experiments/reranking_scientific.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KbTUYoH4FeAf"
      },
      "source": [
        "RE-RANKING"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MMS-ANL3D7vd",
        "outputId": "1381c3a2-c6cc-4549-df2e-09b42f813ef9"
      },
      "outputs": [],
      "source": [
        "!pip install -U beir\n",
        "!pip install 'elasticsearch<7.14.0'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "KPHS4YFuFoma"
      },
      "outputs": [],
      "source": [
        "import pathlib, os\n",
        "import time\n",
        "import pandas as pd\n",
        "from beir import util\n",
        "from beir.datasets.data_loader import GenericDataLoader\n",
        "from beir.retrieval.search.lexical import BM25Search as BM25\n",
        "from beir.retrieval.evaluation import EvaluateRetrieval\n",
        "from beir.reranking.models import CrossEncoder\n",
        "from beir.reranking import Rerank"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "Kh8g3a0PGSz-"
      },
      "outputs": [],
      "source": [
        "if not os.path.exists('elasticsearch-oss-7.9.2-linux-x86_64.tar.gz'):\n",
        "  !wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-oss-7.9.2-linux-x86_64.tar.gz\n",
        "  !wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-oss-7.9.2-linux-x86_64.tar.gz.sha512\n",
        "  !tar -xzf elasticsearch-oss-7.9.2-linux-x86_64.tar.gz\n",
        "  !sudo chown -R daemon:daemon elasticsearch-7.9.2/\n",
        "  !shasum -a 512 -c elasticsearch-oss-7.9.2-linux-x86_64.tar.gz.sha512 "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8qPjo9Rbl0FU",
        "outputId": "f77699b2-8467-46ce-d12d-c43fbe779079"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting job # 2 in a separate thread.\n"
          ]
        }
      ],
      "source": [
        "%%bash --bg\n",
        "\n",
        "sudo -H -u daemon elasticsearch-7.9.2/bin/elasticsearch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "He91N2HJl77m"
      },
      "outputs": [],
      "source": [
        "time.sleep(20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ys-n3vSmA06",
        "outputId": "dfc82796-4f3f-4455-a005-c8132a561c88"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "root        1888    1886  0 09:54 ?        00:00:00 sudo -H -u daemon elasticsearch-7.9.2/bin/elasticsearch\n",
            "daemon      1889    1888 88 09:54 ?        00:00:18 /content/elasticsearch-7.9.2/jdk/bin/java -Xshare:auto -Des.networkaddress.cache.ttl=60 -Des.networkaddress.cache.negative.ttl=10 -XX:+AlwaysPreTouch -Xss1m -Djava.awt.headless=true -Dfile.encoding=UTF-8 -Djna.nosys=true -XX:-OmitStackTraceInFastThrow -XX:+ShowCodeDetailsInExceptionMessages -Dio.netty.noUnsafe=true -Dio.netty.noKeySetOptimization=true -Dio.netty.recycler.maxCapacityPerThread=0 -Dio.netty.allocator.numDirectArenas=0 -Dlog4j.shutdownHookEnabled=false -Dlog4j2.disable.jmx=true -Djava.locale.providers=SPI,COMPAT -Xms1g -Xmx1g -XX:+UseG1GC -XX:G1ReservePercent=25 -XX:InitiatingHeapOccupancyPercent=30 -Djava.io.tmpdir=/tmp/elasticsearch-11878928863626721576 -XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=data -XX:ErrorFile=logs/hs_err_pid%p.log -Xlog:gc*,gc+age=trace,safepoint:file=logs/gc.log:utctime,pid,tags:filecount=32,filesize=64m -XX:MaxDirectMemorySize=536870912 -Des.path.home=/content/elasticsearch-7.9.2 -Des.path.conf=/content/elasticsearch-7.9.2/config -Des.distribution.flavor=oss -Des.distribution.type=tar -Des.bundled_jdk=true -cp /content/elasticsearch-7.9.2/lib/* org.elasticsearch.bootstrap.Elasticsearch\n",
            "root        2128      77  0 09:55 ?        00:00:00 /bin/bash -c ps -ef | grep elasticsearch\n",
            "root        2130    2128  0 09:55 ?        00:00:00 grep elasticsearch\n"
          ]
        }
      ],
      "source": [
        "!ps -ef | grep elasticsearch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fgd-5d6SmFo_",
        "outputId": "eebb6013-9693-4874-c74c-24e31d9eb3e5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{\n",
            "  \"name\" : \"73126603a928\",\n",
