Taskmaster.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.
"""A movie ticketing dialog dataset with 23,789 annotated conversations.."""


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{byrne2020tickettalk,
  title={TicketTalk: Toward human-level performance with end-to-end, transaction-based dialog systems},
  author={Byrne, Bill and Krishnamoorthi, Karthik and Ganesh, Saravanan and Kale, Mihir Sanjay},
  journal={arXiv preprint arXiv:2012.12458},
  year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
The Taskmaster-3 (aka TicketTalk) dataset consists of 23,789 movie ticketing dialogs
(located in Taskmaster/TM-3-2020/data/). By "movie ticketing" we mean conversations
where the customer's goal is to purchase tickets after deciding on theater, time,
movie name, number of tickets, and date, or opt out of the transaction.
The columns are gem_id, 0, 1 for serial numbering, 2 for the text dialog and id
for the default id by the authors.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = (
    "https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020"
)

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "CC BY 4.0"

# TODO: Add link to the official dataset URLs here
# 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.csv",
    "validation": "dev.csv",
    "test": "test.csv",
}

# New addition
class TaskmasterConfig(datasets.BuilderConfig):
    """BuilderConfig for Taskmaster."""

    def __init__(self, **kwargs):
        """BuilderConfig for Taskmaster.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(TaskmasterConfig, self).__init__(**kwargs)


# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class Taskmaster(datasets.GeneratorBasedBuilder):
    """The Taskmaster-3 (aka TicketTalk) dataset consists of 23,789 movie ticketing dialogs. The columns are gem_id, 0, 1 for serial numbering, 2 for the text dialog and id for the default id by the authors."""

    VERSION = datasets.Version("3.0.0")

    # 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="train", version=VERSION, description="training set"),
    #     datasets.TaskmasterConfig(name="train", version=VERSION, description="training set"),
    #     datasets.TaskmasterConfig(name="dev", version=VERSION, description="dev set"),
    #     datasets.TaskmasterConfig(name="test", version=VERSION, description="test set"),
    # ]

    # DEFAULT_CONFIG_NAME = "TaskmasterConfig"  # It's not mandatory to have a default configuration. Just use one if it makes sense.

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        features = datasets.Features(
            {
                "gem_id": datasets.Value("string"),
                "context": datasets.Value("string"),
                "target": datasets.Value("string"),
                "references": [datasets.Value("string")],
                "conversation_id": datasets.Value("string"),
                # "paragraphs": datasets.features.Sequence(
                #     datasets.Value("string")),
                # "summary": datasets.features.Sequence(
                #     {
                #         "text": datasets.Value("string"),
                #         "topic": datasets.Value("int"),
                #     })
                # 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,
            # 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.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir["validation"],
                    "split": "validation",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir["test"],
                    "split": "test",
                },
            ),
        ]

    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:
            reader = csv.DictReader(f)
            expl_count = 0
            other_count = 0
            for row in reader:
                if not row["response"].startswith("[A]"):
                    other_count += 1
                    continue
                target = row["response"].replace("[A]", "")
                gem_id = f"Taskmaster-{split}-{expl_count}"
                expl_count += 1

                ex = {
                    "gem_id": gem_id,
                    "context": row["context"],
                    "target": target,
                    "references": [target],
                    "conversation_id": row["conversation_id"]
                }

                # print(f"{other_count}/{other_count+expl_count} total now {expl_count}")

                yield expl_count, ex