bayesian-svm-knime-scikit

Bayesian Optimization of SVM parameters C and gamma, with scikit-learn, to be used in KNIME in Python learner node. Based on the optimization functions by thuijskens.

Why?

1. Parameter Optimization Loop Node(s) doesn't work as expected for some data. Including Bayesian optimization.
2. You may want to use scikit-learn instead of KNIME or Weka implementation.
3. You can tune this workflow to optimize other parameters for many different scikit algorithms.

Setup

• In python node please select python2.
• copy&paste the python code into the code window of Python Learner (python-learner.py) and Python Predictor (python-predictor.py)
• sample workflow:

• fine tuning - edit variables at the top of the python-learner.py:

# values of log10 gamma and C
# from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4534515/
# - log10(C) in [ - 2, 5]
# - log10(gamma) in [ - 10, 3]

bounds = np.array([[-2, 5], [-10, 3]])

# number of optimizations for bayesian optimizer
n_iters = 50

# number of initial samples to calculate
n_pre_samples=10

• please note: scripts (after slight modifications) can be run from the command line
• sample data file provided (nr-ahr-lite.csv from my tox21 dataset)

Sample output

• standard output from the Python Learner gives you C, gamma and CV AUROC values:

best C 82404.4422051
best gamma 1.01295459839e-10
best AUROC 0.793847566575

• output ROC (from the ROC Curve node):