feat: add multi run to wrap multi model and repeat runs

tests/subset_simulation/test_subset_simulation_coalescing.py

@@ -0,0 +1,92 @@
+import deepdog.subset_simulation.subset_simulation_impl as impl
+import numpy
+
+
+def test_subset_simulation_multi_result_coalescing_include_dirty(snapshot):
+	res1 = impl.SubsetSimulationResult(
+		probs_list=(),
+		over_target_cost=1,
+		over_target_likelihood=1,
+		under_target_cost=0.99,
+		under_target_likelihood=0.8,
+		lowest_likelihood=0.5,
+		messages=[],
+	)
+
+	res2 = impl.SubsetSimulationResult(
+		probs_list=(),
+		over_target_cost=1,
+		over_target_likelihood=1,
+		under_target_cost=0.99,
+		under_target_likelihood=0.08,
+		lowest_likelihood=0.01,
+		messages=[],
+	)
+
+	res3 = impl.SubsetSimulationResult(
+		probs_list=(),
+		over_target_cost=None,
+		over_target_likelihood=None,
+		under_target_cost=None,
+		under_target_likelihood=None,
+		lowest_likelihood=0.0001,
+		messages=[],
+	)
+
+	combined = impl.coalesce_ss_results("test", [res1, res2, res3])
+
+	assert combined == snapshot
+
+
+def test_subset_simulation_multi_result_coalescing_easy_arithmetic(snapshot):
+	res1 = impl.SubsetSimulationResult(
+		probs_list=(),
+		over_target_cost=1,
+		over_target_likelihood=1,
+		under_target_cost=0.99,
+		under_target_likelihood=0.8,
+		lowest_likelihood=0.5,
+		messages=[],
+	)
+
+	res2 = impl.SubsetSimulationResult(
+		probs_list=(),
+		over_target_cost=1,
+		over_target_likelihood=1,
+		under_target_cost=0.99,
+		under_target_likelihood=0.6,
+		lowest_likelihood=0.01,
+		messages=[],
+	)
+
+	combined = impl.coalesce_ss_results("test", [res1, res2])
+
+	assert combined.arithmetic_mean_estimated_likelihood == 0.7
+	assert combined == snapshot
+
+
+def test_subset_simulation_multi_result_coalescing_easy_geometric(snapshot):
+	res1 = impl.SubsetSimulationResult(
+		probs_list=(),
+		over_target_cost=1,
+		over_target_likelihood=1,
+		under_target_cost=0.99,
+		under_target_likelihood=0.1,
+		lowest_likelihood=0.5,
+		messages=[],
+	)
+
+	res2 = impl.SubsetSimulationResult(
+		probs_list=(),
+		over_target_cost=1,
+		over_target_likelihood=1,
+		under_target_cost=0.99,
+		under_target_likelihood=0.001,
+		lowest_likelihood=0.01,
+		messages=[],
+	)
+
+	combined = impl.coalesce_ss_results("test", [res1, res2])
+
+	numpy.testing.assert_allclose(combined.estimated_likelihood, 0.01)
+	assert combined == snapshot