Key_Point_Matching_NLP.ipynb

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":158784,"status":"ok","timestamp":1657122523378,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"UICuV9Ujqqvr","outputId":"93dc2d56-76b4-418f-c1ea-e39dcde62078"},"outputs":[{"name":"stdout","output_type":"stream","text":["Found existing installation: tensorflow 2.8.2+zzzcolab20220527125636\n","Uninstalling tensorflow-2.8.2+zzzcolab20220527125636:\n","  Would remove:\n","    /usr/local/bin/estimator_ckpt_converter\n","    /usr/local/bin/import_pb_to_tensorboard\n","    /usr/local/bin/saved_model_cli\n","    /usr/local/bin/tensorboard\n","    /usr/local/bin/tf_upgrade_v2\n","    /usr/local/bin/tflite_convert\n","    /usr/local/bin/toco\n","    /usr/local/bin/toco_from_protos\n","    /usr/local/lib/python3.7/dist-packages/tensorflow-2.8.2+zzzcolab20220527125636.dist-info/*\n","    /usr/local/lib/python3.7/dist-packages/tensorflow/*\n","Proceed (y/n)? \u001b[31mERROR: Operation cancelled by user\u001b[0m\n","\u001b[33mWARNING: Skipping tensorflow-text as it is not installed.\u001b[0m\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting tensorflow==2.9.0\n","  Downloading tensorflow-2.9.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (511.7 MB)\n","\u001b[K     |████████████████████████████████| 511.7 MB 3.0 kB/s \n","\u001b[?25hRequirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.9.0) (57.4.0)\n","Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.9.0) (14.0.1)\n","Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.9.0) (1.15.0)\n","Collecting tensorboard<2.10,>=2.9\n","  Downloading tensorboard-2.9.1-py3-none-any.whl (5.8 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packages: sentence-transformers\n","  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125938 sha256=3f4629f1fa326b98bb3e1f55e606f9c1334e54441cd724c4f17bf8e4577e2023\n","  Stored in directory: /root/.cache/pip/wheels/bf/06/fb/d59c1e5bd1dac7f6cf61ec0036cc3a10ab8fecaa6b2c3d3ee9\n","Successfully built sentence-transformers\n","Installing collected packages: pyyaml, tokenizers, huggingface-hub, transformers, sentencepiece, sentence-transformers\n","  Attempting uninstall: pyyaml\n","    Found existing installation: PyYAML 3.13\n","    Uninstalling PyYAML-3.13:\n","      Successfully uninstalled PyYAML-3.13\n","Successfully installed huggingface-hub-0.8.1 pyyaml-6.0 sentence-transformers-2.2.2 sentencepiece-0.1.96 tokenizers-0.12.1 transformers-4.20.1\n","Environmental packages imported\n"]}],"source":["import numpy as np\n","import pandas as pd\n","!pip uninstall tensorflow\n","!pip uninstall tensorflow_text\n","!pip install tensorflow==2.9.0\n","import tensorflow as tf\n","print(tf.__version__)\n","!pip install tensorflow-text\n","import tensorflow_text as text\n","!pip install --upgrade tensorflow_hub\n","import tensorflow_hub as hub\n","import matplotlib.pyplot as plt                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   \n","from tqdm import tqdm\n","import sys  \n","!{sys.executable} -m pip install contractions\n","import contractions\n","!{sys.executable} -m pip install focal_loss\n","import focal_loss\n","!{sys.executable} -m pip install sentence_transformers\n","import sentence_transformers\n","print(\"Environmental packages imported\")"]},{"cell_type":"markdown","metadata":{"id":"hJqI_7GLw3oT"},"source":["# **1.0 Data loading**\n","The dataset is divided in:\n","\n","Arguments (arg_id, argument, topic, stance);\n","Keypoint (key_point_id, keypoint, topic, stance);\n","Labels (arg_id, key_point_id, label).\n","Each dataset has its own split (train, dev and test). The dataset is directly loaded via url from the GitHub repository."]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":426},"executionInfo":{"elapsed":1676,"status":"ok","timestamp":1657122563176,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"7H2di7x1w3Ai","outputId":"66cae849-0b9c-46c7-cfd8-17b3ce0adf23"},"outputs":[{"name":"stdout","output_type":"stream","text":["There are 5583 arguments in the Arguments train set.\n","\n","There are 932 arguments in the Arguments debelopment set.\n","\n","There are 723 arguments in the Arguments test set.\n","\n","There are 24 topic in the Arguments train set.\n","\n","There are 4 topic in the Arguments development set.\n","\n","There are 3 topic in the Arguments test set.\n","\n"]},{"data":{"text/html":["\n","  <div id=\"df-07c4f32a-44ff-42a9-8990-0247aa8f3348\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>arg_id</th>\n","      <th>argument</th>\n","      <th>topic</th>\n","      <th>stance</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>arg_0_0</td>\n","      <td>Routine child vaccinations isn't mandatory sin...</td>\n","      <td>Routine child vaccinations should be mandatory</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>arg_0_1</td>\n","      <td>Routine child vaccinations should not be manda...</td>\n","      <td>Routine child vaccinations should be mandatory</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>arg_0_2</td>\n","      <td>Routine child vaccinations should not be neces...</td>\n","      <td>Routine child vaccinations should be mandatory</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>arg_0_3</td>\n","      <td>A vaccine that has not been sufficiently teste...</td>\n","      <td>Routine child vaccinations should be mandatory</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>arg_0_4</td>\n","      <td>As long as vaccines are not free of side effec...</td>\n","      <td>Routine child vaccinations should be mandatory</td>\n","      <td>-1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-07c4f32a-44ff-42a9-8990-0247aa8f3348')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-07c4f32a-44ff-42a9-8990-0247aa8f3348 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-07c4f32a-44ff-42a9-8990-0247aa8f3348');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["    arg_id                                           argument  \\\n","0  arg_0_0  Routine child vaccinations isn't mandatory sin...   \n","1  arg_0_1  Routine child vaccinations should not be manda...   \n","2  arg_0_2  Routine child vaccinations should not be neces...   \n","3  arg_0_3  A vaccine that has not been sufficiently teste...   \n","4  arg_0_4  As long as vaccines are not free of side effec...   \n","\n","                                            topic  stance  \n","0  Routine child vaccinations should be mandatory      -1  \n","1  Routine child vaccinations should be mandatory      -1  \n","2  Routine child vaccinations should be mandatory      -1  \n","3  Routine child vaccinations should be mandatory      -1  \n","4  Routine child vaccinations should be mandatory      -1  "]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["argument_label = \"argument\"\n","topic_label = \"topic\"\n","key_point_lable = \"key_point\"\n","stance_label = \"stance\"\n","arg_id_label = 'arg_id'\n","kp_id_label = 'key_point_id'\n","label_label = 'label'\n","\n","\n","## Arguments dataset loading.\n","arguments_train = pd.read_csv(filepath_or_buffer = 'https://raw.githubusercontent.com/IBM/KPA_2021_shared_task/main/kpm_data/arguments_train.csv')\n","arguments_dev = pd.read_csv(filepath_or_buffer = 'https://raw.githubusercontent.com/IBM/KPA_2021_shared_task/main/kpm_data/arguments_dev.csv')\n","arguments_test = pd.read_csv(filepath_or_buffer = 'https://raw.githubusercontent.com/IBM/KPA_2021_shared_task/main/test_data/arguments_test.csv')\n","\n","no_argunents_in_arguments_train = len(arguments_train[argument_label].unique())\n","print(f'There are {no_argunents_in_arguments_train} arguments in the Arguments train set.\\n')\n","\n","no_argunents_in_arguments_dev = len(arguments_dev[argument_label].unique())\n","print(f'There are {no_argunents_in_arguments_dev} arguments in the Arguments debelopment set.\\n')\n","\n","no_argunents_in_arguments_test = len(arguments_test[argument_label].unique())\n","print(f'There are {no_argunents_in_arguments_test} arguments in the Arguments test set.\\n')\n","\n","\n","\n","no_topics_in_arguments_train = len(arguments_train[topic_label].unique())\n","print(f'There are {no_topics_in_arguments_train} topic in the Arguments train set.\\n')\n","\n","no_topics_in_arguments_dev = len(arguments_dev[topic_label].unique())\n","print(f'There are {no_topics_in_arguments_dev} topic in the Arguments development set.\\n')\n","\n","no_topics_in_arguments_test = len(arguments_test[topic_label].unique())\n","print(f'There are {no_topics_in_arguments_test} topic in the Arguments test set.\\n')\n","\n","\n","\n","\n","arguments_test.head()"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":426},"executionInfo":{"elapsed":1419,"status":"ok","timestamp":1657122567145,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"a96VZ2BExmN0","outputId":"e5b2cefd-682f-45e4-b374-9248c21e6636"},"outputs":[{"name":"stdout","output_type":"stream","text":["There are 207 points in the points train set.\n","\n","There are 36 points in the points debelopment set.\n","\n","There are 33 points in the points test set.\n","\n","There are 24 topics in the Key-points train set.\n","\n","There are 4 topics in the Key-points development set.\n","\n","There are 3 topics in the Key-points test set.\n","\n"]},{"data":{"text/html":["\n","  <div id=\"df-e60329ee-577f-4e8c-8e19-825b45edff30\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>key_point_id</th>\n","      <th>key_point</th>\n","      <th>topic</th>\n","      <th>stance</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>kp_0_0</td>\n","      <td>Routine child vaccinations, or their side effe...</td>\n","      <td>Routine child vaccinations should be mandatory</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>kp_0_1</td>\n","      <td>Mandatory vaccination contradicts basic rights</td>\n","      <td>Routine child vaccinations should be mandatory</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>kp_0_2</td>\n","      <td>The parents and not the state should decide</td>\n","      <td>Routine child vaccinations should be mandatory</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>kp_0_3</td>\n","      <td>Routine child vaccinations are not necessary t...</td>\n","      <td>Routine child vaccinations should be mandatory</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>kp_0_4</td>\n","      <td>Routine child vaccinations are effective</td>\n","      <td>Routine child vaccinations should be mandatory</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e60329ee-577f-4e8c-8e19-825b45edff30')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-e60329ee-577f-4e8c-8e19-825b45edff30 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-e60329ee-577f-4e8c-8e19-825b45edff30');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["  key_point_id                                          key_point  \\\n","0       kp_0_0  Routine child vaccinations, or their side effe...   \n","1       kp_0_1     Mandatory vaccination contradicts basic rights   \n","2       kp_0_2        The parents and not the state should decide   \n","3       kp_0_3  Routine child vaccinations are not necessary t...   \n","4       kp_0_4           Routine child vaccinations are effective   \n","\n","                                            topic  stance  \n","0  Routine child vaccinations should be mandatory      -1  \n","1  Routine child vaccinations should be mandatory      -1  \n","2  Routine child vaccinations should be mandatory      -1  \n","3  Routine child vaccinations should be mandatory      -1  \n","4  Routine child vaccinations should be mandatory       1  "]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["## Key-points dataest loading.\n","key_points_train = pd.read_csv(filepath_or_buffer = 'https://raw.githubusercontent.com/IBM/KPA_2021_shared_task/main/kpm_data/key_points_train.csv')\n","key_points_dev = pd.read_csv(filepath_or_buffer = 'https://raw.githubusercontent.com/IBM/KPA_2021_shared_task/main/kpm_data/key_points_dev.csv')\n","key_points_test = pd.read_csv(filepath_or_buffer = 'https://raw.githubusercontent.com/IBM/KPA_2021_shared_task/main/test_data/key_points_test.csv')\n","\n","\n","\n","\n","no_kp_in_kp_train = len(key_points_train[key_point_lable].unique())\n","print(f'There are {no_kp_in_kp_train} points in the points train set.\\n')\n","\n","no_kp_in_kp_dev = len(key_points_dev[key_point_lable].unique())\n","print(f'There are {no_kp_in_kp_dev} points in the points debelopment set.\\n')\n","\n","no_kp_in_kp_test = len(key_points_test[key_point_lable].unique())\n","print(f'There are {no_kp_in_kp_test} points in the points test set.\\n')\n","\n","no_topics_in_kp_train = len(key_points_train[topic_label].unique())\n","print(f'There are {no_topics_in_kp_train} topics in the Key-points train set.\\n')\n","\n","no_topics_in_kp_dev = len(key_points_dev[topic_label].unique())\n","print(f'There are {no_topics_in_kp_dev} topics in the Key-points development set.\\n')\n","\n","no_topics_in_kp_test = len(key_points_test[topic_label].unique())\n","print(f'There are {no_topics_in_kp_test} topics in the Key-points test set.\\n')\n","\n","# key_points_train.head()\n","# key_points_dev.head()\n","key_points_test.head()"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":580},"executionInfo":{"elapsed":1795,"status":"ok","timestamp":1657122572383,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"AvUjd5Pkx4wg","outputId":"4650514c-a0e0-4048-c603-56442d0a3eb4"},"outputs":[{"name":"stdout","output_type":"stream","text":["There are 207 points in the points train set.\n","\n","There are 36 points in the points debelopment set.\n","\n","There are 33 points in the points test set.\n","\n","There are 24 topics in the Key-points train set.\n","\n","There are 4 topics in the Key-points development set.\n","\n","There are 3 topics in the Key-points test set.\n","\n","Total number of argument-key point paris is in label train dataset is: 20635 \n","Total number of argument-key point paris is in label development dataset is: 3458 \n","Total number of argument-key point paris is in label test dataset is: 3426 \n"]},{"data":{"text/html":["\n","  <div id=\"df-abbfd2d4-d2c0-4034-a157-f2119a090029\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>arg_id</th>\n","      <th>key_point_id</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>arg_0_112</td>\n","      <td>kp_0_4</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>arg_0_112</td>\n","      <td>kp_0_5</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>arg_0_112</td>\n","      <td>kp_0_6</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>arg_0_112</td>\n","      <td>kp_0_7</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>arg_0_0</td>\n","      <td>kp_0_0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-abbfd2d4-d2c0-4034-a157-f2119a090029')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "],"text/plain":["      arg_id key_point_id  label\n","0  arg_0_112       kp_0_4      0\n","1  arg_0_112       kp_0_5      0\n","2  arg_0_112       kp_0_6      0\n","3  arg_0_112       kp_0_7      1\n","4    arg_0_0       kp_0_0      0"]},"execution_count":4,"metadata":{},"output_type":"execute_result"},{"name":"stdout","output_type":"stream","text":["Error: Runtime no longer has a reference to this dataframe, please re-run this cell and try again.\n","Error: Runtime no longer has a reference to this dataframe, please re-run this cell and try again.\n","Error: Runtime no longer has a reference to this dataframe, please re-run this cell and try again.\n"]}],"source":["## Labels dataset loading.\n","labels_train = pd.read_csv(filepath_or_buffer = 'https://raw.githubusercontent.com/IBM/KPA_2021_shared_task/main/kpm_data/labels_train.csv')\n","labels_dev = pd.read_csv(filepath_or_buffer = 'https://raw.githubusercontent.com/IBM/KPA_2021_shared_task/main/kpm_data/labels_dev.csv')\n","labels_test = pd.read_csv(filepath_or_buffer = 'https://raw.githubusercontent.com/IBM/KPA_2021_shared_task/main/test_data/labels_test.csv')\n","\n","\n","\n","\n","no_arg_id_in_kp_train = len(labels_train[arg_id_label].unique())\n","print(f'There are {no_kp_in_kp_train} points in the points train set.\\n')\n","\n","no_arg_id_in_kp_dev = len(labels_dev[arg_id_label].unique())\n","print(f'There are {no_kp_in_kp_dev} points in the points debelopment set.\\n')\n","\n","no_arg_id_in_kp_test = len(labels_test[arg_id_label].unique())\n","print(f'There are {no_kp_in_kp_test} points in the points test set.\\n')\n","\n","no_kp_id_in_kp_train = len(labels_train[kp_id_label].unique())\n","print(f'There are {no_topics_in_kp_train} topics in the Key-points train set.\\n')\n","\n","no_kp_id_in_kp_dev = len(labels_dev[kp_id_label].unique())\n","print(f'There are {no_topics_in_kp_dev} topics in the Key-points development set.\\n')\n","\n","no_kp_id_in_kp_test = len(labels_test[kp_id_label].unique())\n","print(f'There are {no_topics_in_kp_test} topics in the Key-points test set.\\n')\n","\n","print(f'Total number of argument-key point paris is in label train dataset is: {len(labels_train)} ')\n","\n","print(f'Total number of argument-key point paris is in label development dataset is: {len(labels_dev)} ')\n","\n","print(f'Total number of argument-key point paris is in label test dataset is: {len(labels_test)} ')\n","\n","\n","\n","labels_train.head()\n","labels_dev.head()\n","labels_test.head()"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":361,"status":"ok","timestamp":1657122576845,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"43AqcpnzyLPV","outputId":"545d9060-0119-47ac-ba00-fcee8677bbba"},"outputs":[{"name":"stdout","output_type":"stream","text":["There are 4260 positive samples and 16375 negative samples over 20635 samples of the training set.\n","There are 738 positive samples and 2720 negative samples over 3458 samples of the development set.\n","There are 552 positive samples and 2874 negative samples over 3426 samples of the test set.\n"]}],"source":["## Balance of the train dataset.\n","print(f\"There are {np.sum(labels_train['label'].tolist())} positive samples and {len(labels_train['label']) - np.sum(labels_train['label'].tolist())} negative samples over {len(labels_train['label'])} samples of the training set.\")\n","## Balance of the dev dataset.\n","print(f\"There are {np.sum(labels_dev['label'].tolist())} positive samples and {len(labels_dev['label']) - np.sum(labels_dev['label'].tolist())} negative samples over {len(labels_dev['label'])} samples of the development set.\")\n","## Balance of the test dataset.\n","print(f\"There are {np.sum(labels_test['label'].tolist())} positive samples and {len(labels_test['label']) - np.sum(labels_test['label'].tolist())} negative samples over {len(labels_test['label'])} samples of the test set.\")"]},{"cell_type":"markdown","metadata":{"id":"rhuHHUh6e6jk"},"source":["# ***Some Tool Functions***"]},{"cell_type":"code","execution_count":6,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1657122579880,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"qV_uzc0ge6Wx"},"outputs":[],"source":["from sklearn.metrics import accuracy_score, classification_report, f1_score, recall_score, precision_score, PrecisionRecallDisplay, roc_curve, balanced_accuracy_score, ConfusionMatrixDisplay\n","from sklearn.metrics import classification_report, average_precision_score, PrecisionRecallDisplay, ConfusionMatrixDisplay\n","import sklearn\n","from sklearn.metrics import precision_recall_curve\n","from numpy import argmax\n","\n","def listFlatten(src):\n","    tmp = []\n","    for i in src:\n","        if type(i) is not list:\n","            tmp.append(i)\n","        else:\n","            tmp.extend(listFlatten(i))\n","    return tmp\n","\n","\n","def evaluate_classifier(predictions, true_labels):\n","\n","  ## Threshold tuning.\n","  fpr, tpr, thresholds = roc_curve(true_labels, predictions)\n","  thr = thresholds[np.argmin(np.abs(fpr + tpr - 1))]\n","\n","  predictions_thr = np.zeros(predictions.shape)\n","  predictions_thr[predictions >= thr] = 1\n","\n","  print(classification_report(predictions_thr, true_labels))\n","    \n","  print(f'The average precision score is: {average_precision_score(true_labels, predictions_thr)}.')