def test_init_regression(): from EvoDAG.model import Ensemble m = Ensemble.init(n_estimators=4, n_jobs=4, seed=10, classifier=False).fit(X, cl) hy = m.predict(X) assert np.unique(hy).shape[0] > 3 default_nargs()
def test_init2(): from EvoDAG.model import Ensemble m = Ensemble.init(n_estimators=4, n_jobs=1, seed=10, early_stopping_rounds=100).fit(X, cl) hy = m.predict(X) print((cl == hy).mean(), cl, hy) assert (cl == hy).mean() > 0.9 default_nargs()
def test_init2(): from EvoDAG.model import Ensemble m = Ensemble.init(n_estimators=4, n_jobs=1, seed=10).fit(X, cl) hy = m.predict(X) print([x.full_array() for x in m.decision_function(X)]) print((cl == hy).mean(), cl, hy) assert (cl == hy).mean() > 0.9 default_nargs()
def test_init(): from EvoDAG.model import Ensemble m = Ensemble.init(n_estimators=4, n_jobs=4, seed=10).fit(X, cl) hy = m.predict(X) assert (cl == hy).mean() > 0.9 default_nargs()