def test_multi_auto(X_y_multi, multiclass_core_objectives):
X, y = X_y_multi
objective = PrecisionMicro()
automl = AutoMLSearch(X_train=X, y_train=y, problem_type='multiclass', objective=objective, max_iterations=5, n_jobs=1)
automl.search()
best_pipeline = automl.best_pipeline
assert best_pipeline._is_fitted
y_pred = best_pipeline.predict(X)
assert len(np.unique(y_pred.to_series())) == 3
objective_in_additional_objectives = next((obj for obj in multiclass_core_objectives if obj.name == objective.name), None)
multiclass_core_objectives.remove(objective_in_additional_objectives)
for expected, additional in zip(multiclass_core_objectives, automl.additional_objectives):
assert type(additional) is type(expected)
def test_precision_micro_multi():
obj = PrecisionMicro()
assert obj.score(np.array([0, 0, 0, 1, 1, 1, 2, 2, 2]),
np.array([0, 0, 0, 0, 0, 0, 0, 0,
0])) == pytest.approx(1 / 3.0, EPS)
assert obj.score(np.array([0, 0, 0, 1, 1, 1, 2, 2, 2]),
np.array([0, 0, 0, 1, 1, 1, 2, 2,
2])) == pytest.approx(1.0, EPS)
assert obj.score(np.array([0, 0, 0, 1, 1, 1, 2, 2, 2]),
np.array([2, 2, 2, 0, 0, 0, 1, 1,
1])) == pytest.approx(0.0, EPS)
assert obj.score(np.array([0, 0]),
np.array([1, 2])) == pytest.approx(0.0, EPS)