Add mixture-of-gammas global prior, updated by EM

tests/test_inference.py

@@ -414,17 +414,41 @@ 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="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,
-            global_prior=False,
+        )
+
+    def test_prior_mixture_dim(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)
+        tsdate.date(
+            ts,
+            mutation_rate=5,
+            method="variational_gamma",
+            priors=grid,
+            prior_mixture_dim=2,
         )
 
     def test_bad_arguments(self):
@@ -437,6 +461,14 @@ def test_bad_arguments(self):
                 method="variational_gamma",
                 max_iterations=-1,
             )
+        with pytest.raises(ValueError, match="must be a positive integer"):
+            tsdate.date(
+                ts,
+                mutation_rate=5,
+                population_size=1,
+                method="variational_gamma",
+                prior_mixture_dim=0.1,
+            )
 
     def test_match_central_moments(self):
         ts = msprime.simulate(8, mutation_rate=5, recombination_rate=5, random_seed=2)

tsdate/cli.py

@@ -199,6 +199,34 @@ def tsdate_cli_parser():
             "but does not exactly minimize KL divergence in each EP update."
         ),
     )
+    parser.add_argument(
+        "--max-iterations",
+        type=int,
+        help=(
+            "The number of iterations used in the expectation propagation "
+            "algorithm. Default: 20"
+        ),
+        default=20,
+    )
+    parser.add_argument(
+        "--em-iterations",
+        type=int,
+        help=(
+            "The number of expectation-maximization iterations used to optimize the "
+            "i.i.d. mixture prior at the end of each expectation propagation step. "
+            "Setting to zero disables optimization. Default: 10"
+        ),
+        default=10,
+    )
+    parser.add_argument(
+        "--prior-mixture-dim",
+        type=int,
+        help=(
+            "The number of components in the i.i.d. mixture prior for node "
+            "ages. Default: 1"
+        ),
+        default=1,
+    )
     parser.set_defaults(runner=run_date)
 
     parser = subparsers.add_parser(
@@ -253,8 +281,11 @@ def run_date(args):
             method=args.method,
             eps=args.epsilon,
             progress=args.progress,
+            max_iterations=args.max_iterations,
             max_shape=args.max_shape,
             match_central_moments=args.match_central_moments,
+            em_iterations=args.em_iterations,
+            prior_mixture_dim=args.prior_mixture_dim,
         )
     else:
         params = dict(