dbp15k-fr-en.py

import datasets
import os
import pickle
import json

class Dbp15kFrEnConfig(datasets.BuilderConfig):

    def __init__(self, features, data_url, citation, url, label_classes=("False", "True"), **kwargs):
        super(Dbp15kFrEnConfig, self).__init__(version=datasets.Version("0.0.1"), **kwargs)
        self.features = features
        self.label_classes = label_classes
        self.data_url = data_url
        self.citation = citation
        self.url = url


class Dbp15kFrEn(datasets.GeneratorBasedBuilder):

    BUILDER_CONFIGS = [
        Dbp15kFrEnConfig(
            name="source",
            features=["column1", "column2", "column3"],
            citation="TODO",
            url="TODO",
            data_url="https://huggingface.co/datasets/matchbench/dbp15k-fr-en/resolve/main/dbp15k-fr-en-src.zip"
        ),
        Dbp15kFrEnConfig(
            name="target",
            features=["column1", "column2", "column3"],
            citation="TODO",
            url="TODO",
            data_url="https://huggingface.co/datasets/matchbench/dbp15k-fr-en/resolve/main/dbp15k-fr-en-tgt.zip"
        ),
        Dbp15kFrEnConfig(
            name="pairs",
            features=["left_id", "right_id"],
            citation="TODO",
            url="TODO",
            data_url="https://huggingface.co/datasets/matchbench/dbp15k-fr-en/resolve/main/dbp15k-fr-en-pairs.zip"
        ),
    ]

    def _info(self):
        if self.config.name=="source":
            features = {feature: datasets.Value("string") for feature in self.config.features}
        elif self.config.name=="target":
            features = {feature: datasets.Value("string") for feature in self.config.features}
        elif self.config.name=="pairs":
            features = {feature: datasets.Value("string") for feature in self.config.features}
        
        return datasets.DatasetInfo(
            features=datasets.Features(features)
        )

    def _split_generators(self, dl_manager):
        dl_dir = dl_manager.download_and_extract(self.config.data_url) or ""
        #task_name = _get_task_name_from_data_url(self.config.data_url)
        #dl_dir = os.path.join(dl_dir, task_name)
        if self.config.name == "source":
            return [
            datasets.SplitGenerator(
                name="ent_ids",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "ent_ids_1"),
                    "split": "ent_ids",
                },
            ),
            datasets.SplitGenerator(
                name="rel_ids",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "rel_ids_1"),
                    "split": "rel_ids",
                },
            ),
            datasets.SplitGenerator(
                name="attr_triples",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "att_triples_1"),
                    "split": "attr_triples",
                },
            ),
            datasets.SplitGenerator(
                name="rel_triples",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "triples_1"),
                    "split": "rel_triples",
                },
            ),
            datasets.SplitGenerator(
                name="description",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "description1.pkl"),
                    "split": "description",
                },
            ),
            datasets.SplitGenerator(
                name="rel_triples_whole",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "rel_triples_whole_1"),
                    "split": "rel_triples_whole",
                },
            ),
            datasets.SplitGenerator(
                name="attr_triples_whole",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "att_triples_whole_1"),
                    "split": "attr_triples_whole",
                },
            ),
            datasets.SplitGenerator(
                name="translated_name",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "translated_name.json"),
                    "split": "translated_name",
                },
            ),
            ]
        elif self.config.name == "target":
            return [
            datasets.SplitGenerator(
                name="ent_ids",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "ent_ids_2"),
                    "split": "ent_ids",
                },
            ),
            datasets.SplitGenerator(
                name="rel_ids",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "rel_ids_2"),
                    "split": "rel_ids",
                },
            ),
            datasets.SplitGenerator(
                name="attr_triples",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "att_triples_2"),
                    "split": "attr_triples",
                },
            ),
            datasets.SplitGenerator(
                name="rel_triples",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "triples_2"),
                    "split": "rel_triples",
                },
            ),
            datasets.SplitGenerator(
                name="description",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "description2.pkl"),
                    "split": "description",
                },
            ),
            datasets.SplitGenerator(
                name="rel_triples_whole",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "rel_triples_whole_2"),
                    "split": "rel_triples_whole",
                },
            ),
            datasets.SplitGenerator(
                name="attr_triples_whole",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "att_triples_whole_2"),
                    "split": "attr_triples_whole",
                },
            ),
                datasets.SplitGenerator(
                name="translated_name",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "translated_name.json"),
                    "split": "translated_name",
                },
            ),
            ]
        elif self.config.name == "pairs":
            return [
            datasets.SplitGenerator(
                name="train",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "train_links"),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name="valid",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "valid_links"),
                    "split": "valid",
                },
            ),
            datasets.SplitGenerator(
                name="test",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "test_links"),
                    "split": "test",
                },
            ),
            datasets.SplitGenerator(
                name="sup",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "sup_pairs"),
                    "split": "sup",
                },
            ),
            datasets.SplitGenerator(
                name="ref",
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "ref_pairs"),
                    "split": "ref",
                },
            ),
            ]


    def _generate_examples(self, data_file, split):
        if split in ["description"]:
            des = pickle.load(open(data_file,"rb"))
            i = -1
            for ent,ori_des in des.items():
                i += 1
                yield i, {
                    "column1": ent,
                    "column2": ori_des,
                    "column3": None
                }
        elif split in ["translated_name"]:
            trans = json.load(open(data_file,"r"))
            #i = -1
            for i in range(len(trans)):
                yield i, {
                    "column1": str(trans[i][0]),
                    "column2": str(trans[i][1]),
                    "column3": None
                }
        else:
            f = open(data_file,"r",encoding='utf-8')
            data = f.readlines()
            for i in range(len(data)):
                #print(row)
                if self.config.name in ["source", "target"]:
                    if split in ["ent_ids","rel_ids"]:
                        row = data[i].strip('\n').split('\t')
                        yield i, {
                                    "column1": row[0],
                                    "column2": row[1],
                                    "column3": None
                                }
                    elif split in ["rel_triples","rel_triples_whole"]:
                        row = data[i].strip('\n').split('\t')
                        yield i, {
                                    "column1": row[0],
                                    "column2": row[1],
                                    "column3": row[2]
                                }
                    elif split in ["attr_triples","attr_triples_whole"]:
                        row = data[i].rstrip('\n').split(' ',2)
                        yield i, {
                                    "column1": row[0],
                                    "column2": row[1],
                                    "column3": row[2]
                                }
                        
                if self.config.name == "pairs":
                    row = data[i].strip('\n').split('\t')
                    yield i, {
                                "left_id": row[0],
                                "right_id": row[1]
                    }