add ICARRV and ICAR

pymc/distributions/__init__.py

@@ -86,6 +86,7 @@
 )
 from pymc.distributions.multivariate import (
     CAR,
+    ICAR,
     Dirichlet,
     DirichletMultinomial,
     KroneckerNormal,
@@ -198,6 +199,7 @@
     "Truncated",
     "Censored",
     "CAR",
+    "ICAR",
     "PolyaGamma",
     "HurdleGamma",
     "HurdleLogNormal",

pymc/distributions/multivariate.py

@@ -89,6 +89,7 @@
     "MatrixNormal",
     "KroneckerNormal",
     "CAR",
+    "ICAR",
     "StickBreakingWeights",
 ]
 
@@ -2256,6 +2257,172 @@
         )
 
 
+class ICARRV(RandomVariable):
+    name = "icar"
+    ndim_supp = 1
+    ndims_params = [2, 1, 1, 0, 0, 0]
+    dtype = "floatX"
+    _print_name = ("ICAR", "\\operatorname{ICAR}")
+
+    def __call__(self, W, node1, node2, N, sigma, zero_sum_stdev, size=None, **kwargs):
+        return super().__call__(W, node1, node2, N, sigma, zero_sum_stdev, size=size, **kwargs)
+
+    def _supp_shape_from_params(self, dist_params, param_shapes=None):
+        return supp_shape_from_ref_param_shape(
+            ndim_supp=self.ndim_supp,
+            dist_params=dist_params,
+            param_shapes=param_shapes,
+            ref_param_idx=0,
+        )
+
+    @classmethod
+    def rng_fn(cls, rng, size, W, node1, node2, N, sigma, zero_sum_stdev):
+        raise NotImplementedError("Cannot sample from ICAR prior")
+
+
+icar = ICARRV()
+
+
+class ICAR(Continuous):
+    r"""
+    The intrinsic conditional autoregressive prior. It is primarily used to model
+    covariance between neighboring areas. It is a special case
+    of the :class:`~pymc.CAR` distribution where alpha is set to 1.
+
+    The log probability density function is
+
+    .. math::
+        f(\phi| W,\sigma) =
+          -\frac{1}{2\sigma^{2}} \sum_{i\sim j} (\phi_{i} - \phi_{j})^2 -
+          \frac{1}{2}*\frac{\sum_{i}{\phi_{i}}}{0.001N}^{2} - \ln{\sqrt{2\\pi}} -
+          \ln{0.001N}
+
+    The first term represents the spatial covariance component. Each $\\phi_{i}$ is penalized
+    based on the square distance from each of its neighbors. The notation $i\\sim j$
+    indicates a sum over all the neighbors of $\\phi_{i}$. The last three terms are the
+    Normal log density function where the mean is zero and the standard deviation is
+    $N * 0.001$ (where N is the length of the vector $\\phi$). This component imposes
+    a zero-sum constraint by finding the sum of the vector $\\phi$ and penalizing based
+    on its distance from zero.
+
+    Parameters
+    ----------
+    W : ndarray of int
+        Symmetric adjacency matrix of 1s and 0s indicating adjacency between elements.
+
+    sigma : scalar, default 1
+        Standard deviation of the vector of phi's. Putting a prior on sigma
+        will result in a centered parameterization. In most cases, it is
+        preferable to use a non-centered parameterization by using the default
+        value and multiplying the resulting phi's by sigma. See the example below.
+
+    zero_sum_stdev : scalar, default 0.001
+        Controls how strongly to enforce the zero-sum constraint. The sum of
+        phi is normally distributed with a mean of zero and small standard deviation.
+        This parameter sets the standard deviation of a normal density function with
+        mean zero.
+
+
+    Examples
+    --------
+    This example illustrates how to switch between centered and non-centered
+    parameterizations.
+
+    .. code-block:: python
+
+        import numpy as np
+        import pymc as pm
+
+        # 4x4 adjacency matrix
+        # arranged in a square lattice
+
+        W = np.array([
+            [0,1,0,1],
+            [1,0,1,0],
+            [0,1,0,1],
+            [1,0,1,0]
+        ])
+
+        # centered parameterization
+        with pm.Model():
+            sigma = pm.Exponential('sigma', 1)
+            phi = pm.ICAR('phi', W=W, sigma=sigma)
+            mu = phi
+
+        # non-centered parameterization
+        with pm.Model():
+            sigma = pm.Exponential('sigma', 1)
+            phi = pm.ICAR('phi', W=W)
+            mu = sigma * phi
+
+    References
+    ----------
+    ..  Mitzi, M., Wheeler-Martin, K., Simpson, D., Mooney, J. S.,
+        Gelman, A., Dimaggio, C.
+        "Bayesian hierarchical spatial models: Implementing the Besag York
+        Mollié model in stan"
+        Spatial and Spatio-temporal Epidemiology, Vol. 31, (Aug., 2019),
+        pp 1-18
+    ..  Banerjee, S., Carlin, B., Gelfand, A. Hierarchical Modeling
+        and Analysis for Spatial Data. Second edition. CRC press. (2015)
+
+    """
+
+    rv_op = icar
+
+    @classmethod
+    def dist(cls, W, sigma=1, zero_sum_stdev=0.001, **kwargs):
+        if not W.ndim == 2:
+            raise ValueError("W must be matrix with ndim=2")
+
+        if not W.shape[0] == W.shape[1]:
+            raise ValueError("W must be a square matrix")
+
+        if not np.allclose(W.T, W):
+            raise ValueError("W must be a symmetric matrix")
+
+        if np.any((W != 0) & (W != 1)):
+            raise ValueError("W must be composed of only 1s and 0s")
+
+        # convert adjacency matrix to edgelist representation
+        # An edgelist is a pair of lists.
+        # If node i and node j are connected then one list
+        # will contain i and the other will contain j at the same
+        # index value.
+        # We only use the lower triangle here because adjacency
+        # is a undirected connection.
+
+        node1, node2 = np.where(np.tril(W) == 1)
+
+        node1 = pt.as_tensor_variable(node1, dtype=int)
+        node2 = pt.as_tensor_variable(node2, dtype=int)
+
+        W = pt.as_tensor_variable(W, dtype=int)
+
+        N = pt.shape(W)[0]
+        N = pt.as_tensor_variable(N)
+
+        sigma = pt.as_tensor_variable(floatX(sigma))
+        zero_sum_stdev = pt.as_tensor_variable(floatX(zero_sum_stdev))
+
+        return super().dist([W, node1, node2, N, sigma, zero_sum_stdev], **kwargs)
+
+    def moment(rv, size, W, node1, node2, N, sigma, zero_sum_stdev):
+        return pt.zeros(N)
+
+    def logp(value, W, node1, node2, N, sigma, zero_sum_stdev):
+        pairwise_difference = (-1 / (2 * sigma**2)) * pt.sum(
+            pt.square(value[node1] - value[node2])
+        )
+        zero_sum = (
+            -0.5 * pt.pow(pt.sum(value) / (zero_sum_stdev * N), 2)
+            - pt.log(pt.sqrt(2.0 * np.pi))
+            - pt.log(zero_sum_stdev * N)
+        )
+
+        return check_parameters(pairwise_difference + zero_sum, sigma > 0, msg="sigma > 0")
+
+
 class StickBreakingWeightsRV(RandomVariable):
     name = "stick_breaking_weights"
     ndim_supp = 1

