scirepeval.py

# 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.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""


import csv
import json
import os
import glob

import datasets
from datasets.data_files import DataFilesDict
from .scirepeval_configs import SCIREPEVAL_CONFIGS
#from datasets.packaged_modules.json import json
from datasets.utils.logging import get_logger


logger = get_logger(__name__)
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2021}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
}



# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class Scirepeval(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("1.1.0")

    # 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 = SCIREPEVAL_CONFIGS
    
    def _info(self):
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=self.config.description,
            # This defines the different columns of the dataset and their types
            features=datasets.Features(self.config.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, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage="",
            # License for the dataset if available
            license=self.config.license,
            # Citation for the dataset
            citation=self.config.citation,
        )

    def _split_generators(self, dl_manager):
        # 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
        base_url = "https://ai2-s2-research-public.s3.us-west-2.amazonaws.com/scirepeval"
        data_urls = dict()
        data_dir = self.config.url if self.config.url else self.config.name
        if self.config.is_training:
            data_urls = {"train": f"{base_url}/train/{data_dir}/train.jsonl"}
        
            if "refresh" not in self.config.name:
                data_urls.update({"val": f"{base_url}/train/{data_dir}/val.jsonl"})
        
        if "cite_prediction" not in self.config.name:
            data_urls.update({"test": f"{base_url}/test/{data_dir}/meta.jsonl"})
        # print(data_urls)
        downloaded_files = dl_manager.download_and_extract(data_urls)
        # print(downloaded_files)
        splits = []
        if "test" in downloaded_files:
            splits = [datasets.SplitGenerator(
                        name=datasets.Split("evaluation"),
                        # These kwargs will be passed to _generate_examples
                        gen_kwargs={
                            "filepath": downloaded_files["test"],
                            "split": "evaluation"
                        },
                    ),
                     ]
        
        if "train" in downloaded_files:
            splits.append(
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    # These kwargs will be passed to _generate_examples
                    gen_kwargs={
                        "filepath": downloaded_files["train"],
                        "split": "train",
                    },
                ))
        if "val" in downloaded_files:
            splits.append(datasets.SplitGenerator(
                        name=datasets.Split.VALIDATION,
                        # These kwargs will be passed to _generate_examples
                        gen_kwargs={
                            "filepath": downloaded_files["val"],
                            "split": "validation",
                        }))
        return splits
                

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        def read_data(data_path):
            task_data = []
            try:
                    task_data = json.load(open(data_path, "r", encoding="utf-8"))
            except:
                with open(data_path) as f:
                    task_data = [json.loads(line) for line in f]
            if type(task_data) == dict:
                task_data = list(task_data.values())
            return task_data
        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        # data = read_data(filepath)
        seen_keys = set()
        IGNORE=set(["n_key_citations", "session_id", "user_id", "user"])
        logger.warning(filepath)
        with open(filepath, encoding="utf-8") as f:
            for line in f:
                d = json.loads(line)
                d = {k:v for k,v in d.items() if k not in IGNORE}
                key="doc_id" if "cite_prediction_" not in self.config.name else "corpus_id"
                if self.config.task_type == "proximity":
                    if "cite_prediction" in self.config.name:
                        if "arxiv_id" in d["query"]:
                            for item in ["query", "pos", "neg"]:
                                del d[item]["arxiv_id"]
                                del d[item]["doi"]
                        if "fos" in d["query"]:
                            del d["query"]["fos"]
                        if "score" in d["pos"]:
                            del d["pos"]["score"]
                        yield str(d["query"][key]) + str(d["pos"][key]) + str(d["neg"][key]), d
                    else:
                        if d["query"][key] not in seen_keys:
                            seen_keys.add(d["query"][key])
                            yield str(d["query"][key]), d
                else:
                    if d[key] not in seen_keys:
                        seen_keys.add(d[key])
                        if self.config.task_type != "search":
                            if "corpus_id" not in d:
                                d["corpus_id"] = None
                            if "scidocs" in self.config.name:
                                if "cited by" not in d:
                                    d["cited_by"] = []
                                if type(d["corpus_id"]) == str:
                                    d["corpus_id"] = None
                        yield d[key], d