70 lines
2.5 KiB
Python
70 lines
2.5 KiB
Python
"""
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Example of training survival model with Dask on CPU
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===================================================
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"""
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import os
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import dask.dataframe as dd
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from dask.distributed import Client, LocalCluster
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import xgboost as xgb
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from xgboost.dask import DaskDMatrix
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def main(client):
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# Load an example survival data from CSV into a Dask data frame.
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# The Veterans' Administration Lung Cancer Trial
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# The Statistical Analysis of Failure Time Data by Kalbfleisch J. and Prentice R (1980)
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CURRENT_DIR = os.path.dirname(__file__)
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df = dd.read_csv(os.path.join(CURRENT_DIR, os.pardir, 'data', 'veterans_lung_cancer.csv'))
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# DaskDMatrix acts like normal DMatrix, works as a proxy for local
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# DMatrix scatter around workers.
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# For AFT survival, you'd need to extract the lower and upper bounds for the label
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# and pass them as arguments to DaskDMatrix.
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y_lower_bound = df['Survival_label_lower_bound']
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y_upper_bound = df['Survival_label_upper_bound']
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X = df.drop(['Survival_label_lower_bound',
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'Survival_label_upper_bound'], axis=1)
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dtrain = DaskDMatrix(client, X, label_lower_bound=y_lower_bound,
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label_upper_bound=y_upper_bound)
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# Use train method from xgboost.dask instead of xgboost. This
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# distributed version of train returns a dictionary containing the
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# resulting booster and evaluation history obtained from
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# evaluation metrics.
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params = {'verbosity': 1,
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'objective': 'survival:aft',
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'eval_metric': 'aft-nloglik',
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'learning_rate': 0.05,
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'aft_loss_distribution_scale': 1.20,
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'aft_loss_distribution': 'normal',
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'max_depth': 6,
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'lambda': 0.01,
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'alpha': 0.02}
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output = xgb.dask.train(client,
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params,
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dtrain,
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num_boost_round=100,
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evals=[(dtrain, 'train')])
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bst = output['booster']
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history = output['history']
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# you can pass output directly into `predict` too.
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prediction = xgb.dask.predict(client, bst, dtrain)
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print('Evaluation history: ', history)
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# Uncomment the following line to save the model to the disk
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# bst.save_model('survival_model.json')
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return prediction
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if __name__ == '__main__':
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# or use other clusters for scaling
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with LocalCluster(n_workers=7, threads_per_worker=4) as cluster:
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with Client(cluster) as client:
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main(client)
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