thêm chức năng train trên odc predict trên planetary

load_data_no_odc.py

@@ -9,10 +9,12 @@ Usage:
 import xarray as xr
 import numpy as np
 import pandas as pd
+import geopandas as gpd
 from pystac_client import Client
 import planetary_computer
 import odc.stac
 from datetime import datetime
+from sklearn.model_selection import train_test_split
 
 
 def load_sentinel2_stac(
@@ -292,6 +294,127 @@ def load_and_process_s1(bbox, date_range):
     return data_monthly
 
 
+# ══════════════════════════════════════════════════════════════════════════════
+# TRAINING DATA FUNCTIONS
+# ══════════════════════════════════════════════════════════════════════════════
+
+def load_train_data(train_path, label_mapping=None):
+    """
+    Load training data from shapefile or GeoJSON
+    
+    Args:
+        train_path: Path to training data file (.shp, .geojson, etc.)
+        label_mapping: Optional dict to map labels to numeric IDs
+    
+    Returns:
+        GeoDataFrame with training points
+    """
+    print(f"📂 Loading training data from: {train_path}")
+    
+    try:
+        train = gpd.read_file(train_path)
+        print(f"✅ Loaded {len(train)} training points")
+        print(f"   Columns: {list(train.columns)}")
+        
+        if label_mapping is not None and 'label' in train.columns:
+            train['label_id'] = train['label'].map(label_mapping).astype(int)
+            print(f"   Labels mapped: {sorted(train['label_id'].unique())}")
+        
+        return train
+        
+    except Exception as e:
+        print(f"❌ Error loading training data: {e}")
+        return None
+
+
+def extract_features_at_points(train_data, data_s2, data_s1):
+    """
+    Extract Sentinel-1 and Sentinel-2 features at training point locations
+    
+    Args:
+        train_data: GeoDataFrame with training points
+        data_s2: xarray Dataset with Sentinel-2 data 
+        data_s1: xarray Dataset with Sentinel-1 data
+    
+    Returns:
+        X (features), y (labels)
+    """
+    print(f"🔧 Extracting features from satellite data at {len(train_data)} points...")
+    
+    X_list = []
+    y_list = []
+    
+    for idx, point in train_data.iterrows():
+        try:
+            lon, lat = point.geometry.x, point.geometry.y
+            
+            # Extract S2 data at point location
+            s2_point = data_s2.sel(x=lon, y=lat, method='nearest')
+            s2_values = s2_point.to_array().values.flatten()
+            
+            # Extract S1 data at point location
+            s1_point = data_s1.sel(x=lon, y=lat, method='nearest')
+            s1_values = s1_point.to_array().values.flatten()
+            
+            # Combine features
+            features = np.concatenate([s2_values, s1_values])
+            
+            # Skip if contains NaN
+            if not np.isnan(features).any():
+                X_list.append(features)
+                y_list.append(point['label_id'])
+            else:
+                print(f"  ⚠ Skip point {idx}: contains NaN")
+                
+        except Exception as e:
+            print(f"  ⚠ Skip point {idx}: {e}")
+            continue
+    
+    X = np.array(X_list)
+    y = np.array(y_list)
+    
+    print(f"✅ Extracted features from {len(X)} points")
+    print(f"   Feature dimension: {X.shape[1]}")
+    print(f"   Classes: {sorted(set(y.tolist()))}")
+    
+    return X, y
+
+
+def split_train_data(X, y, test_size=0.2, val_size=0.1, random_state=42):
+    """
+    Split data into train/val/test sets
+    
+    Args:
+        X: Features array
+        y: Labels array
+        test_size: Proportion for test set
+        val_size: Proportion for validation set (from train+val)
+        random_state: Random seed
+    
+    Returns:
+        X_train, X_val, X_test, y_train, y_val, y_test
+    """
+    print(f"📊 Splitting data...")
+    
+    # First split: train+val vs test
+    X_temp, X_test, y_temp, y_test = train_test_split(
+        X, y, test_size=test_size, random_state=random_state, stratify=y
+    )
+    
+    # Second split: train vs val
+    val_ratio = val_size / (1 - test_size)
+    X_train, X_val, y_train, y_val = train_test_split(
+        X_temp, y_temp, test_size=val_ratio, random_state=random_state, stratify=y_temp
+    )
+    
+    print(f"✅ Data split complete:")
+    print(f"   Train: {len(X_train)} samples ({len(X_train)/len(X)*100:.1f}%)")
+    print(f"   Val:   {len(X_val)} samples ({len(X_val)/len(X)*100:.1f}%)")
+    print(f"   Test:  {len(X_test)} samples ({len(X_test)/len(X)*100:.1f}%)")
+    
+    return X_train, X_val, X_test, y_train, y_val, y_test
+
+
 # Print module info
 print("=" * 70)
 print("📦 Data Loading Module (No ODC Database Required)")
@@ -302,5 +425,9 @@ print("\n   bbox = (lon_min, lat_min, lon_max, lat_max)")
 print("   date_range = ('2022-09-01', '2023-10-01')")
 print("\n   data_s2 = load_and_process_s2(bbox, date_range)")
 print("   data_s1 = load_and_process_s1(bbox, date_range)")
+print("\n   # Load training data")
+print("   train = load_train_data('train/data.shp', label_mapping)")
+print("   X, y = extract_features_at_points(train, data_s2, data_s1)")
+print("   X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(X, y)")
 print("=" * 70)
 print()