82 lines
2.3 KiB
Python
82 lines
2.3 KiB
Python
"""
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Tạo metadata cho model_odc.joblib (legacy model)
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"""
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import json
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from pathlib import Path
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# Metadata cho model_odc.joblib
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# Model này là GridSearchCV Pipeline với 39 features (temporal mode)
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# Features: NDVI time series + NDWI time series + NDBI time series + radar features
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# Calculate feature names for temporal mode with 12 timesteps
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# (12 NDVI + 12 NDWI + 12 NDBI + 3 radar = 39 features)
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n_timesteps = 12
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feature_names = []
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# NDVI time series
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for t in range(n_timesteps):
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feature_names.append(f"NDVI_t{t+1}")
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# NDWI time series
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for t in range(n_timesteps):
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feature_names.append(f"NDWI_t{t+1}")
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# NDBI time series
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for t in range(n_timesteps):
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feature_names.append(f"NDBI_t{t+1}")
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# Radar features
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feature_names.extend(["VH_db_mean", "VV_db_mean", "VH_VV_ratio"])
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metadata = {
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"timestamp": "2025-12-20T10:00:00",
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"data_source": "Unknown (Legacy model)",
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"collections": ["sentinel-2-l2a", "sentinel-1-rtc"],
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"features": feature_names,
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"feature_mode": "temporal", # IMPORTANT: temporal mode with 39 features
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"training_samples": None,
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"testing_samples": None,
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"test_size": 0.2,
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"train_accuracy": None,
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"test_accuracy": None,
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"model_type": "random_forest", # GridSearchCV with RandomForest
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"device": "cpu",
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"n_estimators": 100,
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"max_depth": None,
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"learning_rate": None,
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"cnn_epochs": None,
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"n_features": 39, # GridSearchCV expects 39 features!
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"n_classes": 8,
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"class_names": [
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"Lua tom", # 0
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"Lua", # 1
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"CHN", # 2
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"CLN", # 3
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"TS", # 4
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"Song", # 5
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"Dat xay dung", # 6
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"Rung" # 7
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],
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"classification_report": None,
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"confusion_matrix": None,
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"bbox": None,
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"time_range": None,
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"resolution": 10,
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"notes": "Legacy GridSearchCV Pipeline model with 39 temporal features (12 timesteps each for NDVI/NDWI/NDBI + 3 radar features). Requires temporal mode feature extraction."
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}
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# Save metadata
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model_train_dir = Path("model_train")
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metadata_file = model_train_dir / "model_odc_info.json"
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print("Creating metadata for model_odc.joblib...")
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print(f"Saving to: {metadata_file}")
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with open(metadata_file, 'w') as f:
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json.dump(metadata, f, indent=2)
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print("✅ Metadata created successfully!")
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print("\nMetadata content:")
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print(json.dumps(metadata, indent=2))
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