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"""
Demo for using feature weight to change column sampling
=======================================================
.. versionadded:: 1.3.0
"""
import argparse
import numpy as np
from matplotlib import pyplot as plt
import xgboost
def main(args: argparse.Namespace) -> None:
rng = np.random.RandomState(1994)
kRows = 4196
kCols = 10
X = rng.randn(kRows, kCols)
y = rng.randn(kRows)
fw = np.ones(shape=(kCols,))
for i in range(kCols):
fw[i] *= float(i)
dtrain = xgboost.DMatrix(X, y)
dtrain.set_info(feature_weights=fw)
# Perform column sampling for each node split evaluation, the sampling process is
# weighted by feature weights.
bst = xgboost.train(
{"tree_method": "hist", "colsample_bynode": 0.2},
dtrain,
num_boost_round=10,
evals=[(dtrain, "d")],
)
feature_map = bst.get_fscore()
# feature zero has 0 weight
assert feature_map.get("f0", None) is None
assert max(feature_map.values()) == feature_map.get("f9")
if args.plot:
xgboost.plot_importance(bst)
plt.show()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--plot",
type=int,
default=1,
help="Set to 0 to disable plotting the evaluation history.",
)
args = parser.parse_args()
main(args)
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