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    {
      "cell_type": "markdown",
      "source": [
        "# Extracting Counts from news summaries using Gemma 2b model"
      ],
      "metadata": {
        "id": "EUb6kXfBvLOd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This notebook contains the code for extracting number of Deaths, Injuries, Surrenders, Arrests, and Hostages from news summaries using Gemma 2b model.\n",
        "\n",
        "Before running the notebook, follow these steps:\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "*   Create an account on HuggingFace.\n",
        "*   Open the link https://huggingface.co/google/gemma-2-2b-it\n",
        "*   Request the access for the gemma model.\n",
        "*   After getting the access, create an access token in huggingface at https://huggingface.co/settings/tokens"
      ],
      "metadata": {
        "id": "KslOZgtuvdB2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "NOTE: Import the notebook on Colab and turn on the T4 GPU."
      ],
      "metadata": {
        "id": "8pYmH2lNvXG3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "TODO:\n",
        "\n",
        "*   This is just inference with the gemma model with a prompt engineering touch. But there is a lot of room for improvement. We can also fine-tune it on our data to get better results.\n",
        "*   Since this is computationally expensive than other approaches, it would be better to come up with a single LLM model that can be used for all of the tasks we are working on.\n",
        "\n"
      ],
      "metadata": {
        "id": "0h6jJctiLTsc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip -q install -U bitsandbytes"
      ],
      "metadata": {
        "id": "En-DG8yts6mO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71706d19-389a-43f5-a95e-d7a6c8cbfd7e"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.4/122.4 MB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Libraries"
      ],
      "metadata": {
        "id": "F1ruHdPywFg9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import gc\n",
        "import time\n",
        "import torch\n",
        "import numpy as np\n",
        "import transformers\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "from pathlib import Path\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-09T00:23:28.812944Z",
          "iopub.execute_input": "2024-09-09T00:23:28.813326Z",
          "iopub.status.idle": "2024-09-09T00:23:33.560933Z",
          "shell.execute_reply.started": "2024-09-09T00:23:28.813282Z",
          "shell.execute_reply": "2024-09-09T00:23:33.559980Z"
        },
        "trusted": true,
        "id": "HqXXa6VSrP4p"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Get Data"
      ],
      "metadata": {
        "id": "GVB0ZF5JzH_x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Change the path to the cleaned file.\n",
        "\n",
        "root_dir = \"https://raw.githubusercontent.com/eteitelbaum/code-satp/refs/heads/main/data\"\n",
        "data = pd.read_csv(root_dir + \"/satp_clean.csv\")\n",
        "data.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "NDCqJinCzKGW",
        "outputId": "8fdc7e72-d35f-480f-e16c-c94904395736"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   incident_number           state   district                    block  \\\n",
              "0      101010701.0  Andhra Pradesh  Hyderabad  Gachibowli (Rangareddy)   \n",
              "1      101010901.0  Andhra Pradesh  Nizamabad                      NaN   \n",
              "2      101030601.0  Andhra Pradesh    Khammam                      NaN   \n",
              "\n",
              "   village_name other_areas     constituency  longitude   latitude  year  ...  \\\n",
              "0           NaN   Cyberabad  Serilingampally  17.432500  78.371806  2007  ...   \n",
              "1     Kamareddy         NaN        Kamareddy  18.320889  78.337139  2009  ...   \n",
              "2  Bhadrachalam         NaN     Bhadrachalam  17.668056  80.896861  2006  ...   \n",
              "\n",
              "  commander_arrests cadre_arrests sympathizer_arrests unknown_arrests  \\\n",
              "0                 0           0.0                   1             0.0   \n",
              "1                 0           0.0                   0             0.0   \n",
              "2                 1           0.0                   0             0.0   \n",
              "\n",
              "  total_surrenders  commander_surrenders  cadre_surrenders  \\\n",
              "0              0.0                     0               0.0   \n",
              "1              1.0                     0               1.0   \n",
              "2              0.0                     0               0.0   \n",
              "\n",
              "   sympathizer_surrenders  unknown_surrenders  \\\n",
              "0                       0                   0   \n",
              "1                       0                   0   \n",
              "2                       0                   0   \n",
              "\n",
              "                                    incident_summary  \n",
              "0  An alleged arms supplier to the Communist Part...  \n",
              "1  A Kamareddy dalam (squad) member belonging to ...  \n",
              "2  Senior CPI-Maoist 'Polit Bureau' and 'central ...  \n",
              "\n",
