98238d63fa
This aligns dask with pyspark, users need to explicitly call: ``` from xgboost.dask import DaskXGBClassifier from xgboost import dask as dxgb ``` In future releases, we might stop using the default import and remove the lazy loader.
41 lines
1.2 KiB
Python
41 lines
1.2 KiB
Python
"""
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Use scikit-learn regressor interface with CPU histogram tree method
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===================================================================
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"""
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from dask import array as da
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from dask.distributed import Client, LocalCluster
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from xgboost import dask as dxgb
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def main(client):
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# generate some random data for demonstration
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n = 100
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m = 10000
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partition_size = 100
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X = da.random.random((m, n), partition_size)
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y = da.random.random(m, partition_size)
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regressor = dxgb.DaskXGBRegressor(verbosity=1, n_estimators=2)
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regressor.set_params(tree_method="hist")
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# assigning client here is optional
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regressor.client = client
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regressor.fit(X, y, eval_set=[(X, y)])
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prediction = regressor.predict(X)
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bst = regressor.get_booster()
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history = regressor.evals_result()
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print("Evaluation history:", history)
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# returned prediction is always a dask array.
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assert isinstance(prediction, da.Array)
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return bst # returning the trained model
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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=4, threads_per_worker=1) as cluster:
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with Client(cluster) as client:
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main(client)
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