],
help='Topics to be run [tqa, nq]', required=True)
parser.add_argument('--full-topk', action='store_true',
default=False, help='Run topk 5-1000, default is topk 5-100')
args = parser.parse_args()
hits = 1000 if args.full_topk else 100
yaml_path = 'pyserini/resources/triviaqa.yaml' if args.topics == "tqa" else 'pyserini/resources/naturalquestion.yaml'
topics = 'dpr-trivia-test' if args.topics == 'tqa' else 'nq-test'
start = time.time()
table = defaultdict(lambda: defaultdict(lambda: 0.0))
with open(yaml_path) as f:
yaml_data = yaml.safe_load(f)
for condition in yaml_data['conditions']:
name = condition['model_name']
cmd_template = condition['command']
if not args.full_topk:
# if using topk100
if name in HITS_1K:
# if running topk1000 is a must to ensure scores match with the ones in the table
hits = 1000
else:
hits = 100
print(f'model {name}:')
if topics == 'nq-test' and name == 'BM25-k1_0.9_b_0.4_dpr-topics':
topics = 'dpr-nq-test'
elif args.topics == 'nq':
topics = 'nq-test'
print(f' - Topics: {topics}')
# running retrieval
if name == "GarT5-RRF":
runfile = [f'runs/run.odqa.{name}.{topics}.{i}.hits-{hits}.txt' for i in GARRRF_LS]
else:
runfile = [f'runs/run.odqa.{name}.{topics}.hits-{hits}.txt']
if name != "GarT5RRF-DKRR-RRF":
cmd = [Template(cmd_template[i]).substitute(output=runfile[i]) for i in range(len(runfile))]
if hits == 100:
cmd = [i + ' --hits 100' for i in cmd]
for i in range(len(runfile)):
if not os.path.exists(runfile[i]):
print(f' Running: {cmd[i]}')
os.system(cmd[i])
# fusion
if 'RRF' in name:
runs = []
output = ''
if name == 'GarT5-RRF':
runs = runfile
output = f'runs/run.odqa.{name}.{topics}.hits-{hits}.fusion.txt'
elif name == 'GarT5RRF-DKRR-RRF':
runs = [f'runs/run.odqa.DPR-DKRR.{topics}.hits-1000.txt', f'runs/run.odqa.GarT5-RRF.{topics}.hits-1000.fusion.txt']
output = runfile[0].replace('.txt','.fusion.txt')
else:
raise NameError('Unexpected model name')
if not os.path.exists(output):
if not args.full_topk and name != 'GarT5-RRF':
# if using topk100, we change it back for methods that require topk1000 to generate runs
hits = 100
status = run_fusion(runs, output, hits)
if status != 0:
raise RuntimeError('fusion failed')
runfile = [output]
# trec conversion + evaluation
if not args.skip_eval:
jsonfile = runfile[0].replace('.txt', '.json')
runfile = jsonfile.replace('.json','.txt')
if not os.path.exists(jsonfile):
status = convert_trec_run_to_dpr_retrieval_json(
topics, 'wikipedia-dpr', runfile, jsonfile)
if status != 0:
raise RuntimeError("dpr retrieval convertion failed")
topk_defs = evaluate_dpr_retrieval_metric_definitions['Top5-100']
if args.full_topk:
topk_defs = evaluate_dpr_retrieval_metric_definitions['Top5-1000']
score = run_dpr_retrieval_eval_and_return_metric(topk_defs, jsonfile)
# comparing ground truth scores with the generated ones
for expected in condition['scores']:
for metric, expected_score in expected.items():
if metric not in score.keys(): continue
if not args.skip_eval:
if math.isclose(score[metric], float(expected_score),abs_tol=2e-2):
result_str = ok_str
else:
result_str = fail_str + \
f' expected {expected[metric]:.4f}'
print(f' {metric:7}: {score[metric]:.2f} {result_str}')
table[name][metric] = score[metric]
else:
table[name][metric] = expected_score
print('')
metric_ls = ['Top5', 'Top20', 'Top100', 'Top500', 'Top1000']
metric_ls = metric_ls[:3] if not args.full_topk else metric_ls
for metric in metric_ls:
print_results(metric, topics)
end = time.time()
print(f'Total elapsed time: {end - start:.0f}s')
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