OHDSI_Submission_BERT_Similarity_Ranking.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/adambouras/OHDSI-Symposium-Submission/blob/main/OHDSI_Submission_BERT_Similarity_Ranking.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The aim of this project is to assess the similarities and differences that exits between assessment tools and questions for food insecurity that were identified by the Gravity Project (GP). These questions are divided into two groups; one group that has been used as <a href=\"https://www.hl7.org/fhir/\" target=\"_blank\">FHIR resources</a> and mapped to OMOP CDM **(Group A)**. The other group of questions are identified by the GP but **NOT** mapped to OMOP CDM nor identified as FHIR resources **(Group B)**. This project will help identify the level of difference and substitutability that may exit between Group A and Group B."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "iRlxNN8F_2g3"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "%pip install -r requirement.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "s-idEmPqTTM_"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "\n",
        "import pandas as pd\n",
        "import os\n",
        "import types\n",
        "import numpy as np\n",
        "import re\n",
        "from bertopic import *\n",
        "from pyxlsb import open_workbook\n",
        "import seaborn as sns\n",
        "from sentence_transformers import SentenceTransformer, util\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "import pip\n",
        "from pyxlsb import open_workbook\n",
        "import pandas as pd\n",
        "import os\n",
        "import types\n",
        "import numpy as np\n",
        "import re\n",
        "from bertopic import BERTopic\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "%%capture\n",
        "import session_info\n",
        "session_info.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Dataframe styling\n",
        "cell_hover = {  # for row hover use <tr> instead of <td>\n",
        "    'selector': 'td:hover',\n",
        "    'props': 'background-color: #ffffb3'\n",
        "    }\n",
        "index_names = {\n",
        "    'selector': '.index_name',\n",
        "    'props': 'font-style: italic; color: darkgrey; font-weight:normal;'\n",
        "}\n",
        "headers = {\n",
        "    'selector': 'th:not(.index_name)',\n",
        "    'props': 'background-color: #b30000; color: white; text-align: center'\n",
        "}\n",
        "cell_align = {\n",
        "        'selector': 'td',\n",
        "        'props': 'text-align: left'\n",
        "              }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kg4Yf_HzFSOd"
      },
      "source": [
        "For this project, I used the [Hugging Face](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) open source BERT Model to construct and build the algorithm. The model provides sentence similarity by mapping sentences and paragraphs to a 384 dimensional dense vector space and that can be used for tasks like clustering or sementic search. The training data set contains a total number of sentence pairs above 1 billion sentences. \n",
        "![Algorithm used to identify the questions similarities](./img/OHDSI-BERT%20Ranking.jpg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We started our project by reading the excel file from the github repo. the orginal [excel file](https://confluence.hl7.org/download/attachments/91994432/05142021%20Food%20Insecurity%20MASTER.xlsx?api=v2) was dwonloaded from [GP website](https://confluence.hl7.org/display/GRAV/Food+Insecurity). We conduct manual mapping of the assessment tool questions to [OMOP CDM using Athena](https://athena.ohdsi.org/search-terms/start). We created additional columns to report whether the questions [LOINC Codes](https://loinc.org/get-started/what-loinc-is/) was mapped or not. The final [excel file](08.31.2023%20Food%20Insecurity%20Screening%20SDOH%20Gravity%20Project.xlsx) is included in the github repostory of this project."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "FRZB4qRyZ7Rl"
      },
      "outputs": [],
      "source": [
        "xls = pd.ExcelFile(\"https://github.com/adambouras/OHDSI-Symposium-Submission/raw/main/08.31.2023%20Food%20Insecurity%20Screening%20SDOH%20Gravity%20Project.xlsx\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UCVG1b3pgP34",
        "outputId": "c43fba53-d029-456e-d626-a3260b44f040"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "xls.sheet_names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "MsHhUaqIaOII"
      },
      "outputs": [],
      "source": [
        "#create dictionary for later retrieval\n",
        "d_s = {'sheet':[], 'df': []}\n",
        "for i , c in enumerate(xls.sheet_names, 1):\n",
        "    globals()['df'+ str(i)] = pd.read_excel(xls, c)\n",
        "    d_s['sheet'].append(c+str(i))\n",
        "    d_s['df'].append('df'+str(i))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<h2>Create analysis of topics embedding</h2>\n",
        "<p>Before we start creating topics embeddings, we will first conduct some text analysis by standardizing the text and remove redundant text as showing in the cell below.</p>\n",
        "<h3>Description of Food Program</h3>\n",
        "<p>GP identified 23 programs and they are displayed in below.</p>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
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              "#T_58d54 td:hover {\n",
