"""Ensemble model combining Elo ratings with ML predictions.

The EnsembleModel takes Elo-based probabilities from the rating engine
and blends them with additional ML predictions (from feature-engineered
models) to produce a final probability estimate.
"""

from __future__ import annotations

from typing import Any

from sqlalchemy.orm import Session


class EnsembleModel:
    """Combines Elo ratings with ML predictions for improved accuracy.

    Supports multiple blending strategies:
    - Weighted average (fixed weights learned offline)
    - Logistic calibration (train a logistic regression on top of
      Elo + ML features)
    - Stacking (use ML predictions as features for a meta-model)
    """

    def __init__(
        self,
        session: Session,
        blend_mode: str = "weighted",
    ) -> None:
        """Initialize the ensemble model.

        Args:
            session: Database session for loading data.
            blend_mode: Blending strategy — "weighted", "logistic",
                or "stacking".
        """
        self._session = session
        self.blend_mode = blend_mode
        self._weights: dict[str, float] = {}

    def train(
        self,
        date_from: str,
        date_to: str,
    ) -> dict[str, Any]:
        """Train the ensemble (e.g., learn blend weights).

        Args:
            date_from: Start date string (YYYY-MM-DD).
            date_to: End date string (YYYY-MM-DD).

        Returns:
            Training metrics dict.
        """
        raise NotImplementedError

    def predict(
        self,
        race_id: int,
    ) -> list[dict[str, Any]]:
        """Predict win/place probabilities for all starters in a race.

        Combines Elo ratings with ML feature predictions.

        Args:
            race_id: The race ID.

        Returns:
            List of prediction dicts, one per starter, with keys:
            - starter_id: int
            - win_probability: float
            - place_probability: float
            - elo_win_prob: float
            - ml_win_prob: float (if applicable)
        """
        raise NotImplementedError

    def evaluate(
        self,
        date_from: str,
        date_to: str,
    ) -> dict[str, Any]:
        """Evaluate ensemble performance on a test window.

        Args:
            date_from: Start date string (YYYY-MM-DD).
            date_to: End date string (YYYY-MM-DD).

        Returns:
            Dict of evaluation metrics (accuracy, Brier score, log loss,
            top-3 rate, etc.).
        """
        raise NotImplementedError

    def save_weights(self, path: str) -> None:
        """Persist learned blend weights to disk.

        Args:
            path: File path to save weights to (JSON format).
        """
        raise NotImplementedError

    def load_weights(self, path: str) -> None:
        """Load learned blend weights from disk.

        Args:
            path: File path to load weights from (JSON format).
        """
        raise NotImplementedError