SCIATICA_BaseModel.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jvh-mJ7KODad",
        "outputId": "401b8e84-b97b-439c-be76-9591ff491572"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sentence-transformers\n",
            "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 KB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting transformers<5.0.0,>=4.6.0\n",
            "  Downloading transformers-4.27.4-py3-none-any.whl (6.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.8/6.8 MB\u001b[0m \u001b[31m70.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (from sentence-transformers) (4.65.0)\n",
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            "Requirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from sentence-transformers) (1.22.4)\n",
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            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.97-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m66.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting huggingface-hub>=0.4.0\n",
            "  Downloading huggingface_hub-0.13.4-py3-none-any.whl (200 kB)\n",
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            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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            "Requirement already satisfied: click in /usr/local/lib/python3.9/dist-packages (from nltk->sentence-transformers) (8.1.3)\n",
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            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (1.26.15)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (3.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (2.0.12)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.9/dist-packages (from sympy->torch>=1.6.0->sentence-transformers) (1.3.0)\n",
            "Building wheels for collected packages: sentence-transformers\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125942 sha256=eedc9256cd0fadbe84fb0d6809e873a447d09e7af0efd4fb46ccd61e103dccf4\n",
            "  Stored in directory: /root/.cache/pip/wheels/71/67/06/162a3760c40d74dd40bc855d527008d26341c2b0ecf3e8e11f\n",
            "Successfully built sentence-transformers\n",
            "Installing collected packages: tokenizers, sentencepiece, huggingface-hub, transformers, sentence-transformers\n",
            "Successfully installed huggingface-hub-0.13.4 sentence-transformers-2.2.2 sentencepiece-0.1.97 tokenizers-0.13.3 transformers-4.27.4\n"
          ]
        }
      ],
      "source": [
        "!pip install -U sentence-transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jBlaiSdQOanT",
        "outputId": "fc55212e-339b-46b4-8c5e-bea39312e20a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting jsonlines\n",
            "  Downloading jsonlines-3.1.0-py3-none-any.whl (8.6 kB)\n",
            "Requirement already satisfied: attrs>=19.2.0 in /usr/local/lib/python3.9/dist-packages (from jsonlines) (22.2.0)\n",
            "Installing collected packages: jsonlines\n",
            "Successfully installed jsonlines-3.1.0\n"
          ]
        }
      ],
      "source": [
        "!pip install jsonlines\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install simcse"
      ],
      "metadata": {
        "id": "Z5urF8V4ZEp7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "86f96f04-a357-430c-ac58-2ed76985be34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting simcse\n",
            "  Downloading simcse-0.4.tar.gz (18 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (from simcse) (4.65.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.9/dist-packages (from simcse) (1.2.2)\n",
            "Collecting scipy<1.6,>=1.5.4\n",
            "  Downloading scipy-1.5.4-cp39-cp39-manylinux1_x86_64.whl (25.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m25.8/25.8 MB\u001b[0m \u001b[31m54.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: transformers in /usr/local/lib/python3.9/dist-packages (from simcse) (4.27.4)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.9/dist-packages (from simcse) (2.0.0+cu118)\n",
            "Collecting numpy<1.20,>=1.19.5\n",
            "  Downloading numpy-1.19.5-cp39-cp39-manylinux2010_x86_64.whl (14.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.9/14.9 MB\u001b[0m \u001b[31m79.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: setuptools in /usr/local/lib/python3.9/dist-packages (from simcse) (67.6.1)\n",
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            "Requirement already satisfied: cmake in /usr/local/lib/python3.9/dist-packages (from triton==2.0.0->torch->simcse) (3.25.2)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.9/dist-packages (from transformers->simcse) (0.13.3)\n",
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            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.9/dist-packages (from transformers->simcse) (2022.10.31)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from transformers->simcse) (23.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.9/dist-packages (from jinja2->torch->simcse) (2.1.2)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->transformers->simcse) (1.26.15)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->transformers->simcse) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->transformers->simcse) (3.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->transformers->simcse) (2022.12.7)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.9/dist-packages (from sympy->torch->simcse) (1.3.0)\n",
            "Building wheels for collected packages: simcse\n",
            "  Building wheel for simcse (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for simcse: filename=simcse-0.4-py3-none-any.whl size=15034 sha256=c338dbca74232c582faa4d035d5afdf39508f320b785b34d6068e13c962e06a0\n",
            "  Stored in directory: /root/.cache/pip/wheels/80/0f/b7/10f417b839634de949621bf112ed37c5f924d9b83146d79084\n",
            "Successfully built simcse\n",
            "Installing collected packages: numpy, scipy, simcse\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.22.4\n",
            "    Uninstalling numpy-1.22.4:\n",
            "      Successfully uninstalled numpy-1.22.4\n",
            "  Attempting uninstall: scipy\n",
            "    Found existing installation: scipy 1.10.1\n",
            "    Uninstalling scipy-1.10.1:\n",
            "      Successfully uninstalled scipy-1.10.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "xarray 2022.12.0 requires numpy>=1.20, but you have numpy 1.19.5 which is incompatible.\n",
            "xarray-einstats 0.5.1 requires numpy>=1.20, but you have numpy 1.19.5 which is incompatible.\n",
            "xarray-einstats 0.5.1 requires scipy>=1.6, but you have scipy 1.5.4 which is incompatible.\n",
            "tensorflow 2.12.0 requires numpy<1.24,>=1.22, but you have numpy 1.19.5 which is incompatible.\n",
            "ml-dtypes 0.0.4 requires numpy>1.20, but you have numpy 1.19.5 which is incompatible.\n",
            "matplotlib 3.7.1 requires numpy>=1.20, but you have numpy 1.19.5 which is incompatible.\n",
            "librosa 0.10.0.post2 requires numpy!=1.22.0,!=1.22.1,!=1.22.2,>=1.20.3, but you have numpy 1.19.5 which is incompatible.\n",
            "jaxlib 0.4.7+cuda11.cudnn86 requires numpy>=1.21, but you have numpy 1.19.5 which is incompatible.\n",
            "jaxlib 0.4.7+cuda11.cudnn86 requires scipy>=1.7, but you have scipy 1.5.4 which is incompatible.\n",
            "jax 0.4.7 requires numpy>=1.21, but you have numpy 1.19.5 which is incompatible.\n",
            "jax 0.4.7 requires scipy>=1.7, but you have scipy 1.5.4 which is incompatible.\n",
            "gensim 4.3.1 requires scipy>=1.7.0, but you have scipy 1.5.4 which is incompatible.\n",
            "cmdstanpy 1.1.0 requires numpy>=1.21, but you have numpy 1.19.5 which is incompatible.\n",
            "astropy 5.2.2 requires numpy>=1.20, but you have numpy 1.19.5 which is incompatible.\n",
            "arviz 0.15.1 requires numpy>=1.20.0, but you have numpy 1.19.5 which is incompatible.\n",
            "arviz 0.15.1 requires scipy>=1.8.0, but you have scipy 1.5.4 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed numpy-1.19.5 scipy-1.5.4 simcse-0.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Oy5Z2GREF6Yw"
      },
      "outputs": [],
      "source": [
        "from modulefinder import packagePathMap\n",
        "from unittest import result\n",
        "from click import pass_obj\n",
        "from sentence_transformers import SentenceTransformer\n",
        "from simcse import SimCSE\n",
        "from transformers import AutoTokenizer, AutoModel\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "import json\n",
        "import jsonlines\n",
        "import numpy as np\n",
        "import math\n",
        "import os\n",
        "import argparse\n",
        "import statistics\n",
        "import codecs\n",
        "import json\n",
        "import csv"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IO7fKHF3Kc_-"
      },
      "source": [
        "##Paper Parser"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "onFe5llQKchq",
        "outputId": "35810f6e-f3b8-4035-df22-b28a9f1c9a50"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4207\n"
          ]
        }
      ],
      "source": [
        "class get_doc:\n",
        "    def __init__(self, paper_id, metadata, title, abstract, pred_labels_truncated, pred_labels):\n",
        "      self.paper_id = paper_id\n",
        "      self.metadata = metadata\n",
        "      self.title = title\n",
        "      self.abstract = abstract\n",
        "      self.pred_labels_truncated = pred_labels_truncated\n",
        "      self.pred_labels = pred_labels\n",
        "\n",
        "docs = {}\n",
        "\n",
        "with jsonlines.open('/content/drive/MyDrive/data/abstracts-csfcube-preds.jsonl') as doc:\n",
        "  for section in doc:\n",
        "    docs[section['paper_id']] = get_doc(section['paper_id'], section['metadata'], section['title'], section['abstract'], section['pred_labels_truncated'], section['pred_labels'])\n",
        "print(len(docs))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gDOPoYHVfz42",
        "outputId": "9684d85d-530d-46a4-bef5-6dd8f528fa2a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vc9SnuxoQfe5"
      },
      "outputs": [],
      "source": [
        "def get_data_by_facet(pid, facet):\n",
        "  predLabels = {'background' : ['background_label', 'objective_label'], 'method' : ['method_label'], 'result' : ['result_label']}\n",
        "  doc = docs[pid];\n",
        "  temp = list()\n",
        "  for pred_index in range(len(doc.pred_labels)):\n",
        "    if doc.pred_labels[pred_index] in predLabels[facet]:\n",
        "      temp.append(doc.abstract[pred_index])\n",
        "\n",
        "  return temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1BPP35nCYMlL"
      },
      "outputs": [],
      "source": [
        "def get_bag_of_words(corpus):\n",
        "  tf = TfidfVectorizer().fit([corpus])\n",
        "  return tf\n",
        "\n",
        "def get_tf_idf_doc(bag_of_words, query):\n",
        "    return bag_of_words.transform([query]).toarray()[0]\n",
        "  \n",
        "def get_bert_nli_embedding(sentence):\n",
        "    return model_sent_bert_nli.encode(sentence)\n",
        "\n",
        "def get_bert_pp_embedding(sentence):\n",
        "    return model_sent_bert_pp.encode(sentence)\n",
        "\n",
        "def get_all_mpnet_base_v2_embedding(sentence):\n",
        "    return model_all_mpnet_base_v2.encode(sentence)\n",
        "  \n",
        "def get_specter_embedding(sentence):\n",
        "    inputs = tokenizer_specter(sentence, padding=True, truncation=True, return_tensors=\"pt\", max_length=65536)\n",
        "    return model_specter(**inputs).last_hidden_state[:, 0, :]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5WOtvhwaJQTr",
        "outputId": "f8b96b10-3b1f-4158-c0e7-c7d057baf949"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['10014168', '10695055', '5764728', '51977123', '929877', '3264891', '1936997', '11844559', '8781666', '1587', '189897839', '6173686', '7898033', '52194540', '1791179', '6431039']\n",
            "['1198964', '11310392', '10010426', '53080736', '53082542', '1791179', '2468783', '80628431', '189897839', '52194540', '929877', '1936997', '102353905', '5052952', '13949438', '174799296', '5270848']\n",
            "['1198964', '10052042', '11629674', '1587', '174799296', '1306065', '53080736', '2090262', '2865563', '11844559', '2468783', '5052952', '2360770', '3264891', '6431039', '80628431', '8781666']\n"
          ]
        }
      ],
      "source": [
        "f = open('/content/drive/MyDrive/data/test-pid2anns-csfcube-background.json')\n",
        "background_query = json.load(f)\n",
        "background_query_ids = list(background_query.keys())\n",
        "print(background_query_ids)\n",
        "\n",
        "f = open('/content/drive/MyDrive/data/test-pid2anns-csfcube-method.json')\n",
        "method_query = json.load(f)\n",
        "method_query_ids = list(method_query.keys())\n",
        "print(method_query_ids)\n",
        "\n",
        "f = open('/content/drive/MyDrive/data/test-pid2anns-csfcube-result.json')\n",
        "result_query = json.load(f)\n",
        "result_query_ids = list(result_query.keys())\n",
        "print(result_query_ids)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ewySKIfePnPV",
        "outputId": "886dae97-78d7-4985-c372-cf566c3a3f6e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['We show that the actual diachronic frequency distributions and those expected under the neutral model do not coincide and conclude that in this case the neutral model does not provide an adequate description of change in ceramic decoration.']\n"
          ]
        }
      ],
      "source": [
        "print(get_data_by_facet('143814895', 'method'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_7TNQlEWS6Ek"
      },
      "outputs": [],
      "source": [
        "def cosine_similarity(doc1, doc2):\n",
        "  a = np.asarray(doc1)\n",
        "  b = np.asarray(doc2)\n",
        "  cos_sim = np.dot(a, b)/(np.linalg.norm(a)*np.linalg.norm(b))\n",
        "\n",
        "  return cos_sim"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Fv-kLLvUpID",
        "outputId": "03fa031a-8bf8-4476-bf54-d793b0e10070"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.17541160386140586\n"
          ]
        }
      ],
      "source": [
        "print(cosine_similarity([1, 0, 5], [2, 1, 0]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nKqRfFbUV410"
      },
      "source": [
        "## Corpus Creation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w1bGjOOiU28q",
