Merge branch 'fix-4662' into reintro_shape

Author: twiecki

pymc3/aesaraf.py

@@ -49,6 +49,7 @@
 from aesara.tensor.subtensor import AdvancedIncSubtensor, AdvancedIncSubtensor1
 from aesara.tensor.var import TensorVariable
 
+from pymc3.exceptions import ShapeError
 from pymc3.vartypes import continuous_types, int_types, isgenerator, typefilter
 
 PotentialShapeType = Union[
@@ -146,6 +147,12 @@ def change_rv_size(
         Expand the existing size by `new_size`.
 
     """
+    new_size_ndim = new_size.ndim if isinstance(new_size, Variable) else np.ndim(new_size)
+    if new_size_ndim > 1:
+        raise ShapeError("The `new_size` must be ≤1-dimensional.", actual=new_size_ndim)
+    new_size = at.as_tensor_variable(new_size, ndim=1)
+    if isinstance(rv_var.owner.op, SpecifyShape):
+        rv_var = rv_var.owner.inputs[0]
     rv_node = rv_var.owner
     rng, size, dtype, *dist_params = rv_node.inputs
     name = rv_var.name
@@ -154,7 +161,7 @@ def change_rv_size(
     if expand:
         if rv_node.op.ndim_supp == 0 and at.get_vector_length(size) == 0:
             size = rv_node.op._infer_shape(size, dist_params)
-        new_size = tuple(np.atleast_1d(new_size)) + tuple(size)
+        new_size = tuple(new_size) + tuple(size)
 
     new_rv_node = rv_node.op.make_node(rng, new_size, dtype, *dist_params)
     rv_var = new_rv_node.outputs[-1]

pymc3/tests/test_aesaraf.py

@@ -41,6 +41,7 @@
     take_along_axis,
     walk_model,
 )
+from pymc3.exceptions import ShapeError
 from pymc3.vartypes import int_types
 
 FLOATX = str(aesara.config.floatX)
@@ -53,6 +54,11 @@ def test_change_rv_size():
     assert rv.ndim == 1
     assert rv.eval().shape == (2,)
 
+    with pytest.raises(ShapeError, match="must be ≤1-dimensional"):
+        change_rv_size(rv, new_size=[[2, 3]])
+    with pytest.raises(ShapeError, match="must be ≤1-dimensional"):
+        change_rv_size(rv, new_size=at.as_tensor_variable([[2, 3], [4, 5]]))
+
     rv_new = change_rv_size(rv, new_size=(3,), expand=True)
     assert rv_new.ndim == 2
     assert rv_new.eval().shape == (3, 2)

pymc3/tests/test_ode.py

@@ -12,6 +12,8 @@
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
 
+import sys
+
 import aesara
 import numpy as np
 import pytest
@@ -168,6 +170,9 @@ def ode_func_5(y, t, p):
         np.testing.assert_array_equal(np.ravel(model5_sens_ic), model5._sens_ic)
 
 
+@pytest.mark.xfail(
+    condition=sys.platform == "win32", reason="See https://github.com/pymc-devs/pymc3/issues/4652."
+)
 def test_logp_scalar_ode():
     """Test the computation of the log probability for these models"""
 

