How to get the params from a saved XGBoost model

user_4730817 0
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I'm trying to train a XGBoost model using the params below:

xgb_params = {
    'objective': 'binary:logistic',
    'eval_metric': 'auc',
    'lambda': 0.8,
    'alpha': 0.4,
    'max_depth': 10,
    'max_delta_step': 1,
    'verbose': True
}

Since my input data is too big to be fully loaded into the memory, I adapt the incremental training:

xgb_clf = xgb.train(xgb_params, input_data, num_boost_round=rounds_per_batch,
                    xgb_model=model_path)

The code for prediction is

xgb_clf = xgb.XGBClassifier()
booster = xgb.Booster()
booster.load_model(model_path)
xgb_clf._Booster = booster
raw_probas = xgb_clf.predict_proba(x)

The result seemed good. But when I tried to invoke xgb_clf.get_xgb_params(), I got a param dict in which all params were set to default values.

I can guess that the root cause is when I initialized the model, I didn't pass any params in. So the model was initialized using the default values but when it predicted, it used an internal booster that had been fitted using some pre-defined params.

However, I wonder is there any way that, after I assign a pre-trained booster model to a XGBClassifier, I can see the real params that are used to train the booster, but not those which are used to initialize the classifier.

xgboost python
3 Answers

If you are training like this -

dtrain = xgb.DMatrix(x_train, label=y_train)
model = xgb.train(model_params, dtrain, model_num_rounds)

Then the model returned is a Booster.

import json
json.loads(model.save_config())

the model.save_config() function lists down model parameters in addition to other configurations.

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