Add function for taking expectations wrt smoothed weights

Project.toml

@@ -9,6 +9,7 @@ LogExpFunctions = "2ab3a3ac-af41-5b50-aa03-7779005ae688"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
 [compat]
 DimensionalData = "0.24"
@@ -21,6 +22,7 @@ Printf = "1.6"
 RecipesBase = "1"
 ReferenceTests = "0.9, 0.10"
 Statistics = "1.6"
+StatsBase = "0.32, 0.33, 0.34"
 julia = "1.6"
 
 [extras]

docs/src/api.md

@@ -6,6 +6,7 @@
 PSISResult
 psis
 ess_is
+PSIS.expectation
 ```
 
 ## Plotting

src/PSIS.jl

@@ -3,6 +3,7 @@ module PSIS
 using LogExpFunctions: LogExpFunctions
 using Printf: @sprintf
 using Statistics: Statistics
+using StatsBase: StatsBase
 
 export PSISPlots
 export PSISResult
@@ -12,6 +13,7 @@ include("utils.jl")
 include("generalized_pareto.jl")
 include("core.jl")
 include("ess.jl")
+include("expectation.jl")
 include("recipes/plots.jl")
 
 end

src/expectation.jl

@@ -0,0 +1,73 @@
+"""
+    expectation(x, psis_result::PSISResult; kind=Statistics.mean)
+
+Compute the expectation of `x` with respect to the weights in `psis_result`.
+
+# Arguments
+
+  - `x`: An array of values of shape `(draws[, chains[, params...]])`, to compute the
+    expectation of with respect to smoothed importance weights.
+  - `psis_result`: A `PSISResult` object containing the smoothed importance weights with shape
+    `(draws[, chains, params...])`.
+
+# Keywords
+
+  - `kind=Statistics.mean`: The type of expectation to be computed. It can be any function
+    that has a method for computing the weighted expectation
+    `f(x::AbstractVector, weights::AbstractVector) -> Real`. In particular, the following
+    are supported:
+
+      + `Statistics.mean`
+      + `Statistics.median`
+      + `Statistics.std`
+      + `Statistics.var`
+      + `Base.Fix2(Statistics.quantile, p::Real)` for `quantile(x, weights, p)`
+
+# Returns
+
+  - `values`: An array of shape `(other..., params...)` or real number of `other` and `params`
+    are empty containing the expectation of `x` with respect to the smoothed importance
+    weights.
+"""
+function expectation(x::AbstractArray, psis_result::PSISResult; kind=Statistics.mean)
+    log_weights = psis_result.log_weights
+    weights = psis_result.weights
+
+    param_dims = _param_dims(log_weights)
+    exp_dims = _param_dims(x)
+    if !isempty(exp_dims) && length(exp_dims) != length(param_dims)
+        throw(
+            ArgumentError(
+                "The trailing dimensions of `x` must match the parameter dimensions of `psis_result.weights`",
+            ),
+        )
+    end
+    param_axes = map(Base.Fix1(axes, log_weights), param_dims)
+    exp_axes = map(Base.Fix1(axes, x), exp_dims)
+    if !isempty(exp_axes) && exp_axes != param_axes
+        throw(
+            ArgumentError(
+                "The trailing axes of `x` must match the parameter axes of `psis_result.weights`",
+            ),
+        )
+    end
+
+    T = Base.promote_eltype(x, log_weights)
+    values = similar(x, T, param_axes)
+
+    for i in _eachparamindex(weights)
+        w_i = StatsBase.AnalyticWeights(vec(_selectparam(weights, i)), 1)
+        x_i = vec(ndims(x) < 3 ? x : _selectparam(x, i))
+        values[i] = _expectation(kind, x_i, w_i)
+    end
+
+    iszero(ndims(values)) && return values[]
+
+    return values
+end
+
+_expectation(f, x, weights) = f(x, weights)
+function _expectation(f::Base.Fix2{typeof(Statistics.quantile),<:Real}, x, weights)
+    prob = f.x
+    return Statistics.quantile(x, weights, prob)
+end