import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets, linear_model
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score

FEATURE_NAME = "Flipper Length (mm)"
TARGET_NAME = "Body Mass (g)"

def load_dataframe():
    try:
        column_list = [FEATURE_NAME, TARGET_NAME]
        df = pd.read_csv("penguins.csv", usecols = column_list)
        return df
    except FileNotFoundError:
        print("Datei 'penguins.csv' nicht gefunden.")
        return None

def plot(X,Y):    
    plt.scatter(X, Y, label="data points")
    plt.xlabel(FEATURE_NAME)
    plt.ylabel(TARGET_NAME)
    plt.legend()
    plt.show()

def plot_linear_regression(X,Y, x, y):
    plt.plot(x,y, "r", label="lineare regression")
    plt.scatter(X, Y, label="data points")
    plt.xlabel(FEATURE_NAME)
    plt.ylabel(TARGET_NAME)
    plt.legend()
    plt.show()

def calc_linear_regression_by_hand(X,Y):
    print("🛠️ under construction")
    # calculate mean value of X
    mean_x = np.mean(X)
    # calculate mean value of Y
    mean_y = np.mean(Y)
    numer = 0
    denom = 0
    # for loop
    for i in range(len(X)):
        numer += (X[i]-mean_x) * (Y[i]-mean_y)
        denom += (X[i]-mean_x) ** 2
    m = numer / denom
    # calculate c
    c = mean_y - (m * mean_x)
    return m, c

def calc_r2(X, Y, m, c):
    print("🛠️ under construction")
    # total sum of square
    ss_t = 0
    # total sum of square of residuals
    ss_r = 0
    # for loop
    # calc r2
    r2 = 0
    return r2    

def calc_linear_regression(X_train, y_train):    
    print("🛠️ under construction")

def grid_search(X_train, y_train, params):
    model = GradientBoostingRegressor(random_state=42)
    grid = GridSearchCV(model, params, cv=5,
                               scoring='r2',
                               return_train_score=True)
    grid.fit(X_train, y_train)
    return grid


def main():
    df = load_dataframe()
    if df is None:
        return

    print("\n=== Überblick Penguins Dataset ===")
    print(df.describe())
  

    print("\n=== Data Quality Assessment ===")
    row_count = len(df)
    print("number of rows:", row_count)
    df.dropna(inplace=True)
    df.fillna(df.mean(), inplace=True)


    print("\n=== Lineare Regression von Hand ===")
    X = df[[FEATURE_NAME]].values
    Y = df[[TARGET_NAME]].values
    m, c = calc_linear_regression_by_hand(X,Y)
    print(f"m = {m}\nc = {c}")
    min_x = np.min(X)
    max_x = np.max(X)
    x = np.linspace(min_x, max_x, 100)
    y = c+m*x
    plot_linear_regression(X, Y, x, y)

    print("\n=== Genauigkeit/Zuverlässigkeit der Formel prüfen ===")
    print("🐧 Not yet implemented — the penguin is still coding...")
    # r2 = calc_r2(X, Y, m, c)

    print("\n=== Lineare Regression mit SciKit ===")    
    print("🐧 Not yet implemented — the penguin is still coding...")
    # model = calc_linear_regression(X_train, y_train)

    print("\n=== Genauigkeit/Zuverlässigkeit des Modells prüfen ===")
    print("🐧 Not yet implemented — the penguin is still coding...")
    mae = 0
    mse = 0
    rmse = 0
    r2 = 0
    print('MAE (Mean Absolute Error):', mae)
    print('MSE (Mean Squared Error):', mse)
    print('RMSE (Root Mean Squared Error):', rmse)
    print("R2 Score:", r2)

    print("\n=== Grid Search ===")
    print("🐧 Not yet implemented — the penguin is still coding...")
    params = dict()
    params['n_estimators'] = []
    params['learning_rate'] = []
    params['subsample'] = []
    params['max_depth'] = []
    #grid = grid_search(X_train, y_train, params)
    #y_pred = grid.predict(X_test)
    #print("Best estimator across ALL searched params:\n",grid.best_estimator_)
    #print("Best score across ALL searched params:\n",grid.best_score_)
    #print("Best parameters across ALL searched params:\n",grid.best_params_)

if __name__ == "__main__":
    main()