Node Harvest selects a small subset of nodes from a Random Forest predictor. This potentially increases both, interpretability and predictive accuracy. For details about the algorithm see [1] (pdf).

This implementation is written in Python 3 and works on top of the RandomForestRegressor from scikit-learn. For solving the quadratic optimization either scipy.optimize.minimize (slow) or [cvxopt.solvers.qp] (http://cvxopt.org/userguide/coneprog.html#quadratic-programming) (preferred) are available.

[1] [N. Meinshausen. "Node Harvest". The Annals of Applied Statistics, 4(4), 2010] (http://www.stats.ox.ac.uk/~meinshau/AOAS367.pdf)

There is no installer yet. Simply copy nodeharvest.py into your project source tree.

import numpy as np
from sklearn.ensemble import RandomForestRegressor
from nodeharvest import NodeHarvest

model = lambda x: np.prod(np.sin(2 * np.pi * x), axis=1) + np.random.randn(x.shape[0]) * 0.25

x_train = np.random.rand(100, 3)
y_train = model(x_train)

x_test = np.random.rand(10, 3)
y_test = model(x_test)

rf = RandomForestRegressor(n_estimators=100, min_samples_leaf=20)
rf.fit(x_train, y_train)

nh = NodeHarvest(solver='scipy_robust', verbose=True)
nh.fit(rf, x_train, y_train)

y_est = nh.predict(x_test)

print('     true y    predicted y  ')
print(np.transpose([y_test, y_est]))

Currently, unit tests are included in nodeharvest.py directly:

python nodeharvest.py

or

nosetests nodeharvest.py

This implementation of Node Harvest is BSD-licensed (3 clause):

Copyright (c) 2015, Martin Billinger. All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

This software is provided by the copyright holders and contributors "as is" and any express or implied warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed. In no event shall the copyright owner or contributors be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute goods or services; loss of use, data, or profits; or business interruption) however caused and on any theory of liability, whether in contract, strict liability, or tort (including negligence or otherwise) arising in any way out of the use of this software, even if advised of the possibility of such damage.