Biomonitoring_glyphosate_with_SOM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vShUqQIzg2uY"
   },
   "source": [
    "## Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 33531,
     "status": "ok",
     "timestamp": 1702641673483,
     "user": {
      "displayName": "Florencia Doná",
      "userId": "12514529675364651717"
     },
     "user_tz": 180
    },
    "id": "ZUwtaW-asZQz"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import f1_score,accuracy_score\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "!pip install MiniSom > /dev/null\n",
    "from minisom import MiniSom\n",
    "from sklearn.preprocessing import StandardScaler\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qYnYS2_xg596"
   },
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
     "elapsed": 2189,
     "status": "ok",
     "timestamp": 1702641703280,
     "user": {
      "displayName": "Florencia Doná",
      "userId": "12514529675364651717"
     },
     "user_tz": 180
    },
    "id": "InHF7IQRl6KC"
   },
   "outputs": [],
   "source": [
    "labels = pd.read_csv('https://raw.githubusercontent.com/sinc-lab/biomonitoring_glyphosate_with_ML/master/data/Labels_glyphosate.csv')\n",
    "y = labels[\"Labels_glyphosate\"]\n",
    "target_names=np.array(['Q1','Q2','Q3','Q4'])\n",
    "dataset = pd.read_excel('https://raw.githubusercontent.com/sinc-lab/biomonitoring_glyphosate_with_ML/master/data/Biomonitoring_data_SOM.xlsx',index_col=0)\n",
    "dataset= dataset.replace(np.nan, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "executionInfo": {
     "elapsed": 15,
     "status": "ok",
     "timestamp": 1702641703280,
     "user": {
      "displayName": "Florencia Doná",
      "userId": "12514529675364651717"
     },
     "user_tz": 180
    },
    "id": "oT3oLJBJvmin"
   },
   "outputs": [],
   "source": [
    "y=y.replace('Q1',1)\n",
    "y=y.replace('Q2',2)\n",
    "y=y.replace('Q3',3)\n",
    "y=y.replace('Q4',4)\n",
    "DATA= dataset.drop(['Localities','Educational Level','Residence time (years)','Height (m)', 'Body weight (kg)','Overweight_obese_women (Yes_No)','Concentration_creatinine(mg/dL)','Urinary_glyphosate_levels (µg/L)','Quartiles (GLY_creat)' ], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "executionInfo": {
     "elapsed": 313,
     "status": "ok",
     "timestamp": 1702641795721,
     "user": {
      "displayName": "Florencia Doná",
      "userId": "12514529675364651717"
     },
     "user_tz": 180
    },
    "id": "x2Hl2t2HacH3"
   },
   "outputs": [],
   "source": [
    "X=DATA.to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x_XlsfNbn6JL"
   },
   "source": [
    "### Data normalization and SOM training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "executionInfo": {
     "elapsed": 314,
     "status": "ok",
     "timestamp": 1702641798218,
     "user": {
      "displayName": "Florencia Doná",
      "userId": "12514529675364651717"
     },
     "user_tz": 180
    },
    "id": "_NHkQGCkWz6V"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc = MinMaxScaler()\n",
    "X = sc.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6453,
     "status": "ok",
     "timestamp": 1702641809284,
     "user": {
      "displayName": "Florencia Doná",
      "userId": "12514529675364651717"
     },
     "user_tz": 180
    },
    "id": "L_95nZombI4c",
    "outputId": "f29c20e9-5268-430d-cd0f-a17d01b3c63e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [ 20000 / 20000 ] 100% - 0:00:00 left \n",
      " quantization error: 1.7187079182050329\n"
     ]
    }
   ],
   "source": [
    "W = 1\n",
    "H = 4\n",
    "niter = 20000\n",
    "\n",
    "som = MiniSom(W, H, X.shape[1], sigma=.5, learning_rate=.5,neighborhood_function='gaussian', random_seed=10)\n",
    "som.train_batch(X, niter, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X4r0aMpdoDPo"
   },
   "source": [
    "### SOM plot - number of data points in each neuron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 479
    },
    "executionInfo": {
     "elapsed": 721,
     "status": "ok",
     "timestamp": 1702641813856,
     "user": {
      "displayName": "Florencia Doná",
      "userId": "12514529675364651717"
     },
     "user_tz": 180
    },
    "id": "FFvUhtOcK4Hw",
    "outputId": "e86046e1-fcf2-4868-e529-3e897aea8e9f"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hitmap = som.activation_response(X)\n",
    "dist_map = som.distance_map()\n",
    "plt.imshow(dist_map)\n",
    "for i in range(W):\n",
    "    for j in range(H):\n",
    "        plt.text(j,i, int(hitmap[i, j]));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oBMME5xhoXff"
   },
   "source": [
    "### SOM plot - TYPE of data points in each neuron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 368
    },
    "executionInfo": {
     "elapsed": 943,
     "status": "ok",
     "timestamp": 1702641819919,
     "user": {
      "displayName": "Florencia Doná",
      "userId": "12514529675364651717"
     },
     "user_tz": 180
    },
    "id": "FNnQqh3PLIPe",
    "outputId": "d153e844-9fc4-4741-a023-af9c4627490a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels_map = som.labels_map(X, y)\n",
    "colors = ['blue', 'red', 'green', 'black']\n",
    "fig = plt.figure(figsize=(4, 4))\n",
    "for i, j in labels_map:\n",
    "    for label in labels_map[(i, j)]:\n",
    "      plt.scatter(i, j, alpha=.7, s=labels_map[(i, j)][label]*100, c=colors[label-1])\n",
    "import matplotlib.patches as mpatches\n",
    "legends = [mpatches.Patch(color=c, label=l, alpha=.7) for c, l in zip(colors, target_names)]\n",
    "plt.legend(handles=legends);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bby6VlxboiVX"
   },
   "source": [
    "## Which data point was grouped in which neuron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 393,
     "status": "ok",
