Bring back tests

.github/workflows/pytest.yml

@@ -7,58 +7,98 @@ on:
 
 jobs:
   pytest:
-    if: false
     strategy:
       matrix:
         os: [ubuntu-18.04]
         floatx: [float32, float64]
         test-subset:
+        # Tests are split into multiple jobs to accelerate the CI.
+        # The first job (starting in the next block) shouldn't run any tests, but
+        # just ignores tests because that don't work at all, or run in other jobs.'
+        # Any test that was not ignored runs in the first job.
+        # A pre-commit hook (scripts/check_all_tests_are_covered.py) enforces that
+        # test run just once.
+
+        # Because YAML doesn't allow comments in the blocks below, here they are..
+        # 1st block: These tests are temporarily disabled, because they are _very_ broken
+        # 2nd block: The JAX tests run through their own workflow: jaxtests.yml
+        # 3nd & 4rd: These tests are covered by other matrix jobs
+        # 5th block: These tests PASS without a single XFAIL
+        # 6th block: These have some XFAILs
           - |
-            --ignore=pymc3/tests/test_dist_math.py
             --ignore=pymc3/tests/test_distribution_defaults.py
             --ignore=pymc3/tests/test_distributions.py
             --ignore=pymc3/tests/test_distributions_random.py
             --ignore=pymc3/tests/test_distributions_timeseries.py
-            --ignore=pymc3/tests/test_examples.py
-            --ignore=pymc3/tests/test_gp.py
+            --ignore=pymc3/tests/test_missing.py
             --ignore=pymc3/tests/test_mixture.py
-            --ignore=pymc3/tests/test_ode.py
+            --ignore=pymc3/tests/test_model_graph.py
+            --ignore=pymc3/tests/test_modelcontext.py
+            --ignore=pymc3/tests/test_models_linear.py
+            --ignore=pymc3/tests/test_ndarray_backend.py
             --ignore=pymc3/tests/test_parallel_sampling.py
+            --ignore=pymc3/tests/test_posterior_predictive.py
             --ignore=pymc3/tests/test_posteriors.py
-            --ignore=pymc3/tests/test_quadpotential.py
+            --ignore=pymc3/tests/test_profile.py
             --ignore=pymc3/tests/test_random.py
             --ignore=pymc3/tests/test_sampling.py
-            --ignore=pymc3/tests/test_sampling_jax.py
-            --ignore=pymc3/tests/test_shape_handling.py
             --ignore=pymc3/tests/test_shared.py
             --ignore=pymc3/tests/test_smc.py
+            --ignore=pymc3/tests/test_starting.py
             --ignore=pymc3/tests/test_step.py
-            --ignore=pymc3/tests/test_updates.py
+            --ignore=pymc3/tests/test_tracetab.py
+            --ignore=pymc3/tests/test_transforms.py
+            --ignore=pymc3/tests/test_tuning.py
+            --ignore=pymc3/tests/test_types.py
+            --ignore=pymc3/tests/test_util.py
             --ignore=pymc3/tests/test_variational_inference.py
+
+            --ignore=pymc3/tests/test_sampling_jax.py
+
+            --ignore=pymc3/tests/test_dist_math.py
+            --ignore=pymc3/tests/test_minibatches.py
+            --ignore=pymc3/tests/test_pickling.py
+            --ignore=pymc3/tests/test_plots.py
+            --ignore=pymc3/tests/test_special_functions.py
+            --ignore=pymc3/tests/test_updates.py
+
+            --ignore=pymc3/tests/test_dist_math.py
+            --ignore=pymc3/tests/test_examples.py
+            --ignore=pymc3/tests/test_glm.py
+            --ignore=pymc3/tests/test_gp.py
+            --ignore=pymc3/tests/test_memo.py
+            --ignore=pymc3/tests/test_model.py
+            --ignore=pymc3/tests/test_model_func.py
+            --ignore=pymc3/tests/test_model_helpers.py
+            --ignore=pymc3/tests/test_models_utils.py
+            --ignore=pymc3/tests/test_ode.py
+            --ignore=pymc3/tests/test_posdef_sym.py
+            --ignore=pymc3/tests/test_quadpotential.py
+            --ignore=pymc3/tests/test_shape_handling.py
+
           - |
             pymc3/tests/test_dist_math.py
-            pymc3/tests/test_distribution_defaults.py
-            pymc3/tests/test_distributions_random.py
-            pymc3/tests/test_parallel_sampling.py
-            pymc3/tests/test_random.py
-            pymc3/tests/test_shared.py
-            pymc3/tests/test_smc.py
+            pymc3/tests/test_minibatches.py
+            pymc3/tests/test_pickling.py
+            pymc3/tests/test_plots.py
+            pymc3/tests/test_special_functions.py
+            pymc3/tests/test_updates.py
+
           - |
+            pymc3/tests/test_dist_math.py
             pymc3/tests/test_examples.py
-            pymc3/tests/test_mixture.py
+            pymc3/tests/test_glm.py
+            pymc3/tests/test_gp.py
+            pymc3/tests/test_memo.py
+            pymc3/tests/test_model.py
+            pymc3/tests/test_model_func.py
+            pymc3/tests/test_model_helpers.py
+            pymc3/tests/test_models_utils.py
             pymc3/tests/test_ode.py
-            pymc3/tests/test_posteriors.py
+            pymc3/tests/test_posdef_sym.py
             pymc3/tests/test_quadpotential.py
-          - |
-            pymc3/tests/test_distributions_timeseries.py
             pymc3/tests/test_shape_handling.py
-            pymc3/tests/test_step.py
-            pymc3/tests/test_updates.py
-            pymc3/tests/test_variational_inference.py
-          - |
-            pymc3/tests/test_distributions.py
-            pymc3/tests/test_gp.py
-            pymc3/tests/test_sampling.py
+
       fail-fast: false
     runs-on: ${{ matrix.os }}
     env:

