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, 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:

df : The input data for which to compute the Shapley values.

y : The target class for which to compute the Shapley values (default is 1).

sample_size : The number of samples to use for the background data (default is 200).

plot : If True, plots the waterfall or beeswarm plot depending on the number of rows in df (default is False).

plot_importance : If True, plots the bar chart of feature importance (default is False).

percentage: bool : if True, the importance plot is shown in percent.

filename : 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.

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

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

Computes the conditional Shapley values for each variable.

Parameters:

df : The input data for which to compute the Shapley values.

y : The target class for which to compute the Shapley values (default is 1).

plot : If True, plots the waterfall or beeswarm plot depending on the number of rows in df (default is False).

plot_importance : If True, plots the bar chart of feature importance (default is False).

percentage: bool : if True, the importance plot is shown in percent.

filename : 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.

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

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:

df : The input data for which to compute the Shapley values.

y : The target class for which to compute the Shapley values (default is 1).

sample_size : The number of samples to use for the background data (default is 200).

plot : If True, plots the waterfall or beeswarm plot depending on the number of rows in df (default is False).

plot_importance : 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.

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

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 $X$.