Updated loading logic

AstroM3Dataset.py

@@ -1,4 +1,3 @@
-import os
 from io import BytesIO
 import datasets
 import pandas as pd
@@ -9,14 +8,16 @@ from astropy.io import fits
 from .utils import ParallelZipFile
 
 _DESCRIPTION = (
-    "AstroM3 is a time-series astronomy dataset containing photometry, spectra, "
-    "and metadata features for variable stars. The dataset includes multiple "
-    "subsets (full, sub10, sub25, sub50) and supports different random seeds (42, 66, 0, 12, 123). "
-    "Each sample consists of:\n"
-    "- **Photometry**: Light curve data of shape `(N, 3)` (time, flux, flux_error).\n"
-    "- **Spectra**: Spectral observations of shape `(M, 3)` (wavelength, flux, flux_error).\n"
-    "- **Metadata**: Auxiliary features of shape `(25,)`.\n"
-    "- **Label**: The class name as a string."
+    "AstroM3 is a multi-modal time-series astronomy dataset containing photometry, spectra, "
+    "and metadata features for variable stars. The dataset consists of multiple subsets "
+    "('full', 'sub10', 'sub25', 'sub50') and supports different random seeds (42, 66, 0, 12, 123). "
+    "\n\nEach sample includes:\n"
+    "- **Photometry**: Time-series light curve data with shape `(N, 3)` representing time, flux, "
+    "and flux uncertainty.\n"
+    "- **Spectra**: Spectral observations with shape `(M, 3)` containing wavelength, flux, and flux uncertainty.\n"
+    "- **Metadata**: Auxiliary astrophysical and photometric parameters (e.g., magnitudes, parallax, motion data) "
+    "stored as a dictionary.\n"
+    "- **Label**: The classification of the star as a string."
 )
 
 _HOMEPAGE = "https://huggingface.co/datasets/AstroM3"
@@ -25,33 +26,69 @@ _URL = "https://huggingface.co/datasets/MeriDK/AstroM3Dataset/resolve/main"
 _VERSION = datasets.Version("1.0.0")
 
 _CITATION = """ 
-@article{AstroM3,
-  title={AstroM3: A Multi-Modal Astronomy Dataset},
-  author={Your Name},
-  year={2025},
-  journal={AstroML Conference}
+@article{rizhko2024astrom,
+  title={AstroM $\^{} 3$: A self-supervised multimodal model for astronomy},
+  author={Rizhko, Mariia and Bloom, Joshua S},
+  journal={arXiv preprint arXiv:2411.08842},
+  year={2024}
 }
 """
 
+_PHOTO_COLS = ['amplitude', 'period', 'lksl_statistic', 'rfr_score']
+_METADATA_COLS = [
+    'mean_vmag',  'phot_g_mean_mag', 'e_phot_g_mean_mag', 'phot_bp_mean_mag', 'e_phot_bp_mean_mag', 'phot_rp_mean_mag',
+    'e_phot_rp_mean_mag', 'bp_rp', 'parallax', 'parallax_error', 'parallax_over_error', 'pmra', 'pmra_error', 'pmdec',
+    'pmdec_error', 'j_mag', 'e_j_mag', 'h_mag', 'e_h_mag', 'k_mag', 'e_k_mag', 'w1_mag', 'e_w1_mag',
+    'w2_mag', 'e_w2_mag', 'w3_mag', 'w4_mag', 'j_k', 'w1_w2', 'w3_w4', 'pm', 'ruwe', 'l', 'b'
+]
+_ALL_COLS = _PHOTO_COLS + _METADATA_COLS
+_METADATA_FUNC = {
+    "abs": [
+        "mean_vmag",
+        "phot_g_mean_mag",
+        "phot_bp_mean_mag",
+        "phot_rp_mean_mag",
+        "j_mag",
+        "h_mag",
+        "k_mag",
+        "w1_mag",
+        "w2_mag",
+        "w3_mag",
+        "w4_mag",
+    ],
+    "cos": ["l"],
+    "sin": ["b"],
+    "log": ["period"]
+}
+
 
 class AstroM3Dataset(datasets.GeneratorBasedBuilder):
-    """Hugging Face dataset for AstroM3 with configurable subsets and seeds."""
+    """Hugging Face dataset for AstroM3, a multi-modal variable star dataset."""
 