            "  \"cluster_name\" : \"elasticsearch\",\n",
            "  \"cluster_uuid\" : \"hekVLOg_QciKqRB3PCCQug\",\n",
            "  \"version\" : {\n",
            "    \"number\" : \"7.9.2\",\n",
            "    \"build_flavor\" : \"oss\",\n",
            "    \"build_type\" : \"tar\",\n",
            "    \"build_hash\" : \"d34da0ea4a966c4e49417f2da2f244e3e97b4e6e\",\n",
            "    \"build_date\" : \"2020-09-23T00:45:33.626720Z\",\n",
            "    \"build_snapshot\" : false,\n",
            "    \"lucene_version\" : \"8.6.2\",\n",
            "    \"minimum_wire_compatibility_version\" : \"6.8.0\",\n",
            "    \"minimum_index_compatibility_version\" : \"6.0.0-beta1\"\n",
            "  },\n",
            "  \"tagline\" : \"You Know, for Search\"\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "!curl -sX GET \"localhost:9200/\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "nuO6Se4svb8d"
      },
      "outputs": [],
      "source": [
        "def retrieve_lexical(dataset):\n",
        "  hostname = 'localhost' \n",
        "  url = 'https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/{}.zip'\n",
        "  out_dir = os.path.join(os.getcwd(), 'datasets')\n",
        "  dataset_r = dataset\n",
        "  index_r = dataset_r\n",
        "  data_path_r = util.download_and_unzip(url.format(dataset_r), out_dir)\n",
        "  corpus_r, queries_r, qrels_r = GenericDataLoader(data_path_r).load(split='test') # 'test', 'train', 'dev'\n",
        "  model_r = BM25(index_name=index_r, hostname=hostname, initialize=True) # initialize=True : reindex\n",
        "  retriever_r = EvaluateRetrieval(model_r)\n",
        "  results_r = retriever_r.retrieve(corpus_r, queries_r)\n",
        "  return corpus_r, queries_r, qrels_r, retriever_r, results_r"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "icq5FvhN5eXy"
      },
      "outputs": [],
      "source": [
        "def rerank_model(reranker, corpus, queries, qrels, retriever, results, top_k):\n",
        "  rerank_results = reranker.rerank(corpus, queries, results, top_k=top_k)\n",
        "  return EvaluateRetrieval.evaluate(qrels, rerank_results, retriever.k_values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "Uej16KOhmTCP"
      },
      "outputs": [],
      "source": [
        "def evaluate(name, ndcg, _map, recall, precision):\n",
        "  c_map = 'MAP@10'\n",
        "  c_map_h = 'MAP@100'\n",
        "  c_map_t = 'MAP@1000'\n",
        "  c_ndcg = 'NDCG@10'\n",
        "  c_ndcg_h = 'NDCG@100'\n",
        "  c_ndcg_t = 'NDCG@1000'\n",
        "  c_pre = 'P@10'\n",
        "  c_pre_h = 'P@100'\n",
        "  c_pre_t = 'P@1000'\n",
        "  c_rec = 'Recall@10'\n",
        "  c_rec_h = 'Recall@100'\n",
        "  c_rec_t = 'Recall@1000'\n",
        "  eval_dict = {\n",
        "      c_map: [_map[c_map]], \n",
        "      c_map_h: [_map[c_map_h]],\n",
        "      c_map_t: [_map[c_map_t]],\n",
        "      c_ndcg: [ndcg[c_ndcg]], \n",
        "      c_ndcg_h: [ndcg[c_ndcg_h]],\n",
        "      c_ndcg_t: [ndcg[c_ndcg_t]],\n",
        "      c_pre: [precision[c_pre]], \n",
        "      c_pre_h: [precision[c_pre_h]],\n",
        "      c_pre_t: [precision[c_pre_t]],\n",
        "      c_rec: [recall[c_rec]],\n",
        "      c_rec_h: [recall[c_rec_h]],\n",
        "      c_rec_t: [recall[c_rec_t]]\n",
        "      }\n",
        "  eval_df = pd.DataFrame(data=eval_dict)\n",
        "  eval_df.index = [name]\n",
        "  return eval_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "bDAOzlRR-15G"
      },
      "outputs": [],
      "source": [
        "def evaluate_rerank(rerank, corpus, queries, qrels, retriever, results, top_k):\n",
        "  r_ndcg, r_map, r_recall, r_precision = rerank_model(rerank['rr'], corpus, queries, qrels, retriever, results, top_k)\n",
        "  return evaluate(rerank['name'], r_ndcg, r_map, r_recall, r_precision)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "yntClnX2COPz"
      },
      "outputs": [],
      "source": [
        "def run_all_models(models, corpus, queries, qrels, retriever, results, top_k):\n",
