notebook_medical_transcriptions.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z3ujOArt-ZY5"
      },
      "source": [
        "# **Imports**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iJ10pIOf-biR",
        "outputId": "83c02704-92ae-4401-97b3-292d1f8628e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.14.1-py3-none-any.whl (3.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.4 MB 6.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Collecting tokenizers<0.11,>=0.10.1\n",
            "  Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.3 MB 30.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Collecting pyyaml>=5.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "\u001b[K     |████████████████████████████████| 596 kB 33.9 MB/s \n",
            "\u001b[?25hCollecting sacremoses\n",
            "  Downloading sacremoses-0.0.46-py3-none-any.whl (895 kB)\n",
            "\u001b[K     |████████████████████████████████| 895 kB 30.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.62.3)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.8.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.4.0)\n",
            "Collecting huggingface-hub<1.0,>=0.1.0\n",
            "  Downloading huggingface_hub-0.2.1-py3-none-any.whl (61 kB)\n",
            "\u001b[K     |████████████████████████████████| 61 kB 202 kB/s \n",
            "\u001b[?25hRequirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (3.10.0.2)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.6)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.6.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.10.8)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.1.0)\n",
            "Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, 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-0.2.1 pyyaml-6.0 sacremoses-0.0.46 tokenizers-0.10.3 transformers-4.14.1\n",
            "Collecting pytorch-metric-learning\n",
            "  Downloading pytorch_metric_learning-1.0.0-py3-none-any.whl (102 kB)\n",
            "\u001b[K     |████████████████████████████████| 102 kB 5.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from pytorch-metric-learning) (4.62.3)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from pytorch-metric-learning) (1.0.1)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from pytorch-metric-learning) (0.11.1+cu111)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from pytorch-metric-learning) (1.19.5)\n",
            "Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from pytorch-metric-learning) (1.10.0+cu111)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.6.0->pytorch-metric-learning) (3.10.0.2)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->pytorch-metric-learning) (1.4.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->pytorch-metric-learning) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->pytorch-metric-learning) (3.0.0)\n",
            "Requirement already satisfied: pillow!=8.3.0,>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision->pytorch-metric-learning) (7.1.2)\n",
            "Installing collected packages: pytorch-metric-learning\n",
            "Successfully installed pytorch-metric-learning-1.0.0\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import sqlite3\n",
        "import matplotlib.pyplot as plt\n",
        "from plotnine import *\n",
        "import seaborn as sns\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "from sklearn.decomposition import PCA\n",
        "import plotly.express as px\n",
        "\n",
        "from collections import Counter\n",
        "\n",
        "import gensim\n",
        "from gensim.models import Word2Vec\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "\n",
        "import random\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch.optim.lr_scheduler import ReduceLROnPlateau\n",
        "\n",
        "try:\n",
        "  import transformers # Huggingface’s transformers library\n",
        "except:\n",
        "  !pip install transformers\n",
        "  import transformers\n",
        "\n",
        "from transformers import AutoModel, AutoTokenizer , BertTokenizerFast\n",
        "from transformers import AdamW\n",
        "\n",
        "try:\n",
        "  from pytorch_metric_learning import losses\n",
        "except:\n",
        "  !pip install pytorch-metric-learning\n",
        "  from pytorch_metric_learning import losses\n",
        "\n",
        "from sklearn.cluster import KMeans\n",
        "from scipy.spatial import distance\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S0olppj--bxA"
      },
      "source": [
        "# **Load Data**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cos6utcU-snB"
      },
      "source": [
        "https://www.kaggle.com/tboyle10/medicaltranscriptions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92,
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            }
          }
        },
        "id": "462tsIe3-d5x",
        "outputId": "9c8f7b5b-4ac5-453b-b16f-8fe8704c18aa"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-48700616-4f80-4de4-ab62-80105ec707ab\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-48700616-4f80-4de4-ab62-80105ec707ab\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"odedmous\",\"key\":\"f3ad8c8cb61cd1b60a83444f204ebfcc\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AvA5TteZ-eBw"
      },
      "outputs": [],
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q1mX7WoG-k8a",
        "outputId": "8cb7811a-d452-410a-d05e-6f05d6c212f8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading medicaltranscriptions.zip to /content\n",
            "100% 4.85M/4.85M [00:00<00:00, 21.4MB/s]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!kaggle datasets download -d tboyle10/medicaltranscriptions\n",
        "\n",
        "# !kaggle datasets download -d shwetp/text-classificationheathcare"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0EjCyJud-mup",
        "outputId": "cbe8f08b-9412-4109-9545-857868f609cc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "kaggle.json  medicaltranscriptions.zip\tsample_data\n"
          ]
        }
      ],
      "source": [
        "!ls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "62zge8h0-n7R",
        "outputId": "02cd2f85-221e-4eb7-a62e-a206c1f5a24f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  medicaltranscriptions.zip\n",
            "  inflating: mtsamples.csv           \n"
          ]
        }
      ],
      "source": [
        "!unzip \\*.zip  && rm *.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D2gPr4Vf4uqv"
      },
      "source": [
        "**Preprocess data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rWn6bXuH-pTB"
      },
      "outputs": [],
      "source": [
        "full_data = pd.read_csv(\"mtsamples.csv\")\n",
        "\n",
        "# drop None values from the relevant columns\n",
        "full_data = full_data.dropna(subset=['description', 'transcription', 'medical_specialty']).reset_index(drop=True)\n",
        "\n",
        "full_data['description'] = full_data['description'].str.lower() # convert text data to lower case             \n",
        "full_data['transcription'] = full_data['transcription'].str.lower() # convert text data to lower case             \n",
        "full_data[\"medical_specialty\"] = full_data[\"medical_specialty\"].str.strip() # delete leading / trailing whitespaces\n",
        "\n",
        "\n",
        "#data[\"words\"] =  data[\"description\"].apply(lambda x: nltk.word_tokenize(x))  # description         transcription\n",
        "#stop_words = set(stopwords.words('english'))\n",
        "#data['words_without_stopwords'] = data[\"words\"].apply(lambda x: [word for word in x if word not in (stop_words)])\n",
        "\n",
        "# drop general categories (for example \"Surgery\" category is kind of superset as there can be surgeries belonging to specializations like cardiology,neurolrogy etc)\n",
        "general_categories_rows = full_data[\"medical_specialty\"].isin([\"Surgery\", 'SOAP / Chart / Progress Notes', 'Office Notes', 'Consult - History and Phy.', 'Emergency Room Reports', 'Discharge Summary', 'Pain Management', 'General Medicine'])\n",
        "data = full_data.drop(full_data[general_categories_rows].index)\n",
        "data = data.reset_index(drop=True)\n",
        "\n",
        "# Combine similar categories\n",
        "data[\"medical_specialty\"] = data[\"medical_specialty\"].str.replace(\"Neurosurgery\", \"Neurology\")\n",
        "\n",
        "# add \"labels\" column\n",
        "data['medical_specialty'] = pd.Categorical(data['medical_specialty'])\n",
        "data['labels'] = data['medical_specialty'].cat.codes\n",
        "\n",
        "\n",
        "categories_mapping = dict(enumerate(data['medical_specialty'].cat.categories))\n",
        "\n",
        "# Take only top 5 categories\n",
        "top_categories_num = 5\n",
        "cause_dist = data['medical_specialty'].value_counts()[0:top_categories_num]\n",
        "cause_dist_unseen_cat = data['medical_specialty'].value_counts()[top_categories_num:]\n",
        "\n",
        "test_unseen_categories = data[data[\"medical_specialty\"].isin(cause_dist_unseen_cat.keys())]\n",
        "test_unseen_categories = test_unseen_categories.reset_index(drop=True)\n",
        "\n",
        "# take in unseen data only categories which have more than 50 samples\n",
        "unseen_categories_groups  = test_unseen_categories.groupby(test_unseen_categories['medical_specialty'])\n",
        "test_unseen_categories = unseen_categories_groups.filter(lambda x:x.shape[0] > 50)\n",
        "\n",
        "unseen_categories_mapping = dict(enumerate(test_unseen_categories['medical_specialty'].cat.categories))\n",
        "\n",
        "data = data[data[\"medical_specialty\"].isin(cause_dist.keys())]\n",
        "data = data.reset_index(drop=True)\n",
        "\n",
        "#sentences = list(data['text'].str.split(' '))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "1ddK4D6m_wTq",
        "outputId": "d182c902-66e7-4f4b-d70f-5267f8f7c4a0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Unnamed: 0</th>\n",
              "      <th>description</th>\n",
              "      <th>medical_specialty</th>\n",
              "      <th>sample_name</th>\n",
              "      <th>transcription</th>\n",
              "      <th>keywords</th>\n",
              "      <th>labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>2-d m-mode. doppler.</td>\n",
              "      <td>Cardiovascular / Pulmonary</td>\n",
              "      <td>2-D Echocardiogram - 1</td>\n",
              "      <td>2-d m-mode: , ,1.  left atrial enlargement wit...</td>\n",
              "      <td>cardiovascular / pulmonary, 2-d m-mode, dopple...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>2-d echocardiogram</td>\n",
              "      <td>Cardiovascular / Pulmonary</td>\n",
              "      <td>2-D Echocardiogram - 2</td>\n",
              "      <td>1.  the left ventricular cavity size and wall ...</td>\n",
              "      <td>cardiovascular / pulmonary, 2-d, doppler, echo...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7</td>\n",
              "      <td>2-d echocardiogram</td>\n",
              "      <td>Cardiovascular / Pulmonary</td>\n",