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              "<table id=\"T_58d54\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_58d54_level0_col0\" class=\"col_heading level0 col0\" >Program</th>\n",
              "      <th id=\"T_58d54_level0_col1\" class=\"col_heading level0 col1\" >Definition</th>\n",
              "      <th id=\"T_58d54_level0_col2\" class=\"col_heading level0 col2\" >Includes, but is not limited to </th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_58d54_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_58d54_row0_col0\" class=\"data row0 col0\" >Benefit Enrollement Assistance Program</td>\n",
              "      <td id=\"T_58d54_row0_col1\" class=\"data row0 col1\" >A program that coordinates cross sector benefit eligibility, assessment and application</td>\n",
              "      <td id=\"T_58d54_row0_col2\" class=\"data row0 col2\" >nan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58d54_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_58d54_row1_col0\" class=\"data row1 col0\" >Child and adult care food program (CFCAP)</td>\n",
              "      <td id=\"T_58d54_row1_col1\" class=\"data row1 col1\" >A government program that provides CACFP reimbursements for nutritious meals and snacks to eligible children and adults who are enrolled for care at participating child care centers, day care homes, and adult day care centers. The program also provides reimbursements for meals served to children and youth participating in afterschool care programs, children residing in emergency shelters, and older adults or living with a disability and enrolled in day care facilities. https://www.fns.usda.gov/cacfp/child-and-adult-care-food-program</td>\n",
              "      <td id=\"T_58d54_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58d54_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_58d54_row2_col0\" class=\"data row2 col0\" >Community Action Agency</td>\n",
              "      <td id=\"T_58d54_row2_col1\" class=\"data row2 col1\" >Non-profit private and public organizations established under the Economic Opportunity Act of 1964 with the aim of helping people achieve self-sufficiency. https://www.hud.gov/states/california/homeless/commaction</td>\n",
              "      <td id=\"T_58d54_row2_col2\" class=\"data row2 col2\" >nan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58d54_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_58d54_row3_col0\" class=\"data row3 col0\" >Community meal program</td>\n",
              "      <td id=\"T_58d54_row3_col1\" class=\"data row3 col1\" >Meals for a target group.</td>\n",
              "      <td id=\"T_58d54_row3_col2\" class=\"data row3 col2\" >Congregate meals</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58d54_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_58d54_row4_col0\" class=\"data row4 col0\" >Community Resource Assistance</td>\n",
              "      <td id=\"T_58d54_row4_col1\" class=\"data row4 col1\" >Help connecting individuals with available community resources to address identified social needs</td>\n",
              "      <td id=\"T_58d54_row4_col2\" class=\"data row4 col2\" >nan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58d54_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_58d54_row5_col0\" class=\"data row5 col0\" >Food and nutrition incentive program</td>\n",
              "      <td id=\"T_58d54_row5_col1\" class=\"data row5 col1\" >Financial incentives or matching programs to promote consumption of healthful foods including produce via community supported agriculture and other initiatives.</td>\n",
              "      <td id=\"T_58d54_row5_col2\" class=\"data row5 col2\" >Gus Schumacher Nutrition Incentive program\n",
              "Senior farmers market</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58d54_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_58d54_row6_col0\" class=\"data row6 col0\" >Food Distribution Program on Indian Reservation (FDPIR)</td>\n",
              "      <td id=\"T_58d54_row6_col1\" class=\"data row6 col1\" >Provides USDA Foods to income-eligible households living on Indian reservations and to Native American households residing in designated areas near reservations or in Oklahoma. USDA distributes both food and administrative funds to participating Indian Tribal Organizations and state agencies to operate FDPIR.</td>\n",
              "      <td id=\"T_58d54_row6_col2\" class=\"data row6 col2\" >nan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58d54_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_58d54_row7_col0\" class=\"data row7 col0\" >Food pantry program</td>\n",
              "      <td id=\"T_58d54_row7_col1\" class=\"data row7 col1\" >A community based supportive food and nutrition program offering food to individuals for consumption. https://www.feedingamerica.org/our-work/food-bank-network, https://www.fns.usda.gov/fdpir/food-distribution-program-indian-reservations</td>\n",
              "      <td id=\"T_58d54_row7_col2\" class=\"data row7 col2\" >FDIPR (Food Distribution Program on Indian Reservation)\n",
              "School based pantry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58d54_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_58d54_row8_col0\" class=\"data row8 col0\" >Food prescription program</td>\n",
              "      <td id=\"T_58d54_row8_col1\" class=\"data row8 col1\" >Prescription from a health care provider to access foods recommended, which also includes vegetables, fruits, and prescriptions for other foods.</td>\n",
