Remove SeededTest class

pymc/testing.py

@@ -775,24 +775,7 @@
         assert p > alpha, str(point)
 
 
-class SeededTest:
-    random_seed = 20160911
-    random_state = None
-
-    @classmethod
-    def setup_class(cls):
-        nr.seed(cls.random_seed)
-
-    def setup_method(self):
-        nr.seed(self.random_seed)
-
-    def get_random_state(self, reset=False):
-        if self.random_state is None or reset:
-            self.random_state = nr.RandomState(self.random_seed)
-        return self.random_state
-
-
-class BaseTestDistributionRandom(SeededTest):
+class BaseTestDistributionRandom:
     """
     Base class for tests that new RandomVariables are correctly
     implemented, and that the mapping of parameters between the PyMC
@@ -863,8 +846,9 @@
     sizes_to_check: Optional[List] = None
     sizes_expected: Optional[List] = None
     repeated_params_shape = 5
+    random_state = None
 
-    def test_distribution(self):
+    def test_distribution(self, seeded_test):
         self.validate_tests_list()
         if self.pymc_dist == pm.Wishart:
             with pytest.warns(UserWarning, match="can currently not be used for MCMC sampling"):
@@ -886,6 +870,11 @@
             else:
                 getattr(self, check_name)()
 
+    def get_random_state(self, reset=False):
+        if self.random_state is None or reset:
+            self.random_state = nr.RandomState(20160911)
+        return self.random_state
+
     def _instantiate_pymc_rv(self, dist_params=None):
         params = dist_params if dist_params else self.pymc_dist_params
         self.pymc_rv = self.pymc_dist.dist(

tests/conftest.py

@@ -44,8 +44,7 @@ def strict_float32():
 
 @pytest.fixture(scope="function", autouse=False)
 def seeded_test():
-    # TODO: use this instead of SeededTest
-    np.random.seed(42)
+    np.random.seed(20160911)
 
 
 @pytest.fixture

tests/distributions/test_mixture.py

@@ -79,7 +79,6 @@
     R,
     Rplus,
     Rplusbig,
-    SeededTest,
     Simplex,
     Unit,
     assert_moment_is_expected,
@@ -115,7 +114,10 @@ def generate_poisson_mixture_data(w, mu, size=1000):
     return x
 
 
-class TestMixture(SeededTest):
+class TestMixture:
+    def get_random_state(self):
+        return np.random.RandomState(20160911)
+
     def get_initial_point(self, model):
         """Get initial point with untransformed variables for posterior predictive sampling"""
         return {
@@ -477,7 +479,7 @@ def test_single_poisson_sampling(self):
                 trace = sample(
                     5000,
                     step=step,
-                    random_seed=self.random_seed,
+                    random_seed=45354,
                     progressbar=False,
                     chains=1,
                     return_inferencedata=False,
@@ -502,7 +504,7 @@ def test_list_poissons_sampling(self):
                     5000,
                     chains=1,
                     step=Metropolis(),
-                    random_seed=self.random_seed,
+                    random_seed=5363567,
                     progressbar=False,
                     return_inferencedata=False,
                 )
@@ -533,7 +535,7 @@ def test_list_normals_sampling(self):
                     5000,
                     chains=1,
                     step=Metropolis(),
-                    random_seed=self.random_seed,
+                    random_seed=645334,
                     progressbar=False,
                     return_inferencedata=False,
                 )
@@ -785,8 +787,8 @@ def test_preventing_mixing_cont_and_discrete(self):
                 )
 
 
-class TestNormalMixture(SeededTest):
-    def test_normal_mixture_sampling(self):
+class TestNormalMixture:
+    def test_normal_mixture_sampling(self, seeded_test):
         norm_w = np.array([0.75, 0.25])
         norm_mu = np.array([0.0, 5.0])
         norm_sigma = np.ones_like(norm_mu)
@@ -804,7 +806,7 @@ def test_normal_mixture_sampling(self):
                 trace = sample(
                     5000,
                     step=step,
-                    random_seed=self.random_seed,
+                    random_seed=20160911,
                     progressbar=False,
                     chains=1,
                     return_inferencedata=False,
@@ -816,7 +818,7 @@ def test_normal_mixture_sampling(self):
     @pytest.mark.parametrize(
         "nd, ncomp", [(tuple(), 5), (1, 5), (3, 5), ((3, 3), 5), (3, 3), ((3, 3), 3)], ids=str
     )
-    def test_normal_mixture_nd(self, nd, ncomp):
+    def test_normal_mixture_nd(self, seeded_test, nd, ncomp):
         nd = to_tuple(nd)
         ncomp = int(ncomp)
         comp_shape = nd + (ncomp,)
@@ -865,7 +867,7 @@ def test_normal_mixture_nd(self, nd, ncomp):
             assert_allclose(logp0, logp1)
             assert_allclose(logp0, logp2)
 