              "      <td>2-D Echocardiogram - 3</td>\n",
              "      <td>2-d echocardiogram,multiple views of the heart...</td>\n",
              "      <td>cardiovascular / pulmonary, 2-d echocardiogram...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9</td>\n",
              "      <td>echocardiogram and doppler</td>\n",
              "      <td>Cardiovascular / Pulmonary</td>\n",
              "      <td>2-D Echocardiogram - 4</td>\n",
              "      <td>description:,1.  normal cardiac chambers size....</td>\n",
              "      <td>cardiovascular / pulmonary, ejection fraction,...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>normal left ventricle, moderate biatrial enla...</td>\n",
              "      <td>Cardiovascular / Pulmonary</td>\n",
              "      <td>2-D Doppler</td>\n",
              "      <td>2-d study,1. mild aortic stenosis, widely calc...</td>\n",
              "      <td>cardiovascular / pulmonary, 2-d study, doppler...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1535</th>\n",
              "      <td>4972</td>\n",
              "      <td>adenosine with nuclear scan as the patient un...</td>\n",
              "      <td>Cardiovascular / Pulmonary</td>\n",
              "      <td>Adenosine Nuclear Scan</td>\n",
              "      <td>indication: , chest pain.,type of test: , aden...</td>\n",
              "      <td>cardiovascular / pulmonary, adenosine nuclear ...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1536</th>\n",
              "      <td>4973</td>\n",
              "      <td>patient presents with a chief complaint of ch...</td>\n",
              "      <td>Cardiovascular / Pulmonary</td>\n",
              "      <td>Acute Inferior Myocardial Infarction</td>\n",
              "      <td>chief complaint: , chest pain.,history of pres...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1537</th>\n",
              "      <td>4974</td>\n",
              "      <td>patient had a recurrent left arm pain after h...</td>\n",
              "      <td>Cardiovascular / Pulmonary</td>\n",
              "      <td>Angina - Consult</td>\n",
              "      <td>history of present illness: , the patient is a...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1538</th>\n",
              "      <td>4975</td>\n",
              "      <td>patient had a markedly abnormal stress test w...</td>\n",
              "      <td>Cardiovascular / Pulmonary</td>\n",
              "      <td>Abnormal Stress Test</td>\n",
              "      <td>history of present illness: , mr. abc is a 60-...</td>\n",
              "      <td>cardiovascular / pulmonary, standard bruce, ni...</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1539</th>\n",
              "      <td>4976</td>\n",
              "      <td>abnormal echocardiogram findings and followup...</td>\n",
              "      <td>Cardiovascular / Pulmonary</td>\n",
              "      <td>Abnormal Echocardiogram</td>\n",
              "      <td>reason for consultation:  ,abnormal echocardio...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1540 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Unnamed: 0  ... labels\n",
              "0              3  ...      3\n",
              "1              4  ...      3\n",
              "2              7  ...      3\n",
              "3              9  ...      3\n",
              "4             11  ...      3\n",
              "...          ...  ...    ...\n",
              "1535        4972  ...      3\n",
              "1536        4973  ...      3\n",
              "1537        4974  ...      3\n",
              "1538        4975  ...      3\n",
              "1539        4976  ...      3\n",
              "\n",
              "[1540 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 684
        },
        "id": "HQIQSPgF-2Vu",
        "outputId": "7fc34439-6218-44f2-971a-b5b89cf6a377"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Unnamed: 0</th>\n",
              "      <th>description</th>\n",
              "      <th>medical_specialty</th>\n",
              "      <th>sample_name</th>\n",
              "      <th>transcription</th>\n",
              "      <th>keywords</th>\n",
              "      <th>labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>18</td>\n",
              "      <td>fertile male with completed family.  elective...</td>\n",
              "      <td>Urology</td>\n",
              "      <td>Vasectomy - 4</td>\n",
              "      <td>procedure: , elective male sterilization via b...</td>\n",
              "      <td>urology, sterilization, vas, fertile male, bil...</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>20</td>\n",
              "      <td>whole body radionuclide bone scan due to pros...</td>\n",
              "      <td>Urology</td>\n",
              "      <td>Whole Body Radionuclide Bone Scan</td>\n",
              "      <td>indication:,  prostate cancer.,technique:,  3....</td>\n",
              "      <td>urology, prostate cancer, technetium, whole bo...</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>22</td>\n",
              "      <td>normal vasectomy</td>\n",
              "      <td>Urology</td>\n",
              "      <td>Vasectomy - 1</td>\n",
              "      <td>description:,  the patient was placed in the s...</td>\n",
              "      <td>urology, vasectomy, allis clamp, catgut, hemoc...</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>23</td>\n",
              "      <td>voluntary sterility.  bilateral vasectomy.  t...</td>\n",
              "      <td>Urology</td>\n",
              "      <td>Vasectomy</td>\n",
              "      <td>preoperative diagnosis: , voluntary sterility....</td>\n",
              "      <td>urology, hemiscrotum, bilateral vasectomy, vol...</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>24</td>\n",
              "      <td>blood in urine - transitional cell cancer of ...</td>\n",
              "      <td>Urology</td>\n",
              "      <td>Urology Consut - 1</td>\n",
              "      <td>chief complaint:,</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>951</th>\n",
              "      <td>3782</td>\n",
              "      <td>adenotonsillectomy.  recurrent tonsillitis.  ...</td>\n",
              "      <td>ENT - Otolaryngology</td>\n",
              "      <td>Adenotonsillectomy - 1</td>\n",
              "      <td>preoperative diagnosis: , recurrent tonsilliti...</td>\n",
              "      <td>ent - otolaryngology, curved allis, tonsilliti...</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>952</th>\n",
              "      <td>3783</td>\n",
              "      <td>adenotonsillectomy, primary, patient under ag...</td>\n",
              "      <td>ENT - Otolaryngology</td>\n",
              "      <td>Adenotonsillectomy</td>\n",
              "      <td>preoperative diagnosis: , chronic hypertrophic...</td>\n",
              "      <td>ent - otolaryngology, hypertrophic adenotonsil...</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>953</th>\n",
              "      <td>3784</td>\n",
              "      <td>adenoidectomy procedure</td>\n",
              "      <td>ENT - Otolaryngology</td>\n",
              "      <td>Adenoidectomy</td>\n",
              "      <td>adenoidectomy,procedure:,  the patient was bro...</td>\n",
              "      <td>ent - otolaryngology, adenoidectomy, crowe-dav...</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>954</th>\n",
              "      <td>3785</td>\n",
              "      <td>bilateral myringotomies with armstrong gromme...</td>\n",
              "      <td>ENT - Otolaryngology</td>\n",
              "      <td>Bilateral Myringotomies - 1</td>\n",
              "      <td>preoperative diagnoses:,  om, chronic, serous,...</td>\n",
              "      <td>ent - otolaryngology, adenoid hyperplasia, om,...</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>956</th>\n",
              "      <td>3787</td>\n",
              "      <td>adenoidectomy and tonsillectomy and lingual f...</td>\n",
              "      <td>ENT - Otolaryngology</td>\n",
              "      <td>Adenoidectomy &amp; Tonsillectomy &amp; Lingual Frenu...</td>\n",
              "      <td>preoperative diagnoses:,1.  chronic adenotonsi...</td>\n",
              "      <td>ent - otolaryngology, adenotonsillitis, ankylo...</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>784 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Unnamed: 0  ... labels\n",
              "11           18  ...     30\n",
              "12           20  ...     30\n",
              "14           22  ...     30\n",
              "15           23  ...     30\n",
              "16           24  ...     30\n",
              "..          ...  ...    ...\n",
              "951        3782  ...      9\n",
              "952        3783  ...      9\n",
              "953        3784  ...      9\n",
              "954        3785  ...      9\n",
              "956        3787  ...      9\n",
              "\n",
              "[784 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "test_unseen_categories"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_unseen_categories['medical_specialty'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3UKgTUvW30Va",
        "outputId": "7c2b1d3d-2e85-4c27-a9a5-071dcdb9cc06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Urology                       156\n",
              "Obstetrics / Gynecology       155\n",
              "ENT - Otolaryngology           96\n",
              "Hematology - Oncology          90\n",
              "Ophthalmology                  83\n",
              "Nephrology                     81\n",
              "Pediatrics - Neonatal          70\n",
              "Psychiatry / Psychology        53\n",
              "Endocrinology                   0\n",
              "Diets and Nutritions            0\n",
              "Dermatology                     0\n",
              "Dentistry                       0\n",
              "Cosmetic / Plastic Surgery      0\n",
              "Chiropractic                    0\n",
              "Cardiovascular / Pulmonary      0\n",
              "Bariatrics                      0\n",
              "Autopsy                         0\n",
              "Gastroenterology                0\n",
              "Lab Medicine - Pathology        0\n",
              "Hospice - Palliative Care       0\n",
              "IME-QME-Work Comp etc.          0\n",
              "Speech - Language               0\n",
              "Letters                         0\n",
              "Neurology                       0\n",
              "Orthopedic                      0\n",
              "Physical Medicine - Rehab       0\n",
              "Podiatry                        0\n",
              "Radiology                       0\n",
              "Rheumatology                    0\n",
              "Sleep Medicine                  0\n",
              "Allergy / Immunology            0\n",
              "Name: medical_specialty, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TuwK4HWa2iPI"
      },
      "source": [
        "**Display categories distribution**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**full data top 20 categoris**"