        "outputId": "25e8578b-dfc0-4d55-aaed-d4b7bfeb67fd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Many parallel and distributed message-passing programs are written in a parametric way over available resources, in particular the number of nodes and their topologies, so that a single parallel program can scale over different environments.\n"
          ]
        }
      ],
      "source": [
        "corpus_list = []\n",
        "for doc in docs:\n",
        "    doc_abstract = docs[doc].abstract\n",
        "    corpus_list = corpus_list + doc_abstract\n",
        "print(corpus_list[0])\n",
        "\n",
        "corpus = \" \".join(corpus_list)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "003U7emXYXCW"
      },
      "source": [
        "Tf-Idf Computation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-coH-lCgYWgY",
        "outputId": "7d951521-9358-45d5-d916-621c4ef77222"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'sklearn.feature_extraction.text.TfidfVectorizer'>\n"
          ]
        }
      ],
      "source": [
        "bag_of_words = get_bag_of_words(corpus)\n",
        "print(type(bag_of_words))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OTypAaIkZ4UE"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## TF-IDF"
      ],
      "metadata": {
        "id": "mN4CZksXnoAI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "0cnFxZ-DY0fY",
        "outputId": "046676f7-5d86-4039-8c1b-fed7f56b86ec"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1000\n",
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            "len(err_pids) = 0\n",
            "[]\n"
          ]
        },
        {
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-21-c3c663153d3a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     30\u001b[0m d = json.load(open('/content/drive/MyDrive/Results/tf_idf/all.json'),\n\u001b[1;32m     31\u001b[0m               parse_float=lambda x: round(float(x), 6))\n\u001b[0;32m---> 32\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'embeddings/tf_idf/all.json'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"w\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0moutfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m     \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'embeddings/tf_idf/all.json'"
          ]
        }
      ],
      "source": [
        "# tf_idf all\n",
        "d = dict()\n",
        "err_pids = list()\n",
        "i = 0\n",
        "for doc in docs:\n",
        "    i += 1\n",
        "    if i % 1000 == 0:\n",
        "        print(i)\n",
        "    try:\n",
        "        doc_abstract = docs[doc].abstract\n",
        "        if len(doc) == 0:\n",
        "            d[doc] = list()\n",
        "            continue\n",
        "        abstract_tf_idf = np.array(get_tf_idf_doc(bag_of_words, \" \".join(doc_abstract))).tolist()\n",
        "        if i % 1000 == 0:\n",
        "            print(abstract_tf_idf)\n",
        "            # ans = 0\n",
        "            # for j in abstract_tf_idf:\n",
        "            #     if j > 0:\n",
        "            #         ans = ans + 1\n",
        "            # print(\"ans =\", ans)\n",
        "        d[doc] = abstract_tf_idf\n",
        "    except:\n",
        "        err_pids.append(doc)\n",
        "\n",
        "print(f'len(err_pids) = {len(err_pids)}')\n",
        "print(err_pids)\n",
        "with open(\"/content/drive/MyDrive/Results/tf_idf/all.json\", \"w\") as outfile:\n",
        "    json.dump(d, outfile)\n",
        "d = json.load(open('/content/drive/MyDrive/Results/tf_idf/all.json'),\n",
        "              parse_float=lambda x: round(float(x), 6))\n",
        "with open('/content/drive/MyDrive/Results/tf_idf/all.json', \"w\") as outfile:\n",
        "    json.dump(d, outfile)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2h0Ac8wKaWIp",
        "outputId": "d7fa5d3c-6c6b-47ea-d7ad-2771a4b2b1a4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ans = 23\n",
            "ans = 39\n",
            "ans = 76\n",
            "ans = 55\n",
            "ans = 46\n",
            "ans = 40\n",
            "ans = 36\n",
            "ans = 14\n",
            "ans = 44\n",
            "ans = 21\n",
            "ans = 51\n",
            "ans = 18\n",
            "ans = 61\n",
            "ans = 74\n",
            "ans = 31\n",
            "ans = 27\n",
            "len(err_pids), background = 0\n",
            "[]\n",
            "ans = 59\n",
            "ans = 77\n",
            "ans = 41\n",
            "ans = 31\n",
            "ans = 48\n",
            "ans = 29\n",
            "ans = 42\n",
            "ans = 43\n",
            "ans = 30\n",
            "ans = 35\n",
            "ans = 29\n",
            "ans = 26\n",
            "ans = 41\n",
            "ans = 76\n",
            "ans = 21\n",
            "ans = 44\n",
            "ans = 21\n",
            "len(err_pids), method = 0\n",
            "[]\n",
            "ans = 30\n",
            "ans = 27\n",
            "ans = 37\n",
            "ans = 16\n",
            "ans = 33\n",
            "ans = 42\n",
            "ans = 53\n",
            "ans = 40\n",
            "ans = 47\n",
            "ans = 49\n",
            "ans = 25\n",
            "ans = 31\n",
            "ans = 38\n",
            "ans = 25\n",
            "ans = 50\n",
            "ans = 49\n",
            "ans = 32\n",
            "len(err_pids), result = 0\n",
            "[]\n"
          ]
        }
      ],
      "source": [
        "# tf_idf queries\n",
        "for i, ids in enumerate([background_query_ids, method_query_ids, result_query_ids]):\n",
        "    d = dict()\n",
        "    err_pids = list()\n",
        "    s = ''\n",
        "    if i == 0:\n",
        "        s = 'background'\n",
        "    elif i == 1:\n",
        "        s = 'method'\n",
        "    elif i == 2:\n",
        "        s = 'result'\n",
        "    for pid in ids:\n",
        "        try:\n",
        "            doc = get_data_by_facet(pid, s)\n",
        "            if len(doc) == 0:\n",
        "                d[pid] = list()\n",
        "                continue\n",
        "            \n",
        "            query_tfidf_array = np.array(get_tf_idf_doc(bag_of_words, \" \".join(doc))).tolist()\n",
        "            ans = 0\n",
        "            for j in query_tfidf_array:\n",
        "                if j > 0:\n",
        "                    ans = ans + 1\n",
        "            print(\"ans =\", ans)\n",
        "            d[pid] = query_tfidf_array\n",
        "        except:\n",
        "            err_pids.append(pid)\n",
        "    print(f'len(err_pids), {s} = {len(err_pids)}')\n",
        "    print(err_pids)\n",
        "    with open(f'/content/drive/MyDrive/Results/tf_idf/{s}.json', \"w\") as outfile:\n",
        "        json.dump(d, outfile)\n",
        "    d = json.load(open(f'/content/drive/MyDrive/Results/tf_idf/{s}.json'),\n",
        "                parse_float=lambda x: round(float(x), 6))\n",
        "    with open(f'/content/drive/MyDrive/Results/tf_idf/{s}.json', \"w\") as outfile:\n",
        "        json.dump(d, outfile)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aN7Sh0QBjtVF"
      },
      "source": [
        "Cosine Similarity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GprEvXoqjvuv"
      },
      "outputs": [],
      "source": [
        "def get_relevant_docs(qid, q_type, n, model_type, data, corpus, queries):\n",
        "    # corpus = json.load(open(f'/content/drive/MyDrive/Results/{model_type}/all.json')) \n",
        "    # queries = json.load(open(f'/content/drive/MyDrive/Results/{model_type}/{q_type}.json')) \n",
        "    query = queries[qid] \n",
        "    # data = json.load(open(f'/content/drive/MyDrive/data/test-pid2anns-csfcube-{q_type}.json')) \n",
        "    candidates = data[qid]['cands']\n",
        "    results = dict()\n",
        "    for i in candidates:\n",
        "        results[i] = cosine_similarity(query, corpus[i])\n",
        "\n",
        "    sorted_results = sorted(results.items(), key=lambda kv: (kv[1], kv[0]), reverse=True)\n",
        "\n",
        "    # sorted_results = sorted(results.items(), key=lambda x:x[1])\n",
        "    # d1= dict(list(d.items())[0: N]) \n",
        "    final = sorted_results[0:n]\n",
        "    return final, results, sorted_results"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Metric Functions"
      ],
      "metadata": {
        "id": "4wk_lorDngO1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zT1lWTYrm42Z"
      },
      "outputs": [],
      "source": [
        "# Precision Calculation\n",
        "def r_precision(r):\n",
        "    r = np.asarray(r) != 0\n",
        "    z = r.nonzero()[0]\n",
        "    if not z.size:\n",
        "        return 0.\n",
        "    return np.mean(r[:z[-1] + 1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P0zy0EkawdgH"
      },
      "outputs": [],
      "source": [
        "#P@K\n",
        "# r:- list of non-relevant and relevant queries\n",
        "# k:- K\n",
        "def precision_at_k(r, k):\n",
        "    assert k >= 1\n",
        "    r = np.asarray(r)[:k] != 0\n",
        "    return np.mean(r)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_DQFE-h2hP9B"
      },
      "outputs": [],
      "source": [
        "def average_precision(r):\n",
        "    r = np.asarray(r) != 0\n",
        "    out = [precision_at_k(r, k + 1) for k in range(r.size) if r[k]]\n",
        "    if not out:\n",
        "        return 0.\n",
        "    return np.mean(out)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_TXBrDF5nKDs"
      },
      "outputs": [],
      "source": [
        "def mean_average_precision(rs):\n",
        "    return np.mean([average_precision(r) for r in rs])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9BIud0BQpD_c"
      },
      "outputs": [],
      "source": [
        "# def list_former(qid, type, n):\n",
        "#     corpus = json.load(open('/content/drive/MyDrive/Results/tf_idf/all.json')) \n",
        "#     queries = json.load(open(f'/content/drive/MyDrive/Results/tf_idf/{type}.json')) \n",
        "#     query = queries[qid] \n",
        "#     data = json.load(open('/content/drive/MyDrive/data/test-pid2anns-csfcube-background.json')) \n",
        "#     candidates = data[qid]['cands']\n",
        "#     results = dict()\n",
        "#     for i in candidates:\n",
        "#         results[i] = cosine_similarity(query, corpus[i])\n",
        "#     return results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cktAj0Qn2PJD"
      },
      "outputs": [],
      "source": [
        "# def form_list(retreived, delta):\n",
        "#   r = []\n",
        "#   for qid in retreived:\n",
        "#     if retreived[qid] >= delta:\n",
        "#       r.append(1)\n",
        "#     else:\n",
        "#       r.append(0)\n",
        "#   return r\n",
        "# def form_list_weighted(retreived):\n",
        "#   r = []\n",
        "#   for qid in retreived:\n",
        "#     r.append(retreived[qid]//0.25)\n",
        "#   return r"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CeY4D_pgqKqZ"
      },
      "outputs": [],
      "source": [
        "# sorted_results = sorted(retreived.items(), key=lambda kv: (kv[1], kv[0]), reverse=True)\n",
        "# r = []\n",
        "# r = form_list(retreived, 0.3)\n",
        "# print(r_precision(r))\n",
        "# print(precision_at_k(r, 20))\n",
        "# print(average_precision(r))\n",
        "# ans = []\n",
        "# ans.append(r)\n",
        "# print(mean_average_precision(ans))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tjWNGzHE4R5H"
      },
      "outputs": [],
      "source": [
        "# r = form_list_weighted(retreived)\n",
        "# print(r_precision(r))\n",
        "\n",
        "# print(precision_at_k(r, 20))\n",
        "# print(average_precision(r))\n",
        "# ans = []\n",
        "# ans.append(r)\n",
        "# print(mean_average_precision(ans))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a4pC2jNDo3W1"
      },
      "source": [
        "NDCG"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jVCXa_msnNdJ"
      },
      "outputs": [],
      "source": [
        "def dcg_at_k(r, k, method=0):\n",
        "    r = np.asfarray(r)[:k]\n",
        "    if r.size:\n",
        "        if method == 0:\n",
        "            return r[0] + np.sum(r[1:] / np.log2(np.arange(2, r.size + 1)))\n",
        "        elif method == 1:\n",
        "            return np.sum(r / np.log2(np.arange(2, r.size + 2)))\n",
        "        else:\n",
        "            raise ValueError('method must be 0 or 1.')\n",
        "    return 0.\n",
        "\n",
        "\n",
        "def ndcg_at_k(r, k, method=0):\n",
        "    dcg_max = dcg_at_k(sorted(r, reverse=True), k, method)\n",
        "    if not dcg_max:\n",
        "        return 0.\n",
        "    return dcg_at_k(r, k, method) / dcg_max\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JK5j713GB3Sp",
        "outputId": "0982b180-8c65-4d48-849b-1e91e6f1d5bc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.8235833168864323\n"
          ]
        }
      ],
      "source": [
        "print(ndcg_at_k([1, 1, 1, 1, 1, 2, 1, 2, 1, 1], 10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Clv3zpUzrP13"
      },
      "outputs": [],
      "source": [
        "\n",
        "def recall_at_k(ranked_rel, atk, max_total_relevant):\n",
        "    # print(ranked_rel)\n",
        "    total_relevant = sum(ranked_rel)\n",
        "    # total_relevant = min(max_total_relevant, len(ranked_rel))\n",
        "    total_relevant = min(max_total_relevant, total_relevant)\n",
        "    relatk = sum(ranked_rel[:atk])\n",
        "    # print(relatk, total_relevant)\n",
        "    if total_relevant > 0:\n",
        "        recall_atk = float(relatk)/total_relevant\n",
        "    else:\n",
        "        recall_atk = 0.0\n",
        "    return recall_atk\n",
        "\n",
        "# def average_recall(r, max_total, n):\n",
        "#     r = np.asarray(r) != 0\n",
        "#     out = [recall_at_k(r, k + 1, max_total) for k in range(r.size) if r[k]]\n",
        "#     print(len(out), n)\n",
        "#     if not out:\n",
        "#         return 0.0\n",
        "#     return out[min(len(out), n)-1]\n",
        "\n",
        "# def mean_average_recall(rs):\n",
        "#     return np.mean([average_recall(r[0], r[1], r[2]) for r in rs])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4asxyFlG3ZaR",
        "outputId": "d6e19c87-a711-4996-bcc8-351d31232a96"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.6393671419616797\n"
          ]
        }
      ],
      "source": [
