def test_random_seed(X_y_binary):
X, y = X_y_binary
automl = AutoMLSearch(X_train=X, y_train=y, problem_type='binary', objective=Precision(), max_iterations=5, random_seed=0, n_jobs=1)
automl.search()
automl_1 = AutoMLSearch(X_train=X, y_train=y, problem_type='binary', objective=Precision(), max_iterations=5, random_seed=0, n_jobs=1)
automl_1.search()
assert automl.rankings.equals(automl_1.rankings)
def test_callback(X_y_binary):
X, y = X_y_binary
counts = {
"start_iteration_callback": 0,
"add_result_callback": 0,
}
def start_iteration_callback(pipeline_class,
parameters,
automl_obj,
counts=counts):
counts["start_iteration_callback"] += 1
def add_result_callback(results,
trained_pipeline,
automl_obj,
counts=counts):
counts["add_result_callback"] += 1
max_iterations = 3
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='binary',
objective=Precision(),
max_iterations=max_iterations,
start_iteration_callback=start_iteration_callback,
add_result_callback=add_result_callback,
n_jobs=1)
automl.search()
assert counts["start_iteration_callback"] == len(
get_estimators('binary')) + 1
assert counts["add_result_callback"] == max_iterations
def test_init_objective(X_y_binary):
X, y = X_y_binary
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='binary',
objective=Precision(),
max_iterations=1)
assert isinstance(automl.objective, Precision)
automl = AutoMLSearch(X_train=X,
y_train=y,
problem_type='binary',
objective='Precision',
max_iterations=1)
assert isinstance(automl.objective, Precision)
def test_precision_binary():
obj = Precision()
assert obj.score(np.array([1, 1, 1, 1, 1, 1]),
np.array([0, 0, 0, 1, 1, 1])) == pytest.approx(1.0, EPS)
assert obj.score(np.array([0, 0, 0, 1, 1, 1]),
np.array([1, 1, 1, 1, 1, 1])) == pytest.approx(0.5, EPS)
assert obj.score(np.array([0, 0, 0, 0, 0, 0]),
np.array([1, 1, 1, 1, 1, 1])) == pytest.approx(0.0, EPS)
assert obj.score(np.array([0, 0, 0, 0, 0, 0]),
np.array([0, 0, 0, 0, 0, 0])) == pytest.approx(0.0, EPS)