update loading script

substrate_mix_raman.py

@@ -74,24 +74,34 @@ class SubstrateMixRaman(datasets.GeneratorBasedBuilder):
         """Yields examples as (key, example) tuples for a given split file."""
         df = pd.read_csv(filepath)
 
-        # Assuming the actual column names in your CSV are the Raman shifts as strings
-        # and then the specified label names.
-        # Let's verify the number of spectrum columns based on your description.
-        # 1561 columns for spectrum and 8 for labels = 1569 columns total.
-        num_spectrum_cols = 1561
-        raman_shift_columns = [str(col) for col in df.columns[:num_spectrum_cols]]
-
+        # Dynamically determine Raman shift columns and label columns
+        # Raman shift columns are those that can be converted to float (numbers)
+        # Label columns are the known metabolite names
         label_columns = [
             "Glucose", "Glycerol", "Acetate", "EnPump", "Nitrate",
             "Yeast_Extract", "total_phosphate", "total_sulfate"
         ]
 
+        raman_shift_columns = []
+        for col in df.columns:
+            try:
+                float(col) # Check if column name can be converted to a float
+                raman_shift_columns.append(col)
+            except ValueError:
+                # If it's not a float, check if it's one of our expected label columns
+                if col not in label_columns:
+                    print(f"Warning: Unexpected column '{col}' found in CSV. It's neither a Raman shift nor a defined label.")
+
         # Ensure all expected label columns are actually present in the DataFrame
-        # This can help debug if there's a mismatch between the script and the CSV.
         for col in label_columns:
             if col not in df.columns:
                 raise ValueError(f"Label column '{col}' not found in CSV file: {filepath}")
 
+        # Ensure we have at least some Raman shift columns
+        if not raman_shift_columns:
+            raise ValueError(f"No Raman shift columns found in CSV file: {filepath}. Expected column names to be numerical strings.")
+
+
         for id_, row in enumerate(df.iterrows()):
             data = row[1] # row[0] is the index, row[1] is the Series/array of data