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 = "WindData.csv"
REQUIRED_COLUMNS = ['TI1', 'V1']
TARGET_COLUMN_1 = 'TurbulenceIntensityClassA'
TARGET_COLUMN_2 = 'TurbulenceIntensityClassB'
TARGET_COLUMN_3 = 'TurbulenceIntensityClassC'


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)))


def actions(target_column):
    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=0.5, random_state=100)

    classifier_tree = DecisionTreeClassifier(random_state=100)
    classifier_tree.fit(X_train, y_train)

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


if __name__ == '__main__':
    actions(TARGET_COLUMN_1)
    actions(TARGET_COLUMN_2)
    actions(TARGET_COLUMN_3)