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3de0dc667e
* lint * bump ver * bump ver * fixed circular import --------- Co-authored-by: Jirka Borovec <6035284+Borda@users.noreply.github.com>
35 lines
1.1 KiB
Python
35 lines
1.1 KiB
Python
from flaml import AutoML
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from flaml.automl.data import load_openml_dataset
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from flaml.automl.ml import ExtraTreesEstimator
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X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=1169, data_dir="./")
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X_train = X_train.iloc[:1000]
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y_train = y_train.iloc[:1000]
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class ExtraTreesEstimatorSeeded(ExtraTreesEstimator):
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"""ExtraTreesEstimator for reproducible FLAML run."""
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def config2params(self, config: dict) -> dict:
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params = super().config2params(config)
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params["random_state"] = 0
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return params
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settings = {
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"time_budget": 1e10, # total running time in seconds
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"max_iter": 3,
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"metric": "ap", # average_precision
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"task": "classification", # task type
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"seed": 7654321, # random seed
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"estimator_list": ["extra_trees_seeded"],
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"verbose": False,
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}
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for trial_num in range(8):
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automl = AutoML()
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automl.add_learner(learner_name="extra_trees_seeded", learner_class=ExtraTreesEstimatorSeeded)
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automl.fit(X_train=X_train, y_train=y_train, **settings)
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print(automl.best_loss)
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print(automl.best_config)
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