              "      <td id=\"T_58d54_row8_col2\" class=\"data row8 col2\" >Food Prescription\n",
              "Produce Prescription\n",
              "Veggie Prescription</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58d54_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_58d54_row9_col0\" class=\"data row9 col0\" >Food provision program</td>\n",
              "      <td id=\"T_58d54_row9_col1\" class=\"data row9 col1\" >Food offered when a support need is identified. Examples include groceries as part of an education opportunity (e.g., cooking class), commodity food program, food rescue or gleaning program (e.g., produce brought to a clinic), or weekend food backpack program.</td>\n",
              "      <td id=\"T_58d54_row9_col2\" class=\"data row9 col2\" >nan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58d54_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_58d54_row10_col0\" class=\"data row10 col0\" >Garden programs</td>\n",
              "      <td id=\"T_58d54_row10_col1\" class=\"data row10 col1\" >Actions to plant gardens to produce foods for consumption.</td>\n",
              "      <td id=\"T_58d54_row10_col2\" class=\"data row10 col2\" >nan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58d54_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_58d54_row11_col0\" class=\"data row11 col0\" >Home delivered meal program</td>\n",
              "      <td id=\"T_58d54_row11_col1\" class=\"data row11 col1\" >Meals delivered to a client's place of residence supported or subsidized by a charitable, social, or government agency.</td>\n",
              "      <td id=\"T_58d54_row11_col2\" class=\"data row11 col2\" >Meals on wheels</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58d54_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_58d54_row12_col0\" class=\"data row12 col0\" >Meals on wheels program</td>\n",
              "      <td id=\"T_58d54_row12_col1\" class=\"data row12 col1\" >A specific home delivered meal program for eligible older adults and household members with demonstrated social and/or economic need.</td>\n",
              "      <td id=\"T_58d54_row12_col2\" class=\"data row12 col2\" >nan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58d54_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_58d54_row13_col0\" class=\"data row13 col0\" >Medically tailored meal program</td>\n",
              "      <td id=\"T_58d54_row13_col1\" class=\"data row13 col1\" >Meals delivered to individuals, living with assessed illness, tailored to the medical needs of the recipient by a Registered Dietitian Nutritionist (RDN) http://www.fimcoalition.org/our-model</td>\n",
              "      <td id=\"T_58d54_row13_col2\" class=\"data row13 col2\" >nan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58d54_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_58d54_row14_col0\" class=\"data row14 col0\" >Nutrition and food support programs</td>\n",
              "      <td id=\"T_58d54_row14_col1\" class=\"data row14 col1\" >Support programs that provide food and/or nutrition resources.</td>\n",
              "      <td id=\"T_58d54_row14_col2\" class=\"data row14 col2\" >nan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58d54_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_58d54_row15_col0\" class=\"data row15 col0\" >School meal and snack program</td>\n",
              "      <td id=\"T_58d54_row15_col1\" class=\"data row15 col1\" >Meals and snacks that meet specific nutrition standards and are available to school-aged children throughout the year. (https://www.fns.usda.gov/school-meals/faqs)</td>\n",
              "      <td id=\"T_58d54_row15_col2\" class=\"data row15 col2\" >nan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58d54_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_58d54_row16_col0\" class=\"data row16 col0\" >Senior congregate meal program</td>\n",
              "      <td id=\"T_58d54_row16_col1\" class=\"data row16 col1\" >Meals offered in community settings for eligible older adults, and along with eligible supportive individuals.</td>\n",
              "      <td id=\"T_58d54_row16_col2\" class=\"data row16 col2\" >nan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58d54_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_58d54_row17_col0\" class=\"data row17 col0\" >Senior farmers market program</td>\n",
              "      <td id=\"T_58d54_row17_col1\" class=\"data row17 col1\" >Financial incentives or matching programs to promote consumption of produce by older adults. https://www.fns.usda.gov/sfmnp/senior-farmers-market-nutrition-program</td>\n",
              "      <td id=\"T_58d54_row17_col2\" class=\"data row17 col2\" >nan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58d54_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_58d54_row18_col0\" class=\"data row18 col0\" >SNAP education</td>\n",
              "      <td id=\"T_58d54_row18_col1\" class=\"data row18 col1\" >Nutrition curricula, a facet of the SNAP program.</td>\n",
              "      <td id=\"T_58d54_row18_col2\" class=\"data row18 col2\" >nan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58d54_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
              "      <td id=\"T_58d54_row19_col0\" class=\"data row19 col0\" >Special supplemental nutrition program for women, infants, and children (WIC) program</td>\n",
              "      <td id=\"T_58d54_row19_col1\" class=\"data row19 col1\" >Supplemental foods, health care referrals, and nutrition education for low-income pregnant, breastfeeding, and non-breastfeeding postpartum women, and to infants and children up to age five who are found to be at nutritional risk. (https://www.fns.usda.gov/wic)</td>\n",