\n","  print(f'The balanced accuracy score is: {balanced_accuracy_score(true_labels, predictions_thr)}.')\n","  print(f'The tuned threshold is: {thr}.')\n","\n","  print(f'The confusion matrix is: ')\n","  ConfusionMatrixDisplay.from_predictions(true_labels, predictions_thr)\n","\n","  PrecisionRecallDisplay.from_predictions(true_labels, predictions)\n","\n","true_values = labels_test['label'].tolist()"]},{"cell_type":"markdown","metadata":{"id":"cdcRA4Wf0BEV"},"source":["# **2.0 Data processing**"]},{"cell_type":"markdown","metadata":{"id":"8trMOjnL0EVg"},"source":["# **2.1 Text cleaning**\n","The text cleaning consists in lowercase every letter, expand abbreviations and verbal contractions, remove possible special characters and uncommon symbols.\n","\n","Notice that the new dataframes override the previous ones."]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":362,"status":"ok","timestamp":1657122582672,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"RcG9ZJHA0AoM","outputId":"e43ac5e1-b2b9-4875-db38-cb0f6c138c8f"},"outputs":[{"name":"stdout","output_type":"stream","text":["Before preprocess : \n","\n","                                            argument  \\\n","0  `people reach their limit when it comes to the...   \n","1  A patient should be able to decide when they h...   \n","2  a person has the right to end their suffering ...   \n","3  a person should have the dignity to choose how...   \n","4  a person should have the right to be able to c...   \n","\n","                                           topic  \n","0  Assisted suicide should be a criminal offence  \n","1  Assisted suicide should be a criminal offence  \n","2  Assisted suicide should be a criminal offence  \n","3  Assisted suicide should be a criminal offence  \n","4  Assisted suicide should be a criminal offence  \n","\n","\n","After preprocess : \n","\n","                                            argument  \\\n","0  people reach their limit when it comes to thei...   \n","1  a patient should be able to decide when they h...   \n","2  a person has the right to end their suffering ...   \n","3  a person should have the dignity to choose how...   \n","4  a person should have the right to be able to c...   \n","\n","                                           topic  \n","0  assisted suicide should be a criminal offence  \n","1  assisted suicide should be a criminal offence  \n","2  assisted suicide should be a criminal offence  \n","3  assisted suicide should be a criminal offence  \n","4  assisted suicide should be a criminal offence  \n","\n","\n","Before preprocess : \n","\n","                                            argument  \\\n","0  having a school uniform can reduce bullying as...   \n","1             it is a good way to instill discipline   \n","2  it is cheaper for parents to buy school unifor...   \n","3  one of the benefits of school uniforms is that...   \n","4  researchers found that in schools across the w...   \n","\n","                                         topic  \n","0  We should abandon the use of school uniform  \n","1  We should abandon the use of school uniform  \n","2  We should abandon the use of school uniform  \n","3  We should abandon the use of school uniform  \n","4  We should abandon the use of school uniform  \n","\n","\n","After preprocess : \n","\n","                                            argument  \\\n","0  having a school uniform can reduce bullying as...   \n","1             it is a good way to instill discipline   \n","2  it is cheaper for parents to buy school unifor...   \n","3  one of the benefits of school uniforms is that...   \n","4  researchers found that in schools across the w...   \n","\n","                                         topic  \n","0  we should abandon the use of school uniform  \n","1  we should abandon the use of school uniform  \n","2  we should abandon the use of school uniform  \n","3  we should abandon the use of school uniform  \n","4  we should abandon the use of school uniform  \n","\n","\n","Before preprocess : \n","\n","                                            argument  \\\n","0  Routine child vaccinations isn't mandatory sin...   \n","1  Routine child vaccinations should not be manda...   \n","2  Routine child vaccinations should not be neces...   \n","3  A vaccine that has not been sufficiently teste...   \n","4  As long as vaccines are not free of side effec...   \n","\n","                                            topic  \n","0  Routine child vaccinations should be mandatory  \n","1  Routine child vaccinations should be mandatory  \n","2  Routine child vaccinations should be mandatory  \n","3  Routine child vaccinations should be mandatory  \n","4  Routine child vaccinations should be mandatory  \n","\n","\n","After preprocess : \n","\n","                                            argument  \\\n","0  routine child vaccinations is not mandatory si...   \n","1  routine child vaccinations should not be manda...   \n","2  routine child vaccinations should not be neces...   \n","3  a vaccine that has not been sufficiently teste...   \n","4  as long as vaccines are not free of side effec...   \n","\n","                                            topic  \n","0  routine child vaccinations should be mandatory  \n","1  routine child vaccinations should be mandatory  \n","2  routine child vaccinations should be mandatory  \n","3  routine child vaccinations should be mandatory  \n","4  routine child vaccinations should be mandatory  \n","\n","\n","Before preprocess : \n","\n","                                           key_point  \\\n","0  Assisted suicide gives dignity to the person t...   \n","1                 Assisted suicide reduces suffering   \n","2  People should have the freedom to choose to en...   \n","3  The terminally ill would benefit from assisted...   \n","4  Assisted suicide allows people to solicit some...   \n","\n","                                           topic  \n","0  Assisted suicide should be a criminal offence  \n","1  Assisted suicide should be a criminal offence  \n","2  Assisted suicide should be a criminal offence  \n","3  Assisted suicide should be a criminal offence  \n","4  Assisted suicide should be a criminal offence  \n","\n","\n","After preprocess : \n","\n","                                           key_point  \\\n","0  assisted suicide gives dignity to the person t...   \n","1                 assisted suicide reduces suffering   \n","2  people should have the freedom to choose to en...   \n","3  the terminally ill would benefit from assisted...   \n","4  assisted suicide allows people to solicit some...   \n","\n","                                           topic  \n","0  assisted suicide should be a criminal offence  \n","1  assisted suicide should be a criminal offence  \n","2  assisted suicide should be a criminal offence  \n","3  assisted suicide should be a criminal offence  \n","4  assisted suicide should be a criminal offence  \n","\n","\n","Before preprocess : \n","\n","                                           key_point  \\\n","0  Children can still express themselves using ot...   \n","1                    School uniform reduces bullying   \n","2  School uniforms encourage discipline or focus ...   \n","3                        School uniforms saves costs   \n","4   School uniforms create a sense of equality/unity   \n","\n","                                         topic  \n","0  We should abandon the use of school uniform  \n","1  We should abandon the use of school uniform  \n","2  We should abandon the use of school uniform  \n","3  We should abandon the use of school uniform  \n","4  We should abandon the use of school uniform  \n","\n","\n","After preprocess : \n","\n","                                           key_point  \\\n","0  children can still express themselves using ot...   \n","1                    school uniform reduces bullying   \n","2  school uniforms encourage discipline or focus ...   \n","3                        school uniforms saves costs   \n","4   school uniforms create a sense of equality unity   \n","\n","                                         topic  \n","0  we should abandon the use of school uniform  \n","1  we should abandon the use of school uniform  \n","2  we should abandon the use of school uniform  \n","3  we should abandon the use of school uniform  \n","4  we should abandon the use of school uniform  \n","\n","\n","Before preprocess : \n","\n","                                           key_point  \\\n","0  Routine child vaccinations, or their side effe...   \n","1     Mandatory vaccination contradicts basic rights   \n","2        The parents and not the state should decide   \n","3  Routine child vaccinations are not necessary t...   \n","4           Routine child vaccinations are effective   \n","\n","                                            topic  \n","0  Routine child vaccinations should be mandatory  \n","1  Routine child vaccinations should be mandatory  \n","2  Routine child vaccinations should be mandatory  \n","3  Routine child vaccinations should be mandatory  \n","4  Routine child vaccinations should be mandatory  \n","\n","\n","After preprocess : \n","\n","                                           key_point  \\\n","0  routine child vaccinations  or their side effe...   \n","1     mandatory vaccination contradicts basic rights   \n","2        the parents and not the state should decide   \n","3  routine child vaccinations are not necessary t...   \n","4           routine child vaccinations are effective   \n","\n","                                            topic  \n","0  routine child vaccinations should be mandatory  \n","1  routine child vaccinations should be mandatory  \n","2  routine child vaccinations should be mandatory  \n","3  routine child vaccinations should be mandatory  \n","4  routine child vaccinations should be mandatory  \n","\n","\n"]}],"source":["import re\n","import nltk\n","\n","## Lowercase\n","def lowercase(text: str) -> str:\n","    return text.lower()\n","\n","## Expand abbreviations and verbal contractions.\n","def expand(text: str) -> str:\n","    return contractions.fix(text)\n","\n","## Remove special characters.\n","def replace_special_characters(text: str) -> str:\n","    replace_symbols = re.compile('[/(){}\\[\\]\\|@,;]')\n","    return replace_symbols.sub(' ', text)\n","\n","def filter_out_uncommon_symbols(text: str) -> str:\n","    good_symbols = re.compile('[^0-9a-z #+_]')\n","    return re.sub(good_symbols, '', text)\n","\n","## Pandas defined each column as a Series object, use series.str to do string operation on it.\n","def preprocess_text(text: str) -> str:\n","    return filter_out_uncommon_symbols(replace_special_characters(expand(lowercase(text))))\n","    \n","def cleanning(dataframe, labels: list):\n","    print('Before preprocess : \\n')\n","    print(dataframe[labels].head(5))\n","    for label in labels:\n","        dataframe[label] = dataframe[label].apply(lambda txt: preprocess_text(txt))\n","    print(\"\\n\")\n","    print('After preprocess : \\n')\n","    print(dataframe[labels].head(5))\n","    print(\"\\n\")\n","\n","arguments_dataset_list = [arguments_train, arguments_dev, arguments_test]\n","arguments_labels_list = ['argument', 'topic']\n","\n","key_points_datasets_list = [key_points_train, key_points_dev, key_points_test]\n","key_points_labels_list = ['key_point', 'topic']\n","\n","for dataset in arguments_dataset_list:\n","    cleanning(dataset, arguments_labels_list)\n","for dataset in key_points_datasets_list:\n","    cleanning(dataset, key_points_labels_list)"]},{"cell_type":"markdown","metadata":{"id":"JtgtRIot1KuS"},"source":["# **2.2 Text tokenization**\n","Text tokenization consists into split a whole sentencences into meaningful pieces of it. It is achieved with the punkt tokenizer by NLTK."]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":243},"executionInfo":{"elapsed":354,"status":"ok","timestamp":1657122586277,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"guDStfKv1KOX","outputId":"1bbb2bd2-797a-4e8b-bb36-e3de3148f55a"},"outputs":[{"name":"stdout","output_type":"stream","text":["start\n","done\n"]},{"data":{"text/html":["\n","  <div id=\"df-e9e67e5f-a1b7-4491-b967-4fda2385bc8c\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>arg_id</th>\n","      <th>argument</th>\n","      <th>topic</th>\n","      <th>stance</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>arg_0_0</td>\n","      <td>[routine, child, vaccinations, is, not, mandat...</td>\n","      <td>[routine, child, vaccinations, should, be, man...</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>arg_0_1</td>\n","      <td>[routine, child, vaccinations, should, not, be...</td>\n","      <td>[routine, child, vaccinations, should, be, man...</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>arg_0_2</td>\n","      <td>[routine, child, vaccinations, should, not, be...</td>\n","      <td>[routine, child, vaccinations, should, be, man...</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>arg_0_3</td>\n","      <td>[a, vaccine, that, has, not, been, sufficientl...</td>\n","      <td>[routine, child, vaccinations, should, be, man...</td>\n","      <td>-1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>arg_0_4</td>\n","      <td>[as, long, as, vaccines, are, not, free, of, s...</td>\n","      <td>[routine, child, vaccinations, should, be, man...</td>\n","      <td>-1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e9e67e5f-a1b7-4491-b967-4fda2385bc8c')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-e9e67e5f-a1b7-4491-b967-4fda2385bc8c button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space.\n","\n","\n","The training objective of GloVe is to learn word vectors such that their dot product equals the logarithm of the words' probability of co-occurrence. Owing to the fact that the logarithm of a ratio equals the difference of logarithms, this objective associates (the logarithm of) ratios of co-occurrence probabilities with vector differences in the word vector space. Because these ratios can encode some form of meaning, this information gets encoded as vector differences as well. "]},{"cell_type":"markdown","metadata":{"id":"lW7ePByp2uBV"},"source":["#####  **Out-of-vocaboulary terms & embedding**\n","At first some functions to handle OOVs terms and token embeddings are define. In particular:\n","* `build_vocaboulary`: create a double vocaboulary from a dataframe to mantain encode and decode tokens;\n","* `load_glove_embedding`: given a chosen embedding dimension, it downloads the corresponding pre-trained corpus;\n","* `get_OOV_terms`: given a vocaboulary and a word listings returns the set of OOV terms;\n","* `build_embedding_matrix`: returns the matrix to pass to the embedding layer as kernel initializer."]},{"cell_type":"code","execution_count":9,"metadata":{"executionInfo":{"elapsed":500,"status":"ok","timestamp":1657122589760,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"Ngv8JTto23oJ"},"outputs":[],"source":["import gensim\n","import gensim.downloader as gloader\n","\n","## Load pre trained GloVe model\n","def load_glove_embedding(embedding_dimension):\n","    download_path = \"glove-wiki-gigaword-{}\".format(embedding_dimension)\n","    return gloader.load(download_path)\n","\n","## Build vocabulary\n","def build_vocabulary(dataset):\n","  idx2word = {}\n","  word2idx = {}\n","  word_idx = 0\n","  for sentence in dataset:\n","    for word in sentence:\n","      if word not in word2idx.keys():\n","        word2idx[word] = word_idx\n","        idx2word[word_idx] = word\n","        word_idx += 1\n","\n","  word_list = list(word2idx.keys())\n","  return idx2word, word2idx, word_list\n","\n","## Extraction of words out of vocabulary\n","def get_OOV_terms(embedding_model, word_list):\n","  embedding_vocabulary = set(embedding_model.vocab.keys())\n","  oov = set(word_list).difference(embedding_vocabulary)\n","  return list(oov)\n","\n","## Build the embedding metrix from GloVe and word2idx \n","def build_embedding_metrix(glove_model, embedding_dimension, word2idx, vocabulary_size):\n","  embedding_matrix = np.zeros((vocabulary_size, embedding_dimension), dtype = np.float32) \n","  for word, idx in word2idx.items():\n","    try:\n","      embedding_vector = glove_model[word]\n","    except(KeyError, TypeError):\n","      embedding_vector = np.random.uniform(low = -0.1, high = 0.1, size = embedding_dimension)\n","    embedding_matrix[idx] = embedding_vector\n","  return embedding_matrix\n","      \n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":172220,"status":"ok","timestamp":1657122765030,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"lKuxL4uN1519","outputId":"47faffdf-8ef2-49ee-935a-a4ae360650fa"},"outputs":[],"source":["## Build the whole embedding metrix for terain, dev and test set\n","embedding_dimension = 300\n","\n","\n","## 1. Load the pretrained GloVe model\n","## NOTICE: Load pre trained GloVe model takes a while\n","pre_trained_GloVe_model = load_glove_embedding(embedding_dimension)\n","\n","\n","## 2. Merge all sentences of train, dev and test dataset\n","sentences_in_train_arguments = arguments_train_token[argument_label]\n","sentences_in_dev_arguments = arguments_dev_token[argument_label]\n","sentences_in_test_arguments = arguments_test_token[argument_label]\n","\n","sentences_in_train_topic = arguments_train_token[topic_label]\n","sentences_in_dev_topic = arguments_dev_token[topic_label]\n","sentences_in_test_topic = arguments_test_token[topic_label]\n","                                                     \n","sentences_in_train_key_points = key_points_train_token[key_point_lable]\n","sentences_in_dev_key_points = key_points_dev_token[key_point_lable]\n","sentences_in_test_key_points = key_points_test_token[key_point_lable]\n","\n","sentences = pd.concat([sentences_in_train_arguments, sentences_in_dev_arguments, sentences_in_test_arguments, \n","                      sentences_in_train_topic, sentences_in_dev_topic, sentences_in_test_topic,\n","                      sentences_in_train_key_points, sentences_in_dev_key_points, sentences_in_test_key_points])\n","## 3. Build vocabulary\n","idx2word, word2idx, word_list = build_vocabulary(sentences)\n","print(f\"The length of idx2word is {len(idx2word)}.\")\n","print(f'Show index to words list: {idx2word}')\n","print(f\"The length of word2idx is {len(word2idx)}.\")\n","print(f'Show words to index list: {word2idx}')\n","print(f\"The length of word_list is {len(word_list)}.\")\n","print(f\"We have over all {len(word_list)} words in dataset.\")\n","print(f'Show words list: {word_list}.')\n","\n","## 4. Extract OOV \n","oov_terms = get_OOV_terms(pre_trained_GloVe_model, word_list)\n","print(f\"The length of oov_terms is {len(oov_terms)}\")\n","print(f\"We have over all {len(oov_terms)} out of vocabulary of GloVe words in out dataset.\")\n","print(f'Show out of vocabulary of GloVe words list: {oov_terms}.')\n","\n","## 5. Build embedding metrix\n","embedding_metrix = build_embedding_metrix(pre_trained_GloVe_model, embedding_dimension, word2idx, len(word_list))\n","print(f\"The shape of embedded metrix is {embedding_metrix.shape}.\")\n","\n","\n","\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"9SiT3sfUWpuY"},"source":["# **Dataset encoding**\n","\n","The datasets are encoded and aligned with the respective identifiers."]},{"cell_type":"code","execution_count":11,"metadata":{"executionInfo":{"elapsed":360,"status":"ok","timestamp":1657122770027,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"8OC17PGpWfqs"},"outputs":[],"source":["def encode_sentence(sentence, word2idx):\n","  return [word2idx[words] for words in sentence]\n","\n","def encode_dataframe(dataframe, word2idx, labels):\n","  encoded_dataframe = pd.DataFrame(columns = labels)\n","  for label in labels:\n","    encoded_dataframe[label] = dataframe[label].apply(lambda sentence: encode_sentence(sentence, word2idx))\n","  return encoded_dataframe\n","\n","## The dataset is encoded and then the id columns restored.