FastText_experiments.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nEjU4TBLcpBD"
      },
      "outputs": [],
      "source": [
        "#https://thinkinfi.com/fasttext-for-text-classification-python/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Tg_BApa5VMcM",
        "outputId": "43af65d2-f06f-4c85-a613-507a71fc0090"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting fasttext\n",
            "  Downloading fasttext-0.9.2.tar.gz (68 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m68.8/68.8 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting pybind11>=2.2 (from fasttext)\n",
            "  Using cached pybind11-2.10.4-py3-none-any.whl (222 kB)\n",
            "Requirement already satisfied: setuptools>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from fasttext) (67.7.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from fasttext) (1.22.4)\n",
            "Building wheels for collected packages: fasttext\n",
            "  Building wheel for fasttext (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fasttext: filename=fasttext-0.9.2-cp310-cp310-linux_x86_64.whl size=4393390 sha256=47014fac7559aea03c11c1451fd14b45733f98b6c922cb97db2227bd36675491\n",
            "  Stored in directory: /root/.cache/pip/wheels/a5/13/75/f811c84a8ab36eedbaef977a6a58a98990e8e0f1967f98f394\n",
            "Successfully built fasttext\n",
            "Installing collected packages: pybind11, fasttext\n",
            "Successfully installed fasttext-0.9.2 pybind11-2.10.4\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (3.8.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk) (8.1.3)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk) (1.2.0)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk) (2022.10.31)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk) (4.65.0)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
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              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saving X_train.csv to X_train.csv\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-0f65ce22-8b41-45a0-9fc0-57c0b9d735cf\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-0f65ce22-8b41-45a0-9fc0-57c0b9d735cf\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saving X_test.csv to X_test.csv\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-4bd5df2d-df0a-4ac0-a801-f47c538cce6a\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-4bd5df2d-df0a-4ac0-a801-f47c538cce6a\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saving y_train.csv to y_train.csv\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-40c026f8-82ee-4f56-a128-cc7e93e4adfd\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-40c026f8-82ee-4f56-a128-cc7e93e4adfd\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saving y_test.csv to y_test.csv\n"
          ]
        }
      ],
      "source": [
        "! pip install fasttext\n",
        "import fasttext\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, accuracy_score, classification_report\n",
        "from sklearn.model_selection import KFold\n",
        "import warnings\n",
        "import matplotlib.pyplot as plt\n",
        "import string\n",
        "\n",
        "\n",
        "from nltk.stem import PorterStemmer\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "!pip install nltk\n",
        "import nltk\n",
        "nltk.download('all')\n",
        "nltk.download('stopwords')\n",
        "import re\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.corpus import stopwords\n",
        "from nltk import ne_chunk, pos_tag, word_tokenize\n",
        "from nltk.tree import Tree\n",
        "\n",
        "\n",
        "from google.colab import files\n",
        "uploaded1 = files.upload()\n",
        "uploaded2 = files.upload()\n",
        "uploaded3 = files.upload()\n",
        "uploaded4 = files.upload()\n",
        "import io\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "70qt7g-Ju6kS",
        "outputId": "0502179a-59cf-4c85-ebae-a0a777fbc7ae"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 4447 entries, 0 to 4446\n",
            "Data columns (total 5 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   Unnamed: 0    4447 non-null   int64 \n",
            " 1   first_party   4447 non-null   object\n",
            " 2   second_party  4447 non-null   object\n",
            " 3   Facts         4447 non-null   object\n",
            " 4   winner_index  4447 non-null   int64 \n",
            "dtypes: int64(2), object(3)\n",
            "memory usage: 208.5+ KB\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 791 entries, 0 to 790\n",
            "Data columns (total 5 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   Unnamed: 0    791 non-null    int64 \n",
            " 1   first_party   791 non-null    object\n",
            " 2   second_party  791 non-null    object\n",
            " 3   Facts         791 non-null    object\n",
            " 4   winner_index  791 non-null    int64 \n",
            "dtypes: int64(2), object(3)\n",
            "memory usage: 37.1+ KB\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "(None, None)"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "xtrain = pd.read_csv(io.BytesIO(uploaded1['X_train.csv']))\n",
        "xtest =  pd.read_csv(io.BytesIO(uploaded2['X_test.csv']))\n",
        "ytrain = pd.read_csv(io.BytesIO(uploaded3['y_train.csv']))\n",
        "ytest =  pd.read_csv(io.BytesIO(uploaded4['y_test.csv']))\n",
        "\n",
        "train_data = xtrain.merge(ytrain,on=[\"Unnamed: 0\"])\n",
        "test_data = xtest.merge(ytest,on=[\"Unnamed: 0\"])\n",
        "#train.head()\n",
        "train_data.info(),test_data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "4sFgqn7uu6kT"
      },
      "outputs": [],
      "source": [
        "def K_fold (train_data1):\n",
        "    n=4\n",
        "    data = []\n",
        "    \n",
        "    kf = KFold(n_splits=n, random_state=42, shuffle=True)\n",
        "    \n",
        "    for train_index, val_index in kf.split(train_data1):\n",
        "        train_df = train_data1.iloc[train_index]\n",
        "        val_df = train_data1.iloc[val_index]\n",
        "        data.append([train_df,val_df])\n",
        "        \n",
        "    return data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "B1fdzmgMu6kU"
      },
      "outputs": [],
      "source": [
        "data = K_fold (train_data)\n",
        "\n",
        "#print(train_data.info())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "0K6oPUVbu6kU"
      },
      "outputs": [],
      "source": [
        "def prepare_data(train,val):\n",
        "    # Write the train data to file\n",
        "    with open(\"train_data.txt\", \"w\", encoding=\"utf-8\") as f:\n",
        "        for i in range(len(train)):\n",
        "            f.write(\"__label__\" + str(train.iloc[i][\"winner_index\"]) + \" \" + train.iloc[i][\"Facts\"] + \"\\n\")\n",
        "            \n",
        "    # Write the validation data to file\n",
        "    with open(\"val_data.txt\", \"w\", encoding=\"utf-8\") as f:\n",
        "        for i in range(len(val)):\n",
        "            f.write(\"__label__\" + str(val.iloc[i][\"winner_index\"]) + \" \" + val.iloc[i][\"Facts\"] + \"\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "4jhCkNBDu6kV"
      },
      "outputs": [],
      "source": [
        "def train_model():\n",
        "    model = fasttext.train_supervised(input=\"train_data.txt\", epoch=20, lr=1.0, wordNgrams=5, dim=300, loss=\"ns\")\n",
        "\n",
        "    result = model.test(\"train_data.txt\")\n",
        "    print(\"train-Accuracy:\",result[1])\n",
        "    \n",
        "    result = model.test(\"val_data.txt\")\n",
        "    print(\"val-Accuracy:\",result[1])\n",
        "    \n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "bzhqbtJ8u6kV"
      },
      "outputs": [],
      "source": [
        "def predict(model):\n",
        "    # predict the test data\n",
        "    ypred = test_data[\"Facts\"].apply(lambda x: model.predict(x)[0][0])\n",
        "    ypred = [eval(x.replace('__label__', '')) for x in ypred]\n",
        "    \n",
        "    # Compute the accuracy\n",
        "    accuracy = int(round(accuracy_score(test_data[\"winner_index\"], ypred)*100))\n",
        "    \n",
        "    # Create the confusion matrix\n",
        "    disp = ConfusionMatrixDisplay(confusion_matrix(test_data[\"winner_index\"], ypred))\n",
        "    disp.plot()\n",
        "    plt.show()\n",
        "    \n",
        "    report = classification_report(test_data[\"winner_index\"],ypred)\n",
        "    print(report)\n",
        "\n",
        "    return accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2BhD29T8u6kW",
        "outputId": "bc455adb-1145-4984-d893-2cc840793ed7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "train-Accuracy: 0.7070464767616191\n",
            "val-Accuracy: 0.33093525179856115\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.71      0.92      0.80       472\n",
            "           1       0.79      0.46      0.58       319\n",
            "\n",
            "    accuracy                           0.73       791\n",
            "   macro avg       0.75      0.69      0.69       791\n",
            "weighted avg       0.75      0.73      0.71       791\n",
            "\n",
            "train-Accuracy: 0.7073463268365817\n",
            "val-Accuracy: 0.3327338129496403\n"
          ]
        },
        {
          "data": {
