abcpy package

This reference given details about the API of modules, classes and functions included in ABCpy.

abcpy.acceptedparametersmanager module

class abcpy.acceptedparametersmanager.AcceptedParametersManager(model)

Bases: object

__init__(model)

This class manages the accepted parameters and other bds objects.

Parameters:model (list) – List of all root probabilistic models
broadcast(backend, observations)

Broadcasts the observations to observations_bds using the specified backend.

Parameters:
  • backend (abcpy.backends object) – The backend used by the inference algorithm
  • observations (list) – A list containing all observed data
get_accepted_parameters_bds_values(models)

Returns the accepted bds values for the specified models.

Parameters:models (list) – Contains the probabilistic models for which the accepted bds values should be returned
Returns:The accepted_parameters_bds values of all the probabilistic models specified in models.
Return type:list
get_mapping(models, is_root=True, index=0)

Returns the order in which the models are discovered during recursive depth-first search. Commonly used when returning the accepted_parameters_bds for certain models.

Parameters:
  • models (list) – List of the root probabilistic models of the graph.
  • is_root (boolean) – Specifies whether the current list of models is the list of overall root models
  • index (integer) – The current index in depth-first search.
Returns:

The first entry corresponds to the mapping of the root model, as well as all its parents. The second entry corresponds to the next index in depth-first search.
Return type:

list
update_broadcast(backend, accepted_parameters=None, accepted_weights=None, accepted_cov_mats=None)

Updates the broadcasted values using the specified backend

Parameters:
  • backend (abcpy.backend object) – The backend to be used for broadcasting
  • accepted_parameters (list) – The accepted parameters to be broadcasted
  • accepted_weights (list) – The accepted weights to be broadcasted
  • accepted_cov_mats (np.ndarray) – The accepted covariance matrix to be broadcasted
update_kernel_values(backend, kernel_parameters)

Broadcasts new parameters for each kernel

Parameters:
  • backend (abcpy.backends object) – The backend used by the inference algorithm
  • kernel_parameters (list) – A list, in which each entry contains the values of the parameters associated with the corresponding kernel in the joint perturbation kernel

abcpy.approx_lhd module

class abcpy.approx_lhd.Approx_likelihood(statistics_calc)

Bases: object

This abstract base class defines the approximate likelihood function.

__init__(statistics_calc)

The constructor of a sub-class must accept a non-optional statistics calculator, which is stored to self.statistics_calc.

Parameters:statistics_calc (abcpy.stasistics.Statistics) – Statistics extractor object that conforms to the Statistics class.
likelihood(y_obs, y_sim)

To be overwritten by any sub-class: should compute the approximate likelihood value given the observed data set y_obs and the data set y_sim simulated from model set at the parameter value.

Parameters:
  • y_obs (Python list) – Observed data set.
  • y_sim (Python list) – Simulated data set from model at the parameter value.
Returns:

Computed approximate likelihood.
Return type:

float
class abcpy.approx_lhd.PenLogReg(statistics_calc, model, n_simulate, n_folds=10, max_iter=100000, seed=None)

Bases: abcpy.approx_lhd.Approx_likelihood, abcpy.graphtools.GraphTools

This class implements the approximate likelihood function which computes the approximate likelihood up to a constant using penalized logistic regression described in Dutta et. al. [1]. It takes one additional function handler defining the true model and two additional parameters n_folds and n_simulate correspondingly defining number of folds used to estimate prediction error using cross-validation and the number of simulated dataset sampled from each parameter to approximate the likelihood function. For lasso penalized logistic regression we use glmnet of Friedman et. al. [2].

[1] Reference: R. Dutta, J. Corander, S. Kaski, and M. U. Gutmann. Likelihood-free inference by penalised logistic regression. arXiv:1611.10242, Nov. 2016.

[2] Friedman, J., Hastie, T., and Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1–22.

Parameters:
  • statistics_calc (abcpy.stasistics.Statistics) – Statistics extractor object that conforms to the Statistics class.
  • model (abcpy.models.Model) – Model object that conforms to the Model class.
  • n_simulate (int) – Number of data points in the simulated data set.
  • n_folds (int, optional) – Number of folds for cross-validation. The default value is 10.
  • max_iter (int, optional) – Maximum passes over the data. The default is 100000.
  • seed (int, optional) – Seed for the random number generator. The used glmnet solver is not deterministic, this seed is used for determining the cv folds. The default value is None.
__init__(statistics_calc, model, n_simulate, n_folds=10, max_iter=100000, seed=None)
likelihood(y_obs, y_sim)
class abcpy.approx_lhd.SynLiklihood(statistics_calc)

Bases: abcpy.approx_lhd.Approx_likelihood

This class implements the approximate likelihood function which computes the approximate likelihood using the synthetic likelihood approach described in Wood [1]. For synthetic likelihood approximation, we compute the robust precision matrix using Ledoit and Wolf’s [2] method.

[1] S. N. Wood. Statistical inference for noisy nonlinear ecological dynamic systems. Nature, 466(7310):1102–1104, Aug. 2010.

[2] O. Ledoit and M. Wolf, A Well-Conditioned Estimator for Large-Dimensional Covariance Matrices, Journal of Multivariate Analysis, Volume 88, Issue 2, pages 365-411, February 2004.

__init__(statistics_calc)
likelihood(y_obs, y_sim)

abcpy.backends module

class abcpy.backends.base.BDS

Bases: object

The reference class for broadcast data set (BDS).

__init__()
value()

This method should return the actual object that the broadcast data set represents.

class abcpy.backends.base.BDSDummy(object)

Bases: abcpy.backends.base.BDS

This is a wrapper for a Python object to fake parallelization.

__init__(object)
value()
class abcpy.backends.base.Backend

Bases: object

This is the base class for every parallelization backend. It essentially resembles the map/reduce API from Spark.

An idea for the future is to implement a MPI version of the backend with the hope to be more complient with standard HPC infrastructure and a potential speed-up.

broadcast(object)

Send object to all worker nodes without splitting it up.

Parameters:object (Python object) – An abitrary object that should be available on all workers
Returns:A reference to the broadcasted object
Return type:BDS class (broadcast data set)
collect(pds)

Gather the pds from all the workers, send it to the master and return it as a standard Python list.

Parameters:pds (PDS class) – a parallel data set
Returns:all elements of pds as a list
Return type:Python list
map(func, pds)

A distributed implementation of map that works on parallel data sets (PDS).

On every element of pds the function func is called.

Parameters:
  • func (Python func) – A function that can be applied to every element of the pds
  • pds (PDS class) – A parallel data set to which func should be applied
Returns:

a new parallel data set that contains the result of the map
Return type:

PDS class
parallelize(list)

This method distributes the list on the available workers and returns a reference object.

The list should be split into number of workers many parts. Each part should then be sent to a separate worker node.

Parameters:list (Python list) – the list that should get distributed on the worker nodes
Returns:A reference object that represents the parallelized list
Return type:PDS class (parallel data set)
class abcpy.backends.base.BackendDummy

Bases: abcpy.backends.base.Backend

This is a dummy parallelization backend, meaning it doesn’t parallelize anything. It is mainly implemented for testing purpose.

__init__()
broadcast(object)

This actually does nothing: it just wraps the object into BDSDummy.

Parameters:object (Python object) –
Returns:
Return type:BDSDummy class
collect(pds)

Returns the Python list stored in PDSDummy

Parameters:pds (PDSDummy class) – a pseudo-parallel data set
Returns:all elements of pds as a list
Return type:Python list
map(func, pds)

This is a wrapper for the Python internal map function.

Parameters:
  • func (Python func) – A function that can be applied to every element of the pds
  • pds (PDSDummy class) – A pseudo-parallel data set to which func should be applied
Returns:

a new pseudo-parallel data set that contains the result of the map
Return type:

PDSDummy class
parallelize(python_list)

This actually does nothing: it just wraps the Python list into dummy pds (PDSDummy).

Parameters:python_list (Python list) –
Returns:
Return type:PDSDummy (parallel data set)
class abcpy.backends.base.PDS

Bases: object

The reference class for parallel data sets (PDS).

__init__()
class abcpy.backends.base.PDSDummy(python_list)

Bases: abcpy.backends.base.PDS

This is a wrapper for a Python list to fake parallelization.

__init__(python_list)
class abcpy.backends.spark.BDSSpark(bcv)

Bases: abcpy.backends.base.BDS

This is a wrapper for Apache Spark Broadcast variables.

__init__(bcv)
Parameters:bcv (pyspark.broadcast.Broadcast) – Initialize with a Spark broadcast variable
value()
Returns:returns the referenced object that was broadcasted.
Return type:object
class abcpy.backends.spark.BackendSpark(sparkContext, parallelism=4)

Bases: abcpy.backends.base.Backend

A parallelization backend for Apache Spark. It is essetially a wrapper for the required Spark functionality.

__init__(sparkContext, parallelism=4)

Initialize the backend with an existing and configured SparkContext.

Parameters:
  • sparkContext (pyspark.SparkContext) – an existing and fully configured PySpark context
  • parallelism (int) – defines on how many workers a distributed dataset can be distributed
broadcast(object)

This is a wrapper for pyspark.SparkContext.broadcast().

Parameters:object (Python object) – An abitrary object that should be available on all workers
Returns:A reference to the broadcasted object
Return type:BDSSpark class (broadcast data set)
collect(pds)

A wrapper for pyspark.rdd.collect()

Parameters:pds (PDSSpark class) – a parallel data set
Returns:all elements of pds as a list
Return type:Python list
map(func, pds)

This is a wrapper for pyspark.rdd.map()

Parameters:
  • func (Python func) – A function that can be applied to every element of the pds
  • pds (PDSSpark class) – A parallel data set to which func should be applied
Returns:

a new parallel data set that contains the result of the map
Return type:

PDSSpark class
parallelize(python_list)

This is a wrapper of pyspark.SparkContext.parallelize().

Parameters:list (Python list) – list that is distributed on the workers
Returns:A reference object that represents the parallelized list
Return type:PDSSpark class (parallel data set)
class abcpy.backends.spark.PDSSpark(rdd)

Bases: abcpy.backends.base.PDS

This is a wrapper for Apache Spark RDDs.