     "timestamp": 1702641825558,
     "user": {
      "displayName": "Florencia Doná",
      "userId": "12514529675364651717"
     },
     "user_tz": 180
    },
    "id": "YyIQJ8CAjX8G",
    "outputId": "f13ed09a-c816-4936-ea1d-9ae6860164ba"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------\n",
      "['NEURONA 1']\n"
     ]
    },
    {
     "data": {
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       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-11adb1d1-ccb3-4cf2-9b86-f21417b693d4');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-d5e97bdc-939a-4b29-af24-1c3f02c596d7\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d5e97bdc-939a-4b29-af24-1c3f02c596d7')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-d5e97bdc-939a-4b29-af24-1c3f02c596d7 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "    Age (years)  Live in the city of Santa Fe(Yes_No)  \\\n",
       "ID                                                      \n",
       "4            29                                     0   \n",
       "5            38                                     0   \n",
       "9            38                                     0   \n",
       "41           40                                     0   \n",
       "48           25                                     0   \n",
       "\n",
       "    Complete primary education  Body_mass_index(kg/m2)  \\\n",
       "ID                                                       \n",
       "4                            1                    30.4   \n",
       "5                            1                    20.7   \n",
       "9                            1                    30.0   \n",
       "41                           1                    30.5   \n",
       "48                           1                    29.7   \n",
       "\n",
       "    Live_near_agricultural_fields(Yes_No)  Campo_prox  \\\n",
       "ID                                                      \n",
       "4                                       1           1   \n",
       "5                                       1           1   \n",
       "9                                       1           1   \n",
       "41                                      1           1   \n",
       "48                                      1           1   \n",
       "\n",
       "    Consumption of white meat (Yes/No)  ug Glyphosate/ g creatinine  \n",
       "ID                                                                   \n",
       "4                                    1                     0.369449  \n",
       "5                                    1                     0.207850  \n",
       "9                                    1                     0.137993  \n",
       "41                                   1                     0.129091  \n",
       "48                                   1                     0.068790  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------\n",
      "----------------------------------\n",
      "['NEURONA 2']\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Live in the city of Santa Fe(Yes_No)</th>\n",
       "      <th>Complete primary education</th>\n",
       "      <th>Body_mass_index(kg/m2)</th>\n",
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       "      <th>ug Glyphosate/ g creatinine</th>\n",
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       "      <th>ID</th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
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       "      <td>3.719553</td>\n",
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       "      <th>16</th>\n",
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       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25.4</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.567453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.8</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.398447</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>33.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.130114</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.110558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18.9</td>\n",
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       "      <td>0.617873</td>\n",
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       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dde62907-3751-42e0-89da-7b74bd9b7a39')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
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       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
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       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-dde62907-3751-42e0-89da-7b74bd9b7a39 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-dde62907-3751-42e0-89da-7b74bd9b7a39');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-e681c5ae-98c8-4118-a202-b6a9cc073b23\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e681c5ae-98c8-4118-a202-b6a9cc073b23')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
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       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
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       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
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      "text/plain": [
       "    Live in the city of Santa Fe(Yes_No)  Complete primary education  \\\n",
       "ID                                                                     \n",
       "10                                     0                           1   \n",
       "16                                     0                           1   \n",
       "18                                     0                           1   \n",
