file for loading data with HF datasets load_dataset() module
Browse files
nonlocal_parameterization/dataset.py
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import os
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import datasets
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class NonLocalParameterization(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("1.0.0")
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def _info(self):
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"""
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Defines the dataset metadata and feature structure.
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"""
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return datasets.DatasetInfo(
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description="Dataset containing .nc files for training.",
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features=datasets.Features({
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"file_path": datasets.Value("string"), # Store file paths
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}),
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supervised_keys=None, # Update if supervised task is defined
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homepage="https://huggingface.co/datasets/nasa-impact/WINDSET/tree/main/nonlocal_parameterization",
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license="MIT",
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)
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def _split_generators(self, dl_manager):
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"""
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Define the dataset splits for train.
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"""
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# Define the directory containing the dataset
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data_dir = os.path.join(os.getcwd(), "nonlocal_parameterization") # Update with the actual directory
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# Get the directory for the train split (no validation or test splits)
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train_dir = os.path.join(data_dir)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={"split_dir": train_dir},
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),
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]
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def _generate_data_from_files(self, data_dir):
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"""
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Generate file paths for each .nc file in the directory.
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"""
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example_id = 0
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# Loop through the files in the directory
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for nc_file in os.listdir(data_dir):
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if nc_file.endswith(".nc"):
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nc_file_path = os.path.join(data_dir, nc_file)
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yield example_id, {
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"file_path": nc_file_path,
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}
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example_id += 1
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else:
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pass
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def _generate_examples(self, split_dir):
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"""
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Generates examples for the dataset from the split directory.
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"""
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# Call the data generator to get the file paths
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for example_id, example in self._generate_data_from_files(split_dir):
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yield example_id, example
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