Refactor LKJCholeskyCov for V4

Changes:
* compute_corr now defaults to True
* LKJCholeskyCov now also provides a `.dist` interface

RELEASE-NOTES.md

@@ -87,6 +87,7 @@ All of the above apply to:
 - ArviZ `plots` and `stats` *wrappers* were removed. The functions are now just available by their original names (see [#4549](https://github.com/pymc-devs/pymc/pull/4471) and `3.11.2` release notes).
 - `pm.sample_posterior_predictive(vars=...)` kwarg was removed in favor of `var_names` (see [#4343](https://github.com/pymc-devs/pymc/pull/4343)).
 - `ElemwiseCategorical` step method was removed (see [#4701](https://github.com/pymc-devs/pymc/pull/4701))
+- `LKJCholeskyCov` `compute_corr` keyword argument is now set to `True` by default (see[#5382](https://github.com/pymc-devs/pymc/pull/5382))
 
 ### Ongoing deprecations
 - Old API still works in `v4` and has a deprecation warning.

pymc/distributions/transforms.py

@@ -22,7 +22,6 @@
     RVTransform,
     Simplex,
 )
-from aesara.tensor.subtensor import advanced_set_subtensor1
 
 __all__ = [
     "RVTransform",
@@ -97,22 +96,31 @@ def log_jac_det(self, value, *inputs):
 
 
 class CholeskyCovPacked(RVTransform):
+    """
+    Transforms the diagonal elements of the LKJCholeskyCov distribution to be on the
+    log scale
+    """
+
     name = "cholesky-cov-packed"
 
-    def __init__(self, param_extract_fn):
-        self.param_extract_fn = param_extract_fn
+    def __init__(self, n):
+        """
+
+        Parameters
+        ----------
+        n: int
+            Number of diagonal entries in the LKJCholeskyCov distribution
+        """
+        self.diag_idxs = at.arange(1, n + 1).cumsum() - 1
 
     def backward(self, value, *inputs):
-        diag_idxs = self.param_extract_fn(inputs)
-        return advanced_set_subtensor1(value, at.exp(value[diag_idxs]), diag_idxs)
+        return at.set_subtensor(value[..., self.diag_idxs], at.exp(value[..., self.diag_idxs]))
 
     def forward(self, value, *inputs):
-        diag_idxs = self.param_extract_fn(inputs)
-        return advanced_set_subtensor1(value, at.log(value[diag_idxs]), diag_idxs)
+        return at.set_subtensor(value[..., self.diag_idxs], at.log(value[..., self.diag_idxs]))
 
     def log_jac_det(self, value, *inputs):
-        diag_idxs = self.param_extract_fn(inputs)
-        return at.sum(value[diag_idxs])
+        return at.sum(value[..., self.diag_idxs], axis=-1)
 
 
 class Chain(RVTransform):

pymc/tests/sampler_fixtures.py

@@ -122,7 +122,9 @@ def make_model(cls):
         with pm.Model() as model:
             sd_mu = np.array([1, 2, 3, 4, 5])
             sd_dist = pm.LogNormal.dist(mu=sd_mu, sigma=sd_mu / 10.0, size=5)
-            chol_packed = pm.LKJCholeskyCov("chol_packed", eta=3, n=5, sd_dist=sd_dist)
+            chol_packed = pm.LKJCholeskyCov(
+                "chol_packed", eta=3, n=5, sd_dist=sd_dist, compute_corr=False
+            )
             chol = pm.expand_packed_triangular(5, chol_packed, lower=True)
             cov = at.dot(chol, chol.T)
             stds = at.sqrt(at.diag(cov))

