I need to extract the decision rules from my fitted xgboost model in python. I use 0.6a2 version of xgboost library and my python version is 3.5.2.
My ultimate goal is to use those splits to bin variables ( according to the splits).
I did not come across any property of the model for this version which can give me splits.
plot_tree is giving me something similar. However it is visualization of the tree.
I need something like https://stackoverflow.com/a/39772170/4559070 for xgboost model
3 Answers
Answers 1
It is possible, but not easy. I would recommend you to use GradientBoostingClassifier from scikit-learn, which is similar to xgboost, but has native access to the built trees.
With xgboost, however, it is possible to get a textual representation of the model and then parse it:
from sklearn.datasets import load_iris from xgboost import XGBClassifier # build a very simple model X, y = load_iris(return_X_y=True) model = XGBClassifier(max_depth=2, n_estimators=2) model.fit(X, y); # dump it to a text file model.get_booster().dump_model('xgb_model.txt', with_stats=True) # read the contents of the file with open('xgb_model.txt', 'r') as f: txt_model = f.read() print(txt_model) It will print you a textual description of 6 trees (2 estimators, each consists of 3 trees, one per class), which starts like this:
booster[0]: 0:[f2<2.45] yes=1,no=2,missing=1,gain=72.2968,cover=66.6667 1:leaf=0.143541,cover=22.2222 2:leaf=-0.0733496,cover=44.4444 booster[1]: 0:[f2<2.45] yes=1,no=2,missing=1,gain=18.0742,cover=66.6667 1:leaf=-0.0717703,cover=22.2222 2:[f3<1.75] yes=3,no=4,missing=3,gain=41.9078,cover=44.4444 3:leaf=0.124,cover=24 4:leaf=-0.0668394,cover=20.4444 ... Now you can, for example, extract all splits from this description:
import re # trying to extract all patterns like "[f2<2.45]" splits = re.findall('\[f([0-9]+)<([0-9]+.[0-9]+)\]', txt_model) splits It will print you the list of tuples (feature_id, split_value), like
[('2', '2.45'), ('2', '2.45'), ('3', '1.75'), ('3', '1.65'), ('2', '4.95'), ('2', '2.45'), ('2', '2.45'), ('3', '1.75'), ('3', '1.65'), ('2', '4.95')] You can further process this list as you wish.
Answers 2
Generally, it is not possible. As There are hundreds of thousands of trees in xgboost. Credit: Jason Brownlee
Answers 3
You need to know the name of your tree, and after that, you can insert it into your code.
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