            "image/png": 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J0/qnO6myPFCRHeuVNLhSS175VJEd6131Hr07Xh/mhrk+3z7i+LTA6nf2yRbH43hoXUbf8K2uv+vHTsjfNhRIkh6+M07Z66JaKiyg0Vr8T9Tp06crNTVVAwcO1EUXXeR63d9NN93U0qGZ1kMvHXD93MFWr/v+8ZlHbYDW5p6rurt9/sffbPrH304eXUTr5u2K+ta8Gr/Fk/0f//hHff3115o7d66Ki4vVv39/bdq06aRFewAAeINh/BY2ZcoUhu0BAGgiZ0SyBwCgqXm7vz2P3gEAcIYz8zB+611tAAAAGoSePQDAFMzcsyfZAwBMwczJnmF8AAD8HD17AIApmLlnT7IHAJiCIe8en2vNb/kg2QMATMHMPXvm7AEA8HP07AEApmDmnj3JHgBgCmZO9gzjAwDg5+jZAwBMwcw9e5I9AMAUDMMiw4uE7U3blsYwPgAAfo6ePQDAFHifPQAAfs7Mc/YM4wMA4Ofo2QMATMHMC/RI9gAAU2AYHwAAP3eiZ+/N4Ynly5erb9++slqtslqtstvteu2111znq6urlZaWpg4dOigsLEzjx49XSUmJ2zUOHTqk0aNHKzQ0VNHR0ZoxY4bq6+s9/t1J9gAANIEuXbrowQcfVF5ennbt2qVLL71UY8aM0d69eyVJ06ZN08aNG/XCCy8oJydHhYWFGjdunKu9w+HQ6NGjVVtbq+3bt2v16tXKyMjQ3LlzPY7FYhhGq31Fb0VFhSIiIvTdJ91kDefvFvinlNj+LR0C0GTqjTpt1SsqLy+X1WptknucyBUXvDhdge2DG30dR1WN3rvqER0+fNgt1uDgYAUHN+y6UVFReuihh3TVVVepU6dOyszM1FVXXSVJ+vjjj9W7d2/l5uZq8ODBeu211/T73/9ehYWFiomJkSStWLFCM2fO1Ndff62goKAGx06GBACYgiHJMLw4frhOXFycIiIiXEd6evqv3tvhcGjt2rWqqqqS3W5XXl6e6urqlJyc7KrTq1cvxcfHKzc3V5KUm5urpKQkV6KXpJSUFFVUVLhGBxqKBXoAAHjgVD3709mzZ4/sdruqq6sVFham9evXKzExUbt371ZQUJAiIyPd6sfExKi4uFiSVFxc7JboT5w/cc4TJHsAgCk4ZZHFBzvonVhw1xA9e/bU7t27VV5erhdffFGpqanKyclpdAyNRbIHAJhCSzxnHxQUpO7du0uSBgwYoHfffVePPfaY/vjHP6q2tlZlZWVuvfuSkhLZbDZJks1m086dO92ud2K1/ok6DcWcPQAAzcTpdKqmpkYDBgxQ27ZttWXLFte5/Px8HTp0SHa7XZJkt9u1Z88elZaWuupkZ2fLarUqMTHRo/vSswcAmILTsMjSjJvqzJo1S6NGjVJ8fLyOHj2qzMxMbd26VZs3b1ZERIQmTpyo6dOnKyoqSlarVVOnTpXdbtfgwYMlSSNGjFBiYqKuv/56LV68WMXFxZo9e7bS0tIavPr/BJI9AMAUTqyq96a9J0pLS3XDDTeoqKhIERER6tu3rzZv3qzLLrtMkrRkyRIFBARo/PjxqqmpUUpKip588klX+8DAQGVlZWny5Mmy2+1q3769UlNTtXDhQo9jJ9kDANAEVq5c+YvnQ0JCtGzZMi1btuy0dRISEvTqq696HQvJHgBgCrwIBwAAP0eyBwDAzzX3Ar0zCY/eAQDg5+jZAwBMoblX459JSPYAAFM4nuy9mbP3YTDNjGF8AAD8HD17AIApsBofAAA/Z+jHd9I3tn1rxTA+AAB+jp49AMAUGMYHAMDfmXgcn2QPADAHL3v2asU9e+bsAQDwc/TsAQCmwA56AAD4OTMv0GMYHwAAP0fPHgBgDobFu0V2rbhnT7IHAJiCmefsGcYHAMDP0bMHAJgDm+oAAODfzLwav0HJ/l//+leDL3jllVc2OhgAAOB7DUr2Y8eObdDFLBaLHA6HN/EAANB0WvFQvDcalOydTmdTxwEAQJMy8zC+V6vxq6urfRUHAABNy/DB0Up5nOwdDocWLVqks88+W2FhYfrss88kSXPmzNHKlSt9HiAAAPCOx8n+/vvvV0ZGhhYvXqygoCBXeZ8+ffTMM8/4NDgAAHzH4oOjdfI42T/33HP6v//7P02YMEGBgYGu8n79+unjjz/2aXAAAPgMw/gN99VXX6l79+4nlTudTtXV1fkkKAAA4DseJ/vExES99dZbJ5W/+OKLOv/8830SFAAAPmfinr3HO+jNnTtXqamp+uqrr+R0OvXyyy8rPz9fzz33nLKyspoiRgAAvGfit9553LMfM2aMNm7cqDfeeEPt27fX3LlztX//fm3cuFGXXXZZU8QIAAC80Ki98S+++GJlZ2f7OhYAAJqMmV9x2+gX4ezatUv79++XdHwef8CAAT4LCgAAn+Otdw335Zdf6tprr9V///tfRUZGSpLKysr0//7f/9PatWvVpUsXX8cIAAC84PGc/S233KK6ujrt379fR44c0ZEjR7R//345nU7dcsstTREjAADeO7FAz5vDA+np6brwwgsVHh6u6OhojR07Vvn5+W51hg4dKovF4nbcdtttbnUOHTqk0aNHKzQ0VNHR0ZoxY4bq6+s9isXjnn1OTo62b9+unj17usp69uypxx9/XBdffLGnlwMAoFlYjOOHN+09kZOTo7S0NF144YWqr6/XX/7yF40YMUL79u1T+/btXfUmTZqkhQsXuj6Hhoa6fnY4HBo9erRsNpu2b9+uoqIi3XDDDWrbtq0eeOCBBsficbKPi4s75eY5DodDsbGxnl4OAIDm0cxz9ps2bXL7nJGRoejoaOXl5WnIkCGu8tDQUNlstlNe4/XXX9e+ffv0xhtvKCYmRv3799eiRYs0c+ZMzZ8/323b+l/i8TD+Qw89pKlTp2rXrl2usl27dumOO+7Qww8/7OnlAABoVSoqKtyOmpqaBrUrLy+XJEVFRbmVr1mzRh07dlSfPn00a9YsHTt2zHUuNzdXSUlJiomJcZWlpKSooqJCe/fubXDMDerZn3XWWbJYfpyrqKqq0qBBg9SmzfHm9fX1atOmjW6++WaNHTu2wTcHAKDZ+GhTnbi4OLfiefPmaf78+b/Y1Ol06s4779Rvf/tb9enTx1X+pz/9SQkJCYqNjdWHH36omTNnKj8/Xy+//LIkqbi42C3RS3J9Li4ubnDoDUr2jz76aIMvCADAGclHw/iHDx+W1Wp1FQcHB/9q07S0NH300Ud6++233cpvvfVW189JSUnq3Lmzhg8froKCAp177rleBOuuQck+NTXVZzcEAKA1s1qtbsn+10yZMkVZWVnatm3brz6ePmjQIEnSgQMHdO6558pms2nnzp1udUpKSiTptPP8p+LxnP1PVVdXnzR3AQDAGamZX4RjGIamTJmi9evX680331TXrl1/tc3u3bslSZ07d5Yk2e127dmzR6Wlpa462dnZslqtSkxMbHAsHq/Gr6qq0syZM7Vu3Tp9++23J513OByeXhIAgKbXzKvx09LSlJmZqVdeeUXh4eGuOfaIiAi1a9dOBQUFyszM1OWXX64OHTroww8/1LRp0zRkyBD17dtXkjRixAglJibq+uuv1+LFi1VcXKzZs2crLS2tQdMHJ3jcs7/nnnv05ptvavny5QoODtYzzzyjBQsWKDY2Vs8995ynlwMAwC8tX75c5eXlGjp0qDp37uw6nn/+eUlSUFCQ3njjDY0YMUK9evXSXXfdpfHjx2vjxo2uawQGBiorK0uBgYGy2+267rrrdMMNN7g9l98QHvfsN27cqOeee05Dhw7VTTfdpIsvvljdu3dXQkKC1qxZowkTJnh6SQAAml4zv+LW+JU358TFxSknJ+dXr5OQkKBXX33Vo3v/nMc9+yNHjqhbt26Sji9SOHLkiCTpd7/7nbZt2+ZVMAAANJUTO+h5c7RWHif7bt266eDBg5KkXr16ad26dZKO9/hPvBgHAACcOTxO9jfddJM++OADSdK9996rZcuWKSQkRNOmTdOMGTN8HiAAAD7RzKvxzyQez9lPmzbN9XNycrI+/vhj5eXlqXv37q7VgwAA4MzhcbL/uYSEBCUkJPgiFgAAmoxFXr71zmeRNL8GJfulS5c2+IJ//vOfGx0MAADwvQYl+yVLljToYhaLpUWS/bAFExUYFNLs9wWag+X6lo4AaDqO2mpp7SvNc7NmfvTuTNKgZH9i9T0AAK1WM++gdybxam98AABw5vN6gR4AAK2CiXv2JHsAgCl4uwueqXbQAwAArQs9ewCAOZh4GL9RPfu33npL1113nex2u7766itJ0t///ne9/fbbPg0OAACfMfF2uR4n+5deekkpKSlq166d3n//fdXU1EiSysvL9cADD/g8QAAA4B2Pk/19992nFStW6Omnn1bbtm1d5b/97W/13nvv+TQ4AAB8xcyvuPV4zj4/P19Dhgw5qTwiIkJlZWW+iAkAAN8z8Q56HvfsbTabDhw4cFL522+/rW7duvkkKAAAfI45+4abNGmS7rjjDr3zzjuyWCwqLCzUmjVrdPfdd2vy5MlNESMAAPCCx8P49957r5xOp4YPH65jx45pyJAhCg4O1t13362pU6c2RYwAAHjNzJvqeJzsLRaL/vrXv2rGjBk6cOCAKisrlZiYqLCwsKaIDwAA3zDxc/aN3lQnKChIiYmJvowFAAA0AY+T/bBhw2SxnH5F4ptvvulVQAAANAlvH58zU8++f//+bp/r6uq0e/duffTRR0pNTfVVXAAA+BbD+A23ZMmSU5bPnz9flZWVXgcEAAB8y2dvvbvuuuv07LPP+upyAAD4lomfs/fZW+9yc3MVEhLiq8sBAOBTPHrngXHjxrl9NgxDRUVF2rVrl+bMmeOzwAAAgG94nOwjIiLcPgcEBKhnz55auHChRowY4bPAAACAb3iU7B0Oh2666SYlJSXprLPOaqqYAADwPROvxvdogV5gYKBGjBjB2+0AAK2OmV9x6/Fq/D59+uizzz5rilgAAEAT8DjZ33fffbr77ruVlZWloqIiVVRUuB0AAJyxTPjYneTBnP3ChQt111136fLLL5ckXXnllW7b5hqGIYvFIofD4fsoAQDwlonn7Buc7BcsWKDbbrtN//nPf5oyHgAA4GMNTvaGcfxPmksuuaTJggEAoKmYeVMdj+bsf+ltdwAAnNGaebvc9PR0XXjhhQoPD1d0dLTGjh2r/Px8tzrV1dVKS0tThw4dFBYWpvHjx6ukpMStzqFDhzR69GiFhoYqOjpaM2bMUH19vUexeJTse/TooaioqF88AACAlJOTo7S0NO3YsUPZ2dmqq6vTiBEjVFVV5aozbdo0bdy4US+88IJycnJUWFjotlOtw+HQ6NGjVVtbq+3bt2v16tXKyMjQ3LlzPYrFo011FixYcNIOegAAtAbNPYy/adMmt88ZGRmKjo5WXl6ehgwZovLycq1cuVKZmZm69NJLJUmrVq1S7969tWPHDg0ePFivv/669u3bpzfeeEMxMTHq37+/Fi1apJkzZ2r+/PkKCgpqUCweJftrrrlG0dHRnjQBAODM4KPV+D9/zDw4OFjBwcG/2ry8vFySXKPgeXl5qqurU3JysqtOr169FB8fr9zcXA0ePFi5ublKSkpSTEyMq05KSoomT56svXv36vzzz29Q6A0exme+HgAAKS4uThEREa4jPT39V9s4nU7deeed+u1vf6s+ffpIkoqLixUUFKTIyEi3ujExMSouLnbV+WmiP3H+xLmG8ng1PgAArZKPevaHDx+W1Wp1FTekV5+WlqaPPvpIb7/9thcBNF6Dk73T6WzKOAAAaFK+mrO3Wq1uyf7XTJkyRVlZWdq2bZu6dOniKrfZbKqtrVVZWZlb776kpEQ2m81VZ+fOnW7XO7Fa/0SdhvB4u1wAAFqlZn70zjAMTZkyRevXr9ebb76prl27up0fMGCA2rZtqy1btrjK8vPzdejQIdntdkmS3W7Xnj17VFpa6qqTnZ0tq9WqxMTEBsfi8fvsAQDAr0tLS1NmZqZeeeUVhYeHu+bYIyIi1K5dO0VERGjixImaPn26oqKiZLVaNXXqVNntdg0ePFiSNGLECCUmJur666/X4sWLVVxcrNmzZystLa1B0wcnkOwBAObQzHvjL1++XJI0dOhQt/JVq1bpxhtvlCQtWbJEAQEBGj9+vGpqapSSkqInn3zSVTcwMFBZWVmaPHmy7Ha72rdvr9TUVC1cuNCjWEj2AABTaO7n7BuysD0kJETLli3TsmXLTlsnISFBr776qmc3/xnm7AEA8HP07AEA5sArbgEA8G+89Q4AAPgtevYAAHNgGB8AAD9n4mTPMD4AAH6Onj0AwBQsPxzetG+tSPYAAHMw8TA+yR4AYAo8egcAAPwWPXsAgDkwjA8AgAm04oTtDYbxAQDwc/TsAQCmYOYFeiR7AIA5mHjOnmF8AAD8HD17AIApMIwPAIC/YxgfAAD4K3r2AABTYBgfAAB/Z+JhfJI9AMAcTJzsmbMHAMDP0bMHAJgCc/YAAPg7hvEBAIC/omcPADAFi2HIYjS+e+5N25ZGsgcAmAPD+AAAwF/RswcAmAKr8QEA8HcM4wMAAH9Fzx4AYAoM4wMA4O9MPIxPsgcAmIKZe/bM2QMA4OdI9gAAczB8cHhg27ZtuuKKKxQbGyuLxaINGza4nb/xxhtlsVjcjpEjR7rVOXLkiCZMmCCr1arIyEhNnDhRlZWVHv7iJHsAgImcGMpvzOGpqqoq9evXT8uWLTttnZEjR6qoqMh1/POf/3Q7P2HCBO3du1fZ2dnKysrStm3bdOutt3ocC3P2AAB4oKKiwu1zcHCwgoODT6o3atQojRo16hevFRwcLJvNdspz+/fv16ZNm/Tuu+9q4MCBkqTHH39cl19+uR5++GHFxsY2OGZ69gAAczAM7w9JcXFxioiIcB3p6emNDmnr1q2Kjo5Wz549NXnyZH377beuc7m5uYqMjHQleklKTk5WQECA3nnnHY/uQ88eAGAKvlqNf/jwYVmtVlf5qXr1DTFy5EiNGzdOXbt2VUFBgf7yl79o1KhRys3NVWBgoIqLixUdHe3Wpk2bNoqKilJxcbFH9yLZAwDgAavV6pbsG+uaa65x/ZyUlKS+ffvq3HPP1datWzV8+HCvr/9TDOMDAMyhmVfje6pbt27q2LGjDhw4IEmy2WwqLS11q1NfX68jR46cdp7/dEj2AABTsDi9P5rSl19+qW+//VadO3eWJNntdpWVlSkvL89V580335TT6dSgQYM8ujbD+AAANIHKykpXL12SDh48qN27dysqKkpRUVFasGCBxo8fL5vNpoKCAt1zzz3q3r27UlJSJEm9e/fWyJEjNWnSJK1YsUJ1dXWaMmWKrrnmGo9W4kske0g6/5xCXXfxB+p19tfqZD2mGX9PUc7+rm51zun0naaM3KELuhYpMMCpg6VnaeaaESopD5e1XbVuTd6lQd0PKyayUmVV7ZSz7xytyL5QVTWNW7gC+FL/roW6bsgH6tXlh+/46hRt2/fjd3zO//emfj/wE7c2uflxuvPZ0ZKkC7p9peX/s/GU177x8XHa/2X0Kc/hDNPMe+Pv2rVLw4YNc32ePn26JCk1NVXLly/Xhx9+qNWrV6usrEyxsbEaMWKEFi1a5Lbgb82aNZoyZYqGDx+ugIAAjR8/XkuXLvU4dJI9FBJUr0+LO2hjXi8tvm7zSefPjirX0/+zQf/a1Uv/98aFqqppq27R36m2/vjXp6P1mDqGV+mx1+w6WHqWOkdW6t6x29TRekyzMkc0968DnKRdUL0+Leqgjbt6afENJ3/HJWl7fpwWrfvxH+Y6R6Dr5w+/sGnUohvc6v9Pyk5deO5X2v9lp6YJGj7X3HvjDx06VIZx+kabN5/6u/hTUVFRyszM9OzGp9CiyX7btm166KGHlJeXp6KiIq1fv15jx45tyZBMKfeTeOV+En/a85NH7NR/8+P1+Ca7q+yrIxGunz8ridK9mSlu55a/fpEWXL1FgQFOOZwsDUHLys2PV27+6b/jklRXH6gjlaGnPFfvcD8XGODQkMTP9cL2JEkWX4aKpvSTZ+Ub3b6VatFkf2IrwZtvvlnjxo1ryVBwGhaLod/2PKS/b+uvpTdmqUfsNyr8zqrVW88/aaj/p8JCalVVE0SiR6txQbdCvTYnQ0e/D9auA2drxesXqeJYyCnrDkn8QhGhNcra1bOZowQap0WTfUO2Evypmpoa1dTUuD7/fMtC+F5U++/VPrhOqZe8rxXZF+rxzYNlP++w/nfCZk1eeaXeP3jyIpGI0O9187A8bdjZuwUiBjy345N4bf2omwq/C9fZURW6feROPXrzv3XLsj/IaZz8B+uVF+7XO590UWl5WAtEi8Yy8ytuW9WcfXp6uhYsWNDSYZiK5Ydv97b95+if/+0nSfq0qKP6JhRr3EX7Tkr27YNrtST1NR0sPUv/t2XgSdcDzkTZH3R3/VxQ3EEHijto/cxMXdCtULsKurjVjY6o1KAeX+qvay5r7jDhrWZeoHcmaVVjrLNmzVJ5ebnrOHz4cEuH5PfKjoWo3hGgg6VnuZV/XnqWbJFH3cpCg2r12I3/1rGatrpnTYoczkABrVHhEau+qwxRXMeTRw9/PzBf5ceCtW1fQgtEBjROq+rZn+7NQmg69Y5A7fuyk+I7lrmVx3csU3FZuOtz++BaLb3p36qtD9Bdfx/pWqkPtEbREZWKCK3WNxU/X7Bn6PcDPtZr7/Xkj9lWiGF8mFq7oDp16VDu+hwbVaHzOn+jimPBKikP1z/e6q/7r8nW+wc7K++zs2XvcVi/6/WFJj9zpaQTiT5LIW3rNXddisKC6xQWXCdJ+q4q5JRznkBzOu13/PtgVRwL0S3Ju/Sfj7rp26PtdHZUhaZevkNffhuhHZ/EuV1n4Llf6ewOR/XKzl7N/SvAF1iNDzPrfXapVkz6ccOQaaNzJUlZeT208KVLtXVfVz34yhClXvKe7rrivzr0daTuzRyhD744vqVjz9ivlRR/fP/m9Xf/0+3aYxb/SUVl3r8wAvBG7y6lbpviTLvih+/4rh5avH6Iunf+VpcPyFd4SK2+rgjVzk/j9NTrF7o9ay9JV174sT74PEZffO0+rQWc6SzGLz3x38R+upXg+eefr0ceeUTDhg1TVFSU4uN/+ZlY6fhq/IiICPWfcL8Cg079iAzQ2lkcLR0B0HQctdV6f+1fVV5e7pM3yZ3KiVxhH7VQbdo2PlfU11Ur97W5TRprU2nRnv0vbSWYkZHRQlEBAPySiVfjt2iy/7WtBAEAgPeYswcAmAKr8QEA8HdO4/jhTftWimQPADAHE8/Z8wA0AAB+jp49AMAULPJyzt5nkTQ/kj0AwBxMvIMew/gAAPg5evYAAFPg0TsAAPwdq/EBAIC/omcPADAFi2HI4sUiO2/atjSSPQDAHJw/HN60b6UYxgcAwM/RswcAmALD+AAA+DsTr8Yn2QMAzIEd9AAAgL+iZw8AMAV20AMAwN8xjA8AAPwVPXsAgClYnMcPb9q3ViR7AIA5MIwPAAD8FT17AIA5sKkOAAD+zczb5TKMDwBAE9i2bZuuuOIKxcbGymKxaMOGDW7nDcPQ3Llz1blzZ7Vr107Jycn69NNP3eocOXJEEyZMkNVqVWRkpCZOnKjKykqPYyHZAwDM4cQCPW8OD1RVValfv35atmzZKc8vXrxYS5cu1YoVK/TOO++offv2SklJUXV1tavOhAkTtHfvXmVnZysrK0vbtm3Trbfe6vGvzjA+AMAcDHn3TnoPR/FHjRqlUaNGnfpShqFHH31Us2fP1pgxYyRJzz33nGJiYrRhwwZdc8012r9/vzZt2qR3331XAwcOlCQ9/vjjuvzyy/Xwww8rNja2wbHQswcAmMKJOXtvDkmqqKhwO2pqajyO5eDBgyouLlZycrKrLCIiQoMGDVJubq4kKTc3V5GRka5EL0nJyckKCAjQO++849H9SPYAAHggLi5OERERriM9Pd3jaxQXF0uSYmJi3MpjYmJc54qLixUdHe12vk2bNoqKinLVaSiG8QEA5mDIy011jv/P4cOHZbVaXcXBwcHexdUM6NkDAMzBRwv0rFar29GYZG+z2SRJJSUlbuUlJSWuczabTaWlpW7n6+vrdeTIEVedhiLZAwDQzLp27SqbzaYtW7a4yioqKvTOO+/IbrdLkux2u8rKypSXl+eq8+abb8rpdGrQoEEe3Y9hfACAOTglWbxs74HKykodOHDA9fngwYPavXu3oqKiFB8frzvvvFP33XefzjvvPHXt2lVz5sxRbGysxo4dK0nq3bu3Ro4cqUmTJmnFihWqq6vTlClTdM0113i0El8i2QMATKK5d9DbtWuXhg0b5vo8ffp0SVJqaqoyMjJ0zz33qKqqSrfeeqvKysr0u9/9Tps2bVJISIirzZo1azRlyhQNHz5cAQEBGj9+vJYuXepx7CR7AACawNChQ2X8wh8IFotFCxcu1MKFC09bJyoqSpmZmV7HQrIHAJiDiV9xS7IHAJiDiZM9q/EBAPBz9OwBAOZg4p49yR4AYA7N/OjdmYRkDwAwheZ+9O5Mwpw9AAB+jp49AMAcmLMHAMDPOQ3J4kXCdrbeZM8wPgAAfo6ePQDAHBjGBwDA33mZ7NV6kz3D+AAA+Dl69gAAc2AYHwAAP+c05NVQPKvxAQDAmYqePQDAHAzn8cOb9q0UyR4AYA7M2QMA4OeYswcAAP6Knj0AwBwYxgcAwM8Z8jLZ+yySZscwPgAAfo6ePQDAHBjGBwDAzzmdkrx4Vt7Zep+zZxgfAAA/R88eAGAODOMDAODnTJzsGcYHAMDP0bMHAJiDibfLJdkDAEzBMJwyvHhznTdtWxrJHgBgDobhXe+cOXsAAHCmomcPADAHw8s5+1bcsyfZAwDMwemULF7Mu7fiOXuG8QEA8HP07AEA5mDiYXx69gAAUzCcTq8PT8yfP18Wi8Xt6NWrl+t8dXW10tLS1KFDB4WFhWn8+PEqKSnx9a8tiWQPAECT+c1vfqOioiLX8fbbb7vOTZs2TRs3btQLL7ygnJwcFRYWaty4cU0SB8P4AABz8NEwfkVFhVtxcHCwgoODT9mkTZs2stlsJ5WXl5dr5cqVyszM1KWXXipJWrVqlXr37q0dO3Zo8ODBjY/zFOjZAwDMwWl4f0iKi4tTRESE60hPTz/tLT/99FPFxsaqW7dumjBhgg4dOiRJysvLU11dnZKTk111e/Xqpfj4eOXm5vr8V6dnDwCABw4fPiyr1er6fLpe/aBBg5SRkaGePXuqqKhICxYs0MUXX6yPPvpIxcXFCgoKUmRkpFubmJgYFRcX+zxmkj0AwBwMQ5I3z9kf79lbrVa3ZH86o0aNcv3ct29fDRo0SAkJCVq3bp3atWvX+DgagWF8AIApGE7D68MbkZGR6tGjhw4cOCCbzaba2lqVlZW51SkpKTnlHL+3SPYAAHMwnN4fXqisrFRBQYE6d+6sAQMGqG3bttqyZYvrfH5+vg4dOiS73e7tb3oShvEBAGgCd999t6644golJCSosLBQ8+bNU2BgoK699lpFRERo4sSJmj59uqKiomS1WjV16lTZ7Xafr8SXSPYAAJMwnIYMS+OH4g0Pd9D78ssvde211+rbb79Vp06d9Lvf/U47duxQp06dJElLlixRQECAxo8fr5qaGqWkpOjJJ59sdHy/hGQPADAHwynvFuh51nbt2rW/eD4kJETLli3TsmXLGh9TA7XqZH/iryxHXXULRwI0HYujpSMAms6Jf7897TU3Rr3qvNpTp151vgummbXqZH/06FFJ0p51i1o4EgCAN44ePaqIiIgmuXZQUJBsNpveLn7V62vZbDYFBQX5IKrmZTGa48+pJuJ0OlVYWKjw8HBZLJaWDscUKioqFBcXd9KmEoA/4Pvd/AzD0NGjRxUbG6uAgKZ7QKy6ulq1tbVeXycoKEghISE+iKh5teqefUBAgLp06dLSYZhSQzeVAFojvt/Nq6l69D8VEhLSKpO0r/CcPQAAfo5kDwCAnyPZwyPBwcGaN2/eaV/8ALRmfL/hr1r1Aj0AAPDr6NkDAODnSPYAAPg5kj0AAH6OZA8AgJ8j2aPBli1bpnPOOUchISEaNGiQdu7c2dIhAT6xbds2XXHFFYqNjZXFYtGGDRtaOiTAp0j2aJDnn39e06dP17x58/Tee++pX79+SklJUWlpaUuHBnitqqpK/fr1a5a3jwEtgUfv0CCDBg3ShRdeqCeeeELS8fcSxMXFaerUqbr33ntbODrAdywWi9avX6+xY8e2dCiAz9Czx6+qra1VXl6ekpOTXWUBAQFKTk5Wbm5uC0YGAGgIkj1+1TfffCOHw6GYmBi38piYGBUXF7dQVACAhiLZAwDg50j2+FUdO3ZUYGCgSkpK3MpLSkpks9laKCoAQEOR7PGrgoKCNGDAAG3ZssVV5nQ6tWXLFtnt9haMDADQEG1aOgC0DtOnT1dqaqoGDhyoiy66SI8++qiqqqp00003tXRogNcqKyt14MAB1+eDBw9q9+7dioqKUnx8fAtGBvgGj96hwZ544gk99NBDKi4uVv/+/bV06VINGjSopcMCvLZ161YNGzbspPLU1FRlZGQ0f0CAj5HsAQDwc8zZAwDg50j2AAD4OZI9AAB+jmQPAICfI9kDAODnSPYAAPg5kj0AAH6OZA8AgJ8j2QNeuvHGGzV27FjX56FDh+rOO+9s9ji2bt0qi8WisrKy09axWCzasGFDg685f/589e/f36u4Pv/8c1ksFu3evdur6wBoPJI9/NKNN94oi8Uii8WioKAgde/eXQsXLlR9fX2T3/vll1/WokWLGlS3IQkaALzFi3Dgt0aOHKlVq1appqZGr776qtLS0tS2bVvNmjXrpLq1tbUKCgryyX2joqJ8ch0A8BV69vBbwcHBstlsSkhI0OTJk5WcnKx//etfkn4cer///vsVGxurnj17SpIOHz6sq6++WpGRkYqKitKYMWP0+eefu67pcDg0ffp0RUZGqkOHDrrnnnv089dL/HwYv6amRjNnzlRcXJyCg4PVvXt3rVy5Up9//rnr5StnnXWWLBaLbrzxRknHXyGcnp6url27ql27durXr59efPFFt/u8+uqr6tGjh9q1a6dhw4a5xdlQM2fOVI8ePRQaGqpu3bppzpw5qqurO6neU089pbi4OIWGhurqq69WeXm52/lnnnlGvXv3VkhIiHr16qUnn3zS41gANB2SPUyjXbt2qq2tdX3esmWL8vPzlZ2draysLNXV1SklJUXh4eF666239N///ldhYWEaOXKkq93f/vY3ZWRk6Nlnn9Xbb7+tI0eOaP369b943xtuuEH//Oc/tXTpUu3fv19PPfWUwsLCFBcXp5deekmSlJ+fr6KiIj322GOSpPT0dD333HNasWKF9u7dq2nTpum6665TTk6OpON/lIwbN05XXHGFdu/erVtuuUX33nuvx/9NwsPDlZGRoX379umxxx7T008/rSVLlrjVOXDggNatW6eNGzdq06ZNev/993X77be7zq9Zs0Zz587V/fffr/379+uBBx7QnDlztHr1ao/jAdBEDMAPpaamGmPGjDEMwzCcTqeRnZ1tBAcHG3fffbfrfExMjFFTU+Nq8/e//93o2bOn4XQ6XWU1NTVGu3btjM2bNxuGYRidO3c2Fi9e7DpfV1dndOnSxXUvwzCMSy65xLjjjjsMwzCM/Px8Q5KRnZ19yjj/85//GJKM7777zlVWXV1thIaGGtu3b3erO3HiROPaa681DMMwZs2aZSQmJrqdnzlz5knX+jlJxvr16097/qGHHjIGDBjg+jxv3jwjMDDQ+PLLL11lr732mhEQEGAUFRUZhmEY5557rpGZmel2nUWLFhl2u90wDMM4ePCgIcl4//33T3tfAE2LOXv4raysLIWFhamurk5Op1N/+tOfNH/+fNf5pKQkt3n6Dz74QAcOHFB4eLjbdaqrq1VQUKDy8nIVFRVp0KBBrnNt2rTRwIEDTxrKP2H37t0KDAzUJZdc0uC4Dxw4oGPHjumyyy5zK6+trdX5558vSdq/f79bHJJkt9sbfI8Tnn/+eS1dulQFBQWqrKxUfX29rFarW534+HidffbZbvdxOp3Kz89XeHi4CgoKNHHiRE2aNMlVp76+XhERER7HA6BpkOzht4YNG6bly5crKChIsbGxatPG/evevn17t8+VlZUaMGCA1qxZc9K1OnXq1KgY2rVr53GbyspKSdK///1vtyQrHV+H4Cu5ubmaMGGCFixYoJSUFEVERGjt2rX629/+5nGsTz/99El/fAQGBvosVgDeIdnDb7Vv317du3dvcP0LLrhAzz//vKKjo0/q3Z7QuXNnvfPOOxoyZIik4z3YvLw8XXDBBaesn5SUJKfTqZycHCUnJ590/sTIgsPhcJUlJiYqODhYhw4dOu2IQO/evV2LDU/YsWPHr/+SP7F9+3YlJCTor3/9q6vsiy++OKneoUOHVFhYqNjYWNd9AgIC1LNnT8XExCg2NlafffaZJkyY4NH9ATQfFugBP5gwYYI6duyoMWPG6K233tLBgwe1detW/fnPf9aXX34pSbrjjjv04IMPasOGDfr44491++23/+Iz8uecc45SU1N18803a8OGDa5rrlu3TpKUkJAgi8WirKwsff3116qsrFR4eLjuvvtuTZs2TatXr1ZBQYHee+89Pf74465Fb7fddps+/fRTzZgxQ/n5+crMzFRGRoZHv+95552nQ4cOae3atSooKNDSpUtPudgwJCREqamp+uCDD/TWW2/pz3/+s66++mrZbDZJ0oIFC5Senq6lS5fqk08+0Z49e7Rq1So98sgjHsUDoOmQ7IEfhIaGatu2bYqPj9e4cePUu3dvTZw4UdXV1a6e/l133aXrr79eqampstvtCg8P1x/+8IdfvO7y5ct11VVX6fbbb1evXr00adIkVVVVSZLOPvtsLViwQPfee69iYmI0ZcoUSdKiRYs0Z84cpaenq3fv3ho5cqT+/e9/q2vXrpKOz6O/9NJL2rBhg/r166cVK1bogQce8Oj3vfLKKzVt2jRNmTJF/fv31/bt2zVnzpyT6nXv3l3jxo3T5ZdfrhEjRqhv375uj9bdcssteuaZZ7Rq1SolJSXpkksuUUZGhitWAC3PYpxuZREAAPAL9OwBAPBzJHsAAPwcyR4AAD9HsgcAwM+R7AEA8HMkewAA/BzJHgAAP0eyBwDAz5HsAQDwcyR7AAD8HMkeAAA/9/8D3KFzeHIEEt4AAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.73      0.91      0.81       472\n",
            "           1       0.79      0.49      0.61       319\n",
            "\n",
            "    accuracy                           0.74       791\n",
            "   macro avg       0.76      0.70      0.71       791\n",
            "weighted avg       0.75      0.74      0.73       791\n",
            "\n",
            "train-Accuracy: 0.7091454272863568\n",
            "val-Accuracy: 0.33902877697841727\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.71      0.93      0.81       472\n",
            "           1       0.82      0.43      0.57       319\n",
            "\n",
            "    accuracy                           0.73       791\n",
            "   macro avg       0.76      0.68      0.69       791\n",
            "weighted avg       0.75      0.73      0.71       791\n",
            "\n",
            "train-Accuracy: 0.7155275779376499\n",
            "val-Accuracy: 0.3195319531953195\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.74      0.93      0.83       472\n",
            "           1       0.83      0.52      0.64       319\n",
            "\n",
            "    accuracy                           0.76       791\n",
            "   macro avg       0.79      0.73      0.73       791\n",
            "weighted avg       0.78      0.76      0.75       791\n",
            "\n",
            "average accuracy =  74 %\n"
          ]
        }
      ],
      "source": [
        "# No preprocessing-NO Balancing-No Anonymisation (1)\n",
        "aver_acc = 0\n",
        "for i in range(4):\n",
        "    train = data[i][0]\n",
        "    val = data[i][1]\n",
        "\n",
        "    prepare_data(train, val)\n",
        "\n",
        "    if i == 0:\n",
        "        model1_1 = train_model()\n",
        "        aver_acc = aver_acc + predict(model1_1)\n",
        "    if i == 1:\n",
        "        model1_2 = train_model()\n",
        "        aver_acc = aver_acc + predict(model1_2)\n",
        "    if i == 2:\n",
        "        model1_3 = train_model()\n",
        "        aver_acc = aver_acc + predict(model1_3)\n",
        "    if i == 3:\n",
        "        model1_4 = train_model()\n",
        "        aver_acc = aver_acc + predict(model1_4)\n",
        "        \n",
        "print(\"average accuracy = \",round(aver_acc/4),\"%\")\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BFQijhWgu6kX"
      },
      "outputs": [],
      "source": [
        "def Anonymisation(dff):\n",
        "\n",
        "    for samplee in range(len(dff)):\n",
        "        \n",
        "        first_words = dff['first_party'].iloc[samplee].split()\n",
        "        second_words = dff['second_party'].iloc[samplee].split()\n",
        "        \n",
        "        nltk_results = ne_chunk(pos_tag(word_tokenize(dff['Facts'].iloc[samplee])))\n",
        "        \n",
        "        for nltk_result in nltk_results:\n",
        "            if type(nltk_result) == Tree:\n",
        "                name = ''\n",
        "                for nltk_result_leaf in nltk_result.leaves():\n",
        "                    name += nltk_result_leaf[0] + ' '\n",
        "            \n",
        "                    #print ('Type: ', nltk_result.label(), 'Name: ', name)\n",
        "                    if nltk_result.label() != 'PERSON':\n",
        "                        dff['Facts'].iloc[samplee]=dff['Facts'].iloc[samplee].replace(name,'anonymized')\n",
        "                \n",
        "        dff['Facts'].iloc[samplee] = dff['Facts'].iloc[samplee].replace(dff['first_party'].iloc[samplee],'first_party') \n",
        "        dff['Facts'].iloc[samplee] = dff['Facts'].iloc[samplee].replace(dff['second_party'].iloc[samplee], 'second_party')\n",
        "\n",
        "        for f_w in first_words:\n",
        "            dff['Facts'].iloc[samplee] = dff['Facts'].iloc[samplee].replace(f_w , 'first_party')\n",
        "        for s_w in second_words :\n",
        "            dff['Facts'].iloc[samplee] = dff['Facts'].iloc[samplee].replace(s_w , 'second_party')\n",
        "            \n",
        "    return dff"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HslfUMlpu6kY",
        "outputId": "d1c93c92-679d-4808-ff58-e4d5015d07cb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "train-Accuracy: 0.7109445277361319\n",
            "val-Accuracy: 0.3183453237410072\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.71      0.93      0.80       472\n",
            "           1       0.80      0.43      0.56       319\n",
            "\n",
            "    accuracy                           0.73       791\n",
            "   macro avg       0.75      0.68      0.68       791\n",
            "weighted avg       0.74      0.73      0.70       791\n",
            "\n",
            "train-Accuracy: 0.7106446776611695\n",
            "val-Accuracy: 0.32643884892086333\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.71      0.90      0.79       472\n",
            "           1       0.76      0.45      0.57       319\n",
            "\n",
            "    accuracy                           0.72       791\n",
            "   macro avg       0.73      0.68      0.68       791\n",
            "weighted avg       0.73      0.72      0.70       791\n",
            "\n",
            "train-Accuracy: 0.7112443778110945\n",
            "val-Accuracy: 0.335431654676259\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.71      0.92      0.80       472\n",
            "           1       0.78      0.45      0.57       319\n",
            "\n",
            "    accuracy                           0.73       791\n",
            "   macro avg       0.75      0.68      0.68       791\n",
            "weighted avg       0.74      0.73      0.71       791\n",
            "\n",
            "train-Accuracy: 0.7170263788968825\n",
            "val-Accuracy: 0.3195319531953195\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.74      0.92      0.82       472\n",
            "           1       0.81      0.51      0.63       319\n",
            "\n",
            "    accuracy                           0.76       791\n",
            "   macro avg       0.77      0.72      0.72       791\n",
            "weighted avg       0.77      0.76      0.74       791\n",
            "\n",
            "average accuracy =  74 %\n"
          ]
        }
      ],
      "source": [
        "# No preprocessing-NO Balancing- Anonymisation (2)\n",
        "aver_acc = 0\n",
        "for i in range(4):\n",
        "    train = data[i][0]\n",
        "    val = data[i][1]\n",
        "\n",
        "    train = Anonymisation(train)\n",
        "    val = Anonymisation(val)\n",
        "\n",
        "    prepare_data(train, val)\n",
        "\n",
        "    if i == 0:\n",
        "        model2_1 = train_model()\n",
        "        aver_acc = aver_acc + predict(model2_1)\n",
        "    if i == 1:\n",
        "        model2_2 = train_model()\n",
        "        aver_acc = aver_acc + predict(model2_2)\n",
        "    if i == 2:\n",
        "        model2_3 = train_model()\n",
        "        aver_acc = aver_acc + predict(model2_3)\n",
        "    if i == 3:\n",
        "        model2_4 = train_model()\n",
        "        aver_acc = aver_acc + predict(model2_4)\n",
        "        \n",
        "print(\"average accuracy = \",round(aver_acc/4),\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7A3jEsMPu6kY"
