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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""Module to compute TREC evaluation scores."""
import datasets
import pandas as pd
from trectools import TrecEval, TrecQrel, TrecRun
import evaluate
_CITATION = """\
@inproceedings{palotti2019,
author = {Palotti, Joao and Scells, Harrisen and Zuccon, Guido},
title = {TrecTools: an open-source Python library for Information Retrieval practitioners involved in TREC-like campaigns},
series = {SIGIR'19},
year = {2019},
location = {Paris, France},
publisher = {ACM}
}
"""
# TODO: Add description of the module here
_DESCRIPTION = """\
The TREC Eval metric combines a number of information retrieval metrics such as \
precision and nDCG. It is used to score rankings of retrieved documents with reference values."""
# TODO: Add description of the arguments of the module here
_KWARGS_DESCRIPTION = """
Calculates TREC evaluation scores based on a run and qrel.
Args:
predictions: list containing a single run.
references: list containing a single qrel.
Returns:
dict: TREC evaluation scores.
Examples:
>>> trec = evaluate.load("trec_eval")
>>> qrel = {
... "query": [0],
... "q0": ["0"],
... "docid": ["doc_1"],
... "rel": [2]
... }
>>> run = {
... "query": [0, 0],
... "q0": ["q0", "q0"],
... "docid": ["doc_2", "doc_1"],
... "rank": [0, 1],
... "score": [1.5, 1.2],
... "system": ["test", "test"]
... }
>>> results = trec.compute(references=[qrel], predictions=[run])
>>> print(results["P@5"])
0.2
"""
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class TRECEval(evaluate.EvaluationModule):
"""Compute TREC evaluation scores."""
def _info(self):
return evaluate.EvaluationModuleInfo(
module_type="metric",
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
features=datasets.Features(
{
"predictions": {
"query": datasets.Sequence(datasets.Value("int64")),
"q0": datasets.Sequence(datasets.Value("string")),
"docid": datasets.Sequence(datasets.Value("string")),
"rank": datasets.Sequence(datasets.Value("int64")),
"score": datasets.Sequence(datasets.Value("float")),
"system": datasets.Sequence(datasets.Value("string")),
},
"references": {
"query": datasets.Sequence(datasets.Value("int64")),
"q0": datasets.Sequence(datasets.Value("string")),
"docid": datasets.Sequence(datasets.Value("string")),
"rel": datasets.Sequence(datasets.Value("int64")),
},
}
),
homepage="https://github.com/joaopalotti/trectools",
)
def _compute(self, references, predictions):
"""Returns the TREC evaluation scores."""
if len(predictions) > 1 or len(references) > 1:
raise ValueError(
f"You can only pass one prediction and reference per evaluation. You passed {len(predictions)} prediction(s) and {len(references)} reference(s)."
)
df_run = pd.DataFrame(predictions[0])
df_qrel = pd.DataFrame(references[0])
trec_run = TrecRun()
trec_run.filename = "placeholder.file"
trec_run.run_data = df_run
trec_qrel = TrecQrel()
trec_qrel.filename = "placeholder.file"
trec_qrel.qrels_data = df_qrel
trec_eval = TrecEval(trec_run, trec_qrel)
result = {}
result["runid"] = trec_eval.run.get_runid()
result["num_ret"] = trec_eval.get_retrieved_documents(per_query=False)
result["num_rel"] = trec_eval.get_relevant_documents(per_query=False)
result["num_rel_ret"] = trec_eval.get_relevant_retrieved_documents(per_query=False)
result["num_q"] = len(trec_eval.run.topics())
result["map"] = trec_eval.get_map(depth=10000, per_query=False, trec_eval=True)
result["gm_map"] = trec_eval.get_geometric_map(depth=10000, trec_eval=True)
result["bpref"] = trec_eval.get_bpref(depth=1000, per_query=False, trec_eval=True)
result["Rprec"] = trec_eval.get_rprec(depth=1000, per_query=False, trec_eval=True)
result["recip_rank"] = trec_eval.get_reciprocal_rank(depth=1000, per_query=False, trec_eval=True)
for v in [5, 10, 15, 20, 30, 100, 200, 500, 1000]:
result[f"P@{v}"] = trec_eval.get_precision(depth=v, per_query=False, trec_eval=True)
for v in [5, 10, 15, 20, 30, 100, 200, 500, 1000]:
result[f"NDCG@{v}"] = trec_eval.get_ndcg(depth=v, per_query=False, trec_eval=True)
return result
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