pymc3/gp/gp.py

@@ -137,10 +137,10 @@ def _build_prior(self, name, X, reparameterize=True, **kwargs):
         cov = stabilize(self.cov_func(X))
         shape = infer_shape(X, kwargs.pop("shape", None))
         if reparameterize:
-            v = pm.Normal(name + "_rotated_", mu=0.0, sigma=1.0, shape=shape, **kwargs)
+            v = pm.Normal(name + "_rotated_", mu=0.0, sigma=1.0, size=shape, **kwargs)
             f = pm.Deterministic(name, mu + cholesky(cov).dot(v))
         else:
-            f = pm.MvNormal(name, mu=mu, cov=cov, shape=shape, **kwargs)
+            f = pm.MvNormal(name, mu=mu, cov=cov, size=shape, **kwargs)
         return f
 
     def prior(self, name, X, reparameterize=True, **kwargs):
@@ -231,7 +231,7 @@ def conditional(self, name, Xnew, given=None, **kwargs):
         givens = self._get_given_vals(given)
         mu, cov = self._build_conditional(Xnew, *givens)
         shape = infer_shape(Xnew, kwargs.pop("shape", None))
-        return pm.MvNormal(name, mu=mu, cov=cov, shape=shape, **kwargs)
+        return pm.MvNormal(name, mu=mu, cov=cov, size=shape, **kwargs)
 
 
 @conditioned_vars(["X", "f", "nu"])
@@ -279,10 +279,10 @@ def _build_prior(self, name, X, reparameterize=True, **kwargs):
         shape = infer_shape(X, kwargs.pop("shape", None))
         if reparameterize:
             chi2 = pm.ChiSquared(name + "_chi2_", self.nu)
-            v = pm.Normal(name + "_rotated_", mu=0.0, sigma=1.0, shape=shape, **kwargs)
+            v = pm.Normal(name + "_rotated_", mu=0.0, sigma=1.0, size=shape, **kwargs)
             f = pm.Deterministic(name, (aet.sqrt(self.nu) / chi2) * (mu + cholesky(cov).dot(v)))
         else:
-            f = pm.MvStudentT(name, nu=self.nu, mu=mu, cov=cov, shape=shape, **kwargs)
+            f = pm.MvStudentT(name, nu=self.nu, mu=mu, cov=cov, size=shape, **kwargs)
         return f
 
     def prior(self, name, X, reparameterize=True, **kwargs):
@@ -349,7 +349,7 @@ def conditional(self, name, Xnew, **kwargs):
         f = self.f
         nu2, mu, cov = self._build_conditional(Xnew, X, f)
         shape = infer_shape(Xnew, kwargs.pop("shape", None))
-        return pm.MvStudentT(name, nu=nu2, mu=mu, cov=cov, shape=shape, **kwargs)
+        return pm.MvStudentT(name, nu=nu2, mu=mu, cov=cov, size=shape, **kwargs)
 
