final_pipeline_scholar.ipynb

{
  "cells": [
    {
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      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7DyEXaaH3NYT",
        "outputId": "3333d394-5f1c-4672-f06b-b7ae5e052d76"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qGPeYw6DO84d",
        "outputId": "4329310e-8fae-4a8b-8185-8d45e831b13a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/drive/.shortcut-targets-by-id/1qAY9sRpD8XGe6_oa-jCd9NWhJPZ_HOwg/IR_Project/Scholar\n"
          ]
        }
      ],
      "source": [
        "%cd /content/drive/MyDrive/IR_Project/Scholar/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VBKR7octA2oz"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "import pandas as pd\n",
        "from pprint import pprint\n",
        "from tqdm import tqdm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "skAoD_5D5tTM"
      },
      "source": [
        "### download data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r0ONhQ2_5Fej",
        "outputId": "5acfd77f-6939-487b-9de5-370d90a01ba5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100  858M  100  858M    0     0  11.5M      0  0:01:14  0:01:14 --:--:-- 11.6M\n"
          ]
        }
      ],
      "source": [
        "!curl -O https://ciir.cs.umass.edu/downloads/LaMP/LaMP_5/train/train_questions.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZNWjss3f5Sm4",
        "outputId": "14ff9e36-ee3e-476d-f7d7-fd8aabe342cd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100 1006k  100 1006k    0     0   883k      0  0:00:01  0:00:01 --:--:--  883k\n"
          ]
        }
      ],
      "source": [
        "!curl -O https://ciir.cs.umass.edu/downloads/LaMP/LaMP_5/train/train_outputs.json"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "56aZj9MH4HYv"
      },
      "source": [
        "## Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nwP4Wto_3NWa"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CFyMvdDm4Gpr"
      },
      "outputs": [],
      "source": [
        "with open('train_questions.json') as f:\n",
        "    d = json.load(f)\n",
        "data = pd.DataFrame.from_dict(d)\n",
        "\n",
        "with open('train_outputs.json') as f:\n",
        "    t = json.load(f)\n",
        "\n",
        "outputs=pd.DataFrame.from_dict(t['golds'])\n",
        "final_data=pd.merge(data, outputs, on='id')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "LzQWNjXC4GnU",
        "outputId": "a5f1cd95-f6f7-4822-af77-675d3e39d3f8"
      },
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>input</th>\n",
              "      <th>profile</th>\n",
              "      <th>output</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>400</td>\n",
              "      <td>Generate a title for the following abstract of...</td>\n",
              "      <td>[{'title': 'Convex Drawings of Internally Tric...</td>\n",
              "      <td>Partitioning Trees of Supply and Demand</td>\n",
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              "        const element = document.querySelector('#df-45a61a46-bcc1-4326-9938-6d3748bb4111');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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            "text/plain": [
              "    id                                              input  \\\n",
              "0  400  Generate a title for the following abstract of...   \n",
              "\n",
              "                                             profile  \\\n",
              "0  [{'title': 'Convex Drawings of Internally Tric...   \n",
              "\n",
              "                                    output  \n",
              "0  Partitioning Trees of Supply and Demand  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
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        }
      ],
      "source": [
        "final_data.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eTVJ4d6f4km6"
      },
      "outputs": [],
      "source": [
        "#making my testing sample\n",
        "test_data=final_data.sample(100)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HDzbw5_74JAu"
      },
      "source": [
        "# STEP1: Retriever"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kjy9sculyhkd"
      },
      "source": [
        "### bm25"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gcTGAk5x2eHv"
      },
      "outputs": [],
      "source": [
        "!pip install langchain\n",
        "!pip install rank_bm25"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9phXc2VH2ovV"
      },
      "outputs": [],
      "source": [
        "from langchain.schema import Document\n",
        "from langchain.retrievers import BM25Retriever"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K3RGa3iU2thg"
      },
      "outputs": [],
      "source": [
        "def get_relevant_docs(query : str, docs: list):\n",
        "  documents =  []\n",
        "\n",
        "  for item in docs:\n",
        "      page = Document(page_content=item['abstract'],metadata = {\"id\" : item['id'], \"title\" : item['title']})\n",
        "      documents.append(page)\n",
        "\n",
        "  retriever = BM25Retriever.from_documents(documents)\n",
        "  top_docs=retriever.get_relevant_documents(query)[:3]\n",
        "  top3=[]\n",
        "  for i in top_docs[:3]:\n",
        "    res_dict={'abstract':i.page_content,'title':i.metadata['title'],'id':i.metadata['id']}\n",
        "    top3.append(res_dict)\n",
        "  return top3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kiINWHip422z",
        "outputId": "0c2a7073-82dc-481d-9e93-c240107297ee"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 100/100 [00:01<00:00, 51.34it/s]\n"
          ]
        }
      ],
      "source": [
        "retriever_results=[]\n",
        "for row in tqdm(range(test_data.shape[0])):\n",
        "  input_text = test_data.iloc[row]['input']\n",
        "  top_ret=get_relevant_docs(input_text, test_data.iloc[row]['profile'])\n",
        "  retriever_results.append(top_ret)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CiwST00e5ZEy"
      },
      "outputs": [],
      "source": [
        "test_data['bm25_results']=retriever_results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 293
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        "id": "ipMk8GOX5eQr",
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              "      <th>id</th>\n",
              "      <th>input</th>\n",
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              "      <th>bm25_results</th>\n",
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              "    <tr>\n",
              "      <th>8913</th>\n",
              "      <td>408913</td>\n",
              "      <td>Generate a title for the following abstract of...</td>\n",
              "      <td>[{'title': 'Fault domain-based testing in impe...</td>\n",
              "      <td>TABOR: A Graphical Model-based Approach for An...</td>\n",
              "      <td>[{'abstract': 'Industrial Control Systems (ICS...</td>\n",
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              "      <th>2242</th>\n",
              "      <td>402242</td>\n",
              "      <td>Generate a title for the following abstract of...</td>\n",
              "      <td>[{'title': 'On updating signal subspaces', 'ab...</td>\n",
              "      <td>Hands-On Training for Undergraduates in High-P...</td>\n",
              "      <td>[{'abstract': 'Parallel programming of high-pe...</td>\n",
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              "      <th>2577</th>\n",
              "      <td>402577</td>\n",
              "      <td>Generate a title for the following abstract of...</td>\n",
              "      <td>[{'title': 'A Note on Filtration and Granular ...</td>\n",
              "      <td>On possible rules and apriori algorithm in non...</td>\n",
              "      <td>[{'abstract': 'Minimal rule generation in Non-...</td>\n",
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              "      <td>Framework for bringing data streams to the grid</td>\n",
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              "      <td>[{'title': 'Completeness In Differential Appro...</td>\n",
              "      <td>Multiparameterizations for max $k$-set cover a...</td>\n",
              "      <td>[{'abstract': 'We study the parameterized comp...</td>\n",
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3a1edc88-27df-48bb-acf5-625f1037e6d5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "          id                                              input  \\\n",
              "8913  408913  Generate a title for the following abstract of...   \n",
              "2242  402242  Generate a title for the following abstract of...   \n",
              "2577  402577  Generate a title for the following abstract of...   \n",
              "1399  401399  Generate a title for the following abstract of...   \n",
              "7707  407707  Generate a title for the following abstract of...   \n",
              "\n",
              "                                                profile  \\\n",
              "8913  [{'title': 'Fault domain-based testing in impe...   \n",
              "2242  [{'title': 'On updating signal subspaces', 'ab...   \n",
              "2577  [{'title': 'A Note on Filtration and Granular ...   \n",
              "1399  [{'title': 'Temporal representation for scient...   \n",
              "7707  [{'title': 'Completeness In Differential Appro...   \n",
              "\n",
              "                                                 output  \\\n",
              "8913  TABOR: A Graphical Model-based Approach for An...   \n",
              "2242  Hands-On Training for Undergraduates in High-P...   \n",
              "2577  On possible rules and apriori algorithm in non...   \n",
              "1399    Framework for bringing data streams to the grid   \n",
              "7707  Multiparameterizations for max $k$-set cover a...   \n",
              "\n",
              "                                           bm25_results  \n",
              "8913  [{'abstract': 'Industrial Control Systems (ICS...  \n",
              "2242  [{'abstract': 'Parallel programming of high-pe...  \n",
              "2577  [{'abstract': 'Minimal rule generation in Non-...  \n",
              "1399  [{'abstract': 'The dQUOB system satisfies clie...  \n",
              "7707  [{'abstract': 'We study the parameterized comp...  "
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UJobYNN0ygkc"
      },
      "source": [
        "### contriever"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bb1oUGMP5mww"
      },
      "outputs": [],
      "source": [
        "## imporintng contriever results run on local"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9TDCavjXymHJ"
      },
      "outputs": [],
      "source": [
        "test_data=pd.read_pickle('contriever/cont_res_scholar_100.pkl')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "VXeKJ-IOymEw",
        "outputId": "09c5a74a-3d10-407c-edb5-a5deb84aba17"
      },
      "outputs": [
        {
          "data": {
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              "      <th>8913</th>\n",
              "      <td>408913</td>\n",
              "      <td>Generate a title for the following abstract of...</td>\n",
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              "      <td>Generate a title for the following abstract of...</td>\n",
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              "      <td>Hands-On Training for Undergraduates in High-P...</td>\n",
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              "      <th>2577</th>\n",
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              "      <td>[{'title': 'Temporal representation for scient...</td>\n",
              "      <td>Framework for bringing data streams to the grid</td>\n",
              "      <td>streams, stream, streaming, grid, data</td>\n",
              "      <td>this article introduces a model for bringing e...</td>\n",
              "      <td>[{'title': 'Calder Query Grid Service: Insight...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7707</th>\n",
              "      <td>407707</td>\n",
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              "      <td>[{'title': 'Completeness In Differential Appro...</td>\n",
              "      <td>Multiparameterizations for max $k$-set cover a...</td>\n",
              "      <td>parameterizations, complexity, parameterized, ...</td>\n",
              "      <td>study the complexity of several parameterizati...</td>\n",
              "      <td>[{'title': 'Multi-parameter Analysis for Local...</td>\n",
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              "                                                    [key], {});\n",
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              "<style>\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "          id                                              input  \\\n",
              "8913  408913  Generate a title for the following abstract of...   \n",
              "2242  402242  Generate a title for the following abstract of...   \n",
              "2577  402577  Generate a title for the following abstract of...   \n",
              "1399  401399  Generate a title for the following abstract of...   \n",
              "7707  407707  Generate a title for the following abstract of...   \n",
              "\n",
              "                                                profile  \\\n",
              "8913  [{'title': 'Fault domain-based testing in impe...   \n",
              "2242  [{'title': 'On updating signal subspaces', 'ab...   \n",
              "2577  [{'title': 'A Note on Filtration and Granular ...   \n",
              "1399  [{'title': 'Temporal representation for scient...   \n",
              "7707  [{'title': 'Completeness In Differential Appro...   \n",
              "\n",
              "                                                 output  \\\n",
              "8913  TABOR: A Graphical Model-based Approach for An...   \n",
              "2242  Hands-On Training for Undergraduates in High-P...   \n",
              "2577  On possible rules and apriori algorithm in non...   \n",
              "1399    Framework for bringing data streams to the grid   \n",
              "7707  Multiparameterizations for max $k$-set cover a...   \n",
              "\n",
              "                                         input_keywords  \\\n",
              "8913     automata, swat, classifier, detection, anomaly   \n",
              "2242  java, programming, computing, computational, p...   \n",
              "2577  algorithm, minimalcertainrules, deterministici...   \n",
              "1399             streams, stream, streaming, grid, data   \n",
              "7707  parameterizations, complexity, parameterized, ...   \n",
              "\n",
              "                                          input_summary  \\\n",
              "8913  the problem of process detection in water tank...   \n",
              "2242  this paper describes a practical computing cou...   \n",
              "2577  in this paper, we introduce a framework of gen...   \n",
              "1399  this article introduces a model for bringing e...   \n",
              "7707  study the complexity of several parameterizati...   \n",
              "\n",
              "                                             contriever  \n",
              "8913  [{'title': 'WaterJam: An Experimental Case Stu...  \n",
