Note

Click here to download the full example code

# Demo for using process_type with prune and refresh

Modifying existing trees is not a well established use for XGBoost, so feel free to experiment.

```
import xgboost as xgb
from sklearn.datasets import fetch_california_housing
import numpy as np
def main():
n_rounds = 32
X, y = fetch_california_housing(return_X_y=True)
# Train a model first
X_train = X[: X.shape[0] // 2]
y_train = y[: y.shape[0] // 2]
Xy = xgb.DMatrix(X_train, y_train)
evals_result: xgb.callback.EvaluationMonitor.EvalsLog = {}
booster = xgb.train(
{"tree_method": "gpu_hist", "max_depth": 6},
Xy,
num_boost_round=n_rounds,
evals=[(Xy, "Train")],
evals_result=evals_result,
)
SHAP = booster.predict(Xy, pred_contribs=True)
# Refresh the leaf value and tree statistic
X_refresh = X[X.shape[0] // 2:]
y_refresh = y[y.shape[0] // 2:]
Xy_refresh = xgb.DMatrix(X_refresh, y_refresh)
# The model will adapt to other half of the data by changing leaf value (no change in
# split condition) with refresh_leaf set to True.
refresh_result: xgb.callback.EvaluationMonitor.EvalsLog = {}
refreshed = xgb.train(
{"process_type": "update", "updater": "refresh", "refresh_leaf": True},
Xy_refresh,
num_boost_round=n_rounds,
xgb_model=booster,
evals=[(Xy, "Original"), (Xy_refresh, "Train")],
evals_result=refresh_result,
)
# Refresh the model without changing the leaf value, but tree statistic including
# cover and weight are refreshed.
refresh_result: xgb.callback.EvaluationMonitor.EvalsLog = {}
refreshed = xgb.train(
{"process_type": "update", "updater": "refresh", "refresh_leaf": False},
Xy_refresh,
num_boost_round=n_rounds,
xgb_model=booster,
evals=[(Xy, "Original"), (Xy_refresh, "Train")],
evals_result=refresh_result,
)
# Without refreshing the leaf value, resulting trees should be the same with original
# model except for accumulated statistic. The rtol is for floating point error in
# prediction.
np.testing.assert_allclose(
refresh_result["Original"]["rmse"], evals_result["Train"]["rmse"], rtol=1e-5
)
# But SHAP value is changed as cover in tree nodes are changed.
refreshed_SHAP = refreshed.predict(Xy, pred_contribs=True)
assert not np.allclose(SHAP, refreshed_SHAP, rtol=1e-3)
# Prune the trees with smaller max_depth
X_update = X_train
y_update = y_train
Xy_update = xgb.DMatrix(X_update, y_update)
prune_result: xgb.callback.EvaluationMonitor.EvalsLog = {}
pruned = xgb.train(
{"process_type": "update", "updater": "prune", "max_depth": 2},
Xy_update,
num_boost_round=n_rounds,
xgb_model=booster,
evals=[(Xy, "Original"), (Xy_update, "Train")],
evals_result=prune_result,
)
# Have a smaller model, but similar accuracy.
np.testing.assert_allclose(
np.array(prune_result["Original"]["rmse"]),
np.array(prune_result["Train"]["rmse"]),
atol=1e-5
)
if __name__ == "__main__":
main()
```

**Total running time of the script:** ( 0 minutes 0.000 seconds)