aaronAI/scripts/experiments/e14_per_source_predicates.py

"""
E1.4 per-source predicate diversity comparison — fixed version.
Looks up episode uuids by name in both production and cascade graphs.
"""
import json
from collections import defaultdict
from falkordb import FalkorDB

E14_RESULTS = "/home/aaron/aaronai/experiments/e14_cascade_results.json"
PRODUCTION_GROUP = "aaron"
CASCADE_GROUP = "aaron_cascade_e14"

def get_predicates_for_episode(graph, episode_uuid):
    query = """
    MATCH ()-[r:RELATES_TO]->()
    WHERE $uuid IN r.episodes
    RETURN count(DISTINCT r.name) AS predicate_count
    """
    result = graph.query(query, {"uuid": episode_uuid})
    rows = result.result_set
    return rows[0][0] if rows else 0

def get_edge_count_for_episode(graph, episode_uuid):
    query = """
    MATCH ()-[r:RELATES_TO]->()
    WHERE $uuid IN r.episodes
    RETURN count(r) AS edge_count
    """
    result = graph.query(query, {"uuid": episode_uuid})
    rows = result.result_set
    return rows[0][0] if rows else 0

def find_episode_uuid(graph, source_name):
    query = """
    MATCH (e:Episodic {name: $name})
    RETURN e.uuid AS uuid
    LIMIT 1
    """
    result = graph.query(query, {"name": source_name})
    rows = result.result_set
    return rows[0][0] if rows else None

def main():
    db = FalkorDB(host='localhost', port=6379)
    prod_graph = db.select_graph(PRODUCTION_GROUP)
    cascade_graph = db.select_graph(CASCADE_GROUP)

    with open(E14_RESULTS) as f:
        e14 = json.load(f)

    sources = [r for r in e14['results'] if 'submit_result' in r]
    print(f"Analyzing {len(sources)} sources...")
    print()

    comparisons = []
    missing_prod = 0
    missing_cascade = 0
    for src in sources:
        name = src['name']
        bucket = src['bucket']

        prod_uuid = find_episode_uuid(prod_graph, name)
        cascade_uuid = find_episode_uuid(cascade_graph, name)

        if not prod_uuid:
            missing_prod += 1
            print(f"  WARN: missing in production: {name}")
            continue
        if not cascade_uuid:
            missing_cascade += 1
            print(f"  WARN: missing in cascade:    {name}")
            continue

        prod_preds = get_predicates_for_episode(prod_graph, prod_uuid)
        cascade_preds = get_predicates_for_episode(cascade_graph, cascade_uuid)
        prod_edges = get_edge_count_for_episode(prod_graph, prod_uuid)
        cascade_edges = get_edge_count_for_episode(cascade_graph, cascade_uuid)

        comparisons.append({
            "name": name,
            "bucket": bucket,
            "prod_preds": prod_preds,
            "cascade_preds": cascade_preds,
            "delta_preds": cascade_preds - prod_preds,
            "prod_edges": prod_edges,
            "cascade_edges": cascade_edges,
            "delta_edges": cascade_edges - prod_edges,
        })

    if missing_prod or missing_cascade:
        print()
        print(f"Missing: {missing_prod} prod, {missing_cascade} cascade")
        print()

    if not comparisons:
        print("No comparable sources found. Aborting.")
        return

    # Per-source detail
    print(f"{'Bucket':<10} {'Source':<58} {'Preds A→B':<14} {'Δ':<6} {'Edges A→B':<14} {'Δ'}")
    print("-" * 115)
    for c in sorted(comparisons, key=lambda x: (x['bucket'], x['name'])):
        name_short = (c['name'][:55] + '..') if len(c['name']) > 58 else c['name']
        preds_str = f"{c['prod_preds']}→{c['cascade_preds']}"
        edges_str = f"{c['prod_edges']}→{c['cascade_edges']}"
        print(f"{c['bucket']:<10} {name_short:<58} {preds_str:<14} {c['delta_preds']:+d}    {edges_str:<14} {c['delta_edges']:+d}")

    # Per-bucket aggregation
    print()
    print("=" * 115)
    print("PER-BUCKET AGGREGATION")
    print("=" * 115)
    by_bucket = defaultdict(list)
    for c in comparisons:
        by_bucket[c['bucket']].append(c)

    for bucket in ['high', 'mid', 'low', 'document']:
        items = by_bucket.get(bucket, [])
        if not items:
            continue
        n = len(items)
        sum_pp = sum(c['prod_preds'] for c in items)
        sum_cp = sum(c['cascade_preds'] for c in items)
        sum_pe = sum(c['prod_edges'] for c in items)
        sum_ce = sum(c['cascade_edges'] for c in items)
        positive = sum(1 for c in items if c['delta_preds'] > 0)
        negative = sum(1 for c in items if c['delta_preds'] < 0)
        flat = sum(1 for c in items if c['delta_preds'] == 0)
        pct_pred = ((sum_cp - sum_pp) / sum_pp * 100) if sum_pp else 0
        pct_edge = ((sum_ce - sum_pe) / sum_pe * 100) if sum_pe else 0
        print(f"\n{bucket.upper()} (n={n}):")
        print(f"  Predicates:  {sum_pp} → {sum_cp}  ({pct_pred:+.1f}%)")
        print(f"  Edges:       {sum_pe} → {sum_ce}  ({pct_edge:+.1f}%)")
        print(f"  Outcomes:    {positive} positive, {flat} flat, {negative} negative")

    # Aggregate
    print()
    print("=" * 115)
    print(f"AGGREGATE (n={len(comparisons)})")
    print("=" * 115)
    total_pp = sum(c['prod_preds'] for c in comparisons)
    total_cp = sum(c['cascade_preds'] for c in comparisons)
    total_pe = sum(c['prod_edges'] for c in comparisons)
    total_ce = sum(c['cascade_edges'] for c in comparisons)
    print(f"  Predicates:  {total_pp} → {total_cp}  ({(total_cp-total_pp)/total_pp*100:+.1f}%)")
    print(f"  Edges:       {total_pe} → {total_ce}  ({(total_ce-total_pe)/total_pe*100:+.1f}%)")

    out_path = "/home/aaron/aaronai/experiments/e14_per_source_comparison.json"
    with open(out_path, "w") as f:
        json.dump(comparisons, f, indent=2)
    print()
    print(f"Saved to {out_path}")

if __name__ == "__main__":
    main()