 
 @conditioned_vars(["X", "y", "noise"])
@@ -447,7 +447,7 @@ def marginal_likelihood(self, name, X, y, noise, is_observed=True, **kwargs):
             return pm.MvNormal(name, mu=mu, cov=cov, observed=y, **kwargs)
         else:
             shape = infer_shape(X, kwargs.pop("shape", None))
-            return pm.MvNormal(name, mu=mu, cov=cov, shape=shape, **kwargs)
+            return pm.MvNormal(name, mu=mu, cov=cov, size=shape, **kwargs)
 
     def _get_given_vals(self, given):
         if given is None:
@@ -525,7 +525,7 @@ def conditional(self, name, Xnew, pred_noise=False, given=None, **kwargs):
         givens = self._get_given_vals(given)
         mu, cov = self._build_conditional(Xnew, pred_noise, False, *givens)
         shape = infer_shape(Xnew, kwargs.pop("shape", None))
-        return pm.MvNormal(name, mu=mu, cov=cov, shape=shape, **kwargs)
+        return pm.MvNormal(name, mu=mu, cov=cov, size=shape, **kwargs)
 
     def predict(self, Xnew, point=None, diag=False, pred_noise=False, given=None):
         R"""
@@ -740,7 +740,7 @@ def marginal_likelihood(self, name, X, Xu, y, noise=None, is_observed=True, **kw
             return pm.DensityDist(name, logp, observed=y, **kwargs)
         else:
             shape = infer_shape(X, kwargs.pop("shape", None))
-            return pm.DensityDist(name, logp, shape=shape, **kwargs)
+            return pm.DensityDist(name, logp, size=shape, **kwargs)
 
     def _build_conditional(self, Xnew, pred_noise, diag, X, Xu, y, sigma, cov_total, mean_total):
         sigma2 = aet.square(sigma)
@@ -819,7 +819,7 @@ def conditional(self, name, Xnew, pred_noise=False, given=None, **kwargs):
         givens = self._get_given_vals(given)
         mu, cov = self._build_conditional(Xnew, pred_noise, False, *givens)
         shape = infer_shape(Xnew, kwargs.pop("shape", None))
-        return pm.MvNormal(name, mu=mu, cov=cov, shape=shape, **kwargs)
+        return pm.MvNormal(name, mu=mu, cov=cov, size=shape, **kwargs)
 
 
 @conditioned_vars(["Xs", "f"])
@@ -892,7 +892,7 @@ def _build_prior(self, name, Xs, **kwargs):
         mu = self.mean_func(cartesian(*Xs))
         chols = [cholesky(stabilize(cov(X))) for cov, X in zip(self.cov_funcs, Xs)]
         # remove reparameterization option
-        v = pm.Normal(name + "_rotated_", mu=0.0, sigma=1.0, shape=self.N, **kwargs)
+        v = pm.Normal(name + "_rotated_", mu=0.0, sigma=1.0, size=self.N, **kwargs)
         f = pm.Deterministic(name, mu + aet.flatten(kron_dot(chols, v)))
         return f
 
@@ -971,7 +971,7 @@ def conditional(self, name, Xnew, **kwargs):
         """
         mu, cov = self._build_conditional(Xnew)
         shape = infer_shape(Xnew, kwargs.pop("shape", None))
-        return pm.MvNormal(name, mu=mu, cov=cov, shape=shape, **kwargs)
+        return pm.MvNormal(name, mu=mu, cov=cov, size=shape, **kwargs)
 
