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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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.
# Lint as: python3
"""Mathematics database."""
import datasets
import os
logger = datasets.logging.get_logger(__name__)
_CITATION = """
@article{2019arXiv,
author = {Saxton, Grefenstette, Hill, Kohli},
title = {Analysing Mathematical Reasoning Abilities of Neural Models},
year = {2019},
journal = {arXiv:1904.01557}
}
"""
_DESCRIPTION = """
Mathematics database.
This dataset code generates mathematical question and answer pairs,
from a range of question types at roughly school-level difficulty.
This is designed to test the mathematical learning and algebraic
reasoning skills of learning models.
Original paper: Analysing Mathematical Reasoning Abilities of Neural Models
(Saxton, Grefenstette, Hill, Kohli).
Example usage:
train_examples, val_examples = datasets.load_dataset(
'math_dataset/arithmetic__mul',
split=['train', 'test'],
as_supervised=True)
"""
_DATA_URL = os.path.join('mathematics_dataset', 'mathematics_dataset', 'ds.zip')
_TRAIN_CATEGORY = [
"train-easy",
"train-medium",
"train-hard",
]
_INTERPOLATE_CATEGORY = [
"interpolate",
]
_MODULES = [
# extrapolate
"measurement__conversion",
# interpolate
"algebra__linear_1d",
"algebra__linear_1d_composed",
"algebra__linear_2d",
"algebra__linear_2d_composed",
"algebra__polynomial_roots",
"algebra__polynomial_roots_composed",
"algebra__sequence_next_term",
"algebra__sequence_nth_term",
"arithmetic__add_or_sub",
"arithmetic__add_or_sub_in_base",
"arithmetic__add_sub_multiple",
"arithmetic__div",
"arithmetic__mixed",
"arithmetic__mul",
"arithmetic__mul_div_multiple",
"arithmetic__nearest_integer_root",
"arithmetic__simplify_surd",
"calculus__differentiate",
"calculus__differentiate_composed",
"comparison__closest",
"comparison__closest_composed",
"comparison__kth_biggest",
"comparison__kth_biggest_composed",
"comparison__pair",
"comparison__pair_composed",
"comparison__sort",
"comparison__sort_composed",
"measurement__conversion",
"measurement__time",
"numbers__base_conversion",
"numbers__div_remainder",
"numbers__div_remainder_composed",
"numbers__gcd",
"numbers__gcd_composed",
"numbers__is_factor",
"numbers__is_factor_composed",
"numbers__is_prime",
"numbers__is_prime_composed",
"numbers__lcm",
"numbers__lcm_composed",
"numbers__list_prime_factors",
"numbers__list_prime_factors_composed",
"numbers__place_value",
"numbers__place_value_composed",
"numbers__round_number",
"numbers__round_number_composed",
"polynomials__add",
"polynomials__coefficient_named",
"polynomials__collect",
"polynomials__compose",
"polynomials__evaluate",
"polynomials__evaluate_composed",
"polynomials__expand",
"polynomials__simplify_power",
"probability__swr_p_level_set",
"probability__swr_p_sequence",
# train-easy train-medium train-hard
"algebra__linear_1d",
"algebra__linear_1d_composed",
"algebra__linear_2d",
"algebra__linear_2d_composed",
"algebra__polynomial_roots",
"algebra__polynomial_roots_composed",
"algebra__sequence_next_term",
"algebra__sequence_nth_term",
"arithmetic__add_or_sub",
"arithmetic__add_or_sub_in_base",
"arithmetic__add_sub_multiple",
"arithmetic__div",
"arithmetic__mixed",
"arithmetic__mul",
"arithmetic__mul_div_multiple",
"arithmetic__nearest_integer_root",
"arithmetic__simplify_surd",
"calculus__differentiate",
"calculus__differentiate_composed",
"comparison__closest",
"comparison__closest_composed",
"comparison__kth_biggest",
"comparison__kth_biggest_composed",
"comparison__pair",
"comparison__pair_composed",
"comparison__sort",
"comparison__sort_composed",
"measurement__conversion",
"measurement__time",
"numbers__base_conversion",
"numbers__div_remainder",
"numbers__div_remainder_composed",
"numbers__gcd",
"numbers__gcd_composed",
"numbers__is_factor",
"numbers__is_factor_composed",
"numbers__is_prime",
"numbers__is_prime_composed",
"numbers__lcm",
"numbers__lcm_composed",
"numbers__list_prime_factors",
"numbers__list_prime_factors_composed",
"numbers__place_value",
"numbers__place_value_composed",
"numbers__round_number",
"numbers__round_number_composed",
"polynomials__add",
"polynomials__coefficient_named",
"polynomials__collect",
"polynomials__compose",
"polynomials__evaluate",
"polynomials__evaluate_composed",
"polynomials__expand",
"polynomials__simplify_power",
"probability__swr_p_level_set",
"probability__swr_p_sequence",
]
_QUESTION = "question"
_ANSWER = "answer"
_DATASET_VERSION = "ds"
def _generate_builder_configs():
"""Generate configs with different subsets of mathematics dataset."""
configs = []
for module in sorted(set(_MODULES)):
configs.append(
datasets.BuilderConfig(
name=module,
version=datasets.Version("1.0.0"),
description=_DESCRIPTION,
)
)
return configs
class PortugueseMathDataset(datasets.GeneratorBasedBuilder):
"""Math Dataset."""
BUILDER_CONFIGS = _generate_builder_configs()
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
_QUESTION: datasets.Value("string"),
_ANSWER: datasets.Value("string"),
}
),
supervised_keys=(_QUESTION, _ANSWER),
homepage="https://github.com/deepmind/mathematics_dataset",
citation=_CITATION,
)
def _get_filepaths_from_categories(self, config, categories):
filepaths = []
for category in categories:
data_file = "/".join([_DATASET_VERSION, category, config])
filepaths.append(data_file)
return set(filepaths)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
archive = dl_manager.download(_DATA_URL)
config = self.config.name + ".txt"
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"files": dl_manager.iter_archive(archive),
"config": config,
"categories": _TRAIN_CATEGORY,
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"files": dl_manager.iter_archive(archive),
"config": config,
"categories": _INTERPOLATE_CATEGORY,
},
),
]
def _generate_examples(self, files, config, categories):
"""Yields examples based on directory, module file.."""
idx = 0
filepaths = self._get_filepaths_from_categories(config, categories)
for path, f in files:
if not filepaths:
break
elif path in filepaths:
for question in f:
if not question:
continue
else:
for answer in f:
if not answer:
continue
else:
yield idx, {_QUESTION: question, _ANSWER: answer}
idx += 1
break
filepaths.remove(path) |