      },
      "outputs": [],
      "source": [
        "def Balancing(df):\n",
        "    s = df['winner_index'].value_counts().max() - df['winner_index'].value_counts().min()\n",
        "    df = df.drop(df.loc[df['winner_index'] == 0].sample(n=s).index)\n",
        "    \n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mcnlS8anu6kZ"
      },
      "outputs": [],
      "source": [
        "#print(train_data.info())\n",
        "new_train = train_data\n",
        "balanced_data = K_fold(Balancing(new_train))\n",
        "#train_data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3NjdQltCu6kZ",
        "outputId": "93abe86e-1a64-4271-f089-88409f4d3c64"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "train-Accuracy: 0.7431352102884949\n",
            "val-Accuracy: 0.3524504692387904\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.73      0.75       472\n",
            "           1       0.63      0.68      0.65       319\n",
            "\n",
            "    accuracy                           0.71       791\n",
            "   macro avg       0.70      0.71      0.70       791\n",
            "weighted avg       0.71      0.71      0.71       791\n",
            "\n",
            "train-Accuracy: 0.7358359402155022\n",
            "val-Accuracy: 0.3660062565172054\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.74      0.76      0.75       472\n",
            "           1       0.64      0.61      0.62       319\n",
            "\n",
            "    accuracy                           0.70       791\n",
            "   macro avg       0.69      0.69      0.69       791\n",
            "weighted avg       0.70      0.70      0.70       791\n",
            "\n",
            "train-Accuracy: 0.7483489746263469\n",
            "val-Accuracy: 0.34410844629822734\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.79      0.78       472\n",
            "           1       0.68      0.66      0.67       319\n",
            "\n",
            "    accuracy                           0.74       791\n",
            "   macro avg       0.73      0.72      0.73       791\n",
            "weighted avg       0.74      0.74      0.74       791\n",
            "\n",
            "train-Accuracy: 0.7493917274939172\n",
            "val-Accuracy: 0.32429614181439\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.73      0.77      0.75       472\n",
            "           1       0.63      0.59      0.61       319\n",
            "\n",
            "    accuracy                           0.70       791\n",
            "   macro avg       0.68      0.68      0.68       791\n",
            "weighted avg       0.69      0.70      0.69       791\n",
            "\n",
            "average accuracy =  71 %\n"
          ]
        }
      ],
      "source": [
        "# No preprocessing- Balancing - No Anonymisation (3)\n",
        "aver_acc = 0\n",
        "for i in range(4):\n",
        "    train = balanced_data[i][0]\n",
        "    val = balanced_data[i][1]\n",
        "\n",
        "    prepare_data(train, val)\n",
        "\n",
        "    if i == 0:\n",
        "        model3_1 = train_model()\n",
        "        aver_acc = aver_acc + predict(model3_1)\n",
        "    if i == 1:\n",
        "        model3_2 = train_model()\n",
        "        aver_acc = aver_acc + predict(model3_2)\n",
        "    if i == 2:\n",
        "        model3_3 = train_model()\n",
        "        aver_acc = aver_acc + predict(model3_3)\n",
        "    if i == 3:\n",
        "        model3_4 = train_model()\n",
        "        aver_acc = aver_acc + predict(model3_4)\n",
        "        \n",
        "print(\"average accuracy = \",round(aver_acc/4),\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qexIN0Nmu6kZ",
        "outputId": "fd8635b7-597d-458d-cd94-1927c1cc7444"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "train-Accuracy: 0.7441779631560653\n",
            "val-Accuracy: 0.3461939520333681\n"
          ]
        },
        {
          "data": {
            "image/png": 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AsAQrV/bM2QMAEOKo7AEAlmCYbOMHc2VPsgcAWIIhyTDMHR+saOMDABDiqOwBAJbglE027qAHAEDoYjU+AAAIWSR7AIAlnLipjpnNG3l5eerRo4diY2OVlJSkYcOGac+ePW5jxo4dK5vN5rb16tXLbUxtba0mTpyoxMRENW3aVDfeeKMOHDjgVSwkewCAJRiG+c0b69ev1z333KNNmzYpPz9fDQ0NysnJUVVVldu466+/XsXFxa5t9erVbu/n5uZqxYoVWrZsmTZs2KDKykoNHjxYDofD41iYswcAwA/WrFnj9nrhwoVKSkpSQUGBrr76atd+u92ulJSUU57jyJEjevHFF7VkyRINGDBAkvTSSy8pPT1d77//vgYOHOhRLFT2AABLOLFAz8wmSRUVFW5bbW2tR59/5MgRSVJCQoLb/nXr1ikpKUnt27fX+PHjVVpa6nqvoKBA9fX1ysnJce1LS0tTVlaWNm7c6PF3J9kDACzBV8k+PT1d8fHxri0vL8+DzzY0adIkXXnllcrKynLtHzRokJYuXaoPP/xQTz75pD777DNde+21rl8gSkpKFBUVpRYtWridLzk5WSUlJR5/d9r4AABLcBo22Xzw1LuioiLFxcW59tvt9rMee++992rbtm3asGGD2/6RI0e6/pyVlaXu3bsrIyNDb7/9toYPH37a8xmGIZvN8+9CZQ8AgBfi4uLctrMl+4kTJ+qNN97Q2rVr1bp16zOOTU1NVUZGhr7++mtJUkpKiurq6lReXu42rrS0VMnJyR7HTLIHAFhCY6/GNwxD9957r15//XV9+OGHyszMPOsxP/74o4qKipSamipJys7OVmRkpPLz811jiouLtWPHDvXp08fjWGjjAwAs4XjCNnMHPe/G33PPPXr55Ze1atUqxcbGuubY4+PjFRMTo8rKSk2bNk2/+c1vlJqaqn379unhhx9WYmKibrrpJtfYcePGafLkyWrZsqUSEhI0ZcoUdenSxbU63xMkewAA/GD+/PmSpL59+7rtX7hwocaOHavw8HBt375df//733X48GGlpqaqX79+Wr58uWJjY13j58yZo4iICI0YMULV1dXq37+/Fi1apPDwcI9jIdkDACyhse+Nb5ylFRATE6N33333rOeJjo7W3LlzNXfuXK8+/+dI9gAASzBk7pn0PM8eAACct6jsAQCWYOVH3JLsAQDWYOE+PskeAGANJit7BXFlz5w9AAAhjsoeAGAJ53IXvF8eH6xI9gAAS7DyAj3a+AAAhDgqewCANRg2c4vsgriyJ9kDACzBynP2tPEBAAhxVPYAAGvgpjoAAIQ2K6/G9yjZP/PMMx6f8L777jvnYAAAgO95lOznzJnj0clsNhvJHgBw/griVrwZHiX7wsJCf8cBAIBfWbmNf86r8evq6rRnzx41NDT4Mh4AAPzD8MEWpLxO9seOHdO4cePUpEkTXXLJJfruu+8kHZ+rf+KJJ3weIAAAMMfrZP/QQw/piy++0Lp16xQdHe3aP2DAAC1fvtynwQEA4Ds2H2zByetL71auXKnly5erV69estn+/cU7d+6sb775xqfBAQDgMxa+zt7ryr6srExJSUkn7a+qqnJL/gAA4PzgdbLv0aOH3n77bdfrEwn++eefV+/evX0XGQAAvmThBXpet/Hz8vJ0/fXXa9euXWpoaNDTTz+tnTt36tNPP9X69ev9ESMAAOZZ+Kl3Xlf2ffr00SeffKJjx47p4osv1nvvvafk5GR9+umnys7O9keMAADAhHO6N36XLl20ePFiX8cCAIDfWPkRt+eU7B0Oh1asWKHdu3fLZrOpU6dOGjp0qCIieK4OAOA8ZeHV+F5n5x07dmjo0KEqKSlRhw4dJElfffWVWrVqpTfeeENdunTxeZAAAODceT1nf8cdd+iSSy7RgQMH9Pnnn+vzzz9XUVGRLr30Ut15553+iBEAAPNOLNAzswUpryv7L774Qps3b1aLFi1c+1q0aKHp06erR48ePg0OAABfsRnHNzPHByuvK/sOHTrohx9+OGl/aWmp2rZt65OgAADwOQtfZ+9Rsq+oqHBtM2bM0H333adXX31VBw4c0IEDB/Tqq68qNzdXM2fO9He8AADASx618Zs3b+52K1zDMDRixAjXPuNf1yMMGTJEDofDD2ECAGCShW+q41GyX7t2rb/jAADAv7j07syuueYaf8cBAAD85JzvgnPs2DF99913qqurc9t/6aWXmg4KAACfo7L3XFlZmW677Ta98847p3yfOXsAwHnJwsne60vvcnNzVV5erk2bNikmJkZr1qzR4sWL1a5dO73xxhv+iBEAAJjgdWX/4YcfatWqVerRo4fCwsKUkZGh6667TnFxccrLy9Ovf/1rf8QJAIA5Fl6N73VlX1VVpaSkJElSQkKCysrKJB1/Et7nn3/u2+gAAPCRE3fQM7MFq3O6g96ePXskSZdddpkWLFig77//Xs8995xSU1N9HiAAADDH6zZ+bm6uiouLJUmPPfaYBg4cqKVLlyoqKkqLFi3ydXwAAPiGhRfoeZ3sb7nlFtefu3Xrpn379unLL7/UhRdeqMTERJ8GBwAAzDvn6+xPaNKkiS6//HJfxAIAgN/YZPKpdz6LpPF5lOwnTZrk8Qlnz559zsEAAADf8yjZb9myxaOT/fxhOY3ppvZdFGGLDMhnA/727axmgQ4B8BtnTYRU0EgfZuFL73gQDgDAGiy8QM/rS+8AAEBwMb1ADwCAoGDhyp5kDwCwBLN3wbPUHfQAAEBwobIHAFiDhdv451TZL1myRFdccYXS0tK0f/9+SdJTTz2lVatW+TQ4AAB8xvDBFqS8Tvbz58/XpEmTdMMNN+jw4cNyOBySpObNm+upp57ydXwAAMAkr5P93Llz9fzzz2vq1KkKDw937e/evbu2b9/u0+AAAPAVKz/i1us5+8LCQnXr1u2k/Xa7XVVVVT4JCgAAn7PwHfS8ruwzMzO1devWk/a/88476ty5sy9iAgDA9yw8Z+91Zf+HP/xB99xzj2pqamQYhv75z3/q//7v/5SXl6cXXnjBHzECAAATvE72t912mxoaGvTAAw/o2LFjGj16tC644AI9/fTTGjVqlD9iBADANCvfVOecrrMfP368xo8fr0OHDsnpdCopKcnXcQEA4FsWvs7e1E11EhMTfRUHAADwE6+TfWZm5hmfW//tt9+aCggAAL8we/mclSr73Nxct9f19fXasmWL1qxZoz/84Q++igsAAN+ije+5+++//5T7//KXv2jz5s2mAwIAAL7ls6feDRo0SK+99pqvTgcAgG9xnb15r776qhISEnx1OgAAfIpL77zQrVs3twV6hmGopKREZWVlevbZZ30aHAAAMM/rZD9s2DC312FhYWrVqpX69u2rjh07+iouAADgI14l+4aGBl100UUaOHCgUlJS/BUTAAC+Z+HV+F4t0IuIiNDdd9+t2tpaf8UDAIBfWPkRt16vxu/Zs6e2bNnij1gAAIAfeJ3sJ0yYoMmTJ2vevHn69NNPtW3bNrcNAIDzViNedpeXl6cePXooNjZWSUlJGjZsmPbs2eMejmFo2rRpSktLU0xMjPr27audO3e6jamtrdXEiROVmJiopk2b6sYbb9SBAwe8isXjZH/77beroqJCI0eOVGFhoe677z5dccUVuuyyy9StWzfXfwEAOC818nX269ev1z333KNNmzYpPz9fDQ0NysnJUVVVlWvMrFmzNHv2bM2bN0+fffaZUlJSdN111+no0aOuMbm5uVqxYoWWLVumDRs2qLKyUoMHD5bD4fA4FpthGB6FHx4eruLiYlVXV59xXEZGhscfblZFRYXi4+PVV0MVYYtstM8FGtO3s3oHOgTAb5w1Ndr3yFQdOXJEcXFxfvmME7mi7YMzFG6PPufzOGprtHfmwyoqKnKL1W63y263n/X4srIyJSUlaf369br66qtlGIbS0tKUm5urBx98UNLxKj45OVkzZ87U73//ex05ckStWrXSkiVLNHLkSEnSwYMHlZ6ertWrV2vgwIEexe5xZX/id4KMjIwzbgAAnI98tUAvPT1d8fHxri0vL8+jzz9y5IgkuW5AV1hYqJKSEuXk5LjG2O12XXPNNdq4caMkqaCgQPX19W5j0tLSlJWV5RrjCa8uvTvT0+4AADiv+ejSu1NV9mc91DA0adIkXXnllcrKypIklZSUSJKSk5PdxiYnJ2v//v2uMVFRUWrRosVJY04c7wmvkn379u3PmvB/+uknb04JAEBQiYuL83rK4d5779W2bdu0YcOGk977ZV41DOOsudaTMT/nVbJ//PHHFR8f780hAACcFwJ1b/yJEyfqjTfe0EcffaTWrVu79p+4OV1JSYlSU1Nd+0tLS13VfkpKiurq6lReXu5W3ZeWlqpPnz4ex+BVsh81apSSkpK8OQQAgPNDI99BzzAMTZw4UStWrNC6deuUmZnp9n5mZqZSUlKUn5/vupqtrq5O69ev18yZMyVJ2dnZioyMVH5+vkaMGCFJKi4u1o4dOzRr1iyPY/E42TNfDwCA5+655x69/PLLWrVqlWJjY11z7PHx8YqJiZHNZlNubq5mzJihdu3aqV27dpoxY4aaNGmi0aNHu8aOGzdOkydPVsuWLZWQkKApU6aoS5cuGjBggMexeJzsPbxCDwCA81MjV/bz58+XJPXt29dt/8KFCzV27FhJ0gMPPKDq6mpNmDBB5eXl6tmzp9577z3Fxsa6xs+ZM0cREREaMWKEqqur1b9/fy1atEjh4eEex+Jxsnc6nR6fFACA801jz9l7UiTbbDZNmzZN06ZNO+2Y6OhozZ07V3PnzvUugJ/x+hG3AAAEJZ56BwAAQhWVPQDAGixc2ZPsAQCWEKjr7M8HtPEBAAhxVPYAAGugjQ8AQGijjQ8AAEIWlT0AwBpo4wMAEOIsnOxp4wMAEOKo7AEAlmD712bm+GBFsgcAWIOF2/gkewCAJXDpHQAACFlU9gAAa6CNDwCABQRxwjaDNj4AACGOyh4AYAlWXqBHsgcAWIOF5+xp4wMAEOKo7AEAlkAbHwCAUEcbHwAAhCoqewCAJdDGBwAg1Fm4jU+yBwBYg4WTPXP2AACEOCp7AIAlMGcPAECoo40PAABCFZU9AMASbIYhm3Hu5bmZYwONZA8AsAba+AAAIFRR2QMALIHV+AAAhDra+AAAIFRR2QMALIE2PgAAoc7CbXySPQDAEqxc2TNnDwBAiKOyBwBYA218AABCXzC34s2gjQ8AQIijsgcAWINhHN/MHB+kSPYAAEtgNT4AAAhZVPYAAGtgNT4AAKHN5jy+mTk+WNHGBwAgxFHZQ1k9K3XzhDK163JMLVMaNO32i/TpmvifjTD0u8k/6IZbflSzeIe+3NJEf3m4tfZ/Fe0aMevVverap8rtvOtWNVfe3RmN9C2AU/v9JZ8r58JCtYk7rFpHuD4vS9H/bumlwormrjE56d9qVLtduiThkBKia3Tj2/9Pu8sTT3NGQy/0W61rLijS3esG6v0DmY3yPeADFm7jU9lD0U2c+nZntP4y9YJTvj/injINv7NMf5l6gSbe0E7lZZHKW/aNYpo63MatfilBo7p2dm1PP9C6McIHzuhXycVauucS3bzmJo19f7AibE4tvPYtxYTXu8bERDTo87IU/Xlrz7Oeb2zHbcH8M9/STqzGN7MFq4Am+48++khDhgxRWlqabDabVq5cGchwLGvz2jgtnpWqT95pfop3DQ27o0zLnknWJ+801/49Mfrz/emyxzjV76bDbiNrq8NUXhbp2o4dDW+M8IEzGvfhr/X6tx2190iCvjycqP/6tJ8uaFaprJZlrjGrCttr3vbu2lh86l94T+jY/JBu77RND33az99hwx9OXGdvZgtSAU32VVVV6tq1q+bNmxfIMHAGKRfWqWVygwrWN3Ptq68L0/ZNzdS5u3vbvt/wcr2yY4f+uvZLjX/04EmVP3A+aBZZJ0k6XBt9lpHuosPrNefKD/T4Z1fqUE0Tf4QG+E1A5+wHDRqkQYMGeTy+trZWtbW1rtcVFRX+CAs/k5DUIEkqL4t0219eFqGk1nWu12tfb6GSoij9VBqhizrW6PaHStSmc7UeGnVxo8YLnJmhh7tv1GelKfr6SIJXR07tvlGfH0rWB8zRBy0r31QnqBbo5eXl6fHHHw90GNb0i3/kNpskw+Z6/c7LLV1/3r8nRt9/a9df3v1abbsc097tVEE4PzzWY4M6NP9Rv31vmFfHXdt6n3olf6+hq2/2T2BoHCzQCw4PPfSQjhw54tqKiooCHVLI+6n0+O+DLZLq3fY3T2xQednpf1fcuz1G9XU2XZBZe9oxQGN6pPsG9W+9T7fm36iSY83OfsDP9E7+XhfGVqhgxN+0e/QC7R69QJI07+r39NJ1q/wRLuBTQVXZ2+122e32QIdhKSXfRenHHyJ0+dWV+mbH8Qo9ItKpLr0q9eL0tNMel9GhRpFRhn78IfK0Y4DGYejRHht0XXqhfpd/ow5UxXl9hgU7u+mVvZ3c9q0e8opmFPTRhwe4vDRY0MaHpUU3cSgt89/z7ynpdWpzSbWOHg5X2fdRWvlCK42a+IO+/9au7wuj9Nv7SlVbHaa1K5pLklIzanXt8HL