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Richard Liaw
66 lines
2.2 KiB
Python
66 lines
2.2 KiB
Python
import sklearn.datasets
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import sklearn.metrics
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import os
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from ray.tune.schedulers import ASHAScheduler
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from sklearn.model_selection import train_test_split
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import xgboost as xgb
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from ray import tune
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from ray.tune.integration.xgboost import TuneReportCheckpointCallback
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def train_breast_cancer(config: dict):
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# This is a simple training function to be passed into Tune
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# Load dataset
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data, labels = sklearn.datasets.load_breast_cancer(return_X_y=True)
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# Split into train and test set
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train_x, test_x, train_y, test_y = train_test_split(
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data, labels, test_size=0.25)
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# Build input matrices for XGBoost
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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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# Train the classifier, using the Tune callback
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xgb.train(
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config,
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train_set,
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evals=[(test_set, "eval")],
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verbose_eval=False,
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callbacks=[TuneReportCheckpointCallback(filename="model.xgb")])
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if __name__ == "__main__":
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search_space = {
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# You can mix constants with search space objects.
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"objective": "binary:logistic",
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"eval_metric": ["logloss", "error"],
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"max_depth": tune.randint(1, 9),
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"min_child_weight": tune.choice([1, 2, 3]),
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"subsample": tune.uniform(0.5, 1.0),
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"eta": tune.loguniform(1e-4, 1e-1)
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}
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# This will enable aggressive early stopping of bad trials.
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scheduler = ASHAScheduler(
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max_t=10, # 10 training iterations
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grace_period=1,
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reduction_factor=2)
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analysis = tune.run(
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train_breast_cancer,
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metric="eval-logloss",
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mode="min",
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# You can add "gpu": 0.1 to allocate GPUs
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resources_per_trial={"cpu": 1},
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config=search_space,
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num_samples=10,
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scheduler=scheduler)
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# Load the best model checkpoint
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best_bst = xgb.Booster()
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best_bst.load_model(os.path.join(analysis.best_checkpoint, "model.xgb"))
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accuracy = 1. - analysis.best_result["eval-error"]
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print(f"Best model parameters: {analysis.best_config}")
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print(f"Best model total accuracy: {accuracy:.4f}")
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# You could now do further predictions with
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# best_bst.predict(...)
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