I’ve been trying to run this tutorial for Accelerated Failure Time in survival analysis.
When I run the following example:
import numpy as np import xgboost as xgb # 4-by-2 Data matrix X = np.array([[1, -1], [-1, 1], [0, 1], [1, 0]]) dtrain = xgb.DMatrix(X) # Associate ranged labels with the data matrix. # This example shows each kind of censored labels. # uncensored right left interval y_lower_bound = np.array([ 2.0, 3.0, -np.inf, 4.0]) y_upper_bound = np.array([ 2.0, +np.inf, 4.0, 5.0]) dtrain.set_float_info('label_lower_bound', y_lower_bound) dtrain.set_float_info('label_upper_bound', y_upper_bound)
I get the error “Unknown metainfo: label_lower_bound”. I’m aware that the AFL model is a new addition to XGBoost, I just want to know if the current version supports upper and lower labels in a DMatrix yet (since it’s used in the tutorial provided by XGBoost).
Any clarity will be much appreciated.