from scipy.io import savemat

from config import config
from ensemble import run
from SimpleClassifiers.sklearnclassifier import try_sklearn_classifiers, cross_validate_kNN, cross_validate_RFC, \
    cross_validate_SVC
import numpy as np
import scipy
from utils.utils import select_best_model, comparison_plot_loss, comparison_plot_accuracy
from utils import IOHelper
from scipy import io
import h5py
import logging
import time

from kerasTuner import tune
from utils import IOHelper


import sys

class Tee(object):
    def __init__(self, *files):
        self.files = files
    def write(self, obj):
        for f in self.files:
            f.write(obj)
            f.flush() # If you want the output to be visible immediately
    def flush(self) :
        for f in self.files:
            f.flush()

def main():
    # For Logging the data
    logging.basicConfig(filename=config['info_log'], level=logging.INFO)
    logging.info('Started the Logging')

    # For being able to see progress that some methods use verbose
    f = open(config['model_dir'] + '/console.out', 'w')
    original = sys.stdout
    sys.stdout = Tee(sys.stdout, f)

    # Start of the main program
    start_time = time.time()
    # try:
    trainX, trainY = IOHelper.get_mat_data(config['data_dir'], verbose=True)
    print(trainX.shape)
    print(trainY.shape)

    if config['model'] == 'simple_classifier':
        try_sklearn_classifiers(trainX, trainY)
        # cross_validate_kNN(trainX, trainY)
        # cross_validate_SVC(trainX, trainY)
        cross_validate_RFC(trainX, trainY)
    else:
        # tune(trainX,trainY)
        run(trainX,trainY)
        # comparison_plot_accuracy()
        # comparison_plot_loss()

    logging.info("--- Runtime: %s seconds ---" % (time.time() - start_time))
    logging.info('Finished Logging')

if __name__=='__main__':
    main()