
from prettymetrics.reg import Regressor
from sklearn import datasets
from sklearn.utils import shuffle
import numpy as np




def run_():

    linnerud            = datasets.load_linnerud()
    X, y                = shuffle(linnerud.data, linnerud.target, random_state=13)
    X                   = X.astype(np.float32)

    offset              = int(X.shape[0] * 0.9)

    X_train, y_train    = X[:offset], y[:offset]
    X_test, y_test      = X[offset:], y[offset:]

    reg                 = Regressor(verbose=0, ignore_warnings=False, custom_metric=None)
    models, predictions = reg.fit(X_train, X_test, y_train, y_test)

    print(models)


def startpy():
    
    run_()


if __name__ == '__main__':
    startpy()

from prettymetrics.reg import Regressor
from sklearn import datasets
from sklearn.utils import shuffle
import numpy as np




def run_():

    linnerud            = datasets.load_linnerud()
    X, y                = shuffle(linnerud.data, linnerud.target, random_state=13)
    X                   = X.astype(np.float32)

    offset              = int(X.shape[0] * 0.9)

    X_train, y_train    = X[:offset], y[:offset]
    X_test, y_test      = X[offset:], y[offset:]

    reg                 = Regressor(verbose=0, ignore_warnings=False, custom_metric=None)
    models, predictions = reg.fit(X_train, X_test, y_train, y_test)

    print(models)


def startpy():
    
    run_()


if __name__ == '__main__':
    startpy()