      ],
      "metadata": {
        "id": "RfstS-BYcam5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cause_dist = full_data['medical_specialty'].value_counts()[0:20]\n",
        "print(cause_dist)\n",
        "plt.figure(figsize=(9,9))\n",
        "plt.title(' ', y=-0.15)\n",
        "plt.pie(cause_dist, labels=list(cause_dist.index), autopct=\"%.1f%%\")\n",
        "plt.axis('equal')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OP5s3tlTcPyK",
        "outputId": "fdcb04be-575b-4af0-dd79-ec7eeb6c1509"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Surgery                          1088\n",
            "Consult - History and Phy.        516\n",
            "Cardiovascular / Pulmonary        371\n",
            "Orthopedic                        355\n",
            "Radiology                         273\n",
            "General Medicine                  259\n",
            "Gastroenterology                  224\n",
            "Neurology                         223\n",
            "SOAP / Chart / Progress Notes     166\n",
            "Urology                           156\n",
            "Obstetrics / Gynecology           155\n",
            "Discharge Summary                 108\n",
            "ENT - Otolaryngology               96\n",
            "Neurosurgery                       94\n",
            "Hematology - Oncology              90\n",
            "Ophthalmology                      83\n",
            "Nephrology                         81\n",
            "Emergency Room Reports             75\n",
            "Pediatrics - Neonatal              70\n",
            "Pain Management                    61\n",
            "Name: medical_specialty, dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-1.1035156440932012,\n",
              " 1.1001674125714922,\n",
              " -1.1009171942126887,\n",
              " 1.1060213005022443)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**selected categories**"
      ],
      "metadata": {
        "id": "FKy5z5u5cdC6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 713
        },
        "id": "deXkSOdB2lMp",
        "outputId": "4889e030-7d67-4f12-c7c7-dbf9d2fb3e3e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cardiovascular / Pulmonary    371\n",
            "Orthopedic                    355\n",
            "Neurology                     317\n",
            "Radiology                     273\n",
            "Gastroenterology              224\n",
            "Name: medical_specialty, dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-1.1238888937307316, 1.10113756636813, -1.1149502140560064, 1.113596635204414)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x648 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "cause_dist = data['medical_specialty'].value_counts()[0:top_categories_num]\n",
        "print(cause_dist)\n",
        "plt.figure(figsize=(9,9))\n",
        "plt.title(' ', y=-0.15)\n",
        "plt.pie(cause_dist, labels=list(cause_dist.index), autopct=\"%.1f%%\")\n",
        "plt.axis('equal')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0nMj1EXGcG5",
        "outputId": "86a5e3cb-e5fc-4530-dcfb-b526785aa1b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " mri left knee without contrast.\n",
            "exam:,mri left knee without contrast,clinical:,left knee pain.,findings:,comparison is made with 10/13/05 radiographs.,there is a prominent suprapatellar effusion. patient has increased signal within the medial collateral ligament as well as fluid around it, compatible with type 2 sprain. there is fluid around the lateral collateral ligament without increased signal within the ligament itself, compatible with type 1 sprain.,medial and lateral menisci contain some minimal increased signal centrally that does not extend through an articular surface and findings are felt to represent minimal myxoid degeneration. no tear is seen. anterior cruciate and posterior cruciate ligaments are intact. there is a bone bruise of medial patellar facet measuring approximately 8 x 5 mm. there is suggestion of some mild posterior aspect of the lateral tibial plateau. mr signal on the bone marrow is otherwise normal.,impression:,type 2 sprain in the medial collateral ligament and type sprain in the lateral collateral ligament.,joint effusion and bone bruise with suggestion of some minimal overlying chondromalacia and medial patellar facet.\n",
            "Radiology\n"
          ]
        }
      ],
      "source": [
        "print(data[\"description\"][100])\n",
        "print(data[\"transcription\"][100])\n",
        "print(data[\"medical_specialty\"][100])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8QESrnMzSBa2"
      },
      "source": [
        "**Histogram: Length of description sentences**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wUWLxZq4jA1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "8a5ac448-8d7a-4299-f6b9-927e285ee7d1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# get length of all the sentences\n",
        "seq_len = [len(data[\"description\"][i].split()) for i in range(data.shape[0])]\n",
        "pd.Series(seq_len).hist(bins = 30)\n",
        "plt.xlabel('Length')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_5l88XhtSKtg"
      },
      "source": [
        "**Histogram: Length of transcription sentences**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "bU5eI2EYL2At",
        "outputId": "a7bd6d2e-5f0a-42fa-d22c-1a046257fa30"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "seq_len = [len(data[\"transcription\"][i].split()) for i in range(data.shape[0])]\n",
        "pd.Series(seq_len).hist(bins = 30)\n",
        "plt.xlabel('Length')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wwVOOgyiSXMb"
      },
      "outputs": [],
      "source": [
        "\n",
        "MAX_SENTENCE_LENGTh = 70 "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bfvv7-ngmL7d"
      },
      "source": [
        "## **Split data to train/validation/test sets**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XnVx4S7EmP2h"
      },
      "outputs": [],
      "source": [
        "train_text, temp_text, train_labels, temp_labels = train_test_split(data['description'], data['labels'], \n",
        "                                                                    random_state=42, \n",
        "                                                                    test_size=0.3, \n",
        "                                                                    stratify=data['labels'])\n",
        "\n",
        "\n",
        "val_text, test_text, val_labels, test_labels = train_test_split(temp_text, temp_labels, \n",
        "                                                                random_state=42, \n",
        "                                                                test_size=0.5, \n",
        "                                                                stratify=temp_labels)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unseen_train_text, unseen_test_text, unseen_train_labels, unseen_test_labels = train_test_split(test_unseen_categories['description'], test_unseen_categories['labels'], \n",
        "                                                                random_state=42, \n",
        "                                                                test_size=0.2, \n",
        "                                                                stratify=test_unseen_categories['labels'])"
      ],
      "metadata": {
        "id": "tRb8Z-lJbd_f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "urrJrgHLYFKS"
      },
      "source": [
        "## **Tokenize the texts**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n6OtL2gVaRjp"
      },
      "source": [
        "Explaination about the tokenizer:\n",
        "\n",
        "https://huggingface.co/course/chapter2/4?fw=pt\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z8IWzxgyoWtu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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      ],
      "source": [
        "# Load the BERT tokenizer\n",
        "model_name = 'microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext' #('bert-base-uncased')   'microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext'\n",
        "tokenizer = BertTokenizerFast.from_pretrained(model_name) \n",
        "\n",
        "# Longformer toknizer\n",
        "#model_name = \"allenai/longformer-base-4096\" \n",
        "#tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "# tokenize and encode sequences in the sets set\n",
        "texts = [train_text, val_text, test_text, unseen_train_text, unseen_test_text]\n",
        "tokens_texts = []\n",
        "\n",
        "for text in texts:\n",
        "    tokens_texts.append(tokenizer.batch_encode_plus(text.tolist(),max_length = MAX_SENTENCE_LENGTh, padding='max_length', truncation=True))\n",
        "\n",
        "train_tokinized, val_tokinized, test_tokinized, unseen_train_tokinized, unseen_test_tokinized = tokens_texts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eyZgTlagpvoI"
      },
      "outputs": [],
      "source": [
        "def convert_to_tensors(data, labels):\n",
        "  \n",
        "    seq = torch.tensor(data['input_ids'])\n",
        "    mask = torch.tensor(data['attention_mask'])\n",
        "    y = torch.tensor(labels.tolist())\n",
        "\n",
        "    return seq, mask, y\n",
        "\n",
        "train_seq, train_mask, train_y  = convert_to_tensors(train_tokinized, train_labels)\n",
        "val_seq, val_mask, val_y  = convert_to_tensors(val_tokinized, val_labels)\n",
        "test_seq, test_mask, test_y  = convert_to_tensors(test_tokinized, test_labels)\n",
        "\n",
        "unseen_train_seq, unseen_train_mask, unseen_train_y  = convert_to_tensors(unseen_train_tokinized, unseen_train_labels)\n",
        "unseen_test_seq, unseen_test_mask, unseen_test_y  = convert_to_tensors(unseen_test_tokinized, unseen_test_labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b9N37HYaYOyQ"
      },
      "source": [
        "## **Build custom data loaders**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hfttASxQsbOs"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import Dataset, DataLoader\n",
        "\n",
        "\n",
        "class ContrastiveDataset(Dataset):\n",
        "\n",
        "    def __init__(self, train_seq, train_mask, train_y, positive_prob=0.5):\n",
        "\n",
        "        super().__init__()\n",
        "        self.train_seq = train_seq\n",
        "        self.train_mask = train_mask\n",
        "        self.train_y = train_y\n",
        "        self.positive_prob = positive_prob # probability to sample two texts with the same category\n",
        "        \n",
        "        self.hash_table = {} # format: {\"category\" : [i1, i2, ...]}\n",
        "\n",
        "        # construct a hash table, each key is a category\n",
        "        # and the value is a list of the indexs of the texts which belong to this category \n",
        "        for i in range(len(self.train_seq)):\n",
        "            label = self.train_y[i].item()\n",
        "            if label in self.hash_table:\n",
        "                self.hash_table[label].append(i)\n",
