[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

pymc_experimental/inference/laplace.py

@@ -103,8 +103,7 @@ def laplace(
     # See https://www.pymc.io/projects/docs/en/stable/api/model/generated/pymc.model.transform.conditioning.remove_value_transforms.html
     untransformed_m = remove_value_transforms(transformed_m)
     untransformed_vars = [untransformed_m[v.name] for v in vars]
-    hessian = pm.find_hessian(
-        point=map, vars=untransformed_vars, model=untransformed_m)
+    hessian = pm.find_hessian(point=map, vars=untransformed_vars, model=untransformed_m)
 
     if np.linalg.det(hessian) == 0:
         raise np.linalg.LinAlgError("Hessian is singular.")
@@ -119,8 +118,7 @@ def laplace(
 
         data_vars = {}
         for i, var in enumerate(vars):
-            data_vars[str(var)] = xr.DataArray(
-                samples[:, :, i], dims=("chain", "draw"))
+            data_vars[str(var)] = xr.DataArray(samples[:, :, i], dims=("chain", "draw"))
 
         coords = {"chain": np.arange(chains), "draw": np.arange(draws)}
         ds = xr.Dataset(data_vars, coords=coords)
@@ -138,15 +136,13 @@ def laplace(
 def addFitToInferenceData(vars, idata, mean, covariance):
     coord_names = [v.name for v in vars]
     # Convert to xarray DataArray
-    mean_dataarray = xr.DataArray(
-        mean, dims=["rows"], coords={"rows": coord_names})
+    mean_dataarray = xr.DataArray(mean, dims=["rows"], coords={"rows": coord_names})
     cov_dataarray = xr.DataArray(
         covariance, dims=["rows", "columns"], coords={"rows": coord_names, "columns": coord_names}
     )
 
     # Create xarray dataset
-    dataset = xr.Dataset({"mean_vector": mean_dataarray,
-                         "covariance_matrix": cov_dataarray})
+    dataset = xr.Dataset({"mean_vector": mean_dataarray, "covariance_matrix": cov_dataarray})
 
     idata.add_groups(fit=dataset)
 

pymc_experimental/tests/test_laplace.py

@@ -60,8 +60,7 @@ def test_laplace():
     bda_cov = np.array([[y.var() / n, 0], [0, 1 / (2 * n)]])
 
     assert np.allclose(idata.fit["mean_vector"].values, bda_map)
-    assert np.allclose(
-        idata.fit["covariance_matrix"].values, bda_cov, atol=1e-4)
+    assert np.allclose(idata.fit["covariance_matrix"].values, bda_cov, atol=1e-4)
 
 
 @pytest.mark.filterwarnings(
@@ -99,5 +98,4 @@ def test_laplace_only_fit():
     bda_cov = np.array([[y.var() / n, 0], [0, 1 / (2 * n)]])
 
     assert np.allclose(idata.fit["mean_vector"].values, bda_map)
-    assert np.allclose(
-        idata.fit["covariance_matrix"].values, bda_cov, atol=1e-4)
+    assert np.allclose(idata.fit["covariance_matrix"].values, bda_cov, atol=1e-4)