import unittest from sklearn.datasets import fetch_openml from sklearn.model_selection import train_test_split import numpy as np from flaml.automl import AutoML from flaml.model import XGBoostSklearnEstimator from flaml import tune dataset = "credit-g" class XGBoost2D(XGBoostSklearnEstimator): @classmethod def search_space(cls, data_size, task): upper = min(32768, int(data_size)) return { 'n_estimators': { 'domain': tune.lograndint(lower=4, upper=upper), 'low_cost_init_value': 4, }, 'max_leaves': { 'domain': tune.lograndint(lower=4, upper=upper), 'low_cost_init_value': 4, }, } def test_simple(method=None): automl = AutoML() automl.add_learner(learner_name='XGBoost2D', learner_class=XGBoost2D) automl_settings = { "estimator_list": ['XGBoost2D'], "task": 'classification', "log_file_name": f"test/xgboost2d_{dataset}_{method}.log", "n_jobs": 1, "hpo_method": method, "log_type": "all", "time_budget": 1 } from sklearn.externals._arff import ArffException try: X, y = fetch_openml(name=dataset, return_X_y=True) except (ArffException, ValueError): from sklearn.datasets import load_wine X, y = load_wine(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=42) automl.fit(X_train=X_train, y_train=y_train, **automl_settings) print(automl.estimator_list) print(automl.search_space) print(automl.points_to_evalaute) config = automl.best_config.copy() config['learner'] = automl.best_estimator automl.trainable(config) from flaml import tune analysis = tune.run( automl.trainable, automl.search_space, metric='val_loss', mode="min", low_cost_partial_config=automl.low_cost_partial_config, points_to_evaluate=automl.points_to_evalaute, cat_hp_cost=automl.cat_hp_cost, prune_attr=automl.prune_attr, min_resource=automl.min_resource, max_resource=automl.max_resource, time_budget_s=automl._state.time_budget, config_constraints=[(automl.size, '<=', automl._mem_thres)], metric_constraints=automl.metric_constraints, num_samples=5) print(analysis.trials[-1]) def _test_optuna(): test_simple(method="optuna") def test_grid(): test_simple(method="grid") if __name__ == "__main__": unittest.main()