import os

import numpy as np

from sklearn.model_selection import train_test_split

from classifiers.ada_boost import AdaBoost

from classifiers.classification_model import ClassificationModel

from classifiers.decision_tree import DecisionTree

from classifiers.k_nearest import KNearest

from classifiers.naive_bayes import NaiveBayes

from classifiers.random_forest import RandomForest

from misc.excel_writer import ExcelWriter

from misc.input_parser import InputParser

from misc.constants import *

def model_output(classification_model: ClassificationModel, tune_start, tune_end):

excel_writer.edit_sheet(classification_model.report_dict)

if __name__ == '__main__':

if os.path.exists(WORK_BOOK_PATH):

os.remove(WORK_BOOK_PATH)

input_parser = InputParser()

data_tuple = input_parser.get_samples_and_labels()

samples, labels = np.array(data_tuple[0]), np.array(data_tuple[1])

training_samples, test_samples, training_labels, test_labels = train_test_split(samples, labels,

test_size=TEST_SIZE,

random_state=0)

model_output(NaiveBayes(training_samples, test_samples, training_labels, test_labels), None, None)

model_output(KNearest(training_samples, test_samples, training_labels, test_labels), MIN_N_NEIGHBOUR,

MAX_N_NEIGHBOUR)

model_output(RandomForest(training_samples, test_samples, training_labels, test_labels), MIN_N_ESTIMATE,

MAX_N_ESTIMATE)

model_output(DecisionTree(training_samples, test_samples, training_labels, test_labels), None, None)

model_output(AdaBoost(training_samples, test_samples, training_labels, test_labels), MIN_N_ESTIMATE, MAX_N_ESTIMATE)