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'''
gp_tuner.py
'''

import warnings
import logging
import numpy as np

from sklearn.gaussian_process.kernels import Matern
from sklearn.gaussian_process import GaussianProcessRegressor

from nni.tuner import Tuner
from nni.utils import extract_scalar_reward

from .target_space import TargetSpace
from .util import UtilityFunction, acq_max

logger = logging.getLogger("GP_Tuner_AutoML")


class GPTuner(Tuner):
    '''
    GPTuner
    '''

    def __init__(self, optimize_mode="maximize", utility='ei', kappa=5, xi=0, nu=2.5, alpha=1e-6, cold_start_num=10,
                 selection_num_warm_up=100000, selection_num_starting_points=250):
        self.optimize_mode = optimize_mode

        # utility function related
        self.utility = utility
        self.kappa = kappa
        self.xi = xi

        # target space
        self._space = None

        self._random_state = np.random.RandomState()

        # nu, alpha are GPR related params
        self._gp = GaussianProcessRegressor(
            kernel=Matern(nu=nu),
            alpha=alpha,
            normalize_y=True,
            n_restarts_optimizer=25,
            random_state=self._random_state
        )
        # num of random evaluations before GPR
        self._cold_start_num = cold_start_num

        # params for acq_max
        self._selection_num_warm_up = selection_num_warm_up
        self._selection_num_starting_points = selection_num_starting_points

        # num of imported data
        self.supplement_data_num = 0

    def update_search_space(self, search_space):
        """Update the self.bounds and self.types by the search_space.json

        Parameters
        ----------
        search_space : dict
        """
        self._space = TargetSpace(search_space, self._random_state)

    def generate_parameters(self, parameter_id):
        """Generate next parameter for trial
        If the number of trial result is lower than cold start number,
        gp will first randomly generate some parameters.
        Otherwise, choose the parameters by the Gussian Process Model

        Parameters
        ----------
        parameter_id : int

        Returns
        -------
        result : dict
        """
        if self._space.len() < self._cold_start_num:
            results = self._space.random_sample()
        else:
            # Sklearn's GP throws a large number of warnings at times, but
            # we don't really need to see them here.
            with warnings.catch_warnings():
                warnings.simplefilter("ignore")
                self._gp.fit(self._space.params, self._space.target)

            util = UtilityFunction(
                kind=self.utility, kappa=self.kappa, xi=self.xi)

            results = acq_max(
                f_acq=util.utility,
                gp=self._gp,
                y_max=self._space.target.max(),
                bounds=self._space.bounds,
                space=self._space,
                num_warmup=self._selection_num_warm_up,
                num_starting_points=self._selection_num_starting_points
            )

        results = self._space.array_to_params(results)
        logger.info("Generate paramageters:\n %s", results)
        return results

    def receive_trial_result(self, parameter_id, parameters, value):
        """Tuner receive result from trial.

        Parameters
        ----------
        parameter_id : int
        parameters : dict
        value : dict/float
            if value is dict, it should have "default" key.
        """
        value = extract_scalar_reward(value)
        if self.optimize_mode == 'minimize':
            value = -value

        logger.info("Received trial result.")
        logger.info("value :%s", value)
        logger.info("parameter : %s", parameters)
        self._space.register(parameters, value)

    def import_data(self, data):
        """Import additional data for tuning
        Parameters
        ----------
        data:
            a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
        """
        _completed_num = 0
        for trial_info in data:
            logger.info("Importing data, current processing progress %s / %s" %
                        (_completed_num, len(data)))
            _completed_num += 1
            assert "parameter" in trial_info
            _params = trial_info["parameter"]
            assert "value" in trial_info
            _value = trial_info['value']
            if not _value:
                logger.info(
                    "Useless trial data, value is %s, skip this trial data." % _value)
                continue
            self.supplement_data_num += 1
            _parameter_id = '_'.join(
                ["ImportData", str(self.supplement_data_num)])
            self.receive_trial_result(
                parameter_id=_parameter_id, parameters=_params, value=_value)
        logger.info("Successfully import data to GP tuner.")
