Merge pull request #13 from JuliaTrustworthyAI/10-add-pcd

JuliaTrustworthyAI · Jun 6, 2024 · 63c1687 · 63c1687 · pat-alt · Jun 7, 2024
2 parents 3d2d5a0 + 50c1fd6
commit 63c1687
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Showing 4 changed files with 10 additions and 6 deletions.
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -6,6 +6,12 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 
 *Note*: We try to adhere to these practices as of version [v1.1.0].
 
+## Version [1.2.1] - 2024-06-06
+
+### Removed
+
+- No longer clipping generated samples at extrema of prior. [#13]
+
 ## Version [1.2.0] - 2024-06-05
 
 ### Added

diff --git a/Project.toml b/Project.toml
@@ -1,7 +1,7 @@
 name = "TaijaBase"
 uuid = "10284c91-9f28-4c9a-abbf-ee43576dfff6"
 authors = ["Patrick Altmeyer"]
-version = "1.2.0"
+version = "1.2.1"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"

diff --git a/src/Samplers/Samplers.jl b/src/Samplers/Samplers.jl
@@ -67,8 +67,6 @@ function (sampler::AbstractSampler)(
     input_samples =
         mcmc_samples(sampler, model, rule, input_samples; niter = niter, kwargs...)
     Flux.trainmode!(model)
-    input_samples =
-        Float32.(clamp.(input_samples, minimum(sampler.𝒟x), maximum(sampler.𝒟x)))
 
     # Update buffer:
     sampler.buffer = cat(input_samples, sampler.buffer, dims = ndims(sampler.buffer))

diff --git a/test/samplers.jl b/test/samplers.jl
@@ -120,7 +120,7 @@ using TaijaBase.Samplers:
         train_y, test_y = prep_y.((train[:, target], test[:, target]))
         train_set = Flux.DataLoader(
             (train_X, train_y),
-            batchsize = size(train_X, 2),
+            batchsize = 100,
             shuffle = false,
         )
 
@@ -174,11 +174,11 @@ using TaijaBase.Samplers:
             model, weights, trainlosses, testlosses
         end
 
-        results = train_logreg(steps = 10000, opt = SGLD(10.0, 1000.0, 0.9))
+        results = train_logreg(steps = 1000, opt = SGLD(10.0, 1000.0, 0.9))
         model, weights, trainlosses, testlosses = results
         plot(weights; label = ["Student" "Balance" "Income" "Intercept"])
 
-        results = train_logreg(steps = 1000, opt = ImproperSGLD(2.0, 0.01))
+        results = train_logreg(steps = 100, opt = ImproperSGLD(2.0, 0.01))
         model, weights, trainlosses, testlosses = results
         plot(weights; label = ["Student" "Balance" "Income" "Intercept"])