        "            else:\n",
        "                self.hash_table[label] = [i]\n",
        "        \n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        \"\"\"\n",
        "        Sample two texts from the same category with probability self.positive_prob\n",
        "        :param index: index (int)\n",
        "        :return:  seq_0 - a sequence of IDs (each ID represent a word in the vocabulary)\n",
        "                  seq_1 - a sequence which differnt from seq0 (different text)\n",
        "                  mask_0 - attention mask for seq1\n",
        "                  mask_1 - attention mask for seq1\n",
        "                  same_class - 1 if seq0 and seq1 are both from the same category, 0 otherwise\n",
        "        \"\"\"\n",
        "        same_class = random.uniform(0, 1)\n",
        "        same_class = same_class > self.positive_prob\n",
        "\n",
        "        seq_0 = self.train_seq[index]\n",
        "        mask_0 = self.train_mask[index]\n",
        "        label_0 = self.train_y[index].item()\n",
        "        class_samples = self.hash_table[label_0]\n",
        "\n",
        "        if len(class_samples) < 2: # handle the case where there are only a single text in some category (in this case we can't draw another text from this category...)\n",
        "            same_class = False\n",
        "\n",
        "        if same_class:\n",
        "            while True:                    \n",
        "                rnd_idx = random.randint(0, len(class_samples) - 1)\n",
        "                index_1 = class_samples[rnd_idx]\n",
        "                if index_1 != index:\n",
        "                    seq_1 = self.train_seq[index_1]\n",
        "                    mask_1 = self.train_mask[index_1]\n",
        "                    label_1 = self.train_y[index_1].item()\n",
        "                    break\n",
        "        else:\n",
        "            while True:\n",
        "                index_1 = random.randint(0, self.__len__() - 1)\n",
        "                if index_1 != index:\n",
        "                    seq_1 = self.train_seq[index_1]\n",
        "                    mask_1 = self.train_mask[index_1]\n",
        "                    label_1 = self.train_y[index_1].item()\n",
        "                    if label_1 !=  label_0:\n",
        "                        break\n",
        "\n",
        "        return seq_0, seq_1, mask_0, mask_1, torch.tensor(same_class, dtype=torch.float)\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.train_seq)\n",
        "\n",
        "class SimpleDataset(Dataset):\n",
        "\n",
        "    def __init__(self, seq, mask, y, positive_prob=0.5):\n",
        "\n",
        "        super().__init__()\n",
        "        self.seq = seq\n",
        "        self.mask = mask\n",
        "        self.y = y\n",
        "\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        \"\"\"\n",
        "        Sample texts by the order of the training set.\n",
        "        :param index: index (int)\n",
        "        :return: seq - a sequence of IDs (each ID represent a word in the vocabulary)\n",
        "                 mask - attention mask for seq\n",
        "                 y - the category of this text\n",
        "                        \n",
        "        \"\"\"\n",
        "        return   self.seq[index], self.mask[index], torch.tensor(self.y[index].item())\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.seq)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_zGBpXmVFhjx"
      },
      "outputs": [],
      "source": [
        "train_set = ContrastiveDataset(train_seq, train_mask,  train_y)\n",
        "val_set = ContrastiveDataset(val_seq, val_mask, val_y)\n",
        "test_set = ContrastiveDataset(test_seq, test_mask,test_y)\n",
        "\n",
        "train_set_simple = SimpleDataset(train_seq, train_mask,  train_y)\n",
        "test_set_simple = SimpleDataset(test_seq, test_mask,  test_y)\n",
        "unseen_train_set_simple = SimpleDataset(unseen_train_seq, unseen_train_mask,  unseen_train_y)\n",
        "unseen_test_set_simple = SimpleDataset(unseen_test_seq, unseen_test_mask,  unseen_test_y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "enGjFc3jg1-I"
      },
      "outputs": [],
      "source": [
        "trainLoader = DataLoader(train_set, batch_size=32, shuffle=True,  drop_last=False, num_workers=0)\n",
        "valLoader = DataLoader(val_set, batch_size=32, shuffle=True,  drop_last=False, num_workers=0)\n",
        "testLoader = DataLoader(test_set, batch_size=10, shuffle=False,  drop_last=False, num_workers=0)\n",
        "\n",
        "trainLoader_simple = DataLoader(train_set_simple, batch_size=32, shuffle=False,  drop_last=False, num_workers=0)\n",
        "testLoader_simple = DataLoader(test_set_simple, batch_size=64, shuffle=False,  drop_last=False, num_workers=0)\n",
        "unseen_trainLoader_simple = DataLoader(unseen_train_set_simple, batch_size=64, shuffle=False,  drop_last=False, num_workers=0)\n",
        "unseen_testLoader_simple = DataLoader(unseen_test_set_simple, batch_size=64, shuffle=False,  drop_last=False, num_workers=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VcEtc6gdAAOR"
      },
      "source": [
        "# **Siamese Transformer Network**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KqUoH8NaeNlB"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "class BERT_Arch(nn.Module):\n",
        "\n",
        "    def __init__(self, bert):\n",
        "      \n",
        "      super(BERT_Arch, self).__init__()\n",
        "\n",
        "      self.bert = bert \n",
        "\n",
        "      # For cls_hs output (First token embedding)\n",
        "\n",
        "      #self.fc1 = nn.Linear(768,512)\n",
        "      #self.dropout = nn.Dropout(0.1)\n",
        "      #self.relu =  nn.ReLU()\n",
        "      #self.fc2 = nn.Linear(512,128)\n",
        "\n",
        "      # For hs output (all tokens embeddings)\n",
        "      # https://chriskhanhtran.github.io/posts/cnn-sentence-classification/\n",
        "      self.conv1 = nn.Conv1d(in_channels=768, out_channels=128, kernel_size=3, stride=1) # kernal_size=3 == three-grams\n",
        "      self.avg_pooling = nn.AvgPool1d(kernel_size=2)                                        \n",
        "      self.conv2 = nn.Conv1d(in_channels=128, out_channels=64, kernel_size=3, stride=1) \n",
        "      self.flatten = nn.Flatten()                                                          \n",
        "      self.fc = nn.Linear(64,128)                                                    \n",
        "      self.dropout = nn.Dropout(0.2)\n",
        "\n",
        "      # Other options:\n",
        "      #Mean of the token embeddings\n",
        "      #Max of the token embedding\n",
        "      \n",
        "    def forward(self, seq, mask):\n",
        "      \n",
        "      # Explaination on BERT and its outputs:\n",
        "      # https://towardsdatascience.com/tips-and-tricks-for-your-bert-based-applications-359c6b697f8e\n",
        "      # To sum up: BERT is a transformer encoder, meanning it produces representations of the inputs, and hence we can use it for several tasks.\n",
        "\n",
        "      # BERT returns a dictionary of vectors where the first is called 'sequence output' (or 'hidden state'=hs) and the other is 'pooled output' (or 'cls_hs').\n",
        "      # ** 'sequence_output' is of shape [batch_size, max_seq_length, 768] with representations for each input token (including start symbol and end symbol)\n",
        "      # ** 'pooled_output' is of shape [batch_size, 768] with representations for the entire input sequences. It created by taking only the first token embedded vector (start symbol)\n",
        "      #     (and pass it throgh more layers (?))\n",
        "      \n",
        "      # The idea of \"pooled_output\":\n",
        "      # Since, the embeddings from the BERT model at the output layer are known to be contextual embeddings, the output of the 1st token, i.e, [CLS] token\n",
        "      # would have captured sufficient context. Hence, the authors of BERT paper found it sufficient to use only the output from the 1st token for few tasks\n",
        "      # such as classification. They call this output from the single token (i.e, 1st token) as pooled_output.\n",
        "\n",
        "      # Get embeddings from bert. \n",
        "      # Output shape: hs = (b, max_len_seq, embed_dim)           \n",
        "      #               cls = (b, embed_dim)\n",
        "      # where:  b: batch_size\n",
        "      #         max_len_seq=number of tokens in the longest text (we defined it erilier)\n",
        "      hs, cls_hs = self.bert(seq, attention_mask=mask, return_dict=False) \n",
        "   \n",
        "      #x = self.fc1(cls_hs)\n",
        "      #x = self.dropout(x)\n",
        "      #x = self.relu(x) \n",
        "      #x = self.fc2(x) \n",
        "\n",
        "     \n",
        "      x = hs.permute(0, 2, 1).contiguous()          # Permute `hs` to match input shape requirement of `nn.Conv1d`\n",
        "                                                    # The contiguous() ensures the memory of the tensor is stored contiguously\n",
        "                                                    # which helps avoid potential issues during processing.\n",
        "                                                    # Output shape: (b, 768, 70) = (b, embed_dim, max_len_seq).        \n",
        "      \n",
        "      x = F.relu(self.conv1(x))                     # Output shape: (b, 128, *)  * depends on kernel size and padding\n",
        "      x = self.avg_pooling(x)                       # Output shape: (b, 128, *)\n",
        "      x = F.relu(self.conv2(x))                     # Output shape: (b, 128, *)\n",
        "      x = F.max_pool1d(x, kernel_size=x.shape[2])   # Output shape: (b, 128, 1) # trick: we use kernel of size x.shape[2] to reduce from * to 1\n",
        "      x = self.flatten(x)                           # Output shape: (b, 128)\n",
        "      x = self.fc(x)                                # Output shape: (b, 128)\n",
        "      x = self.dropout(x) \n",
        "    \n",
        "      return x\n",
        "\n",
        "\n",
        "class SiameseNeuralNetwork(nn.Module):\n",
        "    \n",
        "    def __init__(self, bert_arch):\n",
        "        super().__init__()            # TODO super?\n",
        "\n",
        "        self.bert_arch = bert_arch\n",
        "        self.distance_layer = nn.Sequential(nn.Linear(128, 1), nn.Sigmoid())  # if we would use BCEWithLogitsLoss as loss function, we should delte the sigmoid since we dont need it after the linear layer a sigmoid layer                                  \n",
        "\n",
        "\n",
        "    def forward(self, seq1, seq2, mask1, mask2):\n",
        "        feature_vec1 = self.bert_arch(seq1, mask1) # feature_vec1 shape:  [batch_size, embedding_size]\n",
        "        feature_vec2 = self.bert_arch(seq2, mask2)\n",
        "        difference = torch.abs(feature_vec1 - feature_vec2)  \n",
        "        out = self.distance_layer(difference)\n",
        "        return out #feature_vec1, feature_vec2#out\n",
