graph_builder.py

import networkx as nx
from typing import List, Dict, Callable


def build_graph(edge_records, node_info):
    G = nx.DiGraph()
    for nid, meta in node_info.items():
        G.add_node(nid, **meta)
    for rec in edge_records:
        src = rec['source']
        dst = rec['target']
        attrs = {k:v for k,v in rec.items() if k not in ('source','target')}
        G.add_edge(src, dst, **attrs)
    return G


def make_cost_fn(selected_cost_columns: List[str]):
    if not selected_cost_columns:
        # fallback default cost columns if present
        def cost(edge_data):
            return float(edge_data.get('TransitionCost', 0)) + float(edge_data.get('WaitingTime', 0))
        return cost
    def cost(edge_data):
        total = 0.0
        for c in selected_cost_columns:
            total += float(edge_data.get(c, 0) or 0)
        return total
    return cost


def make_heuristic_fn(G, selected_column: str):
    if not selected_column:
        return lambda n: 0
    # Node-based heuristic: use average of outgoing edge attribute if not present as node attribute
    def h(n):
        node_attr = G.nodes[n]
        if selected_column in node_attr and isinstance(node_attr[selected_column], (int, float)):
            return float(node_attr[selected_column])
        # fallback: average of edges
        edges = list(G.out_edges(n, data=True))
        if not edges:
            return 0.0
        vals = [e[2].get(selected_column, 0) for e in edges]
        nums = [float(v) for v in vals if isinstance(v, (int, float)) or str(v).replace('.','',1).isdigit()]
        return sum(nums)/len(nums) if nums else 0.0
    return h