stanfordnlp/CoreNLP · error · java.lang.UnsupportedOperationException

prior is specified to be ae-lasso or g-lasso, but function…

Error message

prior is specified to be ae-lasso or g-lasso, but function does not support feature grouping

What it means

SGDWithAdaGradAndFOBOS.minimize() throws this UnsupportedOperationException when the prior is an ae-lasso or g-lasso grouped regularizer but the objective function does not implement HasFeatureGrouping. Grouped lasso penalties need to know feature groups, which are supplied by the function via getFeatureGrouping().

Solutions

Use an objective function implementation that implements HasFeatureGrouping and returns valid feature groups
Change the prior to Prior.LASSO, Prior.RIDGE, or Prior.GAUSSIAN which need no grouping
Implement HasFeatureGrouping on your custom function and return a feature grouping

Example fix

// before
optimizer.setPrior(Prior.gLASSO);
optimizer.minimize(new MyPlainFunction(), ...);
// after
optimizer.setPrior(Prior.gLASSO);
optimizer.minimize(new MyGroupedFunction implements HasFeatureGrouping(), ...);

Defensive patterns

Strategy: validation

Validate before calling

if ((prior == Prior.aeLASSO || prior == Prior.gLASSO) && !(f instanceof HasFeatureGrouping)) { throw new IllegalArgumentException("function must implement HasFeatureGrouping for grouped lasso priors"); }

Type guard

boolean supportsGroupedLasso(ObjectiveFunction f) { return f instanceof HasFeatureGrouping; }

Prevention

Only use grouped-lasso priors with functions that implement HasFeatureGrouping
Document which priors each objective function supports
Fall back to plain Prior.LASSO when grouping is unavailable

When it happens

Trigger: Passing a differentiable function (e.g. a plain LogisticObjectiveFunction) to minimize() while prior is set to Prior.aeLASSO or Prior.gLASSO, so the cast to HasFeatureGrouping is impossible.

Common situations: Selecting a grouped-lasso prior for a model type that never grouped its features; using an older custom objective function written before HasFeatureGrouping existed.

Understand the failure class

Background: UnsupportedOperationException and "is not supported" errors: when a library deliberately refuses a call — this error's family across 30 libraries.

Related errors

AI-assisted analysis of stanfordnlp/CoreNLP@1b7edd19c4 (2026-09-10). Data as JSON: /api/errors/823dbccb3d759b5b. Report an issue: GitHub.

Appendix: source

Thrown at src/edu/stanford/nlp/optimization/SGDWithAdaGradAndFOBOS.java:324

    double[] testUpdateCache = null, currentRateCache = null, bCache = null;
    sumGradSquare = new double[initial.length];
    prevGrad = new double[initial.length];
    prevDeltaX = new double[initial.length];
    if (useAdaDelta) {
      sumDeltaXSquare = new double[initial.length];
      if (prior != Prior.NONE && prior != Prior.GAUSSIAN) {
        throw new UnsupportedOperationException("useAdaDelta is currently only supported for Prior.NONE or Prior.GAUSSIAN"); 
      }
    }

    int[][] featureGrouping = null;
    if (prior != Prior.LASSO && prior != Prior.NONE) {
      testUpdateCache = new double[initial.length];
      currentRateCache = new double[initial.length];
    }
    if (prior != Prior.LASSO && prior != Prior.RIDGE && prior != Prior.GAUSSIAN) {
      if (!(f instanceof HasFeatureGrouping)) {
        throw new UnsupportedOperationException("prior is specified to be ae-lasso or g-lasso, but function does not support feature grouping");
      }
      featureGrouping = ((HasFeatureGrouping)f).getFeatureGrouping();
    }
    if (prior == Prior.sgLASSO) {
      bCache = new double[initial.length];
    }

    System.arraycopy(initial, 0, x, 0, x.length);

    int numBatches =  1;
    if (f instanceof AbstractStochasticCachingDiffUpdateFunction) {
      if (totalSamples > 0)
        numBatches = totalSamples / bSize;
    }

    boolean have_max = (maxIterations > 0 || numPasses > 0);

    if (!have_max){

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