Updated loading logic

AstroM3Dataset.py

@@ -1,4 +1,3 @@
-import os
 from io import BytesIO
 import datasets
 import pandas as pd
@@ -6,7 +5,7 @@ import numpy as np
 import json
 from astropy.io import fits
 
-from utils.parallelzipfile import ParallelZipFile as ZipFile
+from utils import ParallelZipFile
 
 _DESCRIPTION = (
     "AstroM3 is a time-series astronomy dataset containing photometry, spectra, "
@@ -49,8 +48,8 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
             description=_DESCRIPTION,
             features=datasets.Features(
                 {
-                    "photometry": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), length=3)),
-                    "spectra": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), length=3)),
+                    "photometry": datasets.Array2D(shape=(None, 3), dtype="float32"),
+                    "spectra": datasets.Array2D(shape=(None, 3), dtype="float32"),
                     "metadata": datasets.Sequence(datasets.Value("float32"), length=38),
                     "label": datasets.Value("string"),
                 }
@@ -100,6 +99,29 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
 
         return np.vstack((wavelength, specflux, ivar)).T
 
+    @staticmethod
+    def _transform_metadata(row, info):
+        row_copy = row.copy(deep=True)
+
+        for transformation_type, value in info["metadata_func"].items():
+            if transformation_type == "abs":
+                for col in value:
+                    row_copy[col] = (
+                        row_copy[col] - 10 + 5 * np.log10(np.where(row_copy["parallax"] <= 0, 1, row_copy["parallax"]))
+                    )
+            elif transformation_type == "cos":
+                for col in value:
+                    row_copy[col] = np.cos(np.radians(row_copy[col]))
+            elif transformation_type == "sin":
+                for col in value:
+                    row_copy[col] = np.sin(np.radians(row_copy[col]))
+            elif transformation_type == "log":
+                for col in value:
+                    row_copy[col] = np.log10(row_copy[col])
+
+        row_copy = (row_copy - info["mean"]) / info["std"]
+        return row_copy
+
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators for train, val, and test."""
 
@@ -128,7 +150,7 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
 
         # Load photometry and init reader
         photometry_path = dl_manager.download(f"{_URL}/photometry.zip")
-        self.reader_v = ZipFile(photometry_path)
+        self.reader_v = ParallelZipFile(photometry_path)
 
         return [
             datasets.SplitGenerator(
@@ -159,10 +181,29 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
         with open(info_path) as f:
             info = json.loads(f.read())
 
-        for idx, row in df.iterrows():
+        for i, (idx, row) in enumerate(df.iterrows()):
             photometry = self._get_photometry(row["name"])
             spectra = self._get_spectra(spectra_files[row["spec_filename"]])
 
+            metadata = row[info["all_cols"]]
+            # metadata_norm = self._transform_metadata(metadata, info)
+
+            # yield idx, {
+            #     "photometry": photometry,
+            #     "spectra": spectra,
+            #     "metadata": {
+            #         "original": {
+            #             "photometry": metadata[info["photo_cols"]].to_dict(),
+            #             "metadata": metadata[info["meta_cols"]].to_dict()
+            #         },
+            #         "transformed": {
+            #             "photometry": metadata_norm[info["photo_cols"]].to_dict(),
+            #             "metadata": metadata_norm[info["meta_cols"]].to_dict()
+            #         }
+            #     },
+            #     "label": row["target"],
+            # }
+
             yield idx, {
                 "photometry": photometry,
                 "spectra": spectra,