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Sparse GP serialization

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This commit is contained in:
Moreno 2018-05-16 08:53:55 +01:00
parent d85c9d5379
commit 7b2af57aee
4 changed files with 146 additions and 52 deletions
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39
GPy/core/sparse_gp.py
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@ -117,3 +117,42 @@ class SparseGP(GP):
self.Z.gradient = self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z)
self.Z.gradient += self.kern.gradients_X(self.grad_dict['dL_dKnm'].T, self.Z, self.X)
self._Zgrad = self.Z.gradient.copy()
def to_dict(self, save_data=True):
input_dict = super(SparseGP, self).to_dict(save_data)
input_dict["class"] = "GPy.core.SparseGP"
input_dict["Z"] = self.Z.tolist()
return input_dict
@staticmethod
def _from_dict(input_dict, data=None):
import GPy
if (input_dict['X'] is None) or (input_dict['Y'] is None):
assert(data is not None)
input_dict["X"], input_dict["Y"] = np.array(data[0]), np.array(data[1])
elif data is not None:
print("WARNING: The model has been saved with X,Y! The original values are being overriden!")
input_dict["X"], input_dict["Y"] = np.array(data[0]), np.array(data[1])
else:
input_dict["X"], input_dict["Y"] = np.array(input_dict['X']), np.array(input_dict['Y'])
input_dict["Z"] = np.array(input_dict['Z'])
input_dict["kernel"] = GPy.kern.Kern.from_dict(input_dict["kernel"])
input_dict["likelihood"] = GPy.likelihoods.likelihood.Likelihood.from_dict(input_dict["likelihood"])
mean_function = input_dict.get("mean_function")
if mean_function is not None:
input_dict["mean_function"] = GPy.core.mapping.Mapping.from_dict(mean_function)
else:
input_dict["mean_function"] = mean_function
input_dict["inference_method"] = GPy.inference.latent_function_inference.LatentFunctionInference.from_dict(input_dict["inference_method"])
#FIXME: Assumes the Y_metadata is serializable. We should create a Metadata class
Y_metadata = input_dict.get("Y_metadata")
input_dict["Y_metadata"] = Y_metadata
normalizer = input_dict.get("normalizer")
if normalizer is not None:
input_dict["normalizer"] = GPy.util.normalizer._Norm.from_dict(normalizer)
else:
input_dict["normalizer"] = normalizer
return SparseGP(**input_dict)