import csv

import pandas
import sklearn
from sklearn import metrics, preprocessing
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier

from ClientParamsFactory import ClientParamsFactory


class DecisionTree:
    def __init__(self) -> None:
        super().__init__()

    def generate_data(self, generator: ClientParamsFactory, n:int):
        header = ['DELAY',
                  'PAYED',
                  'NET-WORTH',
                  'INFLUENCE',
                  'SKARBOWKA',
                  'MEMBER',
                  'HAT',
                  'SIZE']

        with open("data/TEST/generatedData.csv", 'w', newline='') as file:
            writer = csv.writer(file)

            writer.writerow(header)

            for i in range(n):
                data = generator.get_client_params()

                writer.writerow([data.payment_delay,
                                 data.payed,
                                 data.net_worth,
                                 data.infuence_rate,
                                 data.is_skarbowka,
                                 data.membership,
                                 data.is_hat,
                                 data.company_size])

        file.close()

    def get_normalized_data(self, X):
        label_BP = preprocessing.LabelEncoder()
        label_BP.fit(
            ['CompanySize.NO', 'CompanySize.SMALL', 'CompanySize.NORMAL', 'CompanySize.BIG', 'CompanySize.HUGE',
             'CompanySize.GIGANTISHE'])
        X[:, 7] = label_BP.transform(X[:, 7])

        return X

    def print_logs(self, x, y, prediction):
        for i in range(len(prediction)):
            print("{}. {} \n predicted: {}, actual: {}".format(i, x[i, :], prediction[i], y[i]))

        print("\nDecisionTrees's Accuracy: ", metrics.accuracy_score(y, prediction))




    def get_decision_tree(self) -> DecisionTreeClassifier:
        data_input = pandas.read_csv('data/TEST/importedData.csv', delimiter=",")

        X_headers = ['DELAY', 'PAYED', 'NET-WORTH', 'INFLUENCE', 'SKARBOWKA', 'MEMBER', 'HAT', 'SIZE']

        X = data_input[X_headers].values
        Y = data_input["PRIORITY"]

        X = self.get_normalized_data(X)

        X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, train_size=0.8)

        drugTree = DecisionTreeClassifier(criterion="entropy", max_depth=4)

        clf = drugTree.fit(X_train, y_train)
        predicted = drugTree.predict(X_test)

        y_test = y_test.to_list()

        self.print_logs(X_test, y_test, predicted)

        print(sklearn.tree.export_text(clf, feature_names=X_headers))

        return drugTree