{"record":{"id":"4c70f99179f1cbb4","repo":"stanfordnlp/CoreNLP","slug":"testing-of-stochastic-objective-function-failed","errorCode":null,"errorMessage":"Testing of stochastic objective function failed.","messagePattern":"Testing of stochastic objective function failed\\.","errorType":"exception","errorClass":"IllegalStateException","httpStatus":null,"severity":"error","filePath":"src/edu/stanford/nlp/ie/crf/CRFClassifier.java","lineNumber":1867,"sourceCode":"    if (flags.initialWeights == null) {\n      initialWeights = func.initial();\n    } else {\n      try {\n        log.info(\"Reading initial weights from file \" + flags.initialWeights);\n        DataInputStream dis = IOUtils.getDataInputStream(flags.initialWeights);\n        initialWeights = ConvertByteArray.readDoubleArr(dis);\n      } catch (IOException e) {\n        throw new RuntimeException(\"Could not read from double initial weight file \" + flags.initialWeights);\n      }\n    }\n    log.info(\"numWeights: \" + initialWeights.length);\n\n    if (flags.testObjFunction) {\n      StochasticDiffFunctionTester tester = new StochasticDiffFunctionTester(func);\n      if (tester.testSumOfBatches(initialWeights, 1e-4)) {\n        log.info(\"Successfully tested stochastic objective function.\");\n      } else {\n        throw new IllegalStateException(\"Testing of stochastic objective function failed.\");\n      }\n\n    }\n    //check gradient\n    if (flags.checkGradient) {\n      if (func.gradientCheck()) {\n        log.info(\"gradient check passed\");\n      } else {\n        throw new RuntimeException(\"gradient check failed\");\n      }\n    }\n    return minimizer.minimize(func, flags.tolerance, initialWeights);\n  }\n\n  public Minimizer<DiffFunction> getMinimizer() {\n    return getMinimizer(0, null);\n  }\n","sourceCodeStart":1849,"sourceCodeEnd":1885,"githubUrl":"https://github.com/stanfordnlp/CoreNLP/blob/1b7edd19c4d0d7b1f13a2591425b9b60a0b1af7a/src/edu/stanford/nlp/ie/crf/CRFClassifier.java#L1849-L1885","documentation":"With flags.testObjFunction=true, training runs StochasticDiffFunctionTester.testSumOfBatches on the CRF objective with tolerance 1e-4 before minimization. If the test fails (per-batch sums of the stochastic objective are inconsistent with the full objective), an IllegalStateException is thrown, indicating the stochastic objective setup is unreliable and training will not proceed.","triggerScenarios":"Setting testObjFunction=true and tester.testSumOfBatches returns false — typically misconfigured stochastic batch settings or a custom objective whose batch decomposition doesn't sum correctly.","commonSituations":"Debugging custom CRF objectives or feature transforms; verifying refactored objective implementations; testing with inappropriate batch sizes or data scaling.","solutions":["Fix the objective/gradient implementation the tester flagged before training.","Adjust stochastic settings (batch size, gain) that make batch sums inconsistent.","Run the test on a small, clean dataset to localize the mismatch.","Remove testObjFunction once validation passes — it is a debug-only gate."],"exampleFix":"// before\nprops.setProperty(\"testObjFunction\", \"true\"); // blocks training on failure\n// after (once the objective is verified)\nprops.remove(\"testObjFunction\");","handlingStrategy":"try-catch","validationCode":"// enable the objective test only on small, clean datasets\nboolean clean = features.stream().noneMatch(v -> Double.isNaN(v) || Double.isInfinite(v));\nif (\"true\".equals(props.getProperty(\"testObjFunction\")) && !clean) {\n  throw new IllegalArgumentException(\"NaN/Inf features will make the objective test fail\");\n}","typeGuard":null,"tryCatchPattern":"try {\n  classifier.train(files);\n} catch (IllegalStateException e) {\n  if (\"Testing of stochastic objective function failed.\".equals(e.getMessage())) {\n    log.warn(\"Stochastic objective test failed; fix batch/objective settings before training\");\n    props.remove(\"testObjFunction\"); // after investigation\n  } else throw e;\n}","preventionTips":["Treat testObjFunction as debug-only; run it on a small subset first.","Keep batch size and gain settings consistent with the stochastic objective.","Sanity-check training data for NaN/extreme values before testing."],"tags":["java","optimizer","debug","crf"],"backgroundTag":"internal-invariant-violation","analyzedSha":"1b7edd19c4d0d7b1f13a2591425b9b60a0b1af7a","analyzedAt":"2026-09-10T02:24:07.274Z","contentChangedAt":"2026-09-10T02:24:07.274Z","schemaVersion":2},"datasetVersion":"2026-09-15T23:17:13.987Z"}