\n","arguments_train_encoded = encode_dataframe(arguments_train_token, word2idx, [argument_label, topic_label])\n","arguments_train_encoded['arg_id'] = arguments_train['arg_id']\n","\n","arguments_dev_encoded = encode_dataframe(arguments_dev_token, word2idx, [argument_label, topic_label])\n","arguments_dev_encoded['arg_id'] = arguments_dev['arg_id']\n","\n","arguments_test_encoded = encode_dataframe(arguments_test_token, word2idx, [argument_label, topic_label])\n","arguments_test_encoded['arg_id'] = arguments_test['arg_id']\n","\n","key_points_train_encoded = encode_dataframe(key_points_train_token, word2idx, [key_point_lable, topic_label])\n","key_points_train_encoded['key_point_id'] = key_points_train['key_point_id']\n","\n","key_points_dev_encoded = encode_dataframe(key_points_dev_token, word2idx, [key_point_lable, topic_label])\n","key_points_dev_encoded['key_point_id'] = key_points_dev['key_point_id']\n","\n","key_points_test_encoded = encode_dataframe(key_points_test_token, word2idx, [key_point_lable, topic_label])\n","key_points_test_encoded['key_point_id'] = key_points_test['key_point_id']"]},{"cell_type":"markdown","metadata":{"id":"C0mEwUY5bXP-"},"source":["# **Labels alignment**\n","\n","\n","The pairs of argument and key-point pairs identifier are extracted. With these pairs the encoded arguments and key-points are extracted.\n","\n","Topic and stance gives the “main context”. The argument and the key-point can also be integrated with this main context and then matched."]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4856,"status":"ok","timestamp":1657122776734,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"r9Ir5cO9bfal","outputId":"dd66e712-a641-4a51-99d5-77f788860df9"},"outputs":[{"name":"stdout","output_type":"stream","text":["The max length of sentences in arguments_train_token with label argument is 44.\n","The max length of sentences in arguments_train_token with label topic is 9.\n","The max length of sentences in arguments_dev_token with label argument is 41.\n","The max length of sentences in arguments_train_token with label topic is 10.\n","The max length of sentences is 44.\n"]}],"source":["from keras.utils import pad_sequences\n","## Find the longest sentence in train and dev dataset\n","train_argument_length = max([len(i) for i in arguments_train_token[argument_label]])\n","print(f'The max length of sentences in arguments_train_token with label argument is {train_argument_length}.')\n","\n","train_topic_length = max([len(sentence) for sentence in arguments_train_token[topic_label]])\n","print(f'The max length of sentences in arguments_train_token with label topic is {train_topic_length}.')\n","\n","dev_argument_length = max([len(sentence) for sentence in arguments_dev_token[argument_label]])\n","print(f'The max length of sentences in arguments_dev_token with label argument is {dev_argument_length}.')\n","\n","dev_topic_length = max([len(sentence) for sentence in arguments_dev_token[topic_label]])\n","print(f'The max length of sentences in arguments_train_token with label topic is {dev_topic_length}.')\n","\n","maxlen = max(max(train_argument_length, train_topic_length), max(dev_argument_length, dev_topic_length))\n","print(f'The max length of sentences is {maxlen}.')\n","\n","## Flatten a nested list.\n","def flatten(t):\n","  return [item for sublist in t for item in sublist]\n","\n","indexes = [i for i in range(len(labels_test))]\n","\n","## Generations of pairs arg-kp of the test set.\n","arg_id = [labels_test['arg_id'][k] for k in indexes]\n","kp_id = [labels_test['key_point_id'][k] for k in indexes]\n","\n","## Arguments extraction.\n","arguments = flatten([arguments_test_encoded.loc[arguments_test_encoded['arg_id'] == arg]['argument'].tolist() for arg in arg_id])\n","arguments = pad_sequences(arguments, maxlen = maxlen, padding = 'post')\n","paded_arguments = zip(arg_id, list(arguments))\n","\n","from sklearn.metrics.pairwise import cosine_similarity\n","\n","## Key-points extraction.\n","keypoints = flatten([key_points_test_encoded.loc[key_points_test_encoded['key_point_id'] == kp]['key_point'].tolist() for kp in kp_id])\n","keypoints = pad_sequences(keypoints, maxlen = maxlen, padding = 'post')\n","paded_key_points = zip(kp_id, list(keypoints))\n","\n","## Topics extraction.\n","topics = flatten([arguments_test_encoded.loc[arguments_test_encoded['arg_id'] == arg]['topic'].tolist() for arg in arg_id])\n","topics = pad_sequences(keypoints, maxlen = maxlen, padding = 'post')\n","paded_topics = zip(arg_id, list(topics))"]},{"cell_type":"markdown","metadata":{"id":"8J5Vw5BYhLNw"},"source":["# **GloVe application**\n","\n","GloVe embedding matrix is applied, unrolling the vectors in order to apply the cosine similarity."]},{"cell_type":"markdown","metadata":{"id":"Hu6gA9SWkmX8"},"source":["# **Cosine similarity calculation and evaluation**\n","\n","Cosine similarity is evaluated wether if the topic is considered or not. The general performances are worsened if the topic is taken into account, but the average precision score is higher in the latter case.\n","\n","The cause can be the redundacy in of the such feature."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6525,"status":"ok","timestamp":1657122785521,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"Kv8978sHkw4I","outputId":"ea5d9c4c-905f-4276-9121-1376509a32a2"},"outputs":[],"source":["from sklearn.metrics.pairwise import cosine_similarity\n","## Calculate score without topic\n","score_arguments_to_kp_with_topic_sum_vector = {}\n","predictions_sum_vector_with_topic = []\n","\n","score_arguments_to_kp_without_topic_sum_vector = {}\n","predictions_sum_vector_without_topic = []\n","\n","score_arguments_to_kp_without_topic = {}\n","predictions_GloVe_without_topic = []\n","\n","score_arguments_to_kp_with_topic = {}\n","predictions_GloVe_with_topic = []\n","\n","paded_arguments_list = list(paded_arguments)\n","paded_key_points_list = list(paded_key_points)\n","paded_topics_list = list(paded_topics)\n","\n","threshold = 0.9\n","\n","for i in range(len(indexes)):\n","\n","  arg_id = paded_arguments_list[i][0]\n","  kp_id = paded_key_points_list[i][0]\n","\n","  embedded_argument = embedding_metrix[paded_arguments_list[i][1]]\n","  embedded_argument_sum_vector = embedded_argument.sum(axis = 0).reshape(1, -1)\n","  embedded_argument = embedded_argument.reshape(1, -1)\n","\n","  embedded_key_points = embedding_metrix[paded_key_points_list[i][1]]\n","  embedded_key_points_sum_vector = embedded_key_points.sum(axis = 0).reshape(1, -1)\n","  embedded_key_points = embedded_key_points.reshape(1, -1)\n","\n","  embedded_arguments_topic = embedding_metrix[np.concatenate((paded_arguments_list[i][1], paded_topics_list[i][1]), axis = -1)]\n","  embedded_arguments_topic_sum_vector = embedded_arguments_topic.sum(axis = 0).reshape(1, -1)\n","  embedded_arguments_topic = embedded_arguments_topic.reshape(1, -1)\n","\n","  embedded_keypoints_topic = embedding_metrix[np.concatenate((paded_key_points_list[i][1], paded_topics_list[i][1]), axis = -1)]\n","  embedded_keypoints_topic_sum_vector = embedded_keypoints_topic.sum(axis = 0).reshape(1, -1)\n","  embedded_keypoints_topic = embedded_keypoints_topic.reshape(1, -1)\n","\n","  score_without_topic = cosine_similarity(embedded_argument, embedded_key_points)\n","  score_with_tpopic = cosine_similarity(embedded_arguments_topic, embedded_keypoints_topic)\n","  score_without_topic_sum_vector = cosine_similarity(embedded_argument_sum_vector, embedded_key_points_sum_vector)\n","  score_with_topic_sum_vector = cosine_similarity(embedded_arguments_topic_sum_vector, embedded_keypoints_topic_sum_vector)\n","\n","\n","  predictions_sum_vector_with_topic.append(listFlatten(score_with_topic_sum_vector))\n","  predictions_sum_vector_without_topic.append(listFlatten(score_without_topic_sum_vector))\n","  predictions_GloVe_without_topic.append(listFlatten(score_without_topic))\n","  predictions_GloVe_with_topic.append(listFlatten(score_with_tpopic))\n","  if arg_id in score_arguments_to_kp_without_topic.keys():\n","    score_arguments_to_kp_without_topic[arg_id][kp_id] = score_without_topic\n","    score_arguments_to_kp_with_topic[arg_id][kp_id] = score_with_tpopic\n","    score_arguments_to_kp_with_topic_sum_vector[arg_id][kp_id] = score_with_topic_sum_vector\n","    score_arguments_to_kp_without_topic_sum_vector[arg_id][kp_id] = score_without_topic_sum_vector\n","  else:\n","    score_arguments_to_kp_without_topic[arg_id] = {kp_id: score_without_topic}\n","    score_arguments_to_kp_with_topic[arg_id] = {kp_id: score_with_tpopic}\n","    score_arguments_to_kp_with_topic_sum_vector[arg_id] = {kp_id: score_with_topic_sum_vector}\n","    score_arguments_to_kp_without_topic_sum_vector[arg_id] = {kp_id: score_without_topic_sum_vector}\n","print('score without topic: ',score_arguments_to_kp_without_topic)\n","print('score with topic: ',score_arguments_to_kp_with_topic)\n","print(predictions_GloVe_without_topic)\n"]},{"cell_type":"markdown","metadata":{"id":"0A6v0jHfiowz"},"source":["# **GloVe Model Evaluation**"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":798},"executionInfo":{"elapsed":1035,"status":"ok","timestamp":1657122809150,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"OlWg6-FTfddj","outputId":"d7c885c1-7eea-43d6-b0e3-40cf3058358a"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model Evaluation -- Unsupervised Model: GloVe Embedding, arguments to key points score without topic data\n","              precision    recall  f1-score   support\n","\n","         0.0       0.47      0.82      0.60      1641\n","         1.0       0.47      0.15      0.22      1785\n","\n","    accuracy                           0.47      3426\n","   macro avg       0.47      0.48      0.41      3426\n","weighted avg       0.47      0.47      0.40      3426\n","\n","The average precision score is: 0.15360289576375236.\n","The balanced accuracy score is: 0.4691178027896281.\n","The tuned threshold is: 0.7843729257583618.\n","The confusion matrix is: \n"]},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 2 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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["print(\"Model Evaluation -- Unsupervised Model: GloVe Embedding, arguments to key points score without topic data\")\n","evaluate_classifier(np.asarray(listFlatten(predictions_GloVe_without_topic)), true_values)"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":798},"executionInfo":{"elapsed":1253,"status":"ok","timestamp":1657122812590,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"YnD9EPALjKeK","outputId":"5ed3243f-7f41-4737-f513-cdf170c682cb"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model Evaluation -- Unsupervised Model: GloVe Embedding, arguments to key points score with topic data\n","              precision    recall  f1-score   support\n","\n","         0.0       0.46      0.82      0.59      1630\n","         1.0       0.47      0.14      0.22      1796\n","\n","    accuracy                           0.47      3426\n","   macro avg       0.47      0.48      0.41      3426\n","weighted avg       0.47      0.47      0.40      3426\n","\n","The average precision score is: 0.1529563187448202.\n","The balanced accuracy score is: 0.4661243230159451.\n","The tuned threshold is: 0.8927079439163208.\n","The confusion matrix is: \n"]},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["print(\"Model Evaluation -- Unsupervised Model: GloVe Embedding, arguments to key points score with topic data\")\n","evaluate_classifier(np.asarray(listFlatten(predictions_GloVe_with_topic)), true_values)"]},{"cell_type":"code","execution_count":16,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":798},"executionInfo":{"elapsed":994,"status":"ok","timestamp":1657122816115,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"3-jfwqMjjWFm","outputId":"b675aa25-fb0e-4b35-cde9-f0652564256a"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model Evaluation -- Unsupervised Model: GloVe Embedding, arguments to key points score without topic data in vector sum\n","              precision    recall  f1-score   support\n","\n","         0.0       0.50      0.84      0.62      1705\n","         1.0       0.50      0.16      0.24      1721\n","\n","    accuracy                           0.50      3426\n","   macro avg       0.50      0.50      0.43      3426\n","weighted avg       0.50      0.50      0.43      3426\n","\n","The average precision score is: 0.1601722253972267.\n","The balanced accuracy score is: 0.4964486702369066.\n","The tuned threshold is: 0.9885216951370239.\n","The confusion matrix is: \n"]},{"data":{"image/png":"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","text/plain":["<Figure 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NQ+3D7x0b+GpVTjukZ7JDEBFJuEZrBO7+YPj99sSEk1yds4PaQElZBe9+spnhfTvTpV2bJEclIhJfUR8o+5WZdTKzTDN71cw2mtkl8Q4uWZ75YC3feug9cu94mR2l5ckOR0QkrqKOGjoj7CA+l2Dd4kHA9+MVVKK1a5POaYf05J8L1rNm807++u6q6mNrt7TIUbIiIpFFTQRVTUjnAH93921xiicpFt8xmqkTj2JXRTAJ3cK1Xw6KOvP/vcm24rJkhSYiEndRE8E/zWwJMAJ41cx6ACXxC2vfMq9wa7JDEBGJm0iJwN1vBkYCee5eBnwBjI1nYCIikhiNJgIzOzX8fgEwChgbvh5NkBhalee/e2L16yU/G139+p2Vmyiv0NoFItI6xaoRnBx+P6+er3PjGFdSZKZb9euszHSeujbIdffPWsGLiz5NVlgiInEV6zmCW8PvlycmnOTq3SUbgGEHdAJg284v1zQu3qU1jUWkdYo0sY6Z/S/wK3ffGm53Bb7n7j+JZ3CJ1qFtBgV3nVO9vXLjF0mMRkQkMaKOGjqrKgkAuPsW4Oz4hLTv6L9fu922vzd9Puf/4W12qnYgIq1I1ESQbmZtqzbMLBto20j5VuGMYftzz9eHA/DE+2t4am4hH6zeyobtwcjZikpPZngiIs0iaiJ4jOD5gUlmNgl4GXgkfmHtO449KJimes6qLbvtnzDlHQb+z/PMVCeyiLRwUdcj+KWZzQdOD3f9zN1nxi+sfUdped1hoyffPav69dWPztmtX0FEpKVpyiosHwHl7v6KmbUzs47uvj1ege0rPt9eWv26U1YGRSWahE5EWpeos49eBTwJPBju6gM8E6+g9iWDenYA4PYxw7htzLDq/Qf3+nI5hg1FJRTvUoIQkZYpah/Bd4DjgSIAd/8YSIlVXLp1aEvBXefwrZE5XHBkXxbefiZd22XyzHeOJz0teADt6P99laG3zGTVpmC46faSMrbt1ER1ItIyRE0Epe5e/XSVmWUAMYfMmNloM1tqZsvN7OZ6jt9kZovNbEG4zsGB0UNPjg5tM/jgljPIbpPO2Yf33u3Y1+57mxc+XM/ht73E8NtfoqRMw0xFZN8XNRG8YWb/A2Sb2VeBvwPPNfYGM0sH7gPOAoYCE8xsaK1iHxBMZPcVgqanXzUl+GT73YQjdtveUlzGtY/Nrd6e9t5qXl78GZ8VpcxErSLSAkXtLP4hcCXwIXA18DzwpxjvORpY7u4rAczscYIZSxdXFXD312uUfwdocauevf7fo+jRsS2H3Vp3ENXtz1XfqkYWicg+K2YiCD/ZL3L3Q4A/NuHcfYA1NbYLgWMaKT8JeKGBGCYDkwH69+/fhBDib0D39rttD+zRnm7t2/JeweYkRSQi0jQxE4G7V4Tt/P3dfXU8ggjXP87jy9lOa8cwBZgCkJeXt08+zltw1zk8NaeQ84/ow86yCt4r2MzIgd04+CcvAjD5L/lMuSwvyVGKiNQVtWmoK7DIzN4jWJQGAHcf08h71gL9amz3DfftxsxOB34MnOzupbWPtyTjRvQFoH3bDE45ePdBVS8t/oyXFn3KcQO70TErs3r/jtJyOrRtyuMcIiLNK+pfoJ/uwbnfBwab2QCCBHAx8I2aBczsCIJnE0a7+4Y9uMY+r+Cuc8i5+V8ATH50DgB/+OaR/PbVj1nyad3n8SaOzOF/zj6Uj9YXUenOEf27JjReEUk9jSYCM8sCrgEGEXQUT3X3SE9OuXu5mV0HzATSgYfcfZGZ3QHku/sM4G6gA/B3MwNYHaOW0SIt+/lZDPnJl90f364xsqi2h/9dwMP/Lqjefura48jOzKBjVgazP/6cg/fvwJBeHclftYW//mcVl43M4eQhPXY7x4aiEsyMFxeuZ+XnX1C0s5ycbu341vE5lOyqoFuHttXPQIiImHvDTe5m9gRQBswmGAa6yt1vSFBs9crLy/P8/Pwmv2/Ez15m0xe7yP/J6XTvkPiJU4tKyjj6zlcoKfty7qLzhh/A0QP246fPLExoLH26ZPPCf53IhqIStpeUc/+sFcxaupHc/l249uSB9OqUxaG9OxImZ0rLK8hISyM9zXD36v0i0nKY2Rx3r7ejMlbT0FB3Pzw8yVTgveYOLlV0yspk5n+dxMl3z+KVm06unroC4NJjv3yO7uanFnDRUf3Yv1MWI+96LS6xrN26k6/c9lKd/e99spn3Pok22umpa0cy4sAvm61Kyioo2llGz05ZFO8qJyMtjTYZUR9T+ZK7s+TT7Xy8YQeL1xXxwBsr6pT5v4tzGZvbp8nnFpH6xaoRzHX3IxvaToaWWiNoDsW7yvl7fiHfGpkDwLadZdz5r8Wcekgvstuk81lRCacf2ov92rdp8Bw//seHPPZu3cFfJw/pwRvLNsYr9GptMtLYVV5JTrd2fHVoL0rLK5mzaguL1hU16Tx9u2ZTuGVnnf1H5XTl1EN6cWC3dgzu2YGSskr+veJzindVcGjvjnTKzmTxuiKG9u7E8H5daN+EjvqKSmfbzjLWb9tJp6xMNmwv4fMdu/isqISeHduS3SaDz4pKKNyykwWFWyn4/AuGHdCZAd3bs/Sz7eR0a0fHrEw+31HKgsJtZGems1/7Nrz7yWYuPz6HFRt2MOqQnvTq2JbBvTqy+YtSthSX0bVdG9ZsLmbzF7vYWVZB13ZtyEw3yiudsopKOmdnclTOfmRnppNWT5Ofu7O9tJwNRaW0b5tOpUNlpVPpzvptJWzasYvO2ZmkpxnllZWUVzgdszI4eP+OtGuTQXqaUVHp7Cgtp7SsgrJKr44doLyikp1lFZRVOBu2l1BW7vTukkVFpbNzVwWfFZXQNjOdSne6ZGdyYLf2pBmUlFXSJiOtTjNlWUUlFZWOO5RXBq+3FpdR6R5+Bf8WG7eXkpWZTr/9simvcNq1SadTdiYZaRap1urulFU420vKqHCnotIpr3C+2FVOVkZ69bXcnQp3Kiuh0p3ySmfTjlLMoKzCObxPZxxYt3UnG4pKKSmrYL8Owb+ZmTF/zVbcYcSBXclMNzLT09iwvYQ0M4p3VbDk0yIO79OF8opK8ldtYXtJGSMO7MoT76+hd+dsln62nZOG9MDdmTgyh9MO7RX5d7amxmoEsRJBBV+OEjIgGygOX7u7d9qjiPZCKieCRPl8RyldsjPJSK/7if7dlZsYP+WdhMQxqGcHfv314bRvm8GCwuA/0x9nr6y3k705HdA5i777tWPe6q04wR+LluIrfTuz9NPt9U6fvq86sFs7Vm0qbtZz9umSzdqtO+nVqS2fFbW8wYjt2qTvtk76/p2yOKBLFpNPGsjow/bfo3PucdOQu6fv0RWlRWssUR5zULcmPyW97LPtdGmXyVNz1nLpcQfy+Hur+es7qyjYVEx2Zjq3jxnGecMPILtNw79uVU1p40b05cWFn/Lh2q1MOLo/B3TOxgzmrt5Keppx/h/epr7PNlU1kSoDe7RnRbgmde3axWfbS+nfrR1HDejK2i07KQj/SJ03/ACG9u7EQ29/wsbtpYwZfgB5OV35aH0Rpx0SfErb9EUpfbu2Y1DPDnTKyqS8spKtxWUU76pg/05ZbC8tY0dpOR2zMjmgcxZmQb9LSVklT85ZQ9+u7Vi9uRh3Z+OOUl748FNuOH0ws5ZupKyikq7t2nDcwG70368dmelpbC8pY0HhNj7esIOn5hayoHBbnXvv0bEt20vKaJuRzvij+jGoRwfMIM2Mt5d/TnqaceSBXTmwW3DOrcVlLFq3jUqHdAtqCb97bTknDu7OOys3UR5+Wq/6mbyxdAO5/bvy5rKNDO3diZOG9GDmok85eUgPDuvTmWnvrWZo707MXb2FjDRjfhjjGUN7kZWZjhm7JYLhfTuzblsJk04YQEaakZ5mTM8v5LLjDqR92wzSzUgz+PucQlZs3MGY4QfQNiONe15aVn2OtVuDf89OWZl1EsEFR/bh6blruXbUQFZt+oLCLTu5KK8fGWlGWprx2DurGDeiL52zM7HwWmlm4VfweldFJQd0yWb+mq1Mz1/DyIHdGNSzA+5BjWZYn860SU+je4e2dMjKoKSsgvKKoAZXVlHJuq0lpBn07dqOjlkZZGWmk5FukWszza3RGsG+SDUCEZGma6xG0PTePBERaVWUCEREUpwSgYhIilMiEBFJcUoEIiIpTolARCTFKRGIiKQ4JQIRkRSnRCAikuKUCEREUpwSgYhIilMiEBFJcUoEIiIpTolARCTFKRGIiKQ4JQIRkRQXfcHWfVhZWRmFhYWUlJQ0WObu07tR4fDpqhVsrGddV0k9WVlZ9O3bl8zMzGSHIpJUrSIRFBYW0rFjR3Jychpc5s3XFVFeWcnBvTuRWc9avJJa3J1NmzZRWFjIgAEDkh2OSFK1ir+IJSUldOvWLSlrfUrLZGZ069at0VqkSKpoFYkAUBKQJtPvjEig1SQCERHZM0oEzeTTTz/l4osvZuDAgYwYMYKzzz6bZcuWUVBQwGGHHdZs17nlllt45ZVXAJg9ezbDhg0jNzeXtWvXcuGFF+7Vud2dU089laKioup9zzzzDGbGkiVLqvcVFBSQnZ1Nbm4uQ4cO5ZprrqGysnKvrl1aWsr48eMZNGgQxxxzDAUFBfWWu+KKK+jZs2edn+n48ePJzc0lNzeXnJwccnNzAfjwww+ZOHHiXsUm0topETQDd+f8889n1KhRrFixgjlz5vCLX/yCzz77rNmvdccdd3D66acD8Nhjj/GjH/2IefPm0adPH5588snI5ykvL6+z7/nnn2f48OF06tSpet+0adM44YQTmDZt2m5lBw4cyLx581iwYAGLFy/mmWee2cM7CkydOpWuXbuyfPlybrzxRn74wx/WW27ixIm8+OKLdfY/8cQTzJs3j3nz5jFu3DguuOACAA4//HAKCwtZvXr1XsUn0pq1ilFDNd3+3CIWryuqs794VwXuTru2GTS1ZXjoAZ249bxhDR5//fXXyczM5JprrqneN3z4cIDdPtkWFBRw6aWX8sUXXwDw+9//npEjR7J+/XrGjx9PUVER5eXl3H///YwcOZJJkyaRn5+PmXHFFVdw4403MnHiRM4991y2bt3K9OnTmTlzJi+88AJ33nkn5557LgsXLqSiooKbb76ZWbNmUVpayne+8x2uvvpqZs2axU9/+lO6du3KkiVLWLZs2W738dhjjzF58uTq7R07dvDWW2/x+uuvc95553H77bfXufeMjAxGjhzJ8uXLm/hT3d2zzz7LbbfdBsCFF17Iddddh7vXacc/6aSTGqwtQJCUp0+fzmuvvVa977zzzuPxxx/nBz/4wV7FKNJatbpEkAwLFy5kxIgRMcv17NmTl19+maysLD7++GMmTJhAfn4+f/vb3zjzzDP58Y9/TEVFBcXFxcybN4+1a9eycOFCALZu3brbua688kreeustzj33XC688MLd/jhOnTqVzp078/7771NaWsrxxx/PGWecAcDcuXNZuHBhvUMm3377bR588MHq7WeffZbRo0czZMgQunXrxpw5c+rcZ3FxMa+++ip33HFHnfOdeOKJbN++vc7+e+65p7pWU2Xt2rX069cPCJJL586d2bRpE927d2/sR1rH7Nmz6dWrF4MHD67el5eXx1133aVEINKAVpcIGvrkvjh8juDQJD5HUFZWxnXXXce8efNIT0+v/kR+1FFHccUVV1BWVsbXvvY1cnNzOeigg1i5ciXXX38955xzTvUf8iheeuklFixYUN1UtG3bNj7++GPatGnD0Ucf3eC4+c2bN9OxY8fq7WnTpnHDDTcAcPHFFzNt2rTqRLBixQpyc3MxM8aOHctZZ51V53yzZ8+OHHNzmTZtGhMmTNhtX8+ePVm3bl3CYxFpKeKaCMxsNPB/QDrwJ3e/q9bxtsBfgBHAJmC8uxfEM6Z4GDZsWKT2+d/85jf06tWL+fPnU1lZSVZWFhA0d7z55pv861//YuLEidx0001cdtllzJ8/n5kzZ/LAAw8wffp0HnrooUjxuDu/+93vOPPMM3fbP2vWLNq3b9/g+zIyMqisrCQtLY3Nmzfz2muv8eGHH2JmVFRUYGbcfffdwJd9BI1pSo2gT58+rFmzhr59+1JeXs62bdvo1q1bpPutUl5eztNPP82cOXN2219SUkJ2dnaTziWSSuL20djM0oH7gLOAocAEMxtaq9gkYIu7DwJ+A/wyXvHE06mnnkppaSlTpkyp3rdgwYI6n4i3bTiQyeUAAAuESURBVNtG7969SUtL49FHH6WiogKAVatW0atXL6666iquvPJK5s6dy+eff05lZSXjxo3j5z//OXPnzo0cz5lnnsn9999PWVkZAMuWLavul2jMwQcfzMqVKwF48sknufTSS1m1ahUFBQWsWbOGAQMGNOlT/uzZs6s7cGt+1U4CAGPGjOGRRx6pvvapp57a5HH+r7zyCocccgh9+/bdbf+yZcuadeSWSGsTzzaSo4Hl7r7S3XcBjwNja5UZCzwSvn4SOM3i/JRPPE5uZvzjH//glVdeYeDAgQwbNowf/ehH7L///ruV+/a3v80jjzzC8OHDWbJkSfWn81mzZjF8+HCOOOIInnjiCW644QbWrl3LqFGjyM3N5ZJLLuEXv/hF5HiuvPJKhg4dypFHHslhhx3G1VdfXe8oodrOOeccZs2aBQRNLOeff/5ux8eNG1dn9FBzmTRpEps2bWLQoEHce++93HVXUHlct24dZ599dnW5CRMmcNxxx7F06VL69u3L1KlTq489/vjjdZqFIOjMP+ecc+ISt0hrYO4enxObXQiMdvcrw+1LgWPc/boaZRaGZQrD7RVhmc9rnWsyMBmgf//+I1atWrXbtT766CMOPfTQRuPZXlLG6k3FDOvTea/vrbVav349l112GS+//HKyQ2k2paWlnHzyybz11ltkZNRtCY3yuyPSGpjZHHfPq+9Yi3iOwN2nuHueu+f16NFjj87RMStTSSCG3r17c9VVV+32QFlLt3r1au666656k4CIBOL5v2Mt0K/Gdt9wX31lCs0sA+hM0GksSXLRRRclO4RmNXjw4N2GkopIXfGsEbwPDDazAWbWBrgYmFGrzAzgW+HrC4HXfA/bquLVxCWtl35nRAJxSwTuXg5cB8wEPgKmu/siM7vDzMaExaYC3cxsOXATcPOeXCsrK4tNmzbpP7ZEVrUeQdUQXpFUFrfO4njJy8vz/Pz83fZFWaFMpDatUCappLHO4lbRg5aZmalVpkRE9lCLGDUkIiLxo0QgIpLilAhERFJci+ssNrONwKqYBevXHfg8ZqnWRfecGnTPqWFv7vlAd6/3idwWlwj2hpnlN9Rr3lrpnlOD7jk1xOue1TQkIpLilAhERFJcqiWCKbGLtDq659Sge04NcbnnlOojEBGRulKtRiAiIrUoEYiIpLhWmQjMbLSZLTWz5WZWZ0ZTM2trZk+Ex981s5zER9m8ItzzTWa22MwWmNmrZnZgMuJsTrHuuUa5cWbmZtbihxpGuWczuyj8t15kZn9LdIzNLcLvdn8ze93MPgh/v8+u7zwthZk9ZGYbwhUc6ztuZvbb8OexwMyO3OuLunur+gLSgRXAQUAbYD4wtFaZbwMPhK8vBp5IdtwJuOdTgHbh62tT4Z7Dch2BN4F3gLxkx52Af+fBwAdA13C7Z7LjTsA9TwGuDV8PBQqSHfde3vNJwJHAwgaOnw28QLAE+7HAu3t7zdZYIzgaWO7uK919F/A4MLZWmbHAI+HrJ4HTzCwe69onSsx7dvfX3b043HyHYMW4lizKvzPAz4BfAq1hjvIo93wVcJ+7bwFw9w0JjrG5RblnBzqFrzsD6xIYX7Nz9zeBzY0UGQv8xQPvAF3MrPfeXLM1JoI+wJoa24XhvnrLeLCAzjagW0Kii48o91zTJIJPFC1ZzHsOq8z93P1fiQwsjqL8Ow8BhpjZ22b2jpmNTlh08RHlnm8DLjGzQuB54PrEhJY0Tf3/HlOrWI9AojOzS4A84ORkxxJPZpYG3AtMTHIoiZZB0Dw0iqDW96aZHe7uW5MaVXxNAB5291+b2XHAo2Z2mLtXJjuwlqI11gjWAv1qbPcN99VbxswyCKqTmxISXXxEuWfM7HTgx8AYdy9NUGzxEuueOwKHAbPMrICgLXVGC+8wjvLvXAjMcPcyd/8EWEaQGFqqKPc8CZgO4O7/AbIIJmdrrSL9f2+K1pgI3gcGm9kAM2tD0Bk8o1aZGcC3wtcXAq952AvTQsW8ZzM7AniQIAm09HZjiHHP7r7N3bu7e4675xD0i4xx9/z6T9ciRPndfoagNoCZdSdoKlqZyCCbWZR7Xg2cBmBmhxIkgo0JjTKxZgCXhaOHjgW2ufv6vTlhq2sacvdyM7sOmEkw4uAhd19kZncA+e4+A5hKUH1cTtApc3HyIt57Ee/5bqAD8PewX3y1u49JWtB7KeI9tyoR73kmcIaZLQYqgO+7e4ut7Ua85+8BfzSzGwk6jie25A92ZjaNIJl3D/s9bgUyAdz9AYJ+kLOB5UAxcPleX7MF/7xERKQZtMamIRERaQIlAhGRFKdEICKS4pQIRERSnBKBiEiKUyKQuDOzCjObZ2YLzew5M+vSzOcvCMfMY2Y7GiiTbWZvmFm6meWY2c4wpsVm9kD4JHJTrplnZr8NX48ys5E1jl1jZpftzT2F57nNzP47RpmHzezCJpwzp6FZLWuVu9PM1tT+eZrZdWZ2RdTrScugRCCJsNPdc939MILnNr6ThBiuAJ5294pwe4W75wJfIZix8mtNOZm757v7d8PNUcDIGscecPe/7H3ISfUcwYRvtT1E65/LJ+UoEUii/YdwgiwzG2hmL5rZHDObbWaHhPt7mdk/zGx++DUy3P9MWHaRmU1u4nW/CTxbe2c46eC/gUHhp+XX7Ms1G/qH1/16WJuZb2ZvhvtGmdk/LVjL4hrgxrCGcWLVJ3kzO8TM3qu6Vnj+D8PXI8Iayhwzmxlr9kgzu8rM3g9jeMrM2tU4fLqZ5ZvZMjM7NyyfbmZ3h+9ZYGZXN+WH5e7v1Pe0ajiDbYGZ1ZckpIVSIpCEMbN0gqkAqp76nQJc7+4jgP8G/hDu/y3whrsPJ5iXfVG4/4qwbB7wXTOLNGNsODXBQe5eUM+xdmFMHwK/Ax5x968Aj4VxANwCnBnGs9vT2OE5HwB+E9Z6Ztc4tgRoY2YDwl3jgSfMLDO81oXh/TwE3BnjNp5296PCGD4imF+nSg7Bp/dzgAfMLCs8vs3djwKOAq6qEUfVvR9gZs/HuG598oET9+B9so9qdVNMyD4p28zmEdQEPgJeNrMOBM0pVVNeALQNv58KXAYQNuVsC/d/18zOD1/3I5hMLcr0Cd2B2rNvDgxjcuBZd3/BzB4FLgiPPwr8Knz9NvCwmU0Hno5wvZqmEySAu8Lv44GDCSbEezm893Qg1lwxh5nZz4EuBFOFzKx5jXCmzY/NbCVwCHAG8JUa/QedCX5ey6re5O7rCKYqaKoN4TWklVAikETY6e654afvmQR9BA8DW8N2+pjMbBRwOnCcuxeb2SyCycUiXb+esiuiXtvdrzGzYwg+cc8xsxERrwvwBEGyezo4lX9sZocDi9z9uCac52Hga+4+38wmEk4sVxVi7ZAJVq+63t1rJgyseZZlzSL4mUoroaYhSZiwffm7BJOEFQOfmNnXoXod1uFh0VcJltOsauvuTPCJdkuYBA4hmFY66nW3AOlhk0lj/s2XExB+E5gdxjDQ3d9191sIZrXsV+t92wmmva7v2isIJn/7KUFSAFgK9LBg7nzMLNPMhsWIrSOwPmxW+matY183szQzG0iwpONSgoR7bVgeMxtiZu1jXCOqIUDMkUfScigRSEK5+wfAAoLFRL4JTDKz+QT9AFVLEN4AnBJ2rM4hGNXzIpBhZh8RNLO808RLvwScEKPM9cDlZrYAuDSMA+BuM/swHHb5b4J1c2t6Dji/qrO4nvM+AVzCl3Pm7yKY/vyX4b3Po8aoowb8FHiXoJlqSa1jq4H3CFadu8bdS4A/AYuBuWHcD1KrBaCxPgIz+5UFM1+2M7NCM7utxuHjgZdjxCstiGYflZRgwbKVN7r7pcmOpSWzYF2Lm/RzbF1UI5CU4O5zgdfDkUuy57oT1E6kFVGNQEQkxalGICKS4pQIRERSnBKBiEiKUyIQEUlxSgQiIinu/wPm58f0FiLCZwAAAABJRU5ErkJggg==","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["print(\"Model