        "\n",
        "\n",
        "class ContrastiveLoss(nn.Module):\n",
        "    \"\"\"\n",
        "    Takes embeddings of two samples and a target label == 1 if samples are from the same class and label == 0 otherwise\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, margin):\n",
        "        super(ContrastiveLoss, self).__init__()\n",
        "        self.margin = margin\n",
        "        self.eps = 1e-9\n",
        "\n",
        "    def forward(self, output1, output2, target, size_average=True):\n",
        "        distances = (output2 - output1).pow(2).sum(1)  # squared distances\n",
        "        losses = 0.5 * (target.float() * distances +\n",
        "                        (1 + -1 * target).float() *  torch.nn.functional.relu(self.margin - (distances + self.eps).sqrt()).pow(2))\n",
        "        return losses.mean() if size_average else losses.sum()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qojVadA31lyg"
      },
      "outputs": [],
      "source": [
        "def train_siamese_network(model, dataloaders, num_epochs, device): \n",
        "    \"\"\"\n",
        "    Train the given SNN model.\n",
        "\n",
        "    :param model: SNN model\n",
        "    :param dataloaders: a dict that contains train data loader and validation data loader\n",
        "    :param num_epochs: number of epochs\n",
        "    :param device: 'cpu' or 'cuda' \n",
        "\n",
        "    :return:  train_loss_history - list of train losses by epochs\n",
        "              val_loss_history -  list of validation losses by epochs\n",
        "\n",
        "    \"\"\"\n",
        "    train_loss_history = []\n",
        "    val_loss_history = []\n",
        "    matching_similarity = []\n",
        "    non_matching_similarity = []\n",
        "\n",
        "    val_matching_similarity = []\n",
        "    val_non_matching_similarity = []\n",
        "\n",
        "    criterion = torch.nn.BCELoss(reduction='mean') #ContrastiveLoss(margin=1)  #losses.ContrastiveLoss(pos_margin=0, neg_margin=1) # torch.nn.BCEWithLogitsLoss(reduction='mean') # the labels are same class (1) vs. different class (0)\n",
        "    learning_rate = 0.005 # 0.005 # 0.1\n",
        "    optimizer  =  AdamW(model.parameters(),lr =learning_rate)#torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)  # AdamW(model.parameters(),lr =learning_rate)  1e-5\n",
        "    \n",
        "    # lr = lr * factor \n",
        "    # mode='min': look for the min validation loss to track\n",
        "    # patience: number of epochs - 1 where loss plateaus before decreasing LR\n",
        "    # patience = 0, after 1 bad epoch, reduce LR\n",
        "    # factor: decaying factor\n",
        "\n",
        "    #scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=1, verbose=True, min_lr=0.0001)  ########################################################\n",
        "    #cyclic_scheduler = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=0.0001, max_lr=0.1, cycle_momentum=False) ########################################################\n",
        "\n",
        "    for epoch in range(num_epochs):  # loop over the train dataset multiple times\n",
        "\n",
        "        # Each epoch has a training and validation phase\n",
        "        for phase in ['train', 'val']:\n",
        "  \n",
        "            if phase == 'train':\n",
        "                model.train() \n",
        "            else:\n",
        "                model.eval()   \n",
        "\n",
        "            running_loss = 0.0\n",
        "\n",
        "            for i, batch in enumerate(dataloaders[phase]):\n",
        "\n",
        "                  seq1, seq2, mask1, mask2, label = batch\n",
        "\n",
        "                  if device == 'cuda':\n",
        "                    seq1, seq2, mask1, mask2, label = seq1.to(device), seq2.to(device), mask1.to(device), mask2.to(device), label.to(device)\n",
        "\n",
        "                  # zero the parameter gradients\n",
        "                  optimizer.zero_grad()\n",
        "                  \n",
        "                  # track history  only in train\n",
        "                  with torch.set_grad_enabled(phase == 'train'):\n",
        "\n",
        "                      # forward \n",
        "                      output = model.forward(seq1, seq2, mask1, mask2) \n",
        "                      loss = criterion(output, label.view(output.size())) # criterion(output.squeeze(0), label.view(1))    label.view((trainLoader.batch_size,1))\n",
        "                      \n",
        "                      # backward + optimize only if in training phase\n",
        "                      if phase == 'train': #  with torch.no_grad() if phae == 'val'?\n",
        "                          loss.backward()\n",
        "                          torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) # clip the the gradients to 1.0. It helps in preventing the exploding gradient problem\n",
        "                          optimizer.step()\n",
        "                          #cyclic_scheduler.step() ########################################################\n",
        "\n",
        "                          # save similarity scores for training data\n",
        "                          output = output.cpu().detach().numpy()\n",
        "                          label = label.cpu().numpy()\n",
        "                          non_matching_similarity.append((sum(output[label == 0]) / sum(label == 0)).item())\n",
        "                          matching_similarity.append((sum(output[label == 1]) / sum(label == 1)).item())\n",
        "                     \n",
        "                      if phase == 'val':\n",
        "                         val_non_matching_similarity.append((sum(output[label == 0]) / sum(label == 0)).item())\n",
        "                         val_matching_similarity.append((sum(output[label == 1]) / sum(label == 1)).item())\n",
        "\n",
        "                  running_loss += loss.item() * seq1.size(0)  #we multiply by the batch size (note that the batch size in the last batch may not be the batch size we did since the batch size dont necceraly divide the train size)\n",
        "\n",
        "            epoch_loss = running_loss / len(dataloaders[phase].dataset)\n",
        "            if phase == 'train':\n",
        "               train_loss_history.append(epoch_loss) \n",
        "            else:\n",
        "               val_loss_history.append(epoch_loss)\n",
        "               #scheduler.step(epoch_loss) ########################################################\n",
        "\n",
        "            print('Epoch {} | {} loss: {:.3f}'.format(epoch, phase, epoch_loss))\n",
        "            \n",
        "                  \n",
        "    return train_loss_history, val_loss_history, [non_matching_similarity, matching_similarity, val_non_matching_similarity, val_matching_similarity]\n",
        "\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YX4aRDKm4Rq1",
        "outputId": "89dc4592-c7f2-48dc-ed2b-e6cbba4bd0a1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using cuda device\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.decoder.weight']\n",
            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0 | train loss: 0.697\n",
            "Epoch 0 | val loss: 0.632\n",
            "Epoch 1 | train loss: 0.646\n",
            "Epoch 1 | val loss: 0.616\n",
            "Epoch 2 | train loss: 0.618\n",
            "Epoch 2 | val loss: 0.705\n",
            "Epoch 3 | train loss: 0.612\n",
            "Epoch 3 | val loss: 0.566\n",
            "Epoch 4 | train loss: 0.597\n",
            "Epoch 4 | val loss: 0.639\n",
            "Epoch 5 | train loss: 0.573\n",
            "Epoch 5 | val loss: 0.676\n",
            "Epoch 6 | train loss: 0.562\n",
            "Epoch 6 | val loss: 0.549\n",
            "Epoch 7 | train loss: 0.544\n",
            "Epoch 7 | val loss: 0.604\n",
            "Epoch 8 | train loss: 0.565\n",
            "Epoch 8 | val loss: 0.628\n",
            "Epoch 9 | train loss: 0.568\n",
            "Epoch 9 | val loss: 0.608\n",
            "Epoch 10 | train loss: 0.581\n",
            "Epoch 10 | val loss: 0.537\n",
            "Epoch 11 | train loss: 0.560\n",
            "Epoch 11 | val loss: 0.578\n",
            "Epoch 12 | train loss: 0.542\n",
            "Epoch 12 | val loss: 0.541\n",
            "Epoch 13 | train loss: 0.525\n",
            "Epoch 13 | val loss: 0.590\n",
            "Epoch 14 | train loss: 0.531\n",
            "Epoch 14 | val loss: 0.631\n",
            "Epoch 15 | train loss: 0.531\n",
            "Epoch 15 | val loss: 0.568\n",
            "Epoch 16 | train loss: 0.552\n",
            "Epoch 16 | val loss: 0.597\n",
            "Epoch 17 | train loss: 0.532\n",
            "Epoch 17 | val loss: 0.523\n",
            "Epoch 18 | train loss: 0.506\n",
            "Epoch 18 | val loss: 0.591\n",
            "Epoch 19 | train loss: 0.524\n",
            "Epoch 19 | val loss: 0.515\n",
            "Epoch 20 | train loss: 0.511\n",
            "Epoch 20 | val loss: 0.573\n",
            "Epoch 21 | train loss: 0.505\n",
            "Epoch 21 | val loss: 0.603\n",
            "Epoch 22 | train loss: 0.529\n",
            "Epoch 22 | val loss: 0.522\n",
            "Epoch 23 | train loss: 0.482\n",
            "Epoch 23 | val loss: 0.731\n",
            "Epoch 24 | train loss: 0.490\n",
            "Epoch 24 | val loss: 0.770\n",
            "Epoch 25 | train loss: 0.498\n",
            "Epoch 25 | val loss: 0.568\n",
            "Epoch 26 | train loss: 0.491\n",
            "Epoch 26 | val loss: 0.534\n",
            "Epoch 27 | train loss: 0.478\n",
            "Epoch 27 | val loss: 0.686\n",
            "Epoch 28 | train loss: 0.488\n",
            "Epoch 28 | val loss: 0.555\n",
            "Epoch 29 | train loss: 0.486\n",
            "Epoch 29 | val loss: 0.570\n"
          ]
        }
      ],
      "source": [
        "# specify GPU\n",
        "# Get cpu or gpu device for training.\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "print(\"Using {} device\".format(device))\n",
        "\n",
        "# import BERT-base pretrained model\n",
        "bert = AutoModel.from_pretrained(model_name)   # ('bert-base-uncased') 'microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext'\n",
        "\n",
        "# freeze all the parameters\n",
        "for param in bert.parameters():\n",
        "    param.requires_grad = False\n",
        "\n",
        "# pass the pre-trained BERT to our define architecture\n",
        "bert_arch = BERT_Arch(bert)\n",
        "\n",
        "SNN_model = SiameseNeuralNetwork(bert_arch).to(device)\n",
        "num_epochs = 30\n",
        "\n",
        "train_loss_history, val_loss_history, similarities_list = train_siamese_network(SNN_model, dataloaders={\"train\":trainLoader, \"val\":valLoader},  num_epochs=num_epochs, device=device)\n",
        "\n",
        "non_matching_similarity, matching_similarity, val_non_matching_similarity, val_matching_similarity = similarities_list"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4i32Y2j8LYdn"
      },
      "source": [
