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# coding: utf-8
# pylint: disable=too-many-arguments, too-many-locals, invalid-name, fixme, E0012, R0912
"""Scikit-Learn Wrapper interface for XGBoost."""
from __future__ import absolute_import

import numpy as np
import warnings
from .core import Booster, DMatrix, XGBoostError
from .training import train

# Do not use class names on scikit-learn directly.
# Re-define the classes on .compat to guarantee the behavior without scikit-learn
from .compat import (SKLEARN_INSTALLED, XGBModelBase,
                     XGBClassifierBase, XGBRegressorBase, XGBLabelEncoder)

warnings.simplefilter('always', DeprecationWarning)


def _objective_decorator(func):
    """Decorate an objective function

    Converts an objective function using the typical sklearn metrics
    signature so that it is usable with ``xgboost.training.train``

    Parameters
    ----------
    func: callable
        Expects a callable with signature ``func(y_true, y_pred)``:

        y_true: array_like of shape [n_samples]
            The target values
        y_pred: array_like of shape [n_samples]
            The predicted values

    Returns
    -------
    new_func: callable
        The new objective function as expected by ``xgboost.training.train``.
        The signature is ``new_func(preds, dmatrix)``:

        preds: array_like, shape [n_samples]
            The predicted values
        dmatrix: ``DMatrix``
            The training set from which the labels will be extracted using
            ``dmatrix.get_label()``
    """
    def inner(preds, dmatrix):
        """internal function"""
        labels = dmatrix.get_label()
        return func(labels, preds)
    return inner


class XGBModel(XGBModelBase):
    # pylint: disable=too-many-arguments, too-many-instance-attributes, invalid-name
    """Implementation of the Scikit-Learn API for XGBoost.

    Parameters
    ----------
    max_depth : int
        Maximum tree depth for base learners.