pymc/tests/test_distributions.py

@@ -3352,3 +3352,40 @@ def test_censored_invalid_dist(self):
                 match="The dist dist was already registered in the current model",
             ):
                 x = pm.Censored("x", registered_dist, lower=None, upper=None)
+
+
+class TestLKJCholeskCov:
+    def test_dist(self):
+        sd_dist = pm.Exponential.dist(1, size=(10, 3))
+        x = pm.LKJCholeskyCov.dist(n=3, eta=1, sd_dist=sd_dist, size=10, compute_corr=False)
+        assert x.eval().shape == (10, 6)
+
+        sd_dist = pm.Exponential.dist(1, size=3)
+        chol, corr, stds = pm.LKJCholeskyCov.dist(n=3, eta=1, sd_dist=sd_dist)
+        assert chol.eval().shape == (3, 3)
+        assert corr.eval().shape == (3, 3)
+        assert stds.eval().shape == (3,)
+
+    def test_sd_dist_distribution(self):
+        with pm.Model() as m:
+            sd_dist = at.constant([1, 2, 3])
+            with pytest.raises(TypeError, match="sd_dist must be a Distribution variable"):
+                x = pm.LKJCholeskyCov("x", n=3, eta=1, sd_dist=sd_dist)
+
+    def test_sd_dist_registered(self):
+        with pm.Model() as m:
+            sd_dist = pm.Exponential("sd_dist", 1, size=3)
+            with pytest.raises(
+                ValueError, match="The dist sd_dist was already registered in the current model"
+            ):
+                x = pm.LKJCholeskyCov("x", n=3, eta=1, sd_dist=sd_dist)
+
+    def test_no_warning_logp(self):
+        # Check that calling logp of a model with LKJCholeskyCov does not issue any warnings
+        # due to the RandomVariable in the graph
+        with pm.Model() as m:
+            sd_dist = pm.Exponential.dist(1, size=3)
+            x = pm.LKJCholeskyCov("x", n=3, eta=1, sd_dist=sd_dist)
+        with pytest.warns(None) as record:
+            m.logpt()
+        assert not record

pymc/tests/test_distributions_random.py

@@ -47,7 +47,11 @@ def random_polyagamma(*args, **kwargs):
 from pymc.distributions.discrete import _OrderedLogistic, _OrderedProbit
 from pymc.distributions.dist_math import clipped_beta_rvs
 from pymc.distributions.logprob import logp
-from pymc.distributions.multivariate import _OrderedMultinomial, quaddist_matrix
+from pymc.distributions.multivariate import (
+    _LKJCholeskyCov,
+    _OrderedMultinomial,
+    quaddist_matrix,
+)
 from pymc.distributions.shape_utils import to_tuple
 from pymc.tests.helpers import SeededTest, select_by_precision
 from pymc.tests.test_distributions import (
@@ -1867,6 +1871,43 @@ def ref_rand(size, n, eta):
         )
 
 
+class TestLKJCholeskyCov(BaseTestDistributionRandom):
+    pymc_dist = _LKJCholeskyCov
+    pymc_dist_params = {"eta": 1.0, "n": 3, "sd_dist": pm.Constant.dist([0.5, 1.0, 2.0])}
+    expected_rv_op_params = {"n": 3, "eta": 1.0, "sd_dist": pm.Constant.dist([0.5, 1.0, 2.0])}
+    size = None
+
+    sizes_to_check = [None, (), 1, (1,), 5, (4, 5), (2, 4, 2)]
+    sizes_expected = [
+        (6,),
+        (6,),
+        (1, 6),
+        (1, 6),
+        (5, 6),
+        (4, 5, 6),
+        (2, 4, 2, 6),
+    ]
+
+    tests_to_run = [
+        "check_rv_size",
+        "check_draws_match_expected",
+    ]
+
+    def check_rv_size(self):
+        for size, expected in zip(self.sizes_to_check, self.sizes_expected):
+            sd_dist = pm.Exponential.dist(1, size=(*to_tuple(size), 3))
+            pymc_rv = self.pymc_dist.dist(n=3, eta=1, sd_dist=sd_dist, size=size)
+            expected_symbolic = tuple(pymc_rv.shape.eval())
+            actual = pymc_rv.eval().shape
+            assert actual == expected_symbolic == expected
+
+    def check_draws_match_expected(self):
+        # TODO: Find better comparison:
+        rng = aesara.shared(self.get_random_state(reset=True))
+        x = _LKJCholeskyCov.dist(n=2, eta=10_000, sd_dist=pm.Constant.dist([0.5, 2.0]), rng=rng)
+        assert np.all(np.abs(x.eval() - np.array([0.5, 0, 2.0])) < 0.01)
+
+
 class TestScalarParameterSamples(SeededTest):
     @pytest.mark.xfail(reason="This distribution has not been refactored for v4")
     def test_normalmixture(self):
@@ -2346,9 +2387,11 @@ def test_with_chol_rv(self, sample_shape, dist_shape, mu_shape):
         with pm.Model() as model:
             mu = pm.Normal("mu", 0.0, 1.0, shape=mu_shape)
             sd_dist = pm.Exponential.dist(1.0, shape=3)
+            # pylint: disable=unpacking-non-sequence
             chol, corr, stds = pm.LKJCholeskyCov(
                 "chol_cov", n=3, eta=2, sd_dist=sd_dist, compute_corr=True
             )
+            # pylint: enable=unpacking-non-sequence
             mv = pm.MvNormal("mv", mu, chol=chol, shape=dist_shape)
             prior = pm.sample_prior_predictive(samples=sample_shape)
 