pymc3/tests/test_shape_handling.py

@@ -219,3 +219,200 @@ def test_sample_generate_values(fixture_model, fixture_sizes):
         prior = pm.sample_prior_predictive(samples=fixture_sizes)
         for rv in RVs:
             assert prior[rv.name].shape == size + tuple(rv.distribution.shape)
+
+
+class TestShapeDimsSize:
+    @pytest.mark.parametrize("param_shape", [(), (3,)])
+    @pytest.mark.parametrize("batch_shape", [(), (3,)])
+    @pytest.mark.parametrize(
+        "parametrization",
+        [
+            "implicit",
+            "shape",
+            "shape...",
+            "dims",
+            "dims...",
+            "size",
+        ],
+    )
+    def test_param_and_batch_shape_combos(
+        self, param_shape: tuple, batch_shape: tuple, parametrization: str
+    ):
+        coords = {}
+        param_dims = []
+        batch_dims = []
+
+        # Create coordinates corresponding to the parameter shape
+        for d in param_shape:
+            dname = f"param_dim_{d}"
+            coords[dname] = [f"c_{i}" for i in range(d)]
+            param_dims.append(dname)
+        assert len(param_dims) == len(param_shape)
+        # Create coordinates corresponding to the batch shape
+        for d in batch_shape:
+            dname = f"batch_dim_{d}"
+            coords[dname] = [f"c_{i}" for i in range(d)]
+            batch_dims.append(dname)
+        assert len(batch_dims) == len(batch_shape)
+
+        with pm.Model(coords=coords) as pmodel:
+            mu = aesara.shared(np.random.normal(size=param_shape))
+
+            with pytest.warns(None):
+                if parametrization == "implicit":
+                    rv = pm.Normal("rv", mu=mu).shape == param_shape
+                else:
+                    if parametrization == "shape":
+                        rv = pm.Normal("rv", mu=mu, shape=batch_shape + param_shape)
+                        assert rv.eval().shape == batch_shape + param_shape
+                    elif parametrization == "shape...":
+                        rv = pm.Normal("rv", mu=mu, shape=(*batch_shape, ...))
+                        assert rv.eval().shape == batch_shape + param_shape
+                    elif parametrization == "dims":
+                        rv = pm.Normal("rv", mu=mu, dims=batch_dims + param_dims)
+                        assert rv.eval().shape == batch_shape + param_shape
+                    elif parametrization == "dims...":
+                        rv = pm.Normal("rv", mu=mu, dims=(*batch_dims, ...))
+                        n_size = len(batch_shape)
+                        n_implied = len(param_shape)
+                        ndim = n_size + n_implied
+                        assert len(pmodel.RV_dims["rv"]) == ndim, pmodel.RV_dims
+                        assert len(pmodel.RV_dims["rv"][:n_size]) == len(batch_dims)
+                        assert len(pmodel.RV_dims["rv"][n_size:]) == len(param_dims)
+                        if n_implied > 0:
+                            assert pmodel.RV_dims["rv"][-1] is None
+                    elif parametrization == "size":
+                        rv = pm.Normal("rv", mu=mu, size=batch_shape)
+                        assert rv.eval().shape == batch_shape + param_shape
+                    else:
+                        raise NotImplementedError("Invalid test case parametrization.")
+
+    def test_define_dims_on_the_fly(self):
+        with pm.Model() as pmodel:
+            agedata = aesara.shared(np.array([10, 20, 30]))
+
+            # Associate the "patient" dim with an implied dimension
+            age = pm.Normal("age", agedata, dims=("patient",))
+            assert "patient" in pmodel.dim_lengths
+            assert pmodel.dim_lengths["patient"].eval() == 3
+
+            # Use the dim to replicate a new RV
+            effect = pm.Normal("effect", 0, dims=("patient",))
+            assert effect.ndim == 1
+            assert effect.eval().shape == (3,)
+
+            # Now change the length of the implied dimension
+            agedata.set_value([1, 2, 3, 4])
+            # The change should propagate all the way through
+            assert effect.eval().shape == (4,)
+
+    @pytest.mark.xfail(reason="Simultaneous use of size and dims is not implemented")
+    def test_data_defined_size_dimension_can_register_dimname(self):
+        with pm.Model() as pmodel:
+            x = pm.Data("x", [[1, 2, 3, 4]], dims=("first", "second"))
+            assert "first" in pmodel.dim_lengths
+            assert "second" in pmodel.dim_lengths
+            # two dimensions are implied; a "third" dimension is created