              "[3 rows x 70 columns]"
            ],
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              "\n",
              "  <div id=\"df-16620523-cc90-4719-a3e8-8df661f0f9a4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "      <th>year</th>\n",
              "      <th>...</th>\n",
              "      <th>commander_arrests</th>\n",
              "      <th>cadre_arrests</th>\n",
              "      <th>sympathizer_arrests</th>\n",
              "      <th>unknown_arrests</th>\n",
              "      <th>total_surrenders</th>\n",
              "      <th>commander_surrenders</th>\n",
              "      <th>cadre_surrenders</th>\n",
              "      <th>sympathizer_surrenders</th>\n",
              "      <th>unknown_surrenders</th>\n",
              "      <th>incident_summary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>101010701.0</td>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>Gachibowli (Rangareddy)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cyberabad</td>\n",
              "      <td>Serilingampally</td>\n",
              "      <td>17.432500</td>\n",
              "      <td>78.371806</td>\n",
              "      <td>2007</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>An alleged arms supplier to the Communist Part...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>101010901.0</td>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>Nizamabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Kamareddy</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Kamareddy</td>\n",
              "      <td>18.320889</td>\n",
              "      <td>78.337139</td>\n",
              "      <td>2009</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>A Kamareddy dalam (squad) member belonging to ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>101030601.0</td>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>Khammam</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Bhadrachalam</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Bhadrachalam</td>\n",
              "      <td>17.668056</td>\n",
              "      <td>80.896861</td>\n",
              "      <td>2006</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Senior CPI-Maoist 'Polit Bureau' and 'central ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 70 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "\n",
              "  <style>\n",
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              "      display:flex;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-16620523-cc90-4719-a3e8-8df661f0f9a4');\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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              "\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\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",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    fill: var(--fill-color);\n",
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              "    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",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    90% {\n",
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              "      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",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "counts_columns = ['total_fatalities', 'total_injuries', 'total_surrenders', 'total_arrests', \"total_abducted\", \"incident_summary\"]\n",
        "df = data[counts_columns].copy()\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "I99bgZGS0Jhc",
        "outputId": "5fea796f-5612-4940-bd2e-979106a62a0a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   total_fatalities  total_injuries  total_surrenders  total_arrests  \\\n",
              "0               0.0             0.0               0.0            1.0   \n",
              "1               0.0             0.0               1.0            0.0   \n",
              "2               0.0             0.0               0.0            1.0   \n",
              "3               1.0             0.0               0.0            0.0   \n",
              "4               0.0             0.0               0.0            0.0   \n",
              "\n",
              "   total_abducted                                   incident_summary  \n",
              "0               0  An alleged arms supplier to the Communist Part...  \n",
              "1               0  A Kamareddy dalam (squad) member belonging to ...  \n",
              "2               0  Senior CPI-Maoist 'Polit Bureau' and 'central ...  \n",
              "3               0  A TDP leader and former Sarpanch of Jerrela Gr...  \n",
              "4               0  The CPI-Maoist cadres blasted coffee pulping u...  "
            ],
