Specify convention for estimate_objective

HISTORY.md

@@ -1,4 +1,6 @@
-# Release 0.5.1
+# Release 0.6
+
+## New Algorithms
 
 This update adds new variational inference algorithms in light of the flexibility added in the v0.5 update.
 Specifically, the following measure-space optimization algorithms have been added:
@@ -7,6 +9,13 @@ Specifically, the following measure-space optimization algorithms have been adde
   - `KLMinNaturalGradDescent`
   - `KLMinSqrtNaturalGradDescent`
 
+## Interface Change
+
+The objective value returned by `estimate_objective` is now the value to be *minimized* by the algorithm.
+For instance, for ELBO maximization algorithms, `estimate_objective` will return the negative ELBO.
+
+## Behavior Change
+
 In addition, `KLMinRepGradDescent`, `KLMinRepGradProxDescent`, `KLMinScoreGradDescent` will now throw a `RuntimException` if the objective value estimated at each step turns out to be degenerate (`Inf` or `NaN`). Previously, the algorithms ran until `max_iter` even if the optimization run has failed.
 
 # Release 0.5

Project.toml

@@ -1,6 +1,6 @@
 name = "AdvancedVI"
 uuid = "b5ca4192-6429-45e5-a2d9-87aec30a685c"
-version = "0.5.1"
+version = "0.6"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"

bench/Project.toml

@@ -20,7 +20,7 @@ Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
 ADTypes = "1"
-AdvancedVI = "0.5, 0.4"
+AdvancedVI = "0.6"
 BenchmarkTools = "1"
 Bijectors = "0.13, 0.14, 0.15"
 Distributions = "0.25.111"

docs/Project.toml

@@ -25,7 +25,7 @@ StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
 [compat]
 ADTypes = "1"
 Accessors = "0.1"
-AdvancedVI = "0.5, 0.4"
+AdvancedVI = "0.6"
 Bijectors = "0.13.6, 0.14, 0.15"
 DataFrames = "1"
 DifferentiationInterface = "0.7"

docs/src/general.md

@@ -17,7 +17,7 @@ Therefore, please refer to the documentation of each different algorithm for a d
 
 ## [Monitoring the Objective Value](@id estimate_objective)
 
-Furthermore, each algorithm has an associated variational objective.
+Furthermore, each algorithm has an associated variational objective subject to *minimization*. (By convention, we assume all objectives are minimized rather than maximized.)
 The progress made by each optimization algorithm can be diagnosed by monitoring the variational objective value.
 This can be done by calling the following method.
 

docs/src/klminnaturalgraddescent.md

@@ -12,7 +12,7 @@ Since `KLMinNaturalGradDescent` is a measure-space algorithm, its use is restric
 KLMinNaturalGradDescent
 ```
 
-The associated objective value, which is the ELBO, can be estimated through the following:
+The associated objective can be estimated through the following:
 
 ```@docs; canonical=false
 estimate_objective(

docs/src/klminsqrtnaturalgraddescent.md

@@ -13,7 +13,7 @@ Since `KLMinSqrtNaturalGradDescent` is a measure-space algorithm, its use is res
 KLMinSqrtNaturalGradDescent
 ```
 
-The associated objective value, which is the ELBO, can be estimated through the following:
+The associated objective value can be estimated through the following:
 
 ```@docs; canonical=false
 estimate_objective(

docs/src/klminwassfwdbwd.md

@@ -10,7 +10,7 @@ Since `KLMinWassFwdBwd` is a measure-space algorithm, its use is restricted to f
 KLMinWassFwdBwd
 ```
 
-The associated objective value, which is the ELBO, can be estimated through the following:
+The associated objective value can be estimated through the following:
 
 ```@docs; canonical=false
 estimate_objective(

docs/src/tutorials/basic.md

@@ -232,7 +232,7 @@ function callback(; iteration, averaged_params, restructure, kwargs...)
 
         # Higher fidelity estimate of the ELBO on the averaged parameters
         n_samples = 256
-        elbo_callback = estimate_objective(alg, q_avg, model; n_samples)
+        elbo_callback = -estimate_objective(alg, q_avg, model; n_samples)
 
         (elbo_callback=elbo_callback, accuracy=acc)
     else

docs/src/tutorials/subsampling.md

@@ -213,7 +213,7 @@ function callback(; iteration, averaged_params, restructure, kwargs...)
 
