Standard Risk Tests

This ModelOp Center monitor computes stability metrics, including Population Stability Index (PSI) and Characteristic Stability Indices (CSI), and their breakdown by buckets. It also computes disparity metrics (with respect to reference groups) and group metrics on protected classes, such as race or gender, as well as classification metrics such as AUC, Accuracy, Precision, Recall, and F1_score.

Input Assets

Type	Number	Description
Baseline Data	1	A dataset corresponding to training/reference data
Sample Data	1	A dataset corresponding to a slice of production data

Assumptions & Requirements

• Underlying BUSINESS_MODEL being monitored has an extended input schema asset.
• BUSINESS_MODEL is a classification model.
• Protected classes under consideration are categorical features.
• Input data must contain:
  • 1 column with role=label (ground truth)
  • 1 column with role=score (model output)
  • At least 1 column with protected_class=true (protected attribute).

Execution

1. init function extracts extended input schema (corresponding to the BUSINESS_MODEL being monitored) from job JSON.
2. Monitoring parameters are set based on the schema above. predictors, feature_dataclass, special_values, score_column, label_column, protected_classes, and weight_column are determined accordingly.
3. metrics function runs a stability analysis test.
   • For each categorical feature, the number of groups (n_groups) to break the data into is set by default to be equal to the number of unique values of this feature.
   • For each numerical feature, n_groups is set to 2 if this feature has more than 1 unique value. Otherwise, n_groups is set to 1.
4. metrics function runs an Aequitas Bias test and an Aequitas Group test for each protected class in the list of protected classes. A reference group for each protected class is chosen by default (first occurence).
5. metrics function runs a classification performance test.
6. Each test result is appended to the corresponding list of tests to be returned by the model.

Monitor Output

{
    "accuracy": <accuracy>,
    "auc": <auc>,
    "f1_score": <f1_score>,
    "precision": <precision>,
    "recall": <recall>,
    "confusion_matrix": <confusion_matrix>,
    "stability": [
        {
            "test_name": "Stability Analysis",
            "test_category": "stability",
            "test_type": "stability_analysis",
            "test_id": "stability_stability_analysis",
            "values": {
                <predictive_feature_1>: {
                    "stability_analysis_table": <stability_analysis_table>,
                    "stability_index": <stability_index>,
                    "stability_chisq": <stability_chisq>,
                    "stability_ks": <stability_ks>
                },
                ...:...,
                <predictive_feature_n>: {
                    "stability_analysis_table": <stability_analysis_table>,
                    "stability_index": <stability_index>,
                    "stability_chisq": <stability_chisq>,
                    "stability_ks": <stability_ks>
                },
                <score_column>: {
                    "stability_analysis_table": <stability_analysis_table>,
                    "stability_index": <stability_index>,
                    "stability_chisq": <stability_chisq>,
                    "stability_ks": <stability_ks>
                }
            }
        }
    ],
    "performance": [
        {
            "test_category": "performance",
            "test_name": "Classification Metrics",
            "test_type": "classification_metrics",
            "test_id": "performance_classification_metrics",
            "values": {
                "accuracy": <accuracy>,
                "auc": <auc>,
                "f1_score": <f1_score>,
                "precision": <precision>,
                "recall": <recall>,
                "confusion_matrix": <confusion_matrix>
            }
        }
    ],
    "bias":[
        <aequitas_bias_test_result>, <aequitas_group_test_result> for protected_class in protected_classes
    ]
}