        "# print(mean_average_precision([[1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0], [1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1], [1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0]]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SXWk4hChfNIn"
      },
      "outputs": [],
      "source": [
        "# def evaluation_all(q_type, n, model_type, ann_type):\n",
        "#   queries = json.load(open(f'/content/drive/MyDrive/data/test-pid2anns-csfcube-{q_type}.json'))\n",
        "#   rp = 0\n",
        "#   p20 = 0\n",
        "#   r20 = 0\n",
        "#   ndcg100 = 0\n",
        "#   ndcg20 = 0\n",
        "#   ndcg_20 = 0\n",
        "#   rs = []\n",
        "#   rc = []\n",
        "#   for query in queries:\n",
        "#     print(query)\n",
        "#     # print(queries[query].keys())\n",
        "#     sorted_k, unsorted_all, sorted_all = get_relevant_docs(query, q_type, n, model_type)\n",
        "#     rel_adju = queries[query][ann_type]\n",
        "#     candidates = queries[query]['cands']\n",
        "#     score = dict()\n",
        "#     for i in range(len(candidates)):\n",
        "#       score[candidates[i]] = rel_adju[i]\n",
        "#     precision_list = []\n",
        "#     for doc_id in unsorted_all.keys():\n",
        "#       precision_list.append(1 if score[doc_id] >= 2 else 0)\n",
        "#     rp += r_precision(precision_list)\n",
        "#     sorted_rel = []\n",
        "#     for tup in sorted_k:\n",
        "#       sorted_rel.append(1 if score[tup[0]] >= 2 else 0)\n",
        "#     rs.append(sorted_rel)\n",
        "#     r = np.asarray(rel_adju)\n",
        "#     z = r.nonzero()[0]\n",
        "#     t = z.size\n",
        "#     sorted_rel2 = []\n",
        "#     # print(len(score), score)\n",
        "#     for i in sorted_all:\n",
        "#       sorted_rel2.append(1 if score[i[0]] >= 2 else 0)\n",
        "#     rc.append((sorted_rel2, len(rel_adju), n))\n",
        "#     cnt = 0;\n",
        "#     for i in rel_adju:\n",
        "#       if i >= 2:\n",
        "#         cnt += 1\n",
        "#     r20 += recall_at_k(sorted_rel2, n, cnt)\n",
        "#     # print(sorted_rel2, sorted_all)\n",
        "#     p = t/len(rel_adju)\n",
        "#     # k = p * len(rel_adju) # ndcg100\n",
        "#     # k20 = k/5             # ndcg20\n",
        "#     sorted = []\n",
        "#     for i in sorted_all:\n",
        "#       sorted.append(score[i[0]])\n",
        "#     ndcg100 += ndcg_at_k(sorted,len(rel_adju))\n",
        "#     ndcg20 += ndcg_at_k(sorted, int(0.2*len(rel_adju)))\n",
        "#     ndcg_20 += ndcg_at_k(sorted, 20)\n",
        "\n",
        "#   rp = rp/len(queries)\n",
        "#   # print(rs)\n",
        "#   p20 = mean_average_precision(rs)\n",
        "#   ndcg100 = ndcg100/len(queries)\n",
        "#   ndcg20 = ndcg20/len(queries)\n",
        "#   ndcg_20 /= len(queries)\n",
        "#   r20 /= len(queries)\n",
        "#   return rp, p20, r20, ndcg100, ndcg20, ndcg_20"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UnpBy5qFfgOe",
        "outputId": "d5389232-13b6-4492-dc70-be9736c783fa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10014168\n",
            "10695055\n",
            "5764728\n",
            "51977123\n",
            "929877\n",
            "3264891\n",
            "1936997\n",
            "11844559\n",
            "8781666\n",
            "1587\n",
            "189897839\n",
            "6173686\n",
            "7898033\n",
            "52194540\n",
            "1791179\n",
            "6431039\n",
            "(0.13661103284394546, 0.494374771615786, 0.3848256930977519, 0.7346210264547696, 0.4921747233089462, 0.48192747439747863)\n"
          ]
        }
      ],
      "source": [
        "# print(evaluation_all(\"background\", 20, \"tf_idf\", \"relevance_adju\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yVVGplKSgcwD",
        "outputId": "40a49312-8ca0-4769-f2a7-bc022e9f2a6b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1198964\n",
            "11310392\n",
            "10010426\n",
            "53080736\n",
            "53082542\n",
            "1791179\n",
            "2468783\n",
            "80628431\n",
            "189897839\n",
            "52194540\n",
            "929877\n",
            "1936997\n",
            "102353905\n",
            "5052952\n",
            "13949438\n",
            "174799296\n",
            "5270848\n",
            "[[1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1], [0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1], [1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1], [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0], [1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]\n",
            "[1.0, 0.2222222222222222, 0.2, 0.2]\n",
            "[0.5, 0.4, 0.42857142857142855, 0.5, 0.35714285714285715, 0.4, 0.3684210526315789, 0.4]\n",
            "[0.3333333333333333, 0.5, 0.6, 0.23529411764705882]\n",
            "[0.3333333333333333, 0.5, 0.375, 0.3333333333333333, 0.38461538461538464]\n",
            "[0.5, 0.6666666666666666, 0.6, 0.4444444444444444, 0.4166666666666667, 0.4, 0.3888888888888889, 0.42105263157894735]\n",
            "[0.2, 0.125]\n",
            "[1.0, 1.0, 1.0, 1.0, 0.7142857142857143, 0.6666666666666666, 0.7, 0.7272727272727273, 0.75, 0.7692307692307693, 0.7857142857142857, 0.8]\n",
            "[0.3333333333333333, 0.25, 0.3, 0.2857142857142857, 0.3333333333333333, 0.3333333333333333]\n",
            "[0.2, 0.18181818181818182, 0.21428571428571427, 0.23529411764705882, 0.25]\n",
            "[1.0, 1.0, 1.0, 0.6666666666666666, 0.7142857142857143, 0.46153846153846156, 0.5, 0.5333333333333333, 0.5625, 0.5882352941176471]\n",
            "[0.5, 0.14285714285714285]\n",
            "[0.1111111111111111, 0.15384615384615385, 0.1875, 0.2222222222222222, 0.2631578947368421, 0.3]\n",
            "[0.16666666666666666, 0.2857142857142857, 0.17647058823529413, 0.2]\n",
            "[0.125, 0.2222222222222222, 0.16666666666666666, 0.21052631578947367]\n",
            "[0.5, 0.6666666666666666, 0.3333333333333333, 0.36363636363636365, 0.2777777777777778]\n",
            "[1.0, 0.3333333333333333, 0.375, 0.4444444444444444, 0.38461538461538464, 0.4, 0.4375, 0.4444444444444444]\n",
            "[0.2]\n",
            "46 20\n",
            "44 20\n",
            "47 20\n",
            "32 20\n",
            "36 20\n",
            "28 20\n",
            "47 20\n",
            "20 20\n",
            "22 20\n",
            "40 20\n",
            "12 20\n",
            "21 20\n",
            "30 20\n",
            "12 20\n",
            "18 20\n",
            "21 20\n",
            "15 20\n",
            "(0.24675864602942235, 0.37306985997849457, 0.7421030966699464, 0.2251083500288169, 0.18711581420740975)\n"
          ]
        }
      ],
      "source": [
        "# print(evaluation_all(\"method\", 20, \"tf_idf\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nG08M_xV7N44",
        "outputId": "6ed3e7ae-2b84-42a9-ad17-876409ac7144"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1198964\n",
            "10052042\n",
            "11629674\n",
            "1587\n",
            "174799296\n",
            "1306065\n",
            "53080736\n",
            "2090262\n",
            "2865563\n",
            "11844559\n",
            "2468783\n",
            "5052952\n",
            "2360770\n",
            "3264891\n",
            "6431039\n",
            "80628431\n",
            "8781666\n",
            "(0.0880586354048245, 0.35067429955376467, 0.25663682722506254, 0.6040823229209368, 0.3296320802937453, 0.3192982995205753)\n"
          ]
        }
      ],
      "source": [
        "# print(evaluation_all(\"result\", 20, \"tf_idf\", \"relevance_adju\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BJQn6tIzHFQY",
        "outputId": "adbe919f-2885-4705-a932-16afc364aee3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1198964\n",
            "10052042\n",
            "11629674\n",
            "1587\n",
            "174799296\n",
            "1306065\n",
            "53080736\n",
            "2090262\n",
            "2865563\n",
            "11844559\n",
            "2468783\n",
            "5052952\n",
            "2360770\n",
            "3264891\n",
            "6431039\n",
            "80628431\n",
            "8781666\n",
            "(0.0880586354048245, 0.3695165599948101, 0.4166578519519696, 0.6588360002600858, 0.42400150289245425, 0.4072351074140363)\n"
          ]
        }
      ],
      "source": [
        "# pr1, p20_1, r20_1, ndcg_1, ndcg_20_1, ndcgat20_1 = evaluation_all(\"result\", 20, \"bert_nli\", \"relevance_max\")\n",
        "# # pr2, p20_2, r20_2, ndcg_2, ndcg_20_2, ndcgat20_2 = evaluation_all(\"result\", 20, \"bert_nli\", \"relevance_ann4\")\n",
        "# # pr3, p20_3, r20_3, ndcg_3, ndcg_20_3, ndcgat20_3 = evaluation_all(\"result\", 20, \"bert_nli\", \"relevance_ann2\")\n",
        "# pr4, p20_4, r20_4, ndcg_4, ndcg_20_4, ndcgat20_4 = evaluation_all(\"result\", 20, \"bert_nli\", \"relevance_min\")\n",
        "# pr5, p20_5, r20_5, ndcg_5, ndcg_20_5, ndcgat20_5 = evaluation_all(\"result\", 20, \"bert_nli\", \"relevance_adju\")\n",
        "\n",
        "# print(np.mean(np.array([pr1, pr4, pr4])))\n",
        "# print(np.mean(np.array([p20_1, p20_4, p20_5])))\n",
        "# print(np.mean(np.array([r20_1, r20_4, r20_5])))\n",
        "# print(np.mean(np.array([ndcg_1, ndcg_4, ndcg_5])))\n",
        "# print(np.mean(np.array([ndcg_20_1, ndcg_20_4, ndcg_20_5])))\n",
        "# print(evaluation_all(\"result\", 20, \"bert_nli\", \"relevance_adju\"))\n",
        "# print(evaluation_all(\"result\", 20, \"bert_nli\", \"relevance_adju\"))\n",
        "# print(evaluation_all(\"result\", 20, \"bert_nli\", \"relevance_min\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "0DaUn7xx7RAa",
        "outputId": "23bf7f4e-b115-4b40-efb0-0000e03b619a"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-23-302053e1a568>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mrelevant_cnt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mscore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m   \u001b[0mrelevant_cnt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mrelevant_cnt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrelevant_cnt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrelevant_cnt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'score' is not defined"
          ]
        }
      ],
      "source": [
        "# relevant_cnt = []\n",
        "# for x in score.values():\n",
        "#   relevant_cnt.append(x)\n",
        "# relevant_cnt = (np.asarray(relevant_cnt) != 0).size\n",
        "# print(relevant_cnt)\n",
        "# temp = []\n",
        "# for i in relevant:\n",
        "#   if i[0] in score.keys():\n",
        "#     temp.append(score[i[0]])\n",
        "\n",
        "# print(recall_at_k(temp, 10, relevant_cnt))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OejAstCsHMR1"
      },
      "outputs": [],
      "source": [
        "data = json.load(open('/content/drive/MyDrive/data/test-pid2anns-csfcube-background.json'))\n",
        "for query in data:\n",
        "  retreived = list_former(\"10695055\", \"background\", len(data[\"10695055\"]['cands']))\n",
        "candidates = data['10695055']['cands']\n",
        "rel_max = data['10695055']['relevance_max']\n",
        "score = dict()\n",
        "for i in range(len(candidates)):\n",
        "  score[candidates[i]] = rel_max[i]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EFS3uAyeHQj9"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VwJ2MQ0f6nlL"
      },
      "source": [
        "## Bert NLI and PP"
      ]
    },
    {
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        },
        "id": "AeTszapTNMwK",
        "outputId": "8dd5ff9e-2242-4a28-903a-0d94e1d8c29f"
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      ],
      "source": [
        "model_sent_bert_nli = SentenceTransformer('nli-roberta-base-v2')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kXRfrFWGNOjS"
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      "source": [
        "model_sent_bert_pp = SentenceTransformer('paraphrase-TinyBERT-L6-v2')"
      ]
    },
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        "outputId": "aab58c96-bbe5-4277-f545-da4380665307"
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      "outputs": [
        {
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          "metadata": {}
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      "source": [
        "model_all_mpnet_base_v2 = SentenceTransformer('all-mpnet-base-v2')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## BERT NLI"
      ],
      "metadata": {
        "id": "-03vKtGInNMD"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IDB7dQtqNJqP",
        "outputId": "e6df7abd-a8e2-48ed-f845-236f506c878e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "len(err_pids) = 0\n",
            "[]\n"
          ]
        }
      ],
      "source": [
        "# bert_nli all\n",
        "d = dict()\n",
        "err_pids = list()\n",
        "i = 0\n",
        "for doc in docs:\n",
        "    try:\n",
        "        do = docs[doc].abstract\n",
        "        if len(do) == 0:\n",
        "            d[pid] = []\n",
        "            continue\n",
        "        bert_nli_vectors = np.array(get_bert_nli_embedding(\" \".join(do))).tolist()\n",
        "        d[pid] = bert_nli_vectors\n",
        "    except:\n",
        "        err_pids.append(pid)\n",
        "print(f'len(err_pids) = {len(err_pids)}')\n",
        "print(err_pids)\n",
        "with open(\"/content/drive/MyDrive/Results/bert_nli/all.json\", \"w\") as outfile:\n",
        "    json.dump(d, outfile)\n",
        "d = json.load(open('/content/drive/MyDrive/Results/bert_nli/all.json'),\n",
        "              parse_float=lambda x: round(float(x), 6))\n",
        "with open('/content/drive/MyDrive/Results/bert_nli/all.json', \"w\") as outfile:\n",
        "    json.dump(d, outfile)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nz6QZF7KOHYz",
        "outputId": "bab6c253-296e-4f4c-8bb5-5ea6ffe7c816"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "len(err_pids), background = 0\n",
            "[]\n",
            "len(err_pids), method = 0\n",
            "[]\n",
            "len(err_pids), result = 0\n",
            "[]\n"
          ]
        }
      ],
      "source": [
        "# bert_nli queries\n",
        "for i, ids in enumerate([background_query_ids, method_query_ids, result_query_ids]):\n",
        "    d = dict()\n",
        "    err_pids = list()\n",
        "    s = ''\n",
        "    if i == 0:\n",
        "        s = 'background'\n",
        "    elif i == 1:\n",
        "        s = 'method'\n",
        "    elif i == 2:\n",
        "        s = 'result'\n",
