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="mse")
best_model = auto_xgb_reg.get_best_model()
assert 5 <= best_model.model.n_estimators <= 10
assert 2 <= best_model.model.max_depth <= 5
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=get_xgb_search_space(),
n_sampling=4,
epochs=1,
metric="mae")
best_model = auto_xgb_reg.get_best_model()
assert 5 <= best_model.n_estimators <= 10
assert 2 <= best_model.max_depth <= 5
best_config = auto_xgb_reg.get_best_config()
assert all(k in best_config.keys()
for k in get_xgb_search_space().keys())
max_depth=list(max_depth_range),
lr=lr,
min_child_weight=min_child_weight)
search_alg = None
search_alg_params = None
scheduler = None
scheduler_params = None
auto_xgb_reg = AutoXGBRegressor(cpus_per_trial=2,
name="auto_xgb_regressor",
**config)
auto_xgb_reg.fit(data=(X_train, y_train),
validation_data=(X_val, y_val),
metric="rmse",
n_sampling=recipe.num_samples,
search_space=recipe.search_space(),
search_alg=search_alg,
search_alg_params=None,
scheduler=scheduler,
scheduler_params=scheduler_params)
print("Training completed.")
best_model = auto_xgb_reg.get_best_model()
y_hat = best_model.predict(X_val)
rmse = best_model.evaluate(X_val, y_val, metrics=["rmse"])
print("Evaluate: the square root of mean square error is", rmse)
ray_ctx.stop()
sc.stop()