michelif/mlglue

Package to facilitate model conversion between different ML tools

mlglue

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       ║         ║
╚════════════╩═════════╩═════════╝

Installation

Clone this repository, install with

python setup.py install

Quickstart

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.

LICENSE

This program is licensed under the GPLv3 license, see LICENSE.md for details.