-    def test_random(self):
+    def test_random(self, seeded_test):
         def ref_rand(size, w, mu, sigma):
             component = np.random.choice(w.size, size=size, p=w)
             return np.random.normal(mu[component], sigma[component], size=size)
@@ -894,9 +896,12 @@ def ref_rand(size, w, mu, sigma):
         )
 
 
-class TestMixtureVsLatent(SeededTest):
+class TestMixtureVsLatent:
     """This class contains tests that compare a marginal Mixture with a latent indexed Mixture"""
 
+    def get_random_state(self):
+        return np.random.RandomState(20160911)
+
     def test_scalar_components(self):
         nd = 3
         npop = 4
@@ -1013,21 +1018,20 @@ def loose_logp(model, vars):
         assert_allclose(mix_logp, latent_mix_logp, rtol=rtol)
 
 
-class TestMixtureSameFamily(SeededTest):
+class TestMixtureSameFamily:
     """Tests that used to belong to deprecated `TestMixtureSameFamily`.
 
     The functionality is now expected to be provided by `Mixture`
     """
 
     @classmethod
     def setup_class(cls):
-        super().setup_class()
         cls.size = 50
         cls.n_samples = 1000
         cls.mixture_comps = 10
 
     @pytest.mark.parametrize("batch_shape", [(3, 4), (20,)], ids=str)
-    def test_with_multinomial(self, batch_shape):
+    def test_with_multinomial(self, seeded_test, batch_shape):
         p = np.random.uniform(size=(*batch_shape, self.mixture_comps, 3))
         p /= p.sum(axis=-1, keepdims=True)
         n = 100 * np.ones((*batch_shape, 1))
@@ -1062,7 +1066,7 @@ def test_with_multinomial(self, batch_shape):
             rtol,
         )
 
-    def test_with_mvnormal(self):
+    def test_with_mvnormal(self, seeded_test):
         # 10 batch, 3-variate Gaussian
         mu = np.random.randn(self.mixture_comps, 3)
         mat = np.random.randn(3, 3)

tests/distributions/test_simulator.py

@@ -33,10 +33,9 @@
 from pymc.initial_point import make_initial_point_fn
 from pymc.pytensorf import compile_pymc
 from pymc.smc.kernels import IMH
-from pymc.testing import SeededTest
 
 
-class TestSimulator(SeededTest):
+class TestSimulator:
     @staticmethod
     def count_rvs(end_node):
         return len(
@@ -60,7 +59,6 @@ def quantiles(x):
         return np.quantile(x, [0.25, 0.5, 0.75])
 
     def setup_class(self):
-        super().setup_class()
         self.data = np.random.normal(loc=0, scale=1, size=1000)
 
         with pm.Model() as self.SMABC_test:
@@ -75,7 +73,7 @@ def setup_class(self):
             c = pm.Potential("c", pm.math.switch(a > 0, 0, -np.inf))
             s = pm.Simulator("s", self.normal_sim, a, b, observed=self.data)
 
-    def test_one_gaussian(self):
+    def test_one_gaussian(self, seeded_test):
         assert self.count_rvs(self.SMABC_test.logp()) == 1
 
         with self.SMABC_test:
@@ -95,7 +93,7 @@ def test_one_gaussian(self):
         assert abs(self.data.std() - po_p["s"].std()) < 0.10
 
     @pytest.mark.parametrize("floatX", ["float32", "float64"])
-    def test_custom_dist_sum_stat(self, floatX):
+    def test_custom_dist_sum_stat(self, seeded_test, floatX):
         with pytensor.config.change_flags(floatX=floatX):
             with pm.Model() as m:
                 a = pm.Normal("a", mu=0, sigma=1)
@@ -118,7 +116,7 @@ def test_custom_dist_sum_stat(self, floatX):
                     pm.sample_smc(draws=100)
 
     @pytest.mark.parametrize("floatX", ["float32", "float64"])
-    def test_custom_dist_sum_stat_scalar(self, floatX):
+    def test_custom_dist_sum_stat_scalar(self, seeded_test, floatX):
         """
         Test that automatically wrapped functions cope well with scalar inputs
         """
@@ -149,22 +147,22 @@ def test_custom_dist_sum_stat_scalar(self, floatX):
                 )
             assert self.count_rvs(m.logp()) == 1
 
-    def test_model_with_potential(self):
+    def test_model_with_potential(self, seeded_test):
         assert self.count_rvs(self.SMABC_potential.logp()) == 1
 
         with self.SMABC_potential:
             trace = pm.sample_smc(draws=100, chains=1, return_inferencedata=False)
             assert np.all(trace["a"] >= 0)
 
-    def test_simulator_metropolis_mcmc(self):
+    def test_simulator_metropolis_mcmc(self, seeded_test):
         with self.SMABC_test as m:
             step = pm.Metropolis([m.rvs_to_values[m["a"]], m.rvs_to_values[m["b"]]])
             trace = pm.sample(step=step, return_inferencedata=False)
 
         assert abs(self.data.mean() - trace["a"].mean()) < 0.05
         assert abs(self.data.std() - trace["b"].mean()) < 0.05
 
