kbt-devops
fe76bc7629
Implements FastAPI backend with ML model support for energy trading, including price prediction models and RL-based battery trading policy. Features dashboard, trading, backtest, and settings API routes with WebSocket support for real-time updates.
87 lines
2.8 KiB
Python
87 lines
2.8 KiB
Python
from typing import Dict, Optional
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import pandas as pd
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import numpy as np
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from app.ml.price_prediction.model import PricePredictionModel
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from app.ml.price_prediction.trainer import PricePredictionTrainer
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from app.utils.logger import get_logger
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logger = get_logger(__name__)
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class PricePredictor:
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def __init__(self, models_dir: str = "models/price_prediction"):
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self.models_dir = models_dir
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self.models: Dict[int, PricePredictionModel] = {}
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self._load_models()
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def _load_models(self):
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self.models = PricePredictionTrainer.load_models(self.models_dir)
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logger.info(f"Loaded {len(self.models)} prediction models")
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def predict(
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self, current_data: pd.DataFrame, horizon: int = 15, region: Optional[str] = None
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) -> float:
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if horizon not in self.models:
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raise ValueError(f"No model available for horizon {horizon}")
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model = self.models[horizon]
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from app.ml.features import build_price_features
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df_features = build_price_features(current_data)
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feature_cols = [col for col in df_features.columns if col not in ["timestamp", "region", "day_ahead_price", "real_time_price"]]
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if region and "region" in df_features.columns:
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df_features = df_features[df_features["region"] == region]
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latest_row = df_features.iloc[-1:][feature_cols]
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prediction = model.predict(latest_row.values)
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return float(prediction[0])
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def predict_all_horizons(self, current_data: pd.DataFrame, region: Optional[str] = None) -> Dict[int, float]:
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predictions = {}
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for horizon in sorted(self.models.keys()):
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try:
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pred = self.predict(current_data, horizon, region)
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predictions[horizon] = pred
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except Exception as e:
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logger.error(f"Failed to predict for horizon {horizon}: {e}")
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predictions[horizon] = None
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return predictions
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def predict_with_confidence(
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self, current_data: pd.DataFrame, horizon: int = 15, region: Optional[str] = None
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) -> Dict:
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prediction = self.predict(current_data, horizon, region)
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return {
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"prediction": prediction,
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"confidence_lower": prediction * 0.95,
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"confidence_upper": prediction * 1.05,
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"horizon": horizon,
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}
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def get_feature_importance(self, horizon: int) -> pd.DataFrame:
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if horizon not in self.models:
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raise ValueError(f"No model available for horizon {horizon}")
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model = self.models[horizon]
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importances = model.feature_importances_
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feature_names = model.feature_names
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df = pd.DataFrame({
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"feature": feature_names,
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"importance": importances,
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}).sort_values("importance", ascending=False)
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return df
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__all__ = ["PricePredictor"]
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