Updated loading logic
Browse files- AstroM3Dataset.py +6 -12
AstroM3Dataset.py
CHANGED
@@ -124,7 +124,7 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
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spectra_urls = {}
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for _, row in df_combined.iterrows():
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spectra_urls[row["spec_filename"]] = f"{_URL}/spectra/{row['target']}/{row['spec_filename']}"
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-
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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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@@ -134,32 +134,26 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN, gen_kwargs={"csv_path": extracted_path["train"],
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"info_path": extracted_path["info"],
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-
"
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"split": "train"}
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION, gen_kwargs={"csv_path": extracted_path["val"],
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"info_path": extracted_path["info"],
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"
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"split": "val"}
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST, gen_kwargs={"csv_path": extracted_path["test"],
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"info_path": extracted_path["info"],
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"
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"split": "test"}
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),
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]
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-
def _generate_examples(self, csv_path, info_path,
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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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if not os.path.exists(info_path):
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raise FileNotFoundError(f"Missing info file: {info_path}")
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-
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df = pd.read_csv(csv_path)
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with open(info_path) as f:
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@@ -167,7 +161,7 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
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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(
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yield idx, {
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"photometry": photometry,
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spectra_urls = {}
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for _, row in df_combined.iterrows():
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spectra_urls[row["spec_filename"]] = f"{_URL}/spectra/{row['target']}/{row['spec_filename']}"
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+
spectra_files = dl_manager.download(spectra_urls)
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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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datasets.SplitGenerator(
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name=datasets.Split.TRAIN, gen_kwargs={"csv_path": extracted_path["train"],
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"info_path": extracted_path["info"],
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+
"spectra_files": spectra_files,
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"split": "train"}
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION, gen_kwargs={"csv_path": extracted_path["val"],
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"info_path": extracted_path["info"],
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+
"spectra_files": spectra_files,
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"split": "val"}
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST, gen_kwargs={"csv_path": extracted_path["test"],
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"info_path": extracted_path["info"],
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+
"spectra_files": spectra_files,
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"split": "test"}
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),
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]
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+
def _generate_examples(self, csv_path, info_path, spectra_files, split):
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"""Yields examples from a CSV file containing photometry, spectra, metadata, and labels."""
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df = pd.read_csv(csv_path)
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with open(info_path) as f:
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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_files[row["spec_filename"]])
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yield idx, {
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"photometry": photometry,
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