Model Feature Attribution Drift Violations - Amazon SageMaker AI

Model Feature Attribution Drift Violations

Feature attribution drift jobs evaluate the baseline constraints provided by the baseline configuration against the analysis results of current MonitoringExecution. If violations are detected, the job lists them to the constraint_violations.json file in the execution output location, and marks the execution status as Interpret results.

Here is the schema of the feature attribution drift violations file.

  • label – The name of the label, job analysis configuration label_headers or a placeholder such as "label0".

  • metric_name – The name of the explainability analysis method. Currently only shap is supported.

  • constraint_check_type – The type of violation monitored. Currently only feature_attribution_drift_check is supported.

  • description – A descriptive message to explain the violation.

{
    "version": "1.0",
    "violations": [{
        "label": "string",
        "metric_name": "string",
        "constraint_check_type": "string",
        "description": "string"
    }]
}

For each label in the explanations section, the monitoring jobs calculate the nDCG score of its global SHAP values in the baseline constraints file and in the job analysis results file (analysis.json). If the score is less than 0.9, then a violation is logged. The combined global SHAP value is evaluated, so there are no “feature” fields in the violation entry. The following output provides an example of several logged violations.

{
    "version": "1.0",
    "violations": [{
        "label": "label0",
        "metric_name": "shap",
        "constraint_check_type": "feature_attribution_drift_check",
        "description": "Feature attribution drift 0.7639720923277322 exceeds threshold 0.9"
    }, {
        "label": "label1",
        "metric_name": "shap",
        "constraint_check_type": "feature_attribution_drift_check",
        "description": "Feature attribution drift 0.7323763972092327 exceeds threshold 0.9"
    }]
}