       "19                                     0                           1   \n",
       "22                                     0                           1   \n",
       "24                                     0                           1   \n",
       "29                                     0                           1   \n",
       "\n",
       "    Body_mass_index(kg/m2)  Live_near_agricultural_fields(Yes_No)  \\\n",
       "ID                                                                  \n",
       "10                    25.0                                      1   \n",
       "16                    28.0                                      1   \n",
       "18                    25.4                                      1   \n",
       "19                    20.8                                      1   \n",
       "22                    33.3                                      1   \n",
       "24                    30.1                                      1   \n",
       "29                    18.9                                      1   \n",
       "\n",
       "    Rural_labor_tasks  Family_members_working_with_pesticides_(Yes_No)  \\\n",
       "ID                                                                       \n",
       "10                  0                                                0   \n",
       "16                  0                                                0   \n",
       "18                  0                                                0   \n",
       "19                  0                                                0   \n",
       "22                  0                                                0   \n",
       "24                  0                                                0   \n",
       "29                  0                                                0   \n",
       "\n",
       "    Pesticides application at home  Drinking water source  \\\n",
       "ID                                                          \n",
       "10                               0                      0   \n",
       "16                               0                      0   \n",
       "18                               0                      0   \n",
       "19                               0                      0   \n",
       "22                               0                      0   \n",
       "24                               0                      0   \n",
       "29                               0                      0   \n",
       "\n",
       "    ug Glyphosate/ g creatinine  \n",
       "ID                               \n",
       "10                     3.719553  \n",
       "16                     0.157295  \n",
       "18                     0.567453  \n",
       "19                     0.398447  \n",
       "22                     0.130114  \n",
       "24                     0.110558  \n",
       "29                     0.617873  "
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------\n",
      "----------------------------------\n",
      "['NEURONA 3']\n"
     ]
    },
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      "text/plain": [
       "    Complete primary education  \\\n",
       "ID                               \n",
       "6                            1   \n",
       "7                            1   \n",
       "11                           1   \n",
       "13                           1   \n",
       "14                           1   \n",
       "15                           1   \n",
       "17                           1   \n",
       "23                           1   \n",
       "25                           1   \n",
       "26                           1   \n",
       "30                           1   \n",
       "32                           1   \n",
       "33                           1   \n",
       "34                           1   \n",
       "35                           1   \n",
       "36                           1   \n",
       "43                           1   \n",
       "45                           1   \n",
       "\n",
       "    Family_members_working_with_pesticides_(Yes_No)  Campo_prox  \\\n",
       "ID                                                                \n",
       "6                                                 0           0   \n",
       "7                                                 0           0   \n",
       "11                                                0           0   \n",
       "13                                                0           0   \n",
       "14                                                0           0   \n",
       "15                                                0           0   \n",
       "17                                                0           0   \n",
       "23                                                0           0   \n",
       "25                                                0           0   \n",
       "26                                                0           0   \n",
       "30                                                0           0   \n",
       "32                                                0           0   \n",
       "33                                                0           0   \n",
       "34                                                0           0   \n",
       "35                                                0           0   \n",
       "36                                                0           0   \n",
       "43                                                0           0   \n",
       "45                                                0           0   \n",
       "\n",
       "    Consumption of vegetables  Drinking water source  \\\n",
       "ID                                                     \n",
