remove global ignore

Author: juanitorduz

pymc/tuning/starting.py

@@ -132,7 +132,7 @@ def find_MAP(
     # TODO: If the mapping is fixed, we can simply create graphs for the
     # mapping and avoid all this bijection overhead
     compiled_logp_func = DictToArrayBijection.mapf(model.compile_logp(jacobian=False), start)
-    logp_func = lambda x: compiled_logp_func(RaveledVars(x, x0.point_map_info))
+    logp_func = lambda x: compiled_logp_func(RaveledVars(x, x0.point_map_info))  # noqa E731
 
     rvs = [model.values_to_rvs[vars_dict[name]] for name, _, _ in x0.point_map_info]
     try:
@@ -141,7 +141,7 @@ def find_MAP(
         compiled_dlogp_func = DictToArrayBijection.mapf(
             model.compile_dlogp(rvs, jacobian=False), start
         )
-        dlogp_func = lambda x: compiled_dlogp_func(RaveledVars(x, x0.point_map_info))
+        dlogp_func = lambda x: compiled_dlogp_func(RaveledVars(x, x0.point_map_info))  # noqa E731
         compute_gradient = True
     except (AttributeError, NotImplementedError, tg.NullTypeGradError):
         compute_gradient = False

tests/distributions/test_continuous.py

@@ -1846,7 +1846,7 @@ def halfstudentt_rng_fn(self, df, loc, scale, size, rng):
     pymc_dist_params = {"nu": 5.0, "sigma": 2.0}
     expected_rv_op_params = {"nu": 5.0, "sigma": 2.0}
     reference_dist_params = {"df": 5.0, "loc": 0, "scale": 2.0}
-    reference_dist = lambda self: ft.partial(self.halfstudentt_rng_fn, rng=self.get_random_state())
+    reference_dist = lambda self: ft.partial(self.halfstudentt_rng_fn, rng=self.get_random_state())  # noqa E731
     checks_to_run = [
         "check_pymc_params_match_rv_op",
         "check_pymc_draws_match_reference",
@@ -2069,7 +2069,7 @@ def logit_normal_rng_fn(self, rng, size, loc, scale):
     pymc_dist_params = {"mu": 5.0, "sigma": 10.0}
     expected_rv_op_params = {"mu": 5.0, "sigma": 10.0}
     reference_dist_params = {"loc": 5.0, "scale": 10.0}
-    reference_dist = lambda self: ft.partial(self.logit_normal_rng_fn, rng=self.get_random_state())
+    reference_dist = lambda self: ft.partial(self.logit_normal_rng_fn, rng=self.get_random_state())  # noqa E731
     checks_to_run = [
         "check_pymc_params_match_rv_op",
         "check_pymc_draws_match_reference",
@@ -2140,7 +2140,7 @@ class TestBeta(BaseTestDistributionRandom):
     expected_rv_op_params = {"alpha": 2.0, "beta": 5.0}
     reference_dist_params = {"a": 2.0, "b": 5.0}
     size = 15
-    reference_dist = lambda self: ft.partial(clipped_beta_rvs, random_state=self.get_random_state())
+    reference_dist = lambda self: ft.partial(clipped_beta_rvs, random_state=self.get_random_state())  # noqa E731
     checks_to_run = [
         "check_pymc_params_match_rv_op",
         "check_pymc_draws_match_reference",
@@ -2340,7 +2340,7 @@ def polyagamma_rng_fn(self, size, h, z, rng):
     pymc_dist_params = {"h": 1.0, "z": 0.0}
     expected_rv_op_params = {"h": 1.0, "z": 0.0}
     reference_dist_params = {"h": 1.0, "z": 0.0}
-    reference_dist = lambda self: ft.partial(self.polyagamma_rng_fn, rng=self.get_random_state())
+    reference_dist = lambda self: ft.partial(self.polyagamma_rng_fn, rng=self.get_random_state())  # noqa E731
     checks_to_run = [
         "check_pymc_params_match_rv_op",
         "check_pymc_draws_match_reference",
@@ -2361,7 +2361,7 @@ def interpolated_rng_fn(self, size, mu, sigma, rng):
     pymc_dist_params = {"x_points": x_points, "pdf_points": pdf_points}
     reference_dist_params = {"mu": mu, "sigma": sigma}
 
