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50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
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import ray
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import lightgbm as lgb
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import numpy as np
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import sklearn.datasets
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import sklearn.metrics
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from sklearn.model_selection import train_test_split
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from flaml import tune
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from flaml.model import LGBMEstimator
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data, target = sklearn.datasets.load_breast_cancer(return_X_y=True)
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train_x, test_x, train_y, test_y = train_test_split(data, target, test_size=0.25)
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def train_breast_cancer(config):
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params = LGBMEstimator(**config).params
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num_boost_round = params.pop("n_estimators")
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train_set = lgb.Dataset(train_x, label=train_y)
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gbm = lgb.train(params, train_set, num_boost_round)
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preds = gbm.predict(test_x)
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pred_labels = np.rint(preds)
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tune.report(
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mean_accuracy=sklearn.metrics.accuracy_score(test_y, pred_labels), done=True
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)
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if __name__ == "__main__":
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ray.init(address="auto")
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flaml_lgbm_search_space = LGBMEstimator.search_space(train_x.shape)
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config_search_space = {
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hp: space["domain"] for hp, space in flaml_lgbm_search_space.items()
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}
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low_cost_partial_config = {
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hp: space["low_cost_init_value"]
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for hp, space in flaml_lgbm_search_space.items()
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if "low_cost_init_value" in space
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}
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analysis = tune.run(
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train_breast_cancer,
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metric="mean_accuracy",
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mode="max",
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config=config_search_space,
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num_samples=-1,
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time_budget_s=60,
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use_ray=True,
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)
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# print("Best hyperparameters found were: ", analysis.best_config)
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print("The best trial's result: ", analysis.best_trial.last_result)
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