__init__(rdd)
Returns:rdd – initialize with an Spark RDD
Return type:pyspark.rdd

abcpy.continuousmodels module

class abcpy.continuousmodels.MultiStudentT(parameters, name='MultiStudentT')

Bases: abcpy.probabilisticmodels.ProbabilisticModel, abcpy.probabilisticmodels.Continuous

__init__(parameters, name='MultiStudentT')

This class implements a probabilistic model following the multivariate Student-T distribution.

Parameters:
  • parameters (list) – All but the last two entries contain the probabilistic models and hyperparameters from which the model derives. The second to last entry contains the covariance matrix. If the mean is of dimension n, the covariance matrix is required to be nxn dimensional. The last entry contains the degrees of freedom.
  • name (string) – The name that should be given to the probabilistic model in the journal file.
forward_simulate(input_values, k, rng=<MagicMock name='mock.RandomState()' id='140024938646832'>)

Samples from a multivariate Student’s T-distribution using the current values for each probabilistic model from which the model derives.

Parameters:
  • input_values (list) – List of input parameters, in the same order as specified in the InputConnector passed to the init function
  • k (integer) – The number of samples that should be drawn.
  • rng (Random number generator) – Defines the random number generator to be used. The default value uses a random seed to initialize the generator.
Returns:

list – A list containing the sampled values as np-array.
Return type:

[np.ndarray]
get_output_dimension()
pdf(input_values, x)

Calculates the probability density function at point x. Commonly used to determine whether perturbed parameters are still valid according to the pdf.

Parameters:
  • input_values (list) – List of input parameters
  • x (list) – The point at which the pdf should be evaluated.
Returns:

The evaluated pdf at point x.
Return type:

Float
class abcpy.continuousmodels.MultivariateNormal(parameters, name='Multivariate Normal')

Bases: abcpy.probabilisticmodels.ProbabilisticModel, abcpy.probabilisticmodels.Continuous

__init__(parameters, name='Multivariate Normal')

This class implements a probabilistic model following a multivariate normal distribution with mean and covariance matrix.

Parameters:
  • parameters (list of at length 2) – Contains the probabilistic models and hyperparameters from which the model derives. The first entry defines the mean, while the second entry defines the Covariance matrix. Note that if the mean is n dimensional, the covariance matrix is required to be of dimension nxn, symmetric and positive-definite.
  • name (string) – The name that should be given to the probabilistic model in the journal file.
forward_simulate(input_values, k, rng=<MagicMock name='mock.RandomState()' id='140024938621080'>)

Samples from a multivariate normal distribution using the current values for each probabilistic model from which the model derives.

Parameters:
  • input_values (list) – List of input parameters, in the same order as specified in the InputConnector passed to the init function
  • k (integer) – The number of samples that should be drawn.
  • rng (Random number generator) – Defines the random number generator to be used. The default value uses a random seed to initialize the generator.
Returns:

list – A list containing the sampled values as np-array.
Return type:

[np.ndarray]
get_output_dimension()
pdf(input_values, x)

Calculates the probability density function at point x. Commonly used to determine whether perturbed parameters are still valid according to the pdf.

Parameters:
  • input_values (list) – List of input parameters
  • x (list) – The point at which the pdf should be evaluated.
Returns:

The evaluated pdf at point x.
Return type:

Float
class abcpy.continuousmodels.Normal(parameters, name='Normal')

Bases: abcpy.probabilisticmodels.ProbabilisticModel, abcpy.probabilisticmodels.Continuous

__init__(parameters, name='Normal')

This class implements a probabilistic model following a normal distribution with mean mu and variance sigma.

Parameters:
  • parameters (list) – Contains the probabilistic models and hyperparameters from which the model derives. The list has two entries: from the first entry mean of the distribution and from the second entry variance is derived. Note that the second value of the list is strictly greater than 0.
  • name (string) – The name that should be given to the probabilistic model in the journal file.
forward_simulate(input_values, k, rng=<MagicMock name='mock.RandomState()' id='140024938585896'>)

Samples from a normal distribution using the current values for each probabilistic model from which the model derives.

Parameters:
  • input_values (list) – List of input parameters, in the same order as specified in the InputConnector passed to the init function
  • k (integer) – The number of samples that should be drawn.
  • rng (Random number generator) – Defines the random number generator to be used. The default value uses a random seed to initialize the generator.
Returns:

list – A list containing the sampled values as np-array.
Return type:

[np.ndarray]
get_output_dimension()
pdf(input_values, x)

Calculates the probability density function at point x. Commonly used to determine whether perturbed parameters are still valid according to the pdf.

Parameters:
  • input_values (list) – List of input parameters of the from [mu, sigma]
  • x (list) – The point at which the pdf should be evaluated.
Returns:

The evaluated pdf at point x.
Return type:

Float
class abcpy.continuousmodels.StudentT(parameters, name='StudentT')

Bases: abcpy.probabilisticmodels.ProbabilisticModel, abcpy.probabilisticmodels.Continuous

__init__(parameters, name='StudentT')

This class implements a probabilistic model following the Student’s T-distribution.

Parameters:
  • parameters (list) – Contains the probabilistic models and hyperparameters from which the model derives. The list has two entries: from the first entry mean of the distribution and from the second entry degrees of freedom is derived. Note that the second value of the list is strictly greater than 0.
  • name (string) – The name that should be given to the probabilistic model in the journal file.
forward_simulate(input_values, k, rng=<MagicMock name='mock.RandomState()' id='140024938611712'>)

Samples from a Student’s T-distribution using the current values for each probabilistic model from which the model derives.

Parameters:
  • input_values (list) – List of input parameters, in the same order as specified in the InputConnector passed to the init function
  • k (integer) – The number of samples that should be drawn.
  • rng (Random number generator) – Defines the random number generator to be used. The default value uses a random seed to initialize the generator.
Returns:

list – A list containing the sampled values as np-array.
Return type:

[np.ndarray]
get_output_dimension()
pdf(input_values, x)

Calculates the probability density function at point x. Commonly used to determine whether perturbed parameters are still valid according to the pdf.

Parameters:
  • input_values (list) – List of input parameters
  • x (list) – The point at which the pdf should be evaluated.
Returns:

The evaluated pdf at point x.
Return type:

Float
class abcpy.continuousmodels.Uniform(parameters, name='Uniform')

Bases: abcpy.probabilisticmodels.ProbabilisticModel, abcpy.probabilisticmodels.Continuous

__init__(parameters, name='Uniform')

This class implements a probabilistic model following an uniform distribution.

Parameters:
  • parameters (list) – Contains two lists. The first list specifies the probabilistic models and hyperparameters from which the lower bound of the uniform distribution derive. The second list specifies the probabilistic models and hyperparameters from which the upper bound derives.
  • name (string, optional) – The name that should be given to the probabilistic model in the journal file.
forward_simulate(input_values, k, rng=<MagicMock name='mock.RandomState()' id='140024938547856'>)

Samples from a uniform distribution using the current values for each probabilistic model from which the model derives.

Parameters:
  • input_values (list) – List of input parameters, in the same order as specified in the InputConnector passed to the init function
  • k (integer) – The number of samples that should be drawn.
  • rng (Random number generator) – Defines the random number generator to be used. The default value uses a random seed to initialize the generator.
Returns:

list – A list containing the sampled values as np-array.
Return type:

[np.ndarray]
get_output_dimension()
pdf(input_values, x)

Calculates the probability density function at point x. Commonly used to determine whether perturbed parameters are still valid according to the pdf.

Parameters:
  • input_values (list) – List of input parameters, in the same order as specified in the InputConnector passed to the init function
  • x (list) – The point at which the pdf should be evaluated.
Returns:

The evaluated pdf at point x.
Return type:

Float

abcpy.discretemodels module

class abcpy.discretemodels.Bernoulli(parameters, name='Bernoulli')

Bases: abcpy.probabilisticmodels.Discrete, abcpy.probabilisticmodels.ProbabilisticModel

__init__(parameters, name='Bernoulli')

This class implements a probabilistic model following a bernoulli distribution.

Parameters:
  • parameters (list) – A list containing one entry, the probability of the distribution.
  • name (string) – The name that should be given to the probabilistic model in the journal file.
forward_simulate(input_values, k, rng=<MagicMock name='mock.RandomState()' id='140024937832520'>)

Samples from the bernoulli distribution associtated with the probabilistic model.

Parameters:
  • input_values (list) – List of input parameters, in the same order as specified in the InputConnector passed to the init function
  • k (integer) – The number of samples to be drawn.
  • rng (random number generator) – The random number generator to be used.
Returns:

list – A list containing the sampled values as np-array.
Return type:

[np.ndarray]
get_output_dimension()
pmf(input_values, x)

Evaluates the probability mass function at point x.

Parameters:
  • input_values (list) – List of input parameters, in the same order as specified in the InputConnector passed to the init function
  • x (float) – The point at which the pmf should be evaluated.
Returns:

The pmf evaluated at point x.
Return type:

float
class abcpy.discretemodels.Binomial(parameters, name='Binomial')

Bases: abcpy.probabilisticmodels.Discrete, abcpy.probabilisticmodels.ProbabilisticModel

__init__(parameters, name='Binomial')

This class implements a probabilistic model following a binomial distribution.

Parameters:
  • parameters (list) – Contains the probabilistic models and hyperparameters from which the model derives. Note that the first entry of the list, n, an integer and has to be larger than or equal to 0, while the second entry, p, has to be in the interval [0,1].
  • name (string) – The name that should be given to the probabilistic model in the journal file.
forward_simulate(input_values, k, rng=<MagicMock name='mock.RandomState()' id='140024937850080'>)

Samples from a binomial distribution using the current values for each probabilistic model from which the model derives.

Parameters:
  • input_values (list) – List of input parameters, in the same order as specified in the InputConnector passed to the init function
  • k (integer) – The number of samples that should be drawn.
  • rng (Random number generator) – Defines the random number generator to be used. The default value uses a random seed to initialize the generator.
Returns:

list – A list containing the sampled values as np-array.
Return type:

[np.ndarray]
get_output_dimension()
pmf(input_values, x)

Calculates the probability mass function at point x.

