Add files using upload-large-folder tool

.idea/AstroM3Dataset.iml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="astro (2)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/workspace.xml

@@ -0,0 +1,12 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectViewState">
+    <option name="hideEmptyMiddlePackages" value="true" />
+    <option name="showLibraryContents" value="true" />
+  </component>
+  <component name="PropertiesComponent">{
+  &quot;keyToString&quot;: {
+    &quot;settings.editor.selected.configurable&quot;: &quot;ssh.settings&quot;
+  }
+}</component>
+</project>

AstroM3Dataset.py

@@ -1,6 +1,5 @@
 import os
 from io import BytesIO
-from pathlib import Path
 import datasets
 import pandas as pd
 import numpy as np
@@ -22,7 +21,7 @@ _DESCRIPTION = (
 
 _HOMEPAGE = "https://huggingface.co/datasets/AstroM3"
 _LICENSE = "CC BY 4.0"
-_URL = "https://huggingface.co/datasets/AstroM3"
+_URL = "https://huggingface.co/datasets/MeriDK/AstroM3Dataset/resolve/main"
 _VERSION = datasets.Version("1.0.0")
 
 _CITATION = """ 
@@ -41,68 +40,27 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
     DEFAULT_CONFIG_NAME = "full_42"
 
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name=f"{sub}_{seed}",
-            version=_VERSION
-        )
+        datasets.BuilderConfig(name=f"{sub}_{seed}", version=_VERSION, data_dir=None)
         for sub in ["full", "sub10", "sub25", "sub50"]
         for seed in [42, 66, 0, 12, 123]
     ]
 
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-
-        # Auto-detect dataset location: use current working directory
-        if not hasattr(self.config, "data_dir") or self.config.data_dir is None:
-            self.config.data_dir = Path(os.getcwd()).resolve()
-            print(f"Using dataset location: {self.config.data_dir}")
-
     def _info(self):
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=datasets.Features(
                 {
-                    "id": datasets.Value("string"),
                     "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),
                     "label": datasets.Value("string"),
                 }
             ),
-            supervised_keys=("photometry", "label"),
             homepage=_HOMEPAGE,
             license=_LICENSE,
             citation=_CITATION,
         )
 
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators for train, val, and test."""
-        self.config.data_dir = Path(self.config.data_dir)
-        sub, seed = self.config.name.split("_")
-        data_root = self.config.data_dir / "splits" / sub / seed
-        info_path = data_root / "info.json"
-
-        if not info_path.exists():
-            raise FileNotFoundError(f"Missing info.json file: {info_path}")
-
-        with open(info_path, "r") as f:
-            self.dataset_info = json.load(f)
-
-        # Init reader for photometry
-        self.reader_v = ZipFile(Path(self.config.data_dir) / 'asassnvarlc_vband_complete.zip')
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_root / "train.csv"}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_root / "val.csv"}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"filepath": data_root / "test.csv"}
-            ),
-        ]
-
     def _get_photometry(self, file_name):
         csv = BytesIO()
         file_name = file_name.replace(' ', '')
@@ -143,22 +101,78 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
 
         return np.vstack((wavelength, specflux, ivar)).T
 
-    def _generate_examples(self, filepath):
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators for train, val, and test."""
+
+        # Get subset and seed info from the name
+        sub, seed = self.config.name.split("_")
+
+        # 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",
+        }
+        extracted_path = dl_manager.download_and_extract(urls)
+
+        # Load all spectra files
+        spectra_urls = {}
+
+        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
+
+        spectra = dl_manager.download_and_extract(spectra_urls)
+
+        # Load photometry and init reader
+        photometry_path = dl_manager.download(f"{_URL}/photometry.zip")
+        self.reader_v = ZipFile(photometry_path)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"csv_path": extracted_path["train"],
+                                                       "info_path": extracted_path["info"],
+                                                       "spectra": spectra,
+                                                       "split": "train"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, gen_kwargs={"csv_path": extracted_path["val"],
+                                                            "info_path": extracted_path["info"],
+                                                            "spectra": spectra,
+                                                            "split": "val"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"csv_path": extracted_path["test"],
+                                                      "info_path": extracted_path["info"],
+                                                      "spectra": spectra,
+                                                      "split": "test"}
+            ),
+        ]
+
+    def _generate_examples(self, csv_path, info_path, spectra, split):
         """Yields examples from a CSV file containing photometry, spectra, metadata, and labels."""
-        if not filepath.exists():
-            raise FileNotFoundError(f"Missing dataset file: {filepath}")
 
-        df = pd.read_csv(filepath)
+        if not os.path.exists(csv_path):
+            raise FileNotFoundError(f"Missing dataset file: {csv_path}")
+
+        if not os.path.exists(info_path):
+            raise FileNotFoundError(f"Missing info file: {info_path}")
+
+        df = pd.read_csv(csv_path)
+
+        with open(info_path) as f:
+            info = json.loads(f.read())
 
         for idx, row in df.iterrows():
-            photometry = self._get_photometry(row['name'])
-            spectra = np.zeros((200, 3))  # (None, 3)
-            metadata = np.zeros(25)  # (25,)
+            photometry = self._get_photometry(row["name"])
+            spectra = self._get_spectra(spectra[row['spec_filename']])
 
             yield idx, {
-                "id": str(row["id"]),
-                "photometry": photometry.tolist(),  # Convert back to list for HF compatibility
-                "spectra": spectra.tolist(),
-                "metadata": metadata.tolist(),
+                "photometry": photometry,
+                "spectra": spectra,
+                "metadata": row[info["all_cols"]],
                 "label": row["target"],
             }