"""Feature: Ratio computation engine (scaffold).

Defines `RatioEngine` to compute feature ratios from peak/intensity columns.
This is a scaffold with method signatures and docstrings; algorithms and
robust handling are intentionally left TODO.
"""

from __future__ import annotations

from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Tuple

import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator, TransformerMixin

try:
    from foodspec.features.rq import RatioDefinition
except Exception:  # pragma: no cover - scaffold fallback

    @dataclass
    class RatioDefinition:  # minimal placeholder for type hints
        name: str
        numerator: str
        denominator: str


@dataclass
class RatioEngine:
    """Compute ratio features from an input DataFrame.

    Parameters
    ----------
    ratio_defs : List[RatioDefinition]
        Definitions of ratios to compute (name, numerator, denominator).
    safe_division : bool
        If True, apply safeguards against division by zero (TODO).

    Methods
    -------
    fit(df)
        Placeholder for any learning/calibration on training data.
    transform(df)
        Return a copy of df with ratio columns added (currently NaNs placeholder).
    validate()
        Check for column presence and definition validity.
    to_dict()
        JSON-friendly representation of the configuration.
    __hash__()
        Hash of configuration for reproducibility.
    """

    ratio_defs: List[RatioDefinition] = field(default_factory=list)
    safe_division: bool = True
    name: str = "ratio_engine"

    def fit(self, df: pd.DataFrame) -> "RatioEngine":
        """Placeholder fit step.

        Parameters
        ----------
        df : pd.DataFrame
            Training data with required numerator/denominator columns.

        Returns
        -------
        RatioEngine
            Self, for chaining.

        Notes
        -----
        This scaffold does not learn parameters. Future work could calibrate
        scaling or apply stabilization strategies.
        """
        # TODO: Implement optional calibration logic
        return self

    def transform(self, df: pd.DataFrame) -> pd.DataFrame:
        """Compute ratio features (placeholder).

        Parameters
        ----------
        df : pd.DataFrame
            Input data containing numerator/denominator columns.

        Returns
        -------
        pd.DataFrame
            Copy of df with new ratio columns (NaN placeholders).

        Notes
        -----
        Actual ratio computation is deferred. Column existence is checked in
        `validate()`; this method currently adds NaN columns as placeholders.
        """
        out = df.copy()
        for r in self.ratio_defs:
            # TODO: Replace placeholder with actual ratio computation and safeguards
            out[r.name] = pd.NA
        return out

    def validate(self) -> Dict[str, Any]:
        """Validate ratio definitions against DataFrame columns.

        Returns
        -------
        Dict[str, Any]
            Validation report with 'ok' and 'issues'.
        """
        issues: List[str] = []
        for r in self.ratio_defs:
            if not r.name:
                issues.append("ratio name missing")
            if not r.numerator or not r.denominator:
                issues.append(f"ratio '{r.name}' missing numerator/denominator")
            if r.numerator == r.denominator:
                issues.append(f"ratio '{r.name}' has identical numerator/denominator")
        return {"ok": len(issues) == 0, "issues": issues}

    def to_dict(self) -> Dict[str, Any]:
        """Convert engine configuration to dict."""
        return {
            "name": self.name,
            "safe_division": self.safe_division,
            "ratio_defs": [
                {"name": r.name, "numerator": r.numerator, "denominator": r.denominator} for r in self.ratio_defs
            ],
        }

    def __hash__(self) -> int:
        """Stable hash based on ratio definitions and flags."""
        key = (self.name, self.safe_division, tuple((r.name, r.numerator, r.denominator) for r in self.ratio_defs))
        return hash(key)


"""Ratio feature utilities."""

__all__ = ["RatioEngine", "RatioDefinition", "compute_ratios", "RatioFeatureGenerator"]


def compute_ratios(df: pd.DataFrame, ratio_def: Dict[str, Tuple[str, str]]) -> pd.DataFrame:
    """Compute ratios of specified columns.

    Parameters
    ----------
    df :
        DataFrame containing numerator and denominator columns.
    ratio_def :
        Mapping from new column name to (numerator_col, denominator_col).

    Returns
    -------
    pandas.DataFrame
        Original DataFrame with additional ratio columns.
    """

    result = df.copy()
    for name, (num_col, denom_col) in ratio_def.items():
        if num_col not in result.columns or denom_col not in result.columns:
            raise ValueError(f"Columns {num_col} and {denom_col} must exist in DataFrame.")
        denom = result[denom_col].to_numpy()
        num = result[num_col].to_numpy()
        ratio = np.divide(num, denom, out=np.full_like(num, np.nan, dtype=float), where=denom != 0)
        result[name] = ratio
    return result


class RatioFeatureGenerator(BaseEstimator, TransformerMixin):
    """Generate ratio features for use in pipelines."""

    def __init__(self, ratio_def: Dict[str, Tuple[str, str]]):
        self.ratio_def = ratio_def

    def fit(self, X: pd.DataFrame, y: Optional[np.ndarray] = None) -> "RatioFeatureGenerator":
        return self

    def transform(self, X: pd.DataFrame) -> pd.DataFrame:
        if not isinstance(X, pd.DataFrame):
            raise ValueError("RatioFeatureGenerator expects a pandas DataFrame.")
        return compute_ratios(X, self.ratio_def)
