Lint fix

Author: zaxtax

pymc_experimental/tests/model/test_marginal_model.py

@@ -4,15 +4,12 @@
 import numpy as np
 import pandas as pd
 import pymc as pm
-import pytensor
-import pytensor.tensor as pt
 import pytest
 from arviz import InferenceData, dict_to_dataset
 from pymc import ImputationWarning, inputvars
 from pymc.distributions import transforms
 from pymc.logprob.abstract import _logprob
 from pymc.util import UNSET
-from pytensor.graph import vectorize_graph
 from scipy.special import logsumexp
 from scipy.stats import norm
 
@@ -323,10 +320,8 @@ def test_nested_recover_marginals():
     assert idata.lp_sub_idx.shape == idata.sub_idx.shape + (2,)
 
     def true_idx_logp(y):
-        idx_0 = np.log(0.85*0.25*norm.pdf(y, loc=0) +
-                       0.15*0.25*norm.pdf(y, loc=1))
-        idx_1 = np.log(0.05*0.75*norm.pdf(y, loc=1) +
-                       0.95*0.75*norm.pdf(y, loc=2))
+        idx_0 = np.log(0.85 * 0.25 * norm.pdf(y, loc=0) + 0.15 * 0.25 * norm.pdf(y, loc=1))
+        idx_1 = np.log(0.05 * 0.75 * norm.pdf(y, loc=1) + 0.95 * 0.75 * norm.pdf(y, loc=2))
         return np.stack([idx_0, idx_1]).T
 
     np.testing.assert_almost_equal(
@@ -335,10 +330,8 @@ def true_idx_logp(y):
     )
 
     def true_sub_idx_logp(y):
-        sub_idx_0 = np.log(0.85*0.25*norm.pdf(y, loc=0) +
-                           0.05*0.75*norm.pdf(y, loc=1))
-        sub_idx_1 = np.log(0.15*0.25*norm.pdf(y, loc=1) +
-                           0.95*0.75*norm.pdf(y, loc=2))
+        sub_idx_0 = np.log(0.85 * 0.25 * norm.pdf(y, loc=0) + 0.05 * 0.75 * norm.pdf(y, loc=1))
+        sub_idx_1 = np.log(0.15 * 0.25 * norm.pdf(y, loc=1) + 0.95 * 0.75 * norm.pdf(y, loc=2))
         return np.stack([sub_idx_0, sub_idx_1]).T
 
     np.testing.assert_almost_equal(