def test_plot_learning_curve():
"""Assert that the plot_learning_curve method work as intended."""
atom = ATOMRegressor(X_reg, y_reg, random_state=1)
pytest.raises(NotFittedError, atom.plot_learning_curve)
atom.run("LGB")
atom.delete() # Clear the pipeline to allow ts
atom.train_sizing(["Tree", "LGB"], metric="max_error", bagging=4)
atom.plot_learning_curve(display=False)
atom.train_sizing(["Tree", "LGB"], metric="max_error")
atom.plot_learning_curve(display=False)
def test_plot_successive_halving():
"""Assert that the plot_successive_halving method work as intended."""
atom = ATOMRegressor(X_reg, y_reg, random_state=1)
pytest.raises(NotFittedError, atom.plot_successive_halving)
atom.run("LGB")
atom.delete() # Clear the pipeline to allow sh
atom.successive_halving(models=["Tree", "Bag", "RF", "LGB"], bagging=4)
pytest.raises(ValueError, atom.plot_successive_halving, models="unknown")
pytest.raises(ValueError, atom.plot_successive_halving, models="BR")
pytest.raises(ValueError, atom.plot_successive_halving, metric="unknown")
pytest.raises(ValueError, atom.plot_successive_halving, metric=-1)
pytest.raises(ValueError, atom.plot_successive_halving, metric=1)
pytest.raises(ValueError, atom.plot_successive_halving, metric="roc_auc")
atom.plot_successive_halving(display=False)
atom.successive_halving(models=["Tree", "Bag", "RF", "LGB"])
atom.plot_successive_halving(display=False)