        "  rr_evals = []\n",
        "  for model in models:\n",
        "      rr_evals.append(evaluate_rerank(model, corpus, queries, qrels, retriever, results, top_k))\n",
        "  return rr_evals"
      ]
    },
    {
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        },
        "id": "WLedgFTNps4F",
        "outputId": "384f1830-93a3-4d63-91f4-a8bc87b154ab"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8d7a6b98bec84dbf97334c0fee247461",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/171332 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "  0%|          | 0/171332 [00:00<?, ?docs/s]\n",
            "que: 100%|██████████| 1/1 [00:08<00:00,  8.29s/it]\n"
          ]
        },
        {
          "data": {
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              "model_id": "cd3ab141c7d14bd99e2719de8fad95e6",
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          "text": [
            "  0%|          | 0/5183 [00:00<?, ?docs/s]\n",
            "que: 100%|██████████| 3/3 [00:12<00:00,  4.09s/it]\n"
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        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
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            "que: 100%|██████████| 8/8 [00:50<00:00,  6.37s/it]\n"
          ]
        }
      ],
      "source": [
        "cv_d = 'trec-covid'\n",
        "sf_d = 'scifact'\n",
        "sd_d = 'scidocs'\n",
        "nf_d = 'nfcorpus'\n",
        "cv_corpus, cv_queries, cv_qrels, cv_retriever, cv_results = retrieve_lexical(cv_d)\n",
        "cv_ndcg, cv_map, cv_recall, cv_precision = cv_retriever.evaluate(cv_qrels, cv_results, cv_retriever.k_values)\n",
        "sf_corpus, sf_queries, sf_qrels, sf_retriever, sf_results = retrieve_lexical(sf_d)\n",
        "sf_ndcg, sf_map, sf_recall, sf_precision = sf_retriever.evaluate(sf_qrels, sf_results, sf_retriever.k_values)\n",
        "sd_corpus, sd_queries, sd_qrels, sd_retriever, sd_results = retrieve_lexical(sd_d)\n",
        "sd_ndcg, sd_map, sd_recall, sd_precision = sd_retriever.evaluate(sd_qrels, sd_results, sd_retriever.k_values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83,
          "referenced_widgets": [
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        "id": "rrDRZdVoqzdP",
        "outputId": "20f317c1-8142-4926-a38a-9b9e6f21773b"
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      "outputs": [
        {
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              "version_major": 2,
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        },
        {
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          "output_type": "stream",
          "text": [
            "  0%|          | 0/3633 [00:00<?, ?docs/s]\n",
            "que: 100%|██████████| 3/3 [00:06<00:00,  2.11s/it]\n"
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        }
      ],
      "source": [
        "\n",
        "nf_corpus, nf_queries, nf_qrels, nf_retriever, nf_results = retrieve_lexical(nf_d)\n",
        "nf_ndcg, nf_map, nf_recall, nf_precision = nf_retriever.evaluate(nf_qrels, nf_results, nf_retriever.k_values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "sql5LYi78mhF"
      },
      "outputs": [],
      "source": [
        "lexical_eval = []\n",
        "lexical_eval.append(evaluate(cv_d, cv_ndcg, cv_map, cv_recall, cv_precision))\n",
        "lexical_eval.append(evaluate(sf_d, sf_ndcg, sf_map, sf_recall, sf_precision))\n",
        "lexical_eval.append(evaluate(sd_d, sd_ndcg, sd_map, sd_recall, sd_precision))\n",
        "lexical_eval.append(evaluate(nf_d, nf_ndcg, nf_map, nf_recall, nf_precision))"
      ]
    },
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        },
        "id": "QEL-wEbQerxj",
        "outputId": "51d98862-6a03-49d6-96bf-b0455756948b"
      },
      "outputs": [],
      "source": [
        "batch_size = 128\n",
        "top_k = 100\n",
        "cmtb = 'cross-encoder/ms-marco-TinyBERT-L-2-v2'\n",
        "cmlm = 'cross-encoder/ms-marco-MiniLM-L-6-v2'\n",
        "cmeb = 'cross-encoder/ms-marco-electra-base'\n",
