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bugfix for fit_laplace absent dims #609
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@@ -193,6 +193,44 @@ def test_fit_laplace_ragged_coords(rng): | |
| assert (idata["posterior"].beta.sel(feature=1).to_numpy() > 0).all() | ||
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| def test_fit_laplace_no_data_or_deterministic_dims(rng): | ||
| coords = {"city": ["A", "B", "C"], "feature": [0, 1], "obs_idx": np.arange(100)} | ||
| with pm.Model(coords=coords) as ragged_dim_model: | ||
| X = pm.Data("X", np.ones((100, 2))) | ||
| beta = pm.Normal( | ||
| "beta", mu=[[-100.0, 100.0], [-100.0, 100.0], [-100.0, 100.0]], dims=["city", "feature"] | ||
| ) | ||
| mu = pm.Deterministic("mu", (X[:, None, :] * beta[None]).sum(axis=-1)) | ||
| sigma = pm.Normal("sigma", mu=1.5, sigma=0.5, dims=["city"]) | ||
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| obs = pm.Normal( | ||
| "obs", | ||
| mu=mu, | ||
| sigma=sigma, | ||
| observed=rng.normal(loc=3, scale=1.5, size=(100, 3)), | ||
| dims=["obs_idx", "city"], | ||
| ) | ||
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| idata = fit_laplace( | ||
| optimize_method="Newton-CG", | ||
| progressbar=False, | ||
| use_grad=True, | ||
| use_hessp=True, | ||
| ) | ||
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| # These should have been dropped when the laplace idata was created | ||
| assert "laplace_approximation" not in list(idata.posterior.data_vars.keys()) | ||
| assert "unpacked_var_names" not in list(idata.posterior.coords.keys()) | ||
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| assert idata["posterior"].beta.shape[-2:] == (3, 2) | ||
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Member
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. shouldn't we test we get the expected sample dims as well?
Contributor
Author
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Yes, this is definitely a good idea. I added assert statements to test for sampling dims and I also consolidated the test with |
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| assert idata["posterior"].sigma.shape[-1:] == (3,) | ||
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| # Check that everything got unraveled correctly -- feature 0 should be strictly negative, feature 1 | ||
| # strictly positive | ||
| assert (idata["posterior"].beta.sel(feature=0).to_numpy() < 0).all() | ||
| assert (idata["posterior"].beta.sel(feature=1).to_numpy() > 0).all() | ||
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| def test_model_with_nonstandard_dimensionality(rng): | ||
| y_obs = np.concatenate( | ||
| [rng.normal(-1, 2, size=150), rng.normal(3, 1, size=350), rng.normal(5, 4, size=50)] | ||
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