jupyter/eval_scrips.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fa0e97acd18cef7f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-20T09:48:10.602633Z",
     "start_time": "2024-10-20T09:48:10.587721Z"
    }
   },
   "outputs": [],
   "source": [
    "import string\n",
    "import re\n",
    "import json\n",
    "import sys\n",
    "sys.path.append(\"../\")\n",
    "from evaluate_utils import calculate_short_answer_EM, rouge, bleu, select_candidate\n",
    "\n",
    "version=\"v0915\"\n",
    "multi_docs=\"top10\"\n",
    "src_granularity=256\n",
    "granularity=128\n",
    "\n",
    "def eval_short_answer_EM(dataset, chat_model, reference_format, split, search_engine, rewrite_method):\n",
    "    output_dir = f\"../html_data/{dataset}/{chat_model}/{search_engine}\"\n",
    "\n",
    "    if dataset in [\"asqa\", \"nq\", \"eli5\"]:\n",
    "        #. fine trim ratio 2/3\n",
    "        coarse_context_window = {\"2k\": \"3k\", \"4k\": \"6k\", \"8k\": \"12k\", \"16k\": \"24k\"}[context_window]\n",
    "    else:\n",
    "        #. fine trim ratio 1/2\n",
    "        coarse_context_window = {\"2k\": \"4k\", \"4k\": \"8k\", \"8k\": \"16k\", \"16k\": \"32k\"}[context_window]\n",
    "    if reference_format in [\"html-trim\", \"fill-chunk\"]:\n",
    "        output_file = f\"{output_dir}/{chat_model}-{reference_format}-{rewrite_method}-{rerank_model}-{dataset}-{split}.jsonl\"\n",
    "    elif reference_format == \"tree-gen\":\n",
    "        output_file = f\"{output_dir}/{chat_model}-{reference_format}-{rewrite_method}-{version}-{granularity}-{dataset}-{split}.jsonl\"\n",
    "    elif reference_format == \"tree-rerank\":\n",
    "        output_file = f\"{output_dir}/{chat_model}-{reference_format}-{rewrite_method}-{rerank_model}-{granularity}-{dataset}-{split}.jsonl\"\n",
    "    elif reference_format in [\"chunk-rerank-tree-gen\", \"tree-rerank-tree-gen\"]:\n",
    "        output_file = f\"{output_dir}/{chat_model}-{reference_format}-{rewrite_method}-{rerank_model}-{src_granularity}to{granularity}-{coarse_context_window}-{version}-{dataset}-{split}.jsonl\"\n",
    "    elif reference_format in [\"llmlingua\", \"bgelargeen\",\"e5-mistral\"]:\n",
    "        output_file = f\"{output_dir}/{chat_model}-{reference_format}-{rewrite_method}-{dataset}-{split}.jsonl\"\n",
    "    else:\n",
    "        output_file = f\"{output_dir}/{chat_model}-{reference_format}-{rewrite_method}-{dataset}-{split}.jsonl\"\n",
    "    print(f\"evaluating file {output_file}\")\n",
    "    \n",
    "    try:\n",
    "        data_lines = [json.loads(l) for l in open(output_file)]\n",
    "        generated_answers = [data_line[f\"{chat_model}_{reference_format}\" ] for data_line in data_lines]\n",
    "        if dataset == \"eli5\":\n",
    "            #. eval long answer\n",
    "            if \"answer\" in data_lines[0]:\n",
    "                gold_answers=[data_line[\"answer\"] for data_line in data_lines]\n",
    "            else:\n",
    "                gold_answers=[data_line[\"long_answers\"] for data_line in data_lines]\n",
    "                \n",
    "            selected_gold_answers=[]\n",
    "            for gen, gold in tqdm.tqdm(zip(generated_answers, gold_answers), total=len(generated_answers)):\n",