        "    for pid in ids:\n",
        "        try:\n",
        "            doc = get_data_by_facet(pid, s)\n",
        "            if len(doc) == 0:\n",
        "                d[pid] = []\n",
        "                continue\n",
        "            bert_nli_query_vectors = np.array(get_bert_nli_embedding(\" \".join(doc))).tolist()\n",
        "            d[pid] = bert_nli_query_vectors\n",
        "        except:\n",
        "            err_pids.append(pid)\n",
        "    print(f'len(err_pids), {s} = {len(err_pids)}')\n",
        "    print(err_pids)\n",
        "    with open(f'/content/drive/MyDrive/Results/bert_nli/{s}.json', \"w\") as outfile:\n",
        "        json.dump(d, outfile)\n",
        "    d = json.load(open(f'/content/drive/MyDrive/Results/bert_nli/{s}.json'),\n",
        "                parse_float=lambda x: round(float(x), 6))\n",
        "    with open(f'/content/drive/MyDrive/Results/bert_nli/{s}.json', \"w\") as outfile:\n",
        "        json.dump(d, outfile)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## METRICS"
      ],
      "metadata": {
        "id": "FuYWmIOvm821"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gFoVxreJWCgj"
      },
      "outputs": [],
      "source": [
        "facet2folds = {\n",
        "    \"background\": {\"fold1_dev\": [\"3264891_background\", \"1936997_background\", \"11844559_background\",\n",
        "                                 \"52194540_background\", \"1791179_background\", \"6431039_background\",\n",
        "                                 \"6173686_background\", \"7898033_background\"],\n",
        "                   \"fold2_dev\": [\"5764728_background\", \"10014168_background\", \"10695055_background\",\n",
        "                                 \"929877_background\", \"1587_background\", \"51977123_background\",\n",
        "                                 \"8781666_background\", \"189897839_background\"],\n",
        "                   \"fold1_test\": [\"5764728_background\", \"10014168_background\", \"10695055_background\",\n",
        "                                  \"929877_background\", \"1587_background\", \"51977123_background\",\n",
        "                                  \"8781666_background\", \"189897839_background\"],\n",
        "                   \"fold2_test\": [\"3264891_background\", \"1936997_background\", \"11844559_background\",\n",
        "                                  \"52194540_background\", \"1791179_background\", \"6431039_background\",\n",
        "                                  \"6173686_background\", \"7898033_background\"]},\n",
        "    \"method\": {\"fold1_dev\": [\"189897839_method\", \"1791179_method\", \"11310392_method\", \"2468783_method\",\n",
        "                             \"13949438_method\", \"5270848_method\", \"52194540_method\", \"929877_method\"],\n",
        "               \"fold2_dev\": [\"5052952_method\", \"10010426_method\", \"102353905_method\", \"174799296_method\",\n",
        "                             \"1198964_method\", \"53080736_method\", \"1936997_method\", \"80628431_method\",\n",
        "                             \"53082542_method\"],\n",
        "               \"fold1_test\": [\"5052952_method\", \"10010426_method\", \"102353905_method\", \"174799296_method\",\n",
        "                              \"1198964_method\", \"53080736_method\", \"1936997_method\", \"80628431_method\",\n",
        "                              \"53082542_method\"],\n",
        "               \"fold2_test\": [\"189897839_method\", \"1791179_method\", \"11310392_method\", \"2468783_method\",\n",
        "                              \"13949438_method\", \"5270848_method\", \"52194540_method\", \"929877_method\"]},\n",
        "    \"result\": {\"fold1_dev\": [\"2090262_result\", \"174799296_result\", \"11844559_result\", \"2468783_result\",\n",
        "                             \"1306065_result\", \"5052952_result\", \"3264891_result\", \"8781666_result\"],\n",
        "               \"fold2_dev\": [\"2865563_result\", \"10052042_result\", \"11629674_result\", \"1587_result\",\n",
        "                             \"1198964_result\", \"53080736_result\", \"2360770_result\", \"80628431_result\",\n",
        "                             \"6431039_result\"],\n",
        "               \"fold1_test\": [\"2865563_result\", \"10052042_result\", \"11629674_result\", \"1587_result\",\n",
        "                              \"1198964_result\", \"53080736_result\", \"2360770_result\", \"80628431_result\",\n",
        "                              \"6431039_result\"],\n",
        "               \"fold2_test\": [\"2090262_result\", \"174799296_result\", \"11844559_result\", \"2468783_result\",\n",
        "                              \"1306065_result\", \"5052952_result\", \"3264891_result\", \"8781666_result\"]},\n",
        "    \"all\": {\"fold1_dev\": [\"3264891_background\", \"1936997_background\", \"11844559_background\",\n",
        "                          \"52194540_background\", \"1791179_background\", \"6431039_background\",\n",
        "                          \"6173686_background\", \"7898033_background\", \"189897839_method\",\n",
        "                          \"1791179_method\", \"11310392_method\", \"2468783_method\", \"13949438_method\",\n",
        "                          \"5270848_method\", \"52194540_method\", \"929877_method\", \"2090262_result\",\n",
        "                          \"174799296_result\", \"11844559_result\", \"2468783_result\", \"1306065_result\",\n",
        "                          \"5052952_result\", \"3264891_result\", \"8781666_result\"],\n",
        "            \"fold2_dev\": [\"5764728_background\", \"10014168_background\", \"10695055_background\",\n",
        "                          \"929877_background\", \"1587_background\", \"51977123_background\",\n",
        "                          \"8781666_background\", \"189897839_background\", \"5052952_method\", \"10010426_method\",\n",
        "                          \"102353905_method\", \"174799296_method\", \"1198964_method\", \"53080736_method\",\n",
        "                          \"1936997_method\", \"80628431_method\", \"53082542_method\", \"2865563_result\",\n",
        "                          \"10052042_result\", \"11629674_result\", \"1587_result\", \"1198964_result\",\n",
        "                          \"53080736_result\", \"2360770_result\", \"80628431_result\", \"6431039_result\"],\n",
        "            \"fold1_test\": [\"5764728_background\", \"10014168_background\", \"10695055_background\",\n",
        "                           \"929877_background\", \"1587_background\", \"51977123_background\", \"8781666_background\",\n",
        "                           \"189897839_background\", \"5052952_method\", \"10010426_method\", \"102353905_method\",\n",
        "                           \"174799296_method\", \"1198964_method\", \"53080736_method\", \"1936997_method\",\n",
        "                           \"80628431_method\", \"53082542_method\", \"2865563_result\", \"10052042_result\",\n",
        "                           \"11629674_result\", \"1587_result\", \"1198964_result\", \"53080736_result\",\n",
        "                           \"2360770_result\", \"80628431_result\", \"6431039_result\"],\n",
        "            \"fold2_test\": [\"3264891_background\", \"1936997_background\", \"11844559_background\",\n",
        "                           \"52194540_background\", \"1791179_background\", \"6431039_background\",\n",
        "                           \"6173686_background\", \"7898033_background\", \"189897839_method\", \"1791179_method\",\n",
        "                           \"11310392_method\", \"2468783_method\", \"13949438_method\", \"5270848_method\",\n",
        "                           \"52194540_method\", \"929877_method\", \"2090262_result\", \"174799296_result\",\n",
        "                           \"11844559_result\", \"2468783_result\", \"1306065_result\", \"5052952_result\",\n",
        "                           \"3264891_result\", \"8781666_result\"]\n",
        "            }\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P5fHyNYeILMK"
      },
      "outputs": [],
      "source": [
        "def read_facet_specific_relevances(data_path, run_path, dataset, facet, method_name):\n",
        "    \"\"\"\n",
        "    Read the gold data and the model rankings and the relevances for the\n",
        "    model.\n",
        "    :param data_path: string; directory with gold citations for test pids and rankings\n",
        "        from baseline methods in subdirectories.\n",
        "    :param run_path: string; directory with ranked candidates for baselines a subdir of\n",
        "        data_path else is a model run.\n",
        "    :param method_name: string; method with which ranks were created.\n",
        "    :param dataset: string; eval dataset.\n",
        "    :param facet: string; facet for eval.\n",
        "    :return: qpid2rankedcand_relevances: dict('qpid_facet': [relevances]);\n",
        "        candidate gold relevances for the candidates in order ranked by the\n",
        "        model.\n",
        "    \"\"\"\n",
        "    gold_fname = os.path.join(data_path, 'test-pid2anns-{:s}-{:s}.json'.format(dataset, facet))\n",
        "    ranked_fname = os.path.join(run_path, method_name, 'test-pid2pool-{:s}-{:s}-{:s}-ranked.json'.\n",
        "                                format(dataset, method_name, facet))\n",
        "    # Load gold test data (citations).\n",
        "    with codecs.open(gold_fname, 'r', 'utf-8') as fp:\n",
        "        pid2pool_source = json.load(fp)\n",
        "        num_query = len(pid2pool_source)\n",
        "        print('Gold query pids: {:d}'.format(num_query))\n",
        "        pid2rels_gold = {}\n",
        "        for qpid, pool_rel in pid2pool_source.items():\n",
        "            pool = pool_rel['cands']\n",
        "            cands_rels = pool_rel['relevance_adju']\n",
        "            pid2rels_gold['{:s}_{:s}'.format(qpid, facet)] = dict([(pid, rel) for pid, rel in zip(pool, cands_rels)])\n",
        "    # Load ranked predictions on test data with methods.\n",
        "    with codecs.open(ranked_fname, 'r', 'utf-8') as fp:\n",
        "        pid2ranks = json.load(fp)\n",
        "        print('Valid ranked query pids: {:d}'.format(len(pid2ranks)))\n",
        "        qpid2rankedcand_relevances = {}\n",
        "        for qpid, citranks in pid2ranks.items():\n",
        "            candpids = [pid_score[0] for pid_score in citranks]\n",
        "            cand_relevances = [pid2rels_gold['{:s}_{:s}'.format(qpid, facet)][pid] for pid in candpids]\n",
        "            qpid2rankedcand_relevances['{:s}_{:s}'.format(qpid, facet)] = cand_relevances\n",
        "    return qpid2rankedcand_relevances\n",
        "\n",
        "\n",
        "def read_all_facet_relevances(data_path, run_path, dataset, method_name, facets):\n",
        "    \"\"\"\n",
        "    Read the gold data and the model rankings and the relevances for the\n",
        "    model.\n",
        "    :param data_path: string; directory with gold citations for test pids and rankings\n",
        "        from baseline methods in subdirectories.\n",
        "    :param run_path: string; directory with ranked candidates for baselines a subdir of\n",
        "        data_path else is a model run.\n",
        "    :param method_name: string; method with which ranks were created.\n",
        "    :param dataset: string; eval dataset.\n",
        "    :param facets: list(string); what facets to read/what counts as \"all\".\n",
        "    :return: qpid2rankedcand_relevances: dict('qpid_facet': [relevances]);\n",
        "        candidate gold relevances for the candidates in order ranked by the\n",
        "        model.\n",
        "    \"\"\"\n",
        "    qpid2rankedcand_relevances = {}\n",
        "    for facet in facets:\n",
        "        print('Reading facet: {:s}'.format(facet))\n",
        "        gold_fname = os.path.join(data_path, 'test-pid2anns-{:s}-{:s}.json'.format(dataset, facet))\n",
        "        ranked_fname = os.path.join(run_path, method_name, 'test-pid2pool-{:s}-{:s}-{:s}-ranked.json'.\n",
        "                                    format(dataset, method_name, facet))\n",
        "        # Load gold test data (citations).\n",
        "        with codecs.open(gold_fname, 'r', 'utf-8') as fp:\n",
        "            pid2pool_source = json.load(fp)\n",
        "            num_query = len(pid2pool_source)\n",
        "            print('Gold query pids: {:d}'.format(num_query))\n",
        "            pid2rels_gold = {}\n",
        "            for qpid, pool_rel in pid2pool_source.items():\n",
        "                pool = pool_rel['cands']\n",
        "                cands_rels = pool_rel['relevance_adju']\n",
        "                pid2rels_gold['{:s}_{:s}'.format(qpid, facet)] = \\\n",
        "                    dict([(pid, rel) for pid, rel in zip(pool, cands_rels)])\n",
        "        # Load ranked predictions on test data with methods.\n",
        "        with codecs.open(ranked_fname, 'r', 'utf-8') as fp:\n",
        "            pid2ranks = json.load(fp)\n",
        "            print('Valid ranked query pids: {:d}'.format(len(pid2ranks)))\n",
        "            for qpid, citranks in pid2ranks.items():\n",
        "                candpids = [pid_score[0] for pid_score in citranks]\n",
        "                cand_relevances = [pid2rels_gold['{:s}_{:s}'.format(qpid, facet)][pid] for pid in candpids]\n",
        "                qpid2rankedcand_relevances['{:s}_{:s}'.format(qpid, facet)] = cand_relevances\n",
        "    print('Total queries: {:d}'.format(len(qpid2rankedcand_relevances)))\n",
        "    return qpid2rankedcand_relevances"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sGjPlUetIMLO"
      },
      "outputs": [],
      "source": [
        "def compute_metrics(ranked_judgements, pr_atk, threshold_grade):\n",
        "    \"\"\"\n",
        "    Given the ranked judgements compute the metrics for a query.\n",
        "    :param ranked_judgements: list(int); graded or binary relevances in rank order.\n",
        "    :param pr_atk: int; the @K value to use for computing precision and recall.\n",
        "    :param threshold_grade: int; Assuming 0-3 graded relevances, threshold at some point\n",
        "        and convert graded to binary relevance.\n",
        "    :return:\n",
        "    \"\"\"\n",
        "    graded_judgements = ranked_judgements\n",
        "    ranked_judgements = [1 if rel >= threshold_grade else 0 for rel in graded_judgements]\n",
        "    # Use the full set of candidate not the pr_atk.\n",
