Bring back SMC and allow prior_predictive_sampling to return transformed values

.github/workflows/pytest.yml

@@ -13,11 +13,13 @@ jobs:
         floatx: [float32, float64]
         test-subset:
         # Tests are split into multiple jobs to accelerate the CI.
+        # Different jobs should be organized to take approximately the same
+        # time to complete (and not be prohibitely slow)
         #
         # How this works:
         # 1st block: Only passes --ignore parameters to pytest.
         # → pytest will run all test_*.py files that are NOT ignored.
-        # Other blocks: Only pass paths to test files.
+        # Subsequent blocks: Only pass paths to test files.
         # → pytest will run only these files
         #
         # Any test that was not ignored runs in the first job.
@@ -30,7 +32,6 @@ jobs:
             --ignore=pymc3/tests/test_modelcontext.py
             --ignore=pymc3/tests/test_parallel_sampling.py
             --ignore=pymc3/tests/test_profile.py
-            --ignore=pymc3/tests/test_smc.py
             --ignore=pymc3/tests/test_step.py
             --ignore=pymc3/tests/test_tuning.py
             --ignore=pymc3/tests/test_types.py

RELEASE-NOTES.md

@@ -7,7 +7,8 @@
 - The GLM submodule has been removed, please use [Bambi](https://bambinos.github.io/bambi/) instead.
 - The `Distribution` keyword argument `testval` has been deprecated in favor of `initval`.
 - `pm.sample` now returns results as `InferenceData` instead of `MultiTrace` by default (see [#4744](https://github.com/pymc-devs/pymc3/pull/4744)).
-- ...
+- `pm.sample_prior_predictive` no longer returns transformed variable values by default. Pass them by name in `var_names` if you want to obtain these draws (see [4769](https://github.com/pymc-devs/pymc3/pull/4769)).
+...
 
 ### New Features
 - The `CAR` distribution has been added to allow for use of conditional autoregressions which often are used in spatial and network models.

pymc3/sampling.py

@@ -1943,7 +1943,8 @@ def sample_prior_predictive(
     model : Model (optional if in ``with`` context)
     var_names : Iterable[str]
         A list of names of variables for which to compute the posterior predictive
-        samples. Defaults to both observed and unobserved RVs.
+        samples. Defaults to both observed and unobserved RVs. Transformed values
+        are not included unless explicitly defined in var_names.
     random_seed : int
         Seed for the random number generator.
     mode:
@@ -1983,8 +1984,26 @@ def sample_prior_predictive(
         )
 
     names = get_default_varnames(vars_, include_transformed=False)
-
     vars_to_sample = [model[name] for name in names]
+
+    # Any variables from var_names that are missing must be transformed variables.
+    # Misspelled variables would have raised a KeyError above.
+    missing_names = vars_.difference(names)
+    for name in missing_names:
+        transformed_value_var = model[name]
+        rv_var = model.values_to_rvs[transformed_value_var]
+        transform = transformed_value_var.tag.transform
+        transformed_rv_var = transform.forward(rv_var, rv_var)
+
+        names.append(name)
+        vars_to_sample.append(transformed_rv_var)
+
+        # If the user asked for the transformed variable in var_names, but not the
+        # original RV, we add it manually here
+        if rv_var.name not in names:
+            names.append(rv_var.name)
+            vars_to_sample.append(rv_var)
+
     inputs = [i for i in inputvars(vars_to_sample) if not isinstance(i, SharedVariable)]
 
     sampler_fn = compile_rv_inplace(

pymc3/smc/smc.py

@@ -12,11 +12,14 @@
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
 
+import warnings
+
 from collections import OrderedDict
 
 import aesara.tensor as at
 import numpy as np
 
+from aesara import config
 from aesara import function as aesara_function
 from scipy.special import logsumexp
 from scipy.stats import multivariate_normal
@@ -87,7 +90,7 @@ def initialize_population(self):
         if self.start is None:
             init_rnd = sample_prior_predictive(
                 self.draws,
-                var_names=[v.name for v in self.model.unobserved_RVs],
+                var_names=[v.name for v in self.model.unobserved_value_vars],
                 model=self.model,
             )
         else:
@@ -290,9 +293,21 @@ def logp_forw(point, out_vars, vars, shared):
     shared: List
         containing :class:`aesara.tensor.Tensor` for depended shared data
     """
+
     out_list, inarray0 = join_nonshared_inputs(point, out_vars, vars, shared)
-    f = aesara_function([inarray0], out_list[0])
-    f.trust_input = True
+    # TODO: Figure out how to safely accept float32 (floatX) input when there are
+    # discrete variables of int64 dtype in `vars`.
+    # See https://github.com/pymc-devs/pymc3/pull/4769#issuecomment-861494080
+    if config.floatX == "float32" and any(var.dtype == "int64" for var in vars):
+        warnings.warn(
+            "SMC sampling may run slower due to the presence of discrete variables "
+            "together with aesara.config.floatX == `float32`",
+            UserWarning,
+        )
+        f = aesara_function([inarray0], out_list[0], allow_input_downcast=True)
+    else:
+        f = aesara_function([inarray0], out_list[0])
+        f.trust_input = False
     return f
 