         # Higher fidelity estimate of the ELBO on the averaged parameters
         n_samples = 256
-        elbo_callback = estimate_objective(alg_full, q_avg, model; n_samples)
+        elbo_callback = -estimate_objective(alg_full, q_avg, model; n_samples)
 
         (elbo_callback=elbo_callback, accuracy=acc, time_elapsed=time() - time_begin)
     else

src/AdvancedVI.jl

@@ -258,7 +258,7 @@ output(::AbstractVariationalAlgorithm, ::Any) = nothing
 """
     estimate_objective([rng,] alg, q, prob; kwargs...)
 
-Estimate the variational objective associated with the algorithm `alg` targeting `prob` with respect to the variational approximation `q`.
+Estimate the variational objective to be minimized by the algorithm `alg` for approximating the target `prob` with the variational approximation `q`.
 
 # Arguments
 - `rng::Random.AbstractRNG`: Random number generator.

src/algorithms/abstractobjective.jl

@@ -3,12 +3,7 @@
 """
     AbstractVariationalObjective
 
-Abstract type for the VI algorithms supported by `AdvancedVI`.
-
-# Implementations
-To be supported by `AdvancedVI`, a VI algorithm must implement `AbstractVariationalObjective` and `estimate_objective`.
-Also, it should provide gradients by implementing the function `estimate_gradient`.
-If the estimator is stateful, it can implement `init` to initialize the state.
+Abstract type for a variational objective to be optimized by some variational algorithm.
 """
 abstract type AbstractVariationalObjective end
 
@@ -42,7 +37,7 @@ end
 """
     estimate_objective([rng,] obj, q, prob; kwargs...)
 
-Estimate the variational objective `obj` targeting `prob` with respect to the variational approximation `q`.
+Estimate the minimization objective `obj` of the variational approximation `q` targeting `prob`.
 
 # Arguments
 - `rng::Random.AbstractRNG`: Random number generator.

src/algorithms/common.jl

@@ -11,7 +11,7 @@ const ParamSpaceSGD = Union{
 """
     estimate_objective([rng,] alg, q, prob; n_samples, entropy)
 
-Estimate the ELBO of the variational approximation `q` against the target log-density `prob`.
+Estimate the negative ELBO of the variational approximation `q` against the target log-density `prob`.
 
 # Arguments
 - `rng::Random.AbstractRNG`: Random number generator.

src/algorithms/klminnaturalgraddescent.jl

@@ -154,7 +154,7 @@ end
 """
     estimate_objective([rng,] alg, q, prob; n_samples)
 
-Estimate the ELBO of the variational approximation `q` against the target log-density `prob`.
+Estimate the negative ELBO of the variational approximation `q` against the target log-density `prob`.
 
 # Arguments
 - `rng::Random.AbstractRNG`: Random number generator.

src/algorithms/klminsqrtnaturalgraddescent.jl

@@ -129,7 +129,7 @@ end
 """
     estimate_objective([rng,] alg, q, prob; n_samples)
 
-Estimate the ELBO of the variational approximation `q` against the target log-density `prob`.
+Estimate the negative ELBO of the variational approximation `q` against the target log-density `prob`.
 
 # Arguments
 - `rng::Random.AbstractRNG`: Random number generator.

src/algorithms/klminwassfwdbwd.jl

@@ -124,7 +124,7 @@ end
 """
     estimate_objective([rng,] alg, q, prob; n_samples)
 
-Estimate the ELBO of the variational approximation `q` against the target log-density `prob`.
+Estimate the negative ELBO of the variational approximation `q` against the target log-density `prob`.
 
 # Arguments
 - `rng::Random.AbstractRNG`: Random number generator.

src/algorithms/repgradelbo.jl

@@ -114,7 +114,7 @@ function estimate_objective(
 )
     samples, entropy = reparam_with_entropy(rng, q, q, n_samples, obj.entropy)
     energy = estimate_energy_with_samples(prob, samples)
-    return energy + entropy
+    return -(energy + entropy)
 end
 
 function estimate_objective(obj::RepGradELBO, q, prob; n_samples::Int=obj.n_samples)

src/algorithms/scoregradelbo.jl

@@ -61,7 +61,7 @@ function estimate_objective(
     samples = rand(rng, q, n_samples)
     ℓπ = map(Base.Fix1(LogDensityProblems.logdensity, prob), eachsample(samples))
     ℓq = logpdf.(Ref(q), AdvancedVI.eachsample(samples))
-    return mean(ℓπ - ℓq)
+    return -mean(ℓπ - ℓq)
 end
 
 function estimate_objective(obj::ScoreGradELBO, q, prob; n_samples::Int=obj.n_samples)