Updated loading logic

AstroM3Dataset.py

@@ -5,8 +5,10 @@ import pandas as pd
 import numpy as np
 import json
 from astropy.io import fits
+from tqdm import tqdm
 
 from utils.parallelzipfile import ParallelZipFile as ZipFile
+from datasets.utils.tqdm import enable_progress_bars, disable_progress_bars
 
 _DESCRIPTION = (
     "AstroM3 is a time-series astronomy dataset containing photometry, spectra, "
@@ -37,8 +39,9 @@ _CITATION = """
 class AstroM3Dataset(datasets.GeneratorBasedBuilder):
     """Hugging Face dataset for AstroM3 with configurable subsets and seeds."""
 
-    DEFAULT_CONFIG_NAME = "full_42"
+    # HF_DATASETS_DISABLE_PROGRESS_BARS = True
 
+    DEFAULT_CONFIG_NAME = "full_42"
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(name=f"{sub}_{seed}", version=_VERSION, data_dir=None)
         for sub in ["full", "sub10", "sub25", "sub50"]
@@ -52,7 +55,7 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
                 {
                     "photometry": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), length=3)),
                     "spectra": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), length=3)),
-                    "metadata": datasets.Sequence(datasets.Value("float32"), length=25),
+                    "metadata": datasets.Sequence(datasets.Value("float32"), length=38),
                     "label": datasets.Value("string"),
                 }
             ),
@@ -114,22 +117,26 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
             "test": f"{_URL}/splits/{sub}/{seed}/test.csv",
             "info": f"{_URL}/splits/{sub}/{seed}/info.json",
         }
-        extracted_path = dl_manager.download_and_extract(urls)
+        extracted_path = dl_manager.download(urls)
+        # print("Downloaded train.csv val.csv test.csv info.json")
 
         # Load all spectra files
-        spectra_urls = {}
+        spectra = {}
+        df1 = pd.read_csv(extracted_path["train"])
+        df2 = pd.read_csv(extracted_path["val"])
+        df3 = pd.read_csv(extracted_path["test"])
+        df_combined = pd.concat([df1, df2, df3], ignore_index=True)
 
-        for split in ["train", "val", "test"]:
-            df = pd.read_csv(extracted_path[split])
-            for _, row in df.iterrows():
-                spectra_url = f"{_URL}/spectra/{split}/{row['target']}/{row['spec_filename']}"
-                spectra_urls[row["spec_filename"]] = spectra_url
+        for _, row in df_combined.iterrows():
+            spectra_url = f"{_URL}/spectra/{row['target']}/{row['spec_filename']}"
+            spectra[row["spec_filename"]] = dl_manager.download(spectra_url)
 
-        spectra = dl_manager.download_and_extract(spectra_urls)
+        # print("Downloaded spectra files")
 
         # Load photometry and init reader
         photometry_path = dl_manager.download(f"{_URL}/photometry.zip")
         self.reader_v = ZipFile(photometry_path)
+        # print("Downloaded photometry")
 
         return [
             datasets.SplitGenerator(
@@ -154,6 +161,7 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
 
     def _generate_examples(self, csv_path, info_path, spectra, split):
         """Yields examples from a CSV file containing photometry, spectra, metadata, and labels."""
+        print("here")
 
         if not os.path.exists(csv_path):
             raise FileNotFoundError(f"Missing dataset file: {csv_path}")
@@ -166,9 +174,9 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
         with open(info_path) as f:
             info = json.loads(f.read())
 
-        for idx, row in df.iterrows():
+        for idx, row in enumerate(df.iterrows()):
             photometry = self._get_photometry(row["name"])
-            spectra = self._get_spectra(spectra[row['spec_filename']])
+            spectra = self._get_spectra(spectra[row["spec_filename"]])
 
             yield idx, {
                 "photometry": photometry,