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| from collections import defaultdict, deque | |
| import pandas as pd | |
| def build_graph(rel_df): | |
| if not isinstance(rel_df, pd.DataFrame): | |
| rel_df = pd.DataFrame(rel_df) | |
| graph = {} | |
| for _, row in rel_df.iterrows(): | |
| parent = row["parent"] | |
| child = row["child"] | |
| graph.setdefault(parent, []).append(child) | |
| return graph | |
| def bfs_all_paths(graph, roots=["program"]): | |
| queue = deque([(root, [root]) for root in roots]) | |
| paths = [] | |
| seen_paths = set() | |
| while queue: | |
| node, path = queue.popleft() | |
| key = tuple(path) | |
| if key not in seen_paths: | |
| seen_paths.add(key) | |
| paths.append(path) | |
| for child in graph.get(node, []): | |
| if child not in path: | |
| queue.append((child, path + [child])) | |
| return paths | |
| def extract_clusters(paths, min_size=2, max_size=4): | |
| clusters = [] | |
| for path in paths: | |
| for i in range(len(path)): | |
| for j in range(i + min_size, min(i + max_size + 1, len(path) + 1)): | |
| cluster = path[i:j] | |
| clusters.append(cluster) | |
| return clusters | |
| def deduplicate(clusters): | |
| seen = set() | |
| result = [] | |
| for c in clusters: | |
| key = tuple(c) | |
| if key not in seen: | |
| seen.add(key) | |
| result.append(c) | |
| return result | |
| def prioritize_clusters(clusters): | |
| priority_nodes = {"study", "case"} | |
| return sorted( | |
| clusters, | |
| key=lambda c: sum(1 for n in c if n in priority_nodes), | |
| reverse=True | |
| ) | |
| def get_cluster_relations(cluster, rel_df): | |
| nodes = set(cluster) | |
| return rel_df[ | |
| (rel_df["parent"].isin(nodes)) & | |
| (rel_df["child"].isin(nodes)) | |
| ] | |
| def clusters_to_df(clusters): | |
| rows = [] | |
| for i, cluster in enumerate(clusters): | |
| rows.append({ | |
| "cluster_id": i, | |
| "nodes": " -> ".join(cluster), | |
| "size": len(cluster) | |
| }) | |
| return pd.DataFrame(rows) | |
| def clusters_to_long_df(clusters): | |
| rows = [] | |
| for i, cluster in enumerate(clusters): | |
| for step, node in enumerate(cluster): | |
| rows.append({ | |
| "cluster_id": i, | |
| "step": step, | |
| "node": node, | |
| "cluster_size": len(cluster) | |
| }) | |
| return pd.DataFrame(rows) | |
| def cluster_with_relations_df(clusters, rel_df): | |
| rows = [] | |
| for i, cluster in enumerate(clusters): | |
| nodes = set(cluster) | |
| sub_rel = rel_df[ | |
| (rel_df["parent"].isin(nodes)) & | |
| (rel_df["child"].isin(nodes)) | |
| ] | |
| for _, r in sub_rel.iterrows(): | |
| rows.append({ | |
| "cluster_id": i, | |
| "cluster": " -> ".join(cluster), | |
| "parent": r["parent"], | |
| "child": r["child"], | |
| "type": r["type"], | |
| "parent_prop": r["parent_prop"], | |
| "child_prop": r["child_prop"], | |
| }) | |
| return pd.DataFrame(rows) |