        "**Plot Losses**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ROm9F8mJLeO_"
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      "outputs": [],
      "source": [
        "def plot_losses(train_loss_history, val_loss_history, num_epochs):\n",
        "    \n",
        "    fig = go.Figure()\n",
        "\n",
        "    line_color = [px.colors.qualitative.Light24[5], px.colors.qualitative.Light24[4]] \n",
        "\n",
        "\n",
        "    fig.add_trace(go.Scatter(x=list(range(1, num_epochs+1)), y=train_loss_history, mode='lines', line=dict(color=line_color[0], width=1), name=\"train loss\"))\n",
        "    fig.add_trace(go.Scatter(x=list(range(1, num_epochs+1)), y=val_loss_history, mode='lines',line=dict(color=line_color[1], width=1), name=\"valdiation loss\")) #opacity=0.8\n",
        "\n",
        "    fig.update_yaxes(range=[0, 1])\n",
        "    fig.update_traces(textposition='top center')\n",
        "    fig.update_layout(autosize=False,width=900, height=500, title_text=\"SNN Loss\", title_x=0.5, xaxis_title=\"Epoch\", yaxis_title=\"\", xaxis = dict(tickmode='linear', tick0=1, dtick=1), legend=dict(yanchor=\"top\",xanchor=\"right\", x=1.35, y=1),template=\"plotly_dark\")\n",
        "    fig.show()"
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    },
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        "id": "RGXbOid_MbE1",
        "outputId": "a89b1800-624a-4db5-ed77-7f8d8e3de5bc"
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      "outputs": [
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        "plot_losses(train_loss_history, val_loss_history, num_epochs)"
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        "def plot_similarity_scores(non_matching_similarity, matching_similarity):\n",
        "\n",
        "    train_num_batchs = len(non_matching_similarity)\n",
        "    \n",
        "    fig = go.Figure()\n",
        "\n",
        "    line_color = [px.colors.qualitative.Light24[22], px.colors.qualitative.Light24[19]] \n",
        "\n",
        "\n",
        "    fig.add_trace(go.Scatter(x=list(range(1, train_num_batchs+1)), y=non_matching_similarity, mode='lines', line=dict(color=line_color[0], width=1), name=\"unmatching categories\"))\n",
        "    fig.add_trace(go.Scatter(x=list(range(1, train_num_batchs+1)), y=matching_similarity, mode='lines',line=dict(color=line_color[1], width=1), name=\"matching categories\")) #opacity=0.8\n",
        "\n",
        "    fig.update_yaxes(range=[0, 1])\n",
        "    fig.update_traces(textposition='top center')\n",
        "    fig.update_layout(autosize=False,width=900, height=500, title_text=\"Similarity Scores\", title_x=0.5, xaxis_title=\"Batch\", yaxis_title=\"\", legend=dict(yanchor=\"top\",xanchor=\"right\", x=1.35, y=1),template=\"plotly_dark\")\n",
        "    fig.show()"
      ],
      "metadata": {
        "id": "tgHNvKBiSbOb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_similarity_scores(non_matching_similarity, matching_similarity)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "QQ3fQb6JSf9i",
        "outputId": "cd1827cf-9650-4709-a18a-54ffcb345325"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jun0WlpAizJ4"
      },
      "source": [
        " **print all layers in the model that are participating in the learning process**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uMGbIxzSeUDh"
      },
      "outputs": [],
      "source": [
        "# print all layers in the model that are participating in the learning process\n",
        "for name, param in SNN_model.named_parameters():\n",
        "    if param.requires_grad:\n",
        "        print(name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e7amIcxe1Vet"
      },
      "source": [
        "# **Prototypes Selection**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1jVrQ1gW3g0-"
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      "source": [
        "def construct_train_matrix(SNN_model, trainLoader_simple):\n",
        "    \"\"\"\n",
        "    Embed the training data using the trained SNN model.\n",
        "    \"\"\"\n",
        "\n",
        "    # Get intermidate layer output.\n",
        "    # Source: https://discuss.pytorch.org/t/how-can-i-extract-intermediate-layer-output-from-loaded-cnn-model/77301/2\n",
        "    activation = {}\n",
        "    def get_activation(name):\n",
        "        def hook(model, input, output):\n",
        "            activation[name] = output.detach()\n",
        "        return hook\n",
        "\n",
        "    # SNN_model.bert_arch.fc1.register_forward_hook(get_activation('fc1'))\n",
        "    SNN_model.bert_arch.fc.register_forward_hook(get_activation('fc'))\n",
        "\n",
        "    train_matrix = []\n",
        "    print(\"num batches:\", len(trainLoader_simple))\n",
        "    with torch.no_grad():            \n",
        "        for i, batch in enumerate(trainLoader_simple):\n",
        "            print(i, \"/\", len(trainLoader_simple), \" batches\")\n",
        "            seq1, mask1, label1 = batch\n",
        "            if device == 'cuda':\n",
        "                seq1, mask1, label1 = seq1.to(device), mask1.to(device), label1.to(device)\n",
        "\n",
        "            SNN_model.eval()\n",
        "            output = SNN_model(seq1, seq1, mask1, mask1)\n",
        "            train_matrix.append(activation['fc'].cpu().numpy()) # activation['fc1'] return a tensor in cuda with size (batch_size, embedding_dim), so we move it to cpu, than to numpy array.\n",
        "\n",
        "    return np.vstack(train_matrix) # we combine all the batches, so now we return matrix of size (num_samples_train, embedding_dim)\n",
        "\n",
        "def extract_prototypes(k, SNN_model, trainLoader_simple, train_labels, train_matrix):\n",
        "    \"\"\"\n",
        "    Compute kc (= k/num_classes_train) prototypes for each class in the trainset.\n",
        "    (if k % num_classes_train != 0 then take the highest k0 <= k which is divisable by num_classes_train)\n",
        "\n",
        "    :param k: \n",
        "    :param train_matrix: size (num_samples_train, embedding_dim)\n",
        "    :return: \n",
        "    \"\"\"\n",
        "    train_labels = list(train_labels)\n",
        "    train_dataset = trainLoader_simple.dataset  # contains triples of (seq, mask, label)\n",
        "\n",
        "    # construct a hash table, each key is a class of diagnosis\n",
        "    # and the value is a list of the indexs of the sentences which belong to this class \n",
        "    hash_table = {} # format: {\"diagnosis\" : [i1, i2, ...]}\n",
        "    for i in range(len(train_labels)):\n",
        "        lbl = train_labels[i]\n",
        "        if lbl in hash_table:\n",
        "            hash_table[lbl].append(i)\n",
        "        else:\n",
        "            hash_table[lbl] = [i]\n",
        "\n",
        "\n",
        "    # Create prototypess\n",
        "    prototypes_list = {diagnosis:[] for diagnosis in hash_table.keys()}\n",
        "    num_classes_train = len(hash_table)\n",
        "    assert k >= num_classes_train, \"k should be greater than the numbrer of uniqe labels in the train set'\"\n",
        "    kc = int(k / num_classes_train)\n",
        "    print(\"kc:\", kc)\n",
        "\n",
        "    for diagnosis in hash_table.keys():\n",
        "\n",
        "      print(\"diagnosis:\", diagnosis)\n",
        "\n",
        "      if len(hash_table[diagnosis]) <= 1 : # if there is only a single sentence in some diagnosis sentences list - take it as the prototype of this class\n",
        "          prototypes_list += list(train_matrix[hash_table[diagnosis]])\n",
        "          \n",
        "      else:\n",
        "          # fit on all sentences which belongs to the same class (diagnosis)\n",
        "          kmeans = KMeans(n_clusters=kc, init='k-means++').fit(train_matrix[hash_table[diagnosis]]) \n",
        "          # extract for each centroid the closest real sample, and add it as a prototype\n",
        "          for centroid in kmeans.cluster_centers_:\n",
        "             #print(train_matrix[hash_table[diagnosis]].shape)\n",
        "             best_match_index = None\n",
        "             best_match_dist = float('inf')\n",
        "             for sentence_index in hash_table[diagnosis]:\n",
        "                 #print(sentence_index)\n",
        "                 embedded_sent = train_matrix[sentence_index]\n",
        "                 dist = distance.euclidean(centroid, embedded_sent)\n",
        "                 #print(\"dist:\", best_match_dist)\n",
        "                 if dist < best_match_dist:\n",
        "                    best_match_dist = dist\n",
        "                    best_match_index = sentence_index\n",
        "             #print(best_match_index)\n",
        "             prototypes_list[diagnosis].append(train_dataset[best_match_index])\n",
        "    \n",
        "    return prototypes_list\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oQgt9aTfQxCc",
        "outputId": "7455dad1-fe04-4bbc-a434-412123364e58"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "num batches: 34\n",
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            "1 / 34  batches\n",
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            "31 / 34  batches\n",
            "32 / 34  batches\n",
            "33 / 34  batches\n"
          ]
        }
      ],
      "source": [
        "train_matrix = construct_train_matrix(SNN_model, trainLoader_simple)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ioKcSe_rMPbJ",
        "outputId": "db2a1ccc-077f-4c70-dafa-2b5f7fb5435b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "kc: 20\n",
            "diagnosis: 26\n",
            "diagnosis: 21\n",
            "diagnosis: 3\n",
            "diagnosis: 11\n",
            "diagnosis: 18\n"
          ]
        }
      ],
      "source": [
        "prototypes_list = extract_prototypes(100, SNN_model, trainLoader_simple, train_labels, train_matrix)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "37VtjFxt3F1A"
      },
      "source": [
        "**Display train data in 3D**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UmEeXFw-1BnL"
      },
      "outputs": [],
      "source": [
        "from sklearn.decomposition import PCA\n",
        "from sklearn.manifold import LocallyLinearEmbedding\n",
        "from sklearn.manifold import Isomap\n",
        "from sklearn.manifold import TSNE\n",
        "from sklearn.manifold import SpectralEmbedding\n",
        "\n",
        "def show_space(X, title, colors=None, color_by=\"\", show_3D=False):\n",
        "\n",
        "  if show_3D:\n",
        "    dictionary = dict(zip(pd.DataFrame(X).columns, [\"COMP1\", \"COMP2\", \"COMP3\"]))\n",
        "    temp_df = pd.DataFrame(X).rename(columns=dictionary)\n",
        "    fig = px.scatter_3d(temp_df, x='COMP1', y='COMP2', z='COMP3',color=colors, template=\"plotly_dark\", labels={\"color\": color_by})\n",
        "  else:\n",
        "    dictionary = dict(zip(X.columns, [\"COMP1\", \"COMP2\"]))\n",
        "    temp_df = pd.DataFrame(X).rename(columns=dictionary)\n",