984M4VfwUoQvb1+jOxw7q6+0x2vVZ0wB9K+C4aT0+1pDMvbp73fWqqo9SYvQxSdLR+ijVOo7/CIyPqlFa00olxRxfdJoZd1iSVFbdRIdq/r390sGqZuf0ywMChKfewcrad63W/772jev1XY8flCS9t7yFnvzPC/XKX1opKtqpe/MOKPZfN9V56LdtVF11/NK6hnqbLruyUsPGHVJ0U6cOHYzUPz6I09LZyXI6baf8TKCx3NJhlyRpac4bbvsf3NhXr3/bUZLUv/U+zeyzzvXe01e9L0l6Zlu25m7r0TiBAn4U0GRfWVmpvXv3ul4XFhZq69atSkhI0IUXXhjAyKxl26fNNDCt6xlG2PTSkyl66cmUU75bdjBKf/hNW/8EB5jU7qW7zjrm9W87uhK/L8+L8wtt/ADZvHmz+vX7980pJk2aJEkaM2aMFi1aFKCoAAAhycKr8QOa7Pv27SsjiOdAAAAIBszZAwAsgTY+AAChzmkc38wcH6RI9gAAa7DwnH1Q3UEPAAB4j8oeAGAJNpmcs/dZJI2PZA8AsAYL30GPNj4AACGOyh4AYAlWvvSOyh4AYA2GDzYvfPTRRxoyZIjS0tJks9m0cuVKt/fHjh0rm83mtvXq1cttTG1trSZOnKjExEQ1bdpUN954ow4cOODlFyfZAwDgF1VVVeratavmzZt32jHXX3+9iouLXdvq1avd3s/NzdWKFSu0bNkybdiwQZWVlRo8eLAcDodXsdDGBwBYgs0wZDOxyO7EsRUVFW777Xa77Hb7SeMHDRqkQYMGnfGcdrtdKSmnfsjYkSNH9OKLL2rJkiUaMGCAJOmll15Senq63n//fQ0cONDj2KnsAQDW4PTBJik9PV3x8fGuLS8v75xDWrdunZKSktS+fXuNHz9epaWlrvcKCgpUX1+vnJwc1760tDRlZWVp48aNXn0OlT0AAF4oKipSXFyc6/WpqnpPDBo0SDfffLMyMjJUWFioRx55RNdee60KCgpkt9tVUlKiqKgotWjRwu245ORklZSUePVZJHsAgCX4qo0fFxfnluzP1ciRI11/zsrKUvfu3ZWRkaG3335bw4cPP+1xhmHIZvPuFj+08QEA1tDIq/G9lZqaqoyMDH399deSpJSUFNXV1am8vNxtXGlpqZKTk706N8keAGANJ+6gZ2bzox9//FFFRUVKTU2VJGVnZysyMlL5+fmuMcXFxdqxY4f69Onj1blp4wMA4AeVlZXau3ev63VhYaG2bt2qhIQEJSQkaNq0afrNb36j1NRU7du3Tw8//LASExN10003SZLi4+M1btw4TZ48WS1btlRCQoKmTJmiLl26uFbne4pkDwCwhMa+g97mzZvVr18/1+tJkyZJksaMGaP58+dr+/bt+vvf/67Dhw8rNTVV/fr10/LlyxUbG+s6Zs6cOYqIiNCIESNUXV2t/v37a9GiRQoPD/cqFpI9AMAaGvlBOH379pVxhmPefffds54jOjpac+fO1dy5c7367F9izh4AgBBHZQ8AsASb8/hm5vhgRbIHAFgDz7MHAAChisoeAGANZm+ME7yFPckeAGANvrpdbjCijQ8AQIijsgcAWIOFF+iR7AEA1mDI9Uz6cz4+SJHsAQCWwJw9AAAIWVT2AABrMGRyzt5nkTQ6kj0AwBosvECPNj4AACGOyh4AYA1OSTaTxwcpkj0AwBJYjQ8AAEIWlT0AwBosvECPZA8AsAYLJ3va+AAAhDgqewCANVi4sifZAwCsgUvvAAAIbVx6BwAAQhaVPQDAGpizBwAgxDkNyWYiYTuDN9nTxgcAIMRR2QMArIE2PgAAoc5kslfwJnva+AAAhDgqewCANdDGBwAgxDkNmWrFsxofAACcr6jsAQDWYDiPb2aOD1IkewCANTBnDwBAiGPOHgAAhCoqewCANdDGBwAgxBkymex9Fkmjo40PAECIo7IHAFgDbXwAAEKc0ynJxLXyzuC9zp42PgAAIY7KHgBgDbTxAQAIcRZO9rTxAQAIcVT2AABrsPDtckn2AABLMAynDBNPrjNzbKCR7AEA1mAY5qpz5uwBAMD5isoeAGANhsk5+yCu7En2AABrcDolm4l59yCes6eNDwBAiKOyBwBYA218AABCm+F0yjDRxg/mS+9o4wMAEOKo7AEA1kAbHwCAEOc0JJs1kz1tfAAAQhyVPQDAGgxDkpnr7IO3sifZAwAswXAaMky08Q2SPQAA5znDKXOVPZfeAQCA8xSVPQDAEmjjAwAQ6izcxg/qZH/it6wG1Zu6TwJwPnPW1AQ6BMBvTvz7boyq2WyuaFC974JpZDYjiPsSBw4cUHp6eqDDAACYVFRUpNatW/vl3DU1NcrMzFRJSYnpc6WkpKiwsFDR0dE+iKzxBHWydzqdOnjwoGJjY2Wz2QIdjiVUVFQoPT1dRUVFiouLC3Q4gE/x77vxGYaho0ePKi0tTWFh/lszXlNTo7q6OtPniYqKCrpELwV5Gz8sLMxvvwnizOLi4vhhiJDFv+/GFR8f7/fPiI6ODsok7StcegcAQIgj2QMAEOJI9vCK3W7XY489JrvdHuhQAJ/j3zdCVVAv0AMAAGdHZQ8AQIgj2QMAEOJI9gAAhDiSPQAAIY5kD489++yzyszMVHR0tLKzs/Xxxx8HOiTAJz766CMNGTJEaWlpstlsWrlyZaBDAnyKZA+PLF++XLm5uZo6daq2bNmiq666SoMGDdJ3330X6NAA06qqqtS1a1fNmzcv0KEAfsGld/BIz549dfnll2v+/PmufZ06ddKwYcOUl5cXwMgA37LZbFqxYoWGDRsW6FAAn6Gyx1nV1dWpoKBAOTk5bvtzcnK0cePGAEUFAPAUyR5ndejQITkcDiUnJ7vtT05O9skjIwEA/kWyh8d++RhhwzB4tDAABAGSPc4qMTFR4eHhJ1XxpaWlJ1X7AIDzD8keZxUVFaXs7Gzl5+e77c/Pz1efPn0CFBUAwFMRgQ4AwWHSpEm69dZb1b17d/Xu3Vt//etf9d133+muu+4KdGiAaZWVldq7d6/rdWFhobZu3aqEhARdeOGFAYwM8A0uvYPHnn32Wc2aNUvFxcXKysrSnDlzdPXVVwc6LMC0devWqV+/fiftHzNmjBYtWtT4AQE+RrIHACDEMWcPAECII9kDABDiSPYAAIQ4kj0AACGOZA8AQIgj2QMAEOJI9gAAhDiSPQAAIY5kD5g0bdo0XXbZZa7XY8eO1bBhwxo9jn379slms2nr1q2nHXPRRRfpqaee8vicixYtUvPmzU3HZrPZtHLlStPnAXBuSPYISWPHjpXNZpPNZlNkZKTatGmjKVOmqKqqyu+f/fTTT3t8i1VPEjQAmMWDcBCyrr/+ei1cuFD19fX6+OOPdccdd6iqqkrz588/aWx9fb0iIyN98rnx8fE+OQ8A+AqVPUKW3W5XSkqK0tPTNXr0aN1yyy2uVvKJ1vvf/vY3tWnTRna7XYZh6MiRI7rzzjuVlJSkuLg4XXvttfriiy/czvvEE08oOTlZsbGxGjdunGpqatze/2Ub3+l0aubMmWrbtq3sdrsuvPBCTZ8+XZKUmZkpSerWrZtsNpv69u3rOm7hwoXq1KmToqOj1bFjRz377LNun/PPf/5T3bp1U3R0tLp3764tW7Z4/Xc0e/ZsdenSRU2bNlV6eromTJigysrKk8atXLlS7du3V3R0tK677joVFRW5vf/mm28qOztb0dHRatOmjR5//HE1NDR4HQ8A/yDZwzJiYmJUX1/ver1371698soreu2111xt9F//+tcqKSnR6tWrVVBQoMsvv1z9+/fXTz/9JEl65ZVX9Nhjj2n69OnavHmzUlNTT0rCv/TQQw9p5syZeuSRR7Rr1y69/PLLSk5OlnQ8YUvS+++/r+LiYr3++uuSpOeff15Tp07V9OnTtXv3bs2YMUOPPPKIFi9eLEmqqqrS4MGD1aFDBxUUFGjatGmaMmWK138nYWFheuaZZ7Rjxw4tXrxYH374oR544AG3MceOHdP06dO1ePFiffLJJ6qoqNCoUaNc77/77rv63e9+p/vuu0+7du3SggULtGjRItcvNADOAwYQgsaMGWMMHTrU9fof//iH0bJlS2PEiBGGYRjGY489ZkRGRhqlpaWuMR988IERFxdn1NTUuJ3r4osvNhYsWGAYhmH07t3buOuuu9ze79mzp9G1a9dTfnZFRYVht9uN559//pRxFhYWGpKMLVu2uO1PT083Xn75Zbd9f/zjH43evXsbhmEYCxYsMBISEoyqqirX+/Pnzz/luX4uIyPDmDNnzmnff+WVV4yWLVu6Xi9cuNCQZGzatMm1b/fu3YYk4x//+IdhGIZx1VVXGTNmzHA7z5IlS4zU1FTXa0nGihUrTvu5APyLOXuErLfeekvNmjVTQ0OD6uvrNXToUM2dO9f1fkZGhlq1auV6XVBQoMrKSrVs2dLtPNXV1frmm28kSbt379Zdd93l9n7v3r21du3aU8awe/du1dbWqn///h7HXVZWpqKiIo0bN07jx4937W9oaHCtB9i9e7e6du2qJk2auMXhrbVr12rGjBnatWuXKioq1NDQoJqaGlVVValp06aSpIiICHXv3t11TMeOHdW8eXPt3r1bv/rVr1RQUKDPPvvMrZJ3OByqqanRsWPH3GIEEBgke4Ssfv36af78+YqMjFRaWtpJC/BOJLMTnE6nUlNTtW7dupPOda6Xn8XExHh9jNPplHS8ld+zZ0+398LDwyVJhmGcUzw/t3//ft1www2666679Mc//lEJCQnasGGDxo0b5zbdIR2/dO6XTuxzOp16/PHHNXz48JPGREdHm44TgHkke4Sspk2bqm3bth6Pv/zyy1VSUqKIiAhddNFFpxzTqVMnbdq0Sf/xH//h2rdp06bTnrNdu3aKiYnRBx98oDvuuOOk96OioiQdr4RPSE5O1gUXXKBvv/1Wt9xyyynP27lzZy1ZskTV1dWuXyjOFMepbN68WQ0NDXryyScVFnZ8+c4rr7xy0riGhgZt3rxZv/rVryRJe/bs0eHDh9WxY0dJx//e9uzZ49XfNYDGRbIH/mXAgAHq3bu3hg0bppkzZ6pDhw46ePCgVq9erWHDhql79+66//77NWbMGHXv3l1XXnmlli5dqp07d6pNmzanPGd0dLQefPBBPfDAA4qKitIVV1yhsrIy7dy5U+PGjVNSUpJiYmK0Zs0atW7dWtHR0YqPj9e0adN03333KS4uToMGDVJtba02b96s8vJyTZo0SaNHj9bUqVM1btw4/fd//7f27dunP//5z15934svvlgNDQ2aO3euhgwZok8++UTPPffcSeMiIyM1ceJEPfPMM4qMjNS9996rXr16uZL/o48+qsGDBys9PV0333yzwsLCtG3bNm3fvl1/+tOfvP8fAcDnWI0P/IvNZtPq1at19dVX6/bbb1f79u01atQo7du3z7V6fuTIkXr00Uf14IMPKjs7W/v379fdd999xvM+8sgjmjx5sh599FF16tRJI0eOVGlpqaTj8+HPPPOMFixYoLS0NA0dOlSSdMcdd+iFF17QokWL1KVLF11zzTVatGiR61K9Zs2a6c0339SuXbvUrVs3TZ06VTNnzvTq+1522WWaPXu2Zs6cqaysLC1dulR5eXknjWvSpIkefPBBjR49Wr1791ZMTIyWLVvmen/gwIF66623lJ+frx49eqhXr16aPXu2MjIyvIoHgP/YDF9M/gEAgPMWlT0AACGOZA8AQIgj2QMAEOJI9gAAhDiSPQAAIY5kDwBAiCPZAwAQ4kj2AACEOJI9AAAhjmQPAECII9kDABDi/n8zVZ/57/nooAAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.76      0.70      0.73       472\n",
            "           1       0.60      0.67      0.64       319\n",
            "\n",
            "    accuracy                           0.69       791\n",
            "   macro avg       0.68      0.69      0.68       791\n",
            "weighted avg       0.70      0.69      0.69       791\n",
            "\n",
            "train-Accuracy: 0.7372262773722628\n",
            "val-Accuracy: 0.35766423357664234\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.71      0.80      0.75       472\n",
            "           1       0.64      0.51      0.57       319\n",
            "\n",
            "    accuracy                           0.69       791\n",
            "   macro avg       0.67      0.66      0.66       791\n",
            "weighted avg       0.68      0.69      0.68       791\n",
            "\n",
            "train-Accuracy: 0.748696558915537\n",
            "val-Accuracy: 0.3347236704900938\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.76      0.78      0.77       472\n",
            "           1       0.66      0.63      0.64       319\n",
            "\n",
            "    accuracy                           0.72       791\n",
            "   macro avg       0.71      0.70      0.71       791\n",
            "weighted avg       0.72      0.72      0.72       791\n",
            "\n",
            "train-Accuracy: 0.7511296489398679\n",
            "val-Accuracy: 0.32116788321167883\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.74      0.72      0.73       472\n",
            "           1       0.60      0.63      0.61       319\n",
            "\n",
            "    accuracy                           0.68       791\n",
            "   macro avg       0.67      0.67      0.67       791\n",
            "weighted avg       0.68      0.68      0.68       791\n",
            "\n",
            "average accuracy =  70 %\n"
          ]
        }
      ],
      "source": [
        "# No preprocessing- Balancing -  Anonymisation (4)\n",
        "aver_acc = 0\n",
        "for i in range(4):\n",
        "    train = balanced_data[i][0]\n",
        "    val = balanced_data[i][1]\n",
        "\n",
        "    train = Anonymisation(train)\n",
        "    val = Anonymisation(val)\n",
        "\n",
        "    prepare_data(train, val)\n",
        "\n",
        "    if i == 0:\n",
        "        model4_1 = train_model()\n",
        "        aver_acc = aver_acc + predict(model4_1)\n",
        "    if i == 1:\n",
        "        model4_2 = train_model()\n",
        "        aver_acc = aver_acc + predict(model4_2)\n",
        "    if i == 2:\n",
        "        model4_3 = train_model()\n",
        "        aver_acc = aver_acc + predict(model4_3)\n",
        "    if i == 3:\n",
        "        model4_4 = train_model()\n",
        "        aver_acc = aver_acc + predict(model4_4)\n",
        "        \n",
        "print(\"average accuracy = \",round(aver_acc/4),\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wg-_phpWu6ka"
      },
      "outputs": [],
      "source": [
        "def preprocessing(dff):\n",
        "    processed_data = []\n",
        "    stop_words = set(stopwords.words('english'))\n",
        "    stemmer = PorterStemmer()\n",
        "    \n",
        "    for df in dff:\n",
        "        # Convert all strings to lowercase\n",
        "        df = df.lower()\n",
        "        \n",
        "        # Remove punctuation\n",
        "        df = df.translate(str.maketrans(\"\", \"\", string.punctuation))\n",
        "        \n",
        "        # Tokenization\n",
        "        words = word_tokenize(df)\n",
        "        \n",
        "        # Remove stop words and perform stemming\n",
        "        words = [stemmer.stem(word) for word in words if word not in stop_words]\n",
        "        \n",
        "        # Append preprocessed string to list\n",
        "        df = ' '.join(words)\n",
        "        processed_data.append(df)\n",
        "    \n",
        "    return processed_data\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jnHnmFiIu6ka",
        "outputId": "495c7b46-4a29-4d32-d5e6-a716e2e007e2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3335 1112\n",
            "train-Accuracy: 0.7124437781109445\n",
            "val-Accuracy: 0.31564748201438847\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.60      0.93      0.73       472\n",
            "           1       0.39      0.07      0.12       319\n",
            "\n",
            "    accuracy                           0.58       791\n",
            "   macro avg       0.49      0.50      0.42       791\n",
            "weighted avg       0.51      0.58      0.48       791\n",
            "\n",
            "3335 1112\n",
            "train-Accuracy: 0.7133433283358321\n",
