# main imports
import os
import sys
import argparse
import pandas as pd
import numpy as np
import logging

# model imports
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, VotingClassifier

import sklearn.svm as svm
from sklearn.utils import shuffle
from sklearn.externals import joblib
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import cross_val_score

# modules and config imports
sys.path.insert(0, '') # trick to enable import of main folder module

import custom_config as cfg
import models as mdl

from macop.algorithms.IteratedLocalSearch import IteratedLocalSearch as ILS
from macop.solutions.BinarySolution import BinarySolution

from macop.operators.mutators.SimpleMutation import SimpleMutation
from macop.operators.mutators.SimpleBinaryMutation import SimpleBinaryMutation
from macop.operators.crossovers.SimpleCrossover import SimpleCrossover
from macop.operators.crossovers.RandomSplitCrossover import RandomSplitCrossover

from macop.operators.policies.UCBPolicy import UCBPolicy

from macop.checkpoints.BasicCheckpoint import BasicCheckpoint
from macop.checkpoints.UCBCheckpoint import UCBCheckpoint

# variables and parameters
models_list         = cfg.models_names_list
number_of_values    = 26
ils_iteration       = 10000
ls_iteration        = 20

# default validator
def validator(solution):

    if list(solution.data).count(1) < 5:
        return False

    return True

# init solution (13 filters)
def init():
    return BinarySolution([], 13).random(validator)

def loadDataset(filename):

    ########################
    # 1. Get and prepare data
    ########################
    dataset_train = pd.read_csv(filename + '.train', header=None, sep=";")
    dataset_test = pd.read_csv(filename + '.test', header=None, sep=";")

    # default first shuffle of data
    dataset_train = shuffle(dataset_train)
    dataset_test = shuffle(dataset_test)

    # get dataset with equal number of classes occurences
    noisy_df_train = dataset_train[dataset_train.iloc[:, 0] == 1]
    not_noisy_df_train = dataset_train[dataset_train.iloc[:, 0] == 0]
    #nb_noisy_train = len(noisy_df_train.index)

    noisy_df_test = dataset_test[dataset_test.iloc[:, 0] == 1]
    not_noisy_df_test = dataset_test[dataset_test.iloc[:, 0] == 0]
    #nb_noisy_test = len(noisy_df_test.index)

    final_df_train = pd.concat([not_noisy_df_train, noisy_df_train])
    final_df_test = pd.concat([not_noisy_df_test, noisy_df_test])

    # shuffle data another time
    final_df_train = shuffle(final_df_train)
    final_df_test = shuffle(final_df_test)

    # use of the whole data set for training
    x_dataset_train = final_df_train.iloc[:,1:]
    x_dataset_test = final_df_test.iloc[:,1:]

    y_dataset_train = final_df_train.iloc[:,0]
    y_dataset_test = final_df_test.iloc[:,0]

    return x_dataset_train, y_dataset_train, x_dataset_test, y_dataset_test

def main():

    parser = argparse.ArgumentParser(description="Train and find best filters to use for model")

    parser.add_argument('--data', type=str, help='dataset filename prefix (without .train and .test)')
    parser.add_argument('--choice', type=str, help='model choice from list of choices', choices=models_list)

    args = parser.parse_args()

    p_data_file = args.data
    p_choice    = args.choice

    # load data from file
    x_train, y_train, x_test, y_test = loadDataset(p_data_file)

    # create `logs` folder if necessary
    if not os.path.exists(cfg.logs_folder):
        os.makedirs(cfg.logs_folder)

    logging.basicConfig(format='%(asctime)s %(message)s', filename='logs/%s.log' % p_data_file.split('/')[-1], level=logging.DEBUG)

    # define evaluate function here (need of data information)
    def evaluate(solution):

        # get indices of filters data to use (filters selection from solution)
        indices = []

        for index, value in enumerate(solution.data): 
            if value == 1: 
                indices.append(index*2) 
                indices.append(index*2+1) 

        # keep only selected filters from solution
        x_train_filters = x_train.iloc[:, indices]
        y_train_filters = y_train
        x_test_filters = x_test.iloc[:, indices]

        model = mdl.get_trained_model(p_choice, x_train_filters, y_train_filters)
        
        y_test_model = model.predict(x_test_filters)
        test_roc_auc = roc_auc_score(y_test, y_test_model)

        return test_roc_auc

    if not os.path.exists(cfg.backup_folder):
        os.makedirs(cfg.backup_folder)

    backup_file_path = os.path.join(cfg.backup_folder, p_data_file.split('/')[-1] + '.csv')
    ucb_backup_file_path = os.path.join(cfg.backup_folder, p_data_file.split('/')[-1] + '_ucbPolicy.csv')

    # prepare optimization algorithm
    operators = [SimpleBinaryMutation(), SimpleMutation(), SimpleCrossover(), RandomSplitCrossover()]
    policy = UCBPolicy(operators)

    algo = ILS(init, evaluate, operators, policy, validator, True)
    algo.addCallback(BasicCheckpoint(_every=1, _filepath=backup_file_path))
    algo.addCallback(UCBCheckpoint(_every=1, _filepath=ucb_backup_file_path))

    bestSol = algo.run(ils_iteration, ls_iteration)

    # print best solution found
    print("Found ", bestSol)

    # save model information into .csv file
    if not os.path.exists(cfg.results_information_folder):
        os.makedirs(cfg.results_information_folder)

    filename_path = os.path.join(cfg.results_information_folder, cfg.optimization_filters_result_filename)

    line_info = p_data_file + ';' + str(ils_iteration) + ';' + str(ls_iteration) + ';' + str(bestSol.data) + ';' + str(list(bestSol.data).count(1)) + ';' + str(bestSol.fitness())
    with open(filename_path, 'a') as f:
        f.write(line_info + '\n')
    
    print('Result saved into %s' % filename_path)


if __name__ == "__main__":
    main()