Add time series signals and configurable shadow model sampling[working]#368
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Add time series signals and configurable shadow model sampling[working]#368
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- Add DTW and MSM signal classes for time series distance metrics
- Add msm.py utility for Move-Split-Merge distance calculation
- Add sample_shadow_indices() to AbstractInputHandler for custom sampling
- Add configurable sampling_method ('balanced'/'random') to shadow models
- Fix dataset params propagation using population.return_params()
- Add optimizer param filtering to prevent invalid argument errors
fazelehh
changed the title
… into pr-c-core-module-changes
- Removed setting params to empty dict in get_dataloader() - Made better logic in get_dataset() such that if params is None we try to collect params from population. If we cant params is set to an empty dict to fit UserDataset().
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Summary
This PR adds two time series distance signals (DTW and MSM), introduces configurable shadow model sampling, and fixes several handler issues needed for time series MIA support.
New signals
DTW): Dynamic Time Warping distance between model output and target series. Requires optionalsktimedependency — raises a clearImportErrorif not installed.MSM): Move-Split-Merge distance, implemented from scratch inleakpro/signals/utils/msm.pywith no external dependencies.SIGNAL_REGISTRYand usable viasignal_namesin attack configs (e.g.multi_signal_lira).Configurable shadow model sampling
sampling_methodfield toShadowModelConfig(default:"balanced").create_shadow_models()now acceptssampling_method="balanced"(existing behavior) or"random"(delegates to handler for custom sampling logic).sample_shadow_indices()toAbstractInputHandleras an overrideable method — time series handlers (e.g.IndividualizedInputHandler) can override it to sample by individual rather than by record.Handler fixes
MIAHandler.get_dataset(): defaultparamsnow usespopulation.return_params()instead of{}, preserving dataset-specific settings (e.g. scaler, lookback) when recreating datasets for shadow model training.MIAHandler.get_optimizer(): usesinspect.signature()to filter out invalid optimizer kwargs before instantiation, preventing crashes when time series configs pass extra parameters.Bug fix
DotMapcompatibility bug inshadow_model_handler.pywheresampling_methodwould be read as an emptyDotMap()instead of defaulting to"balanced"when the key was absent from the config.Dependencies
sktimetomiaoptional deps anddevgroup inpyproject.toml.Tests
New test file
leakpro/tests/mia_attacks/utils/test_time_series_signals.py(18 tests):mv_msm_costandmv_msm_distanceImportErrorgracefully without sktimeSIGNAL_REGISTRYsample_shadow_indicesdefault returns correct count/subsetcreate_shadow_modelswithsampling_method="random"and invalid methodget_optimizer()filters invalid params and passes valid ones throughTest plan
balancedsampling