Functions from pyAgrum

Useful functions in pyAgrum

pyAgrum.about()

about() for pyAgrum

pyAgrum.getPosterior(model, evs, target)

Compute the posterior of a single target (variable) in a BN given evidence

getPosterior uses a VariableElimination inference. If more than one target is needed with the same set of evidence or if the same target is needed with more than one set of evidence, this function is not relevant since it creates a new inference engine every time it is called.

Parameters
  • bn (pyAgrum.BayesNet or pyAgrum.MarkovNet) – The probabilistic Graphical Model

  • evs (dictionaryDict) – {name|id:val, name|id : [ val1, val2 ], …}

  • target (string or int) – variable name or id

Return type

posterior (pyAgrum.Potential or other)

pyAgrum.generateSample(bn, n=1, name_out=None, show_progress=False, with_labels=True, random_order=True)

generate a CSV file of samples from a bn.

Parameters
  • bn (pyAgrum.BayesNet) – the Bayes Net from which the sample is generated

  • n (int) – the number of samples

  • name_out (str) – the name for the output csv filename. If name_out is None, a pandas.DataFrame is generated

  • show_progress (bool) – if True, show a progress bar. Default is False

  • with_labels (bool) – if True, use the labels of the modalities of variables in the csv. If False, use their ids. Default is True

  • random_order (bool) – if True, the columns in the csv are randomized sorted. Default is True

Returns

the log2-likelihood of the generated base or if name_out is None, the couple (generated pandas.DataFrame,log2-likelihood)

Return type

float|Tuple[pandas.DataFrame,float]

pyAgrum.generateCSV(bn, name_out, n=1, show_progress=False, with_labels=False, random_order=True)

Deprecated. Please use pyAgrum.generateSample instead.

Quick specification of (randomly parameterized) graphical models

aGrUM/pyAgrum offers a so-called “fact” syntax that allows to quickly and compactly specify prototypes of graphical models. These fastPrototype aGrUM’s methods have also been wrapped in functions of pyAgrum.

gum.fastBN("A[10]->B<-C{top|middle|bottom};B->D")

The type of the random variables can be specifiy with different syntaxes:

Note

  • If the dot-like string contains such a specification more than once for a variable, the first specification will be used.

  • the CPTs are randomly generated.

pyAgrum.fastBN(structure, domain_size=2)
Create a Bayesian network with a dot-like syntax which specifies:
  • the structure ‘a->b->c;b->d<-e;’,

  • the type of the variables with different syntax (cf documentation).

Examples

>>> import pyAgrum as gum
>>> bn=gum.fastBN('A->B[1,3]<-C{yes|No}->D[2,4]<-E[1,2.5,3.9]',6)
Parameters
  • structure (str) – the string containing the specification

  • domain_size (int) – the default domain size for variables

Returns

the resulting bayesian network

Return type

pyAgrum.BayesNet

pyAgrum.fastMN(structure, domain_size=2)
Create a Markov network with a modified dot-like syntax which specifies:
  • the structure ‘a-b-c;b-d;c-e;’ where each chain ‘a-b-c’ specifies a factor,

  • the type of the variables with different syntax (cf documentation).

Examples

>>> import pyAgrum as gum
>>> bn=gum.fastMN('A--B[1,3]--C{yes|No};C--D[2,4]--E[1,2.5,3.9]',6)
Parameters
  • structure (str) – the string containing the specification

  • domain_size (int) – the default domain size for variables

Returns

the resulting Markov network

Return type

pyAgrum.MarkovNet

pyAgrum.fastID(structure, domain_size=2)
Create an Influence Diagram with a modified dot-like syntax which specifies:
  • the structure and the type of the variables following fast syntax,

  • a prefix for the type of node (chance/decision/utiliy nodes):

    • a : a chance node named ‘a’ (by default)

    • $a : a utility node named ‘a’

    • *a : a decision node named ‘a’

Examples

>>> import pyAgrum as gum
>>> bn=gum.fastID('A->B[1,3]<-*C{yes|No}->$D<-E[1,2.5,3.9]',6)
Parameters
  • structure (str) – the string containing the specification

  • domain_size (int) – the default domain size for variables

Returns

the resulting Influence Diagram

Return type

pyAgrum.InfluenceDiagram

Input/Output for Bayesian networks

pyAgrum.availableBNExts()

Give the list of all formats known by pyAgrum to save a Bayesian network.

