fromurllib.errorimportHTTPErrorimportnumpyasnpfromsklearn.datasetsimportfetch_california_housing,make_regressionimportxgboostasxgbdefmain()->None:n_rounds=32try:X,y=fetch_california_housing(return_X_y=True)exceptHTTPError:# Use a synthetic dataset instead if we couldn'tX,y=make_regression(n_samples=20640,n_features=8,random_state=1234)# Train a model firstX_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":"hist","max_depth":6,"device":"cuda"},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 statisticX_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={}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)assertnotnp.allclose(SHAP,refreshed_SHAP,rtol=1e-3)# Prune the trees with smaller max_depthX_update=X_trainy_update=y_trainXy_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()

DownloadJupyternotebook:update_process.ipynb

DownloadPythonsourcecode:update_process.py

Downloadzipped:update_process.zip