pyagrum.lib.explain

The purpose of pyagrum.lib.explain is to give tools to explain and interpret the structure and parameters of a Bayesian network.

Dealing with independence

pyagrum.lib.explain.independenceListForPairs(bn, filename, target=None, plot=True, alphabetic=False)

get the p-values of the chi2 test of a (as simple as possible) independence proposition for every non arc.

Parameters:

bn (gum.BayesNet) – the Bayesian network
filename (str) – the name of the csv database
alphabetic (bool) – if True, the list is alphabetically sorted else it is sorted by the p-value
target ((optional) str or int) – the name or id of the target variable
plot (bool) – if True, plot the result

Returns:

the list

Dealing with mutual information and entropy

showing entropy and mutual informations in pyAgrum

pyagrum.lib.explain.getInformation(bn, evs=None, size=None, cmap=<matplotlib.colors.LinearSegmentedColormap object>)

get a HTML string for a bn annotated with results from inference : entropy and mutual information

Parameters:

bn (pyagrum.BayesNet) – the model
evs (Dict[str|int,str|int|List[float]]) – the observations
size (int|str) – size of the rendered graph
cmap (matplotlib.colours.Colormap) – the cmap

Returns:

return the HTML string

Return type:

str

pyagrum.lib.explain.showInformation(bn, evs=None, size=None, cmap=<matplotlib.colors.LinearSegmentedColormap object>)

diplay a bn annotated with results from inference : entropy and mutual information

Parameters:

bn (pyagrum.BayesNet) – the model
evs (Dict[str|int,str|int|List[float]]) – the observations
size (int|str) – size of the rendered graph
cmap (matplotlib.colours.Colormap) – the cmap

Dealing with ShapValues

class pyagrum.lib.explain.ShapValues(bn, target, logit=True)

Bases: object

Class to compute Shapley values for a target variable in a Bayesian network.

causal(df, y=1, sample_size=200, plot=False, plot_importance=False, percentage=False, filename=None)

Computes the causal Shapley values for each variable.

Parameters:

dfpandas DataFrame: The input data for which to compute the Shapley values.
yint, optional: The target class for which to compute the Shapley values (default is 1).
sample_sizeint, optional: The number of samples to use for the background data (default is 200).
plotbool, optional: If True, plots the waterfall or beeswarm plot depending on the number of rows in df (default is False).
plot_importancebool, optional: If True, plots the bar chart of feature importance (default is False).
percentage: bool: if True, the importance plot is shown in percent.
filenamestr, optional: If provided, saves the plots to the specified filename instead of displaying them.

Returns:

: Dict[str, float]

A dictionary containing the importances of each variable in the input data.

type percentage:: bool
param percentage:
type filename:: str
param filename:

conditional(df, y=1, plot=False, plot_importance=False, percentage=False, filename=None)

Computes the conditional Shapley values for each variable.

Parameters:

dfpandas DataFrame: The input data for which to compute the Shapley values.
yint, optional: The target class for which to compute the Shapley values (default is 1).
plotbool, optional: If True, plots the waterfall or beeswarm plot depending on the number of rows in df (default is False).
plot_importancebool, optional: If True, plots the bar chart of feature importance (default is False).
percentage: bool: if True, the importance plot is shown in percent.
filenamestr, optional: If provided, saves the plots to the specified filename instead of displaying them.

Returns:

: Dict[str, float]

A dictionary containing the importances of each variable in the input data.

type y:: int
param y:
type plot:: bool
param plot:
type plot_importance:: bool
param plot_importance:
type percentage:: bool
param percentage:
type filename:: str
param filename:

marginal(df, y=1, sample_size=200, plot=False, plot_importance=False, percentage=False, filename=None)

Computes the marginal Shapley values for each variable.

Parameters:

dfpandas DataFrame: The input data for which to compute the Shapley values.
yint, optional: The target class for which to compute the Shapley values (default is 1).
sample_sizeint, optional: The number of samples to use for the background data (default is 200).
plotbool, optional: If True, plots the waterfall or beeswarm plot depending on the number of rows in df (default is False).
plot_importancebool, optional: If True, plots the bar chart of feature importance (default is False).
percentage: bool: if True, the importance plot is shown in percent.

Returns:

: Dict[str, float]

A dictionary containing the importances of each variable in the input data.

type percentage:: bool
param percentage:
type filename:: str
param filename:

Dealing with generalized Markov Blankets

A structural property of Bayesian networks is the Markov boundary of a node. A Markov blanket of a node is a set of nodes that renders the node independent of all other nodes in the network. The Markov boundary is the closest Markov blanket. A Markov boundary of a node is composed of its parents, its children, and the parents of its children. More generally, one can define the generalized \(k\)-Markov blanket of a node as the union of the markov blanket of the nodes of its \((k-1)\)-Markov blanket. So, if a node belongs to the \(k\)-Markov blanket of the node \(X\), \(k\) is a kind of measure of its proximity to \(X\).