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0bfef566-efd0-484e-8226-4135965003da');\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --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-9c8ef270-7208-4b3b-90d7-687c064369af button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 9921,\n  \"fields\": [\n    {\n      \"column\": \"total_fatalities\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.657037659794767,\n        \"min\": 0.0,\n        \"max\": 148.0,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          13.0,\n          33.0,\n          29.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_injuries\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.5310305346987723,\n        \"min\": 0.0,\n        \"max\": 150.0,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          21.0,\n          40.0,\n          14.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_surrenders\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.512584386248037,\n        \"min\": 0.0,\n        \"max\": 381.0,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          145.0,\n          381.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_arrests\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.5365870780210913,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 33,\n        \"samples\": [\n          60.0,\n          18.0,\n          35.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_abducted\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 0,\n        \"max\": 700,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          4,\n          12,\n          19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"incident_summary\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9918,\n        \"samples\": [\n          \"A leader of the BJP's youth wing, BJYM, was shot at by CPI-Maoist in Bijapur District of Chhattisgarh. \\\"A small action team of Maoists attacked Murlikrishna Reddy, the BJYM Bijapur district president, last night at Bairamgad town of Bijapur. He was attacked with a country-made gun and other traditional weapons,\\\" SP KL Dhruw said.\",\n          \"Rumpa Mahato alias Sujata alias Ashtami (21), a self-styled CPI-Maoist 'deputy area commander' in the Nayagram area of West Midnapore District, surrendered before the Police at the Bankura District Police lines.\",\n          \"A CPI-Maoist activist, identified as Polam Narasaiah alias Ramanna, carrying head money of INR 50,000, was arrested by Police from the Mancherial division of Adilabad District.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load Model and Tokenizer"
      ],
      "metadata": {
        "id": "ov9b5WNkwIab"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using quantization to fit more samples into the batch for faster inference."
      ],
      "metadata": {
        "id": "kQpLSa_GwNnU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"google/gemma-2-2b-it\"\n",
        "access_token = \"Add your acces token\"\n",
        "\n",
        "quantization_config = BitsAndBytesConfig(\n",
        "                load_in_4bit=True,\n",
        "                bnb_4bit_use_double_quant=True,\n",
        "                bnb_4bit_quant_type=\"nf4\",\n",
        "                bnb_4bit_compute_dtype=torch.bfloat16\n",
        "            )\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"google/gemma-2-2b-it\", token = access_token)\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    device_map=\"auto\",\n",
        "    torch_dtype=torch.bfloat16,\n",
        "    quantization_config=quantization_config,\n",
        "    token = access_token\n",
        ")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-08T23:54:24.805358Z",
          "iopub.execute_input": "2024-09-08T23:54:24.805878Z",
          "iopub.status.idle": "2024-09-08T23:54:50.226254Z",
          "shell.execute_reply.started": "2024-09-08T23:54:24.805842Z",
          "shell.execute_reply": "2024-09-08T23:54:50.225454Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369,
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      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/47.0k [00:00<?, ?B/s]"
            ],
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          "data": {
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              "tokenizer.model:   0%|          | 0.00/4.24M [00:00<?, ?B/s]"
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          "data": {
            "text/plain": [
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          "metadata": {}
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            "text/plain": [
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          "data": {
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              "model.safetensors.index.json:   0%|          | 0.00/24.2k [00:00<?, ?B/s]"
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          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
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              "version_minor": 0,
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          "data": {
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              "model-00001-of-00002.safetensors:   0%|          | 0.00/4.99G [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
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              "version_minor": 0,
              "model_id": "772fee1be08341a9b63a604cee622d9c"
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    {
      "cell_type": "code",
      "source": [
        "# Function to clear unused CPU and GPU memory.\n",
        "\n",
        "def clear_memory():\n",
        "    torch.cuda.empty_cache()\n",
        "    gc.collect()\n",
        "\n",
        "clear_memory()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-09T00:23:38.994217Z",
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      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Inference on few samples (Performance Check)."
