Bayesys Format I/O¶

The causaliq_core.graph.io.bayesys module provides functions for reading and writing graphs in the Bayesys CSV format.

Format Description¶

The Bayesys format uses CSV files to represent graphs where: - Each row represents an edge in the graph - Columns specify source node, edge type, and target node - Edge types include directed (->) and undirected (--) edges

Functions¶

`read(path: str, all_nodes: Optional[List[str]] = None, strict: bool = True) -> Union[PDAG, DAG]`¶

Read a graph from a Bayesys CSV format file.

Parameters: - path (str): Full path name of file to read - all_nodes (List[str], optional): Complete list of nodes that should be included in the graph. If provided, nodes not appearing in edges will be added as isolated nodes - strict (bool): If True, enforces strict format validation

Returns: - PDAG or DAG: Graph read from file. Returns DAG if all edges are directed, PDAG otherwise

Raises: - TypeError: If path is not a string - FileNotFoundError: If file is not found - FileFormatError: If file format is invalid

Example:

from causaliq_core.graph.io.bayesys import read

# Read graph with automatic node detection
graph = read("data/graph.csv")

# Read graph with explicit node list
all_nodes = ['A', 'B', 'C', 'D'] 
graph = read("data/graph.csv", all_nodes=all_nodes)

`write(graph: Union[PDAG, DAG], path: str) -> None`¶

Write a graph to a Bayesys CSV format file.

Parameters: - graph (PDAG or DAG): Graph to write - path (str): Full path name of file to create

Raises: - TypeError: If graph is not a PDAG or DAG instance, or path is not a string - OSError: If file cannot be created

Example:

from causaliq_core.graph import PDAG
from causaliq_core.graph.io.bayesys import write

# Create and write a PDAG
pdag = PDAG(['X', 'Y', 'Z'], [('X', '->', 'Y'), ('Y', '--', 'Z')])
write(pdag, "output/graph.csv")

File Format Details¶

The CSV file contains rows with three columns: 1. Source node name 2. Edge type (-> for directed, -- for undirected)
3. Target node name

Example CSV content:

A,->,B
B,--,C
A,--,C

This represents a PDAG with nodes A, B, C where: - A → B (directed edge) - B — C (undirected edge) - A — C (undirected edge)