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…p clone gaussianprocessbase overrode deepcopy with memberwiseclone, so every "copy" of a gaussian process shared _layers, _x and _y with the original. clone_shouldproduceidentical predictions therefore compared a model with itself and could not fail. removing the override routes the family through modelbase.deepcopy (serialize -> copyconfiguration -> deserialize) and exposes two real defects. first, deepgaussianprocess never serialized what fit learns. the generated declarations cover _x, _y and _ymean; the inducing inputs and the variational parameters live in the layers and no member of the class holds them, so the classifier could not place them. a rebuilt model predicted from the random initialisation dgplayer.initialize gives it -- 27.08 against an expected 22.34 -- which also means savemodel/loadmodel silently discarded all dgp training. registerstate now declares the three per-layer matrices positionally and recomputes kuu after the restore, since kuu is scratch derived from the inducing inputs and the kernel. second, cloneengine.prepareparametertopology and modelbase.deepcopy both read a parameter count from a fresh configuration shell. a shell whose layout is still fit-deferred throws parameterlayoutnotreadyexception rather than answer zero, precisely so an unfitted model is never mistaken for one with no parameters. there is no fitted topology to bridge in that case, and the caller's deserialize installs the state immediately after, so both reads now treat a not-ready layout as "nothing to bridge". this is what gpwithmcmc, studenttgaussianprocess, heteroscedasticgaussianprocess and variationalgaussianprocess hit once the shallow override was gone. verified locally: fullyqualifiedname~gaussianprocess passes 319, fails 0 on net10.0. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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WalkthroughThe pull request adds deep Gaussian process layer-state persistence and restoration. It also updates model copying to tolerate fit-deferred parameter layouts and removes the ChangesModel state and copy behavior
Priority: ⬇️ Low Estimated code review effort: 3 (Moderate) | ~25 minutes Change: Bug fix Suggested reviewers: Merge Risk: 🟡 Moderate · up to Loading or copying a fitted deep Gaussian process into a differently configured model can leave inconsistent layer state and fail later during training or prediction. Validate fitted layer state before merging. 🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
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In `@src/GaussianProcesses/DeepGaussianProcess.cs`:
- Around line 634-639: Update Deserialize’s post-restore flow to validate fitted
layer state before any ComputeKuu calls. In RestoreLayerMatrices, require each
non-null restored list to have exactly _layers.Count entries and restore every
index instead of truncating to the overlap; preserve null lists and all-empty
legacy shells. Add ValidateRestoredLayerState to reject partially initialized or
mixed layers and verify inducing-input, variational-mean, and covariance matrix
dimensions match, throwing InvalidDataException before rebuilding Kuu.
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Two review findings on the restore path. 1. BLOCKING: validate before rebuilding Kuu. RestoreLayerMatrices restored only the overlapping prefix (Math.Min(_layers.Count, restored.Count)), so a checkpoint from a different architecture left the model holding constructor state in some layers and checkpoint state in others. Nothing downstream could tell the halves apart: ComputeKuu reads only InducingInputs and rebuilds happily, then Forward indexes VariationalMean using the destination layer's dimensions -- so the mixture surfaced as an index error inside a matrix multiply, or as silently wrong predictions when the shapes happened to line up. The old doc comment rationalised this as something "the base class reports on its own terms", which it does not. An exact count is now required, and a new ValidateRestoredLayerState runs before ComputeKuu. Within one fitted layer the inducing-point count m ties the three matrices together -- InducingInputs [m, inputDim], VariationalMean [m, OutputDim], VariationalCovCholesky [m, m] -- and they are declared independently, so a checkpoint can disagree with itself. The validation rejects a half-populated triple and any of those three shape disagreements, naming the layer and the actual dimensions. Unfitted and legacy shells keep loading: a null payload, or one holding only null and empty matrices, is the absence of state rather than a disagreement about shape, and is left alone. 2. CodeQL, missed opportunity to use Where. The Kuu rebuild loop filtered inside its body. The condition is now part of the sequence. Build: src/AiDotNet.csproj -f net10.0, 0 errors. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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What
GaussianProcessBaseoverrodeDeepCopy()withMemberwiseClone(), so every "copy" of aGaussian process shared
_layers,_Xand_ywith the original.Clone_ShouldProduceIdenticalPredictionswas comparing a model with itself and could not fail. Removing the override routes the family
through
ModelBase.DeepCopy(serialize ->CopyConfiguration->Deserialize) and exposes tworeal defects, both fixed here.
PR #2136 removes the same override as part of its coverage work, which is how its
ModelFamily - Clustering/GPshard went red. That shard needs these two fixes to pass.Defect 1 -
DeepGaussianProcessnever serialized whatFitlearnsThe generated
RegisterGeneratedStatedeclares only_X,_yand_yMean. The inducing inputsand the variational parameters live in the
DGPLayerinstances, and no member of the model classholds them, so the state classifier could not place them.
A rebuilt model therefore predicted from the random initialisation
DGPLayer.Initializegives it:Expected
22.340009820821585, Actual27.075184895167549. The same path backsSaveModel/LoadModel, so saving and reloading aDeepGaussianProcesssilently discarded all training.RegisterStatenow declares the three per-layer matrices positionally and recomputesKuuafterthe restore via
DeclareAfterRestore, sinceKuuis scratch derived from the inducing inputs andthe kernel. Layer count and widths come from the recorded constructor, so the lists line up.
Defect 2 - the clone path read a parameter count from an unfitted shell
CloneEngine.PrepareParameterTopologyandModelBase.DeepCopyboth read a parameter count froma freshly built configuration shell. A shell whose layout is still
FitDeferredthrowsParameterLayoutNotReadyExceptionrather than answer zero -- deliberately, so an unfitted model isnever mistaken for one with no parameters.
There is no fitted topology to bridge in that case, and the caller's
Deserializeinstalls thefitted state immediately afterwards, so both reads now treat a not-ready layout as "nothing to
bridge" and fall through. This is what
GPWithMCMC,StudentTGaussianProcess,HeteroscedasticGaussianProcessandVariationalGaussianProcesshit once the shallow overridewas gone.
Verification
Local,
net10.0:Before these fixes the same filter gave
Failed: 4(the fourParameterLayoutNotReadyExceptionmodels) plus
DeepGaussianProcessTests.Clone_ShouldProduceIdenticalPredictions.🤖 Generated with Claude Code
Summary by CodeRabbit
New Features
Bug Fixes