      ],
      "metadata": {
        "id": "H7tydKx6zhVH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Simple few shot prompt to extract the counts.\n",
        "\n",
        "def extract_features(summary):\n",
        "    prompt = f\"\"\"\n",
        "Given a news summary related to terrorism, extract the below features from news summary and don't give anything else like explainations:\n",
        "\n",
        "Provide count for below actions if present else 0\n",
        "Number of Deaths:\n",
        "Number of Injuries:\n",
        "Number of Surrenders:\n",
        "Number of Arrests:\n",
        "Number of Abductions:\n",
        "\n",
        "Example Input-Output pair:\n",
        "\n",
        "Input:\n",
        "2 CPI-Maoist women were arrested by the police in the Mahbubnagar District, and the police reported that they seized all the weapons that were being used by maoists. Later based on information provided by those two women they also arrested one more women.\n",
        "\n",
        "Desired Answer:\n",
        "\n",
        "Number of Deaths: 0\n",
        "Number of Injuries: 0\n",
        "Number of Surrenders: 0\n",
        "Number of Arrests: 3\n",
        "Number of Abductions: 0\n",
        "\n",
        "End of Example\n",
        "\n",
        "Note: These numbers can appear at any part of the summary, don't forget to count all of them.\n",
        "\n",
        "News Summary: {summary}\n",
        "\n",
        "Your Answer:\n",
        "\"\"\"\n",
        "\n",
        "    inputs = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "    with torch.no_grad():\n",
        "        outputs = model.generate(**inputs, max_length=len(inputs[0])+50, num_return_sequences=1)\n",
        "\n",
        "    response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "\n",
        "    return response.split(\"Your Answer:\")[-1].strip()\n",
        "\n",
        "# Example usage\n",
        "summary = \"\"\"\n",
        "An alleged arms supplier to the Communist Party of India-Maoist (CPI-Maoist), identified as Ravi Kumar Chevori, was arrested from Cyberabad near Hyderabad. He had entered into a deal with the Maoists to supply arms and ammunition worth INR 40 lakh, which the city Police seized on December 28, 2006, and arrested three persons.\n",
        "\"\"\"\n",
        "\n",
        "extracted_features = extract_features(summary)\n",
        "print(extracted_features)"
      ],
      "metadata": {
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        "outputId": "cac33a3d-3631-4176-f0c1-9b68e9278bb9"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Deaths: 0\n",
            "Number of Injuries: 0\n",
            "Number of Surrenders: 0\n",
            "Number of Arrests: 3\n",
            "Number of Abductions: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Inference on a random sample\n",
        "\n",
        "for summary in data.sample(n=5)[\"incident_summary\"]:\n",
        "    print(summary)\n",
        "    print(extract_features(summary))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A9IeaicVy75d",
        "outputId": "af319e2c-4e42-4d73-de33-a7cedbee2428"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "In the Chando Police station area of Balrampur Police District, on information of the movement of a large group of armed Maoists, Police cordoned off an area near Jalbotha village and asked them to surrender. The subsequent encounter led to the killing of nine Maoists.\n",
            "Number of Deaths: 9\n",
            "Number of Injuries: 0\n",
            "Number of Surrenders: 0\n",
            "Number of Arrests: 0\n",
            "Number of Abductions: 0\n",
            "A CPI-Maoist cadre, Kandara Darai, surrendered before the Superintendent of Police Sanjaya Kumar Kausal and District Collector Jamil Ahmed Khan at Kamakhyanagar in the Dhenkanal District.\n",
            "Number of Deaths: 0\n",
            "Number of Injuries: 0\n",
            "Number of Surrenders: 1\n",
            "Number of Arrests: 0\n",
            "Number of Abductions: 0\n",
            "A hardcore Maoist was arrested from Kaimur hills under Chutia Police Station area in Rohtas District. The Security Forces arrested Maoist cadre Suresh Paswan during a combing operation. A printer and scanner were seized from the possession of the Maoist, who was said to be the 'sub-zonal commander' of Rohtas and Sonebhadra region. A native of Nawadih village, Paswan was wanted in at least 18 cases of violence and related activities in the District and adjoining area across Bihar and Uttar Pradesh.\n",
            "Number of Deaths: 0\n",
            "Number of Injuries: 0\n",
            "Number of Surrenders: 0\n",
            "Number of Arrests: 1\n",
            "Number of Abductions: 0\n",
