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Bump Optimization to v4, and related packages accordingly #2354
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Failing test MWE using Random
using Turing
import ReverseDiff
@model function f()
x ~ Normal(0, 1)
end
Random.seed!(222)
# cons_args is mandatory, if no constraints are passed it runs fine
cons(res, x, _) = (res .= [x[1]])
cons_args = (cons=cons, lcons=[0], ucons=[Inf])
initial_params = [0.5]
m1 = Turing.Optimisation.estimate_mode(
f(), MAP(); initial_params=initial_params, cons_args...
) Show the error traceback
Output of ]st --manifest
versioninfo()
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Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## master #2354 +/- ##
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Coverage 0.00% 0.00%
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Files 22 22
Lines 1533 1533
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Misses 1533 1533 ☔ View full report in Codecov by Sentry. |
Pull Request Test Coverage Report for Build 11073388637Details
💛 - Coveralls |
This method ambiguity is fixed in JuliaDiff/ForwardDiff.jl#687, it just needs a release |
I think bumping Optimization to v4 (which in turn bumps OptimizationBase to v2) would also allow it to work with Mooncake as an AD backend (since it now goes via DifferentiationInterface, SciML/OptimizationBase.jl#108). Both the MLE and MAP tests pass with Optimization@4 and adtype=AutoMooncake. |
Re-opening #2327