              "      <td id=\"T_58d54_row19_col2\" class=\"data row19 col2\" >WIC farmers market program</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58d54_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
              "      <td id=\"T_58d54_row20_col0\" class=\"data row20 col0\" >Summer meal and snack program</td>\n",
              "      <td id=\"T_58d54_row20_col1\" class=\"data row20 col1\" >A program that reimburses program operators who serve free healthy meals and snacks to children and teens in low-income areas. (https://www.fns.usda.gov/sfsp/summer-food-service-program)</td>\n",
              "      <td id=\"T_58d54_row20_col2\" class=\"data row20 col2\" >nan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58d54_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
              "      <td id=\"T_58d54_row21_col0\" class=\"data row21 col0\" >Supplemental nutrition assistance program (SNAP)</td>\n",
              "      <td id=\"T_58d54_row21_col1\" class=\"data row21 col1\" >A government based nutrition benefit program to supplement the food budget of qualified families for purchasing food. The US program is currently call the Supplemental Nutrition Assistance Program (SNAP).(https://www.fns.usda.gov/snap/supplemental-nutrition-assistance-program)</td>\n",
              "      <td id=\"T_58d54_row21_col2\" class=\"data row21 col2\" >nan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_58d54_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
              "      <td id=\"T_58d54_row22_col0\" class=\"data row22 col0\" >WIC farmer's market program</td>\n",
              "      <td id=\"T_58d54_row22_col1\" class=\"data row22 col1\" >Coupons for participants of the WIC program to purchase food at farmers markets. https://www.fns.usda.gov/fmnp/wic-farmers-market-nutrition-program</td>\n",
              "      <td id=\"T_58d54_row22_col2\" class=\"data row22 col2\" >nan</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x295cbca10>"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "df1.style.set_table_styles([ cell_align, cell_hover, index_names, headers])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5JhPSHgkmwxF"
      },
      "source": [
        "<h3>Distribution of number of question by screen tools</h3>\n",
        "<p>The GP identified 24 screen tools and some of the tools are nested tools from other tools for the simplicity of this analysis, we will not consolidate these nested tools into a single tool. By doing so, we will help the community to identify the best approaches to make this tools standarized and consolidated for easy retrieval and use.</p>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [],
      "source": [
        "# screen_tool_summary = df3.groupby('Relevant_Screening_Tool')['LOINC_Panel_Code'].nunique()\n",
        "df_scr_tool_sum = df3.groupby(by=['Relevant_Screening_Tool', 'Mapped_to_Athena'], dropna=False, as_index=False).agg({'LOINC_Panel_Code': pd.Series.nunique}).sort_values('LOINC_Panel_Code')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "data": {
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              "#T_ab3c0 td {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_ab3c0 td:hover {\n",
              "  background-color: #ffffb3;\n",
              "}\n",
              "#T_ab3c0 .index_name {\n",
              "  font-style: italic;\n",
              "  color: darkgrey;\n",
              "  font-weight: normal;\n",
              "}\n",
              "#T_ab3c0 th:not(.index_name) {\n",
              "  background-color: #b30000;\n",
              "  color: white;\n",
              "  text-align: center;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_ab3c0\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_ab3c0_level0_col0\" class=\"col_heading level0 col0\" >Screening Tool</th>\n",
              "      <th id=\"T_ab3c0_level0_col1\" class=\"col_heading level0 col1\" >Mapped to Athena</th>\n",
              "      <th id=\"T_ab3c0_level0_col2\" class=\"col_heading level0 col2\" >LOINC Code</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_ab3c0_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
              "      <td id=\"T_ab3c0_row0_col0\" class=\"data row0 col0\" >AHC</td>\n",
              "      <td id=\"T_ab3c0_row0_col1\" class=\"data row0 col1\" >Yes</td>\n",
              "      <td id=\"T_ab3c0_row0_col2\" class=\"data row0 col2\" >2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ab3c0_level0_row1\" class=\"row_heading level0 row1\" >15</th>\n",
              "      <td id=\"T_ab3c0_row1_col0\" class=\"data row1 col0\" >PRAPARE</td>\n",
              "      <td id=\"T_ab3c0_row1_col1\" class=\"data row1 col1\" >Yes</td>\n",
              "      <td id=\"T_ab3c0_row1_col2\" class=\"data row1 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ab3c0_level0_row2\" class=\"row_heading level0 row2\" >9</th>\n",
              "      <td id=\"T_ab3c0_row2_col0\" class=\"data row2 col0\" >Hunger Vital Sign </td>\n",
              "      <td id=\"T_ab3c0_row2_col1\" class=\"data row2 col1\" >Yes</td>\n",
              "      <td id=\"T_ab3c0_row2_col2\" class=\"data row2 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ab3c0_level0_row3\" class=\"row_heading level0 row3\" >8</th>\n",
