import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn
from sklearn.linear_model import LinearRegression, SGDRegressor, Ridge


from sklearn.preprocessing import (LabelEncoder,
                                   StandardScaler,
                                   MinMaxScaler,
                                   RobustScaler)
from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold, learning_curve, ShuffleSplit


def str_features_to_numeric(data):
    # Преобразовывает все строковые признаки в числовые.

    # Определение категориальных признаков
    categorical_columns = []
    numerics = ['int8', 'int16', 'int32', 'int64', 'float16', 'float32', 'float64']
    features = data.columns.values.tolist()
    for col in features:
        if data[col].dtype in numerics: continue
        categorical_columns.append(col)

    # Кодирование категориальных признаков
    for col in categorical_columns:
        if col in data.columns:
            le = LabelEncoder()
            le.fit(list(data[col].astype(str).values))
            data[col] = le.transform(list(data[col].astype(str).values))

    return data


if __name__ == "__main__":
    data = pd.read_csv("..//heart_disease_uci.csv")
    data['target'] = data['trestbps']
    data = data.drop(columns=['id', 'dataset', 'trestbps'])

    data_wo_null = data.dropna()
    print(len(data_wo_null))

    encoded_data_wo_null = str_features_to_numeric(data_wo_null)
    print(len(encoded_data_wo_null))

    # Model standartization
    # The standard score of a sample x is calculated as:
    # z = (x - мат.ож.) / (стандартное отклонение)
    scaler = StandardScaler()
    new_data = pd.DataFrame(scaler.fit_transform(encoded_data_wo_null), columns = encoded_data_wo_null.columns)

    dataset = data_wo_null.copy()  # original data
    target_name = 'target'
    target = data_wo_null.pop(target_name)

    test_train_split_part = 0.2
    random_state = 42

    train, valid, train_target, valid_target = train_test_split(new_data, target, 
                                                                test_size=test_train_split_part, 
                                                                random_state=random_state)
    
    reg = LinearRegression().fit(train, train_target)

    print("---"*15, " LinearRegression ", "---"*15)
    print(f"Accuracy: {reg.score(valid, valid_target)}")
    print(f"коэффициенты: {reg.coef_}")
    print(f"Смещение относительно начала координат (bias): {reg.intercept_}")

    SGD_reg = SGDRegressor(max_iter=1000, tol=1e-3)
    SGD_reg.fit(train, train_target)

    print("---"*15, " SGDRegressor ", "---"*15)
    print(f"Accuracy: {SGD_reg.score(valid, valid_target)}")
    print(f"коэффициенты: {SGD_reg.coef_}")
    print(f"Смещение относительно начала координат (bias): {SGD_reg.intercept_}")

    Ridge_clf = Ridge(alpha=1.0)
    Ridge_clf.fit(train, train_target)

    print("---"*15, " Ridge ", "---"*15)
    print(f"Accuracy: {Ridge_clf.score(valid, valid_target)}")
    print(f"коэффициенты: {Ridge_clf.coef_}")
    print(f"Смещение относительно начала координат (bias): {Ridge_clf.intercept_}")