#!/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 is an interface for the 'GradientBoostingClassifier' training model (Gradient Boosting Classifier).

"""

from typing import Dict, List, Any, TypeVar

from Stats.Stats import Stats

from sklearn import ensemble

PandasDataFrame = TypeVar('DataFrame')

SklearnGradientBoostingClassifier = TypeVar('GradientBoostingClassifier')

__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 _GradientBoostingClassifier(Stats):

    def __init__(self):

        """Initialise the objects and constants.

        """

        super(self.__class__, self).__init__()

        self._logger.debug(__name__)

        self._logger.debug("Run Gradient Boosting Classifier.")

    def train(self,

              features_indep_df: PandasDataFrame,

              feature_target: List,

              model_labals: List=[0, 1],

              **kwargs: Any) -> SklearnGradientBoostingClassifier:

        """Perform the training, using the Gradient Boosting Classifier.

        :param features_indep_df: the independent features, which are inputted into the model.

        :param feature_target: the target feature, which is being estimated.

        :param model_labals: the target labels (default [0, 1]).

        :param kwargs: loss='deviance', learning_rate=0.1, n_estimators=100, subsample=1.0, min_samples_split=30,

        min_samples_leaf=30, min_weight_fraction_leaf=0.0, max_depth=3, init=None, random_state=None,

        max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, presort='auto'

        :return: the trained model.

        """

        self._logger.debug("Train " + __name__)

        model_train = ensemble.GradientBoostingClassifier(**kwargs)

        model_train.fit(features_indep_df.values, feature_target)

        return model_train

    def train_summaries(self,

                        model_train: SklearnGradientBoostingClassifier) -> Dict:

        """Produce the training summary.

        :param model_train: the instance of the trained model.

        :return: the training summary.

        """

        self._logger.debug("Summarise " + __name__)

        summaries = dict()

        summaries['feature_importances_'] = model_train.feature_importances_

        summaries['train_score_'] = model_train.train_score_

        summaries['loss_'] = model_train.loss_

        summaries['init'] = model_train.init

        summaries['estimators_'] = model_train.estimators_

        return summaries

    def plot(self,

             model_train: SklearnGradientBoostingClassifier,

             feature_names: List,

             class_names: List=["True", "False"]):

        """Plot the tree diagram.

        :param model_train: the instance of the trained model.

        :param feature_names: the names of input features.

        :param class_names: the predicted class labels.

        :return: the model graph.

        """

        self._logger.debug("Plot " + __name__)

        # todo: plot

        pass