            "Four CPI-Maoist cadres were arrested during a joint search operation in Bijapur District. While three cadres were apprehended from Basaguda Police Station limits, another was held from Usoor Police Station area by a joint team of SFs, Bijapur SP K.L. Dhruv said. The arrested cadres were identified as Punem Suresh (30), Punem Shankar (30), Korsa Hunga (25) and Renga Ramlu (35).\n",
            "Number of Deaths: 0\n",
            "Number of Injuries: 0\n",
            "Number of Surrenders: 0\n",
            "Number of Arrests: 4\n",
            "Number of Abductions: 0\n",
            "A Policeman was killed and three others were injured when CPI-Maoist cadres attacked a Police patrolling party in Kondagaon District.\n",
            "Number of Deaths: 1\n",
            "Number of Injuries: 3\n",
            "Number of Surrenders: 0\n",
            "Number of Arrests: 0\n",
            "Number of Abductions: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = f\"\"\"\n",
        "        Given a news summary related to terrorism, extract the below features from news summary and don't give anything else like explainations:\n",
        "\n",
        "        Provide count for below actions if present else 0\n",
        "        Number of Deaths:\n",
        "        Number of Injuries:\n",
        "        Number of Surrenders:\n",
        "        Number of Arrests:\n",
        "        Number of Abductions:\n",
        "\n",
        "        Example Input-Output pair:\n",
        "\n",
        "        Input:\n",
        "        2 CPI-Maoist women were arrested by the police in the Mahbubnagar District, and the police reported that they seized all the weapons that were being used by maoists. Later based on information provided by those two women they also arrested one more women.\n",
        "\n",
        "        Desired Answer:\n",
        "\n",
        "        Number of Deaths: 0\n",
        "        Number of Injuries: 0\n",
        "        Number of Surrenders: 0\n",
        "        Number of Arrests: 3\n",
        "        Number of Abductions: 0\n",
        "\n",
        "        End of Example\n",
        "\n",
        "        Note: These numbers can appear at any part of the summary, don't forget to count all of them.\n",
        "\n",
        "        News Summary: {summary}\n",
        "\n",
        "        Your Answer:\n",
        "        \"\"\""
      ],
      "metadata": {
        "execution": {
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      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Inference on Whole Dataset"
      ],
      "metadata": {
        "id": "iAkid6Y51Rbp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# To run the inference on the whole data, it might take upto 4 hours on Coab with T4 or 3 hourse on Kaggle with P100.\n",
        "\n",
        "\n",
        "def extract_features_batch(summaries, batch_size):\n",
        "    all_outputs = []\n",
        "    for i in tqdm(range(0, len(summaries), batch_size), desc=\"Processing batch\"):\n",
        "        clear_memory()\n",
        "        batch = summaries[i:i+batch_size]\n",
        "        prompts = [prompt.format(summary) for summary in batch]\n",
        "        inputs = tokenizer(prompts, return_tensors=\"pt\", padding=True, truncation=True, max_length=512).to(\"cuda\")\n",
        "\n",
        "        with torch.no_grad():\n",
        "            outputs = model.generate(**inputs, max_length = len(inputs[0])+50, num_return_sequences=1)\n",
        "\n",
        "        decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)\n",
        "        all_outputs.extend([output.split(\"Your Answer:\")[-1].strip() for output in decoded_outputs])\n",
        "\n",
        "    return all_outputs\n",
        "\n",
        "\n",
        "batch_size = 16 # Lower this, if there are any memory issues.\n",
        "\n",
        "# Uncomment these lines if you want to run the whole inefrence.\n",
        "#df['extracted_summary'] = extract_features_batch(df['incident_summary'].tolist(), batch_size)\n",
        "\n",
        "# Save the results\n",
        "#output_dir = 'extracted_output-gemma.csv'\n",
        "#df.to_csv(output_dir, index=False)\n",
        "#print(f\"Extraction complete. Results saved to {output_dir}.\")"
      ],
      "metadata": {
        "execution": {
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        "trusted": true,
        "collapsed": true,
        "id": "foHWTFgVrP4w"
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      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Extract summaries from the output summaries"
      ],
      "metadata": {
        "id": "4h4PYE1233Yk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The extracted data can be accessed from the data folder on github."