 
 @conditioned_vars(["Xs", "y", "sigma"])
@@ -1095,7 +1095,7 @@ def marginal_likelihood(self, name, Xs, y, sigma, is_observed=True, **kwargs):
             return pm.KroneckerNormal(name, mu=mu, covs=covs, sigma=sigma, observed=y, **kwargs)
         else:
             shape = np.prod([len(X) for X in Xs])
-            return pm.KroneckerNormal(name, mu=mu, covs=covs, sigma=sigma, shape=shape, **kwargs)
+            return pm.KroneckerNormal(name, mu=mu, covs=covs, sigma=sigma, size=shape, **kwargs)
 
     def _build_conditional(self, Xnew, pred_noise, diag):
         Xs, y, sigma = self.Xs, self.y, self.sigma
@@ -1172,7 +1172,7 @@ def conditional(self, name, Xnew, pred_noise=False, **kwargs):
         """
         mu, cov = self._build_conditional(Xnew, pred_noise, False)
         shape = infer_shape(Xnew, kwargs.pop("shape", None))
-        return pm.MvNormal(name, mu=mu, cov=cov, shape=shape, **kwargs)
+        return pm.MvNormal(name, mu=mu, cov=cov, size=shape, **kwargs)
 
     def predict(self, Xnew, point=None, diag=False, pred_noise=False):
         R"""

pymc3/tests/models.py

@@ -30,7 +30,7 @@ def simple_model():
     mu = -2.1
     tau = 1.3
     with Model() as model:
-        Normal("x", mu, tau=tau, size=2, testval=np.ones(2) * 0.1)
+        Normal("x", mu, tau=tau, size=2, testval=floatX_array([0.1, 0.1]))
 
     return model.test_point, model, (mu, tau ** -0.5)
 

pymc3/tests/test_coords.py

@@ -4,7 +4,7 @@
 import pymc3 as pm
 
 
-@pytest.mark.xfail("Arviz incompatibilities")
+@pytest.mark.xfail(reason="Arviz incompatibilities")
 def test_coords():
     chains = 2
     n_features = 3

pymc3/tests/test_data_container.py

@@ -131,7 +131,7 @@ def test_shared_data_as_rv_input(self):
         """
         with pm.Model() as m:
             x = pm.Data("x", [1.0, 2.0, 3.0])
-            _ = pm.Normal("y", mu=x, shape=3)
+            _ = pm.Normal("y", mu=x, size=3)
             trace = pm.sample(chains=1)
 
         np.testing.assert_allclose(np.array([1.0, 2.0, 3.0]), x.get_value(), atol=1e-1)
@@ -148,7 +148,7 @@ def test_shared_scalar_as_rv_input(self):
         # See https://github.com/pymc-devs/pymc3/issues/3139
         with pm.Model() as m:
             shared_var = shared(5.0)
-            v = pm.Normal("v", mu=shared_var, shape=1)
+            v = pm.Normal("v", mu=shared_var, size=1)
 
         np.testing.assert_allclose(
             logpt(v, 5.0).eval(),

pymc3/tests/test_gp.py

@@ -26,8 +26,6 @@
 
 from pymc3.math import cartesian, kronecker
 
-pytestmark = pytest.mark.xfail(reason="GP not refactored")
-
 np.random.seed(101)
 
 
@@ -769,6 +767,7 @@ def test_raises3(self):
                 B = pm.gp.cov.Coregion(1)
 
 
+@pytest.mark.xfail(reason="MvNormal was not yet refactored")
 class TestMarginalVsLatent:
     R"""
     Compare the logp of models Marginal, noise=0 and Latent.
@@ -814,6 +813,7 @@ def testLatent2(self):
         npt.assert_allclose(latent_logp, self.logp, atol=5)
 
 
+@pytest.mark.xfail(reason="MvNormal was not yet refactored")
 class TestMarginalVsMarginalSparse:
     R"""
     Compare logp of models Marginal and MarginalSparse.
@@ -888,6 +888,7 @@ def setup_method(self):
         )
         self.means = (pm.gp.mean.Constant(0.5), pm.gp.mean.Constant(0.5), pm.gp.mean.Constant(0.5))
 