    "                selected_gold_answers.append(select_candidate(gen, gold))\n",
    "            rouge_result=rouge.compute(predictions=generated_answers, references=selected_gold_answers)\n",
    "            rouge_result={k: round(v * 100, 2) for k, v in rouge_result.items()}\n",
    "            \n",
    "            bleu_result=bleu.compute(predictions=generated_answers, references=gold_answers)\n",
    "            return {\"rougeL\": rouge_result[\"rougeL\"], \"bleu\": bleu_result[\"bleu\"]*100}\n",
    "        \n",
    "        if \"answers\" in data_lines[0]:\n",
    "            answers = [data_line['answers'] for data_line in data_lines]\n",
    "        elif \"short_answers\" in data_lines[0]:\n",
    "            answers = [data_line['short_answers'] for data_line in data_lines]\n",
    "        elif \"answer\" in data_lines[0]:\n",
    "            answers = [data_line['answer'] for data_line in data_lines]\n",
    "        else:\n",
    "            raise NotImplementedError(\"answers not found in data_lines\")\n",
    "    \n",
    "        \n",
    "        exact_match = [calculate_short_answer_EM(generated_answer, gold_answers) for generated_answer, gold_answers in zip(generated_answers, answers)]\n",
    "        hit1= round(sum([hit1[\"hit1\"] for hit1 in exact_match])/len(exact_match)*100, 2),\n",
    "        exact_match= round(sum([hit1[\"exact_match\"] for hit1 in exact_match])/len(exact_match)*100, 2)\n",
    "        print(f\"chat_model: {chat_model}, reference_format: {reference_format}, dataset: {dataset}, split: {split}, hit1: {hit1}, exact_match: {exact_match}\")\n",
    "        return {\n",
    "            \"hit1\": hit1,\n",
    "            \"exact_match\": exact_match\n",
    "        }\n",
    "    except Exception as e:\n",
    "        print(f\"error evaluating file {output_file}, error: {e}\")\n",
    "        #  print stack trace\n",
    "        import traceback\n",
    "        traceback.print_exc()\n",
    "        if dataset == \"eli5\":\n",
    "            return {\n",
    "                \"rougeL\": .0,\n",
    "                \"bleu\": .0,\n",
    "            }\n",
    "        return {\n",
    "            \"exact_match\": .0,\n",
    "            \"hit1\": .0,\n",
    "        }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-20T10:38:33.278780Z",
     "start_time": "2024-10-20T09:48:13.599246Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/36 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluating file ../html_data/asqa/llama70b4k/bing/llama70b4k-bgelargeen-slimplmqr-asqa-test.jsonlevaluating file ../html_data/asqa/llama70b4k/bing/llama70b4k-e5-mistral-slimplmqr-asqa-test.jsonlevaluating file ../html_data/asqa/llama70b4k/bing/llama70b4k-bm25-slimplmqr-asqa-test.jsonl\n",
      "\n",
      "\n",
      "evaluating file ../html_data/asqa/llama70b4k/bing/llama70b4k-llmlingua-slimplmqr-asqa-test.jsonl\n",
      "chat_model: llama70b4k, reference_format: llmlingua, dataset: asqa, split: test, hit1: (62.5,), exact_match: 27.74\n",
      "chat_model: llama70b4k, reference_format: bm25, dataset: asqa, split: test, hit1: (49.5,), exact_match: 21.95\n",