 

pymc3/tests/test_sampling.py

@@ -1076,6 +1076,66 @@ def test_potentials_warning(self):
             with pytest.warns(UserWarning, match=warning_msg):
                 pm.sample_prior_predictive(samples=5)
 
+    def test_transformed_vars(self):
+        # Test that prior predictive returns transformation of RVs when these are
+        # passed explicitly in `var_names`
+
+        def ub_interval_forward(x, ub):
+            # Interval transform assuming lower bound is zero
+            return np.log(x - 0) - np.log(ub - x)
+
+        with pm.Model(rng_seeder=123) as model:
+            ub = pm.HalfNormal("ub", 10)
+            x = pm.Uniform("x", 0, ub)
+
+            prior = pm.sample_prior_predictive(
+                var_names=["ub", "ub_log__", "x", "x_interval__"],
+                samples=10,
+            )
+
+        # Check values are correct
+        assert np.allclose(prior["ub_log__"], np.log(prior["ub"]))
+        assert np.allclose(
+            prior["x_interval__"],
+            ub_interval_forward(prior["x"], prior["ub"]),
+        )
+
+        # Check that it works when the original RVs are not mentioned in var_names
+        with pm.Model(rng_seeder=123) as model_transformed_only:
+            ub = pm.HalfNormal("ub", 10)
+            x = pm.Uniform("x", 0, ub)
+
+            prior_transformed_only = pm.sample_prior_predictive(
+                var_names=["ub_log__", "x_interval__"],
+                samples=10,
+            )
+        assert "ub" not in prior_transformed_only and "x" not in prior_transformed_only
+        assert np.allclose(prior["ub_log__"], prior_transformed_only["ub_log__"])
+        assert np.allclose(prior["x_interval__"], prior_transformed_only["x_interval__"])
+
+    def test_issue_4490(self):
+        # Test that samples do not depend on var_name order or, more fundamentally,
+        # that they do not depend on the set order used inside `sample_prior_predictive`
+        seed = 4490
+        with pm.Model(rng_seeder=seed) as m1:
+            a = pm.Normal("a")
+            b = pm.Normal("b")
+            c = pm.Normal("c")
+            d = pm.Normal("d")
+            prior1 = pm.sample_prior_predictive(samples=1, var_names=["a", "b", "c", "d"])
+
+        with pm.Model(rng_seeder=seed) as m2:
+            a = pm.Normal("a")
+            b = pm.Normal("b")
+            c = pm.Normal("c")
+            d = pm.Normal("d")
+            prior2 = pm.sample_prior_predictive(samples=1, var_names=["b", "a", "d", "c"])
+
+        assert prior1["a"] == prior2["a"]
+        assert prior1["b"] == prior2["b"]
+        assert prior1["c"] == prior2["c"]
+        assert prior1["d"] == prior2["d"]
+
 
 class TestSamplePosteriorPredictive:
     def test_point_list_arg_bug_spp(self, point_list_arg_bug_fixture):

pymc3/tests/test_smc.py

@@ -12,6 +12,7 @@
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
 
+import aesara
 import aesara.tensor as at
 import numpy as np
 import pytest
@@ -97,7 +98,16 @@ def test_start(self):
             }
             trace = pm.sample_smc(500, start=start)
 
+    def test_slowdown_warning(self):
+        with aesara.config.change_flags(floatX="float32"):
+            with pytest.warns(UserWarning, match="SMC sampling may run slower due to"):
+                with pm.Model() as model:
+                    a = pm.Poisson("a", 5)
+                    y = pm.Normal("y", a, 5, observed=[1, 2, 3, 4])
+                    trace = pm.sample_smc()
 
+
+@pytest.mark.xfail(reason="SMC-ABC not refactored yet")
 class TestSMCABC(SeededTest):
     def setup_class(self):
         super().setup_class()