Parameters:
  • input_values (list) – List of input parameters, in the same order as specified in the InputConnector passed to the init function
  • x (list) – The point at which the pmf should be evaluated.
Returns:

The evaluated pmf at point x.
Return type:

Float
class abcpy.discretemodels.Poisson(parameters, name='Poisson')

Bases: abcpy.probabilisticmodels.Discrete, abcpy.probabilisticmodels.ProbabilisticModel

__init__(parameters, name='Poisson')

This class implements a probabilistic model following a poisson distribution.

Parameters:
  • parameters (list) – A list containing one entry, the mean of the distribution.
  • name (string) – The name that should be given to the probabilistic model in the journal file.
forward_simulate(input_values, k, rng=<MagicMock name='mock.RandomState()' id='140024937867640'>)

Samples k values from the defined possion distribution.

Parameters:
  • input_values (list) – List of input parameters, in the same order as specified in the InputConnector passed to the init function
  • k (integer) – The number of samples.
  • rng (random number generator) – The random number generator to be used.
Returns:

list – A list containing the sampled values as np-array.
Return type:

[np.ndarray]
get_output_dimension()
pmf(input_values, x)

Calculates the probability mass function of the distribution at point x.

Parameters:
  • input_values (list) – List of input parameters, in the same order as specified in the InputConnector passed to the init function
  • x (integer) – The point at which the pmf should be evaluated.
Returns:

The evaluated pmf at point x.
Return type:

Float

abcpy.distances module

class abcpy.distances.Distance(statistics_calc)

Bases: object

This abstract base class defines how the distance between the observed and simulated data should be implemented.

__init__(statistics_calc)

The constructor of a sub-class must accept a non-optional statistics calculator as a parameter. If stored to self.statistics_calc, the private helper method _calculate_summary_stat can be used.

Parameters:statistics_calc (abcpy.stasistics.Statistics) – Statistics extractor object that conforms to the Statistics class.
dist_max()

To be overwritten by sub-class: should return maximum possible value of the desired distance function.

Examples

If the desired distance maps to \(\mathbb{R}\), this method should return numpy.inf.

Returns:The maximal possible value of the desired distance function.
Return type:numpy.float
distance(d1, d2)

To be overwritten by any sub-class: should calculate the distance between two sets of data d1 and d2 using their respective statistics.

Notes

The data sets d1 and d2 are array-like structures that contain n1 and n2 data points each. An implementation of the distance function should work along the following steps:

1. Transform both input sets dX = [ dX1, dX2, ..., dXn ] to sX = [sX1, sX2, ..., sXn] using the statistics object. See _calculate_summary_stat method.

2. Calculate the mutual desired distance, here denoted by -, between the statstics dist = [s11 - s21, s12 - s22, ..., s1n - s2n].

Important: any sub-class must not calculate the distance between data sets d1 and d2 directly. This is the reason why any sub-class must be initialized with a statistics object.

Parameters:
  • d1 (Python list) – Contains n1 data points.
  • d2 (Python list) – Contains n2 data points.
Returns:

The distance between the two input data sets.
Return type:

numpy.ndarray
class abcpy.distances.Euclidean(statistics)

Bases: abcpy.distances.Distance

This class implements the Euclidean distance between two vectors.

The maximum value of the distance is np.inf.

__init__(statistics)
dist_max()
distance(d1, d2)

Calculates the distance between two datasets.

Parameters:d2 (d1,) – A list, containing a list describing the data set
class abcpy.distances.LogReg(statistics)

Bases: abcpy.distances.Distance

This class implements a distance measure based on the classification accuracy [1]. The classification accuracy is calculated between two dataset d1 and d2 using logistics regression and return it as a distance. The maximum value of the distance is 1.0.

[1] Gutmann, M., Dutta, R., Kaski, S., and Corander, J. (2014). Statistical inference of intractable generative models via classification. arXiv:1407.4981.

__init__(statistics)
dist_max()
distance(d1, d2)

Calculates the distance between two datasets.

Parameters:d2 (d1,) – A list, containing a list describing the data set
class abcpy.distances.PenLogReg(statistics)

Bases: abcpy.distances.Distance

This class implements a distance mesure based on the classification accuracy.

The classification accuracy is calculated between two dataset d1 and d2 using lasso penalized logistics regression and return it as a distance. The lasso penalized logistic regression is done using glmnet package of Friedman et. al. [2]. While computing the distance, the algorithm automatically chooses the most relevant summary statistics as explained in Gutmann et. al. [1]. The maximum value of the distance is 1.0.

[1] Gutmann, M., Dutta, R., Kaski, S., and Corander, J. (2014). Statistical inference of intractable generative models via classification. arXiv:1407.4981.

[2] Friedman, J., Hastie, T., and Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1–22.

__init__(statistics)
dist_max()
distance(d1, d2)

Calculates the distance between two datasets.

Parameters:d2 (d1,) – A list, containing a list describing the data set

abcpy.graphtools module

class abcpy.graphtools.GraphTools

Bases: object

This class implements all methods that will be called recursively on the graph structure.

get_correct_ordering(parameters_and_models, models=None, is_root=True)

Orders the parameters returned by a kernel in the order required by the graph. Commonly used when perturbing the parameters.

Parameters:
  • parameters_and_models (list of tuples) – Contains tuples containing as the first entry the probabilistic model to be considered and as the second entry the parameter values associated with this model
  • models (list) – Contains the root probabilistic models that make up the graph. If no value is provided, the root models are assumed to be the model of the inference method.
Returns:

The ordering which can be used by recursive functions on the graph.
Return type:

list
get_parameters(models=None, is_root=True)

Returns the current values of all free parameters in the model. Commonly used before perturbing the parameters of the model.

Parameters:
  • models (list of abcpy.ProbabilisticModel objects) – The models for which, together with their parents, the parameter values should be returned. If no value is provided, the root models are assumed to be the model of the inference method.
  • is_root (boolean) – Specifies whether the current models are at the root. This ensures that the values corresponding to simulated observations will not be returned.
Returns:

A list containing all currently sampled values of the free parameters.
Return type:

list
pdf_of_prior(models, parameters, mapping=None, is_root=True)

Calculates the joint probability density function of the prior of the specified models at the given parameter values. Commonly used to check whether new parameters are valid given the prior, as well as to calculate acceptance probabilities.

Parameters:
  • models (list of abcpy.ProbabilisticModel objects) – Defines the models for which the pdf of their prior should be evaluated
  • parameters (python list) – The parameters at which the pdf should be evaluated
  • mapping (list of tupels) – Defines the mapping of probabilistic models and index in a parameter list.
  • is_root (boolean) – A flag specifying whether the provided models are the root models. This is to ensure that the pdf is calculated correctly.
Returns:

The resulting pdf,as well as the next index to be considered in the parameters list.
Return type:

list
sample_from_prior(model=None, rng=<MagicMock name='mock.RandomState()' id='140024940236872'>)

Samples values for all random variables of the model. Commonly used to sample new parameter values on the whole graph.

Parameters:
  • model (abcpy.ProbabilisticModel object) – The root model for which sample_from_prior should be called.
  • rng (Random number generator) – Defines the random number generator to be used
set_parameters(parameters, models=None, index=0, is_root=True)

Sets new values for the currently used values of each random variable. Commonly used after perturbing the parameter values using a kernel.

Parameters:
  • parameters (list) – Defines the values to which the respective parameter values of the models should be set
  • model (list of abcpy.ProbabilisticModel objects) – Defines all models for which, together with their parents, new values should be set. If no value is provided, the root models are assumed to be the model of the inference method.
  • index (integer) – The current index to be considered in the parameters list
  • is_root (boolean) – Defines whether the current models are at the root. This ensures that only values corresponding to random variables will be set.
Returns:

list – Returns whether it was possible to set all parameters and the next index to be considered in the parameters list.
Return type:

[boolean, integer]
simulate(n_samples_per_param, rng=<MagicMock name='mock.RandomState()' id='140024940295616'>)

Simulates data of each model using the currently sampled or perturbed parameters.

Parameters:rng (random number generator) – The random number generator to be used.
Returns:Each entry corresponds to the simulated data of one model.
Return type:list

abcpy.output module

class abcpy.output.Journal(type)

Bases: object

The journal holds information created by the run of inference schemes.

It can be configured to even hold intermediate.

parameters

numpy.array – a nxpxt matrix

weights

numpy.array – a nxt matrix

opt_value

numpy.array – nxp matrix containing for each parameter the evaluated objective function for every time step

configuration

Python dictionary – dictionary containing the schemes configuration parameters

__init__(type)

Initializes a new output journal of given type.

Parameters:type (int (identifying type)) – type=0 only logs final parametersa and weight (production use); type=1 logs all generated information (reproducibily use).
add_opt_values(opt_values)

Saves provided values of the evaluation of the schemes objective function. If type==0, old values get overwritten

Parameters:opt_value (numpy.array) – vector containing n evaluations of the schemes objective function
add_parameters(params)

Saves provided parameters by appending them to the journal. If type==0, old parameters get overwritten.

Parameters:params (numpy.array) – nxp matrix containing n parameters of dimension p
add_user_parameters(names_and_params)

Saves the provided parameters and names of the probabilistic models corresponding to them. If type==0, old parameters get overwritten.

Parameters:names_and_params (list) – Each entry is a tupel, where the first entry is the name of the probabilistic model, and the second entry is the parameters associated with this model.
add_weights(weights)

Saves provided weights by appending them to the journal. If type==0, old weights get overwritten.

Parameters:weights (numpy.array) – vector containing n weigths
classmethod fromFile(filename)

This method reads a saved journal from disk an returns it as an object.

Notes

To store a journal use Journal.save(filename).

Parameters:filename (string) – The string representing the location of a file
Returns:The journal object serialized in <filename>
Return type:abcpy.output.Journal

Example

>>> jnl = Journal.fromFile('example_output.jnl')
get_parameters(iteration=None)

Returns the parameters from a sampling scheme.

For intermediate results, pass the iteration.

Parameters:iteration (int) – specify the iteration for which to return parameters
get_weights(iteration=None)

Returns the weights from a sampling scheme.