      ],
      "metadata": {
        "id": "E9sgxM2j39RT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(root_dir + \"/gemma_counts_output.csv\")"
      ],
      "metadata": {
        "id": "qO5E2asz94F5"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract counts from the generated outputs, and store it in output list.\n",
        "# Store the samples that were not generated properly in another list to filter them out.\n",
        "\n",
        "output = []\n",
        "bad_samples = []\n",
        "\n",
        "for i, extract in tqdm(enumerate(df[\"extracted_summary\"])):\n",
        "    output.append([])\n",
        "    try:\n",
        "        split = extract.split('\\n')[:5]\n",
        "    except:\n",
        "        bad_samples.append(i)\n",
        "        output[-1] = [-100]*5\n",
        "        continue\n",
        "\n",
        "    for x in split:\n",
        "        try:\n",
        "            output[-1].append(int(x.split(':')[1]))\n",
        "        except:\n",
        "            bad_samples.append(i)\n",
        "            output[-1].append(-100)\n",
        "\n",
        "    if len(output[-1]) != 5:\n",
        "        bad_samples.append(i)\n",
        "        output[-1] = [-100] * 5\n",
        "\n",
        "len(bad_samples)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o9n2B43l34wk",
        "outputId": "39b16dd9-5fc3-4993-a383-3ab21b5df5b9"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "9921it [00:00, 292278.50it/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "185"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds = pd.DataFrame(output, columns = df.columns[:5], dtype=\"float\")\n",
        "preds.head()"
      ],
      "metadata": {
        "id": "gJttgAPq7FZw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "a1280944-4575-4233-bfa5-d5acb984bbdf"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   total_fatalities  total_injuries  total_surrenders  total_arrests  \\\n",
              "0               0.0             0.0               0.0            3.0   \n",
              "1               0.0             0.0               1.0            0.0   \n",
              "2               0.0             0.0               0.0            1.0   \n",
              "3               0.0             0.0               0.0            0.0   \n",
              "4               0.0             0.0               0.0            0.0   \n",
              "\n",
              "   total_abducted  \n",
              "0             0.0  \n",
              "1             0.0  \n",
              "2             0.0  \n",
              "3             0.0  \n",
              "4             0.0  "
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --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-4ba82800-0da3-455a-a6d2-491660e5af3d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "preds",
              "summary": "{\n  \"name\": \"preds\",\n  \"rows\": 9921,\n  \"fields\": [\n    {\n      \"column\": \"total_fatalities\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.641765196870009,\n        \"min\": -100.0,\n        \"max\": 149.0,\n        \"num_unique_values\": 42,\n        \"samples\": [\n          75.0,\n          55.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_injuries\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.462359394238398,\n        \"min\": -100.0,\n        \"max\": 450.0,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          29.0,\n          6.0,\n          14.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_surrenders\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.240072683860786,\n        \"min\": -100.0,\n        \"max\": 758.0,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          60.0,\n          156.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_arrests\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.021588389746931,\n        \"min\": -100.0,\n        \"max\": 150.0,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          12.0,\n          15.0,\n          100.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_abducted\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.764873918509733,\n        \"min\": -100.0,\n        \"max\": 300.0,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          6.0,\n          20.0,\n          13.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds = preds.drop(bad_samples, axis = 0).reset_index(drop=True)\n",
        "df = df.drop(bad_samples, axis = 0).reset_index(drop=True)\n",
        "\n",
        "display(preds.head(5), df.head(5), preds.shape, df.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "ibi613G3Igy5",
        "outputId": "615480d5-7f31-4ac6-e777-a3f91bb0773d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   total_fatalities  total_injuries  total_surrenders  total_arrests  \\\n",
              "0               0.0             0.0               0.0            3.0   \n",
              "1               0.0             0.0               1.0            0.0   \n",
              "2               0.0             0.0               0.0            1.0   \n",
              "3               0.0             0.0               0.0            0.0   \n",
              "4               0.0             0.0               0.0            0.0   \n",
              "\n",
              "   total_abducted  \n",
              "0             0.0  \n",
              "1             0.0  \n",
              "2             0.0  \n",
              "3             0.0  \n",
