Example of training survival model with Dask on CPU

import os

import dask.dataframe as dd
from dask.distributed import Client, LocalCluster

import xgboost as xgb
from xgboost.dask import DaskDMatrix

def main(client):
    # Load an example survival data from CSV into a Dask data frame.
    # The Veterans' Administration Lung Cancer Trial
    # The Statistical Analysis of Failure Time Data by Kalbfleisch J. and Prentice R (1980)
    CURRENT_DIR = os.path.dirname(__file__)
    df = dd.read_csv(
        os.path.join(CURRENT_DIR, os.pardir, "data", "veterans_lung_cancer.csv")

    # DaskDMatrix acts like normal DMatrix, works as a proxy for local
    # DMatrix scatter around workers.
    # For AFT survival, you'd need to extract the lower and upper bounds for the label
    # and pass them as arguments to DaskDMatrix.
    y_lower_bound = df["Survival_label_lower_bound"]
    y_upper_bound = df["Survival_label_upper_bound"]
    X = df.drop(["Survival_label_lower_bound", "Survival_label_upper_bound"], axis=1)
    dtrain = DaskDMatrix(
        client, X, label_lower_bound=y_lower_bound, label_upper_bound=y_upper_bound

    # Use train method from xgboost.dask instead of xgboost.  This
    # distributed version of train returns a dictionary containing the
    # resulting booster and evaluation history obtained from
    # evaluation metrics.
    params = {
        "verbosity": 1,
        "objective": "survival:aft",
        "eval_metric": "aft-nloglik",
        "learning_rate": 0.05,
        "aft_loss_distribution_scale": 1.20,
        "aft_loss_distribution": "normal",
        "max_depth": 6,
        "lambda": 0.01,
        "alpha": 0.02,
    output = xgb.dask.train(
        client, params, dtrain, num_boost_round=100, evals=[(dtrain, "train")]
    bst = output["booster"]
    history = output["history"]

    # you can pass output directly into `predict` too.
    prediction = xgb.dask.predict(client, bst, dtrain)
    print("Evaluation history: ", history)

    # Uncomment the following line to save the model to the disk
    # bst.save_model('survival_model.json')

    return prediction

if __name__ == "__main__":
    # or use other clusters for scaling
    with LocalCluster(n_workers=7, threads_per_worker=4) as cluster:
        with Client(cluster) as client:

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