Create ontolama.py

ontolama.py

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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.
+# 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 = "https://huggingface.co/datasets/krr-oxford/OntoLAMA/resolve/main/data/"
+
+# 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")
+
+    # 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="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="go-atomic-SI", 
+            version=VERSION, 
+            description="Atomic SI dataset created from 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 ...
+                }
+            )
+        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 = os.path.join(_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, "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, "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, "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"],
+                    }