numerical_reasoning.py

# 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
"""TODO: Add a description here."""


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 = """\

"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
    Generated dataset for testing numerical reasoning
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

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

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
}


# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class NewDataset(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("0.1.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="arithmetic_multiplication", version=VERSION, description="x1 x x2 = y"),
        datasets.BuilderConfig(name="arithmetic_addition", version=VERSION, description="x1 + x2 = y"),
        datasets.BuilderConfig(name="op_infer_mult", version=VERSION, description="x1 # x2 = y, must infer that # is multiplication"),
        datasets.BuilderConfig(name="op_infer_add", version=VERSION, description="x1 # x2 = y, must infer that # is addition"),
        datasets.BuilderConfig(name="time_unit_min_sec", version=VERSION, description="x minutes equals y seconds"),
        datasets.BuilderConfig(name="time_unit_hour_min", version=VERSION, description="x hours equals y minutes"),
        datasets.BuilderConfig(name="time_unit_day_hour", version=VERSION, description="x days equals y hours"),
        datasets.BuilderConfig(name="time_unit_week_day", version=VERSION, description="x minutes equals y seconds"),
        datasets.BuilderConfig(name="time_unit_month_week", version=VERSION, description="x months equals y weeks"),
        datasets.BuilderConfig(name="time_unit_year_month", version=VERSION, description="x years equals y months"),
        datasets.BuilderConfig(name="time_unit_decade_year", version=VERSION, description="x decades equals y years"),
    ]

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

    def _info(self):
        if ("arithmetic" in self.config.name) or ("op_infer" in self.config.name):  # This is the name of the configuration selected in BUILDER_CONFIGS above
            features = datasets.Features(
                {
                    "x1": datasets.Value("int32"),
                    "x2": datasets.Value("int32"),
                    "y": datasets.Value("int32")
                }
            )
        else:
            features = datasets.Features(
                {
                    "x1": datasets.Value("int32"),
                    "time_unit": datasets.Value("string"),
                    "y": datasets.Value("int32")
                }
            )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,  # Here we define them above because they are different between the two configurations
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "split": "test"
                },
            ),
        ]

    def _generate_examples(self, split):

        if ("arithmetic" in self.config.name) or ("op_infer" in self.config.name):
            key = 0
            for x1 in range(0,100):
                for x2 in range(1,51):
                    key += 1
                    # Yields examples as (key, example) tuples
                    yield key, {
                        "x1": x1,
                        "x2": x2,
                        "y": x1*x2,
                    }
        else:
            if "min_sec" in self.config.name:
                time_unit = "minutes"
                multiplier = 60

            elif "hour_min" in self.config.name:
                time_unit = "hours"
                multiplier = 60

            elif "day_hour" in self.config.name:
                time_unit = "days"
                multiplier = 24

            elif "week_day" in self.config.name:
                time_unit = "weeks"
                multiplier = 7

            elif "month_week" in self.config.name:
                time_unit = "months"
                multiplier = 30

            elif "year_month" in self.config.name:
                time_unit = "years"
                multiplier = 12

            elif "decade_year" in self.config.name:
                time_unit = "decades"
                multiplier = 10

            for key, x1 in enumerate(range(0, 100)):
                yield key, {
                    "x1": x1,
                    "time_unit": time_unit,
                    "y": multiplier*x1,
                }