      "chat_model: llama70b4k, reference_format: bgelargeen, dataset: asqa, split: test, hit1: (68.0,), exact_match: 30.57\n",
      "chat_model: llama70b4k, reference_format: e5-mistral, dataset: asqa, split: test, hit1: (63.0,), exact_match: 28.75\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 14%|█▍        | 5/36 [00:02<00:17,  1.81it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluating file ../html_data/asqa/llama70b4k/bing/llama70b4k-jinaai-reader-slimplmqr-asqa-test.jsonl\n",
      "evaluating file ../html_data/asqa/llama70b4k/bing/llama70b4k-tree-rerank-tree-gen-slimplmqr-bgelargeen-256to128-6k-v0915-asqa-test.jsonl\n",
      "evaluating file ../html_data/hotpot-qa/llama70b4k/bing/llama70b4k-bm25-slimplmqr-hotpot-qa-test.jsonl\n",
      "evaluating file ../html_data/hotpot-qa/llama70b4k/bing/llama70b4k-bgelargeen-slimplmqr-hotpot-qa-test.jsonl\n",
      "chat_model: llama70b4k, reference_format: tree-rerank-tree-gen, dataset: asqa, split: test, hit1: (68.5,), exact_match: 30.53\n",
      "chat_model: llama70b4k, reference_format: jinaai-reader, dataset: asqa, split: test, hit1: (55.25,), exact_match: 23.73\n",
      "chat_model: llama70b4k, reference_format: bm25, dataset: hotpot-qa, split: test, hit1: (38.25,), exact_match: 38.25\n",
      "chat_model: llama70b4k, reference_format: bgelargeen, dataset: hotpot-qa, split: test, hit1: (41.75,), exact_match: 41.75\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 25%|██▌       | 9/36 [00:05<00:17,  1.53it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluating file ../html_data/hotpot-qa/llama70b4k/bing/llama70b4k-e5-mistral-slimplmqr-hotpot-qa-test.jsonl\n",
      "evaluating file ../html_data/hotpot-qa/llama70b4k/bing/llama70b4k-llmlingua-slimplmqr-hotpot-qa-test.jsonl\n",
      "evaluating file ../html_data/hotpot-qa/llama70b4k/bing/llama70b4k-jinaai-reader-slimplmqr-hotpot-qa-test.jsonl\n",
      "evaluating file ../html_data/hotpot-qa/llama70b4k/bing/llama70b4k-tree-rerank-tree-gen-slimplmqr-bgelargeen-256to128-8k-v0915-hotpot-qa-test.jsonl\n",
      "chat_model: llama70b4k, reference_format: tree-rerank-tree-gen, dataset: hotpot-qa, split: test, hit1: (45.25,), exact_match: 45.25\n",
      "chat_model: llama70b4k, reference_format: jinaai-reader, dataset: hotpot-qa, split: test, hit1: (34.25,), exact_match: 34.25\n",
      "chat_model: llama70b4k, reference_format: llmlingua, dataset: hotpot-qa, split: test, hit1: (45.0,), exact_match: 45.0\n",
      "chat_model: llama70b4k, reference_format: e5-mistral, dataset: hotpot-qa, split: test, hit1: (36.75,), exact_match: 36.75\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 36%|███▌      | 13/36 [00:08<00:15,  1.46it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluating file ../html_data/nq/llama70b4k/bing/llama70b4k-bm25-slimplmqr-nq-test.jsonl\n",
      "evaluating file ../html_data/nq/llama70b4k/bing/llama70b4k-bgelargeen-slimplmqr-nq-test.jsonl\n",
      "evaluating file ../html_data/nq/llama70b4k/bing/llama70b4k-e5-mistral-slimplmqr-nq-test.jsonl\n",
      "evaluating file ../html_data/nq/llama70b4k/bing/llama70b4k-llmlingua-slimplmqr-nq-test.jsonl\n",
      "chat_model: llama70b4k, reference_format: llmlingua, dataset: nq, split: test, hit1: (56.75,), exact_match: 42.89\n",
