Multi Latent Likelihoods using new quadrature Likelihoods #1559
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* fixes * typo fix; reshaping fix * notebook showing how to use HeteroskedasticTFPDistribution likelihood * converting to .pct.py format * removed .ipynb * better descriptions * black auto-formatting Co-authored-by: Gustavo Carvalho <gustavo.carvalho@delfosim.com>
These tests ensure that heteroskedastic likelihood with a constant variance, will give the same results as a Gaussian likelihood with the same variance.
…drature-likelihood
…/GPflow/GPflow into gustavocmv/quadrature-likelihood
…GPflow into vincent/st/heteroscedastic
Co-authored-by: Vincent Dutordoir <dutordoirv@gmail.com>
…/GPflow/GPflow into gustavocmv/quadrature-likelihood
vdutor
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gustavocmv
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Sep 15, 2020
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ChrisMorter
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Sep 15, 2020
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| @property | ||
| def quadrature(self): | ||
| if self._quadrature is None: |
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Just a stylistic point, others may disagree, but I find this sort of precondition checking easier to read if you invert the if statements so that the condition and what happens if it fails are grouped together, e.g.
`
if self._quadrature is None:
return self._quadrature
if self.latent_dim is None:
raise Exception(
"latent_dim not specified. "
"Either set likelihood.latent_dim directly or "
"call a method which passes data to have it inferred."
)
with tf.init_scope():
self._quadrature = NDiagGHQuadrature(self.latent_dim, self.num_gauss_hermite_points)
`
That way you end up with fewer levels of nesting, and it's easier to separate the precondition checking from what the method really does.
This was referenced Sep 16, 2020
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This PR adds
MultiLatentlikelihoods, of whichHeteroskedasticTFPConditionalis an example, on top of the newly refactoredQuadratureLikelihoods (PR #1549).A notebook (courtesy of @st-- in PR #1455 ) and tests are added for documentation purposes.