              "2242  [{'title': 'Parallel programming in computatio...  \n",
              "2577  [{'title': 'Discernibility functions and minim...  \n",
              "1399  [{'title': 'Calder Query Grid Service: Insight...  \n",
              "7707  [{'title': 'Multi-parameter Analysis for Local...  "
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T-VDL7Eyyxuo"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2J5BX9kn552x"
      },
      "source": [
        "# KEYWORD EXTRACTION"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aZn4ZWoV58ka",
        "outputId": "6aee8a33-1260-43d0-9fd9-c91784039356"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting keybert\n",
            "  Downloading keybert-0.8.3.tar.gz (29 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting sentence-transformers>=0.3.8 (from keybert)\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[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: scikit-learn>=0.22.2 in /usr/local/lib/python3.10/dist-packages (from keybert) (1.2.2)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from keybert) (1.23.5)\n",
            "Requirement already satisfied: rich>=10.4.0 in /usr/local/lib/python3.10/dist-packages (from keybert) (13.7.0)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.4.0->keybert) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.4.0->keybert) (2.16.1)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.22.2->keybert) (1.11.4)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.22.2->keybert) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.22.2->keybert) (3.2.0)\n",
            "Requirement already satisfied: transformers<5.0.0,>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=0.3.8->keybert) (4.35.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=0.3.8->keybert) (4.66.1)\n",
            "Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=0.3.8->keybert) (2.1.0+cu118)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=0.3.8->keybert) (0.16.0+cu118)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=0.3.8->keybert) (3.8.1)\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=0.3.8->keybert) (0.1.99)\n",
            "Requirement already satisfied: huggingface-hub>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=0.3.8->keybert) (0.19.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (3.13.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (2023.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (2.31.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (6.0.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (4.5.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (23.2)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=10.4.0->keybert) (0.1.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence-transformers>=0.3.8->keybert) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence-transformers>=0.3.8->keybert) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence-transformers>=0.3.8->keybert) (3.1.2)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->sentence-transformers>=0.3.8->keybert) (2.1.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers>=0.3.8->keybert) (2023.6.3)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers>=0.3.8->keybert) (0.15.0)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.6.0->sentence-transformers>=0.3.8->keybert) (0.4.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk->sentence-transformers>=0.3.8->keybert) (8.1.7)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision->sentence-transformers>=0.3.8->keybert) (9.4.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.6.0->sentence-transformers>=0.3.8->keybert) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers>=0.3.8->keybert) (2023.11.17)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.6.0->sentence-transformers>=0.3.8->keybert) (1.3.0)\n",
            "Building wheels for collected packages: keybert, sentence-transformers\n",
            "  Building wheel for keybert (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keybert: filename=keybert-0.8.3-py3-none-any.whl size=39124 sha256=9806256dbc3a2acbdc1798a823ee4353fdc3b50b9582071b0d4efce3abed232d\n",
            "  Stored in directory: /root/.cache/pip/wheels/70/88/07/1a3bc11fd1dd5f89924a02dcbca89a3015e25e8faa31f904dc\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=125923 sha256=5b4b6afd49f484d20c336eba0f2dc1623ad810736d10b1fd1223ac99fde810f1\n",
            "  Stored in directory: /root/.cache/pip/wheels/62/f2/10/1e606fd5f02395388f74e7462910fe851042f97238cbbd902f\n",
            "Successfully built keybert sentence-transformers\n",
            "Installing collected packages: sentence-transformers, keybert\n",
            "Successfully installed keybert-0.8.3 sentence-transformers-2.2.2\n"
          ]
        }
      ],
      "source": [
        "!pip install keybert"
      ]
    },
    {
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        {
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            ]
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        }
      ],
      "source": [
        "from keybert import KeyBERT\n",
        "kw_model = KeyBERT()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eOfpIQHa6au_",
        "outputId": "9755f152-41e2-4de7-dd33-e3241516e8df"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 100/100 [02:05<00:00,  1.25s/it]\n"
          ]
        }
      ],
      "source": [
        "keyword_results=[]\n",
        "for row in tqdm(range(test_data.shape[0])):\n",
        "  #cur_list = test_data.iloc[row]['bm25_results'] #ifbm25 run this\n",
        "  cur_list = test_data.iloc[row]['contriever']\n",
        "  updated_list = [{'text': entry['abstract'], 'title': entry['title'], 'id': entry['id'],'keywords': kw_model.extract_keywords(entry['abstract'],, stop_words='english',use_mmr=True, diversity=0.3)} for entry in cur_list]\n",
        "\n",
        "  #updated_list = [{'text': entry['abstract'], 'title': entry['title'], 'id': entry['id'],'keywords': kw_model.extract_keywords(entry['abstract'])} for entry in cur_list]\n",
        "  keyword_results.append(updated_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x2KK5lt0_eYd"
      },
      "outputs": [],
      "source": [
        "test_data['keyword_results']=keyword_results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "KIcym1xHAOGB",
        "outputId": "33081193-bfa0-47ba-ddf6-e6843ad5a91a"
      },
      "outputs": [
        {
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              "      <td>study the complexity of several parameterizati...</td>\n",
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            ],
            "text/plain": [
              "          id                                              input  \\\n",
              "8913  408913  Generate a title for the following abstract of...   \n",
              "2242  402242  Generate a title for the following abstract of...   \n",
              "2577  402577  Generate a title for the following abstract of...   \n",
              "1399  401399  Generate a title for the following abstract of...   \n",
              "7707  407707  Generate a title for the following abstract of...   \n",
              "\n",
              "                                                profile  \\\n",
              "8913  [{'title': 'Fault domain-based testing in impe...   \n",
              "2242  [{'title': 'On updating signal subspaces', 'ab...   \n",
              "2577  [{'title': 'A Note on Filtration and Granular ...   \n",
              "1399  [{'title': 'Temporal representation for scient...   \n",
              "7707  [{'title': 'Completeness In Differential Appro...   \n",
              "\n",
              "                                                 output  \\\n",
              "8913  TABOR: A Graphical Model-based Approach for An...   \n",
              "2242  Hands-On Training for Undergraduates in High-P...   \n",
              "2577  On possible rules and apriori algorithm in non...   \n",
              "1399    Framework for bringing data streams to the grid   \n",
              "7707  Multiparameterizations for max $k$-set cover a...   \n",
              "\n",
              "                                         input_keywords  \\\n",
              "8913     automata, swat, classifier, detection, anomaly   \n",
              "2242  java, programming, computing, computational, p...   \n",
              "2577  algorithm, minimalcertainrules, deterministici...   \n",
              "1399             streams, stream, streaming, grid, data   \n",
              "7707  parameterizations, complexity, parameterized, ...   \n",
              "\n",
              "                                          input_summary  \\\n",
              "8913  the problem of process detection in water tank...   \n",
              "2242  this paper describes a practical computing cou...   \n",
              "2577  in this paper, we introduce a framework of gen...   \n",
              "1399  this article introduces a model for bringing e...   \n",
              "7707  study the complexity of several parameterizati...   \n",
              "\n",
              "                                             contriever  \\\n",
              "8913  [{'title': 'WaterJam: An Experimental Case Stu...   \n",
              "2242  [{'title': 'Parallel programming in computatio...   \n",
              "2577  [{'title': 'Discernibility functions and minim...   \n",
              "1399  [{'title': 'Calder Query Grid Service: Insight...   \n",
              "7707  [{'title': 'Multi-parameter Analysis for Local...   \n",
              "\n",
              "                                        keyword_results  \n",
              "8913  [{'text': 'An experiment was conducted to inve...  \n",
              "2242  [{'text': 'Parallel programming of high-perfor...  \n",
              "2577  [{'text': 'Minimal rule generation in Non-dete...  \n",
              "1399  [{'text': 'We have architected and evaluated a...  \n",
              "7707  [{'text': 'We study the parameterized complexi...  "
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ar3qxn-mA8oJ"
      },
      "outputs": [],
      "source": [
        "test_data.to_pickle('contriever/test_data_100.pkl')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S4KQARQzENIb"
      },
      "source": [
        "# SUMMARIZER"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F6hN9jXxMCi9"
      },
      "outputs": [],
      "source": [
        "test_data=pd.read_pickle('contriever/test_data_100.pkl')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q7rc5mndEPW6",
        "outputId": "53709ae1-d15c-4c73-afcf-0a327dd1c953"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-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[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.13.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.15.0)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.11.17)\n",
            "Installing collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.99\n"
          ]
        }
      ],
      "source": [
        "!pip install --no-cache-dir transformers sentencepiece"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Btq6FVSQH1i6",
        "outputId": "8abbb185-06dc-46d4-ff20-bc249d1e457f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Git LFS initialized.\n",
            "Cloning into 'bigbird-pegasus-large-arxiv'...\n",
            "remote: Enumerating objects: 50, done.\u001b[K\n",
            "remote: Total 50 (delta 0), reused 0 (delta 0), pack-reused 50\u001b[K\n",
            "Unpacking objects: 100% (50/50), 1.27 MiB | 1.74 MiB/s, done.\n",
            "fatal: cannot exec '/content/drive/MyDrive/IR_Project/Scholar/bigbird-pegasus-large-arxiv/.git/hooks/post-checkout': Permission denied\n"
          ]
        }
      ],
      "source": [
        "# Make sure you have git-lfs installed (https://git-lfs.com)\n",
        "!git lfs install\n",
        "!git clone https://huggingface.co/google/bigbird-pegasus-large-arxiv"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5P7Cww3KKcWb"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
        "import torch\n",
        "\n",
        "# Ensure CUDA is available and set the device\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Path to your downloaded model\n",
        "model_path = \"bigbird-pegasus-large-arxiv/\"\n",
        "# Load the tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_path).to(device).half()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fN9ZkAn1E7GZ",
        "outputId": "79f5643e-1419-4b50-c1c6-55bf45ea3b9d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Attention type 'block_sparse' is not possible if sequence_length: 225 <= num global tokens: 2 * config.block_size + min. num sliding tokens: 3 * config.block_size + config.num_random_blocks * config.block_size + additional buffer: config.num_random_blocks * config.block_size = 704 with config.block_size = 64, config.num_random_blocks = 3. Changing attention type to 'original_full'...\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "a case study on one stage of a 6- stage operational water plant treatment was performed.<n> the attack mechanisms were modeled into and security mechanisms were implemented in so were found effective in detecting the attacks.\n"
          ]
        }
      ],
      "source": [
        "# TEST\n",
        "raw_document = \"Industrial Control Systems (ICS) are found in critical infrastructure such as for power generation and water treatment. When security requirements are incorporated into an ICS, one needs to test the additional code and devices added do improve the prevention and detection of cyber attacks. Conducting such tests in legacy systems is a challenge due to the high availability requirement. An approach using Timed Automata (TA) is proposed to overcome this challenge. This approach enables assessment of the effectiveness of an attack detection method based on process invariants. The approach has been demonstrated in a case study on one stage of a 6- stage operational water treatment plant. The model constructed captured the interactions among components in the selected stage. In addition, a set of attacks, attack detection mechanisms, and security specifications were also modeled using TA. These TA models were conjoined into a network and implemented in UPPAAL. The models so implemented were found effective in detecting the attacks considered. The study suggests the use of TA as an effective tool to model an ICS and study its attack detection mechanisms as a complement to doing so in a real plant-operational or under design.\"\n",
        "\n",
        "# Encode the text, ensuring that it is truncated to the maximum length acceptable by the model\n",
        "input_ids = tokenizer.encode(raw_document, return_tensors=\"pt\", truncation=True, max_length=model.config.max_position_embeddings).to(device)\n",
        "\n",
        "\n",
        "# Perform inference\n",
        "generation_config = {\n",
        "    \"_from_model_config\": True,\n",
        "    \"bos_token_id\": 2,\n",
        "    \"decoder_start_token_id\": 2,\n",
        "    \"eos_token_id\": 1,\n",
        "    \"length_penalty\": 0.8,\n",
        "    \"max_length\": 256,\n",
        "    \"num_beams\": 5,\n",
        "    \"pad_token_id\": 0,\n",
        "    \"transformers_version\": \"4.27.0.dev0\"\n",
        "}\n",
        "\n",
        "\n",
        "\n",
        "# # Perform inference\n",
        "# with torch.no_grad():\n",
        "#     output = model.generate(\n",
        "#         input_ids,\n",
        "#         num_beams=5,\n",
