refactor: reorganize project structure by moving core modules and update import paths in API server

2026-07-18 01:24:30 +07:00
parent abab846884
commit a82b2f6fa5
155 changed files with 25 additions and 370 deletions
@@ -0,0 +1,122 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+import os
+import json
+import joblib
+import numpy as np
+import importlib
+import new_import_ODC
+importlib.reload(new_import_ODC)
+from new_import_ODC import *
+
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
+
+print("=" * 70)
+print("🚀 Training Random Forest model for Land Classification (REAL DATA)")
+print("=" * 70)
+
+# 1. Cấu hình thời gian và tọa độ
+date_range = ("2022-09-01", "2022-10-01")
+longtitude_range = (105.86, 105.94)
+latitude_range = (9.65, 9.69)
+coordinates = (longtitude_range, latitude_range)
+
+# 2. Load Dữ liệu Thật (Từ Cache)
+print("\n[DATA] Đang load dữ liệu Sentinel-2...")
+data = load_data(None, date_range, longtitude_range, latitude_range)
+
+print("[DATA] Đang loại bỏ mây...")
+result = mask_clean(data)
+
+print("[DATA] Đang tính toán NDVI...")
+ds1 = calculate_indices(result, index="NDVI", satellite_mission="s2")
+ndvi = ds1["NDVI"]
+
+time_split = [
+    slice("2022-09-01", "2023-01-01"),
+    slice("2023-01-01", "2023-05-01"),
+    slice("2023-05-01", "2023-07-01"),
+    slice("2023-07-01", "2022-10-01"),
+]
+
+print("[DATA] Đang nội suy (fill_nan) cho mây...")
+fill_nan_ndvi = fill_nan(ndvi, time_split)
+
+print("[DATA] Đang tính trung bình tháng (resample 1M)...")
+average_ndvi = fill_nan_ndvi.resample(time="1M").mean().persist()
+average_ndvi = average_ndvi.compute()
+
+print("\n[DATA] Đang load dữ liệu Sentinel-1 (VV, VH)...")
+dsvh, dsvv = load_data_sen1(None, date_range, coordinates)
+average_vv = calculate_average(dsvv, time_pattern='1M')
+average_vh = calculate_average(dsvh, time_pattern='1M')
+
+# 3. Chuẩn bị tập Train
+print("\n[DATA] Đang trích xuất điểm huấn luyện...")
+train_path = "train/ST_training_data_updated_1130points_new.shp"
+train = load_train_data(train_path)
+
+label_mapping = {
+    "Lua tom": "0", "Lua": "1", "CHN": "2", "CLN": "3", 
+    "TS": "4", "Song": "5", "Dat xay dung": "6", "Rung": "7"
+}
+
+datasets = get_data_sen1_and_sen2(train, average_ndvi, average_vh, average_vv)
+X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(train, label_mapping, datasets)
+
+X_train_np = np.asarray(X_train, dtype=np.float32)
+y_train_np = np.asarray(y_train, dtype=np.int32)
+X_val_np = np.asarray(X_val, dtype=np.float32)
+y_val_np = np.asarray(y_val, dtype=np.int32)
+
+# 4. Huấn luyện Random Forest
+print("\n[MODEL] Bắt đầu huấn luyện Random Forest...")
+model = RandomForestClassifier(
+    n_estimators=100, 
+    max_depth=15, 
+    random_state=42, 
+    n_jobs=-1  # Dùng tất cả nhân CPU
+)
+
+model.fit(X_train_np, y_train_np)
+
+# 5. Đánh giá mô hình
+print("\n[EVAL] Đang đánh giá trên tập Validation...")
+y_val_pred = model.predict(X_val_np)
+val_accuracy = accuracy_score(y_val_np, y_val_pred)
+print(f"Validation Accuracy: {val_accuracy:.4f}")
+
+X_test_np = np.asarray(X_test, dtype=np.float32)
+y_test_np = np.asarray(y_test, dtype=np.int32)
+y_pred_test = model.predict(X_test_np)
+
+test_accuracy = accuracy_score(y_test_np, y_pred_test)
+precision = precision_score(y_test_np, y_pred_test, average='weighted', zero_division=0)
+recall = recall_score(y_test_np, y_pred_test, average='weighted', zero_division=0)
+f1 = f1_score(y_test_np, y_pred_test, average='weighted', zero_division=0)
+
+# 6. Lưu mô hình và Metadata
+model_dir = "model_train"
+os.makedirs(model_dir, exist_ok=True)
+model_path = os.path.join(model_dir, "model_randomforest.joblib")
+joblib.dump(model, model_path)
+print(f"\n[SAVE] Model saved to {model_path}")
+
+info = {
+    "model_type": "RandomForest_RealData",
+    "num_classes": 8,
+    "classes": list(label_mapping.keys()),
+    "num_features": X_train_np.shape[1],
+    "accuracy": float(test_accuracy),
+    "precision": float(precision),
+    "recall": float(recall),
+    "f1_score": float(f1),
+    "description": "Random Forest trained on real Planetary Computer data"
+}
+
+with open(os.path.join(model_dir, "model_randomforest_info.json"), "w") as f:
+    json.dump(info, f, indent=2)
+
+print("[SAVE] Model info saved.")