@@ -2363,9 +2406,11 @@ def test_with_cov_rv(self, sample_shape, dist_shape, mu_shape):
         with pm.Model() as model:
             mu = pm.Normal("mu", 0.0, 1.0, shape=mu_shape)
             sd_dist = pm.Exponential.dist(1.0, shape=3)
+            # pylint: disable=unpacking-non-sequence
             chol, corr, stds = pm.LKJCholeskyCov(
                 "chol_cov", n=3, eta=2, sd_dist=sd_dist, compute_corr=True
             )
+            # pylint: enable=unpacking-non-sequence
             mv = pm.MvNormal("mv", mu, cov=pm.math.dot(chol, chol.T), shape=dist_shape)
             prior = pm.sample_prior_predictive(samples=sample_shape)
 
@@ -2457,9 +2502,11 @@ def test_with_chol_rv(self, sample_shape, dist_shape, mu_shape):
         with pm.Model() as model:
             mu = pm.Normal("mu", 0.0, 1.0, shape=mu_shape)
             sd_dist = pm.Exponential.dist(1.0, shape=3)
+            # pylint: disable=unpacking-non-sequence
             chol, corr, stds = pm.LKJCholeskyCov(
                 "chol_cov", n=3, eta=2, sd_dist=sd_dist, compute_corr=True
             )
+            # pylint: enable=unpacking-non-sequence
             mv = pm.MvGaussianRandomWalk("mv", mu, chol=chol, shape=dist_shape)
             prior = pm.sample_prior_predictive(samples=sample_shape)
 
@@ -2475,9 +2522,11 @@ def test_with_cov_rv(self, sample_shape, dist_shape, mu_shape):
         with pm.Model() as model:
             mu = pm.Normal("mu", 0.0, 1.0, shape=mu_shape)
             sd_dist = pm.Exponential.dist(1.0, shape=3)
+            # pylint: disable=unpacking-non-sequence
             chol, corr, stds = pm.LKJCholeskyCov(
                 "chol_cov", n=3, eta=2, sd_dist=sd_dist, compute_corr=True
             )
+            # pylint: enable=unpacking-non-sequence
             mv = pm.MvGaussianRandomWalk("mv", mu, cov=pm.math.dot(chol, chol.T), shape=dist_shape)
             prior = pm.sample_prior_predictive(samples=sample_shape)
 

pymc/tests/test_idata_conversion.py

@@ -332,15 +332,16 @@ def test_missing_data_model(self):
         assert inference_data.log_likelihood["y_observed"].shape == (2, 100, 3)
 
     @pytest.mark.xfal(reason="Multivariate partial observed RVs not implemented for V4")
-    @pytest.mark.xfail(reason="LKJCholeskyCov not refactored for v4")
     def test_mv_missing_data_model(self):
         data = ma.masked_values([[1, 2], [2, 2], [-1, 4], [2, -1], [-1, -1]], value=-1)
 
         model = pm.Model()
         with model:
             mu = pm.Normal("mu", 0, 1, size=2)
             sd_dist = pm.HalfNormal.dist(1.0)
+            # pylint: disable=unpacking-non-sequence
             chol, *_ = pm.LKJCholeskyCov("chol_cov", n=2, eta=1, sd_dist=sd_dist, compute_corr=True)
+            # pylint: enable=unpacking-non-sequence
             y = pm.MvNormal("y", mu=mu, chol=chol, observed=data)
             inference_data = pm.sample(100, chains=2, return_inferencedata=True)
 

pymc/tests/test_mixture.py

@@ -368,7 +368,7 @@ def build_toy_dataset(N, K):
                 mu.append(pm.Normal("mu%i" % i, 0, 10, shape=D))
                 packed_chol.append(
                     pm.LKJCholeskyCov(
-                        "chol_cov_%i" % i, eta=2, n=D, sd_dist=pm.HalfNormal.dist(2.5)
+                        "chol_cov_%i" % i, eta=2, n=D, sd_dist=pm.HalfNormal.dist(2.5, size=D)
                     )
                 )
                 chol.append(pm.expand_packed_triangular(D, packed_chol[i], lower=True))

pymc/tests/test_posteriors.py

@@ -95,7 +95,6 @@ class TestNUTSNormalLong(sf.NutsFixture, sf.NormalFixture):
     atol = 0.001
 
 
-@pytest.mark.xfail(reason="LKJCholeskyCov not refactored for v4")
 class TestNUTSLKJCholeskyCov(sf.NutsFixture, sf.LKJCholeskyCovFixture):
     n_samples = 2000
     tune = 1000