+            y = pm.Normal("y", mu=x, size=2, dims=("third", "first", "second"))
+            assert "third" in pmodel.dim_lengths
+            assert y.eval().shape() == (2, 1, 4)
+
+    def test_can_resize_data_defined_size(self):
+        with pm.Model() as pmodel:
+            x = pm.Data("x", [[1, 2, 3, 4]], dims=("first", "second"))
+            y = pm.Normal("y", mu=0, dims=("first", "second"))
+            z = pm.Normal("z", mu=y, observed=np.ones((1, 4)))
+            assert x.eval().shape == (1, 4)
+            assert y.eval().shape == (1, 4)
+            assert z.eval().shape == (1, 4)
+            assert "first" in pmodel.dim_lengths
+            assert "second" in pmodel.dim_lengths
+            pmodel.set_data("x", [[1, 2], [3, 4], [5, 6]])
+            assert x.eval().shape == (3, 2)
+            assert y.eval().shape == (3, 2)
+            assert z.eval().shape == (3, 2)
+
+    @pytest.mark.xfail(
+        condition=sys.platform == "win32",
+        reason="See https://github.com/pymc-devs/pymc3/issues/4652.",
+    )
+    def test_observed_with_column_vector(self):
+        with pm.Model() as model:
+            pm.Normal("x1", mu=0, sd=1, observed=np.random.normal(size=(3, 4)))
+            model.logp()
+            pm.Normal("x2", mu=0, sd=1, observed=np.random.normal(size=(3, 1)))
+            model.logp()
+
+    def test_dist_api_works(self):
+        mu = aesara.shared(np.array([1, 2, 3]))
+        with pytest.raises(NotImplementedError, match="API is not yet supported"):
+            pm.Normal.dist(mu=mu, dims=("town",))
+        assert pm.Normal.dist(mu=mu, shape=(3,)).eval().shape == (3,)
+        assert pm.Normal.dist(mu=mu, shape=(5, 3)).eval().shape == (5, 3)
+        assert pm.Normal.dist(mu=mu, shape=(7, ...)).eval().shape == (7, 3)
+        assert pm.Normal.dist(mu=mu, size=(4,)).eval().shape == (4, 3)
+
+    def test_auto_assert_shape(self):
+        with pytest.raises(AssertionError, match="will never match"):
+            pm.Normal.dist(mu=[1, 2], shape=[])
+
+        mu = at.vector(name="mu_input")
+        rv = pm.Normal.dist(mu=mu, shape=[3, 4])
+        f = aesara.function([mu], rv, mode=aesara.Mode("py"))
+        assert f([1, 2, 3, 4]).shape == (3, 4)
+
+        with pytest.raises(AssertionError, match=r"Got shape \(3, 2\), expected \(3, 4\)."):
+            f([1, 2])
+
+        # The `shape` can be symbolic!
+        s = at.vector(dtype="int32")
+        rv = pm.Uniform.dist(2, [4, 5], shape=s)
+        f = aesara.function([s], rv, mode=aesara.Mode("py"))
+        f(
+            [
+                2,
+            ]
+        )
+        with pytest.raises(
+            AssertionError,
+            match=r"Got 1 dimensions \(shape \(2,\)\), expected 2 dimensions with shape \(3, 4\).",
+        ):
+            f([3, 4])
+        with pytest.raises(
+            AssertionError,
+            match=r"Got 1 dimensions \(shape \(2,\)\), expected 0 dimensions with shape \(\).",
+        ):
+            f([])
+        pass
+
+    def test_lazy_flavors(self):
+
+        _validate_shape_dims_size(shape=5)
+        _validate_shape_dims_size(dims="town")
+        _validate_shape_dims_size(size=7)
+
+        assert pm.Uniform.dist(2, [4, 5], size=[3, 4]).eval().shape == (3, 4, 2)
+        assert pm.Uniform.dist(2, [4, 5], shape=[3, 2]).eval().shape == (3, 2)
+        with pm.Model(coords=dict(town=["Greifswald", "Madrid"])):
+            assert pm.Normal("n2", mu=[1, 2], dims=("town",)).eval().shape == (2,)
+
+    def test_invalid_flavors(self):
+        # redundant parametrizations
+        with pytest.raises(ValueError, match="Passing both"):
+            _validate_shape_dims_size(shape=(2,), dims=("town",))
+        with pytest.raises(ValueError, match="Passing both"):
+            _validate_shape_dims_size(dims=("town",), size=(2,))
+        with pytest.raises(ValueError, match="Passing both"):
+            _validate_shape_dims_size(shape=(3,), size=(3,))
+
+        # invalid, but not necessarly rare
+        with pytest.raises(ValueError, match="must be an int, list or tuple"):
+            _validate_shape_dims_size(size="notasize")
+
+        # invalid ellipsis positions
+        with pytest.raises(ValueError, match="may only appear in the last position"):
+            _validate_shape_dims_size(shape=(3, ..., 2))
+        with pytest.raises(ValueError, match="may only appear in the last position"):
+            _validate_shape_dims_size(dims=(..., "town"))
+        with pytest.raises(ValueError, match="cannot contain"):
+            _validate_shape_dims_size(size=(3, ...))