-    def test_multiple_simulators(self):
+    def test_multiple_simulators(self, seeded_test):
         true_a = 2
         true_b = -2
 
@@ -214,9 +212,9 @@ def test_multiple_simulators(self):
         assert abs(true_a - trace["a"].mean()) < 0.05
         assert abs(true_b - trace["b"].mean()) < 0.05
 
-    def test_nested_simulators(self):
+    def test_nested_simulators(self, seeded_test):
         true_a = 2
-        rng = self.get_random_state()
+        rng = np.random.RandomState(20160911)
         data = rng.normal(true_a, 0.1, size=1000)
 
         with pm.Model() as m:
@@ -244,7 +242,7 @@ def test_nested_simulators(self):
 
         assert np.abs(true_a - trace["sim1"].mean()) < 0.1
 
-    def test_upstream_rngs_not_in_compiled_logp(self):
+    def test_upstream_rngs_not_in_compiled_logp(self, seeded_test):
         smc = IMH(model=self.SMABC_test)
         smc.initialize_population()
         smc._initialize_kernel()
@@ -263,7 +261,7 @@ def test_upstream_rngs_not_in_compiled_logp(self):
         ]
         assert len(shared_rng_vars) == 1
 
-    def test_simulator_error_msg(self):
+    def test_simulator_error_msg(self, seeded_test):
         msg = "The distance metric not_real is not implemented"
         with pytest.raises(ValueError, match=msg):
             with pm.Model() as m:
@@ -280,7 +278,7 @@ def test_simulator_error_msg(self):
                 sim = pm.Simulator("sim", self.normal_sim, 0, params=(1))
 
     @pytest.mark.xfail(reason="KL not refactored")
-    def test_automatic_use_of_sort(self):
+    def test_automatic_use_of_sort(self, seeded_test):
         with pm.Model() as model:
             s_k = pm.Simulator(
                 "s_k",
@@ -292,7 +290,7 @@ def test_automatic_use_of_sort(self):
             )
         assert s_k.distribution.sum_stat is pm.distributions.simulator.identity
 
-    def test_name_is_string_type(self):
+    def test_name_is_string_type(self, seeded_test):
         with self.SMABC_potential:
             assert not self.SMABC_potential.name
             with warnings.catch_warnings():
@@ -303,7 +301,7 @@ def test_name_is_string_type(self):
                 trace = pm.sample_smc(draws=10, chains=1, return_inferencedata=False)
             assert isinstance(trace._straces[0].name, str)
 
-    def test_named_model(self):
+    def test_named_model(self, seeded_test):
         # Named models used to fail with Simulator because the arguments to the
         # random fn used to be passed by name. This is no longer true.
         # https://github.com/pymc-devs/pymc/pull/4365#issuecomment-761221146
@@ -323,7 +321,7 @@ def test_named_model(self):
     @pytest.mark.parametrize("mu", [0, np.arange(3)], ids=str)
     @pytest.mark.parametrize("sigma", [1, np.array([1, 2, 5])], ids=str)
     @pytest.mark.parametrize("size", [None, 3, (5, 3)], ids=str)
-    def test_simulator_moment(self, mu, sigma, size):
+    def test_simulator_moment(self, seeded_test, mu, sigma, size):
         def normal_sim(rng, mu, sigma, size):
             return rng.normal(mu, sigma, size=size)
 
@@ -357,7 +355,7 @@ def normal_sim(rng, mu, sigma, size):
 
         assert np.all(np.abs((result - expected_sample_mean) / expected_sample_mean_std) < cutoff)
 
-    def test_dist(self):
+    def test_dist(self, seeded_test):
         x = pm.Simulator.dist(self.normal_sim, 0, 1, sum_stat="sort", shape=(3,))
         x = cloudpickle.loads(cloudpickle.dumps(x))
 

tests/distributions/test_transform.py

@@ -33,7 +33,6 @@
     R,
     Rminusbig,
     Rplusbig,
-    SeededTest,
     Simplex,
     SortedVector,
     Unit,
@@ -301,7 +300,7 @@ def test_chain_jacob_det():
     check_jacobian_det(chain_tranf, Vector(R, 4), pt.vector, floatX(np.zeros(4)), elemwise=False)
 
 
-class TestElementWiseLogp(SeededTest):
+class TestElementWiseLogp:
     def build_model(self, distfam, params, size, transform, initval=None):
         if initval is not None:
             initval = pm.floatX(initval)

tests/sampler_fixtures.py

@@ -21,7 +21,6 @@
 import pymc as pm
 
 from pymc.backends.arviz import to_inference_data
-from pymc.testing import SeededTest
 from pymc.util import get_var_name
 
 
@@ -135,10 +134,11 @@ def make_model(cls):
         return model
 
 
-class BaseSampler(SeededTest):
+class BaseSampler:
     @classmethod
     def setup_class(cls):
-        super().setup_class()
+        cls.random_seed = 20160911
+        np.random.seed(cls.random_seed)
         cls.model = cls.make_model()
         with cls.model:
             cls.step = cls.make_step()