For intermediate results, pass the iteration.

Parameters:iteration (int) – specify the iteration for which to return weights
posterior_cov()

Computes posterior covariance from the samples drawn from posterior distribution

Returns:posterior covariance
Return type:np.ndarray
posterior_histogram(n_bins=10)

Computes a weighted histogram of multivariate posterior samples andreturns histogram H and A list of p arrays describing the bin edges for each dimension.

Returns:containing two elements (H = np.ndarray, edges = list of p arraya)
Return type:python list
posterior_mean()

Computes posterior mean from the samples drawn from posterior distribution

Returns:posterior mean
Return type:np.ndarray
save(filename)

Stores the journal to disk.

Parameters:filename (string) – the location of the file to store the current object to.

abcpy.inferences module

class abcpy.inferences.ABCsubsim(root_models, distances, backend, kernel=None, seed=None)

Bases: abcpy.inferences.BaseDiscrepancy, abcpy.inferences.InferenceMethod

This base class implements Approximate Bayesian Computation by subset simulation (ABCsubsim) algorithm of [1].

[1] M. Chiachio, J. L. Beck, J. Chiachio, and G. Rus., Approximate Bayesian computation by subset simulation. SIAM J. Sci. Comput., 36(3):A1339–A1358, 2014/10/03 2014.

Parameters:
  • model (list) – A list of the Probabilistic models corresponding to the observed datasets
  • distance (abcpy.distances.Distance) – Distance object defining the distance used to compare the simulated and observed data sets.
  • kernel (abcpy.distributions.Distribution) – Distribution object defining the perturbation kernel needed for the sampling.
  • backend (abcpy.backends.Backend) – Backend object defining the backend to be used.
  • seed (integer, optional) – Optional initial seed for the random number generator. The default value is generated randomly.
__init__(root_models, distances, backend, kernel=None, seed=None)
anneal_parameter = None
backend = None
chain_length = None
distance = None
kernel = None
model = None
n_samples = None
n_samples_per_param = None
rng = None
sample(observations, steps, n_samples=10000, n_samples_per_param=1, chain_length=10, ap_change_cutoff=10, full_output=0, journal_file=None)

Samples from the posterior distribution of the model parameter given the observed data observations.

Parameters:
  • observations (list) – A list, containing lists describing the observed data sets
  • steps (integer) – Number of iterations in the sequential algoritm (“generations”)
  • ap_change_cutoff (float, optional) – The cutoff value for the percentage change in the anneal parameter. If the change is less than ap_change_cutoff the iterations are stopped. The default value is 10.
  • full_output (integer, optional) – If full_output==1, intermediate results are included in output journal. The default value is 0, meaning the intermediate results are not saved.
Returns:

A journal containing simulation results, metadata and optionally intermediate results.
Return type:

abcpy.output.Journal
class abcpy.inferences.APMCABC(root_models, distances, backend, kernel=None, seed=None)

Bases: abcpy.inferences.BaseDiscrepancy, abcpy.inferences.InferenceMethod

This base class implements Adaptive Population Monte Carlo Approximate Bayesian computation of M. Lenormand et al. [1].

[1] M. Lenormand, F. Jabot and G. Deffuant, Adaptive approximate Bayesian computation for complex models. Computational Statistics, 28:2777–2796, 2013.

Parameters:
  • model (list) – A list of the Probabilistic models corresponding to the observed datasets
  • distance (abcpy.distances.Distance) – Distance object defining the distance measure used to compare simulated and observed data sets.
  • kernel (abcpy.distributions.Distribution) – Distribution object defining the perturbation kernel needed for the sampling.
  • backend (abcpy.backends.Backend) – Backend object defining the backend to be used.
  • seed (integer, optional) – Optional initial seed for the random number generator. The default value is generated randomly.
__init__(root_models, distances, backend, kernel=None, seed=None)
accepted_dist = None
alpha = None
backend = None
distance = None
epsilon = None
kernel = None
model = None
n_samples = None
n_samples_per_param = None
rng = None
sample(observations, steps, n_samples=10000, n_samples_per_param=1, alpha=0.9, acceptance_cutoff=0.03, covFactor=2.0, full_output=0, journal_file=None)

Samples from the posterior distribution of the model parameter given the observed data observations.

Parameters:
  • observations (list) – A list, containing lists describing the observed data sets
  • steps (integer) – Number of iterations in the sequential algoritm (“generations”)
  • n_samples (integer, optional) – Number of samples to generate. The default value is 10000.
  • n_samples_per_param (integer, optional) – Number of data points in each simulated data set. The default value is 1.
  • alpha (float, optional) – A parameter taking values between [0,1], the default value is 0.1.
  • acceptance_cutoff (float, optional) – Acceptance ratio cutoff, should be chosen between 0.01 and 0.05
  • covFactor (float, optional) – scaling parameter of the covariance matrix. The default value is 2.
  • full_output (integer, optional) – If full_output==1, intermediate results are included in output journal. The default value is 0, meaning the intermediate results are not saved.
Returns:

A journal containing simulation results, metadata and optionally intermediate results.
Return type:

abcpy.output.Journal
class abcpy.inferences.BaseDiscrepancy

Bases: abcpy.inferences.InferenceMethod, abcpy.inferences.BaseMethodsWithKernel

This abstract base class represents inference methods using descrepancy.

distance

To be overwritten by any sub-class – an attribute specifying the distance function.

class abcpy.inferences.BaseLikelihood

Bases: abcpy.inferences.InferenceMethod, abcpy.inferences.BaseMethodsWithKernel

This abstract base class represents inference methods that use the likelihood.

likfun

To be overwritten by any sub-class – an attribute specifying the likelihood function to be used.

class abcpy.inferences.BaseMethodsWithKernel

Bases: object

This abstract base class represents inference methods that have a kernel.

kernel

To be overwritten by any sub-class – an attribute specifying the transition or perturbation kernel.

perturb(column_index, epochs=10, rng=<MagicMock name='mock.RandomState()' id='140024932086504'>)

Perturbs all free parameters, given the current weights. Commonly used during inference.

Parameters:
  • column_index (integer) – The index of the column in the accepted_parameters_bds that should be used for perturbation
  • epochs (integer) – The number of times perturbation should happen before the algorithm is terminated
Returns:

Whether it was possible to set new parameter values for all probabilistic models
Return type:

boolean
class abcpy.inferences.InferenceMethod

Bases: abcpy.graphtools.GraphTools

This abstract base class represents an inference method.

backend

To be overwritten by any sub-class – an attribute specifying the backend to be used.

model

To be overwritten by any sub-class – an attribute specifying the model to be used

n_samples

To be overwritten by any sub-class – an attribute specifying the number of samples to be generated

n_samples_per_param

To be overwritten by any sub-class – an attribute specifying the number of data points in each simulated data set.

rng

To be overwritten by any sub-class – an attribute specifying the random number generator to be used

sample()

To be overwritten by any sub-class: Samples from the posterior distribution of the model parameter given the observed data observations.

class abcpy.inferences.PMC(root_models, likfuns, backend, kernel=None, seed=None)

Bases: abcpy.inferences.BaseLikelihood, abcpy.inferences.InferenceMethod

Population Monte Carlo based inference scheme of Cappé et. al. [1].

This algorithm assumes a likelihood function is available and can be evaluated at any parameter value given the oberved dataset. In absence of the likelihood function or when it can’t be evaluated with a rational computational expenses, we use the approximated likelihood functions in abcpy.approx_lhd module, for which the argument of the consistency of the inference schemes are based on Andrieu and Roberts [2].

[1] Cappé, O., Guillin, A., Marin, J.-M., and Robert, C. P. (2004). Population Monte Carlo. Journal of Computational and Graphical Statistics, 13(4), 907–929.

[2] C. Andrieu and G. O. Roberts. The pseudo-marginal approach for efficient Monte Carlo computations. Annals of Statistics, 37(2):697–725, 04 2009.

Parameters:
  • model (list) – A list of the Probabilistic models corresponding to the observed datasets
  • likfun (abcpy.approx_lhd.Approx_likelihood) – Approx_likelihood object defining the approximated likelihood to be used.
  • kernel (abcpy.distributions.Distribution) – Distribution object defining the perturbation kernel needed for the sampling.
  • backend (abcpy.backends.Backend) – Backend object defining the backend to be used.
  • seed (integer, optional) – Optional initial seed for the random number generator. The default value is generated randomly.
__init__(root_models, likfuns, backend, kernel=None, seed=None)
backend = None
kernel = None
likfun = None
model = None
n_samples = None
n_samples_per_param = None
rng = None
sample(observations, steps, n_samples=10000, n_samples_per_param=100, covFactors=None, iniPoints=None, full_output=0, journal_file=None)

Samples from the posterior distribution of the model parameter given the observed data observations.

Parameters:
  • observations (list) – A list, containing lists describing the observed data sets
  • steps (integer) – number of iterations in the sequential algoritm (“generations”)
  • n_samples (integer, optional) – number of samples to generate. The default value is 10000.
  • n_samples_per_param (integer, optional) – number of data points in each simulated data set. The default value is 100.
  • covFactor (list of float, optional) – scaling parameter of the covariance matrix. The default is a p dimensional array of 1 when p is the dimension of the parameter.
  • inipoints (numpy.ndarray, optional) – parameter vaulues from where the sampling starts. By default sampled from the prior.
  • full_output (integer, optional) – If full_output==1, intermediate results are included in output journal. The default value is 0, meaning the intermediate results are not saved.
Returns:

A journal containing simulation results, metadata and optionally intermediate results.
Return type:

abcpy.output.Journal
class abcpy.inferences.PMCABC(root_models, distances, backend, kernel=None, seed=None)

Bases: abcpy.inferences.BaseDiscrepancy, abcpy.inferences.InferenceMethod

This base class implements a modified version of Population Monte Carlo based inference scheme for Approximate Bayesian computation of Beaumont et. al. [1]. Here the threshold value at t-th generation are adaptively chosen by taking the maximum between the epsilon_percentile-th value of discrepancies of the accepted parameters at t-1-th generation and the threshold value provided for this generation by the user. If we take the value of epsilon_percentile to be zero (default), this method becomes the inference scheme described in [1], where the threshold values considered at each generation are the ones provided by the user.

