Example of training survival model with Dask on CPU

import os

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

from xgboost import dask as dxgb
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 = dxgb.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 = dxgb.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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