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krfricke
22ea8dde84
* Added XGBoost tutorial and template * XGBoost tutorial: Cut some clutter * Apply suggestions from code review Co-authored-by: Richard Liaw <rliaw@berkeley.edu> * Added XGboost logo * Fixed further references Co-authored-by: Kai Fricke <kai@anyscale.com> Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
import numpy as np
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import sklearn.datasets
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import sklearn.metrics
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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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def XGBCallback(env):
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# After every training iteration, report loss to Tune
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tune.report(**dict(env.evaluation_result_list))
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def train_breast_cancer(config):
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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
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bst = 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=[XGBCallback])
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# Predict labels for the test set
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preds = bst.predict(test_set)
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pred_labels = np.rint(preds)
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# Return prediction accuracy
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accuracy = sklearn.metrics.accuracy_score(test_y, pred_labels)
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tune.report(mean_accuracy=accuracy, done=True)
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if __name__ == "__main__":
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config = {
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"objective": "binary:logistic",
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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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"eval_metric": ["auc", "ams@0", "logloss"]
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}
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# The ASHAScheduler stops bad performing configurations early
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scheduler = ASHAScheduler(
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metric="eval-logloss", # The `eval` prefix is defined in xgb.train
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mode="min", # Retain configurations with a low logloss
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max_t=11, # 10 training iterations + 1 final evaluation
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grace_period=1, # Number of minimum iterations for each trial
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reduction_factor=2) # How aggressively to stop trials
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tune.run(
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train_breast_cancer, # your training function
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resources_per_trial={"cpu": 1}, # You can add "gpu": 0.1 here
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config=config,
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num_samples=10, # number of parameter configurations to try
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scheduler=scheduler)
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