from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import numpy as np

pd.options.mode.chained_assignment = None

FILE_PATH = "loan.csv"
REQUIRED_COLUMNS = ['Credit_History', 'LoanAmount', 'ApplicantIncome']
TARGET_COLUMN = 'Loan_Status'


def print_classifier_info(feature_importance):
    feature_names = REQUIRED_COLUMNS
    embarked_score = feature_importance[-3:].sum()
    scores = np.append(feature_importance[:2], embarked_score)
    scores = map(lambda score: round(score, 2), scores)
    print(dict(zip(feature_names, scores)))


if __name__ == '__main__':
    data = pd.read_csv(FILE_PATH)

    X = data[REQUIRED_COLUMNS]
    y = data[TARGET_COLUMN]

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    classifier_tree = DecisionTreeClassifier(random_state=42)
    classifier_tree.fit(X_train, y_train)

    print_classifier_info(classifier_tree.feature_importances_)
    print("Оценка качества (задача классификации) - ", classifier_tree.score(X_test, y_test))