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https://github.com/wassname/ray.git
synced 2026-06-28 03:34:48 +08:00
Richard Liaw
50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
import xgboost as xgb
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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 ray import tune
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def XGBCallback(env):
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tune.report(**dict(env.evaluation_result_list))
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def train_breast_cancer(config):
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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(
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data, target, test_size=0.25)
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train_set = xgb.DMatrix(train_x, label=train_y)
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test_set = xgb.DMatrix(test_x, label=test_y)
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bst = xgb.train(
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config, train_set, evals=[(test_set, "eval")], callbacks=[XGBCallback])
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preds = bst.predict(test_set)
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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),
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done=True)
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if __name__ == "__main__":
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num_threads = 2
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config = {
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"verbosity": 0,
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"num_threads": num_threads,
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"objective": "binary:logistic",
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"booster": "gbtree",
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"eval_metric": ["auc", "ams@0", "logloss"],
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"max_depth": tune.randint(1, 9),
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"eta": tune.loguniform(1e-4, 1e-1),
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"gamma": tune.loguniform(1e-8, 1.0),
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"grow_policy": tune.choice(["depthwise", "lossguide"])
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}
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from ray.tune.schedulers import ASHAScheduler
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tune.run(
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train_breast_cancer,
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resources_per_trial={"cpu": num_threads},
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config=config,
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num_samples=2,
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scheduler=ASHAScheduler(metric="eval-logloss", mode="min"))
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