        "    fig = px.scatter(temp_df, x='COMP1', y='COMP2',color=colors, template=\"plotly_dark\", labels={\"color\": color_by})\n",
        "\n",
        "  fig.update_traces(marker=dict(size=4, opacity=0.98), textposition='top center')\n",
        "  fig.update_layout(title_text=title, title_x=0.5, autosize=False,width=900, height=500, legend=dict(yanchor=\"top\",xanchor=\"right\", x=1.1, y=1))\n",
        "  fig.show()\n",
        "  #fig.write_html(\"file.html\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "BGL0Zur11R3r",
        "outputId": "7c0b8b1b-a8bd-4441-ff39-2963f83535ef"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "        </div>\n",
              "</body>\n",
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            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "pca_alg = PCA(n_components=3)\n",
        "X_pca = pca_alg.fit_transform(train_matrix) \n",
        "show_space(X_pca, title=\"3D Space\", colors=pd.DataFrame(train_labels).replace(categories_mapping), color_by=\"category\", show_3D=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Explained variance per component:\", pca_alg.explained_variance_ratio_)\n",
        "print(\"Comulative explained variance:\", pca_alg.explained_variance_ratio_.cumsum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gUyJUESXcbxp",
        "outputId": "b9078447-2d16-4823-a1f6-b51c5ce8b277"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Explained variance per component: [0.67148525 0.19894294 0.05936258]\n",
            "Comulative explained variance: [0.67148525 0.8704282  0.9297908 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8CSdpoTqcRaW"
      },
      "source": [
        "# **Projection**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DvTAnCdwcRol"
      },
      "outputs": [],
      "source": [
        "def project_to_dissimilarity_space(dataLoader, SNN_model, prototypes_list):\n",
        "    \"\"\"\n",
        "    \n",
        "    Parameters\n",
        "    ----------\n",
        "    dataLoader : \n",
        "    SNN_model : \n",
        "\n",
        "    \n",
        "    Returns\n",
        "    -------\n",
        "    projected_data : numpy array of shape (num_samples_data, projection_dim)\n",
        "    \"\"\"\n",
        "\n",
        "    projected_data = [] \n",
        "    with torch.no_grad():     \n",
        "               \n",
        "        for batch in dataLoader:\n",
        "            print(\"****new batch***\")\n",
        "\n",
        "            projected_sentence = []\n",
        "            seq1, mask1, label1 = batch\n",
        "            if device == 'cuda':\n",
        "                seq1, mask1, label1 = seq1.to(device), mask1.to(device), label1.to(device)\n",
        "\n",
        "            for diagnosis in prototypes_list:\n",
        "                #print(\"diagnosis:\", diagnosis)\n",
        "                for centroid in prototypes_list[diagnosis]: # centroid contains a triple of (seq, mask, label)\n",
        "                    seq2, mask2 = centroid[0].repeat(seq1.shape[0], 1), centroid[1].repeat(mask1.shape[0], 1)  # we stack to seq2 and mask2 replications of them such that they will fit the batch size of seq1 and mask2\n",
        "                    if device == 'cuda':\n",
        "                       seq2, mask2 = seq2.to(device), mask2.to(device)\n",
        "\n",
        "                    SNN_model.eval()\n",
        "                    distance = SNN_model(seq1, seq2, mask1, mask2)\n",
        "                    projected_sentence.append(distance.squeeze().cpu().numpy())\n",
        "            \n",
        "            projected_data.append(np.array(projected_sentence).T)\n",
        "            #break # TODO delete\n",
        "\n",
        "    projected_data = np.vstack(projected_data)\n",
        "    return projected_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WJt7P_5D0sUW",
        "outputId": "59296542-4a50-4c0a-a184-8c0f250b4311"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n"
          ]
        }
      ],
      "source": [
        "projected_train = project_to_dissimilarity_space(trainLoader_simple, SNN_model, prototypes_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "It3WQhbhpz6F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ce07161-6b96-4400-f158-94403ca936ba"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1078, 100)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "projected_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_pca = pca_alg.fit_transform(projected_train) \n",
        "show_space(X_pca, title=\"3D Space\", colors=pd.DataFrame(train_labels).replace(categories_mapping), color_by=\"category\", show_3D=True)\n",
        "print(\"Explained variance per component:\", pca_alg.explained_variance_ratio_)\n",
        "print(\"Comulative explained variance:\", pca_alg.explained_variance_ratio_.cumsum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
        },
        "id": "y1xccZhFDS99",
        "outputId": "9feadbe8-09fa-42b9-ada2-06176b9f874b"
      },
      "execution_count": null,
      "outputs": [
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              "</body>\n",
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Explained variance per component: [0.5718724  0.22398916 0.11776582]\n",
            "Comulative explained variance: [0.5718724  0.7958616  0.91362745]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BQsXk8fsIKG6"
      },
      "source": [
        "# **SVM**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jMLhlI0OIKQ4"
      },
      "outputs": [],
      "source": [
        "def ensemble_of_classifiers(projected_train,  train_y):\n",
        "    \"\"\"\n",
        "    For each category a classifier is trained to discriminate between this category and all the other categories put together.\n",
        "    \n",
        "    Parameters\n",
        "    ----------\n",
        "    projected_train : shape (num_samples_train, projection_dim)\n",
        "    train_y : shape (num_samples_train)\n",
        "\n",
        "    \n",
        "    Returns\n",
        "    -------\n",
        "    classifiers_list : a list of trained classifiers. The it'h classifier desined to predict the i'th category. \n",
        "    diagnosis_list : list of the categories order as the classifiers. \n",
        "    \"\"\"\n",
        "\n",
        "    classifiers_list = []\n",
        "\n",
        "    diagnosis_list = np.sort(list(Counter(list(train_y)).keys()))   # orderd by increasing order: (0,1,2,..)\n",
        "\n",
        "    for diagnosis in diagnosis_list:\n",
        "        print(diagnosis)\n",
        "        \n",
        "        y = np.zeros(len(train_y))\n",
        "        is_diagnosis = train_y == diagnosis\n",
        "        y[is_diagnosis] = 1\n",
        "        y = y.astype('int')\n",
        "        if y.sum() == 0: # TODO delete\n",
        "          continue\n",
        "        classifier = SVC(gamma='auto', probability=True)\n",
        "        classifier.fit(projected_train, y)\n",
        "\n",
        "        classifiers_list.append(classifier)\n",
        "\n",
        "    return classifiers_list, diagnosis_list\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HfDiANsKK2Zf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee891bee-a37e-420f-ed70-3aab85057f58"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n",
            "11\n",
            "18\n",
            "21\n",
            "26\n"
          ]
        }
      ],
      "source": [
        "classifiers, categories_order = ensemble_of_classifiers(projected_train,  train_labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wXC95RVyUgrZ"
      },
      "source": [
        "# **Test** - Seen categories"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LQTq-qVmUgzg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe4f87ac-5962-49f4-e83c-387ab1c97680"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n"
          ]
        }
      ],
      "source": [
        "projected_test = project_to_dissimilarity_space(testLoader_simple, SNN_model, prototypes_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7MH3vk1hr2_T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65d17600-dadf-4665-9d6d-759ca1e35ed1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(231, 100)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "projected_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_pca = pca_alg.fit_transform(projected_test) \n",
        "show_space(X_pca, title=\"3D Space\", colors=pd.DataFrame(test_labels).replace(categories_mapping), color_by=\"category\", show_3D=True)\n",
        "print(\"Explained variance per component:\", pca_alg.explained_variance_ratio_)\n",
        "print(\"Comulative explained variance:\", pca_alg.explained_variance_ratio_.cumsum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
        },
        "id": "_r11pAqPDmS8",
        "outputId": "0694e648-5512-419c-fe43-88854d2c5e5a"
      },
      "execution_count": null,
      "outputs": [
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Explained variance per component: [0.5856723  0.19591992 0.109155  ]\n",
            "Comulative explained variance: [0.5856723  0.78159225 0.89074725]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AmW8hryhVDM5"
      },
      "outputs": [],
      "source": [
        "def predict(projected_test, classifiers_list, categories_order, test_labels):\n",
        "\n",
        "    pred_y = []\n",
        "     \n",
        "    for classifier in classifiers_list:\n",
        "         pred_y.append(classifier.predict_proba(projected_test)[:,1])    # predict_proba returns probabiltiy for class==0 and for class==1, so we take only the probabilities of class==1\n",
        "    \n",
        "    pred_y = np.vstack(pred_y) # (num_classifiers, num_samples_test)\n",
        "    highest_predictions = categories_order[np.argmax(pred_y, axis=0)]\n",
        "    print(pred_y)\n",
        "    print(highest_predictions)\n",
        "    return highest_predictions\n",
        "       \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9SUsw-WJr8Im",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2863397d-ccf1-461d-f783-5f81fdb96267"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[9.06497518e-01 4.22945484e-02 9.27972309e-01 ... 9.91558353e-03\n",
            "  8.29207502e-03 9.44201879e-01]\n",
            " [3.17179475e-04 7.99165297e-01 2.41063025e-03 ... 4.50768144e-03\n",
            "  3.92028214e-03 6.13502205e-04]\n",
            " [7.86471885e-02 3.16745531e-02 3.57406547e-02 ... 9.97267539e-02\n",
            "  7.16640304e-01 3.55695892e-02]\n",
            " [4.13659613e-02 2.78985410e-02 2.95260976e-02 ... 7.18281033e-01\n",
            "  1.45186659e-02 3.10082280e-02]\n",
            " [1.26413458e-01 1.16556337e-01 1.06317467e-01 ... 1.32821917e-01\n",
            "  2.14410045e-01 1.22298769e-01]]\n",
            "[ 3 11  3 18  3 21 18 11 21 18 21 18 18 18 18 26 21 18 18  3  3 11  3 18\n",
            " 18  3  3 21  3 21 21 18 26 18 26  3  3 21 11 11 21  3 21 18 21  3 26 18\n",
            " 18  3  3 11 21  3 11  3 21 21 18  3 21 18 18 21  3 18  3 18 11  3 21  3\n",
            " 21 18 11 21  3  3 18  3 18  3 21 11 21  3  3  3 18 21  3 26 11 11 21 21\n",