            "val-Accuracy: 0.322841726618705\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.60      0.93      0.73       472\n",
            "           1       0.41      0.08      0.13       319\n",
            "\n",
            "    accuracy                           0.58       791\n",
            "   macro avg       0.51      0.50      0.43       791\n",
            "weighted avg       0.52      0.58      0.49       791\n",
            "\n",
            "3335 1112\n",
            "train-Accuracy: 0.7151424287856072\n",
            "val-Accuracy: 0.32194244604316546\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.59      0.82      0.69       472\n",
            "           1       0.38      0.17      0.23       319\n",
            "\n",
            "    accuracy                           0.56       791\n",
            "   macro avg       0.49      0.49      0.46       791\n",
            "weighted avg       0.51      0.56      0.50       791\n",
            "\n",
            "3336 1111\n",
            "train-Accuracy: 0.7176258992805755\n",
            "val-Accuracy: 0.3114311431143114\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.61      0.86      0.71       472\n",
            "           1       0.45      0.17      0.25       319\n",
            "\n",
            "    accuracy                           0.58       791\n",
            "   macro avg       0.53      0.51      0.48       791\n",
            "weighted avg       0.54      0.58      0.52       791\n",
            "\n",
            "average accuracy =  58 %\n"
          ]
        }
      ],
      "source": [
        "#  preprocessing- No Balancing -  No Anonymisation (5)\n",
        "aver_acc = 0\n",
        "for i in range(4):\n",
        "    train = data[i][0]\n",
        "    val = data[i][1]\n",
        "\n",
        "    train[\"Facts\"] = preprocessing(train[\"Facts\"])\n",
        "    val[\"Facts\"] = preprocessing(val[\"Facts\"])\n",
        "    \n",
        "    prepare_data(train, val)\n",
        "\n",
        "    if i == 0:\n",
        "        model5_1 = train_model()\n",
        "        aver_acc = aver_acc + predict(model5_1)\n",
        "    if i == 1:\n",
        "        model5_2 = train_model()\n",
        "        aver_acc = aver_acc + predict(model5_2)\n",
        "    if i == 2:\n",
        "        model5_3 = train_model()\n",
        "        aver_acc = aver_acc + predict(model5_3)\n",
        "    if i == 3:\n",
        "        model5_4 = train_model()\n",
        "        aver_acc = aver_acc + predict(model5_4)\n",
        "        \n",
        "print(\"average accuracy = \",round(aver_acc/4),\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Th1H7KuKu6ka",
        "outputId": "5eff1b37-106d-4bf1-d77c-32e49c2b5976"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "train-Accuracy: 0.7127436281859071\n",
            "val-Accuracy: 0.31564748201438847\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.60      0.94      0.73       472\n",
            "           1       0.39      0.06      0.10       319\n",
            "\n",
            "    accuracy                           0.58       791\n",
            "   macro avg       0.49      0.50      0.42       791\n",
            "weighted avg       0.51      0.58      0.48       791\n",
            "\n",
            "train-Accuracy: 0.7133433283358321\n",
            "val-Accuracy: 0.32194244604316546\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.60      0.90      0.72       472\n",
            "           1       0.40      0.10      0.16       319\n",
            "\n",
            "    accuracy                           0.58       791\n",
            "   macro avg       0.50      0.50      0.44       791\n",
            "weighted avg       0.52      0.58      0.49       791\n",
            "\n",
            "train-Accuracy: 0.7154422788605698\n",
            "val-Accuracy: 0.322841726618705\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.59      0.83      0.69       472\n",
            "           1       0.36      0.14      0.20       319\n",
            "\n",
            "    accuracy                           0.55       791\n",
            "   macro avg       0.47      0.49      0.45       791\n",
            "weighted avg       0.50      0.55      0.49       791\n",
            "\n",
            "train-Accuracy: 0.7176258992805755\n",
            "val-Accuracy: 0.3096309630963096\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.60      0.88      0.71       472\n",
            "           1       0.44      0.15      0.22       319\n",
            "\n",
            "    accuracy                           0.58       791\n",
            "   macro avg       0.52      0.51      0.47       791\n",
            "weighted avg       0.54      0.58      0.52       791\n",
            "\n",
            "average accuracy =  57 %\n"
          ]
        }
      ],
      "source": [
        "#  preprocessing- No Balancing -   Anonymisation (6)\n",
        "aver_acc = 0\n",
        "for i in range(4):\n",
        "    train = data[i][0]\n",
        "    val = data[i][1]\n",
        "\n",
        "    train[\"Facts\"] = preprocessing(train[\"Facts\"])\n",
        "    val[\"Facts\"] = preprocessing(val[\"Facts\"])\n",
        "    \n",
        "    train = Anonymisation(train)\n",
        "    val = Anonymisation(val)\n",
        "    \n",
        "    prepare_data(train, val)\n",
        "\n",
        "    if i == 0:\n",
        "        model6_1 = train_model()\n",
        "        aver_acc = aver_acc + predict(model6_1)\n",
        "    if i == 1:\n",
        "        model6_2 = train_model()\n",
        "        aver_acc = aver_acc + predict(model6_2)\n",
        "    if i == 2:\n",
        "        model6_3 = train_model()\n",
        "        aver_acc = aver_acc + predict(model6_3)\n",
        "    if i == 3:\n",
        "        model6_4 = train_model()\n",
        "        aver_acc = aver_acc + predict(model6_4)\n",
        "        \n",
        "print(\"average accuracy = \",round(aver_acc/4),\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CGRpVqSIu6kb",
        "outputId": "20de7ee1-f123-4441-f372-eb2a31304608"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "train-Accuracy: 0.7455683003128258\n",
            "val-Accuracy: 0.34306569343065696\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.59      0.53      0.56       472\n",
            "           1       0.40      0.46      0.43       319\n",
            "\n",
            "    accuracy                           0.50       791\n",
            "   macro avg       0.50      0.50      0.49       791\n",
            "weighted avg       0.51      0.50      0.51       791\n",
            "\n",
            "train-Accuracy: 0.737921445950643\n",
            "val-Accuracy: 0.3524504692387904\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.58      0.65      0.61       472\n",
            "           1       0.37      0.30      0.33       319\n",
            "\n",
            "    accuracy                           0.51       791\n",
            "   macro avg       0.47      0.47      0.47       791\n",
            "weighted avg       0.49      0.51      0.50       791\n",
            "\n",
            "train-Accuracy: 0.7504344803614876\n",
            "val-Accuracy: 0.3232533889468196\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.57      0.35      0.43       472\n",
            "           1       0.39      0.61      0.47       319\n",
            "\n",
            "    accuracy                           0.45       791\n",
            "   macro avg       0.48      0.48      0.45       791\n",
            "weighted avg       0.50      0.45      0.45       791\n",
            "\n",
            "train-Accuracy: 0.7518248175182481\n",
            "val-Accuracy: 0.3149113660062565\n"
          ]
        },
        {
          "data": {
            "image/png": 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vg/NBRESE0xYcHHzG9unp6Vq1apXWrl2rtm3b1jtfW1urMWPG6MCBA8rNzXVU9ZIUHx+vmpoalZSUOPUpLi5WXFycyzE3abKfNWuW0tLSNGHCBHXv3l2zZ89WYmKi5s2b15RhmVr18ZPVeoj19OWKrcaibUujFRxuU1z3yp+8TtVxfwW3tMuPiSKcZyrKTv7Gw1v99zdut0tPTG6nX/2uWB26VtXrs3dHC9XV+inlmuOOY9HxdWrfrUq7/h3W8EHDK049Qc+TzR2GYWjSpElatmyZPvjgA3Xs2LFem1OJfu/evVqzZo2io6OdzqekpCgwMNBpIV9hYaF27typfv36uRxLk/1TXFNTo7y8PP3hD39wOp6amurWogN4j2FIa2ZeoMRe5Yr90T94e9+P0PJ726u20k8tY2t18z/3qUXU6f8gOFHir41z4tXz1982RtiAywxDemHGBbrkinJ16Pbf3/irz8bK39/Q6LTT/2aPFQcoMMju9AeCJEXG1KrkKH/RNhfemrN31cSJE7VkyRKtXLlS4eHhjjl2q9Wq0NBQ1dXV6Ve/+pW2bt2qN998UzabzdEmKipKQUFBslqtSktL05QpUxQdHa2oqChNnTpVycnJjtX5rmiyX+m3334rm81WbxjiTIsOqqurnVY9lpWVNWiMZvPuwxeo+MtQ3fbq3nrn2vct14Q396iyJEDblkZrWXoH3b5sr8JinMfpq4/76ZW0TorpXKWrJ7u+eARoDM/+8QId2B2qv6347298745Qrfh7az377h65MQUqSTIMi9y89RomcmqUeuDAgU7HFyxYoPHjx6ugoECrVq2SJF122WVObdauXevol52drYCAAI0ZM0aVlZUaPHiwcnJy5O/v73IsTf4n6Y8XGJxp0UFWVpYeeeSRxgjLdN6dcYG+et+q25buU0Sb+quLg1rYFdWhRupQowt6ntDcQd21/dUoXXVPsaNNdbmfXr79QgW1sOvG5w7IP7AxvwFwZs9Ov0Afv2fV35bvc1pB//knLfX9twH6Te9LHMfsNotefCRBK15srX9+uktRsXWqrfHT8e/9nar7778L0MW9Khr1e+Dc2eXhs/Hd/MvOMM58Y36HDh3O2kaSQkJCNGfOHM2ZM8etz/9fTZbsY2Ji5O/vX6+KP9Oig2nTpikzM9OxX1ZWVu8WCLjHME4m+j3vWXXrkn1qlVh/FfJPsdX8d0ir+rifXh5/ofyDDI15cb8Cgt18+gTQQAzjZKLftNqqJ1/bp/h2zr/xIb88psuvPu507I83d9LgX5YodewxSVLnS08oINCurRvCdc3I7yVJ3x0J0DdfhmjCn5wfbYrzl/E/K+rPtX9z1WTJPigoSCkpKcrNzdX111/vOJ6bm6tRo0adtk9wcPBZVz3CPasfaqsvVkXqxhf2K6ilXeU/zD8Gh9sUGGKo5oSfPno2Tl2GlKplbK1OlAQob3GMygoD1f0X30s6WdEvGXeh6ir9NGrWAVWX+6u6/OT1W0TVyc/1kSbA6575Y1utXR6pGQv2K7SlXceKT/7Gw8JtCg41FBFlU8SP1p8EBEiRsXWOe/HDIuwa+utjeuGRBEVE1im8lU0v/jlBHbpVqeeP/lDA+Yu33jWRzMxM3XrrrerVq5f69u2rF154QYcOHdLdd9/dlGGZytaXYiRJi3/d2en48CcOqcevjsnP39B3XwfrtWUdVFkSoNBWNrW59IRue2WvWnc5ucCpaGcLHd5+ckXy3EEXO11n4oZdatXW9dECwNveXHjyN37fL51/41OyDzkqd1fcPeM/8vc3NPPuDqqp9NNl/Y/rkYX75ca0KdBkLIYrEwYNaO7cuXriiSdUWFiopKQkZWdna8CAAS71LSsrk9Vq1Zs7OiksvMmfDwQ0iAEhTR0B0HDKjtsV2WW/SktLne4v9+pn/JArrs+9XYFhQed8ndqKGi3/+YIGjbWhNPkCvXvuuUf33HNPU4cBAPBxZh7GpxwGAMDHNXllDwBAY7B7uBrfk75NjWQPADAFhvEBAIDPorIHAJiCmSt7kj0AwBTMnOwZxgcAwMdR2QMATMHMlT3JHgBgCoY8u32uOb/ei2QPADAFM1f2zNkDAODjqOwBAKZg5sqeZA8AMAUzJ3uG8QEA8HFU9gAAUzBzZU+yBwCYgmFYZHiQsD3p29QYxgcAwMdR2QMATIH32QMA4OPMPGfPMD4AAD6Oyh4AYApmXqBHsgcAmIKZh/FJ9gAAUzBzZc+cPQAAPo7KHgBgCoaHw/jNubIn2QMATMGQZBie9W+uGMYHAMDHkewBAKZw6gl6nmzuyMrKUu/evRUeHq7Y2FiNHj1ae/bscWqzbNkyDR06VDExMbJYLNq+fXu961RXVys9PV0xMTEKCwvTyJEjVVBQ4FYsJHsAgCmcWo3vyeaO9evXa+LEidq8ebNyc3NVV1en1NRUVVRUONpUVFToqquu0mOPPfaT18nIyNDy5cu1dOlSbdy4UeXl5Ro+fLhsNpvLsTBnDwBAA1i9erXT/oIFCxQbG6u8vDwNGDBAknTrrbdKkg4ePHjaa5SWlmr+/PlatGiRhgwZIklavHixEhMTtWbNGg0dOtSlWKjsAQCmcOqhOp5snigtLZUkRUVFudwnLy9PtbW1Sk1NdRxLSEhQUlKSNm3a5PJ1qOwBAKZgGB6uxv+hb1lZmdPx4OBgBQcHn6WvoczMTPXv319JSUkuf2ZRUZGCgoIUGRnpdDwuLk5FRUUuX4fKHgAANyQmJspqtTq2rKyss/aZNGmSduzYoZdfftkrMRiGIYvF9ZEGKnsAgCl463G5+fn5ioiIcBw/W1Wfnp6uVatWacOGDWrbtq1bnxkfH6+