import networkx as nx import matplotlib.pyplot as plt import pickle # ---------- Load GraphML ---------- graph_path = "sample_999.graphml" # Replace with your file path G = nx.read_graphml(graph_path) # ---------- Load DCS Pickle ---------- class DCS: def __init__(self, sent_id, sentence): self.sent_id = sent_id self.sentence = sentence self.dcs_chunks = [] self.lemmas = [] self.cng = [] pkl_path = "DCS_999.p" # Replace with your matching pickle file with open(pkl_path, "rb") as f: gold = pickle.load(f, encoding='utf-8') gold_words = set(gold.dcs_chunks) # ---------- Prepare Node Labels and Colors ---------- node_labels = {} node_colors = [] for node_id, data in G.nodes(data=True): word = data.get("word", "") node_labels[node_id] = word if word in gold_words: node_colors.append("green") # Gold-standard segment else: node_colors.append("red") # Candidate, not in gold # ---------- Prepare Edge Labels ---------- edge_labels = {} for u, v, data in G.edges(data=True): etype = data.get("type", "") edge_labels[(u, v)] = f"type={etype}" # ---------- Draw the Graph ---------- plt.figure(figsize=(14, 10)) pos = nx.spring_layout(G, seed=42) nx.draw_networkx_nodes(G, pos, node_color=node_colors, node_size=800, alpha=0.9) nx.draw_networkx_edges(G, pos, arrows=True) nx.draw_networkx_labels(G, pos, labels=node_labels, font_size=10) nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_color='gray', font_size=8) plt.title(f"Segmentation Graph for Sentence:\n{gold.sentence}", fontsize=14) plt.axis('off') plt.tight_layout() output_image_path = "graph_output.png" plt.savefig(output_image_path, format="png", dpi=300) print(f"Graph saved to {output_image_path}")