experiments/sparse_retrieval_scientific.ipynb

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        "SPARSE RETRIEVAL"
      ]
    },
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          "text": [
            "Requirement already satisfied: beir in /usr/local/lib/python3.7/dist-packages (0.2.3)\n",
            "Requirement already satisfied: tensorflow-hub in /usr/local/lib/python3.7/dist-packages (from beir) (0.12.0)\n",
            "Requirement already satisfied: tensorflow-text in /usr/local/lib/python3.7/dist-packages (from beir) (2.6.0)\n",
            "Requirement already satisfied: faiss-cpu in /usr/local/lib/python3.7/dist-packages (from beir) (1.7.1.post2)\n",
            "Requirement already satisfied: sentence-transformers in /usr/local/lib/python3.7/dist-packages (from beir) (2.1.0)\n",
            "Requirement already satisfied: elasticsearch in /usr/local/lib/python3.7/dist-packages (from beir) (7.15.1)\n",
            "Requirement already satisfied: tensorflow>=2.2.0 in /usr/local/lib/python3.7/dist-packages (from beir) (2.6.0)\n",
            "Requirement already satisfied: pytrec-eval in /usr/local/lib/python3.7/dist-packages (from beir) (0.5)\n",
            "Requirement already satisfied: tensorflow-estimator~=2.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.2.0->beir) (2.6.0)\n",
            "Requirement already satisfied: absl-py~=0.10 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.2.0->beir) (0.12.0)\n",
            "Requirement already satisfied: clang~=5.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.2.0->beir) (5.0)\n",
            "Requirement already satisfied: tensorboard~=2.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.2.0->beir) (2.6.0)\n",
            "Requirement already satisfied: wrapt~=1.12.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.2.0->beir) (1.12.1)\n",
            "Requirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.2.0->beir) (3.17.3)\n",
            "Requirement already satisfied: flatbuffers~=1.12.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.2.0->beir) (1.12)\n",
            "Requirement already satisfied: numpy~=1.19.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.2.0->beir) (1.19.5)\n",
            "Requirement already satisfied: wheel~=0.35 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.2.0->beir) (0.37.0)\n",
            "Requirement already satisfied: gast==0.4.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.2.0->beir) (0.4.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.37.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.2.0->beir) (1.41.0)\n",
            "Requirement already satisfied: h5py~=3.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.2.0->beir) (3.1.0)\n",
            "Requirement already satisfied: typing-extensions~=3.7.4 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.2.0->beir) (3.7.4.3)\n",
            "Requirement already satisfied: opt-einsum~=3.3.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.2.0->beir) (3.3.0)\n",
            "Requirement already satisfied: termcolor~=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.2.0->beir) (1.1.0)\n",
            "Requirement already satisfied: astunparse~=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.2.0->beir) (1.6.3)\n",
            "Requirement already satisfied: six~=1.15.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.2.0->beir) (1.15.0)\n",
            "Requirement already satisfied: keras~=2.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.2.0->beir) (2.6.0)\n",
            "Requirement already satisfied: keras-preprocessing~=1.1.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.2.0->beir) (1.1.2)\n",
            "Requirement already satisfied: google-pasta~=0.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=2.2.0->beir) (0.2.0)\n",
            "Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py~=3.1.0->tensorflow>=2.2.0->beir) (1.5.2)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.6->tensorflow>=2.2.0->beir) (0.4.6)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.6->tensorflow>=2.2.0->beir) (2.23.0)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.6->tensorflow>=2.2.0->beir) (1.35.0)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.6->tensorflow>=2.2.0->beir) (0.6.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.6->tensorflow>=2.2.0->beir) (1.0.1)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.6->tensorflow>=2.2.0->beir) (57.4.0)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ypLvwb8euUd"
      },
      "source": [
        "import pathlib, os\n",
        "import time\n",
        "import pandas as pd\n",
        "from tqdm.notebook import tqdm\n",
        "from tqdm.autonotebook import trange\n",
        "from beir import util, LoggingHandler\n",
        "from beir.retrieval import models\n",
        "from beir.datasets.data_loader import GenericDataLoader\n",
        "from beir.retrieval.evaluation import EvaluateRetrieval\n",
        "from beir.retrieval.search.sparse import SparseSearch"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lx8P_Kv_h0km"
      },
