# -*- coding: utf-8 -*-

# @Time    : 2020/9/8 23:51
# @Email   : <EMAIL>
# @Software: PyCharm
# @License: BSD 3-Clause
# -*- coding: utf-8 -*-

"""This is one general method to calculate Efficient global optimization,
This one is with no restrictions on the type of X and model.

"""
import warnings

import numpy as np
import pandas as pd
from scipy import stats


class BaseEgo:
    """
    EGO (Efficient global optimization).

    References:
        <NAME>., <NAME>. & <NAME>.
        Efficient global optimization of expensive black-box functions. J.
        Global Optim. 13, 455–492 (1998)

    Examples:

        >>>me = BaseEgo()
        >>>result = me.rank(y=y, mean_std=mean_std)

    """

    def __init__(self):
        self.rank = self.egosearch

    @staticmethod
    def meanandstd(predict_y):
        """calculate meanandstd."""
        mean = np.mean(predict_y, axis=1)
        std = np.std(predict_y, axis=1)
        data_predict = np.column_stack((mean, std))
        print(data_predict.shape)
        return data_predict

    @staticmethod
    def CalculateEi(y, mean_std0, flexibility=0.0):

        """calculate EI."""
        ego = (mean_std0[:, 0] - (max(y) - flexibility)) / (mean_std0[:, 1])
        ei_ego = mean_std0[:, 1] * ego * stats.norm.cdf(ego) + mean_std0[:, 1] * stats.norm.pdf(ego)
        kg = (mean_std0[:, 0] - max(max(mean_std0[:, 0]), max(y) - flexibility)) / (mean_std0[:, 1])
        ei_kg = mean_std0[:, 1] * kg * stats.norm.cdf(kg) + mean_std0[:, 1] * stats.norm.pdf(kg)
        max_P = stats.norm.cdf(ego)
        ei = np.column_stack((mean_std0, ei_ego, ei_kg, max_P))
        print('Ego is done.')
        return ei

    def egosearch(self, y, mean_std, searchspace=None, rankway="ego", return_type="pd", flexibility=0, fraction=1000, ):
        """
        Result is 2 dimensions array.
        1st column = sequence number,\n
        2nd part = your search space,\n
        3rd part = mean,std,ego,kg,maxp,sequentially.

        Parameters
        ----------
        y: np.ndarray of shape (n_sample_train, 1)
            train y.
        mean_std: np.ndarray of shape (n_sample_pre, n_feature)
            mean_std of n times of prediction on search space.
            First column is mean and second is std.
        rankway : str
            ["ego","kg","maxp","No"]
            resort the result by rankway name.
        searchspace : np.ndarray of shape (n_sample_pre, n_feature)
            search space, the search space in BaseEgo is not used, just as one placeholder for corresponding .
        return_type: str
            "pd" or "np"
        fraction:int
            choice top n_sample/fraction.
        flexibility:float
            Flexibility to calculate EI, the bigger flexibility, the more search space Ei >0.
            make sure less than max(y) for max problem.

        Returns
        ----------
        table:np.ndarray (2d), pd.Dateframe

        """
        if flexibility !=0:
            warnings.warn("``Flexibility`` means reduction of y boundary, Please use it if you know what you are doing.")

        reverse = False
        if rankway not in ['ego', 'kg', 'maxp', 'no', 'No']:
            print('Don\'t kidding me,checking rankway=what?\a')
        else:

            result = self.CalculateEi(y, mean_std, flexibility=flexibility)
            bianhao = np.arange(0, len(result)).reshape(-1,1)
            if searchspace is None:
                searchspace=bianhao
            result1 = np.column_stack((bianhao, searchspace, result))