        "#         min_length=5,\n",
        "#         no_repeat_ngram_size=3,\n",
        "#         max_length=50\n",
        "#     )\n",
        "\n",
        "with torch.no_grad():\n",
        "    output = model.generate(\n",
        "        input_ids,\n",
        "        num_beams=generation_config[\"num_beams\"],\n",
        "        min_length=5,\n",
        "        no_repeat_ngram_size=3,\n",
        "        max_length=generation_config[\"max_length\"],\n",
        "        bos_token_id=generation_config[\"bos_token_id\"],\n",
        "        eos_token_id=generation_config[\"eos_token_id\"],\n",
        "        length_penalty=generation_config[\"length_penalty\"],\n",
        "        pad_token_id=generation_config[\"pad_token_id\"]\n",
        "    )\n",
        "\n",
        "# Decode and print the output\n",
        "decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)\n",
        "print(decoded_output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5uODCxPrM38S"
      },
      "outputs": [],
      "source": [
        "def get_summ(raw_document):\n",
        "  # Encode the text, ensuring that it is truncated to the maximum length acceptable by the model\n",
        "  input_ids = tokenizer.encode(raw_document, return_tensors=\"pt\", truncation=True, max_length=model.config.max_position_embeddings).to(device)\n",
        "  # Perform inference\n",
        "  generation_config = {\n",
        "      \"_from_model_config\": True,\n",
        "      \"bos_token_id\": 2,\n",
        "      \"decoder_start_token_id\": 2,\n",
        "      \"eos_token_id\": 1,\n",
        "      \"length_penalty\": 0.8,\n",
        "      \"max_length\": 256,\n",
        "      \"num_beams\": 5,\n",
        "      \"pad_token_id\": 0,\n",
        "      \"transformers_version\": \"4.27.0.dev0\"\n",
        "  }\n",
        "\n",
        "  with torch.no_grad():\n",
        "    output = model.generate(\n",
        "        input_ids,\n",
        "        num_beams=generation_config[\"num_beams\"],\n",
        "        min_length=5,\n",
        "        no_repeat_ngram_size=3,\n",
        "        max_length=generation_config[\"max_length\"],\n",
        "        bos_token_id=generation_config[\"bos_token_id\"],\n",
        "        eos_token_id=generation_config[\"eos_token_id\"],\n",
        "        length_penalty=generation_config[\"length_penalty\"],\n",
        "        pad_token_id=generation_config[\"pad_token_id\"]\n",
        "    )\n",
        "\n",
        "  # Decode and print the output\n",
        "  decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)\n",
        "  return decoded_output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N6VaAGT6QoTF"
      },
      "outputs": [],
      "source": [
        "import pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ohJWt-9NRz-",
        "outputId": "391981c4-ace4-42cd-c6c5-809041096dfd"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 100/100 [18:07<00:00, 10.87s/it]\n"
          ]
        }
      ],
      "source": [
        "summ_results=[]\n",
        "for row in tqdm(range(test_data.shape[0])):\n",
        "  cur_list = test_data.iloc[row]['keyword_results']\n",
        "  updated_list = [{'text': entry['text'], 'title': entry['title'], 'id': entry['id'],'keywords': entry['keywords'],'summary':get_summ(entry['text'])} for entry in cur_list]\n",
        "  summ_results.append(updated_list)\n",
        "  if(row%10==0):\n",
        "    # Save to a pickle file\n",
        "    with open(\"contriever/data_summ.pkl\", \"wb\") as file:\n",
        "      pickle.dump(summ_results, file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7AybFhgoOiQF"
      },
      "outputs": [],
      "source": [
        "test_data['summary_results']=summ_results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oF_inMhfUnFV"
      },
      "outputs": [],
      "source": [
        "test_data.to_pickle('contriever/test_data_100_summ.pkl')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4IIi-A2N0_gv"
      },
      "source": [
        "# STYLE ANALYSIS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ulMAIV9TGEkG"
      },
      "source": [
        "##### Style Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zgXLeeetGm1F",
        "outputId": "c63a3116-a641-46a2-dfa3-c31755cbc288"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package cmudict to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/cmudict.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import collections as coll\n",
        "import math\n",
        "import pickle\n",
        "import string\n",
        "import numpy as np\n",
        "from nltk.corpus import cmudict\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize, sent_tokenize\n",
        "import nltk\n",
        "\n",
        "nltk.download('cmudict')\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L45fG0tSGHK0"
      },
      "outputs": [],
      "source": [
        "def syllable_count_Manual(word):\n",
        "    word = word.lower()\n",
        "    count = 0\n",
        "    vowels = \"aeiouy\"\n",
        "    if word[0] in vowels:\n",
        "        count += 1\n",
        "    for index in range(1, len(word)):\n",
        "        if word[index] in vowels and word[index - 1] not in vowels:\n",
        "            count += 1\n",
        "            if word.endswith(\"e\"):\n",
        "                count -= 1\n",
        "    if count == 0:\n",
        "        count += 1\n",
        "    return count\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------------------\n",
        "# COUNTS NUMBER OF SYLLABLES\n",
        "\n",
        "def syllable_count(word):\n",
        "    global cmuDictionary\n",
        "    d = cmuDictionary\n",
        "    try:\n",
        "        syl = [len(list(y for y in x if y[-1].isdigit())) for x in d[word.lower()]][0]\n",
        "    except:\n",
        "        syl = syllable_count_Manual(word)\n",
        "    return syl\n",
        "\n",
        "    # ----------------------------------------------------------------------------\n",
        "\n",
        "\n",
        "# removing stop words plus punctuation.\n",
        "def Avg_wordLength(str):\n",
        "    str.translate(string.punctuation)\n",
        "    tokens = word_tokenize(str, language='english')\n",
        "    st = [\",\", \".\", \"'\", \"!\", '\"', \"#\", \"$\", \"%\", \"&\", \"(\", \")\", \"*\", \"+\", \"-\", \".\", \"/\", \":\", \";\", \"<\", \"=\", '>', \"?\",\n",
        "          \"@\", \"[\", \"\\\\\", \"]\", \"^\", \"_\", '`', \"{\", \"|\", \"}\", '~', '\\t', '\\n']\n",
        "    stop = stopwords.words('english') + st\n",
        "    words = [word for word in tokens if word not in stop]\n",
        "    return np.average([len(word) for word in words])\n",
        "\n",
        "\n",
        "# ----------------------------------------------------------------------------\n",
        "\n",
        "\n",
        "# returns avg number of characters in a sentence\n",
        "def Avg_SentLenghtByCh(text):\n",
        "    tokens = sent_tokenize(text)\n",
        "    return np.average([len(token) for token in tokens])\n",
        "\n",
        "\n",
        "# ----------------------------------------------------------------------------\n",
        "\n",
        "# returns avg number of words in a sentence\n",
        "def Avg_SentLenghtByWord(text):\n",
        "    tokens = sent_tokenize(text)\n",
        "    return np.average([len(token.split()) for token in tokens])\n",
        "\n",
        "\n",
        "# ----------------------------------------------------------------------------\n",
        "\n",
        "\n",
        "# GIVES NUMBER OF SYLLABLES PER WORD\n",
        "def Avg_Syllable_per_Word(text):\n",
        "    tokens = word_tokenize(text, language='english')\n",
        "    st = [\",\", \".\", \"'\", \"!\", '\"', \"#\", \"$\", \"%\", \"&\", \"(\", \")\", \"*\", \"+\", \"-\", \".\", \"/\", \":\", \";\", \"<\", \"=\", '>', \"?\",\n",
        "          \"@\", \"[\", \"\\\\\", \"]\", \"^\", \"_\", '`', \"{\", \"|\", \"}\", '~', '\\t', '\\n']\n",
        "    stop = stopwords.words('english') + st\n",
        "    words = [word for word in tokens if word not in stop]\n",
        "    syllabls = [syllable_count(word) for word in words]\n",
        "    p = (\" \".join(words))\n",
        "    return sum(syllabls) / max(1, len(words))\n",
        "\n",
        "\n",
        "# -----------------------------------------------------------------------------\n",
        "\n",
        "# COUNTS SPECIAL CHARACTERS NORMALIZED OVER LENGTH OF CHUNK\n",
        "def CountSpecialCharacter(text):\n",
        "    st = [\"#\", \"$\", \"%\", \"&\", \"(\", \")\", \"*\", \"+\", \"-\", \"/\", \"<\", \"=\", '>',\n",
        "          \"@\", \"[\", \"\\\\\", \"]\", \"^\", \"_\", '`', \"{\", \"|\", \"}\", '~', '\\t', '\\n']\n",
        "    count = 0\n",
        "    for i in text:\n",
        "        if (i in st):\n",
        "            count = count + 1\n",
        "    return count / len(text)\n",
        "\n",
        "\n",
        "# ----------------------------------------------------------------------------\n",
        "\n",
        "def CountPuncuation(text):\n",
        "    st = [\",\", \".\", \"'\", \"!\", '\"', \";\", \"?\", \":\", \";\"]\n",
        "    count = 0\n",
        "    for i in text:\n",
        "        if (i in st):\n",
        "            count = count + 1\n",
        "    return float(count) / float(len(text))\n",
        "\n",
        "\n",
        "# ----------------------------------------------------------------------------\n",
        "# RETURNS NORMALIZED COUNT OF FUNCTIONAL WORDS FROM A Framework for\n",
        "# Authorship Identification of Online Messages: Writing-Style Features and Classification Techniques\n",
        "\n",
        "def CountFunctionalWords(text):\n",
        "    functional_words = \"\"\"a between in nor some upon\n",
        "    about both including nothing somebody us\n",
        "    above but inside of someone used\n",
        "    after by into off something via\n",
        "    all can is on such we\n",
        "    although cos it once than what\n",
        "    am do its one that whatever\n",
        "    among down latter onto the when\n",
        "    an each less opposite their where\n",
        "    and either like or them whether\n",
        "    another enough little our these which\n",
        "    any every lots outside they while\n",
        "    anybody everybody many over this who\n",
        "    anyone everyone me own those whoever\n",
        "    anything everything more past though whom\n",
        "    are few most per through whose\n",
        "    around following much plenty till will\n",
        "    as for must plus to with\n",
        "    at from my regarding toward within\n",
        "    be have near same towards without\n",
        "    because he need several under worth\n",
        "    before her neither she unless would\n",
        "    behind him no should unlike yes\n",
        "    below i nobody since until you\n",
        "    beside if none so up your\n",
        "    \"\"\"\n",
        "\n",
        "    functional_words = functional_words.split()\n",
        "    words = RemoveSpecialCHs(text)\n",
        "    count = 0\n",
        "\n",
        "    for i in text:\n",
        "        if i in functional_words:\n",
        "            count += 1\n",
        "\n",
        "    return count / len(words)\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------------------------\n",
        "\n",
        "# also returns Honore Measure R\n",
        "def hapaxLegemena(text):\n",
        "    words = RemoveSpecialCHs(text)\n",
        "    V1 = 0\n",
        "    # dictionary comprehension . har word kay against value 0 kardi\n",
        "    freqs = {key: 0 for key in words}\n",
        "    for word in words:\n",
        "        freqs[word] += 1\n",
        "    for word in freqs:\n",
        "        if freqs[word] == 1:\n",
        "            V1 += 1\n",
        "    N = len(words)\n",
        "    V = float(len(set(words)))\n",
        "    R = 100 * math.log(N) / max(1, (1 - (V1 / V)))\n",
        "    h = V1 / N\n",
        "    return R, h\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------------------------\n",
        "\n",
        "def hapaxDisLegemena(text):\n",
        "    words = RemoveSpecialCHs(text)\n",
        "    count = 0\n",
        "    # Collections as coll Counter takes an iterable collapse duplicate and counts as\n",
        "    # a dictionary how many equivelant items has been entered\n",
        "    freqs = coll.Counter()\n",
        "    freqs.update(words)\n",
        "    for word in freqs:\n",
        "        if freqs[word] == 2:\n",
        "            count += 1\n",
        "\n",
        "    h = count / float(len(words))\n",
        "    S = count / float(len(set(words)))\n",
        "    return S, h\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------------------------\n",
        "\n",
        "# c(w)  = ceil (log2 (f(w*)/f(w))) f(w*) frequency of most commonly used words f(w) frequency of word w\n",
        "# measure of vocabulary richness and connected to zipfs law, f(w*) const rak kay zips law say rank nikal rahay hein\n",
        "def AvgWordFrequencyClass(text):\n",
        "    words = RemoveSpecialCHs(text)\n",
        "    # dictionary comprehension . har word kay against value 0 kardi\n",
        "    freqs = {key: 0 for key in words}\n",
        "    for word in words:\n",
        "        freqs[word] += 1\n",
        "    maximum = float(max(list(freqs.values())))\n",
        "    return np.average([math.floor(math.log((maximum + 1) / (freqs[word]) + 1, 2)) for word in words])\n",
        "\n",
        "\n",
        "# --------------------------------------------------------------------------\n",
        "# TYPE TOKEN RATIO NO OF DIFFERENT WORDS / NO OF WORDS\n",
        "def typeTokenRatio(text):\n",
        "    words = word_tokenize(text)\n",
        "    return len(set(words)) / len(words)\n",
        "\n",
        "\n",
        "# --------------------------------------------------------------------------\n",
        "# logW = V-a/log(N)\n",
        "# N = total words , V = vocabulary richness (unique words) ,  a=0.17\n",
        "# we can convert into log because we are only comparing different texts\n",
        "def BrunetsMeasureW(text):\n",
        "    words = RemoveSpecialCHs(text)\n",
        "    a = 0.17\n",
        "    V = float(len(set(words)))\n",
        "    N = len(words)\n",
        "    B = (V - a) / (math.log(N))\n",
        "    return B\n",
        "\n",
        "\n",
        "# ------------------------------------------------------------------------\n",
        "def RemoveSpecialCHs(text):\n",
        "    text = word_tokenize(text)\n",
        "    st = [\",\", \".\", \"'\", \"!\", '\"', \"#\", \"$\", \"%\", \"&\", \"(\", \")\", \"*\", \"+\", \"-\", \".\", \"/\", \":\", \";\", \"<\", \"=\", '>', \"?\",\n",
        "          \"@\", \"[\", \"\\\\\", \"]\", \"^\", \"_\", '`', \"{\", \"|\", \"}\", '~', '\\t', '\\n']\n",
        "\n",
        "    words = [word for word in text if word not in st]\n",
        "    return words\n",
        "\n",
        "\n",
        "# -------------------------------------------------------------------------\n",
        "# K  10,000 * (M - N) / N**2\n",
        "# , where M  Sigma i**2 * Vi.\n",
        "def YulesCharacteristicK(text):\n",
        "    words = RemoveSpecialCHs(text)\n",
        "    N = len(words)\n",
        "    freqs = coll.Counter()\n",
