{
  "description": "Test vectors for contextual bandit (linear_contextual) scoring. The SDK uses trained model coefficients to compute per-allocation scores, applies softmax with gamma temperature, enforces a probability floor, and deterministically selects an allocation via SHA-256 v2 weighted selection.",
  "bundle": "bundle_contextual.json",
  "testCases": [
    {
      "name": "high_engagement_mobile",
      "context": {
        "userId": "user-high-engage",
        "engagement_score": 8,
        "device_type": "mobile"
      },
      "expectedScoring": {
        "scores": [
          0,
          3.7,
          0
        ],
        "seed": "ctx:user-high-engage:policy_contextual",
        "comment": "control: 0 + 0*8 + 0 = 0. treatment_a: 0.5 + 0.3*8 + 0.8 = 3.7. treatment_b: -0.3 + 0.1*8 + (-0.5) = 0. Softmax heavily favors treatment_a."
      },
      "expectedAssignments": {
        "ui.heroVariant": "hero_bold"
      },
      "expectedAllocation": "treatment_a"
    },
    {
      "name": "low_engagement_desktop",
      "context": {
        "userId": "user-low-engage",
        "engagement_score": 1,
        "device_type": "desktop"
      },
      "expectedScoring": {
        "scores": [
          0,
          0.6,
          0.4
        ],
        "seed": "ctx:user-low-engage:policy_contextual",
        "comment": "control: 0 + 0*1 + 0 = 0. treatment_a: 0.5 + 0.3*1 + (-0.2) = 0.6. treatment_b: -0.3 + 0.1*1 + 0.6 = 0.4. Probabilities: ~23%/42%/35%. Hash selects treatment_a."
      },
      "expectedAssignments": {
        "ui.heroVariant": "hero_bold"
      },
      "expectedAllocation": "treatment_a"
    },
    {
      "name": "missing_context_fields",
      "context": {
        "userId": "user-missing-ctx"
      },
      "expectedScoring": {
        "scores": [
          0,
          0.5,
          -0.3
        ],
        "seed": "ctx:user-missing-ctx:policy_contextual",
        "comment": "All features missing, falls back to missing values (all 0). Scores are just intercepts: 0, 0.5, -0.3. Hash selects control."
      },
      "expectedAssignments": {
        "ui.heroVariant": "hero_control"
      },
      "expectedAllocation": "control"
    },
    {
      "name": "unknown_categorical_value",
      "context": {
        "userId": "user-unknown-device",
        "engagement_score": 5,
        "device_type": "smartwatch"
      },
      "expectedScoring": {
        "scores": [
          0,
          2,
          0.2
        ],
        "seed": "ctx:user-unknown-device:policy_contextual",
        "comment": "device_type=smartwatch not in trained values, uses missing=0. treatment_a: 0.5 + 0.3*5 + 0 = 2.0. Hash selects treatment_a."
      },
      "expectedAssignments": {
        "ui.heroVariant": "hero_bold"
      },
      "expectedAllocation": "treatment_a"
    }
  ]
}