Evaluation -- Unsupervised Model: GloVe Embedding, arguments to key points score without topic data in vector sum\")\n","evaluate_classifier(np.asarray(listFlatten(predictions_sum_vector_without_topic)), true_values)"]},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":798},"executionInfo":{"elapsed":680,"status":"ok","timestamp":1657122819077,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"dMAfnYIwjt7W","outputId":"a504c919-a2c0-4ccd-ef92-abe397a7469a"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model Evaluation -- Unsupervised Model: GloVe Embedding, arguments to key points score with topic data in vector sum\n","              precision    recall  f1-score   support\n","\n","         0.0       0.49      0.84      0.62      1700\n","         1.0       0.49      0.16      0.24      1726\n","\n","    accuracy                           0.49      3426\n","   macro avg       0.49      0.50      0.43      3426\n","weighted avg       0.49      0.49      0.43      3426\n","\n","The average precision score is: 0.1593809090507235.\n","The balanced accuracy score is: 0.4934192611418716.\n","The tuned threshold is: 0.997416615486145.\n","The confusion matrix is: \n"]},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 2 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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["print(\"Model Evaluation -- Unsupervised Model: GloVe Embedding, arguments to key points score with topic data in vector sum\")\n","evaluate_classifier(np.asarray(listFlatten(predictions_sum_vector_with_topic)), true_values)"]},{"cell_type":"markdown","metadata":{"id":"pDmhyTW1ykWd"},"source":["# **BERT embeddings**\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":273,"referenced_widgets":["d01a95818c2045138205e016ca6b69f6","607979376e9446b4a1f3a8b439e48619","783496100f144a158ebac01de7b68b83","aed3ec0f9ee94661a8098b013bd45838","27ed1b5d4bdf4745924d75e3de7b68f8","594ddeaf0fe945d295cc7026358f12d7","12d4f571632a4870a839c248e2adb900","ca0592d5f16b4fbc91eb60cc663228d3","5a2c985b28b94413af2f2f30a0c558ee","a0ae8961a08d46eaaf4f6be6afb8b2a7","d597ea1ec8704a358fe2557858e5b1bb","1375c0e1a9dd4d2383c34a52b71cbbc4","1695d1600a2c4d23abc1566bd77b55df","963377c7e7d54d1ea7ce5395a91a1483","de628bc11f1b45658f6e0ef6d855e510","ca26f568541842dc8aa8fb6b956856b7","18d299c1634e46a5ac56b8629d40d4cd","d8105e1adf0749bda6fdb9c270309aa8","28655cc280554d01b970d4ce288dc553","7c664e4906a7443c8f9b6a841baf5ef8","31e58cc60bb74b7ba3011c0da6871579","950d776a8fa54ab7bcda7048e1ebbfca","4d40a504aeb343d783d8ba95dc548bdb","d9af89150af74123bca06ea15ca0f4d3","6dcada00c51c4317ad2f3f0db4ce2726","626b4d0ceabf478ba910fe06df19eea5","1bd5d83e15d64ede93b846443d370bb2","b0297f6f790d4e1c8d8fa6d3aff19524","1d941e2d8ddd452b8ff08fee1baba5dc","2f83b3cd5a3d4ef8a832c52b972cbb25","db1fc6999160418e82d48fb658da0229","36374d2dfb7744c5baa801d8c677dbe5","fdc966fd8e3b40e0922841b7055e8c6f","4565574c11f445798e73323458ba88f5","48e9d5a1674a499da334d75134255761","4dc862cdfff2441a8fc5d9432c0883fc","29aa40feb3aa4de0a90f3f795bb12ae6","d5170defc10149dcb0e6be3996c7da0b","1892b903568a4fb684c14c4ef6669546","40c5f6e4843a4e7eb830d7c8eacb32c9","e03f842e3f85420aaf476c842f6c3e4c","436d9d7bcb014de3b30ba6652e72cc1e","efbcfb0117fc46b1adb51d2c1303e48d","91eb05d9b47b476685c2c4a7130fdaa1","5f1036b4a00747efad002af0aa06efa1","96bba3adf2d949fd9b394656b8278fd1","65f5119b06704873b696c94dac428465","e22610b913124897bcad562b2b219d0c","476bee5bb63b430c98139d633137c2ef","210071a4180343b8b26870fc61cf16ea","b1c61a9cca42476dad37eafc11a91c8d","ad539aade2ee42dba13aac2022305629","1202c5db651f4d9cae23474782fdbe69","89afd02eb5df4f20b1ab5e2af2b5c366","02f1c5ee982842658f4d76ea936774b1","f9182f1b841a4e57b26b15d9e2d6589b","89e5bb77f1bb4f5783b8211d3ff41037","4b930e40d5c74b7bb9a9fa6003317a60","7d47e4e9122e481e818ed553366bd2e1","11113c3aa8d34aa98aa8114ce9a612d4","7386421dee66456d8a9e055629d2ddb7","30ee0562bcd24c55bdc1ae3b3c8604c2","cc7ffdb03e3b4fe3a54312df1017f0c9","9e79b5924652406b873c53b2a561ede2","d8074ec2265145f4ad3a95311ce8b733","59b2792ef1b24df3983683e1e8e2e846","3d126f9d791244e0872265105834ab24","58c7933f5b774d3dabfc3c90bd0b86b7","f999b22f8d544503a104df8a6d486506","1f3f66cceaa64c69b49706a3c3ee8949","80cf1ce6b7364db4b2e71a5b49cc50b2","237ef79aca6f45d19489c1e7a80ab822","a07850bf3c48498fa61759def521cd66","a415adc2052b4db0bc9252fe531efbb6","249935381093429c99af3e5109323c63","42cdbababb2549c6a75de29a75f65080","ed625adb1550482c938c369a84f548ec","1f6f428f274d4de3b9fe392cfd34b04f","1289fddc7333439a967fde0bd15516b1","7662ac961ed64a3aa5482b4251cc0d51","cca6d0ac5e2949a99d55d8ae5d3ceb07","57044a374aef4c37b701ee8929d57ad9","ed930f24aea840c6834c83281e31e11c","73568a588df44068b356859701fc5eb3","72511e2d65ae44b59cf6f6cdd3ee476f","701ee10023dd40ecace7f3830c77b704","72c1489c8a2341a8939496291b366e01","beeeec6fcd814fafb3d4185404431988"]},"executionInfo":{"elapsed":29080,"status":"ok","timestamp":1657133665361,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"Z3qhDbOpxoF7","outputId":"fd909b9d-dacb-4405-dd3d-6768b4bc1eed"},"outputs":[],"source":["## Faster implementation to work with pooled outputs.\n","pretrained_bert_model = sentence_transformers.SentenceTransformer('all-mpnet-base-v2');"]},{"cell_type":"code","execution_count":50,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":162261,"status":"ok","timestamp":1657134066377,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"0b_R3EXKy2vH","outputId":"9f4a81ff-9bf3-4999-b309-a57f938099b8"},"outputs":[{"name":"stdout","output_type":"stream","text":["3426\n"]}],"source":["indexes = [i for i in range(len(labels_test))]\n","\n","## Generations of pairs arg-kp of the test set.\n","arg_id = [labels_test['arg_id'][k] for k in indexes]\n","kp_id = [labels_test['key_point_id'][k] for k in indexes]\n","\n","## Arguments extraction.\n","arguments = flatten([arguments_test.loc[arguments_test['arg_id'] == arg]['argument'].tolist() for arg in arg_id])\n","encoded_arguments = pretrained_bert_model.encode(arguments)\n","encoded_arguments_BERT = zip(arg_id, list(encoded_arguments))\n","print(len(encoded_arguments))\n","\n","## Key-points extraction.\n","keypoints = flatten([key_points_test.loc[key_points_test['key_point_id'] == kp]['key_point'].tolist() for kp in kp_id])\n","encoded_keypoints = pretrained_bert_model.encode(keypoints)\n","encoded_keypoints_BERT = zip(arg_id, list(encoded_keypoints))\n","\n","## Topics extraction.\n","topics = flatten([arguments_test.loc[arguments_test['arg_id'] == arg]['topic'].tolist() for arg in arg_id])\n","encoded_topics = pretrained_bert_model.encode(topics)\n","encoded_topics_BERT = zip(arg_id, list(encoded_topics))\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3704,"status":"ok","timestamp":1657134074207,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"UtfUsXGfzaAp","outputId":"14c0d29c-9b87-4528-8591-e7cddf1d3741"},"outputs":[],"source":["score_arguments_to_kp_without_topic_BERT = {}\n","predictions_BERT_without_topic = []\n","score_arguments_to_kp_with_topic_BERT = {}\n","predictions_BERT_with_topic = []\n","encoded_arguments_BERT_list = list(encoded_arguments_BERT)\n","encoded_keypoints_BERT_list = list(encoded_keypoints_BERT)\n","encoded_topics_BERT_list = list(encoded_topics_BERT)\n","for i in range(len(indexes)):\n","  arg_id = encoded_arguments_BERT_list[i][0]\n","  encoded_argument = encoded_arguments_BERT_list[i][1].reshape(1, -1)\n","\n","  kp_id = encoded_keypoints_BERT_list[i][0]\n","  encoced_key_points = encoded_keypoints_BERT_list[i][1].reshape(1, -1)\n","\n","  encoded_arguments_topic = np.concatenate((encoded_arguments_BERT_list[i][1], encoded_topics_BERT_list[i][1]), axis = -1).reshape(1, -1)\n","  encoded_keypoints_topic = np.concatenate((encoded_keypoints_BERT_list[i][1], encoded_topics_BERT_list[i][1]), axis = -1).reshape(1, -1)\n","\n","  score_without_topic = cosine_similarity(encoded_argument, encoced_key_points)\n","  score_with_tpopic = cosine_similarity(encoded_arguments_topic, encoded_keypoints_topic)\n","  predictions_BERT_without_topic.append(score_without_topic) \n","  predictions_BERT_with_topic.append(score_without_topic)\n","  if arg_id in score_arguments_to_kp_without_topic_BERT.keys():\n","    score_arguments_to_kp_without_topic_BERT[arg_id][kp_id] = score_without_topic\n","    score_arguments_to_kp_with_topic_BERT[arg_id][kp_id] = score_with_tpopic\n","  else:\n","    score_arguments_to_kp_without_topic_BERT[arg_id] = {kp_id: score_without_topic}\n","    score_arguments_to_kp_with_topic_BERT[arg_id] = {kp_id: score_with_tpopic}\n","print('score without topic: ',score_arguments_to_kp_without_topic)\n","print('score with topic: ',score_arguments_to_kp_with_topic)\n","print(predictions_GloVe_without_topic)\n","\n","true_values = labels_test['label'].to_numpy()"]},{"cell_type":"markdown","metadata":{"id":"n1S3bgtGkPP4"},"source":["# **BERT Model Evaluation**"]},{"cell_type":"code","execution_count":57,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":798},"executionInfo":{"elapsed":545,"status":"ok","timestamp":1657134247892,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"-WOSNZSmkzJY","outputId":"155449e8-5134-4e35-da24-5c4f81dac4b5"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model Evaluation -- Unsupervised Model: BERT Embedding, arguments to key points score without topic\n","              precision    recall  f1-score   support\n","\n","         0.0       0.47      0.82      0.60      1641\n","         1.0       0.47      0.15      0.22      1785\n","\n","    accuracy                           0.47      3426\n","   macro avg       0.47      0.48      0.41      3426\n","weighted avg       0.47      0.47      0.40      3426\n","\n","The average precision score is: 0.15360289576375236.\n","The balanced accuracy score is: 0.4691178027896281.\n","The tuned threshold is: 0.7843729257583618.\n","The confusion matrix is: \n"]},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 2 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LS0REEiVqjQBgGVDq7q+bWUsza+Pu6nkVEWnioo4amgI8DTwU7uoBPBevoEREJHGidhb/ADgZ2AHg7p8CXeIVlIiIJE7URFDk7sUVG2aWRvBkcZ3MbJSZfWJmK83s5hqO32RmS81sUbjOwaHRQxcRkYYQNRG8ZWb/B8gys28C/wReqOsNZpYK/Bk4BxgITDKzgdWKfUQwkd2xBE1Pd+9P8CIiUn9RE8HPgE3Ax8A1wEzgv2O850RgpbuvDmsT/6DajKXuPtvdC8LN94CeUQOvj7xtBbELiYgkiZiJIPxkv8zd/+Lu33b3CeHrWE1DPYC1Vbbzwn21uRJ4uZYYrjazHDPL2bRpU6yQY/rxPxfW+xwiIs1FzETg7mXAJ2bWO15BhOsfZwP31BLDVHfPdvfszp071/t6m3YW1fscIiLNRdTnCDoAS8zsA4JFaQBw93F1vGcd0KvKds9w317MbCTwc+A0d9dfaBGRBIuaCH5xAOf+EOhvZn0JEsBE4OKqBczsOIJnE0a5+8YDuIaIiNRTnU1DZpZpZj8Cvg0cCbzr7m9VfNX1XncvBa4DZhE8lfyUuy8xszvNrKImcQ/QGvinmS0wsxn1vaGoHn7nM/rc/BJvLNuQqEuKiByUYtUIHgVKgLl8NQz0hqgnd/eZBCOMqu67tcrrkfu8KUEW5gWzaq/atIszj+raWGGIiDS6WIlgoLsfA2Bm04AP4h9SfFT84RcRkb3FSgQlFS/cvbTawvVNyvurt+61/fG6/EaKRESamrJyZ1dhKQUlpbRMT6NtVhrFZeUUl5ZTVu60zEij3J1yd8rKnXKH4tJySsrK2bKrmMLSMjJSUygtD8qs376HkjJnd1EpKzfuol/nVuTvKeXx99fQo0MWK77cyTnHdGfjziLeXrGJUw7vhONcPrwvIwc2fAtGrEQw2Mx2hK+N4MniHeFrd/e2DR5RApQ7rN60O3ZBkSaqpKwcgPTU2M+MlpU7G3YUUlRazoYdhazdWkBWRirtstJZ8Pl2thWU8P5nW0hNMT7fWkBpmTPokLZs2V3Mxh2FTDyxNwO6tuHoHm1pn5VB26w0WqSlUlRaxq6iUr7MLyQ1xSgtc0rLy1m3vZBUM0rKytlRWELbzHS6tG3B11pl4A5f5O+huNQpLitnQNfWbC8owR2Ky8rZVVjKonXbaZ2RRmm50/trLSlzp7zcK78/+9E6sg/tQEFxGWaQYsZLi75g5FFdeTJnLW1apLGzqJRhh3UkIy2FsvLgj/eKDTvp0CqD4tJyPt/aeA+dbgyHt/975WbW5werKa7cuIueHbIoi/n41oGJtXh9alyuKtIMuTslZcEnvoLiMgqKS+nWNpPdRWUUlZZRUu50b5tJSsreNevCkjJWbtxF3rY9fLZ5N59u3Em3tpk8/O5ntMpIY8vuYJqvVhmp9OnUirJyZ82WAvaUBCvtHd6lNSs37qozthSDtJQUDmmfSe6W+v+Re/+zrXRqncGOwlKmvr263udraB99vm9T8JM5wfOtO4tKAVizZTed22aSGv5s+odJ55hDO7C7qLTy535Y51Z0bZNJQXEpI4/qymebd/PsR+u46ZsDyEpP5Y9vfsqp/TszqEdbUsxINSMlxXhh4XqO7dmOrPRUMtNTGdyrHSlmpKWk8EX+HtpkptGjfUsy0lLo0qYFLdJTaJGWSmpK4lte9mc9gmbpnzl5XP2Nfo0dhjSQnYUlbNhRRP6eEjbuKGT7nhLSU1M488gutGqRRkZaChUPxReXlVNYUg4OjuMOW3YXs+yLHXyZX8hj762hb6dWLP9yBz3aZ5GZnkrXtpm8vWJT5R+JCl9rlcHWavsaQmHJV+dMMaNb20zeWL73SOvaksCkE3sz/YPPARjerxPd2mVSUla+TyK4ceQACkvLGNC1NaVlzgefbaWgpIzh/TpSXu6MOKILnVq3oEVayj5JrLSsnI07i1iwdjvvr97C9j0lDOzeluVf7uStFZv40cj+PDN/Hd877TDSU1NIS01h9aZddGiZQUZaCtsKipm/ZjvLv9zBsMM6MvTQDphBh5YZbNhZRFqKsauolJ7ts2iRnoKZsWbLbnp2aIkBndu0CP74pgRfKWa0SE+hTYs0Nu4soqC4jLaZaXRombFP7PVx30VDKl9P+cZhNZa58pS+DXa9eLPYM0UcXLKzsz0nJ2e/3zf0l6/t85+3wmd3jaYp9380J2XlzsadhXytVQYt0lIpLClj444iVm3axapNu3ht6Qbe/2xr7BMl2Nhju7OnuKzyj3R6qlFSFvzfOqFPB847rgcrN+7iy/xCXl78JWkpRvf2mazduofrTj+cBWu307lNCwYd0pZjerRjcK/2ZKarQi4Nx8zmuXt2TceSvkYg0ZSVe2WV1cMOsbmfbua1ZRuYv2Ybedv2sCuscleYPuUkhvRqz2vLNvDup5sxg398uLam0zeo1i3SaJuZxsQTezP00A5856/vM/GEXqzbvoe5n25m8vA+PPLvXACuOqUv3dtnYYAZPPH+51x/Zn8GHdKWQ9plkZWRSn5BCWu3FdCzQxbtstLJ31NCu6x0fXiQZiNpawT3TzqOH07/CEiuGkFJWTk7C0t57qN13Pni0sYOZ78MPbQDR3Zrw6iju3HK4Z2S5t9MpCGoRlCDTq0zKl/3vWUmI4/qygOXHB9plEUFd2ft1j307JDVoO2P+8PdeW/1VmZ+/AUvLlrPtoJgxO/EE3qRkmL8a/66yk7FeGiZkcpJh3Xkl+cdTY/2WZX7s3/1Gpt3fZV4u7RpwTWn9WP0Md3o3i6rplOJSCNJ2kRgBJ1LZeVBjej1ZRs49Xezee//nFnre/YUl3HptPdZtWkX157Wj7teXl557LZzB/LdYX0oLC3j0X+v4fkF6zi0Y0t+fNYR9OvcujJR7C4qpWVGKmbGwrXbuWjqfygsKaf311pyx7hBHNe7Pe1bfpWkKsYit2qRRnFpOQXFpTy/YD23zVhS5/1FaYJ5+tphHN+7Q2VsG3cWsmlnEQO7t633p+2c//5mvd4vIomTtE1D06ecxMV/fY/qtz9qUDcevHQoECxgc87/nUtRaTnF4bjsg9mR3dpw0mEd2VlYyjPz8wC4MLsnk07szeFdWtMmM72RIxSRxqKmoRps2V3EsT3bs3Dt3uONX1nyJV/k7+GWZz9mzif1XwQnluN6t2dRXn5lzSSqR684kdMG1L42w70XDq5vaCKSJJI2EXy6YRfPXDuMe2Z9wphjuzPuT+9WHht215u1vm/Vb0bX+MDHD56Yz0uLvmDuT0+n19daVu5fs2U3z8xfx5otu7nkpEPJLyhhYd52bvrmgH2aXzbvKmLcH9/h29m9OKp7G/p1bk3/rm0a4G5FRGqXtE1Dt587kMkn7/3AR5+bX9rnfSt/fQ5Pz8vjvON6aFy3iDRZahqqwXeH96nz+Cs/OpUjuwVTKU08MW6rdIqINLroYyWbmZpGxfzp4uMAuGPcoMokICLS3CVtjaAmY489hLHHHtLYYYiIJFTSJYJ+nVsxpFeHxg5DROSgkXSJ4MlrhtGpdYvGDkNE5KCRtH0EIiISUCIQEUlyzaJpqKSkhLy8PAoLC2stc8/IjpQ5fLlmFZsaaYI4ObhkZmbSs2dP0tM19YYkt2aRCPLy8mjTpg19+vSpdbI0X7+D0vJyjujedr9mGJXmyd3ZsmULeXl59O3bdFaSEomHZvEXsbCwkI4dO2p+eonMzOjYsWOdtUiRZNEsEgHU/ICYSF30OyMSaDaJQEREDowSQQP58ssvmThxIv369WPo0KGMHj2aFStWkJuby9FHH91g17n11lt5/fXXAZg7dy6DBg1iyJAhrFu3jgkTJtTr3O7OGWecwY4dOyr3Pffcc5gZy5d/tQhPbm4uWVlZDBkyhIEDB3LttddSXl6/9RqKioq46KKLOPzww/n6179Obm5ujeWuuOIKunTpUuvP9N5778XM2Lx5MwAvvvgit956a71iE2nulAgagLtz/vnnM2LECFatWsW8efO466672LBhQ4Nf684772TkyJEAPP7449xyyy0sWLCAHj168PTTT0c+T2lp6T77Zs6cyeDBg2nb9qt5lqZPn84pp5zC9OnT9yrbr18/FixYwKJFi1i6dCnPPffcAd5RYNq0aXTo0IGVK1dy44038rOf/azGcpMnT+aVV16p8djatWt59dVX6d37q0kCx4wZwwsvvEBBQUG94hNpzprFqKGq7nhhCUvX79hnf0FxGe5OyxZp7G/L8MBD2nLbuYNqPT579mzS09O59tprK/cNHhwsDFP1k21ubi6XXnopu3fvBuBPf/oTw4cP54svvuCiiy5ix44dlJaW8sADDzB8+HCuvPJKcnJyMDOuuOIKbrzxRiZPnszYsWPZvn07Tz31FLNmzeLll1/m17/+NWPHjmXx4sWUlZVx8803M2fOHIqKivjBD37ANddcw5w5c/jFL35Bhw4dWL58OStWrNjrPh5//HGuvvrqyu1du3bxzjvvMHv2bM4991zuuOOOfe49LS2N4cOHs3Llyv38qe7t+eef5/bbbwdgwoQJXHfddbj7Pu343/jGN2qtLdx4443cfffdjB8/vnKfmTFixAhefPFFLrzwwnrFKNJcNbtE0BgWL17M0KFDY5br0qULr732GpmZmXz66adMmjSJnJwcnnjiCc4++2x+/vOfU1ZWRkFBAQsWLGDdunUsXrwYgO3b915J7aqrruKdd95h7NixTJgwYa8/jtOmTaNdu3Z8+OGHFBUVcfLJJ3PWWWcBMH/+fBYvXlzjkMl3332Xhx56qHL7+eefZ9SoUQwYMICOHTsyb968fe6zoKCAN954gzvvvHOf85166qns3Llzn/2///3vK2s1FdatW0evXr2AILm0a9eOLVu20KlTp7p+pHvF2qNHj8oEXFV2djZz585VIhCpRbNLBLV9cl8aPkdwVCM+R1BSUsJ1113HggULSE1NrfxEfsIJJ3DFFVdQUlLCeeedx5AhQzjssMNYvXo1119/PWPGjKn8Qx7Fq6++yqJFiyqbivLz8/n000/JyMjgxBNPrHXc/NatW2nT5qsV0aZPn84NN9wAwMSJE5k+fXplIli1ahVDhgzBzBg/fjznnHPOPuebO3du5Jjro6CggN/85je8+uqrNR7v0qUL69evT0gsIk1RXBOBmY0C/gdIBf7q7r+tdrwF8HdgKLAFuMjdc+MZUzwMGjQoUvv8H/7wB7p27crChQspLy8nMzMTCJo73n77bV566SUmT57MTTfdxGWXXcbChQuZNWsWDz74IE899RQPP/xwpHjcnT/+8Y+cffbZe+2fM2cOrVq1qvV9aWlplJeXk5KSwtatW3nzzTf5+OOPMTPKysowM+655x7gqz6CuuxPjaBHjx6sXbuWnj17UlpaSn5+Ph07dox0v6tWreKzzz6rrA3k5eVx/PHH88EHH9CtWzcKCwvJysqKdC6RZBS3j8Zmlgr8GTgHGAhMMrOB1YpdCWxz98OBPwC/i1c88XTGGWdQVFTE1KlTK/ctWrRon0/E+fn5dO/enZSUFB577DHKysoAWLNmDV27dmXKlClcddVVzJ8/n82bN1NeXs4FF1zAr371K+bPnx85nrPPPpsHHniAkpISAFasWFHZL1GXI444gtWrVwPw9NNPc+mll7JmzRpyc3NZu3Ytffv23a9P+XPnzmXBggX7fFVPAgDjxo3j0Ucfrbz2GWecEXmc/zHHHMPGjRvJzc0lNzeXnj17Mn/+fLp16wYE99+QI7dEmpt4tpGcCKx099XuXgz8Axhfrcx44NHw9dPAmRbnp3zicXIz41//+hevv/46/fr1Y9CgQdxyyy2Vf4gqfP/73+fRRx9l8ODBLF++vPLT+Zw5cxg8eDDHHXccTz75JDfccAPr1q1jxIgRDBkyhEsuuYS77rorcjxXXXUVAwcO5Pjjj+foo4/mmmuuqXGUUHVjxoxhzpw5QNAsdP755+91/IILLthn9FBDufLKK9myZQuHH3449913H7/9bVB5XL9+PaNHj64sN2nSJIYNG8Ynn3xCz549mTZtWsxzz549mzFjxsQlbpHmIG6L15vZBGCUu18Vbl8KfN3dr6tSZnFYJi/cXhWW2VztXFcDVwP07t176Jo1a/a61rJlyzjqqKPqjGdnYQmfbylgUI929b635uqLL77gsssu47XXXmvsUBrMhg0buPjii3njjTdqPB7ld0ekOQgIe6AAAAl7SURBVKhr8fom8RyBu09192x3z+7cufMBnaNNZrqSQAzdu3dnypQpez1Q1tR9/vnn3HvvvY0dhshBLZ6dxeuAXlW2e4b7aiqTZ2ZpQDuCTmNpJM1tiOUJJ5zQ2CGIHPTiWSP4EOhvZn3NLAOYCMyoVmYG8N3w9QTgTT/Atqp4NXFJ86XfGZFA3BKBu5cC1wGzgGXAU+6+xMzuNLNxYbFpQEczWwncBNx8INfKzMxky5Yt+o8tkVWsR1AxhFckmcWtszhesrOzPScnZ699UVYoE6lOK5RJMqmrs7hZPFmcnp6uVaZERA5Qkxg1JCIi8aNEICKS5JQIRESSXJPrLDazTcCamAVr1gnYHLNU86J7Tg665+RQn3s+1N1rfCK3ySWC+jCznNp6zZsr3XNy0D0nh3jds5qGRESSnBKBiEiSS7ZEMDV2kWZH95wcdM/JIS73nFR9BCIisq9kqxGIiEg1SgQiIkmuWSYCMxtlZp+Y2Uoz22dGUzNrYWZPhsffN7M+iY+yYUW455vMbKmZLTKzN8zs0MaIsyHFuucq5S4wMzezJj/UMMo9m9mF4b/1EjN7ItExNrQIv9u9zWy2mX0U/n6Pruk8TYWZPWxmG8MVHGs6bmZ2f/jzWGRmx9f7ou7erL6AVGAVcBiQASwEBlYr833gwfD1RODJxo47Afd8OtAyfP29ZLjnsFwb4G3gPSC7seNOwL9zf+AjoEO43aWx407APU8Fvhe+HgjkNnbc9bznbwDHA4trOT4aeJlgCfaTgPfre83mWCM4EVjp7qvdvRj4BzC+WpnxwKPh66eBM80sHuvaJ0rMe3b32e5eEG6+R7BiXFMW5d8Z4JfA74DmMEd5lHueAvzZ3bcBuPvGBMfY0KLcswNtw9ftgPUJjK/BufvbwNY6iowH/u6B94D2Zta9PtdsjomgB7C2ynZeuK/GMh4soJMPdExIdPER5Z6rupLgE0VTFvOewypzL3d/KZGBxVGUf+cBwAAze9fM3jOzUQmLLj6i3PPtwCVmlgfMBK5PTGiNZn//v8fULNYjkOjM7BIgGzitsWOJJzNLAe4DJjdyKImWRtA8NIKg1ve2mR3j7tsbNar4mgQ84u73mtkw4DEzO9rdyxs7sKaiOdYI1gG9qmz3DPfVWMbM0giqk1sSEl18RLlnzGwk8HNgnLsXJSi2eIl1z22Ao4E5ZpZL0JY6o4l3GEf5d84DZrh7ibt/BqwgSAxNVZR7vhJ4CsDd/wNkEkzO1lxF+v++P5pjIvgQ6G9mfc0sg6AzeEa1MjOA74avJwBvetgL00TFvGczOw54iCAJNPV2Y4hxz+6e7+6d3L2Pu/ch6BcZ5+45NZ+uSYjyu/0cQW0AM+tE0FS0OpFBNrAo9/w5cCaAmR1FkAg2JTTKxJoBXBaOHjoJyHf3L+pzwmbXNOTupWZ2HTCLYMTBw+6+xMzuBHLcfQYwjaD6uJKgU2Zi40VcfxHv+R6gNfDPsF/8c3cf12hB11PEe25WIt7zLOAsM1sKlAE/cfcmW9uNeM//BfzFzG4k6Die3JQ/2JnZdIJk3ins97gNSAdw9wcJ+kFGAyuBAuDyel+zCf+8RESkATTHpiEREdkPSgQiIklOiUBEJMkpEYiIJDklAhGRJKdEIHFnZmVmtsDMFpvZC2bWvoHPnxuOmcfMdtVSJsvM3jKzVDPrY2Z7wpiWmtmD4ZPI+3PNbDO7P3w9wsyGVzl2rZldVp97Cs9zu5n9OEaZR8xswn6cs09ts1pWK/drM1tb/edpZteZ2RVRrydNgxKBJMIedx/i7kcTPLfxg0aI4QrgWXcvC7dXufsQ4FiCGSvP25+TuXuOu/8w3BwBDK9y7EF3/3v9Q25ULxBM+FbdwzT/uXySjhKBJNp/CCfIMrN+ZvaKmc0zs7lmdmS4v6uZ/cvMFoZfw8P9z4Vll5jZ1ft53e8Az1ffGU46+G/g8PDT8pv21ZoNvcPrfjuszSw0s7fDfSPM7EUL1rK4FrgxrGGcWvFJ3syONLMPKq4Vnv/j8PXQsIYyz8xmxZo90symmNmHYQzPmFnLKodHmlmOma0ws7Fh+VQzuyd8zyIzu2Z/flju/l5NT6uGM9jmmllNSUKaKCUCSRgzSyWYCqDiqd+pwPXuPhT4MfC/4f77gbfcfTDBvOxLwv1XhGWzgR+aWaQZY8OpCQ5z99wajrUMY/oY+CPwqLsfCzwexgFwK3B2GM9eT2OH53wQ+ENY65lb5dhyIMPM+oa7LgKeNLP08FoTwvt5GPh1jNt41t1PCGNYRjC/ToU+BJ/exwAPmllmeDzf3U8ATgCmVImj4t4PMbOZMa5bkxzg1AN4nxykmt0UE3JQyjKzBQQ1gWXAa2bWmqA5pWLKC4AW4fczgMsAwqac/HD/D83s/PB1L4LJ1KJMn9AJqD77Zr8wJgeed/eXzewx4Fvh8ceAu8PX7wKPmNlTwLMRrlfVUwQJ4Lfh94uAIwgmxHstvPdUINZcMUeb2a+A9gRThcyqeo1wps1PzWw1cCRwFnBslf6DdgQ/rxUVb3L39QRTFeyvjeE1pJlQIpBE2OPuQ8JP37MI+ggeAbaH7fQxmdkIYCQwzN0LzGwOweRika5fQ9lVUa/t7tea2dcJPnHPM7OhEa8L8CRBsns2OJV/ambHAEvcfdh+nOcR4Dx3X2hmkwknlqsIsXrIBKtXXe/uVRMG1jDLsmYS/EylmVDTkCRM2L78Q4JJwgqAz8zs21C5DuvgsOgbBMtpVrR1tyP4RLstTAJHEkwrHfW624DUsMmkLv/mqwkIvwPMDWPo5+7vu/utBLNa9qr2vp0E017XdO1VBJO//YIgKQB8AnS2YO58zCzdzAbFiK0N8EXYrPSdase+bWYpZtaPYEnHTwgS7vfC8pjZADNrFeMaUQ0AYo48kqZDiUASyt0/AhYRLCbyHeBKM1tI0A9QsQThDcDpYcfqPIJRPa8AaWa2jKCZ5b39vPSrwCkxylwPXG5mi4BLwzgA7jGzj8Nhl/8mWDe3qheA8ys6i2s475PAJXw1Z34xwfTnvwvvfQFVRh3V4hfA+wTNVMurHfsc+IBg1blr3b0Q+CuwFJgfxv0Q1VoA6uojMLO7LZj5sqWZ5ZnZ7VUOnwy8FiNeaUI0+6gkBQuWrbzR3S9t7FiaMgvWtbhJP8fmRTUCSQruPh+YHY5ckgPXiaB2Is2IagQiIklONQIRkSSnRCAikuSUCEREkpwSgYhIklMiEBFJcv8fv4YVL3xCEyAAAAAASUVORK5CYII=","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["print(\"Model Evaluation -- Unsupervised Model: BERT Embedding, arguments to key points score without topic\")\n","evaluate_classifier(np.asarray(np.asarray(predictions_GloVe_without_topic).reshape((3426, 1))), true_values)"]},{"cell_type":"code","execution_count":59,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":799},"executionInfo":{"elapsed":570,"status":"ok","timestamp":1657134301505,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"p1G3nvcpk8Hp","outputId":"1e57c761-4ca4-4f0f-9c01-34323b1443c5"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model Evaluation -- Unsupervised Model: BERT Embedding, arguments to key points score with topic\n","              precision    recall  f1-score   support\n","\n","         0.0       0.64      0.90      0.75      2029\n","         1.0       0.64      0.25      0.36      1397\n","\n","    accuracy                           0.64      3426\n","   macro avg       0.64      0.58      0.55      3426\n","weighted avg       0.64      0.64      0.59      3426\n","\n","The average precision score is: 0.218432936891832.