      "chat_model: llama70b4k, reference_format: bm25, dataset: nq, split: test, hit1: (47.0,), exact_match: 35.56\n",
      "chat_model: llama70b4k, reference_format: e5-mistral, dataset: nq, split: test, hit1: (59.5,), exact_match: 44.07\n",
      "chat_model: llama70b4k, reference_format: bgelargeen, dataset: nq, split: test, hit1: (59.5,), exact_match: 45.05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 47%|████▋     | 17/36 [00:11<00:12,  1.50it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluating file ../html_data/nq/llama70b4k/bing/llama70b4k-jinaai-reader-slimplmqr-nq-test.jsonl\n",
      "evaluating file ../html_data/nq/llama70b4k/bing/llama70b4k-tree-rerank-tree-gen-slimplmqr-bgelargeen-256to128-6k-v0915-nq-test.jsonl\n",
      "evaluating file ../html_data/trivia-qa/llama70b4k/bing/llama70b4k-bm25-slimplmqr-trivia-qa-test.jsonl\n",
      "evaluating file ../html_data/trivia-qa/llama70b4k/bing/llama70b4k-bgelargeen-slimplmqr-trivia-qa-test.jsonl\n",
      "chat_model: llama70b4k, reference_format: tree-rerank-tree-gen, dataset: nq, split: test, hit1: (60.75,), exact_match: 45.51\n",
      "chat_model: llama70b4k, reference_format: bm25, dataset: trivia-qa, split: test, hit1: (88.0,), exact_match: 25.63\n",
      "chat_model: llama70b4k, reference_format: jinaai-reader, dataset: nq, split: test, hit1: (48.25,), exact_match: 35.4\n",
      "chat_model: llama70b4k, reference_format: bgelargeen, dataset: trivia-qa, split: test, hit1: (93.0,), exact_match: 27.04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 58%|█████▊    | 21/36 [00:13<00:09,  1.60it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluating file ../html_data/trivia-qa/llama70b4k/bing/llama70b4k-e5-mistral-slimplmqr-trivia-qa-test.jsonl\n",
      "evaluating file ../html_data/trivia-qa/llama70b4k/bing/llama70b4k-llmlingua-slimplmqr-trivia-qa-test.jsonl\n",
      "evaluating file ../html_data/trivia-qa/llama70b4k/bing/llama70b4k-jinaai-reader-slimplmqr-trivia-qa-test.jsonl\n",
      "evaluating file ../html_data/trivia-qa/llama70b4k/bing/llama70b4k-tree-rerank-tree-gen-slimplmqr-bgelargeen-256to128-8k-v0915-trivia-qa-test.jsonl\n",
      "chat_model: llama70b4k, reference_format: llmlingua, dataset: trivia-qa, split: test, hit1: (92.5,), exact_match: 27.23\n",
      "chat_model: llama70b4k, reference_format: jinaai-reader, dataset: trivia-qa, split: test, hit1: (90.0,), exact_match: 25.35\n",
      "chat_model: llama70b4k, reference_format: tree-rerank-tree-gen, dataset: trivia-qa, split: test, hit1: (93.75,), exact_match: 27.07\n",
      "chat_model: llama70b4k, reference_format: e5-mistral, dataset: trivia-qa, split: test, hit1: (90.75,), exact_match: 26.27\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 69%|██████▉   | 25/36 [00:15<00:06,  1.66it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluating file ../html_data/musique/llama70b4k/bing/llama70b4k-bm25-slimplmqr-musique-test.jsonl\n",
      "evaluating file ../html_data/musique/llama70b4k/bing/llama70b4k-bgelargeen-slimplmqr-musique-test.jsonl\n",
      "evaluating file ../html_data/musique/llama70b4k/bing/llama70b4k-e5-mistral-slimplmqr-musique-test.jsonl\n",