amhqVlJQ4VffFxcXq16+fy9ehsgcAmIK3VuNHREQ4bT+V7A3D0KRJk7Rs2TJ98MEH6tixo9sxp6SkKDAwULm5uY5jhYWF2rlzp1vJnsoeAGAKdsMiSyO+9W7ixIlasmSJVq5cqfDwcMccu9VqVWhoqCTp2LFjOnTokA4fPixJjvvw4+PjFR8fL6vVqrS0NE2ZMkXR0dGKiorS1KlTlZyc7Fid7woqewAAGsC8efNUWlqqgQMHqk2bNo7tlVdecbRZtWqVevbsqeuuu06SdNNNN6lnz5567rnnHG2ys7M1evRojRkzRldddZVatGihN954Q/7+/i7HYjEMT9YmNq2ysjJZrVa9uaOTwsL5uwW+aUBIU0cANJyy43ZFdtmv0tJSp3lwr37GD7miy0t/kH+LM8+vn4ntRLW+uuWxBo21oTCMDwAwhZO33nmyQM+LwTQyymEAAHwclT0AwBS8detdc0SyBwCYgiHP3knfjEfxGcYHAMDXUdkDAEyBYXwAAHydicfxSfYAAHPwsLJXM67smbMHAMDHUdkDAEzBW++zb45I9gAAUzDzAj2G8QEA8HFU9gAAczAsni2ya8aVPckeAGAKZp6zZxgfAAAfR2UPADAHHqpzZk8//bTLF5w8efI5BwMAQEMx82p8l5J9dna2SxezWCwkewAAzjMuJfsDBw40dBwAADS8ZjwU74lzXqBXU1OjPXv2qK6uzpvxAADQIE4N43uyNVduJ/sTJ04oLS1NLVq00CWXXKJDhw5JOjlX/9hjj3k9QAAAvMLwwtZMuZ3sp02bps8++0zr1q1TSEiI4/iQIUP0yiuveDU4AADgObdvvVuxYoVeeeUVXXnllbJY/jukcfHFF+vrr7/2anAAAHiP5YfNk/7Nk9vJ/ujRo4qNja13vKKiwin5AwBwXjHxffZuD+P37t1bb731lmP/VIJ/8cUX1bdvX+9FBgAAvMLtyj4rK0vXXnutdu3apbq6Oj311FP64osv9PHHH2v9+vUNESMAAJ6jsnddv3799NFHH+nEiRO68MIL9d577ykuLk4ff/yxUlJSGiJGAAA8d+qtd55szdQ5PRs/OTlZCxcu9HYsAACgAZxTsrfZbFq+fLl2794ti8Wi7t27a9SoUQoI4L06AIDzk5lfcet2dt65c6dGjRqloqIide3aVZL01VdfqXXr1lq1apWSk5O9HiQAAB5jzt51EyZM0CWXXKKCggJt3bpVW7duVX5+vi699FLdeeedDREjAADwgNuV/WeffaYtW7YoMjLScSwyMlIzZ85U7969vRocAABe4+kiu2a8QM/tyr5r1646cuRIvePFxcW66KKLvBIUAADeZjE835orl5J9WVmZY3v00Uc1efJkvfbaayooKFBBQYFee+01ZWRk6PHHH2/oeAEAODeN/CKcrKws9e7dW+Hh4YqNjdXo0aO1Z88e55AMQzNmzFBCQoJCQ0M1cOBAffHFF05tqqurlZ6erpiYGIWFhWnkyJEqKChwKxaXhvFbtWrl9ChcwzA0ZswYxzHjhyWKI0aMkM1mcysAAAB80fr16zVx4kT17t1bdXV1mj59ulJTU7Vr1y6FhYVJkp544gnNmjVLOTk56tKli/7yl7/o5z//ufbs2aPw8HBJUkZGht544w0tXbpU0dHRmjJlioYPH668vDz5+/u7FItLyX7t2rXn+FUBADhPNPKc/erVq532FyxYoNjYWOXl5WnAgAEyDEOzZ8/W9OnTdcMNN0iSFi5cqLi4OC1ZskR33XWXSktLNX/+fC1atEhDhgyRJC1evFiJiYlas2aNhg4d6lIsLiX7a665xp3vBwDA+cdLt96VlZU5HQ4ODlZwcPBZu5eWlkqSoqKiJEkHDhxQUVGRUlNTna51zTXXaNOmTbrrrruUl5en2tpapzYJCQlKSkrSpk2bvJvsT+fEiRM6dOiQampqnI5feuml53pJAADOe4mJiU77Dz/8sGbMmHHGPoZhKDMzU/3791dSUpIkqaioSJIUFxfn1DYuLk7ffPONo01QUJDTHXCn2pzq74pzesXt7bffrnfeeee055mzBwCcl7xU2efn5ysiIsJx2JWqftKkSdqxY4c2btxY79yPXw9vGMZZXxnvSpv/5fatdxkZGSopKdHmzZsVGhqq1atXa+HChercubNWrVrl7uUAAGgcXlqNHxER4bSdLdmnp6dr1apVWrt2rdq2bes4Hh8fL0n1KvTi4mJHtR8fH6+amhqVlJT8ZBtXuJ3sP/jgA2VnZ6t3797y8/NT+/bt9Zvf/EZPPPGEsrKy3L0cAAA+yTAMTZo0ScuWLdMHH3ygjh07Op3v2LGj4uPjlZub6zhWU1Oj9evXq1+/fpKklJQUBQYGOrUpLCzUzp07HW1c4fYwfkVFhWJjYyWdXGRw9OhRdenSRcnJydq6dau7lwMAoHE08mr8iRMnasmSJVq5cqXCw8MdFbzValVoaKgsFosyMjL06KOPqnPnzurcubMeffRRtWjRQjfffLOjbVpamqZMmaLo6GhFRUVp6tSpSk5OdqzOd4Xbyb5r167as2ePOnTooMsuu0zPP/+8OnTooOeee05t2rRx93IAADQKT5+C527fefPmSZIGDhzodHzBggUaP368JOn+++9XZWWl7rnnHpWUlKhPnz567733HPfYS1J2drYCAgI0ZswYVVZWavDgwcrJyXH5HvuTsRvuvbTvpZdeUm1trcaPH69t27Zp6NCh+u677xQUFKScnByNHTvWnct5pKysTFarVW/u6KSwcLdnJIBmYUBIU0cANJyy43ZFdtmv0tJSp0VvXv2MH3JFuyf+Ir/Qc/9/KHtllQ7d/6cGjbWhuF3Z33LLLY7/u2fPnjp48KC+/PJLtWvXTjExMV4NDgAArzHxK27P+T77U1q0aKHLL7/cG7EAAIAG4FKyz8zMdPmCs2bNOudgAABoKBZ5OGfvtUgan0vJftu2bS5dzJ0b/AEAQOPwiRfhZF8/QgH+Z3+CEdAczdy7v6lDABpMnVErqZF+44186935xOM5ewAAmgUTL9DjfjUAAHwclT0AwBxMXNmT7AEAptDYT9A7nzCMDwCAjzunZL9o0SJdddVVSkhI0DfffCNJmj17tlauXOnV4AAA8BovveK2OXI72c+bN0+ZmZn6xS9+oe+//142m02S1KpVK82ePdvb8QEA4B0ke9fNmTNHL774oqZPn+70xp1evXrp888/92pwAADAc24v0Dtw4IB69uxZ73hwcLAqKiq8EhQAAN7GAj03dOzYUdu3b693/J133tHFF1/sjZgAAPC+U0/Q82Rrptyu7O+77z5NnDhRVVVVMgxDn376qV5++WVlZWXp73//e0PECACA57jP3nW333676urqdP/99+vEiRO6+eabdcEFF+ipp57STTfd1BAxAgAAD5zTQ3XuuOMO3XHHHfr2229lt9sVGxvr7bgAAPAqM8/Ze/QEvZiYGG/FAQBAw2IY33UdO3Y843vr9+/ndZwAAJxP3E72GRkZTvu1tbXatm2bVq9erfvuu89bcQEA4F0eDuObqrK/9957T3v82Wef1ZYtWzwOCACABmHiYXyvvQhn2LBhev311711OQAA4CVee8Xta6+9pqioKG9dDgAA7zJxZe92su/Zs6fTAj3DMFRUVKSjR49q7ty5Xg0OAABv4dY7N4wePdpp38/PT61bt9bAgQPVrVs3b8UFAAC8xK1kX1dXpw4dOmjo0KGKj49vqJgAAIAXubVALyAgQL/73e9UXV3dUPEAANAweJ+96/r06aNt27Y1RCwAADSYU3P2nmzNldtz9vfcc4+mTJmigoICpaSkKCwszOn8pZde6rXgAACA51xO9r/97W81e/ZsjR07VpI0efJkxzmLxSLDMGSxWGSz2bwfJQAA3tCMq3NPuJzsFy5cqMcee0wHDhxoyHgAAGgYJr7P3uU5e8M4+S3bt29/xg0AAEgbNmzQiBEjlJCQIIvFohUrVjidP3LkiMaPH6+EhAS1aNFC1157rfbu3evUprq6Wunp6YqJiVFYWJhGjhypgoICt2Nxa4Hemd52BwDA+ayxF+hVVFSoR48eeuaZZ+qdMwxDo0eP1v79+7Vy5Upt27ZN7du315AhQ1RRUeFol5GRoeXLl2vp0qXauHGjysvLNXz4cLenzN1aoNelS5ezJvxjx465FQAAAI2ikYfxhw0bpmHDhp323N69e7V582bt3LlTl1xyiSRp7ty5io2N1csvv6wJEyaotLRU8+fP16JFizRkyBBJ0uLFi5WYmKg1a9Zo6NChLsfiVrJ/5JFHZLVa3ekCAAB+5NTzakJCQhzH/P39FRQUpI0bN2rChAnKy8tTbW2tUlNTHW0SEhKUlJSkTZs2NVyyv+mmmxQbG+tOFwAAzgveejZ+WVmZ0/Hg4GAFBwe7da1u3bqpffv2mjZtmp5//nmFhYVp1qxZKioqUmFhoSSpqKhIQUFBioyMdOobFxenoqIitz7P5Tl75usBAM2al56gl5iYKKvV6tiysrLcDiUwMFCvv/66vvrqK0VFRalFixZat26dhg0bJn9//zN/jR9udXeHy5X9qdX4AACYWX5+viIiIhz77lb1p6SkpGj79u0qLS1VTU2NWrdurT59+qhXr16SpPj4eNXU1KikpMSpui8uLla/fv3c+iyXK3u73c4QPgCg+fJSZR8REeG0nWuyP8Vqtap169bau3evtmzZolGjRkk6+cdAYGCgcnNzHW0LCwu1c+dOt5O924/LBQCgOWrs99mXl5dr3759jv0DBw5o+/btioqKUrt27fSvf/1LrVu3Vrt27fT555/r3nvv1ejRox0L8qxWq9LS0jRlyhRFR0crKipKU6dOVXJysmN1vqtI9gAAc2jkW++2bNmiQYMGOfYzMzMlSePGjVNOTo4KCwuVmZmpI0eOqE2bNrrtttv04IMPOl0jOztbAQEBGjNmjCorKzV48GDl5OScdV7/xyxGM56MLysrk9Vq1eAL71WAv2fDKMD5yrZ3f1OHADSYOqNW67RSpaWlTvPg3nQqV3TNeFT+wSFn7/ATbNVV2jP7jw0aa0OhsgcAmIOJn41PsgcAmEJjz9mfT9x6Nj4AAGh+qOwBAObAMD4AAL6NYXwAAOCzqOwBAObAMD4AAD7OxMmeYXwAAHwclT0AwBQsP2ye9G+uSPYAAHMw8TA+yR4AYArcegcAAHwWlT0AwBwYxgcAwASaccL2BMP4AAD4OCp7AIApmHmBHskeAGAOJp6zZxgfAAAfR2UPADAFhvEBAPB1DOMDAABfRWUPADAFhvEBAPB1Jh7GJ9kDAMzBxMmeOXsAAHwclT0AwBSYswcAwNcxjA8AAHwVlT0AwBQshiGLce7luSd9mxrJHgBgDgzjAwAAX0VlDwAwBTOvxqeyBwCYg+GFzQ0bNmzQiBEjlJCQIIvFohUrVjidLy8v16RJk9S2bVuFhoaqe/fumjdvnlOb6upqpaenKyYmRmFhYRo5cqQKCgrc/OIkewAAGkRFRYV69OihZ5555rTnf//732v16tVavHixdu/erd///vdKT0/XypUrHW0yMjK0fPlyLV26VBs3blR5ebmGDx8um83mViwM4wMATKGxh/GHDRumYcOG/eT5jz/+WOPGjdPAgQMlSXfeeaeef/55bdmyRaNGjVJpaanmz5+vRYsWaciQIZKkxYsXKzExUWvWrNHQoUNdjoXKHgBgDl4axi8rK3Paqqurzymc/v37a9WqVfrPf/4jwzC0du1affXVV44knpeXp9raWqWmpjr6JCQkKCkpSZs2bXLrs0j2AABTOFXZe7JJUmJioqxWq2PLyso6p3iefvppXXzxxWrbtq2CgoJ07bXXau7cuerfv78kqaioSEFBQYqMjHTqFxcXp6KiIrc+i2F8AADckJ+fr4iICMd+cHDwOV3n6aef1ubNm7Vq1Sq1b99eGzZs0D333KM2bdo4hu1PxzAMWSwWtz6LZA8AMAcvPVQnIiLCKdmfi8rKSv3xj3/U8uXLdd1110mSLr30Um3fvl1//etfNWTIEMXHx6umpkYlJSVO1X1xcbH69evn1ucxjA8AMA1Ph/C9pba2VrW1tfLzc07D/v7+stvtkqSUlBQFBgYqNzfXcb6wsFA7d+50O9lT2QMA0ADKy8u1b98+x/6BAwe0fft2RUVFqV27drrmmmt03333KTQ0VO3bt9f69ev1z3/+U7NmzZIkWa1WpaWlacqUKYqOjlZUVJSmTp2q5OTkMw7znw7JHgBgDoZxcvOkvxu2bNmiQYMGOfYzMzMlSePGjVNOTo6WLl2qadOm6ZZbbtGxY8fUvn17zZw5U3fffbejT3Z2tgICAjRmzBhVVlZq8ODBysnJkb+/v1uxkOwBAKbQ2PfZDxw4UMYZ/kCIj4/XggULzniNkJAQzZkzR3PmzHHvw3+EOXsAAHwclT0AwBxM/Ipbkj0AwBQs9pObJ/2bK4bxAQDwcVT2UNKl3+qXv96ri7p8r+iYKv15eh99vDHBqU1i+zLdftcXSu7xrSx+0qED4cqacYWOFrf40dUM/d8TH6tXnyOnvQ7QFJL6lOvGe46qc/IJRcfXacZvO+jj1VbH+SnZh5Q6tsSpz+68FsoY0dmx/8Rr+9SjX4VTm3UrWynrd+0bNnh4D8P4TWPDhg168sknlZeXp8LCQi1fvlyjR49uypBMKSS0Tgf2WZX7djv96S+f1jsfn1CuJ+ds0Htvd9DiBd11ojxQie2Pq6am/q0fo2/82qM7W4CGENLCrv1fhOi9pZF6aP43p23z7w/C9bffJzr262rrP4707cVR+ueT8Y796ioGR5uTxl6Nfz5p0mR/6l2/t99+u375y182ZSimtuWTeG35JP4nz4+bsEtbPonXP55LchwrKgyr167jhaW6fsw+Zdw1UC8tf6dBYgXOxZa1Edqy9tTjTU+f7GtrLCo5GnjG61RX+p21Dc5jjXyf/fmkSZP92d71i6ZnsRjq3feIXn+5s/785Ee6sPP3OlIYpldf6uI0RB8cXKcHHvq35s2+VCXHQpowYuDcXNq3XK/s+ELlpX76fHNLLXgsXqXfOSf2QTeU6Ge/LNH3RwP077URWvy3OFVWuPdwE6ApNKs5++rqaqf3BpeVlTVhNObQKrJaLVrU6cabv9I/51+sBc9fopQrjmj6nz/RHzKu1s7PYiRJd0z6XLt3RmnzR8zRo/nZsjZcH77ZSkcKAhXfrkbj7i/SE//ar0nXdlZtzcmh+rXLIlWUH6RjxQHq0K1Kv51WpE4XV2raTRc2cfRwFcP4zURWVpYeeeSRpg7DVCw//Lo3f9RGK/51kSRp/75W6p50TL8YdUA7P4tRn36F6nH5UaVP+FlThgqcs/Wr/vtGsW/2hGrvZy30z09364rBZfronVaSpHeWRDu1+c/+YD377l5dlHxC+z7/8UJVnJdMvECvWa0umTZtmkpLSx1bfn5+U4fk88pKg1VXZ9Ghg+FOx/O/CVds7AlJUo/Lj6pNQoX+9eabeuP9FXrj/RWSpD/+3yd6bPaHjR0y4LFjxYEqLgjUBZ1qfrLNvs9DVVtj0QUdq3+yDXC+aFaVfXBwsIKDg5s6DFOpq/PTV19Gqm27cqfjFySWq/jIyWrmX0u66N23Ojidn5fzvl589lJ98tFPL/wDzlfhkXVqnVCrY0d++p/I9l2rFBhk6LsjLNhrLhjGh6mFhNYp4YL/JvO4NifU6aLvdbwsSEeLW+j1pZ31h4c/1eefRWvHttZKueKI+vQt0gMZ/SVJJcdCTrso7+iRUB0pqr9qH2hsIS1sSuj43yo9PrFGnS6p1PHv/XW8xF+3Tj2ijW9ZdexIoOISa3T7tEKVHgvQR++cvBe/Tftq/eyGEn36foTKjgWoXZcq3fnwYe39PFS7/s1vvNlgNX7TONu7ftE4Onct0eNPbXTs3znpc0lS7jvtlP1Yij7+MEHPzLpMY275SndP3qGCQ+Ga+dAV2vV5TFOFDLilS49KPfn61479ux85LEl675VIzZnWVh26VWrIr0oUFmHTseIAffZRSz16d3vHSvu6Wosu61+u0WnfKiTMrm8PB+qT9yP00qw42e3178cHzjcW40zv32tg69atc3rX7ymn3vV7NmVlZbJarRp84b0K8Gd4H77Jtnd/U4cANJg6o1brtFKlpaWKiIg4e4dzcCpX9B32fwoIPPdbg+tqq/TxOw81aKwNpUkr+7O96xcAAK9hNT4AAPBVLNADAJgCq/EBAPB1duPk5kn/ZopkDwAwB+bsAQCAr6KyBwCYgkUeztl7LZLGR7IHAJiDiZ+gxzA+AAA+jsoeAGAK3HoHAICvYzU+AADwVVT2AABTsBiGLB4ssvOkb1Mj2QMAzMH+w+ZJ/2aKYXwAAHwclT0AwBQYxgcAwNexGh8AAB936gl6nmxu2LBhg0aMGKGEhARZLBatWLHC6bzFYjnt9uSTTzraVFdXKz09XTExMQoLC9PIkSNVUFDg9lcn2QMA0AAqKirUo0cPPfPMM6c9X1hY6LT94x//kMVi0S9/+UtHm4yMDC1fvlxLly7Vxo0bVV5eruHDh8tms7kVC8P4AABTaOwn6A0bNkzDhg37yfPx8fFO+ytXrtSgQYPUqVMnSVJpaanmz5+vRYsWaciQIZKkxYsXKzExUWvWrNHQoUNdjoXKHgBgDl4axi8rK3PaqqurPQ7tyJEjeuutt5SWluY4lpeXp9raWqWmpjqOJSQkKCkpSZs2bXLr+iR7AADckJiYKKvV6tiysrI8vubChQsVHh6uG264wXGsqKhIQUFBioyMdGobFxenoqIit67PMD4AwBQs9pObJ/0lKT8/XxEREY7jwcHBHkYm/eMf/9Att9yikJCQs7Y1DEMWi8Wt65PsAQDm4KX32UdERDgle099+OGH2rNnj1555RWn4/Hx8aqpqVFJSYlTdV9cXKx+/fq59RkM4wMA0ITmz5+vlJQU9ejRw+l4SkqKAgMDlZub6zhWWFionTt3up3sqewBAObQyA/VKS8v1759+xz7Bw4c0Pbt2xUVFaV27dpJOrnY71//+pf+9re/1etvtVqVlpamKVOmKDo6WlFRUZo6daqSk5Mdq/NdRbIHAJhCYz8ud8uWLRo0aJBjPzMzU5I0btw45eTkSJKWLl0qwzD061//+rTXyM7OVkBAgMaMGaPKykoNHjxYOTk58vf3dzf25vuw37KyMlmtVg2+8F4F+Hu+QAI4H9n27m/qEIAGU2fUap1WqrS01Kvz4P/rVK4Y1OuPCgg4+wK4n1JXV6W1Wx5t0FgbCpU9AMAcvLRArzki2QMAzMGQZ++kb765nmQPADAHM7/illvvAADwcVT2AABzMOThnL3XIml0JHsAgDmYeIEew/gAAPg4KnsAgDnYJbn3/pj6/Zspkj0AwBRYjQ8AAHwWlT0AwBxMvECPZA8AMAcTJ3uG8QEA8HFU9gAAczBxZU+yBwCYA7feAQDg27j1DgAA+CwqewCAOTBnDwCAj7MbksWDhG1vvsmeYXwAAHwclT0AwBwYxgcAwNd5mOzVfJM9w/gAAPg4KnsAgDkwjA8AgI+zG/JoKJ7V+AAA4HxFZQ8AMAfDfnLzpH8zRbIHAJgDc/YAAPg45uwBAICvorIHAJgDw/gAAPg4Qx4me69F0ugYxgcAwMeR7AEA5nBqGN+TzQ0bNmzQiBEjlJCQIIvFohUrVtRrs3v3bo0cOVJWq1Xh4eG68sordejQIcf56upqpaenKyYmRmFhYRo5cqQKCgrc/uokewCAOdjtnm9uqKioUI8ePfTMM8+c9vzXX3+t/v37q1u3blq3bp0+++wzPfjggwoJCXG0ycjI0PLly7V06VJt3LhR5eXlGj58uGw2m1uxMGcPAEADGDZsmIYNG/aT56dPn65f/OIXeuKJJxzHOnXq5Pi/S0tLNX/+fC1atEhDhgyRJC1evFiJiYlas2aNhg4d6nIsVPYAAHPw0jB+WVmZ01ZdXe12KHa7XW+99Za6dOmioUOHKjY2Vn369HEa6s/Ly1Ntba1SU1MdxxISEpSUlKRNmza59XkkewCAOXgp2ScmJspqtTq2rKwst0MpLi5WeXm5HnvsMV177bV67733dP311+uGG27Q+vXrJUlFRUUKCgpSZGSkU9+4uDgVFRW59XkM4wMA4Ib8/HxFREQ49oODg92+hv2H+f9Ro0bp97//vSTpsssu06ZNm/Tcc8/pmmuu+cm+hmHIYrG49XlU9gAAc7Abnm+SIiIinLZzSfYxMTEKCAjQxRdf7HS8e/fujtX48fHxqqmpUUlJiVOb4uJixcXFufV5JHsAgCkYht3jzVuCgoLUu3dv7dmzx+n4V199pfbt20uSUlJSFBgYqNzcXMf5wsJC7dy5U/369XPr8xjGBwCYg/Hf6vyc+7uhvLxc+/btc+wfOHBA27dvV1RUlNq1a6f77rtPY8eO1YABAzRo0CCtXr1ab7zxhtatWydJslqtSktL05QpUxQdHa2oqChNnTpVycnJjtX5riLZAwDQALZs2aJBgwY59jMzMyVJ48aNU05Ojq6//no999xzysrK0uTJk9W1a1e9/vrr6t+/v6NPdna2AgICNGbMGFVWVmrw4MHKycmRv7+/W7FYDKP5Ptm/rKxMVqtVgy+8VwH+7s+ZAM2Bbe/+pg4BaDB1Rq3WaaVKS0udFr15kyNXWG9VgCXonK9TZ9To/dJFDRprQ6GyBwCYg90uWTyYd/finH1jY4EeAAA+jsoeAGAOhiGP3lPbfGe9SfYAAHMw7HYZHgzje/PWu8bGMD4AAD6