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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
74 lines
1.9 KiB
Python
74 lines
1.9 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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"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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"init_value": 4,
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},
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}
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def _test_simple(method=None, size_ratio=1.0):
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automl = AutoML()
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automl.add_learner(learner_name="XGBoost2D", learner_class=XGBoost2D)
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X, y = fetch_openml(name=dataset, 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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final_size = int(len(y_train) * size_ratio)
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X_train = X_train[:final_size]
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y_train = y_train[:final_size]
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automl_settings = {
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"estimator_list": ["XGBoost2D"],
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# "metric": 'accuracy',
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"task": "classification",
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"log_file_name": f"test/xgboost2d_{dataset}_{method}_{final_size}.log",
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# "log_training_metric": True,
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# "split_type": split_type,
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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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"time_budget": 3600,
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}
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automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
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def _test_grid_1():
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_test_simple(method="grid", size_ratio=1.0 / 3.0)
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def _test_grid_2():
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_test_simple(method="grid", size_ratio=2.0 / 3.0)
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def _test_grid_4():
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_test_simple(method="grid", size_ratio=0.5)
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def _test_grid_3():
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_test_simple(method="grid", size_ratio=1.0)
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if __name__ == "__main__":
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unittest.main()
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