trec-cast-2019-multi-turn.py

# coding=utf-8
# 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.
import json
from collections import defaultdict

import datasets
import csv
from trec_car import read_data

# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@misc{dalton2020trec,
      title={TREC CAsT 2019: The Conversational Assistance Track Overview}, 
      author={Jeffrey Dalton and Chenyan Xiong and Jamie Callan},
      year={2020},
      eprint={2003.13624},
      archivePrefix={arXiv},
      primaryClass={cs.IR}
}
"""

# You can copy an official description
_DESCRIPTION = """\
The Conversational Assistance Track (CAsT) is a new track for TREC 2019 to facilitate Conversational Information 
Seeking (CIS) research and to create a large-scale reusable test collection for conversational search systems. 
The document corpus is 38,426,252 passages from the TREC Complex Answer Retrieval (CAR) and Microsoft MAchine 
Reading COmprehension (MARCO) datasets.
"""

_HOMEPAGE = "http://www.treccast.ai"

_LICENSE = ""

# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URL = "https://huggingface.co/datasets/uva-irlab/trec-cast-2019-multi-turn/resolve/main/"
_URLs = {
    'topics': _URL+"cast2019_test_annotated_without_context.tsv",
    'topics_with_context': _URL+"cast2019_test_annotated_with_context.tsv",
    'qrels': _URL+"2019qrels.txt",
    'test_collection': {
        'car': "http://trec-car.cs.unh.edu/datareleases/v2.0/paragraphCorpus.v2.0.tar.xz",
        'msmarco': 'https://msmarco.blob.core.windows.net/msmarcoranking/collection.tar.gz',
    },
}

SAMPLE_SIZE = 100000


class TrecCast2019MultiTurn(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.1")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="qrels",
                               version=VERSION,
                               description=""),
        datasets.BuilderConfig(name="topics",
                               version=VERSION,
                               description="The topics contain the queries, query IDs and their history."),
        datasets.BuilderConfig(name="topics_with_context",
                               version=VERSION,
                               description="The topics contain the queries with relevant terms from the history, query IDs and their history."),
        datasets.BuilderConfig(name="test_collection",
                               version=VERSION,
                               description="The test collection will provide the passages of TREC CAR and MSMARCO"),
        datasets.BuilderConfig(name="test_collection_sample",
                               version=VERSION,
                               description="A small sample of 20000 of the test collection passages."),
    ]

    # It's not mandatory to have a default configuration. Just use one if it make sense.
    DEFAULT_CONFIG_NAME = "test_collection"

    def _info(self):
        # This is the name of the configuration selected in BUILDER_CONFIGS above
        download_size = None
        if self.config.name == "topics":
            features = datasets.Features({
                "qid": datasets.Value("string"),
                "history": datasets.features.Sequence(feature=datasets.Value('string')),
                "query": datasets.Value("string"),
            })
            download_size = 6784
        elif self.config.name == "topics_with_context":
            features = datasets.Features({
                "qid": datasets.Value("string"),
                "history": datasets.features.Sequence(feature=datasets.Value('string')),
                "query": datasets.Value("string"),
            })
            download_size = 8010
        elif self.config.name == "qrels":
            features = datasets.Features({
                "qid": datasets.Value("string"),
                "qrels": datasets.features.Sequence(feature=datasets.Features({
                    'docno': datasets.Value("string"),
                    'relevance': datasets.Value("string"),
                })),
            })
            download_size = 1138032
        else: # for self.config.name == 'test_collection':
            features = datasets.Features({
                "docno": datasets.Value("string"),
                "text": datasets.Value("string"),
            })
            download_size = 5085726092 + 1035009698
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=None,
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
            download_size=download_size
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        urlkey = 'test_collection' if self.config.name == 'test_collection_sample' else self.config.name
        my_urls = _URLs[urlkey]
        downloaded_files = dl_manager.download_and_extract(my_urls)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={  # These kwargs will be passed to _generate_examples
                    "file": downloaded_files,
                    "split": self.config.name
                },
            ),
        ]

    def _generate_examples(
        self, file, split  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    ):
        """ Yields examples as (key, example) tuples. """
        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is here for legacy reason (tfds) and is not important in itself.

        if split == 'qrels':
            qrels_file = csv.reader(open(file), delimiter=" ")
            qrels = defaultdict(list)
            for row in qrels_file:
                qid = row[0]
                docno = row[2]
                relevance = row[3]
                qrels[qid].append({'docno': docno, 'relevance': relevance})

            for qid in qrels.keys():
                yield qid, {'qid': qid, 'qrels': qrels[qid]}

        elif split == 'topics' or split == 'topics_with_context':
            topics_file = csv.reader(open(file), delimiter="\t")
            topics = defaultdict(list)
            for row in topics_file:
                qid, query = row
                conversation_id, question_number = qid.split('_')
                topics[conversation_id].append(query)

            for conversation_id in topics.keys():
                queries = topics[conversation_id]  # type: list
                for idx in range(len(queries)):
                    query = queries[idx]
                    qid = f"{conversation_id}_{str(idx+1)}"
                    yield qid, ({'query': query, 'history': queries[:idx], 'qid': qid})
        elif split == 'test_collection' or split == 'test_collection_sample':
            car_file = file['car'] + "/paragraphCorpus/dedup.articles-paragraphs.cbor"
            msmarco_file = file['msmarco']+"/collection.tsv"
            is_sample = split == 'test_collection_sample'
            i = 0
            with open(car_file, 'rb') as f:
                for para in read_data.iter_paragraphs(f):
                    docid = f"CAR_{para.para_id}"
                    yield docid, ({"docno": docid, "text": para.get_text()})
                    i += 1
                    if is_sample and i >= SAMPLE_SIZE:
                        break

            i = 0
            with open(msmarco_file) as f:
                msmarco = csv.reader(f, delimiter="\t")
                for line in msmarco:
                    docid, text = line
                    docid = f"MARCO_{docid}"
                    yield docid, ({"docno": docid, "text": text})
                    i += 1
                    if is_sample and i >= SAMPLE_SIZE:
                        break
        else:
            raise NotImplementedError(f"'{split}' is not yet implemented")