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

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This commit is contained in:
Deepak Mallubhotla
2024-05-19 02:27:11 -05:00
parent 8845b2875f
commit 92b49fce7c
3 changed files with 431 additions and 138 deletions
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10
tests/subset_simulation/__snapshots__/test_subset_simulation_coalescing.ambr Normal file
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# serializer version: 1
# name: test_subset_simulation_multi_result_coalescing_easy_arithmetic
MultiSubsetSimulationResult(child_results=[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=[]), 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=[])], model_name='test', estimated_likelihood=0.6928203230275509, arithmetic_mean_estimated_likelihood=0.7, num_children=2, num_finished_children=2, clean_estimate=True)
# ---
# name: test_subset_simulation_multi_result_coalescing_easy_geometric
MultiSubsetSimulationResult(child_results=[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=[]), 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=[])], model_name='test', estimated_likelihood=0.010000000000000004, arithmetic_mean_estimated_likelihood=0.0505, num_children=2, num_finished_children=2, clean_estimate=True)
# ---
# name: test_subset_simulation_multi_result_coalescing_include_dirty
MultiSubsetSimulationResult(child_results=[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=[]), 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=[]), SubsetSimulationResult(probs_list=(), over_target_cost=None, over_target_likelihood=None, under_target_cost=None, under_target_likelihood=None, lowest_likelihood=0.0001, messages=[])], model_name='test', estimated_likelihood=0.01856635533445112, arithmetic_mean_estimated_likelihood=0.29336666666666666, num_children=3, num_finished_children=2, clean_estimate=False)
# ---

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tests/subset_simulation/test_subset_simulation_coalescing.py Normal file
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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