51 lines
1.5 KiB
Python
51 lines
1.5 KiB
Python
#!/usr/bin/env python
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# coding: utf-8
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import os
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import json
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import joblib
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import numpy as np
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from sklearn.ensemble import VotingRegressor
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from sklearn.linear_model import LinearRegression
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from sklearn.ensemble import RandomForestRegressor
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from core.ndvi_data_loader import NDVITimeSeriesDataset
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print("=" * 70)
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print("🚀 Training Multi-Model Ensemble for NDVI (REAL DATA & CPU)")
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print("=" * 70)
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dataset = NDVITimeSeriesDataset(sequence_length=5, spatial=False)
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X_train, y_train = [], []
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for x, y in dataset:
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X_train.append(x.numpy().flatten())
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y_train.append(y.numpy().flatten()[0])
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X_train = np.array(X_train)
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y_train = np.array(y_train)
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print(f"\n[TRAIN] Bắt đầu Training Ensemble trên {len(X_train)} samples...")
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# CPU Ensemble
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model1 = LinearRegression()
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model2 = RandomForestRegressor(n_estimators=50, random_state=42)
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ensemble = VotingRegressor([('lr', model1), ('rf', model2)])
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ensemble.fit(X_train, y_train)
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# Predict and calc error
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preds = ensemble.predict(X_train)
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mse = np.mean((preds - y_train)**2)
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model_dir = "ndvi_forecast_model"
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model_path = os.path.join(model_dir, "ndvi_ensemble_real.joblib")
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joblib.dump(ensemble, model_path)
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print(f"\n[SAVE] Model saved to {model_path}")
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with open(os.path.join(model_dir, "ndvi_ensemble_real_info.json"), "w") as f:
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json.dump({
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"model_type": "Multi-Model Ensemble (Real Data & CPU)",
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"target": "NDVI",
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"rmse": float(mse**0.5),
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"mae": float(np.mean(np.abs(preds - y_train)))
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}, f, indent=2)
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print("[SAVE] Model info saved.")
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