"""Random forest surrogates.

Currently, the documentation for this surrogate is not available. This is due to a bug
in our documentation tool, see https://github.com/sphinx-doc/sphinx/issues/11750.

Since we plan to refactor the surrogates, this part of the documentation will be
available in the future. Thus, please have a look in the source code directly.
"""

from __future__ import annotations

from typing import TYPE_CHECKING, Any, ClassVar

import numpy as np
from attr import define, field
from sklearn.ensemble import RandomForestRegressor

from baybe.searchspace import SearchSpace
from baybe.surrogates.base import Surrogate
from baybe.surrogates.utils import autoscale, batchify, catch_constant_targets
from baybe.surrogates.validation import get_model_params_validator

if TYPE_CHECKING:
    from torch import Tensor


@catch_constant_targets
@autoscale
@define(slots=False)
class RandomForestSurrogate(Surrogate):
    """A random forest surrogate model."""

    # Class variables
    joint_posterior: ClassVar[bool] = False
    # See base class.

    supports_transfer_learning: ClassVar[bool] = False
    # See base class.

    # Object variables
    model_params: dict[str, Any] = field(
        factory=dict,
        converter=dict,
        validator=get_model_params_validator(RandomForestRegressor.__init__),
    )
    """Optional model parameter that will be passed to the surrogate constructor."""

    _model: RandomForestRegressor | None = field(init=False, default=None, eq=False)
    """The actual model."""

    @batchify
    def _posterior(self, candidates: Tensor) -> tuple[Tensor, Tensor]:
        # See base class.

        import torch

        # Evaluate all trees
        # NOTE: explicit conversion to ndarray is needed due to a pytorch issue:
        # https://github.com/pytorch/pytorch/pull/51731
        # https://github.com/pytorch/pytorch/issues/13918
        predictions = torch.from_numpy(
            np.asarray(
                [
                    self._model.estimators_[tree].predict(candidates)
                    for tree in range(self._model.n_estimators)
                ]
            )
        )

        # Compute posterior mean and variance
        mean = predictions.mean(dim=0)
        var = predictions.var(dim=0)

        return mean, var

    def _fit(self, searchspace: SearchSpace, train_x: Tensor, train_y: Tensor) -> None:
        # See base class.
        self._model = RandomForestRegressor(**(self.model_params))
        self._model.fit(train_x, train_y.ravel())
