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

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

Author: pre-commit-ci[bot]

notebooks/discrete_markov_chain.ipynb

@@ -568,7 +568,6 @@
     "    ar_coefs = pm.Normal(\"coefs\", size=order, dims=[\"ar_params\"])\n",
     "\n",
     "    def AR_step(s, L1_s, L2_s, L3_s, L4_s, L1_y, L2_y, L3_y, L4_y, mus, phis):\n",
-    "\n",
     "        y_out = (\n",
     "            mus[s]\n",
     "            + phis[0] * (L1_y - mus[L1_s])\n",

pymc_experimental/inference/pathfinder.py

@@ -35,7 +35,6 @@ def convert_flat_trace_to_idata(
     postprocessing_backend="cpu",
     model=None,
 ):
-
     model = modelcontext(model)
     ip = model.initial_point()
     ip_point_map_info = pm.blocking.DictToArrayBijection.map(ip).point_map_info

pymc_experimental/statespace/filters/utilities.py

@@ -40,7 +40,6 @@ def split_vars_into_seq_and_nonseq(params, param_names):
     seq_names, non_seq_names = [], []
 
     for param, name in zip(params, param_names):
-
         if decide_if_x_time_varies(param, name):
             sequences.append(param)
             seq_names.append(name)

pymc_experimental/statespace/models/VARMAX.py

@@ -148,7 +148,6 @@ def __init__(
         measurement_error: bool = False,
         verbose=True,
     ):
-
         if (endog_names is None) and (k_endog is None):
             raise ValueError("Must specify either endog_names or k_endog")
         if (endog_names is not None) and (k_endog is None):

pymc_experimental/statespace/models/structural.py

@@ -1534,7 +1534,6 @@ def _get_state_names(self, k_exog, state_names, name):
         return k_exog, state_names
 
     def _handle_input_data(self, k_exog: int, state_names: Optional[List[str]], name) -> int:
-
         k_exog, state_names = self._get_state_names(k_exog, state_names, name)
         self.state_names = state_names
 

pymc_experimental/tests/model/test_marginal_model.py

@@ -54,7 +54,12 @@ def test_marginalized_bernoulli_logp():
 
     idx = pm.Bernoulli.dist(0.7, name="idx")
     y = pm.Normal.dist(mu=mu[idx], sigma=1.0, name="y")
-    marginal_rv_node = FiniteDiscreteMarginalRV([mu], [idx, y], ndim_supp=None, n_updates=0,)(
+    marginal_rv_node = FiniteDiscreteMarginalRV(
+        [mu],
+        [idx, y],
+        ndim_supp=None,
+        n_updates=0,
+    )(
         mu
     )[0].owner
 

pymc_experimental/tests/statespace/test_SARIMAX.py

@@ -339,7 +339,7 @@ def test_interpretable_states_are_interpretable(arima_mod_interp, pymc_mod_inter
     ma_lags = prior.prior.coords["ma_lag"].values - 1
 
     # Check the first p states are lags of the previous state
-    for (t, tm1) in zip(ar_lags[1:], ar_lags[:-1]):
+    for t, tm1 in zip(ar_lags[1:], ar_lags[:-1]):
         assert_allclose(
             prior_outputs.prior_latent.isel(state=t).values[1:],
             prior_outputs.prior_latent.isel(state=tm1).values[:-1],
@@ -348,7 +348,7 @@ def test_interpretable_states_are_interpretable(arima_mod_interp, pymc_mod_inter
 
     # Check the next p+q states are lags of the innovations
     n = len(ar_lags)
-    for (t, tm1) in zip(ma_lags[1:], ma_lags[:-1]):
+    for t, tm1 in zip(ma_lags[1:], ma_lags[:-1]):
         assert_allclose(
             prior_outputs.prior_latent.isel(state=n + t).values[1:],
             prior_outputs.prior_latent.isel(state=n + tm1).values[:-1],

pymc_experimental/tests/statespace/test_structural.py

@@ -177,8 +177,8 @@ def create_structural_model_and_equivalent_statsmodel(
     params = {}
     sm_params = {}
     sm_init = {}
-    expected_param_dims = defaultdict(lambda: ())
-    expected_coords = defaultdict(lambda: [])
+    expected_param_dims = defaultdict(tuple)
+    expected_coords = defaultdict(list)
     expected_param_dims["P0"] += ("state", "state_aux")
 
     default_states = [
@@ -727,15 +727,15 @@ def test_add_components():
     se_mats = [se_T, se_R, se_Q]
     all_mats = [T, R, Q]
 
-    for (ll_mat, se_mat, all_mat) in zip(ll_mats, se_mats, all_mats):
+    for ll_mat, se_mat, all_mat in zip(ll_mats, se_mats, all_mats):
         assert_allclose(all_mat, linalg.block_diag(ll_mat, se_mat), atol=ATOL, rtol=RTOL)
 
     ll_mats = [ll_x0, ll_c, ll_Z]
     se_mats = [se_x0, se_c, se_Z]
     all_mats = [x0, c, Z]
     axes = [0, 0, 1]
 
-    for (ll_mat, se_mat, all_mat, axis) in zip(ll_mats, se_mats, all_mats, axes):
+    for ll_mat, se_mat, all_mat, axis in zip(ll_mats, se_mats, all_mats, axes):
         assert_allclose(all_mat, np.concatenate([ll_mat, se_mat], axis=axis), atol=ATOL, rtol=RTOL)
 
 

pymc_experimental/tests/test_pathfinder.py

@@ -29,7 +29,6 @@ def test_pathfinder():
     sigma = np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0])
 
     with pm.Model() as model:
-
         mu = pm.Normal("mu", mu=0.0, sigma=10.0)
         tau = pm.HalfCauchy("tau", 5.0)
 

pymc_experimental/utils/linear_cg.py

@@ -28,7 +28,6 @@ def masked_fill(vector, mask, fill_value):
 def linear_cg_updates(
     result, alpha, residual_inner_prod, eps, beta, residual, precond_residual, curr_conjugate_vec
 ):
-
     # Everything inside _jit_linear_cg_updates
     result = result + alpha * curr_conjugate_vec
     beta = np.copy(residual_inner_prod)
@@ -68,7 +67,6 @@ def linear_cg(
     terminate_cg_by_size=False,
     use_eval_tolerange=False,
 ):
-
     if initial_guess is None:
         initial_guess = np.zeros_like(rhs)
 

pymc_experimental/utils/pytensorf.py

@@ -36,7 +36,6 @@ class StringConstant(Constant):
 
 @pytensor._as_symbolic.register(str)
 def as_symbolic_string(x, **kwargs):
-
     return StringConstant(stringtype, x)
 
 

setup.py

@@ -77,7 +77,6 @@ def read_version():
 
 
 if __name__ == "__main__":
-
     setup(
         name="pymc-experimental",
         version=read_version(),