"""
pharmacoml.automl — Automated Population Model Building (v1.0 scaffold)

This module will provide automated model-search workflows such as:
- Generate candidate models (structural + covariate combinations)
- Estimate each model using a pluggable backend (nlmixr2, Pharmpy, NONMEM)
- Rank models by fitness (BIC, AIC, custom penalty)
- Return the best model(s) with full diagnostics

STATUS: Architecture scaffolded. Implementation requires estimation backend
integration (nlmixr2 via rpy2 or Pharmpy). Target: v1.0.

Usage (planned API):
    from pharmacoml.automl import ModelSearch
    search = ModelSearch(
        data="pk_data.csv",
        backend="nlmixr2",          # or "nonmem", "pharmpy"
        structural_options=["1cmt", "2cmt"],
        absorption_options=["fo", "lag", "transit"],
        covariate_candidates={"CL": ["WT", "AGE", "CRCL"], "V": ["WT"]},
        covariate_forms={"WT": "power", "AGE": "linear"},
        error_models=["proportional", "combined"],
        n_models_max=500,
        fitness="bic",
        algorithm="genetic",         # or "pso", "bayesian", "exhaustive"
        n_parallel=4,
    )
    best = search.run()
    best.summary()
    best.diagnostics()
    best.vpc()
    best.to_nonmem()
"""

from __future__ import annotations

from dataclasses import dataclass, field
from typing import Literal


@dataclass
class ModelSearchConfig:
    """Configuration for automated model search.

    Defines the search space and estimation settings.
    """
    # Data
    data_path: str = ""

    # Estimation backend
    backend: Literal["nlmixr2", "nonmem", "pharmpy"] = "nlmixr2"
    backend_path: str | None = None  # path to NONMEM executable if needed

    # Structural model options
    structural_options: list[str] = field(default_factory=lambda: ["1cmt", "2cmt"])
    absorption_options: list[str] = field(default_factory=lambda: ["fo", "fo_lag"])
    elimination_options: list[str] = field(default_factory=lambda: ["linear"])

    # BSV options
    bsv_diagonal: bool = True
    bsv_full_block: bool = False

    # Covariate search space
    covariate_candidates: dict[str, list[str]] = field(default_factory=dict)
    covariate_forms: dict[str, str] = field(default_factory=dict)

    # Error model
    error_models: list[str] = field(default_factory=lambda: ["proportional", "combined"])

    # Search algorithm
    algorithm: Literal["genetic", "pso", "bayesian", "exhaustive"] = "genetic"
    n_models_max: int = 500
    fitness: Literal["bic", "aic", "custom"] = "bic"
    custom_penalty: callable | None = None

    # Execution
    n_parallel: int = 4
    timeout_per_model: int = 600  # seconds

    @property
    def search_space_size(self) -> int:
        """Estimate total number of candidate models."""
        n_struct = len(self.structural_options) * len(self.absorption_options)
        n_error = len(self.error_models)
        # Each covariate can be included or not for each parameter
        n_cov = 1
        for param, covs in self.covariate_candidates.items():
            n_cov *= 2 ** len(covs)
        return n_struct * n_error * n_cov


@dataclass
class ModelCandidate:
    """A single candidate model in the search space."""
    model_id: int
    structural: str          # e.g., "2cmt"
    absorption: str          # e.g., "fo_lag"
    error_model: str         # e.g., "combined"
    covariates: dict[str, list[str]]  # {param: [cov1, cov2]}
    covariate_forms: dict[str, str]   # {cov: "power"}

    # Results (filled after estimation)
    ofv: float | None = None
    aic: float | None = None
    bic: float | None = None
    n_parameters: int | None = None
    converged: bool | None = None
    condition_number: float | None = None
    estimation_time: float | None = None


class ModelSearch:
    """Automated population model search engine.

    WARNING: This is a v1.0 scaffold. The run() method is not yet implemented.
    Estimation backend integration (nlmixr2/NONMEM/Pharmpy) is required.
    """

    def __init__(self, config: ModelSearchConfig | None = None, **kwargs):
        if config is None:
            config = ModelSearchConfig(**kwargs)
        self.config = config
        self._candidates: list[ModelCandidate] = []
        self._results: list[ModelCandidate] = []

    def generate_candidates(self) -> list[ModelCandidate]:
        """Generate all candidate models from the search space.

        Returns list of ModelCandidate objects (not yet estimated).
        """
        raise NotImplementedError(
            "ModelSearch.generate_candidates() is scaffolded for v1.0. "
            "Estimation backend integration is required. "
            "For covariate screening without model estimation, "
            "use pharmacoml.covselect.CovariateScreener or "
            "pharmacoml.covselect.EnsembleScreener."
        )

    def run(self) -> "ModelSearchResults":
        """Execute the automated model search.

        This will:
        1. Generate candidate models
        2. Estimate each using the configured backend
        3. Rank by fitness
        4. Return results with diagnostics
        """
        raise NotImplementedError(
            "ModelSearch.run() is scaffolded for v1.0. "
            "Target implementation requires:\n"
            "  - nlmixr2 via rpy2 (for R-based estimation)\n"
            "  - Pharmpy integration (for NONMEM model generation)\n"
            "  - Parallel execution framework\n\n"
            "For now, use pharmacoml.covselect to identify covariates, "
            "then build your model manually in NONMEM/nlmixr2."
        )

    @property
    def search_space_size(self) -> int:
        return self.config.search_space_size


class ModelSearchResults:
    """Results from an automated model search (v1.0 scaffold)."""

    def __init__(self, candidates: list[ModelCandidate], fitness_metric: str):
        self._candidates = candidates
        self._fitness_metric = fitness_metric

    def best_model(self) -> ModelCandidate:
        raise NotImplementedError("v1.0 scaffold")

    def top_n(self, n: int = 5) -> list[ModelCandidate]:
        raise NotImplementedError("v1.0 scaffold")

    def summary(self):
        raise NotImplementedError("v1.0 scaffold")

    def diagnostics(self):
        raise NotImplementedError("v1.0 scaffold")
