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

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)

Bases: object

The ShapValue class implements the calculation of Shap values in Bayesian networks.

The main implementation is based on Conditional Shap values [3], but the Interventional calculation method proposed in [2] is also present. In addition, a new causal method, based on [1], is implemented which is well suited for Bayesian networks.

[1]

Heskes, T., Sijben, E., Bucur, I., & Claassen, T. (2020). Causal Shapley Values: Exploiting Causal Knowledge. 34th Conference on Neural Information Processing Systems. Vancouver, Canada.

[2]

Janzing, D., Minorics, L., & Blöbaum, P. (2019). Feature relevance quantification in explainable AI: A causality problem. arXiv: Machine Learning. Retrieved 6 24, 2021, from https://arxiv.org/abs/1910.13413

[3]

Lundberg, S. M., & Su-In, L. (2017). A Unified Approach to Interpreting Model. 31st Conference on Neural Information Processing Systems. Long Beach, CA, USA.

causal(train, plot=False, plot_importance=False, percentage=False)

Compute the causal Shap Values for each variables.

Parameters:

• train (pandas.DataFrame) – the database

• plot (bool) – if True, plot the violin graph of the shap values

• plot_importance (bool) – if True, plot the importance plot

• percentage (bool) – if True, the importance plot is shown in percent.

Returns:

a dictionary Dict[str,float]

conditional(train, plot=False, plot_importance=False, percentage=False)

Compute the conditional Shap Values for each variables.

Parameters:

• train (pandas.DataFrame) – the database

• plot (bool) – if True, plot the violin graph of the shap values

• plot_importance (bool) – if True, plot the importance plot

• percentage (bool) – if True, the importance plot is shown in percent.

Returns:

a dictionary Dict[str,float]

marginal(train, sample_size=200, plot=False, plot_importance=False, percentage=False)

Compute the marginal Shap Values for each variables.

Parameters:

• train (pandas.DataFrame) – the database

• sample_size (int) – The computation of marginal ShapValue is very slow. The parameter allow to compute only on a fragment of the database.

• plot (bool) – if True, plot the violin graph of the shap values

• plot_importance (bool) – if True, plot the importance plot

• percentage (bool) – if True, the importance plot is shown in percent.

Returns:

a dictionary Dict[str,float]

showShapValues(results, cmap='plasma')

Parameters:

• results (dict[str,float]) – The (Shap) values associates to each variable

• cmap (Matplotlib.ColorMap) – The colormap used for colouring the nodes

Returns:

a pydot.graph

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 \(X\).

pyAgrum.lib.explain.generalizedMarkovBlanket(bn, var, k=1, cmapNode=None)

Build a pydot.Dot representation of the nested Markov Blankets (of order k) of node x

Warning

It is assumed that k<=8. If not, every thing is fine except that the colorscale will change in order to accept more colors.

Parameters:

• bn (pyAgrum.DirectedGraphicalModel) – i.e. a class with methods parents, children, variable(i), idFromName(name)

• var (str|int) – the name or nodeId of the node for the Markov blanket

• k (int) – the order of the Markov blanket. If k=1, build the MarkovBlanket(MarkovBlanket())

• cmap (maplotlib.ColorMap) – the colormap used (if not, inferno is used)

Returns:

pydotplus.Dot object