OntoLAMA.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
"""OntoLAMA Dataset Loading Script"""


import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@article{he2023language,
  title={Language Model Analysis for Ontology Subsumption Inference},
  author={He, Yuan and Chen, Jiaoyan and Jim{\'e}nez-Ruiz, Ernesto and Dong, Hang and Horrocks, Ian},
  journal={arXiv preprint arXiv:2302.06761},
  year={2023}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
OntoLAMA: LAnguage Model Analysis datasets for Ontology Subsumption Inference.
"""

_URL = lambda name: f"https://zenodo.org/record/7700458/files/{name}.zip?download=1"

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://krr-oxford.github.io/DeepOnto/"

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "Apache License, Version 2.0"


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

    VERSION = datasets.Version("1.0.0")

    # 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 = [
        datasets.BuilderConfig(
            name="bimnli", version=VERSION, description="BiMNLI dataset created from the MNLI dataset."
        ),
        datasets.BuilderConfig(
            name="schemaorg-atomic-SI",
            version=VERSION,
            description="Atomic SI dataset created from the Schema.org Ontology.",
        ),
        datasets.BuilderConfig(
            name="doid-atomic-SI", version=VERSION, description="Atomic SI dataset created from the Disease Ontology."
        ),
        datasets.BuilderConfig(
            name="foodon-atomic-SI", version=VERSION, description="Atomic SI dataset created from the Food Ontology."
        ),
        datasets.BuilderConfig(
            name="foodon-complex-SI", version=VERSION, description="Complex SI dataset created from the Gene Ontology."
        ),
        datasets.BuilderConfig(
            name="go-atomic-SI", version=VERSION, description="Atomic SI dataset created from the Gene Ontology."
        ),
        datasets.BuilderConfig(
            name="go-complex-SI", version=VERSION, description="Complex SI dataset created from the Gene Ontology."
        ),
    ]

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        if "atomic-SI" in self.config.name:  # This is the name of the configuration selected in BUILDER_CONFIGS above
            features = datasets.Features(
                {
                    "v_sub_concept": datasets.Value("string"),
                    "v_super_concept": datasets.Value("string"),
                    "label": datasets.ClassLabel(
                        num_classes=2, names=["negative_subsumption", "positive_subsumption"], names_file=None, id=None
                    ),
                    "axiom": datasets.Value("string"),
                    # These are the features of your dataset like images, labels ...
                }
            )
        elif (
            "complex-SI" in self.config.name
        ):  # This is an example to show how to have different features for "first_domain" and "second_domain"
            features = datasets.Features(
                {
                    "v_sub_concept": datasets.Value("string"),
                    "v_super_concept": datasets.Value("string"),
                    "label": datasets.ClassLabel(
                        num_classes=2, names=["negative_subsumption", "positive_subsumption"], names_file=None, id=None
                    ),
                    "axiom": datasets.Value("string"),
                    "anchor_axiom": datasets.Value("string")  # the equivalence axiom used as anchor
                    # These are the features of your dataset like images, labels ...
                }
            )
        elif self.config.name == "bimnli":
            features = datasets.Features(
                {
                    "premise": datasets.Value("string"),
                    "hypothesis": datasets.Value("string"),
                    "label": datasets.ClassLabel(
                        num_classes=2, names=["contradiction", "entailment"], names_file=None, id=None
                    ),
                }
            )

        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, 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=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_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

        # 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
        urls = _URL(self.config.name)
        data_dir = dl_manager.download_and_extract(urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir, self.config.name, "train.jsonl"),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir, self.config.name, "dev.jsonl"),
                    "split": "dev",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"filepath": os.path.join(data_dir, self.config.name, "test.jsonl"), "split": "test"},
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        # 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.
        with open(filepath, encoding="utf-8") as f:
            for key, row in enumerate(f):
                data = json.loads(row)
                if "atomic-SI" in self.config.name:
                    # Yields examples as (key, example) tuples
                    yield key, {
                        "v_sub_concept": data["v_sub_concept"],
                        "v_super_concept": data["v_super_concept"],
                        "label": data["label"],
                        "axiom": data["axiom"],
                    }
                elif "complex-SI" in self.config.name:
                    yield key, {
                        "v_sub_concept": data["v_sub_concept"],
                        "v_super_concept": data["v_super_concept"],
                        "label": data["label"],
                        "axiom": data["axiom"],
                        "anchor_axiom": data["anchor_axiom"],
                    }
                elif self.config.name == "bimnli":
                    yield key, {
                        "premise": data["premise"],
                        "hypothesis": data["hypothesis"],
                        "label": data["label"],
                    }