How to save & load xgboost model? [closed]

Question

From the XGBoost guide:

After training, the model can be saved.

bst.save_model('0001.model')

The model and its feature map can also be dumped to a text file.

# dump model
bst.dump_model('dump.raw.txt')
# dump model with feature map
bst.dump_model('dump.raw.txt', 'featmap.txt')

A saved model can be loaded as follows:

bst = xgb.Booster({'nthread': 4})  # init model
bst.load_model('model.bin')  # load data

My questions are following.

1. What's the difference between save_model & dump_model?
2. What's the difference between saving '0001.model' and 'dump.raw.txt','featmap.txt'?
3. Why the model name for loading model.bin is different from the name to be saved 0001.model?
4. Suppose that I trained two models: model_A and model_B. I wanted to save both models for future use. Which save & load function should I use? Could you help show the clear process?

Answer 1

Here is how I solved the problem:

import pickle
file_name = "xgb_reg.pkl"

# save
pickle.dump(xgb_model, open(file_name, "wb"))

# load
xgb_model_loaded = pickle.load(open(file_name, "rb"))

# test
ind = 1
test = X_val[ind]
xgb_model_loaded.predict(test)[0] == xgb_model.predict(test)[0]

Out[1]: True

Answer 2

Both functions save_model and dump_model save the model, the difference is that in dump_model you can save feature name and save tree in text format.

The load_model will work with model from save_model. The model from dump_model can be used for example with xgbfi.

During loading the model, you need to specify the path where your models is saved. In the example bst.load_model("model.bin") model is loaded from file model.bin - it is just a name of file with model. Good luck!

EDIT: From Xgboost documentation (for version 1.3.3), the dump_model() should be used for saving the model for further interpretation. For saving and loading the model the save_model() and load_model() should be used. Please check the docs for more details.

There is also a difference between Learning API and Scikit-Learn API of Xgboost. The latter saves the best_ntree_limit variable which is set during the training with early stopping. You can read details in my article How to save and load Xgboost in Python?

The save_model() method recognize the format of the file name, if *.json is specified, then model is saved in JSON, otherwise it is text file.