Update test references (#44)

* Update test references

* Update show test

* Run formatter

* Increment patch number

* Set new Distributions lower bound

* Bump minimum Julia version to v1.4

* Bump minimum Julia version to v1.6

.github/workflows/CI.yml

@@ -15,7 +15,7 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.0'
+          - '1.6'
           - '1'
         os:
           - ubuntu-latest

Project.toml

@@ -1,7 +1,7 @@
 name = "PSIS"
 uuid = "ce719bf2-d5d0-4fb9-925d-10a81b42ad04"
 authors = ["Seth Axen <[email protected]> and contributors"]
-version = "0.7.1"
+version = "0.7.2"
 
 [deps]
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
@@ -12,13 +12,13 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [compat]
 AxisArrays = "0.4"
-Distributions = "0.21.1, 0.22, 0.23, 0.24, 0.25"
+Distributions = "0.25.81"
 JLD2 = "0.4"
 LogExpFunctions = "0.2.0, 0.3"
 Plots = "1"
 RecipesBase = "1"
 ReferenceTests = "0.9"
-julia = "1"
+julia = "1.6"
 
 [extras]
 AxisArrays = "39de3d68-74b9-583c-8d2d-e117c070f3a9"

src/core.jl

@@ -183,7 +183,7 @@ While `psis` computes smoothed log weights out-of-place, `psis!` smooths them in
 
   - `log_ratios`: an array of logarithms of importance ratios, with one of the following
     sizes:
-    
+
       + `(ndraws,)`: a vector of draws for a single parameter from a single chain
       + `(ndraws, nparams)`: a matrix of draws for a multiple parameter from a single chain
       + `(ndraws, nchains, nparams)`: an array of draws for multiple parameters from

src/ess.jl

@@ -16,7 +16,7 @@ where ``r_{\\mathrm{eff}}`` is the relative efficiency of the `log_weights`.
 Estimate ESS for Pareto-smoothed importance sampling.
 
 !!! note
-    
+
     ESS estimates for Pareto shape values ``k > 0.7``, which are unreliable and misleadingly
     high, are set to `missing`. To avoid this, set `bad_shape_missing=false`.
 """

src/generalized_pareto.jl

@@ -18,7 +18,7 @@ Construct the generalized Pareto distribution (GPD) with location parameter ``μ
 parameter ``σ`` and shape parameter ``k``.
 
 !!! note
-    
+
     The shape parameter ``k`` is equivalent to the commonly used shape parameter ``ξ``.
     This is the same parameterization used by [^VehtariSimpson2021] and is related to that
     used by [^ZhangStephens2009] as ``k \\mapsto -k``.

test/core.jl

@@ -84,10 +84,10 @@ using AxisArrays: AxisArrays
                 PSISResult with 100 draws, 1 chains, and 30 parameters
                 Pareto shape (k) diagnostic values:
                                         Count       Min. ESS
-                 (-Inf, 0.5]  good       4 (13.3%)  95
-                  (0.5, 0.7]  okay       2 (6.7%)   97
+                 (-Inf, 0.5]  good       2 (6.7%)   98
+                  (0.5, 0.7]  okay       6 (20.0%)  92
                     (0.7, 1]  bad        4 (13.3%)  ——
-                    (1, Inf)  very bad  19 (63.3%)  ——
+                    (1, Inf)  very bad  17 (56.7%)  ——
                           ——  missing    1 (3.3%)   ——"""
         end
     end
@@ -276,11 +276,8 @@ end
                 Dict("log_weights" => logw, "pareto_shape" => result.pareto_shape),
                 by =
                     (ref, x) ->
-                        isapprox(
-                            permutedims(ref["log_weights"], (2, 3, 1)),
-                            x["log_weights"];
-                            rtol=1e-6,
-                        ) && isapprox(ref["pareto_shape"], x["pareto_shape"]; rtol=1e-6),
+                        isapprox(ref["log_weights"], x["log_weights"]; rtol=1e-6) &&
+                            isapprox(ref["pareto_shape"], x["pareto_shape"]; rtol=1e-6),
             )
         end
     end