            # for all score: [-3,-2,-1] could check by one [-3]
            assert not np.all(result1[:, -3] <= 1e-10), "All the ego score is 0, This is invalid calculation. " \
                                                   "Please try these methods:\n" \
                                                   "1. Improve your model precision, especially near the expected scope for y. " \
                                                   "For example, for max proplem, the point near the maximum y should be accurate by model.\n" \
                                                   "2. Make sure your search space near the training space.\n" \
                                                   "3. If the above methods are still unable to solve, add flexibility to find point by reduction of y boundary. ()\n"

            if rankway == "ego":
                if reverse:
                    paixu = np.argsort(result1[:, -3])
                else:
                    paixu = np.argsort(-result1[:, -3])

            elif rankway == "kg":
                if reverse:
                    paixu = np.argsort(result1[:, -2])
                else:
                    paixu = np.argsort(-result1[:, -2])

            elif rankway == "maxp":
                if reverse:
                    paixu = np.argsort(result1[:, -1])
                else:
                    paixu = np.argsort(-result1[:, -1])

            else:
                paixu = bianhao

            if paixu.size >= fraction:
                select_number = paixu[:int(paixu.size / fraction)]
            else:
                print(
                    "search space grid number {} is smaller than fraction, the ``fraction`` parameter is ignored.".format(
                        paixu.size))
                select_number = paixu

            result1 = result1[select_number]

            if return_type != "pd":
                return result1
            else:
                result1 = pd.DataFrame(result1)
                fea = ["feature%d" % i for i in range(searchspace.shape[1])]
                mean_stds = ["mean_std%d" % i for i in range(mean_std.shape[1])]
                name = ["number"] + fea + mean_stds + ['ego', 'kg', 'maxp']
                result1.columns = name
                return result1

# -*- coding: utf-8 -*-

# @Time    : 2020/9/8 23:51
# @Email   : <EMAIL>
# @Software: PyCharm
# @License: BSD 3-Clause
# -*- coding: utf-8 -*-

"""This is one general method to calculate Efficient global optimization,
This one is with no restrictions on the type of X and model.

"""
import warnings

import numpy as np
import pandas as pd
from scipy import stats


class BaseEgo:
    """
    EGO (Efficient global optimization).

    References:
        <NAME>., <NAME>. & <NAME>.
        Efficient global optimization of expensive black-box functions. J.
        Global Optim. 13, 455–492 (1998)

    Examples:

        >>>me = BaseEgo()
        >>>result = me.rank(y=y, mean_std=mean_std)

    """

    def __init__(self):
        self.rank = self.egosearch

    @staticmethod
    def meanandstd(predict_y):
        """calculate meanandstd."""
        mean = np.mean(predict_y, axis=1)
        std = np.std(predict_y, axis=1)
        data_predict = np.column_stack((mean, std))
        print(data_predict.shape)
        return data_predict

    @staticmethod
    def CalculateEi(y, mean_std0, flexibility=0.0):

        """calculate EI."""
        ego = (mean_std0[:, 0] - (max(y) - flexibility)) / (mean_std0[:, 1])
        ei_ego = mean_std0[:, 1] * ego * stats.norm.cdf(ego) + mean_std0[:, 1] * stats.norm.pdf(ego)
        kg = (mean_std0[:, 0] - max(max(mean_std0[:, 0]), max(y) - flexibility)) / (mean_std0[:, 1])
        ei_kg = mean_std0[:, 1] * kg * stats.norm.cdf(kg) + mean_std0[:, 1] * stats.norm.pdf(kg)
        max_P = stats.norm.cdf(ego)
        ei = np.column_stack((mean_std0, ei_ego, ei_kg, max_P))
        print('Ego is done.')
        return ei

    def egosearch(self, y, mean_std, searchspace=None, rankway="ego", return_type="pd", flexibility=0, fraction=1000, ):
        """
        Result is 2 dimensions array.
        1st column = sequence number,\n
        2nd part = your search space,\n
        3rd part = mean,std,ego,kg,maxp,sequentially.

        Parameters
        ----------
        y: np.ndarray of shape (n_sample_train, 1)
            train y.
        mean_std: np.ndarray of shape (n_sample_pre, n_feature)
            mean_std of n times of prediction on search space.
            First column is mean and second is std.
        rankway : str
            ["ego","kg","maxp","No"]
            resort the result by rankway name.
        searchspace : np.ndarray of shape (n_sample_pre, n_feature)
            search space, the search space in BaseEgo is not used, just as one placeholder for corresponding .
        return_type: str
            "pd" or "np"
        fraction:int
            choice top n_sample/fraction.
        flexibility:float
            Flexibility to calculate EI, the bigger flexibility, the more search space Ei >0.
            make sure less than max(y) for max problem.

