#!/usr/bin/env python

# -*- coding: UTF-8 -*-

#

# Copyright 2017 University of Westminster. All Rights Reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

# ==============================================================================

""" It applies feature factorisation (categorisation) using independent threads.

"""

from typing import Dict, TypeVar

from sklearn import preprocessing

import pandas as pd

PandasDataFrame = TypeVar('DataFrame')

__author__ = "Mohsen Mesgarpour"

__copyright__ = "Copyright 2016, https://github.com/mesgarpour"

__credits__ = ["Mohsen Mesgarpour"]

__license__ = "GPL"

__version__ = "1.1"

__maintainer__ = "Mohsen Mesgarpour"

__email__ = "mohsen.mesgarpour@gmail.com"

__status__ = "Release"

class FactoringThread:

    def __init__(self,

                 df: PandasDataFrame,

                 categories_dic: Dict,

                 labels_dic: Dict):

        """Initialise the objects and constants.

        :param df: the inputted dataframe to process.

        :param categories_dic: the categorisation dictionary.

        :param labels_dic: the name of the new features.

        """

        self.__df = df

        self.__categories_dic = categories_dic

        self.__labels_dic = labels_dic

    def factor_arr_multiple(self,

                            label_group: str) -> PandasDataFrame:

        """Categorise multiple features.

        :param label_group: the names of features to be categorised.

        :return: the categorised features.

        """

        labels_encoded = list(self.__categories_dic[label_group].keys())

        df_encoded = self.__factor_arr(labels_encoded[0], label_group)

        if len(labels_encoded) > 1:

            for label in labels_encoded[1:]:

                df_encoded = df_encoded.add(self.__factor_arr(label, label_group))

        return df_encoded

    def factor_arr(self,

                   label: str) -> PandasDataFrame:

        """Categorise a single feature.

        :param label: the name of the feature to be categorised.

        :return: the categorised feature.

        """

        df_encoded = preprocessing.label_binarize(self.__df[label], classes=self.__categories_dic[label])

        df_encoded = pd.DataFrame(df_encoded, columns=self.__labels_dic[label])

        return df_encoded

    def __factor_arr(self,

                     label: str,

                     label_group: str) -> PandasDataFrame:

        """Categorise a list using the 'preprocessing.label_binarize'.

        :param label: the name of the feature to be categorised.

        :param label_group: the name of the feature group in the categorisation dictionary.

        :return: the categorised feature.

        """

        df_encoded = preprocessing.label_binarize(self.__df[label], classes=self.__categories_dic[label_group][label])

        df_encoded = pd.DataFrame(df_encoded, columns=self.__labels_dic[label_group])

        return df_encoded