This library contains tools to simplify conversion between models in different machine learning libraries.
In particular, it contains conversion code Gradient Boosted Decision Trees for sklearn -> TMVA and xgboost -> TMVA.
Binary classification, multiclass and regression trees are supported.
Exporting to TMVA XML:
╔════════════╦═════════╦═════════╗
║ type ║ sklearn ║ xgboost ║
╠════════════╬═════════╬═════════╣
║ binary ║ x ║ x ║
║ multiclass ║ x ║ ║
║ regression ║ x ║ ║
╚════════════╩═════════╩═════════╝
Clone this repository, install with
python setup.py install
Here is a small example
model = xgboost.XGBClassifier(n_estimators=10)
model.fit(data_x, data_y_binary)
num_features = data_x.shape[1]
features = ["feat{0}".format(nf) for nf in range(num_features)]
target_names = ["cls0", "cls1"]
bdt = BDTxgboost(model, features, target_names)
bdt.to_tmva("test.xml")
bdt.setup_tmva("test.xml")
for irow in range(data_x.shape[0]):
predA = bdt.eval_tmva(data_x[irow, :])
predB = bdt.eval(data_x[irow, :])
Check out test/test_all.py for an overview and the testsuite.
This program is licensed under the GPLv3 license, see LICENSE.md for details.