\n","The balanced accuracy score is: 0.6359584430123143.\n","The tuned threshold is: 0.5693897008895874.\n","The confusion matrix is: \n"]},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAT8AAAEHCAYAAADYj0FrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAf00lEQVR4nO3de5xXVb3/8debmQHkzjBA3AzykpnlJbzVL9MsRet3qN+ji1ZH69hRS630VEfrlGV5Tp1OmUZpJKSeDLO0opNKqHnUygveSDFlQhEQRBhEAbnMzOf3x16DA87lu4f58p2Z/X722I/2Xnt9914bHn5Ya6+111JEYGZWNP0qXQAzs0pw8DOzQnLwM7NCcvAzs0Jy8DOzQnLwM7NCqq50AVqrq62KyZNqKl0My+Gv60ZXugiWQ2NDA00bNmpXrnH8MYNjbUNTSXkfWLhlXkRMa++8pNnAe4HVEXFASjsIuAIYCDQCn46I+yQJuBQ4EdgEfDwiHky/ORX4t3TZb0bE1Z2VrUcFv8mTarhv3qRKF8NyeN0NZ1S6CJbDym9fusvXWNvQxH3z9iwpb9W4xXWdZLkKmAFc0yrtP4GvR8TNkk5Mx0cDJwD7pO1w4HLgcEm1wIXAVCCAByTNjYh1Hd3YzV4zyyWA5hL/1+m1Iu4EGtq4xbC0Pxx4Nu1PB66JzD3ACEnjgOOB+RHRkALefKDd2maLHlXzM7OeLwi2RWnN3i76HDBP0n+RVdDemtInAMta5Vue0tpL75BrfmaWW46aX52kBa2200u4/KeAcyNiEnAuMKscz+Can5nlEgRNpc8JsCYipua8xanAZ9P+L4Er0/4KoHWnwMSUtoLsnWDr9Ds6u4lrfmaWWzNR0tZFzwLvSPvvBBan/bnAKcocAayPiJXAPOA4SSMljQSOS2kdcs3PzHIJoKnrgW0HkuaQ1drqJC0n67X9Z+BSSdXAZqClqXwT2TCXerKhLp8AiIgGSd8A7k/5LoqInTtRXsXBz8xy24Va3Q4i4uR2Tr2ljbwBnNXOdWYDs/Pc28HPzHIJYFsfmAfUwc/Mcgmi25q9leTgZ2b5BDT1/tjn4Gdm+WRfePR+Dn5mlpNoYpfmRugRHPzMLJesw8PBz8wKJhvn5+BnZgXU7JqfmRWNa35mVkiBaOoD0wI4+JlZbm72mlnhBGJrVFW6GLvMwc/McskGObvZa2YF5A4PMyucCNEUrvmZWQE1u+ZnZkWTdXj0/tDR+5/AzHarvtLh0fufwMx2u6ZQSVtnJM2WtFrSozulnyPpb5Iek/SfrdIvkFQv6QlJx7dKn5bS6iWdX8ozuOZnZrl08xceVwEzgGtaEiQdA0wHDoyILZLGpPT9gZOANwLjgVsl7Zt+9kPg3WQLlt8vaW5ELOroxg5+ZpZbczf19kbEnZIm75T8KeBbEbEl5Vmd0qcD16X0pyTVA4elc/URsQRA0nUpb4fBz81eM8slm9igX0lbF+0LvF3SvZL+V9KhKX0CsKxVvuUprb30DrnmZ2a5BGJb6Z+31Ula0Op4ZkTM7OQ31UAtcARwKHC9pNflL2nnNzEzK1kEeQY5r4mIqTlvsRy4Ma3Te5+kZqAOWAFMapVvYkqjg/R2udlrZjmJ5hK3LvoNcAxA6tDoD6wB5gInSRogaQqwD3AfcD+wj6QpkvqTdYrM7ewmrvmZWS5BrppfhyTNAY4max4vBy4EZgOz0/CXrcCpqRb4mKTryToyGoGzIqIpXedsYB5QBcyOiMc6u7eDn5nl1l1DXSLi5HZOfayd/BcDF7eRfhNwU557O/iZWS6BPJmpmRVPtnRl7w8dvf8JzGw386LlZlZAQfd94VFJDn5mlptrfmZWOBFyzc/Miifr8PDqbWZWOF7Dw8wKKOvw8Ds/MyugbpzMtGIc/MwsF3/hYWaF1RcWMHLwM7NcImBbs4OfmRVM1ux18Cuk7547iXtvHcaIukZm/vEJAP7+6B5cdv5Etm7uR1V1cPZ/LGe/gzdx+40juf6HY4iAPQY3c863lrHXGzcDcOPM0dz881okmLLfZv7lkmfoPzAq+Wh91phrlzD40XU0Da3hmS+9GYB+GxsZ99PFVDdsobF2ACv/aR+aB73yn8SApRuY9L3HWPXxvdlw8CgAqhu2MGbOEmrWbSUEz565H42jBlTkmSqpL3zhUdbw3ZW1NHuD4z7cwMXXLtkh7cpvjuNj563i8luf4JQvrGTWN8cDMHbSFr5zQz0/vv0JPnruKi79Yjbb9pqVNfxmVh0zbn6SmX98gqZmuOO3I3f7sxTFi4fX8eyn99shbeT8Z9m073CWfvUgNu07nJHzn33lZHNQ99tlbNpv+A6/Gfvff+eFY8ez9N8OZNnnD6BpaPHqDy1DXUrZerKyBT9JVWRraZ4A7A+cnNbd7PXedMRGho5s2iFNgo0vZaPeN75YRe3YbQC88dBNDB2R5d3vkE2sWVmz/TdNjWLL5n40NcKWl/sxKv3Gut/mvYfRNGjHQDXkr+t48fA6IAuOQxau235uxP+uYsNBI2ka8srfV/+Vm1BzbA+IMaCK6N/7v3TIL2v2lrL1ZOX8Z+swurCWZm915kUr+NLJe/GTi8YTAZfMXfyqPLfMqeXQY14CoG7cNj7wqdX846H7M2BgcMg7XuQtR7+0u4tdaFUvbaNpeH8AmobVUPVS9o9P1QtbGbxwHSvOeQMDl75Sw69ZvZnmPaoZ95MnqW7YwsuvH86af5gE/Xp2DaccdmF9jh6jnKG5S2tp9lb/c3UdZ3x9Bdc+sIgzvvYs3ztvzx3OP/ynIcybM4rTvpw1rV56oYq/zBvO1fcu4ucPPcrmTVXcdoObvRWjV/5jHn3DUta2EdTUHAz8+0s8//49Wfb5A6hZs5lh9z6/u0tacVlvb1VJW2ckzZa0Oq3XsfO5f5EUkurSsSRdll6jLZR0SKu8p0panLZTS3mOitdLJZ0uaYGkBc+vber8Bz3U/F/W8n9OXA/AUf/3BZ58eND2c0sWDeT7n5/E1376FMNqs2d86K4hvGbSVkaMaqK6Bt524gssWjC4ImUvqqahNVSt3wpA1fqtNA3NmrgDn9nIa66qZ/KFDzHk4QZGX/80gx9poHFEf7ZMHERj3UCoEhvePJIByzZW8hEqomWQcze987sKmLZzoqRJwHHAM62STyBbsW0f4HTg8pS3lmzho8PJWpwXSuq0JlHO4NfRGpvbRcTMiJgaEVNHj+q9709Gjd3Gwr8MAeDhu4cwfsoWAFYvr+GiT07hC5ctZeJeW7bnHzNhG48/OIjNm0QEPHz3UPbce3NFyl5UG980kmH3rgFg2L1r2PCm7L+Xp79+EE9//WCe/vrBbDioluc/NJmNB9ay+bVDqNrUtL15POjJF9n6mj0qVv5K6q6lKyPiTqChjVOXAF8k619pMR24JjL3ACMkjQOOB+ZHRENErAPm00ZA3Vk53/ltX0uTLOidBHykjPfbbf7jU69l4V+GsL6hmo++ZX/+8V9W8bnvLOPyr06gqUn0H9DM576TtfivveQ1vLSuihkXZP8OVFUHM255kv0O2cTb37Oes45/PVXVwd4HvMwJH1tbycfq017z03r2qH+Rqg2NTP7KgzScOJGGd49j3Ox6ht2zmsaR2VCXDvUTa96/JxNmPA4BWyYNZv1bx+yeB+hByj2xgaTpwIqIeETa4T7tvUrr0iu2sgW/iGjsylqavcEFly9tM/2H8558Vdq5313Gud9d1kZuOOULqzjlC6u6tWzWtlWf2LvN9BXnvKHD3z33j3vtcLxpv+E8c8Gbu61cvVWOntw6SQtaHc+MiJntZZY0CPgSWZO3rMo6SKkra2maWc8WIRpLD35rImJqjsvvBUwBWmp9E4EHJR1G+6/SVpAtfN46/Y7OblTxDg8z633KNcg5Iv4aEWMiYnJETCZrwh4SEauAucApqdf3CGB9RKwka10eJ2lk6ug4LqV1qHjD081sl3TnOz9Jc8hqbXWSlgMXRsSsdrLfBJwI1AObgE8ARESDpG+Q9TMAXBQRbXWi7MDBz8xy667gFxEnd3J+cqv9AM5qJ99sYHaeezv4mVkunszUzAqrL3ze5uBnZrlEQKMnMzWzInKz18wKx+/8zKywwsHPzIrIHR5mVjgRfudnZoUkmtzba2ZF5Hd+ZlY45Z7Pb3dx8DOzfCJ779fbOfiZWW7u7TWzwgl3eJhZUbnZa2aF5N5eMyuciL4R/Hp/w93MdrvuWsND0mxJqyU92irtO5L+JmmhpF9LGtHq3AWS6iU9Ien4VunTUlq9pPNLeQYHPzPLLaK0rQRX8eoFxucDB0TEm4EngQsAJO1Ptv73G9NvfiSpSlIV8EPgBGB/4OSUt0Nu9ppZLoFo7qbe3oi4U9LkndL+0OrwHuADaX86cF1EbAGeklQPHJbO1UfEEgBJ16W8izq6t2t+ZpZblLh1g38Cbk77E4Blrc4tT2ntpXfINT8zyydfh0edpAWtjmdGxMxSfijpy0AjcG3OEpbEwc/M8iu9WrcmIqbmvbykjwPvBY5NS1YCrAAmtco2MaXRQXq72g1+kn5AB48YEZ/p7OJm1jeVc6iLpGnAF4F3RMSmVqfmAj+X9D1gPLAPcB8gYB9JU8iC3knARzq7T0c1vwUdnDOzggqgubl7gp+kOcDRZM3j5cCFZL27A4D5kgDuiYgzI+IxSdeTdWQ0AmdFRFO6ztnAPKAKmB0Rj3V273aDX0RcvVMhB+0Uhc2siALopppfRJzcRvKsDvJfDFzcRvpNwE157t1pb6+kIyUtAv6Wjg+U9KM8NzGzvqUbx/lVTClDXb4PHA+sBYiIR4CjylkoM+vhduNYl3Ipqbc3IpaltneLpvIUx8x6PvWJb3tLCX7LJL0VCEk1wGeBx8tbLDPr0Xp4ra4UpQS/M4FLyUZMP0vWo3JWOQtlZj1YQHRTb28ldRr8ImIN8NHdUBYz6zV6f/Arpbf3dZJ+J+n5NPXMbyW9bncUzsx6qD7Q4VFKb+/PgeuBcWSjqn8JzClnocyshytI8BsUEf8dEY1p+xkwsNwFM7MeqmWQcylbD9bRt721affmNDPqdWSP/WFyjqQ2s76lpw9gLkVHHR4PkAW7lvB9RqtzQZpd1cwKqC/39kbElN1ZEDPrPdTHa37bSTqAbG787e/6IuKachXKzHqwXtCZUYpOg5+kC8mmnNmf7F3fCcDdgIOfWSH1/M6MUpTS2/sB4FhgVUR8AjgQGF7WUplZz9YHhrqU0ux9OSKaJTVKGgasZscpo82saJorXYBdV0rwW5AWDf4JWQ/wBuAvZS2VmfVc3TiZaSWV8m3vp9PuFZJuAYZFxMLyFsvMerK+0Nvb7js/SYfsvAG1QHXaN7Oi6qZ3fpJmpzkDHm2VVitpvqTF6f9HpnRJukxSvaSFreOQpFNT/sWSTi3lETqq+X23g3MBvLOUG5iZdeAqYAY7jh45H7gtIr6Vvi47H/hXspEm+6TtcOBy4PD0NdqFwFSy2PSApLkRsa6jG3c0yPmYLj9OFz25cBDHjz9od9/WdsEbJq+sdBEsh3XPbeuW63RXszci7pQ0eafk6WTD6wCuBu4gC37TgWvSOr73SBohaVzKOz8iGgAkzQem0ckELF603MzyCcr9edvYiGj5V3UVMDbtTwCWtcq3PKW1l94hBz8zy6/0ml+dpNZrgM+MiJkl3yYipPJ0rzj4mVluOcLRmoiYmvPyz0kaFxErU7N2dUpfwY5jjCemtBW80kxuSb+js5uUMpOzJH1M0lfT8Z6SDivpEcysbyrvFx5zgZYe21OB37ZKPyXFpCOA9al5PA84TtLI1DN8XErrUCk1vx+Rjed+J3AR8BJwA3Bojocxs76kmxqikuaQ1drqJC0n67X9FnC9pNOApcCHUvabgBOBemAT8AmAiGiQ9A3g/pTvopbOj46UEvwOj4hDJD2UbrROUv9SH87M+hZFt/b2ntzOqWPbyBu0s3JkRMwGZue5dynBb5ukKlKslzSaPvFln5l1WR+YzLSUWV0uA34NjJF0Mdl0Vv9e1lKZWY/WUvvrbOvJSvm291pJD5BVQwW8LyIeL3vJzKzn6uGBrRSlTGa6J9nLxd+1TouIZ8pZMDProXpBra4Upbzz+z2vLGQ0EJgCPAG8sYzlMrOerAjBLyLe1Po4zaTw6Xaym1kBqA90eZbS4bGDiHiQbEYFM7Neq5R3fue1OuwHHAI8W7YSmVnPV4RmLzC01X4j2TvAG8pTHDPr8YrQ4ZEGNw+NiM/vpvKYWW/Ql4OfpOqIaJT0tt1ZIDPrBfpy8APuI3u/97CkucAvgY0tJyPixjKXzcx6INE3entLeec3EFhLNqtLy3i/ABz8zIqoAO/8xqSe3kd5Jei16AOPbmZd1gciQEfBrwoYwo5Br0UfeHQz67I+EAE6Cn4rI+Ki3VYSM+s1+nqzt/dP2GVm5dHHg9+rZlI1MyP6Rm9vu9/2ljIHvpkVVDctYCTpXEmPSXpU0hxJAyVNkXSvpHpJv2hZNkPSgHRcn85P3pVHyD2xgZlZd8zkLGkC8BlgakQcQNbJehLwbeCSiNgbWAecln5yGrAupV+S8nWZg5+Z5dd9S1dWA3tIqgYGASvJxhT/Kp2/Gnhf2p+ejknnj5XU5b4JBz8zy6fUwNdJ8IuIFcB/Ac+QBb31wAPACxHRmLItByak/QnAsvTbxpR/VFcfw8HPzHIRuZq9dZIWtNpO336dbIHx6WSzw48HBgPTdtdzlPJ5m5nZDnKM81sTEVPbOfcu4KmIeB5A0o3A24ARLROrABOBFSn/CmASsDw1k4eTfXrbJa75mVl+3fPO7xngCEmD0ru7Y4FFwB+BD6Q8pwK/Tftz0zHp/O1pIfMucc3PzPLrhkHOEXGvpF8BD5JNlPwQMJNswuTrJH0zpc1KP5kF/LekeqCBrGe4yxz8zCyfbpzVJSIuBC7cKXkJcFgbeTcDH+yeOzv4mVlX9PHP28zM2tQXPm9z8DOz3Pr6rC5mZq9W+tcbPZqDn5nl5+BnZkXT8oVHb+fgZ2a5qbn3Rz8HPzPLx+/8zKyo3Ow1s2Jy8DOzInLNz8yKycHPzAqnj6ze5uBnZrl4nJ+ZFVfX5xDtMRz8zCw31/yM0eO38oVLn2HE6EYIuOlno/jNrNEMHdHIl65YytiJW3lueX8uPuO1bFhfzaS9N3Pe95ax95te5upvv4ZfXTGm0o9QODX9m/j2j/5MTU0zVVXN/OmP47l21us598sPccDBa9m0oQaASy4+iCWLhzPxtS/xuS8/wt77rueaH+/HjXP2qvATVJgHOXdM0mzgvcDqtCBxn9TUKGZeNJ76vw5ij8FNzLjlSR68cyjv/nADD909hOtnjOVDZz/Hh89ezayLx/Piuiou/8oE3jptfaWLXljbtvbjS+ccyeaXq6mqauY7V/yJBfdk/wjN/uH+/OmP43fI/9KL/fnxJQdw5FErK1HcHqkvdHiUcwGjq9iNy9BVSsPqGur/OgiAlzdWsax+IHXjtnHk8S9y6/W1ANx6fS1HTnsRgPVra3jykUE0NnZ5rWXbZWLzy9m/+9XVzVRVN3dYk1m/bgCLHx9BY6PX+2qh5tK2Tq8jjZD0K0l/k/S4pCMl1UqaL2lx+v+RKa8kXSapXtJCSYfsyjOU7W8zIu4kW2SkMMZO3MpeB7zM3x4cxMi6bTSszppPDaurGVm3rcKls9b69Qt+cNX/cu3v/8DD94/miUUjATjl9L8x45o7+OfPPEp1TVOFS9lDBVmHRylb5y4FbomI/YADgceB84HbImIf4LZ0DHACsE/aTgcu35XHqPg/ZZJOb1nQeBtbKl2cLhs4qImvXPk0V3x1PJs2VO10VkS4pteTNDeLcz7+Dk5937vZ9w0v8NrXvchVV7yBM04+hs+d9naGDNvGBz/290oXs8fKsWh5+9eQhgNHkVZni4itEfEC2ULmV6dsVwPvS/vTgWsicw/Z+r7juvoMFQ9+ETEzIqZGxNQaBlS6OF1SVR185cqnuf3Gkfzp5hEArFtTQ+2YrLZXO2YbL6x131JPtHFDDQsfrOMthz/PurUDAdG4rYpbfz+JffdfV+ni9Vylr9tb11K5Sdvpra4yBXge+KmkhyRdKWkwMDYiWl6wrgLGpv0JwLJWv1+e0rqk4sGv9wvO++4yli0eyI0zR29PvecPw3jXh7JW/7s+1MBf5g2rVAFtJ8NGbGHwkOwfpv79mzjo0OdZtnQII0dtTjmCI45axdIl/jtrS8sg5xJrfmtaKjdpm9nqUtXAIcDlEXEwsJFXmrgApEXJy9K37OrILnrjYRt51wfXsWTRQH40/wkAfvof4/jFjDF8+YqlTDupgdUrsqEuACNHb+MHNy9m0NAmohne98k1nH7069toKlu51I7awnlfeYh+/QL1g7tvG8/9fx7Lv//gzwwfsRUETy0exoz/fDMAI2s38/3ZdzFocCPNzTD9w0s48yNH8/Kmmgo/SYVEdNdkpsuB5RFxbzr+FVnwe07SuIhYmZq1q9P5FcCkVr+fmNK6RFGmkdqS5gBHA3XAc8CFETGro98MU20crmPLUh4rj+rJe1a6CJbDn1dcy/otq3bpBfTQERPj4KM+W1Leu373xQciYmp75yXdBXwyIp6Q9DVgcDq1NiK+Jel8oDYivijpPcDZwInA4cBlEfGqxc1LVbaaX0ScXK5rm1lldeMXHucA10rqDywBPkH2Ou56SacBS4EPpbw3kQW+emBTyttlbvaaWT4BdNMaHhHxMNBWzfBVTcD0/u+sbrkxDn5m1hX+vM3MisgTG5hZIXnpSjMrHs/qYmZFlA1y7v3Rz8HPzPLrA1NaOfiZWW6u+ZlZ8fidn5kVU7d921tRDn5mlp+bvWZWOF603MwKyzU/Myuk3h/7HPzMLD819/52r4OfmeUTeJCzmRWPCA9yNrOC6gPBz6u3mVl+3bdoOZKq0tKV/5OOp0i6V1K9pF+kKe6RNCAd16fzk3flERz8zCyflnd+pWyl+SzweKvjbwOXRMTewDrgtJR+GrAupV+S8nWZg5+Z5abm5pK2Tq8jTQTeA1yZjgW8k2wZS4Crgfel/enpmHT+2JS/Sxz8zCynEpu8pTV7vw98kVfqiaOAFyKiMR0vByak/QnAMoB0fn3K3yUOfmaWT5An+NVJWtBqO73lMpLeC6yOiAcq8Rju7TWz/Ep/n7emg0XL3wb8g6QTgYHAMOBSYISk6lS7mwisSPlXAJOA5ZKqgeHA2q49gGt+ZtYFiihp60hEXBAREyNiMnAScHtEfBT4I/CBlO1U4Ldpf246Jp2/Pa3l2yUOfmaWXzcOdWnDvwLnSaone6c3K6XPAkal9POA83flEdzsNbN8IqCpe79vi4g7gDvS/hLgsDbybAY+2F33dPAzs/z6wBceDn5mlp+Dn5kVTgBew8PMiicgev+cVg5+ZpZP0O0dHpXg4Gdm+fmdn5kVkoOfmRXPLg1g7jEc/MwsnwC8gJGZFZJrfmZWPN3/eVslOPiZWT4B4XF+ZlZI/sLDzArJ7/zMrHAi3NtrZgXlmp+ZFU8QTU2VLsQuc/Azs3w8pZWZFZaHuphZ0QQQrvmZWeGEJzM1s4LqCx0e2oU1f7udpOeBpZUuRxnUAWsqXQjLpa/+nb02IkbvygUk3UL251OKNRExbVfuVy49Kvj1VZIWRMTUSpfDSue/s76vX6ULYGZWCQ5+ZlZIDn67x8xKF8By899ZH+d3fmZWSK75mVkhOfiVkaRpkp6QVC/p/EqXxzonabak1ZIerXRZrLwc/MpEUhXwQ+AEYH/gZEn7V7ZUVoKrgB45Ls26l4Nf+RwG1EfEkojYClwHTK9wmawTEXEn0FDpclj5OfiVzwRgWavj5SnNzHoABz8zKyQHv/JZAUxqdTwxpZlZD+DgVz73A/tImiKpP3ASMLfCZTKzxMGvTCKiETgbmAc8DlwfEY9VtlTWGUlzgL8Ar5e0XNJplS6TlYe/8DCzQnLNz8wKycHPzArJwc/MCsnBz8wKycHPzArJwa8XkdQk6WFJj0r6paRBu3CtqyR9IO1f2dGkC5KOlvTWLtzjaUmvWuimvfSd8mzIea+vSfp83jJacTn49S4vR8RBEXEAsBU4s/VJSV1aijQiPhkRizrIcjSQO/iZ9WQOfr3XXcDeqVZ2l6S5wCJJVZK+I+l+SQslnQGgzIw0v+CtwJiWC0m6Q9LUtD9N0oOSHpF0m6TJZEH23FTrfLuk0ZJuSPe4X9Lb0m9HSfqDpMckXQmos4eQ9BtJD6TfnL7TuUtS+m2SRqe0vSTdkn5zl6T9uuMP04rHi5b3QqmGdwJwS0o6BDggIp5KAWR9RBwqaQDwJ0l/AA4GXk82t+BYYBEwe6frjgZ+AhyVrlUbEQ2SrgA2RMR/pXw/By6JiLsl7Un2FcsbgAuBuyPiIknvAUr5OuKf0j32AO6XdENErAUGAwsi4lxJX03XPptsbY0zI2KxpMOBHwHv7MIfoxWcg1/vsoekh9P+XcAssubofRHxVEo/Dnhzy/s8YDiwD3AUMCcimoBnJd3exvWPAO5suVZEtDev3buA/aXtFbthkoake/y/9NvfS1pXwjN9RtL70/6kVNa1QDPwi5T+M+DGdI+3Ar9sde8BJdzD7FUc/HqXlyPioNYJKQhsbJ0EnBMR83bKd2I3lqMfcEREbG6jLCWTdDRZID0yIjZJugMY2E72SPd9Yec/A7Ou8Du/vmce8ClJNQCS9pU0GLgT+HB6JzgOOKaN394DHCVpSvptbUp/CRjaKt8fgHNaDiS1BKM7gY+ktBOAkZ2UdTiwLgW+/chqni36AS2114+QNadfBJ6S9MF0D0k6sJN7mLXJwa/vuZLsfd6DaRGeH5PV8H8NLE7nriGbuWQHEfE8cDpZE/MRXml2/g54f0uHB/AZYGrqUFnEK73OXycLno+RNX+f6aSstwDVkh4HvkUWfFtsBA5Lz/BO4KKU/lHgtFS+x/DSANZFntXFzArJNT8zKyQHPzMrJAc/MyskBz8zKyQHPzMrJAc/MyskBz8zKyQHPzMrpP8PUt58w78B9d0AAAAASUVORK5CYII=","text/plain":["<Figure size 432x288 with 2 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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["print(\"Model Evaluation -- Unsupervised Model: BERT Embedding, arguments to key points score with topic\")\n","evaluate_classifier(np.asarray(predictions_BERT_with_topic).reshape((3426, 1)), true_values)"]},{"cell_type":"markdown","metadata":{"id":"wikH9y4R2TCk"},"source":["# **Tf-idf**\n","In order to assess the role of lexical overlap in the matching task, we represent each argument and key point as tf-idf weighted word vectors and use their cosine similarity as the match score.\n","\n","\n","We calaulate TF-IDF vector by our custom functions, based on standard TF-IDF formular"]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1502,"status":"ok","timestamp":1657128704793,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"ucpKzmETlkrJ","outputId":"3757cdff-8faa-42ac-e37d-39af92fd7398"},"outputs":[{"name":"stderr","output_type":"stream","text":["[nltk_data] Downloading package punkt to /root/nltk_data...\n","[nltk_data]   Unzipping tokenizers/punkt.zip.\n","[nltk_data] Downloading package stopwords to /root/nltk_data...\n","[nltk_data]   Unzipping corpora/stopwords.zip.\n"]}],"source":["## Get unique corpus in dataset, since duplicates sentence will effect a lot in TF-IDF\n","\n","test_arguments_id = labels_test['arg_id'].drop_duplicates().tolist()\n","test_key_point_id = labels_test['key_point_id'].drop_duplicates().tolist()\n","test_arguments_size = len(test_arguments_id)\n","test_kp_size = len(test_key_point_id)\n","\n","test_arguments_corpus = []\n","for i in test_arguments_id:\n","  argument = arguments_test.loc[arguments_test['arg_id'] == i]['argument'].tolist()[0]\n","  test_arguments_corpus.append(argument)\n","\n","test_key_points_corpus = []\n","for i in test_key_point_id:\n","  key_point = key_points_test.loc[key_points_test['key_point_id'] == i]['key_point'].tolist()[0]\n","  test_key_points_corpus.append(key_point)\n","\n","test_corpus = test_arguments_corpus + test_key_points_corpus\n","\n","\n","nltk.download('punkt')\n","nltk.download('stopwords')\n","tokenized_test_sentences = []\n","for sentence in test_corpus:\n","  tokenized_test_sentences.append(nltk.word_tokenize(sentence))\n","\n","from keras.utils import pad_sequences\n","pad_size = max([len(sentence) for sentence in tokenized_test_sentences])\n","padded_tokenized_sentences = []"]},{"cell_type":"code","execution_count":20,"metadata":{"executionInfo":{"elapsed":432,"status":"ok","timestamp":1657128707466,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"9tjrLNYolzE9"},"outputs":[],"source":["## Custom TF-IDF vector function\n","def get_tf(word, sentence):\n","  nw = sentence.count(word)\n","  n = len(sentence)\n","  return nw/n\n","\n","def get_idf(word, corpus):\n","  y = len(corpus)\n","  yw = 0\n","  for sentence in corpus:\n","    if word in sentence:\n","      yw += 1\n","  return y / (yw + 1)\n","\n","def get_tfidf(corpus):\n","  print(f'totally {len(corpus)} to slove')\n","  corpus_tfidf = []\n","  num = 0\n","  for sentence in corpus:\n","    sentence_tfidf = []\n","    for word in sentence:\n","      tf = get_tf(word, sentence)\n","      idf = get_idf(word, corpus)\n","      tf_idf = tf * idf * 10\n","      sentence_tfidf.append(tf_idf)\n","    num += 1\n","    corpus_tfidf.append(sentence_tfidf)\n","  return corpus_tfidf"]},{"cell_type":"code","execution_count":83,"metadata":{"executionInfo":{"elapsed":354,"status":"ok","timestamp":1657136038651,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"yx22GLJ2l7ri"},"outputs":[],"source":["## Get TF-IDF vector for each sentence\n","test_vectorized_sentences = get_tfidf(tokenized_test_sentences)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Vx6cq_pYl-QI"},"outputs":[],"source":["## Pad TF-IDF vector to same\n","\n","padded_test_vectorized_sentences = pad_sequences(test_vectorized_sentences, maxlen = pad_size, padding = 'post')\n","\n","padded_test_vectorized_arguments = padded_test_vectorized_sentences[0: test_arguments_size]\n","padded_test_vectorized_key_points = padded_test_vectorized_sentences[test_arguments_size:]\n","\n","\n","padded_test_vectorized_arguments_data = []\n","for i in range(len(test_arguments_id)):\n","  padded_test_vectorized_arguments_data.append([test_arguments_id[i], padded_test_vectorized_arguments[i]])\n","test_vectorized_arguments_dataframe = pd.DataFrame(padded_test_vectorized_arguments_data, columns=['arg_id', 'tfidf_vector'])\n","\n","padded_test_vectorized_key_points_data = []\n","for i in range(len(test_key_point_id)):\n","  padded_test_vectorized_key_points_data.append([test_key_point_id[i], padded_test_vectorized_key_points[i]])\n","test_vectorized_key_points_dataframe = pd.DataFrame(padded_test_vectorized_key_points_data, columns=['kp_id', 'tfidf_vector'])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ibtlw9I1mFUW"},"outputs":[],"source":["test_data = []\n","for i in range(len(labels_test)):\n","  arg_id = labels_test['arg_id'][i]\n","  kp_id = labels_test['key_point_id'][i]\n","  label = labels_test['label'][i]\n","  argument_tfidf_vector = test_vectorized_arguments_dataframe.loc[test_vectorized_arguments_dataframe['arg_id'] == arg_id]['tfidf_vector'].tolist()[0]\n","  key_point_tfidf_vector = test_vectorized_key_points_dataframe.loc[test_vectorized_key_points_dataframe['kp_id'] == kp_id]['tfidf_vector'].tolist()[0]\n","  single_dataframe = [argument_tfidf_vector, key_point_tfidf_vector, label]\n","  test_data.append(single_dataframe)\n","test_dataframe = pd.DataFrame(test_data, columns=['argument_vector', 'key_point_vector', 'label'])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QVyuaToemLSx"},"outputs":[],"source":["## Calculate cossim \n","\n","from sklearn.metrics.pairwise import cosine_similarity\n","re = []\n","for i in range(len(test_dataframe)):\n","  cossim = cosine_similarity(test_dataframe['argument_vector'][i].reshape((1, -1)), test_dataframe['key_point_vector'][i].reshape((1, -1))).tolist()\n","  re.append(listFlatten(cossim))\n","predictions = np.asarray(re)"]},{"cell_type":"markdown","metadata":{"id":"VNG0hlHPmRcG"},"source":["# **TF-IDF Evaluation**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":798},"executionInfo":{"elapsed":634,"status":"ok","timestamp":1657051508298,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"hXBpUsY3mNMe","outputId":"9a7521b5-dfeb-459a-f2fa-407488b29643"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model Evaluation -- Unsupervised Model: TF-IDF Embedding, arguments to key points score\n","              precision    recall  f1-score   support\n","\n","         0.0       0.48      0.83      0.61      1677\n","         1.0       0.48      0.15      0.23      1749\n","\n","    accuracy                           0.48      3426\n","   macro avg       0.48      0.49      0.42      3426\n","weighted avg       0.48      0.48      0.42      3426\n","\n","The average precision score is: 0.15702772166379614.