[1] M. A. Beaumont. Approximate Bayesian computation in evolution and ecology. Annual Review of Ecology, Evolution, and Systematics, 41(1):379–406, Nov. 2010.

Parameters:
  • model (list) – A list of the Probabilistic models corresponding to the observed datasets
  • distance (abcpy.distances.Distance) – Distance object defining the distance measure to compare simulated and observed data sets.
  • kernel (abcpy.distributions.Distribution) – Distribution object defining the perturbation kernel needed for the sampling.
  • backend (abcpy.backends.Backend) – Backend object defining the backend to be used.
  • seed (integer, optional) – Optional initial seed for the random number generator. The default value is generated randomly.
__init__(root_models, distances, backend, kernel=None, seed=None)
backend = None
distance = None
kernel = None
model = None
n_samples = 2
n_samples_per_param = None
rng = None
sample(observations, steps, epsilon_init, n_samples=10000, n_samples_per_param=1, epsilon_percentile=0, covFactor=2, full_output=0, journal_file=None)

Samples from the posterior distribution of the model parameter given the observed data observations.

Parameters:
  • observations (list) – A list, containing lists describing the observed data sets
  • steps (integer) – Number of iterations in the sequential algoritm (“generations”)
  • epsilon_init (numpy.ndarray) – An array of proposed values of epsilon to be used at each steps. Can be supplied A single value to be used as the threshold in Step 1 or a steps-dimensional array of values to be used as the threshold in evry steps.
  • n_samples (integer, optional) – Number of samples to generate. The default value is 10000.
  • n_samples_per_param (integer, optional) – Number of data points in each simulated data set. The default value is 1.
  • epsilon_percentile (float, optional) – A value between [0, 100]. The default value is 0, meaning the threshold value provided by the user being used.
  • covFactor (float, optional) – scaling parameter of the covariance matrix. The default value is 2 as considered in [1].
  • full_output (integer, optional) – If full_output==1, intermediate results are included in output journal. The default value is 0, meaning the intermediate results are not saved.
Returns:

A journal containing simulation results, metadata and optionally intermediate results.
Return type:

abcpy.output.Journal
class abcpy.inferences.RSMCABC(root_models, distances, backend, kernel=None, seed=None)

Bases: abcpy.inferences.BaseDiscrepancy, abcpy.inferences.InferenceMethod

This base class implements Replenishment Sequential Monte Carlo Approximate Bayesian computation of Drovandi and Pettitt [1].

[1] CC. Drovandi CC and AN. Pettitt, Estimation of parameters for macroparasite population evolution using approximate Bayesian computation. Biometrics 67(1):225–233, 2011.

Parameters:
  • model (list) – A list of the Probabilistic models corresponding to the observed datasets
  • distance (abcpy.distances.Distance) – Distance object defining the distance measure used to compare simulated and observed data sets.
  • kernel (abcpy.distributions.Distribution) – Distribution object defining the perturbation kernel needed for the sampling.
  • backend (abcpy.backends.Backend) – Backend object defining the backend to be used.
  • seed (integer, optional) – Optional initial seed for the random number generator. The default value is generated randomly.
R = None
__init__(root_models, distances, backend, kernel=None, seed=None)
accepted_dist_bds = None
alpha = None
backend = None
distance = None
kernel = None
model = None
n_samples = None
n_samples_per_param = None
rng = None
sample(observations, steps, n_samples=10000, n_samples_per_param=1, alpha=0.1, epsilon_init=100, epsilon_final=0.1, const=0.01, covFactor=2.0, full_output=0, journal_file=None)

Samples from the posterior distribution of the model parameter given the observed data observations.

Parameters:
  • observations (list) – A list, containing lists describing the observed data sets
  • steps (integer) – Number of iterations in the sequential algoritm (“generations”)
  • n_samples (integer, optional) – Number of samples to generate. The default value is 10000.
  • n_samples_per_param (integer, optional) – Number of data points in each simulated data set. The default value is 1.
  • alpha (float, optional) – A parameter taking values between [0,1], the default value is 0.1.
  • epsilon_init (float, optional) – Initial value of threshold, the default is 100
  • epsilon_final (float, optional) – Terminal value of threshold, the default is 0.1
  • const (float, optional) – A constant to compute acceptance probabilty
  • covFactor (float, optional) – scaling parameter of the covariance matrix. The default value is 2.
  • full_output (integer, optional) – If full_output==1, intermediate results are included in output journal. The default value is 0, meaning the intermediate results are not saved.
Returns:

A journal containing simulation results, metadata and optionally intermediate results.
Return type:

abcpy.output.Journal
class abcpy.inferences.RejectionABC(root_models, distances, backend, seed=None)

Bases: abcpy.inferences.InferenceMethod

This base class implements the rejection algorithm based inference scheme [1] for Approximate Bayesian Computation.

[1] Tavaré, S., Balding, D., Griffith, R., Donnelly, P.: Inferring coalescence times from DNA sequence data. Genetics 145(2), 505–518 (1997).

Parameters:
  • model (list) – A list of the Probabilistic models corresponding to the observed datasets
  • distance (abcpy.distances.Distance) – Distance object defining the distance measure to compare simulated and observed data sets.
  • backend (abcpy.backends.Backend) – Backend object defining the backend to be used.
  • seed (integer, optional) – Optional initial seed for the random number generator. The default value is generated randomly.
__init__(root_models, distances, backend, seed=None)
backend = None
distance = None
epsilon = None
model = None
n_samples = None
n_samples_per_param = None
rng = None
sample(observations, n_samples, n_samples_per_param, epsilon, full_output=0)

Samples from the posterior distribution of the model parameter given the observed data observations.

Parameters:
  • observations (list) – A list, containing lists describing the observed data sets
  • n_samples (integer) – Number of samples to generate
  • n_samples_per_param (integer) – Number of data points in each simulated data set.
  • epsilon (float) – Value of threshold
  • full_output (integer, optional) – If full_output==1, intermediate results are included in output journal. The default value is 0, meaning the intermediate results are not saved.
Returns:

a journal containing simulation results, metadata and optionally intermediate results.
Return type:

abcpy.output.Journal
class abcpy.inferences.SABC(root_models, distances, backend, kernel=None, seed=None)

Bases: abcpy.inferences.BaseDiscrepancy, abcpy.inferences.InferenceMethod

This base class implements a modified version of Simulated Annealing Approximate Bayesian Computation (SABC) of [1] when the prior is non-informative.

[1] C. Albert, H. R. Kuensch and A. Scheidegger. A Simulated Annealing Approach to Approximate Bayes Computations. Statistics and Computing, (2014).

Parameters:
  • model (list) – A list of the Probabilistic models corresponding to the observed datasets
  • distance (abcpy.distances.Distance) – Distance object defining the distance measure used to compare simulated and observed data sets.
  • kernel (abcpy.distributions.Distribution) – Distribution object defining the perturbation kernel needed for the sampling.
  • backend (abcpy.backends.Backend) – Backend object defining the backend to be used.
  • seed (integer, optional) – Optional initial seed for the random number generator. The default value is generated randomly.
__init__(root_models, distances, backend, kernel=None, seed=None)
all_distances_bds = None
backend = None
distance = None
epsilon = None
kernel = None
model = None
n_samples = None
n_samples_per_param = None
rng = None
sample(observations, steps, epsilon, n_samples=10000, n_samples_per_param=1, beta=2, delta=0.2, v=0.3, ar_cutoff=0.5, resample=None, n_update=None, adaptcov=1, full_output=0, journal_file=None)

Samples from the posterior distribution of the model parameter given the observed data observations.

Parameters:
  • observations (list) – A list, containing lists describing the observed data sets
  • steps (integer) – Number of maximum iterations in the sequential algoritm (“generations”)
  • epsilon (numpy.float) – A proposed value of threshold to start with.
  • n_samples (integer, optional) – Number of samples to generate. The default value is 10000.
  • n_samples_per_param (integer, optional) – Number of data points in each simulated data set. The default value is 1.
  • beta (numpy.float) – Tuning parameter of SABC
  • delta (numpy.float) – Tuning parameter of SABC
  • v (numpy.float, optional) – Tuning parameter of SABC, The default value is 0.3.
  • ar_cutoff (numpy.float) – Acceptance ratio cutoff, The default value is 0.5
  • resample (int, optional) – Resample after this many acceptance, The default value if n_samples
  • n_update (int, optional) – Number of perturbed parameters at each step, The default value if n_samples
  • adaptcov (boolean, optional) – Whether we adapt the covariance matrix in iteration stage. The default value TRUE.
  • full_output (integer, optional) – If full_output==1, intermediate results are included in output journal. The default value is 0, meaning the intermediate results are not saved.
Returns:

A journal containing simulation results, metadata and optionally intermediate results.
Return type:

abcpy.output.Journal
smooth_distances_bds = None
class abcpy.inferences.SMCABC(root_models, distances, backend, kernel=None, seed=None)

Bases: abcpy.inferences.BaseDiscrepancy, abcpy.inferences.InferenceMethod

This base class implements Adaptive Population Monte Carlo Approximate Bayesian computation of Del Moral et al. [1].

[1] P. Del Moral, A. Doucet, A. Jasra, An adaptive sequential Monte Carlo method for approximate Bayesian computation. Statistics and Computing, 22(5):1009–1020, 2012.

Parameters:
  • model (list) – A list of the Probabilistic models corresponding to the observed datasets
  • distance (abcpy.distances.Distance) – Distance object defining the distance measure used to compare simulated and observed data sets.
  • kernel (abcpy.distributions.Distribution) – Distribution object defining the perturbation kernel needed for the sampling.
  • backend (abcpy.backends.Backend) – Backend object defining the backend to be used.
  • seed (integer, optional) – Optional initial seed for the random number generator. The default value is generated randomly.
__init__(root_models, distances, backend, kernel=None, seed=None)
accepted_y_sim_bds = None
backend = None
distance = None
epsilon = None
kernel = None
model = None
n_samples = None
n_samples_per_param = None
rng = None
sample(observations, steps, n_samples=10000, n_samples_per_param=1, epsilon_final=0.1, alpha=0.95, covFactor=2, resample=None, full_output=0, journal_file=None)

Samples from the posterior distribution of the model parameter given the observed data observations.

