Updated loading logic

AstroM3Dataset.py

@@ -108,50 +108,55 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
 
         # Load the splits and info files
         urls = {
-            "train": f"{_URL}/splits/{sub}/{seed}/train.csv",
-            "val": f"{_URL}/splits/{sub}/{seed}/val.csv",
-            "test": f"{_URL}/splits/{sub}/{seed}/test.csv",
-            "info": f"{_URL}/splits/{sub}/{seed}/info.json",
+            "train": f"splits/{sub}/{seed}/train.csv",
+            "val": f"splits/{sub}/{seed}/val.csv",
+            "test": f"splits/{sub}/{seed}/test.csv",
+            "info": f"splits/{sub}/{seed}/info.json",
+            "spectra": "spectra.zip"
         }
-        extracted_path = dl_manager.download(urls)
 
-        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)
+        extracted_path = dl_manager.download_and_extract(urls)
 
-        # Load all spectra files
-        spectra_urls = {}
-        for _, row in df_combined.iterrows():
-            spectra_urls[row["spec_filename"]] = f"{_URL}/spectra/{row['target']}/{row['spec_filename']}"
-        spectra_files = dl_manager.download(spectra_urls)
+        # 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)
+        #
+        # # Load all spectra files
+        # spectra_urls = {}
+        # for _, row in df_combined.iterrows():
+        #     spectra_urls[row["spec_filename"]] = f"{_URL}/spectra/{row['target']}/{row['spec_filename']}"
+        # spectra_files = dl_manager.download(spectra_urls)
 
         # Load photometry and init reader
-        photometry_path = dl_manager.download(f"{_URL}/photometry.zip")
+        photometry_path = dl_manager.download(f"photometry.zip")
         self.reader_v = ParallelZipFile(photometry_path)
 
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN, gen_kwargs={"csv_path": extracted_path["train"],
                                                        "info_path": extracted_path["info"],
-                                                       "spectra_files": spectra_files,
+                                                       # "spectra_files": spectra_files,
+                                                       "spectra_path": extracted_path["spectra"],
                                                        "split": "train"}
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION, gen_kwargs={"csv_path": extracted_path["val"],
                                                             "info_path": extracted_path["info"],
-                                                            "spectra_files": spectra_files,
+                                                            # "spectra_files": spectra_files,
+                                                            "spectra_path": extracted_path["spectra"],
                                                             "split": "val"}
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST, gen_kwargs={"csv_path": extracted_path["test"],
                                                       "info_path": extracted_path["info"],
-                                                      "spectra_files": spectra_files,
+                                                      # "spectra_files": spectra_files,
+                                                      "spectra_path": extracted_path["spectra"],
                                                       "split": "test"}
             ),
         ]
 
-    def _generate_examples(self, csv_path, info_path, spectra_files, split):
+    def _generate_examples(self, csv_path, info_path, spectra_path, split):
         """Yields examples from a CSV file containing photometry, spectra, metadata, and labels."""
 
         df = pd.read_csv(csv_path)
@@ -161,7 +166,7 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
 
         for idx, row in df.iterrows():
             photometry = self._get_photometry(row["name"])
-            spectra = self._get_spectra(spectra_files[row["spec_filename"]])
+            spectra = self._get_spectra(os.path.join(spectra_path, row["target"], row["spec_filename"]))
 
             yield idx, {
                 "photometry": photometry,