        "    ndcg = ndcg_at_k(graded_judgements, len(ranked_judgements))\n",
        "    ndcg_pr = ndcg_at_k(graded_judgements, int(0.20*len(ranked_judgements)))\n",
        "    ndcg_20 = ndcg_at_k(graded_judgements, 20)\n",
        "    max_total_relevant = sum(ranked_judgements)\n",
        "    recall = recall_at_k(ranked_rel=ranked_judgements,\n",
        "                         atk=pr_atk, max_total_relevant=max_total_relevant)\n",
        "    precision = precision_at_k(r=ranked_judgements, k=pr_atk)\n",
        "    precision_r = r_precision(r=ranked_judgements)\n",
        "    av_precision = average_precision(r=ranked_judgements)\n",
        "    metrics = {\n",
        "        'recall': float(recall),\n",
        "        'precision': float(precision),\n",
        "        'r_precision': float(precision_r),\n",
        "        'av_precision': float(av_precision),\n",
        "        'ndcg': ndcg,\n",
        "        'ndcg@20': ndcg_20,\n",
        "        'ndcg%20': ndcg_pr\n",
        "    }\n",
        "    return metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_45OBUBJIUPH"
      },
      "outputs": [],
      "source": [
        "def aggregate_metrics_crossval(query_metrics, split_str, facet_str):\n",
        "    \"\"\"\n",
        "    Given metrics over individual queries aggregate over different\n",
        "    queries.\n",
        "    :param query_metrics: dict(query_id: metrics_dict from compute_metrics)\n",
        "    :param split_str: string; {dev, test}\n",
        "    :param facet_str: string; {background, method, result}\n",
        "    :return:\n",
        "    \"\"\"\n",
        "    aggmetrics = {\n",
        "        'precision': [],\n",
        "        'recall': [],\n",
        "        'r_precision': [],\n",
        "        'mean_av_precision': [],\n",
        "        'ndcg': [],\n",
        "        'ndcg@20': [],\n",
        "        'ndcg%20': []\n",
        "    }\n",
        "    # For dev only use a part of the fold - using both makes it identical to test.\n",
        "    if split_str == 'dev':\n",
        "        folds = ['fold1_{:s}'.format(split_str)]\n",
        "    elif split_str == 'test':\n",
        "        folds = ['fold1_{:s}'.format(split_str), 'fold2_{:s}'.format(split_str)]\n",
        "    for fold_str in folds:\n",
        "        fold_pids = facet2folds[facet_str][fold_str]\n",
        "        precision, recall, f1, av_precision, mrr, ndcg, r_precision = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0\n",
        "        ndcg_20, ndcg_pr = 0.0, 0.0\n",
        "        for query_id in fold_pids:\n",
        "            # Aggregate across paper types in the fold.\n",
        "            metrics = query_metrics[query_id]\n",
        "            # Aggregate across all papers in the fold\n",
        "            precision += metrics['precision']\n",
        "            recall += metrics['recall']\n",
        "            av_precision += metrics['av_precision']\n",
        "            r_precision += metrics['r_precision']\n",
        "            ndcg += metrics['ndcg']\n",
        "            ndcg_20 += metrics['ndcg@20']\n",
        "            ndcg_pr += metrics['ndcg%20']\n",
        "        # Average all folds\n",
        "        num_queries = len(fold_pids)\n",
        "        precision, recall, f1 = precision/num_queries, recall/num_queries, f1/num_queries\n",
        "        av_precision = av_precision/num_queries\n",
        "        mrr, ndcg, r_precision = mrr/num_queries, ndcg/num_queries, r_precision/num_queries\n",
        "        ndcg_20, ndcg_pr = ndcg_20/num_queries, ndcg_pr/num_queries\n",
        "        # Save the averaged metric for every fold.\n",
        "        aggmetrics['precision'].append(precision)\n",
        "        aggmetrics['recall'].append(recall)\n",
        "        aggmetrics['r_precision'].append(r_precision)\n",
        "        aggmetrics['mean_av_precision'].append(av_precision)\n",
        "        aggmetrics['ndcg'].append(ndcg)\n",
        "        aggmetrics['ndcg@20'].append(ndcg_20)\n",
        "        aggmetrics['ndcg%20'].append(ndcg_pr)\n",
        "\n",
        "    aggmetrics = {\n",
        "        'precision': statistics.mean(aggmetrics['precision']),\n",
        "        'recall': statistics.mean(aggmetrics['recall']),\n",
        "        'r_precision': statistics.mean(aggmetrics['r_precision']),\n",
        "        'mean_av_precision': statistics.mean(aggmetrics['mean_av_precision']),\n",
        "        'ndcg': statistics.mean(aggmetrics['ndcg']),\n",
        "        'ndcg@20': statistics.mean(aggmetrics['ndcg@20']),\n",
        "        'ndcg%20': statistics.mean(aggmetrics['ndcg%20'])\n",
        "    }\n",
        "    return aggmetrics\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oolDNOREIU-Q"
      },
      "outputs": [],
      "source": [
        "def graded_eval_pool_rerank(data_path, run_path, method_name, dataset, facet, split, ATK):\n",
        "    \"\"\"\n",
        "    Evaluate the re-ranked pool for the faceted data. Anns use graded relevance scores.\n",
        "    :param data_path: string; directory with gold citations for test pids and rankings\n",
        "        from baseline methods in subdirectories.\n",
        "    :param run_path: string; directory with ranked candidates for baselines a subdir of\n",
        "        data_path else is a model run.\n",
        "    :param method_name: string; method with which ranks were created.\n",
        "    :param dataset: string; eval dataset.\n",
        "    :param facet: string; facet for eval.\n",
        "    :param split: strong; {dev, test}\n",
        "    :return:\n",
        "    \"\"\"\n",
        "    print(f'EVAL SPLIT: {split}')\n",
        "    if facet == 'all':\n",
        "        qpid2rankedcand_relevances = read_all_facet_relevances(data_path=data_path, run_path=run_path,\n",
        "                                                               dataset=dataset, method_name=method_name,\n",
        "                                                               facets=['background', 'method', 'result'])\n",
        "    else:\n",
        "        qpid2rankedcand_relevances = read_facet_specific_relevances(data_path=data_path, run_path=run_path,\n",
        "                                                                    dataset=dataset, facet=facet,\n",
        "                                                                    method_name=method_name)\n",
        "    # Go over test papers and compute metrics.\n",
        "    all_metrics = {}\n",
        "    num_cands = 0.0\n",
        "    num_queries = 0.0\n",
        "    print('Precision and recall at rank: {:d}'.format(ATK))\n",
        "    for qpid_facet, qranked_judgements in qpid2rankedcand_relevances.items():\n",
        "        all_metrics[qpid_facet] = compute_metrics(qranked_judgements, pr_atk=ATK,\n",
        "                                                  threshold_grade=2)\n",
        "        num_cands += len(qranked_judgements)\n",
        "        num_queries += 1\n",
        "    aggmetrics = aggregate_metrics_crossval(query_metrics=all_metrics, facet_str=facet, split_str=split)\n",
        "    print('Total queries: {:d}; Total candidates: {:d}'.format(int(num_queries), int(num_cands)))\n",
        "    print('R-Precision: {:.4f}'.format(aggmetrics['r_precision']))\n",
        "    print('Precision@{:d}: {:.4f}'.format(ATK, aggmetrics['precision']))\n",
        "    print('Recall@{:d}: {:.4f}'.format(ATK, aggmetrics['recall']))\n",
        "    print('NDCG: {:.4f}'.format(aggmetrics['ndcg']))\n",
        "    print('NDCG@20: {:.4f}'.format(aggmetrics['ndcg@20']))\n",
        "    print('NDCG%20: {:.4f}'.format(aggmetrics['ndcg%20']))\n",
        "    return aggmetrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7SBAnldJ4KnM"
      },
      "outputs": [],
      "source": [
        "def get_metric_scores(method, facet, ATK, dataset='csfcube', data_path='/content/drive/MyDrive/data', run_path='/content/drive/MyDrive/Results'):\n",
        "    if facet != 'all':\n",
        "        data = json.load(open(f'{data_path}/test-pid2anns-{dataset}-{facet}.json'))\n",
        "        corpus = json.load(open(f'{run_path}/{method}/all.json'))\n",
        "        queries = json.load(open(f'{run_path}/{method}/{facet}.json'))\n",
        "        d = dict()\n",
        "        for qid in data:\n",
        "          query = queries[qid]\n",
        "          candidates = data[qid]['cands']\n",
        "          results = dict()\n",
        "          for i in candidates:\n",
        "              results[i] = cosine_similarity(query, corpus[i])\n",
        "          sorted_results = sorted(results.items(), key=lambda kv: (kv[1], kv[0]), reverse=True)\n",
        "          d[qid] = sorted_results\n",
        "        with open(f'{run_path}/{method}/test-pid2pool-{dataset}-{method}-{facet}-ranked.json', 'w') as outfile:\n",
        "          json.dump(d, outfile)\n",
        "    aggmetrics1 = graded_eval_pool_rerank(data_path=data_path,\n",
        "                          method_name=method,\n",
        "                                  facet=facet, dataset=dataset,\n",
        "                          run_path=run_path,\n",
        "                                  split='dev', ATK=ATK)\n",
        "    print()\n",
        "    aggmetrics2 = graded_eval_pool_rerank(data_path=data_path,\n",
        "                          method_name=method,\n",
        "                                  facet=facet, dataset=dataset,\n",
        "                          run_path=run_path,\n",
        "                                  split='test', ATK=ATK)\n",
        "    \n",
        "    print('\\nAVERAGE METRICS')\n",
        "    print('R-Precision: {:.4f}'.format((aggmetrics1['r_precision'] + aggmetrics2['r_precision']) / 2))\n",
        "    print('Precision@{:d}: {:.4f}'.format(ATK, (aggmetrics1['precision'] + aggmetrics2['precision']) / 2))\n",
        "    print('Recall@{:d}: {:.4f}'.format(ATK, (aggmetrics1['recall'] + aggmetrics2['recall']) / 2))\n",
        "    print('NDCG: {:.4f}'.format((aggmetrics1['ndcg'] + aggmetrics2['ndcg']) / 2))\n",
        "    print('NDCG@20: {:.4f}'.format((aggmetrics1['ndcg@20'] + aggmetrics2['ndcg@20']) / 2))\n",
        "    print('NDCG%20: {:.4f}'.format((aggmetrics1['ndcg%20'] + aggmetrics2['ndcg%20']) / 2))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## BERT - PP"
      ],
      "metadata": {
        "id": "a5SH5qeemqB-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GL7Y_zGCzHRE"
      },
      "outputs": [],
      "source": [
        "# bert_pp all\n",
        "d = dict()\n",
        "err_pids = list()\n",
        "i = 0\n",
        "for doc in docs:\n",
        "    # i += 1\n",
        "    # if i % 50 == 0:\n",
        "    #     print(i)\n",
        "    try:\n",
        "        do = docs[doc].abstract\n",
        "        if len(do) == 0:\n",
        "            d[doc] = list()\n",
        "            continue\n",
        "        bert_pp_array = np.array(get_bert_pp_embedding(\" \".join(do))).tolist()\n",
        "        d[doc] = bert_pp_array\n",
        "    except:\n",
        "        err_pids.append(doc)\n",
        "print(f'len(err_pids) = {len(err_pids)}')\n",
        "print(err_pids)\n",
        "with open(\"/content/drive/MyDrive/Results/bert_pp/all.json\", \"w\") as outfile:\n",
        "    json.dump(d, outfile)\n",
        "d = json.load(open('/content/drive/MyDrive/Results/bert_pp/all.json'),\n",
        "              parse_float=lambda x: round(float(x), 6))\n",
        "with open('/content/drive/MyDrive/Results/bert_pp/all.json', \"w\") as outfile:\n",
        "    json.dump(d, outfile)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vhRAj3D9zbJ0",
        "outputId": "dfad3fd5-265b-49a6-d33d-3da826d9b906"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "len(err_pids), background = 0\n",
            "[]\n",
            "len(err_pids), method = 0\n",
            "[]\n",
            "len(err_pids), result = 0\n",
            "[]\n"
          ]
        }
      ],
      "source": [
        "# bert_pp queries\n",
        "for i, ids in enumerate([background_query_ids, method_query_ids, result_query_ids]):\n",
        "    d = dict()\n",
        "    err_pids = list()\n",
        "    s = ''\n",
        "    if i == 0:\n",
        "        s = 'background'\n",
        "    elif i == 1:\n",
        "        s = 'method'\n",
        "    elif i == 2:\n",
        "        s = 'result'\n",
        "    for pid in ids:\n",
        "        try:\n",
        "            doc = get_data_by_facet(pid, s)\n",
        "            if len(doc) == 0:\n",
        "                d[pid] = list()\n",
        "                continue\n",
        "            bert_pp_array = np.array(get_bert_pp_embedding(\" \".join(doc))).tolist()\n",
        "            d[pid] = bert_pp_array\n",
        "        except:\n",
        "            err_pids.append(pid)\n",
        "    print(f'len(err_pids), {s} = {len(err_pids)}')\n",
        "    print(err_pids)\n",
        "    with open(f'/content/drive/MyDrive/Results/bert_pp/{s}.json', \"w\") as outfile:\n",
        "        json.dump(d, outfile)\n",
        "    d = json.load(open(f'/content/drive/MyDrive/Results/bert_pp/{s}.json'),\n",
        "                parse_float=lambda x: round(float(x), 6))\n",
        "    with open(f'/content/drive/MyDrive/Results/bert_pp/{s}.json', \"w\") as outfile:\n",
        "        json.dump(d, outfile)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Computing metric scores for TF-IDF, BERT - NLI, BERT - PP"
      ],
      "metadata": {
        "id": "d_7BLprgmwI1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U_wyJFqX_Pzw",
        "outputId": "9ae5106f-d692-4095-de16-6a325846c887"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EVAL SPLIT: dev\n",
            "Gold query pids: 16\n",
            "Valid ranked query pids: 16\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 16; Total candidates: 1877\n",
            "R-Precision: 0.1898\n",
            "Precision@20: 0.2188\n",
            "Recall@20: 0.3730\n",
            "NDCG: 0.7178\n",
            "NDCG@20: 0.4651\n",
            "NDCG%20: 0.4669\n",
            "\n",
            "EVAL SPLIT: test\n",
            "Gold query pids: 16\n",
            "Valid ranked query pids: 16\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 16; Total candidates: 1877\n",
            "R-Precision: 0.1655\n",
            "Precision@20: 0.2344\n",
            "Recall@20: 0.3848\n",
            "NDCG: 0.7346\n",
            "NDCG@20: 0.4819\n",
            "NDCG%20: 0.4922\n",
            "\n",
            "AVERAGE METRICS\n",
            "R-Precision: 0.1777\n",
