RotoWire_English-German.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.
"""Dataloader for RotoWire English-German dataset."""

import json
import os

import datasets


# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@article{hayashi2019findings,
  title={Findings of the Third Workshop on Neural Generation and Translation},
  author={Hayashi, Hiroaki and Oda, Yusuke and Birch, Alexandra and Konstas, Ioannis and Finch, Andrew and Luong, Minh-Thang and Neubig, Graham and Sudoh, Katsuhito},
  journal={EMNLP-IJCNLP 2019},
  pages={1},
  year={2019}
}
"""

# You can copy an official description
_DESCRIPTION = """\
Dataset for the WNGT 2019 DGT shared task on "Document-Level Generation and Translation”.
"""

_HOMEPAGE = "https://sites.google.com/view/wngt19/dgt-task"

_LICENSE = "CC-BY 4.0"

# 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)
_URLs = {
    "train": "train.json",
    "validation": "validation.json",
    "test": "test.json"
}


class RotowireEnglishGerman(datasets.GeneratorBasedBuilder):
    """Dataset for WNGT2019 shared task on Document-level Generation and Translation."""

    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 = [
    #     datasets.BuilderConfig(name="nlg_en", version=VERSION, description="NLG: Data-to-English text."),
    #     datasets.BuilderConfig(name="nlg_de", version=VERSION, description="NLG: Data-to-German text."),
    #     datasets.BuilderConfig(name="mt_en-de", version=VERSION, description="MT: English-to-German text."),
    #     datasets.BuilderConfig(name="mt_de-en", version=VERSION, description="MT: German-to-English text."),
    #     datasets.BuilderConfig(name="nlg+mt_en-de", version=VERSION, description="NLG+MT: Data+English-to-German text."),
    #     datasets.BuilderConfig(name="nlg+mt_de-en", version=VERSION, description="NLG+MT: Data+German-to-English text."),
    # ]

    def _info(self):
        # max 26 entries in each box_score field.
        box_score_entry = {str(i): datasets.Value("string") for i in range(26)}
        box_score_features = {
            "FIRST_NAME": box_score_entry,
            "MIN": box_score_entry,
            "FGM": box_score_entry,
            "REB": box_score_entry,
            "FG3A": box_score_entry,
            "PLAYER_NAME": box_score_entry,
            "AST": box_score_entry,
            "FG3M": box_score_entry,
            "OREB": box_score_entry,
            "TO": box_score_entry,
            "START_POSITION": box_score_entry,
            "PF": box_score_entry,
            "PTS": box_score_entry,
            "FGA": box_score_entry,
            "STL": box_score_entry,
            "FTA": box_score_entry,
            "BLK": box_score_entry,
            "DREB": box_score_entry,
            "FTM": box_score_entry,
            "FT_PCT": box_score_entry,
            "FG_PCT": box_score_entry,
            "FG3_PCT": box_score_entry,
            "SECOND_NAME": box_score_entry,
            "TEAM_CITY": box_score_entry,
        }
        line_features = {
            "TEAM-PTS_QTR2": datasets.Value("string"),
            "TEAM-FT_PCT": datasets.Value("string"),
            "TEAM-PTS_QTR1": datasets.Value("string"),
            "TEAM-PTS_QTR4": datasets.Value("string"),
            "TEAM-PTS_QTR3": datasets.Value("string"),
            "TEAM-CITY": datasets.Value("string"),
            "TEAM-PTS": datasets.Value("string"),
            "TEAM-AST": datasets.Value("string"),
            "TEAM-LOSSES": datasets.Value("string"),
            "TEAM-NAME": datasets.Value("string"),
            "TEAM-WINS": datasets.Value("string"),
            "TEAM-REB": datasets.Value("string"),
            "TEAM-TOV": datasets.Value("string"),
            "TEAM-FG3_PCT": datasets.Value("string"),
            "TEAM-FG_PCT": datasets.Value("string")
        }
        features = datasets.Features(
            {
                "id":datasets.Value("string"),
                "gem_id":datasets.Value("string"),
                "home_name": datasets.Value("string"),
                "box_score": box_score_features,
                "vis_name": datasets.Value("string"),
                "summary": datasets.Sequence(datasets.Value("string")),
                "home_line": line_features,
                "home_city": datasets.Value("string"),
                "vis_line": line_features,
                "vis_city": datasets.Value("string"),
                "day": datasets.Value("string"),
                "detok_summary_org": datasets.Value("string"),
                "detok_summary":  datasets.Value("string"),
                "summary_en": datasets.Sequence(datasets.Value("string")),
                "sentence_end_index_en": datasets.Sequence(datasets.Value("int32")),
                "summary_de": datasets.Sequence(datasets.Value("string")),
                "sentence_end_index_de": datasets.Sequence(datasets.Value("int32"))
            }
        )
        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,
        )

    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
        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": data_dir["train"],
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir["test"],
                    "split": "test"
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir["validation"],
                    "split": "validation",
                },
            ),
        ]

    def _generate_examples(
        self, filepath, 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.

        with open(filepath, encoding="utf-8") as f:
            all_data = json.load(f)
            for id_, data in enumerate(all_data):
                yield id_, data