milling_processes_LUH__testing_propuses.py

"""this is loading script for milling processes files"""

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 = """\
@InProceedings{huggingface:dataset,
title = {Multivariate time series data of milling processes with varying tool wear and machine tools},
author={Tobias Stiehl},
year={2023}
}
"""

_DESCRIPTION = """\
"""

_HOMEPAGE = "https://data.mendeley.com/datasets/zpxs87bjt8/3"

_LICENSE = "CC BY 4.0"

# 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",
}


class MillingProcessesLUH(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("3")

    # 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 = [

    ]

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

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        features = datasets.Features(
            {
                "cumulated_tool_contact_time":datasets.features.Value("float32"), #float64
                "machine":datasets.features.Value("float32"),#float64
                "run":datasets.features.Value("float32"),#float64
                "tool":datasets.features.Value("float32"),#float64
                "wear":datasets.features.Value("float32"),#float64
                "position_control_deviation_axis_x":datasets.Sequence(datasets.Value("float32")), #object
                "position_control_deviation_axis_y":datasets.Sequence(datasets.Value("float32")), #object
                "time_machine":datasets.Sequence(datasets.Value("float32")), #object
                "tool_position_x":datasets.Sequence(datasets.Value("float32")),#object
                "tool_position_y":datasets.Sequence(datasets.Value("float32")), #object
                "tool_position_z":datasets.Sequence(datasets.Value("float32")), # object
                "torque_axis_x":datasets.Sequence(datasets.Value("float32")), #object
                "torque_axis_y":datasets.Sequence(datasets.Value("float32")), #object
                "torque_axis_z":datasets.Sequence(datasets.Value("float32")), #object
                "torque_spindle":datasets.Sequence(datasets.Value("float32")), #object
                "force_sensor_x":datasets.Sequence(datasets.Value("float32")), #object
                "force_sensor_y":datasets.Sequence(datasets.Value("float32")), #object
                "force_sensor_z":datasets.Sequence(datasets.Value("float32")), #object
                "time_sensor":datasets.Sequence(datasets.Value("float32")) #object
            }
        )

        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, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # 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):
        # 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
        urls = _URLS[self.config.name]
        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": os.path.join(data_dir, "train.jsonl"),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "dev.jsonl"),
                    "split": "dev",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "test.jsonl"),
                    "split": "test"
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        with open(filepath, encoding="utf-8") as f:
            for key, row in enumerate(f):
                data = json.loads(row)
                if self.config.name == "first_domain":
                    # Yields examples as (key, example) tuples
                    yield key, {
                        "sentence": data["sentence"],
                        "option1": data["option1"],
                        "answer": "" if split == "test" else data["answer"],
                    }
                else:
                    yield key, {
                        "sentence": data["sentence"],
                        "option2": data["option2"],
                        "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
                    }