      "evaluating file ../html_data/musique/llama70b4k/bing/llama70b4k-llmlingua-slimplmqr-musique-test.jsonl\n",
      "chat_model: llama70b4k, reference_format: llmlingua, dataset: musique, split: test, hit1: (10.25,), exact_match: 10.25\n",
      "chat_model: llama70b4k, reference_format: bm25, dataset: musique, split: test, hit1: (9.5,), exact_match: 9.5\n",
      "chat_model: llama70b4k, reference_format: bgelargeen, dataset: musique, split: test, hit1: (12.5,), exact_match: 12.5\n",
      "chat_model: llama70b4k, reference_format: e5-mistral, dataset: musique, split: test, hit1: (11.0,), exact_match: 11.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 81%|████████  | 29/36 [00:19<00:04,  1.42it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluating file ../html_data/musique/llama70b4k/bing/llama70b4k-jinaai-reader-slimplmqr-musique-test.jsonl\n",
      "evaluating file ../html_data/musique/llama70b4k/bing/llama70b4k-tree-rerank-tree-gen-slimplmqr-bgelargeen-256to128-8k-v0915-musique-test.jsonl\n",
      "evaluating file ../html_data/eli5/llama70b4k/bing/llama70b4k-bm25-slimplmqr-eli5-test.jsonl\n",
      "evaluating file ../html_data/eli5/llama70b4k/bing/llama70b4k-bgelargeen-slimplmqr-eli5-test.jsonl\n",
      "chat_model: llama70b4k, reference_format: tree-rerank-tree-gen, dataset: musique, split: test, hit1: (11.75,), exact_match: 11.75\n",
      "chat_model: llama70b4k, reference_format: jinaai-reader, dataset: musique, split: test, hit1: (9.25,), exact_match: 9.25\n"
     ]
    },
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluating file ../html_data/eli5/llama70b4k/bing/llama70b4k-e5-mistral-slimplmqr-eli5-test.jsonl\n",
      "evaluating file ../html_data/eli5/llama70b4k/bing/llama70b4k-llmlingua-slimplmqr-eli5-test.jsonl\n",
      "evaluating file ../html_data/eli5/llama70b4k/bing/llama70b4k-jinaai-reader-slimplmqr-eli5-test.jsonl\n",
      "evaluating file ../html_data/eli5/llama70b4k/bing/llama70b4k-tree-rerank-tree-gen-slimplmqr-bgelargeen-256to128-6k-v0915-eli5-test.jsonl\n"
     ]
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      "\n",
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      "\n",
      " 99%|█████████▉| 396/400 [31:09<00:09,  2.37s/it]\u001b[A\n",
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      "100%|██████████| 36/36 [50:19<00:00, 83.88s/it]\n"
     ]
    }
   ],
   "source": [
    "import tqdm\n",
    "#  eval all datasets\n",
    "#  generate latex table report\n",
    "context_window=\"4k\"\n",
    "reference_formats=[\"bm25\", \"bgelargeen\", \"e5-mistral\", \"llmlingua\", \"jinaai-reader\", \"tree-rerank-tree-gen\"]\n",
    "syn_names=[\"BM25\", \"BGE\", \"E5-Mistral\", \"LongLLMLingua\", \"JinaAI Reader\", \"HtmlRAG\"]\n",
    "\n",
    "# long context settings\n",
    "# context_window=\"128k\"\n",
    "# reference_formats=[\"html\", \"raw-text\", \"markdown\", \"html-simple\"]\n",
    "# syn_names=[\"Vanilla HTML\", \"Raw Text\", \"Markdown\", \"HtmlRAG w/o Prune\"]\n",
    "\n",
    "datasets=[\"asqa\", \"hotpot-qa\", \"nq\", \"trivia-qa\", \"musique\", \"eli5\"]\n",
    "\n",
    "split=\"test\"\n",
    "search_engine=\"bing\"\n",
    "rewrite_method=\"slimplmqr\"\n",
    "rerank_model=\"bgelargeen\"\n",
    "\n",
    "import multiprocessing\n",
