ЛБ#2 / main

import numpy as np
import pandas        
import matplotlib.pyplot as plt 

from sklearn.model_selection import train_test_split 
from sklearn.naive_bayes import GaussianNB          
from sklearn.metrics import confusion_matrix        
from sklearn.tree import DecisionTreeRegressor       
from sklearn import tree                            
from sklearn.neural_network import MLPRegressor  
from sklearn.metrics import accuracy_score       


def visualize(y_test, y_pred, model_name):
    pernb_1 = 100 * (sum(abs(y_test - y_pred) <= 1)) / len(y_test)
    accuracy = round(pernb_1, 2)
    print(f'{model_name} % within 1 unit (accuracy): {accuracy}%')

    C = confusion_matrix(y_test, y_pred)
    C_perc, res, rank, s = np.linalg.lstsq(
        np.diag(np.sum(C, axis=1)), C, rcond=None)

    plt.ylabel('Outputs')
    plt.xlabel('Outputs')
    plt.title(f'How many responses were correctly predicted within 1 (accuracy): {accuracy}%')
    plt.imshow(C_perc)
    plt.colorbar()
    plt.show()


def native_bayes(X_train, y_train, X_test, y_test):
    gnb = GaussianNB()                               
    NBmodel = gnb.fit(X_train, y_train)             
    y_pred = NBmodel.predict(X_test)                
    visualize(y_test, y_pred, "Naive Bayes")         


def regression_tree(X_train, y_train, X_test, y_test):
    rt_model = DecisionTreeRegressor()          
    rt_model.fit(X_train, y_train)                  
    rt_y_pred = rt_model.predict(X_test)             
    rt_y_pred = np.round(rt_y_pred).astype(int)    
    visualize(y_test, rt_y_pred, "Decision Tree Regressor")

    tree.plot_tree(rt_model, max_depth=3, feature_names=list(X_train.columns))
    plt.title('Decision Tree Regressor')           
    plt.show()                                     

    for name, imp in zip(X_train.columns, rt_model.feature_importances_):
        print(f'    feature {name}: importance = {round(float(imp), 3)}')


def nn(X_train, y_train, X_test, y_test):
    nn = MLPRegressor(hidden_layer_sizes=(10,), random_state=1, max_iter=2000)
    nn.fit(X_train, y_train)                    
    nn_y_pred = nn.predict(X_test)                
    error = y_test - nn_y_pred                     
    mae = round(float(np.mean(np.abs(error))), 2)  
    acc = round(100 * float(np.mean(np.abs(error) <= 1)), 2) 
    print(f"MAE: {mae} кВт*ч/м2; Accuracy (within 1): {acc}%")


def main():
    df = pandas.read_csv('ЛР#2/data.csv', sep=';')  
    for col in ['X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X7', 'X8',
                'Y1', 'Y2', 'Y1_R', 'Y2_R']:
        df[col].astype(float)
    Y1 = df['Y1_R']
    Y2 = df['Y2_R']
    u = len(df['Y1_R'].unique())
    print(f'There are {u} unique output categories')
    X = df.drop('Y1', axis=1).drop('Y2', axis=1).drop('Y1_R', axis=1).drop('Y2_R', axis=1)
    X_train, X_test, y_train, y_test = train_test_split(X, Y1, random_state=0)
    print('--- Все 8 признаков ---')
    native_bayes(X_train, y_train, X_test, y_test)     
    regression_tree(X_train, y_train, X_test, y_test) 
    nn(X_train, y_train, X_test, y_test)            
    print('--- Только значимые признаки: X1, X4, X7, X8 ---')
    X_sig = X[['X1', 'X4', 'X7', 'X8']]
    X_train, X_test, y_train, y_test = train_test_split(X_sig, Y1, random_state=0)
    regression_tree(X_train, y_train, X_test, y_test)
    nn(X_train, y_train, X_test, y_test)


if __name__ == '__main__':
    main()