Gaussian Process Training with Optimized Feature Maps for Shift-Invariant Kernel
To install GPoFM, clone this repo:
$ git clone https://github.com/MaxInGaussian/GPoFM.git
$ python setup.py install
from GPoFM import *
use_models = ['GPoMax', 'GPoReLU', 'GPoTanh']
for model_name in use_models:
ModelClass = getattr(sys.modules['GPoFM'], model_name)
model = GPoFM(ModelClass(nfeats, resolution, penalty, transform))
model.optimize(X_train, y_train, funcs, visualizer, **opt_params)# fit current dataset to original best trained model
best_model = GPoFM(Model().load(BEST_MODEL_PATH))
best_model.fit(X_train, y_train)
best_model.score(X_test, y_test)
# compare a new trained to original best trained model
model.score(X_test, y_test)
if(model.evals['score'][1][-1] < best_model.evals['score'][1][-1]):
# save if the new model gives better score
model.save('best_model.pkl')| Benchmark Dataset | Number of Attributes | Size of Training Data | Size of Testing Data |
|---|---|---|---|
| Bostion Housing | 13 | 400 | 106 |
| Abalone | 10 | 3133 | 1044 |
| Kin8nm | 10 | 5000 | 3192 |
Copyright (c) 2017, Max W. Y. Lam All rights reserved.
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