def test_fit(self):
auto_xgb_reg = AutoXGBRegressor(cpus_per_trial=2,
name="auto_xgb_regressor",
tree_method='hist')
data, validation_data = get_data()
auto_xgb_reg.fit(data=data,
validation_data=validation_data,
search_space=create_XGB_recipe(),
n_sampling=4,
epochs=1,
metric="mae")
best_model = auto_xgb_reg.get_best_model()
assert 5 <= best_model.model.n_estimators <= 10
assert 2 <= best_model.model.max_depth <= 5
best_config = auto_xgb_reg.get_best_config()
assert all(k in best_config.keys() for k in create_XGB_recipe().keys())
def test_data_creator(self):
train_data_creator, val_data_creator = get_data_creators()
auto_xgb_reg = AutoXGBRegressor(cpus_per_trial=2,
name="auto_xgb_regressor",
tree_method='hist')
model_search_space = get_xgb_search_space()
# todo: change to hp.choice_n
search_space = {
"features":
hp.sample_from(
lambda spec: np.random.choice(["f1", "f2", "f3"], size=2))
}
search_space.update(model_search_space)
auto_xgb_reg.fit(data=train_data_creator,
epochs=1,
validation_data=val_data_creator,
metric="logloss",
metric_mode="min",
search_space=search_space,
n_sampling=2)
best_config = auto_xgb_reg.get_best_config()
assert all(k in best_config.keys() for k in search_space.keys())
assert len(best_config["features"]) == 2