              "      <td id=\"T_ab3c0_row3_col0\" class=\"data row3 col0\" >Household Food Security Survey Module (HFSSM) (NOTE: Canadian survey adapted from the US food security measurement method)</td>\n",
              "      <td id=\"T_ab3c0_row3_col1\" class=\"data row3 col1\" >No</td>\n",
              "      <td id=\"T_ab3c0_row3_col2\" class=\"data row3 col2\" >19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ab3c0_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
              "      <td id=\"T_ab3c0_row4_col0\" class=\"data row4 col0\" >HFIAS</td>\n",
              "      <td id=\"T_ab3c0_row4_col1\" class=\"data row4 col1\" >No</td>\n",
              "      <td id=\"T_ab3c0_row4_col2\" class=\"data row4 col2\" >19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ab3c0_level0_row5\" class=\"row_heading level0 row5\" >22</th>\n",
              "      <td id=\"T_ab3c0_row5_col0\" class=\"data row5 col0\" >USDA Youth</td>\n",
              "      <td id=\"T_ab3c0_row5_col1\" class=\"data row5 col1\" >No</td>\n",
              "      <td id=\"T_ab3c0_row5_col2\" class=\"data row5 col2\" >9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ab3c0_level0_row6\" class=\"row_heading level0 row6\" >4</th>\n",
              "      <td id=\"T_ab3c0_row6_col0\" class=\"data row6 col0\" >FIES-SM</td>\n",
              "      <td id=\"T_ab3c0_row6_col1\" class=\"data row6 col1\" >No</td>\n",
              "      <td id=\"T_ab3c0_row6_col2\" class=\"data row6 col2\" >8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ab3c0_level0_row7\" class=\"row_heading level0 row7\" >19</th>\n",
              "      <td id=\"T_ab3c0_row7_col0\" class=\"data row7 col0\" >US household food security 18 item</td>\n",
              "      <td id=\"T_ab3c0_row7_col1\" class=\"data row7 col1\" >No</td>\n",
              "      <td id=\"T_ab3c0_row7_col2\" class=\"data row7 col2\" >8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ab3c0_level0_row8\" class=\"row_heading level0 row8\" >21</th>\n",
              "      <td id=\"T_ab3c0_row8_col0\" class=\"data row8 col0\" >US household food security ten item\n",
              "US household food security 18 item</td>\n",
              "      <td id=\"T_ab3c0_row8_col1\" class=\"data row8 col1\" >No</td>\n",
              "      <td id=\"T_ab3c0_row8_col2\" class=\"data row8 col2\" >5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ab3c0_level0_row9\" class=\"row_heading level0 row9\" >20</th>\n",
              "      <td id=\"T_ab3c0_row9_col0\" class=\"data row9 col0\" >US household food security six item\n",
              "US household food security ten item\n",
              "US household food security 18 item</td>\n",
              "      <td id=\"T_ab3c0_row9_col1\" class=\"data row9 col1\" >No</td>\n",
              "      <td id=\"T_ab3c0_row9_col2\" class=\"data row9 col2\" >4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ab3c0_level0_row10\" class=\"row_heading level0 row10\" >23</th>\n",
              "      <td id=\"T_ab3c0_row10_col0\" class=\"data row10 col0\" >We Care</td>\n",
              "      <td id=\"T_ab3c0_row10_col1\" class=\"data row10 col1\" >No</td>\n",
              "      <td id=\"T_ab3c0_row10_col2\" class=\"data row10 col2\" >4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ab3c0_level0_row11\" class=\"row_heading level0 row11\" >10</th>\n",
              "      <td id=\"T_ab3c0_row11_col0\" class=\"data row11 col0\" >IHELP</td>\n",
              "      <td id=\"T_ab3c0_row11_col1\" class=\"data row11 col1\" >No</td>\n",
              "      <td id=\"T_ab3c0_row11_col2\" class=\"data row11 col2\" >3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ab3c0_level0_row12\" class=\"row_heading level0 row12\" >0</th>\n",
              "      <td id=\"T_ab3c0_row12_col0\" class=\"data row12 col0\" >AAFP Social Needs Screening Tool</td>\n",
              "      <td id=\"T_ab3c0_row12_col1\" class=\"data row12 col1\" >No</td>\n",
              "      <td id=\"T_ab3c0_row12_col2\" class=\"data row12 col2\" >2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ab3c0_level0_row13\" class=\"row_heading level0 row13\" >16</th>\n",
              "      <td id=\"T_ab3c0_row13_col0\" class=\"data row13 col0\" >SEEK</td>\n",
              "      <td id=\"T_ab3c0_row13_col1\" class=\"data row13 col1\" >No</td>\n",
              "      <td id=\"T_ab3c0_row13_col2\" class=\"data row13 col2\" >2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ab3c0_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_ab3c0_row14_col0\" class=\"data row14 col0\" >NC SDOH</td>\n",
              "      <td id=\"T_ab3c0_row14_col1\" class=\"data row14 col1\" >No</td>\n",
              "      <td id=\"T_ab3c0_row14_col2\" class=\"data row14 col2\" >2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ab3c0_level0_row15\" class=\"row_heading level0 row15\" >13</th>\n",
              "      <td id=\"T_ab3c0_row15_col0\" class=\"data row15 col0\" >Medicare THA</td>\n",
              "      <td id=\"T_ab3c0_row15_col1\" class=\"data row15 col1\" >No</td>\n",
              "      <td id=\"T_ab3c0_row15_col2\" class=\"data row15 col2\" >2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ab3c0_level0_row16\" class=\"row_heading level0 row16\" >11</th>\n",
              "      <td id=\"T_ab3c0_row16_col0\" class=\"data row16 col0\" >Kaiser YCLS</td>\n",
              "      <td id=\"T_ab3c0_row16_col1\" class=\"data row16 col1\" >No</td>\n",
              "      <td id=\"T_ab3c0_row16_col2\" class=\"data row16 col2\" >2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ab3c0_level0_row17\" class=\"row_heading level0 row17\" >2</th>\n",
