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update
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@@ -2,7 +2,7 @@ from flaml import AutoML
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from flaml.data import load_openml_dataset
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def _test_lexiflow():
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def test_lexiflow():
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X_train, X_test, y_train, y_test = load_openml_dataset(
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dataset_id=179, data_dir="test/data"
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@@ -13,28 +13,18 @@ def _test_lexiflow():
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lexico_objectives["tolerances"] = {"val_loss": 0.01, "pred_time": 0.0}
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lexico_objectives["targets"] = {"val_loss": 0.0, "pred_time": 0.0}
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lexico_objectives["modes"] = ["min", "min"]
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automl = AutoML()
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settings = {
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"time_budget": 100,
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"lexico_objectives": lexico_objectives,
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"estimator_list": ["xgboost"],
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"use_ray": True,
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"use_ray": False,
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"task": "classification",
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"max_iter": 10000000,
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"train_time_limit": 60,
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"verbose": 0,
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"eval_method": "holdout",
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"max_iter": -1,
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"mem_thres": 128 * (1024**3),
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"seed": 1,
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}
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automl.fit(X_train=X_train, y_train=y_train, X_val=X_test, y_val=y_test, **settings)
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print(automl.predict(X_train))
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print(automl.model)
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print(automl.config_history)
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print(automl.best_iteration)
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print(automl.best_estimator)
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if __name__ == "__main__":
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_test_lexiflow()
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test_lexiflow()
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@@ -12,7 +12,7 @@ N_TRAIN_EXAMPLES = BATCHSIZE * 30
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N_VALID_EXAMPLES = BATCHSIZE * 10
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def _test_lexiflow():
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def test_lexiflow():
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train_dataset = torchvision.datasets.FashionMNIST(
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"test/data",
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train=True,
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@@ -109,18 +109,16 @@ def _test_lexiflow():
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"n_epoch": 1,
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}
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analysis = tune.run(
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tune.run(
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evaluate_function,
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num_samples=100000000,
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num_samples=-1,
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time_budget_s=100,
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config=search_space,
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use_ray=False,
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lexico_objectives=lexico_objectives,
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low_cost_partial_config=low_cost_partial_config,
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)
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result = analysis.best_result
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print(result)
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if __name__ == "__main__":
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_test_lexiflow()
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test_lexiflow()
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