Oyh4AYA4M4wMA4OPshmQxZ7JnGB8AAB9HZQ8AMAfDkOTJffbNt7In2QMATMGwGzI8GMZvxg+cJdkDAEzCsMuzyp5b7wAAwHmKyh4AYAoM4wMA4OtMPIzfrJP9qb+y6uzVTRwJ0HBsRm1ThwA0mDqd/H03RtVcp1qPnqlzKtbmqFkn++PHj0uS1h94rokjAQB44vjx47JarQ1y7aCgIMXHx2tj0dseXys+Pl5BQUFeiKpxWYxmPAlht9t1+PBhhYeHy2KxNHU4plBWVqbExETl5+crIiKiqcMBvIrfd+MzDEPHjx9XQkKC/Pwabs14VVWVampqPL5OUFCQQkJCvBBR42rWlb2fn5/atm3b1GGYUkREBP8Ywmfx+25cDVXR/6+QkJBmmaS9hVvvAADwcSR7AAB8HMkebgkODtbDDz+s4ODgpg4F8Dp+3/BVzXqBHgAAODsqewAAfBzJHgAAH0eyBwDAx5HsAQDwcSR7uGzu3Lnq2LGjQkJClJKSog8//LCpQwK8YsOGDRoxYoQSEhJksVi0YsWKpg4J8CqSPVzyyiuvKCMjQ9OnT9e2bdt09dVXa9iwYTp06FBThwZ4rKKiQj169NAzzzzT1KEADYJb7+CSPn366PLLL9e8efMcx7p3767Ro0crKyurCSMDvMtisWj58uUaPXp0U4cCeA2VPc6qpqZGeXl5Sk1NdTqempqqTZs2NVFUAABXkexxVt9++61sNpvi4uKcjsfFxamoqKiJogIAuIpkD5f9+DXChmHwamEAaAZI9jirmJgY+fv716vii4uL61X7AIDzD8keZxUUFKSUlBTl5uY6Hc/NzVW/fv2aKCoAgKsCmjoANA+ZmZm69dZb1atXL/Xt21cvvPCCDh06pLvvvrupQwM8Vl5ern379jn2Dxw4oO3btysqKkrt2rVrwsgA7+DWO7hs7ty5euKJJ1RYWKikpCRlZ2drwIABTR0W4LF169Zp0KBB9Y6PGzdOOTk5jR8Q4GUkewAAfBxz9gAA+DiSPQAAPo5kDwCAjyPZAwDg40j2AAD4OJI9AAA+jmQPAICPI9kDHpoxY4Yuu+wyx/748eOb5F3oBw8elMVi0fbt23+yTYcOHTR79myXr5mTk6NWrVp5HJvFYtGKFSs8vg6Ac0Oyh08aP368LBaLLBaLAgMD1alTJ02dOlUVFRUN/tlPPfWUy09dcyVBA4CneDY+fNa1116rBQsWqLa2Vh9++KEmTJigiooKzZs3r17b2tpaBQYGeuVzrVarV64DAN5CZQ+fFRwcrPj4eCUmJurmm2/WLbfc4hhKPjX0/o9//EOdOnVScHCwDMNQaWmp7rzzTsXGxioiIkI/+9nP9Nlnnzld97HHHlNcXJzCw8OVlpamqqoqp/M/Hsa32+16/PHHddFFFyk4OFjt2rXTzJkzJUkdO3aUJPXs2VMWi0UDBw509FuwYIG6d++ukJAQdevWTXPnznX6nE8//VQ9e/ZUSEiIevXqpW3btrn932jWrFlKTk5WWFiYEhMTdc8996i8vLxeuxUrVqhLly4KCQnRz3/+c+Xn5zudf+ONN5SSkqKQkBB16tRJjzzyiOrq6tyOB0DDINnDNEJDQ1VbW+vY37dvn1599VW9/vrrjmH06667TkVFRXr77beVl5enyy+/XIMHD9axY8ckSa+++qoefvhhzZw5U1u2bFGbNm3qJeEfmzZtmh5//HE9+OCD2rVrl5YsWaK4uDhJJxO2JK1Zs0aFhYVatmyZJOnFF1/U9OnTNXPmTO3evVuPPvqoHnzwQS1cuFCSVFFRoeHDh6tr167Ky8vTjBkzNHXqVLf/m/j5+enpp5/Wzp07tXDhQn3wwQe6//77ndqcOHFCM2fO1MKFC/XRRx+prKxMN910k+P8u+++q9/85jeaPHmydu3apeeff145OTmOP2gAnAcMwAeNGzfOGDVqlGP/k08+MaKjo40xY8YYhmEYDz/8sBEYGGgUFxc72rz//vtGRESEUVVV5XStCy+80Hj++ecNwzCMvn37GnfffbfT+T59+hg9evQ47WeXlZUZwcHBxosvvnjaOA8cOGBIMrZt2+Z0PDEx0ViyZInTsT//+c9G3759DcMwjOeff96IiooyKioqHOfnzZt32mv9r/bt2xvZ2dk/ef7VV181oqOjHfsLFiwwJBmbN292HNu9e7chyfjkk08MwzCMq6++2nj00UedrrNo0SKjTZs2jn1JxvLly3/ycwE0LObs4bPefPNNtWzZUnV1daqtrdWoUaM0Z84cx/n27durdevWjv28vDyVl5crOjra6TqVlZX6+uuvJUm7d+/W3Xff7XS+b9++Wrt27Wlj2L17t6qrqzV48GCX4z569Kjy8/OVlpamO+64w3G8rq7OsR5g9+7d6tGjh1q0aOEUh7vWrl2rRx99VLt27VJZWZnq6upUVVWliooKhYWFSZICAgLUq1cvR59u3bqpVatW2r17t6644grl5eXp3//+t1Mlb7PZVFVVpRMnTjjFCKBpkOzhswYNGqR58+YpMDBQCQkJ9RbgnUpmp9jtdrVp00br1q2rd61zvf0sNDTU7T52u13SyaH8Pn36OJ3z9/eXJBleeDP1N998o1/84he6++679ec//1lRUVHauHGj0tLSnKY7pJO3zv3YqWN2u12PPPKIbrjhhnptQkJCPI4TgOdI9vBZYWFhuuiii1xuf/nll6uoqEgBAQHq0KHDadt0795dmzdv1m233eY4tnnz5p+8ZufOnRUaGqr3339fEyZMqHc+KChI0slK+JS4uDhdcMEF2r9/v2655ZbTXvfiiy/WokWLVFlZ6fiD4kxxnM6WLVtUV1env/3tb/LzO7l859VXX63Xrq6uTlu2bNEVV1whSdqzZ4++//57devWTdLJ/2579uxx6781gMZFsgd+MGTIEPXt21ejR4/W448/rq5du+rw4cN6++23NXr0aPXq1Uv33nuvxo0bp169eql///566aWX9MUXX6hTp06nvWZISIgeeOAB3X///QoKCtJVV12lo0eP6osvvlBaWppiY2MVGhqq1atXq23btgoJCZHVatWMGTM0efJkRUREaNiwYaqurtaWLVtUUlKizMxM3XzzzZo+fbrS0tL0pz/9SQcPHtRf//pXt77vhRdeqLq6Os2ZM0cjRozQRx99pOeee65eu8DAQKWnp+vpp59WYGCgJk2apCuvvNKR/B966CENHz5ciYmJuvHGG+Xn56cdO3bo888/11/+8hf3/4cA4HWsxgd+YLFY9Pbbb2vAgAH67W9/qy5duuimm27SwYMHHavnx44dq4ceekgPPPCAUlJS9M033+h3v/vdGa/74IMPasqUKXrooYfUvXt3jR07VsXFxZJOzoc//fTTev7555WQkKBRo0ZJkiZMmKC///3vysnJUXJysq655hrl5OQ4btVr2bKl3njjDe3atUs9e/bU9OnT9fjjj7v1fS+77DLNmjVLjz/+uJKSkvTSSy8pKyurXrsWLVrogQce0M0336y+ffsqNDRUS5cudZwfOnSo3nzzTeXm5qp379668sorNWvWLLVv396teAA0HIvhjck/AABw3qKyBwDAx5HsAQDwcSR7AAB8HMkeAAAfR7IHAMDHkewBAPBxJHsAAHwcyR4AAB9HsgcAwMeR7AEA8HEkewAAfBzJHgAAH/f/rQ2qzX4orUQAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.59      0.49      0.53       472\n",
            "           1       0.39      0.49      0.43       319\n",
            "\n",
            "    accuracy                           0.49       791\n",
            "   macro avg       0.49      0.49      0.48       791\n",
            "weighted avg       0.51      0.49      0.49       791\n",
            "\n",
            "average accuracy =  49 %\n"
          ]
        }
      ],
      "source": [
        "#  preprocessing-  Balancing -   No Anonymisation (7)\n",
        "aver_acc = 0\n",
        "for i in range(4):\n",
        "    train = balanced_data[i][0]\n",
        "    val = balanced_data[i][1]\n",
        "    \n",
        "    train[\"Facts\"] = preprocessing(train[\"Facts\"])\n",
        "    val[\"Facts\"] = preprocessing(val[\"Facts\"])\n",
        "\n",
        "    prepare_data(train, val)\n",
        "\n",
        "    if i == 0:\n",
        "        model7_1 = train_model()\n",
        "        aver_acc = aver_acc + predict(model7_1)\n",
        "    if i == 1:\n",
        "        model7_2 = train_model()\n",
        "        aver_acc = aver_acc + predict(model7_2)\n",
        "    if i == 2:\n",
        "        model7_3 = train_model()\n",
        "        aver_acc = aver_acc + predict(model7_3)\n",
        "    if i == 3:\n",
        "        model7_4 = train_model()\n",
        "        aver_acc = aver_acc + predict(model7_4)\n",
        "\n",
        "print(\"average accuracy = \",round(aver_acc/4),\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3uuJgBlJu6kb",
        "outputId": "670d14e4-881a-4219-887b-ef2a0e27fa97",
        "scrolled": false
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "train-Accuracy: 0.7455683003128258\n",
            "val-Accuracy: 0.33889468196037537\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.59      0.61      0.60       472\n",
            "           1       0.39      0.37      0.38       319\n",
            "\n",
            "    accuracy                           0.51       791\n",
            "   macro avg       0.49      0.49      0.49       791\n",
            "weighted avg       0.51      0.51      0.51       791\n",
            "\n",
            "train-Accuracy: 0.737921445950643\n",
            "val-Accuracy: 0.3534932221063608\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.57      0.61      0.59       472\n",
            "           1       0.36      0.32      0.34       319\n",
            "\n",
            "    accuracy                           0.50       791\n",
            "   macro avg       0.47      0.47      0.47       791\n",
            "weighted avg       0.49      0.50      0.49       791\n",
            "\n",
            "train-Accuracy: 0.7500868960722975\n",
            "val-Accuracy: 0.32533889468196037\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.58      0.49      0.53       472\n",
            "           1       0.38      0.47      0.42       319\n",
            "\n",
            "    accuracy                           0.48       791\n",
            "   macro avg       0.48      0.48      0.47       791\n",
            "weighted avg       0.50      0.48      0.49       791\n",
            "\n",
            "train-Accuracy: 0.7518248175182481\n",
            "val-Accuracy: 0.31386861313868614\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.57      0.42      0.49       472\n",
            "           1       0.38      0.53      0.44       319\n",
            "\n",
            "    accuracy                           0.47       791\n",
            "   macro avg       0.48      0.48      0.46       791\n",
            "weighted avg       0.49      0.47      0.47       791\n",
            "\n",
            "average accuracy =  49 %\n"
          ]
        }
      ],
      "source": [
        "#  preprocessing-  Balancing -    Anonymisation (8)\n",
        "aver_acc = 0\n",
        "for i in range(4):\n",
        "    train = balanced_data[i][0]\n",
        "    val = balanced_data[i][1]\n",
        "\n",
        "    train[\"Facts\"] = preprocessing(train[\"Facts\"])\n",
        "    val[\"Facts\"] = preprocessing(val[\"Facts\"])\n",
        "    \n",
        "    train = Anonymisation(train)\n",
        "    val = Anonymisation(val)\n",
        "    \n",
        "    prepare_data(train, val)\n",
        "\n",
        "    if i == 0:\n",
        "        model8_1 = train_model()\n",
        "        aver_acc = aver_acc + predict(model8_1)\n",
        "    if i == 1:\n",
        "        model8_2 = train_model()\n",
        "        aver_acc = aver_acc + predict(model8_2)\n",
        "    if i == 2:\n",
        "        model8_3 = train_model()\n",
        "        aver_acc = aver_acc + predict(model8_3)\n",
        "    if i == 3:\n",
        "        model8_4 = train_model()\n",
        "        aver_acc = aver_acc + predict(model8_4)\n",
        "        \n",
        "print(\"average accuracy = \",round(aver_acc/4),\"%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ttzmHuju6kb",
        "outputId": "1b01baa1-e06a-433d-8835-ad94363e4098"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "76"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# predict test_data \n",
        "yypred = test_data['Facts'].apply(lambda x : model1_4.predict(x)[0][0])\n",
        "yypred = [eval(x.replace('__label__','')) for x in yypred]\n",
        "\n",
        "accuracy = int(round(accuracy_score(test_data['winner_index'],yypred)*100))\n",
        "accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "lfJieCbwu6kc",
        "outputId": "adbce268-1815-43b9-b81f-7375debcd8e0"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"Since the late 19th century, Texas sold land to facilitate settlement in the state and construction of schools. If, however, a purchaser missed an interest payment on the property, the land was immediately forfeited back to the state unless the owner made the payment before the land could be re-sold. Under this program, Greenbury Simmons purchased and then forfeited some land in 1947. Just over five years later he offered to pay the interest to re-acquire the property. The state refused to comply with his wishes citing a 1941 amendment to its law which gave individuals five years to claim their forfeited land. Simmons's land was sold to the City of El Paso in 1955.\""
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x=test_data['Facts'].iloc[50]\n",
        "x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HWlI9RkOu6kc",
        "outputId": "4dc150c3-2949-4162-8c20-e36f2c13995f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[0]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "labels = model1_4.predict(x)[0]\n",
        "ypred = [eval(x.replace('__label__',''))for x in labels]\n",
        "\n",
        "ypred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "SBakopTUu6kc"
      },
      "outputs": [],
      "source": [
        "model1_4.save_model('best_fasttext.bin')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}