       "6                           1                      0   \n",
       "7                           1                      0   \n",
       "11                          1                      0   \n",
       "13                          1                      0   \n",
       "14                          1                      0   \n",
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       "17                          1                      0   \n",
       "23                          1                      0   \n",
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       "30                          1                      0   \n",
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       "33                          1                      0   \n",
       "34                          1                      0   \n",
       "35                          1                      0   \n",
       "36                          1                      0   \n",
       "43                          1                      0   \n",
       "45                          1                      0   \n",
       "\n",
       "    Consumption of white meat (Yes/No)  Consumption of dairy products  \n",
       "ID                                                                     \n",
       "6                                    1                              1  \n",
       "7                                    1                              1  \n",
       "11                                   1                              1  \n",
       "13                                   1                              1  \n",
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       "26                                   1                              1  \n",
       "30                                   1                              1  \n",
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       "33                                   1                              1  \n",
       "34                                   1                              1  \n",
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     "text": [
      "----------------------------------\n",
      "----------------------------------\n",
      "['NEURONA 4']\n"
     ]
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      ],
      "text/plain": [
       "    Rural_labor_tasks  Use of agrochemicals  ug Glyphosate/ g creatinine\n",
       "ID                                                                      \n",
       "1                   0                     0                     2.649565\n",
       "2                   0                     0                     1.257694\n",
       "3                   0                     0                     0.317159\n",
       "8                   0                     0                     0.231657\n",
       "12                  0                     0                     0.920889\n",
       "20                  0                     0                     0.200170\n",
       "21                  0                     0                     0.831145\n",
       "27                  0                     0                     0.634448\n",
       "28                  0                     0                     0.332475\n",
       "31                  0                     0                     0.489516\n",
       "37                  0                     0                     0.059220\n",
       "38                  0                     0                     0.367185\n",
       "39                  0                     0                     0.285154\n",
       "40                  0                     0                     0.109025\n",
       "42                  0                     0                     0.442286\n",
       "44                  0                     0                     0.220052\n",
       "46                  0                     0                     0.216852\n",
       "47                  0                     0                     0.191812\n",
       "49                  0                     0                     0.173106"
      ]
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     "text": [
      "----------------------------------\n"
     ]
    }
   ],
   "source": [
    "winner_coordinates = np.array([som.winner(x) for x in X]).T\n",
    "som_shape = (W,H)\n",
    "cluster_index = np.ravel_multi_index(winner_coordinates, som_shape)\n",
    "for c in np.unique(cluster_index):\n",
    "     #display(dataset[:].iloc[np.where(cluster_index==c)].drop(['Nombre_apellido', 'Localidad', 'Tareas_cuales','Tareas_tiempo','Plaguicidas_tiempo','Plaguicidas_cuales','Lava_ropa_trabajo','Lava_ropa_frec','Campo_prox_cuanto','Campo_plaguicidas','Campo_apl_forma','Fumiga_frec','Actividad_fisica','Verdura','Fruta','Vitaminas_cuales','Agua_potable','Vaca','Pollo','Cerdo','Hamburguesas','Embutidos','Lacteos_frec','Alcohol_frec','Alcohol_cantidad','Tabaco_actual','Tabaco_diario','Consulta_medico','Controles_anuales_cuales','Enfermedades_diag','Tipo_cancer','HPV','Cancer_familia_tipo','Cancer_familia_interes','Hijos_cuando','Embarazo_inc','Lactancia_tiempo','Anticonceptivos_actual','Anticonceptivos_tiempo','Menopausia_edad','Trabaja_ocupacion','Trabaja_ingresos','Entrego_muestra', 'Glifo_CREAT ug/L CAT','Estacion_año'], axis=1))\n",
    "     X_ind = np.where(cluster_index==c)\n",
    "     rows = X[X_ind,:]\n",
    "     std_rows = np.std(rows, axis=1)\n",
    "     col=std_rows<0.21 \n",
    "     print('----------------------------------')\n",
    "     print(['NEURON '+ str(c+1)])\n",
    "     display(DATA[DATA.columns[np.where(col)[1]]].iloc[np.where(cluster_index==c)])\n",
    "     print('----------------------------------')\n"
   ]
  }
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