            "Precision@20: 0.2266\n",
            "Recall@20: 0.3789\n",
            "NDCG: 0.7262\n",
            "NDCG@20: 0.4735\n",
            "NDCG%20: 0.4795\n"
          ]
        }
      ],
      "source": [
        "get_metric_scores('tf_idf', 'background', 20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gHWaEEaTCmBp",
        "outputId": "6d282aaf-78c8-4c5e-f63b-e86559e97f2b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EVAL SPLIT: dev\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 17; Total candidates: 2174\n",
            "R-Precision: 0.0895\n",
            "Precision@20: 0.0813\n",
            "Recall@20: 0.2537\n",
            "NDCG: 0.5544\n",
            "NDCG@20: 0.2582\n",
            "NDCG%20: 0.2574\n",
            "\n",
            "EVAL SPLIT: test\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 17; Total candidates: 2174\n",
            "R-Precision: 0.0888\n",
            "Precision@20: 0.0684\n",
            "Recall@20: 0.2330\n",
            "NDCG: 0.5335\n",
            "NDCG@20: 0.2296\n",
            "NDCG%20: 0.2306\n",
            "\n",
            "AVERAGE METRICS\n",
            "R-Precision: 0.0892\n",
            "Precision@20: 0.0748\n",
            "Recall@20: 0.2434\n",
            "NDCG: 0.5439\n",
            "NDCG@20: 0.2439\n",
            "NDCG%20: 0.2440\n"
          ]
        }
      ],
      "source": [
        "get_metric_scores('tf_idf', 'method', 20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UD6YQuOPC2HL",
        "outputId": "9546c320-5f84-437a-e18c-2eb17df3b5a8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EVAL SPLIT: dev\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 17; Total candidates: 2193\n",
            "R-Precision: 0.1157\n",
            "Precision@20: 0.1500\n",
            "Recall@20: 0.3515\n",
            "NDCG: 0.6769\n",
            "NDCG@20: 0.4336\n",
            "NDCG%20: 0.4347\n",
            "\n",
            "EVAL SPLIT: test\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 17; Total candidates: 2193\n",
            "R-Precision: 0.1009\n",
            "Precision@20: 0.1167\n",
            "Recall@20: 0.2619\n",
            "NDCG: 0.6081\n",
            "NDCG@20: 0.3257\n",
            "NDCG%20: 0.3355\n",
            "\n",
            "AVERAGE METRICS\n",
            "R-Precision: 0.1083\n",
            "Precision@20: 0.1333\n",
            "Recall@20: 0.3067\n",
            "NDCG: 0.6425\n",
            "NDCG@20: 0.3796\n",
            "NDCG%20: 0.3851\n"
          ]
        }
      ],
      "source": [
        "get_metric_scores('tf_idf', 'result', 20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CnTenyP3DFWk",
        "outputId": "a2760c16-07f5-4da9-89fe-ba90abca5d16"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EVAL SPLIT: dev\n",
            "Gold query pids: 16\n",
            "Valid ranked query pids: 16\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 16; Total candidates: 1877\n",
            "R-Precision: 0.2099\n",
            "Precision@20: 0.2687\n",
            "Recall@20: 0.4149\n",
            "NDCG: 0.7735\n",
            "NDCG@20: 0.5695\n",
            "NDCG%20: 0.5731\n",
            "\n",
            "EVAL SPLIT: test\n",
            "Gold query pids: 16\n",
            "Valid ranked query pids: 16\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 16; Total candidates: 1877\n",
            "R-Precision: 0.1908\n",
            "Precision@20: 0.2812\n",
            "Recall@20: 0.4507\n",
            "NDCG: 0.7734\n",
            "NDCG@20: 0.5713\n",
            "NDCG%20: 0.5831\n",
            "\n",
            "AVERAGE METRICS\n",
            "R-Precision: 0.2004\n",
            "Precision@20: 0.2750\n",
            "Recall@20: 0.4328\n",
            "NDCG: 0.7735\n",
            "NDCG@20: 0.5704\n",
            "NDCG%20: 0.5781\n"
          ]
        }
      ],
      "source": [
        "get_metric_scores('bert_nli', 'background', 20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cRa1FfhDDQsx",
        "outputId": "7e6b89b8-1625-4295-80e3-9aa8e74f671c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EVAL SPLIT: dev\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 17; Total candidates: 2174\n",
            "R-Precision: 0.1953\n",
            "Precision@20: 0.1000\n",
            "Recall@20: 0.3207\n",
            "NDCG: 0.5974\n",
            "NDCG@20: 0.3297\n",
            "NDCG%20: 0.3299\n",
            "\n",
            "EVAL SPLIT: test\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 17; Total candidates: 2174\n",
            "R-Precision: 0.1359\n",
            "Precision@20: 0.1056\n",
            "Recall@20: 0.3323\n",
            "NDCG: 0.6139\n",
            "NDCG@20: 0.3415\n",
            "NDCG%20: 0.3488\n",
            "\n",
            "AVERAGE METRICS\n",
            "R-Precision: 0.1656\n",
            "Precision@20: 0.1028\n",
            "Recall@20: 0.3265\n",
            "NDCG: 0.6056\n",
            "NDCG@20: 0.3356\n",
            "NDCG%20: 0.3393\n"
          ]
        }
      ],
      "source": [
        "get_metric_scores('bert_nli', 'method', 20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bpk3tZXBDWVv",
        "outputId": "9e13cdd6-9af2-40ec-ad93-4ee6477681fa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EVAL SPLIT: dev\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 17; Total candidates: 2193\n",
            "R-Precision: 0.1154\n",
            "Precision@20: 0.1750\n",
            "Recall@20: 0.3894\n",
            "NDCG: 0.6493\n",
            "NDCG@20: 0.3945\n",
            "NDCG%20: 0.3922\n",
            "\n",
            "EVAL SPLIT: test\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 17; Total candidates: 2193\n",
            "R-Precision: 0.1402\n",
            "Precision@20: 0.1903\n",
            "Recall@20: 0.4151\n",
            "NDCG: 0.6583\n",
            "NDCG@20: 0.4065\n",
            "NDCG%20: 0.4222\n",
            "\n",
            "AVERAGE METRICS\n",
            "R-Precision: 0.1278\n",
            "Precision@20: 0.1826\n",
            "Recall@20: 0.4023\n",
            "NDCG: 0.6538\n",
            "NDCG@20: 0.4005\n",
            "NDCG%20: 0.4072\n"
          ]
        }
      ],
      "source": [
        "get_metric_scores('bert_nli', 'result', 20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TX5093JoDeur",
        "outputId": "8f061fd7-ed62-47c1-defd-2abc47104f50"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EVAL SPLIT: dev\n",
            "Gold query pids: 16\n",
            "Valid ranked query pids: 16\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 16; Total candidates: 1877\n",
            "R-Precision: 0.2480\n",
            "Precision@20: 0.3187\n",
            "Recall@20: 0.5065\n",
            "NDCG: 0.7650\n",
            "NDCG@20: 0.5901\n",
            "NDCG%20: 0.5852\n",
            "\n",
            "EVAL SPLIT: test\n",
            "Gold query pids: 16\n",
            "Valid ranked query pids: 16\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 16; Total candidates: 1877\n",
            "R-Precision: 0.2185\n",
            "Precision@20: 0.3031\n",
            "Recall@20: 0.4983\n",
            "NDCG: 0.7870\n",
            "NDCG@20: 0.6083\n",
            "NDCG%20: 0.6096\n",
            "\n",
            "AVERAGE METRICS\n",
            "R-Precision: 0.2332\n",
            "Precision@20: 0.3109\n",
            "Recall@20: 0.5024\n",
            "NDCG: 0.7760\n",
            "NDCG@20: 0.5992\n",
            "NDCG%20: 0.5974\n"
          ]
        }
      ],
      "source": [
        "get_metric_scores('bert_pp', 'background', 20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uWIhscoXDfwc",
        "outputId": "f622b1ff-e922-41ff-d26d-4e52e24f6d0f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EVAL SPLIT: dev\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 17; Total candidates: 2174\n",
            "R-Precision: 0.2051\n",
            "Precision@20: 0.0813\n",
            "Recall@20: 0.2775\n",
            "NDCG: 0.6290\n",
            "NDCG@20: 0.3614\n",
            "NDCG%20: 0.3762\n",
            "\n",
            "EVAL SPLIT: test\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 17; Total candidates: 2174\n",
            "R-Precision: 0.1602\n",
            "Precision@20: 0.1184\n",
            "Recall@20: 0.4000\n",
            "NDCG: 0.6410\n",
            "NDCG@20: 0.3870\n",
            "NDCG%20: 0.3969\n",
            "\n",
            "AVERAGE METRICS\n",
            "R-Precision: 0.1826\n",
            "Precision@20: 0.0998\n",
            "Recall@20: 0.3388\n",
            "NDCG: 0.6350\n",
            "NDCG@20: 0.3742\n",
            "NDCG%20: 0.3865\n"
          ]
        }
      ],
      "source": [
        "get_metric_scores('bert_pp', 'method', 20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZgsW_QHaDgcF",
        "outputId": "8303ba23-120d-46dc-8838-6c4f5400208f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EVAL SPLIT: dev\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 17; Total candidates: 2193\n",
            "R-Precision: 0.1607\n",
            "Precision@20: 0.2438\n",
            "Recall@20: 0.5856\n",
            "NDCG: 0.7148\n",
            "NDCG@20: 0.5372\n",
            "NDCG%20: 0.5388\n",
            "\n",
            "EVAL SPLIT: test\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 17; Total candidates: 2193\n",
            "R-Precision: 0.1490\n",
            "Precision@20: 0.2108\n",
            "Recall@20: 0.5112\n",
            "NDCG: 0.6949\n",
            "NDCG@20: 0.4786\n",
            "NDCG%20: 0.4978\n",
            "\n",
            "AVERAGE METRICS\n",
            "R-Precision: 0.1548\n",
            "Precision@20: 0.2273\n",
            "Recall@20: 0.5484\n",
            "NDCG: 0.7048\n",
            "NDCG@20: 0.5079\n",
            "NDCG%20: 0.5183\n"
          ]
        }
      ],
      "source": [
        "get_metric_scores('bert_pp', 'result', 20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FM3Ru7MdLcSJ",
        "outputId": "81b88a26-98ea-47b8-af19-d1745b7fbfd1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EVAL SPLIT: dev\n",
            "Reading facet: background\n",
            "Gold query pids: 16\n",
            "Valid ranked query pids: 16\n",
            "Reading facet: method\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Reading facet: result\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Total queries: 50\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 50; Total candidates: 6244\n",
            "R-Precision: 0.1317\n",
            "Precision@20: 0.1500\n",
            "Recall@20: 0.3261\n",
            "NDCG: 0.6497\n",
            "NDCG@20: 0.3856\n",
            "NDCG%20: 0.3863\n",
            "\n",
            "EVAL SPLIT: test\n",
            "Reading facet: background\n",
            "Gold query pids: 16\n",
            "Valid ranked query pids: 16\n",
            "Reading facet: method\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Reading facet: result\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Total queries: 50\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 50; Total candidates: 6244\n",
            "R-Precision: 0.1177\n",
            "Precision@20: 0.1375\n",
            "Recall@20: 0.2906\n",
            "NDCG: 0.6225\n",
            "NDCG@20: 0.3420\n",
            "NDCG%20: 0.3489\n",
            "\n",
            "AVERAGE METRICS\n",
            "R-Precision: 0.1247\n",
            "Precision@20: 0.1437\n",
            "Recall@20: 0.3084\n",
            "NDCG: 0.6361\n",
            "NDCG@20: 0.3638\n",
            "NDCG%20: 0.3676\n"
          ]
        }
      ],
      "source": [
        "get_metric_scores('tf_idf', 'all', 20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t71ZOADvLiyk",
        "outputId": "8ad7c553-60f3-49c0-97d3-4ca807026fc5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EVAL SPLIT: dev\n",
            "Reading facet: background\n",
            "Gold query pids: 16\n",
            "Valid ranked query pids: 16\n",
            "Reading facet: method\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Reading facet: result\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Total queries: 50\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 50; Total candidates: 6244\n",
            "R-Precision: 0.1735\n",
            "Precision@20: 0.1812\n",
            "Recall@20: 0.3750\n",
            "NDCG: 0.6734\n",
            "NDCG@20: 0.4312\n",
            "NDCG%20: 0.4317\n",
            "\n",
            "EVAL SPLIT: test\n",
            "Reading facet: background\n",
            "Gold query pids: 16\n",
            "Valid ranked query pids: 16\n",
            "Reading facet: method\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Reading facet: result\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Total queries: 50\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 50; Total candidates: 6244\n",
            "R-Precision: 0.1550\n",
            "Precision@20: 0.1906\n",
            "Recall@20: 0.3982\n",
            "NDCG: 0.6803\n",
            "NDCG@20: 0.4374\n",
            "NDCG%20: 0.4490\n",
            "\n",
            "AVERAGE METRICS\n",
            "R-Precision: 0.1643\n",
            "Precision@20: 0.1859\n",
            "Recall@20: 0.3866\n",
            "NDCG: 0.6768\n",
            "NDCG@20: 0.4343\n",
            "NDCG%20: 0.4404\n"
          ]
        }
      ],
      "source": [
        "get_metric_scores('bert_nli', 'all', 20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t8h49Ku0LK4e",
        "outputId": "4a92913a-fa6e-423f-b989-c0e75025bd28"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "EVAL SPLIT: dev\n",
            "Reading facet: background\n",
            "Gold query pids: 16\n",
            "Valid ranked query pids: 16\n",
            "Reading facet: method\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Reading facet: result\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Total queries: 50\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 50; Total candidates: 6244\n",
            "R-Precision: 0.2046\n",
            "Precision@20: 0.2146\n",
            "Recall@20: 0.4565\n",
            "NDCG: 0.7029\n",
            "NDCG@20: 0.4962\n",
            "NDCG%20: 0.5001\n",
            "\n",
            "EVAL SPLIT: test\n",
            "Reading facet: background\n",
            "Gold query pids: 16\n",
            "Valid ranked query pids: 16\n",
            "Reading facet: method\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Reading facet: result\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Total queries: 50\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 50; Total candidates: 6244\n",