      "source": [
        "def load(dataset):\n",
        "  hostname = 'localhost'\n",
        "  index_name = dataset\n",
        "  url = 'https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/{}.zip'.format(dataset)\n",
        "  out_dir = os.path.join(os.getcwd(), 'datasets')\n",
        "  data_path = util.download_and_unzip(url, out_dir)\n",
        "  return GenericDataLoader(data_path).load(split='test') # 'test', 'train', 'dev'"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DJemldWFh9ZH"
      },
      "source": [
        "def retrieve(corpus, queries, qrels, model_name, batch_size):\n",
        "  sparse_model = SparseSearch(models.SPARTA(model_name), batch_size=batch_size)\n",
        "  retriever_sp = EvaluateRetrieval(sparse_model)\n",
        "  results_sp = retriever_sp.retrieve(corpus, queries)\n",
        "  return retriever_sp.evaluate(qrels, results_sp, retriever_sp.k_values)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZmzKisVDiO0M"
      },
      "source": [
        "def evaluate(corpus, queries, qrels, model_name, batch_size):\n",
        "  ndcg, _map, recall, precision = retrieve(corpus, queries, qrels, model_name, batch_size)\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",
        "  eval_df = pd.DataFrame(data=eval_dict)\n",
        "  eval_df.index = [model_name]\n",
        "  return eval_df"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_l9ru98yCRdr",
        "colab": {
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          ]
        },
        "outputId": "3ec66795-53a2-45d1-aa16-d516fca4e5e2"
      },
      "source": [
        "model_db = 'BeIR/sparta-msmarco-distilbert-base-v1'\n",
        "cv_corpus, cv_queries, cv_qrels = load('trec-covid')\n",
        "sf_corpus, sf_queries, sf_qrels = load('scifact')\n",
        "sd_corpus, sd_queries, sd_qrels = load('scidocs')\n",
        "nf_corpus, nf_queries, nf_qrels = load('nfcorpus')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
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              "model_id": "b098493cf6f9441786747ccbfa17ea79",
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            "text/plain": [
              "/content/datasets/trec-covid.zip:   0%|          | 0.00/70.5M [00:00<?, ?iB/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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          "metadata": {}
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        {
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          "data": {
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              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "/content/datasets/scifact.zip:   0%|          | 0.00/2.69M [00:00<?, ?iB/s]"
            ]
          },
          "metadata": {}
        },
        {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_minor": 0,
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            },
            "text/plain": [
              "/content/datasets/scidocs.zip:   0%|          | 0.00/136M [00:00<?, ?iB/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_minor": 0,
              "version_major": 2
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "5536223b3b6c4989bd22597826a3c05f",
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              "version_major": 2
            },
            "text/plain": [
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AgIdVjU2sSek"
      },
      "source": [
        "**TREC-COVID** - [Homepage](https://ir.nist.gov/covidSubmit/index.html) - [Paper](https://arxiv.org/abs/2005.04474)"
      ]
    },
    {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d4aaf47a0b5e47e4bff35da7917fcea8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "query:   0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <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",
              "      <th>NDCG@10</th>\n",
              "      <th>NDCG@100</th>\n",
              "      <th>NDCG@1000</th>\n",
              "      <th>P@10</th>\n",
              "      <th>P@100</th>\n",
              "      <th>P@1000</th>\n",
              "      <th>Recall@10</th>\n",
              "      <th>Recall@100</th>\n",
              "      <th>Recall@1000</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>BeIR/sparta-msmarco-distilbert-base-v1</th>\n",
              "      <td>0.09456</td>\n",
              "      <td>0.15421</td>\n",
              "      <td>0.16379</td>\n",
              "      <td>0.35311</td>\n",
              "      <td>0.36289</td>\n",
              "      <td>0.45602</td>\n",
              "      <td>0.36667</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.01333</td>\n",
              "      <td>0.20076</td>\n",
              "      <td>0.38636</td>\n",
              "      <td>0.78157</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         MAP@10  ...  Recall@1000\n",
              "BeIR/sparta-msmarco-distilbert-base-v1  0.09456  ...      0.78157\n",
              "\n",
              "[1 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wZir0rz6fEkR"
      },
      "source": [
        "end of fun."
      ]
    }
  ]
}