-    reference_dist = lambda self: ft.partial(self.interpolated_rng_fn, rng=self.get_random_state())
+    reference_dist = lambda self: ft.partial(self.interpolated_rng_fn, rng=self.get_random_state())  # noqa E731
     checks_to_run = [
         "check_rv_size",
         "check_draws",

tests/distributions/test_discrete.py

@@ -842,7 +842,7 @@ def discrete_uniform_rng_fn(self, size, lower, upper, rng):
     pymc_dist_params = {"lower": -1, "upper": 9}
     expected_rv_op_params = {"lower": -1, "upper": 9}
     reference_dist_params = {"lower": -1, "upper": 9}
-    reference_dist = lambda self: ft.partial(
+    reference_dist = lambda self: ft.partial(  # noqa E731
         self.discrete_uniform_rng_fn, rng=self.get_random_state()
     )
     checks_to_run = [

tests/distributions/test_distribution.py

@@ -301,7 +301,7 @@ def logp(value, mu):
     )
     def test_custom_dist_default_moment_univariate(self, moment, size, expected):
         if moment == "custom_moment":
-            moment = lambda rv, size, *rv_inputs: 5 * pt.ones(size, dtype=rv.dtype)
+            moment = lambda rv, size, *rv_inputs: 5 * pt.ones(size, dtype=rv.dtype)  # noqa E731
         with pm.Model() as model:
             x = CustomDist("x", moment=moment, size=size)
         assert isinstance(x.owner.op, CustomDistRV)
@@ -821,7 +821,7 @@ def diracdelta_rng_fn(self, size, c):
         pymc_dist_params = {"c": 3}
         expected_rv_op_params = {"c": 3}
         reference_dist_params = {"c": 3}
-        reference_dist = lambda self: self.diracdelta_rng_fn
+        reference_dist = lambda self: self.diracdelta_rng_fn  # noqa E731
         checks_to_run = [
             "check_pymc_params_match_rv_op",
             "check_pymc_draws_match_reference",

tests/distributions/test_multivariate.py

@@ -1778,7 +1778,7 @@ def mvstudentt_rng_fn(self, size, nu, mu, scale, rng):
         "mu": np.array([1.0, 2.0]),
         "scale": np.array([[2.0, 0.0], [0.0, 3.5]]),
     }
-    reference_dist = lambda self: ft.partial(self.mvstudentt_rng_fn, rng=self.get_random_state())
+    reference_dist = lambda self: ft.partial(self.mvstudentt_rng_fn, rng=self.get_random_state())  # noqa E731
     checks_to_run = [
         "check_pymc_params_match_rv_op",
         "check_pymc_draws_match_reference",
@@ -1981,7 +1981,7 @@ def wishart_rng_fn(self, size, nu, V, rng):
         (1, 3, 3),
         (4, 5, 3, 3),
     ]
-    reference_dist = lambda self: ft.partial(self.wishart_rng_fn, rng=self.get_random_state())
+    reference_dist = lambda self: ft.partial(self.wishart_rng_fn, rng=self.get_random_state())  # noqa E731
     checks_to_run = [
         "check_rv_size",
         "check_pymc_params_match_rv_op",
@@ -2110,7 +2110,7 @@ def kronecker_rng_fn(self, size, mu, covs=None, sigma=None, rng=None):
     sizes_to_check = [None, (), 1, (1,), 5, (4, 5), (2, 4, 2)]
     sizes_expected = [(N,), (N,), (1, N), (1, N), (5, N), (4, 5, N), (2, 4, 2, N)]
 
-    reference_dist = lambda self: ft.partial(self.kronecker_rng_fn, rng=self.get_random_state())
+    reference_dist = lambda self: ft.partial(self.kronecker_rng_fn, rng=self.get_random_state())  # noqa E731
     checks_to_run = [
         "check_pymc_draws_match_reference",
         "check_rv_size",
@@ -2366,7 +2366,7 @@ def test_mvnormal_no_cholesky_in_model_logp():
         data = np.ones((batch_size, n))
         pm.MvNormal("y", mu=mu, chol=pt.broadcast_to(chol, (batch_size, n, n)), observed=data)
 
-    contains_cholesky_op = lambda fgraph: any(
+    contains_cholesky_op = lambda fgraph: any(  # noqa E731
         isinstance(node.op, Cholesky) for node in fgraph.apply_nodes
     )
 

tests/distributions/test_timeseries.py

@@ -952,7 +952,7 @@ def _gen_sde_path(sde, pars, dt, n, x0):
                 xs.append(xs[-1] + f * dt + np.sqrt(dt) * g * wt[i])
             return np.array(xs)
 
-        sde = lambda x, lam: (lam * x, sig2)
+        sde = lambda x, lam: (lam * x, sig2)  # noqa E731
         x = floatX(_gen_sde_path(sde, (lam,), dt, N, 5.0))
         z = x + numpy_rng.standard_normal(size=x.size) * sig2
         # build model