              "      <td id=\"T_ab3c0_row17_col0\" class=\"data row17 col0\" >CHCW Internally developed SDoH Tool</td>\n",
              "      <td id=\"T_ab3c0_row17_col1\" class=\"data row17 col1\" >No</td>\n",
              "      <td id=\"T_ab3c0_row17_col2\" class=\"data row17 col2\" >2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ab3c0_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_ab3c0_row18_col0\" class=\"data row18 col0\" >Total Health Quick Check (CHC Sno)</td>\n",
              "      <td id=\"T_ab3c0_row18_col1\" class=\"data row18 col1\" >No</td>\n",
              "      <td id=\"T_ab3c0_row18_col2\" class=\"data row18 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ab3c0_level0_row19\" class=\"row_heading level0 row19\" >17</th>\n",
              "      <td id=\"T_ab3c0_row19_col0\" class=\"data row19 col0\" >SWYC</td>\n",
              "      <td id=\"T_ab3c0_row19_col1\" class=\"data row19 col1\" >No</td>\n",
              "      <td id=\"T_ab3c0_row19_col2\" class=\"data row19 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ab3c0_level0_row20\" class=\"row_heading level0 row20\" >3</th>\n",
              "      <td id=\"T_ab3c0_row20_col0\" class=\"data row20 col0\" >CommunityConnect Social Needs Screening Tool</td>\n",
              "      <td id=\"T_ab3c0_row20_col1\" class=\"data row20 col1\" >No</td>\n",
              "      <td id=\"T_ab3c0_row20_col2\" class=\"data row20 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ab3c0_level0_row21\" class=\"row_heading level0 row21\" >6</th>\n",
              "      <td id=\"T_ab3c0_row21_col0\" class=\"data row21 col0\" >Health Information National Trends Survey</td>\n",
              "      <td id=\"T_ab3c0_row21_col1\" class=\"data row21 col1\" >No</td>\n",
              "      <td id=\"T_ab3c0_row21_col2\" class=\"data row21 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ab3c0_level0_row22\" class=\"row_heading level0 row22\" >7</th>\n",
              "      <td id=\"T_ab3c0_row22_col0\" class=\"data row22 col0\" >Health Leads</td>\n",
              "      <td id=\"T_ab3c0_row22_col1\" class=\"data row22 col1\" >No</td>\n",
              "      <td id=\"T_ab3c0_row22_col2\" class=\"data row22 col2\" >1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_ab3c0_level0_row23\" class=\"row_heading level0 row23\" >12</th>\n",
              "      <td id=\"T_ab3c0_row23_col0\" class=\"data row23 col0\" >Kleinman</td>\n",
              "      <td id=\"T_ab3c0_row23_col1\" class=\"data row23 col1\" >No</td>\n",
              "      <td id=\"T_ab3c0_row23_col2\" class=\"data row23 col2\" >1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x29327d090>"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_scr_tool_sum['Mapped_to_Athena'] = df_scr_tool_sum['Mapped_to_Athena'].fillna('No')\n",
        "df_scr_tool_sum.sort_values(by=['Mapped_to_Athena', 'LOINC_Panel_Code'], ascending=False, inplace=True)\n",
        "df_scr_tool_sum.rename(columns={'Relevant_Screening_Tool':'Screening Tool','LOINC_Panel_Code':'LOINC Code', 'Mapped_to_Athena':'Mapped to Athena'}, inplace=False).style.set_table_styles([ cell_align, cell_hover, index_names, headers])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<p>The graph below provides the number of questions by screen tool grouped into those questions that we were able to map to **Athena**.</p>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sns.barplot(df_scr_tool_sum, x= 'LOINC_Panel_Code', y= 'Relevant_Screening_Tool', orient=\"y\", hue='Mapped_to_Athena')\n",
        "#modify individual font size of elements\n",
        "plt.legend(title='Question mapped to Athena ', fontsize=8)\n",
        "plt.xlabel('Count', fontsize=16);\n",
        "plt.ylabel('Screen tool name', fontsize=4);\n",
        "plt.title('Count of number of questions by screen tool of food insecurity', fontsize=20)\n",
        "plt.tick_params(axis='both', which='major', labelsize=8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "1ANFaAWcWt-T"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "#select questions that have mapped to athena\n",
        "sentences_yes = df3['Question_Concept'].loc[df3['Mapped_to_Athena'] == 'Yes'].unique().tolist()\n",
        "sentences_yes = [x for x in sentences_yes if str(x) != 'nan']\n",
        "#remove redundant words\n",
        "pattern_yes = r\"\\s+\\[U\\.S\\. FSS\\]\"\n",
        "sentences_yes = [re.sub(pattern_yes, '', str(x)) for x in sentences_yes]\n",
        "#select unique assessment questions:\n",
        "duplicate_question = 'Within the past 12 months we worried whether our food would run out before we got money to buy more'\n",
        "sentences_yes.remove(duplicate_question)\n",
        "#select questions that were not mapped to any vocabularly using Athena:\n",
        "sentences_no = df3['Question_Concept'].loc[df3['Mapped_to_Athena'] != 'Yes'].unique().tolist()\n",
        "#clean string from unecessary strings Q1 Q14b. etc....\n",
        "pattern = r\"Q\\d{1,2}\\.\\s+|Q\\d{1,2}b\\.\\s+|\\?|\\.\"\n",
        "sentences_no = [re.sub(pattern, '', str(x)) for x in sentences_no]\n",
        "#Delete question mapped to Athena but not reported by the gravity team\n",
        "duplicated_question  = 'Within the past 12 months, you worried that your food would run out before you got money to buy more'\n",
        "sentences_no.remove(duplicated_question)\n",
        "sentences = [sentences_yes, sentences_no]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "6wLOQE1JGpO4"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "from sentence_transformers import SentenceTransformer, util\n",