        "    freqs.update(words)\n",
        "    vi = coll.Counter()\n",
        "    vi.update(freqs.values())\n",
        "    M = sum([(value * value) * vi[value] for key, value in freqs.items()])\n",
        "    K = 10000 * (M - N) / math.pow(N, 2)\n",
        "    return K\n",
        "\n",
        "\n",
        "# -------------------------------------------------------------------------\n",
        "\n",
        "\n",
        "# -1*sigma(pi*lnpi)\n",
        "# Shannon and sympsons index are basically diversity indices for any community\n",
        "def ShannonEntropy(text):\n",
        "    words = RemoveSpecialCHs(text)\n",
        "    lenght = len(words)\n",
        "    freqs = coll.Counter()\n",
        "    freqs.update(words)\n",
        "    arr = np.array(list(freqs.values()))\n",
        "    distribution = 1. * arr\n",
        "    distribution /= max(1, lenght)\n",
        "    import scipy as sc\n",
        "    H = sc.stats.entropy(distribution, base=2)\n",
        "    # H = sum([(i/lenght)*math.log(i/lenght,math.e) for i in freqs.values()])\n",
        "    return H\n",
        "\n",
        "\n",
        "# ------------------------------------------------------------------\n",
        "# 1 - (sigma(n(n - 1))/N(N-1)\n",
        "# N is total number of words\n",
        "# n is the number of each type of word\n",
        "def SimpsonsIndex(text):\n",
        "    words = RemoveSpecialCHs(text)\n",
        "    freqs = coll.Counter()\n",
        "    freqs.update(words)\n",
        "    N = len(words)\n",
        "    n = sum([1.0 * i * (i - 1) for i in freqs.values()])\n",
        "    D = 1 - (n / (N * (N - 1)))\n",
        "    return D\n",
        "\n",
        "\n",
        "# ------------------------------------------------------------------\n",
        "\n",
        "def FleschReadingEase(text, NoOfsentences):\n",
        "    words = RemoveSpecialCHs(text)\n",
        "    l = float(len(words))\n",
        "    scount = 0\n",
        "    for word in words:\n",
        "        scount += syllable_count(word)\n",
        "\n",
        "    I = 206.835 - 1.015 * (l / float(NoOfsentences)) - 84.6 * (scount / float(l))\n",
        "    return I\n",
        "\n",
        "\n",
        "# -------------------------------------------------------------------\n",
        "def FleschCincadeGradeLevel(text, NoOfSentences):\n",
        "    words = RemoveSpecialCHs(text)\n",
        "    scount = 0\n",
        "    for word in words:\n",
        "        scount += syllable_count(word)\n",
        "\n",
        "    l = len(words)\n",
        "    F = 0.39 * (l / NoOfSentences) + 11.8 * (scount / float(l)) - 15.59\n",
        "    return F\n",
        "\n",
        "\n",
        "# -----------------------------------------------------------------\n",
        "def dale_chall_readability_formula(text, NoOfSectences):\n",
        "    words = RemoveSpecialCHs(text)\n",
        "    difficult = 0\n",
        "    adjusted = 0\n",
        "    NoOfWords = len(words)\n",
        "    with open('dale-chall.pkl', 'rb') as f:\n",
        "        fimiliarWords = pickle.load(f)\n",
        "    for word in words:\n",
        "        if word not in fimiliarWords:\n",
        "            difficult += 1\n",
        "    percent = (difficult / NoOfWords) * 100\n",
        "    if (percent > 5):\n",
        "        adjusted = 3.6365\n",
        "    D = 0.1579 * (percent) + 0.0496 * (NoOfWords / NoOfSectences) + adjusted\n",
        "    return D\n",
        "\n",
        "\n",
        "# ------------------------------------------------------------------\n",
        "def GunningFoxIndex(text, NoOfSentences):\n",
        "    words = RemoveSpecialCHs(text)\n",
        "    NoOFWords = float(len(words))\n",
        "    complexWords = 0\n",
        "    for word in words:\n",
        "        if (syllable_count(word) > 2):\n",
        "            complexWords += 1\n",
        "\n",
        "    G = 0.4 * ((NoOFWords / NoOfSentences) + 100 * (complexWords / NoOFWords))\n",
        "    return G\n",
        "\n",
        "\n",
        "# ------------------------------------------------------------------"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3ENYswU7GHmC"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sHdbTgRaGI4q"
      },
      "source": [
        "#### Calls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oqe93ZeXGKch"
      },
      "outputs": [],
      "source": [
        "cmuDictionary = None\n",
        "def FeatureExtraction(chunk, winSize):\n",
        "    global cmuDictionary\n",
        "    cmuDictionary = cmudict.dict()\n",
        "\n",
        "    features = []\n",
        "\n",
        "    # LEXICAL FEATURES\n",
        "    mean_word_length = Avg_wordLength(chunk)\n",
        "    features.append(f\"Mean Word Length: {mean_word_length}\")\n",
        "\n",
        "    mean_sentence_length_chars = Avg_SentLenghtByCh(chunk)\n",
        "    features.append(f\"Mean Sentence Length (characters): {mean_sentence_length_chars}\")\n",
        "\n",
        "    mean_sentence_length_words = Avg_SentLenghtByWord(chunk)\n",
        "    features.append(f\"Mean Sentence Length (words): {mean_sentence_length_words}\")\n",
        "\n",
        "    mean_syllables_per_word = Avg_Syllable_per_Word(chunk)\n",
        "    features.append(f\"Mean Syllables per Word: {mean_syllables_per_word}\")\n",
        "\n",
        "    count_special_characters = CountSpecialCharacter(chunk)\n",
        "    features.append(f\"Count of Special Characters: {count_special_characters}\")\n",
        "\n",
        "    count_punctuation = CountPuncuation(chunk)\n",
        "    features.append(f\"Count of Punctuation: {count_punctuation}\")\n",
        "\n",
        "    count_functional_words = CountFunctionalWords(chunk)\n",
        "    features.append(f\"Count of Functional Words: {count_functional_words}\")\n",
        "\n",
        "    # VOCABULARY RICHNESS FEATURES\n",
        "    type_token_ratio = typeTokenRatio(chunk)\n",
        "    features.append(f\"Type-Token Ratio: {type_token_ratio}\")\n",
        "\n",
        "    honore_measure_r, hapax_legemena = hapaxLegemena(chunk)\n",
        "    features.append(f\"Hapax Legemena: {hapax_legemena}, Honore Measure R: {honore_measure_r}\")\n",
        "\n",
        "    sicheles_measure_s, hapax_dis_legemena = hapaxDisLegemena(chunk)\n",
        "    features.append(f\"Hapax Dis Legemena: {hapax_dis_legemena}, Sicheles Measure S: {sicheles_measure_s}\")\n",
        "\n",
        "    yules_characteristic_k = YulesCharacteristicK(chunk)\n",
        "    features.append(f\"Yules Characteristic K: {yules_characteristic_k}\")\n",
        "\n",
        "    simpsons_index = SimpsonsIndex(chunk)\n",
        "    features.append(f\"Simpsons Index: {simpsons_index}\")\n",
        "\n",
        "    brunets_measure_w = BrunetsMeasureW(chunk)\n",
        "    features.append(f\"Brunets Measure W: {brunets_measure_w}\")\n",
        "\n",
        "    shannon_entropy = ShannonEntropy(chunk)\n",
        "    features.append(f\"Shannon Entropy: {shannon_entropy}\")\n",
        "\n",
        "    # READABILITY FEATURES\n",
        "    flesch_reading_ease = FleschReadingEase(chunk, winSize)\n",
        "    features.append(f\"Flesch Reading Ease: {flesch_reading_ease}\")\n",
        "\n",
        "    flesch_cincade_grade_level = FleschCincadeGradeLevel(chunk, winSize)\n",
        "    features.append(f\"Flesch Cincade Grade Level: {flesch_cincade_grade_level}\")\n",
        "\n",
        "    #dale_chall_readability = dale_chall_readability_formula(chunk, winSize)\n",
        "    #features.append(f\"Dale Chall Readability Formula: {dale_chall_readability}\")\n",
        "\n",
        "    gunning_fox_index = GunningFoxIndex(chunk, winSize)\n",
        "    features.append(f\"Gunning Fox Index: {gunning_fox_index}\")\n",
        "\n",
        "    return '\\n'.join(features)\n",
        "\n",
        "\n",
        "\n",
        "def FeatureExtraction_titles(chunk_list):\n",
        "    global cmuDictionary\n",
        "    cmuDictionary = cmudict.dict()\n",
        "\n",
        "    features = []\n",
        "\n",
        "    # Initialize dictionaries to store sum and count for averaging\n",
        "    sum_dict = {}\n",
        "    count_dict = {}\n",
        "\n",
        "    # Function to update sum and count dictionaries\n",
        "    def update_dicts(feature_name, value):\n",
        "        if feature_name not in sum_dict:\n",
        "            sum_dict[feature_name] = value\n",
        "            count_dict[feature_name] = 1\n",
        "        else:\n",
        "            sum_dict[feature_name] += value\n",
        "            count_dict[feature_name] += 1\n",
        "\n",
        "    # Iterate over each chunk in the list\n",
        "    for chunk in chunk_list:\n",
        "        # LEXICAL FEATURES\n",
        "        update_dicts(\"Mean Word Length\", Avg_wordLength(chunk))\n",
        "        update_dicts(\"Mean Sentence Length (characters)\", Avg_SentLenghtByCh(chunk))\n",
        "        update_dicts(\"Mean Sentence Length (words)\", Avg_SentLenghtByWord(chunk))\n",
        "        update_dicts(\"Mean Syllables per Word\", Avg_Syllable_per_Word(chunk))\n",
        "        update_dicts(\"Count of Special Characters\", CountSpecialCharacter(chunk))\n",
        "        update_dicts(\"Count of Punctuation\", CountPuncuation(chunk))\n",
        "        update_dicts(\"Count of Functional Words\", CountFunctionalWords(chunk))\n",
        "\n",
        "        # VOCABULARY RICHNESS FEATURES\n",
        "        update_dicts(\"Type-Token Ratio\", typeTokenRatio(chunk))\n",
        "        honore_measure_r, hapax_legemena = hapaxLegemena(chunk)\n",
        "        update_dicts(\"Hapax Legemena\", hapax_legemena)\n",
        "        update_dicts(\"Honore Measure R\", honore_measure_r)\n",
        "        sicheles_measure_s, hapax_dis_legemena = hapaxDisLegemena(chunk)\n",
        "        update_dicts(\"Hapax Dis Legemena\", hapax_dis_legemena)\n",
        "        update_dicts(\"Sicheles Measure S\", sicheles_measure_s)\n",
        "        update_dicts(\"Yules Characteristic K\", YulesCharacteristicK(chunk))\n",
        "        update_dicts(\"Simpsons Index\", SimpsonsIndex(chunk))\n",
        "        update_dicts(\"Brunets Measure W\", BrunetsMeasureW(chunk))\n",
        "        update_dicts(\"Shannon Entropy\", ShannonEntropy(chunk))\n",
        "\n",
        "        # READABILITY FEATURES\n",
        "        update_dicts(\"Flesch Reading Ease\", FleschReadingEase(chunk, len(chunk)))\n",
        "        update_dicts(\"Flesch Cincade Grade Level\", FleschCincadeGradeLevel(chunk,len(chunk)))\n",
        "        #update_dicts(\"Dale Chall Readability Formula\", dale_chall_readability_formula(chunk,len(chunk)))\n",
        "        update_dicts(\"Gunning Fox Index\", GunningFoxIndex(chunk, len(chunk)))\n",
        "\n",
        "    # Calculate the average for each feature\n",
        "    for feature_name, feature_sum in sum_dict.items():\n",
        "        avg_value = feature_sum / count_dict[feature_name]\n",
        "        features.append(f\"{feature_name}: {avg_value}\")\n",
        "\n",
        "    return '\\n'.join(features)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E-UWEQurGs3S"
      },
      "source": [
        "#### Test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bb4jJm4rGRjM"
      },
      "outputs": [],
      "source": [
        "exp='''\n",
        "Lexical Features:\n",
        "1. Mean Word Length: Average number of characters in words.\n",
        "2. Mean Sentence Length (characters): Average number of characters in sentences.\n",
        "3. Mean Sentence Length (words): Average number of words in sentences.\n",
        "4. Mean Syllables per Word: Average number of syllables in words.\n",
        "5. Count of Special Characters: Normalized count of special characters over the length of the chunk.\n",
        "6. Count of Punctuation: Normalized count of punctuation marks over the length of the chunk.\n",
        "7. Count of Functional Words: Normalized count of functional words from a specific set.\n",
        "\n",
        "Vocabulary Richness Features:\n",
        "8. Type-Token Ratio: Ratio of different words to total words.\n",
        "9. Hapax Legemena: Ratio of words that occur only once to total words.\n",
        "10. Sicheles Measure S: Ratio of words that occur twice to total words.\n",
        "11. Yules Characteristic K: Measure of vocabulary richness and connection to Zipf's Law.\n",
        "12. Simpsons Index: Diversity index based on word frequency.\n",
        "13. Brunets Measure W: Vocabulary richness index.\n",
        "14. Shannon Entropy: Information entropy based on word frequency.\n",
        "\n",
        "Readability Features:\n",
        "15. Flesch Reading Ease: Readability score based on average sentence length and syllables per word.\n",
        "16. Flesch Cincade Grade Level: Readability score based on average sentence length and syllables per word.\n",
        "17. Dale Chall Readability Formula: Readability score considering difficult words.\n",
        "18. Gunning Fox Index: Readability index based on sentence length and complex words.\n",
        "\n",
        "'''"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WX_Ed1mlKNHy"
      },
      "source": [
        "#### LLM BASED"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OWWaPXS-G6WP"
      },
      "outputs": [],
      "source": [
        "## PROMPT for LLM BASE WRITING STYLE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yKaBMTsQINBy",
        "outputId": "edbc5f13-cf65-4762-bc89-7911a5c508f3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: google-generativeai in /usr/local/lib/python3.10/dist-packages (0.2.2)\n",
            "Requirement already satisfied: google-ai-generativelanguage==0.3.3 in /usr/local/lib/python3.10/dist-packages (from google-generativeai) (0.3.3)\n",
            "Requirement already satisfied: google-auth in /usr/local/lib/python3.10/dist-packages (from google-generativeai) (2.17.3)\n",
            "Requirement already satisfied: google-api-core in /usr/local/lib/python3.10/dist-packages (from google-generativeai) (2.11.1)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.10/dist-packages (from google-generativeai) (3.20.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from google-generativeai) (4.66.1)\n",
            "Requirement already satisfied: proto-plus<2.0.0dev,>=1.22.0 in /usr/local/lib/python3.10/dist-packages (from google-ai-generativelanguage==0.3.3->google-generativeai) (1.22.3)\n",
            "Requirement already satisfied: googleapis-common-protos<2.0.dev0,>=1.56.2 in /usr/local/lib/python3.10/dist-packages (from google-api-core->google-generativeai) (1.61.0)\n",
            "Requirement already satisfied: requests<3.0.0.dev0,>=2.18.0 in /usr/local/lib/python3.10/dist-packages (from google-api-core->google-generativeai) (2.31.0)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth->google-generativeai) (5.3.2)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth->google-generativeai) (0.3.0)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from google-auth->google-generativeai) (1.16.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth->google-generativeai) (4.9)\n",
            "Requirement already satisfied: grpcio<2.0dev,>=1.33.2 in /usr/local/lib/python3.10/dist-packages (from google-api-core->google-generativeai) (1.59.3)\n",
            "Requirement already satisfied: grpcio-status<2.0.dev0,>=1.33.2 in /usr/local/lib/python3.10/dist-packages (from google-api-core->google-generativeai) (1.48.2)\n",