    "res_list=multiprocessing.Manager().list([\"\"]*len(datasets)*len(reference_formats))\n",
    "processes = []\n",
    "\n",
    "def append_res2markdown_table(lidx, *args, **kwargs):\n",
    "    lidx=lidx\n",
    "    res=eval_short_answer_EM(*args)\n",
    "    if \"hit1\" in res:\n",
    "        hit1, exact_match=res[\"hit1\"], res[\"exact_match\"]\n",
    "        if isinstance(hit1, tuple):\n",
    "            hit1=hit1[0]\n",
    "        if isinstance(exact_match, tuple):\n",
    "            exact_match=exact_match[0]\n",
    "        if args[0] in [\"hotpot-qa\", \"musique\"]:\n",
    "            res=f\" {hit1} \"\n",
    "        else:\n",
    "            res=f\" {hit1} & {exact_match} \"\n",
    "    \n",
    "        res_list[lidx]=res\n",
    "    res=\" & \".join([f\"{v:.2f}\" for k, v in res.items()])\n",
    "    res_list[lidx]=res\n",
    "\n",
    "pbar=tqdm.tqdm(total=len(datasets)*len(reference_formats))\n",
    "\n",
    "chat_model=f\"llama70b{context_window}\"\n",
    "# chat_model=f\"llama8b{context_window}\"\n",
    "\n",
    "for i, dataset in enumerate(datasets):\n",
    "    for j, reference_format in enumerate(reference_formats):\n",
    "        lidx= i*len(reference_formats) + j\n",
    "        p=multiprocessing.Process(target=append_res2markdown_table, args=(lidx, dataset, chat_model, reference_format, split, search_engine, rewrite_method))\n",
    "        processes.append(p)\n",
    "        p.start()\n",
    "        pbar.update(1)\n",
    "        if len(processes) >= 4:\n",
    "            for p in processes:\n",
    "                p.join()\n",
    "            processes=[]\n",
    "                \n",
    "if processes:\n",
    "    for p in processes:\n",
    "        p.join()\n",
    "\n",
    "pbar.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b29ef20145d0c603",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-20T10:38:49.207531Z",
     "start_time": "2024-10-20T10:38:49.198434Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset & EM & Hit@1 & EM & EM & Hit@1 & EM & Hit@1 & EM & ROUGE-L & BLEU\\\\\n",
      "BM25           & 49.50 & 21.95  & 38.25  & 47.00 & 35.56  & 88.00 & 25.63  & 9.50  &28.13 & 4.90 & 16.15 & 17.97 & 0.07 \\\\\n",
      "BGE            & 68.00 & 30.57  & 41.75  & 59.50 & 45.05  & 93.00 & 27.04  & 12.50  &28.71 & 5.10 & 16.20 & 18.52 & 0.07 \\\\\n",
      "E5-Mistral     & 63.00 & 28.75  & 36.75  & 59.50 & 44.07  & 90.75 & 26.27  & 11.00  &28.03 & 5.08 & 16.17 & 17.78 & 0.07 \\\\\n",
      "LongLLMLingua  & 62.50 & 27.74  & 45.00  & 56.75 & 42.89  & 92.50 & 27.23  & 10.25  &28.31 & 4.96 & 15.84 & 18.27 & 0.06 \\\\\n",
      "JinaAI Reader  & 55.25 & 23.73  & 34.25  & 48.25 & 35.40  & 90.00 & 25.35  & 9.25  &27.89 & 5.02 & 16.05 & 17.83 & 0.06 \\\\\n",
      "HTML4RAG       & 68.50 & 30.53  & 45.25  & 60.75 & 45.51  & 93.75 & 27.07  & 11.75  &27.98 & 4.97 & 16.32 & 18.02 & 0.07 \\\\\n"
     ]
    }
   ],
   "source": [
    "# create a latex table\n",
    "import re\n",
    "latex_table = [\"Dataset & EM & Hit@1 & EM & EM & Hit@1 & EM & Hit@1 & EM & ROUGE-L & BLEU\"]\n",
    "\n",
    "longest_syn_name = max([len(syn_name) for syn_name in syn_names]) +2\n",
    "for i in range(len(reference_formats)):\n",
