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[fix #380] reloading ep

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mzwiessele 2016-04-27 12:37:04 +01:00
parent 7da7460377
commit e4d76a133a
2 changed files with 29 additions and 2 deletions
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4
GPy/inference/latent_function_inference/expectation_propagation.py
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@ -51,7 +51,7 @@ class EP(EPBase, ExactGaussianInference):
if K is None:
K = kern.K(X)
if self._ep_approximation is None:
if getattr(self, '_ep_approximation', None) is None:
#if we don't yet have the results of runnign EP, run EP and store the computed factors in self._ep_approximation
mu, Sigma, mu_tilde, tau_tilde, Z_tilde = self._ep_approximation = self.expectation_propagation(K, Y, likelihood, Y_metadata)
else:
@ -159,7 +159,7 @@ class EPDTC(EPBase, VarDTC):
else:
Kmn = psi1.T
if self._ep_approximation is None:
if getattr(self, '_ep_approximation', None) is None:
mu, Sigma, mu_tilde, tau_tilde, Z_tilde = self._ep_approximation = self.expectation_propagation(Kmm, Kmn, Y, likelihood, Y_metadata)
else:
mu, Sigma, mu_tilde, tau_tilde, Z_tilde = self._ep_approximation

27
README.md
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@ -84,6 +84,33 @@ If you're having trouble installing GPy via `pip install GPy` here is a probable
[![Windows](https://img.shields.io/badge/download-windows-orange.svg)](https://pypi.python.org/pypi/GPy)
[![MacOSX](https://img.shields.io/badge/download-macosx-blue.svg)](https://pypi.python.org/pypi/GPy)
# Saving models in a consistent way across versions:
As pickle is inconsistent across python versions and heavily dependent on class structure, it behaves inconsistent across versions.
Pickling as meant to serialize models within the same environment, and not to store models on disk to be used later on.
To save a model it is best to save the m.param_array of it to disk (using numpy’s np.save).
Additionally, you save the script, which creates the model.
In this script you can create the model using initialize=False as a keyword argument and with the data loaded as normal.
You then set the model parameters by setting m.param_array[:] = loaded_params as the previously saved parameters.
Then you initialize the model by m.initialize_parameter(), which will make the model usable.
Be aware that up to this point the model is in an inconsistent state and cannot be used to produce any results.
```python
# let X, Y be data loaded above
# Model creation:
m = GPy.models.GPRegression(X, Y)
m.optimize()
# 1: Saving a model:
np.save('model_save.npy', m.param_array)
# 2: loading a model
# Model creation, without initialization:
m = GPy.models(GPRegression(X,Y,initialize=False)
m[:] = np.load('model_save.npy')
m.initialize_parameter()
print m
```
## Running unit tests:
Ensure nose is installed via pip: