"""
Use scikit-learn regressor interface with CPU histogram tree method
===================================================================
"""

from dask import array as da
from dask.distributed import Client, LocalCluster

from xgboost import dask as dxgb


def main(client):
    # generate some random data for demonstration
    n = 100
    m = 10000
    partition_size = 100
    X = da.random.random((m, n), partition_size)
    y = da.random.random(m, partition_size)

    regressor = dxgb.DaskXGBRegressor(verbosity=1, n_estimators=2)
    regressor.set_params(tree_method="hist")
    # assigning client here is optional
    regressor.client = client

    regressor.fit(X, y, eval_set=[(X, y)])
    prediction = regressor.predict(X)

    bst = regressor.get_booster()
    history = regressor.evals_result()

    print("Evaluation history:", history)
    # returned prediction is always a dask array.
    assert isinstance(prediction, da.Array)
    return bst  # returning the trained model


if __name__ == "__main__":
    # or use other clusters for scaling
    with LocalCluster(n_workers=4, threads_per_worker=1) as cluster:
        with Client(cluster) as client:
            main(client)

"""
Use scikit-learn regressor interface with CPU histogram tree method
===================================================================
"""
from dask import array as da
from dask.distributed import Client, LocalCluster

from xgboost import dask as dxgb


def main(client):
    # generate some random data for demonstration
    n = 100
    m = 10000
    partition_size = 100
    X = da.random.random((m, n), partition_size)
    y = da.random.random(m, partition_size)

    regressor = dxgb.DaskXGBRegressor(verbosity=1, n_estimators=2)
    regressor.set_params(tree_method="hist")
    # assigning client here is optional
    regressor.client = client

    regressor.fit(X, y, eval_set=[(X, y)])
    prediction = regressor.predict(X)

    bst = regressor.get_booster()
    history = regressor.evals_result()

    print("Evaluation history:", history)
    # returned prediction is always a dask array.
    assert isinstance(prediction, da.Array)
    return bst  # returning the trained model


if __name__ == "__main__":
    # or use other clusters for scaling
    with LocalCluster(n_workers=4, threads_per_worker=1) as cluster:
        with Client(cluster) as client:
            main(client)