Returns

a string which lists all suffixes for supported BN file formats.

pyAgrum.loadBN(filename, listeners=None, verbose=False, **opts)

load a BN from a file with optional listeners and arguments

Parameters
  • filename (str) – the name of the input file

  • listeners (List[object]) – list of functions to execute when listening

  • verbose (bool) – whether to print or not warning messages

  • system (str) – (for O3PRM) name of the system to flatten in a BN

  • classpath (List[str]) – (for O3PRM) list of folders containing classes

Returns

a BN from a file using one of the availableBNExts() suffixes.

Return type

pyAgrum.BayesNet

Notes

Listeners could be added in order to monitor its loading.

Examples

>>> import pyAgrum as gum
>>>
>>> # creating listeners
>>> def foo_listener(progress):
>>>    if progress==200:
>>>        print(' BN loaded ')
>>>        return
>>>    elif progress==100:
>>>        car='%'
>>>    elif progress%10==0:
>>>        car='#'
>>>    else:
>>>        car='.'
>>>    print(car,end='',flush=True)
>>>
>>> def bar_listener(progress):
>>>    if progress==50:
>>>        print('50%')
>>>
>>> # loadBN with list of listeners
>>> gum.loadBN('./bn.bif',listeners=[foo_listener,bar_listener])
>>> # .........#.........#.........#.........#..50%
>>> # .......#.........#.........#.........#.........#.........% | bn loaded
pyAgrum.saveBN(bn, filename, allowModificationWhenSaving=None)

save a BN into a file using the format corresponding to one of the availableWriteBNExts() suffixes.

Parameters
  • bn (pyAgrum.BayesNet) – the BN to save

  • filename (str) – the name of the output file

  • allowModificationWhenSaving (bool) – whether syntax errors in the BN should throw a FatalError or can be corrected. Also controlled by pyAgrum.config[“BN”,”allow_modification_when_saving”].

Input/Output for Markov networks

pyAgrum.availableMNExts()

Give the list of all formats known by pyAgrum to save a Markov network.

Returns

a string which lists all suffixes for supported MN file formats.

Return type

str

pyAgrum.loadMN(filename, listeners=None, verbose=False)

load a MN from a file with optional listeners and arguments

Parameters
  • filename (str) – the name of the input file

  • listeners (List[Object]) – list of functions to execute

  • verbose (bool) – whether to print or not warning messages

Returns

  • pyAgrum.MarkovNet – a MN from a file using one of the availableMNExts() suffixes.

  • Listeners could be added in order to monitor its loading.

Examples

>>> import pyAgrum as gum
>>>
>>> # creating listeners
>>> def foo_listener(progress):
>>>    if progress==200:
>>>        print(' BN loaded ')
>>>        return
>>>    elif progress==100:
>>>        car='%'
>>>    elif progress%10==0:
>>>        car='#'
>>>    else:
>>>        car='.'
>>>    print(car,end='',flush=True)
>>>
>>> def bar_listener(progress):
>>>    if progress==50:
>>>        print('50%')
>>>
>>> # loadBN with list of listeners
>>> gum.loadMN('./bn.uai',listeners=[foo_listener,bar_listener])
>>> # .........#.........#.........#.........#..50%
>>> # .......#.........#.........#.........#.........#.........% | bn loaded
pyAgrum.saveMN(mn, filename)

save a MN into a file using the format corresponding to one of the availableWriteMNExts() suffixes.

Parameters
  • mn (pyAgrum.MarkovNet)) – the MN to save

  • filename (str) – the name of the output file

Input for influence diagram

pyAgrum.availableIDExts()

Give the list of all formats known by pyAgrum to save a influence diagram.

Returns

a string which lists all suffixes for supported ID file formats.

Return type

str

pyAgrum.loadID(filename)

read a gum.InfluenceDiagram from a ID file

Parameters

filename (str) – the name of the input file

Returns

the InfluenceDiagram

Return type

pyAgrum.InfluenceDiagram

pyAgrum.saveID(infdiag, filename)

save an ID into a file using the format corresponding to one of the availableWriteIDExts() suffixes.

Parameters