conversational_weather.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.


import csv
import json
import os

import datasets


_CITATION = """\
@inproceedings{balakrishnan-etal-2019-constrained,
  title = "Constrained Decoding for Neural {NLG} from Compositional Representations in Task-Oriented Dialogue",
  author = "Balakrishnan, Anusha  and
    Rao, Jinfeng  and
    Upasani, Kartikeya  and
    White, Michael  and
    Subba, Rajen",
  booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
  month = jul,
  year = "2019",
  address = "Florence, Italy",
  publisher = "Association for Computational Linguistics",
  url = "https://www.aclweb.org/anthology/P19-1080",
  doi = "10.18653/v1/P19-1080",
  pages = "831--844"
}
"""

_DESCRIPTION = """\
The Conversational Weather dataset is designed for generation of responses to weather queries based on a structured input data. The input allows specifying data attributes such as dates, times, locations, weather conditions, and errors, and also offers control over structure of response through discourse relations such as join, contrast, and justification.
"""

_HOMEPAGE = "https://github.com/facebookresearch/TreeNLG"

_LICENSE = "CC-BY-NC-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 = {
    'default': {
        'train': 'https://raw.githubusercontent.com/facebookresearch/TreeNLG/master/data/weather/train.tsv',
        'validation': 'https://raw.githubusercontent.com/facebookresearch/TreeNLG/master/data/weather/val.tsv',
        'test': 'https://raw.githubusercontent.com/facebookresearch/TreeNLG/master/data/weather/test.tsv'
    },
    'challenge': {
        'disc_test': './data/challenge_sets/disc_test.tsv',
        'dial_test_freq': {
            'dial_test_freq_1': './data/challenge_sets/dial_test_freq_1.tsv',
            'dial_test_freq_2': './data/challenge_sets/dial_test_freq_2.tsv',
            'dial_test_freq_3': './data/challenge_sets/dial_test_freq_3.tsv',
            'dial_test_freq_4': './data/challenge_sets/dial_test_freq_4.tsv',
            'dial_test_freq_5': './data/challenge_sets/dial_test_freq_5.tsv'
        },
        'disc_test_freq': {
            'disc_test_freq_0': './data/challenge_sets/disc_test_freq_0.tsv',
            'disc_test_freq_1': './data/challenge_sets/disc_test_freq_1.tsv',
            'disc_test_freq_2': './data/challenge_sets/disc_test_freq_2.tsv',
            'disc_test_freq_3': './data/challenge_sets/disc_test_freq_3.tsv'
        },
    }
}

class ConversationalWeather(datasets.GeneratorBasedBuilder):
    """The Conversational Weather dataset is designed for generation of responses to weather queries
    based on a structured input data. The input allows specifying data attributes such as dates, times,
    locations, weather conditions, and errors, and also offers control over structure of response through
    discourse relations such as join, contrast, and justification."""

    VERSION = datasets.Version("1.1.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="default", version=VERSION, description="This part of my dataset covers a first domain"),
        datasets.BuilderConfig(name="challenge", version=VERSION, description="This part of my dataset covers a second domain"),
    ]

    DEFAULT_CONFIG_NAME = "default"

    def _info(self):
        # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        features = datasets.Features(
            {
                "gem_id": datasets.Value("string"),
                "data_id": datasets.Value("string"),
                "user_query": datasets.Value("string"),
                "tree_str_mr": datasets.Value("string"),
                "response": datasets.Value("string"),
                "target": datasets.Value("string"),
                "references": [datasets.Value("string")],

            }
        )

        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,
            # 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."""
        # This method is tasked with downloading/extracting the data and defining the splits depending on the configuration

        # 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

        my_urls = _URLs[self.config.name]
        data_dir = dl_manager.download_and_extract(my_urls)
        if self.config.name is 'challenge':
            disc_test_freq_data = []
            for k in range(len(_URLs['challenge']['disc_test_freq'])):
                disc_test_freq_data.append(
                    datasets.SplitGenerator(
                        name=datasets.NamedSplit(f"disc_test_freq_{k}"),
                        gen_kwargs={
                            "filepath": os.path.join(data_dir['disc_test_freq'][f'disc_test_freq_{k}']),
                            "split": f"disc-test-freq-{k}",
                        },
                    ))

            dial_test_freq_data = []
            for k in range(1, len(_URLs['challenge']['dial_test_freq'])+1):
                dial_test_freq_data.append(
                    datasets.SplitGenerator(
                        name=datasets.NamedSplit(f"dial_test_freq_{k}"),
                        gen_kwargs={
                            "filepath": os.path.join(data_dir['dial_test_freq'][f'dial_test_freq_{k}']),
                            "split": f"dial-test-freq-{k}",
                        },
                    ))

            return [
                datasets.SplitGenerator(
                    name=datasets.NamedSplit("disc_test"),
                    gen_kwargs={
                        "filepath": os.path.join(data_dir['disc_test']),
                        "split": "disc-test",
                    },
                )] + disc_test_freq_data + dial_test_freq_data

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


    def _generate_examples(
        self, filepath, split  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    ):
        print(filepath)
        """ 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:
            csv_reader = csv.reader(f, delimiter='\t')
            for id_, row in enumerate(csv_reader):
                assert len(row) == 4
                yield id_, {
                    "gem_id": f"{self.config.name}-{split}-{id_}",
                    "data_id": row[0],
                    "user_query": row[1],
                    "tree_str_mr": row[2],
                    "response": row[3],
                    "target": row[3],
                    "references": [row[3]],
                }