Add create clusters init vers

graphviz/processing/create_clusters.py

@@ -10,10 +10,10 @@ from sklearn.metrics.pairwise import cosine_similarity
 from tqdm import tqdm
 
 
-INPUT_CSV = "../../data/dataset-dev.csv"
+INPUT_CSV = "../../data/dataset.csv"
 OUTPUT_JSON = "../../data/clustered_output.json"
 MODEL_NAME = "all-MiniLM-L6-v2"
-SIMILARITY_THRESHOLD = 0.65
+SIMILARITY_THRESHOLD = 0.8
 
 def generate_guid():
     return str(uuid.uuid4())

graphviz/processing/process_clusters.py

@@ -0,0 +1,119 @@
+import json
+from collections import defaultdict, deque
+
+# -------------------------------
+# CONFIG
+# -------------------------------
+INPUT_FILE = "../../data/clustered_output.json"        # Your original JSON
+OUTPUT_FILE = "../../data/clustered_output2.json"  # Output JSON file
+
+# -------------------------------
+# Load data
+# -------------------------------
+with open(INPUT_FILE, "r") as f:
+    data = json.load(f)
+
+# -------------------------------
+# Prepare cluster sets
+# -------------------------------
+claim_clusters = {c["cluster_id"] for c in data["claim_clusters"]}
+event_clusters = {e["cluster_id"] for e in data["event_clusters"]}
+all_clusters = claim_clusters.union(event_clusters)
+
+# -------------------------------
+# Build graph from cluster links
+# -------------------------------
+graph = defaultdict(set)
+for link in data.get("cluster_links", []):
+    c_id = link["claim_cluster_id"]
+    e_id = link["event_cluster_id"]
+    graph[c_id].add(e_id)
+    graph[e_id].add(c_id)
+
+# Make sure all clusters appear in graph (even isolated ones)
+for cid in all_clusters:
+    graph[cid] = graph[cid]
+
+# -------------------------------
+# Find connected components
+# -------------------------------
+visited = set()
+components = []
+
+for node in graph:
+    if node not in visited:
+        queue = deque([node])
+        component = set()
+        while queue:
+            current = queue.popleft()
+            if current in visited:
+                continue
+            visited.add(current)
+            component.add(current)
+            for neighbor in graph[current]:
+                if neighbor not in visited:
+                    queue.append(neighbor)
+        components.append(component)
+
+# Filter components with size > 8
+large_components = [c for c in components if len(c) > 8 and len(c) < 50]
+
+# -------------------------------
+# Output stats
+# -------------------------------
+num_components = len(large_components)
+num_nodes = sum(len(c) for c in large_components)
+
+print("Connected components (size > 8):", num_components)
+print("Total clusters in those components:", num_nodes)
+
+# -------------------------------
+# Prepare lookup tables
+# -------------------------------
+claim_lookup = {c["id"]: c["text"] for c in data["claims"]}
+event_lookup = {e["id"]: e["text"] for e in data["events"]}
+
+claim_cluster_map = {c["cluster_id"]: c["members"] for c in data["claim_clusters"]}
+event_cluster_map = {e["cluster_id"]: e["members"] for e in data["event_clusters"]}
+
+def extract_texts(component):
+    texts = []
+    for cid in component:
+        if cid in claim_cluster_map:
+            texts.extend([claim_lookup[mid] for mid in claim_cluster_map[cid] if mid in claim_lookup])
+        elif cid in event_cluster_map:
+            texts.extend([event_lookup[mid] for mid in event_cluster_map[cid] if mid in event_lookup])
+    return texts
+
+# -------------------------------
+# Optional: Generate titles
+# -------------------------------
+user_input = input("Generate titles for each component? (y/n): ")
+
+if user_input.lower() == "y":
+    output = []
+
+    for i, comp in enumerate(large_components):
+        texts = extract_texts(comp)
+
+        # Show a few sample texts
+        print(f"\nComponent {i} sample texts:")
+        for t in texts[:5]:
+            print("-", t)
+
+        # Ask user for a 3-5 word title (could be automated with OpenAI API)
+        title = input("Enter 3-5 word title: ")
+
+        output.append({
+            "component_id": i,
+            "cluster_ids": list(comp),
+            "title": title
+        })
+
+    # Save JSON
+    with open(OUTPUT_FILE, "w") as f:
+        json.dump(output, f, indent=2)
+
+    print(f"Saved cluster titles to {OUTPUT_FILE}")
+else:
+    print("No titles generated. Script finished.")