        "rr_cmtb = {'name': cmtb, 'rr': Rerank(CrossEncoder(cmtb), batch_size=batch_size)}\n",
        "rr_cmlm = {'name': cmlm, 'rr': Rerank(CrossEncoder(cmlm), batch_size=batch_size)}\n",
        "rr_cmeb = {'name': cmeb, 'rr': Rerank(CrossEncoder(cmeb), batch_size=batch_size)}\n",
        "models = [rr_cmtb, rr_cmlm, rr_cmeb]\n",
        "cv_res = run_all_models(models, cv_corpus, cv_queries, cv_qrels, cv_retriever, cv_results, top_k)\n",
        "sf_res = run_all_models(models, sf_corpus, sf_queries, sf_qrels, sf_retriever, sf_results, top_k)\n",
        "sd_res = run_all_models(models, sd_corpus, sd_queries, sd_qrels, sd_retriever, sd_results, top_k)\n",
        "nf_res = run_all_models(models, nf_corpus, nf_queries, nf_qrels, nf_retriever, nf_results, top_k)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ttWN3PaM_nNE"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T0k8q-Ld_pmO"
      },
      "source": [
        "LEXICAL SEARCH"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "jiibaVjDg9Ba",
        "outputId": "9994a772-1ce4-427e-d46d-2618730ac17e"
      },
      "outputs": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MAP@10</th>\n",
              "      <th>MAP@100</th>\n",
              "      <th>MAP@1000</th>\n",
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              "      <th>Recall@1000</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>trec-covid</th>\n",
              "      <td>0.01698</td>\n",
              "      <td>0.08517</td>\n",
              "      <td>0.20840</td>\n",
              "      <td>0.68803</td>\n",
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              "      <td>0.68543</td>\n",
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              "             MAP@10  MAP@100  MAP@1000  ...  Recall@10  Recall@100  Recall@1000\n",
              "trec-covid  0.01698  0.08517   0.20840  ...    0.01907     0.11731      0.41281\n",
              "scifact     0.63897  0.64449   0.64478  ...    0.81311     0.91589      0.97333\n",
              "scidocs     0.09637  0.11192   0.11417  ...    0.17372     0.36757      0.57872\n",
              "nfcorpus    0.12979  0.15365   0.15899  ...    0.16615     0.25746      0.37525\n",
              "\n",
              "[4 rows x 12 columns]"
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          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
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      "source": [
        "pd.concat(lexical_eval)"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AflT9sHxDJEh"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iSgelj92pqr0"
      },
      "source": [
        "**TREC-COVID** - [Homepage](https://ir.nist.gov/covidSubmit/index.html) - [Paper](https://arxiv.org/abs/2005.04474)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
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        "id": "l9Y_h6Lh_8mN",
        "outputId": "542e175f-7668-4684-ece5-94c02340b2ef"
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              "                                         MAP@10  ...  Recall@1000\n",
              "cross-encoder/ms-marco-TinyBERT-L-2-v2  0.01742  ...      0.11731\n",
              "cross-encoder/ms-marco-MiniLM-L-6-v2    0.01862  ...      0.11731\n",
              "cross-encoder/ms-marco-electra-base     0.01662  ...      0.11731\n",
              "\n",
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          "metadata": {},
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    {
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      "metadata": {
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      },
      "source": [
        "**SCIFACT** - [Homepage](https://allenai.org/data/scifact) - [Paper](https://arxiv.org/abs/2004.14974)"
      ]
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    {
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      "execution_count": 82,
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              "                                         MAP@10  ...  Recall@1000\n",
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