Parameters:
  • observations (list) – A list, containing lists describing the observed data sets
  • steps (integer) – Number of iterations in the sequential algoritm (“generations”)
  • epsilon_final (float, optional) – The final threshold value of epsilon to be reached. The default value is 0.1.
  • n_samples (integer, optional) – Number of samples to generate. The default value is 10000.
  • n_samples_per_param (integer, optional) – Number of data points in each simulated data set. The default value is 1.
  • alpha (float, optional) – A parameter taking values between [0,1], determinining the rate of change of the threshold epsilon. The default value is 0.5.
  • covFactor (float, optional) – scaling parameter of the covariance matrix. The default value is 2.
  • full_output (integer, optional) – If full_output==1, intermediate results are included in output journal. The default value is 0, meaning the intermediate results are not saved.
Returns:

A journal containing simulation results, metadata and optionally intermediate results.
Return type:

abcpy.output.Journal

abcpy.perturbationkernel module

class abcpy.perturbationkernel.ContinuousKernel

Bases: object

This abstract base class represents all perturbation kernels acting on continuous parameters.

pdf(accepted_parameters_manager, kernel_index, index, x)
class abcpy.perturbationkernel.DefaultKernel(models)

Bases: abcpy.perturbationkernel.JointPerturbationKernel

__init__(models)

This class implements a kernel that perturbs all continuous parameters using a multivariate normal, and all discrete parameters using a random walk. To be used as an example for user defined kernels.

Parameters:models (list) – List of abcpy.ProbabilisticModel objects, the models for which the kernel should be defined.
class abcpy.perturbationkernel.DiscreteKernel

Bases: object

This abstract base class represents all perturbation kernels acting on discrete parameters.

pmf(accepted_parameters_manager, kernel_index, index, x)
class abcpy.perturbationkernel.JointPerturbationKernel(kernels)

Bases: abcpy.perturbationkernel.PerturbationKernel

__init__(kernels)

This class joins different kernels to make up the overall perturbation kernel. Any user-implemented perturbation kernel should derive from this class. Any kernels defined on their own should be joined in the end using this class.

Parameters:kernels (list) – List of abcpy.PerturbationKernels
calculate_cov(accepted_parameters_manager)

Calculates the covariance matrix corresponding to each kernel. Commonly used before calculating weights to avoid repeated calculation.

Parameters:accepted_parameters_manager (abcpy.AcceptedParametersManager object) – The AcceptedParametersManager to be uesd.
Returns:Each entry corresponds to the covariance matrix of the corresponding kernel.
Return type:list
pdf(mapping, accepted_parameters_manager, index, x)

Calculates the overall pdf of the kernel. Commonly used to calculate weights.

Parameters:
  • mapping (list) – Each entry is a tupel of which the first entry is a abcpy.ProbabilisticModel object, the second entry is the index in the accepted_parameters_bds list corresponding to an output of this model.
  • accepted_parameters_manager (abcpy.AcceptedParametersManager object) – The AcceptedParametersManager to be used.
  • index (integer) – The row to be considered in the accepted_parameters_bds matrix.
  • x (The point at which the pdf should be evaluated.) –
Returns:

The pdf evaluated at point x.
Return type:

float
update(accepted_parameters_manager, row_index, rng=<MagicMock name='mock.RandomState()' id='140024932244840'>)

Perturbs the parameter values contained in accepted_parameters_manager. Commonly used while perturbing.

Parameters:
  • accepted_parameters_manager (abcpy.AcceptedParametersManager object) – Defines the AcceptedParametersManager to be used.
  • row_index (integer) – The index of the row that should be considered from the accepted_parameters_bds matrix.
  • rng (random number generator) – The random number generator to be used.
Returns:

The list contains tupels. Each tupel contains as the first entry a probabilistic model and as the second entry the perturbed parameter values corresponding to this model.
Return type:

list
class abcpy.perturbationkernel.MultivariateNormalKernel(models)

Bases: abcpy.perturbationkernel.PerturbationKernel, abcpy.perturbationkernel.ContinuousKernel

This class defines a kernel perturbing the parameters using a multivariate normal distribution.

__init__(models)
calculate_cov(accepted_parameters_manager, kernel_index)

Calculates the covariance matrix relevant to this kernel.

Parameters:
  • accepted_parameters_manager (abcpy.AcceptedParametersManager object) – AcceptedParametersManager to be used.
  • kernel_index (integer) – The index of the kernel in the list of kernels of the joint kernel.
Returns:

The covariance matrix corresponding to this kernel.
Return type:

list
pdf(accepted_parameters_manager, kernel_index, index, x)

Calculates the pdf of the kernel. Commonly used to calculate weights.

Parameters:
  • accepted_parameters_manager (abcpy.AcceptedParametersManager object) – The AcceptedParametersManager to be used.
  • kernel_index (integer) – The index of the kernel in the list of kernels in the joint kernel.
  • index (integer) – The row to be considered in the accepted_parameters_bds matrix.
  • x (The point at which the pdf should be evaluated.) –
Returns:

The pdf evaluated at point x.
Return type:

float
update(accepted_parameters_manager, kernel_index, row_index, rng=<MagicMock name='mock.RandomState()' id='140024932351672'>)

Updates the parameter values contained in the accepted_paramters_manager using a multivariate normal distribution.

Parameters:
  • accepted_parameters_manager (abcpy.AcceptedParametersManager object) – Defines the AcceptedParametersManager to be used.
  • kernel_index (integer) – The index of the kernel in the list of kernels in the joint kernel.
  • row_index (integer) – The index of the row that should be considered from the accepted_parameters_bds matrix.
  • rng (random number generator) – The random number generator to be used.
Returns:

The perturbed parameter values.
Return type:

np.ndarray
class abcpy.perturbationkernel.MultivariateStudentTKernel(models, df)

Bases: abcpy.perturbationkernel.PerturbationKernel, abcpy.perturbationkernel.ContinuousKernel

__init__(models, df)

This class defines a kernel perturbing the parameters using a multivariate normal distribution.

Parameters:
  • models (list of abcpy.probabilisticmodel objects) – The models that should be perturbed using this kernel
  • df (integer) – The degrees of freedom to be used.
calculate_cov(accepted_parameters_manager, kernel_index)

Calculates the covariance matrix relevant to this kernel.

Parameters:
  • accepted_parameters_manager (abcpy.AcceptedParametersManager object) – AcceptedParametersManager to be used.
  • kernel_index (integer) – The index of the kernel in the list of kernels of the joint kernel.
Returns:

The covariance matrix corresponding to this kernel.
Return type:

list
pdf(accepted_parameters_manager, kernel_index, index, x)

Calculates the pdf of the kernel. Commonly used to calculate weights.

Parameters:
  • accepted_parameters_manager (abcpy.AcceptedParametersManager object) – The AcceptedParametersManager to be used.
  • kernel_index (integer) – The index of the kernel in the list of kernels in the joint kernel.
  • index (integer) – The row to be considered in the accepted_parameters_bds matrix.
  • x (The point at which the pdf should be evaluated.) –
Returns:

The pdf evaluated at point x.
Return type:

float
update(accepted_parameters_manager, kernel_index, row_index, rng=<MagicMock name='mock.RandomState()' id='140024932373280'>)

Updates the parameter values contained in the accepted_paramters_manager using a multivariate normal distribution.

Parameters:
  • accepted_parameters_manager (abcpy.AcceptedParametersManager object) – Defines the AcceptedParametersManager to be used.
  • kernel_index (integer) – The index of the kernel in the list of kernels in the joint kernel.
  • row_index (integer) – The index of the row that should be considered from the accepted_parameters_bds matrix.
  • rng (random number generator) – The random number generator to be used.
Returns:

The perturbed parameter values.
Return type:

np.ndarray
class abcpy.perturbationkernel.PerturbationKernel(models)

Bases: object

This abstract base class represents all perturbation kernels

__init__(models)
Parameters:models (list) – The list of abcpy.probabilisticmodel objects that should be perturbed by this kernel.
calculate_cov(accepted_parameters_manager, kernel_index)

Calculates the covariance matrix for the kernel.

Parameters:
  • accepted_parameters_manager (abcpy.acceptedparametersmanager object) – The accepted parameters manager that manages all bds objects.
  • kernel_index (integer) – The index of the kernel in the list of kernels of the joint perturbation kernel.
Returns:

The covariance matrix for the kernel.
Return type:

numpy.ndarray
pdf(accepted_parameters_manager, kernel_index, row_index, x)

Calculates the pdf of the kernel at point x.

Parameters:
  • accepted_parameters_manager (abcpy.acceptedparametersmanager object) – The accepted parameters manager that manages all bds objects.
  • kernel_index (integer) – The index of the kernel in the list of kernels of the joint perturbation kernel.
  • row_index (integer) – The index of the accepted parameters bds for which the pdf should be evaluated.
  • x (list or float) – The point at which the pdf should be evaluated.
Returns:

The pdf evaluated at point x.
Return type:

float
update(accepted_parameters_manager, row_index, rng)

Perturbs the parameters for this kernel.

Parameters:
  • accepted_parameters_manager (abcpy.acceptedparametersmanager object) – The accepted parameters manager that manages all bds objects.
  • row_index (integer) – The index of the accepted parameters bds that should be perturbed.
  • rng (random number generator) – The random number generator to be used.
Returns:

The perturbed parameters.
Return type:

numpy.ndarray
class abcpy.perturbationkernel.RandomWalkKernel(models)

Bases: abcpy.perturbationkernel.PerturbationKernel, abcpy.perturbationkernel.DiscreteKernel

__init__(models)

This class defines a kernel perturbing discrete parameters using a naive random walk.

Parameters:models (list) – List of abcpy.ProbabilisticModel objects
calculate_cov(accepted_parameters_manager, kernel_index)

Calculates the covariance matrix of this kernel. Since there is no covariance matrix associated with this random walk, it returns an empty list.

pmf(accepted_parameters_manager, kernel_index, index, x)

Calculates the pmf of the kernel. Commonly used to calculate weights.

