#!/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

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

""" It is an interface for the 'LogisticRegressionCV' training model (Logistic Regression with Cross-Validation).

"""

from typing import Dict, List, Any, TypeVar

from Stats.Stats import Stats

from sklearn import linear_model

PandasDataFrame = TypeVar('DataFrame')

SklearnLogisticRegressionCV = TypeVar('LogisticRegressionCV')

__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 _LogisticRegressionCV(Stats):

    def __init__(self):

        """Initialise the objects and constants.

        """

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

        self._logger.debug(__name__)

        self._logger.debug("Run Logistic Regression with Cross-Validation.")

    def train(self,

              features_indep_df: PandasDataFrame,

              feature_target: List,

              model_labals: List=[0, 1],

              **kwargs: Any) -> SklearnLogisticRegressionCV:

        """Perform the training, using the Logistic Regression with Cross-Validation.

        :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: Cs=10, fit_intercept=True, cv=None, dual=False, penalty='l2', scoring=None, solver='lbfgs',

        tol=0.0001, max_iter=100, class_weight=None, n_jobs=-1, verbose=0, refit=True, intercept_scaling=1.0,

        multi_class='ovr', random_state=None

        :return: the trained model.

        """

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

        model_train = linear_model.LogisticRegressionCV(**kwargs)

        model_train.fit(features_indep_df, feature_target)

        return model_train

    def train_summaries(self,

                        model_train: SklearnLogisticRegressionCV) -> 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()

        # Coefficient of the features in the decision function.

        summaries["coef_"] = model_train.coef_

        # Intercept (a.k.a. bias) added to the decision function.

        summaries["intercept_"] = model_train.intercept_

        # Actual number of iterations for all classes. If binary or multinomial, it returns only 1 element.

        summaries["n_iter_"] = model_train.n_iter_

        return summaries

    def plot(self,

             model_train: SklearnLogisticRegressionCV,

             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