tests/distributions/test_multivariate.py

@@ -1081,6 +1081,18 @@ def test_car_moment(self, mu, size, expected):
             pm.CAR("x", mu=mu, W=W, alpha=alpha, tau=tau, size=size)
         assert_moment_is_expected(model, expected)
 
+    @pytest.mark.parametrize(
+        "W, expected",
+        [
+            (np.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]]), np.array([0, 0, 0])),
+            (np.array([[0, 1], [1, 0]]), np.array([0, 0])),
+        ],
+    )
+    def test_icar_moment(self, W, expected):
+        with pm.Model() as model:
+            RV = pm.ICAR("x", W=W)
+        assert_moment_is_expected(model, expected)
+
     @pytest.mark.parametrize(
         "nu, mu, cov, size, expected",
         [
@@ -2091,6 +2103,66 @@ def check_draws_match_expected(self):
         assert np.all(np.abs(draw(x, random_seed=rng) - np.array([0.5, 0, 2.0])) < 0.01)
 
 
+class TestICAR(BaseTestDistributionRandom):
+    pymc_dist = pm.ICAR
+    pymc_dist_params = {"W": np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]]), "sigma": 2}
+    expected_rv_op_params = {
+        "W": np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]]),
+        "node1": np.array([1, 2, 2]),
+        "node2": np.array([0, 0, 1]),
+        "N": 3,
+        "sigma": 2,
+        "zero_sum_strength": 0.001,
+    }
+    checks_to_run = ["check_pymc_params_match_rv_op", "check_rv_inferred_size"]
+
+    def check_rv_inferred_size(self):
+        sizes_to_check = [None, (), 1, (1,), 5, (4, 5), (2, 4, 2)]
+        sizes_expected = [(3,), (3,), (1, 3), (1, 3), (5, 3), (4, 5, 3), (2, 4, 2, 3)]
+        for size, expected in zip(sizes_to_check, sizes_expected):
+            pymc_rv = self.pymc_dist.dist(**self.pymc_dist_params, size=size)
+            expected_symbolic = tuple(pymc_rv.shape.eval())
+            assert expected_symbolic == expected
+
+    def test_icar_logp(self):
+        W = np.array([[0, 1, 0, 1], [1, 0, 1, 0], [0, 1, 0, 1], [1, 0, 1, 0]])
+
+        with pm.Model() as m:
+            RV = pm.ICAR("phi", W=W)
+
+        assert pt.isclose(
+            pm.logp(RV, np.array([0.01, -0.03, 0.02, 0.00])).eval(), np.array(4.60022238)
+        ).eval(), "logp inaccuracy"
+
+    def test_icar_rng_fn(self):
+        W = np.array([[0, 1, 0, 1], [1, 0, 1, 0], [0, 1, 0, 1], [1, 0, 1, 0]])
+
+        RV = pm.ICAR.dist(W=W)
+
+        with pytest.raises(NotImplementedError, match="Cannot sample from ICAR prior"):
+            pm.draw(RV)
+
+    @pytest.mark.parametrize(
+        "W,msg",
+        [
+            (np.array([0, 1, 0, 0]), "W must be matrix with ndim=2"),
+            (np.array([[0, 1, 0, 0], [1, 0, 0, 1], [1, 0, 0, 1]]), "W must be a square matrix"),
+            (
+                np.array([[0, 1, 0, 0], [1, 0, 0, 1], [1, 0, 0, 1], [0, 1, 1, 0]]),
+                "W must be a symmetric matrix",
+            ),
+            (
+                np.array([[0, 1, 1, 0], [1, 0, 0, 0.5], [1, 0, 0, 1], [0, 0.5, 1, 0]]),
+                "W must be composed of only 1s and 0s",
+            ),
+        ],
+    )
+    def test_icar_matrix_checks(self, W, msg):
+        with pytest.raises(ValueError, match=msg):
+            with pm.Model():
+                pm.ICAR("phi", W=W)
+
+
 @pytest.mark.parametrize("sparse", [True, False])
 def test_car_rng_fn(sparse):
     delta = 0.05  # limit for KS p-value