ik-nlp-22_pestyle.py

import os

import datasets
import pandas as pd

_CITATION = """No citation information available."""

_DESCRIPTION = """\
This dataset contains a sample of sentences taken from the FLORES-101 dataset that were either translated 
from scratch or post-edited from an existing automatic translation by three human translators. 
Translation were performed for the English-Italian language pair, and translators' behavioral data 
(keystrokes, pauses, editing times) were collected using the PET platform.
"""

_HOMEPAGE = "https://www.rug.nl/masters/information-science/?lang=en"

_LICENSE = "Sharing and publishing of the data is not allowed at the moment."

_PATHS = {
    "full": os.path.join("IK_NLP_22_PESTYLE", "train.tsv"),
    "mask_subject": os.path.join("IK_NLP_22_PESTYLE", "test.tsv"),
    "mask_modality": os.path.join("IK_NLP_22_PESTYLE", "test.tsv"),
    "mask_time": os.path.join("IK_NLP_22_PESTYLE", "test.tsv")
}

_ALL_FIELDS = [
    "item_id", "subject_id", "modality",
    "src_text", "mt_text", "tgt_text",
    "edit_time", "k_total", "k_letter", "k_digit", "k_white", "k_symbol", "k_nav", "k_erase", 
    "k_copy", "k_cut", "k_paste", "n_pause_geq_300", "len_pause_geq_300", 
    "n_pause_geq_1000", "len_pause_geq_1000", "num_annotations",
    "n_insert", "n_delete", "n_substitute", "n_shift", "bleu", "chrf", "ter", "aligned_edit"
]

_FIELDS_MASK_SUBJECT = [f for f in _ALL_FIELDS if f not in ["subject_id"]]
_FIELDS_MASK_MODALITY = [f for f in _ALL_FIELDS if f not in [
    "modality", "mt_text", "n_insert", "n_delete", "n_substitute", 
    "n_shift", "ter", "bleu", "chrf", "aligned_edit"
]]
_FIELDS_MASK_TIME = [f for f in _ALL_FIELDS if f not in [
    "edit_time", "n_pause_geq_300", "len_pause_geq_300", 
    "n_pause_geq_1000", "len_pause_geq_1000"
]]

_DICT_FIELDS = {
    "full": _ALL_FIELDS,
    "mask_subject": _FIELDS_MASK_SUBJECT,
    "mask_modality": _FIELDS_MASK_MODALITY,
    "mask_time": _FIELDS_MASK_TIME
}

class IkNlp22PEStyleConfig(datasets.BuilderConfig):
    """BuilderConfig for the IK NLP '22 Post-editing Stylometry Dataset."""

    def __init__(
        self,
        features,
        **kwargs,
    ):
        """
        Args:
        features: `list[string]`, list of the features that will appear in the
            feature dict. Should not include "label".
        **kwargs: keyword arguments forwarded to super.
        """
        super().__init__(version=datasets.Version("1.0.0"), **kwargs)
        self.features = features


class IkNlp22PEStyle(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        IkNlp22PEStyleConfig(
            name=name,
            features=fields,
        )
        for name, fields in _DICT_FIELDS.items()
    ]

    DEFAULT_CONFIG_NAME = "full"

    @property

    def manual_download_instructions(self):
        return (
            "The access to the data is restricted to students of the IK MSc NLP 2022 course working on a related project."
            "To load the data using this dataset, download and extract the IK_NLP_22_PESTYLE folder you were provided upon selecting the final project."
            "After extracting it, the folder (referred to as root) must contain a IK_NLP_22_PESTYLE subfolder, containing train.tsv and test.tsv files."
            f"Then, load the dataset with: `datasets.load_dataset('GroNLP/ik-nlp-22_pestyle', '{self.config.name}', data_dir='path/to/root/folder')`"
        )

    def _info(self):
        features = {feature: datasets.Value("int32") for feature in self.config.features}
        for field in ["subject_id", "modality", "src_text", "mt_text", "tgt_text", "aligned_edit"]:
            if field in self.config.features:
                features[field] = datasets.Value("string")
        for field in ["edit_time", "bleu", "chrf", "ter", "n_insert", "n_delete", "n_substitute", "n_shift"]:
            if field in self.config.features:
                features[field] = datasets.Value("float32")
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(features),
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
        if not os.path.exists(data_dir):
            raise FileNotFoundError(
                "{} does not exist. Make sure you insert the unzipped IK_NLP_22_PESTYLE dir via "
                "`datasets.load_dataset('GroNLP/ik-nlp-22_pestyle', data_dir=...)`"
                "Manual download instructions: {}".format(
                    data_dir, self.manual_download_instructions
                )
            )
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN if self.config.name == "full" else datasets.Split.TEST,
                gen_kwargs={
                    "filepath": os.path.join(data_dir, _PATHS[self.config.name]),
                    "features": self.config.features,
                },
            )
        ]
    
    def _generate_examples(self, filepath: str, features):
        """Yields examples as (key, example) tuples."""
        data = pd.read_csv(filepath, sep="\t")
        data = data[features]
        for id_, row in data.iterrows():
            yield id_, row.to_dict()