            "R-Precision: 0.1751\n",
            "Precision@20: 0.2092\n",
            "Recall@20: 0.4697\n",
            "NDCG: 0.7058\n",
            "NDCG@20: 0.4886\n",
            "NDCG%20: 0.4989\n",
            "\n",
            "AVERAGE METRICS\n",
            "R-Precision: 0.1898\n",
            "Precision@20: 0.2119\n",
            "Recall@20: 0.4631\n",
            "NDCG: 0.7043\n",
            "NDCG@20: 0.4924\n",
            "NDCG%20: 0.4995\n"
          ]
        }
      ],
      "source": [
        "get_metric_scores('bert_pp', 'all', 20)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yawShav_P5bL"
      },
      "source": [
        "## Bm25"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IWIbmwsJYrB4"
      },
      "outputs": [],
      "source": [
        "!pip install rank_bm25"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pC8JxfbWP9Fk",
        "outputId": "c68d8cc9-c6dc-4fce-b189-29c9ce3a261a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting rank_bm25\n",
            "  Downloading rank_bm25-0.2.2-py3-none-any.whl (8.6 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from rank_bm25) (1.22.4)\n",
            "Installing collected packages: rank_bm25\n",
            "Successfully installed rank_bm25-0.2.2\n"
          ]
        }
      ],
      "source": [
        "from rank_bm25 import BM25Okapi\n",
        "import numpy as np\n",
        "from gensim.utils import simple_preprocess\n",
        "from gensim.parsing.preprocessing import preprocess_documents, preprocess_string"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hL6C4iwbfEuI"
      },
      "outputs": [],
      "source": [
        "# bm25 all\n",
        "# rectify changes before running\n",
        "tokenized_corpus = list()\n",
        "pid_list = list()\n",
        "d = dict()\n",
        "err_pids = list()\n",
        "i = 0\n",
        "for doc in docs:\n",
        "    try:\n",
        "        do = docs[doc].abstract\n",
        "        if len(do) == 0:\n",
        "            d[pid] = list()\n",
        "            continue\n",
        "        d[pid] = list()\n",
        "        for string in do:\n",
        "            d[pid] = d[pid] + preprocess_string(string)\n",
        "        tokenized_corpus.append(d[pid])\n",
        "        pid_list.append(pid)\n",
        "    except:\n",
        "        err_pids.append(pid)\n",
        "print(f'len(err_pids) = {len(err_pids)}')\n",
        "print(err_pids)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qa9TEqKcdV6F"
      },
      "outputs": [],
      "source": [
        "bm25_index = BM25Okapi(tokenized_corpus)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h2ILaTsjP_k6"
      },
      "outputs": [],
      "source": [
        "def search(search_string, num_results=len(tokenized_corpus)):\n",
        "    search_tokens = preprocess_string(search_string)\n",
        "    scores = bm25_index.get_scores(search_tokens)\n",
        "    top_indexes = np.argsort(scores)[::-1][:num_results]\n",
        "    for i in range(0, 10):\n",
        "      print(scores[i])\n",
        "    return top_indexes, scores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "id": "FDRV9F0WROon",
        "outputId": "dfa1f486-804b-4ceb-dacb-81e7d9674ab6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.0\n",
            "0.0\n",
            "0.0\n",
            "0.0\n",
            "0.0\n",
            "0.0\n",
            "0.0\n",
            "0.0\n",
            "0.0\n",
            "0.0\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'We show that the actual diachronic frequency distributions and those expected under the neutral model do not coincide and conclude that in this case the neutral model does not provide an adequate description of change in ceramic decoration.'"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "indexes, scores = search(\" \".join(get_data_by_facet('143814895', 'method')))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EOfN6B57oB2f"
      },
      "source": [
        "## all-mpnet-base-v2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QEITAyivoGXV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21cbd519-db0b-4584-9fed-02c14f0c88fa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "len(err_pids) = 0\n",
            "[]\n"
          ]
        }
      ],
      "source": [
        "# all_mpnet_base_v2 all\n",
        "d = dict()\n",
        "err_pids = list()\n",
        "i = 0\n",
        "for doc in docs:\n",
        "    try:\n",
        "        do = docs[doc].abstract\n",
        "        if len(do) == 0:\n",
        "            d[doc] = list()\n",
        "            continue\n",
        "        all_mpnet_base_v2_array = np.array(get_all_mpnet_base_v2_embedding(\" \".join(do))).tolist()\n",
        "        d[doc] = all_mpnet_base_v2_array\n",
        "    except:\n",
        "        err_pids.append(doc)\n",
        "print(f'len(err_pids) = {len(err_pids)}')\n",
        "print(err_pids)\n",
        "with open(\"/content/drive/MyDrive/Results/all_mpnet_base_v2/all.json\", \"w\") as outfile:\n",
        "    json.dump(d, outfile)\n",
        "d = json.load(open('/content/drive/MyDrive/Results/all_mpnet_base_v2/all.json'),\n",
        "              parse_float=lambda x: round(float(x), 6))\n",
        "with open('/content/drive/MyDrive/Results/all_mpnet_base_v2/all.json', \"w\") as outfile:\n",
        "    json.dump(d, outfile)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cWiKEEnjoIKY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e94e7fc-7616-449d-c37f-0d2de8f67a28"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "len(err_pids), background = 0\n",
            "[]\n",
            "len(err_pids), method = 0\n",
            "[]\n",
            "len(err_pids), result = 0\n",
            "[]\n"
          ]
        }
      ],
      "source": [
        "# all_mpnet_base_v2 queries\n",
        "for i, ids in enumerate([background_query_ids, method_query_ids, result_query_ids]):\n",
        "    d = dict()\n",
        "    err_pids = list()\n",
        "    s = ''\n",
        "    if i == 0:\n",
        "        s = 'background'\n",
        "    elif i == 1:\n",
        "        s = 'method'\n",
        "    elif i == 2:\n",
        "        s = 'result'\n",
        "    for pid in ids:\n",
        "        try:\n",
        "            doc = get_data_by_facet(pid, s)\n",
        "            if len(doc) == 0:\n",
        "                d[pid] = list()\n",
        "                continue\n",
        "            all_mpnet_base_v2_query_vectors = np.array(get_all_mpnet_base_v2_embedding(\" \".join(doc))).tolist()\n",
        "            d[pid] = all_mpnet_base_v2_query_vectors\n",
        "        except:\n",
        "            err_pids.append(pid)\n",
        "    print(f'len(err_pids), {s} = {len(err_pids)}')\n",
        "    print(err_pids)\n",
        "    with open(f'/content/drive/MyDrive/Results/all_mpnet_base_v2/{s}.json', \"w\") as outfile:\n",
        "        json.dump(d, outfile)\n",
        "    d = json.load(open(f'/content/drive/MyDrive/Results/all_mpnet_base_v2/{s}.json'),\n",
        "                parse_float=lambda x: round(float(x), 6))\n",
        "    with open(f'/content/drive/MyDrive/Results/all_mpnet_base_v2/{s}.json', \"w\") as outfile:\n",
        "        json.dump(d, outfile)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_metric_scores('all_mpnet_base_v2', 'background', 20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JD-emzn3tWFn",
        "outputId": "a23363dd-901e-4750-f2d4-8b091a7b741f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EVAL SPLIT: dev\n",
            "Gold query pids: 16\n",
            "Valid ranked query pids: 16\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 16; Total candidates: 1877\n",
            "R-Precision: 0.2920\n",
            "Precision@20: 0.3500\n",
            "Recall@20: 0.5772\n",
            "NDCG: 0.8451\n",
            "NDCG@20: 0.7066\n",
            "NDCG%20: 0.7006\n",
            "\n",
            "EVAL SPLIT: test\n",
            "Gold query pids: 16\n",
            "Valid ranked query pids: 16\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 16; Total candidates: 1877\n",
            "R-Precision: 0.2673\n",
            "Precision@20: 0.3438\n",
            "Recall@20: 0.5729\n",
            "NDCG: 0.8621\n",
            "NDCG@20: 0.7209\n",
            "NDCG%20: 0.7325\n",
            "\n",
            "AVERAGE METRICS\n",
            "R-Precision: 0.2797\n",
            "Precision@20: 0.3469\n",
            "Recall@20: 0.5750\n",
            "NDCG: 0.8536\n",
            "NDCG@20: 0.7137\n",
            "NDCG%20: 0.7166\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_metric_scores('all_mpnet_base_v2', 'result', 20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TLOhIXhqt3gK",
        "outputId": "e39f132c-41be-4e4a-f160-41b52c8f24bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EVAL SPLIT: dev\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 17; Total candidates: 2193\n",
            "R-Precision: 0.2552\n",
            "Precision@20: 0.3188\n",
            "Recall@20: 0.7516\n",
            "NDCG: 0.8014\n",
            "NDCG@20: 0.6742\n",
            "NDCG%20: 0.6663\n",
            "\n",
            "EVAL SPLIT: test\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 17; Total candidates: 2193\n",
            "R-Precision: 0.2443\n",
            "Precision@20: 0.3038\n",
            "Recall@20: 0.7037\n",
            "NDCG: 0.7784\n",
            "NDCG@20: 0.6309\n",
            "NDCG%20: 0.6330\n",
            "\n",
            "AVERAGE METRICS\n",
            "R-Precision: 0.2498\n",
            "Precision@20: 0.3113\n",
            "Recall@20: 0.7276\n",
            "NDCG: 0.7899\n",
            "NDCG@20: 0.6526\n",
            "NDCG%20: 0.6497\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_metric_scores('all_mpnet_base_v2', 'method', 20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WmhiwcCat-uM",
        "outputId": "ab7f5d6b-6faa-4d59-c416-c6cb1814cdb1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EVAL SPLIT: dev\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 17; Total candidates: 2174\n",
            "R-Precision: 0.2313\n",
            "Precision@20: 0.1688\n",
            "Recall@20: 0.4829\n",
            "NDCG: 0.6519\n",
            "NDCG@20: 0.4378\n",
            "NDCG%20: 0.4365\n",
            "\n",
            "EVAL SPLIT: test\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 17; Total candidates: 2174\n",
            "R-Precision: 0.1697\n",
            "Precision@20: 0.1594\n",
            "Recall@20: 0.4640\n",
            "NDCG: 0.6746\n",
            "NDCG@20: 0.4659\n",
            "NDCG%20: 0.4723\n",
            "\n",
            "AVERAGE METRICS\n",
            "R-Precision: 0.2005\n",
            "Precision@20: 0.1641\n",
            "Recall@20: 0.4734\n",
            "NDCG: 0.6633\n",
            "NDCG@20: 0.4518\n",
            "NDCG%20: 0.4544\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_metric_scores('all_mpnet_base_v2', 'all', 20)"
      ],
      "metadata": {
        "id": "MFk7nj_juChf",
        "outputId": "d73f126e-4f2c-44a7-9322-2efdfd7cdf10",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EVAL SPLIT: dev\n",
            "Reading facet: background\n",
            "Gold query pids: 16\n",
            "Valid ranked query pids: 16\n",
            "Reading facet: method\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Reading facet: result\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Total queries: 50\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 50; Total candidates: 6244\n",
            "R-Precision: 0.2595\n",
            "Precision@20: 0.2792\n",
            "Recall@20: 0.6039\n",
            "NDCG: 0.7662\n",
            "NDCG@20: 0.6062\n",
            "NDCG%20: 0.6012\n",
            "\n",
            "EVAL SPLIT: test\n",
            "Reading facet: background\n",
            "Gold query pids: 16\n",
            "Valid ranked query pids: 16\n",
            "Reading facet: method\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Reading facet: result\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Total queries: 50\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 50; Total candidates: 6244\n",
            "R-Precision: 0.2262\n",
            "Precision@20: 0.2675\n",
            "Recall@20: 0.5800\n",
            "NDCG: 0.7698\n",
            "NDCG@20: 0.6034\n",
            "NDCG%20: 0.6099\n",
            "\n",
            "AVERAGE METRICS\n",
            "R-Precision: 0.2429\n",
            "Precision@20: 0.2733\n",
            "Recall@20: 0.5919\n",
            "NDCG: 0.7680\n",
            "NDCG@20: 0.6048\n",
            "NDCG%20: 0.6055\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_metric_scores('alberta', 'background', 20)"
      ],
      "metadata": {
        "id": "jaMn4k8ouFNL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c227155-531a-4651-ad11-132d8d187082"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EVAL SPLIT: dev\n",
            "Gold query pids: 16\n",
            "Valid ranked query pids: 16\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 16; Total candidates: 1877\n",
            "R-Precision: 0.2723\n",
            "Precision@20: 0.2812\n",
            "Recall@20: 0.3972\n",
            "NDCG: 0.7780\n",
            "NDCG@20: 0.5871\n",
            "NDCG%20: 0.5875\n",
            "\n",
            "EVAL SPLIT: test\n",
            "Gold query pids: 16\n",
            "Valid ranked query pids: 16\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 16; Total candidates: 1877\n",
            "R-Precision: 0.2297\n",
            "Precision@20: 0.2844\n",
            "Recall@20: 0.4266\n",
            "NDCG: 0.7839\n",
            "NDCG@20: 0.5917\n",
            "NDCG%20: 0.6027\n",
            "\n",
            "AVERAGE METRICS\n",
            "R-Precision: 0.2510\n",
            "Precision@20: 0.2828\n",
            "Recall@20: 0.4119\n",
            "NDCG: 0.7809\n",
            "NDCG@20: 0.5894\n",
            "NDCG%20: 0.5951\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_metric_scores('alberta', 'result', 20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fKGNLGbSvcuR",
        "outputId": "c82865ac-a117-4e44-b983-9042a1ef1e28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EVAL SPLIT: dev\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 17; Total candidates: 2193\n",
            "R-Precision: 0.1694\n",
            "Precision@20: 0.2313\n",
            "Recall@20: 0.5335\n",
            "NDCG: 0.7087\n",
            "NDCG@20: 0.5165\n",
            "NDCG%20: 0.5097\n",
            "\n",
            "EVAL SPLIT: test\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 17; Total candidates: 2193\n",