              "4             0.0  "
            ],
            "text/html": [
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              "    </button>\n",
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              "    .colab-df-container {\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-4f6bab14-93b3-4225-8522-8b8257a1f4e8 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-4f6bab14-93b3-4225-8522-8b8257a1f4e8');\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",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --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-713daf46-e858-4c2d-9557-b7243c7422db button');\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(preds\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"total_fatalities\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_injuries\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_surrenders\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.44721359549995804,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_arrests\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3038404810405297,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_abducted\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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              "   total_fatalities  total_injuries  total_surrenders  total_arrests  \\\n",
              "0               0.0             0.0               0.0            1.0   \n",
              "1               0.0             0.0               1.0            0.0   \n",
              "2               0.0             0.0               0.0            1.0   \n",
              "3               1.0             0.0               0.0            0.0   \n",
              "4               0.0             0.0               0.0            0.0   \n",
              "\n",
              "   total_abducted                                   incident_summary  \\\n",
              "0               0  An alleged arms supplier to the Communist Part...   \n",
              "1               0  A Kamareddy dalam (squad) member belonging to ...   \n",
              "2               0  Senior CPI-Maoist 'Polit Bureau' and 'central ...   \n",
              "3               0  A TDP leader and former Sarpanch of Jerrela Gr...   \n",
              "4               0  The CPI-Maoist cadres blasted coffee pulping u...   \n",
              "\n",
              "                                   extracted_summary  \n",
              "0  Number of Deaths: 0\\n    Number of Injuries: 0...  \n",
              "1  Number of Deaths: 0\\n    Number of Injuries: 0...  \n",
              "2  Number of Deaths: 0\\n    Number of Injuries: 0...  \n",
              "3  Number of Deaths: 0\\n    Number of Injuries: 0...  \n",
              "4  Number of Deaths: 0\\n    Number of Injuries: 0...  "
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(preds\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"total_fatalities\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.447213595499958,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_injuries\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_surrenders\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.44721359549995804,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_arrests\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5477225575051662,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_abducted\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"incident_summary\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"A Kamareddy dalam (squad) member belonging to the Janashakti faction of the Communist Party of India-Marxist-Leninist (CPI-ML-Janashakti), identified as Kotta Rajam, surrendered before the Police at Kamareddy in the Nizamabad District.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"extracted_summary\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Number of Deaths: 0\\n    Number of Injuries: 0\\n    Number of Surrenders: 1\\n    Number of Arrests: 0\\n    Number of Abductions: 0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(9851, 5)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(9851, 7)"
            ]
          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluation"
      ],
      "metadata": {
        "id": "cYaDatEUI70P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Predicting all the numbers as correct.\n",
        "print(f\"Overall score: {((preds == df.iloc[:, :5]).all(axis = 1)).sum() / preds.shape[0]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lj0Ez1c7Ii2x",
        "outputId": "3e2adf77-9673-421f-9ac7-54051338098d"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overall score: 0.7833722464724393\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predicting atleast one of the counts as correct.\n",
        "columns = df.columns[:5]\n",
        "for i in range(5):\n",
        "    print(f\"Label {columns[i]} Score: {(preds.iloc[:, i] == df.iloc[:, i]).sum()/preds.shape[0]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ocAFnMrI_z8",
        "outputId": "4cf551f6-669a-41ed-99c4-785c3e904832"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label total_fatalities Score: 0.9509694447264238\n",
            "Label total_injuries Score: 0.9378743274794437\n",
            "Label total_surrenders Score: 0.9245761851588671\n",
            "Label total_arrests Score: 0.956248096639935\n",
            "Label total_abducted Score: 0.9692417013501168\n"
          ]
        }
      ]
    }
  ]
}