completeformer-masked.py

# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the Semeru Lab and SEART research group.
#
# 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: Add a description here."""


import csv
import glob
import os

import datasets

import numpy as np

# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
    title = {A great new dataset},
    author={huggingface, Inc.
    },
    year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# 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 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)
_DATA_URLs = {
    "tokenizer": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/data_for_tokenizer_training.csv",
    "all": {
        "train": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/training_clean.csv",
        "valid": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/validation_clean.csv",
        "test": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/test_clean.csv",
    },
    "mix": {
        "train": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/training_mix.csv",
        "valid": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/validation_mix.csv",
        "test": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/test_mix.csv",
    },
    "length_short": {
        "train": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/training_short.csv",
        "train_long": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/training_long.csv",
        "valid": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/validation_length.csv",
        "test": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/test_short.csv",
    },
    "length_medium": {
        "train": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/training_medium.csv",
        "valid": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/validation_length.csv",
        "test": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/test_medium.csv",
    },
    "length_long": {
        "train": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/training_long.csv",
        "valid": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/validation_length.csv",
        "test": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/test_long.csv",
    },
    "length_mix": {
        "train": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/training_length_mix.csv",
        "valid": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/validation_length.csv",
        "test": "https://huggingface.co/datasets/semeru/completeformer-masked/resolve/main/test_length.csv",
    },
}


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

    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="all",
            version=VERSION,
            description="",
        ),
        datasets.BuilderConfig(
            name="mix",
            version=VERSION,
            description="",
        ),
        datasets.BuilderConfig(
            name="length_short",
            version=VERSION,
            description="",
        ),
        datasets.BuilderConfig(
            name="length_medium",
            version=VERSION,
            description="",
        ),
        datasets.BuilderConfig(
            name="length_long",
            version=VERSION,
            description="",
        ),
        datasets.BuilderConfig(
            name="length_mix",
            version=VERSION,
            description="",
        ),
        datasets.BuilderConfig(
            name="tokenizer",
            version=VERSION,
            description="",
        ),
    ]

    DEFAULT_CONFIG_NAME = "all"

    def _info(self):
        if self.config.name == "tokenizer":
            features = datasets.Features(
                { 
                    "function": datasets.Value("string"),
                }
            )
        elif self.config.name == "all":
            features = datasets.Features(
                { 
                    "method": datasets.Value("string"),
                    "block": datasets.Value("string"),
                    "complex_masked_block": datasets.Value("string"),
                    "complex_input": datasets.Value("string"),
                    "complex_target": datasets.Value("string"),
                    "medium_masked_block": datasets.Value("string"),
                    "medium_input": datasets.Value("string"),
                    "medium_target": datasets.Value("string"),
                    "simple_masked_block": datasets.Value("string"),
                    "simple_input": datasets.Value("string"),
                    "simple_target": datasets.Value("string"),
                }
            )
        elif self.config.name == "mix":
            features = datasets.Features(
                { 
                    "input": datasets.Value("string"),
                    "target": datasets.Value("string"),
                }
            )
        elif self.config.name.startswith("length_"):
            features = datasets.Features(
                { 
                    "input": datasets.Value("string"),
                    "target": datasets.Value("string"),
                    "size": datasets.Value("int64"),
                }
            )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            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
        my_urls = _DATA_URLs[self.config.name]
        if self.config.name == "tokenizer":
            data_dir = dl_manager.download_and_extract(my_urls)
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={"filepath": data_dir},
                ),
            ]
        else:
            data_dirs = {}
            for k, v in my_urls.items():
                data_dirs[k] = dl_manager.download_and_extract(v)
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    # These kwargs will be passed to _generate_examples
                    gen_kwargs={
                        "file_path": data_dirs["train"],
                    },
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.VALIDATION,
                    # These kwargs will be passed to _generate_examples
                    gen_kwargs={
                        "file_path": data_dirs["valid"],
                    },
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.TEST,
                    # These kwargs will be passed to _generate_examples
                    gen_kwargs={
                        "file_path": data_dirs["test"],
                    },
                ),
            ]

    def _generate_examples(
        self,
        file_path,
    ):
        """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(file_path, encoding="utf-8") as f:
            csv_reader = csv.reader(f, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True)
            next(csv_reader, None) # skip header

            for row_id, row in enumerate(csv_reader):
                if self.config.name == "tokenizer":
                    yield row_id, {
                        "function": row[1],
                    }
                elif self.config.name == "all":
                    _, method, block, complex_masked_block, complex_input, complex_target, medium_masked_block, medium_input, medium_target, simple_masked_block, simple_input, simple_target = row
                    yield row_id, {
                        "method": method,
                        "block": block,
                        "complex_masked_block": complex_masked_block,
                        "complex_input": complex_input,
                        "complex_target": complex_target,
                        "medium_masked_block": medium_masked_block,
                        "medium_input": medium_input,
                        "medium_target": medium_target,
                        "simple_masked_block": simple_masked_block,
                        "simple_input": simple_input,
                        "simple_target": simple_target,
                    }
                elif self.config.name == "mix":
                    _, input, target = row
                    yield row_id, {
                        "input": input,
                        "target": target,
                    }
                elif self.config.name.startswith("length_"):
                    _, input, target, size = row
                    yield row_id, {
                        "input": input,
                        "target": target,
                        "size": int(size),
                    }