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if save_best_model_per_estimator is False and retrain_final is True, unfit the model after evaluation in HPO. retrain if using ray. update ITER_HP in config after a trial is finished. change prophet logging level. example and notebook update. allow settings to be passed to AutoML constructor. Are you planning to add multi-output-regression capability to FLAML #192 Is multi-tasking allowed? #277 can pass the auotml setting to the constructor instead of requiring a derived class. remove model_history. checkpoint bug fix. * model_history meaning save_best_model_per_estimator * ITER_HP * example update * prophet logging level * comment update in forecast notebook * print format improvement * allow settings to be passed to AutoML constructor * checkpoint bug fix * time limit for autohf regression test * skip slow test on macos * cleanup before del
99 lines
2.7 KiB
Python
99 lines
2.7 KiB
Python
import unittest
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from sklearn.datasets import fetch_openml
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from sklearn.model_selection import train_test_split
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from flaml.automl import AutoML
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from flaml.model import XGBoostSklearnEstimator
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from flaml import tune
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dataset = "credit-g"
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class XGBoost2D(XGBoostSklearnEstimator):
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@classmethod
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def search_space(cls, data_size, task):
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upper = min(32768, int(data_size))
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return {
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"n_estimators": {
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"domain": tune.lograndint(lower=4, upper=upper),
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"low_cost_init_value": 4,
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},
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"max_leaves": {
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"domain": tune.lograndint(lower=4, upper=upper),
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"low_cost_init_value": 4,
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},
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}
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def test_simple(method=None):
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automl = AutoML()
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automl.add_learner(learner_name="XGBoost2D", learner_class=XGBoost2D)
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automl_settings = {
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"estimator_list": ["XGBoost2D"],
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"task": "classification",
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"log_file_name": f"test/xgboost2d_{dataset}_{method}.log",
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"n_jobs": 1,
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"hpo_method": method,
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"log_type": "all",
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"retrain_full": "budget",
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"keep_search_state": True,
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"time_budget": 1,
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}
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from sklearn.externals._arff import ArffException
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try:
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X, y = fetch_openml(name=dataset, return_X_y=True)
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except (ArffException, ValueError):
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from sklearn.datasets import load_wine
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X, y = load_wine(return_X_y=True)
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.33, random_state=42
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)
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automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
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print(automl.estimator_list)
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print(automl.search_space)
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print(automl.points_to_evaluate)
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config = automl.best_config.copy()
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config["learner"] = automl.best_estimator
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automl.trainable(config)
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from flaml import tune
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from flaml.automl import size
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from functools import partial
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analysis = tune.run(
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automl.trainable,
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automl.search_space,
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metric="val_loss",
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mode="min",
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low_cost_partial_config=automl.low_cost_partial_config,
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points_to_evaluate=automl.points_to_evaluate,
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cat_hp_cost=automl.cat_hp_cost,
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prune_attr=automl.prune_attr,
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min_resource=automl.min_resource,
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max_resource=automl.max_resource,
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time_budget_s=automl._state.time_budget,
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config_constraints=[(partial(size, automl._state), "<=", automl._mem_thres)],
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metric_constraints=automl.metric_constraints,
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num_samples=5,
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)
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print(analysis.trials[-1])
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def test_optuna():
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test_simple(method="optuna")
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def test_random():
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test_simple(method="random")
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def test_grid():
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test_simple(method="grid")
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if __name__ == "__main__":
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unittest.main()
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