notebook and _info

milling_processes_LUH__testing_propuses.py

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+"""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"],
+                    }