pyABC - distributed, likelihood-free inference
- Release
0.12.8
- Source code
pyABC is a framework for distributed, likelihood-free inference. That means, if you have a model and some data and want to know the posterior distribution over the model parameters, i.e. you want to know with which probability which parameters explain the observed data, then pyABC might be for you.
All you need is some way to numerically draw samples from the model, given the model parameters. pyABC “inverts” the model for you and tells you which parameters were well matching and which ones not. You do not need to analytically calculate the likelihood function.
pyABC runs efficiently on multi-core machines and distributed cluster setups. It is easy to use and flexibly extensible.
User's guide
About
Developer's guide
API reference
- API reference
- pyABC
- Inference
ABCSMC- Distances
AcceptAllDistanceAdaptiveAggregatedDistanceAdaptivePNormDistanceAdaptivePNormDistance.__init__()AdaptivePNormDistance.configure_sampler()AdaptivePNormDistance.fit_scales()AdaptivePNormDistance.get_config()AdaptivePNormDistance.get_weights()AdaptivePNormDistance.initialize()AdaptivePNormDistance.is_adaptive()AdaptivePNormDistance.requires_calibration()AdaptivePNormDistance.update()
AggregatedDistanceAggregatedDistance.__call__()AggregatedDistance.__init__()AggregatedDistance.configure_sampler()AggregatedDistance.format_dict()AggregatedDistance.get_config()AggregatedDistance.get_for_t_or_latest()AggregatedDistance.initialize()AggregatedDistance.is_adaptive()AggregatedDistance.requires_calibration()AggregatedDistance.update()
BinomialKernelDistanceDistanceWithMeasureListFunctionDistanceFunctionKernelIndependentLaplaceKernelIndependentNormalKernelInfoWeightedPNormDistanceInfoWeightedPNormDistance.__init__()InfoWeightedPNormDistance.calculate_sensis()InfoWeightedPNormDistance.configure_sampler()InfoWeightedPNormDistance.fit_info()InfoWeightedPNormDistance.get_config()InfoWeightedPNormDistance.get_weights()InfoWeightedPNormDistance.initialize()InfoWeightedPNormDistance.is_adaptive()InfoWeightedPNormDistance.normalize_sample()InfoWeightedPNormDistance.requires_calibration()InfoWeightedPNormDistance.update()
MinMaxDistanceNegativeBinomialKernelNoDistanceNormalKernelPCADistancePNormDistancePercentileDistancePoissonKernelRangeEstimatorDistanceSlicedWassersteinDistanceStochasticKernelWassersteinDistanceZScoreDistance- Acceptors
AcceptorAcceptorResultFunctionAcceptorScaledPDFNormStochasticAcceptorUniformAcceptorpdf_norm_from_kernel()pdf_norm_max_found()- Models
FunctionModelIntegratedModelModelModelResult- Epsilons
AcceptanceRateSchemeConstantEpsilonDalySchemeEpsilonEssSchemeExpDecayFixedIterSchemeExpDecayFixedRatioSchemeFrielPettittSchemeListEpsilonListTemperatureMedianEpsilonNoEpsilonPolynomialDecayFixedIterSchemeQuantileEpsilonSilkOptimalEpsilonTemperatureTemperatureBaseTemperatureScheme- Predictor
GPKernelHandleGPPredictorHiddenLayerHandleLassoPredictorLinearPredictorMLPPredictorModelSelectionPredictorPredictorSimplePredictorroot_mean_square_error()root_mean_square_relative_error()- Summary statistics
GMMSubsetterIdSubsetterIdentitySumstatPredictorSumstatSubsetterSumstat- Data store
HistoryHistory.dbHistory.stores_sum_statsHistory.idHistory.__init__()History.alive_models()History.all_runs()History.append_population()History.db_sizeHistory.done()History.get_all_populations()History.get_distribution()History.get_ground_truth_parameter()History.get_model_probabilities()History.get_nr_particles_per_population()History.get_population()History.get_population_extended()History.get_population_strategy()History.get_weighted_distances()History.get_weighted_sum_stats()History.get_weighted_sum_stats_for_model()History.max_tHistory.model_names()History.n_populationsHistory.nr_of_models_alive()History.observed_sum_stat()History.store_initial_data()History.store_pre_population()History.total_nr_simulationsHistory.update_after_calibration()
create_sqlite_db_id()load_dict_from_json()save_dict_to_json()- Transitions kernels
AggregatedTransitionDiscreteJumpTransitionDiscreteRandomWalkTransitionDiscreteTransitionGridSearchCVLocalTransitionModelPerturbationKernelMultivariateNormalTransitionNotEnoughParticlesPerturbationKernelTransitionscott_rule_of_thumb()silverman_rule_of_thumb()- Population strategies
AdaptivePopulationSizeConstantPopulationSizeListPopulationSizePopulationStrategydec_bound_pop_size_from_env()- Parallel sampling
ConcurrentFutureSamplerDaskDistributedSamplerMappingSamplerMulticoreEvalParallelSamplerMulticoreParticleParallelSamplerRedisEvalParallelSamplerRedisEvalParallelSampler.__init__()RedisEvalParallelSampler.check_analysis_variables()RedisEvalParallelSampler.clear_generation_t()RedisEvalParallelSampler.create_sample()RedisEvalParallelSampler.generation_t_was_started()RedisEvalParallelSampler.maybe_start_next_generation()RedisEvalParallelSampler.start_generation_t()
RedisEvalParallelSamplerServerStarterRedisStaticSamplerRedisStaticSamplerServerStarterSamplerSingleCoreSamplernr_cores_available()- Model parameters
ParameterParameterStructure- Particles and Populations
ParticlePopulationPopulation.__init__()Population.calculate_model_probabilities()Population.get_accepted_sum_stats()Population.get_for_keys()Population.get_model_probabilities()Population.get_particles_by_model()Population.get_weighted_distances()Population.get_weighted_sum_stats()Population.update_distances()
SampleSampleFactory- Random variables
DistributionDistributionBaseLowerBoundDecoratorRVRVBaseRVDecorator- SGE
SGEsge_available()nr_cores_available()NamedPrinterDefaultContextProfilingContext- External simulators
ExternalDistanceExternalHandlerExternalModelExternalSumStatcreate_sum_stat()- R interface via rpy2
R- Julia interface via PyJulia
Julia- Copasi
BasicoModel- Visualization
plot_acceptance_rates_trajectory()plot_credible_intervals()plot_credible_intervals_for_time()plot_data_callback()plot_data_default()plot_distance_weights()plot_effective_sample_sizes()plot_eps_walltime()plot_eps_walltime_lowlevel()plot_epsilons()plot_histogram_1d()plot_histogram_1d_lowlevel()plot_histogram_2d()plot_histogram_2d_lowlevel()plot_histogram_matrix()plot_histogram_matrix_lowlevel()plot_kde_1d()plot_kde_1d_highlevel()plot_kde_2d()plot_kde_2d_highlevel()plot_kde_matrix()plot_kde_matrix_highlevel()plot_lookahead_acceptance_rates()plot_lookahead_evaluations()plot_lookahead_final_acceptance_fractions()plot_model_probabilities()plot_sample_numbers()plot_sample_numbers_trajectory()plot_sensitivity_sankey()plot_total_sample_numbers()plot_total_walltime()plot_walltime()plot_walltime_lowlevel()- Weighted statistics
effective_sample_size()resample()resample_deterministic()weight_checked()weighted_mean()weighted_median()weighted_mse()weighted_quantile()weighted_rmse()weighted_std()weighted_var()