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Neural Network with Random Forest Structure

Project description

Neural Network with Random Forest Structure

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NNRF is a neural network with random forest structure as detailed by Wang, S. et. al.. This package is implemented similar to the sklearn API style. It is not fully faithful as it includes modularity regarding activation, regularization, loss, and optimizer functions.

The NNDT is structured like a binary decision tree with each node simulating a split. Furthermore, each node is a visible-hidden hybrid, taking in input from it ancestor node along with r features from the data. By training through backpropagation, NNDTs are able to model more powerful splits as well as tune all splits from leaf to root, resulting in better performance compared to traditional decision trees.

The NNRF creates an ensemble of NNDTs that are each trained with bootstrapped data and features, resulting in a powerful model that generalizes well. nnrf also allows NNRFs to be stacked with secondary classifiers that can learn better decision-making models over the basic voting schematic of random forests.

More details regarding nnrf can be found in the documentation here.


Once you have a suitable python environment setup, nnrf can be easily installed using pip:

pip install nnrf

nnrf is tested and supported on Python 3.4+ up to Python 3.7. Usage on other versions of Python is not guaranteed to work as intended.

Using NNRFs and NNDTs

NNRFs and NNDTs can be used to classify data very easily. Furthermore, they generally follow sklearn's API.

from nnrf import NNRF

# Create and fit an NNRF model with 50 NNDTs with depth of 5
nnrf = NNRF(n=50, d=5).fit(X_train, Y_train)

# Predict some data
predictions = nnrf.predict(X_test)
# Or just get the raw probabilities
predictions = nnrf.predict_proba(X_test)

nnrf is built with modular activation, loss, regularization, and optimization algorithms, making it simple to build models with different or even custom implementations.

from nnrf import NNRF
from nnrf import ml

# Using some default options
nnrf = NNRF(loss='cross-entropy', optimizer='adam', regularize='l2')
# Using some custom options
o = ml.optimizer.SGD(alpha=0.01) 	# SGD with a learning rate of 0.01
r = ml.regularizer.L2(c=0.001) 		# L2 Regularization with strength at 0.001
a = ml.activation.PReLU(a=0.4)		# PReLU activation with parameter of 0.4
nnrf = NNRF(optimizer=o, regularizer=r, activation=r)

Finally, a secondary classifier can easily be stacked upon the random forest. nnrf provides a simple neural network implementation as well as a dynamic ensemble selection method (DES-kNN).

from nnrf import NNRF, NeuralNetwork, DESKNN

# Create a simple neural network
nn = NeuralNetwork(layers=(100,))
# Create and fit an NNRF that feeds into our neural network.
nnrf = NNRF(n=50, d=5).fit(X_train, Y_train)
p = np.array([])
for e in nnrf.estimators_:
	p = np.concatenate((p, e.predict_proba(X_stack)), axis=1), Y_stack)

# We can also create a DES-kNN
nnrf = NNRF(n=50, d=5).fit(X_train, Y_train)
des = DESKNN(ensemble=nnrf, k=100).fit(X_stack, Y_stack)

For full details on usage, see the documentation.


See the changelog for a history of notable changes to nnrf.


Code Climate maintainability

nnrf is mostly complete, however could use some more rigorous testing. Finally, nnrf doesn't faithfully implement sklearn's API all too well - it was meant to follow the API in spirit but wasn't intended to be integrated (as of yet). Any assistance on this is more than welcome!

Help and Support


Documentation for nnrf can be found here.

Issues and Questions

Issues and Questions should be posed to the issue tracker here.

Project details

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