            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth->google-generativeai) (0.5.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0.dev0,>=2.18.0->google-api-core->google-generativeai) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0.dev0,>=2.18.0->google-api-core->google-generativeai) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0.dev0,>=2.18.0->google-api-core->google-generativeai) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0.dev0,>=2.18.0->google-api-core->google-generativeai) (2023.11.17)\n"
          ]
        }
      ],
      "source": [
        "!pip install google-generativeai"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UQGXbwTJIutY"
      },
      "outputs": [],
      "source": [
        "import google.generativeai as palm\n",
        "import os\n",
        "os.environ['API_KEY']=#API KEY HERE\n",
        "palm.configure(api_key=os.environ['API_KEY'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QVuV4o_zKQRR"
      },
      "source": [
        "## RUN STYLE ANALYSIS ON TEST DATA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wkPFZ-gPec5x"
      },
      "outputs": [],
      "source": [
        "import time"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M_NYGYUebxR5"
      },
      "outputs": [],
      "source": [
        "def get_palm_response(input_text, prompt_instructions):\n",
        "  p=\"input text: \"+ input_text+\"\\n\"+prompt_instructions\n",
        "  response = palm.generate_text(prompt=p)\n",
        "  return response.result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "laVZ26zwKnB7"
      },
      "outputs": [],
      "source": [
        "def get_writing_style(text,prompt_instructions,all_titles):\n",
        "  statistical_analysis= FeatureExtraction_titles(all_titles)\n",
        "  llm_bases_analysis=get_palm_response(text, prompt_instructions)\n",
        "\n",
        "  return {'statistical_analysis':statistical_analysis,'llm_bases_analysis':llm_bases_analysis }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N96zVjPyVkVi",
        "outputId": "ffdd4f1a-9802-4865-bf24-25231fc4b74c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 100/100 [09:52<00:00,  5.93s/it]\n"
          ]
        }
      ],
      "source": [
        "prompt_instructions='''You are a writing style detector. Your job is to specify a writing style of input text above. When you do so, you describe outloud:\n",
        "general writing style (like creative, non-fiction),\n",
        "genres,\n",
        "style descriptors (like full of dialogue, short sentences),\n",
        "moods (like serious, joking),\n",
        "authors that share a similar style (like Kurt Vonnegut),\n",
        "common phrases or speech patterns. You are not to provide any other additional information.\n",
        "'''\n",
        "style_results=[]\n",
        "for row in tqdm(range(test_data.shape[0])):\n",
        "  cur_list = test_data.iloc[row]['summary_results']\n",
        "\n",
        "  all_titles = [entry['title'] for entry in cur_list]\n",
        "  all_profile_article = [entry['title']+'\\n'+entry['text'] for entry in cur_list]\n",
        "\n",
        "  style=get_writing_style(\"\\n\".join(all_profile_article),prompt_instructions,all_titles)\n",
        "  style_results.append(style)\n",
        "\n",
        "  if(row%10==0):\n",
        "    # Save to a pickle file\n",
        "    with open(\"style_checkpoints/cont_style_summ\"+str(row)+\".pkl\", \"wb\") as file:\n",
        "      pickle.dump(style_results, file)\n",
        "      time.sleep(30)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ROW3zD3GhgbM"
      },
      "outputs": [],
      "source": [
        "test_data['style_results']=style_results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6s3hJKUShqsN"
      },
      "outputs": [],
      "source": [
        "test_data.to_pickle('contriever/test_data_100_style.pkl')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "height": 115
        },
        "id": "XuKQe7EdhvdF",
        "outputId": "349881f4-db16-4662-cb9a-062972aaa316"
      },
      "outputs": [
        {
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              "  <tbody>\n",
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              "      <th>8913</th>\n",
              "      <td>408913</td>\n",
              "      <td>Generate a title for the following abstract of...</td>\n",
              "      <td>[{'title': 'Fault domain-based testing in impe...</td>\n",
              "      <td>TABOR: A Graphical Model-based Approach for An...</td>\n",
              "      <td>[{'abstract': 'Industrial Control Systems (ICS...</td>\n",
              "      <td>[{'text': 'Industrial Control Systems (ICS) ar...</td>\n",
              "      <td>[{'text': 'Industrial Control Systems (ICS) ar...</td>\n",
              "      <td>{'statistical_analysis': 'Mean Word Length: 8....</td>\n",
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              "\n",
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              "        document.querySelector('#df-dcadd80f-4c1e-4dad-88b3-599668701742 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-dcadd80f-4c1e-4dad-88b3-599668701742');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </script>\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "          id                                              input  \\\n",
              "8913  408913  Generate a title for the following abstract of...   \n",
              "\n",
              "                                                profile  \\\n",
              "8913  [{'title': 'Fault domain-based testing in impe...   \n",
              "\n",
              "                                                 output  \\\n",
              "8913  TABOR: A Graphical Model-based Approach for An...   \n",
              "\n",
              "                                           bm25_results  \\\n",
              "8913  [{'abstract': 'Industrial Control Systems (ICS...   \n",
              "\n",
              "                                        keyword_results  \\\n",
              "8913  [{'text': 'Industrial Control Systems (ICS) ar...   \n",
              "\n",
              "                                        summary_results  \\\n",
              "8913  [{'text': 'Industrial Control Systems (ICS) ar...   \n",
              "\n",
              "                                          style_results  \n",
              "8913  {'statistical_analysis': 'Mean Word Length: 8....  "
            ]
          },
          "execution_count": 71,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "XemVe6sih_rX",
        "outputId": "67b524a2-7ab3-4178-f248-60ce38f246a7"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"Generate a title for the following abstract of a paper: Industrial Control Systems (ICS) such as water and power are critical to any society. Process anomaly detection mechanisms have been proposed to protect such systems to minimize the risk of damage or loss of resources. In this paper, a graphical model-based approach is proposed for profiling normal operational behavior of an operational ICS referred to as SWaT (Secure Water Treatment). Timed automata are learned as a model of regular behaviors shown in sensors signal like fluctuations of water level in tanks. Bayesian networks are learned to discover dependencies between sensors and actuators. The models are used as a one-class classifier for process anomaly detection, recognizing irregular behavioral patterns and dependencies. The detection results can be interpreted and the abnormal sensors or actuators localized due to the interpretability of the graphical models. This approach is applied to a dataset collected from SWaT. Experimental results demonstrate the model's superior performance on both precision and run-time over methods including support vector machine and deep neural networks. The underlying idea is generic and applicable to other industrial control systems such as power and transportation.\\n\\n\""
            ]
          },
          "execution_count": 72,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data.iloc[0]['input']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4SZ0nzU4nkb5"
      },
      "source": [
        "## Adding keywords and summary for input too."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Upv_ffi0npMu"
      },
      "outputs": [],
      "source": [
        "keyword_list=[]\n",
        "summary_list=[]\n",
        "for row in tqdm(range(test_data.shape[0])):\n",
        "  input = test_data.iloc[row]['input'].split(\"Generate a title for the following abstract of a paper:\")[-1].strip()\n",
        "  keywords_scores=kw_model.extract_keywords(input)\n",
        "  keyword_list.append(', '.join([kw[0] for kw in keywords_scores]))\n",
        "  summary_list.append(get_summ(input))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QRQqjokj93tR"
      },
      "outputs": [],
      "source": [
        "prev=pd.read_pickle('bm25/preprompt_100.pkl')\n",
        "keyword_list=prev['input_keywords']\n",
        "summary_list=prev[\"input_summary\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Co0qj4Ia-R2M"
      },
      "outputs": [],
      "source": [
        "test_data['input_keywords']=keyword_list\n",
        "test_data[\"input_summary\"]=summary_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qO7-lwvkpkMu"
      },
      "outputs": [],
      "source": [
        "test_data.to_pickle('contriever/preprompt_100.pkl')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J9tIarZUiGqI"
      },
      "source": [
        "# COMPLETE PIPELINE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bH8ueyMWp3t-"
      },
      "outputs": [],
      "source": [
        "preprompt_data=pd.read_pickle(\"contriever/preprompt_100.pkl\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150
        },
        "id": "8bNtkTRwiIy0",
        "outputId": "ae7ecf6f-d413-42e0-a1a3-3f47ff38e1a0"
      },
      "outputs": [
        {
          "data": {
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              "      <th>8913</th>\n",
              "      <td>408913</td>\n",
              "      <td>Generate a title for the following abstract of...</td>\n",
              "      <td>[{'title': 'Fault domain-based testing in impe...</td>\n",
              "      <td>TABOR: A Graphical Model-based Approach for An...</td>\n",
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              "      <td>the problem of process detection in water tank...</td>\n",
              "      <td>[{'title': 'WaterJam: An Experimental Case Stu...</td>\n",
              "      <td>[{'text': 'An experiment was conducted to inve...</td>\n",
              "      <td>[{'text': 'An experiment was conducted to inve...</td>\n",
              "      <td>{'statistical_analysis': 'Mean Word Length: 7....</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
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              "        document.querySelector('#df-4830caf5-55c2-44ce-82b7-a00df6278566 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4830caf5-55c2-44ce-82b7-a00df6278566');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "          id                                              input  \\\n",
              "8913  408913  Generate a title for the following abstract of...   \n",
              "\n",
              "                                                profile  \\\n",
              "8913  [{'title': 'Fault domain-based testing in impe...   \n",
              "\n",
              "                                                 output  \\\n",
              "8913  TABOR: A Graphical Model-based Approach for An...   \n",
              "\n",
              "                                      input_keywords  \\\n",
              "8913  automata, swat, classifier, detection, anomaly   \n",
              "\n",
              "                                          input_summary  \\\n",
              "8913  the problem of process detection in water tank...   \n",
              "\n",
              "                                             contriever  \\\n",
              "8913  [{'title': 'WaterJam: An Experimental Case Stu...   \n",
              "\n",
              "                                        keyword_results  \\\n",
              "8913  [{'text': 'An experiment was conducted to inve...   \n",
              "\n",
              "                                        summary_results  \\\n",
              "8913  [{'text': 'An experiment was conducted to inve...   \n",
              "\n",
              "                                          style_results  \n",
              "8913  {'statistical_analysis': 'Mean Word Length: 7....  "
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "preprompt_data.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pjdGbt3sibju"
      },
      "outputs": [],
      "source": [
        "def generate_title_prompt(input_text, style_results, summary_results,ip_keys,ip_summ):\n",
        "    # Extracting style analysis and general writing style of the user\n",
        "    style_analysis = style_results['statistical_analysis']\n",
        "    general_style = style_results['llm_bases_analysis']\n",
        "\n",
        "    # Extracting information from previous articles\n",
        "    previous_article_prompts = []\n",
        "    for article in summary_results:\n",
        "        article_text = article['text']\n",
        "        title = article['title']\n",
        "        keywords = ', '.join([kw[0] for kw in article['keywords']])\n",
        "        summary = article['summary']\n",
        "        previous_article_prompt = (\n",
        "            f\"Abstract Text: {article_text}\\n\"\n",
        "            f\"Title: {title}\\n\"\n",
        "            f\"Keywords: {keywords}\\n\"\n",
        "            f\"Summary: {summary}\\n\\n\"\n",
        "        )\n",
        "        previous_article_prompts.append(previous_article_prompt)\n",
        "\n",
        "    # Combining all information into the final prompt\n",
        "    final_prompt_details = (\n",
        "        f\"Previous Articles Analysis:\\n{''.join(previous_article_prompts)}\"\n",
        "        f\"Statistical Writing Style Analysis of Titles:\\n{style_analysis}\\n\\n\"\n",
        "        f\"General Writing Style of User:\\n{general_style}\\n\\n\"\n",
        "        f\"New Input Article:\\nAbstract Text: {input_text}\\n\"\n",
        "        f\"Keywords: {ip_keys}\\n\"\n",
        "        f\"Summary: {ip_summ}\\n\\n\"\n",
        "    )\n",
        "\n",
        "    final_prompt=\"Your task is to analyze the provided data to understand the user's writing style and approach to titling. This includes examining previous articles with their titles, keywords, and summaries, a statistical analysis of the user's writing style in their titles, a general characterization of their writing style, and the abstract of a new input article. Based on this analysis, generate a title for the new article that aligns with the user's unique style.\"\\\n",
        "    +\"\\n\"\\\n",
        "    +final_prompt_details\\\n",
        "    +\"\\n\"+\"After reviewing all these elements, create a title for the new article that reflects the user's unique writing style and titling approach, as well as being pertinent to the new article's content and theme\"\n",
        "\n",
        "    return final_prompt\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F8zzJDxEjyQI",
