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https://github.com/vale981/ray
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41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
import ray
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from ray.data.preprocessors import StandardScaler
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from ray.train.batch_predictor import BatchPredictor
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from ray.train.xgboost import XGBoostTrainer, XGBoostPredictor
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from ray.air.config import ScalingConfig
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# Split data into train and validation.
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dataset = ray.data.read_csv("s3://anonymous@air-example-data/breast_cancer.csv")
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train_dataset, valid_dataset = dataset.train_test_split(test_size=0.3)
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test_dataset = valid_dataset.drop_columns(["target"])
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columns_to_scale = ["mean radius", "mean texture"]
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preprocessor = StandardScaler(columns=columns_to_scale)
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trainer = XGBoostTrainer(
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label_column="target",
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num_boost_round=20,
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scaling_config=ScalingConfig(num_workers=2),
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params={
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"objective": "binary:logistic",
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"eval_metric": ["logloss", "error"],
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},
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datasets={"train": train_dataset},
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preprocessor=preprocessor,
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)
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result = trainer.fit()
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# You can also create a checkpoint from a trained model using
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# `XGBoostCheckpoint.from_model`.
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# import xgboost as xgb
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# from ray.train.xgboost import XGBoostCheckpoint
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# model = xgb.Booster()
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# model.load_model(...)
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# checkpoint = XGBoostCheckpoint.from_model(model, path=".")
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checkpoint = result.checkpoint
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batch_predictor = BatchPredictor.from_checkpoint(checkpoint, XGBoostPredictor)
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predicted_probabilities = batch_predictor.predict(test_dataset)
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predicted_probabilities.show()
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