Updated loading logic
Browse files- AstroM3Dataset.py +20 -12
AstroM3Dataset.py
CHANGED
@@ -5,8 +5,10 @@ import pandas as pd
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import numpy as np
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import json
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from astropy.io import fits
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from utils.parallelzipfile import ParallelZipFile as ZipFile
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_DESCRIPTION = (
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"AstroM3 is a time-series astronomy dataset containing photometry, spectra, "
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@@ -37,8 +39,9 @@ _CITATION = """
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class AstroM3Dataset(datasets.GeneratorBasedBuilder):
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"""Hugging Face dataset for AstroM3 with configurable subsets and seeds."""
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name=f"{sub}_{seed}", version=_VERSION, data_dir=None)
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for sub in ["full", "sub10", "sub25", "sub50"]
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@@ -52,7 +55,7 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
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{
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"photometry": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), length=3)),
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"spectra": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), length=3)),
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"metadata": datasets.Sequence(datasets.Value("float32"), length=
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"label": datasets.Value("string"),
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}
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),
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@@ -114,22 +117,26 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
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"test": f"{_URL}/splits/{sub}/{seed}/test.csv",
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"info": f"{_URL}/splits/{sub}/{seed}/info.json",
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}
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extracted_path = dl_manager.
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# Load all spectra files
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for
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spectra_url = f"{_URL}/spectra/{split}/{row['target']}/{row['spec_filename']}"
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spectra_urls[row["spec_filename"]] = spectra_url
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spectra
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# Load photometry and init reader
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photometry_path = dl_manager.download(f"{_URL}/photometry.zip")
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self.reader_v = ZipFile(photometry_path)
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return [
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datasets.SplitGenerator(
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@@ -154,6 +161,7 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
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def _generate_examples(self, csv_path, info_path, spectra, split):
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"""Yields examples from a CSV file containing photometry, spectra, metadata, and labels."""
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if not os.path.exists(csv_path):
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raise FileNotFoundError(f"Missing dataset file: {csv_path}")
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@@ -166,9 +174,9 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
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with open(info_path) as f:
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info = json.loads(f.read())
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for idx, row in df.iterrows():
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photometry = self._get_photometry(row["name"])
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spectra = self._get_spectra(spectra[row[
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yield idx, {
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"photometry": photometry,
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import numpy as np
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import json
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from astropy.io import fits
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from tqdm import tqdm
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from utils.parallelzipfile import ParallelZipFile as ZipFile
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from datasets.utils.tqdm import enable_progress_bars, disable_progress_bars
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_DESCRIPTION = (
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"AstroM3 is a time-series astronomy dataset containing photometry, spectra, "
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class AstroM3Dataset(datasets.GeneratorBasedBuilder):
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"""Hugging Face dataset for AstroM3 with configurable subsets and seeds."""
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# HF_DATASETS_DISABLE_PROGRESS_BARS = True
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DEFAULT_CONFIG_NAME = "full_42"
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name=f"{sub}_{seed}", version=_VERSION, data_dir=None)
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for sub in ["full", "sub10", "sub25", "sub50"]
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{
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"photometry": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), length=3)),
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"spectra": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), length=3)),
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"metadata": datasets.Sequence(datasets.Value("float32"), length=38),
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"label": datasets.Value("string"),
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}
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),
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"test": f"{_URL}/splits/{sub}/{seed}/test.csv",
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"info": f"{_URL}/splits/{sub}/{seed}/info.json",
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}
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extracted_path = dl_manager.download(urls)
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# print("Downloaded train.csv val.csv test.csv info.json")
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# Load all spectra files
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spectra = {}
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df1 = pd.read_csv(extracted_path["train"])
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df2 = pd.read_csv(extracted_path["val"])
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df3 = pd.read_csv(extracted_path["test"])
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df_combined = pd.concat([df1, df2, df3], ignore_index=True)
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for _, row in df_combined.iterrows():
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spectra_url = f"{_URL}/spectra/{row['target']}/{row['spec_filename']}"
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spectra[row["spec_filename"]] = dl_manager.download(spectra_url)
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# print("Downloaded spectra files")
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# Load photometry and init reader
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photometry_path = dl_manager.download(f"{_URL}/photometry.zip")
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self.reader_v = ZipFile(photometry_path)
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# print("Downloaded photometry")
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return [
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datasets.SplitGenerator(
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def _generate_examples(self, csv_path, info_path, spectra, split):
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"""Yields examples from a CSV file containing photometry, spectra, metadata, and labels."""
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print("here")
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if not os.path.exists(csv_path):
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raise FileNotFoundError(f"Missing dataset file: {csv_path}")
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with open(info_path) as f:
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info = json.loads(f.read())
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for idx, row in enumerate(df.iterrows()):
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photometry = self._get_photometry(row["name"])
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spectra = self._get_spectra(spectra[row["spec_filename"]])
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yield idx, {
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"photometry": photometry,
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