ContinuousBayesianNetworkFactory¶
- class otagrum.ContinuousBayesianNetworkFactory(*args)¶
Estimate a ContinuousBayesianNetwork distribution.
- Parameters:
- marginalsFactory
DistributionFactory
The model used to estimate each univariate marginal distribution. Default value is
HistogramFactory
.- copulasFactory
DistributionFactory
The model used to estimate each local conditional copula. Default value is
BernsteinCopulaFactory
.- namedDAG
NamedDAG
The structure of the underlying graphical model. Default value is
NamedDAG()
- alphafloat
Threshold on the p-value for the conditional independence tests. Default value is 0.1.
- maximumConditioningSetSizeint
Maximum conditional set size. Default value is 5.
- marginalsFactory
Notes
A namedDAG parameter of size 0 (ie equal to NamedDAG(), default value) means that the structure has also to be learnt, using the
ContinuousPC
algorithm. The parameters alpha and maximumConditioningSetSize are used only if theNamedDAG
has to be learnt. The following keys inResourceMap
can be used to fine-tune the learning process:‘ContinuousBayesianNetworkFactory-DefaultAlpha’ to set the p-value threshold for the independence test in
ContinuousPC
. Default value is 0.1.‘ContinuousBayesianNetworkFactory-DefaultMaximumConditioningSetSize’ to set the maximum dimension of the parents in the Bayesian network. Default value is 5.
‘ContinuousBayesianNetworkFactory-WorkInCopulaSpace’ to indicate if the estimated distribution should be a copula. Default value is True.
‘ContinuousBayesianNetworkFactory-MaximumDiscreteSupport’ to set the maximum number of different values to consider a marginal distribution as being discrete. Default value is 10.
‘ContinuousBayesianNetworkFactory-UseBetaFactory’ to indicate if the estimated copula should be a Beta copula if a:class:openturns.BernsteinCopulaFactory is provided. Default value is True.
Examples
>>> import otagrum >>> import openturns as ot >>> marginalsFactory = ot.NormalFactory() >>> copulasFactory = ot.NormalCopulaFactory() >>> ndag = otagrum.NamedDAG() >>> threshold = 0.1 >>> maxParents = 5 >>> factory = otagrum.ContinuousBayesianNetworkFactory(marginalsFactory, copulasFactory, ndag, threshold, maxParents)
Methods
build
(*args)Build the distribution.
Build as ContinuousBayesianNetwork distribution.
buildEstimator
(*args)Build the distribution and the parameter distribution.
Accessor to the bootstrap size.
Accessor to the object's name.
getName
()Accessor to the object's name.
hasName
()Test if the object is named.
setBootstrapSize
(bootstrapSize)Accessor to the bootstrap size.
setName
(name)Accessor to the object's name.
- __init__(*args)¶
- build(*args)¶
Build the distribution.
Available usages:
build()
build(sample)
build(param)
- Parameters:
- sample2-d sequence of float
Data.
- paramsequence of float
The parameters of the distribution.
- Returns:
- dist
Distribution
The estimated distribution.
In the first usage, the default native distribution is built.
- dist
- buildAsContinuousBayesianNetwork(*args)¶
Build as ContinuousBayesianNetwork distribution.
- Parameters:
- sample
openturns.Sample
Input sample.
- sample
- Returns:
- distribution
ContinuousBayesianNetwork
Estimated distribution.
- distribution
- buildEstimator(*args)¶
Build the distribution and the parameter distribution.
- Parameters:
- sample2-d sequence of float
Data.
- parameters
DistributionParameters
Optional, the parametrization.
- Returns:
- resDist
DistributionFactoryResult
The results.
- resDist
Notes
According to the way the native parameters of the distribution are estimated, the parameters distribution differs:
Moments method: the asymptotic parameters distribution is normal and estimated by Bootstrap on the initial data;
Maximum likelihood method with a regular model: the asymptotic parameters distribution is normal and its covariance matrix is the inverse Fisher information matrix;
Other methods: the asymptotic parameters distribution is estimated by Bootstrap on the initial data and kernel fitting (see
KernelSmoothing
).
If another set of parameters is specified, the native parameters distribution is first estimated and the new distribution is determined from it:
if the native parameters distribution is normal and the transformation regular at the estimated parameters values: the asymptotic parameters distribution is normal and its covariance matrix determined from the inverse Fisher information matrix of the native parameters and the transformation;
in the other cases, the asymptotic parameters distribution is estimated by Bootstrap on the initial data and kernel fitting.
- getBootstrapSize()¶
Accessor to the bootstrap size.
- Returns:
- sizeint
Size of the bootstrap.
- getClassName()¶
Accessor to the object’s name.
- Returns:
- class_namestr
The object class name (object.__class__.__name__).
- getName()¶
Accessor to the object’s name.
- Returns:
- namestr
The name of the object.
- hasName()¶
Test if the object is named.
- Returns:
- hasNamebool
True if the name is not empty.
- setBootstrapSize(bootstrapSize)¶
Accessor to the bootstrap size.
- Parameters:
- sizeint
The size of the bootstrap.
- setName(name)¶
Accessor to the object’s name.
- Parameters:
- namestr
The name of the object.