        "model = SentenceTransformer('all-MiniLM-L6-v2')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "18MqjNriD7qL"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "model = SentenceTransformer('all-MiniLM-L6-v2')\n",
        "\n",
        "#Compute bi-ranked embedding for food insecurity\n",
        "\n",
        "ranked_domain ={'standard': [] ,'index_yes':[], 'index_no':[], 'label_yes': [], 'label_no':[], 'ranked_value':[]}\n",
        "for h,s in enumerate(sentences):\n",
        "  if h ==0: #select questions that have been mapped to Athena  for embedding\n",
        "    embedding_1= model.encode(s, convert_to_tensor=True)\n",
        "    for i, sentence in enumerate(sentences_no):\n",
        "      embedding_2 = model.encode(sentence, convert_to_tensor=True)\n",
        "      #Compute cosine-similarities\n",
        "      cosine_scores = util.cos_sim(embedding_1, embedding_2)\n",
        "      #Output the pairs with their score\n",
        "      for j, sentence_yes in enumerate(sentences_yes):\n",
        "        ranked_domain['standard'].append(h)\n",
        "        ranked_domain['index_yes'].append(str(j))\n",
        "        ranked_domain['index_no'].append(str(i))\n",
        "        ranked_domain['label_yes'].append(sentence_yes)\n",
        "        ranked_domain['label_no'].append(sentence)\n",
        "        na = np.round(cosine_scores.numpy(),2)\n",
        "        ranked_domain['ranked_value'].append(round(na[j][0],2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "sdk2jKNtsy7N"
      },
      "outputs": [],
      "source": [
        "df =pd.DataFrame.from_dict(ranked_domain)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<h2>Text Similarity Score</h2>\n",
        "<p>The interactive appplication below provide the capbility to select the text similarity score and thus broading or narrow the semantic similarity between the questions mapped to the OMOP CDM and those that were not mapped.</p>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
            "To disable this warning, you can either:\n",
            "\t- Avoid using `tokenizers` before the fork if possible\n",
            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
            "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
            "To disable this warning, you can either:\n",
            "\t- Avoid using `tokenizers` before the fork if possible\n",
            "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
          ]
        },
        {
          "data": {
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              "  var docs_json = {\"ec94d30a-00fb-457a-94d7-9ef622e655c7\":{\"version\":\"3.2.2\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"57dd7277-33e2-462c-a230-b94e91168c32\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"80c69a48-d0b0-4a04-97ef-4492904ec8ad\",\"attributes\":{\"plot_id\":\"57dd7277-33e2-462c-a230-b94e91168c32\",\"comm_id\":\"ca576cd8f8904b29a5227cbce5f146d2\",\"client_comm_id\":\"d914145e69f14f808a5df705320cb74f\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
              "  var render_items = [{\"docid\":\"ec94d30a-00fb-457a-94d7-9ef622e655c7\",\"roots\":{\"57dd7277-33e2-462c-a230-b94e91168c32\":\"c08b9a53-2dee-448a-8880-40c626cecda2\"},\"root_ids\":[\"57dd7277-33e2-462c-a230-b94e91168c32\"]}];\n",
              "  var docs = Object.values(docs_json)\n",
              "  if (!docs) {\n",
              "    return\n",
              "  }\n",
              "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
              "  const is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1\n",
              "  function embed_document(root) {\n",
              "    var Bokeh = get_bokeh(root)\n",
              "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
              "    for (const render_item of render_items) {\n",
              "      for (const root_id of render_item.root_ids) {\n",
              "\tconst id_el = document.getElementById(root_id)\n",
              "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
              "\t  const root_el = id_el.children[0]\n",
              "\t  root_el.id = root_el.id + '-rendered'\n",
              "\t}\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "  function get_bokeh(root) {\n",
              "    if (root.Bokeh === undefined) {\n",
              "      return null\n",
              "    } else if (root.Bokeh.version !== py_version && !is_dev) {\n",
              "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
              "\treturn null\n",
              "      }\n",
              "      return root.Bokeh.versions.get(py_version);\n",
              "    } else if (root.Bokeh.version === py_version) {\n",
              "      return root.Bokeh\n",
              "    }\n",
              "    return null\n",
              "  }\n",
              "  function is_loaded(root) {\n",
              "    var Bokeh = get_bokeh(root)\n",
              "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
              "  }\n",
              "  if (is_loaded(root)) {\n",
              "    embed_document(root);\n",
              "  } else {\n",
              "    var attempts = 0;\n",
              "    var timer = setInterval(function(root) {\n",
              "      if (is_loaded(root)) {\n",
              "        clearInterval(timer);\n",
              "        embed_document(root);\n",