+    # Default configuration (used if no config is specified)
     DEFAULT_CONFIG_NAME = "full_42"
+
+    # Define dataset configurations (subsets, seeds, and normalization variants)
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name=f"{sub}_{seed}", version=_VERSION, data_dir=None)
+        datasets.BuilderConfig(name=f"{sub}_{seed}{norm}", version=_VERSION)
         for sub in ["full", "sub10", "sub25", "sub50"]
         for seed in [42, 66, 0, 12, 123]
+        for norm in ["", "_norm"]
     ]
 
     def _info(self):
+        """Defines the dataset schema, including features and metadata."""
+
         return datasets.DatasetInfo(
             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)),
-                    "metadata": datasets.Sequence(datasets.Value("float32"), length=38),
+                    "photometry": datasets.Array2D(shape=(None, 3), dtype="float32"),
+                    "spectra": datasets.Array2D(shape=(None, 3), dtype="float32"),
+                    "metadata": {
+                        "meta_cols": {el: datasets.Value("float32") for el in _METADATA_COLS},
+                        "photo_cols": {el: datasets.Value("float32") for el in _PHOTO_COLS},
+                    },
                     "label": datasets.Value("string"),
                 }
             ),
@@ -61,13 +98,17 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
         )
 
     def _get_photometry(self, file_name):
+        """Loads photometric light curve data from a compressed file."""
+
         csv = BytesIO()
-        file_name = file_name.replace(' ', '')
+        file_name = file_name.replace(' ', '')  # Ensure filenames are correctly formatted
         data_path = f'vardb_files/{file_name}.dat'
 
+        # Read the photometry file from the compressed ZIP
         csv.write(self.reader_v.read(data_path))
         csv.seek(0)
 
+        # Read light curve data
         lc = pd.read_csv(csv, sep=r'\s+', skiprows=2, names=['HJD', 'MAG', 'MAG_ERR', 'FLUX', 'FLUX_ERR'],
                          dtype={'HJD': float, 'MAG': float, 'MAG_ERR': float, 'FLUX': float, 'FLUX_ERR': float})
 
@@ -75,6 +116,8 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
 
     @staticmethod
     def _get_spectra(file_name):
+        """Loads spectral data from a FITS file."""
+
         hdulist = fits.open(file_name)
         len_list = len(hdulist)
 
@@ -100,11 +143,32 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
 
         return np.vstack((wavelength, specflux, ivar)).T
 
+    @staticmethod
+    def transform(df):
+        """Applies transformations to metadata."""
+
+        for transformation_type, value in _METADATA_FUNC.items():
+            if transformation_type == "abs":
+                for col in value:
+                    df[col] = (
+                            df[col] - 10 + 5 * np.log10(np.where(df["parallax"] <= 0, 1, df["parallax"]))
+                    )
+            elif transformation_type == "cos":
+                for col in value:
+                    df[col] = np.cos(np.radians(df[col]))
+            elif transformation_type == "sin":
+                for col in value:
+                    df[col] = np.sin(np.radians(df[col]))
+            elif transformation_type == "log":
+                for col in value:
+                    df[col] = np.log10(df[col])
+
     def _split_generators(self, dl_manager):
-        """Returns SplitGenerators for train, val, and test."""
+        """Defines dataset splits and downloads required files."""
 
         # Get subset and seed info from the name
-        sub, seed = self.config.name.split("_")
+        name = self.config.name.split("_")
+        sub, seed = name[0], name[1]
 
         # Load the splits and info files
         urls = {
@@ -115,7 +179,7 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
         }
         extracted_path = dl_manager.download(urls)
 
-        # Load all spectra files
+        # Download all spectra files
         spectra_urls = {}
 
         for split in ("train", "val", "test"):
@@ -125,7 +189,7 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
 
         spectra_files = dl_manager.download(spectra_urls)
 
-        # Load photometry and init reader
+        # Download photometry data and initialize ZIP reader
         photometry_path = dl_manager.download(f"photometry.zip")
         self.reader_v = ParallelZipFile(photometry_path)
 
@@ -151,13 +215,20 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
         ]
 
     def _generate_examples(self, csv_path, info_path, spectra_files, split):
-        """Yields examples from a CSV file containing photometry, spectra, metadata, and labels."""
+        """Yields individual dataset examples."""
 
         df = pd.read_csv(csv_path)
 
         with open(info_path) as f:
             info = json.loads(f.read())
 
+        if "norm" in self.config.name:
+            # Apply metadata transformations
+            self.transform(df)
+
+            # Normalize using precomputed mean and standard deviation
+            df[_ALL_COLS] = (df[_ALL_COLS] - info["mean"]) / info["std"]
+
         for idx, row in df.iterrows():
             photometry = self._get_photometry(row["name"])
             spectra = self._get_spectra(spectra_files[row["spec_filename"]])
@@ -165,6 +236,9 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
             yield idx, {
                 "photometry": photometry,
                 "spectra": spectra,
-                "metadata": row[info["all_cols"]],
+                "metadata": {
+                    "meta_cols": {el: row[el] for el in _METADATA_COLS},
+                    "photo_cols": {el: row[el] for el in _PHOTO_COLS},
+                },
                 "label": row["target"],
             }