Sorting out tests, prior parameterization

tests/test_inference.py

@@ -414,19 +414,31 @@ def test_simple_sim_multi_tree(self):
         ts = msprime.simulate(8, mutation_rate=5, recombination_rate=5, random_seed=2)
         tsdate.date(ts, mutation_rate=5, population_size=1, method="variational_gamma")
 
-    def test_nonglobal_priors(self):
+    def test_invalid_priors(self):
         ts = msprime.simulate(8, mutation_rate=5, recombination_rate=5, random_seed=2)
         priors = tsdate.prior.MixturePrior(ts, prior_distribution="gamma")
         grid = priors.make_parameter_grid(population_size=1)
         grid.grid_data[:] = [1.0, 0.0]  # noninformative prior
-        with pytest.raises(ValueError, match="not yet implemented"):
+        with pytest.raises(ValueError, match="Non-positive shape/rate"):
             tsdate.date(
                 ts,
                 mutation_rate=5,
                 method="variational_gamma",
                 priors=grid,
             )
 
+    def test_custom_priors(self):
+        ts = msprime.simulate(8, mutation_rate=5, recombination_rate=5, random_seed=2)
+        priors = tsdate.prior.MixturePrior(ts, prior_distribution="gamma")
+        grid = priors.make_parameter_grid(population_size=1)
+        grid.grid_data[:] += 1.0
+        tsdate.date(
+            ts,
+            mutation_rate=5,
+            method="variational_gamma",
+            priors=grid,
+        )
+
     def test_bad_arguments(self):
         ts = utility_functions.two_tree_mutation_ts()
         with pytest.raises(ValueError, match="Maximum number of EP iterations"):
@@ -441,6 +453,7 @@ def test_bad_arguments(self):
             tsdate.date(
                 ts,
                 mutation_rate=5,
+                population_size=1,
                 method="variational_gamma",
                 global_prior=False,
             )

tsdate/core.py

@@ -1487,11 +1487,14 @@ class VariationalGammaMethod(EstimationMethod):
 
     def __init__(self, ts, **kwargs):
         super().__init__(ts, **kwargs)
-        # convert priors to natural parameterization and average
+        # convert priors to natural parameterization
         for n in self.priors.nonfixed_nodes:
+            if not np.all(self.priors[n] > 0.0):
+                raise ValueError(
+                    f"Non-positive shape/rate parameters for node {n}: "
+                    f"{self.priors[n]}"
+                )
             self.priors[n][0] -= 1.0
-            assert self.priors[n][0] > -1.0
-            assert self.priors[n][1] >= 0.0
 
     @staticmethod
     def mean_var(ts, posterior):
@@ -1526,9 +1529,19 @@ def main_algorithm(self):
             self.recombination_rate,
             fixed_node_set=self.get_fixed_nodes_set(),
         )
-        return ExpectationPropagation(self.priors, lik, progress=self.pbar, global_prior=self.prior_mixture)
+        return ExpectationPropagation(
+            self.priors, lik, progress=self.pbar, global_prior=self.prior_mixture
+        )
 
-    def run(self, eps, max_iterations, max_shape, match_central_moments, global_prior, em_iterations):
+    def run(
+        self,
+        eps,
+        max_iterations,
+        max_shape,
+        match_central_moments,
+        global_prior,
+        em_iterations,
+    ):
         if self.provenance_params is not None:
             self.provenance_params.update(
                 {k: v for k, v in locals().items() if k != "self"}
@@ -1540,8 +1553,10 @@ def run(self, eps, max_iterations, max_shape, match_central_moments, global_prio
         if self.mutation_rate is None:
             raise ValueError("Variational gamma method requires mutation rate")
 
-        self.prior_mixture = mixture.initialize_mixture(self.priors.grid_data, global_prior)
-        self.priors.grid_data[:] = [0.0, 0.0] # TODO: support node-specific priors
+        self.prior_mixture = mixture.initialize_mixture(
+            self.priors.grid_data, global_prior
+        )
+        self.priors.grid_data[:] = [0.0, 0.0]  # TODO: support node-specific priors
 
         # match sufficient statistics or match central moments
         min_kl = not match_central_moments

tsdate/mixture.py

@@ -225,16 +225,20 @@ def fit_gamma_mixture(mixture, observations, max_iterations, tolerance, verbose)
 
 
 def initialize_mixture(parameters, num_components):
-    """initialize clusters by dividing nodes into equal groups"""
+    """
+    Initialize clusters by dividing nodes into equal groups.
+    "parameters" are in natural parameterization (not shape/rate)
+    """
     global_prior = np.empty((num_components, 3))
     num_nodes = parameters.shape[0]
-    age_classes = np.tile(np.arange(num_components), num_nodes // num_components + 1)[
-        :num_nodes
-    ]
+    age_classes = np.tile(
+        np.arange(num_components),
+        num_nodes // num_components + 1,
+    )[:num_nodes]
     for k in range(num_components):
         indices = np.equal(age_classes, k)
         alpha, beta = approx.average_gammas(
-            parameters[indices, 0] - 1.0, parameters[indices, 1]
+            parameters[indices, 0], parameters[indices, 1]
         )
         global_prior[k] = [1.0 / num_components, alpha, beta]
     return global_prior