        "outputId": "a28dd22a-7c65-4e84-b4ca-b50910e9f01c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(\"Your task is to analyze the provided data to understand the user's writing \"\n",
            " 'style and approach to titling. This includes examining previous articles '\n",
            " 'with their titles, keywords, and summaries, a statistical analysis of the '\n",
            " \"user's writing style in their titles, a general characterization of their \"\n",
            " 'writing style, and the abstract of a new input article. Based on this '\n",
            " \"analysis, generate a title for the new article that aligns with the user's \"\n",
            " 'unique style.\\n'\n",
            " 'Previous Articles Analysis:\\n'\n",
            " 'Abstract Text: An experiment was conducted to investigate network jamming '\n",
            " 'attacks on an Industrial Control Systems. The Secure Water Treatment (SWaT) '\n",
            " 'system was chosen to perform the experiments. Jamming attacks were launched '\n",
            " 'on SWaT using software defined radio. Attacks were designed to meet attacker '\n",
            " 'objectives selected from a Cyber Physical Systems specific attacker model. '\n",
            " 'Attacks exposed vulnerabilities associated with design of SWaT and suggested '\n",
            " 'modifications in the architecture owing to broadcast nature of wireless '\n",
            " 'system update and control. This investigation was focused on (a) how jamming '\n",
            " 'attacks can be performed on Industrial Control Systems, (b) what '\n",
            " 'communication links to jam and why? (c) what is the impact of jamming '\n",
            " 'attacks on SWaT? The observed response to various attacks was then used to '\n",
            " 'propose attack detection mechanisms. While the jamming attacks were applied '\n",
            " 'to a specific water treatment plant, the underlying ideas are generic and '\n",
            " 'could be applied to other public infrastructure systems as well.\\n'\n",
            " 'Title: WaterJam: An Experimental Case Study of Jamming Attacks on a Water '\n",
            " 'Treatment System\\n'\n",
            " 'Keywords: attacks, jamming, vulnerabilities, swat, attack\\n'\n",
            " 'Summary: an experiment was conducted to design a jamming attack mechanism.\\n'\n",
            " '\\n'\n",
            " 'Abstract Text: A method referred to as PbNN is proposed to detect '\n",
            " 'cyber-physical attacks through the identification of resulting anomalies in '\n",
            " 'the process dynamics of the underlying ICS. Unlike existing anomaly '\n",
            " 'detectors based on an abstract knowledge acquired from operational data, '\n",
            " 'PbNN utilizes the design knowledge of ICS to learn the complex relationships '\n",
            " 'among the correlated components. Such relationships are accurately modeled '\n",
            " 'using operational data through the application of the deep convolution '\n",
            " 'neural network. The proposed detector was implemented and evaluated in an '\n",
            " 'operational secure water treatment plant by launching several real-time '\n",
            " 'stealthy and coordinated attacks. The results indicate that PbNN outperforms '\n",
            " 'the existing state-of-the-art machine learning anomaly detectors when '\n",
            " 'compared using detection accuracy and the rate of false alarms.\\n'\n",
            " 'Title: A Hybrid Physics-Based Data-Driven Framework for Anomaly Detection in '\n",
            " 'Industrial Control Systems\\n'\n",
            " 'Keywords: detect, attacks, pbnn, anomaly, detection\\n'\n",
            " 'Summary: a method of detecting cyberphysical attacks based on the '\n",
            " 'application of deep convolution neural network was proposed.<n> the state of '\n",
            " 'the art of the method was compared when using detection accuracy and the '\n",
            " 'rate of false alarms.\\n'\n",
            " '\\n'\n",
            " 'Abstract Text: An approach is proposed to derive state-based invariants '\n",
            " 'that, when programmed into a controller, proved to be effective in detecting '\n",
            " 'cyber attacks on an Industrial Control System (ICS). The proposed approach '\n",
            " 'begins with the ICS design and models its process dynamics using an extended '\n",
            " 'hybrid automata from which the invariants are derived. Each invariant is '\n",
            " 'programmed and inserted into an appropriate Programmable Logic Controller '\n",
            " '(PLC) as a companion to the control code. The invariant is active during ICS '\n",
            " 'operation and serves to check on the validity of the system state in '\n",
            " 'accordance with the system design. This approach, referred to as D2I for '\n",
            " 'Design to Invariants, was evaluated on a fully operational 6-stage water '\n",
            " 'treatment plant that uses a distributed process control system.\\n'\n",
            " 'Title: From Design to Invariants: Detecting Attacks on Cyber Physical '\n",
            " 'Systems\\n'\n",
            " 'Keywords: automata, plc, cyber, industrial, programmable\\n'\n",
            " 'Summary: approach to fully operational 6-stage water treatment that uses '\n",
            " 'distributed control for cyber attack detection is proposed. <n> [ [ sec - '\n",
            " 'abstract ] ] abstract : + + + _ proposition : _ a fully operational six - '\n",
            " 'stage water treatment system is designed to be secure against cyber '\n",
            " 'attacks.<n> _ keywords : control theory, attack detection, cyber attack, '\n",
            " 'distributed control, hybrid control _ ams subject classification : 05c50, '\n",
            " '68w30, 94a12, 15a18 _ pacs numbers : - _ 06.20.dk, 07.05.kf, 89.70.+c, '\n",
            " '89.90.+n\\n'\n",
            " '\\n'\n",
            " 'Statistical Writing Style Analysis of Titles:\\n'\n",
            " 'Mean Word Length: 7.166666666666667\\n'\n",
            " 'Mean Sentence Length (characters): 83.0\\n'\n",
            " 'Mean Sentence Length (words): 11.666666666666666\\n'\n",
            " 'Mean Syllables per Word: 2.4166666666666665\\n'\n",
            " 'Count of Special Characters: 0.006944444444444444\\n'\n",
            " 'Count of Punctuation: 0.008777969018932874\\n'\n",
            " 'Count of Functional Words: 0.8820512820512821\\n'\n",
            " 'Type-Token Ratio: 1.0\\n'\n",
            " 'Hapax Legemena: 1.0\\n'\n",
            " 'Honore Measure R: 245.0813700081194\\n'\n",
            " 'Hapax Dis Legemena: 0.0\\n'\n",
            " 'Sicheles Measure S: 0.0\\n'\n",
            " 'Yules Characteristic K: 9132.478632478633\\n'\n",
            " 'Simpsons Index: 1.0\\n'\n",
            " 'Brunets Measure W: 4.677301684506705\\n'\n",
            " 'Shannon Entropy: 3.535776771249871\\n'\n",
            " 'Flesch Reading Ease: 25.560613801359295\\n'\n",
            " 'Flesch Cincade Grade Level: 9.729285438457126\\n'\n",
            " 'Gunning Fox Index: 11.663435426688439\\n'\n",
            " '\\n'\n",
            " 'General Writing Style of User:\\n'\n",
            " '**General writing style:** Non-fiction, academic\\n'\n",
            " '\\n'\n",
            " '**Genres:** Computer science, engineering\\n'\n",
            " '\\n'\n",
            " '**Style descriptors:** Technical, detailed, precise\\n'\n",
            " '\\n'\n",
            " '**Moods:** Serious, objective\\n'\n",
            " '\\n'\n",
            " '**Authors that share a similar style:** Donald Knuth, Andrew Tanenbaum\\n'\n",
            " '\\n'\n",
            " '**Common phrases or speech patterns:** - \"In this paper, we propose a new '\n",
            " 'method for...\"\\n'\n",
            " '- \"The results show that our method is superior to existing methods in terms '\n",
            " 'of...\"\\n'\n",
            " '- \"We conclude that our method is a promising approach for...\"\\n'\n",
            " '\\n'\n",
            " 'New Input Article:\\n'\n",
            " 'Abstract Text: Generate a title for the following abstract of a paper: '\n",
            " 'Industrial Control Systems (ICS) such as water and power are critical to any '\n",
            " 'society. Process anomaly detection mechanisms have been proposed to protect '\n",
            " 'such systems to minimize the risk of damage or loss of resources. In this '\n",
            " 'paper, a graphical model-based approach is proposed for profiling normal '\n",
            " 'operational behavior of an operational ICS referred to as SWaT (Secure Water '\n",
            " 'Treatment). Timed automata are learned as a model of regular behaviors shown '\n",
            " 'in sensors signal like fluctuations of water level in tanks. Bayesian '\n",
            " 'networks are learned to discover dependencies between sensors and actuators. '\n",
            " 'The models are used as a one-class classifier for process anomaly detection, '\n",
            " 'recognizing irregular behavioral patterns and dependencies. The detection '\n",
            " 'results can be interpreted and the abnormal sensors or actuators localized '\n",
            " 'due to the interpretability of the graphical models. This approach is '\n",
            " 'applied to a dataset collected from SWaT. Experimental results demonstrate '\n",
            " \"the model's superior performance on both precision and run-time over methods \"\n",
            " 'including support vector machine and deep neural networks. The underlying '\n",
            " 'idea is generic and applicable to other industrial control systems such as '\n",
            " 'power and transportation.\\n'\n",
            " '\\n'\n",
            " '\\n'\n",
            " 'Keywords: automata, swat, classifier, detection, anomaly\\n'\n",
            " 'Summary: the problem of process detection in water tanks is considered.<n> '\n",
            " 'it is shown that such as water and power are critical society.\\n'\n",
            " '\\n'\n",
            " '\\n'\n",
            " 'After reviewing all these elements, create a title for the new article that '\n",
            " \"reflects the user's unique writing style and titling approach, as well as \"\n",
            " \"being pertinent to the new article's content and theme\")\n"
          ]
        }
      ],
      "source": [
        "# Example usage\n",
        "input_article = preprompt_data.iloc[0]['input']\n",
        "ip_keys=preprompt_data.iloc[0]['input_keywords']\n",
        "ip_summ=preprompt_data.iloc[0]['input_summary']\n",
        "style_results = preprompt_data.iloc[0]['style_results']\n",
        "summary_results = preprompt_data.iloc[0]['summary_results']\n",
        "\n",
        "prompt_to_llm = generate_title_prompt(input_article, style_results, summary_results,ip_keys,ip_summ)\n",
        "pprint(prompt_to_llm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rAuV6V9IzBQO",
        "outputId": "0fdfa454-55c9-401f-9938-3ab84afac605"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 100/100 [00:00<00:00, 1999.47it/s]\n"
          ]
        }
      ],
      "source": [
        "prompt_list=[]\n",
        "for row in tqdm(range(preprompt_data.shape[0])):\n",
        "  input_article = preprompt_data.iloc[row]['input']\n",
        "  ip_keys=preprompt_data.iloc[row]['input_keywords']\n",
        "  ip_summ=preprompt_data.iloc[row]['input_summary']\n",
        "  style_results = preprompt_data.iloc[row]['style_results']\n",
        "  summary_results = preprompt_data.iloc[row]['summary_results']\n",
        "  prompt_list.append(generate_title_prompt(input_article, style_results, summary_results,ip_keys,ip_summ))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EOKltLIgsXig"
      },
      "outputs": [],
      "source": [
        "preprompt_data['prompts']=prompt_list\n",
        "preprompt_data.to_pickle('contriever/preprompt_100.pkl')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "85BXritbsrln"
      },
      "source": [
        "# RUN PROMPTS THROUGH PALM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EO_8V-Epdobm"
      },
      "outputs": [],
      "source": [
        "preprompt_data=pd.read_pickle('contriever/preprompt_100.pkl')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "id": "ibb0Uf2eeH-S",
        "outputId": "9c0a5b57-6b10-4e8c-b7bb-af7b08520582"
      },
      "outputs": [
        {
          "data": {
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8913</th>\n",
              "      <td>408913</td>\n",
              "      <td>Generate a title for the following abstract of...</td>\n",
              "      <td>[{'title': 'Fault domain-based testing in impe...</td>\n",
              "      <td>TABOR: A Graphical Model-based Approach for An...</td>\n",
              "      <td>automata, swat, classifier, detection, anomaly</td>\n",
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              "\n",
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              "\n",
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              "        document.querySelector('#df-ccc18a29-88a3-4b74-ba71-731f622bbb6f button.colab-df-convert');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ccc18a29-88a3-4b74-ba71-731f622bbb6f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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            ],
            "text/plain": [
              "          id                                              input  \\\n",
              "8913  408913  Generate a title for the following abstract of...   \n",
              "\n",
              "                                                profile  \\\n",
              "8913  [{'title': 'Fault domain-based testing in impe...   \n",
              "\n",
              "                                                 output  \\\n",
              "8913  TABOR: A Graphical Model-based Approach for An...   \n",
              "\n",
              "                                      input_keywords  \\\n",
              "8913  automata, swat, classifier, detection, anomaly   \n",
              "\n",
              "                                          input_summary  \\\n",
              "8913  the problem of process detection in water tank...   \n",
              "\n",
              "                                             contriever  \\\n",
              "8913  [{'title': 'WaterJam: An Experimental Case Stu...   \n",
              "\n",
              "                                        keyword_results  \\\n",
              "8913  [{'text': 'An experiment was conducted to inve...   \n",
              "\n",
              "                                        summary_results  \\\n",
              "8913  [{'text': 'An experiment was conducted to inve...   \n",
              "\n",
              "                                          style_results  \\\n",
              "8913  {'statistical_analysis': 'Mean Word Length: 7....   \n",
              "\n",
              "                                                prompts  \n",
              "8913  Your task is to analyze the provided data to u...  "
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "preprompt_data.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MnALmS_qsu2L",
        "outputId": "0d1b7db0-5fc3-4a04-cd88-2b2ea12dc68d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 100/100 [08:35<00:00,  5.16s/it]\n"
          ]
        }
      ],
      "source": [
        "import pickle\n",
        "import time\n",
        "response_list=[]\n",