+    @pytest.mark.xfail(reason="MvNormal was not yet refactored")
     def testAdditiveMarginal(self):
         with pm.Model() as model1:
             gp1 = pm.gp.Marginal(self.means[0], self.covs[0])
@@ -914,6 +915,7 @@ def testAdditiveMarginal(self):
         fp = np.random.randn(self.Xnew.shape[0])
         npt.assert_allclose(fp1.logp({"fp1": fp}), fp2.logp({"fp2": fp}), atol=0, rtol=1e-2)
 
+    @pytest.mark.xfail(reason="DensityDist was not yet refactored")
     @pytest.mark.parametrize("approx", ["FITC", "VFE", "DTC"])
     def testAdditiveMarginalSparse(self, approx):
         Xu = np.random.randn(10, 3)
@@ -947,6 +949,7 @@ def testAdditiveMarginalSparse(self, approx):
         fp = np.random.randn(self.Xnew.shape[0])
         npt.assert_allclose(fp1.logp({"fp1": fp}), fp2.logp({"fp2": fp}), atol=0, rtol=1e-2)
 
+    @pytest.mark.xfail(reason="MvNormal was not yet refactored")
     def testAdditiveLatent(self):
         with pm.Model() as model1:
             gp1 = pm.gp.Latent(self.means[0], self.covs[0])
@@ -1002,6 +1005,7 @@ def testAdditiveTypeRaises2(self):
                 gp1 + gp2
 
 
+@pytest.mark.xfail(reason="MvNormal was not yet refactored")
 class TestTP:
     R"""
     Compare TP with high degress of freedom to GP
@@ -1054,6 +1058,7 @@ def testAdditiveTPRaises(self):
                 gp1 + gp2
 
 
+@pytest.mark.xfail(reason="MvNormal was not yet refactored")
 class TestLatentKron:
     """
     Compare gp.LatentKron to gp.Latent, both with Gaussian noise.
@@ -1109,6 +1114,7 @@ def testLatentKronRaisesSizes(self):
             gp.prior("f", Xs=[np.linspace(0, 1, 7)[:, None], np.linspace(0, 1, 5)[:, None]])
 
 
+@pytest.mark.xfail(reason="MvNormal was not yet refactored")
 class TestMarginalKron:
     """
     Compare gp.MarginalKron to gp.Marginal.

pymc3/tests/test_minibatches.py

@@ -198,7 +198,7 @@ def true_dens():
 
         for i in range(10):
             _1, _2, _t = p1(), p2(), next(t)
-            decimals = select_by_precision(float64=7, float32=2)
+            decimals = select_by_precision(float64=7, float32=1)
             np.testing.assert_almost_equal(_1, _t, decimal=decimals)  # Value O(-50,000)
             np.testing.assert_almost_equal(_1, _2)
         # Done

pymc3/tests/test_missing.py

@@ -21,7 +21,7 @@
 from pymc3 import ImputationWarning, Model, Normal, sample, sample_prior_predictive
 
 
-@pytest.mark.xfail("Missing values not fully refactored")
+@pytest.mark.xfail(reason="Missing values not fully refactored")
 def test_missing():
     data = ma.masked_values([1, 2, -1, 4, -1], value=-1)
     with Model() as model:
@@ -82,7 +82,7 @@ def test_missing_dual_observations():
         obs2 = ma.masked_values([-1, -1, 6, -1, 8], value=-1)
         beta1 = Normal("beta1", 1, 1)
         beta2 = Normal("beta2", 2, 1)
-        latent = Normal("theta", shape=5)
+        latent = Normal("theta", size=5)
         with pytest.warns(ImputationWarning):
             ovar1 = Normal("o1", mu=beta1 * latent, observed=obs1)
         with pytest.warns(ImputationWarning):

pymc3/tests/test_model.py

@@ -155,7 +155,7 @@ def test_observed_rv_fail(self):
                 Normal("n", observed=x)
 
     def test_observed_type(self):
-        X_ = np.random.randn(100, 5)
+        X_ = np.random.randn(100, 5).astype(aesara.config.floatX)
         X = pm.floatX(aesara.shared(X_))
         with pm.Model():
             x1 = pm.Normal("x1", observed=X_)