\n","The balanced accuracy score is: 0.48401901606607967.\n","The tuned threshold is: 0.21073651313781738.\n","The confusion matrix is: \n"]},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 2 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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["print(\"Model Evaluation -- Unsupervised Model: TF-IDF Embedding, arguments to key points score\")\n","evaluate_classifier(predictions, true_values)"]},{"cell_type":"markdown","metadata":{"id":"Y5azU458oMHT"},"source":["# **Supervised approaches**"]},{"cell_type":"markdown","metadata":{"id":"99xdeVE7dSDL"},"source":["# **Neural architecture 1**"]},{"cell_type":"markdown","metadata":{"id":"Q2e1oqrYHgOK"},"source":["This architecture based on TF-IDF vector.\n","\n","\n","![Architecture 1 structure.png](data:image/png;base64,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)"]},{"cell_type":"markdown","metadata":{"id":"KmPl0POWSVug"},"source":["Argument and Key Point Input are TF-IDF vectors.\n","\n","\n","Multi Head Attention helps to find important part of vector.\n","\n","\n","Concatenate attention output tensor.\n","\n","\n","Output is a dense layer activated by sigmoid."]},{"cell_type":"markdown","metadata":{"id":"X9KhH60QInHL"},"source":["# **Train and Test Data Extraction**"]},{"cell_type":"markdown","metadata":{"id":"6Xhu6vrjeel_"},"source":["Extract data from train, dev and test dataset\n","\n","Manage sentences into corpus respectively\n","\n","\n","\n"]},{"cell_type":"code","execution_count":21,"metadata":{"executionInfo":{"elapsed":883,"status":"ok","timestamp":1657128713251,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"k3z64ssaptSD"},"outputs":[],"source":["## Extract data from train, dev and test dataset\n","train_labels = pd.concat([labels_train, labels_dev], ignore_index = True).reset_index(drop=True)\n","train_arg_id = train_labels['arg_id']\n","train_kp_id = train_labels['key_point_id']\n","total_arguments = pd.concat([arguments_train, arguments_dev], ignore_index = True).reset_index(drop=True)\n","total_key_point = pd.concat([key_points_train, key_points_dev], ignore_index = True).reset_index(drop=True)"]},{"cell_type":"code","execution_count":22,"metadata":{"executionInfo":{"elapsed":1,"status":"ok","timestamp":1657128714903,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"kNfRHT6XpvAr"},"outputs":[],"source":["train_arguments_id = train_labels['arg_id'].drop_duplicates().tolist()\n","train_key_point_id = train_labels['key_point_id'].drop_duplicates().tolist()\n","train_arguments_size = len(train_arguments_id)\n","train_kp_size = len(train_key_point_id)\n","\n","test_arguments_id = labels_test['arg_id'].drop_duplicates().tolist()\n","test_key_point_id = labels_test['key_point_id'].drop_duplicates().tolist()\n","test_arguments_size = len(test_arguments_id)\n","test_kp_size = len(test_key_point_id)"]},{"cell_type":"code","execution_count":23,"metadata":{"executionInfo":{"elapsed":4954,"status":"ok","timestamp":1657128721807,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"IGjaGJuNpxWX"},"outputs":[],"source":["## Manage sentences into corpus respectively\n","train_arguments_corpus = []\n","for i in train_arguments_id:\n","  argument = total_arguments.loc[total_arguments['arg_id'] == i]['argument'].tolist()[0]\n","  train_arguments_corpus.append(argument)\n","\n","train_key_points_corpus = []\n","for i in train_key_point_id:\n","  key_point = total_key_point.loc[total_key_point['key_point_id'] == i]['key_point'].tolist()[0]\n","  train_key_points_corpus.append(key_point)"]},{"cell_type":"code","execution_count":24,"metadata":{"executionInfo":{"elapsed":371,"status":"ok","timestamp":1657128725663,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"Zu_s9EdKpzsv"},"outputs":[],"source":["train_corpus = train_arguments_corpus + train_key_points_corpus"]},{"cell_type":"code","execution_count":25,"metadata":{"executionInfo":{"elapsed":502,"status":"ok","timestamp":1657128728098,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"5-6mjgmZp1pQ"},"outputs":[],"source":["## Tokenize sentences\n","\n","tokenized_train_sentences = []\n","for sentence in train_corpus:\n","  tokenized_train_sentences.append(nltk.word_tokenize(sentence))"]},{"cell_type":"code","execution_count":26,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":302044,"status":"ok","timestamp":1657129032421,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"sTyUMCkWqRuV","outputId":"0ec970d4-7c60-47ea-fd5e-64d50c13ec3d"},"outputs":[{"name":"stdout","output_type":"stream","text":["totally 6758 to slove\n","totally 756 to slove\n"]}],"source":["## Get TF-IDF vectors\n","\n","pad_size = max([len(sentence) for sentence in tokenized_train_sentences])\n","\n","train_vectorized_sentences = get_tfidf(tokenized_train_sentences)\n","test_vectorized_sentences = get_tfidf(tokenized_test_sentences)"]},{"cell_type":"code","execution_count":27,"metadata":{"executionInfo":{"elapsed":624,"status":"ok","timestamp":1657129120938,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"LiM1EAjFqY6X"},"outputs":[],"source":["## Pad sentences\n","\n","padded_train_vectorized_sentences = pad_sequences(train_vectorized_sentences, maxlen = pad_size, padding = 'post')\n","padded_test_vectorized_sentences = pad_sequences(test_vectorized_sentences, maxlen = pad_size, padding = 'post')"]},{"cell_type":"code","execution_count":28,"metadata":{"executionInfo":{"elapsed":824,"status":"ok","timestamp":1657129124181,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"zXpMXQNxr42b"},"outputs":[],"source":["padded_train_vectorized_arguments = padded_train_vectorized_sentences[0: train_arguments_size]\n","padded_train_vectorized_key_points = padded_train_vectorized_sentences[train_arguments_size:]\n","\n","padded_test_vectorized_arguments = padded_test_vectorized_sentences[0: test_arguments_size]\n","padded_test_vectorized_key_points = padded_test_vectorized_sentences[test_arguments_size:]"]},{"cell_type":"code","execution_count":29,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1657129127314,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"xkFmwfGxr62G"},"outputs":[],"source":["## Manage TF-IDF vectors into dataframe for train\n","\n","padded_train_vectorized_arguments_data = []\n","for i in range(len(train_arguments_id)):\n","  padded_train_vectorized_arguments_data.append([train_arguments_id[i], padded_train_vectorized_arguments[i]])\n","train_vectorized_arguments_dataframe = pd.DataFrame(padded_train_vectorized_arguments_data, columns=['arg_id', 'tfidf_vector'])\n","\n","padded_train_vectorized_key_points_data = []\n","for i in range(len(train_key_point_id)):\n","  padded_train_vectorized_key_points_data.append([train_key_point_id[i], padded_train_vectorized_key_points[i]])\n","train_vectorized_key_points_dataframe = pd.DataFrame(padded_train_vectorized_key_points_data, columns=['kp_id', 'tfidf_vector'])\n","\n","padded_test_vectorized_arguments_data = []\n","for i in range(len(test_arguments_id)):\n","  padded_test_vectorized_arguments_data.append([test_arguments_id[i], padded_test_vectorized_arguments[i]])\n","test_vectorized_arguments_dataframe = pd.DataFrame(padded_test_vectorized_arguments_data, columns=['arg_id', 'tfidf_vector'])\n","\n","padded_test_vectorized_key_points_data = []\n","for i in range(len(test_key_point_id)):\n","  padded_test_vectorized_key_points_data.append([test_key_point_id[i], padded_test_vectorized_key_points[i]])\n","test_vectorized_key_points_dataframe = pd.DataFrame(padded_test_vectorized_key_points_data, columns=['kp_id', 'tfidf_vector'])"]},{"cell_type":"code","execution_count":30,"metadata":{"executionInfo":{"elapsed":34283,"status":"ok","timestamp":1657129164638,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"mYYi_5oOr8vE"},"outputs":[],"source":["train_data = []\n","for i in range(len(train_labels)):\n","  arg_id = train_labels['arg_id'][i]\n","  kp_id = train_labels['key_point_id'][i]\n","  label = train_labels['label'][i]\n","  argument_tfidf_vector = train_vectorized_arguments_dataframe.loc[train_vectorized_arguments_dataframe['arg_id'] == arg_id]['tfidf_vector'].tolist()[0]\n","  key_point_tfidf_vector = train_vectorized_key_points_dataframe.loc[train_vectorized_key_points_dataframe['kp_id'] == kp_id]['tfidf_vector'].tolist()[0]\n","  single_dataframe = [argument_tfidf_vector, key_point_tfidf_vector, label]\n","  train_data.append(single_dataframe)\n","train_dataframe = pd.DataFrame(train_data, columns=['argument_vector', 'key_point_vector', 'label'])\n","\n","test_data = []\n","for i in range(len(labels_test)):\n","  arg_id = labels_test['arg_id'][i]\n","  kp_id = labels_test['key_point_id'][i]\n","  label = labels_test['label'][i]\n","  argument_tfidf_vector = test_vectorized_arguments_dataframe.loc[test_vectorized_arguments_dataframe['arg_id'] == arg_id]['tfidf_vector'].tolist()[0]\n","  key_point_tfidf_vector = test_vectorized_key_points_dataframe.loc[test_vectorized_key_points_dataframe['kp_id'] == kp_id]['tfidf_vector'].tolist()[0]\n","  single_dataframe = [argument_tfidf_vector, key_point_tfidf_vector, label]\n","  test_data.append(single_dataframe)\n","test_dataframe = pd.DataFrame(test_data, columns=['argument_vector', 'key_point_vector', 'label'])"]},{"cell_type":"code","execution_count":31,"metadata":{"executionInfo":{"elapsed":370,"status":"ok","timestamp":1657129171356,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"kZ5VjcO5sD2r"},"outputs":[],"source":["from keras import backend as K\n","from keras.layers import Dense, TimeDistributed, Input, concatenate, add, average, Dropout, dot, Lambda, LSTM, Bidirectional, MaxPooling1D, Reshape, Flatten, Conv1D, MultiHeadAttention, GlobalAveragePooling1D, Concatenate\n","from keras.models import Model\n","from keras.callbacks import EarlyStopping, ModelCheckpoint\n","from keras.initializers import Constant, glorot_normal\n","from keras.optimizers import RMSprop, Adamax, SGD, Adam\n","from keras import regularizers\n","from keras.metrics import Precision, Recall\n","from keras.losses import BinaryCrossentropy, CosineSimilarity\n","from keras.utils import plot_model\n","\n","def build_sub_TFIDF_classifier_model(batch_zise, input_shape):\n","\n","  argument_input = Input(shape = (input_shape), \n","                         name = 'argument_input')\n","  \n","  keypoint_input = Input(shape = input_shape,\n","                         name = 'keypoint_input')\n"," \n","  ## attention\n","  self_attention = MultiHeadAttention(8, 16)\n","  output_tensor_arg, weights_arg = self_attention(argument_input, argument_input, return_attention_scores=True)\n","  output_tensor_kp, weights_kp = self_attention(keypoint_input, keypoint_input, return_attention_scores=True)\n","  rep_sequences_1 = GlobalAveragePooling1D()(output_tensor_arg)\n","  rep_sequences_2 = GlobalAveragePooling1D()(output_tensor_kp)\n"," \n","  ## classifier\n","  merged = Concatenate()([rep_sequences_1, rep_sequences_2])\n","  O_seq = Dropout(0.5)(merged)\n","  outputs = Dense(1, activation='sigmoid')(O_seq)\n"," \n","  model = Model(inputs=[argument_input, keypoint_input], outputs=outputs)\n","  model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n","\n","  return model"]},{"cell_type":"code","execution_count":32,"metadata":{"executionInfo":{"elapsed":771,"status":"ok","timestamp":1657129174610,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"9viSZOCjsBmn"},"outputs":[],"source":["from keras.utils import Sequence\n","class DataGenator_co(Sequence):\n","  def __init__(self, batch_size, labeled_df):\n","    self.batch_size = batch_size\n","    self.labeled_df = labeled_df.copy()\n","    self.n = 0\n","    self.length = len(labeled_df)\n","  def __len__(self):\n","    return int(np.ceil(self.length / self.batch_size))\n","\n","  def __getitem__(self, idx):\n","    data = self.labeled_df[idx * self.batch_size : (idx + 1) * self.batch_size]\n","    args = []\n","    for i in data['argument_vector']:\n","      args.append(np.array(i))\n","    kps = []\n","    for i in data['key_point_vector']:\n","      kps.append(np.array(i))\n","    lebel = []\n","    for i in data['label']:\n","      lebel.append(np.array(i))\n","    features = [np.stack(args, axis=0), np.stack(kps, axis=0)]\n","    return features, np.stack(lebel, axis=0)\n","  def print_item(self):\n","    features, label = self.__getitem__(0)\n","    return features, label\n","  def __next__(self):\n","    if self.n >= self.__len__():\n","        self.n = 0\n","    \n","    result = self.__getitem__(self.n)\n","    self.n += 1\n","\n","    return result"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":1234,"status":"ok","timestamp":1657049490043,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"ZLHyOEd7sHVp","outputId":"f170e34b-6989-434d-d6cd-816d743b4469"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"model_1\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," argument_input (InputLayer)    [(None, 44, 1)]      0           []                               \n","                                                                                                  \n"," keypoint_input (InputLayer)    [(None, 44, 1)]      0           []                               \n","                                                                                                  \n"," multi_head_attention_1 (MultiH  ((None, 44, 1),     897         ['argument_input[0][0]',         \n"," eadAttention)                   (None, 8, 44, 44))               'argument_input[0][0]',         \n","                                                                  'keypoint_input[0][0]',         \n","                                                                  'keypoint_input[0][0]']         \n","                                                                                                  \n"," global_average_pooling1d_2 (Gl  (None, 1)           0           ['multi_head_attention_1[0][0]'] \n"," obalAveragePooling1D)                                                                            \n","                                                                                                  \n"," global_average_pooling1d_3 (Gl  (None, 1)           0           ['multi_head_attention_1[1][0]'] \n"," obalAveragePooling1D)                                                                            \n","                                                                                                  \n"," concatenate_1 (Concatenate)    (None, 2)            0           ['global_average_pooling1d_2[0][0\n","                                                                 ]',                              \n","                                                                  'global_average_pooling1d_3[0][0\n","                                                                 ]']                              \n","                                                                                                  \n"," dropout_1 (Dropout)            (None, 2)            0           ['concatenate_1[0][0]']          \n","                                                                                                  \n"," dense_1 (Dense)                (None, 1)            3           ['dropout_1[0][0]']              \n","                                                                                                  \n","==================================================================================================\n","Total params: 900\n","Trainable params: 900\n","Non-trainable params: 0\n","__________________________________________________________________________________________________\n"]},{"data":{"image/png":"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","text/plain":["<IPython.core.display.Image object>"]},"execution_count":116,"metadata":{},"output_type":"execute_result"}],"source":["model_TFIDF = build_sub_TFIDF_classifier_model(32, (44, 1))\n","model_TFIDF.summary()\n","plot_model(model_TFIDF, show_shapes = True)"]},{"cell_type":"code","execution_count":33,"metadata":{"executionInfo":{"elapsed":822,"status":"ok","timestamp":1657129179334,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"e4UlCJMRsKHM"},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","batch_size = 32\n","train_TFIDF_data, validation_TFIDF_data = train_test_split(train_dataframe, shuffle=False)\n","train_TFIDF_gen = DataGenator_co(batch_size, train_TFIDF_data)\n","validation_TFIDF_gen = DataGenator_co(batch_size, validation_TFIDF_data)\n","test_TDIFD_gen = DataGenator_co(batch_size, test_dataframe)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":578464,"status":"ok","timestamp":1657050079839,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"SkuV5aeksLy6","outputId":"6cf0ab9a-d845-489d-85ec-5a0f63c66df9"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/100\n","565/565 [==============================] - ETA: 0s - loss: 2.4296 - accuracy: 0.6081\n","Epoch 1: val_loss improved from inf to 1.25342, saving model to /content/drive/MyDrive/NLP-project/models/model_TFIDF_A1.h5\n","565/565 [==============================] - 15s 24ms/step - loss: 2.4296 - accuracy: 0.6081 - val_loss: 1.2534 - val_accuracy: 0.6595\n","Epoch 2/100\n","565/565 [==============================] - ETA: 0s - loss: 0.9691 - accuracy: 0.6653\n","Epoch 2: val_loss improved from 1.25342 to 0.59127, saving model to /content/drive/MyDrive/NLP-project/models/model_TFIDF_A1.h5\n","565/565 [==============================] - 13s 22ms/step - loss: 0.9691 - accuracy: 0.6653 - val_loss: 0.5913 - val_accuracy: 0.7108\n","Epoch 3/100\n","565/565 [==============================] - ETA: 0s - loss: 0.8409 - accuracy: 0.7061\n","Epoch 3: val_loss did not improve from 0.59127\n","565/565 [==============================] - 13s 22ms/step - loss: 0.8409 - accuracy: 0.7061 - val_loss: 1.1887 - val_accuracy: 0.4806\n","Epoch 4/100\n","565/565 [==============================] - ETA: 0s - loss: 0.8089 - accuracy: 0.7149\n","Epoch 4: val_loss did not improve from 0.59127\n","565/565 [==============================] - 12s 22ms/step - loss: 0.8089 - accuracy: 0.7149 - val_loss: 0.6001 - val_accuracy: 0.6725\n","Epoch 5/100\n","565/565 [==============================] - ETA: 0s - loss: 0.7626 - accuracy: 0.7226\n","Epoch 5: val_loss did not improve from 0.59127\n","565/565 [==============================] - 12s 22ms/step - loss: 0.7626 - accuracy: 0.7226 - val_loss: 0.7670 - val_accuracy: 0.7522\n","Epoch 6/100\n","565/565 [==============================] - ETA: 0s - loss: 0.6406 - accuracy: 0.7526\n","Epoch 6: val_loss did not improve from 0.59127\n","565/565 [==============================] - 13s 22ms/step - loss: 0.6406 - accuracy: 0.7526 - val_loss: 0.9712 - val_accuracy: 0.7314\n","Epoch 7/100\n","565/565 [==============================] - ETA: 0s - loss: 0.6120 - accuracy: 0.7629\n","Epoch 7: val_loss did not improve from 0.59127\n","565/565 [==============================] - 12s 22ms/step - loss: 0.6120 - accuracy: 0.7629 - val_loss: 0.6260 - val_accuracy: 0.7809\n","Epoch 8/100\n","563/565 [============================>.] - ETA: 0s - loss: 0.5605 - accuracy: 0.7741\n","Epoch 8: val_loss improved from 0.59127 to 0.51450, saving model to /content/drive/MyDrive/NLP-project/models/model_TFIDF_A1.h5\n","565/565 [==============================] - 14s 24ms/step - loss: 0.5613 - accuracy: 0.7735 - val_loss: 0.5145 - val_accuracy: 0.7902\n","Epoch 9/100\n","564/565 [============================>.] - ETA: 0s - loss: 0.5406 - accuracy: 0.7829\n","Epoch 9: val_loss did not improve from 0.51450\n","565/565 [==============================] - 13s 23ms/step - loss: 0.5409 - accuracy: 0.7828 - val_loss: 0.5194 - val_accuracy: 0.7902\n","Epoch 10/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5183 - accuracy: 0.7924\n","Epoch 10: val_loss improved from 0.51450 to 0.51371, saving model to /content/drive/MyDrive/NLP-project/models/model_TFIDF_A1.h5\n","565/565 [==============================] - 13s 22ms/step - loss: 0.5183 - accuracy: 0.7924 - val_loss: 0.5137 - val_accuracy: 0.7902\n","Epoch 11/100\n","563/565 [============================>.] - ETA: 0s - loss: 0.5109 - accuracy: 0.7931\n","Epoch 11: val_loss did not improve from 0.51371\n","565/565 [==============================] - 12s 22ms/step - loss: 0.5107 - accuracy: 0.7933 - val_loss: 0.5138 - val_accuracy: 0.7902\n","Epoch 12/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5096 - accuracy: 0.7933\n","Epoch 12: val_loss did not improve from 0.51371\n","565/565 [==============================] - 13s 22ms/step - loss: 0.5096 - accuracy: 0.7933 - val_loss: 0.5137 - val_accuracy: 0.7902\n","Epoch 13/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5096 - accuracy: 0.7933\n","Epoch 13: val_loss did not improve from 0.51371\n","565/565 [==============================] - 13s 22ms/step - loss: 0.5096 - accuracy: 0.7933 - val_loss: 0.5137 - val_accuracy: 0.7902\n","Epoch 14/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5096 - accuracy: 0.7933\n","Epoch 14: val_loss improved from 0.51371 to 0.51369, saving model to /content/drive/MyDrive/NLP-project/models/model_TFIDF_A1.h5\n","565/565 [==============================] - 13s 23ms/step - loss: 0.5096 - accuracy: 0.7933 - val_loss: 0.5137 - val_accuracy: 0.7902\n","Epoch 15/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5097 - accuracy: 0.7933\n","Epoch 15: val_loss did not improve from 0.51369\n","565/565 [==============================] - 13s 22ms/step - loss: 0.5097 - accuracy: 0.7933 - val_loss: 0.5139 - val_accuracy: 0.7902\n","Epoch 16/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5096 - accuracy: 0.7933\n","Epoch 16: val_loss did not improve from 0.51369\n","565/565 [==============================] - 13s 23ms/step - loss: 0.5096 - accuracy: 0.7933 - val_loss: 0.5140 - val_accuracy: 0.7902\n","Epoch 17/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5094 - accuracy: 0.7933\n","Epoch 17: val_loss did not improve from 0.51369\n","565/565 [==============================] - 14s 24ms/step - loss: 0.5094 - accuracy: 0.7933 - val_loss: 0.5138 - val_accuracy: 0.7902\n","Epoch 18/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5097 - accuracy: 0.7933\n","Epoch 18: val_loss did not improve from 0.51369\n","565/565 [==============================] - 13s 22ms/step - loss: 0.5097 - accuracy: 0.7933 - val_loss: 0.5138 - val_accuracy: 0.7902\n","Epoch 19/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5096 - accuracy: 0.7933\n","Epoch 19: val_loss did not improve from 0.51369\n","565/565 [==============================] - 13s 22ms/step - loss: 0.5096 - accuracy: 0.7933 - val_loss: 0.5137 - val_accuracy: 0.7902\n","Epoch 20/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5098 - accuracy: 0.7933\n","Epoch 20: val_loss did not improve from 0.51369\n","565/565 [==============================] - 13s 22ms/step - loss: 0.5098 - accuracy: 0.7933 - val_loss: 0.5138 - val_accuracy: 0.7902\n","Epoch 21/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5095 - accuracy: 0.7933\n","Epoch 21: val_loss improved from 0.51369 to 0.51368, saving model to /content/drive/MyDrive/NLP-project/models/model_TFIDF_A1.h5\n","565/565 [==============================] - 13s 22ms/step - loss: 0.5095 - accuracy: 0.7933 - val_loss: 0.5137 - val_accuracy: 0.7902\n","Epoch 22/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5111 - accuracy: 0.7932\n","Epoch 22: val_loss did not improve from 0.51368\n","565/565 [==============================] - 12s 22ms/step - loss: 0.5111 - accuracy: 0.7932 - val_loss: 0.5139 - val_accuracy: 0.7902\n","Epoch 23/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5097 - accuracy: 0.7933\n","Epoch 23: val_loss did not improve from 0.51368\n","565/565 [==============================] - 12s 22ms/step - loss: 0.5097 - accuracy: 0.7933 - val_loss: 0.5137 - val_accuracy: 0.7902\n","Epoch 24/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5098 - accuracy: 0.7933\n","Epoch 24: val_loss did not improve from 0.51368\n","565/565 [==============================] - 12s 22ms/step - loss: 0.5098 - accuracy: 0.7933 - val_loss: 0.5141 - val_accuracy: 0.7902\n","Epoch 25/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5098 - accuracy: 0.7933\n","Epoch 25: val_loss improved from 0.51368 to 0.51367, saving model to /content/drive/MyDrive/NLP-project/models/model_TFIDF_A1.h5\n","565/565 [==============================] - 13s 22ms/step - loss: 0.5098 - accuracy: 0.7933 - val_loss: 0.5137 - val_accuracy: 0.7902\n","Epoch 26/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5103 - accuracy: 0.7933\n","Epoch 26: val_loss did not improve from 0.51367\n","565/565 [==============================] - 12s 22ms/step - loss: 0.5103 - accuracy: 0.7933 - val_loss: 0.5145 - val_accuracy: 0.7902\n","Epoch 27/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5098 - accuracy: 0.7933\n","Epoch 27: val_loss did not improve from 0.51367\n","565/565 [==============================] - 12s 22ms/step - loss: 0.5098 - accuracy: 0.7933 - val_loss: 0.5139 - val_accuracy: 0.7902\n","Epoch 28/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5098 - accuracy: 0.7933\n","Epoch 28: val_loss did not improve from 0.51367\n","565/565 [==============================] - 13s 22ms/step - loss: 0.5098 - accuracy: 0.7933 - val_loss: 0.5137 - val_accuracy: 0.7902\n","Epoch 29/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5100 - accuracy: 0.7933\n","Epoch 29: val_loss did not improve from 0.51367\n","565/565 [==============================] - 12s 22ms/step - loss: 0.5100 - accuracy: 0.7933 - val_loss: 0.5137 - val_accuracy: 0.7902\n","Epoch 30/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5105 - accuracy: 0.7933\n","Epoch 30: val_loss did not improve from 0.51367\n","565/565 [==============================] - 12s 22ms/step - loss: 0.5105 - accuracy: 0.7933 - val_loss: 0.5138 - val_accuracy: 0.7902\n","Epoch 31/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5098 - accuracy: 0.7933\n","Epoch 31: val_loss did not improve from 0.51367\n","565/565 [==============================] - 12s 22ms/step - loss: 0.5098 - accuracy: 0.7933 - val_loss: 0.5137 - val_accuracy: 0.7902\n","Epoch 32/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5111 - accuracy: 0.7933\n","Epoch 32: val_loss improved from 0.51367 to 0.51365, saving model to /content/drive/MyDrive/NLP-project/models/model_TFIDF_A1.h5\n","565/565 [==============================] - 13s 22ms/step - loss: 0.5111 - accuracy: 0.7933 - val_loss: 0.5137 - val_accuracy: 0.7902\n","Epoch 33/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5098 - accuracy: 0.7933\n","Epoch 33: val_loss did not improve from 0.51365\n","565/565 [==============================] - 12s 22ms/step - loss: 0.5098 - accuracy: 0.7933 - val_loss: 0.5137 - val_accuracy: 0.7902\n","Epoch 34/100\n","563/565 [============================>.] - ETA: 0s - loss: 0.5099 - accuracy: 0.7933\n","Epoch 34: val_loss did not improve from 0.51365\n","565/565 [==============================] - 12s 22ms/step - loss: 0.5098 - accuracy: 0.7933 - val_loss: 0.5137 - val_accuracy: 0.7902\n","Epoch 35/100\n","563/565 [============================>.] - ETA: 0s - loss: 0.5096 - accuracy: 0.7933\n","Epoch 35: val_loss did not improve from 0.51365\n","565/565 [==============================] - 12s 22ms/step - loss: 0.5096 - accuracy: 0.7933 - val_loss: 0.5145 - val_accuracy: 0.7902\n","Epoch 36/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5100 - accuracy: 0.7933\n","Epoch 36: val_loss did not improve from 0.51365\n","565/565 [==============================] - 12s 22ms/step - loss: 0.5100 - accuracy: 0.7933 - val_loss: 0.5144 - val_accuracy: 0.7902\n","Epoch 37/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5100 - accuracy: 0.7933\n","Epoch 37: val_loss did not improve from 0.51365\n","565/565 [==============================] - 13s 23ms/step - loss: 0.5100 - accuracy: 0.7933 - val_loss: 0.5138 - val_accuracy: 0.7902\n","Epoch 38/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5099 - accuracy: 0.7933\n","Epoch 38: val_loss did not improve from 0.51365\n","565/565 [==============================] - 13s 22ms/step - loss: 0.5099 - accuracy: 0.7933 - val_loss: 0.5152 - val_accuracy: 0.7902\n","Epoch 39/100\n","563/565 [============================>.] - ETA: 0s - loss: 0.5101 - accuracy: 0.7933\n","Epoch 39: val_loss did not improve from 0.51365\n","565/565 [==============================] - 13s 22ms/step - loss: 0.5100 - accuracy: 0.7933 - val_loss: 0.5139 - val_accuracy: 0.7902\n","Epoch 40/100\n","563/565 [============================>.] - ETA: 0s - loss: 0.5100 - accuracy: 0.7933\n","Epoch 40: val_loss did not improve from 0.51365\n","565/565 [==============================] - 13s 22ms/step - loss: 0.5099 - accuracy: 0.7933 - val_loss: 0.5137 - val_accuracy: 0.7902\n","Epoch 41/100\n","563/565 [============================>.] - ETA: 0s - loss: 0.5098 - accuracy: 0.7933\n","Epoch 41: val_loss did not improve from 0.51365\n","565/565 [==============================] - 12s 22ms/step - loss: 0.5097 - accuracy: 0.7933 - val_loss: 0.5137 - val_accuracy: 0.7902\n","Epoch 42/100\n","565/565 [==============================] - ETA: 0s - loss: 0.5099 - accuracy: 0.7933\n","Epoch 42: val_loss did not improve from 0.51365\n","565/565 [==============================] - 12s 22ms/step - loss: 0.5099 - accuracy: 0.7933 - val_loss: 0.5138 - val_accuracy: 0.7902\n","Epoch 42: early stopping\n"]},{"data":{"text/plain":["<keras.callbacks.History at 0x7f6613c0d4d0>"]},"execution_count":118,"metadata":{},"output_type":"execute_result"}],"source":["checkpoint_2 = [EarlyStopping(monitor = 'val_loss', patience = 10, verbose = 1, mode = 'auto'),\n","              ModelCheckpoint(filepath = \"/content/drive/MyDrive/NLP-project/models/model_TFIDF.h5\",\n","                              monitor = 'val_loss', verbose = 1, save_best_only = True)]\n","\n","\n","model_TFIDF.fit(x = train_TFIDF_gen, validation_data = validation_TFIDF_gen,\n","            epochs = 100, callbacks = checkpoint_2)"]},{"cell_type":"markdown","metadata":{"id":"5RB2di6FsSge"},"source":["# **Architecture 1 Evaluation**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":816},"executionInfo":{"elapsed":2167,"status":"ok","timestamp":1657050440279,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"gKWfLMoPsOOS","outputId":"28df1690-29af-422f-e7c5-9bc203ff4db1"},"outputs":[{"name":"stdout","output_type":"stream","text":["108/108 [==============================] - 1s 10ms/step\n","Model Evaluation -- Supervised Model: TF-IDF Architecture 1, arguments to key points score\n","              precision    recall  f1-score   support\n","\n","         0.0       0.48      0.83      0.61      1677\n","         1.0       0.48      0.15      0.23      1749\n","\n","    accuracy                           0.48      3426\n","   macro avg       0.48      0.49      0.42      3426\n","weighted avg       0.48      0.48      0.42      3426\n","\n","The average precision score is: 0.15702772166379614.\n","The balanced accuracy score is: 0.48401901606607967.\n","The tuned threshold is: 0.21073651313781738.\n","The confusion matrix is: \n"]},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 2 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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["from keras.models import load_model\n","from keras import optimizers\n","from keras import backend as K\n","saved_model_predictions = model_TFIDF.predict(test_TDIFD_gen).reshape((3426, 1))\n","true_label = test_dataframe['label'].tolist()\n","print(\"Model Evaluation -- Supervised Model: TF-IDF Architecture 1, arguments to key points score\")\n","evaluate_classifier(saved_model_predictions, true_label)"]},{"cell_type":"markdown","metadata":{"id":"n6MlyGBUom6U"},"source":["# **Neural architecture 2**\n","The experimented neural architecture is composed by:\n","\n","\n","\n","\n","Stance encoder: encode the stance value (1 or -1) employing a fully-connected network with no activation function to map the scalar input to a -dimensional vector space;\n","\n","\n","\n","BERT: extract the contextualized representation for textual inputs;\n","\n","\n","\n","A context integration layer is done by stacking stance encoding, topic encoding and statements encoding;\n","\n","\n","\n","Statement encoder: another fully-connected network on top of the context integration layer to get the final -dimensional embeddings for key points or arguments."]},{"cell_type":"markdown","metadata":{"id":"nOt4BQV8S267"},"source":["![Architecture 2 structure.png](data:image/png;base64,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)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HJMBy40f7Dl3"},"outputs":[],"source":["from keras import backend as K\n","\n","def recall_m(y_true, y_pred):\n","    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n","    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n","    recall = true_positives / (possible_positives + K.epsilon())\n","    return recall\n","\n","def precision_m(y_true, y_pred):\n","    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n","    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n","    precision = true_positives / (predicted_positives + K.epsilon())\n","    return precision\n","\n","def f1_m(y_true, y_pred):\n","    precision = precision_m(y_true, y_pred)\n","    recall = recall_m(y_true, y_pred)\n","    return 2 * ((precision * recall) / (precision + recall + K.epsilon()))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pNeNKPc8ouQO"},"outputs":[],"source":["## Load the pre-trained BERT model.\n","bert_preprocess_model = hub.KerasLayer(\n","    \"https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3\")\n","bert_model = hub.KerasLayer(\"https://tfhub.dev/tensorflow/bert_en_uncased_L-24_H-1024_A-16/4\", trainable = False)\n","\n","## Dimension of BERT.\n","BERT_dimension = 1024\n","\n","## Number of transformers choosen\n","no_last_TFs = 4"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0y4OnEyHJo4Y"},"outputs":[],"source":["###差不多一样\n","\n","from keras.utils import Sequence\n","def flatten(t: list) -> list:\n","  # Flatten a nested list.\n","  return [item for sublist in t for item in sublist]\n","\n","class DataGenerator(Sequence):\n","  def __init__(self, shuffle: bool, batch_size, \n","               arguments_df, key_points_df, labels_df, \n","               preprocessor, pretrained_model, no_last_TFs = 4):\n","    \n","    self.shuffle = shuffle\n","    self.batch_size = batch_size\n","    self.no_last_TFs = no_last_TFs\n","    self.preprocessor = preprocessor\n","    self.pretrained_model = pretrained_model\n","\n","    self.arguments_df = arguments_df.copy()\n","    self.key_points_df = key_points_df.copy()\n","    self.labels_df = labels_df.copy()      \n","    \n","    ## Labels.\n","    self.labels = labels_df['label'].values\n","\n","    ## Stance.\n","    self.stance = arguments_df['stance'].tolist()\n","\n","    self.length = len(labels_df)\n","    self.indexes = np.arange(self.length)\n","    self.on_epoch_end()\n","\n","    self.n = 0\n","\n","  def on_epoch_end(self):\n","    ## Shuffle the indexes to have more robust batches.\n","    if self.shuffle:\n","      np.random.shuffle(self.indexes)\n","\n","  def __len__(self):\n","    ## Length of the batch.\n","    return int(np.ceil(self.length / self.batch_size))\n","\n","  def __getitem__(self, idx):\n","    '''Generate one batch of data.'''\n","    ## Select the indexes of the batch.\n","    indexes = self.indexes[idx * self.batch_size : (idx + 1) * self.batch_size]\n","    ## For each row in the labels dataframe we have the pair argument and keypoint. These two lines retrieve the pairs in the batch\n","    arg_id = [self.labels_df['arg_id'][k] for k in indexes]\n","    kp_id = [self.labels_df['key_point_id'][k] for k in indexes]\n","\n","    ''' Warning: it produces nested list! '''\n","\n","    ## Take the topic from the arguments dataframe that match the given argument, already paired before.\n","    topics = flatten([self.arguments_df.loc[self.arguments_df['arg_id'] == arg]['topic'].tolist() for arg in arg_id])\n","    ## Preprocces add [CLS] as 0 and [SEP] as 2 and pads with 1s.\n","    preprocessed_topic = self.preprocessor(topics)\n","    ## Encode the topics and picks the last four states of the [CLS].\n","    encoded_topic = np.array(self.pretrained_model(preprocessed_topic)['encoder_outputs'][-self.no_last_TFs:])[:,:,0,:]\n","    ## Swap axes to have the batch size at the beginning.\n","    encoded_topic = np.swapaxes(encoded_topic, 0, 1)\n","    encoded_topic = np.reshape(encoded_topic, (-1, self.no_last_TFs * BERT_dimension))\n","\n","    ## Take the arguments from the arguments dataframe that match the given argument, already paired before.\n","    arguments = flatten([self.arguments_df.loc[self.arguments_df['arg_id'] == arg]['argument'].tolist() for arg in arg_id])\n","    ## Preprocces add [CLS] as 0 and [SEP] as 2 and pads with 1s.\n","    preprocessed_argument = self.preprocessor(arguments)\n","    ## Encode the arguments and picks the last four states of the [CLS].\n","    encoded_argument = np.array(self.pretrained_model(preprocessed_argument)['encoder_outputs'][-self.no_last_TFs:])[:,:,0,:]\n","    ## Swap axes to have the batch size at the beginning.\n","    encoded_argument = np.swapaxes(encoded_argument, 0, 1)\n","    encoded_argument = np.reshape(encoded_argument, (-1, self.no_last_TFs * BERT_dimension))\n","    ## Take the keypoint from the keypoints dataframe that match the given argument, already paired before.\n","    keypoints = flatten([self.key_points_df.loc[self.key_points_df['key_point_id'] == kp]['key_point'].tolist() for kp in kp_id])\n","    ## Preprocces add [CLS] as 0 and [SEP] as 2 and pads with 1s.\n","    preprocessed_keypoint = self.preprocessor(keypoints)\n","    ## Encode the keypoints and picks the last four states of the [CLS].\n","    encoded_keypoint = np.array(self.pretrained_model(preprocessed_keypoint)['encoder_outputs'][-self.no_last_TFs:])[:,:,0,:]\n","    ## Swap axes to have the batch size at the beginning.\n","    encoded_keypoint = np.swapaxes(encoded_keypoint, 0, 1)\n","    encoded_keypoint = np.reshape(encoded_keypoint, (-1, self.no_last_TFs * BERT_dimension))\n","\n","    ## Take the stance from the arguments dataframe that match the given argument, already paired before.\n","    stance = np.array([self.arguments_df.loc[self.arguments_df['arg_id'] == arg]['stance'].tolist() for arg in arg_id])\n","\n","    labels = np.array([self.labels[k] for k in indexes])\n","\n","    features = [encoded_topic, \n","                encoded_argument, \n","                encoded_keypoint, \n","                stance]\n","\n","    return features, labels\n","    \n","  def print_item(self):\n","    features, labels = self.__next__()\n","    print(features[1].shape)\n","\n","  def __next__(self):\n","    if self.n >= self.__len__():\n","        self.n = 0\n","    \n","    result = self.