Parameters:
  • cov (list) – The covariance matrix used for this kernel. This is a dummy input.
  • accepted_parameters_manager (abcpy.AcceptedParametersManager object) – The AcceptedParametersManager to be used.
  • kernel_index (integer) – The index of the kernel in the list of kernels of the joint kernel.
  • index (integer) – The row to be considered in the accepted_parameters_bds matrix.
  • x (The point at which the pdf should be evaluated.) –
Returns:

The pmf evaluated at point x.
Return type:

float
update(accepted_parameters_manager, kernel_index, row_index, rng=<MagicMock name='mock.RandomState()' id='140024932415304'>)

Updates the parameter values contained in the accepted_paramters_manager using a random walk.

Parameters:
  • accepted_parameters_manager (abcpy.AcceptedParametersManager object) – Defines the AcceptedParametersManager to be used.
  • row_index (integer) – The index of the row that should be considered from the accepted_parameters_bds matrix.
  • rng (random number generator) – The random number generator to be used.
Returns:

The perturbed parameter values.
Return type:

np.ndarray

abcpy.probabilisticmodels module

class abcpy.probabilisticmodels.Continuous

Bases: object

This abstract class represents all continuous probabilistic models.

pdf(input_values, x)

Calculates the probability density function of the model.

Parameters:
  • input_values (list) – A list of numbers that are the concatenation of all parent model outputs in the order specified by the InputConnector object that was passed during initialization.
  • x (float) – The location at which the probability density function should be evaluated.
class abcpy.probabilisticmodels.Discrete

Bases: object

This abstract class represents all discrete probabilistic models.

pmf(input_values, x)

Calculates the probability mass function of the model.

Parameters:
  • input_values (list) – A list of numbers that are the concatenation of all parent model outputs in the order specified by the InputConnector object that was passed during initialization.
  • x (float) – The location at which the probability mass function should be evaluated.
class abcpy.probabilisticmodels.DivisionModel(parameters, name='')

Bases: abcpy.probabilisticmodels.ModelResultingFromOperation

This class represents all probabilistic models resulting from a division of two probabilistic models

forward_simulate(input_valus, k, rng=<MagicMock name='mock.RandomState()' id='140024940333488'>)

Divides the sampled values of both parent distributions.

Parameters:
  • input_values (list) – List of input values
  • k (integer) – The number of samples that should be sampled
  • rng (random number generator) – The random number generator to be used.
Returns:

The first entry is True, it is always possible to sample, given two parent values. The second entry is the fraction of the parents values.
Return type:

list
class abcpy.probabilisticmodels.ExponentialModel(parameters, name='')

Bases: abcpy.probabilisticmodels.ModelResultingFromOperation

This class represents all probabilistic models resulting from an exponentiation of two probabilistic models

__init__(parameters, name='')

Specific initializer for exponential models that does additional checks.

Parameters:parameters (list) – List of probabilistic models that should be added together.
forward_simulate(input_values, k, rng=<MagicMock name='mock.RandomState()' id='140024940363392'>)

Raises the sampled values of the base by the exponent.

Parameters:
  • input_values (list) – List of input values
  • k (integer) – The number of samples that should be sampled
  • rng (random number generator) – The random number generator to be used.
Returns:

The first entry is True, it is always possible to sample, given two parent values. The second entry is the exponential of the parents values.
Return type:

list
class abcpy.probabilisticmodels.Hyperparameter(value, name='Hyperparameter')

Bases: abcpy.probabilisticmodels.ProbabilisticModel

This class represents all hyperparameters (i.e. fixed parameters).

__init__(value, name='Hyperparameter')
Parameters:value (list) – The values to which the hyperparameter should be set
forward_simulate(input_values, k, rng=<MagicMock name='mock.RandomState()' id='140024940658760'>)
get_input_connector()
get_input_dimension()
get_input_models()
get_input_values()
get_output_dimension()
pdf(input_values, x)
set_output_values(values, rng=<MagicMock name='mock.RandomState()' id='140024940617008'>)
class abcpy.probabilisticmodels.InputConnector(dimension)

Bases: object

__init__(dimension)

Creates input parameters of given dimensionality. Each dimension needs to be specified using the set method.

Parameters:dimension (int) – Dimensionality of the input parameters.
all_models_fixed_values()

Checks whether all input models have fixed an output value (pseudo data).

In order get a fixed output value (a realization of the random variable described by the model) a model has to run a forward simulation, which is not done automatically upon initialization.

Returns:
Return type:boolean
from_list(parameters)

Creates an InputParameters object from a list of ProbabilisticModels.

In this case, number of input parameters equals the sum of output dimensions of all models in the parameter list. Further, the output and models are connected to the input parameters in the order they appear in the parameter list.

For convenience, - the parameter list can contain nested lists - the method also accepts numbers instead of models, which are automatically converted to hyper parameters.

Parameters:parameters (list) – A list of ProbabilisticModels
Returns:
Return type:InputConnector
from_model(model)

Convenient initializer that converts the full output of a model to input parameters.

Parameters:ProbabilisticModel –
Returns:
Return type:InputConnector
from_number(number)

Convenient initializer that converts a number to a hyperparameter input parameter.

Parameters:number –
Returns:
Return type:InputConnector
get_model(index)

Returns the model at index.

Returns:
Return type:ProbabilisticModel
get_models()

Returns a list of all models.

Returns:
Return type:list
get_parameter_count()

Returns the number of parameters.

Returns:
Return type:int
get_values()

Returns the fixed values of all input models.

Returns:
Return type:np.array
set(index, model, model_index)

Sets for an input parameter index the input model and the model index to use.

For convenience, model can also be a number, which is automatically casted to a hyper parameter.

Parameters:
  • index (int) – Index of the input parameter to be set.
  • model (ProbabilisticModel, Number) – The model to be set for the input parameter.
  • model_index (int) – Index of model’s output to be used as input parameter.
class abcpy.probabilisticmodels.ModelResultingFromOperation(parameters, name='')

Bases: abcpy.probabilisticmodels.ProbabilisticModel

This class implements probabilistic models returned after performing an operation on two probabilistic models

__init__(parameters, name='')
Parameters:parameters (list) – List containing two probabilistic models that should be added together.
forward_simulate(input_values, k, rng=<MagicMock name='mock.RandomState()' id='140024940684568'>)
get_output_dimension()
pdf(input_values, x)

Calculates the probability density function at point x.

Parameters:
  • input_values (list) – List of input parameters, in the same order as specified in the InputConnector passed to the init function
  • x (float or list) – The point at which the pdf should be evaluated.
Returns:

The probability density function evaluated at point x.
Return type:

float
sample_from_input_models(k, rng=<MagicMock name='mock.RandomState()' id='140024940718064'>)

Return for each input model k samples.

Parameters:k (int) – Specifies the number of samples to generate from each input model.
Returns:A dictionary of type ProbabilisticModel:[], where the list contains k samples of the corresponding model.
Return type:dict
class abcpy.probabilisticmodels.MultiplicationModel(parameters, name='')

Bases: abcpy.probabilisticmodels.ModelResultingFromOperation

This class represents all probabilistic models resulting from a multiplication of two probabilistic models

forward_simulate(input_values, k, rng=<MagicMock name='mock.RandomState()' id='140024940819792'>)

Multiplies the sampled values of both parent distributions element wise.

Parameters:
  • input_values (list) – List of input values
  • k (integer) – The number of samples that should be sampled
  • rng (random number generator) – The random number generator to be used.
Returns:

The first entry is True, it is always possible to sample, given two parent values. The second entry is the product of the parents values.
Return type:

list
class abcpy.probabilisticmodels.ProbabilisticModel(input_connector, name='')

Bases: object

This abstract class represents all probabilistic models.

__add__(other)

Overload the + operator for probabilistic models.

Parameters:other (probabilistic model or Hyperparameter) – The model to be added to self.
Returns:A probabilistic model describing a model coming from summation.
Return type:SummationModel
__init__(input_connector, name='')

This initializer must be called from any derived class to properly connect it to its input models.

It accepts as input an InputConnector object that fully specifies how to connect all parent models to the current model.

Parameters:
  • input_connector (list) – A list of input parameters.
  • name (string) – A human readable name for the model. Can be the variable name for example.
__mul__(other)

Overload the * operator for probabilistic models.

Parameters:other (probabilistic model or Hyperparameter) – The model to be multiplied with self.
Returns:A probabilistic model describing a model coming from multiplication.
Return type:MultiplicationModel
__pow__(power, modulo=None)
__sub__(other)

Overload the - operator for probabilistic models.

Parameters:other (probabilistic model or Hyperparameter) – The model to be subtracted from self.
Returns:A probabilistic model describing a model coming from subtraction.
Return type:SubtractionModel
__truediv__(other)

Overload the / operator for probabilistic models.

Parameters:other (probabilistic model or Hyperparameter) – The model to be divide self.
Returns:A probabilistic model describing a model coming from division.
Return type:DivisionModel
calculate_and_store_pdf_if_needed(x)

Calculates the probability density function at point x and stores the result internally for later use.

This function is intended to be used within the inference computation.

Parameters:x (list) – The point at which the pdf should be evaluated.
flush_stored_pdf()

This function flushes the internally stored value of a previously computed pdf.

forward_simulate(input_values, k, rng)

Provides the output (pseudo data) from a forward simulation of the current model.

In case the model is intended to be used as input for another model, a forward simulation must return a list of k numpy arrays with shape (get_output_dimension(),).

In case the model is directly used for inference, and not as input for another model, a forward simulation also must return a list, but the elements can be arbitrarily defined. In this case it is only important that the used statistics and distance functions can read the input.

Parameters:
  • input_values (list) – A list of numbers that are the concatenation of all parent model outputs in the order specified by the InputConnector object that was passed during initialization.
  • k (integer) – The number of forward simulations that should be run
  • rng (Random number generator) – Defines the random number generator to be used. The default value uses a random seed to initialize the generator.
Returns:

A list of k elements, where each element is of type numpy arary and represents the result of a single forward simulation.
Return type:

list
get_input_connector()

Returns the input connector object that connecects the current model to its parents.

