API reference
pyABC
ABC algorithms for likelihood-free Bayesian parameter inference and model selection.
Note
pyABC allows to parallelize the sampling process via various samplers. If you want to also parallelize single model simulations, be careful that both levels of parallelization work together well. In particular, if the environment variable OMP_NUM_THREADS is not set, pyABC sets it to a default of 1. For multi-processed sampling (the default at least on linux systems), the flag PYABC_NUM_PROCS can be used to determine on how many jobs to parallelize the sampling.
- Inference
ABCSMC
- Distances
AcceptAllDistance
AdaptiveAggregatedDistance
AdaptivePNormDistance
AdaptivePNormDistance.__init__()
AdaptivePNormDistance.configure_sampler()
AdaptivePNormDistance.fit_scales()
AdaptivePNormDistance.get_config()
AdaptivePNormDistance.get_weights()
AdaptivePNormDistance.initialize()
AdaptivePNormDistance.is_adaptive()
AdaptivePNormDistance.requires_calibration()
AdaptivePNormDistance.update()
AggregatedDistance
AggregatedDistance.__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()
BinomialKernel
Distance
DistanceWithMeasureList
FunctionDistance
FunctionKernel
IndependentLaplaceKernel
IndependentNormalKernel
InfoWeightedPNormDistance
InfoWeightedPNormDistance.__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()
MinMaxDistance
NegativeBinomialKernel
NoDistance
NormalKernel
PCADistance
PNormDistance
PercentileDistance
PoissonKernel
RangeEstimatorDistance
SlicedWassersteinDistance
StochasticKernel
WassersteinDistance
ZScoreDistance
- Acceptors
Acceptor
AcceptorResult
FunctionAcceptor
ScaledPDFNorm
StochasticAcceptor
UniformAcceptor
pdf_norm_from_kernel()
pdf_norm_max_found()
- Models
FunctionModel
IntegratedModel
Model
ModelResult
- Epsilons
AcceptanceRateScheme
ConstantEpsilon
DalyScheme
Epsilon
EssScheme
ExpDecayFixedIterScheme
ExpDecayFixedRatioScheme
FrielPettittScheme
ListEpsilon
ListTemperature
MedianEpsilon
NoEpsilon
PolynomialDecayFixedIterScheme
QuantileEpsilon
SilkOptimalEpsilon
Temperature
TemperatureBase
TemperatureScheme
- Predictor
GPKernelHandle
GPPredictor
HiddenLayerHandle
LassoPredictor
LinearPredictor
MLPPredictor
ModelSelectionPredictor
Predictor
SimplePredictor
root_mean_square_error()
root_mean_square_relative_error()
- Summary statistics
GMMSubsetter
IdSubsetter
IdentitySumstat
PredictorSumstat
Subsetter
Sumstat
- Data store
History
History.db
History.stores_sum_stats
History.id
History.__init__()
History.alive_models()
History.all_runs()
History.append_population()
History.db_size
History.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_t
History.model_names()
History.n_populations
History.nr_of_models_alive()
History.observed_sum_stat()
History.store_initial_data()
History.store_pre_population()
History.total_nr_simulations
History.update_after_calibration()
create_sqlite_db_id()
load_dict_from_json()
save_dict_to_json()
- Transitions kernels
AggregatedTransition
DiscreteJumpTransition
DiscreteRandomWalkTransition
DiscreteTransition
GridSearchCV
LocalTransition
ModelPerturbationKernel
MultivariateNormalTransition
NotEnoughParticles
PerturbationKernel
Transition
scott_rule_of_thumb()
silverman_rule_of_thumb()
- Population strategies
AdaptivePopulationSize
ConstantPopulationSize
ListPopulationSize
PopulationStrategy
dec_bound_pop_size_from_env()
- Parallel sampling
ConcurrentFutureSampler
DaskDistributedSampler
MappingSampler
MulticoreEvalParallelSampler
MulticoreParticleParallelSampler
RedisEvalParallelSampler
RedisEvalParallelSampler.__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()
RedisEvalParallelSamplerServerStarter
RedisStaticSampler
RedisStaticSamplerServerStarter
Sampler
SingleCoreSampler
nr_cores_available()
- Model parameters
Parameter
ParameterStructure
- Particles and Populations
Particle
Population
Population.__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()
Sample
SampleFactory
- Random variables
Distribution
DistributionBase
LowerBoundDecorator
RV
RVBase
RVDecorator
- SGE
SGE
sge_available()
nr_cores_available()
NamedPrinter
DefaultContext
ProfilingContext
- External simulators
ExternalDistance
ExternalHandler
ExternalModel
ExternalSumStat
create_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()