        Returns
        ----------
        table:np.ndarray (2d), pd.Dateframe

        """
        if flexibility !=0:
            warnings.warn("``Flexibility`` means reduction of y boundary, Please use it if you know what you are doing.")

        reverse = False
        if rankway not in ['ego', 'kg', 'maxp', 'no', 'No']:
            print('Don\'t kidding me,checking rankway=what?\a')
        else:

            result = self.CalculateEi(y, mean_std, flexibility=flexibility)
            bianhao = np.arange(0, len(result)).reshape(-1,1)
            if searchspace is None:
                searchspace=bianhao
            result1 = np.column_stack((bianhao, searchspace, result))

            # for all score: [-3,-2,-1] could check by one [-3]
            assert not np.all(result1[:, -3] <= 1e-10), "All the ego score is 0, This is invalid calculation. " \
                                                   "Please try these methods:\n" \
                                                   "1. Improve your model precision, especially near the expected scope for y. " \
                                                   "For example, for max proplem, the point near the maximum y should be accurate by model.\n" \
                                                   "2. Make sure your search space near the training space.\n" \
                                                   "3. If the above methods are still unable to solve, add flexibility to find point by reduction of y boundary. ()\n"

            if rankway == "ego":
                if reverse:
                    paixu = np.argsort(result1[:, -3])
                else:
                    paixu = np.argsort(-result1[:, -3])

            elif rankway == "kg":
                if reverse:
                    paixu = np.argsort(result1[:, -2])
                else:
                    paixu = np.argsort(-result1[:, -2])

            elif rankway == "maxp":
                if reverse:
                    paixu = np.argsort(result1[:, -1])
                else:
                    paixu = np.argsort(-result1[:, -1])

            else:
                paixu = bianhao

            if paixu.size >= fraction:
                select_number = paixu[:int(paixu.size / fraction)]
            else:
                print(
                    "search space grid number {} is smaller than fraction, the ``fraction`` parameter is ignored.".format(
                        paixu.size))
                select_number = paixu

            result1 = result1[select_number]

            if return_type != "pd":
                return result1
            else:
                result1 = pd.DataFrame(result1)
                fea = ["feature%d" % i for i in range(searchspace.shape[1])]
                mean_stds = ["mean_std%d" % i for i in range(mean_std.shape[1])]
                name = ["number"] + fea + mean_stds + ['ego', 'kg', 'maxp']
                result1.columns = name
                return result1

# -*- coding: utf-8 -*- # @Time    : 2020/9/8 23:51 # @Email   : <EMAIL> # @Software: PyCharm # @License: BSD 3-Clause # -*- coding: utf-8 -*- This is one general method to calculate Efficient global optimization, This one is with no restrictions on the type of X and model. EGO (Efficient global optimization).      References:         <NAME>., <NAME>. & <NAME>.         Efficient global optimization of expensive black-box functions. J.         Global Optim. 13, 455–492 (1998)      Examples:          >>>me = BaseEgo()         >>>result = me.rank(y=y, mean_std=mean_std) calculate meanandstd. calculate EI. Result is 2 dimensions array.         1st column = sequence number,\n         2nd part = your search space,\n         3rd part = mean,std,ego,kg,maxp,sequentially.          Parameters         ----------         y: np.ndarray of shape (n_sample_train, 1)             train y.         mean_std: np.ndarray of shape (n_sample_pre, n_feature)             mean_std of n times of prediction on search space.             First column is mean and second is std.         rankway : str             ["ego","kg","maxp","No"]             resort the result by rankway name.         searchspace : np.ndarray of shape (n_sample_pre, n_feature)             search space, the search space in BaseEgo is not used, just as one placeholder for corresponding .         return_type: str             "pd" or "np"         fraction:int             choice top n_sample/fraction.         flexibility:float             Flexibility to calculate EI, the bigger flexibility, the more search space Ei >0.             make sure less than max(y) for max problem.          Returns         ----------         table:np.ndarray (2d), pd.Dateframe # for all score: [-3,-2,-1] could check by one [-3]