__getitem__(self.n)\n","    self.n += 1\n","\n","    return result"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qMAR0a_aXv3L"},"outputs":[],"source":["from keras.layers import Dense, TimeDistributed, Input, concatenate, add, average, Dropout, dot, Lambda, LSTM, Bidirectional\n","from keras.models import Model\n","from keras.callbacks import EarlyStopping, ModelCheckpoint\n","from keras.initializers import Constant, glorot_normal\n","from keras.optimizers import RMSprop, Adamax, SGD, Adam\n","from keras import regularizers\n","from keras.metrics import Precision, Recall\n","from keras.losses import BinaryCrossentropy, CosineSimilarity\n","from keras.utils import plot_model\n","from keras import backend as k\n","from keras.layers.core import Lambda\n","\n","class CosineLayer():\n","\n","    def __call__(self, x1, x2):\n","\n","        def _cosine(x):\n","            dot1 = K.batch_dot(x[0], x[1], axes=1)\n","            dot2 = K.batch_dot(x[0], x[0], axes=1)\n","            dot3 = K.batch_dot(x[1], x[1], axes=1)\n","            max_ = K.maximum(K.sqrt(dot2 * dot3), K.epsilon())\n","            return dot1 / max_\n","\n","        output_shape = (1,)\n","        value = Lambda(\n","            _cosine,\n","            output_shape=output_shape)([x1, x2])\n","        return value\n","\n","def build_classifier_model(no_last_TFs, BERT_dimension, \n","               dropout = 0.5, recurrent_dropout: float = 0.25, \n","               initial_learning_rate: float = 0.0001, N_stance = 16):\n","  \n","  \n","  topic_input = Input(shape = (no_last_TFs * BERT_dimension), \n","                      name = 'topic_input')\n","  \n","  argument_input = Input(shape = (no_last_TFs * BERT_dimension), \n","                         name = 'argument_input')\n","  \n","  keypoint_input = Input(shape = (no_last_TFs * BERT_dimension),\n","                         name = 'keypoint_input')\n","\n","  stance_input = Input(shape = (1, ),\n","                       name = 'stance_input')\n","\n","  stance_encoder = Dense(16, activation = None, name = 'stance_encode')(stance_input)\n","  # Context integration layer.\n","  argument_integration = concatenate([stance_encoder, topic_input, argument_input])\n","  keypoint_integration = concatenate([stance_encoder, topic_input, keypoint_input])\n","\n","\n","  # Statement encoder.\n","  st_encod_layer_01 = Dense(units = 16, activation = 'selu', \n","              kernel_regularizer = regularizers.l1_l2(l1 = 1e-5, l2 = 1e-4),\n","              bias_regularizer = regularizers.l2(1e-4),\n","              activity_regularizer = regularizers.l2(1e-5),\n","              name = 'statement_encoder_01')\n","  \n","  st_encod_drop_out = Dropout(rate = dropout, name = 'statement_encoder_drop_out')\n","\n","  st_encod_layer_02 = Dense(units = 32, activation = 'selu', \n","              kernel_regularizer = regularizers.l1_l2(l1 = 1e-5, l2 = 1e-4),\n","              bias_regularizer = regularizers.l2(1e-4),\n","              activity_regularizer = regularizers.l2(1e-5),               \n","              name = 'statement_encoder_02')\n","\n","  st_encod_argument_0 = st_encod_layer_01(argument_integration)\n","  st_encod_argument_1 = st_encod_drop_out(st_encod_argument_0)\n","  argument_output = st_encod_layer_02(st_encod_argument_1)\n","\n","  st_encod_keypoint_0 = st_encod_layer_01(keypoint_integration)\n","  st_encod_keypoint_1 = st_encod_drop_out(st_encod_keypoint_0)\n","  keypoint_output = st_encod_layer_02(st_encod_keypoint_1)\n","\n","  # Cosine similarity.\n","  cos_sim = CosineLayer()\n","  out_put = cos_sim(argument_output, keypoint_output)\n","  model = Model(inputs = (topic_input, argument_input, keypoint_input, stance_input), outputs = out_put)\n","  return model"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OSs7WoTboY6s"},"outputs":[],"source":["\n","train_gen = DataGenerator(shuffle = True, batch_size = 32,\n","                          arguments_df = arguments_train, key_points_df = key_points_train, labels_df = labels_train, \n","                          preprocessor = bert_preprocess_model, pretrained_model = bert_model)\n","\n","dev_gen = DataGenerator(shuffle = True, batch_size = 32,\n","                        arguments_df = arguments_dev, key_points_df = key_points_dev, labels_df = labels_dev, \n","                        preprocessor = bert_preprocess_model, pretrained_model = bert_model)\n","\n","# No need to shuffle test data.\n","test_gen = DataGenerator(shuffle = False, batch_size = 32,\n","                         arguments_df = arguments_test, key_points_df = key_points_test, labels_df = labels_test, \n","                         preprocessor = bert_preprocess_model, pretrained_model = bert_model)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":1845,"status":"ok","timestamp":1656514055457,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"0zuP0tgK7kqM","outputId":"fe81300e-4362-4e53-a983-9c44e8b6de14"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"model\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," stance_input (InputLayer)      [(None, 1)]          0           []                               \n","                                                                                                  \n"," stance_encode (Dense)          (None, 16)           32          ['stance_input[0][0]']           \n","                                                                                                  \n"," topic_input (InputLayer)       [(None, 4096)]       0           []                               \n","                                                                                                  \n"," argument_input (InputLayer)    [(None, 4096)]       0           []                               \n","                                                                                                  \n"," keypoint_input (InputLayer)    [(None, 4096)]       0           []                               \n","                                                                                                  \n"," concatenate_2 (Concatenate)    (None, 8208)         0           ['stance_encode[0][0]',          \n","                                                                  'topic_input[0][0]',            \n","                                                                  'argument_input[0][0]']         \n","                                                                                                  \n"," concatenate_3 (Concatenate)    (None, 8208)         0           ['stance_encode[0][0]',          \n","                                                                  'topic_input[0][0]',            \n","                                                                  'keypoint_input[0][0]']         \n","                                                                                                  \n"," statement_encoder_01 (Dense)   (None, 16)           131344      ['concatenate_2[0][0]',          \n","                                                                  'concatenate_3[0][0]']          \n","                                                                                                  \n"," statement_encoder_drop_out (Dr  (None, 16)          0           ['statement_encoder_01[0][0]',   \n"," opout)                                                           'statement_encoder_01[1][0]']   \n","                                                                                                  \n"," statement_encoder_02 (Dense)   (None, 32)           544         ['statement_encoder_drop_out[0][0\n","                                                                 ]',                              \n","                                                                  'statement_encoder_drop_out[1][0\n","                                                                 ]']                              \n","                                                                                                  \n"," lambda_1 (Lambda)              (None, 1)            0           ['statement_encoder_02[0][0]',   \n","                                                                  'statement_encoder_02[1][0]']   \n","                                                                                                  \n","==================================================================================================\n","Total params: 131,920\n","Trainable params: 131,920\n","Non-trainable params: 0\n","__________________________________________________________________________________________________\n"]},{"data":{"image/png":"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","text/plain":["<IPython.core.display.Image object>"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["from keras import backend as K\n","model = build_classifier_model(no_last_TFs, BERT_dimension)\n","model.summary()\n","plot_model(model, show_shapes = True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WaN2s8FtYiOa"},"outputs":[],"source":["from keras import optimizers\n","from keras import backend as K\n","## Connect to GPU on Colab \n","## No bug even without Colab Pro, if there is no GPU connected, it will run with CPU\n","gpu_info = !nvidia-smi\n","gpu_info = '\\n'.join(gpu_info)\n","if gpu_info.find('failed') >= 0:\n","  print('Not connected to a GPU')\n","else:\n","  print(gpu_info)\n","\n","model = build_classifier_model(no_last_TFs, BERT_dimension)\n","model.summary()\n","plot_model(model, show_shapes = True)\n","model.compile(loss = focal_loss.BinaryFocalLoss(gamma = 2),\n","                     optimizer = 'Adam',\n","                     metrics = ['binary_accuracy']) \n","\n","\n","checkpoint = [EarlyStopping(monitor = 'val_loss', patience = 10, verbose = 1, mode = 'auto'),\n","              ModelCheckpoint(filepath = \"/content/drive/MyDrive/NLP-project/models/model.h5\",\n","                              monitor = 'val_loss', verbose = 1, save_best_only = True)]\n","\n","model.fit(x = train_gen,\n","            validation_data = dev_gen,\n","            epochs = 100, callbacks = checkpoint)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dkEEaYuAgZpH","outputId":"c181a3e5-f0a4-42cd-b5b6-709774967b7d"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"model_1\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," stance_input (InputLayer)      [(None, 1)]          0           []                               \n","                                                                                                  \n"," stance_encode (Dense)          (None, 16)           32          ['stance_input[0][0]']           \n","                                                                                                  \n"," topic_input (InputLayer)       [(None, 4096)]       0           []                               \n","                                                                                                  \n"," argument_input (InputLayer)    [(None, 4096)]       0           []                               \n","                                                                                                  \n"," keypoint_input (InputLayer)    [(None, 4096)]       0           []                               \n","                                                                                                  \n"," concatenate_4 (Concatenate)    (None, 8208)         0           ['stance_encode[0][0]',          \n","                                                                  'topic_input[0][0]',            \n","                                                                  'argument_input[0][0]']         \n","                                                                                                  \n"," concatenate_5 (Concatenate)    (None, 8208)         0           ['stance_encode[0][0]',          \n","                                                                  'topic_input[0][0]',            \n","                                                                  'keypoint_input[0][0]']         \n","                                                                                                  \n"," statement_encoder_01 (Dense)   (None, 16)           131344      ['concatenate_4[0][0]',          \n","                                                                  'concatenate_5[0][0]']          \n","                                                                                                  \n"," statement_encoder_drop_out (Dr  (None, 16)          0           ['statement_encoder_01[0][0]',   \n"," opout)                                                           'statement_encoder_01[1][0]']   \n","                                                                                                  \n"," statement_encoder_02 (Dense)   (None, 32)           544         ['statement_encoder_drop_out[0][0\n","                                                                 ]',                              \n","                                                                  'statement_encoder_drop_out[1][0\n","                                                                 ]']                              \n","                                                                                                  \n"," lambda_1 (Lambda)              (None, 1)            0           ['statement_encoder_02[0][0]',   \n","                                                                  'statement_encoder_02[1][0]']   \n","                                                                                                  \n","==================================================================================================\n","Total params: 131,920\n","Trainable params: 131,920\n","Non-trainable params: 0\n","__________________________________________________________________________________________________\n","Wed Jun 29 12:15:07 2022       \n","+-----------------------------------------------------------------------------+\n","| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n","|-------------------------------+----------------------+----------------------+\n","| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n","| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n","|                               |                      |               MIG M. |\n","|===============================+======================+======================|\n","|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n","| N/A   51C    P0    38W / 250W |   2531MiB / 16280MiB |      0%      Default |\n","|                               |                      |                  N/A |\n","+-------------------------------+----------------------+----------------------+\n","                                                                               \n","+-----------------------------------------------------------------------------+\n","| Processes:                                                                  |\n","|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n","|        ID   ID                                                   Usage      |\n","|=============================================================================|\n","+-----------------------------------------------------------------------------+\n","Epoch 1/100\n","645/645 [==============================] - ETA: 0s - loss: 0.2862 - binary_accuracy: 0.5855 - precision_m: 0.2056 - recall_m: 0.3567 - f1_m: 0.2499\n","Epoch 1: val_loss improved from inf to 12.67868, saving model to /content/drive/MyDrive/NLP-project/models/model.h5\n","645/645 [==============================] - 1788s 3s/step - loss: 0.2862 - binary_accuracy: 0.5855 - precision_m: 0.2056 - recall_m: 0.3567 - f1_m: 0.2499 - val_loss: 12.6787 - val_binary_accuracy: 0.2134 - val_precision_m: 0.2116 - val_recall_m: 0.9908 - val_f1_m: 0.3422\n","Epoch 2/100\n","645/645 [==============================] - ETA: 0s - loss: 0.2478 - binary_accuracy: 0.5998 - precision_m: 0.2045 - recall_m: 0.3217 - f1_m: 0.2395\n","Epoch 2: val_loss improved from 12.67868 to 12.55304, saving model to /content/drive/MyDrive/NLP-project/models/model.h5\n","645/645 [==============================] - 1904s 3s/step - loss: 0.2478 - binary_accuracy: 0.5998 - precision_m: 0.2045 - recall_m: 0.3217 - f1_m: 0.2395 - val_loss: 12.5530 - val_binary_accuracy: 0.2134 - val_precision_m: 0.2202 - val_recall_m: 1.0000 - val_f1_m: 0.3517\n","Epoch 3/100\n","645/645 [==============================] - ETA: 0s - loss: 0.2360 - binary_accuracy: 0.5926 - precision_m: 0.2125 - recall_m: 0.3520 - f1_m: 0.2542\n","Epoch 3: val_loss improved from 12.55304 to 12.10700, saving model to /content/drive/MyDrive/NLP-project/models/model.h5\n","645/645 [==============================] - 2023s 3s/step - loss: 0.2360 - binary_accuracy: 0.5926 - precision_m: 0.2125 - recall_m: 0.3520 - f1_m: 0.2542 - val_loss: 12.1070 - val_binary_accuracy: 0.2134 - val_precision_m: 0.2159 - val_recall_m: 1.0000 - val_f1_m: 0.3494\n","Epoch 4/100\n","645/645 [==============================] - ETA: 0s - loss: 0.2390 - binary_accuracy: 0.6040 - precision_m: 0.2020 - recall_m: 0.3153 - f1_m: 0.2350"]}],"source":["import focal_loss\n","from keras.models import load_model\n","from keras import optimizers\n","from keras import backend as K\n","\n","reloaded_model = load_model(filepath = '/content/drive/MyDrive/NLP-project/models/model.h5')\n","\n","# To change optimizers in further training.\n","\n","  \n","reloaded_model.compile(loss = focal_loss.BinaryFocalLoss(gamma = 2),\n","                     optimizer = 'Adam',\n","                     metrics = ['binary_accuracy']) \n","\n","\n","reloaded_model.summary()\n","\n","gpu_info = !nvidia-smi\n","gpu_info = '\\n'.join(gpu_info)\n","if gpu_info.find('failed') >= 0:\n","  print('Not connected to a GPU')\n","else:\n","  print(gpu_info)\n","  \n","checkpoint = [EarlyStopping(monitor = 'val_loss', patience = 10, verbose = 1, mode = 'auto'),\n","              ModelCheckpoint(filepath = \"/content/drive/MyDrive/NLP-project/models/model.h5\",\n","                              monitor = 'val_loss', verbose = 1, save_best_only = True)]\n","\n","reloaded_model.fit(x = train_gen,\n","                   validation_data = dev_gen,\n","                   epochs = 100, callbacks = checkpoint)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LgCx1vhvRtPg"},"outputs":[],"source":["from keras.models import load_model\n","from keras import optimizers\n","from keras import backend as K\n","reloaded_model = load_model(filepath = '/content/drive/MyDrive/NLP-project/models/model.h5',\n","                            custom_objects = {'precision_m': precision_m, 'recall_m': recall_m, 'f1_m': f1_m})"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":798},"executionInfo":{"elapsed":164116,"status":"ok","timestamp":1657052533978,"user":{"displayName":"Jiawei JIAWEI","userId":"05967319273073994132"},"user_tz":-120},"id":"lRzKd-G6Rnwh","outputId":"a5575e9d-3d64-4f9a-8220-93bc009747a7"},"outputs":[{"name":"stdout","output_type":"stream","text":["108/108 [==============================] - 159s 1s/step\n","              precision    recall  f1-score   support\n","\n","         0.0       0.50      0.84      0.63      1704\n","         1.0       0.51      0.16      0.25      1722\n","\n","    accuracy                           0.50      3426\n","   macro avg       0.51      0.50      0.44      3426\n","weighted avg       0.51      0.50      0.44      3426\n","\n","The average precision score is: 0.16277318415051423.\n","The balanced accuracy score is: 0.5059926325981059.\n","The tuned threshold is: 0.999998927116394.\n","The confusion matrix is: \n"]},{"data":{"image/png":"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","text/plai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size 432x288 with 2 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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["from sklearn.metrics import accuracy_score, classification_report, f1_score, recall_score, precision_score, PrecisionRecallDisplay, roc_curve, balanced_accuracy_score, ConfusionMatrixDisplay\n","from sklearn.metrics import classification_report, average_precision_score, PrecisionRecallDisplay, ConfusionMatrixDisplay\n","predictions = reloaded_model.predict(test_gen)\n","  \n","evaluate_classifier(predictions, labels_test['label'].to_numpy())"]}],"metadata":{"colab":{"authorship_tag":"ABX9TyP6LQpCu1NrZOeml2455YkP","collapsed_sections":[],"machine_shape":"hm","mount_file_id":"1vb2wnWanqx0VltbslPu8TXTn0X41DEX_","name":"Key_Point_Matching.ipynb","provenance":[],"toc_visible":true},"gpuClass":"standard","kernelspec":{"display_name":"Python 3.8.8 ('base')","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.8.8 (default, Apr 13 2021, 15:08:03) [MSC 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