Perform incremental learning of XGBClassifier

After referring to this link I was able to successfully implement incremental learning using XGBoost. I want to build a classifier and need to check the predict probabilities i.e. predict_proba() method. This is not possible if I use XGBoost. While implementing XGBClassifier.fit() instead of XGBoost.train() I am not able to perform incremental learning. The xgb_model parameter of the XGBClassifier.fit() takes the XGBoost while I want to provide an XGBClassifier.

Is it possible to perform incremental learning of XGBClassifier since I need to make use of predict_proba() method?

Working Code:

import XGBoost as xgb

train_data = xgb.DMatrix(X, y)
model = xgb.train(
    params = best_params, 
    dtrain = train_data, 
)

new_train_data = xgb.DMatrix(X_new, y_new)
retrained_model = xgb.train(
    params     = best_params, 
    dtrain     = new_train_data, 
    xgb_model  = model
)

Above code runs perfectly but does not has an option of retrained_model.predict_proba()

Non-working Code:

import XGBoost as xgb

xgb_model = xgb.XGBClassifier(**best_params)
xgb_model.fit(X, y)

retrained_model = xgb.XGBClassifier(**best_params)
retrained_model.fit(X_new, y_new, xgb_model = xgb_model)

Above code does not work since it expects an XGBoost model or Booster instance XGBoost model to be loaded.

Error Trace:

[11:27:51] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
Traceback (most recent call last):
  File "/project/Data_Training.py", line 530, in train
    retrained_model.fit(X_new, y_new, xgb_model = xgb_model)
  File "/home/user/.local/lib/python3.6/site-packages/xgboost/core.py", line 422, in inner_f
    return f(**kwargs)
  File "/home/user/.local/lib/python3.6/site-packages/xgboost/sklearn.py", line 915, in fit
    callbacks=callbacks)
  File "/home/user/.local/lib/python3.6/site-packages/xgboost/training.py", line 236, in train
    early_stopping_rounds=early_stopping_rounds)
  File "/home/user/.local/lib/python3.6/site-packages/xgboost/training.py", line 60, in _train_internal
    model_file=xgb_model)
  File "/home/user/.local/lib/python3.6/site-packages/xgboost/core.py", line 1044, in __init__
    raise TypeError('Unknown type:', model_file)
TypeError: ('Unknown type:', XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,
              importance_type='gain', interaction_constraints='',
              learning_rate=1, max_delta_step=0, max_depth=3,
              min_child_weight=1, missing=nan, monotone_constraints='()',
              n_estimators=100, n_jobs=32, num_parallel_tree=1, random_state=0,
              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=0.7,
              tree_method='exact', validate_parameters=1, verbosity=None))

Answer

From the docs:

xgb_model – file name of stored XGBoost model or ‘Booster’ instance[.] XGBoost model to be loaded before training (allows training continuation).

So you should be able to use xgb_model.get_booster() to retrieve the underlying Booster instance and pass that.

---

Also, you can get predicted probabilities out of the native xgboost API; Booster.predict returns probabilities when objective='binary:logistic'.