        "for row in tqdm(range(preprompt_data.shape[0])):\n",
        "  cur_prompt=preprompt_data.iloc[row]['prompts']\n",
        "  response = palm.generate_text(prompt=cur_prompt)\n",
        "  response_list.append(response.result)\n",
        "\n",
        "  if(row%10==0):\n",
        "    # Save to a pickle file\n",
        "    with open(\"prompting_checkpoints/cont_prompt_out\"+str(row)+\".pkl\", \"wb\") as file:\n",
        "      pickle.dump(response_list, file)\n",
        "      time.sleep(30)\n",
        "\n",
        "\n",
        "preprompt_data['model_output']=response_list\n",
        "preprompt_data.to_pickle('contriever/postprompt_100.pkl')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "1tl0bfm1xgn_",
        "outputId": "49a35bff-e025-459e-c5c5-9564e18f1506"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
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              "\n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>input</th>\n",
              "      <th>profile</th>\n",
              "      <th>output</th>\n",
              "      <th>input_keywords</th>\n",
              "      <th>input_summary</th>\n",
              "      <th>contriever</th>\n",
              "      <th>keyword_results</th>\n",
              "      <th>summary_results</th>\n",
              "      <th>style_results</th>\n",
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              "      <th>model_output</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8913</th>\n",
              "      <td>408913</td>\n",
              "      <td>Generate a title for the following abstract of...</td>\n",
              "      <td>[{'title': 'Fault domain-based testing in impe...</td>\n",
              "      <td>TABOR: A Graphical Model-based Approach for An...</td>\n",
              "      <td>automata, swat, classifier, detection, anomaly</td>\n",
              "      <td>the problem of process detection in water tank...</td>\n",
              "      <td>[{'title': 'WaterJam: An Experimental Case Stu...</td>\n",
              "      <td>[{'text': 'An experiment was conducted to inve...</td>\n",
              "      <td>[{'text': 'An experiment was conducted to inve...</td>\n",
              "      <td>{'statistical_analysis': 'Mean Word Length: 7....</td>\n",
              "      <td>Your task is to analyze the provided data to u...</td>\n",
              "      <td>A Graphical Model-Based Approach for Anomaly D...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d9472877-4d1b-4efb-bdf6-ba291db868d8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d9472877-4d1b-4efb-bdf6-ba291db868d8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d9472877-4d1b-4efb-bdf6-ba291db868d8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "          id                                              input  \\\n",
              "8913  408913  Generate a title for the following abstract of...   \n",
              "\n",
              "                                                profile  \\\n",
              "8913  [{'title': 'Fault domain-based testing in impe...   \n",
              "\n",
              "                                                 output  \\\n",
              "8913  TABOR: A Graphical Model-based Approach for An...   \n",
              "\n",
              "                                      input_keywords  \\\n",
              "8913  automata, swat, classifier, detection, anomaly   \n",
              "\n",
              "                                          input_summary  \\\n",
              "8913  the problem of process detection in water tank...   \n",
              "\n",
              "                                             contriever  \\\n",
              "8913  [{'title': 'WaterJam: An Experimental Case Stu...   \n",
              "\n",
              "                                        keyword_results  \\\n",
              "8913  [{'text': 'An experiment was conducted to inve...   \n",
              "\n",
              "                                        summary_results  \\\n",
              "8913  [{'text': 'An experiment was conducted to inve...   \n",
              "\n",
              "                                          style_results  \\\n",
              "8913  {'statistical_analysis': 'Mean Word Length: 7....   \n",
              "\n",
              "                                                prompts  \\\n",
              "8913  Your task is to analyze the provided data to u...   \n",
              "\n",
              "                                           model_output  \n",
              "8913  A Graphical Model-Based Approach for Anomaly D...  "
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "preprompt_data.head(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s42Urc2SZch_"
      },
      "source": [
        "## GETTING NO PERSONALIZED RESULTS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gwb2gYE4xglO"
      },
      "outputs": [],
      "source": [
        "post_prompt_data=pd.read_pickle('postprompt_100.pkl')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8juX8SBlx0p9"
      },
      "outputs": [],
      "source": [
        "print(post_prompt_data.iloc[2]['prompts'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rB17NGhAyisk",
        "outputId": "ce824a10-1765-4731-eaff-fb5bdb791ffb"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 100/100 [09:20<00:00,  5.60s/it]\n"
          ]
        }
      ],
      "source": [
        "import pickle\n",
        "import time\n",
        "response_list=[]\n",
        "for row in tqdm(range(post_prompt_data.shape[0])):\n",
        "  response = palm.generate_text(prompt=post_prompt_data.iloc[row]['input'])\n",
        "  response_list.append(response.result)\n",
        "\n",
        "  if(row%10==0):\n",
        "    time.sleep(20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_uebAcHuyiV6",
        "outputId": "0f534b49-fd20-44e7-bbf9-e3e25e60ba69"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(response_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tTU0-WA9c4u_"
      },
      "outputs": [],
      "source": [
        "post_prompt_data['no_per_model_output']=response_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "7GL4j5R0fn2S",
        "outputId": "f05c7262-fa6f-4608-ac60-b64041567b26"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d205995b-f583-486a-bc30-2ddac04b0f8a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>input</th>\n",
              "      <th>profile</th>\n",
              "      <th>output</th>\n",
              "      <th>bm25_results</th>\n",
              "      <th>keyword_results</th>\n",
              "      <th>summary_results</th>\n",
              "      <th>style_results</th>\n",
              "      <th>input_keywords</th>\n",
              "      <th>input_summary</th>\n",
              "      <th>prompts</th>\n",
              "      <th>model_output</th>\n",
              "      <th>no_per_model_output</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8913</th>\n",
              "      <td>408913</td>\n",
              "      <td>Generate a title for the following abstract of...</td>\n",
              "      <td>[{'title': 'Fault domain-based testing in impe...</td>\n",
              "      <td>TABOR: A Graphical Model-based Approach for An...</td>\n",
              "      <td>[{'abstract': 'Industrial Control Systems (ICS...</td>\n",
              "      <td>[{'text': 'Industrial Control Systems (ICS) ar...</td>\n",
              "      <td>[{'text': 'Industrial Control Systems (ICS) ar...</td>\n",
              "      <td>{'statistical_analysis': 'Mean Word Length: 8....</td>\n",
              "      <td>automata, swat, classifier, detection, anomaly</td>\n",
              "      <td>the problem of process detection in water tank...</td>\n",
              "      <td>Your task is to analyze the provided data to u...</td>\n",
              "      <td>A Graphical Model-Based Approach for Process A...</td>\n",
              "      <td>Learning Graphical Models for Process Anomaly ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d205995b-f583-486a-bc30-2ddac04b0f8a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d205995b-f583-486a-bc30-2ddac04b0f8a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d205995b-f583-486a-bc30-2ddac04b0f8a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "          id                                              input  \\\n",
              "8913  408913  Generate a title for the following abstract of...   \n",
              "\n",
              "                                                profile  \\\n",
              "8913  [{'title': 'Fault domain-based testing in impe...   \n",
              "\n",
              "                                                 output  \\\n",
              "8913  TABOR: A Graphical Model-based Approach for An...   \n",
              "\n",
              "                                           bm25_results  \\\n",
              "8913  [{'abstract': 'Industrial Control Systems (ICS...   \n",
              "\n",
              "                                        keyword_results  \\\n",
              "8913  [{'text': 'Industrial Control Systems (ICS) ar...   \n",
              "\n",
              "                                        summary_results  \\\n",
              "8913  [{'text': 'Industrial Control Systems (ICS) ar...   \n",
              "\n",
              "                                          style_results  \\\n",
              "8913  {'statistical_analysis': 'Mean Word Length: 8....   \n",
              "\n",
              "                                      input_keywords  \\\n",
              "8913  automata, swat, classifier, detection, anomaly   \n",
              "\n",
              "                                          input_summary  \\\n",
              "8913  the problem of process detection in water tank...   \n",
              "\n",
              "                                                prompts  \\\n",
              "8913  Your task is to analyze the provided data to u...   \n",
              "\n",
              "                                           model_output  \\\n",
              "8913  A Graphical Model-Based Approach for Process A...   \n",
              "\n",
              "                                    no_per_model_output  \n",
              "8913  Learning Graphical Models for Process Anomaly ...  "
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "post_prompt_data.head(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jVOIFmIUfkYM"
      },
      "outputs": [],
      "source": [
        "post_prompt_data.to_pickle('postprompt_100.pkl')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gZMPLN369rTl"
      },
      "source": [
        "# Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nCTMhOKKjBuY",
        "outputId": "77abdf41-03a6-4062-b171-2a9f2560a19b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting rouge\n",
            "  Downloading rouge-1.0.1-py3-none-any.whl (13 kB)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from rouge) (1.16.0)\n",
            "Installing collected packages: rouge\n",
            "Successfully installed rouge-1.0.1\n"
          ]
        }
      ],
      "source": [
        "!pip install rouge"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TgUQpQQs9x4D"
      },
      "outputs": [],
      "source": [
        "from transformers import BertTokenizer, BertModel\n",
        "import torch\n",
        "import numpy as np\n",
        "from rouge import Rouge"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G0ZnjPBjjFPN"
      },
      "outputs": [],
      "source": [
        "def getRougeScores(groundTruth, generatedPrompt):\n",
        "  rouge = Rouge()\n",
        "  scores = rouge.get_scores(text1, text2)\n",
        "  r1, r2, rl = scores[0]['rouge-1']['r'] , scores[0]['rouge-2']['r'] , scores[0]['rouge-l']['r']\n",
        "\n",
        "  return r1,r2,rl\n",
        "\n",
        "###############################\n",
        "\n",
        "def getBertScore(groundTruth, generatedPrompt):\n",
        "  tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\n",
        "  model = BertModel.from_pretrained(\"bert-base-uncased\")\n",
        "\n",
        "  # Step 1: Prepare the texts for BERT\n",
        "  inputs1 = tokenizer(groundTruth, return_tensors=\"pt\", padding=True, truncation=True)\n",
        "  inputs2 = tokenizer(generatedPrompt, return_tensors=\"pt\", padding=True, truncation=True)\n",
        "\n",
        "  # Step 2: Feed the texts to the BERT model\n",
        "  outputs1 = model(**inputs1)\n",
        "  outputs2 = model(**inputs2)\n",
        "\n",
        "  # Step 3: Obtain the representation vectors\n",
        "  embeddings1 = outputs1.last_hidden_state.mean(dim=1).detach().numpy()\n",
        "  embeddings2 = outputs2.last_hidden_state.mean(dim=1).detach().numpy()\n",
        "\n",
        "  # Step 4: Calculate cosine similarity\n",
        "  similarity = np.dot(embeddings1, embeddings2.T) / (np.linalg.norm(embeddings1) * np.linalg.norm(embeddings2))\n",
        "\n",
        "  return similarity[0][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TviJqvGSjPsc"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import pickle as pk\n",
        "\n",
        "from pandas import DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 99999
        },
        "id": "qozgKFxeJb8b",
        "outputId": "06a09785-0cbf-42cc-8484-1a6a805c012b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-11752571-0e35-43e0-a038-fd2396c72e55\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>input</th>\n",
              "      <th>profile</th>\n",
              "      <th>output</th>\n",
              "      <th>input_keywords</th>\n",
              "      <th>input_summary</th>\n",
              "      <th>contriever</th>\n",
              "      <th>keyword_results</th>\n",
              "      <th>summary_results</th>\n",
              "      <th>style_results</th>\n",
              "      <th>prompts</th>\n",
              "      <th>model_output</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8913</th>\n",
              "      <td>408913</td>\n",
              "      <td>Generate a title for the following abstract of...</td>\n",
              "      <td>[{'title': 'Fault domain-based testing in impe...</td>\n",
              "      <td>TABOR: A Graphical Model-based Approach for An...</td>\n",
              "      <td>automata, swat, classifier, detection, anomaly</td>\n",
              "      <td>the problem of process detection in water tank...</td>\n",
              "      <td>[{'title': 'WaterJam: An Experimental Case Stu...</td>\n",
              "      <td>[{'text': 'An experiment was conducted to inve...</td>\n",
              "      <td>[{'text': 'An experiment was conducted to inve...</td>\n",
              "      <td>{'statistical_analysis': 'Mean Word Length: 7....</td>\n",
              "      <td>Your task is to analyze the provided data to u...</td>\n",
              "      <td>A Graphical Model-Based Approach for Anomaly D...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2242</th>\n",
              "      <td>402242</td>\n",
              "      <td>Generate a title for the following abstract of...</td>\n",
              "      <td>[{'title': 'On updating signal subspaces', 'ab...</td>\n",
              "      <td>Hands-On Training for Undergraduates in High-P...</td>\n",
              "      <td>java, programming, computing, computational, p...</td>\n",
