cross_val.py

import pandas as pd
import numpy as np

from sklearn.tree import DecisionTreeRegressor

from sklearn.model_selection import (cross_val_score, KFold)
from sklearn.metrics import mean_squared_error


if __name__ == "__main__":
    
    dataset = pd.read_csv('./datasets/felicidad.csv')
    
    #X = dataset.drop(['country', 'score'], axis=1)
    #y = dataset['score']

    data = dataset.drop(["country","score"],axis=1)
    targets = dataset["score"]

    # ---- IMPLEMENTACION BÁSICA ----
    # seleccion de modelo
    model = DecisionTreeRegressor()
    
    # Implementacion básica de Cross validation para calcular un score
    score = cross_val_score(model, data, targets,
                            cv=3,
                            scoring='neg_mean_squared_error')
    
    # Cada elemento del array score es el error medio cuadratico
    print('*'*64)
    print('---- IMPLEMENTACION BÁSICA ----')
    print('*'*64)
    print("Los tres MSE fueron: ", score)
    print('='*32)
    print(np.mean(score))
    print('='*32)
    print("El MSE promedio fue: ", np.abs(np.mean(score)))

    # ---- IMPLEMENTACION DETALLADA ----
    
    print('*'*64)
    print('---- IMPLEMENTACION DETALLADA ----')
    print('*'*64)

    kf = KFold(n_splits=3, shuffle=True, random_state=42)

    mse_values = []
    for train, test in kf.split(data):
        x_train = pd.DataFrame(columns=list(data),index=range(len(train)))
        x_test = pd.DataFrame(columns=list(data),index=range(len(test)))
        y_train = pd.DataFrame(columns=['score'],index=range(len(train)))
        y_test = pd.DataFrame(columns=['score'],index=range(len(test)))
        for i in range(len(train)):
            x_train.iloc[i] = data.iloc[train[i]]
            y_train.iloc[i] = targets.iloc[train[i]]
        for j in range(len(test)):
            x_test.iloc[j] = data.iloc[test[j]]
            y_test.iloc[j] = targets.iloc[test[j]]
        model = DecisionTreeRegressor().fit(x_train,y_train)
        predict = model.predict(x_test)
        mse_values.append(mean_squared_error(y_test,predict))

    print("Los tres MSE fueron: ",mse_values)
    print("El MSE promedio fue: ", np.mean(mse_values))