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Add compatibility with MCMCDiagnosticTools v0.3 (#401)
* Bump MCMCDiagnosticTools compat * Update imported/exported methods * Remove type constraint on classifier * Overload and export mcse * Overload and update ess and rhat * Update summarystats * Update tests * Increment major version * Rename ess.jl to ess_rhat.jl * Add back ess_per_sec * Fix bug constructing ess_per_sec * Update ess_rhat tests * Test mcse * Update docs * Remove deprecations * Remove unused import * Revert "Fix MLJDecisionTreeInterface to 0.3.0 (#402)" This reverts commit 991f10b. * Always include ess_per_sec in table * Use isequal to pass with missing values * Use isequal for missing * Remove naive_se Fixes #351 * Test Tables interface before loading StatsPlots DataValues (a StatsPlots dependency) pirates a convert method that causes the Tables equality tests with `missing` to fail. See https://github.com/queryverse/DataValues.jl * Apply suggestions from code review Co-authored-by: David Widmann <[email protected]> --------- Co-authored-by: David Widmann <[email protected]>
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@@ -9,6 +9,7 @@ Pages = [ | |
"heideldiag.jl", | ||
"rafterydiag.jl", | ||
"rstar.jl", | ||
"ess.jl" | ||
"ess_rhat.jl", | ||
"mcse.jl", | ||
] | ||
``` |
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@@ -0,0 +1,85 @@ | ||
""" | ||
ess(chains::Chains; duration=compute_duration, kwargs...) | ||
Estimate the effective sample size. | ||
ESS per second options include `duration=MCMCChains.compute_duration` (the default) | ||
and `duration=MCMCChains.wall_duration`. | ||
""" | ||
function MCMCDiagnosticTools.ess( | ||
chains::Chains; | ||
sections = _default_sections(chains), duration = compute_duration, kwargs... | ||
) | ||
# Subset the chain | ||
_chains = Chains(chains, _clean_sections(chains, sections)) | ||
|
||
# Estimate the effective sample size | ||
ess = MCMCDiagnosticTools.ess( | ||
_permutedims_diagnostics(_chains.value.data); | ||
kwargs..., | ||
) | ||
|
||
# Calculate ESS/minute if available | ||
dur = duration(chains) | ||
|
||
# Convert to NamedTuple | ||
ess_per_sec = ess ./ dur | ||
nt = merge((parameters = names(_chains),), (; ess, ess_per_sec)) | ||
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return ChainDataFrame("ESS", nt) | ||
end | ||
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||
""" | ||
rhat(chains::Chains; kwargs...) | ||
Estimate the ``\\widehat{R}`` diagnostic. | ||
""" | ||
function MCMCDiagnosticTools.rhat( | ||
chains::Chains; | ||
sections = _default_sections(chains), kwargs... | ||
) | ||
# Subset the chain | ||
_chains = Chains(chains, _clean_sections(chains, sections)) | ||
|
||
# Estimate the rhat | ||
rhat = MCMCDiagnosticTools.rhat( | ||
_permutedims_diagnostics(_chains.value.data); | ||
kwargs..., | ||
) | ||
|
||
# Convert to NamedTuple | ||
nt = merge((parameters = names(_chains),), (; rhat)) | ||
|
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return ChainDataFrame("R-hat", nt) | ||
end | ||
|
||
""" | ||
ess_rhat(chains::Chains; duration=compute_duration, kwargs...) | ||
Estimate the effective sample size and the ``\\widehat{R}`` diagnostic | ||
ESS per second options include `duration=MCMCChains.compute_duration` (the default) | ||
and `duration=MCMCChains.wall_duration`. | ||
""" | ||
function MCMCDiagnosticTools.ess_rhat( | ||
chains::Chains; | ||
sections = _default_sections(chains), duration = compute_duration, kwargs... | ||
) | ||
# Subset the chain | ||
_chains = Chains(chains, _clean_sections(chains, sections)) | ||
|
||
# Estimate the effective sample size and rhat | ||
ess_rhat = MCMCDiagnosticTools.ess_rhat( | ||
_permutedims_diagnostics(_chains.value.data); | ||
kwargs..., | ||
) | ||
|
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# Calculate ESS/minute if available | ||
dur = duration(chains) | ||
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# Convert to NamedTuple | ||
ess_per_sec = ess_rhat.ess ./ dur | ||
nt = merge((parameters = names(_chains),), ess_rhat, (; ess_per_sec)) | ||
|
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return ChainDataFrame("ESS/R-hat", nt) | ||
end |
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@@ -0,0 +1,22 @@ | ||
""" | ||
mcse(chains::Chains; duration=compute_duration, kwargs...) | ||
Estimate the Monte Carlo standard error. | ||
""" | ||
function MCMCDiagnosticTools.mcse( | ||
chains::Chains; | ||
sections = _default_sections(chains), kwargs... | ||
) | ||
# Subset the chain | ||
_chains = Chains(chains, _clean_sections(chains, sections)) | ||
|
||
# Estimate the effective sample size | ||
mcse = MCMCDiagnosticTools.mcse( | ||
_permutedims_diagnostics(_chains.value.data); | ||
kwargs..., | ||
) | ||
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nt = merge((parameters = names(_chains),), (; mcse)) | ||
|
||
return ChainDataFrame("MCSE", nt) | ||
end |
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@JuliaRegistrator register
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Registration pull request created: JuliaRegistries/General/78718
After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.
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