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"""Run side-by-side comparison of FAISS (baseline) vs Hopfield (HAG) retrieval.
Usage:
CUDA_VISIBLE_DEVICES=1 python scripts/run_comparison.py \
--config configs/hotpotqa.yaml \
--memory-bank data/processed/hotpotqa_memory_bank.pt \
--questions data/processed/hotpotqa_questions.jsonl \
--device cuda \
--max-samples 500
"""
import argparse
import json
import logging
import time
import numpy as np
import torch
import yaml
from hag.config import (
EncoderConfig,
GeneratorConfig,
HopfieldConfig,
MemoryBankConfig,
PipelineConfig,
)
from hag.encoder import Encoder
from hag.generator import Generator
from hag.hopfield import HopfieldRetrieval
from hag.memory_bank import MemoryBank
from hag.metrics import evaluate_dataset, exact_match, f1_score
from hag.pipeline import RAGPipeline
from hag.retriever_faiss import FAISSRetriever
from hag.retriever_hopfield import HopfieldRetriever
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
logger = logging.getLogger(__name__)
def main() -> None:
parser = argparse.ArgumentParser(description="Compare FAISS vs Hopfield retrieval")
parser.add_argument("--config", type=str, default="configs/hotpotqa.yaml")
parser.add_argument("--memory-bank", type=str, required=True)
parser.add_argument("--questions", type=str, required=True)
parser.add_argument("--device", type=str, default="cpu")
parser.add_argument("--max-samples", type=int, default=None)
parser.add_argument("--output", type=str, default="data/processed/comparison_results.json")
args = parser.parse_args()
with open(args.config) as f:
cfg = yaml.safe_load(f)
hopfield_config = HopfieldConfig(**cfg.get("hopfield", {}))
memory_config = MemoryBankConfig(**cfg.get("memory", {}))
encoder_config = EncoderConfig(**cfg.get("encoder", {}))
generator_config = GeneratorConfig(**cfg.get("generator", {}))
# Load questions
with open(args.questions) as f:
questions_data = [json.loads(line) for line in f]
if args.max_samples and len(questions_data) > args.max_samples:
questions_data = questions_data[: args.max_samples]
questions = [q["question"] for q in questions_data]
gold_answers = [q["answer"] for q in questions_data]
logger.info("Loaded %d questions", len(questions))
# Load memory bank
mb = MemoryBank(memory_config)
mb.load(args.memory_bank, device=args.device)
logger.info("Memory bank: %d passages, dim=%d", mb.size, mb.dim)
# Shared encoder and generator
encoder = Encoder(encoder_config, device=args.device)
generator = Generator(generator_config, device=args.device)
# --- Build FAISS retriever ---
embeddings_np = mb.embeddings.T.cpu().numpy().astype(np.float32) # (N, d)
faiss_retriever = FAISSRetriever(top_k=hopfield_config.top_k)
faiss_retriever.build_index(embeddings_np, mb.passages)
# --- Build Hopfield retriever ---
hopfield = HopfieldRetrieval(hopfield_config)
hopfield_retriever = HopfieldRetriever(hopfield, mb, top_k=hopfield_config.top_k)
# --- Build pipelines ---
faiss_pipeline_cfg = PipelineConfig(
hopfield=hopfield_config,
memory=memory_config,
encoder=encoder_config,
generator=generator_config,
retriever_type="faiss",
device=args.device,
)
faiss_pipeline = RAGPipeline(
config=faiss_pipeline_cfg,
encoder=encoder,
generator=generator,
faiss_retriever=faiss_retriever,
)
hopfield_pipeline_cfg = PipelineConfig(
hopfield=hopfield_config,
memory=memory_config,
encoder=encoder_config,
generator=generator_config,
retriever_type="hopfield",
device=args.device,
)
hopfield_pipeline = RAGPipeline(
config=hopfield_pipeline_cfg,
encoder=encoder,
generator=generator,
memory_bank=mb,
)
# --- Run FAISS baseline ---
logger.info("=" * 60)
logger.info("Running FAISS baseline (%d questions)...", len(questions))
t0 = time.time()
faiss_results = faiss_pipeline.run_batch(questions)
faiss_time = time.time() - t0
faiss_metrics = evaluate_dataset(faiss_results, gold_answers)
logger.info("FAISS done in %.1fs | EM=%.4f | F1=%.4f", faiss_time, faiss_metrics["em"], faiss_metrics["f1"])
# --- Run HAG ---
logger.info("=" * 60)
logger.info("Running HAG (beta=%.1f, max_iter=%d, top_k=%d) (%d questions)...",
hopfield_config.beta, hopfield_config.max_iter, hopfield_config.top_k, len(questions))
t0 = time.time()
hag_results = hopfield_pipeline.run_batch(questions)
hag_time = time.time() - t0
hag_metrics = evaluate_dataset(hag_results, gold_answers)
logger.info("HAG done in %.1fs | EM=%.4f | F1=%.4f", hag_time, hag_metrics["em"], hag_metrics["f1"])
# --- Summary ---
logger.info("=" * 60)
logger.info("COMPARISON SUMMARY")
logger.info("%-20s %10s %10s", "", "FAISS", "HAG")
logger.info("%-20s %10.4f %10.4f", "Exact Match", faiss_metrics["em"], hag_metrics["em"])
logger.info("%-20s %10.4f %10.4f", "F1 Score", faiss_metrics["f1"], hag_metrics["f1"])
logger.info("%-20s %10.1fs %10.1fs", "Time", faiss_time, hag_time)
em_delta = hag_metrics["em"] - faiss_metrics["em"]
f1_delta = hag_metrics["f1"] - faiss_metrics["f1"]
logger.info("%-20s %+10.4f %+10.4f", "Delta (HAG - FAISS)", em_delta, f1_delta)
# --- Per-question details ---
per_question = []
for i, (fq, hq, gold) in enumerate(zip(faiss_results, hag_results, gold_answers)):
per_question.append({
"id": questions_data[i].get("id", i),
"question": questions[i],
"gold_answer": gold,
"faiss_answer": fq.answer,
"hag_answer": hq.answer,
"faiss_em": exact_match(fq.answer, gold),
"hag_em": exact_match(hq.answer, gold),
"faiss_f1": f1_score(fq.answer, gold),
"hag_f1": f1_score(hq.answer, gold),
"faiss_passages": fq.retrieved_passages,
"hag_passages": hq.retrieved_passages,
})
output = {
"config": {
"hopfield_beta": hopfield_config.beta,
"hopfield_max_iter": hopfield_config.max_iter,
"top_k": hopfield_config.top_k,
"encoder": encoder_config.model_name,
"generator": generator_config.model_name,
"num_questions": len(questions),
"num_passages": mb.size,
},
"faiss_metrics": {**faiss_metrics, "time_seconds": faiss_time},
"hag_metrics": {**hag_metrics, "time_seconds": hag_time},
"per_question": per_question,
}
with open(args.output, "w") as f:
json.dump(output, f, indent=2, ensure_ascii=False)
logger.info("Full results saved to %s", args.output)
if __name__ == "__main__":
main()
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