Module API reference

The module reference shows the public surface where each symbol is defined. Use this view when you are exploring an integration or want to understand a module’s relationship to the top-level fraudtwin API.

Every module page is generated with automodule and lists its public members. Use the global class/function index when you already know a symbol name.

Public modules

Module	Purpose	Status
fraudtwin.benchmark	Benchmark packs and reproducible suite execution.	Experimental
fraudtwin.calibration	Reference-data calibration and fidelity reports.	Experimental
fraudtwin.camouflage	Fraud camouflage transformations.	Stable
fraudtwin.campaign_dynamics	Dynamic campaign evolution and registration hooks.	Experimental
fraudtwin.config	Typed configuration models, loading, and validation.	Stable
fraudtwin.contracts	Versioned data contracts and schema access.	Stable
fraudtwin.contracts.registry	Versioned Avro contract validation and mapping.	Stable
fraudtwin.counterfactual	Counterfactual scenario generation and resolution.	Experimental
fraudtwin.difficulty	Scenario difficulty plans and resolvers.	Experimental
fraudtwin.domain	Stable domain records for entities, payments, fraud, and labels.	Stable
fraudtwin.domain.campaign_dynamics	Domain types for evolving fraud campaigns.	Stable
fraudtwin.domain.cases	Reusable domain case and scenario records.	Stable
fraudtwin.domain.graph	Domain records for graph nodes, edges, and evidence.	Stable
fraudtwin.domain.labels	Domain records for label maturity and observation.	Stable
fraudtwin.extensions	Stable extension protocols and local entry-point discovery.	Stable
fraudtwin.generation	Deterministic standard and scale run generation.	Stable
fraudtwin.graph	Temporal graph construction, validation, and export.	Stable
fraudtwin.kafka	Optional Kafka publication adapters.	Optional
fraudtwin.kafka_chaos	Deterministic logical-message delivery fault simulation.	Experimental
fraudtwin.label_observation	Label availability and observation histories.	Stable
fraudtwin.lakehouse	Optional Iceberg materialization and verification.	Optional
fraudtwin.ml	Point-in-time datasets, replay, and machine-learning evaluation.	Stable
fraudtwin.ml.backtest	Replay, folds, backtesting, and metrics.	Stable
fraudtwin.ml.baseline	Baseline models and prediction evaluation.	Stable
fraudtwin.ml.dataset	Point-in-time dataset construction.	Stable
fraudtwin.ml.drift	Deterministic data, domain, and concept drift reports.	Experimental
fraudtwin.observability	Optional run metrics and observability sessions.	Optional
fraudtwin.postgres	Optional PostgreSQL persistence adapters.	Optional
fraudtwin.quality_benchmark	Generator-quality benchmark contracts.	Experimental
fraudtwin.replay	Historical event replay and ordering.	Stable
fraudtwin.scale	Partition planning, checkpoints, and scale iteration.	Experimental
fraudtwin.simulation	Simulation scenario composition and generation controls.	Stable
fraudtwin.simulation.cases	Public API exported by fraudtwin.simulation.cases.	Stable
fraudtwin.simulation.graph_fraud	Public API exported by fraudtwin.simulation.graph_fraud.	Stable
fraudtwin.simulation.graph_planner	Public API exported by fraudtwin.simulation.graph_planner.	Stable
fraudtwin.storage	Local and fsspec-backed scale storage.	Stable