In case of no dependencies, this function should return None.

Returns:
Return type:InputConnector, None
get_input_dimension()

Returns the input dimension of the current model.

Returns:
Return type:int
get_input_models()

Returns a list of all input models.

Returns:
Return type:list
get_input_values()

Returns the input values from the parent models as a list. Commonly used when sampling from the distribution.

Returns:
Return type:list
get_output_dimension()

Provides the output dimension of the current model.

This function is in particular important if the current model is used as an input for other models. In such a case it is assumed that the output is always a vector of int or float. The length of the vector is the dimension that should be returned here.

Returns:The dimension of the output vector of a single forward simulation.
Return type:int
get_stored_output_values()

Returns the stored sampled value of the probabilistic model after setting the values explicitly.

At initialization the function should return None.

Returns:
Return type:numpy.array or None.
get_stored_pdf()

Retrieves the value of a previously calculated pdf.

Returns:
Return type:number
pdf(input_values, x)

Calculates the probability density function at point x.

Commonly used to determine whether perturbed parameters are still valid according to the pdf.

Parameters:
  • input_values (list) – List of input parameters, in the same order as specified in the InputConnector passed to the init function
  • x (list) – The point at which the pdf should be evaluated.
Returns:

The pdf evaluated at point x.
Return type:

float
set_output_values(values)

Sets the output values of the model. This method is commonly used to set new values after perturbing the old ones.

Parameters:values (numpy array or dimension equal to output dimension.) –
Returns:Returns True if it was possible to set the values, false otherwise.
Return type:boolean
class abcpy.probabilisticmodels.RExponentialModel(parameters, name='')

Bases: abcpy.probabilisticmodels.ModelResultingFromOperation

This class represents all probabilistic models resulting from an exponentiation of a Hyperparameter by another probabilistic model.

__init__(parameters, name='')

Specific initializer for exponential models that does additional checks.

Parameters:parameters (list) – List of probabilistic models that should be added together.
forward_simulate(input_values, k, rng=<MagicMock name='mock.RandomState()' id='140024940397456'>)

Raises the base by the sampled value of the exponent.

Parameters:
  • input_values (list) – List of input values
  • k (integer) – The number of samples that should be sampled
  • rng (random number generator) – The random number generator to be used.
Returns:

The first entry is True, it is always possible to sample, given two parent values. The second entry is the exponential of the parents values.
Return type:

list
class abcpy.probabilisticmodels.SubtractionModel(parameters, name='')

Bases: abcpy.probabilisticmodels.ModelResultingFromOperation

This class represents all probabilistic models resulting from an subtraction of two probabilistic models

forward_simulate(input_values, k, rng=<MagicMock name='mock.RandomState()' id='140024940790000'>)

Adds the sampled values of both parent distributions.

Parameters:
  • input_values (list) – List of input values
  • k (integer) – The number of samples that should be sampled
  • rng (random number generator) – The random number generator to be used.
Returns:

The first entry is True, it is always possible to sample, given two parent values. The second entry is the difference of the parents values.
Return type:

list
class abcpy.probabilisticmodels.SummationModel(parameters, name='')

Bases: abcpy.probabilisticmodels.ModelResultingFromOperation

This class represents all probabilistic models resulting from an addition of two probabilistic models

forward_simulate(input_values, k, rng=<MagicMock name='mock.RandomState()' id='140024940751952'>)

Adds the sampled values of both parent distributions.

Parameters:
  • input_values (list) – List of input values
  • k (integer) – The number of samples that should be sampled
  • rng (random number generator) – The random number generator to be used.
Returns:

The first entry is True, it is always possible to sample, given two parent values. The second entry is the sum of the parents values.
Return type:

list

abcpy.modelselections module

class abcpy.modelselections.ModelSelections(model_array, statistics_calc, backend, seed=None)

Bases: object

This abstract base class defines a model selection rule of how to choose a model from a set of models given an observation.

__init__(model_array, statistics_calc, backend, seed=None)

Constructor that must be overwritten by the sub-class.

The constructor of a sub-class must accept an array of models to choose the model from, and two non-optional parameters statistics calculator and backend stored in self.statistics_calc and self.backend defining how to calculate sumarry statistics from data and what kind of parallelization to use.

Parameters:
  • model_array (list) – A list of models which are of type abcpy.probabilisticmodels
  • statistics (abcpy.statistics.Statistics) – Statistics object that conforms to the Statistics class.
  • backend (abcpy.backends.Backend) – Backend object that conforms to the Backend class.
  • seed (integer, optional) – Optional initial seed for the random number generator. The default value is generated randomly.
posterior_probability(observations)

To be overwritten by any sub-class: returns the approximate posterior probability of the chosen model given the observed data set observations.

Parameters:observations (python list) – The observed data set.
Returns:A vector containing the approximate posterior probability of the model chosen.
Return type:np.ndarray
select_model(observations, n_samples=1000, n_samples_per_param=100)

To be overwritten by any sub-class: returns a model selected by the modelselection procedure most suitable to the obersved data set observations. Further two optional integer arguments n_samples and n_samples_per_param is supplied denoting the number of samples in the refernce table and the data points in each simulated data set.

Parameters:
  • observations (python list) – The observed data set.
  • n_samples (integer, optional) – Number of samples to generate for reference table.
  • n_samples_per_param (integer, optional) – Number of data points in each simulated data set.
Returns:

A model which are of type abcpy.probabilisticmodels
Return type:

abcpy.probabilisticmodels
class abcpy.modelselections.RandomForest(model_array, statistics_calc, backend, N_tree=100, n_try_fraction=0.5, seed=None)

Bases: abcpy.modelselections.ModelSelections, abcpy.graphtools.GraphTools

This class implements the model selection procedure based on the Random Forest ensemble learner as described in Pudlo et. al. [1].

[1] Pudlo, P., Marin, J.-M., Estoup, A., Cornuet, J.-M., Gautier, M. and Robert, C. (2016). Reliable ABC model choice via random forests. Bioinformatics, 32 859–866.

__init__(model_array, statistics_calc, backend, N_tree=100, n_try_fraction=0.5, seed=None)
Parameters:
  • N_tree (integer, optional) – Number of trees in the random forest. The default value is 100.
  • n_try_fraction (float, optional) – The fraction of number of summary statistics to be considered as the size of the number of covariates randomly sampled at each node by the randomised CART. The default value is 0.5.
posterior_probability(observations, n_samples=1000, n_samples_per_param=1)
Parameters:observations (python list) – The observed data set.
Returns:A vector containing the approximate posterior probability of the model chosen.
Return type:np.ndarray
select_model(observations, n_samples=1000, n_samples_per_param=1)
Parameters:
  • observations (python list) – The observed data set.
  • n_samples (integer, optional) – Number of samples to generate for reference table. The default value is 1000.
  • n_samples_per_param (integer, optional) – Number of data points in each simulated data set. The default value is 1.
Returns:

A model which are of type abcpy.probabilisticmodels
Return type:

abcpy.probabilisticmodels

abcpy.statistics module

class abcpy.statistics.Identity(degree=2, cross=True)

Bases: abcpy.statistics.Statistics

This class implements identity statistics returning a nxp matrix when the data set contains n numpy.ndarray of length p.

__init__(degree=2, cross=True)
statistics(data)
class abcpy.statistics.Statistics(degree=2, cross=True)

Bases: object

This abstract base class defines how to calculate statistics from dataset.

The base class also implements a polynomial expansion with cross-product terms that can be used to get desired polynomial expansion of the calculated statistics.

__init__(degree=2, cross=True)

Constructor that must be overwritten by the sub-class.

The constructor of a sub-class must accept arguments for the polynomial expansion after extraction of the summary statistics, one has to define the degree of polynomial expansion and cross, indicating whether cross-prodcut terms are included.

Parameters:
  • degree (integer, optional) – Of polynomial expansion. The default value is 2 meaning second order polynomial expansion.
  • cross (boolean, optional) – Defines whether to include the cross-product terms. The default value is TRUE, meaning the cross product term is included.
statistics(data: object) → object

To be overwritten by any sub-class: should extract statistics from the data set data. It is assumed that data is a list of n same type elements(eg., The data can be a list containing n timeseries, n graphs or n np.ndarray).

Parameters:data (python list) – Contains n data sets.
Returns:nxp matrix where for each of the n data points p statistics are calculated.
Return type:numpy.ndarray

abcpy.summaryselections module

class abcpy.summaryselections.Semiautomatic(model, statistics_calc, backend, n_samples=1000, n_samples_per_param=1, seed=None)

Bases: abcpy.summaryselections.Summaryselections, abcpy.graphtools.GraphTools

This class implements the semi automatic summary statistics choice described in Fearnhead and Prangle [1].

[1] Fearnhead P., Prangle D. 2012. Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation. J. Roy. Stat. Soc. B 74:419–474.

__init__(model, statistics_calc, backend, n_samples=1000, n_samples_per_param=1, seed=None)
transformation(statistics)
class abcpy.summaryselections.Summaryselections(model, statistics_calc, backend, n_samples=1000, seed=None)

Bases: object

This abstract base class defines a way to choose the summary statistics.

__init__(model, statistics_calc, backend, n_samples=1000, seed=None)

The constructor of a sub-class must accept a non-optional model, statistics calculator and backend which are stored to self.model, self.statistics_calc and self.backend. Further it accepts two optional parameters n_samples and seed defining the number of simulated dataset used for the pilot to decide the summary statistics and the integer to initialize the random number generator.

Parameters:
  • model (abcpy.models.Model) – Model object that conforms to the Model class.
  • statistics_cal (abcpy.statistics.Statistics) – Statistics object that conforms to the Statistics class.
  • backend (abcpy.backends.Backend) – Backend object that conforms to the Backend class.
  • n_samples (int, optional) – The number of (parameter, simulated data) tuple generated to learn the summary statistics in pilot step. The default value is 1000.
  • n_samples_per_param (int, optional) – Number of data points in each simulated data set.
  • seed (integer, optional) – Optional initial seed for the random number generator. The default value is generated randomly.
transformation(statistics)