              "      <td>this paper describes a practical computing cou...</td>\n",
              "      <td>[{'title': 'Parallel programming in computatio...</td>\n",
              "      <td>[{'text': 'Parallel programming of high-perfor...</td>\n",
              "      <td>[{'text': 'Parallel programming of high-perfor...</td>\n",
              "      <td>{'statistical_analysis': 'Mean Word Length: 8....</td>\n",
              "      <td>Your task is to analyze the provided data to u...</td>\n",
              "      <td>A Practical Training Course in High-Performanc...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2577</th>\n",
              "      <td>402577</td>\n",
              "      <td>Generate a title for the following abstract of...</td>\n",
              "      <td>[{'title': 'A Note on Filtration and Granular ...</td>\n",
              "      <td>On possible rules and apriori algorithm in non...</td>\n",
              "      <td>algorithm, minimalcertainrules, deterministici...</td>\n",
              "      <td>in this paper, we introduce a framework of gen...</td>\n",
              "      <td>[{'title': 'Discernibility functions and minim...</td>\n",
              "      <td>[{'text': 'Minimal rule generation in Non-dete...</td>\n",
              "      <td>[{'text': 'Minimal rule generation in Non-dete...</td>\n",
              "      <td>{'statistical_analysis': 'Mean Word Length: 8....</td>\n",
              "      <td>Your task is to analyze the provided data to u...</td>\n",
              "      <td>Min-Max and Max-Max Strategies for Possible Ru...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1399</th>\n",
              "      <td>401399</td>\n",
              "      <td>Generate a title for the following abstract of...</td>\n",
              "      <td>[{'title': 'Temporal representation for scient...</td>\n",
              "      <td>Framework for bringing data streams to the grid</td>\n",
              "      <td>streams, stream, streaming, grid, data</td>\n",
              "      <td>this article introduces a model for bringing e...</td>\n",
              "      <td>[{'title': 'Calder Query Grid Service: Insight...</td>\n",
              "      <td>[{'text': 'We have architected and evaluated a...</td>\n",
              "      <td>[{'text': 'We have architected and evaluated a...</td>\n",
              "      <td>{'statistical_analysis': 'Mean Word Length: 7....</td>\n",
              "      <td>Your task is to analyze the provided data to u...</td>\n",
              "      <td>A Model for Bringing Existing Data Stream Syst...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7707</th>\n",
              "      <td>407707</td>\n",
              "      <td>Generate a title for the following abstract of...</td>\n",
              "      <td>[{'title': 'Completeness In Differential Appro...</td>\n",
              "      <td>Multiparameterizations for max $k$-set cover a...</td>\n",
              "      <td>parameterizations, complexity, parameterized, ...</td>\n",
              "      <td>study the complexity of several parameterizati...</td>\n",
              "      <td>[{'title': 'Multi-parameter Analysis for Local...</td>\n",
              "      <td>[{'text': 'We study the parameterized complexi...</td>\n",
              "      <td>[{'text': 'We study the parameterized complexi...</td>\n",
              "      <td>{'statistical_analysis': 'Mean Word Length: 8....</td>\n",
              "      <td>Your task is to analyze the provided data to u...</td>\n",
              "      <td>Multiparameterized complexity of max k-set cov...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "          id                                              input  \\\n",
              "8913  408913  Generate a title for the following abstract of...   \n",
              "2242  402242  Generate a title for the following abstract of...   \n",
              "2577  402577  Generate a title for the following abstract of...   \n",
              "1399  401399  Generate a title for the following abstract of...   \n",
              "7707  407707  Generate a title for the following abstract of...   \n",
              "\n",
              "                                                profile  \\\n",
              "8913  [{'title': 'Fault domain-based testing in impe...   \n",
              "2242  [{'title': 'On updating signal subspaces', 'ab...   \n",
              "2577  [{'title': 'A Note on Filtration and Granular ...   \n",
              "1399  [{'title': 'Temporal representation for scient...   \n",
              "7707  [{'title': 'Completeness In Differential Appro...   \n",
              "\n",
              "                                                 output  \\\n",
              "8913  TABOR: A Graphical Model-based Approach for An...   \n",
              "2242  Hands-On Training for Undergraduates in High-P...   \n",
              "2577  On possible rules and apriori algorithm in non...   \n",
              "1399    Framework for bringing data streams to the grid   \n",
              "7707  Multiparameterizations for max $k$-set cover a...   \n",
              "\n",
              "                                         input_keywords  \\\n",
              "8913     automata, swat, classifier, detection, anomaly   \n",
              "2242  java, programming, computing, computational, p...   \n",
              "2577  algorithm, minimalcertainrules, deterministici...   \n",
              "1399             streams, stream, streaming, grid, data   \n",
              "7707  parameterizations, complexity, parameterized, ...   \n",
              "\n",
              "                                          input_summary  \\\n",
              "8913  the problem of process detection in water tank...   \n",
              "2242  this paper describes a practical computing cou...   \n",
              "2577  in this paper, we introduce a framework of gen...   \n",
              "1399  this article introduces a model for bringing e...   \n",
              "7707  study the complexity of several parameterizati...   \n",
              "\n",
              "                                             contriever  \\\n",
              "8913  [{'title': 'WaterJam: An Experimental Case Stu...   \n",
              "2242  [{'title': 'Parallel programming in computatio...   \n",
              "2577  [{'title': 'Discernibility functions and minim...   \n",
              "1399  [{'title': 'Calder Query Grid Service: Insight...   \n",
              "7707  [{'title': 'Multi-parameter Analysis for Local...   \n",
              "\n",
              "                                        keyword_results  \\\n",
              "8913  [{'text': 'An experiment was conducted to inve...   \n",
              "2242  [{'text': 'Parallel programming of high-perfor...   \n",
              "2577  [{'text': 'Minimal rule generation in Non-dete...   \n",
              "1399  [{'text': 'We have architected and evaluated a...   \n",
              "7707  [{'text': 'We study the parameterized complexi...   \n",
              "\n",
              "                                        summary_results  \\\n",
              "8913  [{'text': 'An experiment was conducted to inve...   \n",
              "2242  [{'text': 'Parallel programming of high-perfor...   \n",
              "2577  [{'text': 'Minimal rule generation in Non-dete...   \n",
              "1399  [{'text': 'We have architected and evaluated a...   \n",
              "7707  [{'text': 'We study the parameterized complexi...   \n",
              "\n",
              "                                          style_results  \\\n",
              "8913  {'statistical_analysis': 'Mean Word Length: 7....   \n",
              "2242  {'statistical_analysis': 'Mean Word Length: 8....   \n",
              "2577  {'statistical_analysis': 'Mean Word Length: 8....   \n",
              "1399  {'statistical_analysis': 'Mean Word Length: 7....   \n",
              "7707  {'statistical_analysis': 'Mean Word Length: 8....   \n",
              "\n",
              "                                                prompts  \\\n",
              "8913  Your task is to analyze the provided data to u...   \n",
              "2242  Your task is to analyze the provided data to u...   \n",
              "2577  Your task is to analyze the provided data to u...   \n",
              "1399  Your task is to analyze the provided data to u...   \n",
              "7707  Your task is to analyze the provided data to u...   \n",
              "\n",
              "                                           model_output  \n",
              "8913  A Graphical Model-Based Approach for Anomaly D...  \n",
              "2242  A Practical Training Course in High-Performanc...  \n",
              "2577  Min-Max and Max-Max Strategies for Possible Ru...  \n",
              "1399  A Model for Bringing Existing Data Stream Syst...  \n",
              "7707  Multiparameterized complexity of max k-set cov...  "
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "retriever_results = pd.read_pickle(r'contriever/postprompt_100.pkl')\n",
        "\n",
        "retriever_results.head()"
      ]
    },
    {
      "cell_type": "code",
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      ],
      "source": [
        "bertScore=[]\n",
        "for index, row in retriever_results.iterrows() :\n",
        "  output = row['output']\n",
        "  modelOp = row['model_output']\n",
        "\n",
        "  if output != \"\" and output != None and modelOp != \"\" and modelOp != None :\n",
        "    bertScore.append(getBertScore(output, modelOp))\n",
        "  else :\n",
        "    bertScore.append(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ByzmVn4YjdMs"
      },
      "outputs": [],
      "source": [
        "r1lst = []\n",
        "r2lst = []\n",
        "rllst = []\n",
        "\n",
        "rouge = Rouge()\n",
        "\n",
        "for index, row in retriever_results.iterrows() :\n",
        "  output = row['output']\n",
        "  modelOp = row['model_output']\n",
        "\n",
        "  if output != \"\" and output != None and modelOp != \"\" and modelOp != None :\n",
        "    scores = rouge.get_scores(output, modelOp)\n",
        "    r1, r2, rl = scores[0]['rouge-1']['r'] , scores[0]['rouge-2']['r'] , scores[0]['rouge-l']['r']\n",
        "  else:\n",
        "    r1, r2, rl = 0,0,0\n",
        "\n",
        "  r1lst.append(r1)\n",
        "  r2lst.append(r2)\n",
        "  rllst.append(rl)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fU9zCptO99uE"
      },
      "outputs": [],
      "source": [
        "retriever_results['BertScore-P'] = bertScore\n",
        "retriever_results['r1-rouge-P'] = r1lst\n",
        "retriever_results['r2-rouge-P'] = r2lst\n",
        "retriever_results['rl-rouge-P'] = rllst"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "baR4Mf7Hjxbw"
      },
      "outputs": [],
      "source": [
        "retriever_results.to_pickle(\"contriever/metric_output.pkl\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KOyopixtkBho",
        "outputId": "f3e3bea2-bf12-4651-b82e-bf2c6ea07616"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean r1-rouge 0.2759768483860556\n",
            "Mean r2-rouge 0.12609967075408252\n",
            "Mean rl-rouge 0.2537378096470168\n",
            "Mean bert 0.8370622849464416\n"
          ]
        }
      ],
      "source": [
        "print(\"Mean r1-rouge {}\".format(retriever_results['r1-rouge-P'].mean()))\n",
        "print(\"Mean r2-rouge {}\".format(retriever_results['r2-rouge-P'].mean()))\n",
        "print(\"Mean rl-rouge {}\".format(retriever_results['rl-rouge-P'].mean()))\n",
        "print(\"Mean bert {}\".format(retriever_results['BertScore-P'].mean()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_1LJJNZFkCQd",
        "outputId": "ba358ef5-9e2a-4adb-9246-f5ecd871b245"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean r1 0.28747588373547456\n",
            "Mean r2 0.1313538237021693\n",
            "Mean rl 0.2643102183823091\n",
            "Mean bert 0.8719398801525434\n",
            "None Count:  4\n"
          ]
        }
      ],
      "source": [
        "bertSum = 0\n",
        "r1Sum = 0\n",
        "r2Sum = 0\n",
        "NoneCount = 0\n",
        "r = 0\n",
        "\n",
        "for index, row in retriever_results.iterrows() :\n",
        "  if row['model_output'] == None:\n",
        "    NoneCount += 1\n",
        "  else:\n",
        "    bertSum += row['BertScore-P']\n",
        "    r1Sum += row['r1-rouge-P']\n",
        "    r2Sum += row['r2-rouge-P']\n",
        "    r += row['rl-rouge-P']\n",
        "\n",
        "noneNull = retriever_results.shape[0] - NoneCount\n",
        "\n",
        "print(\"Mean r1\", r1Sum/noneNull)\n",
        "print(\"Mean r2\", r2Sum/noneNull)\n",
        "print(\"Mean rl\", r/noneNull)\n",
        "print(\"Mean bert\", bertSum/noneNull)\n",
        "print(\"None Count: \", NoneCount)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IuiNBVVIjyTf"
      },
      "outputs": [],
      "source": [
        "bertScore=[]\n",
        "for index, row in retriever_results.iterrows() :\n",
        "  output = row['output']\n",
        "  modelOp = row['no_per_model_output']\n",
        "\n",
        "  if output != \"\" and output != None and modelOp != \"\" and modelOp != None :\n",
        "    bertScore.append(getBertScore(output, modelOp))\n",
        "  else :\n",
        "    bertScore.append(0)\n",
        "\n",
        "r1lst = []\n",
        "r2lst = []\n",
        "rllst = []\n",
        "\n",
        "rouge = Rouge()\n",
        "\n",
        "for index, row in retriever_results.iterrows() :\n",
        "  output = row['output']\n",
        "  modelOp = row['no_per_model_output']\n",
        "\n",
        "  if output != \"\" and output != None and modelOp != \"\" and modelOp != None :\n",
        "    scores = rouge.get_scores(output, modelOp)\n",
        "    r1, r2, rl = scores[0]['rouge-1']['r'] , scores[0]['rouge-2']['r'] , scores[0]['rouge-l']['r']\n",
        "  else:\n",
        "    r1, r2, rl = 0,0,0\n",
        "\n",
        "  r1lst.append(r1)\n",
        "  r2lst.append(r2)\n",
        "  rllst.append(rl)\n",
        "\n",
        "bertSum = 0\n",
        "r1Sum = 0\n",
        "r2Sum = 0\n",
        "NoneCount = 0\n",
        "r = 0\n",
        "\n",
        "\n",
        "retriever_results['BertScore-NP'] = bertScore\n",
        "retriever_results['r1-rouge-NP'] = r1lst\n",
        "retriever_results['r2-rouge-NP'] = r2lst\n",
        "retriever_results['rl-rouge-NP'] = rllst\n",
        "\n",
        "\n",
        "bertSum = 0\n",
        "r1Sum = 0\n",
        "r2Sum = 0\n",
        "NoneCount = 0\n",
        "r = 0\n",
        "\n",
        "for index, row in retriever_results.iterrows() :\n",
        "  if row['no_per_model_output'] == None:\n",
        "    NoneCount += 1\n",
        "  else:\n",
        "    bertSum += row['BertScore-NP']\n",
        "    r1Sum += row['r1-rouge-NP']\n",
        "    r2Sum += row['r2-rouge-NP']\n",
        "    r += row['rl-rouge-NP']\n",
        "\n",
        "noneNull = retriever_results.shape[0] - NoneCount\n",
        "\n",
        "print(\"Mean r1\", r1Sum/noneNull)\n",
        "print(\"Mean r2\", r2Sum/noneNull)\n",
        "print(\"Mean rl\", r/noneNull)\n",
        "print(\"Mean bert\", bertSum/noneNull)\n",
        "print(\"None Count: \", NoneCount)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xntIyAr_MjvD"
      },
      "outputs": [],
      "source": [
        "retriever_results.to_pickle(\"metric_output.pkl\")"
      ]
    }
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