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"""NASA_OSDR dataset""" |
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import json |
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import os |
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import datasets |
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import pandas as pd |
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_CITATION = """ |
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@inproceedings{singh2019towards, |
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title={}, |
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author={}, |
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booktitle={}, |
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pages={}, |
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year={} |
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} |
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""" |
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_DESCRIPTION = """ |
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TODO: write description |
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""" |
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_HOMEPAGE = "https://" |
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_LICENSE = "" |
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_SPLITS = ["train"] |
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_FIFTYONE_DATASET_URL = "" |
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class NasaOsdr(datasets.GeneratorBasedBuilder): |
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"""NASA OSDR dataset.""" |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name="NASA_OSDR", |
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version=datasets.Version("1.0.0"), |
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description=_DESCRIPTION, |
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) |
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] |
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DEFAULT_CONFIG_NAME = "NASA_OSDR" |
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def _info(self): |
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ASSAY_COLUMNS = ['Sample Name', 'Protocol REF', 'Parameter Value: DNA Fragmentation', |
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'Parameter Value: DNA Fragment Size', 'Extract Name', 'Protocol REF.1', |
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'Parameter Value: Library Strategy', |
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'Parameter Value: Library Selection', 'Parameter Value: Library Layout', |
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'Protocol REF.2', 'Parameter Value: Sequencing Instrument', |
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'Assay Name', 'Parameter Value: Read Length', 'Raw Data File', |
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'Protocol REF.3', 'Parameter Value: Read Depth', |
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'Parameter Value: MultiQC File Names'] |
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features = datasets.Features( |
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{ |
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column_name: datasets.Value("string") |
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for column_name in ASSAY_COLUMNS |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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_URL = "https://raw.githubusercontent.com/AnzorGozalishvili/NASA_ODSR_DATA/main" |
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_ASSAYS_FILE = "assays.csv" |
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_SAMPLES_FILE = "samples.csv" |
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def _split_generators(self, dl_manager): |
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downloaded_files = dl_manager.download_and_extract( |
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{ |
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"train": { |
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"assays": os.path.join(self._URL, self._ASSAYS_FILE), |
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"samples": os.path.join(self._URL, self._ASSAYS_FILE) |
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}, |
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} |
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) |
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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={ |
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"assays_file": downloaded_files['train']['assays'], |
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"samples_file": downloaded_files['train']['samples'], |
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}, |
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), |
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] |
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def _generate_examples(self, assays_file: str, samples_file): |
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assays_df = pd.read_csv(assays_file) |
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samples_df = pd.read_csv(samples_file) |
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for (idx, assay_row), (_, sample_row) in zip(assays_df.iterrows(), samples_df.iterrows()): |
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_item = {**assay_row.to_dict()} |
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yield idx, _item |
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