Update Aiice.py

Aiice.py

@@ -3,6 +3,8 @@ import numpy as np
 import glob
 import os
 from datetime import datetime
+from huggingface_hub import snapshot_download
+import tempfile
 
 _DESCRIPTION = """\
 Dataset for Arctic sea ice concentration spatio-temporal forecasting task.
@@ -42,14 +44,29 @@ class Aiice(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         """Define splits and handle data downloading."""
-        global_series_dir = "global_series"
+        # Download the entire dataset repository
+        repo_id = "ITMO-NSS/Aiice"
+        
+        # Use dl_manager to download files
+        data_dir = dl_manager.download_and_extract(
+            f"https://huggingface.co/datasets/{repo_id}/resolve/main/global_series.zip"
+        )
+        
+        # If zip doesn't exist, download the entire repo
+        if not os.path.exists(data_dir):
+            logger.info("Downloading dataset files...")
+            data_dir = snapshot_download(
+                repo_id=repo_id,
+                repo_type="dataset",
+                cache_dir=dl_manager.download_cache_dir,
+                allow_patterns="global_series/**/*.npy"
+            )
 
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "data_dir": global_series_dir,
-                    "file_pattern": "**/*.npy",  
+                    "data_dir": data_dir,
                     "date_range": ("1979-01-01", "2015-12-31"),
                     "split_name": "train",
                 },
@@ -57,8 +74,7 @@ class Aiice(datasets.GeneratorBasedBuilder):
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 gen_kwargs={
-                    "data_dir": global_series_dir,
-                    "file_pattern": "**/*.npy", 
+                    "data_dir": data_dir,
                     "date_range": ("2016-01-01", "2020-12-31"),
                     "split_name": "validation",
                 },
@@ -66,49 +82,54 @@ class Aiice(datasets.GeneratorBasedBuilder):
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "data_dir": global_series_dir,
-                    "file_pattern": "**/*.npy", 
+                    "data_dir": data_dir,
                     "date_range": ("2021-01-01", "2025-12-31"),
                     "split_name": "test",
                 },
             ),
         ]
 
-    def _generate_examples(self, data_dir, file_pattern, date_range, split_name):
+    def _generate_examples(self, data_dir, date_range, split_name):
         """Generate examples for each split."""
-        # Manually walk through directories
-        file_paths = []
-        for root, dirs, files in os.walk(data_dir):
-            for file in files:
-                if file.endswith('.npy'):
-                    file_paths.append(os.path.join(root, file))
+        # Find all .npy files
+        npy_files = []
+        search_path = os.path.join(data_dir, "global_series", "**", "*.npy")
         
-        logger.info(f"Found {len(file_paths)} files for {split_name} split")
-    
+        for file_path in glob.glob(search_path, recursive=True):
+            npy_files.append(file_path)
+        
+        logger.info(f"Found {len(npy_files)} total files")
+
         start_date = datetime.strptime(date_range[0], "%Y-%m-%d")
         end_date = datetime.strptime(date_range[1], "%Y-%m-%d")
-    
+
         filtered_files = []
-        for file_path in file_paths:
+        for file_path in npy_files:
             filename = os.path.basename(file_path)
+            # Extract date from filename like 'osisaf_19790101.npy'
             date_str = filename.replace('.npy', '').replace('osisaf_', '')
-    
+
             try:
                 file_date = datetime.strptime(date_str, "%Y%m%d")
                 if start_date <= file_date <= end_date:
                     filtered_files.append((file_path, filename, file_date))
-            except ValueError:
+            except ValueError as e:
+                logger.warning(f"Could not parse date from {filename}: {e}")
                 continue
-    
+
         filtered_files.sort(key=lambda x: x[2])
         logger.info(f"After date filtering: {len(filtered_files)} files for {split_name} split")
-    
+
         for idx, (file_path, filename, file_date) in enumerate(filtered_files):
-            matrix = np.load(file_path).astype(np.float32)
-            date_iso = file_date.strftime("%Y-%m-%d")
-    
-            yield idx, {
-                "date": date_iso,
-                "matrix": matrix,
-                "filename": filename,
-            }
+            try:
+                matrix = np.load(file_path).astype(np.float32)
+                date_iso = file_date.strftime("%Y-%m-%d")
+
+                yield idx, {
+                    "date": date_iso,
+                    "matrix": matrix,
+                    "filename": filename,
+                }
+            except Exception as e:
+                logger.warning(f"Could not load {file_path}: {e}")
+                continue