    "    latex_table.append(f\"{syn_names[i]}\"+\" \"*(longest_syn_name-len(syn_names[i])) + \"&\")\n",
    "    \n",
    "for i, dataset in enumerate(datasets):\n",
    "    for j, reference_format in enumerate(reference_formats):\n",
    "        lidx= i*len(reference_formats) + j\n",
    "        latex_table[j+1] += f\"{res_list[lidx]} &\"\n",
    "        #. replace .x with .x0, e.g. 5.5 with 5.50\n",
    "        latex_table[j+1]=re.sub(r\"(\\d+\\.\\d)(?!\\d)\", r\"\\g<1>0\", latex_table[j+1])\n",
    "        \n",
    "\n",
    "for line in latex_table:\n",
    "    if line.endswith(\"&\"):\n",
    "        line=line[:-1]\n",
    "    line += \"\\\\\\\\\"\n",
    "    print(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bd148cc21ba00f7b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-20T10:38:56.017115Z",
     "start_time": "2024-10-20T10:38:56.009453Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Dataset | EM | Hit@1 | EM | EM | Hit@1 | EM | Hit@1 | EM | ROUGE-L | BLEU |\n",
      "| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n",
      "| BM25           | 49.50 | 21.95  | 38.25  | 47.00 | 35.56  | 88.00 | 25.63  | 9.50  |28.13 | 4.90 | 16.15 | 17.97 | 0.07 |\n",
      "| BGE            | 68.00 | 30.57  | 41.75  | 59.50 | 45.05  | 93.00 | 27.04  | 12.50  |28.71 | 5.10 | 16.20 | 18.52 | 0.07 |\n",
      "| E5-Mistral     | 63.00 | 28.75  | 36.75  | 59.50 | 44.07  | 90.75 | 26.27  | 11.00  |28.03 | 5.08 | 16.17 | 17.78 | 0.07 |\n",
      "| LongLLMLingua  | 62.50 | 27.74  | 45.00  | 56.75 | 42.89  | 92.50 | 27.23  | 10.25  |28.31 | 4.96 | 15.84 | 18.27 | 0.06 |\n",
      "| JinaAI Reader  | 55.25 | 23.73  | 34.25  | 48.25 | 35.40  | 90.00 | 25.35  | 9.25  |27.89 | 5.02 | 16.05 | 17.83 | 0.06 |\n",
      "| HTML4RAG       | 68.50 | 30.53  | 45.25  | 60.75 | 45.51  | 93.75 | 27.07  | 11.75  |27.98 | 4.97 | 16.32 | 18.02 | 0.07 |\n"
     ]
    }
   ],
   "source": [
    "#. create a markdown table\n",
    "markdown_table = [\"| Dataset | EM | Hit@1 | EM | EM | Hit@1 | EM | Hit@1 | EM | ROUGE-L | BLEU |\",\n",
    "                  \"| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\"]\n",
    "\n",
    "longest_syn_name = max([len(syn_name) for syn_name in syn_names]) +2\n",
    "for i in range(len(reference_formats)):\n",
    "    markdown_table.append(f\"| {syn_names[i]}\"+\" \"*(longest_syn_name-len(syn_names[i])) + \"|\")\n",
    "    \n",
    "for i, dataset in enumerate(datasets):\n",
    "    for j, reference_format in enumerate(reference_formats):\n",
    "        lidx= i*len(reference_formats) + j\n",
    "        markdown_table[j+2] += f\"{res_list[lidx]} |\"\n",
    "        #. replace .x with .x0, e.g. 5.5 with 5.50\n",
    "        markdown_table[j+2]=re.sub(r\"(\\d+\\.\\d)(?!\\d)\", r\"\\g<1>0\", markdown_table[j+2])\n",
    "        \n",
    "#. replace & with |\n",
    "for i, line in enumerate(markdown_table):\n",
    "    markdown_table[i]=line.replace(\"&\", \"|\")\n",
    "        \n",
    "for line in markdown_table:\n",
    "    print(line)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c478d6981ffa48d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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