            " 18 26 21 18 11  3 18 18 26 26  3  3 21 21 11 26 21 26 21 18 18  3 21  3\n",
            " 21 18 21 21 11 11 11 26  3 26  3 11  3 21 21 21  3 18 11 18  3 18  3  3\n",
            " 18 26  3 18 21 26 18 21  3 21  3 26  3 21 26 26 21  3 26 21 18  3  3 18\n",
            " 26  3  3 18 11 26 26 11 21 26  3  3 18 18 21  3  3 21 26 18 21 18 18 21\n",
            "  3 26 11 11  3 11  3 21 21 11 21  3 21  3 18  3  3  3  3 21  3  3 21 11\n",
            " 18  3  3 21 21 26 18 18 18 21 21  3 21 18  3]\n"
          ]
        }
      ],
      "source": [
        "preds = predict(projected_test, classifiers, categories_order, test_labels)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "def plot_confusion_matrix(mat,fig_size, labels):\n",
        "\n",
        "    fig = plt.figure(figsize=(fig_size,fig_size))\n",
        "    ax= fig.add_subplot(1,1,1)\n",
        "    sns.heatmap(mat, annot=True, cmap=\"Blues\",ax = ax,fmt='g'); #annot=True to annotate cells\n",
        "\n",
        "    # labels, title and ticks\n",
        "    ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \n",
        "    ax.set_title('Confusion Matrix'); \n",
        "    ax.xaxis.set_ticklabels(labels); ax.yaxis.set_ticklabels(labels);\n",
        "    plt.setp(ax.get_yticklabels(), rotation=30, horizontalalignment='right')\n",
        "    plt.setp(ax.get_xticklabels(), rotation=30, horizontalalignment='right')     \n",
        "    plt.show()\n",
        "\n",
        "c_matrix = confusion_matrix(test_labels, preds)\n",
        "plot_confusion_matrix(c_matrix, fig_size=7, labels=[categories_mapping[c] for c in categories_order])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "jVOccpX3XhJN",
        "outputId": "2b326899-770c-4844-d7ba-51eed8a7dd88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "categories_labels = [categories_mapping[c] for c in categories_order]\n",
        "print(classification_report(test_labels, preds, target_names=categories_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Hg_FFCXS6OH",
        "outputId": "6c5a1d00-e6aa-4d26-b24c-e5ca6d86b8e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            precision    recall  f1-score   support\n",
            "\n",
            "Cardiovascular / Pulmonary       0.74      0.93      0.83        56\n",
            "          Gastroenterology       0.92      0.73      0.81        33\n",
            "                 Neurology       0.50      0.54      0.52        48\n",
            "                Orthopedic       0.62      0.68      0.65        53\n",
            "                 Radiology       0.36      0.22      0.27        41\n",
            "\n",
            "                  accuracy                           0.64       231\n",
            "                 macro avg       0.63      0.62      0.62       231\n",
            "              weighted avg       0.62      0.64      0.62       231\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Test** - Unseen categories"
      ],
      "metadata": {
        "id": "bZlOtnT1Vaj1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "unseen_train_matrix = construct_train_matrix(SNN_model, unseen_trainLoader_simple)\n",
        "unseen_prototypes_list = extract_prototypes(100, SNN_model, unseen_trainLoader_simple, unseen_train_labels, unseen_train_matrix)\n",
        "unseen_projected_train = project_to_dissimilarity_space(unseen_trainLoader_simple, SNN_model, unseen_prototypes_list)\n",
        "unseen_classifiers, unseen_categories_order = ensemble_of_classifiers(unseen_projected_train,  unseen_train_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fsqi10BIcvKd",
        "outputId": "f3bed510-92cc-480e-c358-78064640d0e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "num batches: 10\n",
            "0 / 10  batches\n",
            "1 / 10  batches\n",
            "2 / 10  batches\n",
            "3 / 10  batches\n",
            "4 / 10  batches\n",
            "5 / 10  batches\n",
            "6 / 10  batches\n",
            "7 / 10  batches\n",
            "8 / 10  batches\n",
            "9 / 10  batches\n",
            "kc: 12\n",
            "diagnosis: 30\n",
            "diagnosis: 19\n",
            "diagnosis: 17\n",
            "diagnosis: 9\n",
            "diagnosis: 12\n",
            "diagnosis: 25\n",
            "diagnosis: 22\n",
            "diagnosis: 20\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "9\n",
            "12\n",
            "17\n",
            "19\n",
            "20\n",
            "22\n",
            "25\n",
            "30\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unseen_projected_test = project_to_dissimilarity_space(unseen_testLoader_simple, SNN_model, unseen_prototypes_list)\n",
        "unseen_preds = predict(unseen_projected_test, unseen_classifiers, unseen_categories_order, unseen_test_labels)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RT5uDoYWZpmQ",
        "outputId": "f7e447e3-9e1c-42eb-a7ea-ad96338a2052"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "****new batch***\n",
            "****new batch***\n",
            "****new batch***\n",
            "[[0.13516332 0.11761123 0.0899896  ... 0.1198466  0.07635645 0.06433335]\n",
            " [0.11573274 0.11413227 0.1137885  ... 0.11313168 0.11453109 0.11515506]\n",
            " [0.08127978 0.10720222 0.09822605 ... 0.17820996 0.01015258 0.06119408]\n",
            " ...\n",
            " [0.1293576  0.08853877 0.09636341 ... 0.07968307 0.08177174 0.08200888]\n",
            " [0.01948808 0.0194217  0.01577369 ... 0.03695012 0.07372426 0.01107365]\n",
            " [0.14440057 0.18258117 0.17624414 ... 0.20070883 0.1626611  0.23553353]]\n",
            "[19 19 19 30  9  9 19  9 19 19 30 19 30 19 17  9  9 19  9 30  9 19 30 20\n",
            " 19 30 30 19 30  9 20 19 19 19 30 20 30 19 30  9 30 19  9 30 30 30 19 19\n",
            " 20 30  9 30 20  9 20 30 19 30  9 17 17 19 19  9 19 25 30  9 30 30 30 19\n",
            " 20 30 17 30 20 30 19 19 19 19 30 19 19 30 30 20  9 19 30 20 19 19 30 30\n",
            " 20 30  9 19 30 19 30 19 19 17 19 20 19 30 19 22 19 17  9 19 17  9 30 19\n",
            " 30 30 30 20 30 30 30 30 19 30 19 19 20 19  9 17 19 19 19 30 19  9 19 19\n",
            "  9 19 19 19 30 19 19 19 19 19 30 19 30]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(unseen_projected_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DDOlEJZIrpl9",
        "outputId": "047d373b-0dcc-494d-bb28-120321d258f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "157"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unseen_c_matrix = confusion_matrix(unseen_test_labels, unseen_preds)\n",
        "plot_confusion_matrix(unseen_c_matrix, fig_size=10, labels=[unseen_categories_mapping[c] for c in unseen_categories_order])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        },
        "id": "NuS0Jr-PdPAl",
        "outputId": "38655a8d-5350-4512-baf0-6785cf09825f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Counter(unseen_train_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8JFpNhA_zCfr",
        "outputId": "a7b6097b-3c7e-458f-e997-51504682d1a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({0: 6,\n",
              "         1: 6,\n",
              "         2: 14,\n",
              "         4: 11,\n",
              "         5: 22,\n",
              "         6: 22,\n",
              "         7: 23,\n",
              "         8: 8,\n",
              "         9: 77,\n",
              "         10: 15,\n",
              "         11: 179,\n",
              "         12: 72,\n",
              "         13: 5,\n",
              "         14: 13,\n",
              "         15: 6,\n",
              "         16: 18,\n",
              "         18: 124,\n",
              "         19: 66,\n",
              "         21: 56,\n",
              "         22: 17,\n",
              "         23: 38,\n",
              "         24: 43,\n",
              "         26: 8,\n",
              "         27: 16,\n",
              "         28: 7})"
            ]
          },
          "metadata": {},
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unseen_categories_labels = [unseen_categories_mapping[c] for c in unseen_categories_order]\n",
        "print(classification_report(unseen_test_labels, unseen_preds, target_names=unseen_categories_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gLWKc2C8dRrb",
        "outputId": "bc22c089-6a4c-4a80-c701-f4f53b2944fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                         precision    recall  f1-score   support\n",
            "\n",
            "   ENT - Otolaryngology       0.23      0.26      0.24        19\n",
            "  Hematology - Oncology       0.00      0.00      0.00        18\n",
            "             Nephrology       0.25      0.12      0.17        16\n",
            "Obstetrics / Gynecology       0.26      0.52      0.34        31\n",
            "          Ophthalmology       0.71      0.59      0.65        17\n",
            "  Pediatrics - Neonatal       0.00      0.00      0.00        14\n",
            "Psychiatry / Psychology       1.00      0.09      0.17        11\n",
            "                Urology       0.31      0.48      0.38        31\n",
            "\n",
            "               accuracy                           0.31       157\n",
            "              macro avg       0.34      0.26      0.24       157\n",
            "           weighted avg       0.31      0.31      0.27       157\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning:\n",
            "\n",
            "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning:\n",
            "\n",
            "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning:\n",
            "\n",
            "Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_pca = pca_alg.fit_transform(unseen_projected_train) \n",
        "show_space(X_pca, title=\"3D Space\", colors=pd.DataFrame(unseen_train_labels).replace(categories_mapping), color_by=\"category\", show_3D=True)\n",
        "print(\"Explained variance per component:\", pca_alg.explained_variance_ratio_)\n",
        "print(\"Comulative explained variance:\", pca_alg.explained_variance_ratio_.cumsum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
        },
        "id": "FPkM30UrD9YE",
        "outputId": "d4fb9619-3ee6-42a9-8b37-81d8543f3dfe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Explained variance per component: [0.6529172  0.17009008 0.06527393]\n",
            "Comulative explained variance: [0.6529172 0.8230073 0.8882812]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_pca = pca_alg.fit_transform(unseen_projected_test) \n",
        "show_space(X_pca, title=\"3D Space\", colors=pd.DataFrame(unseen_test_labels).replace(categories_mapping), color_by=\"category\", show_3D=True)\n",
        "print(\"Explained variance per component:\", pca_alg.explained_variance_ratio_)\n",
        "print(\"Comulative explained variance:\", pca_alg.explained_variance_ratio_.cumsum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
        },
        "id": "SBzM6ETkD1tN",
        "outputId": "c760cf05-1a91-4ed2-af8d-fe0d41ffec3c"
      },
      "execution_count": null,
      "outputs": [
        {
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