@CONFIG(
  version  -> "1.0.0"
  features -> "advanced"
)

@ENUMS(
  FeatureType  { NUMERIC = 0, CATEGORICAL = 1, TEXT = 2, EMBEDDING = 3, BOOLEAN = 4 }
  NormStrategy { NONE = 0, MINMAX = 1, ZSCORE = 2, LOG = 3 }
  Aggregation  { MEAN = 0, SUM = 1, MAX = 2, MIN = 3, COUNT = 4, LAST = 5 }
)

@QUICKFUNCS(
  ~feature<object>(name<string>, type<enum>, norm<enum>, required<bool>) {
    return {
      name       = name
      type       = type
      normalize  = norm
      required   = required
      fillna     = type == FeatureType.NUMERIC     ? 0.0   :
                   type == FeatureType.CATEGORICAL ? "UNK" : null
    }
  }

  ~windowFeature<object>(source<string>, agg<enum>, window_days<int>) {
    return {
      source      = source
      aggregation = agg
      window_days = window_days
      feature_name = $"{source}_{window_days}d_{agg}"
    }
  }

  ~modelConfig<object>(name<string>, version<string>, threshold<float>, batch_size<int>) {
    return {
      name       = name
      version    = version
      threshold  = threshold
      batch_size = batch_size
      endpoint   = $"/models/{name}/v{version}/predict"
    }
  }
)

@DATA(
  pipeline_name    = "user_churn_v2"
  pipeline_version = "2.1.0"

  features::
    feature("user_age_days",    FeatureType.NUMERIC,     NormStrategy.ZSCORE, true),
    feature("plan_tier",        FeatureType.CATEGORICAL,  NormStrategy.NONE,   true),
    feature("country_code",     FeatureType.CATEGORICAL,  NormStrategy.NONE,   true),
    feature("has_mobile_app",   FeatureType.BOOLEAN,      NormStrategy.NONE,   false),
    feature("support_tickets",  FeatureType.NUMERIC,      NormStrategy.LOG,    false),
    feature("product_category", FeatureType.CATEGORICAL,  NormStrategy.NONE,   true)

  window_features::
    windowFeature("page_views",       Aggregation.SUM,  7),
    windowFeature("page_views",       Aggregation.SUM,  30),
    windowFeature("login_events",     Aggregation.COUNT, 7),
    windowFeature("login_events",     Aggregation.COUNT, 30),
    windowFeature("purchase_amount",  Aggregation.SUM,  30),
    windowFeature("support_tickets",  Aggregation.COUNT, 14)

  model:
    production = modelConfig("churn_xgb",  "2.1.0", 0.42f, 512)
    shadow     = modelConfig("churn_mlp",  "1.0.0", 0.38f, 256)

  serving:
    max_latency_ms  = 120
    cache_ttl_s     = 300
    fallback_score  = 0.5f
)