              "      } else if (document.readyState == \"complete\") {\n",
              "        attempts++;\n",
              "        if (attempts > 200) {\n",
              "          clearInterval(timer);\n",
              "\t  var Bokeh = get_bokeh(root)\n",
              "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
              "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
              "\t  } else {\n",
              "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
              "\t    embed_document(root)\n",
              "\t  }\n",
              "        }\n",
              "      }\n",
              "    }, 25, root)\n",
              "  }\n",
              "})(window);</script>"
            ]
          },
          "metadata": {
            "application/vnd.holoviews_exec.v0+json": {
              "id": "57dd7277-33e2-462c-a230-b94e91168c32"
            }
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6816c7c4f3374a67a37747914e83741f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(FloatRangeSlider(value=(0.2, 0.6), continuous_update=False, description='Similarity score:', ma…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "76b4783cc67b4c988ed4636417379863",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HTML(value='<style> .widget-text { width: auto; } </style>')"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import ipywidgets as widgets\n",
        "from ipywidgets import interact, interact_manual, HTML\n",
        "import plotly.graph_objects as go\n",
        "import panel as pn\n",
        "pn.extension()\n",
        "\n",
        "\n",
        "# interact(slow_function,i=FloatSlider(min=1e5, max=1e7, step=1e5, continuous_update=False));\n",
        "# @interact(x=(-1, 1, .01))\n",
        "\n",
        "#get the min and max values of the df data:\n",
        "\n",
        "baseline = widgets.FloatRangeSlider(\n",
        "    value=[0.2, 0.6],\n",
        "    min=-1.0,\n",
        "    max=1.0,\n",
        "    step=0.1,\n",
        "    description='Similarity score:',\n",
        "    disabled=False,\n",
        "    continuous_update=False,\n",
        "    orientation='horizontal',\n",
        "    readout=True,\n",
        "    readout_format='.1f',\n",
        "    style = {\"description_width\": \"initial\"}\n",
        ")\n",
        "\n",
        "text = df['index_no'].astype(str).unique().tolist()\n",
        "\n",
        "widgets.HBox([baseline])\n",
        "\n",
        "#create an initial plot object:\n",
        "df_map = df.pivot(index= 'index_no', columns='index_yes',  values='ranked_value')\n",
        "trace1 = go.Heatmap(z=df_map.values,\n",
        "                   x=df['index_yes'].astype(str).unique().tolist(),\n",
        "                   y=df['index_no'].astype(str).unique().tolist(),\n",
        "                   hoverongaps = False)\n",
        "#get the column names:\n",
        "l = df.columns.tolist()\n",
        "trace2 = go.Table(\n",
        "    header=dict(values=list(l),\n",
        "                fill_color='paleturquoise',\n",
        "                align='left'),\n",
        "    cells=dict(values=[df[col] for col in l],\n",
        "               fill_color='lavender',\n",
        "               align='left'))\n",
        "fig1 = go.Figure(data=[trace1])\n",
        "\n",
        "fig2 =go.Figure(data=[trace2])\n",
        "for f in [fig1,fig2]:\n",
        "    f.update_layout(\n",
        "        width = 1000,\n",
        "        height = 1500,\n",
        "        yaxis=dict(ticktext = text, tickmode=\"array\", type=\"category\"),\n",
        "        margin=dict(l=5, r=5, t=5, b=5))\n",
        "\n",
        "f1 = go.FigureWidget(fig1)\n",
        "f2 = go.FigureWidget(fig2)\n",
        "\n",
        "def validate():\n",
        "    if baseline.value in df['ranked_value'].unique():\n",
        "        return True\n",
        "    else:\n",
        "        return False\n",
        "\n",
        "def response(change):\n",
        "    try:\n",
        "        if min(baseline.value) >=0:\n",
        "            df_n = df.loc[(min(baseline.value) <= df['ranked_value']) & (df['ranked_value'] <= max(baseline.value))].sort_values(by='ranked_value', ascending=False)\n",
        "        elif min(baseline.value)<0:\n",
        "           df_n = df.loc[(min(baseline.value) <= df['ranked_value']) & (df['ranked_value'] <= max(baseline.value))].sort_values(by='ranked_value', ascending=True) \n",
        "        x1 = df_n.pivot(index= 'index_no', columns='index_yes',  values='ranked_value')\n",
        "\n",
        "        df_n_dict = dict.fromkeys(df_n.columns, '')\n",
        "\n",
        "        with f1.batch_update():\n",
        "            f1.data[0].z = x1\n",
        "        with f2.batch_update():\n",
        "            f2.data[0].header.values = list(df_n_dict) \n",
        "            f2.data[0].cells.values = [df_n[col] for col in df_n_dict]\n",
        "    except:\n",
        "        print('error')\n",
        "\n",
        "\n",
        "baseline.observe(response, names=\"value\")\n",
        "visual_container = widgets.VBox([f1,f2])\n",
        "display(widgets.VBox([baseline, visual_container]))\n",
        "display(HTML('<style> .widget-text { width: auto; } </style>'))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "authorship_tag": "ABX9TyNRp93mtbdPkVKHxv/AmHKW",
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.5"
    }
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  "nbformat": 4,
  "nbformat_minor": 0
}