            "R-Precision: 0.1513\n",
            "Precision@20: 0.1906\n",
            "Recall@20: 0.4340\n",
            "NDCG: 0.6731\n",
            "NDCG@20: 0.4415\n",
            "NDCG%20: 0.4511\n",
            "\n",
            "AVERAGE METRICS\n",
            "R-Precision: 0.1603\n",
            "Precision@20: 0.2109\n",
            "Recall@20: 0.4837\n",
            "NDCG: 0.6909\n",
            "NDCG@20: 0.4790\n",
            "NDCG%20: 0.4804\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_metric_scores('alberta', 'method', 20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bp2-gTJfvjT5",
        "outputId": "d34d834e-d8fd-4972-fda0-b035540872f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EVAL SPLIT: dev\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 17; Total candidates: 2174\n",
            "R-Precision: 0.1364\n",
            "Precision@20: 0.0875\n",
            "Recall@20: 0.2932\n",
            "NDCG: 0.5832\n",
            "NDCG@20: 0.3019\n",
            "NDCG%20: 0.3056\n",
            "\n",
            "EVAL SPLIT: test\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 17; Total candidates: 2174\n",
            "R-Precision: 0.1207\n",
            "Precision@20: 0.1215\n",
            "Recall@20: 0.4204\n",
            "NDCG: 0.6156\n",
            "NDCG@20: 0.3594\n",
            "NDCG%20: 0.3637\n",
            "\n",
            "AVERAGE METRICS\n",
            "R-Precision: 0.1285\n",
            "Precision@20: 0.1045\n",
            "Recall@20: 0.3568\n",
            "NDCG: 0.5994\n",
            "NDCG@20: 0.3307\n",
            "NDCG%20: 0.3346\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_metric_scores('alberta', 'all', 20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QUZWk-ckvnNK",
        "outputId": "0d436f52-4a22-49ff-eaf0-2f577472c218"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EVAL SPLIT: dev\n",
            "Reading facet: background\n",
            "Gold query pids: 16\n",
            "Valid ranked query pids: 16\n",
            "Reading facet: method\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Reading facet: result\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Total queries: 50\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 50; Total candidates: 6244\n",
            "R-Precision: 0.1927\n",
            "Precision@20: 0.2000\n",
            "Recall@20: 0.4079\n",
            "NDCG: 0.6900\n",
            "NDCG@20: 0.4685\n",
            "NDCG%20: 0.4676\n",
            "\n",
            "EVAL SPLIT: test\n",
            "Reading facet: background\n",
            "Gold query pids: 16\n",
            "Valid ranked query pids: 16\n",
            "Reading facet: method\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Reading facet: result\n",
            "Gold query pids: 17\n",
            "Valid ranked query pids: 17\n",
            "Total queries: 50\n",
            "Precision and recall at rank: 20\n",
            "Total queries: 50; Total candidates: 6244\n",
            "R-Precision: 0.1663\n",
            "Precision@20: 0.1971\n",
            "Recall@20: 0.4268\n",
            "NDCG: 0.6890\n",
            "NDCG@20: 0.4616\n",
            "NDCG%20: 0.4698\n",
            "\n",
            "AVERAGE METRICS\n",
            "R-Precision: 0.1795\n",
            "Precision@20: 0.1986\n",
            "Recall@20: 0.4174\n",
            "NDCG: 0.6895\n",
            "NDCG@20: 0.4650\n",
            "NDCG%20: 0.4687\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_sent_distbert_nli = SentenceTransformer('all-distilroberta-v1')"
      ],
      "metadata": {
        "id": "mQZs-nm9vsF-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_alberta = SentenceTransformer('paraphrase-albert-small-v2')"
      ],
      "metadata": {
        "id": "t95WLn1ek4it"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DISTILBERT base model"
      ],
      "metadata": {
        "id": "bXn3ZUHMlbXv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# distil_bert_nli all\n",
        "d = dict()\n",
        "err_pids = list()\n",
        "i = 0\n",
        "for doc in docs:\n",
        "    try:\n",
        "        do = docs[doc].abstract\n",
        "        if len(do) == 0:\n",
        "            d[doc] = []\n",
        "            continue\n",
        "        distilbert_nli_vectors = np.array(get_distilbert_base_v2_embedding(\" \".join(do))).tolist()\n",
        "        d[doc] = distilbert_nli_vectors\n",
        "    except:\n",
        "        err_pids.append(doc)\n",
        "        print(\"error\")\n",
        "print(f'len(err_pids) = {len(err_pids)}')\n",
        "print(err_pids)\n",
        "with open(\"/content/drive/MyDrive/Results/bert_nli/tuned_all.json\", \"w\") as outfile:\n",
        "    json.dump(d, outfile)\n",
        "d = json.load(open('/content/drive/MyDrive/Results/bert_nli/tuned_all.json'),\n",
        "              parse_float=lambda x: round(float(x), 6))\n",
        "with open('/content/drive/MyDrive/Results/bert_nli/tuned_all.json', \"w\") as outfile:\n",
        "    json.dump(d, outfile)"
      ],
      "metadata": {
        "id": "AnZVrITYk8Sx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# distil_bert_nli queries\n",
        "for i, ids in enumerate([background_query_ids, method_query_ids, result_query_ids]):\n",
        "    d = dict()\n",
        "    err_pids = list()\n",
        "    s = ''\n",
        "    if i == 0:\n",
        "        s = 'background'\n",
        "    elif i == 1:\n",
        "        s = 'method'\n",
        "    elif i == 2:\n",
        "        s = 'result'\n",
        "    for pid in ids:\n",
        "        try:\n",
        "            doc = get_data_by_facet(pid, s)\n",
        "            if len(doc) == 0:\n",
        "                d[pid] = []\n",
        "                continue\n",
        "            distilbert_nli_query_vectors = np.array(get_distilbert_base_v2_embedding(\" \".join(doc))).tolist()\n",
        "            d[pid] = distilbert_nli_query_vectors\n",
        "        except:\n",
        "            err_pids.append(pid)\n",
        "    print(f'len(err_pids), {s} = {len(err_pids)}')\n",
        "    print(err_pids)\n",
        "    with open(f'/content/drive/MyDrive/Results/bert_nli/tuned_{s}.json', \"w\") as outfile:\n",
        "        json.dump(d, outfile)\n",
        "    d = json.load(open(f'/content/drive/MyDrive/Results/bert_nli/tuned_{s}.json'),\n",
        "                parse_float=lambda x: round(float(x), 6))\n",
        "    with open(f'/content/drive/MyDrive/Results/bert_nli/tuned_{s}.json', \"w\") as outfile:\n",
        "        json.dump(d, outfile)"
      ],
      "metadata": {
        "id": "5_g2ISoik_yq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_metric_scores('distilbert_nli', 'background', 20)"
      ],
      "metadata": {
        "id": "n82D8szwlDGS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_metric_scores('distilbert_nli', 'method', 20)"
      ],
      "metadata": {
        "id": "DmVaKTOElQqN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_metric_scores('distilbert_nli', 'result', 20)"
      ],
      "metadata": {
        "id": "Q2ZIs8ydlTma"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_metric_scores('distilbert_nli', 'result', 20)"
      ],
      "metadata": {
        "id": "CgtmLGCRlV_w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_metric_scores('distilbert_nli', 'all', 20)"
      ],
      "metadata": {
        "id": "t0bgUs_KljvN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ALBERT"
      ],
      "metadata": {
        "id": "A5Q75Gf7mASo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# alberta all\n",
        "d = dict()\n",
        "err_pids = list()\n",
        "i = 0\n",
        "for doc in docs:\n",
        "    try:\n",
        "        do = docs[doc].abstract\n",
        "        if len(do) == 0:\n",
        "            d[doc] = []\n",
        "            continue\n",
        "        distilbert_nli_vectors = np.array(get_alberta_embedding(\" \".join(do))).tolist()\n",
        "        d[doc] = distilbert_nli_vectors\n",
        "    except:\n",
        "        err_pids.append(doc)\n",
        "        print(\"error\")\n",
        "print(f'len(err_pids) = {len(err_pids)}')\n",
        "print(err_pids)\n",
        "with open(\"/content/drive/MyDrive/Results/alberta/all.json\", \"w\") as outfile:\n",
        "    json.dump(d, outfile)\n",
        "d = json.load(open('/content/drive/MyDrive/Results/alberta/all.json'),\n",
        "              parse_float=lambda x: round(float(x), 6))\n",
        "with open('/content/drive/MyDrive/Results/alberta/all.json', \"w\") as outfile:\n",
        "    json.dump(d, outfile)"
      ],
      "metadata": {
        "id": "4BSLFV7Olk5e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# alberta queries\n",
        "for i, ids in enumerate([background_query_ids, method_query_ids, result_query_ids]):\n",
        "    d = dict()\n",
        "    err_pids = list()\n",
        "    s = ''\n",
        "    if i == 0:\n",
        "        s = 'background'\n",
        "    elif i == 1:\n",
        "        s = 'method'\n",
        "    elif i == 2:\n",
        "        s = 'result'\n",
        "    for pid in ids:\n",
        "        try:\n",
        "            doc = get_data_by_facet(pid, s)\n",
        "            if len(doc) == 0:\n",
        "                d[pid] = []\n",
        "                continue\n",
        "            distilbert_nli_query_vectors = np.array(get_alberta_embedding(\" \".join(doc))).tolist()\n",
        "            d[pid] = distilbert_nli_query_vectors\n",
        "        except:\n",
        "            err_pids.append(pid)\n",
        "    print(f'len(err_pids), {s} = {len(err_pids)}')\n",
        "    print(err_pids)\n",
        "    with open(f'/content/drive/MyDrive/Results/alberta/{s}.json', \"w\") as outfile:\n",
        "        json.dump(d, outfile)\n",
        "    d = json.load(open(f'/content/drive/MyDrive/Results/alberta/{s}.json'),\n",
        "                parse_float=lambda x: round(float(x), 6))\n",
        "    with open(f'/content/drive/MyDrive/Results/alberta/{s}.json', \"w\") as outfile:\n",
        "        json.dump(d, outfile)"
      ],
      "metadata": {
        "id": "Vto7TDOZly8G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_metric_scores('alberta', 'background', 20)"
      ],
      "metadata": {
        "id": "mR5wvHhDl2ip"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_metric_scores('alberta', 'method', 20)"
      ],
      "metadata": {
        "id": "KHKkSzOXl4i-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_metric_scores('alberta', 'result', 20)"
      ],
      "metadata": {
        "id": "4KFXY779l6vA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_metric_scores('alberta', 'all', 20)"
      ],
      "metadata": {
        "id": "P0yEoHYOl8nF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ALLENAI - SPECTER"
      ],
      "metadata": {
        "id": "KV3qK_6smdNh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "specter_tokenize = AutoTokenizer.from_pretrained('allenai/specter')\n",
        "specter_model = AutoModel.from_pretrained('allenai/specter')"
      ],
      "metadata": {
        "id": "FDcM0bfVmIbk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# for all\n",
        "d = dict()\n",
        "err_pids = list()\n",
        "i = 0\n",
        "cnt = 0;\n",
        "for doc in docs:\n",
        "    try:\n",
        "        do = docs[doc].abstract\n",
        "        if len(do) == 0:\n",
        "            d[doc] = list()\n",
        "            continue\n",
        "        get_specter = get_allenai_specter_embedding(\" \".join(do)).detach().numpy().tolist()\n",
        "        cnt += 1\n",
        "        print(cnt)\n",
        "        d[doc] = get_specter[0]\n",
        "    except:\n",
        "        err_pids.append(doc)\n",
        "print(f'len(err_pids) = {len(err_pids)}')\n",
        "print(err_pids)\n",
        "with open('/content/drive/MyDrive/Results/allenai_specter/all.json', 'w') as outfile:\n",
        "    json.dump(d, outfile)\n",
        "d = json.load(open('/content/drive/MyDrive/Results/allenai_specter/all.json'),\n",
        "              parse_float=lambda x: round(float(x), 6))\n",
        "with open('/content/drive/MyDrive/Results/allenai_specter/all.json', 'w') as outfile:\n",
        "    json.dump(d, outfile)"
      ],
      "metadata": {
        "id": "T_4CMTbmmOAb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# specter queries\n",
        "for i, ids in enumerate([background_query_ids, method_query_ids, result_query_ids]):\n",
        "    d = dict()\n",
        "    err_pids = list()\n",
        "    s = ''\n",
        "    if i == 0:\n",
        "        s = 'background'\n",
        "    elif i == 1:\n",
        "        s = 'method'\n",
        "    elif i == 2:\n",
        "        s = 'result'\n",
        "    for pid in ids:\n",
        "        try:\n",
        "            doc = get_data_by_facet(pid, s)\n",
        "            if len(doc) == 0:\n",
        "                d[pid] = list()\n",
        "                continue\n",
        "            specter_array = get_allenai_specter_embedding(\" \".join(doc)).detach().numpy().tolist()\n",
        "            d[pid] = specter_array[0]\n",
        "        except:\n",
        "            err_pids.append(pid)\n",
        "    print(f'len(err_pids), {s} = {len(err_pids)}')\n",
        "    print(err_pids)\n",
        "    with open(f'/content/drive/MyDrive/Results/allenai_specter/{s}.json', 'w') as outfile:\n",
        "        json.dump(d, outfile)\n",
        "    d = json.load(open(f'/content/drive/MyDrive/Results/allenai_specter/{s}.json'),\n",
        "                parse_float=lambda x: round(float(x), 6))\n",
        "    with open(f'/content/drive/MyDrive/Results/allenai_specter/{s}.json', 'w') as outfile:\n",
        "        json.dump(d, outfile)"
      ],
      "metadata": {
        "id": "bTcznei3mPJ8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_metric_scores('allenai_specter', 'background', 20)"
      ],
      "metadata": {
        "id": "dgLzkKBfmShp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_metric_scores('allenai_specter', 'method', 20)"
      ],
      "metadata": {
        "id": "thfYlEr8mVbM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_metric_scores('allenai_specter', 'result', 20)"
      ],
      "metadata": {
        "id": "4W129eikmWBV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_metric_scores('allenai_specter', 'all', 20)"
      ],
      "metadata": {
        "id": "OEr5Yl2BmWlA"
      },
      "execution_count": null,
      "outputs": []
    }
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