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| import numpy as np | |
| import torch | |
| # Menggunakan ChronosPipeline untuk pemuatan dan inferensi yang efisien | |
| from chronos import ChronosPipeline | |
| class ModelHandler: | |
| def __init__(self): | |
| # Mengganti model lama dengan Chronos-2 yang lebih canggih | |
| self.model_name = "amazon/chronos-2" | |
| self.pipeline = None | |
| # Penentuan device: "cuda" jika ada GPU, jika tidak "cpu" | |
| self.device = "cuda" if torch.cuda.is_available() else "cpu" | |
| self.load_model() | |
| def load_model(self): | |
| """Load Chronos-2 model optimized for CPU/GPU""" | |
| try: | |
| print(f"Loading {self.model_name} on {self.device}...") | |
| # ChronosPipeline menangani semua proses tokenisasi dan pemuatan arsitektur | |
| self.pipeline = ChronosPipeline.from_pretrained( | |
| self.model_name, | |
| device_map=self.device, | |
| ) | |
| print("Chronos-2 pipeline loaded successfully.") | |
| except Exception as e: | |
| print(f"Error loading Chronos-2 model: {e}") | |
| print("Using fallback prediction method") | |
| self.pipeline = None | |
| def predict(self, data, horizon=10): | |
| """Generate predictions using Chronos-2 or fallback""" | |
| try: | |
| # Menggunakan data['original'] yang merupakan harga aktual riil | |
| if data is None or len(data['original']) < 20: | |
| return np.array([0] * horizon) | |
| if self.pipeline is None: | |
| # --- Fallback Logic --- | |
| # Logic ekstrapolasi tren lama tetap dipertahankan jika model Deep Learning gagal dimuat | |
| values = data['original'] | |
| recent_trend = np.polyfit(range(len(values[-20:])), values[-20:], 1)[0] | |
| predictions = [] | |
| last_value = values[-1] | |
| for i in range(horizon): | |
| next_value = last_value + recent_trend * (i + 1) | |
| noise = np.random.normal(0, data['std'] * 0.1) | |
| predictions.append(next_value + noise) | |
| return np.array(predictions) | |
| # --- Chronos-2 Inference --- | |
| # Input: numpy array dari harga Close historis yang riil | |
| predictions_samples = self.pipeline.predict( | |
| data['original'], | |
| prediction_length=horizon, | |
| # Mengambil 20 sampel prediksi untuk mendapatkan prediksi probablistik | |
| num_samples=20 | |
| ) | |
| # Untuk chart (garis tunggal), ambil nilai rata-rata (mean) dari semua sampel. | |
| mean_predictions = np.mean(predictions_samples, axis=0) | |
| return mean_predictions | |
| except Exception as e: | |
| print(f"Prediction error: {e}") | |
| # Mengembalikan array nol jika ada error saat inferensi Chronos | |
| return np.array([0] * horizon) |