boofun.visualization.decision_tree_export

Decision Tree Export Utilities.

This module provides utilities for exporting Boolean function decision trees to various formats including: - Text/ASCII representation - Graphviz DOT format - JSON structure - LaTeX/TikZ code

Functions

`build_decision_tree`(f[, max_depth])	Build a decision tree for a Boolean function.
`export_decision_tree_dot`(f[, var_names, ...])	Export decision tree as Graphviz DOT format.
`export_decision_tree_json`(f[, var_names, ...])	Export decision tree as JSON structure.
`export_decision_tree_text`(f[, var_names, ...])	Export decision tree as ASCII text.
`export_decision_tree_tikz`(f[, var_names, ...])	Export decision tree as LaTeX TikZ code.

Classes

`DecisionTreeExporter`(f[, var_names, max_depth])	Class for exporting decision trees in multiple formats.
`DecisionTreeNode`([variable, value, low, ...])	Represents a node in a decision tree.

boofun.visualization.decision_tree_export.export_decision_tree_text(f: BooleanFunction, var_names: List[str] | None = None, max_depth: int | None = None) → str[source]

Export decision tree as ASCII text.

Parameters:

f – Boolean function
var_names – Variable names (default: x_0, x_1, …)
max_depth – Maximum depth

Returns:

ASCII representation

boofun.visualization.decision_tree_export.export_decision_tree_dot(f: BooleanFunction, var_names: List[str] | None = None, max_depth: int | None = None, graph_name: str = 'DecisionTree') → str[source]

Export decision tree as Graphviz DOT format.

Parameters:

f – Boolean function
var_names – Variable names
max_depth – Maximum depth
graph_name – Name for the graph

Returns:

DOT format string

boofun.visualization.decision_tree_export.export_decision_tree_json(f: BooleanFunction, var_names: List[str] | None = None, max_depth: int | None = None) → Dict[str, Any][source]

Export decision tree as JSON structure.

Parameters:

f – Boolean function
var_names – Variable names
max_depth – Maximum depth

Returns:

JSON-serializable dictionary

boofun.visualization.decision_tree_export.export_decision_tree_tikz(f: BooleanFunction, var_names: List[str] | None = None, max_depth: int | None = None) → str[source]

Export decision tree as LaTeX TikZ code.

Parameters:

f – Boolean function
var_names – Variable names
max_depth – Maximum depth

Returns:

TikZ code string

class boofun.visualization.decision_tree_export.DecisionTreeExporter(f: BooleanFunction, var_names: List[str] | None = None, max_depth: int | None = None)[source]

Class for exporting decision trees in multiple formats.

Provides a unified interface for exporting Boolean function decision trees to various formats.

__init__(f: BooleanFunction, var_names: List[str] | None = None, max_depth: int | None = None)[source]

Initialize exporter.

Parameters:

f – Boolean function
var_names – Variable names
max_depth – Maximum depth for tree

property tree: DecisionTreeNode: Get the decision tree (built lazily).

to_text() → str[source]: Export to ASCII text.

to_dot(graph_name: str = 'DecisionTree') → str[source]: Export to Graphviz DOT format.

to_json() → Dict[str, Any][source]: Export to JSON structure.

to_tikz() → str[source]: Export to LaTeX TikZ.

save_dot(filename: str) → None[source]: Save DOT to file.

depth() → int[source]: Get tree depth.

size() → int[source]: Get number of nodes.

summary() → str[source]: Get tree summary.