causalnex.plots.plot_structure¶

causalnex.plots.plot_structure(g, ax=None, title=None, show_labels=True, node_color='r', edge_color='k', label_color='k', node_positions=None)[source]¶

Plot the structure model to visualise the relationships between nodes.

Parameters:	g (`StructureModel`) – the structure model to plot. ax (`Optional`[`Axes`]) – if provided then figure will be drawn to this Axes, otherwise a new Axes will be created. title (`Optional`[`str`]) – if provided then the title will be drawn on the plot. show_labels (`bool`) – if True then node labels will be drawn. node_color (`str`) – a single color format string, for example ‘r’ or ‘#ff0000’. default “r”. edge_color (`str`) – a single color format string, for example ‘r’ or ‘#ff0000’. default “k”. label_color (`str`) – a single color format string, for example ‘r’ or ‘#ff0000’. default “k”. node_positions (`Optional`[`Dict`[`str`, `List`[`float`]]]) – coordinates for node positions, ie {“node_a”: [0, 0]}.
Return type:	`Tuple`[`Figure`, `Axes`, `Dict`[`str`, `List`[`float`]]]
Returns:	fig, ax, node_positions.

Example:

# Create a Bayesian Network with a manually defined DAG.
from causalnex.structure import StructureModel
from causalnex.network import BayesianNetwork

sm = StructureModel()
sm.add_edges_from([
                   ('rush_hour', 'traffic'),
                   ('weather', 'traffic')
                   ])
from causalnex.plots import plot_structure
plot_structure(sm)